diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 20e0b44..a24f2ec 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -9,21 +9,21 @@ jobs:
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
 
     - name: Cache python dependencies
       id: cache-pip
-      uses: actions/cache@v1
+      uses: actions/cache@v4
       with:
         path: ~/.cache/pip
         key: pip-pre-commit-${{ hashFiles('**/setup.json') }}
         restore-keys:
           pip-pre-commit-
 
-    - name: Set up Python 3.8
-      uses: actions/setup-python@v2
+    - name: Set up Python 3.9
+      uses: actions/setup-python@v5
       with:
-        python-version: '3.8'
+        python-version: '3.9'
 
     - name: Install python dependencies
       run:
@@ -39,15 +39,15 @@ jobs:
 
     strategy:
       matrix:
-        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v2
+      uses: actions/checkout@v4
 
     - name: Cache python dependencies
       id: cache-pip
-      uses: actions/cache@v1
+      uses: actions/cache@v4
       with:
         path: ~/.cache/pip
         key: pip-pre-commit-${{ hashFiles('**/setup.json') }}
@@ -55,7 +55,7 @@ jobs:
           pip-pre-commit-
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index 1834f25..f329de5 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -18,10 +18,10 @@ jobs:
 
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v2
       with:
-        python-version: '3.8'
+        python-version: '3.9'
     - run: python .github/workflows/check_release_tag.py $GITHUB_REF setup.json
 
   pre-commit:
@@ -41,10 +41,10 @@ jobs:
         restore-keys:
           pip-pre-commit-
 
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v2
       with:
-        python-version: '3.8'
+        python-version: '3.9'
 
     - name: Install python dependencies
       run:
@@ -60,7 +60,7 @@ jobs:
 
     strategy:
       matrix:
-        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']
 
     steps:
     - name: Checkout
@@ -100,10 +100,10 @@ jobs:
     steps:
     - name: Checkout source
       uses: actions/checkout@v2
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v2
       with:
-        python-version: '3.8'
+        python-version: '3.9'
     - name: Build package
       run: |
         pip install wheel
diff --git a/.gitignore b/.gitignore
index 174d009..81de26c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -50,6 +50,9 @@ coverage.xml
 # Visual Studio Code
 .vscode/
 
