From d36377a99a2afbe4bf80c0db93835c96a160a306 Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Mon, 29 Jan 2024 11:48:49 -0500 Subject: [PATCH 1/9] change robustness function --- docs/notebooks/figanos_docs.ipynb | 64 +++++++------------------ docs/notebooks/figanos_multiplots.ipynb | 61 +++++++++++++++++++---- 2 files changed, 68 insertions(+), 57 deletions(-) diff --git a/docs/notebooks/figanos_docs.ipynb b/docs/notebooks/figanos_docs.ipynb index 61c1a298..44ce187a 100644 --- a/docs/notebooks/figanos_docs.ipynb +++ b/docs/notebooks/figanos_docs.ipynb @@ -722,44 +722,7 @@ "execute": "never" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/sarbou1/PycharmProjects/figanos/figanos/matplotlib/utils.py:865: UserWarning: Colormap warning: More than one variable group found. Use the cmap argument.\n", - " warnings.warn(\n", - "/home/sarbou1/PycharmProjects/figanos/figanos/matplotlib/plot.py:2034: UserWarning: Only first variable of Dataset is plotted.\n", - " warnings.warn(\"Only first variable of Dataset is plotted.\")\n", - "/home/sarbou1/micromamba/envs/figanos/lib/python3.11/site-packages/dask/core.py:121: RuntimeWarning: invalid value encountered in divide\n", - " return func(*(_execute_task(a, cache) for a in args))\n", - "/home/sarbou1/micromamba/envs/figanos/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:1662: UserWarning: The following kwargs were not used by contour: 'Over 0.8'\n", - " result = super().contourf(*args, **kwargs)\n", - "/home/sarbou1/PycharmProjects/figanos/figanos/matplotlib/utils.py:221: UserWarning: Attribute \"description\" not found.\n", - " warnings.warn(f'Attribute \"{string}\" not found.')\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Ensemble plot - hatchmap and gridmap')" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAGFCAYAAACPGJsxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOyddZwVVd/Av2dmbmzvsrCwdKOUgCAqoIAd2K2P9ehjJ4rdgZiIweOrPsZjYIuILWEA+iDdsXTssp23Zs77x9yO3buwlMyXz2XvPXPmzJm5c+f8zq+OkFJKLCwsLCwsLA5IlL3dAQsLCwsLC4u9hyUIWFhYWFhYHMBYgoCFhYWFhcUBjCUIWFhYWFhYHMBYgoCFhYWFhcUBjCUIWFhYWFhYHMBYgoCFhYWFhcUBjCUIWFhYWFhYHMBoe7sDFhYWFhYWO8PFJ19MRXXFLreT0yGH//73v03Qo/0TSxCwsLCwsNgvqaiu4IsXvtjlds5+5Owm6M3+iyUIWFhYWFjsv0h9b/dgv8cSBCwsLCws9lMkSGNvd2K/x3IWbCKGDx+OECLp+jNmzEAIwcMPP7z7OtUI9rX+JEPHjh3p2LHj3u5Gg7z99tsIIXj77bf3dlcOGC6//HKEEKxfv36/OP769esRQnD55Zfv1n5ZWMRjlwWBwA1c36tfv35N0FWL/YH9ZXBOhsYKdxYWFnsav0ZgV18HOE1mGujSpQuXXHJJ3G2tWrVqqsNYWFhY7POMHTuWu+++mzZt2uztrvy9kVgDeRPQZIJA165d9yu1soWFhcXuIj8/n/z8/L3dDQuLpNgrPgJCCIYPH86OHTu48sorycvLIyUlhcMPP5wZM2bE1N+2bRu33HIL3bp1IyUlhWbNmtGnTx+uv/56KisrI+p6PB6ef/55BgwYQFpaGhkZGQwbNoyvvvoqpt2AHa+goIBnn32W7t27k5KSQs+ePZk0aRIAXq+XBx98kE6dOuF0Ounbty/ff/99wnNzuVyMGTOGdu3a4XQ66dOnD//5z38adX2Kioq47bbb6Nq1Kw6Hg+bNm3P22WezZMmSpNsIqLWboj9Lly7l/PPPJy8vD4fDQadOnbjtttsoLS0N1gmYiDZs2MCGDRsiTEO7W0Csqanh9ttvp02bNjgcDvr27cunn34aU2/VqlWMGTOGAQMGkJubi9PppHv37tx9991UV1dH1BVCMHPmzOD7wCvahrto0SIuueQS2rZti8PhID8/nxNPPJEpU6bE7evPP//M0KFDSUtLIzc3l8suu4ySkpKIOuH24uXLl3PqqaeSnZ1NTk4OF154IcXFxQD88ccfHHfccWRmZpKTk8PVV19NTU1NRFsej4eXXnqJE044gXbt2uFwOMjLy+Oss85i/vz5Mf0L92f44osvGDRoEKmpqbRq1YrrrruOsrKy+r+MMKZPn86VV15Jjx49SE9PJz09nYEDB/J///d/ces39rkA5r156qmnkpGRQVZWFieffHKjficBfD4fY8eOpUuXLjidTrp27crYsWMpKCiI+70HTGDl5eXcfPPNtGvXDk3Tgn4giXwEdF1n3LhxdO3aNeI4hhF/Vhs4TkVFBddddx35+fmkpaVx1FFHMW/ePAC2b9/OZZddRl5eHqmpqZxwwgmsWbMmpq0vvviCCy+8kK5du5KamkpWVhbDhg3js88+i6kbfg8uWbKEk046iaysLDIzMxk1ahTLli1r9DXeHQgkAn2XXwc6ey1qoLy8nCFDhpCZmcnFF19MUVERH330ESeccAJ//fUXvXv3BqC2tpYhQ4awfv16jj/+eM4880w8Hg8FBQW8/fbbjBkzhszMTADcbjcnnngiM2bMoH///vzzn//E6/UydepUTj/9dF566SVuvPHGmL7cfvvt/PHHH4waNQpVVZk0aRIXXXQROTk5vPLKKyxZsoSTTz4Zl8vFBx98wGmnncaKFSvo1KlTTFvnnnsuixYt4txzz8Xr9fLxxx/zz3/+k8LCQu65554Gr8vatWsZPnw4W7Zs4fjjj+eMM86gqKiIzz77jO+//56ff/6ZwYMHJ32dd7U/s2bN4vjjj8ftdnPOOefQsWNH5syZw/jx45k6dSqzZ88mNzeX7OxsHnroIcaPHw/ArbfeGmxj+PDhSfe3sXi9Xo4//nhKS0s566yzqK2tZdKkSZx33nl89913HH/88cG6n3/+OW+++SYjRoxg+PDhGIbBnDlzGDduHDNnzuSXX37BZrMB8NBDD/H222+zYcMGHnrooWAb4f4ugQerYRiMGjWKHj16UFRUxB9//MGbb77JqFGjIvo6ZcoUvv76a0aNGsV1113HL7/8wrvvvsvatWv57bffYs5t3bp1HHnkkQwcOJCrrrqKuXPnMmnSJDZt2sS4ceM47rjjOO644/jXv/7FjBkzeOONNwB4/fXXg22UlpZy6623MmzYME4++WRycnIoKCjgq6++4ttvv+WXX35h0KBBMcf+9NNP+fHHHzn33HM59thjmTlzJv/+97+ZPXs2s2fPJiUlpcHvZty4caxZs4bDDz+cM888k/Lycr777juuueYaVq5cyXPPPRezT7LPBYAlS5YwZMgQqqurOeuss+jWrRt//vknQ4YM4ZBDDmmwf+FceeWV/Pe//6VLly7ccMMNuN1uxo8fz+zZsxPu43a7GTlyJFVVVYwaNQq73U7Lli3rPc6//vUv/vOf/9CpUyduuOEGXC4Xzz//PLNmzUq4j8fj4bjjjsPlcnH++edTWFjIxx9/zLHHHsusWbM48cQTadWqFZdccglr1qxhypQpnHrqqSxduhRVVYPt3HPPPdjtdoYOHUp+fj47duzgq6++4pxzzmHChAncdNNNMccuKChgyJAhHHbYYVx//fWsXr2aL774gt9++41Zs2Zx8MEHJ3F1dydW1ECTIHeRdevWSUB26dJFPvTQQ3Ff3377bcQ++C07119/vdR1PVj+xhtvSEBec801wbKvvvpKAvK2226LOXZlZaV0u93Bz/fee68E5MMPPywNw4ioN3DgQGm32+WWLVuC5ZdddpkEZLdu3WRRUVGwfM6cORKQ2dnZcujQobK6ujq47aOPPpKAvPnmmyP6cvTRR0tA9uzZU1ZWVgbLt23bJvPz86WmaXLt2rXB8unTp0tAPvTQQxHtHHnkkVLTNPnDDz9ElK9cuVJmZGTIPn36xFyHeDRFf3Rdl926dZOA/O677yLav+eeeyQg//nPf0aUd+jQQXbo0CGpPu4qHTp0kIA8/fTTI+6Dn376SQLyhBNOiKi/efPmiHoBHnnkEQnI9957L6I8cA3jUVhYKNPT02VaWpqcN29ezPZNmzYF37/11lsSkJqmyd9++y1Y7vP55PDhwyUgZ8+eHSwP/KYAOX78+GC5YRjy5JNPDt6bX375ZXCbx+ORffv2lTabTW7fvj1Y7nK55ObNm2P6t2TJEpmeni6PPfbYiPJAXwH5008/RWy74oorJCAfffTRuNckmoKCgpgyr9crjzvuOKmqqtywYUPEtsY8F6QMfT/R31vg3gTkunXrGuxn4H4ZOHCgrK2tDZZv27ZNtmrVSgLysssui9gncO8df/zxEfsECDxbwo8f+I0dcsghEc+UzZs3y+bNm9d7nHPPPVd6vd5g+VNPPRW8D2677baI5911110nAfn5559HtBX+ew9QVVUl+/TpI7OysmRNTU2wPPwevP/++yP2eeeddyQgR44cGdPenubUoSdI7+ziXX6NGjUqbvsff/yxPP3002Xbtm1lamqq7NOnj3z11Vcj7k8ppZw6dars16+fdDgcskuXLvKVV16J294zzzwjO3ToIB0Ohxw4cKCcPn16U1+SnaLJBIH6XrfcckvkQUGmpaXJqqqqiHKv1ys1TZMDBgwIlgUEgXvvvbfefui6LnNycmTXrl0jfhTR7bz00kvBssCP9e23346p37lzZwnImTNnRpT7fD5ps9nk0UcfHVEeeCi9//77MW0988wzEpCPPfZYsCzewDtv3ry4g2uA22+/XQJy8eLFcbc3dX9++eUXCciTTjoppo3q6mqZm5srU1JSIgbXvSEIxBtwOnToIJs1a5ZUOyUlJRKQl19+eUR5fYLA008/LQH54IMPNth+YHC99NJLE26bMGFCsCzwm+rcuXPMA+fdd9+VgBwxYkRMW48++qgEkn64jBo1StrtdunxeGL6c9xxx8XU37Jli7TZbLJLly5JtZ+Izz77LO7vrjHPhQ0bNkhA9u3bN6b9qqoqmZ2dnbQgcPnll0tATp48OWbb2LFj6x2gFy5cGLfNeIJAQJD67LPPYuo/9thj9R5n/fr1EeUbN26UgExPT48QKqQM/W6jJxmJeO655yQgZ8yYESwL3IM5OTkx7RuGIXv37i0BuXHjxqSOsbs4degJ0juraJdfiQSBwYMHy/POO09++OGHctq0afKBBx6QmqbJO+64I1hn1qxZUtM0eeWVV8pp06bJxx57TCqKIl9//fWItp555hlps9nkM888I3/++Wd5wQUXSKfTKRctWrRbr1EyNJlp4IQTTuC7775Lun63bt1IT0+PKNM0jZYtW1JeXh4sO+qoo2jVqhVjx45lwYIFnHLKKQwdOpQ+ffpEhHatXLmSsrIyWrduzSOPPBJzvB07dgCwYsWKmG39+/ePKcvPz6egoCAm9FFVVfLy8tiyZUvc8xo2bFjCsgULFsTdJ8CcOXMA0+YXz64e6PuKFSsiVKT1sSv9CdiQ46n209LSGDhwIN9//z2rVq1Kuj/xGD9+fMR3DqaNNZkwxOzs7LgmmrZt28aodaWUvPXWW7z99tssWbKEioqKCNvs1q1bk+7zn3/+CRBhemiIAQMGxO0nEHP+AIcccgiKEunGE3BAixeSG9gWfW8uWLCAp59+mt9++43t27fj9XojthcXF8c4tsW7b1q3bk2XLl1YsWIFVVVVZGRkxNQJp6qqimeffZYvv/yStWvXxvgvxLveyT4XFi5cCMDQoUNj2khPT6dfv34J/QqiCbR15JFHxmyLVxYg4HOTLIHj1PebjEd2djYdOnSIKAt8X926dSMtLS3utuj7oKioiKeeeopvv/2WDRs2UFdXF7E93vfRv3//mPaFEAwdOpQlS5awcOFC2rVrl7Dvux25e00DU6ZMoUWLFsHPI0aMoLq6mpdffpnHH38ch8PBo48+yoABA3jzzTeDdTZu3MiDDz7IlVdeiaIouN1uHn/8cW699VbuuOMOAI4++mj69OnDE088EfRJ21vsNR+BrKysuOWapqHrekS92bNn89BDDzFlyhS++eYbwHyA3nPPPVx//fUAQce1pUuXsnTp0oTHjX4YAUEfg+h+1Lct+mEaIC8vL6YsYDesqKh/cYzAOUydOpWpU6cmrBfvHBKxK/0JOGImsnsGwkIbaqchxo8fz4YNGyLKhg8fnpQgUN99FO2AdfPNN/Pyyy/Trl07TjvtNPLz83E4HAA88sgjuN3upPscGJQaEx4Wr6+B+yz8ng+wM/clEHFvzpo1i5EjRwKm0BIYaIUQfPnllyxcuDDuece7b8C8F1asWEFlZWW9goDH42H48OHMmzeP/v37849//IPc3Fw0TWP9+vW88847cY+b7HMhcM/V189kqaysRFEUcnNzG9VOXl5eo/JMVFRUoCgKzZs3b9Rx6rtvkr0PSktLGTRoEBs3bmTIkCEce+yxZGdno6oqCxYsYPLkyY2+DwLntHeRwO4TBMKFgAD9+/fH5XJRWlpKs2bNmDZtGk899VREnYsvvpjXX3+d+fPnc+ihhzJr1iwqKiq48MILg3VUVeX888/nueeeQ0q5V3OW7Bcphjt27Mg777yDrussXryYH374gQkTJnDDDTcEPakDP4izzz47rsf4nqKoqChGQi4sLAQSP+QCBM4hkVPj3upPoH40gfJ4D6PGsCeyvxUVFfHKK6/Qt29fZs+eTWpqanDb9u3b42qR6iM7OxswZ137cgKlJ554ArfbzW+//caQIUMits2ZMyc4S42mqKgobnmy3/nkyZOZN28eV111VYTzIsCkSZN45513kj2FuATu3Yb6mQyZmZkYhkFJSUnMIF1fO419cGdlZWEYBsXFxTEDTGP6uzO8+eabbNy4kccff5z77rsvYttTTz3F5MmT4+7X0PVt6Bnyd+TXX3+lWbNm5OXlsXLlSjweT4zTZM+ePQFYvnw5hx56KMuXLwfgoIMOiqlXVVXFli1bgtrBvcF+lWJYVVX69evHmDFj+PDDDwGCYYEHH3wwmZmZzJ07N+FsfU/w66+/JixrKMNiIBqgPk/lPdmfgMkknoq1traWuXPnkpKSQo8ePYLlqqrGnd3ubQoKCpBScuyxx0YIARD/GgFBj+t453PYYYcB8MMPPzRxT5uWtWvX0qxZsxghoLa2Nhh+Fo9412Tr1q2sXbuWLl26NGgWWLt2LQCnnXZaUm03lkBUQLxoi+rq6gbNXvHaiue5X583f2MJHKe+3+TuYme/j/nz58fVQP7+++8AjY7OaHqkuejQLr4ShW9GM3fuXN566y1uu+02VFUNhtMGJgYBcnJygJCWt6ysDIfDERNtE11vb7HPCwJLliyJURtDSCINXFhN07juuuvYsGEDd9xxR1xhYMmSJQkl3KbiiSeeoKqqKqKfzz//PJqmcdFFF9W772GHHcbgwYP58MMP+eijj2K2G4YRjG3fE/0ZMmQIXbp04dtvv+Wnn36K2DZ27FiKi4u58MILsdvtwfJmzZpRXFyMy+VqVD93NwEb66xZsyJ+9Js3b+buu++Ou0+zZs2CdaK57LLLSE9P57nnnos76CTyIdnTdOjQgbKysghzma7r3HHHHUG/mXj8+OOP/PzzzxFl999/P16vl8suuyyp40LsQD1z5swYDcHO0L59e4466igWLVrE+++/H7HtySefjOtzkYiLL74YgMceeyzivt2+fTsvvvjiLvc1wKWXXgrAo48+GjG4btmypUmPE49E38cHH3wQNLfGo6ysLEbt/e6777J48WJGjhy5d/0DAjRBiuHVq1c3eJjt27dz9tlnc9hhh3HXXXdFbEukHQovj1dHSlnv/nuKJjMNrFmzpt7EMTubVOann35i9OjRDBkyhIMOOojc3NxgHHRKSkqECv2RRx5h3rx5TJgwgalTp3L00UfTokULtmzZwuLFi1m4cCGzZ89OaPdqCjp37kzv3r05++yzg3H7RUVFPPHEE3Tu3LnB/T/88ENGjBjBBRdcwPjx4zn00ENxOp1s3LiR2bNns2PHjkYNsrvSH0VRePvttznhhBM4+eSTOffcc+nQoQN//PEH06ZNo0uXLjEPiZEjRzJ37lxGjRrFsGHDgnHL8Zy69iT5+fmcffbZfPbZZwwcOJBjjjmGwsJCvv76a0aOHElBQUHMPiNHjuTTTz/l3HPP5eSTTw46h51yyink5eXx7rvvcsEFF3DYYYdx2mmn0aNHD4qLi/njjz/o2LEjX3755Z4/0ShuuukmfvjhB4YOHcp5552H0+lkxowZbNmyheHDhyd0qDvllFOC33m7du2YOXMms2fP5pBDDgk6O9XHqFGj6NixI08//TRLliyhd+/erFy5kq+//pozzjgjbhKbxvLKK68wZMgQLr30Ur788ku6devG//73P/7880+GDRuW9Cz72GOP5eKLL+b999+nT58+nH766bjdbj7++GMGDx7MlClTYpw2d4bhw4dzxRVX8NZbb9GnTx/OPPNM3G43H330EYcffjhff/31Lh8jEf/4xz8YN24cN910E9OnT6dDhw4sWrSIn376ibPOOovPP/887n7Dhg1jwoQJzJkzh0GDBrFq1Sq++OILsrKyePnll3dbf/c03bp1q3d7RUUFJ510EqmpqXz11VfBfCOBGX10oq3A58D2nJwcXC4XLpcLp9MZrBcQWAP19hq7GnaQTPhg9GGAmPC7ANHhZ8uWLZO33HKL7N+/v8zNzZUOh0N27txZXn755XLZsmUx+/t8Pvnaa6/JIUOGyMzMTOlwOGT79u3liSeeKCdOnBgRChMvxCdAfaFj8ULkAvVra2vlHXfcIdu0aSPtdrvs1auXfOONN2LaSJRHQEopS0tL5f333y979+4tU1JSZHp6uuzWrZu86KKLYmKDE9GU/Vm0aJE855xzZPPmzaXNZpMdOnSQN998s9yxY0dM3aqqKnn11VfL/Px8qShKo8KYGkt9oYrxvr+qqio5evRo2bFjR+lwOGS3bt3kY489Jj0eT9x70uv1yjFjxsj27dtLTdPihnfNnz9fnnfeebJly5bSZrPJ/Px8edJJJ8mvv/46WCcQkvfWW2/F9DPedQ/8pqKPlah+Q8f59NNP5YABA2Rqaqps3ry5PO+88+TatWvj3v/hbXz++efy0EMPlU6nU+bl5clrrrlGlpSUxBw3EQUFBfLss8+WLVq0kKmpqXLQoEFy0qRJCc+hMc+FAIsXL5Ynn3yyTE9PlxkZGfKkk06Sixcvrve3HQ+v1ysfe+wx2alTJ2m322Xnzp3lk08+Kf/444+4IdANhckmOr7P55Njx46VnTt3jjjOmjVrEoYPJjpOouuV6P5ZsGCBPP7442VOTo7MyMiQRx99tPzpp5/i3jfhbSxatEieeOKJMiMjQ6anp8tTTjlFLlmyJOG570lOPfIY6f1l3S6/EoUPSillXV2dPOqoo2R+fn5MGKfL5ZJ2u10+//zzEeUzZsyQgJw7d66UUspp06ZJICbnyMMPPywzMjLihrzvSYSUft2Exd+K4cOHM3PmTKyv16IxvP3228FZq7UkLrzxxhtcffXVvPrqq1x33XV7uzt7jPXr19OpUycuu+yyfXr57FFDjuXLp+KnrG4MZz5za9w09D6fj7POOotffvmFX375hb59+8bUOemkkygvL4/w7br22mv56quv2Lx5czB8sGXLllx77bVBLaqu6/Tt25c+ffocuOGDFhYWFvsK27dvp2XLlhG22i1btvD444+jqiqnnnrqXuydxd7ihhtuYMqUKTz99NPU1tYGc72A6fGfmZnJgw8+yFFHHcXVV1/NxRdfzO+//87rr7/Oa6+9FjQpORwO7r//fu69915atGjBgAEDeOONNygoKNjrQgBYgoCFhYUFTz31FFOnTmXYsGHk5eWxceNGvv76a6qqqnj44Yf3Dac4izjs3oRCgQXmxowZE7Nt+vTpDB8+nCOOOILJkydz77338u6779K2bVsmTJjAVVddFVF/9OjRSCmZMGEChYWF9OnTh2+++aZRSal2F5YgYGFhccBz4oknsmzZMqZOnUpZWVlwpdHrr7++wegai73J7hUEks1xcvLJJ3PyySfXW0cIwZ133smdd97ZBD1rWiwfAQsLCwuL/ZJRQ0by5ZP/3uV2znzujrg+AgcKlkbAwsLCwmL/Re57Ccz2NyxBwMLCwsJiP2X3rjVwoGAJAhYWFhYW+ye7efXBA4V9PsWwhYWFhYWFxe7D0ghYWFhYWOy/WBqBXcYSBCwsLCws9lMs00BTYJkGLCwsLCwsDmAsjYCFhYWFxX6KFTXQFFiCgIWFhYXF/onEMg00AUkLAiKtExje5FuWBnhKQHrB3hwUu/mlNYReB3oVZuXAAiAirIIguLqxNDClwUDDCqjpYMsIVfdVgq8KtAzQMuMf01sJehVpGTnUeJzgLgJ0EBrYc0GJc5l0L3jLQXpitwk7PfoOxO5MTeKELfYEus/LlvUrqCgpDBUKGziag4i1kImYkqZFYoC7xPxNOZojFHvDOzVBp6RhgBDm4joSpLfK/7vWwfAACgjV/N2GHzilFSLOdYronjCrCgGogrD1e5C6REr/KSjmsVHwT+jMbWbFsH2k2ZVgM4rZppRme+axhNllf30w20MVEX3CIOL6+SoLzd9wQ2gO+hw6rOF6FjEc0q0V//3vf/d2NyySIHmNgOFFaX5U4u2BH6EAqXuRJTPMwubHoThyaSiRcXD7tg+DDamaBlIipeFfTleGLaurYHc4SM9sRst2XXDX1bC5YBked6U5cDc7GlG7Bunagsjsg8jsHTxW4GESLmsYO6ZRU1VoChKtz+CwHgZ/Tv8C3IWQdyKqI8esV7cdWTHfFAIAtEz6DhxMyzadad2hB/2OPAFnanrD19Nij7Bm6Vw+e/0x1q9aYBYoTv/gm4OaNwKh2OpvYDdIBNLwohdOBylR849DOJonPrwI/TZEfX2Js00IgZQy+BfAWzAJMJCKHdRUUOyI9I7gq0VWrwEM/wisIVLywZ6DLF8EnjLUtieh2FKRhgQJik2gqAJDl8H+KTaBYhcITaDXGehuiS1dQfgHZmlIDI9