+# idea editor
+.idea
+
 # Translations
 *.mo
 *.pot
@@ -105,3 +108,24 @@ ENV/
 
 # vi swap files
 *.swp
+.DS_Store
+examples/aic_4
+examples/aic_6
+examples/bayesian_analysis_4
+examples/bayesian_analysis_6
+examples/chain.h5
+examples/data_rahman.zip
+examples/dic_4
+examples/dic_6
+examples/data_manager/gpumd/compute.out
+examples/data_manager/gpumd/dump.sample.xyz
+examples/data_manager/gpumd/model.xyz
+examples/data_manager/gpumd/run.in
+examples/data_manager/gpumd/thermo.out
+examples/data_manager/lammps/config.lammpsdata
+examples/data_manager/lammps/dump.sample.lammpstrj
+examples/data_manager/lammps/sample.lammps
+examples/07_example_negative_log_likelihood copy.ipynb
+examples/08_example_optimize_prior .ipynb
+examples/stress_flux_hacked.npy
+sportran/md/maxlike copy.py
diff --git a/examples/06_example_bayesian_regression.ipynb b/examples/06_example_bayesian_regression.ipynb
new file mode 100644
index 0000000..7dc424a
--- /dev/null
+++ b/examples/06_example_bayesian_regression.ipynb
@@ -0,0 +1,1215 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "# sys.path.append('../../sportran/')\n",
+    "import sportran as st"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import CubicSpline\n",
+    "def model(x, y):\n",
+    "    return CubicSpline(np.concatenate([-x[::-1], x[1:]]), np.concatenate([y[::-1], y[1:]]))\n",
+    "\n",
+    "def model_wishart(x, y):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    y00, y01, y11 = y.reshape(3,l)\n",
+    "    yy = np.array([[y00, y01], [y01, y11]]).T\n",
+    "    # yy = np.einsum('tab,tbc->tac', np.transpose(yy, axes=(0,2,1)), yy)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dc=np.load('data/bayesian/CsF/dc_minimal.npy', allow_pickle=True).item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TODO: Laplace approximation around the maximum likelihood --> Hessian --> Covariance matrix --> Standard error on the mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 966,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import opt_einsum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1056,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LOG2=np.log(2)\n",
+    "from scipy.special import multigammaln\n",
+    "multig = multigammaln(0.5*3, 2)\n",
+    "def log_likelihood_wishart(w, model, omega, omega_fixed, data_, nu, ell):\n",
+    "        '''\n",
+    "        Logarithm of the Wishart probability density function.\n",
+    "        '''        \n",
+    "        n = ell\n",
+    "        p = 2\n",
+    "\n",
+    "        # Compute scale matrix from the model (symmetrize to ensure positive definiteness)\n",
+    "        spline = model(omega_fixed, w)\n",
+    "        V = spline(omega)\n",
+    "        V = opt_einsum.contract('wba,wbc->wac', V, V) / n # equiv to V.T@V for each frequency\n",
+    "\n",
+    "        # The argument of the PDF is the data\n",
+    "        X = data_ \n",
+    "        \n",
+    "        # Determinant of X\n",
+    "        a, b, d = X[...,0,0], X[...,0,1], X[...,1,1]\n",
+    "        detX = a*d - b**2\n",
+    "        \n",
+    "        # Determinant and inverse of V\n",
+    "        a, b, d = V[...,0,0], V[...,0,1], V[...,1,1]\n",
+    "        invV = (1/(a*d - b**2)*np.array([[d, -b],[-b, a]])).transpose(2,0,1)\n",
+    "        detV = a*d - b**2\n",
+    "\n",
+    "        # Trace of the matrix product between the inverse of V and X\n",
+    "        trinvV_X = opt_einsum.contract('wab,wba->w', invV, X)\n",
+    "\n",
+    "        # if detV.min() < 0 or detX.min() < 0:\n",
+    "        #         print(detV.min(), detX.min())\n",
+    "\n",
+    "        # Sum pieces of the log-likelihood\n",
+    "        log_pdf = 0.5*(-n*p*LOG2 - n*np.log(detV) + (n-p-1)*np.log(detX) - trinvV_X) - multig\n",
+    "        \n",
+    "        return np.sum(log_pdf)\n",
+    "\n",
+    "def log_likelihood_offdiag(w, model, omega, omega_fixed, data_, nu, ell):\n",
+    "        '''\n",
+    "        Logarithm of the Variance-Gamma probability density function.\n",
+    "        '''\n",
+    "        import scipy.special as sp\n",
+    "        spline = model(omega_fixed, w)\n",
+    "        rho = np.clip(spline(omega), -0.98, 0.98)\n",
+    "        _alpha = 1/(1-rho**2)\n",
+    "        _beta = rho/(1-rho**2)\n",
+    "        _lambda = 0.5*ell*nu\n",
+    "        _gamma2 = np.abs(_alpha**2 - _beta**2)\n",
+    "        _lambda_minus_half = _lambda-0.5\n",
+    "\n",
+    "        # Data is distributed according to a Variance-Gamma distribution with parameters (notation as in Wikipedia):\n",
+    "        # mu = 0; alpha = 1/(1-rho**2); beta = rho/(1-rho**2); lambda = ell*nu/2\n",
+    "        # Its expectation value is ell*nu*rho\n",
+    "        z = data_*ell*nu\n",
+    "        absz = np.abs(z)\n",
+    "        # z = data \n",
+    "        # print([np.sum(i) for i in [_lambda*np.log(_gamma2), _lambda_minus_half*np.log(absz), \n",
+    "        #       np.log(np.abs(sp.kv(_lambda_minus_half, _alpha*absz))), _beta*z,\n",
+    "        #        0.5*np.log(np.pi), np.log(sp.gamma(_lambda)), _lambda_minus_half*np.log(2*_alpha)]])\n",
+    "\n",
+    "        print('1',np.min(_lambda_minus_half))\n",
+    "        print('2',np.min(_alpha*absz))\n",
+    "        print('3',np.min(sp.kv(_lambda_minus_half, _alpha*absz)))\n",
+    "\n",
+    "\n",
+    "        log_pdf = _lambda*np.log(_gamma2) + _lambda_minus_half*np.log(absz) + np.log(np.abs(sp.kv(_lambda_minus_half, _alpha*absz))) + \\\n",
+    "                _beta*z - 0.5*np.log(np.pi) - np.log(sp.gamma(_lambda)) - _lambda_minus_half*np.log(2*_alpha)\n",
+    "\n",
+    "        res = np.sum(log_pdf)\n",
+    "        return res\n",
+    "\n",
+    "def log_likelihood_diag(w, model, omega, omega_fixed, data_,  ell):\n",
+    "        spline = model(omega_fixed, w)\n",
+    "        rho = np.clip(spline(omega), 1e-6, 1e6)\n",
+    "\n",
+    "        # Data is distributed according to a Chi-squared distribution with parameters (notation as in Wikipedia):\n",
+    "        # Its expectation value is ell*rho\n",
+    "        z = data_*ell/rho\n",
+    "        absz = np.abs(z)\n",
+    "        # z = data \n",
+    "        log_pdf = (ell / 2 - 1)*np.log(absz) - absz/2 - np.log(rho)\n",
+    "\n",
+    "        res = np.sum(log_pdf)\n",
+    "        return res + log_prior_diag(w)\n",
+    "\n",
+    "def log_prior_diag(w):\n",
+    "        # Uniform prior\n",
+    "        if np.all((w>=1e-6)&(w<=1e6)):\n",
+    "                return 1\n",
+    "        else:\n",
+    "                return -np.inf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1057,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.optimize as opt\n",
+    "\n",
+    "def do_mle(data_, w0, model, omega, omega_fixed, nu=2, ell=3, solver = 'CG'):    \n",
+    "    res = opt.minimize(fun = lambda w, model, omega, omega_fixed, data_, nu, ell: -log_likelihood_wishart(w, model, omega, omega_fixed, data_, nu, ell),\n",
+    "                       x0 = w0,  \n",
+    "                       args = (model, omega, omega_fixed, data_, nu, ell),\n",
+    "                       method = solver)\n",
+    "    params = res.x  \n",
+    "    return params\n",
+    "\n",
+    "def do_mle_od(data_, w0, model, omega, omega_fixed, nu=2, ell=3, solver = 'CG'):    \n",
+    "    res = opt.minimize(fun = lambda w, model, omega, omega_fixed, data_, nu, ell: -log_likelihood_offdiag(w, model, omega, omega_fixed, data_, nu, ell),\n",
+    "                       x0 = w0,  \n",
+    "                       args = (model, omega, omega_fixed, data_, nu, ell),\n",
+    "                       method = solver)\n",
+    "    params = res.x   \n",
+    "    return params\n",
+    "\n",
+    "def do_mle_d(data_, w0, model, omega, omega_fixed, ell=3, solver = 'CG'):    \n",
+    "    res = opt.minimize(fun = lambda w, model, omega, omega_fixed, data_, ell: -log_likelihood_diag(w, model, omega, omega_fixed, data_, ell),\n",
+    "                       x0 = w0,  \n",
+    "                       args = (model, omega, omega_fixed, data_, ell),\n",
+    "                       method = solver, tol = 1e-10, jac = '3-point')\n",
+    "    params = res.x  \n",
+    "    return params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1058,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lIYQQQoiblnu+tnBSboR/pUzytqEtPqOtod3OR0uKnUXZa2eIt4kFNG6nNN75AsoJSR4fxsrGuzqXcMElNtp5j0H3fAHQeJfLxMbSpO8da5lpoZwA+4UZdKTIPLiX2HCy6XYA7usFkrduze/6dNHlar4WINFaMwQNEYir+SrXCrUskfsOD5FJdLZEjzqMy86UXbxNGm27lSRjo89uu+22htuXL1/ueN9Lly413D527FhPzkkIIYQQ4mailUY5waoru8qTxX2/aFXrQ+C8ONNFQ8X+UE5AVNzdV6tLf3MF9/WlC6i9G7kRTDt4F0sEM05tTGrJa5shsZL9wjS60xX4Ivd8ER0ogimbKO8RRIovPXeZ5y4usGDfeB6rr+VrfWIChfPSTFf30alLUyWe/eY5Fk7MdP046sdYcOpfTxZr2R5h4UbWx7XCYtADuLJs27V4pc5e08FiZlpilwc3JLDRZ3fffXfD7XPnznW87/nz5+tfj4yMsH///p6dlxBCCCHEzcJ5aYby9yepnpxfe2PRE+GMg3epRDBXxXmpT6UQHVjqN1H50RT+la3vi9AzK2IXOlB4F0u1pqE9FFy70RxUeRGVZ6fwWjTObEXZ65/GoeyAE1eLTBZcIqU5O33jfKL5G2UiOlBopfGv9vY5vnK1TOFSibMvTffu9aM1Ual5sC/qYhzr0enWJTG6yXGGNvA87AQS2Oiz4eFhjh49Wr/9zDPPdLSf7/u88MIL9dv33Xdfz89NCCGEEGK3i+yAcL62AAim7C0+m5tHcP3Gz1pVt25B5b6Wry/yqq8ubNl5rOWZ8/N87nvneXZiY8E33eFV+YQXkbPDtWtSmiR/eF03ztSEc1WUe+N1EBY6z6CZLnXW06L6yhzVM6ufY601QaTqmSZhpJiYrXC5i+yIYjXAv1QimHWa9t1oTnN+ZvXPKir6XfcS6Vbp26urBIbLEtgQG/Tud7+7/vX58+eZmJhYc5+nn366odHoe9/73r6cmxBCCCHErnYTNwgVoN3+LubC+SqVZyapnpxrepW8E24Q8cOJeWwv4gfn5/HCxqyLyA5wzxeIOiw9KLkBL13Jc3qyyEzJXdWUUlVDjl5xODrtsm9h/WVCnT5e99U89oszlJ++VitlURr7uamO78dQmrHC2ucZzDQPVLw2XeaFS3me/8tzhEWfS/M2M+VmP0ujbRGP8hXOS7NEdtDReZfdkNkmZVjV0+2DVwXH57WpUkPZjVibBDY2wQc/+MGG25///OfX3GflNh/4wAd6eUpCCCGEEEJsmajoYb84g3u+0FXPhs2ile5o4W7//QxRJcC/bhPOdp4BsFx1RfmIvywYp7XGeXEGb6KI/fw0Olr7nE5cLeIGipIbMjFnc/JakfyyhqLehSLG4s98rNh+kR52OQFk+XkvlVWEy/qbVJ6dQjmdBQaWpBe8pgGYTl42jh9SWLy/hatlqifnmgYbFo/Y00EvK5/XTmjg1akyeSfg7HSFSLUPzHb6q2OtM+i2k0hgYxM89thj3HvvvfXbn/nMZ7hw4ULL7Z955hm+/OUv12+/5z3v4Y477ujrOQohhBBCiJ0jKvs4L8/iTRT7FhjQWhNV/KZjcXWoCBfcjhbazdjPTxPOVfEmikQLLv61Cs4rsy17D6xX5HSXsRHOV3FemqH07cuUv3e1q6ajYb77K+xaa4rVNgt9faOUR0d6zawNw6iNF11xCJ45P3fjdpssJh0qdFBbkK+/X4fmzPUSz11c4Fp+dbCn29Kkgcn1l5CFq16frV+vwZTDna+3Hgub34QMisqKDCNPMs46JoGNTWCaJr/3e79Xv23bNu973/u4cuXKqm1PnDjBhz/8YdRidM40TZ588slNO1chhBBCCLH9OS/OEMw4tRKFdVxV7+TatHc2X2u8+ez1huwFrTX289PYL0xT+s5lSn9zpWUZQMv7X3Y8/3KZ6ul5gikH+4XOSxR6TfkR9t/PEMwuTqkIFM6J7huf6kBRPTVP9eTcmsGBb52a4i9evLau8+1G8nr750dHGu9yidJ3r1D+3jXC+Sp6nYGNghNQWlygX8mv7kfRTRZIr5uhbsRryxqXtjNbdjlzvUTe6T5It/K3suKGvDZV4sXLnTdsna94nJ0uU3T8bZkN1S+dDcm9SXz1q1/lN37jN1b9e7nc2PTl8ccfJ51Or9ruD//wD/mFX/iFpsd+3/vex6/92q/x2c9+FoBTp05x11138fjjj/Pggw8SBAE//OEP+cpXvkIQ3Ija/sEf/AEPPPDARh6WEEIIIYTYZZaPqg2mHWJjqz+bbtRSk0jlhATTDokD2dptOyAq31i06UDhvDzL0E/fsq77CZY1Y9RhLUvETMcxrN6NLe1EON0ku8Ct/Zz/7vU5Tk0WefiWER65dbTtcbyJAv7k2ovgSGnOXN+cSS0DM60bXqbcCOfEbL0pplYa++9nyP3YgXXd11pZBn4XzUd73WSzF+v8S/M214su2YTFfYeHWN5hNYgU52d716R4Yq67Y4WR4vWZ2mtvwfbJJCwGUjfHkv/meJQdKpVKDSNWW5mcnGy5fzt/9Ed/RLlc5k/+5E+AWubG5z73uabbGobBb/7mb/LEE0+seT5CCCGEEEJ0o32bxNUart63KD+Jyj7WQGIjpwVA5ZnrxIaTZB7Zh2GsPk/lBESVgNieNIbZ/+BH2Q147mJt2sbTr8/xpqMjWG3ud/nUEP9664XpypIRtGa4EhJeKKKPj2LENye5/rbrVcJUp5M+1rZZ4aireYfJQpW9A0lu3ZOr/7sfKQwgbpk0eflsSKgU14u1jBPbjyhVQwbTcbww4sKsTdnb2skj1wqNz6PjRzjbKOuln6QUZRNZlsUXv/hFvvSlLzX03Fjp0Ucf5b//9//eUL4ihBBCCCFEr3TdJrGDBaL93FTb/g3Kq2UGdFLeERY8VGV17wnlR1SevY7z8izumY2NRu1UZeVUkR6l96/8kWarEQdnPcKLJdzXWoym7VFlwfKHYKzjmOW/63/5zJJmLz2lNVfzVZSGqZJXnyRTcHxevJTnhUv5rktBOinbWNmz4+piD5GLczaFalBvmLpVpjucnLMbScbGMh/5yEf4yEc+0vf7+cVf/EV+8Rd/kZMnT3LixAkmJyexLIuDBw/y5je/mWPHjvX9HIQQQgghbnqbW+nQllaa4LqNETeJ78305T6iio97No+ZjkEfehLqSBNM2SQODzT9vvvaAkGTco92x4vsAO1FWCNJvFBhTxSIhbXFoz9pk75nT9v9N2pitsLf/GhzFot787WFuKaW6ZG+t/Vj63UmQre6bQDaS2GkVj3+MNIkY7WJIktemypz+3i2Ybt2r4jXptZfFpTvctIL1DKBLs2vb5KOWE0CG1vo3nvvbZu5IYQQQggh+mgb9dXzL5ZwzxcAyL5pL7E9ve+Z4bw02/8FaZufaTdBDaj18nBfXUArTXQ0x3+bKjAw5fDWeILxgdSa+wcd9LlYbuUV+6ofMlP2YDze1XF6pfrawurHsLigV353kalmJT3NhJHC9kIG0lvzmDvx/KU847nOSp6uNmle2kqh3XSaRWeut2890AmtNYZhcOZ6iZtgCuumkVIUIYQQQgghtthSUAOgeqo/JRYbCWroTvftYSaB+3q+Pj3l4gtTVBd7BXTanLGTBp5t929RVrP0EHW04vs9KlEpLl799y+X0eHKPhzgX6vgX2meXVBZcIk1yVTRupb9Uj0z3zLApLXm5LUiZ6bKvHBxAe/S2ov4qWKV16ZKlN21gwIVL+DE1QKvTZWI1MZShmYrnZWZtHoO16sX41fPz9ZelxLU6C0JbAghhBBCiJvTNipFWa7bEY3+tQrK7W8mhne5vHoh30T1zELDxJQlah1NFXVw4/7coH0DRDeIUD1bKWouzFY4s1iacOtklbGCT8qNODDnEUzZBHNVyn9zdcP3NFr0ufOSzd6FG+UuE3N228dbPd068HX1B63PyXlxBv9q62BP2Q1xFxfukYYrZ+banTqOH3Jx3iHvBJyaXDsIcuZ6GcePyDsB1/JVLs3bXJy3CReDHF4Yrfk8d2urS3aamavcXGNYN4uUogghhBBCCLHDVU/Pk31oX1/vI5ip1ke+tj2XE7OYmcZSBvvZqX6dFj84P8ezEwscGknz4YcPr1l2oZyg3sA0c/84ZiaODlU9kFJwAqbLNwINGTci495YcLun5jFb3Ee7KSgraaXZP18LAu0pNGY8TBVdbt2z9s96xRGZLrlNvxMpTXlxRGmziN6JqwXGso3lHRfnHfYPtS6JKnVQurHyHJZMFhvPczyX5NRkEa3hjn05RrPJZd9df3RiZaxru8QTyn0ORN6MJLAhhBBCCCG2DR0qlBdhZmId9wXYddaReRDON1/QdiOq+HjnC5jZBMljQ+s+r8gJVwU2lNe/kZPPTtQmiFzLV7m84HDLWPuAQPX0AlG5tih3Ts6ROj6Cc2KWmQWHKwsO/gaajlZPts9yaLCOVXa7rJn5it/y3EtuyCtXixweSXN4ZHVz2tpY0N6MfO12GslU0aVcDeovr7PTFR49lmy/0wrbJF7RsdM96NUhGklgQwghhBBCbAtaaexnrxM5IclbBkndObLVp7QldKQJCy6x4bUbZPaS8+LsYklLFWuws+aMW01VQw7MeoyUA+aH4pQ7yCII8zeCQFHRx35hGui8d8dGKTfETK1vGRZca11Kcm5m7Z4iV/PVpoGNXrG9cF0TQnpdgtLaTguBiE5Jjw0hhBBCCNGVWtp+7xci4YxD5NRStDtpXLhT+ZMV7Oem8NssUp2XZnt2X9VXF1AdLDaX9+kIppov8nWoup5u0k/lv7vGyGL2xVgxwJrbeOZKvzkvr/+5VU7rEoatTnCq+iFX8+t8bWzSyfcjrLHRRqiiNyRjQwghhBBCdCyyA5znp9ChJn3/HuLjvbv6289yhe1CR6o+9SQseMT3ZTBiq681Lm+cuR7+ZAVvolifhBIVvLbb6w6mPVRPz7dtXNlXTVakWuvaONZlgvMFTo6nuWNfbpNOrHtRyUe5Ie6Z7n+Wkd19NkQz/VjgX5x3MJvEJ2wvJNHkNb5bTJfa/26JzSGBDSGEEEII0TH3bB7l1xbBzkuzDP30LRs73msL+NdtkkcHwWp91VZ5EdVX5tCRIn3vHqxsvOW23QoLHt6FIrHhJMnbmvSW6KGVAQvlRVhrLPq00qB00wBIM8qPVo2MbTapZLmNjkbdCnMVjwtzjZklV/JVzp6e5vWZMj+Ntanns7LPybW8w1zF48BQmr2DjWVF1ZPzBPPbJ/OlNU2pGpKMmSTj7X+ebhCRTa5eXr5yrUi8ze92L5y8ViST2Nzne4m3aWU0oh0JbAghhBBCiI6pNa5ORuXa1ejYnvTa0yncEO9ybaSme75A6o7hltt6r+frvRGqJ+fIvfVAdyfehv1cbWJHOFclNprCGuqucWG/RBUfMxXDfn4KVQlIvWEUI25i5loHdbTW2D+83vV96RXZMuFcbxpJ9lO7nhgX5xwYHNjEs6GeHQPghxFX8rWf4cSczd7BJMune4R5l4l19PRQWjNf8UgnYuQWgwiRUsyUvfX0nF3Tmetliot9S96wL8dIdn2/G8EaDVlbvVP4kWK20llGhONvfoCh5IaUZMLJtiCBDSGEEEII0RNRxcd+9jpaQ/LYEKnbh9tuv6r0pE01xPIxmlGpu6kLrTRbTIXzbn8DG11cuK480xigqL5am/5hNMv3X+QvuLx+pUA1UNy2J9v0CnpTK8dibmAyiNaaK3mHlBMwnEmgdG0MacIyGcsl2Mj4ziVqu8ztbMFbUdqj9eo2EvN296/jC3M2s4vlN3cfGGQwHefSvLOqJKdXisuasb62YlpJs2eg13kZl+d73NB1e79sxAbs3mInIYQQQgixLu75AuW/vYJ7vtDdfmfz9QmW3kSx9ye2xbTShHPVjhpxAignwL9aRq1xJVl53V3x1W0uzZ+9kGe24lPxQs50MVKyV70bAK4Vqpy8VuTVqTKlasCVBYdL8w6vz1QaFsob8cKlfNN/j0W6PkZVu6t/7lvZx6VUDXj5Sp6z0+V1B2ZmlwUwTl8vYfth34Iaa1lYEZjxQrWuYA207h26ngkr7ZS7/F3b7k5Ndv47bmjNgLN7y2YkY0MIIYQQQtTpQNWDEt5EkeTRAYw1auvr+3bQgHIn884V8C6VMCyD3I8fxGyTDaGVxn5uCuUrYtdtsm/e33rbJgvw9bqSd+qdJcIuahN6WXpyNV+FdO0sJmYruMteFxfnHB44cmOUrBdETMzZWKbBsfEsMbP9ddc9hdpCN2rz2NKewtCgWP1z9a+Uu3osvXR6MdBUDfyGAEUzUYeBj4nZreuNUt6BJRivTW3d87+Vck7IoVkPa6E32W7bkWRsCCGEEEKIupXBiY1O59hNlkbQ6kjjX2x/pTRccOtNVsM1JpJ0k78fRoqpYhXHX72o1NuoPCNXrQUV1jqj87O1LI4F268FRNowOyyPOTTrcXimxdjXfjSiaMJdo6HkWpkrs2WPvNO4CG32nNtbkIHi+CELdm+zRFb+Crx4OU+pGrQNYInO5JyQo9MesV3+Vi6BDSGEEEIIsSVWr+d3ziJmzeyUFkEG7TXbr/PIxtnpMhfnHV65Wly16Kt87yojkzth0sYNyxsvThdbBCOovTKsDhe5iUDVSlKaUH3IMihVA/K2x/LXb7vGpp16barMuZkyoVL121vNDyNOXitydrq/mSJeqOoZLmL9DK05NOv1vPfJdiSBDSGEEEKIbUL5EVFl96YK7zQ6VG37WXR1LK2Jyj6VZ7ufWLLcUiBAAyeuFhq+t5Qh0olQKfKOT6R2zmVcswdPRTDd28BPsepz+nqJ16YrTLUJzKzXXMXn+Yt5Zsvuqoak63W9WF13JsRk0e1T0svNsPTefIN2tOszNZZIjw0hhBBCiG1AeRH2DydRviJ1xzDJW4e2+pRuasGUTfXkHEYqRvYt+zETnfUZgVojTu98Y/NU73wB70KTK9DR+lcd61/oak5dK1ENIgZSMe45uDWvtbkum14ealVesskipTGt2kL8/MyNzIWL8w77h9J9uc9eZIAsuTS//uCO6lNpyBqTocU6Ddg7rw/KeklgQwghhBBiG/AuFutX3N3XC1sX2NjCBcY2ahGB88ocALoaUn1ljth4ZwtWHSnsH02tKlVpGtQAKs9ObexE18HxI6qLPSDKbkikFNYaTTvXq1nwRWmNUppzXTa+THWRkdJPZ66XuH1vjoob4q9zLO7KiSI3u15lo4hGnZZv7QZSirJLvf7666TTaQzDwDAMfvu3f3urT0kIIYQQbegdOGHgZhEuuLivNR8vumrb2eq2nw6zMoC0mUufUClevlJoOa51K9h+uKpRZzuOH/HK1SIX5nqXRbFTbNVoWdEdrSLMoLejcrc7ydjYpT72sY/hutsjXU8IIYTYLXSkwQDD7Edag+Rib7o+5L9HpR1wJX4LX2pBpFkrlHJ+psJcxePQSJrDI5ke3bNGazBWPOeOH3LyarHvwZ35XdI7x9CaQTtiwA6xlCYyDcrZGKWshZZ6kk1hKg2+j+MXCEOHIHLxVO0/V3tUcXHxeXN4nFx0y1af7qaRwMYu9F/+y3/hO9/5DrfddhsXLlzY6tMRQgghdoUw7+K8NINhmWQf2YeZiW/1KfVFtNZo0g3QShMtuJi5OGZqYx9Dp0suJ64WGUjFKFUDbt2T5c59Az060/VbGgkr1qcaRMxWaq/Bq/lqTwIboVKcnizhBhF37M0xkk3Wv3dlwdmUjJVuy262o5wTcmh29djQIScinIdr40kqGVlerpdWESpwUYFLEFYJQxdfVWtBC+3x09GbiIdgaZg083w/8ferD7IstuQYuyOY1il55e0yc3NzPPHEE8RiMf7dv/t3vP/979/qUxJCCCF2BefFGXSk0WFE9dUFsg/t2+pT6rlwwa33lugVrTXBpI1yApQdEMxWMeImAz9xaEPH/W/PXSFcVj9+arLEgbenGEjdCDgVnQDlhi1rr8OCuyrDouwG/Oj1WW5zg4ZjdStSirwdMJCKkYx33nh0K+Rtn7FcErPFFfdQKfxQkUn0f+kQ9KGM53rBxfFrPUVem64wkvG4fW+OmGmuezpIK69PlzkwnOrpMbeDnBNydLr12NCYgqPTHpf3IcGNlbRGhwFRUCUMq0ShS06n2K9HiEWaeKh5hle4YEyh2yS9qMDDohaUy+pk6w0XOYZLZEBkQjza/TmB8qrbZf7Vv/pXzM3N8S//5b/k/vvv3+rTEUIIIXYNvaxJYJTfXXXmyqnVYldPzff0uGHeBQ3V043H1YHCv1YhNrz2h/OWx26yIL284NQnfFyat3nqxUnSXsTPRTEG06uDFFHRx/5R4/jVv319jssZE2eywptvHWnZVNOftDGzcRJHB5uWJr0+XaFQDYiZBg/dMtIyaLAdnJ+1ma/4vPHA4KrvzZbd+kSOIyNpDvWsNKRTmvUvyWr7VrzG/jV5J+B6weXIaO8fy7ztM7/LGoMaWnNotnVQo74dcGjW4+zRm6csZSnLIq0TJCODeKhRocup4FU87VHVLg4eoRE17HdHdIC7gxsZZvGYiV5jZe4YHpnFgEZGJ8noJGlSpIwkKTNFwkwRj6WJWWmsRAornuZVK07e9qnOOPzELl/67+5Hd5P5zne+wxe/+EUOHTrE7/zO7zA319srLkIIIcRupEMFWmP0+Kq68iOCyQrWcJJYj67gKj9CuxHmQLzeK6B6ap5g1iF1+zCJI92XYoTzVZwXZ4DeTyWxn59u/c1I9eYSotYcnPNI+grjiA8Ha//8/z5zhTuuV7GU5qxp8Mito612b+B4IWQSAJSqISPZRMu7dl8vgGmQPLo6IFCo1oJFodL1jIjlStWAIFKMZhOr+j5shaXzXS5SqmHM6JV8lf1DqVXBnnDxuYz1YbJKGGli1vp+PhfnbG7dk2v6vbmK15fAxm40aEeryk9aiSkYsCNKuV2wzNSaWKTxvRLVoEQQVvGiWmlIVbtU8XDx0AZ8yHuUEZ0FwCXicmoxYNripWvTGBzPcOP9IaFjpEmSNlIkjRRJM0XcSrEwMEgxniKImUQmPGD8bAcPQTMxZxOi+RI+MXZvk+pd8IrbmU6dOsWJEyeYnJzEsiwOHTrEI488wm233bau47muy8c+9jEA/v2///fkcjkJbAghhBBriMo+9gvTEGkyb9pLbLR3KeT2j6ZQ1dqHyPRdoyQOb6z/gw4i7GcmUb4ivi9D+p49qIqPP1mr3a++utBVYENrDRqcl2a31ZjVbg1VQobLtZ9z4vQCvHEcgMPXq7UmezTP7liPUjWg7AbsHUwRt2qLePe1fD2woYPmq7+VP9+yG3D6eq0XR2+yILrLalhry4LjE0aaZHx1oOL8rL3Yy0QzW/aYLLhUg4iYaXD3wcGel6s8fynPI7eMrGvfqZLHrXtybR/vdggqbXcDdneL4UE73PaBDa1CIr9KFFQJwtp//mIDzkGd5i3hHcQijQE8G7vIK7HLjQdY8bJxDK8e2EgSw9ImkdH4fmBogwxJ0iTJxgaYS8cIYyaBZZA1j/Fm8xbMeBrTal4CFyz+141526+//yngKtt7YtNGbO9X3CZTSnHmzBmef/75+n8vv/wy1Wq1vs13v/tdfuqnfmrd9/GVr3yFT3/605w4caLp99/2trfx5JNPdn0fv/u7v8vrr7/Oz/3cz/GhD31o3ecnhBBC3Eyqp+fri1HnxRkG33W0Z8deCmoAVM8sbDiw4V0qo/zauQbTDqo6RaJJpkAndKSwn59Glf01gxrl70+SOJglfc+ept93X88TG0/3LCtlLW4Q1a7gL665c9Wo6XbmBoIZaXf1Mf1I1YMRFS/kDftX/+w7HfO6fEzolXy1bWCj7AaEkVrMHFm9CL84ZzNVctk/mGyZndCNouPz6lQZgJEmDXIXbB/QVLywIZsjVJqJWbvpPhs1WayuvVGXJJzROavL36Vut+81HYWooEoYVAkDh73GKINRklikiYWKvzC+R7VNY01XDxBfVnqY1Wu/t9mmh2sZBDGT0DJ4I3eAFSNupbDiGax4GtNKYCzLamrMZ4v1ZWGet2+eka8S2Fj0oQ99iG9961vYdn/mUUdRxEc/+lG+8IUvtN3uBz/4Ae9617v45Cc/yac//emOjn369Gn+8A//kFQqxX/4D/+hB2crhBBC3ByWN47Uff4wriMNSmM0uQre0f5+42I7KvlEhcbR7tXT86TuGl3zKrR/rdLVWFJ/0iZ+eIDY0OqeGN7FEt7FEoPvONLx8YB6kKbxH9s/B9cLVS4tOCQsg9HhONXk6vKhMO8SG9lYkOXItLt4OpoF2yOXjDFTupE6nncaFws6iGqlTAZESnN2utz2+J02rCy7Aacma8EUA5qW00yV3MX/ez3pf3F+2fSOlY9zyYuXC3hNgjgVL1zVz6IX/D40FN2MY+8WUZfjrbvdvhs6imq9LCJNPFSU/QUmw0lc5eJqFwcX12h83b7Tv5dRdaPZs5Fsf3620fi+miPNqB4gbaRJmjdKQ2KxDFY8hZVIE1hxzi/bZ5A3bPix9kKobp7XtwQ2Fr3wwgt9C2oAfOITn2gIamQyGR5//HEefPBBfN/n2Wef5c///M8JggClFL/7u7/L6Ogon/jEJ9oeV2vNr/7qrxIEAf/z//w/c+zYsb49BiGEEEKsX+WZSbQbkrp7jMTBxivryosIF7q/Kr2y9MG/ViG2J018b/sFrqp0fxVP2QE0CWwsKX33SlfH86+UiYqNdebexfajUi8tOLV9I83++Vpgxkk1Bjfs56fJvXV/V+eyXNpTxBav1r4+09mIzvLfXSPz0D7MlMVMyaXYpF9FOxUvIJesZTpESqE0xC2TiWVBBk0tsNNOJyU3th8yU3Jbfj/qIObSLKjRTxsplVqwPVSzAxi1PhvVoHnGj7ihnI0x5HT+cypl17nE1JrQqxAGDuFieYinXDxVpao9HFwS2uL/4/94fZfXzTITicX3nlb9LIzG95msTuIs/ltaJ0iTqvWzWApaxDJcGEkRxAxCy0Cbt/AGblnfY9pi/eh7s11JYKOJZDLJ/fffz8MPP0ylUuFP//RPN3S8b3zjG3zmM5+p37777rv55je/yZEjjVc2Xn75Zd797nczOTkJwBNPPMFjjz3Gfffd1/LYn/vc5/j+97/PnXfeyW/8xm9s6DyFEEIIsT5aaZznpwmLHkbMIPfjq0eZLpWmVE/NNwQ2dKSxn72ODnuTMRIVvHpgo+wG/D8vT+IFivc+cIC9A9trDGU3WSOtZJqUjbhn8+s+3oDTfcaBChT2yzMMvPUgRbf7oNGpayXeemwML4h48UoBgNvHs6syO66uEdjQWrfN1gmV4tS14lqJMduOG64/+HB2unlwyg0U5zoMXN3sSlmLcJ6OGoiGJpSzqzOptFKo0CMKHILAqfW0iKocYg+Ho1HiYa1R51cTPyJvNrnYvPiyDgjRaIzFf8jS4j1NQ5pa0CJKJJiPxwgW+1ncbryJ2ywLM5HGMJs3jXbWfqg7wkg2zoKzu6b0tCKBjUW//Mu/zJEjR3j44Ye57777iMdrUfMvfOELGwpsKKX45Cc/Wb+dyWT42te+tiqoAfDAAw/w5S9/mbe//e0oper7fu1rX2t67KmpKX7zN38TgM9+9rMkEq27dgshhBA3A+WFBNdtYmNprIHN+7sYTNmEi9kHOtR45wsd7xvOOiivP1eNn359rl5C8fWXr/M//sT6mpQDtZSBHaLfZUWL90LeCbAMg4vzNlU/4sit6+txsXS2F+dvLOjOz9ok1jENRLdJb5iv+G2DGkrrjstkNsrQmkE7YsAOsZQmMg3K2VjTciS7T78fojPaMLg2nuTodPuRrxrNwmCc4XJIPNLYQZET0as4iyUiylj93A6EBplwuH47q1PkaZ1FrwxNFZ+YVcuoMK0cxzhKwkqTiKWJxdK1fhbxNOayoMXUsmMYDNHbGVjb11g2waV5p2cNlLczCWws+tSnPtWX4377299uaBT68Y9/vG25yNve9jY+/OEP86UvfQmAr3/965w7d47jx4+v2vZf/+t/TaFQ4Jd+6Zd417ve1fuTF0IIIXYY5+VZoqKPcaHIwE8exrA2Jw03yjemOvtXO78S3GnDyab3W25yJW7Zwvb1ZVeruy2PEO1dmne4Xmws6fj2yWne3GzjDuMTK3ta+J3UhSzj+BElt3XGSbugB8Bs2Wv7/V7JOSGHZr1VGQBDTgSFGXKjcSoZWaZsF4bWeHGTi4MBh8sWcb36fbWKz/fip3ln4V7ii0tM04iYSebbvv5X9rMY0CkyOkmGFKnF8pCElSZhpYnFU5jxDBPx9LImnGn28WCPHunuYxgGx8azLTOXdhN5x+izv/iLv2i4/dGPfnTNfX7lV36lHtgAeOqpp3jiiSdWbXfhwgUA/uzP/ow/+7M/a3vM3/md3+F3fud3AHjxxRd58MEH1zwPIYQQYqeJirWFvg414by7Zq+JddvgfNRg2iacq3Y1nrUZtY6yid1sruKzYPvsNTf2c+3EyqAGwL4Fv6Psgn5lRWy0tGL5tJZ+yTlh+yv/geLotMflfUhwYxNopdBRQOTbhIujT73IwVMu42qQ+6Kj9Qkh342/xncTM9yq9nJrNE6SGB4hF61ZLpozKENTCT1GdO15yzWZJpLQMTKLPS1SZpqB+AhXhxK1aSIxg2HzTQxvUkD6ZjGSSXDnvhwTs/3//d5K8m7RZ9/4xjfqX99+++3cfvvta+7z9re/nVQqhevW/mB+/etfbxrYEEIIIcTGaK1ZsH1ScYtssvOPRVpD6duXSRwdIHXHSNf365yYAyCYcki9ofv9O6G2WerxdMllruyxfyjFWK51E9KVVDXEiJmsMegFuJGVMnmlQHq9J7oBg3ZIJ3kxW5UW7kdbOyHB0JpDs+3LGaB2gf/QrMfZoxa6kydetKRVRBS4hL7NKINkIpN4qIkCj+/qH2HjEhnNXxeGDolHN8rnszqFMjQT1jQT1vSq7U1tUI75JKwBgpiJb8a4j3uJxVLE4lnMRBoztrpEsNi7hytaGMkkeOhonFiwFe+Mm0MCG31UKBS4fPly/fajjz7a0X6JRIKHH36Y73//+wANpSzL/af/9J+oVFpH5icnJ3nPe94DwD//5/+cj33sYwC84Q3bY/yQEEIIsdUuL9TKCQzg/iPDDHWxr1Ya72KJ5G1DrLcBRae9IPwrZfxrnV+Nf22qzLdOTTWfBrEB3qVS8zGtawgiVc8GKM9UGMsl6KQ+w79SpvrqAmbS6utYzoLjc73oMprdun5l/RiT2kgzWWg9DWUzDNpRRw0oodaocsCOKOVkudKOoTXxUON7Jcr+An5YxVVVqtpZ7G3hoRd7W/y89wh7de1dTmFQTjroDstENDBAtnHsqZUhHksTi6eJxzIY8RRl02T5sOMcMrFxy2kNkYfpVxiL5rb6bPpG3in66MyZMw23m/XJaOX222+vBzby+TxTU1Ps3984umyt4w0PD9e/3r9/f8/LT86dO7fufcfHx9m7d28Pz0YIIYTo3lI5gQauLjisZ0io7rIPwiprXJHWoaL66sKqf6/6tYVwOhHDCyO8QDGYjqG8iO//4Do6E4NWzSfbnLIXRFSDiKF0vGHCho7UuqeNuCtGamq95sMGqD9u5UVcmu9PGnUQKV6dqi3FtrIPyfLRrr1UdgMGUnEcf+sbcA7Y3QVvBu3wpg9s6DAg8h2CwCYMHbyoFrhIqRhvC99ILNIYwEvWNU7GzzfuvOJ3rGxU64ENE5MMSWxqfVVi2iRDioyRrvW2sNKkYjkujC6OPY0ZaOM4b6Dz9YzoP60UvtIEocIPFVlvhv3+JZLKIaUcMtohg0uM2u//YGX135Ld4uZ+p+iziYmJhttHjx7teN+V205MTKwKbGy1D3zgA+ve97d+67f47d/+7Z6dixBCCLFRS9kNyovAADOxPfrm6yYL0qLj1xfjt4/nuDBvEynNgaEUt2BwaNajmgy4cKixx0jJDXjpcoHDRY+xJvcVRIoTVwtEGvYPprh1T/bGNxfH0fqRqi3CNRwbz5KIdf9zqgYRbhAxkkm0HU+63Fyl85GFZhexpmv5zRzsqLk03/z++pWRcmqyxKPHxjbaFqYnrC5LcLrdfqdZ6m+hfJusSpJTCeKhIh5q/jZ6jgIVPKN5sC1Hqt77Apr3s1gupeO4MSjFLIKYQRAzuZs3oWMxrEQWw4ova8h5w24Ze7rjaE2oNHF3ASsoEwttEqFNYjFgkVYOGaq4Os4X1c/Wd7vbmOUN5pk2B969JLDRR6VSqeH26Ohox/uOjDTW25bL5RZbCiGEEKITUcUnKvnE92VaTksJ56o4L8+CCdlH9mNma+PfDbPN4nsLVoyvTZfrSRfnll3pv150uWWsFoxIewpDafSyc//GietMFV2uz7r81HCOVLwxKDFVdFlaK02VXG7dk0VrzcV5m6nQ5/7I5HqxSmFxeseFOZs37B/s+vxfuVpEA/sGktw2fmNE6mShymShyvhAsv441iPZRblMuykivVZwgqZNR/tvewQIona/Rz3YftvRGkuB8h0q3gJ+6OBFzmKpSBUbl8CoBS5/Ingj+6ND9V39hI9nts4gsvFQKExMNJAzsozrYdJmmqSZJmnVykNiiSxWIo1h1d7Lriw7RgzJnt4KOgoxAhvTrxALbeKhTSKySUYOZznKhN6PHyo08MvmXzNitM7mihkhtd/v2u+K3SbAFWFi0z4AtpNJYKOPVva/SKU6fyGl042NXdr10hBCCCFEe8qPsJ+dQitNOFclc/940+3sF2cWdwD7R1MYcROtNNmHWi8Agun+XtMMZqur/m29F7Knii5oTSzUzJY9jow2ZnSEanVAYL7iM13yMEoeZ0yjofFlbTyppuAEJGImmURnHy2XjjBd9tg7lCJv+4xlE1xeqP0srxdd9g+lSK4jG6Qb0yV3U0s0Ck7rrJN+NhTdDtkaAOVsrDbStUOl7PZfqqjQI/RswsCpBy4C5fMP1H3EA4Wl4bI5xw8SLzfu2KRMZLmcTpGnVn5lamOxTCS12N8iTSKW4cJwiihuLZaJZDm+rmK67mitmbd98nZAqBQx02QkG2cs23n21a6lNYQehl/BCiqYYZUJ8xb8SONHij3BFO/Uz5Ch9VjlKZXF0zf+3lRIM0LrdWCciCQBkZkkYZmE1gjn9HE8M0MQyxLGskTxLDqeg3iai7EJ4Iu9fNTbxvZ/t9jBlqaaLEkkOm9IlUw2dguvVld/qFnLrbfeuua88o146qmnuuobstz4ePMPlEIIIcRywYxD9fQ8Vi5B5k3jLTMt1jzOtUq9UWengQitNHpxdKfzcuuGa+GCi5lodl6aubJPNYg4MJQits5zX29fi1YOz3jkqhF0OJhk3r7xIbzZ4vtqvsrVfLXWgPXwEOkOgxtLTk+WiJRmptT4uWmu7HFoJM1s2cPrQ5lGqNSmjDcFuJavsie3dY1Jt0lcg1LWIpynowaioQnl7NaXg6koIBZEZFSMRKiJB4rX/LNM6TkqVAmM5hk/pv8GLFqPPV3O0ia+qSmnLfyYQRAzOGS8gXFLE4tnMOPppmUim537k7d9JubsVe8DC47PpXmHY+NZRjJb9zrvKx2hwpCqqjUy9iPFkeqrjIaztdIQXSsNidMYuPtBtIeI2us4iUnGah3UAMjRuOYrkaOMg2Okcc0MnpnBs2oBizCWQyVyPJDIYZhLvytDzHGgZw97J5HARh+tzNDw/c5rQz2v8UW/MoNjOzh+/Dj33HPPVp+GEEKIHSiyA6on5zBMg/T9ezBbjFp1Xp4FIMy7+NcqJI92X/YAnU8faUWto1yh7Ib1MpFqEHHnvoENncN6DVYCRksh+YEYdtpisIsGjgu2t+ZV2Kv52gdxTW3KTLelKZFa6t3R+BxdyVfJJGOcn+1P8MELNm/0aTWIKDjBlgUYJgvVWrbOFtOGwbXxJEen24981cC18eTmjHqNIkLfJvLthlIRRznYVHGNgLvDw9wV3pgq+ErcIW+1LxMvG1XGdO13PkuKEZ0jbaRJmWmSVoZELE0snsFKZDGtBIZpcnnZ/gZ7Oo09boq87XN2pnXmQKg0Z6cr3Lkvt+OCG1projAg6c4RCyrEw0qtNETZZOpBC5eX1e08px+o73e3OcmtxrW2x85RpUit3K6yogzEJYFDmqqZwTUz+GaGSmIvb0jlSMRMEpZJ2XwXp9b4PbjJ82TqJLDRR7lcruH2ygyOdlZmaKw8lhBCCLGTVU/OEZVqAX/3bIHMfXvW3EeVOr9AALWgSGw0RbqDY3dKac18xSOTiJFdHoxpsmK9Vrjxt3zB7u7cl/ihwgsikvH1Xbk2dC1DAyDjRkwcvHGhZPWHYb3q385OV7oagRr1uJzitane9RgruwGlasD4YIqEZVLa5Akos2WPWKspNX22FHzaDiqZGJf3waFZr2nmRmjWghqVTG+WKVopoqBK5NsEQS14cac+wkAUJx5q3MjhS6kfrN5x2VO1ukyk8YKjpU1ypMkY6cUeFxny2RyFZIogZhKZ8EbjnT15PFtBa81Eh9lNE7M2Dx2Nb5uyFK0ijMDGWiwPiYcVkpFNKrJ52niIUpTAjxRDVPiI9a22x8oZ1Yb3elunm0YVIoxawMLIMJaJkYnX3nMSVobn9c+h4rVMC8zmr/HhDTzem5kENvpocLDxikU+33kqaaFQaLg9MLA1V3mEEEKIfoiWBSmCKRt6GHxYLlxwcV9bwMzEe3K8idkKcxUfA3jTLSMk1lle0gk/VPzJDy9x+EqBIyNpDo1k1t4JGspQzRUlqYmweeDh0rzdl6aWQaSY3gaZAlCbDgK1ka53Hxzi0sLmznuYt/0tLUfZTiqZGGePWgzYEYN2iKU0kWlQysYoZ62uMzVMVSsRSYSagjfDjD9FVTvYiw06tdH4ur/DGyajhwGwSGFoY9U2DedreHiLJSJ+3GTIPMj9xjDxeK05pxFLrioVaV9wsLPM237HPWBCpVmwfcZy/c830SrC8G1Mv4yvYIbRepnI3cEp3qjOkaXaMqPBjO7ApzawocLa2fEDVDENSFoWiZjJgnGYM6TxrSxhPEsUz6ETWXQsDUbt9bCy+D7k8AYesWhHAht9dNtttzXcvnz5costV7t06VLD7WPHjvXknIQQQoidyr9ukzg6iDXY3eIwmHJIHhvqyTksjRzVwFSxytHRbMvOjB0vzVrsf2qyWM8quJKvdhzYePbCQsvvGSvuS2nN+ZkK8+vMKFnLuZkKxU3OjFhLyQ2bNkndDN2MrN3ttGFQysUo5dZejmilUIFD4NkEoY0X2lSVg6McsjrJz/o3ygPyVp6L8au1Gy1+CctGlf2LgQ0TgwGdIkSRNdJkjAwpK0PCShOPZ4klspjxNOcaAhdJ1j+zZ+fJ2939Di/YwYYDG1prgkjhhYogjDjinCYZVkgpu1YaslgesvQUX9HjvKB+sr5/aETkzPaZSgM4zCwGNiIsCgwQYdVLQzwzix/LEcZzi0GLHG+OJRqOUOSODT1O0TsS2Oiju+++u+H2uXPnOt73/Pnz9a9HRkbYv7//XY6FEEKIreJdLqG9iNhICjMbx0w3/4hiPzdF7u2HMBMW4XwV/0qZ+P4s8f1ZdNDddItwfmPp+f2eNGF7G5/WsW+h/UJ6ruz1LagBbLugxpLnL/a2IavYOBUGKK9CRiUYUAkSgSIeav5aPUOBCqpZRoUBHo2v3wHd/Mp7SifIkiZjpvGzWa4lE/gxkyBmcLf1rqbNOUVNt4HANbfXCiOoYvqlWk+LoEIiqpCKbNLaJqMd/t/oES6zb2kHftp6aVVjzuVWNt0sr8jAqPWzyOCYGVwzi2dlyCX3cVdyoN7P4lXzH7c97e1RXCNakcBGHw0PD3P06NF6psYzzzzT0X6+7/PCCy/Ub9933319OT8hhBBiu3Bfqy00vYslDMsg9+iBpuUjWmn8K2WSx4aw/742mtWbcUjbAd5Esav7dF6a3fiJA0R6VXPSshssjkJtbabkMlfxGN+bWpWuDGCabDh6MlRp3yh0vb0/hOia1hi+j+sXCQIbL3RwlY2tqtg4uEbt9+XHgzewP1qWrp/QKLP174GNR4TCMEz8mEHKGua4eQtJK0siVisVMZMZTKvx/aSw7GtZsLYX6zLoEzPAdOax/DKxsIyKIs6Yd+CFEX6oeGN0ln9gvtz2GI39LAwqOs2I0bx5qUsC30wykorXelnETAzzNl5gD1F8oNbPwlr99yS1+J/YHSSw0Wfvfve7+eM//mOgloUxMTGxZlnJ008/3dBo9L3vfW9fz1EIIYTYTnSkcc8XWzcUXbbYv5Z3uJKvMjxd5o37B+h0iRLMOB1PSrk4bzNb9jg03PxKcFhorKa3/ZDTi/0cWt5/pOrN+CZfnuKxA0OsPPeEHXL3hf6NI71a6CxjxQs7zxwpuSFKa8xt0jhQbC6tQkLPJvQr+IFDEFV5SN9JIlTEA801c44fJFYsaFe8VEorGnUO6jQL1Ba0aZ2sNek0a+UiSStDIpHj9WSaKGaBYQAZxhnt46O8+Yxk4yw4nQdB3+J9nzfMXa3frpLge9HR+u0Sa5fVDVDrgRMzDRKWyRXjFuYMH9/KEiyWh6j4QK2nxWLQ4s6GI6QJFstMxM1BAht99sEPfrAe2AD4/Oc/z+///u+33efzn/98w+0PfOAD/Tg1IYQQW8S7XMK/UCJ+IEvqzpvvg1dUXvsDsvbbLaaN+pW8K4vTHgpOQKkaMpjurEno0hjZtXhhVB+Teblts0mNFyjiMZPL886aYz3dZWUzuWqE1otrsmWyrxWY7+gsOzda6r40pNuSmEvzNrftkWluu5WhNIlQkwgUM+5VCsECjrapaAcHb1Wg4lH3KElqv5etykSWWNrENxWljFVr1BkzOWDexbh1d9OsiyUbL9oSAGiNigIst1CbIBKUSUYV9kYOV7kPj7X7G2VwOb5iBGoaH4uIiNp0p/JiYMMjjk0ax8hStbJ4Vo7AyhLGB1DJEd6cyGKatReUw9vY3Ha/YqeRwEafPfbYY9x7772cPHkSgM985jP86q/+6qrGokueeeYZvvzlL9dvv+c97+GOO6QpjRBC7BZa6xtlF5dKJA7nejaxAyDMu0Rln8TBHEZse9aMK7d9eQTUppn4k83Tjlvxw943hOz0mJfmHa4XXTIJa1tnK6S9/jfNnC55JCyT6dL2mIYiuqdCn8ir1EpGggpu5GBrmwPRCG8JbmQeT8RnuGhN1W60adSZ1DcCGzd6XWRImxkSsQzxeJb4YpNOwzS50nCEYVmw9IqKMP0ypl8mFpTro0+/bzyMF2q8KOKAnuUfWd9btevPmi5fUz+GpvXfFQPFY+YLWGgUBjZpbCNL1cxyJBuDeJpEzCRl5fih+T9ALLk6ortoe/71EtuZvE/0mWma/N7v/R4///M/D4Bt27zvfe/jr/7qrzhy5EjDtidOnODDH/4warHhjmmaPPnkk5t+zkIIITaPcqOeBTZUNcR5YRqtISp6ZO670blBR7UcAsPavovulaqnepivoDXFqk/MMskmev/xZ2lUqtM20+TmsZRJI7YprdFhQCo0SEUGiUBB4POD8AUqy/pdrJRcsdwcVGkWL8LXGdogR4qsUQtcFIbSOIkkfrzWA+NN5j/s16MSUUAYBthRDC9S+IHiuHuSg9E1stom02L06XejO6kuNtsstygTuc2Y4n3mM/x39TBOk84UCSLuyFSYyzzMdGIQEpn6yFOg3gZUiH6RwMair371q/zGb/zGqn8vl8sNtx9//HHS6dVpdH/4h3/IL/zCLzQ99vve9z5+7dd+jc9+9rMAnDp1irvuuovHH3+cBx98kCAI+OEPf8hXvvIVguDGH5I/+IM/4IEHHmh6TCGEEGIl71Kp3n4imHJgsfd0VPaxX5gGIPvQXqzBjY3h23JtYjMVL8DAIJts/IhzfiLP1etlDODeQ0Orvr8daK1xgoh0fHtnfYidQwUeoVcm8Ct4kUM1snGUQxkHzwh4r/dwfeypBvLJMpHROqtnqf+FAoK4wZA1xu2mImVlScazxJIDWIk0mDeiHd7if2Ljoigk7uax/BLxsEwyrJCOKqS1vRi48DitbuEF/Uh9nzcaRfaac22PO0AVZzGwUSFNgFWbIGJkaiUiZq4+9vS+eIr5MMVCVREqRcw0Gc3GGc0mMIxxKQsSW2b7/VXfIqVSqWHEaiuTk5Mt92/nj/7ojyiXy/zJn/wJUMvc+NznPtd0W8Mw+M3f/E2eeOKJNc9HCCGE0EoTFbyW406rJ+fQQW2x4pyYY+AnDm3m6fWFajJxZN72yM/W/v2N+wcYztyoB796oQDUFm8X5mzuPTS0GafZMQ2cnS5TcAKG0nHuOjC41ackdggV+oReGSMIOKBHSS72v3g9ushzsddX77AsZlYyHfZHw4v/bDCgUxSMWicDS5vkSJM1MmTMLKlYlkQ8x9l0miBm1Bt17uVg/x/kzUBr9NIIVL9MYjFw8bJxJ/kojR8qYtrjY9bX2h5m0HBY3uSnRHbVNjYpbCND1cjiWjnGcoMMJnMkYybJmMkL5j9tWSICMAaMyVuU2GYksLFJLMvii1/8Iu9973v59Kc/Xe+5sdKjjz7Kk08+yTvf+c5NPkMhhBCbYmPTO5uqnpwjmG7dVi2q3AgCqOra/S3WQzkBYcEjPp7BiPe/Orp6cvUVyOXjVV+bKvPWY2NN9w2jzvtMLDUObabbnh6zZRelYe/A6oyZUGkKi+dfrAYtJpFo5sp+V1NKxO5gaE1QLeH75cWeFza2tilrB9eoNePN6iQPej9R32fQXHuQZdGoUk2Y+PFao87jxt0oK0YseaPfxUrdt58VAGiFCjwcFcMLFV6oGHcvcSw4R0bb5LCJN8l3OB3tocp+ACLi+MRI0Pp9PIdDwqoFKBIxE9u8hZcYJIgPECUG0YkBDKtxCTjQ20cqxJaQwMaij3zkI3zkIx/p+/384i/+Ir/4i7/IyZMnOXHiBJOTk1iWxcGDB3nzm9+85ihYIYQQYqXlQQ0viDAWx+PpSLUNeHRKRwr/chkjZhI/lCO4VsE9XyA2liZ97xgosJ+bQvmKYMQm+0itmlprjbIDzFSs541Mo3L75VUv4kduEDFvt57gMm/7dNPe+/xsbXSr0prcilKYv7+Ub7jdbBJtqRpybra7hqpi59BKEfk2oVfBCyqMqQEORUMkQk081DyVeIF5c8Xzv+yium14BETEF5teDC5OIIlriwEyi806syQXMy9iyRxmLMnEsuBFXLIv1k9rjMDG8krEghKJoEQqqpWKZLVNFod5PcifqXfVdxk1Shwyr7c97EBDBoZBQedIGmGtKaeVw7Ny+LEBwvgAYWIQncjyJnN545McLjey9KTITexWEtjYIvfeey/33nvvVp+GEEKIXSRve5ydrmAacPehIRJnFvCv26u2CxdcqqfnMTMxMvePrxl08CaKeBcXSy4tg+qrCwAEUzaJIzl0oFB+LXshzN/IcPDOF/AulDBTMXI/dgAjZqKcAO/1QlePqxpE+GHEUDoOGESFzanYt721s1suzdscHW3ebK/1Pg73HOw+j/v09fZlr2IH0JpYpDE8j7x7nWpYoapqo1LLOCjjRkTr3vAobwhvXEsf1BnmaR7Yii0GL+bSEbFECj9u4MbGeEvsZzCtRNPMi62mtWbe9snbQb1Xw0g2zlg2gbEde8xoDaGL6ZWI+SUSYYlr7GNKDS1mYET8j+Y3yNI6y2vQaAw0l/Tq944AiwpZHDNL1cyRSI5zRzJLIm6RjJlMmP+obZnINvzJCbEpJLAhhBBC7BKvTdcWPZGGidkK97WY/rHUSFRVQ/zLZZLH2vebqAc1AHcxqLFE2SFGzFixfRHvQhEd1hZpyg3xJyskjw7ivDRLZHeezO4GEa9cLaA0HB5Jc3gkQ7iwfcaIXi+6DKbk45RoFIU+kVfG92vZF1Vl82j0RgYCC1ND0XD4XnJZWXKT1WhxxSJ4RGWZNzPkjOyynhcDtcyLeArDNFlYcQyL3o2S7qW87TMxZxOuSE1acHwuzTscG88ysqxHzmaJlMILamUiCS/PYe8smahMRtkMUFlVApJX91FYFpwo6SxZo/X7UwqflBFhxhMkYyZY+zmpHsGPDxAlBlCJQXQs1RC4iAOjPX+kQuw+8pdYCCHETS1ccAnnq8QP5rCyzRcBOlRgGhjmzrkW5nY4dtQ9XyC2L9Pw2CdmK0zM2tx/eIi9g2vX6a86ZpOMDO1FtSanXQQ1AK4sOPWyjKv5KodHusuO2AwLTRqZrqWTC9JK96Ehi+idxeyLZKApVqeZDaZwlE1Z21SN1SVMDwVHMHUtU2dApzC0gTZWP8dZnSJnZBiID3N9IFHrfxE3Scbu5R7jvr4/rH7L2z5nZ1qXVIVKc3a6wp37cr0PbkQh+GViXpF4UCIZlklFFbK6QlpX+c/Rz7EUZTpEgXdZr7Y93BCNGXElMoxQokIO28xStQbwrGVlIskBHogt77EzQIU9vX2MQtykJLAhhBDipqUDVc9eCKadptNC/Os27qk5jHSM7Jv3YyasVdvsdNWTc+TeegCoZUj83y/VJoC9OlXi19/Z2EVCR50utjV528cyTQbTcaKCR9CkLGYtK6/obkdei2k0GzVZqPbluKI7KgqI3Fr2hRuUqSqbtIrzE/6dmIsvz7+35rkYv1q70SJoVTBsxhcDG5FlccTYi2FYpK0syXiOeGIAK5nDXNbYcWUGxk6ntWZirrP3gYlZm4eOxrsrS9EK069geiUcZTGrh3DDWhbG28NnOM7ltqUaWVzsxbGnzaaJwI1SEdvM4Sf2cDSVJhk3ScYs5qzHyFtWZ5FLIURPSWBDCCHETWt5P4hW00KWpm9oJ8S/WCJ150hfz0lrDZo1s0O00kRlHyvXItW8iw/WUenG1eVryxbTQcdBjNWuF10uzddS6e8+MMggYA2vngaynXX66EtunybNbP+Yzu6xLPsiGSheq56hrEpUtIPdpLRgQKcw9Z3128N69SJ4qe9FzsySNrMEw6OcT6Xw4ybKNDjEW/v6kLajedvvOFgZKs2C7TOWa3zfUEoTBD4DzpVag86wRFpVyKkKOWwsav1+zuijvKjeXN/PNSyMNVqNDGHjkCYZM7FiA5zTx/GsAfz4AEFiEJUYhHim4f31QIePXQjRXxLYEEIIIToUVVpPyOgF5YaUn74GQOr2IZLHhltu67w0QzjvEhvqbbCg2+uMwbSNNbj6HJaCGgDnZio8dEt/A0K94vghQaQWG5WK3UarkMi18b0SXljBiSrYyua+8CjHo3317b6fmKVg2i1/IcqGS0hEDIvAMkjHhzhmHiVt5UgmatkXRqJxXKqGNm0lbw75LkvRCvl5bqtcJh2V+RH3Mh+mCJQmic/HrO+23XdlmUhxMQNDAzZpbCOHY+ZwYwP4sVrg4lBqD0diyXrgYo53dHW+QoitI4ENIYQQO5YOFMoJMAe3aRf9LlVPz9e/ds8XSdw21PRx6VARzteWSGGxtxNCuv05hvNu/VxaiRZ7RfhXy10de2K2QrHaff+K9XL8iFeuFtHALaMZ4j0eUdsNLf01OmJozaAdMWCHWEoTmQblbIxy2iQRapK+wvVKvBa8RkXbVKiiV77EDcgbZeBGYGNYZygsWxhb2qxnX2TNHMn4ABOjKaJEHGUaQIZ9jG3KY97JQqW62j4TFrlbvwLAiegQAfsB8EjgESdJ8/cHhySBmWQslVgsEzEJrXv4kXkXKjkAZvOSwu03O0YI0SkJbAghhNgUyo/wr5SxcnHi+5rXLndDR5rKs9dR1ZDEoRzpu3fIoqLNenVVgEBpsG6swiI7IJyvEhvpvqHndrA0JSVv+xSrAfuGUqTjzRcY1SBiprw5Y12XeOGNRdelBYfje3N9vLf2AaSp4s1+bX9tOSfk4KxHfMVaeciJ0GiMxZ/xghFyPTnX9kdeWJxAElgGXtxgv3WYjDlGMp4jlhzESmRWjUzdvJDbDqQ1RuhiuAXifolEWCQVlrnmH6bEeMeHWR64GDTs+vunAVxjL0lDYVsDeLEB/PgQYWIIlRzEiNWajh5vOFqK7sIqQoidRAIbQgghNoV7Zp5gpta/IffWWNPyhW4EM069L4Z/rdKTwIYOFc7fzxA5Ael7xoiPb58JHFppnOenUL7CTG1tA9MgUkwVXXLJGCPZzqcWVP2QkhtyYbF5YN7xedPR5iUqQXhzL0E2O6izrS3vf+ErJr1LhL7DXcExzBbRCmPZvw/qNIamnqlhaIMB0uSMHFkrSyo+QCoxxJl0ZjH7AgyO0H4IskArDN/G8goUdYZ8lMYNFW4Q8QvRNxmltGqX0Ay4qjoPbIybRS5aR3GtAWLJA9yVGiAZM0nETGaMdzfdZ+fn7gkh1kMCG0IIITbFUlADwLtUInNf5x9um9F9mEThXy7VSzucl2YZ+ulbGr6/VslFP0UFD+XXFvvK7eCxa83FuQoVL+LoaIbBNXpG2F7Ii5cL2H4tWBQPFAfnPOyXZ1Zt+/p0ud4w84Ejwy2zLpbzwohXrhUbGmJ66wheaK03r+yoj9UgVxactTe6yWilUL5D4JVw/TLVqEJFVRhSad4R3FPfbtJyuD860jKo0XBMoJKJc691N1YsTTw5gJnMYjYpRbi5Q2ktaE0YeKSqMySCIsmgSCYqkVNlBqgQW/yp/Z26l2v6DfXdqmaiaYThuHGVDPfjsHbWWcw0CI/+BFOLv+/xxf+EEKIZCWwIIYRYk/IjCBVmZnd/rAwL2+0q+foX8JGGqVLt8Zy+XuLRY+0zWv7f01NcnFtcbGvN4RmXtKcIpp1VgYTlU0BmSi63jK1dWjSZr3Y15UO16DHx+kyFO/cNdH6gJtxQ4fghmcTWfQzazN4h247WxMPa9JGkF3HSe4WSqlDCJjRWBO0MCIzGn9UhNUaazjKFDKCcjZPLHV9z25uVVhGGVybmF0n4BZJhiWeNB3ADjRtGHGCOD1t/2/YYwysbdeosh4w5fGJUyFExc1StAbz4ILdqxenK2ud1bDy7K3onCSE2hwQ2hBBCtKXckMoPr6MDRfquURKHN7aoFFvdFFIzV/HRWjM+kGQpeLIU1Ng/5zFa6v2iO+qyGeZ0qXl2zILdm8k0p64V15zU4gT9GeN6s9BKEQUOgVvCDco4YZmyKvOm8FaORXvr230vkadkVlsep2Q4RCiUZeHFDQaDQegiYWvQDinlbu6PvFpr3KBWJjJSvcSof51MVGJAlxdHpDb+fn4/uo0qtVK8Au0Dlw5JDCvGWDJBKm6SilnMW4/yw9iPrxqNCjAA3Jn1mZi1m45+jZkGx8azjGQ6L3MTQoib+11eCCHEmrxzBXRQSzeunlnYtoEN71KJYMomcXSQxIF1NiftYO3tni+QvG0Iw+ztlUTdTTrDBsyWPc7P1q6uag17B2+khBtK9yWosXRf3aj6vS81Wi7SawdJvECKEzqyrAeG75a47E1QVhVKVAiaZGDUJpDcCGwM6ywlbgQ2MjrJoJEja+ZIxwdIJAZ4LZVBx2rlIwevOmSizl9Q6mZ5HlWE6ZWwvAKJoEgqKJJRZWbVIH8b3Vvf7KeMy9xpTrQ91BAVyouBDYcURXJUjTS2OYAbG8SLDxEkhlGpIYxYrV9SY05M+x5KI5kEDx2Ns2D7LNgBoVLETJPRbJzR7O6YciWE2FwS2BBCCNHWUoPO7UJHGtUkjd89mwegenKO+P5MZx+M1zFS05soYmbi6w+etBDONbtirel1K7yloAbAxJxdD2zcMlnl+vjqxUjvz2D7kImq3dOBR+CW8PwSTljGVhV+xn+ApKpNDJkzXL6XvNb2RbNg1F6DfszAi5scsm5h0DpAMjmIlRzAiq2+Ur/8qXKUYqSLV6XT5YjRbU0rglBRDTVuEOEHAT9W/VsGVC3zwmwSnTV1Y4ldkebTfiqkKRsDONYAI9lB0qkcqZhJMm5xxvylpvts5L3BMAzGcknGchtrJC02LrIMrC6ChUJsRxLYEEIIsfk6+Pyko1p/B2swgZWrLXS01jgvTNcbfLY9fh9X4/6VUkeBjWDGQTkBicMDGDGz7bZRqTclFuuVdSNuud66HABqP/+Kt0mBrt0aTdkhTFWbQFKpzrDgz1JRZUq6gmOs/t1ztENycbE8rLOrfv+yOsWgkSNn5kjHB0klhxsmkMAhuskDu2pqDkWdv0CumrrFUn6b0hpCF8vNE/cLpIICmbBETpUYpMI3oke5yP765mPWPClav38MGTeCmcmYSdnay3mOU40N1kakJodRySGwbvRQyiz+J24OfswgLYENscNJYEMIIcSWCGYcUJrYvubZFdVT87XGlaZB7u2HMBMWquyvHdToIe/y6nGFAEQa5YaYqdZ/RqOih/PyLADKDknfs9i8s9Vnxw0s5Fs12uxWPGx/nHMzFeablG9cL1ZxvJCDI5mOJqSstGB7nJ+pkE3GeMP+QawelfmU3YCyGy72ElnNXTZZx9CaQTtiwA6xlCYyDcrZECNroXdxWrxSEZFbwfeLVP0SaWXxYHALicXXwrOxac7FLtc2bvFjWDBsRnSO0AQ3EeeN1u3ErDTJ5CCx5CBmfHUGxkZyKKZiBtVAk+7gl6aKZipmsB1bh+rQB6/Mgs7hBhHVQHHAv8SPq+fbBiqGjTLoG4GNgs6x31ioHRMok6Vi5HCswVrwIjHEA5lBknFr8b12mFlu6/OjE0KIzSWBDSGEEJsumHYIpmvNKtNh84akS9/XShNcrZA8NoReY+HdLb1G7b37Wr7pv0eVgPLT10jdPkTy2HDzfc8X6l/7k5UbgY0uz8+wFjM9Wqzh3CDi9GSJqE/p9nrZ1fdmQY1SNcBe7IdR8UIeONK+IedKFS/k7HRtRELJDZkpuxwYWmvJqpkpeUyX24/fPT1ZQtN6AslksbZ/zgk5NOsRW/EjHHIiwnm4Np6kktn5H5nMUOE4M7h+CTssUVZlilRQxo3fq2GV5S3h0frtUbU61yGuYwyRY8DKkYkN4g7s4dV0hsiqPWsj3LNqn14azsX5u2qVdxFrO/JVofk7QkZy6b6eT1sqwvBKxNw8iaBAKiySqzftrOIR44+jn2fplyyDScpqn701QoWYaZCOW6TiFhPGA1y1IEyOoJNDGNbq1+raw1WFEGJn2/l/pQXnz5/na1/7Gs899xyvvPIKs7OzLCwskEgkOHr0KD/5kz/JP//n/5wHH3xwq09VCCFW6aQhqe5TM4TqmYUN7e+eL7YMbHQqmHHwL5dQ3upmmeWnrzH007e03f/ivI0fbV0PAXtZk89qm0BRoUVw4fXpcsPtUjXAC1XbY0VKMzFnt/z+kqVXTbvRqjkn5Oi013J5HFNwdNrj8j52THBDRyGhW8LzSuyJcuwLsyQDTTzS/FnyZeylcpImD7q4OIHEwiQyIBcf4hbjIOnYAKnEIInkIGY8jWE2llb1t9Vro7Fsgr+fd/i2CvkJYk0zN6qLQY3rpuahbJ+na2iNETpYbh47ijGtBmsZGH7Ee9TfcNiYbblrkpAMHs5i6CG/rCjHI07JGKBiDlK1BvHiwwTJYVRqmIdjy7OQjrNUILZ7c4tEP6ldnJUmbh474y+0aOvP/uzP+Lf/9t+u+nff9zl9+jSnT5/mc5/7HP/m3/wbfud3fmcLzlAIIbYf1eOpGxUvZLbscXQ007aUIly4kWUQKsXs6VkGU3GScQulNUprYssWjcGMg7ID/GuVpscruxvreVF0fOJt+3/0oGGJ1rQq3/bCxgBG3gmA9pNZwh7Vghtac2i2dVCjvh1waNbj7NHtVZailUL7VTyvWMvCiEqUVJkSDnoxC+Ph4Bi3RzfKDkZVDttqLOcytMEgGQaNAbLxAS4NxgmTCYKYAUaWg4xv6uNai2HUxoGena7wJXxuxeQWTBIY+GguobiIQgF3jud6N2FDKQyvQNxdIOkXSIdFBlSRAV0itfiaPalu4yX9UH2XgpFtG9iIMBmPe5Tjg6TiJul4huf1zxGlRtBNRqUK0Q+hJa+zXnFSFhl3M0O9YokENnaBbDbL+9//ft7xjnfwwAMPcPDgQcbGxpienuYHP/gB/8v/8r9w/vx5PvWpT3HgwAE+9rGPbfUpCyHE+nSaubHede869/NDxZ/+8FJ9ROlIJs5DZcUtVgw3iLheqBKfLHLXaLbedwPg+Ys3Sl0ePDLM6cki/uKifSQT59h4DuflWSKluZp30MDhkXRD4GOjzkyV2y7sl34kZXf9Y2B73ZPuSt7pyXEG7WhV+UkrMQUDdkQptzUfnVQUELol9oYDpINaY89SkOcbiecbN1zxZC6YlYZ0ir16GB/NgDlAJj5EIjlILDXYUL7QvoXs9jCSSXDnvhwTszYTSjGxomtHzDQ4Pp5lJLOObI3AxXTzJLw8F42DlEKLqh8RC23+mfWX7c/LKDe8jxTIoYEKWUrmAI41hBsbwk8MEyVHUMkc+w1zWStQCDnc/TkLIcRNTgIbu8AnPvEJPvGJT6z697GxMe6++27+0T/6R9xzzz1MTk7yB3/wBxLYEEJsW/6VMv7VMvFDO2qGAa9NletBDahlHbw+U+XA3sHa94KI06em2XNwtF7rHq7oiXHiagG1bEGUdwJeuJTn1rEMfqi4XryR6XHrWG9HzbaLOwShIpYwOTXZopHqFpir9GaCzIDdXbbLoB1uSmAj8qv41TyuX6QSlSiqMmUcMODD3o8xpGvzKuK0fh2Y2mCIHIlYlqmBBG7CxIsbJKy7ObZLsgBGMgkeOhpnwfZZsANCpYiZJqPZOKPZRNtMDa01ynNIujMk/AKZsEAuKjKoy2S48bs2Eb2dPHsXb6XwiZGg+evGJUFkJtiTTpCKW6QTJpH1IM/GH2mYOCKEEKL3JLBxExgeHuZDH/oQn/nMZ7h48SLz8/OMjXXfxE4IIfpJh4rqq7WeF1GLpp1dHI31lU+sL7WgVY8LL4ioLpu8cXnB4c7FrysrSkhUi7u+ON+YnTBVdNcV2Fha9HXr5atFjoxsYfPFPrJa/dB7tP2atCYRalKeIuUrrngXOa0ncI0VgZtlL+UFo1IPbCSIMaDSRIZiyBggZw2Qjg+RSA5hpXKYZm1CzXxvz3pbMQyDsVySsVzzyTdEAZabJ+blUaHHaX2MahDhBhEPGq/ziHmi7fFHjDJX9FJgw2BBD5IxPErmELY1RDVW63sRpkYx4mkwDG7v7UMUQgjRAQls3CTi8RtXCpLJFn/8hRDbmtYa7SuMhNm7mvGO7niT7ibsYQNMXZtEYsRN4uOZ3h13B8vbPuMD65uNcCXfTXFCD3pybJKoy7Gy3W6/nIpComoR1yvihEVKUYmYNniv/3B9mwXLwI23zkaxtEkpHrCQiOEmTNyEyT3xn8KQbAAipYk70yTdedJBnmxUYFAVGeBGYLBKgr+LbpR55HXrpsU+MUrGAKlkisPpNOm4WZtAEvsgZpMA4c54xQsh+m16NMFtkzuhoG/3kcDGFjp16hQnTpxgcnISy7I4dOgQjzzyCLfd1tvZ4q7r8rWvfQ2Au+66i1xuZ6V4CyFq3FPz+Ndt4vsyZO7fXs38eqLNyqDbOI53oYh3sVY6kX1oL7GxDjMO1hHEiezOek+YkcYMFFu5BJore2tvtEFa75x+h+VsjCGn8yZvpWxnH5usSJPyIqadixTDPEVVamjoCYABFiYKhUltoTy2bLRqRicZMgYYsAZrWRipIaxkDsM0uc7yw1gdn/+uEFSxqvMk/DxzaoAr0RjVIMKPFP/Y/Dv2Ga2zvdL4pPBwqV3gWWCACmnKxiAVawg3PoyfGCFMjaATOTCMWuPYTXpoQoidr5qyqCZN0t7WTSu7WUlgYwWlFGfOnOH555+v//fyyy9Trd6IvH33u9/lp37qp9Z9H1/5ylf49Kc/zYkTzdMf3/a2t/Hkk09u6D6UUkxNTfHcc8/x5JNP8vrrr2MYBr//+7+/7mMKIbaODhT+9dp4y2DaQbkhZmqT3sJ3yCJ1uaWgBkD11DwDP9m+GZ/SGqX0uv4oOi/OwJG1s0LuuOKQHFQwujUZJAu2vzhxRCwpZS3CeTpqIBqaUM42BhG0UkSBg18tUPWL7FE57vDHiS92Sz0dn2LSWmj5OxQZirzhkIsN4CZM/PgID5lvJp4exkzszvKfjmhNGAYknSmSfp50kGcgKjKoi2S4EZx7WR3jpB6s357Xg00DGxEmRQYom4MczsYhmSUdt0jFRzhp/pNNeUhCiJtHKRsj7fWmF5TonAQ2lvnQhz7Et771LWzb7svxoyjiox/9KF/4whfabveDH/yAd73rXXzyk5/k05/+dFf38RM/8RN8//vfX/Xvt9xyC//7//6/8/73v7+r4wkhtge9ora/p2Uba975Zt1RfyIoeo2RHEGkOHmtiB8qbrVgHyYXZivMVjwODac5NNI+EKGqIQRrPx8978/Qpc0Kajh+xEzZXXvDbUAbBtfGkxydbj/yVQPXxhIEXgXPLeD4BcpRkYIu4Rk3eqVE0X7ujvbUb4/pHJMs1G9b2mSYHAPWINnYIKnkMNf3DjdMJElyoJcPcXvTGvwKMXeehJfnNW6hGFi4gSKrK/xT61ttdx9bMYFklhFmjDJlcxgnNoyXGCFMjaKTgxiL5SPSYUwIIXYnCWws88ILL/QtqAG16SXLgxqZTIbHH3+cBx98EN/3efbZZ/nzP/9zgiBAKcXv/u7vMjo62nTiSTcGBwf5F//iX/COd7xjg49ACCE2JlKaS/M2odLcMpYhGet/Gv1aQaBrhSre4janJksMjQ8wvViycSVf5dBImrWCLulX85DYQTUYffTKteJWn0JXKpkYl/fBoVmvaeaGMsCLGYzOV/jrxNON31zxdM8b5frXgWUwFtvLccsiHR8kmRwmlsphmDdZ6QjUAhiBQ6I6Q8JbIBMUGFAFhnSpYcLI2SiDTa3MrkSGAIs4q0uFXOIUjSHKsT0czaZJxy3ScYtk7BEmjDev2l5+K4UQYveTwEYLyWSS+++/n4cffphKpcKf/umfbuh43/jGN/jMZz5Tv3333XfzzW9+kyNHjjRs9/LLL/Pud7+byclJAJ544gkee+wx7rvvvo7u56//+q+JogilFDMzM3znO9/hySef5F/+y3/Jf/yP/5G//Mu/5M4771z7QEKIbWbF1f4duoC+Xqwysxg0iJTmrgO1NHLlhlR+eL3drhuitW7ZcHX5mFZDa/x1ZMOYbkjCBD/e/nnR6MXpJDvz+duNtFIUDYeZXJ6sGzIcJkFrXCNgUKfZowdJBxpIkNVJbGN1n5KcTtf6YSSGuDSSxE2YhDETyDDOwU1/TFvKt4m58yjf4zwHqfoRVT/iTZzhIfNU211HjTLX9FL/IIMp9hA3NBVrCCc+Uut/kR6DeKb+HngT5bcIsSWujSc5NNv//kxCbJQENpb55V/+ZY4cOcLDDz/MfffdV58k8oUvfGFDgQ2lFJ/85CfrtzOZDF/72tdWBTUAHnjgAb785S/z9re/HaVUfd+l5p9rSadv1OQODg5y/PhxPvShD/HWt76V8+fP8/73v5+TJ09iWTfhFSMhxJa7XrxRolCs3iiNcF/Po9uWc3QeCJgre+Qdn/1DKQZStfdx99Q8yePDHfUlCVXjeXTcDLODSpPJgstkwWUsm2B8QCZUbTqtSQaatBdRqc7xavg6ecqExmJwywQSNzZ/KLiNPdGNHg571CCmaTNsDJKLDZFODpFIj2DEbzyXlU16KFsucDHdeZLuAplggVxUYFgXSS/2wKiQ4pnoPfXN5xhsdSRsUpSMIZLpHMcymXoGxjXr5/v+MIQQ7ekdeiFlKxmbUHm6MBhntLS6xHRqLMlIKSDZQYnsbiOBjWU+9alP9eW43/72txsahX784x/n2LFjLbd/29vexoc//GG+9KUvAfD1r3+dc+fOcfz48XXd/9jYGP/23/5bPvKRj/Dqq6/y7W9/m5/5mZ9Z17GEEKITF+dsnr0wzy1jWR49tnZVezjfm54MXhhxbra2tJy3/fp9+9dt/Os2Vi6OmUuQvqf1OZ2dXt/SNBZpokgTWWt/CJy3fQbTMqKzn7RShF4Fz81j+3kcVeEfeg8S07XnZ8pQzCYLbQ4AFcunkLJwkxZuwuRQ/K0cjK0e9bmb6SjArM5TCS3mogxVX+EEIR/Wf8240brsKIdLEh9vMVI0zxAV0hSNISrWMNXECEFylCg1CvHaGGIL2IXznoQQoqfchMnUWKJpYEPdxHEoCWxsgr/4i79ouP3Rj350zX1+5Vd+pR7YAHjqqad44okn1n0Ob3nLW+pfv/zyyxLYEGIXKrsBGhhMbf6CWfkR/qUSZipG/HCOv3jxGlDLUDg2nmXvQGpTzqPshm2/H1UCokpAbCxFbM9Gp05oCk5AYnGhe+v1KsqASwfSVFNrZ8VFPWom2qvj7HTKc3CrCzh+gVJUYEGXCIzG14NNlSFqzWDHdK6WZWMAGobIMmQOko0NkU4O1yaTxBJc2/yHsjVUhOkWiLnzpP0FsmGeIVVgkAoG8KI6zkn9QH3zeXOIcZoHNqokKBjDHMoaqFSGTNwinRjmpCUTSIQQW+fS/hS3TK3vQsrkeJI9hYBiNsZ4of3EkyDW3+hCNWnu2JLkfpLAxib4xje+Uf/69ttv5/bbb19zn7e//e2kUilct/bL9/Wvf31DgY0wvPHhrlWduRBi57pWqPLnL1xFa3j/gwe5dU92U+/ffXWBYNoBwEg2Luqnim4PAhsa56UZgtnq2pt2IMq79bIUrdcXGLi6UOVqoYppwHiuVopgajg84/L60SyxSPPaVLnl/tcKvXksNyMVeJhulfEwQ8pXpD3Fs8YZXo0tC0M0+VM3Z5QY1Bm8uEE1meJ+8z5iyRzx1DBm7CbJoNEaw69QiuJUAo3jR1heiQ+rv8RqU081ZpQayq3m9SC+EaNgDFG2hnHio7iJUaJlPTAk+0IIsZ3YmRinj+W4e6L7zMzCQJzCQO3vxFqBjWIuxlgxIOUrZkcSjOdXb3/hYJqUpzgwL/1LekUCG31WKBS4fPly/fajjz7a0X6JRIKHH364Prp1eSnLejz99I1O7u3KYIQQO9NfvXK9fuX+qZeu8f99bONNgnWgCPMuKlg9lWClpaAGgHehSCxURKaB7lGTzGC2SlRaeyZ8p/fmT9r4k51NwVq6qL/S1cXAhNLUp6gAxENNLFTEI03YJpuiF5kWsztkrOpG6CggcApU3QXKUZG8KlIxquxRA9zj38hGHLcGeXVlfsViJsawOUQuNoQ3uJcz6Uz9dZnlts18KJtvsYwk6c6TDuYZCPOM6AIpfP5r9A6mGQXAIAnWYupKs8NgYpom45kk6YRJJm6h4o/w97FH5aqhEKJjTsoi4679maKX5obj5JyI2ZHE2hv3imFw4WAaU2mimNk0sFFNWVRTFvvyPmaPMy9v1ndlCWz02ZkzZxpud9Mn4/bbb68HNvL5PFNTU+zfv79hG9/3uXbtGrfd1vrD2ZUrV3jyyScBGBgY4LHHHuv4HNo5d+7cuvcdHx9n7969PTkPIXYq73KJKO+SvHUIa2iNRpJr/M1bXoKxzgSEVewXp4mKawcTVopKPndedvASJucPdVjuscY5q8rqOlKAqh9SqAaMZRMkOhgdG0QKyzQwWyzG2jX8ipTm9ekybhhx+3iu7f3cfrWK1ecSEdsLOT/bvxHlW0LregbGWec0c2qeIhX08qdr8esFo0KEwqJWCjSuBsnqFCPGEAOxIdLJ0Vo5SfzGh9ldW7SjNV6ocPwIxw8Zdy9yf3CCIcotP+DuMYpM61pgQ2OwoAcYM0qUyFE0h7HjI7iJUcLUHlRqCAwTuSwihNiIiwdS3H2h/3+3lFHLoLw2nqQ4EGdmtO93uYo2DaLFILofN0m0aObZj79Lu/Zv3RoksNFnExMTDbePHj3a8b4rt52YmFgV2HAchze+8Y188IMf5P3vfz9vetObGB+vJX9evnyZb37zm/xv/9v/xvz8PAC///u/z+Bg687k3fjABz6w7n1/67d+i9/+7d/uyXkIsRNFZR/3tTwAwUyVxIEssf1Z4hvu+9AbOlAtgxphwSOccYgfzGLlWl8BSfqKoUpI7FqF8sXmH2SCSFF2A4YHknTbklFrzanJEqHSzJY97j883Hb76ZLLhTmbVNzkvkNDWObqe0z5rbuITxWrFBYnuZyaLLW9r34HNRw/5OS11o0bdwKtNZHv4FUXsP0FDkRD3OmNYy7+6F6KlyhYrdOFE8SYTvnEUgNUkybVZIb7rZugf1ToY1bnSLjzZIN5BqIClg74/0Xvqm+SJGLYal0GBTBq2AwmY2QSFplEjNesnyWWzIIlHw1F99xE7f203XuoEBhGyyzItSRjJl6H49jPHclgKvAT/Wn2bKctstXOM08u709x/Iqz9oY9cn1Pkluv33zlrvLXq89KpcYPv6OjnYcMR0ZGGm6Xy80/pPi+z5e+9KWGZqMrpVIpfu/3fo//6X/6nzq+fyFE/4RzjX9wlqZ2DL7jCMa2mLrQemFuPzcFQHC9Qu4nD7ft25PyFfGrNmqweY+Nk9eKeKEiW/a4b393QdeKF9ZLPRw/anvOABfmasEVN1BMFV0OjWQ6vq9Iaa7kt8+HhHMzlR13RUZFAYGTx3EXKIcFFnSBqrEseBbt4436RleGcT3IJLXgX0xbjBiDDFnDZBLDpNIjmPEM+SbBqfXSWjNv++TtgFApYqbJSDbOWDaxNb2ptMaPFLYfsb9yhjF/sqGZ50oJAnxq9d9zDNX/PcSkYAxRNEew46N4yTHC9B6MRIa7Go4g44eFWK+54Th7Cs0zC3c63aO3P3eDQYbRbKJhZHw7YZ8/R13Zm2KsGDBcCYiHa/819uOb97mukrEIYyZX9qWIhZqBakjO2dzyn60igY0+q1QarzalUp030EunG6/crjwWwNDQEN/5znf4zne+w9NPP82VK1eYnp4mCAKGh4d54xvfyDvf+U7+6T/9p11liwghtoZyQqzBTawDXSaYdQimHBIHsx31s1C+at2AYtFYMYA9rR/P0tWXshuite5qAel3eOWmGbfLfa8VNu9KSydqgZztSytFItBkF8tKDM/lq8b30MtrfVY81bNG7UKApvYBeDRxkHutQVLpUWLJAYweBjFWyts+E3P2qp4oC47PpXmHY+NZRjJ9/L1UEVZ1nrg7S9afZ0YN8lJ4S/18jpuTHDWutj3EGCUWrPHFDIwxXuQf4Kf2oFPDsOJnd7PWXwvRL1GP+kltRQ+KzXJl3/qaiM8OJxgv+Fv6vnVkJM1Z06i/JyvLYHY0gTJh30L3Jbv9cnVvqh7UKWdry/xcF5klO50ENvpsaarJkkSi8w9GyWTj1ZNqdfXVQsMweMc73sE73vGO9Z3gBjz11FNd9QxZbqlcRgixPr2+gKxDhfPSLADBVB/rX1s0AFlP46zXZxqDvb3qLbKS1rWxtaI1HXh4zgKOn6cU5pnXRd4R3MMhtWdxixjZZJIKq3+OOZ1mxBxiMDnC+dEkXtJabO6ZZmAT5mrkbZ+zM61LXkKlOTtd4c59uZ4EN6IoJGFPkfLmyPrzDEULDFNsmEgS1/t5Xt24GDGrh7hjWWAjwCJvDFO2RnDio3ipMY6m93I0duP8PDbeQFgIsbkmx5ObWrKwWa7sSxG0yFpwE2bbEqb5ofiaU0j6LZOw+B9+7BbcQPFnP7oxFCI/EN9WgY1SrvXS3jKNXT8eXgIbfbYyQ8P3O3/xe17j+J+VGRxb7fjx49xzzz1bfRpC7C5bdElCOetLoY0WXIw+1bAup/vUMbxUDSgta7wqOqA12qtSdq5T8fPkVYGSseKDuAEzZokj9cBGrbmnawaMGkMMWcNkEyMkM6OYiRt/2zY7fKS1ZmKus0DexKzNQ0fjnWcVaQ1ehVh1lik1xEKYwPEiwijg16xvYrYpJho3CvWvE5ZJPn6I04aJmxwjSO1BLzbzFELsHoVcbN0lC0HM6KgkYito40b2QDNX9qW4YxOCOQ8eHealy4V17z/cJLCtLKOeUbLd7R9M7fox8xLY6LNcrrFz/soMjnZWZmisPJYQYmN0EIFhbJOeFuvndjCOtV/sF2ewMjvvT4mm1rj09PX2TUBvbH3zUmGA7yxwKBom62nSXsSszvPD5KnaBi3W+TNGETduUE1aOCmTg/EH2ZdI9LWkpFvztt92JO9yodIs2D5juSa9KLTCqBZIVGdI+/MMhvOM6AJpahcovhU9Qp5bFje2WNAD7DEaX3sRBgWGKFqjVOKj3JXLkknGiVkmMEypvr8Q24822k+V2u22+rFro/W45g0fuy9HvaFVJscqG7zwc+tYdkOBjVb8+M4o7ts7kJTAhtiYlRNI8vl8x/sWCoWG2wMDA704JSEEtX4S1RNzGDGDzCP7sbLxro+hI0UwU8UaTKxr/40of3+SgR8/yIJSfOm5Kx3tExY8gusV4vuzxEZuZJMpN1zVzLQVrTUaGsalRk5vMh4mZiuM5ZJNr4pslOOvPse5itdky5ubVgrlO1Sr89j+AvmoQIEy2oDj3o8xoGsNV8cZxNBGQ8+MpI4zagwxGBsmnRwlmRnlfGz5cxnfdr0d8nZ3mUoLdsBYJoYTamwvwvFChr2rvDP6PnFaBxjHjSKvLlsdXGec0EhSio3ixMfw0+NE6VEM88bHst7MLxNCbIZeNdgUop19g0mmS+v77BKzdv+LVAIbfXbbbbc13L58+XKLLVe7dOlSw+1jx2SCvBC9stRPQvsa92ye7Jv2dn2M6ul5gikHwzIYePshjLjV69Nsyz2b59uB21ETTa10fZqJf7XC4DuPYlgGOlTYz16vNQJd6xiL41VtL+TWPVn2tZh0sl6zFZ/Zis8jt44QW8dV/XZXla6umGgyX/FIDndW3rebL0IaWpPyFbY9wzX/MguqgGMs+9C07HPQjFFkaDGwYWFxGwfAtMjFR0hmxrAS2W2VjdGJUHXXRDbhTPHw1e/xf0bvI6L2++6TJG61DmpUSBOPxzmUSZFN1Mar2rF3YK8oadn9HzmFEGJ7uHQgxd4Fn7TX2d+AXnwOiExjw6Pgj4xmGgIbFw+k62Ndzx5tP+ltS6Z7bTIJbPTZ3Xff3XD73LlzHe97/vz5+tcjIyPs37+/Z+clhLhBrTP6HUzVakJ1pPGvVkjeNrTGHr0VzlWZNTur69QrpmgoN8TKxgmm7I6CGlDLcKh4tcyHC3N2zwMbSxwvYjDd/QJZKd0yyKNXdBZVenWw42agAg/fmSfyHe4JDpH2FaaGs1aFq/HplqvrhI5RTkTMpOI4KYtq0mSf+cjmnnwfdBtAG8AhTsQeikxTG9+eJ0eIiYWiyCAFa4RKfAwvuYcwM46O1z5sHu752QshtotOSlGK2RjlbIzDM/3pJpQfiDFSDgktg1i0m0Py67P8c4CdjnHhUIy7J1o3jt6OVv7NctIWp4+t3arg6GimIdN2t5LARp8NDw9z9OjReqbGM88809F+vu/zwgsv1G/fd999fTk/IURveBPFpoEN5QRUT82DaZC+dwwz2du3XTPSjBV83KSJne7+2LqLDz9usP7xqpthYs5mwW4M9JTdgIHU5pYJbSehZ+M6c5T9efJRnqJRa5RpaoOHvL2Yi1kHe1Xja3dApxk1RxiIj5BKjxFLDWKYJrOb/gj6JAownTn2RRUWGOt4t9vNawDsM/KUY+NkkzGyCYvnrJ/HSA+DdfO+1oS4mXVSinJ9PIkyDZjZ+P01axZ6fU+SYi6OlzB5w6Xup5u1yyi4vifJgTmPyDKoJswNjRCtZGIMLJawhjdBecR2cHBZhmo5s7nZxZtJAhub4N3vfjd//Md/DNSyMCYmJtYsK3n66acbGo2+973v7es5CnFz2/gf1lZTO6qnFwgLtYwQ92yBzH17mm639h00/+f9sx7Zcq1PgJ22uLI3hbIMtNK4ry0QlXxSd4xg9qDBp97kooy843N+pkI6YfHG/YNYpkHR8TkzVW66/cqgBsCpyRK3j2f7Ngp2W9GaRKjJuBGXnXNcjK5gG8uuDC57mStDM2eU2a+HUQbEEjnutI6RTgwvlpVsrylcGxIFWM4sSXeGnD/PcDTPMCVMNBEGF3gPLk0agq6QIMDJHOFH6YdIpce4z1r+O9Wf7CUh+q2Stja0SN1s1aTZcfnAZlGmQSUTW3PsZy//DKlmzUINAyfdn0VrfjBOJW2hTIO9CxvrT3V9T4LEdYWlNJf39/69s5rcWSWR/bYy6DY/3Ps+ZtuFBDY2wQc/+MF6YAPg85//PL//+7/fdp/Pf/7zDbc/8IEP9OPUhBB9FuZvLCyDKRvWG9hoYdAO6y0Ls9WI8YLP9GgC+0dTROXahyz7hWkyD4w3P79IEUSKuLX9Pgi8thjAKLshU8Uqh0bSLYMa7Zyf7f7K1XJbOXWmHa0UYbWAU50nGWru9w8TX8zAmYwF2LHm6c5JHWfMGCY/GMfJpHCTJtowGOPezTz9/lARnu9TDAxsL8L2Qv5R+JcMG83TjS00P2M+z/+j3sZaAc5b943gZfbBmlsKsXNI08uNKWcsZkaTqM3+OW7B89bx9JI1hDGT80fa94NYLz9ucmVf74MlW/UZaW4ozp5id02u16LM3ftLL4GNTfDYY49x7733cvLkSQA+85nP8Ku/+qurGosueeaZZ/jyl79cv/2e97yHO+64Y1POVQixSfqUQTBWDBgrBkTHGv+wOy+vLiIoL1T5/jNXMCsBx8azjA9s36vOV/JVDnTY7LPXTl7rZCRs/+koInAWsN15iuECczpPYNSCLgMqzcPRofq2+9UwLy9+vVRWMhgfJZXZg5XMYZgmCtjRXUbqI1anyfqzDIdzjOo8L6g7eUnfU99s1hximNWBjRCLvDFMGBvkSNzkug1hk9SemGlwbDzLSB+m9Qix1apJiwGnefD26t4kh2e21/SorR6rutLCYK30I96kVNOLmyTXUcK5lEWzW0bodvIQlh6zHzNJdNAQvZVzfQqYDKT6u2RuNax3ZizZ88DGbiaBjU1gmia/93u/x8///M8DYNs273vf+/irv/orjhw50rDtiRMn+PCHP4xa7NRumiZPPvnkpp+zEDebyA4wExZGh1ckwuL6P+zpSOGeL3S2baBWNb7sBR0qJr51AaMSoKllNSwPbIRKUbADBtMxErHtUY9ZcDprlLpbGEqT8RRZN+LF6otcY5bIWPaBb9lFl7JZxcYlSwo3bpBIjvFA7EGSmT1Yyf580NtUWuOFCs91OFJ5haFwjjG9QJLVH/j2G/mGT4gzeoRbjSnyxjBFaww7OU6Q3kuUHgGj9to+CBzYo1mwfRbsgFApYqbJaDbOaDZxU3STFzenhaE4e/Mbf2/VhoGxi2r+lvpNTI4nOTi7vr/3k+NJbpvsPnx8ZV+KASekmrQYL/gMl3szUn3dOnj7s9MWhVyMQ+v8WV3ZlyLrRlSTVtP+IBcOphlPJJiM63U/H+t1+3iWdj+EILbxbI5cKkbZbf88LyVa7MklyFP7LDCciVNwJPCxRAIby3z1q1/lN37jN1b9e7ncmPr8+OOPk06vvnL4h3/4h/zCL/xC02O/733v49d+7df47Gc/C8CpU6e46667ePzxx3nwwQcJgoAf/vCHfOUrXyEIbrxA/+AP/oAHHnhgIw9LCLEG5UdUfjCJmbLI/dhBjA7+SDmLo1PXw7/WWRfuqOJjPz+N7kPTTvtHU1TblFi8er1MxQtJxkwePDJcW9ht8WfWs9M7q3t5t1QU4NvzVNxZxsIsd3n7WHolxuKayFr9OjC0wSgDjMRGubYnicpkiBabsWXIbuLZ91hQJWZPk3ZnOKOPMu0nCZXGIuIx6zRmmxfjHqNIJm6RS8bIJi2CxJt4PvHWNUfRGobBWC7JWG7tfhtC7AbzQ/FdnZbeDS9hklw2IezckQym0gRxc9MX0to0KOVqjYgn9yS3PrCxQrNXzKUDtXXRegMberFPSSvVlIV96yCFi9G6n49+fYRxUmY942RmpHVm3/RYon7u+YFljabb/ArefXCQkSkPL1Tctqf2N/2WsSyOpRk/nGXfYIq/Pj3dk8exG0hgY5lSqdQwYrWVycnJlvu380d/9EeUy2X+5E/+BKhlbnzuc59ruq1hGPzmb/4mTzzxxJrnI4ToDeVG+FfKHY1tXe+FqVpTz3xH27qnF/oS1FiL1ro+1tULFRUvJBW36v8meqMWyFig4s6SD+eZp4RezDt21Cj3sq++7X41zIQ1g6VNxowhhq1Rsqk9JDKjmLHaB6Qde81GhcTsGVLVaXLBLKPRPIPLSkeuRnGucQsAERbzepBxowjUPqgWGSRvjVFO7MFN7UNl9nCfTCcRYtN1EyIpZWMM2tv3b0pkGfUg8ZZqkS3W60W63kZttjQrXkvrfBr2DCSZK29CUMowuLw/haFrAZpWCrkYVqSxlGZuqLPSxrhl8Ib9gw3/lkjF+PG3HcRMWJydXrvv2B171x4Hu1tIYGMTWZbFF7/4Rd773vfy6U9/ut5zY6VHH32UJ598kne+852bfIZCCOX1t1Gkf7Hzfg1RaXvUNoeR5uWpAmGLyS9LHD9Ea8iuGGmrtObqggNAF9Nld52l0pK0E/Cs9xxzFFBLBdQrPgtNmwUiFBgmTsoikzzEw/9/9v4zVrbsPM9FnzFmqrxy2Hl3ZgeyW2w2RVKWlUjZh6aCJYsGrnB85HNlwbABAxLkABmGDUuyIdvABSxDkK3zQ1e2cY8h2ZZFSRSPAiUqUCLZZLMDe/fOceValWeeY9wfs1auVauqVq2w964HbHCvqhlGzTjGO77v/awpzNwkUp6O1KCBaKeUNIOYZhAz5d3lO9SfYnTpps+JCld0Kmw4puSm8SzLZoKfmSHOzYE5irAYMWLYrJeOVhy8P+vwwq3+hY1+x7h35zJkA8VMtXu6TaVoMl9Ol/Ht3kf5p0L8GAKr4zbFVue0mdcuT/L5lePzmrpxPsfT99M+Q2DJzagi0efZ72XppSl787wfCiEONuIVYigVSQofTUUNOHiS7W++doGxOw3ilYfaUatnRsLGNn7kR36EH/mRHzny/Xz605/m05/+NG+//TZvvvkmCwsLGIbB2bNnee211w4sBTtixIiTI1738d7aa8TZK716a8DgUSHDZrHmdRU1Vuo+N9e2cmKfmyvu+H6h6hH2oWi8t9zg6ZmHM41CaE2plVBsxRiJItIRy6KCrS0uh+ObqSXKjlFy7zGxtMGMmGDcmuLmpE3o2O0Zuww2xT3Ln3riAKO1TM5fphStMqHW+eXkr5CQdsoCshhG52vDx6Ysp0gyM7yvUCDvmJiGRPNKByvQESNGDIvlSZv1kkXRPZqIisCS+0YiHMTilMPlxd4Gaa2sQTNvkg33DlxbGYO8n+z4e2nKJhMqVnscfDazRl8iyDBp5gwy4XAiOhdmnKFVPOnEg9n+jMlDW3Llcp5MoHaUbj1syfmcvXdSYL1kMd6IyYQKJaDDa/lY6OdukE5vkxtSCM6OZ3HvPD5vzJGwcYK89NJLvPTSI1Beb8SIx4jW68eTyxivHdxxOy7ho5uoESZqh6gBqTCxc5n+GpoozXsPoZ9Gvhlybi3E2jFtYzLBDCEx27uNZ9QEa7KBpQ2mxQTj5hS53DRWdmLTC+Jhs0rVWqO9Gjn3AYVghclklQlqezpsM1RZYgqACkUCLAwUa2KSmjmdmnvm5tFOaXPwc3By2IgRI4bF1qzy6YtGcDPDGYCvj1k7hA2EYH3M5pPvP8Nvv7V44PpXL+aIDTGwQHMYbp/Jkh1idKnXFmd6/SW6j9/8YMahkTt4ID5bcljZFqWqpMDNdl9vpuCw2uw9sjXvmLxyYZy3H9S2+jVCcPN8DjtUKAnP3nV73t5p5+LUyVSSO0lGwsaIESNGnEJaX1s56Sb0hPc4+25ojRNpLNdjpXmL56PLyH26hnb7dZsAbtZg2r7EB53z2NkJxEOaWiIij9irsxAXN1NLvpl3eEVe6breGbFOxZih4JgUHJPXze9FZMce2uMwYsSIY/SWFoLYEJi9COZ9Nur8RJbn5os9CRuJPBlRI7AkbtYYqrBxMDsP5NqYxUR9y9Xp7HiGb+yzZq04vJSm3akol6ZzfQkbAN/xvlm+432z/H9+9+qOz8Mjjrx5br7Ie0vd/TBMKQ5M+e2VYsbkO56bHcq2HiZGwsaIESNGDIjWmqobkrNNHOv0D8qCKKEVxoznbGQfHTLV5UV7SrJljo3Eb+K6K5yPxpkLMliJRqGZ5dy+osZ2tIR7cw5aZHionCG0QnrrZNxFisEyk8kaYzSo6TxfUX91c7ElJvesGmBRFlNUrRnczBx2fp5XrO0zSf2FKY8YMeL0sTxlc27ldPhCDYTWXJw82tLYB3ownDCr4xZz6wfHCkaWJDIFVpz2AIqOxUn0BswDKl0Nq0XGLkPQv/zsNF+4utbz+sWMySfff+ZAYeP5syUeVDzWW73Ha+b3SUv5P7/lCeRjWPFoJGyMGDFixIDcXG2y2gwxpOCbLo4PvB2lNVcW67hhwqWnizxd3Ds4PCyJUrz5oEaiNFN5m2fmij2Hnfrx/nm8/byAH0ZUFOA3V6iHq6wlZRoiTRHKRU9zPkkNLSWCLL3lZJsKiq2EeuF0v36VSivjzDa+wUx4j2m1htOh7sqYaJHFx2uLE8tMss4YFXOGpjNLkJ1HZSdAnCLL/REjTiGGFBQzJlX39NQ3Cqydbwnflnt8HdbGLFpZA7/HvP9uhJbEPoFKYI8iCzMOZ1YDgg7nbA/tiY71koWZaMxYM7ajYs3e3oLnGFjxwxWxOcgwP2NKJnI73+8vnx/nfsXj5mqr4zqfeGFuoBKsedvk2bkif36z3PM658Y7p5vsEDUeI33jdPesRowYMeIUs9pMB/WJ0qzWg7ZrQP+s1H3qftpB+Oqf3eepixNdl9dac2/dJYgVSY9hi2vNcHPZcivkQpRQ9Q7fga64j5awIZTGdF0WWzcoJ2XWaWx1CrZ1DhZkhQ8kl9CAErCP/2VHSq34dAkbWkPYxG4t0ogk1+IZWmEa5vyEfMA5sX9YtofD+UyIn52g6JjknQmuyk8fV8tHjBhxhASOQXnMothKWJ1IBYwnH3g70kBWpvqPPVuZOHxliJOkWjS7lvU8DFHo4TXXyJVmMTtVfOpxt9WiRS1vogW8cKvzAHw3WgqWpxzQmrEBKtYchienCzs8NgahFy+Pfnnp/BhiV4SraUi+5enpfYWNl86NDSRsDMLutnXkMQqtPUU9qxEjRox4eEnaTp4qSNKOR6Lxrqz3tG5zm0/FWDOm/gd3uy6/1gxYqPl9tW93Osk7C7W+1n9U0UoReVVmwgzjviAXKAId8oXM7X07kCWdI2sVuTvh4GYM5hY9JqLeew7qpGcklUK4a2TcRUrhMtNqlSKpYdodPccb6i9tLrqgp3hGPADSvtE646ybMzSdOcLcGXRmjMkTyDN/WNBa4dfX0EmCXRzHtB4/M7eTIpECY0j56o8qoofDszzlsLxNtS+P9ZausB+LUw6NfOcBaHnM4sza1uB2vWThRIq8N3w/iXjAMq1awML0cBMJHVMSxAq0pnLtTQgT/OwSs+977VDb7UV8mR/LcOMAJ/KcffTDxQ9eGu8rUqETkTncyMCsJfdNd5nK28wUHVYbh0+/OsxT6sa9uzSrC+RL85vG448zI2FjxIgRjyxJIySph1hzOcSQX3idiNY8vDdWwJAkaFbXPXK2QTHT3Tyr3+7Vcs+zGpogVtjG3t8e9Vmp5FEiCT281jLVYJVVtYYvIj4RfoCCmgEgi82UKlCWaWWWjLaZlVOU7Gmy+VlMJy1Fu5Et6yrFRB9n0VXHK2xspJU0vJBvbn2eWb2KTefZuHlRRqDRCAwhWLfP8w0JbnaeOD8PnWYQR+xL6NVRbnq/BvE65uy5E27RiO0kcUhQK4MUZMamkfJku8WPukSoBFTG9n8fVoomVqwwY83KpE1sSs6sBuQZTNjY73iulyzC3eVNexRoI2OrVG1l8Spxs0Fu7jzj2ZmB2gjw/vNjfGOhzmrYgna0HF5EkoQYxnCjW0JTYm9LL708leNPg+5V2M5NZOD+UJuxB8c8/T5l2xFC8EMfOs9KPeDXXj+ag/NgxuFyl++v3LrB7//e67gVl+Scx9jsU50XfNQfLNsYCRsjRox4JFFBQutLS2ilicseuQ8M3unoFXejkkmsuL7c2PSfeOXCOJkTMBe9sZJ6gIznLMYOEFcAvn6vgpSCZ2eLx9C640OrhLC5Rt1foRyvUhHbSsm2X/j3ZZlLbWEjEfCkcYk5KyGXncXMlLrOhNyXmnNJ7z2H+1JTGOiX9IaIXOzmA/L+EjeTOa5G05szQt8qPWzRWdRokKNszPD0mEUmkyNrGwgxQZ3RYHxQEm/bgCEZeQecNvxaGcL0fgjkOtmxx6+KwLFykHggBCuThxNPVRKl5xVNMjsL7I2SWpp2MA4p7nutMtFSaiDZat6g+IG9104vETGQmmBmLQMd7FrhkPMPWiXUVm7i1QycwgTSsKgVTASa6epGKurDM+p9br7Il2+nkbCZ3cLUkFietJlbD0mkQB0Q8eKYBhd6MJw9ICCmI3fmM7QOKHf7R3/8tc1/Bw+WYT9h4zFiJGyMGDHikSS830C3w5Cj5eOuS653mGreKbc4N5Gj4BhsdCL8KCHRmvwAIZ66h96O0nrTA6TqRh2jNnbjtVMkvnav2nebThNaKexIU/QVBS+hFazzm/ZX0i879FOEBtdIWCmkOeSuIzHFU4z1uL8lU+BFmmwPHUQPzZIpeLr3n3MwQR2n+YBisMR0vMI49a39qZD3mN78e1FPMSuqKATrYpyKOUszM5+mlTipoDV869oRI04p4ZbIp9yAnm/6IyLIGnCKzEMfKrQiCj2iVh3dTu90K4twcbCTGoc+67fegljxjPEElr1zABs0dqaaqg6pLb1UQdkYOx9FDGWjukC4uIpyJ/CTNXKTZ9AC1kv2NmHjcGxUDIkCF9FDP+MwzBQdPvHCHNFqxNkOppmzJYelQ+6jPG7TzBrEpuTF1cHTTJ6cyW96cLz2RH9v1UQKWrmD+4bJQaawjyEjYWPEiBEnik40CBDDNuLalVvtfm0FcyaLfb73aASdaKKlFjJrYk6mVR+01oSJwtmV2rK99cGuKiIVN6Li1pgvZbg8nacZxHxjoYbS8PTM0czdl3fVdg+PaLb4tKS0qDgkaK5Q89P0kr8SfoAJnR7bPEVsbRJui1TI6wwzcpqxzAzZ/BzCslkdcN/jBYs/8Ty+C7NryVeF5k+ImSgcwmdBa7wooe7HJF6dTwS/u+mP0YlzYg00GEJQcExW7Of5ivU0SX4ePeQQ5xEjRvTOvbkMF5Z3eyU9PLPnAzHI1PUunj9T4qlAcvNBfcfnbnUV7e/0/tCHMMiuLV3fFJlCfx1rrv9SsJ4jiQ2BmWh8W/YQsTJIS1Mxv7n6gOVahfz5CxTG0yg7f2XL+FkHeyP1Zgrt6Jg+fZLGcxYIHwE8N1dkZX2NyuoVkILEeQbDOroS3i+dG6M2U+1t4QGvt+AQFX4KTjq0/vjzc/yZXSZnG7x0tjTw9gbBPobU69PKSNgYMWLEiRHeb+C9m856FL/1HDIzxEfSrvd0tOYRrXmY09mu+wnvNVDNCOdyiXjdJ7iTdp4K3zyPLNq8u1in7sfMlfYPl93vXbpU97k8nePtB1vGnddXm8wU+hxg9vCuvrHLrfsoSgjGJxhar5Ui9us03SXK0QpruobeiPsVcF+uM5GkwoZEclFN0zQiJq0Zcrk5zExxaEZbU3mbr5Zdfl/F/CXMjpEbXlvUWJSaD+b7ON9aIVprZN0FxsIlvhg/x7Iea/9MA9vofF5bZFiVs9SceV4qFsk5Zts9vbiPu8aIhwnN0Q2BVyZsZishgVsldl3MXA4nN35Ee+uPJA7wK2ugNc74JKbd/2BzWJTHLKZq+zxXtSYMWgghsNqePLtpZg0Wph3ObjPLRGtCv4EQct/1NlBJTOg3sOzskQ4k+6FWMJmtHG2lrMvTOebXYyI34l5lK9Vrt6gBfd4jWnNpauuYJ/Vt79ABTWgztsmts1kKXkJ9gIodnaIzN0qPNvLmpolr6NXQQYQQCvfWHQrf1Fv64LmJwUT2S1M53m9nUKseGcvgnYVyOqJMNEG9Qm7qzEDbHTonYGx9vn1M847JJ16Y62md3X4v6pBdkx/60PnDbeAhZiRsjBgx4sTYEDUAvHfK5F/t7SWwgfJiohUXczqLkT/YQwIgqYcHCihxxSeu7JxJ869WqT9T2izL2ruB505qHWaQNrb5sPGg2t1wbNgIpcn7qV/Gl+Ov44pt52BX/+W+LPO0cZlW1qCZNZjNvMbMEXVyhBA8OZPn6nKT/0bIZSSXkNgIQjR3UNxGoYBnZwrdy7OpBMNdIdcWMmbUKg5b18wdxllux8trBAt6iifEEnXyrBmz1J0z+PmzkBnf7NR1Hx6NeBh5Yz5m6s4KZxg7cAA8CFolxLXUHjeuNbAzRYQcjk+QVgqtEqS59cxWSUQUethOvut+gvo6tCPigmoFc7Y/YSP0myShj50rYZiHi1jqln8fuDXienr89ITGzvQWmVdZXyKutG2JJ+l6bv3KCjpKSGSL3MzZU1ERoTyWVjEZa+7zThvWM1ikJTeBTTFoGMyPDVcg+tQHzvBrr9+n0oMfRK9H5n3zRb4SejsGw0l4yMocA0Q25B2TqIN3mE6GX8HmUWdx2mF+bUuYi58sQZwex5fOjcG93qvgzRQdZounQ+g8CUbCxogRI04EHe18+cXr/ZUv1VrT+uoyyo0R16vYF4vInIV1Nt9bXe9+0RovPPwLu1Pd892pK2HSuZLJZlMO3YrhsNhnydlBSPwGzeYSL0RnKAUCqcHF5o8yeztyjjaZkdOM2zNkC3PctI+vtOZEzubZuQI3V1vcVIqb7DynphQ8PZPfnG3bQGuNGybUvYist8h3RX+I1aUCwDlR5msacpZBMWNy1/oIK5nMpj/GiMeD2s0rjLcyhHGCOZfpKAaEfpOoXkUYBtmJ2b6ECaXiPX8bQxA2tFa45UWIFTKfJVuaTp+t5RVIFInVJDd9dv/1t4fT9xkxppKIqFIBwA9D8l320zda4dXL6DjGLo5tihoAUa26r7AR2Duf8621Gg7pZ2GtgjXbWdjwG+Wtd6jSREETOzv8cPfAb6CSkGx+6uCFgUZ9gbfjBh9V850rzAwhFWU3gVcnrtUPXvC40ZpvXH+XxnqNwvjBwlMvR2Yqb5O1DTjaoJiHlj2X1xFcb4fhb7x6nt9+axF3V18ysCULz47xwflxjDGH78oZvH6nwlwpw2wxQ22f7Y3Yy0jYGDFixIng3+j+qNaJJq74GCUbaXeYFYgUyk07uVppgttpx0ZmDMypLgPaI3rR3at4NIOY5+a7DzB72f3dcounH7HKJL2ik5iwtUbNW2Y1WaUuUv+Ic6HFuE6rluRwmFZF1mSDKV1i2pqlkJ3Fyk2e6KzlRM7mgxct1lsh662IWClMKZnMW0zm7VRwUwrprpBzHzAeLvHZ+EO4Op25zpLBMvaKGgpBWUywbs7RyJ7nQ6VxjM3fmT81QteIYyTaGtRHQWvnoFYr/GaFpJneOzqJCdwamcLJ28KGXn0z4kK1PChBHHmbIoWOErRKhhYdsnPf22b1o4TW4r2hbTvw6ig3FXqDyk5TyW5pDJ4jqRZNCm7C6kwGY2Xbl9tWi0yBFacfqDjaPLdHSeBWqV77BkJBcHaW8bnulseBW8W9dQcAPzHJTc4PqSWdj99k3ubWWutIRA23uQbR4SYy/FaFG6+v4jVSEb44OUh6wM7fPmjqyGE5CX3AsSRBdMh010NOck30kTbqt9bbqUP7990uTOb43z96if/4Rzf3fJdYEueJNBqzCHz7c6MKTYMwEjZGjBhxIkSLza7fe2+tEq16yIxJ4VvO9mwuGtystYWN48+trLgRdS8+tHHTWjNkuhBScUPmShlyA1ROeZjQoU+rscB6uMKyLhOLdody2ym81y7Hmsg0N/05+wM8lcshjzEqoxeEEEwVHKbapmxaKaS7Rm71PuPhIrO7Ukvm9So3SWeOPTKUdYlx0WRVTFGx5mhlzxLnz8C2sPnjLxw84lSzq/Me+s09A18Vno4p3k5h6geNmUKvTuy7WLnuYu/2wf9xEoXuZtoO0J8fgxAszKRh47YpO1YkWhu3qBYsnr6fntMk2edcaoXfqqJVgp0rpV4kSRoZ4+TGkEZv6Zob1B/cRLTHleHCChwgbDRW7241JdibcqlVAlKilWJ94QrK84jURSxrgGe4BmtbVOPbmTrzsUNNxjwVdk/NSuKQtZtvYBkmWl3cEsO33UfNm9f230CPXYuk0YJ2ppR39+6AwkaKVgq/scaNuMIz2ct885OT/MXNdeKzecyFNAr0tjWY2JWouH1N9X4exDH0r6bzTv/prruaZe3qN5p9Tnxcns5DcLC40qwt4t66BRoWc8D7j85jxJrp/36Ruf7u/YeZR7u3PGLEiIeWaDV9oSk/Jl73saZ7fJifsKm8HyVDcaS+spR2lMvNkA9dTru7qp0Oc8qiK/tGK0U20hTdhIKbUI/W+XPn7fTLDudvTOcxnAK3pjO4mQ1n+Qwnn1HegXZqSc2PmGjd5iPxl8h0iRs+L1a5pc+Sd0xKGZNvWB/HypWgz0HIiBEbRG4H0fiIn4taKbRO+h48u+VFpLPTiFklEZFbxXLyCMMkqqbRfaG/nv6OfZ5/oSWx4uPP7w+r6wcvdAhWJp10YNtcRwUBOuzsX9FaXdyMfPFbW2mCqunhuT7ZqbkdviYHoePheT/5zXWSRgtlCRpjOZLVKgCBX8aaPYdXX0NHEa2pEvmx/iM9PJlwy04H9gcJG36tjDJdEi0IzGrnSKYBq32tTtjMtM1Tb9mtLQGgx80FZucbNXCrKDfATRLevXaTb//OT/Dy+XEcKfkv/+tdViOfhtv/+fJ8j2/cvEclWUU8eX6zmspuTsCDsytGySapHyzW5h1zs+zqU7OFNI2nDyxDtt2xutO6fYsN7/IH37gO3/3RvvbTD84zE/2vc6lItNRCeTGL0w4c7SPrRBkJGyNGjDgywoUm8aqLfaG0WS71wHUWW/jfKO/88ASrb2wQVwNE7eCwxChRxGp47Y03Z/807yzUaAUPpzGXVjFBY5Wqv8RyssJfCz/ImE6nszKUcLRJ0C7HamjJrJhk0p4lV5jHdNL89KMPvh4ArZF+lWzzLvlwhd+JX93MEgixyBh7O18KwZqYZN2aJ8hd4kOFCeTmzNLJVXl43EgiHyGMvgZ7p5nQbxI1apupHoMSdRhcxaFH6DawMrm0EonWBF4NNNjZIm55CWKFUcyRKfTmxwCgw5hk10Ddr5RTv42WhzU23vO2FqccnnzgIjUszDjMldN7L468o51h3mcQvGYGTMf7V886EKVRSRr54DVX0wiAfYg9t/t7UmmCZoXs+MHh7aHfYPnttxGHTQNok8ThVttjhX/3/taXWhMFrc00ntbNm+S/abew0eHcHeZ0hvHm6CdxXdglbPzBn//5wJteG7NQIjWWXauGzLB1/pvVB/sKBxvcCRY6fp64W2+/Vju1Jd8uK1oZs6iuxztekJWlq0zMP3tge+8vLJIkCtsQ1PappjKZt3HM0xUnmPvADMHdOuaYAze3+otBzoRtgpwGvucDZ6l5EWPZAZ7zPQpSoo9bZZCJqdw3zRLeqmHO7DLK7yL0bkdYBoWPnkEnGvGVeyNhY8TpxvM8fud3fofPfe5zfPnLX+bGjRu0Wi3Gx8f5wAc+wA/+4A/yt//23yabPV0h2yMePpTSXF1pUMpYnB3vfj0pP8Z7J33hRCseY5+41NM+vLfXDtVGL0r4iz+/x/zdJmfHs0M1EpXvlA9c5l7F21GCbjeDNqcZJA+dqKEiH7exRCVYYmlXislducb7k4tAWo71KX0W34BSdg6nMNPZeO60ELZwGvcY8x8wmyxSYOt8l9TTlNtVS5YZJ8TEJEk9Mqx5mtlzRIWzYGyJZKcy8mSIJJFPHPlDraxxWAK3mqYPCIEzNdWOONDYTv70TU/uw+5Wbhhj9kscuiRxhJ0tIoSkmdt777n1GraWBC2PuBQghNiqmuK2NsWUpOHCNmFDJTEqCTGtbPu49tAL3xigK42Kuld7UEm0GSUS2pLrF3JIlf57rhwS+g2iavVYHZfzjkkriKkaUSpsHOJy8utlYAK3sgzsPzDrlPqxZxmVbPwD362lY6Li9J7lokoVkRueOO/Xur/T4/AgE+rDnbz1e+/gtEymzfmenj/vvnX3wGX2Q0tBebz9bN913t27d7sKG4HvEld2RVv1eBpUdafsHy2uQQ/CRuCn1814ziYI9h7nVy6M4wwhAnXYyKxJ9rlUkLozn2FuPcRzJFbRgspOoVRK0ZdXxknR7TFhTWd7j1reb/uGRBjwfa+cJVq7c6htnWZOcc9xRK/Mzc3RaOwtdbW2tsYf/MEf8Ad/8Af8/M//PL/xG7/BM888cwItHPGo8Gc3ynz59jpCwP/rmy/uKSmllSa4WUW1Iozi0b5I9ht7vP2gRlT2uQdkLGPT62AQlNakE+knP9BR/eRtnxRa40Qa3azwrv8Oq1TRG4du1yG8Y6zylHmZRs6gkTMZs15m7JQOKBOlqHsx483rvC94m4kuHuXnxSplPUbeNihlM7xu/lVkbhJt7rwOtUqIghamnUMaj+6rWKkYv7wKGpLAJzcxLEPBw7HpiaA1wdrWwEuPxTi58Z63o5UiiX1MKwOivwFAFLqgdX/lWrXGaxxO/N1OEgcE62XQoKKA7Nhses7WVzeXicXOZ09S3zX42idCRKsEb20prdqRy5AdmxlauzcImhWyY1tRCPGuQVhUqfa1vQ1DUbNUwMn3H/INYPToB6V3LaY7TOVqf0OwOPyzcaOijN+qbkZQuNkEZi4fettd6VJNLBDJ0caoaYjXaiRRBt8q77hWAEg0raX7CEuizl4+ypbsOL8rEzazlZ3RfF6nFLIeCNzqYZoFgCkkU3mb3fJYpkM51wNJNO76EpmxqY6paV6rTNiqU5g6h2Ecvp/YypncbIuxF3f1IU5nj+JkGc/ZfPSpvYLmo8Kj25t6jGg0GjiOww/8wA/wfd/3fbz22mtMTExw9+5d/uN//I/84i/+IleuXOG7v/u7efvtt8nnh1/zfsTjwZdvp/FrWsMXrq7xN17daYYVr7oEt1KH8milT9OnIbHeiti4wpdq/h5hw31zDefSwbmZizWPO2WXYsbkhTOloykh+wiglSJyy5wNCoy7GifWtBD8Uaa6Z1lTS+bENJPOHLniGW5Zp7PWulYJRnMRw1vnjfgyzXao/HOETBidRY11xlg157Hy53m1MI65aWyX6zjf6FaWIUyIjAb5mbMPTZRAv4RubXPCdWuQdjIoFRM0Kghj/856XGv0Lmxojbe+hI4SQsfqqwpE6Dc2B91qLNrcZxL7xGGAnSl0LuEaNIldD7Jb89ix72JnBquiFDSqmxtSbkBgVak8qHIxOXxkTdCqbhppKtenHcgEwKLlcybq5f7vLujqIab9bSeuN7GzY8OpsqTTa09KE5VE6fOlj2dfkoRt89fhdNm3p7TE5Sre/BqttXWEaRyJ+NSNm3aLF0X6xr5hp+2azNust7be0V6zjFPxMLOFfcvm9oJygx3X4CZao8OEVmUBeGnP13LXszmOPMp3b5C4g5c7Xxu3kFozXd32TOzjFRBGEa7vMV4s4TcPjiQdlCAMWF5bY+xCf+vpIMKvl/cI2XkzYuXae5hCsN6sMvPkNw2xtR3acaRbf7hQSvH5v/gLVlcrTBYPkSJ3yhkJG48Af//v/33+2T/7Z8zNze34fGJigl/4hV/g8uXL/ON//I+5ffs2v/ALv8A//If/8IRaOuJRIu6Qzxst7Z8DvBt9Au71GwR3Di4Pd6echnY2/Jj1VnioyI9HDaUSwuYqFW+BpWQFX4RMhy/jqHQWII/DjCqxKutktc2cMct4Zh6nMHs6oxO0Rnplss17TIQLzKoVbGIUgi8lc2yEgN9ja7avSZYV4wy1zDmCwgVoz7r3NBzUemsWM1FEkYdlP6LeGqfI6davldH+8KqDxLGPbpeE1EGEVurAgbC7vkRuYjZNj9jYTltM0SrBX1sDrUl8j9zkPEkcEjTWAYGZzRG7TeoyFdo2xkHaC2F80F+x8/zEtQZJ2OAQGyRwazi5sY6iw8beajJiOevzijf4fjZI4hC/VmYlqTN+8X1pOtEQ0CjEkBLFvLVlMhNT+OX0/JqlIrQl+DBoorXGypc6rlu+8rX286K/Z4QQYk8UiFJ7DSYb165CNJEKdFZ/pVO1Sli782Za7WYfrxEhBFlL4naI3GjJhHcmAlRcx5Nb37uNVdy1BezSBLmVdXTsEPkV5JSkXC1zrktazqDEfufJmJxt0FTx5oTJ9cYdEjW4qAGAEKyN2TuEDS8L7DMfVDNixtpi43om5L/899/Ca4S8+PLljst3irQYJPri6s27+G7IV/9wieTSsxhmbxEWGcsg6mBye96sUs3aZCzJYm34k1/nJrLcXR+eG5c5kSH0BoukOSn2myO5fv8uV95OI9LefuPdY2zR8XIKe5gj+uU//If/0PX7n/iJn+Df/Jt/Q7lc5rOf/exI2BgxYh9urjZZaQQYu14MwSHN+A5LojTBCbj9b0clEX5jmYq/yJJaJWwbfW6Mru7INS60hQ3PljzjPMfFrI2VnRjOrOeQiROFXb/DrHud2WSRHHtz+SWac6xxizOYQpDJlnjd+EvE+Tl0ZmJ4URanaPB/YmhNGLQwTBPDPJpInmGKGgDJHv+Hg8+jDiICr9Fx0cCtbV4LG54JQb28mUIQbbT/dNiU7Etcq+PkOk2N989BR1T7Eb6/nP7RDKjde4+Zpz84lH3vRxy6xKGPZ5ko1aPYkCj8tW3pPfU0FapVX6Z18wYA4bk5Otqudknl6EYxY1L3dkZJxWH3wWTc6m8Q5zcrKJ2uM1t0qBiSqMOkRxrB1vl3BIYm3iZqKKVoXk/LrfrVFhXDYqZtwuqur/GN379LYfIJLk+f7autByH2u9oE1IoRxrhFZEjWq37X6AqVxNTXbh+4P2UI3IxBoe2dtTDJvqaON50WF5SDl0mo2zG0M+ne+fptyO0UxAqOyXjORlg737u2Kfc5A1v3WRg0sZ2tqBjfTZ85Wmma1QeMTT9x4O/a2Fen/Zlyy/R0WHz/K2f47TfuMlUq8E0Xx/nijd4iWLRKqCxeIwl8SmeewMnufWY5T4+RVH1UmJB7+WDj3WOnj67Dzdv3BlrvYWMkbDwGmKbJs88+yxe/+EUePHhw0s0Z8ZiS1EPC+w3M2dyhTZBS9ulZiIMX6USiFCttt/HdE08bs16t4PDl7wYRSTZSgI4bI9EU3BjVqvL76i9INqy/dx1XoQWembBYsmnkDSJTAllOlV2XSkjcKgthlpoX0QoTPiIecFne7rh4gmRFTDNVzJIvlsjbBghBxPNDaMwj3KvYRa+/1GusoVo+kRBkpmd7nhncsz+VgBCIXX4XSXx4UUOrhCQOUCrp27thR1uC/WZ7dx6twK1uihrbWTL7my3OO2bHikJaJUeWHhRH3pZZZRuvtoqKD7u/7gakunHImfRteMuLYEgQkJmYwjAzqCQiWC/TlDG+H+F1ETZUD1d/6/bNzZ/jP1imc67E8Djw+PdZ5lS5PgzZl17rne/ImoxYsHzyyuC+5ZHBYPH+Openz3JuPMPVxeHsN/Z9/q//+j86fqdkWnYXOLBfUV26TrzS2zv79pkMNvDxj19E/s69fZeLpOaW7TKXzWD16eXTlfbp3vhJUdDCdgpopQjc6maM0LDeWEeR0vv2W6/D7RXM8+M4ZmpS30uKWqO6sHmeasF7zD7/4T3LSMek8C3dK9n0y+Pz9j8ZRsLGCfLOO+/w5ptvsrCwgGEYnDt3jg996EM88URvimg/LC0tAVAqdQ51HDHiqHG/towKFeGDJqVvP99xmfDeXhPcTsRVH9Ucboc86qFD163ayaOCCj1ajUWeDCeY8W0EoMhgOpJkm0W7oSXzYpopZ55c8QzCck5XBTGtwa+Rbd5lMrjPnFomwOIryf/GRjfujp7jm0lDMjVQZoI16wzN3HniwlkwLAxg2K5ED2XHRmvCoIkQsj+zyx5RLX9zP0GzQm58rvsKHQhaFeJGEwxJbmpnBYSDKjNsJ468tIrH9vYlEd7KUt9t6kSvkSObBqfb2yE0DdmfwCqF4MxYZo/lrVdb7bj8MNhuyLqBGsCTQPm7omKOO7qpHX3g19bJT51tR9VsK1bR5b2hgFAobN1lILpr/aN22ulWMva0oHc/IQU8sLbevZltIUtR0LnP4JiSIFYI2hECvXQX3IggO7hoUL73DbITsz2LGgAIQSIFQgienClwa9fXe45FD8gezWu70aotps+fbSVSY/f0XTthFHH3xgoAy/errFTKNGuLuLdvgyk5N/bN+64blFe2/vAPP2l1WIqZox2SB+HJelwdFyNhYxdKKd59912+8pWvbP739a9/Hc/beqh+/vOf59u//dsH3sev/dqv8dM//dO8+eabHb//2Mc+xs/+7M8eah/b+epXv8qtW+nj8iMf+chQtjlixD7ZtPsur8KtQXFc6VzCz7tycIcgrgW0vrx84HK9orUmOYWpAEJrSq2EYivGUJpEChp5k3reQA951iMOmrQaC6yGS6xSBQGF6Blm2SrHelHNcFuuMC9nmMycIVucR3RwPD9JdBxgNR5Q8u4xFy8wxs6wapuYSRqskwq8ZTnFNeNpGtmzBIULiIfd50JrlE5IogAhJKY9vKlUv1XZGhRNaKxDmPgdiNJEQYvIbWA42X3TGlQSEQUulpNDGhbxRrWOOJ1tzGwrO9pPOH9QXoOpaUwzk6YbaT00UWM/WmsLEB3cxjUj3HzMuvJw6Wl9RWv0WKl16AwQ4bbytT8D4LyeGV7WTvv62S3O2KYk7NLGFTPgfDTkkIYhsm4MNz1rO36jjD1ASlJlpbdyq1prwqBz+y1DIoVAGZpSxoRjsElI1qo0y9V9vxcqjcLaz5x4qjCcGMdCP6ke+0S4ug8eADsnQpNynXrhHqXJPp1Ej5Ak2fkM/Nwf/BnB7fXUtDhMaC3dADqXvX1yJs/NVtixGtFRkrcNihmTxjYxxTblYJVKenwuB1HA/VvDq6Z1mhkJG9v4wR/8QT73uc/Rah2NKpkkCT/6oz/KL//yL3dd7s/+7M/4ru/6Ln7qp36Kn/7pnz70fn/yJ38SACklP/ZjP3bo7Y0YcWh6Kl2qCRONvcvwwu9B/OgdzZWlBjUvYuoU1TkvuDHnVgPMXf3lMTchLsODGYdm7nCP7zho0Ww8YCVcoCzahnHbDvVtY5WXkotEhqCRMzibeYGp3Dch5Cl6bWiNFyVU3YiqF/HR8Iu8T+zfKU6QXLBbFPJzjOUscpZBWXwHcMxl4Y6oH+VWV7aiAQToicmhRVdsn+kN69WhCBsqiYhDr2Mbw/X0Po/9aFO42IHWeOsrECtis0l2Ymf+s4oPMQOn04iDAJA5BzPb22+NI78txmT6KhkL9CRq7GbVCJiSNgV1iu7JU0Qq3Ox/Zy9YPmd7qsyS4jfKO95dtiHJCKOrsNGNysLVPZ8dxxArFpq6EdESSV/RPytXv4J0bCaKz2x92GVQmDRdvKbbd/pB7XZvKdOvv/ENoi4m5JYpyZhGe//HNHg9YDe1O1f3pDzMj6XX4LAG2BtVXMQRvNX8O52Fje0ljhOx/+/Y3aLV619l7MKzO3w9tiOFQLWPSyl78ERKfd1juuBQ8yIsQ6Ba+0fZFhyTc+NZQONFiu975Sx/eqPMWqPzxNuwEELwAx88z3tLDZ6ZK6C0Jmeb/QlSffL6O984sm2fNkZvw228/vrrRyZqAPz4j//4DlEjl8vxwz/8w7zyyiuEYchf/MVf8N//+38niiKUUvzMz/wMk5OT/PiP//jA+/y5n/s5Pv/5zwPwd//u3+X973//YX/GiBGHRveQ9nF9pclaM2Qqb/MyM4T3GvhXK+ieRJHeaPgxtba5Wrl1dDNX/VBwYy4uB/t2SUwFF5cD7s7Rt7hhxoqxVoLZavLr4o/TDzvsKKcd8uYYNycdvIzRNsl0TkdN+DjAadxlzLtLEsf8dvLa5ld3mOV9xk5ho0qJFessjex5ouJ5MobFmeNu8zGgknhnioOGsFbFmj1Y2FAqJmzVMCynN8Gi0zhOazR6j7fFvh19rfDKy5BoIrv7ezeOAuxdwkaSRFuz+bHCbxxNIpRyA7TTWyTPdjHGtLMY5tFWUtIC3nUavByMDT1d6iQ4KPqhG2tm/4ORBdPrS9hImjudSixDdr4XttHsIhxEy+W9fkU9t2ZwPJlsllbti1aIaoWsmEvM6ym8av+pTL0IKb0cg5YVMxakzwTVZSA9bOL4kIPeDikP3/W+/U0pm+V7TJzpHHGwhejwr3Yay6CHps8LUYi0TK8XJaxnQia8dKJoYbr7M1A3fKp332P2mVc7fv/933SW//XGAihFoXWL//o/3uGjH36ZJ8/vHzViGZLpdhW73aJa023R8jxqrSZbP1KQtQyenCnw+p0KcehTXbyKEBI/uETGGf5zfDJv89GnOloFHwmePzzvodPOSNjYB8dx+MAHPsCrr75Ks9nkv/yX/3Ko7f3Wb/0WP//zP7/59wsvvMDv/M7vcOHCzpvz61//Op/85CdZWFgA0miLj3/84wMJEr/zO7/DP/2n/xSAF198kX/7b//tIX7BiMcNrTTuGysklYDMcxPY54sHr4MmURpDQtc34wEzE7FSrDXTQVq5FaZl4q5U+mh9b3QqWduJ4+o2Ca05t7q/qLG5HHBuNeDqxYPTUlTQotF4wIWwxIVg4xzazNhpOdYNSjrHnDlPKX9ms5LJqXAU0RrhVcg1bzMd3GNWr2K0z0iCwOIVonbpv7vM4WOxIuepZM7jFy+indPoKzT8K0rrDjP+iaK1ch+EIDMxs68hp19bQ/sRCS5yxup/QK417voiOkowCnkyhckDVwkDd8tfYFdZwI2KIJt/qwStkh2eGXuP4c6/tReSFAIMwz78TKg++DnxZqbGB/ytsPs4cDFMZ6Ac+W5UjV3pI+3bv1MZz90YUpAojVIxXuXo/DUGpdMMc69iR71P3xFIhaGjpiFjakbEWDLctL2cbeJ2KKc5KIFUOKo3f4lqq8y1aoWSsnjg9PaWUEITCcUda6+Nbbyy893eyx2zmg3JxAZaa67ax1OOUyXxHkPcQVi99jooReniszjZsa6l5KOlNdTck4hBJ3R2CSnvmy+yEuwVZw7zlEraxyRnm+Rskxi4Y7lILagWexhmNvcXiy5N5fnb33KZd29e48t/mqZSfPazX+Tv/53+02HevHaFP/7DzhYAG3zbczN8/Wt/jqq0GMta/PHrr/OJj32s730dNbVmg6+8/XYvr6bHjpGwsY2/9bf+FhcuXODVV1/l/e9/P5aVvoh++Zd/+VDChlKKn/qpn9r8O5fL8ZnPfGaPqAHw8ssv86u/+qt867d+K0qpzXU/85nP9LXPL3/5y/zQD/0QSZJw4cIFPvvZz5LLPeT54yOOlXjVJS6nKq/37npHYUMrTbTUQuYszHGHt+7Xqd2vM1NweGq2y8zvAW9RteslHidqoIfVqYgw6INSK9mTfrIfpoJiK6Fe2HtkVOC200wWWRXV9LPkHBd43+YyTySz+CLmjHmGsfxZjOzYqSnLqpSm7kfM1d/iqfA9SnSeXTTQXGSFJfsi41mL8VyRN5y/BcN0jR82WpEcYtYvClrEfgsrW+zNQyPRgCZorJObmO/cpG1eC2GzRna8v7J2od9Atz0IkkYLtgkbOtlv8NV7Vzqu1YmbTbJTc0ij9yeBX11LpxIHLJe5QVTdbbu5l0Duc+Nu27cSGjngaLphxLRkTE129sXwKysdP99C8OR0nmsrzTRS5oRLWPdKxhw81eOwZG0D75DXDgLuWV5vwkaPt4RpSMzdNckPyRW7wcvbhLlu6FbIggUL9DYL3JQxi+M+97U7NDEpkZrbYy5+lNBoHo/x49qbXyJ3+eKht6PbA/nanavMvu+1A5aG1W98BdFntRrYRyg0oHLvPVZ1iyfMi5jbqqIIoHX7Fvb7ep8M0Frz9pXrez73NzoyuyZeBqmKUsxYLC8e3lftIFEDYLaYYTpJSAoOGUty9Z37fOIYdI0/+errvPP2Lc5dnOZTPfgr/vbv/zHrK8cj6D1sjISNbfzLf/kvj2S7v//7v7/DKPQf/IN/wJNPPrnv8h/72Mf4oR/6If7bf/tvAPzmb/4m169f5+mnn+5pf1euXOGTn/wkzWaTmZkZfvd3f7ejiDLi8UUFCdGDBsaYgznVeXCkWgcbywXXqwR36ggB5quz3Ku4lIDVZsCFqRy20XmAOcx0kj3b7mvp3l6yrSDezPM8Soqt/jpopVa8KWwkoUer8YCVYGHTAHSnZ8YKH4ufJTIN6nmTbO4ZXrTfd2rEDPw6cavK9WiCuhehgUnhUpJ7RQ0fm0V5hkr2IlPFS0wP0STzqHErK3siEnpFq2Qz3SHwQsz5ztWFOq7bYZ9aJalvwDaUF9Dy0rKDztR0T+KJ2le82GtQ2VpMty0yfXraJIqgWSE7NkMcuoStHiooRSc7ePecnffWbcvlyXCwpJErTvffq3sYgI/n2sf8IRE1gBNVpy1D4nH4GfpA7LONHn7be7vOezFjDrVk5ljbt6DmRVxxGpSUxaoxfI8BLY4nQgZ2pqY0rZ3HPokHC8nf2KJ7uzdj0+2UMhb1Tka93q7ItH3WFwM+x1qNvWaRS6sr4EdgaqJmDXZ7iyWK2sK1nvexVl0nPAUVRYaJZUisobkO98bXX78BwJ1rKyy+tMqZ6Zmuy49Ejf0ZCRvHwP/8n/9zx98/+qM/euA6f+fv/J1NYQPg13/91zdNQLtx584dPvGJT7C2tkaxWOSzn/0szz33XP+NHvFI4729RryevuCL33oOOWCZqeBOmsqgNfjXqzu+S5RmP0v6Xjrhu1mp+zyopsZQFyaPN/qo4kZ86dbRFzM1+hR8DKUZb0QUmhH/S/8hoYg7dpYLOssZc55rkzaxY2/Oohz7mEFrosjDkBbSkJjNJQqtO8yG95mkRoMcryd/dbNlt/Q8H+YKAOuMs2yfo5m/RJI/A21B5mGKytEqGVjUAIjDbWHfWqcpEu3olKDW//XpNysod/9BTFBewzzTWRTXOhUatNYDDbR6LXm6HRVF6X4r6x0MiI/2SjhMtMUG/ZZpHTaGFIRe/eAFO3Ac6XjGEMpUnkaUgGtOk6fCfN/XUN3Yec0MU9SAdBCXsQxqXkTDiGkYJz9INaUgPoSedMVp8LIcR5maSmbnc8bvUIa4l/YchmLWxJACKaHcPAIvr33mJirvXdnzWXn14PtfVZr0OrKPopO/Xh41qvXagcJGN47CNPZhYiRsHAO/9Vu/tfnvp556iqeeeurAdb71W7+VTCaD3zZ8+c3f/M0DhY2VlRU+8YlPcP/+fTKZDJ/5zGd49dXOhjwjHm82RA2A8EGTzFPje5Y5KEAh04zY8QhRkPN7640Et/vvXN9cS2fuH1Q9ZooOmR5fvG4Y86DiUciYnBnLoDUs13009LyN4yLpswOV9xWF9gDxojXNdWOrJGVBZ5k35xnLn8PKjiOk5KS7IH59GV2vg/J4kbcZEzvzrYu4TFGnzBiGEESZOd4wP0pQvIR2DvZ4GZTQb5IELlauiGkdXfRHP34LgVsjdlvYpXGiVh0dJchdUQ5ubZXc+Bxohe4j316rBK+y0tc6uwnd2paZ4nH1o6IEb325x6pKw+Utp95zqD5AXG9S8atMbHtGRkLt8eI4LlqNVBTrJa1mEEKhsPXhor+GnV4RCoWpT8czvmpEXLdbPBsMp0xyt8oT3cg7Jo3DVAwaAMfs77qQAiZyNhV3MBGgJRNulFqpuSvsVOV6jLwcy1rUvAhTCvK2mU7UDIhAkN+seHGMJuUdmqzUruiP/W65YzRkHYTrd+/w9MVLx7Kvu0sLLK2u8YFnnzsSI9ERw2UkbBwx1WqVu3e3Qtc+8pGP9LSebdu8+uqr/Omf/inAjlSWTtRqNf7KX/krXLt2Dcuy+NVf/VW+7du+bfCGjxjRhWIrJrfcJLw4jm2mHUfVijAHyAMdhCBKehYlrizWCRNNuRVScEwqrZCF2ul0iG7kTcbc3qeqtvdJnkrmWJRVzhjzjBXObRqAnjRhnFBxIypuxJP+MlnSCIFVOcEYO4WNZSY4k4uZLRU3Q659XjrS9ikVE1VS87rADzHnek/vOCoCt0pcS0PQw/JWqohq7bxutReixxRueYmD2bpagla1Z1Ej9BuY1q7KEVoT17eFwu667ZWKiQMX0x5+ZNVhxJjDEO7nodEFvWsQoQVEHcpo9GPcOChaQ6V+NKIGwJLlczHceb6vOA0yWnK5j/Qb05A9mzofxC27xYt+iYJj0gxOWtY9PNecJq8yAcBCwWfMPbgLn7ON1HybVMjfHRXTj5SUsVK/k+1pmZYhiRKFFAKrg4BhGhJrAN/UvGMOLGwMg2LGImcbSCEQQvQsSm8vTwr9RSEdZYru0BlUg9y23oaZMdAuu9r7Pn73977E0//nwcJGHCf83p/9Gbnc4BMWn/nMnwBw78ESP/hXv3vg7fRKuVrj//6N3yafz/Ldf/ljONZITOmHkbBxxLz77rs7/u7VJwPS6I4NYaNSqbC0tMT8/F7zN9/3+d7v/V7eeOMNpJT8yq/8Cp/61KcO1/ARDx1aa+JVD2FKzMneS9j1uRPm1kOmamk4/a21Fs/Np0ZTg6SX9Ipa7c15PVYKa5e4Em77e70VsnhKRQ2Aet4gLtOTgahHiELhyAz1vEmUO8f7MxdOXszQGtFapdC8hY58fj/equjkYZMVqbARaZNECJbFLGXnArXsGYJWBAoc3UKI8WNpbhxsE1e2dSyTOMCvrIHWOOOThK0GOoowC0Wc3NHNuGutNkWNXnBXFvqOYEjC3nPoo0qVqM9IIm95MV3X7P13HJbtqS3DHBwPyn4h/arDoXzTqfGaN3HELYK793sRwIZHw4hpQF/CxjBxZfpOkkec4rJ9gHaUVI2I28XUgDM0FGPbuvAVI2Qi2etbYxqSnC0AjdnJ86rHQzOWtShmTBaqO9+f0wUbN0zIWkbHEPicbeDv5zFywoxlbbqV/jJ6fJdujy6ZzNvEStHwY3K2gXnS7+MDSOJUlDqqq7devkt+fB7D2HttTuUd1poBQsDHX5jbu3KXRqkeJ9HiQPHeO/d7bW5Xlu5Vh7Kdg9jw2yjT5EsTb/GtH/zQsez3UWEkbBwxN2/e3PH3xYu9OyrvXvbmzZt7hI0kSfibf/Nv8oUvfAGAf/fv/h2f+tSnaDb3N5YpFIYTCnn9+l4n5F6ZmZlhdrY/5/0R3QnvNfDfS2ee8x+c3dcUdA99vNEKbrIpakDqPTEoa82AxVrqmXFmrHtbexE2gjjh7Qc1nG0vvIdpAgRAC8HKuM2Z9bBrf1OheddeJjt2FpXPnZxnxmaDEszGfUqt25yJ71NsR2IkSL7A+zbLsfrYKCShsGnZF6jOfBuiXVo0WH2weS3GtQZObvwkfskmQX1902Qx2BY1EdfqRyps9O2B0K+oEYf9VwnpcR/vOQ0uRTkyqh1N9TCZVD7M9HgJPFQzwj0ihDh8Od+HiGAf1bslEyIRMJFYWLtSgtIUn8O9HaTovA1DSoqZ0z1434+CY+yJsBiEvGNgSAchwDENwKDg9B+m8s47Nw9eqAPBvcWB1tvOQVdHYAz2LPfv3idYX2XmmVd3F0jBNiVnx9NJuMndJqanlCRJ+OLX38DzA/7SBz9INnNEk4htblx7MBI2+mQkbBwx9frOTurk5OQ+S+5lYmLnDE6jsXf26969e/zGb/zG5t8/8RM/wU/8xE903e6wOgHf//3fP/C6//yf/3P+xb/4F0Npx4iUDVEDwHunTPEvDz+kvugNliYBoLRmtRFgm5KJnMX1tqtzK3CZKjj7VlDplbtll2iXiv+whB8nkY/RrPOEWyQXph2IiASrg/tqJBQPpm2cwvs6BLUfH4nS2LXbzLpXOZMsYHdw8DBQXGKZe+ZFJnIWWd+iKdOZGZGxNkUNYHgqlFaEQQvDsDB2p1B0Wny/z7tcO255kezkHKJt3KmSmChoYWXySNn/azVcXyfOuWnFj8bRuZ2rJMJfO6gs6ODUjZi3jDqvuUcffXAa0WIrNb0mI3LH5O9wkL/FxrM4OsKoupOimDHxo52/K2ebuO2UJbudHmFIQZOYrDodnhtHwR3b5Q48tvffIIg0mOWwW+k5NXYiZ1PzIpTWWIbke14+s/lds4do0iF7x3blPafB81aJhh2TyMEO0mTeZr1d3vZ7Xj7L7RvlXUs8XEaXX7vy7mZEhe+HfM93fvvJNqhHGnX34IUeEUbCxhGzO3Ii04e6l83unMXuFoUxYsR2dB/lweJVl2bZw5rJ4jw5fmRtur/ubnpbvHBmZ530IEoOLWx40cPVaddJjNdYZMW7x5Iuk8PmhfBb2HjRe4T8iXmD59Q5cjKHsDLUCzaNvIE+zt7NNpI4Yt1LWG9F1LyIj4j7XJKdy9+tiilW7AtMli4xlSmBELQWu1TuGJKu4dXLKNcnAjAlmYkZDHO4s0E6jAndOk5+HLRODS1jRdxqkp8+O1DvU7k+nlw7ujAjrfFWl46nvMVjyhW7weUoRyAUy6bPE9HxpGDcsV1e9EsHL9gDvkxIOlwkt60WzwVHZ+DbjX5n1cdzFumFLljNhcz6Do1CjOcmD52wYZuSsB35VMwMYFYx4lSRd8xtRqLw9Gx/99RxprbUjZgHxcOl7ubstCLMp1+7wLnxLHduDNh3OaE+z26++vp7m/++e2MFvvMEG9Mjnu/z4M5uQenRZSRsHDEbVU02sO3eO9jOLvddz9sbjn/58uXHKgxzRG/sF3IcV3zErnzjpJmmkyT1EHMuj7AkwbUq4cJwhbTthp23y62d7T2mS/ik7xStFJFbpty6x0KylJZnBRDQImBRVjirJvFsiVsYYzr7ASp2lkr3zR4tfp1c/SazwR0c5fIr6rvZEF9u6jOb5VhjJEtynnLmEn7pCbSdDuoEqZmkXzueF6tytz1zY0VQXyc3Od++yPRmedRNBrz4kiidhUqSYCvlIlYoFSONwQYgqtmbl8zAnPQNcAzcsls8k5zMALxpxLxtDFZOtRu7S37uZsNL4rDcs1zWjajjJGrdiLnuNHl6SFU9+sGQouecekiFkMl82n9ySbjtuISJouAeLGpslPU96gHk9sHtBpYhYVd253jWohUmCNLolEeJDePRx5nP/cmfsLxUYWZ2/MBlh13qt8Me+l+jhzY5poGZtPjCV96m2ewjcuB0aBkHUmvu7yX19ffe5etvXePCxTm+48Pf3Pe24yjhzWtXeOrCJfKZ/VO2M5axGbn26qWdEVtvdCj7+yjzaD0lTyG7IzTCsHeX5yDYafC2O4LjpPn1X//1vsxQtzMzM3iN5hGDES408d7pPrhUzZBoxSVaejTD1pZOyDjUjhTN2j2uhldpiPbgdddLO6cdVnMadyxLYKedaskJzNBpDV6FQuMmc+EdZvS2KAvBZjlWgLKY5D3jWZq5c0Sli9DBIAzAr6+jvV3PvuMSs4IIlUR45RVQCmt8HDuzNfCN64c0uNz9Ox5x8WDVDJiJT69L+5oZ8szDFby1FwFNGXPXcrG0pC4H9zLqhyWru6lsxTiedhwXU3mb7YWZGkbMbatFThi8rMfRwKLlcyYafh690WFAOJG3YdcrSgjBRO7h8B/Yj/0eieM5i9VG70bGR4FtSDyVPjC6laWVRyQqXH93AYBG5YhF7SGydudNvng1YOz80+ToTQD8jd/+AqH/cKQG98sXv/rGvt/9yRfeAuAblTu8+PTTzE5O9bXtwIv54z98k6+NXeV//xufQu4juE7kbOp+xLc8Pc0zszvF5zh5NI/7foyEjSNmt1Hn7giObuyO0BiW6eewePrpp3nxxRdPuhkjeuQgUQNAefFDIWq4UcIYGq3TCMWqG22WtDs1JDHjrma8EZMPFDdlQsPeeU+b2uCcnGMqdwG7MIOQkgAIWhXiVgvp2GTHjsFkV2taYUK+dpVn/beYZP9Z56eNZczcLBN5m1LGpCIOLiutveF2XpWKSaIAy871FKLqrWxVg4gqVcSkxHLyaJUMPVxoe1lApWKkkHujRA7BdadJU8a84o0PbZv9EIkBj9dQctkfPTpVT3nPbmyL0jia51pTxhTU490FzNppWoBSGi9KqMkIXyqmJxxuJ+l7sFINexY2+o1ocUxJ0I72KmWsNGJjQJJB78sT5DRUDLFMidKp99x4zurgFJViSLGjdO7jjFpvoh2TOzeXeX7uzMErwOFFjWMI6w2iYKDSqi23N1Fqqbzat7CxQbPmU200mBzrbFxuyFQA/fATqY/jcnmNOwsLvPjMMwPt72Hm8X6rHQOl0s6810ql96DyarW64+9i8WTCa0c8nIT3G1hzOUSPplYA/rXq4Ds8whfPUm3ni+NO2aXcDGkF8akaK2mlCFqrrLXusqxW+XTwMTLtqIuLahpbm4TEzIlJZjMXyBfPIsy9URlxPU0DUm5AnPUw7SOI1tIa5Va4H2RYb4UEseJ5AiY7hNKXKfG2fpHEyCKKJS6PHeAdoDVJEiKliZCdrz8dRLRWHmCVxrAz+w8GkjgkDlpYTgFpWmil8NaWINFE2Sa58a0ycV59taefHq6vY53Jo/XeMOjArWFYfcyQdtBVtErwG2WUmwo6wjExs3mS4PARQxUjYkAftxNj0fKpGCHPBIU9FRt6wTIksdKPbtrlCYRcv5tpkFGSF4ISEsE1+3g8vKQU5Pp4Jx01hhRpqosGwxBMFRxAEBnptdYSCQ0jppgc3F2uHhBZU5cRsdCYWuCZCZP51EjSkGLfNJNul8aaHTAdOkRCsWYMJh6bR1wKtxuGFJhSEO9JnT3eNjlWO0JSSOhiyT2ZtxnPQdOPqfuPVvTSBtvFtseNz37+T/j+7/6uY9lXy/f4rd/7I3w/5Du+9bWe1lG6N1HNDwL+52f+kCRS3Lz1gHPnH68I+ZGwccQ88cQTO/6+e7ez0V4n7ty5s+PvJ598cihtGvF44L27TrTkkv9Qh/rgp5zd3Zyl+t5O22mqeJIELWr1OzyIHtDclmpy01jmhSStThNbJq84L0NhHMPp3VQwSSJMhiRsaI1sLTHWuMHZ6A5ZAr6WfIq4/Sq4yRkUAolmnTGWnUs0Ck/S8CJo529q14eS7hop4TXWUC0fTEluusuMTqKIKhXMuX1mRLXGX1+FRBEbLvmZswRuFdr59toLaXn3ADBLhXSfvR4KpejUgY5r9X1n7fY0P/IJajsNUVUS4ZdXdpiA6iAmCmo9t+1RY8H0UAJqRsR0n2ksQgiytoHW0Oh1MDFk/WPFPNlw+aPCl4qvZ2oIID6GGX9TSnJOW9QY8tjpsO13LMlk1mbd2nWNidQU9jXv4EojB7VAC3jXqTOmLJyCgSHlphfIILTGE9bqdXyhmCw6+FHS03sxZxvtCAQTo9eoiSPSGgoZk+qesvGnVcAUnKAOdCq5f6+3yYSDqDbqjBeHY4A8KL0YbEbBcCJ2/vT1r7K6mE4ife73vjiUbQJcuX2Tm3fuk7QLCJSXGyNhY8RweeGFF3b8ff369Z7XvXHjxua/JyYmmJ+fH1q7RjwexJXBZ4ijRHG37CIEXJzKDbFVB7NYPf35plrFePVFlr27LOly2vHb1em5Y6wyl7tMtWji2xJD9Hcco0SzWvXwqumgYCJvMZW3+zMR0xrZWqbUuM656A5FdqYaPcES10jFF8vJ8br9LejCGVRmfGuh+r3dG6VbT3dTYIgVoXewmaJf79yhSJIANszlEoXXKKNana+NjSiXXgm82g6vjUHwK+Wt9rUJ17tUfjlmbtst5uMMmaFWguhv0FE1ItQhBgMZq/vAy5CC5IBKMg0jLfMpAan7a8yC5bNonv7n0UHU7YhSmEaHbU9/OUwKg2lI4j7MH7P20UVq1I7Sh2TA67fTkfWlwpcB52QW0cOGu50d2zIwJiQFNIaUe8re7odjGpwpHb9n21TBodwu/TmRf7h9Qx4VBn0026aE3i0DD+T/96uf45P/28f6Wme5vEYpf7Ip+r//xS+ydK/a93r37myVXQ/c4U3S/f7vfmVo23pYGQkbR8z4+DgXL17cjNT44hd7U+bCMOT111/f/Pv973//kbRvxIj9uFtusdpM31waGG9FTNSPpvN4Z73F+8+NAYI4UR0jNE4LmSBhvBkTt9b5jPW19MNtvQOhYV5MM5e5SKZ0hiWj/858rDR+lKA0NKIYtx1DsO6G3Cm7PDmT724opzXNIMap3+IV/ysUaXVeDDhjNQmLWSbzNgYJbngGyyr0aAl2MDo5uLOtvbBzBMiuXv1+osYgJI0mXp9iyM7GqD2ixmlj1QyZjYdvfHic9NPx9jeqg+xaKRAJVzOpSeyr3WbeNcgQrMhCaIkWiuVMhaSgj6wiQaeB61FET6zkQgwtaYYxt6zOz4N+MYToOboJjq5i461s66isSLqSGfLgbhAM2UFRP6VkLYOZooPWaRWH/TmZ33Paj2L2AJF3GGQsI01F0WLP/TqVt6m4EbYpU5FyiNe+SjSf/8JXYPJ5AOrluzRvrPJ59WRaTaTDw+Mzn/0CP/w3Pjm8RgBvXu2vgsiVt3dP+uyPGkJ/QSnFX7z15qG38ygzEjaOgU9+8pP84i/+IpBGYdy8efPAtJI//uM/3mE0+qlPfepI2zhixG42RA2A1UaA91Z/IYdrzYByM2Qyb3NhsnukQitIqHsxpax1YpVLuqHikGbtHk/4Y8yHGzNdJaaMAmWZDo6LOss56zxjpYvIPlJNdlNphQRhsu9MXaw0V5ebPDtX2CluaA1umbt+lnIrIkwUZxB8q7GrtC6wLGZYzjyBV3oanDzz7fVbq0tp2ofZIj9zbuDfsINee4ud/BOOsqd5yLGjPsFUqJMM1O53396QSpEexJLls7LhM9ChkQdFjYgQ8l4BqXcOuObXLhKtB6xOLzE0ta8DK2bAbOxQMyJc0fsx07FCqBgRJwit0Ei0NAANGsw4IvF9guoDbmtNy4twtCKjFEJrhNY0JuZRpo3QGhnHFNcXyGYkGwcy1gmTfjo5k80IlGkTOTlUpJAKrKBFyVska09vG4AIJpbvoYUg45jE2TwRCQiB0IpcbQ1BhGXZaCFBCDQJImn7zEpJrw8AwxDHLmzkbYOhBkLtw3ENtrOWgddjxMdhcMzT468yKFaX6ilHhRCCsezRR7kUHJMwVsRKM1HYmSaVtU2y9tENG1v1AFLvS/y793FMyTe+focXn+lcfTHwhv8O/uM/OjrR4E//+G2y2SzPXXri4IX34WvvvctXv3R1iK169BgJG8fAX//rf31T2AD4pV/6Jf71v/7XXdf5pV/6pR1/f//3f/9RNG3EMaMTDVojDvFiVG1XbnmEYb2dSBr9RWvcb5cve1D1KDg9GK95IaWs1cFI7GTQShG4ZcrNOzxQSyRCIdQF5nl2c5n3Jee5Z9SZzl/Ezk8jeslX1prAq6FVgpMb32GuqbXm5lqLsz207+Zqiw9etDC8MsX6Nc6GtxmjybXkE4SkuaqLTNIgSwGPFTHDcuYybulpcApEQYuwXkWYDbJjsySxvxWBECu0SvY1/jw+Tvsc2ohO3LRbPBHlCYVi0TweofKeNXg0jwih6JbY73qzlMOZlQuIyRb6gLGFThKMMMYIY6wYbBUjlEIoRaGyjlecRMZpEoLj1iAoM+fewdeaB0KQqIhZFGgFKJLx7wQjh9ACETWg9r/QJKAT0tF8es9mx78FAGnMIu2nNtuThNfRySpx9U8B6OToMBv/v5FG6ravkhph/X8hxrd+qFARsp6GOAvhYMYzOGrLO0eFawT11xGFnZGlRvUvAHhgXefCxN/ENNqisA5x1r5EEtdpZJ9i0knTbBPAav8njGkM+xl0TeEIjRPeQyfrbOX8CRAShcRq3WFO+biTH0Dn8tTrMUJHZFeugWGCaYBpkBHTWFYJZRgo0zyU4bXqcCAf5qfVRN7GewhSQE8DWUuSs9PIhvHs8ZRknys57eico0WIDQPd08PqKUrvPCy//7tf5rkfHVzY+PM/eafvdXpJeXuUGAkbx8DHP/5xXnrpJd5++20Afv7nf54f+7Ef22MsusEXv/hFfvVXf3Xz77/21/4azzyGJXseNZQf0/ryEjpUZN8/jTW7N4pBx4rwfgOZszp+H9cC3NeXQUPug7OYEw9HmPl7y43eFz7hZ7CKQxq1OzwI7lITrR1tumYs8Vr8NIFjUi2YUHiGc312NsKgSVxLj4efKLJjW8ZO5Va4V9gRuuMMdKw09t0v8KrYqd4/I+7zFzr19ik5Fl/P/GWs/ATa2eknEVbW00ndWBHaNeQAZc56oZdUlE4kkU8cjjraDyNlM6RihGhSw8Rjp5996jRS46CVBJJCK4+uXAcUmgSVeQIhbMqxYm4hS1L7v4EtATiTew7Tnt78O+/WGA9eoZBJBdAkXkbHSyThtfTvDvt1Igep0jxypWJC3T2NRO96WAhED1E2W0t07AQfcDw1Gjfe30unHq1xzfd5Pp8KG9ur2yx6NzaFjU47FUiEhkTHBEkNW25757UfjUnygCSuUfS+DRmmqUZaNQjqb+zYopV7Fsveet6Gt1ZR059CS4UWirhxnah1BYSJkCZIkxlpkJg2mXwqhkROBm1IypmQqfDoZ9CPQuaPjL0h8VIIpBCofcSex2todBDiUKavIx5fDlPY68Hy8vAa8ggzEjaOASkl/+pf/Su+93u/F4BWq8X3fM/38NnPfpYLFy7sWPbNN9/kh37oh1BKba77sz/7s8fe5hH9oxOFf62KjhIyz0wgd5Vv869VUX7adXW/vsrYJy7t2YZ/tUL4IE1tKHzzPEZp58vTe2stjfpob6P07Rf2bGPEAGhNJki4VX2Te2qBRKg9PTlHW5w3znL1jIXKDC4oRfWt6hjK9WFbWfJKq3tUjCQhh4+Lg8JkWU3ArqCKacPlcinHZN5uh8yWOneOt32YBEFfwkZ369CdbJQ87Re/vHp6zfEfc+7aLhfD7ullhzEMHQQdx9iujxkEmNpCWTnAQGiwo4S5lTdAReRLz29GQ0ACchxpj3XfeBshTJQw0Elatt1MTIS0aUUKkwwJ3e9frXd+L3bfvB3ZkjuE6KXL1q4YlMpKKOCBexOw22kiEpHaqLIR9RAaAdpqAhohXKScRIgtc0mBhZRTAKyHTaZyZ4i2pf1IYSLl9LZ1dLu9pfa/NbEMSUScCiciIY0dsdkvh0TsyfvRPGhdJdYR53LPkDP3VlAQYmv2fPex7rgPYSORoNJ96aRFEu+tXCeBtWCZmcwFAvNllpIcyUqW0PBI4utkhQHCYLZxlfzYi2jTJCMNDKOMKGZQtoPY/XP2uT9sS3LAZXQoEqFZy/ZnjiCEIHtEJXof1QrOI0YMm/XawUbsnfj66zcOXugRYiRsbON//I//wT/6R/9oz+eNxs7Z5h/+4R8mm93rKP1v/s2/4Qd+4Ac6bvt7vud7+Ht/7+/xC7/wCwC88847PP/88/zwD/8wr7zyClEU8ed//uf82q/9GlG09Vb7uZ/7OV5++eXD/KwRx0R4r0F4L71WdKzJf9Psju9VD4aYG6IGgH+9Sv6DO0u1qm05hTo63caFg3Kc4yGpNGPNmIlGTDZU3LICkl2zWXN6kvnsJbKlMwjDHHaVwh3EqsvWtWZONrE0lIhY0GME7Y78KpMsOU/QGnsaMiW6FfjVWqGSg3vO7uoiGJLsxOyByx4Jow7vqaRmRBx1ZVCRJBhRhIxjDK1IpEVsFzBjidAC2y1DUm8LFAlax8xUv7C5vhz/FvLONyHas/tRZFEO04pkWp3fsS/DGO+rbdKYJEnW2n9tH5TvF5IuQRjpQF0YtOw6oZFBC42Z2EhjGsN6CoTc9JrY+LeWkrWJNZJM+l7QOqZofxRlSJRhoqREGwbKkAScQQuBlhIta5sP0q9kDc5OfJxM2/vifsXt0Mag/d8G38kld8tkNRKKlafSyJMVDQVl8Hzg45gGQZwAE6xc+vYd6wCsPrn19/lxgVHZGlBX5r9rszqL2xonZxmYKDw/RiUJCEliVMnZJmGkEF6RWHwQT3kocw6Ek3qEoDEzIVLnCS0QpKlPSsZI4yzoCE0MOgZh0XbwSBshdoq5oosYsurfZ9W/jzP2CkKCoU3MRKN1a3NwnoRXWWkEzGTOUw9XsNxrzNgvYBQ+TmQEREaI3fgGMlonsR1EJosu5BGT48ixAsKQmEISH9Fb5sZYCyX0ho7TE4YUzBScTfNcx5J44Qk4tY4YCnJXD+txS1M4Lt68doWr1/eKpCOOnpGwsY16vb6jxOp+LCws7Lt+N/79v//3NBoN/vN//s9AGrnxn/7Tf+q4rBCCf/JP/gk/+ZM/eWB7RpwOgttb5z9e6xBCP3RLeM1C1aOYMSlmes/zTJRmpeGTtQzGt5lPxkqhNVjG8RtjHTdhq0y5cRsZxzwfvbT5+fuSc9w2VnG0xQXjHJOlyxjZ46utbnbw6NgYREqhsNr/NhFYIsaXOf5k4nsJI420LDK70k32oDVueQl6MYlTGlRC0Oye35rEIXHoYjkFpDF6pTyqtGRMxYhoyQSrz/wSGYTYrRbZICCrs4ROIQ331xIjVpjuvXTg2RYptgsGCSCMSZxoK11B6TWUOqiizZYIXE66zTb3d81qmUVYsyAkXsZHmTFLpSqBiMkWv43ENFG2SWzbjOkJrG1mDCu5SaCK3xYAwmKJmsyy7Ezv2c+WaLCzrbViZ1PfxO2cFjH0VCABTaNz9ZnBtykwLQMw0FqSbKseYNqSCE0ks9Qn52kaCa2giEzS85ZREq3OYif2ruCPDNH4Xyc4X6Ywkx6bxrLAjixEopBxjL5UI46aaKXRCkQ8galfBJWgVQwqQusIrQPQQfr/YitaT+u9kxUr/h1W/DtbP03mMbSJEZtk4jxhuEIS3UorSjSBNeA2gIWQBXThA9hjz5KYAi01QTXAkCFivIjYE/bRH7HRvyKZt03MbX2C8axFFCsSDdOFvdfcw6xHP8xt7xkBY1mLpojJJeYox+iI+OM/7G5CelQVokaMhI1jxTAMfuVXfoVPfepT/PRP//Sm58ZuPvKRj/CzP/uzfOd3fucxt3DEqeKAt+z9dY//58v3cCzJ//HRyz1v9t56a7Oc6vvPjZF3TPwo4e0HNRKleXau+EjWmFdJRLN2jwX/DhWRRtYICS2eJk/aWR23p3kl801k2tEZ/aBVgkoiDNPp+60Veg1UfYmzQZN19qYotfew5+/S1DyBuwqJJgkiIsvByhQI3BoqDLDzJUKvifIDzEIeaVi9iRrbUGGX6A6t8NdXINHDraQyYmg0ZUxuCOUbrjrNzTKkkdDcly0ueRZCaRLTQSiwfUmpfA/DvY9WAVr7aO2xEVtv5Z5B2LPk1EsI2RbhtCJWB4TY6phyrJkyN+6rbvemAGGhCFFGQEjCWq5BvvUsWhgIa64d1dCOisDcFAx7IbIgmNgpRKiMQEmDzNx5ys1tg91QcJjJ95xt4LZnxwvt57QhBTnbpOKecJ3RI0KjuWW3KCiTmbhzetzG6RIh2G4BU+uOlSct5WDenafJEoUZOx3DCYE2DRLTwJ4Zw1zfGrQbM+cxszvj3XZHuGh1F5FoJh2b0rpCts6kPkJaYVhPolUA2kdpD7SLEPld6+/nkRKhVQWla+SjEsTptZ5baRE2fhWwkbKEkHkwc4hMDkoF5PwUslTYZ5vDx5CS+bENcefwo7OjKqP8qDHMo2SbkknHxgwf/mM/un5G7GYkbGzjR37kR/iRH/mRI9/Ppz/9aT796U/z9ttv8+abb7KwsIBhGJw9e5bXXnvtwFKwIx5NdJ/VQO5XPZgsEESKr9yp8EqP6y1tS4m5u+7y/JkSt9dam6aV7y03+MiTUz23oxlEFJz+nMHdLqGsw35NxX6d9fot7sYPiES8YwdawBVzkUu5Z6gULUJbkqW7d0AntFa4a2mpVJnLYGXzxIGLlS2mQkeX9UpLX6Tu2xjt6cYcnULFYfeRMaRkMm/jNraum8h3kaZJ3M7F9L2tEr1xrYE53puXwIG0TTaiyIe25wtxmuLi1x8dB/NHgQeWR0mZZHoVN1JLBIQGESuyjXXQCbO1d0iUi9YBSvtE2sMZ/xhCljCcFwGY9YvE4X3i+N4B+9hWpk9IUqOYTs8EAyFMhDBYNWvkLRstFIawMOQ8SkqQksQwaExmiTMZYtsmjGeIEwX4VIyQpuPRnP8AwJ50CRnE9FNQJbL2F/qG6UMgJYznbKSIMKSgmDFJPSk4lrKcJ8maGbJGuK+wAewyfd3/3SmQOPen0JM19hgSDYCQAqQgU7KwbQfp2bSCmERpli99EIDzE+k7REcxC2tN0HcQCRixZMKfQ8aFtvDnolWTHaYaYqcwrlWl/a8QpdZAraXBSD5QBe6Ckf0I4fgLYCVYpsKOPBLLgIx1RDPy+290PGux1hb37B6qv+Vsg5p3/F4bnSp/jIbIJ09j/T7FyZ3pgovLq/ssPWLEXkbCxgny0ksv8dJLLx284IjHguBm7eCFOiCURq20OMxruRXurAe+VOu9p//OgzovnR/DOmQpsoWqTxgr7GGkwmhNwUtYr93idd5NP9vVvBk9znz2EnbpHMuHTKEIvfpmqVTl+gReAFqT+MGeKAatIU4UYaJRSnHeuMF720rIfot8m6vqWXSX8ymACxPZPbMV2gt2iBk9IxhKz9JbWTr0NkYMl1ho3nLqvOZNIBKFGQYYUYyMY4RKECr1H0hTQCKE9TRyw3dCQxxdBRRJfIdEx3t3sNuXQHYRBoWNEBmUiIhNDy0USirsZBYEJFKiTTNN57Ascjl7s8RhptgiXEn/HZGBdpSVIQWJ0gS5tM0CtmehHIiyQfkJUh886FUiQQ09mK3zfTeWsZBC7EgXHJRHcVJThvR0ziCN3GiuxWSGIGxsx+9QXWQ7wjLRmfT8adJaOivjzwFwfiILCHSi0I0mulyDRhMhz+DZTaQyMJSBSqoHtkPKSfL+OG17ESzexNQeccVCCAchHLAs4so68vwc0jm6iMyMlQrumcjo6V0uhWCumGGpfrRloUOxda5ytpmKkI+mTdlDjXfn7h5h47137nPxyZmOy+uH0H02SRK85hE6BD/mjISNESOOiwM6l8GtzsJGXPYIbtYwJjtX4riw4jMmI5g9wF+hD26X94sa2IsG7qy1eGr28OGwa82QgjP4Y0lHIdOuYKIe4cSacTHO69smZixtcEGeZar4BGZ+/NDt3dyv2jWDqreiGABQCVbtNjOtq7wXX8DTG+HJgmvbzAwDbFTuAtnAwI8VnYJ4pICMZZDtJ35+c28DkIx6f75MSNDkVedrUwmNPKSZwXWnydPBIe4hpVPRIoww4giRxMw2r+FOfQgrtsgbeUS0hopuba7SDs7YgdDbgvpFWjlCax+lO0cJaBISEaNkQi1bR0obR72IMi0SyyHKZAhzGcJ8hmIyjr2pDGztJ86mURRvZKu84uU77KU/ej0Ta2ZIRktEtknRLXVdU6NoZVtDm9a1TUmU7H8Pm4cUeG1TcsNuMpW3eRTnoq2ovyhBKg5MHLxYPzStmKYVo0LNTbtzikkpa1H39h/ECEMixkswnno5yWqWqJWwGcH0zJNY9Tn06jqi0UD7Hjp20aqFUnUgQsjxze1pHaM3FA7d9gihCQnES38KtwykeZbg7LdDPsYsQKZkHNq/Y9svImebPUVrbHDYa70XykbIGZkhowzWsoNV6jpJ5KOoTvbB3ZuPTtTG733xiyfdhEeakbAxYgRpGkhwo4oOEpynx/eUaj2ZRkFSC2h9dQWAuNr5ZVxwEyjsnImqexFLdZ+pvE1J66HkIX7tbmXf75IhquZe2Md0a5uotc5a4xb31CJ/Lfwgjk5TLsZ0jvPJJA0ZcN6+RGHsEtLqs0PcE52Pr0hCJhf+iAvxbbLtgdwCUzTZGrzdNJ5gRrskZhaEAWNTmOUyBSMd+BQMEwMTS0iysYFltGetq7W+fUAGosupfVy6WlecBqXE4smw8/H+WqbKmTjD2WhvtaxeqRjdZ3BEkiAUyJbGCCWmkmQai2jlA1G7vOXWydKACm4x7v5VAIyMRIseZmrbwoZGo0QCxhhSZxHOc2DYKMsmsm2ibAbPuYw2DSAd1DVyLSAHPL/Ptjlls6Q6/Z8NDerkvULHKIBIBqxOL3FWTu67pZzdXzRAxjSIkv6fdfthm5KwLaSumgHetMIyjCPr5DlHVP5zO/E+5XcypkTo/gbDQu2X7nQIBDwo+qxon3AfAbjomCit8SPVTo/qdx8CY7IEk3tNrHWiUOs1fB2RhIskrsRu+qCNneleO0hAWBQbM9Au+Bf5X0IH7zJjThBnxmhOTeGPl/aWqH2I0QLeceqYWjKetbH3lBM+nViGJO+YA/XhJvP2ZlqQYx79/TqiN66/27kAxYjhcApGbyNGnDzRQnOzqokKkz1lVk+CuOLT/NIgof2abyymv2W9FTLfish1cC+HNIxvseZ1nTncIIj375SpPv1ButHrlpRK8OoPWHBvsyaq6YcCvmHeZzYaQwONnMEF+QzNOMTM5PYVNZSKCVs1pOVgZwaZNd9qtdAJMnZxlI9JzLPy6o4lDcAWMRg2lhRYxSxJq3MnyzIEZ8YyOLkSKonwVnaWng6r+4tNnUiiYRkOatCasDFY+tTDRnzAVanEEIZMcYzleRhhiBHHiCTaTBHRRKAjpHWZs3FqLpvLSBJ9G633MyMEtIfWMUK0X/VtYUMIC7DS/xcGWhpoaaIMk8BxSOx2uVABkPrtrFzeGzGm3cN1lqtGxHhyFEJjr2zzMrChaTWRYRoNIJEoFIvTK8QFnXordAlky9l9dqeGrAo6prEpbBw1Y1kLR0mCXQP1dTNkMk6vMVceXkR4z2nwUfb6PeVsE6Tu66bTQ2jPIAghGM/aBFbCamO4kQLCkBgzE5uuUFU3ZD0QuK1nMcKQbBhAGEISpqambS8bYews4a3jJZQqQ1hGhFCsQ0kUkMYUOlMimZtBXjyLGDCyIjggZWc3RxXAocTOlJSHgblS50jdXthIC1Ja4/RT43fEocgcg+g7Yn9GwsaIEUBwZ1up1vJWrmdwu0Z4t4F1tkDm6fFjb5fWmljp9iz9YD3h+19a4NnvvNzxu7ofU/cPP2voRcPrLBykkcSRT616k3vRXTwR7jksC7LCUsmgXrLxRYS/GiKApOFCfoJO01B+dQ0dRCS4GDNWV8PPTmgNsdJEiWJCrWNvS/K/wxwzrHNVP0lL5AmMEhlzq7OiWr3mFnc4/6q/465a+3inaAhr1b62FYUueoDomoeR3sS2A5bSYMQRph8iowipYsLsHIYykdrgTE1gmbc2t9Rxa7vLSwqHtGbk9s8sBDYIE8N+lvXCPaKMyVnGQWi0fD7NZ+qDu3bvqWnd2L3Xe5bLeNKboe2xZFILiDMasqkh8lu6RiJBPDaxSb1RzFis6ZBiY2cX8q7lYmuJ1IIb9kHleA/GF/ukPwkgE+/w3DyQiQBN9+d6YB7doLfXMuqBoQ7vBCIgcWzk+FZkYM2JsM0fQt9fIdQZfFYwvQxOWEAly3s2oXWTJG5CE1QTjLX/g6AIuhBijWsyeaurecti3udMK0MsNeXswYJ6KWNR9yOkEH2Vrx+Ex+duFluCaxf9e8ThmS06VL0Iy5Bk+4zeGzFcRsLGiBH7oBOFf60KpP4X9oUi0tn7wEqUZqHqkU+G0CHZ2LfWCAFXlhrUvIj5UobL04Plnt+6to58bpLO1ksnh1Ixkd/CsnNI8+COjB0qxms+n43+kHBXdROAKT3GucwlcmPnKbdTNFSwc+DdLuaxBx1s9ZDDVo3s2GyHpfYi3TXiRpm7fo5S22fDJYMttjr199UFbltPEYtCR1HlNLD99/dKHAxnsPvoINJAFqWxQgMnMBCaNCQ8qaDiVbaPxDSQNS6CSK99ISeAla57qIWrhPYskRHgWw6GLiANC2WYxJZF7DhoQ25e5MtPbMzlarTbbmMPbE9rAFg2Hr6c9EFpyJhFx2eumGG9FfacOrNhcjqZt9P1eLQFkVjuPTCR0LzrNDosPVzWsgGXgiyq2ZvpayQD8tMmrO39biHvM+M5eGZMyxwsqqO0rRLIfpE7UgimCw5elJC3Tfa7F8u5gImGhYXkptXCkYfvVVSciLVsgJQ2PHWerYS5EJ2sYd5+DdbW0V4Flayj9c5zKOQElpzCagEtSO7fJmj9FtKcRpamEE9fRBZ3RjrWnZiW1USJthB1AKWsScYyMKQ4MT+Jh8+GcsRpwTYNZosjQeM0MBI2Roxgn8HurvQMHSbQQdj43DtLvLfU4AMPPF49O7aZC6n8eF+vDq01Nx7UyN9uULRNsi9uhduWmwE3Vps7IheW6v4OYcPfp9xfuRnQCvbOot/4zevM9FHC9Tjw11fQUUJsNMjPnO046NdKYbseZ5oGRS/9zU+Ys7xnpjmKhpacl/PMFJ7AKhzT74sDrPX3OOPf4CxrBFhc09+x+bWLQwGfUDjEZg7k4R6zca2Bk92bXw2cWE9MK4UKh5XW8pChQUYRVhBgRBEiiZirfo3J3LMU8y/vHMxuBFrJEtI8SxLdQCdb6UNa++20EBCyCMg0bUQ4CGGhhYE2TJRpEtsWbxabxNYKQkPoTUA+DxzebHODQCoKwtwMpT2q1AZTSuI+o436wZCC/Sqils2QqXa6xKLp8US49/h5MsHvYfZ+QwDKWMbmjHzONjGlQAiBVRV9VWgZ0Ru+oUAK4qKLXe+eOqhRBOfLWEbndMyGE9NwDneSNkL+40RT6OLPlbGMA8PUEwMeTHm4vsLJykMP8iOpWMnvL0wKQ2I+dR6eOs9C1UNpjV2tU1grY3l1dFxBmDurVKjoDlo3SKIGSfkWlL+KNOaQ2VnExXMYZ6fT39KXji+wzUdXCBwxYsTxMBI2RhwJyo/x3lpDK032pWmM/N4Zee+dNaLFFvYTY2SeGj/+Rg6J95bS2Y0gUlTckMl8Gu7qv7dOXPaROZP8qzs9O+5XPN79fJ1xN+YD58fhG1sv9Gsr+4fwKq1xw5h3F9LUGWtbCkgrjLuue9S8/aAPvwWt0Bsjj0QThR6WszXAUElEs3qHe8Ft8srh2eiDm9+9lFzgrrHGJfMSY+OXkXbvho1x5BP7Lna2gGH1kbuqNUbjAVONK9hJnSV1nkXmGZMN8gTMaxePDKYhsAwLVw43Pqa1snBwjs4x4q0+BmVdNYhEIxJJtgFT6++Sz8yidbCjxKkGkvguqOe6z9ALC8N6joRroFyEsIgMj8jRKKl5MLnO2fhZkJ1HA/csl/gIc3dfz1Z4Nlsk63XYx5DHG1nboOEfjbCRsQxsJfcVf29bLVwR48oEv0PUQb/7ckyDyby1IyvCbhv1Ldk+T/vpwHvNPD4h0BMJj0PAvZ03CGkSlS1gr3ARyYDgfJnCzNGVN00R/XusdMFxTJz+siGHSjheYn28xHwpg2VIoiCiWV9AN0ysZg4R39u1hkIli6jmInzj68RXJlDzH0VcGCNTMB+aesOnoZWOKTf9zHpNXxoxYsQWI2FjxJHgX61sVvHw3l6j8M1ndnyftCLChTTpL7hZw3lybCiVO4bJIM1Jtg0+o5XUzyBpRJvGpBs8qHqUSKOc71dcnu5hwKK05q371R1+FvPlrZmYldrR1oE/iF4MSA8iCVqs125yN763mW5SN1zKcYMpXcS3BO7YJK/kPgFG/4O8cL0MGnzfJz9zrvNJ3vYz4kRhVm7wgvsVSm0vgy+rl9uLGdzRZ5kVVUzToGCaR9cxOkWixiOFBhnFWL6PEUXIJELrEHSAtJ5GGKn/w6SXJ4n+BG3n9tmQxHCePXh/QiCcp2iWmtt60e1zmxHgnlxHVgmQfXpvDIoQnTvtxhD2P11wtleS3YMSsGQNL7UmfYQIOoVP+VJx1WmSVQYr5tE+nxfzPoXAwBUJq2bADIObDvbKaXhj23kDc6ZBpRZAxUEoIzUKnUjTT/aL1BhxMBtlWC3HwpqBNJ/VJ1n5MPrOItotk8RLoHde21o1yLhPIa5aBKZLlC+TzdYxzp28KftpZyKXVjLRpGltI0aM6I+RsDHiSIiWt/Lvk/reXqYOTsah/KRIavv3tJMeB61rjWCPSWfRfTiP4+5fHPk1livvcF8to4Xe2WPWcNup0xifoZWRh5v92dhxots+Jnu3pdHU3JC1hkfZTThPzEeMVNSotv3nFZJAZGgY0ywXi+D6Q+vkB+vlIW1pxCYaSDQyEUglsf0WIlxrR19s3Zvb7y6tPQRpCpAQAmHsTgcyEO2UEVH4NoTobSAptYEMQR1iRvaRlblOmbg9DGpGRK1LKd9hncuaE3ElU0+39+gdxq4IQ1KYs2FOs5X78/gNCi1TwjHY4RizkzCblj42oxh14y56dYUkWkarGtK6tJli58Q5zJXbRO7nSN6bRubOIp5/GmNskOpjjz6mIZkfG7xs+KOAUqP8vRGDMxI2RjxyqCBGmHLg0mS9orUmWnIZb0RUC0d/K4XJ/mHTD+VAR0NJmbi6wlfd6+ln2zrkpja4ZJxjauwpjExx6KbeGkXQqCA2Qv91ghm7jEfLXFEXyGmDHBAJh1VdYlrUeUc/T2gUUGZms7HaHfJM7EN5Mk8JWmMEEbbvI+MIoTYiMBRG5uWtxVREog4wOdQ+GkUsY6q5CnPqLNiTKMsicjIktg0iLWvpV+Lt+siBWJFF4GwzrD1kSkQv3LJbHf0kThueTMiqkzNhe5hvv15MGndjSkHWNrANSbnV+SLeHknjmL2/V00piJUme8TlDx/mczZMcraBFxoIIchaxxP9JSwT431PwvuexASSe0tEdYGnGmTDIgBJeAUApdZQzTX48jso6wJi5jzGc5ePvK82Yi9q17MikafnLlp75ys9L3vaIr1HnDwjYWPEI0Vwp45/tYLMmhQ+cgbRRyesX6IlF+/tNc6uBvQ0LjnFz1+lYrSKMcyjD19WKmaskfBEWCKvTVwc3jBuoET6Ys3rDJfsyxTHLiOto5t185YXARCJjxO7OO2prpA809qn3jZldBB8zXwF08mio1N8Eh8nNKDaERiJxGmtQdJCa3/T/2JPyVSdgGgPsMTulJKN6AsbbVgo08bLZEicBgho5jyc+CyTHYSBdSNEqv7KEwq987l00zpYtlPt9I2oi8DZjTUzZExZTMZ776lql4iCXjnIFLFXrttNnojyxGjy27ooxyH+wKl+TB8JpiEZy7aviX2EjZxtYsjUQWYi1/szebaUIUoUtikfOfXhNF4ngrTySjE8ua69cWG+XR0uIPRaBMsJdm23iJyQRLdh4TbJ4psY2QuIV17AyD3ekQrHiRZQyURM+BY1JyIeQNg4smfyEZlWj3g8GAkbI04H+9Xh7BP/alpxQHkx4f0GzuWxA9dJGiGJ2yn0bVeDdj33vbe3asfNlwOSY8pP70TVjQbOx1RJhLe2DEpjFPNkCpNDbl17P3FIrXqTO+Ftvi16gbxOq5jkcHgmOUPZaHE++ySZsbOIIZS4Awjr1c5faIURt8goD4O96Tx5EeCKArYpMaUAzO1+kSOOCw0iUVh+iBmGiCQEFSKMMaR5dnOxRC2hu0RglIMFZh2XxHBIZEwiYxw1Q2LZxJkMse0c6vmjZH8pYVrs7Lg1jO6ht4nQrBkBBocTapsyZnJXiH7FCLljH650rylFOng9JBIITc27soEUApGH8cCmYceofTYvhcDt8/iP6A8pBWcGCI+XQuCYnZ/l3uicPfLYWRP7sgmXPkl8awEe3CMO74H2NpfRuomKTcx3zxIaEaHtoSc9CjOj4clRs5ILWMkOnru0ngkphSaWkizlhxO5akixmZ6dHaIh74jHh9FVM+LUoLyYaNXFmskhs4e/NJV3cJ6e1prWV46jwsPRTlfFSrPS6P6Curfuku9QrjZoVjbNKZNGC4YsbKSGoDe4E98jEgkIeNO4w3mVChtVGTJXeo7JUmFoOfZJHODXynuU/0Rp4iRmRq11Xg8DX2RJzBy5IYkrI3pEg0hARopMqwIqAhWgtceG+8WOu2ibsCFEbsd3QlgIHJAW2rCIsyWahQi9rYRnnOutcs2KeXDHz8u7XQ0rdxNZWypZcMCsVygV79oNlICCbRB5nZcPhcLW/YkLX85VDl7omJkuOnhhQsYycA2Fa7U7zB0eobGlMaTmuhheNajUo+J4RGrHlDuNXQ7B7hafRIR2KWtR99Jre3x3ZIeAe0WPCd+ibsfE7sMRwpGxjH0r7IzoESEwnzwHT57DiD5I8s4NdOUBWmawcp9AyDQSzk4MbC8DDyaIFwLkjIcqnMbYmEeIQwn6cGvMRWj2FZ77ZbrgUHVDDCnJ26M+2HY87xgMdB4BRsLGiFND6/XlNNLiToPCXzp7+Ny5fdbXShMttZAZE2PMRsf7dbB2ft768hLmbI7sC5MPZU7og6rX8XM9YGj7QURulZX6de6pxT2GoMuyxl1Zp2ZqPJlgZyaxhtgTD2rrELY7o1oRJ4oggaTtrBcIC2dbgcYAh9DIoY8hFWcEkOh2FEaAjEO0NYtBIS2XqhVx9B7dxECt0wiDWEYkMkYIC0PMkNgOUTZDsiuFqWX6FOPBOklLPVSziAq6bRB8sCOoEgmqj+CqiowIpUIAG0Fhne6U95wG7/cPjlA7GoZ375pSUswc/HwNpWJtJqQVJrgPqYmybUgsQ5KzDdzwcL9BCEHBMWkGMTnbxNynbHC/9FNysuiklaGEgEIHEd21Elyr/Tu3BQltpCECNA+IXjpuxnMWS7WH8/o6jQjLxnzleZqrT5G/O4/YJwrN1A5nli9Qrb1H69zxeAM9HFLb6UKLwbx99sMyJDPF3vthWj8+Z+3W1eOYhH34GQkbI04FOkw2IyyUH6PcGCPfX956rwQ3qpvlV/Ov9V5+bEMQMUo2zqXdFRLAGEJJzlg9/LmFWT/heuV17tJ+CG976dna5LJ5kfHSEyxV1rtuJ4lDIr+JlcljmDsHjFol+I0yWkOmNImU6aMsiXyEYaLDGKEirLiFo3087eBS3Fy/pbOYIiEQWWIzD6PojKNhI5XECzDDEJmEaB2kwoSONxbBECWE2T4/QiJkFq22p0cIhLARwgZpk1g29VJ1a6SPQ7sW4dDZPujqhACEFFTG1jnTnKf7IF/TyjYH0gFMQ5K1TKp0zonypcKXCZkTNN48zdimJE406hR0hBcsjxIWWoJbSpg0HPzIO3TbxnM2YzlraDKTaUgylkHS45BPCEEx0/97+6rd5ENiAiXgrunRITvwxDCl5PxEjvuVw6VrjdhCJwrn/tS+osYGQkjGvYsU3vgsjbNP4s5OH2m7RrEhI0Y8/IyEjRGng92z9UMQCXZsLkwQUiBMuSlqAPjv7R+KraPOIkO84mKf7a9UWVzZP4Rs45fWvJD3lg6o1HAU7Pc21xq/WUYrhZ0fxzC7TDNrTd5XTFdDCr6ibGa4u+3pkhqCPkFp/DLCtNCqS89Va8KgRVStgIbE9cjPnN1xjfiNdZSbHlOfMrnxOfzmOkmjhUxaZGMPa9sAMCsCpM6jaHtmGFlaxumvEPFQoUHECZYfoo0CRmJiJjYiWScJbwH7R91r3UIwg0YTywhplJAigzItYschyubQQ5qB7kbDiCkmg70WdRYaqk7eKyD1XnFBiYRWtonu0wpnbVsqjCFTc8DKLpNHv+1XIE6oaz60YKsBtpO1JNX2v819fI5ytslk3qbihrSCrYgAKQR5x4RdwWxHfRQfmD75okUsFZEx3HfdMK+B3DGFgjeMmGvjqYFu2FJ7hI1R4YNHi+ZqTLHHetdC5tEyS+7eH1Fceor1J56BAf3ERowY8egzEjZGHDs6Svb6X+z26RyisBEtt/DeWkNYBvkPz+/4Lql3TozXsaL5xcV9t3kU6RvvLp6AqNEFt7qM9lNxwA9Xyc+c27OMVopW/T4Nd5nvCp7f7FS/GJ/nbeMuRZ3lLPNkJs7j5Ma2tuvtOu7beq5efXVTtAAgUSRxgGFthSeqbSVWtRcSiEVEdZ2C9pAdhs8aQc5QYNqcoMfro0M7EsP2/LapZ4BW/maKiJn5MIhUiNBifwEpjcJwUIamWaiBQftZsNfnZdHymY0djC5xr1rAAQEWXblqN5hJHC6Gu6um9Ia2oWk1kWFa0lVoiRaKyIrS9BMB62bYsTJJJ27ZrT3GmBnLYCxnsb3+cdzrj9bgmAZBfPCUeLfbJGMZqT+Em966Tru0pKB7OPceH4ghDMINKZkqOARRQsHpr0szP5ZBVru0WRz0iwZEsJWSccS7emjocik4pkGU5hFSGiAiZIvH+QCfHkS1N1FjA8N+BhW9RxJdZ+zqXXT1o+j3XxyV+hwxYsQeRsLGiGNDBTE6UrhfXUEFuzrWR9jfcN9MjSJ1mOBf680sL7zfGKq4sp3FWmevi9PGhqgB7DHhVCqmWb3LXf8GDeGBgAdybtMQ3z2YWgAA2ohJREFUFCfLN6kXCaSxo/ORRP5eUWMXO0SNjfWSGKNDfzZRmjBRFCsVcuz1QoiwCGQOZWaRo05Q39y3PM6HWVAgEzBiA9utQlJpp4qovWVVaUdgiDS1RIgMaTlVq11O1SZxHMJsHmX2/gq6b3lMxzZGlxFQS8YUOkRctGRveftKQE32Vv7GNiVhp7J0ApQDgdN5O4HoTRQNpGLN7MORtEcsQ2AZJq0BPB3ytollik0BIe+YCMHmvTVddFjtYmLsieHlGMTbrrqsZZAdoNxsv8+Eu7Y7sOh1EJM5m9VmeuzGst0H75kjLGN+Gtnw7JjMO6iMHukTDzmi33S57eK4MMmELxG8FaEuNpD5x+teGDFiRHdGwsYjgNaaK1eu8KUvfYkvfelLfPnLX+brX/86YRhufn/SRGse3hsrnHRTVLO3QUtc3t8wMK7u33Ffa6bHfKbosN8U1J3y0eXqap0OmoQ43Ms+Dju3USURjeptbgc3cUWw4ye+Yd5mzJhlbczCzUjCJXPPEVDJYMZwUa2KnUnTf0QSImKPprI3y4I1yZIT6TnTCHzhEBl5MEYhqwcRCYW1vZqG0lhegBUETLYeUMicRVqXtn1dQaluVSgkia4TZEAZCm2AGH8WfQhhqZfKJN1YNyKe6mN5IcTmc3O/R1bGMjoLG0eEM8TBrJSiY+RSy+ouPBQy5g4zSWPXRhzTIGsbePuIJstmwHSSztbey7h7Ug4y+5QG3eCu5XIxyoGA23aLcY73/o6GVcKkA45lMF1wUFofmAKSelmYNPzTZbR5lAiRlhT2j/AcjDgedJ+lfptFm2zyIsq7hpn9GEI4ZCIHfSOP61xDTAp0tr8okBGPBmqkco7YxUjYeAS4c+cOL7zwwkk3oyvu11ZOugkpPSor8fpgNblrXkTNi9AaZkt7nZ0X9qlMMgyioEVYqYCEzOTMHsPNXgncGnGtvvNDrVhffZfb0W0CEe0QNISGC+IMc+PPcDff2c26q6dGLyiNDOqMV97iUniNN9ULuHqCpP0ICzHxcFDSIjHzm2kQIw5Agwwisl6IjIJ2edXWprGniu+gEwe2CRtCbveXEQiRQUgHZdrEmWzqhyEE2x019CFSDm7YLdaNQ0Yu9LD7/EZFB1Jvge1eDCeNZUhKu2bxa0bEWJJ+tthD5RY4+DAkUnO/4DHnDj5I6JZekgjNW06Ni+M5yl64Q9iwDEnBMbsOW5fNAE8mxELjyoTxfhvX4fFvCLEj+uMkyfQRdVLKWrhhQqJ0X+s9TAz7rGQtE9q3tfkQVjY7ao7rLtDjwY5UugOZDImfeRl35RJWc5yxjXW1wlz9c6aXQ/y5l2mc690MfsSjwf/3v37mpJsw4pQxEjYeMc6dO8eHP/xhyuUyX/jCF458f1prtBcjHBNhHCbU/3R0LHvmgObeXGt1FDburh9dtEZYWU/blUBQXyc3eWag7WwXNQwtmI8zTCQGv8Y7hGKb8Z4WXJLnmBl/BiNb7LSpTZJGC/ITA7VHJCF20uKDS/8P5rYw/jw+VQqYUmAbksjY68swYhexwohBKgszsTATm8D9EkLsP++hdQA6JpGayIzQlsZmhmhDxDhiU89IqM0R+VGaYxpSMD+WwUkMjMbe/Rz3E0q39+iYRjsCbCe37BZnoyy+TKj2mD5TN+IDf0jLTlghYDrYGw2xO0KjEwctoQUkHS6ZrL2RttalgaL9G3rd2e59d/hMyr37HCSt5bgRCGaLGcJEPXapKbs5qHLRBlnbwI5kWk3rITjHjyqFGZNoMcDqwUA0kgGlWQthSHLn06oobnUB4844RuMtdJKmGjtLf0q29jzLzz6HeMzvh8cJPQrgGrGLkbDxCDA1NcWv//qv8+EPf5gzZ9LB7M/8zM8ci7Dhv7tO+KCJMWaTf23+WM2clBsR3GtgjjlY871VuEjcIc3CnkBOTd07YPCyrUl6gPz5HZvSMWOx5JlkatPT4IXkPG+YtzG15KKap2DPkJ+9dMCWtgjcKnG9WwrDTmTs4SStzeoma3KMeVKPFI1ASEnBMh5d7wxNVxPKXtY3ghDb85BRCNpHqxbSPIu0LmwuJmQJdHXP6kI4SGMWYc3QKlRJrI2IAYMofzSlVQ+iYoTMxJ07w/ctj3F1uBLRhpRI1fngnrarLBKaO3Z/QqkrE5btgJKyeE82+lJrpvL2o3uv7aKQ6dw16uJbeyIYUpB9HEtVC2haMYXIxDcUgdHj6EaMBI3TgDAkwfky5t35riVfNYrgfBlrV0ppbtxGFZuoP6+yZVmsUN47zL29zvrTr0CHiaWDeMim10aMGNGBkbDxCFAsFvm+7/u+E9l3+CAdqCa1kKQaYE70/zIBBnqjuG+ukjQiQhoUijZG/nCDmqPncK/N+oD51EJrSq2EYivGUJpEChp5k3re2ON7oHREU5W5J5cZN/I8l0xtfve8ukAzicnJ/z97fxpk51Um+L7/9Y57zDk1D9ZgW5IlS0Y2tuURLHCVbQEGTJ9ud1NUX3M/cPoS4dMOosIR/QVjOoo4/aUcVdSBOHG4FB3nGBvDLexu3AVNVVFgDAiwsLGxNWDJSo2pHPb8Tut+2KmduTN3Zu7M3FNmPr8Ih/Pd+x1Wpvaw1vM+61l9YJqwwA51PUGNMIrwwogwCFhbWcSxzMNinCTHnV1kon5QxhxdouVNedRcNtQNICrOsmyoBqMUEs+Ollcp0YVytgXVrzw9vT6GkUaFWZSKoU2X0I3hJcqFPa/YBRJ+vPG/4CKdtQskIpNkNPOrK2sESw5sdJJmZadciXmMmwG5TFhJy69H3GlMd2Fq/YjitGKi4VKWtGmg2QI4HdI8AZxNFYmFRjmo0WEBJzG/1KBDlvO47/XXzNzwjRKlTcOkBmvX0TFMA+OOg0Rv9KDP/7Y8hRKIwnP0vp1D+7fA9pkruc1lJbyMHMlWEaucBDZEw2i/NTlh4ViJYKxEmJnMYPDPZTF3Lm6qw2Jkfjq04GMKLfr7TJXKB2y8VMKadunufEgwDGcHXfKA1j6ZiYBGOHH3a1hlOGMMsyHq45xVJLtxgMSFyTv1jewEGKVxRsbGOJGLsVFHgEFBucQpD8w9bP7oXo/Zv4Pi8MUVPcJQHqTzXcz2Fza0STrfRS4axSnlCI0Upk5gRw7oiMB/uzKNoRatsxStHKEVElkaz4hjq+va2qszDEXCMcnOE7zzleb3sYllkTXcUmjde37lvuJmKpoRzXhBuJZBd3xyoKIVvOlm6A8dRrr8pr0GJ087819xscGUlXLnf7FFadv+flBQnP7FJpaV1KCD7hsjezmAERcVmeXCor0lkgPWjEyNWqLrrmG0q5v+E68TBqcB0HocffKfCTM3Ye7f1exfo+0GUi7D2RJKKXoSUjBdrG4S2GijN954g2PHjjE0NIRpmmzcuJGbb76Zbdu2tbtpbTNW8PADTWKWpVYjLyT3y/M1ZoLM7BGHYyVKp8dnPN4uBa/2oG007/Pzk8MNv14qH7DlQmnWsYIVwZYLRUasEX5tHicyp80z1w4XzDwXbYNIgZFtfBuNsIQdZNnhHyevY7ytD1Wey+g4ylB4E6ubKMcmzI9Bk5bh7Qi6nKkx/whPkSykCIp/wLV3oKyJzowyUEayKitDKaeSjRG4Ll4yhZ66/Kky2j5KMZXCUIqka+EFEX7YmgFLoDTWxPyCwCj/ETrlrt304NTdH9jPT/7xtTlnwS2o7XOcJzA1I2mPaExz3i42bBA/mJ6Z0Zc1A7JmwCarOcuowtwv74sJj8GMQzTxh7XqrBW1mDujpqGIIk1fqnNWcEi6i+sGZpyAHlXOkBo366vxIsR0yjRIrXVgrWYyhWxhg/MgleD83ptZ+1aKqPj7iUd9onFFfsgjuX7lZPLVErNN1veUMytXy3RBIWYjgY1poijizTff5Fe/+lXlv9dee41CYXI1ix//+Mfce++9i77G888/z1NPPcWxY8dqPn/o0CGefvrpJV2jLZZYd2K84PPmufLd2CtnRrmld92Mffz3MrUvU+OzPBz3CMeXuJpCA7Xy60Zp2Hhp9qBGZT8U1wfr+LV5vPJYXDtsiTbgGt1o06gMCqJc41Z0KdfPyGJPdGTO6XUcUG/SQwZFedBgm3E8NTkNQhd9iK3sL23DY8b0k1kpG2X2o6MMinImTUQEZjeGcghtFz+RIHCbM4iaGhRoFNNQnO7Ps/5ifVPaSkaEGxnYtiJrzF8Yc7rjTpbrvPIqL2dTzVuxqBH2bNvJyOg4v/vNqVn3GTY91kflv13JWFpwaCwRUHJCogD63JV7F9Azo/IqOF6IAlKORWb6OrTzqPddsK4rhqZzBh+uZSy6LVrB8UQWDUtftWjqeVdw3Fo0jzINLt6wh96TScyR32K6e7Dih0ifU4wH52Du+ubLXqd8pgjRbhLYmOITn/gEL7/8MrncQtahql8Yhjz22GN84xvfmHO/n/3sZ9x33308+eSTPPXUU01pSyc6eXnyLvPRd0e45caZgQ3p9NSnL7RnTD+ZTRyHa6I1nDdG2RytxzW6oY6l8Gb7p/AK4wTFWkUNNaafIxblMacNHALtMKK62N6t8EqzfyzpYucEqhoq0jj5HLbfvaAImGH2o8PL5GLjhJYGE1DdQHezWlrxpjvOvmLjr+PXOzVAwR+cDAOhi0qDLs780yUdiwhNd9yGGi/JjBnwWmwMgHVW59QSWaxzVpF4ZGJrxal5CovW0w92LRPXWv5TLub7VQ1DkZ4oGJqdks3U6KGCUs1c12fpHMvAC8pfHPUsh+pbmhFrcZ/JSsn3uWi8ke1biV3upit7LWqiEHTXpfUE2ePk16/cAK0QokwCG1McPXq0aUENgMcff7wqqJFIJHj00Uc5cOAAnufx6quv8p3vfAff94miiC996Uv09fXx+OOPN61NHUM3ppPjhxFnRwvELIN13TE6J7EcWtmW3nBhX+D7gmtIOkVUHZ3ZuegoxB8dq34M8IOIWJghWWN0WcLBM5O8PrgHJ5bCO3dmSW1YFrTGzeWwigVUWCSK8kCI4ewpByfq5DsJium1tGMuSXGJGQGNUDIizhoFBqzaWSmmqTBRpGOzpyIHSs+6CkYjWKaBZSiKfjmYV1ILywaod2oElOtFvOPWt/JQT8Lhwnixsj2wiOkRRTMiNrF2a8ZZWHHjRqygNdsZ8tbk3/iCVVrydVaLvoTDcM5DKYhH838XJByT8aIijPSCp7QMJF0uZcv/Not57Qkxm+JAD4n1o+gTGjdIEAXnSZz7H6Su7ODiDbvb3TwhRBNJYGMWruty4403cvDgQbLZLN/61reWdL6XXnqJZ555prK9Z88efvCDH7B58+aq/V577TUeeOABhobKxSmfeOIJDh8+zL59+5Z0/eXq6mCg3jneJy9lGcmX5/u6lklv0iFb8rmc8ehPOXMOcFYSc4FBFEe5KLXwO29hUMIwbJRR7gRH4ZS51lFIMVL4QUR59myMlJoMbBSJ4VkpMMv/JmGpgL+S0ykjiGUyWMURoqgAhGimhyQWNjjUqjq4MGJ62BgkIhOj09am7GDd8dk/FxYbMvJUhKMNVAJGHZ/YsEHBCLlselzD3MtTW4YBhJiGomviM6vRS2nbpsGatEsYaeKOyWICr+dSBTZk42iluZhYWAChK2aRLwVoIDVtULzUu/nnUkXCQkTBCMkYAcmJrk6rw3+dmB7uWialoPy9mpr2fWiZBmsnlslUV+Zvu1KKtV0x/DBacBFS1zZZN3GterJDhFgIO26irx/HO3aBKPMS4BEV32DwTQW3Hmh389pKugZiJZPAxhSf/vSn2bx5MwcPHmTfvn3YdvlL/xvf+MaSAhtRFPHkk09WthOJBN///vdnBDUA9u/fz3PPPcddd91FFEWVY7///e8v+vrNUnhrGJ1KooyJT8kG9hq3DRU4dyXPC6+VAzwff99G1nfPnip+tf94NagBMDRWoCdh8/rZcgHR8+NFbt3W1/ABQj201jOKAYaBR3H0MmhNrKcf017kUrk1hIRA/UGccDH/eH5I8dJFsAwSA+tRqtw5VaGHE2ZxdYmS7kFPtCPAJEcMpQwCKzVjudgoX8TLF2dcZlnSGjefxSoWKcXXYQcOduSgw4gwmi0jRaGjHMrsn+X5mXy7umjfCSeHVmBoONjCFUOaqRXv1nomCMz3sTH9HfT72Djp0CaVsogMOB8vEKLn7VQmHAvLLBdTXdsVq2twHLPNShB4IZwlTjPxTM0fu+ee8jIb0zBY0xUjiCLi0wLXH/7wrbz88qtVjy3kdRAYmvec9tdMSU8N3jQxK2gh+pI240WFZSji9tIDCoZSi56uJAEN0UxOzCLaHuC/Fkfr8o0bnX8djsXhptWVuZFxAtKehVYw5kixX7FydcY3bYf44he/2JTz/uhHP6oqFPr5z3+e7du3z7r/oUOHeOSRR3j22WcBePHFFzl+/Dg7d+5sSvsWa+y//ZHcBhPr+l6KaRsnihYwlJ6mxu25n/30NF6s3PF58bVzfPbu7XgLXOXEm7a6ghdEuC1epq8UhPx+aJxSEBEFPlqHmHaM0vgVuJqePnaFxMCGJV9L64BcNMzvjIh7ohvqPm5Jxd+CCK+Qocu/Qu/477kUTi4Jm1Z5Srq7UhA0MHvrmtu/7Giwi3mcfBYVFKsyMhLGVphYPUAZUyuYKZSRBCNOZLsUkwm0ZZEaD+sqIBqpkGjajKOVcCfGMhb/S7SjgsHV5UKnX9lXmiuWR8Iof80GddYOuTr1xDDqv+M/kHIZL/iMF5dXh9U2Dewag9t1AwMzHlNKYZvG0lbMaXHKhlUJ3uiGBBEawTQMemVJSLHCXX2rGwO9GHvuJPr9P6N1uTh9OPwb1PEk5vYt7Wtgi51LFsnaFkUrpI5ZZkIsW/LyboHvfve7VduPPfbYvMd89rOfrdr+3ve+18gmNYz2I7zXh3nv5Aj//Pblhp7bz0wOtrOlcoq+DmbrmS5uQOOFEe9cyHDyUpYwmtlhHsl7/OqPV3jj7FjN53Ud+dLvDucpBRGhX6Rw+QLFy5co5UfRpclBiF7E3dapzFCzyY/jBHlOmkOcMC9QoL5ghU/EiLH4wIYRFFhz+VVuGvshhWhaajMhcbtcmM+1jBUX1IiNj5G6cIbk+XewR/+I9i4TRVmYUhxVR5PBuMhQKHsDOr6FwsB15NZuITc4SKGnC21boCAXzzL/CEyX91shf097Sh0JNbHs60LUM15dzPKc810kUJpLZmtqOBjG3O1Pxyy64vac02pawbaa96LsavPvthi2aUxkpKyQN+sqVZ6uNWmlfZetZOa6PnKbDjK5jGxA+O4viIZH29iq1tIKxt0Az5SKvWJlk8BGC7z00kuVn3fs2MGOHTvmPeauu+4iFpucmvDiiy82pW2NoCgvLXp2eGmFV2v2E7RGRXV8EC+yk3HqUpbhnMfFTIlzozOnQfzhfIYg0mRKAefHqp/3gpDXzozOe43MxF3U0vhIJTMlGMvU30itCfwCOpoZ/NC+x+BwiWvP5NkQxtkbbiKmbSKl+bn1zozpLzOOR3PKzi3qTn8QRiRKl0iFo6QmioJmdQ8AHjYZs4e8uwbbXP6rKgAYQYCby6B8sAoG8fEYlmcRRRk0te6UK5SRJlQ+udg446lRsl1ZcgM9FHpS6Fkyh7QDmcQ40SxFJiMVkkmMo6fddC0YSwuOtYtlqOqUdFVe9rXeujoTh8xrIeerx+l0nt/FxojqnInXiNVF1BzZLEqV63HMV0fInDjHrYf2LLk9tSQdq5KFsdhgkponiLNYUwNmtTJFFkuGCivf1CyXcvCwcyMb0ZTssKyzPL8XGi2/ZoCg/yau/rtpnSd449foevqXQohlQ6aiNNno6CinT5+ubN922211Hec4DgcPHuSnP/0pQNVUlk5kRZA+M8Ibb7wBgHtp7nnXiUSSZHLuAnoK2HquSLIYMty9iDt1dXxfTa/JsakvMeu+mVJ1YcczV/IUg7nToktBiB+WG6IXmUJdGL9ElC+BaZAYLNey0KHP2JUTnPRPcsjfxRq9FgAbi5uC7QwbRVynjxOpiK2jCrtGDNMn4pSdY9RcWPq6H0blaTUaHGxSavLvUsKlZCXBXAFV7jW4hSx2LgNhgUiXAJN0bGPldp02eoB3K4coI4lSSUI7RikRJ3LMiX6UZiEdYe1A1s5ieGD7NkobaBXh2355+kmNU71nt7+mwGIkFpid0S7BtEKtBTuqe3oJQF/SIecF5SKLiytLUT9TQTizbaahKjWGYm5zpiOUC0q6aCBXCvGChWeDGU0aNMZsg+64jR9quuK1X3fFetfJnuAbEb4hg6OVzlCKTb2z9w86yZl0gQ3ZGIGhuRyrP6OsN2FzJVd+v04v6LsSjF6zmcFCAZ0v96ej8DzRpXHMtc1fHl0I0Ror75Orw7z55ptV2wupk7Fjx45KYGNkZITz58+zbt26hravkfSbZ9j7+N0APH7Hn8+577333MM99947eWyNfmEqH2JNdM77x3wKvx+e/YStuHkyrY2XsnN32McLPm+em1ITZJFtjPITHZMwwsuOUipd5oR3gqLyQcGvrZNs8waJgHNWkYKZIKYSEMGV4hgjLnQFNn2hg4kiRHPF9Bi3/PozNbTGCrKoyGdcd1UezhAnSZFLqp9TqZvxi8u7g2/6Hm52DMMroKMCmpDqYU6A1jmUSgGgVALDXEtkOXjxOIFrNS4PTkHkQsldXnUTGm0hwYNmGjcCckZAMrK4uIglRKeucLJYhqrvxZXecR2Zt/8w4/HpRTqbpx3VTqqZRnkZUpg6jaD20r9nUwXW52J4ZsRIrP5AzPlkkZwddvLN+0XL2SFJv/x3uzLlb7Jz9waOvznUrmaJOhStiJM9C4+eJhyTSDtEWpNegYENgIvX7WTd6yOE4RXs5AMY0TqCUhbLXRmZpUKsdivzk6uDnDx5smp7y5b6ixVN3/fkyZOzBjZ+//vfMz4+OYh+7733Kj///Oc/r9p3z549dHV10WhJc4l3AKd1Dq1pdxy9s9mlnZ+lpQxP5F0wVvDrSiv/w/nxxqUo6wg/Gud45vfkVanqb5VTJd5OjJMNo0pa/FVBpCn6IWM64AxT7uiHYETl9Pw5izXqCMvPEtd51MRv4+JTwkYBhmkxbg5idXcRZpY2FamtNBg+xEeHicIRZr1nq2y0LuGbFr7tEVoRmP0rcmAzF18toYDjFHkjpKtGyeFR1ycRmIybAflZpuRcZRqqMr2iqRS86WZwtEFJRWyi9XdvD+zezbHfHG/5dRdv5ifgPR84wHvnLnDirXOzHtWIlauuFlS1TYPYPIU7s07IO079n19XPzPH3IUtz9wqTtzEKyxtCsL5ZJHBvEtgRIyu8uDq6qFWTKbGbJ8gyjTQBw5inOjBpK8cgh2NweASCqgLITrGyvgE62BTgw0AfX19dR/b21u9XGMmM3tdhs997nP80z/9U83nbr/99qrtH//4x9w7JVuiUXJha78YzlzJczlbYkNPnK0NON98dUC11py+kmdotIhZR7+7Rib4gqlIo8Msp9QZsmZ1jQ9TG2yJ1pEyBhiPohnf5EGkKXizL+QaaSh4IXFnZnBD6RDLzxLThUpA46q0yqONHhzLpBXjyUYzfZ9YdhSjlEMDQep67MBFodBmCOHIlL3LdTKUkSZw4ngxm8hRoEqV55e7dt5bP+FkWcfMZY4vJEoUjYDLBW/OP3FPwiZmm5X3bt4ISETNu/OmFZQaFNRZjGQszpGH7uL/fu7HbWvDUu3YtIW9O6/jr9/6dlOvY5sG/anGTolzbRMrMIg57S9PluxyyY03r3BtYGjOpVbI8ttCTKHSKbxtReKnyttWZOOPZKGnrc0SQjSABDaaLJutzjKYWhB0PvF4fM5zdRq1ezOvv/46AO5r89fYWIqSH3J2tJyBcOpyjk1jM4MqeS+opCI3ytBEgdFGBC3morSmNxMwMOrzFpC1JzuYhlaVgIZhzv4WLvqzBzWu0hP7Ve7S6AjbzxDXBabfbdUoCipBYKeITUuJ12Fn3rkEQINTzOHkxsAvEFFCM7luieNblZoZyuhGqRgYXWAmKcVdAldRVyRrFQkbND2kaERcipUYLEwbgKrywGpqUCMds8gUq19nKbec7XF1CdD37AJdkY2zhPXsSmZUeeUHSnP1XZSMWWQnrj99hYSlmK/A73Sb1qxv2LU7VZ0zblquJ+E0NXAmhGiMqZ+qtW5aJfscMiOXoDBIFJxGFS4QDqVhy8ylpoUQy4cENpqsWKy+4+E49U/XcN3qzn6hMHtxwH/8x39cULsaLTAgt6mXPbv3EOV9skOzpxnPZiFDx9K0op3+xTyRV516G2r43XtjC27HVeOF6vTbeocfUehTyo5gWA5uontB68LpKMIfPcfubDexsHzcbjbyO+s0BTw2R2voMgYxzLnn6vuhpt6YTqTLy95GGsIgYJ2qDmpEGBSMBKGVnHXEUakD0mGSw+dR3jgRwazTS3Q0ijIHiIjwnBJBYjuRzbJbM6re1+cVy6MvWNq0sVN2s6tfzpR0ZwY2pvOV5pg7xsFiL4uOvSg43ZWnGIQMm5MZI6ZSrOmK4QdRQwMbU12ySpWpOZfiS3tPqXQMnZE77qtRM7OwGjFNaC5xx6AUhJPXauBNhBHXp7tUfn/lbVktZCVYyjTExGZN+Prv0VE5Gzr649sS2BBimZPARpNNz9DwvPqna5RK1R3b6RkcnUIDZwddYl5E/rcXCYYX0Zmebx7INGOFmXN+M//03ozHFnJWTXm6ybtX8uRLAePTB1F1nqw4ehntBUSUMCwb250/O0VrjTdyllOFt7miMiT1Hq6jfGfWwODGcCcZE8w665gE0cLS5Yv+1f0NcjpGShUIMSgYSSIruaDgTLuYQYBVKlBKpDF8sHwLpZNEXJm5s3IwjF60maYYcwicMbTFsp5ZUm/T58/jmd07bpaAiKzZuYMCreCCVWSdX/7sLVrlto66PkadC8cUrajmKjOOaeA0cJnQ6U7bebrjDoERkV3iwEsZxox/aaMB7+PB9V2MDGcJvPk/Y2KWCTWXQoaewSSjl2rXtVB1RxXn/33616YZvrCA5bVFWyUdCy+ICEJNb9yBBv7TlayIoWSRWGhwJSZ1Q1YC2zRIuha50sKzRk3LQLld6EL5RRYGQ4RvnsTavb3RzRRCtIgENposlUpVbU/P4JjL9AyN6efqBNoyON1vk01Y9GaCxQU1KurvdF+dhnJVwQ/JzXM3tx4jeY/zY0u7y6m9yXb4uczcgQ0NKiowpIa4XByv/Al+Y51ku7eWsbTDae8SvnJYyD3iBcaJqmRVAgxrWQQ0TN8jlhnFKGUI8VCY2P56jIm/ljYUcAYAZaRQRg+RlaQUswgdTXm3zlh1o520oq4Mh4UuDdwuQ1YRNzKwLIPzyfL7+WKixJoxB6PupYAWx7EMvImMsoSzsK/YyDEYjjenVpFSilSstV/5lmnQk3Ao+SHpade+/97b+cef/YoLZ0dnHNfsrIBFm+M9kuqOkV3id0fDdOifrx5KKfqSE9mqTfhozrhBI2MlogP0Jhy64zZDo7Uj19Nj0VNrihlr+9HvjqJ1OcganXsTfd01qCYGsIUQzSPv3CabvvrIyMjILHvONDo6WrWdTqcb0aSGcXf24N80QDbR/vjY62fHOH6p/hokWmt+/dZxcuffo5iZXEb27Quzn6MYLOYO6iw9Mw1GWOKCPsVr5ltcNqqLzKZUkrc2WJwfcPEXkVO/0HGBSYShIGYbJFyHyE51bFDD8kqkhs+TPncC9/JxdOkyISXK1QoCVDQ5TUKpBIa9kyhxA4WerWT70+R7TcL41aDGylHvq6TWv+rv3XEuWwsfUJ+ZyGrwVETe6JwsjlBpjrs5znQV8M2J9YwUXDKbP2XKtUwcyyDhWPQklra865JM/EOnXIuBlMvaLrchGRsLlXIt+lMuzrSVpAZ6+vjkAx9u6LXiqfb8vffs38rtt93YlmuvZJ35DSSWH8Vg2iXpWgym3erAqaFQXZPTT6JomPD15bTylBBiqvaPSFe4bdu2VW2fPn267mPffffdqu3t2zsrPc4aiNOoZTGyryy8JsdU9RYJPX4xixeEmPiMDJeDGGE2D+n+eY/1l1IxVGsII9DQHdmc5TQnzfMzdlsb9dLPWiwzjnIXP/XIMgz8sP6BpmNo7AXeXW6H1KWzRMHYnMuxon00ESW7SGD5RI4zJR1Busq15I2QU06OgQXW3jhvFxkxPXwVsdNL0a5oUa151nbNO27N//dXqryMcl9yictfL9WUl3zMnvnv0rFZESx+yozr2hSyjcsqsk2FP/VjtEazlIIPvP9W3j13tmHX7WR6KemAQrSJa5m4Vu3vJ6O/BzKTNx2j4ZNovRPVqVWMhRCzkndtk+3Zs6dq+/jx+iPBJ06cqPzc29vLunXrGtauRhgv+FzJ1XeX90quxOtnxzg3S6ogtCZBIFsK8ELNWK46ZTgMSuRHzlMYu4iOFn7nOQxKhMHsf4v8yAXSgcVuL831fpp1UXUmz0DUxe7oetYa12AZS6+lYpuq7piTocB2GrssYiNYpRKJ0eFydktJ4WYdMNbO3FE5GNZ6DHc3QXoX2bRJpjuDl/SJXFZlLKOVQ4+SEREpatakaBVDqRnZEb2JmYEFa2J1G8Xky2Kh00Waq3Uv1qUMUAfWdjewJZ2rK25XgixtD1R1iHoDYhuvmf9mgRCNMr2/M30J+3ml+yo/RtFFordONaBVQohW66Qe3YrU09PDli1bKpkar7zySl3HeZ7H0aNHK9v79u1rSvuW4tRwDu3WF9i4OsUjWwroSzmzRs7bpTg2DF65rGLJHCOW6pt1Xx1F5SDMRDTfK2bxR0fKA/DktKCEBrJjFPJnOehfhzERS7w+3Mgx6zSOtlnDemwjMa2S/dIGOFqXB3vRPIMXRe27ue1i+h7x8RGUV66ZAYq03oRSE4MKwyXCQCkHZfaB2UPJNQmccKL4Z+dMh5jNqOnTEzY/Zb6ef/+FsIy54+BXsz62eQtbyrloLazQ7WxSrk3KtSgFEYZStTM2uiE+YmKaCs+I6E+5xOzWx/eX+m/TznvmSik+cPv7+d7I/2xodsT0a3QCyzBY1x1D64msoM5c/GmGTvj7Hbjhes7+8WftboZYNRT9KZdcKSDhmAvO+jL6uzGG1kJUDmhEly5i7t7RjIYKIZpIMjZa4IEHHqj8fOLECU6ePDnvMT/5yU+qCo0+9NBDTWlbOxS9Dhx8TmlTmJ99KctSfpT8xbPkLp+rZHZcDWoARLnJu9ZWFDJeeI+fj/8zb5tneducnG6TMQKu09ex0diBYyQbtzyfjrC8MfxSnmCe6TmGgrhjLvzORoOZQUBy5FKlZkbkDU8ENQA0OpxSl0YZGLEb8dLXkuvpIdsLfjJE2yybzIyssfQit0uRM0IuWJMjtNMNXLp1bBHFRfNWyJjr4xsRQ6mlFl9UuJY5yzQU8CzNlS6fTCxgqKtI3DYb9t6LFnCahFOuwxF3zEVljOzctx0cE2tND3373se261qbzdfX3c2//9cP879+9lNY7sK6EfUMuo15gmitZCi1pCUlhRCtEbdNBlLuorPwVN9WDPta7NQnMRMPUsq397taCLFwndN7WMEefvjhqu2vf/3r8x4zfZ+PfexjjWxSk2lG8h6Z4vJYQWE+gZcnP3oBrzBOMJYpBzGCiFJuYsA9LX5gRRH58AKvqdc5ZZ5DT9R3+I11ihFV4vfOOG87WQJj/kFVGNR5i1BHWN44ae8iCZ2nS00uo2gocEyFbSgsQ2GbirhtknKttgc1EiOXcC+9gy5emigAWk2pOAqDiIiinSOTHCPb43VMMOO007igQKtcMku8Z+c5axd418kzbDZnFY66KTifLHGyJ0/GmexINmUqv4LRmM/5ZKlSVLRRhs0S4cR7fervUYthKGJ2jQBMnYWCb7juBtbccCt9G/dgWTHuvuXgotrcEJ1UckHiD0KIOk0PRqs9W9A992HaWzCUQelie9olhFg8CWy0wOHDh9m7d29l+5lnnuHUqdnn773yyis899xzle0HH3yQa6+9tqltbKRzY0X+cD7DG0PjrQtuaE0pP1oONuj5U9sX0v8tXRlGFzz80bGqx6OgevBiRRHF8BLH1O84bg4Rqsl2JLXLBr2Bt51snXfsNVpHFIcvzbObxvQypLyLJHQONTHKcAiIK4+YbZByLWK2OXF32CRum9hmG0YAGpx8tjwQCsEqGCjWMGNkpFwMawOGuxc/dS25LodsdwYvGXREMGOqUaP5r+8ri1itZDYFI0SrcoduyC5y0Sq17e950Vomef0Tao3fp9ZdiBS84Y4zlCpyLtncZT+3DyYrU8iuGUjMeH5tV6zyc2qWu5edMF1h2ZnjTyZFNYVYXrSCy3GPSGmuxDxCE7zeyX5ebLS7SRF2IUSzSI2NFjAMgy9/+ct85CMfASCXy3HkyBH++3//72zevLlq32PHjvHII48QRVHl2KeffrrlbV6Kd4cn72KfuJjlwJbequeb8TVRuppNAWgdEUs1sHDZPA2ORybrA5efWK8xbFYvFxvXDlvjO7EK9oLTq/1SHmabTqI1RpAjEeUwpq0REmFQMJLYdpxOiAJYXonY+DD4WSICrNIuLDXxmlAQqgQaH8PsR5n9eI5F0fGJ7AhUY+ovdJqsEZCKLDwV4ejZXxcjpkdxGdQNWajTTr4lS682Uq3spoRjVRVQLhnRvNkac6vv/WqbBv/m/VsYGiuwYzCF51UXbr3z2kFyF8/ghQHWLNNyVqRmj0GWyRhHYlZC1Gc47jEcn/wMj61V6EsROsxgFP+A/5aBvfua9jVQCLEgEtiY4oUXXuALX/jCjMczmUzV9qOPPko8PnPliq985St8/OMfr3nuI0eO8LnPfY6/+Zu/AeCNN95g9+7dPProoxw4cADf9/n5z3/O888/j+9P3gX+y7/8S/bv37+UX6utwjqi3Xkv4A/nM5SCxQ9ig7HxyWtm8pDqR0chURRiWjWq2Teg5+f6ik0Xi3R5XSgUN4Sb+WfjzfJz2maTXk/cGSTVvY58cWjB5/dHRmY8pgEviHDDLCly055TFIwkgZVqe89WRSGJsRGM4hghparQixGMgT0Z7DKcXfh2VA5mOBGolTGFaS5vu1l6Qptx0+cWrw8/rP3a95XmiumzwV/6Sjn1iJTG0M177YRK846TJWMuYvDf4kFlb8JhJF/u8DqWQcLpnCK7AN0Jm+6JlWCisPqrvD+dYFNvnHOZxmeOSKZHZ5jz30H+jYRYFNu18PP/QFh6AwDz4jaQwIYQy4YENqYYHx+vWmJ1NkNDtQep4+PjNR+/6q/+6q/IZDL83d/9HVDO3Pja175Wc1+lFH/xF3/BE088MW97lrMgijj23tj8O05Rz2oCUehTGL4AocZMxYmlBwDwvTxBMY9hLf6lb0eanB7mdDTMHu/9KMoDnp3hOl43z9BNDwnVh2mYEGm8Um6eM9ZQ41f0gggvjIg0+MRIqzwKXQ5oqASBnaqs1NIupueRGD1PFObRRNTKNdC6PFgMDI+SUyJ0wilrta2ODnmoNMMTU0xMQ+HPkZRRmFhtJB1ZDARLW5a3mX9d2zToc2yYZdVXz4h4zR1bdCNaPaBOuhZJ10Trq+PEzn1txlyXzdsGOHPqMms39TDQM/uqTp3ung8caPg5TVsR+ssk3aIBJK4hxOIZyTjhREJh6L+HVSii4rG5DxJCdIRVlKPafqZp8s1vfpNnn322qubGdLfddhs//OEP+fKXv9zC1jXPcHb2lPNL4wtLR9da84tTV+bdr3DxPITljmyYLY+0dBTiXblClCtUpq0shB1pvPAyv1Ov85Z5hlEjzx/McpArrwJO2nk2qp2kjUFMNXF3N9IEowsL3Eyn/DyJ0kUIipWZKREG4yQoqARZZw2B09X2oAYRqMAkDHPoadNjlIphWJtRsRsppTYwnhol31UgjEUzF6BfZa7UsWTyZcvjlJOnaCxgWkqj/6x1nC82RzV6Xesctkmiq0ZGVQ3tWZlCTQRUGnft/+VTH2rKwPOhD97Lpx99gI/ff7jxJ59Ljd/l+hs2Lfp0e3deN+Mxc4lL8n7gAzcv6fh6bFzT2pVp5tKwVbaEWIXU9duBq0uy+0Rv/7GNrRFCLIRkbEzxmc98hs985jNNv86nPvUpPvWpT/H6669z7NgxhoaGME2TDRs2cMstt7B9+/amt6FV/FDzzsXquhMFP6Rn4uf5liSd7u0LCw9IVNri5essBFXdKbQjTS66wlvmEN60wp9vWO/hGD2MGD6oBncogwLxMItN+ZrdKkdponqmYxlgddHOSRtOPoubuULgdhPaA7h+HIUiNPvR4WXAxDAHUNYAJcfEdwO0FUz8eVdvx3t68djRtM9QUGCdHyMVzv2RPG4ExKL6pkQsh7+wsg3+7Sce4mv/1wt17d+TcBjNt3kVlyXq7+5l1/XXUCgW+eMfz9d1zNZr18y7j2EYpBOppTavIT5422384Y3nG3Y+yzYI/cVPVbx+6zZ+yC+X3I6cE3B1BqBnVrfHXkIWYKOpVR4sFmIpjGQM09pAGLwLQDR6mc6aiCiEmE3nfBOvQnv37p0zc2M5OnNl/uUv3x3Os64rhgYuTp8DriOKuRGiICSW6sWw7KqnR/ItGMqHEfmR86TNBFdypzmuTuNZ1YNRW5tsYB0p+hgxG9ymoEgszOJMC1vYBCTMCMNy25ZqrKKI+PgVzMIoIR4hYJQsbLZU9jGs9Wijm9BOU3B9QkdDXSvBrHwXrRKZaX8LZShivSZOxiBWMinOMS/lrF0gHVnY2uC4k2VXKd3sJjfVQgvOJx2TXMnADyO64/b8B3SoVCJJKpGsO7DxwN13N7lFjbXQQsmL0XXdLrh0uunXmSpScDqdJ+lbjLkzP/ct1yAotb/gcSzukB1tzso8rZwStnomD4lOo1L9MDoR2AguoMMItZoKMQuxTElgQyyaX8xQyo/hxrsqk3rPjs4ywX6abClgOFvCD6u7Ll4xWy7+CRTCyyT71ze20fPR0BVZbBiPkY4MvuOcr8rSsLXJBr2OlOrDVI19++jAIxZmcJl5R7qkXDyrC9Noz1vW8krExi6jgyyasKp2ho5G0VEJZbhEKqQUA99xoYHLlK4Eb7mZWYtmmoaBY85/TyhQmtdj1bV8bLM80E84JnlC/DDCUArTUIQLzIjylcZdQvHQSDV3KKKUYk3aRQPGSiwkMMuvtJhAQdNWKWzDn33GYFpBLNnX8sAGQMGOKNhTPts68GV4YN/1/PDc0jNUamnpsrYKClZIPDDxjYii2f6gkVgdjO2b4de/BkDrHOGZc1jXbGxzq4QQ85HAhli0IJMjiI8DGjfRs+Djz9eor+Hnpkxb8ea+yx94BcLAw4k34K61BjcKiUcR14WThff2B9fwT87vJwIaa0mp/oYHF/xQUwpCUrqAq6qDAZ5y8KwuIqN9d6fTF88QhlmiGvfPlJFGmWvw7QDPLRKVZ8qsOGOmT3fY+n+D+YYQJSOiy7GJYWClFQPapeAHxEwThuu/ztUgyCknx/6wm5If8UcnhzVlKdp6alxoVV7KdW0Q45JZYlMTVnNRagVXEFiJwZpWasefr01pBXMFGJIrqNDhe6kCSd+iYIcr8rtFdCajrxvDGCSKLpUfGDoHEtgQouNJYEMsWTCWWXBgo9adZK+YYbblIXQUEkaTz4WBR+nKZdDVS73OapZOoNLgRD4XucBp4xKOYbMtHMCemFG5RvexPdpCXHU3PKARhT6FQF2tcUqWOCmKGET42JTsLiKjvsKKDadBBeCUbLTqAqbWNjEwzAG0PUgprgicqCOnmjQyGPG2k6UvdNjhJRtyvsWanoXxByeDEVNknQCtFKaClGsveLCVdCzijomh4GxQ4PK4R8YMWO/HUEphKHDqTMO9YJW4YJWDls0IbKxkqc3b4fJ7DTnXSoqRNDNLQKkmZreIJYsMyLid9/0iVj7lDEKxHNjQfn3ZyEKI9pLAhmiL6QVFdRThj4zW3NcrjOOPjXE0c4kw2YdpOXi50QUN3rxipqqnb2gwoyLnuMCQObnKSgmfP5hn2aTXcc4qklEBKdW/kF9tXlEY4AYZ4hQJdRcFykt4RhhkVBrTNInMpS3ruRhGGJIYvQS+RxTfhaUnggLWOqJgCJSDYa0lcNIUYhpt63JkqI3O2gU2tmLwrOCK5eGpkN2lruZfbxYDKZcL45Nz50tGxOVEY6b82BOBC98BM23geiY9lk3ab97XxFyD7/asgtJGChKpgYYFNpplxr9ZJwUFaralkxq4/PX1dANn290MIZouWr8J+8JWDHsjkZlA65GWLzsuhFgYCWyItpiesRFFtQtwhkEJf2K51EzeR0VXSPSuQy+0dkAui51KY0cQRBn+aJxj1MzN2C+l4wybIXkjW+MsSxOFAU6QJcFk5L9b5ShqB8s0cC0TpSwaPotYg+GB7dsobaBVhG/7RA6gwPQ84mOX0UGGaKJ6hhnkwewuH68cDHcXxbhB4EblqFCHOGcVFxTYuGyVGAgWHzTyWh7Iqb6e3aLiZSnXIuVaOAWTZi+9M1utjLhtYhmKINKk3OXxVbWkuh+JxmZnrYYshFtu28Uvf/4WAPtu2saZ0xfa3KKF61uT4srFxn/fNMOuvZtx7OXxXhRiqexrBlGjgygMTA2FbEA8vXyLVguxGsg3lGg5v5TDL2Sx4ylsNwk6wsuN1dy3OHypalv7i0hJ1ZAOTNYPa7KB5gfO8Rm7dOskg3otcaOr4RH5KAyxgyxpCqhpA1UFpB0FRnMWE1MeJAspDF19fjeAqBCig/fQ/rkZ9TOi8Bym2U3JKuA5HtpWE9kZnXO3IlhEkCFjBFxxfa4rdcaymCtRT8JmtAGrFymlWNMVI4w0N9+ykxMnh5q20sNS9CcdhnPlrJn+1MKDE6stMWUxZvtMvvmGvUQ6IgwjbrtxP//P6f8+6znWburhwnujTWrh4n3ontt4+X/+jNHh+VcUa7fNG9dTLNb3HpQ722K5M0yTgpMj7pXruAUZDct7ITIhVjxZu0g0VGH8MrlzZ8idO4NfmpkREUUB3pUr6IJX/r+OKBXGifIzC4mWD1j8bUelNU7gsd1z2V1I0eOZbIz66I0m6yQMRr1cq69li7qWhNFNI8sShpFGlTJ0B5dIkq8KaoSY5M1ucu4aaNJKJ8qDdL5rRlDjKgMT09yCMnumPGpiWOvx42sY7xqjlPLQE5kdK8VYo5fnbSIv2fjb7mZfc3tmKddmXXd9xQvnyyowlMI2DXbt2M6fPfKRBrSu8eKOxdquGGu7YrjWwgOUlqlwrPJX8U1behrcusZLJjunbophGNx24wHuuOl9mPOsKvShu25ny441XLtnWgHANn+2DfT08ejHH2rY+ZyY3K8SolGCeAEdFQn907hnz7W7OUKIecg3oGiYMCgR5SanWXgjV7DXTSm2qDWFi+erjonCgGAsQ/0UXjGLLs5eV8CJoKTHeU9d4IqVxWMrA8HOiaMVu8PNvMsVUqof12h89fgw0pSCiCDSpNGoKZkFEQZFM0VoJZs781uXMzXm7bUrhWnvIIh+h7IGKCbThDGj7Z19AaftfHl5wwbGYZz+PpRVR7HdJbIWsTzpXK7e/b3v8EF+9MOjDT13vfbdtI3f/eZUzeeWNkVIMZh2SfcnuOe6QU4ctSnmOjf4dsfBm6ofaOlnxeI/NbtTaY588F4A3vn9txvUHtr2WTlbRsSHD9/a4pbU1tJlYYVoklj2OKWxnwOgVBewtb0NEkLMSTI2RMME3rQU1Wn9Gt8vzLhFGwYLLHwYRvgjIzMeVlrjhD658DyvqWP8zjzBlYk6GW+bQ4REjBk+f7AzZK04/cZGXNXYoEYUReBlyXsBwUSmSY44EQYRBgUzTc5dQ9DsoAblmhqzZWrMoGy8rp3k+rsJ4xLUaIaueDmLoSdR//zcC3ZpSf8Wteo9OLHuxZ+wCRaarb5r2w7+189+qjmNaSOFwjFNlFLcd+8t7W5OTaat+Lf/+k9JJ1bYNK4lfhgnUo0p9HzHXXtJ9y49G6avu7Pe40IsZ8ZAT+VnrceJMp0/ZUyI1UwyNkTL6GhmWcxaQYr6TwjpyGKcYU6rCzWLgQIkdJxfO5fRTZryEUUaI8iR1jlMIgIgTzloYhgGWbMXZdroFkYMbH9hc/3NyCJQnXuXeDHyRjhjudeRtk5DUaTcxtSfqIdjGvhhVF2ot43z3m1TsbYrxmjeoxQ0vETuinHNhk3tbkJNn/13H593usdqZFqNuT90YNceDuzaw9//8H9y5tTlRZ+nkdMpZ7lAfbtJjQ2xAhjrB+Atm6upk9HZCxi7trW3UUKIWUnGhlh23Eix0Y+zv9TNbr+LLFlGjeqghq1NNkVr2aV3scnY0ZSgRqQ1eDnS/iW6dAZzYj2TLpXHMiDhmCQcE0yndUGNCJyMj+Mt7Dak0s35KDhvta/YYz4VzngsbOGqJo5poJQiZjd3MHjjwe21n1CQnLaaSCNfhWb/wpa9TTjWoqdsKPmqajsJagghVhtlGhhm3+QDY6Nta4sQYn6SsSE6nwZba3yd5QrlDI/94YHK09eG6zllXgSgN0rTSx8JoxtDNacjHmmN8vOkdA6L6sGzRhEYMZK22dIMDUJw8hF2YQQdXgTnWiBR9+FaNecOetTgQELWDEiF5Y+ti9YsBWeBs3YBrHKdii1+/X+HWgrGzABJPWKOSeMruCxNzDZhcb/ODG53P/nh+up1WKYhM5wEAKqZMarFvMgUS56OUrEaykqsht9RiCmU2QXhxFLSxYXUhBNCtJoENkRn0mDpkJLOMsIo540RIqPco1Ia8pRI4KLRpEhxTbSJmErjNKEY6FWRLhcF7YlGcZleG0RRMuL4VoqoSQGVWlQIdtHGLZYIvbfRE1kjUXgF0+yv+zy+3RnTUHwVYc+RPXLSzrGVBIHSnLMKM/rYJ5wcGdPHV5o+HHQDRtMXamSdOJaBtwynU1wzkOSPfoLL4xnGi5P/5k6D0umn6orZMDEdOWYZhDIiEkIIscwoN8nVLl8USo0NITqZBDZEYzSgArqjDbpCiysMc8oYYsyo/QWiFbxjXqBXDTBslvCVpovBJV9/NmGk8YIIf6JWQQEHV00GNsoBjXRrAxqBxim6uMFEsTnDKd8K1ROD7XAcTYCq4y0eqZBoYSU56rbQof+x2BgHC72zPl8yIt52s5MPTHvZaaXxG5wlUlAhPYlybQzTgHTMJtK6EtjIGgGpaHl8lBo7u9h+OYl3wca2DMbyHo5lErNrBzYK1uLTO7riFgnHwlBgGEoCGytVS/9Z54lUrqKXmKw6IkSLdKVhIlEj0pKxIUQnWx69cdHxSvnRhR2gwdAhoS5i6JAbg824lAMDWXO4ZlDD0RaDuo8kXRTsBCWaW78hjDRGkCeui+R1F1c71TlipMkTKgfPThOpFr2NNKgA3JKLXSqV0zWujkeVhWGuRQfnwekh3zsIQY50frLds500F882dSWUt9wMu0rpuvaNOmm+goIRwyNrhGxyXWK2iaMN1BjVA6gaba61Ikmdl2yKK5bHZdPjxm4XJuoSxm2TePfcqzDkrZAR1ycRmFyKl2BKKZs1XXH+OOfRCsuc/I1K5uIzXAyjk14YK8NiX6PtFLPKdWs0mrVdnTbRSwixEhmDPXB2YkMXCMcymN319WmEEK0lgQ3REMF4tubjXiFDMDYCRkioPYphlqzKM6ZyFIxy1oOtTQ4GWyrHbIr6+QXHAUjrOL26mxhpXCPZkkrrVwMa6Sk1NBKUyBHDVOBaFnlzTetqaGhQPsRKMSw/Igr+SBiOoIwUprOPwPQougV0OoViJ9HVIn8OZBgnWUjVXPo1UiG5eBbdpGyNqzJmwIjp0Rs2+UINYBqKMNKMmX51ZghgGQaRBt+IMCKFpyJKs9QmiTudVWjxolkiYwYMpFzeXciBCi4mp9QyuRrYSDp0p+bv2A2limzIxgiV5mKidk0UWT1B1EspxZq0SzEIObJ/fbubM5O8lIVYcVRvN4azD8PsQRm9+IFBZ33DCyGuksCGaBwdoqMSSW2zPurB1Sax8/Bj+yzvmpfK+9T4NvBVyBWVZUB3UVQhnmFybbQNS8WxlduyzmKkQft50jo7oyhol8qjrWTlDnSrkoBVAPF8HCt0iILThMFQ5eo6yhLoU+TTfRN/I3NGu7QDWTuL4YHt2yhtoFWEb/vl6SfSEa+SdC3GC3PUG1FwJl0gVjA4qXI1/37vJQvsjtIEhsZqQgrKQut7JBwTxzLojtn0JJyGpLAP7DhQ134ZJ+BET44ITSQLm9QUTEydWrYBnhY32zYNHNsgHbPn37nVOmB2iDLkjSZEIynTIOy9DccrB/Pz/lDHFQYXQpRJYEMsWjG6wmhkUaJEjiJZowgmbA/XsjdaW9mvS8+e7h7XDr26i/esAmeMsFIfIU5Ps5tfRWvQXo5eZq7y4BlxPCuF1eqBRwDJbBoDgygcIgrOVj1t4uDFnPkHFgoiF0puZxQIXQ7mGp/4piYX8yj5k8EF2zSwTIVtGpg9Rd7RORRw7UhqQde1TAUT/0zmLNMv3AUGNizToD/pkrcbtBwKYJgWWgd17RsYHTDa6zCx63sp/mEEDVyYJZNFiI60TONvQixFaHuVAqK6JMFDITqVBDbEop02LlCsMWgZUdUp/Gkdx9QGSR0jTYK4jmOrOIZyMJUN6ur3RXsGQBoIvAL904Ia5YBGa4uCVkSQzCUxMNBRjsg/XXnKwCSKD5Dp7pdO5jT1voLedfJs9aqXgY3Zk//OC/2zxm1zykGqvBrLIl7OSikSjkUQRQykXIp+44IRy1k92Qz9a9MMX1gehd3cLV2YXS6nTvwC35TAjxBCdDI9pZC2CqTjJUSnksCGaChX27i4vGfmKRnlGgR5FWcPN6I6tABgwQtJ62JlYBphUHD6CFWbUp01xLNxTG2BDom8t7k6SjYwKAxuJ7Q6MA27w6VcC9s08MOIi1aJK6bHTYUeABzTaMqSp9XqG8BapsIyTWzTaEpgo91THpo1jE91xZdNYAPA6nElqLECtPv9JIRovtjwW5TyL4P2iOXWwI7b290kIUQNEtgQi7Yu6mNruAEbBwsbw3BQykYpxZAxdcUS1bGJBXkvJIg0o6SItEFKFdoe1HBzDvbE+qtReAmtJ/+WQXq9BDUWafrKGldrG7iWiTstqNGO4War3iOyTOTiNP3P1vAXwMr5d5bYwUwSUBGidVRYRIcTteIiWRFFiE4lE8XEonUZa0mba4mZvVhmCkM5HRzCmKngl4MaABpFwUyRcwbbF9QA7IKJG0zWJCnFUyh3AIXCtLopprrb1rblpN7si7hj4tqGTOkRbbF155rKz3e8/0D7GrJSyPtYCNEM5uR94HrrSwkhWk8yNsSqVPIDuqJxMsTxsLFNRcw2qb8kY+OZRUXcmyw2WbIK+IkQP7kGu5gmcKUO9ww1BjI9CZu4bVEK5p/GYZtLj+3Odl985dwvbyzHXFjx05Xsw3fewdG+N+jr6Wbz2g5cvrRD1J0tswLedCvgVxBixdFTM2UlsCFEx5KMDbHqlPyQdDRGnBKDaoyEEZSLP7aR8iBZ7CpvaE3AOKWkVxm4+7E4WlKP65Jy7VlXFBGNs9iXY1fcxjLKuV2behPz7r+SObbN7fsPcP3Wbe1uilghDNSyypys1x137W13E8QqpqzJPqLWssKcEJ1KAhtiVSkFIalojNjEOiwKTcpo73KLKtCk8pNzNqPgDKrwe1JXLsjtOzGvdg1iFltzwlCKdd0xNvTG6YpLvZjmadHrotM/oyQgvCJsWb+x3U0Qq5h2pn5XScaGEJ1KAhti1Sj5IelwlDiTgQzfiFG02li3IoRELo2aeCtG0ThRcBaNJvKGSQ2fa1/bppKxQcfSHT+yrGVhd5WnF34V9ViOr4vaJDYxkxQBFqJ11JTAhtZeG1sihJiLBDbEqlCuqTFSydQACAyXgt3bvkZFkMglMHU5xVHrAEp/qDxtYJLvGWxX68RCrMaBV4N+Z8OQr6FlrdNf+xIAaAtZtUWsKO7UjA0fraVOlBCdSHqUYsUr+QHd0Sguk/MifSNGwe5rX6O0Jp6NY0UTX5YatPcm0ZQUxyC9nshaGfV9x0yZk9osK3E+vRBieZOMErGixFzABpVEGT1QlKwNITrRyhg1CVGDBgpeiIoCbDUZMPCNOAW7p23tQkMsG8OOnMpDYXgSHWUndzFTFFNd7WhdbUvso142PbpNm97QmX9nIRZDxlGizWQwL8TKpHrSxHr/P5XtwLwgAyghOpBkbKwgL730EkeOHGH9+vXEYjGuueYaHnvsMV5//fV2N63lIg25UkAQaXwshnU5SOCZibYHNdycgxNOLt3qcQHtX6hsm7hk12xqR+uaRis44eRaek1jlaRCL88aGwsjWSli0VbJ54AQonmMaUvDh8HK/94VYjmSwMYK8R/+w3/goYce4sUXX+T8+fOUSiXeffdd/s//8//k5ptv5r/+1//a7ia2TBBpQi9fdfcsMl1y9kB7C4VqcPI2bhCvPFS0ctiFM5VtA4P8wBbpjC/AbAGMmG3gm+V5sLbZ2I+6xXRpdIP+SaU7JcQs2vyx2Ql1JTqhDdA57RCiEZRShFMyf3Uo38RCdCIJbKwA//v//r/z13/91wA8/PDDHD16lIsXL/Lyyy+zd+9eSqUSf/7nf84rr7zS5pY2nxeEON4Y/YzRRTlDwLUMYrZJaLRxacmJoEbMT1QeKlkFvGRAoX8LJuUpGkF6A6EtS2AuVnVXQxHvsQhT4LoGFxLNWdb3nFWo/Jy1W78MnGQztIZMMxBCiNUr8t8l9N4iKB1DjY63uzlCiBpkitgyd/nyZb74xS8CcP/99/Od73yncqfkwx/+MP/0T//EDTfcwPnz5/mP//E/8rOf/aydzW0aDXieR7cew5mIqqdVAUwXbcXnPrgFjZse1PDMIqWkBwoCxyWzbgex3Hhn1dVoMA1ESmM0KnWhDoahuNhd4uLVJX6bUO9rzAg4nyjiRAZXYp1fJFWG56KdVDPvp6yiF7dkRAjRWlHunwmjEQCskdtgc3+bWySEmE4yNpa5b37zm2QyGQD+83/+zzM6O319fXzhC18A4JVXXuE3v/lNy9vYbEGkiUpZBvQVnCmrioTKRpltLlapwclbM4IaWl1CTV0uTCmKqTZOk2kFBW+6GTxjhS2TpmA05nMp4REaHTKyWmFjHiVLwoplQLJ6hFi51JR7wSro/JsYQqxG0ltc5v7+7/8egJ07d3LTTTfV3OeRRx6ZsX8jBFH7O3GeHxD3R+glg5pyu84zE+SdfiJltq9xlUyN5JR2FYmMy1iZs6QunMDymjM9olPljZB37Xy7m9ESKyy2IERt7f8aEEKI5lOTgQ0dhG1siBBiNjIVpU3eeOMNjh07xtDQEKZpsnHjRm6++Wa2bdu2oPP8+te/BuDWW2+ddZ9NmzaxceNGzp49y9GjR5fU7qlKfkS2FBCzTSyjtcO4INJ4fsAarmAwmQGgURStbnyz/dNP3JxTVSjUM4v4zjjxK2eJ0IT4xIffJbPuOhkFL4FrGxS8cicjbrcmkDW9GOly+udbTm0VzSNTGcRiyOtGrFZqSmADydgQoiNJYGOKKIp48803+dWvflX577XXXqNQmCwO+OMf/5h777130dd4/vnneeqppzh27FjN5w8dOsTTTz9d1zXOnj1bmYayffv2Offdtm0bZ8+e5a233lpwm+cSaSh4IXGnNcENraEYhPihBhQFHJKqCECgHIp2T3uzNAA0xLJu9ZKuZpFSIk/q4mnCKYEYP712RY80u+I254JiU69hmwbaLqeBO6mpYa7mMZSiN+lQ8EKSrsVy+kds3A12uVUvWE4v/VVBSR6uEM0xNbARScaGEJ1IAhsTPvGJT/Dyyy+Ty+Wacv4wDHnsscf4xje+Med+P/vZz7jvvvt48skneeqpp+bc9/Lly5Wf16xZM+e+V58fHh6ur8ELoIGiH5Jym/ty8oMAMyzg68ksiDGSxPDxrSSemZzj6BaJIJ6LYYdu5aGSVaCUKJG++C4hk1F+5fbPWlfjglVkg9/mrJMGcC0DGrRQSN6o3ZE4ky7QV7TJOAFRg2JaeWP+Ricdi6Qz+2teA6HSmBPFUktmY0IuiwkgRqrxQYhWjmcNGT0vmJR7WNmknocQLTb1ppmWwIYQnUgCGxOOHj3atKAGwOOPP14V1EgkEjz66KMcOHAAz/N49dVX+c53voPv+0RRxJe+9CX6+vp4/PHHZz3n1PbGYrFZ9wOIx8uD5Gw2u7RfZBaRBj/U2GbjByBBGGIHWfoooND4mJRwMA1F3LbJqbmDOi0TaRLZBFY0WbC0aOfxEh7pS2cI9WTmgmmkyPStnfVU51ZIYGMxjrtZdpZSAJxwcvgqoiuyuGTWXtIkb4fk7aV3MnwjwooMCkbIeasBtU8UnO4q0F2yyDgBjVoMJulYZIsBQaTpjs++NPC4E9DlWRSskGKDgipCiOaSgIUQnUkZU2psSMaGEB1JAhs1uK7LjTfeyMGDB8lms3zrW99a0vleeuklnnnmmcr2nj17+MEPfsDmzZur9nvttdd44IEHGBoaAuCJJ57g8OHD7Nu3r+Z5p3aA5pv32orOUhBF2GbjpoEEQYAT5uidCGhc1aNyjFkxHLNzcm5VAIlcClNPvqUKTg4/HpAaPk8YTgaUTBUjs2ZzrdNURIryLfFV2McdMX3ecstTrDJmUPX/Zhp3AobjHmdG8g1LR/DMiEuJ+taYrTepQinF2q4YkQZzjuyNc8kiV2IGnhnJdIFVSEohtIb8mYVYJaoyNprfJxFCLJwENiZ8+tOfZvPmzRw8eJB9+/Zh2+U7od/4xjeWFNiIoognn3yysp1IJPj+978/I6gBsH//fp577jnuuusuoiiqHPv973+/5rlTqVTl56l1QGopFoszjmm0RsROtIYoKOFGedKUqgIaABqD0Ep0VlDDg1Q+jZqyyFDezRDEygPKqcu6mthkB7c2ddRxwsmxw2vPtJyiERKbNh9kMfGZVgQyatFXA0odTinFvMlRCkrWysjUkIKFoi4r4GUir3UhOlPQvR6LtSjl4LuK2fMlhRDtIoGNCV/84hebct4f/ehHVYVCP//5z89Z6PPQoUM88sgjPPvsswC8+OKLHD9+nJ07d87Yd2BgoPLzxYsX52zH1ef7+/sX1P6FWEp/LIg0fhgRhBHr1CjmtBKQGoVnJvGsFLqDeq9mUZEsppnao87Fxgljk0P5zOBGklcsVGmU/MA16AZmtdRSnKUWxWzyRkhinuIUp+08W/wEACUjwo1mBpZGTI/37AL7itV1QwxDEXbA0sBCrCarckbDavydhRAt4fcNEiutB6AYG6VhBcSEEA3TObe9V6jvfve7VduPPfbYvMd89rOfrdr+3ve+V3O/jRs3VjIwTp48Oec5T506BcCuXbvmvf5iWcbCXk5RFIKXIyplyXvllU40ipyerBcSYVA002TdtZSsdOcENTTYeZNksYurQQ1NRDYxRhjTqLA6uJDrW0tu7bWEdv0x/rxqzRzOU878tWUuWiXO2gXO2UXes2ZmB12yShx3cxSNmRkCfQlnxmPtknAmAzhzTeMQQgghhLhKmZORU6PGzR0hRPvJO7PJXnrppcrPO3bsYMeOHfMec9ddd1UVA33xxRdn3fd973sfAK+++uqs+5w9e5azZ89W7d9ohmLewqFaawiKmN44sdIlevyLdOlx0uSr9iuoOL6yKVjd5Ny1HZelQaSJZV3i3uS0nlAFZFMZIgdi2XESF98hPj5SdZheYOCnnoBDI4R13ObUCobsIu/ZBcIFrrDh2iaDaXfG40YbUq5TrkXCMXEsg4HUzDaJ5WtVZiiIFWep9bCUdOuEaIqpJTZUo5ZgE0I0lHwDNtHo6CinT5+ubN922211Hec4DgcPHqxsT53KMt2RI0cAOH78OL/5zW9q7vPtb3+78vNHPvKRutqwEAqI2dUf8pEuTy8pBRGGlyFeukS3d56ucISkzuFMSeEzCXHwsU1FwjGJuw4FZwDfTHRcZrEKIJlJ4YSTgSff8Mils2gL3HwWM3MWTYTKnSMxdmXR18obIb+Nj3LOLs6/8xSd9jcDcK2ZnQDXav3Hj1KKvqTLmnQMu4PqtCxHuoXTi5T8UwlRRWpxCNFaztglSmP/X4qjX0cN/z/tbo4QogbpLjbRm2++WbVdq07GbKZmdoyMjHD+/Pma+/3Zn/1ZZTrKk08+OeNuz5UrV/jKV74CwK233trwjA0DTVIVifujuKVh4qVLmMURsqWAvBdSCiJUFGDPMhcxwqBoJIi7NnHbxOrg6QFGCVLZrqqVT4p2nkK6AIbCzWWwxs6gJ0ILCkVkLDyqn59SI8NXmvfsuQvDCtEO/ZLxIkRH0h0Z3hZieVMKdDQMOoOOWpNRK4RYGAlsNNH0uhdbtmyp+9jp+85WQ2NwcJD/9J/+EwA/+MEP+OQnP8lvf/tbLl++zD/8wz9w7733cv78eSzL4r/8l/+ywN9gbt0qywZ1mR4yxCni4mET4KjqIIY3pXa0RlHUFhmdIGsPkHXX4tndaNXBdWw1ODmLVKEbVVUkNIOX8EFBLDuGNf5eVYcySq6jmO6udcY5veGON6TZrbQSutF5W9aln09/0kEpWNcd4+C2Ne1ujhBCCNEa8an1wgK0L8VDheg0HTyaXP7Gx6sHqH19fXUf29vbW7WdyWRm3fcLX/gCp06d4m//9m954YUXeOGFF6qedxyHr3/969xxxx11X78eMbyaj1uEgC5PetcRRQ1XiOFF4EflBVxjrkvMWAaLZQWaRD6JFU22NSIil8ygJx6KZ0YxsueqMzUSayl09dY64/w6N2mlNgXnrYVNl+kU76ULDOZdClZI3qod2Jg+zWq5W8oc/rhjsdGx+Nfvrz9IK4QQQix3Kj4tS7FYAluGUUJ0EnlHNlE2m63anloQdD7xeHzOc0331a9+lQcffJCvfvWrHD16lJGREdatW8d9993H448/zr59++pveJ1K2iKvHTQQakWoVbm2hlZEYZ6r9/EjwF9uo3UNZkmRKHZVZWn4ZolCoggTY934+AhG7nx1UCO1nkK6pw2Nbo43YtUBOl9Vr3zyppOhVGM1lOUgZ4fkuvNz7tOX7JxVXcTs1gz0cvrE3MteC7EaqOX2fSvEchCv7sPrQhGVTrapMUKIWiSw0UTFYvVdbMepf4DkutWR4UJh/joLDz30EA899FDd11gqz+7GcyazEhTl8b4JzDf7vqMLn0Xg5h3coDq4lHezBLGwKqMicGI4OUV5ko0iTG+gmFr49JNGatT86nMTq6BMlzdCrlgefYHDkF0gazYmHbNgdOZUkHas3iIW7n17buDkqbOMjeQI/cZPjpK6BaKTLHX1FCHEwpiOjY8JlPsqulg7a1kI0T4S2Gii6Rkanlf/h2CpVKranp7B0QmUYWAscAnTTmeUIFFMYejJ6QeRCsnFs+gacSk/Fsfo2og9fha/ayOlZLop7TpvF1nn15/x02wn7BxnE0WKwdKDEVkjwERx3Jk7K0mIudiWxb/+6AMA/PXXvz3P3mI5mzXWWOPx+YLoSjWyRlA5yN1OHX3TQIjlTClQNuiJfk9JAhtCdBoJbDTR1dVKrpqewTGX6Rka088lGiwCp2AT8xNVD3tWgWLCq5TZVVFEcuQi2b61ld51KZnGi1+HbmKQ56xVIEDjq4i1QYxEu9dQV9CXchgaXfqKLW/GZq8fMx9Hlmylp6s5wTQhlh1JYugYEmARK5HCRjPRl/f89jZGCDGDjAqaqKurq2p7ZGSk7mNHR0erttNpGbw0hZ5YxjWTqgpqaCJysXGKycmghuWVSF04QeRdIX3xTFUnulFBjfN27eBXpMpTQy5bXsNT4hfb/WznFA2lFIZS9C7D+hdJdzKe3B1fegHdPdvrX0ZaiHZrxIC3kbMwGjujozMiK82cNiX1O8RqpowkqDTK6CfSMoQSotNIxkYTbdu2rWr79OnTdR/77rvvVm1v3769IW0SU4QQy7s44bQpQ2aRYqII5mQHzs1lsMfPElIukhlGWRJjl8n3DDSsORkz4D1r6RkQ9TKUWpbztOO2WSnouRzvCnbHbRTlv3/KXfpH8EqbDiaEEEJ0onDwIeKl8k3LzMAQy2BtPyFWFekRN9GePXuqto8fP173sSdOnKj83Nvby7p16xrWrlVPg1UwSGe6qoIalSyNVKkqqJEcuYQ1/h4Rkyt/mEaSQld/Q5t1wSqiGzBO18AZe3Klj3xPSMKZOYBWChKuRVfMxl7klI6E09opMY5l0JOwUUoty6AGlAMaPQmHrri9bH+HVlPyVSWWBXk/X7Ucg+ZCzCeaugLc8lwMTogVTTI2mqinp4ctW7ZUMjVeeeWVuo7zPI+jR49WtpuxVOuqpMHwIV5IYurql37JKlCKe5VlXAFUGJK6fIYwql4OVLkDZPrWNKN5i+ZYBl4w+S17wbpafFZhxBXrSrULj5qGIhWzUDXqdl4ySzMfnKY7bpP3Gr+aiW0a+OHMXsOadOcUUG20rBOQ8suvy0iVXw2LDTitJH1rUji23BcTkxoZD1xy8VCJZQixaugpgQ0dyJtfiE4jveYme+CBByo/nzhxgpMnT857zE9+8pOqQqOtXMJ1pVKBJpaNkcp3VwU1QhWQTYxRSlYHNYwwJHnxeFVQw8AgSm8iV2dQo2S0JpxfMMIZ8561gvN2ifN2cd5BQDTL881u/1yDCdNYfR2GMSdgzPUpWCFn0uUpSTHbIGaXX5g9ieVXT2Spbjy4nU888KF2N2NlkmyhxpDEhJokG02sRFbxImHp9wTF3+JeOd/u5gghppHARpM9/PDDVdtf//rX5z1m+j4f+9jHGtmkjjdiNnAJrQicnEU624MTupWHNZqCkyXXlSNymHHXLTJNlDG5xK6JQ7F/G8VUF0Vj/gyF12JjC16+dLH943ft/KzPzTdlY9wJCIzO65nb5irsFCs4nyxxuqtA0YoqDw6kXDb1JhpSj2O52bZpo2RrLFbnva1FA832ud7sos7NLEwqRKczM8fx8z8gKPxPzPEz7W6OEGIaCWw02eHDh9m7d29l+5lnnuHUqVOz7v/KK6/w3HPPVbYffPBBrr322qa2sdO868w+UK9bBHbeJD3eRcxPVj1Vsgpk0mP4ibAqoGEG1Ut3ZQc3YWJhWt1k1u0gcMqBkd/Fxue89Dm7iGdE5OsIgDRCSc2eWdEzz6ob55LlzKDpneG33MUvwTqb/ikrmMRbXJtDCCGEEGJJ1JS+iw7a1w4hRE0S2GgywzD48pe/XNnO5XIcOXKEM2dmRnqPHTvGI488QhRFlWOffvrplrW1U6ilVNCMwJoIaMS9VNUUjcDwyCTHKKW8quKgaEgNXyB26TjxzOjkw4ZBdu1OMoMbZ6RtX62BMN2Y6TM0ZWWTEwvK2mj8nbB5i3tO/Fq2aVSCG65lkjEX94U9vSbE1H/KuGMxkHLpTTgdXTuiK2bX/FnMbRXm2IhWqTcLoR0vwkZfUxIihOhYypjsU2ndmptXQoj6rb7c5lm88MILfOELX5jxeCZTfef60UcfJR6Pz9jvK1/5Ch//+MdrnvvIkSN87nOf42/+5m8AeOONN9i9ezePPvooBw4cwPd9fv7zn/P888/j+5NZA3/5l3/J/v37l/JrLUv+HBkIs4rAKpnES4kZKyiEKqDo5gldPaMT6hRyuKPnCClPfzGzFzASKSKz/NbQsyyl+Y6T5TovzfT4xlm7UFWzYiExmqX1Z/WMnwZS7tzTUKYEL5SCpGuhtcZoYn2LmG3idvja7+mYVfNnIUSHk6BA002v5yTEqmJO9gm0ZGwI0XGk1z5hfHy8aonV2QwNDc16/Fz+6q/+ikwmw9/93d8B5cyNr33tazX3VUrxF3/xFzzxxBPztmclWlDCRqixSzYxLzGjwxWpkIKbqxnQMIKA5JUhwjDL1Ji7RmMXC5SS6TkvO24GvBYbxdCKG4vdC2hw41mzBCKuFp2sZcgucMX16Wey7ohSnVXwLWabFP3W3xFRStE1zxQeIYRoJ1lOVYg2MGUqihCdrLNvna4gpmnyzW9+k2effbaq5sZ0t912Gz/84Q+rpq+ImVQATs6mK9ND3EtWBTUiInKxDNmuLGGsOqihtCZ55SKJS8cJw+ppIqaKUxzYMW9QA2BNVwxf6cVll8xiMd1UQyn6kjNXy6j12FRn7SLBLNNpFmJ6IKQrPhkrXep0k/55fgex/KXSM7PfhBBCiI5kTcnYQAIbQnQaydiY8JnPfIbPfOYzTb/Opz71KT71qU/x+uuvc+zYMYaGhjBNkw0bNnDLLbewffv2prdh2dJgeOB6MewpK5xcFaqQ4iwZGlclRi+jS5ergggGJmFqLZl0T91NUUB/ymUkU5pzv6xR/xffbNXmB1MuwzmPaOIO3UWrxDbPwjYNVKJ2AGHe2hrTDMc8+ovlQMJIzIcav1Y6ZpEpVv8+hlKVzIqYbRK3LRKOSRiBYxlklvDF30nZIytJp9zptV2Tu99/c7ubIYQQQtRFO1OyObU/+45CiLaQwEab7N27d87MjYV44403qoIkGzdu5Oabb2bbtm0NOf9inLELREqz1Uss6nitmKxfEYFVMnC9OKae+ZINDJ+SUyB0NZGhMXR1YVCFRk8MkvPdAySLV4gIUSiU3UOub+2stTTmErcNRubZx68zK8IzIrLTVlFZky7XyLBNg7VdLl4QMVrwGTY9YrbJgOsylvRnTN0pFzadPygwdY/huFfZHo57VYENQyl6EjYJx8RQioIfVhXVHEg5+KHGmijIapkGVhMWPZEwx8ry6KceIBmTjA0hGkkCwkI00ZTlx7UENoToOBLYWMaef/55nnrqKY4dO1bz+UOHDvH0009z7733trZhQD4V4uQW38F6085wQy6N7dm4frxmwTLfLBHESvjWZIbGFdNnIHBAaxJjVzALV9BOmmz/OgC0oYji/ZilHIXedZUlXOejqJ4qMttvttB74e/ZBUoqImP6VSftTzo4U6IDpmEQdwxs02C86JNJBBCbbEnGCnACgzHTr3uKydQOsFZwKeFVtlOuRbYUoJRiXXessmJKOmaTnrFSiMI26/+3Doza7TMNRRiVn4vPUR9ErAzLLaiho87IdBFCCNEeUXcXVuIwSjloJUMoITqNvCuXoTAMeeyxx/jGN74x534/+9nPuO+++3jyySd56qmnmtKWt9wMu0rz16SYasz0ecfJcnOhd+aTEbjDFolsL2k7WfVUqOHtUoCVHGdd3MRyjfLgPJgy4PA9kiMjqNIoESEhYHijqGhNJSsj3zMADCzsF1UKy1AEYYRjGZXshKUKlOaKVQ4oOJZB90TRSneWlAfLNOhLzgzGnIrnCNAUVP3FNnsSsxfI7EnYxGwTy1SVoEajFM2IvBViBYoL1mRqyEDKZbzo45gGjiXlf4QQQgjROVQ6ieXuqGxrfUmypIToIBLYWIYef/zxqqBGIpGoLB3reR6vvvoq3/nOd/B9nyiK+NKXvkRfXx+PP/54w9uSWUANCYC33Qxj5rRjJmpnOL6LE8QYLE4ESqaMu0MVknOznF17hbSyWFvqqjqFU8jjZi5Typ9Au2urMiciImLZMQpdNQIpC5CwTULLYDDlMjOHY+ksQ80a0JiPVpA35g5quJZBKSgXO7VNY57inmrOVVWWRMGZdIHAjTifK1Yetk2D/hpBGyGEEEKIdjOn3dSK/AhzgTXNhBDNI4GNZeall17imWeeqWzv2bOHH/zgB2zevLlqv9dee40HHnigsjztE088weHDh9m3b1/D2vKOm6FL1Z+tUTBCxiYCITrSGB5Yvo3rx1A1Fui54If0xD18xyOyy1MYlKVg2vg9deksUTA28XB1sMFUCUrdg3jx6uyPely2SljaoCe0MQ0FCkylpkTn64vSB0pjTSuEcXXaReVMLVhi1LFMNKA1DKbbHEBQYLkK1ysHW7rr+N2tJa6yIhpv6pK86bh8nSx3Dz98L9/97j+2uxlCCNGRDLu6HxKGGglrCNE5ZKSwjERRxJNPPlnZTiQSfP/7358R1ADYv38/zz33HMbE9IvpxzZCaYHLhb5hj2MUNImLLutPryeV7ybmJ2YENYpWnos9Z3l9/WlKKY/IoRJDMEsl0heGqvYPneq5+gqFaabxe7dxZuNGvHiS3AIzSwBOOXnecbMopYjNMjVi+l8grJHFcSkxOd3i8sTUi6RjkXQs0t02a7tirOuKYS2igOlCKFUeiMYnioC2QsmcazlcxWDaZWNPvEbdjrKrS74qoFeWf+04fUmHlGvRk7BXfF2Uvddf2+4mNF1/d3e7m7A0qygjXNLfhWg9pRSl8W9RHP0/KI48g3HhQrubJISYQm6xLSM/+tGPqgqFfv7zn59zedhDhw7xyCOP8OyzzwLw4osvcvz4cXbu3Nn0tlZoUAFYvsngpTU4Yazy1HCg6bfUxG4RJbuA7wRcSo+gDAAFAVheCTc7Sj57lt7sUSwzjZncDmb5XMV0L4n8RQxMRt00sTXXoU2TghHyjjNOXJsUVcjNhV6UUgte7jLtWpUO8+W4V/WcVuVgxUDgMmL6lIzqgfxg2kWbcC4sQgjnrCJ5O2KXYZGLBZTsCFutrPjiuWSRNXmXnB2St+ar+aGYq38edyzWmQZKlQuois5SXjFn5Qec7rrnRtb09re7GWI+Ut+1bj0DCUYv59vdDCGWnygLeuK9U/Lm3lcI0VIS2FhGvvvd71ZtP/bYY/Me89nPfrYS2AD43ve+xxNPPNHQdkVq5hKrUTYiPhInFaYwdPlOrhNWD/ovBQHjTpbBmEFkUwkeOKUC3RfO4IxdYETDhvR2/CjkTOH18j5KEx+/Qim2AVMpPENR6t9GYLucToxQDEskIpNzVhEUFFTIuu4YJ+N5bsx3caXk4ejqQbKnInwVkYzKb4nilHoVlxMeAwWHkhkxHJv5JXbKznPGLhBM6VVbRnnAd7Vmht8TcSkzkbmRhhNmbjF/6lnNXS+jtcbdgHF34Rkys5EpKKtXrdWQ2uHG63a1uwlikVr6Clpg0LwZjDoDwPXuJ4SYbkqGqde4vo4QYukksLGMvPTSS5Wfd+zYwY4dO+bYu+yuu+4iFotRLJaLNL744osNDWx0x23eijLsLqQxfYXl2zh+jJ5imi5z5nxEjSYbGyWXzOKnQnocg/UXHTynXAMjUJqesyfRY2/iAR4wPnoZjUZTDowobYKOsAyFbRl4YVRZtjUVs7hYKjGdZRhYcXgnnuNSpkiqaLHdm6y7UTRC3rXz7CqlUShOOJOBh+G4x6jrEyo9o5dsKEWErgpqpGIWPfHqu9i2abCh52q2SuO72gnHJO+ZeEFE7xyrnQghhJiq8YWgW2GhmYdCiMZQyq7EMJUvgQ0hOokENpaJ0dFRTp8+Xdm+7bbb6jrOcRwOHjzIT3/6U4CqqSxLpUMNo2BeiXG2mORa28Y2IBdqpnYUtdb4Zo5x+21s7xLxkVGSF7LosIBPkUvuenqv+VMi0+T37jhqcAOpsTcrx0eEWNrBjA2S7dvE8OAW1vo9dE3UvXDjBlZkMOr6dMVtsqXqL5qUW/0y70s6jBkB75kFNhcTKEMzkvYpehG/jY/V/F1Do75OZHfcnnG9Sc28d6gYSMmKImJl0ctwwNkpOnXg25j6Pp2RybMa1VvbQ0qAiBVLTfbxdOi3sSFCiOkksLFMvPnmm1XbC6mTsWPHjkpgY2RkhPPnz7Nu3bolt2nNGUUi6wEF0Jc4XigRNzR5baDcG8i5GbKOSXL8DEYxgzNabkMl7DDR8bninWM4+wb5rddQ8iLo7qWXJMqI4afX4K/fjLtxDdlSyFjBJxYpmPJd8seuPFZk1CxUaRkzVxsxDYO+pANJuBx4RErjmGY5PWSJZiuC2W6h0pgT04V8Y66CnkIIsXJ0Znhn5evQuJoQS6amDp3C+eqICSFaSQIby8TJkyertrds2VL3sdP3PXnyZEMCG0H+f+Db1cu9eoCp0/w8PMn6UhorvhE7CDDnuHujtCIVFoknXfJ2wFjB59zBP6083x23Ucog5SryXkiRiDBe7jVlnIDQgHBisD79Mmu6YnPeIfSsyUH+1SVYF6IrbjOaL0dEEh28lvnZVIHN2QQazVCq2O7mCFGXTqmxIVayJY7AJTVBiNVFTenrhTIVRYhOIoGNZWJ8fLxqu6+vr+5je3t7q7YzmUxD2jQrXSSKAi7qcWAjAQZBmEdphakdNC44CeKD64n6+gkH1xBY5ZdiwrFIOBYFP+RKzsM2VWVqh1KKtV0uWsNFPEYjH2/aFBGFoj/pkC2FJN2FLWs6kHK5nC1VhlIa6J1nxYekaxJFNpHWMzJDOknBjjjenQUgkppxQohVoulhB0lNEGJ1Mab09SLJ2BCik0hgY5nIZrNV27FYbJY9Z4rH43Oea7EMbWNOLGeitEJjojHwJ15Wvg75Ze4U64wk570xrrjXoBwXXSqwdetWrrnmGqB2xzNum2zsidd4ZnJ5UM+s3aGMOxZxZ+Evbds0WN9d65qzU8yc6tKpJKAhOlV3f4Kx4c5cevLmW69vdxOaot5aCSvf8iwe2unk5SVWKmW6KKMblIM2F9ZnFEI0lwQ2lomrq5pc5ThzZxJM5brVRSULhUJD2uQfuBdv/caaz908bXs7W9k+ZdtxlkcwQAjRfEc+fA+vv/MOA329/PB//LLdzan4V48cZqCn/uw4ITqBJJEI0Tz5jfvpGr4fgEz6MtKbFaJzSGBjmZieoeF59Ve6LE1b/nR6BsdiJRJxksnk/DsKITpGJ66W0Z1Kc8dN72M0Mz7/zi0kQY3O0NF3/zu6cUKIhpsyBVpFBiAF2YXoFJKcvkykUqmq7ekZHHOZnqEx/VxCrBaWKx95rRJPLfw+lkyPaJ1ODHAJWeJYiE6nrOmBDSFEp5B35DLR1dVVtT0yMlL3saOjo1Xb6XS69o5CrHAHD+5qdxNWhVsP7eETRw63uxlCrFgSBBSiPaxihqD0OkHx1zhjb7e7OUKIKWQqyjKxbdu2qu3Tp0/Xfey7775btb19+/ZZ9lyY3GUfvTZCmRIfE8tDOiVTp1rh5hv2trsJYh4yMBZCiIWzs1cI8q8AlIuI0pg+tRBi6WREukzs2bOnavv48eN1H3vixInKz729vaxbt64hbUpeWENwrJvspfrrfQghhBBi+ZHpS0KAnrrqng7a1xAhxAwS2Fgmenp62LJlS2X7lVdeqes4z/M4evRoZXvfvn0NbZcduSRPr5PghhBCCNEiEmQQoj2UPVk/Smvp+wrRSSSwsYw88MADlZ9PnDjByZMn5z3mJz/5SVWh0Yceeqjh7VIYuO/1o0OpDC2EEMuVki6BmINMXxICiDlTNnzp+wrRQaQXs4w8/PDDVdtf//rX5z1m+j4f+9jHGtmkCjtyyV2WlDwhhBBCCLEyqarABlCqf5VCIURzSWBjGTl8+DB7904W5XvmmWc4depUZfull17iyJEjrF+/nlgsxvr16/n2t79def7BBx/k2muvbV4DR9zmnVuIBpD0bSGEEEIslkrEq7Z1vtSmlgghppPAxjJiGAZf/vKXK9u5XI4jR45w5swZ/sN/+A889NBDvPjii5w/f55SqcT58+crAzmlFE8//XRT26cis6nnF0KITmaYkqrfVm3+80vcVIiVTzk2Uz9sdFECG0J0CglsLDNHjhzhc5/7XGX7jTfeYOfOnfz1X/81ADfeeCP3338/9pTiRlAObOTz+aa2TRthU88v2u++wwfb3QQhOtbd9xxodxMqVmV20ir8lYUQraVMA5icjqJLfvsaI4SoIoGNZeiv/uqv+Hf/7t9Vtj1vsirzsWPHePnll/H98getUopkMkkURfzH//gfm9uwXolar3S7tu1odxOEINnVedPe/uRPbuOGHU2c6ic6ntTWFGJ1MJN3Yycfwk49TJDsbndzhBATJLCxDJmmyTe/+U2effZZ1q1bN+t+t912Gz/84Q956qmngPISsb/5zW+a0ibfKJEcsObfUQgh6qSM2l9R9993qMUtmd+OzVvm30kIIcSyFyW2YzrXYdrbCK1Yu5sjhJggI9Fl7FOf+hR/8zd/w/nz51m7di1PPPEEpmmyYcMGbrnlFrZv3w7Addddx//2v/1vAPz93/89N910U0PboYkobRrGNp35dxZCiDrpqPYyeusHBlvckuVnJS/NuZBfbSX/HYQQ7REZk99NWmZhC9ExJLDRRm+88QbHjh1jaGgI0zTZuHEjN998M9u2bav7HL/+9a+B8oopTzzxRM19Nm3axMaNGzl79ixHjx5tSNuv8o0SpU3DpAYlqCGEEJ2iU2tsLCbQoKZVBV3Ir9bKv0Mr/+TtCNh06mtKiFaLptSU0768L4ToFBLYmCKKIt58801+9atfVf577bXXKBQKlX1+/OMfc++99y7pOs8//zxPPfUUx44dq/n8oUOHePrpp+e9ztmzZ8lkMgCV7IzZbNu2jbNnz/LWW28tqs215NdexLohIZkaosJyDAKv9l12IYRYDN3EqqCS0CGEWChn+F8o+mdAe8RLu2CTFFYXohNIYGPCJz7xCV5++WVyuVzTrhGGIY899hjf+MY35tzvZz/7Gffddx9PPvlkpT5GLZcvX678vGbNmjnPefX54eHh+hs8j8SAPVEdWoiyvoE0F4fG2t0MMZdldHNpthobQnQKiYsIsQppH/TETc9I5qII0SkksDHh6NGjTQ1qADz++ONVQY1EIsGjjz7KgQMH8DyPV199le985zv4vk8URXzpS1+ir6+Pxx9/vOb5prY3Fpu7eFE8Hgcgm80u/RcRQggh2qSTsiwaHSesd7pHO6aFSL0SIcqUMic3pMiGEB1DAhs1uK7LjTfeyMGDB8lms3zrW99a8jlfeuklnnnmmcr2nj17+MEPfsDmzZur9nvttdd44IEHGBoaAuCJJ57g8OHD7Nu3b8Y5p3Zs5utwyNxYIcRsnJiFVwza3QzRQqtlkDr/d2OLGiKEWDmMycCGjuS7U4hOIXm+Ez796U/zta99jaNHj5LJZPjFL37BV7/6Ve67774lnzuKIp588snKdiKR4Pvf//6MoAbA/v37ee655zAmUrCnHztVKpWq/Dy1DkgtxWJxxjFCCCFEp5tePFR0Pvk3EyuaMeW+sGRsCNExJGNjwhe/+MWmnftHP/pRVaHQz3/+83MW+zx06BCPPPIIzz77LAAvvvgix48fZ+fOnVX7DQwMVH6+ePHinG24+nx/f/+C2y+EaK6ewSSjl5o7FU4IMdNSE1caPXxfLZk0QixrxtSpKJKxIUSnkIyNFvjud79btf3YY4/Ne8xnP/vZqu3vfe97M/bZuHFjJQPj5MmTc57v1KlTAOzatWveawuxYnVo3vmD993FtXs2Ek/Z7W6KWCVkeqIQQiySOXlfWEtgQ4iOIYGNFnjppZcqP+/YsYMdO3bMe8xdd91VVRD0xRdfrLnf+973PgBeffXVWc919uxZzp49W7W/EKJ57ju8sKXfetJdfPiOO9iwcWD+nYUQQgjRPtbUhHcJbAjRKSSw0WSjo6OcPn26sn3bbbfVdZzjOBw8ODk4mjqVZaojR44AcPz4cX7zm9/U3Ofb3/525eePfOQjdV1fiBWpRWneu7bNH7wUQojpJJNGiM6nXRdl9KLMtWD2tbs5QogJEthosjfffLNqe3qdjLlMzewYGRnh/PnzM/b5sz/7s8p0lCeffHJGp+jKlSt85StfAeDWW2+VjA2xusmgQQigdi0Hqe+wMjQrOCJBFyHK/A0bcbv/HLfrUVTX/e1ujhBiggQ2mmx67YstW7bUfez0fWvV0RgcHOQ//af/BMAPfvADPvnJT/Lb3/6Wy5cv8w//8A/ce++9nD9/Hsuy+C//5b8s4jeYXT5fIJfLLeo/3/ca2hYhhBBCCCGazTAng8CGNufYUwjRSrIqSpONj49Xbff11Z+y1tvbW7WdyWRq7veFL3yBU6dO8bd/+7e88MILvPDCC1XPO47D17/+de644466r12PN954nffODi3q2K1br+Gaa65paHuEEEKUyb11IYRoDjUlsGFqC621ZLwJ0QEkY6PJstls1fbUgqDzicfjc55rqq9+9at8//vf54EHHmDt2rU4jsOWLVv48z//c371q1/x6U9/emENF0KsCKZd/phft7l3nj2FEMuFDKKEaB+DiKB0jKD4K/zCz4jyxXY3SQhBh2VsvPfee007d3d3N+l0umnnn02xWP1h5zhO3ce6rlu1XSgU5tz/oYce4qGHHqq/cUKIFe/jH/0A5y5eZPf2Oer7KLm/v9LIsFc0mgRThCgzLYWX/+HkdqYfkvE5jhBCtEJHBTY2b97ctHP/5//8n/mLv/iLpp1/NtMzNDyv/toSpVKpant6Bke73XDDXtat37ioYx3HbnBrVrb+dWmGz9eeiiTEXNb09rOmt7/dzRBCNJAU8hSifQzHBkwgBCAqlSQFXogO0FGBjZXo6oolV03P4JjL9AyN6edqt0QiTjKZbHczVoWurqQENoAP3vc+/uePft3uZghRt56BBKOX8wDs2LW+za0Rq4FkVgjRAsoBPdFPL/ntbYsQApDARtN1dXVVbY+MjNR97OjoaNV2O6bSiM4gHdWyeCzGus09nD8z2u6mVHHiJl6hfOdm74FtbW6N6CTbtm2EbVDyPA7ddKBl15X7+a2xYcPglK3O+KtLNocQzaew0ZQDG0oCG0J0hI4KbKzEL+Nt26oHOadPn6772Hfffbdqe/v27Q1pkxCisdZv6mfz+rWUPJ+De25Y9Hla8Qm48j5lO5tSitv3H2h3M0SDxVM2iVSM2/bvb3dThBBtoJTN1WGLDiWwIUQn6KjAxkq0Z8+equ3jx4/XfeyJEycqP/f29rJu3bqGtUuI5apT45/7r9/d7iaIWdx+5w288i9vtLsZbbV+Sy/nTtefMSjm9u//9cM1HlVI6FCIVUJNGUL5QfvaIYSokFo3TdbT08OWLVsq26+88kpdx3mex9GjRyvb+/bta3jbhBCNoWQNio5lmIprNmxqdzPa7k/vuavdTQDAMKTbsVytxKxaIRZLTb03HIbta4gQokJ6GC3wwAMPVH4+ceIEJ0+enPeYn/zkJ1WFRmUZ19WtEzqUW7YPzr+TWBIJj4hGueXA5JSotRt72teQVaX9n9PNZtRZ7ykZTzS5JUK02dSMjVAyNoToBBLYaIGHH65OWf36178+7zHT9/nYxz7WyCYJsWAfuvNQu5uw4q38YdHCSCbM4l2zYSO33XkD19+wifvvXWHvXSmm3DZRnUH2nnTX/DsJsYyF3buwkx/BTn2SUt/OdjdHCIEENlri8OHD7N27t7L9zDPPcOrUqVn3f+WVV3juuecq2w8++CDXXnttU9soxHxirtvuJixZJ2S+pHtj7W6CWAUMw+Dg7hs4fOgQ6URnLRXeUSRIIoRYBL9rENPZiWlvIXIkkCdEJ5DARgsYhsGXv/zlynYul+PIkSOcOXNmxr7Hjh3jkUceIYqiyrFPP/10y9oqOpMs97pyfPqTH2l3E1a8ZNdkEG7TNTKFasXpgAClEGJ100Y0uRFKH02ITiCrokx44YUX+MIXvjDj8UwmU7X96KOPEo/HZ+z3la98hY9//OOznv/IkSN87nOf42/+5m8AeOONN9i9ezePPvooBw4cwPd9fv7zn/P888/j+5PLRv3lX/4l+2U5ObEEazZ0c3ForN3NEKJl7r/vEP/wjz/HcSzuve1mfF8Ku01nrJJpPq0MCtuORamwtLn29cZs2hHslgC7EJOUWX6zaq1RgUaqZAnRfhLYmDA+Pl61vOpshoaGZj1+Pn/1V39FJpPh7/7u74By5sbXvva1mvsqpfiLv/gLnnjiiXnPK8RcHnnwfv76699udzM6grsCptOI+a0fGOTTnzxS2b4y1r7AnowFm6CRf9QGZn/ce/dBXn751YadTwjRuWIX3qE4/gJoDyezDvZ8qN1NEmLVk6koLWSaJt/85jd59tlnq2puTHfbbbfxwx/+sGr6iljdllobYmpq/mp2zfqNpHtnZlwJ0SydOmsiqlGqtt4VL0RtO7dsbdm1OqFekBCrmTI06BKgQcuqKEJ0AsnYmPCZz3yGz3zmMy251qc+9Sk+9alP8frrr3Ps2DGGhoYwTZMNGzZwyy23sH379pa0Q4jVxjAM/peP/gmXx0b47nf/sd3NEaJlVus0gnkDAErRyPWIlOrcYJYQooHMySGUxp9jRyFEq0hgo4327t07Z+aGEKLxHNtmw8Cadjej49yw/xreeO2PAFy7ZyPv/P5sy9sgd6HFardK409CLD/OlCGUZGwI0RFkKoroCPG0wzXXrm3a+e/5wIG6991x/fqmtUOITnXoppvYvW8L19+wiXvef3O7myOaTIqHNsdSY3OdHNvTWst0JSGucpzKj1pLxoYQnUACG6IjuK7Fg/fe07Tz7915Xd37plKJprVjsVZrGrloHce2+eBtt3H40CFcW2qyCCGEELNRrj1ly0PraNZ9hRCtIYEN0Rk6aOAuQYQO18F3NBvNMNvdgtaS954QQohlITb1BoCGote2pgghyiSwIcQyILUHVqc//ZNDLbmO6pBpCfI6L5P4juhUkbxHhQBAJaozG3Wh1KaWCCGukuKhoiNIP745+tem2t2ExlvBL5bpA/trNmxq+jWEEEIIsTAqFkOZA4CNUg5RoOVusRBtJu9Bsexs2T7Y7iYsC9uvX8+ffvCudjej8WRcLoRYRm6/84Z2N0EI0WDKNLC6/w1u17/GSX+CIJ5sd5OEWPUksCGWnWu3b2l3E5aFP737LrpT6XY3Y1WQ2hBiNot5aUhSzcpy/bbtLb2eZGUJ0RqRCid/DuV9J0S7SWBDrBhrN/a0uwlNIwNnIYQQV7XjO0G+h4SoFhmTgQ0dtLEhQghAAhuiQzSiw7Rl87oGtKRzO2+333kDjmuu6ABOPWJxZ/6dhOgQi7l53qEfQUIIIaZQhXcICr/AL/wL1uWL7W6OEKueBDaEWAa01rxv9w189tOf4JMPfLjdzWmrO29+X7ubIIQQdTOaUPG4XdNNDIm6CTGp8HuC4r8QFn+BOX6l3a0RYtWTwIZYdmZbKULmFa8OPemudjdBCNFCM7Po5LO+HeQ7VohqSpmTG6HMRRGi3SSwIZaV99++m5jrzr+jEEKIFUqyBubSqdMphVhxlDX5cxTOvp8QoiUksCGWTLXwVbRj8+wrokhnToiVRdLeBSz/TIFIMkyEWJGqMjYksCFE20lgQyyZYRqs3dSzpHPUE5ToW5Oir7t7SdcRQgjRmXbvk6W8G2G5B4KEWDamZGzoSKaiCNFuEtgQS6c1D3/4PtZsaF7Q4b4P3cwnH5y7aGakoyVfZ9t1jVlZpdGWmo2yZ8+2BrVkbj296ZZcR6wOkQzQWqrdWW+2Y82/EyA1NoQQHcGaskpbVGhfO4QQgAQ2RIOYpklXd3IJZ5i7o7p+YBDbmrvTG4aLD2z0Dib52Efv5k/uunPWzr0TN2s+Ptc5G2Wpd+Bu2r2nQS2Z2+0H9hNP2ShVroeymm3ZPtiQ8+w/uGPW57buXNOQawjRyZobcJEgyUJIsFGIKZKpyo9RlGljQ4QQIIENsQy4CYt0Yv4gQRjWP7/RcauDFA8evpuNa9ZhGAamUfttoZTi8IdvYePWfjZs7Zv3Gju2b+LPHn2QI0funPGcG7foGUxy7Z6Ndbd5KeYLCjVKKpHk337yIf7s3x7hlr37WnLNq7r7Ey293nzuv/tObj1UO6B0zwcOcHWsds8HDsx5njvfd5D77791xuMHbt7Jh+68fanN7GhSY6N52p2d0TLL7teUwIEQy0b/ZKay1lmigmRtCNFOrRntiFWh0R3lnv4EGzYNcsPOnRizBBumMs2FZVQ89NAd/Ob1t9i2ZQPdqckpFLt3bOeXP3+r5jHXb93G9VvL0zpe/pd/4fibQ3NeI5VIkpoWlOkZSPDoww8B8PbpP/LO788uqN2dzrFtHNtu+XUH1/QwNpxv+XVn49g2N9+wl1d/9vsZz+3deR3d6fJrbvPa9fOey7ZnflTfcdP7lt7Iq5o0+NuyfZDTJy815+TLRKfGD2plga3I2gwr8FcSQnQGY10v1ruHMcx+lNFHKRgl1u5GCbGKSWBDLFmz+sLprgQfeP/MO9W1mJZi//XX89tfHa/7/FvXb2Tr+pkZE+lEqsbeMzUikLMiBxJ1WrOhm1LJ76hgxGIt5rVQT0BjuXvg3rv5u8svkhsvtbspbdMpb/FWrl4lViallGRRCTGFYdp4qR3E/PINrCA/AlJqTIi2ka6OWBE+/tEPEndbGydfzUGJet14cPusz8ViDv/24w+1sDViVk16KZumybr180/buiqK5D0lRD1WzVQiITpc4BQrP0cFGVYJ0U7yDhRLduiOvfPuk+opBx1MW7H3QI0VOpbQSbMcgzV9/R3X0atn+sxKd9f7bm53E1piOQW5mt3WzdsGmnp+IUR7LKfPOSFaJXJ9tNZE4QjO5eF2N0eIVU1GXmLRrtuzifffvpt9114PwMZ1s6/Q8LE//QA3HtzORx66h3tuuYU/faDxRQ8Nw2DH9eX0/njaqaRev+/91zX8WvMxLcW+a5t33bWbepp27kY7cPPOdjdhSXoHkyS7XPrWpHBizZ29t9wHDlt3ruGDh+qbPlaLYXRWcHIl67RAcNOs0F9zuX9WCLFSxApnKY3+Nd74/4Ua+0m7myPEqiY1NsSiHdyzlxtuuKGyvXvbDk5uf4/Ll8fIj3tV+3an0lV377dv3Ay8Muf5Hae+ApTdfZPFOf/k7rvwbvexTJMLV4YplIoT16qW7Fr6tJVrr9las3ioaSs+8tA9xFy35nHxxOTjix1cfPDQrbz4D/9MZqTzK3DfduN+lFL85pfvtLspi9Lb18Wf3n0XAD/9za9r1nFZ9oPEBjQ/3RvjoQ/cu/QTtdDaTT1ceG+Unv4Eoyug1stSLfvX8SL1DCYZvZRrdzOEEMuQMdhFNFTu82qdJ7o8ijHQ095GCbFKScaGaBjDMDhy3wf483/1sUWfY9t164ByxsPt7zsw6373HT6IYZaXbf3gHe+ves6xbQzDYP3AYFVQ44679qIUmLbBvXfesvDGTbtBds2GjQyu75r5O+xcz4aB6uyVfTeVp9+YluLO9y99NYu+7m4+/ckjSz5PK5imyaEDN818YmIQ1b+2vmKtU+3et2XBx3T1xRd8zHS37N3HNdeuZe3GniWfS8zU6hobD3/4Pj720bt55Mj9Lb3uvNoUXzAMg3Wbe9pz8TZ68L672t2EFUUK1YrVxBroATW53Hw0dLF9jRFilZOMDdFR7r/zDt7Z8kf6enqqlmCdbte2HVyzYROGYdS9tOiBXXvYvH49lmnNee7Z7LxuU9W2YRjce+gWnvvOj6oer3XX8+6bb2Hb5k0kYwn6urtnPL+affCuW3nu+cm/Ybo3RmakXIwr0eXMyP4B+OBtt3HLjXv55n/9b3Vf54HDdy65rY5t8+C99wDw11//9pLPV69Gpp3fcuNe/vjOhYad76p16/trPt7JCfOmabJxzbp2N6OjfOxD93F++BLf+//9c7ub0jI96ZkBarF4qzTxR6xWSmEavYThRNZfZqy97RFiFZO4uugopmmya9sO1vTWHiRNFXPduoMaV/V39y4qqLF52wC37d8/4/E1ff1smlYscbbl8DavXb9sgxpbd85eP2Wppv9b9/SkuPXQHq65di3333do1uPSiRSx5OS//55rd8x5nf7u3kW1b6X10df09XPvB2tk0CzCh+5/P07cpKc/wR0Hl56J1EmuZlmtNleDPXfde2PlsfffvruNLRLLz0r71BRibsqZ7Ntpb7R9DRFilZOMDSHmkexy+cjhD876/EcPf7Dq7v2awfqXt+xk/WvTDAx0EwQhd72/taub3HxDeaWdYqk0534PfPhOfvHb37FuTT+b165vRdNWhBt2XMs//s/fLPk81225huv+7TVz7rNchzh337yI6WoryI3X7sKxHcIwZPe2uYOGQkylpAiwWGV0Ty9MlDwLw8tYfoCyZYglRKvJu06IBrj10B6OHn2L/oEu9u5o/GooawZaHywxDMXhQ7NnTHSC9QODfHSOoNNVu/bOLCArOk86maza7u1anhlOUy3ntPxd12xvdxNqSvck5t9JtI0bl66lWF2MazYQnjOACPCJ3h3C3LnwWmBCiKWRbx8h5pFKz1908uYb9layDJbCMCEKJ7dt16S7L8lNuzonFfyOu/by05+83u5m1GXL9kFMy+SO9y1hmkQHDEwHehY3jWa5sS2L+++/lTfePsGua7cteKpZJ5JVORvjng8c4J//6beYlsEH73z//AcskWkpwqAx/3g6ihpynuXinjsPtrsJQrSUmYwTGgNEUblwqL54GSSwIUTLSWBDiBpuuW0Xv/z5W5i24u7bW9dJiyWri2X+vz/9iZZdu143XreLIIzwfL/jlnBN98YrS+Bu3NrPkfs+0JLrbtrQvBokAKlEklR3jOxYsanXaYaFDg13btnKzi1bm9IWsUwp2LvzOjavW49lWSRjS1/haD4f/+gHOT98id+/eZLhC9mmX285qHc54FpLrAux0hnxjSh/LYa1iVIqJQMsIdpA3neiI6wZ7Kw70u/fdyPXbNyI6ziLKja6Esw2IDUMo5Kd0mmBjfs/cDsv//gVLMvgA4cac1d3ts784Q/fws9/8Tv6+ru4Yfu1DbnWXLZtX8/vfnOq6ddphQ1bV0Ydmlp6BhKMXs63uxkrUis/i9f09bOmr583//DHJZ9LGVKnXYiVrnTt9STf3QCAGwTo8ArKlPe+EK0kgY0VQGvNW2+9xS9+8Qt+8Ytf8Mtf/pLXXnsNz/Mqz7fanv1b+f1r7wKw9drad7Pv+cABfvrTYyTTMW4/cKCFravPmr75V2YRnWVt/wCf/uSRhp7zhutqF068fus2rt+6OlfOWIq77rmRXcu0GGU87VDIzFx+eKoHD9/Nf/1/ftCiFgnROu3oSwixXMR7LaLTIYY2MbVFbjQg2e+0u1lCrCoS2FgB3n33Xfbs2dPuZlQ5dNMBfD8gDCPunmVFjb07r2PvzsYX2lxN1m3u5fyZkbr27R1MMnIpV/e5O6C0RNvdctsuNq2R1VauUgbcedeN8+84hxuv29Wg1rRed09i3sBGT7qrRa0R023espbR4eWd1dSI2IEEIIRoPcM0yMbHSOX70FEB88wI9K9td7OEWFUkR2qF2bhxIw8//DB33313W9vh2i4fvuMO/vTuu1oyH3q1+vDdt7Nlxxqu3bORjdfMnWHy8J/ct6Bzt7Jr7CYmY6zXXzuZBWGaZtV+qUTrVkPYuLWf9+9b2iAeYP2WyWlW6d7YnPvqOf7qnbCE4r/65IcWHJhIp2QFC9Eat+6/kbWbetrdDCHEKmWpIUqZZymN/S1q+F/Q4eoqHCxEu0lgYwXo7+/ne9/7HkNDQ7z33nu88MILfOhDH2p3s0QLpBMpjnzwXj58xx2Y88zjjsdi3HLbLlSHvOvNKXNPH7j/TtZt7mXvgW1cu3mycKRtWezcXZ6zumnbAAM9y682wwcP3UrPQIJ0b4z7P7D45XMP7Jr8t+tbk2pQ6xamv3vhtXBu2beXeKq8usme/Y0rCmq7k0Gv/rWrsw6OqObaLp/80w+3uxkzLGRVlOW8PLAQq509EEMHQ4BG6xzRidPtbpIQq4pMRVkB0uk0H/3oR9vdDNEAagkTQNavG+T0yUtz7vP+fTdyYNdu/ts//jNn/zi86Gs1wm0H9ld+3jCwhk/8Se1g3P133sn9d7aqVY3Xk+7i0YcfWvJ50okUf/Int3Nm6Dzvu6Fzlv+dj2u7PPrJB8jm84sKjMzm8H3v5x9++Cqm2ZrlP4UQy5/tmvilcP4dhVgEc6CH0FxHFJ4DQJ8/C9dd095GCbGKdMi9WyEEwK7dk3e0ewYWlsK///pdrN3UQyxpc+8Hb5p1P8e2+cDt7yeedjBtg/0Hd9C/Ls3261tbS6Kvu7ul11uouaaFtMv2jZu555ZbSCcam7FR7zKOi+XabkODGlD+W3z2332cf/9vPraoQr9btg82tD2NZNry1bwUU6e2XbdzSxtb0h7Nfj8vZ524hLpYWYzkZF8q8M+ifb+NrRFidZGMjTZ74403OHbsGENDQ5imycaNG7n55pvZtk1WW1iN3rf7BsYzOXK5AnffenBBx9qWVXcadncqzb//Xz425cLl//31H769oGsK0U7GIpfRXLuph8N33N7g1jTOPXfdxI9+eBSAm25p/lLCK80D99/JT1/9DT296XmXYu60IIDU/RRieVPXb4NfvgaEoIuEb57CulEK5QvRChLYmCaKIt58801+9atfVf577bXXKBQKlX1+/OMfc++99y7pOs8//zxPPfUUx44dq/n8oUOHePrpp5d8HbG8mIbBh++4o93NAGDdWlnuVqxMd73/fcRjcxdybadd23Zg3W9R9Ers2b6zIedUTUrQ3HfjDn7zy3eacu7F2jCwhkcevL/dzWiowx++hR/+j1/Wta+siiJE+5jdSSJrI2FQrq+hh88AEtgQohUksDHFJz7xCV5++WVyufqXxFyoMAx57LHH+MY3vjHnfj/72c+47777ePLJJ3nqqaea1h7RXkaHLar6wfvex7/89Biprhi33rj0FUGmcuImXqE8t3lwvSyJ2UlkINR5dm5pXKHVZrpl7z4uXR7hvVOXgc6e4tMoU1c6qodlLr2rdf3WbfyQ+gIbQog2698EF8qBjTA8i3nuEsb6lf/ZKES7SWBjiqNHjzY1qAHw+OOPVwU1EokEjz76KAcOHMDzPF599VW+853v4Ps+URTxpS99ib6+Ph5//PGmtksIgN3bd7K7QXeIp/vwfbfxP374KqalOrbYoxu3KBUCAK7bsTwGlqIxDFMRhRLgWSjbsvjo4Q9y5sI5Llwe5sbrrm93k5pq74Ft3Lxv74KOibkuG7b2MfTulSa1qjXaPW2nf22a4QuZtrZBiHqYe7YRXXodHY0DEJ6WwIYQrSCBjVm4rsuNN97IwYMHyWazfOtb31ryOV966SWeeeaZyvaePXv4wQ9+wObNm6v2e+2113jggQcYGhoC4IknnuDw4cPs27dvyW0QopFS3TGyY0UAegaTc+67df1GPvvvPt6KZi3aA39yJz/75W/p7U2ze9uOdjenZdo9YBHL3+a169m8trUFiFthejbTPbfcsqjzfPTwBxnNZPi/v/1yI5oFQP+6NMPnWzfQb3dm1+G7b+XZ537Y1jYIUQ/DNDGSO8EzMN39hHaMKBzDMKUwtBDNJO+wKT796U/zta99jaNHj5LJZPjFL37BV7/6Ve67774lnzuKIp588snKdiKR4Pvf//6MoAbA/v37ee655yqF8aYfK0Sn+NAHbyOWtImnbA7fdWu7m7NkGwbW8Mk//TD33Xb7ogtTCrEcSDCrtQzDaPhKUB+++1BDz9fpBnr62t0EIeqmbtqNmbgDw+zGjlxy52WZYSGaTTI2pvjiF7/YtHP/6Ec/qioU+vnPf57t27fPuv+hQ4d45JFHePbZZwF48cUXOX78ODt3NmeagOgMy22wsWFgDf+vf/Nwu5shhFjB+gekJk8t///27jw6qvL+H/h77p19yb6HhKxkXyABkkAIO7Ipi1Arfv1qK1ppa4+W+vXb1p9Va63Lsf26oMUea23dilsVqlQFFBUpoBBAEBKWAAkQIIHss93fH5Ehk8wkk2QydyZ5v87xnHnufZ57PxxDmPu5z/N5/H3LbKKRTKkW0RR8HqaLUQAATX0I7NGXIChFmSMjGr74StJH3n77baf2Lbfc0ueYlStXOrXfeecdb4ZEREReICoDKyEZCCqmFUIQAZ1RhfLx/dv6mojIH2ji7bDDDru1Fmh8H/Zd++QOiWhY44wNH9mwYYPjc2pqKlJT+16/X15eDq1Wi/b2zhoG69evx+rVq4csRiIKTBk5o/Dt/pNyhzHiZOYmoP5cI0qLvbuDkK8oBECyd37WGVXyBtNNbtoYZCSlQFAoIIp8w0lEgUetU8LS8QlsrV8DAKRmPcTWdCj0epkjIxqemNjwgcbGRtTU1DjaJSUlHo1Tq9UoKirC559/DgBOS1n8QVVV1YDHRkZGIioqyovRDA9yF2ejwFQxYQIMBj0MOi2++vpbtFzqkDukYan7X88ZpaXyBOIl02cUY9PHO6FQKDC9YmBFMd1JSI7Aie+2gB0olVL+rygqFZMqRDRwQkocbPsqAdggSa2wfb0fykne/X1LRJ3k/9YwAhw4cMCp3Z86GampqY7ERkNDA06fPo2YmBivxjdQixYtGvDY++67D7/5zW+8FgvRSKZSKlFaUAgA+Orrb+UNhgJGZlIKYr/XuQVhsNHk1WvPmFyKT1Q7cPZ0Q0An2sqKx+LtU5sh2YGUjOG36wsRDS0xJhxSVTqs7QcBANa2wxDqUyFEshgukbcxseEDR44ccWonJiZ6PLZ73yNHjrhMbHzzzTe4dOmSo33y5JVp6V9++aVT3+zsbAQFsRgbEdFleqPGsXXxSOLthMZlBq0O8yqmYG/Vt/h0854huYcvxEZEYv68STjf2IiCMZlyh+M1YeFBuHC2We4wiEYERX4OsOM4ILUBsMK2vxLC1Klyh0U07DCx4QNdEw4AEBbmeZY2NDTUqd3U5HrP+lWrVuGTTz5xea6023TpzZs3Yyp/oRLRd4JDDXKHILtpU4qx4V+fQbIDZZNz5Q6H/Mjo2HiMjo2XOwyvmjRuLGpPnUNbs9lRZ4WIhoYYZIBkzIC1aTcAwGatgWLvYSjz0uUNjGiYYWLDB5qbnd+KaLVaj8fqdLperyWnd955Z8Dbz0ZGRno5msAkCNyYSC5dawCISgUqKsbKHBEAH5ZYKZ+aj88+rYRKJWJqabHvbuynEmPisHzpLFitNkSHR7jsE2C7MfsF1g3yP6OSI2DUG3DjtQthlySs/ctbcodENOwJxdkQPjkFu70eAGA7uxdC6ygIel0fI4nIU36X2Oi6hMLbgoODYTINzbTb3lze1eQytVrt8ViNRuPUbmtrc9lvy5Yt/Y5rsNLS0pCTk+Pz+w4no2PjoNYqYW63whSqZfV/H5o1uQzbgyoRGmxCQUaW3OH4XH56JlLiE6AUldB2+z0zUoUHh/Z6ns/oFMgUApBbkIzxuXkAAFEU0du/ODqTGm1NZkc7JjwCtWfPDHGURMOTICohJBXAfmQTADskAB3VrdDlMbFB5C1+l9hISEgYsms//PDDuOeee4bs+u50n6FhNpvd9Oypo8O56Fr3GRwU2ERRxOKFU1FdcwLZaX1vAUzeo9NqMXXCBLnDkJVRzyUoNLIFhYycbRfjEsMxpbgfuzFIEqZOH4u9+w8jJXkUgo0ml4mNkHA9Gs+3ejFSouFJmRIH1I6B3WaFSj8LCrMOLedqYYjw/IUnEbnnd4mN4choNDq1u8/g6E33GRrdr0WBLyIkDBEhrI5NROQLU6ePxSdbvoaoEjB9cs/kpqiScc2Ri1lBGr18X9VyUtORk9p7HYD09ETsOH/QRxERBTZhwlhYDxihsHa+qNSciIDFdAEqDR/JiAaLf4t8oPsOJA0NDR6PbWxsdGrLsZSGiHwjyMQZFERDLSc1HaNj4yAKInTfzagcN2EMvvrPIQBAxRQ/qLfznfTseBRkZsgdBhF5iaBWAanNsB/SQ5BEiDbAumMf7BOyIfRjqToR9eR3iY3hWGgsOTnZqV1TU+Px2OPHjzu1U1JSvBITEfmfCXn5qDp8Em3NFuSNTe57gA+NLxk+W10SdV+GNTEvH0FGAzQqNdISR8sUVU+zJ02SOwQi8jKtUYWmmLMwnNTB0vxPSPbzwLYmKKZMgkLBovJEA+V3iY3hKDs726ldVVXl8djq6mrH59DQUMTExHgtLiLyL1qNBjdcuwBtHe0INvrX7KwJeflyh0A0ZARB6HPJBRGRt5hilbBUfdaZ1ABgsxyFYrsJypJCeQMjCmBMC/pASEgIEhMTHe1t27Z5NM5sNmPXrl2Odl5entdjIyL/olap/C6pIar4TwWRT/jTlsLc35ho6CgUECeOhSBEOg5Zm/fCWvmtjEERBTZ+W/WRefPmOT5XV1fjyJEjfY7ZunWrU6HRBQsWDElsRERE5Ae8sBrX3s8lvYXFaVAogNDIgdX4iY+OGtA4opFO0Ggg5pVAobj8d0+C9exO2L49KmtcRIGKiQ0fWbx4sVP7+eef73NM9z6LFi3yZkhERERDStHHW/+up1O7zGwcLrIL/KdehzuTxo7Dj35wLa67eu6AxsdHxSAzLwFBYTpUTCv0bnBEw5wYGQoxaSKAy4VDbbCc+A+sx07JGRZRQGJiw0dmzpyJ3NxcR/upp57C0aPuM7Lbtm3DunXrHO358+cjPZ3rf4mIKHD0VRB83rwyRMeHoLA4DXERw+/N/+RxRXKH4NBbkkkQBAjCwL8SzigpxX8tXYjctDEDvgbRSKVMHQUxbgIA8bsjHbBWf4GOc5fkDIso4DCx4SOCIOB3v/udo93S0oKFCxfixIkTPfpWVlZi2bJlsNvtjrEPPfSQz2IlIoobHeb4nJOfJF8gNKwlxY3CtfNmY9LYcXKHMiRUSvc12oPCdENyT8FNAsMbu87FRUUP+hpE1JMqOwXK8CJcLrSj1E6AcGIU2pos8gZGFEC4K0oXb731Fu6+++4ex5uampzaK1asgE7X8wvJo48+iiVLlri9/sKFC7Fq1SqsWbMGALB//35kZWVhxYoVKCwshMViwZdffok33ngDFsuVX2SPPPIICgoKBvrHIiLqt5mTS/CJeic0ajUm5gX27x+NRu3U1mo0MkVCFNj8rbAx0XCiHJsJ7LRB6tBDqS0C7ICiKgJtKeegC1bJHR6R32Nio4tLly45ba/qTm1trdvxfXnyySfR1NSEv/3tbwA6Z26sXbvWZV+FQoF77rkHq1ev7vO6RETeZNIbsWDqVLnD8AqDVofUjFgcOVyH0anRfDgD/Gv3jREkMTUKNdVnAQCl47mF8lCKHx2OU8fPyx0GUb8oi3PQdNoM5Sk7FBCgtKsg7T8Pq6ERyoJMucMj8mtciuJjoijipZdewuuvv+5Uc6O7kpISfPTRR07LV4iIaGCumlKOVT9cjvlTK+QOhUawWZNKkVuYjEnluUhLHJrCov3dFcVfpGbGuj1nMvRvx5ZrrinHVRWTBxsSkSxMMWq0JJyGBDtslhrYWj+CtX4HrF/uljs0Ir/GGRtd3HTTTbjpppt8cq/ly5dj+fLl2LdvHyorK1FbWwtRFBEXF4fx48cjJSXFJ3EQERGRb2g1GlSMHy93GD6RlhWHqgOuZ7h2l5mbgMzUZFQfrOtxbubs8b3WKnFlVFQs2js6+jWGyJ8Yo9RokU5BuW8jgM6ae9bmSmBrK8SSCVCo+AhH1B3/VsgsNze315kbRERERIEmPDQEVeg7sZGQHIEZpaWoO1ff45xSLSBjdPJQhOeRrLxEJMbFYt+3VTh1jMtayLcM0TpYWyfCdmQbJKkFAGDtqIL9syYoC0oghAXLHCGRf+FSFCKiYUSjZYGxoWKz2eUOgShghJgCv5bNxMICpCWORkx0uNyh0AilTI6DMncaBOHKTmV22xlYvv4I1kPHZYyMyP8wsUFENIxMKR0HxXe/2fOLuKTNm2Jj+v9wY9BrhyASIv8gqtx/jUwZlYCoON+/UR6dFuW1a2lV6r47EQ0xMToM4sQZEJVJjmOS1AJrzVaYv64GArSuDpG3MbFBRDSMxEfFYOmi6Zg7txSTCsfJHc6wUlJQiPAYE3RGFa66qsRtv7SsOACAKVSLnJR0X4VH5HPZue4LoAqCgGXz5yC7YGiKpLoze/IkAIBa5ZvZawp+kyYfEA06KMsnQ2nIh2NbK8EAlbUALQeUsHRYZY2PyB+wxgYR0TATHR4hdwjDklqlwnUL5/bZb87kyRifdxEmgwGiKPogMiJ5BJuMXruWSa/3ynUUis6HPkEQoBAAaYhXkOUWJGPv10eH9iZEABSiAGVpIXAgCLba3VDp50IhaGFo08L6jRlt4TXQJYbIHSaRbJhnJiIi8rKw4OB+7+QwHI2OjZc7BPJzSYmdM5yMegNyCpK8em1DEJeC0fCjzEqBWDofrcFXkoGiDVBUbYZly2bYzzXIGB2RfJjYICIiGuYkyFP4NNhowqTyXMQmhmLu3FJZYiB55Y25shzLEKTpcT4n9cr5qRMm+CQmokCn1GtgzLShKbYONoUV1rbPAXsTbNYTMO/+N6w7KiGx4DWNMExsEBER0ZApzMzGkjmzkDIqQe5QSAYRIWGYMq0AqRmxWDCnHKGRBsc5nVHF5Vo+EBzunWU+5GcUCpjiVLCmn4FkretyogPWi7th+eRD2I6dki08Il9jYoOIiIjID0jDdHeDvLQMXDWlHBEhYZg5pQQ6owpqnYjZM9wX4SXvmT2Ns6WGM41JA9WUmVCaCgFc2cnHbj8DS9UmWD/9AvZLzbLFR+QrXABMRERERD4RFRaOH3x/sdxh+MTU6WOxZdPXcoeBYKP3irySf1KIApQT86E4Mwr2A7ths5787owEq7kKil2tMGdUwBirdBTYJRpuOGODiIiIiMjLutYPIfIFMToMqoppUEaWQqEIchxX6spgqotFx14dmuvNwDCdHUYjG2dsEBERDXOKbu8xRIHvNWhgFIorz0RJ8aPkDYaIelIooCxIh9CRCPuufZCsdoiqRACA1mKEdEwB8753IUSnQpmdInOwRN7DxAYREdEwp1apEBSmw6ULbdAaVEiIjpU7JApQ8+aVYfe+b5GUGItgo0nucHxCAU7dp8AjaDQQyopg6bCi6fg5mJoiAADW9u2w2+pgr62D/Uw1xLg0iBnJMkdLNHhMbBAREY0AS+bPxOFjR5E8KgECZ2wMidLJOdj22X4AQN7Y4fmgkBQ3CklxI2umhgRO26fApdIooRoDtDXVQTpqh9BR6Thnt9XBfqIOttrDEKJSIWYmQyHy3wcKTExsEBERjQAGrQ6FmdlyhzGsFWZkwW6XYLFaMSE3r9/jU1NG4WztRQCAWue8DapGr0RHqxUAkJTMGTeBTBABu03uKGik0ZlUQD5gPTgZ9tqDsNvPOs7ZbadhrzsN+5lDECJSocxL71x3RhRAmJIjIiIi8gJBEFCck4vSgkKIotj3gG4KxmQiNSMW4TEmzJ1d5nRu7qwyhITrEZsYipKCAm+FTF4mfPe/XaN3/e5QEIHkNP9ITCkUQEqGf8RCvqPMTIKqYjaU0aUQxCinc3Z7PewNZ9G+V4emOgvsNrtMURL1H2dsDANtbW344IMPsHHjRuzYsQPV1dVoaWlBSEgI8vPzsXTpUtx8883Q6XRyh0pERERuiKKIq6aUuzwXHxWDFUsW+Dgi6q+5V5Xh8LEa5GeMcXn+mqun4vDR4z6NSSWq3J6bMqEYHe1f4NTx8z6MiOSmEAUo89Ih2VJh/fY4pDNVsNvqAABK7QQIFiO0tUZYTnfAZtkJITUaYniIvEET9YGJjWEgOjoaTU1NPY6fO3cOmzZtwqZNm/DUU0/h3XffRXo6tx4jIiIaDsZNGIOv/nMIAFAyOafXvhH9fCjx91noao0Ic4f86zm0Go1Tu68aJHERUT5PbLirqaM3aWDQ6rBo9gw88/w/fBoT+QeFKECVnQxkJ3cmOOobICjjHOdFSxvMl7bD9rUIuyoRivjRUKYlyhgxkXtcijIMNDU1QaPR4Pvf/z5ee+01VFdX48KFC9i9ezduv/12KBQKHDx4ELNnz0ZLS4vc4RIREZEXTMzLR+nkHEyekoexGVku+ygUQHC4HuPzcn0c3RATAGOwdlCXUGsH/36vMDPLcZ2E5IgBXUNUypNFUqn7v1yKhi9lxmioJheiPaMOTUH1sMMGW8fX3521wWY5CuuxLTBv2gDrfyphb2mVNV6i7jhjYxj48Y9/jHvvvRfR0dFOx0NDQ7FmzRokJSXhf/7nf3Ds2DGsWbMGv/jFL2SKlIiIiLxFEASMy+p9psYt/70YapX7pQj+JnlMDI4eOg0AKJ7gOllzmUanQvPF9gHfS6UWMXlSPjZ9/NWAr6FWqXDd0tk4e/4CkuLiB3SNmTMmYOPG7QOOgcibtEYVtOmA1VwP7LJDYTZAkq68GLXbz8N+6TywbT+U6kTYUzKhHhUuY8REnThjYxh4+umneyQ1urrrrrsQHt75C+f999/3VVhERERE/TK9ZCKy8hIxdny621ko3pSVkobE1Ki+O/bCpDciNSFxQAVjASAtcfSg7k80FJRqJVSlBVBNXghlaBEEsfuzhgVW8xGo6uLQtleDppMWWM1WWWIlAjhjY0RQKpUYM2YMtm3bhlOnTskdDhEREZFLWo0G00tKfHrPsqICnDz2Yb+3YI2MDRqagIj8iKBRQyjKAZAD67FaoOYYrOYaAGYIqhQoBBN0ZgBnTLCeqIKl4ysogmMhZKdA6FaDhmgoMbEhs/3796OyshK1tbUQRRHx8fEoLi5GcnKyV+9z+nTntM6gIP4jTERERIEnLSsOVQdqAQBlpfkw6HTYsOGLQV83PDgUS66Zjjfe2uRRf4UAhEUaMWfqpEHfmyiQKJPigKQ4CG1jYd9XBZvCefmV3fwN7NaTwPmTwGeVUKpGAVFxENNHQyFyoQANLSY2urHb7Thw4AB27tzp+G/Pnj1oa2tz9Nm8eTOmTp06qPu88cYbePDBB1FZWenyfFlZGR566KFB3wcAvvrqKxw9ehQAUOLjtyBERERE3jBt4kTotLthMOiRlZwKQRCcanJ0ZzTp0NzoWQ2O6HDPC3+u+uFyj/tSJ1GpgM0qyR0GeYmg00EYnwclgPamOljqBWgatLBbjlzpJHXAaq4GTlbDduoriOo4ICoWYloikxw0JPhT1cXSpUsRFBSE3Nxc3HTTTXj66afx5ZdfOiU1Bstms+Hmm2/GsmXL3CY1AOCLL77AjBkzcO+99w76nqtXrwbQWWTs1ltvHfT1iIiIyE8N42dHtUqFKcXjUZSV49jCNCMlyW3/ipIiKNWd/SaU9r9ex0BrZnRVXpHv+Dy+JHPQ1/OmkuIrscWNDhvSe/3o5mVDen2Sj9akgilFhLKwDcqYEoiq0ej+7lySWmDtOAzrqa9hrjShqdqG1kYLIA3jX1jkc5yx0cWuXbuGfDvUO++8Ey+++KKjrdfrsWLFChQWFsJsNmP79u148803YbFYYLfb8dvf/hZhYWG48847B3S/Rx55BJs3bwYA/OhHP0JeXp43/hhEREREfi0iJAwrls1Fu7kDESH9e3DXGa/sJKPAwLdjzR+TCbVKBavNhuyUtAFfZ6BCIvQuj6u1SqQmJDracTGRqD1+YUhjmTKtAJ99ugcanQpLF87E319lQftAJNnsaK63QtGogcIuQhJskEI6YIxUQpmbBiANYlMr7AerYW8+DbvtNC5nXEXVGKjsOmgadUAjYGnfAYXtNBARDSE9AUIA7eBE/oeJDTc0Gg3y8/NRVFSE5uZm/P3vfx/0NTds2ICnnnrK0c7OzsYHH3yAhIQEp3579uzBvHnzUFvbuY509erVmDlzZr+TEh988AF+9atfAQBycnLw2GOPDfJPQERERBQ4jHoDjHpDv8ep1Fe+IkuDnAaTmZzq1M5KTcG+3Z1LhDW6gX0VNwRpML44G+mJSXjtnx+gqcH17OKKSUUux1636KoB3Xcw8tIykJmUClEQHDNuKLA015uhORkOk71bUdAWwFLXgY5R52GMVEMw6SGMzwOQB9uFRkiHa2BvPQ1BPcYxRJIk2Dv2Q7JfAGoPA3UaiMpYKEyRENJHQzC5TsoRucPERhc33ngjEhISUFRUhLy8PKi+yxq++OKLg05s2O12/PKXv3S09Xo93nvvvR5JDQAoKCjAunXrUF5eDrvd7hj73nvveXy/HTt2YNmyZbDZbEhISMD7778PvZ6/IIiIiIjkFBUWjtLJOThVV4/i/JwBXeOm713j+Lxi8Ty0W8x48W9XvidqdEpMmJiNUVGxPcbm56dBK9NuFSolHz0CVXO9GYaaGCjcVDJQ2TVQ1sSgGadhjFQ7jothIcDEEAD5sHTY0HquDkKjDtoWS2dS4zKpAzbLMeDCMWD7VxDFaMAYCyk3A+oBJgBpZOFPSRcPPPDAkF37448/dqqpcccddyAlJcVt/7KyMixbtgyvv/46AGD9+vWoqqpCWlrf0xgPHjyIefPmobm5GZGRkfjwww9dJlCIiIiIAtmomGjHZ1GpgBggMwHGZeVgnIdlPzQ6JTrarG7Pi6IIg6hzOpZfkIb8dP+q6UGBS7LZoTkZ7japcZkCAjQnwyGFXXRZIFSlEaGKF4F4K2yNTVB+Wwip9SxstjMAuu63bIPNVguhVQ/1NzFoV7XAYmyBStkIdbSB28iSS4Hx238YePvtt53at9xyS59jVq5c6dR+5513+hxz/PhxzJo1C+fOnYPJZML777+PjIyMfsVKREREFAg0Kg3mzJmI5DExWDB/csAucSidnANBBILCdD3OzZ1dNuT3789yG51RhcSUyCGMhvxNc70Vqu7LT9xQ2TVoOec+EXeZGBIE5cR8qKbNhHriYijDx39XePTKfQRVEgBAazHA1BAF4ch+mLe+Ccvmj2H9cg+sJ8+yACk5cMaGj2zYsMHxOTU1Fampqb307lReXg6tVov29s6tytavX+/Y4cSVs2fPYtasWTh58iS0Wi3ee+89FBX1XFtJRERENFykJY5GWuJoucMYlM4ZHJ3LUp55/h9O5+KjYpCVl4gDe2vkCM2lrPQU1ByplzsM8hFFYz9nSDRogGjPEw6CSQ9hbBaALCgtFtgP10A6dwY2TZTjYVWSrLBbTwCwwmY7BTSfAg7uge1QEERlJGAKgyIpHmJoUP9ipWGDiQ0faGxsRE3NlX+MSkpKPBqnVqtRVFSEzz//HAB63R724sWLmDNnDg4fPgyVSoV169ahoqJicIETERFRQImLjHJqK72wZSkFHqmfb7H7+9I7UGfG0MAo7P37PdLZv+9ZGy7HqlQQs1MBpEIJoKO1DuYLgOpsk8v+kv0SrOZLwPlqKBprYAm/BmZDK4QgK7RBApQaPu6OFPw/7QMHDhxwantSJ+Oy1NRUR2KjoaEBp0+fRkxMjFOf9vZ2XH311di9ezcEQcBLL72EBQsWDD5wIiIiCihajQbTZ4zDoSM1yMtK5wMoDYogAnabixPddsBNz47H4W9O+SQm8j1JcPVD4L3+vdHoVdDoAYwKg73jWkiHayA11MNmOQfJ3uDUV1AmQGUxQttoBBoBc8smmK2nIKjDIYWGQ0yKh2DQei028i9MbPjAkSNHnNqJiYluevbUve+RI0ecEhs2mw3f+9738OmnnwIAHn/8cSxYsADNzc1ur2k0Gj2+f2+qqqoGPDYyMhJRUVF9dyQiIqJ+yUpJQ1aK5y9RRhK1Vglze+eb5MTR0W77hUcE+yqkIaFQKPru1IWrGR4KBTCxNAfbPtvf53iNWt1nn/5YsGAS1q//3KvXpIGTQjqAln4MCO0A4N2fCQAQNGogNw1AGpQA7GfPw378FKTWBtis9RBUzs9NkvU4JHsD7O31QB1gqxMgCGEQVGGAMQSK0XEQw7h0ZbhgYsMHLl265NQOCwvzeGxoaKhTu6nJeRrWiRMn8O677zrad911F+66665er9nf6YnuLFq0aMBj77vvPvzmN7/xShxEREREnpgzqwRbPtsFvV6DsrFjnc5VTCvE1k93Q2tQo3z8OJki7NzdxWaVtyDi5Cl5CDaZkBQ3CocO1+D8GdfLAC6LjY7EPhz1yr2nTCvA6Nh4r1yLvMMYqYSlrsOjAqIWoQOGCN88YgpR4RCiwgEASpsdHS1WtDbVQdGkhqZZ0WNGB2CH3X4O9o5zQAeACwZYwm+ARd8KGC1QGyWoDSqXO7qQ/2Niwwe6z57Qaj2fAqXTOVfH7m0mBhERERG5lxgThxuvjXN5LjdtDLK/m+ki5xKeqVPHYdPHuyBJnQ/5cijIuLIX7bL5s/HcC2/22n90bKxX7ps3Nhl5aa538wsJ16PxfKtX7uOOIUiD9laz7Iklf6MQBXSMOg9lTUyvW75KsKNj1HmoRO/P1uiLQhSgDVJDG9QZiWSzQTg2DdKZekgdF2Cz1QOwOI0RlHFQ2wzQNhmAJsDath0dHbshiuFQaIKB0FAISTEQ+vHsRvLxu8TGyZMnh+zawcHBMJlMQ3Z9dy7vanKZuh/T9TTd9mlua2tzaiclJXltBgYRERGNXKJKgM1iBwDExUfIHI08/KEmSWZyKkxXGyBBwqgo7yQM+tLbd0lxBBWgTR+TgEljx/XYmWYwlBoB1g67164nF2OkGs04Dc3JcJczNyxCBzpGnYcx0vdJDVcUogAxNQFITQAAKC1W2GrqgPpzkNobYbOdg6B0nhlkt54CpBbYrC2dtU9bAJz8bvmKMgTQB8OWkgRNqL5zrRb5Fb9LbCQkJAzZtR9++GHcc889Q3Z9d7rP0DCbzR6P7ejocGp3n8Ehp3feeadfhVC7iozk/udERET+ZPasidiydRd0OjUmF3O7eDnFR8X03cmLgkyGfvUXuj3USV56bvdFEkUheC9eT9x247VeTZTIyRiphhR2Ec3nrECDBgq72FkoNLRz+YkcMzU8pVApoeya6LDZYW61oK2lDmgSoWrVAdZaFyO/W75iPgeYFdAcKYNNANq1LZDERmjMdRDiowOiVodks6PlnKXvjgHK7xIbw1H3Yp3dZ3D0pvsMDW8V/vSGtLQ05OTkyB0GEREReUFKfAJSrhu6F0wkH41OiY62zqKpY0Yn9Tifk5KOPZWHcfF8KzLz5PkZEJUKjMvKluXe5DmFKMAYrQaiJVzZ0tV/ExruKEQBGpMGGhOAGADogP3CXEg1tZCaLsJuuQi7/Ty6blurECOgUKggSoChLQS2jjpYWrfBdgawKowQxFAoVEGAKQiKuEiIESHy/OFcaK43Q3MyHIZzw3fzBiY2fCAoyDmD19DQvZCNe42NjU5tOZbSEBEREVHgWjh3Cr7afwDJifEw6nvOzhAEAd+/Zi7aLWYYtH3PDg4xOX+3HexMi9BIA2aUT4SuyyznxNQo1FSfBQDEJISgvdXzGc9EAyGEBQNhV3ZEkiwW2GvOQDp3HlL7RUAMhwQJiu/2O7bb6q70lZphszZ35kHaAJwFrAoDEDQZHZEREA02qIwC1FrR58tYmuvNMPRRH2U48LvExnCsF5GcnOzUrqmp8Xjs8ePHndopKSleiYmIiIiIAkOQaXAzdqPDIzB3SnmvfURRhEH0bMlziCkImXkJOHbkNDIyE6FSDu6RIjVlFKLDneu6TC0pxr/N2yDZJcycXIr3/v3JoO4hJ7VGhLnDBgDIzEvAwb0nZI6IPKFQqSCmjgJSRzmO2az16Lhkha1ZAe3xDkChA6Q2l+MlqQVqKQaa8+HAeUCSLOi4+CIUQhAElRHQmYDwUAjxERBUqiH5M0g2OzQnw4d9UgPww8TGcJSd7TytrqqqyuOx1dXVjs+hoaGIifHtmksiIiIikldBRib27atGy6UOZOUlyh0OAGBGSSlQcqUdPSoEZ042eq2GhUlvxNKrZg3+Qn7gqjll+OTzXdAbNCjOzWViI4CJSgH6MDUQBiBxPJRSEexnGyDV1QPNlyBZLsFmb/gu2aGCQgh1jJVs9ZCkJki2JthtANoBNACoEiEIIVCIQZBMsbCMToTaKEClHvzsjuZ6K0webNM7HDCx4QMhISFITEx0zNTYtm2bR+PMZjN27drlaOfl5Q1JfERERETkv1RKJa5fMg9tHe0INvrnsuT506bg64MHEBcViQ0bvpA1Fp1RBXOHFTaLi5ngvUwONxn0QxJPQnQsbliyAADQ3m1jAApsCoUAMTociA53HFNKdkjnGmGrv4gmwxkIbWqo2/UQ2s+6uYqts56H/TyUrTEwHOvcktoqmCG1bIVCagd0RiAkqHN2Rz82k1A0joykBsDEhs/MmzcPzz33HIDOWRhHjhzpc1nJ1q1bnQqNLliwYEhjJCIiIiL/pFapoB6i6ep9iRsdhtrjFwAAhYVjXPbRabUoKxw7oOt7UqOjP++tg4INmD99CjosZuw+cBD79xxz2zc9Ox6HvzkFY4gWOanp/bgLkWsKhQBFZBiEyDB0/o2VALTA3hAE5ckSoKkJkrkZdvtFSPaL6JptU4hXdo5U2tVoNx8B7E1AB4BGAMcAhcIEQQiCQmkEdEZY0tOhNighKnsuN1HYR852zUxs+MjixYsdiQ0AeP755/Hwww/3Oub55593ai9atGgoQiMiIiIicmvW5FJ8rv8aBr0OBWMyvXLN8BgTzp9ugqhSIG+MdxMKGo0KOq0WOq0WUydMcEpsdC/nN3vSJEzIvwSDTu8ywdK1PgbRYAihQRBCnQvvSu0dsJ2qBy40AO3NMOu1UEodUNk0kOztnUmNbiSpCTZbE2ADFNYI6KoqAABmsR2SpQpi+zFAY4AUHARIST74k/kHJjZ8ZObMmcjNzcW+ffsAAE899RRuvfXWHoVFL9u2bRvWrVvnaM+fPx/p6cwiExEREVHvDHrPp6p7wqg3YM7kyQMaK6pcz7W4ZtY07K+uQmJsLDQq702XF0QFyooL+zWm+y4v3al1IsxtTG6Q9ym0Gii7FCjtfDhvgrWjEZbzLVBZciCZmyHZm2C3XwTg/HOoEK4sgVHbtLC0n4e141DnDI9LgEp1BjDO89UfR1bDvzyqnxAEAb/73e8c7ZaWFixcuBAnTvQsHlRZWYlly5bBbrc7xj700EM+i5WIiIiIAktqRiwAwBSqRVZyqqyxlE/Nh0IBKARg2rRil310Wi2Kc3IRFRbu8vxAzJw9HksXT0d4cGjfnfthwVVTkJoZ2+N4eHT/d6vxZNlNYkpkn31oeFNqROjigqAsK4JqagXU0xdAPe17UGVdBWXERCj1ORBVSVCoI2FTWB3jJNt5p+vYLYcg2Vt8Hb4sOGOji7feegt33313j+NNTc5TgFasWAGdi6Itjz76KJYsWeL2+gsXLsSqVauwZs0aAMD+/fuRlZWFFStWoLCwEBaLBV9++SXeeOMNWCwWx7hHHnkEBQUFA/1jEREREdEwN3vyJDQWNMFkMEAQ5H13mZ+eifDgEAiCiNgI7zyk91Lz0yFjtOuZ0CGRBjTWdz7cxY8Ox6nj5132c0mhQGxEJGLLI/HMwX84nVq+4CrsP3IYn27e4/HlVEol8sYmY+/XR13fTgBKiwpQc+Qjz2OkEUEQlUB8VOd/XUkNMLfbYGmxQ10dCmW7Anbb5RkeFlha/w2V4RooFMN7TgMTG11cunTJaXtVd2pra92O78uTTz6JpqYm/O1vfwPQOXNj7dq1LvsqFArcc889WL16dZ/XJSIiIqKRSxAEhAUHyx2GQ3xUjE/vNyZnlNtzC2ZOwa59+zEqLgZHjnlvq1VBEKBWqfs9bkrxeEwpHo+29na88Ld3nc5dvbAcESFh3gqRRgKFAmqdEmodgIgru2hKNjukCw2wn2mAuXUPVEKWfDH6wPBO2/ghURTx0ksv4fXXX0dubq7bfiUlJfjoo4+clq8QEREREVFnUU9TqA5xo8MwOj0K5UVFbvsGG02YXlKCMYlJvgtwgEZF9VzyQjQQClGAEBkOZW4aNBMSoBh7Ca3R7racDXycsdHFTTfdhJtuuskn91q+fDmWL1+Offv2obKyErW1tRBFEXFxcRg/fnyfW8ESEREREY1UK29cOqBxKlU/H38UvS+CMei0A4pjoGITQ1FX0+DTe9LwoBAF6CPk2TLaF5jYkFlubm6vMzeIiIiISD6hfrS8gwavOC8Xhw6chN0mIT5p8IVLR0XFIiI2COfq+l6S7g2RkUxsELnCpShERERERG6kJ4xGVFwwFAJQUCTvbiM0eMFGE5ZcMw2Tp+Rh/rSKPvsr4Hq72q6WzZs9oFgUir6v3WOMB/EMF/qg/tcvoZGLMzaIiIiIiNwQBAHL5s+ROwzyoujwCESHR3jtenLvQjNQ0aNCcOZko9xh9DAqOQJtbR0YV5CFDzf+R+5wKEAE5t9CIiIiIiIaMTIzkhyfTaE+rGvRZYJE6eQcx+f8Innq4Q1klgcAhEYaehybU1GGuNFhiIwNwsSy7MGG5jUF2WNw3cK5AVHslfwHZ2wQEREREZFfK8jIxIXGi2hqakX5xHGyxFCYkQUJgM1qQ1F2jtO5zLwEfLvvBBQCYLcNXQy56enYvbOqX2OuXTId0eEReOb5fzgdN+mNWDx7pqO9/YtvvBLjYNml3gu2ErnCxAYREREREfk1lVKJ2ZMm+fy+XWdICIKAoqwcl/1mlJRiRkkp7HY73v1oM04dP4+rrirxWhxpWXEYHR+LYKOpX+N0RpVXl90Q+SsmNoiIiIiIiLxAEAQsmj3D69edM3lyn31SM2JR/W2d88F+LF0JCtPh0oW2/oZG5BdYY4OIiIiIiMiFAZa0kMWsSWUomew8o6Rr/MHhesdnnVHVY/xQrACZMq3A+xclcoGJDSIiIiIiogAnimKPpTJdt4edMWUi1DoRKo2IWdNdLZPxbmbjxyuXIy8to8fxnIIkzJhVjKy8RK/ej0Y2LkUhIiIiIiIKEC6XnHggNiISK29Y6va8N2dsFE0c4/icXTAa3+w57mhPnTABAHD23Hnv3ZBGPM7YICIiIiIickGt6blkwyf31YmOzxq987voWZPKMKk81/s3HWRiIzzaiIKiVIwdn47xOXmO46mJCYMMjKhvTGwQERERERF9J29sMgBAIQBTyny3tWx0fIjj84xpEyAqFRBVAmZOm+DUTxRFFGZme3RNRT+KhEyc4JwsMQRpoDV4nthRCAImjytCWeFYiOKVxExiTBxyC5MRHm102ikmPjra42v3x5w5E5FflIKgMB1GJQfOjjA5BUmIjA2SO4yAxaUoRERERERE35k8rghR4WEw6PVIiI4dsvsour1iLi68kqxIiU/Af38/EgCg02oHfI+wcM+3hx2TmITmslY0tbSgtKAQWo0GL7+9Hu0tFue4Ff1ftlIxfnyPY8nxoxAebcT5M839u9h3MvMSUFd7HhfPtzodT0scjbTE0SgfBxw4UoWTR88N6Pq+NnXCBHx7/Cg+qtshdygBiTM2iIiIiIiIviMIAjKTU4c0qeEJnVY7oKRG+dR8KARAa1BhysRij8cJgoDinFxMmzARWo2m86CLGR/TZxRBIQCC2ONUvwiCgGmTJvQ84WHSZEZJKW5YssD5moOMaTibO7dU7hCGFGdskMfOnz/fa5vI3509exZr1qxxtFetWoWoqCgZIyLyHH9+KdDxZ5gC3VD/DNu9VL0zPz0TGUnJEBQiVErvP+5lJqci6toICIICL7/2geO4NID4g40uZpQMYovd7iH0ZymOr5hCtWhqaB/UNeKTwnHqmOfPYiGRBqhUSlgszrNvhtPzHBMb5LGGhoZe20T+rr6+Hvfff7+jvWzZMn6ppoDBn18KdPwZpkDnbz/D5VPzsXVLJQBg8pQ8p3MalWZI7x0WHNzjmE7X/3tqNRpMnpKHzz7dCwBQa5UYHRPnOF9YnIbdO6sGHugQmliWje1ffONoj0qOQOm4Aqx78+Nex101YxLWvdF7H6+TJChFJSwWq9Ph4fQ8x8QGERERERFRgMlPz4RRZ4AkSUhNSBySe2i1agAtbs9n5SXiwN4aiCoBk8ePHdA9CjKykBQ/CgeOVCMtMdGp8OiksePQ0WHGsWOn0dZk7jUGAJg0Oc9lH1fmzJmIjRu3DyhmAEgfPRrFOf3fnSYqNHzA9xyoqOgwhAUP78KkTGwQEREREREFoJRRQ7uVavnEcVh3wv3sgqkTJiApIR4hxiCXszg8FWw0oSS/0OW56SUlQAnwzPP/cB1jcTHUahW0Gg3y0jN6vc+4CWNw7FgtcnNSkZY4GhvR/8SG1qBCcVGm62U06Nzd5sypxn5fFwBCgzxMPvRj1U9QmA5l48ZCo9IgPSNmQHEFAiY2iIiIiIiIfExQOFe6DDIYZIrEvajQcOQWJmPf7qMAOnci6UoQBKTED21ypS8qpRKTxxW5PtmtxsaE3DyUFhS67KrWiAiNMLlNSuSNTUawyYiCjKxe4yktzsc7pz51ea7rlr6uRIWGI3lMDI4eOt1rP09Fx4fg2nmzHe248EivXNcfMbFBRERERETkYyqlEpm5CTh04CQSkiMRERImd0gulRcVQafVoMNidjurIlD0Vkw0LCoIFSVFeGf9FlgtNtisztMiphT33LLWFceOMt2MyRmFksL8PsfPq5gCVABbv9qJyl1HPLqnOwMp6BqomNggIiIiIiKSwYzSUszw8104BUHAhLy+H8iHg4iQMNz8/WtglyS88Mo7sHbYvXLdgqJU97NK3FCK7h/VJQ/XooykxIYgdwBERERERERE/kAUO7fJHTt2jOPYqOQIj8eHmoKh0lxZZjQqOWJAiaHekhJKD7fxzctO6/d9AxVnbBAREREREZFf67r1a0FRqkdjui89EQTP3+sXZeegrb0Dbe3tKB9f7PE4QRCwYF45vjlUhay0FMRH9SzYGZsYirqaK1uthkT2r75KWVEBThz9EJIdGJ0WheNVZ53OJyRHICTEhIyklH5dN5AxsUFERERERER+rSS/wOXn3qh6Wc7RF1EUUTHes7oa3cVFRCEuIsrt+XlTp2D3twegVathsdqQndZzZoUgOCdlgsP1aG3uwJQpYxEeHIqrF5bj3IUG5KSNwdqqt5z6Xj1z+oDiDmRMbBAREREREZFfE0URk8aO69eYpLh46IwqtDVbEB7tentWOWg1mj4LsRaMycTuXYdhs0oIjzHhuoVznc6PiorFqKjYIYwysDCxQURERERERMOOIAhYfs1snDhdh5QEebel7S+dVourF1bgRG0d8sdk9NpXa1ChvcUCAAiLMvoiPL/D4qFEREREREQ0LBn1BmSlpEGjcr0Nqz+Li4jCxPwC6LTaXvvNmj4Rao0IjV6JGeUTfRSdf+GMDSIiIiIiIqIAlRgTh5U3LpU7DFlxxgYRERERERERBSzO2CCPmc1mp3ZNTQ32798vUzRE/VdVVdVrm8if8eeXAh1/hinQ8Wd4+KmrPeH4bLE3Dvtnm5qaGqd29+e7QKaQJEmSOwgKDE899RTuuOMOucMgIiIiIiKiQXryySfx05/+VO4wvIJLUchjQUFBcodAREREREREXjCcnu+Y2CCPhYSEyB0CERERERERecFwer7jUhTyWGNjIz755BNHOyEhARpN4G2bRERERERENNJ0dHTgxIkrdUUqKiqGTXKDiQ0iIiIiIiIiClhcikJEREREREREAYuJDSIiIiIiIiIKWExsEBEREREREVHAYmKDiIiIiIiIiAIWExtEREREREREFLCY2CAiIiIiIiKigMXEBhEREREREREFLCY2iIiIiIiIiChgMbFBRERERERERAGLiQ0iIiIiIiIiClhMbBARERERERFRwFLKHQAFjv3796OyshK1tbUQRRHx8fEoLi5GcnKy3KERERFRALBardi2bRuOHz+Ouro6iKKI6OhoREdHIz8/H1FRUXKHSNRDfX09duzYgWPHjuHixYsQRRGhoaHIyMjAuHHjYDQa5Q6RyKt27tyJgwcPora2FjqdDvHx8SgrK0NMTIzcobnFxAb16Y033sCDDz6IyspKl+fLysrw0EMPYerUqb4NjMgDkiShuroa+/btw4kTJ3Dp0iXo9XqEhYWhoKAAeXl5EEVR7jCJiIa148eP44EHHsDbb7+NhoYGt/0yMzNxxx134Pbbb/dhdESuvf/++3jsscewZcsWSJLkso9Go8HixYvx61//Gjk5OT6OkEYSu92OAwcOYOfOnY7/9uzZg7a2NkefzZs3D+qZ7LnnnsPjjz+O6urqHudEUcSMGTPw2GOPIT8/f8D3GCoKyd3fUhrxbDYbbrnlFrz44ot99hUEAb/85S/x4IMPDn1gRH1oamrCe++9h3fffRebNm1CfX29276hoaG4+eabsXr1asTGxvowSqKB27NnD4qLi2G1Wh3HKioqsGXLFvmCInLjiSeewL333ovW1laP+s+fPx/r168f4qiI3LPZbLj11lvxwgsveDxGpVLhiSeewE9+8pMhjIxGqqVLl2Ljxo1oaWnptd9AExutra1YunQpPvjggz77qtVqPPnkk7jtttv6fZ+hxBkb5Nadd97plNTQ6/VYsWIFCgsLYTabsX37drz55puwWCyw2+347W9/i7CwMNx5553yBU0jXlNTE6KiotDe3u5R/4aGBjzxxBN48cUX8ec//xmLFy8e4giJBudy0rlrUoPIX91999147LHHHG1BEDBx4kTMmDEDcXFx0Gg0OHfuHPbt24ctW7bgxIkTMkZL1GnVqlU9khpTp051/NxaLBZUV1fjn//8Jw4dOgQAsFgs+OlPf4qgoCDceOONcoRNw9iuXbv6TGoMlN1ux4oVK5ySGqGhofiv//ovZGdno6mpCZ988gk2bNgASZJgNptx++23IyIiAkuXLh2SmAaCMzbIpQ0bNmDBggWOdnZ2Nj744AMkJCQ49duzZw/mzZuH2tpaAJ1fWHbv3o28vDyfxkt0WWNjI0JDQ52OpaSkoKKiAhkZGYiIiEB7ezv27t2LN998E+fOnXP0E0UR69atY3KD/Nrjjz+OX/ziFz2Oc8YG+ZtHHnkE99xzj6M9YcIErF27FgUFBW7HbN++Hbt37/a7N4E0cmzfvh0lJSWOdkhICN58801Mnz69R19JkvD444/j7rvvdhwLCwvDsWPHYDKZfBIvjQxJSUk4fvw4gM7lT/n5+SgqKkJzczP+/ve/O/oNZMbGM8884zTTqLy8HP/85z97fJ/etGkTFi9ejEuXLgEAjEYjqqur/ac2kkTUjc1mk/Lz8yUAEgBJr9dL1dXVbvt//vnnkiAIjv4LFizwYbREzhoaGiQAUlBQkPSzn/1M2rNnj9u+LS0t0sqVKx0/uwCk0NBQqb6+3ocRE3muurpa0uv1EgApMjJSCg8Pd/zsVlRUyB0ekcP+/fsljUbj+PmcOnWq1NLSIndYRH1atWqV0/eCN998s99jXnnlFR9ESiPJvffeK61du1batWuXZDabHcf/8pe/OP3sbd68uV/XbW5ulqKjox3jY2NjpYaGBrf9X331Vaf7/eQnPxngn8j7uN0r9fDxxx87FQq94447kJKS4rZ/WVkZli1b5mivX78eVVVVQxojkTtKpRL33HMPjh49ij/+8Y+9FjfS6/VYu3Ytrr/+esexhoYGrFmzxhehEvXbrbfe6qhT8MQTT7ASP/mtn/70p+jo6AAABAcH45VXXoFer5c5KqK+7dy50/E5KirKo1mc3Yvd7tmzx+tx0cj2wAMPYOXKlRg3bhxUKpXXrvvKK6/gzJkzjvZ9992HkJAQt/2vu+46TJw40dH+85//jObmZq/FMxhMbFAPb7/9tlP7lltu6XPMypUrndrvvPOON0Mi8pjRaMTDDz+MsLAwj8c89thjUCgUjjaL1pE/euGFF/Dxxx8DAGbOnIkbbrhB5oiIXDtw4AA2bdrkaP/85z9ncWYKGBcuXHB8Tk1Ndfp+4E56errbaxD5s67PfXq93ullnztdn/va29s9KjjqC0xsUA8bNmxwfE5NTUVqamqfY8rLy6HVah1tPhhSIImLi0NWVpaj7WqLKyI5nTlzBqtXrwYAaLVaPPvsszJHROTe2rVrHZ8FQcAPfvADGaMh6p+uL0Y8LdbY/Y2139QcIOpFe3u7UxK6tLTUo9ows2bNcmr7y3MfExvkpLGxETU1NY521+JJvVGr1SgqKnK0uy5lIQoEXaf0D1XVaaKB+slPfoKGhgYAwL333ou0tDSZIyJy78MPP3R8LigoQHx8vIzREPVPWVmZ4/P+/ftx+vTpPsdcnk132ZQpU7weF5G3HTx40LFkEPD8uS8xMdHp97q/PPcxsUFODhw44NTuz5fnrjM7GhoaPPqHgMhfHDt2zPE5JiZGvkCIuvnnP/+JN954AwCQk5PjckcUIn/R3Nzs9F2itLQUQOdWmK+++ioWLFiApKQkaDQaREREID8/Hz/+8Y+5ow/5jR/96EeOGgY2mw0/+9nPIPWyieT58+fxq1/9ytEuKCjo8UabyB9567nv4MGDsNvtXotroJjYICdHjhxxaicmJno8tnvf7tci8lefffYZzp4962hf/iJOJLeLFy9i1apVAACFQoE//elPXi0aRuRte/bscfqCm5mZicrKShQXF+P666/Hhg0bcPz4cZjNZpw/fx579+7FmjVrMG3aNMyYMQOnTp2SMXoiICMjA7///e8d7X/84x+YNWsWtm7dCqvV6jje3NyMV199FePHj3cUzY+IiMCrr77qUV0OIrl567mvra3NL15oK+UOgPzL5X2JL+tPAcbuex03NTV5JSaiofboo486tZcvXy5TJETO7r77btTW1gLoLNY1adIkmSMi6l19fb1Tu7GxERUVFWhsbHQcCw4ORlBQEM6ePes0DXrTpk0YP348Nm/ejIyMDF+FTNTDXXfdhZCQEPz85z9HY2MjPv74Y3z88cfQarWIioqC1WrF6dOnnZJ4M2fOxJ/+9KdedxIk8ifD7bmPMzbISffiR10LgvZFp9P1ei0if/Tqq6/ivffec7QLCwtxzTXXyBgRUadPP/0Uzz//PAAgOjoajzzyiMwREfWtawIDAB588EHHseuvvx779u1z1PNqamrC+vXrkZOT4+hfV1eHJUuWOLY1JpLLD37wAxw7dgy33XabYwZGe3s7ampqUFtb60hqGAwGPP7449i4cSOTGhRQhttzHxMb5KS9vd2prVarPR6r0Wic2m1tbV6JiWio7N+/H7feequjrVQq8fzzz0MQ+KuR5NXe3o6VK1c61nX/8Y9/7HVfeSJ/0f3LrcViAQA88MADePnll52SGCqVCvPnz8e2bdswceJEx/FvvvkGzzzzjG8CJnLjX//6FyZPnow//elPvdbYaGlpwerVq5GVldWjiCiRPxtuz3389k5OumfqzGazx2O7TicFembyiPxJXV0d5s+f7/Ql/Pe//z2Ki4tljIqo0/33349Dhw4BAObMmYPrrrtO5oiIPOPqjV9ZWRl+/etfux1jMpnw8ssvQ6m8skL6//7v/4YkPiJP3H///Zg/fz727dsHoLPuxtq1a1FdXY329nY0NTVh9+7deOCBBxxT8g8dOoRZs2bhhRdekDN0Io8Nt+c+JjbISdctL4GembzedM/Udb8Wkb+4cOEC5syZg+PHjzuO3Xrrrfj5z38uY1REnfbs2YPHH38cQOcXhWeffVbmiIg8ZzKZehy74447+iymmJqaiquvvtrRPnXqFL755huvx0fUl1dffRW/+c1vHO1rrrkGX3/9NVauXImUlBRoNBoYjUYUFBTg3nvvxe7dux07REiShNtuuw1ff/21TNETeW64PfcxsUFOgoKCnNoNDQ0ej+2+rtbVlxsiuV26dAlXXXUV9u7d6zi2YsUKPjySX7DZbPjhD3/oqLx/3333ITk5WeaoiDzX/XsEAEybNs2jsd37ffXVV16JichTVqsVd999t6MdGxuLl19+ude30YmJiXj99dcdyTur1Yr/9//+35DHSjRYw+25j4kNctL9C3RNTY3HY7u+/QbAAkrkd5qbmzF37lzs2LHDcezaa6/FX//6V9bVIL/whz/8Abt27QIA5OXlcRYRBZzLb64v02g0iIqK8mjs6NGjndrdd1ghGmpbt27FyZMnHe2bb74ZBoOhz3FFRUVOW8Vv3LiRBXDJ73nruU+n0yEmJsZrcQ0Uv8mTk+zsbKf25X25PVFdXe34HBoa6hc/4ESXtba2Yv78+fjiiy8cx66++mq88sorEEVRxsiIOp0+fRr33XcfAEAQBKxdu9ap5gBRIEhNTXUqKtefKvvd+/ZnWjSRN1RWVjq1+1N3q2tfi8XiqJNE5K+89dyXmZnpFy8I+Y2JnISEhCAxMdGRsdu2bZtH48xms+MtI9D5ppHIX7S1tWHhwoX49NNPHcfmzp2LdevWQaVSyRgZ0RWnT592vOETRRE33HBDn2NOnTrl+Lx9+3akpaU52rNmzeISK/I5URSRl5eHnTt3Auhc/me1Wj1K0l24cMGpHR4ePiQxErnT0tLi1O5P3YDuMzv8YZcIot5kZmZCrVY7ioZ6+tx34sQJp+8f/vLcx8QG9TBv3jw899xzADqzcUeOHOlzWcnWrVud3qwsWLBgSGMk8lRHRwcWLVqETZs2OY7NnDkTb731Vr+2tSLyJYvF4vQ2xBPt7e1OY3Jzc70dFpFHrr76akdiQ5IkVFZWYty4cX2O615wkUtaydcu73By2enTpz0eW1dX59RmYo78nVarxfTp0/HBBx8A6ExsNDc395nQ+/e//+3U9pfnPvnnjJDfWbx4sVP7+eef73NM9z6LFi3yZkhEA2I2m7F06VKnX8DTpk3Du+++26/p0URE5Llrr73Wqf2Pf/yjzzF2ux1vvPGGo61WqzFp0iSvx0bUm66z3gDgww8/9GiczWZzeoGi0WiQkJDg1diIhkLX577W1la8/PLLfY7p+tyn0Wgwd+7cIYmtv5jYoB5mzpzp9KbvqaeewtGjR93237ZtG9atW+doz58/H+np6UMaI1FfrFYrrrvuOmzYsMFxrLy8HO+9955f7LVN1F1hYSEkSerXf12LLVZUVDide+edd+T7w9CIlpWVhXnz5jnazzzzDI4cOdLrmKefftppxtHy5cv5u5p8rry8HHq93tF+7bXXetTdcOXpp592KrxYUVHBn18KCNdff71Tgef777+/x44nXb322mvYvn27o33LLbf4xVavABMb5IIgCPjd737naLe0tGDhwoU4ceJEj76VlZVYtmwZ7Ha7Y+xDDz3ks1iJXLHZbLjhhhvw9ttvO46VlZXhX//6l0fVzYmIaHAeffRRR2Hm5uZmzJ49GwcOHHDZ98UXX3TaAUir1eLee+/1SZxEXWm1WqxatcrRtlgsmDdvnlPh8a4kScKaNWuwevVqp+Pd20T+ymg04te//rWjXVdXh2uuucZlcmPTpk247bbbHG2DweA0Vm4KSZIkuYMg//TjH/8Ya9ascbQNBgNWrFiBwsJCWCwWfPnll3jjjTdgsVgcfR577DH+MidZSZKEm2++GX/9618dx0pKSvDvf//bL/bYJvKmpKQkx5ZrFRUV2LJli7wBEXXx3HPP4fbbb3e0VSoVFi1ahEmTJsFkMqG2thbvvvuu0xbcQGei47//+799HS4RAODixYsoKyvDN99843R86tSpmD59OuLj4x11kN599118++23Tv1uuukm/OUvf/FlyDQCvPXWW7j77rt7HG9qasLZs2cd7bi4OJezhR599FEsWbLE5bXtdjsWLVqE9957z3EsLCwMN954I7KystDc3IwtW7Zg/fr1uJw6UCgUeO2117B8+fLB/tG8hokNcstms+Hmm2/G3/72tz77KhQK3HPPPU4zPYjksHXrVkyZMsXpmLtf8r355JNPEB8f783QiLyOiQ3yd48//jj+93//F1artc++Go0Gzz77LG6++WYfREbk3qlTp7BkyRL85z//6de4H/7wh3juuee4VTd53Ysvvjio341/+ctfcNNNN7k939LSgsWLF3tUV0atVuMPf/iD0+wmf8ClKOSWKIp46aWX8Prrr/daXb+kpAQfffQRkxrkF2w2W49jtbW1qK6u7td/XWciERHRwKxevRo7duzAnDlzHEtTulOpVPje976H3bt3M6lBfiE+Ph5ffPEFnn322T63shQEAXPmzMHGjRvx5z//mUkNCkgGgwEbN27EM88843ZHKkEQMHPmTGzfvt3vkhoAZ2xQP+zbtw+VlZWora2FKIqIi4vD+PHjuR0b+ZUtW7Zg2rRpg77O0aNHkZSUNPiAiIYQZ2xQIKmvr8fnn3+O2tpaNDY2IjQ0FElJSSgvL/eb4nNErpw8eRI7d+7EqVOncPHiRYiiiJCQEKSmpmL8+PEIDg6WO0Qir9qxYwcOHDiAuro66HQ6xMfHo6ysDLGxsXKH5hYTG0REREREREQUsLgUhYiIiIiIiIgCFhMbRERERERERBSwmNggIiIiIiIiooDFxAYRERERERERBSwmNoiIiIiIiIgoYDGxQUREREREREQBi4kNIiIiIiIiIgpYTGwQERERERERUcBiYoOIiIiIiIiIAhYTG0REREREREQUsJjYICIiIiIiIqKAxcQGEREREREREQUsJjaIiIiIiIiIKGAxsUFEREREREREAYuJDSIiIiIiIiIKWExsEBEREREREVHAYmKDiIiIiIiIiAIWExtEREREREREFLCY2CAiIiIiIiKigMXEBhEREREREREFLCY2iIiIiIiIiChgMbFBRERERERERAGLiQ0iIiIiIiIiClhMbBARERERERFRwGJig4iIiIiIiIgCFhMbRERERERERBSwmNggIiIiIiIiooDFxAYRERERERERBSwmNoiIiIiIiIgoYDGxQUREREREREQBi4kNIiIiIiIiIgpYTGwQERERERERUcBiYoOIiIiIiIiIAhYTG0REREREREQUsJjYICIiIiIiIqKAxcQGEREREREREQWs/w/htm2xqFa7mAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "npar = 9\n",
+    "nsam = 10000\n",
+    "\n",
+    "omega = np.linspace(0, 10, nsam, endpoint=True)\n",
+    "omega_fixed = np.linspace(0, 10, npar, endpoint=True)\n",
+    "w0 = np.random.randn(npar*3)\n",
+    "\n",
+    "diag = np.abs(omega**2) + 1\n",
+    "off_diag = np.sin(2*np.pi*omega/16)\n",
+    "\n",
+    "diag_noise = np.random.chisquare(df = 6, size = diag.shape)/6\n",
+    "diag_noise2 = np.random.chisquare(df = 6, size = diag.shape)/6\n",
+    "off_diag_noise = 0.5*np.random.normal(size=off_diag.shape)\n",
+    "\n",
+    "truth = np.array([diag, off_diag, off_diag, diag*1.4]).T.reshape(nsam,2,2)\n",
+    "truth = opt_einsum.contract('tba,tbc->tac', truth, truth)\n",
+    "\n",
+    "data_ = np.array([diag*diag_noise, off_diag + off_diag_noise, off_diag + off_diag_noise, diag*1.4*diag_noise2]).T.reshape(nsam,2,2)\n",
+    "data_ = opt_einsum.contract('tba,tbc->tac', data_, data_)\n",
+    "\n",
+    "guess = np.transpose(np.array([[pd.Series(data_[:,i,j]).rolling(window=50,closed='left',min_periods=0).mean().to_numpy() for i in range(2)] for j in range(2)]), axes = (2,0,1))\n",
+    "guess[0]=guess[1]\n",
+    "guess = np.array([guess[j] for j in [np.argmin(np.abs(omega-omega_fixed[i])) for i in range(len(omega_fixed))]])\n",
+    "\n",
+    "assert np.min(np.linalg.det(data_)) > 0\n",
+    "\n",
+    "true_params = np.array([np.abs(omega_fixed**2) + 1, \n",
+    "                        np.sin(2*np.pi*omega_fixed/16), \n",
+    "                        np.sin(2*np.pi*omega_fixed/16), \n",
+    "                        (np.abs(omega_fixed**2) + 1)*1.4]).T.reshape(omega_fixed.size,2,2)\n",
+    "# true_params = opt_einsum.contract('tba,tbc->tac', true_params, true_params)\n",
+    "true_params = np.concatenate([true_params[:,0,0], true_params[:,0,1], true_params[:,1,1]])\n",
+    "# data_ = np.array([np.sin(2*np.pi*omega/4),np.sin(2*np.pi*omega*2/4),np.sin(2*np.pi*omega*1.5/4),np.sin(2*np.pi*omega*3/4)]).T.reshape(nsam,2,2) + np.random.randn(nsam, 2, 2)\n",
+    "\n",
+    "true_spline = model_wishart(omega_fixed, true_params)\n",
+    "splined_y = true_spline(omega)\n",
+    "splined_y = opt_einsum.contract('tba,tbc->tac', splined_y, splined_y)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "for i in range(2):\n",
+    "    for j in range(2):\n",
+    "        pl, = ax.plot(omega, data_[:,i,j], alpha = 0.5)\n",
+    "        ax.plot(omega, truth[:,i,j], color = pl.get_color(), markeredgecolor='black', markeredgewidth=0.5)\n",
+    "        ax.plot(omega, splined_y[:,i,j], ls = '--')\n",
+    "        # ax.plot(omega, true_spline(omega)[:,i,j], ls = '--')\n",
+    "        ax.plot(omega_fixed, guess[:,i,j], 'o', color = pl.get_color())\n",
+    "ax.set_yscale('symlog')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1059,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = do_mle(data_, true_params, model_wishart, omega, omega_fixed, solver = 'Nelder-Mead')\n",
+    "# params = do_mle(data_, true_params + np.random.normal(scale=3, size=true_params.shape), model_wishart, omega, omega_fixed, solver = 'CG')\n",
+    "# guess = np.concatenate([guess[:,0,0], guess[:,0,1], guess[:,1,1]])\n",
+    "# params = do_mle(data_, guess, model_wishart, omega, omega_fixed, solver = 'CG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1060,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x302196510>]"
+      ]
+     },
+     "execution_count": 1060,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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aqqYaHwr4+fmV+rb8cePGKSUlRVOmTHHUpk2bpsWLFxe5v+CdATk5OaW+W6DgAw0L3jlQUYsWLSr1gw8LatiwoVN7AQAAAFAJDm6SlkyUju8y1sM6SUNfl5p2NacvVGk1PhQoq8cee0xvvfWWjhw5IklatWqVMjMzi/ymPTAw0LDOysoqdShQ8M6AgteqqDZt2qhDhw5OvSYAAACAKuh8irTyWSn+c2Pdv64U/azU5U7J08uc3lDlucUzBcrC19dXw4YNc6yzsrL0yy+/FLm34EjCmTNnSv05Z8+edXzt4+Pj1IcMAgAAAHADeRZpy3vSW10LBwJXjpEe+Unqdg+BAErEnQJFaNu2rWF9/PjxIve1bNnSsD506FChWnEOHjzo+LpVq1Zl7BAAAACAWzu0RVoyQUpJMNZDo6Shr0nNin4uGlAQdwoUoeCoQEZGRpH72rdvb1jv3bu3VNfPysrSsWPHir0OAAAAABQp7bj09QPSh4OMgYB/XWnILOm+dQQCKBNCgSKkpKQY1iEhIUXu69Spk2G9efPmUl1/27ZtslgsjnXHjh3L2CEAAAAAt5JnkbbOlv7dVdox33is8x3Sw3FS93sZFUCZMT5QhI0bNxrWxY0EtGzZUu3atdOePXsk2R9KaLPZ5OHhUeL1V65caVhf/AwDAAAAADA4tFVaOkFK3mmsh3aUhrwmNe9hTl+oEbhToIBdu3ZpzZo1jnWLFi1KfLXfyJEjHV8fPHhQK1asKPH6FotFH330kWMdHh6url15NQgAAACAAtJOSIsekj4caAwE/OpKMTOle9cRCKDCanQokJWVJZvNVur9aWlpuuOOO2S1Wh21cePGlXjOAw88YHhzwOTJk5Wbm1vs/lmzZuno0aOO9aOPPnrJOwsAAAAAuBFrnrTtfemtLtIv84zHOt0uPbJd6nGf5MWN36i4Gh0KbNmyRVdeeaXmz5+v9PT0EvfGxcWpV69ehtcPhoWFacKECSWe16xZMz300EOOdXx8vEaPHq3s7OxCez/77DNNnTrVsQ4PD9fDDz9cyl8NAAAAgBrv8I/S7P7S0olS1rn8euNI6c5l0sh3pcBGprWHmqfKREsLFy7U5MmTC9XPnz9vWI8ePbrQ2wEkacaMGbrhhhsK1Xfs2KHRo0erVq1auvrqq9WpUyc1bdpUQUFBys7O1uHDh7Vu3Tpt2rTJcF5gYKC+/fZbBQYGXrL3adOmacOGDdq+fbsk6auvvtKmTZs0ZswYtWrVSmfOnNHSpUu1fv16xzl+fn76/PPP5e/vf8nrAwAAAKjh0k9Kq56Tfp5rrPsFSdc+JXW7hzsD4BJV5t+q1NRU7du375L7Ln6VX8HzS5KRkaGVK1cWeshfUdq1a6dPP/1UV1111SX3SlKtWrW0ePFiDR06VD/99JMk6ejRo3r11VeL3F+nTh19/PHH6tOnT6muDwAAAKCGsuZJcR9Jq6dJWWeNx6Jula5/QarT2JTW4B5q9PhAmzZtdP/996tz587y8rr0qzmioqL0zjvv6Keffip1IPCn0NBQbdmyRS+88IJCQ0OL3OPr66uRI0dqx44dhgcUAgAAAHBDR+Kk96+TlkwwBgKNOkjjlko3/IdAAC7nYSvLk/iqsfT0dP388886ePCgjh8/royMDHl7eys4OFhNmzZV9+7d1bBhQ6d8Vl5enjZt2qS9e/cqJSVFderUUdOmTdW3b1/Vr1/fKZ9R0K5duxQZGelYJyQkqEOHDi75LAAAAAAVkH5KWv289NMnki76dsy3jnTtk1J3HiLobsz8fs5t/k2rXbu2+vTpUym37Ht5ealv377q27evyz8LAAAAQDVhzZN++lha/YKUecZ4rOPN0sBpUp2i7zoGXMVtQgEAAAAAMM3ROPuYwLGfjfWGEdLQWVILnjcGcxAKAAAAAICrZJy2jwrEfazCowJPXBgV8DGtPYBQAAAAAACczWqVfv7E/prBQqMCN0nXT5OCwkxpDbgYoQAAAAAAONPRn6SlE+0jAxdr2E4aMktqybPHUHUQCgAAAACAM2ScltZMk7Z/JOOoQKDUf4rU435GBVDlEAoAAAAAQEVYrdIv86SVU6XM08ZjkaOkgS9KQU3M6Q24BEIBAAAAACivY7/Y3ypwdLuxHtJWGjJTatXPlLaA0iIUAAAAAICyyjwjrXlR+vEDGUYFfGpL/R+Xejwgefua1h5QWoQCAAAAAFBaVqu0Y7608lkp45TxWIeR0sCXpLrh5vQGlAOhAAAAAACURtIOaclE6cg2Y73B5fZRgdbXmtMXUAGEAgAAAABQksyz9lGB7R9INmt+3aeW1G+y1PMhRgVQbREKAAAAAEBRrFZpx2cXRgVOGo+1HyENekmq29SU1gBnIRQAAAAAgIKS4qWlE6XDW431Bm0ujApcZ05fgJMRCgAAAADAnzLPSmtfln58v/CowDWTpF4PSd5+prUHOBuhAAAAAADYbNKOz6WVz0jpJ4zHIv4qDXpZCm5mTm+ACxEKAAAAAHBvyQn2UYFDm431+q2lITOkNgPM6QuoBIQCAAAAANxT1jlp7SvSttmSLS+/7h0gXTNR6v0IowKo8QgFAAAAALgXm02K/1Ja8bSUftx4rN0wafArUnBzc3oDKhmhAAAAAAD3kbJLWjJROrTJWK/fSoqZIV1+vTl9ASYhFAAAAABQ82WlSutelba+V2BUwF/qe2FUwMffvP4AkxAKAAAAAKi5bDZp51f2UYG0FOOxtkPtowL1LjOnN6AKIBQAAAAAUDOlJNrfKnDwB2O9Xkv7qMAVA83pC6hCCAUAAAAA1CxZqdL66dKWd4sYFZgg9f4/RgWACwgFAAAAANQMNpuUsEBa/pSUlmw81nbIhVGBFqa0BlRVhAIAAAAAqr/je+yjAgc2GuvBl9lHBdoONqcvoIojFAAAAABQfWWfzx8VsFry615+Ut9/SFf/XfIJMK8/oIojFAAAAABQ/dhs0q6F9lGB80nGY5cPkmJeleq3Mqc3oBohFAAAAABQvZz41T4q8McGYz24+YVRgRhz+gKqIUIBAAAAANVDdpq0YYa0+e3CowJ9HpX6PMaoAFBGhAIAAAAAqjabTUpcJC17Ujp/zHiszfVSzHSpQWtTWgOqO0IBAAAAAFXXid+k2EnS/nXGet3m9ucGtB0ieXiY0hpQExAKAAAAAKh6ctKl9X+OCuTm17187W8U6PMPybeWef0BNQShAAAAAICqw2aTEr+xv1Ug9YjxWOtoachMRgUAJyIUAAAAAFA1nPxdWjpJ2r/WWK/bTBr8itRuGKMCgJMRCgAAAAAwV066tGGWtOnfhUcFev+f1HcCowKAixAKAAAAADCHzSbtXiwte6KIUYHrpJiZUkgbc3oD3AShAAAAAIDKd2qffVRg32pjPaipNPhlKeKvjAoAlYBQAAAAAEDlycmQNr4mbXpTysvJr3v6SL0fka6ZKPnWNq8/wM0QCgAAAABwPZtN2rPEPipw7pDxWKtr7W8VCLncnN4AN0YoAAAAAMC1Tu2TYh+X9q401oPCpUEvS+2HMyoAmIRQAAAAAIBr5GRI3/9T+uGNAqMC3lKvh6VrJkl+gaa1B4BQAAAAAICz2WzSr7HSsselswVGBVr2s48KNGxrTm8ADAgFAAAAADjP6f1S7BTp9+XGep0w+6hAh5GMCgBVCKEAAAAAgIrLzbSPCnz/hpSXnV/39JZ6Pij1myz51TGtPQBFIxQAAAAAUDG/LpNiJ0tnDxrrLfpKQ2ZJjdqZ0xeASyIUAAAAAFA+p/+wv2Lwt1hjvU6YNOglqcMNjAoAVRyhAAAAAICyyc2UfviXtPH1IkYFHpD6Pc6oAFBNuFUokJeXp19//VW7du3SsWPHlJaWpsDAQIWEhOiqq65Su3bt5EGSCQAAABTvt+X2UYEzB4z1Fn3tbxVoFGFKWwDKp0qFAlarVbt379b27dsd/+zYsUOZmZmOPWvXrlX//v1Lfc1Tp05p4cKFWrJkidatW6dz584Vu7dJkya677779Oijj6pu3bqluv66det07bXXlrqfi/3444/q2rVruc4FAAAAKtWZA/ZRgV+XGuuBofZRgchRjAoA1VCVCQVGjRql5cuXKz093WnX3L17t6KiomSxWEq1/9ixY3ruuef0wQcfaP78+erTp4/TegEAAACqpdwsadOb0sbXJEtWft3DK39UwD/IvP4AVEiVCQXi4uKcGghIUnZ2dqFAICIiQtdcc41at26tkJAQnT9/XnFxcVq4cKHS0tIkSYcPH9bgwYO1cuVK9erVq0yfedlll8nbu3T/s/r7+5fp2gAAAECl+n2ltHSSdOYPY/2yq+1vFWjc3py+ADhNlQkFLubn56eoqCh16dJFaWlpmjdvXoWu17hxY917770aN26cWrduXeSe119/XXfddZe+/fZbSVJ6errGjh2rhIQE+fn5lfqz1q1bpxYtWlSoXwAAAMBUZw5Ky5+U9nxnrAc2lga+KHW8iVEBoIaoMqHA2LFj1axZM3Xp0kUdO3aUj4+PJGnOnDnlDgUCAwM1ffp0Pfzww6pVq1aJexs0aKAFCxYoOjpaGzZskCTt3btXX3zxhcaOHVuuzwcAAACqFUu2fVRgw2uSJf+5XvLwknqMl/pPkfxL9+wtANVDlQkFXnjhBadfs02bNpo8eXKp93t7e+vVV19V7969HbXvvvuOUAAAAAA13++rpNhJ0un9xnrzXvZRgdBIc/oC4FJVJhSoKnr27KnatWs7nm+wb98+kzsCAAAAXOjsYWn5E9LuxcZ67UbSwGlS1C2MCgA1GKFAAR4eHoZQwNkPPwQAAACqBEu2tPktaf3MAqMCnlL38dK1TzAqALgBQoEC0tPTdeLECcc6NDTUxG4AAAAAF9i72v5WgdMF7opt1lMaOksK7WhOXwAqHaFAAV9//bVsNptjXdZXEj755JNKTEzUwYMHlZ6eruDgYIWGhqpXr14aNGiQhg8fLi8vL2e3DQAAAFza2cP2twrs/tZYr91Quv4FKepWydPTnN4AmIJQ4CJ5eXl67bXXDLWbb765TNf47LPPDOsTJ07oxIkT2rlzp2bPnq1WrVrp9ddf1/Dhwyvcb0n27t1b7nMbNmyoRo0aObEbAAAAmMqSYx8V2DBTys3Ir3t4St3ula59UgoINq09AOYhFLjIjBkz9MsvvzjWf/3rX3XllVeW+Tr16tVTUFCQ0tPTdfr0aVmtVsex/fv3a8SIEXryySf10ksvOaPtIo0YMaLc506dOlXPPfec03oBAAAoixyLVbEJSVqWkKzUrFwF+ftocGSoYiLD5OvNT7HLbN9a+6jAqd+N9abdpaGvSWFR5vQFoEogFLhgzZo1euaZZxzroKAgvfnmm6U6t0GDBrr99ts1dOhQdevWTfXr13ccO3funNasWaPXXntNP/zwg6P+8ssvq1GjRvr73//uvF8EAABANbcqMUVTFsbrZFqOoR6bkKxpgYmaPipK0RGNTequmjl31D4qkLjIWK8VYh8V6HQbowIAxJ8CkhITE3XjjTcqLy/PUfvggw902WWXXfLcLl266MiRI3rzzTc1aNAgQyAgSXXr1tXIkSO1ceNGTZs2zXDs8ccf1+HDh53ziwAAAKjmViWmaPy8uEKBwJ9OpuXovrlxWr07pZI7q2YsOdL3b0hvdTMGAn+OCjyyXbpyNIEAAEncKaADBw5o4MCBOnPmjKP28ssv68YbbyzV+XXq1CnVPg8PDz399NM6evSo3nvvPUlSdna2ZsyYoX//+99lb/wSFi1apDZt2pTr3IYNGzq5GwAAgJLlWKyasjBeeVZbifvyrDY9viBem6ZEM0pQlP3r7KMCJ38z1pt2k4bMkpp0NqMrAFWYW4cCR48eVXR0tI4ePeqoTZkyRU888YTLPvPFF1/Uxx9/rMxM+7tgFy9e7JJQoE2bNurQoYPTrwsAAOAKsQlJxd4hUNDJtBzFJiRpeOdwF3dVjaQek5Y/Je1aaKzXaiANeF7qzJ0BAIrmtn8yJCcn67rrrtP+/fsdtUcffVSvvPKKSz+3QYMG6tevn2N98OBBJSUlufQzAQAAqrplCcll2r98V9n211h5udIPb0r/7logEPCQut4tPbxdumoMgQCAYrnlnw7Hjx9XdHS0fvst/7aqBx54QP/85z8r5fPbtm1bqB8AAAB3lpqVW7b9mRYXdVKN/LFBeq+PtPIZKTc9vx7eVbpvrTTsdalW/eLPBwC54fjAqVOnNGDAACUmJjpqd999t95+++1K6yEgIMCwzsjIKGYnAACAewjy9ynb/gC3+2tsvtQkacXTUsL/jPWA+tKA56QruTMAQOm51Z+mZ86c0YABA7Rz505HbezYsZo9e7Y8PDwqrY+UFOMTc0NCQirtswEAAKqiwZGhii3DCMGgDqEu7KaKysuVtr4nrXtVykm76ICH1GWcFP0sdwYAKDO3CQXOnTungQMH6pdffnHUbrvtNn300UfyrOQkdePGjY6vfXx8FB7OQ3IAAIB7i4kM07TAxFI9bDAk0FcxkWGV0FUV8sdGaelE6cQeY73JVdLQ16Twq8zpC0C15xb3FaWlpSkmJkbbt2931G666SbNnTu30gOB2NhY7d2717G++uqrVatWrUrtAQAAoKrx9fbU9FFR8vIs+e5NL08PTR8V5T6vIzyfLC24R/p4mDEQCKgn/eVf0j2rCQQAVEiN/9M0IyNDQ4cO1ebNmx21G264QfPnz5eXl1eFrv3nawVLKykpSePHjzfUxo0bV6EeAAAAaoroiMaaPaaLQgJ9izweEuir2WO6KDqicSV3ZoK8XGnz2/a3Cuz86qIDF0YFHvnJ/n95dgCACqrR4wPZ2dkaMWKENmzY4KgNHz5cn3/+uby9K/5L/+KLL/TRRx9pwoQJGjx4sHx9i/4PmCStWrVKd911lw4fPuyoderUSWPGjKlwHwAAADVFdERjbZoSrdiEJC3flazUTIuCArw1qEOoYiLD3OMOgQM/2EcFjica602ulIa8JjXtYk5fAGqkKhMKLFy4UJMnTy5UP3/+vGE9evToQk/vl6QZM2bohhtuMNS+/PJLrVy50lD7+eefFRERUabeLr7dv6ANGzZow4YNCg4O1tVXX62oqCiFhYWpTp06ysjI0B9//KGVK1dqx44dhvNCQ0O1aNGiSh9fAAAAqOp8vT01vHO4hnd2s+cunU+xv14w/gtj3T9YGjBVuupvkmfF7nQFgIKqTCiQmpqqffv2XXLfsWPHij2/oLy8vEK1Q4cOlb25Ujh79qyWLFmiJUuWXHJvz549NW/ePLVo0cIlvQAAAKAaybNIP74vrX1Zyi7wd9qrxkrRz0m1G5jSGoCar8qEAtVR165ddeedd2rLli3as2ePbDZbsXs9PDzUq1cvPfjgg7r11lsr/DwDAAAA1AAHN0lLJkrHdxnrYZ3sowLNupnTFwC34WEr6TtZlNrZs2f1888/69ChQzp58qQyMzPl5+en4OBgtWjRQt27d1fdunVd9vm7du1SZGSkY52QkKAOHTq47PMAAABQAWnHpZXPSjs+M9b960rRz0pd7mRUAHAjZn4/x50CThIcHKxrr73W7DYAAABQleVZpO0fSGteLDwqcOUd0oDnpdoh5vQGwC0RCgAAAACV4dAWackEKSXBWA/tKA19XWrW3Zy+ALg1QgEAAADAldJOXBgVmG+s+9eVrntG6noXowIATEMoAAAAALhCnkXa/uGFUYFzxmOdR9tHBQIbmtMbAFxAKAAAAAA426Gt0tIJUvJOY71xR2noLKl5T3P6AoACCAUAAAAAZ0k7Ia16TvplnrHuV1e67mn7qIAXfwUHUHXwJxIAAABQUda8C6MC06SsAqMCnW6Xrn9eCmxkTm8AUAJCAQAAAKAiDv9oHxVI2mGsN46UhsySLutlTl8AUAqEAgAAAEB5pJ+0jwr8PNdY9wuSrn1K6nYPowIAqjz+lAIAAADKwponxc2RVr8gZZ01Hou6Vbr+BalOYzM6A4AyIxQAAAAASutInLTkH1LSL8Z6o/b2UYEWV5vSFgCUF6EAAAAAcCnpp6TVz0s/fSLJll/3rSNd+6TU/V7Jy8e09gCgvAgFAAAAgOJY8+xBwOrnpcwzxmMdb5YGTpPqhJrTGwA4AaEAAAAAUJSjcdKSidKxn4z1hhHS0FlSiz7m9AUATkQoAAAA4IZyLFbFJiRpWUKyUrNyFeTvo8GRoYqJDJOvt6fZ7Zkr47T9zoC4j2UcFQiU+j8h9RjPqACAGoNQAAAAwM2sSkzRlIXxOpmWY6jHJiRrWmCipo+KUnSEGz4932qVfv7E/prBQqMCN0nXT5OCwkxpDQBchVAAAADAjaxKTNH4eXHKs9qKPH4yLUf3zY3T7DFd3CsYOPaztGSCfWTgYg3b2d8q0LKvOX0BgIu5+b1hAAAA7iPHYtWUhfHFBgJ/yrPa9PiCeOVYrJXUmYkyTkvfPSbNvtYYCPgGSgNflO7/nkAAQI3GnQIAAABuIjYhqdDIQHFOpuUoNiFJwzuHu7grk1it0i/zpJVTpczTxmORo+yBQFATc3oDgEpEKAAAAOAmliUkl2n/8l3JNTMUOPbLhVGB7cZ6SFtpyEypVT9T2gIAMxAKAAAAuInUrNyy7c+0uKgTk2Sekda8KP34gQxvFfCpLfV/XOrxgOTta1p7AGAGQgEAAAA3EeRfttfoBQXUkL8qWq3SjvnSymeljFPGYx1GSgNfkurWwDsiAKAUasif9AAAALiUwZGhii3DCMGgDqEu7KaSJO2QlkyUjmwz1htcbh8VaH2tOX0BQBVBKAAAAOAmYiLDNC0wsVQPGwwJ9FVMZFgldOUimWeltS9JP/5Xsl30FgWfWlK/yVLPhxgVAADxSkIAAAC34evtqemjouTl6VHiPi9PD00fFSVf72r4V0WrVfr5U+nfXaRts42BQPvh0sM/Sn0eIxAAgAuq4Z/0AAAAKK/oiMaaPaaLQgKL/qY4JNBXs8d0UXRE40ruzAmSd0ofxUjfPChlnMyvN2gj3bFQuvkTqW5T8/oDgCqI8QEAAAA3Ex3RWJumRCs2IUnLdyUrNdOioABvDeoQqpjIsOp3h0DmWWndK4XvDPCpJV0zSer1kOTtZ1p7AFCVEQoAAAC4IV9vTw3vHK7hnavxU/dtNmnH59LKZ6T0E8ZjEX+VBr0sBTczpzcAqCYIBQAAAFD9JCdISydKhzYb6/VbS0NmSG0GmNMXAFQzhAIAAACoPrLOSWv/HBXIy697B0jXTJR6P8KoAACUAaEAAAAAqj6bTYr/UlrxtJR+3His3TBp8CtScHNzegOAaoxQAAAAAFVbyi5pyUTp0CZjvV5LachM6fLrzekLAGoAQgEAAABUTVmp0rpXpa3vFRgV8Jf6XhgV8PE3rz8AqAEIBQAAAFC12GzSzv9JK56S0lKMx9oOtY8K1LvMnN4AoIYhFAAAAEDVcXy3fVTg4PfGer0WUswM6YpBprQFADUVoQAAAADMl5UqrZ8ubXm38KhAn39IV/+dUQEAcAFCAQAAAJjHZpMSFkjLn5LSko3H2g65MCrQwpTWAMAdEAoAAADAHMf3SEsnSgc2GuvBl9lHBdoONqcvAHAjhAIAAACoXNnn80cFrJb8upef1Ocxqc+jkk+Aae0BgDshFAAAAEDlsNmkXQvtowLnk4zHLh8kxbwq1W9lTm8A4KYIBQAAAOB6J361jwr8scFYD25+YVQgxpy+AMDNEQoAAADAdbLTpA0zpM1vFzEq8Kh9XIBRAQAwDaEAAAAAnM9mkxIXScuelM4fMx5rc70UM11q0NqU1gAA+QgFAAAA4FwnfpNiJ0n71xnrdZvbnxvQdojk4WFKawAAI0IBAAAAOEdOurRhprTpLcmam1/38pWu/rvU5x+Sby3z+gMAFEIoAAAAgIqx2aTEb+xvFUg9YjzWOloaMpNRAQCooggFAAAAUH4n99pHBfatMdbrNpMGvyK1G8aoAABUYW4bCmzfvl179uzRsWPHFBAQoPDwcPXu3VuhoaEVvrbVatWmTZu0b98+JSUlqW7dugoPD1ffvn1Vr149J3QPAABgspx0aeNr0g9vGkcFPH2kq/9P6jtB8q1tXn8AgFKpUqGA1WrV7t27tX37dsc/O3bsUGZmpmPP2rVr1b9//3J/xnvvvadZs2Zp3759hY55eXkpOjpaM2fOVFRUVJmvbbFYNH36dL3zzjs6duxYoeO+vr76y1/+olmzZqlFixblaR8AAMBcNpu0e7G07IkiRgWuk2JmSiFtzOkNAFBmVSYUGDVqlJYvX6709HSXXD8jI0OjRo3SsmXLit2Tl5enFStWaN26dXrzzTc1fvz4Ul8/JSVFw4YN0/bt24vdk5OTowULFmjlypX65JNPNHz48DL9GgAAAEx1ap+0dJK0b7WxHtRUGvyyFPFXRgUAoJqpMqFAXFycywIBq9Wq0aNHGwKBevXqacyYMWrfvr3Onz+v9evXa8mSJbLZbMrJydEDDzygkJAQjRo16pLXz8zM1PDhww2BQHh4uO644w61bt1ap06dUmxsrDZs2CBJSk1N1a233qo1a9aoV69ezv8FAwAAOFNOhn1UYNObUl5Oft3TR+r9iHTNREYFAKCaqjKhwMX8/PwUFRWlLl26KC0tTfPmzavQ9d59910tWrTIse7bt6+++eYbw3z/xIkTtWbNGo0cOVKpqamy2WwaN26c+vbtq0aNGpV4/WeffVZbt251rG+88UbNmzdPfn5+jtqUKVM0f/58jRs3Trm5ucrKytItt9yi3377Tf7+/hX69QEAALiEzSbtWWIfFTh3yHisVX9pyCwp5HJTWgMAOIen2Q38aezYsZo9e7bi4uJ0/vx5bdu2Te+++66io6MrdN309HRNmzbNsQ4LC9O3335b5AP/rrvuOv3nP/9xrNPS0gznFuXIkSN66623HOuoqCjNnz/fEAj86fbbb9cLL7zgWB8+fFhvv/12mX49AAAAleLUPunTm6QvRhsDgaBw6aaPpTGLCAQAoAaoMqHACy+8oHvvvVdXXXWVfHx8nHbd+fPnKyUlxbGeOnWqgoODi91/6623qkePHo71f//7X6WlpRW7/91331VWVpZjPWPGjBL7nzhxosLDwx3rN9544xK/AgAAgEqUkyGteUl6p6e0d2V+3dNbuvpR6aFtUocRPDsAAGqIKhMKuMrXX3/t+LpWrVq6/fbbL3nOvffe6/g6KyurxIcTXnz9yy67TAMHDizx2t7e3rrzzjsd6yNHjpT4cEIAAIBKYbNJe5ZK7/SQNswwPjug5TXSA5uk65+X/ALN6xEA4HQ1OhTIysrSmjVrHOtevXqpTp06lzzv+uuvN6y/++67Ivf98ccf2r17t2M9YMAAeZQiNS/t9QEAACrF6f3S/Fukz2+Tzl40KlAnTLrxI2nst1LDtub1BwBwmSr5oEFn2bNnj7Kzsx3rnj17luq85s2bKzw8XEePHpUkxcfHF7lvx44dhnVpr9+9e3d5e3vLYrGUeH0AAACXys2Uvn9D+v6fUl7+35nk6S31fFDqN1nyu/QPVAAA1VeNDgUu/im+JLVp06bU57Zu3doRCuzZs0dWq1WensYbK8p7fX9/fzVp0kSHDtmT+MTExFL3BQAA4BS/LpNiJ0tnDxrrLfra3yrQqJ05fQEAKlWNDgX2799vWDdv3rzU5168NzMzU8nJyWrSpIlTr/9nKFDwOgAAAC5z+g/7KwZ/izXWA0OlQS9JkaN4iCAAuJEaHQqkpqYa1vXr1y/1uQVfWXj+/HmXXT83N1fZ2dlFvsawvPbu3Vvucxs2bKhGjRo5rRcAAFAF5GZJP/xL+v51yZL/5iR5eks97pf6T2FUAADcUI0OBQq+StDf37/U5wYEBJR4LVdc35mhwIgRI8p97tSpU/Xcc885rRcAAGCy35bbRwXOHDDWW/SVhsyUGkWY0hYAwHw1OhTIysoyrH19fUt9bsFv0DMzMyv9+gAAABVy5oB9VODXpcY6owIAgAtqdChQ8Cf3OTk5xews7OK3FkiFf7Jf3PVLe7dAaa4PAABQLrlZ0qY3pY2vGUcFPLykng9I/R6X/IPM6w8AUGXU6FAgMDDQsC74k/2SFPzJfcFrFXf90oYCpbl+RSxatKhMb1u4WMOGDZ3aCwAAqES/r5SWTpLO/GGsX3a1/a0Cjdub0xcAoEqq0aFAUJAxAT9z5kypzz179qxhXadO4QfvFHX94ODgMl/fx8fHqc8TkOyvR+zQoYNTrwkAAKqwMwel5U9Ke74z1ms3so8KdLyJUQEAQCE1OhRo2bKlYf3nKwBL4+DB/Hf2BgQEKDQ0tFTXL1grzfVbtWpV6r4AAAAMLNn2UYENr0mWi+5E9PCSeoy3v1XAv655/QEAqrQaHQq0b2+8Pa4sr+nbt2+f4+t27drJ09OzVNfv16/fJa+dlZWlY8eOFXsdAACAUtm7yj4qcHq/sd68l31UIDTSnL4AANVG4e90a5B27doZ3giwefPmUp13+PBhHT161LHu2LFjkfs6depkWJf2+tu2bZPFYrnk9QEAAIp09rD0xR3SvFHGQKB2Q2nkf6Q7YwkEAAClUqNDAX9/f1133XWO9ebNm5WWlnbJ81asWGFYDxs2rMh9LVu2VLt27RzrVatWyWazXfL6K1euLNX1AQAADCzZ9jcKvNVN2r04v+7hKfW4X3p4u9TpVp4dAAAotRodCkjSyJEjHV9nZGTo008/veQ577//vuNrPz8/xcTElOr6Bw8eLBQoFGSxWPTRRx851uHh4erateslewIAAG5u72rp3d7S6heMzw5o1lMav0GKmS4FBJvWHgCgeqrxocDtt9+uRo0aOdbPP/98oTcLXOzzzz/X1q1bHet77rmnxNcFPvDAA4Y3B0yePFm5ubnF7p81a5ZhNOHRRx+VB2k+AKAGybFY9c0vR/XAvDiN/u8WPTAvTt/8clQ5FqvZrVVP545IX46V5t0gnbro+Ui1G0oj3r0wKsAoIgCgfGp8KBAYGKinn37asU5KStLw4cOLDAbWrFmj8ePHO9a1a9c2nFuUZs2a6aGHHnKs4+PjNXr0aGVnZxfa+9lnn2nq1KmOdXh4uB5++OGy/HIAAKjSViWmqPerq/X3z39RbEKyfth7SrEJyfr757+o96urtXp3itktVh+WHGnj6/ZRgcRv8usenlL3++yjAp1vl4p4GDIAAKXlYSvNEHwlWLhwoSZPnlyofv78eR0/ftyxbtKkiQICAgrtmzFjhm644YYir221WjVixAgtXpw/e1e/fn2NHTtWERERSktL07p16/Tdd985ngng4eGhzz//XDfffPMle8/IyFC/fv20fft2Ry08PFxjxoxRq1atdObMGS1dulTr1693HPfz89OqVavUp0+fS16/NHbt2qXIyPwHCiUkJKhDhw5OuTYAAKWxKjFF4+fFKc9a/F8tvDw9NHtMF0VHNK7EzqqhfWvtbxU49bux3rS7NPQ1KSzKnL4AAC5h5vdzVeaVhKmpqYbXABbn4lf5FTy/OJ6envrss880cuRIx0P+Tp8+rTfeeKPI/b6+vvrnP/9ZqkBAkmrVqqXFixdr6NCh+umnnyRJR48e1auvvlrk/jp16ujjjz92WiAAAIDZcixWTVkYX2IgIEl5VpseXxCvTVOi5evNT7gLOXdUWv6klLjIWK8VIl3/gtTpNu4MAAA4ldv8V6V27dpavny53n77bbVq1arIPZ6enhowYIC2bt2qBx98sEzXDw0N1ZYtW/TCCy8oNDS0yD2+vr4aOXKkduzYYXhAIQAA1V1sQpJOpuWUau/JtBzFJiS5uKNqxpIjff/GhVGBRfl1D0+p273SI9ulK0cTCAAAnK7KjA9Uth9//FG7d+9WUlKSAgICFB4ert69eyssLKzC187Ly9OmTZu0d+9epaSkqE6dOmratKn69u2r+vXrO6H7whgfAACY6YF5cYpNSC71/iEdQ/XO6C4u7Kga2b/OPipw8jdjvWk3acgsqUlnM7oCAFQixgdM0K1bN3Xr1s0l1/by8lLfvn3Vt29fl1wfAICqJjWr+DfvFLk/0+KiTqqR1GPS8qekXQuN9VoNpAHPS525MwAA4HpuGwoAAADnCfL3Kdv+ADf+K0herrTlXWn9dCkn7aIDHlLXu6TrnpZquebOQgAACnLj/yIDAABnGRwZWqbxgUEdin7+To33xwb7qMCJPcZ6eBf7WwWaXGlOXwAAt0UoAAAAKiwmMkzTAhNL9bDBkEBfxURW/Bk+1UpqkrTiaSnhf8Z6QH1pwHPSlWMYFQAAmIL/+gAAgArz9fbU9FFR8vL0KHGfl6eHpo+Kcp/XEeblSpvekt7qWiAQ8JC63Ck9Eid1+RuBAADANNwpAAAAipVjsSo2IUnLEpKVmpWrIH8fDY4MVUxkWKFv7KMjGmv2mC56fEF8kXcMhAT6avqoKEVHNK6s9s114HtpyUTpxG5jvcmV9lGBcN6+AAAwH6EAAAAo0qrEFE1ZWPgb/NiEZE0LTCzyG/zoiMbaNCVasQlJWr4rWamZFgUFeGtQh6KDhBrpfLJ9VGDnV8Z6QD0peqp01VjJ08uc3gAAKIBQAAAAFLIqMUXj58Upz2or8vjJtBzdNzdOs8d0KRQM+Hp7anjncA3vHF4ZrVYdeRZp23+kta9IOecvOuBhHxGInspbBQAAVY4bxPUAAKAscixWTVkYX2wg8Kc8q02PL4hXjsVaSZ1VYQd+kP7TV1r+pDEQCOss3bNa+su/CAQAAFUSdwoAAACD2ISkUr1FQLLfMRCbkOR+dwX86XyKtPIZKf4LY90/WIp+VuoyjlEBAECVRigAAAAMliUkl2n/8l3J7hcK5FmkH9+X1r4sZacaj101Vop+TqrdwJTWAAAoC0IBAABgkJqVW7b9mRYXdVJFHdwsLZ0opSQY62GdpCGvSc26mdMXAADlQCgAAAAMgvx9yrY/wE3+OpF2XFr5rLTjM2Pdv+6FUYE7GRUAAFQ7bvJfcQAAUFqDI0MVW4YRgkEdQl3YTRWQZ5G2fyCtebHwqMCVd0gDnpdqh5jTGwAAFUQoAAAADGIiwzQtMLFUDxsMCfRVTGRYJXRlkkNbpCUTpZSdxnpoR2no61Kz7ub0BQCAk/BKQgAAYODr7anpo6Lk5elR4j4vTw9NHxUlX+8a+NeJtBPSogelDwcZAwG/utKQWdJ96wkEAAA1Qg38rzgAAKio6IjGmj2mi0ICfYs8HhLoq9ljuig6onEld+ZieRZp62zp312kXz41Hus8WnokTup+L88OAADUGIwPAACAIkVHNNamKdGKTUjS8l3JSs20KCjAW4M6hComMqzm3SFweJu05B9ScoFRgcYdpaGzpOY9zekLAAAXIhQAAADF8vX21PDO4RreOdzsVlwn/aS0aqr08zxj3S9Iuu5pqevdkhd/ZQIA1Ez8Fw4AALgna560/UNpzTQp65zxWKfbpOtfkAIbmdMbAACVhFAAAAC4n8M/SksnSEk7jPXGkfYHCV7Wy5y+AACoZIQCAADAfaSflFY9J/0811j3C5KufUrqdg+jAgAAt8J/9QAAQM1nzZPi5kirX5CyzhqPRd1qHxWoU8PepAAAQCkQCgAAgJrtSJz9rQJJvxjrjdrbRwVaXG1KWwAAVAWEAgAAoGZKPyWtfl766RNJtvy6bx3p2iel7vdKXj6mtQcAQFVAKAAAAGoWa549CFj9vJR5xnis483SwGlSnVBzegMAoIohFAAAADXH0ThpyUTp2E/GesMIaegsqUUfc/oCAKCKIhQAAADVX8Zp+0ME4+bIOCoQKPV/QuoxnlEBAACKQCgAAACqL6vV/nrBVc9JmaeNxzreJF0/TQoKM6U1AACqA0IBAABQPR37WVoywT4ycLGG7exvFWjZ15y+AACoRggFAABA9ZJxWlozTdr+kQqNCvR7XOr5AKMCAACUEqEAAACoHqxW6Zd59lGBjFPGY5GjpIEvSkFNTGkNAIDqilAAAABUfcd+uTAqsN1YD7nCPirQqp8pbQEAUN0RCgAAgKor84y05kXpxw9kGBXwqS31f1zq8YDk7WtaewAAVHeEAgAAoOqxWqUd86WVzxYeFegwUhr4klQ33JzeAACoQQgFAABA1ZK0Q1oyUTqyzVhvcLk0ZKbU+lpz+gIAoAYiFAAAAFVD5llp7UvSj/+VbNb8uk8tqd9kqedDjAoAAOBkhAIAAMBcVqsU/7m04hkp46TxWPvh0qCXpbpNzekNAIAajlAAAACYJ3mnfVTg8BZjvUEbKWaG1CbanL4AAHAThAIAAKDyZZ6V1r0ibZttHBXwDpD6TZJ6PSx5+5nWHgAA7oJQAAAAVB6bTYr/wj4qkH7ceCziL9KgV6TgZub0BgCAGyIUAAAAlSM5QVo6UTq02Viv31oaMkNqM8CcvgAAcGOEAgAAwLWyzklr/xwVyMuvewdI10yUej/CqAAAACYhFAAAAK5hs0nxX0orni48KtBumDT4FSm4uTm9AQAASYQCAADAFVJ22d8qcGiTsV6vpTRkpnT59eb0BQAADAgFAACA82SlSutelba+V2BUwF/qe2FUwMffvP4AAIABoQAAAKg4m03a+T9pxVNSWorxWNuh0uCXpXotTGkNAAAUj1AAAABUzPHd9lGBg98b6/VaSDEzpCsGmdIWAAC4NEIBAABQPtnn80cFrJb8ure/1Ocf0tV/Z1QAAIAqjlAAAACUjc0mJSywv1XgfJLx2BUx9rcK1G9pTm8AAKBMPM1uoDK0aNFCHh4eFfqnf//+RV77wIED5b7m//73v8r9HwIAgIo6vkf6+C/SgruNgUDwZdJtn0u3f04gAABANcKdAqUUHBxsdgsAAJgn+7y0frq05V3jqICXn9TnManPo5JPgGntAQCA8nGLUKBFixby9i7bL/XYsWPKzMx0rG+77bZSndekSRMFBJTuL0WBgYFl6gkAgEpns0m7FkrLn5bOHzMeu3ygFDNdqt/KnN4AAECFuUUosG7dujLtz87OVnh4uCMUaNCggUaMGFGqcz/99NNiRw0AAKhWTvwqLZ0o/bHBWA9uLg2eLrWNkTw8zOkNAAA4hVuEAmW1aNEinTp1yrEeM2aM/Pz8TOwIAIBKlJ0mbZghbX67iFGBR+3jAowKAABQIxAKFOG///2vYX333Xeb1AkAAJXIZpMSF0nLn5JSjxqPtbnePirQoLUprQEAANcgFCjgwIEDWr16tWPdo0cPRUZGmtgRAACV4MRvUuwkaf86Y71ucynmVantEEYFAACogQgFCvjwww9ls9kc63vuucfEbgAAcLGcdGnDTGnTW5I1N7/u5Std/Xepzz8k31rm9QcAAFyKUOAiVqtVc+bMcaxr166tW265xbyGAABwFZtN2v2ttOxJKfWI8VjraGnITEYFAABwA55mN1CVrFixQocPH3asb7nlFtWpU6dM13jjjTfUo0cPhYSEyMfHRyEhIYqIiNDf/vY3zZ07V9nZ2c5uGwCAsjm5V5p3g/TlWGMgULeZdMs86Y4FBAIAALgJ7hS4SMEHDJZndOCbb74xrE+dOqVTp05pz549+uSTTzRp0iS9+OKLLh9L2Lt3b7nPbdiwoRo1auTEbgAAVUJOurTxNemHN42jAp4+0tX/J/WdIPnWNq8/AABQ6QgFLjhx4oS+/fZbx7p9+/bq1atXua4VFBSk4OBgZWVl6dSpU8rLy3McS0lJ0b333qsNGzZozpw58vR0zc0aI0aMKPe5U6dO1XPPPee0XgAAJrPZpD3fScuekM4dNh5rfZ0UM1MKaWNObwAAwFSMD1zwySefKDc3/6cmZXkNYWBgoO666y4tWrRIycnJOnfunA4ePKiUlBSlpqZq+fLlGjp0qOGcuXPnasKECU7rHwCAIp3aJ316o/TFHcZAIChcuvkT6Y6FBAIAALgx7hS44MMPP3R87evrq7Fjx5bqvLCwMB09elRBQUFFHq9Vq5YGDhyogQMHas6cObr77rtltVol2Z8/cNttt6l79+4V/wUAAHCxnAz7qMCmN6W8nPy6p4/U+2HpmkmMCgAAAEIBSdq8ebMSExMd6+HDhyskJKRU5/r5+cnPz69Ue8eNG6eUlBRNmTLFUZs2bZoWL15ctoZLYdGiRWrTpnw/+WnYsKGTuwEAVBqbTdqz5MKowCHjsVb9pSGzpJDLTWkNAABUPYQCcs4DBkvrscce01tvvaUjR+xPe161apUyMzMVEBDg1M9p06aNOnTo4NRrAgCquFP7pGVTpN9XGOtB4dKgl6X2wyUPD3N6AwAAVZLbP1MgLS1NX375pWN92WWXacCAAS77PF9fXw0bNsyxzsrK0i+//OKyzwMAuIGcDGnNS9I7PY2BgKe3dPWj0kPbpA4jCAQAAEAhbn+nwOeff660tDTH+s4773TZGwH+1LZtW8P6+PHjLv08AEANtmeptOxx6WyBUYGW19hHBRq2Lfo8AAAAEQrogw8+cHzt6empO++80+WfWXBUICMjw+WfCQCoYU7vl2KnSL8vN9brhNlHBTqM5M4AAABwSW4dCuzatUtbtmxxrAcOHKjmzZu7/HNTUlIM69I+1BAAAOVmSt+/IX3/TykvO7/u6S31fFDqN1nyq2NaewAAoHpx61Dg4rsEJOnuu++ulM/duHGjYd2yZctK+VwAQDX36zIpdrJ09qCx3qKvfVSgUTtz+gIAANWW24YCOTk5mjt3rmPdsGFDDR8+3OWfu2vXLq1Zs8axbtGiRblfHQgAcBNnDthHBX6LNdYDQ6VBL0mRoxgVAAAA5eK2ocA333yjkydPOtZjx46Vj49Pma6RlZUlPz8/eZTyL2JpaWm64447ZLVaHbVx48aV6TMBAG4kN0v64V/S969Llqz8uoeX1PMBqf8URgUAAECFuO0rCZ0xOrBlyxZdeeWVmj9/vtLT00vcGxcXp169ehlePxgWFqYJEyaU+XMBAG7gt+XSOz2kdS8bA4HL+kj3f2+/Q4BAAAAAVJBb3ilw6NAhrVy50rHu3bu3IiIiynWtHTt2aPTo0apVq5auvvpqderUSU2bNlVQUJCys7N1+PBhrVu3Tps2bTKcFxgYqG+//VaBgYEV+rUAAGqYMwelZU9Ivy4x1gMbSwNfkjreyKgAAABwGrcMBT766CPDLfz33HNPha+ZkZGhlStXGsKG4rRr106ffvqprrrqqgp/LgCghsjNkjb9W9o4q+hRgX6PS/5B5vUHAABqJLcLBWw2mz766CPHuk6dOrr55pvLda02bdro/vvv15YtW7Rz507l5eWVuD8qKkr333+/xo0bp4CAgHJ9JgCgBvp9pbR0knTmD2P9sqvtbxVo3N6cvgAAQI3ndqGAh4eHDhw44JRrNW3aVO+++64kKT09XT///LMOHjyo48ePKyMjQ97e3goODlbTpk3VvXt3NWzY0CmfCwCoIc4clJY/Ke35zliv3Uga+KIUdTOjAgAAwKXcLhRwldq1a6tPnz7q06eP2a0AAKo6S7a06U1pw2uSJTO/7uEl9Rhvf6uAf13z+gMAAG6DUAAAgMq0d5W0dLJ0ep+x3ryXfVQgNNKcvgAAgFsiFAAAoDKcPSwtf0LavdhYr93wwqjALYwKAACASkcoAACAK1mypc1vSetnFhgV8JS63yf1f0IKCDatPQAA4N4IBQAAcJW9q6XYydKpvcZ6s57S0FlSaEdz+gIAALiAUAAAAGc7d8T+VoHEb4z1WiHSwGlS1K2Sp6c5vQEAAFyEUAAAYKoci1WxCUlalpCs1KxcBfn7aHBkqGIiw+TrXc2+cbbkSFveltbPkHIz8usenlK3e6Rrn2JUAAAAVCmEAgAA06xKTNGUhfE6mZZjqMcmJGtaYKKmj4pSdERjk7oro31rpaWTpFO/G+tNu0tDX5PCoszpCwAAoATV7EcwAICaYlViisbPiysUCPzpZFqO7psbp9W7Uyq5szI6d1T6apw0d4QxEKjVQBr+tnTXcgIBAABQZREKAAAqXY7FqikL45VntZW4L89q0+ML4pVjsVZSZ2VgyZG+f0N6q5u06+v8+p+jAo/ESVfewbMDAABAlcb4AACgUlz87IB9J9KKvUOgoJNpOYpNSNLwzuEu7rAM9q+Xlk6UTv5mrDftJg2ZJTXpbEpbAAAAZUUoAABwueKeHVBay3clV41QIPWYtPwpaddCY71WA2nA81Ln0dwZAAAAqhVCAQCAS/357IBLjQqUJDXT4sSOyiEvV9ryrrR+upSTdtEBD6nrXdJ1T0u16pvWHgAAQHkRCgAAXKa0zw64lKAAE/9z9ccG+1sFTuwx1sO72EcFwq8ypy8AAAAnIBQAALhMbEJSuUcGLjaoQ6gTuimj1CRpxdNSwv+M9YD60oDnpCvHMCoAAACqPUIBAIDLLEtIrvA1QgJ9FRMZ5oRuSikvV9r6H2ndK4VHBbqMk6KfZVQAAADUGIQCAACXSc3KrdD5Xp4emj4qSr7elfQT+QPfS0smSid2G+tNrpSGvmYfGQAAAKhBCAUAAC4T5O9T7nNDAn01fVSUoiMaO7GjYpxPllY8I+380lgPqCdFT5WuGit5erm+DwAAgEpGKAAAcJnBkaGKLcMIQdvGddS6UW0N6hCqmMgw198hkGeRts2W1r4s5Zy/6ICHPQiInirVbuDaHgAAAExEKAAAcJmYyDBNC0ws1cMGQwJ9tfiRPpU4KvCDtHSidDzRWA/rbB8VaNq1cvoAAAAwEY9NBgC4jK+3p6aPipKXp0eJ+yr12QHnU6SF90lzhhgDAf9gaejr0r1rCAQAAIDbIBQAALhUdERjzR7TRSGBvkUeDwn01ewxXVz/7IA8i7TlXemtrlL8F8ZjV46RHvlJ6nY3zw4AAABuhfEBAIDLRUc01qYp0YpNSNLyXclKzbQoKMC78p4dcHCzfVQgJcFYD42y3x3QrJtrPx8AAKCKIhQAAFQKX29PDe8cruGdwyvvQ9OOSyuflXZ8Zqz715Win5W63MmdAQAAwK0RCgAAap48i7T9A2nNS1L2OeOxK++QBjwv1Q4xpzcAAIAqhFAAAFCzHNoiLZkopew01kM7XhgV6G5OXwAAAFUQoQAAoGZIOyGtmir98qmx7ldXin5G6noXowIAAAAFEAoAAKo3a560/UNpzTQpq8CoQOfR9lGBwIbm9AYAAFDFEQoAAKqvw9ukJROk5HhjvXFHaegsqXlPc/oCAACoJggFAADVT/pJ+6jAz/OMdb8g6bqnpa53S178Jw4AAOBS+BsTAKD6sOZJcR9Jq18oPCrQ6Tbp+hekwEbm9AYAAFANEQoAAKqHI9ulJf+QknYY640jpSGzpMt6mdMXAABANUYoAACo2tJPSquek36ea6z7BUnXPiV1u4dRAQAAgHLib1EAgKrJmifFzbkwKnDWeCzqVvuoQJ3GZnQGAABQYxAKAACqniNx0tIJ0rGfjfVG7e2jAi2uNqcvAACAGoZQAABQdaSfklY/L/30iSRbft23jnTtk1L3eyUvH9PaAwAAqGkIBQAA5rPm2YOA1c9LmWeMxzreLA2cJtUJNac3AACAGoxQAABgrqNx0pKJ0rGfjPWGEdLQWVKLPub0BQAA4AYIBQAA5sg4bX+IYNwcGUcFAqX+T0g9xjMqAAAA4GKEAgCAymW12l8vuOo5KfO08VjkjdLAF6WgMFNaAwAAcDeEAgCAynPsZ2nJBPvIwMVC2tpHBVpeY05fAAAAbopQAADgehmnpTUvSts/VKFRgX6PSz0fYFQAAADABIQCAADXsVqlX+bZRwUyThmPdbhBGvSSFNTElNYAAABAKAAAcJVjv0hLJ0pHfjTWQ66QhsyUWvU3oysAAABchFAAAOBcmWekNS9J2z+QbNb8uk9tqf/jUo8HJG9f8/oDAACAA6EAAMA5rFZpx3xp5VQp46TxWIeR0sCXpLrh5vQGAACAIhEKAAAqLmmHtGSidGSbsd7gcvuoQOtrzekLAAAAJSIUcKHt27drz549OnbsmAICAhQeHq7evXsrNDTU7NYAwDkyz0prX5J+/G+BUYFaUr/JUs+HGBUAAACowtwqFPDw8CjXeTNnztTEiRNLvf+9997TrFmztG/fvkLHvLy8FB0drZkzZyoqKqpc/QCA6axWKf5zaeWzUvoJ47H2w6VBL0t1m5rTGwAAAErNrUIBV8vIyNCoUaO0bNmyYvfk5eVpxYoVWrdund58802NHz++EjsEACdI3mkfFTi8xVhv0EaKmSG1iTanLwAAAJSZ24YCjRo1Up06dUq1t169epfcY7VaNXr0aEMgUK9ePY0ZM0bt27fX+fPntX79ei1ZskQ2m005OTl64IEHFBISolGjRpX71wEAlSbrnLT2ZWnbbOOogHeA1G+S1OthydvPvP4AAABQZm4bCkyfPl3jxo1z2vXeffddLVq0yLHu27evvvnmG0OgMHHiRK1Zs0YjR45UamqqbDabxo0bp759+6pRo0ZO6wUAnMpmk+K/kFY8I6UfNx6L+Is06BUpuJk5vQEAAKBCPM1uoCZIT0/XtGnTHOuwsDB9++23Rd5hcN111+k///mPY52WlmY4FwCqlOQE6aMY6evxxkCgfitp9ALplnkEAgAAANUYoYATzJ8/XykpKY711KlTFRwcXOz+W2+9VT169HCs//vf/yotLc2VLQJA2WSdk5Y9If3nGunQ5vy6d4B03dPSg1ukyweY1x8AAACcglDACb7++mvH17Vq1dLtt99+yXPuvfdex9dZWVklPpwQACqNzSbFfym91U3a8o5ky8s/1m6Y9PA26ZpJPDsAAACghiAUqKCsrCytWbPGse7Vq1epHmB4/fXXG9bfffed03sDgDJJSZTmDJUW3iul5d/9pHotpdH/k279VApubl5/AAAAcDq3fdCgs+zZs0fZ2dmOdc+ePUt1XvPmzRUeHq6jR49KkuLj413SHwBcUlaqtO5Vaet7xjsDvP2lvhOk3v8n+fib1x8AAABcxm3vFPj00091zTXXqFGjRvL19VX9+vV1xRVX6JZbbtF7772n1NTUUl1n9+7dhnWbNm1K3UPr1q0dX+/Zs0dWq7WE3QDgZDabFP+V9FZXacvbxkCg7VDpoa1Sv8kEAgAAADWY294psGrVKsP6zJkzOnPmjH7//Xd9+eWXmjJlih5//HE9/vjj8vQsPjvZv3+/Yd28eelvrb14b2ZmppKTk9WkSZNSn1+SvXv3lvvchg0b8opEoKY7vltaMlE6+L2xXq+FFDNDumKQKW0BAACgcrltKCBJtWvXVv369ZWbm6tTp04pNzfXcezcuXN68skntXbtWn3zzTcKCAgo8hoF7yioX79+qT+/4CsLz58/X4buSzZixIhynzt16lQ999xzTusFQBWSfT5/VMBqya97+0t9/iFd/XfuDAAAAHAjbjU+4Ovrq5tvvlmfffaZDh06pLS0NB06dEhJSUlKS0vTxo0bdccdd8jDw8NxzsqVKzV69GjZbLYir1nwVYL+/qX/y3TBoIHXEgJwGZtN2vk/+1sFNr9lDASuiLG/YrD/4wQCAAAAbsat7hQ4cuSIGjZsWOQxX19f9enTR3369NHo0aM1atQoZWRkSLK/cvCrr77SzTffXOi8rKysQtcpLT8/4yu9MjMzS30uAJTa8T3S0onSgY3GevBlUsx0qW2MOX0BAADAdG4VChQXCBQ0ePBgffjhh7r11lsdtWnTphUZChS8MyAnJ6fU/Vz81gKp8J0DFbFo0aIyPfTwYqX93wlAFZd9Xlo/Q9ryjvHOAC8/qc9jUp9HJR/n/bkDAACA6setQoGyuOWWW/T6669r27ZtkqSEhAQdOHBALVq0MOwLDAw0rAveOVCSgncGFLxWRbRp00YdOnRw2vUAVCM2m7RrobT8aen8MeOxywfa7w6o38qc3gAAAFCluNUzBcrqhhtuMKw3b95caE9QUJBhfebMmVJf/+zZs4Z1nTp1St8cABTlxG/SJ8Ol/91lDASCm0u3fibd/iWBAAAAABy4U6AEbdu2NayPHz9eaE/Lli0N60OHDpX6+gcPHnR8HRAQoNDQ0DJ2CKAy5Fisik1I0rKEZKVm5SrI30eDI0MVExkmX+8qkq1mp0kbZkqb35as+W9SkZevdPWj9nEB31qmtQcAAICqiVCgBAVn/P988ODF2rdvb1jv3bu31Nfft2+f4+t27drJ07OKfHMBwGFVYoqmLIzXyTTj80JiE5I1LTBR00dFKTqisUndyT4qkLhIWv6UlHrUeKzN9fZRgQatTWkNAAAAVR/fhZYgJSXFsA4JCSm0p127doY3DhQ1YlCUw4cP6+jR/L/Ad+zYsZxdAnCVVYkpGj8vrlAg8KeTaTm6b26cVu9OKfK4y538XZo7QvpqnDEQqNtcuuVTafRXBAIAAAAoEaFACTZuNL6+q+CogGR/+8B1113nWG/evFlpaWmXvPaKFSsM62HDhpWzSwCukGOxasrCeOVZbSXuy7Pa9PiCeOVYrJXUmaScdGnVc9I7vaT96/LrXr7SNZOkh7ZKEcMkD4/K6wkAAADVEqFAMU6cOKHPP//csQ4ICFCfPn2K3Dty5EjH1xkZGfr0008vef3333/f8bWfn59iYnhPOFCVxCYkFXuHQEEn03IUm5Dk4o50YVTgG+mt7tL3/zQ+O6B1tPTgFum6p3l2AAAAAErNLUKB3NxcWSyWS2+8wGKxaMyYMYaf+N9yyy3y9/cvcv/tt9+uRo0aOdbPP/98oTcLXOzzzz/X1q1bHet77rnHqa8jBFBxyxKSy7R/+a6y7S+zk3uleTdIX46VUo/k1+s2k26ZJ92xgFEBAAAAlJlbhAJHjx5Vu3bt9J///OeSrwz8/fffFR0dreXLlztqtWrV0vPPP1/sOYGBgXr66acd66SkJA0fPrzIYGDNmjUaP368Y127dm3DuQCqhtSs3Etvunh/ZumDxzLJSZdWvyC920vatya/7ukj9Z1wYVTgL4wKAAAAoFzc5u0D+/bt0/33369HHnlEvXr1UufOndWyZUsFBQXJYrEoKSlJ33//vdasWSOrNX822MvLS19++aWaN29e4vUfeughrVy5UosXL5YkbdiwQa1bt9bYsWMVERGhtLQ0rVu3Tt99951sNvuMsoeHhz788ENeRQhUQUH+PmXbH+DkP05tNmnPd9KyJ6Rzh43HWl0rDZklhbRx7mcCAADA7bhNKPCn3NxcbdiwQRs2bLjk3iZNmmjOnDm6/vrrL7nX09NTn332mUaOHKmVK1dKkk6fPq033nijyP2+vr765z//qZtvvrlM/QOoHIMjQxVbhhGCQR2cGO6d2ifFTpb2rjLWg8Klwa9IEX/lzgAAAAA4hVuMD9SvX19///vf1aNHD8PrA4vTunVrTZ8+Xbt27SpVIPCn2rVra/ny5Xr77bfVqlWrIvd4enpqwIAB2rp1qx588MFSXxtA5YqJDFNI4KX/vJCkkEBfxUSGVfxDczKkNS9K7/Q0BgKePlKfx6SHf5TaDycQAAAAgNO4xZ0CQUFBjp/YZ2dna8eOHdq/f7+Sk5OVnp4uLy8v1a1bV6GhoerWrZuaNm1a7s/y8PDQgw8+qAcffFA//vijdu/eraSkJAUEBCg8PFy9e/dWWJgTvnkA4FK+3p6aPipK982NK/G1hF6eHpo+Kkq+3hXIWG026delUuwU6dwh47FW/aWYmVLDK8p/fQAAAKAYbhEKXMzPz0/du3dX9+7dXf5Z3bp1U7du3Vz+OQAKy7FYFZuQpGUJyUrNylWQv48GR4YqJjKs1N/AR0c01uwxXfT4gvgiX08YEuir6aOiFB3RuPyNntonLZsi/b7CWK/TRBr8stR+BHcGAAAAwGXcLhQAUPOtSkzRlIWFv5GPTUjWtMDEMn0jHx3RWJumRCs2IUnLdyUrNdOioABvDepQtoChkNxMaePr0g9vSHkX9enpLfV6SLpmsuTHq0oBAADgWoQCAGqUVYkpGj+v+Fv+T6bl6L65cZo9pkupgwFfb08N7xyu4Z3DndPkr7H2BwmeLTAq0PIa+1sFGrZ1zucAAAAAl+AWDxoE4B5yLFZNWRhf4jMAJCnPatPjC+KVY7GWuM/pTu+XPr1Z+uxWYyBQJ0y68SNp7LcEAgAAAKhU3CkAoMaITUgqcva/KCfTchSbkOS8n/6XJDdT+v4N6ft/SnnZ+XVPb6nng1K/yZJfHdf3AQAAABRAKACgxliWkFym/ct3Jbs+FPh12YVRgYPGeou+9lGBRu1c+/kAAABACQgFANQYqVm5ZdufaXFRJ5LOHLC/YvC3WGM9MFQa9JIUOYq3CgAAAMB0hAIAaowgf5+y7Q9wwR+BuVnSD/+Svn9dsmTl1z28pJ4PSP0el/yDnP+5AAAAQDkQCgCoMQZHhiq2DCMEgzqEOreB31ZIsZPsdwlc7LI+0pCZUuP2zv08AAAAoIIIBQDUGDGRYZoWmFiqhw2GBPoqJjLMOR985qC07Anp1yXGemBjaeBLUscbGRUAAABAlcQrCQHUGL7enpo+KkpeniV/A+7l6aHpo6Lk613BPwJzs6T1M6W3uxsDAQ8vqedD0sPbpaibCAQAAABQZREKAKhRoiMaa/aYLgoJ9C3yeEigr2aP6aLoiMYV+6DfV0nv9pLWvmh8dkDz3tL9G6XBL/PsAAAAAFR5jA8AqHGiIxpr05RoxSYkafmuZKVmWhQU4K1BHUIVExlWsTsEzh6yjwrs+c5Yr91IGviiFHUzdwYAAACg2iAUAFAj+Xp7anjncA3vHO6cC1qypU1vShtekyyZ+XUPT6n7eOnaJyT/us75LAAAAKCSEAoAwKXsXSUtnSyd3mesN+8lDZklhUaa0xcAAABQQYQCAFCcs4el5U9Iuxcb67UbXhgVuIVRAQAAAFRrhAIAUJAlW9r8lrRhlpSbkV/38JS63yf1f0IKCDatPQAAAMBZCAUA4GJ7V0uxk6VTe431Zj3sowJhUeb0BQAAALgAoQAASNK5I9LyJ6XEb4z1WiHSwGlS1K2SJ29xBQAAQM1CKADAvVlypC1vS+tnFB4V6HaPdO1TjAoAAACgxiIUAOC+9q2Vlk6STv1urDftLg2dJYV1MqcvAAAAoJIQCgBwP+eOSiueknZ9bazXaiBd/4LU6XZGBQAAAOAWCAUAuA9LjrT1XWnddCk3Pb/u4Sl1vUu67mkpoJ55/QEAAACVjFAAgHvYv94+KnDyV2M9vKs09DWpSWdT2gIAAADMRCgAoGZLPSYtf0ratdBYD6gvXf+81PkORgUAAADgtggFANRMebnSlnel9dOlnLSLDnjkjwrUqm9aewAAAEBVQCgAoOb5Y4N9VODEHmM9vIs0ZJYUfpU5fQEAAABVDKEAgJojNUla8bSU8D9jPaC+NGCqdOVYRgUAAACAixAKAKj+8nKlrf+R1r1SeFSgyzgp+llGBQAAAIAiEAoAqN4OfC8tmSid2G2sN7nS/laB8C7m9AUAAABUA4QCAKqn88nSimeknV8a6wH1pOip0lVjJU8vc3oDAAAAqglCAQDVS55F2jZbWvuylHP+ogMe9iAgeqpUu4Fp7QEAAADVCaEAgOrj4Cb7qMDxXcZ6WGf7qEDTrqa0BQAAAFRXhAIAqr7zKdLKZ6X4z411/2D7QwS7jGNUAAAAACgHQgEAVVeeRfrxffuoQHaq8diVY6QBz0m1Q0xpDQAAAKgJCAUAVE0HN0tLJ0opCcZ6aJQ09HWpWTdz+gIAAABqEEIBAFVL2nFp5VRpx3xj3b/uhVGBOxkVAAAAAJyEUABA1ZBnkbZ/IK15Sco+Zzx25R3SgOcZFQAAAACcjFAAgPkObZWWTJBSdhrroR0vjAp0N6cvAAAAoIYjFABgnrQT0qqp0i+fGut+daXoZ6SudzEqAAAAALgQoQCAymfNk7Z/KK2ZJmUVGBXoPNo+KhDY0JzeAAAAADdCKABAORarYhOStCwhWalZuQry99HgyFDFRIbJ19vTuR92eJt9VCA53lhv3FEaOktq3tO5nwcAAACgWIQCgJtblZiiKQvjdTItx1CPTUjWtMBETR8VpeiIxhX/oPST9lGBn+cZ635B0nVPS13vlrz4IwkAAACoTE7+ESCA6mRVYorGz4srFAj86WRaju6bG6fVu1PK/yHWPOnH/0r/vqpwINDpNumROKnHeAIBAAAAwASEAoCbyrFYNWVhvPKsthL35VltenxBvHIs1rJ/yJHt0vvX2scFLn52QKMO0p2x0sj3pMBGZb8uAAAAAKfgR3OAm4pNSCr2DoGCTqblKDYhScM7h5fu4umnpNXPST99Yqz7BUnXPil1u5c7AwAAAIAqgL+VA25qWUJymfYv35V86VDAmif99LG06nkp66zxWNSt0vUvSHWc8HwCAAAAAE5BKAC4qdSs3LLtz7SUvOFInLR0gnTsZ2O9UXtpyCypxdVl7BAAAACAq7llKGCz2bRv3z4lJCTo8OHDSk1NVa1atVS/fn116tRJHTt2lJeXl9ltAi4V5O9Ttv0BxfxxkXFaWvXchVGBi55P4FtHuvYJqft9klfZPgsAAABA5XCbUOD8+fNavHixvv32W61Zs0YnTpwodm+9evV05513auLEiQoLC7vktQ8cOKCWLVuWq6+vvvpKN954Y7nOBSpicGSoYsswQjCoQ6ixYLXaRwVWPy9lnjEe63izNHCaVKfAOQAAAACqFLcIBc6fP69GjRopKyurVPvPnDmj119/XXPmzNF///tfjRw50sUdApUvJjJM0wITS/WwwZBAX8VEXhSQHY2TlkyUjv1k3NgwQho6S2rRx8ndAgAAAHAFtwgF8vLyCgUCrVq1Ur9+/dS2bVuFhIQoKytLO3fu1IIFC3Ty5ElJ0unTp3XTTTfpq6++KlMw0KRJEwUEBJRqb2BgYOl/IYAT+Xp7avqoKN03N67E1xJ6eXpo+qgo+Xp72kcFVr8gxc2RcVQgUOr/hNRjPKMCAAAAQDXiFqHAn4KCgnTnnXfqrrvuUlRUVJF7Xn/9dT366KN6//33JdkDhbvvvlt9+/ZVSEhIqT7n008/Vf/+/Z3VNuAy0RGNNXtMFz2+IL7IOwZCAn01fVSUots2lOI+tj87IPO0cVPkjdLAF6WgS4/aAAAAAKha3CIU8Pb21pQpUzRp0iTVr1+/xL21atXS7NmzlZ6ervnz50uyjxO88847evbZZyujXaBSRUc01qYp0YpNSNLyXclKzbQoKMBbgzqEKiYyTL7Hd0gf3C4d3W48MaStfVSg5TXmNA4AAACgwtwiFAgMDNQrr7xSpnNmzpypzz77TDab/Rbp7777jlAANZavt6eGdw7X8M7h+cWM09KyidL2D2UYFfCpLfWfIvW4X/L2rfReAQAAADiPW4QC5dGkSRNFREQoMTFRkrRv3z6TOwIqidUq/fKptGqqlHHKeKzDDfZRgbrhRZ8LAAAAoFohFCjBxQ8BTE9PN7EToJIc+0VaOlE68qOxHnKFNGSm1Kq/GV0BAAAAcBFCgRIcOHDA8XVoKO9bRw2WeUZa85K0/QPJZs2v+9SW+k2Wej7IqAAAAABQA3ma3UBV9f333+v48eOOda9evUp97htvvKEePXooJCREPj4+CgkJUUREhP72t79p7ty5ys7OdkXLQNlZrdLPn0r/7ir9+L4xEGg/Qnp4m9TnUQIBAAAAoIbiToFizJgxw7C++eabS33uN998Y1ifOnVKp06d0p49e/TJJ59o0qRJevHFF3XPPfc4pdei7N27t9znNmzYUI0aNXJiN6iIHItVsQlJWpaQrNSsXAX5+2hw5IU3A3hXINdLirePChzeaqw3uNw+KtD62oo1DgAAAKDKIxQowmeffabFixc71p07d9bw4cPLdI2goCAFBwcrKytLp06dUl5enuNYSkqK7r33Xm3YsEFz5syRp6fzb9gYMWJEuc+dOnWqnnvuOaf1gvJblZiiKQvjdTItx1CPTUjWtMBETR8VpeiIxmW7aOZZae1L0o//LTAqUOvCqMBD3BkAAAAAuAnGBwrYtWuX7rvvPsfa29tb77///iW/cQ8MDNRdd92lRYsWKTk5WefOndPBgweVkpKi1NRULV++XEOHDjWcM3fuXE2YMMElvw5Uf6sSUzR+XlyhQOBPJ9NydN/cOK3enVK6C1qt0i/zpbe6SttmFxgVGC49tE3q8xiBAAAAAOBGCAUukpSUpKFDhyotLc1Re/XVV9W1a9cSzwsLC9PRo0f1wQcfaPjw4Wrc2PiT21q1amngwIH67rvv9NFHHxkChjfeeEPbtm1z7i8E1V6OxaopC+OVZ7WVuC/PatPjC+KVY7GWuE/JO6WPYqRFD0jpJ/LrDdpIdyyUbv5ECm7mhM4BAAAAVCeMD1xw+vRpDRo0SAcPHnTU7rvvvlL9JN/Pz09+fn6l+pxx48YpJSVFU6ZMcdSmTZtmGFdwhkWLFqlNmzblOrdhw4ZO7QVlF5uQVOwdAgWdTMtRbEKShncOL3ww65y09uXCdwZ4B0j9Jkm9Hpa8S/fvLgAAAICah1BAUmpqqgYPHqydO3c6aqNHj9a7777rks977LHH9NZbb+nIkSOSpFWrVikzM1MBAQFO+4w2bdqoQ4cOTrseKteyhOQy7V++K9kYCthsUvwX0opnpPTjxs0Rf5EGvcKdAQAAAAAYH0hLS1NMTIx+/PFHR+3GG2/Uxx9/7JIHAEqSr6+vhg0b5lhnZWXpl19+cclnoXpKzcot2/5MS/4iZZd9VODr8cZAoH4rafQC6ZZ5BAIAAAAAJLn5nQIZGRkaOnSoNm3a5Kj99a9/1fz58+Xl5eXSz27btq1hffz48WJ2wh0F+fuUbX+At31UYN2r0tb/SLb8t13IO0C6ZoLU+/8YFQAAAABg4LahQGZmpv7yl79ow4YNjlpMTIy++uor+fiU7Ruy8ig4KpCRkeHyz0T1MTgyVLGlHiGw6e6gH6W3bpPSCryJoN0wadDLUr3LnN4jAAAAgOrPLUOB7OxsjRgxQmvWrHHUBgwYoIULF8rXt3Jex5aSYvzmLSQkpFI+F9VDTGSYpgUmXvJhg1d4HNYrfh+rS1yi8UC9llLMDOmKgS7sEgAAAEB153bPFMjJydGoUaO0YsUKR+3aa6/Vt99+K39//0rrY+PGjYZ1y5YtK+2zUfX5entq+qgoeXl6FHk8UBl6ynuelvo+oS66KBDw9peufUp6cAuBAAAAAIBLcqtQwGKx6NZbb9WSJUsctb59+2rx4sVOffL/pezatctwl0KLFi3K/fpA1FzREY01e0wXhQRefPeKTX/1/EGr/SbqXu+l8va46DWDbYdID22V+k2WfCov4AIAAABQfbnN+EBeXp7uuOMOff31145a7969tXTpUtWuXbvc183KypKfn588PIr+iW5BaWlpuuOOO2S15n8zN27cuHJ/Pmq26IjG2jQlWrEJSYr/abNuTP6XInLijZvqtZAGT5faDjalRwAAAADVl1vcKWCz2XT33Xfriy++cNR69uypZcuWKTAwsELX3rJli6688krNnz9f6enpJe6Ni4tTr169DK8fDAsL04QJEyrUA2o237x0DU95R88cGW8MBLz9pf5PSg9uJRAAAAAAUC5ucafA999/r48//thQO3TokK688soyXWf9+vUKDw8vVN+xY4dGjx6tWrVq6eqrr1anTp3UtGlTBQUFKTs7W4cPH9a6desMrz6UpMDAQH377bcVDiZQQ9lsUsICacXT0vkk47ErYqTBr0j1eRYFAAAAgPJzi1AgLy+vUO3YsWNlvk5ubm6JxzMyMrRy5UqtXLnyktdq166dPv30U1111VVl7gNu4PgeaelE6YDxgZQKvkyKmS61jTGnLwAAAAA1iluEAq7Upk0b3X///dqyZYt27txZZABxsaioKN1///0aN25cpT7cENVEdpq0frq05R3Jasmve/lJfR6T+jwq+fDvDQAAAADncItQoH///rLZbC65dtOmTfXuu+9KktLT0/Xzzz/r4MGDOn78uDIyMuTt7a3g4GA1bdpU3bt3V8OGDV3SB6o5m03a9bW0/CnpfIG7WC4faL87oH4rc3oDAAAAUGO5RShQWWrXrq0+ffqoT58+ZreC6uTEb/ZRgT/WG+t1m+ePCpTy7RYAAAAAUBaEAoBZstOkDTOlzW9L1oueV+HlK139qH1cwLeWae0BAAAAqPkIBYBSyLFYFZuQpGUJyUrNylWQv48GR4YqJjJMvt5lfLOnzSYlfiMtf1JKPWo81uZ6+90BDVo7r3kAAAAAKAahAHAJqxJTNGVhvE6m5RjqsQnJmhaYqOmjohQd0bh0Fzv5u7R0krR/rbFet5k0+FWp3VBGBQAAAABUmjL+iBNwL6sSUzR+XlyhQOBPJ9NydN/cOK3enVLyhXLSpVXPSe/0MgYCXr5S34nSQ9ukiGEEAgAAAAAqFaEAUIwci1VTFsYrz1rymyvyrDY9viBeORZr4YN/jgq81V36/p/GZwe0jpYe3CJFP8OzAwAAAACYgvEBoBixCUnF3iFQ0Mm0HMUmJGl45/CLinul2MnSvtXGzXWbSYNfkdpxZwAAAAAAcxEKAMVYlpBcpv3LdyXbQ4GcdGnja9Kmf0t5F4UKnj7S1f8n9Z0g+dZ2crcAAAAAUHaEAkAxUrNyL73p4v0ZudLuxdKyJ6Rzh40HW10rDZkphVzuxA4BAAAAoGIIBYBiBPn7lHpvC48kPXHmDemLbQUuEm4fFYj4K6MCAAAAAKocQgGgGIMjQxV7iRECf2XrQe9vNN7rO/llWPIPePpIvR+WrpnEqAAAAACAKotQAChGTGSYpgUmFvOwQZuu94zTVJ9P1NTjpPFQq/5SzEyp4RWV0SYAAAAAlBuvJASK4evtqemjouTlabztv7lHij70man3fV83BgJ1mkg3zZHGLCIQAAAAAFAtEAoAJYiOaKzZY7ooJNBXfsrRY97/00rfybrO6xfHHquHt3T136WHf5Q6jOTZAQAAAACqDcYHgEuIjmiszTfkKOe7p1Q746jhmLXFNfIcOktq2Nak7gAAAACg/AgFgJKc/kNaNkU+vy2T4V0EdcKkQS/Js8MN3BkAAAAAoNoiFACKkpsp/fAvaePrUl52ft3TW+r5oNRvsuRXx7z+AAAAAMAJCAWAgn5dJsVOls4eNNZb9JWGzJIatTOnLwAAAABwMkIB4E9nDkixU6TfYo31wFBp0EtS5ChGBQAAAADUKIQCQG6WfVTg+9clS1Z+3cNL6vmA1O9xyT/IvP4AAAAAwEUIBVDt5Visik1I0rKEZKVm5SrI30eDI0MVExkmX+9LvHXztxX2UYEzfxjrl/WRhsyUGrd3XeMAAAAAYDJCAVRrqxJTNGVhvE6m5RjqsQnJmhaYqOmjohQd0bjwiWcOSsuekH5dYqwHNpYGviR1vJFRAQAAAAA13iV+jApUXasSUzR+XlyhQOBPJ9NydN/cOK3enZJfzM2S1s+U3u5uDAQ8vKSeD0kPb5eibiIQAAAAAOAWuFMA1VKOxaopC+OVZ7WVuC/PatPjC+K1aUq0fP9YI8VOkk7vN25q3lsaOktq3MGFHQMAAABA1UMogGopNiGp2DsECvJLO6qTH9ykJkmrjAdqN5IGvihF3cydAQAAAADcEqEAqqVlCcmX3OOrXN3jtUSPeC9SQNJFAYKHp9R9vHTtE5J/XRd2CQAAAABVG6EAqqXUrNwSj1/juUPPeX+sVp4FwoPmvaQhs6TQSBd2BwAAAADVA6EAqqUgf58i6010Uk/7zNMQr22GeqpXPQX95RWp062MCgAAAADABbx9ANXS4MhQw9pXuXrQ6xut8ptkCATybB76yDJIGwfFSp1vIxAAAAAAgItwpwCqpZjIME0LTNTJtBz18dyp573nqLVnkmHPdusVejZ3nI7XvkKbrmprUqcAAAAAUHURCqDKyLFYFZuQpGUJyUrNylWQv48GR4YqJjJMvt7Gm1p8vT31RkxDpX4zWUM8txqOnbQF6ZXc27XQ2keenl6aPSqq0PkAAAAAAEIBVBGrElM0ZWF8odcMxiYka1pgoqaPilJ0RGN70ZIjbXlbfdbPkDwzHHvzbB6am3e9XrfcqFQFKiTQ13geAAAAAMCAUACmW5WYovHz4pRntRV5/GRaju6bG6fZY7oo2m+3tGSidOp3w57T9TvrvdoPKNHWUn0CvDWoQ9F3GAAAAAAA8hEKwFQ5FqumLIwvNhD4U0PrSeV9OU6ybTIeqNVAuv4F1e90u570JAAAAAAAgLIgFICpYhOSCo0MXMxHFt3pFau/ey9UbVv2RUc8pG53S9c9LQXUc32jAAAAAFADEQrAVMsSkos91stzl17wnqPLPY8aD4R3lYbOkppc6eLuAAAAAKBmIxSAqVKzcgvVGuu0nvL5VH/12myon7YFakH9e3Xv3c9KjAoAAAAAQIURCsBUQf4+jq+9ZdE4r+V61HuBAj2yHHWrzUPz867TTMstujqkDYEAAAAAADgJoQBMNTgyVLEJyerluUvPe8/RFQVGBX6xttIzuXdpp62VJGlQh1Az2gQAAACAGolQAKaKuUzyC3hHg23fG+qnbYGaYblVX+T1l032OwNCAn0VExlmRpsAAAAAUCMRCsAcebnS1v/Id92rGmw77yhbbR76LO86zbTcrLOq46h7eXpo+qgo+XozOgAAAAAAzkIogMp34HtpyUTpxG5DeZda64mccYq3tTbUQwJ9NX1UlKIjGldmlwAAAABQ4xEKoEJyLFbFJiRpWUKyUrNyFeTvo8GRoYqJDCv8U/3zydKKZ6SdXxrrAfWk6Km6POoO3Z14XMt3JSs106KgAG8N6lDMtQAAAAAAFUYogHJblZiiKQvjdTItx1CPTUjWtMDE/J/u51mkbbOltS9LOecv2ukhXTVWip4q1W4gX0nDO4dreOfwSv11AAAAAIC7IhRAuaxKTNH4eXHKs9qKPH4yLUf3zY3Tl4Ot6pLwsnR8l3FDWGdp6GtS066ubxYAAAAAUCRCAZRZjsWqKQvjiw0EJKmhzmqK13x1WWN8q4D8g6XoZ6Uu4yRPL5f2CQAAAAAoGaEASu3P5wd8+P0fhUYG/uSlPI3xWql/eH+lII9M48Erx0gDnpNqh7i+WQAAAADAJREKuNCuXbsUHx+vY8eOycvLS+Hh4eratatatmxpdmtlVtzzAy7W1WOPpvnMUYTnIUP9kF8bNb/jPalZN1e3CQAAAAAoA0IBF/jf//6nadOmKT4+vsjjvXv31ksvvaT+/ftXbmPldKnnB4TonKb4fKYbvTYY6udstTTTcosOht+suQQCAAAAAFDl8J43J8rLy9Odd96pm266qdhAQJI2bdqk6OhoPfPMM5XYXfmU9PwAL+VprNdyrfGbUCgQ+NLST9dlv6Z5ederTi2/ymoXAAAAAFAG3CngRI899pjmzJnjWNeqVUujR49W586dlZOTo61bt2rBggXKzc2V1WrViy++qPr16+uxxx4zr+ki/PnsgGUJydp3Iq3IkYGrPH7TNJ+P1MHzoKG+y3qZnsm9Uz/ZrnDUBnUIdXnPAAAAAICyIxRwkiVLlujf//63Y92+fXstW7ZMzZo1M+zbsWOHhgwZomPHjkmSJk6cqAEDBqhjx46V2u/x48f1zjvvONYPPvigguuHaObyPfp400Hl5FmLPK+BzmmK92e6ydt4Z0CqrZZmWm7Wp3kDZL3oBpSQQF/FRIa55heBcivq979Ro0YmdoTKxO+/e+P3333xe+/e+P13b/z+oyQeNput+PfKoVSsVquuvPJKx8hArVq1tHPnTrVq1arI/Zs2bVLfvn1ltdq/8R42bJgWL15coR527dqlyMhIxzohIUEdOnQo9f4PYrfpn5tP6Xx2XpH7PWXVaK9Vmuj9pep6ZBiOfWW5RtMtt+mk6hrqXp4emj2mi6IjGpfnlwQXKuu/L6hZ+P13b/z+uy9+790bv//ujd//qs/M3yOeKeAEq1evNjxD4P/+7/+KDQQk+4MGb7rpJsf6u+++0969e13aY0kCWnfXC+uOFxsIXOXxm771fVrTfOYYAoFE62UalT1Vkyz3FwoEQgJ9CQQAAAAAoIojFHCCr7/+2rC+5557LnnOvffea1gvWrTImS2Vnre/QkY8UeSh+krVdO/ZWuj3nCI9DzjqqbYATc39m/6S86LibG0N53RuVlf/urWzNk2JJhAAAAAAgCqOZwo4wZIlSxxft27dWq1bt77kOX379pW/v7+ysrIk2e8WmDhxost6vPjhgWczcnXy7HmFP/ixvALry8PDw7DXU1bd7rVak7y/KDQqsCCvr17NvU0nFFzoM0ICffXl+N7y9SZrAgAAAIDqgFCggs6ePatDhw451j179izVeb6+vurSpYt++OEHSSrxFYYVtSoxRVMWxhd6i4B3nQaF9nb22KtpPh+q40V3BkjSbmszPZt7p360tSvyM7w8PTR9VBSBAAAAAABUI4QCFbR7927Duk2bNqU+t3Xr1o5Q4MyZM0pOTlZoqHNf37cqMUXj58Upz1ry8yTrKVWTvb/Qbd5rDfXztgC9brlRn+QNVJ68ijw3JNBX00dFMS4AAAAAANUMoUAF7d+/37Bu3rx5qc8tuHf//v1ODQVyLFZNWRhfYiDgKatu9Vqryd6fK9gj3XBsYV4fvZJ7m06oXqHz2jauo9aNamtQh1DFRIZxhwAAAAAAVEOEAhWUmppqWNevX7/U59arZ/xm+/z5807pSZL27t2rdX+kFRoZuFiUxz5N8/lInTyNwcYeazM9mztO22wRRZ4XEuirxY/0IQgAAAAAgGqOUKCC0tLSDGt/f/9SnxsQEFDitSpixIgRChnxhGq3vbrQsWCd12TvL3Sr11p5euTfRXDeFqA3LKP0cd5AWYr5V8PTQzw7AAAAAABqCEKBCvrz7QF/8vX1LfW5fn5+hnVmZqZTevqTp19tw9rDMSrwhep5GAOIRXm99VLu6CJHBS727uguPDsAAAAAAGoIQoEKKnhnQE5O8bfrF5SdnW1YF7xzoKKs2fnPCOjosV/TfD5U5wKjAr9Zw/Ws5U5tsba/5PX+dXMnDYp07oMQAQAAAADmIRSooMDAQMO64J0DJSl4Z0DBa1XEokWLdMQrVLO/P6BJ3l/odq81hlGBNJu//mkZpY/zBhU7KnCxoZGhGn5VU6f1BwAAAAAwH6FABQUFBRnWZ86cKfW5Z8+eNazr1KnjjJYkSW1atdLQrDiN9n9awTI+wPCbC6MCxy8xKvCnoZGhevuOLk7rDQAAAABQNRAKVFDLli0N60OHDpX63IMHDxrWrVq1ckpPkqSF98jbtkfBF5V+t4brWcs4bbZ2KNUlfLw89M+bO2tYpybO6wsAAAAAUGUQClRQ+/bGWfy9e/eW+tx9+/Y5vq5Xr55CQ8s/r1/w+QR7d++U6tvfEGDx8te8vOv1RXZvWeQl6WARV8jXtJ6/bu9+mfq3bSQf7zPatav0dz+geij472lZ/r1F9cfvv3vj99998Xvv3vj9d2/8/ld9BX9PCn5/50oeNpvNdultKMlll13muEOgdevWpfp/spycHNWtW9fxDIJrrrlG69evL3cP33zzjUaMGFHu8wEAAAAAVcOiRYs0fPjwSvksXjbvBEOGDHF8vW/fPu3fv7+E3XYbN240PJRw2LBhLukNAAAAAIDiEAo4wciRIw3r999//5LnFNzDT/kBAAAAAJWN8QEnsFqt6tSpkxISEiRJtWvX1s6dOws9hPBPmzdvVp8+fWS1WiVJQ4cO1XfffVehHs6ePWsYP2jWrJn8/PwqdE0AAAAAgOtlZ2fr8OHDjnW/fv0UHBxcKZ9NKOAkixcv1l//+lfHukOHDoqNjVWzZs0M++Lj4zVkyBAdPXpUkuTp6amffvpJnTp1qtR+AQAAAAAgFHCihx56SO+8845jXbt2bY0ePVqdO3dWbm6utmzZov/973/Kzc117Jk58//bu/ugqK77f+BvQBZ5FkQFFkHBScQapIkPiBEnCSLWVHzGRiXa1FQn6UQTJzXWzNjWaqPt1Do1OqaKkRqfGnyOxic0pGpVRlE0VkUEFFQEFBZYWJbz/eP3yx0usLALe3fh7vs1szOey7nnnsvxczh8uA9rsWTJEnt0l4iIiIiIiBwckwJWZDQaMW/ePKSlpbVZ18nJCUuXLsWqVats0DMiIiIiIiKi5vigQStycXHB9u3bsXv3bgwePNhkvZiYGJw8eZIJASIiIiIiIrIrXimgoJycHFy7dg1FRUVwcXFBcHAwhg0bhvDwcHt3jYiIiIiIiIhJASIiIiIiIiJHxdsHiIiIiIiIiBwUkwJEREREREREDopJASIiIiIiIiIHxaQAERERERERkYNiUoCIiIiIiIjIQTEpQEREREREROSgutm7A2QfN27cwLVr11BUVAQXFxdotVoMHToU/fv3t3fXyExCCOTm5iInJweFhYWoqKiAh4cH/P39MWTIELz00ktwcXGxdzepi7t8+TJu3bqFoqIiuLu7Q6vVIjY2FoGBgfbuGhGZSck4bmhowLlz55Cbm4vi4mL4+vpCq9Vi9OjR8PPzs0LvqT2MRiP+97//4caNGygqKoJOp4OXlxcCAgLw8ssvY+DAgXBycrJ3N6mLY/yriCCHsnfvXhEVFSUAtPiJjY0VGRkZ9u4mmVBRUSF27NghkpOTRa9evUyOIwDh5+cnPvzwQ1FUVGRW23l5ea2219pn7969Cp85/ai9Y7R27VqLjrNx40YRERHRYlsuLi4iISFBZGdnK3SWROpnNBpFTk6O2LZtm3j//fdFTEyMcHd3l8VaR38eKxnHBoNBrFy5UgQHB7fYvkajEVOnThV5eXkdOge1UmL8nz59KjZv3iySkpKEr69vqz8TgoODxYoVK8SzZ8/Mbj8jI6PdP4MuXbpk4XdI3ZQYf1uu4xj/6sOkgIOor68Xc+fONWticHZ2FsuXL7d3l6mJiooK0b17d4snen9/f5Gent5m+0wKdA1KJwWqqqpEYmKiWW1qNBqxadMmhc+YhBAiLCys3WP/42fMmDEtts3Yt70pU6YIT0/PNr+/7U0KKB3Hjx49EkOHDjWrfR8fH7F///52nYdaKTH+N2/eFN26dbM4hvv27SsyMzPNOgaTAtahVPzbai5n/KsTbx9wEIsXL8a2bduksoeHB2bNmoXo6GjU1dXhv//9L77++msYDAY0NDRg5cqV8Pf3x+LFi+3XaZIxGo3Q6/WybeHh4RgzZgxefPFFBAQEQK/X4/r16/j666/x9OlTAEBZWRmmT5+OvXv3YvLkyWYfLzg4GO7u7mbV9fLyMv9EyGp69+4Nb29vs+qacxlfQ0MDZs2ahWPHjsn2mzNnDgYNGoTKykqcPXsWR44cgRACdXV1WLhwIQICAjB16tR2nwfZRo8ePezdBfr/srKyUFVVpUjbSsdxTU0NkpKScPnyZWmbVqvF7NmzERERgdLSUhw9ehTfffcdAKCiogIzZ87E6dOnMXLkSOufcBekxPjX1taivr5eti0yMhJxcXGIiIhAQEAAKisrkZWVhfT0dOh0OgBAYWEhEhMTceLECYvHJywsDN26mferRPfu3S1qW82UjP/GlFjHMf5VzN5ZCVLe4cOHZVm7QYMGiYKCgmb1rl69KrsMyNnZWVy7ds0OPaaWlJeXS1nXDz74oNVLPquqqsT8+fNl4+7n5ydKSkpM7tM0w8zbSDqnxmOUmppq1bb/8Y9/yNofPXq0KCsra1bv1KlTwsfHR6rn5eUlHj9+bNW+kNyYMWNERESERZ+ml6Lu2rWrxbabxn5wcLDZxzh69KiNvxPq0PjKDzc3NzFs2DCxYMECMXv27A7Pw0rH8ZIlS2TtT5s2Tej1+mb1duzYIVxdXWV/ka6pqbH4fNRIifG/cuWKACD69Okjli9fLu7evWuy7tOnT8XEiRNlxxowYECL49hY0ysFeGl4+ygV/7ZYxzH+1YtJAZUzGo2yZwh4eHiI3Nxck/X/85//CGdnZ6n+m2++acPeUmsqKyvF0qVLRWlpqdn7vPXWW7LJ+/e//73JukwKdA1KJQV0Op3o06eP1HZQUJAoLy83WX/nzp2yvrz//vtW6wt1nF6vFz179pTGp2fPniYX/Ix92/v000/F5s2bRVZWlqirq5O2p6amdmgslI7jwsJC2W1sUVFRsv43tXr1aln7f/nLXyw6H7VSYvzv3LkjPvvsM1FVVWVWfYPBIOLi4mTH+/LLL1vdh0kB61Aq/pWeyxn/6sakgModP35cFpBLly5tc5/k5GTZPnfu3LFBT0kJDx8+FE5OTtJYDhs2zGRd/mLQNSiVFNi8ebOsbXPuMR4xYoRUv3v37qKystJq/aGO2bVrl2w8Fy1aZLIuY7/z6OgvBUrH8bJly2TtHzt2rNW2DQaD0Gq1Uv2QkBCLzsfRdHT8LXXu3DnZ8aZPn95qfSYFlNXZkwKMf3VzBqnavn37ZOVf/epXbe4zf/58WXn//v3W7BLZUHBwMCIjI6Vybm6uHXtDnVnjucLDwwNvvfVWm/s0niv0er3sHmayr3/+85+y8jvvvGOnnpAtKR3HjdsPCwtDQkJCq21369YN8+bNk8oPHjyQ3YtM9hUTEwNPT0+pzDUCtYbxr25MCqjckSNHpH9HREQgIiKizX1Gjx4teyDM4cOHFekb2Ubjh8fY4sE21PXo9XqcPn1aKo8cOdKsBxiOHTtWVuZc0Tncv38fp06dksojRozA4MGD7dgjsgWl4zgvLw8//PCDVI6PjzfrPfecJzovJycnWVKAawQyhfGvfkwKqNizZ89QUFAglWNiYszaT6PR4JVXXpHK165ds3rfyHbu378v/TswMNB+HaFO69atW6itrZXK5s4VoaGh0Gq1UplzReewdetWCCGksjlXiFHXp3QcZ2dny8rmtj98+HDZE+o5T3QeVVVVKCkpkcpcI5ApjH/1Y1JAxRpn9ABgwIABZu/b+IqC8vJyPHr0yGr9Itv5/vvv8eTJE6lsyetg1q1bhxEjRiAgIACurq4ICAhAZGQk3n77baSlpckWn2QfO3bsQFxcHHr37g2NRgN/f3+88MILSE5OxqZNm1BRUWFWO9aaK27duoWGhgaz9yXra2hokL1+1tPTE8nJyfbrENmM0nHc3va7d++O4OBgqXzz5k2z+0XK2rdvnyyBaOkr45YtW4bo6Gj4+flBo9Ggd+/eiIqKwq9//Wukp6fDaDRau8tkAWuu4xj/6sekgIrdu3dPVg4NDTV736Z1m7ZFXcOaNWtk5RkzZpi974EDB3Dx4kWUlpaivr4epaWluHXrFrZv346UlBSEhYU1u2+ZbOvkyZPIzMxESUkJDAYDysvLcefOHezZswcLFy5EaGgoVq9e3eYv6taaK2pqaphAtLPjx4+jsLBQKicnJ5t1CXljTAh2TUrHsbXa53qiczAajfjrX/8q22bJGgEAdu7ciezsbDx79gwGgwElJSW4fv06Nm/ejKlTp+KFF17AgQMHrNltsoA113GMf/VjUkDFmv6V0N/f3+x9/fz8ZOXKykqr9IlsZ+fOnTh06JBUjo6ORlJSkkVt+Pj4IDQ0FL1794aLi4vsa48fP8b8+fORkpLCvw7bkaenJ/r27YvAwEC4urrKvvb8+XMsW7YMiYmJqKmpMdkG5wr1aLrAa8+tA0wIdk1Kx7G12jcYDEwsdQJr1qzB1atXpfLEiRPx05/+1OJ2/Pz8EBYWhoCAADg7y3+tuHfvHiZNmoTf/e53He0utZO11nGMf/VjUkDFdDqdrNz44YFtcXd3b7Ut6txu3LiBd999Vyp369YNX3zxRbMf2E15eXnhl7/8Jfbv349Hjx7h+fPnyM/Px+PHj1FRUYFvv/0WEyZMkO2TlpaGjz76SJHzoOY0Gg1mzJiBnTt3oqCgADqdDgUFBSguLoZOp0NmZiZmz54tewDQiRMnMGvWLNlloo1xrlCHkpISHDx4UCoPGjTI4suBf8SEYNejdBxznlCP06dP49NPP5XKPj4+WL9+vVn79uzZE7/5zW9w7NgxlJaWoqysDPfv30dJSQnKysqQnp6OUaNGyfZZtWoV/v73v1v1HKhlSq3jGP/qx6SAiun1ellZo9GYva+bm5us3NpfGalzKS4uxoQJE2ST7p///GcMHTq01f2CgoLw8OFDbNmyBUlJSejTp4/s6x4eHkhISMDhw4eRmpoqSzCsW7cOFy9etO6JUIsePHiA3bt3Y+bMmejbt6/saxqNBq+++irS0tLwzTffwMPDQ/ravn37sHfv3hbb5FyhDtu3b4fBYJDKlryGkAnBrk/pOOY8oQ43b97EtGnTZPf7b9myBWFhYW3u+8orr+DBgwdYv349xo0b1+yvxb6+vpg8eTIyMzPxxz/+Ufa13/72t7Jbm8j6lFzHMf7Vj0kBFWuaxaurqzN736aX9jTN8lHnVFZWhnHjxiE/P1/a9u6775q1cHdzc4OPj49Zx5k7dy5WrVol29Z0AUDK6NWrl1n1EhMTsXXrVtk2U2PEuUIdGo+3RqNBSkqKWfsxIagOSscx54mu7/79+0hISEB5ebm0bdWqVZg2bZpZ+3t7e5v1F2InJycsX74cCxYskLbV1tY2e84RWZeS6zjGv/oxKaBijd9PDzTP8rWmaRavaVvU+VRUVCAxMRHXr1+Xts2aNQsbN25U5HiLFy9GSEiIVD558iSzv51McnIyhg8fLpVzcnJkr6j8EeeKru/8+fOypzonJSUhICDArH2ZEFQHpeOY80TX9vDhQ7zxxht4+PChtG3p0qX45JNPFDvmypUrZb8ANn7OEdmfJes4xr/6MSmgYk0XeY0zw2159uyZrGzp06vJtnQ6HcaPH49Lly5J26ZNm4Yvv/yyzecItJdGo8Gbb74plfV6veyhRdQ5TJkyRVY+f/58szqcK7o+azxg0FxMCHZOSsextdp3dXVtdjkxKevRo0d4/fXXZU9+X7RoEVavXq3ocXv27IkxY8ZI5fz8fBQXFyt6TDKfJes4xr/6MSmgYv3795eVCwoKzN638eXnABAeHm6VPpH1VVdXY8KECTh37py0beLEifjqq6+aPSDM2l588UVZ+cmTJ4oejyxnzhhZa65wd3dHYGCghT2kjtLpdNizZ49UDgsLQ3x8vGLHY0Kwc1I6jq3VPtcTtvXkyRO88cYbuH37trRt4cKF+Nvf/maT43Od0LmZOz6Mf/VjUkDFBg0aJCvfvXvX7H1zc3Olf/v5+XGh30nV1NTg5z//Ob777jtp2/jx47F3795mr6dTQtP7wqqrqxU/JlnGnDGy1lwxcOBAxa5MIdN27dole7DovHnzFB8HLvQ7H6XjuL3t6/V6FBUVmWyHlFNaWor4+HjZrUXvvPMONmzYYLM+cJ3QuZk7Pox/9ePqTcV69OiB0NBQqdzSZcMtqaurQ1ZWllR+6aWXrN436rja2lpMmjQJp0+flrbFx8cjPT3doqfCdsTjx49lZXPvYSbbMWeMBg4cKPs/Y+5cUVhYKLs/lXOFfWzZskX6t7OzM+bNm6f4MbnQ73yUjuMhQ4bIyua2f/HiRdTX17fZPllXeXk54uPjZc8ZSklJwebNm2WvrFUa1wmdm7njw/hXPyYFVO5nP/uZ9O/c3FzZ/WSmZGZmyh4g0vgyUeoc6urqMHXqVBw/flza9tprr+HgwYMWvTu2ozIzM2XlppeXkf2ZM0bdu3fH66+/LpXPnz9v1nuEG///AzhX2MONGzdw4cIFqZyQkCBLBiuFC/3OR+k47t+/PwYOHCiVT548CSFEm+2fOHHCrPbJep4/f46EhATZbT2/+MUvmr09xBYa/wxydXWFVqu16fGpdeau4xj/6sekgMpNnjxZVv7iiy/a3KdpnUmTJlmzS9RB9fX1mDlzJo4cOSJtGz16NA4dOmTT17zcuHFDdpVCv379MGDAAJsdn9pWUlKCXbt2SWV3d3e8+uqrLdZtPFdUV1djx44dbbbfeK5wc3PD+PHjO9Bbao/GVwkA/+/SYFtgQrBzUjqOG7efn5/fLKHQVH19PVJTU6WyVqvF0KFD2+wTtd+PDx6+fPmytG369OlIS0uzeULg6NGjssvMR40aBQ8PD5v2gUyzdB3H+Fc5QapmNBrF4MGDBQABQHh6eop79+6ZrH/u3Dnh7Ows1Z8wYYINe0ttqa+vF8nJydL4ABCxsbGisrKyQ+3W1NSIhoYGs+tXVlaK6OhoWT9WrFjRoT5Q6+rq6oTBYDC7vsFgEOPGjZON0dy5c03Wr6ysFL1795bqBgUFifLycpP1d+7cKWv7vffes+R0yApqa2tFQECANAa9evUSdXV1ih83JydH9nOiX79+ih/TUaSmpsriKiMjw6L9lY7jgoIC4ebmJtWPiopq9f/c6tWrZe2vXbvWovNxNB0d/6qqKhEXFydrY8qUKRb97DClurraovpFRUWib9++sr5s27atw/1Qs46Mvy3WcYx/dWNSwAEcPHhQFpQ/+clPREFBQbN62dnZQqvVSvWcnZ3F1atX7dBjaklDQ4N4++23ZWMZExMjKioqOtx2RkaGGDJkiNixY4fQ6XSt1r18+bIs0fTjwrOjiQlqXV5enoiIiBCbNm0SZWVlrda9fft2s4Whh4eHyM/Pb3W/9evXy/aJi4tr8ReKU6dOCR8fH1mysbi4uCOnR+2wZ88e2Xh99NFHFrfBhGDn0tFfCoVQPo4//PBDWfvTp08Xer2+Wb2vvvpKaDQaqZ5WqxU1NTUWn48j6cj46/V6MXbsWNn+SUlJVksUpqamiri4OHHgwAFRW1vbat0TJ040SwgMGTJEGI1Gq/RFrToy/rZaxzH+1ctJCDNuCKEu77333sPnn38ulT09PTFr1ixER0fDYDDgwoUL+Pe//w2DwSDVWbt2LZYsWWKP7lILMjMzERcXJ9sWHBxs8S0DZ8+ebXZP35kzZ/Daa68BADw8PDBq1CgMGTIEISEh8PHxQW1tLQoLC3HmzBnZqw8BwMvLCxkZGbwkTGH379+XLtF2dXXFyJEjER0djf79+8PHxwf19fUoLi7G999/j9OnT6OhoUHa18XFBQcOHMCECRNaPUZDQwMmTZqEQ4cOSdv8/f2RkpKCyMhI6HQ6nDlzBocPH5buJXRycsKuXbswY8YMBc6aWpOYmIhvv/1WKt+8eRORkZEWtXHmzBksWrQIH3/8MZKSkuDp6WmyblZWFubOnYucnBxpW1BQEG7fvg0vLy/LT8CBpaen4+OPP262vbKyUvYmB1Nz/Jo1azBlypQW21Y6jqurqzFmzBjZ5elarRZz5sxBeHg4ysvL8c033+Ds2bPS193c3HDy5EmTty85GiXGPy0tDSkpKbJtoaGhFr+JyNRT5bdt2yY9xLRHjx4YNWoUoqKiEBQUBG9vb1RXVyMvLw8nTpxAdna2bN/AwECcP38e/fr1s6gvaqXE+NtqHcf4VzH75iTIVurr68WcOXNk2T1THycnJ/HJJ5/Yu8vUREZGhlnj19YnLy/Pam0PHDhQZGVl2f6b4YDy8vLaNUbBwcHi+PHjZh9Hp9M1+2uTqY9GoxEbNmxQ8KzJlPz8fNkl/LGxse1qp3Hse3h4iLFjx4olS5aIdevWia1bt4qNGzeKZcuWidjY2Gbj7+XlJS5dumTlM3MMTf8iaOknNTW11faVjuPi4mLx8ssvm9W+t7e3SE9P78B3S32UGP+Otvnjx9p9jomJEXfv3lXwu9n1KDH+tlzHMf7ViUkBB7N79+5mlww1nbxPnTpl725SC5RMChQWFooFCxaI6Oho4eLi0mYbUVFR4vPPP7f4HkNqv+fPn4sPPvhAjBgxQnZJnqlPRESE+Oyzz1q9n9iUhoYGsWHDBhEeHt5i287OziI+Pl5cuXLF6udJ5lmxYoVsTLZu3dqudpgQtA+lkwJCKB/HdXV14g9/+IMIDAxssX2NRiMmT57c6nOMHFVXTApcv35dzJs3T0RGRgonJ6dW23BychKxsbHiX//6l6ivr1fwO9k1KTH+tl7HMf7Vh7cPOKicnBxcu3YNRUVFcHFxQXBwMIYNG4bw8HB7d43srKqqCleuXEF+fj6ePHmC6upqdOvWDT169EBISAiGDx+OXr162bubDq22thbZ2dm4d+8eHj16hKqqKri4uMDX1xeBgYEYNmwYQkJCrHKsS5cu4YcffkBxcTHc3d2h1WoRGxuLoKAgq7RPlhNCoH///sjPzwcAeHt7o7i4uNVL/0158OAB/vSnP+HChQu4fv06jEZjq/WjoqKwYMECzJ0716ZvO6GOUTKOjUYjzp07h7t37+Lx48fw9vZGSEgIRo8eDX9/fyv0njqbZ8+e4cqVKygoKMDTp09RU1MDNzc39OjRA/369cPw4cPh6+tr7246LFuu4xj/6sGkABERETEhSERE5KCYFCAiIiIiIiJyUM727gARERERERER2QeTAkREREREREQOikkBIiIiIiIiIgfFpAARERERERGRg2JSgIiIiIiIiMhBMSlARERERERE5KCYFCAiIiIiIiJyUEwKEBERERERETkoJgWIiIiIiIiIHBSTAkREREREREQOikkBIiIiIiIiIgfFpAARERERERGRg2JSgIiIiIiIiMhBMSlARERERERE5KCYFCAiIiIiIiJyUEwKEBERERERETkoJgWIiIiIiIiIHBSTAkREREREREQOikkBIiIiIiIiIgfFpAARERERERGRg2JSgIiIiIiIiMhBMSlARERERERE5KCYFCAiIiIiIiJyUEwKEBERERERETkoJgWIiIiIiIiIHBSTAkREREREREQOikkBIiIiIiIiIgfFpAARERERERGRg2JSgIiIiIiIiMhBMSlARERERERE5KCYFCAiIiIiIiJyUEwKEBERERERETkoJgWIiIiIiIiIHBSTAkREREREREQOikkBIiIiIiIiIgf1f4RgH5bwRzHEAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(true_params,params, 'o')\n",
+    "xmin, xmax = np.min(np.concatenate([true_params,params])), np.max(np.concatenate([true_params,params]))\n",
+    "delt = 0.01*(xmax-xmin)\n",
+    "plt.plot([xmin - delt,xmax + delt], [xmin - delt,xmax + delt])\n",
+    "# plt.ylim(xmin,xmax)\n",
+    "# plt.plot([-1000,200],[-1000,200])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1061,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "215703.2291812817"
+      ]
+     },
+     "execution_count": 1061,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-log_likelihood_wishart(params, model_wishart, omega, omega_fixed, data_, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1062,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "217194.2018930231"
+      ]
+     },
+     "execution_count": 1062,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-log_likelihood_wishart(true_params, model_wishart, omega, omega_fixed, data_, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# It makes sense that I am not getting to the `true_params`: the `-log_likelihood` is not minimal! TODO: fix this bug. The loglike must be minimal at the true parameters\n",
+    "\n",
+    "## Capire dove cacchio va messo $\\ell$ per far quadrare i conti\n",
+    "\n",
+    "## Implementare l'informazione di Fisher: https://stats.stackexchange.com/questions/249078/what-is-the-fishers-information-matrix-for-the-wishart-distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1064,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "opt_spline = model_wishart(omega_fixed, params)\n",
+    "th = opt_spline(omega)\n",
+    "th = opt_einsum.contract('tba,tbc->tac', th, th)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "for i in range(2):\n",
+    "    for j in range(2):\n",
+    "        pl, = ax.plot(omega, data_[:, i,j], alpha = 0.2)\n",
+    "        pl, = ax.plot(omega, truth[:, i,j], alpha = 1, color = pl.get_color(), lw = 2)\n",
+    "        ax.plot(omega, th[:, i,j], alpha = 1, ls = '--')\n",
+    "ax.set_yscale('symlog')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "npar = 5\n",
+    "nsam = 1000\n",
+    "\n",
+    "omega = np.linspace(0.1, 10, nsam, endpoint=True)\n",
+    "omega_fixed = np.linspace(0.1, 10, npar, endpoint=True)\n",
+    "\n",
+    "truth = np.abs(omega**2) + 1\n",
+    "true_params = np.abs(omega_fixed**2) + 1\n",
+    "noise = np.random.chisquare(df=6, size=truth.shape)/6\n",
+    "data_ = truth*noise\n",
+    "\n",
+    "w0 = true_params + np.random.normal(size=true_params.shape)*0.05"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2.5\n",
+      "2 22.18117431901184\n",
+      "3 0.0\n",
+      "1 2.5\n",
+      "2 22.18117431901184\n",
+      "3 0.0\n",
+      "1 2.5\n",
+      "2 22.18117431901184\n",
+      "3 0.0\n",
+      "1 2.5\n",
+      "2 22.18117431901184\n",
+      "3 0.0\n",
+      "1 2.5\n",
+      "2 22.18117431901184\n",
+      "3 0.0\n",
+      "1 2.5\n",
+      "2 22.18117431901184\n",
+      "3 0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/1h/l4hl5gdn4j55pcw3tbrrx2c80000gn/T/ipykernel_33834/4079297062.py:57: RuntimeWarning: divide by zero encountered in log\n",
+      "  log_pdf = _lambda*np.log(_gamma2) + _lambda_minus_half*np.log(absz) + np.log(np.abs(sp.kv(_lambda_minus_half, _alpha*absz))) + \\\n",
+      "/Users/paolo/micromamba/envs/lammps/lib/python3.11/site-packages/scipy/optimize/_numdiff.py:590: RuntimeWarning: invalid value encountered in subtract\n",
+      "  df = fun(x) - f0\n"
+     ]
+    }
+   ],
+   "source": [
+    "params_od = do_mle_od(data_, w0, model_od, omega, omega_fixed, solver='CG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  1.06441419,   7.68272051,  26.51352885,  57.61680625,\n",
+       "        100.95297957]),\n",
+       " array([  1.01    ,   7.630625,  26.5025  ,  57.625625, 101.      ]))"
+      ]
+     },
+     "execution_count": 364,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "params_od, true_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 300.0)"
+      ]
+     },
+     "execution_count": 366,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "pl, = ax.plot(omega, data_)\n",
+    "ax.plot(omega_fixed, true_params, 'o', color = pl.get_color(), markeredgecolor='black', markeredgewidth=0.5)\n",
+    "spl = model_od(omega_fixed, params_od)\n",
+    "ax.plot(omega, spl(omega), c = 'red')\n",
+    "spl = model_od(omega_fixed, true_params)\n",
+    "ax.plot(omega, spl(omega), c = 'green', ls = '--')\n",
+    "\n",
+    "ax.set_ylim(0,300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0.  ,   6.25,  25.  ,  56.25, 100.  ])"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using multicomponent code.\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux=st.HeatCurrent([(dc['qflux']), (dc['ele_flux'])],\n",
+    "                      DT_FS=1,\n",
+    "                      TEMPERATURE=dc['Temeprature'],\n",
+    "                      VOLUME=dc['Volume'],\n",
+    "                      UNITS='metal'\n",
+    "                     )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using multicomponent code.\n",
+      "-----------------------------------------------------\n",
+      "  RESAMPLE TIME SERIES\n",
+      "-----------------------------------------------------\n",
+      " Original Nyquist freq  f_Ny =     500.00000 THz\n",
+      " Resampling freq          f* =      20.00000 THz\n",
+      " Sampling time         TSKIP =            25 steps\n",
+      "                             =        25.000 fs\n",
+      " Original  n. of frequencies =        100001\n",
+      " Resampled n. of frequencies =          4001\n",
+      " min(PSD)          (pre-filter&sample) =      0.00000\n",
+      " min(PSD)         (post-filter&sample) =      0.00018\n",
+      " % of original PSD Power f<f* (pre-filter&sample)  = 99.707 %\n",
+      " fPSD not calculated before resampling\n",
+      " -----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample=flux.resample(fstar_THz=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You must install the tqdm library to use progress indicators with emcee\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCMC with 4 parameters and 8 walkers\n",
+      "Running up to 10000 steps\n",
+      "[45.52486617 35.7572916  41.62978853 51.13327919]\n",
+      "[55.47486796 35.31634649 52.96643866 55.05730845]\n",
+      "[57.27564388 47.7028527  62.21841209 61.9089177 ]\n",
+      "[59.42962059 48.70385826 59.03486027 58.49996813]\n",
+      "MCMC autocorrelation time = [61.70592068 55.98921351 56.47722514 65.94391729]\n",
+      "MCMC burn in = 395; thin = 83\n",
+      "-----------------------------------------------------\n",
+      "  BAYESIAN ANALYSIS\n",
+      "-----------------------------------------------------\n",
+      "  L_01   =           0.000303 +/-   0.000016\n",
+      "-----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample.bayesian_analysis(model, 4, n_steps = 10000, burn_in = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = flux_resample.bayes.sampler.chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x1159a2bd0>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for x in chain:\n",
+    "    plt.plot(x[:,0], '.')\n",
+    "    plt.plot(x[:,1], '.')\n",
+    "    plt.plot(x[:,2], '.')\n",
+    "    plt.plot(x[:,3], '.')\n",
+    "plt.axvline(395)\n",
+    "# plt.xscale('log')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "You must install the tqdm library to use progress indicators with emcee\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCMC with 6 parameters and 16 walkers\n",
+      "Running up to 2000000 steps\n",
+      "emcee: Exception while calling your likelihood function:\n",
+      "  params: [ 0.18021792  0.24366431 -0.26732124 -0.59426797 -0.85500721 -0.85363719]\n",
+      "  args: (array([   0,    1,    2, ..., 3998, 3999, 4000]), array([   0,  800, 1600, 2400, 3200, 4000]), array([ 0.04921777,  0.44522278,  1.13556658, ..., -1.0155539 ,\n",
+      "       -0.45174781, -0.39013564]), 2, 3)\n",
+      "  kwargs: {}\n",
+      "  exception:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Traceback (most recent call last):\n",
+      "  File \"/Users/paolo/micromamba/envs/lammps/lib/python3.11/site-packages/emcee/ensemble.py\", line 640, in __call__\n",
+      "    return self.f(x, *self.args, **self.kwargs)\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/paolo/Software/sportran_bayes/sportran/md/bayes.py\", line 717, in log_posterior_offdiag\n",
+      "    return self.log_prior_offdiag(w) + self.log_likelihood_offdiag(w, omega, omega_fixed, data, nu, ell)\n",
+      "                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/Users/paolo/Software/sportran_bayes/sportran/md/bayes.py\", line 671, in log_likelihood_offdiag\n",
+      "    log_pdf = _lambda*np.log(_gamma2) + _lambda_minus_half*np.log(absz) + np.log(sp.kv(_lambda_minus_half, _alpha*absz)) + \\\n",
+      "                                                                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mflux_resample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbayesian_analysis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlog_like\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43moff\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m bay\u001b[38;5;241m=\u001b[39mflux_resample\u001b[38;5;241m.\u001b[39mbayes\n",
+      "File \u001b[0;32m~/Software/sportran_bayes/sportran/current/current.py:384\u001b[0m, in \u001b[0;36mCurrent.bayesian_analysis\u001b[0;34m(self, model, n_parameters, is_restart, n_steps, backend, burn_in, thin, mask, log_like, parallel, ncpus)\u001b[0m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    377\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbayes \u001b[38;5;241m=\u001b[39m BayesFilter(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcospectrum, model, n_parameters, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mN_EQUIV_COMPONENTS,\n\u001b[1;32m    378\u001b[0m                          is_restart \u001b[38;5;241m=\u001b[39m is_restart,\n\u001b[1;32m    379\u001b[0m                          n_steps \u001b[38;5;241m=\u001b[39m n_steps,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    382\u001b[0m                          thin \u001b[38;5;241m=\u001b[39m thin,\n\u001b[1;32m    383\u001b[0m                          mask \u001b[38;5;241m=\u001b[39m mask)\n\u001b[0;32m--> 384\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbayes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_mcmc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlog_like\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlog_like\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffdiag \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbayes\u001b[38;5;241m.\u001b[39mparameters_mean[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbayes\u001b[38;5;241m.\u001b[39mfactor\n\u001b[1;32m    387\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffdiag_std \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbayes\u001b[38;5;241m.\u001b[39mparameters_std[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbayes\u001b[38;5;241m.\u001b[39mfactor\n",
+      "File \u001b[0;32m~/Software/sportran_bayes/sportran/md/bayes.py:237\u001b[0m, in \u001b[0;36mBayesFilter.run_mcmc\u001b[0;34m(self, n_parameters, n_steps, is_restart, mask, filename, n_walkers, log_like)\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_restart : todo \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    236\u001b[0m disc \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m--> 237\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msample\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoord\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterations\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn_steps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprogress\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# Only check convergence every 100 steps\u001b[39;49;00m\n\u001b[1;32m    239\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miteration\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m250\u001b[39;49m\u001b[43m:\u001b[49m\n\u001b[1;32m    240\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mcontinue\u001b[39;49;00m\n",
+      "File \u001b[0;32m~/micromamba/envs/lammps/lib/python3.11/site-packages/emcee/ensemble.py:409\u001b[0m, in \u001b[0;36mEnsembleSampler.sample\u001b[0;34m(self, initial_state, log_prob0, rstate0, blobs0, iterations, tune, skip_initial_state_check, thin_by, thin, store, progress, progress_kwargs)\u001b[0m\n\u001b[1;32m    406\u001b[0m move \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_random\u001b[38;5;241m.\u001b[39mchoice(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_moves, p\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_weights)\n\u001b[1;32m    408\u001b[0m \u001b[38;5;66;03m# Propose\u001b[39;00m\n\u001b[0;32m--> 409\u001b[0m state, accepted \u001b[38;5;241m=\u001b[39m \u001b[43mmove\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpropose\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstate\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    410\u001b[0m state\u001b[38;5;241m.\u001b[39mrandom_state \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrandom_state\n\u001b[1;32m    412\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tune:\n",
+      "File \u001b[0;32m~/micromamba/envs/lammps/lib/python3.11/site-packages/emcee/moves/red_blue.py:93\u001b[0m, in \u001b[0;36mRedBlueMove.propose\u001b[0;34m(self, model, state)\u001b[0m\n\u001b[1;32m     90\u001b[0m q, factors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_proposal(s, c, model\u001b[38;5;241m.\u001b[39mrandom)\n\u001b[1;32m     92\u001b[0m \u001b[38;5;66;03m# Compute the lnprobs of the proposed position.\u001b[39;00m\n\u001b[0;32m---> 93\u001b[0m new_log_probs, new_blobs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_log_prob_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[38;5;66;03m# Loop over the walkers and update them accordingly.\u001b[39;00m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, (j, f, nlp) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;28mzip\u001b[39m(all_inds[S1], factors, new_log_probs)\n\u001b[1;32m     98\u001b[0m ):\n",
+      "File \u001b[0;32m~/micromamba/envs/lammps/lib/python3.11/site-packages/emcee/ensemble.py:496\u001b[0m, in \u001b[0;36mEnsembleSampler.compute_log_prob\u001b[0;34m(self, coords)\u001b[0m\n\u001b[1;32m    494\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    495\u001b[0m         map_func \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmap\u001b[39m\n\u001b[0;32m--> 496\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(map_func(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_prob_fn, p))\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    499\u001b[0m     \u001b[38;5;66;03m# perhaps log_prob_fn returns blobs?\u001b[39;00m\n\u001b[1;32m    500\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    504\u001b[0m     \u001b[38;5;66;03m# l is a length-1 array, np.array([1.234]). In that case blob\u001b[39;00m\n\u001b[1;32m    505\u001b[0m     \u001b[38;5;66;03m# will become an empty list.\u001b[39;00m\n\u001b[1;32m    506\u001b[0m     blob \u001b[38;5;241m=\u001b[39m [l[\u001b[38;5;241m1\u001b[39m:] \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m results \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(l) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m]\n",
+      "File \u001b[0;32m~/micromamba/envs/lammps/lib/python3.11/site-packages/emcee/ensemble.py:640\u001b[0m, in \u001b[0;36m_FunctionWrapper.__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    638\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m    639\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 640\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    641\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m:  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[1;32m    642\u001b[0m         \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtraceback\u001b[39;00m\n",
+      "File \u001b[0;32m~/Software/sportran_bayes/sportran/md/bayes.py:717\u001b[0m, in \u001b[0;36mBayesFilter.log_posterior_offdiag\u001b[0;34m(self, w, omega, omega_fixed, data, nu, ell)\u001b[0m\n\u001b[1;32m    716\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlog_posterior_offdiag\u001b[39m(\u001b[38;5;28mself\u001b[39m, w, omega, omega_fixed, data, nu \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m6\u001b[39m, ell \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m):\n\u001b[0;32m--> 717\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlog_prior_offdiag(w) \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_likelihood_offdiag\u001b[49m\u001b[43m(\u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43momega\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43momega_fixed\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mell\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Software/sportran_bayes/sportran/md/bayes.py:671\u001b[0m, in \u001b[0;36mBayesFilter.log_likelihood_offdiag\u001b[0;34m(self, w, omega, omega_fixed, data_, nu, ell)\u001b[0m\n\u001b[1;32m    669\u001b[0m absz \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mabs(z)\n\u001b[1;32m    670\u001b[0m \u001b[38;5;66;03m# z = data \u001b[39;00m\n\u001b[0;32m--> 671\u001b[0m log_pdf \u001b[38;5;241m=\u001b[39m _lambda\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mlog(_gamma2) \u001b[38;5;241m+\u001b[39m _lambda_minus_half\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mlog(absz) \u001b[38;5;241m+\u001b[39m np\u001b[38;5;241m.\u001b[39mlog(\u001b[43msp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkv\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_lambda_minus_half\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_alpha\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mabsz\u001b[49m\u001b[43m)\u001b[49m) \u001b[38;5;241m+\u001b[39m \\\n\u001b[1;32m    672\u001b[0m     _beta\u001b[38;5;241m*\u001b[39mz \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m0.5\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mlog(np\u001b[38;5;241m.\u001b[39mpi) \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39mlog(sp\u001b[38;5;241m.\u001b[39mgamma(_lambda)) \u001b[38;5;241m-\u001b[39m _lambda_minus_half\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mlog(\u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m_alpha)\n\u001b[1;32m    674\u001b[0m res \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msum(log_pdf)\n\u001b[1;32m    675\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample.bayesian_analysis(model, 6, log_like='off')\n",
+    "bay=flux_resample.bayes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'bay' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mbay\u001b[49m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'bay' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "bay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1462b6f90>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x750 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s=bay.omega[-1]/flux_resample.freqs_THz[-1]\n",
+    "cubic_mean_spline=bay.model(bay.omega_fixed, bay.parameters_mean)\n",
+    "cubic_max_spline=bay.model(bay.omega_fixed, bay.parameters_mean+bay.parameters_std)\n",
+    "cubic_min_spline=bay.model(bay.omega_fixed, bay.parameters_mean-bay.parameters_std)\n",
+    "f, ax=plt.subplots(figsize=[3,2.5])\n",
+    "ax.plot(flux_resample.freqs_THz, cubic_mean_spline(bay.omega), color='red', label='Best Bayesian',\n",
+    "        linewidth=3, zorder=1)\n",
+    "ax.fill_between(flux_resample.freqs_THz, \n",
+    "                cubic_max_spline(bay.omega), cubic_min_spline(bay.omega), color='red', alpha=0.3, zorder=1)\n",
+    "Nf=5\n",
+    "ax.plot(flux_resample.freqs_THz, st.md.tools.filter.runavefilter(bay.noisy_data, Nf),\n",
+    "        linewidth=1, color='black', zorder=0,\n",
+    "        label='Window filtered', alpha=0.4)\n",
+    "ax.set_xlabel(r'$\\omega$ [THz]')\n",
+    "ax.set_ylabel(r'$\\rho$')\n",
+    "plt.legend(loc='best', fancybox=True, framealpha=0.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sdata=np.loadtxt('data/bayesian/mock_data/mock_data_sin.dat').T\n",
+    "\n",
+    "s_noise=sdata[1, 1:]*np.ones((2,2,(sdata[1,1:]).shape[0]))\n",
+    "true_s=(np.sin(sdata[0]/2.2 - np.pi/4)*0.98 + np.sin(-sdata[0]/2.2 - np.pi/4)*0.98)/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4001,)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sdata[1, ].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f917b407f70>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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3EhERhYrMglL8e1ksZEXFGzO6o1Ft1pzzlpAPGsiyedGrixcvOryfsjIWKyMKJHvPZWFt3GUAwBsrTng8aBCIl7uuXOMGSjReCNnFJYmIiALL/9acwqrjlwAAOknEF7P6evX4lm5eBMqYxxUhPz2BiDzEz8+g5zMLvXtAN78dnn57Xb0rfjgl200t8Sze/CciIgoMfx5Irfz3ymNckcibQj7TYPTo0SFZUIzIn6TlFuPUpXwMb98AYZJ3YpmB/rX3dPNdPS/OnLfX6Gdb1+aqqiK/1IDaETqLAYtA/5sRERFR8AmFGxDMNCAinyosNWDSp9sxe/5+vLjomK+b4zGBPh/fUXqDYvSzrd/+1WVx6PmfdXjk10Oea5QGe/t51iMgIiIKbZZuXoTCTQ0GDYjIpxYfSkVOUXlNkMUWlhf0hEA/v3s6Q8qbF8mlBhk/7zkPAFgde8ni1JFA/5sx7EBERKGo1CDj9KV8GGTF9sbklxg0ICKHnbmcj9OX8q1vZOdFZ0GpeTFSb/B2VHjV8Ut4YsFhXM4r8e6B/YS1T4OsGP8xKoJIREREFPju+nYfJnyyDY/+5t1sQnezNHQMhWREBg2IyCHb4tMx4ZNtmPjpNmw6ddnl/YXCibbC30cv4qW/gncKhqd5ItATSp8/IiIib0tIL8C+pCwAwNq4yyjWe/9mkaqqOHExD0V6g4f275Hd+hUGDYjIIf/86QAUtfwEee8PB3zdnICz+XS6W/YTaB2UO6Y7eKIuhLPtKimT8X+Lj+Pu7/fh7JUCN7eKiIgoOBSZZJQaFO9PUfhwXTyu/2w7Rr63xSdBi2DAoAEROaTUYOfJ3s6rWl/d6PVkYcKFB1Jwx7d7nH69v6zowrvwVUzfigX7krFgXzK2xafj4V8O2rcPvp9ERBRi/KHv+3zzWQBARkEp5m1PdHo/lsZn/vA7elrIL7lIRORO2YV6PO+FVSA8vRrD2SsFqBcV5tFjOMw/YikAgIXV1oo+w0wDIiKigJCSVeTrJgQkZhoQkU8FW3Q2yULl/0CTW1yGDSev+LoZNpWUyTiemosyVmQmIiLye76O/xeUOl/XQAnhJReZaUBEFIACrYPyRGxIVVXc/s0eHEnJwbgujfDtPQM8cBQiIiIKFs4GDVRVxewf9rm5NYGDmQZE5Bl2phAITlxOHk3Jwb0/7McXm886Pf8/0C66TQV6+6tz9neJv1yAIyk5AIANJ6+g1OB4cSN3ZrrM25aIu77biz2Jme7bKRERUQCJu5iLedsSkZ5fqvm8r8cvpWXWMxN3nc3A+I+24okFh42yGPckZiElq1jzNcGWNauFmQZEFHBu/Xo3Sg0KNp26gsFtY9DvmhiH9xFE19yaivUyFuxLRpsGNTGmcyNfNwcGRYWiqBBF53tW07+Z6dJJvhyIxF/Ox1urTgIAtp/JQNK7k822CYExBRERhbDCUgNu/nI3istkrIm7hL8eHhpwF9Szvt0LoLxe0ciODXFzvxYAgJRsy7UQfB0I8QZmGhCRTznTmVRfwWHTKf+Yd683KNiVkIHc4jKvHK+iEOKpS3nYdOoyFJOJdm+vOonXV5zA7B/241hqjlfaZMtPu5Ncer1pVone3pU8rLA30yUxo9BqVguzC4iIKNStP3EZxWXlWX8Hz2drb+TjC2xHCknvOptR9bpQiAxYwaABEXmGv59c3dy+R349hFnz9uIf8/e7db/WnL2Sjymf7cC9PxzAJxvPGD33857zlf9+a+VJr7XJmv8sP+HW/S0+dMGt+7Pl76MXvXo8IiKiQKJojK2cmYbqL6r/NtbqLQdaNoUzGDQgInKDDScve/V4qgq8u/oUDFczDD4zCRpUp/ezlQVyivT435pTmLvlLJIyCjUHGVpMt/rjQIrx8x6OUz35+5HKf5sOEOwZLwihMKogIqKQ5e/3iwDH2lg9u0C28sJA+L1dxZoGREQBSAXsngphkP2rN/tySwK+3pYIAHhvzWmM69LYa8c2TS909jo+FAYIREREwcaR7rv6tpyeQETkQ766+2rvqT8Y+ogyP8s0qAgYVHBXlkZGgXal5upM/55u+/Qxi4CIiMgmR2oKVLc1Pt09x3dgYFd9U1kJggGhCxg0ICLP4EWU0+zqlhzouwwh0tE9+fthrx3L1sf7/h/3Wy4CRUREFKKcvRlzz/f77N42JasIWYV65w5UjXFNg9AYS1nC6QlE5Bl29gr+ElpQVTVo55wb/CzTwFm2PlKHknNs78M9TTFj+snZcPIKNpz0j5U9iIiIfMV0aOXpS+9FB1Px3MKjiI7UYcXjI9CqfpTTx6+elRAMmaeuYKYBERF81xnsSsjAcwuPGi3rYw9H0vvK3FTTYI6VYove4MpvceJiHm6cuxNPLDDORnBXnChI401ERERu5enaAM8tPAoAyCsx4K1V5qs2OVQIsdq/rRVCDAXMNCAin/LVxZY/nPv1BgWz5u0FUB4ZP/3mREToJLteq6r2v3fuqmnw4fp4ZBbqUaQ34PGxHdyyT3e7mFOMd1afQoNa4XhxYmdEhpW/n/f9uB9puSUAcnzWNgYWiIgomGkNrbydaVBdUkaR2WPOHt/a9IRQ6N8ZNCCikGQa6fZFDOFKfonRzxdzStCmQU23H8edNQ1+2JUEADiRlue2fdrLnrsTzy08il0JmQCAlvWicO/wNgBwNWBgzl3rRwfyOtRERETe4g83bexWra2KlbFUQP1OTuL0BCLyKX+51HImXe6d1SetdiKOqkips4cjR/VETYPYC94PGtijImAAAN/vPOfDlhAREZFpUN3Z1RPcxpHVE6q1NcTrIDJoQEShyfTc70xf8PXWRGw74/wSQKb9liPV9h0JcgRLgUdH/0be/LWD5C0mIiLyrAC6+DZactHKuCsUxgAMGhCRZwTYGdTZ1LLNp654vKgPOceeP0uAfUz9iqqqiL2Qi/ySMl83hYiI/JQvaxpocWz1hKp/c3oCEZEneOkM6q655M6my/24+zyK9LJb2uAIR1obaBfG7mpvanaxe3ZkB3uabM82ZbKCZUcu4HCy/VknvvLqsjhMmbMDkz7djlKD978DREQUeHx9ge3Y6gnlG7+x4gQ+33zWQy0KDAwaEJFP+Sp13rTTcKUT+8zHSxEGG4t/iwCP5OcWl+GVJcfxn7/jUFhq0NzmvTWn8OTvR3Djl7sQfznfyy10zM97zgMoD86sPJbm49YQEZGv2ZN56euaBo4cX1WBU5fy8N0O1kji6glERC76elui14/p60i9t+QWl+FSbgk6Nq7l66ZY9faqkza3+WBdfOW/a4RLeHFiZ7Nt5m0vH5ioKvDemtP49p7+7mukB+UUcYoCERGZM701FEjjFxXAmcsFvm6GX2CmARH5lL+kzlvqxPy1bzuSkmP3tn7yFjusoNSA6z7eigmfbMOH6+J9fnfCmrwS7cwBS77ckmBzm0BK+fffvwwREfkTX/cXDk1P8HVj/QiDBkQUkvy5H7C3k8oO0ru7T/5+GJdyS/Dz7vO4nFcKAPh881lkFOid2t+ig6kWnwuWlSWIiIj8kVkhRB9fiTt2eH8eLXoXpycQkU/56pLNtNPy57vYlpy9Epwpc0mZRXhp8TE0q1vD6PF957Ic3tegtzdUBh60MGTgHr4eBBIRBbv9SVlIyijE9N7NEa4L7Pu+5zMLUTcqHHVqhHn92NZ6q3dXnzLell1bJQYNiMgznLyDq6qqx+/+XskvQUJ6oclxPXpInwrEu+lbTqfjpr4tXN6PtYABAOw9l4nsIj1mDmxlFqQgIiLyB6cu5eHWr3dDVYGTafl4dWpXXzfJad/vSML3O8+hQa0IrHlqBBrUivB1kwAAyZlF+Gqr+dTBIB4eOiSww1RE5L/svQo3uaB19OLd0QyB85mFGPXeFizYl2yyH/I33pjTvzbuMuZsOosHfj7g8WMRERE543+rT1WOj77f6b+V/O0ZS1W0P6OgFF/ZUd/HFVlFelzJKzF6zFJmXHJWkdljHBtWYdCAiPyKp0/Q764+heIy84tRX6RXax1SVlSUaLQvWJTJit3blhrs39ZVsRfyHNreG8kbgZghQkRE7ufN/tBRiqJaHUNZG16lZhd7oEVV0vNLMfTdTUaP6S2MQ0SNLldRVew4k+6JpgUcBg2IyKfMl+Jx7OJdcHBWekq2eSQZ8J9o8uTPtmPdictu3ac/XXquOHbR7m39cZBU8fn0RoxpW3w6HvjpAOIv53v+YERERA5acjgVHf+1GtO/2Ilivaw5pvP1+MqgGLcgMb0QCw+kmG+oMVgqkxX8ecByMeVQwpoGRORXPN25WAoy+EtNg1OXgvsCcctp+yP2pX6YcaGogOSmKMyl3BK8uiwWNcIlvD69u2ZBqHUnLiPuYh52vjTWPQf1EH/5/hARkfc8/cdRAMCx1Fws2JeMWpG+ubQsKZPx7fZEiFrpAhqeX3QMTevUwPAODSof0xofZjq5alMwYqYBEfmU+VI8vmmHz0PhbtDjP2sx8ZNtyC4Mjk7OH/8kqqri4PksxF10bDqDlleWHMe6E5ex7MhFfLrhjMXtLuR4Nn2TiIjIVacumfeLquqdcd3XWxPxwbp4vLfmtN2vufO7vUY/c0agdQwaEJFf8XQim6VOwfcJdK7LLzHg1KV8zN1y1uhxf+oIHWqKH/5JVAAz5+21uZ09Np66Uvlvfy5sVeFkWh4mfbods+btQZafBaZ+3JWEYe9uwgdr7R8wEhFRlYyCUhgcqDtkD1vduLvGJx9viHd5H340VPJLDBoQkU+ZpoOFUpqzpwIV+85leWS/7uBIcb+E9AIPtsQ5qgro/bDWgjc8/MtBnEzLw66ETHy83niA5sugm6KoeO3vOFzIKcbnm8/iIjMziIgc8vaqk+j/5gbMmrfX7YWh3dE/5JeUuaEl1mlNbQilMaktDBoQUUixdMkaVB2Di8tY+otMP7ubDZSvbhGqkjKriogud6CgpTvsOpuBhQdSNFffkE0+4AwaEBE55pttiQCAfUlZ2JPo3I0HrbGGuwIQ//zJ88si21kOIWSxECIR+ZS/1DQIpktBdnzOsWdwUzGwCnU5RZ6/61PhSEoOZn1bPiUkMaMQL07s7LVjExGFmoyCUrftS4V947qcIj1eWHQM+SUGvHtTD1xTv6bR884GMhzDwZM1zDQgIr/ijjQ2VVXxr6XHMeHjbdh0yr7lC92djudLokkkJmBrGniZPVkE7pg3SY75z99xlf/+ckuC2fNB9NUlInLY5tNX0OO1tbj+0+3IKXIsQ09vUHDTl7vc1xgnz8efbDiDdScuY3diJka9vwV3f7/PK1MSqvOnsZI/YtCAiNxOUVQcTc21a1vzNX1dP/6Osxn4ZU8yTl/Ox70/mKS0WegVLB02EC9I2O85J4RnHrjMk98TWzUkgqGIKRGRNdYuaGfP34/8UgNOpOXhy63mgVVrlh6+gIPns+0+lqPsXT3ht33JRj9vi0/H+3YUtk3KKMTyo85Pl7uYU4xFB1ORXag3u+ECsH+pjtMTiMjtNp++YnsjC9xxej6QlG3xOX+oabD86EVM7dXMY/vX6vjINiUQI0Rk9t3lX5GIgoFBVmBQVESGSXa/xtr4R8tJjWUSTQtUu6Lba2twU98WFp+vGK5oHfGn3efx32ndrI7PJn+2HYV62en2DX13EwCgR/M6eOuG7k7vJxQw04AoQO06m4E1sWl+mVa/OyHT7m3Naxq4YXqCU6/RfpUn3t/HFxzGxZxizwUqzHrf8gcOJGXhjRUnNNdS9ho/jmf4usjhquNpPj2+K/zvLEREFLjySsow4ZNt6P7aWiw9fMHu1zk6Zpm/M8nsMXfedyiTVfy+P8XmdpaOmZpdbDWg70rAoLrjF3I1C+1SFQYNiALQjjMZmPXtXjz0yyF8t8P/1nd35QLCLRcfzlyNW3jJxlNXMGveHhSUGlxrk4mt8elu3V91WoUQS8pk3PzVbny34xwmfrIdK45dRJ6X5wsC7r2D4W6Fbv4bO+qRXw9ZfO7voxex82yGF1vjGD+MXRIRBazvtp9DQnohDIqKp/444uvmeJylsYGiql6cOmjeBk5brMKgAVEAenbhkcp/v7nypO8aUk1KVhG+3pqAhPQCh15n2lG44+LD2kneUjTb2mF3JWTiu+3uDc7oPLjEgdb0hOMXjGtMPPbbYcyev99jbQhEA9/e6OsmWPTEgsO449u9DmXxeNNfh1J9dmwGLIgo2JxIs50RWCYr+NrBGgb+ytLYLKtQj+1nPHeTxVYbOG2xCmsaEAWgwlL3pGO5i6qquOu7vUjKLMK87YmY0tOF+fpuOD9bK1zj7KX68Qs5Tr5S2+W8Eo+ldJt2fIKgfWFlWvyI/N//LT6GdU+PQqlBRu3IMF83p9LZK44FC92JhaqIKNjYc636+75kvLP6lNuP7cotDWfPx5aOecNcN67s4ATGDKow04CIXJZfakBSZhEAIKNA79icedOaBm5ZctHx1ySmF1p9PtrNF2gfrIuHrHhm/pxmBWD2fEHhYk4Jxn20Fb1fX+/QPNdgxo82EYWify+LM3ssUE+Hgh8UcGZfYh2DBkTkdq6ceLVeu+NMBr7YfBbZhfatP+zM4T9cZ31pn5oR7k/MOpGWb9d2wXTB7wfjgoCmlxUkZxVBvjrP9fZvdqPjv1Zj4QHbhab82durTmLQ2xvw464ks+dsffqD59tBRFTB82c2xcJcTl/00/4wNtAaawXT+MtVDBoQBSBfnVt3JdhXiM2RU2xKVpHV16ZmF+HO7/bi/bWn8eJfx8xe7+gcNEvR7Owi6wGJkjL3Twmxt/Ceo33W9jPGfycB/nNh5QfjgqCyJzELeoOC5xeZfzcCxfnMQnyzLRGX80rx2t/md85s4aCOiIKNs6c1R/pY2Y/Onf4wNtBaQYmFEKswaEAUiHx0dr3z271u3+ecTWetPl99dYh1Jy7bt1ONk3xmQSkWH0pFZkGp5ktspcYtPOj+Qm92Bw3ccCx/GRuUGLikERk7n1lkeyMr/OSjTUQhokxWsORwKg4kZfm6KS6xvMyw9weZ/jA9gQEC61gIkYjs5o0TqqN3DQ+ez4aqqkYdjukeVFXFzHl7EH/ZcrE2Dy5mYJG3Clqq8J9iccuPXvR1E0JamaxgyaELaFA7HGM7N/Z1c9zCXwJiRBQaPlh7Gl9vS4QgACseH45uzeq4/RjOntYceZ2B0xOMaE5P8JOxkz9gpgERuZ8rNQ1Mf7axr10JmfjApB6B6Yk/Pb/UasAA0C4e6GlFevsyDe79YT+mf77D6eOk55di1jz3Z4lQ4Pli81m88Ncx3PvDAew6qz3d6OyVfEz+bDtu/Wo3ruSXeLmF5mx+MzmmIyIv+npbIoDy8ck7q9y/eoG3WM40cJ43plV4itbb4aF61QGJQQOiAOSpk6uqqjiQlIXLea5dKLgSmXWmw/lis/E6xab7sGfeni+CBpai/Ka2xqfjaGquh1tDoeCTDWcq//3iYu06CI8vOIK4i3nYl5TlkwGxo9lGvBNERL7iiXpHgHdqtXgiaOAs/5ieYP5+XMgp9kFL/BODBkRU6d3Vp3DzV7sx/qOtyLJzpQItggthjcJSAz7dcAa/70t2utM07Qft2Y3og7OhP3XYFHoMsvbn72Ra1ZKpSw5fQOwF/w5YcXoCEfmKp04/3jitGazcRl9x7CK+3Z7osaBIhYrxou9DBtaLaBNrGhAFJE9FZCtS7vJKDPh+xzk8N6GTU/tx5c7f6A+2VP47pma4144feyHP9kZuxqABBYJbv96NE69P9NrxVNV4fiuXXCQiMqa3UFRY67o3v6QMH66LhyAAz17XCbWuLiFtaQyyOyETP1xd/vZyXglemdzVrja5ci7210wDqsJMAyLSlOlCpoG7vLXqpMPZBpfzSjB/Z5LRY/buYfuZdIeO5SpbyzwSeZKqAv9eGou+b6zH/J3nLG5XpJeRX1LmvXa5+nqO+4jISzw1jcDWbtfGXbJ7X59sOIMfdiVh/s4kfLaxaoqapaBBRcAAAOZtt9w3uJMfxAzw295kXzfBrzFoQBSAvHFyDdS1z19dFmv2WFJGoV2vveu7fXYvg+gOK46lee1YRKYu5ZXg5z3nkVWox3+Xn7C6rScHU64HCQLzXEVEgc9X0xMKHBirVF+6+purGaWA/dmO9o6hXOEHMQNsOHnF103wawwaEHmRqqoo0hsCYpDrSpqWu349AY53yGvjLps99sXms3a//sD5bAePSBT8vJl55HghRGP+cMeKiMiT3DHOsrcY8wuLtIvmanG2WYF+3i4uk3HXd8G9ShWDBkRe9Nrfcej66lo8+fsRl/bjjXOrI9PtPRkDcUvHaKHgm5YA77eIPMKb9TccPVIAxGCJiBxiGjy1t8C0I6dDxc7z+r6kLAf26piVx9Mw4eNtuJxX6rFjeMulXN8vUexJLIRI5CUlZTJ+2n0eAPD30Yt4fkIntIyJ8nGrLHMk02DuFvvv5DvK3qKGBlmBTtKOg3JJNgp1ry6LxalL+Va3UVXVYjGq6umtnuZoEIDfbyLyFUfOV6nZRdAbFLRtWMvl49q6M2+QFSw6mIq6UZYLSnvizOnMjZfTl633TeQfGDQg8hK9bFzpNre4DC2d3JdXqsw60Jt8vTXR6Gd3dURJmUVoVte+uXTvrD6Ff0+xr8IvUaipCFhaY7pqgbe4PF2LMQMi8hFLpx9VVfHR+njsTczC0+M7Iipcwi1f70aZrOCLWX1xfY+mrh3Xxnnvq60J+GBdvEvH0PLt9kTsSczEo2Pao0+reubtcvsRyV9wegIRaXLlxO/Omg27EjLt2u67HeeQnFnk8vECfV4dkbN8tdyU6VEdzRzgIJWI/M2B89mYs+ks9iVlYea8PXhu4VHoDQpUFXjk10M2X+/q6diegIGjx4i9kIs3V57EhpNXcPNXu51sGQUqZhoQhaiSMhlFehmKquLx3w4jz2RJNX9dr/ZynuU5Y+ezPF/hlyhYKSpwMafY68c1XfHE4ekJ/nmqIqJQoKqYv/Mcvth8FlN7NcNrU7sBAHaezTDa7JwXViBwlKMB2q3xVctSe7PODfkHBg2IApCrN8OzCvWYOmcHLuYWQxIEzQq6/tofDHp7o8OvceSiwt5iQ0T+yJUsnyMpOVh13D3LgBpkBXpZQVS49WFGmawgJcu1QAVrGhCRr6hA5ZK183cm4e4hrdGmQU3N7Rzbr+fPa+4OuKoqg7jBjNMTiAKQqyn0X21NwIWcYqiq5SV37L340Kq+eyg5x5XmOc1Sk9mHUahwZcB269e7UayXXW5DSZmMaZ/vRLfX1uLnPdZrKbjjbhUHqUTkL1KzXZ8mCfC8Rv6HQQOiEHTGjkq1sRdy7drXP3864GpzPM6Ru6+saUCh7I8DKS7vY+HBVJxIy4OqAv9eGmv7BSYcXz2BiMg3LJ2vTLMW3T20cEcmgquBidYvrTR7jGOo4MWgAVEIsmf1haTMIhSWGmxut/HUFXc0yS3iuWwPhThfXkBXBOcSrhQYPf711gTH9uNoIUTekiMiHzE9X7kr49HWaW3lMQtTyRw4Hzpyrt13Lgtr4y7Z3idPx0GLQQMiL3HvidQ7odwNJy975Tju8ubKky7vg0FyIudYmmnwzupTVl7j3btlsRdyccEHxR6JKDhZzDRwcTBh64Le3pWlrB7DgXPnrV/vxrFU+zJQKTgxaEDkLW4MGrjaGdn7cncGOvYmZmL7mXT/vyvIqAEFMF9+v5w5ttZLXP0VLL3++x3nMGXODoz7cCuS/LCSOREFHtPzjaXxmT8OLfx1lSzyTwwaEHmJP1X49sWcs9u+2YO7vtuHRQdTvX9wB2QV6n3dBCKn+fIs40xNQ62XuPo7WApevL6ivMJ5cZmMd61kPxAR2cveC293T09whx92Jbl1f38dSsXLS467dZ/kPxg0IPIS/wro+i7m/fyiY14/piNv/WO/HfZYO4iCmbXBs6VVEjwxPcGePV7KK3H5uEREnhrbObtbR163+NAFJ49CoYhBAyIvcWe/4otL/nMBnM6blsMLBAoNb169m+5vymRF83Ht6Qm2z5bHUnNww9ydeObPIyg1GC8Tac8gnhW+iQLT4eRs9H9zA679cAvScn1fn8RS4DMhvUDzcaJApfN1A4i8RVVV/LI3GYeTs/HI6PZo36iWr5vkNJdrGjjx+vt+2O/aQX2IdxWt08GABshFQyEXdYUC1EEh6giFqINCRF/9b22hGBEoQwT0iBT0iEAZIlH+XwkWLggB6BGGEoSjBOEoVcv/XYowFCESuWrN8v+h/L95iEKOWgsZqIMral2UIMK7b0SAyygoxY+7z/vs+NayBspkBZFhktnjWgECewKsd367F3klBhxOzkGDWsafE3umgjFmQBSYZv+wHzlFZcgoKMVbK0/i81l9fdoeS2ebZUcuembHblAmK5yiRQ5j0IBCxrHU3Mo1w/edy8KOF8d69fh+XwDQhkQ3Zhr8suc8bunfAhE684sIci8BChoiF62Ey2glXEEr8QqaIAuNhWw0EnLQSMhGfeRDFPzv85mnRuGKWheX1Xq4grq4rMYgRW2IZLURktVGuKg2QBm7sUq+rsdh7RRnkLWfdPa0mFdStRzsmliTZcD876NMRG6SU1RW+e8tp9Od3o+iqNh+NgPN69Zw6SaS6dhuyaEL+H7HOaf3V7lfD57Ilh25iO/c0EYKLRxtUchYfrQq6pua7f2UNo5jq/xraSxUVcVdQ1r7uilBIxqFaC9cQEcxFR2EC2gtXEIr4QpaClcQKZTZ3oEfihaKEC0UoT2079jIqoA01Eey0gjn1cY4qzbHGbU54pUWuIQYhNr9ZEsX5t5iK9NAi2YhRAd/DbN10u14jcD5CUQBz5WbMR9viMecTWcRLolY9/RItG5Q08k2GP+8+LBn6gTsSsiwazt73pJFB1NcbA1pEYN8pM+gAZGXuDPRQHDxYsgfhsv/XhbHoIETdDCgo5CK7uI5dBZS0EFIRUcxFY2FHJf3nadGIVutVTldIBc1kafWRA5qoUCtUTnNoEQNQynCUYLy/8oWyuMIUBGOMkRWTmu4+l/oUVMoQR0Umk2HqCMUIsbOzAdJUNECGWghZWAojOfy56s1cPZqACFebYHjShucUK9BAaJcfp/8la+Xz6o4utZAvsyRQogeWnKxOn84BxKRa1w5VczZdBYAoJcVjP5gC6LCJQxuWx/f3t0fomj/GcJTZ13T85gzq9NY4uoYkoA6KEAP8Rw6C8noLKago5CCsvDz6O/rhnkQgwZEXuLOVDPeJAsNYTCgo5CCHuI59BDOXQ0UJCNCMNh+sYlMtTZSqqX1X1br4opar/y/qOdXNQR0MKA+8qpNochBYyEbTZGJlkI6WopX0AyZVgMLtYVi9BHOoo941ujxBKUp4tTWOK60QazaBrFKG+QHSSDB4M5RpRPUq8kEWq0oMzhQCNHFc6U9rz9wPhtb49MxqmNDl46lJatQj/1JWRjWvgFqRXCYReQp7oyTFullbDp1BZtOXcG4ro0daINnzrsHzmd7ZL/kuGgUopuYhJ5CInqIiegpJKKVaD41Jk6QNV4dPNibEXmLh8fzqqoiJasYG09dxtB2DdCpSW2L2zLo4J/qoAD9xHj0F+PRT4xHLyHBoakFeWoNnFFb4IzSHAlqMySrjZGsNkKK2jCg7rAboMNlxOCyGmPxexMGA5oL6eV1GoQraCNcQgchFR3EC2gqZFncdzsxDe2QhmnSbgCAogqIV1vgoNIRB5SOOKB2RIraCIF4L1pWtC/MvcXaxbrl1RM8sOSinbu85/t9OPLqeNSNCtd8vkxW8M6qU0jMKMDL13dBx8ba59TCUgNqhEkQRQGKouLGuTuRlFmEAa3rYeFDQx35VYjIx85cKXAoaJCUWeT2NmQWlLp9n9VxDGiNijbCJfQXT2OAcBr9xHi0E9N83Si/wKABkZc4OjRWVRVHUnLQODoSzerWsLn9i38dw58HUit/PvrqdagTFaa5rb2paZ7uWI6k5Hj2AH6uCTIxVIxDf/E0+ovx6CjaNxdSr0qIV1siVmmNeLVl5Tz+y6iHQLzYdUYZdEhSmyJJbWr2XEV9h/biBXQUUtFNOI9u4jlEC+a1TERBRWchBZ3FFNyBjQCAK2rd8gCC0gm7lG44rbaAGgArFPu+poHl54rLtO/AOFPTwFagwZF3YU9iJiZ2N/8MAeV1cL7fWV4sLDG9ENteGGO2zXtrTmHulgSM6dQQ3/9jAOIu5lVeROxPykapQWbBVyIP8USxQH+4oP7rUKrtjVzgD7+jv5Ago7twrjxIIMajv3gaDYQ8u16bqdZGnNIap9RWiFdbYL9eB+AdzzbYhxg0oJDh65OkozfU5m5JwPtrT6NmuIS1T49Ei3pVd4pNf5WcIr1RwAAAer2+DknvTtbct73vRUWbPZV+d8tXuzyyX38VjQIMEU9gmBiHYWKsXdHrUlWH02pLxCptcFxtg+NKG8SrLaGHdkCIgDzUxCG1Iw7JHSsfE6CglXClcppHd+EceojnUEcwv0vUSMjB9dI+XC/tAwBkqNHYo3TFTqUbdirdkeynmQiyr6cnWDlPFJQYT6k5nJwNRQWaawREbf0WM77YaXJc+9vhiCXVCpolZ2nfTZy7JQEAsPl0Oo6m5vr8b0AUShz5qucWl+G/f8ehxCDjP1O7ea5RFpiel2Iv5KJ78zqVP+cWl+HHXUlo3aCmx2sOhHZNg/JMguHicQwXYzFEPIFojXGAqRy1Jo4pba+Ow9riuNIGF9AA1ccCetV3Sx57A4MGFDJ8veKhoxHx99eeBgAU6mV8tC4eH93Wu/I508rfRXrtu3hX8kvQqHakYw3V4Kn3rszHd0Y9TQcD+ovxGCkewzAxFj2EczYL/GWqtatS5ZVOiFXbMEDgBipEnFeb4LzaBCuUIQDKAwlthbTyKSFCvMU0xAZCHqZIezBF2gMASFUbYKfcHVuVntiu9PSbmgiyj09y1q6X80urggYbTlzG/T8dAAD8e0pXh49zNDXX6Ge9ydQHR94Fa2+Z5EAxNAC4kleC+rWM64L4ut8hCmaWvl6qqmL5sTQkZxbinqGtUTsyDJ9uOFO5soG1mVy2vvUXcpxbfcu0rVPm7MBnM/tgWq9mAID/Lo/D4kPl7ZvUvYlTxwAAvYX6MdX5+iaat8UgD8PFWAwXj2OYFIvmQqbN15xWWuCA0gn7lU44pHbw25sF3sSgAZGXuDJ4vJJvfX6bpQ7AUucRah2GN8UgD6PFIxgrHcFI8ZjNCPZ5pRH2KF1xQC0PEpxTmyDUOyZvUSEiQW2OBLk5/kR56nkM8tDvak2JIeIJzUBPCyEDt+m24DZsQZkq4YDSCZuVXtik9MFZtTl89ffzeSFEK5fr1TMNKgIGAPDGihNm26qqiu1n0vHllgSM7mS7UGG66fnRTW+DxBMlUUA6cD4bTyw4DAA4n1mE92/pVTnVCADWxF1yet8v/XXMqddpjQGfWHC4MmhQETAAgNWxzrfv9OV8lFiYDhY6VHQRkjFWPIxrpUPoLSRYvWGjVyUcU9th/9UgwUGlI3JRy4vtDQwMGlDI8PX4z5VxrK2l1CylmrnjLpeiqCgxhHoHZI2KrsJ5XCsewljpCHrZ6Jwy1GjsuprmvlPphlS1kRfbSrZkIRrrlf5Yr5QvnFQ+peQkhohxGCbGoYNJ3YkwQcYQ6QSGSCfwMhYgRWmITUpvbFL6YpfSDWVe7GZlH2fuVE1nMn/ONBvA6n4A3PVd+dSQXQmZCNc5Vk9ChYqjKTlYdDAVU3o2xaC29a0eyxJHll2ztEdmGhB5kIXvV0WmJgAsPJiK92/pZfcubY0Vt5/JsHtfvrLooPWaCIHwOzgqAnoMEeNwrXgYY6XDNrMJTiqtsEPpjp1Kd+xVOqMYrmflBjsGDYi8xJV5ts6+1NLr7J3PpkLF3d/vw46zwdfBuEKAgt5CAiZK+zBJ3Ke59E6FUlWHPUpXbFN6YqfSPWCK6lG5PNTCWmUA1ioDAACNkI2hYhxGSscwWjyCGKHAaPuWYjruEdfjHqxHnhqFDUpfrJUHYKvS0+NLWqZku7+KtyOsnaccOYeZbmtPum11sgLc+OUuyIqKn/ecx4F/jUODWtrvvbV2ORUzMN2/p5fNIQphnvx+ZRfqUTtSB50UeP11ioUaLMGmFoowVjyCSdJejBKPIUqwnJWbpsZgu9wDO5Tu2K10Qzrqeq+hQYJBAyINsqJi37ksdGhcq3KweSGnGKuPp2Fcl8Zo3aCmw/t05Y6TrUwDe15nkBUsPXIR9SysqKBl19lMBgyuEqFgoHgKE8T9mCjtt7qsX5oag81yb2xS+mCn0o0R7CByBfWwVBmOpcpwiFDQS0jAGOkwxopH0F1MMto2WijCjdIO3CjtQJEagS1KL6yRB2KT0tsjS2D+d7l5qr83WTtPLTyYgim9miI60vP1OfKKy4wKEvZ/cwN+uW+Qw/txtKaBFlszRnKLyrBgfzJ6tqiDoe0auHw8InKNAAEfr4/HpxvPoFfLulj88FC3nAu8KZhDldEoxLXiIVwv7cNI8RgiLCxLragCjqrtsFHug01KH5xQrwGnfrqGQQMiDS8vPo4/DqSgQa1wbH9hLCLDRNz57V6cyyjEN9sSsfv/rvVqJ2LaAZimz1lKp6v+uq+2JuCDdfEAgOhI+776vr5z6WsCFAwQTmO6tAsTpP1Wl+E5pLTHRrkvNil9cFJtBXZOwU+BiMNqBxw2dMBHuBWNkI3R0hFcKx7GSPEYagj6ym2jhNLKFRlKVR22KT3xtzwUG5S+QRNUsjZQPZycg6d+P4Lv/zHAjv24NuTVevWd3+11+Fiig3PaTAvUArYzzF7861jl/OodL44xWiXHXsmZRTibno+RHRoG5F1RImdZ/Hq5eNX86cYzAICjKTnYePIyruvmfGFCX/DUile+Eo0CTJAOYJK4D8PF4wgXtKfMFqoR2K70xEalDzbLfZCBOprbkXMYNKCQoTWgs+SPAykAgIwCPRYfTsXkHk1xLqMQQHlRwjNX8tG5SbRDxzdfFsyR19qqaaBNUVVcyi1BSZlcGTAAgDyT5c8s2ZNo+W568CovoDNd2oWp0i6L8+JkVcA+pQtWKwOwVh6Ay4jxcjvJ31xBPfwpj8Gf8hjUQAlGiccwSdqHseJh1BaqKm5HCAaMlw5hvHQIhWoE1in9sUweih1KDxgCuFtWbNxW33Tqin07cnG862xmlilbgWF7Bua2Mg2qF2T7fkcSXp3q2GoSWYV6TPx0G4r0MmYNaoW3b+jh0OuJTl/KR3QNHZrWMV/+1N954tLYdKh4Ka/EA0fxLFcKKfqLcJRhjHgYM6SdGCseRoSgPW7NVmthndwfq5UB2KV052pTHhS4oxMiL8ktLnNLMSvTO1qO3E1z9vh/HkjB11sTnXtxiGkhXME0cRdmSDvR0aTYXQW9KmGX0h2rlYFYL/dDFhwLHFHoKEYk1igDsUYZiHCUYagYi0nifoyXDhjVQagplOIGaSdukHYiS62FlfJgLJOH4qDaMShrXxgcKIjoNDddSdjKNLDrvOzhG37zd56rXHL3t73JDBqQQ37dex6vLIlFVLiEFY8PR9uGgVUx3mLgLsQT/VKznVsW0tcEKBgonMZ0aQcmS3tRx8LqU5lqbayVB2CVMgh7lC4BHWwPJHyXiWxwJWCwNzET/14Wiw6NauPJcR2c3q/pnTOzsayFDpIBA+tqoQhTpD24WdqG/mK85jayKmCH0gN/y0OxXumHPDhez4JCmx5h2KL0wRalD1423Ich4glME3dhorQP0dUyEGKEAtyl24C7dBuQqjbAInkkFskjA2aFjVnf7sGKx0ZYDYhO/2Knzf24ep3troCszaCB5qPGr3Ek68GZFX7y7cwaI9LyypJYAECRXsYbK05g/uyBPm6RY4IrCT90dRBScYO0A9OlnRazOzPUaKyWB2KVMgj7lM6QIXm5lcSgAZENK4+lOb1c423f7AEAxF8uQNM6xvOWrXV2sRdyjX42TXG1d/UDMidAwSDxFG6RtuB6cZ/RvPPqDivtsVQehpXyYM6LI7eRIWGH0gM7lB74t2E2xohHMF3aibHiEaOCTi2EDDylW4yndIuxS+6KhfIorFYGenwFBlekZBXjq20JVreJu2i5LkgFV6cXODT1y8pzOqemJ5hmlGm7kleC73cmWd0/kTddyAm8u9Pemrp/Jb8Eiw9dwDAWK3WbWijCVGk3bpO2oLeo3W8Uq+FYq/THUnk4dijdmVHgY3z3iWw4kZaHE2m2B7q2bI03XpbPUlrdhZxizDC5G1esl1GkNyAqvPwra3onjUEE25oiEzdJ23CLtBXXiNpzqxOUplgqD8MyZRiS1cZebiGFmlKEV05hqI0iTJD2Y5q4C8PEWEhC1Xd8qHQCQ6UTeF39ASvkwVgoj8IhtQP8MQd3XdwlDGlX36V9yLYKAdjgyMut1SUQbQUNXNj/U38cwa4E4ztq/vfXJAo9m08bjw8EAI/8cggHzmcjQhd8U8a8S8UA4TRu023B9eJezSUSK7I7l8jDsU7pj6IgKRQcDBg0oJC1/Uw6nvr9CFrGROHHeweiTg3vFk+xNOD8cstZGExGvacv52PQWxvx8/2D0LtlXY+3LViEowzXiQdwq7QFw8VYiIL5u56t1sJSeRgWySMRp7YGh+7kC/mIwiJ5FBbJo9AI2bhJ2o6bpa1oJ6ZVblNbKMZM3WbM1G1GgtIUv8tjsEgeiWw/qq2RkF6IhPRCl/ahuFj2QHZbIUTrz9tzGEsBDNOAAeDc9ASiQJVbXIYtp69gSNv6aBTtPxeGO8+afzcPnM8GAJQavFCTJQg1vNqn3WLSp1V3XGmNxfIIrJCHIB11vdtAsguDBhSy7vpuHwAgs1CPedsS8dyETh49nunY0dKAs6Kolan8UgMe++0Qdrw41uy5xxcccrF1waWFkI5Z0kbcKm3RXCZRUQVsU3riT3kUNij9WG2X/MoV1MOX8jR8KU9FX+EMbpG2Yoq0x2gFhnZiGl4Rf8NzuoVYqQzCr4ZrcVDtiGAIerl60e+u5cZs1zSwfRxnl488kpKDR345iLpR4fjh3gFoVNt/LqqI3OEf8/fhcHIOmtetge0vjLGZ2eOQACp2cCm3BE3qBN/3W4CCEeJx3CltwFjxMHSCecAlR62JJfJw/CmPxkn1Gh+0khzBoAEFrdziMqw4dhH9r4lBpya1rW6795x24RV3Mp+n63ivVlER13Q6QmgujWhMhILR4hHcKW3AaPGoZlZBktIYC+VRWCyPQBpcS6Em8jwBh9SOOGToiNcNd2GiuB+3SFsxVDpRuUWEUIYbpR24UdqBU0pL/Cpfi6XycOQjyoftdo3sYqqBK9MbzmUUoqRMRsuYKLOgwe/7ktG0bg2M6tgQAPD5prM29+ds/OL+H/cjo0CPi7kleHf1KXx0a2/ndkTkhwyygsPJOQDKp2QeTc1Bn1b1PHpMQfBeDQRHfLIhHu/e1NPXzXCbusjHzdI23CltQGvxstnziipgh9IdC+VRWKf0RynCfdBKcgaDBhS0nlhwGFvj0xEZJmL/K+N83RwzFjsvG51aSVl5fQMq1wC5uFXajFm6TWghZJg9X6KGYaUyCH8YxmCf2hnBcCeWQk8xIrFEGYElygi0MFzBbdIW3C5tRkOhqmhqZzEFb4g/4P90C7BMHopf5WsRq7b1XaOd5OqqjP9dfsL2RldVPw9vP5NemYEGAO0aGq+U8tLi4wCARQ8NQf/WMZhjEjTQynBw5CJFqBakyCioKtC68aR2DRaiQGX6tXC1jokZjW5e0DiuP0jLLfF1E9xARS8hAXfpNmCquNuoqG+FqhWBRiFVbeiDNpKrGDSgoFVReLCkTMGyIxd9f6moWv2xkq3K4SPe22w0oAxN5cV07tatwwRxP8IF8ykdiUoT/Cpfi0XyKOQisNaeJrImVW2EDw234jPDjRgnHsQd0gYMl+Iqn48SSitrHxxR2mG+YQJWKYNRFiBdvtsvIOz0z58OGP1sqTbDa3/HYeUTI+zap6srQbhrH0S2BPPH7OyVAocKpHpTdlHgjuciUYpp0i7cJa1HDzHJ7HlZFbBJ6YNf5XHYpvSEAhaSDGSBMYIgcpGtQej+pGy8vvwEHhjZ1mtzyyx10Lb6tfR882qzoSIcZZgq7sZs3Rp01+igDKqI9Uo//CKPwy6lG1R2UBTEyqDDamUQViuD0MaQhpnSJtwibUU9oaBym95iAj4Nn4tX1N/wi2EcfpOv9fslRL15kVy95kBJmX0pDgbZ/vY58ptYDGz76cUOkbM8/hU32f/kz7Z7+IDOO5aai882nvF1MxzSHOm4W7cOt0ubUUcoMns+XY3GH/IYLDCMxQUwqyBYMGhAIePnPeetPv/9znM4kZaL3x8Y4pHjmxdC1O41gzna76yGyMEdug24Q9qAhhqFDdPUGCwwjMUf8mhcRowPWkjkW+fUpnjbcAc+NNyCSeI+3KnbgP5ifOXzjYQcPBO2CI/qlmKFMgTzDRP8duqCrzIN7GWpPqKg8YSi8buUlGkXu7XEv98NskVVVWQXlaFujTD3FvsLYKYFQrfFp6N/a/f13ZfyjFP+/X3Vg4/Wx9veyOdU9BPica9uNSaK+42WBa6wV+mMXwzjsEYZGDCZbWQ//kUpJKiqanFVguo8WVDQNEjgZEmDkNJdSMRs3RpMFXdrTkHYIXfDT/J12Kj0hQzJBy0k8i+lCMdSZTiW6oejm5CEe6S1mC7tqpxjGiEYcJO0HTdJ27Ff6YgfDBOxVukPgx8NB6Z/sdNrx3J3kNZ0f7/sOY/eLetiYvcmlUGFJxYc1n6xhetJd60GQb7x0l/H8ceBFEzo1hhf39Xf183xC6Yf6c82ncWQdg0wpJ3rBYqXHbmA5Czzu99OC/G1UMNgwPXiHtyrW4NeYqLZ8wVqJBbLI/CLPA7xaksftJC8xX9GCUQhxuL0hBAfIEqQcZ14ALN1azBQPG32fIkahiXycPwgT8BptZUPWkgUGOLU1njB8CDeNczETGkT7tKtRxMhu/L5AWI8BoTHI02NwY+G6/CbfC3yUNPKHoOPu5MaTHf39bbyQfZP9w7EyKurLqw7YV5R3JF9UuAoKZPxx4EUAMDauMtIySpCy5jAXdnEFlVVcfxCLprXrYH6tSIceu3LS45j83OjXW7Dk78fcXkfBNRDHmZKm3C3Sb9R4bzSCPPliVgkj0RBAK/WQ/Zj0IDcLj2/FI/9dgh5JQZ8entvdGxsfblDb/CHQZf5gosWpid4vil+KQoluF3ajHt1qzVXQbik1sNPhvFYII9FNqJ90EKiwJSFaHwhz8DX8hRMEvfhH7q16CdWzaFtKmThpbDf8ZhuKf6Qx+B7w8SQmYf63MKjaFQ7ovKC3h5a0xAqaE1HAICX/jqGXf93LbafSXe4jSEeRw5oepOlQHKLy+Cv92Ld8TF7f+1pzN2SgJia4dj87GjUiQrTPpbGwfR+PoUgVHQQUjFbWo0bpR2I1FgFYbfcFd/LE7FR6cvChiGGQQNyu/fWnMLec+Vp/o/9dgjrnh7l4xb5B7NOkvMTAJTXK/iHbg3ulDZoFtQ5pLTHfMNErFYG+lUKNVGgMUCH5cpQLNcPRU8hAf/QrcWUalN/agkluE+3GvdIa7FSGYx5huv9tu6BO939/T4kvTvZLfuydPrWXy2eWH1JR1MCBFzIKdaYymZ/pzBn4xk8fm0Hu7cncqe5WxIAAFmFeszfdQ5PjeuouZ3WZ7rYwVof3nL4vPld9uCjYqBwCg/qVuBayXz6VKmqw9/yUMyXJ+KE2tr7zSO/wBE4ud2yIxcr/x1/ucDKlt7jD3dqTDtJFeVz737dm4xb+7fEzf1aaG4XrNoJF3C/tAo3StsRIRiMnitTJaxSBmG+YSKOqO191EKi4HVMbYdnyh7BO5iJu3XrcZe0HnWF8iUGdYKC6dIuTJd2YZfcFd/Ik7FV6cXVSOxgaeUHe6adHUrOxnc7ElFmsjqDI1MoPlwfz6AB+YVMK0tDa30dsgr1WHbkAqb3bu7BVjlu8eELvm6Cx4hQMF48gId0K9BHPGv2fLoajV8M4/GrPM7vV90hz2PQgNzPBzVjvt2eiLlbEjCtVzP8Z1o3s+cduQzPKrS9Zq7gxC9p2kmWlMmVc+/2ncvCpO5NUDNC5xcBDs9R0V84jQd1KzFeOmj2bL5aA7/JYzHfMBGX4HpBJCKyLh318KHhVsw1TMMt0lbcL61CK7EqhX6odAJDpROIV5pjnjwZy+Rh0EM75ThUWD37Wzh/y3ac2Peds1CI10d9wr5zWUjLLcaUns0gseq/xyiKih93J+FSbgkeGtUO9WqG+7pJHmfpI/3k70esBg1CveaTu0RAjxul7fintBJtxUtmz59UWuI7+Xr8LQ8N+fM9VWHQgILCmytPAgB+2JWEe4a2RpsGzhfzWrAv2eY2Cw+kYOXxNIzr0hhvzOju1HEyTYIT6fmlQRs0KI9mH8SDuuXoqxHNvqTWw3zDRPwmX4t8FtQxUy8qDNlF5nMLidylGJH4SZ6AX+TxmCDux4O6FegtJlQ+31G8gPfFb/C87k/MN0zEL/K4oPquZtsRLK5grZi6pawAS7UO7OGL7LMjKTm49evdAICE9EI8M147zdyfLTtyAb/uScbN/Vvg1v7+WkkAWBN3Cf9dfgIAcDG3BHNm9nF4H6cu5eHRXw+hZoQO39zVH03qRLq7mW7l7MX/6cv5bm5JaIlGAe6UNmC2bi0aCrlmz++Uu+FreQq2KT3hkzuA5NeYa0h+S29QsPzoRZy6lOfQ6y5kF5s95kgHVWrHvLpvd5xDWm4Jft5zHsdSc+zar2kTTNcir7iTE0zTE8JRhpnSRmwIfw5fh39sFjA4rbTAc2UPYkTpp/hanhpUFyHupJNcP1XPGsSVJsg2BSJWK4MwQ/86bi59FevlfkbPNxJy8GLY79gZ8Tie1/2O+jAfeAaiF/86Zve2cRfzLK6rbun87coqDaZ9R1JGIT7fdAZnrFxApeUW4+Yvd2H6FztxPrPQ6v5TsopQUGo8Rey1ZbGV//5s4xnTl/i9UkN5Jt++pCy8sOgY8kt8E3S1Z+hR/bO0/OhFK1ta9uivh5CQXohjqbl4Y+UJp/bh7rv4JWUycou133dnj1Rsx9LZZK4ZMvAv3c/YHfE4Xgj70yhgIKsCVsiDMbX0TdxR9gq2Kb3AgAFpYaYB+a3/LI/Db3uTESYJ2Pr8GDSrW8Ou11maU+opZy4XoGeLug6/7te9541+rrh7FQyZBpEoxUxpEx7UrdBcqmeP0gVfG6ZgC+dJ28UdmcENHVz+ikKdgANqZxwo64y2hou4X1qFm6TtiLhaTTtaKMajur9xn7Qaf8ij8Y1hSkCvuODoMoiWLqQtBQdMg8SOqN6nqaqKu77fi5SsYszfmYRJPZpovuY/f8fhwNUCbs8vOoY/Hxyi2ab31p7C11sT0bROJNY8NRJ1apSnIvtrUboKx1NzsTo2DTf0aY4OGis05RUbB0Eu55WgdqQP0qy91J8npFcFhlYeS8MXsxzfx8WcEtzz/T5EhUt458YeqBvl2jSJhQdTseTwBXw2sw+u79HU6DlnxzmufI9CUXshFQ/r/sY0cTfCBOPvdIkahj/l0fhWvh7JamMftTBwzRrUCjf3a4Eb5+7ydVO8hkEDK+Li4nDs2DFcvHgRkiShefPm6N+/P9q0aePrpvk1d8Unf9tbPk2gTFbx1dYEvD5dexqAeaVp1zj6enuDFKbtjL1gnEEx/H+b8cWsvgGdZ1ATxbhLWo/7davQQDD+/WRVwGplIL4xTMExtZ2PWhiYRGv50A6YObBV5fSbmQNbYsG+lMrn3r2xB15afNwtx6Hgkqg2w8uG+/Gh4RbM1q3B3dJ6RF9d6SRSKMM9uvWYJW3C38pQfGmYirNqCx+32Hcs3a21p6aBxX1W+3dOURlSssqz6TIL9Thjodjw2riqIIhWrQRVVfGP+fuw/Uz58rZpuSX4cVcSnrhaRNGZuj3eojcouOXrXSgpU/DngVTsf+Vaq8tg+pI94wN/madfXCZja3x5PZMGtSKcnnpZnUFR8civh9y2OomBQQO7dBOS8KhuKSaK+yEKxu9ZtloLP8nX4UfDdcji8tVOqxkuoWvT0Hr/GDTQsGjRIrzxxhs4dkw7XXHo0KF46623MHr0aO82LISVlMk4npqLE2m5mNarOWqESxa3VVUV5zKsp2Na42j/XX3zoyk5qBkhoX0j8zsf9uz20d8OYVyXwIv4RqMAs6W1mK1bU1mBvUKpqsNCeRS+kacwmu0kdwyHVQBvzuiOm/o2R9uGtRAmCZVBg8gwEbUi2R2QdZmogw8Mt+Frw1TcIW3AfbrVlWmuYYKMm6TtuEnajrVyf8w1TMPREFv5JCWrCDvOZmg+58qFoSeuKY+l5lYGDCpczKma2uen1+AAyufvl5QpAICMglIU6mXUijA+f/nLND/TVny34xxqRkh4ZHR7u7MnfeHnPectBg1yi8oQESYiMszyOMwmZhp4RG/hLB7TLcE4jWUTU9UGmGeYjD/lUSiGf9e8CASq6t/nSU/gKLEaWZZx//3344cffrC63a5du3Dttdfi5ZdfxhtvvOGdxoW4S3mluGHuThgUFbsSMvHp7VWFgkwHVCqA5xYeNXosu8j+IlcbTjqWplrR+f11MBXPLjwKSRTw54OD0e+aGOPN7O7rAqdTjEEe7tOtwt3SetQWjGtJFKvh+FW+Ft8YpuAK6vmohcHBXXfRJFFA/9ZVn8sfZg/AmthLmDmwFVI1aoEQaclHFL6Sp2G+PBG3SFvxoLQCLautuDBBOoAJ0gHslLthrjwNO5XuCIU5sq+vsDyX3F0XO6angr2WVlywQavgb6AOgE2bHX85Hy/7SdaUabBoydXl+5KzivHTvQPLt/F6q5z35/4UvLj4GFrWi8Lyx4ajTpRzUz6cDerYyjQo8fNpNZ4ySDiJx3RLMEKKNXvutNICXximY6UyGDJcCPSQERXuywINFAwaVPP0008bBQyioqJwxx13oHfv3tDr9di7dy/++usvlJWVQVEUvPnmm4iJicHTTz/tu0b7IXd8h0wLN22LrxqQLjty0ThoYPpiFTh43nge/RebE0y3sujUJeeq8z57NVAhKyqeX3gMm54bbdIse6cxOHV4r2qEbDygW4E7pI2oIRgHZPLVGvhJHo/vDZOQyXV9/YfGB2t0p0YY3akRAOBCDoMG5JhShOMXeTwWyGMxRdyNh3XL0VmsmvIyTIrDMCkOR5S2mGuYjvVKv5CtYeJvN0h/359i9lgg9D32mD1/v9+czyz93auPaQLJC1cLhiZnFWHe9kQ8N6GTw/v4ZEM8VhxLs/h8YnoB2jaspfmcrCgWX5eUUYibvwqd+eWAihHicTymW4pB4imzZ2OV1phjuAHrQvi860mqah6wjK6hg+VPduBj0OCqlStXYs6cOZU/d+3aFWvWrEHLlsbL9Bw9ehTXX389Ll4sr3D73HPPYdy4cejRo4dX2xvs7v1hv9XnT13KQ6ROQmuNpRU/32y+pJ8nqVChNxh3ZBdznR+wJNmodO1LzZGOh3TLcau0BRGCcaGpHLUm5hsmYr48AXnQ7vCDXafGtT2yJJQ7AnFBcj1gl1eu74IP1p1GqcHyAJPcR4aEZcpw/K0fimvFw3hEt8xopZTeYiK+Cf8YJ5WW+MIwA6uUQVA4iHWKt2oNmGY3lRpkfLQuHlfyS/HixM4Wl/QrKZPx295kNKtbAxO7axdp9DR/CRgAdt4ssLGJqqrYcPIKYmqGo981/pO1d/aKdj0Na46n5uKTDdZX43jqjyP4+7Hhms+VyeZv1owvduK9m3virZUnkVFgf1Zp4FJxrXgIj+uWGi2LW+GQ0h6fGW7AFqU3AiHD6/7hbfDtjnO+bobD+rSqa3ae7NOyHk77qD3ewF4bgKIoePnllyt/joqKwvLly80CBgDQq1cvLFy4EKIoar6W3KN6JWAtEz/ZjvEfb8Wh5Gyz9D/TLANPi7uYh5u+NI5ua6Us2XsXx9bv7guthTS8p/saWyKewV26DUYBgww1Gu+W3Y7hpZ/iU/mmkA0YAMBDo9t6ZL/uCBp01KgwXl1F1XRr7hlyjUPHfNaOtd0fH9sejaPdv7JDiGUN+gUVIjYo/XCj/r+4Xf8vbJONg+ldxBR8Hj4H68JfwA3idkgIrVRif7+7XP07Y/r1+XN/Cr7eloglhy9U3m3W8sHa03h9xQk89MtBzQKMIccN0dqvtyXinz8dwE1f7sLB84H9nu49l2lzm2Opxsu4FutlXMotAaA9zedISg4e+uUgDnl57OdtIhRcL+7BqvCX8V34h2YBg91yV8zUv4Ib9f/FFqUPAiFgAARuXz2pexOzd1hyx1JXfoyZBgA2btxoVPTwiSeeQNu2lgf/Q4cOxS233II//vgDALBixQqcPXsW7duHVtEnXyuTVTzzxxGsf2aUT9vx0+7zZo9pnTYC8U5vByEVj+qWYqq4G5JJBd5Laj18bZiCBfJYlIDL+QGem9/m6t3FER0aYJKNu35D2ta3uZ86Di7BZU+hrKhwnUdSt11NtRaE4EnX9j4Be5Su2KN0RXdDIh7VLcMkqSp7rL14ER+Hf4mnlL8wV56OxfIIlIXAcOTu7/d55TjuqMZveir797K4yn9bC35Uv2P43+VxWPnECJfbYsq5X883g3l3nNveXV2Vev7KkliseWqk6zv1kVXHHUveLig14PpPtyM5qwjPju+I3OIyze0S0wtROyI4zyESZEwVd+Mx3VK0Fy+aPb9F7oXPDdNxQO3sg9a5zl9XPrGmS9No6CTR7Fwb7EEDZhoAWLJkidHP999/v83X/POf/zT6eenSpe5sUkDz5lJNSZlFXjuWI1zJNPAH3YRz+DLsY6yPeAEzpF1GAYMUpSFeLrsPI0s/wXx5EgMG1TjT+d012LG79874+b5B0EnWT/eiKMBmf+fgh9iet0OF6nffjRphEpY9OgwzejfzdVMCXqzaFg+XPY3rSv+Hv+UhUNSqD8U14hX8L2weNkc8gzul9YhAKKQWO+fVZbFQVdWudHfnv09Vfxt/Hseb/noL9iVj9Pub8amNtHd32Z+UhYd/OYglh1NtbmvX38uBYxeUGmxv5CXOFDM8lJzj0Pa/7T2P5Kzycd6H6+OtprHn+9F74w5hMOA2aTM2hT+LT8LnmgUM1sn9MLX0Tfyj7MWADRgA/psPEW5jzASYj/l0DBoEv5UrV1b+u127dmjXzvYa8iNGjEBkZNWcvhUrVnikbVr+2J+Mnv9Zi0d+Peg36/tas+GE5dUIVFXFwfPZyC3Sjh7bwy/fAo3zRkZBqffb4aC+Qjy+D3sPKyNeMbozCACJShM8V/Ygxug/xG/ytdDDuarJZOy562wXkvLWAN6lJbQ02BN1V1Xrd0YfG9MeDWo5luHg6nJrCx4YjJ4t6uKDW3rhX5O7uLQvKhevtsQTZY9jnP59/CWPgEGtGn60EDLwZth8bIt4CvdJqxAJ/z9XettPu8/j+UXH8MyfR21uq3ihU4x3oHZLdqEe0z/fgUFvb/DIlIU3V55EUmYRPt4QjzSL9YTc957c8tVurI69hKf/OGrWr5eUyXhtWSwe/fUQLuQUu3184q/BnCK9AT/tTnL7fkNxVZ8I6HGXtA5bIp7G/8Lm4RrxSuVziipguTwYE0vfxQNlz+K46pkpkQRMcKImi07y0y+om4R80CAnJwfJyVVLDw0ePNiu14WHh6Nfv36VP1ef3uBJa2LT8OJfx5FXYsCq45csrgntS6ad2v0/HcCRlBzNbf/zdxxu+nIXJn66DfklzgUO/GU95uoC67ShYogYh1/D3sLiiP9grHTE6NlTSks8rn8M4/QfYJE8CoYQSCP2JkEEHhltPVDprc+TrQGuo980e6drWNvvcxM6Yemjwxw8svOD67sGX4PeLesCAHSSiPtHcFDmTolqMzxb9nB58NEwBnq1KlDVWMjBv8N+wY6IJ/GQ9DdqIvQuGKxZdDAVm05dsbld53+vcflYtr67WYX2Z4V8uvEMjqbm4nJeKe78dq+rTbPqgocvMk2X9Iu9YDz//ufd5/Hj7vNYeTwNLyw6alcAx5GbP97M5HTEK0ti8Wq1KSzu4p+/rWfUQAnul1Zie8RTeCPsBzQXquo/GFQRf8kjMF7/Hh4vewKn1FZeaVPD2l7IJPXTP7IAWCw8aqnJkhjcl9XB/dvZ4eTJk0Y/O1KXoHpGQnZ2Ni5duuS2dlny0C+HjH6Ov+x49VpfeHGRdlDlx6v1ANJyS/DV1vKiLqWGwC+OJYoCkv106kQVFaPEo1gY/l8sCH8LwyTjDv+Y0gYP6J/GJP07WK4MZbVzOzjT94mCgEfGtMedg70zCLDG1gDX0btmoihgQrfGNrerYSPDwd/XQr5/eBtfNyHgpKiN8bLhnxhd+jF+NIxHqVqVudRAyMNLYb9jZ8QTeEJajGj4X3FYf2ZrLXt72PrGaX0nr+SVaG67tVoNBL1seUWTK/klOJmWZ1f7LPH0/OgrecaZBQ1qGV9UfbsjsfLfO89m+k2mQalBxnMLj2L2/H0eGZssOXzBrfv7YvNZTP98B9ZZyVQNFrVRhEekpdgZ8QT+FfYrGgk5lc/pVQm/GcZijP5DPFv2MBLU5l5t2+on3V+TxJS/BsJUAA+OdOymQbuG5iu6BZOQv2WYmJho9HOrVvYP3E23TUxMRJMm3l1iqCJCnZJVhJoROsTUdCyN11usDRQqnL5UHgD5z9+ORav9cXqCAOCpPw77uhmaBCgYLx7EY7ql6Cmazw88oHTEHMMN2Kr0hN+GgP2UMwM6USgvBvjmjB7of00MnvrjiNk23vqI2840UNG8bg27lzQTBWDOzL7Yn5SFOyzcYVRVFe/f3BOzrNyBdFe69cyBLbFgn/n69NU58zdsVT/KyRbRRTTAa4bZ+NwwAw/oVuIOaSOihPILs7pCIZ4JW4T7dSvxozwB3xsmIhvRPm4xAYDpdN+8kjJc98k2zW3t+f5ezCnGhE+2Ib/EgH9N7mI1w8fanXnLU6Lc05cV6o3nzYuCAFlR8fryOJxIy8PlPPun1qiq6nCQo2JrRVHx99GLCNeJNovcAsDbK09i0cHyGgwttifijRndHTqut72/NpgXritXF/mYrVuD2dJaRAvGgZwSNQwL5LH4xjAFabBdpNhTJC8E7P35nsC4LrZvenx7d398t+Mcru/ZFEpmss3tA1nIBw3y8oyj2jExMXa/tl4947SV/Hz3rc9+9uxZ2xsBuHTpEj79Ox0f78pAzXARn17fFD3bNkOjRo3sev25jELE1Ay3a7k1W1RVxdurTqJIb54pkJxVhHdXn0KfVnUxoZt2B1fR19sa1AcCQRAcLvjjaSIUTBb34FHdMnQWzd/jnXI3fC7PwG6lKxgscI4AweZF9ZhODbH5dNWdt+p37CylwnkrMGZPpsGcWX1w49xdVrerIAoCwnUihrVvgJEdG2pWXVdVYGj7Bvj1/kHILtLjxMU8zN2SYLaNO7w+vTtGd2qEB38+aHEbZ7NFqvvniDbYey7LbOkwd2pWJxIXc7Xv7AaidNTDW4Y78aVhGu7XrcLd0jrUEsp/v2ihGI/rluJeaTV+kcfhW8NkpKOubxscYOy5QDV62ua2xs//tjcZOSa1iSo2sSdo8PH6eOSXlF+Qv7nypNPTgjzdc5n+LipUrIm9VJk1aWt7o9eqwNkr+WYFnbfFp2Nkx4aar6l433/Ze75yOsCcmX1strt6+37ecx4n0/K8Ov867mIuujWr47Xj+bMGyMX9ulW4U1pfeY6rUKhG+NU5zp8v6L1BFMvHMHqD5Ruf47o2xriu5cGFn1cF/vWLNSGfb1xQYJzeX724oS01atSwui9XzJgxA927dzf7n6n33n8fH+8qr2tQqFcw850FmDt3rtV9bzx5GXd9txfXfbwVYz7Ygms/3IpMNxTp252YiXnbtSvbyoqKr7Ym4MGfDyIhXft9CqaTkyPzPT1NBwNulrZiffjzmBP+uVnAYJPcGzeW/gd3lL2C3Uo3hFrA4Nb+Ldy6vx/vHWD1edMhpD2p91oDzz8fHGJ7tQMH2XNt3rO55YHfixONKzhXn3bw9Z398JmVwe2w9g0wpWczPDiyHTo3qY0InYhv7iqvG1PTTUtphUmixaBlBWfSm00rJj84qh1u7ufez1V13ZtHo02QpkFmIRrvGW7HsNLP8KnhRuSpVVkcNYVSPKhbie0RT+I13Y9oAttrvlO5vQ4WILR3ekJBqQGz5+8zWhbQlGI70RAXLRQwVFXVoTn/shsjrDvPZmB3gvFnzHT3qgpsOGk5hf6vQ5bT9mVV1cyw+sd8y0tzVvxdqtcPeHyB41mNB85nY0+ia0UpHXmrP14f79KxgkFjZOFV3U/YHvEkHtItNwoY5Kk18JlhBoaXfop3DHf4RcDg1/sHeWU5RE8e4as7+7q8jwidA5fKQT58DvmgQUmJcZQvPNz+9P6ICOO5bMXFbi7AI0rQ1W9RXinNIpPlPuqVLxG2JzET325PNCsuqKoq7vvxALafyaish5BRUIpvthtP03DGngT7BnBzNydoPu7svCZ/nJ7gD8JRhlnSRmwOfxYfhH2NdqLx+sir5QGYXPoW7i17AYfUjj5qpe89NMr2aimOaN+oNppEWw4+mk43rn69aemzrPX4wDYxWOvm9bptDc6b16thMcgRJgl4YGRbjOlUfoesblQYJlZLm60RLuG6rrZT/epEhWH1kyNw6o2JuO7qBX5MzXCHaz6Ynk/+MbS1Q6+vrnOT2kY/T+1lvBSjaBI0EAUBijsWaLfgwZHu/cz6o1zUwseGmzGs9DO8V3YrstRalc9FCmWYrVuLbRFP4W3dt2gh2C4OGOr2OniBaOtaoSJt+a2VJ40yp6qLvZCH5Mwim+cVWVGx86z5+GHz6Svo+upajP94m9GNDWt7c3bMXiYrWBt3CWevlI+Llh25gDu+3YuZ8/ZgTWxVvSqtX8Xae/XZRsvLQMqKivR88xs21U8dKVkm9Qe8eFFSvejjL3vO4+FfLGdo2RKhc+/KPIGkhXAFb+m+w7aIp3Cvbg1qCFU3lbLVWvig7BYML/0MHxlu9avpV8PaN/D4zbyv7uzr8jGsTcvudbWocYXXp3dzeDWmj27tbfe2QR4zYNDANLNAr7f/DnFpqfHJ3jTzwDUCGt3yOprf/xUaTH/R8lam3zbFgHwlHDPn7cGbK0/ipcXHjZ6WLQxk03JcT3O1NyL51yHttY2DKdPAlyJRitnS6vIBddh3aClWDehkVcBSeSjGl76Hh8ueRpwa2gXcnruuI9o2rGV7QzvZ8xk2HUBXvwi3dyWQOwaVX0B3aFzbrXNTbV3n3tS3BQQBaF7X/Fw3vXdzSKKAL+7oi89m9sHyx4abLeGo9f7cPtA8GCAIgtn55M0ZPRwKkvRqaZwR8drUrna9Tiso8sUdfTGyY0Pc3K8F9r1yLV6caLxMpum8T1HwbB0KSRRCJliajyjMlWdgeOlneKtsFtLVqr9ruCBjlm4TtoQ/gw/CvkJb4aKVPYU2R7OS7N18wT7rc3jv/XG/zfPKegvF7mbP34/iMhlnrxRgzib7pmxaztyy3oiP18fjwZ8PYuIn23AhpxhP/n6k8rmHql0sm01PUJ0v1GrPtA2zqW5e/N73/O86yIqKhPQC/GtpLFbHGhf7duTXjnbDFNhA00ZIwwdhX2FL+DO4Q7cREUJVPYx0tQ7eKpuFYaWf4XP5BuTBPzPHrH22f75voEv7blg7AhO7N3VpHwDQop79114jOjTEHw8OsWvbirHa+K6NMe/u/k61LdiEfNCgVi3jCwbTzANrTDMLTPfliuHPfYMarXsBAGp2Gobnf96OQ0fNVyB49tlnjX7u27sX0GVc5YBy5bE0s9docUeasz1rsldnMCmO6GzH649LLvpCTRTjIelv7Ih4Eq+F/YwmQnblc2WqhD8Mo3Gt/gM8VfYYzqieS532FUkUHLqAblg7Ao+N7eCRtkzuaX9HWP1jbznTwPiJ1vWrBhi3D2iJlyZ1Nn2JU2x1vpFhEgRBwE/3DcSjY4zvdlcMgKPCdZjWqxlaxpgXBzT9jj89rqNDSzqZFl+zRFXLgwz1osIQrhPx470D7Q5qam3WrmEt/HTvQHxwSy80qh1p9ncyPfcJEGxeKH11Zz88P6GT9Y0sEIXQCRpUKEIk5slTMLz0U7xadg8uqlX1h3SCgpulbdgQ/jzmhH2GTkJwF6Nyhmk2jBbjkgbWt7d3GsDZKwW4ZGFVhQqnLtleMeHA+fJMifySMvxsoX4AYPlC1lZzK+qoGBQVM7/ZY3E7rZoGzg6fLN3Eqc70nOmuorD20BsUfLohHuM/2mpxG1lRMcdKNkUFh1K8A1xHIQWfhc3BhvDncLO0DTqhaqybpsbgtbJ7MLz0U8yTp6AI9k+J9gVrp41OjWtbftIBnlw9wfTrIgrl/bmjxtuRJVm+/+C++xnyhRCjo41TgbKzsy1saS4nJ8fo59q13fMFAoCELD3Cq9XB+TM2Fy2bmn9oGzVuDKCqzXFXShFTx/hbcj6zENdcvciw1N3YM6CwxdFdmEatVx5PQ4cNjs97C7XBs6loFGC2tBazdWtQVzBemqxU1eEPeQy+NkzBBWgXVgoW+18Zh5ia4Vgbewk7zmbY3N4TVYEr9vj42PY4fSkf5zIKba40YM/FrLWPeJgk4qFR7ZCcVYTf9rp2sfTJbb1x2zd7ygeCM/tYnCvbrmEtPD+hM76oPtXIju+h6W9qqfCjO7RvVAvbXxwLWVZRJ8r+u1zOfCpM/4SCaB7oaVg7wigVeWL3Jpi3zblpYaY1FIDygE+qh9eo9welCMdP8gT8Lo/FjdJ2PCItQ6ur2VSioGKqtAdTpT1YK/fHHMMMxKrOFdQLNmcuu69QM2DfBa8nPLfwKNbGWa4hYGnQ7khrk02nBFjZj6I6nyVprdZDRkEpXllyHAfPG49Jvf22f2Yjw2P9iUv40I56BY7UpQhU3YVEPKZbhonSfrPnkpWGmCtPx2J5BPQInKwLaxf0Onuj+LaO4eJQzNoYyjTIpvX71I0KMyvi6nRb3LIX/xU6oT8L2rQxTs9OTrZ/0H3+vHG0u21bzw5OtE7MWufh7WeML5hGf7AFW05fsbg9oBHNVlSzTABbPlhn/wX/8qMXzSqkA8AnG2xHrKlcfeTiBd3v2BnxJJ4O+8soYFCshuNbwySMKP0UrxpmB33AAKia1/btPfalkTkS5IoMs+9UWfE1qhsVjl/uH4TVT5mvcezM2Mme17ijs+rfOgbrnh6JVU+MMJu3b4s9v5bpecZTWULNrk6fqBWhcyhgADi7bKZ5TQPTv9nH1eZF/ntKV6ePBWhndYmCgNend3NuhwFIjzD8Lo/FWP2HeEb/EBIU4+yeCdIBrIj4F+aH/Q99BRZhW3rkInKK9Mgu1Nt1AWfro+mri0BrAQPAsbowzjD9vVVVdfpOqbVsjbdXnsTauMvIKDCeMuurYI0lP++xnPVRnX+12r36C6fwQ9j/sCLiX2YBgwSlKZ7RP4Sx+g/xuzw2oAIGgPU+ytHsYlMVH39Xxy7WXm+aydgouvzn7+7pj1oROvRoXgf3DGmt3T5n2sJMg+DWtavxPFd7lzoEgISEqoveevXqoUkT22vlups9qWqqCjz66yHEvT7R4vYV3/1Sg4wTF/Pw7MKjSM0uxhez+tpMy8ku1OOVpcetbmPKmWq/lgRzZ6SlCTLxoG4Fbpc2GxXUAYB8tQZ+ksfjO8P1yPKjgjreZDqP3hJ7s2tWPD4c/10eh/1Jxnd8mtetgZwiPQo1lhitoHUEa0V7LH2WvZmS6kzqHmDfRYRZCRYHf63W9WuidoQO+aUG1K8ZjkyNVUq6N4+2a93y6m2q3nR3rJ6gqqrZ32x4hwb47Z+DkFdswIRujZ0+FlD+2TUNuIgCcPeQ1kZV1UOBATosVkZiqX44Jon78JhuCbpUWyFmjHQUY6Sj2CV3xRz5hpBeUrb36+sBAHcNvkbz+V/3JuOtG3ogKaMQB0zucJvyt4vXCpbOldaWTHNs/8Y/qwBEJ2+/WXsPFx/WXnXBn+7Y2wrgBDcVw8RYPK5bisHiSbNnTykt8blhBlYpg6AE4P3Zni3Ka8dY66LCXF6y0z2f5UFtYnAkJUfzuQidhB/vHYhFB1NxU9/mlePDa7s0Rux/JwAANp/SLqRbz8EbDgCCvmsJvE+ym9WtWxetWlUV4tq9e7ddr9Pr9Th4sKo4To8ePdzeNnv8ameU19qFDVBRWEvFrV/vwQ1zdyExvRB6g4J//nRAc/nA7EI9DiRlQVZUvL3qJFYdv6SxV+84mWZ7PmQwaCVcxtu6edgW8RRm69YaBQxy1Jr4qOxmDCv9FO8bbg/ZgIEj7I2Sd7ewxOCk7k0Q9/pEo8daNzAuZqR1Udi7ZV0MbVcfAHDvMONMJ0sDQhXl6fYVrteomeCJAHf1Off3D7deNNOeawjT98PRAbBOEvH7g4Px2Jj2WPDAYKPnRnVsiEP/Ho8ljwyzmTZ5X7Xf5fXpxnUwnHkbTQNQiqo9HBrargEmdm9S+T7Y8xEMkwTMn228jKfW1Jpgn0tpiwIRK5XBuF7/Dv6pfwZHFePMv6HSCSwIfwuLwv+L0eIRhF64uYq1u8PnMwtxj5Ul/yq4c2lDd85pthQ0mPr5Dly2UVvBHlpLLjp7peBMMFhRgdOX3DvVhByh4lrxIJaGv4pfw98xCxgcVdrin/pnMEn/DlYoQwIyYABUfaKt9SvuyjS4zsYyyJb0bFEH9w9vg0FtY6xuN6pjQ8yZ2QejOzWy+PzYzo0QoRNR52rBzqhwCY+OaW9xn5belgg3TdnwVyGfaQAA119/Pb766isA5dkDiYmJNqcabN++3aho4pQpUzzaRksu5jrWCVpaq1kUBOxPysZRjWjdZxvP4F+TuyC3uAz1a0WgpEzGpE+341JeCW4f0BILD2qvhuAtt3xlX6AnULUXUvGobhmmibsgCcaDjHQ1Gt8aJuMXeRwK4c7VO4KfpZoGLWNqICXLeG646aA2XCfinyPLzxFvTO+GTzeewcTuTdC5SbTJ68wJAvDzfYOQU6RH/VrGqXOWhpCqCnx/zwD8sCsJg9vGaK5e4An3DW+D0jIZpbKCx20UjXTmEsKZZbi6NauDbs20AznWsjiqe2Z8R0SFS6gVocPtA1ri30tjq560awUM45+vqW9c9DEqXMLw9g1s7seeIdf2F8aa/V5adzbdUZcmGKgQsV7pj/X6fhglHsPjuiXoL1ZNT+gvxuOH8PdwTGmDzw0zsF7pBzVAB/aeMOr9LXZt50c3vI1Ya9aH607jlcldsS7uEga1qY9W9c2LtdpifqGvOh2wdSZbQ1FV3PGt5UKN/spfPy/2EqFczWRaii6i+TTmvUpnfG6Yge1KDwTD7eamdcrHGNZ+E52zKTZXVXwkujevgxEdGphNrbZmYOsY/PlQ+SoIFdOvnSWKAr7/R3lgXlFU7DmXiZb1oirfAy2WvvMdm7ivtp0/YtAAwA033FAZNACAefPm4Z133rH6mnnz5hn9PGPGDE80zW1qRehQrJdxz/fadxBEQUBOkfZyk3sSM3HL17txODkHj4xuhzYNalZWQ/59f4rma8h13YRzeEy3FJM0iupcVGPwlWEq/pDHoBSOrTkbiqLCJRSZZNtUv8j69u7+ePL3w2jbsBZ+mD0AN8zdheSsIrNMgAqrnhiBxtHlVY/vGtIad1mYE2eJJApmAQPrVLSqH4VXrSwb6IkKxJFhEp65zr4K//ZmDYzs2BDb4tPRMqYGBraxfofAEY4su1QzQodnLfxezryPtSPD8MltvfHXoVTcOfgahEkiujevg5cmdcbuhEw8Plb7joU9F/pN6kSizKS+jE4UzQbhdUNwSTPrBGxVemGrvicGiyfxuLQEw6SqqRs9xXP4JvxjnFJa4gvDdKxUBgfsXUFf8PT0BEsZjrZYOw8dS83FAz8dwN5zWagbFYa9L1/rcODSNGiQWaB3+sy74aTj6f2KqprVOQgEgbrKlQ4GTBd34RHdMrQTzVcj2yb3wOeGGdindvFB6zwjKlyqXJHJs5kGVZ+Ju4e0dihoUP1L52o7qhNFAUPb2Q74hyoGDQCMGzcO3bt3R2xs+d2mOXPm4IEHHjArklhh9+7dWLhwYeXPkydPRocOnlm6zV3q1QzDCStp/KJg+eRwqloq3NwtCXj5evcs70ba+gmn8ZhuKcZIR82eO6c0xpfyNCyRR6CMX1+7NImOxJbnR6Pzv9cYPV69nxnXtbHRVINVT47AhexidGysPb+/+lQBaxy9A+VKES/TYz1xbQdsPX3FZoaAu1TMgbTlm7v6YXdCJnq1rOtyZ//a1K747/ITiKkZjieudc/vac/fTGsAPKNPc8zo09zosYdGtcNDo9qZbVt5LJOfOzWujYs5xcgvNVjdLrqG+Xf/P9M8VwSxRb0aGNu5EX7dm+y3c9ktE7BH6Yo9Slf0NcTjMd1SjJWOVD7bWUzBnPDP8ZTyF+YapmOZMhQGnlttcmV6QmZBKe798QAyC0oxZ2YfzW36vrHe6GcBAr7fec7mvm01qyLbMqeoDFtPp9udGj3q/c3ILzEYTW0CgDdWnsCojs4VGnam/oi9X78dZzIwrH19vynMpqrA/9ac8nUz7BaOMtwibcVD0nK0vLo6S3Xr5H743DADx1TL5/dAtOLx4YipGV5ZTNjSx6eOG4LU1T/KrnxKQ31qnjexZwQgiiLefvttTJs2DQBQWFiIqVOnYvXq1WjZsqXRtseOHcMtt9wC5epaOaIo4q233vJ6mx0VExVuMZMAKI+u2ZtpZAi4QWMgsF5U57TSAl8YZmClMggyHE/pDmVhOkGzOKK1jqZWhA6dqqeZOdknuevuvz3fuNb1jespPDO+I54Z39Etx7fkyzv64pk/j6Jjk9q400JxNVORYRLGdNaeW+io2cPaYFTHhqgXFY56dk5N8Csmn8G1T4/E8wuPmk350kkipvRsihXH0jCoTQw6Na5t9pmwVHvDXvWiwqA3KJr1b3a8OBYA8MDIthAEATfO3YnLeaVm2/m7Q2pH3Fv2ArobzJdGayem4cPwr/Ck8he+kqfhL3kEs7iscKUg30fr4yunQv5j/n6zC3FLrtjxmbM2PDG9gHYkAHY+s3wZxvfXnjZ6PCWr2KsXLfbWQbjzu734v0md8aCVoKU3xV7IxdHUXF83w6YaKMFMaTMe0K1AE8F0uUsBK5VB+MIwA6fUVhb2ENhM+xFLQSd3FOSsvoueLa33X7f0a2HUL1ZvlaWvX6fGwT1VwBcYNLhq6tSpeOSRRzB37lwAQFxcHLp06YI77rgDvXv3RllZGfbs2YNFixahrKxqPc///e9/6NWrl6+abbeWMVG478cDFp+XBMHuiLQsM2jgLgIUXCcexMO6v9FbNF+C8qjSFl8YpnPerQe4M6XNEsfHktrfLXsGijMHtsIve88jMb2wckk/T5vUoykm9TAvyuhNbZ1c7cESe/5kLetFoXF0BC7nlaJJdCSaXp2q4oljVfj09j54enxHtKwXBUEQ8PDodth39a7puC6uBWF6tqiDN6Z3R/N6NfDYb4ewJ1G79k2Leo7PAfdHsWpbPFT2NDoaUvCobhmmiLsr68W0EtPxtvgdntL9he8NE/GrPA75CI7f250cXJHZyK97q+aE5xaX2ZVJdfxCrl0X+a8ssX8lJ3dd63vzPqfiQKDjndWn/CZo4O8Bg7rIxz3SOtyjW4sYocDoOYMqYok8HF/K05CoOrYUMdmnUW3Lfeidg1vhzRk9jIMG1acnmHyRnxnfEZkFpbhvuPXadOQ4Bg2q+eyzz5Cfn4+ff/4ZQHnGwTfffKO5rSAIeOmll/Dcc895s4lOC7dR0fPbHefQs2Vdu/blzqrJoSocZZgh7cCD0grNeXJ7lc74wjAd25SeCIaiOvZ6dnxHfLjeO2uqD+/gf/PWLH217Bko1giXsPrJESgsle0uCEjm7LmQEEUBv94/CKuOX8Lknk2dLkKodYfS0l9aEgWj5TBHdWiIN6Z3Q1JmkdUpELbc2r8F3ru5KvD9+wNDcDmvBDO+2In0/FJ8ZiF9PBjEqy3xZNlj+ES4CY9Iy3CDtAM6ofxquJGQg5fCfscjumX4VR6H7w0TkY56Pm6x/3DnMrCX8+0r6GxtimWFM1cKbG7jbt6cAsBET/dqhgzcr1uF26XNiBKMM1lKVR3+lEfja3kqUlXnpqAEg4o6RNW542PoSrZC9SxO0/53Ru/mThU5JdsYNKhGkiT89NNPmDJlCt54443KGgemBg8ejLfeegtjx471cgudZ08H/84q87R4LYE3p9V/1EIRZkkbca9ujVnqGxCcRXUcERHmvWyKR0ZZXk7Hk6x9FS09NaC1fQUDI3SSUysSUBV7p5S0b1QbT1zrWvqjK9caoig4XIBTi9bnsXF0JDY/Nxp6WUF0ZPAXWDynNsXzhofwqXwjHpKW4xZpGyKE8ozCaKEYD+uW415pNf6SR+AbeQqSVN9m1/gDR+542/LbXvNq9N7hnot9b06pLjCpd0LO6Sik4EHdckwTdyNMMJ6SVahGYIE8Ft8YpuAKA4W4rX9Ls6CBO6IG9u6iQ6PyfnZw25jKLLjZw1pb3N7FRR3ICgYNNNx666249dZbERsbi2PHjuHixYuQJAnNmjXDgAEDbC7H6I/s6d/T7Fy+kTUNHNcQOZitW4M7pQ2IFoqMnlNUAauVAfjKMA3H1cD7bAWimQNboU6U5y+G3DGYbFQ7wuqqCeRe3rwA0EpQ8JdErsgwSbMWCOA/bXS3VLUR/mW4D58YbsZs3WrcVe18HSEYMEu3GbdLW3i+RmBmHJ40yVQoKZNx/4/7K1eDcpYnVq4hz+gnnMbDur8xTjps9lymWhs/GCbgJ/k65MK9094Cmb19YsWUPbvZOIWIAtClaTRuG1BeW+7j23rjs41n0Lp+TYzr0rhqNyb78cbU01DFoIEV3bt3R/fu3X3dDLdwZwe/4thFt+0r2LUW0vCAtBI3Sdsr71xVKFXDrt65mhw0d65GdGiAyDAJ6084vpSUP/NWF2T6Nd3x4hg0rB3B7AEv8maqsdaxAnVpsmCSgTp433A7vjRMw0xpE+7Tra7MDBMFFZOlfZgs7cMOuRu+kqdhh9IdoTSNDAiONPl52xMRd9H2lAdbeI3i3wQoGCMewcO6vzFANJ/+mKo2wDeGyfhTHo0SOLIUsmfcN7wNvtthe6WQcJ0IvcGF4iJ20gxua2z3zxFt8eZK+zKWLe2jQodGtbD88eEIk8TKIEDTOjXwzo09zfdjMnAyrXFA7sMkjhCx8pj5vHlnpWQVu21fwaqHkIgvwj7BpvDnMEu3yShgkKdGYa5hGoaXfoqXDfcHTcAAAH6+bxC6N3O+iru/3rFxZx/kyL6iwnUMGHiZNz+BWseaPbSqkvwIJ+pu/GtyaE5t8oQCRGGePAUjSz/BC2X/RIJifK4eLsXhl/B3sCL8lfJiijBfeSJYKYqKlKwi2xv6MXcEDADgh11JbtmPJ6yJveSWSveBKBxluEnchjXhL+H78A/MAgYnlZZ4Uv8IRpd+hJ/kCX4RMACAGmESHhtje/pkTxdXzDH11g2WbpLa1ys6GnC39bmMDJPsyhow3YuzNYbINmYaELmJCAXjxIO4T7cag0Tz9Ygvq3XxnWESfpOvRUEQV+P2tzulgTRe8rf3LhT1bOHegZg1WoOs7s2j8cltvXEiLQ/327kUXXVhNoremuInzjY9wvCnPAYL5VGaq910F5PwefgcpKoNMN8wAX/KY4J+xQVZUfF/i+1fqSCY+fOUzYd+OYif7xvo62Z4VV3k4w5pI+7RrUMjIcfs+b1KZ3xpmIYtSi/4Y4bQ3UOvQY0wCecyC63e8HPXp65nizr47PY+aN2gpubzWrEArdpmWtfq9aLCkF1UZv4ErH9voiKcvzxlpoHnMGhA5KIolOAWaStmS2vQWjRPy09QmuIreSqWycOgR/AUFXtxYmd8sy3BrEPwxUX6GzMcm0bkrT4lXBLRrmFNJKQXVj5m7f1pFWN8oVE7kqdob/hiVl+8ufIEBraJwZhOri1f6Aitj6EgCJjRpzlm9Gnu3D4d/GwHUlDN11SIWKsMwFp9fwwWT+IhaTlGS0crn28hZODfYb/iKd1i/CmPxnx5AlJV732evElRVew4m+HrZpAdnvnzqO2NgkBb4SLulVbjJmk7agh6s+fXyf3wlWEqDqkdfdA6+1UsP/jFrL5YeWylxe3clUFSLyrcYsAA0O6n7J3ybC2eVmoyteKZ8R3x0fp4CALw+rRudu0fMO/DmGngORyREjmpKTJxj24dZkobUUcwT9M8pLTHV4apWK/0gxqEM4FcvSv+2tSu+O/yE0aPOXrB8+ntvWGQVdxg5QLLl0FnQRDw470DMfx/m+3aPipch3l398fSwxdw24CWDt81JudM7tkUk3t6f5qQJ6o8e+PjHihxhvuHt8G3dswNdpyAPUpX7FG6oovhPB7ULcdkcW9lFfbaQjHu063GP6Q1WKMMwHeG6/3+QsVRORbuHpL/Ce5LKBVDxBO4T1qlWdywVA3DYnk4vpWvR4LqXCA22Nm6xtbKiDPI9tVScGRp1sfGtEfHxrXQsHYketm5BDxgPhb1RiFEf51K62kMGhA5qKeQgPt0qzFZ3FO5pncFWRWwWhmI7w2Tgm6QaK8a4cZz8JvWicTANjFYdsS4gOaNfVqgXlQ4nvrjSOVjjaMj7T7Oy9d3xvTetgcBvr6T2qKeY2nK47s2xviujW1vSAHPIwMPR+eVBkwIwHFhOs8H3U6q1+CpssfwLmbiHt06zKoWRJaqFU08rLTHt4brsUYZABmBX6fkLTuXaCbf8+ZSxt4SBgOmiLtxv24VuonnzZ7PUKPxs2E8fpHHIRPem3LmTe46c9uqRaD1rFYGgdZ21sZf9wy5xuhnURQwsbvrwXtOT/AcBg2I7KCDAePFg5itW4OB4mmz5/PVGvhdHoMf5QlIVRv6oIXeZ6kziDIJGuz+v2sBwCxoAMH8+qZL09q4vkcTrDp+yeJx5/9jAKLCJQxsE+Nwm4n8SdM69gfJ7OVvw6VWMVFI9lHBPG8OHi+hPv5nmIk5hhtwk7QN90qr0abadLU+4ll8Ef4ZUtUG+NFwHf6UR3NZN/KKyCAqptsQObhd2oQ7dBsrVzSpLl5pjm/l67FMHoZShPughd7jyA2RPq3q4q7B12hOVbF1lnTlNKqVaTCtVzPIiorHr+3g/I6rM5ue4J7dkjkGDYisqOigZuk2oamQZfZ8eeGrifhDHu13xQ1fndIVr684YXtDN4sKt++0otURCYKAL2b1xftrT2PulgTzDQC0a1gLrep7970O1VQ08qyBbWIwrH197DybaVe1bHuIfnaX5aVJnfHIr4d8cmxfTG0tQiR+lq/Dr/I4XCsewn261RgsVt2VbyFk4JWw3/CsbiGWysPws3wd4tTW3m8ohYzAzzRQ0Vc4g7t163C9uBfhgvkqJdvkHvhWvh7blJ7wv9Ap8MPsAYi7mIf315rfdPKG927qiQ6Na2sHDWxOT7DvGFoZC6ZBgxphEj6b2ce+HdrJNCzhjWCxn3WzXsOgAZGZ8g7qHt06TLLQQR1UOuBbw/VYp/T321TTWi5Un3VFi3o1NB9vXrcGLuRULdcpwLyTqXjsodHtsDMhE0kZhXhxYme8vMS9VbodPd+HagdBniUIAn6+dxCKymS3fV8d/qx6eHaCL6cH+XLihQIR65X+WK/vj27COdynW42p4u7KugeRQhlu123B7botOKh0wI+G67BaGYQyDsvIzQI10yACekyTduFuaR16iElmz5eqOiyVh+M7eRLi1ZZ27bNT49o4fTnfzS21bXSnRhjaroHNoEFMTfuzI6Jr2F9Y23q/YGt6gr1LLpo/ZjqNwRvT4bxR0yBUsXciuioSpZgm7cI90jrNOXJ6VcIqZRB+NEzAYdVNaVUe5Ku5yoPaxGBEhwbYfiYDD45qW/n4P0e0wX+qFT4UBMHsTmBFECE6MgzLHh0GAEjNdn9qc/DO4qZAI4qCWwN83ogZOBII8GXNBF/XM6kQp7bBM2WP4H+4HXfp1uN2aTMaCHmVz/cTz6Bf+Bmkqz/jd3ksfjNcizTU92GLKZhEhkkQBP/5PtjSQkjHndIG3CZtRj2hwOz5NDUGvxjG4Q95DDICqF5BuIUaK2GSgDJZRc1wCfPu7mf3/l65vgt2nc1weclPm4FmV6YneGE50v6t66F2pA75JQZ0aFTLZo0Gch6DBhTy2gkXcLu0GbdIW1FXKDR7Pk2Nwa+Ga/G7PDagOqiGtSNcev3kHk1xIacYR1JyLG6jdXIWBAE/3TsQmYV6NKhV1QadyUoAAsxTqf01QDywTQx2JWS6ZV/N6mpnYhC5Q88WdX3dBCO+zTTwr6uky4jBB4bb8JnhRlwv7sXdunXoK56tfL6hkIfHdUvxsPQ31iv98Is8DruUbkG5+g55T4QXCoK6SoKMMeIR3C5twljxCETB/Lu7W+6KH+XrsF7p57cZns5Y+9RINK1TA4qqoqYDAeROTWpj+ePD8dXWBPOaUWacH1y5MuXNdHqCJ6Z6Rugk/PHAEGw+fQXTejVz+/6pCoMGFJIioMf14l7crtuMQeIpzW0qOqgNSl8YAvCrMqpjI/RoXgfHL+Q6/FqdKOCLO/oCAH7clYTX/o5z6PWCIBgFDLS3Me+MPNGhaKX7NXfwwv2+4W2wNT4dSRmF+Pi23g634fkJnfD+2tPo06ouV0Ygj+raLBqPjWmPjaeu4GRans3t3bXWtyWOLLkVKvQIw1JlOJbqh6O7kIi7pfWYJu1CpFC+jKFOUDBJ2o9J0n6cVxrhD3k0FsmjcAX1fNxyCkQRYSIE+GeGXXOk41bdFtwmbdEsbFikRmCxPBw/ydfZPQXBGl/chH7CjoJ/pqtO2atL02iM6dTIjqCBZe5KNLBnlYX2jTxT/LVrs2h0bRbtkX1r8dTv4e8C70qIyAWdhWTcLm3CDdKOymWxqitUI7BYHoGf5OtwRm3hgxa6jyQK+Ovhocgu0mPQ2xsdeu2P9w6s/Pc9Q1tjWPsGGPfRVqNtXL3YECCYdeDu6tD/PaUr3lhxApIo4K0ZPQBUXbg3qxOJu02W+rGldmQYljwyzOn2PDqmPe4ecg2iwnWcb0ce99yETnhuQie0fmmlr5tCNsSqbfGC4UG8bZiFW6UtuFPagFZieuXz14hX8IL4J57RLcJmpQ9+l0dji9I7qO60kmdF6iS/ChjoYMC14iHMlDZjpHhMM6sgUWmCn+Xx+EseiTzU9EEr3eehatM0tbiaTm9PRpW1Q7irECIAfHp7bzz5+5HKn3u1qIOHR7fDI78eQoROwrs39bB/Z37mk9t645k/jyCmZgSen9DJ183xCQYNKOhFoQRTpd2YKW1Cb1G7Iv9JpRUWyGOwRB6BfD9bBcEV4ToRjaMdW9Ztcs+mGNa+gdFjnoiqamYauOl6+t5hrdGlaW3E1AxH5ybl0edHx7THlJ5NUb9WhE+KRNaOtL9oEZF/86dLEMsCKckhB7XxjTwV38qTMVo8gjukjRgtHoF09YJKJygYLx3EeOkgLqn18Kc8Cn/Ko5GqNvJxy8nf+cvqCW2ENNwibcUt0lY0FMwzIPWqhHXKACyQx3hsWo4vzgk1wqwH+DxxG6FR7QhcyS81e7xNg5o4l2E8DddWhqfW8y9M1L5ont67OerXjMC9P+5HhCTi3Zt6okvTaGx9fgwidCIaOTge9Scz+jTHsPYNUCNc8lmhcV8Lzd+agp4IBUPEONwobcdEcT9qCuYnz0I1An/LQ/G7PAZH1Xbwx2V6LPn4tl54+g/zpXPcoZadSyaqKnDbgJb48urSiN2cSA0zfcftmTtnT2BBEAQMbdfA7PFr6gf2HQsid/P0GNqXF+62pkj5IwUiNil9sUnpiybIxC3SVtym24IWQkblNk2EbDyhW4rHpGXYrXTFYnkE1igDUAjWSyFzxXrZZ9MTolGAqdIe3ChtRz/xjOY2iUoTLJDHYrE8ApkBVDfKXtUzCXSi4FDhwvdu7okXFh2zuo3pOXbnS2NxOa8EN87dVdWGq/+dM7MPpszZYbS9aCM2YzrmenBkW9w7rI3FDYd3aIC9/3ctRFFAnasrPLSMCY6bca7WCgt0DBpQUGkvpOImaTtmSDvRVMjS3OaI0g4L5LFYIQ8O2EHW4Lb+UVn74dHtkJxZhMzCUrx9g2NpZ4Jg3hmx6C1RcFGhYubAVliwL9nrxw70oqOXUB9z5BvxuTwDw8VY3C5twnjxYOUywKKgYpgUh2FSHN5Q52Ot0h9L5OHYofSAwuKJdNVSF+a7O0MHA0aKx3CTtA3jxEOIEAxm25SqOqxWBuJ3eSz2KF0QSDdtXBFdIwxZhXqjx6yNe27t39Jm0MBU87o1cDmvxOQY5Qfp3tw8KGM708DYY2PbI1Ije6L6dvUcWDqSAgeDBhTw6iMXU6XduFHajp7iOc1tctUoLJGH43d5LE6prbzcQvfz5Lz4Xi3r2r1tdGRYZcFER2nVNNDKNDCdXuHodAsicq++reph3YnLdm3btkEtHEt1vBirq7a/MAYn7CgEGQhUiNiu9MR2pSfqIxc3Sttxu7QZ7cS0ym2ihFLcIO3EDdJOXFbrYpk8DIvlEUHR31EgUNFNSMKN0g5Mk3aioaD93TuptMQieRT+kkcgB7W93Ebfqx2pMw8auBgw0Sr27NYRooWlsSn0MGhAASkaBZggHcAUcQ+GibHQCYrZNmWqhC1KLyyWR2CT0gelCJ7Ip+TBk/ZN/ZprPm5PmpwjBMG8s9T6rcIkEQsfGoLFhy7ghj7NLa51TESOc2b6wL+ndLU7aNCrZV0sOXzB8YM46NC/x+NAUhYKSg2Y1L0paoRLQRM0qC4TdTBPnoJ58mT0E+Jxo7QDU6TdRoV9Gws5eEC3Eg/oVuKk0grL5cFYqQzGebWJD1tOwaijkIIp0m5MFXejjah9TshQo7FMHoa/5BE4oV6DUMkq0PLgyHZ4eclxt+5zZIeG6NYsGnEX83CPhSLPVt9xW4UQ7RinAeX1Eii4MWhAAaM2ijBePIAp0h4MF49XpmiaOqa0wWJ5BJbLQ4Jyfhzg2rq51rx7Yw9E6LSL9tzct4VR0MDRaw2zqQiV/1d9G+3fa0DrGAxoHePgEYnIFmfmObeMiUL8m5Nw4HwWZs3ba3N7Ty+7+OLEzoipGY7ruhlfFAf3pYmAg2onHDR0wn8Nd2OseBg3StsxRjyCsGp9YxcxGV3EZLyAPxGrtMZKeTBWKIOQonLpV3JOGyENU8TdmCrtRkdROyBYquqwXumHxfIIbFN6BuSy1Z5wY9/mZkEDZ4ZzDWpV3QQTr66UdTmvBK2u1g5wJBvA5pKLVqaRvja1K95edRKjOjbE0Hb+MW2WPIffYvJr0SjEaPEIpkp7MFI8qjk3DgDS1BgsvZqOGehLJdpDtDE9YUI39w8IbR3TUYJgnpTHrDci72rsZGGncJ2oWWxUiyeDBi9f3xl3D2ntsf0HAj3CsEYZiDXKQNRDHqZIe3CTtN1staDuYhK6i0l4Eb/jqNIWK+VBWKUMRqra0Ectp8Cgop1wERPEA5gs7UE38bzFLQ8oHbFEHo7l8mDkITTXsrfGHTd8wiQBc+/oZ/RYZJhktdBz9cNWLEld9ZxjNQ2qj9xmD2uD2VpFESkoMWhAfqcZMjBOOojx4kEMFk8a3TWpLl2Nxmp5EFbIg7Ff7eSR5Xn8la3r9zkzjesMvHtjD7y02L0pca5eBwgw76w8lUFBRObqRoXhodHtPH4c2Xz2mNs8MNJy+0Nx7m02ovGzfB1+lq9DGyENk8U9mCztQRcxxWi7XmIieomJeBkLcFJphXVKP2yQ++G42gbBnqNBtglQ0Ec4i+ukgxgvHjCqn2HqqNIWK+TBWCkPxkXYF0gMVa7ee+nRvA7m3tHX5moE1g5z3/A2xkEDG8c0vWEUgqdVuopBA/IDKroK5zFeLF+DuruYZHHLLLUW1sgDsVwZgn1KZ8iwvv5tsLI1GDad939L/5Z2BQ28uSST1q/AvojIO1Y9MQKNoiO8siyh6qN1F0P9fHJObYrP5RvwuXwD2gupmCzuxRRpDzqYpJRXTGF4UrcEl9R62CD3xXqlP3YrXaFHmI9aT94WAT2GiHG4TjyA8dIhNBQsFzCtqJWxQhmC5ACZ6nJjn+aIu+jbOidaYzdHLsJbxUQ5tXyhtWKLto4f6udRqsKgAflENAowTIzDSPEYRkrH0FzItLhtplobG+R+WKEMxm6lK+fGwfFBuCdWW1BdDDEIgmAWdWcEm8g7ujaLDspjkbazagt8KrfAp/JN6CCkYoq0B5PFPWgvGi/H10TIxp26jbgTG1GgRmKn0h3blJ7YqvREqtrIR60nz1DRVkjDKPEoRonHMFg8gUihzOLWsUprrJP7Y6UyCAmqdsFkf/bkuA74dOMZnL6c77M2aA3FPJER5chy1o7WNKDQxasv8goRCnoKiRglHsVI6Rh6C2chCZYvOs8pjbFe6Y91cj8cUjtyzWkTnrpv5+0bgmZVedk7EQWdW/u3xJLDF3A4Ocerx/X16WR0p4bYcjrdt43QcEZtgY8NN+Nj3Iy2wkWMEw9inHQI/YV4iNX65VpCCSZIBzBBOgCgvF/epvTENqUn9ihdUYgavvoVyEk1UYyhYlxloKClaPnzaVBF7FW6YJ3SHxvkvriAwK59cU39mnh+QicsPuT51VwscXmM44Fzmu02cVxG5Rg0II8QoaCLkIxB4snK/9UVCq2+5rDSHuvlflin9MNZtTl4onKvl6/vjLdXnfJ1M4w4Eg0nosAUGSZhySPDEHshF1Pm7Kh8vEfzOjh+wXIKtKt8eT5pUCsCIzr4Z9CgukS1Gb6Rm+EbeSpikIex0mGMFw9ihHgcUUKp0bZtxMtoI67HPVgPvSrhsNoBe5Su2Kt0xiGlA0rg+aku5JiaKEZ/MR6DxRMYLJ5EDyFRc4nqCoVqBLYqvbBe7odNSh/kBlkxw6Z1auDE6xPQ9dW1vm5KJU+cpqxNR3D0+ByXUQUGDcgtJMjoKpzHIPEkBosnMEA8bbRutJYMNRrblR7YJvfEdqUnMoJ0eURPcCYj4IGR7TBr0DUY9NYGFOq1i0s6MuVgYBvXl0A07YtYCJEodPwwewD6vbnB181wu1v6tcB9I9pgx5kMXzfFIVmIxiJ5FBbJoyrnt48Sj2GEeNxsGkO4IGOQcAqDxPJAtF6VcExth31KZ+xVuuCg0gEFcHzuNbkmGgXoK569OhazHSQAgBPKNdiq9MRWpRcOKh1RFuSXBlHhnv39ujWL9nntBIfYGHY1jo40+tkT010pMAT3mYE8REUzZKK3eBa9xQT0EhPQQzhndlfCVJkq4ZDaAVvl8s7phHpNSK14oKVzk9q4d3gbvLDomGMvdHIaQa0InUtLJ/5830C8v/Y0BrSOwZC2bliT1zTTwPU9EpEF9w9vg10JmXhuQkdfNwWA56cjOXK3zZ3ev6UXAARc0KC6UoRji9IHW5Q+AIDmSMdwKRYjxaMYLsaa3RQIF2T0F+LRX4zHI/gbsiogXm2Jw0o7HFXb46jSDmfU5iFbvNgTJMjoLKSgt3gWfcSz6COcsbrKQYUctSa2Kz2wVemFbXJPXEE9L7Q2dDh6U8eR06CzZzTrNQ2s77V53Rr4x9DWWHQwFQ+ObIswKbTH7aGMQQOyQUVzZJRXVxbOo4d4Dr3FBDQScmy+0qCKiFXbYI/SBXuVLtivdOKdBxOiIGBUR8fnCdaO1EEnCjAo7itCEC6JmNKzmdVtRnRoiBEd3Dev0bSzYqYBkef8a0pXXzfBiMe/7T4+nXiqRszzEzrh/bWnPbNzCy6gIf6Qx+APeQxEKOglJGCIeAIDxVPoL55GLaHEaHtJUNFFKF+VYRY2AyhPfY9V2+CI0g5xSmucUlshUW3K4sZ2CEcZOggX0FVMQhchGd3EJLtu1gBArhqFvUoX7FG6Yo/SBafUVqwT5UGOfu0dCW564pQytJ3tG0D/mdYN/5nWzQNHp0DCMzVdpaIe8tFWSEN78SK6COevBgqSEW1jmkGFMlXCMbXt1c6pCw4qHVkoyQ7OjGtFUcCSR4Zh6uc7zJ57aJTj666/OLEzBreNQZ0a3l1eizUNiEKXI9/3J67tgM82nvFcYzxA8dFSk56mQMRhtQMOyx0wV55uND1xkHgKA8RTmjWMagqlRlMagPJpDWfVFjiptsRppSVOqa1wVmmONMSEZCaiBBkthStoK6ShnXARncVkdBXOo71wEWGC9rRCU1lqLRxUOmE3gwQ+4aslZq0xzer65b5BePXvWHRpGo1pva3fLCKqwKBBCBGgoAHy0EzIQAshA22ENLQR09BWuIQ2QprNQoWmUpSGOKK2wxGlPQ4r7RGntkYpwj3U+iDm5IVyjxbGNSA+n9UHrevXRDcby5tpHe7h0Y4HGhylFU03fYRBAyLS0rC240X2fH068b9LB8+QIeG42hbH5bb4Vp4MAQo6CBfQS0xAb6F8CmNnIVlzbn24UB5w6IrzqD5zoUQNQ5LapPJ/iWoTJClNcBENcEmtF9DZCRHQo6mQieZCBpoLGWgjXEJbIQ1thTRcI1xCuJ3BAaD8Zk2cek3lOOyw2gHJaiP4/tPvHx4c1RaHk3Pw/IRObt/369O7YX9SNpYfNa734Y/TE0wN79AAm54d7aa9UagI3LMuVdLBgHrIR30hH/WFXNRHPmKEPDQQctFUyEIzZKKpUP6/CMHg1DFS1QY4qVyDE2orHFXa4ajSDpksXOgyQXBt3u2LEzvjg3WnMax9A0zu0TTglyz01RxkomD08W298PQfRwEAb93Q3cetMefI992ZUizuOh82rROJt2/sgdnz9zv0Ok/dcPT307wKEfFqS8TLLbEQowEAkShFV+E8+ohn0UNMRGchBe2s3D2PFMrQWUhBZ6SYPaeoAtJRB2lqfVxU6yNNrY80NQZZam1koTay1OjK/xYhAt65gFYRCT3qo3zsVV/IQ30hDw1R/u/GQhZaXA0SNBScWzGkVNUhXm2BE0prnFRb4ZjSljdrrvrH0Nb4YVeS2eP/N6mL247RvlEtnL1SUPlzw1oRmDOzD05czEVCetVNN0cKSgPeWeraz08ZFCAYNPBTj+mW4hpdLUiQESbICIMBUShBLaEEUShBTZSgllB89b8ltndop1w1ColqM5xSWuKk2gonlWtwWm2JPNR02zGoikFWXRoAPjy6He4acg1qRQTmV9l0UO/vg2GiQDKjd3PoRBGyomJaLz9MQRWAkR0bYlu87WUJfVnvpEa4hDGdGjn8OkcvHuwViMHVEkTgkNoRh+SOwNU4QTjKylPwhWR0vjodspOYgsY2aiaJgorGyEFjIQe9kWB121I1DLmoiSI1AkWIRCEiUKRGohCRKEYEylQJCkQYIEGGePXf5an8YZChgwwdDAgXyv+rg4wo/H97dx4fVXX/f/w9k30PIXsChBD2Lew7IgKioIIsLriggOJSFEW/1mrVWpdWra3WrfpVW/d9xwVFrbYu1Z9VofqtIJuCqIBC2ALJ/P4AhszkTmbJvXPvzLyej0dr7sxdPgnJnHM/95zP2a1s107laKeytVNZrn3/DWeEQEsaPS594ynUSk+5vvJU6j+NHfQfTwfqP7SgIt/6qbD+f3Uev/96X3fgECP6VjADnz4OdUzSP9Uz2Zoqwz97MrXe01ZrPKX62lOmVZ5Sfd1Ypq89ZdqiHJGTjJ7/27jN8KddmJ2mH+uCFziSFLMJA0nKz/StocBvHmAel8ulo5yYLNjP5ZJuOb5Wtb9ZEnTfpAh6vaZ9nkR4E+DEmwcnqVeKvvB00BeeDlKTmQtZ2qkq10ZV7Z862dH9napc36mDa6MKXeEtZZfm2qNi/eS4xqXB49JGtdG3nkKt9RTr68Zyfe0p00pPudZ4Shg9EMOC/dm3zUrVpu31UYkFMFPs3m2gmR2eNG3y5GqTd3herr7ztNF6T6HWe9rqW0+hNngKWMGgBVNqy/Xsv9cH39FE/k/bfzullw7rXqxh1y2NyvXs1KUkR6M6F+qdr37U9AGVSmYpHyCh5GeGdnPUmqViW8tp9/4O+gi3xHZlaLmnSss9VfteaPIAP031KnVtVrlrk0q1WWWuTSp3bVKJ6ye1cW1TgbaqwLUt7BpNZvrJk6UfPXnapFz96MnVj548/ejJ07eeQn3rKdK3cVCXAYGTgsEKId558gDNuPO9g/tH4RMmFkcnwXn4xHKoJxtG6529Bdqzf8jcHk+ydihNdcrQdk+6dihddZ50bVeGtnoytUm52qXwC0XB1x+P7xf1pIERKz/g7epwBrrufbMH6fttu1WWlx7dgADEjEjyiXYv4VrZhtWDzLZbqVrjKdUaT2mL+yVrr/K1XQWurfunEByc2pnp2r3va9duJalBSWr0/s+9/78uebRXSdqjZO9/93iStFdJ2qE0bfdkqE4Z2qYM1R342pOhn5SjPXSt41KoHyfBUgBF2b599XBGJDnpoQ8SD59sDnXX3slK3dvB7jAQBUZNQCK1C8lJbpVHYT4igNgVSQLAbdLApUiXUDuyd5nOe/Tf5gTRRE46Xbdg9ipZP2rfE35JzhsuAlMZPa0Pd3BSWrJbu/c2X+EjnGtKwZMArenfdSnOjui4ROpTwjqMBQZs5v9h7lF0p1/ec8rAKF4NAHwd02Sd8JkDKw33cbtc6l0R3oo9yRFkDTJTk/Tq+aPDPs5ISpJbj8wbasq5mhpa3db0cwLx5pmzR4S1/+LzRinZhmlQwfJZN87oqyS3SzXF2Zo9oioaIQGGSFcDNms2FcHjiWrWYFyPkuhdDAD8XD65h3clmUuO6K7HP/qm2T5Jbpf+dHytxt70dsjnTYrgBuCti8aoOMd3qlRrHlIP62T+Db7b5dLTZw/Xsbf/0/RzA7HIvx9V2SZDfdvlh3WOTkXZ+0c0hTaCwBNg+YRgI5PCnX46fUCljuxdqpQkt1JCnKflPwqCgQYwA0kDwGauKI/3ofEA4CSF2Wm6bVb/Fvdxu6TqovCG5kby1NA/YeBUuUxRQJQV56Tp+22hreoUq8wYxt9owVSYzFT+3mE/picANvOfq7tvegK39gBwQCQ1DSIZaWDEaUsnupr8PxAt54/rYncIAZm1AkFLHzODOxZYEkukNVPCwscFTEDSALCZf7+2sdFD0RoAaCKSBEBykklJAwdW0bNxBUokqMLs0JZHdYL+7dtEdJx/cvLBOUOUk5asmuJsLRzvmzQJMDsh7EKI0ckZ8IGB1iNpgISy4LDOuvm4vpo7sqPl12pa3KslxiMNrGPXkj0sPwYgUpGMNLCyqFl1YZbSku3pQrlcLL2G6DMrCWeV+08bpLRktyryM3TJEd0iOof/58zIzoX6/KrD9foFh6iw2VKJka2eAMQqkgZIKG2zUjW1X6X6RZiFDtXckR31++l9NG9U8OSEf9+v0WNth9CuZn9kTaFG1hRKki6e2NWmKADYxT9xmJGSFPKx7ggSAJEkGowY3QSMqClUhY1LxTr79g3x6m+nD7Y7hIDGdC3Wp1dM0FsXjQl7GecDq0hFIxdnR76PHCPMQNIACcXqD87LJnXXr47srgsndFVacpIm9CwNekyzkQZxmqZ2uVx6YM5g/d9vJ+rsMTV2hwMgyvIzU3XN1F4a0KGN/nR8bcBK4I/MG6rSXN+ChEkRjTSIvIvTdCj24SF8jkebWQkRIByjuxTZHUKL0lOSQlph4NeTe3i/vnxyD+8qUuH8VXVom2X4erA+nH9Rw4zU0JOngJ0ox4mEZNUc1cl9ylWad7CzG8r9v1HnL167gy6XS2nJNJBAopo1pINmDenQ4j7DOrXVSwtGasBvX/e+FslMg6RWDKe+d/YgLXzs3yrKSdMvxhonOUO9b3e5zB2y7JKLJ4eIOifPiw/37+u0EVWqaJOhnPRkDe9U6H19XI8SPf3/vpW0b2Sqv19P7qEbX/s/TehRotoASzoGWz2hICtVx9SW67l/r9fRfcubTXuwgnP/5RBLSBogoVj9wdk2gkJBzQoheiiECCA2HdKlSG//9wdJ0gmD20d8Hv8pWpFMT2hNTYM+lfl648IxAd/3yGNrXQHaCCByLpfLcATRJRO76csN27RlR73uOGlAs/dPH9lRp/vVxPIfWRDKQ6k/HlerX0/uoQKDxATgVCQNAJP0a58f0rA4f/4dT4/H2ox+ZUGmNm2vt+z8ABLXdcf21qXPfK60ZLcuOty82iWRrJ7QJtPaDnmoEbnUvMJ6q65LIUTEqT6Vefrsm59tu35xbroWnzeqVecIZdSDy+VS2yiMMGh6PaC1qGkQx247sb/dITiWFWUDhnRsa8p5PJKlQyKundpLqfurft8wvY91FwKQcMrzM3T/aYN118kDW/UUzf8jMJI5/KnJbt07e2DEMQRjNDy5W2mOZddriluA0JwyrOWpMHCWO08aoAWHddZjZwwN6zgn3RM7sSqVg348iGEkDeLYpD5ldofgPBa2LJEMhTWqvt1ocSHEnuV5euOCQ/TyeaM0Y2A7S68FAJHw/6iOdKbB2G4lrQ8mgEUGIymuPbZ3s9eseMpnRiHEtGS3KctGdiw0LgjnBHNHVtsdAsJQnp+hC8Z30ZDq5g9hWvoMsLN+tP+l47SWNUDSADCL0RrGwaro+s+N23eM9VnzdgWZ6l6Wa+1FAMS9Hk0+R4Z0LLDsOpFMT7CSxyOV5Kbr9QtG+7xeFKUhx2a0EQ/OHdL6k+znpCe9TVGZ3kQ2/xt/esUEXX1MT918XF97AwlidJeDhRUzHfL759S/T8QWkgZISFYkgiNZEixQcW8+3wHEgj8dX6v2BZnqWJil300zb7qTf10Xpy4x6F/HJtIwLxjfRalh1MQx46eRmuQ2rS105r8O7JSRYt4N86nDOignPUUnD6vS1H6Vpp3XCv8zsZs6F2erbVaq/nb6YLvDAUxDIUTAJJEs72U0bLWx0d6q3AAQqs4lOfr7xYeafl7/CuTRSBqEc4kDtQv8B5MZfXYHOm3Xkhz938ZtKsxO0/xDOumovuW65qX/6PUvvjctzmjwePa3WQ4cl23V8sqJqLYyP6T9xnUv0brNO/S76X005bZ/WBuUA5XkpmvJBYfYHYYPJy+XidhB0gAJ5cDHZrBpA5GIpKaBUefPI57aAEhsDX6LnVsxPSE1ya36hkbvdrDExE0z+up/nvpMPSvyAtaDMTpDoNPeNqu/Vnxfp9p2+UpNdqtjYZb6d2gTNGlgKpOaQse2WXGQMyjMTtWPdfaveNQmxMKm95xqXfHRQOLgnxlwPJIGgEmS3BEst2jwmtWFEAHA6fxyBopgNdsQruF7kWA3vtMGVGpqv4oWlzwMZxRARmqSJvbyXSveqdMwgtkXt/PaLudFFL73fnmYbn3jK732n4368rttdoeDWBSbHytwGGoaICJWFryykpX9sZQIpicYBRSNQogA4GT+o8FaM2Xrb6cPVnZasrqUZPu87r8kZCiXcLtdPrH435SGMwzYaMRbKEebOX3NjOH7Hu2rbeFE8ZCDT3a7dMGErnrszGF2hwID8fA7BoSCpAEi8tiZw5STzkCVpoyGzwZrS4y6fvumJ5A1AJC4inLS1K5g35K0ZXnp6tg28mX9Rncp0rKrDtdrCw/R76YdXBLx5uNqffaL5HO3eXIjnGObv9bafECy26X8zJTWnSQC/iMmnCIeahpQ4wjhal5rxZ44EF9IGsSAX4ytsTsEQ3wG+YqkpkGfyrxmr+0rKhX4mHAqbANALHK5XHp47lBdNqm7Hj1jqNwm1TQ4blB7PTx3iJ45e7hG1BT6vhnBJUK5JQ0nGRHKvi3tcdPMvkpLjn4b4dQb27h6ChyD38svj+hmdwiWa1+QaXcIQTnzrxOxhruPGNCujfM/kGKFlU/wQ61pcOdJ/dWlJFvzRnVUH4NqxC11cnLTk/XAHJbwARD/2hVkau6oanVoxSgDI8NrCtWvfZtmr5vROhieI4wTm3HvHWo755G5N9V2JCuCKc5JszuEhFaWn2F3CJa78ugeykzdt7zk701cdhZwGsaXI2IxmPS2lFFNg+y05n9iE3uVaWKvsoDnCTSc8oLxXbTgsM6RBwgACCjaRQhDvWHPy0jRzzv3hHzeaD/0P/B9DKoq0LsrfozuxYNIZmSeraxYqcppaopztPTCMdq2a486l+TYHY4hp44EQmzh0xQJJTmSYoUh6lGW2+y1nuXNXwumkUKIABB1kcyAaHZPFE5NA4MEsVHioneF7zQ2l0vKMkhIhxuCM9c7MF+3UmfeyCF+lOalOzZhAJiFpEEsiPMbyLwM46JNhdmhrQkcjkm9Az/hj1RV20wtOKyzYYPhcrm8w9ZC5fFQCBEAoi2yp3H+yzY2P0egs4ZaCLGqsPkUxay0ZF04vothLZ1Qv49ESBjEk4ww+xLAAfQoYQaSBjEgnD/22nb5SnK7Qp5XldPC04pgzPoQMlp1oLooS/976iCTrrDPawtHe5/OmDli7uXzRuuC8V0Cvp+REmbSQC0XQgSARDWx58Eq/YOrWr/0b9PRYNMHVLb6fEbC+Tz333VwxwJN7Fnmt8++vX5xWGd9fPl4n/fCXdUoEYaPx4vUZHer+mzBDKtua9m5rcavMWA9ahrEgHCefjxz9nA1NHpCnseXk56sbbv3trjPqM6Fys1I0UufbQg5jnAYDQm9cUZfdTFxqNebi8aoY6G5xbRCFW5btm+kAQDA35VH99Sm7btVv7dRv5/e+qJjt5zQTxc/+ZnyMlJ0/rjwa8a05mbF6NDUZN8k82NnDNV7X28K+SSVNhROjodlDWPFu5eMVd+rXrPk3KkWFLLkZt4ZeBAFMzDSIAaE87fucrnCKvwTSiNx0eFdlZvefAqBWW1BNAq0lOWlhxCH5WGEhCc/AGCsNC9dT8wfrufOHakqExLBnYqy9dRZw3Xv7EHKzwx/Slyzkgbh1DQw+Kw/urbcOzrtkC5FQdvHvMwU1bbLlyT1bZevzsXZoQcgc9tfkgfWCzSdM5YcN7Cd3SEAiAAjDWJAfqZ1jYTR1AB/Vs+vDxSCmTfx/ucy6txcfUwvU87dWh4PlW4BIBa19pM7Oy1Zz587Qh+t2aJJfYxr8Pg3D4/MG6r3V23SoKoCuVyuVicuALMY9bXo3gCxiZEGDjWxV4mkfU8OBnZo/bzNQJLdkf8KmPW574Sif/8zsZuOGxQ4+53sdum8KC13WFOc7YCfCAAgmFDuuQO1cYEO7VySoxMGtzcc4WckIzVJh3Yt9i7xG85NWaMJOQPyDgjEqt8N/9EKjHJpGX+jMAMjDRzqgvFd9evSjirLT9fuvY2WXSeUJQj3dUCs+8Qx6uCYfdPs32nz/wA9a0wnw+PGdS/WvFHVapOVqjWbdpgc1UG/m9Zblz6zTL0r8jS1fwWZeACIQUajxAJ9nlvVkfdv7w7vWaJXl2+05mJy/g0JI/fsY/S7Ycbvy+VH9dBjH61r/YkAhIykgUO5XC7vfE1LkwaRLExtMqN1qT1yyhA2l4bsryhsZdLguEHtNX1AO7ld+/7tGTIKAM7XuieckR0brGn0j8mM0QShoNlCKMwYFZBt4SoSAIwxPSEGWHnvHEpNg73R6nFYyBkJiJYluV3eJyI8GQEA56suzPYWFM5NT1Z+GIXqCrLSLIkp2Mi6g69Ht20vybXm+w1X+4LorzABALGOpEEMsPIGMpSaBvUWjnSQAt/QW1nroKW+0sAObbxfnzysQ9BzcX8PAIkpNdmtJ84cpjNHV+uxM4fJbZCIz0n3fSpalJOmBWNrVJAV/moNkoI+SWhW+NcBQwCKctJ0wfgudochSRpRUxjW/lNqyy2KxFms+C0xOqcVv45OqI0FxDvG9yS4UEYa7GlotHTYodH0BMnk1RPC2PeWE/rp1qUr1Lk4W6PC7Fy09toAgNjSt12++u5f9tDIH2bWatY9H0iSfj25h04f2TFKke3TGKABj+aItjcXjdFHqzdH7XotOXdsjR75cG3I+1s1IiQRRCthRSFEwHokDWKAlc16KIUQrR5p4ICyCj7K8zN03bG97Q4DABAHRtQU6tEzhmrbrr0a173Y8uv5N6mBZhh6PB7dML2PLnryM8tjyk5Ldsy0u4LM8EZ4NDRa2weSpE5FWXK7XBrfo0S3v7XS8usZseIG33CkgelXgT//vr1D/vQQ40gaJLhQCiFaWYhRamGkgYXXNLPRCjYsLtJrleWla8PPuyI8GgDgFEP3F9Q1Q7A2x//mPNBIA0maPqBSJbnpOuXeDyOOZ8bASknBh5075b7FqMvRt12+Pl33k+H+DRY/Lf/0ignK218LY9m3P0eUNJjcp0zH9q/Q6fd/ZHZ4pnPAbJm41700V+0LMrV28w71rcxTZiq3e2g9ahrEACszhKF8kKQmu6z9kI9CT8IpTzjC8eDcIXaHAACIcS213y6XS6O7FKlPZV7E5587qjqk/ZzcDF87tVfA9/Y2WHuXm54SXld89vCqZq/lpCdrbLcSkyIyEQkCW7jdLj111nDdckI//W0OfUmYg6RBDLCqwIvLJS30K0yUn9m88vOozkWWXN8bR6DXLexhJJn4m29VmJ2Ksq05MQAgZgVrc5pPT7D2zi3U5e+iXayusk3G/uv6xWEQRmoLnQKrV5BqOtoylH+qtDCTDE5jRf0BRi80V5STpqP7lntHsQCtFdufPAhb15IcSdKImrZ6cv5w1RT73pj2KMv12X7sjKFKMfMO20A0pif4n2tCj1JlpiZJkgZ3LDDxSgAA2MivwRvQZEWgEA+xRLRHGvzvqYOM4zD4btOSkwz3zUhJUpcSaxP4gfpAcYsbfCAmMcklBpjVnqQlu7X4vFHa09Co9BTjBtJf2+x9VYOtrExrR3uZlZasp84arvdWbtKUfhXRDwAAAAv4N6m17fJ15iHV+ueKTfr825+9r3fe/xDBLMH6CdFu6ruWGn9/Rn2OQFMEHj1jqD5as8XMsJppWlqqQ2GmpdeKNlY1AOIHSYMEk+R2KckdWsIgGLOmDwQasmjqkosG5+pelqvufiMrIjp3q88AAEBogrU5/m2zyyX98ojukqR/rd6sRz5cq6P6lIc8rcA0DmksjcIwGlF5ZO9S9W2Xr39ZvFRk03+v3PQU9Wufr0/W/mTpNaPFaNoAaQQgNpE0gI9w5oVFa/1dAADQeoOqCjSoqvmUPDNa86CrOjgka2D0wKOlhxTR7upM7lMeP0kDuwMAYBpqGsQAs564h3Ie/6Fk0Zg6EOgaZhZCtLKoYiyuzAAAiE3htjnRulkPNBT9skn7Rjk4pakMN4xoD7F3yI/JFGV56c1eM+uB05Am9aimD6g05ZwAAmOkQQwwq8Fvzed0tDPtDGIAACB8kfQYrLpRHdO1yLsko103w/7JCqPkRUv9LIsXTwhbLBVOHG2w+pZZP85bT+inhz5Yq0FVBSrJbZ6cAGAuRhqgRdGpqBwbDSDTMQAATueklqpofzFlyTltfaA4Thra3vB1pzX9pw6rsuS8wzq1Nf2cbrdL47oXm35eSSrOTdfC8V00snOhJecH4IukQQyI6vSEMBpH/4b3/tOMlzeKhEP6FiEJFur1x/b2fn32mE7WBgMAiGsx1Dz6cHS77pJ+c3Qvw7ectgJAqcGQfzMU51hz3iS3k//hAYSK6QkxwKyP21ASAoF2MXrd/8n7mK6RZZNjpTmJ9CnJ+B4lunFGX23ZXq+Th3UwOSoAAOxn1Mdo2mzW722MXjBhystICfie00YaxBr/qR/8PIHYxEgDtOjAjfL4HiXe1wKtZxwpt8HpImlUOhdntz6YFgSanhAsl+ByuTR9QKXmja5Weoo5y10CABJT2PlrGzPzTW8Y63bvDbjf5ZN7aMHYmmiE1Mwfj6uN+jUn9ylTapJblx7Zrdl7sXRPnRFCn8b/9zWWvj8ABzHSIAaE+oR79vCq1l8swKf5uO4lmjOyo5av/1mX7F/v2SypBusjBzOma5G+/mG71m7e4X3t4XlDNeia180MDQCAmFZdmGXbtZt2X7a3kDSYM7KjJOmWpSusDqmZnPSWu8KNFlRC/POJ/U0/px3yMlK0p6FRe1v4GQUr3Fiel671P+/S6SM6mh0eABORNIgDBVmpumB8F1OWnGm25OL+/ya5Xbp8cg/f90JIZvz19ME69d4PW9wn3KfvNcXZum/2II258S2f19MCjIBoOkoCAIBE0qGtfUmDprLSgnc5F03oohtf+28Uojko2MjGaD8Zj5UpmyHzH2ng9wN/+fzRWrd5h3qW50YxKADhImkQA4I1IG8uGtPifLxw9CzP079Wbwlp31BWEzikS/PldpoaHqBab0v5iLRkd0gJi/89daDyMlLUt11+0H1bwykVoQEA8c+sZZijoWnzOK578AT+uWM7a2KvMo37w9sWRiX1LM/V8vVblZWaFLT6fmOCTMKPZIUolyuEKZr+1/HbzstIUV5FXtjXBhBd1DSIAcE+kFubMLj1hH5KS3arX/t8nTjEd8khq++HQ2lwQj6X33ZWWrIGVhUoJYLpD07U0cYhpgAAtCTYLWeoVfRrTK5PZNTHuPuUgbro8K567MxhQUc7JkjOICIeT/CfT7DpCQBiAyMNoKP6luuovuWSpHVNagREg8dj3KDQSO9z9TE9dcXzyzWwqkBH9Cq1OxwAgN2C3INF8sQ40uz9gVoEgU/rzBvG8vwMnXNoaIUX6Y60TrNfAX6gQEyKj0ewcc7lcunBOUM0vkeJ/nR8rXKDFO2JFjM6A70jGJJ24LL+V3dq56Q1Th5WpRXXHKnHzhiq5DgZMQEAiH0nD+2g88Z1bnEf/1b5+EHtIr7egVGVFfkZ6l4WxfnvMfEUI3r9n6HVBd6vTx9ZFfb0BACxibuQGDGyc6HuPmWgjqmtiPmb4+uO7a2UJJc6FWXprDGdLJ8CEevcblfM/5sDAOLL1VN6KTc9vOmR/zOxm3dkY7ieO2eEVl8/Sf+4ZKy6leZEdI5ImJUy6FWxL9Hxa7+i0rHmppm1mtS7TKcM66BThlUF3d+//+JfcBtAbHDGI2uEJaKhhyEK59400jiO7FWmaf0rleR2hTzHsanJfYw7HNxWAwDinWNzyAZdAv9Y22Sl6tYT+mnXngYt+c/GsE5v19x4swohPnv2CO3e2xjSKhJOVpGfodtmhb5kZLPZCeQMgJgU259csJxVVZpTkyMb5HJ033KdGkJmGwAAOFMEzwvktmlsrFk3uclJ7sScZujUJBeAsCTgp1fsi+ZQ9ZaGkZkVR6jnuWF6H91yQj9lpCYZHmf1j6U8P8PaCwAAEIQVTd0F47t4vz46wPSBiT2DFOM1CCzQg4dfjD1YCyHQ9fxFOtKgtQ8/GsNMGhTnpCknxkcThCPYz7d7aevrT4zq3PLy3QCsR9IgBlk7PcH6hES05rOZ/Z30qsjTkb1ZwQAA4FyRtLCjOxfqd9N669xDa3T1Mb1Mu3CgLkWvijzdeVJ/LRzXJeTrRTKd0QwZQZZk9JfkdumRM4ZaFI3zBOvTzRraXt3LcpXkdumG6X1CfsDz+JnD1L99vmYPr9LYbsUmRAqgNRInFQrHaPBL28fSyLXbTuyvhz5Yq8ueXWZ3KAAAmMLlcum4Qe1NP++w6rYB35vYq0wTw8hP2FXT4ORhHXTH2yu0a09jSPs3NHrUqyJPozoX6p2vfrQ4OufLTE3Wi78Yqb2NjUpLTlKfynwt/vw7SdL0AZUBjxvcsUBPnz0iWmECCIKRBnFuSu3BYX8XTujSwp7R4z/Uz6x+gFX1F3yu4XLZ1nEBACBWVtOZPqBSE4JNaQhDSwMNTh3WwbTr+CvIStWLvxilP5/YL6T9W1s4saY4u1XHRyrSqEPpeyW5XUpL3jdio2tpjv56+mBdNqm7rjy6Z4RXBRBtjDSIc78+qqcy05KVm54S0tI40RBKgxrN5ZTCFSP9NQAAbHPjjL6mnq/p9AT/ZvjKo3tqyX82av3Pu0y95gE1xdmqKc7WuQ9/EnTfveEWQfAzqnOhJvYs1bsrftSvJnVv1bmc6pAuRTqkC3UKgFhC0iAGhfOUoSArVddO7W1hNMFlpiZpR32Dd7swO83nff/v5qG5QwyXJArWDEfrZp6cAQDALk5tg6yuV9RS38flcqlHea5h0iDaif6s1NZ1rV0ul+48eYBJ0Vhv9ogq/eXvX0tqeToKgNhG0iAGWVkI0QpJfi22fzEj/47AiJpCy2NqDUYaAAAcK7a6CCFLjZHlCn83rY/dIUTVuWNrtHHrLm3duUe/ibSIJgDHI2kAH1bcD8db/yUatRMAAMBBKUnObnur2mbq2mN7a3gnZz/4CGRox8hGCeSmp+hPx4dW7wFA7IqNtC18xEoRpFDF3HcTcwEDAOJFnHUBQpbs8JEGh/cq9UkYxFJf7bpje6t920y7wwDgYM7+BIahWJueEK14qWkAAADsEMujEE8YbP5ymwDiC0kD2C6GkvGSYuvpAQAA0WD38wy7r0/XAEA8I2kQg2LtpjV4Ox5b309sRQsAiCfBnmjPH9PJ+/WhXc1b1i7Guh5e0Qo7Rn88ABASkgYxKJrTE8K91JG9S71fnzikfUTnCKRLSU6L70draGCsdpwAAPFvar8KLRzXRScMbpdQlfztbpuP7V/ps+3ErsKZo6vtDgFAjGL1BJjqyqN7qrFRSk5y6aIJXUM6pqWG/qYZffWHJf/VmK5Fqm2Xb06QrWR3xwQAkLiCtUEpSW6dN65zdIKBV01xtt0htOjUYR30yyO765ThVbrv3VW6591VdocEIIaQNICpinPSdefJA3xe8wSZoFCRnxHwvWkDKjVtQGXA95uKXiFEsgYAADQV1ZICDmuGx3YrbvaaHQ8Yrjiqh6564T+SpLyMFP28c4/3vcLsNEn7+lxpKQw0BhAekgaw3TmH1uj5T9dr8/Z6/fKIbnaHExQjDQAAiJ4rj+rhs53ibn7Ta3chRCeYPbxKZXkZyklP1iMfrtWLn23wvkffBUBrkDTws3nzZn3++ef673//q82bN0uS8vPzVV1drcGDBysvL8/mCGNPsIa8KCdNr19wiDbV7VbnIHULWkJ7CABAfDm6b7lmj+jo81pykrNafKcshe1yuTSx177aUo98uNbnPYeECCBGJXzSwOPx6L333tNTTz2lJUuWaNmyZQE//N1utyZOnKhFixbp0EMPjXKkzhNq1jqUdqogK1UFWamtiidaYm31CgAAYlVOevOuakpS85EGdjbNTrwfp68CwEwJnzQ4+eST9dBDD4W0b2NjoxYvXqzFixdr3rx5uu2225SSkmJxhM7ltKx1tBpImmEAgF2cei+Ya3Bzb5Vkd/Mfgp19EqNrl+SkRz+QFjj19wZAbEj4pEFdXZ3Pdm5uroYPH67BgwerpKREqampWrt2rRYvXqyPP/7Yu9/dd9+tLVu26PHHH496NtfK61lyapsacqt+TjS8AAD4+uWR3fX3r35UQ6NHC8bWWHqtZIORBgHZ1GifN66zXvp8g+p279VFh4e2mhQAOFXCJw0OmDhxoubOnaujjjpKqanNh8n/5je/0VNPPaXTTjtN27ZtkyQ9+eSTuvfeezVnzpyoxuqUuXN2s+vefWCHApuuDACAM3UpydEL547U+p92Gq4mYKYUh9U0MFKen6ElF4zWxq271bcy+vWw6CsCMFPCr7kyZswYffjhh3r55Zc1bdo0w4TBAdOmTdOTTz7p89o111xjdYiOFXpNg/hquErz0vWHmX11ZO9SPTF/mN3hAAASiJOX/e1RnqtxPUrkNpg+YCajmgaBHNWnzPt1WZ41UwYC9XLK8jJU2y7fEfUFUpMTvssPoBUS/hPk/PPP16BBg0Lef8KECZowYYJ3e9WqVfrPf/5jRWgBOaHxCUe0kt3+PxWrOgeSdGz/St0+a4AGVTHqAACAaApn9YSThnbQsf0rNLiqQPfODr2/F+t6VfiObjhuYHubIgEQDxI+aRCJcePG+WyvXLkyqtd3ypA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+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(sdata[0],sdata[1])\n",
+    "plt.plot(sdata[0],true_s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 0/2000000 [00:00<?, ?it/s]/Users/enricodrigo/opt/anaconda3/lib/python3.8/site-packages/emcee/moves/red_blue.py:99: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  lnpdiff = f + nlp - state.log_prob[j]\n",
+      "  0%|          | 5/2000000 [00:00<13:35:21, 40.88it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MCMC with 8 parameters and 16 walkers\n",
+      "Running up to 2000000 steps\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 1005/2000000 [00:23<13:03:38, 42.52it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[52.52052256 45.29009383 46.06495915 39.15516239 31.32441985 43.18288245\n",
+      " 39.75463365 42.24679089]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 2000/2000000 [00:46<13:00:10, 42.68it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[59.93213359 64.39415448 61.3360152  72.8807202  44.65552738 60.20953229\n",
+      " 62.27225641 72.11608274]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 3000/2000000 [01:10<12:57:59, 42.78it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[63.48797952 73.40219032 68.21241101 79.33816729 58.44895418 62.61355588\n",
+      " 75.09302373 90.79058931]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 4000/2000000 [01:35<13:05:01, 42.38it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 67.89079957  74.00902168  86.91704156  83.82339647  74.79126866\n",
+      "  72.23661195  82.98389665 103.42432584]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 5000/2000000 [02:00<12:50:28, 43.16it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 70.02579636  82.52547107  87.88187256  93.60501833  77.50394872\n",
+      "  83.04917987  81.42823692 110.87267164]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 6005/2000000 [02:25<15:10:46, 36.49it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 86.88981206  86.5459709  107.47736025 100.562028    93.82918505\n",
+      " 107.33162043 110.88082035 101.28398463]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 7005/2000000 [02:48<15:11:42, 36.43it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 82.08140325 101.42590051  99.41576366 106.71939166 103.50220326\n",
+      " 110.11573001 105.94016955 109.31752572]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 8005/2000000 [03:12<15:15:36, 36.26it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 84.22420095  94.75212313  99.3201634  104.97825411 100.86375138\n",
+      " 112.89598529 109.49118536 104.49414648]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 9005/2000000 [03:36<15:23:41, 35.92it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 87.31856378 100.55528496 108.54280858  95.34413573 106.62617805\n",
+      " 111.39837253 116.66214893 107.45528937]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|          | 10000/2000000 [04:00<12:53:28, 42.88it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 94.94666575 101.09596165 113.93457187  94.49916734 105.08628408\n",
+      " 111.52025455 126.01107395 108.57378042]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  1%|          | 11000/2000000 [04:24<13:04:13, 42.27it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[101.05536735 102.13101542 113.2559194   96.74134884 104.27528048\n",
+      " 107.78577403 122.23352706 113.48545363]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  1%|          | 12000/2000000 [04:48<13:04:45, 42.22it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[103.13952734 100.06935884 118.82605796 100.19908656 107.07361816\n",
+      " 104.29966163 120.46947372 118.85606441]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  1%|          | 12250/2000000 [04:54<13:17:06, 41.56it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "900  discard\n",
+      "MCMC autocorrelation time = [103.67815919 102.95652762 119.76512969 100.52440087 108.18139875\n",
+      " 104.50237345 121.36627086 118.64874278]\n",
+      "MCMC burn in = 900; thin = 150\n"
+     ]
+    }
+   ],
+   "source": [
+    "npr=8\n",
+    "bay=st.md.bayes.BayesFilter((s_noise[:,:,:])*3,model,n_parameters=npr, n_components=3)\n",
+    "\n",
+    "bay.run_mcmc_scratch(n_parameters=npr,\n",
+    "             n_steps=2000000,\n",
+    "             is_restart=False,\n",
+    "             mask=None,\n",
+    "             filename='chain_sin_{}.h5'.format(npr),\n",
+    "             n_walkers=None,\n",
+    "             log_like='off')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f9920a6a850>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x750 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s=bay.omega[-1]/sdata[0,-1]\n",
+    "cubic_mean_spline=bay.model(bay.omega_fixed, bay.parameters_mean)\n",
+    "cubic_max_spline=bay.model(bay.omega_fixed, bay.parameters_mean+bay.parameters_std)\n",
+    "cubic_min_spline=bay.model(bay.omega_fixed, bay.parameters_mean-bay.parameters_std)\n",
+    "f, ax=plt.subplots(figsize=[3,2.5])\n",
+    "ax.plot(sdata[0][1:], cubic_mean_spline(bay.omega), color='red', label='Bayesian ',\n",
+    "        linewidth=3, zorder=1)\n",
+    "ax.plot(sdata[0][1:], true_s[1:], color='green', label='True',\n",
+    "        linewidth=2, zorder=1)\n",
+    "ax.fill_between(sdata[0][1:], \n",
+    "                cubic_max_spline(bay.omega), cubic_min_spline(bay.omega), color='red', alpha=0.3, zorder=1)\n",
+    "Nf=0\n",
+    "ax.plot(sdata[0][1:], st.md.tools.filter.runavefilter(bay.noisy_data, Nf),\n",
+    "        linewidth=1, color='black', zorder=0,\n",
+    "        label='Signal', alpha=0.4)\n",
+    "ax.set_xlabel(r'$\\omega$ [arbitrary units]')\n",
+    "ax.set_ylabel(r'$\\rho$')\n",
+    "plt.legend(loc='best', fancybox=True, framealpha=0.0)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/06_example_maxlike copy.ipynb b/examples/06_example_maxlike copy.ipynb
new file mode 100644
index 0000000..effce9a
--- /dev/null
+++ b/examples/06_example_maxlike copy.ipynb	
@@ -0,0 +1,881 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "# sys.path.append('../../sportran/')\n",
+    "import sportran as st\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import CubicSpline, interp1d\n",
+    "def model_scalar(x, y):\n",
+    "    return CubicSpline(np.concatenate([-x[::-1], x[1:]]), np.concatenate([y[::-1], y[1:]]))\n",
+    "\n",
+    "def model_wishart(x, y):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    y00, y01, y11 = y.reshape(3,l)\n",
+    "    yy = np.array([[y00, y01], [np.zeros_like(y01), y11]]).T\n",
+    "    # yy = np.einsum('tab,tbc->tac', np.transpose(yy, axes=(0,2,1)), yy)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    # return interp1d(xx, yy)) \n",
+    "    return CubicSpline(xx, yy) #, bc_type = 'clamped')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LOG2=np.log(2)\n",
+    "from scipy.special import multigammaln\n",
+    "multig = multigammaln(0.5*3, 2)\n",
+    "def log_likelihood_wishart(w, model, omega, omega_fixed, data_, nu, ell):\n",
+    "        '''\n",
+    "        Logarithm of the Wishart probability density function.\n",
+    "        '''        \n",
+    "        n = ell\n",
+    "        p = 2\n",
+    "\n",
+    "        # Compute scale matrix from the model (symmetrize to ensure positive definiteness)\n",
+    "        spline = model(omega_fixed, w)\n",
+    "        V = spline(omega)\n",
+    "        V = opt_einsum.contract('wba,wbc->wac', V, V) # equiv to V.T@V for each frequency\n",
+    "\n",
+    "        # The argument of the PDF is the data\n",
+    "        X = data_ \n",
+    "        \n",
+    "        # Determinant of X\n",
+    "        a, b, d = X[...,0,0], X[...,0,1], X[...,1,1]\n",
+    "        detX = a*d - b**2\n",
+    "        \n",
+    "        # Determinant and inverse of V\n",
+    "        a, b, d = V[...,0,0], V[...,0,1], V[...,1,1]\n",
+    "        invV = (1/(a*d - b**2)*np.array([[d, -b],[-b, a]])).transpose(2,0,1)\n",
+    "        detV = a*d - b**2\n",
+    "\n",
+    "        # Trace of the matrix product between the inverse of V and X\n",
+    "        trinvV_X = opt_einsum.contract('wab,wba->w', invV, X)\n",
+    "\n",
+    "        # if detV.min() < 0 or detX.min() < 0:\n",
+    "        #         print(detV.min(), detX.min())\n",
+    "\n",
+    "        # Sum pieces of the log-likelihood\n",
+    "        log_pdf = 0.5*(-n*p*LOG2 - n*np.log(detV) + (n-p-1)*np.log(detX) - trinvV_X) - multig\n",
+    "        \n",
+    "        return np.sum(log_pdf)\n",
+    "\n",
+    "def log_likelihood_offdiag(w, model, omega, omega_fixed, data_, nu, ell):\n",
+    "        '''\n",
+    "        Logarithm of the Variance-Gamma probability density function.\n",
+    "        '''\n",
+    "        import scipy.special as sp\n",
+    "        spline = model(omega_fixed, w)\n",
+    "        rho = np.clip(spline(omega), -0.98, 0.98)\n",
+    "        _alpha = 1/(1-rho**2)\n",
+    "        _beta = rho/(1-rho**2)\n",
+    "        _lambda = 0.5*ell*nu\n",
+    "        _gamma2 = np.abs(_alpha**2 - _beta**2)\n",
+    "        _lambda_minus_half = _lambda-0.5\n",
+    "\n",
+    "        # Data is distributed according to a Variance-Gamma distribution with parameters (notation as in Wikipedia):\n",
+    "        # mu = 0; alpha = 1/(1-rho**2); beta = rho/(1-rho**2); lambda = ell*nu/2\n",
+    "        # Its expectation value is ell*nu*rho\n",
+    "        z = data_*ell*nu\n",
+    "        absz = np.abs(z)\n",
+    "        # z = data \n",
+    "        # print([np.sum(i) for i in [_lambda*np.log(_gamma2), _lambda_minus_half*np.log(absz), \n",
+    "        #       np.log(np.abs(sp.kv(_lambda_minus_half, _alpha*absz))), _beta*z,\n",
+    "        #        0.5*np.log(np.pi), np.log(sp.gamma(_lambda)), _lambda_minus_half*np.log(2*_alpha)]])\n",
+    "\n",
+    "        log_pdf = _lambda*np.log(_gamma2) + _lambda_minus_half*np.log(absz) + np.log(np.abs(sp.kv(_lambda_minus_half, _alpha*absz))) + \\\n",
+    "                _beta*z - 0.5*np.log(np.pi) - np.log(sp.gamma(_lambda)) - _lambda_minus_half*np.log(2*_alpha)\n",
+    "\n",
+    "        res = np.sum(log_pdf)\n",
+    "        return res\n",
+    "\n",
+    "def log_likelihood_diag(w, model, omega, omega_fixed, data_,  ell):\n",
+    "        spline = model(omega_fixed, w)\n",
+    "        rho = np.clip(spline(omega), 1e-6, 1e6)\n",
+    "\n",
+    "        # Data is distributed according to a Chi-squared distribution with parameters (notation as in Wikipedia):\n",
+    "        # Its expectation value is ell*rho\n",
+    "        z = data_*ell/rho\n",
+    "        absz = np.abs(z)\n",
+    "        # z = data \n",
+    "        log_pdf = (ell / 2 - 1)*np.log(absz) - absz/2 - np.log(rho)\n",
+    "\n",
+    "        res = np.sum(log_pdf)\n",
+    "        return res + log_prior_diag(w)\n",
+    "\n",
+    "def log_prior_diag(w):\n",
+    "        # Uniform prior\n",
+    "        if np.all((w>=1e-6)&(w<=1e6)):\n",
+    "                return 1\n",
+    "        else:\n",
+    "                return -np.inf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scipy.optimize as opt\n",
+    "\n",
+    "def do_mle_od(data_, w0, model, omega, omega_fixed, nu=2, ell=3, solver = 'BFGS'):    \n",
+    "    res = opt.minimize(fun = lambda w, model, omega, omega_fixed, data_, nu, ell: -log_likelihood_offdiag(w, model, omega, omega_fixed, data_, nu, ell),\n",
+    "                       x0 = w0,  \n",
+    "                       args = (model, omega, omega_fixed, data_, nu, ell),\n",
+    "                       method = solver)\n",
+    "    params = res.x   \n",
+    "    return params, res\n",
+    "\n",
+    "def do_mle_d(data_, w0, model, omega, omega_fixed, ell=3, solver = 'BFGS'):    \n",
+    "    res = opt.minimize(fun = lambda w, model, omega, omega_fixed, data_, ell: -log_likelihood_diag(w, model, omega, omega_fixed, data_, ell),\n",
+    "                       x0 = w0,  \n",
+    "                       args = (model, omega, omega_fixed, data_, ell),\n",
+    "                       method = solver)\n",
+    "    params = res.x  \n",
+    "    return params, res\n",
+    "\n",
+    "def do_mle_wishart(data_, w0, model, omega, omega_fixed, nu=2, ell=3, solver = 'CG'):  \n",
+    "    res = opt.minimize(fun = lambda w, model, omega, omega_fixed, data_, nu, ell: -log_likelihood_wishart(w, model, omega, omega_fixed, data_, nu, ell),\n",
+    "                       x0 = w0,  \n",
+    "                       args = (model, omega, omega_fixed, data_, nu, ell),\n",
+    "                       method = solver)\n",
+    "    params = res.x  \n",
+    "    return params, res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Diagonal, off-diagonal estimates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "npar = 4\n",
+    "nsam = 1000\n",
+    "\n",
+    "omega = np.linspace(0, 10, nsam, endpoint=True)\n",
+    "omega_fixed = np.linspace(0, 10, npar, endpoint=True)\n",
+    "w0 = np.random.randn(npar*3)\n",
+    "\n",
+    "diag = np.abs(omega**2) + 1\n",
+    "off_diag = (1+1+np.cos(2*np.pi*omega*0.1))/3 * 3\n",
+    "\n",
+    "diag_noise = np.random.chisquare(df = 6, size = diag.shape)/6\n",
+    "off_diag_noise = 0.5*np.random.normal(size=off_diag.shape)\n",
+    "\n",
+    "data_diag = diag*diag_noise\n",
+    "data_offdiag = off_diag + off_diag_noise\n",
+    "\n",
+    "guess_diag = pd.Series(data_diag).rolling(window=50, closed = 'left', min_periods = 0).mean().to_numpy()\n",
+    "guess_diag[0] = guess_diag[1]\n",
+    "guess_diag = np.array([guess_diag[j] for j in [np.argmin(np.abs(omega-omega_fixed[i])) for i in range(len(omega_fixed))]])\n",
+    "# guess_diag += np.random.normal(scale = 1, size = guess_diag.shape) \n",
+    "\n",
+    "guess_offdiag = pd.Series(data_offdiag).rolling(window=50, closed = 'left', min_periods = 0).mean().to_numpy()\n",
+    "guess_wishart[0] = guess_offdiag[1]\n",
+    "guess_offdiag = np.array([guess_offdiag[j] for j in [np.argmin(np.abs(omega-omega_fixed[i])) for i in range(len(omega_fixed))]])\n",
+    "guess_offdiag += np.random.normal(scale = 0.4, size = guess_offdiag.shape) \n",
+    "\n",
+    "true_params_diag = np.abs(omega_fixed**2) + 1\n",
+    "true_params_offdiag = np.sin(2*np.pi*omega_fixed/16) \n",
+    "                       \n",
+    "diag_spline = model_scalar(omega_fixed, true_params_diag)\n",
+    "offdiag_spline = model_scalar(omega_fixed, true_params_offdiag)\n",
+    "splined_diag = diag_spline(omega)\n",
+    "splined_offdiag = offdiag_spline(omega)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "for ydata, ytrue, yspline, yguess in zip([data_diag, data_offdiag], [diag, off_diag], \n",
+    "                                         [splined_diag, splined_offdiag], [guess_diag, guess_offdiag]):\n",
+    "    pl, = ax.plot(omega, ydata, alpha = 0.5)\n",
+    "    ax.plot(omega, ytrue, color = pl.get_color(), markeredgecolor='black', markeredgewidth=0.5)\n",
+    "    ax.plot(omega, yspline, ls = '--')\n",
+    "    # ax.plot(omega, true_spline(omega)[:,i,j], ls = '--')\n",
+    "    ax.plot(omega_fixed, yguess, 'o', color = pl.get_color())\n",
+    "ax.set_yscale('symlog')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/1h/l4hl5gdn4j55pcw3tbrrx2c80000gn/T/ipykernel_34858/1648416007.py:62: RuntimeWarning: divide by zero encountered in log\n",
+      "  log_pdf = _lambda*np.log(_gamma2) + _lambda_minus_half*np.log(absz) + np.log(np.abs(sp.kv(_lambda_minus_half, _alpha*absz))) + \\\n",
+      "/Users/paolo/micromamba/envs/lammps/lib/python3.11/site-packages/scipy/optimize/_numdiff.py:590: RuntimeWarning: invalid value encountered in subtract\n",
+      "  df = fun(x) - f0\n"
+     ]
+    }
+   ],
+   "source": [
+    "solver = 'BFGS'\n",
+    "params_d, res_d = do_mle_d(data_diag, guess_diag, model_scalar, omega, omega_fixed, solver = solver)\n",
+    "\n",
+    "data_offdiag = np.loadtxt('/Users/paolo/data_offdiag.txt')[:nsam]\n",
+    "guess_offdiag = pd.Series(data_offdiag).rolling(window=70, closed = 'left', min_periods = 0).mean().to_numpy()\n",
+    "guess_offdiag[0] = guess_offdiag[1]\n",
+    "guess_offdiag = np.array([guess_offdiag[j] for j in [np.argmin(np.abs(omega-omega_fixed[i])) for i in range(len(omega_fixed))]])\n",
+    "\n",
+    "params_od, res_od = do_mle_od(data_offdiag, guess_offdiag, model_scalar, omega, omega_fixed, solver = solver)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 10.0)"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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/Ozs78fbbb6cN8/LLL8f+9nq9+OUvf6mLbN1Qw9pDNldGK5Bu/pIZk0P1ZUxqlR3Z9BVO2fc3XLP+Ypy77U5UNvnSB0piUGlO+ocMQO+Vc71LVka3LUzq+2X0+xKEiVS29ficonpGZBWZMAQkBMl4pcAVV1yB8vLy2PWjjz6acrJAPHPmzMHq1atj19dff73q4wgJQiusNgAxUxq90paSx1LTHtLS4922MW8kKhvTHLnHpMY8qDTxiEipc3OHBTpvqSKYqRcl81r9IcW3/cgMBTCRDjljCquNPwh1ULtMJJPxSoGCggI8+OCDsevq6mqce+65vIqB5cuX46abbopd5+fnJ4QlCCIzUD+4SR/ewYYwqPWH2PXeoqmobumSlYrTwaC8UP5xNy4ng8lDSjBhYBGKD58PrvWALtMn0xn+epbBjuXo+JG9Yn9PS3N8odUh3xkEQQhCeoOswjKnD3z00Ue49957U+63t7cnXF955ZXIzU09U/rpp5/GBRdcwBv3bbfdhqVLl+LTTz8FAHz11VcYOXIkrr76aowbNw4dHR1YuXIlFixYENOcMQyD1157jY4ijMMILTFpotNg0+yxkkJaizLGpT+1EAPaf4KHjXrKDzu8WB0+AhEu9TxwMYpy3XAqXPL3upzwupxgGGXe+pPRYuogFAcH600O3S55OnM58qt9VYtlVdZxzNBScFy0LZk6zN5KASPINKsDue8j1ucYPuaxUF9MEIS1sIxSoK2tLeEYQCGqqqoEwwvhcDjw7rvv4vzzz8fSpUsBAE1NTXjuued4n/d4PHj22WdxySWXpBecIAhboVRBwTBMTGkoJYqhzT2nnRwsmoI9zWGJKfXEXpzrThk0Spl8MgJ/E9JxO6M5Z4f8s76MErRoNsLtdCRYCxAEQdgNaVslM1OLlKnvpRbLKAX0Jj8/H4sXL8Z//vMf/P3vf8fevXtTnnE4HJg1axaeeeYZTJ482XghJXDa0NNQ5CmCg0ldxSr1lmqaVp/cPrjoiIti126HW9P4+SjyFCWkWeQp0j1NOzG131RM7KP+iMzuPOY4DueOjCDfGV3tKskV/8YXHjMwYVJd4HVhYEmq5U4yZXmehOv4b+x1JprHXzFtSEp4L8+q7fj+RQlH9XVzbuS62N9OuMAwDoS5IHLdTjgdDNwoxHFDB6GLHcora56jlNek9rzItbFuxOVg4GV66lt8mgCQ7yxD2cSbAfY3AIBenr74SzAfETZpgu+chBAbwqG2Jqyq3ImPVzvhj9MdpPseRmK1lXy94StzSsiybNMU2vNKGEW2FjVZPgWyNZMIIkuwjFLgmmuuwTXXXKNrGgzD4NZbb8Wtt96KtWvXYtu2baiurkZubi4GDhyI6dOno3///rrKwIecRrnEW6KfIAYSYkMIhAPwOr1wO60z8TESjuPAgeNV8BCJ2G0o4mDDcLOB2HWnIy9FIdCN2+nEsNI+GFbaB8eUh/HQgu8QBpDjjpr/JyPFdJWRaCogdT8x32NamgQbMdgUSkIoC9xOe9dLWglRBilxCEIapCSwF/S9iHRYRilgNFOnTsXUqVPNFiMr4TgOLV0t4MChM9yJ3rm9BSfGmTqwDbNhtARawHEcir3F8Dg96QPpQGbmrvm42Z59/BHGjc6ICwDP9oGkGcgRfYtx9sRhmL9nbcxBYEoQSbMWvofoa8tBrlKAJpOEWZTmm9N/GAXHcdjbuhcMw2B40XByjkgQBpGpY3CCH3svhRC2hDv8v24ibMREaczBF/KB5Vhw4NASaDFbHN3Jtm4l4CpEfd4otHr7w+fpjWCElRx2+oiotZKQUkAKiYYC2gyg9XcWZq2Bvkej7QNiaLVwwzCMpQdvVpbNrpw6rhwMA/Qu9GLiwGKzxdGV7U3bsbhiMT7f9zl2tewyWxzZTRWt0NqH5q5m+EI+s8UwBGqXiWSy1lKAIMwkxIbMFoGQDQM56g3W4UaXowQcxyHoD0oON7i0AA6GiVMKyHc0qOZ5rdAjXTVDGKGwQsoOj823D6iFBozW5qhBJRjbrwguBwOHwhNKjKAldBBramrws0FTUOgpVBTHigMrYn8vq1yGI0qP0Eo807GzwiDT2ojtTduxfP9yuB1uXHTERSjN0dZPlx2xS/m0i5xWJ7tHPYTlyLROhiCCEU5WuXY7HSjMccGRASaydn4Fu/sUEKMhsB/zd8/HtsZtZotCqMDjckhWCJhRF7vYduz1f4tNjRuxpGKJ8QJYFQOHOWk/Ow25YizfvxxAdNHmm0PfmCyN9mgxvs6U40XFlAjZrGAgSwGCIGyJmc02h6jpN99A28UG4eE6EHTmAYwDwbD0rQPdlOT1bB1IOZIwTafMcVzCSFCvLlzJJEPtXmAlodPll7CjQXmpCb0b3321E7Tk8HKiY7kIdnZ8g/yOUlR1VGFEyYiUE0CI7EHPQX5jsOeUp9rOWt3SsTJhNowtjVvMFoOQSWug1WwRdCeTJr/kZ0QbSClgAWh1nJCK3mUlM0uisW/ljnSiNNwMDgzavP3REM6RHUdJrrDjMCl9X4JPge6LuGzgOA4t4QPY1BxCLwBS3JTp2ecKKVhSntNPhBSM8CmgJ0IDPjbJ4WV7sB3eXK8lB4jUN5oHDbK1YW3NWqyvWy8rDMdx2NG8A52hTkzsM9GQ46CVQPUz87HLN7Zi/2VHSClAEETGEkFYn5kkB4DhkLxGy3AcnFx00sWAQxBuwaMIhXAyDHLcWk5IUwf3vkgD9vm/Q0GTC+68XJzS6ecJl1nIHTQo3T5Q31mPFQdWoNBTiFOHnKooDitgVzNRlmOx8sBKNPgbcOLAEzGgYIDZIlkCoeJvl0G/XZGrEACAiraKmCm7P+zHCQNP0FosRVBZIYjMxt5LIQRBJBAIR9DRFZI9Ec0UzFYWO7keh4IRONHByj8qzO1yQMwgXO5Uje/5quBPsb+3eaTJaIUpoh4yCMWp1NHgwr0L0eBvwL7WfdhYv1G5YCYjZwJgpVWa3S27sb1pOxr8DZi/e77Z4hCELDiOw9cHv45d692G0ESfEMNKbTuhP6QUICSTCY1DIBJAc1czOkOdZouiORGWQ0tnCL5gBK1+655ukAnlSAhn3KkSQVe+rKMIu/Gk2cuuePtABmBUyXE75Xt07366M9zTtuxp2aOhVCJpW/4bG1fn97ftNywtO2H9MmIf9LaisctEPZP78kwk+XvZpZwZTTbnCykFiKyiI9iBEBtCR6gDETZitjia0hXqeZ+QjMnoyLKB6Feci2+//koPsSyHns29i+2xFAg48xEMy0uNQarZuqIOKu0MwFozBA6caRLx7Z2248kD0gfo1vn2egy+1E7Ysm3yTBM7giCyjWye+Ithv5EPkdnoWE8feeQR9M7rjb75fdE3vy9cThcYhoHX68WAAQNwxhln4JVXXkEoZN1VdjvxzJNP4Jknn8D+ykqzRTEEBxcBgx5ljA95sjset9MhYVKSftaSaCnA4/1ellRqAtkTLZUCWZRtBEFkMKRAIojMhhwNWgBqaI2nT3kfOJjowL+9vR3V1dWorq7GkiVLMHv2bCxZsgSlpaUmS2lv/v7XPwMApp94MiaNG60uMhtUkXgrgZAjB/6IA4C87QO8k1HZ7QP/84kKCmOmqsmrtnaZILt1PnlAvflx+vByFVJqVk5o1cW+0LezHnYZE1LZIYjMgiwFiKxk877NOHDoAGpqauDz+VBZWYkbbrgBALBu3TrcfvvtJktoTaw/qTNmkMKXioOL8yfgzEcgLH97isflSPsGksyb457hf17mfnmG0XUfbfRIQnNKF1+qXgWWAmaanVv9pAClkweadBDZBnf4f4alZxMFBGEO1AZnF6QUIAgAQ4YMwUsvvYRTT40eIfb++++jo6PDZKkIUSzWVwWcRfC7ihFy5sHnKJB9AoSDYeDkdW6XGI8Wpw9YewppPq40zh61ggZc2f7+mUmm+2WwuhKOyG4ibATzd8/Hyz+9jB1NO2L3qb+RRjYrykgpQBBxnHHGGQCAYDCIXbt2pfxeV1eH1157DRdccAHGjRuH4uJi5ObmYtSoUbj++uuxZcsW3njPPvtsMAyDe+65J+W32ura6Eosw+DYY4/lDT9mzBgwDIP/vv5f2e/U3NyM3//+9xg5ciRycnLQv39/XP8/12Pj+vRHHf3444947LHHcPLJJ2Po0KEY3KcERwzphzNPPRn/fPZvvIqT22+5Af2Kc2PXF559BrxuZ+wdhw8fnvD8hvUbEtIYWp6Yhs8myhmOASION4LOPPg4r+zw/AoBZST4FDBpACtn5d9qXXCmT2ri0WIAZKlBlEW/3fam7fh83+foCNebLQphIfjqjp6TNzUx6z2prPXVYm/rXmu1JzZkZ/NOVHVUIcSG8MX+L8wWh7AR5FOAIOKI74wikVTz73vvvRdvvPFG7LqoqAjhcBh79uzBnj178NZbb+Htt9/GhRdemBBu1qxZWLhwIZYvX54S59df9pxJvH79erS0tKCkpCR2r6qqCjt37gQAnDTjJFnvU1FRgZkzZ6LysLM/j8eDzs5OfDrvUyxeuBgv/fdl0fDHHHNM7G+Hw4HCoiK0trTgx3Vr8eO6tZj73rv44NPP0KdPeey5oqIi9Cnvi/q6WgBASUkpvF5P7Pc+ffokpDHz+JmJaRQWobW1J40P5ryDTxctQenwQbLe3UyUbB2QqhSQa2avxfYBvnjMMvdXitA402avQdiQ9mA7lu+Ptv07OxtxdNGlJktEZAt2aafrOuvw0a6PwIHDcf2Ow7H9+BdIiPTU+6UpHqUoX5KfsUt5SgdZTfBDlgIZAMuyaA90oa2rE60a/tfW1Yn2QFfsP63iF4s3/jeWZQ1fQly8eDEA8K5oA8Dw4cPx4IMPYv369ejo6EBraysCgQA2b96MK6+8EoFAAL/+9a9RVVWVEO6UU04BAGzcuBFNTU0Jv3371bcAopNplmWxcuXKhN+7FQlDhw7FsOHDJL9LJBLBxRdfjMrKSpSWluL999+Hz+dDa2srvlr3FY6eejTuuEncd8Jpp52G1157DZWVlQgEAti1vxr7aprw2ltzMGr0Edi+bSvu+93/JoR54qm/Y9Ouitj1q2/Nwf6Dh1BTU4OamhqsWbMm4fmZs2YmpLEjKY2d27fh7jt+K/m9paFtwXJHuuDgoooAjgNCso8iZOAU6GyTY5LmUkB8G4IVzV+tJJGS/BEaKxkxhpKXhoAjSlqd041qX7XZIghC3916GPlNrDI5+ubQNzFZ1tSsSfM0QRB6QJYCFkBto+wLBfHHZS/D5XCB0VDPw4FFmA3Hrt0ON7QZunMIsT1O2eLljk/zz6fdgGL5VtiK2L9/P5544onYBPycc85Br169Up57+OGHU+45HA5MmDABb731FlpaWrBw4UK89tprePDBB2PPTJ48GWVlZWhqasLKlStx8i9Pjv32zZffAAB+97vf4bHHHsPy5ctx3nnnxX7vlqlbsSCVuXPnYt26dQCADz74IOYvAQDGjBuDd+e/i1OmnYLWllbBOJYuXZp0J4zc3Fycec65mHLMsfjZ5An4bOGnOHhgPwYNHiJLvm7mL5qPYm+xaBoLF3yC/fv3Y8gQZWkkoMkYiDv8HwMHG4I30gFEAJZxoMNZIrtOOxzQdlassaPBTIZWPszDfhIThL5wnLGOBq1CIBIwW4QEsvEb8CEnH8JsGM1dzSjLKYPT4dRRKkIvyFKAyBrite9HDj8SgwcORr9+/ZCfn4+hQ4fi5ZejpvRjx47Fv//9b0VpnHXWWQCAb775JuE+wzCYMWMGACRsITiw/wD2V+zH6NGjcfXVV6f8DgArVqwAIF8pMGfOHADACSeckKAQ6CYvLw+3/u42WXHG03/AQIw/ciI4jsO61d8rjkdqGqtWrUr6VWmnrTRc3OQxLgpvpCPhmbC8UwgBAC7RrQPJ5nvicUVVFdZESHarLVbqrSdQG7/V9Bg0gCayiUxRJMoluZ6TlQnRDcdx+GTPJ/hg5wdYXLHYbHFUkc39GVkKEFlJfR3/nqurr74as2fPRk5OjmDYjRs3Yvbs2fjmm29QUVGBjo6OlM7x4MGDKeFmzZqFefPmYfny5XgEjwAAvln5Tey3kSNHYsiQIdiyZQtqa2vRt29fVFRUoKKiAoB8pUC3lcCsWbMEnzlxxomicbAsizlz5mDOnDnYsGED6uvr0dXVlfJcVdUhyXIlN7gsy+Kdd95JmwZfnppF/Bt4wj50Sxth3LJPHQAAp0M//SzfAFa2XwLoq2gYUCJc3+TCMPxKhkzu6DXYPaArivNeQTD1W2Oya8KXyfWCMBd/2I8dTTvQP78/+ub3NVscWVhxi53WaFn3WwItqPHVAAAq2ioQioTgdro1i58wBlIKZAD5bg/+fNoNKPEUw6VhJQxFgmgNtsWuy7ylcGhgEsRxLBq7evbVx8sdjoTQEoyas+e7PbzhtaDWV4sSbwncDjdqamrwySef4P7778ebb76JI488Er///e955Obw/AvP467f3RX1d4Do5Kq4uBheb3Sfg9/vR1tbG3w+X0r47kn9tm3bUFtTiz59+8T8CXRP3E855RS88cYbWLFiBS677LKY1cDIkSMxePBgtAaETf2TqaurAwAMHDhQ8Jn+A/sL/tbZ2Ymzzz47ZqkARB0VlpaWweWOfq+W5iaEQiF0dnZKlis5jasvujrB2aJQGnx5ajocB0/EBxw+wi7MeCBXJ+BgGNGV3+SOW+5gRfxpvZfEhX+68OhB+GTjIXhdTsw8ohxrK5qEH9YZIUWCrDiyYBCpPTQhJbIbvolZpqzAL6lYgkMdh+BgHPjNkb+B12nQflANIGVZFKn5EOESnSuzUGAySZgObR/IABwOBwq9OSjKyUOxhv8V5eSh0JsT+0+r+MXijf/NoePqaTcMw6B///646aabMG/ePDAMg/vuu4/3lIAffvoBd995N1iWxcUXX4w1a9agq6sLzc3NMSd6//jHPwDwd+oTJkxA375RbXm3H4Fvv/oWDMPEFAbdyoHu9Lv/FVvtl/KOSn7785//jBUrViA3NxfPPvssKisrcaC+BdsqDmHTrgps2lWBo4+dCkD5IOa5p5/D119+nZBGZZ22aWgFX065I3444jq/gAI9q9yjCKUs8qd/RIkjvcQwaqfAQ3rl4eYZI3HticNRnOe2nEl8N9liKkyD4MxGSGlF3z27UfP904U91BG1IGQ5FruaU494JjIXs8dr6bC6fGZBSgGCOMzMmTNx1VVXgeM4/Pa3v005kvDDuR8iEongiLFH4PW3XsfUqVPh8SRaM9TU1KRNAwC+Xvk19uzag6pDVRg7fmzsmL5u5UC3MiDZn4CcDry8PHpMoJjZffUhYa/Y3T4JHnroIfzud7/DkCFDUiZIdbW1kuXhY/6H83VPQ088cf4EIowbYUVbB3SYdKZ1NGgNXE6H5u+f0tdLUqIkKTw0zDSrWRDoMQmkiSXBD5ULPsTqCwfrOhpUM5GiSZj5KPkGUsMk93MsZy9LAavWOaMhpQBBxPHQQw/B6XRi27ZteOONNxJ+O3QwqvUef+R4wYnGsmXLROPvntx/8+U3sa0D8fv6Bw8ejFGjRmHPnj1YunRp7GjDbmWCHI49NnrOb7z5fzLdFgt8HDhwAAAwZcoU3t/3V1Zi3949guG7J1ZinUrVwSpVaZiNN9KzpSHs8ICV2Q8ygOBRhFrBNym12kSVUAnP57TWINxKshCEtbFW3SUyFSXl7MfaH7GuZl3KfQdD08lMgL4iYWmM1t6NHDkSl156KQDg8ccfRyjUc3RiUVERAGDblm28jelnn32GlStXisbfvQ2gsqIS7/73XQDAiTMTnf11Kw7+9Kc/AYiehtC/v/DefyG63+Obb77hlcvv9+M//yd8ykJxcfSYwI0bN/L+/udHHuS9303h4fxqa20RfKaouEhVGvogUTPOReBiexwihuCWXV6dDiZpQqdFeecSJv1aHUloZYsDKcgZ/yh6VQvmz+CyPLNFSCC1fnC2L1d2R2hioI3ikD5uJkPKi+xkTc2alCMkk9sLO6+821l2tZBSgCCSeOCBB8AwDCoqKvDqq6/G7p9yenSyvmPbDtx5+51oaoo6RvP5fJg9ezYuuugi9OrVSzTu0aNHY9CgQQCAH9f+CKfTieknTk94pltxsHr16mi6Mk8d6ObCCy/E0UcfHft77ty5sS0RO7fvxBXnX4GG+gbB8L/4xS8AAE888QQ++ugjhMNhAEBlRQVuue7X+GTeXJSUlAqGHztuPABg7vtzBB0RnnLaKarSUII2026AZZxoyBuFVm9/hBw5CHLym1NppvPyjiS0Akpk1Pu9ehcIO7myQ54qYcKAIozok4+i3HgHtDoMeHQaQ2Xz4CwzyMzvp7ellZHlnib2hBK6woknRCVvubPb9gEiCikFCEthaGcokNaRRx6JX/3qVwCizvYCgahG9ORTTsZ5F50HAHh59svo1asXSktLUVxcjJtvvhnjxo3DI488kjbd+En+xMkTY6vlfL/zXUvF5XLhgw8+wODBg9HU1ISLLroI+fn5KCkpwUnHnIR1q9fh+Zf+KRj+iSeeQN++fdHe3o4LL7wQubm5GDW4H6ZNGod5H76PB/70KMYdeaRg+KuvvQEAsPCT+Sjv3QuDBg3CsGHDcPJJJ8eeuf/h+1HetzwhjSOGSE/DbFiHG13uEgRcBQqPItR/NtqdQnx5774ndTLMJBs0yAhrFaYOK0NxrhsOhsEvjuwn+qzV302qeG6nA+dOHojrThwu+Ex3udC17TVw3kFbY+RBihdz4T19QMdvQjoAohtdyxm1K7aElAIEwcMf//hHAFEnfbNnz47d/8//+w8ef/pxTJw4EV6vF5FIBBMnTsSTTz6Jb7/9FgUFBWnjjp/kx/sT6KZv374YPz66ys4wjCJ/At2MGDECGzZswF133YXhw4eD4zjk5OTgnPPPwYLlC3DGmWcIhh06dCjWrVuH6667DgMGDAAAeL05OP0XZ2LOR5/i9rtTj22M56JLL8cLL72GacdPR15eHqqrq1FZWZng+HDwkMFY8e0KxWlYBY4DWJmjLaeDgUPC7FNJ55oQrUbbB+xOrseJq48fiptnjsC4/omKOLNyQ6sBOk2ECStBpVE5Wk+m9PwWNPGzF0q+l2RHg0ljGbJAsSfyz88iNCfbKo9Z7/vwIw/jtvtuk/Ts1KlTeeV0OBy48bYbcffv7kauOzfl92uuuQbXXHONaNy/+c1v8KvLfpVyrms8W7Zs4f9BQdaVlZXh73//O/7+97/H7tV11gEAWJbDnqZDyHeW8YYdNGgQXnnlldh1fXsX4hfE5y1cIpr2RZdejosuvRyleW54XM5omhyLBn/PtoWBgwbG0uA4DnXtiXvV5i1cArfTgbL8xJMezMTBReCJdCDsLAAYB8JyPQwC8LgcYKD/Iir/hDE7h+0up0NSp6dkki0UwupWB2aSXT1f9iBF2UmIoGPFoE9D6EHyeNlu8xpScEUhpYAF8IV86IM+ZouRfVioDYiwETgdTrPFICTiYrtQEmyAAww63WVo5OSPtLxOJ5TsupNyXF6CoQDP40rGhXr7CcjG1W49B+hW2IplR7Ju0qTDp8u6PNSYTKpPRGaSbhxi6zJsY9HVQtsHLMCifYvQGeJ3xEZkB01dTQizYbPF0I1Ma2NdbAgcAAYcIowTrEx/AgwYeFyMIfnC33XTqD2e5PGNUZMamy2mKMbWA0SCIADYb/WXUI/StpscDdoTUgpYhA11G8wWIS12H9hZuUPjwKE92G62GBKw72RSq6/v4Fg4uB4FTgfyZMftcTkkrfgrg9PpM1ng21u3CpuKnKIktR23cnsphn71KjPRo1/P9O0DVMYIQhy7zxeyFVIKWAR/2K86DqqE9kbMx4BR2KkEmSWrkw3G/g4zbnRE5O/C8rjkNL09b9oWrsGyA4tQH9wlOXTPAJbjuaccq5r7uxSc6KBJfth9omBGhUqjeLCrYiKZTHkPqdi9KhhJStnQuahkWVEk4qCjLol0kFLAKlAnShC2wMn1KAWCzgIEw/LN5LyylAI97O78EjW+gzjQ9SMCbIfgc/ETdi2aFkOaJymJpHnmtHF94XRqofDQF7WTJj4FhJmDMOmDTRooZgM0nMkOaOKX+Si1LKPtA/aElAJE1mI1ywrqYI2DU+TiD2A4wMmGYtedjjzZRxG6HA44RVezpXnx7Yw0SUtQoyMJ9V7929u+Gdt9i9EUqhB+SCSrp40ow8RBxZrLpRbzjjsUyixTTAJMSJOQgi7bBxRY62QTVurr5Xx/q42ZCHXoWQ6tXlbETkuwuux6QkoBgiCyiiDrR4D1KQrr4ELodg/IgUEbmyM7DjErgTAXVCxbPPETeP4DCa01aPeFfNje8gM6Iy2o8K82fdBsN/NnPcS163YIq5VtqyD3c6r5/vQFCCIzUNoXm92HE8ogpQBBWIRs0U6a3VmE4nwCyCV+60DImYcuBQdGiPkTCHMBJWKJotckSWzOIDfN1kBrUgj71QW7ToT2te5DhE31Z2JEPc2WNi/rsKlCiZAH1V+im+SywCq0xiTMRb6HLEIXaHVDPhzHgQMHB0O6Lc3Qq4/PkLFDyJmPCOOBiwuiEwUIh+R1fA6GgdsZX14NyBgmfUoc9JnU1viqsLnjE3gcBRiRe6Lgc8ntHwdOvjwcf1xSSJ3DZE97/EPtD/CFfDhxoPD3SYdUBQJNIqyHHsqf7Kk92kN1hNCLFJN5Hcsa+RSwJzSbImwJx3FoCbSgwd8AX0i9uTVBSIEDEHG4EXDmow35ssNHjyLUXq54kif4/MkpmDgrkGXhvk8QZP3oCNejLrhDOG5aWVSFnPzjGwZub9qunTBE1pNcHLO1ektRuNhJCaBGVju9J2C+RaNVUPzdKPtsCSkFCFsSjAQROuzwTapSwG6dEh+Z8A6ZglGnDvB9czmDbL5nk1fT072JFpP29nCt5GeVOoLUCq0mMVRf1WFG/mXb/FUXR4PZqgVIwgr1P92noMkvoQV23z7QLX+21wdSChCmo6TjjHCpe2AJQk+cbACO7nLHAaGI/E5PzJ+Alsgdk0upgeoVA5ygXHpun5J20qEGxxgKRSFBKSM7LQnPGHomtQUmP92Q1Yn50BeIkukTDCvVe62hdkQdVi/7mVx21UA+BQiCINLBccgJt4IDwDIO+BxFsrsUj9OhcAUtMSVpE8qeZ/gGN6l7+OUj9004cBAaJyTLaLqlgGbxiMek5bBE+iDH+MEQDcDsCcdxiiZHZCkQxW6rpUQPVp/UKiG5HdayXU7OLyr79oQsBQjTMcvJIg1UswwVn9sT6QRzuNNjOA4hjpEdoVFWAsn01C7rlncGTOJMXIGoat7O7nMYLeSn9pCIpyvchde3vI6vDn4lP7DN65NmKGrHrFsPM3GiTOgDlRV7QkoBi5DNpw8o6QSzOb8yGat2I55IR+zviMMNBTsH4HU5NZRILfItBfSscamWAuaWBDIdNQouK9tyuwyY/WE/NjdsRktXi6xwmf5FpZZZs9sxPrRcLbZLOSaUo/QbU9mwJ6QUIAhCd9IdhxeKsJbuRLzhHqVAmHGDlSmr08HA6TBqqJx4nB/v/DbpHmeQaNLn2srLgtbzeanRsWwEqw6tSrhnxUmBHqq39HXXivlAANIG712RLllx0vaBKNas/wQRRc2Yi+8Y4Xjsun0g2+ssKQUIwiYwDIOy3DJ8+9W3ZouiKb5AGE2+IFr9IbNF4cXJBuHigrHrAOeWHYdXxVGEKZ2UPJcCvKtaqR16+kjVj/NFfAqkGWDoTfKrKXnVfR3bsKF+gwbSZDLZPeCyKy6HPPdTpBOIYmVFN0FIQagvTtdHG132VY8ZqKoCIKUAYTH0mAwwDAOGYeB2utE3v2/svzx3Xuw3of9ef/11gUi1k2/Of+fgmT8/Y5HJvrEtI8tyiLDRNAMKjvgzAk+3lQADsIzzsD8BGTARuJys4IRYD9JaCsjcPsB7rKGEbIif7IvVbYbRwvWhuexs3cB73wjzeL4UMmHFIxPeIRvpbhuy/ftl2vvr6aiOMAADP5fVy4bV5TMLOn2AyHj69u0b+5vleiaenb5O+Hy+lGfiyc3N5Y9Uw/bkvbffw6qvV+GeP9yD839xvnYRS8DsZlF6+uZJ6o34Yn+HGQ/YiAxZGBYOZydYxokwx8LNCJQnQ+iRm0n61+xykIzhlgKM+HUmIZSzZqxq0sDMfKR8A7llIxv9RPChpE6RdQGRCcSPtQn7QEoBiyDHsdV1H/8Vtf4KVXFII9HcV8v44zu+xHhTTYy7fx+QOxLzLnv68E3padXU1AAAQmwIzV3Nsfv/9+T/4S9P/CXhGYJIgOPgjlMKBOGWNY1hHF1RXwIMEOT8cEOJUiA1RTk1UatqK2egL5Sm1j4FGkMVONj1A/KcvXAMe57UyHVBTj6r/SZ2U1rQNCcZDka741OTmlzlTXf5zHblgB2UXnaQkdAHNQrBdMorUm7ZE1IK2JBafwX8zl1mi2E4VX6VEahoo1IauOwe60iC41gAjO08uScM1xkGjXmj4Il0IMS2Isg5Ia8gcZo7GJTkUiDtQ8Yb63MiPgVSn5VGpX81AKA9XIuqzn0AymO/+SKNcMINoESOmADsN+kG5A3CzBqu8Q1CrdY+WE0evdFj8N6dh9k+4cz4iVGGvx4hneSynu11366QTwGCEKDbr8DKlStRV1eHh+57CMdPOh7Deg9Dnjsv9tzMmTPBMAweeeQRdIY60eRvQmeoMyGuRx55BAzDYObMmbF7c/47B33z+2LV11GP5X/7y99SfBpUVFTwytbR3oEnH30SY8eORW5uLnr16oWLzj8XP65bo3k+KCHMBeBnW9DFth5WDkjEgv0I63Chy12CLlcxlLg9UKsU6M4SeQNMJu6v9McPWM3QT1aZOUxbqCn2d0NwL3b4lmFb5+doDTakDWv3FU1Z89gMn6jY/VvqhZqvLn/7QGYjVXGkx9YMuVB9INSgdHJv9PYBrepRxivy0kCWAgSRht27d+Oyyy5DbW0tcnJy4HLzVxuO49ARijql6wh1IMeVAwcjrHfLyc1Bn/I+aGluQSgUQl5+HgoLChOecTpTz7avranFaSechn179iEnJwcOhwNNTU34/LNF+GLZUrw5Zy5OOe10FW+sngDrA4PosTQhzg/Aa1DK+jXoEZaT3UE6HYyOq878ESfLyOskMDkMAwVZJ/fFOMG80LIjZgDs71obi3dT83c4F+NlxiH/o9lpZUSHjWbS02YyXi+RcSjdPpDt2KlNIDIfI8uj3cq+3eTVC7IUsAikzbUud955J0pKSvDhwg+xr34f9tTswcYtG1OeS/HMm2bke95F52Hzvs2Y+rOpAIBb77gVNTU1Cf8NHjw4JdwDdz0Aj8eDuYvmwufzoaOjA2vWrMHoI8YgFArh93f+FixrnbVfloskXNul8XWwoejJA4c13hEFearN1gEu6d/0MAJ/p0tBNE5DmygFZUSgvnVFOnnvx6PFuwlFYdoESSQLzXApSPRgpTZQD1kcpBUAoNDRoNFlQ822SguVY0IBGn4+ueNfs7G6fGZBSgGCSIPD4cDSpUtx0syT4HBEq8zoI0arilONEsjpdGLuork4ccaJcDgcYBgGU6dOxX/feRcAcHD/fqxb870q+QggJ9yG0q4DKPftRGGgBmFWfificlpgcNx9PJio+OJyavIWnLAMHDjJxxeKRM+LoromEITlIqgL7kBdcEeKsstcpL+j0nOn9UJMchq4aYeRLZEFWj1LIOikjSbTRIZDpw/YE1IK2AyyKDCeq666CoMGDTIkLSmD4KuuvQp9yvuk3J9w5EQMGToMALB182atRTMEKw2VPJHoVhAGHCKcA3J1Ag6GgdOsFTMm/k8RGWLniVsNa66wNYT24GDXBhzs2oD6UPY5e9Ue65W8bEOXIwlpmAIg8yb/pKQjpJJpZT9bIKUAkRFI6ayUNlInnHCConB6cfTUowV/69e/PwCgpblZ8BkiPQwXgSfO7LwdeSJP8+N1adW8cof/X7vtA8kxGXX6gNTJgrK6qq1fAj4Odq2P/X2oK3ULkdz4lQ6yraYcTv8exg0Qs+30ACuS6d9Aav3L9IlRpr9fppFi4q/hkYTJ14Y7GtSoLGZ7mSalAEGkoby8PP1DGiGlQSooKBD8zemKOkEMhUOayWRV9Gy6PWFfbNgXYZxoi3hkp+jRTCmQiqwxt4RnleSlnuN+JR2zy0ndmR1ILjZWU3AQqch2NKiTHHZDz8UKgrAyZFViT+j0AcKWGDmQ5DsBwIpYeSBm/RPUE/FG2mN/B5wFCAZZMCmtpbCsDBh4XA4ENFCWK+lb4+uHXo4GlfjnF08g/lFpL92v2Iua1gBcTgbDe8m35oglnfRydlvpZBiaXBAKkVBs5J8+YGz9qfXVYuG+hXAxLpw76lwUe4sNTV8IZb5RjK3H1G4QWqDECsFK2E1evaClFYtgt0FoJqBVI+A6vDrv7/In3I//pq2trdJkUqFdpSZNGxhw8IZ9sWufgq0DbhdjrgfueJ8Ch+UIcwFEOH4LEmWWAnq+nzSJhvXKx/gBRZg0qATuw5YZKcoLk9tW8w4fkPdVjVjZ4R046vx9ZO+H10kOOyM3DzU5dEUGn1d8jq5wFzpCHfjq4FfGJg7pJtZ2hyZO2YdSx7RWdzRIZZkfUgoQhATEGpDS0lIAwMEDBwWfWb16teBv3ZMWrQcQVh+QsCyHQCgCKRNAI9/EFemCA5FYuq1sruw4vC4trUu0e/ttvs8R5gKpcaYbxDNMinWO2MBf7pwgZX+ixHdmGAbFuW54XA7Ll3froH0+pfteRn4Z2o4gDz0Gx0J6Hr3qqC/Uo8Q90H5AlzSUkOkTD2pz7Y2e5ZPKhj2h7QM2pG/uMNT6U+/LXRGLr7RMNILkBxKaDC1X3BLSZsSthbvTHZA7UrP0tWTSpEn48MMPsXTJUvh8PuTn5yf8vnz5cnz33XeC4QuLCgEArS2tpg8itEydiY+PJ2J/KAIOiZpJK3QjnkjPADPozIM/LD8O7ZwMKkOopgbZTtQHd6Y8YYRPATll2+hj81L3uROZQvJxl+mfJ5KR71Mgu2qQUBmjiRFhJZSUR6V9ruHbYDSqa2aPwc2GlAI25NVz7+e9X+wthtfplRQHy7Fo8Df0hPUUw+tKDBsIB9Aa7DF7L80phdvhViBxImE2jKaupti11+lFIBIAEB1MxFfKfHc+8t35KXEkI3fgJ4d0ZlCXXHIJHn74YTQ2NuLma27GU889hQEDB8Dv9+PDDz7E7bffjrKyMjQ1NfGGHzt+LD5f8Dm+WPIFDh06hKGDh+rxGpaDt+mV0x7r1Hb7PL0QdhXAG+5AFzyyOxsnw8Cpi/1sVI7oZFx5/AHWB68j6qyyOxZWQXzylYT6drbZ3JnbaQrGJGgLsxWbZYBcce1UIBVg59MHQpEwvj74NTrDnZg+YDopLghF2H37AMEPbR8gpKNT39GtEIgmYb0OqjXQimAkKPj7EUccgT/+8Y8AgCWLlmDKEVMwesBolBSX4JprrsGsWbNw6623Coa/9MpLkZOTg3179mHEsBHo168fhg0bhmHDhuHgQeEtCZbHtgNDBiFnHjq85WjmhE96EEJ7hYARe73To97RoFj6XKKfQfAPKMQG41oObvV2Q6DW6kpNcKltrC5tMc83MtX3Bg/WkkZ/9PjOQt/Uiv272aS0WxpnUfKn2NGyFZsaNmFPyx6sOLBCVlxKt3nZkUx+NyOg/LMnpBQgomTBSEhNI9UWbBP9/bHHHsP/e+P/4ZjjjkFefh4ikQgmT56MF198ER999FHqCQZx+T1i1AjM/WwuzjjrDPTp0weNjY2orKxEZWUlwmEFtuuWwd6FiuOAQDgiO5xLJy9byeVXbIKcfp6lvsOWMpmLf0RtipvqN2FjyzK0hatVxpSKxealtiNt22riaiSthKqHjiQUR6iMWXG1dFvTptjfB9ttvOhAqEbSkZkS288UhZHN2l27yasXtH2AiJKF9eGPD/0Rf3n8L4K/13fWy+rUr7zySpx50Zmx6145veB0RJUBjzzyCB555BHBsMcedyzefP9NlHhL4HF6eJ/hOA5NXU0Is8KKgnkLl0iW11isUMA4pBuuutgAnAiAc+QiwrKIsHK9bjNwaKwUULILUByeva+y09B7Ip0oUYD14etD36E52Ir2roM4uuhSnhBCb6Fga4SG0xozTz/gOM700xdSSdw/kG37z62GpImBXKUAfVIA1lwtpU8jDWqXxEnXblhRIUakh5QCRPZivf46PXaUWQsMeG+G45ATbkdZpAVgPKhyDoDcIZQ+vgT0h5MgdvJAX8xSgP8n4Y+YbpWhM9KIojTydQ/AlQzm7PnVerDexN86WHFiZkWZtERw+wDH2b+yySDTVx8zuRxn4rspeSel281sm382FVsraPsAESUbOuqUrXvWq/1WlClbcHKh2N8OLgxfRH7zqKeDQTmITYybQhXwRRKdXsq3LVDSZKg/fUAPOkOdqO3agxDbc6SLUXPsTKnt6SY/Pd9T3sCR2kPtUOWHQubkNhuGE/EYfVqKGpIViFqcCkNYG6H6S0cSEsmQUsAikKkSASBzZgkKMfP1nWyPM8mAswChiLg0qcfYMXBaZcU2jRht4ZqEByU5GkyKVGx1WvbxqCkSKCgJSoJwHObvno89vtXY2bmcBjIZQHI5jS9bnaFO7G7eLeo4NtuQMjGQPXmwSDNoOoqascxsgzL1vbKVtKcPCDgLJqwNbR8gdCEQDqAz3Amv04s8d57yiLKsH4lw8h3b2R2rfGJXnFKgg5FfZp0OxraDYSN8CsiZbxs1gPSFfGgJtAAMgwDbgSDng5cpSPiMUt9TiczmFBf7rGpqCcuxmLtrLtqD7eif3x/njz6f9zmr6PWshNyykfGnDyS9nlxLgfj7xueJhgU8Qz4nIYLEb5yyfcBmCvaMaZtUQkoBQhdag60AgBAbgtfpjTnc04tM2aso5kTQCmiRxVZsfJ1sGEycZruVzZUfh86nDsTvmdd+4iI/Qn2PktNu76OYlFqWRSW5oVXqdmr6DJ90H87kWl8t2oPtAIBqn/AJFjYby1oSO5VHPSFna4QViB75a2ytJEsBe0JKgSzFSC1emA0nKAWsOCm0CmHO2kqBTMXJ9VgJhBy58IcTPaSng4F+RxEqQmYVM+b0ATV7V9MnpsaJUkrsGn5KvnwyqqSYMRgUI+ULGdgPJVthWfNkBuORVG/k7h7I8nztCHagqavJFkoBvcZjdlspzgaUOANUWj6M/v5y5TTDz4IdIKVAJpHdZTkteld2LeIPs2HRwaqdGqz4N7C61PFbBzodefLNZS20dUBJZyzJp0DK6QPSn5WPgnegQagkrNaGRBUXGiLRtJuQjlZHEmbst4h7rWAkiPd2vIdAJCB6vLBZaKkkzNjvmWEYYUmb7gQhwh6Qo0GLkO2adSGETYIzN7+y0a+AmTg4Fo44C412zjpbBxLRr5NlFYkv4miQ5zdZXq5VOOhS15Qyh//fmPZlcKkKfytxUPeRHqle17MuL3VoVqxkNGU025q2IRAJAIAih5Y0mSIyAT22D1Dd0B9SChCExaCGz1hYxoFOd1n0xAFHDjrCUg2oer6Ty6FnU6p/eUh7JCHPIF/LgX/UOib+jjFmt5rWNQWzyT6FXpw0urfg7w3+BvxY+yM6gh1p41L7Lua0O2mOJNRAJkFnbya2s3ZbYZWfV5mtFRA74YJOtpBHfWc9VlWtQnNXs9miEDxIbT/t7miQiEJKAUIQuw1c5JL572dRxAQzSWiWcSDkzIHfkQ9WphBOhx6O/3hQkTd9i7y89+WInXIEo+iRhDIi5iHdq/INODR1GmjgnObYYWUo8KYqosJsGPN3z8f31d9j0b5FxgmkgPQDwJ7fM3u6mJlotX3Aup1S9qDGCkprE3GWYzFv9zxsqNugKh5CnFj91eDkX9lpEraCfAoQBKLa/fZgO5yME0XeIjgY0peZgZ7diJS4I6x8CbwunU/W0CBXhvXKR2GOG63+EOrbAzxpyEe+pYCcVBTtH+BF0tYsJuEfS1DVURVbdWzwN2gbuWnjNR5lDqevEoYcSomjRz7oezIJoSV6rehKKVdtgTbLn7iU7SgtH3o42RQrU2SZoA008yEIAC2BFkS4CIJsEF3hLrPFIQzCyYXhYoNgDncoYUVKAes3ow4Hgz6FXuR7+RUYUly9JY/zxVac5E4J0nlFTk1L3wGAllMaI6ZHQt8iGwdKmexvxixkWwroJIdVsZOCyUr6Gjvlm53RI59T4ky+tGnfk+1l0vqjWcIQNPVIm64x0KHOaVmRaU9g9uCKdCEn3Ib8UCPc4U6wMjsyB8PA7TSmGdXGkZ5Q3PKRK4e8OppmrznfirMmbYCazJWefnLeDe+dH/vb6+YvT/HtqpoywEF7T9Tp8l5wX6q2YoimLbYPPJuRMniXO8DPutMHNMROeaRWVju9ayaiZ/5b/dtaXT6zyNrtA/X19Vi7di0qKirQ2toKp9OJ0tJSjBkzBkcffTQKCgoMlcfo1Q0l55XanWx4RyUklDydsqgzEEEgxCLf6zR9pSL+FeOPIowoqIMel0P/9+H5JkqTFFxRVhKXjkcSGlVXuyc7cvwl6MH0Ub1Q1x5AZzCMs47qf1gmPW3q9Yva7hjTF2f2B6DtA5mJ1u2yXVeTbYeKbFb6zfXYPkDoT9YpBT777DM888wzWLlypWCD5PV6cf755+PBBx/EhAkTDJaQIIzFiG45wnGIRDgEO1mU5YvtwVe78iAdJxcGE+flvot1y05P/dYBYYm9LgeK89xAqgsAzZGWb4kDfbGBv9yJlRaei4XC2MGcPM/jwhXThog+w4HT9V2MUMRwSD5lInpX1zSFypLgbZqoEPKQ217FlzHjJ8bGtIeSLFCorhmKlvmd9vQBPbYskBJJd7Jm+0AkEsF1112HM888EytWrBAtXIFAAHPmzMGUKVPwwgsvGCglYSXsMJmwNJKzz5yG3hlvJcC4EFJw6pZHJ38ChTkuHD20FH2LcuI6Vx3zKc234qsLMvz3HUZc/qLceKWMAqWAhvljiYXOJBnE+ixZ8tpoYEWTBu0Qsn6RksdqTx/obj8y5XsaaUlE4xDCjhg9gVe9lcVG/aKeZI2lwK233orXXnst4d7MmTNx6qmnYsCAAQiFQtizZw8+/vhj7Ny5EwAQCoXwv//7vygqKsLVV19thtiECRgxcBHr6K04cApFWATCLCIZ1HDGbx0IQb6VgNvJ2N9M9rD4Ur5q8quKWgrw/JTOc3Ch14WBpblo84cwY3Qf7KgUk0abcpjeoaF0tC4JdtoDr1w27d9J6oTNyvlpNWT7FMiyiayuZUnzrNTQfxTVIVsg6NNFQ2uO5OesuH2gNdCKlkALBhcOFnwm25UDWWEpsHr1arz00kux65KSEnzxxRdYsWIFHnzwQVx77bW46aab8PTTT2P79u14+umnE8LfeeedaG9vN1psXnTTUEuoB5nWAWhd+dPlz7Bhw8AwDF5//fWU344ddyz65vfFnP/O0VQmLeA4Ds2dQfgC8o4OUlJSjSphDo6Fg4u+z7HH/hJD+x6Bj9/7QFYc6RwMPvPkExhe1g9XnHORYjm719f0Rkn3rfkkmGEwpCwPRw4sRp8ij+iz+jkaPCyLZjFpZ3WgxJmfHdpsq8loyITWQq+sy5GEWTGy1Aer1Yd4yKcA0Y1S57J6pilGZ6gT7+14Dwv3LsS3h77VUKrMIiua7jfffDPh+tVXX8WsWbN4n2UYBr///e9x6623xu41NTVhwYIFusqYbZp1U+CindAnH32Cay67BkeOPhK5ubkoKCjAcUceh3NOOwcP3fcQFny8AG1tbWZLawp8TW4owtrJ4lgS8VsHOIVVT6+tA4DxR3opczSo4/52DX0KSMGK7W9K/mpWB82uzOY61U13n0hF/pGEdESmGHLyQeu2yWgnqmJQHTQGVfmsMKjVLAU21G1AmI0uBG1q2ERtkQBZoRRYt25d7O/y8nKcf/75acPccsstCdcbN27UXC5COUoauZaWFlzwywtww1U34LNPP8OBAwcQDofh9Xpx6MAhrPluDWa/MBtXX3Y1PvroI0NlHDp8KEYdMQpFxUWq0yXS4+Tij52UP0hyOhg4LDiRjKJoii8/hIavb9ZpKELp6j1uVjIwj5dVj4G9vnlOAzA7o9anQKZj5wmGmnov9t404bce6ZwDahm31QhEDPDYnAFkhU+Bpqam2N8jR46UNKAaPXq0YByE9ZDSuF3/m+ux6utVcDqduPG2G3HDjTdg0rhJcDgcqGqrwo5tO7Bi6QrM+2CeUiGkP8px6ApFwHHRc8nnLpqrLM108thtcCYnD1UkE3AVIcSG4OKCiuJxOozMWO7w/0uXVG5nL8mnQNK1uE+B1N/kDBqSVxnUTTJke0S0BLbyKSBr5TMunAHv1HPsJMN7P9vRIx8MbR5NQMvV+3Qnr1jRikkrqA5mLnpYCoj6JUrTl1jJQsbKZIVSoKysLPa3z+eTFKajoyPhury8XFOZUqDyqiu7du3CooWLAAD3P3Q/br/nduQ4c+A4vPnR5XJhwsQJmDBxAu68507kcDnoRKdu8gTCLMJstBELhFjkesSO6ct8WC4ClguDM6JJ4qKT4IjDjQjnBqdg/4ArwzbNphua8fWnYrmmtjlTs39em87f/AZZr0ms9Ybh2mov7TCJsrKChxfZ4io/6SAbkJMPatuz1JMglGO0HyZCG9R8N6XfyGrbB9LR/Z7ZXiYza2QrwPTp02N/b9myBTU1NWnDfPHFFwnXJ598suZymYmeBd+KlWrDhg2xv39x9i/SPp+bm5tyL94ZYHt7Ox544AGMGTMGubm56N27Ny6+8GL8sPYHSfKEIj0NZoTjRB0NMgwDhmGwcuVKtLe348lHn8QJU07A0F5D0atXL5x99tlYvXp12jRXrlyJyy+/HEOGDMGQsiEY1X8Uzpz5C7z0/L8lK8uSuf2WG9CvOBe333IDAGDO2//FWafNwKShY3HUsPG46vxLsXrVd7Hnw+Ew/vXCv3D6Cadj0pAxmDR0LK65+HKsW/8NApwPHSF+h55tra147LHHcPTRR6OoqAi5ubk4euJ43Hvn7ajct09Uxi6/Hy/87TmcMvVkDO01FMeMnohrL7kK3375teTTFNZ+9w3uveW3+Pkxx+PYYUdg8rCxOOvUWXjhub8rzjtR4kZuWtYmofGlEr8Kmp68kPySKS+tz0TPTitVYu2q4HeVY11iSF7ItWBRLxP5FFAPbR8wDi1PRCG0g+M47GrehX2t4uONbIXa08wgK5QCN998M9zu6JFjkUgEd9xxh+gAqLGxEX/84x9j15MmTcLpp5+uu5yECmS0R9WHqlUl1dLSgpOOPwl//etfUVFRAY/Hg8bGRnz6yac4e9bZeOeNd1TFL0R1dTVmTZ+F555+Dgf3HwTjYNDU1ISFCxfipJNOwrKlS3jDhcNh3HDDDTjllFMwZ84cHDhwAC63C52+Tmz4YQOeeuTPOGPGdBzYL3oGXFpuv+UG/O7WG7Fx/Y8Ax6G1pQWrvvwGV553EZZ8vgiBQAD/c/H5uPN3d2L71u1gORYd7e1YuWw5Lj3rfGza8BNCbDAl3u3btuLEaUfj4Ycfxvr16xEKheB2u7F3z268+drLOHnaFCz4mH/LR0tzMy755Xl49i/PYNeOXQiHwwiHQvhy2XL8+oLL8darb4i+UzgcxqP33oHrLzkHn8//BDVVVXC5XPB3dmLjjz/iiYcfxBkzpuPQgYOq8k4a8XvKtVkR745B0vaBpPTEklcrGpv2PAQeSwFVjgaF41WPssxIzm8tJ+0FOcYaCdKA0d7ILXu2P6rVRAw/312n9DKtzm9q2ISllUvx2b7PsLd1r9niyEKNYlSofKQrN1azFCDlmjSyQikwZswY/PWvf41dv//++zj99NPx9ddfIxzuOWato6MD7777LqZOnYrdu3cDAHr37o13331X8/0oTY1NqKuri/1XWVGJLVu28P7n9/tj/4VCIbAsf2Vjfa2I1B0Q/Y8LdEUfPlyfufoqcPVVCNfzPF9/EGhpSEyjQ0Iawa6EMJH6gynxdqfL1VelpKEHU6dOjX3Dhx94GHt27VEc19+f/Dvq6+rx/vvvw+fzobW1FVu3bsVJJ58ElmXx+9t/j5/W/6SV6DFuu+02eDwezF00F/vq92Ff3T6sWbMGY8aMQSgUwh233cpbNu655x688sor6Nu3L/7973+joaEBe2v3orKxEh8umosJRx2J3bt24rr/uVywbKVj8cIF+HTeXDzz3AvYdbAOG/fvwPK1X+HIyUchHA7jj7+/C48+eD82rv8R78x5B3tq9+Kn/Tsxf/lnGDJ8GPydfjz+wEMpXVRHezuuvvRCVB06hIEDB2LhwoXw+Xxoa2vD19+vxTFTj0MgEMBtN/wGWzal5vkDd/weW37aDI/Xi78+9xT2HNqGnTu+xrq1n+EXv5yFvzz4CJoaGwXf6x9P/AkfvfsmevUpxx//+md8vXUjNlbuwJaqPXj/008x8ajJ2L1rJ265+nrFeZeepJWjw02RloM5ZacPaJZ8Cqn7apMf4Amj4C2EwiS/my/SiJrANtnxW4nzpgyEy8Eg3+vE0F55aZ/X04eFkag5o5tQhlDTkKl5rqnDtpT2XuPTByw0ObJTefjm0DexvxdXLDZREuXY3kJYwySs3GeZSVb4FACAu+66CyUlJbj77rvR0tKCL774Al988QVycnJQXl6OcDiMmpqahIH9aaedhtmzZ2PEiBGay/Pee+/BVdST/b5dPvi28Jshr127Nva3q9iFwqJCFBYWpjzX9uhtaFq0WTTdIU/djfxzr49dN51xefRfgedzBnuQ//HS2HXjwzeiYaH4hHfwU3cBp58bu6485WzR55PT0INhw4bhmmuvwf979f9h25ZtOGHKCZg4aSJOnH4ijjnmGIycOBJjx4+V1AG3tbZh4eKFOPPnZ8bujRs3Dh8v+BjHHn0s9u7ei6cefwozF80UiYU/HZbjBDtKl8uFeZ/NQ68+vWL3pk6dig8++ABHHXUU9u+vxLo13+O4n/Vsl9m8eTOef/555OXlYenSpZg4cSI4jkO9vx5utxvHn3QC3v10Ln4xfRZ+2rgeCxd8issuvjBtHiTT2tqCf738Gi68JFqefBE/Ro4ehX++9iJmHXMCDuyvxGsvvYiPP1+GX5x2Epq7WhAMhjFx8lH487NP4arzLsUPq9ei6lAVBvcvjsX7/16Zjf2VFXC73fj8889x5JFHxn6beNQkvDdvAU6ZPhUH9lfiyccfwZvvfRj7feMP67FkwWcAgMee+Qv+55or4A51ACFg8OABmP3Kczj3vGuxfk1P/Ypn1/ateOe12cjJzcPstz/CEROHwMEADgcDh8ON4088EfMWLcHJ06Zgy8ZNWPbZEvz8rPRbU+xKcokVqytyh53pTh9QMy5WOwQORLqwq3O56Ssfas+FHt47H+cem49P9iyBw5Hqw8SwAXrySYucMR5RzXQ0ZeUBqKTVQto+oCmiXvstPFHW+pQYK9cLMaz8jbIV+ibakBWWAt1ce+21qKiowE033RQbIHR1dWH//v2oqqqKKQTy8/Pxt7/9DYsXL9ZFIaAKKveKee6fz+Gu++9CXn4eOI7DTxt+wr///W9cd911mHncTBw5/Eg8dN9DqKutE43nuOOPwymzTkm5n5ubi1t/dysAYPnS5WhtbZUtYzDCotUf5m3gbrzxRvQp75Nyf+LEiRg+fDgAYOvmRKXQq6++Co7jcNZZZ2HixIm8aRYUFuAXZ50DAIJbENIxcPBgXHDxZSn3hwwbiqHDhwEAfjb9BEw7/gQAQDhO+TbthOPh8XoBANs2b00I/8lH0VMZzjnv/ASFQI/shbjtjrsAAMuXLkZbXJ4v+OhjAED/gQNw0ZWXRm+Ge46liThzcNOdtwu+0/w5b4HjOJx86s8xetwEAKmnDhQUFsby7uvlKwXj0oLkEiE0yVG2Yi4fJR7Gpa5SpevgeR0NajAo6I4hXsrKtr2mKATkOBqUmq8LKz6Gw0TX8GZMAqw5WLSiTMLIVgpYaDVaD3Q9fUDvsmEhjY1dlQJ2Qw9Hg5n27brzyJr9hXFkjaUAACxatAj33XcfNm8WX033+Xy455578NJLL+Hf//43Tj31VP2Fy+5yaAgulwv3/ek+3HL7LViyaAlWf7saG3/ciG3btiEYDKKhvgGzX5iND+d8iEULF2HkxJG88Zw440TBNE6acRIAgGVZrP9xPU479TRJssV//jDLIsJycDkTO+9p06alhuM4MAyDAQMGYN++fWhpbk74/ZtvoiZvn332Gfr16xe7Hz/J4QB0+qInLRzYv1+SvMlMnny04CS1d58+qNi7D5OOPgYA0BmMIBDuSd/pdKKsrBQ11TVobe6Z1AeDQWzdsgkAMGPmLMG0Z5wSrZ8sy2LTxg04+sRJAIBNG6IWLdNOPD7qrJENA1wkFs7PuXHMz46Dy+VK2EbUzfq13wMAvlmxDLOOHgOASxhPRQeGDHy+6Eklhw4cEpRRCGnVnu8pbQd2nIL4xAbGahUWVhpwaC2L0jF5vBxKv771zhU37ztL3TqS6egxCKY81C6uTFawZPsEzGw0zX8u+VL7b6smTjqSUBpZoxR49NFH8cgjj8Sux4wZg7vvvhunnnoqBg4ciFAohD179uCTTz7Bs88+i+bmZuzcuROnn346XnnlFVx77bWaynPppZeifFjPMYdjC8diYhH/Sm68tUJLqAWMwEpP4cMvoPf9qY7a4nEUJ640ly1+FwBQ4ClAjtOb8FtXJAAflxhfr0dfQtl9baJpoLgXOiM9RzoOXbEAXqcndh1iw2gN9Ez+GLcHRlJUXISLLr8IV/7PlSj2FqOrqwufLvsUr/znFSxZtASNDY248MIL8f1P38PtdaeE7z+gv2Dc/Qb0TLzr6sQtDsTga/r4tox043JFq3IoHEq4X1VVBSDqLyP5mE0+OjuVHcOYLyKb87BsBQXRZzqDqRNwJ4/8Lc1NiESik/j+AwYKxt9/YM9vDQ31sb8bG6K+Kvr1j34vJhJnJcC4EGYZeHNyUFxaisb6nnDd1NVGTynp9HWg05c+77r8/rTPWBU2TX/JIHWgL7bgLLf/TXdWN18IwbTlJc0bRskAQusxR4qjQSUDIhlBUuLXabwuK5tozmAJ5E4eaPytHKOVc2rSEy0XVHcth5Z+d4jMJCuUAu+++26CQuDcc8/Fu+++m3DsnNfrxaRJkzBp0iT8+te/xqxZs7Bnzx5wHIebbroJU6ZMwZQpUzSTqaxXGcrLe5QCQ8uHYsKACWnD+eATNGV15BfDWZwjSw6mzwAAgNNTBKcrMawz3AUEExUAjoJioKAYYrAcC/h7JlGOPgMT4mYjITCBfFlyaoFQ45aTk4MZs2ZgxqwZuP3G2/He2+/h4MGDWL5kOc4454zUAKKTIWuNhron1X/9619x3333AUDMpwAAhFkOgVAEec4yAECBV7smQdOckJjnfPnffcsRpxQIIlXZkwzLRvPujgcexrW3/g4eTwc87p4dVx4mD25HDjgO6GSFvHKog2Hix1Z6OglSgI5FPf3pA6lYcfASteRRHlbsmlAH5ad+aLm1yQ7IfS+x57PK0WCGlodsgL5dZpLxPgXC4TDuvffe2HX//v3x9ttv855D382QIUPw3nvvxRrjcDiMhx56SHdZswWrmRXFc9W1V8X+3r1rN+8zYkcaxv8Wr/RJxohumQNiWwY2bdpkQIraUlJaBqcz6hDt0EHhI/+q4n7r1avHCWOv3r0BANVV1XBwbHT7wGG6uKhSIBgIoDVpy0U3vftEv9+u7VE/B06ncYMpKQM3oyc1yTKJHTsm9IvQe6W8S9KllJwXzA+RwLEwsWe4lCBWGkQLocW8Qc/yZOS+VPlWJ9mNHt/ARLcVtiebtg/QvDJzICVBZpDxSoGvv/4aB+MmDb/5zW+Qn59+lfqYY47B8ccfH7tevHixYtNqKdjJZMxIjJYzL7/nqC6Pl39bw7dffSsY/puvonv4HQ4HJk+ZrKlsSjjhhKhjv4ULF0raPmAlPB4Pxk+Ibqn5+ssVgs99tXI5gGieT5w0OXZ/4uSjAABrvv0eDNuzLYFjHAhy0abvh+/X8PoTAIDJx0Z9OHy9fAn8Ph+cplmBcAn/qhkkCh4VpmFcWpDOUoDX0aDQHnGVklplH6PSye2+1n34ofYHBOIsZbTGjhNvu/SBVoAcDUpH67ogNy+3NW7DygMr0dLVoqkcfGTr6QN2Q7ctIjqEE42TyovuZLxS4KefEo/PO/bYYyWHjX82FAph586dmslFGMu+ffuwa+eutM+9//b7sb+POjypTGb1qtX46suvUu77u/x48fkXAQCnnHYKSkpKlAkL7SZcN9xwAxiGQUtLC37/+9+LPhsKhSynODj3wosAAJ/On8frINTX0YF//d8/AACn/vwXKCru2dpy1vm/AgBUHTyE996bj1BubyCnGF1M1EqIZVm89Nw/BdO+4Ipfg2EYtLe24rm/PCyyGsshFArB18F/pKgeiJcPo04fkO9UQLKjweQBhU5zDL2dzWnp9VnJgKjB34DP9n2G1dWr8fXBr2Wllylk6ntphZQyqpVPATsqj6Sg5aRLzUknhzoOYcWBFdjauBXfVkUXL9IpFfT6JlTvMptM295G5TVKxisFfL7EgXpBQYHksMkWBX4bOxLLdrZs2YJjjjoGV15wJd5/+33sr9wfawRCoRA2bdiEO266Ay/+MzqpP+644/Cz6T/jjauouAhXXHIFPvzww9gq8/bt23HBry7Arh274HQ6ce+f7uUNG8OghZTJkyfjd7/7HQDgxRdfxMUXX4wNGzbEGvBIJIJtm7fgH08/iZ9NHo+fNm4wRjCJXHPdjRgydBhCoRB++ctf4rPPPosdHbpl8yZcdsE52F9ZAY/Hg/sffDgh7ORjj8Zpv/w5AOChe/6At994BwHWgU7Oi+qDh/D7m27Dxh9+RI7AVqKxEybiyutuAQDMefNV3HbNjdi6aXNC3m3Z9BP+8fSTOOXo6di2eYusd5PahxrRWUlJIXmgL6oTSPkjXfpyJ8DSLQVEY7HRQCbd+/G9y7radbG/dzbLU2rrVe6S3SdqjdRvSoNA6ci2FMhwQwE9fQep8Snw7aEeK8bKtkpt5ImrT1q3l1QHjYGO2yOkkvGOBktLSxOua2pqJIetrk7cOx6/X1lzLFhXM6nBdrvdYFkWyxYvw7LFywBEzdMLCgrQ3Nyc0FhOmjIJ8+bNg8PhQCTuCLtu7n7gbrz9/97GxRdfDK/Xi5ycHLS2Rk9TYBgGT/3fU5h89GRD3ksKzzzzDDiOw3PPPYcPP/wQH374IXJycpCXn4e21rYE83mrOUosKCzEG3M+xBUXnouDBw/izDPPRE5ODjweD9raok4wvV4vXnjpNUyYeBTYpO/113/+HVeddym2bd6K+27/PR68+w/IyctFe2sbGIbBA395DK//a3aCX4J47nrwMYDj8Nar/8FnnyzEZ58shDcnB3l5uWhva0/aeqBv3ulZGxVtH1DioV9iHikZvGgz4FG/RSMdSuOOfz+pChrLtOEmDkYtkwdxWFEmLcnm7QNqUdOONfgb0j6j5bdRvX2AJqmmYrd2SE15oTZJGhlvKTBq1KiE66VLl0oKF4lEsHz58ti11+vF4MGDNZXNStitcZDLGWecgY1bNuKJZ57Ary74FY4YewS8Xi9aWlqQl5eHEaNG4FcX/gqz35iNZd8sw4ABAwTjKikpwVfffoX7778fQ4YMQSAQQFlZGc48+0x8+sWnuOo3VwmGNQOn04lnn30WP/74I2688UaMGTMGDqcDba1tKC4pxjHTpuKeBx7EF9+sxvHTT9AwZW0a4XHjJ+CbNT/ikUceweTJk+FyuRAIBDB8xAhcfe0N+PL7H3HOeRfwhi0tK8P8he/jrvvuxKgjRsHhcMDldOGEU2Zg9ntv4bJrrhZN2+l04o+P/xVLv/oOl//6SowYPRJOpwPtbe0oLinBcT87Hvfc/0cs+GoJjv3ZVE3e1ww4Bd9Kwe4ByaRdFRf5LWXCLPJunCFKAG2wchstXTYu4QOlG+Tp+c40IZGOZtsHLFyG1aBlWVKaR8GI+HHUViRTy0M2kM66z3A/adSea0LGWwqcdNJJyMvLizkJnDNnDu655x4cdRT/fvFuXnjhBezfvz92PWPGDNETC9RCjaP+jBg1AjfcegNuuPUGAIDH4UFJTgkAoK6zLvacw5FeV1ZUVIQnn3wSTz75ZOyeP+xHe7BdMExFRUXs7wOt9Qm/rdu6Dr4gv8O7+MZOaCVg5cqV6AiE4QvwxwEAU6ZMwezZs0WPJJTL8/95Gc//52XRZ977dB5ynEWiz3y1cTUAIMfl4J3xFRUX4+GHH8bDD/dsEWj1B9EVEt9/yQAo9rK4785rcN+dv0GHoxC+SOL3/XytsONIAPC6og4M//zs0wmieZhcuB254DgOnWzqCQa/f+BB/Pbe/wWLVGsTWShoGkT3+/OhYOYqOw0R5HuM53QdBCS+mrR0+BQLVrO8EcO0QZXGWUR9qfbIdzSoLJxdUFtX5ISXqrBsSzo+uthbzPucXAUoB04TpSlN2sxDlc8LqsNZRcZbCuTk5ODWW2+NXYdCIZx55plYtWoV7/Mcx+Hf//437rnnnoT7ydeEMFlb2ez42naUGUA4wqZVCACAgw2Bib0kB39EfpPndTtlh9GFZMc+Ah/P6WBQXuiVFifTHZd8xIaJageRyacPGDW51ro6qHI0KMORkyaDdpNXevQia/sjE7GTMsxqGF1e09Xz/W370RpolfSsnHjlxkdkD43+RhxoP5DaBxriXym7y2TGWwoAwIMPPohFixZh69boeeOHDh3CCSecgJkzZ2LWrFkYOHAgQqEQ9uzZg08++QQ7duxICH/NNdfg9NNP11XGbC+IaqH8k46V80qqZK3+UPqHALjYHpPKCOMGKymBnofcTgecsg/dVj8g7h5TRyd70nKlMVSBQ13rceSgPLic8hQZ8t36aWspkJJeysA43bWefgisMcGxsiNFKZYd3VgjN4lu9OgPHAntV08aVu57zEIrM2zJdV16lwIAWLRvEdwON64cdyVPotLj4YMUdsYgVIZUnTzCJV9q8y0b/A34YOcH4DgOxw84HlPKp2gSbzpFJbVNUbJCKVBcXIwlS5bgggsuwJo1a2L3V65ciZUrV4qGve666/Diiy/qLCEhFytW4ExdabMiYWmzezi5nrPZA/DITsfr6rEs0P5rKotRqG+r9Ee3YOR6CmXHKeUQLFmnD8id+SnKisyub/K3VKhMz4ABupkrP9Y6QsteZVW2T4FkZYC9XtdUFJ8Lr2OZCrEh/NTwU/oHZUJKASKZbw59EysX31V9p5lSgJBGxm8f6GbgwIFYtWoV/vOf/2DixImizzocDpxxxhlYvHgxXnnlFbhcmac7UbTqZLOBjBXhOA6tgVZEuOS9/xqvBhBwsmE44s587mTl12Ovy/ytA7yrbEz0jmEycEBJXqJSRdKRhApJPqs7vZ2AMmKOBmUoPOSi5tzxeKjNlkgWvrIadOlTsswcRG4eitVLpXVW77GBHo4Ms7J9ylC0Kn9d4S5l6VNZ0oTMm+2K4HQ6cfPNN+Pmm2/GwYMHsW7dOhw6dAitra1wOp0oKSnByJEjMXXqVBQX8ztp0Qua7KlEQfbJbUTWbVuX/qE0BNkgApFA+gc1gUPWjc7icHLxWwdciHDy8sLlcMDlzI78k1ITCrwunHxEb2yrbsfRQ0olbR9QfPyephPg9KcP9NzQth1uCbRgYcPbaAv0x0DvpKg0GhSp5HxlGPWDIl09/Sv4RXsZMtuyRE/k5lXyjqvuaqXlOGd19WrsadmDY/oeo1mcUrFi2ZEqkxr/I3IsfSSZphthKcRx+L76ezT4GzCt/zSU55XrnqbZSLWKUuP3wYw6QPMk/ckqpUA8gwYNwqBBg8wWwzz0WnrLBCTkhdIGMcKq9ERvJ0yeT7vY+K0Dbtnh47cOZDpSjyQ8ZmgZjhkaPakiHBFeAZc78U1rKs9zzTH8YbQodloeURhmw6gNbEc/z3g4Genl0OjtAynpy0hPrwGinu8sJHOOO3vqvVRknz6gs6PBlq4W/FD7AwDgi/1f6JqWFJSU/4PtB9ER6kCuK/FUK60si+yAEW1aZVsl1tetBwDUdtbi+onXSwpHE1Bz0Ot44OR41fhZyGSyVilgNayoeSbMQr+yEGEjaOpq0i1+q8BwHJi4xt3PKlAKpJkcZEKNjR0dZvKRhMmkaw+1ai+NHABwkjw3iIW3rqNBWeisLFSiTJkwoAhbqtpQmufGiN4FeonWI5PuKUhHj7FHzyfm2fqkAU0Be/dhdZ11MaVGn7w+Cb/pPxZUVgH1mqzpzd7WvbG/9dgCYUXUru6H2BC6wl0o9BRadjsLoQ+kFCCyBvlnLVu/E5R7hnBnuFN2PtixaecYBp2esuiRhGwIYZlHETodDFyO7FkxVPKNxX0KyDwLO525I5NeQi0H00r0HVrrSIx2ypfpgzgxZcHp4/ti8uASFOe54ZB92kjmI7dsJCsMM7xoyeZA+4HY3/Wd9Qm/KXY0KNCG6mm0obbNsPJiWLYeqxlmw/hgxwdoCbRgar+pyHPn8T5n5W+niAx7HaVkz6g3Q7DDRJWQj1HtUZhNdnBojhxGwAGIONzoRI7ssF6XM2EwxT/24c8tbczX41PhYn9pmUY86b473wBAz0GTUQMOrfaZN4f2S0lMFWIDcOv3CsIrV3r3aVK+KcMwKC/KsYRjUaPR4zz55GNc5R/ram3E2g2tfXtInXjr3WYyDKN5GlZWRFpZNq3g+547m3eiJdACAFhbs1ZVXEZidvqZAikFCABUobQg1WmZ0ngUC0DwIPX4wnhyLOJPwLaLFSrlTt5XqyY60Tzk+OOXk+9dbDv2+b+TK5ZsdLcU0EhBkg6rFOls6/NUtSUys8rjcmBc/+jRqKX5buS4s0/ZYjRKHQ3qNfmVIg+rcksVwY+a41c7gh1JkfE/1xnqxMe7P8ZHuz5Ca6BVroiGQwuq0qDtA0RGQMd1Ed242CAcXBhhhxdhziF70ONkGLic1lAKREmUP+YHwMLlV273K/9dUp83a2WnOVShS7xaKRmtROp31v6lhFZazVz5S007Az5mGs6Y0A/I64s9rT37/ztCHXA73IImyYR22weMQHUflPnVwFJoWUa+rfo25qNh+f7lGNdrXGJaNv24dpVbK6w08s1qDPcsraTgZ0ldyfZGQX/0zV93xA9PpBN5oWa4I52yw3tcDkuu0JtZLo0eb6bup0/6HVzKXVVHEiY9Im9VwZjCorfik9q97ESvo+MYhkGu25mwzejj3R/jrW1vodZXKzs+K6Pl+E2xYzedLX30WGmlNkcfJJcFCdkvFDbeaWO1r9qQOYyW5YXKHj+kFCBSCEaCaPQ3oj3YbrYoWUtzZwj17QEEw8qPMMzGJo/hWDi5UOw6wMk3W7XEUYQGKyW0Litq/Q2oMSuVk7aRA4PutCRLl2IoEOdTIlmJwaQ+Lxczjv9T4stCq7QJ6WiZh2E2jGX7l2kWX6ah2MGoxMfUTO6zyacAkWFYcKHHitD2AYtgpYFLt5ORbrhDe9F0zm/QGHdv9LJP4Bo0WlH8VnpXq8Ie7iybO0PoW5RF+zFVFg1PxIduV4ocGAQ5eU0cI7h1IFPLrLSeUu+3T+9ci0srhxYDzOrgZjjhRmf4NACFquPTEiXvpyZPZFkZKE5Hh+0DArIodeBGaI8d9iADImVJx5NBlCpEdXc0yNNXiKWplwUKIUxlWyV+rP0R1b5qs0VRjNIFhUw+CcNILLAkRhDGQINAa6DnV/BGepzkBBmP7LRctvKSbd3yrDYXkztovnGCFuayyW1CS+ggGkP7sKbuS9lx6Y3Rjgb1IhsdPhnhS0FPFO9zt9l7WgKFWWbVUwusmnYmsnDvQl6FgJp8VlqO9Bhv0xhef0gpYBGosMtEi+zSOsut9gmtJo8EVInMsfCEfbHLLs4tOworHp2VjQMnofaw5+tIzxMlk9C6zioZKTFx/y+GvO9o1GkAxmLi5FhkOwYhDuWVcSj2KaD3GFKHrpHGvcaSmX2KNKSOA7IpT/ig7QMZhB6FORgM4oXnX8GPhw5ifyiETpZFnsOBzmNPwrU334w//OEP8Hg8usthB7L1vQ1BQtZ6Iz4wYAE4wYFBQO7WATBwKjJdM+q767nnO90D8tKWm43pzHFjHuTlRZs+3dg+f/2UQZo5rRR9ef5EZLVJKjJXr7bv20Pfoq6zDscPOB757nxFcdDEQxzKH2Gs0KdLlUHykYQMo7yuJyvWsrTs2M3ayYhtZFaoK4R6yFKA4CUYDOLqS67GsN7D8NQLr2JpRwd2BAI4EApF/62txaOPPor8/Hycd955CIfD6SNVgJadzpOPP4m++X1j//XK6wWGYZCTk4PJoyfjqouvwsdzP+5J017tPqT09Gkbbh3a9Wf/+gyeefIJ7K+s1D7yOLzhHseYIcYt+1WcDsai3zz1TTTwK6craifCLJe4rzY5Or5y7Av58PHuj9ESqpGcjpUHMhm7qhP3MdM17xw47GzeiZUHVkqOXvIgNksnNEZCeWwcdmwvktt5PbDbBJ4gzIQsBSyCEQ14mA2jNdAKDhy8Tq/gc81NzZh53EzUVEcH10UOBy4oLsHx+XkocDjQwbJY5fNhXmsr2sJhfPzxxxg6dCg2bdqEsrIy3d+DD7n516e8DxgwYBgGra2tqK6qRnVVNZYsWoL333ofCz5ZoKuc2dRN/d/TfwMATD/xZAwZOlS3dJi4AYaSrQMup02+ioXFbA1XoS1cjd6hIwEMMjz9Qx2HsLVti+p4LHkkpc4TrJQ21EJziso25QrF7veivdP8SJGNJveJWPJ7auSLQAi7HkloyW+VBeiR76KOLeVuz9PRWaidIUuBLKIj2IEIFwHLsfCH/bzPBINBzJg6AzXVNXAzDB4s74uVI0fh3vJynJRfgCm5eTgpvwD3lffFipGj8GB5X7gZBlVVVZg4cSKCwWBKnFasbJv3bcbWiq2oqamBz+fDl2u/xIxZMwAAy5Ysw4MPPmiyhPbH6HlVa+4gNOaNQJcz36CtA8aWayvWo3jCXAB7Or9GfXA31jcv1T096YNZ0UgMR6nCIeFIQolxWqfMCMmRnRN1q3wVqSje567XmxqUgZKdrFlAaSKU1ylthYW0ylbIt26sJIvWCB4Jq+UJERbPvuRTDazTN1oLUgpYBCMKaJBNnbAnp3/JJZegtqYWbobBS4MG4YrSUuQ4+ItJrsOBK0pL8dKgQTHFwKWXXppeEIvVRYfDgbHjx+LND97E8JHDAQCzZ8/WZUuElTrkTIRlXPBzXrAy89lqWweMLidaVMmOcH3s77BIW6MEvUxjheOJc2mYZuCk15eSu5KhNk9UeaiWEdboakaDP8LOWEUxwTCM5nXJiLpJY64eqC1MTyYrh6RASgEiRjAYxMKFCwEA9/Upx7Q8aY6dpuXl494+5QCABQsW8FoLaI0ejVtOTg7OOf8cAEB7ezt27diFttY2zPtgHm75zS2YMXUGxgwagyFlQzB21FhcccUV+P777wXje+zRx8AwDGbOnAkAmDt3Ls458xyMHzoew8sG4v/++vfYs7t37MZLz/8HV51/KU6YPAnD+pZi1KBynHbiz/Dggw+ioaFB8L2HDRsGhmHw1n/fRGdnJ57+y+M4ceokFBUWYMCAAbjqqquwb9++2PONDY14/MHHcfyk4zGyfBiOGzsZd/32FtTX1YrmT1dXF1558V8478zTMX74IAzuXYSJo4fhmisuxvJlS1Kev/OWOzCybGDs+sKzz0C/4lwMKinGyLKBOHnSNN50PlvwCa654mJMGjMcg3sXYcyQ/jjnF6fixRdfRCgU4g1z/lk/R7/iXPztyT/DHwjijRdfwmVnnI0TxkzEUf2HYu2q72LP7tu1G4/ecz/OOWEmjhs+BlOHH4FZU47Dhaefjb89/iR279whmg+WgFHoVUDxKrWydKQml64+623Ob8XtAsnovn3AlMGQcWmaOiBWkXSDvwHfHvoWNT7pvjLkQNsH1GOFyZYVZCDsT/KKupXQtIxTdeGFfApYBQsU0L/85S8Ih8MocjhwfnGxrLAXFBfjnw31aAuH8dRfn8Jt996mk5TKkdKgDBg4IPZ3e1s75n80H3/7y99i9/ILooqSA/sP4N3972LOnDl47rnncPvtt4vGe/fdd+Mf//gHGIZBcUkxHEnWF5effxkO7j8IINooFxUVo62tFZs3bcTmTRvx+uuv44svvkDp4FLBNNpaW3H2aTOwdctmeHNy4GAYVFdX46233sIXX3yBVatWocnfhPPPPB/7K/YjNy8XLMuioa4e7/z3dXy/6ht8vep7FOT0Ss447N2zG/9z8fnYu2d3TMbCoiLU19Xi84UL8PnCBfj1dTfiqX/8XyxYUVEhepf3QUNddAW5pKQUbo8H0S/Boax3Yjq+Dh9uuvE2LPu8x/S8sKgIbW2t+H7Vt/h+1bd48803o4orZ27sGVekC67DK9Ndfj9+c8El2LD2B7hcLuQVJCq2vvvya/zvr69DMBCIhnW7kZuXi5qqatRUVWPDD+uR6ynEvX+YIJjPRsIwqQPybFj5SJ2EpLi9Tr2nSTr6IXvPo8nvp+UAzFRTfYE8sMNEl+M4zN89H8FIEBvrN+LGo26Ey2H8sM1y2wfMRuS1zHpn6ekmmVHrVA/spmzK2LIqAt87p1ioSbVU0cmaTy+sLp9ZkKUAEePll18GAFxQXIJcgS0DQuTGKRJeefkVzWVLi0b1+0DlgdjfJWUlKO9bjpt+exMWrVyEnYd2Ym/tXlQ2VmLLji244447AAB33XUX1q9fLxjnDz/8gH/84x+49957sffgXuw4uANbD+3GRVf2bLU4ZuoxePipJ7D8h2+xp7YOO/ZXo7KuBR98vAjHHXccDh06hCuuuEJU9if//Dja29vwxtx3sfngLuyq2Y0lS5agd+/eqK6uxn333Ycbrr4BRUVF+GTZAmw+uBs/HdiJf776H+Tm5WHvnt147h9/S4m3paUFl553Nvbu2Y0TT56J+Z8tRUVtM3bur8HO/TV49C9PIb+gAG+8+hJe/s8LsXCPPvUEVm/fELt+9a052LSrAut37sLq7Rsw/4tFCencfcvtWPb5UgwfMRL/efV17D5Yh10HarGvpglvzvkQI0aMwHfffYdrr702IZw33AYHF93q8earL2Hn1u14/Lm/YdXOLfhm20/4assGHDFuHADgzw88iGAggOkzTsbcFUvw4/7dWL1zC7ZW7cGib77AHffdjUGDB4vmszwyo+Ox0NjNuuisqxEbxGiRtBlOv/RI0RZKAImysAgjGOmxvGv0NypOUkiZaKl8sRhy6kQoEkKEjZgmg9LvqMbRoJlblqyUBmF9smExRQvIUoCI0dTUBAA4Pj9PUfjp+fl4o7kZTc1NWoplGO1t7Zj73lwAQFlZGUaNHoURo0ekPMcwDIYOH4rnnnsO4XAY//rXv/Cvf/0Lz/77Wd54Ozo6cNddd+Gpp56CL+SDL+SD1+vFwME93tlffH02OkPRAYXH4Yn+6/HgpJmn4BezTsKoUaPw448/YvWq1Zg2nd/sPhAI4ON572PQ8Oik1ul04vTTT8dTTz2F6667Du+//z7K+5bjy7VfwlNQBABwu9046/xfYe+O/Xj26Scx94MP8PRfn0yI96m//gUH9lfixJNnYs68T+Fy9TQbRcXFuOm22zF4yFBc+z+X4dlnnsJvbrhZ9ixlxZJlWLrwc5T3LcfcBYsxYGDPtoOcnBz84syzccoJ0zB27FjMnz8fv/1pI448ahLAATlxRxF2+Drx/BuvYubPT4vdKymLWlc0NjRg/74KAMDj//c39OnbFwDgcjBwunMwZvxYjBk/FjmOInnC647+Ttq0HjbJ7YDlewLW16eAFYcPVp68WVo2ISdbNpwsmGXaa8e8MpIdTTuwuXEzij3FGFRo/KkrfAjVyZQiZOquGipXRhArCzpmtxF9gJZppCioLdyHGQlZClgEKzSO3Y71CmRaCXSTz0TDhUPaO+jTk5aWFny14itceOaFsWMY77jjjhQTfz7OOussAMA333wj+IzD4cB9992nWL6CggLMmDEDALB61WrB584973wMGzEy5f4ZZ5wR+/vqa69GWa/UYyNPOTU6id67dw98Pl/sPsdxeOP11wEAN//vHQkKgXh+efavUFhUhKbGBvy04cf0L5XEe/99FwBwwaUXof+AAbzPDBo0CKeccgoAYMUX0S0GTjYAF9ezkjZqzJgEhUA8+fkFsW9aX1sH4PCpAw4rTgFTsUIbYRW42P9JQfj7apOn+pQfo80xjSpfyT6gswEr1F1VMigNav5rG8Kmhk3gOA4tgRZsbthsigxWKGNyMWIiJlVBTZPC7IK+Nz9kKUDEcLlcCIfD6GDZ9A/z4Dt8TrzLbf09j33z+wr+dvHlF+OPf/wjWkOtAICKfRV4/aXX8e1X36JiXwU62jvAJuXRwYMHBeMbNWoUysvL08q0fPFSzHtvLjav/wn19fXwd3amPFNdVS0Y/phjp/Le79u3512nHDOF95k+fXqeaWlpQX5+dC/+1q1bYxYkd9xyo6iixNfRAQA4sH8/xkwZJfgcHz98vwYA8M4bb2HunA/BN9FyMEBra/SbHDwQ3eaRE25LeGbSsT8TTCMnNwfTTjwB3331NW654te45OorMfP00zD56InweDyy5DUSK3Rdmd6BppQ2Xeb50TxUutqb0MbZQ49lDWKLZPba82oUlA/C2ClvpLbRyZNkVaeOqOwXDFldJn8Y5lhLWTz7Mun7agkpBSyCFQpoWVkZqqqq8J2vEyflF8gOv+rwCnNZadJKtPmvlkKf8j4AAAfjgNfrRUlZCY6cdCQuvPRCzJg5A06nEwgBiz5ZhJuvuRmBw47pAKCwqBA5OTlwMA4Eg0E0NzcnrK4nk04hwLIsbr32t5j3wbzYPZfLFXPM1z0Z7urqQqcvVVHQTUFhIe/9+NX9gkL+7+qMeybew39VVVXs78aGekjB7/dLei4+vabGqOKhrbUNQJt4AAB+fzQfvJH2hPtlvXuLhnvk70/h9muux44tWzH72ecx+9nn4fF4MHHKJJz2yzNwyVWXoV8vadsHDC/WcQMoreaEVplbyp+wabR9QMNBqeZ7FtP4WkxIW0DRIOv9Unw56rLjP+mK07wMWqEvTYfkfeJJz6kpY2rC0sQqETu8l9Iypln6EtoPO+QjL1bpOA3A7HKkF3aT1yhIKUDEuOGGG/Doo4/io9YW/G/v3rKcDfpZFvMOr+Jef8P1eomoGZv3bYaDcaB3bnQSWddZF/utu7FoamzC7TfdjkAggBNnnIi7H7gbU46dgtzcXOS785HvzscXX3yB007jN1fvxul0iv7+/n/fxbwP5sHpdOLWu27HJZdfhZHDx8RW5fsW5eCqq67CW2+9ZfiKbSTS4zhp064K9ClPtLAIsX6EOD8cjBtepjC2X9EXke5Xgo1L44XXXsSFF1ydMslxMAz6FHoBRAcbde0BONkg3Gwg4TmnS9zRU/9BA/HekoX47suv8c0XK7Bx3Q/YvmUrfli9Fj+sXosXn3sBL73xFmbMPFWS7AoPBkwfr4RBh5ZFgdN4r7L2W595vCTr2KnLE1/8aa2yIt37mun0S1ZYgwfUgqtktrB+sYaMNIC2PooVNybWAyPKleTtA3IUcFQdLEe6cpzyPekb8kI+BSyC6QMUDvjDH/4Al8uFtrgJvlQ+am1FG8vC5XLhvvuV75+3EksXL0V7WztKSkvw3w//i+knTUdubm7CMzU16s+OXjDvYwDAJVddjt89cA+GjxiRYqZ/qOqQ6nSU0K9fv9jf27ZsSfk9yPnBAYhwIbAIpfwuBW9ODgqLig6nsVV6uLitA2EZMw2Hw4ETTpmBP/7lMXy84jP8sGcLnn3pBQwYNBCtLS347Q3XIhgMpo/IMOzRe2l6hF2a9lBOWuoVFNrMYtXmjul9hM2x4rGI6QMmXprmaDBby56NXlvxkYRGKQP5wlu4XNlNESaWl92/SXmn5DbG6G9kVhtHRCGlABHD4/HEHOc9VV+H1Z3CJvHxfO/z4en66Er72WefnbI/226NazeHDkYn4iNHj0ReHv+JDMuWLVOdTvWhqIn++KOO5P29vb0da9euVZ2OEo488kgUHZ6wz5/7geizLMe/St/dyPd0Lqnl4ZhpxwIAFn28IMVfgzAMIkzUCiOsoClzOaNyFRQW4FcXnY+/Ph89jrG+rg7btkh3FqVn6U7Ubh/ek65jelrCcZxkYfXdLqDO0aD1zx3XJg59B39CcWufptSTLOzQL9lBRjHsLr+t0Dmr9TjSzbblwy6dcBr42nytjra0+re1m7xGQUoBm6Hb/q/D8b7//vvo178fQhyHGw8exDvNzfALTNL8LIu3m5tx06GDCHEcBg4ciA8++CBjKlf3ZHjv7r3o6upK+X3Dhg145513YtdKG9PuVfLtm/lXyR9//HF0tHcoilstLpcLV19zDQDg/XffwurvvhV9vrkpddtAt6+DttYWwXCX/fp/AAD7du/Fv59/VjQNn8+HYDCITk8vNOSNRr13MFgJTVkobvWfAQNX0qkD3pweK5B0Wz6MRf8JVQiA2MYLuSkJjZmk1hFpbYiG/gAMGOR1sa2qJt5ieaKH+LLacYmPmtoz2LJbMsARWybv/dYJI/NDaZvZfZ288mrUSrDdyozd5E1GTH67v1s3asqq1HJvZesVIyClgEWwSqX1eDxYuWZlTDHwRF0tTtmzG0/V1eJrXwd+7OzE174OPHX4/p/ramMKgZ9++knwyDo7csppp8DhcKC5qRm3XntrzPN/MBjE3A/m4uc//zkKBZz7yeHkWTMBAO+9+Q7eff2tmOl6XW0NHnrg93jmmWd4jxEEjGnAHvjDgxg2fATC4TAuv/BcvPjC/6Ehzulge1sbvly2AnfecgvO/WWqf4Ux48YCAOa+PwedPCcqAMDpZ56Bn5/9SwDAnx/5E+6983bs2b0r9nswGMTq1atx3333YdiwYWg4bJkChkEbmyOp9mxY9wMunHUG/jv7FVTs3g3usLKL4zj8sHotHrrnAQBA/4EDMW4Cv9VGItaos3IQWu054HbjnaJCdGo4O+Zr05Q7LEt/J4aMVxA8yzsuknQrZFJX0PZ0fo2aYOoWHKmyKck7WZYCJpRnI9IUbCMNfF2r9O9Ksbv8SrHTe2u1wmtEmlqFJ3rQKi9TJs8Sywd9y8wgc2ZwBDiOgy/oAwsW+e58OBhlOp/SslKs3boWN1x1A5Z+thRtkQjeaG7GG83NKc+6XC6cffbZ+OCDDzJKIQAAI0eNxK2/uxUv/OMFLPx4IRZ+vBBFxUXwd/oRCoUwfPhwPPHEE7jyyitVpXP9b2/C4k8XYffOXXjwrvvw0D0PoLCwCG1t0ZXFG268AW2+Nrz39nsavZk8ysrK8N78hbj2fy7Flk0/4ZE/3o9H/ng/iotLEGEj6GjvOQFg+IiRKeGv/M2vsW71Giz8ZD6WfLYQvfr0gdPpQL8B/fH+Z/Njz/3jxX/i/tvvxoKPPsabr72MN197GXn5+fC4PWhra03YVtCzJQEIhKQfoblr23Y888jjeOaRx+F2u1FQWID2tnaEw2EAUauGf738qnRLAb08DcYhvOlCW9odDqzN8fLLoFni5k/QEpM1NuHqwBYMzT9KUVg7D7riZdfDDDlTsdOklA87l1m7IbWspBxJmOGOBgl52LXOyi1LVPb4IUsBi6BFAe0Kd8EX9sEf9qMjqM7c3OPx4I333kBFQwXuv/16nF5QgLFeLwa73dF/+/bFI488Ap/Ph3nz5slSCOhRGRWtoklo/P70+J/wwssvxE4dCIVCGD5yOO657x6sX78eAwYMUCJuAkXFxfhk2af4zc3XY9CQwXA6nXC6XJh+4sl48dU38M9//1N1GmoZOmwYFq/8Fv+c/SpO/8WZ6NuvHzo7fQiFQhg8dAh+fvYv8bcXnseCZStTwp5/yUV44aXXMO346cjNy0NdTQ0OHTiImsOWF93k5uXi/175N+Z++jkuuuwKDB02HBzLwufrQO8+5Zg1axaefvpp7Ni2BeNLQsgLNgKRAMISfRBMmDwJf3vp37j011fhyElHobRXGdrb2uHN8WL8xAm48fZbseT7lZg2fboWWaYfOs6p6l3abZvQdHCR4jhY6zaEEb0UwqwBVPLAXgsDD1MGSTokKWRGbc89pPrK6A/70RZMfwSsLbIqy0kp3zaY3LGcdIW+7lg/u0SR5B9HQZlQteXNwmXQHu2/8WTW0m6WE+bCsb+7Il0ogrTz1sXweDz43W+vQ9PibxLuj172CVyDRquO30juf/B+3PWHu2SHu/iKi3HxFRcn3Os+knDmzJmxhq8tkDi4+tPDf8Ljjz0uKY2S0hI8+JdH8eBfHoXXkQ8X07Niy3Ecnn/peTz/0vO8YSsqKgAA7V0hNHfxD/C6ZWzqakKYDaf8PmToUNS0+pHvcaIgx80bh8vlwsWXXYGLL7sidi/+6EE344XHkc8b9qJLL8dFl14OAAhzAQRYYSeW0086GSecPCPhXsKRhB11YNoOwRP0IwfNqMMgvPbRBwDEBxh5eXn4+Tln4cxzz4HX7YCDAVi79gsMYOVRjNAkVdg7gn7vIrYyrT7duBVwjZU1Wkxi5QzK9PJ3QChH7xM9Pt3zqeFyEPLQ23zb1NMHqFxZDit7/zfyhKNshSwFrEImlc8MeRermLkaqU1X8+kM++z+ltifreBXQoiR7GDQiiTv6kv8Vz/kujT8+YS+InHxhJLqNCvlOe38E9iVtMc0ajzI0Sd/jV+tt8LpA6lppabNciy6wskObfWVscHfoGv8ejtG1hvBsmPjCUVy72fqN7JvNloOUUeDNiqvZo27hSzKsg2yFCCyBrtWdsVKAcNf14AEIyEg1GNl0MzyHxUpBAMGzphSwACHABnOhAHFGFCci+bOIF5feyDuF/6OnWO0ym+N4lEZDQdOt+0cmlgK2KF862xhEbtvkYFxYU7PsCvXE92uw3Is5u2ah9rOWlQH+qO/dwIA67ROVsk7QhjFdV1iMLUryKuqVqFffj/0y+8Xl7R1ypWVZDEKKx/JS5gDKQWItDADR6DXj1+iyFOEHFeO2eIYhmmOSzgkDJTt0ygbIGdXSyxrQnDBD37HeEK4nIymkxC9dNq9D2+VYJhUDXZ3mlYpFaX5HgQj0hRXUo8BSinzBh+bFf9drTIfEpuYMWAsUx7SkTi3kC71wfaD+LHuRwwpHILJ5ZOlJyiQhFkT3RF98uHscMLDOHDu5KhPmsq2StR21gIAqgObY0oBqxQ+5aeGWEN+PsJsGBX+79DFtmNQztEocPY2WyReJB9JqPC0EiO/0Ue7PsKtk281Je2MRyQrzbTIMiqNdPUkxcEmlT1eSClgEaiAEkLIsRQw0zDekBIct3WgzTJbB7R5c4/TAY7j4GAYlOTy+3XQG05ohV+JgyLB2NQTlSf9hF4qVtxQosWRhLLSUxG/Ef3XJ3s+ARBVDgwpGoKyHP5jWqViVp+b53HhomlDUZZTBpczuoNTyDGwVZzHZeL4ZEfTDjSF9gMAdneuxOTCi1KesZOFhPRvZJ3JkZ3y1+pYcdU/OgawYu9KZU8I8ilAEEowsD2xlIdeMfRuZCNhIG7wrGTrQPcg3Ao4kkRhGMDrdsLtcqQ11dSqm9XLp1DPBo30CYQiIWxr2qaPIGkQHBhIzhcJZd6aY6K06DNoSr+3XgrVvmrhH4UsAyw0+XE4kNAWSTXNVvcONAiOZ2/r3tjfLBcxURJtUOxoUC8rLAnxWknZZCVZ7IjdJtn0vfmxzgg5y7FbhZKDHitQqebFipOwPGaXDUHnc0rFUhquqyX2ZxhOdELeVha301o6a4+FFBS6wCFF68DnU2DZ/mUpDs+Sy7wa5YVY2Fg7ojB+JW2b0neJzxM9lDl6tjOaD8BkRGcFR4PpEG6ZtLMUUFNmFKdrnSzWFCuVnW6kypSsgDJVWWbBfJSCtUYSUURN64XaQJvNO3SVl+v+x155ojUZPiolpJKNFcEu72xdOfUybxaIx+EC3NEtA0pOHXDrMgk3YnAgqJYxIG2FCGULzxmQ+1r3pY2O6+mxY9dWeXujBohibyx4BKRBgz7lx6EZh3VKDA+CR3haWGabY0VzayNkSK6qqhZtVJ57b7dJaTZi9DfSqz+1kjLMypBPAYtABVQdZuyVshpqJAqyfkS4AJyRPBQiaT+75IiTVndVyMNLbgmQWwJfpx91Tb60jyfKwsDtdIDVtIOzXhkwC75vzbef0GrOr6Q4GrTKd7ayTwE5aNEuyJFVcFBrjc8KQPpA2Ir9jl3JtAmp0PtYbU07vl+w0jewkixKEJO/+zc92w++uDmOk10ADXNqau/PrRtkKUBkDTSg4ocDEOL8YMGiK9JpeR8GLYHoyQNycDoY3fbPa41w/qfv9K2CoE8Bxd9Am73oQigVK75N0bp4yXUyp7Z907d9PDwoTUlC+33QdmznpW4fMItMPH3AyrIpQWp7kVLWTMyGTPsGdsMu1jJ6kKnvpRZSChAZCcuxCIQD6ia4shWPmdHIKDcFNmJSwaG1KyQ7tD6nDuhDW6At4VrwGD8d8ltbOwqelQOFlgICZyLIFyo5BospU8RQ8r0t2SYpPJJQKVxc28F33wxSfWYIlXDtZDTaTDwTsNN7m6G4UdQmxW8fsGL7ZFMyJS/FrKYy5R2tDG0fsAhU2LWD4zg0dzUjwkXgcXhQklMiO7wt4QBLGQvGRFGXnwwXQZlvLxAuQperCJGIvHdkwMBpI6XAwY6Dor9L9VSuJUqrRIqoMuJhmJ50y4u8CCsTAfrWCS5t/JrtkdS7WeKJn+VYRNgI3E7xIzLT9V+siq+nJD27kbLNhuN467nd+iYry2tl2eKRrEi12OkDgunFvY+VvkGmtSnxGPFuVvqWUhBcbLHZe2gNWQoQGUeIDSFy+IihIBsUfVbTBkAjBz7Wa5LM3QfuDbfDyYUBfxM87Qdkh3c6GEvpSoRgADR1NWF3y+6Ee92I568VSk2cKb0Gju8uPHoQ8jxO9Cn0YvyAIsVSKfn0kifyOma72SvbITaE93e8j1c2vYItjVtUxVXpX4v2cF3K/fQqFfmkmFEL5JuRgz/5345/D7BZE5dMnjBlCnbZ4kFlSR90O1rSYEsSvUjXn1pJVjMhSwGLILVCtwZake/Oh4MhfY4QVLkzi5xwjzl9K/Jlf12XUx+NgOoJzeHAETaCel8L6v1VqA1UIsymX1XVy0OvptsH+FY7eY4kFBJkcFkebpoxEgCwtqYu6WcOmmwfMKGtUPrtlPgvkDNQTM6LGl9N7O8vD3yJCb0mSI6Ljz3+rzDIO0VVHN1kWhvP5xmbAQCL+HfJxNWzTCtDyRjh1FVRWC7+z8z+BpbhcDanTIw1rNd2+5Z2k9coSClgM748+CW6wl2Snr118q0J1x/u/FDw2Qm9JmDG4Bmiz5065FSMKRuTNt0GfwPm7Z4Xu57abyqm9psau65sq8QX+79IGw8AjCgZgV8M+0XK/W2N27CmZk3senKfyZg+cDoAoNZXiwV7F8R+686HpRVL0djVmBDPzZNuBgMm4Z09Tg+un3g9FlcuRnNXM69cU8qn4PgBxyfc+/rQ1zgQt5J99oizMaRoSMIz25u2Y3X1amw51Iq2rp7J35h+hdhR0w4AGJxzDPp4RsV+6zXge3SEOnjluGr8VSj0FAIA1lU2YeGulagL7gQATBxUjHun3Z7w/LLK6Jnw3+1pTIkLAPqX5OCRk29DniMvdm9vgw9LttSmPBvhQtjY/lHCvSmFl4BhGPzY9h6G9spDZWMn8pwlGJt/RuyZhuBu7O/6gTd9AJhY8Cu4HbkAAG+oFTetPQM4bPlxa/BBfM+OTwnjLvsajCO1XrgcDI4eUgqGYdC/OAetXSF0BqJx5bod8IdSB92j8magyNUv4V6YC+Kn9nkJ94pc/XBMr9NQ29GIrb7PemRxeDGx4DwAwJaOBQiwPSclHF10aezvI/rnYM6ON2LXI/vko7wohydHzHcGlDb+lFmqXl4AtI2pe1CUYtkg2VBAv3yX62hQdXo6x88ersNJqSqKS5ayw0CfHFJJ9ZnBv88mWUK7DWStLG/GKTok61uNaVek1DsrfQMryZKOGl8N9rXuw9iysSjNKVUVV3NXM1YeWIlgJIhRpaN0VRxYiUx9L7XQcjMhGcXmaQZUPqVeVPXUlIrKJDrpUC6THivIwid6pf4gZe+wnLcb1fQlnIcnE23OMqxhx8oIDZTlexJX4eITF7Bx3935JYJsZ9JdPqk5VSca2GBHgyr4jiRUuhWlu556XI64Z6yANUzQBbdrGCSfnHT0snJJh5UnqMJHd2bHAN0MbON4UeFk36rEy2n1k46sSIgNYd7ueVhftx6f7vk0dl/p999QtwHVvmo0djVidfVq1HamLgBlAnapH2ZDSgGbYcV9Q3rGJTUNsTTFzmiVEk6yTKq+jYgcSvOTM34QyWncyR/RsDT298LwVLAym6xeBV7B38SmJocCG9LG3TNsTx64p5crmn7md1Ja79sbUpYHpyOa9yeM6iUxlBRvxonPZLrChg8zBk26nKAhsQIa+b7pZDLi9AE1WEUOLcm0d1JqASA1H/iUeaSkMp4aX00s34UsSJMR9KsCDoFIIOFee7BdnYAS0hWiuas5Y5USdoGUAoTuZIvnU3UrDxqkzwHJ0xkjjgnkS69fMZ8ZvHA4PnJCLRjSsjZ2/VFwmizpPE4HCnN6dkjJyYnW8CFJz6mZPEq1MhBaNdRjlVqrGBmor5N8R1flepyYNLgEZ03sh6MGlqiKX7IcEp/T+lAIuYN8e0xyNNr2YYt3lU6qpQC/ctU0R4OWPKZWHVLeycryJ2Ob0wfoSEJd0Oo7muWAL8SGMHfXXNFnjPCBkO1lkpQCFsHsgiipgzRj4q0gyTCXaMIudE51/G/8Scv7TU3+cAl/q8jnpImJrt9MIOoRffIxpCyP/0cZjGpcAQeiWwcamTKs446QFb6swKOzqTL/sWGGYHCymhUjDSLyupwozfcI1hM531y14sLI1WaRtLQoh6b0QQYkKdT+m6lITklbcJcNJ3A/u7HCIoBRaO8w0JxJH1962fQd9UKtcsvsuUc3+1r3IRgRPy1MDLWWvVQWo5BSgNAcMzSN3Wmsr1uPz/d9LkmuwzdTb2mlcTUgnnQOq2TnPW9+SEu7++7RQ0qjxwAqpDve+vwx2FR+LvzOInwaPg6c3K0D+R7FMkibdav7vimTV8EkbdhZCe5xV4ZebYj6ePn8JmhE8nzQyoMWg0WT0yZagc5wso+SRIQsBbTsS9WUUiv7E1KKlWXTEj311komm3axFBBVwpq5wUzWeEy/9FIe09AykDAPOn3AIpi9D9IqK15q+a7qu9T0OE5kkmKNhkiP9pA7/D+zUOv8rLZwPGoLx+M/Bbfhm60VstL2uhwo8KZp3uT06wIi87+jhnke7yNRtJBoZTqoDKFBUrqzgcVlEZ5w23kAoXSQrvsJE6bkqQEKY5PLyqqqVfip/ifRZ4yYZBh+9JyOaCGPJOfEFjy5Qgilba2pFjMWykcryWImmpYHjbPUbr7P7AhZChCaY4ZZjh7bH0Sf5zU60GfQJb1zB1L3D6iXRTh9AfNt1ePbxHi31fnRhgJZMUQdDPI4RlIjFk9cRqwXCOe+/LdRPiGVmU4sPWWnD6SkzzPgtUKnnmyno29aPalJ3S5g9oRYL5R8e7P2ym6o25D2meTvKXj6gAXKfKaQaXmpu9JQM8UzbR/Qktj2KLExpJhlVZpvsKF+gyw5rAptF5AGKQUsgtkVSpLWXOmAXofJsl5OVcTSTBuXDE19ysqQRp8/dQqmYPBsgcYyN9QMAOgKRVDZ6IvdZ9yNcHjqkC7DpGwd0G4Kp0yhk27rhxhafGe9keJ3Q81gRQu600jOT+nKE/1kTMk/kaTMPi1Bavnb3/UDEk8INeAbW7BuJCO07csqA1kr9AnxaGIpoNM76XMssHaLHmqst1IjS7z0hXwJ3vGlpJ9JdIY60RXu0jUNvcfE2XJMpB36BTOg7QNEFJvXDymTL7nOAeU2GmZs7UjXEYTZMDxO6Xvr+WKT41NATFZ/pBUBtl0glR5yg4248YdLUFM4Hl+5T4STOx4sXHB46uAq2ggACHeMA9s1iDd8jtuJPI9TNA1t4PTdsGkCnE1eR15d47ChbgN2tezCpD6TcESpPIeVasdaWhURLVbHtY7fLJS023JPc9CT9Hltn29hV5QuhKQtNwxM+XxKLWG0qge+kA/vbn8XwUgQU/tNxciSkYalrQVqZdnXug+fV3wOt8ONC0ZfgLKcMo0kU4/oGDhD2ppMeQ+zIUsBQnPSrXDp0REo1aRraf2gziJC+EpeHIkzkDe2vIEGf4PMeNR9n25T2PgVkyDrw47OJdjr/xYHuzaIhh/e9DUciGBA+ybMbP4QIUQn+K6inn25roJtguF7FXjAv3bKqZ/hJUQnbfKhBkZshGlgHyg32xgwmnpFVnPahz/SiVVVq1DfWY9llcv401A8cefUBBaPWcsVvSzGjvkmNIi347vogdmTSbH0zXJCJ/2UAmXh0vFD7Q8x7/Fra9YKPhefHitw9KYd+WzfZ+A4DsFIEF8f/Fr02UMdh/BF5Rc40HZAdbqqTr2y8JiYNw1VJ3yJh6W2NQopBSyC6Z2czSqEkhUw2Sv/FtFiy5Mj1Qx1+f7l6tKX9Qv/0zXBbZLN0kY1rYz9/WnoOPS8k7R86J3vlfScFoO36JRdYTmRmbzd6iiQfmKruI6JBUvKV3+4Q1ka/NFJFkNr0uaVSmHMaO/M2JKmZRxakapI52K/iD4nMxXFIRWWDSvlcTJW6d81Q/LraGdREB+2MyR+wgZfGDXfoCvchTAbTv+gCYgtxETYCD7e/TF2NO/Ap3s/RYgNyYrbynVKDUa/V8bVf42g7QOEZCRX2hRDAf0HI1r5AZCTZrpwshQXOrZPelkKCD3FN4mKcNI6Pk/EhwHtPRYBCyLTJIXrJs/rQo5b+60D/Fpwo9AvJa1WtVJM5BmhcqSwjmjcpnRbswjFE58vrEUGYaI+SjQwWTainTaE1KVQ/vsWRqkiWy+sIoeWmKmQUpJmurZajg+ShMcMPvlKiyMJtzRswVcHv0KRtwgXHXERvE5pCwGicmn4XcUcwXZFEn0OdAQ7UJpTqjpNpVthM7FuSyFb3zsdZClgM9QMvsQ6FSMriB5picapeDHSuIZUzFRajiY//gsrlVC6cyvpE2VOoplgmX9fLJYqlGMjl35fYjzpHAwmyKfxKQlEeuSUbSPM/YQcDcqIQLUMglFrsEpsxYFPsi2TWXTnze7m3Xhl0yuYv3u+aSuPqW2sgCbDpOzSUuluFdRub9IyjBaYXdeNTP/Lg1+CA4fWQCs21W8yLF0tEHIqqhR5i0/p+xStFsXkImWBQt0WXWn9qZXbLCMgpUAWkdGdlYQkpO6fstsAKBRhsb2mDS2dIZjvh1wIaXnTq3Mvut/hk/A0yH0fMaWAHp+HYfT3Ncjx/BW9Ur/iIl0GefELbasQnPvITF/PvYVy45L7+VW5L5AZR6YOcOS8V7rvvaRyCYKRIKo6qrC9abta0TShx1KA/z6hnkzLS73rulbbLxMsBTSQuSXQojoOrZFjgafnSVpxP2oTj0yMdHSbafXZLGj7gEUwu0Br2aEY8S5a+RTgOE6/Rlkk2uSJpJgI6eRbsqUWO2vbu2MWfVZb+OTiYu+WcPSYhDz2RHwoCtTGruVuHSjIccHrkr51QG1OaV3OM+wgAwAqV/sN0SVKSUSaIFp/PqOdzGWKAkFwBUhC+1zXWaeDROlJlVnAUsAkrFY20tWFD3d+iBHFI3B036ONT9/i1hxmK1iNVGbLwShZkrcWmO3rSiuHtlb6llKwWptmFchSwGaoqXiq9w8rTNqITkfKc1o2vlp6beWJSFa8PQoBad+41S++v98f9qM92BOnUOoyczPtE738e2N/H0BfbOaGy0qhd4H6fYXWRLuJa/pY9NZMKBuA6DZBFohGjoLGqKGF3h7PtTTN1Bs1JrNaxasnPXJoN4FTg90mCnWddfi++nu0BlpVxaOlzyGt5Uh5RqO2VakMSvLFrhMzMZ8BRmM1/yNWJa3TY8o/AGQpQBzGSJMho30KyP3NjO0DoiGVRssTrq0rhK1VbaLBPtr1ERgwOHXoqeJnuvO+L39+SvEp0Jg7AuA4HApUYGX7WMhZe2UA9MoT9yegDmt1GN1u8oxCStGWsgIlZ5VKr06a4zhZy/qs6Msb+A0sVga1gAOnaICtiT8JE/NTsnM3mQribEFq/jV2NaLYW6wqDtsgqOCU6aBQZ6RaCsQ7hFWDlSbweqKkL9XSp4DtSHrN/W378dXBr9A/v7858lgEUgpYhEyuiLooAXic4SnRZHOH/8f7vAFyxyUmHCZNxymH3XXSjmfjwGFZ5TIcUXqE4IRQSC6+VUspeRl05qO68Cjc23EdtjbLO6anNM8Nl1Oe4ZPYUEHayquQR+jMrctCKB12KV2l4iBc3+XI0p2+WWeLi6GFWacebViNrwZbGrdgRPEIDC+WZ80DGL9NJraSJk2zZQqpyjI29ovYc3LIkrlRAg4RY1jdlI4mFSK9FzPkTiClOHIzYtwrXQGnnVWOnj4F9LboUhw/TzC7zWs2N2zG5obNZothKrR9gACQGSsQ6TohvbWiasyRxLTnWn4Zjk0Xm7rU4kMnnoQgvWPeU+8DK7NpKi/KzK0DDBBbLtRlH2uybwuF0SSH7p6ApLMMMMPTsfZWUYfj09vhpNj2AQMnfB/t+gg7mnbgs32fIRAJAFCTp9p/f6WTAGvBJf1rLmZ5JBeMV2K+iK0Sq9kSYfR46UD7AUXhJFlrafQuSrYrZMK4Uy5a7eGPiyBtPNZu67Qh7aKgTuU+0yClgEXQal+XCgEkPKKRjDJeQU5Ho1neSGlkNW5Q1DgaTHg2nVyKJg8avCsntn2AQ1FXFRiORas/hK5wRFbUbieDXvnSlAIJb6LaxYbagXs2dkraKMq0IhavSFmwy3hKbR5J2rec9Ex9Z72ClIz1fyBnz6327brE+Dj+y+TQqpRhNinHWiJqKWCjDFm0bxEOth8UfUb399EheiMmZtmyfUApdpoc280huh0hpQChO3p0Vnxav7S+DCQckaNKJhnxGNFNKXmrmtYAQpH0+//FUuDrg8W+TV6oCRPqF+KYqrcxouVbOCBPKTCyTwGcDiln3GqPsUcSxics+ItpaDmBkZae8pW+2N8GTAySi4fS8hIvN41z1WOqT4F0fRX4tw+oStMi1jVaIFkeMWWfxd4pHcv3Lxf93ZTtAwrSlLp9QGtHiXJRt1XH+CMJlaClI0271Sc7KQWNhJQCFsHsCmV2+lqgxDeAFmaoQunwhZfWWUg3sdbju8WfZhBNX0baQooXETl7d+4BALjZLhSHG2VvHRjbr9DU0quVOaAatYZ2JqD6IseEzx6dtnGmxFZQsih51+Jcd+zvvlpt8xGzrJLRfqYLqxbV1hsW6ZftURdTUesrRNdThmQS4cSV5VIn7Xo5r6TtA8qQmwdKzOCVbmVVi6W+r07lPtMgpYDNsENBNmXvDoe0MxqhTlMreZWY3/akqRXiMUkZIrX5w9qIkoCQXBx6dfYcRbiDGywr1jyPE4NL89SLoQBDyrUJFgEcx6EuuBOV/tUIsO2xexJCxv5iwETzR8AsOj4tRTKqyRMu/k8p1i7SlDFaL97LaZeE0tZ6QqzE2mpEn3yU5LlRlu/B4LLEumrkZNPKe0pTZeO31up+rjXQis/2fYavDn6FMCutvVZjXWKlvAIguUl0MJmxfQCQYF1iAaUhkR7dLBsUjj81s5Q1oHxQGdQfOn2AAKBuQis7LTlaS4ne+sW2D8QcDRro5VUuYrKx4DTT3ik7/kvOs3En3TOJ9/koDNQhJ9weS2cnO0iWbEf0LYRDwtaBWAJxaOFx3pxNC/rG3M424WBwPQCgk23GuPxfpA2TUq4EM0aGpUD8ipKC00YyDSu8r5I2McftxLj+RabLkRKHAWfQK4kv3+uM/V2SlzhE645vSeWSmD+HUm8pJvaZqEJK/TB7EC/WxmulWLQ7RrcrRp8+IBU9ZanvrEeQDWJA/gBT3lmOFa2VvokczG5rMgWyFLAIhlREg/ahqjHfVJwmJ8GngIClgGYyaLYSpWN+MVrGLWcVkn/lq5d/T+zvg1xv+JAjS4Ix/QplPS8VhgGunDYEXrdYExl9f6O2d6cr3Vql0RDeH7v2R1plhpabnnUGSFZDi3ZTEwWsCEqUjIkh7PEt9CY+70eVFyDP7UCB14XjhpfyPh/v4HFr41bd5bMaUsu16OkDSuuXRYusFj5WRJ/TYVHI/tvH0lPdUY0Pd36Ij3d/zFtX1b6nHEeqcuLLNMyYl9gRUgpkEyJ1IBMaAiWVXIoyQau0EsKnuZaaVqrs6bYPKLAUEDTWEPiB4UmL51EGrMDWAWl5W5LrRt9CbY8iLM51Y3z/IkwZUoryohxcOW2o8MMqq4waB09awV8a1KfHQNoqhNI80GwA1J2+SHSsgEJLSzmkYIU22goyANItTOKvrb5C102ex4WLjh2EG04ekeCPIV04PbHrAFqtpYBVyrsUpFu+mGe+rkUYo1AjW3y5W35geawcfXnwS/VlSmg8ptFWPC23z1r6+9qobhsJKQVshh0LshEaOrHtA0JyCN2L/SZ3u4Ga91K6aGHhRjcePkuBoq4qeCJ+AEAEDuxiB8qKc2y/QlXHDQkFLc5zI88TNdstznWjMEd4l5WU5F1OBkf0LVAiomFWCOmQUsy0Lopyzx2WHK+EcJKVZ0buiRdLS4OCIik/tf7GSsPJyPfvqr5DIBKQKI/G2wekrsIKKTIkDNily2yCUkSn+iE1XlFLAZuNpXRrD9WcPqDAUbNVxyxy8k/O4oo/7NcsXaXEttCa0MZLRdIWZpvVWTtCPgWyiejyHS+aas0NqLd8gyVhJ37pVwOT2da4TbVM8sLy/w10N5YCa7qiJgbRf/TwwJw20ZS7qffL/BWxvyu5cgTgkSWX3K0DUt5CaAwpOhgX2ZIxrFc+ehUddq5W2wGO42IDVfkKDXlWIUrRchU+nSVLa0Da9gSlFgZp45X0rumekSeL1PoodwVH9UqNUeMtiU4ctWRN9RoUehLbCysPMFkuqkRN+X5mDdgtnFdiiJZ/pYp4i+aF5Ml9SpEy9n2kbh9IJsSGsOrQKvgj4pPstOnH9cGK41A11tPW6k3WhD9VmKyBtg9Ig5QCFsHsAqpn+kZqQhXJwXN7xYEVsuNS19gr06pLmibG9X9y+0IlxyHyTXr4nq0omY7mnCHIa92BLV19ZclV4HWhJE+eEiEqh8HIyG/7nz0vL3d3N+/GksolOskijLaOrgy0FIhLSwsnmWplMBO5Fl6bGjZh+oDpquJVhMqJpx3yW49wRsSrZiHELr5QrLhFQqn1zE/1P2FL4xY9RJKUvm7pmOwAW6sjla3SVknFbvIaBSkF7IYNynFKZTNIZsEOXMRTudnKmG6ULmhwnPB+Zy2IcBHh9NOc4ZzuWDeOcaAldzC+bCxCV5ozmJPpVaCtLwFlcAKTM63LFJf0L98T+pbjdLHz5wJf+ehBjkJAL58CatFTCr2sI9SgRb5r4WhQEyeMfHEY1VelGYintXpLc4+Ikk15o3irisS6tK91n+I0pcoixsb6jarSSoeW2wfkOLjMpjKqFi37QMp3fsinAGE4emn5FTm60bBhkHUmuOijnMDf4k+m/hb9Nb4Dk7u+GJE5WReUReCFOwIRdIWS0khzQgLDAL3y5VsJpMSjOoZujO9c9FopVjvE68bKVg9SzFfj5U9XrfnqmdEIpS2nfZO0qihjsqoneq2aao3aVTctFUOj+vZsnxA/VYVHHgMUUhE2gk31m/B5xedYW7PWEkowPqw6mdDK0ZwQvpBPUfwp6SmUU6v21XLfT6U4lnsfi2KVrVhWhywFLIIdKrYZZkVytN9ipn6i5qZKBphpVsmFnhGWQRmshICJSgF5HWuEFVMKpJ9Q9TzZY9Hg5EIo921HY+5I1HfIf/MBJblwO5XqM+PS45FTybBDbFWAbyOFcCpqfQzYAWWTVHmrONoiXYGnL3pPksyZNCsMZ/BxjXojNPnXUsbjhpXi0wo3QhEWI/vIc3xqRN+/sX4jvq/+HgCwF3vRL68fBhcN5n3WqqbdZmJK/VWwvTDhvoWz16hvL/e7KbF6tdI2GMLaZL1SIBwO47vvvkNlZSWqq6vhdDrRt29f9O3bF0cddRTKy8vNFtEQzFox1xK5+0zF7huN2LF/olsLUn5LfThh0ipzxhThIiKyyaHn6dLOCgxr/h5Dm79HMTcAC/AzWTEN752PUFin7yaYPwJKIFUzUHnmnmZOTiWdPpASRj+ZxLYESQkb+1u1bQSXNuuVWk1osWVC8/2nFhlEih0TqQar5Ff3+2m5bcbrdmJc/yLF4fWmwd+QcN3Y1SioFDAbq9SDeJRuLVHTTofYkKy0Dj8o8TGJ/aNWTmc17K/kLL6wOm8BzSQ0nadYsA5bgaxVClRWVuKxxx7DvHnz0NzcLPjc2LFjcfvtt+OWW27RVR6l+8HsgB77gflWVszWlGrXqej5jeXFLd5h8cXF/x3i87m3fw+A6Hy6i3OnPCuG2+nA4NI87K3XxpQxGXmWFFzc/8fftW4dlfJ2yuWXMACVYykQX470ylJJ8VrjeybnXYj1w8V4wTDa7AKU5KxVc+WqwnBxwbqP/Mp15cIX8uFA+wH+ICb4aFC83YLjD2/ltkUtye/WEeoQflayp/3Mza9kpL+rdnkSZIOywyi3ANNo+wAnTZmvm5NMjeu0FKsiJWNjpXLoEbfS9BN+12EekolkpU+Bf/zjHxg/fjxee+01UYUAAGzfvh0LFy40SDLzsLsDj3TbA7TePqB9PMrCRtj0WmY1PgXCbFj2ezFJ/0aJxuFiu1DcdTB2dwcnbyVoZJ98uJ1KBwfGl0u1wxjxc7b5/1YDx5NcS+gg2sN1suJhwPCWG61M+9PVaSXxxaNITp1dCsS30WtqvsOmjk+wy7/i8BFb+qatJVrI2v299rftx+ubX8fb295Gg78B83fPVxevRSaRMesgnVd6paKFVY5cOoLCSgGj0Mp3kZVR046GIqH0D2mYnh2RO1lVnI6CeLLtW8STafVYK7LOUuDee+/FM888E7t2OByYNm0aTj31VAwYMABerxcNDQ3YvHkzVq5ciQMH+FcdtMYOBdSMgYGcFQEj81BzM1OR6BJW2XN7J5hZ8ulmk0ncPiBPDxi1FJDuxCydRUavzn1wHH5ZH7w4yPXhfVqIcf2LJJmyCxEflm/lQc6EhYMSLwCcirmjOW3EXv+3YBjgYPtQDCocxPsMw0irE6wsPxvKVpTkIGnSJfqMfOWEFtsJNjVGPXF3hBvQEakDMIxfDhmiWU2ZK0a3BdOCvQsAAMFIEGuq16A10Co5LSPeV+37JQe34tY8vdJoD7VrHqedSTu5VHiahlbbB6TGa9WxrpZlRVyZn1yp5cWdSWWasB5ZpRR46qmnEhQCxx13HF566SVMmjRJMMzq1auxYcMGA6RLj657dG3W0MiakHKcYMOrqYWEwglP9Jr/7+Srs0ecjde3vC5XtBiKTh/g5DUTfB1id9707twdu7eTHSRrilzgdWFwWR6affJNFvVFWRmS+uaGmuCJ/PbF/i/w6wm/lh6XyrrlC/ngZJzIc+elxm0J6x5rEOK6DEvLKvnFgUOjvzHhXnNA3OpParxmIKSwsOb2NmOwgqWAnVC6B19NGQtGevpiueUr7fNJP4tNtGWlKzWfDGoLxLZotgfb0R5sR//8/mnfX8oWAV4Tf618MhiRXxomYZW+zGpkjVJg69atePjhh2PXM2fOxMKFC5GXlzrgjGfatGmYNm2a3uJlFOlWZPQanCjZIhB15GejRhGAx+mJdcZSZE9cEZcnY/RIQqFmQtwqIBlvuB1FgZrY9XaZWwfG9iuCQ2c7aaHYhd5UcYSao13ZG1qWh0JHELvrEgflYqtCyUi1HBDjo10fwck4ce6oc3nbEKXx28VZkdR2iTHwMMRkmbpTlpcP6qXlwKGyrTLhXq/cXqKWAmag1rrOKk7IDFEoJCXhD/sRYkNwO1L9zpg5sdNyzKAlZrSHan0KZAPJk3mpFkudoU68t+M9BCNBTCmfguMHHK+bjHqhZqFMb7KtHEola3wK/O///i8CgQAAoLi4GO+8805ahYCRmF1A7W6FkK6j1trRoHqz46RnhSwZwImay0fY5A6G/+9u5M6pI2xEs68XbyXQxBWgliuTFX5c/+g520rlkVPEpU2zOFWrF8nipI8pOYT6L8OXZo7bgT6F3tTUNWgjlFiqLK5YrFsigu8UF0f6epzud22m7EomiIodO1odjmdCkm7hUYojRaP64bSLpPyWAnIs5LTErPEJWQtIR7GjNxWfVo1PgWy19pJqqbG+bn1s8Wd93fq0zytaFDPJUlYPZJcDexUbw8gKpcC2bduwfPny2PXdd9+N/v37myhRZmOW00KzOwft0tf2PeInroq2DwggNECNORpMSiw33LOKJ9dKoG+RF70KUieqauCbzyub44t1xlqQGovRjuWkVen0D7EKcsQX8mlq8irfzFVxUqowysJKDtq3scriYzlWdX5YIT+7kXoygpVkloJaef1hvyqTZ7vllxjaTaK1izfCRRBhI/LiMfmTWE1OoTLafapK2vAqBTV73NyNlIUYLft9q7y31ciK7QMvvfRS7G+Hw4Frr73WRGmUo7YQC3kE7+Zg+0H4Qjoc9WaE5aGYlYCIE0I1ZsipkakJKtFUOI0pGm+Y+MZW4mQy6tGciVoKaJQ9u8tm4kDRFISqN2M7N0Qs9ZQ7EwYYdb629Nm20eNN8eSs18HpPSDXIn6h+iPdIN+4fNd7EKO19ZOeaGHCzavU1LjMqnU0aJWBrFnWCFYpb/FYVdFgVl4F2SByHbkp99OtaBvllV8P5FgIpqvDVhh/WgErf+9sIiuUAkuXLo39PWnSJAwcONBEaaxJta8an+z5xGwxFGP09gHV7ZeIJXi8TMnyJU9WUl+Z4/1TKHw6xCwFeBNIULKkplUXysX28FhZMjiYqD+BuCQUoySocHpqThKQI4DQbe07UaUxJiscGQjJpyyFlLg0UsCpXmXhOHXC2AxB5arS76p0zz1SlbnpZJAqYzAShMfpUSSXUoxwNKgKi4jRjVE+BayqALASITaEXKQqBYSwap5qqYCTM84y0l8HXzuj5zYOy7RfsKblnRXJ+O0DHR0d2LZtW+z6+OOjzjpCoRDeffddnH322Rg2bBi8Xi969+6No446CrfddhtWrlxpqJx2KKBWcOwllobYxF8wfzUUTU2jo1QMlk0fUo0bsggXkf39xJTo9R3yHRMN752PPI9Tdrh08ImpJKekmv4e/lWeQFJlUB6UJy796qtWShQrDTa60VpBlPyO8T4FEuuYRl65FfRBpq1cc5xqJ3x8sh9oP4BXNr2C76u/VxV3LA2BPP1076do6mqSHd5uRxKq9Wlh1XGRGeVeyZGERsgpx+kxoFzhpZk7VYsVKbn5INQmWLFPtDKUX/xkvFJg48aNYNmewcPYsWPx008/4dhjj/3/7P13mB3HdSaMv903h8kRMxjkQAIECVLMokSQkiVZshKltSVZtmzL9jrsep/95LBrS5921/bnn+W8a6/W8tqPuStZmcqyRIkkRFIESYAkCBAZgzQ53ztzc+j6/XHnhu7boaq7OtyZ++qhMLe7uup0dcVT57wHH/rQh/Cd73wH169fR6FQwNLSEk6fPo3/+T//Jx566CG86U1vwtTUlIvSy+HVCdJOsPhWWmV6tgL7vo2caLDZfcAYVsjwqv6CalB/5+Zr3Znr2LXyNGLZKSTSeWYZbhrupLAO4FT/61Ul42EQ1POnc92wjno5TijXBM1SDN+36TCfYx/leAohe44iC6+Mu0Zmt274lpqpG159ws6Tn5fnXuaSj16dfu/q9zTT1dwHGK0hNiIsjSObqLrcahtmyAbbqMOJ6FetPG401o/uO7JyBbVwndiJDe8+sLCwIPudSCTw4IMPIpFI1K51dXWhs7MT8/PztQgFAPDkk0/irrvuwlNPPYX9+/dzlWt5aRn+jnr1p/1pnCFndJ+RiIT5+XnEYjHEYjGu8vCE0WLNFrNnHW4AOwZLq88pn5ZVEUPWynGQJxmRgHVLAYtZjqydRjB7AUOpCxCF7fgheR1F6RWEAyJ29scUd71zkm2F8E8QNvCkZLNfhZ0mjwC9+GaML82SpHlBOcHf4sq8IpelPtQUo27XZyKf0LznlvvAO3e9E9+68i3Ve1WeGbtgh/mx2887CdaTel5gCVXbWD6ru4+dbU+tPMCeNu8VnpBa+R4IyT2xNoGjE0e5yKEFt8f7VsGGVwo0bv4B4A//8A9RLFYGsQ996EP4/d//fRw8eBBAxaXg8ccfx+/93u/hzJnKBn1mZgaPPPIIjh8/zjWE4Re/+EX4O+vVX86U8buP/67+QwIw+O5BPPjggzhy5Ag3WTYDuHMKUOTDZDqplVZxWVBEJddXeNSfYca6VoA9+kC9YAGAjxQxkL6I5LpN0jUyzCTG/qEO+ESF/CY/mdkvzXMu0fte2l+Jom2YFVK1UD6KMnVzVpPg+Q24Lg6cW2hoLaZ5mdWashRwaUHL6gvLzfSYEWbrp+Ya4bAly0B0QLscEOZ6tKt9uL2RcgNmODOqbaM/HsRiqoCgX0RvLIgZjnzSVfcBWri9OXOi7VghIqzlQdnXjCzIGu853W9oy/vWuLoi0k643Q69ig3vPpBKyePcVhUC/+2//Td87nOfqykEACAQCOAd73gHjh07hnvuuad2/ezZs/i7v/s7ZwT2MHia/lqF2gZES75MMdPSIZ5oNv488lKDRMwEkaujJ3MNPlLpc3kEmJUCN22hjTrAvui3HpLQeBLmuRVppYWwdqQTTko4Qrhkpdn/Gz6ccb03aMFsRG1h5+KY1VQXZkRhqlsNOVQ4BfQVboLnTuhkaDIdq/7Dz1rEU++rAlX5iPfemSc5m1N41+FR3Le7D+9/3dZmJbtFFCR2niAATupTTcOJtev5lfM4Pnu8SbnSam3MLnEb66EV1uutjg2vFAiHw03X7r//fnz84x/XfKajowOf+9zn4PfXT/L/5m/+xhb5mODOYQczzBDicClXY1T65vg3cW75nOo9XmVZfiei/sPoVLCJaFBFDDPmb9VsSlKJIpX2tYHMpdrfF6VRlEBBGLhuVt8XC2KoI2ScXlMWlVSyZHRUg3rVZ7NFo3a5LpSp28bVeBccsrBoPZgz89Uzu+Vi4kpzKu2R78BsKWBmDHSRa8Yt9wG9ejJFRGlD2Egn0UqbED1ZuyIB3LurD0OdYe7vxGwpYPKb2m3tw7NeWGRdyCzg+OxxPDv1rKUy3e4rXkebn4UOG959oKOjo+nab/3WbxkuEnbv3o13vetdeOyxxwAAU1NTOHv2LA4cOMBFrp/5mZ/B4I7B2u+oL4p3/OU7dJ8pkzIem37M03wCanCi8xktEo/PHtd8jlf5VvK2chZihMYJinViNcXwvV6EWEyjMzddu3yebGPK5uBIp7ovMLtE1FCrncpaQfv7uje5aCuPLOVq40LYbi4Ae/LRs9Rx7tvbbQJKk6/WaTubKb88BzNg5RQQBZXzD4PHzZjLm4Um0aCHrBtacRHdSpt6q7CDk4EG1YMD2rJoxzGnD4+cgtZ7nV8+j4e3PcyeH8243YJ9twpCCNUpCOs7bqaxgQUbXinQ2dlsfvzQQw9RPfvQQw/VlAIA8PLLL3NTCvT29WJwsK4UiAViMlcGNZSkEp4tWdMmugE7Op/aYslsSC0vDJi6247Gm4rBkSIioWxjzVpHFaJB9WeMFiGB5Qs1LvtVRDFN+pjKPjBS77uOfSHeewBBQEvYSTZAS1pqM3rq9PTgGW6Lhs24sRnQLLlYQH1q3WR44b125CanAKvC0tSi0SVLIC1li9317Rb3AqDdx73W7r26mXCzL6pe5xwlxW6iQTW4OQZogSc/Fs+2rMdp0UbrYMO7D+zevVv2OxQKyTbjeti+fbvstzKSgdNwe3Kk1gR7bHIyeMiVsptSk8Y/9fOSbfKtlsuAopTFamkWpLYY19hQrZv/B5fO1q6dl7aCMMyuY70RdEYCpmU1C37zv9E3pJ8wHT2Nbvh/KxAELbk32iKh/j5abUd53bIyw8Uq5N0WzeZW4Tqh3zCrbXaN3kWCCSspZRkWw+TSPO/U+GBq02GTbF4cO+2GWyfrdqE1rcbooKdcoy2Hp4JOj4ywldqNlW/Udh+gw4a3FNi9ezdCoVAt1KAax4AWlGlzuRxX2TYr2MzqGUzSTPZxLr6SFscXsxNFE6cA42muvjzCeqhHoEyKOJf+Pkokj77ATmyP3K2fgVRGOTIAZFZAUGZ2HTgwrE0wyGsO05ty6SbkqnmxU5OLtyexppZopxuCTghSo2/HSlyk1zft/CIsnAKA9fp2+6TJzrJV3QcMC2B/xCxo3TIske5x5b1xBm5vWDT7hAeHYrcPbVpFUcPSpm6s3cDE2gQO9PGxELYDXlAWV4q3brnHM1+r5W5WbHilgM/nw6FDh3DixAkAwOrqKkqlkoxEUAvLy8uy3319bObPXoNb/pFOlemlk4z1zG2H/rhWudn43SXGgbBaP8vF6yiRimJtqXgV2yN367+e6EN211vxbOk2YH4ayyJtFAEg4AN2D8aZ5KQBzavz7iNap8TK0JL6ZasIrnkSzyqfSpm2buZNPmfhXe22OnVytFNThvAyq6VSkGhtXk23GfMLSTYeg+ZoGI5YClDIWCwXMZ4clz+nYSlgt4kujxNOJ+D2BtgVmHgVT258qntYlzaDLPjXq/8KALicuOx42U64ZXjBPcft8tuQY8O7DwDAu971rtrfhBCcOnWK6rlXXnlF9nvXrl1c5dossKPT8yLV4TVpMpetNDTQPJBQbACaaLpsGlCJ/O/yelhB7UR1NEo4lyrhEhllKnp7XwwBn2i/Eosu+AAA9Tf11mRmvyy65tkq0QfsrB+eZoRqEITmEHZauTkJs9wpXgKPfq3GKaBXL25FH6DBd69+F9Opadm1qkKiqa229qfXhdaczmuu5wVvjft1eG1c4On77jZyJTYrYd5RPGTPu7jOtQMsyiF9qz2Dd/RuFXgKm0Ip8P73v1/2+0tf+pLhM5Ik4Stf+UrtdzAYxOtf/3ruslVB1WndNg/itYG2i1XWQaJB5/yT5DkLQvMJs5EcVogGq+nVy9TYKktlAMBqtoS1XKkWYpAWewf0rQSs1bX+s6xbB62535gXgrEgijx5gMDuYcbkybCyvzGyz9Pmq5pGtxxnFR/q5Hp81GdUdcF93DP3vBqngGFJylN3B04qafKYSk2pPchNBiboNCT98LTOgrrvaynbPbxJokG6mMaXLnwJnz37WSxkKlxXrnE5mVxTtBT3VAvA7TZtNfyq09io7cAqNoVS4Oabb8bb3/722u+/+7u/w5UrV3Sf+du//VuMj9dN+n76p38akUjENhk3FJpOwS2c6tH6qTGakzoBFq2mruQ6N6miDzSs9JgX0lWlCcOuI/SD/wS88llkrp9ACAWwLPxDAR8GO0MA5BtnHvPdcrqAcsOeyilnGmowWCnYCStn8FVUmBa81R/tg8F7Kr6rVSsHNxf/bi88q2C1mDDDKcBF6WQyj5qlgIdCEubK7JxKdp2Kbp6xpRnXktfw6JlHsZhdxGphtWbizrJWsgN2u3S4GRmDJ8zyR7E+T/us233JrRN+r8xlXsOmUAoAwKc+9Sn4fD4AQCqVwlve8hacO3dONe0///M/42Mf+1jtdzgcxic+8Qlb5XO7Y7Ya1E6s3OQUsE7ipG4ipcy1iWhQp9w6/4z1dxRUhgq1on3lPALnvwEkJ3HT6nPYKczWwhLSYLAjZLsPOC+Y++Z0pnDsuXJqxzzrXkUkXmRElhZGjX1Nr/9QWRGYFoMib9UKdH1Rr3iI/dlGZR97iQ3P0j9tJvqAI5Y5Wv7BnMO5WX0WAPKlvKXnjaDZri2IrfXOrOEslXm6sV4jIFgtrOK7V78ru54qpmr33YCZgwaAwlLH4U2bp8ZV/Qy5leOFjTGTDDaMBW3IseGJBqs4ePAg/vZv/xa//uu/DgAYHx/Hbbfdhve85z14/etfj46ODkxPT+Ob3/wmjh8/Lnv2f/2v/4V9+/a5IbYMrdKonTOtbyjDZPQBsxYGvN9R72ldiwPGYlmtLgn06rX5xrbkCxDya0CkCwX4MU5GqN0HBAD9sZCxbDY2J1aFxHzuOm5kX2Yvhyk1kf1jBXTvZ25x7rxFA4eTXKqNv37vNIKyyq3oXdz2pXZjbFeVhahwCujIIgjNRIM0ZViFWcVDbeNE0z45fgO909hsOcutHGpovZpVHTxlBl7YNDXiwvIFzXutsj50G25/U97fye33qULVCsFDIVW9Uq7XsWmUAgDwa7/2a0ilUvjP//k/o1QqoVgs4stf/jK+/OUvq6YPhUL49Kc/jY985CMOS+pNeOGUT7ecFu7keueVjWjmFGDd1LBqEdbLpeQU2L10tHb1ojSKIvwAJYt3VzSAoL9ukWC3wYDeBpnWVPF04mnkPeRr6yWomyral7eVZ3mcWNObt5ovzYwvPTUcGj6VVKlmIBGJaTFsxvSYR/QBIxhZCrDyINgJM5YCLG3VjrR2WFx4Fh57JU2Fl0c2sa0KMy41+gdLzn6PdDGNldwKRuIjEAWRH7cL89K23Q7VsGncB6r47d/+bRw/fhxvfetba+4ESgQCAfzMz/wMTp48iV/8xV90RK52A1WHsl5KUknVKoDVx1Qrf7NQK9u8WRT9czQhBuXuCGZMzACaLXqwnMLI6qu13+fI9sofQpmqnIF4xUogXV5kFZEbNDcPnCdONosEZxe25vOtPycIrbH403UfqN2zailgTr3ltP+nGasJy6RhFsipmqwWGKMPeMF8mdVSwO51gl5bNcMpYBW2cQpYeNyr45pb/uIsVi1WwCv0qha4WtzYGH3Aq6Cpv0K5gC+c/wK+Of5NPDP5DPVzPMAyX2xmbCpLgSoOHz6M733ve1hYWMCPf/xjTE9PI5FIoKenBzt27MAb3vAGxOP846RvFtjV2a4lr+GHN36IjkAHusJdzeVysmSge8hUUSZQfytVv1gV5YhubqxMwRqnoBW3CzkGMpdqrgIJEsM06aMuJ+AT0RMNAgBm8mcREGLYHtrLJCs7VCZuxnUH84aPsJdRf7Rh062RidV1k9lmrW5HopIbL4Zi4p4iNRryIZMvV8Vw9IROjezPKQIuVcWni8psJvcBCOwnSQ4QDRr5vHtp4Zot2es+4CSPAq0VSKucdntNHj04pUSoYqMQFFahZXlLpeBmXC/yxtmls8iXKxZHZ5bO4MGxB7mFJGyDDzalUqCKgYEBvOc973FbDAB0g/pm7xBVgp2l8hKWcktN91uJaEVZqq4UOjdZT/do37aZpFA5sRJFbgQD6Uu1X+fINhCGybgvHpRp12/kjmN7R10pIJ8Y7APv5YOuiwLXwvjVitku4eT45OQJmBL7BjtwZTGFXJHSvLwp+oA9clmFG3OQaZsUFS4YXUsBjxINahVhR9luEA1ajj7ASMRoNd822GE2JCErNNn4CUGmmEHErx4dzI1180JmAc9MPoMHRh/gLg//Mdg567OqQsALaI8B6tjUSoE27IFbRIOuug9YNN3TPiVp2FOozIms0psdB5UbWKX5brwwj0gxWVEVCA2uA5Soug7IyvRIjL59wx1YvLLM+JS+kGrmhVo6gvo3Y1IlcQdbX1EndnPUmociLzoiNzkiQR8OjnRhLVdEcqnhrgMHUlqcAk5E7PAS0aAERk4BM0SDHN7NNNGghluG3QtZPbNnN9wHtEDNKaBVvxvsgIXFosEppaKR5Qevtnw1eRVXk1dxqP8QgCHT+fCul9OLp7GrexfXPD0Nk9VnJRIIC9pKADpsOk4Br2KjTVK8YHXy99pz5sqSo3Hh1mzS2XyiLpeVXatfGUyV9GDygTzv78T17rsxLY7ghfLNWCVR6jJiIT+iQef1k2rL32rVNtbxPTt7sbVX/RSCFV7v55akc/DVCIjjyiFNOYwUQJzLczN0liangGmZzCuJWAhszXAKOLFYNeQUoLHecGixmyuxKwV4nIraYjWxwTYIrfQ+dsl6evG06vUm10cHJ4659FzzRYvFaylm9d7Ly8oxFpdXqyGEraTfLGhbCrQQWmngb4RThE1udnLmdyS6Pxszrv2pdnL+3MwPIZbuRod/kEo2MwOnoFF2I4piBJPxW/HRwjsQLIYBvEBdxkAHXRjCudUchjrDjn/ncMCHd9y6BS+tBLGUKjhadgU0p9kcT9I55aXeJ8xtIu16PyuLDGpfVUH5k1LR6bAPvyfM6SlBQJr8wlndB5yAWTLD6vWmd3TxG3iKaJBy/KDlKdCykPBSm9eDq2sfmw5Wmr6Rh7gB3JTFynrai+3ZLZm8WBdeQNtSYBPBbvbWKniT3VGX6yJhILVFgw3RAq5kn2HIjw7N5apZCsjTLKcLKJaBNdCfqouCgP5YUPWesrleWUjr5GTiQ6ryDGr3EfeXJMbv6K6M9N/A8gmiLtM8Qz4OK1yswm2yPy/5s7KM96Igap6w8cjfLLRk0CIatN19QC/6gAlLARY42o6tbKxU+CzchpdPgpXwokyAc4dubnDCuDmWGT7HKyShUTkebXdeQ1spsIHBe0Fh9nnHOAVMlOO2OW4trV7S9XvqZFlAmZTYBKPEcrqApVQeRKXsxvqOF+YhQMJCKl8XlhI9sSB8YmsMQ2qbTa0NqOEps6rvgrm8+IO9PFXzbJV8qld4kFPZzk9glEyoSkHqP7WSmYBq/ZnkTqEqjybfJgsnM22F+ZEmqHEr6LoPmPgKPNwHeCsenFKiqKFM6ELL8oQdEQnUnvfSKbQpMFQH9/HDZHaunRC7bHHLXbHqYdJNL2/E3W4HXkXbfaAN7rCLFEw3HUwqBcwqOhj8WSlzNEyh7hfLWArlAzOJHGaSOcwtLeFwr1ooqko+wXIat8x9EwUxDH9pK45hF1gCV6kRDBpB/RXYF3V6nALq6XksHDfuRNTYB4xqivei3q18zCoi7SjP9gWYh5ouK6eAah5GG3YXX5g3GRst3IyvblekATU0K3y0me1bAV7cfFl1/VDCKUvXVoWpNsCx2fBaS+uhxPEAzIt9xgtojSO6TQA7wkHR+s3ZDVazTQsFuQYW5YX68/JUNM/QwgynwEyybiq6mi2i2X2AAAToz1yGAIKQlMU+cQpS53kI/lWqMkJ+EV2R1tFL8u4+7P25taDLKcBhM87VcknXUkf9pmD0XC0dv4bjOneKcmxyLaQradrYmfXfdxOG7gMUc8FmXNxanW8tle3B+naKwV0PrN/Ejv7aCv72lk/2HVZE2w0Wa6p0Uc+FVL9uvVwHXkLrrMjbaFl42VeL5yI7Wyhjfi2H7mgQXZGAwQBFi8bTV/ZY27RpjMpXY/AlkDCQvli7dlbaBgIBga6XqXLu7wiBnxc8n2+oHRaQcFWqCRCaCOdYc2flnqDLkz10m3ZeOu3fIxs0RxYRJpuNqsuERjt0wvxZbax0wkJK9Skeft0Gj3NxHzBLNKilPHNT8W1mfmXo50zudhwV5dUcmZ732AZDq6064rrpwborkyKWi9cwm+60vSynoWUhZXYt6HZbZhlnjZQCLPDKGsRraFsKeAS2aLQ5N3q3Bw89mPW1Ne2XrPLc+dlVTCdyODu9ilKZbUFptmaVryzbKBK1a4wTuGZ6Cb2Zq4gWE7Ur58h2pryNXAfMhBGzExvDeNG+t5D1CS1+BEK/gNHMu3LBMbhqsq9SXmWsk1/llbdhGpt8vFmhZilipODhsXmZSc3gxOwJbotTIxPrzb5w1fputN/Sanv18pqnESwbK7ffiRCCbCmLQtmOSD6Vd7uRO46J3Mv42qWvYa24pkjh7lrbS5ayPGVR7aecLaC5KgVapG87jbalQAthsy8Q9OA0p4BaPrlifWJOZIvq6TQXgRr5NqSvbpJZTwXl7gN8QAjBzsQxLK3/niR9SJIY9fOdkQBCfp+5sk091QzVetTjFHBNK0AU/1qRxZkxRID9lgJ2T+p1M1f9VEZQfiraT6e5yPKQdsrMN2jsd1a+odVwfUbplRutXCmHb4x/AxKRMLE2gffufa/lMljvu+o64qDSnSJjS/CCuT1PKPuCk9D8xhqXp1JTOLVwylbiypXixLoIBDdWb9A91CLL6828mbWkFNi81caEtqXABoZbg4dbBE48T/1tB8sCi6t4fOpIJAWMJV+s/Wa1EhikIBhU2/uQ2v9xAGtIQlWzbZNF897YrbcnHvmaWfxXijU+zWcxddSTyUqflSnJLCsnzCgjLchu8jSGKm+K0FBeMmdnOh3lEK72cuJyrcyZ9AxlFgZ1ykiu50XzX16wo21rKuEtKFu8WN9OkjRqykBZL/OZeSqFgDI/Gvc9L655eZNS87bWcrKNqLrAMpTfdh+wH21LAY+gFRqolwcdO0N1qZbnkROc5lfWXzSy15F6+m0rxxAoZ4BAGEX4cEkapc7R7xPQEwtSpy9KOaTLi3riOAKe+3iWvOrfT/nyZiqDZmFlP7wSRsnyAo2LFAzl8fCl18qbIl8vbYjKknxjwdtSwNVT+ZqVinMybIlt0b3vpW/vJVm8AJZTd9511wpr10Y42Xa8EjHBkTW4TYdy1fsSkZApZQzT0lrStscQdbSVAhsYm6nRu20lYNXHUS+N3hPsJ65MyTWl2J74ce3vy2QEBQSoc+qPhyBSTpYSKeNC5gcoSJmaFLy+GXNIQob5vSqj8hFrChoaeGMRAhicnnDYwNnuPmDwfYRa+AHnNuleMx83tXltaKKW3AcYog+YOYX2ItGgnYv7t+96O/c8mU7gNdqXXrszWz51PWok89pGeKO5Q7gBXtZnFAVZgpU2rWa1YOecYoWnRolsKdsS0SVaHW2lgEewkRqskbkUEylLQ1pBhxndCqeAGxM8i7FiFVUNKKv22UxIQqP0T+38f5CbugmZtSdwRtrBlOdgR5gqnSAAydJ0TSGgJ4/dICDUigx3UKmXgM87Mupu0lwIy6T2rPX2ZPy8stlYXXwSQTm+SrV7VkD1vIemKRZrLVNzgwMvy+peYJf7yFjHGEI+fZcuL61RrL6zlU201xQCgDdksrt90J0AO1MPyjWY15S1TWmcGMtMykE7BmaK+lYCbfBBWymwkeH+PMEVftGPoqRO4Ac4OzHSMF87MxBTpLEkh4YSRvDhqfQOzJfvY8qtI+xHJEBPMFgmCoZim6tUqP1r7+ZaTb9gVKbRqwsCsHswbl6oWjkEZiwOWMzPeZt62wEaWW2VSiVztZPTq9ljeHWhAwNxe8NvqSlezfNP1HN1Cqy+vTzanNkytKJ0OHaa6RHQcgLoZGDt+RaBnaR9RrD8jRjztQo7+oEXOClatU3Tfg/ebbwVx0Mn0CYa9Aic6NBWNzpmOxGvd/OLOjosYv40yInFH2tu8nwrv3n4p/GSM5UrYjaZZX6O1koAqGweJJSartvZU3i5D9QfUl6wR/qfPDSMQ6Nd6AzTu3GYASEEj197HP/82j/jwvIFzXR6Vj0AnwUjj/HI+ikLhaUARwWT1nh1Yv5FldT8wZs0iyd4W6Y4xYWjer2qkDIpw1J2Ccl8kjo91dxiQpRWX3h7qX3roSQ1z5NaaJVvUpbKzBYdvCzIvIxW+X604Ol6y/J8q3xvp9FWCrQQrJ6uOdUJ7DqBEQXt5mp2k+D0AMud5IeiLCts66T2b+WveGEeI2uvYmZ2kvlN/KKAXgaCQQAoE/lihzQKZQu0F8dq7gPaa2kKIan3isZ5bemOIBYyNvzyi9Y2qBNrE7icuIxMKYMnbjxRu173r69Dta2bjL9uVz+l64+8lRvm38WrvsOmxzUX12WspvsmC7Emg/I+hUgXVy7iixe+iM+f/zzm0nPGD1DClUW0xZN+Wv9rT8SZtwA3LQXswufPfx5fOP8F5Eo5APZb71kCQ5PgPZfRWbTR9QM3QDsGmuEnKErFJkLaNvTRdh9ow3Y4RQ7iNb9RfqHCKv+qTorKNSN3Nwb5s8Op1zCQHsevpX6E/+57CN/BCHVOLASDVUhKpQDht49gPfnnG31A1X/AAPU3NytLR9iPeMiPVL6EwY4QAqtZPPC/YrI0P/zzDNAZU31+MbtIV5CBgHb6vxvVDW3ZhkSDEGSNkbY90Vr8qCtV6MowA5pNsluKZhrwVkbT1IfRtzSreLASkvCH138IoKJAenLiSXzwpg/qygDYt+Fimr89QNbWqtCyFHDTpZIHEvkETi6cxL1b7uWeN0+wbLrtcqvwCreAHqyGJGTBlcQVPH79ccQCMbxv7/uay91gYwAvtJUCXoEN7VPZ6J3StLplpuOm1tPsCbzRvca6qxENNnxHFj9u2vRaufilPPoy1wAAfkiYJb1MOQx06BNZKSEIgqr7gJ3Q6yFm3Dcq30qrzvmddtG2P0EQcHCkE/mShHBABJbyVM+ZgS5b/QaakI3ehSc/pRW3CcO8TSwq3SSMbMqLVRai/4wTVhlaZfDqJ6v5VUvPK0GjCOFantbYRzt2Wozp3irjFIv7gFPgVXcLmQVHyvTCqbkerPQ9O0l/rYKnpUAjvnftewCAtcIaXpx9kfvctVHRdh/YwLDLTMlpJHIJzKRmDBdpEtwzrbUa4sh0uTQ3LZVZf7gvcxniupniMonhZbKXOpd42I9okF0H2WQpYHcbZOYUYIwEoZuXMxBFAZGgz5ySg5Pgbi62mTkFqDoZQ/nUQ4W6UsXVuvPwQkrXfNaGKDM0SgOrRIO012mgJ4sXTLNZFAC2kMV5uG0boUTcUwrYXW/VOYdu7jEny3xmHpcSl0w9WylVvVw1mc2O31qbY6+E6TP9Xpws9/Qwn5lvzq9FFH5Oo20p4BGY8ZdpWTC8xkpuBV+88EUqnzkzJzu86lSy69uQestQnRSbim2eMFL5ElL5EuIhP/WGRJkbATCYrhPLPS69DiVRBG0cgUFGKwGgsnEuq1oK2NcPdC0FbCuVDl6fxOhOmitgJpCy2frIvP2Ms99EIpJtZbpzyuaQFZmGgkUPRm2Ux3cwtBRQ1GuJlPDc9HOWy1WCxaXFSQWCXZYoG2YttQ4tv2knxqeLKxcxl5lDIp+wJX/e7U357VdyK/jqxa+2jJVBK7Vdni4NrfJ9Wh1tS4ENDLc2Eby1j9QkOiaKNU1QSKPE0UvD6fTRKOXEShanp5L4P8euIVMomf42vZlriBeWar+/K91N/axPFNAXY1cKAABRfHueYzvrYoPplLymyWGRhxUNJ94e0PjblQ/3/Enjn+ZlrDcHNvcBS4sbnfHKjfHe64oqI5gh56rdp7IysaZ4UGJybRIn508yPWMXLixfwBfPfxEvzb3EJT8WxY0dftk5aRWPX3scL8+9XA8J6aG+pgc33QeKUlGVa4bXRoxpnjZR5PHZ47a0J820ZqPmGDxndizjNt+bJPo2fC8OJKBuW9i1EtpKgTZsh1NaOjfdByTbtJ3N6eWbU/38plYqYQNzhTJOXFsxMWlWHti1fLR2ZYb04DoZos6hzwTBYBVqlgJ2NqcabwNTpAEWWBDeoTnNTP0quRMEaPV7/cW2pkwqFjB2o14m3wWVJXNJjUd5tE0aU3cvL6x0/WZVZLeyyKa5T5WHgZuA1foukzI+d+5zeGHmBd10tBuvqlyL2UU8ceMJLOWW8MLMC1grrFmSk6ZMU88y1N+V7DO4nLiM52eex2Rq0hZ57IIXOQV4oeY+QNFGzfSXolRkfsZNNI1jHh6TaUAtv6Wlk4rC0YP92AtoKwU8BKuLFNb8mMEpO7sGMVPuA9xeyr4BRi9r+mIJUjkTkx8h8El57Fypm6yeITsAgYC2QQyZcB0AKhtLtegD3KC2xtBYdxBCzBkxchC4VScvO08nrBDuuc0pYAV6G3NXLAVMbF7t4tPgraDhPR+rQUuRzfNbJvNJvDT3ElYLfEgHT86fxJcufEl2Tct0nOmUj4Gp3SrRoNrjeSld+/viykWq/L0CFk4Bp+YTXm2Yu/uADeOko0pqrSatu1DUztNtfh+33AfM5hdYzeLu3/4X07J4HW1OgTZsh1ODjmnzJQ6LSdVDPQ5+to050GnKtSGZnABihUUkQ1sATKEIHy5KWw1Kang2ZI5gsAo7ow+wLjV4sm4LgsB3scO1i5ndbDeAc0hCpnbLZG1qvCm0On4pvzMt/4hauRKRtD+PxTZAY8bJm9vBKbcXM4tCHqb/ZnkL7DBHXsmtaN+k7DMExBZOA1a0qqLUKrTmDM2QhC5v+LiAaZo0sZZrkZN2r8tphreFEEL9yay8fyKfQDKfbC67jSa0lQIegtMkPhsNpjb3nAZa+9wHDPJjGFDNjIEEwGp4FI9t+wMcu/o1DCCJAgLrd4wzNEMwWIMgqEYfsHMo1w9JSJ8PD9NiWVrSXN9uTGks45PqRmx9E2R5M9nijMtWytMq04kQel5emLK6DxjmZ1FpQFWGDVEGvAQ3+7luvgZyVcc5MxsdN6BFNLgRcCVxBS/OvOi2GNxglyLVi+2SBk7MW0Dr1o/TaCsF2uCO5lN0ZzqjU4OLGuwbcOqLWfXwNnpPylFhLmcvHwAuz6ewQjqwgo7KZcE4p6BPNE0wWEVZzX3AJa2AqHLTaIvc3C4I1XNG4Elep4SV7TZ1SlZLAQvmf/YqWtndGKx8KT3LJrsXPWb88mly5QUzm369Z7jMJwYiaZHoVuXiOXfqvatWHxEgyJ6zs42xtGu3F/heU9pQkzHDwbrjWMyJuROIB+Jc8rLFfUAnT34uqga3DZSiWmncbMs0yloj0k9lfixlt9GMNqeAh8CdU0CR3qr5s9lO5BQxiqnBjZh7rnlxzFysqXvmczb/9UqShPHFVHM5Bs1p71AHfKL5NidA3X2gFSwFaPJjQ2tNYBXaQRWZ1yvEVbPWhs5Ks5DSqnva9tCkwLHgPlCFkrhTFATLdWpXFBbPgLOodroPeAYtbri40U5VtaDpPuAx5YUVpIrKNUgznPquSiWaVe4MFnB9R55ZmZw/nOAUcCK/jYK2UmATodUIZpwol58Wl+40w5Qv9foj6qc59AOqxOC/VSlPwj0T/4B7zvwhtpeuq5Srn9nBkU76wjTg1qJGra7tIkijhZbVAfdyTNR5pW7s09J7dQJ3un1KRAIhBEG/iL54EADQHw8i4BOdkYVDEY19y82v6kT0AUMZOJRBvajWeV89SwFFYUywzfx/g69lWOFFBv1WqTsubkAOvKvWaT8N941ra3KjuqU4lKu9N+93aI3m6Tja7gMbGBtJS0wDN6MPuBUOke0Ts71rd3YCscIi3rj2bXQFwvh5PIIsqu4A+kqB0e4IemNBXF/KMJUpk1ZV0aKV1nQxMmgtjjX5PjQVBY0TmbqCIVFYopbLiQ1KY252ofpNrZ6QmvERb3xW7W+9dHq5GcG0Lkkl68b33jsYx44+goCPj7aKpo3xX1zyy89MJAEr7gNUlgImyQrtmLudUGLwhlabs6oIscRJ4sF1FYv7gFNwp54olGgufz9eFre80CrKG97YrO9thLalgIfgBLGRF2DXe5jdmJuyMKCIPmBUKus9tXi99NvmiowsYg6mz9f+fp7c3KAQgCGnwKGtXQwlqYMIzQudRsK9RHESJSmPRHESEthPStRO/vWsAcxZCijMDNf/LUp5vLr0YzMZqubrBTTZMZgIkURdloNjIXtPNchPRfbJtUmcWjiFYlm/HTdukgRBQNAv1sYFR06rXOKLoQFvxZny3eww9TdSVnDlFNCzFNAY3JwkPvaSCXarET6zRB/YyJshN96sLJWRL+cdK48vf5D7ChJqGTZus/UU2pYCGxhucQrwJ6Oig9mQhFzKpk7HWh6fYZuAoCzRL2qD5RR6shO1398v3wVQbrwjQR/2DMZRKutLHvSLKJS0ZSIGi/ArWSubanaY6z7qlgJLhWtNl912T+ANL5ozKmFFDmGdOcGqgmY5t4xvjn8TALCYXcTD2x7WTqxjoMPD+kL3vomTeJpSeYGOH0L/mt59M/OaWU4Br1gKCIIg+0ROu/247n+t8bhXxq8qvBh9wGt1ZAcK5QK+cvErSOQT1M+YthQwYe2kfNYNolCjZ7i6D2z8Jmc72pYCHkIrDaKEEKSLaU9NRm4SZbGfQnEpliEfss7cT/fAYPpCjXf6ujSIV8ku2X0BBILGOx8c6YJfNB5ajLZSaqc1DK/AHao8AybzIiAclAD1iuDLUm79SSVreXNKDot9k1kQynqjYz2meBODSBEnZk/U/j6/fF4zHaC/8be8gTKxyfXSnKXb3rTeTc+YxcAqgofLmFYetvnRaoCaU2CDwEq9eqnNV1Ei6pYCmw/WFXUsOL14mkkhsC6ALXDbastMhCBCCPU46vb7bRa0LQU2MOycvI5NH8PJhZMYjA7ifXvfJ7NCaFpMOTSJuuXXzxtaPs/qm2RlXWtmConQfQkBEgZTF2q/v1B+GKQpioB2XR8a7aIoxRiq0nJsSqxkglxO8tf7hiiw62Od6kd2llPd1PIIScgTQb85/XijnYCWiMp2JlnYXOqZWzpJdmUFjf3IyXUe60m/E5YChu4DDKeAVkC7+bezH2pZc1gJR7pZNhJa7gNuwovKE95I5BKOlWXFpcjTc4PBY5ulD3sFbUsBL4Fz27ezM51cOAkAmM/M48baDaZnbSNMcTPeqmTjYsnsWKt4jrbeu7MTCJXTAIASRHyl/MbmRBqcAt3RALoiASY5taG2GHTHQowQYurMjF9bZ9vAuAGWTZNVBZ6l9yWNf1Z+DHWGaoqBrT0Rc5lxhtZmyDZGdxPv4oV2VwVvyxSlVYYZZbdZokE7qtWMewmty+G3xr+Ffzz9j7iavCq77tm53uBxJ3k6eIBFKbCxN1nOvputYyZlKFsaBeJcZk79WcJvPjHtPkCr4GuRvtjqaCsF2qCG1uCRKeozzDtmKWAm+oCNAyLvybdGNCg7aqN7lsXcupFg8BwZwwK6NQRqznCwI1y/bbCeNHXf5qZUPTFTWwxb5eRQlmQVnp4iBfU+UatCRuHtJszyiyJu29qF28a6MNYbpSpHqL2jvhyGX5rhNQjRsRRwYLFvJhyWQY4WJaLPSm1Tz6JIMFISmIEmp0C1bKc6OeVwpFdf+XIe/3r1X02LoKoEs+BHzZKOFV7bWGtFH8iWsnhu6jmcWTzjuMxu1JGZEltts2nGUmwhs4CLKxc9125plBJOu1JtdrTdB9rgDrc6r11EJ3aVrQeJlCAKzd3zzHQSr00l0RkroiMc0DhLV5dQgkQl5XTHYawURGwv38AJab9Gvs05hfwiuqO8rATU38TNicGMTkDrEda8aDafvGCGpZy5DMZ34bkh0yrb7xPh99mvJ7diVOSm+4DXFpRKcH9/A0srHuWZ4kFgyMNMOq7waJPZaBsMLUuBoxNHsVZYAwB0BDuwrXObg1K1oQbqcVQjmdlx+IfXf4ibe2829SwNzMrF0xVoo/VrN9BWCngIZv0PvQ6n5J5OTTM/w2sgU/oJW3nlRHESp0oz2Bq6HQExXCsrX5Tw+Jk5zKfzuJFYxX27+5jKkShjEi77B3Cy8DoEyC3Ik271RELzJnWgM8xETGVMNKh1IkpdhH7+rCEJGfKua7fVr7Pieu4FXQm8NhkKgGpbq17yylhmJ3FfDSb1KKzWR3aEzGsqn/PG2M12a2jab+DiwqO+ddufgSUDK3SVfZREg25wCtCmZSrLI+OP3agqBADg5fmXsa1zm+fmCq7YJN+1EXSEuDrPU7QHI+JgK9jQ7bEF0XYf2MBwbeIzOGHZLNBb1BvViETKuJE7IbuWyMhDAjLFdQahni/n1/KQCEEeQV3j2kYIAjAYD1HLU3tIB+o+1e5BEIQmkfVeIV1eQlnjBEc9koF2ZoniFBLFSSo53QP9hpGVU0Bt86TV/o0UU43PUZ0+GEQfYHUfsDIe6lk2ubJRqlm50z/rBp+9ltsFy0m9Kfc0g3pxRClFKUsbzWBxaWhDDjfam1PEnCzl8Xim8TmuikLavCgGbbMEiO3+5C20LQU8BLsG0eOzx3Fp5RIK5YKlfDbiooLXaYxd41rjgOkXRRRR3/RIhO3kTjJ402hxGWWImF+jeRl5mt5oEAFG02tDSwGVBO7puSoFiwJQppChTIq4lHkK+zqjqvethvqq0mB5CSzmfawLATv8uRvlYb0nk4P5U5o399Z9b4tVYmipxmkBxzpO8ACPxXvTb8sKJf37drtDNELTUsCiu5DbPv12KdDaMEarbPY8Kycl0WDtvo1tWoSIMtjDj89n5iFAwFBsSPW+HkdOU9p2n3UEbaVAC4G1UxAQrORWcHz2uE0StT5Ma205nCLRl1X51ycKaLQVKEv67gBXF9ONuRhuqLclX0RPdgL9ZADHhAOYIX31m02kgvIT0sHOMJSwK8Y1r8lBTTrjNbAAmt1XsjQFSYP8yRZwnC95ZWX3iYbVExfaPJqica7DJGei6agg1bRucQqolWGmzO5oAPGQH6l8CaNMkR74Q3dTrkGqWAWPELh6eXhhsdzkPuDQwjywmsUDv/q/AQDTAB4C8OxnfhnFTmfai11zVxsbA1baB2+Xq2+OfxM94R7EA3FTZdMoRYQKqy5z3s9OPQsA+MmdP4mdXTvVn6NUnHpWebPB0HYf8BDsaPQruRXueRqhpbgRbBSFx0aGNCwafYrdSVmiz40AIESC1gsHyyl0Zyvm6duEBQRqWmGNEhqUBJGgD51hE/pFg3lV/e3cbTvNG0StlzCQk2skA87gdQqvlo9Jtnoz4eDoMjZOMtwdrvW9npiSSJNyQcVaqAb02JqtKiZpTqJ4fAdBEHBwtBN3bO/G9j5nNnlqyhQaywjZbwMlAU0eSnjFfUDLIsCrm2OrJIzV60bDME9l5GaDO/VkvU8yleYg+V0tH5XsVnIrmFibMJ+nAWjGAb28tKKScOdNafdNy2hbCmxgEBDOYdRMyuElJYAKuLgP2DQYlUlBczguM9Xr+vCr8chg+kKN2C9BYpggA4b5VTHcGYYZT2FjosHmFHY3JaPJj7Y7VcXUjD7AJSQh24k3D7DIrevKwriB5Ulw1+jCQOM+EBBFHBrtwlq+iL5YSDWNHVDn1HDQ3JwBrGO8KAgI+X2e3XQCKi4rBkoCM3DSfcCsAkeWBwuPDWP9sHLktNFGK8Jr62GavkSzj7CbU6Dd551BWymwwSEK/I1BvDaoWQEPE2Qr+RihTEq1Tqqs9jKNc3sNBJJGLDQBEgZTF2q/XyM7QIw2xxXbA/hEAf2sBIO1PIwmGrVNkeplxyBQug8Y57MxQEhF8UgoP4xZsiRVokGL34HFPDsS9CES9KneZW0PVsMp2uanbXRyrqJEsToXOLXQ0yKMY1GymBnzrVgK8HZJMxN9wE5Uxw5by9CyFDBot144TGl1uLGJ48Ed4hkQ5U9zc6eXQcMpsBHf28touw94CKzmjIb5EeLOZM9hMeUUWDSVuvlQZlH3j6JLX0YjOaTCfYBITDWptcTsyd5AqJxeL0/AWWlHQ5HrZpbKkgQCCMBARwhiwwJq10CsnsTYFEAXpInHYP26QbbUYAxJSHO/Di0pjWwI3Adbq6pA2acN64mxCEubUQ3CJiskcHW52KGhm6N8Vpsu1A1lrZfGckOwiqpIb2Q5YKZMXU4Bi/HMm5PpKAUoBzZW/gsW2Jm3WWi5D7RUu3cLHq2iVj3Uqkeucl5+msNFs0pS3uNcG9bQVgpscJRtIDtTduynJp7CK/OvcC/HCXBjMVfJhkfeZVLUHAtLBkSDMllQkVEt+VDqbO3vy2QEGdCc/FdOSIc66gSDB0Y6MaRCOKgFM9ti+90HDO5TawUMTqJU86bMWiUDvgsdc4tzWzcMNnx4PosrHgpFhjx0DBNacaPi1ALdjG+4EZEgj5N8r29Q7DxUsOQCZDH6wGZGu074gcr03kMHAGrKdbffoaU4yDYB2koBD8EOYqGyxE8poHfCdmz6WFM6rd9eAjf3AZvesUxKmpuACtGg8qbWjkHdIiJcSqA7N1X7fUraTSkZwUA8iHBAzaSaE2xXAPDnQajCUHSLc2xwPaxbieRxPfsCfnDjB9YytAEszO7MeXOw8GFxH1CFUJOEsVzz0DultFwfJogGG++ZKtNBRQbrKZVRhJmN5D5AnYeN38tJS4FWVKC1GtypYxNWbg6vT63OO26sp6mIBk2QLvIOSejlvUaroK0UaCGYafBOWArwTu807Dgx1BzQNa5HfT2q18uk7j7QxCnAYIdcWdBLTXl05OcgrQ/4i6QTU6S/6UmtHLf3RanLVwMNn23zFWKvtQCj+4B2cv3vb1XzvqUrjJBfxEz+DCLxOdcmQ7k7jHEfqF7jEc7NKnjEfSYaf1spV698PZmcqFOnlKFOgNVdr+ldOby6kyEJeRANsoCHe45VWFYetHD7bkMdTq9HN8KYyYtjQ4vbxeyzbfBHm2hwg8MLSoGm5z3UuXlNEGbMU6sYCO5BSOxAprzSdE9CsZ7P+sBc3VCyKAUAddP7hdh+nFjtwvbCZSRIrDlB/WnZr2jIh754EKslJhHkMJxnVAR2yHpAa9NOu5k32vxbVQr4fSLeuHcAF5YvIxrkG9bNTBWH/HL9ctCvr292cwzgS1xk4mTKQrkSkbTrzuLr8Dj5tqNMLuWYWIwahV+kOck3KkOSnLMU0AP1uOahudsKLJF9tngduNnnbC/T5Q2/nXnbxinAKTuzJI9tokFvoa0U2MAghNizsDDom14mFmyCSdGaTUtZT5HqCXoC25FVUQgA6+4DtTIV9yQV5wGdiViNQC5bLGM2F8AsbjYSWAaa+OI0y8yor0dVGQKoa6d5tiRTym+O/gNWFQMBv4BYyNkhXClztQX6fSJuG+vGqdllDHSE4BdF9bbIjcODgyk0jxMKQmzjjGQ11XdjnK2V6eEhvgrmEHkGJ3w86ttJixkzfcZJf2hbwh3a1C5bXTHQhntoRdcX29wHQFpi7thMaCsFNjhKkpWjXDk2osaO16BkJYvKgKs+6Da6DyhTsFkKVP2B5c/MreYMn5P/C/hEASPdoaa8WGE00bix+dGSqDrhNd3XfAX9vtIKEa92D8YwPl+JSrGzX8+KpILhrjBKYmftt9538sIYYlUGoTkmB125ForV88F0YrHJnS/GoWZgpm6MOASoLAWMTsH0lLi8OQV0ZNEzD660c/4WMZaIBltwY7XZ0K5ja0o13kpIAYLp8ZbGfYBKPuXZGSHUitF2e3IGbaWAh8B7EiUg3nAfaDpE34Cdm/rggu3dy6QEQiqm2Mqhs8wUfYBAIvUNSbwwj5HkK5jLbIOAARCGyas/HoJP5LOrFRhpTWw/oGmBzbrtWK/kgfh6FApSCT2pmrShAdIsYmqcAhY3PJY2FJQhCWklUfuTLQf6Tb4u2Z/NnYOFFGojgIfFmxWiwVZ11TALJqJBC7wcrGXxfNZNOM2C76rlks2obZJNFGe27Vq1ytLa2NPUGU1IQhqozmkcOQVatW96CW2iwQ0OO9wHNpLpHLeQhE0DOHQHb9piJRQbHqoO6pV/S6ycAg1qheHUWfTlbuA94rN4i++E5jNC0x9YDztoffgVBPY9uN3mZoYhCSnzMaodXsQ9dqDGhyAIGOwIY7AzXJOXGweHjUzotFVr5RQXqLSFSh6VdLwUZXqwcxNJ9U04K3id9G9WcwfQtWbhEH3ACLrfk/c8q5Od7oax4RaTib+HLQU2MzbS+m2jwc6Nr9rG3u1+RMMp0IazaCsFPATuIQkJ4eo+UMvXhMWCV8FrULL67bSWZPJFo7yMMquP7Hpyv5RDX+ZK7fpVaYuOYPIyOiMBRAI+2WaolrTpWX15BADQ1UBrnfLwAY8tnNk8eJzaeGJxty7CjdUbmEpNqd5Tu+QmWWnNDcpi/YmigJHuMACgJxZAJEgbntOezZDd46zX3UH0wOOU3w6iQSfr1Gx+jWOV17+zEpryttZrcIHjJHxuzE80m2qX503a72DEacIKK6f9dnEK0MDt77XZ0HYf2ODgaSlgZHZLCIEgCC3VibmdfNLmY6k4xcDMdGhOIJGKlIPpCxDX3UrSCGGcjFA9DwDDneH1n5LBhp4ChpYCaqZm1oo0guEJvuptdTNvI7TaAhvQlvnbV77tCTl45kHDoXLzlk4ERiKYTme5y6FO1MiYnoMcemlaqQ2zym7En9BqlgJ6ZbFGVeECDzedan2ovq9OH7QbrdTf3IF15R/PZ80coBlyLZkcF9S4QWjz4sUpYCkkYbvtO4K2pYCHYMfJgB2cAmbQ2PE3YuduCj5gw8JBmRtRK1jzdJ2scxBIGEqdq10/Le1EmXIYCPlFdEcDOqWYAfsQZFfr0Zv32L+lVvrKdTVLc694FBAGRSL7SSdBUSpyUVaaXRzRbPZZEPL7HHMH8RynQIsM5WYUKYYLaA7vrtcPnCQa1IIgyKOkMLUxD7QNowgeHhly22ijGU3DjbctBcyAhqeGl2VfG3RoWwpscNjhPqCFqpbT65v+RmsGs+RZ/E+NtAdd0vRHXQaWoVoiBF3ZSYRLa5XfEPCatNPgqXqhw11hhSmpPowmEqOgfJr7LE7NS28fp7XJU5e44l2uBm1RObgP2NTP9LYiLO1e7d505ir+9+n/7TjplRqcDGsoe8bCd5OIpL3B9fi46zUwWwoo0tsdfYC7UlmvrXLi4bACW0ISbjDYycXCG+4QDdKk8W7bIRQhbt3gFKByH6CxFFAqWnWU3Kby5zwutMJ+hjfalgIbHLYQDXI4dXETLBuSn9j+E1TpJIPBqFSWIEl1BQRTbdQSN8tNm0+13OH02dq1K2QLUohQPe8XhTobfa1kHt9UTxmiZmpmX1tqlGSzLjor4PPueifajeNSQAyYy9+snKQuh24yiigFThMl2emDTuUTqrFR9tL4rgqiYbrKUJ+2EA3qqOAuLF+wnH8j9OTVmxMbFaQ8x0UniAatutqwthm74fl+5jqsH/DwfFYtDKDRMxKRMJ+ZR0EqqN7nwQ3C/CyN+4AZBTlDSEJX4P7ZheNoWwp4CFZOFbRQIvwsBYzMeHQXWI0aUJfnNVEQIREJyWwRpfwa9ndru1j4RFryMDka6+Li3Bq+99osuiIBDAzz5HhgMznvyM9AzE7Ufr8q7aJ+dnt/FD4xUS8bBIKyHTAOoJXoA9p6SbXs+G7B2Ed8q8z2dbNV7+pjWU/sLJNsmjC998qJmePkXTonK7a7D6j0vlbepNAqhbTAg2hQL48zS2cM82cBjwMCljzM+FPblbfXnjcLOw557IJbinXWcr1EfkdA8OSNJ3Fp5RL3MlUtBTzAKWD0mJvuAyJElOENF2yn0FYKbGAQQlCW+DdoO5QXTkKAgFyxjLPTq+jwR9ApLAFB9bR+Qb2LNNbBWmENx2ae1kzznVMzAIDldAGr8yn4ws3y0JyY04ynmt+GEBTFMK4GDmBH8TJyJIhJMqBZZmOOAoA9A3GsUaR2BDY1L/Nu4WoCGZ0geBesp9FWzdndip9N7ctopOy0CawkZ6260XECZt5NeYJlR/04OVfqWgroDH52RR+wZCng8TWGXdjIfZQHzJiw265oUXgXGsmophBohGmiQcG8KbxtnAIMxzxutH1BaLb02OhoKwU8BO4hCUGcdR9g3Ey4BUEQMJOssoUTXFtKY1AjKh+NpcCTN56kfrtktoDesHE6PTQyI9MP1gSrQgd+mL8ZgrQXnUIatIwEuwZiiIWyWMsrczSS0/i+oEN+o85Ua5CpJWgvfrXar2H92yivXX2KZRNMoE1mSiufGQIkFl9EzTw4WRs4ObY5GteeAq28OTP6dsp3M+M+YJTGyZNfpzk0mC0FPKIgAepKEhYlqCNooe7mXj15u5LMrpOt1qfdnAI0MNOf3JxjvMB95DTaSoEWgplBgSfRoEQklKSSdicl1X+8PSgLEBo2l/qyihANyUaa4rPD7s1rvQx6lQBwcT5ViUAAH5ZJJ/WTt2/rAbCokat5mD2Zt6tqaeShrm+jBuCBuUYqSzj1wgV8eWoSN4pFZCQJUVGE+GeP4cjPPowD9x+A6DPesLsRP5nHKaNb41STwonBHUDXfcCqpYAVToEWUA5Y3aQaKQmuJK5gPDmOW/puwZb4FtVnrMpkBVw4BXhaCljx5aY9XTRZxqmFU5jPzCOsNOtzGTTv7RVytFYYEwBrijnu9ayTndX69AnmXGEBfpwCamOql9uJU1GFvIS2UsBl2DmA87YUuLhyEVeTV/H60ddrlqcnC006JyAKYm0DSBr+XzutoLkYbrjQBJaTbv1weBVIxkVqXCUYWDuLr0z1gJJXsIZYyI+RrjCmC8p8Kb6i4XiqH39AXasMbhoXU8M99UPsMprRSpsZP6SyhKOfP4qjnz+Kpeml5gQvXMC5Fy6gb6QPRz54BEc+dASiWNm4q21oNYmCKMWyWxuvGcSCEIAQCGUJQkn+DlKwvoAikgRfrk76RCCA+EUQUXR8QWOnKwOPk28vg7evcWN++XIe37v2PQAV89/fOPwbVGU4aSngNKcAK2xxz9CaJSnawmx6lrc4lkFT/2rrlc0D9o2pmTbtpuuLaRcAlUUmNacAp+gDVp7bvG3aWbSVAhscWqa9ZlGUijg6cZRrnk5D91REscnyiT6umxY9fgCdh9ZlU1wmKhdV0JWbwjuv/jVulUbxZ+JunJV2UBe9pTtMrS1lrSXB4KmyUgsCAMRGSwGODzkxfZmZJAu5Ah79g0dx8smTAIBOUcQjXd24LxZFXBSRkiQ8l07ja8kklqaX8NW/+CrGXxnHR/74IwiGg6plWt0oaLYvQiAWy/CncvBnCgikc/Cn80jcPAoSD4GAQMwXsfPLL8CXL0HMF+HLFbF7LQdkCwgUSvCXJCT2b8Hkb9ajiAif+TzOf/nnQUpFPFRSHx9f+m/vB/avpy+V8cZf+Ht1EUUBxOdD9y2jOPV776pd3/rdkxh9/DSkoA/lUAA7ywTFgA/FoB/RyHXMvvhD9PzczyG0sx4SdPipsyhHAkitPQtfRxzBpXkEhRTK0SDKoQCwvth3zcKBEKzkVuTXWkhJoKpkZIiYo+c+sJhdbEorCqJh/XjFfYB2juMZNrBVOQXcPN2kqbMqkbISNbdDh2T3iiUW7/TMYMjeVvcBD5Abq7ll0owT85l55Mo5O0VThRkrxlZHWyngNrRDnFsGIYSr+4BhebVzd/XNnFcg34QoBFN8j6qlgJH81gZsygUZ5TUlhlNnQADsFaewVwjjLHZQlRf0i+iLBVULYuMz0IBgtBhVN5/mAbNWYazvrCctl7UZw/ghlSX88x/8M1598lUEBAG/NzCIR7q6EBblE98bYnH8Vv8AvpZM4k8X5isKhD8APvqpj6rWv9bGRplWkCSEV7OIpbMIJrMIrGZRjIch3X2olmbsWy9jy1Nn4E9XlABiqTnv43/yART3dFQLwbZvv6L73sX+DoXAEkjOeIFBs3gWJAJBKjVZGgSTGURnE7XfXbK7N7Dy4o/R+Y53AOtKAaEs4ea/fwIAMIHKifOe9f8AQBIFrBwaw/L/95u1eu0/Po6+l66h2BFGqSOMSN8QiqECivEwih1hFDsiKMZDgEi3sDHqW6cWT2EmPaN8iOpZL0D5PfPlPJ6bfo46vZ7bh9I0tyyV6VxvHKw3HmV5OnyYCjbj6eJm3MhUYaaNO+HGYvUZ2fMm5RWFZss2nrw/VO4DakoBAxmem3oOSzkVi0YH0OYUaMNVWA2RpAavnER4yX1A1tEVWn+lpUCVU8AM2N5Tz4xemUZb46+8Ei6toid7A1h/r1ekPU3PaGGoM2ywedZ/PxqiQb1U0aAP0aAPmUL9NJeAm/dAszyyZsH67ZrdK9RQbRO8XNVY3AeOfv5oTSHwma1bcU80ppk2Ior4UE8PdoeC+NXJSZx88iSOfuEo7vnYPU1pJUgQ80WEltPI98YghQKVG+ksDv3Zt3HzdAIdqxlEM3kIClGXbtuGG3ffWvvtz+QRm07ovoc/k0dh/Z2loPEUJpYV1gB+/otmyS/fFCqVBGoQwnV/ZbGgr7wVJQKIguyUsvPiLEaOnpWlG1E898r/+wgSB0YrPwjBwb/+HoqdERS6o8h3RVHoXv+vK4pSdwzlgLbf6Xhi3PCdWgm5Ug5Xk1c177NYCigXzSVSQgABQxm8Mj9TRx9wOtoGh7R2PO8WNqOSgzdoSYRZ8uAFO124VN0HKPPiFZJQ9TmDNu2WQgDYnAq2tlJgA0OPGdyu8tb/0L7nATR2dKVUSgWAKIqmfLG07hKKZzXzNPHccOpMbct6TRrCDTJE9ZwoCBjsCDWW3iyPxW8qGFgKEBDcvKUTJycSdVcCns2oqWgdWTTqvspRrfKEOZlsQpVHAAB+b2BQVyHQiHuiMfzuwCD+eH4OP/rcU/i9B34SI5deQ2QuifBcEtNrj6M0PYMHk6sAgJc/+QiSN1c2oVIogN6T1yGWtTc+wdWsrH+VoiHVdOWAD6VYqPm+KGDibbeB+EWUQwGUwwHM5ktYIQKKAT9KfhGdo91ofKr83rdi7/t/EYvFBL4/+QQkvwjiE2WamlK0HqOU+H149jO/XM+gykNQlhATwuj2xTFdlJuPT7/pIFYOjUEsliDmS7g2lUSgWIK/WMYwGcMDWwfgH6iHBBUkgpUDo/BnCuiVIpDW1lBcW4XY4N5QioRkJyuBlLG1Q7Gjrnjwp/MYfOGybvqFu3bhtY+9o/a795VriE0tI98bR74vjlxvHIWeGMi6EsRL47oReMvauKFXLh5pQwE7yilg4pRfgCDrnyx52MkqTvusmgxL2SU8P/O86bLdBK37gBfQKgoMx3kydKpF92DNBULfKrhxCigtTjm7w7XSfORVtJUCLqPxpM+OBu2oUsDGAY0n9LSeym/gE+g4BZrezpbXlcthdGrukwoYSF2opf2S9CB1Sf0dIfh1iNS4uA8oFpzNZVRcGAY7Q5hJ5GrX7GpJcqcSDSUA5Ss7NTnRfoOzz53F0vQSOkUR7+3qMn6gAY90deF/LC5gcWYZL/3mf8GD8XjtXlGRNrScrv/wiSj0xBBeXJOlKQd8KHRFUeyKIL21T+bruHDXbqS39lYUANX/oqEmi4Bgw9+Xf+GNsnuX5tawmKoTA27vi8pO0ElvF0KDeyGmZpAraEfhqI3LAlDsVGfozANYRgGAPJ/sSA+yIz2136+O10879kUfwvt+8gFZ+lIshJP/7yMAUCOp+/KFL2EpOQtfpgB/Jg8p4ENvQ39cet1OFDsjCKzl4E/lEE2XgOQaAqkcAms5iGVJphQIJjOa71qTIxKU/R44Po6RJ+XWCEQACt0x5Htj8A0NofhHt9VvShLCi2vI98RBdKwOWhF6prdNSoH1edcO6z+tfIxO87iUZeewxpC3lVPJ71z5juMm47xAs4E18h3f2Bun1n43KksBk21Xba3gNlEujfuAm1DWmVSWcOrYWbw05z0SUl5oKwVaCMydh9CfWGwmVDp6tbPrm5IpT0osQ3MQpvGtVygFVLOqXxzIXIKfVLZtKRLGv5bvBnCKSswtnfXNhF3kZkbuAzVTe5U7XodRffl9fNoUDd8FABz7+jEAwCNd3YhQ+phXEVlXJDy6soLHkgmZUqARRBBkp9cEBOMfuA8XF9JY64wg1RlFx0gXto50ybQr/Q1/54a7kBs2VlrwWMx43j9aECAF/ZCCfhS7o/Xr66++eOcuLN65q3a5P9JfJ7wjBL5sEeVw3YS9GAvj8s++HsFEGsFEBqFEBqFkFoFEuvbdCl0N5QAIJpoVCQIBQitphFbSwPg84PcD6zqY8OIa7vut/wMAyPfEkBvoRHagA7nBTuQGOpEb6EB6tBeFXvU2ZCesthllexlPjGMwMojd3bub8q4pBYxOyzmNZVaVtEbku1WwnKp6wdVAma5MykgVU3aI5Aho3ltrveL05suNzV6lzbGVaykkoQmln9kIMm5unqnWwFSGAkpTAW8dGCpRfW/DiE0bCG2lgMuQ+evZ0OlLxDmiwSpUWZ4brrk9CMjdB5oHKWVaVQ3resLahNKUDf93VNsk600hQ2tnar++K92NLEIUXq5AdzSAsOEpH4G+IoNiIjEiGlRpJ7PJHDd/fDV5OCQBABCVhcZodwQoEIiCgDu29WD8nCJvG94rkMggfn0RybM3AAD3xaIGT6jj/lgMj66sYIJISNw8guxQF7KDXbjzjp9CeagX/5o5jkJ3tGKGvw5CCOYf2I9zDafkkXCg6UXNmjVaDu3koRjyquXrWOkYphcElKPyU/9idxQT77xDdi3ijyBbykIolRFMZiEpyPGSN40AgoDQUgqh5RSCq1l5mT4R/r4+YJ1/MLRU33BVFQddF+XkhJNvOYRLv3Sk9nvk8dMIJdLIBrbNVgABAABJREFUrisNcoOdyPd1yNoSDzCzkqvE1G7EWmENj19/HA9JD2EgOiC750VlPA8zaTv7hBP9zUniZVtAUUVecR/wLDis1exaw9rdB0zzEXggJKEbECCoRmx6KB7HN1ZX3RXOJrSVAhsYEiRHN+BmNaBOQznA6ZEgaikFqqifCNHB3ARU/UPN/Ev9mXApiUA5U5Pt69Lrqcvb0mQqre4XR5SscYwwmmbqShAaCwnr5cuULdzaaz2fsd4I7ugdxO7eMaTK0xxybjgdlCREpxOIX1tAx/VFxK8vInZ9EaF1k/HSUsWEP85oJVBFbH2hudgbxSuffF/t+hsPvAXpYhr5S+e0HjWEmUUsj4UE9Wkj57HLSm565pZmfcYBgPh9yPc1n97feNfrZL/FQgnBlTTC60qCgXIUgq+uQFQqDdSQG5S7Wgw/e6FJcSD5xIqVwXAXssNduPzhB2o8BmbBrBQwsCKr4qmJp/DT+39ado3WUoAXCCGGA6qeLLREgzSKhcXsIo5NH0M0oK+AtFQ39BOuDDyUAm5uYqgsBTzCmO5WPbGW6zSngFVLAa4++JRzG682peaC5aW9gRJEIqoRmyaKxbZSoA17QGv+awZO8gkAfBfP1xbTmF3NYagzjJ39dKRotNBbAHWFupDMJ2u//aJfNz3riZCWcRvNkKv2XHOdV35nfF34J+nt2EuuoQcpzJA+KvliIT86I/JhwcokJAjam3gBAgQ9/8eq0sGGNQ5rluwbCpU40YKAHX1xDMbDWEtYWRAThBfWgJhUa5udl+bwuk9+RfOR6LoyICWZWwCl1xdO4Wi46R5Lv1erd9cWsR5Zi1g9wTa6zhNS0I/cUBdyQxUXj1Qghjc0lLtwzx4c/exvILSUQnhxDeGFVUTmVxFeWEV4fhXhhTVkB+XuIeGF5sWVWJYQnU0gOptAMRbCpV94sHav59Ub2P+/n6ooDIa61hUH3cisy6UVkcJOyxLlPadPpGnakFOkht8a/xayJWPlkBWYnZNa3VLA6BvSuDs6qqhqgwl2HqyxKigawcuF1m5OAd5t7vHPPk4dsWmjoK0U8BB4N2jJ5AbAKqz6UZbKEmaSFR/X2WQOW3siCHA0JZUPcHJZAmIAb9vxNlxMXMTBvoOGp5hai4yJ5Sx+cHYW28Jp1fu8oFWTC6k8smUfTmE3Reo6tnSFoe7F3zyYN5+0s00chvMMaXaXsBM05dDPjWym33qIpnLYMrmEkaklbJlcxraZZQTXcrj2vz4GrOt6UjsHIIlCJXSdAvmuKIb7u3Bhah7H0hm8Icbuz/1cutKO+0f7m95H64RafRHSDDMLDis8F7QnLl5c1PL2ObW62EsX03h26lmZXMTvkykOdEEIZo4cQHg+ichCRYkQWpGPmVkFx0R0ZgWRhVVEFlaB0xNNWeZ647j8kTdg4Z56+NVAIgOE2OZDszG9AXqiQV6wSlCmNXYLgiL6AIViwYxCwClzbKU7paAWTZYmX5fGBsPv7A0jAddA1Q8UaZyMAAJYH8PdaHt2Ke4bQ+x6DVJZwvc/+30AbBGbWh1tpcAGhtNEWrpaTqYFlTxtWSLgSWTdyM5LFGf3giBgV/cu7OquE3jpKQb0rDFS+RK+9sqU4mpzPRgNuDXvgaqFaMMiTW08JSA1pYq8IP1vEPKL6FX4IdsNPUsByemj3IZ6zZfzOL98HiPxEXQGG82d6SbH2sbT5Ct0n5nElqfOouviDB6aVzdTC1+aBPq2Aqic4KZ2DEAslJDa3o/09n6k1v8rdMdw4JnX8KPf+jQeSybw7/v7mcgGs5KEryUr1jP3vee+pvtWlY9uuQ/QLgb5n6zxP/FxE6cXT+Ng30FzDwsCrv7MvbJLYqGE8PwqIrMJROeSKCrCUEbmktBDeDklI1gEIbjnY5+Fr1hGZrgL6fXIEJmRHmS2dCMz0oNyxHjcY1nMn1s+h9X8quMbDj046XJDlZcLmwEulgIudj+j+jeKPOAk3Bmn2Ms00w6tuJ655T6gpkinzouTTsDuNlGUiljILKA/0i+L7GYGZ587i4WpBVMRm1oZbaWAy7CTaNCtcCOqA6GHtIGsp2N6m/baIoPl9VTT6hHu0WftL2dw08w3sFrajkvYinJtkUBjJRDRqBs1pwcCNY4DZuhmsW4p4NDpR2MxRamIJ288iag/ip878HPMedGeQgcKJYxMLqFrJQXsuLN2Pzy/iuFnL+jmEZhehICx2u+X/ujfABqb/QP3H0DfSB+WppfwtWQSH+rpUU2nhseSSaxKEvpG+3Dg/gNN76FpKWCzv6KVxRHN825vvlkXlF7agFqBFPQjs7UXma29UON5nnjH7Vi+dRsis0lE5xKIzCYRmU0ivLAKsVypg8yWevsOrOUQSOcBAPEbS4jfaM518XU7cfp3fqr2O7SwioCvCHKzBGG9T7Es5q8kruBK4gr1O1sGRVPVtRSgHGRbjWCtyVKAg1Lg+ZnnkcgnLOdjCkaGAhTf0UtrMS/A81FoPABulgJE+ZOv+8Dnz38eAHB48HBlQWchaysRm1oZbaVAC4F1MG81syinwG7mrh3flZlQykI1EBgrbLcmX0a8tIC3+RbwOnIJnyu/iSpvvyhgoCNknJAjBEX0AZ8IlGVN1tk2o7aeypQymEnXSdCaXCa0PohG35OSq1h7/izKz/wAb3/+OfRMLMEnEZR8Io6+8zCwfmKZ3L9F9lwqHsb01j7MbO1D9107sLprEENb9gDVEHSApkIAAESfiCMfPIKv/sVX8acL89gdClKZwz2fTuNTC/MAgCMfPAJRxY1Ha5xR6xNq1eV5okEPjV2A9jjr5mLfyTrK93cg39/RdF0oSwgtrSE6k0Cu4b6RZQEAFDvkXBnbv/ESRn/4Gi5EHkVo504E9+zGlngSiZFKSMXsUKesv7ndRsyYTdtVjht50YKHUsA1hQCM13R6Y6nj3C0udQlmZa+lNRm7YpmFm0SRGVWZvGRxAgTEln3KyfmTEAXR0hizMLkAwHzEplZFWyngMngReKjBSxpQPYZ/p9FY50pZWCdOfaJBtZM+dlSfEQxO5kWpiK2JF5FYTzZOGjaWAtG1ZR/qDEO0Ib6xnrJWFAQc2NKN+evAQEcIa7kSMoV6fVYnLDv7SCO0vj0BafgIdHkp33n0e69iy1NnsXbjb7G2/l6N3vn+soSuq/NIHai4A2S3dOPGT92OtV2DeCoUxmpXtKaBuG93nTSSpW6OfPAIxl8Zx8knT+JXJyfxewODeG9Xl6oWPCtJeCyZxKcW5lEkBIffdBhHPnhENV+m2OUq15xerJL66soVNJlwqoS+EwR100e9kxW3x1W3QXwicoNdyCmIDFf3DuPpf/63GFwqo3xtAtHpFURnVhCdTiA6swJfviSzLACA6PRKJc9sFrmzZ5E7exZjQM0upxzw4fm/+XkUeuPr6XKITi4jO9xlOUqCXTDDKaC8Z9dBg20WCC4TQPJGK/VxN2Q1Q9rqtJxm3WztkJOaaJBX9AGNOc0OWJU5n123LttEVgJAWymwoeFZMhyXUQkzWIWxrLruA0R7kbFYuIJUeQHbw/cg5qtv5NTOT2njwOql2r/4A2TLKST8fpQg4pS0Wyd1HaIgYKizmVXeSJamNzExBt8y2oXset28OpFoKsVktoZQ20tr7a9ZfNNC2QLGrs0jJi3i1YP1bX9oJY2O64uqz+SDfkxt65d/XUHA+IcfAACsjqsZUbP3M9En4iN//BHgD4CTT57EH83P4b8vLuC9XV24PxZDTBCRJhKeS6fxtXWXAQA4/KbD+MgffQSixuSoaSlAOf6YiqtNrI9vhvXn7WGsDQaUw0FkdnUhOaoItyoRhJZTTRELfHn9zSMRRRS665Y2pbPncc9vfw6ST0R2SzfSo71Ib+1BZrQX6a29yGzpAeFJjKOUh6Kxmj0kYCUapIUrlgI683UrwJBTwCCE8uYAfbtyRXGhM2/R9C9XiAZ5RR9Qys5hHtcsy8K3jUyvoDNbwiLMR2xqVbSVAg149dVXceedd6JUqk8cDz74II4ePeqeUBbgKfcBD/mxySZNCrFU3QfWHzQKSZgrr+Fy5ke4reMR+gI1QGqWAioDNCG4beL/4vn1Hn1eGkMGdO4A/R1Bw+gOZgdYQS8mIfQVLnYTDSrL1lQKCELdakFxz1coYcfFGWy/NIvtV2YxPLMCgQCJvk68erDuo5w4MIrt33ip8kxfH6TbbsLz/RJOD3RjYbgLRBRx144e5gGZdQEYDAfx0U99FEe/cBRH/+UolqaX8OjKCh5dWWlK2zfahyMfPFJxG9BQCBDCZv6nGpLQKdKIdVS/JTXRoAvcLFrfdSGzgIXMgvpzHhpjWwaioOqK8NIf/zTiRT/+Tfh+5McvozA+jouvPIXI5CIic0lkRnsAsf6NSleuV7IrS4hNLiM2uQy8UM+PCAIufvQIpt98S+1aaGEVhZ4YF8sCmm+/GduHct5iDSHsNRh9Q9rDBSewmFVXgtsLtn5gdY3Mm6TQTksBQojp5addlgJ2tkXWbysUyxh4cRxbnjqD3tcmsTtXwhXAdMSmVkVbKbCOcrmMX/7lX5YpBJyG0QDD2oEcVwqsy+f1EzjlJqRRXrXBT4/Rt84poI0yKaqWRQuaiWcseQJbcpeBeOX06mWyV5mL5rNbOiOa9ypPqlgFqF5jgyAovoWi6p0+79Ca+ESITadsdzx/EfvPTmDb9UX4Ss0Lze6lVXQk0iBDlckkuX8LLvzSg0gc3Ir3vfk/YDYziwuvfAPzK9bieZuZrEWfiId/9mEc+cARXPzeCzj///s6JopFpCUJMVGEcHgHjvzswzhw/wFVDoFG6PkE2mmaqJc3rY7Bq0SDZD3MCLMprAl5uS32WmDDyczJEwkicsstiByqbOS/caoTRakIsVBCYFXeb8nKimZIUAAQCEG+R87h8br/9ysIrGaRGelBeqwX6bE+pMf6kBrrQ26gU6Z0MHw3ms2QyfbhiPuAQ32tKBUVVyyykXkMpqyubMJUShl5yX6wfkkn2p2TG2FW2E0GTFO+V+YOsVjGTZ95omYl9khXN36QSpmK2NTKaCsF1vFXf/VXOHHihNtibFh4peMD7BEf9E4yveKjeOvE/639fY0MYpl0KlKob+J7YkGEDc1a1bkRaKYJow2a3mRT/Tb2HCQ3Z6pVDplbQHk1CQj1NDuuzGHn+Jxq+kw0hMndowgU68qCcjiI6bfcCgB4fvZ57OjcYUl6HhB9Ig7dsw+/PrpVdv2Lv/NeDO7eqvFUM6xyl5hdyFolXPLSmMQDXlpsehFWlSzV31LQ32RdEPqVn8P3H4ghOptAdN1SIDa1jOjkMqIzCYhlCemtvbX0/rUsQitpAEB8YgnxiSUAl2r3yyE/Fu7ajXP/7i21a0KxDOIXTQ+IXKIPcGxjTvQ/O6IPuImNEmHEVjA0K7NtkPY51vxprG1bed5yklNAD/50HoM/vojcUCeWb9sOAChHg5i/Zw+2PH0eAHD45m0Yzq5hdjnJHLGpldFWCgC4cuUKPvnJTwIABgYGIEkSlpbU/Xh5w86QhG5ZCngd8nqxJnPNR9HGV69mrTUXRIor2Ld6rJb2ZWkfdd4jXWxcAqzQW2oKEGSLUWVasv6dnPKRrJYi5oroOTOJ3tM30HNqAsnp/wHf/XcBv3VvLdXV3cO46cwEgAonwPVdQ7i2exjXdw9hYaALYX8nctIa+lTKuZK4gu2d2y03mSohnZsg0Nb0U59CmH2H1hhuVKB/cmRWadHeMOjDMgeFwYKdBHy10/5GBw+hVEZkLlk5/V9HZH5V17JAjdNg9788h+FnziM91ovUWB/SW/tqFgY076bXPnSJBm3iFAAqp8mTa5PY10M/Z1nBpuAUcHlOcBdsFjOWTfLNPK/ziFeJBrlFJFSS6sI5okehLKH7tUkMP3MeAy9chq9YxsrBrTWlAICKe5cgYOahA0ju34K3f+Vl/NP/909MEZtaHW2lAIBf/dVfRSaTAQD85V/+JT7+8Y87phRggefdB6odXkVMLykMknlliKoG9wGVCVU3JKFk7D4gf07rDv2oq0yZQgR/WPo5/KLvXzFDIriBQbWSKxEIGhAP+xEPBYwLVJW5+aKZeUPfUsBmNBTds7iKQ9fmcGB8Dt1nJyGW5H2n9NKrEIp31X6P7x/BMw/dgswdO3CqJ46i4rSb6Jyea/aFFl3LWR1n9NxzeEDTAsSj7gM8ERAD6Iv0YTY9q3p/M20grFoK6FnE6C7m/T5kRntl19Z2D+HpR38d0ZkVxCaWEZtYQmzdYiAyvwoASI/JVYqxySUEUjl0n5tG97lp2b3Jwa8jvHcfRv78z0zJ6Aby5Ty+Nf4tSETCleQVewpRvHKrWwrw4BTY6GBp53avkVlPxqm4QSxayLGWB9joPqBzqMCnAIL4tQUMP3MBg89dRCiRkd3uOTOJ8GwSueEuAMDqvi1Y3VeP2vWOn38HEucTeOyxx2QRmzYyNr1S4J/+6Z/wxBNPAADe/OY348Mf/jA+/vGPO1a+LDwe587h1VArhotxm8UukzLTBkxvQKxyCrCAPWyO/v2ptTKeK/0k/rn0FvSGfwQE6GQa6dLnEqiVrzFw07teaPjZKjkFlPdtdR+Q4/2fewYD89rxzLPdEYQX14BARYmy2h3DM2++Fdt6o5ASGSj3C9W6CRr45Lc69IgGadu53e4DWv3Xqyfrphd9DX20N9yLHV07sKNzB16cfZGXaNple2zDaQv0XtHMgWHAh/S2fqS39cuu+3IFRKdWUOiSx8eOTWgfVJTnF5BJJOHrrFsjdF6axc1/+3jNmiAz1o/UWC8yW7qbyA1pF/08+8y15LVafiu5FW756qHVlQJG0JtPN0UfZV5b2Wc9pPmMSWsAas4uk2W7AbtDQt7yl9/FwHF1hWOhI4y5B27SjQrj8/nw2c9+Fh/+8Ifx2GOP1SI2HYlvXOLBTa0UmJubw2//9m8DAMLhMD796U87LkPjKVmruw9UYUUj6QQODx7GmdnHAdDVud6mxclFhpqlaUmSML9Wiadahg8JIQ4BKptbxd48EvShJ0phJaAJ2g2ZPvROiXn1h539MUysZBBbWMOei9PYfWEa8yM9mP/IG2ppLu8fkSkFitEgVm4Zw8qt27B8aAy5oYp2WFhMyzNXecGtPRHMJwm6owF0hNWHWF79wQsnQ5aJBk1qfWjHN2X2Nd9MQ+WkPT6ctESsVk62u0PduHfLvayibWhY8e81WrzynLvL4SDWdg81XX/xzz5U4SqYWEK8wbogkK6M/8E9uyH46gvc2MQSonNJROeSGDhxtXa9GjYxtbUXU2+9FcmbR6ll4/meWv1eKks4dewsvjw1iRvFIjKShKgoIvqJR3Hv+x+gIkDVkrfVow8YjXlKYsjNBpouzsN9gPY51rUw77nGL/pra1SzBzuAfdEHeCK4kkZ0egWJg3U+pNXdQzKlgOQTsXT7dsy94SYs3rHTMEysIAiIRCL40pe+hF/+xC/jW49+C0vTS/jm6qpt7+E2NrVS4N/9u3+HlfVQXJ/4xCewZ88ex2Ww01LAqydhbmJn104MRAYarsiHKbXBT+1aLSQho6UAAVHdT9MQ7il3oCIpIbB0EUQagC86jnJmp27JQkPBo12Rpvz0nm3ygybNmy3WvZ0Ag5MNjTCAtBBLZWy7voDbn1rAW169jr6F+kDes5LCfEPa8X0j2Dc+i9SdO7F0+w6s7h1WDxVGIcxYbxR7ByLIljK64Sx5wA7zb5ZxSDf6gM2miZbr0Tt6Si4wM39spg2EnSdsTpzAlToiSN48Kt/EE4LgShrvEe9AWAzK0mtZFjSGTZy/v+LLXx1HJv7tr+EmMlfhRtjai+AeP3J9dT9anmsKpbJdKks4+vmjOPr5SqjUJjx9Gq88fRp9I+uhUj+kHSpVC0WijD7QWjBqZ5vJHUgbDPOXC4dVppWLVWUxg8w+wYcSrB9c2dmurHyD4HIKAy+OY/D5y+i6MI1iZwQ//vQvAevjwtwD+7Hri8ewuncLZt+wH/P37kGpg85CthE+nw8/9Qs/hde973W4+MRJnPgv/4JjWWuRo7yKTasU+MY3voGvfOUrAICDBw/id37ndxwtP5DKN12jPbmihePuA3oa0EbtLPOJjWmRmrA1vlVB7mgN9ZMHB+q6VkRF/t70JezN/RgH/RG8FL+CE6VHqLIJBXzojQWNE9ILBAAQVfkY9HOQEw1qhIo0MR+946vPY/+ZCYTz6ovAvoVVROeTtcxv7BrCY//pPdgzqG8WRi2KwE4C2KobNK2NAm2cav5htASdX+xmmHaPo2rkS2ag5fPequ1KC4IgcDfbNUrvJUs3GQQBhd44QgfuQTwoj4gw8Y7bkTi4FbEbDXwF65EQqmiMiFBOpZD60Y+wBXKUoyGkRnuQHutD4L48cCjERXSfUFe6FnIFPPoHj+LkkycBAJ2iiEe6unFfLIq4KCIlSXguncbXkkksTS/hq3/xVYy/Mo6P/PFHEAxrz2XK79bq7gOGRIMQua8jWwts72Y1co4RWE/nebsPNPYxL4DHXBdcTmHghXEMPn8JXRdnZFRZwWQW3eema9YC+f4OPPd3v4hCLx9zf9En4tC9N+PNQ8N497Wrxg+0IDalUiCZTOI3fuM3AFQWGH//93+PQMCKKbV5NC6IW919QI81e3JtEkvZJezt2Wucj41zliAIXLWeVUsBWpnV0hku2qsaYkF+cTh5CgDQgSx6sQYxPN30qFpGI11h5jpQm9qUYK5WweDdhaqlgH7G3UtrCBTLWBjurl2LZPKqCoHFgU6M7xvB1ZtHEemNA/N1dwAzzULrEaf6nh0bvoE4W0QKqwsr20+3tIgGDTqtW/6Xc+k5xIMmFjEurvWdrCuf4HOcRd6ojXtxo5Xv70C+vwOLd+6qXRNKZURnEuuuB8vIrhNsCRCQv3RJNR9fJo+uS7PoujSLuWwBOPS22r0tT7yG+MQSUlv71iMv9KIcpVMaVPu9VJbwz3/wz3j1yVcREAT83sAgHunqQlhhBfCGWBy/1T+AryWT+NOF+YoC4Q+Aj37qo9TuBC2vFDAiGhQE3XEgVUihKLW2tYQ+qPwH6n/aHJLQqHyu+arAJ9aVAl4gGrQaknDb109g9xeOad7PDnTCn5YfuPJSCGwWbEqlwO/+7u9ierqygfqVX/kVvP71r3dchpWVZZD5GNL+NNKlysbkgnQBiVBC85n5/DzmF+cRi8UQixmHxvCS+8Bz088BANYKa8zP2mFuLbcVILrh3XLlnGZ+TkywpOkPoCdzDR3Sau3yy2QvIOh978rDQb+I/jjbSY9W/Suvqrpe6BIJKvwfm2MSrv8jL0mQJIxMLmHvuSnsPT+FgfkkLt00ii//3IO1NJdu3op956dQ9Pswf/MIzu8exvndW5BcnyD8PgH7FO4BIsW8x3UTbtn63Z6QhCG/CJZWbXWcMe0+YHJBU4s3b/MpkVl868q3TJ2Ge41Eyi6YJqa0aSFfue1u3VNbvfjrYROVCG7bhi1/8id44cdfqikNwsspWRrlcwPHr6Dv5HXZtVxfvFZGaqwXC/fuhRRsXmpW+WSOfv5oTSHwma1bdcN+RUQRH+rpwe5QEL86OYmTT57E0S8cxcM/+7D6+yrqZTMoBbTSlKUyvnTxS3aI1bJoJfcBM7Iqx0qz78trndFkKUDUSayFUhldF2bQcXUeEz91R+262riVHezE/D17sHDvHqztGnSGmXoDY9MpBZ5++mn8wz/8AwBgaGgIf/qnf+qKHF/7+jeQ+5EPpVQJ/njlM/zJj/8EhYWC5jOBgQB6Xt+DBx98EEeOHHFIUnrQEHi9OPsidnTucEiiZjQT2xkPktmStu8Q+yLDgl9r9VECDCZeqV2/REaQIHTa0OGusKqZv0HJaOIUAGnayqlla0g0qLPAr5vLAYFCCTsvz2DvuSnsuTCFmEIbvHN8FpFyGTm/D/uG4rh48ygysTfi6u5hjI10YSaZRa5otAmk0gpQQXejvIH2bnrRB2hhe/QBi5YCbmz4vHjy7BW4wUHhBU4B3fKtthcB8Pf1ofu978H4zrrF2UApitDEPMrj1xCfXELigJyUUI23ILyUQngphb6T1yH5xBpvAQBEZhIYfvo80mO9wJ07QEgRRz9/FADwewOD1HHA74nG8LsDg/jj+Tkc/fxRHPnAESprgZZXChhxCuj0jbnMHG9xPAfWXmC7axjr6TxncfxCfYtn9ZTeDhDUCVz9qRx6T91A30tX0Xfyeo1Ade71+1HoqYwLK4fGUA76UeiOYv6ePZi/by9SOwdsUwRsNNc7GmwqpUAul8Ov/Mqv1DrlX//1X6O7u9tdoRr7JOf255b7gNV0dg5UgiA/nVaOz2oa0VxJ21LAiUWGUsaezHX0luuLsRPS/mpKaM8qBEG/gEFG03ADyWS/mIkGDb0m1k90CcHP//3jGJpNaKZND3bh7ogP6ZEeBHwiLsTCuHTzVs30QHN34zmvGPU9tydjnnBNKdCyG2fvyM3N0sTBV3LKT7axfRm2ce98Uq6Q4hFkb96OpZ0qSmdCcOOdd8iiIfiz8kONzEiPjLC16+IMdnzt+Pqv70PIpLE0vYROUWSO//1IVxf+x+IClqaWcPa5s7jlDbc0i7jRLAUMGpooiCijtSMsWAXL+tLq3GVmHrfMKcAw7xkRcbq9DiFXbmD0m88gfnIcnZfnIKi8W9/LVzHzpkrfloJ+vPjnH0JuoLNtEWATNpVS4L/+1/+KixcvAgDe+ta34gMf+IDLEinAuY2fXTrLN0OXoDUGpvMlrOaKGIiH4Kf0KRSgxilQGRpZqr86MNc4BWifU0vLyHMwsvB07e8bZADzpKchd23cNByHj8ZGXgHS8P96UHcf0H+mcUMoAOhZXMX+s5PIhYMgW++qlEyAK3u3yJQCkijgxo5BXLp5FJdu2orhg1vQEw1CixmE5q15pQGc2bASQlzXZBOom/+xwO6T32avFPcsABxDo1fOBls8mX0fK+3U65YCVmGqDwoCpt52W/03IQgtpSquB+uhE/MKf16lZcHX16M/PdLVjQhjJIHIuiLh0ZUVHPv6MVWlgBKs0YK8BkP3AdX1zWaCMy5X1M+pJHOLaNDK+MdrnSGupoFGne5LpzH65R+rppX8IlZuGUO+T06gmhtkUx62wYZNoxR49dVX8ed//ucAgEgkgk9/+tOuyvPe97wb3QfG0BXoQrJYiY/++o+/HiPhEc1n5vJzeHrxaSo+Ad4oSwQTKxmUygTbeqMI+u2PL6+HUlnCa1NJSARIZIq4eUsn1XNqCoHaPybGPSfcB+on5kDv2kX0FK9g0l/puiekummmIGjn7fcB+4Y7MGOKAkEt32b1Bqu+QVjPJn5tAf3Hx3Hox5fQv77xXxzoxGuPVLT4EiG4dNMobn/xMsb3j+DSTaO4sm8EuUiddXqYrWh1eTguppyKae4FWF1om97k2ew+UBsaWtYiwTk42abtdjdRS2/oauJyG7FLMcfUNwWhRm64fPsO1SRruwYxd99exCeWEJ1J4kaxMiHdF4uyigwAuD8Ww6MrK1icUo90YuyDb6pY19AOSagPomstqZLeDU4BnTJ5W/c2cQoo6oaal8dkuwom0ug+O4Xuc9MYv/xt7J6bxvRnPlq3Hrr7sCx9oTOCpdt3YPF1O7Fy6xjKOpFF2rAHm0IpUC6X8dGPfhSlUmUD98lPfhI7d+rFdLcfPT29GBgcRF+4D6Fchfht987d2NW1S/OZztVOnBfPOyWiDLOrOcwkKmb0ZYlg/3BHUxraAcY4ZI5xHkvpAqT1dIkM/U5XhKgISagwgWfUDDhijtgg4sr8JIjUASCLedKFG2RIPaECewbiFUXOelUJAl09G+esD63JZOTGIm49ehqlM9/AXdPNvo79C6uITM8hs7Mi5+S2Afz17z8CyW/SdFghh5pUTi6nCCEqbY8dbi8CtYiCWGA6JCF1sRpEgy4RsTq9EHXCmsRJpYDdxJRVlKQSnrj+BO4buc/zijyvy1fF/P37ahwD9/TdgZW73wHM5hFntBKoIrY+duQydRe//hNXEFxOITPaC9++bpBddUJWt5U3VkFjKeAEBAgt0+aUaKxDNzgFzKavWbgxtGEjVyve7x9IZNBzdrKiCDg7hdj0Su1eARUjgc7Lc0jeVDn8JGPDWHz9TUhu68XyrduQ2t7PfrrUBldsCqXAX/3VX+Gll14CABw6dAgf+9jHXJaojsZFvZcnrKmVOtneclqdDNGMz5PTEAWxaSNFiP7+wi/6kSsWIAhCzfy++q7V6ANWQhJWoD0QVh+RCPDo/C6kAx/C/vIp5ECnRRUFAfuGmpU49GgWumI2Lr+mtkHVeqsHnnoNey6qh1CcH+rGhYNbIYZDALKQCAFEAZKoM8EZ1L+qHIqLNPtrrx3EuO0+AHAISeiw+wDt824SDbYavDzmV2HmO15YuQAJEu7dci/3vL0ELeXi1vhWTKYmbSlTCvgQ6IwAs0BKMjeGpNcVe+FonStny5Nn0P/ytfVfX8PFzr9BaNcuBHfswJbIElaH4li8c6eM66BVQMMp4AgEbAgeDcucAibGPd1vSJEdy1jDqz0IEHBT7004v9xwKEkIAsksit11K59t33kF2771sm5enZdn60oBAJf+4zt1ebvacBYbXikwOzuLT37ykwAqpBuf+cxn4Pd757VbRSlAA2dPi3g9aCzzgc434B9f+ToEoYxbRrsQDtQXE05GH7iykMZKJgJ/p4gLZEwj7+aa6e8IIRwUZWWzzOm0nAJKiLkidp+6jm0nr6PsE/Gd99UX1hcObJUpBZJ7h/Ha/hGc2jeKlXUfsnt7OiAgAT/FKVLjNzELt0/dWeGFjQgBsew+4Hj0AferTQYvfEercHTst9ndRIlLK5dw9/DdtuTNCzzLf/eed+MH136AeDCOO4busE0pQAjBwNYBTF2cwrF0Bm+IsccTfy5dCefcP9pfuxadWpGlkVZXkT15EtmTJ7ENgOQT8fT/+fXa/f75JO48dgFL/Z3oS44gs6UHuYEOTyoNvLJG9K6lgDNSWYpkovMNeUvPi5RVgIAHe+9G5+nrmHvxaXRenEXXpVmUwgE8/7e/UEu3cmC0SSmQ64sjcWAUt7/t53B0cAnTHfI1s1fadBsVeGd3bBNmZ2eRyWQAAD6fDx/+8IcNn5mamqr9/cILL2DPnj213z/xEz/BlY9Az5RdCW8OwvZD9a0V60JC6GK2a7kPVKkG1fI4fsmHA7F3IldO4trSM7hpuM5fYMZ9oOmE3Sg9AKlUwGsTOQD92gkFApDG3AQIAsFIV/UUxf7240/l0PfyVQy8OI7eV2/AV6xsGIsBH77/zjtRWo9XfenmrZi7NI3Rt78TP9qZQ6E3jjNTSazmmutzqCuE6WQWpXKz/CG/iN54EJGg9uRn9UTZDqjKZIbrywOWAm65D1xauWSp3M2yGNlo7gOmYUHEzcQpMBofxS/c8gv1ezYpSwkI7nvPfTj55Ek8lkzg3/f3M5ENZiUJX0tW+Jjue89965kSLN+2DYWJJUSnVhBcbQ4nnB3qBGkgJh6eWsIdL16u/PhuZUMj+UTkBjuR2dKNzJZuzD2wH6mdgybflB88YynQwmDhCuFZFlV6zgqDxvZg5l37X7yM3lMT6Lj6dVy8OoWYJKHRwTmQyiG4kq6FDEzuH0F2sBOJm0aQODCKxIHRWqSAhw6/D8WLXwYyC7J3aom5YxNhwysFGlEsFjE+Ps70TC6Xkz1zyy3GDLcskHXaFu8c1JwChvHBjS5UTOIbIRHAR7F2UXMfAAz8BwD4hSAEQURKsWmtKgVs/XYEWD7/ND5eOoVTvrfgC+iDOotCM8lOfzyEkN/X9IoCC6mAiv+7sqwDp67hti88g+4zkxCl5nwDxTJ2XZrBxYMVC4dMPIznf/td+OkD70Dh6vc030YA4BdFHB7rxvhCCivp+puLAnDH9h7VZ5vAyTVAucFy27jAC9YNVk0w7X4H5TejdQtwe6PnBLgpDBysKqeIBhth1UXGbrTi2kEiEg7cfwB9I31Yml7C15JJfKiHcjwH8FgyiVVJQt9oHw7cf6ByURBw6ZeO1NJsk3rwsLQXhWvXULh2DRdf/RGyvXJSw97Ftaa8xbKE6EwC0ZkEAGB177BMKXD7f/0qyqEAskNdyA52IjfUVfl7oBNSWCsGjnXwjj7w+tHX48dT6uzvRuV4EcxEgxb7De+QhLzHGaWlgBrRoFgoITaxhI4r8yh2RLBwb/0QdOD4FQw/c0G3jPiNRSyvKwXK0SCe/+8f0UyrbL+tOG5tdGwqpYAXITu1bvFFKLcOTpQ/m/NVDzMmGJq1Cev/0yyMUrRqKDZW02lTNSQV0LF0Cr3CGj4R+BymxIfxJLZT5C5gtDtSk9fsyl2tPoO5Akrh+vCxZXIZvacnmtLlwwFc2j+KCwe24ureLQrplE79inIbNpsBn4jOcECmFDCzl+wL7MBaeQ4EuaY2RLXQ8dBayCsTqmWlgN2VqpG9a0SDHvluPNEKRINWYNRWNuI3rcKu+iaEQPSJOPLBI/jqX3wVf7owj92hIO6JGkdXej6dxqcW5gEARz54BKJGSOJyZxTRPXcieuedAIBvnu5GoVznRIoEfJjcPoDj9+1D7+IaRpJphBdWoQzkkx3urv0tFkroPqfOhwMA+e4olg7vwIVfe1PtWjCRBiSCQnfMEpEab0sBs9+2cqhg6lF7wSiT6X5LfZ7SnFDXGoDiHhPRoNgYklCCf24ZfWevVsKGTiyhY2IZkalliOXK+LZyYFSmFFjbOShXCsSiWN7dj+TeYazuG8bqnmGUYiFqedrwPja8UuDw4cPMm+0dO3bg+vXrAIAHH3wQR48etUGyZrT8woKbTsA4I+WGsHo4LQqi7kZdEISKtQAaCQMbfe31J8nGtlQiDVYDNn664MJphEhlIZMmITwnHQSQUpNO9mugaiWgAuYNNSEYnFnB/rMT2H9mEqIk4R//43trty8c2Ip7flwhocl3RbF4504s3r0bx3o7sVzQXlDr1XcTO39TUvqXqKbcHrkHydI0JgrPNqdxUCfQ8n19HYQQy5tru01em+2C6CwFWhmN/coJaxInFdpuKAW87j7Qiqieih754BGMvzKOk0+exK9OTuL3Bgbx3q4uVVeCrCThsWQSn1qYR5EQHH7TYRz54BHTMuwaiOG1m0Zxdf8I9g7GcSMeglgoITy/iujMCqIzCURmEsg0KAXCC6u6eYYSGfizcjLmsW+9jG3fOYlywId8Xxz5vg7kBjqQ7+tAdv3f5L5hSCF9KwPDMYuxa5gde71qKUADJ90H7IBhG5AIQktriM4k4L9lBKh+YgKM/cZfYns2r/lox9WFymJ6XXGVuHkUU286iJ7b7sTBI49gql/Eqzce5/Qm4BK9yE54wRLTaWx4pYDXwUI02CqLWGfkVLoPVMqkUQqogUZiAQIaLeP5hSNUWi803CFljK69tp4K+Jfym5BBGKKaUkCo+wgIArC1J9Jw04SlgEQwOrGIWy59Hg+/egxdywnZ7d75JBYGKqaYU9v6ce09d2L58HYk9w0D6ws6aWYV0FAKKF1AjGC36b7bw7/b5ZuF1ywFmixAtMMP6MKpaCpuLop4LXpagWjQCtyyKqGFZU4BnTq101IAAESfiI/88UeAPwBOPnkSfzQ/h/++uID3dnXh/lgMMUFEmkh4Lp3G19ZdBgDg8JsO4yN/9BGIOjwERnHZo0E/bt/WDYmQmgJdCvqR2dqLzNZe1TzzPXGc/tjbEZlfRXguichcsvL3/GrtxDU72Cl7JjJfUST4imVEZ5OIziab8j32Nz+P3FAXACCwmsW+fzqKXH8Hcv0dFUVCTwzo70W+wy/jRGiECEZLAZN9yaubJafdB8zkrxt2kOGeL1dA/PoiIutuLrV/ZxM1LqfUv48Br++rPC8ChW1DCF+40ZR3OeBDels/1nYNQiyUai4wqZ0DuPgrD+OOoTsQ3rIPQuKK8UvrQNVyokX2NZsFbaWAy2jU1Hrdb9EIdnVu9VzlV6tKAaPJSoTYpIhhkbo6phFCmJQCVSJEwri4jK6cQxwVoswSEfGPpZ8E0DyoK9EXCyEc8ENqUJDIOAUMnr/j+Yt44KnXEE9ph4rZdmUGCwO7K3mLIq5+4L6mNHqfQxC0lSEVeeV1Zfc6xKsLHT24rWXnQRTkdL3XzDBdW4xUyl0rrCEWMDaVtoo20aB1bGYSYDuJBqsIhoP46Kc+iqNfOIqj/3IUS9NLeHRlBY+urDQ91zfahyMfPFJxG2AgJlSWWUVAY4OthXI0iMW7djffkCSEltOIzCVR6JbzFgTW9EOuEQHI99WjL4Tnkxh8/rJGWgGF7igm33Ybbrz7dbXr3WcmEQ4Vke/wwR/NoxQPG06apt0HWlaFrYDFbmuKU4DGfYAQ+NN5hBfWEF5cRXhhDchdAfkvn6qV2XlpFof/+Bu6ZQWuTdeUAgBQ2LkFZGkF6bE+pLf1ITPWj7WxXmRGe3SjbfD63k1KOnjbUmAzoq0UcBmyztbifYPXyRrN08oiqr/9gh8FFJofWIf6RtScvI1KAaMcCCQI8K3/TeuQVsaO9Knaz2elQ5hFH/yaSoF1xQiA0e6ICndCg5tEwy1/oYTBuQSmx+qRDSSfqKoQmBrrw4UDY7hwYCtWB/oAok55SIOmk/8mQjhlev3fLGWpPd1qOgGiSgDpPCyHJGQ83WKFtnUQ3UaPRx0reT1PzJ7Ai7MvoivUhaAvaDl/t+Go+4AHLQXc7oeWFXMe4GkQfSIe/tmHceQDR3DxiZM4/4dfxESxiLQkISaKiNyzD/e+/wEcuP+AJoeAEk3EZna2U1FEvr8D+f6Opluv/Jf3wZcpVDZ4i2sILa4hvLiG2HIWgfkEhFJZtikLq5AfViEQgtBKGoIkr7+dX3kB3eemMQBgDwDJL6LQGUWxK4JCZwSFrijO/9qba6bh/lQOwoWrCGXWUOyMQApuhO2AcU9obANm+42lkITlEoLLKQhErgga+cFpkPPP4a6JawgvrDW5oABA6Td/p/Z3Zos+KWeuNwYhKHdHmf/N92IiXQ+vKgoilRVUbXxosTVSG+zYCKPAhkGrs2HbJp9KtspLVUuBRmIVNTQTDVa1lTSCyJ9jcx8wKqB5tI0kLqITlTjMEgR8W7rXuAyBoDceQjjga8qzsX2FskXsPTOB/WcnsfviNAiAv/6D96EUqIYMHAX5RuVUYnHPzTh38xa8ur8Dqa76CYiPhvtBZxYRBH2m5CarCisTEie+AKW4phfT3u7K1OCh6XeLU8BNk/AXZ18EACTzzWbEbejDbiWSGgzbisv92c61gV0KA606FX0iDt17M359dKvs+rN/+BEUOyOqz9DCTeVNORpEels/0tvqyvdYIIZ0Md2UNj3aiys/fW/tlDi4kkZoJY1Auu4Pnu+Ny54JLsvzEUsSwssphJcrrobFWEhGcth1fhr483/A/eu/S5EgCl0RlGJhFOMhlGJhzDx0ACuHxmrPdJ+ZBAQBYmcHEBFRjIUghfwe0qizfV8uCt9iGYG1LIodEZBAZf0pFkoY+/YriJXO4Ob5KQTWsggmswgm0giuZiFIBHP37cXZ//C2Wj6xiSUIPz6NuFZBAIpTUyCBisz53jhyvTHk+zqQ2dKN7Hr4zMyWbmSHuiGFA7hr+C5g9njlXQmpuXXyfH8WqLnzuK1QbUOOtlLAZcjcBzzut2gE2s5tmE5pBaCWRHGxkWhQD5ohCRlBwOg+YGKy6k7VQ2GeJ2NYIPqaYUGoKDeqEQfk+QHhtTUcfuUy9p+dwI7xWfgUoQN3XprFpQOVhVg6HsGXPvwgprf1Y/vAOzGTfw2pkjbjsrZMOvcMF5ty+XhxCmjyN3hmYUMPtydUHkRBrVjvLQVLfZAOG51TwOt8P61oKeBGnbn9nZTQWq9kxvpwfayv6XqgIMG/vIrgSloWEQEAckNdCIoBiIsJiMXmtUmhS74uCCYzst/+bGH9dLquqFxuUAgAwP7PPInonFyRKYkCyuEAyuEgslu6cfITdQLirgvTGHrmwvr9yn9SwA8p4IPk90EK+rC6Z1hmXRGdXAYEQApUuBOIUHFPhChU/vb5UI42WFdJBAIhgETgKxTgL2UQyhYgEoJAMgNBIijFQjJLiNyFCyC5HErL19F7/TrEYglioVz7t9gRxsJ9e2vpB5+7iMEfX6zVkS9bwMXiv+DAWhJioVLXL/3hv8Hq3mEAlcOUXV96vvI+TV+iglBCrsQp9DS7kpWDfuQGOpAb6ETvjv3wdXaCZGuLXRz7n7+kkXsFypCETaDsDty4Z1RCEnqtTzZiw7jJMKCtFFDBtWvXHCtLFpLQw52DBdZjv7KXUR1s/IJ+k665DzR6bTRoK1kGPzNKAfV3ay5zKVXA8dK92CtM4i7xIo5L+6lKGewIIaJCYHzgmR/j7m9+tzJ5qiAXCiC+lpVdG79p1KA0dXcEWhhxCjSnb7pC/yzFk1SWAhqpBoP7sFKaQFHKqt5XA4/e7gXrIa9vSLTaJi2jPI86FuD6YbKt2OjRB4z4ftzuh89OPYv373u/6ee9qGjhUgbNCYOLMNy0KVAK+VAc7m5SCADAq7//bhzoO4ClzCKWFm4guFI5lQ6sZhFMZprcA8SSBMSiQDrTlFetPEW4uYCKS6EoEYiZAgKZAsqKxUfs+hJGf/ia7ju99h/ehoUGpcDt/+0xBFe159HUtj4c/9SHar9Hv38K+x59WpbmpxTPnPqdn8LS63bWfl/7Nz8NUqiY59+mUsbq7iGZUiAyl8TAS1dlacqAzGYp0LB+IgEfSuEA/Dlt90qfwj1g+dZt2N6/D+cC88gNdCI30IliR50X4o1b34hQ/x7g9FHNPJvKaAxJyMMqwuLYq7pu91if3OxoKwVcBkv0Aa/DUaWGlqXAunlUrljGaraInlhQRiQkoNlk3azcjSEJDT8dIdR7WEIIplayIBBwkYzhYrmqrTfOYEdvFP3zM9h+ZQ6v3ndL7frS6EiTQiAbC+P8zaO4cGAM13cPoaxJNEPjJsB2HTBeiCotZ6xwCqiWzzGagV8IMsmj1uZa8cSch6bfbvcBLWwUJawR2kSD1mHEm+H2+89n5jGfmcdgdNBVOVjQ6paRPMCqFDBqZqIgAoKAUjyMUjwM7e0+MPXWW3HLv/0d/ODidxCoKg9Ws/Cn8wikcvCn80hva7BWIAS5/g6UQ34E0gX48s0b3nJYrhRQS9P0SgF5HYhFg76mmCeV3ApqUKYRQqGaUkANSkuLUjSkkbIOf0oe6m/2wZvRG+7FnD+DYkcYxc4I8j0xRIdHMRcp1hj+q1jbPQTpjQ9gYao5XDJgbn/QEWjmt5Dl6YFx2wsytFFHWyngMlg2Ap7vPOvicY/pTMEp0GgpIBGC16aSKJYJOtbyuGW0q5ZOFERbiAaNUCdhNE67kMojV9J7aYX8hGDP4ioenlrCfd94Fr2LFfO+G3u3YaEvDACY37EN2XgMBR/BhYNjSN55J84Pd2ONrFDKrurEUftLqxnrNW/RcFOucB9oduinhhUrALkMVkrgDy+MCUb9ORQQkS9WFmV98ebFFXdliAHvA08LADNQRtWwA06fpm90osFWUNgvZBdMKwWMuF/sgBPRlmSkch70X+YRPcEKBEGAFPRrEiQqEuPEn34QQIULIZNZRSCdgy9bhC9XgC9XbGKwX9s1iOvvvAP+XD2NWCpDLJQhlMoQixVT/UZIAR+kolixZFCDqFg30PRNRRpfbw+EQADlgA8ZsQgp4EM56K+4NgR9yA51ydInbhrBpZ9/A0qRIMqRIEqRIN596wfwg8VnsShWNv1KS4xLv/ggDvQdwLWls/Kyw72QcsvqYlJ8X5qxyCf48PC2hxEQVcxGTcC2sKQe649ttJUCrqOxs7W65pxbB2+y+DPOt2op4BN9WMuWUCxXLqzl5Bt3NU6BRqJBlsHPHNGgPqmhRCTcmvgh+oV+vEp2oqDRRUVJwoHZBO67Nod7r8+jP91s1rfv7A0svGFfpVRRwHf+w7/Hxch1QBCws2snSCpZsYEzCfl3YbcVMFxsGugAzE9TWhoM0xluXhDj/nnzcCemElnEQ37EQ83t2XaiQS33AV4cKLQyrGfjpXG+FX0mvcCUr4QXFrcSxYmpl9AKiha7wWwpYAB1ziSd9CZJOwUIIAEfCt0xoFs7XeLgViQObtVOoIIff+aXK380cAXU/pWaLS6n33QL5u/dCyIKuDE3hM7AGC7knoIkCLhrdx8gCpD8cjP6Pd//PgBgPDGOp65931Cm9PZ+pLf3y65FbrsN+YsXkc8uMr2f3lihG66QYYx59553Yzg2jInVCSbZtFBtU5bdB5yMBtKGKbSVAi5jI3IKWAWdtlT+uxp9QBREw+ftCUloRESlv2CrybRyDTsxg/3iNO4QLuD/lH4CWYRqqap408Up/LtnzzZntI6ZkV6kOuoUNwRAuqcHyFfCGYqCwGQvb/R+ool9ttHiRTlhWDqwook+YIoXgaGABvCYDHmQ/DmBSNCHPYPanMrc2eQpq8Q9SwFrIRxZsdHcB1g2PQIELrLpKQUECJ7oh0XJfHhYJ8golXCaU8CL6yveClHWb2XWCsQRax1RAIEA+PSH9PL66T0A5DJxhP0dyAmV30oTfSWstEGzp/rWQ3YbP+8X17d2Cu4sr8GLfXKzo60UcBksnAJe70BODjrKuqgqBdSIBgkhtXpWcx+grVeZAocQGaeAsbx6+VYgEYId6VcrcoJgjnQjkBVw/41J3DmxiD+/ewxVKpsT2wYh4WxtOyUBmNwxiPFb9uDMTQNY7YkhIEaABuK7xvcUOSgEGr+3Zna67gNs5TZxABg8bwhFBkp3BopHADQqqFrv1NUqeIQk5L3AVEqjzL5O+unkeFs3FZAM/NNbEZ4lGuTE8Oj1uRcAMiU9D3LvwQn3gUZ4cVPE21KAVSlgxVLAi2D9wl6LgGH2nhK83AaqsPq9q883r9u9bd3k1XZuJ9pKAZfROCi3wsJDD3aZ46p6sytdDBrcB/RQIxpseL7xFN8+9wGpqaym/FauYxSLiCQEbLkawMA1AR+Ze6rWQg5vm8Sx3psAACvREM5s6UHB58NrN40icdcupOMRhH0dyJXX1t+lEXJeABalQP15bWiG+dN/yCAt0U/AeTNpJTu3Jg8vjBmejz6gkb9bdSc5bSngwKmeWqx1u+A1SwEI3uiHVr6BG5wCbUsBGywFmCP6eNhSwBTYTu8tR8rifJCn6z7AwIVTVTaZPQBTouY+YPG7q4UkbMNbaCsF3IbHzXtYQHsCZ5yP0YVmNLoP6KFqKSCTkTQrGVhh9LzRtw3Oz+F9TxzHrusRxFaq75CSpbl38hqO9dZ/f+LtdwGigNu2diOkGTlAJkXtL1EUwBAOwTiNCaJBAWqkj43F6rsPsExPTeWYndtUnjMzTxIQPieYLo8ZPCwF+HMKGCzUKBdXdtWtBBZlor3gtbhPFVPGiTjh1v5bcWO14gYVEAO6ZvO8FE5utRUWZIotZing8Cmh1zYgdnCpsPZn727uraDynQM+43ezu9+yH4BRKDVY3AcsyNKIVnYhaoMNbaWAy2hzCqjAoBoEQWhKUh1bVN0HUN/LCYJaSMIGSwGVSdInCihLjblUy6T/Xspv6yuVZSEAxfMv4NDJItSM6hejIby4fRA/Gt0vz1MQMNQRlisEZMXU5RUF+d7ex7QgMW6Z2rmZP4Fqdh8wB9rPRLM+0pwcXVhbeWW8sGwpwNt9QGlgYpVokMPCpVEG4nGTSa+jK9SFd+x6B+Yz8+gN9+L7FERhVmHIKeCBvmjFfUDXUsCuDYEDdSbru+5/IhnUIyFZzJPRHWCjuQ/cvbMXlyYq4+3eIYNoChZBFc1C5TaNNYBVqCkFvABlfXlh3GxDDm+2nE2ERm2xmQHGS6Ad0IxNrvR/qxE76VoKNOznBaib1+nVfV0pIE/PNqARBFcT2HP6DG66PI43nbqBf/zNt2GlvxO5ooTDoWdRCBMEcxXZrvV04oUd/Xhh+yDG+zoBQUA5M4LGwMOiIGCkO6JZYuN7+kQRpQarZS1iQLPQ2tjpFdP0pZoSKy0FFJwCelYIDUqQ7mgAiUzzaWKTNwInvzkn4fakymXDzLnemscL5X16M0w74Lj7AOf6vaX/Fry2+BrXPFmxvXM7tndux3RqWjed0lXMLJz2fzeDbClrnMhD2OynhKyRAmjglKWAVy0MRnvCuGVgBN+51oVoUH17kyqmMJeeQ3+k3/b5Uy1/q7wBNETaNfcBBs4yGvCeS7zOKbAZ0VYKuIyNFJKwCrsne1EQVaIPVP5V4xRoTKoeklC/3pX+9zXzY6MBXCIYnlnBngtTuOXSMfTeuCq7ve/cFF54QycS4y/iLv8FvHS4G0QEPjt2O16J3gzBp2+SO9QZQtBHp+n3iUCpbI5TgDT8vyZMEA0KBpEimtwH9CWQ4ZaRLkwlsuiM+BEN+qlcD0xFH4A51xO3N/Negt0hCbXgLNFgHU4QDdq5aO8OdduWNw0a381okeqE+4BXog8UygXTEQjc2OS13QdscB9gbO9m+4dXLQUAYKAjrKkQAIDjs8dxfPY4YoEYbhu4zVZZ1NqcXrunCklo0Ix9os91ZZMWWi0koVeVX3airRRwGbJGp9M/CCE4MXfCfoEswKlJV/WkXyf6gOzZWn1rnWyruQ/If1cVEHoD2tbrC3jkX55BPJXTTLPr0gyevm8fvrkwiGXxo9h12/eQhx8ny2NASW8wEuATBWzparYSkH+DRkuByt0qKqaLtDD+riZ0AsYLC8GIU0D7+XjYj/3D6uaDNSZcSjNzmjSb0UqgKoPXOAWav6s62RK1ZROHem6UwOmQhLzhlhKnVj6DyTOvRZ3RBtYLfRFgJb+tww2iQcejD3jkG1XhE3zc5w3Wb2W2L3tWKcDwidPFNK6tXrNQlLG1KOvcyHJIooXGiBZK92T3wvCql+u1PtlGWyngOmg5BSbXJrGYXXRCJMuw3tH1HQjUOAX0LQXq/gOC4t/KfUn3tFfNUqBxcA3PJrHvuYuYvmkMxVAlFMxKX1xVIZDqjmP59m04NjaAa7uGMZnIoiwBT+M2PF8uIIxC9S1VJKlfG+4MI6BqJUAaUjcqBeR+r7yjD2jlp7d4MHJhaLYU4EQWWM2/ibPAIrMuY7vnMR16QdPuOU4BWq6ADWwp0Ai3Nx684bVTbbfroxGtZG3oSPSBRqZ5D4yVjbBjY826yefpPnCo/xAurFxAoVxQecIZsG58cyXtQxsecINo0A4+AbV1MyvUOBi81ifbaCsFXEfj4Ko3oV9KXHJCHEuwq4M3nfyh2U+0yilgFPe3PmnSD24+xe6VFMroPj+L8PmzuPvHxxGbXgEAXPvZIC4d2AoASMcjmB7txfD0CqbG+pA49EYkDz2IswPj2DpQwvhEAplCCYtr+Zo8BAKyCBnKFxAFbOkKq97TOn2pvEODUoCBVIBmotLMzcB9oHGSMQqfYy36QDOafM+piAb17tC3/+emnqNOqwe3Ne3s3BrN4LE49ot+xAIxJPPJ5vybSQXW/3HOUqARTnMK8IZZcjJekI0ZBp2Wm/sA5xNBu2BWKeAG0eBmdx/wiT7uBLW1kMsM6bXQH+nHPVvuwXeufIcuL0FA1B91VSnAinw5b5xIB2b6vVmiQVqFR6NSgFfftWscbQWuls2GtlLAZdBaCnjWXKsBvCZdo3FPVPFF11UKNCSt1aOMgEVqTNAEnygguJrAyEs/xC1njmLn1Xn4C81mmnsuTNWUAgDwnUfuRaozgmw0hH3RhxD3DwLFK7X7xaVruEecxUnciZRyQ0xUzPvX32PHQAw+Ud1MVOtdfIqFAlv0AYrtrqZZvd4j+m2a6yJOpahmZZM1sEjrtQWqFXjBfeCOwTtw5/Cd+IdT/0D9XZ3cyMmjDzgcklBXMcfe6t12H2CBE+4DXok+ALSYpYADddZYhlcUN1V4ISShngzMpIWMCgk7wKqktqrAMGNtZsVF4NjMMUOZjFxo3YJqXXisT7ahF0msDUdAu9lvhYVYzVfX5sm+QuykKFvHfUD2rFA1g2p4ViVdOZVqiGcORJbncOtj/xd7L0yrKgQSW3qw3C/3YV8Y7kY2GlovQ143q9kCbi2+ivvEc/h+8HfxZlHJF6HuPhAJ+LCjN6b5fnLSRDmnQON3qVQDvwmc3R2B5mRe31LAfCsTVDOwvqBxdoKjColkuxDW+rtWNBA7QT1Oca3aBouwFj8dcXvhzzIX8mpbRpvtsuQN6w/TSgE9xdEGsRTwGkTwD0noGNGgyhjQCgdXSpgl5gQo5z2VJGajDxBCcGnF2GLYFveB6vemsqZUT6R0uwW8fzjSim3aKrypUtpEoHUfcHshxhPG2lX93+qcAtqWAqqlkcb6lEAkgviVeXReGMe1M59F9uSr2PnYVxHetw+EECS37UEhGkcwU4kIUA76UT58E64d7MPS7TtwOeDHxLJ2SCjZiQUIpOUrGBGWAQBdQgazpBfAkoZ8ddy9sxd+3wy09hWNSoHGAU0U5S4XosAy3BEY7ZC08tIPG6gvQaqYQA+iDWXw6wMVY399pYPqc8o0st/OT3Atr2m3YVgzIhqsp6N0H+BcxxJKAIJc83QSbi+U3JgL9dpAUSri5MJJ54RpQEAMgIDUCAbLDvNVWIEjnAKKeddLEEX+SgFRYMvTrPuN6nUPLFGdng+Nw2uznY4bKQzSxbShTDL3AUVIQrN9gKmdanhSqllxbHbFoBfRVgq4BEEQ8Padb8dsZrZ2zSjskedR9dV1ICShctSpWQpQMvoKAHqW1rBjfBZ7r76MsavXEFqrkM5Ut/bpZ3+Mmd5RrGSKgOjDtfuOYDk/Dum+3Sjduh0HttyGqcXTlcQrGYMS6/JeWUjh9vJrtUn02fIteI3sQqBRKaDyDtGgH4dGu3Bdxw1OK7yiqIg+4BPpT9uoJhJN9wE9okF9GZo4BDR8w2mgHrFCmYYuJ9r8NwOsMhpz83lkCrFJZynAcxMhs0xyeCFkx8bDTTBtenhZCnjYuqPxe5jti25EH3B6k+41Baod3Bys7V3XfcAEz4Tb86CbDPtqUJPFivtAxB9BtqR9+AQYW8uaApNOgN6dymuKujbaSgHX8JM7fhI7unZgLjNXu6bLKdAClgJOhiRUjp1VSwFRZbOrNs6OvXoNRz7zfd1ylp5/Ed8Ze33t99l3fQBnU9/FwZFOxHwinjw/j6try9g7GDeUuVo3kkQwc+0MbhFWavf+rvQeFUee5vc4NNrVRHrYVE7DyzZO0H7Fc6Ig0B2N1zOW/RQE+SXNxQDLZKJUAigebtYJWGtvahYolvJzeH7jQfJX7IzgqV9TnD50Noe6NJLDLHj7oVasiNyxAKCFE0SDtItzM4t4txf+euSkTWkZ2pZf9OP2wdtxfPZ40z0vn2g1buxayVLAiTr10gZRCVEQXY8nv9ndB6yCt2KZR35a7gN8QuvSHLhpWOa1OQVaAm2lgEsI+iu+5jItv55SoAUGXF4dXIsvoAo194EqGt0Hwpk8tl+dx81HT6Pv3BRe/i/vq91b2L2l6VlJFFC4aQe2vvmnEH/gAfxLIgrkmxcuEiGYSWaRyxZQlgjOz65ha4/+Rqp6OnhjOY3biqdrO9yrZAinyC4IUITGUbgPDHSEMNYbBRu0OQVEQYCPY/fX0lXotVqjAAjNlgKcTpXX/zXTXpu9B4SGO5tvglPzE2SBLcpOI6qAKleIS6cZrR6SkBaHBw7jwsoFw5MtVtilIO8IduDw4GFVpYCXF6+Nc57p6AM6dWpX+3Hc1Ntj47MtRIOMSlae7gOC4Dw/jBoc+86Ev/sAD9hBNMjyXUVBVFdOqtSXl8fVzYq2UsBD0HUfaAFLAVqwDgQEBLPJHCRCMNwVrkQfUMkikMig9MQzOPTEs7j3zBSGZlcgNKTrPjcFPFD5O98RxdxwN0SJYGbPLuTuHET+tu3YMXoQt+94CwBAevoK1Jz3JQKs5UoIML0FUChJ8M2/hiF/onbtmHRAI7X8e9+7qw9C3nwbUBIBioIAQcG/4BP8GAndikx5GSulGw0LTONp1kzzFBjNJy14D6hwAZisS53HvLbodApW3pvVB1YL8igudDBc0PFcsDS8YoVTwBuwcxEfC8bwoZs/hC9f+DJWC6vc8rXLfUAvrZctBRrltsVSwKYm4oRLhqc5BTiNfco8WcCXp8f9NarXFE083QdoFfBaIQmtuFbw4qlouZCE7jdpx9FWCriMdkhCYyys5bGarSykCQH6I811ceCFS3jgy8+jAGC3Rj49r002/BLw6K+9BaWAH13+EWwdSqEzrL/Nr5ZalthOGitpJbxyfREPoR5lYJxswTzpMSgN6IoEsLUngrk5jaQU8Ilys+oK0aB8ASFAxEBwLwAgk15Btpykzt/vU1+M6O29jSIW8OQUUEN3NAifKKAsEXSGW3ModFvTzsOFgTeMpHEqSopm+R46HbFT2SxAQMgXQjwY56sUaJDZ8KSTkzm1lxevjRtB05YCLqwvnFa0eKnfAfYoBXjCDZ6JzQCzRIM09wF7og9UYeW7t90HWgOtuRLeQKAlCXKb3IkGvBbZynxWsyUIkoSBuST6XriE7YkXsPXSJP7+N95Ws0Ff2NKrmlc6Fsbqoa1I3roNy4fGatcFCCgF/OvlSUwbzEJZWt+g0j+Uzpdw4kYSya57cLfvBAaFBJ6TDmo/0OA+MNYbNeWv3jip+wS5ebsoiPAp2pSg0cYq7bJZgJHuMKYTOfhEASNd6u4TVhY9TZwCnBciPlHALaOdWM2W0BenY4PXkqBy3fkJzhMbcgsi2BLbmlIeKwuwfT370BXqUjU1V4ObS2jepuGsbc7NsGu8yvby4pUHp4AZUjmr8HKdOgFRELkPDNzXiZpugeqcAm4rC5ycD2nKUt0Im7UUoHy3RnciZfQBs2BySdHhFGi1kISbEW2lgMuQhSTUOY3wska5ipqvrgk/K0UCRNM5jN5YxMjEIkYnljAyuYRgQW52OzCfwMJw5aR9YaQHpXAAgc4u3NjZjdeGejCxYwCLg124fXsPwgE9RtYGcj7ZKZR66kKpzDz5nZxcQUnqxGUyivFyJ4aEZSyRzmpJKhJVrvXFQ4gF/ZAIMdEG6umV/Is+ERDgU6QWG/42LmtbbxTd0SDCARFBP/tiRGkpwGrhz+Q+oPhV/R0N+vHA2N14ee5lhty05Nl8E5xVSwFuPBGNix8HiAYj/gjuGr4Ls+lZTKxNmM6nDTbYufEQoO0T7VX3AQIitxSQvCmnGjZ79AE72rFeG+ZajhrR4Ca0HuAfrla7/9Ka/9thKcDDfUAtJKLX+mQbbaWA65B1Np3+sZEHXKFURjCRQb6/o3bt/qNncPdzF3Sf23Z1vqYUID4Rz//1z+Ott/80XjrzNZydMTJXVfha0UkKAMiXpCaNvN7z6XwJVxdTADpr1+aIumVDPUMRoiBgbJ3AkIrpX1Xa5r+BqqWAtlKgSRyVNxQEAV0RA5cLHVG1LBPq8jiDjkBHhbySYoLSrnsz0lqfEN2eVK2WL8J5E9qa+wCl7GrpqmGfWn1cdkJ+rmUoXYoM2g6vsr2qFAA4WQq4QDTodPQBryltacMns8Api1KvHlJZJb6tIugLolAuGJdnhlNATz4OTTQgsrJd0cNK9AE1eHlc1UKxM4IX//xDwPv/2G1RbEFbKeASPvb7v4//+Du/g+E7hmvXWp1ToAq99xALJYSnJjFy7iI6rswjfm0B8RuLyIz04PinPlRLNzXWD0CuFJBEAXPD3Qjdvg8nBiI4N1LfWBMAxe4odUgyeX2yjcSFkoRIgG4TCRBMLyfxc/6z+Eb+fUhrpms6J8dgZwghf2XzUQm5yNgGFFYPjfUiqHEKyBYU7CevqiLo3KuERTT5MABTPhWaeVGm05XJ+ZMvtxe6yvLvGr6L2qQesGdTSk00aMF9oKpQM6Ogcxq85w7aRacdc5bd86BWe/Ty4pU2gpHXYJdCsyvUhWS+mQ/Ha3Vji0KUp/7NofCGPGHlG+/u3o2h6BAAYLWwitcWX7NcHrP7gIFrAc37ydwHOK3jWKDrPqC0FDApkwD60MNW4IU27TTaSgGX8MSPfoQnfvQjjGwbwX3/5j4c+dAR3YVHK5xI6XXSXZ9/Dr0nryM2uQyx3PyesYkl+DIFlKNBEABT2/qRiocxta0fU2P9mNrWj5mRXpSCfjxy6y0YH7+ATLrYUPh62dTVpBgsa48bZ5AvSYgGffL31Xj15XQRewrn8NO+l/Cx0DP4LfFePIcdhmX4RR9GG/z0JS7jXz0Tn9gcfUCElouFipUAB2mM8jD6FkzuA1aUD1T5OjNJeQ08Fva8ow8YfgZS/ce87NWNGLXsDo/f9MoKdrn6I/0YjA5iPjNvWxm88uLFmt0qRIN2RB+wa+1hx3gZ8oXw4NYH8c3xb9pWBi8IAn9XGJExoo8RNEMStsB6lBUBMYDDg4cBAM9NPWeYnopTwESkLav52RLqkkOYS0JI09zstqVjG81oKwVcwrs6O3E0lcL0jWl89S++ivFXxvHx//FxEEJwbvkcSlIJB/sO1sxU3SYa1DUDL5URnUlAPPsc5ud+hB2vHEV0/wAm33F7LU10egUd1xc18yA+EdHpZaztqVhOrHZF8d//03tVCxYqx97y53VkVx13BHkCusVD5aFSmUBSjG9qz0uEYH55BW8WL4FAQKeQRUTIU5QD7BvqgN9Xd4EwN3hq8yOIgtBMNMh5QWEEZZs2jDZgCQ2ZbaAFjduLXuVmyc2QWFVQWwpQhiRUq+OapUALtCU9GTuCHZr39PDuPe/GP5z6B7MimUbzmMHXfUDzlMuji1dCWpdTwGl47RvaEX3AqfFIlWjQBiUHK6xw3MjeifI1eLcpI6JBKitYhugsrLDaXrmFJBTgBq/zpkBbKeASPtrbh08ODeNryST+dGEeJ588ib/4f/4CBz57AEcnjwIAilIRrxt6HQD3zVgaxyp/oYTtXzuO2MQyYhNLiE6v1E7/lwB0ASiWCzKlwNqOAQwcvwIAkAJ+pLb1YW3XINZ2DmBt5yDSY70g66byFUt57fcVBVFzPKiYFdG8kDWzqmJZgt/gubnVHG6VziEkliAJQILE8Kx0CPDp+6qF/CL2DnZgocEQQiKNbcCMybJc1sqCRMEpIOgRDZqcaI18VfV4NAzy5jknsLFKqFwXXFp0uj0xKspnHad4M3ALMHbr4RGSkNVSwIuqg6HoEHZ07sD55fNMz4mCiIAYQDwQR6qYskk6dbhl0twq7gNmLQWU/DKNcHvtwQotxnW3FahK2HHQw2opIAgC7h+5H8emjzXVD2tECk+0Ewuf2MwGmkcIQer0hC4/t922WNKaXTM55T6wGdFWCriIiCjiQz092B0K4lcnJ3Hs+8fwiT/9BB7+2YcBAC/MvIATsycwFBvC9s7tjsgkFMsIL60hMpNAdCaByGzl32tHbsHMOqmf5BOx4ysvqroBVBG7sST7vXj3buT7OpDaOYjI3r1YKCyblxFC0+k/0fyhQZKn4WtFO6CVJSJTCijN+4tlCcnEEm4Tx2sifbr0LuQQhAB9pcBYTxR+0Scjv+NhKdCYh08Qmk/dwJ9TQFc6g6o2nKOtiGV23vTAuqcKL0yKTQtJ1k2b4AxbthqsKHGqFlytiKHoEN6x6x0I+oK4vnqd+fnqN+Z9Sm8HmN0NWpxTwKycblsi8oIX2hwtREHk365MvP7hwcM41H8IL86+iFfmX5Fnp1GfXo4+wMNSwK1QpjwOFrT6sqWQhAz1oVW+Wlu3ohRowx60lQIewD3RGH53YBB/PD+Ho58/iiMfOALRV+lYZVLGdGoa+RKd2bkRxEIJgdWsjOm/7+Wr2P71lxBeWEUwkYag0k8Hbt4qUwrkBjsRnUnU7ks+ERjbgu4Dt+J8dxaLIzHZ8+mxPqTH+iAIAlIWFAIAdMkEK/doMmn8oZ6bcpLTOzuXFIPbVCKLu3EW/nXzqEXSgUfLbwGgH/ouHvKjLx5sul5ROrAOhNoSi4IAUZB3f71TBtMTrY7IzZOHMjFPX2S78zFxysBhT++2YkA50XthsjakFNBxC9BL34jayar7r8sMURAR9ocBmPte1WeorSQ4bhZcUyB5QAGnhcZx26ylgF4YMy/0aRZoKfw95z4AkTtXhSiIpvqbb/0QghaaXANuW7Ra6Kes9UYT6YCnpUAj95VZmF/H0SmC9dKwki4aluFwd/aK0stutJUCHsEjXV34H4sLWJpawstPv4yDbzgIv+CHX/RDEAQs5Za0H5YIINYbbN+JK4jOJBBMpBFaySCYSNf+9mcLyPfE8Nynf6mWXiyW0XVxRle+3gV5iL/ZN94EX66I1Fgf0tv6kBnpwZ1b78XB4bvwo3P/gkQ+oZoPzcRsNFCoTjx6Vlcq9wSl+4CpAabBUqDBVCBTKKG8NocDvvop3JfKDyKHEDR5u9fl2d4XRWWnUYkOQFBZ5CmVDjQQFH835iCKQpMSQO4+IBOOuWxq6HFV2DQGu71w4QUvLHItWwqAj6WArFzKarFSf6zuAwGfCIA/CZwZWPU5tZPE0AjMnBUmCLLUTFNbxVLAbJveKJYCevCaYkcQBNk6hEueFvqc1f7qhXmVgE9IQl4bQGalAAclg6jhBup2+1cbQ82Oq7ZwERGCklRCiZRQJmUQQrCcXca15DVIRMJsahYL2QUQECxmtPnRWh1tpYBHEBFFvLerC4+urOCrn/sKjvdWwnr1JwnefoIgUhQRKQmIFIB4DohlCWJZgmhWwtMPD+DYW7ci6Asi5AvhZ756EsNXk5plhVbSEAslSMHK588OdMruE0FAvi+OzJbuyn8jPbjYIT/5v/7euzjXAD1EQVTd6N+z5R76TASNwZJhrGl8rq4TILi2lMGbxNdqWS2RDjwp3a58vFEYAEBfPIh4KKAqhkQII6OAPGXldeWWAsroAzKOAVn9mIeerIbmxwZ5M8klaP+04i/n5jLI7UkeaF7E8DLXNgsaSyFaTgG9dKwhCbf3RfHaVBISqSr+nINSRq2QVQwZVv6h/HZ2Rh9waiNih1Lgwa0PYjY9i7nMnKYSnQZ2cwp4YK+nCp/gU33fViGL9Ak+lAW+ikLe0Tk073u0TViBGfcBHtECaPOjne9tiT5gYY2kB6uWC0CljnPlHLKlLPKlPPJSHoVyAfmy+r9FqVjZ/K8rAKp/s4yduamcKblbAW2lgIdwfyyGR1dWUJyvM8x1ZICfelECdMzMcisLOLVYN8m/N1jGsE45kgA8f/pfkR3qRMQfQU8ojPxHDqM01Asy3A8M9AEBedOYv7YMlCnNbW3erCgX/mPh16EzFMHtg7djNj0Luu1i48Cl4T6g87SSQ6A6+C+lC1jLFfGCcBOCYhEjwjKelW6BZLCAFkUBYz2Nm4WKpUD1dLFxbqGtXb3B2SeITSEInY4+YHpBUgN9O7N/DcO3hP09+3Fh5QLXPO2AsuewLkh4h9CqwP7FP2v0gVjIj0Nbu1AsE3SG3Z12ZZYCFtwHjBALxIwTsZZtgrNCDTu7dmIqNYVCWYXfRWlWBXs2lJ3BThzsP4gLyxfwxI0nTOdjN6eAF06A1eATfSiX5Qt5PVm9oERthB18KlbyZPIbVxm33eSHqcKS3zyr+wAnqwRlnlqg7du2fAOGLHVDElJcq14vSAWkCimki2mki2lkShlkS1lkS1nkyjlkipXfuVLOc327ldFWCngIsfWJmWTrnT/f7F7ehHhW/vvCqABRAlbiwEpcQCIOJGKVv1fiQDIGSOJkJVTAOn44sv7HAuBb9CEWiMn+W86L8COGoBBDSFRf7PEaII2yUbLWd/gG0RvsqS1saMSQmVVVwh1QSFZPIxEC0jBIS6Ry7cZyBgAwQ/rwpfIRbBUWMEkGUBdYXbhbR7sQ8jdu0uXyVCwFLJgGKiwFfIIgcxcAlAtDpdKEP6eA3um9+gU5XDn4cWDNEwvE8Kbtb0KqmMJUakoznRdOvqxyCvAKYyXvz/ppq23Zyukvq6UAAESD3phuZQt6U4YCguxfNQzHhmvkuG5uFLTKvmv4LhwsHsS3r3y7Ka2q+wBn32+ZjBbbv+2WAh6FliJDK/qA1+ATfPyVAhzz01UUedi/2imiwUK5YNjf3CAa1HIPc6ovaNVd4xhalspYLaxiIbOAucwc0sU0UsW6AiBVTKEklRyRtw05vLFKaQMAkF5fpPrC9cF4NQJ87w4B+QCQDwjIBYFUBEiFgVREQCpc2fA34tv3iPg2gyW9EmVS6bCrhVXNNJfPRNER7EBnsLPyX6gT8UAc2zu32z74VEISysuoFqk2INGwGFRBbTameEeJEEwlsiiU5IvHikJAX4po0Ifbxrox3mCRpPS1JgQQmRdu8ucbJRDE5oVgo6WAEacA/ZpAO6GhpYAH1x1aItkhqtHCywva8Sb3ARMfTRREHB44jJMLJy3LQx2SFPSmn2rpRJGNU8BxNIil/CZV2SvJTFgKGHzjh7c9jH09+2zZODRFTDF0QVK/z8pl0SqcAmbl1Ium4dU23oqKjEbwDsday5MBuiGDdU7+tUISuq0ssEQ0yPgxvn7564ZpeM7RtOtqvTbgBNt/NSxwrpxDMp/EamEVa4U1XEtew6sLryJZSCJdTJuSow370VYKeAjPpSsdxTdUNw9IRQX801u9N/llShlkShnMZeZq156aeAp/duLPEBSD6Ap1oSfcg+5Qd+2/rlCXLssxLUSIbEOtauJGn3l1SwG9+U0iQGOYhlS+hMVkCg+IZ/GytBcZhGmEAADcs7MXQZ9iIBcgY1iVCEHAgqk1gSSTQRTEpu2TvvsA/w2osfuAPlgk8qKCQQtePt1SgiW2tRqqi8j7R+/H3VvuxunF0zg2fYybfGqwFuazglbekDRaClghGtR6NuQLycmuXIw+wItI0ZY+WeVmsLAzJCCWLQV4kX06Da2NaSO8HH3Ajg20JfeBpmhLbAp9L7QhSwz9OopUNdixseXtPsCLaFDLOkwiElYLq0jkEljJryCRSyBVTGE+O6/umtWG59FWCngEWUnC15JJAEDPG3tclsYaClIBC9kFLGQXZNcFCOgIdqA71I3ecC/6In3oC/ehO9TNFvdbzeezekuFaExtMJQPbkTX0kDtGUkiIL56vpfmU7hNuIQ7xUu4VbyC49JNOC7tb3hY3X0gGvLjpi2dKBM5cUmlLHVLAUEtZqSavIL8+cZ68IuCrqWAsmyz0PUeUE68TT+dWWTwKceGBZ5Bnl44vVQutK1wCvhFv/kFrYn2Sks0qIZWCkmorFMtdmravBpZ+mnK4wmuGymG8cYLfU0LVqMPGIWxc/v0VwtaawatjZAXLKsaIULk3le4knpy4u9oFdjCaM/Y5vTGGdq87PgOZVLG1eRVvDjzIl6ceREr+RWs5FeQzCc9PTa2wY62UsAjeCyZxKokITAQQPxQ3G1xbAEBqbkl3Fi7UbsuQkR3uBt94T70RfqAchwFKYyAEFEd4ESI8sWPIJjYuFrToFaIBivPJTIFZFKruNN/EQAQRBlh0GlJd/RGIQqCCoejXONf4RSoLIJ8Iu2g3/iOclQsBbRDEspXzHy07823rLkPsH1zPhNlkx7DxXWQk4tcQRBUNxxWfa3tWUhyrheV7FhDEnoJVpQCstM0LaUAw4mjVZglK9U1jVaJgW1HX6PhZqBBo2LNjKUAk0LeQ2BVQHpOKWCgjDEDx6xyNG65Ph5aOcCwYS7iySlgxn3AjKVArpTDUm4JS9ml2r//ePofUZDaJ/+bAW2lgAfwfDqNTy3MAwD63twHgXrTtzEgQcJybhnLuWVcSlyqXfchiIivB1GxB1FfDyJiL4JClNoaoH7PCOaWCwSVzfr15QzuFc8hhAoxSg4BvNhoJaAhRX88hI5wQKeERqVA/SRfNDF5Nft+N3MUsJ4ZWk3VHF7MGdhhLuv6YshmqJGvAc3tirUn2XI6QymC00SDXoEV036a93VTCWAXvHwa1sgRYUZOo821V9u4lguPFrma19wHKop5vnXLGs1Fr3w9pYWX3QecIhqklYcXqC0FdGRX5pEpZjCfncdCpmLZu5hdbPv7b3K0lQIuIitJeCyZxKcW5lEkBJ13dqLvJ/rcFsszKKOAVHkOqfIcsB6l0Ycg/ufJ15DMCgiiG1GxB4QQ2UaAbu5XXzjIw3UZgWAmmUO0tIJbfVdrV1+QbkIeirARCpN/nyhgrFc7Xnkz0SCpbeJ9jPHBO8L+9cmg0X1AbFIKyIgIFXdsOWURgKCoHV7D+DW9tcjbyBAFUXXD0Uz46c43YdncVmU2kpWKaLAFTGb13Aes5EW9YeBYRbyJ1NR+qy2q7VQKWI4+YNVSoEX5MdTagl5detJSgLdymlM0F4CdU8ADOgFL8IpSw8y9RmgpfbOlLC6vXMalxCXMZ+axkF1oKwDaaEJbKeAS/nF5CUdTKaxKlcVG552d2PqrWzedlQAryijg3PI52bXx3NMYDt2E0On7MdYx1hzKRGUs5UHAksmXMJ3I4N3iqRr1YYLEcErarZJavqgc6Y7UyAW1JyMNSwHqdbEAUQB2D8TXFSdy5Yf+Alvb9YAFRguLweggBqIDWMgsYEt0BxKpSeSltOGzAJv7AK9e1SyTDVwClIs6JzfgWt/CsqUAb/NZ8Is+oAe/4K+V12qwxClA871srJJm6yKT7gOMQtriPmDAzUADJdGgGeWFkVLADcVXsTOCp77w73XTaFoKaM3t3tIJcN3AN+Zp+lll32LkmXBjLOwN92Jn1068NPcSgMp8ZD58srHCkxU83QdY+naxXMS55XN4fuZ5PH7tccxn5rFWXGOSpY3NibZSwCV8c7US7i8wEEDfm/vQ9xObz22AF/LSGq5nj+NvXj4OoDI5hcVuxHx9iIn9WCtuR0e4R25WKMuBmOAkIDg/l8R2zGC7MF+7+rR0CGUVE77GrUo44MOWzrDifvNmU7m4Edc5BWjdBwQAd2zvQcAnIhryK+4JaI42oFcJJldUBpwCgiDgPXveg2Q+iVeuFnF2/l/q93l2B529vFunvUY16qWQhFqyNMqg5WKgB9YQczRwgmiwxinQApYCSliJPmDGUsCr5Ge0nAiAx90HGqMPSOyWAlYsR9xEK/a9RrCa+tOAa19jtBRwQykwEh9BxB+p/faaNQirPGYtBTLFSiSw2fQsnpl6BpdWLiFfzjOV3UYbQFsp4Bo690fR8/YBxA/F28oAziAgyEoryEorWMRlXL/6PKL+KA70HcCjZx7FnUN3yn0NFRub2t9Ni4767+V0AUupMn4q/BoA4PZcDi+THbiMAxADiWahGtwHtvdFKeJry4qDJAGCj00p0IiOkB/b+iI4vyhga090XSWgvShRRmewEwExgP5IP0RhzqFJ3cJpioe6qpOWAlobB9lmSQBzU+G1kGTJp1ZvFLJq1bHXTa5l9aGomsawsHZYCrTCZo11U2PGLJ9BGEuw21LAq6DhFGiE1zaMIr3JH32ejC4JeusdPWWRpkLQ4b5vlxKEG6cAq6WAnlKgIZTucm4Zs+lZzGZmMZeeQ7KQtCRnG21U0VYKuIS/+ZNPYex1N2Exu4gXZl6omT1JRIJEJJSkUuU/UkJZKtf+rl4vSkUUygUUygXkpTwK5YKnTzPcRqaUwYm5EzgxdwIA4BeCiIi9iPsG0OkfQpmMUudVliRcX84AQge+K92NB8VX8cbyBL6R+iVIQZ+6UmB9sO+LB9EdoVmECSAN37NiKVAlGqSVVJ7wwEgn4vHe+l3FBC6fkKy7VzRLoLinKL8S0UFquK+fN4tU9i9VzJfQExjDSnFCJUcPWQpQvJ8ZSwEz5fCAFTNToHVPVwE527wlSwHKb8Xzm7JaljD7RWvADgUcr3qRKQVMRAMx2px61UWm5aMPeD0koR2uRTZAaVFptq/a4T7ACjXZq0qAqbUpnF46jZnUDHLlnMrTbahBhIigL4iQL4SgL4igLwi/6EdADMAv+OETffCL/sp/QuVfn+iDCBEj8REcGjgEv+DHibkTSOaTEAQBS1jCZVx2+9VsQVsp4BJuH7odt227H9OpaSxll7jkWSwXMbeWhgA/3nfTW/DFi59HtpRFtpRFrpST/ZstZ5EpZuw9AfEwSqSAtfIs1sqzmCmcxuVrfmyJDWNibQIdwQ7c0n+L5rOTiSwKpcria5F04avlN+LFYg/GyRhETGs8ReATBeweiAFQGdBVrBIaFzESAYT1kxGRISRhNaVysqz4MzaJyB8G7gOy4pW8ByoPRwIissVK3ffFtEkKLYjlOHyCXgQKbTi5yKWxFBAgMCsm3Vh8WWGorqKVQhIqZbRyMiwnYuXjr89UvgfqW4t00ywshyRsVApIbUuBRng5+oBP8HmKU6A5M7Y+7kbf5DkH2hIJx4R8hBAs5ZYwnZqu/JeebrsCrEMURET8EcQDcQR9QUT8kdp/YV+48q8/LFMC+AW/6X6xr2cf3rz9zQAqB4uz6VkAQDBufu3pdbSVAi6D54nTWo5gYqmMkBhBLrUV2zu366YnhCBfziNdTMv+SxVTst+bQStZJiVMpiYxmZrEt698GyFfCMOhmzHguwUjodvQF9gBAQLShRLmktX6qA/4V8mW9b80Bh+B4HXbehAJ+JCnWLdVQmU3KgXqnAI+SqWA7ik94wRoPsyPzr0mSwWFK4fKw3uHOnBpbg2iKGBbn3b0hmY57F2w2JG/YZ4OrnFpiAYroUIZOQUULiym61Goy0AD2g2C1vvUSOJawFReCUvuA5QWI7LfPGOn82JW1yF5M3rHgBjw1CJdxinw/2fvPcMjua4z4fdWdc4NNHLOmBw4w5khOcyZoiiZooIlW5Ite3dlrx8H7Tp8az/rddIGe4N3Za9le2WvV7JNRVKUKFGkSIpDcpjDcDgZA2AADHLsBjpVfT8a3eiurnArdVVj+tVDDbrq1r2nbt147jnvMSH6gF3beLVbChBig9C4JV5GAvcBOfdCjX3HDAhDUBpCNFjB9+B5HkvJJYyvjmN2fRajK6O2Gl8qBSfjhN/pR8AZ2PrXVfrbzbpBCEHEHcFScsl0meyghK40akqBbYSLs2uFv0+OLMDdIJ+eEAKPwwOPw4N6r3goxKVECqcmF5Di4kjxcSS5NaT4OPyeNFZSK1hJrWxLa4NkNonRxFsYxVvA6j/AzQTR5NqJK8urqGMZXI/LeDFzFFueXPKDh8/F4HB3FGfXaSUQuA8URx/Q4D5QNlmK5iEemjG/XS/J2YSxMrdRK7YUKIff7cD+zqih5RIQHGo6VHAtkU1r5HvrXKPagWiw2FyZgKg+jfM7/brk0gLahSPNu1TjokGXpYCMD7LSdSNgtUkzATHsZD3/LrraEK+fU6BaXWGk5JaKPmA3pQBDGMPbJ0MYw8zgtJB0Vho8zxsmiymWAhJzyHpmHVdWr+DK6hWMr41fE6EBWcLC7/Qj5Aoh5A7l/s3/5w7Bzbqp87KronI7oKYUsBhWD66EyC/k15IZsMQJLxuBF5HC9WO9OSUCz/NIZBJYTi4XFvgnJk5gMbmI1dSq7SZirUhyqxjbOAl4gTUv8Fo2i+HUE5jJdGAkOaz4/I6WABysHLFf+RW+aMPF8XxhIFTFSylluLD5Px8bQSK7BACoc/YoP6gSanx6eR4UigvNgsjiQOMBpLk03p5928BCtw+kFuB6LQXC7rAuuQplm2T5oqQYsHr8poFwAaWLU0DDaZqV0Qe05C1XhpN12m5BWly/plgK2LSNi8ktK6vNliJmKGP0fCtVRINiKxYL+oXdebTy80yGy2AqPlVQBMxtzFksmXkIu8MIOUOIeCKIuHP/hVwhPDzwMJ4ae8qQMuw6Jm0H1JQCFsPqicHJOJHKprSXRQj8Tj/8Tj921e/CdU3XFRb6GS6D5eQylpJLWEouYXFjEYsbi1hILth+MFfCCsviLS8LYAauwAwcgXPIrO4Gl2xAbvWx9Q3q/C7UB9T5jROgRClQvDehDklYEoJRnICn23MMM+mz8LMxeFnxDZouD2w5TgFR94HSk2ejoJSTk3XixrYbFZUC5TKZt9K02+ZDDMKWobZfC5UCFXtnoz6b/T9RGRxEu/sADezEKWAU0WAexXWnF0a5oBRvjjVZCpjAgl8JFLvBFENoTl7422ZaAbWRAmhQMY4Ckcv5EMOVBA++RBY9fDFGEg3yPI/F5CIm1yYxsjyCqfjUtrKoJciZ8Ec9UUTdUUQ8kdy/7gj6o/0YXRkte8bIcaamFDAPNaWAxbB64a/XFJLjeRBsnRIWD3wOxoF6b32Za0KWz2I5uYz59XnMb8xjYX0B8xvzWEuvoRpBCMB6roL15EhIeM4JLlUPLhUDydSjqz5amKjEJizxAS6nFsiDK1rc0HIKCPMWhl4kBPCwIXSyh+Uz0bGWkuUUENzlBEqLKtgTF6CvH9v/RWmJBtW6DxhlKaAWNCztdttEaEUZ0WCxpYCBm+xCGkFfMNNSwIz5U05eB+Ow3eLedE4BQX10BDswvloeLcUo0EYxKW7HNLAb0aBZB0KaQxIKnlNtKWDBPMaj1H1AzzfWK3+aS2NibQJjK2MYWxnDanpVV352gcfhwc66ndgd242IO4Kp+BSi7qhk/5Oqx2o/CLxWUFMKXOOQ0rbTYGRuDVeXk2gKudHbEADP81QdnyUs6jx1qPPUYQADheuL63G8Pz2FufU5rHOLSHCL2OBWYDu7PwUQJr2lJOCB0WQMca4X9c5umUmrfCHNlXAKbD1HaylQwikgEn1A/kmhMsH8kFwcLzBHN7zErXKNytvMdaadtOFSpFOlxJDq3QdCrlDJb63vrCq8nIqwVUrvU4lvpCXUo5xcxZtAXe4DFpCNGWopoEF54WAcyGaNVQoYGn2gApwCzZtRekxDqT5cEk5G3PpOShFuN5hxsm6kokFN36F5xhSIfV6Nn1xOQSKFleQKRldHMboyism1SdspDNUi4o4g5o0h5o2h3lOPmDcGr8OLhwcfRrO/GYl0Al957yuyeUi1DUMjRVTqxMg+S7CKoaYUsBhmDKIccgNTT7gHI8sj8Dg82MiIRxDQqhTIZDlcXc4xpE6vJNEe9ZVZCqjF1aUsSDaKBle0cI3jM1jnlpDILiLBLWCdW0SSqyKuAgLEuTnEk3MYnX0FTuJFkG1G2NGKINsEhtIclStxH6AtWo/pbPF9XnMrlVVgCG4JN2pWW9GIQVok9bJWSQsGIPMtSigg1FsK6FFKFkNtW6EiGqRIU4k2qsT7ohZ66rx400E9dxlYRUZZIWglUXMwDl3udmrLy6M30ou11BpmE7Nl7VKvpYCiS4RAPLOJCWmVYGLtWO672m3N4GAchq//jI70oSokoUXhZY1SAtHIn+WymIpPYWx1DKMroxVhwDcLQVcQjd5GNPga0OBtwGB0EMlsUnQMydexz+nDPd334AeXfyCZL02kohrsi5pSwGIYq9nNgd9UCtzVdRfGVsfQ6G3EP7z/D6KnCFrdBzhB/85uXtBjIrSUSJddY4gDfjYGPxsrXPv5Q3fgv5/8RySyC4hn55Hi41jnFjWXW0mk+XUsZEawkBkBAYsg2wQfG0Wza5fsc7wGpQBQ1CZUDsik6GSYB6d5Ye9xMvC7WcST0hPNtY5qqAepBdOl5UtbaVSeaB9sOqhbrjzy4ygBQcjjwMpGBgDgd5ePbzxPbykghhZ/S+HvSlkKGPm8HkuBknyrPPqAJk4BxoEjLUfw9NjTqp/VA5aw+KmBn0KWz+LL73y55J6UDz0t1K5BzG7ztPlLrV2qIfpAo68RIVfIlLqsBBu/rdwHjApTKmEpkMwmMbYyhssrlzG2MoYUZ6xSsBJws240+5vRHepG0BVEg7cBHoenJI3H4cm9m0g3Ka7jvkiffGGS5wdVaClwDaKmFLAYplgKbGr6HIwDveFe2bRSJnhawIPOfUAvXKwHAbYBATYXc3GX/wNI8wkcGFzCa9Ov4unR57CUmofdz2J5ZLGSncSLy/8bAIGPqUPY0Yqwo03WLJ2h1gqQkgFazaBcvFDkwWlupYQQ7GwNYW0jg0tzcSTTXMm9YqhZz97bfS+evPykCjno85bNx5hsqg60vuQ0bexw82F0BDvQ6GsUK0gTik87exsCOHN1BTwP9DcGtGW4CeH79EZ6cWPrjYXfSvXSG+nFpaVLsmmUwBDGULNUszkFjHjGqLy0nHTKLTgdjAMD0QEkMgm8NPmSKllkhKQCQxjRDbxeXiC1nAKmWwoQQjV1VyunQNgdxkN9D+VO4i3e3GhxEZDJrOIQflPacLOiKJJ/fn0ep+ZOYWR5BJPxyarzh++P9GNfwz7srN+JC0sXEHaFQUiOHFDOusFoBXQxjAy7WA0HKdWKmlLAYpgxKeTdB2igdmKVAg8e4CtDJsLzAhNSQhBgG3BP9zHc0HYMmVQQI3MrSHCLiGfnQBzLWMssYSW1Yrps2sEjwc0jkZrHVOpdXEm+Di8TRdjRBj9TD1K0EKNVCgiN/9RxChQpBXjtlgIA4GAYRHwusCQhm05INCiFZn8zesI92gUSKEv0wB7LTPNBuxGg6f9ehxfN/ma9IpWgWD6vi8WBzqhkWlrlpdgm4ua2m+Fz+gq/lfpRk69Jt1LA6DmixFJAx8k77YbbVKJBHXlrqVcH4wBDGBxoPKBbKaCVEK4YPHjdm3S7RR+grRcpt4eS72rDAboj2AEna9xhjFEoC0kIxhJrIDUQug9ozofnMb4yjheuvIBnxp/B6fnTBkhXGTgYB5p8TWj2N6PZ14x/c/jfFMi9hRwAy8llyXzkOC6McBWbSczQ5UFVjD3a33ZETSlgMcwYXMUWs1KdSE+IpaCjAauZ2a1ydXIKEEJ3WiyVptTE3lGwJhhoCuBXrvs8/vTHz+Fq8jSmUqcwmXwHaX5ds6xmYy07i7XsLGbT58DCjbCjFQ2ufrS694LV0GaEoXqUBtVipQAHToJmTh+EMtAe5GghaDJsErlG5yKa00gCuhM+xTw0QPWpIa2gNthU6N30CSMtFPtim9GPzIwQYNTppdY6Lbas00IAKQYjiQa1QNFSoAIRH7TkL8opIFOXdnIfyMPOmxvL5lgVEFvr0lqEcDyHq/GrGFkewcjKCFZT1REtwMk40eJvQWugFa3+VsR8sZI+HHFHJJ81IlyjbLqyo6gtGKoUsIlSajuiphSwGFZPCnosBeqcPQiyTZhMnipc02Mp4HYw2EgrPy/kM8jXodyAx3EEdc5u1Dm7sRP3Y2z9dYxvvI44l9t8r3OzSHLiZIxWI4skFjIjeGrhj+EiPvSu9yOZCaoiKgTKQxLKQYlTQIslpvCRMqWA+izpYXI3M6Mf22kRS7PxIIRgR/0OvDX7lnw6E+pKjXKTNpa1Xepfb31luVJFrSayQJH0tKdKVroPqMknf02ujBJ3O4KKKY3kZDJdKSB0HzBFRaweNIrKEk4Bm7gPlMCEeckMH3uae2ZEU1CCcIxWGrM5nsPk2iQuLl/EyPII1jP2PRjKw8W40OJvQV+0D1F3FDFvTLbP6xkPau4D1pRjJ9SUAhbD6kanj/1buKnTZyngYBmAQikgdB/Yui5enzwPpLOl+TKEgZcNw8uGEXP2o7POi3Nz0+A44Nz8BHjPeRDWfkqCFJ/AmaV3AAAMHAg5WhBxtCPItoAVbIxKCJdEmP3lzPVJycSiPfqAHIQLCFr3AU1lqZBDPp9rb5IA6C0Fop4o7u66G6fmT2FybVJ1OVrrV+1CyKgNgrJyTX970bvpE47JeqzDaPpKmVLASEsBlXlLujholKm47vRaCtBwGFDlU2FLA9MtBSjfR3Wft4mSrxhWzydy7kBy31ms7i2JPsCXEg2KcQpk+SwmVidwafkSRpZHsJG137quGA7GgVZ/K9oCbWgLtKHeWw+GMIp8AHlo/Q524LiowXrUlAIWw2zSnjykyHu0Eg1u+XJtntJvxpjnOB2cApRzNic0FaDIRqgUEIIQAg8TwuRsI1bHbsWn2B/gY/6v4WWvBye8Xrzu8QLEXmQzHDJYyoxjKTMOAhYhthkRRztCjlawxAkINPfCjZBcNZa4D/AcQISJjV9gqd2o9YZ7S9jvrcCWyNt7MqWxKMq3tf5oP0LuEL5+7usSCY2ULIe8fDSLGjXtTLjArLQptVYUyyW0FCghGtQTfcDgDTdVmQaZNDOE0SS/USE0VUPmtfXWt1pLAbu4D9A+L9w42glWKwXkoGVtWun3kVL0ZLksrqxdwcWli7i8chnJbLKicqlFg7cB7cF2dAQ70OxrFp1vtSi1jBovab+rHfuZUW5e1wpqSoEqxVB0CGcXz5Zc09IftSoltk7lizadOi0FaDtuOitzwg1x5UdG8IyY6fryegqX5xNowgJ+0/HPCKZS2JNKoXFhN57N/CJY3wW4Ys+CcS2AcaxRyVop8MhiOTuB5ewESJJBkG1Chk+iLVsPN+su39xAPvZ5aVgnrry+tIyxQmsFjXnmJ567uu7Cu3Pv4sXJFymeoctbMR9jsqlIrkZCLcO5WtNTvVArH5WbE0V7rMQi2GhLAT3uA1qeNbKO1Jquy7k4aCUaLM5DD2jcFdTkoxVqiQbNbvNmkUfacWNgxiZKa/2pcfsRk9uykISb5Wa4DN5feB8/Hv8xXpp8ydahA4OuINoDOSVAW6CtLDygGCpB4m3EWFSJdqCq31TQzWs7oKYUsBhaJoXDzYcR88bKlAKVBkH5aYueiZf2yYzEEXd+UynmZ1ZuKVAqdyrD4dJcHEAdft/5dwiSnK/ZEu/HH6R/BoAL2fggst4JZOMASBKMaxmMaxqMawGEyVBKbz54cFjJTuFU/Ds4/R6DrmAXDjcfRneoeysRkTfXL3YfyCkFjB9Vy050VD7PMqxmFnvNE5f99++moKIWTRqQP1kJuoJUhFFGbRAqcTKSX2hplVlOUavnJImafMrIOiLCn8a7D9BuhmhD5ymBirzRRCWbaqJBmygFaE81CxvH2sagDGo3/nLPWaEUSGfTeO3qa/jR6I9weeUy0ly64jLQwME4CkqAjmAHQq6Q6nFRaPFFA9WcMTqjTZg1H/ocPnSGOnFm4QwONB7A3Poc9bM1SwF1qCkFLIaWgdTFutT7Aeo0CxJCbD+p11KAtt9mJYrgIWEmyJdbFwjTvTW+iFSGw3XkHO5lXy1c/+PMT2MO4fxTRXm6wW20gUs2oqPeg52RQSxkRjG68QqWM1foXqQC4HgOIys5dl0n40RXqAv9kX6ks2nwvHQbEoYkJIxwoaUfwrZHyymgpc0aR1BWimtlsqGOPpD/W+OmSyvy8h1vO46vn/s6snwWfZE+XFy6KJqexoVAjJDQioWv3oWW3GLSqEWjXBo7Eg0yIgptGhTXpV1Mv812HxDCbAUh9fuofG07jtV2aUNiUH3qSyrzPnmywAtLF3B55bJtyQLDrjC6Ql3oDHWi1d+qO/x3hrfu4MnqdkoIwe2dt+P2ztsBAI9ffJz+WT2WRzbun2ahphSwGFomdAJiOQNwbgPOFCZmfvP/9HAK0FsKSDwvk0GmTK6tel9MpDA6n4ALafys4ylgc+x9mduBf87eKiMJj4DbgZaQDzFXP3p9N6HfewvOxn+ElexVrGSmsJqdBgd7aK/TXBoXli7gwtIFPH/lebS7jqDLcwPa3HvLohgwJdEHsjBCDVAWfaDM91N9nkb4PRox8EvlQXNybenE4/IBt/6WYjI1nAJ6oLUu8hubem89PrHjE0hlU0hn09JKAaMsBSr17XSYQMpaCqhVChQrfigVzVYqBdRYCtAQ/5VEcDHI910LeaORqJQVkNGgthTI+TraM/qACdDsPqBCmVdp9wGe5zGdmC6sX+yoCGAIgzZ/GzpDnegMdcqGBtQCTZYCakiUibQSyApXMaNglEXXtYKaUqAKIe0PqX7lqMdSoESTzOcWTHqYXWkn7XL3AZIXoSBbSb4QsRTY/Ded5TAylwuVcow5jTrkNnBJ3oHfSf98UcpyEAL0NQTK6tDJeFHP9KDe2QOe5xDn5rCcmUKSW8FKdorqHc1GIpPAucyPcS7xY7iZILo9R9HrvRFNrp1gCFvuPmDABl4IMV6HqLMTi+kx+eeKZKE95bKK/8bv9MPJOLGwsSByt3pmKqqQhLQLBxM+RrHSIuQKAQBmE7OiafVsDqwgUtK70NJlvSUHCbFstYGVkVGLnJXw61WLSkcvMN1SwKRxxJaWAjYkZstD7jtXYvPH8zzmN+ZxfvE8Li5dxGpa2S2s0vA7/TlrgGAn2gPtcLLaiLtpYNo4XgQjFJ12a9PVqvS0CjWlgMVws260BlpVhe8ihNgkjBDB1oacx1R8CiPLI5pzo52ypdwH5CDOKcDj0ly8cG8NXqSR21z8r8yHcIlvlc2zq84Lj3OT9VzyRIpBgG1EgG3E/uBH8NrKP2A+PYKVzCTi3Lz6FzEBSW4VZxNP4WziKXiZCHq8N6DBOVBgbeZ5czgFhOB5Hu3u/eDBIZGlqxtaNnCjWn9ZP1KIPmBHzbkWqHYfkFOmmVAnYvLJtQ2azR0PXvfpoty462ScVD6w+RMczZwCcidMKj+FXAgzsTRayqAtnyq9SOFSC1eadtngbVBVfiWguz8pPG6m5YdoebT+y5TtL99vbKkUsIFZduFvoSxE+luo6VdqsbixWLAIoAnBV2nUe+rRHe5GT6gHMW9M1zt3h7pxeeWyccIJYKZlVSVhdT+5llBTCtgAD/Q+gC+/82Xq9ATqlQIymWkCD6DE7xzARkZn/NeiOdvFeAEAKa7cTCwtSTTIS76PMPoAQDC9msRSYouh9nVuEL+VPox/nXkHf5l9UFbUoMeJ5nA5q78ceHDwMGE0uXagybUDaW4DK9mcMmgy+Q6ysJ4td51bwun49wAALhJAnbMLra69CLqMHyrErA+cjBe93hsRz84DeFP8uaKPrNYf1grYcTGqFlSWAka4D2jMQ0w+KaWAEZt9I+BiXVRKAb2uYma5D9CmsXJBJ0eIprateR1e9IR7ZPO2AkYwhqtKb5OTQLVy26HPC2GXNiQGuXFHrA3oGafW0mu4sHgB5xfPY26DnkSuEiAgaAm0oCfUg+5QN0LukGF539JxC9yTbstJwwE5C2R1eZgBM0Ln1iCOmlLABnAyTjT5mjCdmKZ7gIgvgiva9IX7bwPm2+IsCBgM+u7EOreI8Y3XkeTihXuZjDj5Fw/xwYPngbSAU2B1I4Ox+URZ2hk+it/JfE5CwlzeLEPQ1+AXeGso1z6PUhmcjAf1TC8OBB/BBreCl5f/FsuZSaxkJ5HhrY+rm+LXcDX1Hq6m3kNwI4YQ04GIowNORjl8Dg3UEA06GAcyXI7socnXVLhOTd5TXBQhlrkTVCtUhyTUurk3kCtFTmYaRQ3PKxMN6lmsUFu5yPh6Sj5TlN5QpQCFpYDcM3phRP0rKbik3uve7nvLow/YAJVeyJv93nralZhbiJ2VsqbUpdYshYYCaqMPqBynUtkULi1fwrnFc5hYm6B+rhLwsB60BlrRHepGV6iLKmSgFthtw1ppBWMl8tWlULDJGF9J1JQCNoGak381lgKJVAZep/TiWDOnAHiAbDE4GzPtlubiZDxwMi0lFgmAPNFgyT69COmihzIch1cuLYJjeDSSRTSQZbzHdVPIl3vXjjof3A625OSBphblTiocxI2wow1hRxt4nkPU2YHZ9Hlc3ngZ8az1mvPVzBxWMYeJ1FsIsc2IOjqRzoYM9aErrh7hN7+76264WBcS6QR6I72F6w5C6z5gjjl7XmSpHHLuF9p6h51OtipFNGik/5+TEW+bldwkyLUtKfmMhpyrhBkhCc2MPmCE25xWMi2j38uoeqn0xsIu7gM1mAtZ3g2Jy0pja5bP4srqFZxbPIfLy5ctZdQXIuwK486uO3F75+2o89Th+SvPV6ZgE5u7UQo/NSEJ7aboqAZrUjuhphSoQtCSJL14cQ4nLy2gLeIF3BJ5aZyAeeQHHLJ1QSRv2o1Ni78FPF9MxrYll3CiKSca3BKKgJTJwoMveebliwtYWk/D5edwF/M6GsgKBskV/Ch7EEuy9UoQ9jrRFBSrTCq1AMRVFkX1CIAQphDN4PrQZzCXvoDLGy/j8vpLWM1SWpOYBh4r2SmsZKfwldOvozfci+G6YXSHupHm0gi5QlhJrUg8WQoxokGpe4QQtAbKOR5oLQWsWmfK9dN817DbJCoGQzkFZD6GkUoBuZN4oxQuer4drVKg4KurUeS8hY1o3iZYCpjpPqA2b6kTTdlnVPhRbwco1Ydw/q064i4+/499lKx52LlNqbYUkHgXnucxuz6Lc4vncH7pvH43UwPhc/jQE+5BX6QPn9v9OfREcu5B5xbPVaR8uTr2O/0IuoK4Gr9aGVlk9hVWt9MyhayKBR2tRV4NOdRqyyZQGzqEZmI+eSm3yZ5YWkcmmIbfQAuo3Kl8MadA+YTrIA6keXmf2YHoAALOAPbE9uCx996jKlvoPrCll5Ce9PNkgmMLCbw+tgjGS3CYOYMGktvAdpEZhEkcSzLlulgGvQ3BrQJLZCCb/0h/Rw70zNXFfq8NrgE0uAZwKPgpLGQuY8H5OF69+iY2OPHNd6WQ4TI4t3gO5xbPoc5ThyMtR/DBvg/i7Zm3ReuhN+bH+1M5BuHWiEeEU6BULYCSX+L1ap8Fqszm34aLUbUwMvqA3nJoQQgBS9gy83me56n6ohj3gJGLI1orG71l9oR7cGruFIDcIrgkbwOI+xSfMVIjpzIrUd/nzTam+t1NtIDQg4q7D5htKUCZv2qiQZtYXvGlJnGGQ+v3ET4nyykgUoZw7F5JruDcUm59sJxc1iSTGfA7/eiP9GNfbB+8Tm9B7uLNY6X6ttxGvCvUhd2x3fjns/+spwDV8uiBXcbEYthnjVgdqCkFbAKz3AfyyG2K6bW7tHLknxabbmlk7Ah2YLhuOJdHyVxZLFdp7osJ+ZMv4SaM54Esx2M9lcVTp3Mn7b2YwmFmi9zlba4XV3h5ZulbBhuQcjjAifjo0tWjmnBW4idc9c4e7O+8C5n1FiwmF7GUuYKlzBVscNZOugsbC/j+yPfx/ZHvI+wOYyg6hMHoIIKuYCFN2OtEf2MA6SyHppCn3FJA8vvrB5H422xsF1NYKksBSn9zrVYEWuBgHMiKhCupJksBpshNSwsONR3C/Po8EpkE7uq6q+y+2JgpBRprEDPbvBHjQkHhqvJUzEg+CbXPy32fihMNmq0UoKgXyQgSIubLtegD2iAbt16i7uOpON6bew/nFs/haqIyJ9w0CDgD6Iv0oTfciyZfE9wON8LucGnYWgs+hZI1RqVN3+1qJaV3n1IDPWpKAZtAbcOV23D3em8sz9/gfpFbVMu7DzgYB5JZecI8I+M+y63zsxyPH56+irVkBk5k8MuOb2NyU+gV+PACt1s274HGAHa0BPHOmvaKFCMuy0PN9yfIDd4eJoxmVxjNrl1IcqtYyoxjMTNuuYJgObmMV66+gleuvoK2QBuGokPoDffCyTrRUOx6IXjl4rox3H/X7HlB12Rq/0lLrfuA1nRGa/XFxiAe0v1QEQaevFC7D+hsHz6nDx8e+LBcAdSuCSX9UkIsM90Hyiw3lFwBKE40C3krVIJZyg79Y5tOuQywvjASNPXhIA56SwHeXpYCxbDThkVNvy2+l+WyuLxyGb974nfx0uRLssSmlUTQFURfuA99kT40eBvKlNZCSwha9zcjoeQKrHc+VLWulHFRs5pTwKrDFTv1z0qhphSwCYxyHwg6mhBxtpddZwzWAPIAmCKlgNhYQjMJF6ehNasTs3fIy0AIEZXl2XOzuLwZbeDz7HfQy1zFJHJhD5/KHkRapisE3A7cPty4+Y2U6kv6vqRCQGQwlitFrK24mSCaXDvR5NqJDW4Fi+kxrHEzlpMUTqxNYGJtAs9PPI/ecC+GokNoC7SJTh6lVBGCd9Q9NpszuNtwnWkKGEY/wRtVOQZGH5CFQd9Nz2KlUkSDSjDcUsDgE/ViGElMZlf/WbWouKWADZQCLMNSn2ra2lLAxpZkcrLxPI+ZxAzOLpzF+aXzioc/lULQGcTNHTfDw3rKFAHFEFtDWxFZRNZSgFSfpYBZY2fNUqByqCkFLMbofBwnRxZwNRuHQ4IMUAh5QhDpZwxbCSOXFSkJ7VaeN00Mbm2WAuJvKaWEmFtL4ofvTgEAdpNL+GXHt3GGzw2273A9GOcbZUu7e2cTPJsRHPQsJIUhCeWhcAImc9vDhNDi3o0293746t/EhcULuLR8Ccsp6ywIivkHAs4ABqODGIoOYWf9zkIaWfcBg9dzxbkbsQDY7hMPTZQHNacJevOghdZT4UqAmlPABpuwosSq0xjZN7KculNIOU4BtSjbRFR4M25WPopEg4J51WzFHU17l9ssyXPVbH+Y2a4S6QTOLZ7Ddy99F+Or44aUoxchVwgdwQ70R/oxXDeMg00H8ez4s7LPEJAykmIr5nACcTeYPCpqKaBgtUCVh42VXDXQoaYUsBjffCMXn/XSehydTRn43RSLb0hYChAAvJRmtPxawBnQFX0AxUSDIvMujVKgeGCWmrppF/B82R+58IMvn/UUTqF/hv0RnCQLBqyE20BpfVzXFUVH3RY5l7SJlU6lgIrPwFCmJYQg5o0h5o3hgd4HcGn5Ek7NncKFpQtIZBL0BRqMtfQa3ph5A2/MvIG9DXvxUN9DuKf7HnAlIR5L27feTRwBwBJn0a/KgCoCRxXMo5UiGjT6ZERKJhplpJi7j1rTeLnx1Uj3AeFpv1rLM2r3AQ2WAkZCaCmgWP8i95WIBmlPy2yzAK6wGHZ4775In+i3NWJjU0kYKavRGzqGMCCEFNwDzi6cxdjqmC0Uqi7GhZ5wD/Y17MPP7vxZPDHyBAD6dYJYO7HKfUAOlWbOr8R7M4RRfRio1yVNT2joaw01pYBtQLCWpFQKyJgVyS/USheNd3ffrUPby2/mKe0+oASPw4OByEDueZ4XKBbUD048n18cb8k4NlWPpdmOwpXfznwOl/lm3OP8Dn6YvQ4pSC/MG4NuHOutL7km3Kyqkk9NLW1aYYiNYwTqF2aEEDT7mxF2h3Gs9RgGogP461d/iMsbL1saxeCd2Xfwzuw7+I+v/EcMh46iibkJbe798LpK27fehYjX6cOA75iuPMSg6IdswCTbUefF+MI6AKCvwa87Py1QSzQom06mTozecIjlJ8ftIcR6Zr3wN0tYQ5UWakISKieCZmsatadJhb8N3jzTLNzUWApIbRy1wkyuBD2wi8WCUVBqL22BNhxqOiSp2C53S8ttQOy4KbBb3efB8zzOL57H8xPP44lLT9jCPcBBHOgKdWEgOoCOYAccjANhdxjuIhPb1dQqptamFPMSW0NboeySJXOUOvwzWR6a61JjNQFRXLqzhFWvFNDxbfLfWq/rWVeoqxAesmJujhagphSoQsibHEmY1gt+f2LoE4h4IpqVAvkNeEEpoGG+/ejgRwsmtHrma6lBdWp5A5Mz9Shu5hwY/EX2g/jn9CBW3dLv7mAI7tnVDFZwLK9nkOZVDoQMIchKaAXorHiLLTm2NkIMYbC/YT9uiPTgKP85TCbfwcX1n2B04yQyvDUxhFNcCu8sPQ/geXiZMO7uugeudR4xb6wgvx4cb70DLywXbSgqOP9rVWjkn2sNe8EQAoaQUqLGCkKtpYDU+ORknOgIdYjeoy1HDaTGBppvwoPHamq18DvgErGsEvx0s+7CArot0Cabv5GWAgwYZKGN4EvNgouqHWgkCaV5BxrrMwDY37AfR1qO4OTVk+Xl5C0FNJw2yf1WC9u4Dyg8L+wrRvTRu7ruwmvTr6E/0o/T86cRT8ep5Xmo/yEApQq7YkiGJLTBCXcZjJyHNvPS2i4JCOLpOM4vnseZxTNY3Fg0UDhtYAhTcA3oCfWUuVzxPF92mn528SxoIMuTU6H1gZKCXK8S2qyQs2p4aIRgGZZ6HDcKLGGRgbJSoNHXiJnETOF3cf3tje3FVHwKK8kV7OzciT/AH5giq9W4JpUCPM/j4sWLOHXqFMbHx7GysgKfz4e6ujrs27cPe/bsActWmOBDjeaJqJ+YhZuqiCei6vmy/DYFkRtCdsd2F+JjC+Fm3Qi4AoXfnEC+0pyFLHzKC/3VjTTGF3MnCRGswo8NTGAr5OAiH5Rt/DcPNKDO7yq7roewUZ5ToHwhzTIEWU5MG1s+Z3nZMNazyzJli7N2M4RFu+cA2j0HkOY2MLbxKi6uP4+J5FsqORCMwzq3jO+M5GLz1nvqMVQ3hKMtR9Ed7tacp1UnMrSm33JgGILWiNcokTRB6H+pFhF3BMN1w+gIdshuho3WwIuNkzx46lP1tdRa4e/i8JpSuKf7Hjw99jRYwuLWjltlla4utnx8EQNtiLZK7HmK26rRJ68MYRSZy4X3pfpOR6gjR0Yncl+qjdly00iBSoZGBIwZSweiAxiI5qwE359/nzr/e3vuVUxXVUSDJlqy0CKZTeLZ8Wfx1fe/ijdn3rRFPbX4WzAYHURvuBceh0cy3UB0QBNhK4E9og8A8v0vH3rTaisXWispvXwgkuXrHONoy5Rz13CyTnyg9wMAgPfee0+XPHbGNaMUWF1dxeOPP47HHnsMzzzzDGZnZyXTRqNRfPazn8UXvvAFtLS0VEQ+NU1eym9OLg+pIUXr4MfzpR1VOJHc0HoDvA4vTkFcKVCuzS9FgG2AamxmspHO4PzM2qb1AY8/dv4tbmLexe+lP4tvczdCfFu9hYHGAHa3hajkLrpDKSD94C6mgCBkU4aiWxFnG+oc3bi0fkJWIqWJxcl40Oc7jj7fcaxl5nBi6S+xmBlFglugltlozG/M48XJF/Hy1Mu4ue1mfLD/g7il/RbqDVUewimt9Je6PuBgCTLZXF1GfS6MzSdkJ0mrJ3QjoPaEWFgffqcfB5sOqsrDCOixFABQYikQdJYrBYT513vr8eldny78vrJ6RTJvQy0FdJzeGk00qMd9QAkZTn/0AcUwhiq5BsySoxhy7dXsDYyUQtko0Fpg/PTwTyseZohZUdo6JKGBdammHfA8j9n1Wfzhy3+I7498Hysp61wI84i4IxiMDmIgOoCQS3z9lUeTrwk+pw/7G/erJh8F7OE+oEQymP+eDuJAmtd2sq6qTYCXnEfE+CbEFLg05WlSCugc42jnx0pHe7AjrgmlwOrqKhobG7GxQWcavbi4iD/7sz/DV77yFfz1X/81PvzhD5ssIQAVJ2Ryg4nkIKMz/mh5dvymdQMRzX9XbBdGlkckny/3++PR77sFI+sn4GS8aHbtKiqLViYgneXx6uVFpLO5U+77yUncz74CAPhvri9hNeXF09x1knn43Q7csaNRsl6UBhcCgqaQG4QQXF0ubW9qT95ZkaLyioLi+uv13oS1jJiSq1RpU0JEpjDI+tgoGlwD2B3bjSyJYykzhndnziDJr8k+ZxY4nsOzV57Fs1eeRdgdxn3d9+FD/R/CzvqdVMQ1suGVVJ6M7GwJYWp5HSGvEx4ni+awB9ics8Nee4SZMxosYRVNBo3YnBjuPiDBKUBLNLiaLnUfMEwulLNfy6W1C6gsX7S6D1B8eyOiDxgVLtMu36XSbgxGvzftKaTfVcqnonTCKlQC2OEE3EwQkbWBEPF0HOcWz+HswlksJq13D/A6vBiIDGAwOoiYN0bdlh8efLjwdwop1eXmSRSLUXFLAcoiKsUrwPO8bgUuDdGnFTwJtHNtpYkd7Yhrogay2WyZQqC3txe33HILhoaGEIvFsLGxgXfffRff+MY3MDeXi+2+sLCARx55BI8++qjpigFVlgKyHVfqZEwbJJXrfL40Ipq/gzhkB4cyEz8eCDmasS/4sMQT0s8W5/EfnzyDhXhukgiROH7D+QTye/EXsrvwDHdgM3X5wMQQgiM99XA7pAcQGveBkNeJiM9ZrhSQOakQZ8mmtwYhogOt/KJJDgQMCAF6GwIAAgCakF7rQYJbwGJmFEuZcWR4a8iHlpPL+Mez/4h/PPuP6Av3oSXQgt5wL/xOaRI+hjZkAwX8bgf6G7dOjdsiXjQzAUxPO9ETEyxcZb5BNS1RqUzYDVhUVUIpANBvEIrdB8ROr7SSzzGEoT6VoKn7Si20tHxXNZwCSshzjBRlrrrs/DW1MbmN5hRQA6U61OPjq4W01gqoJcTM10dBAWjDAbcSm88Ml8HoyijOLJzB+Oq45coRB+NAT6gHg9FBtAfbdY9dWjdydrAUoLmv5/TaqL5tufuAxnk2j5qlAD2uCaVAHqFQCJ/97Gfxcz/3c9i7d69omj/7sz/Dr/7qr+LLX/4ygJxC4ed//udx/PhxxGIx0We0oniTKL6pE4eWiUTENV1zXlvPiockzGthlUhUiiFv2Uc3iT3xzhS+enIMrnqABYf7mZMIkhwZ0TLvwxfS/xK8zMKzs86HqE/eLJ2OcE28XnlwqkLmCEkOgdzmlgdfRqsgxklRnCYX3YF+MUCIiM8dIfCz9fCz9Whz7UdbYwLnF89jZGXEELNeLbi4fBEXly/ixMQJdAQ7MFQ3hO5Qd0U1vg6WwW2DTZgjIiaPhGYDao8TRzkwYHJialxP0rb7ShAN8qDrCxy4MqJBDQKIQsrfXTQLlQoZMyHnMkbzjJZ0naFOTKxOwOPw4FgrZQQRgdK6GDTWXjTXDSMKpKgfxbrW0TfVwmjeD1plixq3lBLui2uQUyDvHnBm4QwuLF2wPHoAAUF7oB0D0QH0hnvLCAP1IK9gVeIjEcojNw5UQkFQUE5KtYHNyxWzFChEFBMTxTiFqBZ+Il3RB0BP2FhTClwjSgGHw4Hf+q3fwr/5N/8GdXV1sml9Ph/+6q/+CvF4HF/96lcB5NwJvvSlL+H3fu/3DJVLaqOuBDuYLfIycuQ3ZFK+faImvTombAKCmZUN/MVzF5EfSW9g3kMzWQK/+fv/S/88rqIovCBfKkOd34WmkDKzuyQhZOGd5E6GpU2Wy99fXCmQL0Z4R22oRDozYLlJk0FXqAtdoS64GBdivhgev/g4Xrn6iio5jAIPHmOrYxhbHYOLdaE/0o+h6BCafE2bCqoi2WGCCawOAspqAEMYxdPIEk4BrSbaGhZAXoc0CaOuBRUPrGe3GM79jnJLFK3v6SAOQ90HtoWlgMQ7dIe6cVfXXarqrFC2qLWV+GLcjj7ntNBlKaDwfYT1YvaGidpSQ8wKJG9CX0S8WeAUsKNSwEhOAUIwm5jFj0Z/hOeuPGcL94CYN4bB6CD6I/2ylnx64WAcyGZVKAVIuVKg0nM17bfXS/JLOzbkDpsoFbgWWwqoBbVSQFDXWvgqqh3XhFIgEAjgT/7kT1Q985//83/G1772tcKE8t3vftcEpUBxR1XR6GWSSpvWiw8KmjkF+LwgueeLB518ByyWZSOdxempHJnNjuYQgq7SAVleQSJ908ESrKey+O67U0hlOIAA3eQqrmPOF9J8NXMbvstJnzC5HQx6Y37QDD3S7gPKULsoEXcfENcKiEteelqitnxaRYOLdeFD/R/Ch/o/hNHlUfyHl/8Dzi6cxXJqWVV5RiGVTeH0/Gmcnj+NiDuCoegQdkaOAFBmj9+ueLD3QTx+6XHNz6sNSai5HA2nkHd13SV5T9RSgKfrCxk+UzJu0izO1Cz0qC0FbKRY0mSBQym+XBtzs+IKW01KiuKNo8x9pet2+i66LP4scgcolE95CqnGUkPMUmC7IsNlcHnlMs4tnsOfv/nnqmPAGw2/04/B6CAGo4Oo88gfwhkFJ+NUZQ0hZikgR5RrBmjdmCp1es3z0pYCwsuyVhYKdadXyaEWSlYhxRDWtdV9yQpcE0oBLWhtbcWOHTtw+vRpAMDFixcNL6N4n67WX07urlJZaiBNKcCXuAgU5y+2cDw/vYZkOtfBLsyuoSkQFcinXsAdLUFcmFnD905NYXUjZ77uQAa3MW8V0lzkWvAfMj8r8nR+QAYGGoNgCzFr5b+DlFJA+JxYKvl3LL8n6j5Q0AkIF1LKnAKl7irGnT4W59UaaMV1TdfhYONBTCemcXbhLC4sXUCKU08GZASWkks4efUkXrn6Clrde9HvvQ0t7l3KDxoEKosMietGLmaFJF1qkbcUkAUp/lPbKazaDcqH+j+E1kCrqvxo3QeEpwRiizNav1AhGMJo6l9SqJSlQPHYbvRmi9Z0VS+01pWRJrRGPF+WV4X2vraxFFAgGhTCjpYgWts2z/OYWZ/B2YWzOL90HqmsNfNrHixhcWfXnWjxt8DNuitOJqdWWSlmUl5pokEaYkigssR8esdgs6zadIckpFRECNNleGvcYq1ETSkgg0Bgy4c0Ho8bnr9WS4F8BznYdBBvTL9RkoP0BkMiL6VyJR4Uzq+j8wk4GILGkGdrsC3Kei251bnWNjJlnVzOUkBqLve5WFyYXcPk0jryTTkDB77F3YT7mZOIkjX8TubnsAFpt4Cuej/87uJuoHWALju6L4Pq6AMiAyGzyapczikgZlUgLJ8++kAujY4wZ4Sg2d+MZn8zbmy7EZdXLuPswlnLiI548JhIvo2J5NtwEi9injaEgrvR7Gs2tdztEpIwHy9Z7tMZYimgcsHQ4JMPXSrFKcBR9EXhgsDIxZmDOKhPIej6amVOe7VYCuh1H9CTtyzRoMoFcKU3OmqgjxtI/lmzx2utC35qSwED3AfMGsfVvrvdogc0+ZowXDeMvkgffvnAL+P7I9+XDcNqFtRyFBTms+JrNrL8Abbahm5LAQJqhaFuokGKOnSQym47xcJPSkEoW81SoIYSXL58ufB3c7OxmwdHOFzyW4ulwNGWowg4A3j+yvMld6We0nKUoGYivTgbR2PIs8UpoGaQVSFaPt+3ryyXMfwDBEt8AP+cvRUNZAljvPgpIg+ChqAbTcFShYHSJC0Zx7Xob46XsBSQ2YgIN+AscYgy5ktxCoiXKDitNUkpUJKXSLYOxoH+SD/6I/1IpBO5Rc3iWSxsLFDlbzTS/Dqm1i/g2xcuIOwKYyW1gs/s+gxaAi2a87TbgkIIvfIxoLAUMABqy9ByysLxHNV4IyTPpFlY0MrPMqyxSoEKmYA7GfUEYbqVAhpeLT/WGRmS0GhLASOhi4hLLUO5weOAGeNK8TtxPIc0l9Z1mq6Hs0Ev8u4BVirVi+F3+vFAzwNgCIOoZ8vik8A6BbiWjaZs9IEKdG1a5WRFQxLqHKvNsmrTO/ZqjfSjhrxyu6CmFJDACy+8gJmZmcLvY8coWY9VoNhSQKv7QNAl9JMWz8fowXp0PoH6UHknSqQyeHtuBTF+Gl1N9CH4OJWm9YuJFM7PrOYzQztmMMHHCikzYDHF15c9l0e9343u+hyPgJxcZXJTDEbS78JDajfCEifCjlYsZyYRcMTgZoJgRcZONYR2YqclSvmUpjF+MvI5fdjfuB/7G/djR90OvHL1FXxv5HuWKQiWU8v46pmv4mtnvobrm6+H3+k3lB2ZbjFpn82FFGgmciMWq5XaoFBxCnDmWQqwhEWdpw5+px/xtLwVmp2iD2iyFKD8ptIKV2NPwfO8Farz1SgGQxhRBZBdFIlarQXNKp8Qgjs678DTY0/LP0cZfWB+Yx6PX3pct1LADMhxQNnNPaA33IuhuiG0BdrwUN9DZRw1VrZnteOSGvctwBylEO24qDeSkhqiQalPqCZCiNJ7WRF9gNpVr2xfUrMUqGET/+k//aeS3x/96EcNzf/ChQtYT3OYnV0GACxzy0hxiyAbyh1mPjqv+lRTL6fAgO82XFp/AVk+LUhR2onOXl2Fk3fh7fFlZBhp4pdy9wF6AVfiLlycXSu8UxO/hG+7fw+nuB78euZzWJN/HF4ni9uGGnE1e0FMMtlnWQr3Aal3URpgerw3YINbgYfJhbYT4y8ocAqU3aMwAVXbBnjjzXmL0R/px22dt+HXD/06TkycwGMXH8Oz488izQnbmPngwePk1ZMAgJ9M/AR94T4M1Q2hxd+ie0LaDhAzt1RKXwloseyhDc9JoxRQMkGV+v4sw4JlWDzQ+wBGlkdwNX4V46vjomnN8tPMQ81it2SBSmuSqtdSQC5vhe9vqqUA5Xvd2Xknfjj6Q01l0qKS40wlyhqqG8L5pfMYWxnTJEfxvWfHn9UtD0MYU04NhW1xLb2G84vncWbhDJaSS4aXpxZ7YntQ56lDX6RPkuwzDztZzihBLdHgLR234NWrryoqcNXKsPmH7H07cgroGQPsHH1AiFr0gRoAAF/72tfw+ONbWtD9+/fjoYceMrSMD33oQyAuL0KHPwwAcLeugnWfR3ZN+cQ0cTCB//S7OaVFKcuu+sMMxYF8c+HHEidoVoEbaQ4uR64DXpyNAyL8ZoSUd3LaZWk6y+F7p2eR5PaCcV8Fs9GEv3T9D8TICm5l38aj5D/gA7gfWQnTdwLgvt3NCLhXgARloUVQOs0iIOA48XrlIE9awhAWPnbLHE+caFBi0Ba9Lu0+QAO98cWVH8z942ScuLXjVtzacSuWk8t4cuRJPHbxMbwz9462fHUizaVxZvEMziyeQdAVxFB0CEPRIYTcIclnCCFo8jVhOjFdQUltBo3EqXqgxaecNhJHsVKAIYyhi978IiXmjSHmjeGFiReklQJVbilAi0oRDRbqk/JUTEoOGrk+MvgRuBiXbH5U76fQXM1UXFrFKaDH99joDaqZG94Ml8Hl5cs4s3gGV1av2MI9YCg6hN88/JvoDHXi70//fXkikeqw0sVCy9pGLiSh8HuH3WH87M6fxV+8/RfahRTKQDmu6eUUoC2HB737gJ7+UKloCsXQSupbcx+oAe+99x5+8Rd/sfDb4XDgy1/+MhjGBG1dSUMl1Mf58h1XwhxNU16lz9EOvIxCp2dElAI0lgIcz+Ps9OpmpIFGcKlG/KHjb3CA3Trx/xL3ELKQ7sg39sfQUefDSmZF9L6SHz3N4JJ/FyL4pBl+Q9XkJUY0WFjPlt1S+I5a3Ac0EA1qdYPJI+wO42PDH8PHhj+GS0uX8J2L38F3L34XM+szIjmYj9XUKl6bfg2vTb+GVn8rhuqG0BvuhYstX+jf2XUn/t/7/6/0IqHpN+Zv6IzYWOnZPFi1WJSyFKAxCywmGpRayGitV6EJpdKpp1L9GU2aJVWmk2jgFLDwBFHJrUpVXirY7/NwMa6KvL8uFwu1LjsVij6gpICSswKxu4UWz/N4d+5d/NPZf8KTl5+03D3AzbpxffP1CLvDaAu0gSEMusJdqvKwsp+rdY8lm/9Tld4kRZOkMpRU1lIgR2BNd+gkGSKWoo40WQpUKPqAEDX3gWscU1NTeOCBB7C2tmWA/sUvfhGHDh0ypbySZs5DbKenCrksxPPIbVSNH7TFfTbZzXviS1lRU2S56AObIcTOz6wiXhTF4CPsc/iUY8vv8KuZ2/GN7M1w4cei+Qw0BnCwMyIpd+66PEQO78uelHqVudQlhdwFZYkSDZJNja6wdPnFr/BL0E2IBBWLcyWC3kgvfu26X8OvHPgVvDz1Mr5z8Tt4ZuwZVfGIjcRkfBKT8Un8ZOInOf/KaM6/Mh+aM+wO486uO/Gj0R8VnrH74tRIqA15aQQUyxG5TaugEFoKiGdPhBdKf0rIp2ZhRMf/YXB9S3R9M6MPSD5P6TteDLlvnP+Wai0TlN6jN9yLeDpuibVQNY8zWvuIrCLN4P5g1MZsLb2GH17+If709T/FyPKIIXnqwYHGA/hg3wdxT/c9mFybLOFxkFNG2q29aVE6yylS7fR+lTxZp33vm9puwtfPfV30eaU8tG7Q9UBYh42+RswklA+aapYC1zAWFhZwzz33YHR0tHDtF3/xF/Ebv/EbppT37W9/G40dPXj8TO7Eeo47B3dgBPU+5Q7zseGPid+Q2/fz+kwziYoNYv6EWSo1Q8oHZLmQhAAwMh/HUmLL13wXGcEfOf628Pstrg//PvNpyfdvCrlx184mipME+fpQsoIAgJawRzENDcRDEub+pQlJWIwySwGTlAJm+J2zDIsb227EjW03YjW1ih9c/gG+c+E7eGv2LVWyGYUMl8G5xXM4t3gOAWcAQ9EhHG87jqgnWlavcozMSocbak4/GnwNONpyFI9ffFw5sUYofS+rzV7FwIhYuwjdB6QWv8VKAaMXZnIxsoWg4hTQET5UDYqVArTfuxImqWry1FpXitYYIvOarEIjH3LMgEVyNUcfkMpfqV709hktMmlBhstgZHkEZxfP2so9YKhuCL9z5Hck02kJw1gt4XdF3Qc0KB91ybCZpxnEfCXlKIQSzkON+0CjrxE/NfBTeGbsmRLuC7OiDwihyspD5Fvf2nEr/vnsPyvmW7MUuEaxsrKCe++9F++++27h2ic/+Un8xV8Y5z8kRH9/P1q6B/Dy/GUAQDY5B1dwEY1hr+KzDQ3ysbnFwGtiHMg9WZqHEGKn2XmlgHiHymsTx+YTuDC7it2tYdmJcmwhgdnVrdPhCFbxv13/FW6SUxLM80H8q9SvIgUnIOK3H/E68fDBdjhZgbuGmGyKxGXyypUH97XipZl3CyXomSLFOAW2Bq1yW4GytEWyatKkU6fTaI6r4bmgK4iPDH4EHxn8CC4vX8ZjFx/D45cex9X4VU0y6MVaeg2vz7yOn/7eT+NA4wEcbTmKZDapSMpUDBqrD+U8CEIuac4DI6DGfcAuJy2i/ZkXLF4pOqrRJpxlSgaZ6rLEUkACmkIS6pRNS1vKf1/RZ/OKVcE9vZs1rW0+6o6i3lOP+Y15hFwhrKTEXdvMguWcAhLlK1ql6OwzaqD22/I8j+nENM4unsWFxQtIcfaIHjBcN4zWQKu4W5WINaGSabtdYIT7QKUtBZTmFC1EgzFvTJdMal672d+MgegAXr36atHjyhlosTbTq2xSo4QvhpBs+FrANa8UWFtbw3333YdXX91q2B/5yEfwd3/3d+bwCBSh1I+eaIoQICQalOrVUnkrnv7p6Iv5tbawQyczHC7NxjE5PoEsx2NsPoG7djWL5vHulWVcWUyUvNbD7PNoJ3MAgCxP8MvpX8EUNsMPCiwiWMLil2/rRzLLIZUpUlJIvrc2pUD+sVjADRjk/i5ONJiPJyssXkwuaaUA3aTOABKKHSno5RRQg+5wN37l4K/glw/8Ml65+gr+99v/G2/NvFXiC15JvDnzJt6cebMkfNNw3bDkojoWcGEhnlssuhyVYxjWCrXWKLT3zIQU0WDxCQADRpaHBJA+rdHqy682HJYRadRAynrCTPcBTcowpegDInkWQhLqVVaIfHut7/DhgQ9jOj6NoCuIr575qvo8dHx/qxV4ZZuzzXpVIho0ki9CCbRtZS29hnML53B28awtogc0+5oxVDdEFT1AOEZLhdIErG8zeqFEHFsRHhBK/gtaKzU368btnbeXl0P5rXLrSvOthioZTQHIyUhrbVFuwVyzFLimkEgk8MADD+DFF18sXPvgBz+Ir371q2BZ8/1eSg6r1JhdS3ZE6U5dvo1UD9WmZJuLSrHHRmbjGPTnbiwm0qJ5X5pdw4/PzsBZX3r9b7L3w4ks/q3jn/CfMh/HS9yuorssuFQ9GNc8+EwQdw32oivmx7np1ZI8tLsPyIckLP6MRKepgGz0gbJb8nILBze6jQYdNIf4MmjiZQiDoy1HEU/Fsa9hHy4uXcTZxbOYik8Zkr9aZPkszi+dx/ml83hh4gX0R/rRH+lH1BMtSVfnd6E14kGLy4uYK6irTKXvWemTHbssGqXeu8SqgcK8UutCRqoe5JivafMoSVOhk1FTow9UqI0atSiV2sxqed7FutAR6sBaSimgrkReNju5VQMp2f0ukdBFxc/J9AujNx5yZaWzaYysjODcwjlcWbPePSDqjqIn3IOhuiFE3BHq57TxDtkDg9FB1fO90I3IKiWBkjUGzXcYiA7gto7bdI/Par85TZheIbS44untU5pDEtY4Ba4drK+v48EHH8Tzzz9fuHbffffh0UcfhdOp3kRSC0qIuUCom73kQlcuAwmiQWXTwa2UorLInFBPLSfgRhIhj0h9CjVyAoXc+EIC3zt1VZLm5i+zH8RL3E68zfeV3c2s7ANxrOD6zm4MNoeo45JvSS4NVoFwzMjFiKhVAsnLoW9BSgfjT+7Mhot1YUf9Duyo34GV5ArOLp7F2cWzWE2tKj9sApaSS4XoBU2+JgxFh9Af6Yfb4QYhBF31fhxpacDJKXUklEJU8mSjmiA1vpVYClD0WaOjD6jxZ6UpolILeC2cAnpFM9pf3iyGetGTaw1+2WJQjD6hx1JA0VpQi5WZduTfZTg6jDen38RKagX7G/aXp6ugD7jYCeLk2iTOLp7F2MoYNrIbhpanFg7iwFDdEH71ul9FOpvGu3PvKj8kgBprwkorDOSsFgBgR/0O/GTiJ9Qnu2Jk15V+p3x5Uhv5gjwUYjkZp3Q+lP2Vh3T0ASloiciiZfzQM4aq5Y8oRs1S4BpBMpnEhz70ITzzzDOFa3feeSe++c1vwuUSjylsBkrJ9ZRPq7ZSSp1+beYjVpYKuUQy1fzo2atr2NseFrlbKme2qNNPLa/j8XcmkS1UEI9WMocMWMzwW6etb/P9EiWzGIx14kh3Q0EO4ZgizTCtPyRhcW56KlDOUiCnaFlXKH3reTNDElaSU0AhwxKE3CEcbj6MQ02H0BPqx1+//gRGNl5ChrdmATedmMZ0YhonJk+gO9SNobohdAQ7LJHFDJQsKm2iP5Dqr2one+o4x5Qvrir6AEWehp+MSlhPaAonRVknZpJ5mVGOGZsKK6yurD4RliqfZVh8fPjjWE4uo85TR/VcgbzN4HfK96/59XmcWzyH84vnEc/EDS1DC5p9zRiuG0ZfpK9ANnti4oS2zCjXSPJZGGslcbj5MFr8Lfjx+I9lFfsMYXB75+0lkX/kQCCiFKi0NV3eTUYp9CaVVacxlsB63QfMIsWlbVct/haksinMb8yXXKedt4TyZ7mapcC2RyqVwsMPP4wf/vCHhWu33XYbHnvsMXg8xrDGUyGxSMWALQZNpqZSWSv0YWWJ5BZduYX38npaJk0OeQXA7GoS33lrEunsVslhsoYH2ZfhQBY/yB7CaQWJemJ+3DncWBh0eV7sPbSe8Kl/jiEsOA1mSKzI2JnXE4S8NF13S1YjNJ6E6OOYKM+vMpMwIQR7G/bheLQHR7mfx8X1F3A68S0spSsfOgzImaRdXL6Ii8sX4XP4MLY6hpArVFj4aiOFrIClgJJVkRyngEUmtWZbCmitdjUbShr5KrXA1ZKv3rapa3MiZmyVtxQQvEu+/Wqeh0UNu+jnddE8bQAjzMq9Di/WMzkldr2nXiH1FhyMA/Ve+vR5GNn+4+k4ziycwTuz75RtNqxAPuLNYN2gKvcAJQi/s9y4U6m5+3Dz4Vx5Bm+MCUi5+0DR85WcT5VO+CtFNKvFUkCNG5zUMzQoW1sIijnQeAAD0QFE3BF88/w3y2QKu8Nl16jKtWFEJbNxTSkFMpkMPv7xj+OJJ54oXDt+/Dgef/xxeL3KrP9GQ2gpQLvhKum4RX/KPW9W05brWnl5OFHBhOZ4PBbjKXzrzQkkiwgBg0jgw8wL8CJHyHYP+xpm8RBmEYUY+hsD+OwN3ZhZLY1lT2spoLTKV4pXXjKxbP7Z7j6IhcxlrGVmZfMuL0vcUoBHrg0MNgVwblraB/WGvhjOJ3J/a1nYVcpMtBLIv4uT8aLPexwu9yra6oFzi+cwvjKOqwlrohckMgl8f+T7AIAGbwOG64axt2GvprzsZN5vl82NVJ0UKwX0LGS0vqfRlgKVMIXXnJeF7VKOaLASZVUKZroPGIEbWm/AqblTSHNp3Nl1p3z5lOKYKXeBJ2DxnC3CCDqIA72RXgxFh9AWaBN3i9HZ/uwcTpDOslGFUkCl+4CZVkxK7gN2mUvFoNUs3ww55CIvtAXa0BXqwujKKI62HK2gZNWHa0YpkM1m8alPfQrf+ta3CtduuOEGfO9734PfL09oYxa0cgpoK0v8uvKAo0eq3MJbyBeQK7cU4wsJfPPNCaynt07UncjgL5z/Fe8xK8huPvGj7EFJhUC934W/+7nr8erIAlCiFOBFFBNS7gPy9SFm0i+XHwD42Chirl68vfpNZPlSqwnJ7CTKKrbqrQ+4cdDjwMM97fjGG1cKaeoDLnTV+7CjJYTzF3PXtPmFCidN8dag2X2ggmGjxO6FXCEcajqE3z7821hNr+Kxi4/hsYuPIc2VW7ZUArPrs5idmMWJyRM4t3gOTb4m+Bw+Q+KYGwFF32MbatWlXa22ZKU5uaB2H6Bs06qIBg1eENOApkx6nhY62aTK1LXhFbNkM2jcodlU6P2uWmVR9azK+tVSVsAVwMODD6t+TlEWCUsMLaeRxTwBI8sjls0DxWjxt2Aomose4GLlXVv1cmWIcQrYSdGsBLVk3WXKweKztgpy9BhB3moWeapieg0uVJosBdRYW0nwyNzfc3/h76WNJdUyXCu4JpQCPM/j53/+5/FP//RPhWtHjx7Fk08+iUAgYKFcxb80WgqU5ijZKTmtRIOKMok9X3otq2ApsJRI4Te/8Q7WksWh5Hh80flXuIl9D+8hF3/9JDeEs7y4D7aLZfBHH96DtogXrwgGEE7FXkUxxJVGFlNA4tRKRisgZikglM/tYOF1lcrEEgK3gy1JK2YpMNQcxNmr0n56dtZQq4WwmoWRPw42HcTBpoOIeqIYWR7B2YWzuLJ2BVaA4zk8NfoUgBxxYl+4DwPRAbT4W6RPrCuxiFEcK0qVnCX3LFIYGMUpoJW9WApGn7DYefFuJaeAaDk2cq0wKg+7WwqoUdiqeheBplrLxrjAE7B0HvG09TwBAWcAQ3VDGIoOlZk9y8HD6nN/NWKMttLaQG27kdvQlrVJE/uIk8gTm+u1FKM2ldfw7WhcqIQwxH1ABpJ8YRVyw6h2XBNKgRdeeAF/93d/V3JtbGwMBw4cUJXPc889h7a2NsPkEm5M9HIK5PJTf5JtFiLODlxJvglOZFeef4elRArfeGNCoBAAfsPxKB5mXwCQm/PP8O14mdspWg5LCB7c14LumC+XXlCc2Hii3X1AjTZa8FuEvEutpQBDUBZRvUwkkv9HfqK4bagRQY8DHA+8MboomqqsHLG6tGAhq7osIj3hF8PJODEYHcRgdBBrqTWcWzyHM4tnsJxcroSYZUhlU3h/4X28v/A+/A4/+qP9GIgMIOaNUb/TtQypeikONUR1usHQuQ/QLibt5D5g+kKeUjQ9iznJPMVcsAxyH6DZzNKEu9QDr8MrWbZZsMIqRfJZCUsBpTxXkiuFsLGLG2JzX2VB4x4gxKd3fRpnFs7g5NRJEBDc1HaTLhmM2HiZBaryVIjEgNHkD28kqIkGK2QpZgSnAA00RR8weBCVk+HWjlvx7PizAIA7Ou8wtNxqwDWhFMhmy0neJicnVeeTThtrTlZq0q4iJKGMRlNqbOClLAUUTYKVhJG2PnAQNwZ8t6MuGMfM6ktlqRYTKXzjjSuIJ0u/z8fYH+NfO75d+D3ONeIp7hB4iZe7e1cT2qO+wuZfOLeJWipoNFVVCkkoX5/qYrqKug9QbRDo0npdLI4PNGApkRJVCpQ/L6EV0IhKug8wGk6uA64ADjYdxIHGA5hOTOPs4llcWLyAFJfSLasWxDNxvD37Nt6efRsRdwQDkQEMRAcKp0lmmyqrcR+wi8ZdOnyrOvcBoy0F1NQP1cLLHtUtCiuJBk11HzDhvWjcXe7tuRdPjjwJQgju7r4795yJ7gNGKI3MshSQVCSJXE+kE7iwdAEXli5gOmENyawQ+xv2I+KOULkHCOFiXDjYeBAxbww+hw8Nvly0JSPHArVt3EoXMrWcAn6nv+ya2N9q8xaDx+HBRqY06hEt0SAVZJKqCkmo8j3F+q5SHnrGc00yqRwbd9TtgJNxgoCgL1Ie8ny745pQCtgVXIm5LehDEkotdGWe0Tuvq5l8i+Fn69Hm7YSDvIEMv+XnH09m8OP3yhUCAHCBa8UiH0CUrOEs147HuGPISpzufHBfK3piucFd6h3FFjVySxQ5OCQX6MqLHnHSK2mIEw1KpydERUzsonykH7HxTkMl9OwDCCFo9jej2d+MG1tvxOjKKM4snMH46rhli6Cl5BJenX4Vr06/ikZfIw43Hca+hn2WyJKHHYmqpMbKyyuXFdMUw2i3DVWWAlZwClDkR23ZRllHRiqS/A5pniCajTcNaCI+mHH62BvuxceGPgaGMIh6orrz07sRoHrGTJcNmc+WzCYxsjyC84vnMbE2YQvek6g7io8OfRQf7PsgGnwN+D+n/o/mvAgh6Ap1GSKXXr9tM2E40SByjPRD0SGcXTyLA40HDA/rWgyx8d5IokEzZVdTrmmcAkJOLJMOQAhyPBoD0QHNeVQ7rgmlwK233mrLBWupnQCjyVKgJD9ebsFTnI7H5bk4Ql4nxSkBpVAyKA4vCADxVAZnxhaRSLaLpn+dH8LDqX+PLzq/jF9N/RKSeAtiM/8X7h5EY8iDicX1UpkF6cTfQdsgKkmGJer/L/gtdjIks8sXJxoUW3iKlys3OKqxOBDmaxQqaq4nLEpju3YwDgxGB9EX6UM8Hce5xXM4u3AWi0nrTFBnEjN4YuQJfP/y99Hqb8VAdAA94R64WbfuvBnC4J7uewBQnCgKQqyW3LNo/JUyFU9lt6w9aNqh1CZe6VRCKm9VRINUfdX8vvRQ/0OantPNKaDy1TpDnWj0NeYeFVWsVpaUSxUBGmVaYZg+peecjFOaOK+Kdb9iJ5UbmQ2cnj+Nk1dPYnRl1JBwvHrhYBzoDefcA+7qugu3dd4GAIUwjZpg8Hez4xo5D6oxUCXRIADc3nk77ugy30RczmJJiUhYr0JEDaeAbksBGgW7SdFfpFBzrVSHa0IpYFeUD8LaB+WtvKQVBnlMLm3gW3MTIAS4aafxDLvCTpjluII/+upGGmenV5HOlJLiNGEB04giL/8lvhUfTf0eAAIxo7rjAzH80m39ePT1LTK4/KZEWK9ZUU4DOtmFYCV8i6Vyy33TvMa3/Fk5jgJWQtEgpdHf8q80ZhNBDGZc1yKDqvxo5ZA/XKKG3+nHgcYD2N+wH0dajuAHl3+AJy49gdW0NHmjmeB4DlfWruDK2hU8f+V5dAY70Rfp03yS9GDvg2jyN1GbtdrhFE4IPVYAatOogdlEg7pN2wX5fWL4E2Wn0nb83gFnAA/0PCCvEDXr1FrtgtpAywKl5+SUAqrL1CCimpM9LUqULJ/FxOoEXpx8EV985YtIZBLqhTQBLf4WDNcNozfcWxhHi/u+GRsWrXmKKU/URgQxa0wwvJ4UDk6MJhoUmz+23Gz1WwoYVj8qs9FiKaCJU0BBYSXrUq1ChpoCoaYUsBQlRINgDIg+IN2ni7MeW0ggFsqV/+rlRcBHV65WZDfnmqVECudn1srCA/aQKTzq+n38IHsYv5v5LLjC5ln8bfa2h3H3zqZcyJyi6wVOAUH68nCE0nkrDQpShGPieQkviJ1ayZUlck12QKXfGNjB57uSMpg52BNCsKN+Bw41H8Inhj6B//X2/8LZxbMYWxmzbOOU5bMYWRnByMoIWMLi4tJFOBknukJdcLLybMd5sAxbohAw48TTbBi1oDI7+oAczHYfECVqE+SnRylCLZukoQD9uzkYhyIBp1FjAZX7gMlEg4VylJQCrBPIiN9TetbnNHeBoPV7ZLgMRldGcXYhF0JwI7uh/FAFEHaFC2S1IXdINm0lQ0kqwc7uAzQwgovCLMjVl5NRiD6gkzWf2lJAC6eAiDJaSV6zQxIKUdvoq0NNKWAhhAfYmogGNzuggJ2gHBIah2SaEz2J35KJXkNXfLUYWY7D7GoK52ZXi8TIpWnBPP6v608QIyv4pONp+MgGfi39eUEeWzLEgm7c2tNQOLGnGTDFVQLStgJykCIapIEop4CM/AwhOcsAvviaSL6kVKNMBNe1QzjgAx42iI2sNSfhSlAz+Bu9Ts+X7XQ40RfpQ1+kD4l0AolMAk+PPY2FjQWDS6RHls/i2SvPAsidSnSGOtEf6UdnsFNWQaCWAMyO5qdGhSGSWsgomX7S5qfbfcCGi/Q8qDkFKnRqKvUt1bZf4QmfEb75mi0FKNwHtD67t2EvTs+fRjwdx+Hmw6bIV5JWpg4yXAavXH0FP7z8Qzw99jSWkkua5DEaYXcY7YF2DEYH0eRrsrTNb0eYwSmgtzw1ELUUUIg+oCW8pl5UJPqAhvcxPPpArd9JoqYUsAgrG2mQRJFfK9FpKaDwrCQJH12RoqfcmzcUyuXxo/dncGZuFcLxI4Zl/IPrj9FO5grXnsvuk8yzPuBCb70/ZyGQN/8qKSv/R+lzYiERJaGo5dSj2RebGOSfYQgpiZ7AyJIJilSKZMqtRNK5lWbEEgcCTEOZUsCshaypqMD+1ef04ab2mxDzxjCbmMXZxbM4v3je0hOtDJ/BpeVLuLR8CQ7Gge5QN/rCfegMdZYtTow2SbcCRm2opSwFWvwtCDgDWEuvoTXQSi2XmsUUTVoriAYrBT2ymEn+R6PYqVQ90rgPaIWbdePjwx9HPB1HnacOGU7C5EAGepRedlUEMIRBV6gLn9/3edzReQe+/O6XVeehq23bqI/mYaliWEV1VHrtIUc0aIQpuxHubVq+XRk/AM3a0wT3Abn8q9HC0UrUlAIW4funriK60UaVlud5XJyNYymRQmedT3SwULIUkOpSin1Nyxi/KQLH8Xju3CzemViGs640SRDr+KrrD9HHTBWu/V760/g2Jx5rtz7gRl/MX+i0+RPz4j6c5XmcubqC6ZXSTdfUstgmTNtArDT4ljxPSq+ptRQgJEc2WMKJQLXhF5GlLG+awY/I/tQL6xY14rGtaSH3rNjpOgFBg68BDb4GHGs5hrHVMZxdOJsjwYJ1JFgZLlMI0VVQEET60BksVxDQwI6WAkZtqKX6C0MYfGTwI5iKT6Ez2Ektl3CRqNf80xL2aepAJ/pOTY02sTaqrsqsRIgBi0qNjyuVK9efab6Pm3XrIi5Vw/FAQGyrCACAZl8zBusG0Rfug8fhwa0dt1K7ZJXBhClQaxsU+752VDxIwVBLAZVWcorlSbkWaXy2LI3utZ5yPhIPqH7eCEsB3d+jitp1pVFTCtgEBNInwGvJDGZXc+H8Ls7GJZQC+bjM4tBqKaBNJ0CQznL4/qmrGJmLF67m4UUSv+b4OgbTE4Vr/yX9CP4+e49ofvUBN/oa/CXvXTCtKnrhZ8/O6OZlMJJoUJiTGHGfkuWB8LZYeiklgF4NdJlfsQRrrFnkWKrzs4BcTKmcMmsLhkVPuAc94R6sZ9ZxfvE8rqxdwejKaEXkk4JQQdAR7EDYHcaDfQ8i5Mr5xW5XLTrNJlGOU8Dn9KmOZ6yKoEml5Y8Z0LUxN3Dxq75wymsaQOM+oFbZI0ngpoJoSwyySgGV9W6WVUqWy+LK2hW8OfMm/v1L/95WioA6Tx16w70YjA4i7A5bLQ4A4/vLjrodeGP6DSQyCeyO7ZYv24S5W66NG+UGloci47/B7ye2djKyDCPaghZOAbF5TO/Bml7oUejUlAU1pYBtIGZansd6KitMXA5e7ibASWzvzYjWs57K4utvX8HMpiKjGB4k8TD7E7Rx84Vr/y3zU/if2Q+L5jXcHERMoBAAJKpAlQZDqvObMKDJ+IbJEQ0SkLIIBAyBjKZGuhyRZIoQVzLYayKzAmoJmaTSex1e7G3Yi3+1/1/hsQuP4fzieZxfOo+19JpRompChstgZHkEX3zli/gvr/0XHGk+gts7b8daSl4uO7LRG2UpYHb0ATlYwSlg5Les5KmOESGxaC1ehBsLKxmtld7b6rFWqg7WUmt4a+YtvDz1MsZWx6TDJloAN+tGf6Qfg9FBXN98PcZWxwzJVy50qxxa/C2Yik8pJ9QIJ+vER4c+ipXUCpp8TaaVIwYGDLLISt43+gRarTuNGdEHqMs26fRdCCPcB4xW3uShqBQtJpc1SYZrBTWlgE1AQJT2eiVpxf4WTZwHL6UskIfSOCEsfy2Zxuujs0isBkTTZ+BAHJ7CU/8982H8t8zDoml3tARx544mvLUqtpmm3ABTyq10PQ+xMIElz8velyCbkaljoWWAvJkYiiMgUteNIkeBQtl2CUmoBnzJ3/o3QHpJgeq99aj31uNIyxEsJ5eR5bN4/NLj+uJYG4AMl8GJyRM4MXkCQM50tifcg95wbxmztlw9WuVaYDangFYIF4leh1cybaUWhWahWogG1cIMTgGzrK6MjA2ud0E9tz6HZ8efxdNjT+Pk1ElbKQJYwqIr1IXB6CA6g50FxY9cmzErgkDIFcJKagVAjuyxPdCOqZEtpYDWNYwcfE5fSbQJM1x6JPPTOUWoeW8l97hKEA0qjUNb1r+V2+SqthrSwDtU6egDNp4abYmaUsAukCEaLOtoIunylyTND6UsBXQv1rfKm13bwOW5BNLpmGTqDFg8nj2GjuyreC8zhP+a+QjEeu2+9jBuGWyQPn0hpf/qQU/ML+rmIAY10QeI4N+Qx4lVwWGr3Mk/IQAjMCUQS79VB6To/w04uRIqowgDIqFcqiYQ6FyAyDxrhO9he7AdP7vrZ7Ertgsnp07i3OI5XF65rIngy2hcTVzF1cRVvDT1Euo99QUFQZ2nzrCNv5IpqRoYZimgwm2IBkK5fA7psG807+B3+kt+q2mHYnVN48NZKUZou5KxlRFzqi2rgkOpJZwTRZiKT+G7l76Lp8eexlszb9nKqoggN+YORAbQE+4pCcOah1n1J7cJO9x8GDOJGfDgcbj5MK7Gr5oig5HQOm6r4mqSSqNiMaiWM0fvOCIafYAyT92cArSHQxr6JK3LqhZ5iqGKaFCPEr36l7e6UVMK2AQ5SwHxhi/sZ1le/YeT5BSQ6WsTi+tYXk8rJuR5HmOLCVwVJfQDOsg0NhDHMnJa9wxY/I/sh5HK7IJYL7y+uw5He+tkBxg9UQAAgbWFiqwYNSHIBBnXBzwgTi+uLG6d/iq9h9C9QM4cndaXivbUk8ZlQ64cxfwraMJlus+1jg2NVBoH40BXqAtdoS6ks2mMrIwUOAg4M/x+VGJ+Yx7zG/N4bfo1BF1BDEYH0R3qxqHmQ6ILa1oomZKqAsVn18spoAXC/JRiwQsXRQxhcLTlKGbXZzEcHcZictFQ+YyElVYMYmOMYUSDFGSRqi0FtFpdVdB9gOadOJ7DTGIGoyujGF0ZxV+8/ReGlW8UDjQeQF+kD36HX7H/mdWE5erSyTpxvP24ZForTaANJ+JTeN7od9UTjUMLtBAN5sf8irkPGMApQCNLxS0FalCFmlLAJiBgqE8vOZH9AL9lKiAKtUoBnucxtpDY+g2+5ATPyXgAAPFkBqevrmBtQ/wUc4BcwT+4/hj/yPrwf/hbkEDuuSwcosIeH4jhYGdUXKgibFkKaB0MichfygOakvuAXAmMSN5y2RHkog8opd+SmW6ijnqU61fseULo26jdoc9QQB8hkhK8znJTcifrxGB0sPDfU6NP4Y3pN3A1YY/To9XUKl6ffh3/YvpfwOfw4YbWG5Dls+gMdsLn9KnmYTCqnRllNp3lDVJSbELYToQn/XJpAeBjQx8r6cdLs0uGyQYY6+5B3SektY7ayxYbcw3a4dEqitRsQKrBfUAKqWwK46vjOUXA6ig2MtaFXpXCUHQI9/Xch/t67kNroBXPjD2DMwtnFJ+zg3uOJTLQFqlTNCMUdUa6DxgNLUSDhTlTYs1Xwk1hBMkyr999wKxn9ORhh75bTagpBWwCAkYmXnwpODmWVokOIPWI1HmjWPpe7024tP4CAKDHcwyX5+L4wZkr4ILiCoHd5BL+3vVF1JE11JMsfop5AV/L3o6siAKEALhjRyN2tapj9tWuEiDFP7b+VDy5127mRghDFU2gWC6hPLIhDDfLVhovG32N8gmKBSj7VdkTJ6PyK9uUGuzjLu3mQveON7bdiBMTJ0AIwS3tt8imDblCONx8GF2hLqykVnBh8QLGV8cxGZ9ULbcZSGQS+NHYjwq/G32N2FG3A/sb92MoOqRYJx3BDowsjxgiCxXxHMWCNMsZqxQQbijlwr2JtWuhYs9sokEzFlYxbwxz63MAgKArqMvaRgpmcgqUEQ2Kr97hYT3YWb8Tp+dPK6fXiEoyfhe3teXkcsEaYDI+aQsLJiFCrhD2N+zHrx/6ddVRQvKwwn1A+E3rvfV0eVZgEyRlKaD1RFfRUoDmtNxA94FKcArQfiZRxabAxc4qqxFVUXRUpBFClfuASRwf1wpqSgGbgIBIcwoI2mm2JGy9cDCWUAog17GEHUaqs4ldDTtasdN/HzgOeONyEq+NToKwPMrPQQhuYd7G/3L+dwTIxmY5wJvozykEBHI6GIL7djejt0GcnNAcFJ/jE9HrYlATklCYpzjZjDrLBLloBWWlSuTd4G0o+S2nYiot29iFkZXmj5UyeKCdJPfE9qDeUw+Pw4OYV5qTA8jVW/5bhFwhHGw6iJ/b/XMIuUP409f+FBeXLtrKnHwmMYOZxAyeu/IcmnxNuLn9ZtzcfjOub75e1Gz3xrYbMbc+h2Q2iVQ2patsoxYpRvM5lC2mVIauK0tjQV+iXahJyb+vYR8uLl1EPBPHbR234Z3Zd4wUT1oeibpSu6GhcR/I45b2W+BiXHhr9i1NsilBrzKbFqlsCm/NvIUXJ1/E6MqorcIGFiPkCqEr1IWB6AAavY1oDbRqVggACsr+Cm0kfE4fbum4BReXLmJvbG9FyqSFEVw6lZTBSeStd4z+pkpm9r3hXlxavlRyX45o0Gj3jXx5qt0HVEYfIJv/0yIbLfTUTU0pUFMK2AZqBkWOEyGG0rjLkXMfEMP6hhs/PD2NqU3+ALFUtzNv4i+c34KD5E4N0jyLb2Rvxmmm/ITa52LxwX2taAp5NMlvxFq4NA/5DB1qlAKkNEexAVEpJGE5p4DIAElK/1Aa2Bp8pUoBh4QQYoNrJU+3VOdnAx9LrX6feYLBYkj1QY7nRMvpi/ThcPNhHG4+jIWNBVxYuoCLSxdttXCfTkzj0XOP4tFzj8LBOHCg8QBuaL0BN7beiKG6ITCEQcgVwqd2fAoA8Den/kaXYsAwSwGN7gNSixlVIQlNIm/Si55wDy6vXFZMJyWbi3Xh/t77FdPpMhUVPbw3pq6UYp0Xl0UI0U9MSFGOFLQqBXiex+jKKE5MnsCLky/i1auvWh4RRQo+hw97G/biX+77l1jcWMS5xXOFe1LvT032ZkH/EitzV/0u7KrfZakMgHGKtTwULTANDjPnZCvLKaCkFLi5/WZwPCc6nkryEYgcDopB1abYiOgDclnYZM9dC0kojZpSwDZgCgNqPJlBluMR9Dg2zYRKU2ZFlAI04PnyTbSUK4LwKs9zePfKMp4/P4uMTPnHmPfwLxxvw5HJKQTivBufT/8q3uPXQJAoSklQ53Phof2tCHm1D9B6SO7yJlildgL6Flcl8VLFni3b5CuUV8YpQDH4E/m0AWepRUbY60RL2FNQ9BTnKJq/MFUVhCQUbrCrmRpBzJRd2C7rPHW4vvl6HG7KKQj8Tj++ef6bWE4tV0pMRWS4DF69+ipevfoq/vsb/x11njocaz2GG1tvxLHWY4oWEzSg8aWm2dz1R/p1y1IMo5UCRlvxCPuLmAyD0UFcjV/F+wvvy+alRjFmNEx1HxBaCli54lUoWk3drqZW8crUKwVFwMTahE7hzEPAGUB3qBt9kT60+FvQHmzHdU3X4cdjPy5Jp/ebV9MmwgpZ9ZZZ6egYiu4DBvZlSYVUUZ35nD7c1X0XvvzOlwvX5IgG7UI6qbZcBowmWdW4JdmlbqoVNaWATUCQ0/ytrKfx3mQuNm1/YwANQTeEM76cUkBuMOPAgwgelVQKFF1OZzn84PRVjM2KNJeiEHWNZBGHmbOFMmb4CD6b+rd4j++GEydKHov53fjQcDs8Tn2s3kZbCigrBbQUSArPlisK5OUqdx+QNS2gQpkvLCF4+Lp2zKwm8c+vjhdlJxxcDZ64DR6rVU3kBmsFik8Exa7LPquyTWX5bNlCQ27hUe+tx2d2fQZu1o35jXlcWLqAybVJTCemVZVrNhY2FvDEpSfwxKUnAADDdcMIOANo8beg2d+siRzKiA31bR23UZNz0kJMWRF2h7Gc1Ka0sWJDyjIsbuu8TVEpIAXaU0VdnAIaWL9poTb6gJkM2nqU2Rkug/fn38eLky/ixckX8fbs24YTaxqJBm8D7uy6E/d034Mz82dKFJ35b0A7PtLCEkuBKtrMmB59gGYeVSGDg1Ru68MSlm5jr+NEX57LSoellQKE85iSC4LWNm00x43RB1zbCTWlgE2QYxMFzk2vFq5dmFlDQ9ANVjCAZUUIRrYuyTRqXswCQCopD4DHzFoS4/MJrC8mAIRk32GGj+JZbj+O4WWc59rwmdS/xQTypupFyoOgG9e3NEgqBPZ3RtAQcOOp09KblrzcerrwFoOrmkFTenFBiJIZV/n7Kg1CbqdwcSNebr6Erf+XlkWMNdvJMmiLlLLei008NJMbLaxyHzBzgVeJxWOWz1J/m5I0hCDmjSHmjeF423GwDIs/OfknuLR8CSupFTNF1oRiVnCWsGj2N6Mt0Ia2QBsafA1U7O80bUJpgbijfoeysCohtkm5r+c+fOfCd8rNs7XsJVXpx8y1m7GyXxo5Xgmh1x1AzQJU6RspKgWK5i2e5zG/MY+JtQlMrE3g/57+v1hLr1HLYgWKFQEHGg8U+k+xi0Axygl6lU9r5aCX82O7oWz+KaxDq8N9oJJEg7SuK3raqF2IBnmel517jHZBFS1Dw8FMDVuoKQVsAkJy0QfS2fJBlREsfuUtBaSuE3A8UGYqILHYWIyncXpqFasbaWmhAbRgAfNFv9/hevG/MzG8nLoHKyg1UycE6Kr3oynoLgu1V4xEMovG1i027h7vDRhZfxEA0OG5TlYedcif7hZfUTc5ddR5Mb6QW8Q/sKcFhGzVl9DPXJxoUF46n4veRJX2VJr+1MRcMyyjB2u5TeJ2inOb5bLUCwopEEKws34njrUew9GWo1jYWMDI8gguLV/C/Ma8cgYVRpbPFjYxQG5R1+JvgdfhxfXN12O4bljUDYCmjdG4D2iFmhOJOk8dPr3r00ikE/j7039fuE5jOmm4+4CB/YXab1sqgofB44RZ7gNiqNRiXa4cBgwm1iZwau4UJtYmMLk2iY2s/cIFCtEWaMPtnbfjjs47ShQBxdDbtmhRafN2wB6bmUrJYISlgBpUklNAsu0Q4c/SC7Khj3VYGchBbz9RVF5qtRRQE32Ach2s9dntjppSwCYgYCQbvkPAlCqmFFCKPgDkXAUYXr7RZ7IcXhtdxKuXFxS5C36KeR6/4/5b/AJzI97jugvXn+auQ1agEHCyDAaaQwh58u8iLcdGunTTE3G0o893Ezg+i4ijoyStnjFsa9NeclEegippDnngdbL44FAremP+UvNfQV5aXA+8TsFpFJEeIGkmCj1mc4QQ8CJ5msWYrRZqFm6Gqwg2X0XqBEX+UXX1IGYpoGfRmncxqPfW41DzIawkVzCyMoK11BrenXvXlgqVDJfB+Oo4/uz1PwMABJ1BXNd8HQ43HcbBpoMYqhuCk3HSKQUo481rgdq6YwhTtmClycPoxUwZp4CO/K1caJlpKUATQcKKTRXP81hMLmIqPoXJtUlMrk0ikUnIPG0f1HnqcLTlKD67+7NU4UsllW6C62b2cbNg5w2K0ZvSSkcfUFoHGc4pIGbhqXDoIsspYNJpuGr3AZWKOq1y6rGUsnM/siNqSgGbIO8+IAZG6D4gu1mX32z73eWfPD/4nJ9Zw0/Oz0lYB2yV6UIav+34Kj7r+AFWCMHtzJtY4IOY4sXj6DYE3BjujAJsnEJKYFdbqPT0nhCEHW0SqfV0+NKTfJr8hBwMDCGoD7jRtxlOUY5okBCmTIkha5ZISJmlgJhiQapEsbxVEZwJw83ItFEtsPYEsDIbXTMmpCyv31JADiF3CPsa9uGTOz6JNJfGj8d/jKfHnsbLky/b1td4Nb2KZ8efxbPjzwIAvA4v9sb2ojfSi9XUKpp8TXCxLtFntXAVmAnht7TCUsBI6F3w64o7rYJTQK3ps273AYPGhlQ2hZHlEbw58yam4lO4Gr+KZDZpSN5mg4CgJ9yDJl8TesI9CLvD2N+4H8N1w3TPS2yOypSmFISjSnLWoAyzog/QQM03UmsdpkcxLqWQUpJXVUhCO7kPyECrnE5GhWUHRRG1/iwNe62GrmkQyX0KlaWA4phF8P7UKloj5aH/ppY38MKFOUwuKYca6iTT+J/O/4G9zEguVx5IwgmmSPji0+TdrSHcMtiA8xunsV6ynyjvlF4Xi1sGGzDYGMSKgttCYcBU2bcDbgc66rx4f2qLu6FYFLnBIux1gkNpvHI1m3xGw0BU5j5AM+DJpFH2pZPma2AIAUeh8bYK9KdB5RE99KKSdZDls2XvyhBtrL5KiHljeGTwETwy+AgePfsoTkyewMjyCMZWx5DhMsoZWIT1zDpOXj2Jk1dPAsh9n3pvPVr8LQXiQr/TD8B+p4hCeagWpPbogqKgNvGu0EuY5T4g9p3M6JPLyWW8Pfs23ph+A2/OvIlTc6eQ4rSH7aw0HIwDR1qO4I7OO3Bbx214b+49vDf/3tZ9A0jgaDdNtG3OEqWbDfo0bb3pVvwpvKyd/eqVwBCG6rRfCjSHO0ZxXujlRaE50ReT1ck4keak1/x3dN5BL5PwHezZLGyLmlLAJiAl2+pStAf68Ca5ggyfQtjRUkI0WJaPQgeYXNryJVyIp/CT0RlMpsdlntjC/czL+KLzywiRLeXBGb4DX8scwyp8JWmdLMHtw40YbpYiJywXdE9bGDtacukdLN0krLa/E7I1kG2dKhTnJzEJEuD+PS2Y25gUXKc5kcilEZsclIIJeCksBbbSK5uUKRL6gEDKFcVokhjDOQpssCgwy9evGFkuCwcrdCsxvhxhnh6HB4PRQQxGB5HhMphcm8ToyigWNhYwGZ+UyMUe4MFjbn0Oc+tzeHfuXQC5cGaNvkYsJhexnl5Hg7eh4vGrxVDmW0qhwdJ7EioEB/oQUJpRAWOdSroPqAmbRYssl8Xcxhym4lN4dvxZnJo7JRrL3O6IuCM43nYcN3fcjBtbb0TQFZRMa0S4TiPdqwCFNmP9tFMCK5T0esu0s6WTXtASDQoh5z6gNi9aaAkxWAxFSwEJOUOukCiX0T3d98DFuNARErgN63xfuxxk2RE1pYBNQIqPaAVg4cSA73asZWcRdXRIcAoUclIsayOdxQ/eu4qzV1dB3OtwSM/Pm+Vz+FXH1/B5PF9y/f9m7sQfch8Bj5Ml18NeJx7c14moX9xcV0rK4vHIIcfAV/KM+s69tbEmRf8vJxnwC8d74Xc7kCZOtIQ9mI+nytj6hfKUuw/Imf6XgxDA5yrnFJAuU7kulE5FCUGhMRm9sDIbak58jd6PqCGUE3lYFUQ5BVQSZNJA7jTIwTjQGerEZ3d/FoPRQVxYuoDnrjyH58afw9uzb9uSh0CItfQa1pbXcGn5EoDc+0Y9UTT6GtHka0KjrxF1nrqKyyX8VjQbdDsoxPTCDPInVe4DKtus0Ooqy6lzrSn7zjyHsZUxnFs8h+n4NGbWZzC3PmeKsqESGIgO4Jb2W3BL+y3YE9sjabItrAc17jy0nAK6lQIW9C87b1y0+rTf030P/E4/vnn+m7L5lZVHszG26RioN/KF2KurOXwws17ELAWUXGLFZPU5fRiuH8aJidLQ5X2RPvUyVeBgZjujphSwEeSWJF42DC8bBiDBKUARkjCeymBqeQML8SSSs6ubqZUXQlkwGOAWkN93rPBe/Hb6F/AEdxRgkije+jeFPDja3ISot1QhUN4xxZj4t9I4FSwFtJp/M2RLkpwihm7QcDly8vDg0R3zozvmF01XkhcpvcaALXc3UChb6D6QykgvELdUA9J5KroPyPzKCW/fAVZu4SenwTaUab0C4XD8Tr/oorfSk98dnXdgqG4IQG4DMBAdwOf2fA6LG4t4YeIF/M27f4Px1fGqMW3mwWNhYwELGwuFcIgsYXFi4gSG64exo24HhuuGMRAdgNdRrhA0UTBF2HnhU8mQhJU8VRMqIZU278XjTJbL4uzCWby/8D7OLJzB+/Pv49ziOduHBpQDS1i0BdrQFepCV6gLv3n9b2rKRx3vjT5LASNCEm5nGB3dIegKotHXWFYGjQWjogw2HQO1zs2ynAIaCI1pYDYvilR6J+vEvoZ9mF+fLwlFrAV6DmKu1X5ejJpSwEaQWvvxgogBWY4Hx/EYXUggxac3n80PIMJneVyeT+DN2SUspZZVSkTApcPgki34vdRBHHH/W1zmm/HL6V/BGN9UktLlYNAb8yPsdVGZ/oubtpOiv1Hi3y6djzoQUnzaTgrXtq4oKCN0hFwRvSdrlUjgdpTKk8yUn0ZtvY5ybag7Makujauq6ANGcwoIXFKK4WJdSGXVb4zF2pqDceDG1hvx8tTLJdfNsOJQ0rh7HOX8JAAQ9UTxYN+DGF8dR5bLYio+han4FOY35jGyPGK4nGYiy2dxav4UTs2fKlxjCIPuUDeG63KKgqG6IQxEB1DvqTdlUUFzUqynXNExTXBJF9mfXlNPg8s2qq+UuQ+AEy1vJbWCC4sX8MzYM3hz5k3Mrc9hYWMBf/XuXxkih5XwO/24rvE6PDz4MN6dfVeT+01ZpAAVJHC01iXVSDRol6g+omVI1Idask5A+vRYmEarTFZDK9GgXDo17VtVvaisQqmICXLppTgF9MAqpch2RE0pYCdI9CfhZY7n8fKleZwcWcAGNw93XflGMZ7M4PTUCt6bXMHyehrOSAq0/D3NZAG3Mm/hqfX7MLVyGAAwBeCTqf8PZ/kOpFDUgXmgPuBCd70fDkZu4i07Ii9PUbw5JwQOhiCdVRpk5N9FTAqmsIFT9xwARXNi0QUnkb6nJINwsEvKWAqUbeJFKofKfaCQm9hptLKMVsGIxf6RliM4OXUSDsaBgDOApeSSvgwJcE/XPfjupe9KKpRoJ6JPDH8CXocXHodH9NuYDS0TJsuwaA+2Y1dsFz6545OYWpvCickTeHHyRbw8+TJW06vKmdgMHM/h0vIlXFq+hO+NfK9wPeKOoC/Sh75wH/oifeiP9BvSN1oCLYpp7GwyWUlLAZqyjeorwrzjqTjGVsZwdvEsFjYWsLixiG+c+wZm1mcMKc8OcLNuHGo+BJ7n0RnsRMQdwfUt1+Nw82HdJ3x5GEH8WTY+yq5NlGF317lqhhT5XDE6Q52KCmW7rEOEkNoI67FSKbtm0KvrHYO1hg50MdKuxmpBMxfaaX60G2pKARtBqjsJw+BlsjxOjiwAALI8MDqfQEvEA47nMb64jhNTkxiZjwtOQ6U6wVYiD1K4kTmFPcxlAMDPsk/hP+MmcJtayHf53pInvU4WxwebsO5TICUQgdiJvJBGgGFI7gXl8tGi2ZSwFJAavAvPIWf+dkPrDXhx8kXRdMUaW9pNtbSwuX96G/y4NBsHIcDutjCujEolL7d8EEI5+gABJMzW1KtSKgt1i0nxdnWw8SBa/a3wOX14YeIFaqVAoe5FJqSOUAceGXoECxsL+NHoj1TIWIqoJ1qccWk5CqcthphmG+Aa0RJowUcGP4KPDH4EGS6DU3OnckqCiRdxav5U1fpPA8BScgmvT7+O16dfL7nuYT2o89Qh4okg4o4g7Arj0vIldAQ6JE9WH+p/CK9Pv472QDti3phi2UZvWgx1qaFtJ1ImnTYaczJcBlPxKYytjGFsdSw3RmwsYSG5gHg6rpxBFaI/0o8bW2/EDW034Lqm6+Bm3fjSW18yLH/huKJmHJfiPTE6JKEaGeyeryoZKqDQYwij+H2GokOYWJ3A4sYijrcf1yVDd6hbrYi6oDkkoUFEg2ZGH1Ati8Q6xciwwHboN9WMmlLARpAyvSFC94GidNksh7HlOM5Or2B0PoHk8hy4pLrPSsBjBzOKm5hT8GHLzLmXTOEI8z5e4naVPbOrNYSb+mNg2TTeFbhBah3EyhcH0vlshc1TNwAQUmwpIP6vuLxb2N+4H6fmTmEltSKfUHBRTCVAM+fePtyIxuAKmsMehDzlmwihO8TWL2NPycwgAjMScgsY4SZHysqNEFI4mdXyXlIyxLwx3SZyxRAuosxc9BoBsbHNwTiwv3E/9jfuxy/t/yUsJ5fx0tRLeGnyJZyYOIHpxLQFkhqPjewGJuOTJREavn/5+2AIgxZ/CzqDnegMdaIr1IXOYCfag+1o8bfgg30fpC7D7D6oi+yP8lldZJ0q8lQaA9NcGkvJJaymVrGcXC789/2R7+PK2hVbh+I0AhF3BEdbjuKG1htwQ+sNaPI3KT+kA2XuAyZYCpgdMs9OqISsUj7tWpSJhChzCjgYB+7uvlt13kLsqt+FQ82HdOejBpqjD8hxChjcvo2CVksB1etSFa8rTvBt77WslagpBaoAxWdnPM/j8lwCr48uYmwhgcm1q2BCRZtTXl2j3kEu4wD7HFrJQsn1OT6Eb2duxzmBQiDic+KO4Ua0R3MhCMUXSNqUAsJweyxNBAK1lgIij0htqmXzkRiE5TZnDGHLT3hB8MH9rfjR6WmkMhwyRSSS+SKCHieO9dUXrksqjyjkVvLXlONXYGxONEi7mDSaPIm2LK2LA9FnLIgMYbaJetgdxr3d9+Le7nvB8zzGV8fx52/+OS4sXcDE2gTWM+vKmVQROJ7DxNoEJtYm8NLUS2X3w+4wWvwtaPG3YH59Hn6nH0FXEAFXALOJWdR56gr92S6LQlFYKJqYz+tKcgVXVq9gMj6Jd2bfwVp6DaupVWxkNvA/3/yfoqGxtjMCzgAONR3C9S3X4/rm6zEQHaioubwpnAI6rA/EYIX7QDVtUPQqDQ2ZaynyuKXjFt3lqAVLWKpDMDUwi9BYTKYddTuon6fiFDC7XVdPt7ElakoBG0GsO62nsjizuIpTo0lMryRxdXkD6+ktDgHi4KnOCDPxfjjDObPW7EYbAODj7DP4jOvv8AzxFdKlwOJlbife4vqQ4VsL1x0MwaGuKK7rilIRCQpRPhBQuA+YsNDNaaXz+apRAtDnL3wm/+4sU85CSwjQ1xBA3y0BXFlM4NHXrlDLJFJ60f+LD/CKnAKy1hLGbWrNgJV+nzSLUyPrSfiuDGEMnwzN8lukLbsz1In9jfvRE+4Bz/NYTC7CyTjx4uSLmFibQDKbrJxAFiB/Qi3mq/3N898EQxjUeeoQ88YQdAaxklqBz+mDz+FDhsvAwThQ76lHyB1C2BXWRAJnBCRJyQQzntZFcpbPIplJIplNIpFO4OnRpzG3PofZ9VlMrE3g3bl3kUgnkMgksJ5Zx1++85eaytkucDAOtPhb0BZoQ1ugDf/u6L8z1HxXLwyJPiC4rlcha2ulmwUw8qSaxlKAKh8DJyivw4uwO4zl5DL8Tj+i7qh2uQgR5RVRqrPC+CiSrMxSUKb+1HwbYR0ebj6MfQ37qJ/Xailg5hrSaIXMdod9ZoJrDFmOw0Y6i3SWw3oqi0Qqi+n0BpKZ3PX1dBbJNIeTI/NIL4fAp5X9SnOQWIClo8is7gFh1pHd6AAAPJW9Do/w/1hIc55vxXPZfVhDabit4eYgbuirR1DEdF2sODEJWsJeXCgyRhAngBJO5KKvoliWbHpStFkv+B/m85LZEFNqZuXyUKtMoX03OX92IVQRDQreuTcWxDvxnGVIxGfNBkMOakIS0hg5Gj1Rmam0UAzpZLDVQ6WwxZNBUOepw73d9+Jf7P0XWEuvYSW5glenX8VrV1/Dm7NvYjm5XHH5rATHc5hbn8Pc+lzZvROTJ/Dld79cci2/0A27wrl/3WGEXCFcWLoAB+OAk3GW/sfm/nUwDlxeuQy/ww8n6yxLy/MKsak3vyHHc8jy2dy/XBZLG0sYX8mFrExzaYwsjeDK6hWkuXTuv2zu30Q6AR48EpkE4qk4lpJLWE5tmfULw/j93em/M6B2tw+8Di/2NuzFdU3X4UjzEZyYOFFyGm+5QkDQdBy0jMiQnpeNDtlmNl+LaL5VuHHRFH0AxpweS5mJa3VpeKjvIYwsj6Ar3KXrW7CERcAZwCxmy2SjksXCTe2BxgOqxgcevOx7SR1eGNk/dVmtVGGfMxo1pYBF+OYbk1gfj5Rcc8W0xCima8S9ZAr/Mvs4fpA+hKf5HgDAPML4RvY4HPxp/ITbiwm+VPEQ9blw+1AHmsPiocdUSSliNq+URs59ID/Mq+3EOVO1rV+lsqixHKA5GS6FU8Qs0tgxSDkzRaJBmV+xoAvXdYXATXvQFqlgnHZKGOGLqgUlE5KM8ojW1DUPuQWWFdEHRISghlbiOjFrhYgngognAgSBnbGd+PSuT4PjOYwsj+CNmTfw5vSbeGPmDUysTWgqc7tiPbOO9cw6rsavqn720XOPyt4vbo9/+XbuJF7pm9Nu3sVcK2qQhtfhRYu/BTvrd+JTOz6FwbrBEj4TYThTq6FnLKNVzls1N1iBioQk1Gi+LsXxYZrSmoDuBEAEAVcAexr26JIJyNXVjrodZdETqIkGKerGLtaaBvLTaoZd+RaqBTWlgEW48vTfw3v4YXh7DoKo8KFTBx77yEX8guMJ3M+8AobwGOTG8XTqIPIr+ke5W0CyzSVPeV0sOqI+9Ibq0exTUgiIdThlzaZ49AHB4oDCVECTpYBAGUBjKSAEFbGbUMkhGpKQ3jqBGjLvo3yiXJxN+WKtrzGAuazfGDkNBv1ikkgSDQqSqUalTpSE7yqX93DdsCFl2mbhIQKGMLlwgJE+PDL4CABgOj6NE5Mn8PVzX8dUfArz6/OGsurXsIVavVqH7lA32gPtcLJOtPhbEHKFQAhBV6gLu2LlJMF2Q5n/vwHrIVpLAbvMXdUOPUSDgHKoZyoZbDo/sYRFd7gbbYG2EkW1Uttr9DXm0tGspzW042Otx/DSZKnCVS9XgaL7gE36W41oUBo1pYBFWBs9hbXRU2DDTQhd9yCCh+SYpukbKg8CLzbwQfYlfIp9qhBeMI/9zCUcY04XIgpkwRYaQcTnRFvEi4DbAYCO/EW8E4ltRoXXlNPIRR8o5KKyDxPIuSXotxQQzzWX1sEypiyehe4QclDHKSBQCoCxdbxmWfcBMzctlE1Bbd3ta9iHqfgUAKDFXxqrXkxhI7zW6GtEf6QfO+t3GvL+VkzoesgNm/xNuLPrzsLJeCqbwkxipvDfdGIaiUzCUHlrqMFMhFwh7Intwe7YbuyJ7cGehj2o89Th5amX8cb0GyVpq3WBqyokIWVEHL3RWeww9tkJRiu4d9fvxvvz71OHAKaFVvcBI5Gf9xu8DYrWa/d234ufTPwETf4m9IRzFr000Qe0cAp0BDvwEoy1wlIkGoS4q4jqKGJmHKbVAKCmFLAMP7vXgcfPZbC4PI3FZ/4aySun0fKZA2Cc+j7Jh5gX8PvuJxAi5YvdC1wrvpT5IF7lhoqu8vC5WLSEvTjWV4+JxXUkUtmyZ/WiXCWg7D5AQzSoejAhW4PGlsVA/p45SoE8WIYtM68qzkY4nkqVID3JCdwhRDJQwykglr3Y5GOXxYvcxOh1eAGIhJCUAe17FS84tVgKSF3vDnfjaMtRLCeXy8Io0UQf6An3YH/jfgC5DbFauFm37P2KhL7SoRQASr+Ni3WhPdiO9mA7gNwCJp6Ooyfcg8cuPpZTFqzPbPuQczVUBxjCIOaN4c7OO7GnYQ/2xPagM9ipKsRWNcBI9wEpfh07K7ONhpXcL1rAEAZO1omPDn0Uf/XOX+kRwlC5jEK+7QldN8Vk6430ojfSW5qugv1db7485PllJAk/DdzI07xDTXEgjZpSwCL82xvd+IsPePF/3kzj13+4gcS5F3H1q9No+Zn7QRhhx5HeHraRWRTzmmbBlikEXuGG8H8y9+JJ7jCKYxV01fnQ39GIlGsaAEEqw2kiihFCtL+VmTtRRB+Q4xTQKGeO6bbwS3iXPh+agWdL3QAAcJZ9V/kS1VtBUFgKqAn3JGS43fyfXSGl8OiP9CPsDgNQF/eedoIsrlNZwjWVH5QhDA42HaTKS2yy1bIQvqPzDpxdPIuddTupFjG00Npf9ZoSK32PgCuA4+3HMbueI4HieA6LG4sFAr+59Tmsplaxml7VJUcNNcjBw3owGB3EcN0wphPTiHljqPfUg2VYfH7/5xWfr2aGbY7nSn4bQXxIG7JVjy+8VFn5a3rXUnb+fnqVtWLQ+90l+0CFDQW8Dm9J+Nx82xPOZZUKSajm2xihFFDK3whLATVQlbd9u1zFUFMKWAifk+CXrndhZwODe/9fAvFTF7F04m1Ejx+QfCaMNRxh3setzNu4jX0LHLuGe/AxpDY/5XPZfUgzj2IDLnwrexP+X/YOnOU7C887WYLBpiD2d0QQC7gxl9rA2EauJyQzHDiTBlCGYhIp9y0svd9R58P4Qk7hcaAzuvmMOjlI0TPCE3U1WWkZxBxiGxxDByHlOla2FNh6pkxlIhU6yCYDqbD9HG4+jP2N+8vYrI1ebFFbFBh4WiUWksiIhdpQ3RCG6oZE71lx6lJMkAao/3aqLQsIg3pvPeq99RjCEKKeKD4+9HFMrE3gzMIZvL/wPs4snMGZhTOYSZSHmaqhBiW4WTdi3hju6roLw3XD2FG3A12hrsKm4UtvfUl1nnbeQCpBqBSoZEhCveVIgSUsMvy1Y3Gkp/0ZNS9WerMphU/u+CT++t2/LvzOr7mEay9d0QeEc72G+hdTWunlKjDiUFELSkOBV+9YaAfUlAI2wG09Dvzp3R786+9vYOmFtxC5cV/BWiCIBK4nr+CI4yqOMO9jBzNe8uwsYdBBZnCRbwUArMCPz6W/gNe4QcSLQgt21/vQ1xDAYFMQLsfWIMwUDVTJdFag6dNqvq/sGiCWpjwkYenvu3c14eSlBUR8TvTG/JQSCuUo5koQmhqq0KhqGHgcrEj0AQ0aXiltbOEtZERT5hSQ+0XHM2EVhO/G83zZplINaN+1uNxKEQ2W5S0a4tNgqw5BEWragla/Tt2nRxra696GvXhn9h0whMHtHbeDEFJwO7iz685CuoWNBVxcuogLSxdK/jXaL7aG6oSTcWJH3Y4CAWY6m8bCxgL8Tj8IIVQWAHpgB9NpGmT5UndFPSEJpa5X2n3AiPJs/f0MFM2o97Tr2kTSfYBSXilLlJIyNFpw3tdzH74/8n0AwL0995puVVG6/i69bgfYus9VCDWlgE3wcwec+L0fb2BxYQWJs6Pw7+gBAPQwV/G7rhfQmhH38+d4gjqyUlAKAATPcfsAACGPAwc6o/jt+3Ps499/tzwUVcTRDgd5Cxk+CZ7zgOPVhkWkVQoIN5jK7gPCkIQhjxN37WxSLl4GpOiR8n83zfxZgnRW2QxKsSxBEp/DXTbmGjEECUMsbv0SsRRQMMd2sMUaVwHDvYSlgF0GUqFswsWmWdAzuRsFsUUBrbLCztDrwqBlcd4f6ceu+l1gGRYhV0gyXZ2nDnXNdTjcfLhwjed5zG/M4+LSRVxcuojXp1/Hu3PvYjm5XCM13KaIuCPoDHaiN9KLjcwGGMKgzlOHgDOAXzrwS4V0L06+iLdm3jJFhmrt34CxlgJCviClPKlPaynG7mJ2+eG6Ybwz+w5V3tsB+fpxs27E03FVz5pqKWADgsiC+4BFlgJyh0s94R58ZPAj4HkeTf4mXFq6RCWTFJSU/3r7oRbYReFQLagpBWwCn5Pgs/td+LOXU1h55b2CUmCWD4MI+tksH8JJbgeeyR7Aj5l+xLn3CveCHicGW6PobwygMehGa8SL4eYQLsyI+8QyxIFh/91Y5xbhdXrwfuppU96PxrRZyVKAJl/F9KQo3y0/AhT/0VXvx4UZeeXIsdZj+Ob5bwIAdsd2U8lY56lHvacRlzBSfFNWVnVQfkDJUuD24UY8+toVAMCBjghG08W5S7gP2ARC2XT7dFK2LStiYAsnX7FYz1oWCnKwYuOhVymgjvsjB4YwiHqiqsop5EUIYt4YYt4YjrQcwaHmQ/jR6I8AAOlsGnd23Ykra1cwvjqO0ZVRjK2MYXRlFPMb85rKq6EycLNuDEYH0RnqRFewCx2hDnQFu9AZ6tzkK8nhOxe+o8gwbgZE+3qVrIX1KAWk3pH2JJXWgolmHLmz6068N/ceWgItmFufo8pXtkwbb2ak6uPWjlvxrfPfkqzX/LjqZt1IZpMAUGDZ1y2TTeur4D5gYOhxI941vz7Khz7MZSwox+BBRIpTQEs+Wu7VoIyaUsBGuKvPgT97OYXswlLh2hwfxnm+DSey3XidG8Ar3PCmVUCu4XuccUT9LoQ9ToS9TuwOtSHkjBWez252fIcIyV0eLsYHF+NDil80hmhQ5JrQCkCMaFBT9AG1lgKk/GRdaClAUwPN/mbc23MvVpIr2FVfGgs6HwZHKJqTZXC8+V68PfEVpLj1krKNQJnlg0jlKG1g26M+fOhAG+LJDOrDGxi9sHWPIeJEg3YZhNVYChRbROiFXn82Tf6AIkoBIYxWVphBLqUEPe4fgL2UH07WiaG6IdHY8fF0HOOr45ham8JkfBJX41cxubb5b3zSkE1GDdLwsB5EPBHsrNuJeDoOlmERcAYQdAURcoXgdXjxr/b/K6vFlIRdxmAtECoFtIT7VboulachoVo3y/I7/bi+5XoA2Pb9VWouaPY34+HBh7GWWsOTl58sSfNTAz9VUPJ+oPcD+PH4jxFwBbCvYZ+hchV/U0v6RdlaN3dBK7cRVWhwDQTHYm1fN9GgDgJwo6CGhLHs2SoeR41CTSlgIwRduX+55NbxbBoOfDr1W+AzEQBA2OvEYMiNppAHzSEPdnUQPDt1DtlNhkDhZpvbvE6zCcpyKCEaNFpHqAShEkDoPmCMFKRQTlnuKgem3nCv+A0CgEfJ1ATkoikwxAEvE9lSChSVqVUfo4YTgUZb3bPJ1zC3ni65bndLgTJOAcGk94G9LfjuO1MgBBhsDG6lk6h4O1sKCCHq1mE0oaIFJzFl/sVqRdAgshXv6Xf6MVw3jOG6YdH7X3rzS1hOLWM1tYq19Br2N+7PRUdIzGFuYw6ziVlMxic1hZ7czvCwHrhYF3wOH3xOH/xOP27tuBUxbwxNviY8O/4sAs4AnKwTMW8MHx36KL5x7huYTpRGKlGyBrMadj0lpYEeNy+9Y7RQISFTUNXAapP5Rl9jmaXVgcYDaPY3F343+Zvw8eGPmyFIyamOHfqFlKWAng2oFQp6Keyq34X35nPWykdajmB8dVwyrRSnQA32QU0pYCOs5tdzbjfcDgYeJwuPk0VHXT0afU1oDHrgdZUOLG5nCgwBpKbV+kAu1ricpUAeWjal9ESDwkFMjFOgNI3PpbzZUr1HKHmAKZHNMMKbTW21sD4dDDGVx4VGQ6prA0vsMclKQfi+Wa60Vww0BfHTR5xgCMGjF/WdQBejeENeqclZqMggpNwsz+zwkZV4V92cAhR1UElSMq11xjIswu5wwVT953b/XMn9jcwG/vbU3yLDZbCeWUezvxldoS6spFawnFze+i+1jJXkSuHv1dQq1jPrBVNeu8LNugsb+6ArmKsLVxgRdwQXli7A4/DAzbrR6GvEA70PIOqOosHXAAKCr7z3lUI+DGHwi3t/sfD77dm3FcvuDfeW8EbYEVYyrwecAV3P67FOlJrzhH1Y8rSUsmy1dWkVC3uloLQOsGqTKrQUsEqGYpjBKUBjKahUjmj0AUoLm2Icaz2GkDuEsCuMZn8zrqxekUwrNR/rbS/Fz+tRmNh5fVsp1JQCNsJTF3MhbBo7YtjfsaVpHfKH4Wf9os84WGZzM71pKYAcUV6WA1wOBscHYpvpKEyQNHVMusWIcMMvTgpT+vu6rijeGl9CKsPhYFe0LH3uGXUyE0LgcbKbMpiDLTeEvPVG7jfL5GMX05qNiV+XXnCQkudE3QdU+LWJTW5iSgW7DKRCOTiUnwI1hTwqMlRfbqXqomxRIKZkK1YEGiCWHTgF1IJlWOyO7capuVPUz1SjCWHBRJVxIOgKoifcg5vbb6Z+Pstl8edv/jnSXDr3XzZd+DvDZXBbx23gwCGdzf0upNv8L8tlBeFMS/vEq1dfBQMGLMOi0deI3bHdcDEuOFknnIwTLsaF8dVxnFs8BxfrgpPJXXeyTnxm12dkOR6KQ/jFvDEcaDxQ+L2R2SitJw3f9t6ee1U/s91xpOUITk6dRJ2nDoPRQV15mWEpQEs0aPUGUg7VNA7ZZg1goxP0PPJrJiOjD5Sl0fCeou4DGr6ji3WVjLlyKD6As9zNowZR1JQCNkEizeNv38qZCvTfcV3JPbkO42SIwF+fYLg5hGN99XA5GDjZfDiU8jx2tobQHPLgmTP64m03htyYWZE/aaIJSShM43Gy+MwN3ZhfS6E96i1LL56vPAiA3pgfYa8TSBDEAq6iewax4BKScx8QjLlmuEPkytv8l8Z9QIWlQNlpC6rL9IvaNFQClXIf0DQhCtqW2KLXaEsBPd9e68mZ1oVUMW5uvxlHW47i2fFncWHpgmJ6O7rIKLI6C7612vpmGRYu1gUX6xK9f2/PvboUNMUb975IH+7pvqcszVszb4lGaNBTrqqwmUbw6Vg0PoqG+TJxoX1d03XYUbcDbtatm0BNzzgtqRSgtP6hLVuW2Eyk7q1UNlRig6W0+Rb+rlR95NdeUnJYgWKFbcl1HZYCRsAMTgGtMJRTQPgOKrK2Q3uxGvZb/Vyj+Ns301jaAAKNUbTs66d+zsEwIMWbTULgYAn8bkdBIQDkLAqE8DhZ7GgpDrkl7BAUGkpCEPQ4MNQclE3HlJklKbsPAIDf7UBnvQ+MxIZaSUJhloTkfPt/+kgnjvXG0N9YbvpoFGt9mVKAyLsPCAdp7QOUtCmVmg2s2GmLnUMSCqFXKUALOdM1s1DWVkT6jtmb20psgPRaCuThYl2i8optTs18L9PyFmRbqbavBVIbBMmY8zr6lBZzWNNg4r7IijHY5/QZwqiuq61KvLbRlgKW+OlXkQLeLrBDf5dac5WtvWgtESnX4WpB4z5gGnQWI/u+tW6jCzWlgA3wzEgGv/HDnJnj0P1HwJRt4GUsBQruA/mUpEQZkIeYpQBLiOh1tSAgqPOLnzAVpyo+7adxMTACUm4LHieLqL90o2AYpwDJKwUEJt4MUcXbYEZIQlUxoMU2mlU0ZFQsJGHRwthKBYnwe0md+gL2Npsthl5OAblnj7YcRU+4p5yfoQpXFWWWAgZ/X0PrxMKmV43flgbi7njV8a6GKrCI4N9NSIYkNMn335goTtq+XyXGdqXwt3aymLEaW+6jGjkFKN5JywGAWDup89apzkcITRGYQEr+NRLbdcw3C9Wzwt+GSKR5/M9XUrjv/yWQygLdR/dg+P6jqvLwuNiycH9iG32xawxBSYg+rZ2nfBAQyYfwaIt4C4oBcaWAEWXL51mcvFwGYwcPs87qyi0K8v+K1XvpTz1KAanoA3YddPX4qqqBkSQ5tJBa+HWHugEA9Z56NPmadJUhhB04BYyUYSA6AIYwqkib7AphX61GojNaU3A9edpx02AEqvm9KsIpIEG0bIT7gBisVLpmuEzFy7TrGsAKCLFVpXQAADj7SURBVPtigVNAEEnHSGWYWvcWAKLKWb/Tj+Ntx9HibzGVS0XKxN/IEKGVfna7oMYpYBH+44kkHj+XwdImD9Lem+7BgV+6DVmmPKSUVEN1ORj0xPxgzpXeFyMVZBkCQkpN2nPXCFhCkOF5LUfTm/Ipy0uQOylvCXtwZXEdYv77WhY2Abd8E/a6HEgXhdYrVhLIMRQzhIDTGnMV4pYCOUjnKUwuVRtKLgBytahKKSCyoK6mDZNuTgHK9ljyPRQeua7pOrw+/boOqXKQ2vTd030PZtdnUe+pt1VIQq2TvZMYFyVCCKl+aubCwKy8rfLh1QJJ9wEz6uYaWeNZGX1AL8zgFKAhYhVLZydo/X6WKAWUiKQpq3l/4368NfMWAEiGZ5WVQ7hmsUEfyLc9oaUArTKskkSDALCnYQ/2NOxRnZ8eGPmd7GK1Uq2onhX+NsP/fSenEOjp6cFv/N4f4Wf/3X+Dwy1l7iveqIebg/C52DL3AbHwg0TEVcC9ycLPsnnTnfJnjAJfdm5ujKVAxCe9aXA5GLjKFCRE9E/hBT1eFVvuA+X3Ij4lNwtlFLOKH2w6uFVuXv5i2SkI6aRAbZJq0zG3Un7Vaur0SMsR9EX6dJcpNaGzDItmfzOcrPGbaVtYChhJSJTvpxT8DHZHmaWA0e4DFtZJpU5+7LxBVET1NdkCdCkFKE9B7RR9QKiENDrcZZpLKydSgYHogOj14qgTh5oOGVLWoaZD2B3bjeG6YRxrPaY7P1twCjDinAJ6rFTKDgVkXtMOipE8lNwELJfVPlVlGWqWAhbh6A034nf/v9/BPffcg3MzcTx56qrqDuF1spsm3cVXiWT4QQfLIJ3d0k56nPn4qTrNnzWYpIsTo6mXI+yV3vw0hTxYT5dqY+XcB4p/MQwBOOMtBXpjfsSCLqwsAd314mEmt2QVr4/WQCvu77kf8Uwcw9HhIslFJg/BokdP9AGpa3ZFpaIPFNcJzTP5WPOFZ6pwE1opGEU0CEgTeQrbSTW1cSnY2X1ASjYj+oHSiaXli06TUM3vdV3TdXhq9CkAQG+kV9WzUt+X1iWItp+obZtq+t+hpkNo9DViObmMFyZe0FxmHkZbCtzReQfcrLssrOvx9uMIu8MIu8No8beU3NMqu4t1qQqlKoSixUIFQAhBvace8xvz8Dq8BTc+4doryxnn3qiF68kshZiWOjfUUuAaGfPNQk0pYBF++9/9O9x/X85nx+faHCxUtl2ngwHAlw2EYkSDQDmvgNuxqcFkpDeVNKCzMBAuyOmiDyjB42ThdbFYT5UPsD0xH85cXRWUW/y3sLwtmfSQHuYXIGJbUkIIDnfXoXFlVeQuPbrD3YW/s3nlhYihgJRsWqE3/F4lUSn3AbVKAUsWKgaUacVk62RMdB+QsBQwk0yzUgogzjRGE/NQkVBq21QBV83uA32RPqymVrGWXlN94iz1PctIfk0MSagXhBB0hbowsTZhSH5GKwUYwqDJ14RTKFUKuFm34VYOemEXDpEP9H0AF5YuoCvYVbAUEMpiZNuTe0+pe83+ZqryzUBZ6EiV30mWQ0Fwz+sQD2degzhqSgGLUByiz7upFBDbKOeui3cAF8sAyJZYCuTcByQsBcqUAnlfJ51KAarH+JK04qbpmopHxOssUwpEfU7sbY/g7NU1QRlE9O+cTFswICiDapZtYXJaEba+nvITeqMPVDoeth5UamNkxcLDkpNgHa+p9VRCaCmg570lNwpaO56dYXDzMLKPq20LuogGbXByWAlUs7KDIUyJG5wRoHUJMuu09FojGlRCpepD2L/rvfVYTi5XpOxi+J1+7GvYJ5vGSk6BB/sehJt1q3rGTBg5Lm9kN0p+twXaLJGjWlFTClgEtmjXWe93I+hxAPEccd5aUjCoSwwKzoJSoPS+GKcAALACCwKhUkBYCrVpXbmtgExa6TSalQI+J6aWtwaCoeYg7trZtBmuUVoyKbl5Xp+lwJZZcmUXBjRhXfRyClRTSEKOqxCnQFGd6AnHowZV7f+sAkKlQIY3bsFb6KcC5VE1tXEp2Ll9SMpmwnrMTos8M78JTeSZ7Qha9wEpCzftaxx5aPnWRrVVozkFqgnC+bfR2wi/w4/RlVHbWTXosRSQI8lWev7GthvREeygKtssmDkuzyZmS37XeejDLDb6Go0Wp+pQUwpYhOI+zDIEn7i+ExvvRsAzLF4ZWaDKw+XIZcIwpVtdKU4BIfJEg1sbYBNXESRvKZDfuBrjPgDkuAPen9oyx494nQUXCmGexfqS8gVF8eZOkyibz+YXJvqgXgZjLQVoOQXstPAuRqVCElrhPmCFpYAVpplCpYAeP0yp00NaU+Nqgp05BaRgBvmUGkuBfPuws0KlhlLQug/QRilQW44UtPQ/o8ZXO1oKWInj7cdxHMetFqMMlVqfCGH2es0Kq6Vipd/O+p2YTkwDALpCXdTyPNj7IFysfiLwakf1r36qFERwhO13O9AU8pZYEBTSSuThZJnNMHGl16WUApyAOM+zaSngKFgKlNkKSJRMK2FxTsrmfFqVArvbSonbgp4iP+QySwFp9wEjZCkuo1IL8y1RSck/YtBtKVBFGya9i3vaydOKSXBf45ZpYmeoUzG9GSRulYDwhM/IBa+UpUA1m2LnYWdOASsVFtvh24qhmty6jATtZl/qu1cqQk0xzG6DIVfI1Py1wCr3gUr092LiYDWRhagtBSjdOGlhh3HB6LCBxZv5/kg/dsd2Yyg6hDs676B6fqhuCB0ha60n7IKapYBFYEQ6pnRnlXMfKN3AEhA4JdwHsgKlgCP/vCEO9FsQJz1SnhS0iuFkGXzi+k48eWoKXheL/sZAUZ7S/gNyE4hZIQm15EOdXl9xVBmKWgrYdKFtRUhCKv8/mTZJizpPHe7tvhcz6zPYG9urPgMN0LOY0LoRLCNnMnCzW3ifbXgwbPTGuxJ9XNJSwKbji51gh4W+nUDb/q1wHzCrzDyOth7FyMoIUtlU2b2B6AAm1yYRT8dxY9uNmvK3M6wYKwYiA3CyTsTTcVzXdJ1s2hZ/C6biUwByJ9o0MJpE1Ow6MsJKRi2KQzA7WaeuCBbXOmpKAYsg1i/3NuzFj0Z/JJZaNA/RKANE2n1AqBTIQyokIb2dgHpLAdF8dAxWzWEPPnNjT3meMr/LyytSChigKFEb0VD/Qj4nc54rQi/EfKuraYGu16qBOvqAjMGVmfXVG+lVHcLLSFRqI9IR7MD46jhcrAs94fI+rhWFKCEWnBbWUA4rFQ/VjmqOPqAHUop92pNp6hNsAhxuPoxXr76qSj41MKr9+51+fHrnp/Hld79cds/DevDx4Y9jI7NRFhp3O8CKCDmEEBxoPECV9s6uO/HmzJuIeWNo8DZQ5192TY9SYBuMC8I6cTE1s3+jUFMKWASxbUR/pB/LyWW8dPHJkutSXdjFMhDzSpIKSZiRUgrojD6Qz7Wr3ofR+YREPtYcxwmNJojAqqLkXhFJUTWZZxFCcPNgDE+fbwLnOQ+P05iwgaIuHmKKAhtNMtc3X49Xrr4CABXTFjMSljnbDVZ95zs678Cl5UtoDbTqClEoZVIc8UT0iCdfpkWm8nZWdEhtxMzgFLhWUE3KWkMh8dq0Gy41/eRw82EM1Q3h/fn38fr06/KJDej2etp98clpSZ6EwM26bcU8byTs3g+CrqDqdYne8c8Iy0Q1sIJkU3fo4m1oLagVNaWARRDbRzCE2WRIfbL8pgicDoKsYE4jgCgvAQBkJdjYWZ2cAvmnWsKeHD9BvDyNVeRNQvcBaksBA9wHKonruupwsPMoXpzkMLE2gSMtR3TnWY2cAgebDiLgCsDr8Opm2KXmFNA7aVfiNMOAMvT4Aerp/z6nD7tjuzU/r4SYN4ad9TsxsjyC65uvN62cSmI7keUZ2T/k2mw1kjPmca0qTqTeu8HXgP2N+zG2MibLOk9NNLhZTsgVKiM/1ZOvbJkmrCO0thM1DO5iqOa+pQTTifvErICq6NCKRgYjOQVq0IeaUsAiqOsE0u4DSa58Y+uQVAqI515QIpQ9po5okBCChqAbcyJKAeuHoRzkCAQJZTolFEIDEnFeAWlyJH0ghBjqJyhFcEMIKZnk7aSdZwiD4bphQ/KinTztriipYRMyHezWjltxa8etFRPFbGznRbgclPqslQtiUxU1Iq91LSyU5eaeG1pvwA2tN8g+b6eQhHZGg68Be2J7cHnlsu1C+xXDiv5t+vpHJHs172mFS4VaqJVJSDh8LYx1lUJNKWAR1JxES3WY/Oa/LPiOxCDlYAlSmfLJyuiQhGLy8haxYZdZCpDie8LNXO43z+u0FNh8/4HGAM5NrwEA7tzRpPicUVwAZoMBg6yo48q1CbUTmh0n5RpqqBSkNmKSi2sDu4uwDCfjLMR17wp1GVdQhSE2pvRH+i2QpLLQu+HR4mZTqfHbjHL05GnX0H7FsNMBhanQ8ZosY4x7qRRkFW0GfZ78mJ2HXk6B7abE04Pq2IVsQxhlzsuDL9EKyOV63+7mwt/Fm1SxkIRRvxM7W0NwUWxUS/z0bTYoC8Up5lsoX1AUP6fDUmDz2Tq/C8PNQTy4rwW725TDBLVFvIgFc75+w81BzeWbhXx9XTMn45RNQDehYZUoCapFTjvBqvGwGjkFrMCDfQ8i5Aqhxd+iyBxuZ0TckZLfzf5mNPmVFdFGolgJMRQdqkiZesckavcBlf34WrXUkcJ2DkloifuAHr4Jvf73BkAvz4EwsgaNS08NdKjVpEVQx24v7wcZ9jmBBeVcemJ+fOhAG9JZDn0NW2H7xDgIXCyDnpgfdxzsw8hcHI+/PSkjg7K8xZYCu1pD6HT5MLW8jnTW3MlCaClQfBpfPrAWcwroVwoQQhD1u9DfSLfBJ4TgY4c6sJRIIRawLxGQ3RQ/VkOtUsCK+jOizNp3Vw+rNgd22njTwgqiwWZ/Mz6181Om5V8pRD1R3NV1F07PnwYhBLe031JxGW5quwlZPmdBdqz1WEXK1DsmUceKrylEqwLb8TvpVQoI09pxA63XfaC2NjEO9msd1wgMiHhXQMDtQEedF8vraXxof5tkOkIIemL+clnElAIOBjx4sAxBf2MAjxxqx7npVbw9vlyer0p5Q14nHt7fjpcuzuPlS/Mqn1YHoWwllg9CZWXRwCIRwIGyTO0f1+Vg0BjyaC+8AmCJfvOzaogjS000WIUTkpY2Wu0LrmrcKGuFnd9V0n2gAu3LqPC5dsRAdAAD0QHLyvc5fbiv5z7Lyq8UaMZ7KxjYKQvZ1igjw63EmGLy/C/F7aQVZlsKUIUf1+n2I7QU0ItqHfPNwDViB2w/qLEUkOsw+cbcHvVhV2sY7XXlm34liLkP+N2Oko7SHvVhX3tEUkLpX1uSCjFUZCIf9ZkzUJXFM5WxFCBF3cEIosHtCuHJuJr3faD3ATw88LCpbPKVhliYRjWoRqUCoO6718xpK4vtVN+Vij5gSP7bfOy3G4zmFJBye9iWBHY1VAXE2l6Tj941SNiO7GgpoBZCToEajENNKWAR5CaZwaZA6QUDSAnlIEY06HeVDxwO6uNzOhnq/C7cu7sZe9rCeEjGwkEPhLqXEvcB4WBJcmQlPG8Mp4DdYJRcehZIEXek4r6uYoh6ooW/pQjGaOtLqCSJeWOF58XyrtaNg13bNS2200ZZCdX4rlLty8h2V619rwZx6G0bwn5yW+dt+KmBnypXWhvYbKzclNXav/EwnVNApI13hbowFB1C0BXEPd33qMrPFpwCOrkfUpyxlgI1bKH6VUZVCrkuUB9wY6+TBcsQTCyuAyDwOFk4WYLVjZwvDQ0BIC2EnAJuB5PLX7CudLJSUtOY04n77u1oCWFHizIJn1bIWQoIT3hZkvPj9zhZNIXcuDizpq1Mm068ek+0C/kILQWqcLN4f8/9OD1/Gp2hTngc4u4aUt/xcPNhvHr11a10gve/t+denJk/g45gB9ysfbkh1MKu7bqGcmgxh7yh9Qa8OPkiWMJiT2wP3pp9y3jBIC2bIeS7tTZagwoI2yJDGDT7mzG6MqovX4GyYWf9TpyePw2GMDjQeED0GaNN3w81HcJr06/pyqPaYIX7gBUghOCOrjuo0gr97+2gFBDCcveBKlSim4WaUsAiKHkP+N25T9Pb4MeHezvREAiA43l8+80JrCWzuH9PLpKAsDFr2aCxAvcBn1vcZ9wpaSkg6FA22iQKJXGzW+9WXFftUS/YDRdYhuCGvnr43Q5cmo1jLp7E3TubsR1gVNSA7RB9IOwOqybD2hPbg5A7hIHIQIlSQMixEHKFcH3L9ZL5VKMSRQzb5T22I7QoBfY17EOTrwk+pw8TaxMmSKUNhroPUORl1kbCjgvPat806ZVfC9GgFl6K423H0RZoQ9QTRcAVkHjKWBxsOggX68KLky8Wrm33Mbva2zMt1Bzw2FEpoFd5Y+foOtWOmlLAItD6rBNCEAt4Chvyjx3uBM/zhg7uW5YCuX/zrgPCic0hocmgI97hUL5FNx8ZrnTwcDvFB9P2qBf7ou3YEetE1J9zI/jo4Q5NZdp14jVKLj2cAtUE4XvtbdiLsDuMZDZZmk5nvW7X+iuGHYh87CBDpaAp/johaAm0AAAm16SjzeiF5Oa4EjxrFH11O/jcXivQG9rMrJCEQrAMq5oEUm+ZDsaB/Y37S5QC2x1WhCS0AmoOZjJ8qVLA7PHNjsrPGuhR/Ud+VQo9RIPFA51wUtOywWAFA6fPJW4pID3A2tdkKyUIeehixYkGCSHoi0VRb0AoQDu9fzG0nPA3+7esJAajg5rz2c5Q65Zh1/ahhO26wKqhHFYoUCRDEla43d3YemPh74NNBytadsVR5V1aL9Fgnv9FKV+10LIxqsS8YJQLoV1RreF+zSwznS0l5bPlPK5SpNoa1DzUatYiqApJqIZo0ACCPO+mUoB2YqN7F2u0h+lM6YlZsTJGWFduhzH+37YcdKFtQXB319042HQQH+j9QMHscTuSMIqhzMSNlLrZ5HGtTFB6F+A1VA52Pq2R2ohVAjTjT4OvAR/u/zDu7LoT1zdLuwFtB2y3Puxz+lSlP952HC7WBUKIbDhFtfVkJ6uknnAPgFzbtyrqT6XGI0uiRFhQpipLAYH7gB2htg6L++qDvQ8aLc41jZqdnEVQ0wlkQxIKOQU0DFDL66WaRK9zUylAMbF5nCy8AQ8SGXlSPqsWqUL3gWIITWyNIoWz60JLy4Y84ArgaMvRkmtCH3q7vm+lUHMfqA7YaaFuNvS+q9Ft8p7ue/DU6FPwO/043Hy4ImXqKaMl0IIWtJgsjfWo9rFHOPb6HepCMkc9UfzMzp9BlsuWKBS2E2Hd7Z234+zCWTT6GlUrTaod1fzd5KCKU4CvrFKAZu7R6+bRFerCR4c+CsAYJfO1tDZQQk0pUCHMz8+X/F5YWjAkXyPcB9qjXrx6uSgPFR30p4904rGRUp81MRmsOiAWug+U3itlMN1OTPFi0HuiPTMzgy996Uu4HLiMhCOBw4cPw+9XtwirZuTbdc1SQD3scnIdj8fx6qs5ksiZb8/g85//PBobG00rr8HXYFrecrAbEVNfpA8dwQ4whJH0aa0mKyKrkB+D8zC7/dodwjHJ71Q/H7lZNyDuMakZdtpkuFk39jbstVqMAsxsw5aY8luheFBRpJ0sBZTqSs33s9LiTLifE/6uZtSUAhXC4uJiye+lpWXqZ80edLrqfdjVGsLMahK7AlvhAZUmttaIB2Gv09baWKH7QDGEhHFGEbDYdXGrd/M6OzuL3//930fkxghcDS7s3LkzpxRQ495i47aiFdvdTzOP7XB6Fo/H8dxzzwEAHv32o3jkkUdM3VTVeepwuPkwRpZHcKjpkGnlVANcrMtqEaqyzRYjPwbnobf92nWuooXwe5p1Eq66njToBKr9W9DC6DZcjLL+bXCV7o7txqm5U8ZmqgF6og+YDSpLgSpfSwj3c8Lf1YyaUqAKoGqy0NC3CCG4e1eOUO5Lb9GHK8lwfOF5JSGsmu/SWWmlgNGxTvOw6wBn2Im2fQ5BKorCdxWuO1Q2br2M2VbBru2aFlZZKxxuPixpMm8W7GKZoQYVcR+wcONlp9PjPKq9T2f5bMlvLZYCamFWG3KQ0uV4tX+bYlSq7Qu/jdDVUS+OtRzDWmoNl1cuS5ZZCWw360Sr27odx2arsL1a1jaFUfwDaqG0sHSy9M3Hqk4vN16nuGtMKWBQd+c5/S4r2wnXygmPEGreuzbpVhbVWN/X+jhSg3okMomS3z6HPXzmtfS/sDuMRl/uxLwj2GELy5pqg3AMcTHG1qGTdeJA4wFD89QCO685aBTS10royGrENW0p8N577+Gdd97B5OQkWJZFW1sbDh06hJ6eHqtFo4YRRINqcPNgDM+fmwMA3D4sbvJ121AjTl4QhkExVSxJ3L2zGd96cwIAcMtQqX+v0H3AKNh1gDNMLh37je248L9WQhJWq9jXIuzGKVApKI1xZvc9o08mzYZd5ypaJNKlSgGWMab+9UZa0aIUIITgof6HML8+b6m/9HaCk6G3fKVFtZu+11CDHK5JpcDXv/51/MEf/AHeeecd0fs33HAD/uiP/gi33nprZQWTgNzEXUY0aPIkf6Ajinq/G363A7GAODHfYHMQA7Eg/uHlUVNloUFXvQ8P7mtFMpPFUFOw5N7RlqN4/OLjAICh6JBhZdp1kjDK5KzMUqDKF5a0KLynYL2nt17t2l6EqBY5a6hOS4Ht0LyOtBzB6MooePBl5G5NviacgvX+yNsJ65n1ipRTPMeZOQ46GSea/c2m5b/dIVyLOFnjlQI16Ift1oxVOF2ahWvKfSCbzeKzn/0sHnnkEUmFAAC8+OKLuOOOO/C7v/u7FZTOGBjqPiDSUxiGoDvmR0NwSyFAU6ZVQwAhBP2NAexqDcMhcHdoD7Tj5vabcaDxAI61HjOwUOOyMhJ24BSw3WSgAcLTqIgnYo0gFUa1KwWKif42rmxYKIn5qHEKSJRh8vhT763Hhwc+jDs678CxltI5pT/Sj85QJzwOD+7uuttUOa4VFPfp/ki/hZKUohr7n5moVH2UuQ9UwgWjuqdFwyGnkI66o6LXq31tsZ1wTVkK/Nqv/Rq+8pWvFH77fD588pOfxP79+5FKpXDy5El84xvfQDqdBsdx+MM//EPU1dXh137t16wTWgFeh9dqEUTNqbgqUL0RQrA7ttv4fA0Mu2IkDBt4BZ/2WhnQ8+/JEAYP9D6A9+bew2B0UHUoy2ulvophh5PrZn8zro9ej/9y5r8gcTGh/EAVo95bb7UItkQl+l6zv1n0tJdlWHyg9wOml68G1T4WNfubcUvHLVhJrmBfwz7D8tVrIm6H8e5ahNBtymhOATFUex8yG7d03ILnx59H0BXEvkbxProdDou2C64ZpcATTzyBP//zPy/83rlzJ5588kl0dHSUpHv77bdx//33Y3JyEgDwhS98AXfeeSf27NlTUXlpEfVEMRAdwPnF8zjQeMDQzqVVu0sIAXsNd3K7ThJmuQ8I0RnqxNjKGIAcYdL46rgh5doJXaEudIW6DMmrWibEapFTCoQQdPm6ED8Tt1oU1aDZZNzXcx+evPwkHMSBm9tv1lWeFezWlWhf1d6GjUa11wchBLvqd1WknBrsD2H4PTPcB+y6vpNCT7gHI8sjAGCo4owWu+p3oTfcCyfjNCzst9GoKfG2YM8vZDA4jsPv/M7vFH77fD48/vjjZQoBANi3bx8effRRHD9+HBzHFZ59/PHHKymyKtzZeSfu6LzD8IUcbUcRGyTr/C5EfU4sJtJoCnmwpCJSQbXDruFiKuU+cGv7rfjJxE/gYlzYWb+zRClQbROqGajWBWaNMdje6An34Gd2/AxYhtVtQdYb6cVPJn6CVDaFjmD5PGkGjBgb1Frt1FCDWtC009omoxSVqo80V0pwbQbRoBB2X9Pc1HYTgJzVxPXN15tfoMinFs5Heok8azAP14RS4Omnny7hEPiVX/kV9Pb2Sqa/4YYb8Mgjj+Cf/umfAADf/e53ceHCBfT328dnrRiEEFt1KgICQgg+ergDVxbX0Vnnw+sz+/D27NsAYIrJvp1gp29RDKM2cUohCQOuAO7ruQ8AMLc+Z0iZVsCu37GGGqQQcAUMycfJOPHI4COYik+hNyw9V9oBh5sP49Wrr4IhjCI3TK1Pl6JWH+LQXS81nYAlECoFzDigqTZleNAVLKzH7Ipqq9PtDHseaRqMb33rWyW/P/e5zyk+8wu/8Aslv7/97W8bKVJVgNZ9QGoC9bkcGGwKwuNkcaj5EHbHdmNX/S4caTlipJj2g03HN7Wh86TAZ2srHiNxTSzMa01GF+7ovKPw9+HmwxUpM+wOY7huuGLx0rX2g0NNh/Bg74P46NBHUeepM1iq7Y1rYuwxALV6qg6ks2nlRDpRs5iTB41ViN3qrEYMuoVrwlLgiSeeKPzd19eHvr4+xWeOHz8Oj8eDjY0cS/V3v/tdfOELXzBNxmoGDSmPm3Xr9nOtFth1AfH/t3fn0VGV9x/HP8mQhYQlQWTLT4qgZRPCYakYxLCWsskm1gPUirKURT1WyqEtlCLFqlC0VnaPoJZNQHYLKiFKFSlQSUgI8mNCQmQSAUkgCYRs8/uDXy65WSdhMjNk3q9z5pz5Xp5n5ptwM3Pv9z73eZxWNS8wh572AV9TnPVzeur+UVW15ee4GzwY8qDyC/OVW5Bba0daVffvy8fHR/c1cOwWB2/5rMKdqWg/cWQf4vYB98i351feCEC5av1IgYyMDJ0/f96Ie/bs6VA/f39/devWzYgrWsIQZt5+4FXfv767UyiT0yYaZKQA4FI+Pj7qcE8HdWnSxWMna7obUMgy8/bvakdV9ffElUczl80p4IqRAne4MgWYU8CT1fqiQEJCgimuyrwAxUcUpKenKy0tzWl5ofbqfG9nBfsFS5J6NnesCOUKFAXujNO+uHxKhnfHFyInEAC80d3yGe3tGKHhftX5P3D3sQX7zW21/pJDYmKiKW7ZsqXDfUu2TUxMVLNmpdcfdkRubq4pPnrsuAr9G5TZNu6HOFO8M3mnKQ4NDdU999TMOtQ2q814nls3V/G58ZX2STmXovSb6UacYE/w2KvlrhJeEK4ce44CLgco/rL5d5j8fbJsWbd/z/F+lf+OqyvzfKYy8zIlST/9n58qPqv673X27FlJUsGNAuVfy9fFpIvKz8yX9apVQT8GldknPSddtqTbP2uCT4IC6wRWOwdXsl62ynb5du6nfE4poM6dz25+NuOsbGm3X/e7vO90JejKHb9uZYr/bZ/NOiuFmrdJFe+LSVeTZEu93f60TivIr+z/95LvV9lru0rRPlxeDPe5fP2ybOdtpbbf6X5TfD9sem9Txd9w/35YkYr+bpyx/1ouWowVYXo066H4eM/+fbhD4o+Jsl0q9r1VmKCGAQ0lSWfTz8r2Q8Xf30kpSbJlu+Y73lMV348DLgcoPvPW76AmP4Nd8Z1T8pjmf6//r/Ib1uxtC574XVqecz+cky294n3/+8TvlZGbYcSnC0+rYUBDnUs9J9tV1/zdmH6nwarS90Lx0edS6fO7u5q9llu+fLldt6a5skuyb9++3eG+f/vb30x99+3bV+083n77bdNr8eDBgwcPHjx48ODBgwePu/Px9ttvV/vc0NPU+tsHsrKyTHFgoONXKevWNa+tWfK1qqJBg7JHBQAAAAAA7i616fyu1hcFilYPKOLv7/jySgEB5qHCN27cqHYeISEh1e4LAAAAAPActen8rtbPKVByZEBV7v24efOmKS45cqAqIiMjtWPHDmVkZOjatWtq1qxZlQoUxdXknAIAAAAAALObN28qJSXFiCMjI92YjXPV+qJAvXr1THHJkQMVKTkyoORrVUVISIhGjBhR7f4AAAAAAPfp2rWru1OoEbX+9oGS93qkp6c73DcjI8MU16/v3TPqAwAAAABql1pfFLj//vtNccmlJCqSnJxsilu3bu2UnAAAAAAA8AS1vijQoUMHU1yVNVGtVqvxPDQ0VM2aNXNaXgAAAAAAuFutLwqEhISoZcuWRnz48GGH+uXm5ur48eNG3KlTJ6fnBgAAAACAO9X6ooAkDRkyxHhutVqVmJhYaZ9Dhw6ZJiUcNmxYjeQGAAAAAIC7eEVRYNSoUaZ4zZo1lfYp2WbkyJHOTAkAAAAAALfzsdvtdncnUdMKCwsVHh6uuLg4SVJwcLBOnjxZahLCIocPH9ajjz6qwsJCSdLQoUO1Z88el+ULAAAAAIAreMVIAV9fX7366qtGnJ2dreHDhyslJaVU29jYWI0dO9YoCPj6+mrRokUuyxUAAAAAAFfxipECRWbMmKHly5cbcXBwsMaPH68uXbooLy9P33zzjbZu3aq8vDyjzeLFizVr1ix3pAsAAAAAQI3yqqJAQUGBJk6cqA8//LDStj4+PpozZ45phAEAAAAAALWJV9w+UMRiseiDDz7Q5s2b9dBDD5XbrmfPnvr8888pCAAAAAAAajWvGilQUlxcnGJjY2Wz2WSxWNSiRQv16NFDrVu3dvp7xcfHm94rLCxM3bt3L3eyQwAAgOLy8/N1+PBhJScnKzU1VRaLRU2bNlXTpk3VuXNnNWnSxN0pAmW6dOmSjh49qqSkJF29elUWi0WhoaFq27atunbtqnr16rk7RcBpjh07ptOnT8tms6lu3boKCwtTRESEmjVr5u7UyuXVRQFX2Lp1qxYuXKjY2Ngy/z0iIkKLFi1Snz59XJsY4AC73S6r1aq4uDilpKTo2rVrCgoKUqNGjRQeHq5OnTrJYrG4O00AqNWSk5P1yiuvaPv27UpPTy+3Xbt27fTCCy9o2rRpLswOKN+//vUvLV68WNHR0SrvlCMgIECjRo3S3Llz1bFjRxdnCG9RWFiohIQEHTt2zHjExMToxo0bRpuDBw/e0TnZypUrtWTJElmt1lL/ZrFY1L9/fy1evFidO3eu9nvUFIoCNaSgoECTJk3SunXrKm3r6+urP/zhD1q4cGHNJwZUIjMzU7t379auXbsUFRWlS5culds2NDRUEydO1KxZs9S8eXMXZgncmZiYGHXv3l35+fnGtsjISEVHR7svKaAMS5cu1bx583T9+nWH2rOMMjxBQUGBpkyZovfee8/hPn5+flq6dKlmzpxZg5nBG40ZM0b79+9XdnZ2he2qWxS4fv26xowZo3379lXa1t/fX2+//bamTp1a5fepSXXcnUBt9dJLL5kKAkFBQcZKB7m5uTpy5Ii2bdumvLw8FRYW6i9/+YsaNWqkl156yX1Jw+tlZmaqSZMmysnJcah9enq6li5dqnXr1undd9/VqFGjajhD4M4VFW2LFwQATzR79mwtXrzYiH19ffXwww+rf//+atGihQICAnT58mXFxcUpOjq6zKWWAXeYPn16qYJAnz59jH03Ly9PVqtVO3fu1JkzZyRJeXl5ev7559WgQQM9/fTT7kgbtdTx48crLQhUV2FhocaPH28qCISGhupXv/qVOnTooMzMTH3xxRfau3ev7Ha7cnNzNW3aNDVu3FhjxoypkZyqg5ECNWDv3r0aNmyYEXfo0EH79u3TfffdZ2oXExOjIUOGyGazSbr1ZX/ixAl16tTJpfkCRTIyMhQaGmra1rp1a0VGRqpt27Zq3LixcnJydPLkSW3btk2XL1822lksFm3ZsoXCADzekiVL9Lvf/a7UdkYKwJO8/vrrmjNnjhH/7Gc/0+rVqxUeHl5unyNHjujEiRMedwUK3uXIkSPq2bOnEYeEhGjbtm3q169fqbZ2u11LlizR7NmzjW2NGjVSUlKS6tev75J8Ufu1atVKycnJkm7drtK5c2d169ZNWVlZ+uc//2m0q85IgWXLlplGt/Tu3Vs7d+4sdTwdFRWlUaNG6dq1a5KkevXqyWq1es5cMHY4VUFBgb1z5852SXZJ9qCgILvVai23/VdffWX39fU12g8bNsyF2QJm6enpdkn2Bg0a2F988UV7TExMuW2zs7PtkydPNvZdSfbQ0FD7pUuXXJgxUDVWq9UeFBRkl2S/99577ffcc4+x/0ZGRro7PcBut9vt8fHx9oCAAGPf7NOnjz07O9vdaQEOmT59uunYYNu2bVXus2HDBhdkCm8xb948++rVq+3Hjx+35+bmGtvXrl1r2u8OHjxYpdfNysqyN23a1OjfvHlze3p6erntN27caHq/mTNnVvMncj6vWpLQFQ4cOGCaVPCFF16ocDWDiIgIjR071oj37Nmjs2fP1miOQHnq1KmjOXPm6Ny5c3rrrbcqnAglKChIq1ev1rhx44xt6enpWr58uStSBaplypQpxr3ZS5cuZcZreKTnn39eN2/elCQ1bNhQGzZsUFBQkJuzAhxz7Ngx43mTJk0cGkFYcnLMmJgYp+cF7/XKK69o8uTJ6tq1q/z8/Jz2uhs2bNAPP/xgxPPnz1dISEi57Z966ik9/PDDRvzuu+8qKyvLafncCYoCTrZ9+3ZTPGnSpEr7TJ482RTv2LHDmSkBDqtXr57++te/qlGjRg73Wbx4sXx8fIyYCa7gqd577z0dOHBAkjRgwABNmDDBzRkBpSUkJCgqKsqIX375ZSZyxV3lypUrxvM2bdqYjhHK8+CDD5b7GoCnKn7eFxQUZLpQVp7i5305OTkOTU7oChQFnGzv3r3G8zZt2qhNmzaV9undu7cCAwONmJMq3E1atGih9u3bG3FZy7AA7vbDDz9o1qxZkqTAwECtWLHCzRkBZVu9erXx3NfXV88++6wbswGqrviFBUcndyt5tdRj7rMGypGTk2Mq4D7yyCMOzYMxcOBAU+wp530UBZwoIyND58+fN+Lik6xUxN/fX926dTPi4rcfAHeD4kOwa2p2V+BOzJw501jffd68eXrggQfcnBFQts8++8x4Hh4errCwMDdmA1RdRESE8Tw+Pl5paWmV9ikaxVXksccec3pegDOdPn3auM1Lcvy8r2XLlqbPdU8576Mo4EQJCQmmuCoHncVHFKSnpzv0AQp4iqSkJON5s2bN3JcIUIadO3dq69atkqSOHTuWufIA4AmysrJMxxKPPPKIpFtLtW3cuFHDhg1Tq1atFBAQoMaNG6tz586aMWMGq2bAo/zmN78x7tsuKCjQiy++KHsFi539+OOP+uMf/2jE4eHhpa6mAp7GWed9p0+fVmFhodPyqi6KAk6UmJhoilu2bOlw35JtS74W4Kn+/e9/6+LFi0ZcdBALeIKrV69q+vTpkiQfHx+tWrXKqZMMAc4UExNjOjhs166dYmNj1b17d40bN0579+5VcnKycnNz9eOPP+rkyZNavny5+vbtq/79++vChQtuzB64pW3btnrttdeM+KOPPtLAgQN16NAh5efnG9uzsrK0ceNG9ejRw5hku3Hjxtq4caND8xAA7uSs874bN254xMXgOu5OoDYpWneySFUmayu5lmVmZqZTcgJq2htvvGGKn3zySTdlApQ2e/Zs2Ww2Sbcm9+nVq5ebMwLKd+nSJVOckZGhyMhIZWRkGNsaNmyoBg0a6OLFi6ahq1FRUerRo4cOHjyotm3buiploEy//e1vFRISopdfflkZGRk6cOCADhw4oMDAQDVp0kT5+flKS0szFcEGDBigVatWVbhqF+Apatt5HyMFnKjkJCnFJw+sTN26dSt8LcATbdy4Ubt37zbiLl26aMSIEW7MCLjtyy+/1Jo1ayRJTZs21euvv+7mjICKFT/5l6SFCxca28aNG6e4uDhj/qLMzEzt2bNHHTt2NNqnpqZq9OjRxrKbgDs9++yzSkpK0tSpU40r/zk5OTp//rxsNptREAgODtaSJUu0f/9+CgK4a9S28z6KAk6Uk5Njiv39/R3uGxAQYIpv3LjhlJyAmhIfH68pU6YYcZ06dbRmzRr5+vKxAvfLycnR5MmTjftY33rrrQrXDgY8QckDw7y8PEm31thev369qQDg5+enoUOH6vDhw6Z1r0+dOqVly5a5JmGgAp988okeffRRrVq1qsI5BbKzszVr1iy1b9++1ISDgKeqbed9HL07UckKUW5ursN9iw8BlEpXkABPkpqaqqFDh5oOYF977TV1797djVkBty1YsEBnzpyRJA0aNEhPPfWUmzMCKlfWlaaIiAjNnTu33D7169fX+vXrVafO7TtC//73v9dIfoCjFixYoKFDhyouLk7SrXkGVq9eLavVqpycHGVmZurEiRN65ZVXjKHUZ86c0cCBA/Xee++5M3XAIbXtvI+igBMVX5ZNKl1BqkjJClHJ1wI8xZUrVzRo0CAlJycb26ZMmaKXX37ZjVkBt8XExGjJkiWSbn3Rrlixws0ZAY4pa43rF154odJJ19q0aaPHH3/ciC9cuKBTp045PT/AERs3btSf//xnIx4xYoS+/fZbTZ48Wa1bt1ZAQIDq1aun8PBwzZs3TydOnDBmY7fb7Zo6daq+/fZbN2UPOKa2nfdRFHCiBg0amOKiNbEdUfI+wrIODAB3u3btmn7xi1/o5MmTxrbx48dz0gWPUVBQoOeee86Y4Xr+/Pm6//773ZwV4JiSxxGS1LdvX4f6lmz33//+1yk5AVWRn5+v2bNnG3Hz5s21fv36Cq+EtmzZUps3bzaKX/n5+frTn/5U47kCd6K2nfdRFHCikgee58+fd7hv8auukphoBR4nKytLgwcP1tGjR41tTzzxhN5//33mEYDHePPNN3X8+HFJUqdOnRjBgrtK8bWrpVv3nTZp0sShvj/5yU9MccmVDABXOHTokL7//nsjnjhxooKDgyvt161bN9OSxvv372fCTHg0Z5331a1bV82aNXNaXtXFkbwTdejQwRQXrbnqCKvVajwPDQ31iJ0DKHL9+nUNHTpUX3/9tbHt8ccf14YNG2SxWNyYGXBbWlqa5s+fL0ny9fXV6tWrTfdZA56uTZs2pgmoqjKbdcm2VRnKCjhLbGysKa7KXEPF2+bl5RnzwgCeyFnnfe3atfOIi2scLTlRSEiIWrZsaVSKDh8+7FC/3Nxc48qWdOvqFuApbty4oeHDh+vLL780tg0ePFhbtmyRn5+fGzMDzNLS0owrSxaLRRMmTKi0z4ULF4znR44c0QMPPGDEAwcO5NYYuJTFYlGnTp107NgxSbdu2crPz3eouHXlyhVTfM8999RIjkBFsrOzTXFV7pUuOaLAE2ZkB8rTrl07+fv7GxMMOnrel5KSYjr28JTzPooCTjZkyBCtXLlS0q0qUGJiYqW3Ahw6dMhU0R82bFiN5gg46ubNmxo5cqSioqKMbQMGDNDHH39cpaVXAFfLy8szVeIdkZOTY+rz0EMPOTstoFKPP/64URSw2+2KjY1V165dK+1XcmI2bkOEOxStJFAkLS3N4b6pqammmMIWPFlgYKD69eunffv2SbpVFMjKyqq0EPbpp5+aYk8573P/WIVaZtSoUaZ4zZo1lfYp2WbkyJHOTAmoltzcXI0ZM8b04dW3b1/t2rWrSkNaAQCOe+KJJ0zxRx99VGmfwsJCbd261Yj9/f3Vq1cvp+cGVKb4aCtJ+uyzzxzqV1BQYLoAERAQoPvuu8+puQHOVvy87/r161q/fn2lfYqf9wUEBGjw4ME1kltVURRwsgEDBpiuLv3jH//QuXPnym1/+PBhbdmyxYiHDh2qBx98sEZzBCqTn5+vp556Snv37jW29e7dW7t37/aItVSBsnTp0kV2u71Kj+KTs0VGRpr+bceOHe77YeC12rdvryFDhhjxsmXLlJiYWGGfd955xzTK5cknn+SzGm7Ru3dvBQUFGfGmTZtKzTNQlnfeecc0UVtkZCT7MDzeuHHjTJPBLliwoNTKAsVt2rRJR44cMeJJkyZ5xHKEEkUBp/P19dWrr75qxNnZ2Ro+fLhSUlJKtY2NjdXYsWNVWFho9F20aJHLcgXKUlBQoAkTJmj79u3GtoiICH3yyScOzSAMALgzb7zxhjGJa1ZWln7+858rISGhzLbr1q0zrbIRGBioefPmuSRPoKTAwEBNnz7diPPy8jRkyBDTRMXF2e12LV++XLNmzTJtLxkDnqhevXqaO3euEaempmrEiBFlFgaioqI0depUIw4ODjb1dTcfu91ud3cStdGMGTO0fPlyIw4ODtb48ePVpUsX5eXl6ZtvvtHWrVuVl5dntFm8eDEfgnAru92uiRMn6v333ze29ezZU59++qlHrKEKOFurVq2MpYEiIyMVHR3t3oSA/7dy5UpNmzbNiP38/DRy5Ej16tVL9evXl81m065du0zLxEq3igS//vWvXZ0uYLh69aoiIiJ06tQp0/Y+ffqoX79+CgsLM+Z92bVrl7777jtTu2eeeUZr1651Zcqo5T7++GPNnj271PbMzExdvHjRiFu0aFHmCJU33nhDo0ePLvO1CwsLNXLkSO3evdvY1qhRIz399NNq3769srKyFB0drT179qjotNvHx0ebNm3Sk08+eac/mtNQFKghBQUFmjhxoj788MNK2/r4+GjOnDmmEQaAOxw6dEiPPfaYaVt5H5AV+eKLLxQWFubM1IAaQVEAnmzJkiX6/e9/r/z8/ErbBgQEaMWKFZo4caILMgMqduHCBY0ePVr/+c9/qtTvueee08qVK1lOFk61bt26O/psXLt2rZ555ply/z07O1ujRo1yaA4Nf39/vfnmm6YRNZ6A2wdqiMVi0QcffKDNmzdXOIN1z5499fnnn1MQgEcoKCgotc1ms8lqtVbpUXwEDACgembNmqWjR49q0KBBxu0EJfn5+emXv/ylTpw4QUEAHiMsLExff/21VqxYUemSa76+vho0aJD279+vd999l4IA7jrBwcHav3+/li1bVu7KL76+vhowYICOHDnicQUBiZECLhMXF6fY2FjZbDZZLBa1aNFCPXr0YMkgeJTo6Gj17dv3jl/n3LlzatWq1Z0nBNQwRgrgbnHp0iV99dVXstlsysjIUGhoqFq1aqXevXt7zERVQHm+//57HTt2TBcuXNDVq1dlsVgUEhKiNm3aqEePHmrYsKG7UwSc5ujRo0pISFBqaqrq1q2rsLAwRUREqHnz5u5OrVwUBQAAAAAA8FLcPgAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJeiKAAAAAAAgJf6P7SeqlTBVSzqAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "diag_spline = model_scalar(omega_fixed, params_d)\n",
+    "offdiag_spline = model_scalar(omega_fixed, params_od)\n",
+    "splined_diag = diag_spline(omega)\n",
+    "splined_offdiag = offdiag_spline(omega)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "handles = [[], [], [], []]\n",
+    "labels = [[], [], [], []]\n",
+    "for ydata, ytrue, yspline, yguess,res in zip([data_diag, data_offdiag], [diag, off_diag], \n",
+    "                                         [splined_diag, splined_offdiag], [guess_diag, guess_offdiag], [res_d, res_od]):\n",
+    "    pl, = ax.plot(omega, ydata, alpha = 0.5)\n",
+    "    handles[0].append(pl)\n",
+    "    labels[0] = 'Raw data'\n",
+    "    \n",
+    "    pl, = ax.plot(omega, ytrue, color = pl.get_color(), lw = 3)\n",
+    "    handles[1].append(pl)\n",
+    "    labels[1] = 'Truth'\n",
+    "\n",
+    "    pls, = ax.plot(omega, yspline, ls = '--')\n",
+    "    handles[2].append(pls)\n",
+    "    labels[2] = 'Spline model'\n",
+    "    pl = ax.errorbar(omega_fixed, res.x, np.sqrt(res.hess_inv.diagonal()), \n",
+    "                     color = pls.get_color(), \n",
+    "                     lw = 0, \n",
+    "                     elinewidth = 1,  \n",
+    "                     marker = 'o',\n",
+    "                     markeredgecolor = 'black', \n",
+    "                     markeredgewidth = 0.5,\n",
+    "                     markersize = 4)\n",
+    "    handles[3].append(pl)\n",
+    "    labels[3] = 'Parameters'\n",
+    "    # pl, = ax.plot(omega_fixed, yguess, 'o', color = pl.get_color(), \n",
+    "    #               markeredgecolor = 'black', markeredgewidth = '0.5')\n",
+    "    # handles[3].append(pl)\n",
+    "    # labels[3] = 'Initial guess'\n",
+    "\n",
+    "ax.legend(handles = [tuple(h) for h in handles], labels = labels, loc = 'upper left', fontsize = 6)\n",
+    "# ax.set_yscale('symlog')\n",
+    "# ax.set_xlim(0,3)\n",
+    "ax.set_ylim(0,10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Wishart estimate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10000, 4)\n",
+      "(20, 2, 2)\n",
+      "(20, 2, 2)\n",
+      "data.shape (10000, 2, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "npar = 20\n",
+    "nsam = 10000\n",
+    "\n",
+    "from scipy.linalg import cholesky\n",
+    "import opt_einsum\n",
+    "\n",
+    "omega = np.linspace(0, 10, nsam, endpoint=True)\n",
+    "omega_fixed = np.linspace(0, 10, npar, endpoint=True)\n",
+    "\n",
+    "data_wishart = np.loadtxt('/Users/paolo/data_wishart.txt')[:nsam]\n",
+    "truth_wishart = np.loadtxt('/Users/paolo/truth_wishart.txt')[:nsam]\n",
+    "\n",
+    "print(data_wishart.shape)\n",
+    "\n",
+    "guess_wishart = np.array([pd.Series(data_wishart[:,i]).rolling(window=50, \n",
+    "                                                               closed = 'left', \n",
+    "                                                               min_periods = 0, \n",
+    "                                                               center = True).mean().to_numpy() for i in range(4)]).T.reshape(-1, 2, 2)\n",
+    "\n",
+    "guess_wishart = np.array([guess_wishart[j] for j in [np.argmin(np.abs(omega-omega_fixed[i])) for i in range(len(omega_fixed))]])\n",
+    "print(guess_wishart.shape)\n",
+    "\n",
+    "guess_wishart = np.array([cholesky(g, lower = False) for g in guess_wishart])/np.sqrt(3)\n",
+    "print(guess_wishart.shape)\n",
+    "\n",
+    "data_wishart = data_wishart.reshape(-1,2,2)\n",
+    "truth_wishart = truth_wishart.reshape(-1,2,2)\n",
+    "\n",
+    "print('data.shape', data_wishart.shape)\n",
+    "\n",
+    "# solver = 'BFGS'\n",
+    "# params, res = do_mle_wishart(data_wishart, \n",
+    "#                              np.array([guess_wishart[:,0,0],guess_wishart[:,0,1],guess_wishart[:,1,1]]).reshape(-1), \n",
+    "#                              model_wishart, omega, omega_fixed, solver = solver)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 2, 4001)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(flux_resample.cospectrum.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, 65.0)"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spline = model_wishart(omega_fixed, params)\n",
+    "# guess_spline = model_wishart(omega_fixed, np.array([guess_wishart[:,0,0], guess_wishart[:,0,1], guess_wishart[:,1,1]]).flatten())\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "estimate = spline(omega)\n",
+    "estimate = opt_einsum.contract('wba,wbc->wac', estimate, estimate)*3   # This should be 3*V, the expectation value of X\n",
+    "\n",
+    "# par_mat = np.array([np.array([[p[0], p[1]], [0, p[2]]]) for p in params.reshape(npar, 3)])\n",
+    "# estimate_par = opt_einsum.contract('wba,wbc->wac', par_mat, par_mat)*3   # This should be 3*V, the expectation value of X\n",
+    "\n",
+    "# par_0 = guess_spline(omega)\n",
+    "# par_0 = opt_einsum.contract('wba,wbc->wac', par_0, par_0)*3   # This should be 3*V, the expectation value of X\n",
+    "# par_0 = opt_einsum.contract('wba,wbc->wac', guess_wishart, guess_wishart)*3   # This should be 3*V, the expectation value of X\n",
+    "# par_0 = guess_wishart\n",
+    "\n",
+    "# params_resh = res.x.reshape(3, npar)\n",
+    "# cov_resh = res.hess_inv.diagonal().reshape(3, npar)**0.5\n",
+    "\n",
+    "comp = {(0,0): '$qq$', (0,1): '$cq$', (1,1): '$cc$'}\n",
+    "\n",
+    "ip = 0\n",
+    "for i in range(2):\n",
+    "    for j in range(i, 2):\n",
+    "        pl, = ax.plot(omega, data_wishart[:, i, j], alpha = 0.1, label = f'{comp[i,j]} (raw)')\n",
+    "        ax.plot(omega, truth_wishart[:, i, j], color = pl.get_color(), alpha = 0.4, lw = 3, label = f'{comp[i,j]} (truth)')\n",
+    "        ax.plot(omega, estimate[:, i, j], color = pl.get_color(), ls = '-', label = f'{comp[i,j]} (MaxLike estimate)') \n",
+    "\n",
+    "        # ax.plot(omega_fixed, estimate_par[:, i,j], 'o' , markersize = 8, color = pl.get_color())\n",
+    "        # ax.plot(omega, par_0[:, i,j], ls='dotted', color = pl.get_color())\n",
+    "        # ax.plot(omega_fixed, par_0[:, i,j], '*', markersize = 8, color = pl.get_color())\n",
+    "\n",
+    "        # pl = ax.errorbar(omega_fixed, params_resh[ip], cov_resh[ip], \n",
+    "        #              color = pl.get_color(), \n",
+    "        #              lw = 0, \n",
+    "        #              elinewidth = 1,  \n",
+    "        #              marker = 'o',\n",
+    "        #              markeredgecolor = 'black', \n",
+    "        #              markeredgewidth = 0.5,\n",
+    "        #              markersize = 4)\n",
+    "    \n",
+    "ax.legend(loc = 'center left', bbox_to_anchor = (1,0.5))\n",
+    "# ax.legend(handles = [tuple(h) for h in handles], labels = labels, loc = 'upper left', fontsize = 6)\n",
+    "# ax.set_yscale('symlog')\n",
+    "ax.set_xlim(0,10)\n",
+    "ax.set_ylim(0,65)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Now with actual data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dc=np.load('data/bayesian/CsF/dc_minimal.npy', allow_pickle=True).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using multicomponent code.\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux=st.HeatCurrent([\n",
+    "    dc['qflux'], \n",
+    "    dc['ele_flux']\n",
+    "    ],\n",
+    "                      DT_FS=1,\n",
+    "                      TEMPERATURE=dc['Temeprature'],\n",
+    "                      VOLUME=dc['Volume'],\n",
+    "                      UNITS='metal'\n",
+    "                     )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 2, 100001)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux.cospectrum.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using multicomponent code.\n",
+      "-----------------------------------------------------\n",
+      "  RESAMPLE TIME SERIES\n",
+      "-----------------------------------------------------\n",
+      " Original Nyquist freq  f_Ny =     500.00000 THz\n",
+      " Resampling freq          f* =      20.00000 THz\n",
+      " Sampling time         TSKIP =            25 steps\n",
+      "                             =        25.000 fs\n",
+      " Original  n. of frequencies =        100001\n",
+      " Resampled n. of frequencies =          4001\n",
+      " min(PSD)          (pre-filter&sample) =      0.00000\n",
+      " min(PSD)         (post-filter&sample) =      0.00018\n",
+      " % of original PSD Power f<f* (pre-filter&sample)  = 99.707 %\n",
+      " fPSD not calculated before resampling\n",
+      " -----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample=flux.resample(fstar_THz=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MaxLikeFilter\n",
+      "Optimal number of parameters between 2 and 18 chosen by AIC\n",
+      "n_parameters = 2\n"
+     ]
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "Misshaped `data` for None likelihood.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mflux_resample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaxlike_estimate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_wishart\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Software/sportran_bayes/sportran/current/current.py:447\u001b[0m, in \u001b[0;36mCurrent.maxlike_estimate\u001b[0;34m(self, model, n_parameters, mask, likelihood, solver, guess_runave_window)\u001b[0m\n\u001b[1;32m    445\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m n_par \u001b[38;5;129;01min\u001b[39;00m n_parameters:\n\u001b[1;32m    446\u001b[0m     log\u001b[38;5;241m.\u001b[39mwrite_log(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mn_parameters = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_par\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 447\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaxlike\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmaxlike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    448\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m    449\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    450\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mn_components\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mN_EQUIV_COMPONENTS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    451\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mguess_runave_window\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mguess_runave_window\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m    452\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mn_parameters\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn_par\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m    453\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mlikelihood\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mlikelihood\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m    454\u001b[0m \u001b[43m                     \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    455\u001b[0m \u001b[43m                     \u001b[49m\u001b[43mwrite_log\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    456\u001b[0m     _aic\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmaxlike\u001b[38;5;241m.\u001b[39mlog_likelihood_value \u001b[38;5;241m-\u001b[39m n_par)\n\u001b[1;32m    457\u001b[0m     _filters\u001b[38;5;241m.\u001b[39mappend(deepcopy(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmaxlike))\n",
+      "File \u001b[0;32m~/Software/sportran_bayes/sportran/md/maxlike.py:116\u001b[0m, in \u001b[0;36mMaxLikeFilter.maxlike\u001b[0;34m(self, data, model, n_parameters, likelihood, solver, mask, n_components, guess_runave_window, omega_fixed, write_log)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`data` should be a 1d array (diagonal or off-diagonal estimates) or a 3d array (Wishart matrix estimate)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m3\u001b[39m:\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlikelihood \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwishart\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMisshaped `data` for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlikelihood\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m likelihood.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`data` for a Wishart estimate must be a (2,2,N) array.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[0;31mAssertionError\u001b[0m: Misshaped `data` for None likelihood."
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample.maxlike_estimate(model_wishart, np.arange(2, 20, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux_resample.optimal_nparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 2, 4001)"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux_resample.cospectrum.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "omega_fixed = flux_resample.maxlike.omega_fixed\n",
+    "omega = flux_resample.maxlike.omega\n",
+    "params = flux_resample.maxlike.parameters_mean\n",
+    "params_std = flux_resample.maxlike.parameters_std\n",
+    "\n",
+    "data_wishart = flux_resample.maxlike.data\n",
+    "\n",
+    "spline = model_wishart(omega_fixed, params)\n",
+    "spline_p = model_wishart(omega_fixed, params + params_std)\n",
+    "spline_m = model_wishart(omega_fixed, params - params_std)\n",
+    "\n",
+    "estimate = spline(omega)\n",
+    "estimate = opt_einsum.contract('wba,wbc->wac', estimate, estimate)\n",
+    "\n",
+    "estimate_p = spline_p(omega)\n",
+    "estimate_p = opt_einsum.contract('wba,wbc->wac', estimate_p, estimate_p)\n",
+    "\n",
+    "estimate_m = spline_m(omega)\n",
+    "estimate_m = opt_einsum.contract('wba,wbc->wac', estimate_m, estimate_m)\n",
+    "\n",
+    "comp = {(0,0): 'qq', (0,1): 'cq', (1,1): 'cc'}\n",
+    "\n",
+    "ip = 0\n",
+    "addaxes = []\n",
+    "colors = ['tab:blue', 'tab:red', 'tab:green']\n",
+    "icol = 0\n",
+    "for i in range(2):\n",
+    "    for j in range(i, 2):\n",
+    "        fig, ax = plt.subplots()\n",
+    "        pl, = ax.plot(flux_resample.freqs_THz, data_wishart[i, j, :]*flux_resample.KAPPA_SCALE/2, alpha = 0.3, label = f'(Raw)', color = colors[icol])\n",
+    "        ax.plot(flux_resample.freqs_THz, pd.Series(data_wishart[i, j, :]).rolling(window=50).mean()*flux_resample.KAPPA_SCALE/2, \n",
+    "                alpha = 0.8, label = f'(MA)', color = colors[icol])\n",
+    "        icol += 1\n",
+    "        # ax.plot(omega, truth_wishart[:, i, j], color = pl.get_color(), alpha = 0.4, lw = 3, label = f'{comp[i,j]} (truth)')\n",
+    "        x, y = flux_resample.freqs_THz, estimate[:, i, j]*flux_resample.KAPPA_SCALE/2\n",
+    "        ym, yp = estimate_m[:, i, j]*flux_resample.KAPPA_SCALE/2, estimate_p[:, i, j]*flux_resample.KAPPA_SCALE/2\n",
+    "        ax.plot(x, y, color = 'k', ls = '--') \n",
+    "        ax.fill_between(x, ym, yp, \n",
+    "                        color = 'k', alpha = 0.5) \n",
+    "        ax.set_title(f'{comp[i,j]}')\n",
+    "        ax.legend()\n",
+    "        ax.set_xlim(0,10)\n",
+    "        # ax.set_ylim(0,65)\n",
+    "        ax.set_xlabel('$\\omega/2\\pi$ (THz)')\n",
+    "        ax.set_ylabel(rf'$S_{{{comp[i,j]}}}$ (arb. units)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5, 2, 2)\n",
+      "(5, 2, 2)\n",
+      "(5, 2, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "transpose_to_N22 = lambda arr: np.moveaxis(arr, arr.shape.index(max(arr.shape)), 0)\n",
+    "\n",
+    "# Example usage:\n",
+    "arr1 = np.random.rand(2, 2, 5)\n",
+    "arr2 = np.random.rand(2, 5, 2)\n",
+    "arr3 = np.random.rand(5, 2, 2)\n",
+    "\n",
+    "print(transpose_to_N22(arr1).shape)  # (5, 2, 2)\n",
+    "print(transpose_to_N22(arr2).shape)  # (5, 2, 2)\n",
+    "print(transpose_to_N22(arr3).shape)  # (5, 2, 2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr = np.array([1,2,3])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from scipy.linalg import cholesky"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.random.randn(2,2) + 1j*np.random.randn(2,2)\n",
+    "A = A@A.T.conj()\n",
+    "L = cholesky(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.98759513+0.j        , -0.05933581-2.45849019j],\n",
+       "       [ 0.        +0.j        ,  1.79482252+0.j        ]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "L"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/07_example_negative_log_likelihood.ipynb b/examples/07_example_negative_log_likelihood.ipynb
new file mode 100644
index 0000000..6cfdfec
--- /dev/null
+++ b/examples/07_example_negative_log_likelihood.ipynb
@@ -0,0 +1,1025 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sportran as st\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import CubicSpline, interp1d\n",
+    "def model_scalar(x, y):\n",
+    "    return CubicSpline(np.concatenate([-x[::-1], x[1:]]), np.concatenate([y[::-1], y[1:]]))\n",
+    "\n",
+    "n = 2\n",
+    "def model_wishart_(x, y):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    y00, y01, y11 = y.reshape(3,l)\n",
+    "    yy = np.array([[y00, y01], [np.zeros_like(y01), y11]]).T\n",
+    "    # yy = np.einsum('tab,tbc->tac', np.transpose(yy, axes=(0,2,1)), yy)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    # return interp1d(xx, yy)) \n",
+    "    return CubicSpline(xx, yy) #, bc_type = 'clamped')\n",
+    "\n",
+    "import numpy as np\n",
+    "from scipy.interpolate import CubicSpline\n",
+    "\n",
+    "def model_wishart(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    upper_triangle_indices = np.triu_indices(N)\n",
+    "    y_elements = y.reshape(len(upper_triangle_indices[0]), l)\n",
+    "    \n",
+    "    yy = np.zeros((l, N, N))\n",
+    "    for k, (i, j) in enumerate(zip(*upper_triangle_indices)):\n",
+    "        yy[:, j, i] = y_elements[k]\n",
+    "    \n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n",
+    "\n",
+    "def mini_model_w(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    \n",
+    "    # y.shape should be l*N**2\n",
+    "    yy = y.reshape(l, N**2)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n",
+    "\n",
+    "def mini_model_w_real(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    yy = y.reshape(l, N*(N+1)//2)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dc=np.load('data/bayesian/CsF/dc_minimal.npy', allow_pickle = True).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_bl(array):\n",
+    "    s = array.shape[0]//2\n",
+    "    return np.hstack([array[:s], array[s:]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using multicomponent code.\n",
+      "Using multicomponent code.\n",
+      "-----------------------------------------------------\n",
+      "  RESAMPLE TIME SERIES\n",
+      "-----------------------------------------------------\n",
+      " Original Nyquist freq  f_Ny =     500.00000 THz\n",
+      " Resampling freq          f* =      20.00000 THz\n",
+      " Sampling time         TSKIP =            25 steps\n",
+      "                             =        25.000 fs\n",
+      " Original  n. of frequencies =        100001\n",
+      " Resampled n. of frequencies =          4001\n",
+      " min(PSD)          (pre-filter&sample) =      0.00206\n",
+      " min(PSD)         (post-filter&sample) =    268.60806\n",
+      " % of original PSD Power f<f* (pre-filter&sample)  = 99.707 %\n",
+      " fPSD not calculated before resampling\n",
+      " -----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux = st.HeatCurrent(\n",
+    "    [\n",
+    "        dc['heat_flux'],\n",
+    "        dc['electric_flux']\n",
+    "        # split_bl(dc['heat_flux']),\n",
+    "        # split_bl(dc['electric_flux'])\n",
+    "    ],\n",
+    "    DT_FS=1,\n",
+    "    TEMPERATURE=dc['Temperature'],\n",
+    "    VOLUME=dc['Volume'],\n",
+    "    UNITS='metal'\n",
+    "    )\n",
+    "flux_resample=flux.resample(fstar_THz=20)\n",
+    "flux_resample.filter_psd(0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 466,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using single component code.\n",
+      "Using single component code.\n",
+      "-----------------------------------------------------\n",
+      "  RESAMPLE TIME SERIES\n",
+      "-----------------------------------------------------\n",
+      " Original Nyquist freq  f_Ny =     500.00000 THz\n",
+      " Resampling freq          f* =      20.00000 THz\n",
+      " Sampling time         TSKIP =            25 steps\n",
+      "                             =        25.000 fs\n",
+      " Original  n. of frequencies =         50001\n",
+      " Resampled n. of frequencies =          2001\n",
+      " min(PSD)          (pre-filter&sample) =      0.10953\n",
+      " min(PSD)         (post-filter&sample) =    417.00365\n",
+      " % of original PSD Power f<f* (pre-filter&sample)  = 99.936 %\n",
+      " fPSD not calculated before resampling\n",
+      " -----------------------------------------------------\n",
+      "\n",
+      "-----------------------------------------------------\n",
+      "  CEPSTRAL ANALYSIS\n",
+      "-----------------------------------------------------\n",
+      "  cutoffK = (P*-1) = 7  (auto, AIC_Kmin = 7, corr_factor =  1.0)\n",
+      "  L_0*   =          13.162604 +/-   0.036877\n",
+      "  S_0*   =      620593.471517 +/- 22885.701507\n",
+      "-----------------------------------------------------\n",
+      "  kappa* =         233.877523 +/-   8.624730  S/m\n",
+      "-----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# to check the NLL estimate\n",
+    "flux_c = st.ElectricCurrent(\n",
+    "    split_bl(dc['electric_flux']),\n",
+    "    DT_FS=1,\n",
+    "    TEMPERATURE=dc['Temperature'],\n",
+    "    VOLUME=dc['Volume'],\n",
+    "    UNITS='metal'\n",
+    "    )\n",
+    "flux_resample = flux_c.resample(fstar_THz=20)\n",
+    "flux_resample.filter_psd(0.5)\n",
+    "flux_resample.cepstral_analysis()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Diagonal (thermal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 474,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MaxLikeFilter Initialization\n",
+      "Maximum-likelihood estimation with n_parameters = 16\n",
+      "Spline nodes are equispaced from 0 to the Nyquist frequency.\n",
+      "Optimization terminated successfully.\n",
+      "         Current function value: -7835.506135\n",
+      "         Iterations: 368\n",
+      "         Function evaluations: 12639\n",
+      "         Gradient evaluations: 383\n",
+      "The BFGS solver provides Hessian. Covariance matrix estimated through Laplace approximation.\n",
+      "factor 1\n",
+      "-----------------------------------------------------\n",
+      "  MAXIMUM LIKELIHOOD ESTIMATION\n",
+      "-----------------------------------------------------\n",
+      "  Fixed n_parameters = 16\n",
+      "  kappa* =           0.236849 +/-   0.007326  W/m/K\n",
+      "-----------------------------------------------------\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "  message: Optimization terminated successfully.\n",
+       "  success: True\n",
+       "   status: 0\n",
+       "      fun: -7835.506135395672\n",
+       "        x: [ 7.677e+05  5.450e+05 ...  5.844e+03  3.143e+03]\n",
+       "      nit: 368\n",
+       "      jac: [ 5.638e-05 -3.448e-06 ... -5.250e-05 -4.946e-05]\n",
+       " hess_inv: [[ 1.410e+08  2.437e+08 ... -9.078e+04  6.190e+05]\n",
+       "            [ 2.437e+08  4.259e+08 ... -1.448e+05  9.898e+05]\n",
+       "            ...\n",
+       "            [-9.078e+04 -1.448e+05 ...  6.175e+04 -4.884e+04]\n",
+       "            [ 6.190e+05  9.898e+05 ... -4.884e+04  2.207e+05]]\n",
+       "     nfev: 12639\n",
+       "     njev: 383"
+      ]
+     },
+     "execution_count": 474,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux_resample.maxlike_estimate(\n",
+    "    model_scalar, \n",
+    "    # n_parameters = 'AIC',\n",
+    "    16, \n",
+    "    likelihood='chisquare', \n",
+    "    solver = 'BFGS',\n",
+    "    minimize_kwargs = {\n",
+    "        'tol': 1e-10,\n",
+    "        'jac': '3-point',\n",
+    "        'options': {\n",
+    "            'disp': True, \n",
+    "            'gtol': 1e-4, \n",
+    "            'maxiter': 500, \n",
+    "            'eps': 1e-9,\n",
+    "            }\n",
+    "        },\n",
+    "    # ext_guess=flux_resample.cepf.psd[::len(flux_resample.cepf.psd)//16][:-1]\n",
+    "\n",
+    ")\n",
+    "flux_resample.maxlike.optimizer_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 475,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-----------------------------------------------------\n",
+      "  CEPSTRAL ANALYSIS\n",
+      "-----------------------------------------------------\n",
+      "  cutoffK = (P*-1) = 7  (auto, AIC_Kmin = 7, corr_factor =  1.0)\n",
+      "  L_0*   =          13.603064 +/-   0.040742\n",
+      "  S_0*   =     1000687.651713 +/- 40770.158812\n",
+      "-----------------------------------------------------\n",
+      "  kappa* =           0.308721 +/-   0.012578  W/m/K\n",
+      "-----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample.cepstral_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 476,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(17,)"
+      ]
+     },
+     "execution_count": 476,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.shape(flux_resample.cepf.psd[::len(flux_resample.cepf.psd)//16]*flux_resample.KAPPA_SCALE/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 493,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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3NPNhx44d+M9//mPSp/JixMaVSTt27MBrr71m0r9iQeQKxlU+1QkNDUVQUJC0GY09m7cQ1RanUNmgbdu2Jm3jzHRNrl69atKuaR5pTX755ReTP7DPPfecVedNnTrVZO2e33//3a44iIiIiIjI+YzvPZo0aWLVtB9zEhISqvxLT0+vkrzp0aMH9uzZg9tvv92uuM1p1qyZSfv69etm+/n7+6Nfv35S29w0KuPHhg0bht69e0sJp4MHD1aZNWHcPzw8vEplTXWMq38q398RuQITODbo0qWLSduWrGtCQoJ03KBBg1pnzCvExsaatKvbIs9YQEAAOnXqJLXj4+PtioOIiIiIiJzPuKLf3mr+mtx1113Yt2+fSfLEGhqNBhs2bMDUqVPRv39/NG3aFAEBARAEweRf5V2tKs9WMGY8xanygseZmZnS/Uzr1q3Rrl07qFQqacHh0tJSHDx40OQc4zGGDBkCQRCsfn3GS0/k5+dbfR6Ro3AKlQ1CQ0PRunVrKftd+Y+BJRqNBseOHZPatmR5LTFewAywbU2dgIAA6ZirpxMRERHVL14qDwzuyLUHa0PONXCM/7vdljVnKqtcaZOfn49r167hjz/+wKJFi5CZmYlNmzahf//+2Lp1q9VfPK9duxYvvPACkpKSbI6purVFhw4dirfffhsAkJiYiCtXrkgzI3bu3Cm9HuOpUMOGDcOGDRsAlFfcVCSB9Ho99uzZY9LPFv7+/tJxcXGxTecSOQIrcGw0atQo6TghIcGq7bj37t1r8kdp9OjRdsfRoEEDk3ZaWprV56ampkrH4eHhdsdCRERERO7FS+XBf7X4J+tnZlS1otPpHDZuSEgIbrrpJrz22ms4deoU2rdvD6B816u77rrLqp13v/jiC4wdO9Zs8iYoKAjNmzdHZGQk2rVrJ/0zVjmpZKx///4mCSvjKVCV178xd2zc/9ixYyaVM9YuYFzB+L2oXEVE5ApM4NjovvvuM2kbLyRsSeU+9957r91xVF58bOvWrVadd/HiRSQmJkrtDh062B0LERERERE5lyuq6Js1a4bVq1dLyYnjx49j3rx51Z5z5swZzJgxQ2oHBgZi1qxZ2LNnDwoKClBQUIDr168jISEBly5dwqVLl3Dx4kWrY/Lx8UFMTIzUtpTAMa6m6d69Oxo2bAgAOHr0qDR7wbh/06ZN0blzZ6vjAEzfd+PPg8hVmMCx0YgRI0xWSF+8eDGuXLlisf/BgwexatUqqX3XXXdJWe3KEhMTTeaGDhkyxOK4d9xxh0n7/fffl1ZEr87s2bNN2iNHjqzxHCIiIiIikpfxVKbMTOftIta9e3c8//zzUvv//u//cP78eYv9Fy9eDK1WCwDw8/PDvn37sHDhQgwcONDiWj3VrXljjrl1cFJSUqS42rdvj5YtW0p9jO+ltFot9u7dC8A0+WNr9Q1g+r5XXoiZyBWYwLGRh4cH3n//famtVqsxZswYs+WCsbGxGDduHAwGg3Tue++955A4Bg4ciL59+0rthIQEjBo1CsnJyWb7FxcX48knn8TatWulx4KDgzFlyhSHxENERERERM5jvCNufn4+ioqKnHatuXPnSmts6nS6Kl8CG9u+fbt0/Nhjj5ls422JNctQGDOurklNTcXZs2ctVt+Ye2zHjh3QarXYv3+/9JitCRy9Xo/09HSpHRERYdP5RI7ARYxrYcyYMZg2bRq+/PJLAOU7OXXu3BkTJkxAz549odVqcejQIaxevVrKRgPAwoULrfqDZq2vv/4agwYNkv5479u3D1FRUbj77rvRr18/hIeHQ61WIzY2FmvWrEF2drbJ+f/3f/8nlRYSEREREZFyGc8CAMqXRrj55pudcq2GDRvi6aefxkcffQQA+OOPP3D06FGTL5ArGG8Bbu29jnHSxxp9+/ZFQECANBVqx44dOH78uPS8uWRM5aqdw4cPm2wEY2sC59KlS9IX84BjNqYhshUTOLW0aNEiFBYWYuXKlQDKK3GWLl1qtq8gCJgzZw5efvllh8Zw8803Y9OmTXjooYekhYnLysqwatUqk2lblfn6+uLTTz/FxIkTHRoPERERERE5R58+fUzasbGxTkvgAMDMmTOxePFilJWVAQDeeOMN/PXXX1X6GS9AXN1uUhU0Gg2WLFliUyxeXl4YMGAA/v77bwDlCZwTJ04AKL/XMpeM6dSpE5o3b46UlBQcP34cv//+u/Rcq1atqqwpWpPY2FiTduXPg8gVOIWqllQqFVasWIFff/21SjbcWHR0NLZt22Yy7cqRBg0ahLi4OLz66qs1bvHn7++PSZMm4cSJE3j66aedEg8RERERETlew4YN0bVrV6l95MgRp16vWbNmJl/4bt68GQcOHKjSr3Xr1tLxxo0baxx31qxZJpuqWMt4StTmzZuldUi7du2Kxo0bmz2nIrFjMBjw1VdfVXncFkePHpWO27Zta/K6iVyFFTh2Gj9+PMaPH4+4uDjExsYiJSUFKpUKzZs3R9++fREZGWn1WBEREdVuoWdJWFgY3n33Xbzzzjs4e/YsTp48iczMTBQWFsLPzw9hYWHo0qULevbsCR8fH5vHJyIiIiIi+Y0aNQrx8fEATHdUcpbZs2fj22+/hV6vB1BehbNt2zaTPiNHjpQWE96xYwfef/99sztXqdVqzJkzB59//jk8PDxMpiNZwzjpUlxcLB2bW//G+LmffvoJgOkOUrVJ4Bi/33feeafN5xM5AhM4DtKtW7dqK3FcQRAEdOnSBV26dJE1DiIiIiIicrz7778f//3vfwEAZ8+exbVr15xaCdK2bVs8+OCD+PnnnwGUr12zZ88eDBo0SOozY8YMLF26VJo+9eqrr2LVqlW477770KpVKxQXFyMuLg6///47MjIyAADvvvtujduTV9arVy+EhIQgPz/f5PHqkjGWkjvVJX3MyczMlKZsAeWfA5EcOIWKiIiIiIjIDfTr1w8dOnSQ2sbrujjL3LlzIQiC1H7jjTdMno+IiMCyZcvg6XmjNuDkyZOYP38+Jk+ejOeeew5fffWVlLx5+umnMXfuXJvjUKlUJokjoHyX34rtws2JiIiosltUZGSkzUmvtWvXSlVIrVq1qvaaRM7EBA4REREREZGbmDJlinT8v//9z+nX69atG8aMGSO1d+/ejR07dpj0efDBB7Fz50707t272nFWrVpl8wLGxipX29x8880IDQ2t9pzK1Ta1mT5l/D4/8cQT8PDgbTTJQxBrs+gK0b/i4+NNpo7FxcWZLK5WG9vOpEvHHZoEoXW4v13jERERERHVFfn5+WjZsiWKiooAlFe7WLt9tyvEx8fj8OHDyMjIgI+PD5o1a4bu3bu77TIPFy9eRMeOHSGKInx8fHDt2jWLiyaTfZxxb1nXcA0cIiIiIiIiNxESEoJnnnlGWgvniy++wNKlS2WO6oauXbvWqZvuJUuWSBvNTJ48mckbkhVrv4iIiIiIiNzI7NmzERQUBABYsWIFUlNTZY6obsrNzcU333wDAPD19cVrr70mc0RU3zGBQ4pmtF4aEREREREBCA8Px6xZswAAZWVl+Oijj2SOqG767LPPpKlqL7zwApo3by5zRFTfMYFDRERERETkZl5++WVERkYCKJ9GdfXqVZkjqlsyMjLwySefAABatGjB6htSBK6BQ0RERERE5GZ8fX3x448/4u+//wYAXLlyBW3atJE5qrrj8uXLeOmllwCU71wVGBgoc0RETOAQERERERG5pf79+6N///5yh1EnRUdHIzo6Wu4wiExwChURERERERERkcIxgUOKUrFFHxERERERERHdwAQOEREREREREZHCKW4NnBUrVgAAgoODce+999Z6nI0bNyInJwcA8NhjjzkiNCIiIiIiIiIiWSgugTNx4kQIgoB27drZlcB5/fXXERsbC4AJHCIiIiIiIiJyb4qcQuWodVC4ngoRERERERER1QWKTOAIgiB3CEREREREREREiqHIBI4j6HQ6AICXl5fMkRARERERERER2afOJnCSk5MBAEFBQTJHQrbgrDciIiIiIiKiqupkAue3335Dfn6+tBgyEREREREREZE7k20Xqt27d2P37t0Wn8/JycHbb79t9XgGgwH5+fmIi4vDrl27pMcHDBhgT5hERERERERERLKTLYGza9cuiwkaURSRm5uLt956q1ZjV+w+5eXlhSlTptQ6RiIiIiIiIiIiJZAtgQOUJ1oEQTC73be9W4D7+Pjgq6++QseOHe0ah4iIiIiIiIhIbrIlcEJDQ9GmTZsqj1+9ehWCIEClUqFFixZWj+fl5YXAwEBEREQgOjoaEyZMsOl8IiIiIiIiIiKlki2B88ILL+CFF16o8riHR/m6yhEREbhw4YKrwyIiIiIiIiIiUhxF7kJl7/QpIiIiIiIiIqK6RNY1cMz54YcfAADBwcEyR0JEREREREREpAyKS+A8/vjjcodARERERERERKQoipxCRfUXJ88RERERERERVcUEDhERERERERGRwiluClVlxcXFOHXqFM6ePYu8vDyo1WqbFzl+4403nBQdERERERGRayxbtgyTJk0yeaxLly6IjY2FSqWyaoxdu3Zh6NChUnvDhg0YPXp0jdf64YcfMHHixNoF/q8333wTb731ltTeuXMnhgwZYteYRPWJYhM4cXFxePfdd7F+/XqUlZXZNRYTOEREREREVBedOXMGy5cvx+TJk+UOhYicTJFTqJYsWYLevXtj1apVKC0tlSpuRFG0+R8REREREVFdNn/+fJSWlsodBhE5meIqcNatW4dnn30WACAIgvS4IAgICwtDYGCgXKEREREREREpTnJyMhYtWoRZs2bJHQoROZGiEjiiKGLmzJkAyhM2oiji4YcfxtSpU3HLLbfA19dX5giJiIiIiIiUoWnTpkhLSwMAfPDBB5gyZQoaNGggc1RE5CyKmkJ19OhRJCYmSpU33333HX766ScMGjSIyRsiIiIiIiIjxmt95ubmYsGCBTJGQ0TOpqgEzsmTJ6Xj4cOHV1lhneo+rltERERERGSdgQMHmuwgtXjxYiQnJ8sYERE5k6ISODk5OdLxnXfeKWMkREREREREyvfBBx/Aw6P8tq60tJQ78BLVYYpK4ISHh0vHnLtJRERERERUva5du+Kxxx6T2itWrEB8fLyMERGRsyhqEeOIiAjpODMzU75AiIiIiIjquMTERLvHCA0NRWhoaJ2/htK9/fbb+OWXX1BaWgq9Xo+5c+di/fr1codFRA6mqATOkCFDEB4ejpycHOzcuROvvPKK3CEREREREdVJbdu2tXuM+fPn480336zz11C6Vq1a4bnnnsNHH30EANiwYQP27duHAQMGyBwZETmSoqZQeXl54dlnn4UoitiyZYvJosZERERERERk3rx580yqiGbPni1fMETkFIpK4ADA66+/jv79+8NgMOD+++/HtWvX5A6JiIiIiIhI0Ro0aIA5c+ZI7QMHDuCPP/6QMSIicjTFJXBUKhX++usv3Hnnnbhy5Qp69OiBDz/8EOnp6XKHRkREREREpFgvvPACWrZsKbXnzp0LvV4vY0RE5EiKWgMHAIYNGwYAMBgM8PDwQH5+PubOnYu5c+eiTZs2aNq0KXx9fa0eTxAEbN++3VnhkoOJcgdAREREROSmfH198eabb+LJJ58EAJw9exbLli3DE088IXNkROQIikvg7Nq1C4IgSG1BECCK5bf1iYmJuHr1qtVjiaJoMhYREREREVFdNnHiRHzyySc4c+YMAODNN9/EI488Aj8/P5kjIyJ7KS6BA0BK2Nj6HMkvu6hM7hCIiIiIyApXrlyxe4yatt6uK9dwJyqVCgsWLMA999wDAEhOTsaiRYu4qDFRHaC4BM7jjz8udwhkh0sZRRgkdxBEREREVKOIiAheo466++67MWDAAOzbtw8A8MEHH+Cpp55CgwYNZI6MiOyhuATODz/8IHcIREREREREbm3hwoW49dZbAQB5eXl4//338d///lfmqIjIHorbhYqIiIiIiIjsExMTI02jAoDPP/8cSUlJMkZERPZiAoeIiIiIiKgOWrBgAVQqFQCgtLQUb7zxhswREZE9mMAhIiIiIiKqgzp37oyJEydK7RUrViA+Pl6+gIjILkzgEBERERER1VFvvfWWtIW4wWDAu+++K3NERFRbbpHA+fPPPzFjxgzExMSgdevWCAoKgkqlQseOHav01Wq1OHLkCI4cOYJjx47JEC0REREREZEytGjRAtOnT5faaWlpMkZDRPZQ3C5Uxn766Se8/vrruHr1qvSYKIpmjyt4eXnhsccew8WLFwEAJ06cQPfu3Z0fLDmEmY+UiIiIiIjsMGfOHHzzzTfIycmp9RizZ8+2uXrn/vvvx8KFCy0+P2HCBKk6yFrTp083SUgR1SeKTODodDo8+uij+O233wBUTdQIgmA2eVPh2WefxQsvvABBEPDjjz/iww8/dGq8REREREREShUaGoq5c+filVdeqfUYGRkZyMjIsOmc9PT0ap9PSUmxOQ57klBE7k6RU6gmTJiAX3/9FaIoQhRFeHl54c4778T8+fPx5ZdfQhRFCIJg8fxx48ZJz//111+uCpuIiIiIiEiRnn/+ebRu3VruMIjIDoJYXSmLDH755Rc88sgjUpXN/fffj0WLFqFZs2ZSHw8PDwiCgHbt2uHChQtmx+nTpw+OHz8OQRCQkZGB8PBwV72EeiU+Ph7dunWT2t+t343JYwbVejyNzoA9FzKldsemQWgV5m9XjERERERERKRsle8t4+Li0LVrVxkjUh7FVeDMnz9fOn766aexatUqk+SNtXr16iUdx8XFOSQ2IiIiIiIiIiI5KCqBc+bMGVy8eBGCIKBVq1b49NNPaz1Whw4dpOOEhARHhEdEREREREREJAtFJXCOHz8uHY8fPx4+Pj61His0NFQ6zs3NtScsIiIiIiIiIiJZKWoXKuNVyjt27GjXWCqVSjrWaDR2jUWOU6rV42xqAVQeAjo3C4aXyjSHKEJRSzIRERERERERKYKiEjjGO0vpdDq7xjLeXq5BgwZ2jUWOcya1ADlF5Qk1H081OjYNqrZ/NZuNEREREREREdUbippC1bhxY+nY3nVrTp48KR03adLErrHIcSqSNwCQlFNcY39l7ZFGREREREREJA9FJXC6dOkiHW/cuLHW45SVleHvv/+W2tHR0XbFRUREREREREQkJ0UlcHr16oUmTZpAFEWcP38ey5cvr9U4X3zxBbKysiAIArp06VKrbchJGTiFioiIiIiIiEhhCRwAeOKJJwAAoihi2rRp2Lp1q03nb926FfPmzZPa06dPd2h8RERERERERESuprgEzpw5c9C4cWMIgoCSkhKMGjUK06ZNw4ULF6o9LycnB3PnzsXo0aOh0WggCAI6dOiAyZMnuyhyIiIiIiIiIiLnUNQuVAAQGBiIdevWYcSIESgpKYFer8fXX3+Nr7/+GpGRkejatavUNycnB8888wzi4+Nx6NAh6PV6iP+uehscHIx169aZbCdOREREREREROSOFJfAAcoXHd6wYQMeeeQRpKenAyifUpWQkIDLly9L243n5uZi6dKl0vMVjzdr1gxr1qxBx44d5XkBVGvcdYqIiIiIiIioKsVNoaowdOhQxMbGYuLEifDy8pIeFyvd4YuiKD2mUqnw6KOP4vjx49x5ioiIiIiIiIjqDEVW4FRo1KgRvv/+eyxYsACrV6/G3r17cerUKWRnZyMvLw/+/v5o2LAhOnXqhKFDh+KBBx5AmzZt5A6biIiIiIiIiMihFJ3AqdCkSRM8++yzePbZZ+UOhYiIiIiIiIjI5RQ7hYqIiIiIiIiIiMoxgUNEREREREREpHBM4BARERERERERKRwTOERERERERERECifLIsYrVqwwaT/22GMWn3ME4/GJiIiIiIiIiNyNLAmciRMnQhAEqW2cYKn8nCMwgUNERERERERE7kzWKVSiKLr1+EREREREREREriBLBQ5QfXKFiRciIiIiIiIiohtkSeAYDIZaPUdEREREREREVB9xFyoiIiIiIiIiIoVjAoeIiIiIiIiISOFkWwOHiIiIiIiIHEOj0eDo0aNITk5GVlYW8vLyEBgYiAYNGqBjx47o0aMHfH195Q6TZDRkyBDs3r0bAPD4449j2bJl8gZENmMCh4iIiIiIyA2JoojffvsNy5Ytw549e1BcXGyxr6enJwYMGIBHH30U48ePR2BgoAsjdT+fffYZ8vLyAJQnPoYMGSJrPESAAhM4Wq0WqampAAAPDw+0bNnS5jGSk5OlxZCbN28OT0/FvUxyUxmFpbiYXoQQPy90bR4MQRDkDomIiIiI6qHt27djxowZOH36tFX9dToddu3ahV27dmHOnDl444038PTTT/NeyYLPPvsMV69eldpM4JASKO639bPPPsOcOXMAAGPHjsWqVatsHmPmzJlYs2aNNN7zzz/v0BjJeZS+g3xsUj4AoESjR+MgHzQOZhkqEREREbnWokWLMHPmTOj1eukxQRDQvXt3DB06FM2bN0d4eDgKCgqQnp6OEydOYNeuXSgrKwMAZGZm4vnnn8eAAQPQs2dPmV4FEdlKcQmcFStWQBRFCIKA2bNn12qM2bNnY/Xq1QCAZcuWMYFDTpFfomUCh4iIiIhc6p133sEbb7whtQVBwMMPP4x33nkHkZGRFs9Tq9X49ddf8dZbb+HatWuuCJWIHExRCZzU1FTEx8dDEARERUWhT58+tRqnd+/e6NixI86fP49Tp04hMzMTjRo1cnC0puLj4xEbG4uUlBSoVCq0aNECffr0Qdu2bZ16XUsKCgpw4MABpKSkICMjA4GBgWjcuDFatGiBnj17IiAgQJa4iIiIiIiodrZs2YI333xTant7e+PHH3/EuHHjajw3ICAAkydPxn/+8x+8//77eOedd5wYKRE5g6ISOCdOnJCOBw8ebNdYgwcPxvnz5yGKIo4fP46RI0faG55Zq1evxjvvvIPY2Fizz8fExOC9995z2ZzJvXv3YuHChdi6dSs0Go3ZPiqVCtHR0Xjvvffsfp+JiIiIiMj5iouL8eijj0prfQLATz/9hAceeMCmcby9vfHmm28iOjoaQUFBVp2TmpqKAwcOIC0tDQUFBQgPD0dkZCQGDhwIHx8fm65vyZEjR3Du3DmkpqYiODgYUVFRGDJkCLy8vGweq7i4GMePH8eZM2eQl5cHrVYLf39/NG7cGJGRkejZsyf8/PwcEret/vnnH5w/fx6pqakQRREDBgxA//79zfbVaDQ4ffo0zp07h/T0dJSUlCA4OBiNGzfGLbfcIluxAMlHUQmcy5cvS8ddu3a1a6zOnTubHddR9Ho9nnzyyRq3Xjtw4ACGDx+OefPmOTXLXVxcjOeeew7Lli2DWMNCMnq9Hvv378fRo0eZwCEiIiIicgPffPMNMjIypPajjz5qc/LG2B133FFjnz/++APvvvsujh07ZvYeIzAwEE888QTefPNNhIaGVjvWrl27MHToUKl95coVRERE4JdffsEbb7yBixcvVjknLCwMs2bNwssvvwyVSlVjvBkZGXj99dfx888/o6ioyGI/T09PREdHY+bMmbjvvvukx5ctW4ZJkyZV6f/WW2/hrbfeMjvWDz/8gIkTJ1oco+J9++qrr/Df//63yr3p448/bpLAycrKwm+//Ya1a9di//79KCkpsfg6OnXqhNmzZ+Pxxx/n5ir1hKISOAUFBdJxTX8AamJ8fn5+vl1jmTNjxgyT5I2/vz8mTJiAnj17QqPR4PDhw1izZg20Wi0MBgPeffddhIWFYcaMGQ6PRa1W46677sLu3bulx/z8/DB8+HBER0ejSZMm0Ov1SEtLw8mTJ7Fjxw6T95qIiIiIiJRLFEV88sknUlsQBJOpVI5WVFSEhx56CJs2baqx3//93/9h1apV2Lp1K7p06WLTdV5++WV8/PHHFp/PycnBnDlzsHnzZvz555/VVs2cPn0aw4cPR2ZmZo3X1el02LdvH9q0aWOSwHEGnU6HcePGYd26dVb1//zzzy0miyo7d+4cJk2ahHXr1uGnn37iMhn1gKISOP7+/tKxvQkG4/MdvTXepk2bsHjxYqndpUsXbN68Ga1atTLpd+rUKYwaNQopKSkAyv9AjRgxAjfddJND4xk3bpxJ8ubRRx/FRx99hMaNG5vtr9VqsXHjRtnKBomIiIiIyHqxsbEmCw/fdttt1S5YbA+1Wo2hQ4fin3/+kR4LCgrCmDFj0KdPH4SEhCAjIwObN2+W7kFSUlIwePBgHDt2DK1bt7bqOj/88IOUvGnZsiUefPBBdOjQASUlJTh69Ch+//13qfpk165deOCBB7Bx40azlSalpaW45557TJI3PXr0wO23347IyEj4+fmhqKgI169fx6lTp7B7926o1eoq4wQHB6Ndu3YAgKtXr0Kn0wEAGjRogLCwMLOvIzg4uNrX+fLLL0vJm27dumHMmDGIiIiARqPB2bNnq52C1qxZM9x6663o2bMnGjZsCC8vL6Snp+PgwYP466+/pPj++OMPTJs2DcuXL682FnJ/ikrgGC80fP78ebvGunDhgtlx7WUwGDBv3jyp7e/vjw0bNlRJ3gDlfzRWrVqFgQMHwmAwSOdu2LDBYfF89913+Ouvv6T2rFmzsHDhwmrP8fLycnqmubZEKHwfcSIiIqK6IjHR/ONNmwK+Vuy0WVAA5ORUfdzbG2je3LoYkpOBf29CTTRsCAQG1nx+cTFgNK1I4uEBWJlIQFoaUFpa9fHQ0PJ/CmD8ZS0ADB8+3GnXeu6550ySNw899BA+//xzhIeHm/SbM2cONmzYgIcffhhqtRpZWVmYPHkytm3bZtV13n33XQDApEmT8MUXX1T5cjkhIQH33XcfTp8+DQD4888/8f333+OJJ56oMtYvv/yCK1euACivTvruu+/MToWqUFpainXr1iEpKcnk8bFjx2Ls2LEAgIiICFy9ehUAMH369FpXPP3f//0fPD098cUXX2DKlCk1TnXy8PDAPffcgxdffBGDBg2Ch4eH2X6JiYl45JFHcPDgQQDluzlPnDjRZJoa1T3mfxpkYrxuzaZNm0wW6LKFwWAwSZJ06NDB7tgqbN++3WTB4unTp1eb/Y6JiTFZFX7jxo24dOmSQ2IpLCzEyy+/LLWjo6OxYMECh4xNNWOqiYiIiNxa27bm/x06ZN35339v/vxRo6yPYcAA82OsXm3d+Vu2mD+/e3frY3joIfNjfPaZ9WM42ZEjR0zat9xyi1Ous2fPHpNlIiZMmID//e9/VZI3FcaMGYP//e9/Unv79u3Yvn27VdcyGAwYNWoUvv32W7MzA9q1a4fNmzebfBn/6quvQqvVVum7Y8cO6fiee+6pNnkDAL6+vnjooYfwyiuvWBWrvRYvXoynnnrKqnVqZs2ahXXr1mHIkCEWkzdAeYJp8+bNUsVQxXWoblNUAqdXr15o2LAhRFHEtWvX8NVXX9VqnCVLlkglhqGhoejXr5/DYly7dq1J+8knn6zxnClTppi0rZ3/WJMff/wReXl5UvvDDz+s9peciIiIiIjcT3p6uknbWbsPffTRR9JxeHg4lixZUuM5Y8aMwSijpJ015wDly1wsXry42vuX5s2bm1S+pKenY/369VX6paWlSceO/PLeEXr27ImpU6da3d/Xmuq3fwUHB+PVV1+V2lu2bIFer7cpPnIvirvb/89//gOgfKGul156CX///bdN52/evBkvvfQSBEGAIAh45JFHHJrUMF7Iq127diYZT0sGDhxo8ou4ceNGh8SydOlS6bhjx44YOHCgQ8ZVkho21CIiIiIiqvOys7NN2vZu+GJOfn6+yb3Ok08+afU2448//rh0vH379hp3xQWsX8fnscceM6nQMZfAMV5LtWJKkVI4e4eoESNGSMdqtRpnz5512rVIfopL4MybNw+BgYEQBAFlZWUYPXo0ZsyYgeTk5GrPS05OxosvvogxY8ZAq9VCFEUEBATg9ddfd1hseXl5JouHRUdHW3Wet7c3evfuLbWNp2DVVlZWFk6ePCm1R9lSqkpERERERG6jsLDQpB1ozfpANjpw4IDJEhZ33XWX1ecaT+nKy8uzaj3TkSNHWjV2YGAgYmJipPbRo0er9OnVq5d0vHfvXkybNq1K0ksuxrE7Q7NmzUza169fd+r1SF6KS+A0bNgQy5cvlypo9Ho9Fi1ahLZt2yImJgbTp0/HggULsHjxYixYsADTp09HTEwM2rZti8WLF0Ov10MURahUKqxYscLiTky1UTmbGRUVZfW5xpU6ubm5JmV+tVF5Hmz//v0BlGfnP/30UwwYMADNmjWDj48PmjVrhpiYGLz22mu4ePGiXdclIiIiIiLXqlwJU1RU5PBrnDp1yqTdtWtXq89t0qSJSbumL98B2LQzr3Hf8+fPV6nwmThxosluTkuWLEGLFi0wevRo/N///R+OHz8u29QiW+4ZjWk0GmzYsAFTp05F//790bRpUwQEBEj3yRX/vLy8TM4zXmKD6h5F7UJV4b777sPXX3+N5557DhqNBgCg1+tx+PBhHD582Ow5oihKpWk+Pj5YsmQJ7r33XofGdfnyZZO2tVvkmet7+fJlNG3atNaxnDhxwqTdqVMnrFmzBk8//TSysrJMnktLS0NaWhoOHjyIDz74AE899RQ+/fTTaresq63U1BTEx8dbfP5aUrFJO168EWujRo0QGGp+ez4iIiIiovqq8iLCeXl5CAkJceg1KlesWFq42Bq5ubk19rHli3bjhYwNBgMKCgpMXn/r1q3x3XffYeLEidLW2mVlZdi0aZM0LSwkJASDBw/GuHHjMHbsWJNpV85U0zbj5qxduxYvvPBClV2yrFFqbkc1qjMUmcABgCeeeAI333wznnnmGalMzjhJY6wiAyuKIvr3748lS5aguy0rz1upoKDApB0WZn2yoUGDBibtymWQtsrMzDRp79q1Cy+88IL0XgiCgIYNG8LT0xMZGRlSxlmv12PJkiWIjY3F1q1bza74bo+333kHryWfsfi8XzvTFfNLEm5UEs2fPx+z5r3m0HiIiIiIyIJ/t12uwtovGSdPBsx9YertbX0M+/ZZ3kbcGrffbv512LIG5i+/WN5GXCEqV7gkJiaiTZs2Dr2GIys3iouLa+xjy31IQECASbuoqKhKAmvChAno2LEj5s6da3Ydnvz8fKxfvx7r16/Hyy+/jIULF5qs3eMsnp623XJ/8cUXeO6558w+FxQUhKCgIPj6+prcFyckJEjH1qw/RO5LsQkcoHwu4+HDh7F371789ttv2Lt3L86ePWuydZynpye6du2KQYMG4aGHHpKmEjlD5VJFW1YIr/wHyt6yx8p/YF966SWIoggfHx/MnTsXTz31lDQfMj8/Hz///DNee+015OTkAAD279+PZ555xmSbQMdw3gJdRERERORAERH2nR8cXP7PHi1b2ne+v7/9r8OOqnhXueWWW/DTTz9J7SNHjmDw4MEOvYZxRYpKpUKEHe+rNYsfl5SUWD2eWq02aVtaA6hPnz7YunUrrly5gr/++gu7d+/Gvn37kJKSYtIvPT0dEydOxJUrV0x2uZLbmTNnMGPGDKkdGBiIadOmYfTo0ejZs6fZ91UURe5EXI8oOoFTYeDAgSY7LBUWFqKwsBCBgYG1KkmrrcrlaN42fLtQebqSLX+wzKmcANJqtfDy8sKff/6JYcOGmTwXEhKCZ555BkOGDMHAgQOl8sjly5fj2WefRd++fe2KhYiIiIiInKdysmb79u145ZVXHHqNhkZVT15eXrh48aJTd0/KyMiwuq/x7AMPD48a7wHbtm2LadOmYdq0aQCACxcuYOPGjfj+++9Nlnt4++23cf/999u0Ho8zLV68WCpW8PPzw759+9CjR49qz+GaN/WLWyRwKqsoHXO1yhU3FevzWKOsrMykbe/UJXPVPy+99FKV5I2xzp0745NPPjEpFfzss89Msvn2euP113DvrZb/AB6utAZOv1Y3Mv2NGjXituFERERERJXcdNNNaN26tbQjbkWVSdu2bR12jU6dOknHpaWlSEhIqPUCvNY4ffp0tfcuxuLi4qTjDh062JxY6tChA2bOnIkZM2bglVdewccffwygvHrl559/xoIFC2waz1m2b98uHT/22GM1Jm+Aquu0Ut3GWisbVC7Vs2WBqMoVN/Zu/Vc5gSUIAqZPn17jeY888ojJHNpt27bZFUdlzZo1R9euXS3+a926tck/4+ccuWMYEREREVFd4eHhgZkzZ0ptg8GAt99+26HXGDJkiEliZMOGDQ4dv7K///7bqn5FRUXYv3+/1LZn9oAgCFi4cKHJAs1nzphfv9N4dyfj7dWdyXgLcGuSN4Bp0ofqPiZwbFC5VM+a1dUrVC5ts7eCqHIsnTp1kta8qY6npycGDBggtTMyMqza5s9qTlwCp6hMB63eNX88iYiIiIiUZMqUKSZfeC5btgzr1q2r9XibN282Wfy2UaNGJhUxn332WZVZBI60bds2JCYm1thv5cqVJl+G33PPPXZdV6VSoV27dlLbeH1VY8ZfuOfn59t1TWsZL0BsTbGARqPBkiVLnBkSKQwTODaoXKJYUcJojatXr5q0IyMj7YrF+I8OYNuW5pVXrK+8o5USJWQW4VBCNg5dzoaOSRwiIiIiqmf8/f2xcuVKkwVrH3roIaxZs8amcTQaDd58803cddddVXbGnTdvnnR87do1TJ061eZdjaydpaDVajF9+vRqx09LSzNZZLhx48a4++67q/SzJhFUoaSkBBcuXJDalhZrNn7ceAqXMxnf023cuLHG/rNmzbLptZP7k2UNnD179pi0Bw0aZPE5RzAe3x5dunQxaV+6dMnqc42z2w0aNEBTO1e779q1q0nblh2xKve1ZSqYXK5klq88X6Y1IDm3BBENA2o4g4iIiIiobrn99tvx5ptv4o033gBQvs7m+PHjMWHCBLz11lvVromjVqvx66+/4u23367y5XKFYcOGYdKkSfjhhx8AlG96kpWVhc8//7zaXanKysqwfft2LF26FC1atMAXX3xR42vx8PDAhg0bMGXKFHz++edV7lEuX76MsWPHmix2/P7775tMbTKOu2vXrpg6dSpGjhxptg9Qvr35E088YTI74t577zXbNzo6Wqpw2rVrF3799VeMHz/eqQs7jxw5EufPnwcA7NixA++//75JUq2CWq3GnDlz8Pnnn8PDw8NlU7xIfrIkcIznVwqCAJ1OZ/Y5R6g8vj1CQ0NNFg87ePCgVedpNBocO3ZMajtilfNu3bpBpVJBr9cDgLQ9uDUq9zWeA2o3FyxCrGEFDhERERHVU6+//jqCg4Px0ksvQa/Xw2AwYOXKlfjxxx/Rs2dPDBkyBM2bN0d4eDgKCgqQnp6O48ePY9euXVZNifryyy9x6dIl7N27FwCwadMmbN68GUOHDkVMTAyaN28OHx8f5Ofn4/r16zh16hQOHjwoVfNMnTrVqtfx2muv4e2338Z3332HLVu24MEHH0SHDh1QWlqKo0ePYs2aNSguvrEByqhRozB58mSzYxkMBmzcuBEbN25EaGgoYmJi0Lt3bzRp0gSBgYEoKChAXFwc1q1bZ5IQuvPOO3H77bebHXPChAmYP38+ysrKYDAY8NBDD2Hq1Klo2bIlPD1v3Ea//fbbZquCamPGjBlYunSp9AX7q6++ilWrVuG+++5Dq1atUFxcjLi4OPz+++/S63j33XfNJnmobpJ1F6rqyuVsLdWrTBAEu8cwZ9SoUfjqq68AlFfVXL58ucbpUHv37jWpchk9erTdcYSEhGDw4MHYsWMHACA2NhYGg8GkpNKSEydOSMdeXl5o2bKl3fEQEREREZFrvPDCC+jatStmzJghTe8RRREnTpww+W99S5o1a4a33nrL7BfLvr6+2LZtG6ZNm4bvvvsOAKDX67Ft2zarNkBRqVRWvYZJkyahqKgIn3zyCZKSkvDRRx9Z7Dt48GCsWrXKqi/68/Ly8Oeff+LPP/+stt/w4cPx66+/Wny+ZcuW+Oqrr/DUU09J6+Tk5+dXWQ/Hli/SaxIREYFly5bhP//5j1SEcPLkSZw8edJs/6effhpz585lAqcekW0NHGcmbxw1hjn33XefSfubb76p8ZzKfSyV6dnqgQcekI7z8/OxZcuWGs+5cuUKjh49KrWjo6Ph7+9fzRk2cuIixi68BBERERGRoo0YMQKxsbH43//+h5EjR9b43/ReXl4YPnw4li9fjoSEBEyZMsVissXb2xvffvstDh06hDFjxsDHx6fascPDwzFu3DisXr0an376qdWv4eOPP8bPP/+M9u3bm30+LCwMCxYswPbt26t9fV9++SUmT55s1bqgN910E7799lts3bq1xo1lJk6ciFOnTmHGjBno27cvwsLCLE7PcpQHH3wQO3fuRO/evS326datG1atWsUFjOshQXRWpqMaledcGi+qa2k+pj0qL9prD4PBgB49ekiZ7oCAAJw+fdrifNODBw9iwIAB0rzEu+66y+KCVImJiSbjDB48GLt27bIYS1FREaKiopCeng6gfKu5I0eOwNvb2+I5Y8eOxdq1a6X2t99+iyeeeMJi/5rEx8ejW7duUvu7DbsxebTlNYe2nUk3aY/o0sSkXVSmw6GEbKndsWkQWoX5m5wX0dAfUY3t28WrtozjaB3ujw5N5ImDiIiIiMiYRqPBkSNHkJycjKysLOTn5yMwMBANGjRAx44d0bNnzxoTMZaUlJTgwIEDuHr1KrKysqDX6xEUFISWLVuic+fO6NChQ43VMbt27cLQoUOl9pUrV0zW1Tl8+DDOnTuH1NRUBAcHIyoqCkOHDrU5YZKSkoK4uDgkJiYiNzcXWq0WQUFBaNGiBXr16mX3ZjKuFB8fj8OHDyMjIwM+Pj5o1qwZunfvXmVt1rqi8r1lXFxclbVf6ztZplBVl1BxZLLFGTw8PPD+++9L8xzVajXGjBmDv/76C61atTLpGxsbi3HjxknJGw8PD7z33nsOiyUwMBBvvfUWnn76aQDAqVOnMHbsWKxcuRINGjQw6VtWVoaZM2eaJG86dOiAxx57zGHxEBERERGRPLy9vTFgwACnjO3n54fhw4c7ZewK/fr1Q79+/ewep3nz5mjevLkDIpJf165dmcAgE7IkcAoKCgCUz48MCHC/3YTGjBmDadOm4csvvwRQnins3LkzJkyYgJ49e0Kr1eLQoUNYvXq1NF8SABYuXIgePXo4NJannnoKu3fvxv/+9z8A5YuMRUVFYfz48ejevTs8PT1x8eJF/PbbbybVTYGBgVizZo3TSwCdw7GTqERRxLWcYpTpDIgID4C3p2wzC4mIiIiIiIjMkiWBExoaCkEQEBUVJW2TVqFihycvLy80a9ZMjvCssmjRIhQWFmLlypUAyitxli5daravIAiYM2cOXn75ZYfHIQgCli1bBoPBIC3ClZOTIy20bE6LFi2wdu1ak/K0+iyjsAwX04sAAKVaPbq3DJU3ICIiIiIiIqJKZF3E2NzyOxEREWjbti2GDBni+qBsoFKpsGLFCvz666/VJkKio6Oxbds2vP/++06LxdvbG7/88gt+++039OzZ02K/kJAQzJo1C6dOnULfvn2dFo+zOXCXeQDA5Uy1dJxRUL61oiiKyC4qQ4lG79iLEREREREREdWCbNuIV7fIlaXkjhKNHz8e48ePR1xcHGJjY5GSkgKVSoXmzZujb9++Ni2SFRERYdfrHjduHMaNG4cLFy7gxIkTSElJgUajQXh4OLp06YJbbrkFnp6y7hxvM7l+DC5mFOFadjFUHgJiosLh42nddohEREREREREziDL3byXlxd0Op20t31lNa1grkTdunVTzJSkDh06oEOHDnKH4TSu+Om4ll0MANAbRFzLLkZ77jZFREREREREMpJlClVISAhEUUR6erq0QxORtVyd4NPq3aMajIiIiIiIiOouWRI4HTt2BACUlpbiu+++kyMEUih3mTpHRERERETWGzJkiLRUhiiKiIiIkDskIrcjyxSqESNGYP/+/QCAp59+Gt9//z2ioqJMtrTOyMjA5MmT7b6WIAhMEtUx7jfBjoiIiIiIiMg+siRwpkyZgk8//RSFhYUQRRFHjhzBkSNHpOdFUURhYSGWL19u13VEUWQCpw5SyhJJLBYiIiIiIiIiV5FlClXz5s3x66+/IiiofGFY41K6CsaP1fYfEREREREREVFdINue0iNHjsTly5fx448/4tChQ8jKyoJWq8Xu3bshCAJ8fX1xyy23yBUeKZjg4klUIpgMJCIiIiIiInnJlsABgLCwMEyfPh3Tp0+XHvPwKC8KatGiBXbu3ClXaKQgrKYiIiIiIiKi+k7WBA6RNSrnbxyxBk5RmQ5x1/Ph6SFAo+dW9kRERERERKRsikzgsOKCnO10cj7UZTq5wyAiIiIiIiKyiiwJnF69egEA2rRpg7Vr15o8ZzCwGoJMOSOdx+QNERERERERuRNZEjgnT56EIAgoKiqq8lxkZCQAoG3btti+fburQyM7GQwijl3NRfsmgQj29XLImJUrsly9jTgLwoiIiIiIiEhuiptClZiYCADw9FRcaGSlXLUGJ67lYXCHRjafay5XwvyJ6+SoNcgv0aJFqB+8PT3kDoeIiIiIiIj+JUuWxMPDA6IoWpwuJbi6xIIcTqtzzFQ4EaKZRYz58+EMpVo9jl/NBQAUlGjRo1WovAERERERERGRRJav2IOCgiCKInJycuS4PLlIfolW7hDIBkk5xdJxZmGZjJEQERERERFRZbIkcCIiIgAA+fn5OHTokBwhkAscv5oLvcH+CVBipUlUrL8hIiIiIiKi+kaWKVQxMTE4deoUAGDUqFGYOnUqoqKi4OV1Y9HbwsJCrFixwiHXe+yxxxwyDtlGbxCRrS5D4yBfu8apOoXKruGIiIiIiIiI3I4sCZwnn3wSX3/9NURRRF5eHj788EOT50VRREZGBiZNmuSQ6zGBQ0RERERERETuTJYpVDfffDPeeustaXvoyttEO4qzxiV5CZxERURERERERPWMbPsEv/rqq/j7779x7733omnTplCpVBBFUdphSBRFu/+R+zH3sfGjJCIiIiIiovpOlilUFW677TbcdtttJo95eHhAEAS0a9cOFy5ckCkychgHJF8qL2JM1hNFEZlFZfDzUiHI16v6vi6KiYiIiIiIiGwnawKHyBpKrcBxh8RSQqYaiVlqCAIQHRmOAB/+yhMREREREbkj2aZQVYfTn4jMyyvWYM+FTBy+nA2NzlBj/8QsNYDyJNiljKJq+3JlISIiIiIiIuVS3NfxO3fuBAD4+fnJHAkpgShWndrjDpUvznL8Wi4MBkCjM+ByVhE6NQ22+lydofr3rf6+q0RERERERMqnuATO4MGD5Q6BFIYVWTcYjIpucoo08gVCRERERERELqXIKVRUdzgi9cL0DREREREREdV3iqvAMefMmTPYunUrDh8+jOTkZOTm5qK4uBgRERHYvn27SV+DwYCsrCwA5TtaNWzYUI6QiRSIqTAiIiIiIiJ3pegEzt69e/Haa69h3759Jo9XTKlRqVRVzhFFEb1790ZKSgoEQcCFCxcQGRnpknjJAczkGDiDioiIiIiIiOo7xU6hmjNnDoYOHYp9+/ZBFEWTf4Jgeb8clUqFadOmSX1XrlzpwqjJKSolcFyd0GECiYiIiIiIiOSmyATOyy+/jP/+978wGAxStU3nzp3x+OOPY9asWTUmcR5++GHpeNOmTU6Pl5yrPu86RURERERERAQoMIGzdetWfPLJJ1K7X79+OHLkCOLj4/HDDz/ggw8+qHGMiIgIdOnSBaIo4sSJEygoKHBmyORkrIAhIiIiIiKi+k5xCZw5c+ZIx/feey/27duHPn362DxOxTkGgwFxcXEOi4+IiIiIiIiIyNUUlcBJTEzEiRMnIAgCGjZsiGXLlpldqNganTp1ko4vXrzoqBDJRrZWz+jNnMACHCIiIiIiIqrvFJXAOXjwoHQ8fvx4BAcH13qssLAw6TgnJ8euuMh1zqVVne4mVkrqMKFDRERERERE9Y2iEjhpaWnScbdu3eway8fHRzouLS21ayxyneIyvUlbBBM27k5dpsO17GKU6fQ1dyYiIiIiIiKzPOUOwJhOp5OOazt1qkJeXp50HBISYtdYVL9xF6zaMxhEHE3MgU4vIjW/BP0iw+UOiYiIiIiIyC0pqgKnSZMm0vHVq1ftGst44eJGjRrZNRbJi7tQua/cYg10+vIPsLBUV0NvIiIiIiIiskRRCZyoqCjpePPmzbUex2Aw4O+//5bavXv3tisukhcrYNwXPzkiIiIiIiLHUFQCp1+/fggNDYUoijh+/Dj+/PPPWo2zcuVKJCcnQxAEtG3bFpGRkQ6OlFyKWQCHYCUTERERERGR+1JUAkelUuHhhx8GUL7z0OOPP24yFcoasbGxeOGFF6T21KlTHRoj2YbVM3VXYakW1/NKoNMbLPZRetJIbxCRmKVGcm5xld3OiIiIiIiIlERRCRwAePPNNxEUFARBEJCdnY3+/fvjww8/RFFRUbXnGQwGfP311xg0aBAKCwsBAE2bNsVzzz3nirDJiSrfVvNGW34anQFHE3NwNqUAZ1Krbv3uLq5kqXEpowjnUguRVsDd6oiIiIiISLkUl8Bp1KgRfvzxR3h4eEAQBKjVasydOxeNGjXCbbfdhhdffFHqW1BQgIULF+Kxxx5D06ZNMW3aNBQUFEAURfj4+GD16tXw8/OT78WQQyg1X6PUuFwhJa8Ehn8LbzIKyuQNxg6JWWrp+KwbJ6KIiIiIiKjuU9Q24hXGjBmD5cuX46mnnkJJSQkAoKysDDt27MCOHTsAlFdhZGZmYt68eVJbEAQAQGBgIFauXIn+/fvL8wLIoeSehlWfEzWW8C0hIiIiIiJyLcVV4FR45JFHcPToUQwePFiaMlPxv4IgSMka48dFUcTAgQNx6NAh3HPPPa4PmuzCqVHy4ttPRERERESkXIqswKnQuXNn7NixAydPnsRPP/2EvXv34tSpUygruzFlQ6VSoWPHjhg6dCgeeugh3HrrrTJGTPYwWEggMLGgPELNXYiIiIiIiMiBFJ3AqdCzZ0/07NlTahcXFyMvLw/+/v4IDQ2VLS6qmS3JF4OFzlUXMa59POQYAjM4RERERERELuUWCZzK/P394e/vL3cY5GCWEjOcWqU8gpU1OHKvX0RERERERFRXKHYNHKp/zFXgiKLIips6hMk4IiIiIiKi2mECh4hsxilUREREREREruU2U6ji4+Nx+PBhXLhwAbm5uSgqKkJgYCAaNGiADh06IDo6Gl26dJE7TKqFEo0eelGEB5MCRERERERERGYpOoFTUlKCL774AkuWLEFiYmKN/du2bYtnnnkG06ZNg5+fn/MDJLvll2hx7GoODAagbaMAs30460Z5WIFDRERERETkWoqdQnXo0CH06NEDs2fPxpUrV2pcO0MURVy+fBmzZs1Cz549ceTIERdFStWpKfcSn5IPg6H8+Eqm2sIYzOA4gjPfRa5tQ0RERERE5FyKTODs27cPI0aMQEJCAkRRhPDv1/0VxyEhIWjevDlCQkIgCIJ081hxfPHiRQwbNgz79++X82WQFUo0erlDqBFTE1UJlUpwmL8hc9LyS7H3YibiU/LlDoWIiIiIyO0pLoGTlZWFu+++G8XFxdJjLVu2xPz583H06FGo1Wrk5OQgKSkJOTk5UKvV+OeffzB//ny0atUKQPnNZXFxMe655x5kZWXJ9VLICtZMxVFqcuB6bgn2XczCtezimjvXMZU/Nms/IqV+luQccdfzUaY1IDWvFLlqjdzhEBERERG5NcUlcN544w3k5eVJ1TSvvPIKzp8/j/nz56N3797w8fEx6e/j44NevXph/vz5OH/+PGbPni1V6uTm5mL+/PkyvRJyFEfe85dq9Th8Oduh411IL4TBUL8zExanUNXvt4WMFJXp5A6BiIiIiMitKSqBo9Vq8eOPP0rTM9555x0sXLgQvr6+Vp3v4+ODBQsW4N1334UoihBFET/++CO0Wq0zwyYnc+T6KpcyilBY6vgbSX09Ky2pXDlVv149ERERERGR6ykqgbNv3z4UFRUBADp27IhXX321VuPMnTsXnTt3BgAUFRVh3759DouR5GdPriSzqMxxgRipb5syCeAaOERERERERK6kqATO5cuXpeMHHnig1uMIgoD777/f7LjkfmzJDZRo9EjLL4W+nk9pcraqFTh8v4mIiIiIiJzJU+4AjBkvOBwREWHXWMbncyFj+dg7/UmE9dUdeoOIw1eyodOLCA/0xs2tG9h17SqxsMzEIr41REREREREzqWoChx/f3/puLCw0K6xjM83HpfckXXZgYzCUuj05X2ziyzseFOLREN9SdzYUkVT36aMERERERERyU1RCZymTZtKx/auW7N//36z45JrnUsthNrO3WeszZ9w1pQLVZ5CxfeeiIiIiIjIqRSVwImJiQFQXvGwceNGnD17tlbjnD17FuvXr68yLsnjbGqB3CGQFSovTFwtsXLTugwO8zz1V+V1k4iIiIiIyDaKSuC0aNECvXv3hiAIKCsrw9ixY5GUlGTTGMnJyRg7diw0Gg0EQUDv3r3RokULJ0VM1sgrtm8bd970u0ZNSRit3gCDhTInSxU4/OyIiIiIiIgcQ1EJHAB45513IIoiBEHA+fPn0bNnTyxatKjGNXEKCwuxePFi3Hzzzbhw4YL0+Ntvv+3skMkO1lR9VE4O2LPjUX3eLcmeaU5JOcXYfT4Th6/kmN3hq/6+q3WXVm/Asau5OJiQjYJS+5KwRERERERkP0XtQgUAd9xxByZNmoQffvgBgiAgNzcXM2bMwJw5cxATE4MePXqgcePG8Pf3R3FxMTIyMnDq1CkcPHgQpaWlUvJHEARMmjQJd9xxh9wviexkbdKFMzSc53xaeQJVXaZDSl4JfDxNc7/1ZaFnuYmiiOt5JRBFoGUDPwhOnJd0JUuNXHX5YuCnkvIwsH0ju8ZzZqxERERERPWB4hI4APDNN9+grKwMP//8s/Qf/aWlpdi5cyd27txp9hzjxI0oipgwYQK++eYbV4ZNdVRFaoI5inLFGj28KydwZIqlvrmeV4JzqeXJNFEEWoc7b4e99IJS6bhMa3DadYiIiIiIyDqKm0IFAB4eHvjxxx+xfPlyNG3aVPp239K3/MbPN2vWDCtXrsTKlSv5jW8dwcSJ7QwG0eJ6NfYy92vFz8g1KpI3AHAhvfpppUREREREVLcosgKnwqOPPoqHH34Y69atw4YNG3D48GFcunQJBsONb4M9PDzQvn17REdHY8yYMbj33nuhUqlkjJoczR2SA0oKUV2mw7GruTC48o2z8lLlyVYmVqtjMIjIUpch2NcLvl51528ZP3UiIiIiIvsoOoEDAJ6ennjggQfwwAMPSI/l5+ejqKgIgYGBCAkJkTE6cjZRrLoGjqW8hDUFV+6QDLLX2dQCaHTOnfJi7cLS9eH9drQzqQVIyy+Fp0rAgKiG8FQpslCyRlwXiYiIiIjIsdzyziAkJAQtWrRg8qYucIOv5XOKNEjKKZY7DKvZu217bfBe3XHS8svXntHpRaTml9bQm4iIiIiI6gu3TOBQ/aKE5MD5tEKUObmqxZ0p4COqk8xt2S4XW2Op/HvLJcmIiIiIiOzDBA7VGUKlch5HT+EoLHN9ZYsSXcsuRra6zOQxTpep+/ZezERRmU7uMIiIiIiI6i3FrYGTmpqKrVu3AgACAgJw//332zzGmjVroFarAQAjR45EkyZNHBojuVZtcwOiyG/9jTkyyZKaZzq1h+mbuk+nFxF/PR/9IsOt6s+fCSIiIiIix1JcBc6HH36ISZMmYdKkSTh06FCtxjh8+LA0xmeffebYAEl29twY2pXD4B2pRda+r3wL3Vthae0rcCpXyBERERERkW0Ul8D57bffIIoiBEHA9OnTazVGxXmiKOKXX35xZHgkA0s7HClJfZ9B5A6fEbkWp9URERERETmWohI458+fR2pqKgRBQN++fdGqVatajdOyZUvExMQAAK5du4aEhARHhkkuVuspVI4Ng6rDN5uIiIiIiMipFJXAOX36tHR866232jVWdHS0dBwbG2vXWCSv2uYGHF0BwByFZXxvqDL+TBAREREROZaiEjjJycnScdu2be0ay/j8pKQku8YieVmbiOGCxUTKxd9PIiIiIiL7KCqBU1xcLB0HBgbaNZa/v790XFRUZNdY5DzOvKdzdAVAXV/Sw57XZ+lcro1Tf9X13xciIiIiIldTVAInJCREOs7KyrJrrOzsbOk4ICDArrFIXu5wH8hEBRERERERETmTohI4jRs3lo6PHTtm11jHjx83Oy65n8rf5FuaUuXsGRpM0lh2NVuNfRezcDmT1W51XZlOb1W/yr8vnEFFRERERGQfRSVw+vbtC6D8Bn3z5s3Iz8+v1Tj5+fn4888/pfbNN9/skPhIDiJqW4NT692rOPdDklesQX6JtsZ+haU6lGr1uJyprvYGn2+t+9t7IQtJOcU1dyQiIiIiIodSVAInIiICUVFRAMqTMHPmzKnVOLNnz5aSP61bt0anTp0cFiO5D0dXzNSUfNDqDYi7no9TSXko1VpXpaBkafml+CcxF0ev5Nh0nkZncFJEN1zPK8GBhCxcyVI7/VpU1fm0whr7MFlHRERERORYikrgAMD06dOl46VLl+LVV1+FwWDdDaEoipg3bx6WLl0KABAEAc8//7xT4iTXsfpGUOY5Gley1EjLL0VmYRnOWXGD6yyO2u0n7nrtKuCcTRRFnE0pQHGZHgkZRXUiWVYvcA4VEREREZFdFJfAmTp1Ktq1aweg/Ebtgw8+QN++ffHLL7+gpKTE7DklJSX43//+h759+2LhwoXS45GRkXjuuedcEjcpjysrAEQRuJZ9Y1pJVmGZXeMVa3Q4fi0XcdfzodM7pqJFjoIIZ3wGlccs0zq/4oeIiIiIiEhunnIHUJmXlxc2bNiAmJgYaRrUiRMnMGHCBKhUKnTq1AnNmjVDYGAgioqKkJqainPnzkGv10MURQj/lh+EhYVh48aN8Pb2lvPlkANUzgHINTPDldeNu16Agn/XnvHzVqFdo0AXXp2UgrOQiIiIiIioguISOADQqVMnbNu2DePHj8fly5chCAJEUYROp0NcXBzi4+OlvsYLzlb0a9++PX777Td07NhRjvDJzSlh7Y4Co4WDr+eWMIGjMFq9AVq9Af7eivwTqghK+D0iIiIiIqpLFDeFqkKvXr1w4sQJvPLKKwgODjZ5ThRF6Z+x0NBQzJ07F8ePH0ePHj1cGS45EW8EbeOoNXCcoS5sxa7RGXAwIRsHLmUjkYsoExERERGRiyj66+OgoCAsXLgQr7/+Ov7++2/s3bsXZ86cQXZ2NgoLCxEYGIjw8HB069YNgwYNwsiRI+Hv7y9LrPHx8YiNjUVKSgpUKhVatGiBPn36oG3btrLE4y7Kp7zV7qY+q6gMZ1MLEODjiR4tQ6s87+jED7cXJwC4llMs7bR1KaMIEQ0DZI5ImepCso6IiIiISEkUncCpEBgYiPvvvx/333+/3KFUsXr1arzzzjuIjY01+3xMTAzee+89DBkyxLWB/euLL76ospDz/Pnz8eabb8oST21YSpycvJYHACjTapCSVwIfL8UWlFEdwl2viIiIiIhIDrzjrSW9Xo9JkyZh3LhxFpM3AHDgwAEMHz4cr7/+ugujK5ecnIy5c+e6/LrOZi6fk1usqdrPwRUAchXgKHlKVE1q85ZpdIZqd95iXYd7YMEaEREREZFjuUUFjhLNmDEDy5Ytk9r+/v6YMGECevbsCY1Gg8OHD2PNmjXQarUwGAx49913ERYWhhkzZrgsxmeeeQaFhYUuu56zuPt9oFZvgN4gwtdLBQBIyy/FubQChPh5oWerUGnnNAIyC8tw+noePAQBfSPCEODDP1FEREREREQAEzi1smnTJixevFhqd+nSBZs3b0arVq1M+p06dQqjRo1CSkoKAODll1/GiBEjcNNNNzk9xl9++QUbN24EAHTu3Blnz551+jWdwZ5v8ZVQAVCs0eHw5RzoDSK6twxB42BfxF3PBwBkF2mQXlCGpiG+MkepHKeS8gAABog4l1aI3m0ayBsQ1VqVXz8F/D4SEREREbkzTqGykcFgwLx586S2v78/NmzYUCV5AwA9evTAqlWr4OHhYfZcZ8nOzsYLL7wAAPD19cWiRYucfk1XSsxSY+/FzKpPOOgG0ZH3mZcz1dAbykeMTc6v8nxRmbbKY/YSoNyKHluSaoWljn9viIiIiIiI3BUTODbavn27yZo306dPR2RkpMX+MTExGDdunNTeuHEjLl265NQYZ8yYgYyMDADAa6+9hqioKKdezx7WpBrM3fSXaS2vkSKdZ3s4NsdRkxI3WvBWCRVLVHdw1zYiIiIiIsdiAsdGa9euNWk/+eSTNZ4zZcoUk/a6descGZKJLVu2YOXKlQDKp3bNmjXLadeimvl6quQOweUuZhQ5dXwmBoiIiIiIqD5iAsdGmzZtko7btWuHdu3a1XjOwIED4et7Y52TirVpHE2tVmPq1KkAAEEQ8PXXX8PLy8sp13Ila3eTqtxLCTf6vpW2Ntfoaq4cslVRmQ6HLmfj2NUclOnkr/jJKdIo4r0neVX5fZQlCiIiIiKiuoMJHBvk5eXh2rVrUjs6Otqq87y9vdG7d2+pXd224/Z47bXXkJiYCKC8MmjAgAFOuQ5VZSlf4aky/RXTGWxP4NS0pk1sch6KSnXIVWuRkKG2bl6akzF/Q0RERERE5FhM4Nig8k5OtqwtY1ypk5ubi7S0NIfFBQBHjhyRFitu0qQJFi5c6NDxZVXLZEBtcwiWqkesrQRyteKyG1U36QWlFvsxqUKuxJ83IiIiIiLHYgLHBpcvXzZpt27d2upzK/etPJY9tFotnnjiCRj+re749NNP0aBB/dt+mTeM9QM/ZiIiIiIiqo885Q7AnRQUFJi0w8LCrD63ckKlsLDQITEBwAcffIC4uDgAwO23346HH37YYWPbKjU1FR7e/lUejxezAADXkopNHvdSCdDqy2/JQ0JCEBIS4rBYHJ3Qqc149X0tGFe//qyiMugNIpoE+9bcmZyrfv/oExERERE5HBM4NigqMt1dx3hh4pr4+flVO1ZtnT17Fu+99550jSVLljhk3Np67733zCZwShKOAAD82t1i8rio10BQeQMAHn74ITz88CNVzlXKfaBccQg2rGkjQqxxzZy6KrOwDKeS8gAAmqYGtAqr+nNIrlN5ymE9z2USEREREdmNU6hsUFpqur6It7e31ef6+PiYtEtKSuyORxRFTJkyBWVlZQCAN954A5GRkXaPqyR23fO58IbR6p2yeBPrNHHX86Xj82mOq3BzBJ3egFKt/DuEERERERGR+2IFjg0qV9xoNBqrz61IslSoXJFTG19++SX2798PALjpppvw0ksv2T2mElmb9KjyjX8tMziOzLHIka9RQv2NI163YEvpEQC9QZnZsVKtHkeu5ECjM6BL82A0D7X/d5+IiIiIiOofJnBsEBgYaNKuXJFTncoVN5XHslVSUhLmzp0LoPxG9+uvv4aXl5ddYzrCq6++ihZt2lV5vF+r8ukshyutgeOtEqAxWgNHyapbz4WVNWTJlSw1NLryBcbPpBTUmwQOfyeIiIiIiByLCRwbBAcHm7Rzc3OtPjcvL8+kHRQUZFcszzzzjLQQ8tNPP43+/fvbNZ6jNGvWzOzuXF27NAEApArpJo/7eHmgTGuodkxrKmkyCsrQKMinxn72qC6KNAvbdzviJlYUgcuZRdJiz/WduyUGijU6uUOQhZt9TEREREREiidLAmfy5Mkuu5YgCPjuu+8cMlbbtm1N2teuXbP63KtXr5q07VmrZv369di0aRMAoGnTpliwYEGtx6pLikpNb5RduQvVlUy1dWPU4rqlWj0uWzm+UpRXKwlul2yxhu2vSd5JbSUaPQQB8PVSyRpHbac0EhERERFROVkSOMuWLbN5fYvaEEXRoQmcLl26mLQvXbpk9bkJCQnScYMGDdC0adNax3H58mXpuLi4GL179662v05nmthYtGgRfvzxR6n92muvYeLEibWOx5l0erHGCp0K6QWm6wzxdtGUEm6gnZnQEQT3q85xtqyi8p25BAHo1boBQv2tX3jdXq7eQp6IiIiIqK6TdQqVu/0HfmhoKFq3bi1V3hw8eNCq8zQaDY4dOya1b7rpJofFVFBQgIKCApvOyc3NNZn+VXl6l5Kk5Nm/W5fjuMfPa3ly1D1irQ9ckKu26OS1PADlia246wUY0L6hfMEQEREREZFdZEngDBo0yCUVOM4watQofPXVVwDKq2ouX75c43SovXv3mix4PHr0aKfGWF9V2YWqlglCS6fVbjgmUkgZuI05EREREZF7kyWBs2vXLjku6xD33XeflMABgG+++abGNWi++eYbk/a9995rVwwvvvgiXnzxRav7JyYmmqzfM3/+fLz55pt2xVAf1dVUjFZvwKWMIodWOznivdLqDNAbRKg8TJO9SpgKZgv3TFXbz70+JSIiIiIi5fOQOwB3M2LECHTr1k1qL168GFeuXLHY/+DBg1i1apXUvuuuu9C+fXuzfRMTEyEIgvRvyJAhDotbqQQn3t4q4QayctWOs6cN1mb4y5lqXM9V0lS1G5Jzi2vuRIpU9WdfnjiIiIiIiOoKJnBs5OHhgffff19qq9VqjBkzBklJSVX6xsbGYty4cTAYDNK57733nstipdqxVOFR125AMwpLsf9SFpJyHJ8kOZ9WiOtWVPRkF5Vh38UsnLiWC4Oh6hucWVhm5izzlDor012nixIRERERkbLIuoixuxozZgymTZuGL7/8EgAQHx+Pzp07Y8KECejZsye0Wi0OHTqE1atXQ6vVSuctXLgQPXr0kCvsOs/Z3/i7y9Qda9MFsUn5ToshLb8UafmlaBria/J45ffwxL+L7JZq9VYlfNxRfU3f1Pb3RaMz4GJGIQQIaN8kEF4qfs9ARERERAQwgVNrixYtQmFhIVauXAmgvBJn6dKlZvsKgoA5c+bg5ZdfdmWIVEuOXMTYPVI+zpOWX2rSjrtevmNap2ZBCPb1Mnkur1iLysy9f3WtEopMXcwoRGpe+c+Np0pAhyZBMkdERERERKQM/GqzllQqFVasWIFff/3VZE2cyqKjo7Ft2zaTaVfkHJXv692lYqY+KSjRoqBEK21vTXVY5Yo4K0+rSN4AwLVsroFERERERFTBLSpwRFHE2bNncf36deTl5aG0tNSmxWAfe+wxp8U2fvx4jB8/HnFxcYiNjUVKSgpUKhWaN2+Ovn371rjFuLGIiAinLHLrrHGVLjYpHxENdYhqHOiQ8RzxFrriU3CHJVc0OkOVx9ILSqs85gYvpUbu8HkoBbc6JyIiIiKyTNEJnN27d2Px4sXYsmUL1Gp1rcdxZgKnQrdu3aqtxCF5JGap0SjQByH+XjV3rkFtKnrqYd5MMU5cy4W/tyfaNw6Eh4d8WRRn7rSmZLX50T+VlOfQGApKtbiQVgg/bxU6Nw2W9eeAiIiIiMheikzglJSUYNq0aVixYgWAG1svC4JgUklivLtL5QqTir7cAab+sFRllFuscUwCxwnJmMSsYqTml+Lm1g0Q6GP/r2N1MRaX6XEqKQ89WoXafR13kF2kQTY0CPT1RItQP7nDISsUluocOl5sUj5KtXrkFWsR7OuFVmH+Dh2fiIiIiMiVFLkGziOPPIIVK1ZAFEWTm/KKhExFUqbieeMET8W/+jhlyB25Ir+m9J+EMq0BcdedtyOUsczCMuSoNS65lqtZqnRJy3fs7la2VmLV1RxyTX9jlfAn2HhKlrkpekRERERE7kRxCZzly5fjjz/+kNrt2rXD999/j2vXrkGj0Ug3DVFRUTAYDMjJycE///yDDz74AG3atIEoivDw8MCCBQtgMBig13NNhfrO1mSeo248L2cWISnHdBFWS2MXObjyoDolbrTOiAJyAFRH6Az8aSIiIiIi96a4BM7ChQul45iYGBw/fhwTJ05Ey5Yt4elZdYpJaGgoevXqhVmzZuH8+fOYPn069Ho95s2bh+eee86VoZPMLN2eOeq+zdbEzuXM2q/b5Ex1tCCEXKym34fKlUpyV0UaLFy/sFSLC+mFyDezjT0RERERkZIoKoGTmJiIc+fOAQA8PT3x888/IygoyOrzvby88Nlnn+HZZ5+FKIpYsmQJ1q5d66xwyW3YWIFjoX9d2Za8rk7pIaqOoerGZxBFEf9czcW17GIcTcyBgVU6RERERKRgikrgHDlyBED5WjZ33nknWrdubbFvdd/mLly4EI0bN4YoinjrrbccHicplIUfCUd98Z9RUOaYgZyspl2PPJjBcan6+nYrYQ0cY3ozAekMIvT6G48XaVw3lZGIiIiIyFaKSuBkZGRIx/3796+2b2mp5QUp/f39MXbsWADA6dOnpaoeqp+UdB95NVv+aVXunk+wmBhw9xfmZpT0e2UNVtcQERERkbtTVAKnoKBAOm7SpInZPv7+/hBFEYWFhdWO1a1bN+k4NjbWMQGSw7nintvWSgBnVg4oooqHiQ6Xqqkiypit68QUlemQUVgq+/oy7kDPBA4RERERuTlFJXD8/PykY63W/IKSFWviFBYWQq22XM3g7+8vHaempjooQlIyS2vUWFq8tL5ypylU7hOpc5Vq9biQXjVpfeRKNmKT8nExo0iGqKpX+beOv4ZERERERPZRVALHuOomKyvLbJ+2bdtKxydPnrQ4VlJSknRcVqaAqgdyOnOLlAL178axpvyMOyVF6sJH54h82fm0QlzLLq7yeMXPvLnnnI1VP0RERERErqWoBE6nTp2kY0vJme7du0vHmzZtsjiW8e5TjRs3tj84clu27h5V129LBTeqwLFGxedbt16VqcxCJqGJiIiIiOo7RSVwbrrpJmka1f79+832GT16NIDyb3+/+OILxMXFVemzYMECkwRQv379HB8suQ0WCpjycPNMR+WEXKnWQumVRN4XXMfyZRJH/1qxooeIiIiIqHqecgdgzMvLC7feeiu2bduG1NRU7N27FwMHDjTpM3LkSLRq1QrJyckoLCxEdHQ0HnvsMfTo0QOlpaXYuHEjduzYIVUZ9O7dG507d5bj5ZCbcvcbSTcP32Zx1/PRNMRX7jCcwp1/Ft0x9jqaayMiIiKiOkJRCRwAuO+++7Bt2zaIoohPP/20SgLHy8sLn332Ge6//34IgoDi4mJ8/fXXJn0qbhwq+lL9VpcXMT6fVnVh21Ktvtpz3PHtKNPpUVCiQ1iAt9nntfqaqnAcF4etO4nZtguVrREREREREVF9obgEzvjx45GRkQEA8PDwgEajgbe36U3bfffdh8WLF2PmzJnQarUQRbHKuh6+vr5YtmwZYmJiXBY7KVNdvSnWiyKScly/eK2riaKIo1dyUarVIyzQG52aBskWy7nUqgmzmlSeQmXu75U7cvTvVV39PSUiIiIichTFJXDCw8Mxf/78Gvs9++yzGDZsGBYtWoQdO3bg+vXrEEURrVu3xsiRIzFjxgy0adPGBRGT0tl6X+gu95EGdwnUTrnFWqmqKKdIY7aPq27+HbGYsCjW3XVxiIiIiIjIeRSXwLFF586dsWTJErnDIIUziCJKtXqoy3Ryh+JQdXlqmDFrX2d5VYvy3pO6mqupaXc3d/jxdIcYiYiIiIgquHUCh8gaeoOIw1dyoNVZt06K29zU1TJOd3l5FSonQMx9PrZuFS8n94nUsdzm94qIiIiISKGYwCF5uaA8Ib9Y6/yLyKC+VOBU5m6v29waOHW3LoeIiIiIiJzFQ+4AiKh23CyPUWuVF/w197Ld6b1wdqjFGh3KdNXvROYIDl/E2LHDERERERHVOazAIZdxxU2lQ/BOUlGsmUJlrp9yuC6y5NxinEsthKdKQL+24S67rrtyp6l3RERERESKT+AkJCTgyJEjOHv2LPLy8qBWq/+dgmAdQRDw3XffOTFCslZscr7cIRBg0++PElTZscm9wq+iurff3pdWsc25Ti8iIbPIztGIiIiIiEhJFJvA+fvvvzF//nwcPXq01mOIosgEjkIYDKLbrEXDb+WVzdzno+ScVJU1cFz081WilbfizdbPxN0Si0RERERErqbIBM7s2bPx0UcfAbiRhOF/3Ls3ncF9Pr8TSXlyh0BGhEpTkNx+Fyr3CdWhlPgZ1dfPgoiIiIjck+ISON988w3++9//ArixeKkoimjatClatGiBwMBAOcOjWtK7UQJHr3efWOuFShUstu5CVWUKlovJdXlb3qasojKU6QxoFuwLDw/rImbyg4iIiIjItRSVwNHr9XjttdekxI2XlxdeeuklTJ06Fa1bt5Y5OrKHzmAw+3jl6gqiyqpOQapKFOVP1FhSeRctpckr1uDktTwAQIlGh6jGQfIGREREREREZikqgbNv3z5kZmYCADw8PLB+/XrcfvvtMkdFjuBOFTikbOanULkPpVWunEsrlI4Ts4qtTuA4ekqUwt4WIiIiIiLF8ZA7AGNnz54FUP6N9T333MPkjTsTDRAMOgCAoNfAb+1E9Pt7DIKzT8kcmHKdSsrDpYwip6/35O43yuYXMbb/VSkhseLMz95SIZCzLln5c1LC+0tERERE5M4UVYGTl5cnHQ8ZMkS2OKj2zp8/D++yLNx/aQ48ocPJQd8jOCcWPhc3wQdAj/3TsPfu/XKHqUiZhWXILCxDiJ+X3KEom5snApS4mC8RERERESmfohI4jRs3lo65WLF7+vbD+ejTWIOEhkWIaeWJZsUz4B9047P0Kc2UjgWDDoK2DICPDJEqV3pBqdwhKNrlLHWVxxyREnHVUjV1pRKlrrwOIiIiIiJ3oagETvv27aXjlJQUGSOh2srLSsO2LGAbAEADL49T6N0mCNN6aDG2sxcCvAW0O/VfXOn6LG7Zdj8CCq/gbO93kBI5TubIydWsnS5UuVtRqa7GPvaqiM0RCxBXfp31Je9h62ciR0KISSgiIiIicieKWgNnwIABaNmyJQBgy5YtMkdDjqA1AIeuFOKxdaVo+nEhZmwuhffRr9Fk/QsILEiAIBrQ5Z9XIeg1No/tXZKBqFMfounVP5wQed1VJ29aHfiaCkq12HMxC3svZqGorGqyiMrZ+pbXxR87IiIiIiJXUlQCRxAEzJo1C6IoYt++fdixY4fcIZEDFWmAzw5r0Pb/ivDFH5uRqb6xtXjI2s+A7duA48eBq4mAuqj6wUQRXY7ORcT5b9Ht8CtcHNkNXcspdthYIkSHbUl/OjkfWp0BGp0BZ1MLHDKmMWcvUu3q6xARERERkWsoKoEDAM899xzuvfdeiKKI8ePH4+jRo3KHRA6mNQBL/tGi/eIifHqwDFq9CJ+ydCAjAzh/DjhwAFi/Hli7Fti/H7h4ER55GfAqyykvHxFF9Nr9OBqm7ZXGbH5ljYyviGrjYnoNSboKLs5DlGj00nF+sdbh41f3cpz5UkUR0Btc92baXqEjf8JJ/giIiIiIiCxT1Bo4FX777Tc899xzWLp0KQYMGIAnnngCEydORO/evaFSqeQOj6oxA0AogDIAagCpAH6z0De/DJi5pQw/x2kxc0AcvJtEISQkRHpeKC1Cy6SdCEu+jEbChWqv66XJdUj85J6UXGwiV2iFZtYKSsopRkTDAIeMXxcqfJSQNCIiIiIispbiEjiRkZHSsYeHB7RaLb7++mt8/fXX8PLyQlhYGHx9fa0eTxAEJCQkOCNUMuMJAF2N2ldhOYFT4Z8UAyauicXtHb1xX9+BaNmiOTw8PNBKOIwOHtathRSYX32Ch26oizetjnhFrspHyJn3SMp1XAKHiIiIiIhcS3EJnMTERJOdXyqORVGERqNBWlqaVeMIggBRFB2yiwzVnoe/gIYAsoqrv2vV6EXcdfYY7ki6hPzwJvDrFI6ItieBRh5W7e8cUHgFHhfjYYhoD3h5OyZ4chvlv+tyR1G31IECGyIiIiKiOkVxCRzAMaX5daG8vy5o1NgX1x7xxFfHDHhnD5BborbYd0ygD5oX5qJ5YS6Q+O+DPgD6+wCDfWq8VotjS5F0IgZo3hxoGwk0bwYIilvmSXZx1/MxrJP1VWxyc1XFkMsSQHXkT5PNa9zU8DdZCX+ylRADEREREZElikvgzJ8/X+4QyIFyAzrhaNEoBDUqxJzhediTEI/dCcdQVGnX8LkxXmh+pLTqAGWQfkqvGW6Br5CPxsJ56Wmd6ANPoQwA0FA4hyR9PyApqfyfry/Qti3Qrh0QFOykV+h+DAZAXce2x67NfXepVm/S5s27KXsTWo5Iojs7qcbPnIiIiIjcCRM45FRa/0D4B4cjIjgcTUvL0DAsDF/fcR5PbSzFnxfLkwjD2qrwdg9vCAcs7PjTUoWL+uG4ilvRSDyPxqryBI5eVOGiOAKdhU0AAF8Ump5XWgqcPVv+r3EToH0U0LIV4KHsqhxX3FRqdIaaO7mR2rxnVu+CZSclJQlsiaWmvkp6XURERERE9YHiEjjk5lp5QPRQoVDXBB5lWpSEhklP+fr6oHXr1kjT3IMND63H0uMGvL1Hhf/db4DnVb3Z4fSCgC+yHoC/LhStWwOZHp1w0TACzYRTSBL7oUBsJvX1ycxFu9Mbkd7tZhQ1bGE6UEZ6+T9fPyCqHRDVHvDzc8pbQK5Xm2lW6QWmFV+umkJVXaxMisiM7z8RERERKRgTOORQcaMeRk6j9iiD5SlLud69sc8QhYat1XhleDYaB6wB0jVm+2aEhCNHo8GaA9sxXjMI7dpF4qpqAK6KAwAA3iiQ+nrGq9F23w603bcDhc2aIaXbLUjv2guawKAbA5aWAHFxwJmzQOtWQMdOQFhY5cuSglib1LCUf7E2L8PkiY1qqtBxTRR2cYcYiYiIiIgqMIFDDlWIFtUmbypohBCEhofANyAcx7O6oleP00ADDyDVACTrgDQDYACuhoRhyYG/kFGUj+ziAjxluAPt27WHl1f5j64WARBFAYIgAudurOsSlJqKjql/oP22DUi5ORrnRj1gGoBBDyQmlv9r3Bjo1Alo0QLW3+47T13c5tvp+JYpHj8iIiIiIiL7uG0CR6vVwsvLS+4wyE6+vj7I974T8IoHwryBnv8+oRXx446+WHr6H2Soy6ts4tOS8MXuTXjGcCc6tO8Aby8vpHkVIs3QFM1yrgMZVdd18RANKDOuwDEnIwPIyIAQ5I9WYZcghjdEUvtHAQ8VvEpz0DBtF3Ia90eZf7Pqx3Ejde1muq69Hmdx5PvkiERjUk4xUvNL0SrMDw38vR0QFRERERFR3eUWCZzdu3dj48aNOHz4MC5cuIC8vDwpgRMaGooOHTogOjoao0ePxqBBg+QOl2yk9wjENv0buEVchGDPPADAtpT2mHcuFknqApO+FzJT8PnuTZgmikiL1uGvJsexWO+H7Qd0CLAwflq33lbF0bJoBzoUbwaSAWSm43qfaRi8Plp6Pq7fR8hsPgx6r8BavEqqLWvSBO40/clZsTpi1ydHqikcjc6A82nlC4/HX9ciJircBVGZqvyesfqNiIiIiJRM0Qmcv//+Gy+99BLOnj0rPWb8H9wajQYZGRnIzMzE/v378fHHH6Nr1674+OOPcdttt8kRMnXqBLRsA+j1gE4HaLWARgOUacrXnyktLW9X4YGLwt3oaliDnBI/vPhXGpLyssxeIiE7DV/t/QtedzaABzygVumQf9FgNoFT1LIZSsIaWhV6W2G3dNwx5Ts0Wb/F5Pluh19GduP+ODFkuVXjkes468a7tgsb6/QGeKoqdjtjUsCckkrbuBvMvE2uWliaiIiIiMgdKDaBM3PmTPzf//0fgPKkjSAIFr9hNn4+Li4Od9xxB1588UV8/PHHrgyZACAyEmK7jjiVuxdaQxl6hQ2FyqPSj5lOBxSrgaIioLAIKCwECgqQm+eDfWUvo0QsxZ1dj+Jyzp8o0Zpf3PhiViqCvihC6+dbQ1ABf8WE4gl9NpBkelNYcFMrs+f7IheNhbMoEhsjB1EARHgnFgIBAtBYBQAIRVKV88IzDkKlLapSheNdkgE/dTLyw2+2+65TYYUU9ZotH2WZTo+knBJkFJaiRKNHoyAfdG8ZatP17ElEufrnpvL1DAYRV3OKoTeIiAj3r7E/ERERERHZRpEJnFdeeQWfffYZAEAwuoPq2rUrunfvjiZNmiAgIABqtRoZGRmIjY1FfHy81F8URXz22WdQqVT48MMP5XgJ9drp3H1YnvA2AKBMX4wBTe4x7eDpCQSHlP+rrKQEfnm5GNCtGzTBIfjur1+hLiut2g9A4alCJC1NQqunW2H5sEaY3L4UQq4BiNUCp7VArgFlXaruMBWGBPRSrbwRr+F+3CSsBjaWAjkGIFIFRHsDUZ5m7+D9cy+hsHFPqe1TnIb+m++Ep06Ny12ew+Vu0614l+SltOk29hJFyL7+9LnUQmQWlkntjIIyFJZq7RrTnT6l63klSMgoAgAYRBEeLJ8hIiIiInIoxSVw9u3bh48//lhK3Hh5eWH69OmYNm0aIiIiLJ539epVfPnll1i0aBE0Gg1EUcTHH3+Me+65B7feequLoicAWHn5fel4zbXFVRM41fHzA/z8ENCsOYZ17YbsyEis/fojFKsLzXYvOFKA697Xgckt8Ld/EIaLGuQPao+Ggy4BOQa0DTyAQDEbCYahaCacQqhwDSHCdZMxbvJYA1zQlSdvAOCyHrhcAjT0AG71Bm7yAlQ3bkbb7XoVJzstKJ8u5u2NyPhF8NSpAQCRZz53iwSOO7Em2eSsRIdgQ1bIOHlTQauvGpmcSRlnJu4q1rMBgGvZxYhoaGlVqopgKjUVkFRUQAhERERERBYpLoEzb9486bhZs2bYtGkTevToUeN5bdq0wcKFCzFhwgSMGjUKqampEEUR8+bNw+7du2s8nxxHJ9pXdVAhICAA4x95FIIgYNVXH6BMXWy2X96+PKgCVPh0ZEeotGPQyvMkGnokAOHlU6EaCefRSHW++osdMjNVK8sA/FEKNFdJ06oAoCEuIjz+d2Sf7wJ07ozwtL21fo3kGEq4+TfHU+W6KhRXvwPypdUcR/kREhERERHd4FFzF9fJyMjA/v37IQgCVCoVfv/9d6uSN8a6d++ONWvWwMOj/KXt378fGRkZzgiXXMDfzx+D7xuMVrNbQBWgstgv++9snD56GWeTE5Grb2nbRdL1wBW92acy23fBmYb3QSv6mjx+s+pnDDK8hxFn7odvSbrJcx4684kma/Gm0nZOy9/8m3/RG0Ro9VW3qbfmdIXmlhSHbxMRERERUfUUlcDZu3ev9E36yJEjccstt9RqnH79+uHOO+8EUP7N/N69rJBwlWJt1alOOkP5lDad4UZljiiK2Jm2Cj9dXois0pRqxzyYtx6+rX3Q5qU28PC1/COb+ksaluticag0DMc9JlZJulhkrvrmX4kxw5Ei9sJVseo0PG+hxOw5vurqX48S1Neb5WKNzuZETLFGh/2XsrD3YqbZ522t/nFWtVBtx9Wb2f5JqzdAo6v+fbL3dVResDnuer5d4xERERER1XWKSuCkpaVJx0OHDrVrrCFDhkjHqampdo1F1svWpFV5LL30Gj6Kfxqvn3wA8XmHAABX1WexPulr/JO9FT9eXmB2rDxNFj478xyOZP0NAPCP9EebGW0geFuYliICJ1YdwveGdfi9aV/sGvgXtrX8wWKsavHfBY7bewItqv4q5LaORH6rtgCAHDHS4jiV+e5YAyRcAkTbKzYA5U4Hkou170ZN69Wk5ZfiwKVs7L2YiRKN+YqrqmOWr+2i0RlgqMXHaesn6eqPXqMzoFRb9b0wXs+mtqq+lupfXHFZ1ThsWYOoNirHyN88IiIiIlIyRSVwCgtv3DQ0aNDArrFCQ0Ol46KiIrvGIutlm6mm+Sj+aaSUJKBUr8YPl94EAFwoOC49f1V9FmklV6uctzttNa6qz5k8FtAxAK2fa23xJ1fUiTjz9THsufA7jqWkAjG3IqP5iCr98sRWSDAML2908QKeDETGpJuR3rkHxH8X0E68dbjUvwAtoBH9LL/wLD1QUH6HH1x2BThyBNi0CbiaCCXeFqblm9/Zy11Zk/ioqPAwGIAzqdZXexSU6moblktdzLDu75zBIOJqthqJWWokZqvN9qlrPx9ERERERHWBohYxbtiwoXSclJRk11jXr9/YaSg8PNyusch6ScUXgUDLz+tFHVYlfgYflWkyJFl9EU392kjt3LJ07EpfbXaMoO5BaDGpBa5/d93s8yo/FVLDTuNwyhb4xPmgaauRaJyyDQAgQsA/rd9Gfqo3/EtNf8ZKWjbCxfvvgF9OJprGHUd2ZCeT508Y/oN+qm+qXO+iYQRabdwJ3+RcoJcXogZsQ0rAzdAUAjhwADhzFujRHWjewvIb42J17Qa98nQcY4KZ7azVZqo9zNHpRdiTgBNF16Xvruean9JnTASQnFuCi+n2J7WVl5YkIiIiIqrbFFWB06bNjRv4devW2TWW8fnVbT9OjnUm/1CNfQ5kbsTOtFUmj2UYJVO0Bg2+PP9KlfNmdfsWk6PeAgA0GNgATe5rWqWPbytfRL4eCd8WvrjW8AjiTp/GgbIOyG4SAxECLnV/BfnRDwL33oPigfeanCugvIKmJKwRrgwaCVS68S9EC5w33Iky8cb2yDrRG+rEEPhezQX0AI5qgUVF6L15CXzyc8s75eUAu3cDW7cCViyozRtjU9ZU19g69cjgqrlKZi4j9+d7Id3+6VHWkPt1WsUtgiQiIiIiKqeoCpyBAwfCz88PpaWlOHXqFJYuXYqnnnrK5nG++eYbnDhxAgDg5+eHQYMGOTpUcrCUksvS8ebry5FVZjoV6/We3yLMKwLN/CJwd6up+CdrK2LGPoA9V7fg3PEDAICAzgFo/XxrqPzLd6vSeBWhzLMIh4+fhNeAd9B6YFOIHl7lAwoeQMvWSGt1J5om/VUeg2c/oIYd0JPEfkgS+yEIqWgmnEKG2BmRezaZdtIDAf+k4dYT76PkmRbwDitFnGEssrMAbN8GNGteXpHTIKz2bxhVYabQxiJHJnBE0bZrm5Or1kAQAF8vyzutKY29b6ESl3ri+lNEREREpGSKSuD4+vrigQcewMqVKwEAzz77LMrKyvD8889bPcaXX36JF198EUD51Ilx48bBx8fHGeFSNcJ9miGnLB0irFv59Vz+UeRpshDsFYaDmaYJkW6h/dEqMArqsvK1SIY2HYehTccBAJo/3Q3ff1SAwgapaDalCTw8TYvK1D5Z8FEH4sCBA/AZOhSNGzU2ef5Cz9dQEhiBopAOKGoxEkhMBM5fAPLzqo23EM1QKDZD6LXLCLt6yWwfj1ZAQHj5OG08DiLb0KH8idQUIC0VaNmqPJETFGzFO1Q/WXs/bettd20WJK6N6qZ2VUgvKMXp5PI1eTo2DXJ2SA5jzWsz6e+muRG9QUSZTg9/b0X93yURERER1UOKmkIFAO+//z78/f0hCAL0ej1efPFF9O7dG99++y2Sk5PNnpOcnIxvvvkGffr0wfPPPw+drvxG39/fH++++64rw6d/RQbehDd6/ARflb9V/fWiDn//W3lTor+xPseMLl9gctTbFiscovv3xwNTZ2Lo6MerJG8AoMyrfLqIQa/Hnj17kJ9vunitxq8REm6agfTWd0EtqrFM/BkrOp5AyZBooEULi6UVXhDhAxFt9/xt+UUNvpE4DBOumD4nikDSNWDTn8Dhw0Cx+cVkybpEgTtXTlQkbwDH7P5ULXd7m5y7CVWNP1sGg4jDV7Jx4FI2Lrpo6hkRERERkSWK+0qxRYsW+OWXXzB27Fjo9XqIoogTJ05g6tSpAMp3l2rcuDH8/f1RXFyMjIwM5OXlATC9ifP09MSvv/6KFi2Us3BsfdLQtzlCvRvhuU6f4EpRPHqHDUehLg9/X1+B4zk7zJ5zKOsvXCqMldqNfVujdUDHaq+j8lBh2NDh+HuLDr6JwRAgIC00HumhZwAASQ2PQuOpRvOcntAWlmLnrp24/fbbkaa7jKPZW9DMry0GNL4HHoIHdqT+hlO5ewAAjXxb4s5Bj0OTn4UrF7YiPFmNVFUmokqaws9QnpjxFA3I7HQTArIz4FtYaVejNiog4savl1608KsmGoDLCeW7VbWLArp2gRjgXe1rrk+snZqk1LyEubySG+eaTNk7hcoxUThVRmGZtL351exitG/iPhVSRERERFT3KC6BAwCjR4/GH3/8gcmTJyM9PV3aRUYUReTm5iI3NxeCIJgkbARBkB5r0qQJfvjhB9xxxx1yvYR6r6FPeeKshX8UWvhHAQD8PAPxaLt56NFgIJZffhd+qgDM7LIE78ROkM7LKruxs1TrgA7ScXX38T4+Phg8aDC2bN0CnVYLH63pTVZ66BmcO70fab+moePLXXH05I8o9syTng/0DMXNYUOwI+1X6bEtKSvRr+EdeOfCv7G1LP+fDqUt8Uzy7f8GJSC5zwBc7xmNFicPI2L/dvgW/jvuYNNpeypBBw9oYYBX1RcgGsoXQL5wHrh8GejWEYjqBHgxkWMtpSZFFBqWSyj1M6lO5ZA1OhfNtSMiIiIisoLiplBVuPPOOxEXF4eXXnoJDRo0qDJFwlw7LCwMr7zyCuLi4pi8kYkADzT0aY6uodEW+3QPG4i3e/6GN3v8gjCfJhb7tTKqvjG3FbSxkJAQxMTEAIKA4JIbu1OJBhFpq9Jw/bvr0BfpcXHxWeQXZZqcG593EDP/ua3KmMaJpQoXfJORc3NbIChIqg4RPT2R3OdWHHx2Hs7e+QAyu3ZBQZuWUIum29d7w/zWzT1+/R6dNv0G37wcQKcFTp0E1q8H4uOBf6cDUk2Umy1QUiLD1nVrqh/Luf3lIooiknKKcSmjCNpaLJYkiiL0Bnd5tURERETkThRZgVMhPDwc//3vf/Huu+/iwIEDOHz4MC5cuIDc3FwUFRUhMDAQDRo0QIcOHRAdHY3+/ftzwWKZPdfxY3Tq1AseQvW76QR4hkjHo1s+iY3J31bp0zawm3RszVSaFs1boEePHjh18iQ6Xh+Jsw3/QvI3ySj4p0Dqo83W4tqia2g7py08vMvzl5amdFlyqWEhmnS5G0i+BpyKB/JysSfkDNK9czDMtwfCe8eU362KwCCPD+EtFAMAApGOUjQwGSsk6QoaXSqf7tX81BGk9LgFuQOGACGeQOwp4Pw5oEsXoH17QKXoX1dZiaLTl0upFXNr8zgyiSIn+3ehUub7kFFYVuu1iAwGEUcTc1BYqkPHpkFoFWbdGmBERERERNZwiztCHx8fDB06FEOHDpU7FKqBv1dQjcmbygY2vheXCk7iXME/0mMqwQst/516BVh/c96lcxfk5eXhwtliZHxRiIKUgip9Si6XIPmbZLR6phUED9tv+/935UPc0mgIfCIjgeatceHyFqzNPgQAKFSVYnLaCKlvLiLQBOUJmqZCHLLETiZjRe7dIh17GAxoeeIQWpw6ggY9bsGFkfeV7+F14gRw7jzQpTMQFQV4uM9W0w5hxX2+nKkAZaYh5OcOiSpzOaRLGeYr5ayRWlCKwtLyqrnzaYVM4BARERGRQyl2ChXVH94qX0zt+AE6hfSVHhvd8okap01Zcsstt6BJs+bwDwqx2KfgaAEyfs+o8viwpg+iXVD3Gq9xIOMv6XhT6Xrp+HTgNZN+6YYbVUQNhCswvt0PSU5E+OXzVcYWDAYEZqbBYFxxU1IMHDsGbNgAXLzoun2wZWb1NuIKzRWUF2LZF5xSK1VckaBxdVWVvW91cRmnPBIRERGR87hFBQ7VD4+0nYU/r/+AcJ9mGNRkrMlztuRyPFWeGDR4MAoK8rF26cfITjO//Xzmxkx4N/ZGg0E3pjX1a3QnxrSagrjcA0grSYQgqLAx+Zsq5yYUngZwP66pz+Oa+pzJc8UjBsD//FUgORk5YkT59B4B8BHUCEAG1Chf96e6LcgvDxpp/kUXFwP/HAXOngG6dAUiIwEP5mHlrPaITc5Di1A/s89lF2mQnFPi4ohcz+Ama76IoljrxDARERERkdyYwCHFCPJqgAcjZlp41rabrgD/AAwdOhxFBQVY/90nKK68zfe/ri+/Dq+GXgjsEohwzxZo7Fu+3VS3BjHo1iAGBlGPUO+G2J22BknFF6TzjmfvwpS9Q8yOmeFXhIgBA4H8fOjOxKPwWnMEIwUAECZchlpsAs8SNQKyM82eX9iyDXLadjD7nEStBo4eAc7EA527AO0i6+zUKmtSAwZDzQtdO0t2kQbZRRqzzyXlFFd9UMZchyMLt4yrVVLyqyapaqpmUULKRwkxEBERERFZi1/dk1uozb1548aNMXDIUNz24JPwtLQltx64tvgaSq+XQsz3RkaG6bQqD0GF3uHDMbn9W1ZfN6M0qfwgJAToH4OcdjfWxAlFEnxQAIOfNw5Mm4Mzd41HSUiYyfnJgy1U35ijVpdX5GzYUL4Nub5+TuEQIcLSckbuXm+h0BlUJsmPJCuqjJT6Oipz958XIiIiIqq7FFeBM2zYMIeMo1KpEBwcjNDQUERFRaF3794YMGAA/P25qKQ7qu1NVYf2HZATMxDqwjxs/+17s+uJGEoMuPrJVTScGYnde3Zj+LDhCAszTaqEejdCVFBPXCo8WeM100tM18EpaNIHuLwCANDE4wya4AzKxAAcwjSk3ByN1O590Sz2KNru24ayoGDkt21v3YsTRUTu+RvazoFo0PQ6ko71RW58V6BTJ6B9FODpZd04dYC6TG8xgePOlLr+TWW1SbDK8dIqpjNafN7OmhzOziIiIiIiZ1JcAmfXrl1OmwoRGBiICRMm4I033kDTpk2dcg1yDnt+Jvr27Yv8vHwU5mbj8JZ1Zvtos7U4/2UsOvxnGHbt3oURI0YgOCjYpM8tDUdaTOA09m2NjNLyxM3RrC24tfHdCPMpX+smL7Rzlf4+ghpNVGeRrO8DUaWSEjne6kKorEpXiYi69ici9m4H9gLo4ImGt57BwRYvoORkyf+zd97xjZx1/n/PqMuW5d772lu8vfcku9n0AiFAEkInoeT4EQj1DjhKCJcDjoMDAqElJPQESO+7abvZ3vva696rZMnq0vz+0Fq2bEmWbbls8rz3pddqZp421Xo+8y1w8gTMmwdz54FOF0d7sxNJim+i3+/0JqS/2SaY1Hc7cPtmZ8Dq4ccq0hU7tgvVzB/r2Xa+BQKBQCAQCASCWMxKFypFUcI+sbbFs33wY7PZePDBB1m0aBEvvPDCdO6SYJJMRtJTySo2bd7EikuupGr15qjl+lraef2JP+FyOtixYwcDAwNh2/MMpRHrfXLu9/l4xbdC6dNtvj7+cP67OHw2fnLqs3y5+vM8m7d8VD1zIbBwIWiCljKKSoU7JTViHxI+SqSdlEpvIOEjgxpK33ptqMA5H/JDNlY98n9sOP8/bPZ/H/OJZ+Gpp+DQIXAMRGxXEM50zOfH20WbxTUl40gk8jjNn3z+AKfbbFM0GoFAIBAIBAKB4O3JrLPA+fCHP4wkSTgcDv7xj3+EiTS5ubksXLiQtLQ0dDod/f39NDc3c+rUKdxuNxC01Jg/fz5r1qzB7XbT29vL8ePHaWtrC/XR29vLzTffzKuvvsqaNWtmZD8F00uSMYlNGzfhcrqwWXppqj4ZsVzD2ePs3/4Ma7bdyI5Xd7Dt8m0YDMEMQ4VJldxa+iXe7HyCFkdNcJ2xkrmmFahkNbfP+QKP1vwIgMaBs3z98E2hdn+duYiV6lxym4bSj+c1P0PDlXfimHsVufsfgvZ2unzlKOgZmuYHJ8b50mEq5VcAqGAHtPnhvH/U+HWN/aB1wFwj8+Xn2ev7NJw9A+fOQUkJLFgAqamTOZTTynQbSHjfIenZYxGvVcrwYvHZjA1VaOh14J2llkWTQ/hQCQQCgUAgEAimjlkn4Dz88MOcOXOG9773vQQCAXQ6Hf/v//0/7rjjDubOjZyZx+1288wzz3D//fdz8OBBzp07x/vf/36+9a1vhcocPXqU++67j8cffxxJknA6ndxxxx0cO3ZsunZNMAkS4VWXnZ3NypUr8XndPPfIL6KmFz+x5zVMaRksWLkxJOLoLrghrc26mrVZV4McoMtVg1lVjEoO3kabc6/n+aa/0O1uGdVmrf0EJ9a/QlvpTSx/85Oh9eteehf9aQtJ6bsgKA1LJOVTtLQrizivXE6JtDu8wV3u6Du6KRiw2SS1k0IT/RSBEoD6uuAnLy8YJyc3b6xD9o6jsz/GcU0wLq8fjWr2GUFORDQb7/3Z2BMhO1fEdqdZEBEeVQKBQCAQCASCWcysmz309fVxzTXXcPr0aYqLizl06BA/+MEPooo3ADqdjptvvpl9+/bx1a9+Fb/fz3e/+11++MMfhsosXbqUv//979x///2hN8wnT57kqaeemvJ9EkweKUFvtufNm0flvPlcccsnSIrmriTLqC8EAO63Wtnx6g48nvA00SpJRXlKFVqVfqieBPnGsqh9e/wuenIvxaNLC1sfEm9GoJY8FMqHuFT1Q4xS79CG3gCcipJtqlAFxUMq0BrV79DRH16mrQ1efRWeew5qz0NgtCVPoplz7H9Y89K7yWh7bcr7mgzTEW9GUYLpxXdWd/PW+e4p72+8TEzDuDgsT4Q+IxAIBAKBQCC4mJl1As4XvvAFGhoaUKvVPPHEEyxYMDoAbDQkSeK//uu/uPnmm1EUha9//eucO3curMxXvvIVrrrqqtDy888/P7IZwSwkkS/i16xZQ2FJOVfcegcabXiAX61Oz1W3fYrKpUOudZa+vtEijjLaOkAC5qWsitpvl7sZJInGyo9ObgdSJbjZAHkRbt9N2lEHK186FLkdqwX27oUnnoBjx8AZn1XEeEm2nKbszIOkWE6x5K27kQLjCzg8G4LdJpqz7cH4L27v7HMjmpALVYT7M9Z5my3ndHaMQiAQCAQCgUAgiI9ZJeBYLBb+/ve/I0kS1157LcuWLZtQO9/85jcB8Pv9/Pa3vx21/Z577gl9f+uttybUh2B6SaSAMxjUOK+4nC03fwRJCt4GyeZ0rv/Y3eRHSOPd19sbJuJEmoBKksTarGsoTaqK2O/xvl0A1Fd9hvp5d058B2QJFmo494n3cOgDn6K39MJ4s2SYO9orMlnqvPBt2JiVAIv/8Qdyjx1AGhgIZq168inYtQs6O0e1MRnMPUdD31V+J2mdexPafmIRU/qJIIv82QKBQCAQCAQCwZQzqwScN954A5crmHHlkksumXA7S5YswWQyAfDKK6+M2r5582ZUqqCbSXt7+4T7EUwfiZ4gJhmT2LRpE8WVC1l/zc1kFRRzw8fuJjUzJ2qdvt5etu/YHgqYPXqMQdeqj1d+h7WZV7MyYxurMraFtp+27g99r58/WsAZMJWz/b0ncRmCKe69mpSoQo9fUdPBQnrL53Hog59h38fupvXqNRGVrhzpFNtU32az/CPSqAUgo+YMOaePsuipP7P5/75LxY5nMPR2QWMDbH8l6F5VfQ58k0/PPf/gt8KWk61nJ93mVCGySscvYQ0XMSPdnZ4R7miz4diOZV2kwKS8wYSOJRAIBAKBQCCYSmZVEOPm5qGgsqmTzJSTmpqKzWajqalp1Da9Xk9GRgadnZ1YLJZJ9SOYHlTjTFMcD9lZwaDGgYCfuUvXIKtUY9ax9PXxyvZXuHzrVpJ1mohlTJo0bi37EgAtjhoO9ARFxOaBc7j8A+hVSfh0qVQv/hKVx38UqteTsxFF1nDo0t+T0/wSnQXbcCYVUnr2N2Ht96ctpK7og7h7c6GxCZQA/QUlnKKE034fCioqpJcplcOty3TSAAvlJ9gZuIfifW+E1msddkrf2kHpWzvomL+E4zd/JOhedeAAHDkKJcUwZw5kZMZ1XPPq/om55xCy30N+wxOjthvso+/JWEznxH86umrqmxpXtUSRqCDGPXbP6JXjbXfSLcRmNohKAoFAIBAIBAJBvMwqAcdut4e+t7SMzuQTL36/n84LbiAOR+TJkl4fDD6rimPSLph5JmOBk6RTs7TIjD+gEAjA/vqhgMAVFRVYLBaqq8/FaCFIy/mzpOcWAPDSyy/zrmuuBHV4EOOR5BnKMapMOPw2AgSotZ2gKnUtAA3z78Ror6eg7nE82lSaKj8MgCOlgrqqilAbLkM2emfwem4pey+nV39/qIOldjhzFmprwedFuXBLn1e2Uspo90C91I+ps4mMusj76zUmhe+Izwvnzwc/qWlQXg6lpaDTRayfZK1m4f6vRdw2iNHeGHP7TKEo8WdHmgxWx+StmqaSeOPTDBc/LjYXKq8/gMub+ODdF9dREAgEAoFAIBBcbMwqF6r8/PzQ96effnrC7bz00ku43W4kSSI3Nzdimb6+PgBSUlIm3I8gOlmmyBP8iaKeoAWOSpZYPycDo1aNSa9Bpxl9ya9YsYLs7OiuUwD1Z47x0t9+wwt/+iUuh50Bu51nn32GPktfqEyklMeyJDPHtCS0XGM7wrAKnF79fXa85wg7r38dp6kkYt81i7+EW59FZ8GVVC/9avjGpGRYuRLe9S5YuhT0BgAU1HQqkQOAr9n/i6j72bJifdRtWPrg0MFg0OOdO6G5GQLBbFhqt4Ws5pfIbRz7vjUMzE4BRxAkXquUyRivzKTliz+gsLe2l721vVR32MK2CYscgUAgEAgEAsFsZlYJOGvXrg19P3DgAI888si423A6nXzlK18JLa9bt25UmZaWFmw2G5IkUVRUNLHBCmJSlpWU0Pam8g2/LMts2rSJpOTkiNvrTh/l1X88ghII0NfZxgt/+hUuxwBOp5NXXnmF9o7YcZTmmJaGvr/a/hj/efh9/KHmXvxK0AIgoDYSUBui1m8vfTdv3riLYxt/jk9rjlxIq4WqhUEhZ906SEujJrAVizLi+nYrSMcju7ZYC4rx5KaQTg0SUdKUAwT8mJt2snnXjVz6+ApyX/sJy177OEvf+ixlp38Vvd4F9AOtSIEY7Y9AzKlnP/HcnrNBHFGANqszZH3j8Iy2wpFi2NHEm6FLIBAIBAKBQCCYCmaVgFNZWcnq1auB4A/lO++8kwcffDDu+k1NTVx55ZWcPHkytO5DH/rQqHKvv/566PuiRYsmMWLBdKFSTa1zgk6n49JLL0Wj1Yatrz15mNf++SiKMhSQtbejlRf//CtcTgc+r5fXX3ud2traqCJTZcqysGWbr48jfa9zrO/NcY0xoPgJKGOknZZlKCuHq6/BcfltHMj/Fq8FvkZdYPOFHZXgU0mwSoOiDR9v24pVrJN/wQrVH5knvRBrJKze/Ut0u3rQWB0s6nyAVOuJuPdDVnzoHa0Rt+Wf/xsrX72d7KZY/QtmA8PFjFiix2zD6xcijEAgEAgEAoHg4mRWCTgAP/vZz5AkCUmS8Hq93HXXXSxbtoyf/vSnHD16FK83PH5ER0cHzz//PJ/85CeZP38+b731Vqj+tddey9VXXz2qjz/96U+h75s2bZryfRJMnimIYTwKc4qZjRs3htKKN5w9wetP/DFMvBmkp72Fpx/9FQ57P4GAn71797Bv396I8UNyDaWUJI12Z2q0x5+NqcvVwr3HPsi3j9xCm6MOALfficXTTfNANS+0PEKHM+iapChK0LonOxs2X4Lvhvdzfv5nqZGuDDaWqYLrDEj3JHP2mndhy8nDqzegXuhHIwUzbBXKB4hk+6Knj0X+x2GXG7a74Sd2+N0A7HGDLbq4ZDPPw6NNDS0bbbWh7yqvHYOtHnP3YaoOfpO0rv0s2vtFDLb6uI+PIHEkKohxIpAkcHn9vFXTzWtnO7E4Jh8YeSoZ6UYpLHYEAoFAIBAIBIlkVgUxBlizZg2//OUv+cxnPgMEfwAfO3aMe+65J1TGYDCg0+mw2+34fEOuGIqiIEkSiqKwdOlSHn744VHtHz9+nBdeCL7dl2WZG264YWp36B1KouctqonOEMdZLS83j1WrVrF//z6y8osxpWXS39sVsWxPRxvP/uFnXP2BT2NKy+DY0aM0dljYsHEDWs2QJY8syXx2/v/wWMNP2df9Ymh9s6N6VJuKouBTvNT0H+FY35ssStvI/JRV7Gj/GxZPcBx/rvsBH6v4Nv9z8tM4/EMxPPZ0Pcfnq37Or85+FbvPwkfn/CcVKUuDcXKWLad+0f/iOvQQi+ovZL7SSeSsrsG4yonUryVXG25Fo8WGh6EYUWnUslL1CJz1gmtYwWZ/8POiG+5MgvyhwOBuJZl6/VW05t7Eot4HyeoKWh1VHv0hPbmXonH3se6lG9C5wo+xHPCS3fwSfdmVEY+9YOqIO4jxsO9T6eJY02kPuTodauxj6/zY8aqmEkURqcIFAoFAIBAIBDPHrBNwAO68804yMzO566676OjoCL3VHHyb6XA4RmWXGl7m5ptv5te//jVpaWmj2q6oqKC1Nei+oVKpyMyMLzWyYGaRp8ME5wIVFRXY7XZOnz7FNR/8DM8/+gD9fd0Ry9r6enjmDz/jqg98koKcLNraWnnxxRfZtGkTaalD159a1nJr6ZdIUpt5tf3vAHS6htJpBxQ/B3u28+e6H4S1v6f7+VF9NjuquffY7aPWW73dfOforaHlP9f9N/+59M9DBdQa2td8ktLef5Hcfx6AVKkpKHKlQjLhIkqWdJZs6TQB1DQG1gXFG4AjUbIoJUm4c1LQKE5qlMtpU5bjxQAOCU7V0CcZyLpg85fcX822x+bRMPejo8SbUP+t25HSisF0AyqPDb/WFLnfMfALK4gpYfhhHa+oMZ5T0jswZHUTGMODcLLEK17F3Z4QfAQCgUAgEAgECWTWuVANctNNN3H69Gm++c1vUlBQMKYp+qDL1AsvvMBjjz0WUbyBoPVOTk4OOTk5QryZShI8Z57uNMVLly2luKSEpJRUrvnQXZjSMqKWddr7efYPP6exJugSZbfZePnllzlfez6snCRJbM5+d2i539vDiy2Pcrj3Nf790LtGiTeTpc/TGXG9I7k0rvoL5GfJkGrJks4NiTf2ANREDkDctaCKN/kiOwLfoFHZiBcjw02gOpQqAkr4I6fk3MNR+0/tOUzFG5+j4OdlbHliJVV7vxK1bCx67R4ONvSNXVAATNCFKvHDmBIUZeyxJvJRI6RDgUAgEAgEAkEimZUWOIOkpqbyne98h29/+9ucOHGC/fv3U1dXh8Viwe12k5KSQlZWFkuXLmXNmjWkp6fP9JAFU8REXagmOheTkFi/bj1ul5sO4JoP3sULf/xlVEscr9vFM399iDVXvIuq1Zvx+3zs27uX9vZ2Vq9eHXKpMmsz0UhavErQquCF1j9McITxsafredZlXRO2rq303WS3bp9Yg8e8UWel9VVbiKUJu0mlVrmMCv92eNkNZSooUwcDK8dBfsMT5DY9y+vv2otfEzljWDT6BmZ37JTZRPyiw7AgxvFkoZqgnDGbRJCxxnKxCFkCgUAgEAgEgouTWS3gDCJJEosXL2bx4sUzPRTBDCHPgK2YLMtsvmQzr7zyCgDXfeT/8eJfHqS3I3IGJUVR2PvSE/R1tbPuqptQqzU0NjTQ093DunXryM7ORpZkipPnc952LK4xVJnXccq6Z8L78Lf6/6HCtJRMfX5oXXf+5RNuj8UaUMB2PAPTsOPgNRmwFpWOWb1euQR1nY3SfdthH0G9p1AFc9RQoYY8OaYaIAe8rNpxG3uvenri+yCIiQi8mziCx1LIOgKBQCAQCASCxHBRCDgXAydPnuTYsWO0traiUqkoKChg1apVlJWVTUv/Ho+H06dPc+rUKdrb23E4HKSkpJCTk8OqVasoLy+flnEMkuhYEiOzu0wXGrWGLVu28MrLQRHn2g/9Gy//7Xd0NNVGrXPu8B562prY8p6PkJKeycCAne07tjNv7jyWLF3C+0u+wH+d+FjEukvSNjM3ZQX19pOszNhGcdI8vn74pohlv730b4CCSZPOFw9cGXU8h3p3cGX+B0PLiqymYe5HY7ovRcOXrOPNdV/Hv06HsaeT3JMHyDl1hO45C0GKrrJVG1qpMbSztn8uhrPDrJgCQKMfGv24D2rx3J2LSeqIOQaT9SwpPUfoz1g27vELxibeO3e4zjMTmo8/oFDbZcfrV6jITkarHlvlVVBiWgslej/eSVKYxxegqc+BSa8m26Sf6eEIBAKBQCAQvC0RAs4kefzxx7n33ns5diyyRcWGDRu47777uOyyyxLed0tLC48//jjPPfccO3fuHBXYeTgVFRXcdddd3HXXXeh0uoSPZbYycgI1ER1Ir9OzZeuWkCXOVR/4JK/961Eaz52MWqenvYUnf/djrrjlDnKLy0FROHv2DC2tLaxZvYb/XPIn7jv+EfzKUDyZxakb+VjFtwDYmB3MjqYoClpZjycwlPZJRmZb/u2YtUNxeW4p/SJ/q/+fiGPpcDaMWlez+Ev4VUbKTz8Q93FQkNlfcC/+jgB4PDgysqm95FpqL7kWyR85Lg6ARTXAg/kv4ZcCnDE0ceO5uojluuYs5IzyXlYpDwWDK8cgb/uP6c/+OGRmQlYWZGSAWjzOEoESZ6DgmRYnmvscNPQEn3kBRWFRgXnK+xQWNdE53dZPl80NwNpyFSa9ZoZHJBAIBAKBQPD2Q8x4Jojf7+eOO+6ImKp8OG+99RaXX345//Ef/8G9996bsP5feuklrr766rjdHWpqarjnnnv43e9+x9///neqqqoSNpZZTYJmmUnGJLZu3cr2V7bjBLa+92McfPVZju9+NWodnd5AWlZu2Dq7zcaOHdspKy/n1rKv8KeG74e2bc4ZbWkjSRLL07ewt/t59Kok/t/8/yVdl4NelRRWbl3WNZg1GezsfJIC4xxebhvKPtXjbhvVrqLSUrv48/TmbqT43MNkt7wc2tadewmZ7W+Elvde8QQGewPO5GIG0hYGUwG1tUJDA7S0gs+Loor+KKk2tuKXgqpAcnszelt/xHK95fMAmQOBj6HBiQ89C+QdpGDjYOAqSqU3KZH3gDVA/smddObPwZpbQkCjDVr/pJohIxMyM4KCTkoKYrI9fiZiPTeVYk60M3i+yx763m51TYuAMxYjBeJ3kjfaoHgD0NDjmBXnQyAQCAQCgeDthhBwJsgXvvCFMPHGaDRy++23s2zZMjweD3v37uUf//gHXq+XQCDA9773PdLT0/nCF76QkP4dDkeYeCPLMkuXLmXz5s2UlJSQlpZGX18fe/bs4cknn8TjCQZxPXnyJFu3bmXnzp1UVFQkZCzTgQQsKjBzosU6rnqJdOUyJZu4/PLL2b59O06ng9WX30BqZg67nn2MQMAfVlaWVWx5z4fRGYwR26qrrUXTrOVDC+/Dl2Eh21BEaXJkUe39pV9gXdY15BpKRgk3w1mQuoYFqWsAmGdezc/PBK+1bncrL7f+mW53C1flf5h0XU6ojiVrNY7kEjJbdyArwX3ozVlPa9l7KD39azqKr8OWVoUtbdjYZBkKCoMfvy8o4jQ1QWtQzBnJc+mHQt+3Hoos3iiSRG/Z3MEO8BLcz2ZlK3l48GKgTVlGCXugzofqZRcr+SUBWcaWW4C1oARrYRkdC5dDTXWwGY0G0jMgPT34yUiHpPEFP34nEq/oMF5xYmLZraSo97CERKKlo7FaG29viXYlFQgEAoFAIBC8s5n1As6JEyd4/vnn2b9/Py0tLVgsFlwuV9yWJ5Ikcf78+bELjoNnn32Wn/3sZ6HlqqoqXnjhBYqKisLKHT16lGuvvZbW1mCw1y996Uts27YtocGYy8rK+NSnPsWHP/xh8vLyIpZpbGzk1ltvZffu3QB0dHTw8Y9/nDfeeCNi+UQwFW+ec816kvVq9pzvmbFxmExBEWfHqztwDAxQuXQNprRMXv3nIzjtQ+LE6m03kFVQErMtr8fD2cPnSTGbKVyeClG0BVmSo4o70cjSF4S+D/j6ea7l9wC4/U4+WvGfYWU9hmzOLfs6hef/hN08l+Y5HyCgNtBZdO3YHanUUFwc/Ph90N4eFHNaWgl4XNQa2rFoBkLFD84zsrOjiNWnrOiGHS9rQQk+vSFmV07Sgl+ah8QyORDA3NqEubUJW2MdHQuXo6OfudILeH0GznVcTaCjfagRrQ7S0iA9DVIv/G8yxYzf805jIrfMbLA06Xd5SRnDbSeYRjy2VdZkbLZGtm1z+dAlqybRokAgEAgEAoFAMMSsFXBOnz7NXXfdNUpkGI9woyhKwoPfBgIB/uM//iO0bDQaefrpp0eJNwBLly7lscceY/PmzQQCgVDdp5+efAad7OxsHnzwQT72sY+h0cSetBQXF/Piiy+yevVqzp49C8Cbb77JG2+8wSWXXDLpsUwHEz2NUzGxNJlMbLt8G6++ugObzUZucTnvvvOL7HnyEepqz1MybzFVqzfHbKOno4XUzBxUKjX9Viuvv/Ya2dk5LF22lMyMzMmPUZ0Wcf3Rvjci3hfNlR+kufKDEevEjUo9ZJmjBPjL6Xs5MPBmWJHdi0zUV6bxlcZ3k9TVRsb5s2TUnqW3tBKAHanHOWCq4RJrFev654XV9aPDoxjRttiJhPWCYLZI/gdpUjDujy2QS4uyeqiQxw3tbRhPHsWRnhW0JlKrwWyG1NTgJy0NzKmg1Ya1L/vdBCQ1yG/vCbndFT2e0XCmw7pEkuK/hw/W93HZvKwZC3geiSONFi6dl4VGJQRCgUAgEAgEAsHkmZUCzgsvvMB73/tenE5nRMFmUJwZuW44U5UKd/v27WEBiz/3uc/FzPC0YcMG3ve+9/G3v/0NgGeeeYaamppJuy9t2LCBDRs2xF3eZDLxrW99iw984AOhdc8888xFI+AkirHevsdLUlISV1xxBa++9hp9vb0Ykkzc8oEP8ureI1QsXRNzEul2DvDCH3+FVqdn+aVXUb5wBbIs09nZwcsvvUR+QQGLFy0mPT19wuOTJInKlOVU9x8eta3b3RpmoTMV+FFGiTeDdGgteGQ/ZOczkJ3PzssW0azroczVxzMZ+1Ek+Fv2LvR+LdkBLdmu1FDdTvcCCodb1AzDWlCMDktIvAFYID/LgD8LC6WhdQZLLxt+9d/41RoGsnKwZ+Vhz87DnpVLX2kliuqCQJOUFBJ2UuVGFp//PqqAm4Nb/hTuUvY2o7rTNtNDiI8Rt5g/oOAPKKhVUyfgDP+z4vUHcHr9Y1r9tFqclGREd30UCAQCgUAgEAjiZdYJOHV1ddx66604HI7QJLisrIwtW7aQl5fHfffdB0B6ejqf/exnsVgs1NbWsnPnTiwWC5IkIcsyn/rUp8jKykr4+P71r3+FLd9xxx1j1rnzzjtDAg7AE088wZe+9KWEj20stm3bFracaNey4cwCj4opR6fTs+3ybezctZO21lZkWWbh2kvHrHfwtedxOwdwOwd448k/c+ytHay87BqK5y5CkiRaW1pobWkhLy+fqoVVZGdlT2h86zOvpab/yChLicaBM1Mq4DzX/HuO9EV3zwtICq1rSyjtSaKr6xQ/zXiGgDT6ivlDXjBA9A+BbI+ZSyxVpB6fT6ESOXB0RmEdLkbf86tUD3My8G7alGUAmNqbAVD5vKS0NZPSFlwOyCpe/er9QxUHBmBgAEPrCVap/i+0unTHf3I87wsYDE60eh+O7AV40wtBentY5kwkBs5UWePMHluacPwBhT21Pbi9AYozjMzNMUUtOxvcywQCgUAgEAgEbw9mnYDzve99j/7+fiRJQqPR8L//+798+tOfDok5wwWcb3/726F6Ho+Hhx9+mK997WtYrVaefvppnnzySZYtW5bQ8T377LOh73PmzGHOnDlj1tm8eTN6vR6XK5gK+plnnpkRASc5OTzIysDAQJSS00s8bhKz1QVBrVZz6SWXcPjwEazV+8cs393WxJmDu8PWWbra2f7YQ6Rm5bJ43WWUL1yBSq2mra2VtrZWMjIzmT9vPkVFhUhRYrXMzTFxriPccmJ5xhZKk6s41PsqzzT/NrT+j7X/Ra+7g8vzbkVOcOyXevupsAxY0ajVt1O66hbOdDQTaBx7htuptfJ49m42aFbRUzYXc0s96guBuQHQQ2ZmNT3Mi1h/ofwEUsBPq7Ic0wXBZiQDmdlD1jfDKJAOhi3n+I/heO1Bio27UWUBGoku5nM05TOQlh7MhmU2B92wDLHj+kwZSiB4U02hu9e0CBMxFJxImxxePwZiPy9ieVjFa7nZanHi9gYzqzX2OGILOHG1KBAIBAKBQCAQjM2sEnDcbjd//vOfQ2LNT37yEz796U/HVVer1fLJT36SK664gksvvZSmpiZuvPFGjh49Slpa5Hgg48VisdDY2BhaXrduXdxjW7lyJbt27QIIc8GaTurq6sKWc3Nzo5ScXZRnJc1aAQdAkmRWrFhB8vxcfv/sTvy+yDFEFCXA7hf+QbQpnaWrnTef/isHXn2OBas2MnfpWoymFHq6u9nVvROD0UhlRSVz5pSjHxHwtzjDOErAAUjT5XB53q1oZB3/avxFaP1zLb9HK+u4NPfmie94BFocNRHXL0xdT56hlFfa/gLA082/QZZUnLeN716455IDfLXkFnLdZpJ72ljY8jimllZQgVr2UaU8EbVulfw0OcpJlA5XxO32rMhBwNOk+lHryp56GWxKUEVIl8nKPsSCzF/RsGgLjoxhFlM6XVDISU2FtAv/m83BeEGJQAlQcexH6B2t1Cz5Eq6kQnSOdpa89W8k2eqom/9pGhZ8MjF9xRrGLFEp9tX2olXLrJ+TMSXPjEFLI68/kPC2B/EHFFxeP0m6WfXnWSAQCAQCgUAwC5hVvxD37NmD2+1GkiTKy8vjFm+GU1ZWxl/+8hc2b95MS0sLX/nKV/jNb36TkPGdPn06bHk8cWzmzJkTEnD6+vpob2+fdgHln//8Z9jy+vXrp6yv8cQgGssCpzzr4kj9vGTJYq70GNm1ayf9/aPTZVcfPUBXS2OEmuE47f0ceu15Dr/+IkWVVcxdvpbCOfNxOhwcO3aUEydOUFBQwJw5c8jNy40rrs/azKvZ3vZX+r1DGbz2db+YMAHn5dY/8VzLQxG3pWlz+HjFd6iznwgJOABPNv1qQn39tPAZvl/3QexZBezL+iyXr7w3tG2s+LUZnMfbNjrVOYA9e7SAo8KFidbwlU4lKN5AUIvrCUBPgAL20lG4LFzAcbuhsyP4GT7IZFMwWHJaavD/1LQJWevkND5L6dmgdVVu03Ojtlce/xHd+VsZMMf/rDJqVTg8/jHLTUsQ4wmkCvf4AjT0OKjInthzI9FBkFstTvLMevSasa2hFEVhX10vA24fRelG5uVGt+wRCAQCgUAgELzzmFVmDTU1Q2/vr7/++phlPcPdJ0awceNGLrvsMhRF4W9/+1vIdWmy1NbWhi0XFxfHXXdk2ZFtTTV2u50HHnggtKzVannXu941rWNIBJMNTj3VCWpSU1O56qqrKY/gWldUsYC5y9dFdYMaiaIEaDx3glf+9jv+9tPv8tbz/6CtoQafz0tTUyOvvfYqTz75JEeOHKGjo4MlReaobelUBj46Jzx9eKuzFk/APb4djIDb7+SF1keibl+Uuh5ZkilLXkSqdvJxqVwqL22bKmHJUpSCYhxEDva81/9J7MqI+EEOBbUzigXOMAEnk7Oskn/LFtX9yCPj83RGFzd82XGIMIpC+tEDFD/2COn//Dvap5+Ef/0z+HntNTh2NJiKPYaLo9ptofLI/Sze+8Uxu1v/4rXjM5GJ8x5pswwdx6m0wJnILevyji1ATYSx9jOSqOX0+DncaImr/S67mwF30IKvqdcx3uEJBAKBQCAQCN7mzCoLnN7e3tD3uXPnRiyjVqvx+/243bEnntu2beO1115jYGCA119/nauuumrS4xtpVTGeLEEj3bhstunN9PLFL36Rtra20PKnP/3pKbEAamtrQ9YayQl00dLqJB5PA1mGQADMZjNmc3QR4mJBrVazds1aCgsL2b9/P05HcCJmSDax6br3s3j9Fg6//gK1J0dniIqGc8DGmYO7OHNwF5n5Rdz48S8E1zscnD59il+e2012djaB7HnkFxWTYkoZ1UaZaSE/WvkCXzl0HQElOMHtcbWSZyyb0H76Al4kJHrcbaH2RlKcNI8r84PpyWVJ5o7Ke/nRydGWdVpZz/tLv8CLrY/S5RqKUXNl4S2sylnOC3Uvcah3R2j9Ufk0+Qs/AkDf/q0Y6x4Pa++8fDk2fz5nA9ewUvWHoQ1JMtJ/mKAzEBRiLvzv7jDiyMpmrvQc2dIZ9NJoC6oOZQE50ulgnUjoYY35d3jR06isR8sAyVInVqWAFmUVzmFCU86pwxQc2Rta9uoN2LPy6FywhKY1wzLD6XSQngEZ6ZCRARkZlFf/mvJTP488hiik9B6jP2NpfIXHIcY4PX4M2vHF2RmP+9GA24dqRFapV890Up6VdMFSJvJgA4oSUewdU4CJc9/Ha6Uz4Pbh8PgwamP/yfX6Z4kvmkAgEAgEAoFgVjKrBJxAYOiHvdFojFjGZDLR19dHT08PiqJE/SGdlzf0Nr25OXLQ0vFit9vDlvV6fdx1DSPcI0a2NZU88sgj/PrXvw4tFxcXc++998aoMXHuu+8+ZK0R5/l9SFoD+qLFY9ZRFD+SpOK2227ltts+MGb5i4WC/AKyrs3i2NFjVNdUh2aH5vQsLrvpQyzZcDlHdr5Mw5lj47IsyimKnLa+s7OTljYXh48eI8VsprCwkMLCQtLT00NuVipZTaYun05XEwA/OHknn6j4LovSNqAoCo81/IRa2wneVfwpFpjXRB3DScse/lDzXWRJRY6hZNR2raznm0v+iFGdEhYoucBYwd0L/o+fnv5cWPnrCj/ByozLWZlxOcf7dnHaup+N2TewIGM+OSk6suRV5Bvn8Exz0B1yf/dLXJl3OypZzdnl3yDZehZz73EAFCQ6rvwyqHPo6+vjYHMZxr4aFvT/MdiZWoJ8VfBzAbtSRoFyiGJ5X9R9bgmswimlUdq5PXKBbBVIEhrczJFeC61Okxoo5S22+7+JQrDPpK7wVOgal5O0plpsuSOyg7nd0NYKbc2k0YAPHeWqXzNeslq3xy/gjIOeATcMBN2ExmLwEm/oGZ9liX+EqOEPKFR32NGoo1uyzZaYPMPxB8Ye1GzNuiUQCAQCgUAgmB3MKgFnuPWF1WqNWCYjI4O+vj78fj+NjY2UlIyePAJhblNdXV0JGd9IVyytVht3XZ1OF7bsdI494UkEr7/+OnfeeWdoWaPR8Ne//pWUlNEWGolG8cS5j4oS98wl0fEpphqtRsuqVasoLy/n4MGDdHcPXYvpOflsvfkj9Pd2c2Lv61Qf3YffFzk+y3DKFkSfiFt7u9j53D/IKSojp6iczPwizKnp5Ofnk5+fT05uDjn64pCAA/C7mv/kxsJPkmssZXdXMMvan2rv5ztL/45KjvyIeLLpV3gVDyjBtOTDMaiSua7wEyRrUiPWzTeUk6ROYcDXj1mTydcW/x69akiwXZy2kcVpG0PLg5PxCtOS0Lo+TwdfOng1H6v4NkvSNnFk828pP/kzTJZTtJbejCPlgshlTKKv4AP0AZlvNpPV9lrEMWVIdWRIdRG3AdiUTLYng18q5lO+AsyqRuSRliTZsV3jLlfdi6JI2JVMjF1tEcvYs0ZbxalxsFn+MSppWHBsvwLHvFCogkw55Bvo0mbRm7uBgNqAuecwJutZALJatnN+8T0xxzfIWDJDQc1fKDz/Z7rzt9Ko+xIO9/jclXoHoru/XuzEEo4CcRgeXWSPN4FAIBAIBALBNDOrBJyysiFXjvr6+ohlqqqqQrFydu7cGVXA2b9/KKVzNGue8TLS4iZWHJ6RjHT5GmmRMxUcPHiQG2+8MTROSZJ46KGHpjR4sSAy6enpXHHFFTQ1NXHs2NEwd7yU9Ew2XHMzKy69iuqj+zl7eA/9vZFFx6SUVLIKIl/zAO0NtbTVV9NWXx1aZ0rLIDOviIzcQrLyi0lbmg4jQtE81Rxu1THg6+eR2u/R5+5kTdZVrMu8Fp/i5ZHz3+O0NbqVyhV5H+Cago/FFNq0Kj0fr/guR3pfZ1XG5WHiTSwy9QWj1j1U823+c8mfSNPlcHZFMMaPP+Bjd+eTJKlSWJZ+WWgsZ1Z8G+ngf+LRZ1K78P9RWPOnUADgSFgoIUlpx42Z+1IX8Wz66wA4bt/GpX3zMPT1kNzZRlJXO9ldJzCV9EZtaxBJUjBZOyDKs2PgQhweFS786ACJedIL4eINQHsAngoKyopeQinUYC0sobZwGxZnJUpmFoaMDWy0Bi2dkvur0bj78GpTMdgbcSXlo8gaANLbd1Fx7If0Za+hce7HUdSRs3EBpPQcZcGhbwFgsp6lvfg6MEd2d41+DMZVfEJE01EUlDH7n6rx+eMwC4onILlAIBAIBAKB4J3LrBJwFi8ecrfZty/yJHHdunU89dRTAPzyl7/k9ttvH1WmoaGBv//976HlysrKhIwvOTk8q8l4giOPtLgZ2VaiOX78OFdddVWYUPDAAw9EPF6J5Otf/zoFJXNYWxSclO9tGttdQq2S8PmViz7+TTyTr6KiIgoLC2hoaOTkqZP0D7M00xuTWbx+C4vWXUZHUx1nD++h/vSRsLTki6oWIEtS1Alqe9Po4Ni2vh5sfT3UnToSXPFH0GXq0ZVo0Rfr0RfrMZQYUKepw4SXY307AWhqOEet7QSHe18dc/82Zt8Yl5VUuWkR5aZFY5YbTpI6stXYd4/dTroul68u/C1alZ6nmn/NGx3BjGvVtiNcmnMzOYZi3En5HLlkSLDpT4/u3ndsw8/oLLwKHDZqOvfzbNf3Q9uOm5q4xFrFQEZWMOPUgqXUcRUZVJMbOIaCTL58NPqOxAiC7M5KpkTaSYX0CpIEXcpcsqRzows2DV0TkktBqvGQVlPNMk0Dr33pPujuwtmt4JO1qKWgWJS/5xdUdgwFm+7O3Ux7yY3MP/Qd1F47KZZTlJx7GEv2Gg5vfBC/JmlUt5kjLJhW77iVxsqPoHN20DDvjiHLpxgkSqKI5XYYjIGToI7GQawu43KhGnFwAgEFWRaijkAgEAgEAoEgyKwScPLz8ykvL6e2tpZ9+/bR1dVFVla4qcAtt9zC17/+dQB2797NRz/6Ue6///5QQN5du3bx8Y9/HMeFwLFGo5HNmzcnZHwj3Y76+vrirmuxWMKWTaapSw975swZtm3bRk/PUMron/zkJxNKyz5e8vLyKC4uZmFVDgBtUscYNUCrlvH44g9sOhlmw1RIkmRKS0spKS2htaWVs2fP0tHRPmy7RG5xObnF5Wy45maaqk9Rd/oIzTWnWbSwCoPkxaFoIrbd0RTdDWg47m4X7m4X/QeHBD6VSYW+WI+xwkjOTTlh5eMRbz47/8eYtZlx9T9RVqZfzsHe0TFoet3tPHT+O3y4/Osh8QZgd9ez7Ol6jtvKvszqzCvD6kQScI5s/CXdBZcPrTCaOKiEB5uu0bfwp9UNHOgbGse7PVdyac9yehxzQVFo9K9HxscAWayTf4lBsgw1oJFgjgpvpw6NbZjAaZbYZAwPThxRvAE8TTq0jA7kbs0vQlENxvaRcGEmmaA113DxBiCz/U0y298c1UZq5z6Kzz1M3cJ/G7Ut+YJL1iBqr53yU78AIKX3OHuvfCqqCUui047PRJibQVFoIs+ReOJcjWx3FobyEQgEAoFAIBDMILNKwAG4+uqreeCBB/D7/fz5z3/m7rvvDtteVlbGhz70IR555BEkSeLRRx/lj3/8I1lZWbjdbqxWa+iHsiRJ3HXXXQmzdhnu4gXQ2NgYd92Ghoaw5fLysd9UT4Tq6mq2bt1KZ2dnaN39998/6jjOJt6pcR8kJAoKCigoKKDf1k9NTQ319fW4h1l2abQ6yhcup3zhcrxuFwUGF71RZnU2m43+vp7IG+PAb/MzcHKAgCswSsAZjhJQkGSJHH0JHa7gdV1hWsqcYTFqEsnw3X1/2T0sSF1Dr7uD51p+H1au3n6KZkc1I1FQ+HPdDyg3LSZDN+Qe5EoqpL3oGnKbngfg8OZf05N32aj652yjs4UNF28AntS+zMIrP0imJhtsNuxWK1j7od/Krv5vobW1kaGcZaH8JJSpoUyNBsBpGsqGNcZsvUcpo08pQ4ODnKa9EctYC0rDlp2khgScURz2BOPnFKhghJVHdvMLkQUcy5lR6wYxWc+S2/AkHcXXhdyzIpGw+z3G8YqllcxUgOORLlQur59+p5eMZB2qweM/0gJHUVDNCtlZIBAIBAKBQDAbmHUCzvvf/34eeOABFEXhhz/8IZ/5zGdGBQv+8Y9/zP79+zl9+jSSJBEIBOjoGLL0kCQJRVFYu3ZtQrMtVVVVhS0PxuKJh/Pnz4e+p6WlTUkK79raWrZu3RqWLvy73/0uX/3qVxPe10wxnmxNFxMpphRWLF/B8mXLaG/voLGxkZaW5rDYSRqdHklyR524jkdQjIWhJHZ8ptr7ajE6U0meX0hmWRnqYjVXbfhIQvoeic3lpd85FNhZK+tYmRG0kCk3LeLPdT+g1x20XnL5B/jl2ejX+veOfQiAe5f9A5uvj12dT3Fy3nu5PG8LPo2JntxLR9XpdrWG2o+FgsJr7Y/x3tK7ITUt+BmGJxCgzW5DfaaQefW/GNpgkKBEDdHDGoXoUubTrKxFZ+2jxPZSxDLWotKw5QElmyxptKiFW4FnXBAA9MAcNZ6KZLQVPkiWMVnPklf3T9rK3hOqovLaMQ7Ezui3aN9XKK7+A/u2/QOkaEGdEyNIxLLomehzYrKPl1j9DnehCgQU9tX14vEFyEjWsrw4LWKdi/Vxd7GOWyAQCAQCgWC2M+sEnEsuuSQsnXgk0tPTef3117n77rv561//Omq7SqXiE5/4BD/+8Y/HlSlqLFJTUykuLg5NlHfv3h1XPY/Hw8GDB0PLw2P9JIqGhga2bNkSljL9G9/4Bt/85jcT3leiiRY7RpJgUcHFExcnEZYFkiSTl5dHXl4eirKaru5uWltb6WjvoLcvdpDc/Px81my7gfbGOjqb63A5BiY0hrTcHFR+LX7V6EC7il/B0+DF6Wujp3VIKHyFf5GWmc3C5atYvGIdC1esoWL+IlTqyT1iYk0E55iW8I3Fj/Lvh96FOxB0RVIY2xXvm0duDn3f1fkUPcX/xiU5l1/oT8Gv+Ki3n+TV9sc4ZY1s6RKJt7qe5Yr82yO7kckypJhpWnM36e5TZLWN7ZI2iEsx0a3MpUVZBYDWMUB/XiHJ7a3ISvj+Wi8EuHbKbhyyB7VvPaXsGt1onY/QoXIBJ31oT1qCy3clQZaKhfu/Rvbxv5JEJ/ULP41HP+TO6lfpaKz8KGld+9C5ujAME3ZS+k5SeubX1M+7E+ShVO2DJMoCZyIiwaQFmkk4NQ3/s9Y94A65jfbYh+6zkc/CgFBCBAKBQCAQCATDmHUCTrxkZmbypz/9iR//+Mfs2LGDlpYWFEWhuLiYrVu3joqdkyiuvfZafvWrXwFBq5ra2tox3aHefPPNsIDH119/fULH1NzczNatW8MsML761a8m1PpoKok0ocs06Zifa0KvGT0BfKcgSTLZWdlkZ2XDUvB4PVQYnOw82UBNazd9vX14PEMWOmlpaSxZV8KidVtQFIUBax/d7c10tzXT09ZET3tzXKJORnYxhR0LaU89Sbq9FKfWQkfqSQCKD2/kpO9kxHp93Z3sfPk5dr78HAA6vYEFS1eyeOVaFq1Yy/wlKzAmJTZ4tyRJ5BiKaBw4O2rb3JSVnOs/GKFWOP9q/AXZ+kLyjXP42ekv0O1uiVhuU/a7yDWU8HjD/0XcrhDg2ebfMzdlBWWmhZy1HsSoTmFuynKM6qGYV3VVd40ScM4t/Xdayt/Pkl13kdpzBKcxH72jnXbzJs4k3QYWK1itoASw5RWy7xP3IHvcmFubMDfXY26pR+Uc4JW8Btyyl7fMZ7CqHcxx5lLYuoEK+S0AupR5yHhJrqlHR3hgdQAlSUbKHLKcyXIdAaDq4H+GlbNkruL8ki8CIPtcbHxuKzpXd2h7xfEfk2w9x4l1P4522CdNTDepCdaD+OyDJiJCDRdjoo3hnepOKhAIBAKBQCCIj4tWwBkkJyeH2267bdr6u+mmm0ICDsBvfvMb/uu//itmnd/85jdhy+9+97sTNp62tja2bt1Kbe1Q9qF77rmH+++/P2F9zARqWYoo3kxW0IknQ9JsRavRMnduEZ6kHIqtQUFwYGAAi8XCHIODrq4u3E02rP123G43yanpJKemUzo/GJtGURQG+i30drTQ095CT0cLve0t2K3DgnHLUJa0hiRHGqmOQgACko9kVyY6bwodzfG7abldTo7s3cmRvcFsVpIsM2feQhatXMu2G97LvEXLEnJctuTewh/Ofzds3XtL7mZj9g0c7NnOH2tj358AD5779zHLzE1ZweK0jazM2MaR3tfpcjWzJvMqDvS8zCttfwFgf89L7O+J7N5017wfUZmyjP6MpdhS52O6EE/myKYH6c7fAsDhy/4AAX/QckVRwmf0AT9YLNDTC729BHp76dHp+dtyO89nNAImINxi6LyhnXvys/jv1utxK8l0Mx8UhY3V34s4RuucUlKl7ojbhlPPZujogJxsAmo9J9b+iJWvfzSsTG7jM9Qt+AwD5mAWwMkEAB4vM2W4EjML1QQGJSxwBAKBQCAQCATDuegFnOlm27ZtLFq0iBMnTgDws5/9jE9+8pOjAhwPsnv3bh577LHQ8nXXXRc1rXl9fX1YO5deeimvvfZa1LF0dXWxbds2qquHYlx87nOf43/+53/Gs0szzngmdBqVzLxcE2fbbVM2npkiLUlD34B37ILDSEpKIikpiU0Xsn5lnOvC4wvg9XkZsA8wMDCAw+HA4XTgcDhwOp3kF5ficrpC1jtup4PejhY6O+tx210kSeHxOGRFTYZ9DgCnO/ZMeP+UQICa08epOX0cfXIqyelZZGVmTdrNcVn6JRzv28qh3h0AaCQti1LXA7AifSsOnw27z0LzQA2nrBMbf6o2iwXm1QDoVUbWZV0T2rbAvCYk4MTit9Xf4Kr8D6EA2pXfZfGxH2E3V9KdNyL2zqDb0UixUVZBekbwc4GTPW/wfO3vYvZbZ+jiuOoy0n1BK6Ck7g4M/ZEz6DXN2Yik7McstUZtz6mk0teqpfSxf8daPpe+y66gb85C2opvJK/xqbCy61+8jkOX/I7e3M0Q8IHLiux3M9UyTkCJ7Ow0WTlkMnpKPHF5RhYR+o1AIBAIBAKBYDhCwBknsizz/e9/nxtvvBEIWkDccMMNPP/88xQVFYWVPXbsGO973/tCMX1kWea+++5LyDj6+vq44oorOHXqVGjdXXfdxU9/+tOEtD8ZslN0k24j1sSlKN34thRwtCoVKpUPv3/yszaNWkNqaiqpqalRywQCAZwuJy6XC6fTicvpwuly4nQ6Q2KPY8AR5qbV0x7ZvWi8+FU63nj9dZAkzClmMrMyycrKIjMzE7ull52vPMeiFWupXLAYtSZ6RqNBbiv7MjmGYjpdzazOuCIUh0aSJDbnvBsAp8/OS61/JEWTziU572F/z0v8rT66i0+hsZI+TwfJ6jRuL/8aajmy0FSaXEWyOhW7zxJzjJ6Ai6ebg9Z4qqJP49nyxzH3KxZ2r5XHm34xdkGgWdcTEnBkv4/uigWk1deg8g0Jhook0VM+l57APFbJD5MsdUZs63jgZvQWCxWvPgevPof7b7+no2opvpUGyFJGCU8r3vgEbSXvIq/hSQCqjNkc2vggHl0GbkN2jGDHU0PM4MdxSDzddjfVHfbweopyIXj+pIcXhrDAEQgEAoFAIBAMRwg4E+CGG27grrvu4oEHHgDg5MmTLFiwgNtvv51ly5bh9XrZs2cPjz/+OF7v0ATpv//7v1m6dGlCxvDzn/+co0ePhq174YUXqKioiLuNwsLCmBY+E6E0M4mqvJTxVYrwMn4ywUIvZlSShH+a9l2WZZKMSSQZk2KW8/l8DAwMYB+wU5yfx7lTR6k/d5qW+vN0tjbitPePq1+jKYXk1PTggqJgtVqwWi2cv5DVrebYft54KmjRotXpmb9kRSiOTtWyVRHj6KhlDVfmfzBmvwZ1Mu8q/nRouShpXtSyn6j4LovSNoQm5rGQJRXXF97BX+t/FLPccJ5u+jWX5b437vLDsXn7+FfjAxzujT8QcuvcFJa4q6CzE5skc+TWO5G9HlIba8k8f5qMmjN4jUn4DMFrYU/gLnRY0WHDSSoL5GdQkDgVeBd+9JScHEqjrrP3U7zvTdgHgTQN8kd0YA4XZQbFGwCNo5O1L98EgD2lkr1XPhEz7fhEmGgacbvbF7Ndv6JwpNESsc1EeGeOfO69M5+CAoFAIBAIBIJoCAFngvzf//0fNpuNRx99FAha4vz617+OWFaSJL72ta/xpS99KWH9+/3+UeuGx8GJB58v9mRlIuSZDahV43ujHi0L1TsNSQJ5krPAqQjxo1arMZvNmM1mCvIL2HjpltA2j8dDQ+15Thzex8kjBzh/+jhtDefx+6K7guUUlccURVpqzw2173ZxbP9bHNsfDMIryTIlc+axZNU6qpatYu7CpRSUlCPL47fiyDOUUZw0LxQA2aBK5oPl/85882rkC1Yh8cZMWpt1NXNTltMwcIYC4xxaHOfp83Ri1mTwaO33R5UPxJEtK1RWCbCn6zmO9r1BsXE+R3pfp9sT2xIqR1WGRtLR7AvG2dnv3MeyvPeiLS5Gr1Kht9mgs5PerBx6KxbAlQqqYZZWAG7MuAlmgTsWuDW0fqf5FGXVkcUjFym0J6+nnDfi2rfk/mrSOvfSm7sprvLxMtE04m0WV8ztTs/o5268KAr02N3U9wzgG2FlF00oVOK/TAQCgUAgEAgE7wBmRMDZunVr6LskSWzfvj3itkQwsv1EoVKpeOSRR7j++uu59957QzFxRrJu3Truu+++hO/X24lIc+Sp8hyY7VKRPNsHOAKtVkvl/AVUzl/ATbd9BACny8GRfbs5tGcnZ44epP7cKVyOIZeTnKLI8aIG6Wiqi7pNCQSorz5NffVpnvrLQwDoDEZKKuYxb9EyqpatYv7i5eQXlY4p6siSzB2V3+P19n+gVyexNff9yNLEg2Sn6XJI0wVjEWXpC0PrF6au52DPdh5r+EnkfVICOJxB1zWnw4nT6cDhDLq2uZwuTvIaNUnBQNDn+g+F6tmO2nA2OAm4AgTcAVBA4zVi9GTQ4u3BJ3voy2hD1st06br4oeduBjK6IM3HXP8WilKqMKelkZKXR6bXS6rLjcluRxsIRBUCTxubOex5jW83OyJub1uyhnouwRfQM1eOHMx5JPMO38u+bf/Ar0lshrJIIo4SJTbOpPuKs9zhCNY7MMyCZ2QMHGGDIxAIBAKBQCAYxowIOK+99tqFeAGj3zoObksE8bg/TJb3v//9vP/97+fEiRMcO3aM1tZWVCoV+fn5rF69eswU48MpLS2N+83xt7/9bb797W9PcNSzn8lMWyRJBP+cSQx6I+svuZz1l1wOBO/D6tMn2Pvmq5w4uIfKxSuinqSBfgt2a++4+nM7HZw7fphzxw/z9AVRZ8GKdXz489/AlGwiOTkZY5KRJGPSqIDJJk0a1xfdMcE9HRuvz4vb6WWOspoPpn2HP/Z9CwAloPDLp7+HtjWNvp4unPZ+nHYbzgFb6H9DSjKLb91EffaBiG33H+yn742RwYh7geaI5TvoCH0/T9DKSWcwsuqGa1Fv9uJLdmPUpFFoLWa+J5Mcn59Uvx+jQR96jr5pPsW1L1gjth+QoGbZYlSoaVQ2QEBhrvzyiFKVOOUBDIGhIMlJtjq2/GsFhzc9SE/+FhKB2x/gzBTEyYr2XAk+tyMoMMPLTKA/X0Chx+4mxaBBM07LxkgEAgqHGvuwuX1U5aWQk6KfdJsCgUAgEAgEguljxlyoYgkVEzV/n0kWLVrEokWLZnoYFyWJltguagHnIrPAiQdJkphbtZi5VYuBzwHgdrvp7OwMfSxWCyhKTOub8aA1JFN97tyo9Wq1Bp1Wy9N/+BnpWblk5uaTkZ1LWkYmqemZpGVkYc7IwGAwIsty0JXqwjlRFIWAP4A/4Mfv9+Pz+fB6vXg9XjxeDx63B5fbhdvtprHmLO3N9ThsVpwDdlwDdpwOG73eJrz9Hnz9Pk4GTsbehxwthjxP1O2ybvITerfTQVfxKbSmYFvWpGba007iaLqBJE8GKp8PfXcnlYZksgI+asrbMM9PorTdzaVHbOi9Qzfa7oXJfH3py3y0bSvznQU4lMwIPV7KeV8O7o46Vu7+HazSQJEKJImlb32WN69/A68+I0K98eH3K7RbY7tDTYQTLZHFq8mijPh/kKNNFhQFjFoV6+dkTPqFRIvFicURdG883mwlp0oIOAKBQCAQCAQXEzMi4HzkIx+Z0DbB25OIsR8uQgUmIcZeF99uTwidTkdRUVEoc5vL7aKrswv/gJWiivm0NdTi80YXL8YiNSs34nqfz0tfdwf1Z09Qfzay2yOASq1BrdGiVmtQaTSo1MFHpaIoKIEAiqJww8c/j05viFj/0JsvU31034THD+CzRohRNWjoAZiVPHromVQfAHJxABgSgxQpQKf5NGVdm3D6vPzuNz8iLTuXnHkluFJt7K5KYs/CZJKcfrYc6ufavVbWn7DzxKZU3LKXBwteZJmtjJu6l7FsWD+tyibypWAmrOyDx+G4N/jJlmGVFnmJwrKdn+bA1r+gyFP3p2kqni0hAWaSTUdLI+7w+LE4vKQlRc6EFi9WZ/TYVAKBQCAQCASC2c+MCDgPPfTQhLYJ3p5EjIEzqfZiuzLMZi7OUU8evU5PUVERH7zjM3zwjs9gs/VzYPebHN67i7PHD9NaVx0WR2csUrNyom6zW8Z20fL7vPh9XtwxygQiBBIfxBAhU9Z4GYxtM2hpk2WdS1b/XHwqD2ZHAcf8O4DoIlQ8qEwq1Cmj/wx0mc+R6ijCetpCIOCnp70lmEL+ddBmazGvM5O6LpVnNqbxzMY0Um0+BvRDItARUx1HTHW0dC3ls/aj2JUcWpRLyJecqDwu8o4PcwvrDMBzLnjFhXnLPrJSH6VzxYdBnng8omj4Awq1XQMJbzceYmfGGqbMRUGkFBcIBAKBQCAQiCxUgrcd0+GFVJqZRH33xCaC5VlJESeRkiQmaYOYTClsufI6tlx5HQB2u51jh/dzeM9Ozh4/THtjLZbujojWFJIkYc7Ijtq2LQ4BJx6UGCmC9EmmhPTh6/ehzdKi8mvJsyxG7zWHtmm0k7PGANDl6wCQA2pKutZTl/NmaFt13nY62rpH1fF0euh6qouup7owF2WxaOkWTJd6sJhqRpX9dVYfA4GbWGVbjEkKnivz8SOoPRGkMQ9glFlS+19sbzCizKuCykrQJc7N53xX/CLgeDjWbGVFceqE6w+5UEW//6NlqBtw+7C5fGSZdKhmKAr6xWgxKRAIBAKBQHAxIgQcwYwTacoxZVmoosxvJAkMGhV+RcHtHTt3b0V28oQFnJKMyAIOXMSxe6aY5ORkNmzewobNwSC3AwMDNDU1cPzgPs6eOEJLXQ3dbU1YezoxpWWiVmuitjXeIMnRUALRrxODMUEZlXpVLB14Hxq/AVkJf1wnm9PILiwNunpptcjDLFYGg8T7vB48HieOgIWAy4/P7sPjcobK6fP1pNqLKe3agMZvCBNwlIBC34nRAs5wrE1d7Gr6O9pXDKRtSCflCiO6PF1YmT/lHCTTk8dB81nu1bfy69+cidyYUYKq4D6mec7Se9wPp09DaRksmA/JkxfF+gamxoWob8BDl90d04JushmlIj27PL4Ae+t6CAQg16xnUYF5dKFpYJTr1zvWllAgEAgEAoFgahECjmDGiSyqTHwCMBEXKqNWzfo5weCpr5zqiFlWp5lc8NhY78jFtCc+kpKSmD+/ivnzqwBwuZx0dXfT0txEQ+15UKmiujjF40IVD7GsDvTjdKHSGYzok5PxZzhRpcpoUrSka4qpcl6KThVZuCieu4jiueMPnO71uHHYrAzYrGiTdWS2F4e2FfaspDnjIACuBhe+/ghxeCLgcTnp2NFCx6tgWm4i69osjBXG0PafFj0DwOLzDvJbowQCXqYBdfDuMEq99CqAzwc11XC+BgqLoKoK0tPHvc/TQb8z9rGK7UI1dhkpwpOjxeJkUEdst7pmTMARCAQCgUAgEEwPQsARvO2YrBPBypI0Trb24/JGj3ESud/4eo5qBYQkXKgmiF5voKiwiKLCItat20AgEMBisdDT00Nvby+9fb30W/sJBPwUVVah1euxWfqwW3pwDthxOwfG7QYSq7wh2YQh2YTemIwhafD/5OD/ySYMSaah/5NMyKqg9YxPduNW2zF60sOuJ5VajU6nQ6vVotPqUGvUaDQaNBoNapUalVo1KmsWCgQCgbCMWR6vB5fLhcuVicPhGCVy5fUtpie5FqeuD0eNY1zHY7BP2yEbtkM2jPOMZF2XRfLi5FCg8pNlBv7t88XcsqOXTcftyIPCBRLtK5aTx+ng8WO4yBYgWenA0eQm0NQIubnosjJxL1oy/vHNIDEFnAvSbcxnToTnhj+GFdggbVYnHl+AwjTjmGUFAoFAIBAIBLObWSvgKIrCoUOHOHToEN3d3fT29tLf34/ZbCY9PZ3MzExWrVrFsmXLZnqogkkTKQvVJFqLoaNES8M7fHVakpZNlZljWuJMlJipgIV+kxBkWSY9PZ30YdYagUAAa78V6/oNwf+tVmz9/djtA/h8XjwuBy7HAK4BOx6PC7/Xi8/nxe8NBjRGkpAkCUmSkSQJnSH6hDg9O5/bPv+d2GNUqTDoDRgMBvQGPQa9Ab1eP/TdoEev06PT6VCrYz+q81MNtFqcMctEwuVyYrPb6e/vx2qx0tfXh7r9fbTrT8EVOzEtM+E87KHveDf2k3YYWy8I4TjroOFsA8lLkim9pxSAgCzxxrIU3liWQkGXh/e+1sttr9lx5pZiTyuACwKOUeoFBXKkYyyW/xkcq2LiUOAjGGrPsOzzv8FTZObUHd+h513vA2nyKdUTQTwiTTR6BzwxAyxHDPY+xvOiy+bmZEs/AF7/6JO3pzaYxWxJoRmjduI/B8RjSyAQCAQCgWB6mHUCzr59+/jBD37A9u3b6e/vH7N8amoqV1xxBV/+8pdZuXLlNIxQkGgmkn67JMNIQ09kC4F4LWGGM1sMX0TsiKlDlmXSUtNIS00LW6+g4HA4GBgYwDHgwOF04HK6cLlduN1uPB4PPq8Xn9+P3+9HCSih8yRfEHRUKhUqtQq1So1arUaj1aDVakMWMzqdDp1eFxRodHp0eh1azeSDEA/fh4mg1xvQ6w1kZWaFtdVv7aez83ba3e20z2+jefkRPDYP9n0D1J44TGdzfdx9JC+K7E7WkqXlp+/L5YF3B5jfY+I2j57KCwYoWdI51skPkCx1Do1VsrFB9XPcB9RIPgVdnYXlX78bz8+/ydkPfZHO2z+OEiP20VQz1nNsLBeqY82Wcfc51lk/224Lfa/vdpBrDg8IbXcF3b5OtfazqnR2uqYJBAKBQCAQCIaYNQJOR0cHn/jEJ3j++eeBcPeESBYLg9v7+vp47LHHeOyxx7jhhhv4zW9+Q1ZW1qjygouLsSYmc7KS0alV6DUyx5rDY2pMRBCaLcThESFIMBISScYkkoxJIB4dSEiYzWbMZjOVlZUoSoCu7stobm6m2dBM1arNWLo7OHPoLWqOHQgLijwSTZqG9MuCwoDKr2V53QcY0HdyuvC5UBmvRuZ47gDtgXqua4BBW5rh4k0Ij4Juf1/YKm1bP4t/8C0sf32EAw88CqWlM2aRMxkB1uePXXciInO8z0KLY/zBnRVFod/pw6hTTUvmP4FAIBAIBALBLBFwTp8+zbXXXktjYyOKolxwU5BCIk2sWBOD4o6iKDz99NOsX7+e5557jrlz507L2AWTJ9KP/6xkXYS1Q8iyRHHGoAvLCAFnrP6k2WNxMxxJApNejc0VX+BYgWA6kCSZ7KxssrOyWb58OT3dPdTX15OdX8yqLddRe/IwJ/e9QV9n26i6iy69BG3Aj6kvh6KeNUhImFy5rK75KAfL/0hAHrrWu+R+/jctlS/2WaIP5rAXnJFv3q6K+bBnD5w4AQsXBrNXybPDtQqYfHyrCNXHanIqhZWTrf20W10YtCpWx2G9021309QbtALKMxsm1bfvgjuYWjV7zq9AIBAIBALBdDDjAk5TUxOXXHIJPT09YcJNUlIS69evZ8OGDZSWlpKWlkZycjI2mw2LxUJtbS27d+9mz549DAwMhISc2tpaLr30Ug4cOEBBQcEM750gHkZaWJn0agrTJvEDf4xZiyxJ+EfMfDSq2fEOuSo/hf31vReVJU56spZeu2emh/GOZroESQmJzMxMMjMzWb5iOU2NTeTmF1C5dA3N589w/K0dtDeeB8CUmsHSBVcjN6sitCOT37uMAzueQVJLZF6biUqv4uHUFK63DzDPG7QIcclpNPrW4EXPQp6A3e6I41K0Mi0rNgQX7HbYuxdOnAxmrSovnxVCTjxZqMbd5hgWPzHjbU2SdqsLAKfHT7c98nkZzpFGCwA9dg8ZSTq06omdk94BD0ebLEgSrCpNJ1k34z9jBAKBQCAQCKaNGf/l8/GPfzwk3iiKQlZWFl/96le58847MZkip88dTn9/Pw8++CA//OEP6e7uRpIkOjo6uOOOO0LuWILZzcgpxpLC1Em9WR0rBk6kOc3c3LGvtenApNewuTKL5j4n5zvtMz2cuFhamMqrZyK4uwje1qhkFaWlpZSWltJn6ePc2QpK5i2ivbGWY7u2Uzp/SSi7ViQM5zPpeaEHJaDQ90YfOTfnkLoxlY/m57KrsYXWsvdxZuV3wemA6mqyD5wmy3wYrKMzNQVW6vDpw+O7MGCH/fvg1ElYUAVz5ky5kBNLiIllWReP61WkMmMJP/I06dJjuX8FAuHbB9w+tOqJxYA60tQXErhPt/XHZf0jEAgEAoFA8HZhRgWcF198ke3bt4feEq5evZp//vOf47KcSUlJ4ctf/jK33XYb73nPezhw4AAAL730Etu3b+fyyy+fkrELpo6pjmETvN6GJhTLi1NJ0c9c8NORaFTyhN9OCwQzQVpqGmvXrmXp0iWcOXuWgrJKvJ7YVlkHXnkO5cLE3mfx0fK7Fnpe6SH/w/l8d/GP2Fh1bfBeNSahLFlKdelPyDJdC00+2OWBsxcEERlU61QY6cYRKYjRwAAc2A+nT0HVQlRZmfjNqQk+AlAXI4MUjJEiPA48vgA+fyBM3B7ThWqWBAQb6T42mWENt060TiB2j0AgEAgEAsHFzIzOEn/6058Cwfg1RUVFvPjiixN2eyosLOSFF16gqKgo9KP1Jz/5SaKGKriIGGtyMPKtdIph9og3FyOzY4oYH7PAk+ZtjV5vYNnSZdx4440sWbIUrTZyLKuW2nM0VZ8atd7V4KL23lp+/6Mf8a9/PE5120l+cOIO7jlwBT9p/x312WugSA23Gjn7tY/jXZYMyzWQIjNPHsPicmAA1c432LhtJYs/eRvJp48nYpcTQjwuVMearew634PD47tQRxnTcmfks266XO1G9jPSZXW2CEsCgUAgEAgEFxszZoHT29vLK6+8AgR/zP32t78lNTV1Um2mp6fz29/+lquuugoIWuFYLJZJtyu4uBhraiCPmDyIqcQ7hxS9ZkIZdwTjQ6vRsnDhQubOm8vZM2c5c/ZMyCInEAiw75UnY9Zv2neG35z4ItnvzyLtkjQkWeJ0/37uzprHz5JuwWfIornqLrxrlrDoyDcByJBqUeHCjx4zTeRIJ2hXFtFPUajdwoO70DoGyNn1Kjm7XqVrzUbq7v53+petmrqDEQfx6ipeX4DqDjvlWUkcarTg9YUHyxpMAjDIbNFJRgo6s2VcAoFAIBAIBBcbM/Y++tVXX8Xn8yFJEosXL2bbtm0JafeKK65g8eLFAPh8Pnbs2JGQdgUXD2O93RVzh9lNVX4KK0rSEt7u3BwTek30mCyCxKNRa1i0aBE33nAjC6qqUKnV9Pd24bT3j1nX5/DS+nArtffW4qwLpiqvdZzlOnbzdV0XPzv3Ff6SInFGNxT7ZrX6YdbKD7Ba9TuK5b2skP+IGgcAstdDyZ7Xw/rI2reLNbdfz+JP3gbKzEYOj1fU6Hd5OdnaP0q8gUhCydQ87WJlhoyEPzAL0/4JBAKBQCAQXITMmICzZ8+e0PcPf/jDCW17eHvD+xG8PUlLGnKBkuWxJ0JTNqmJ+z16ZCadZniGmOzhLM9KClvWqGTSkyYW4DQaalUw7Xy0sY4cw8XGbL90tFoty5Yu4/rrr2fFmg3c/G9fZ9G6y5DlsQU1Z52T8989T+sfWvHZg+5DJyy7OW87xlPNv+EjeTl0XYgLk6y0Y5KGAmqrJTeXqX6AGidVhx9D64gcGHxAUsOzz0J93YwIOTaXN+5z6PYGsEcJiDwq1sxkBxaF8V5vI8c1269XgUAgEAgEgtnKjAk4Z86cCX1fu3ZtQttet25d6PupU6PjLAjeXszPTUGjllHJEsuL0sactMxW8/136qSmYDIp4xPA3BwT5VnJZJoix2sRTAyTXs36ORlh64wGI2vXruXd734PN3zwk7zn01+leN6isRtToPfVXqr/vZq+nX1hFiAOGf5pSo5Z/TLlfnJ3H4q4zafR0rTmErDZYPdueO45aKgnfsemyXOyZWyLpHgYOeKR7qKJYrxHJjBSE3uHPusEAoFAIBAIJsuMxcCpr68PfV+5cmVC216xYkXoe0NDQ0LbFsw+knRqNldkogAqWULqjl1+spOasdKUT5SLVcCZrEXTyOM5WUumiH1cGGOkc1eUHhSQxusW8k7AoFXh9Ew8e1K0SyM1NZWtW7Yyb+488kvKOH14H3te/Ce2vp6Y7fltflp+20LfG33kfyQffUHQfeqAXsenYlXsV0AvgW30OW5euQGvcZgFVn8/vPUWnDwFixdDUSEXi+PldMWaGXmvjLxnu2xuumxusi6IoqMscISCIxAIBAKBQDAhZswCp7MzaOZuMBjQ6/VjlB4fBoMBo9GIoiihfgSzh6mYVMiyhCqUciV2ByMzsyQKnVqFUTfxGCsXqwvVbMGoVY3biibochf5gijJMCZiWNPCVE2IlxSaJ2UhNZbYWVBQwHXXXscN7/0A77vr66y49GpU6rHfKzjOObC8aQktnzaYqVl8D7uvepYBUzk+TTKNlcNcc9Nl+EwS3GKAvKE/ewGViua1GymXXqVcepUUmsniDBJ+sFpg55vwwgvQ0hysMMvv0ZHXwUixOlHXSTytHG2y4PUHTW+EC5VAIBAIBAJBYpgxCxy73Y4kSVOWISo1NRWHw4HNZpuS9gUTR6dW4fJO/K3+WIw3Bk4iY+KsKE6jy+bmbPv4r7t36pwmUYdfo5YpSjPQbXOP2jZoMTCyr9mQzlivmdr7YTLo1CqK0o209DknVD+ewyvLMgsWLKC0tJSSslLmLFrJ3pefoPHcyah11OlqFl+6hS5OA2DFw1+UeaxLmcPua14IKgSSRG3VZ7nsyTVDg5mvgXlqOO+HN9zIuTKbUn8WarecYJDj1sBSTik3BVf29cEbb6BKSmLl7/+X1ls+Qut7biOgS+yLh0QwlgVOomIJxx2vxxdAo5JHpREfFHRcXj+HGvrwBRSWFqViNmgiNSMQCAQCgUAguMCMWeC43cFJltE4NW+5DYbgW2PPhdS1gtlLoufQ5ZlD7hA5KaMnWVM5Z9drghPeiZAIC5ypcu+6WIgmyPj8UQScYd9nSkDbVJk56TZ06tmXXWu84pjBYGDD+g28+7238J4772HbLZ8gOTU9YtmKmxdR7FiFwT2UrezFzod58aWX6OntCZ1ony6VV953hhNrf8T5hZ/j7PJvBLdVqOFjRrgysgiTKVWPWlfwyjOkVJ9h/vf+nY1XrKL44V8hOwbGtY/TzcjnQaxnzN7aHk619sflShivJc9gfyNj4AzWru6w4/D48fgCHG2yxNWmQCAQCAQCwTuZGbPACQQC0/L2W8S1mFkK0gzjfns/2csi1ahlYUEKA24/xRHElKkK7DlZxKV6gSk8DiMntLGeQRfT6SjJMNLU60h4uzNxq2RnZ3PN1ddQVlZGUcUCDr3+Isff2kEgELRSKpwzn/XZH0JSJHKsC6jPfgsAa1ILtdYjNL9WzdyiKijsZ2H6OjJ0ebSX3BhsXFEw9xwht/GZ4M5F+QuolRyocOEnKPAEU5C/Ftqu6+lm7g+/Temvf0rdZ+6h6UN3TtnxGA8jnyGyPHJ79Kva5vJhc/nISNZGFL5j9RO13AXhJpoLVbd9yFrOEyEtukAgEAgEAoEgnBmzwBG8M6jMjp0dZqrIMxuoyE5Gqx59iU9FDJx4J7qxyk2n2Ph2zbg03lPrfRtMGnPNevSa6bXASTXG5+oyUQFIlmWqFlTxrnfdxI2338lNn/oyeaWVqNRq1l19c0h4y+qfF1avOv8VDmn+zg+/+Qkefem/+emJu/H4XWEDOrHux+y4+Tj9abGzXxnpC33PP7oP3cBot0ittQ/twX0wS2KtjbSMGW2BM3Ybw0WVyRKywIkSxHiWaukCgUAgEAgEs5YZs8ARvDNQq2afRjhTFjiSBMuKUqNuv5gsPiaLSiXh9yvoNDKaEdfIVB6Ht+OEUa+Zunss0uGSJFhVms4rpzriqD+5A56UlMQll1xCWXkZOYUltDXWkZI2lJpcQiLJlcmAPph6TlEU2v7YhqvJRf0P6ulb38fv3/UjPnDZ50gxpYTqBVQ69l3xT2SfC0WSUWQN8w9+i8Lavw71ne7H1ieR5Gun8q2nI47Pr9bQuGg1bH8FsnNgyWLIyp7UPk+GsWLg+ONQcFRxKNwj+4mmPUdzoRq8yYNC3MXx5HN5/Qy4faQZtchTFQlfIBAIBAKBYAxmXMCx2Ww88sgjU9KuQBCJqbBWGEsYyEjWsiAvJWbfgURFGI2DmZ5+rCxJo7PfTa45sYFgxwxgHWObcGGLzMhjNh4BNFGCWVFhEXm5eRw7doxz586hKEOKgMGTFhJw+vf3Yz9hD22z7rbyxJFf0fZ6O++6/WNUVlVg0plD2wPqoevvzKrvovI7yGt4CoBF1t+SUnorxTv/CHZvxHG5VmZSkHwIFR6aO9eQvf1J0vRtdFTcRMeiDyRm58fB4CVsd/s4226jbyA8BtxgVqhYqOI4aSMtfaLdOoOPtNEWOEEuFh3EH1DYW9eL1xcg16xnUYF57EoCgUAgEAgEU8CMCzidnZ187GMfm+lhCGYRUx2ItyTDSLvVhcvrJ1mvjvrGWZJiT+rzUvW0WYLuGXOyYruK6dSqMYWj4V2Nx50qnjfms40UvYYUfWQ3nNkao2gsBq+X7BQdnf2Jc0OJo+epaznSuYizO0VREjoytVrNihUrKC0tZd/+ffT19gKQ17cYh7YXxSFx7o81o+r5nT7eev5xjp58kdyPZbNo/gb+bfEPIu6bNX1pSMABKG78KxSr4e5k2OWBQx7wXdiogqSNNirkHQCUsTO43gPZp47iabbSt/LmabXIGXxuHG2y4PSMzmwWj4ATj3XJaAucyM+rwfUjs1ANLo7nWe8PKFR3JvbFjNvnRyVJY1qKtlmdIXfLdqtLCDgCgUAgEAhmjFnh36IoSsI/gtlLvBlMpgqNSmZVaRqLCsysLEmLWm5VSTpZJh3zck0Rt1dmmyhMN1CaaaQwbfLZ1CaahWpFcfR9mGqSdInRgLNTgjF5DFoVmcnahLQZidja0OSuy1Ul6czLNVGVlzJ24YuYeAU2halJ056ens5VV17J8hUrUKs1GLypLGp+F46/BvD0u6LWG2i0cf6753nzN8/yx+d/hcViGVWms/AqfOqk0ZVTZLhGHxRyNmhBCyzTgCn6n9CV/f/LvO2fR/3yU8gN9Sz41hdIro6eGj0ROD1+9tf3RhRvIIIrUwTiscAZ+ayKZjwYUMDm8tJqCQ9kP/g3YDzac1Ovg+beiaWzj0S71cXO6m52ne/B5Y18vAaJ57gJBAKBQCAQTAczaoEzlUKLEHEEsdBrVOSaY1vEmI0alhpTATjbHiGAqVpmfm7iJusTvWTNcQaUnQqWFiXmTfSifDPWNC/JevUUZ6eburbNRs2MnoupIGIMnDjrKsrUHW1Jkpk/bz5FRUUcOHCAluZmPO7o4s3QoKB3Ry9/OvA96rdUc/VNt7FkyRJ0uqCA6DFkc/iS37F6x60RqzesuIPu6y9j5fMfiisicJF8AG3vAMaftGF6qZH8f/yFwMIk3NsKOfa+XyEpAbKbnqc/YxldBdvGdQwicbKtH79/cn/7EmkB5/L6OdVmHZ1GfHCI4+iqviexKdtPtFiBYCDz6g47iwvH/yxzef30O71kJOsuSktIgUAgEAgEFx8zJuA89NBDM9W1QDAruRg1x0RNWmRZIi0pfssbo06FojDK0mCq3e8EjAo6HQ0FZcqDRicZk7j0kktpamrCaEyiYskqdj//DyzdsQMs+/v9vPHknzl79C02X3srm7ZuY97c+ahVaqyZK3jl/efIbH0VtceKztWNR59OW8lNIROu7R8MWtIkW86Qe+LPlHT8M2pfOf5T8FYwLo+kgOrEAMYTZ1n35DbYrIMyNQoSe6/4F/a0qkkdj8mKNxBf3KKR2lW0FyZ13QMRrVeGYuBM3QXi8QUIKEpcMc9s7sgxjgaJNMxAQGFfXS8eX4CMZC3LZ9ASUiAQCAQCwTuHGRNwPvKRj8xU14JZzkUaAmXSTNSFaiaZSsEkVgyiJK2apUWpYZmQ4jl8I6+twnTDuOrPRqbS2lCSRh8zrTpOz1tlalyoIlFUVERuXi5HiwrJLirj5J7XOPLmy/i8npj1OurrefyX9/PK0V+Rf1MxpfnzyTblszXvVsjfErWeogqKjbaMJdguXUKD8wtknn6K3s5kqqwPkS7VDxU+4gV7hHNU54dSP5SpkVDIbnl50gJOIojrchop4EQpFi3r1eA1O1VXh8PjY19dLz6/wqICc8KDpUMw3brnQlycHnvs60wgEAgEAoEgUcx4EGPB258lhWaONVtnehiznukUcBI1r55Kt4GxgkhPqM0Ry8Xpk49dNNNM9VUz8hzHe+1MY1I1ADRqDatWrqKsrIzMzCzKq5az56UnaDx3InZFBSy7LFj3Wem8qpXsd2fTaqvny0t/FXffHkMOrSvuBOBwx6VkHn2MjL69FAb2w84oAa01wOohq7PyU7/Abp5HZ9HVcfc7FXj8flxef0zLlVFZqMZ5rhUlGEDY5vKNXXgCnOuw47tgjXSixTolAs7IwMwCgUAgEAgE08GsCGIseHuTnZL4H89vRy626UBZVtIUCzhTb73xdnC5mkrhT5IkdOrwifyg1cFYDE7yjbqxXVgSSUZ6BlddeSWXXn4FV9/+Kba9/xMkm9PHrKd4FRxnHUgqiVZPDS/ueJ7u7u7Qdl/Aw59rf8AX9m/j5dY/RbV88mXn0HXl3Zy5+he0NawGa5Tzs0ILhvDrb97+b6F29cS/s1NAfbeDXTXddNujZ1IblYVqnE8vRYGTrf3jqzOOsgPuxApDkR5FI4/BWzXd9A5MjyVOu9XF6+e6ONJkEfH+BAKBQCB4hyEscASCWUKS9uK5HafKLWE40yGtDJ+YXazToOnIkKNVyyHhJtuki6vO4LxyYZ6Z/fW9UzW0iEiSzNy58ygqKuZwQQH5ZZUc2fkKJ3a/SiAQPeNQ7i25IeHwpPtNel7uJblAS336W9Q5j4fKPdfyEM+1PMSazKtYk3kVEhI5hhL+UvdDzvUfYk3Gldxc8jlOfvlReub+hfJf/S/GlpZQ/YAkc27NjfgCySySh+Ln6Hx9rHjqvZyq+DLG9ADW7FX4NCb8mgiZsaYQRYEjjRa2VeVE3j5iebzXoIJCbwS3I0VREiLcTvWzQ1FGS1YOj59DDX1Rj1kiGQzA3G1z02lzkyNekggEAoFA8I7h4pkxCt42aFUybq/IyzocWYb5eUPpysVL1UELnCgxNCbY5sh6seaKF8s5GK/1w0RYWpjKsRYLWpVMUZxuZ4OWQdIM2nkaDAY2rN/AnPI5pGdmUbF4Jbuf/wdtDTWjyqasTsFYMbRvTZn7aMrcF1yIkr16X/eL7Ot+cdT6XV1Ps6vraQqNlXzgmq/ScMNN+J97kHV/eJLss2epWzyfZvOGoPuWv4hNqp8OjYMW1tV8fqgxWwD/s3DqM/9Nx+U3BR8W08TRJgtWp5f5eSayTUMiwUirj4lY4ESi2+4hK06BMCYJVnBGikqKMnsyXbZbXbi9AbJTdHEFbBYIBAKBQHBxIwQcwbRTkGagvtuBy+unOMOIyzsyk9A7jw1zMqf1x/dkXYemIzbtVHQxcs71dnChmo55pNmoYVNF5risIyaQKXrKyMnJ4dprruHsuTlk5BZQe/II+7c/TX9vFwCyrGLVluuweOvwaOwR2+h7o4+AJ0Da5jRkXXwiSrOjmkfP34cv4KErvwX+XcPSmnKsSV4WplbT5W3nrLGFUucl3N/ZR6F8fHQjBzyoznpY/Pl/o6Lofho+9Gna3n0L/qTkCR+PeOmyBd2ojjVZ2VY1TMAZUW7cMXCirD/aZOHyBdmTtsKZ6vs6oCizRuDtsrnpsrlptjjYMCdzQm0ELgSskkUqdIFAIBAIZj1CwBFMC9kpOjr73WjUMnlmAzkpelxePya9huMXWYBjg3byQsvIAL3izeloitKNnO8MTqZTjRosjtipfidCmAvVLJmQjZfpGve4J9XKBOtNEZIkM3/efEpLSjhaUkJRZRVnD+3m8BsvUr5oOaXa1dCwmtbUozRnHgyrG3AHaH+sHb/NT+cTnaRvTSdjWwbqlLH/hLY568KWj16w8qnnTbiQBO2oqZ6n7Cu4yzVCwPEpcGDoujc0NTH/+1+n4v/+i4ZPfIq6T355Akdi8kz2motlveL1K2jVkYLOxN9+oi+5kc3NxkeFw+3H5w+gVo3PQqvf5eVQQx8AK0rSSNFrJjyGpl4HPQMeStKNpCVpx64gEAgEAoFg3AgBRzAtLMo30212k6LXoJIlVEhoxvlDcyapyk/hVGs/KlliXq5p7AqzjKnI6DTVFKcbcXr8ePwB5uWY2FXTPXalcSLPEnFhMsQKYqzTzJy74qBbzWw7wnq9gbVr1lJZWUlubh5zFq8MuznyLUtJdRRyuuB5/KpgnJae7T34bUFLQb/dT9dTXXQ/103+6nKKr6zEVtYcrGsop9/bi91nGfe4fpl3iMuaCqnyBdvqUBagP96F2WEbVVZttzPn9M/R7bdQs/Qr+LTmcfc3GSadhSrGtmipx8dDoq+5kY+JoAtVgjuZIU60WEMZu0629LN+TsaE2nF4fJxtD16r3Tb3tMQCEggEAoHgnYgQcATTgixLYTEULjbyUw2YDUHxKRHWMtMtqIzsT6eZnHg2HZNylSxRlZ8yrjpjx+II3z7bxIWJEGuPs0w6mnujBHCZYmb7BDc9LZ3LL7+c5pZmjh45Qn//UFYkoyeD5XW3oUgBnAELTz7z01H1FZ9Cy+7ztOw+T375XCrWr2Dj5e8ld04Gu/ufxBNwc6zvzXGN6TNlmfxPVzoFNhWn3Vezeu/PIxfUAsu1FNY9RlLnaQ5e8RfQJiB2TLwkIAtVNHyBADC5Z+xYVl821/is+Ua6ZCkX/s02JjIih3vIhXky2bv6psBCUiAQCAQCwWiEgCMQxEmSLnG3S3qSju4L8SVUqsTJCNHmLcEJyNDP+5IMI60WZ+jN69uBeIKKjoqBE+ZCdXEei2gWOCUZRlKN2mkTcPJTDbRahvqK53DOzTHR3OfA4YmeGWqqKSwopCA/n/O1tZw4fgKn0wGAjAoUFXV7juN2OGK20Vp7jtbacxx8+gXmrVjPpitvZNnKVbxryad4sfVRet0dzE1ZgVFtYlHqBg71bOep5l+PaqfX18vnszOomrsO175H2NrREqE3YKkG9MGLN23gBKYnHsBWvhnmzQXT+ETPeBmeIWqyMXBiSQ2Rnklef2Bcljkjn4OvnOog16xnTlYyAx4fRxotcbcVidn6qJjJcb0dxHCBQCAQCC4GhIAjmHXMlpgZU8m8HBN2lw9vIMDyotSp73DEIdWpVawrz2BndWLckublmkLm82MOZRad3kRdazPpVhdp0qaSJSpzTFid4W/FlxSZCQSG0hAnkgV5pjABZ5BYhzhJp2JpUSq7z/ckfDzjQZJkKuZUUFpaSnV1NadPncLtdhMI+Dm1b2fc7Qz0Wzj02vMceeMlSuYvZtGazVx65bVUVMwlKWkoFfhlue+jKGkeOpWBU5a9vND6h9A2q7eH3V3PQqnMB79ezgdf7mbbgX7Uwzzh+tcUY1K6Qsd2sfI39lWb8NVUQ24uzJ0L+fmARMab27GsWIc/aXKpyP0BBfUFsXnkNecfp3IQq7h3RE5ynz/AWzGujwG3D68/QKpxKOZKpEuu3erC5fVjm4CVycXiQjUbrYIEAoFAIBAkFiHgCAQzgEGrYmNFMNbAeEWE0swk2q0uAHLN8bml6VQyDn+4lYNeo0IlSxOLOTFiyIVpBiQJzrSNLeJMlYATz3GMtacTmfqsLEnDF1ASk/p4gkS0HLpwKMwGDWlJGvoGvBSmG8g26UeJOolAJUsTEsO0ajmhlm2TRa1Ss2D+AiorKjlXfY4zp09z/cc+x6n9b3Lm0G68bldc7QQCfupOHaHu1BG2/+MPzFm8is1Xv5uVq9dRUFiASlZRkbIUgKKkuVxV8CF+X/0tjlt2DTUiSRytNHK0spicHg+37ujlltf7cRfO40jax8hTjrBQegIAo9TLZaofcDzwHjraFGhrg+Rk9GYTy+76IP6kZNquv5nW996ObcHiCR0bv6KEfjCMFAosDs+42hpPDJwWixOvL3ocp711PQQCUJmTTElGUKSKdilONBD6yFtsdjpQzbAFziwS5gUCgUAgeDsze345CwTvMCZq/ZGsU7O0KBW720dhmiGuOlX5KRyoD2YaGa+1yJIiMw63n5rOyOmVIbgvhWlGumxueuyxJ3NTleJ3JlygZkOmlUh7naIferSvKE7D7QvMmkxnKpWERpbJNesxTSLjzVSiVqupWlDF3Mq51JyvITMnn6Ubt3H28B5O7XuDAVv8FkwOWz/H39pBfkklLo8PrVZHSWkJZWVlZKQPBYxdl3VtuIAzjI4MLT99Xy4P3piN2e6mI+P3vNt7FR9vLaQs0Bwqt1j+J16/kV4qwG6n4Om/IykKaruNor8+TNFfH8Y2byGt77mNpts/Ma5Z93BhZeStNtxoxqBV4RzDJS5W4O2RLlTeMdw8B/uu7rCHBJypdugJWuDMRgln9jDc5U4gEAgEAkHiEAKOYMZ5O/7GK81Mor57YML1terYQYazTLq4rT5yzXpSjVpWl6Xj8wfISB6/tUgiJ/8zeb4v9jlXerKW3hECWeDCxHppUSrHmi2oZIn5uUNxUCQpPPD2dB9/g0aFVi3j8QXQaWQ2VWQmfGI3VUHB1Wo18+fNZ27lXOrq68jMzmHF2vWcPnGCk/tep6c9SoyaERhNZvLKKgHweNxUnztH9blzpKSkUFpaRklpCfNSVpKlK6DLHWzz+sI72Jp7Cy7/AL+p/jp19pO4dDIuXVA0fELzIjvKzPyhsZMK79A1sVh+jGrlKiy+QvKP7B01FtPZk+Q8/wRNH7xjXMciTMCJUU6vkccUcGLF3vKNsMDRTiBbYaKv8ZH2NgFFWOCMhaK8Pf+2CwQCgUAw0wgBRzDjqOTwX3lvh998c7LCBZzx/pDNStZh1KnCMoRMhDXl6ZguuKiYDROzdpCQkKT4ZgbxTMxn8q3seKZds22KtrjQTJJOzR57eDyQwflulknHpspMZElCM4FJ72QYPFaF6YZQ0OTSzEF3Fonlxal09LvJNeun5PzLkjTuOCzjal+WmVM+h/Lyclx9naTnlzJn8Uq6Whs5c2AXdaeO4PdHj61SsXglsjz6nPT393Ps2FH+/tAvSUlNZdNVt3LWtAuTLo1Lc96DJEkY1MncUnoPvzj7ZWze3vD6ARv/VlLF8zVHGGxdI7mpkp6Cai8MRA5g3Vo8F/bthTlzICMzrmMwaOmiKApOT/R9leM4v+4YLlEjLVs06vFfL1P9hFGYXWLJIDP5zJoqy0qBQCAQCAThCAFHMONoEpiFabYw2UmqLEusLcvg1TOdk2onJUEuKiP3JtqP9Xj2eirP9mQmVbNxQjacnBQ9jggT58qc5NB3nXpm3aTmZAXHIiFRmmEMrTfpNVPqLiVPNJbTOJGQKCstwZCWg9Vqpbq6mvySCuxX3Ej10X2cObgbm2V0wN2KJaujthkIBDj85os47TZe/NvvyS2Zw8IV69h/5W6Wr1qDXm8gx1DCPVUP8GzzbznQ80pY/VZ/L49e/xwb61+m4sT/Dm04GNmV0a/R0jFvEZw/H/yYU4NCTmkp6KJb5/W7vJj0avbW9cZMNz1SkI+Eyzu1WccSLRKOioGjwMSiZk0ts+kZFlAUZCHqCAQCgUCQcISAI5hxptta4GJBJUsk69XYXePPmpJIJCmxU5XpNMCR5aDlQEV2UFiYTROciTBy/HOyk8kcp0vcVB5+jUoOc99KBEatasw04ypJIvGhmSMzeP2azWZWrVrF0mVLqa+rJ7egiEXrLqO1rprqo/tpOHsMv89HVkEJqZk5UdvraKzFaQ8G/1YCAdrqqmmrq+aVfzxKWnYe85etYePl17Dhssu5vfxr7ELgjQAAhi5JREFU3Fr6JdwBJ788+1WaHecA+GfDL8iZdz9uQw5Zrdsxdx5El+eC9gA4wy+ajvlL8OuGBT+3WuDQQThyBAoKoLwM8vJAksna8QKanm46r7gOS4oeSSKmeAPxWeDEEnAmn6I8Mdd4XfcA5y/E/VKPfMmgDFm+jWQmY7/Eii003cyekQgEAoFA8PZCCDiCGUctBJyoTL0rwNg/syXiN4+PZ94SzwRvLOINILq+PBOPPzBh97HZTl6cWcguJow6FYFAcJKfatRQlZ/CWzWx04zHYfSRMEbeCxq1hsrKSiorK+nt7aW2tpbS+YsZ6LdSd/oohqTkKC0FqTt9JOq2vs42dr/0JLtfehJDsonyBUtYsf5SLrvqBlakXxYScGrtx/nyoeu4Mv9DXL3h50iAedNBVr98G5zywREP1AVFE80yPyrc+Bkh/AX80NQY/OgNUFpCyYM/IfXEEeZ/79+xbtmG473vR155KQHDkHWV7HMRUA9dh4MWODpHO8nWM9jN83Ab88K6inX7Dm7z+QNYnd5psawaJBBQON5ipcvmDls/MmaPghJVLAkoMFNGpTMpmkRKtS4QCAQCgSDxCAFHMONopnP2dZEhz5JjM3IY0TSYkZPbJYVmmi3OsMC7U7lHI8UhvUbGoB1yK4o5cRwx/Ukekd46HkuQsSjOMNLY45hUG8N5OwYJVUkSK8tSsTq8pCdp4xJ4E3mfpCdrSdGrqe+OfJ5iuQilp6eTnp7O8hXLaWluoXROBe1t7QQCka+bQMBP/ZljcY3Labdxcv8uTu7fxaP/919kFxSjX65CtVjBOMeIpJZ4qfVRzJoMNmRfjzVrFa98oJqchqdZvPeLYAnAKS9ZZTWs0f2Bg/KdeAai7IvLiWH3LlJPHAFA9nlJe/l50l5+nly9gWM//jV9Gzey5K3Pkdn+Bn2ZK+ksvBpXUgGapM0sf+2TZHTuDu6jpKJh/ic5v+hukOIT6/0BhQMNfRO2PpzofdHQ6xgl3kQioIRn3hpOUFyemRtzNokmw5+nNpeXcx12knQq5mabZs3fNYFAIBAILkaEgCOYcYQFTnRmy8/ceF0CRhbLTtGTnaLnlVMdwwolcGAjSDGoQ2mM05I0k3JlyDcbaLO66Hd6WZCXEoz/Uds7dsUYTHaCNYvmZ1OGLEvo1CqyU4aEtznZydR3D0S1xihON3KmvT/qpHr8RL9u4pl7qmQVxcXFFBcX43a7aWpqoqGhgc6uzrCLoL3hPK4B+wTGp9DZ0gAtwDOgSlIx7yfzkDUyjzX8hIM928kzlJKmy0VvMOIvuIxlvAYbglY3SZ5W1hgeZP/mX+Buc0JjE3jChYuck4cj75vLSVXjN9B19IXWpXUfJK37YHBhRCZ0WfFTdvqXlJ3+JcfW/x+dRVePuW+tFueExJvGHgfFGcYJB9Rtt7riKqcoSlTrxVj3qNXhxeL0kJOiT2hmv7g6n2JGHvHhz7qjTVZcXj99A8G4bPmphmkdm0AgEAgEbyeEgCOYcVINGvQaFS6vH5NeLd7ODaMo3YjFYQUg1TgzbkCSlNj8IoloTR0how8Ex7q6NB2L00O6UTtqe0yXsRGbZDnY1nDmZCeH4mIkgrejBc1kiXRIyjKTKMtMwh9Q2FXTjWdEFiOtWmZlSTr9Ti9n222TH0OM8zJeUVCn01FRUUFFRQVOp5Om5iaaGpvo6uoi2ZzOonWXUXfqKAP9fWM3FgV9gR5ZM3RP1NqPU2s/Hlp+XAvfKL+SW2pfGqrjbGfl0c9zYOtf8KxcBW1t0FAPLS3g9ZJ74mDkzgpV6HQTG+uS3Z/DfrKCugWfoaP4+ogHWlHAOcEgx+c6bEEBZ7z3lTLYd3wKSKwsVNFcqzy+AIca+/AHFPocXpYVpRK4IEjG8zfP7fPTY/eQnjT6uTY0rtkp8Q6PedTe7xICjkAgEAgEk0AIOIIZR5YlVpSk0mP3kGUaX0DWtzvZJh3lWUk4PP5Qhp9YZJl0IRedUYE3J8HICdFkWp6oaFGSYaShx4EkwZzspFHbB6cuWrVMtmn8sWGK0o2hyX9a0vSIZeM9Fnp1uHCliSJkxe5zdqtGscankiXWlqdjdXo51mQNrVcUMBs0mA2aSQs4ihI7TtNk9GWDwcDcyrnMrZyLy+2itaWVBYuX0dbeRldLA43nTtJYfZKetuZxtTs//1LKm5dwPvcVPOrRViQNP23g08ZmfrR8CbeXm7jbugsJMA40se7F69l99fN4CwqgoADZbce4ezfJ3R2jOwJYMPSzwSFJnNBpKfKlkR3oQxUIt+Lpat1EclI1hpT20MWe3F/D4r1fJK/hSc4u/wZOU2lYnclKEG7f1Ga4guA1Ek2oiSbstFtdIQuybpsbm8vLoUYLiqKwsiRtzCxthxosYwaQnkkXqpFdRzs+s/vpIxAIBALB7EcIOIJZgVGrxpguLseRSJJEeRzCzSClGUn0O714fAEW5pvHLF+ZbRpzwisxcReqSEw0iHFFdjJmowaDRoVRO7FrpTDNSJslOMFNG/EmuyDVgMvrx+0LhLJWjUQ9SeuwFEP4uMdrjaRWySwuNNNmdVGQanhbWquNdXno1CrSjFOajD6mSJOIINwAep2e8vJyysvL8fl9dHR00NrSSmtbK11tLTRVn6Lx3Ala66sJ+GOLEmVz5jDPo6ey+UrOax1YtRasSc3YDO34B/zYjthAgQO7dnIA+HqWjhuLFa4rlrmkpJfVO26lruozZLTvJKfxWfp0q+l639VkvfQiWMMn4v0LtewxGrCoVPwyNY1utYQ2oOYyyxpWO1J5t+ogtVoPvzdV8KPv/A0Ab3oq6pIBpHlqKFODViKz/Q0yn7+S6iVfpmH+nQk5pjA9IkYwiPH4+h952Zxpt+G9YEl2vNnKhorMqP05PL4xxZvguGaOyKnWBQKBQCAQJBoxYxYI3kZo1TKrRrj9xKIg1YDXH6C2ayBmuXjnrPEIEhOd/0qSNCHLmuGYDRoWFqRgc/koTjeGbZNlicocU8z6eWY9td0DeH2BmG4AGclaei4EblbJEmqVhEmvIcek5yT9QwUncCxyUvTkpLz9sk8NEo9AMireRoKnrrEtcBIvHqlVagryCyjILwDA2m+lra2NtrY2mpsaaTl/lpbaM7ScP4vNEp6RS29MIjsvH5XkQqVoSHcWkOQoJt+yBKfGwonGV0bN7B1dbv7aBX+94CVVmnqCS0ru5pJiFZeWqpmTthepSoIFyfS1KOyuUbPukI32dA23LCsZNX6P7OOl9CO8lA73XVj37jd2hrZrei3QCxz2ggr4fDIkB63HKo/9kM7Cq3AmFwOTn/hPJpV2vDVjWuBEaWWk2Gp1DCW+HwyObnV6aep1kG3SkT3sHo83E1e8LmDTQbSRzHYLQIFAIBAIZjtCwBEI3sHIctDCR6OSo1riSFJizd5n+ud7ntlA3tjGSRFRq2TWlqXj8PhJixGTaEFeCidb+5EkWJifgk4dOWDpTByL2TTJi8RsMCqKNcecgNfauDGnmDGnmJk/bz7+gJ+uri7a29vp6OigoeYcTedP01p7ltb6GvLL5iKHZXcaOr8Gbyr+42MHy623KNRbvDxyNCgq5CVLXFKiYl65jj+vy0O3UYf6w5Bp8Y7R0hCXHo38PHHkF+ItLsXcO5R9a+1LN7L7qudwG3Np6raRkjRxgXI6so4rSnShKdr6eK7rQw3BGDntVheb52pCz414d2kaM66PYqRwNdufMwKBQCAQXKwIAUcgEJBr1lPTZcfvV9Cq5VFBYke+NY32FjWel6sX+xtYvUY1ZgYZvUbFypK0uNvMS9WHXLumG7NRQ0mGMSymzEwSTewazlReQ4oSu/2psMCJhUpWkZuTS25OLgCeLVvo6uyio7ODttZWOtvbw8c3on5bfc24+2yzK/ztpA9O+uDpGlTJKsq/WU5HTniMMgmJoqS5tDvq8ShD8W+0ngAbTkQO9l1dkkeN/UY2GvXkOvYBoPY52PzsZQAEJDXNFR+kf9nXkF0uAgZjxHaiEVCUCVvxxFvvRIsVrTqykhfNMmcscaWp1xFmaWN1eEOZ2OIdl4KCxxfA6w+QpJven3ejXKiilLu4n/4CgUAgEMw8QsARCARoVDIritPoG/CQYtBwqGEoy4yEFLdVRDxz28nGkXk7MSgUVGabUMsyTb2OaR/D4oIJmiMNY7Iv20szk6jvHkClkijNHHvCPuoKGta/LBN3OvE15eloVTI7q7vD1k9HDJyJotVoKSgooKCgAJavwOP1IDktaOwdNDY20tfYCxdC5jgH7PR1tk26T8WnkJacT5ornzb9KdLVuWzMvZFFaevJ1OfjC3jwK37e7HyCl1sfYdXZXozuyCfhJ5u6OZz/R+5q3cJHlRNopfBrXlZ8FFc/TO6Rx1H/pJP+RcvpW7OBvlXrsS5bjT9pdADzsLEGpicb00iRO9R/lPKBMRScWLHI4rVmcXr8nGjpIhCAqvyUac32NHKEwgBHIBAIBIKpQQg4AoEAGMri4/SMDpg62dTfxRlGGi9kkIoWIPidyOBR1apl5uWasLu99A3E76bydqEiO5msZB06jRynBU70bYvyzRxrjs+aSC1Lo6ypFCafhSonRU9H//RYVGk1WvKzSqjKXwzAkcZeTtW10N3dTWtLC1fdeidN50/TVl9DX1f7GK1FxlhhpLJrKzqfiXxWISHRcc6HN+Mkubk9vPbUX2moPsOSVev4xnXfZd0mBz27HyBt31vIvqHrud8oc7jSiF8K8LOC7ezuX8uXLeep8jeO3q+aXvD5SD2yn9Qj+yn79U8JqNX0L1rGwYf+gaKNnLFwUhY4CRB+FCUopPS7vGQm61BduGAmE5snXteo6o4hq6dTrf0TEnAURRllgdbR78Lm8lKYZhzT+nB4OwKBQCAQCBKPEHAEAkEYkeaukw1iXJmdTIpeg0GrmnbT/tnM6ON6cVonRXMnGQ/mGDGFRjJygjl8qpidomdJEaNcwubmmDjXEW7lEO16jXW9x+O+NfFA3VCSYaS+e3yWWMP7U6tUZGdlk52VTdWCKi7ftg2bzUZ3Vzd1tdUc27+b2tPHaW+swdIVJVX4COZmbkbnCwb4HjxmgYCf7q4uuru62LX9BXraWzi6bxePPvA/aDQaKhcuZeWtH+Y6nZ5VLY1kvvUSu6r0+FVDgz2Qcp5bUuA/Owt430BLeKf1o4Vk2edDPWCPKt7A5ISSRFDfPYD1QibALJOOpUWpF8Y18TbjDWKcCPwBBfWwc2R3+zh+QRDtd/lYURzZNXSkYBNvNi4IZtlqs7rISNKSatSOLiAQCAQCgSCEmEkJBIKYRHorHe3tqkkf+ZEiSRK55rdv5qSJMlnLpoSMYYJDWJCfwunWYEatRXGkrJ9Osk16tGpbmJuLUTfaciDSvivK5C1wJqIhaNQyG+dkYHf7JiXgjNqGRIophRRTCuXl5Vy+7So8Xg893T2cPH+AF48+TFPNGQbODuBqdEX0AcotnhO1fY/LSU97a9g6r9fLqSMHOHXkAI9eWFdQVMLilHkk7/fSnd+ELk+HdOFgfjdbxc+9VZS4sjAGNNxgc3Fdw/MR+7NmZcGhQ5CTjTYZ/MY0/Joht6qAMrQLKccPY5tXFVPwSTRdNnfY936XlyStesIijKIoNPTEzhIYL26fH0UhphWNL6Aw3AhuuFtn74XMepHHOWJ5HOM62dqP1eGloWeAzZVZaFTTEClcIBAIBIKLFCHgCASCMCJNBkdOaKP9OM9N0dNmdWJ1elmQl5L4wb3NGHmsL6b4zgWpBowaFbIsYTbEbz0zFUQSTEYey/FYCcU6DfHEwJmIFYjXF0Ctkkelm46H4UJgPNeQVqMlLy+PvLwb2LbpBhQUevq6aG5s4Oi+vRw+toP2mgZ6m9uQZZms/OKobXU01xHPdL2lqYGWpgZ4KbisTlKjn6PHWGHEWGkkUB6g1xR0AarxebiuL3Kb+fkHkc99H985LUXyAQB6DQvx61PwGdNJys6jwOUloN/EiltvIaDR0r9wCf2LV2BdtAzboqU4issiphObCkOXfbW9GLQqMpPHKSJdOI+dNjcWx8TcKr3+QEgMsTq9HKjvBWB5cRrpSdqIQvxIoWmiz6TxuFANplQPBKDH7hFiv0AgEAgEMRACjkAgCGOkVUgwKw+j1kVCliVWlqRP0chikzoOF5zZwmzRayZqCZSWdPG4O5giuO5FtsBRQnFLIhGPgONXFNKSNBOKZ6SawIx5eJWJnEsJicy0bDLTslm2dDUf4bN4PB5amps4dewwBnM6PT09+Lyj96e94fy4+wPwDfiwH7NjPxYUbXJvyyXzqkwAVp6LboEklcrkycfC1qU7T4IT6ANawAhknPo9ALLXQ+qRA6QeCYo9iizz6t5qAsbRwZCnKm6L0+OfcIDyMzGCG4/F62e7KM00UpFtYn9db2j94cY+Ll+QE/E57puwpdCI5Sjlxro+T7RY6eh3sTA/BbWwxBEIBAKBYBRCwBEIpgFDnIEfZwOjxBpGCw3TkeUlHpYXp3Ks2YpOI1OWGTs7zWwhI1lLzwVXhJIRY54JQWc2uHFNBSOFEEmSwty+IEYMnBjHZKThRqQU8IqisCAvhbdqemKOcU52Muc7gwLGoAAZSzyKxvAak7HiGr4vWq2WsvI5lJUH3acUFKwWK93d3XT3dNPd1Y3N1k97Y+3EOxyGsWIo+9jza81UF+pZfWaAbaftrDxtQ3aCP0NGlRTfpF5uiiyeDZRXjhJvHB4/B+p78flnx3NtOJMVleq7HWQlh1u0DDYZyVJslAXOBJ8P44mBM5Ium5v6ngEqsk0T6lsgEAgEgrczQsARCKaIypxkqjvsGLQqCtOmL53rZIlkYTAqcOssmedkJOvYMj97pocxLhbkpXC+y45OrSIvJXxilWc2hMSdmeZicueKJCgm6dQ4RmRU041wo4q6jzH2PUkb/mdzZB8QdMUxatWsKk3jQH1f1LZK0o3YXF7sLh/lWcHsbBNJUz78/pzMeYvVt4REamoqqampVFRUAOB2uynOy+bgW29w5thBGmtO43aO39JEpVazUroVb5sdVUBDe+oJqotaqC7S8+crMhjY5cTwm/Nk9wTo+KWd9fkqMsr07FqUjrnAwK+6Oin2+cIbbR59XgD6k8zw3LOQkQkZGZCRDubUiG5K66/fhCctA/v8hdjmVWGftwB7ZRUB/TSm507As/Z4S+SsbJGa9gXC06PHez2NvAfjFfmjCVQtFtf/b+++w6So0v2Bf6tzDpNzYMhZkoAMGFBRkokVxYR51w2m3+p6ddVrDrvXiwpeXQUDoiKgoIiCgoCASs5pmGFy7pnpHOv3RzNFV6fpnume+H6epx/6VJ86dTrUNPX2Oe8JGcAx211wudmoEp8TQgghvQUFcAiJk9xEJVI1MoiFgnb9qt5VAkbbsGxA4tZuEr/pkWRiIYaFSPqbqpHCaFPA7HCj3icZalfoSQGcYCPcElUSXkLZSHmXEQ/9uEDAQK+UwGD2BtpS1TIuh0crTwTTUEbn6CAQMBiZpeNtb9cInA5OoWoVbfBIKpXiooun46KLpwMA3B43BDXH8ePGH7Dh510oPnEELYb6NttJzsiB2pMEmL1TqLTWTBzLXA+j3LvsefMZA4oBHAWAWg+O1XqA/U5gjRGMhMGQPDnk+XKMHDIIFw0ZhSzGhXur3gv+SqQzEDVXw9VsQEHxu0hiTkLMWCBkXBCzVlglqXDIElCnvwzK4tNQFp+Gfu+v3O6sgMGJN15C/cTpkFrrYFVmILX8eyRU/4KmpLFgBSLITaXwCKWwqPNRUTA/qtf0/IFa/+n4X1uHO3gwK5IROOG4PSwOlDfB5nBD5ZfA/mBZM/KSXChIDj8yMuRInRD1W2xO/HbGOx1saIamXUulE0IIIT0ZBXAIiaNwq310V0GnUPknMaYITlwwDIMBqd5fnTcdjWyJ544fM0QS4G4+tap/igqna01IUkuDLj2cqZOjqtmGZosT/UJcRIYcgNNGIGN4pgZFtWZIxQIkqSU46fdWtXUNPCRDEzKpbXtivbGaQtXROLNQIMS0iy/G5ZddiunnPr9VFaX4bfsWVB/9DTt370NVaTE8fqM8UrLyA9rKbLgAxzM3AAwLa7E15DFZBwvLSQssJy3Y/P0ObMYODJMyuJkFgo3fyMg8hAzhsSANef+RO2ogd9RAu/dQ0OMxHhaDzz4PNLwY8Fhy1Wb+BjeLgftfRFXuHDSmFaIh9SK4xaqQz8VX62coFn9r/V5uTtAcOG7/KVShVRis3MpUwUaildSbkdBGnqyQuXJCHPikT06go5UtFMAhhBDS51AAhxDC09bFK9B9cuCQvisvSYm8MHmPGIbB+LwEOFwebgUq/wvWYJ91lm07F5FUJMTQDO8qazZnsClUoc+PNK0MGWFW2Ynk/IvFPsHbiUEbfuX0zBzMvfE2TB/6/7CzqAH1hmYc2L0Te3Zux/EDe1B88gjScgOXKdfY0jGw6nKUqXfDVnokqj4csbPQAxgOYDKA8QCmiIABLIC0CIPqNcFHrUDFAKoIk+uusUJ41oSs5KXISv4InjQpigvvR+nUe+FWhc/v0vo3Np7B8mBtO9zhp1CxLMt93upM/NxPwTSaA0fB2V1unK41QSQQIC9JEWSv0KPBHC5+/5otTppKRQghpE+hAA4hJGo0AqcP6N4DcCIWbvnw0CNwOnbM1gBOsPNkeGbw6XMd4TtypiNdFwkEkIoFsDtDDNmIQLhgEsMAcoUSE6dOx8Sp3mlXHo8HHrcLLSYTGhsa0dDYgMaGRjQ3N0NnyYLzlBusa2vU/XADOHDutgTAwNF6rJ8lQz/WHPAaFQ++D5s/W4xkuQfDUoXI0zIQVAcP4FRnSmAUi5HhcqFSJEJ/pxO7ZVL8KpNhjN2OQ1IJ1qhUEILFO4ZiZJtcgMkNFLshgAMFa1+D9PZqHP/7ayH7LrY1Qlx6EBBaILH1h0OWFPXzj0SwQHyDyYGCZN8t/FfLwwLCc5ukIiGA8Cut+a9qxTDAqRoTqpu9wZ9QC02F+hSJRQLAZ7TP7yWNGJOrb3OkDyGEENJbUACHEBJApxCjyeKEWCRAQpDpKRS/6V2CTZ3J6oVTE/wvWIMuIw42qlwwwaqGmkIVr2XXfae7dTSJ8cgsHW/J6ej7Et1jAoEAAoEECfoEJOgT0B/eBMkutwsGgwFbvlsLuVIFq9nU7j4BQEuBFNfkJQJIRGGzDHcbzVijk2KbSokW+2Yc/MEGt9UbGBDIhDjk9mBokHa+LVDjjay0No8ncrFIqwke3Mgwr4H55DDU5MyCqukExPZGWDQF6H/wdSTWbPfptAcX1YlQceFNODvhbjgUqWAFsftvW7AAo9Hm5I2y8f88eVgWwnPvpEjY9oetvDFw+ltr8AbwrpIVTKhAoDhIxOdIZTMKByQHqe3NR+XysGEDuf7cHhYtVie0cjEEPSh/HSGEkL6BAjiEkACjsnWoNdqRoJDQf2D7AJFQgDStjLuwStFIw05P6i1CXSRKRQIIBKFzh7QlMUSgZti5aVexxn8aHTtftfLIp6MoJEK4WZY3YidcACmaqV4ioQjJScmYd+tduOGWO1FZWoKSo3uxc+cvOHFwH8qKT8Htv/JUGLLc89PWtmlt2KYVAnABaIaj3sEFbwDAY3PjLgAXABgFYDSAEQAUALbtN6LS5oE0XQppuhSSdAnECeKA55ZTY4c4xLLkziRg0P4XMWh/YB4dntMuCNeZkPPJEuSIlsCTLIFxwAjUTJiF0oV/jPi5hxJsqh/LeqdReUfXhN8nmoTH0Qr1UREHCRo53cFPVJvTjV1nGuD2sOiXrEJeoiKiz+DvJY0w2VxIUksxOlsXTbcJIYSQuKMADiEkgFgoQGaYERjRXOSRnmF4phYDUlWQCAUxy6nSI7HeQMPYnARUt9hgsDhgsoUOFARL9jwgVdXaFE+kK0yJhAyXTDZUkmlfvkHWaN46oYDhXYS37ut7/PD9FCBPL8fRyhafNsJPoWoPhmGQmZuP0cMH46KrrgcAOB0OnDlxBEcP7sGhPb/h5JH9qK0oC7kstSwndN4hW3lgLpdd526tBAAGAKiutKO5kp/XRSAVQJIuQdoNaVAN9773AyqCr4DmEgB3D0/FE81NUHk8qBCJ0CwQYKzdjlVqFapEIujcbuyTSXGTtR4zcK5vLkBQ5YC2ag+UpadRPf96OOQpIZ9TJEK9w3bX+QBO4KqE5++3J4ATaXL0kFOoQs258mN1uPHL6fMroBXVmmCwODAmRx92vyaf873eaOeNRiKEEEK6AwrgEEIiMiZXj5IGMxKVEqik9KejNwr1q3uvEcX1plYhhlYhxrGqlrABHH8X9ksI+TpGehk4JlePoloTNHIx1FIRDpY3h63f3stL/wBOq9HZOhwob4ZYwGB0jg47TjcE3T/awXkdvQz2DYCJJRIMGnEBBo24ANcuuBsAYDWbUXTiCE4dPYBjB/fh1NFDqCw9A7UuARcZ70KdqQTNyjI0qM/w2z3d9mgzD4AToR6ze2ArsWGAdjQuyJyKKSlzMOzXxQD+HVC3RCfGbzYh5qWngWnjBbyrNvjnTqSzYOq6KbAqMnBm2F9g1hSgJWHUuWifBxJbA7JPf4Kkis2Q/F8ZWvJHwFgwDOZ+g2DJK4A5tx/cHhbWICtHAd6RKxpZ8CC97+fFP79NLIUa+RnpinlHq1oCtjWaHLC73GH/zvk/J5aNTXJvQgghJFboKowQEpEEpYQSRZIeTSqOfYAqmou7SH/J18jEuMBnpMD4fCHMdhdvpIsv35w90VxrigQMHD5l6bk8ITqFBIX9k9qcPtnZF7Zt5SaSK5UYPmYCho+ZgGvPbXPYbWiorUF6di5sdhuam5pxtvEEjhp/hdsKyBvS8HPxpzHpX2rzOOBoMg5VHEXdhdNwJn8A0qoroT9bBPfvO6ErLca+RidOPnoSjIiBJEUCSaoE0lQpJGkSrizWi8EIGORXBR/FczxdisEA5JZKDPv9HwAAu1gPCESQ2uvOV2zxACdNSD5ZhWT8wGtj76qNaBw8Imj7Nsf5KUn+8ZKdRQ0Yl6eHTiGBpx0BnEhXMHS6PdhbakCL1Yn+KSpk6b2rVQWb9uX2sFzeGpeHRZJKAoPZEVAPaHs0m3/7R6ta0C9ZCYWE/rtMCCGke6BvJEIIIX2CVi5GklqKeqMd+cnBR134X9+1dcEnFgqgkAhhcbghEQl4o9P8p/O0N96hlYuhlYvDBHB8jtGBqIrGZ2pkJLmvoj2W//V+v2QlztSZI95fEHkeWo5EKkN6di4AQCaVQZYqQ2pqKiZgKlen7vBhtDTUoL66MvoDnCOWSCGWylFfV4f6ujoUAfgFABR6SEZdhCNWB7aVFqP1U8e6WNgr7bBX2mGEkdcWI2GgThYjsy54EuS3ByZgitqFeUYTWl8SqdMQWLE+dBInU0rola2M9vPHlezfB111Iyy5BXAkJgEMg0MV3qTB7RmBE+kKhha7Gxa7d4TQ8SojklRSyMTCkPsfqmhGvTF4wCuS47vcHoiEgoDHq5ttcLo9vIBqR7k9LCwOF9QhRjkRQggh4VAAhxBCSJ8xOlsHj4eNaXLuC3L0qG6xIUUt7Zp8Ge1cRlwlE8HiM41GFuUIpWSVFMlqKZfwWdNGbiz/gFZOgiKqAI6oPRGcCPztn6/gb3gFZpMRZ0+fQPHJYyg+dQxFJ46g5NRxmFrCT2EDAG1Sasj33uGwo6r8LAAgkmfLOlgkVDhQASA7yOPF6VJsSdLg+aQEjLexGGQ34QajERq3G+ViMeqEQlSLRNDabZiLIKs8KRiM3b4Ahy54GaaMMYCQ/1/BqiYbqpq8uXdGvfo8xm3yjt5xKRWw5PWHud9A4I6b4B45NaDpeGmxOr0BnBAjeCIJ3gCAm/WfIsVi99nzI32CjfJqMAUfzdMeHg+LnUUNsDndyE1UYECqOmZtE0II6RsogEMIIX2I/1QAUR9cZSxc8Mb/F/hI4jFyiRD5EazaFa/YDm8KVRTHkIqEGJyuRp3RjtzE6FYdS9XIkKWXcwmfG8x2ZLSx9Lz/pXc0y7UDQJZejpJ6fghEIxfDbHfFZEUkpUqNoaPHYejocdw2lmXRWF+LklPHUXrmFMrOnEJp8WmUF59CQ10tVy8tKxcKpRIWiyXoMI+WxrqAbeGUAMgBoAIwEMAQAKNkMlwwQo6KpPNTWX+XMfhdpsYnWjWqv6xG044mSJK907FeDjEFqzxNghRbOSbuvAUA0CAYiEbFMBj0Y2HT5yO7+XvI3A2QOBqReGgTt5/IbIHmyEFojhwEOzgLnhGFUT2njjhY3owpA8QRj+AJxX+KVGWzDc0W74ijUzUdW6o+EhVNVtic3qDp2QYLBXAIIYREjQI4hBDShwgFDC7I0aGmxY4MnYxWWOlE8Xqt+TlwIj8GwwBZegWXXySU3EQFzjbwR3KMyNJy91sTPrfF/+I72lFQMrEQk/snckmV07QyDM/UotxgwfEqYxt7tw/DMEhMTkVicirGTp7GbR+QqoJW6MSv+w5h0469SM3IwugLp8DtccNkMsFkNMFoMsJkNMFkMsHYFDwRdFtMAPaeu21MSsPl19wBRvANgMCVsxzVDrgaXXA1umA5YQk6egcANrd48NTvYtwis2OihsUo3XEUuE9AYFoDlPlUZFl4Gj0INu6JOf0OMg8noCxvHpzS0NOL1If3Y/g//gJLTj4sufmw5BVAn9MPltx82NMyooo4nq7teIDFP29PJAnKYznwy+4KPa2NEEIIiQQFcAghpI9JVEmRqJJ2dTe6pUiTrEbWVueIdwguP0kZEMCJRus1eqglvqOhkIhwyeAUWBwuLt9QJCN5hEIG7giWRo+GTqfDlZcU4uLCydh20rtktVAghFajhVZzPsDldDjw9cChqDh7BoaG6Ebi+FLrkyC1ajD6zE1oUJ2BXdwCi7QBZmkjHGITHHX8qT7vAzgIYBC8o3j6A5ACOFTvxNn1DXjBp65QxCA5QYjh/STQL8xCjUgIcZMbm5zBA2OuRCH6HXgdmYffR3HmTajNuRouXSo8Ci2vnvLMKe7G21+lxpZdJ6N6/rVGG5I6+HerxeqCTnF+BFMkI7cimbpndbhhtDmRqJLyVkvzF4tzgBBCSN9GARxCCCEkDoSdNLqpvVOoUjWyiOqJhB0bgtDaJ2eMlp0WChheAthIAjjRBG8UUiGXQDcSbR1fLJHgfz7+GgBgNhlRWVqMirNnUHH23L+lJagsPYOmxvCjdDT6RADeUVZJpgLeY0rGiBPVL/G2rT534/oJIBfB8/C4XSyqa10wJkuRL/UGOC6ssYbsy3S7EmONwF2SJgwtXYzBpYsBAM1MNhpkI9CkHYmWhOHQ7N8RdH9HehKEThPckranEGWtWAq3TAZ3v/4QDhoISDXtno94ssYIvVLMfX6CrWrlL1wdm9MNoYDBrjMNcHtYZOjkGJqhCVnfPwdPPNQabTDb3cjUySERxSdvFCGEkK5DARxCCCEkDnQKMbdCVZI6fiOeGJ9rNIWk7UTEqRoZdArvyladoXValzNO00dincZpaLoG+8ua4AoT9PG9Do/m8EqVGgOGjsSAoSMDHjO1NKOitNgnsFN8LthTjJamRlx65VWYMO0KmM1m781k5u67G41w2gOnVfnyAChuo3/SlPOfU43ZjRa5ABor/32rAfDzx7X4GcC/AahlDAbqGRQkCFCgP4UCfREKEtaiQC9A5sHgfVJIy3DRt5ehZOBdKB16b9iATMGiVyBuaeLKg1RqWHLyceqRf8IwcUobzyjQ8WojxuclAIhsBE6oGocrmlHdzH9+lU1WLoDDsmzAtElPnGdQGW1OHCzzJt22Od0Ykh46mAQAzRYnztSboFdIkBdBHi9CCCFdjwI4hBBCyDn+P5DnJipQYfCOREiOMgjDMAzG5yfAaHNBF8dgie8lYuK5laEazPaQF4vDMzWdmvsoFocKNy0l1s8lkvZ8PyZtjcCRiYVc4tpwVBotBg0fjUHDRwc8ZmpphlAkglyhRII+IeBxa8UJ/O+LbR6iTZJkKRKM+ZA61Tgw2I7r/w2MMQ3HiBY51PVFOPHOe3D5nSNGG4s9VSz2VAV+4PYDGBXsQAkCSJxNGHjkX9Ac3YRGxXDoUQKluwp2RRpKBt6F5uyLIG5u4gVvAEBkMkJz9CBSKjbAbBsMhyz0kugAIDY0ACwLpz4RYBheIDGSETHBpj253J6A4I2vZosTByuaIBYKMCZHz42E8R/NE+sV8U755AmqMFjbDODsLTPA7WbRYHJAr5R0WlCXEEJI+1EAhxBCuqGhGRocrWwBAIzM1rZRm8SLQiLCqGwdjDZnm8l+gxELBUhQStquGIEUjRS1LYErC/kHEEZl6wAAm47WBG2nOyWuVslEAYlkR2ZpkaCU4ESNkVvOun+KKmQbsR6BE+3LE4uXk2GCLl7FUWm0GJmt5UZX+Os/cCC+/fZb7Nh3BEePn0JVWQkqy0pQVV4KpyOyJbYBYDAuQ7+aMbxt9QA2AzCJ0vBFFDOAGAADQjz2RZ4G2TIGk2x2pLEHkGY+wD229sAJZB3Zhv7aLKQ3NoZsP7vpM2R+swZ1GZeiNvMKmLX9YdINCaz3yX/Q753/gUuhhDUrF9bsXFiHDoKscDI84y9r83l4PECj2cGdx24Pi9LG8DmhDlY0we70wO704HStyWdUjl/bLAtBkDFcFocL9UYHktVSyEOMqrM53ahsskIrF3M5zfyTNLfFd2phvcneoQBOvcmOCoMV6ToZUtSRTc8khBASPQrgEEJIN5Shk0MqEkDAMNDHKABA2hbs8idZLY169E08DEnXQCe3QSxicKSihdvejeIxQbUGmDRyMVqs3iWbE1Tez/TwTC12FfFzvwgEDERCAQqSvUEbqUiALH3oJcr9A1gKqRAKiQj1Rm/gYmSWFgfLgwc+QrXnDXKFm0J1/rFYBMSGZmiglYvxW3FjyKlbKWoZElRWNJocAY/pdFpcffXVGDiuECX154MLHo8HjXU1qGwN6JSe9f5bVoLKsrMwNht47cy46lokpWVB6LSgstHIm6plNNRH9ZwEAO6AN4jje0sB8HWOBofT5LjBaMI8owlyD4siiRhHPEL89bPKcy2cwD0i4N0gbTtFwB6xCGPdDqSWf4/U8u8BAA0pk3F65KOQWashtjVA6LYi5dAGAIDIYob65FGoTx4FfvwO9v2bwLw1BBAmtXkS7S1pxPRhaQCA49UtXGAxGI+Hhd15fpRPdYt3WpXT7UFNC3+/YPEWlmWxv7QJFocbZQYLJhckBnzGnG4Ptp/yvh8MA1zUPwkysRBN55ZE72wej7fPAFBntOPiQZIO584ihBASHAVwCCGkm6KVoogvsVCAnEQFLA7+iJVIkvgKBN798zspz0VekhIl9d50uYPTvYlqh6SrcbC8GQzjzTMDACqpCBfk6LDv3MUfcD75s0wsxLCMtkef+T//EZlaqGVimOwusCx7LmFtZAEcpVQEpUTYZl6bWKaiHZSmRppGBoZhUDggGZuP14asG6pfra+B2O+iWSAQICk1HUmp6Rg5blLAfqaWZlSVn+WCO0OHj4ZcocSAVBXKDVZYHd6pXyxYbPjiQ3wXxfNyA1gZZPvwv2RDlCeHh2HwhUaNLzTnExnbKvgBjqwQq3wfcQGTXmkCI2KQoBMiS8sgXSPEGNVPyP1uC64oECFPd+61OBssbTMg9pzE2NUXwS5LQkPqFNiUWajLvAxG3VBeQEfU3IQpV4yHNTcPtuxcqFIzkZ2RDVt6JhomT4NHzh+Z5z8ty+MBiupMqDMGjoQKliC5xeqC5dzrbnW4YXd5IBPzR+Gcqjk/VYplgZ1FDRHl8/HlPzWsI2FIp998TZvLAxUFcAghJC4ogEM6pKGhIWyZ9By1tbVYvHgxV/7Tn/6ElJSULuwRaS96L9uvuy3zG+y9VOkCc6C0ZWi6FmnazpvWkJ+khFjIQCwUcNMp1DJx0NEE/uVoc4IwfteJrflyWpcZB4CcRAVKwyyFnqyWIk0rg14hifkUs9al6Zubm7F+/bfc9quvngmtVovshPMBgHC5foDQo31aN8vFbSex9qXSaLmkysMztThW1QKFRIhsvYIXJGDAYNjAAkycdnm7pmb5EiVmwSUKvjy5/1LotQB+h3fkjs5ne+ui5KyLRUO9Cw31wAE4seHc9mdu1aIgRwUHA8xvMiHY5MfnJAqYTFIMdxsx4+zXyAWLfkffglOiRYt+BGqzroBDlgT5yWKITEaIjhyC/Mgh6H3a+OvDz8GsUHLvJRA8MXJxXfAgUrA/Nya/AK3bw6K62QaVTASRgAHLepMl+9cJprbFhmS1NOjnxn+fSD73xfVmWBwuFCSreEEl/+fRzQcFcui7sveg97L3oGvLtlEAh3SIwWAIWyY9R11dHZ599lmuPG/ePPry66Hovew9gr2XCi0/gBPquqs1aKGQCJHSwSlgYpGAS/6qU7SdJ0MoYJCbGDjaJ9hFov+WtoIYbQk2IikvURkygJOXpER2ghxS0fkL0rauZf0vWIdlanCyxgSny4PcRAXOBjlWc3MzVqz4DKzbAUYowUUXTeEu+tsiPpcEt60ROLIIViELur8ASNPKkKyWhnz9L7vyKoyechkaTY6wU7OqykvR0hQ8dw3DMLh75EuwOqzYZ9mIE/gFdoEZYL1T1px1/ClAb5+7AUASzk/DqkR4H/dLgjxRDoXVjTtNFUHrbN3chJ82t3YMEGtE0GoESNE4kKvajLGyTchWCzCmMXg2cFYM3GpbhCajCEN2rUPj0NtQmzUjILgCAEP/8Rfo9u+GNSMLtows2NIzYU9JB2ZfClwwGoB3WpTF7obVL4DzW3Fj1KNrWh0sb4ZeKYZUJEROogIa2flz1xWmzTqjHRaHCxk6OTeqq9ZoQ9G5JMkuN8vl2gICRxJ1rzB4aPRd2XvQe9l70LVl2yiAQwghhJzTEy48Aqc+BL/gHpiqRqZODplY2OGVbkZlabGvrAlChmlzZZuOirar/sGUYAGIcEGhcAmSwxyVV0rXypGuPZ+nJ1gAp5W96hRkWcMAgEtu6ysvScHLY6OQCrkpZ6ECS60BnGhG4EhEArg9LFiwGJmlAxD+dXK4PJCcu5gPNjVLIAAkQu+KW6aWZlRXlKK6ogzV5aXn7pfCbrVi0KDBAIDRuACA9/PMgoXNZsPi9U+hCsuDHr/+3G1nBM9NnOANVGTVh84Jc8a3wALOZhfqm73HOApw08UeAzAuyP6VOhFWDlVB6/HgkKcIIw88jsLdT6Js0N1QqgbCqsyBJ9GbxllZUgRFaTEUpfyF3E3Op3E2ZwB0Cgn2lzXxVshq5fawENhtEDU3wZGYDAijC9IZzE4ATrRYnZjc//yKXQFBl3PlFpsTB8qaAAAWx/mlyH0/k/7TwfwDTMGmhvnzeLxj0zoasCWEkL6GAjiEEEJID9K6JHErUZgLIKU0Nl/zOoUE0wYkA4h+ilNb/C/1Isnpw6/PLwuD7B/tReKAFDUOV0Se+DgcmVjIS2rL2s2wlR3C0BQpMnSByZlzEpRgWW8+m9xEBW/UUqjXpnWrWChApl6OCgN/FEiGTh4wMmRCfgJEAgZuluWNPgrFm4sleF4TkZDBBdl6HKn0vmYqjRb9NSPQf8iINttlGAYMGCjkCkgEIojFEjidgYmaI8WIGIhUYmjNGajVM7jidT0y6s3IrnMht9aDpH1N0JfXoyzC9vqF2F6UJMV3qsBRZuPql4NdWY8Pv2iCWipAskaKXxuDJz3eXlGH8i3boNUnQpWQBrEkeMJ67YE9GLvweniEQjiSU2FPToUjKQW21HSceOrliJ6HxeFGaYMFOYkKuD0snO7AoIvN6cZvZ86PnvJdijzc9FKHX+CJDT5oiWOyu7C7xJuwO00rQ/8UVUCeH0IIIcFRACdGjhw5goMHD6KyshJCoRCZmZkYN24c8vPzO7UfHo8HO3bsQFFREaqqqqDVapGZmYnCwkLo9fq2GyCEkD5GIRFySUNTe8Dyt1KREPnJSlQ2WZGToIh5QCWUeB3H/9f6aIMtapkYSqkIZrsLCSpJVP3slxw8qXOqRgqbU4XTtSZkJyjQP0XFSy4cTaoklVQEiVCAUgBOg3fyD+uwQi0NfsEqEQkwIFUd9LFktRTVzYHBAN+4zpB0DXQKMW+lMrGQgVwi5JISq2Qi7oI50v8ISkSCkBfZUwcke1/3Dn5E/vrPV/DnJ19CY12Nd+ROpc8InvJS1FZXoL66KmyAJzk1A3/NXgLWLoDFYoHVaoVFaYE1xYr9/Wz4qeoTHCvfHnGfQgVwqlODB1t2y2WodXsfM9o9cNRZkRS0JvCvpYuxaen5vB0amRB6tRw6rQZ6tQL/fuJeiFSJSDywCQAgcLshq66ErNr7OXKphVBeVAyXNBkm7UDY5SlwyBLhEmtglyXCou7HJYnK/uQ/cJhNaCzIQblEDZs2AXJdAuwpqfDIFTDb3ThS2RLQR5ZlcaLGCKMteEbp0gYLTtbwcxo5PR4u4GO0u6CUiHjn9aHyZm61tepmG2xON8blJcBsd+FAWRMYhsEFOboOB3VsTjdcHpaXD4sQQno6+ovWQV9++SWee+45HDx4MOjjkydPxgsvvICLL744rv1wuVx45ZVXsHjxYlRWBs4Ol0gkmD17Nl5//XXk5eXFtS+EENKTjMjS4nBFC8RCBgUpnbNKU0cVJKu4ZbZ7uoAATjuSCI/P06PZ6oReEfyiGgA3MkUoZJCskp4b4RL8/WYYBnlJSuSFWLVLG0EeIF+jsnUQNMvhaiyPaj9/KWopMvVyGCwOWOzukPX8V6MSChgMTddgX5kBDBiMyGw7947/8uu5iQo0WwOnJGXq5VzQLNrRU8H4Ts8aPvbCgMc9Hg+aGxtQW1WO2upK1FVXoq66ArVVFWisrUJSWgbyM/sDABJVEgxJ1+BIZfO5qUTAvh+/iqo/jwEYCm8gJx9ALoA8hsFxoxwtB4RgBIBumBBuwfmgkrvl/HuTHaZt/+w8LTY3WmwmnK0zQSAA6opeh4hhIDhpQUaQ/Q8a3bjwsW2QqoRQKwVQKwVIUAigUzJIlwM5MiBPIYdQ5sCFq4xQG7xDY3yzaB1/6kWUz78z6CpZgDePTp3RDlGTAfKqcjh0CXDqE1DWaEF2giIgeAMA+0uboJAIIRYJ0GxxQikVYUSWFpVNVi7Y6qvJ4gTLsueSJHtfuyOVLRibe/6Hx9oWG07XmqBTSDAgVYUT1UY43R5k6RVIUvGTj7Msi99LDGg593ntl6xEv7B/LxkI1QkwhjmnukpFkxU1LTbkJCiQRCtTEkJAAZx2c7vduPvuu7Fs2bKw9Xbs2IHLLrsMTzzxBJ577rm49KWmpgazZs3C7t27Q9ZxOBxYtWoVNm7ciI8++ghz586NS18IIaSnUcvEmFSQ2NXd6LP8R7O0Z6SPSChAYhsXN0PSNcjUywNGA0TqghwdTtaYoJWLkdyOC6lYBDeYczmIXG4PtpyoO7/db+iL/7FEAgH0SgkKByRDwDARPf8UjQxTBohhc7qhkIggEQmQoGAgEQm4KTOD0tS81c38jysVC9A/RcUbDdRRAoEA+qRk6JOSMWjEBbzHtAoxhqRrcLLGCLlYiMFpajAMg2y9AgazNxil0emRnJaJxvoauF0h1in3sffczVdSWibMTRnAWm95EubCozejWnsYVokB7ubzoZncMG2HC+cJVCI8kuZNwvoPeyWGBalTDcBl8cBl8cB8rhzIm3h4VojjGM++juRNX0GhGQx7ymQYUibCLk8FGAaVpSU4ZWqBWqvDoN27MP6Jv3L7uSVSuPU6jM3phz3L1gS0a3G4gXPBGLPdhV1FDRAbGuCWyQGZPCCh07EqI290mcF8PiBW02LDoXPBRIvDCoPFwY0mazA5kKSWYrRPUuWKJisXvAGAM3XmsAEcUUIGxPpMHK21Y5jNCbUsugBtNBwuD07XmiASMuifrAr7987mdOPYuVFRjSYHpg9NjVu/CCE9BwVw2umhhx7iBW8UCgUWLFiA0aNHw+Fw4Ndff8WqVavgdDrh8Xjw/PPPIyEhAQ899FBM+2G1WjF37lxe8CYzMxO33HILCgoK0NDQgO+++w5bt24FALS0tGD+/Pn46aefMGnSpJj2hRBCCIlWJAlPY0XTgQuzRJUUkyIM3MjE3mS+AJCmif20vMCl1/mP+49iEgq9Zf+ROW2RiYW8aSwioQDj8vQwWJxIVkkD8jH5X4uOzdUH5EeJJwbeKWtjcvhTxpN9VmF75Ln/AXBuJI+hAZlSG5rqa/HjnhNorKtBQ201GupqvPfP3Txu/siM5NR0mH0Gnlw+/XJk52TDYXfA7rDjn65b0YRfAQAHAFwHbyAnF0AegEwAWgDBF1P3EqrPv+4pTcEDTcEDNoEk4I+68bU4Q4vdWguAvehfsQtji+wY6nBDI1Bg9cpqfL3TG0C4H8B43/457EBNDUzGJrz/z3sgTuoHhUYPlUYDpUoDpVoDpUoNjS4B6dneMNbY26+FqugkPGIJnBoN3Eo1XCo1zvzpUVReckVA31iWhcnu4oI3QmMLpPW1cKnUEChV8MgVAMOg3miHzemGTCzE4YrmoNMMWzWaHTha2QKlVAgRy0KUkAWx/vz4ppM1RozNDfVqtZ/F4YLN6UGt0cblo5IIBXCzLOxOD/olKwOmjJn8Riq1rlSmlAohivJcJoT0HhTAaYdvv/0Wb775JlceOnQoNmzYgOxs/kDZAwcO4Oqrr+amND366KOYPn06RoxoO6lfpP75z3/i119/5co33HADPvnkE0il5/+z8vjjj+PTTz/FHXfcAafTCZvNhhtvvBEnT56ETNb98z0QQgjpvVLUMpwQGuFys9ArQ0+B6klGZWtRXG+GRiaOy3MSChjolRIYzA6oZKKAEQPeCzwGLjcLgQBIjGEfFBIRFJLg/330H2SkkIggEfIDOGlaGWRiIUrqzdw2nUKMJkvoFaMiFWo0A8MwyE5QoKzR4lNXAH1iMsYPSoZYKMDki6djd4l3udrhmVouiXVroKehtgaN9TVobmyAzeHEex9/zj8GGEilUkilUphbmrjt9QACx6e0Ta5WQs+mw8Hakd5YErROpAGccOM2DD6BotMSCU77JFIu9YlbhcrjU2qx47NV60K2n5eqw3fvPgWGdUNq8OaREjgdkDbUAw31AIDvli3Gnt9+gVKthlKlgUKthlyhxG/blJDJFZArlJArlOj/+w5Mfub/cW2zAgFcShWs2Xkw7voNAMIGb2xON8r+8wn0FjNcCiUsLiumytSw2M0oE3mft8Hs5IJB/vs63R643CwOVzZDKhJiTI6OF0hpsTlxutYEjUzMW93O4nBh15kGePximafPLcsOeIM1E/LDB44OlDWhyeKEXCLEpH6JnZb/rJXHw3b6MQkhgSiAEyWPx4MnnniCKysUCqxbty4geAMAo0aNwsqVK1FYWAiPx8Ptu25d6C+6aJSXl+Ott97iyiNHjsSnn34KsTjwF8abb74ZpaWl+Mc//gEAKCsrw9tvv41HHnkkJn0hhBBC2kMoYDA2Vw+D2YkUTe/I8aCWibmluePlgmwdDBYHdEHy/oiEAkzsl4gmixM6hbjTVvgJNk1MJBTwpl2la2VQyUQoM1jgPrcK0ZB0DRrNDugVYpQ0mHlLVkcjXP6k7AQ5L4DTqnVUkk4hwYX9EuBhAa1czAVwWgM9+sRkAMMBAKWlpYBfAMfX04uWoqG2GmkSB349WoTG+np4rM2wNjeipLwKtTU1aDI0wGIKPQZnRPYk/HPC+wAAw6cG7KqogLS2CrKaaohqq/Hrig/wS5OhzdcEANLCPGZTyhG4FpyX23w+ghMqgNNWD0xSKz4qfQsMWDzrE9jytX33LqzfvauNloCFAKb4lBmPB2JjC+rOnIRx/zpINFLoDSzcIjlcIgXcQgXcIgU2fPcdvlveDJFUhr+9/b/IqK3i2vjx3L//T5uKytJiiAUs1jfWYerQTKiVCkgkElQ0WXG8yvteaQ7tw9A3XoRHKoNVrYRarwbkckClwtEHnoDJ5kKjyQGdQowEhQQWpxsHy5t4wRtZ+VkInE64pTKwEik8YjFMVgnMqUooFef/Bvovz94a5LQ63NhbasCYHH1AQKXZ6oRYyPCCrEabEwKG6dCqhAazAwfKmyARCTA+LyHq0Xyh2F3uiFbBI4ScRwGcKP3444+8hMV//etf0a9fqDUKvEmM582bh88/937Rf/PNNzh9+jT69+/f4b4sWbIENtv5XxpeffXVoMGbVo8++ijeeustVFR452a/8cYbFMAhhBDS5dQycVzzTvRGAgETNu+PTCxEmrZzL4wGpqqx09QAAMhJVHDbh2VocLLGBJ1CzPV5Ql4CjDYXktVSCAUMN82pf4oaAAOz3YU0rYybPgMACSoJGk2hV6AKl9tHIRFhdI4O+0ubQtZp6zMoEHjzCbVi3Q4wwsAAWk5+P9x25UQIBAyud7phc7qhlYvBMAzsLjf2nm2C2e6C0+FAS7MBLU2NaDE0otnQiOZz97Pyzv/fkk1MhEmjh2nIcG7bw2s+Q12b4RMvB4Bv4A3kpAFIBtD6yZnXbwksLhsa7DUot59AnfskTKhBC9MMj+l81CE5RNtt9cCqEmGdWgmJw4MXQqQcijRDkibE9iaPHa8c/QdELKD1eCBmWUhZFkluNzwAPv9PLUqKvUma7w3VRnMNXr/vxqCPicUSjC+8BM8uWgZJXS0Sd20LqGMTifDHX36DWCyGSCyBWCKBWCyBqLUsFnP3/+u7rzC8vDSgjWPzb4bq7ffQbHWiX7IyYPphyenjcDoc0NfXYvB/3kKjWgGFSgFGJgMjk8EiEOO3ex8Fo9ZicIYOuUlKXv6gcXl66BQSuNweVGz7HYKGOmQkayGUSgCJBBCLgaQkIC0w5Le/vAluNwuX243iejMG+qyU53B5UNlkhUomiirR8tHKFlQ2WaFXSjAqS9vuaWHF9Wbsr7JCqEmBu6W27R0I6eEogBOlNWv4A2HvvvvuNve55557uAAOAHz11Vd49NFHY9qX3NxcXHFF4PxhXyKRCAsXLsTzzz8PwDuCZ/fu3Rg3blyH+0IIIYSQvk0pFWFMrh5muwvpPsmNg+UPUkpFIUcE+E4/qdPaUdNiQ36SEhk6OWpb7HB6PCiuMwfs11ai6KRzeXvam5dneKYWKWoZdPYa2KtPwmMzQZ43hntcJRNhQIoKGrmYGxnhn0dIKhJiUkEiNh2tgVgiQWJyKhKT205Om66Toarp/I92Lyz5BIbGerQYGtBsaESS2IH6+nreraGxEQ0NjThgNmG2X3sqANfNmINbC1p/UBwO4DIMz9QiTStDndGOkU8NgBVlAEKPwGlqo99Cufe5ayyhV3iKNICjDrHdKGKwN0xKgGrP+YTfodYZDPw0ned0OtBUuxWHNk3A4L02jA62v8eF/b9Gtjz9AyG2b/1pFQZ/XgaBUI7DEhXk6kwks2qwbhtYsRZ/eXQJjhdXYxyA+UH2lwGY99FStE7MEonFEIlEEIrO/3vllTPw0htvQ/Lv15G+7suANn4aOBj/zMwBhCKIRCJolTIkqBWoNbkgFIkgEokhEoswTCnCo+//Bx6RCE5GAIVACKdQiEqxGEUvv4gL5i/AqRojmq3e0T9JKikGpqpgdrhx/MghuJ0ONK/diKwjBwGxBBVaJTRaJersgDk3H0l3LECCSg43I4CHFaDUYEGCUoIsvTcw7PGwcHlY4IcNMFSaoG9owMWqRNicVrjAwNEU4afK5fJm0heJAueAnsOyLJqtzoBzmZCuQgGcKH377bfc/YKCAhQUFLS5T2FhIWQyGTda5ptvvulwAKe4uBjHjh3jytOnTw9IahjM5ZdfzgVwWvtCARxCCCGExEKCUoKEGObcGZ6pxZB0DTe6JidRAbeHDRrAiWR1rY4kzW4NEAkFDDzmJgAA6z6fuydNI2tzNbT2GJiqRqpGhlSNjBtBlD9wCPJ96oRaocjl9sDldGLd76fQ0mSAsbnp3M2A9OzcgPxDeqV3FFKyWgqT8fxF8FXwJkJOPvev/tztVBt9z0sagsvS50JrKwZwImidjo7AsYjCv++s8/x73p4ADgCUy8T4QKfFXLEHVwV53BpFahh5iO1lYuAthQHcuCbTyfMP2oBih3d7uE+Y72LwLqcTLqcTgJXbduzIemxa+wdcVn0g6P7llWfxy8njbT0F7ADwdIjH/t+Sd3D/0JHwiGTwCOVwC8SoanSgqtE7De2emxeg+NRxvAoEvJZ6AOsAjHn8b7ztrYEjiUQCiViMCdMux0P//T+4bPZsjDk3P+1qn/qPPPQo/qKQwu0B5FIx5BIRBAIBhEIhBAIBwDAQCoS4+kwRFu71rjPnZhh4GAZuhkG5Xo+fXngB99xzD4rrzThTZ4ZAAEzqlwSGAaQiAV5//XU0NDTgttWroTabAYaBi2HgZAEIhPj9kouxNysfYBikauQQiYRgGAYCgYC7qVNzMF6uQt7y9yGTiuFgGdg8gEIugVijxrqpU2E2myEQCAL2dbpZyCQiCIVCZGzeDHlDAyASgWEYMAIBPADMI0dCccklGDRoEKwON07UGCERCjAoTc39vTx16hQ8NTXQbN4MRiDw3oRCrh3rtddCIJXyju3fH4lEAtmhQ0BZmTcQdu41BsMAOTnAqFFtfqZgtwNbtkC+b1/bdfs4CuBEoampyTvv+ZyJEydGtJ9EIsHYsWPxyy+/AABvClZ7HTjA/8MbaV8mTJgAkUgE17mlM2PRF0IIIYSQePEPzAgFDCb3T0RxvZk3KiWS/KpioQAud+jRIK0ydHJutSAAEAkZ6OSBU6zslSeglAiQrJYiJ0ER8Hg0xuXp4XB74HB5kKSSoqTBDIlQgJRz08uSVFIMSlOjpMEMuzOyUUQioQAioRQjBuSgvDEZCqkQE/MT4XB7IBIwEAkFKKk3o7TRgky9nJeP5Md9p1FeXc8FfFqDPxZTMwyNjTA2N0HVYsDE5maYTUaYjM0wG1tgNhlhMRnBsiwm9Z+A24f8EfUpLdix9laIW5ohammCyGiEyGzEp688jTp76MTDvkIFcNxSJa5KvguMRwCzqxlWtxl2txVOjw2M24liZwns50Ib7Q3gMOeCRBJn8ACgNejW4EKNFXII2whEub3HDhfAaSsV+BmxHW8IStHP1YK8II83e0JPUfQVbrJhkekg3tw3HxKWhRAshCwgAMCcy7XUaCwN20awmXYulxMulxM2q/eVLi7+Dr/9fBzT/TNDn3O2aB9+aWz7Xcn0uS9kWQhZFmIApvp6fPbpu+iXwwKMAGCEYBkGO8sEACMAAwEW/e+/UV5RjT8BCMyECjz/0Yf4uI3jX375dEy49BLIVywH4H1vW99fl0aLP32xEuWlZ9t8Hj8DCBYieRLA/mtvwOOvLOFWRgSAyiYrJvdPhFgowMyZM6E9dQq/h2hb9eijbZ4f193wByx1s9CsWRnwmPnW2zHl2DGUl5w5NxJMBLFYDEYghFgkgkQshlgsQiqA7/bt470nJDgK4ETBd8QLgKjy2BQUFHABHIPBgOrqaqQFmWMa777IZDJkZGRwgaijR4+2uw+EEEIIIV1BIRFhWIaWF8CJJI/SkHQN9p71jmTwzePhb2CqCjqFGAKGgdHmRLJaGjRHB+uwYHiqDMOydVH1n2G8MzdaTeiXELDM/eC0wJBFdoIC2QkKXm6TC3LaPvbgNA2y9QrIxUIIBAxkgvOBmrwkJfKSAkMbw7N0YMHAnZIEi8N9rq4CKRoZalvsaLI4YHd54PawAdPSLh2cDJPJO5lHLFOCBVAvGRhwjAMnj2F8cxPMphaYjUZYTC2wmE2wWiywWS1wOs6PKXkYwHPwBnI08E6p0gAYMnoKrsi7yef5KJCkknKrim3EephhggTAaXiDOK231le8rQvUNEUGpuovxmDhbgCVAY/bwCBUMmh/oUbgeERCaFgpXPDABTccDP81bSuAE0kYjDkXJBKHCEQ5InwO4c60ZqkQx6ShR+FZPUzYNkKkSuKplLBYKXHgiRCPN7CRrWoX6kLYBWCv4zQeqnkrRA2gxuHN9xUqc0/bYWLgd+PveL/sOF4L8pjJY0KdLbKE7qEmdrEA9lT+gOe2TgMDQAAWDACGBRbt8Pa9sqkEujBtR/KJOFy2CadYYGyQx06e3YSK8ibU14c/y1IiOA7xogBOFM6cOcMr5+TkRLyvf90zZ850KIDT0b60BnD824mWw8GP1PsuaR4tvV6PxMTEDvWHtN/p06fDlknPQe9l70HvZe9B72WcNFtR2mCGSiZGvUeDhvK2h+HILQ64PCya3RIcqY5s7ktFPVBx7n4s3kuR0Y5Ttd4pJVl6BcrY+qjbSGNZsCxQfbY+4iXFo9WajUjucsPm9MBWK0JZnfc1k+N8MKLR7MCJau9kqKHpWhw7xn8+YgDJbg+OVrbA7Dh/iT7npju4+3mJSqjlIl7i6lGZGhwprYPVakaKXIDyuiY0tZigELnAuJywWCzQpGSi5LR32k9uogJWtxwVdQyELTYU1ZmQkZMHtUYHp8OOKU4HXE4HnA4HbDYr4HRCDqCty+RUUX/ck3svzgwZjO9v6A+Bw+692b3//rB7F+CObCRRKUKM2JH2w8KEf3FFN+uBw21FdoISNS1WPOa5Bc1ogB3AkSC7h17T7DyNXYKcSjnqzWzQNirckQVwPCH6AABmhwfSitCvhcfuDUzVh2ijJJLj2z1wlFtD9qE5whFqhhB9OAnAY/XAFuZ5tE7NOw2gOcjjkaQYd9s8qGtwBe1DE8vCHSLQ5q8ECBqEqQVgtrM4UxN6ZJXdxcKK0O9nJIGoKrsTv7nZoKPLtliMMDjsQR7hc53rg/9fU/9rTQKAJRFbvHgxC28gkgXArlmzJuJ9//Wvf/H23bBhQ4f68oc//IHXnsFgiHjf2bNn8/a12Wzt7seiRYt4bdGNbnSjG93oRje60Y1udKMb3ejW0duiRYvafZ3aW7VvvbY+qnUoaCtZmIz3/uRy/oBJ/7Z6al80mlAzkgkhhBBCCCGEkPaha81AFMCJQusqUq0kkshXWZBK+TNWrdZo0p11377odLp270sIIYQQQgghhARD15qBKAdOFPxHuUQzJ89u58/98x8FE4u+RDoKJ5Z9mTZtGr766is0NTWhpaUFaWlpUQWTfFEOHEIIIYQQQgjpm+x2O8rKyrjytGnTurA33RMFcKKgUql4Zf9RMOH4j3LxbysWfYk0gBPLvuh0OsydO7fd+xNCCCGEEEIIIQAwZsyYru5Ct0ZTqKLgPwfPYDBEvG9TUxOvrFaHXrqyM/siFosDplQRQgghhBBCCCGke6EAThTy8/N55daluCNx9uxZXrlfv37doi8d7QchhBBCCCGEEELijwI4URg6dCivfPq0/0r1oRUVFXH39Xo90tLSuqQvNpsNlZWVIdshhBBCCCGEEEJI90MBnCjodDrk5ORw5Z07d0a0n8PhwJ49e7jyiBEjOtyXUaNG8cqR9uW3336Dy+WKaV8IIYQQQgghhBASXxTAidLVV1/N3S8qKsKZM2fa3Gfbtm28hMezZs3qcD/y8/MxePBgrrxp0yawLNvmfhs3buSVY9EXQgghhBBCCCGExBcFcKJ07bXX8srvvfdem/v417nmmmti3pezZ8/ihx9+CFvf5XJh6dKlXDkzMxPjxo2LSV8IIYQQQgghhBASPxTAidL06dMxfPhwrvzmm2+iuLg4ZP2dO3di5cqVXHnmzJkYMGBA0LolJSVgGIa7XXzxxWH78sc//pG3gtTf//53OJ3OkPVff/11VFRUcOUHH3wQDMOEPQYhhBBCCCGEEEK6HgVwoiQQCPDiiy9yZbPZjNmzZ6OsrCyg7sGDBzFv3jx4PB5u3xdeeCFmfcnOzsYDDzzAO96CBQtgt9sD6q5YsQJPP/00V87MzMSf//znmPWFEEIIIYQQQggh8cOwkSROIQEeeOABLF68mCsrlUosWLAAo0ePhtPpxK5du/Dll1/yRsS89tprePTRR0O2WVJSwlsefNq0adiyZUvYflgsFkybNg27d+/mtmVmZuLWW29Fv379YDAYsH79evz888/c41KpFJs2bcKUKVOiecqEEEIIIYQQQgjpIhTAaSe3242FCxfi448/brMuwzB4/PHHeSN3gmlPAAcAqqurMXPmTOzdu7fNumq1Gh9++GFALh9CCCGEEEIIIYR0XzSFqp2EQiE++ugjfP7557ycOP4mTpyITZs2tRm86Yi0tDTs2rUL//3f/420tLSgdSQSCa699locOHCAgjeEEEIIIYQQQkgPQyNwYuTw4cM4ePAgKisrIRQKkZGRgfHjx6Nfv36d2g+3240dO3bg9OnTqKmpgVqtRlZWFgoLC5GQkMDVO3LkCK+/rStS+Y4A6gwejwc7duxAUVERqqqqoNVqkZmZicLCQuj1+k7tS0/CsiyKiopw+PBhlJWVoaWlBQqFAgkJCRg1ahRGjBgBoVDY1d0kPRSdl4REb/fu3Th+/DgqKyshl8uRmZmJyZMnh/xhJZ4cDge2bduGkpIS1NXVISEhAdnZ2Zg2bRoUCkWn96cncLvdOHHiBI4cOYLKykqYTCaoVCokJSVhzJgxGDx4MC38QDqEzkvS3XXW9QV9X3YQS/qUlStXsiNHjmQBBL1NnjyZ3bx5c9z74XQ62eeff57NyMgI2g+JRMJef/31bHFxcdz70lO0tLSwy5cvZ2+88UY2OTk55HsIgNXr9ezDDz/MVlZWxqUvmzdvDnv8cLfff/89Ln3qadr7+r322mtx6xOdl6S3cbvd7OHDh9lly5axf/7zn9mJEyeycrmc97nu6HfekiVL2IKCgqDnjFAoZK+44gr2wIEDsXlCbTCZTOzDDz/MJiQkBO2PSqVi77zzTraurq5T+hNL8Xgv6+vr2XfffZedO3cuq9Vqw/7tzcjIYJ955hm2qakpPk+QZdmlS5e2+7uhJ72n8Xgvi4uL2/3arVy5Mj5P9JzefF6Snq8zry/o+zI2KIDTR7hcLvaOO+6I6ItMIBCwTz75ZNz6Ul1dzY4bNy6ivmg0Gvarr76KW196ipaWFlYmk0X9n5KEhAR29erVMe8PBXA6rr2vX7wCOHReRi83N7fd72Prbdq0aTHpC52Tga677jpWqVS2+fzbG8Axm83sjBkzInqNJRIJ+84778T2Cfo5fvw4O2DAgIj6k5aWxu7YsSOu/YmleLyXR48eZUUiUdTnS3Z2Nrtt27a4PM++EMCJ13nZXQM4vfm8ZNn4BOM6+r0KgL399ttj9hx783nZWdcX9H0ZWyKQPuGhhx7CsmXLuLJCoeBWzXI4HPj111+xatUqOJ1OeDwePP/880hISMBDDz0U035YrVbMnTs3YNWsW265BQUFBWhoaMB3332HrVu3AgBaWlowf/58/PTTT5g0aVJM+9KTuN1u2Gw23rZ+/fph2rRpGDRoEJKSkmCz2XDo0CGsWrUK9fX1AIDGxkbMmzcPK1eujGvuo9zcXIhEkf05kclkcetHT5WSkgK1Wh1R3XhMYaLzsuvodLqu7kKvtWfPHpjN5ri07fF4sGDBAmzYsIHbptfrceutt2Lo0KEwGo34+eef8e2334JlWTgcDvzxj39EUlISrr/++pj3p76+HldffTXOnDnDbRswYABuuukmZGdno7q6GqtWrcL+/fsBeBc/mD17Nnbt2oX+/fvHvD+xFo/30m63w+Vy8bYNGTIEU6dORUFBAZKSkmA0GrFnzx6sXr0aJpMJAFBWVoYZM2Zg48aNcf/7V1BQEHHdnjJtOp7npa+MjAzI5fKI6qpUqrj0obefl9dffz2+//77Tnk/o0XfrZHpjOsL+r6Mg66OIJH4++abb3iRxKFDh7KlpaUB9fbv38+bOiEQCNiDBw/GtC+PPvoory833HADa7PZAuotX76cFYvFvF+8rFZrTPvSkxgMBhbwjnz429/+FnZ4odlsZu+55x7e66zX62P6K4D/r/00pSZ6vq/f0qVLu7QvdF62z7Rp09iCgoKobv6/TH722Wcx6Yv/OZmbmxtxnw4dOhSTPnQ3viOkpFIpO378ePb+++9nb7nllg79OsyyLPvWW2/x2igsLGQbGxsD6v3444+sRqPh6qlUKrampiYGz47vhhtu4PXnb3/7G+t2uwPqvfrqqyzDMFy9CRMmxLwv8RCP93Lfvn0sADY1NZV98skn2dOnT4esW19fz86ZM4d3rP79+wf9O9kR/r/090bxOi/9R+B0RjqAtvSl8zLcLdr3Itrv1YKCAt7/TQCwu3btitnz9D8vo+lXsO+F7qQzri/o+zL2eue3A+G43W5ezhuFQsEWFRWFrP/LL7+wAoGAqz9r1qyY9aWsrIw3TG/kyJGsw+EIWf+ll17inWCvv/56zPrS0xiNRvbxxx9nGxoaIt7n5ptv5r1+zz77bMz6QwGcjusuARw6LzuPzWZjExMTudcuMTExZheAdE4Geuqpp9h3332X3bNnD+8z7f+f8WgvLkwmE5uamsrtn56ezhoMhpD1V6xYwTven//853Y+o+B+++03XvszZswIW/++++7j1f/yyy9j2p94iMd7eerUKfaVV15hzWZzRPWdTic7depU3vE+/PDDaJ9KWH0hgBOv87K7BXD6wnkZzyB5NKqqqnjTIYcPHx7T9nvzeRnv6wv6voyP3vUpJAF++OEH3gfv8ccfb3OfG2+8kbfPqVOnYtKXJ554gtfuhg0bwtZ3Op1sZmYmVz8rKysm/egrKioqeJHj8ePHx6xtuljsuO4SwKHzsvN89tlnvNf6wQcfjFnbdE5GrqMXiu+++y5v/0jm6l944YVcfZlMxhqNxnb2PpD/f6aPHTsWtn5jYyNvJNiUKVNi1pfO1tH3Mlo7duzgHW/evHkxbb83Xyi2pbcFcPrCeRmvYFy0Xn75Zd7x3njjjZi235fPy2Ciub6g78v4EID0amvWrOGV77777jb3ueeee3jlr776KuZ9yc3NxRVXXBG2vkgkwsKFC7lyeXk5L0cHCS8jIwNDhgzhykVFRV3YG9Jd0XnZef7zn//wynfddVcX9YR0hO85o1AocPPNN7e5j+/3qs1m4+UC6Ain04lvv/2WKxcWFmLw4MFh99Hr9by8Ajt27EBNTU1M+tPbTZw4EUqlkivT9yoJpq+cl//93/+Ne+65B2PGjIFYLO6yfnzwwQfcfalUiltuuaXL+tIXRHN9Qd+X8UEBnF7O94NaUFAQUUK8wsJCXqLZb775psP9KC4uxrFjx7jy9OnTwTBMm/tdfvnlvHIs+tKX+Cbm645J5kjXovOy85SUlODHH3/kyhdeeCGGDx/ehT0i7WGz2fDTTz9x5UmTJkWUgDxe58z27dvR3NzMldsKwAbrj8fjwXfffReT/vR2DMPwAjj0vUqCofOy82zduhUnT57kytdccw0SExO7sEd9QyTXF/R9GT8UwOnFmpqaUFpaypUnTpwY0X4SiQRjx47lygcPHuxwXw4cOMArR9qXCRMm8FY3ikVf+pKSkhLuflpaWtd1hHRLdF52ng8++AAsy3LlSEZDku7n+PHjsNvtXDnScyYnJweZmZlcOVbnTHvP4cmTJ/PKdA5Hxmw2o66ujivT9yoJhs7LzuM/spW+WztHJNcX9H0ZPxTA6cV8f1kHENXSZ74jdQwGA6qrq7ukLzKZDBkZGVz56NGjHepHX7J9+3bU1tZy5Xgud/rEE09g9OjR0Ov1kEgkSElJwciRI3Hfffdh9erVcLvdcTt2b7B8+XJMnToVKSkpkEgkSEhIwMCBA3HjjTfinXfeQUtLS1yOS+dl5/B4PFi2bBlXViqVuPHGG7uuQ6TdYvW9evz4cXg8ni7rT15eHm/ZaTqHI7NmzRpeIDbey4jfddddGDp0KLRaLaRSKdLS0jB27Fj89a9/xQ8//BDXY/d0b7zxBi688EIkJSVBLBYjKSkJQ4YMwe23346PP/6Yd2EZa3Redo7m5mZ8+eWXXDkvLw+XXXZZF/aob4j0+oK+L+OHAji9mO/69oA3ohkp/7r+bXVVXzraj77k1Vdf5ZX/8Ic/xO1YK1aswIEDB9DU1ASn04m6ujocOnQI7777Lq6//noMHDgQX3/9ddyO39Nt2rQJ27ZtQ11dHZxOJwwGA06dOoUvvvgCf/zjH5GTk4OXXnopJl9gvui87Bw//PADysrKuPKNN94Y0TDijqCganzE6pyxWq0d/mHEvz8CgQBZWVkR7ScSiZCenh60HRKc2+3Gv/71L962eH6vAt6Re8eOHUNLSwscDgdqamqwd+9evPnmm7jyyisxatQo/PLLL3HtQ0/19ddf47fffkNDQwNcLhcaGhpw/PhxfPTRR7jtttuQm5sbMHojVui87BwrVqyA1WrlynfeeWdE08A7qq8HViO9vqDvy/ihAE4v5v+rfUJCQsT76vV6XtloNHaLvjidzrj+atJbrFixAuvWrePKo0ePxty5c+N6TL1ej9zcXCQlJUEg4P9pOXPmDK655hr813/9V1z70JMplUpkZ2cjLS0tIBlgc3MznnjiCcyYMYP3n5WOovOyc3TFEG8KqsZHd/pe9e+PWq3mTW2Mpj+x6Etv9+qrr2L//v1cec6cObjgggviekyGYZCYmIjc3NyAzw/gHcp/8cUXY8mSJXHtR0+l0WiQk5ODlJQU3i/oAFBTU4N77rkHt912W8x/HKHzsnP4frcKhULeAgvx1JcDq9FcX9D3ZfxQAKcXM5lMvLJvYuK2yOXysG315L70dkeOHMG9997LlUUiEd57772AoEpHJSYm4i9/+Qs2bNiAhoYGNDY2oqSkBHV1dWhsbMTq1atx0UUX8fZ58cUX8b//+78x7UdPJZFI8Ic//AErVqxAaWkpTCYTSktLUVVVBZPJhG3btuGWW27h/Zq0ceNGLFiwgDeEvyPovIy/uro6rF27lisPHTo07tMuWlFQNfa62znj20Y0ffHvD52/4f3000946qmnuLJGo8GiRYvicqysrCw89thj2LJlC5qbm1FfX4+SkhI0Njairq4OH330EUaMGMHVd7lc+POf/xyw6mhfpFKpcOedd+Krr75CdXU1mpubcfbsWdTU1KClpQXff/89Zs6cydvn448/xiOPPBLTftB5GX8HDhzAnj17uPKVV14Z8YiKjuqrgdVory/o+zJ+KIDTi9lsNl5ZIpFEvK9UKuWVO/qrf3fqS29WVVWFmTNn8v64vPzyyxg3blxMjzN27FiUl5dj0aJFuPLKKwOi6lqtFtdeey22bduG5557jvfYY489xptO0leVl5fj888/x/z585Gdnc17TCKRYMqUKfj444+xfv16KBQK7rE1a9Zg5cqVMekDnZfx99FHH8HpdHLleC4dTkHV+Otu54xvf6Lpi39/6PwN7ejRo7jhhht40w7ff/995ObmxvxYc+bMQUlJCV5++WVMmzYtYKplUlISbr31VuzZswf33Xcft93j8eD+++/vFhcWXSU9PR0VFRV4//33MXfuXKSmpvIeVygUuOKKK/DNN99g6dKlvIvON954A7/99lvM+kLnZfz5j2yN53crQIHV9lxf0Pdl/FAApxfzjy46HI6I9/WfDuEfCe3JfemtGhsbceWVV+Ls2bPctnvvvTfmvywB3qGHkUSvGYbBk08+ifvvv5/bZrfbA+bP9kXJyckR1ZsxYwY++OAD3jb/oFh70XkZf77vnUQiwW233RaX41BQtXN0t3PGtz/R9MW/P3T+BldSUoIrrrgCBoOB2/biiy/ihhtuiMvxEhISAqb6BCMWi7FkyRJcffXV3Lba2lr83//9X1z61RNIpVJoNJqI6t5xxx148cUXedti9b0K0HkZb3a7HcuXL+fKKSkpmD17dtyO19cDq+29vqDvy/ihAE4vplKpeGX/SGg4/tFF/7Z6cl96o5aWFsyYMQOHDh3iti1YsKDbDN98/vnneX/wfOfPkrbdeOONmDBhAlc+fPgwbwnH9qLzMr527tzJW61g7ty5SEpKisuxKKjaObrbOePbRjR98e8Pnb+BKioqcNlll6GiooLb9vjjj+Mf//hHF/bqPIZh8Prrr/O20Xdr5B566CHelJtNmzbF7Jd1Oi/ja/Xq1byg6u233x6QOzCW+nJgtSPXF/R9GT8UwOnF/H+J8P1j15ampiZeuaMrpsSqL2KxOGBYXV9nMplw1VVX4ffff+e23XDDDfjwww9jnvemvRITEzFt2jSufPbsWVRVVXVhj3qe6667jlfeuXNnh9uk8zK+uiJ5caQoqNo+3el71b8/JpMJLperXf2J96poPU11dTUuvfRS3mojDz74IF566aUu7FWgIUOGYPDgwVx5165dXdibnkUikWDWrFlc2Waz8ZJUdwSdl/HV2dOnotGbAqsdvb6g78v46R5XdyQu8vPzeeXS0tKI9/UdJgcA/fr16xZ96Wg/ehuLxYKZM2dix44d3LY5c+bg008/jejXgs40aNAgXrm2traLetIzxeP1o/MyfkwmE7744guunJubi+nTp3dhj/goqNo+sTpn5HI50tLSYtoft9vNGy0SjsvlQmVlJVemc/i82tpaXHbZZTh58iS37Y9//CP+53/+pwt7FZrvd4Pdbkdzc3MX9qZnidf/S+i8jJ/i4mJs3ryZK0+ZMiXgfexqvSGwGovrC/q+jB8K4PRiQ4cO5ZVPnz4d8b5FRUXcfb1e3+ETp719sdlsvJPGv52+zGq1Yvbs2di6dSu37aqrrsLKlSvjOpS0vfznjFosli7qSc8Uj9ePzsv4+eyzz3jz3hcuXNhtRsS1oqBq9GL1vTp48OCYfB7a25+SkhJeUl46h70aGhowffp03tTHu+66C2+//XYX9io8+m5tv3i9dnRexs/777/PW4mzO41s9dWTA6uxur6g78v46V7/myQxpdPpkJOTw5UjnXLhcDh4S/P5ZlVvr1GjRvHKkfblt99+4w1xi0VfegO73Y5rrrkGP/30E7dt+vTpWL16ddSZ1TtLTU0NrxyvXCC9VTxePzov4+f999/n7gsEAixcuLALexMcXfhFb/Dgwby/sZGeM2VlZbxf+2J1zrT3HPb9VTWW/enJDAYDpk+fzsv1cNttt+Hdd98FwzBd2LPw/L8bEhMTu6gnPU+8/l9C52V8uN1uLFu2jCtrNBrMmzev6zoURk/9fo3l9QV9X8YPBXB6Od9EWkVFRbz53KFs27aNl9zJd45we+Xn5/OGE27atIkXQQ9l48aNvHIs+tLTORwOXH/99fjhhx+4bZdccgnWrl0bURLTrrJt2zbuvlgsRmZmZhf2pufxff2AwKGp7UHnZXwcOXKEN2T6iiuu4AXTuwsKqkZPJpPh0ksv5co7d+6MaIUR37/XQOzOmSlTpkCr1YY8Tii+57BAIOD9X6Evam5uxhVXXMHLgXLTTTcFLDfd3TgcDt7y15mZmd32R5zuKB7fqwCdl/GyYcMG3oX9zTffDIVC0YU9Cq0nBlZjfX1B35fx032/lUhMXHvttbzye++91+Y+/nWuueaamPfl7NmzbZ44LpcLS5cu5cqZmZkYN25cTPrSU7lcLsyfPx/ffvstt62wsBDr1q3rFsvahfLdd9/xhipedNFF3fZLtzuqq6vDZ599xpXlcjmmTJkSk7bpvIw939E3QPdKsOiLgqrt43vOWCwW3nK2ofh+r0qlUlx11VUx6YtYLOb9Z3Lbtm04fvx42H0MBgNWrVrFlSdNmoTU1NSY9Kcnak3UuXv3bm7bvHnz8PHHH3fr4A3g/VtjNpu58uWXX96FvelZjhw5whtlkJeXh/79+8ekbTov46OnfLf2xMBqvK4v6PsyTljSq7ndbnb48OEsABYAq1Qq2TNnzoSsv2PHDlYgEHD1Z86cGbJucXExVw8AO23atLB9KS0tZaVSKVd/5MiRrMPhCFn/pZde4rX/2muvtfl8ezOXy8XeeOONvNdk8uTJrNFo7HDb0byXFoslqrYrKyvZ7OxsXvvLli3rYI97LofDwTqdzojrO51O9sorr+S9fnfccUfI+nRedi273c4mJSVxr09ycnLY17OrrF+/nvc+XnzxxV3dpU6zdOlS3nPfvHlzVPsbjUY2JSWF2z89PZ01GAwh669YsYJ3vAceeCBs+5s3b+bVv/3228PW//XXX3n1r7rqqrD177vvPl79lStXhq3fnXX0vTSbzezUqVN5bVx33XVR/Y0OJ5r3Mtrv1sOHD7NarbZDz7876ch7abVaWY/HE3F9o9HIjh49mne8Z555Juw+dF5GrqPnZTA1NTWsWCzm2hw1alSH24yXxYsXR/x/tu4gntcX9H0ZHxTA6QPWrl3L+/ANGzaMLS0tDah34MABNjMzk6snEAjY/fv3h2w32gtFlmXZhx9+mLfPvHnzWJvNFlDv008/ZSUSCVcvMzOTtVqtUT3v3sTj8bC3334777WbOHEi29LSEpP2o3kvly5dyk6dOpX9+uuvWbvdHrbdjRs3BgRvRo0axbrd7pj0uycqLi5mCwoK2HfeeYdtbGwMW/fkyZMBFxcKhYI9e/Zs2PbpvOw6X3zxBe+1fOSRR9rVDgVV4ycWFxeLFi3itTF16tSg/yn98ccfWY1Gw9VTKpVsVVVV2Laj/Q8py7Lsddddx9vnwQcfDPp39rXXXmMZhuHqjR07NqoL3+6mI++lzWZjL7/8ct7+c+fOjWnANZr38plnnmFnz57N/vjjj6zL5QpZz+PxsJ9//jmr1+t5bc+aNStm/e4KHXkvN2/ezI4aNYpdvnw5azKZwtbdvXs374fN1ovKti5W6byMXDwCOK+++iqvzTfffLPdbVFg9bx4X1+wLH1fxoMIpNebPXs2/vSnP2Hx4sUAvMNGhwwZggULFmD06NFwOp3YtWsXvvzySzidTm6/V155JSDhU0c999xz2Lp1KzdUeeXKldixYwduvfVW9OvXDwaDAevXr8fPP//M7SOVSvHZZ5916/wu8bZ9+3Z8+OGHvG2lpaW44IILomrn559/jsk0ia1bt2Lr1q3Q6XS46KKLMHLkSKSnp0OtVsNisaC4uBgbN27EgQMHePulpaXhq6++6vbD0uOtqKgI999/P/7yl79g0qRJGD16NPLz86HRaOByuVBVVYXt27fjp59+gsfj4fYTCoX44osvYp5Phc7L2OmKId6ff/45li5dikceeQQzZswIO1R706ZNuPPOO1FWVsZtGzVqFG699da497OzrV69Gn//+98DthuNRl55wYIFQYeIv/rqq7juuuuCtv3AAw9g48aNWLduHQDv38SCggLcdtttGDJkCEwmE7Zs2YJvvvmGyyvFMAw++OCDmCyH6m/JkiXYu3cvSkpKAABvvPEG1q9fj5tuuglZWVmoqanBqlWrsG/fPm6fhIQEfPrpp906QW+reLyXX3zxRUA+r3379mHIkCFR9S2alVXCYVkW69atw7p165CSkoKLLroII0aMQEpKClQqFYxGI06ePIkNGzbg1KlTvH2HDBmCjz76KCb9iLd4nZcHDhzAggULoFAocNFFF2HUqFHIysqCRqOB3W5HWVkZtmzZEpCQVKVSYe3atVCpVB18ZoF6+3nZmT744APuvkwmw4IFCzrluK+++ir27NmDBx98ENOmTQu5hDbLsli5ciXuv/9+3opTs2bNwsUXX9wpfW2Pzri+oO/LOOja+BHpLC6Xi7311lt5EcdQN4Zh2H/84x9tttmeX/pZlmWrqqrYMWPGRNQXtVrNrl69uoPPvufzjzC391ZcXBy0/WhH4LTn2BMnTmRPnz4dnxeoB/F/rSO9ZWRksD/88EPU7dN52XnOnj3Lm4I6efLkdrfV3nNSp9OxM2fOZP/xj3+wixYtYpcuXcq+/fbb7KOPPsqOGjUq4L1MS0sL+Xehp2vv36rW29KlS8O2bzKZAkZwhLpJJBL27bffjqjf7flFkWVZ9ujRo2xBQUFE/UlNTWW3b98eUbvdQTzey4622XoLJ5r38umnn27X8WfOnMnW1NR08BXuPPF4L9v7f6TBgweze/bsiajfdF4GWrVqFVtQUBBw850yA3j//xKs3qpVqyI6zrZt23jt3XzzzR3qd3vPy5SUFPbaa69l//nPf7JvvfUWu2zZMvbNN99k//KXv7ADBgwIeD+HDBnS5kjrrhbv64tW9H0ZWzQCp48QCoX46KOPMGvWLDz33HM4fPhw0HoTJ07ECy+8wMsaHmtpaWnYtWsXXn75ZSxevBjV1dUBdSQSCWbOnIl//etfMVsVgMTGuHHjsHDhQuzatQvHjx8Pu2oRwzCYNGkS/vSnP2H+/Pkhf7noSxISEvC3v/0Nu3btwr59++BwOMLWLygowL333ot7770XOp0ubv2i87Ljli5dyhsxdffdd3d6H5qamvDtt9/yEhGGMnHiRHzyySfIy8uLf8d6IaVSie+//x5LlizBv/71r6CrPAoEAlx66aV47bXXMHr06Lj2Z8iQIThw4ACeeuopLFu2DAaDIWif582bh1dffRXJyclx7Q+JzmWXXYZTp05h165dba4YKhKJcOmll+LPf/4zZs+e3Uk97L769++P+++/H7t27cKhQ4fgdrvD1h85ciTuv/9+3HHHHXFfAKI3n5ctLS0oKipqs15lZWXI/SPhP7K1K75bAaC2thZr1qzBmjVr2qw7c+ZMfPDBB9Dr9Z3Qs+6Pvi9ji2HDXX2RXuvw4cM4ePAgKisrIRQKkZGRgfHjx6Nfv36d2g+3240dO3bg9OnTqKmpgVqtRlZWFgoLC5GQkNCpfSHRa2pqwr59+1BaWor6+npYrVZIpVLodDrk5eVhwoQJvCX7CJ/dbseBAwdw5swZVFdXw2w2QygUQqvVIi0tDePHj0dWVlan94vOy+ixLIv8/HycPXsWAKBWq1FVVQWlUtmu9kpKSnhBsmnTpmHLli1B6x4+fBj//ve/KajaxX7//XccO3YMVVVVkMvlyMzMxOTJk5Gent7pfXE4HNi6dStKSkpQV1cHvV6PnJwcTJs2rd2fSdJ5amtrceDAAZSVlaGhoQE2mw0KhQI6nQ79+/fH+PHjaSXHEMxmM/bt24ezZ8+itrYWFosFIpEIOp0OWVlZmDBhQpddjPW283LZsmVYuHBhu/dfunQp7rjjjrB1jEYj0tPTudXWCgoKcOrUqQ5NZdmyZQsuueQSrnz77bdj2bJlQetu27YN77zzDgVWY4y+LzuGAjiEEEJIL0FBVUIIIST2KLBKugsK4BBCCCGEEEIIIYR0c317KRhCCCGEEEIIIYSQHoACOIQQQgghhBBCCCHdHAVwCCGEEEIIIYQQQro5CuAQQgghhBBCCCGEdHMUwCGEEEIIIYQQQgjp5iiAQwghhBBCCCGEENLNUQCHEEIIIYQQQgghpJujAA4hhBBCCCGEEEJIN0cBHEIIIYQQQgghhJBujgI4hBBCCCGEEEIIId0cBXAIIYQQQgghhBBCujkK4BBCCCGEEEIIIYR0cxTAIYQQQgghhBBCCOnmRF3dAUIIIYSQcFiWRVFREQ4fPoyysjK0tLRAoVAgISEBo0aNwogRIyAUCru6mzF133334d133wUAfPTRR7j11lu7uEc9yzXXXIOvv/4aALBx40ZMnz69i3tECCGEdBzDsizb1Z0ghBBCSN91yy23YPny5QCA5557Dk8++SSMRiPWrVuHtWvX4qeffkJdXV3I/fV6PRYuXIhHH30U6enpndXtuNm9ezcuvPBCeDwejBo1Cnv37oVAIEBJSQny8/Pjdlzf/xIuW7YMCxcu5MpLly7FHXfcEVV7W7ZswSWXXMKVn376aTzzzDMd7WZEjh07hhEjRsDtdmPIkCE4cOAAxGJxpxybEEIIiReaQkUIIYSQLuNyufDdd99x5blz58JoNCIlJQULFizA559/HjZ4AwAGgwH//ve/MXz4cKxZsybeXY67hx56CB6PB4A3oCUQ0H/XojVkyBDcfPPNALzBnHfeeaeLe0QIIYR0HE2hIoQQQkiX2bZtGxobGwEA+fn5GDFiBJqammCz2Xj1+vXrh2nTpmHQoEFISkqCzWbDoUOHsGrVKtTX1wMAGhsbMW/ePKxcuRLXXnttpz+XWFi/fj22b98OABg+fDhmzZrFPSYWi1FQUNBmG42NjTAYDFw5IyMDcrk89p3t5h577DF88sknYFkWL7zwAu666y4oFIqu7hYhhBDSbhTAIYQQQkiXWbt2LXd/zpw5vMc0Gg0WLlyIO++8EyNHjgy6/7///W88+OCDeO+99wAAbrcbd911FwoLC5GUlBS/jseJ7xSjhx9+GAzDcOXMzEycPn06ojaeffZZrrx8+XJcfPHFsexmjzBs2DBceeWV2LBhA2pqarBkyRI88sgjXd0tQgghpN1oTC4hhBBCuoxvAGfu3LkAAJFIhMcffxzFxcV44403QgZvAEChUODdd9/lpssA3ilVixcvjl+n42Tr1q34/fffAQBarRY33nhjF/eo57vnnnu4+4sWLYLL5erC3hBCCCEdQwEcQgghhHSJw4cP48yZMwC8iYgLCwsBACqVCi+99BISEhIibuu1117jjVb55ptvYtvZTrBo0SLu/vz582m6TwzMmTMHiYmJAIDS0lJuZSpCCCGkJ6IADiGEENJHGY1GZGVlgWEYMAwTMIUpUi+88ALXBsMwOHbsWET7+V5MX3311RCJ2j+zOyMjA0OGDOHKRUVFYevPmTOH1+eO3DQaDdxud7v7Dnjz1qxbt44r33DDDR1qryfJy8vr0OtfUlISsm2RSMSN7AK8q2sRQgghPRUFcAghhJA+6plnnkFFRQUAb4Lc119/vV3tjBkzhlf+5ZdfItrPN4Dje5HdXiqVirtvNpvD1t25c2eHj9dq0qRJEAqFHWrjyy+/hMPhAADodLo+mbMmXnwTWm/YsAENDQ1d2BtCCCGk/SiJMSGEENIHHT58mDdl54EHHsDAgQPb1dbYsWN55V9++QV333132H2qqqqwe/duAIBEIsGMGTPadWxfviMx0tLSQtYrLS2FVquFVqtFbW0tjEYj14/s7OyQ+4Wqe/XVV3e4775TvgoLCzs0GqmnycvLi+r5VlVVwWKxRFx/6tSpEAqFcLvdcLlc+P7773k5kwghhJCeou/874AQQgghnL///e9cQleFQoH/+q//andbKSkp0Ol0aGpqAgAcPHiwzX3Wrl0LlmUBAJdeeinUanW7jw8A27dvR21tLVeeNGlSyLo5OTncak5XXHEFNm7cCAC47LLLsH79+pD7RVM3Gi6XCz/99BNXbs0F1Fds2bIl4rq//PILLrvsMq48aNAgpKSkhN1Ho9Fg1KhR2Lt3LwBQAIcQQkiPRVOoCCGEkD5m165d+O6777jyvffe2+EltzMzM7n7beWfAcIvH94er776Kq/8hz/8IaL9Dhw4wN0fNWpU2Lq+gam26kbj8OHDvClfsWy7NykqKsI111wDu90OAEhKSsK3334bUbLn0aNHc/d37doVry4SQgghcUUBHEIIIaSPeeaZZ7j7YrEYjz76aIfbzMrK4u43NzejsbExZF2z2Ywff/wRADqUPLnVihUreAmAR48eHVFOnZqaGt6onXDLldfW1qKmpiaiutFqHRnSavjw4TFrO1YWLlwYdXLhSy65JGbHNxgMmDlzJurr6wF4p7CtWbMGBQUFEe0/YsQI7v6pU6fQ0tISs74RQgghnYUCOIQQQkgfcurUKXz//fdcedasWbzRM+0ll8t55dZcMcF8//333CiKsWPHduj4R44cwb333suVRSIR3nvvPQgEbf8Xx3f0DRA+KONfN5ajZFqncwHegFq4/D19kdPpxPXXX48TJ05w295//31MmTIl4jZycnK4+yzLcsvXE0IIIT0J5cAhhBBC+pD33nuPV77jjjti0i7DMLyy0+kMWdd39amOjL6pqqrCzJkzYTKZuG0vv/wyxo0bF9H+vlOipFIpBg0aFLKubwBHKpW2O+FzMKWlpdz91NTUiIJPnS0lJSXqPEVWqxWVlZUdPvZ9992HzZs3c+WnnnoKt9xyS1RtZGRk8Mpnz57lTasihBBCegIK4BBCCCF9yPLly7n7Op0OV111Vci6paWlXHAhNTUVAwYMCFm3NSFyq1DLarvdbnz77bdcub3Lhzc2NuLKK6/E2bNnuW333nsvHnnkkYjb8A3KDB06NOxKSL7BnmHDhsV0lajW5M8AOpzMOV5eeeWVqIN9W7Zs6fA0qhdffBFLly7lyvPnz8ezzz4bdTu+S8wD3ml+hBBCSE/T/X7iIYQQQkhcHD16lDciorCwEGKxOGT9J598EoWFhSgsLMRHH30Utu3y8nLuPsMwASMeWv3yyy9oaGgA4F0+uj25ZFpaWjBjxgwcOnSI27ZgwQIsWbIkqnbam8A4lvlvAO9IlVYymSymbfdkK1euxJNPPsmVJ02ahKVLlwaM9oqEf6LjaJYhJ4QQQroLCuAQQgghfcTPP//MK0+bNi1s/T179nD3hw0bFrZuWVkZdz8lJQVSqTRoPd/pU+0ZfWMymXDVVVfh999/57bdcMMN+PDDD6OaeuR0OnH8+HGuHC4o43Q6cezYMa4c61WifINo/iOZ+qpff/0Vt912G7fUfH5+Pr7++ut2B7gcDgevHC5wSQghhHRXFMAhhBBC+ogjR47wyuFygDQ1NfECHOFyvlRWVnKrAwHA+PHjQ9btyPLhFosFM2fOxI4dO3htfPrppyGnbIVy9OhRXp6ecAGc48eP8wIAsR6Bo1Qqufu+o3H6qpKSEsyZMwc2mw0AoNVq8c033yA5Obndbfq/rr6vOSGEENJTUACHEEII6SNOnTrFKw8ePDhk3S1btsDj8XBl32XC/e3cuZNXnjx5ctB6R48e5VZc0ul0mDp1apt9bmW1WjF79mxs3bqV23bVVVdh5cqV7RpN4TslCgg/qsa/bqwDOL6rTtXV1cW07Z6mubkZs2bN4pZ3F4lEWLlyJYYOHdqhdv1f1/T09A61RwghhHQFCuAQQgghfYTvNCeGYcIuV71hwwZe3XCjH3744Qde+dJLLw1az3f61MyZMyNOBGy323HNNdfgp59+4rZNnz4dq1evhkQiiagNf775b9LT05GUlBRR3YyMjLB12yM/P5+739zczFtVqy9xuVyYN28eb6TYW2+9hcsvv7zDbVdUVPDKeXl5HW6TEEII6WwUwCGEEEL6CN/ErUqlMuS0I5fLhdWrV3NlqVQaMnGs2+3GN998w5Wzs7MxYcKEoHXbM33K4XDg+uuv5wWJLrnkEqxdu7ZDCX+jSUrsWzfW+W8AYPjw4byy/0ipvuKBBx7Axo0bufLDDz+M++67LyZtnzx5kruv0WiQk5MTk3YJIYSQzkQBHEIIIaSP8M354p/U1dfatWt5U05cLheXTNbft99+y1vZ6qabbgoa7KmpqcGvv/4KAJBIJJgxY0ab/XW5XJg/fz5v2fHCwkKsW7cOcrm8zf3D6S4rUAHAuHHjQh6vr3j99dfx7rvvcuW5c+fitddei1n7vq/p2LFj27WSFSGEENLVKIBDCCGE9BEpKSncfYfDETCtBABYlsXLL78MANDr9QC8gZTq6uqgdZ999lmuLJVK8de//jXosdetW8cFgS655BJoNJqwfXW73bjllluwZs0abtvkyZOxfv36Diegra6u5nKsAOGDMk1NTaiqquLKHc3FEkxSUhJvla/ffvst5sfoztasWYPHHnuMK48ZMwbLly+PalWxtuzevZu7f/HFF8esXUIIIaQzUQCHEEII6SP69+/PK/vmpGm1aNEibonu+fPnc9s3bdoUUPeFF17A3r17ufJ9992HzMzMoMeOZvlwlmVx11134fPPP+e2TZw4ERs2bIBKpQq7bySiSWDcmnS5lU6n6/Dxg7n66qu5+5s3b47LMbqjQ4cO4ZZbbuESZmdmZmLt2rUxXSXqyJEjvIDdVVddFbO2CSGEkM4UWfZAQgghhPR4s2bNwpdffsmVn3zySYwYMQKFhYVwu914++238cgjjwAApk2bhnnz5mHJkiUAgKeffhqFhYXIy8uDwWDAiy++iNdff51ra8CAAXjxxReDHtdiseDHH3/kyrNnzw7bz+3bt+PDDz/kbSstLcUFF1wQ1fP9+eefgwaUfKdPSSQSDBo0KGQbDQ0NvPIXX3yBSy+9FAzDxDTIcP3113NTho4dO4bS0tI+kadlz549vNxMFosF06ZNi6qNUO9zq++//567n5OTEzBljRBCCOkpKIBDCCGE9BE33HADnnnmGZSUlAAADAYDpk6diuTkZFgsFpjNZgCASqXCu+++i6ysLCiVSpjNZhQXF6OgoABpaWmora2Fy+Xi2k1LS8OaNWtCBjR++OEHWK1WAN78I+GWJAe806f8+ebZiZRvzh9fviNwhgwZEnYZcv9Ez8uXL8fy5cuRmJiI+vr6qPsUyoUXXoiBAwdyyXZXr16NBx98MGbt9xQGgwEGgyGqfUK9z618g5a33HIL5b8hhBDSY9EUKkIIIaSPUCqVWLFiRcAy2HV1dVzwJikpCevXr8fAgQOhUCjw/PPPc/U8Hg8qKyt5wZuJEydi69atvBwu/qKZPtUZoklgPGrUqKBLlccjmfE999zD3V+xYkXM2++LSkpKsGvXLgCAQCDAXXfd1cU9IoQQQtqPYUMtK0EIIYSQXqm8vByLFi3CN998g7KyMrhcLvTr1w+zZ8/GQw89hNTUVF79zz77DG+++SYOHjwIp9OJtLQ0jB8/HjfeeCOuv/76sCMaPB4P0tLSuFWt9u/fH5eluKOxa9cuLgiVnZ2N3NzcsPVXrVqFp59+GqdOnYJcLsegQYPw4IMP4qabboppv5qbm5GVlQWTyQSge7xWPd2TTz6JF154AQBw7bXXYvXq1V3cI0IIIaT9KIBDCCGEkLjZvn07CgsLAQC5ubnc9C0S3N///ncuF84999zDW1qbRMdutyM/P59bRWzXrl248MILu7hXhBBCSPvRFCpCCCGExM3atWu5+91h+lR399hjj0GtVgMAPvroI94S5iQ6H374Iff6zZkzh4I3hBBCejwK4BBCCCEkbnzz38yZM6cLe9IzJCYm4u9//zsA7wgS35W+SORcLhdeffVVAN5E1K3TqAghhJCejKZQEUIIIYR0IzabDcOGDcOZM2cglUpx4sSJNvP0EL7FixfjgQceAAD87W9/wxtvvNG1HSKEEEJigJYRJ4QQQgjpRmQyGT755BN8//33AIDi4mIK4ERJIBDg6aefBsMwfXI5dkIIIb0TjcAhhBBCCCGEEEII6eYoBw4hhBBCCCGEEEJIN0cBHEIIIYQQQgghhJBujgI4hBBCCCGEEEIIId0cBXAIIYQQQgghhBBCujkK4BBCCCGEEEIIIYR0cxTAIYQQQgghhBBCCOnmKIBDCCGEEEIIIYQQ0s1RAIcQQgghhBBCCCGkm6MADiGEEEIIIYQQQkg3RwEcQgghhBBCCCGEkG6OAjiEEEIIIYQQQggh3RwFcAghhBBCCCGEEEK6OQrgEEIIIYQQQgghhHRzFMAhhBBCCCGEEEII6eYogEMIIYQQQgghhBDSzVEAhxBCCCGEEEIIIaSbowAOIYQQQgghhBBCSDdHARxCCCGEEEIIIYSQbo4COIQQQgghhBBCCCHdHAVwCCGEEEIIIYQQQro5CuAQQgghhBBCCCGEdHMUwCGEEEIIIYQQQgjp5iiAQwghhBBCCCGEENLNUQCHEEIIIYQQQgghpJujAA4hhBBCCCGEEEJIN0cBHEIIIYQQQgghhJBujgI4hBBCCCGEEEIIId0cBXAIIYQQQgghhBBCujkK4BBCCCGEEEIIIYR0cxTAIYQQQgghhBBCCOnm/j8hu0UDSyYzTAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1140x690 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_chisquare = flux_resample.psd\n",
+    "estimate = flux_resample.NLL_mean\n",
+    "estimate_std = flux_resample.NLL_std\n",
+    "\n",
+    "ip = 0\n",
+    "addaxes = []\n",
+    "\n",
+    "N = data_chisquare.shape[-1]\n",
+    "fs = plt.rcParams['figure.figsize']\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "pl, = ax.plot(flux_resample.freqs_THz, \n",
+    "                flux_resample.psd*flux_resample.KAPPA_SCALE/2, \n",
+    "                alpha = 0.3, \n",
+    "                label = f'(Raw)',\n",
+    "        )\n",
+    "\n",
+    "from sportran.md.tools.filter import runavefilter\n",
+    "ax.plot(flux_resample.freqs_THz, runavefilter(flux_resample.psd, 50)*flux_resample.KAPPA_SCALE/2)\n",
+    "ax.plot(flux_resample.freqs_THz, np.exp(runavefilter(np.log(flux_resample.psd), 50))*flux_resample.KAPPA_SCALE/2)\n",
+    "\n",
+    "# ax.plot(flux_resample.freqs_THz, \n",
+    "#         flux_resample.fpsd*flux_resample.KAPPA_SCALE/2, \n",
+    "#         alpha = 1, \n",
+    "#         label = f'(MA)', \n",
+    "#         lw = 1.5,\n",
+    "#         color = pl.get_color()\n",
+    "#         )\n",
+    "\n",
+    "ymin, ymax = ax.get_ylim()\n",
+    "\n",
+    "x, y = flux_resample.freqs_THz, estimate*flux_resample.KAPPA_SCALE/2 / (np.sqrt(5)/3)\n",
+    "y_std = estimate_std*flux_resample.KAPPA_SCALE/2 \n",
+    "ax.plot(x, \n",
+    "        y, \n",
+    "        color = 'k',\n",
+    "        lw = 1.5,\n",
+    "        label = 'NLL',\n",
+    "        ls = '--')\n",
+    "ax.fill_between(x, \n",
+    "                y-y_std, \n",
+    "                y+y_std, \n",
+    "                color = 'k', \n",
+    "                alpha = 0.5)\n",
+    "\n",
+    "x, y = flux_resample.freqs_THz, flux_resample.cepf.psd*flux_resample.KAPPA_SCALE/2\n",
+    "y_std = flux_resample.kappa_std/flux_resample.kappa * y\n",
+    "ax.plot(x, \n",
+    "        y, \n",
+    "        color = 'r',\n",
+    "        lw = 1.5,\n",
+    "        label = 'Cepstral',\n",
+    "        ls = ':')        \n",
+    "ax.fill_between(x, \n",
+    "                y-y_std, \n",
+    "                y+y_std, \n",
+    "                color = 'r', \n",
+    "                alpha = 0.5)\n",
+    "        \n",
+    "ax.set_ylim(0,ymax)\n",
+    "ax.set_xlabel('$\\omega/2\\pi$ (THz)')\n",
+    "ax.set_ylabel('Diagonal coefficient')\n",
+    "\n",
+    "ax.legend()\n",
+    "# ax.set_xlim(0,1)\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 487,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7671919546429262"
+      ]
+     },
+     "execution_count": 487,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/ (flux_resample.cepf.tau_cutoffK / flux_resample.maxlike.NLL_mean[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 488,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 2)"
+      ]
+     },
+     "execution_count": 488,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux_resample.N_EQUIV_COMPONENTS, flux_resample.N_CURRENTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 489,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 489,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux_resample.ndf_chi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 490,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7453559924999299"
+      ]
+     },
+     "execution_count": 490,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt(5)/3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "self.N_EQUIV_COMPONENTS - self.N_CURRENTS + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 417,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 417,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flux_resample.N_EQUIV_COMPONENTS - flux_resample.N_CURRENTS + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 2 0.6123724356957945\n"
+     ]
+    }
+   ],
+   "source": [
+    "factor = (\n",
+    "                np.sqrt(flux_resample.N_EQUIV_COMPONENTS) / flux_resample.N_CURRENTS / np.sqrt(2)\n",
+    "            )\n",
+    "print(flux_resample.N_EQUIV_COMPONENTS, flux_resample.N_CURRENTS, factor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factor/np.sqrt(3/2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Off-diagonal (Seebeck)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To fix: cepstral estimate of the other parameters is missing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flux_resample.maxlike_estimate(\n",
+    "    model_scalar, \n",
+    "    n_parameters=9, \n",
+    "    likelihood='variance-gamma', \n",
+    "    solver = 'BFGS',\n",
+    "    minimize_kwargs = {\n",
+    "        'tol': 1e-6,\n",
+    "        'jac': '3-point',\n",
+    "        'options': {'disp': True, 'gtol': 1e-3, 'maxiter': 500, 'eps': 1e-5}\n",
+    "    }\n",
+    ")\n",
+    "flux_resample.maxlike.optimizer_res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ase.units._e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.sqrt(ase.units._amu*1e-10/ase.units._e) * 1e10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Wishart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MaxLikeFilter Initialization\n",
+      "Maximum-likelihood estimation with n_parameters = 12\n",
+      "Spline nodes are equispaced from 0 to the Nyquist frequency.\n",
+      "Optimization terminated successfully.\n",
+      "         Current function value: 157458.831853\n",
+      "         Iterations: 259\n",
+      "         Function evaluations: 19345\n",
+      "         Gradient evaluations: 265\n",
+      "The BFGS solver provides Hessian. Covariance matrix estimated through Laplace approximation.\n",
+      "-----------------------------------------------------\n",
+      "  MAXIMUM LIKELIHOOD ESTIMATION\n",
+      "-----------------------------------------------------\n",
+      "  Fixed n_parameters = 12\n",
+      "  S_{00} =     1037077.263318 +/- 69140.726137\n",
+      "  S_{01} =      119445.494420 +/- 36311.972520\n",
+      "  S_{11} =      630121.087176 +/- 35695.842880\n",
+      "-----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample.maxlike_estimate(\n",
+    "    lambda x, y: mini_model_w_real(x, y, 2), \n",
+    "    # np.arange(5, 7), \n",
+    "    12,\n",
+    "    solver = 'BFGS',\n",
+    "    minimize_kwargs = {\n",
+    "        'tol': 1e-8,\n",
+    "        'jac': '3-point',\n",
+    "        'options': {\n",
+    "            'disp': True, \n",
+    "            'gtol': 1e-4, \n",
+    "            'maxiter': 500, \n",
+    "            'eps': 1e-6\n",
+    "            },\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ase\n",
+    "def scale_jc(units='metal'):\n",
+    "    if units == 'metal' or units == 'gpumd':\n",
+    "        Ang_to_m = 1/ase.units.m\n",
+    "        ps_to_s = 1e-12\n",
+    "        return ase.units._e * Ang_to_m / ps_to_s\n",
+    "    else:\n",
+    "        raise NotImplementedError(f\"Units `{units}` not implemented\")\n",
+    "    \n",
+    "def scale_jq(units='metal'):\n",
+    "    if units == 'metal':\n",
+    "        eV_to_J = 1/ase.units.J\n",
+    "        Ang_to_m = 1/ase.units.m\n",
+    "        ps_to_s = 1e-12\n",
+    "        return eV_to_J * Ang_to_m / ps_to_s\n",
+    "    elif units == 'gpumd':\n",
+    "        return ase.units._e**(3/2) / ase.units._amu\n",
+    "    \n",
+    "eV_to_J = 1/ase.units.J\n",
+    "Ang_to_m = 1/ase.units.m\n",
+    "ps_to_s = 1e-12\n",
+    "T = flux_resample.TEMPERATURE\n",
+    "V = flux_resample.VOLUME * Ang_to_m**3\n",
+    "\n",
+    "kappa_scale = 1/ase.units._k/V/T**2 * scale_jq()**2 * ps_to_s/1000\n",
+    "sigma_scale = 1/ase.units._k/V/T * scale_jc()**2 * ps_to_s/1000\n",
+    "mixed_scale = 1/ase.units._k/V/T * scale_jc()*scale_jq() * ps_to_s/1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1140x2070 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unit_factors = {\n",
+    "        (0,0): 0.5*kappa_scale,\n",
+    "        (0,1): 0.5*mixed_scale,\n",
+    "        (1,1): 0.5*sigma_scale/100,\n",
+    "}\n",
+    "\n",
+    "unit_symbol = {\n",
+    "        (0,0): r'$\\mathrm{Wm^{-1}K^{-1}}$',\n",
+    "        (0,1): r'$\\mathrm{Am^{-1}K^{-1/2}}$',\n",
+    "        (1,1): r'$\\mathrm{Scm^{-1}}$',\n",
+    "}\n",
+    "\n",
+    "quantity = {\n",
+    "        0: 'q',\n",
+    "        1: 'c',\n",
+    "}\n",
+    "\n",
+    "data_wishart = flux_resample.cospectrum.real.transpose((2,0,1)) / flux_resample.N_EQUIV_COMPONENTS\n",
+    "fdata_wishart = flux_resample.fcospectrum.real.transpose((2,0,1)) / flux_resample.N_EQUIV_COMPONENTS\n",
+    "estimate = flux_resample.NLL_mean\n",
+    "estimate_std = flux_resample.NLL_std\n",
+    "\n",
+    "ip = 0\n",
+    "addaxes = []\n",
+    "\n",
+    "N = data_wishart.shape[-1]\n",
+    "fs = plt.rcParams['figure.figsize']\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows = N*(N+1)//2, ncols = 1, figsize = (fs[0], 3*fs[1]))\n",
+    "\n",
+    "for ax, i, j in zip(axes, *np.triu_indices(2)):\n",
+    "\n",
+    "        unit_factor = unit_factors[i,j]\n",
+    "\n",
+    "        pl, = ax.plot(\n",
+    "                flux_resample.freqs_THz, \n",
+    "                data_wishart[:, i, j]*unit_factor, \n",
+    "                alpha = 0.2, \n",
+    "                label = f'(Raw)',\n",
+    "                zorder = 0,\n",
+    "                color = 'k',\n",
+    "                )\n",
+    "\n",
+    "        ax.plot(\n",
+    "                flux_resample.freqs_THz, \n",
+    "                # pd.Series(data_wishart[:, i, j]).rolling(window=50).mean()*flux_resample.KAPPA_SCALE/2, \n",
+    "                fdata_wishart[:, i, j]*unit_factor * flux_resample.N_EQUIV_COMPONENTS, # WHY??? \n",
+    "                alpha = 1, \n",
+    "                label = f'(MA)', \n",
+    "                lw = 1.5,\n",
+    "                color = pl.get_color(),\n",
+    "                zorder = 1,\n",
+    "                )\n",
+    "\n",
+    "        x, y = flux_resample.freqs_THz, estimate[:, i, j]*unit_factor \n",
+    "        ym = (estimate[:, i, j]-estimate_std[:, i, j])*unit_factor\n",
+    "        yp = (estimate[:, i, j]+estimate_std[:, i, j])*unit_factor\n",
+    "        # ym, yp = estimate_m[:, i, j]*flux_resample.KAPPA_SCALE/2, estimate_p[:, i, j]*flux_resample.KAPPA_SCALE/2\n",
+    "\n",
+    "        ax.plot(\n",
+    "                x, \n",
+    "                y, \n",
+    "                color = 'r',\n",
+    "                lw = 1.5,\n",
+    "                label = 'NLL',\n",
+    "                ls = '--',\n",
+    "                zorder = 2,\n",
+    "                )\n",
+    "                \n",
+    "        ax.fill_between(\n",
+    "                x, \n",
+    "                ym, \n",
+    "                yp, \n",
+    "                color = 'r', \n",
+    "                alpha = 0.5,\n",
+    "                zorder = 2,\n",
+    "                )\n",
+    "        \n",
+    "        ax.set_xlim(0,10)\n",
+    "        ax.set_ylim(0 if i == j else None)\n",
+    "        \n",
+    "        ax.set_xlabel('$\\omega/2\\pi$ (THz)')\n",
+    "        symb = f\"{quantity[i]}{quantity[j]}\"\n",
+    "        ax.set_ylabel(f'$S^{{{symb}}}$ ({unit_symbol[i,j]})')\n",
+    "\n",
+    "        ax.legend()\n",
+    "\n",
+    "fig.suptitle(f\"MaxLike estimate with {flux_resample.maxlike.n_parameters} parameters\")\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "pl, = ax.plot(\n",
+    "    flux_c_resample.freqs_THz, \n",
+    "    flux_c_resample.psd*flux_c_resample.KAPPA_SCALE/200, \n",
+    "    alpha = 0.3\n",
+    "    )\n",
+    "pl, = ax.plot(\n",
+    "    flux_c_resample.freqs_THz, \n",
+    "    flux_c_resample.fpsd*flux_c_resample.KAPPA_SCALE/200, \n",
+    "    alpha = 1,\n",
+    "    color = pl.get_color(),\n",
+    "    )\n",
+    "ax.plot(\n",
+    "    flux_c_resample.freqs_THz, \n",
+    "    flux_c_resample.cepf.psd*flux_c_resample.KAPPA_SCALE/200,\n",
+    "    color = 'k', \n",
+    "    label = 'cepstrum',\n",
+    "    lw = 2,\n",
+    "    )\n",
+    "ax.fill_between(\n",
+    "    flux_c_resample.freqs_THz, \n",
+    "    flux_c_resample.cepf.psd*flux_c_resample.KAPPA_SCALE/200-flux_c_resample.kappa_std/100,\n",
+    "    flux_c_resample.cepf.psd*flux_c_resample.KAPPA_SCALE/200+flux_c_resample.kappa_std/100,\n",
+    "    color = 'k', \n",
+    "    alpha=0.5,\n",
+    "    )\n",
+    "x, y = flux_resample.freqs_THz, estimate[:, 1,1]*unit_factors[1,1] \n",
+    "ym = (estimate[:, 1,1]-estimate_std[:, 1,1])*unit_factors[1,1]\n",
+    "yp = (estimate[:, 1,1]+estimate_std[:, 1,1])*unit_factors[1,1]\n",
+    "pl, = ax.plot(\n",
+    "    x, \n",
+    "    y,\n",
+    "    color='tab:red',\n",
+    "    label = 'NLL',\n",
+    "    lw = 1.5,\n",
+    "    ls = '--',\n",
+    "    zorder = 2, \n",
+    "    )\n",
+    "ax.fill_between(\n",
+    "    x,\n",
+    "    ym,\n",
+    "    yp,\n",
+    "    color = pl.get_color(), \n",
+    "    alpha = 0.5, \n",
+    "    zorder = 2,\n",
+    ")\n",
+    "ax.set_xlim(0,10)\n",
+    "ax.legend()\n",
+    "ax.set_ylabel(f'$S^{{cc}}$ ({unit_symbol[i,j]})')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "x = flux_resample.freqs_THz\n",
+    "y = flux_resample.cospectrum[0,1]/flux_resample.cospectrum[1,1]/flux_resample.TEMPERATURE\n",
+    "pl, = ax.plot(\n",
+    "    x, \n",
+    "    y, \n",
+    "    alpha = 0.3,\n",
+    "    label = 'Raw',\n",
+    "    )\n",
+    "y = flux_resample.fcospectrum[0,1]/flux_resample.fcospectrum[1,1]/flux_resample.TEMPERATURE\n",
+    "ax.plot(\n",
+    "    x, \n",
+    "    y, \n",
+    "    alpha = 0.3,\n",
+    "    label = 'MA',\n",
+    "    color = pl.get_color(),\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "x, y = flux_resample.freqs_THz, estimate[:, 0,1]/estimate[:, 1,1]/flux_resample.TEMPERATURE\n",
+    "# ym = (estimate[:, 1,1]-estimate_std[:, 1,1])*unit_factors[1,1]\n",
+    "# yp = (estimate[:, 1,1]+estimate_std[:, 1,1])*unit_factors[1,1]\n",
+    "pl, = ax.plot(\n",
+    "    x, \n",
+    "    y,\n",
+    "    color='tab:red',\n",
+    "    label = 'NLL',\n",
+    "    lw = 1.5,\n",
+    "    ls = '--',\n",
+    "    zorder = 2, \n",
+    "    )\n",
+    "# ax.fill_between(\n",
+    "#     x,\n",
+    "#     ym,\n",
+    "#     yp,\n",
+    "#     color = pl.get_color(), \n",
+    "#     alpha = 0.5, \n",
+    "#     zorder = 2,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ase\n",
+    "def scale_jc(units='metal'):\n",
+    "    if units == 'metal' or units == 'gpumd':\n",
+    "        Ang_to_m = 1/ase.units.m\n",
+    "        ps_to_s = 1e-12\n",
+    "        return ase.units._e * Ang_to_m / ps_to_s\n",
+    "    else:\n",
+    "        raise NotImplementedError(f\"Units `{units}` not implemented\")\n",
+    "    \n",
+    "def scale_jq(units='metal'):\n",
+    "    if units == 'metal':\n",
+    "        eV_to_J = 1/ase.units.J\n",
+    "        Ang_to_m = 1/ase.units.m\n",
+    "        ps_to_s = 1e-12\n",
+    "        return eV_to_J * Ang_to_m / ps_to_s\n",
+    "    elif units == 'gpumd':\n",
+    "        return ase.units._e**(3/2) / ase.units._amu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eV_to_J = 1/ase.units.J\n",
+    "Ang_to_m = 1/ase.units.m\n",
+    "ps_to_s = 1e-12\n",
+    "T = flux_resample.TEMPERATURE\n",
+    "V = flux_resample.VOLUME * Ang_to_m**3\n",
+    "\n",
+    "kappa_scale = 1/ase.units._k/V/T**2 * scale_jq()**2 * ps_to_s/1000\n",
+    "sigma_scale = 1/ase.units._k/V/T * scale_jc()**2 * ps_to_s/1000\n",
+    "mixed_scale = 1/ase.units._k/V/T * scale_jc()*scale_jq() * ps_to_s/1000\n",
+    "print(kappa_scale, kappa_scale/flux_resample.KAPPA_SCALE)\n",
+    "print(sigma_scale, sigma_scale/flux_c_resample.KAPPA_SCALE)\n",
+    "print(mixed_scale)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "figure, axis = plt.subplots(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# color = next(axis._get_lines.prop_cycler)['color']  # deprecated\n",
+    "\n",
+    "# New approach using get_prop_cycle()\n",
+    "prop_cycle = axis.get_prop_cycle()\n",
+    "color = next(prop_cycle)['color']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
+    "color = colors[0]  # Access the first color from the color cycle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/08_example_optimize_prior .ipynb b/examples/08_example_optimize_prior .ipynb
new file mode 100644
index 0000000..5f7bc49
--- /dev/null
+++ b/examples/08_example_optimize_prior .ipynb	
@@ -0,0 +1,247 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import sportran as st\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import CubicSpline, interp1d\n",
+    "def model_scalar(x, y):\n",
+    "    return CubicSpline(np.concatenate([-x[::-1], x[1:]]), np.concatenate([y[::-1], y[1:]]))\n",
+    "\n",
+    "n = 2\n",
+    "def model_wishart_(x, y):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    y00, y01, y11 = y.reshape(3,l)\n",
+    "    yy = np.array([[y00, y01], [np.zeros_like(y01), y11]]).T\n",
+    "    # yy = np.einsum('tab,tbc->tac', np.transpose(yy, axes=(0,2,1)), yy)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    # return interp1d(xx, yy)) \n",
+    "    return CubicSpline(xx, yy) #, bc_type = 'clamped')\n",
+    "\n",
+    "import numpy as np\n",
+    "from scipy.interpolate import CubicSpline\n",
+    "\n",
+    "def model_wishart(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    upper_triangle_indices = np.triu_indices(N)\n",
+    "    y_elements = y.reshape(len(upper_triangle_indices[0]), l)\n",
+    "    \n",
+    "    yy = np.zeros((l, N, N))\n",
+    "    for k, (i, j) in enumerate(zip(*upper_triangle_indices)):\n",
+    "        yy[:, j, i] = y_elements[k]\n",
+    "    \n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n",
+    "\n",
+    "def mini_model_w(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    \n",
+    "    # y.shape should be l*N**2\n",
+    "    yy = y.reshape(l, N**2)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n",
+    "\n",
+    "def mini_model_w_real(x, y, N):\n",
+    "    xx = np.concatenate([-x[::-1], x[1:]])\n",
+    "    l = x.size\n",
+    "    yy = y.reshape(l, N*(N+1)//2)\n",
+    "    yy = np.concatenate([yy[::-1], yy[1:]])\n",
+    "    return CubicSpline(xx, yy)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dc=np.load('data/bayesian/CsF/dc_minimal.npy', allow_pickle = True).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def split_bl(array):\n",
+    "    s = array.shape[0]//2\n",
+    "    return np.hstack([array[:s], array[s:]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using multicomponent code.\n",
+      "Using multicomponent code.\n",
+      "-----------------------------------------------------\n",
+      "  RESAMPLE TIME SERIES\n",
+      "-----------------------------------------------------\n",
+      " Original Nyquist freq  f_Ny =     500.00000 THz\n",
+      " Resampling freq          f* =      20.00000 THz\n",
+      " Sampling time         TSKIP =            25 steps\n",
+      "                             =        25.000 fs\n",
+      " Original  n. of frequencies =        100001\n",
+      " Resampled n. of frequencies =          4001\n",
+      " min(PSD)          (pre-filter&sample) =      0.00206\n",
+      " min(PSD)         (post-filter&sample) =    268.60806\n",
+      " % of original PSD Power f<f* (pre-filter&sample)  = 99.707 %\n",
+      " fPSD not calculated before resampling\n",
+      " -----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux = st.HeatCurrent(\n",
+    "    [\n",
+    "        dc['heat_flux'],\n",
+    "        dc['electric_flux']\n",
+    "        # split_bl(dc['heat_flux']),\n",
+    "        # split_bl(dc['electric_flux'])\n",
+    "    ],\n",
+    "    DT_FS=1,\n",
+    "    TEMPERATURE=dc['Temperature'],\n",
+    "    VOLUME=dc['Volume'],\n",
+    "    UNITS='metal'\n",
+    "    )\n",
+    "flux_resample=flux.resample(fstar_THz=20)\n",
+    "flux_resample.filter_psd(0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MaxLikeFilter Initialization\n",
+      "Maximum-likelihood estimation with n_parameters = [5 6]\n",
+      "Optimal number of parameters between 5 and 6 chosen by AIC\n",
+      "n_parameters = 5\n",
+      "Spline nodes are equispaced from 0 to the Nyquist frequency.\n",
+      "Optimization terminated successfully.\n",
+      "         Current function value: 157645.439405\n",
+      "         Iterations: 115\n",
+      "         Function evaluations: 3968\n",
+      "         Gradient evaluations: 128\n",
+      "AIC: -157650.4673367685; Steps since last AIC update: 0\n",
+      "n_parameters = 6\n",
+      "Spline nodes are equispaced from 0 to the Nyquist frequency.\n",
+      "Optimization terminated successfully.\n",
+      "         Current function value: 157481.577063\n",
+      "         Iterations: 136\n",
+      "         Function evaluations: 5180\n",
+      "         Gradient evaluations: 140\n",
+      "AIC: -157487.60993576434; Steps since last AIC update: 0\n",
+      "SHAPE----------------------------------------------------------------\n",
+      "(18,) (18, 18)\n",
+      "-----------------------------------------------------\n",
+      "  MAXIMUM LIKELIHOOD ESTIMATION\n",
+      "-----------------------------------------------------\n",
+      "  Fixed n_parameters = 6\n",
+      "  S_{00} =      964641.265201 +/- 43460.905393\n",
+      "  S_{01} =      164644.042008 +/- 24778.827913\n",
+      "  S_{11} =      640889.880138 +/- 28493.365586\n",
+      "-----------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "flux_resample.maxlike_estimate(\n",
+    "    lambda x, y: mini_model_w_real(x, y, 2), \n",
+    "    # np.arange(5, 7), \n",
+    "    # 5,\n",
+    "    'AIC',\n",
+    "    solver = 'BFGS',\n",
+    "    minimize_kwargs = {\n",
+    "        'tol': 1e-8,\n",
+    "        'jac': '3-point',\n",
+    "        'options': {\n",
+    "            'disp': True, \n",
+    "            'gtol': 1e-4, \n",
+    "            'maxiter': 500, \n",
+    "            'eps': 1e-6\n",
+    "            },\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.plot(flux_resample.maxlike.alpha, flux_resample.maxlike.best_alpha['lev_s'])\n",
+    "plt.axvline(flux_resample.maxlike.best_alpha['alpha_s'], color='red')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/data/bayesian/CsF/dc_minimal.npy b/examples/data/bayesian/CsF/dc_minimal.npy
new file mode 100644
index 0000000..6a0af90
Binary files /dev/null and b/examples/data/bayesian/CsF/dc_minimal.npy differ
diff --git a/examples/data/bayesian/mock_data/mock_data_sin.dat b/examples/data/bayesian/mock_data/mock_data_sin.dat
new file mode 100644
index 0000000..f250cbb
--- /dev/null
+++ b/examples/data/bayesian/mock_data/mock_data_sin.dat
@@ -0,0 +1,4001 @@
+0.	-0.11669098460419569
+0.005	-0.2136503829526069
+0.01	-0.07745268671025325
+0.015	-1.1157217357490687
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+0.025	-0.6665463263034117
+0.03	-1.1265822853532437
+0.035	-0.04244818713633765
+0.04	-0.3470419152919827
+0.045	-0.8216966627728126
+0.05	-0.8954685781896172
+0.055	-0.18565945295525443
+0.06	-0.7597679932011584
+0.065	-0.5251006969795041
+0.07	-0.5657288571868553
+0.075	-0.40365924166989964
+0.08	-0.15766009539345754
+0.085	-1.152587462838372
+0.09	-0.25297039238641
+0.095	-0.6541003386528617
+0.1	-0.2220998857114356
+0.105	-0.02876555702954655
+0.11	-0.8264548892676038
+0.115	-0.48284947998238037
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+0.125	-0.2956746680187379
+0.13	-1.007040117701281
+0.135	-0.6264789433278442
+0.14	-0.05189591781103893
+0.145	-0.4143203396424907
+0.15	-0.6617829439209342
+0.155	-0.7795373713821526
+0.16	-0.5941496738433365
+0.165	-0.3387424469242666
+0.17	-0.23765378827356956
+0.175	-1.5092572464982414
+0.18	-0.36852360232030745
+0.185	-1.2921615081420916
+0.19	-0.9907184640566593
+0.195	-0.3791827104624643
+0.2	-0.94552839370443
+0.205	-0.8530845847155843
+0.21	-0.5745737623828966
+0.215	-0.20938770480210728
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+0.225	0.055881500319555855
+0.23	-1.3208200512286554
+0.235	-0.06348706113488174
+0.24	-1.1542529103229802
+0.245	0.06981392428245492
+0.25	-0.4642905883227284
+0.255	-2.75472313913465
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+0.265	-0.1811094314423234
+0.27	-0.6954187070342553
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+0.305	-0.7342990918210976
+0.31	-0.6520580370466893
+0.315	-0.6017279885847431
+0.32	-0.09721003989889046
+0.325	-1.4475856878112712
+0.33	-0.3624749615533558
+0.335	-0.09462330654258136
+0.34	0.014932040182219644
+0.345	-0.4089251555979876
+0.35	-0.35353853097720545
+0.355	-0.3694568615645186
+0.36	-0.5713334595311383
+0.365	-0.7477726160035552
+0.37	-0.9907181303078098
+0.375	-0.06750769283234133
+0.38	-0.2301454457870875
+0.385	-0.3851617529186222
+0.39	-0.14982138998940367
+0.395	-1.092547409892104
+0.4	-0.35791073443902044
+0.405	-0.7915331476858355
+0.41	-0.8641589103010029
+0.415	-0.09424516618930762
+0.42	-0.8863246192654352
+0.425	-0.38810590181430227
+0.43	-0.388000056583961
+0.435	-0.1855723336983202
+0.44	-0.024366231405337852
+0.445	-0.5560763067149708
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diff --git a/examples/data_manager.ipynb b/examples/data_manager.ipynb
new file mode 100644
index 0000000..f774ec5
--- /dev/null
+++ b/examples/data_manager.ipynb
@@ -0,0 +1,1142 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import sportran as st\n",
+    "\n",
+    "import lammps_logfile as ll\n",
+    "\n",
+    "from ase.io import read\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# From LAMMPS/GPUMD to archive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## LAMMPS input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root = './data_manager/lammps'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LAMMPS printed table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A simple line of pandas is sufficient to read a table file printed by lammps using the command:\n",
+    "\n",
+    "`fix \t\tthermo_print all print 5 \"$(step) $(time) $(econserve) $(pe) $(etotal) $(temp) $(press) $(density) $(enthalpy) $(v_Jx) $(v_Jy) $(v_Jz) $(v_vcm11) $(v_vcm12) $(v_vcm13) $(v_vcm21) $(v_vcm22) $(v_vcm23)\" append sample.lammps screen no title \"step time econserve etotal T P density enthalpy j[1] j[2] j[3] v1[1] v1[2] v1[3] v2[1] v2[2] v2[3]\"`\n",
+    "\n",
+    "The column names of the `pandas.DataFrame` will be the ones defined in by the `title` variable of the LAMMPS `fix print` command. We should be careful and choose a set of keywords we consistently use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timeseries = pd.read_csv(f'{root}/sample.lammps', sep = '\\s+')\n",
+    "\n",
+    "# We split thermodynamic data from the timeseries of the currents (for consistency with GPUMD)\n",
+    "thermo = timeseries.loc[:, ['step', 'time', 'econserve', 'etotal', 'T', 'P', 'density', 'enthalpy']]\n",
+    "timeseries = timeseries.loc[:, ['j[1]', 'j[2]', 'j[3]', 'v1[1]', 'v1[2]', 'v1[3]', 'v2[1]', 'v2[2]', 'v2[3]']] \n",
+    "\n",
+    "# We should also load a lammps data file to get the volume, since it's not present in the timeseries.\n",
+    "structure = read(f'{root}/config.lammpsdata', format = 'lammps-data') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the timeseries to a feather file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timeseries.to_feather(f'{root}/sample.lammps.ft')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read it from file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "timeseries = pd.read_feather(f'{root}/sample.lammps.ft')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>j[1]</th>\n",
+       "      <th>j[2]</th>\n",
+       "      <th>j[3]</th>\n",
+       "      <th>v1[1]</th>\n",
+       "      <th>v1[2]</th>\n",
+       "      <th>v1[3]</th>\n",
+       "      <th>v2[1]</th>\n",
+       "      <th>v2[2]</th>\n",
+       "      <th>v2[3]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>98.860585</td>\n",
+       "      <td>451.412579</td>\n",
+       "      <td>401.256234</td>\n",
+       "      <td>-0.024594</td>\n",
+       "      <td>0.243474</td>\n",
+       "      <td>0.100957</td>\n",
+       "      <td>0.015949</td>\n",
+       "      <td>-0.157896</td>\n",
+       "      <td>-0.065472</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>86.835391</td>\n",
+       "      <td>431.087326</td>\n",
+       "      <td>414.761925</td>\n",
+       "      <td>-0.032700</td>\n",
+       "      <td>0.263837</td>\n",
+       "      <td>0.091336</td>\n",
+       "      <td>0.021207</td>\n",
+       "      <td>-0.171101</td>\n",
+       "      <td>-0.059232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>111.486685</td>\n",
+       "      <td>386.972491</td>\n",
+       "      <td>440.930754</td>\n",
+       "      <td>-0.036475</td>\n",
+       "      <td>0.283372</td>\n",
+       "      <td>0.081802</td>\n",
+       "      <td>0.023654</td>\n",
+       "      <td>-0.183770</td>\n",
+       "      <td>-0.053049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>175.084996</td>\n",
+       "      <td>326.531957</td>\n",
+       "      <td>411.807814</td>\n",
+       "      <td>-0.035150</td>\n",
+       "      <td>0.302389</td>\n",
+       "      <td>0.071609</td>\n",
+       "      <td>0.022795</td>\n",
+       "      <td>-0.196103</td>\n",
+       "      <td>-0.046439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>178.459243</td>\n",
+       "      <td>269.363316</td>\n",
+       "      <td>440.358015</td>\n",
+       "      <td>-0.029682</td>\n",
+       "      <td>0.323938</td>\n",
+       "      <td>0.061421</td>\n",
+       "      <td>0.019249</td>\n",
+       "      <td>-0.210078</td>\n",
+       "      <td>-0.039833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499975</th>\n",
+       "      <td>-258.913911</td>\n",
+       "      <td>36.169839</td>\n",
+       "      <td>-553.521873</td>\n",
+       "      <td>0.445324</td>\n",
+       "      <td>-0.307098</td>\n",
+       "      <td>0.387822</td>\n",
+       "      <td>-0.288798</td>\n",
+       "      <td>0.199157</td>\n",
+       "      <td>-0.251507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499980</th>\n",
+       "      <td>-283.186906</td>\n",
+       "      <td>40.685236</td>\n",
+       "      <td>-500.809352</td>\n",
+       "      <td>0.460423</td>\n",
+       "      <td>-0.355443</td>\n",
+       "      <td>0.366227</td>\n",
+       "      <td>-0.298590</td>\n",
+       "      <td>0.230509</td>\n",
+       "      <td>-0.237503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499985</th>\n",
+       "      <td>-302.987081</td>\n",
+       "      <td>23.344070</td>\n",
+       "      <td>-450.029797</td>\n",
+       "      <td>0.472723</td>\n",
+       "      <td>-0.399537</td>\n",
+       "      <td>0.333738</td>\n",
+       "      <td>-0.306567</td>\n",
+       "      <td>0.259105</td>\n",
+       "      <td>-0.216434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499990</th>\n",
+       "      <td>-329.423859</td>\n",
+       "      <td>16.555491</td>\n",
+       "      <td>-400.434345</td>\n",
+       "      <td>0.482686</td>\n",
+       "      <td>-0.440561</td>\n",
+       "      <td>0.293111</td>\n",
+       "      <td>-0.313028</td>\n",
+       "      <td>0.285709</td>\n",
+       "      <td>-0.190086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499995</th>\n",
+       "      <td>-344.651843</td>\n",
+       "      <td>-0.860378</td>\n",
+       "      <td>-338.985537</td>\n",
+       "      <td>0.489870</td>\n",
+       "      <td>-0.480203</td>\n",
+       "      <td>0.246698</td>\n",
+       "      <td>-0.317687</td>\n",
+       "      <td>0.311418</td>\n",
+       "      <td>-0.159987</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              j[1]        j[2]        j[3]     v1[1]     v1[2]     v1[3]  \\\n",
+       "0        98.860585  451.412579  401.256234 -0.024594  0.243474  0.100957   \n",
+       "5        86.835391  431.087326  414.761925 -0.032700  0.263837  0.091336   \n",
+       "10      111.486685  386.972491  440.930754 -0.036475  0.283372  0.081802   \n",
+       "15      175.084996  326.531957  411.807814 -0.035150  0.302389  0.071609   \n",
+       "20      178.459243  269.363316  440.358015 -0.029682  0.323938  0.061421   \n",
+       "...            ...         ...         ...       ...       ...       ...   \n",
+       "499975 -258.913911   36.169839 -553.521873  0.445324 -0.307098  0.387822   \n",
+       "499980 -283.186906   40.685236 -500.809352  0.460423 -0.355443  0.366227   \n",
+       "499985 -302.987081   23.344070 -450.029797  0.472723 -0.399537  0.333738   \n",
+       "499990 -329.423859   16.555491 -400.434345  0.482686 -0.440561  0.293111   \n",
+       "499995 -344.651843   -0.860378 -338.985537  0.489870 -0.480203  0.246698   \n",
+       "\n",
+       "           v2[1]     v2[2]     v2[3]  \n",
+       "0       0.015949 -0.157896 -0.065472  \n",
+       "5       0.021207 -0.171101 -0.059232  \n",
+       "10      0.023654 -0.183770 -0.053049  \n",
+       "15      0.022795 -0.196103 -0.046439  \n",
+       "20      0.019249 -0.210078 -0.039833  \n",
+       "...          ...       ...       ...  \n",
+       "499975 -0.288798  0.199157 -0.251507  \n",
+       "499980 -0.298590  0.230509 -0.237503  \n",
+       "499985 -0.306567  0.259105 -0.216434  \n",
+       "499990 -0.313028  0.285709 -0.190086  \n",
+       "499995 -0.317687  0.311418 -0.159987  \n",
+       "\n",
+       "[100000 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timeseries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>step</th>\n",
+       "      <th>time</th>\n",
+       "      <th>econserve</th>\n",
+       "      <th>etotal</th>\n",
+       "      <th>T</th>\n",
+       "      <th>P</th>\n",
+       "      <th>density</th>\n",
+       "      <th>enthalpy</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-2810.806235</td>\n",
+       "      <td>-3083.548118</td>\n",
+       "      <td>-2823.832117</td>\n",
+       "      <td>2011.261684</td>\n",
+       "      <td>-392.441199</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2833.976211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.0025</td>\n",
+       "      <td>-2810.806294</td>\n",
+       "      <td>-3083.112614</td>\n",
+       "      <td>-2821.114775</td>\n",
+       "      <td>2028.932428</td>\n",
+       "      <td>-235.167801</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2827.193556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.0050</td>\n",
+       "      <td>-2810.805897</td>\n",
+       "      <td>-3082.615031</td>\n",
+       "      <td>-2823.061548</td>\n",
+       "      <td>2010.003130</td>\n",
+       "      <td>-239.968940</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2829.264433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.0075</td>\n",
+       "      <td>-2810.806471</td>\n",
+       "      <td>-3082.154176</td>\n",
+       "      <td>-2826.750674</td>\n",
+       "      <td>1977.865345</td>\n",
+       "      <td>-267.817847</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2833.673417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.0100</td>\n",
+       "      <td>-2810.806200</td>\n",
+       "      <td>-3081.739821</td>\n",
+       "      <td>-2826.784147</td>\n",
+       "      <td>1974.397324</td>\n",
+       "      <td>-224.982999</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2832.599665</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499975</th>\n",
+       "      <td>249.9875</td>\n",
+       "      <td>-2810.794373</td>\n",
+       "      <td>-3082.953261</td>\n",
+       "      <td>-2825.205822</td>\n",
+       "      <td>1996.016989</td>\n",
+       "      <td>-688.964326</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2843.014653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499980</th>\n",
+       "      <td>249.9900</td>\n",
+       "      <td>-2810.794285</td>\n",
+       "      <td>-3082.957909</td>\n",
+       "      <td>-2822.482228</td>\n",
+       "      <td>2017.144709</td>\n",
+       "      <td>-640.411150</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2839.036023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499985</th>\n",
+       "      <td>249.9925</td>\n",
+       "      <td>-2810.794678</td>\n",
+       "      <td>-3082.987220</td>\n",
+       "      <td>-2822.597515</td>\n",
+       "      <td>2016.478906</td>\n",
+       "      <td>-646.861749</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2839.318050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499990</th>\n",
+       "      <td>249.9950</td>\n",
+       "      <td>-2810.794972</td>\n",
+       "      <td>-3082.977744</td>\n",
+       "      <td>-2822.821297</td>\n",
+       "      <td>2014.672535</td>\n",
+       "      <td>-660.713589</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2839.899884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499995</th>\n",
+       "      <td>249.9975</td>\n",
+       "      <td>-2810.794960</td>\n",
+       "      <td>-3082.890843</td>\n",
+       "      <td>-2821.543483</td>\n",
+       "      <td>2023.895063</td>\n",
+       "      <td>-686.054968</td>\n",
+       "      <td>1.171598</td>\n",
+       "      <td>-2839.277111</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            step         time    econserve       etotal            T  \\\n",
+       "0         0.0000 -2810.806235 -3083.548118 -2823.832117  2011.261684   \n",
+       "5         0.0025 -2810.806294 -3083.112614 -2821.114775  2028.932428   \n",
+       "10        0.0050 -2810.805897 -3082.615031 -2823.061548  2010.003130   \n",
+       "15        0.0075 -2810.806471 -3082.154176 -2826.750674  1977.865345   \n",
+       "20        0.0100 -2810.806200 -3081.739821 -2826.784147  1974.397324   \n",
+       "...          ...          ...          ...          ...          ...   \n",
+       "499975  249.9875 -2810.794373 -3082.953261 -2825.205822  1996.016989   \n",
+       "499980  249.9900 -2810.794285 -3082.957909 -2822.482228  2017.144709   \n",
+       "499985  249.9925 -2810.794678 -3082.987220 -2822.597515  2016.478906   \n",
+       "499990  249.9950 -2810.794972 -3082.977744 -2822.821297  2014.672535   \n",
+       "499995  249.9975 -2810.794960 -3082.890843 -2821.543483  2023.895063   \n",
+       "\n",
+       "                 P   density     enthalpy  \n",
+       "0      -392.441199  1.171598 -2833.976211  \n",
+       "5      -235.167801  1.171598 -2827.193556  \n",
+       "10     -239.968940  1.171598 -2829.264433  \n",
+       "15     -267.817847  1.171598 -2833.673417  \n",
+       "20     -224.982999  1.171598 -2832.599665  \n",
+       "...            ...       ...          ...  \n",
+       "499975 -688.964326  1.171598 -2843.014653  \n",
+       "499980 -640.411150  1.171598 -2839.036023  \n",
+       "499985 -646.861749  1.171598 -2839.318050  \n",
+       "499990 -660.713589  1.171598 -2839.899884  \n",
+       "499995 -686.054968  1.171598 -2839.277111  \n",
+       "\n",
+       "[100000 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "thermo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Atoms(symbols='Cl500Na500', pbc=True, cell=[34.59789548758798, 34.59789548758798, 34.59789548758798], id=..., masses=..., momenta=..., type=...)\n",
+      "Temperature = 1999.05 K\n",
+      "Volume = 41414.18 Ang^3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(structure)\n",
+    "print(f'Temperature = {thermo[\"T\"].mean():.2f} K')\n",
+    "print(f'Volume = {structure.get_volume():.2f} Ang^3')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GPUMD input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output format of GPUMD is much less flexible than LAMMPS', so here we know what to expect when we read a file. The only thing that  can change is the number of columns of the `compute.out` file, which depend on what's in the `run.in` file.\n",
+    "\n",
+    "There are packages (e.g. `gpyumd`) that supposedly can read GPUMD outputs, but they don't seem to be updated anymore. Since we just need to read a couple of tables, I think we can do it from scratch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "root = './data_manager/gpumd'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure = read(f'{root}/model.xyz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    timeseries = pd.read_feather(f'{root}/timeseries.ft')\n",
+    "except FileNotFoundError:\n",
+    "    timeseries = {}\n",
+    "    timeseries_ = pd.read_csv(f'{root}/compute.out',\n",
+    "                             sep = '\\s+',\n",
+    "                             names = [\n",
+    "                                 'jv1[1]', 'jv2[1]', 'jv1[2]', 'jv2[2]', 'jv1[3]', 'jv2[3]',\n",
+    "                                 'jk1[1]', 'jk2[1]', 'jk1[2]', 'jk2[2]', 'jk1[3]', 'jk2[3]',\n",
+    "                                 'v1[1]', 'v2[1]', 'v1[2]', 'v2[2]', 'v1[3]', 'v2[3]'\n",
+    "                                 ]\n",
+    "                                 )\n",
+    "    for ii in [1,2,3]:\n",
+    "        timeseries[f'j[{ii}]'] = timeseries_[f'jk1[{ii}]'] + timeseries_[f'jv1[{ii}]'] + timeseries_[f'jk2[{ii}]'] + timeseries_[f'jv2[{ii}]']\n",
+    "        timeseries[f'v1[{ii}]'] = timeseries_[f'v1[{ii}]'] + timeseries_[f'v1[{ii}]'] + timeseries_[f'v1[{ii}]'] + timeseries_[f'v1[{ii}]']\n",
+    "        timeseries[f'v2[{ii}]'] = timeseries_[f'v2[{ii}]'] + timeseries_[f'v2[{ii}]'] + timeseries_[f'v2[{ii}]'] + timeseries_[f'v2[{ii}]']\n",
+    "    timeseries = pd.DataFrame(timeseries)\n",
+    "    timeseries.to_feather(f'{root}/timeseries.ft')\n",
+    "except Exception as e:\n",
+    "    print(f'Some other exception: {e}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>j[1]</th>\n",
+       "      <th>v1[1]</th>\n",
+       "      <th>v2[1]</th>\n",
+       "      <th>j[2]</th>\n",
+       "      <th>v1[2]</th>\n",
+       "      <th>v2[2]</th>\n",
+       "      <th>j[3]</th>\n",
+       "      <th>v1[3]</th>\n",
+       "      <th>v2[3]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.307160</td>\n",
+       "      <td>-50.682440</td>\n",
+       "      <td>50.684640</td>\n",
+       "      <td>8.807900</td>\n",
+       "      <td>52.03160</td>\n",
+       "      <td>-52.02848</td>\n",
+       "      <td>-9.734899</td>\n",
+       "      <td>146.46300</td>\n",
+       "      <td>-146.46056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6.197453</td>\n",
+       "      <td>-57.130560</td>\n",
+       "      <td>57.132720</td>\n",
+       "      <td>8.981368</td>\n",
+       "      <td>48.65744</td>\n",
+       "      <td>-48.65432</td>\n",
+       "      <td>-9.020685</td>\n",
+       "      <td>139.95560</td>\n",
+       "      <td>-139.95312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.776381</td>\n",
+       "      <td>-57.573560</td>\n",
+       "      <td>57.575760</td>\n",
+       "      <td>8.692182</td>\n",
+       "      <td>45.97476</td>\n",
+       "      <td>-45.97164</td>\n",
+       "      <td>-8.215225</td>\n",
+       "      <td>132.22656</td>\n",
+       "      <td>-132.22412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.068126</td>\n",
+       "      <td>-52.376000</td>\n",
+       "      <td>52.378200</td>\n",
+       "      <td>7.965965</td>\n",
+       "      <td>44.11236</td>\n",
+       "      <td>-44.10928</td>\n",
+       "      <td>-7.356712</td>\n",
+       "      <td>123.72356</td>\n",
+       "      <td>-123.72112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.104579</td>\n",
+       "      <td>-41.995240</td>\n",
+       "      <td>41.997440</td>\n",
+       "      <td>6.861634</td>\n",
+       "      <td>43.06760</td>\n",
+       "      <td>-43.06448</td>\n",
+       "      <td>-6.456287</td>\n",
+       "      <td>114.46780</td>\n",
+       "      <td>-114.46536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99995</th>\n",
+       "      <td>-11.275199</td>\n",
+       "      <td>151.886520</td>\n",
+       "      <td>-151.883200</td>\n",
+       "      <td>-2.732424</td>\n",
+       "      <td>133.54872</td>\n",
+       "      <td>-133.54416</td>\n",
+       "      <td>8.721313</td>\n",
+       "      <td>-255.50924</td>\n",
+       "      <td>255.51268</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99996</th>\n",
+       "      <td>-8.661137</td>\n",
+       "      <td>114.979760</td>\n",
+       "      <td>-114.976480</td>\n",
+       "      <td>-1.979022</td>\n",
+       "      <td>123.92116</td>\n",
+       "      <td>-123.91660</td>\n",
+       "      <td>9.341784</td>\n",
+       "      <td>-276.86204</td>\n",
+       "      <td>276.86548</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99997</th>\n",
+       "      <td>-5.984299</td>\n",
+       "      <td>76.342480</td>\n",
+       "      <td>-76.339160</td>\n",
+       "      <td>-1.205753</td>\n",
+       "      <td>111.79908</td>\n",
+       "      <td>-111.79452</td>\n",
+       "      <td>9.964473</td>\n",
+       "      <td>-297.18272</td>\n",
+       "      <td>297.18620</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99998</th>\n",
+       "      <td>-3.333353</td>\n",
+       "      <td>36.762828</td>\n",
+       "      <td>-36.759532</td>\n",
+       "      <td>-0.367569</td>\n",
+       "      <td>97.48352</td>\n",
+       "      <td>-97.47896</td>\n",
+       "      <td>10.649682</td>\n",
+       "      <td>-316.32052</td>\n",
+       "      <td>316.32400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99999</th>\n",
+       "      <td>-0.774426</td>\n",
+       "      <td>-2.997616</td>\n",
+       "      <td>3.000905</td>\n",
+       "      <td>0.587785</td>\n",
+       "      <td>81.26316</td>\n",
+       "      <td>-81.25856</td>\n",
+       "      <td>11.420167</td>\n",
+       "      <td>-334.06296</td>\n",
+       "      <td>334.06640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100000 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            j[1]       v1[1]       v2[1]      j[2]      v1[2]      v2[2]  \\\n",
+       "0       5.307160  -50.682440   50.684640  8.807900   52.03160  -52.02848   \n",
+       "1       6.197453  -57.130560   57.132720  8.981368   48.65744  -48.65432   \n",
+       "2       6.776381  -57.573560   57.575760  8.692182   45.97476  -45.97164   \n",
+       "3       7.068126  -52.376000   52.378200  7.965965   44.11236  -44.10928   \n",
+       "4       7.104579  -41.995240   41.997440  6.861634   43.06760  -43.06448   \n",
+       "...          ...         ...         ...       ...        ...        ...   \n",
+       "99995 -11.275199  151.886520 -151.883200 -2.732424  133.54872 -133.54416   \n",
+       "99996  -8.661137  114.979760 -114.976480 -1.979022  123.92116 -123.91660   \n",
+       "99997  -5.984299   76.342480  -76.339160 -1.205753  111.79908 -111.79452   \n",
+       "99998  -3.333353   36.762828  -36.759532 -0.367569   97.48352  -97.47896   \n",
+       "99999  -0.774426   -2.997616    3.000905  0.587785   81.26316  -81.25856   \n",
+       "\n",
+       "            j[3]      v1[3]      v2[3]  \n",
+       "0      -9.734899  146.46300 -146.46056  \n",
+       "1      -9.020685  139.95560 -139.95312  \n",
+       "2      -8.215225  132.22656 -132.22412  \n",
+       "3      -7.356712  123.72356 -123.72112  \n",
+       "4      -6.456287  114.46780 -114.46536  \n",
+       "...          ...        ...        ...  \n",
+       "99995   8.721313 -255.50924  255.51268  \n",
+       "99996   9.341784 -276.86204  276.86548  \n",
+       "99997   9.964473 -297.18272  297.18620  \n",
+       "99998  10.649682 -316.32052  316.32400  \n",
+       "99999  11.420167 -334.06296  334.06640  \n",
+       "\n",
+       "[100000 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timeseries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GPUMD saves global thermodynamic quantities to a different file (`thermo.out`), and it usually happens that `compute.out` and `thermo.out` are printed at different rates. So in this case it makes sense to read another table with the thermodynamic quantities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    thermo = pd.read_feather(f'{root}/thermo.ft')\n",
+    "except FileNotFoundError:\n",
+    "    thermo = pd.read_csv(f'{root}/thermo.out', sep = '\\s+', names = ['T', 'K', 'U', 'Px', 'Py', 'Pz', 'Pyz', 'Pxz', 'Pxy', 'Lx', 'Ly', 'Lz'])\n",
+    "except Exception as e:\n",
+    "    print(f'Some other exception: {e}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>T</th>\n",
+       "      <th>K</th>\n",
+       "      <th>U</th>\n",
+       "      <th>Px</th>\n",
+       "      <th>Py</th>\n",
+       "      <th>Pz</th>\n",
+       "      <th>Pyz</th>\n",
+       "      <th>Pxz</th>\n",
+       "      <th>Pxy</th>\n",
+       "      <th>Lx</th>\n",
+       "      <th>Ly</th>\n",
+       "      <th>Lz</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>395.125378</td>\n",
+       "      <td>3603.778879</td>\n",
+       "      <td>-289042.41148</td>\n",
+       "      <td>-1.167067</td>\n",
+       "      <td>-1.215918</td>\n",
+       "      <td>-1.248022</td>\n",
+       "      <td>0.001475</td>\n",
+       "      <td>0.009405</td>\n",
+       "      <td>0.009959</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>378.045897</td>\n",
+       "      <td>3448.003832</td>\n",
+       "      <td>-289780.76109</td>\n",
+       "      <td>-1.291412</td>\n",
+       "      <td>-1.292198</td>\n",
+       "      <td>-1.327587</td>\n",
+       "      <td>-0.002286</td>\n",
+       "      <td>-0.007696</td>\n",
+       "      <td>0.001941</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>352.709011</td>\n",
+       "      <td>3216.916335</td>\n",
+       "      <td>-290148.59903</td>\n",
+       "      <td>-1.361265</td>\n",
+       "      <td>-1.381595</td>\n",
+       "      <td>-1.374045</td>\n",
+       "      <td>-0.004406</td>\n",
+       "      <td>-0.011146</td>\n",
+       "      <td>0.016371</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>334.223087</td>\n",
+       "      <td>3048.313696</td>\n",
+       "      <td>-290353.04438</td>\n",
+       "      <td>-1.373173</td>\n",
+       "      <td>-1.409258</td>\n",
+       "      <td>-1.400900</td>\n",
+       "      <td>-0.017500</td>\n",
+       "      <td>0.007733</td>\n",
+       "      <td>-0.001820</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>321.288342</td>\n",
+       "      <td>2930.341110</td>\n",
+       "      <td>-290513.09910</td>\n",
+       "      <td>-1.414994</td>\n",
+       "      <td>-1.409434</td>\n",
+       "      <td>-1.421372</td>\n",
+       "      <td>-0.000430</td>\n",
+       "      <td>-0.015094</td>\n",
+       "      <td>-0.012644</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8995</th>\n",
+       "      <td>300.752425</td>\n",
+       "      <td>2743.041310</td>\n",
+       "      <td>-293261.94191</td>\n",
+       "      <td>-1.633069</td>\n",
+       "      <td>-1.643559</td>\n",
+       "      <td>-1.656173</td>\n",
+       "      <td>-0.001073</td>\n",
+       "      <td>0.015467</td>\n",
+       "      <td>-0.002861</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8996</th>\n",
+       "      <td>300.716094</td>\n",
+       "      <td>2742.709958</td>\n",
+       "      <td>-293262.24822</td>\n",
+       "      <td>-1.643525</td>\n",
+       "      <td>-1.631987</td>\n",
+       "      <td>-1.667063</td>\n",
+       "      <td>-0.011431</td>\n",
+       "      <td>0.014692</td>\n",
+       "      <td>-0.001572</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8997</th>\n",
+       "      <td>298.985521</td>\n",
+       "      <td>2726.926099</td>\n",
+       "      <td>-293263.35138</td>\n",
+       "      <td>-1.653012</td>\n",
+       "      <td>-1.658252</td>\n",
+       "      <td>-1.667866</td>\n",
+       "      <td>-0.008714</td>\n",
+       "      <td>0.008375</td>\n",
+       "      <td>-0.006385</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8998</th>\n",
+       "      <td>301.947746</td>\n",
+       "      <td>2753.943349</td>\n",
+       "      <td>-293247.86899</td>\n",
+       "      <td>-1.641305</td>\n",
+       "      <td>-1.640563</td>\n",
+       "      <td>-1.664131</td>\n",
+       "      <td>-0.011629</td>\n",
+       "      <td>0.025223</td>\n",
+       "      <td>0.004753</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8999</th>\n",
+       "      <td>300.536647</td>\n",
+       "      <td>2741.073289</td>\n",
+       "      <td>-293249.96380</td>\n",
+       "      <td>-1.669260</td>\n",
+       "      <td>-1.665708</td>\n",
+       "      <td>-1.680731</td>\n",
+       "      <td>-0.009296</td>\n",
+       "      <td>0.011265</td>\n",
+       "      <td>-0.001574</td>\n",
+       "      <td>96.9385</td>\n",
+       "      <td>109.056</td>\n",
+       "      <td>121.173</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>9000 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               T            K             U        Px        Py        Pz  \\\n",
+       "0     395.125378  3603.778879 -289042.41148 -1.167067 -1.215918 -1.248022   \n",
+       "1     378.045897  3448.003832 -289780.76109 -1.291412 -1.292198 -1.327587   \n",
+       "2     352.709011  3216.916335 -290148.59903 -1.361265 -1.381595 -1.374045   \n",
+       "3     334.223087  3048.313696 -290353.04438 -1.373173 -1.409258 -1.400900   \n",
+       "4     321.288342  2930.341110 -290513.09910 -1.414994 -1.409434 -1.421372   \n",
+       "...          ...          ...           ...       ...       ...       ...   \n",
+       "8995  300.752425  2743.041310 -293261.94191 -1.633069 -1.643559 -1.656173   \n",
+       "8996  300.716094  2742.709958 -293262.24822 -1.643525 -1.631987 -1.667063   \n",
+       "8997  298.985521  2726.926099 -293263.35138 -1.653012 -1.658252 -1.667866   \n",
+       "8998  301.947746  2753.943349 -293247.86899 -1.641305 -1.640563 -1.664131   \n",
+       "8999  300.536647  2741.073289 -293249.96380 -1.669260 -1.665708 -1.680731   \n",
+       "\n",
+       "           Pyz       Pxz       Pxy       Lx       Ly       Lz  \n",
+       "0     0.001475  0.009405  0.009959  96.9385  109.056  121.173  \n",
+       "1    -0.002286 -0.007696  0.001941  96.9385  109.056  121.173  \n",
+       "2    -0.004406 -0.011146  0.016371  96.9385  109.056  121.173  \n",
+       "3    -0.017500  0.007733 -0.001820  96.9385  109.056  121.173  \n",
+       "4    -0.000430 -0.015094 -0.012644  96.9385  109.056  121.173  \n",
+       "...        ...       ...       ...      ...      ...      ...  \n",
+       "8995 -0.001073  0.015467 -0.002861  96.9385  109.056  121.173  \n",
+       "8996 -0.011431  0.014692 -0.001572  96.9385  109.056  121.173  \n",
+       "8997 -0.008714  0.008375 -0.006385  96.9385  109.056  121.173  \n",
+       "8998 -0.011629  0.025223  0.004753  96.9385  109.056  121.173  \n",
+       "8999 -0.009296  0.011265 -0.001574  96.9385  109.056  121.173  \n",
+       "\n",
+       "[9000 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "thermo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Atoms(symbols='Li24480Si46080', pbc=True, cell=[96.9385, 109.056, 121.173], group=..., mass=..., vel=...)\n",
+      "Temperature = 300.14 K\n",
+      "Volume = 1281007.64 Ang^3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(structure)\n",
+    "print(f'Temperature = {thermo[\"T\"].mean():.2f} K')\n",
+    "print(f'Volume = {structure.get_volume():.2f} Ang^3')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/setup.json b/setup.json
index 953875f..33d1217 100644
--- a/setup.json
+++ b/setup.json
@@ -1,21 +1,21 @@
 {
     "name": "sportran",
-    "version": "1.0.0rc4",
+    "version": "1.1.0",
     "author": "Loris Ercole, Riccardo Bertossa, Sebastiano Bisacchi",
     "author_email": "loris.ercole@epfl.ch",
     "description": "Cepstral Data Analysis of current time series for Green-Kubo transport coefficients",
     "license": "GPL 3",
     "url": "https://github.com/sissaschool/sportran",
     "keywords": "cepstral data analysis thermal conductivity transport coefficients physics green-kubo",
-    "python_requires": ">=3.7, <4",
+    "python_requires": ">=3.9, <4",
     "classifiers": [
         "Development Status :: 5 - Production/Stable",
         "Programming Language :: Python",
-        "Programming Language :: Python :: 3.7",
-        "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
+        "Programming Language :: Python :: 3.13",
         "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
         "Operating System :: OS Independent",
         "Intended Audience :: Science/Research",
@@ -30,7 +30,9 @@
         "scipy>=1.3.2",
         "matplotlib>=3.1.2",
         "markdown2>=2.0.0",
-        "pillow>=5.4.0"
+        "pillow>=5.4.0",
+        "emcee",
+        "opt_einsum"
     ],
     "extras_require": {
         "pre-commit": [
diff --git a/sportran/current/current.py b/sportran/current/current.py
index 0ba3c8a..5b807f4 100644
--- a/sportran/current/current.py
+++ b/sportran/current/current.py
@@ -9,11 +9,14 @@
 import inspect
 from sportran.md.mdsample import MDSample
 from sportran.md.cepstral import CepstralFilter, multicomp_cepstral_parameters
+from sportran.md.bayes import BayesFilter
+from sportran.md.maxlike import MaxLikeFilter
 from sportran.md.tools.filter import runavefilter
-from sportran.md.tools.spectrum import freq_THz_to_red, freq_red_to_THz
+from sportran.md.tools.spectrum import freq_red_to_THz
 from . import units
 from sportran.utils import log
 from sportran.plotter.current import CurrentPlotter
+import warnings
 