EsQmkAYbPQE1RwIc5oCsC3W2g10mECoomguWKXaDa/e1IghdGGmB4ZHDAl4bElqEiDYnUQXeVoldsQ9gcKM4WaJnZpmAiJHrZZjyFq/FVFYHPFOqV1BzUtOZozbvx/HMTk/tSLWL4ZuKNe+Ao0sos2AQ0nWnA/yAKCgHeCkTzEQh7w0IARD7wAEjrwROv/ZesZnlJd8Hn8/HvR69i6dzpUPiFKZukHYTICAkBSOL2R80biV76P6hZA9umcNbTvzPi9CsYd+tpUPQ9utBA+ghIPN16D+biW8bRsk2npPtnsWeora7g+49f5Ycv3gbdZRbacxDpnZFli0whoGUSQkAU9Q7EUSS656XhxSicDp4K1PwRKPUIAUkdt55t0vAPlMGpOKA6Qa81Z/+GOfBJvQYlqydkdENWrUZkHoTarH+wHV3qyIql+DZMRm05BDW9vdmmBMMnEYpfORd8BkikEdLYSsO8IEIFDDC8EkUTCBWM0jV4dxioWfnoVTtQUlujaE7UFFPIEJowB3SfxPCYbQeEAUP3awYAoQkU1TyWOfiH1MU1C6cSoWpQNLRmHdEyWqFlNsfe9hDU8i14i9dj1JWDlDg7HsHTjzxW7/disS9g+Qg0BUkvOiRS2tavEcB8yMniGUEhAFtuozskazdB+SxCX64wZ3FaNhdedQOHjTyzwRn3deedBtULwzpvB3szcLaGlA4omhNDN6BsFri3AirYssCRZw721StNs0D+2Zx3ei+mT/4PutdLakYWmwtWAjqgmOYOLR2aH8PEN55u9LlaNB2u2mp+/Ow1vvn4TdBrzEKhIFLboTQbAL4a9O3TwZ6VUAgIDLhSNm7Q32MErGIJ+mYYPqjdguHagbBlIDK6oQgltB9g+Fzom74EDLA3Q2gZ5m8u7GEqsnrx6offAHDT9fcAIGu3ohf9AhiIlJYoqe0grS2qLc0c4DGvmdBM9b3hk+geiZaimNdS8c/u/bN2va4CQ1fwbP0TWVsYnNUJWzppvc80tQEOxRQAfKbgAObMX9jMQd/UPvqP6Tcz6B4JhsTwgeoQSAmudXPw7lgFgJbTGV/FpsRmTtWOLe9gJrx54C5A01R8M/HG3b6Qz6gjhjL5kad2uZ0zXn7aWnSo0YSZAYJFUUJAspqAGFLagfNccG8B11bwloFeDZ5tfPjq/Xz46v2mrT/3GCa+NT5uExM/Nr/QlYvmMP6h0eDZAe7t5qtiHgaq3w7qASUFMMw6nh1hJ+QDTxGfTlF45Y2ZweLvP/k3X7411tzHcIHHBYZ7J07Uoqm44aJTMMqX+D8pCGceSmZPlLTWABjuYvTt0xH2LNRWDWsCIoSAZAWCplzDs74BP0F/vJumgLcqsjslf2E481Cb9UM4Tc1DUP0vFPCUIr2ViIxunH7OWWzbsJL89t34atqGYDsvvTo2+L6yvJi7LhuJrCtEryuEkrnoWjpay0FoGa2Rvmo8WxdhVK0DLR1n19NR7eaM3vCB4b9IenUx7oIf/ecZptZV7Ti7DkNNVUDKoLpf0YR/pi/Nmb/NHPiF6lfSAdIng74Dik0gpMTwmvv7KraGrov04ux8FIo9g3NOHMaWdcvRbA569D2Cbn2PQNMs/2mLA4/G3fXRDzv/wypCCGgxAuHXBEQ4CyX5oDSdexRTIEhpF9rf8EHdRqhdA94SqJjbYFs9+h7OxC9+B8Dn8bDwzx+Z//u3/PXbDFO4AHMQV51gb8XIk0/FMHyUFG5m8aoqFGcrXvm/p7jhX3fzyv+ZUucJ515L38OPZdoXb1C8fRNnXnkP7bv2TtADiz2BUb4UALXFkZDWHkUJObUZrhKM0gUIRzZqq+GNNgfEQ4iAnTrspg4foOPd643QMAghkqsfVkfYs0zHvyikqwjf1h9MB1pnPlStQm3WFzJ7IiuWYJSvQFatNIVbew5qbg2qM74mLzO7ORMnL8LjquX2667GqCxA1m3Ft2U6vqi6ijMHIQSGNzRA6x4Do2o1vu1/YdroQieS0m0kWnYbwDQdIE31fsDfQLEBdiW4XbULpG5qHqRPIlSB4ZOmfO6WKI6ATwI4uwzDs2UBek0Jevkm9PJNoKi8v3UeT7z8WaPMjxb7GNZaA01C40wDuUeFHj7+vaSM1QRE14nHzmgLZN1WKJ8D0s0/73qJgUeflrDuykVzGH/vZeaUQU2F7MMR9lxefcMc0K879SAw6uLvrGVy8fX3cuTx53PzdfcGi19+bddVUBZNzxtP3cBfv3wNQkXJHYia2QUA6S5BeitRMzojw43YYYgmsgEk8zMKP1bAiS+2UuOOm6j7Rs02vCULwF0auUFLR8s7Ehy5psAkwKjZjK/EdAgEUJoPQs3sxoSXx8Y2HEV1ZTnffDCeGT99h6zdZvbJmYviyAJVMX2GvLVId1XIXyOKlO7HomXmm4O5x5z1Gx7z4a7YFVMIkKZPAIGIAfzBHkKYnwMaFAFCFUifqQ3wlqzGvX6OWd+Watb31ISuny2VVycvb/A8LRrPHjENHD6EyQ8/scvtnPHq8we0aaBxmQWjVKYRQkCLEQiHfyYRx3QQ01QDDzwpw17uEmTh11A2EyF8nHzRLUEhYOOaJXzw0j1cd1ofrrvgzOD+E+69yFTdK3YzhLD4R6RrKzdcfTcAT737izlDCnRScSCyDjHDBX1VvD/hbm4Y1R3DUwFYQsC+zFV3v8Kltz8HSIziP/Bu/BK9cg2+bdOQdUVIaSCEgvCHzYW/6kUk+YK4bcccK6p+3OPgt7U39FICA2H8l5Kej5re3mwvJR+R08f/W6jGt/UHfOs+xFPwEZ71k/EVzw8bpEUwiuHmG+9p8NqnZ2Zz3rUPo+YdCWoKANJVgl5RgF66BqNiA7J2RxwhQCC0VOwdTkDYW+Grk6aDX8BPwzAHdPxOicEoH2kKCgjQXTJoClAc/mgFQVAIEIpA+PsEmAKJz23aFBQbwpbKGf+4pcFztLD4u7PTzoIx5gB7PY6ByYYORpd7q03HQW8JCEHfwcdy2e3PkZqexeaCZTxx89lg1Ebs07ZzT3oNHMHsHz+msqwUtd156LXboGSm2ZGMQ1CyegZV/YZh8MV/nuSnz1+H1A6ozYcgpQ9j69emd3V6d9RmAyOO8fK/G54pWex5fB4P7790N3N+/sxfIhDpHVBzD0XRnA030AjBNeKejb5/G2UG2Ll9k1FkGJ4aPOu+AgyEsyVKWlvQUpCuEqSnzDQl6AH/FgGOHLQWg3nptZeTP4EE3HLr3WD4kLoP74ap/ggFgdasO1rLPig2p98MaPoQxJgRJWipYfMUv99EwBTgLd+EZ/NsnB0GY2veKRS54NcYRDgYej3ULvsCdA/21oOwtzoIJDzzxOO7fJ4WidkzGoEjmfzwrn+PZ7w6/oDWCCTvI+ApxajZCs7mZlxyEwkBcXfRXVA2BzymqrFrr8O48q4J5DTPB+C6iy6B8t9Cjad2g8y+UPwzmwuWs7lgWbAtvWSOOdPPPw1Z+B1ULcRwNAtuVxSFs6+6n58+fwNqN6Jv3h7p/Fe9Ct211cwvoDpAcVK4aS0t23Vp/IlZ7FY0u53LRj/PGVfczaRXH2DB7J+R1evxVa9HzR2Imt0jdqckBtQGB90E2xu7305ZKRLtI0Gxp2HrcDLeLTOQrkJ0l5lMSaS1x9bmmGC93YKh8OKEZ7n+rEGmEKA6Sel+OsJmQyjmTF8oAW2c+VnqphCgaCD8eQbMKANzm6FLM6RQFQjcoHtwFfyKt2Q99vbDgr4hQhEIxTQZ+KqLcK+b7o8SEAhbCs88bgkAfx8sH4GmoBEJheqgzPSeDz47mh1rOgY2YkYUnhwlWhMgDZ/pBFi3zt+7LMg+gtHPvBasc93Fl0L5r6GDZPQJ5QnIO8lsx70NSsxQJ2rXIWvXgTMfJf8UjC1fQMlvMf06+cKbzNAz6QPFESkM+KrNl5+Hrx/FxCnLYtqw2DfIapbHNfeb98zqxXOY+OhV1JXMBS0VNaNdA3snR7z7N3xbbGES9ZLWBjRcUfp/lKozC6Xz6RiGD9+WGci6QoQ9C71qHUZtISBR0zsEIyxenPBkcp2Iwy0334sQMP7FJ0PtqH7nTN2FXlOMlplv5gWQIIQ5wKOErqdQ8dv9QfrMB7zwJxVCB9fmBfh2LI44rl6xibqlH6Omt0JJaw5IjNoy0FLRS1eBlNjy+6G16IWiKNxx7/0gJc+O3XXbsoXF34HkfQTsrSCtNwhHqKz0Z4yi7zFqNwft+QCBDMD1ESMEVK+A7Z+bQoCSCs1GMvGrRUx8NyQEGIbhzzHgP0iYEBBuP0XLMO2hABl9aNelF7i2YWz/zr+rF58nMjXwqH+MZuKUZUz8ehWi1QmgpmE+mdVQW36U7L7ceO3d9Z+gxT5Btz6H88DEH0Eo6IW/YvhqG96JqPspiTr11k9g44+268f4FahR7/0vFOp/EWoL/7FUVUO6igEwypagF85CVq1FVhXg2zYdT8GnSHcpt9x8L7fcfG/0GSTFixOeZPyLkYLES5N+QWs9AhB4Nk7DW7Hd79DnzwGgmtkIg5dKMUMDA0mJghc0kE083uV1ZAECvXIz3m0L8G5biF6xEb1kBUgDW5vB2Fr0RvFLGwGThMXfAAnB7FW78jrASV4jIBTwbAcMyD0ePMVQtwa8pVD2K7Jcg9QuyIy+CEUzf2dxhIHomZT01ULJz2Y4n9AgYxATJ30atws3XHQ2wRzofiEg7u+5ZKbpKIiAtK7c+9J4/v3Y1Syc/ZP55HHk8ePnr3H0qZeSmp4Vs/urb7wUU+bxuNhcsJy2nQ/Gbk/C3nyA8Of0L9iybgXd+gzmoP5H7bU4bI+rlneeH828WT/77dHhoX0aTmcqrrpq9B1zUfJjE2MlNS6E1wmP85emz4x3yzSkpwJbm5EozuaJZ/jxHAYhJJYbYe+DA3sS/QughmnejFB6YeHIRrpKEfZMlMzOqFlmNIVesgSjYjXezT+gZHZFzezciIMl5tZb7kVKiZrWGtF2JN7N0/BumYmSfjYKmjn7Vwh11p91MJAcCPCbDEJRAPb8Q7DlHYLh8+ArnIteXoCW2wdbs47ouo9rLhnFv19/CyW9ObhqwJGGoprPI0sD8HfEMg00Bck7CyoOU4TPPcb0rA9geKBqKdSt9Q/SAuwtIb0bOFqhxFmwx9A9piBRs9qf2Q8GjzyLS259pt6B5LrT+pgRAGm9ULL7xumkv/2a9VD+F+aCQApPvP0bzfLaUFq0hcdvOJG6msrgLm06Hcypl9xOvyOOT+YyWPipra1i9AWHR5hMTALTYkF6ZhY5zfPJa9uZjt0OYcDQk2mW12anjrdg9g/MnTmZmspybHYHeW06kduqPT6Pm1WLZ7Pkz+mYhnEHwpZm5oZQbKC7kd5K0E3hQGnWBy2nV2SPk1Tlx0Wa0TPezWFCQEps2uDoJEVBIUCJrRM3s2G8/kT9cr1Fi/EWLcSWdwi2vD5mFT0sVFEhOKuWhoxo07tjEXpJSOVu63wmihpaNCt6pr8z/PT563z2xuMgVOzth6GmtkVoBNcOk1IipRfPxumoKc2xtTwUoZlzEMNn+g4E/AgwwFe6DG/hPOztj0VNa8Xzz1kD/b7EHnEWHHw4kx98eJfbOeO1Vy1nwaQIrLJX8gPmk0wzX4rdtKk78k0ve2+xqTko3Q4kkQVazYDswVx+xwsN98FXA8IeKQTEcbhS0ztCekeMmk3I0t+478qR3PrkO7z8wKX4vG5E9iEIWyZGxTK2rFvOa49dDfZcJn45L8mLYfHKg5ebQoA9C1vLIRh12zDqis3VIw0fUhpUV9dSXbGcTWuX8tfMKXz2xuOomo2sZi1p2/lgeg0czmHDz2DD6kV8+vpjuGqrKSmvQWipHHPCyTTPb4/HVcdnb4+PI3BEoTrQWgxCzegQd/NOZbkMI3rxn8D7ZISAyIYStx/zPpEwECc8N1oICPZVA4EI7eNPB0zAUc//AzWqNoQaU+xmWu8m1p4fe9bVTJ7yI77CP/FsmIFwZGNrdwxaSgooYNRV4V77DUgfuu7G3ubQYCQAhF1/JRhJ6C/AEgIOWCRmyneLXSF5QcCWY76MOjPkyHCbnri+KqBiJw9vegANGdgcwzAiMsJF4/G4AN0UOCDiIRV/RidQ0ttjeHshq5Yy/u7zUVUVpfmwoMOYmt4Ow/BhFM5Eugp59Nrj6HPYSHoeejQt23YhM6dFvX06kDmo3xAKls1FCA0lJQclJSduPSHMHPiyZjtGzRaMumJKd2yntGgzi+b8yIcv3xezj6SEHz97LaJMyeiEmjcQRbVjGAZG9Sb0kiWgOdFaHYGqpdbrQR83bXCUej96e/R9FT1YS6N+ISDRrD5aGxAz8If3K56ToX+/wEDoLVyMt3AhtpYhTUDMisEBU52/7WCzmlmmtRqAr2gR0l0OhgfvhinYO56Comi88MKuawMCvPTWx9xy8x10yy5m6dzpeNZ+Ae2GoKTl4177dVDNa8vrTXA9GY1gNuLgugYBHws/t99p3keBa/nc05ZgcMBgmQZ2meQFATUVNXdwpP4yDF03TDu/ry6sjmGaCwyv/6/H9MrXPWZdvRYMF79/P4nff1vAxE++T3j4FfN+Nd/YW8R/UCcwoio5h2AoKtJTjpEzANVmqjsDoUuqYoNWIzC2T2PbxlVs27iKHz79d3B/zeYgI6sZbTv3ZNQ/RpuOhwcgPp+PlQt/Z9GcHyhYPo8d28wZpPSUNWi/VhQNMtqiZrQNlhmGD6NyI0b1Roy6oshFYLRU1NxDEIoNoWjgyELRQmpq3KXoRX8g7FnY2hxVb9pgv5UirkYgYrD026WT1RxIw4tnkykE2NsmoQkI3zegqg/3BQh2inpn4kHven+doBDQyhQCIrYn+mICy/eGOffastqiZbTF8Bn4tv+BXrEW7+Zp2FodwW23hZwHm0IoUDQ7Nz76NvN/+5b/G3s9no2/+r0HDYQ9E+mpRK8uRNhz8e5YCEKipjRHadYDVdWCAo1RY2odhSOT55+xBv4DE8tHoClovGdXPP2oEKiqAmom0paZRBuht0bdViidCb7yendZPt8f8pfSOikBIHyTmh22FkDYTMuc1YGqqqhtjsMwDISvDL1mO1KvBV8tPncZZcXbKCvexuI/f+a4s6/hrH/unFf1/siSudP57PXH2b5pTeQGLRUlvQNq80PMz41UIyuqhpLTGXI6gwRf1SZ8hX+Y2iZfLXrhbLSWh6FmdovYz6grxrtlGsKehb3dSBANCAFx3sdWDP0VNCwMxAgBqc3rj5KJ0SxEFiRa7TBeWXjfwoUAe6u+MfuJcKEiQuMhgseUurk8cCB0T1UV1A5HULtsK7JuB551X6Fmd8fWapAZtdMEvPC8KUz0H3oSz32yiDsvPwGjeitKWj5ai0Pxbp6GXr4WvXxtcB+jchMULcTR6XjU1Ob4KjdhVG9F2NIZPz4Js6KFhUVCdt7FO1wgCPsrop6IcR+qfjWlUb3av3iQAlkD6j3cjO++BASqvWFBI+FD3z/4C0VEqGODmlqhILRcFP+iK4bLP+g4m6O2GIRv6wx+/Ow1/vp1KocOO4VhJ/+DFvlNE5e+L/LhK/fxy9T3zA9aKvhciKxuOFr5My3GHbxE/KDUqDEkesEeLbMdWmY7XKs+Mj3DAF/RPLTskCBg1BXj2RwSApJeQChCIBCRx46TAyPmtMIKpO7F4zcH2Nv5NQGJhICEE/IE6wwE+tOARgDAU58QkITzY+jw5m8hKC8YZties/OxeIuWoVduQi9fhV6xFqTOmIt+pP+QEznl4tvIzE5eC5KI1NQMXvl4Vkz5TVeeh1G9EVvzfuDIQa9Yi2/7n7gLvkdJy8OoKQShYO947C73wWJ/JmA/stgVGhE+SKQWIEADi7cnSrxiVK+EinnmjC7vRCa+lTitqcdVC95y0LIav1Z8PDttmBCgKLEyDYDUPQgMhLM5ttbDTDV1pzPQC2dTWrSBHz97jR8/e43U9GwOG3EGo/5xe9xQxP2VyvJiUwjQUhEZHZFly9Ca90XL7RNr645DTMrYKOFAIGJ+v77qwqAQAKCktgwey6grxrNjEaBiazsC6fMgFb3+1MFRAkC89zLeKC7in1tQCHCHCQFRx2mIiHYbcD+JuS/9b7yFi/BuX4g93y8ENKDtiHZ0DG8/YHM3CwImM4mamo3a4Qi8pa3wbPrdNNArdqoqy/ll6ntBAVGkt+Ha2x/li7efwl1XQ367bhw96lL6Dt61Afql/3wc8fn2O+9DcaTh2TLLFAI0J45OJ6I60rn9zvss08CBSiCPgMUu0TRB3/XoUqM3mUmB/oDa9SBsPPPhXNIzs+ttftLEB803mX0iN0Q9rOM6hEVtMxOtEPOADK9jGBJFs4OWh805IvjwVhQFpdUQZMshGHVFGJVrqK3ezIwpbzNjytvkt+/Owf2H0qF7XzodNHC/1hZ85L/mIqVVSAhoHrr+CWf+0cSbYfuR4XHzgJLaAuHIQbrLALC1HWZurivGs/FncDQDdDwFXwQFBjW3D7YWYVEkcTzqISqELqJ7IriaXfC84vVV95sDooWAekh2ZcOEUQJECgFCgGf7Ijzb/EJAft/QfgEnwJjzDmsn+vcSLQQF/QuEaXpFYMvtjGfzbJAGtlb9cbTsjrtoBd4t/zPbr97GxEf/ae6saJTt2MqyeTNp27kntz01qcmE4/CB/rbbbwPVyQvPmGt+jB4T63BqYWGRPI0XBGKmesljeCqgeJqZ7EfNYNx/ZzQoBHhctfwx7QtQUlBS2yXUBiQjBEQTkQnR/xCU0sC3fRZai34otnQUNfx8CQ5sSkYepJvrmPuqt+IrWcy2javZtnFVxDFSUjPodNAAzrrqXtp0PKjec91XqK2uYN6vU0GxI6sKgkJAuLd70lqZBJ75QRt1mLZAURScnU/B8Ln8nzVTCNg0DeHMBtWBdJlOhcKRg/TWoJcsQc1oi+L0rx8hIo+TDEKN1E5Eq+4DQoDhLsfR/hiUlOYJlx1OavBPMhAlWhMQIwSEazyUqB0DSYQUQYQaLaJ7sX0V4U4Sfqe81B6n4tq2CLVZJ3M5Z0+NGdRv+AADYc/A3ukkVLsdaXhwb/iNzQXLGH3eIYBA2NN56JWvaNmmU3In3gAvPB/pE2BFCBzIWM6CTcGuaQQSRBDEw6hYDFVLQAiOP/c6zrwiuRS9U957HkP3Qc5hcZ0Ed0YAMPtMSCsQKNI9eNZ/hbBlgOIILX0aaCuemUGClt4aLb01UprCjlG7w1zdzVNFnbuMZfNmsuz6mYjUfCZ88Aua3c6+jKH743INT6QQEC4AJGGHro/wMS7i9lFA8WduNGqLcW/4CTI7oGS2R0vNRy9dim/HIqS7DK3ZQfhKV+BZ/y0hBxAVNaMDtvzB6DVFSHcpirMZvvK1YHgQ9iyEPRMMD2paaxRnlnleWqhjQopgBEHAHBAuBIBfeIDE5skEg30yckL0fR6jCWgd7ROQeJA3BQQRpRnwVwkIaTE/XxHaLkHNyCYt46jgJke7Q9Fa9sK7dS4oKvY2g1BtmhkkpNtJ6TISb9k6vEVLwNCRnkoevno4zi6nIJw5wWtmxf5bNAmWILDLNI1pII4jQDBrqOGGop/BVwHCwb0TvmhUCN7iP34GFNT0sEQxcV2sY7sUr46U0rRPR/UTQKh27B3PBKQZthZ1PjFNRj1UhQKqIwvFnhUhIxmeKnzbfkfWbuOms3qjtTuVlyY+n/Cc9zbp/nDJzQXLMGqLgjkehCGQ0dqAoMklvCy+gCjD34QNQEKAp2gJetlqc+EpqSO0FKSnBhxZaC0PxW63m80164VwZOPdPBNf6QocnU7Bu2MB0lcH0jC1BBVr0Jr1wLvpxzhntzn4zmdbQUrXM82sdv5+RDxTDC+ejdMwXOU4OhxjRgdE08DsPqGgGidvQbhmKrRJ4t6+OOQT4DcHJMpREPwYlmtAJNLgBEwJ0V9XfcIvIAWodida12HBetIAoZiaB6mDlt0Jb+ESpCeUCMpTOB81sz1aVldLCLCw2IdoRGbB+rbFbhQC9OoN5nLCGAwYejL/vPuVRifo0Q09Uu8Z4fQVeJNEQ+EafoNQPnMROW4J1e89Fcd7O8afIHpGFRaHHl5XsWdgb38ivpIl6KUL8W38ihuvlbz873037OmeCVMZf/cFrF7yB541n6Dm9kJr1gsFJSQMhJ97lBo75n2ce8TQXZjTcQVf0UKzUHOajXmrzcbdZfhWfYwvjn1BOLJQnDk42o0IlvnKC/Bum4V73beA6UMAmEmk7FnoxYvQS5cCYGvpj34IHzQD94Luxb3BLwR0OgY1tXnofGWDPrKR1CcMhN/Dkpj7zlNoCgHB6IB4ApcgocARrjCI6VO0i0CYIqA+Ajk4zAic8PZME4vUJSgS6a6I2M+o3mq+3OWMHnOfpdK3aAKsqIGmoHEagXiux4HPUUlKZO1GKJuFze7kqrtfpu/hx+1UB1vkd6B42wYMnyvCQ7xRQkAUUkqQIjiYx33wxRvkItoIvGnoYKG3Wm5vUBzoxX9iFP3eyF7vWRRF4fanP+b7T/7Nl28/jb5jIXrxYhAqCAXhyMKWezBKZhsURQmtHR9Y3Q2CX5LhdSOFglGxCV/5eozaEtBdMce0tRyAkpaHZ+MvvDylgPmzvuWtic8jPTVI3Q1CQ6h2FEc2SlpLRErL2H5ntoeiuaB7UNLbYs87JLhNSom3cp2/byrSXYGR1iZGOJVGlBCQ5tcEBAZpASKhWj2MaBV8vG3hGpWoOuFCgCM/QXRAA6YxmahuvM/+vsaLMoj47M/qF9IE+E0lfrWZIgSGx42j4+FInw9QEbY0PNvmIV3lwUONvst08ntunCUQWOwk0vIRaAp2zjQQPR2Kmu0ZvloonQ0oPP7277sUb3zCudexfN4vyNLfoaUZklTfTCzh7CcaGTXORz/Uw2e88dS4Ye3ElEc5xYWjZndDr1yNdBWyY9sGWuR3YF/mhHOv5biz/8UPn0xk1g8fU1y8A2n4kLVFeGqLzErCH4OpqAjVgXDmIOxpSHeVmf0t+oeq2OnW53AKNheDEAweOID/LVqDktoC9/qfuWPchyiKwvuTZ2FvfXi9oWG3j470GB//wjhuH534tra3PxZf8WL0qo34dizAV7IMe9thqBlm6uoITUBHvyYgfKYeGBADWgQIOeQRJSCGCQ5xiWdW8RMQAoIhgomIdsaM02bw5xpPIEnUp7Bzif65CzV0AKEAhgxGXgTWBqhd9q1p2olCyeqIo41fE+O/LW6/8z6EsJz+LCz2FsmvPpjSFrVF7PKt4Rh1W5FVy8FtDhD/uv81+h954i538rrT+4O3FNH6LBTNmVAbkIwQEEgoI8LUqgn3jcKoK0Gv3WKqraWOSGuHmtEx5vgJr2jAb8Jdjm/zVISzFa9+/kfDB94Hqa2t4ufPX2ft0rnUVJWhqho1VeUUF24FGcoFIGypKOnmzP3s8y5j8DFn7ZF8C7ePvo/nn3siRlAIYBgGevFCfCXLAImSmo/W9gh8m341HQM7hGkCAoQPhkGVuIidQQd8DcJuhPgrCsa/6TzbF+PdviBoDohR+0ffs0LE1SwkvKfjmScatAf4GwxftTDCtBESDAyPxLNjE64Nv/tXfTRRszvhaDck1J4R1rb/+JYw8Pdhj6w+OGgAk++5bZfbOePtTxL2dc2aNTz77LPMmTOHJUuWcNBBB7FkyZKIOvVFC23dupX8fHOi0bFjRzZs2BBTp66uDqdz7y1v3wgfgdDDPeQIaCBcG5E1BeAuJrAKVF6bTlw2+gU6H9R/lzrn8bj4dep7pNh91HkBow4h/BcrGSEgYnuCCmEOWtEPdClBukrwFs0BTwUxT8vqjQhbZjB0La4AEPWg1as3oJeYqxxKvZYFs39g/coFbN+0mqxmeXTrPZiD+h/F4j9/4t0X7wOhcd4/72DE6VfUf4J7CI+rFhSF1NQMRl1ye/w6Hhd11ZWkpWdz14OPAPDcU/vGAz4gHCiKgpLXH7XZwWZoYO02PKs+BwS2RI6BAQLaSAECiYwXhqdi2swDg3KUBjPu4B7w0k/JRESHCIYrVaLuWfPGE+GbEpsBIjQV4SqOsE7Fu5ElobrhWrDw6B1hrvyMEDjbtMfRqh2GDzxF63Fv+B29fB1uxYajzWHm/oFlkSVBP43Rd91nmQosGsHu9xFYunQpU6dOZfDgweaCZ3FSbc+ePTum7NJLLyUtLS0oBAQ455xzGD16dESZw+Fo2k43kuQ1AkKAsIGaYor9uttcidBPdvN8Bh19Gsefe32DuQHqY83Sufz6zXusWjSL8pLCwNEhvStqs0HJJV8htk6D8d1xNhuGB1/Bp4CkXZdeHNx/GIcceSLtO/fi5ouHI2u2mKl3tXSEloawZaA4cyClNUrM8m/gK12MUbao/n5E9Mm/3ioGqelZXDHmRXoPHJH8/k3ALdddg2fTNHPBqDDUrM7Y2x6532R0S6QZAJCGD1fBVPBWBcuUtNY4O40M1Qmf0QcG9+islEJEzMiDhM3QY3xLwtX5YVoGaUT5WkgZawqNN/P3awfiagbq00hEPwbCBvhQn6Ok5ag2gtoQXSL10DnpHonhqaN28admoWLz5yAI2VmeeOs3nnj21YguWNqB/Zs9oxHoz+S7b97lds5454uEfQ1fGffyyy9n7ty5MRqBaNavX0+nTp14+umnufPOO4PlHTt25NRTT+XllxNn0t0bJK0RUFSVzOxcykt2YHoUabTv2puBR5/OsJMuwpmavtOdcNVW8/rYG1j210xCDwcNHHmI1A6Q1sUMX2tgLG90+uGInf1/w5+NlesAyUU3jWXYSRdFVFdz+uDzVoO3BnxFwYlW4Fmt5g5Cze4esY+S2gajcrU5NTTciJQ2KKn5iJQWYM8GXy2ydhuGuxQhDJTmhwIaRulCasuX8cqDl3PlmAkcetSoPbY8sl5bFCMEACjprffI8ZuK+sLVpJTcdMl8tKxOGL46fEXzMWq24t2xBFuL3hiGD1/pWqS7DFvLfkGnVakTyq0QMaiGNR7Pfq+EG+KJ64QbUce/LRDpIo2Qg0uDuTICh/abKyLrx/nBRH2OkA9EpGQRkAmCDoRhwo1QwNBBeiVCESj2FGwte6BXbEfqXoQzE8NV6V+Z1OC1J67hkcfe46EnnknOl8HCYg+xM8/aDz74ACEEF1544W7oUdOTtEag7+HHcf1DbzZ5BzauWcLYW04zn6pqOiKlLWR0Q9HSYx5KCWOy422PqpdsutfAPkbNVnzbfwFp8OLny7A7U+vdxeOqZfO65axe8j+m/PdZdGnz5yQIIQ0v3uK/wFWG1nIQijOx+jl6suZZ835EB9t16cl51z5M116HJX9eO8nSv2ayYdVCKkoL6XPYMfQ8dPgeE0R2NwFfgttuuR2hpQDmDMC9+lMzAZEtDekNOb2pzXrgaD3I/BA96453v4YLAjE5GEKz9wb8byPakwamg14gDDa6UkO/DRH2Obo/0TS0lkhE+KD5khKkT/oTMkWeiFD9KZ0l1K2fjV4aWtVSODJJ6X5axDWzzAT7L3tEIzCwH5PvvnGX2znt7S/5+uuvG6yXrEagT58+5ObmMmPGjIjyjh07UlFRQW1tLTabjaOOOopx48bRp0+f+A3tIZomodBOsmbpnzx353kAiJzBKBld4s6Ooj8aPhf4Ss3ZeGqbyLXqI3YKvQ2Xd+oTCgzXDnxFc8BbCQjUvCENCgEAdmcqnQ8+lBdf/QxDawauQvSK1WaWQi0dQ9iQhTPAWwGtRtQrBJj9jZyA2bpeCK5i9Mo1GJXr2LR2Kc/ddxWODqcy/sVdXyO+PnodejS9Dj16tx5jbxHQFAgtJSLVsaPbaXjWfYf01mKu0+vXdSNxb56N1L2AREnJQcvtjqI6QZGxA32UV3/w9laIuD9jBurALmE2/WCIZmAHIfGWbcW14TeQEqGooNhQ7CmozTpia94NRTVPShqmgONaMwM1vTnOtqF1CkI5IWSU/0BYJxrwsQn4TYiAYCP8C3r5NRu+ykpql01GcWbiPPhkhNRwtjsc2h0OQuBa/xt6+Trq1n5PavcTTVMIMPru+/YZ/xKLfRGJbAIfgdWrVzdBX0wWLVrEkiVLeO2112K2nXbaaQwePJj27dtTUFDAE088wdChQ5k/fz6dO3dusj40lr0qCDx35/kAiBbHoDhbAGB4q5BVy6BuG6bzYdDw6H8YR33ppf+DVscF9zcbjKwSu/575CxFequRrkL08hXmQA2I1DY8/Z9vdsrfQc3uiW97IXrxn7EbtXREVQGG9EJKq3pn1oHnr2H4MMpWoleuBp9/dmrLxJY/fLcLAfVx+533IV0leLbPDy4L27XnoRx+7DkMPuZsNG2v3l5Jc/ud98VkCFRtTpzdzgDAu2MpvqL5AOilkWtJGFWb8BUtQknLx97xaBTVPOdE/gDBWb0EYZgF0dp38zs3hQXFLzBIg2BoXjCXgSJwb5oDPjcI1XRa1GvQ3ZXoVYV4NvyJkpaLo91AtIw8kDp6xWb0is14i1bh7H4MWmqOOWir/naVmB9P5OcwQT3GZyJwzoa/jiJAlwhVoGWlABLDVUHtgo9wtB+MltMVoYLhleiVm8zma3dgeOsQaig6yHIgtNjddOvWreFKSfL+++9js9k455xzYrZNmDAh+H7YsGEcf/zxHHTQQTz77LO8+uqrMfX3FHvtSW16XpqDuiz5FR0B0kcwOkFoZOe2wNB1KisqQLHRrmN3snJb0qxFG/JadyQtI4d3nh+NUToPtc0JCVSiiYUAo2o9evEfYbM96NH3SC6/czzZubHJapLhxZfMgfmma25Gundw+qgRlO3YyozvJiN1l+kHULUGX5VfJarYwZ6FktIKJas7qs0ZisrweTCK/8So3khAt9xr4AjOveahJlvAZVfwbp/nD78DYUsDJGuW/smapX/y3otjaNelF9c99CY5zfPrb2gf4PlnnjAFAj/hsqI9rxdas25ITxWoTtAcKIqGlAZ69XZ8hfMwarbhWjoJhIK93TC0rHaYWYfC1fCx9ncR4TxASEgIF0z8n4USJRAAzs5DqVv5I0gdW253HO0GMvahh/l16n/55J3nMWqKqVvxHcKehqPdQOyt++LZugjpraVu6RRsrXrjbD8gZGZIYFYI5hQIM0fIgH+DQciHwZDBHC9CkaavgFcihA1ntxG4Vk8HaeDeMBvprcXeqi+KTfidBwWOLiNRNGfE9Vc0y2HAIhFR4Tg7SVOZOqWUTJo0iZNOOolmzZo1WD8/P5+hQ4fy119/Ncnxd5a9JggoisK51zzEJ2+OB8MNSHNQdLThvqf/TdvOPZNq5+cvXmfzuuUYhi9sNhb/wREjBOwwMx8ecdxFtO/ah0EjTsdub5pYzpdemxDx+bxrHw6+37FtEw/feQOybruZhtW1A8O1A6NsMT7FDrZ0QIC7xNxBS+WSGx7miOPO3W22+U1rl/LUPdfQslkKNVVleN0uFFVD1TQ0m4MWrdpz0kW30KZDd9KzzBv8xjseYPz9l4PuRnprUNLzcXQ6CqO2BF9pAZvWLuW+f47k5S+W7tM+BYHIh3gRELffeZ8/9bQdJTXXHPd0D7dcfT7Pv/AiQrVx9ein+L+nbjQHs6iHUrQQEFyzIqiSj55xB+rH6WhAIPAveCR1iS2rJWrf06hZ9j3eouV4S9by67fdGHH6FYw4/QoqSou474az0Cs241o7E7TIMCXv9iX4yjeS2nUkalomEZkh/YmSIsIjwwb5oDNiILQw3OwhI+RrhAK2nHa4VLu5g6Lg2boQNasNWlouwpaK9NahZuSZv18jFKEgjTCpwMIimn0os+Bvv/3Gxo0befrpp5PeJ0k3vd3KXncW3FX++uVr3njqBpTsXqjNDklqH6NuO/q2adgdKTz21m+7lPmwKTAMg2XzZvLqU/cjXcVBwahlm86ce+3De8Q+f90pnc0nt1BAsSH8M97gFNTwBuuq2d15+YPQgj43X3MpvsJ55owZQKgIW0pwwZmUnufx/LPP7PZz2BPcOvoe3CsmxX34CGcO9taHoaa3iBnkw0P0hRrH5B6WkCdUFrW5njYNn4574zw821eC1BG2NBydh/HCC6adcvS9d+PZNBdf6dpg39Ws9makS8VGALTcrjg6Ho6qKRHHDw9rNDwyqDmI1hSYb0KahYBToylQmB2tK/gdX2kBzs4jcRVMA81BWt9z8GxbgnfbQhydR2LLamMO/mEHeHasZRrY39gzzoJ9+XLMtbvczpnvfZdUXxtyFrzuuut47733KCwsJDW1Yd+yrVu3cvDBB/OPf/xjr4YU7h9G3Ho49KhTeWPczRhVaxsUBIzqDejlS8FTDgjuePazvS4EgKkd6T1wBK9+uvfWH1DS22NUrQNAa3YwtrzeYYMPGK5K9Opt+HYsRS9fxZvjbuKfd70EwITX3gXg1ptvwle8FKOmEOl1gaKhpOf/bYQAML8rNbMDesU6f4GGrdUh2HI6othTE4Xjhz6Hq9XDy/0OBAHVe8BJMLg4VmAwjuO4JwSoNpWUzoNwtB9AXcEcfDvW4Fr5HTdfdRYT3vic5558ijvvux9vTmfca35EzWiJvd1hKFoKem0J7vUz8ZWswVdaAKoNpIE9vx/ONgebWntfNVJ1BrVuQcfHMGFBCDNk0PBKFHv0iZsdVZyZAOi1O9Ba9sZXuAT3+tkIf7nEXPTLXLvAPIYlBFgkpmlMA/VRW1vLN998A8CGDRuorKzk00/NnBhHH300LVqY/mk+n49PP/2UM844I64Q8OGHHzJ16lROOukkWrduTUFBAWPHjkVV1ZgEQ3ua/V4QANOxT9ZsxKjZipIWim83PJXImg0YtVvBXUbQ0dDRgidfn7Jf2K73FK98NIPfv/+I916615+Dfylas+6oLfqgqhqKPRMlNxM1qwOu5Z8xd+ZXLN6ezvgXxgbbGD/hpb14BnuGgPngtjvuwr1+GrK2CO/2hagp2SjO1NAkPuDtH0bcpawhslBKpOEPGwiECKph0Qf+PAIBz3xzfxH8o9pU0rodid6mN9WLv8G79S9uvHAIjo7DkHXluAumoWa2YsIHvwfNNXfcez+pPc/itCN7MOn1sUipg+7Ds/l/+ErW4Gg7gLrVPwOQPuAcFHtq8HwCP6lA7iuhAJrA8IZF6QSuhwDvDtPZUthTsTfrjl62wRQ+/Dz3/Ktodjt33Ht/fNOJhUUMu1cQKCoq4txzz40oC3yePn06w4cPB+D777+nuLiYiy66KLoJADp16sTmzZu59dZbKS8vJzs7m5EjR/Loo4/SqdPe9fna700DADdecxv6ps8BBWwZpmpd9/sdBNDSUdI68MJbnyYVDnigYhgGk98exw+fvxF04BLOHJAG0lcbzB0vUvJwdjz+gF5XfvSY+/CWrsa79U9AIhyZ2PP7YsvpBAL02jI82xahV28H3WOuHdDSHy8cJwthuLYgsJCRNECxiQiTQeAXG3CiC9rQwzz5pQTD66VmyXcYtaWhnRUbWm4XhJbKA4+PT7jo1e23Xo9r4+8RqwUCoDlI7382Ujdwb/wLW/MuqGktEKoIagWkfyliw4epGQjze/AUrca9fg4g0Vr0QmvRC+/2v0D3ccOdT3DwgGGN+AYs9mX2jGmgD1/eefUut3Pm+z/t9r7uy/wtBAGA6y8ahaxcYQ5eig20VISjOUpaOya88eY+7ay2r3LTlefhK1lq5msQwoxRT2mOmtODCa+9tbe7t89w2+jReDbPwqjaahYEjeQhbzk1qyOODkPNzXFm8kAw4VB42GFwgA23u/tn14Glf4UiQhYDv9bAbNis7y5ch2vNr6FGAwiFiVPXxT2nO+4xIygMTzV1y7+M3Kg50DJa4SvbYH62pWDP74Oj5UHByIbwlMgBU0dAuDFcVdSs/B68dWi53Xjpvz/Vf4Et9kv2iCBwaG++uPOqXW7nrA+mWYJAMhX3dUFgT7Bh1SI2FSyl96CREeGFKxfN4a+Zk/n1x6/odnBvnClplBVvo6RwM666apCgahpdeg7ixkfeRrPb9+JZWOwORo+5D0P68BUtNmPiDT0yI2FGG7QWB6Ok5cUXSqM1BAIwTMdClKhtftu5ohE0HQR9CWLaDTntGTpIaeDZthTP5vn+7SrCkcGZF9/AMWdeFdG3gDBw9QWn8NLT96HldsOo2o63cGHUMfyjv+bE1rwbSIm9dW80px3Da2oGhAD39qXolZvQctqj5XalbsU3SHcVR596GRdc/2hjL7nFPs6eEQR68cUd/9zlds76cIYlCCRT8UAWBAzD4Lk7z6FgeSjWU9VsOFPTqaupwtB9cfYSZsy5LR0hFHNQ8FWj5PTklfe/3XOdt9jj3Hb7aFwrPycyFWB4YLwN4czG1qInWla7CA1BUCPgn+mHJw8KrURoTv8VLcypMCzWPjxlcUROAmk68kkDvGWb8JVuQK/ZgXRXmxsVjRGn/oOzrrw3KKzecc99MY6N4x5/hNE3Xo7hKsPeogdSceDdNg9f8YqIekpKDo4Og9AyWiF1SfWCSRHRJyH1ATz65i8JzRQW+yd7ThC4YpfbOevDXw5oQeBv4Sy4O7nx+jvRt3wH3kpESiuUjM5m+KGrlJqaOn8WNQWl1TC09LYx+wce8t7SpRglCzj7vHNj6ljsHLW1VVSVFNGiTad9yvTz2EP3ceeF/pX24k3TDS+ydgeeDTPxCAXhzDYTMikq0l0Fvjqk4TNn65oDNa0FtjYDUYQazCEAEkMGwhEF+EKeiEZgcaFAzoGAg6EiUBzmZ7VVe8hvbwoHhoFn8xJcmxcz/au3mP71u9jbDubFf3+Y0GP/xX+/F1O2bdMaCpb9RUZWM/79zF0YdSXUrfjBDGXsNARb8654i5ajZrenW8f2VJQWkZKeSd/Bx1lCgIXFXsQSBOrBVVuNvulr0GsRGZ2x5R0BgJrRCSm9+LZOQ3oq0NqMTLh2gJSgV63HKFkAQmP4qMv33An8jdmxbRMP/nNoqEAo/sFU8yf/yeOFN7/cK2mO07Oa8cKnS5n8zjOsWzGPjWsWI6VEyTIHO6N6u9+ZFUBB1pUh60yHPkVRcaSk4fKANHSkuxKfqxxfxSZSepyGqjiCs/xgDL8uY1MDB7YHVir0CwaEKykCVRUFZ4e+ODv0xb11FXUFf+LZOIvrz+iFmtUeLacjakqzBsP48tt1Jb9dVwBe/ew4qitK+fCV+5j32ze4Vv2AmtkWhIJeXchmcTTP/Pv9etuzsGgIiTTznVjsEpYgkICK0iLuvnQoGG6UrJ5ozfubGwRII0wIaB0SAqKXajdqtuLb8T/wVYNQuf+VqfvUzDUZbh8dSrsbno//yUfuYfm8X6mtqaRzz0Np2abzHj23rJxc2nXpxaa1S80CaQQTGknAqCnk1kuP5eUPZuyxPoXjTE3n/OseAfyZJK8+GqNiI2pudxztjkBxZODa8CvSVY5wZJJ68OnoXp0Xnhkb09bNV5yGt3AhdSu+JKXHqGD4HoR56RvRGQGJcC4USsh3MSLEkcjUBI7W3bG16kzt8l/xlW7Ct2MZvh3LQCjcdMUCXnpratLXID2rGVffO5HK8mLu/udx6JWbzQ26Fynh9jtuB6nz1NhxKIoGhmH5z1g0DhmVwtJip7B8BKIwDIMbLzoBWbkakCjN+qPl+NMd1yMERLThqcC3dTr4ahBCoc/gY/jHLU8HU/PuL9w+OnIxHr1mC96ipci6ksQ/PjMHLsKehpLaktvvG0vHHv13m5BgGAarl/zJkj9/Yt2KBdRUleGqq0EAV9w5nm59Dqdw01ocqek7vX5EU7Bg9g+8/cwtuF21cben9DoPoZqDYLzV9m7+55l4t80DBFrL3jhb9wv6D2AQucSxP+9/+MqF4c6G4ZkCg/ilgYgMiAr4KovxFK/DW7gW6XMjUlsw/r1fdioE9+Z/XYBRuRElpyuyeiu+ktgV37TcbjjbHx7sw7NPPt7o41jsG+wJH4FTBxzMF6Mv3eV2zv5o9gHtI2AJAlFcf8HxyMqVoDrR8oagpLYKboswB8QRAgzDh77jf8iqAhCCQ4edwkU3PklqetZu73fBivksnPUddmcqDmcqG9csJiUtk5MuuGmXBsCy4m3cf8N5GNVbgoO/sKcjnNlomfkIzYFRV4r0VCN9bv/Lg/TVRapHVDvCnoHizOVfN43hoAHDmmxdh3isXDSHKe8+y9rlfwUd0lLTsxl28sWcetGte23mublgGSsXzmL7pjXUVJVjd6QweORZScXPL5//GxMevMLM5aA5seX1wpbbGUVzYvg81C79HCUlm5QeJ6D4Qwoh4C8QGaoYnZdAGqEVCMO1CAHhQfca1K38Gb1yGyg2nvlg7k6tzAnw8b8fZvpX/vBToaKkNgPDh1FXBkBK3wt5/qmndqpti32HPSUIfD76kl1u55yP/jigBQHLNBCFEIpfY6qAlhYsl4YX37b4QoDhqUDfMRdZtx2A7NxWXP/wf2jXpddu729tbRUv3Hkum9ctj7v9l6nvccJ513PG5WMa3fYN5w/FqDKXh0VLQcvuhJbXG8NdgepMR7Wn+melnSLMIoHBRq8tw1e2Hl9VEYarEllXil5XwsRH/XG/fs2BcGRz633P0K2PORN01VYzf9Z31FaVc/CAo2jdoXtS1+GvmVOY9NYEjJodBFaxFPYM1IzWSN1NbeVmvv/4Fb7/+FVESg7XjnmGvoOPbfR12RXadu6Z9IJa0RzcfyivTF7JZ68/xrTJb+Hd+hferX/5YwwBzYmj0wgEIqQN8P8R/qRDMmzxI4hSCviXlQjmLVBEcDVBIRRSux+Le/sKPJv/x50XH4azyzG8MH5io8/jiOPOZdYPH5naEamb35efEaddEbFAl4VF/ez+FMMHApZGIA6fvPYI0yb/B4SG1vFcpK8GfetPYHgQzjyU1DaIjPbI2m3oZYvBa9qmW7bpzOmXjaH/0JP2SD+3rF/BM6PPwl1Xg5KWj9aiPwIDQ/egOJth1JXg3TYbdBdKWj6vfDIn6bZ//fYDPnjpHoQ9C3vbI1HTcgHw1ezAV7ICW6tD0FKygt7pQMgpjVitcyALqOGuwluxCaO6CKOuDOmtizAzmKvQxVGfKxpCS0HNbM9z//cpd93/GHpFAXr5OmxGNe66UMw+WgpaVlvsrfuhaM6QCl2Cp3gNvqJlGK6KYF172yN48dX9K0GSz+Nh4ZwfmPfbVApWzKeiZLt/FTNhamzsaQh7Ooo9HcWRjnBmoqRkRawVAMR8X4HEP+HagFCkglmhdv0ivFsXmJ8VDSUlFy2nCy/+34eNOofa6grWrVzAlnXLcTrT6HP4sVba778Re0YjcBCf337xLrdzzsf/O6A1ApYgkIBPX3+Mn794g5CrdSIEPQccxXnXPULLNnsuX/S2TWt44voT0HUfWqvBaNldQ8svh6l9DcPAs2YK0lvFS1+tTdqL/obzh2BUbcbZ8yLTvi9Ary3Gve5ncObg6DgczWaPTIQTJghEr5wXEAbiLcpTu/IbjNqSYJma3hItpzPC4USvKkSvLkb66pCemvjSv5aCmtYCLbstSmZ7FNUW7FOEPTws657hdePeNBdfSYFZqDqwteiJsKVxxeWX0XvQyL0ScbCzGIbBtC/fZPaPn1C8fSMed13iykIBRUUodoRmR2hOlLQ8bK16hpbyVkWEMGcWhtIaeysrcW9ZhK9iK/jMYzm7ncQLL/57d5yexX7InhEEevDZbfFz+zeGcz/564AWBPafJ90e5pgzr+LnyeaqegOGHMfAYaPoc/hxGIaP+b99y9qlf9Isry0jT79ir6xd8PoT16HrPmztjkFLD5tFRfnkKYrfXVwojRrYhOK3oRseUJxBIUBxZpvqZ9XmX0RH+lehC0wpw53W/EVhs09BZLIbKcGW2w23pwZHfj/U5l1QNTVY35bTzp9Gz/SE9+xYh2fbQqS7EgBb6wHYW/SMyKITnkgnmNo2zDZuZuWzk9LpSPTWA3Bv+h96xUa8281se6899hvCkcmrXyxm9F338dy4fX89BUVROPasqzn2rFDe9drqCrZvWkPhlnV88P5bSE8t0ltH6xbZbNm8AWl4kK46kGXoVdvwbl9kOnmmZKOk5qJltkJJa44QSvAa6v5liNWUDJwdj6R6wUf+DmgIZw533HOftVqgxR7GMg3sKpYgkICc5vlM/DrWqxk0Bo88k8Ejz9zjfQpn28Y1CHs2alqreusZtcVIXy0iJa9R7V97+wO88tAV+IqXo2a2w73eLwR0HIlQbBH2ZyB29hi2Wl5E3HrYLN2feh5b827YWnQLesCHQuECmfVEcF97847YW3TCqKvCW7YONfdgM6ueQXDWH51uN7Aoj1AEAZlFCoE0JKozhbQeR+Hz6OilZt59z/YlSHclt915F4qqMfru+wisJvjc0/vPIJeankXngw+l88GHcsSx5ySsZxgGv38/iQ9fH4d0V6F7qtErNuPd5k8lrNrQMttgz++L4swKCpvh33lKj1MbjAypra1CQeBMTd/VU7OwsGhCLEFgv0WCqoXMAQHChGNfxQa8W38DBPc+806jWu89aCQIBV/ZWnylq0JCgGozK0T7AoRN8yV+tXLY4jnBfcJC2oKagvDQN9VUYEgBCBkpYAgRzLuvpGbgSO0bTLeLCFvkRhUhzYNfKJC6DAoJIbOB6VRn+CSqpqK27Iq3vCiobXAt/xQUG/0OO5oVO2zoVVu49aZrGf/S30v9rSgKw066iGEnmSpWn8fD2hXzWLVoFhvXLGbd8vnUlK3HV7YeVDtqai5Keivzu1BtSN2Dr2wdE/6TWLX6xVtP8cMnpmOhcGRyy8Ov0eOQI/fE6Vn8nZFWQqGmwBIE9lsE0luDYfjMZCwQGfNftQ3v1l9BqNz9wpc75amupLfFqNoIWgqODn5NQH34R/agaj7U1aA3eiIntYgmwtwywq0Opi3B7/keIWjICPNDIMV/uDAScHgLCgEE5RGELWTWcK3+IdgXodqRhpcFs74Llql5vf/26m/NbqdH38Pp0ffwYNnWDav4ZtIE/vrtB/SqbehV20I7qA60rHbmIkX+Lz/6+vz4VSCLoEC6Kxl/z4VoLQ7mpXe+w8JiZzEzC1oJhXaV/SvNnUWQ3ocNB18d7pWTcK35Er2m0Bw8DTB0D55NMwDBw//3Mx26992pY7z4/nQQGvjqcK3/EaOuJKZOwM4fUWbIiO0JNQMJ9o+IRAgUhL+C5aFtiiZME4EE6ZNm2Ft4Ot2AySDqs1DDtASArUU3lIw8nAedSPohZ5Mx4EK0vB7BQ+o7luOt2E5FaRElhZsSX7y/Ga07dOequ15m4pRVvPTlam5+4n1ufOxdXvxyJROnrEJJyQleRKEI7rj3/oj9be2ONJcHD7PZ+HYs57Zbb9iTp2Hxt8RogteBjaUR2E+54eG3+OuXr/n12w9YufB3PBt/RElvg9bsYLxbZ4HUseUfuUuRDJqm4TjoLDwbZiBri3Ct/RYlPR97/kDTVhxG0PYecBb0mwggNNBHrIQXRYQwIITphBi+McYJIVA3cn+hmtN9RY1yUhShurEH91dTILXL4Iispe6ti/AVrURr3h29ajvSXYln/U/cfclPZgXFRr/BIzn3modoltcmwQH+Xmh2Owf3HxpRFp4BMFwIuOGcQ/0RIfGDk1xrf2TJ3On0Hjhit/TVwsKiYSxBYD/m0KNO5dCjTqWitIgX77uYbRtW4aneAphRD+dc/cAuH2P8c88AUFK4iQeuG4VRvQ3X6inminJdT0HR7MEZfpim3vQyDyOk3ic0IEePDeET/kBYYrgQEEcgiBjg/WaFoG9hPAfGqOPFQwhTEeLatAjPlgXY2/TDs3VRROiicGSipGSjl29kwezvWfC/X5j41Yr4DR5ghAsFRl0pIEFzYMvpgPCvkyB1D0ZtKXrldl558HLufembPZKAy+LvhuUj0BRYgsDfgKxmeTw48UfWLJ3Lsr9mcOhRp9Km40FNeozclu247ZF/8/y9l4HuMlMIYySebYfnro+TWyBAjDNgxEb/f8HQvwQjd9DYHzp0xKAf3Wy0ICIi30sd3FsW4d68gJQex6Omt0TYMvGVrUPNzEdLa4U0DOpWTQ3uZMttOPvhgYi99QA8W+eBz413h2lCsLfph5rdBlVVqF70FUZdOS3bddnbXbXYL5FIS7W/y1iCwN+Irr0G0rXXwN3W/vN3XwRSR8lsj739kSiKFifpTJzBOkHGwYgCEb9+eHlM6quo/AQJiRIKYhQL4aYLTBOBs11fnO1M3wpDB3teR+wtOpomDwNcm+aFNSw57/wL6+vBAcuLr3+GYRjMnTmZHz/7PzYXLMO1ZjogEI50pLsK4cjYretOWFhY1I/lLGiRPNJAOHNwdjwqFKkQQIhgalqhhl6EO+QpUU5/4aOxIUMvKSM/GzIUdijD6gb7ZWrt4zkemn0Le5HY3cB/ijGvYHcD+fcFZorlln2C+33y1feNuJAHFoqicNiIM7nv5W8Z98Ff2FodAoqGdFehZrZhwicL9nYXLfZTJCClscuvAx1LELBIHsXmNwmEjeNhAgBKyAM/eqw3V7iTMS9zhi2Dg3jwZUR/lrEjfdSoH+oT9Q7+McJCMK9A/BeEhAKk6ZCo2lWcrfuhNusKgGf9dCY+ehWFW9bt6lX+W5OZ3ZwJ//mKV75aRWrf80npun+lcrbYx/DnEbAEgV3D+gVaJMXPX7wRXHQpxqEvMJCKsEG2Hht9OHHrh+0T15QQngQgrDhucEF08qIkiPBPNMKiHcL6JIWZjCi18xH4WnTHVfALi+b8yKI5PwbbURQVZ2oGLdt24qB+Qxk84swYW7jHVYthGAdctj1FUXjuqaf3djcsLCywBAGLJLjxkmPQS9eAasfZaXiEACAU/0zZwMwGGJYtMIaoZEIRRA3qMR+it8eZ4cc4AYYfN47QEHHo8L4H2ow2NUQJFUIAqsCW3Ryt/1n4qorxlaxBel3mTMPros5dw7oV81m3Yj7fTnqJ4EUDwlddRLWj5nRG1pWQ0vUEnh37BLePGUPdiq/AV4dwZDH+wzl7ZV0LC4t9mQPFWVDXdaZOncqPP/7IH3/8wfbt26mrqyM3N5cePXowbNgwzj77bDp1anzIuCUIWCRk26Y1PHbjKKS3FmFPx9n9JFSbI6Ryl2D4EnjhJ0G42SDhxkB74V79UTP/+mz+wfbDZ/lx9onQDsbpT0ArEN5GRHcBW2ZzbJnNY4QHwzAwanbgK12HXlOG1D0gJYo9FWFPw3BXYVQXoZeuJa332SAEt958Nd5t8/1CQCbSXcFtl47glY//qP9kLSwOIA6EzILV1dU8++yzTJw4kdLSUnr16kW/fv046qijcDqdlJWVsW7dOp5++mnuvvtuhg8fziOPPMKQIUOSPoYlCFgEMQyDwi0FlBRu4t8vjkMvWQ6Amt0BR4ehgIGvajvS50bL7hAckePOyKNC+hLkkwnuF95GYFXDIDKsYqAo/BgkEAbiBCQEVh+MMUmIqP3CtgfMAjGCSwLBR0S1pQgFJaMlWkbLmLwIenUxtav8yYkMLzXLJoP0ge41d7dnkHrw6dSumIJRvR2Pq9bSClhYHEB06tSJnj17Mm7cOM4880yysrIS1v3zzz+ZNGkSp556Kk888QTXX399UsewBIEDnNvG3IV34+/olYnT5erlG6gt30jEaN5hmCkMJEuU6h1CY2K0/1+4D0IMCUIIY2bo8VT9/orRA3VMnfBt0fkJEhF9fmECkBD+TRIz6sCPXlNC7eqfUFKzcbQ9FPfGuRjuShAKWm5XtJxOKCnZuAqmI13lZrkVZmdhEcbfP4/Al19+mfTs/rDDDuOwww7jkUceYcOGDUkfwxIEDnB82/6KKwScdukdADhT0tmyfgUb1yxm09qlAKiZbVGy2jesk4d6NQEQO7hGxvfH8/6LIoHDYIQPQJRzYb0DerRPQOBvQ5qNQJ1Ezo9RfdWri6ld+SNKSjap3Y9FKBrqwSdF7G94aqld8jlInRb5Hbn63lcbXOp3f2TMw2YGzKcffmwv98Ri/+Pvn1mwMSr+ABkZGfTu3Tvp+pYgcIDzyHPvMG3yf8jMaUHngwfS+eABcQebKe89bwoCioZIyUEv34DSLOSUkoxMUB8J94+xOxClXidmgJdRA3g8h7+En+NoLiLqxulC+OBdn2ATeO+rihICVFvYeYb6VLf2J5A6V46ZwKDhpyfoVP3cef/9eEsL0KsLQcLDz75Nbst2O9WWhcW+yN9dI7AnsASBA5xmeW2SWpPguLP+xbxfp7J90xp8hYsBEIqClt0hoaq93kE1UCVeeGBDsX4xnn711I0SAqLH/UR9DAz0we40YMZI1OXo+jGagHAhIAxfZSHSVYGa2W6nhQAAT9EyPJv/Cn6+/4qhnHzRLYy65PadbrMp8Pl8TLj3InZs30h1eRlaTjsMw/hbajwsLJoKr9fLc889xyeffMKmTZtwuVwR24UQVFRUNLpdSxCwSApnajqjn/mMOy/oB0iUjHzUrDaN1wREj+ExjoYyYltEebyDNXT8cIe+6JBAEaUciOPDEPM5ykGwwfwEYfWDQoAzm5Sux4DQkIaMWCQp0J6vZBUAT7z6eQMHSMwt15xrCgGqnfR+Z2G4a6hd9g3ffPAix5zxT1LTEzsd7W52bFvP6iX+CAjVhnfHam484yCuHP0sA48+ba/1y2L/QkqJ8Tc3DYRz/fXX8+6773Laaadx4oknYrfbm6RdSxCwaCQSFA1n55Hm7C0ZQaARoX6J20iwY0MDcXgTSsODffQh45oVwgWCYOWoOlHoVcXUror0CYg2ZYScJEMfDCMUGnXnfffzzBOhlf3q469fvsaz6U9TCOg7CqO2FPfWJWDoqJoNuz0lqXZ2F2np2Wg2Bz6vOxghIX1u3hx3Ex98/R1aWi5g+Q1YNMzf3UcgnM8++4wXXngh6WiAZLH0cBZJk56ZzfHnXAuGD9eqbzCMJH6AIqRiDwys4a/Y+iLe23rbj1igKMrTP5x49vvoJQ/CB/m4UQRBf4CoE4gnHPiFAjNEMMocEEzNHHqF+zZozXsAcO8VR1NZXgyQtBAA8J+XHza7bHNSveALapf/gF6xlbw2nbjvle/QmmgmsTNsLljGXZcMMoUAEfsIUlOzYr8XCwsLMjIy6Ny5c5O3awkCFo1i1toaDj3qVIy6MtzrZkZurGcQDlYRCQbg+khkEojnmxCtvg+8JDGDdUJhRMr4moaI8MCoxALRzn7+jzGaANUW/1yiTlNLb4GtdT/Q3Xz2evICQAB73kEgFKS7mrzWHTju7Gt48t05PPL6DPLbdW10e02Fz+Ph+bvOB2mQ0mMkmYMvIXPIP0jpenhYLeuxZJEcZvDgrv/bXxg9ejSvvPIKPp+vSdu1TAMWjWLsIw9z0+ndAdArN+PePBdb3sEo9rRGqel3ikSDfaI60WUJiLH9BwP/E7Qd3n54vKKMDGHQa0pMIcAZEgIM3Yd0VSJSMlGEampVhAAM/yqLBtLjwnBXYlQV+ptq/IPqhZffa/Q+e4IJD/yDuppKHO0HYs9ti6+6BPem+fjKtgbr1C79BluLzjjye+7FnlrsH/z9wwfDufnmm9m6dStdu3blqKOOIjs7O2K7EIIXX3yx0e1agoBFo9A0jbaderC5YBkA3qLl+Mo3kdb7TLNC1KC7U1EBydatb4CvL6Y/QQhhIp+AuN1ItAqi/71eXWwmC3Jmk9r9GISigZTULv0a6a6sp+NRp+HM4pKbn0q6/r5MwYr5rF48ByW9Oc42PfEUr6Nu1a+RlYSCUVuGe8NcpKdm73TUwmIf5f333+fZZ59FCMHPP/8c4yy4s4KApYPbz6mtruDLt5/mhvMO45brLtkjx7zv5W+Z+M0GehxiJrqwtewZo6qvNy9A9MZoW0H0+2iSNikQ2a84pgshiFhDIGIpYwBDInVpztbDX+F9VsyXUM2XUefPGJiSTepBxyI0e7Cuo3XfJDtvomW3+9ukFH7mznMBQVq3YSBAy8pHSclEy2pFas/hZA27mKwj/0HmkRchNAeewlV7u8sW+zimMs/Y5Vd9rFmzhmuvvZZ+/fqhaVrcRD2XX345QoiY13fffRdT99lnn6Vjx444nU4GDRrEjBkzkj7fe+65h3POOYeSkhK2bNnCunXrIl4FBQVJtxWOpRHYjyncso6Hrx4e/OypK9ujx1+9xnxQC1tooGpwMaD6BvhEhNvd48zk691VRGrskzl0xOw/zuqH8Y4fqKZXF1Oz4kfUlGxSekT5BEiwNe+MrXmYs49/P8MwQPcgPXVITy2GpxrP1kV4ty/hlmvO5sXXPqu/0/sov3zzHpPeGIf0haIDfDUl2FMzUR0pZA46M8JfUNjA8GmgauBzc/3pPbjslqcYPPLMvXQGFvs2crfb+JcuXcrUqVMZPHiwuYBYAifpzp078/7770eUHXzwwRGfn332We69916efPJJBgwYwOuvv85JJ53En3/+SZ8+fRrsS1lZGVdffTWZmZk7f0JxsASB/ZRNa5fy7B1nhwoUjYuuf2S3HtPjcbFh5QIMw2DBrO8xanYAIDRH/KWDw4mrCQhsI3agTTZsrwFhIFkrREx96f+giISnE33cgBAQ0ASgRDkG1tNPRVFAcYLNCak5AGjNu1Az7yM8Wxc37iT2EUbfeS21S781IyQ0Z/Crq1v1C7YW7UxzCaHAAcMH0gDD40G6TbOA9Ll5+9lb6dproJUR0WKvMGrUKE4/3UzqdfnllzN37ty49VJSUjj88MPjbgNwu908/vjj3Hrrrdxxh5nC/eijj6ZPnz488cQTTJo0qcG+nHDCCfzxxx8cc8wxO3EmibEEgf2QJf+bxisPXQFASnoW19z/f/Tom/gGbCruu+wIqitKI8qcXUdgy8wLFSSabtc3s25MWSInwV0gobCQQAiIRohITUDqQQmiAxrZB0XRUFKzMWpKGPPg/Tz9aOOjB/Ym7k3zAcg49GxUZxqGNPBuW4GwqdhSbBheGZbcSSCERE0ReKWCrUVH7K16AAY1i3/kwetP5cUP/7dXwx4t9j2kZLcnFGqqbJezZs2ioqKCCy+8MFimqirnn38+zz33HFJKRAMPnKuvvpobbriBmpoajjnmmBhnQYABAwY0um+Wj8B+iNftBiAjuzn3v/ztHhECtm5YRXVFGSgqWouDguWuNdNDqrKEQkDU3/BNjR3QGwhPbHD3BH2ICGcMOAkqYcdLcFwhQOpeXBv/algIqKetiOP7t3tLN2DUlCAcmfudEACgpGYDULv6N3w1JaiaQkrHnqS0OwihgpoiUB0CaYCigWIXSB20FBtZA4bjbJmPLac1thadMOrKGXPxoRSsmL93T8piH0M2iY9AUjlRGmDt2rVkZ2djt9s59NBD+fLLLyO2L19uLut+0EEHRZT37NmTqqoqtmzZ0uAxTjrpJAoKChg7dizHHnssgwYNCr4GDhzIoEGDdqrvlkZgP6T/0JOY+M2GPXa8ksJNPH7DCYDE0e4wbLkdcTtS8RWuQMvpgKKqiXcOzOITDN4NZfZr7D710Si3hCR8D4LvNRupBx8LQmlQoo9sJOpz1NoF7s3zAMGz//0t+Tb3Ic4773Len3A3esV2qud/DYC9ZWfSeh6JqmkoKqCCUAWqTUSYj4QAb61ESxGkdupNVWUhdTWVPHf/5bzy6cK9eFYW+xKSpkkxvHr16l3av3///gwaNIhevXpRXl7OxIkTOfPMM/nkk08455xzANO+73A4SEmJzOqZk2OaAktLS2nbtm29x5k+ffou9TMRliBg0SCOlAykX2IW/rzwjpY9cbTsiWhIbRYdwtcEKv2GFvpJ2JUEyxVEt11vs3EEGyEARd3lTHgy6o301ICi8Os376NqNprnd6DfEcfv2kH2IB+8FtBihGw6nsICvCWbyT32InOLIlCFRPdKv2ZAoijgqQWhgq+mgoo/pwTbTOk0iGmT/0ObTj33iCbM4sCgW7duu7T/LbfcEvH5tNNO48gjj+TBBx8MCgJA3ImC9D/IkplEHH300bvUz0RYgoBFvZQVb+OT10JOiK6tC8Fbiy2vB0KJ0gREe/RHlzWS8IE+kUq/MW0EPjd2wE4keDQYIRFROUF51DkGnBAFoDgzMOoq+PLtccE6alY+aQcdx7iH9u0c/LN/+hTprgZA2J1kDT4Pb+kGalfNRstuheEF6QOhSqQOCNNRUNfBp5uZHaUOUklHScvGqKkAJDVLf+STpT8CcNQpl3DhDU/svZO02CdoiqiBpl71UlEUzj77bMaMGUNdXR0pKSnk5OTgcrlwuVw4nc5g3fLyciCkGUiW2tramNUHAZo1a9bo/lqCgEVCbr78eLxFKwEQthSkaseW0QJHxyMSSq8Rdu7dST2CQVwBohHag0QDfNxza8x5xu1gZP+CVYDUPqMwakrAMHCt/wOjrhyJwNA9jTjonue6UV39oYICR4d+qOnNEAo4WnbA2bpjsJ5Q/WkY/E8h1SZQNInuVUCCp0aiqio5R56BUKF05idIr5fU7oOpW7eAX6a+x/9Wr+b58R/vlfO02PvIfTizoIyaOQRCCZcvX07//v2D5cuWLSMjI4M2bdo02GZlZSVjxozhk08+CQoQ0ei6Hre8PixBwCIuN15wOHrlNoQ9DUfnIXg2z0dLyQoKAeED5M7MsiMiAKLLidqWZLRAvaaCXfEPiKfpaEyb8RIoBduLWqMgrEhRFJSMFgAYdeWAglGxleq5k7j91kJszcxwunEP7mvaAb+d3+ZA+jzY0nMQAeWR9AsAfkfMgBZE8a8MqXtMdYhqA0eGwOeRGF4P3opysg4dgXRX49q+Bek1HWZdG5dw+y3n8dQz72K3O+N3x8JiD2MYBp9++im9evUK+gQceeSRZGVl8dFHHwUFAV3X+fjjjzn55JOTMg1cccUVTJs2jauuuoru3btbyxBb7B48Hhe3XTwUo2YHakZLnN2G41o1DTWtGc4Oh8W9WRPevzurLo+3LQnTQHja/4bqRu8XUyeZQb6+HAY7Y7eIEo4CTRhGYIGR0Ma6tb9hzw2FIe0L3PX4vXiLN5o6fUB6XXi2LMWzZRk5wy9FqOYKiwJAmIM/+CMm/KGaigYI8zwVVeCrqaRs5ucJjyldNdSt/oPbLhxI1uCzeHLMo7v3JC32OYzdvMhJbW0t33zzDQAbNmygsrKSTz/9FDBt9rW1tVx++eVceOGFdOnShbKyMiZOnMjcuXP57LNQIjCHw8H999/PvffeS4sWLRgwYABvvPEGBQUFSeUQAPjpp5949dVXufjii5v0HC1B4ABnzMMP8PTDjzHm4QfwVRVSt/JnMHyoWa1JO+hYEJDW+6S93c2kHASTTlqYYMCN2F7fvsmQrGdi9D6A9NZSvXAKqHbS+4/Cs2WJuV1Rzaw7gOJIx1OyHkVLYdGcHykvLcTn9XDI4cftlcQ7d95zA9WLvg0KAYH+CkXFltvOn36ZYPpm868ptQUXfTLAp4Ohm2YCR4okq3Ualalp6LX+dQeEgpaegZqSiru4EKFoSJ9pKtlHNcQWuxEpmyZqoL5I+qKiIs4999yIssDn6dOn07dvXzIzM3n00UfZsWMHdrudgQMH8u2333LCCSdE7Dd69GiklEyYMIHCwkL69OnDN998k1RWQYD8/HyysrIaeW4NYwkCByBjHn4AgKcfDqmUpbuauuXfAwJHp8E4WvYIbmvMOkH10VjzQcxYGhhAGupLnLC8uBqGJFIUx7ad5MVo5Mkauo5RW4avfCuebSvMlLw+N9XzJyM9tTFtGrVl1K36BYCJj34fLP/ktUcQjnRueuDfHDxgWKP6kCz3PPkgj415EE3TuOfJB3EXrqZu9Sx/HxWc7fvgaNcT1eGI/RIFESmFEQKBDK73oAgzuaKUEp9XIA2N/FPOxecW2FMkNpukprCYwuk/oGU2x9m+F9WLpmPUVVH+yztc99t7pPc9jmeefG23nLvFgUfHjh1j7P3RTJ48Oam2hBDceeed3HnnnTvVl4cffpixY8cydOjQuMmEdhZLEDjAEQLueuQBXJv+AiC114loGS3qNWuH05Bn/672LfJgJGUyiNg5oIuON/tWdkJ9H2i7CU/c0HWq5rxPuIRja94Jw12NXrUjvCJadmu07HyEPRWkjuGpQ6h2FHsKCAXP9lX4yrYw4f5LcLTvx/h/J/eASpbbbzqfuvV/cdNp74BQcLTri3vjgrB+tyelU7+Q86MqYr6jiAROAqQRMAtI868ChgGeOklaFnjcAtUGHpegavMOyv/4ATUjh+ZDjkd3u6hNzQBDR0lJx1e+g+qFP1BWvI2c5vlNeu4W+yZNs9bA/pFb74ILLmDhwoW0b9+efv36xV2GOFmhJBxLEDgACWgC7nrkgWCZXlNqZg0MCAH1jW1huf+bevCPS6LZfML6ScT1hSWuCRBX6G+k00F49QSa/8h6/pUMhT0lNPMHvMXrCZyk0OzY2/TC0aY3ihrngRXmSGlv3g7D46Jq7he4Ny3CMIwmC436dtJL1K2dA4qKrXl7fOXbI4SA9ENOwJ7byuySYZ6wNIQ52w9/+X0CAt+r5pAgBbrXLFA10zwgDYmr1hQShCrxlhdT/scPaJk5tBx+HD7dhuK00XzE2WYOAhVqt2yiav7PvPnUTdzx7KdNct4W+y5yDyw6tC/x3HPPMW7cOFq2bImu61RVVTVJu5YgcAAz7qHHTGFA+D283VVU/fUptuYdcHQ4NPEAEh5Gtzv9dOprP3pb2Ki7s8JJwoE7XLsQNso3tKxCovJgM36thCoEWYedi5TgqyrGs3UZel0liiMNe6se2HNb+xsgfjRFlPlEsTuxt+2Fe8N8/vPMzVx554RGCQP3PPkgY++NdLqrLC/mq3efQ2gOMo84B0XRMKRB9YLv0CtNrUXNsl9wDD8/2A8z0RJghOz3AUuB8PsGSD2wKKGZO0BRQ34CQjH/Kiq4thdTNud7tMwccg4/Ho/bzExo6BLNLjB8PmpWz6d61TIAnGnpSZ+vhcX+wrhx47jhhht48cUXmzT3gSUIHOAEEtPc+aCHuhU/o9eW4dm2HE/hKpzt+uFoG7b2dtyZcWDbLnRiZ53zkogk2BVitAWNzkQU9j5aG2DEUadIiS2zOVrGUSClOWuur81EZQIc7Xri2b6av2ZOYdHypWZ7thScnQ5l3H31r1sw9t5HMQyDMfdcy5HdO7JxzRJWLpwFSNJ6DUdRNFPgEArp/U6k4rcPwNCRuhdpBGb8hASVQHSAajoBCgG61xzAFRV0H6i2QOykQCimJkH3gc0h0SuKKPvjRxw52bQeeSw+/2OrrtpHXcEiqlcvNRvBrz3J68SqDQXceMEApO7j6GPO4rxrH673nC32Xxqy3/+d8Hq9nHHGGU2eAMkSBCwAeObRUPa6264/G9fGBbg2/IV7y2IcbXpja90r8uaLF/qWiOhZayInvXhq+GTS+jX0IEg0k66vfjjxx+z69wt4wofVj7BYqCJUL157wRCIyHZjLoeIf4kURSNr8NmU//IO3qKCYLnhquT2m8/HV1XMhLd+jjmFwk1r+fi1R1g2/xeQkh/9KyALeyopXQ/Dlt0qQhOhagpC1ZCGjrPdQTHtBQWCwHUwzAe34j9/oZhmAan7zQJ+gUC1SVNI8HjYNu1H7Fk55Bx5HHVuM25ad7nY8eMnQQEg2L7Pg3vrytDFkpLpU96xBIG/KQeaaeDcc8/lu+++s5Yhttj9vPDqZxiGwW3XnIpn63JcG+bh2jAPJSULW24H7G16oTRmmd2GHPwasuknMLqHMv01MFNvaNCOrhNH3R7dRkBmCfpTxDlGeLeDnvLxBKKAmSDab6Gey1KfQBDoi3vbqmD9jH4nU7t6Dr7SzfhKNwMwevTl2Ju3x3BV0y3Vw7zfvw+G4inODGx5nbA174ialoUQSmT7/nOSBqR0GYSvsoi07gMjrlng2iiqWU/VBKpd+jUiphAgBKh2GXQQDE8uJATYnHayeg/A0bobimZDs0k8lVUUfReKz3a26YzUddxFW9AysrG37EBqh+64d2ylasFMDjn8WO5/4SEev+2R2C/JYr/GjDo9cDQCw4YN44EHHmDr1q0JlyE+66yzGt2uJQhYxEVRFF58/RsMw2D0zRfiKVyFUVuGe/Mi3JsXoWblk3rQCBS1EbdQhCd/4sE7RjHQmCRGkFg7IcP+JnJA9P81B6rY8mBTMnLgDQQnRDgExhv8E/VTiMjZczIREuGCCKGBOdDX2jV/AqDltEbJyEUakbNn96aFuDctRq8u5i9MtbqtRSecHfqipmYnzK8QOK/ANXK06oKzTdc45+Tvk/+a6D5zYSGh+h0ADYFQzY3hq8AaRugekBJS2h+EYhMoikTXwVULQrMhVJWcI09BODIi8hPoukHt2iXUrp4HispGey5PWkKAxd+Ayy67DICNGzfy4YcfxmwXQlgphi2aHkVReOHljwC4+7EH8JRswL1pIXrFNqr+nIS91UE4OgxACQ8OTzgQBzzFoma+cUjaWb8hgSCRSSDRJELGDvLhf4Oz/IhYeH83wvdrjDuBiDyvYFhdQ+1FmyuMUN+ql/5metoBaQcNoeL3D4KfUw8aRu3qOejVpQCo6c1J6XY4tszcBo9hCitmYWCNgPDcANLv5Be8PjLqcwKhThrCX12GhADD9BdQbOY23SswDHBkZdLq1EsihB6fV6du7VJcm9eg11SE2lZtlM/6nBvOmsKdT31Ax+6HAPDAiw/x2C2WcLD/01QJhfYP1q1bt1vatQQBi6R56oFQAqLbb7kE17o/8WxbhqdwJc62h0Q6FjaWaJU5scJAg2GN0SRjEkhw3Ggbf6CuCN+ngVl7Ir+CuNaPOL4/ic5XRNWVBkifC0/JVnxlW/EUbQhmILTndwMtlIM/47Az0FKzqF1lJgCytehEeq+jQucaJQBFJ/8JnojwX4wwIShcQxGe7lmE7R+QA8MXrpSGACH9fgIC1NCFN3zm4B8u5ARcVWwpEp9HULNpHRVzZ8ReKDAlE9WG4aph3G2nc/cLX9Ghe19LCPibcKCZBjp06LBb2rUEAYud4vkX3wPgtuvOxrVpAa6N8/CWbyG153H1hB3WM4rHUb83tEtC4gzUQaEiwTNDCP+m6AExus2wtoUaW6c+q0ejzyVB/YBK3lO8mZrlvyO9sUuRAthy25LS7XAURaHZiH/4d/afoF87kNKpT4SqP/rYQRNJ+KaoaIYIgUUEZviBuv42wtILSxkIGTR3DjgICjWUUAhA1yWKBqowtQGKJkNmBo/A6xboHl+EEKBm5JDStgvONp3RUtOQ0gwx9JZup3zO9zx122nYcvNBSo474TyOO+tfOFOtUEOLfZcdO3bQokWLRu9XXFxM8+bNk6q7f6RTsthneWHiZ2QMugA1Kx+9spDapd9jGHFUdYkc/SA4s4x4H+9zsi9Cg05gBhqcxAbU1OHCggwbBAP7hdcJayc8I148khrsE/U3fP/A5/BfaJgQ4y0ronrRz5FCgFBQM3JxtO5O5uFnkHHIMSiKEopQCMPZeSDODoegpefEdk+J/CvDZ+TxtDRh5yXCr5/f8S/gJxAUxPz7K6o/OkABVTVfSiDVsAKaRmibTQYdCQMaBWmAommk9xpISueetDz5YlqMOIOMHn1MIcCQSMMULuy5rcg+/HiEasNbvBVvyTa++eBFbjunF1++/TQAD064j/ueujnmeljsy5imgV197ct06tSJW265hcWLFzdYt6amhvfee48BAwbw2mvJp9m2NAIWu8zTDz0BPMFNFx2Jr3wLNUu+I633iXE1A3EHynhe+vGIGiwbyvIbYXf3D0wx7UVpDcLLEw34Da4nFKVeb5Dw4wUPQowwIHVCPgyOTNSslmjpOaR06I3iTAudRNiFiZ7NB7antO9lthk2449w8/D7AoSWDw6M3iACtvk431m01sXQQdFEZLdE2LlKgWHI4AAfPP0oM4qqBtrz5xmQZrQBUpB1cG8UBXweU2tg+Ex7haKJYGIiAEeL1rQ8+WLz+ikGNSsXUrViAd9/OpH5NRWU//EDad0OwWL/4UAwDcyePZv777+ffv360aVLF4YMGUKfPn1o0aIFDoeD8vJy1q1bx19//cXvv/9OdnY2d911F9dee23Sx7AEAYsmI+3gY6hZMR1f2SbqVk4jredxEdubQjXeUKRhzL6JnAXBTERi+BCqLdYpL5HAEjhu1Pagx36Cz0kRJQhEq9chNHCrTieZ/U8MOe8lkk4SlJlJe2KPE14/wiUgYA4IF9okETrFcC2M4TOdCQPLCwvFXNohECWgqqFIASHM99HCQHQ/zO1m5IApcAgCSxYbughqF4TwCwOYmoOAP4JQ/HV9AkNXcO3YDkBa556Uz/kBafhwtGwd2wELi71Inz59mDx5MmvXruXdd9/l559/ZtKkSbjd7mCd9u3bM2TIEN577z1GjRqFpjVuaLcEgb8Z3056idyWbTlsxJl7/NgBZ8Ibzx9oOq3tKMCR19ncmKzKnCQEhvCBKNHsO4HzW2Cm6asopGbZDIRqI+vws0J1/G2Gz47j2cmjiRYCwv/GrRfuExEYxwkrwz++h2sBBCi2sP40FI4ZxxwTL7yxXoElfPYe9T7CoTJKQFNt/rwAuj95kGGO2wpmyGAgwkkIUygIN41Ea3zCTy9gKvAKGRWEImP2CeV5kMF9g4oNFVJat8dbsp2adStBGrQ6+Tweui4ytbLFvs2BlFCoS5cuPPLIIzzyyCMAlJWV4XK5yM3NxW6371LbliDwN+LdF+5k9o8fgxB7RRAIkNrzOKr++BDXuv+ZgkDY4CF1H0gDoYVu3PAJbVLZQmXU+3ps9tEDVWBA8RSuRnpdZmy94UVotogBMUZl3kDnGhIUouuF594PF0CCgoF/ti5UvzAQ7k8QDM0LSQyRqvco80AcLYr0NxYYREMCigy2F9op8n2EMODva0AbEGENkgQXGYowHegidB2QwbwB0bJMuEAQLgwEhSIl8lQD7wNaAZ8nsINAd7kw7HaqVizCtWU9em010uf3WPR7Lhb9+AXz+x5N/6En8chrDwPw0DUPY7EPI8E4gFIMh5OTk9NkbVmCwN+IP6d/Yb7Zyz8MRdWwtz4Yz9ZluDYtwNmhX3Cbe+sKXOvnEwgCzzzsDNSUzNCgG95QAvW8RGKuYm/+Dd8W3C/KJBA+UOu1lXi2+9Pu6j5cW1eR0r5XpFNdvFi/uHF/DVzreCNVYNcw+7X0fw7upoLQQgJJ0GEv2p8CU+UdObCbb6SRYL2C6NNSzLqxG+rfL9DxWEdHv1peA2lIDF1GmAgMA1TF7HdAcAgf7OMJBQCKIpFSIKXpSCiERBGgG8I0BwRyEUjTVGDoZjvVGwuomDcz7nUIaxzD7eL1Z2+h5dI/EAIe/NfD9e9jYfE3wRIE/kYcefx5/PXLVB6Y+EOj9737sdCSxOH5AnaGp+5/jLsevx9P4WpcGxYh0ZHuWvSaCvTqsjCjNFTN+wY1PQc1NQslJQM1NQtb8zamHhlzeWRfVTF6TSVGXSV6TTnS6yG1xxG4Ni0jrccRVC+ZTlrPo/HuWI/iTEfNbI4tsxlg+gF4S7aY+9b6X9WlEcJC3dp5eIrWoaZmo6ZmoqZmoOXko9hTEAJ0dx3C5kSJM6jKqNEyrqzg1/1LXYbU/iqm4CFlcAldhAjWEWrYwA8o9tA2JAgtvi0/ujNxMx3GdtAfTREY0Ymc+QfKot6HCwDRs/agKt+fDMjwyWB/VJvp5BfYJyAMhEd4BLsW1q5hiKg6Al0GfA1EUEvg85lChmY3j53esR1VS50YbjPCQmg2v2Yq3KvRvCel7kNR4IGrH05wsSz2JQ4EZ8E9gRU++Dfiohuf5LmPF5Kd27LR+z71wGPBAeDuxx9osH5DKKpCStcjAIl7wxI82wvQq0oiDdJSIn1uc137rSupWzsXw12LECJs0FKo+3/2zjvOcqu8+98j6ZbpfXdmtvfe3XfXvWCDbXrABmPqCwk4EFMNxoVgINgm9BASenEcAgbjAu5l3e319t77Tu9zi3TeP6QjHenqzswWGwP7fD535ko6Xbp6fuep218ms2cdudY9OAM9yHyGvnWPYncfpvv53+MMdNPz4t0M7l7DwM6XEcrUXQiEYeBk++nf8jyZfZvIdxxA5jIRbuNgd7eRPbiNge0ryRzcgZFIBuLuZJLBHSuxB3qRjgMa4wsYfPhl5EbGc93XwtzYY+JC7ZAFhhVwXeW2qO/+/emoRD3R+AWq3SKi9Gg5/7/6brhtyzg1iwYKdBF/gRRAfY+oKYJIjME99Q35jHAZw9CkJdqSKRWA3q5e1nU1lP45y/I+CYmVkJgJizGXv4OJ77yapsuvJFFVgzAt6pZf4LsjVM49ifqzL2b0RW/BsCRf/vENMYt3gl6L5Eh5zJ+/dzoBBE6QT1/9wpeKy2WPkL7yuZtJjZ6ESKSJl/EXUtmspaTHzfCPhQFWRTWVi1+HSJYMPS4hEMkSKpdcjFVZHTBaASVjZ1A2e+mIxpAcNZ6KBWcjTNP9dQgQhknJ5AX0b36OntWPYPd24uRsZF764nyXccsQQHCBiAhZ3CtXOp+pRpirAgbCFIHUILLl98XsOlOPeZdFIjpraxX05zN5Q5NCaNf875F64f4DUAOROkSYuiVDUQX19pTYX69nGBIh9I+rEnDrhSdteNfUvC1LUpJ2KEk7pFMSJ5ejfcUD5Lo7qD/zQuzebpTVYt+29bQ/8wgdzz1GvrcbIeBff3QCDLzWSSKxcY758/dOJ1QDJyhEX/388bWatqobybXsxGoYT75ld9FyZXOWk2qc4h5E5MNmaSWVSy6m+6X7kdmBQhm4BwKqTroYs6Qics39lx47DWEa9K55ougYkqMnUj73TIQwCnbFwjQpX3A2vWsep/Ppu0iOmkj28E6/SNVpl2FV1AZ1pKt31w34jCQB047o+nXGi1BfRVDOcVUMwvSkDCLQufvzjNvYiNC/QMpQdEdPuF3iRf7qwDf01NUmEfChcgIYlsfIjcBAUFcLqL7UcTE7AU/w4EYolIX2BaqcISCfF+6y5HMcfuwBsl2djDrrQkpHNZCvbwBp07t1s6v+MQyyrYc4eN9vqZg4ieqTRgYeT9AJ+munE0DgBL1iJARYlQ3kWnaSrB1TFAiY5TWkm1wQoDNOnTmYpZWkx85iYPtLhQ1ISI+bFYCAIoKDVNMUBnasde0UYqh02hKEoVnvhSYDwjAonbKE7KGdIRAA0PXMHyibdTqp5hlh5hgVtSsyIsxPBTyKMmbvvFTYxPF067YM7AQiIn3hgYIQXiom+9PH5o1DMW4fnBTR/4fGGW0LDVBEJA1ChF0Gwd2Y6+27IKEQ2fjqixgVgSNdqYBpBpYbiYSDncux9+GHyHZ2Mvb8CxCVDX5/1bPmUjtnjtsnkOls58Cjj9Kzcwe9e3az/fQ3M3nWkiKLd4L+0vT3aCPQ0dHBfffdx969exkcDIcXF0Jw/fVHrto9AQRO0CtKn/n017jx/51Lrm1v+IK267f7u5FSurYBMUxTnXP6OwtPeg74Tl9ntFoMA5bY/V2xYwCwezsLJQoR7wN7INKP1kbfpudINU8HQ2CEmw4x1Z41T5A5uD3MXaWk9twrEVYiND3VryDCoBHh2AOKOepAwgAcCoP+FFsfXeSvMVvT0mwiYhsK2goxc61fw5JIx7XkxxPxq3KGlkhIb0N6jF2dUyBA00hgCNeTQFcTCAF525Wm2Lkce/78EJmOTkafcyHJunpPGiF940PTczc0TYlVX8O4y95C17pVtK9ayX/88Iv82+33cIJemyT5+9Lx//nPf+atb30rvb29JJNJEolE6PrRAoER2wisefZBPvL6yfzjmxew+pkHjrijE/TK0re+8G7+8fUT+eW3PgtAd2crbYf2/EXHdMtnbmb0OHenb/tMXCDMBIlaLYKbY+MM9KrL4V20dpzvafe5kVlahVla5ZaRkrzyBIjucDVyBnpCie8Tdc0ul/Mq2b2d4QoCT0/vfgwT7L4O/2LReeji7ZhoeaF5lFVhlrnz6F75IJ1P/943GBQRsXnUDVI/p3T8Qjv21095GuhrqzN9gu+GiZ/hzzALLfpDXReRdviM3M/f4OntDelH91NphxVFgUZU/6/I0MatbAaUVEA3KjQNCXaWXfc/zEBbO6VjxtKy4jF2/O8d5LvaSViShOWqKGxH2ScIcnlBMuEwcOgAAGXjJvC1n9/A135+wl7gBP3l6dprr2XJkiVs2bKFwcFBenp6Qp/u7u6janfkEgEzCU4eOdjJ92/+AKKklvTEM7n9tm8eVccn6PhRf28XG156HIC92zcA8Jl3nQpOnis+9hWWX3zFX3J4gMDJZcEwKZkwm/TEuRhWinxPG/1bXiLXtg+7vxOzrCK8q9V4hZQOdn83RrqM0mlLSDZOAiB7cAf9W17E7utG4iCi2FYGbeV7OgFI1I2hdPoSrIpanFyGgR1rGdy9jnxfR5iBou1EpduU3dMZmoeZTJHraqN/60vkWvdhD7jzCFntF5lH2YwlpJrceQzu30H/5hdxsoNI6fh5GhSzldo8Qh4CWuAh3ZtAOqFNvRaESKsvtCa1xEDFbDKFEnNEJTcxICFKuoeAWlPpCDACZh41FhQieByKjUsvb9tBASeXY99DD5Lr6qRixjx61r/sXzvw6MPUzJpF6cTJmKk0UgpsR2J74CRvC+xsFoQgXd/g9/PVn7lg4LNX3TT0ZE/Qq0p/T6qB7du3c/vttzNlypTj2u6IgYBIlCKzfSQnn0v+0Dqc3gMMbLiL2z9zkPf8y63UjR53XAd2gkZOpeVVLF7+eloP7uYjN/63e9LLR3/Hf7lA4HO3fBGEa82v6HNf/SIAX/nsKxdW9fO3uv0apkXVGZdjpEr9a1ZlHZVLLiDXcQiZd8dbTL8vcxnKZpxCqnlaoMcHUo2TSY6eSGbfFmQ243oXREm1aZhUnnwxiZrAvdJIpCifsYTSibPIHN5dIOKOeBliVY2ibOZJ/jyEAYnqOqpPvoBc+yGcfD4EZIQIMzGZz1A+6xTS48LzKBk7mXTTRAb3boF8BlIloXEYlntLoyGHlaTCyRPyzDSUoEMPaayNKeo5oL7rngChJYw7H6MOUGuCkKhYEGod9cyCUobDDKsdfhzDl5EvKvaRIVxDQOUtYBiuuF/ms+x76EGynZ00n3cB6fpa7O5W+ve66ql8Xy8tLzwPLzzvD0CYJoZluW6jpkmuuwukLBzL3w/P+asgKf++IgsuXryYPXuOv6R35BIBaZOecj5mWT2J8kbs/jYye55hy5pn+MJ7lyHS1Xz4M7cx/9Tzj/sgT9Dw9MHPfS90LJKlyGw/VvVYnvrznXQ+9QtS4+aTz2axvLjUw+3ijhtJMErKMdOlsS9SnTEXIyNVQnrsjNhrwjBct0NNmhDHyJP1YwrreozQSJdSMn6mz2yiRmmqbMnEmaF6bv/ePOpGB0Z6mhRBX2cjVULJhBmxYxSmQenEGYUBdZSFvWd0p3b7wgrOR8vqNgMhMBJRB6h6UW8A/XpwPnwujmn7IAD8LIGK+UfrGl6UQSnxowIqMKDr/ZUhn/qup1lQ0gLHAx1OLseBh10QMOb8CyhpcPOxjznnPHIDg/Ts2kH/3j0MHDgQGozM55GGQb6/DyklZjJJenSjr35wPCPNE9KAE/SXpO9973u8+93vZsyYMZx33nlHnFyoGB2BRKAMs6zeOxCYZfWUznwDdn8b2b3P4fS38v2b3g/CZN7JZ3PlNV+lqnbUcRnkCToychyHy6/4GL+/8wdkD6zn3l9/ExybzK6VXPee0/jct++hpr4JgC/807Vks4Mkk+mCdj5/qysx+PInj1ViIBFmIoiZr2gEQES351Pi6+KFYxiTKF7HL1tEHaEfF+jDFbOPqhBEAX6Ib4tCxqj/1/sxdAt7E/JZsFK4QYAcPHdCr3ldp+8xWkOFTpZaoh5D+PEO3LnFGGrGjNO/pAGMaFwA/Xo0E3XUJiE4L8P3WsUMwLMMV6BDUy3oZAiwszkOPPyACwIuuICS+vpQ+1Y6Tc2MWdTMmIWUDoeefILeXTv9Mma6hAmXvdFXU6hYUFLCp991AgC8VunvKQrAGWecQS6X45JLLsEwDEpKwhJQIQRdXV1H3O7IgYBhxe6yzNI6SqZfjJPPkju0mnzHTtY89xCffdcpvP7Kf+YNV37iiAd1go6OXn76z6x/8VFW3H8HjhMErm87tBcjVYZV00TPwa1cd9VpnHTWZfSuW8m1b/8pRkkF3/2/tQXtHSsA+PxtN+Dk3FSZRqrE3ylHDd7ixK9hphDf/nA642Jl9d2xeyJ+91uQr6AIqNCZ23BSyqJGd9q4Qr+zyLES/9t5MIVaSy/8cLQtQwQgQrjlfG8Gfw207TaF6x43JyHivwO+MWCBtKNIu1K67n5Sujr+cHsy0Gio9tTcwt1iZ3Ps90BA83lREKDqawweg8HWllAb+f5+9tx7N0YiQemY8dTMnu139PVffpFPXXkiM+Frjf7evAauvfZaxCsgyhVSjmwVrapxlEw5p+B8XO18z0EyOx8DO0vzhBl87pt/9MXRJ+j40mB/L9+78X3s2PQy+VymaLn05CWUjJ9LrnM/vWsecTmJRl+/YxXlldXHfXzPPfI7fvz1j1M64xTSY2cDFBjkKYozrjsiOyCNeReKuyN96F3owKAI848y67jANyHL9zjA4GfiC3PXQjfDcAfR/qQEOyu9pD7h+em2AIYBpor1b4fL+W3KYFyO5utfbK1UAqTgHoYnOhRI0M+ZplIJBN+DfgOXwOj6++1obbmGgYEkoLQhAAHR95PU7BZyvd20PP882e4e7MwgTib8+2k49XQqp00PtXMCDIyc7v3+R/nDH/7wivZxypwqPv++Yzec++/Hxr7iY30t08hDDEdFo+p0zA7HqmikZM7bMKsnsH/XJr7wvmX09x65uOIEDU8/+rdr2LL22TAIsCKgy0yQqB9H36anMcvD6hplXFcsn/UXbr/hqMYlpeQLt9/Az37wrwCkxkwNi+LVf19Ujf+MObkMrff/hMH924s+d/Gdev8FZA5sp/VPP8HJZ3xG5zP7yEdE/qtPx4q7aX/ybr+uLuqO03sLQwTnBEg7Q8t92jx00hpVonrDFH74YIT7fXD/Ng7+4cc42Yy3ruDYUuszIprX10sI12felJhJiZnQrPcdb/zaHPT5qHsS/ag+3S8y9vc/3DkFOqLr6d9Gj1kPBUjUWriGgYXqALdtNzZA2AVSejYFYJZV0nj2eYy/7I1UTiu0P+k/dBAp4ZNXuMxfgYCv//KLfP2XX4wf1Al6VUkCtpvW7Jg+f23U39/PihUruPvuu1mxYgUDAwPH1N6R5xrQX5b6aRH+sRuGQXrimVij5tLVfojPX730L+7X/rdIGzav978bZbWAcJXIGpXPPZeeF+4me2AzXSt+FZIGyOwAiYYJJNOlxNG//stNRzWuz3zhQ9gDPdjdrZjl1RhGYKDoi7h13bxG+TbXkKt/84vBySLPXQF51/u3vOC21X6AkK99tH5E/O7v7u0c+a428l1tSDsXerjDkobCH4JicLm2gwD0bX6xKGNUIMAHQt4c1Hh71rvzyLUf8HTknujd8xRw8vjpdlVgnpCUwgFpuwDFtCRW0v0o+4EoGZ4XgjBlsNsX0v8II/hedD4R4KR/vBw/3jil912Gcgq47YXzOkbxG7iSgL0Pat4BdQ04jsBxBLYt/O+qPzVOFX4YAjVDSeNojAgY7tu5g4FD+/nstZew54+/587/uJH+/h7ABQUnwMBfniSu++Cxfv6a6Mtf/jKNjY2ceeaZXH755SxfvpzRo0dzyy23HHWbx5Z0qMhLWX8RpZoXkRx7CoP9Pdz4oXP5w89upav98DF1e4IC+vavnuHrd6yibPEbScocUZl0om4c/ZufCmTDUiJSZWCYCCtJydQlfOunjx/3cTn5QTqfcUVtZbNP8xP4hALZxDw/Ukr6tq922xjsw+5zA2SIaL1iH3BTFg/2A9C/bQ1qTWJ3mEWkXAO7NvrHA7s2ReT+hZIAfyfvXZRA39ZV7jwG+rD7u+N3torp+6AgYOb53i6cAXcevZtX4zgSYQjMhPuxUsJVD2j6fmUboECDEO6tz2cFdl74Za2kDPUpzIDph/T8muW+7v6n3xMdfEQ3BHEqlGgUQcWwVVAfRZ4NZBgQeIxc5gObANc7oMEHFI6348/bOigIvBOUB4AgiFxY1tjM1He8g+lXXcWUd17pqwRannmarg3ryHV18sgffsxn37/Mr3NCTfDaIHW/j+Xz10Lf/OY3uf7667niiit4+OGH2bBhA4888ghXXnklX/ziF/nWt751VO0eme+B/ouUQ5xTl7xrifoZiEQZmZ2Pc98d3+a+O75NMlVCdX0jo8dOYdKMhcxYuIyJ0xf4gVRO0MjpwK6NDK6+BzufC18QBlJKN6KeRlUnXYKRLgPgy9ce3Y5/KNq9bR19a58E6ZAaO51EVYMrCfDHFUSw03W4TjZD9vBu7O52r5ygb8tLlM08GTNdijKSKYiu57XhOBIn00/flpd80UO+u43BvVtJNY7HSKRC1eIYs7RtnIFuD0C41Ld1DalRYzBLK92shFoDAfMP2nRyGQYP7CbfFcyjd9NKKuecjEiVBj+ZkFRAWwdbYg/007Nem0dXG5l9WylpHo+RTHnzVR4B7gJIDygoA0I/wqGvrnAj6SGFL0UQpkTaXqZDLehPnMjeX6OIzj5q5KjOAchcFpGw0PccygZABQAKqw3CYKtQkiL9YEHZzk7GXXgB6fp69zn3GLwRU0dJolTfhgZ2Ao8Nt37b2rV0b9kMQL63N9SWkUiw94E/8Zu+BG9+/+dPvK9O0KtK3/3ud/nUpz7F1772Nf/cjBkzOOuss6isrOQ73/kO11xzzRG3O3JjwepxlEw9J3wyrmaR1lw07uD07CPfsROnvxWZG8DP4woIYVA7qpnp88/gjAv/galzThrpPP6u6dNXLKGns3VEZa3aZioXXfiKAACA++/8Hr//ydciZwVGugSzvJqS8TNINU7wzkv6Nr5Itu2gGxlQqTT0t7Miw8AsqcCsqKZ81ilYpWXurrm/j571z2H3dGJHQghH2xKJJGZZJcnaRspmLvGBRebgLvp3bXLTCw8OUNxC0Z2HVV5NycSZpJsmeM1Leje+QLb1IPnebmRuqHmYWKXlWJU1VM0/BbOkDGFAfqCXrlXPke/uJN/Xq1n3xc/DKq+kdOIMSsZPD2IHaGR6IcgdO2DswpTkOlo4/PC9pEaPoWbxUoxUqab7l7FDVt3HrkhEyiIlSDtPx0vPMrB/L/bgAGZJCfbAABXTplN/8mkYHqdW8QLc+oWd+m1r52Q+bBiYqm3wyyomr9pTjN0wNOmH14+uNlA7Qk+oQn5ggIMrniTX1YmdzbrBroTAKisn3xuA6oqp0/m3b50It16MXg1jwSVzKvnUeycdczu/enzCX4WxYDqd5o9//CPnn18Yr+eBBx7g0ksvLUhENBI6IolAVNwX6zAdd86ra5oGZvU4rKogCqHjOGT3PI3dsR1ppWg7fICnH7iTpx+4E8O0aGiawMyFyzjlnMtPZAErQhe/8xru/MFNhczDo9T4uWQP7UAYJuWzl70iIMBxHP7zlg+z6qk/xVyVOIP9WFV1pEaN1ZiKID1uKgN7twYgAOI5keNg93VRMnkOZkmZX8wqLSPVMIaeg7viB6Zb5+eyOP29pBdM85kRQGrUWAb2bvXVCcXJnYfw5uFNAdMUlE6YxsDurQEIKDoPm3xvF2XT5mKVlfmid6u0jPTosXTuG9k87P5ePxCTlfRsATTDSzuHbyDo5NUPVyBtFxlkDu6le82z1J26HAzTr2v39dC1fhW1S05HejGLdRCgb4CLAYbBQ/vp3b7FP2d7hkw9WzZTOXkKybrRfv04aYLep2LOUUnAmPMvIFnb4McXkNJ1pzSMIPVzGBh4Y7GFDxAsyy1o4uJHJVFIlKYZe/4F/ljaVq+iffUq8r09pOrqSdfV0rV5Mz1bN/NPl06mtKyKjMxRMWUqX77lrvj7d4JeGforE+0fKzU3N/Pkk0/GAoEVK1bQ3NwcU2t4OqqwRKFdw3Dqgug1wj/6/OG12B3bSTQuINk4HwBnoJtc+xbs7n0c2reDQ3u38dgff4owTMoqqqipb2L6/NNZfsm7GD3m2NHgXzudc+l7+OOD92BVN9P9/O9IjJrE7f/1oB91quXALv79Vz8Ciu/sjoUcx+H2z7ydbeueL1om1TiBqsVnIQwjxECs8mpqzriEzqfvw8kOxnMXjyoWLKdkrOsqpHTiAKUTpiNMg+6Xnyw+SCEwkmlqzrgYq6wytBDCsqhecjZdLz1OphigUPNomkD1krMRphECxlZ5NbXLXk/7k/cOO4+qxcspHT/Vc8MTPhgom+yGHe544Ymh55FKU7/8EsyyStdjQLgqAWkH4m/DFNhZ4XoKWA4dLz1Dqr6Z/r3b3TUbN4H6088MhTl2HLDKK6ieM58D999FsraBulOXIYQu1g+GYmj5CdSxEJKyceOpP20Zrc8U3o/OjRuoP60Bw1s/FRY4iCyIbygY2sFrhoFjL1DqAPzIjWrXXzhOSSrhePEHAldMXQKhEhkFkQ4D1YGUUL9gPg0L5yMltK9eTevLL1Mzbz4Dhw4xePgQvZ4qq2fb1oL5fuOO6/nEO75U/H6eoBN0BPSBD3yAG264gUwmw9vf/nYaGxs5dOgQd955J7feeis33XR0m7yRqwZqxlE67Vz3IKZK1L86liLXsgdWkz2wimRTAAKibQI4Ax3k2nfg9B7EyfW5sdi9xsqrall60Tu57KpP/l3r61Qa32L0+dtueEUkAdd95Ro6n/szMlPcfSXVOJ6qJecMOT67v4e2J/+IzMbHQqhcGIAA9ZaPPm8De7bS/XI8ExXJNHXL34BZGqQZjhoNStuh88VHyBzYHdtGumk81SefgzCMos+63d9D66N3++5+Uao5ZTnlE9156L8ZfSx9O7fR/lz8PIxUmoZzX49V5s1DukBCr+/YDu3PPkqysprymfNxnATOQAeH/3wXACVjxlN3+tn+PAJjv0BvnuvtYe999+BksyClK9ZfcjLta9bQtW419acH84gTywsBPdu3cfipGHAmBEYyxfjL3ujbOwgRCBPVVNR3aedxMgPsuvtuprz9bRgewNXvgWt0WKiqADcTYcJ0/KRCChTYWgZEpUJwYrIiJiw3IVHryy4IqFu4kNp5C3DyeVpffB4hoHPTZtL19XzjZy9yglx6NVQDi2dX8i/HQTVw5xPFVQNbt27l1ltv5ZlnnmHt2rXMnDmTtWuDAGy2bXPbbbdxzz33sH79evL5PPPmzeOGG27gvPPOC7U1ceJEdu0q3GwMDAyQThdGd42SlJJPfvKTfOc73yGfD7y/LMvimmuu4etf//pIpxyiowtUXKAjCP8A4wKASP0XDmT3ayCgSQMBMZIDs7QGs7Qm1F6+t4VcywZ6u/fxpzu/yyN/+DH/eOOPmTH/tKOa0l87DRdt6niDgGx2kE9/5CIyB3YCYFXXu8ZxMqqjN7Aqa0I7zzgyyyrdMMTEM1CrqjZWnKEzA6uytmj7hmUFzNOvHLQhBEhhkKisJnNwb+w8ElU1mAkjCKgT436XqKhAWAkoAgQSVTUhABEHJpLVNYUn1TAsi2RFMA+F40OMz3Ln0b1xLd0bVlMxexE5ZbhoGCSrqn0QAJ7RoRHo6w1DkiivwLASfpCdni2b6fEM6ABS1TUFBn6GlltAAOmaIvOQEiNhYaVcEKCWQP8v1Ee4czasCqa+851+EyoMsJq7m3Qo/Hzou/68IyLeBxLTCMTKqq2EFdzUvG34bo2Z1hZaX36Z+iVLqJ0zx1VFJEwaTz+NXF8f3Tt3Mdjayqc/eg6NS8/AMAw+/g9f4pt3Xs8/v/2EROCVIskrn3Ro3bp13HPPPZx66qmunVvEDmlgYIBbbrmF97znPXzqU58ikUjwk5/8hAsuuIA//OEPvOENbwiVf+tb38q1114bOpdKhQ2Zi5EQgttuu43rrruOZ599lo6ODmpraznllFOoq6s76jkem9dADCCA2Pd16FxGgYBmDQQMpVbQr3