 __all__ = ['Current']
 
@@ -44,7 +47,7 @@ class Current(MDSample, abc.ABC):
     # parameters are class-specific (a HeatCurrent may use different ones wrt ElectricCurrent) and case-insensitive
     _current_type = None
     _input_parameters = {'DT_FS', 'KAPPA_SCALE'}
-    _optional_parameters = {'PSD_FILTER_W', 'FREQ_UNITS', 'MAIN_CURRENT_INDEX', 'MAIN_CURRENT_FACTOR'}
+    _optional_parameters = {'PSD_FILTER_W', 'FREQ_UNITS', 'MAIN_CURRENT_INDEX', 'MAIN_CURRENT_FACTOR',}
     _KAPPA_SI_UNITS = ''
     _default_plotter = CurrentPlotter
 
@@ -78,9 +81,8 @@ def __init__(self, traj, **params):
         self.cepf = None
 
     def __repr__(self):
-        msg = type(self).__name__ +\
-              '\n  N_CURRENTS  =  {}\n'.format(self.N_CURRENTS) +\
-              '  KAPPA_SCALE =  {}\n'.format(self.KAPPA_SCALE)
+        msg = (type(self).__name__ + '\n  N_CURRENTS  =  {}\n'.format(self.N_CURRENTS) +
+               '  KAPPA_SCALE =  {}\n'.format(self.KAPPA_SCALE))
         for key in self._input_parameters - {'DT_FS', 'KAPPA_SCALE'}:
             msg += '  {:11} =  {}\n'.format(key, getattr(self, key))
         msg += super().__repr__()
@@ -141,14 +143,14 @@ def set_plotter(cls, plotter=None):
     def initialize_currents(self, j, DT_FS, main_current_index=0, main_current_factor=1.0):
         # check if we have a multicomponent fluid
         j = np.array(j, dtype=float)
-        if (len(j.shape) == 3):
+        if len(j.shape) == 3:
             self.N_CURRENTS = j.shape[0]
-            if (self.N_CURRENTS == 1):
+            if self.N_CURRENTS == 1:
                 self.MANY_CURRENTS = False
                 j = np.squeeze(j, axis=0)
             else:
                 self.MANY_CURRENTS = True
-        elif (len(j.shape) <= 2):
+        elif len(j.shape) <= 2:
             self.N_CURRENTS = 1
             self.MANY_CURRENTS = False
         else:
@@ -185,7 +187,8 @@ def _get_units(cls):
         units_prefix = 'scale_kappa_'
         units_d = {
             name.replace(units_prefix, ''): function for name, function in inspect.getmembers(
-                units_module, predicate=lambda f: inspect.isfunction(f) and f.__name__.startswith(units_prefix))
+                units_module, predicate=lambda f: inspect.isfunction(f) and f.__name__.startswith(units_prefix),
+            )
         }
         if not units_d:
             print(
@@ -236,8 +239,10 @@ def compute_psd(self, PSD_FILTER_W=None, freq_units='THz'):
         # number of degrees of freedom of the chi-square distribution of the psd / 2
         self.ndf_chi = self.N_EQUIV_COMPONENTS - self.N_CURRENTS + 1
         if self.ndf_chi <= 0:
-            raise RuntimeError('The number of degrees of freedom of the chi-squared distribution is <=0. The number of '
-                               'equivalent (Cartesian) components of the input current must be >= number of currents.')
+            warnings.warn(
+                'The number of degrees of freedom of the chi-squared distribution is <=0. The number of '
+                'equivalent (Cartesian) components of the input current must be >= number of currents.', RuntimeWarning,
+            )
 
         if self.MANY_CURRENTS:
             if self.otherMD is None:
@@ -246,7 +251,7 @@ def compute_psd(self, PSD_FILTER_W=None, freq_units='THz'):
         else:
             super().compute_psd(PSD_FILTER_W, freq_units)
 
-    def _compute_psd_multi(self, others, PSD_FILTER_W=None, freq_units='THz', normalize=False, call_other=True):
+    def _compute_psd_multi(self, others, PSD_FILTER_W=None, freq_units='THz', normalize=False, call_other=True,):
         """
         For multi-component (many-current) systems: compute the cospectrum matrix and the transport coefficient.
         The results have almost the same statistical properties.
@@ -272,14 +277,14 @@ def _compute_psd_multi(self, others, PSD_FILTER_W=None, freq_units='THz', normal
 
         self.spectrALL = np.fft.rfft(self.traj, axis=0)
         self.NFREQS = self.spectrALL.shape[0]
-        self.freqs = np.linspace(0., 0.5, self.NFREQS)
+        self.freqs = np.linspace(0.0, 0.5, self.NFREQS)
         self.DF = 0.5 / (self.NFREQS - 1)
         self.DF_THZ = freq_red_to_THz(self.DF, self.DT_FS)
-        self.freqs_THz = self.freqs / self.DT_FS * 1000.
+        self.freqs_THz = self.freqs / self.DT_FS * 1000.0
         self.Nyquist_f_THz = self.freqs_THz[-1]
 
         # calculate the same thing on the other trajectory
-        if (call_other):
+        if call_other:
             for other in others:   # call other._compute_psd_multi (MDsample method)
                 Current._compute_psd_multi(other, [self], PSD_FILTER_W, freq_units, normalize, False)
         else:
@@ -293,9 +298,10 @@ def _compute_psd_multi(self, others, PSD_FILTER_W=None, freq_units='THz', normal
             other_spectrALL.append(other.spectrALL)
 
         # compute the matrix defined by the outer product of only the first indexes of the two arrays
-        covarALL = self.DT_FS / (2. * (self.NFREQS - 1.)) *\
+        covarALL = (self.DT_FS / (2.0 * (self.NFREQS - 1.0)) *
                     np.einsum('a...,b...->ab...', np.array([self.spectrALL] + other_spectrALL),
-                                                  np.array([self.spectrALL] + other_spectrALL).conj())
+                              np.array([self.spectrALL] + other_spectrALL).conj(),
+                             ))
 
         # number of degrees of freedom of the chi-square distribution of the psd / 2
         assert self.ndf_chi == (covarALL.shape[3] - len(other_spectrALL))
@@ -317,7 +323,7 @@ def _compute_psd_multi(self, others, PSD_FILTER_W=None, freq_units='THz', normal
         if (PSD_FILTER_W is not None) or (self.PSD_FILTER_W is not None):
             self.filter_psd(PSD_FILTER_W, freq_units)
 
-    def filter_psd(self, PSD_FILTER_W=None, freq_units='THz', window_type='rectangular', logpsd_filter_type=1):
+    def filter_psd(self, PSD_FILTER_W=None, freq_units='THz', window_type='rectangular', logpsd_filter_type=1,):
         """
         Filter the periodogram with the given PSD_FILTER_W [freq_units].
           - PSD_FILTER_W  PSD filter window [freq_units]
@@ -326,7 +332,7 @@ def filter_psd(self, PSD_FILTER_W=None, freq_units='THz', window_type='rectangul
         """
         super().filter_psd(PSD_FILTER_W, freq_units, window_type, logpsd_filter_type)
 
-        if (window_type == 'rectangular'):
+        if window_type == 'rectangular':
             # try to filter the other currents (if present)
             if self.cospectrum is not None:
                 self.fcospectrum = []
@@ -349,6 +355,122 @@ def initialize_cepstral_parameters(self):
                 raise RuntimeError('self.ndf_chi cannot be None.')
             self.ck_THEORY_var, self.psd_THEORY_mean = multicomp_cepstral_parameters(self.NFREQS, self.ndf_chi)
 
+    def bayesian_analysis(self, model, n_parameters, is_restart=False, n_steps=2000000, backend='chain.h5',
+                          burn_in=None, thin=None, mask=None, log_like='off', parallel=False, ncpus=1,
+                         ):
+        if parallel:
+            self.bayes = BayesFilter_parallel(self.cospectrum, model, n_parameters, self.N_EQUIV_COMPONENTS,
+                                              is_restart=is_restart, n_steps=n_steps, backend=backend, burn_in=burn_in,
+                                              thin=thin, ncpus=ncpus, mask=mask,
+                                             )
+            self.bayes.run_mcmc(log_like=log_like)
+        else:
+            self.bayes = BayesFilter(self.cospectrum, model, n_parameters, self.N_EQUIV_COMPONENTS,
+                                     is_restart=is_restart, n_steps=n_steps, backend=backend, burn_in=burn_in,
+                                     thin=thin, mask=mask,
+                                    )
+            self.bayes.run_mcmc(log_like=log_like)
+
+        self.offdiag = self.bayes.parameters_mean[0] * self.bayes.factor
+        self.offdiag_std = self.bayes.parameters_std[0] * self.bayes.factor
+
+        self.bayesian_log = ('-----------------------------------------------------\n' + '  BAYESIAN ANALYSIS\n' +
+                             '-----------------------------------------------------\n')
+        self.bayesian_log += ('  L_01   = {:18f} +/- {:10f}\n'.format(self.offdiag, self.offdiag_std) +
+                              '-----------------------------------------------------\n')
+        log.write_log(self.bayesian_log)
+        with open('bayesian_analysis_{}'.format(n_parameters), 'w+') as g:
+            g.write('{}\t{}\n'.format(self.offdiag, self.offdiag_std))
+
+    ####################################################################################
+    # MAXLIKE methods
+    def maxlike_estimate(self, model, n_parameters='AIC', mask=None, likelihood='wishart', solver='BFGS',
+                         guess_runave_window=50, minimize_kwargs=None, ext_guess=None, limits=[0, 1], omega_fixed=None,
+                        ):
+        """
+        Perform maximum likelihood estimation and optionally select the optimal number of parameters using AIC.
+        """
+        minimize_kwargs = minimize_kwargs or {}
+
+        # Get the appropriate data based on likelihood type
+        data = self._get_data_by_likelihood(likelihood)
+
+        # Initialize MaxLikeFilter object
+        self.maxlike = MaxLikeFilter(data=data, model=model, n_components=self.N_EQUIV_COMPONENTS,
+                                     n_currents=self.N_CURRENTS, likelihood=likelihood, solver=solver,
+                                     ext_guess=ext_guess, omega_fixed=omega_fixed,
+                                    )
+
+        # Run the maximum likelihood estimation
+        self.maxlike.maxlike(n_parameters=n_parameters, mask=mask, guess_runave_window=guess_runave_window,
+                             minimize_kwargs=minimize_kwargs, limits=limits,
+                            )
+
+        # Extract and scale results
+        self.maxlike.extract_and_scale_results()
+
+        # Access the results from self.maxlike
+        self.NLL_mean = self.maxlike.NLL_mean[0]
+        self.NLL_std = None
+        try:
+            self.NLL_std = self.maxlike.NLL_std[0]
+        except AttributeError:
+            pass
+        # self.NLL_upper = getattr(self.maxlike, "NLL_upper", None)
+        # self.NLL_lower = getattr(self.maxlike, "NLL_lower", None)
+
+        # Store additional results if needed
+        self.optimal_nparameters = getattr(self.maxlike, 'optimal_nparameters', None)
+        self.aic_values = getattr(self.maxlike, 'aic_values', None)
+
+        # Add logging for the MLE results
+        self.mle_log = ('-----------------------------------------------------\n' +
+                        '  MAXIMUM LIKELIHOOD ESTIMATION\n' + '-----------------------------------------------------\n')
+
+        if isinstance(n_parameters, str) and n_parameters.lower() == 'aic':
+            self.mle_log += '  Optimal n_parameters (AIC) = {:d}\n'.format(self.optimal_nparameters)
+        else:
+            self.mle_log += '  Fixed n_parameters = {:d}\n'.format(self.maxlike.n_parameters)
+
+        if likelihood == 'wishart':
+            # Iterate over the upper triangle (including the diagonal)
+            for i in range(self.N_CURRENTS):
+                for j in range(i, self.N_CURRENTS):
+                    mean_val = self.NLL_mean[i, j]
+                    std_val = self.NLL_std[i, j]
+
+                    self.mle_log += (f'  S_{{{i}{j}}} = {mean_val:18f} +/- {std_val:10f}\n')
+        else:
+            mean_val = self.NLL_mean * self.KAPPA_SCALE / 2
+            try:
+                std_val = self.NLL_std * self.KAPPA_SCALE / 2
+            except TypeError:
+                std_val = 0
+
+            self.mle_log += '  kappa* = {:18f} +/- {:10f}  {}\n'.format(mean_val, std_val, self._KAPPA_SI_UNITS)
+            self.NLL_mean = mean_val
+            self.NLL_std = std_val
+
+        self.mle_log += '-----------------------------------------------------\n'
+
+        log.write_log(self.mle_log)
+
+    def _get_data_by_likelihood(self, likelihood):
+        """
+        Get the data to be used for the likelihood estimation based on the provided likelihood type.
+        """
+        likelihood = likelihood.lower()
+        if likelihood == 'wishart':
+            return self.cospectrum.real * self.N_CURRENTS
+        elif likelihood in ['chisquare', 'chisquared']:
+            return self.psd
+        elif likelihood in ['variancegamma', 'variance-gamma']:
+            return self.cospectrum.real[0, 1]   # * self.N_CURRENTS
+        else:
+            raise ValueError('Likelihood must be Wishart, Chi-square, or Variance-Gamma.')
+
+    ################################################################################################################################################
+
     def cepstral_analysis(self, aic_type='aic', aic_Kmin_corrfactor=1.0, manual_cutoffK=None):
         """
         Performs Cepstral Analysis on the Current's trajectory.
@@ -370,32 +492,40 @@ def cepstral_analysis(self, aic_type='aic', aic_Kmin_corrfactor=1.0, manual_cuto
         The log of the analysis can be retried from the variable `self.cepstral_log`.
         """
 
-        self.cepf = CepstralFilter(self.logpsd, ck_theory_var=self.ck_THEORY_var, \
-            psd_theory_mean=self.psd_THEORY_mean, aic_type=aic_type)
+        self.cepf = CepstralFilter(self.logpsd, ck_theory_var=self.ck_THEORY_var, psd_theory_mean=self.psd_THEORY_mean,
+                                   aic_type=aic_type,
+                                  )
         self.cepf.scan_filter_tau(cutoffK=manual_cutoffK, aic_Kmin_corrfactor=aic_Kmin_corrfactor)
         self.kappa = self.cepf.tau_cutoffK * self.KAPPA_SCALE * 0.5
         self.kappa_std = self.cepf.tau_std_cutoffK * self.KAPPA_SCALE * 0.5
 
-        self.cepstral_log = \
-              '-----------------------------------------------------\n' +\
-              '  CEPSTRAL ANALYSIS\n' +\
-              '-----------------------------------------------------\n'
+        self.cepstral_log = ('-----------------------------------------------------\n' + '  CEPSTRAL ANALYSIS\n' +
+                             '-----------------------------------------------------\n')
         if not self.cepf.manual_cutoffK_flag:
-            self.cepstral_log += \
-                '  cutoffK = (P*-1) = {:d}  (auto, AIC_Kmin = {:d}, corr_factor = {:4})\n'.format(self.cepf.cutoffK, self.cepf.aic_Kmin, self.cepf.aic_Kmin_corrfactor)
+            self.cepstral_log += '  cutoffK = (P*-1) = {:d}  (auto, AIC_Kmin = {:d}, corr_factor = {:4})\n'.format(
+                self.cepf.cutoffK, self.cepf.aic_Kmin, self.cepf.aic_Kmin_corrfactor)
         else:
-            self.cepstral_log += \
-                '  cutoffK  = (P*-1) = {:d}  (manual, AIC_Kmin = {:d})\n'.format(self.cepf.cutoffK, self.cepf.aic_Kmin, self.cepf.aic_Kmin_corrfactor)
-        self.cepstral_log += \
-              '  L_0*   = {:18f} +/- {:10f}\n'.format(self.cepf.logtau_cutoffK, self.cepf.logtau_std_cutoffK) +\
-              '  S_0*   = {:18f} +/- {:10f}\n'.format(self.cepf.tau_cutoffK, self.cepf.tau_std_cutoffK) +\
-              '-----------------------------------------------------\n' +\
-              '  kappa* = {:18f} +/- {:10f}  {}\n'.format(self.kappa, self.kappa_std, self._KAPPA_SI_UNITS) +\
-              '-----------------------------------------------------\n'
+            self.cepstral_log += ('  cutoffK  = (P*-1) = {:d}  (manual, AIC_Kmin = {:d})\n'.format(
+                self.cepf.cutoffK, self.cepf.aic_Kmin, self.cepf.aic_Kmin_corrfactor))
+        self.cepstral_log += (
+            '  L_0*   = {:18f} +/- {:10f}\n'.format(self.cepf.logtau_cutoffK, self.cepf.logtau_std_cutoffK) +
+            '  S_0*   = {:18f} +/- {:10f}\n'.format(self.cepf.tau_cutoffK, self.cepf.tau_std_cutoffK) +
+            '-----------------------------------------------------\n' +
+            '  kappa* = {:18f} +/- {:10f}  {}\n'.format(self.kappa, self.kappa_std, self._KAPPA_SI_UNITS) +
+            '-----------------------------------------------------\n')
         log.write_log(self.cepstral_log)
 
-    def resample(self, TSKIP=None, fstar_THz=None, FILTER_W=None, plot=False, PSD_FILTER_W=None,
-                 freq_units='THz', FIGSIZE=None, verbose=True):   # yapf: disable
+    def resample(
+        self,
+        TSKIP=None,
+        fstar_THz=None,
+        FILTER_W=None,
+        plot=False,
+        PSD_FILTER_W=None,
+        freq_units='THz',
+        FIGSIZE=None,
+        verbose=True,
+    ):  # yapf: disable
         """
         Simulate the resampling of the time series.
 
@@ -422,18 +552,30 @@ def resample(self, TSKIP=None, fstar_THz=None, FILTER_W=None, plot=False, PSD_FI
 
         if plot:
             try:
-                axs = self.plot_resample(xf=xf, freq_units=freq_units, PSD_FILTER_W=PSD_FILTER_W, FIGSIZE=FIGSIZE)
+                axs = self.plot_resample(xf=xf, freq_units=freq_units, PSD_FILTER_W=PSD_FILTER_W, FIGSIZE=FIGSIZE,)
                 return xf, axs
             except AttributeError:
                 print('Plotter does not support the plot_resample method')
         else:
             return xf
 
-    def fstar_analysis(self, TSKIP_LIST, aic_type='aic', aic_Kmin_corrfactor=1.0, manual_cutoffK=None, plot=True,
-                       axes=None, FIGSIZE=None, verbose=False, **plot_kwargs):   # yapf: disable
+    def fstar_analysis(
+        self,
+        TSKIP_LIST,
+        aic_type='aic',
+        aic_Kmin_corrfactor=1.0,
+        manual_cutoffK=None,
+        plot=True,
+        axes=None,
+        FIGSIZE=None,
+        verbose=False,
+        **plot_kwargs,
+    ):  # yapf: disable
         from sportran.current.tools.fstar_analysis import fstar_analysis
+
         return fstar_analysis(self, TSKIP_LIST, aic_type, aic_Kmin_corrfactor, manual_cutoffK, plot, axes, FIGSIZE,
-                              verbose, **plot_kwargs)
+                              verbose, **plot_kwargs,
+                             )
 
 
 ################################################################################
diff --git a/sportran/current/units/electric.py b/sportran/current/units/electric.py
index 12e62f6..3a73707 100644
--- a/sportran/current/units/electric.py
+++ b/sportran/current/units/electric.py
@@ -20,7 +20,7 @@ def scale_kappa_real(TEMPERATURE, VOLUME):
     VOLUME      cell VOLUME [A^3]
     Input current is in units of electrons_charge * Angstrom/femtosecond. Current is EXTENSIVE.
     """
-    return constants.charge**2 / TEMPERATURE / constants.kB / VOLUME * 10000. * 1.0e6
+    return constants.charge**2 / TEMPERATURE / constants.kB / VOLUME * 10000.0 * 1.0e6
 
 
 def scale_kappa_metal(TEMPERATURE, VOLUME):
@@ -31,7 +31,7 @@ def scale_kappa_metal(TEMPERATURE, VOLUME):
     VOLUME      cell VOLUME [A^3]
     Input current is in units of electrons_charge * Angstrom/picosecond. Current is EXTENSIVE.
     """
-    return constants.charge**2 / TEMPERATURE / constants.kB / VOLUME * 10000.
+    return constants.charge**2 / TEMPERATURE / constants.kB / VOLUME * 10000.0
 
 
 def scale_kappa_qepw(TEMPERATURE, VOLUME):
@@ -45,7 +45,7 @@ def scale_kappa_qepw(TEMPERATURE, VOLUME):
       tau_{a.u.} = 4.8378 10^{-17} s
 
     """
-    return constants.charge**2 / TEMPERATURE / constants.kB / VOLUME * 10000. * constants.J_PWtoMETAL**2
+    return (constants.charge**2 / TEMPERATURE / constants.kB / VOLUME * 10000.0 * constants.J_PWtoMETAL**2)
 
 
 def scale_kappa_gpumd(TEMPERATURE, VOLUME):
@@ -57,4 +57,4 @@ def scale_kappa_gpumd(TEMPERATURE, VOLUME):
     TEMPERATURE [K]
     VOLUME      cell VOLUME [A^3]
     """
-    return constants.charge**3 / TEMPERATURE / constants.massunit / constants.kB / VOLUME * 1.0e8
+    return (constants.charge**3 / TEMPERATURE / constants.massunit / constants.kB / VOLUME * 1.0e8)
diff --git a/sportran/i_o/read_lammps_dump.py b/sportran/i_o/read_lammps_dump.py
index c3908d5..89d6807 100644
--- a/sportran/i_o/read_lammps_dump.py
+++ b/sportran/i_o/read_lammps_dump.py
@@ -144,7 +144,7 @@ def _read_ckeys(self, group_vectors=True, preload_timesteps=True):
             line = self.file.readline()
             if len(line) == 0:   # EOF
                 raise RuntimeError('Reached EOF, no ckeys found.')
-            values = np.array(line.split())
+            values = line.split()
             if (values[0] == 'ITEM:'):
                 if (values[1] == 'TIMESTEP'):
                     self.current_timestep = int(self.file.readline())
diff --git a/sportran/i_o/read_lammps_log.py b/sportran/i_o/read_lammps_log.py
index c58a7e6..ea79052 100755
--- a/sportran/i_o/read_lammps_log.py
+++ b/sportran/i_o/read_lammps_log.py
@@ -231,7 +231,7 @@ def _read_ckeys(self, run_keyword, group_vectors=True):
             nlines += 1
             if len(line) == 0:   # EOF
                 raise RuntimeError('Reached EOF, no ckeys found.')
-            values = np.array(line.split())
+            values = line.split()
             # find the column headers line
             if (len(values) and (is_string(values[0])) and (values[0] == 'Step')):
                 log.write_log('  column headers found at line {:d}. Reading data...'.format(nlines))
diff --git a/sportran/i_o/read_tablefile.py b/sportran/i_o/read_tablefile.py
index c58be04..ebf4ebd 100644
--- a/sportran/i_o/read_tablefile.py
+++ b/sportran/i_o/read_tablefile.py
@@ -191,7 +191,7 @@ def _read_ckeys(self, group_vectors=True):
             line = self.file.readline()
             if len(line) == 0:   # EOF
                 raise RuntimeError('Reached EOF, no ckeys found.')
-            values = np.array(line.split())
+            values = line.split()
             # text line: read variables names and save indexes in ckey
             if (is_string(values[0]) and (values[0].find('#') < 0)):
                 self.header += line[:-1]
diff --git a/sportran/md/bayes.py b/sportran/md/bayes.py
new file mode 100644
index 0000000..03ac334
--- /dev/null
+++ b/sportran/md/bayes.py
@@ -0,0 +1,855 @@
+# -*- coding: utf-8 -*-
+# Methods to perform a bayesian estimation of the transport coefficients
+
+import numpy as np
+import emcee
+import scipy.special as sp
+from . import aic
+from .cepstral import (dct_coefficients, dct_filter_psd, dct_filter_tau, CepstralFilter, multicomp_cepstral_parameters,)
+from .tools.filter import runavefilter
+from sportran.utils import log
+from multiprocessing import Pool
+import time
+
+__all__ = ['BayesFilter']
+EULER_GAMMA = (
+    0.57721566490153286060651209008240243104215933593992   # Euler-Mascheroni constant
+)
+LOG2 = np.log(2)
+
+
+class BayesFilter(object):
+    """
+    BAYESIAN ANALYSIS based filtering.
+
+    ** INPUT VARIABLES:
+    spectrum        = the original periodogram (if single-component) of spectral matrix (if multi-component)
+    is_offdiag      = If True, estimate the off-diagonal matrix element of the spectral matrix (default = True)
+    is_diag         = If True, estimate the diagonal matrix elements of the spectral matrix (default = False)
+    model           = the function that models the data (for now only spline)
+    n_parameters    = the number of parameters to be used for the fit
+
+    ** INTERNAL VARIABLES:
+    samplelogpsd  = the original sample log-PSD - logpsd_THEORY_mean
+
+    logpsdK  = the cepstrum of the data, \\hat{C}_n (i.e. the DCT of samplelogpsd)
+    aic_min  = minimum value of the AIC
+    aic_Kmin = cutoffK that minimizes the AIC
+    aic_Kmin_corrfactor = aic_Kmin cutoff correction factor (default: 1.0)
+    cutoffK  = (P*-1) = cutoff used to compute logtau and logpsd (by default = aic_Kmin * aic_Kmin_corrfactor)
+    manual_cutoffK_flag = True if cutoffK was manually specified, False if aic_Kmin is being used
+
+    logtau          = filtered log(tau) as a function of cutoffK, L_0(P*-1)
+    logtau_cutoffK  = filtered log(tau) at cutoffK, L*_0
+    logtau_var_cutoffK = theoretical L*_0 variance
+    logtau_std_cutoffK = theoretical L*_0 standard deviation
+    logpsd          = filtered log-PSD at cutoffK
+
+    tau          = filtered tau as a function of cutoffK, S_0(P*-1)
+    tau_cutoffK  = filtered tau at cutoffK, S*_0
+    tau_var_cutoffK = theoretical S*_0 variance
+    tau_std_cutoffK = theoretical S*_0 standard deviation
+    psd          = filtered PSD at the specified cutoffK
+
+    p_aic... = Bayesian AIC weighting stuff
+    """
+
+    def __init__(self, spectrum, model, n_parameters, n_components, is_restart=False, n_steps=2000000,
+                 backend='chain.h5', burn_in=None, thin=None, mask=None,
+                ):
+
+        if not isinstance(spectrum, np.ndarray):
+            raise TypeError('spectrum should be an object of type numpy.ndarray')
+        if spectrum.shape[0] != 2 or spectrum.shape[1] != 2:
+            raise TypeError('spectrum should be a 2x2xN numpy.ndarray')
+
+        self.spectrum = spectrum / n_components
+        self.model = model
+        self.mask = mask
+        self.n_components = n_components
+        self.n_parameters = n_parameters
+        self.is_restart = is_restart
+        self.n_steps = n_steps
+        self.backend = backend
+        self.burn_in = burn_in
+        self.thin = thin
+
+    def __repr__(self):
+        msg = 'BayesFilter:\n'   # + \
+        #   '  AIC type  = {:}\n'.format(self.aic_type) + \
+        #   '  AIC min   = {:f}\n'.format(self.aic_min) + \
+        #   '  AIC_Kmin  = {:d}\n'.format(self.aic_Kmin)
+        # if self.cutoffK is not None:
+        #     msg += \
+        #         '  AIC_Kmin_corrfactor = {:f}\n'.format(self.aic_Kmin_corrfactor) + \
+        #         '  cutoffK = (P*-1) = {:d} {:}\n'.format(self.cutoffK, '(manual)' if self.manual_cutoffK_flag else '(auto)') + \
+        #         '  L_0*   = {:15f} +/- {:10f}\n'.format(self.logtau_cutoffK, self.logtau_std_cutoffK) + \
+        #         '  S_0*   = {:15f} +/- {:10f}\n'.format(self.tau_cutoffK, self.tau_std_cutoffK)
+        return msg
+
+    ################################################
+
+    def run_mcmc(self, n_parameters=None, n_steps=None, is_restart=None, mask=None, filename=None, n_walkers=None,
+                 log_like='off',
+                ):
+
+        # Initialize the parameters if undefined
+        if n_parameters is None:
+            n_parameters = self.n_parameters
+        if n_steps is None:
+            n_steps = self.n_steps
+        if is_restart is None:
+            is_restart = self.is_restart
+        if mask is None:
+            mask = self.mask
+        if filename is None:
+            filename = self.backend
+
+        # Initialize the parameters for cepstral analysis
+        ck_THEORY_var, psd_THEORY_mean = multicomp_cepstral_parameters(self.spectrum.shape[2], self.n_components)
+        # Cepstral analysis for the diagonal elements
+        cepf1 = CepstralFilter(np.log(self.spectrum[0, 0].real), ck_theory_var=ck_THEORY_var,
+                               psd_theory_mean=psd_THEORY_mean, aic_type='aic',
+                              )
+        cepf1.scan_filter_tau(cutoffK=None, aic_Kmin_corrfactor=1.0)
+        cepf2 = CepstralFilter(np.log(self.spectrum[1, 1].real), ck_theory_var=ck_THEORY_var,
+                               psd_theory_mean=psd_THEORY_mean, aic_type='aic',
+                              )
+        cepf2.scan_filter_tau(cutoffK=None, aic_Kmin_corrfactor=1.0)
+        self.sigma1 = cepf1.psd[0]
+        self.sigma2 = cepf2.psd[0]
+
+        nu = 2
+        ell = self.n_components
+        # Define noisy data
+        if mask is not None:
+            noisy_data = (self.spectrum.real[0, 1] / np.sqrt(cepf1.psd * cepf2.psd))[mask]
+        else:
+            noisy_data = self.spectrum.real[0, 1] / np.sqrt(cepf1.psd * cepf2.psd)
+
+        self.factor = np.sqrt(cepf1.psd[0] / cepf2.psd[0])
+
+        # Define initial points for the MCMC
+        try:
+            guess_data = runavefilter(noisy_data, 100)
+        except:
+            guess_data = runavefilter(noisy_data, 10)
+
+        args = np.int32(np.linspace(0, len(noisy_data) - 1, n_parameters, endpoint=True))
+
+        # MCMC sampling
+        # number of walkers must be larger than twice the number of parameters (and often a power of 2)
+        if n_walkers is None:
+            n_walkers = int(2**np.ceil(np.log2(2 * n_parameters)))
+
+        log.write_log('MCMC with {} parameters and {} walkers'.format(n_parameters, n_walkers))
+        log.write_log(f'Running up to {n_steps} steps')
+
+        p0 = guess_data
+        p0 = np.clip(p0[args][np.newaxis, :n_parameters] + np.random.normal(0, 0.1, (n_walkers, n_parameters)), -0.98,
+                     0.98,
+                    )
+
+        omega = np.arange(noisy_data.size)
+        omega_fixed = omega[args]
+
+        self.omega = omega
+        self.omega_fixed = omega_fixed
+
+        # Set up the backend
+        # Don't forget to clear it in case the file already exists
+        backend = emcee.backends.HDFBackend(filename)
+        if not is_restart:
+            backend.reset(n_walkers, n_parameters)
+
+        # Initialize the sampler
+        if log_like == 'off':
+            sampler = emcee.EnsembleSampler(n_walkers, n_parameters, self.log_posterior_offdiag,
+                                            args=(omega, omega_fixed, noisy_data, nu, ell), backend=backend,
+                                           )
+        elif log_like == 'normal':
+            sampler = emcee.EnsembleSampler(n_walkers, n_parameters, self.log_posterior_normal,
+                                            args=(omega, omega_fixed, noisy_data, nu, ell), backend=backend,
+                                           )
+
+        # Run MCMC
+        # We'll track how the average autocorrelation time estimate changes
+        index = 0
+        autocorr = np.empty(n_steps)
+
+        # This will be useful to testing convergence
+        old_tau = np.inf
+
+        # Now we'll sample for up to max_n steps
+        if is_restart:
+            coord = backend.get_chain()[-1]
+            good_idx = None
+            tau = backend.get_autocorr_time(discard=500)
+            burn_in = int(2 * np.max(tau))
+            thin = np.max([1, int(0.5 * np.min(tau))])
+            log.write_log('MCMC autocorrelation time = {}'.format(tau))
+            if good_idx is None:
+                samples = backend.get_chain(discard=burn_in, flat=True, thin=thin)
+            else:
+                samples = backend.get_chain(discard=burn_in, flat=True, thin=thin)[:, good_idx]
+                n_parameters_ = samples.shape[1]
+                self.n_parameters = n_parameters_
+
+            # Compute marginalized errors
+            rho = []
+            rho_min = []
+            rho_max = []
+            print(sampler.iteration)
+            self.acceprance_fraction = np.mean(sampler.acceptance_fraction)
+            for i in range(self.n_parameters):
+                mcmc = np.percentile(samples[:, i], [16, 50, 84])
+                q = np.diff(mcmc)
+                rho.append(mcmc[1])
+                rho_min.append(mcmc[1] - q[0])
+                rho_max.append(mcmc[1] + q[1])
+
+            # The estimated parameters are the values of rho as a function of frequency
+            self.parameters_mean = np.array(rho)
+            self.parameters_args = args
+            self.parameters_std = 0.5 * (np.array(rho_max) - np.array(rho_min))
+            self.sampler = sampler
+            self.noisy_data = noisy_data
+            if log_like == 'normal':
+                self.aic = (
+                    2 * self.log_likelihood_normal(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) -
+                    2 * self.n_parameters)
+            elif log_like == 'off':
+                self.aic = (
+                    2 * self.log_likelihood_offdiag(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) -
+                    2 * self.n_parameters)
+                burn_in = int(2 * np.max(tau))
+                thin = np.max([1, int(0.5 * np.min(tau))])
+            return
+        else:
+            coord = np.copy(p0)
+
+        todo = True
+        if is_restart:
+            todo = False
+        disc = 0
+        for sample in sampler.sample(coord, iterations=n_steps, progress=True, store=True):
+            # Only check convergence every 100 steps
+            if sampler.iteration % 250:
+                continue
+
+            # Compute the autocorrelation time so far
+            # Using tol=0 means that we'll always get an estimate even
+            # if it isn't trustworthy
+            tau = sampler.get_autocorr_time(tol=0, discard=disc)
+            autocorr[index] = np.mean(tau)
+            index += 1
+            if sampler.iteration % 2000 == 0:
+                print(tau)
+
+            if todo and sampler.iteration > 1000:
+                s_old = np.ones(n_parameters)
+                for i in range(100, int(sampler.iteration / 2) + 1, 100):
+
+                    s = sampler.get_autocorr_time(tol=0, discard=i)
+
+                    if np.all(abs((s - s_old) / s) * 100 < 5):
+                        disc = i
+                        todo = False
+                        print(s)
+                        break
+                    s_old = s
+
+            # Check convergence
+            converged = np.all(tau * 100 < sampler.iteration)
+            converged &= np.all(np.abs(old_tau - tau) / tau < 0.015)
+            if converged:
+                break
+            old_tau = tau
+
+        # Compute chains auto-correlation time to estimate convergence
+        # If AutocorrError, probably the chain is too short. You can still use ~2*max(tau) as burn_in
+        good_idx = None
+        try:
+            tau = sampler.get_autocorr_time(discard=disc)
+            burn_in = int(6 * np.max(tau))
+            thin = np.max([1, int(1.5 * np.min(tau))])
+            log.write_log('MCMC autocorrelation time = {}'.format(tau))
+        except emcee.autocorr.AutocorrError:
+            log.write_log('The chain is probably too short')
+            burn_in = int(sampler.iteration * 0.3)
+            thin = int(np.max([int(0.05 * sampler.iteration), 10]))
+        except ValueError:
+            log.write_log(f'There is something wrong with tau: tau = {tau}')
+            good_idx = ~np.isnan(tau)
+            tau = tau[good_idx]
+            log.write_log('Fixed MCMC autocorrelation time = {}'.format(tau))
+            burn_in = int(6 * np.max(tau))
+            thin = np.max([1, int(1.5 * np.min(tau))])
+
+        log.write_log('MCMC burn in = {}; thin = {}'.format(burn_in, thin))
+
+        if good_idx is None:
+            samples = sampler.get_chain(discard=burn_in, flat=True, thin=thin)
+        else:
+            samples = sampler.get_chain(discard=burn_in, flat=True, thin=thin)[:, good_idx]
+            n_parameters_ = samples.shape[1]
+            self.n_parameters = n_parameters_
+
+        # Compute marginalized errors
+        rho = []
+        rho_min = []
+        rho_max = []
+        for i in range(self.n_parameters):
+            mcmc = np.percentile(samples[:, i], [16, 50, 84])
+            q = np.diff(mcmc)
+            rho.append(mcmc[1])
+            rho_min.append(mcmc[1] - q[0])
+            rho_max.append(mcmc[1] + q[1])
+
+        # The estimated parameters are the values of rho as a function of frequency
+        self.parameters_mean = np.array(rho)
+        self.parameters_args = args
+        self.parameters_std = 0.5 * (np.array(rho_max) - np.array(rho_min))
+        self.sampler = sampler
+        self.noisy_data = noisy_data
+        if log_like == 'normal':
+            self.aic = (2 * self.log_likelihood_normal(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) -
+                        2 * self.n_parameters)
+        elif log_like == 'off':
+            self.aic = (2 * self.log_likelihood_offdiag(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) -
+                        2 * self.n_parameters)
+            self.dic = (
+                -2 * self.log_likelihood_offdiag(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) +
+                4 * sampler.get_log_prob(discard=burn_in, flat=True, thin=thin).mean())
+        with open('aic_{}'.format(self.n_parameters), 'w+') as g:
+            g.write('{}\t{}\n'.format(self.n_parameters, self.aic))
+        with open('dic_{}'.format(self.n_parameters), 'w+') as g:
+            g.write('{}\t{}\n'.format(self.n_parameters, self.dic))
+
+    ################################################
+    # Helper functions
+
+    # The log-likelihood function
+    # def log_likelihood_offdiag(self, w, omega, omega_fixed, data_, nu, ell):
+    #     spline = self.model(omega_fixed, w)
+    #     rho = np.clip(spline(omega), -0.99, 0.99)
+
+    #     one_frac_rho2 = 1/(1-rho**2)
+
+    #     # Data is distributed according to a Variance-Gamma distribution with parameters:
+    #     # mu = 0; alpha = 1/(1-rho**2); beta = rho/(1-rho**2); lambda = ell*nu/2
+    #     # Its expectation value is ell*nu*rho
+    #     data = ell*data_
+    #     z = data - ell*nu*rho
+
+    #     log_pdf = -np.log(sp.gamma(0.5*nu)) + 0.5*(nu-1)*np.log(np.abs(z)) - 0.5*np.log(2**(nu-1)*np.pi/one_frac_rho2) + rho*z*one_frac_rho2 +\
+    #             np.log(sp.kv(0.5*(nu-1), np.abs(z)*one_frac_rho2))
+    #     return np.sum(log_pdf)
+
+    def run_mcmc_scratch(self, n_parameters=None, n_steps=None, is_restart=None, mask=None, filename=None,
+                         n_walkers=None, log_like='off',
+                        ):
+
+        # Initialize the parameters if undefined
+        if n_parameters is None:
+            n_parameters = self.n_parameters
+        else:
+            self.n_parameters = n_parameters
+        if n_steps is None:
+            n_steps = self.n_steps
+        if is_restart is None:
+            is_restart = self.is_restart
+        if mask is None:
+            mask = self.mask
+        if filename is None:
+            filename = self.backend
+
+        # Initialize the parameters for cepstral analysis
+
+        nu = 2
+        ell = self.n_components
+        # Define noisy data
+        if mask is not None:
+            noisy_data = (self.spectrum[0, 1])[mask]
+        else:
+            noisy_data = self.spectrum[0, 1]
+
+        # Define initial points for the MCMC
+        try:
+            guess_data = runavefilter(noisy_data, 200)
+        except:
+            guess_data = runavefilter(noisy_data, 100)
+
+        args = np.int32(np.linspace(0, len(noisy_data) - 1, n_parameters, endpoint=True))
+
+        # MCMC sampling
+        # number of walkers must be larger than twice the number of parameters (and often a power of 2)
+        if n_walkers is None:
+            n_walkers = int(2**np.ceil(np.log2(2 * n_parameters)))
+
+        log.write_log('MCMC with {} parameters and {} walkers'.format(n_parameters, n_walkers))
+        log.write_log(f'Running up to {n_steps} steps')
+
+        p0 = guess_data
+        if log_like == 'normal' or log_like == 'off':
+            p0 = np.clip(p0[args][np.newaxis, :n_parameters] + np.random.normal(0, 0.2, (n_walkers, n_parameters)), -40,
+                         40,
+                        )
+
+        if log_like == 'diag':
+            p0 = np.clip(p0[args][np.newaxis, :n_parameters] + np.random.normal(0, 10, (n_walkers, n_parameters)), 0,
+                         1e6,
+                        )
+
+        omega = np.arange(noisy_data.size)
+        omega_fixed = omega[args]
+
+        self.omega = omega
+        self.omega_fixed = omega_fixed
+
+        # Set up the backend
+        # Don't forget to clear it in case the file already exists
+        backend = emcee.backends.HDFBackend(filename)
+        if not is_restart:
+            backend.reset(n_walkers, n_parameters)
+
+        # Initialize the sampler
+        if log_like == 'off':
+            sampler = emcee.EnsembleSampler(n_walkers, n_parameters, self.log_posterior_offdiag,
+                                            args=(omega, omega_fixed, noisy_data, nu, ell), backend=backend,
+                                           )
+        elif log_like == 'normal':
+            sampler = emcee.EnsembleSampler(n_walkers, n_parameters, self.log_posterior_normal,
+                                            args=(omega, omega_fixed, noisy_data, nu, ell), backend=backend,
+                                           )
+
+        elif log_like == 'diag':
+            sampler = emcee.EnsembleSampler(n_walkers, n_parameters, self.log_posterior_diag,
+                                            args=(omega, omega_fixed, noisy_data, ell), backend=backend,
+                                           )
+
+        # Run MCMC
+        # We'll track how the average autocorrelation time estimate changes
+        index = 0
+        autocorr = np.empty(n_steps)
+
+        # This will be useful to testing convergence
+        old_tau = np.inf
+
+        # Now we'll sample for up to max_n steps
+        if is_restart:
+            coord = backend.get_chain()[-1]
+            good_idx = None
+            tau = backend.get_autocorr_time(discard=1000)
+            burn_in = int(6 * np.max(tau))
+            thin = np.max([1, int(1.5 * np.min(tau))])
+            log.write_log('MCMC autocorrelation time = {}'.format(tau))
+            if good_idx is None:
+                samples = backend.get_chain(discard=burn_in, flat=True, thin=thin)
+            else:
+                samples = backend.get_chain(discard=burn_in, flat=True, thin=thin)[:, good_idx]
+                n_parameters_ = samples.shape[1]
+                self.n_parameters = n_parameters_
+
+            # Compute marginalized errors
+            rho = []
+            rho_min = []
+            rho_max = []
+            for i in range(self.n_parameters):
+                mcmc = np.percentile(samples[:, i], [16, 50, 84])
+                # mcmc = np.percentile(samples[:, i], [50-95/2, 50, 50+95/2])
+                q = np.diff(mcmc)
+                # print(mcmc[1], q[0], q[1])
+                rho.append(mcmc[1])
+                rho_min.append(mcmc[1] - q[0])
+                rho_max.append(mcmc[1] + q[1])
+            # The estimated parameters are the values of rho as a function of frequency
+            self.parameters_mean = np.array(rho)
+            self.parameters_args = args
+            self.parameters_std = 0.5 * (np.array(rho_max) - np.array(rho_min))
+            self.sampler = sampler
+            self.noisy_data = noisy_data
+            if log_like == 'normal':
+                self.aic = (
+                    2 * self.log_likelihood_normal(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) -
+                    2 * self.n_parameters)
+            elif log_like == 'off':
+                self.aic = (
+                    2 * self.log_likelihood_offdiag(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) -
+                    2 * self.n_parameters)
+                burn_in = int(2 * np.max(tau))
+                thin = np.max([1, int(0.5 * np.min(tau))])
+            elif log_like == 'diag':
+                self.aic = (2 * self.log_likelihood_diag(self.parameters_mean, omega, omega_fixed, noisy_data, ell) -
+                            2 * self.n_parameters)
+                burn_in = int(2 * np.max(tau))
+                thin = np.max([1, int(0.5 * np.min(tau))])
+            return
+        else:
+            coord = np.copy(p0)
+
+        todo = True
+        disc = 0
+        for sample in sampler.sample(coord, iterations=n_steps, progress=True, store=True):
+            # Only check convergence every 100 steps
+            if sampler.iteration % 250:
+                continue
+
+            # Compute the autocorrelation time so far
+            if sampler.iteration % 1000 == 0:
+                print(tau)
+
+            # Using tol=0 means that we'll always get an estimate even
+            # if it isn't trustworthy
+            tau = sampler.get_autocorr_time(tol=0, discard=disc)
+            autocorr[index] = np.mean(tau)
+            index += 1
+
+            # Check convergence
+            converged = np.all(tau * 100 < sampler.iteration)
+            converged &= np.all(np.abs(old_tau - tau) / tau < 0.015)
+
+            if todo and sampler.iteration % 500 == 0 and sampler.iteration > 1000:
+                s_old = np.ones(n_parameters)
+                for i in range(100, int(sampler.iteration / 2) + 1, 100):
+
+                    s = sampler.get_autocorr_time(tol=0, discard=i)
+
+                    if np.all(abs((s - s_old) / s) * 100 < 2):
+                        disc = i
+                        todo = False
+                        break
+                    s_old = s
+            if converged:
+                break
+            old_tau = tau
+
+        # Compute chains auto-correlation time to estimate convergence
+        # If AutocorrError, probably the chain is too short. You can still use ~2*max(tau) as burn_in
+        good_idx = None
+        try:
+            print(disc, ' discard')
+            tau = sampler.get_autocorr_time(discard=disc)
+            burn_in = max(int(6 * np.max(tau)), disc)
+            thin = np.max([1, int(1.5 * np.min(tau))])
+            log.write_log('MCMC autocorrelation time = {}'.format(tau))
+        except emcee.autocorr.AutocorrError:
+            log.write_log('The chain is probably too short')
+            burn_in = max(int(sampler.iteration * 0.3), disc)
+            thin = int(np.max([int(1.5 * sampler.iteration), 10]))
+        except ValueError:
+            log.write_log(f'There is something wrong with tau: tau = {tau}')
+            good_idx = ~np.isnan(tau)
+            tau = tau[good_idx]
+            log.write_log('Fixed MCMC autocorrelation time = {}'.format(tau))
+            burn_in = max(int(2 * np.max(tau)), disc)
+            thin = np.max([1, int(1.5 * np.min(tau))])
+        # if self.burn_in is not None:
+        #     burn_in = self.burn_in
+        # else:
+        #     self.burn_in = burn_in
+        # if self.thin is not None:
+        #     thin = self.thin
+        # else:
+        #     self.thin = thin
+        log.write_log('MCMC burn in = {}; thin = {}'.format(burn_in, thin))
+
+        if good_idx is None:
+            samples = sampler.get_chain(discard=burn_in, flat=True, thin=thin)
+        else:
+            samples = sampler.get_chain(discard=burn_in, flat=True, thin=thin)[:, good_idx]
+            n_parameters_ = samples.shape[1]
+            self.n_parameters = n_parameters_
+
+        # Compute marginalized errors
+        rho = []
+        rho_min = []
+        rho_max = []
+        for i in range(self.n_parameters):
+            mcmc = np.percentile(samples[:, i], [16, 50, 84])
+            # mcmc = np.percentile(samples[:, i], [50-95/2, 50, 50+95/2])
+            q = np.diff(mcmc)
+            # print(mcmc[1], q[0], q[1])
+            rho.append(mcmc[1])
+            rho_min.append(mcmc[1] - q[0])
+            rho_max.append(mcmc[1] + q[1])
+
+        # The estimated parameters are the values of rho as a function of frequency
+        self.parameters_mean = np.array(rho)
+        self.parameters_args = args
+        self.parameters_std = 0.5 * (np.array(rho_max) - np.array(rho_min))
+        self.sampler = sampler
+        self.noisy_data = noisy_data
+        if log_like == 'normal':
+            self.aic = (2 * self.log_likelihood_normal(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) -
+                        2 * self.n_parameters)
+        elif log_like == 'off':
+            self.aic = (2 * self.log_likelihood_offdiag(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) -
+                        2 * self.n_parameters)
+            self.dic = (
+                -2 * self.log_likelihood_offdiag(self.parameters_mean, omega, omega_fixed, noisy_data, nu, ell) +
+                4 * sampler.get_log_prob(discard=burn_in, flat=True, thin=thin).mean())
+        elif log_like == 'diag':
+            self.aic = (2 * self.log_likelihood_diag(self.parameters_mean, omega, omega_fixed, noisy_data, ell) -
+                        2 * self.n_parameters)
+            self.dic = (-2 * self.log_likelihood_diag(self.parameters_mean, omega, omega_fixed, noisy_data, ell) +
+                        4 * sampler.get_log_prob(discard=burn_in, flat=True, thin=thin).mean())
+        with open('aic_{}'.format(self.n_parameters), 'w+') as g:
+            g.write('{}\t{}\n'.format(self.n_parameters, self.aic))
+        with open('dic_{}'.format(self.n_parameters), 'w+') as g:
+            g.write('{}\t{}\n'.format(self.n_parameters, self.dic))
+
+    ################################################
+    # Helper functions
+
+    # The log-likelihood function
+    # def log_likelihood_offdiag(self, w, omega, omega_fixed, data_, nu, ell):
+    #     spline = self.model(omega_fixed, w)
+    #     rho = np.clip(spline(omega), -0.99, 0.99)
+
+    #     one_frac_rho2 = 1/(1-rho**2)
+
+    #     # Data is distributed according to a Variance-Gamma distribution with parameters:
+    #     # mu = 0; alpha = 1/(1-rho**2); beta = rho/(1-rho**2); lambda = ell*nu/2
+    #     # Its expectation value is ell*nu*rho
+    #     data = ell*data_
+    #     z = data - ell*nu*rho
+
+    #     log_pdf = -np.log(sp.gamma(0.5*nu)) + 0.5*(nu-1)*np.log(np.abs(z)) - 0.5*np.log(2**(nu-1)*np.pi/one_frac_rho2) + rho*z*one_frac_rho2 +\
+    #             np.log(sp.kv(0.5*(nu-1), np.abs(z)*one_frac_rho2))
+    #     return np.sum(log_pdf)
+
+    def log_likelihood_wishart(self, w, omega, omega_fixed, data_, nu, ell):
+        """
+        Logarithm of the Wishart probability density function.
+        """
+        from scipy.special import multigammaln
+
+        spline = self.model(omega_fixed, w)
+        V = spline(omega)
+        # TODO: convert the notation from wikipedia to Baroni
+        X = data_ * ell * nu
+        n = ell
+        p = 2
+        a, b, d = X[..., 0, 0], X[..., 0, 1], X[..., 1, 1]
+        detX = a * d - b**2
+
+        # detX = np.linalg.det(X)
+
+        a, b, d = V[..., 0, 0], V[..., 0, 1], V[..., 1, 1]
+        invV = (1 / (a * d - b**2) * np.array([[d, -b], [-b, a]])).transpose(2, 0, 1)
+        trinvV_X = np.trace(invV @ X, axis1=1, axis2=2)
+        detV = a * d - b**2
+        # trinvV_X = np.trace(np.linalg.inv(V)@X)
+
+        log_pdf = 0.5 * (
+            (n - p - 1) * np.log(detX) - trinvV_X - n * p * LOG2 - n * np.log(detV)) - multigammaln(0.5 * n, 2)
+
+        return np.sum(log_pdf)
+
+    def log_likelihood_offdiag(self, w, omega, omega_fixed, data_, nu, ell):
+        """
+        Logarithm of the Variance-Gamma probability density function.
+        """
+        spline = self.model(omega_fixed, w)
+        rho = np.clip(spline(omega), -0.98, 0.98)
+        _alpha = 1 / (1 - rho**2)
+        _beta = rho / (1 - rho**2)
+        _lambda = 0.5 * ell * nu
+        _gamma2 = _alpha**2 - _beta**2
+        _lambda_minus_half = _lambda - 0.5
+
+        # Data is distributed according to a Variance-Gamma distribution with parameters (notation as in Wikipedia):
+        # mu = 0; alpha = 1/(1-rho**2); beta = rho/(1-rho**2); lambda = ell*nu/2
+        # Its expectation value is ell*nu*rho
+        z = data_ * ell * nu
+        absz = np.abs(z)
+        # z = data
+        log_pdf = (_lambda * np.log(_gamma2) + _lambda_minus_half * np.log(absz) +
+                   np.log(sp.kv(_lambda_minus_half, _alpha * absz)) + _beta * z - 0.5 * np.log(np.pi) -
+                   np.log(sp.gamma(_lambda)) - _lambda_minus_half * np.log(2 * _alpha))
+
+        res = np.sum(log_pdf)
+        return res
+
+    def log_likelihood_diag(self, w, omega, omega_fixed, data_, ell):
+        spline = self.model(omega_fixed, w)
+        rho = np.clip(spline(omega), 1e-6, 1e6)
+
+        # Data is distributed according to a Chi-squared distribution with parameters (notation as in Wikipedia):
+        # Its expectation value is ell*rho
+        z = data_ * ell / rho
+        absz = np.abs(z)
+        # z = data
+        log_pdf = (ell / 2 - 1) * np.log(absz) - absz / 2 - np.log(rho)
+
+        res = np.sum(log_pdf)
+        return res
+
+    def log_likelihood_normal(self, w, omega, omega_fixed, data_, nu, ell):
+        spline = self.model(omega_fixed, w)
+        rho = np.clip(spline(omega), -0.98, 0.98)
+
+        log_pdf = -((data_ - rho)**2)
+        return np.sum(log_pdf)
+
+    # The log-prior function
+    def log_prior_offdiag(self, w):
+        # Uniform prior
+        if np.all((w >= -1) & (w <= 1)):
+            return 1
+        else:
+            return -np.inf
+
+    # The log-prior function
+    def log_prior_diag(self, w):
+        # Uniform prior
+        if np.all((w >= 1e-6) & (w <= 1e6)):
+            return 1
+        else:
+            return -np.inf
+
+    # The log-posterior function
+    def log_posterior_offdiag(self, w, omega, omega_fixed, data, nu=6, ell=3):
+        return self.log_prior_offdiag(w) + self.log_likelihood_offdiag(w, omega, omega_fixed, data, nu, ell)
+
+    # The log-posterior function
+    def log_posterior_diag(self, w, omega, omega_fixed, data, ell=3):
+        return self.log_prior_diag(w) + self.log_likelihood_diag(w, omega, omega_fixed, data, ell)
+
+    # The log-posterior function
+    def log_posterior_normal(self, w, omega, omega_fixed, data, nu=6, ell=3):
+        return self.log_prior_offdiag(w) + self.log_likelihood_normal(w, omega, omega_fixed, data, nu, ell)
+
+    def initialize_cepstral_distribution(self, ck_theory_var=None, psd_theory_mean=None):
+        """
+        Initialize the theoretical distribution of the cepstral coefficients.
+        The samplelogpsd must has been already set.
+
+        Input parameters:
+            ck_theory_var   = the theoretical variance of cepstral coefficients, \\sigma*^2(P*,N)
+            psd_theory_mean = the theoretical bias of log-PSD, \\lambda_l
+
+        If ck_theory_var and/or psd_theory_mean are not specified, the default theoretical values will be used.
+        """
+        NF = self.samplelogpsd.size
+        N = 2 * (NF - 1)
+
+        if psd_theory_mean is None:
+            # by default the THEORETICAL means are the one component ones:
+            # ck THEORY mean:
+            #    - EULER_GAMMA - log(2)   for k = {0, N/2}
+            #    - EULER_GAMMA            otherwise
+            self.logpsd_THEORY_mean = -EULER_GAMMA * np.ones(NF)
+            self.logpsd_THEORY_mean[0] = -EULER_GAMMA - np.log(2)
+            self.logpsd_THEORY_mean[-1] = -EULER_GAMMA - np.log(2)
+        else:
+            self.logpsd_THEORY_mean = psd_theory_mean
+
+        # set theoretical errors
+        if ck_theory_var is None:
+            # by default the THEORETICAL variances are the one component ones:
+            # ck THEORY variances:
+            #    (pi^2)/3/N   for k = {0, N/2}
+            #    (pi^2)/6/N   otherwise
+            self.logpsdK_THEORY_var = (1.0 / N * np.concatenate(
+                ([np.pi**2 / 3], [np.pi**2 / 6.0] * (NF - 2), [np.pi**2 / 3])))
+            self.logpsdK_THEORY_std = np.sqrt(self.logpsdK_THEORY_var)
+            # logtau THEORY variances:  (we assume to be summing ck up to K, included)
+            #    (pi^2)/3/N*(2*K+1)   for K = {0, N/2-1}
+            #    (pi^2)/3             for K = N/2
+            self.logtau_THEORY_var = (1.0 / N * np.concatenate(
+                (np.pi**2 / 3.0 * (2 * np.arange(NF - 1) + 1), [np.pi**2 / 3.0 * N])))
+            self.logtau_THEORY_std = np.sqrt(self.logtau_THEORY_var)
+        else:
+            self.logpsdK_THEORY_var = ck_theory_var
+            self.logpsdK_THEORY_std = np.sqrt(self.logpsdK_THEORY_var)
+            self.logtau_THEORY_var = np.zeros(NF)
+            self.logtau_THEORY_var[0] = self.logpsdK_THEORY_var[0]
+            for K in range(1, NF - 1):
+                self.logtau_THEORY_var[K] = (self.logtau_THEORY_var[K - 1] + 4.0 * self.logpsdK_THEORY_var[K])
+            self.logtau_THEORY_var[-1] = (self.logtau_THEORY_var[-2] + self.logpsdK_THEORY_var[-1])
+            self.logtau_THEORY_std = np.sqrt(self.logtau_THEORY_var)
+
+    def scan_filter_tau(self, cutoffK=None, aic_Kmin_corrfactor=1.0, correct_mean=True):
+        """
+        Computes tau as a function of the cutoffK (= P*-1).
+        Also computes psd and logpsd for the given cutoffK.
+        If cutoffK is None, aic_Kmin * aic_Kmin_corrfactor will be used.
+
+        Input parameters:
+            cutoffK = (P*-1) = cutoff used to compute logtau and logpsd (by default = aic_Kmin * aic_Kmin_corrfactor)
+            aic_Kmin_corrfactor = aic_Kmin cutoff correction factor (default: 1.0)
+            correct_mean = fix the bias introduced by the log-distribution (default: True)
+
+        self.tau_cutoffK will contain the value of tau for the specified cutoff cutoffK
+
+        If cutoffK is out of range, the maximum K will be used.
+        """
+        if cutoffK is not None:
+            if not isinstance(cutoffK, int) or (cutoffK < 0):
+                raise ValueError('cutoffK must be a positive integer.')
+            if aic_Kmin_corrfactor != 1.0:
+                raise ValueError(
+                    'If you specify cutoffK manually, the AIC will not be used, hence aic_Kmin_corrfactor will be ignored.'
+                )
+        self.aic_Kmin_corrfactor = aic_Kmin_corrfactor
+
+        if cutoffK is None:
+            self.cutoffK = int(round(self.aic_Kmin * self.aic_Kmin_corrfactor))
+            self.manual_cutoffK_flag = False
+        else:
+            self.cutoffK = cutoffK
+            self.manual_cutoffK_flag = True
+
+        if self.cutoffK >= self.samplelogpsd.size:
+            log.write_log('! Warning:  cutoffK ({:}) is out of range.'.format(self.cutoffK))
+            # log.write_log('! Warning:  cutoffK ({:}) is out of range. The maximum frequency ({:}) will be used.'.format(self.cutoffK, self.samplelogpsd.size - 1))
+            # self.cutoffK = self.samplelogpsd.size - 1
+
+        # COS-filter analysis with frequency cutoff K
+        self.logtau = dct_filter_tau(self.samplelogpsd)
+        self.logpsd = dct_filter_psd(self.samplelogpsd, self.cutoffK)   # that is log(psd) for the chosen cutoffK
+        self.psd = np.exp(self.logpsd)
+        self.tau = np.exp(self.logtau)
+        self.tau_THEORY_std = self.tau * self.logtau_THEORY_std
+
+        if self.cutoffK < self.samplelogpsd.size:
+            self.logtau_cutoffK = self.logtau[self.cutoffK]
+            self.logtau_var_cutoffK = self.logtau_THEORY_var[self.cutoffK]
+            self.logtau_std_cutoffK = self.logtau_THEORY_std[self.cutoffK]
+            self.tau_cutoffK = self.tau[self.cutoffK]
+            self.tau_std_cutoffK = self.tau_THEORY_std[self.cutoffK]
+            self.tau_var_cutoffK = self.tau_std_cutoffK**2
+        else:
+            self.logtau_cutoffK = np.NaN
+            self.logtau_var_cutoffK = np.NaN
+            self.logtau_std_cutoffK = np.NaN
+            self.tau_cutoffK = np.NaN
+            self.tau_var_cutoffK = np.NaN
+            self.tau_std_cutoffK = np.NaN
+
+        if correct_mean:
+            self.logpsd = self.logpsd + self.logpsd_THEORY_mean
+            self.logtau = self.logtau + self.logpsd_THEORY_mean[0]
+            self.logtau_cutoffK = self.logtau_cutoffK + self.logpsd_THEORY_mean[0]
+
+    def scan_filter_psd(self, cutoffK_LIST, correct_mean=True):
+        """Computes the psd and tau as a function of the cutoff K.
+        Repeats the procedure for all the cutoffs in cutoffK_LIST."""
+        self.cutoffK_LIST = cutoffK_LIST
+        self.logpsd_K_LIST = np.zeros((self.samplelogpsd.size, len(self.cutoffK_LIST)))
+        self.psd_K_LIST = np.zeros((self.samplelogpsd.size, len(self.cutoffK_LIST)))
+        self.logtau_K_LIST = np.zeros(len(self.cutoffK_LIST))   # DEFINED AS log(PSD[0]), no factor 0.5 or 0.25
+        self.tau_K_LIST = np.zeros(len(self.cutoffK_LIST))
+
+        for k, K in enumerate(self.cutoffK_LIST):
+            # COS-filter analysis with frequency cutoff K
+            self.logpsd_K_LIST[:, k] = dct_filter_psd(self.samplelogpsd, K)
+            self.logtau_K_LIST[k] = self.logpsd_K_LIST[0, k]
+            self.psd_K_LIST[:, k] = np.exp(self.logpsd_K_LIST[:, k])
+            self.tau_K_LIST[k] = np.exp(self.logtau_K_LIST[k])
+
+            if correct_mean:
+                self.logpsd_K_LIST[:, k] = (self.logpsd_K_LIST[:, k] + self.logpsd_THEORY_mean)
+                self.logtau_K_LIST[k] = (self.logtau_K_LIST[k] + self.logpsd_THEORY_mean[0])
diff --git a/sportran/md/maxlike.py b/sportran/md/maxlike.py
new file mode 100644
index 0000000..c74d4f1
--- /dev/null
+++ b/sportran/md/maxlike.py
@@ -0,0 +1,589 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import scipy.special as sp
+
+# from scipy.special import multigammaln
+from scipy.optimize import minimize
+from scipy.linalg import cholesky
+import opt_einsum
+from sportran.utils import log
+from .tools.filter import runavefilter
+
+EULER_GAMMA = (
+    0.57721566490153286060651209008243104215933593992   # Euler-Mascheroni constant
+)
+LOG2 = np.log(2)
+
+
+class MaxLikeFilter:
+    """
+    Maximum-likelihood estimate of the Onsager or transport coefficient.
+
+    Parameters:
+    - data: The noisy data (spectral matrix or one of its components).
+    - model: Function that models the data (e.g., spline function).
+    - n_parameters: Number of parameters for the fit or 'AIC' for automatic selection.
+    - n_components: Number of independent samples the data is generated from.
+    - n_currents: Number of independent flux types.
+    - likelihood: Type of likelihood function to use (
+                                                        'wishart',
+                                                        'chisquare',
+                                                        'variancegamma'
+                                                      ).
+    - solver: Optimization solver (e.g., 'BFGS').
+    - omega_fixed: Fixed frequencies for the model nodes.
+    """
+
+    def __init__(self, data=None, model=None, n_parameters=None, n_components=None, n_currents=None, likelihood=None,
+                 solver=None, omega_fixed=None, ext_guess=None, alpha=10**(np.linspace(-10, -3, 10000)),
+                ):
+        """
+        Initialize the MaxLikeFilter class with the provided parameters.
+        """
+        log.write_log('MaxLikeFilter Initialization')
+
+        self.data = data
+        self.model = model
+        self.alpha = alpha
+        self.n_parameters = n_parameters
+        self.n_components = n_components
+        self.n_currents = n_currents
+        self.solver = solver
+        self.omega_fixed = omega_fixed
+        self.omega = np.arange(data.shape[-1]) if data is not None else None
+        self._data_prepared = False
+
+        # Set likelihood function
+        self.log_like = self._get_likelihood_function(likelihood)
+
+        # Store optimization results
+        self.parameters_mean = None
+        self.parameters_std = None
+        self.parameters_cov = None
+        self.optimizer_res = None
+        self.log_likelihood_value = None
+        self.aic_values = None
+        self.optimal_nparameters = None
+
+        # DEBUG
+        self.ext_guess = ext_guess
+
+    def _get_likelihood_function(self, likelihood):
+        """
+        Get the likelihood function based on the provided likelihood type.
+        """
+        if likelihood is None:
+            return None
+        likelihood = likelihood.lower()
+        likelihoods = {
+            'wishart': self.log_likelihood_wishart,
+            'chisquare': self.log_likelihood_diag,
+            'chisquared': self.log_likelihood_diag,
+            'variancegamma': self.log_likelihood_offdiag,
+            'variance-gamma': self.log_likelihood_offdiag,
+        }
+        if likelihood in likelihoods:
+            return likelihoods[likelihood]
+        else:
+            raise NotImplementedError('Supported likelihoods: wishart, chisquare, variance-gamma')
+
+    def _validate_parameters(self):
+        """
+        Ensure that all necessary parameters are set before running maxlike.
+        """
+        assert (self.n_parameters is not None), 'Number of parameters (n_parameters) must be provided'
+        assert self.solver is not None, 'Solver must be provided'
+        assert self.data is not None, '`data` must be provided'
+        assert self.log_like is not None, 'Likelihood must be set'
+        assert self.model is not None, 'Model must be provided'
+
+    def maxlike(self, data=None, model=None, n_parameters=None, likelihood=None, solver=None, mask=None,
+                n_components=None, n_currents=None, guess_runave_window=50, omega_fixed=None, write_log=True,
+                minimize_kwargs=None, limits=None,
+               ):
+        """
+        Perform the maximum-likelihood estimation.
+        """
+        minimize_kwargs = minimize_kwargs or {}
+
+        # FIXME
+        self.limits = limits
+
+        # Update instance variables if provided
+        self._update_parameters(data, model, n_parameters, likelihood, solver, n_components, n_currents, omega_fixed,)
+
+        # Validate necessary variables
+        self._validate_parameters()
+
+        if write_log:
+            log.write_log(f'Maximum-likelihood estimation with n_parameters = {self.n_parameters}')
+
+        # Prepare data
+        self._prepare_data(mask)
+
+        # Prepare n_parameters
+        n_parameters_list = self._prepare_n_parameters(self.n_parameters)
+
+        if isinstance(n_parameters_list, int):
+            # Run with fixed number of parameters
+            self._run_maxlike_fixed_parameters(n_parameters_list, guess_runave_window, minimize_kwargs, write_log)
+        else:
+            # Run over a range of n_parameters and select optimal one via AIC
+            self._run_maxlike_aic(n_parameters_list, guess_runave_window, minimize_kwargs, write_log)
+
+    def _update_parameters(self, data, model, n_parameters, likelihood, solver, n_components, n_currents, omega_fixed,):
+        """
+        Update class parameters with new values if provided.
+        """
+        if data is not None:
+            self.data = data
+            self.omega = np.arange(data.shape[-1])
+        if model is not None:
+            self.model = model
+        if n_parameters is not None:
+            self.n_parameters = n_parameters
+        if likelihood is not None:
+            self.log_like = self._get_likelihood_function(likelihood)
+        if solver is not None:
+            self.solver = solver
+        if n_components is not None:
+            self.n_components = n_components
+        if n_currents is not None:
+            self.n_currents = n_currents
+        if omega_fixed is not None:
+            self.omega_fixed = omega_fixed
+
+    def _prepare_data(self, mask):
+        """
+        Prepare data for processing, applying mask if provided.
+        """
+        if mask is not None:
+            self.data = self.data[mask]
+
+        # Validate data shape
+        self._validate_data_shape()
+
+        self.omega = np.arange(self.data.shape[-1])
+
+        if not self._data_prepared:
+            # Perform the axis transformation only once
+            self._orig_data = np.copy(self.data)
+            self.data = np.moveaxis(self.data, -1, 0)
+            self._data_prepared = True
+
+    def _validate_data_shape(self):
+        """
+        Validate the shape of the input data.
+        """
+        if len(self.data.shape) == 3:
+            assert (self.log_like == self.log_likelihood_wishart), 'Misshaped `data` for likelihood'
+            assert (self.data.shape[0] == self.data.shape[1]), 'Data for a Wishart estimate must be a (n,n,N) array.'
+        elif len(self.data.shape) != 1:
+            raise ValueError('`data` should be a 1D or 3D array')
+
+    def _prepare_n_parameters(self, n_parameters):
+        """
+        Prepare the number of parameters array based on input.
+        """
+        if isinstance(n_parameters, int):
+            return n_parameters
+        elif isinstance(n_parameters, (list, np.ndarray)):
+            n_parameters = np.asarray(n_parameters)
+            assert np.issubdtype(n_parameters.dtype, np.integer), '`n_parameters` must be an integer array-like'
+            log.write_log((f'Optimal number of parameters between {n_parameters.min()} '
+                           f'and {n_parameters.max()} chosen by AIC'))
+            return n_parameters
+        elif isinstance(n_parameters, str) and n_parameters.lower() == 'aic':
+            n_parameters = np.arange(3, 40)
+            log.write_log('Optimal number of parameters between 3 and 40 chosen by AIC')
+            return n_parameters
+        else:
+            raise ValueError('Invalid value for n_parameters')
+
+    def _run_maxlike_fixed_parameters(self, n_parameters, guess_runave_window, minimize_kwargs, write_log):
+        """
+        Run maximum likelihood estimation with a fixed number of parameters.
+        """
+        self.n_parameters = n_parameters
+        self._initialize_spline_nodes(write_log)
+        if self.ext_guess is not None:
+            guess_data = self.ext_guess
+        else:
+            guess_data = self.guess_data(self._orig_data, self.omega, self.omega_fixed, self.n_components,
+                                         self.n_currents, window=guess_runave_window,
+                                        )
+
+        # Perform optimization
+        self._optimize_parameters(guess_data, minimize_kwargs, write_log)
+
+    def _run_maxlike_aic(self, n_parameters_list, guess_runave_window, minimize_kwargs, write_log):
+        """
+        Run maximum likelihood estimation over a range of parameters and choose the best
+        one with AIC.
+        """
+        _aic = []
+        _aic_max = -np.inf
+        _steps_since_last_aic_update = 0
+
+        for n_par in n_parameters_list:
+            log.write_log(f'n_parameters = {n_par}')
+            self.n_parameters = int(n_par)
+            self.omega_fixed = None   # Reset omega_fixed to recompute spline nodes
+            self._initialize_spline_nodes(write_log)
+            guess_data = self.guess_data(self._orig_data, self.omega, self.omega_fixed, self.n_components,
+                                         self.n_currents, window=guess_runave_window,
+                                        )
+
+            self._optimize_parameters(guess_data, minimize_kwargs, write_log=False)
+            _new_aic = self.log_likelihood_value - n_par
+            _aic.append(_new_aic)
+
+            if _new_aic > _aic_max:
+                _aic_max = _new_aic
+                self.optimal_nparameters = n_par
+                self.best_parameters_mean = self.parameters_mean.copy()
+                self.best_parameters_std = self.parameters_std.copy()
+                self.best_parameters_cov = self.parameters_cov.copy()
+                self.best_omega_fixed = self.omega_fixed.copy()
+                self.best_log_likelihood_value = self.log_likelihood_value
+                _steps_since_last_aic_update = 0
+            else:
+                _steps_since_last_aic_update += 1
+
+            if _steps_since_last_aic_update > 5:
+                break
+
+            if write_log:
+                log.write_log((f'AIC: {_new_aic}; Steps since last AIC '
+                               f'update: {_steps_since_last_aic_update}'))
+
+        self.aic_values = np.array(_aic)
+        # After the loop, set the parameters to the best found
+        self.n_parameters = self.optimal_nparameters
+        self.parameters_mean = self.best_parameters_mean
+        self.parameters_std = self.best_parameters_std
+        self.parameters_cov = self.best_parameters_cov
+        self.omega_fixed = self.best_omega_fixed
+        self.log_likelihood_value = self.best_log_likelihood_value
+
+    def _initialize_spline_nodes(self, write_log):
+        """
+        Initialize spline nodes for the model.
+        """
+        if self.omega_fixed is None:
+            if write_log:
+                log.write_log('Spline nodes are equispaced from 0 to the Nyquist frequency.')
+            args = np.int32(np.linspace(0, self.data.shape[0] - 1, self.n_parameters, endpoint=True))
+            self.omega_fixed = self.omega[args]
+        assert self.omega_fixed.shape[0] == self.n_parameters
+
+    def guess_data(self, data, omega, omega_fixed, ell, nu, window=10):
+        """
+        Moving average of the input data as initial guess for the parameter estimation.
+        """
+        try:
+            guess_data = self._compute_moving_average(data, window)
+        except Exception as e:
+            log.write_log(f'Guessing data failed with exception: {e}. Window changed to 10.')
+            guess_data = self._compute_moving_average(data, 10)
+
+        return self._process_guess_data(guess_data, omega, omega_fixed, ell, nu)
+
+    def _compute_moving_average(self, data, window):
+        """
+        Compute the moving average of the data.
+        """
+        shape = data.shape
+        return np.array([runavefilter(c, window) for c in data.reshape(-1, shape[-1])]).reshape(shape)
+
+    def _process_guess_data(self, guess_data, omega, omega_fixed, ell, nu):
+        """
+        Process guess data based on the likelihood.
+        """
+        indices = [np.argmin(np.abs(omega - omega_fixed[i])) for i in range(len(omega_fixed))]
+        guess_data = np.array([guess_data[..., idx] for idx in indices])
+
+        if self.log_like == self.log_likelihood_wishart:
+            guess_data = self._process_wishart_guess_data(guess_data, nu)
+        elif self.log_like == self.log_likelihood_diag:
+            guess_data = np.log(guess_data)
+
+        return guess_data.flatten()
+
+    def _process_wishart_guess_data(self, guess_data, nu):
+        """
+        Process the guess data for Wishart likelihood.
+        """
+        guess_data = np.array([cholesky(g, lower=False) for g in guess_data])
+        upper_triangle_indices = np.triu_indices(nu)
+        nw = self.omega_fixed.shape[0]
+        return self._flatten_wishart_parameters(guess_data, upper_triangle_indices, nw, nu)
+
+    def _flatten_wishart_parameters(self, guess_data, upper_triangle_indices, nw, nu):
+        """
+        Flatten the Wishart parameters for optimization.
+        """
+        guess_params = np.zeros((nw, nu * (nu + 1) // 2))
+        for idx, (i, j) in enumerate(zip(*upper_triangle_indices)):
+            guess_params[:, idx] = guess_data[:, i, j]
+        return guess_params
+
+    def _optimize_parameters(self, guess_data, minimize_kwargs, write_log):
+        """
+        Perform the optimization to find the parameters that maximize the likelihood.
+        """
+
+        self._used_guess_data = guess_data, self.data
+        res = minimize(fun=self.log_like, x0=guess_data,
+                       args=(self.model, self.omega, self.omega_fixed, self.data, self.n_currents, self.n_components,
+                            ), bounds=self.limits, method=self.solver, **(minimize_kwargs or {}),
+                      )
+        self._store_optimization_results(res, write_log)
+
+    def _optimize_alpha(self, res):
+        """
+        Assume a gaussian prior (alpha/pi)**(P/2) e**(-alpha*||w||**2) and maximize
+        the marginal distribution of alpha. We sample the posterior distribution
+        assuming it is Gaussian
+        (see https://en.wikipedia.org/wiki/Bernstein–von_Mises_theorem)
+        and compute p(D|alpha) reweighting the posterior at alpha=0: see
+        reweight_alpha and reweight_logev_alpha_vec.
+        """
+
+        w = res.x
+
+        cov = res.hess_inv
+        if self.solver == 'L-BFGS-B':
+            cov = cov.todense()
+
+        samples = generate_samples_mc_alpha(w, cov)
+        dic_alpha, self.alpha_plot = reweight_logev_alpha_vec(alpha=self.alpha, samples=samples)
+        parameters_mean, parameters_cov = reweight_alpha(alpha=dic_alpha['alpha_s'], samples=samples)
+
+        return dic_alpha, parameters_mean, parameters_cov
+
+    def _store_optimization_results(self, res, write_log):
+        """
+        Store the results of the optimization.
+        """
+
+        if hasattr(res, 'hess_inv'):
+            if write_log:
+                log.write_log(f'The {self.solver} solver provides Hessian. '
+                              'Covariance matrix estimated through Laplace approximation.')
+
+            self.best_alpha, self.parameters_mean, self.parameters_cov = (self._optimize_alpha(res=res))
+
+            try:
+                cov = res.hess_inv.todense()
+            except AttributeError:
+                cov = res.hess_inv
+            self.parameters_cov = cov
+
+            self.parameters_mean = res.x
+            self.parameters_std = np.sqrt(np.abs(self.parameters_cov.diagonal()))
+        else:
+            if write_log:
+                log.write_log((f'The {self.solver} solver does not provide Hessian. '
+                               'No covariance matrix output.'))
+            self.parameters_mean = res.x
+            self.parameters_std = None
+            self.parameters_cov = None
+
+        self.optimizer_res = res
+        self.log_likelihood_value = -self.log_like(res.x, self.model, self.omega, self.omega_fixed, self.data,
+                                                   self.n_currents, self.n_components,
+                                                  )
+
+    def log_likelihood_wishart(self, w, model, omega, omega_fixed, data_, nu, ell, eps=1e-3):
+        """
+        Logarithm of the Wishart probability density function.
+        """
+        n = ell
+        p = nu
+
+        # Compute scale matrix from the model
+        V = scale_matrix(model, w, omega, omega_fixed, p)
+        X = data_
+
+        if n < p:
+            S = np.linalg.svd(X, hermitian=True, compute_uv=False)
+            detX = np.array([np.prod(s[abs(s) > eps]) for s in S])
+        else:
+            detX = np.linalg.det(X)
+
+        invV = np.linalg.inv(V)
+        detV = np.linalg.det(V)
+
+        trinvV_X = opt_einsum.contract('wab,wba->w', invV, X)
+
+        log_pdf = 0.5 * ((n - p - 1) * np.log(detX) - trinvV_X - n * np.log(detV))
+
+        return -np.sum(log_pdf)
+
+    def log_likelihood_diag(self, w, model, omega, omega_fixed, data, M, ell):
+        """
+        Negative of the logarithm of the Chi-squared probability density function.
+
+        :param array like: data is the PSD
+        :param int: nu is the number of thermodynamically independent fluxes (usally
+                    useful for thermal transport)
+        """
+
+        # Number of degrees of freedom
+        dof = int(ell - M + 1)
+
+        # Model for the spectrum
+        spline = model(omega_fixed, w)
+        spectrum = np.exp(spline(omega))
+
+        # Log-likelihood of data = spectrum * chi^2(dof) / dof
+        log_pdf = 0.5 * (np.log(data**(dof - 2) / spectrum**dof) - dof * data / spectrum)
+
+        # Return the negative log-likelihood
+        return -np.sum(log_pdf)
+
+    def log_likelihood_offdiag(self, w, model, omega, omega_fixed, data_, nu, ell):
+        """
+        Negative of the logarithm of the Variance-Gamma probability density function.
+        """
+        spline = model(omega_fixed, w)
+        rho = np.clip(spline(omega), -0.98, 0.98)
+        _alpha = 1 / (1 - rho**2)
+        _beta = rho / (1 - rho**2)
+        _lambda = 0.5 * ell * nu
+        _gamma2 = _alpha**2 - _beta**2
+        _lambda_minus_half = _lambda - 0.5
+
+        z = data_ * nu * ell
+        absz = np.abs(z)
+        term1 = _lambda * np.log(_gamma2)
+        term2 = _lambda_minus_half * np.log(absz)
+        term3 = np.log(sp.kv(_lambda_minus_half, _alpha * absz))
+        term4 = _beta * z
+        term5 = -_lambda_minus_half * np.log(2 * _alpha)
+        log_pdf = term1 + term2 + term3 + term4 + term5
+        return -np.sum(log_pdf)
+
+    def extract_and_scale_results(self):
+        """
+        Extract results and scale matrices according to the likelihood.
+        """
+        omega_fixed = self.omega_fixed
+        params = self.parameters_mean
+        params_cov = self.parameters_cov
+        omega = self.omega
+
+        if self.log_like == self.log_likelihood_wishart:
+            self.NLL_mean = (scale_matrix(self.model, params, omega, omega_fixed, self.n_currents) / self.n_currents)
+
+            self.NLL_std = (scale_matrix_std_mc(self.model, params, omega, omega_fixed, self.n_currents,
+                                                self.parameters_cov, size=1000,
+                                               ) / self.n_currents)
+        else:
+            _NLL_spline = self.model(omega_fixed, params)
+            self.NLL_mean = np.exp(_NLL_spline(omega))
+
+            try:
+                err = np.random.multivariate_normal(np.zeros(params_cov.shape[0]), params_cov, size=1000)
+                samples = [self.model(omega_fixed, params + e)(omega) for e in err]
+                self.NLL_std = np.std([np.exp(s) for s in samples], axis=0)
+            except TypeError:
+                pass
+            except AttributeError:
+                pass
+
+    def __repr__(self):
+        return 'MaxLikeFilter:\n'
+
+
+def scale_matrix(model, w, omega, omega_fixed, n):
+    """
+    Compute the scale matrix from the model.
+    """
+    elements = model(omega_fixed, w)(omega)
+    is_complex = elements.dtype == np.complex128
+
+    # Precompute the upper triangle indices
+    triu_indices = np.triu_indices(n)
+
+    # Preallocate L
+    L = np.zeros((n, n, omega.shape[0]), dtype=elements.dtype)
+
+    # Assign elements to L using vectorized operations
+    for idx, (i, j) in enumerate(zip(*triu_indices)):
+        L[i, j] = elements[:, idx]
+
+    # Compute the scale matrix S using einsum
+    S = np.einsum('jiw,jkw->wik', L.conj() if is_complex else L, L)
+
+    return S
+
+
+def scale_matrix_std_mc(model, w, omega, omega_fixed, n, cov_w, size=1000):
+    """
+    Compute the standard deviation of the scale matrix via Monte Carlo sampling.
+    """
+    sample = w + np.random.multivariate_normal(mean=np.zeros_like(w), cov=cov_w, size=size)
+    sample_S = np.stack([scale_matrix(model, ww, omega, omega_fixed, n) for ww in sample])
+    S_std = sample_S.std(axis=0)
+    return S_std
+
+
+def reweight_logev_alpha_vec(samples, alpha):
+    """
+    samples: shape is (N, P): N number of samples, P number of parameters
+    array: array of alpha to test
+    """
+    M = samples.shape[1]
+    means = np.mean(np.exp(-alpha[:, None] * np.linalg.norm(samples, axis=1)**2), axis=1)
+    l = np.where(means > 1e-300)[0]
+    truth_mean = np.log(means[l]) + M / 2 * np.log(alpha[l] * 2 / np.pi)
+    dic_alpha = {}
+    dic_alpha['lev_s'] = truth_mean
+    dic_alpha['alpha_s'] = alpha[np.argmax(dic_alpha['lev_s'])]
+
+    return dic_alpha, alpha[l]
+
+
+def reweight_alpha(alpha, samples):
+    """
+    samples: shape is (N, P): N number of samples, P number of parameters
+    alpha: scalar
+    """
+    # Compute the squared norms
+    norm_samples = np.linalg.norm(samples, axis=1)**2
+
+    # Use log-sum-exp trick to prevent underflow in the exponential terms
+    max_exp_term = np.max(-alpha * norm_samples)
+
+    exp_term = np.exp(-alpha * norm_samples - max_exp_term)
+
+    # Weight denominator
+    weight_denominator = np.mean(exp_term, axis=0)
+
+    # Compute the weighted mean
+    truth_mean = (np.mean(samples.T[:, :] * exp_term, axis=1,)) / weight_denominator
+
+    # Compute the weighted covariance with log-sum-exp normalization
+    weighted_samples = samples.T[:, None, :] * samples.T[None, :, :]
+
+    # Adjust the covariance by scaling with the exponential
+    truth_cov = (np.mean(weighted_samples * exp_term, axis=-1,)) / weight_denominator
+
+    # Subtract the outer product of the means
+    truth_cov = truth_cov - np.outer(truth_mean, truth_mean)
+
+    return truth_mean, truth_cov
+
+
+def generate_samples_mc_alpha(w, cov_w, size=1000):
+    """
+    samples shape is (N, P): N number of samples, P number of parameters
+    w: parameters mean as estimated by self.maxlike
+    cov_w: array PxP
+    """
+
+    sample = w + np.random.multivariate_normal(mean=np.zeros_like(w), cov=cov_w, size=size)
+
+    return sample
diff --git a/sportran/md/mdsample.py b/sportran/md/mdsample.py
index f8acaec..90c3d13 100644
--- a/sportran/md/mdsample.py
+++ b/sportran/md/mdsample.py
@@ -70,22 +70,21 @@ def __init__(self, traj=None, spectr=None, psd=None, freqs=None, DT_FS=1.0):
         self.initialize_psd(freqs=freqs, psd=psd, DT_FS=DT_FS)
 
     def __repr__(self):
-        msg = 'MDSample:\n' + \
-              '  DT_FS:  {}  fs\n'.format(self.DT_FS) + \
-              '  traj:   {}  steps  *  {} equivalent components\n'.format(self.N, self.N_EQUIV_COMPONENTS) + \
-              '          {}  fs\n'.format(None if self.traj is None else self.DT_FS * self.N)
+        msg = ('MDSample:\n' + '  DT_FS:  {}  fs\n'.format(self.DT_FS) +
+               '  traj:   {}  steps  *  {} equivalent components\n'.format(self.N, self.N_EQUIV_COMPONENTS) +
+               '          {}  fs\n'.format(None if self.traj is None else self.DT_FS * self.N))
         if self.spectr is not None:
             msg += '  spectr: {}  frequencies\n'.format(self.NFREQS)
         if self.psd is not None:
-            msg += '  psd:    {}  frequencies\n'.format(self.psd.size) + \
-                   '      DF =      {}  [omega*DT/(2*pi)]\n'.format(self.DF) + \
-                   '                {}  [THz]\n'.format(self.DF_THZ) + \
-                   '      Nyquist Frequency = {}  [THz]\n'.format(self.Nyquist_f_THz)
+            msg += ('  psd:    {}  frequencies\n'.format(self.psd.size) +
+                    '      DF =      {}  [omega*DT/(2*pi)]\n'.format(self.DF) +
+                    '                {}  [THz]\n'.format(self.DF_THZ) +
+                    '      Nyquist Frequency = {}  [THz]\n'.format(self.Nyquist_f_THz))
         if self.fpsd is not None:
-            msg += '  fpsd:   {}  frequencies\n'.format(self.fpsd.size) +\
-                   '      PSD_FILTER_W  = {} [omega*DT/(2*pi)]\n'.format(self.PSD_FILTER_W) +\
-                   '                    = {} [THz]\n'.format(self.PSD_FILTER_W_THZ) +\
-                   '      PSD_FILTER_WF = {} frequencies\n'.format(self.PSD_FILTER_WF)
+            msg += ('  fpsd:   {}  frequencies\n'.format(self.fpsd.size) +
+                    '      PSD_FILTER_W  = {} [omega*DT/(2*pi)]\n'.format(self.PSD_FILTER_W) +
+                    '                    = {} [THz]\n'.format(self.PSD_FILTER_W_THZ) +
+                    '      PSD_FILTER_WF = {} frequencies\n'.format(self.PSD_FILTER_WF))
         if self.acf is not None:
             msg += '  acf:    {}  lags\n'.format(self.NLAGS)
         return msg
@@ -119,7 +118,7 @@ def set_plotter(cls, plotter=None):
                                dir(cls)):   # same as [name for name in dir(plotter) if name.startswith('plot_')
             obj = getattr(cls, funcname)
             if callable(obj):
-                #print('deleting {} from class {}'.format(obj, cls))
+                # print('deleting {} from class {}'.format(obj, cls))
                 try:
                     delattr(cls, funcname)
                 except AttributeError:
@@ -130,7 +129,7 @@ def set_plotter(cls, plotter=None):
             obj = getattr(plotter, funcname)
             if callable(obj):
                 add_method(cls)(obj)
-                #print('{} added to class {}'.format(obj, cls))
+                # print('{} added to class {}'.format(obj, cls))
 
     #############################################
     ###################################
@@ -150,12 +149,12 @@ def initialize_traj(self, array):
             raise TypeError('Input trajectory must be an array.')
         if array is not None:
             array = np.array(array, dtype=float)
-            if (len(array.shape) == 1):
+            if len(array.shape) == 1:
                 self.MANY_EQUIV_COMPONENTS = False
                 self.traj = array[:, np.newaxis]
-            elif (len(array.shape) == 2):
+            elif len(array.shape) == 2:
                 self.MANY_EQUIV_COMPONENTS = True
-                if (array.shape[0] % 2 == 1):
+                if array.shape[0] % 2 == 1:
                     self.traj = array[:-1]
                     log.write_log('Trajectory has an odd number of points. Removing the last one.')
                 else:
@@ -196,12 +195,12 @@ def initialize_psd(self, freq_psd=None, psd=None, freqs=None, DT_FS=None):
         """
         # frequencies
         if freq_psd is not None:   # use freq_psd variable
-            if (len(freq_psd) == 2):   # (freqs, psd) tuple was passed
+            if len(freq_psd) == 2:   # (freqs, psd) tuple was passed
                 if (freqs is not None) or (psd is not None):
                     raise ValueError('Too many arguments.')
                 frequencies = freq_psd[0]
                 array = freq_psd[1]
-            elif (len(freq_psd) > 2):   # array used as psd or freqs
+            elif len(freq_psd) > 2:   # array used as psd or freqs
                 if psd is None:   # only psd was passed
                     if freqs is not None:
                         raise ValueError('Too many arguments.')
@@ -214,7 +213,7 @@ def initialize_psd(self, freq_psd=None, psd=None, freqs=None, DT_FS=None):
                     array = psd
             else:
                 raise ValueError('arguments not valid')
-        else:   #ignore freq_psd variable
+        else:   # ignore freq_psd variable
             frequencies = freqs
             array = psd
 
@@ -236,10 +235,10 @@ def initialize_psd(self, freq_psd=None, psd=None, freqs=None, DT_FS=None):
         # frequencies
         self.NFREQS = self.psd.size
         if frequencies is None:   # recompute frequencies
-            self.freqs = np.linspace(0., 0.5, self.NFREQS)
+            self.freqs = np.linspace(0.0, 0.5, self.NFREQS)
         else:
             self.freqs = np.array(frequencies, dtype=float)
-            if (self.freqs.size != self.NFREQS):
+            if self.freqs.size != self.NFREQS:
                 raise ValueError('Number of frequencies different from PSD array size.')
 
         # freqs conversions to THz
@@ -265,7 +264,7 @@ def compute_trajectory(self):
         if self.spectr is None:
             raise ValueError('Spectrum not defined.')
         full_spectr = np.append(self.spectr, self.spectr[-2:0:-1].conj())
-        self.traj = np.real(np.fft.ifft(full_spectr))   #*np.sqrt(self.NFREQS-1)
+        self.traj = np.real(np.fft.ifft(full_spectr))   # *np.sqrt(self.NFREQS-1)
         self.N = self.traj.size
 
     def compute_spectrum(self):
@@ -277,7 +276,7 @@ def compute_spectrum(self):
         self.NFREQS = self.spectr.size
         self.DF = 0.5 / (self.NFREQS - 1)
 
-    def compute_psd(self, PSD_FILTER_W=None, freq_units='THz', method='trajectory', DT_FS=None, normalize=False):
+    def compute_psd(self, PSD_FILTER_W=None, freq_units='THz', method='trajectory', DT_FS=None, normalize=False,):
         # overridden in HeatCurrent (will call, at the end, this method)
         """
         Compute the periodogram from the trajectory or the spectrum.
@@ -286,7 +285,7 @@ def compute_psd(self, PSD_FILTER_W=None, freq_units='THz', method='trajectory',
         """
         if DT_FS is not None:
             self.DT_FS = DT_FS
-        if (method == 'trajectory'):
+        if method == 'trajectory':
             if self.traj is None:
                 raise ValueError('Trajectory not defined.')
             self.freqs, self.psdALL = periodogram(self.traj, detrend=None, axis=0)
@@ -296,15 +295,15 @@ def compute_psd(self, PSD_FILTER_W=None, freq_units='THz', method='trajectory',
             self.NFREQS = self.freqs.size
             self.DF = 0.5 / (self.NFREQS - 1)
             self.DF_THZ = freq_red_to_THz(self.DF, self.DT_FS)
-        elif (method == 'spectrum'):
+        elif method == 'spectrum':
             if self.spectr is None:
                 raise ValueError('Spectrum not defined.')
             self.psd = self.DT_FS * np.abs(self.spectr)**2 / (2 * (self.NFREQS - 1))
-            self.freqs = np.linspace(0., 0.5, self.NFREQS)
+            self.freqs = np.linspace(0.0, 0.5, self.NFREQS)
         else:
             raise KeyError('method not understood')
 
-        self.freqs_THz = self.freqs / self.DT_FS * 1000.
+        self.freqs_THz = self.freqs / self.DT_FS * 1000.0
         self.Nyquist_f_THz = self.freqs_THz[-1]
         if normalize:
             self.psd = self.psd / np.trapz(self.psd) / self.N / self.DT_FS
@@ -316,7 +315,7 @@ def compute_psd(self, PSD_FILTER_W=None, freq_units='THz', method='trajectory',
         if (PSD_FILTER_W is not None) or (self.PSD_FILTER_W is not None):
             self.filter_psd(PSD_FILTER_W, freq_units)
 
-    def filter_psd(self, PSD_FILTER_W=None, freq_units='THz', window_type='rectangular', logpsd_filter_type=1):
+    def filter_psd(self, PSD_FILTER_W=None, freq_units='THz', window_type='rectangular', logpsd_filter_type=1,):
         """
         Filter the periodogram with the given PSD_FILTER_W [freq_units].
           - PSD_FILTER_W  PSD filter window [freq_units]
@@ -329,7 +328,7 @@ def filter_psd(self, PSD_FILTER_W=None, freq_units='THz', window_type='rectangul
             if freq_units in ('THz', 'thz'):
                 self.PSD_FILTER_W_THZ = PSD_FILTER_W
                 self.PSD_FILTER_W = freq_THz_to_red(PSD_FILTER_W, self.DT_FS)
-            elif (freq_units == 'red'):
+            elif freq_units == 'red':
                 self.PSD_FILTER_W = PSD_FILTER_W
                 self.PSD_FILTER_W_THZ = freq_red_to_THz(PSD_FILTER_W, self.DT_FS)
             else:
@@ -337,15 +336,15 @@ def filter_psd(self, PSD_FILTER_W=None, freq_units='THz', window_type='rectangul
         else:
             pass   # try to use the internal value
         if self.PSD_FILTER_W is not None:
-            self.PSD_FILTER_WF = int(round(self.PSD_FILTER_W * self.NFREQS * 2.))
+            self.PSD_FILTER_WF = int(round(self.PSD_FILTER_W * self.NFREQS * 2.0))
         else:
             raise ValueError('Filter window width not defined.')
 
-        if (window_type == 'rectangular'):
+        if window_type == 'rectangular':
             self.fpsd = runavefilter(self.psd, self.PSD_FILTER_WF)
 
             # filter log-psd
-            if (logpsd_filter_type == 1):
+            if logpsd_filter_type == 1:
                 self.flogpsd = runavefilter(self.logpsd, self.PSD_FILTER_WF)
             else:
                 self.flogpsd = np.log(self.fpsd)
@@ -370,8 +369,17 @@ def compute_gkintegral(self):
         self.tau = integrate_acf(self.acf)
         self.taum = np.mean(self.tau, axis=1)   # average tau
 
-    def resample(self, TSKIP=None, fstar_THz=None, FILTER_W=None, plot=False, PSD_FILTER_W=None,
-                 freq_units='THz', FIGSIZE=None, verbose=True):   # yapf: disable
+    def resample(
+        self,
+        TSKIP=None,
+        fstar_THz=None,
+        FILTER_W=None,
+        plot=False,
+        PSD_FILTER_W=None,
+        freq_units='THz',
+        FIGSIZE=None,
+        verbose=True,
+    ):  # yapf: disable
         """
         Simulate the resampling of the time series.
 
@@ -394,7 +402,7 @@ def resample(self, TSKIP=None, fstar_THz=None, FILTER_W=None, plot=False, PSD_FI
         xf : a filtered & resampled time series object
         ax : an array of plot axes, optional (if plot=True)
         """
-        return resample_timeseries(self, TSKIP, fstar_THz, FILTER_W, plot, PSD_FILTER_W, freq_units, FIGSIZE, verbose)
+        return resample_timeseries(self, TSKIP, fstar_THz, FILTER_W, plot, PSD_FILTER_W, freq_units, FIGSIZE, verbose,)
 
 
 ################################################################################
diff --git a/sportran/plotter/plotter.py b/sportran/plotter/plotter.py
index 3a39f75..2960682 100644
--- a/sportran/plotter/plotter.py
+++ b/sportran/plotter/plotter.py
@@ -16,14 +16,15 @@
 
 # list of colors
 colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
-
+iter_colors = iter(colors)
 ################################################################################
 
 
-class Plotter():
+class Plotter:
     """
     Plotter abstract class. Essentially empty.
     """
+
     _plot_style = None
     pass
 
@@ -98,7 +99,7 @@ def plot_periodogram(current, PSD_FILTER_W=None, *, freq_units='THz', freq_scale
     if (PSD_FILTER_W is not None) or (current.PSD_FILTER_W is not None):
         current.filter_psd(PSD_FILTER_W, freq_units)
     else:   # use a zero-width (non-filtering) window
-        current.filter_psd(0.)
+        current.filter_psd(0.0)
     if kappa_units:   # plot psd in units of kappa - the log(psd) is not converted
         psd_scale = 0.5 * current.KAPPA_SCALE
     else:
@@ -109,17 +110,17 @@ def plot_periodogram(current, PSD_FILTER_W=None, *, freq_units='THz', freq_scale
         plt.subplots_adjust(hspace=0.1)
     if freq_units in ('THz', 'thz'):
         axes[0].plot(current.freqs_THz, psd_scale * current.fpsd, **plot_kwargs)
-        axes[0].set_xlim([0., current.Nyquist_f_THz])
+        axes[0].set_xlim([0.0, current.Nyquist_f_THz])
         if mode == 'log':
             axes[1].plot(current.freqs_THz, current.flogpsd, **plot_kwargs)
-            axes[1].set_xlim([0., current.Nyquist_f_THz])
+            axes[1].set_xlim([0.0, current.Nyquist_f_THz])
             axes[1].set_xlabel(r'$f$ [THz]')
     elif freq_units == 'red':
         axes[0].plot(current.freqs / freq_scale, psd_scale * current.fpsd, **plot_kwargs)
-        axes[0].set_xlim([0., 0.5 / freq_scale])
+        axes[0].set_xlim([0.0, 0.5 / freq_scale])
         if mode == 'log':
             axes[1].plot(current.freqs / freq_scale, current.flogpsd, **plot_kwargs)
-            axes[1].set_xlim([0., 0.5 / freq_scale])
+            axes[1].set_xlim([0.0, 0.5 / freq_scale])
             axes[1].set_xlabel(r'$f$ [$\omega$*DT/2$\pi$]')
     else:
         raise ValueError('Frequency units not valid.')
@@ -143,10 +144,8 @@ def plot_cospectrum_component(current, idx1, idx2, *, axis=None, FIGSIZE=None, f
     """
     if axis is None:
         figure, axis = plt.subplots(1, figsize=FIGSIZE)
-    color1 = next(axis._get_lines.prop_cycler)['color']
-    color2 = next(axis._get_lines.prop_cycler)['color']
-    axis.plot(current.freqs_THz, np.real(current.fcospectrum[idx1][idx2]) * current.KAPPA_SCALE * 0.5, c=color1)
-    axis.plot(current.freqs_THz, np.imag(current.fcospectrum[idx1][idx2]) * current.KAPPA_SCALE * 0.5, c=color2)
+    axis.plot(current.freqs_THz, np.real(current.fcospectrum[idx1][idx2]) * current.KAPPA_SCALE * 0.5)
+    axis.plot(current.freqs_THz, np.imag(current.fcospectrum[idx1][idx2]) * current.KAPPA_SCALE * 0.5)
 
     if f_THz_max is None:
         f_THz_max = current.freqs_THz[_index_cumsum(np.abs(current.fcospectrum[idx1][idx2]), 0.95)]
@@ -154,9 +153,9 @@ def plot_cospectrum_component(current, idx1, idx2, *, axis=None, FIGSIZE=None, f
         f_THz_max = min(f_THz_max, current.freqs_THz[-1])
     axis.set_xlim([0, f_THz_max])
     if k_SI_max is None:
-        k_SI_max = np.max(
+        k_SI_max = (np.max(
             np.abs(current.fcospectrum[idx1][idx2])[:int(current.NFREQS * f_THz_max / current.freqs_THz[-1])] *
-            current.KAPPA_SCALE * 0.5) * 1.3
+            current.KAPPA_SCALE * 0.5) * 1.3)
     if k_SI_min is None:
         k_SI_min = -k_SI_max
     axis.set_ylim([k_SI_min, k_SI_max])
@@ -191,8 +190,8 @@ def plot_ck(current, *, axis=None, label=None, FIGSIZE=None):
 
     if axis is None:
         figure, axis = plt.subplots(1, figsize=FIGSIZE)
-    color = next(axis._get_lines.prop_cycler)['color']
-    axis.plot(current.cepf.logpsdK, 'o-', c=color, label=label)
+    l, = axis.plot(current.cepf.logpsdK, 'o-', label=label)
+    color = l.get_color()
 
     axis.plot(current.cepf.logpsdK + current.cepf.logpsdK_THEORY_std, '--', c=color)
     axis.plot(current.cepf.logpsdK - current.cepf.logpsdK_THEORY_std, '--', c=color)
@@ -215,8 +214,8 @@ def plot_L0_Pstar(current, *, axis=None, label=None, FIGSIZE=None):
     """
     if axis is None:
         figure, axis = plt.subplots(1, figsize=FIGSIZE)
-    color = next(axis._get_lines.prop_cycler)['color']
-    axis.plot(np.arange(current.NFREQS) + 1, current.cepf.logtau, '.-', c=color, label=label)
+    l, = axis.plot(np.arange(current.NFREQS) + 1, current.cepf.logtau, '.-', label=label)
+    color = l.get_color()
     axis.plot(np.arange(current.NFREQS) + 1, current.cepf.logtau + current.cepf.logtau_THEORY_std, '--', c=color)
     axis.plot(np.arange(current.NFREQS) + 1, current.cepf.logtau - current.cepf.logtau_THEORY_std, '--', c=color)
     axis.axvline(x=current.cepf.aic_Kmin + 1, ls=':', c=color)
@@ -245,11 +244,11 @@ def plot_kappa_Pstar(current, *, axis=None, label=None, FIGSIZE=None, pstar_max=
     """
     if axis is None:
         figure, axis = plt.subplots(1, figsize=FIGSIZE)
-    color = next(axis._get_lines.prop_cycler)['color']
+    l, = axis.plot(np.arange(current.NFREQS) + 1, current.cepf.tau * current.KAPPA_SCALE * 0.5, 'o-', label=label)
+    color = l.get_color()
     axis.fill_between(
         np.arange(current.NFREQS) + 1, (current.cepf.tau - current.cepf.tau_THEORY_std) * current.KAPPA_SCALE * 0.5,
         (current.cepf.tau + current.cepf.tau_THEORY_std) * current.KAPPA_SCALE * 0.5, alpha=0.3, color=color)
-    axis.plot(np.arange(current.NFREQS) + 1, current.cepf.tau * current.KAPPA_SCALE * 0.5, 'o-', c=color, label=label)
     axis.axvline(x=current.cepf.aic_Kmin + 1, ls=':', c=color)
     axis.axvline(x=current.cepf.cutoffK + 1, ls='--', c=color)
     axis.axhline(y=current.kappa, ls='--', c=color)
@@ -279,8 +278,18 @@ def plot_kappa_Pstar(current, *, axis=None, label=None, FIGSIZE=None, pstar_max=
     axis.yaxis.set_minor_locator(MultipleLocator(dy2))
     return axis
 
-def plot_cepstral_spectrum(current, *, freq_units='THz', freq_scale=1.0, axes=None, kappa_units=True, FIGSIZE=None, mode='log',
-                           **plot_kwargs):   # yapf: disable
+
+def plot_cepstral_spectrum(
+    current,
+    *,
+    freq_units='THz',
+    freq_scale=1.0,
+    axes=None,
+    kappa_units=True,
+    FIGSIZE=None,
+    mode='log',
+    **plot_kwargs
+):  # yapf: disable
     """
     Plots the cepstral spectrum.
 
@@ -303,17 +312,17 @@ def plot_cepstral_spectrum(current, *, freq_units='THz', freq_scale=1.0, axes=No
         psd_scale = 1.0
     if freq_units in ('THz', 'thz'):
         axes[0].plot(current.freqs_THz, current.cepf.psd * psd_scale, **plot_kwargs)
-        axes[0].set_xlim([0., current.Nyquist_f_THz])
+        axes[0].set_xlim([0.0, current.Nyquist_f_THz])
         if mode == 'log':
             axes[1].plot(current.freqs_THz, current.cepf.logpsd, **plot_kwargs)
-            axes[1].set_xlim([0., current.Nyquist_f_THz])
+            axes[1].set_xlim([0.0, current.Nyquist_f_THz])
             axes[1].set_xlabel(r'$f$ [THz]')
     elif freq_units == 'red':
         axes[0].plot(current.freqs / freq_scale, current.cepf.psd * psd_scale, **plot_kwargs)
-        axes[0].set_xlim([0., 0.5 / freq_scale])
+        axes[0].set_xlim([0.0, 0.5 / freq_scale])
         if mode == 'log':
             axes[1].plot(current.freqs / freq_scale, current.cepf.logpsd, **plot_kwargs)
-            axes[1].set_xlim([0., 0.5 / freq_scale])
+            axes[1].set_xlim([0.0, 0.5 / freq_scale])
             axes[1].set_xlabel(r'$f$ [$\omega$*DT/2$\pi$]')
     else:
         raise ValueError('Units not valid.')
@@ -384,24 +393,26 @@ def plot_resample(x, xf, PSD_FILTER_W=None, *, freq_units='THz', axes=None, FIGS
     TSKIP = int(x.Nyquist_f_THz / xf.Nyquist_f_THz)
 
     from sportran.current import Current
+
     plot_kappa_units = isinstance(x, Current)
     if not axes:
         figure, axes = plt.subplots(2, sharex=True, figsize=FIGSIZE)
         axes = plot_periodogram(x, PSD_FILTER_W=PSD_FILTER_W, freq_units=freq_units, axes=axes, mode=mode,
-                                kappa_units=plot_kappa_units)   # this also updates x.PSD_FILTER_W
-    xf.plot_periodogram(freq_units=freq_units, freq_scale=TSKIP, axes=axes, mode=mode, kappa_units=plot_kappa_units)
+                                kappa_units=plot_kappa_units,
+                               )   # this also updates x.PSD_FILTER_W
+    xf.plot_periodogram(freq_units=freq_units, freq_scale=TSKIP, axes=axes, mode=mode, kappa_units=plot_kappa_units,)
     if freq_units in ('THz', 'thz'):
         axes[0].axvline(x=fstar_THz, ls='--', c='k')
-        axes[0].set_xlim([0., x.Nyquist_f_THz])
+        axes[0].set_xlim([0.0, x.Nyquist_f_THz])
         if mode == 'log':
             axes[1].axvline(x=fstar_THz, ls='--', c='k')
-            axes[1].set_xlim([0., x.Nyquist_f_THz])
+            axes[1].set_xlim([0.0, x.Nyquist_f_THz])
     elif freq_units == 'red':
         axes[0].axvline(x=0.5 / TSKIP, ls='--', c='k')
-        axes[0].set_xlim([0., 0.5])
+        axes[0].set_xlim([0.0, 0.5])
         if mode == 'log':
             axes[1].axvline(x=0.5 / TSKIP, ls='--', c='k')
-            axes[1].set_xlim([0., 0.5])
+            axes[1].set_xlim([0.0, 0.5])
     return axes
 
 
@@ -425,8 +436,8 @@ def plot_psd(jf, j2=None, j2pl=None, f_THz_max=None, k_SI_max=None, k_tick=None,
             f_THz_max = maxT
 
     if k_SI_max is None:
-        k_SI_max = np.max(
-            jf.fpsd[:int(jf.freqs_THz.shape[0] * f_THz_max / jf.freqs_THz[-1])] * jf.KAPPA_SCALE * 0.5) * 1.3
+        k_SI_max = (np.max(jf.fpsd[:int(jf.freqs_THz.shape[0] * f_THz_max / jf.freqs_THz[-1])] * jf.KAPPA_SCALE * 0.5) *
+                    1.3)
 
     figure, ax = plt.subplots(1, 1)   # figsize=(3.8, 2.3)
     ax.plot(jf.freqs_THz, jf.psd * jf.KAPPA_SCALE * 0.5, lw=0.2, c='0.8', zorder=0)
@@ -434,9 +445,9 @@ def plot_psd(jf, j2=None, j2pl=None, f_THz_max=None, k_SI_max=None, k_tick=None,
     if j2 is not None:
         plt.axvline(x=j2.Nyquist_f_THz, ls='--', c='k', dashes=(1.4, 0.6), zorder=3)
     if j2pl is not None:
-        plt.plot(j2pl.freqs_THz, j2pl.cepf.psd * j2pl.KAPPA_SCALE * 0.5, c=colors[1], zorder=1)
+        plt.plot(j2pl.freqs_THz, j2pl.cepf.psd * j2pl.KAPPA_SCALE * 0.5, c=colors[1], zorder=1,)
     try:
-        plt.plot(jf.freqs_THz, np.real(jf.fcospectrum[0][0]) * jf.KAPPA_SCALE * 0.5, c=colors[3], lw=1.0, zorder=1)
+        plt.plot(jf.freqs_THz, np.real(jf.fcospectrum[0][0]) * jf.KAPPA_SCALE * 0.5, c=colors[3], lw=1.0, zorder=1,)
     except:
         pass
 
diff --git a/tests/README.md b/tests/README.md
deleted file mode 100644
index 919c1a0..0000000
--- a/tests/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-This folder contains various tests.
-They are run by running 'tox' in the main folder of the project, where tox.ini is located. 'tox' is installed with 'pip install tox'.
-If you have issues running the tests with errors such 'You are using pip version 8.1.1, however version 19.2.1 is available.', please consider upgrading virtualenv with 'pip install virtualenv --upgrade'. Simply upgrading pip in this case does not work.