tklTdglbuGQplD68keeIl//+w/MO/U83nbh75IQ9OEmEZO0LGS4zh8+hNvpG/rGpAOZmkF1SefR++ml8j7Bovank465Ls7irbnP0J2HmegT6sfFjHluztIeIy+mD7Z7u0In9S23HZ/H9LJI0wrVCcqTs53dxIbrk865Lx5qMQ6QmM8ChxIO4/dP9Q8OklWB4BF/wmpYtnOYeZh50O7Yv2/cpPLd3X48+jZ8HLQnuPOw7IK7Sp15urk8+TVPCJjAMj3dFBSVxP6WRoi/DPNdHYWnUe+rw/Htn0vDF0KoFMxLwIdtKjsg+o1ZDsqnbE3FyRIUeBNoMYclMcfi0pipGwKuvceZOJll5KoqgVkyOsgUVbGxMsvZ/udd9KzfTtWSQmjTlrCN++8HoBv/e8XuOZt/1rY+Qn6q6BLL72Uyy+/HICrr76aF154IXS9pKSEHTt2UKMB3wsvvJDNmzdz2223FQCB0aNHc9ppx7Zhraur45JLLjmmNnQ6+tRFSo4HwyueNRFBZt8qsvtdEJBqnh9vaxCqG3M98rZIjZ5NonocA9seZs2zD7Lm2Qcxy0fznTufG/F0wGVy6196DNNMMG3eaccts9PfCq178TG+e/MHPJewJJXzTqdk3GQAcl1tAAjTonTqPASu25208+S62od9RPK9Xagba5ZVUDZ1PpnDe8gccMVodm9nYRtRJt4TMNBU0wRSo8bSt3U1dm83IMn3dpGoqnPF4WbA7/VIefo8ymfMA6B3kzePzvagQylCoXbV/1xPMA+rvILymfMYPLCXQc8IMNcVBkVRYzl3Hp3+9ZIx40k3jaVn4xryPd0uGOjtwqqtC7UBwc7eMCDrjVWYFlWz5wLQtX4t0s6T7WgnmXBc/3oZZP5znMAdUqXhBUiUV1Azdy59+/bRtzuYh8oRoIzvIAATQsgQoCkbN56yMWPoWLeWXLc7j3x3F8masBSn2GMSt+dQ5wwhyTsuo1c7fMuUkfpBgCIhJI4Xs0AlFlLzd9tTSY5cdZttC+oXKKml9Nu3/XVz2H3PPW7dhEXNrBk+UPjHN93I7T/+BP/+P9dhmQYffesJQHC86ZVOOjScytk0zRAIAHfnvnDhQp58cgi7pRHSb3/7W84991yqq6v57W9/O2z5N7/5zUfcx7FxOh0MDFnOAwH7V7sgYMxCUs3zQcrYH3hBH0T6iUP2qQrKZl+OPdDBwOb7sHsP8ZFLp4GTcysYJvUNTZSUVSCEIJsZ5OD+3e71Ip2XlldRUVVHurSc+qbxNE+YwYTpC6hvHE/74X10tOxn4oyFNE+YPoJF+Oul7RtX8o2bP0i+swWEoGz6fMpmLPJ/IE4+h8xmKJ0yl/Jp8zC8TIalE2fSu3UNAzs3IvN5hGUVfWby3R2YpeWUz1hEeuxkhDAonTCNXFcrPetfJN/TEfJz10k9Q/meTlKjxlAxezGJajfUbNnEKQzs2U7PhpXkezpJVtcFTDfy+3ZyeZxMhvJpcymfMQ8z5c6jbPJMejetoW/7Rpy8thuPeT/ku9x5VM5ZSMl4dx5lE6eR7Wile+1L5Lo6C8YdnUuuq4N0YzPV8xeTrKlHCCifOIX+3dvpWvsyua5OUrV1IUM9AMtymZftzaNq1hwqZ83DSqcRQlIxbSZd69fQs2UTuYyNlTQxAGm4wXf03XeuqxOrrJy6BQupmDQJYRhUT5vKYFsbrS+9RKbDnYerUtAt9fFvcKazk9KmZmoXLiZVV4cAqqZMpmfnDlpXriLT2UGqtjZ0T0NrIm3fUHEoIJn3ghHZUon8XZsARwaSg6h7ogIBqm0FYFQ7KviQq3KQoXVOJhwf7DiOoPXFF8n39VE+YQLp2hpaX16FPTBArq+Pj/345+46JZPMfOeb+e7/fZ5/esuX+d7/fZ5/fMuXi0/qBI2IXNXAsbcTFfcfj/aeeuopZs2aVXDtl7/8JT/84Q9JJBKceeaZfO1rX2PevHlF23rrW9/KM888wymnnMJb3/rWIft1gWu899iQ9UZqLJioGUfpjHPjeWZE/x9HmX2rye57OQABofrRH2lY91vQ1xCU69xDZvuj7oGRwEhVABKZzyDtrBuTFUCYYCUxrBKElUKBBaO0DgQ4PYewBzvBznoy1OIdm5VNLJi/iHw+x7gpczj/TR8kXVo+9EBf49Tb1c7//de/8uxjd/uufYnqBqpPOxfLC0fs76RyWWQu57v1Rcke6ENYiYIQrjrle7sxS8sQhlloMYbL5C0vKZKv04/cklx3J8mq6tDYVHlp2ziZPjcoUBGA7+SyyPwI5pHQ5hFhMKF5aOSDle5OElXVsYxNWeFnuzpJVVeHpOnKGM/J22T7+kiUV4YkCbqo2s5mcXI5zJJy71wYcOf7+zCsBGYqiSECIzk/sA6Q6+kmWVaGME2/vv67zHV3kq6pIhQJ0AkYrz4PGWG4QoCdt8n19fv2DnEM28llMQyBkUhE1rKQqYNrFOjbK4hAZWBrO311XRkLOh4gcIFDENnQkYFaQM3fcYQ/frUW2e5utv/2rsKb6XVYUluDkUzRt/8ARsLCyeUpGVVPurqKzq07XIBVMxprXC03/esf+f5vr+Mjbz76ULGvJXo1jAUXzq7kmvdMPOZ2vvajDJs2bRq2nFIN6MaCcfTNb36TT3ziEzz66KOceeaZ/vlrrrmGU089lfHjx7N9+3a+/OUvc+jQIVauXMnkyZNj29q1axdNTU0kk8lYQ8MoTZhw5GrxI5YI6C+wYcX6HoVAwJiITYBqtIjhYaifuL60a46T90GAVTOR9KTl/rV8XyuDWx7EKGugZOp5CBVxpdjYm+aF5uo4eZz+Duy+VmS2H5EsxbDSZA6sxu4+wEtPuvHxVz/zAPf88t+ZuXAZ7//MtymvKm7A9lqkXZtXc9uXPkTOi/cvEklKxk+jbMYiEmVhBqlum5FIgmKOMaDQLC1DJRiKZgFU9zZRUVn4POm73QgI0NtQ5BrBeQe62F9IhGlgJCuCRmXhjQ/NI4bMEuX3Hw8KhXDnob7HSS5SNdWhY92XHlwwkKquDjEcnaQwSVdVRtqVvoGeBMxkEjOZLBin8NoXZWW+vtw0pZcB0C1r265RXaqy0mOahXMVwl1rtYkqtpVwwYyaR8BQhZCYlolRqRtvFjZiasBRZ+Aycu8UAFDzj95Zywx7AwgBthPuMWcbIRsCQ7iSEscJVB1KMqDuV+emzRx8+hm/TmlTI7VzZlHeOAorlfDH42QybL/vQQbbXFXJwOFWBg63YpWkMRIWHe0HkS37ePzeX8TaMZygoel47OWnTZt2HFpx6bHHHuPTn/40n/zkJ0MgAAiFAF6+fDkXXnghM2fO5NZbb+V73/tebHs6Yz8aJj8SOibVQNyuJvpSGDiwnnwUBEDhyz4WYQx7KWI74IAwMauaSU8OQECut43s9scwSqopmaaBgCJUqIsWGGYCo2IUVsWoUJ+Jhsk4jhMYrHXuIbNnJRtffpJPXXESN/7HA4wed+xpMl8pyufzvPTE3Tzz0G/ZsOppNxINYJZXUzn/dFL1jX5ZfWcX5xniXizSkX6/RWFbsaLhCFgoMBLUd/bCNQjzY+Nr/vl63QBsHL08sZiYOmzMFjPeGACj6ujroHb3TsS9DdykPdEdsQIBQlO7qBJGzL1SO2N1zvQYZcJyMA1BLi9Cbevfh5Mf6tKD6HnDcJm2E7mf+viiNhPB/MMngp2/VoYAcIC7qzc8dqzWNG8riUWw8wdXIqDItx/w3REVCAmvRcvKle4YEgkmveFiktVVWL5UwnsOASyDqFdP6ag6Zr3xfFe9Jm1e+OFv+J//vJF5V72Fb/3sGvIDg/zL//tPv/wPfncdjuRvRlpwvEgC9tH/lH06Xq7nq1ev5vLLL+eNb3xjKAxwMWpqamLZsmW8+OLI3E5N0+Tpp5/mlFNOKbj24osvcsoppxyVamDkQEAw7M6fyAsns281TvseEuNOIt3s5qIvysiHkBAQueR3FyliWEnKF18ROmf3tZI7uBYjXUl6ytlFQUAc8y8sFF/PNIOHyKibQKJuArnWXQxsfYzv3/wBbvzhI7F9vtr0hX+/ASHgM+/9BI/8/r959uHf0nZor39dJNOkmidSNm0+VmkFiCBJTVy2yRFRZIsWJ5aPY5hxRmF+kzKcelclyUGzHI+CgKP1MIo+bzrTHgoIxzEz/by+w41GwNOZnu7bH7XKH+oWaJiggBRT1ClhOeRtw92tm0F43mDMsoAZR+elgEyxtXaN9NzvI70f0cA/cfpgQ60lrgcABpgxINAUEtOS5GyDnCNIWjaGcNUDClDYnpuhxI0fkMsLfz10A0TbEUx9yxtx8nlS5SVIKTA8+wRfjSMh0zvApjsCAy/Dsmg6eT7NC2aQNG0c6ZCzTZpPmc++Z15m02/vJ9vTx6j5hbplJzPIy0//mXsf/S/KRtfyz+/7j5Et4t8wSV55Y8GR0rZt27joootYvHgxP//5zwvAXzEaoXZ+2LK5XA7TNIteH4qOKsRwlOJUBJm9q8nsfZnU2EWkPBAQ10ZRcfCQYoChx6PI7m1lYPMDmLWTSI89A8MaWhIwZMNFXurFyiTqJ5BrH8+hfdvZsPJJZi1aNrK+jxN94Rs3+N+dTD+D+7eQa9lLvreDT/3pJ+4Fw8SqbiDVNJH0uGmYiSBVcDESwtXdCiuBEGJIZlgAAnwm6maPM4YQwxfbWQvDFfw4OdfWw3UHVFvt+Ock2oY61ueh9xsHQqI7Vd9ILpcrsH3Q2yi2O3bbFDheSmjDtEIAQe87VDdmXE4ui5FIeOdE0bJBv9E2JPlMjkQq4VvfG4bj68/D4xaxP0c7b3vzMP0+RgwWVRtZNQ9dEqIAkgyNxdDmJHHBgACEPYgjUuivQ922QdkpGI4uUVApkN1rKmshuGAAQBiOPx41DiOZxEgmsR2wDIllOtqcXQmVDgJGL5pL45K5JEyBKdwAR7Y0cBCMmj+bju176D/cBoagv6WVR37/Y2YvPpP/eeg2Gvpqefye/2Mt/+e3973ST2Cl03zojV85soU+QcedDh48yIUXXkhjYyN33XUXySHsoXTav38/K1as4N3vfveQbe/fv98/3rRpU4FH2+DgID/60Y+OWnVw9HEEFMX84Af3KBCwkNTYeaGyBU1GX5RxW5mR6CAi5e3eVvq3PIhR2Uxy7JIhmc6QNJKdsIi/bpZWk2/fTUfL/sI6ryB97pZryezdRLZ9P3ZvZ2wOgspF55AYPQ5DCyxTQDFv83xfNy0P/B/psZOpOems2GrK6C2qAlDU+cLjDOzdzqgL30pCGYtprnjqWNWLShGEKdn/O9cae8xbrw61X8yjIHou39vNwft+S+n4ydSeGujx4kBAsd192zNP0L97O82vfwtWeUXR8krqWJgPSbLrf34BwOQr31N0vP7Y0H4eEt+ob/fvf0f5xMk0LV9eoDbQdfNxPxkp4cATT9K9fSfT3vpG0lXlOFKQdwQJ08GQwhe5R0X6Om35xS8BmHn1VZE5Bv0q98So8SFAtruH7b/9HRWTJzHmzOVa/cJB6yDA0EDVnkefpHPbTqa89Y0YlRX+dWUsaJlgeIMxNTfHpGePYQqHrGNimRJDOGTyBo70bBC0exD0HXw3tZDE+lpZpSXIfJ55734ThmVhCollOEgJtjQwhYMpoOtQJwPtnZjJJNKx6d69nzt/cKM/5w2AMARNJy8AR7L/+VVs/O39fORzPyy8GX9HJOUrn2ugv7+fe++9F3AN97q7u/nNb34DwFlnnUV5eTmve93rOHz4MLfffjvr168P1VcxA379619zzz33cPHFF9Pc3Mz27dv5yle+gmmaBQGGdPrBD37ATTfd5G28BFdffXVBGSklpmkWtTMYjo7dUb4YCBgXsQmIKUvkxTKUsVhhv8XfmHZvK/2bH8AoqaZ0ytJ4dUCMFGMoGok+PFomc2AjyVQJp50/tMvH8aTnH/09nU/+Ro0II12GVTOaVNMkjHQZXU/9HgCzogZDGD5DidNfK5IymOrA3p0AZFv2Ew1rXKD7Vu5+js4MpOtGCAzs20lipgsUC5i9QUFwP7Xjd2MOuGT3dWOVV8YyuaF0+QN7dwFughx3R1hYuBiIAPeH59aF/r07qZoVAN448FMolRDkezv941xPN8nKwAiw2E8gipmVX//Awf24QlK1gw7bG8TNR0ln+vYfBKB7525SC2a7u2sBtmMUiObj9LGZzu7ge1c3qQJjRn1NChMBCSHp2bUTgP79BwDXVsGRYUt/KLQVkFJgGhJbOvTucw1c+3fvJDF7PiCwHemrFHI2pCynQOqixpF3gjwYjnR3+Y4n8nfPuQUVMFAAKQhVLDxbDaUigHnvepMraUBiGg6W4WAKh5xjYgn3u0SQNG2qmuqZftFSEimLbMamd+9eug+2Ix1J3aQxlDc3IDGxpUGmu5u2TTv41uevxEgmaB4zlbLKWs5/yweZe9I5hTfpb5jkSF/iR0mHDx/mbW97W+icOn7kkUeYOHEiq1atAuCNb3xj4fi8h2rSpEns3buXj3/843R2dlJdXc25557LzTffzKRJk4r2f/XVV3P22WcjpeTcc8/lu9/9LrNnzw6VSSaTTJ8+/aijCx6hRGDoBR/cvYrMHhcEpMfNL3iLDW3oF6MNGAkgiFAIBMw4H2Em4ndDI3h2RsL8i5XNtmwHO8vS17/3Vc2BYLhZaABJatxMymed6o+vZ80T/kD7t62mcv4yEJEdrAi/pHVy8jk3oiCuvjJzaC/pxnGx5XXGHg7HK6hccAbJutFkWw/g2FkM05PWRI3gdEswTVfes3G132H3htXUnLQsfgxFJDVOPkfPJm0eB/dR0jy2cMIxbSoaOLAXJzPojWENFdNm+qqnuH71Z1Axs461a/wCnWtXM+qMQH2kHn8j7tnV5tGxznVjsgcH6du3j/KxY0N9KtKjBuqgpG/fXmwvbWnr6rXUzZ6OmbB8hqarBwqZsNtO25pgHu1r1tC4dGnRe6EYd2gXl8/RtmadP4/evfupGDfGZ7h+SmFiQIGnMujbt4/8oBsO+fCq9VROn0ki5b7e/BwDUjCYCzItql29b4ehz02tmw5cvP4t07Wn8NdWuNcSloNBIF2QCATBc6sAhS0NT5XhSSeEQ9XoauZdutTL+ChJpkxqp0ygdsoEBK5kwwUiDiaSmeefTHbpQnasWEnXnoPs27UJ6ThsWrWCKReczic/cQc/ufszXH3p8AZrJ2homjhx4rB6/JHo+U877TQeffTRI+5/woQJvsj/kUceYfHixVRUVAxT68jo2NIQazS4JwICYEhGX9BOhOmHQMEIx6eDgLKZ54ckAUeqryygIerH2QoM7lkJCN74vs8eY8dHRkvOfAOLll3CF967lI49G7Cq6hFCYPd1kdm/HbWamX3b6CspwyqvwiyvxCqrDKlPpCd/dgb7yfd1Y/d1k2nZ76fZtSqqkbZNrrsTq7wynJ8WkHbxHbFhCErHT6Vk3GRyHS1uoJ/aeoxkGiNd6gIH3T89lyXf2+1+erro3xnMo3/XNqzSMqzKKhLllVgVwTz03bs90E++p5tcTw+Dh/a5yXS8Qh0vP0e+twurvJJERSVWWbkf+jaYj02+r5d8bze5nm56tm7yubuTzdL27JOUNI7BqqggWVmJWVLqSxl8K/ZsjlyPWz/X3UXvju3+j6Bnx3assnKSlZUkKqtIVlQgksnQujmOO49sdw+5nm76D+wPzaPlhRfIdbuShWRlBYnycgxL6eyFP49sby+57h6y3d10btrsz8POZtn7+FOUj2kiWVVBqrLSFW3LYC2kdHX57hx6yHR10b09mEfXtu1YZWWkqqq8uVQUuAHajiTfP0C2u5tsdw/9+8PzOPzcC+S6u0hVVZCurCBRUY5hmiGmbNs2uZ5eMl29ZLq76dDnkcly4El3HiltHsJwWa/lSUlyWQdbmJge4LQjgMc0AvsDJfp3pGdQ6NkkGIYr6leMXmhvK0EkGZQKixySsmjlDUOrL/3yellHCh8YpEosZpzvWo8f3LCTbQ+7kVSTpSX89O5Pv8L75NcOvVaMBV8NOuuseHXssdLIAwrVjaN89nmx1wZ3r2Jw98ukxi8kPW5BYQG9iyK9FR3FCEYnpQcCNj2AWVJNaQQEAGGgEYX+x/CLCTE7O4+Tc3Pa9668i3mnns8/3vDfR9/4MVDLgV188f1nBltRIQLlMuCKAggtfNXis0iPUUEtJF0vPsrg/p3+dbO0nPJZS+h68TEaLnoHZrrEbelIQFL0ulIf2DaDB/cwsHcHNaecDVIgDOjfu52OZx8LNzjMPOpOP4uy8UrUJml96jH69wTz8PPNRgep9MelpTSd/3osL25Cvq+PAw/cgz3QH1s+rs2y8RNpWHqWn9mvd+cODj35eNE+49ocvfwsKiZM9EX4B594nN5dI5+HVVrKhNdf7Md/yPX1seue+8j3j3weFRMnMP7cZdiOu5Pu2bmD/Y9pmRFHMI/ms86kctJE77Rk/2OP07NzV9CEYSCHmEeirJQpl15IsrwMKQWZ3j623/2nIecRbbNq0gTGn7sUcNUdndt2svuRFZQ01DPQ0gpC0HzyQkYtmIlhuMaCSjURLI3GwLWdPqgARnH2DIXnFLM3RLgNRYF6IrggEeRzDi2bd9HX1kmms5t8Jot0JJneAfKDGYyEyfy3X0RJVeFu8ao3/FvBuVeaXo2AQnNnVfKBdxcXq4+UHn6mePbB1xr94he/4D/+4z/YvHkzg540T6fu7u6YWkPT0RkLas+2AgHp8QtJjw9AQHi3r8k34+RvDCEtiDKSGGDg9A0DAoaiGBHmMHaIsSTtHH0bHsTJDmBVNwNw2buLG4C80tTQNIEZC5ayadUKb4Ax2mbtVMX8paSbgx+UEIKqRWciHZtcewvpMZNIjR5LunEspePiI2C59bQepEQ6TkGUPb+sbhyISbppIukml/G5clhJ6bhJyHyezhdXUHva2XS+9HQkxW94HrUnL6Vs/CRN3SGoP205LY7NwL49bqGo1Z7GxIxUisZzLvJBAECivIzGcy/iwIP3uX3HPSBam6VjxzHqjGUIld4XqJzkzuPwM0+F+4yOwaNRp59B5cSJwVoJweily5C2Td/e4edhplKMvfBCrNIy75QkUVbGuIsuYPd9f8LODD+PivFjGXOmyzxNwzW6q5g4icZ8noMrnh7RPJqWnU7V5AkeFnV3yGPPWspex6Znt+u6KoeYh5VOMeni80mUlbuGeECqvIzJF5/HtnseKDoPvc3KCWOZcM7pSMW+paBq8iTG5G32PfGM3+f+51bSuWsv/Yda/LoTzz2DmqkTfSmCTkoCoIv9IWD0umogFhCIAFAAOBGPDKX7zg1mOLRxF/teWE8+kw3qG+4PxbAMGqaPZ+KyhVjpkr8I0z9Brw794he/4AMf+ABXX301Tz31FO973/uwbZu7776b6upqrrrqquEbiaGjU157L+piIAAiemd1okg7oU+kfmE74Y/d20rfRg0EWIkh20UUth3tZ7jrUVIgwO7vJD1tuRvKGKhvHD/kMr7S9NGbfoKRjg+Xq1PlguWUjPMia+m7KtOk+pTzqD/vzVTOP41UQ1NB3ej6ZNtbOHD3L2h57B6knS8KAqR0nRmk4/4XwgUGISNBKUAKyiZOZ8xbr6Zk7ERGXXAZ9UvPQSQSCMui7vSzAaheeDL1S8+hYso0fxeuyLBMRi8/h9JxE4ovghCY6RKaL7iEZFVVwb1OVFbRdMElrhRkCDFH2fgJNJ55NoZl+iBAzbdy6jRGLx3ejXT00mVUTZ3mDSu4H6Zl0Hz2WZQP5SIkBFY6zfiLL/aN9pQluxCSdHUlE1//OqyS9JDzqJw4gXHnnoXpqRYCH3monjaNpuXDz6P5zKVUT5samochXBfDceeeReXEIX4fQmCVpJn8hov8eeiJg1LVVUy59KLh5zFpPOPPPRMprJCYXwhJ7fSpjD3rjFB5HQQA7HzkaXr2Du314wMCJKZwfOmAiNm1xL1Lop4RjvTcCvN5XvrV/Tz3379n14qXfRBglaQorauifHQd1eMbqZs8lpOXvJGTxl2OyA/w63vdDcgv7/nkkOP+WyCJwDkOn78Wuu2227j++uv57ne/C8A//uM/8uMf/5gdO3bQ0NBAefnRhbY/aomADwImaCAgZoMR2mEPZWQQ7UO1JyhqXxACAbOGlwQMayfg9XUklG3fy8CmhwEwayeSrGxA5AfJt+7kxg+dy3XfuZdKLwHOq01WMsk373iZb3/xKjavfto/nx4/k8HdGwEonTqf9Fgt8mHoDQUCgUi6ubJ1vXmc5CTb3kLbij9hVdZQd8aFBXEbirn2DWVoqBsQGgaY5WUky8tI1TcgHYlVWkbZ+Kv9dordY2EYjFp6FvvuvcvNfudR43mvo+Xpx7H7+2k8+3w/RHBU0i0EJCsraTz7fPbdFy9CTFRW0bjsTEQR41Ah3KQ72a4u11AwhprOPofycQGD1CPlqXk0LV/Ozo6O0Dy0Cow5/3xSVRWBRiiy7umqCsZdcB47fv/H2DEkqyppPmsZUhiBsR6KYbkLoubRtjp+HnUL5lEzdXJR1y5hGIw9ezlbf3c32a7wPEYtWUB5UyNmKkmqMizm1tciXVXOpIvOYcvv7o3tI1VVyfizl2GYAtMIJATuThxA0jhzAnZPFwdeWhfbxpjFs6gZ31Tg9WEKx29LOg4v/fpPDHT2AFA/ZQwzzj8ZM+EZK0oDkKRM202E5EkKHOmqKdxER25gIwV1shmbVXfcT7a3v2BM+YEM+YFM6Ny9G4PQteNPngmXwJWvvzV2Tn9r9Eq7D76WaMuWLSxduhTTNDFN01cDVFRU8JnPfIaPf/zj/Mu//MsRtztiIKD/CAZ3r2JwVwQEQKzqQNUtUBVEKdb/S/sfuWz3ttK3wQUBZbOPUB0Q20eRwQ9B0s6R3b8GzARlsy/AqnAZfqJuPMkxc+nat5br37ecr//qRZJeop5Xm6xkkk989Q4+ckmwi3RyWRouvto9iGHGxSy+o8f6+QIQkCi8HyENkSDkXqj/9/MDhNQMwQfASAXrWcwoMfryNkzBuEvfVGD9PuaSy9n9m1/7yZV0hhcev8TJZwvO18xfSMfql7EzgwjDCCUAUrvxYHwCmc/5uviyMWMYdcrJ7PjdXUx805tIlJcX7BALxmEYrki8CMl8NjQHFyCF21RGn3FkZzJYlgh8tH0jt3A5J5crtFMAMAycbG7IOfjzGAzm0bBwLrXTp5KscHc10nF8RhklNRaZK96PncmQSLhzN5D+zk8XxwvhBZaK2il448tm3IBPlgoopD0XSiVgGq74XlHrtn107W8l5zHrk9/1OlJVFdhSkHcMd7cfeibg4NqttGzcTn4wizAEmd5+pO1glaQKmL6iycsX0Tx/Kk4ux0BnFz0H2tj14mZ2P7+R2792OWPnT+HtF99edH1O0F8fVVVVkfF++2PGjGH9+vWcffbZgGtA29bWdlTtHrHXwMAuDQRMiBgGRvX6I7EBKFYgWkhrM69AQGk1ZSOQBBROhGMzEhTuy7xvvasO8EGANoeSiUsw0+UMbHuG//3Pm7nymq8eZWfHhz7/nft47I8/48n7f032wHZyk2b7qXqhkGnGUbHrOgioXxaAAD0OQFygHvdAhtwEpYMXXKgwgl0gWQobWUXbjvPjLxbSF8BMpph0xdWhvqKpZ1Vbuc6OUEdWWTm18xdQO39BoTmLBgJ0Rlza3EznJtfroOHkk0lUVDL9qquGFZipudmZQRwFBKKoR0qynZ2UNY4qYP66q5xKI+w3qrVjD2bIDw5ipdOYwrWSV656+lwyHR34Afn1RXccMh0dsZEAdbIHB6iaNJ72jVuYecVbSJQo41OvH7Nw7aLtDA4xj/xgBpkZxCpxJVpmyA0voP7WLs+WRSC9QbvfHQbbO7GlwFQifc+aXwh8NUBuMEN+UHk9uH9yAxn/PdOx9xANlVUM5A3yjus6KB2b3t17OLRpNz2HOsj2DcSKb/IDGQzTJFVRQkl1BRUN1aQqSkikU4ya0gw4kDJJN9ZQ21hN06xxPP2TP7PlsZc5Z+mHCxf9b4wkf19eAyeddBKrV6/moosu4rLLLuOmm27CcRwSiQRf/epXOfXUU4+q3SMCAgO7VjG482VKp59BqinI1hQy7Avt/CPH+qWYXVxRcKBdyPe20rf+AYySKlIzznVtAvyGjmAycW/t4QaqiuZz9K1/wAUBcy7AqmiIrZJqmsHg7pd5+sHf8M6P3vKqxhOI0tjJs7n86s/y5P2/BsAZ6AMNCBRMM7LrDzFVbRoKBCSqaqhbGpYE6Pr+YdUyMW0XxBWIkVq47mw5ch0tlDQ2h64HY9cj2YWZWRyjjvIUCB6XXFcnAFZZOXWLFlPS1Eymo510Ta0vJpYyfJ91UOGOSyAMg7LmZgyr8Cc41FoJAdlOdwzCNKmdM4eaObPp37+flpdecl3qFHME3+0tNB4hyXhtJMvLaDx5EeXNTbSsXU/rmg1I2ybT0UWiKe3H4ReGEwJPhvCAAFA1fgx1ixYB0PLSSnp27yXT0Vk0N4Ciwc5ORp+8mDFLVayLwI4gPOf4RoSAysY6MASp8jKaT1lIxdhGWlZv4NDqjW7K5s4OUqWjQ/p603sekqYbcXOgvdObRzNjT1mAIRz2PLeGjh37GGjvJGnYZB2TpGF7MRBEYAcgoL+tyx23ZTJ20XTGLpxG557DbH9qDYPdfRxct4NcxsZMJslnsnTs2EtvS4cPOoyEiZGwkLbj+qMLQcXoWuonNTFqxnjSFaVefALpuQ6qNbFRUQzVubJSizPefQ5P/vRBfvjVj/Bvv3qZ+564nqVzP0bzhOnFb8ZfK0kKgN3fMn3uc5/zUxHffPPN7Nq1i0984hPYts3JJ5/MD37wg6Nq94iAgMxnKJ0RBgEQ4akFagBVuXi7BdL5aL9eiUz3YQbX3AdAasrpJDzddfxAhqGRciatuJRhEFA+VwMBRZpLNs0gs3sVm15ewazFy+MLvUpU7qXyBehZ9zSppgmR3XkM6JHxjBFcEND6RDwI8Jsshn30l/twonBfxB4jWcrnaHvyARCC0sZGhNZhMHbhg4BCNYf0y0br+WUIjp1cjlGnnUHF5Cmu37eARKqW/MAAHevWkKyqoWraNA1wEPqfHxigY/06xl10ESUNYduRgZYWWleupPnM5SRK0/649foAuZ4eambPom7eXBKlaQAqJ02kYsJ4urdvp/+AGynQF59HwI4jBXY2x5jlp1E9dTKG4QrLRy9ZSP3smRxetZZMdw9lTaMju3GJjSsdyOWypGprGbVkEWWj6n0py9jzzmGgpYWWF18mPzCA5e3y9f799kzLDV6kAYC4e2REXh66iL+0voapFyynanyTfz/GnjKfpnnT2P/Sega6eqlsHhXawftr4Aiy/YOUNVQz8bR5VIwOUobPueQMug+2s+uZNeQHBkl565zJmz4YADcyZ39nH2MWTGXM4lkkSkvI25KOg51+/oXelk56WzpDc0hXV1A3dQIVo2vZ9vCz5AYymAmTyafNYOqSqRiWQU6aSCkwRS5YI8Lhoh0hfLWHKVzAWVaZZs6581nz55f51i2X09vSxRM/up+pp82gr7OfTF+GfDZPsiTJjPMW8w+XfJO/Vvp7kwicdtppfsji6upqfv/735PJZMhkMlRqkUmPlEYcRyDZMI6K+echpQOyyNs9pqXY1kd6TqN8Twu9az11wOwLwpKAV5BCkot8jt51IwcBAP3bnie7fz3Xfftexk2Z88oOdhj63//8Eg/f9V+kGidSOutkEr5rmVZIiOCfEocKfL29omx7C62P/3lIEDAkRXbgUZKyIEaRf17VsbM5Wp94gFx3B6PPvpBUXSCZiZMIQIwgaIj2/bb0647jM5zQdBSg8K5DMP6QxFq7rs6Hw+7KcBsiCGATHYO6Ht11Syn9IDnFdtPScXwJVVRtYgjI56Xfh95GLhJRD/B3pFFVgD5OvZ0C4VMECLjnvHYi/vrheepthO+d767n2FiWm+RHb8OPNuiEVVOh9r2R2rbExkLKsITFdw3EZrC9k9K6SgzDYPUfnqRz90EMy6Rq7GiaFs1E5h3sXA7DNKlsbmCwu5etDz5Lf1snCMHkJZNZfPY0LMMhIWyyjrtHyzomWSdspKvP3/bexaZwMJw8O17ewc41e+lt7wUgVZYi01fcnqRx1nhmnX8yDoJ3XHwbAL++91ocKY7Z2PDViCMwa2YV735XcXfmkdIzz437q4gj8L73vY/rr78+NiTxrl27uOmmm/jRj350xO0eca4BIQx31x8Hw0JvTFXeO4yTEmjlhmKm+e4ABJTPKQQBI4MyI6OievBshsEtTyESJZTNORmrsiG+YIRy7XuwEsm/OAgAGOhzRZipsVOxSiIgQGiZBENMNLyrFwIybRoIWHYBRsKKB3JCwhBiO51ZR3f8RqTP6EvfyRWCAB+0xIAMfVcfZ3oChHMuFBuzBwIU8y7c5RZKJKIGi1HpRMhewZBIETDguD70tNcQZsKGN9GhDA6FN49gnF7YX4JEOpYlcKQMiemDmPoB0zaE9BMTGZFVK+Y9MRTp89UlAXEALXqsdupSupM0hMSwAl9+6YmRQ7HpDYH+8Oo2BH4IYFNgkfcSFbnM1zIcf3wHtuxn4/2uV06yNE22f5BUVTmLr3x9aKxOPs+BNVvZ8fgLZHpcb4C6cfWcdMkC6qoMhMiTsS0wIGnkSRo23fk0DsJn+KpPB89OwdsPH9x6gBfvfsFbFKhsqKKvs9cHAdVNNUxcMIGq5joSXmCmp37+IAc37KZzbyupilI2PnAmU85cdOSg/i9MQ6XH/lujn/zkJ3z4wx+OBQKtra389Kc/fXWAgKKoxXdhAYoaCxYFBVDAUHxJQFk15UUkAUNJ+aP2B8XE3EORzOfIbHoUIUxS4+eTGCEIQAD5LKUVVSPv7DjQ9d+8gS/9800F59/6wet5+sHf0Lv2KVLnvj2+8hAgAMIgoH65dj+ijMeLARB7jSJMucg9iQMBLY+7IKDxnAtJ1+uSgBjxf5H+CsZUZIzhc1HbgpE/S1H7BH9HHWJ4oqCPUMoFES6rKK5M5CdYFNwov3rLCP9eTBFdNxHKuKePV/V1pPpaJYFwpCZR8Oct0CUEBmEDTgfhZxMEsD0jPstwfFDgjkfEJkty+5ChwD9K/6/+m0qVgCuCd38fTqi/0ZMa2agMNfvdSG/jT5kDdp6dT6+ia18L2d4BPw6AMASjJ9Wz6LzZVNW4qpOcp6owvLBHtjTozidc40IkMvJiVOoAda8G+gIvkGRJip7WbqSUlFSWsPwdS0mXK0DhqnYcBGdceT4v/eEpug60M9jbT9f+Vlq37WPBW8+htLaaX97zyde8C6KKI/D3RHEJ0sB1LXx1kg65owi9TeN0wD44iNv5M4yYT7uW726hb93DLgiIkQRE2y023GifRwoCfHXAvELDwPhOta+pUvq6O0be4StIpeVVJOqaybXuw870YymXRl0agH8KIJQeONvWSusTAQjwvQOKSQOGoGJgLE4iEDJI10DAqLMuJFnb4F0L91cANkcAOMLjCDO6YtdVX4YhY8X0OkVF6dHz0X6GcyMcqoxqXo/PP1Qd3R5BLZb+U8/bhs/wo33otaJqDr2c1MtF6ioJg542GcI74LxjBOl9zeC8GqNlBGF+o+550c2AIkcDWwIXFNgyGLH6bgrpgwFTuIaThuEmFiIhOOejb2b78xvZ9Ywbj6B9+z52PLHScwc0SKSTVDbVM2HuOKbMG0XCFL4BoB2jah10LH8do4wujvGNnz+JsfMns+qeZzm0xc3COH7eeOZfsMCTYoTrSSkwE3DSm5f5sQ32rtnB5kdWsvJ/HuLUqy8J2XecoL8cff/73+f73/8+4IKAK664gpLIvRkcHGTnzp0FWRJHSkcABCJbxWIkZWxe+WKgwG890qSUYFU2UDb3PMySqgAEROsO91KPe+uMkGQ+R+/6EYKAIn1YVaPI9nVwaM82Ro+bEl/oGOjaf3k7gzvXgjCoPuNSvnztV2KlAYpKp8ylq3Ufg7s2UDFziX9+OClJyDBwmQvKoqLv4VQ0RyKN0cupPpxcjsOPFaoD1G7VLSt9tz2dER2hbSgAvXv2gBCUjx3rj6P4vERoZ+9mDZQcevFl6ufOwkqnfQAQTa1bjASQGxykbe1GRp+00D8HAdNV54Z6zHv3uCGJqyaMDVmYK4tzCDNwRSHpgJcxUMdUEhVj36/hr7saj29zMJghPzhAuro6pGJQ62YICkAABDtxA4lhSH9Hm7eFr9JQdQIGLkBqsfxRz4Rkz3NraF4wg0RJYGisyumSAf8eCHyGbSIZ7M+x9+UtTDxtLg6mvzPvPNDmgwBhCNq2ueGTJ50+l3FLZmIZDiVmjpSZ5+C2QwA0TxkF3u7cNQp0X5Y56babo9C+QkoRUpnYKgqh18a4+ZNo2X6Ik998CvVjG1x1CEEdJQ3Q2zVw1UBj5k4mWVbK2j+u4KU7HuTk917Ga51cKdRfehSvLDU3N7NkifuuXrt2LTNmzKChIcyLkskks2bN4v3vf/9R9XEUNgLFr7m6ORF6gwwLCvyL8f0kKuuDtuPqDvcQjIQBRGWoaJKAvqMHAQAy5+roLC0Dm6L+/h5a9++i7eAeOtsOUlZZTeO4aYydPHvEroaLJ07myY3PAjAn5Q7k+m/dAMCXrikEBLdc/x/80xsmkW076I09EHHr89AZcRQEFNMhRnX50XNDiuVF4bOlj8HJ5Tj06ANkuzpoOudCknUNWj0ZbgdAY0ZDUTE9vJSS/Y88AsD0q96N4XG74rrqsLgfoP/QYdpWr6Vnx06mv/2NBTp0IuOLqgQAtv/hfrI9vVSMbaa8qSHYbUfqoBn66S9GgWTHnx8DYMEHrkA3LzAM6ZpxxLhQqjlGAyLpfavSOsBxA0GFQYUQks13ufOYeukFlI4eVbBWcWR4O3LbE4EbSOz+PqyyMoQE24G8IxAILNPxrebVuB1EKLFPz4FW9r+0gbate1h05evDUkLCSYN0AKDnDnjpzocZ7O6jbsJoKpuDd8KWh1/wv0tHUlJdzqwLT6KmsQbTyFNi5jxJguSp370EwJuufR1CuIzcFE4ACHAKdu865XO2a5dDsMtXZUaPr+OSf369v3565MLQPdTm6qplXDBRN6mJ6nGj6dxziMObd0HYzOE1SX/rqoHLL7+cyy+/3D++/vrrmTz52A0kdTqygEJupEztRPjYf+nHSQ4ioMA/PUJwcLQ2BiMixTHUO3U4EDDC5y5zYBO5lp1U1oyibvQ4Hv3jz/jjz29noK8bx7GL1quoquOd//RlFi27eNg+3vnRW3hhw2ryXS2sbu9iQxH7AEWGYWCkSrF7OoPpaCAgeutCcQI8EBBVs8Tp+UfC/OP6C8YZAIGhQIDbR3gnqWNG3bAvjsIMKxhz3759ACSrq+nft4/ycWMj85SxjEx/wXZu3gpAfmAQO5fDUqoUbXx6nUBS4Cb4kfkc+YEBADo2b6W8qSHUfqh/zf7A3eG51zt37fOL9OzdR+W4sUF/SBzhPvi6KF53z1PXQ14NGhgZipn7gESbR9vGbT4QUBTyipCBHYKD8GMwdOzYx0BrG4dWrWf2299AsqIc03B36XnbIG8b2No6Gt41FQdBSji0fjsAuX7vfiQTRXMC6CQ9Y73MoO3bABxYv4P6sa67oS0FY+ZOYudzG6mf3ETT7InUjXGvKQAA7rN2cPthv93D2w8zanKjv1t31xt/h1+wnp4aZO1j66lqqGT94+s590MXYXrPlSkc1z7Ct2EwPLsGiSlcDwRHgCHxQYcf8lhI//zMC07m2R/fw64Vq+ATQy7NCXqV6cc//vEr0u5R2AgMczlmV+hfUFTExqDA8DC69Yg0Fd/P0OMb0ktBxICAkRoGxtDgzpdJJNN86b8f45ff8oL5GCZmeS1WIoWRLMFIlWCkyzBSpchchmzrXnpa9/Cft3yYhWe8jg9e9/0hpQOGYfCN79/L9d+8ofi8IpSoaySzfzv5vh6s8uK+pyMFAcUkAXGkM/6h6vjt5jUQcO4FpOvrCxiyXkdvR6p2pIh9/MLnwpEMy8eOZfpVV2Fnsxx88klKmsf4UoFi0gD/GMh099C5bQfgWot3bNhMw/w5/vVovagPvWlIDm/YhOP5onds3UHzkjkkKytcozJNEqIM2myNWRsITzWx2h/wgRdWUzW+GRAhxh99ZlR7ypMjarToULjj16UqanyKDq/dEppH4+K5pKvK/XXXKWr06DgCcDj44mo/8M/2B55g5ptf56+kZQaMVk8VrNZDSvd+tG7Z5Y4/n+fQum2MWzzD67PwRaLvph0EmbzJwbXB/Ti4cTezTp9KaVUZWcdi/MIpTFk8CctwvOcoT9axyDomliFICBsDmw0rtqCMvdat2ErdpEaEEC4TFgGoNQn61sFXf2cfu9fs8t9ju1fvZPJJUzFxQgDP7SIABLrXga2p0WK9eiRIJGbyte89ECex+luja665hk9+8pOMHz+ea665ZsiyQgi++c1vHnEfR+01EPTs/Y+RDBw3UKDf5yF2n0X7ixtvDMl8jt61xwcEAMjcIKMnz8ZKpnnyT3cAbh6C9PhZJKpUDILwjjTdPJXezS+S2b2Gl5+6ny9cfQbXfef+UDCgoShOHRClkkmzyezfTveap6g9/XXhMXv3bTgQoMrqYx+JG2ecpEC/jyH9cC4MAlJ1o0B7QQ/nIqeu+oBA6y/eALCwnUQ6SdmYMRx49FEQkKysIF1VSUl9HSX1NV6bDt2795Pt7iHT1UOmq5vB9o5QO4deXEXXjl2kqqtIVlaQqqykYsxokl6gmmz/IP0HD5Dp6iHb1cNAZzcDbZ2Uj66n91ArQkDXnv00zJmOacSPFenQvXMPpQ11WOXlCAEz33JxmOkTBGDRd53RoD2GJ+J3HBNd9+/2UximGYIXcqa7h46tO9316OzCTKdD5Tbf9SdK6qpJVVWSrKwgWVlJ5fhmn0EOtHXQ39rqrWUPg+2dZLp7/foDrR2s/58/UlJTRbqqnHR1JWUNtZTU1aiR0LNnH4OdPQx29TDQ2UN/W2foGd3z7Brat+2mpKaSkqpySqvLqR7bQLLUjaaY7R+kY28L/Z19DHT2kunsore10x+DEIInf/UElQ1VlNWUU1ZTSnlNOU2T6xGGK0Hoa+2g/3AbfZ299HX00d3aS29nkESo83A3D/34USrrKiirLqOipoyaxiqqRqnEV5LDOw7T09FHb0c/Pe199LR0IYQL8gA2r9jAwc37Kastp6y6jMqaUurH11NalvDGKUkI21sVQc6TNvhxFAi8CJR75fo/PQMSppwV2BC9lulvPaDQ3Xffzfvf/37Gjx/PH/7wB4p5DcBfEgj4IyCWSRcV6UcLRgoUdU8sAjzivg/Zb7TMcQQB/ou3tJq929fztU9cxjmXXc0jf/wFuZYd5FrcnaJIlmJW1JKsaybVOAmRLKF/xyoyu9dQMmURMp+jY9darn/vUr726xdJJtNF+xxKHaDTDd+9gVRNPVZ1PbnWA7Q89L/Un/0mN9St8EBARwvtKoHQUpVFsDD2/kiZ/1AUZxsAcZKABpQIW/nhq/p+W5E2lEhYUsS9bohnM3h+JXWzp1M9dSLb/u/39O7eg1WSZtIlF2g7SUF+oJ8Dz75YdJ7ScRhobWegtR2AivFjqJo01r9upRJ0bN1FlybKH3PKAhrmzSgIQ6zE97oLmcu4BRWNtVilZTjenMwI4DF9u4CwEVkUVDtSdxUM1szRDP2EkKGkRIpSlRU0Lp7nAwPpOLSt3+Suj5SkKsvp3X+ITGc3tTOnkigtCb3cDFNw4PnV5AcGi66nAgkAidI0sy47118HgFzfALueerlofek49B7uoPewC9hqJzbRMLWZnGOSMGyMZJLDm/fQsTM+BbF0JLnBHG17Wmnb0wrAvPPn42C6qbURJCzY8NTmIQP69HX009fhgoNUWYqlbzst6EMY9PdmWPvohiHmIek61EnXoU4ARk8eTePUxgDkSUHeuw+6nYACABD2vACQtvvSTVX8ZRKlnaAw7dixw/++c+fOV6SP4wcEIAADEVDgXz4KUBDKTx/tK46G6TeOnFwAAirmuyDgaPzbo1Sx6GJ61z7M7i1r2LN1LW//0A3MXnwmTz34vzz0p//F6e8m37aXfNte+jc/5082MWoC6UlzMISFUVJO/8Zn+Pk3PsX7P/PtoSdyBFRzxiV0PHUf+c4WBnZvoWzyLAAy7S10PBWTRVAKoi6B0R38sJIghgZqSgOivAN8m4Da4H4UAwDFxfXCfRxlmKEpGwTVVtSWIJiHe2wmkzSesohDz69k8usvJFlVibKmB2iYNQ3DMNjz+DPxEweEYdCwYC6D7R2MO2cZhhm4wxmmyZTXneWLOg0jnDEPApGvIaS/DVK+9apcqrwM7z2OKSSmIUNW+uAFzSHwwVdzEJqawJaG79eugw63vqv/j6oH3HUT4WNvsevmzqJu7iwEbra/nX9+hPpZ06maMtGrEzww6Zoqpl9+Plv+8KCbOKiIWbgwBFY6xezLzyNdXeG34UhB09zJmGbYiK8Y1Uwaw4yLTnMDOXngKZGULHz9yay7/zkObzswbBsLL1rA2DnjcV313Ae5qq6cs95xKo/d8SzZgeyQ80iVJFn+D6dRWlPu73ANJJPnj0MYgpf/tHrYMTRPG82S1y/GMoP1dNUCgWdC4KEgEYaD7Qgc6UYuVAaS9dPG03OonZYtu4ft8y9NEsHfU66BV4pGDgQE8cw3+myLyP8iZYcSM/sFNDAQKjuULGikz4SScuYLQUDc+GK7GqaMYSWpXPQ68r0d9L58P3f+4Ebe9L7P8ab3fpY3vfezAGSzg6x57iFeevweXnr6z2A75A7vouPBXSAMhOV6G7QeOPYf5Q3fvYGb/smVHNz4vRsQpvsCSDdPdMfSUQQEQCEIcMLAICoB0sFAsXsdrSNlECcg2+UGC0qGwgaHrcJDxoFSb08HC0qsXbgeYRAjY+q6pHbG1VOnUjPNdQEVaDtij6qnTUUKk72PrQj1M2rRXA6vXMvkS86jrDFsKKfHEAgAkQwZkKnxqOtRxg7Bejgh5lw4z8BvXvo7QdtRbnhePgFHhK7rpOY7nF62qHEmYCSSTLjgXMgN+nNXa6HsJEqrKph5+QVs/MOD5PoHY0U2iZI0c950LqmKcvSXi+nNoX7mFDBMtjz4bNFxNkwby4zzT8G0PObpOF6gIIFlGcy/5GTW3v8CB7fESwYAFl+8kLGzxuLgMl5XHO/q7Muqyzn7nafx2B3PkunLEI3oLoQgVZrirHecSmlVGUr1pdPkeWNImvDcvcXBwNgZTZx0yfxQuGQF3Bxt9x+b0tkDgLYXgKn7gCvhaJo7tWh/ryWSI37p/21QLpfjpz/9Kc888wwHDhygqamJ0047jauuuopkjHfaSGjEMUDVrqtAlCuO4GNEjvVmYtuOlxsLY+SfaD193NLO0bOmEAQcl+dKm6NVXkPlqW9BJFL87kdf4a6f/JtfLJlMs2TZ6/ngdd/j+3dv5Ru/WcfbP3IziYZxmGWurtAoreTKa75yHAalj09gpFxVg8xlQiCg9vRwKmHpgB4pUAhiQUBMF0UZsM6kfDVDPgwComGDXUal1QPfcj0oM7yuYqQqDT0AkPqolLyO9pEyeNHWTpvIuHOWYlgmoxfNZeobzqdxyQLmf+DKAhAQjKcwGiIoH/HA79vfucf8VnSxOIR1/ipTXuH8pN+m7bgBe/KOURDgRkkDihlpAqE1GAmZyQRWeXnBeb2N0uoyGucVz5jXOH8a6cpybzzxgLNu2kRK66qKtjHl9LkkLG99hUPStLEMd6fhSLBMwYxls4rWr2yopHnWuGBewiFp2FiG7f13qKwpYeqi8bH1JZIpiyZ4IEADRSp2Am50wwlzmqlqqCg6jnnLp2GZmsGmZyCogwFleGh6IMV2wuGWpXTob+0gP+CqMjYPAaBeKyQJ/z6P9vPXQps3b2bGjBl8+MMf5oUXXiCXy/HCCy/w4Q9/mBkzZrBp06ajavfIVAPqmZHFX/5xFOvuJ7Xv6lgV0XaIoRMFjQ1PxcCAzOfoWe2BgAUxNgFxYxtu7EOsiZlKUnXam+l69i7+dOd32bTqKa79+m+wIvrfdGk551z6Hs659D3Dzu1ISUkDFJVOnktm/05aH7kLDEGiuj4EAiAKnpT4N363ORIqFlAoqg5I1Tf4ovo418D4tqMNKzVAMG5/KkOMezj/drd+JMqe5k5YM2Uilc2jMUtKhm3LBzieq56y1He/6+GGAY3ZR3f+6rwpHGw9EE0IEET6VtblXnHbCUCHkjoEfurh9S0wFIyb29BTL2rwqeYvJAy0dwXATXsxCAED7d1uPzGASIUIth0Y6OwJF9AehN72HlJV5Z6KRTFKtcbu/HvaAkNFt4nAWK+3vRchHRdYIxGG9N33IABRve29KJWpsoeQ0rXr6G3vCdWJzglASIee9r7YMQD0tPdSVl3qi/9VOGG1zgB2XoJhkPduuECyZ+VmtjyxtuAeAJjJ46s5PkHHTv/v//0/kskkGzduZOrUQGKzZcsWLrvsMj7ykY/w8MMPH3G7I88KoiPu4Xb+0apDSRKKHVOE2eiNjeQTQzKfo2fVAzh9nVQuuoBE9TCGgXFjHeq6Oh2RTBiJJFVL34ZVN5adm1Zy44fOITvYX1jxONAXv3MDN3z3htC5G78XHN/4kRtJ1jRQMX+pu06OQ9m0BSPK6hgn0o9+itWLvrSFCIOAxhgQ4Jf3Pr7nQaht6X8MI5AMhET8hvrIUHnfDc+QWqS6I98m6LuLoUCAPsZC2wavLScAQIE7GL7fu6GP2Uuva3jlTeH4HyHwxf6qXjR4jqqrdsKqHR0EjJT0n4LUjv3xFakXXStX1y7oa+30desl1RWUVLu7YulI+j0r/uhcwF0DgSTb0+sbvwHUjB+NmbD8Qfa3dwWBgyIxBRKeT35fW5c/OStpUT8hSB/t2A79Xf3+TlsxdLWbV987Dnf786ioLaWyttSfR1dLN5bhhD6mcEL1+zoHcLR5jJ5Yh5Uw/eeiq9UFK67rqDuWhHCDLKlxtWzehcgN+H0YAlp3BXENotSx8wCrn3mg6PXXCingcyyfvxZ69tln+fKXvxwCAQDTpk3j5ptv5plnitsoDUVHFVnwqFz0InrhYLcfvh49jtM5HxFFKioQYPd2UrFIswkYKp7BkXZZDF4JgSEElQvPp+v5P9J2cDfXvu9svv2r546twxi6+aM3FZy78R/D56SdI7N/O7f89Cmuu+o0BnZuoKRpbDx+KmJp74v1I7vtuJ1/1DgP4iUBbtlgN+OHDdbah+I7zpAFtGLMpvYMaABDD7cbbiMIyuP2ObTLYvRaNKWugfQT4agXvcQVxycMG1saQYpfASrclKqnZ9ADN7FO3jEQ2nVbGjgyCJNrebHwhSdedvXAbh9KreHbIGgMUEkGlNeBrQzNZGAoWBA9UK2DdhwtoyIPKgplTFT1hH6fwM7lSJaXMe7UedRNm4AhJO1bd7HrmTUMdPUgHQdMQVT1oSDHQLvLxGsmNDL59LmUN1RjD2bY89Im9ry8lb62LhKmHTJyVOF+FVPtaevBMA0mLZ7MlJOmkEgn6W7pYuOTG2nZeZje9h4qa4LY73p+BHCZfW9HP6WVaRaeOZVxs5oA2LPxAKse20pPez+mzGMYQgNfwvf3dxB0trnSgKZJ9cw/axo1oyrIDubY8OxONr2wi+7WHhf04SYoEgj/ftsYGFLSuruFrc9uZvoZM2ieMQZpGJz85jOQUpDzsjI99aP7yPZnEKaBncvzX1//J771f5t5rdKRqqT+2qm5OXCzjZJhGDQ2Nh5Vu0cl+9Ff9CMJJKPEmsGJEQKCYdou5oteMF7DE7nmc/SsetADARdiVdYXFiaekevJd/TvQ1KRG9a7/knsbtcgx8kVdy16pUkIQdXiM6mpbwLDINNyACebxUgmi4rOowy+mLi9mG2A+l9MEhA/zsJ+Q4+TDM4UGiLGp/tVjFu5v6lY94oc6e2kQ+OQoe9R5m8aEktTQ+jW+AoEpMx8oArQdP+5vGJEXltIP7tdiEl7KXDBZfS2E8Sol95OP+8YIcYoccv4KXrVeIWj2QMEzFSNS79fthNE7BNKTeCtXxwgEL7HQhhM6WQpL5GI9CMIjARTLz6bVGUFlqVyGAhqp06kZvJ4Wjdux85kMEvToTWCQDJgWYIFbzmbqqZ6v30jnWTK0rmMXTiFtu37QmsRZSoOgurmWmadOZuS8sB9t2pUFWe85WTa9rZj5+OjhCpAMDiQYdG5M5g8bwyWGTD7SbMbmTSjga1r9pMZyFFanvQ9OfQ8AwYS0xScd8XJjB5b7T17knTaYsFZ05i2eAJ7txwK7EI8QOAgMIWNLd0kSfVjqpl71iySZSXu7wkJHiAVliCXs8n2u+8jJUWpn9wcO7cT9JehG264geuvv56FCxeGwgxv27aNL37xi9xwww1D1C5OQkbNWItQavR4qpacF3utqO41wvz9cjE7Nr3ckMcjpAKGlc/RvfIB7N4OFwRUNYS5xrE6xCsaRmne9fwffRCgaPr80/nYzT+LzUdwrKTUAVFpgE75bJaPvXEaACUTZ1C98AxAYwQ+ryiUCkQpCgaKPRsyHwEBdQ2aLj/cQVwMfP97TF/KcDA0h1hgEsTm1/8HSWjCjE2P4580nZAfvr5r18epxPaOdC3zLWH7PtxBRr0g9a0r+XB367YUJAyXgQfMPmDY6hnX+1B19fJKFeBItz913gnt1t3x633rXgoq258OYJTHQdz9UqBBd4cMg5AAdCiphxpjzgmjbJVDQF9TBbL8PpUYPSKFUX3o66VUAaqeqXlpRMkynJCxpA7iVDtKFK/61ceq9+WvdaS+fl6RHg0wrkzUpVOnEKCNAGRfIqRJHpQ9gS0NbFuyd81Odr24hcGefoQhOOODl/Ouy78Zuz5D0b3f/yh/+MMfjrjekdCU6bVc/o6Zx9zO1pfrX/GxHg+69NJLeemllzh8+DBz585l1KhRHD58mLVr1zJ69GgWL17slxVC8Pvf/35E7R65sWDcjjtWlKz9KI6Ex0b7iEoKRtrMECAgUd0QNj4qqFAEIAzD5Iv1r6h/+yoXBAiBWVGHWVJGvq+Lzauf5tNXLuGit32Ecy67mmT6+AXyyPd0MrB3Ky8+eQ9LlhVmEHEch1s//VYASiZMo3L+aQXjj0pAjsRYME4HHgcChIg3Hgvv6EcOQgrmIMMAQZFi7LrlcFSApXa3wt0EkzJt/9FQjFmP7+4zDM2wT+nyHe9FrFy1oj7QQriDdXW7aMwgmIjO9JESGwNbertaR5Cy3B2qMvhTOv+QQa42S6WmUPIPQwTSCKnAUYQBAeFQtcQbWboSknB51Q647aYs2/daiOJxMwLofPsFWagOiHpNOJ4kRIXrtT1mjvDSChdIHDXpjm99L7zy7hqpYx8Aevfdn48Mj3co0j0EgnP666cQGKswxOq7DiqCRjRpkyYlcH9f6jkVvlTHBEwhyDsSwzSYsHASzbPGs+KnfybbnyHbEzaWfC2R5G8/6ZBOvb29TJ8+nenTXW+abDZLdXU1y5YtA6Cnp2eo6kXp6HMNDMWYtRf3cAw8VqQfBziGOheRPOgUAgGLLyRR1VDwsik6qOj3EVaJo/7tqxjYvpKSyYsonbIgfG3HKga2r+aun3yNu37yNWpHjeGyqz7Fqee+aUR9Ryk72M+//cubaDmwi2zGTfbyX7f8I/N+u4Fbfvw1Bvdt56v/+nM6Wg/w7euv4sCuzaQax1O9aFnhvGJUIFERv34+VjUjNFVSvjCBUJhhRwwEi9yK6HJHpQFR2wV1vlimvWhqYInLWCxT+jtWwBfLQxCpzfBE1or0MuBKC6TE1dXG/CAUU0sYtsvgtYkK6fgMUu3udQmCPve8NJAIsrbp7/BNz2As7mUZl35XIvxAQoDvyaCTfj8V8zcMSTTngeojLu6BLklxvN2oCowUNY7UYx/EkaDweVHx+nVS65BzDAzh+GNXDNEP2KTttBPC8TIguj8EN5dAMJ+8Y/r2BDop+wq/ffCBiUozrAMKFX8gABJuEiAI5yBwx2D7O3ldshCyx9CkFX4wIaFUTkEyIv9eIjBNVz2yY81e1j60Bsd2mL34LN73rv/2292zbR3jpsyJvQ8n6JWnR7yMqMebjiz7YGib5P4bkqkWuxbDuAs25AVv+phz0bYix3EgQPVVzLahWBCcojSCci6jX0nJ1EWUTl5Q0H7ZlAWUTJ5H5sB2svu30d6yn5/c+nF++9+38NGbf3LEP7yWg7vZt3Nj6JxZUUMyXcqNH7kJgM9cdwXdL7uBb9LNE6k++Zwh5ijRmb9/WhSu01D2A1EQkG6oJ/qQRJm3fz5yXX/hx5HyXtDva1xgISM0XuExfyfYDWtiayWSth1BzjGxHUHCdHz9u0on6+6oDa1d6Y8h9HwifObv1nUt1ZW0ALzogN4LOpM3sAyJiRPSo+t9Ctyyyk7ANOzA8M9w90+K6eoqBh3IhBi9lJim9FUFvg2D4SYEsgwnxLR9KQQB0/XXOsKwFVjx52pILC1yvBpHNEyy6kute1QdoLsAuuc1gOcxREe6YEBn4rY0MHF8CYBqS70kfYAQ85vXJSamCFQNui2JiQKVefKO6Y/F39VLV2riSzI8uwVTOCHQIIRnTCrdZEbKMyAua6Gh7qyQmhTAswvwJTnu/+7OAZ68axWdh3swLZMPfPa7LDnzDQD87t6P8vhPH2CwZ5AL3vL/ePP7rytchL8QHS+t7t8zHVFkwZEwx4KdPRRn9nqZmHKxUoI4yUDcOPI5ul96ALuvg4olAQhQ/RSby7BzLHY9TjqBJwnYupLSqRFJQIShmhiUjp1K6bipOE6evvUv0L17I7d87BKWX3wF7/inLw+ZhVCn0c2TWXrRO1j3wqNkBvsYPWYK/3zLL/3rjuPQvfoZhGlRs/QiUrVBoJti848ah8beI62NOBuNQhBQ+EzEif91EKD+x7neDaU6iFMLRMkQknTCLtjRQ8BsdR2+Evdn7SBEK0DOMf2xKEYPLqNTIndVxsb01QtZ2/Qt/d3UvAGzdMfg9S+EPxYFTtR3xTjyjuVnwlMeA1lv9yo1nXewxoFEQM1FRRd0564lqonZuav1U+J3G+GBlPg1120SlFdDnBSgELiFRfJKUmMgObRpN+v//DzLP/QGrFLLG2t4l+yCWgGOK2kxTXeHbwoHR8CTv3wcIWDZlWeCp1ZQQX3cdQpIXwf1385k+e23H+K0189j4pwmbw7BGNX/pJH3AJvpufq56iZbikASoEBBxNbBkO6YpLe7N0XwjKk+7v7J8wjg0qtP8jwJXAAjkSANhBG4J3a2D/LMveto2dcJwKhJo/j8N54I5TbZt24Hgz1uJMi2w3tj7+lfglxoO9Jd218n3X777Vx55ZWMHj2a22+/fciyQgg+8YlPHHEfRxcxQuMIxXaA4fKRY1mEkUSYfGjnHgcw1PkI4AiBgMUREEBh+aI0kucrRkoC7tj7t3kgYFoYBMRJVvTzlmVRNf80yibPouPZB3jivl+x4pG7+PovnqO0tHh0MUVWMsm7/vlrsddu+sGNtDzwv+DYlE5fEAIBoWlFDARjxx5XL0ZyEO8iKGOfnaHE/0EfhRKKOLuFOOPBYq5qAihN5n1xuls2uK5etLpe35GCjG2FmLXuwhfS5YOfAyD63AkCYzhl4a8SL6h2DGH7KgLlk68+ecfwd8cqWZDKTZ+1TXKRuSiAoEsxdOM7A0nWk3ioqI1I1yMi65gFiYr0NdKTGumkRzdUoEonV5ohfXCVjxgM+gGCvPGq3b+hGUVue8oNjNO59zDpaWPQo+ipPkzhkMAhYcKgbXnzcK/nMnm6Dne5/WVzmCnTVxvoa+OPSbj5Glwg797DQzvbAFj9xBYmz2ksqo4SAizhkMQmJw2yjuWPN9SXcHyJgoFk0ElgILEM2z+X87wdVGjjfCZH20FXV2znciSSFiBxpOkBLrePg7s7eP6hLXQccstWj67kozf8L2MmFhrffehj/8tvSv6VsZNnc+Yl7yq4foJeOfrkJz/JsmXLGD16NJ/85CeHLPuKAwFXvBThWrKQSRypmKYAEMRIEWJVBUTKe999dUCcJECnodqM62OY8lHm3r9tFf0eCCibuiBSuLBOuL57YFVU03D+2+hZ9xz929dx27Vv4frv/3mEg44nx3Gw+13jn9LxUwv7phAEDKUCiAMGRUHAuRdQ0lCP473r4ph33BLraoCiNgPDjCN8PvyQJkyHpOmQMOyAaUvXAE9R1OraloG1u2K+vkuW17cLGLydms5MhLuPcXdoKkyxe82tY2h2A46fNlbFDtANDJOeuF8BEsCPL+Ba9LvlTMOhLJEladg+ox+wE0jpkLZymj7bLW85FlnHJGt7UgRvMU3pSjVMDej4onlv56lSGIMmYdDKA6StvD9W1YcppK+7F969MH2g40lTkKHnwV1P6OvoIdPr2sPsemETjdOakNrO15dseLtoISTliSxZJ2D2+9Zs99d19+odTD9lsqcuCPfnJ6LSVDLKVW/js9sA6O8epKejn4qaUh+4qOdGrbNa84Rw/DgXUQBkEMRxEIYkbeTIOpYvjUkINwbFoLQ82ZJk48ogg+WGl/Yz99QJQVtSsGP9AV58ZCsDvVkAJs5YxLv++auxAMB/ztKlXPHRW4pe/8vR334cAcdxYr8fTzpiG4Fghx7P8YcSLQeF9AsxbQ/V7xDkSwJ6hwEB+hii7Y4EcFBknt65/m2r6N8SAQExTCuO+ccdVs49BXuwj/27NvGnO7/HRW//xyKDDNOjd/+U//3RV3Ey/ZjlVTSc92Zu+sjNfHrnBnrWPEfLQ7+l5tTzKWkK0uEWkwQcicW++u7kwuqAVF0DjhO8UHXmP3TbYQlArPogRgTt76R0IBHTvqkxUMXoo6F8lRhYUcIIVAUgNIO2AAyodnRG4DLywPpdSk84LkSIqfr6cw8EqHGp3byyARjIW67xlzcWV61heG54JqVWjrJE1u9bMVXLcCgh5zNYcJmUOnZ3pqa2k5de/4J83sQ0AimCSlrkyOCH6tsSeJIKtZbKsFK9vPOO4bvvGZ5kYnDAxs7bJEtTvsWAq2P31lSBLY/xZXoH2Pb0ejdeiCPpPtzFoQ27aJjcjEwn3LF6O2choMTIYUvDl25k8pKB7h62Pr/Nv2+bn9tOwCFokwAA6xZJREFU4+QGSqvKcBKG73kQ3bWrOeYG8+zacpjOw+7uWhiCVU9sY8k5UymvTGIQNqLUSVcvKGPCpHD851wgQ1KYMLAwfFCaz0n6uvpY/UyQoGz107tpnlxPVU0J+3Z08PSfNtHfm0EYglPOeRNv+9AXKa+qBaC3q50n7vslWzb/H5Pnj2XZmbfxvX+9jF4vTfLiC+fw1nfeETuHvxT9PagGXg0acRyBdON4ak49v5AhS1mUSUeNtYoy9JidV/T8UCQlSDtH94sBCEgOETb4aIxLijKqyPkCEFBk51wMBBRTGziOQ8v9v0bYNl+/YyWl5VVDjvfxe3/Br7/zeRACkUgisxkQAquylpu/fQ+H9+3g36+7AqRk1Plvxqpw24vLLaBLBMJ6+uL9y3yOg4/8mWxXJ03nXkC6Xs8iOLQwJqrP11MG62WC7+GLur+/7g1QEH3OE2WnrbzLoJVRnWZNrYwDHQSO4/rDJ7wQrYpB6z77/ph8xhkwTCWOVzp9IKQ6UDtq8JiLpz8OjZlwwiPLcEJ2CPocs7ZJZSpDmZkNtePGL3DZak7zV1fGcf35JJawA7WHNy3FrNQOPk68H0f6vJRkQFnf294cBJKkF+Hv8M4WXrrrKQzTIF1ZRnldBVOXzydVXoYQksGefrY8vpq+9h4Gu/tCoXfBFY+q15qVSlBWXUbtuHpmLZuJYbjMdv/mg+xYtYve9l4G+waLvmuEgHR5msraMqYuHMfY6aP9VVzz+GYO7e6gp72fbCZf0LciwzRYctZkZp0ywX8PKtDlS2yEZKAvS7I0ifp1KAa3b1sbT9+zlrFT66mqK2PmKRNQ0eV2b2ph88q9dLZ0k+nPxU8iQqef/1be8dEvk0ymcRyHVU/9iTv/85N0tmpugkoKqlFZdQm3/ipshDwUvRpxBCZOr+Pit8095nb2ran6q4gj8NBDD7F7927e+973Flz7yU9+woQJEzjnnHOOuN2R5xrwqJgIdjjR7FCi5Ohu2S8jtM8QpIOAymFAQLHxqnPFPqGxRsalyoRAwLRjBwF6/6ZpUH3SWTiOzS+++Zkh5/f4vb/gju9eD4bJ6EvfxajXvYPSKXOwKqrJd7Vx3dVLyWWD1K5ONia6YYxEII5i18rO+iCg+bwLKGlwXQQNoXbMQ7dhqJS4RiF4HA6AgB4YKAwC1LmgLZfxZR3TBQJecBy1I9at3qWkAARIAqt5tXOLurLZ0nABg+My7qTpiuYThq0ZoAXx/3VbAwjAhcoUqK7HgYCUmccU0vVo8Ha/GduVGOSkyaCTICdN+vNJevNJX/QvPVuHrFdPSrce4FvAK127JWySpu0BIPc+KeNKZemuQiinzLwPhFTsfzf6YtgzQOCK6ZOGTaarh3UPrnTnaDv0d/RQP2E0ZZVBCN+SihIaJo2iv6OnAAS49yq4B/lMjoGefppnT0AKw08uVD9pNMKyGOwtDgLUfR/oGcRKGDRPCSKRCiGYPK+Zvq4BHwRE+1bk2A4bXtwLTlj/r9uwSNtm7bO7OLSr3T/nuxZmc0xfNIZTL57NtJMnAQb9PYM8efc6nn9gA4d2tY0IBJRXpfnar1/i7R++ifvv+A6fedcsPnrpJP7zlg+HQQCE1sQw3efr4rcNrZ8+Qa88feELX+DQoUOx11paWvjCF75wVO0eMRCAKLMW8eeHqV94kuKAIOa6OnZ0EHDShSRqGuKBQwygiGX0xerFMH69bp8CAdM9EBA3h+jcRyAJUNfswX76t7tofOWK+/jGZ9/Bb374Jbauey6kN+psO+SBAIPaM1+PYVgYhkHVvFNoOPeNVC85Cxyb797wXhCCumWvI9UwKpy2OWLUNCw40kjmsxx4+AEfBChJQBzzN03XNc0wwp9ouWifcQmDFJPXAYBSBQh1PdK2CivsRPzcXcZoBrt2Agt4FZ1PCDxmHk74E7cubiAaw//vEICI0kQupIbQXeoU84yGC3bHHlY5uJICzxdd093bjqAnl6Inl6Ivl6Q3lyRjW74aJOdJBlJm3p97wghC5hoeANBBjiVsf10sYSOQPiCyDMdPv6snvDE9ewdVPmnYJEw3VW+JlXdF24NZnv7fp3xdP8C8ixYzaf5YH2C46wFj5kxi3oWLChdbf3YMQaosxWn/sIyy2go/JgBAwjJY/IaTaJw6fGz2sdNHc8ZlC0l4YY4d7z5W1ZVx0btOoqQ8iRgmzeQFb18AhvvcmLjrkdTBoGlyyrlTaZ5Y65/r6xpgw7M76esepLy6lI0v7GblQxu5/2fP8rvvPcmu9QfI5xwq68tpGF/LJe+8hvd+6t+55su/ZMoCV92XLkty0tmX8bVfvcjXfrmB79xwEZ946xzuu+Pb9HYOUNNQzqKzpnDJe04mVRqvKXZsybip9Zz3pg8Mu1avNnnWNsf8GYq2bt3Khz/8YRYuXIhlWcydGy+BuPfee1m0aBHpdJqpU6fyve99L7bcrbfeysSJE0mn05x88sk8+uijI57vunXrOOmkk2KvLV68mHXr1o24LZ2OOg1x+Hx42xZ9GRZzMYu9LsLtF5TTjp18ju4XNBCgSwKiY1V1i80hNLgipwu4mfuvb+sq+jd7IGDqgqGZ/3AUU7fjxScY2L01dH7z6qfZvPppHvrdf4EwmDxrMe/4yM08evdPkdKh5vSLSFbVFTRfMm4yXaueQuZzJBuaSDU0aZ1FdPcxwCc6n5CPdj7HwUceIKeBABEz/2Ihg+OS/xSrU5BSN9qm1l5g5OWeM4YQMQ3lYqiruqLBeBSD1q36w+N1GWneMUh6uep9f3cL+nMJkqbtt5V3ZMgo0O/fY4RSelIDR4noXR27RJA0bD9krB8Nz3NbtB3TNdSzTR/QKFe2wGPCIGm6qgFHGiRM2+tfhR724i0I2/fHF0gfIAghSXlgQqj4/SIcyU8IiXQMz8ZBUJFw6GrrdXfoHs27aDGT5jQDEkdrX+2UJ8wdC0Kw5k8vxd6vRCrB8ncsxaqoAAJbCKWTTyckC15/EvKeFzi09WBsG83TRnP6pfORwsSW3vqIIKhQVW0pF115Evf97HkyA/G78uVvmEVVXRmONnbf/VE4vu2DnjDq0f9bze4trTGtuVQzuoJF58/mbVfeGXt91qIVBee+9E+L2L+jnaraUhafNZkJM1yPIfXMjR5THdvn2z9y8yuSFv24kCyMznm8ad26ddxzzz2ceuqpOI4Ta7D39NNPc/nll3PVVVdx++23s2LFCj72sY+RTCb5wAcCAHXrrbdy3XXXccstt7B48WJ++MMfcvHFF/Pcc88xb968YccihKCrqyv2WkdHB7Ydn/diODqiOAJDX1dbkwgXlzL2UrGqQhTh0REDMR0EVJ18YfFUwqJIv8PMZzgVhqJ+DwSUaZKAovWLXCxW1slmOXT/r0MixapFSymbOB0pIdvVxuDeHQzs3c729S9wy8cucQsZJqm6xqLtloyfSv/2DSTriu+GijN+9T+8qE4ux4GHXRDQdN4FlNQ3FJQNJaeJ7PDjsogVi/5XrFzsLfPBjSw4V4z0+cb5yivRtm+kpxkLupKDIPa/qqOu2Y6rH3aj24H0LNlTZj5k22AKqTHnINKdy0gkluXaAOjSCRW+WKkzlCrCZ6ACXw3iHjsITR+d9Bg+HuBImznfFVHZFJhCkvfUEoEEwq1meVkUXcYfrJ+KjKekIIr8+PxIOlr6eO4PL4buQ+2ocl8lIWTgPaDqCiGpGVVe9D5aSZOK6jTgMuisY/r3x5YGWdsiYdiU11Zw2Djkpwn2nwNDUF1fBoYJEg9cmZ70Q6lwBBXVJSSSZlEgUDuqAgc3+I+6L2reivQcEptXHWD3llbKq9OccsEMrITJmDFXUVFVz+ixk6mubxpxTBFFvV3t7N/ZTlVdKW/+4Kl+r/5ckYweV1UABCpqSl67IOBVoksvvZTLL78cgKuvvpoXXnihoMzNN9/M4sWL+e//dqMwnnPOOezevZsvfvGLvO9978MwDDKZDP/6r//Kxz/+cd8N8KyzzmLevHl8+ctf5o47hjfEPPXUU/nud7/Lm9/8ZvQshFJKvve973HqqacOUbs4HWUcAQoYsz+guC1tTMyBUB0Zz2z061FJQNdIQECkvaGMBIvt9ocq17dlFX2bV1I2YxHlGggYWQfDU++W1T4IKJ04ncoFp4deAMmqOnAc+ndsxKysRQD57nZwbLrXPkvVvMKHQgioXnAaqfrRpJomFHUVjAMCcSBACBcEtL3wLPbAQEgdUNh3YewAIRgSABTmqY/JRxCpO4yUNjQXvx+N4au48r54XgRR8yzD0UTvWn3vetCGFr5VaNHjvLHnHJOEYZP0GKMwpc9slZtg0LeD4dVR1wGSJr6tgivmD7vyJQwXBCirfBVaVoEH4UkPctL0XetM4apnctKzm5Au8LIMm4RwyDomjgA8SYAiJVUwkL7LnmUEUQyFAf35hH+vDSFBSCqSGey8zZP/9zTZ/qxv+Q/Q29ZNw+jSwLVRBLYbrm2HRXdLEFtd1VUGe/3dA+TyDqZl+qoFFXVR18H3tPUEIECTGErHBSj686JsDIQIYhrIfJ6+7kG/nDvfYB4iocVe8NY4zn8ln7f58/+s4cDuTizL4HXvWEh5dSmnnvnjgrJHQo//8efc8R/Xg4RTzp3qTU+EgAjAzEVjeP7hbaFzn77t2NyVXw2K9wU6fjQc6MpkMjz88MN89atfDZ2/8sor+eEPf8jKlStZsmQJTz31FF1dXbzzne/0y5imyT/8wz9w2223uR5Ew/CJm266iXPOOYf58+dz9dVX09TUxP79+/nZz37G5s2bj0jNoNPRAQGIZ5Qy+CGEy4r488HlkOTAb04zplHHTj5H1/MPYPd0UHXK8CAgqmaIC1wz5JwiZdX3Xh0ETF8YP+Bh2hqOymctJt08AauqBsNQkdKC69mOFtpX/Amrsoba0y/ESCRof/ZhMgd20b9tPYP7dtJw7hsxkqnwHAwoHTdJO3HkIMC/J/kcA/v3UX/SKRimgTCt0PVicx9OXVAs8A8EtymaPTCubLH+ouFug34DX3hdV652s35QHC/Fru4BoIIA+eJ4AtdEtfu2MULA12Xghu9ZkPYM7DDdHexgPoEhvPjyqFwDhqcvt0la7vxdD4NgPikz7/uYqwx6YGMbgv580o8/r2LaKwt2FSIXbGzc7Id6vgJTODiGACfwhtANK5UxpDIylATRBi0jiKCnu2p2t3RhJS1mLZvBwe0tHNxyAICOlj4mESTIUTH2dfF6d1sABBqnNNI0uZ7Nz21zXd4k9LT1UT260gcgCWH7eQOUeqbHCyJkWiZTT56CELDluW3YeZuuw12+Osf2fPmjz09H24D/DFTUlDLn1Ans297K7k0tAAz2ZTFrU6HnT3erBNi/s4MHfrOWfM5m9NgqrvvWM8cl+di175hOf7drDHzem+cwYWoNKsw0hJloZ9tAqK6VMKgbPe6Yx/BKkoRXXDUwHG3bto1sNsusWbNC52fPng3Ahg0bWLJkCRs2bABg5syZBeV6enrYt28fY8eOZSg6/fTTeeihh/j0pz/NZz7zGRzHwTAM//xpp512VHM4qlwDRV0BdaYRc23I2yUiyU1iVArSztF9JCCgyByKjVsvE1dPUe+WVfRtckFAxYyFwZARBWWPmDTphxAGyZr4OcaBAIDaU8+lZ8NKeje9jDPYz6F7f8WoC9+KVVZRmEAowtSjVGigFxlqLoeUkvIJE4tOJxznPdymfr3Amj+mnWiENqUqMEN148cRl+EtCgD07woEqEiBOijwd/3eTtsRKtyu4wfziT5niomr8qq+275B3haUmDlvnVymV+5lERzMu/c2ZebJ2ibKENJARFQJwjfSSwjXYE9NS0+j6+rnBelEIMpWvugqu54riQiS4SDxk+W4BoVOEKEQ1x0x4bkEmoZD0sjjIDyffUFHtsQ3CrQMmxJTMjiYZ+PT20k31HLWVedgmILxc8bTfqiLjU9upKetJwRYVGpeUzhIxx1Xb1sP9RNGMXvZdGoaqwAYP3ssuzbsZ9NTm+lt66ZiVDUqrDJapEEpBflcnuxAlslLpjDj1Mkk0ilM4TBt4Vg2PLeDHS/vJp+zSabCz4vvuy4FnS29lFelWbBsMhNnN2KaghkLmmg72M1Lj21j/bM7aRo7v8AVVEmWHr9nE5tXH0QYgis+9hWWX3xF/EN8NOSkgQyGCc2T6vzn0E9w5Um+bAzaW/oor0qTSidoO9TDvKWTeeaJ99PbNcgFl/7q+I3pNUjHEqino6MDgOrq6tD5mpoaANrb2/1yqVSKkpKSouWGAwIAS5cuZcWKFQwMDNDR0UF1dTWlpccGGo9KIhCnO1YUihAYEekPZ6gXYvxa4wLpSgKee4B8TwfVpxaCgCHDG0vtv970MACgUF0g6N38Mn2bVlI+U5MERMvHzV0fXxxQOgIqBgIUVcxaRMnYybQ8fBdIh8N//g1j3vJer+/I7rrI7j+OWevDBxBev1GAGFcv3t4g2F2H7AeGmLtKCVx4Pnwcl+UvKpFQKgA9Wp0y6lP6eQgCuPiAVAaMNwBtamfthOwFFKmYBY4Mj01JDlKmyzgFAsvTYdvSjTqX9yMKBpIJBRbyjgsMcByEcK/lbBOwSJmDDNgJ0obrEZCVJra3E1fqBT/OvrbqWcckIWySRh4hvGMjD9gM2hZJ0ybtqQJshGuNLwJVhhv+Fj+Ij5p7QtghWwd7MMuc5dMYlGly0iAp8zhCUDu6isVvWkp/e1ewc/VUJXlvDVSSnjlnzqCittIDDC5AkobBuNnjaJ7ezGDvoA9wAsmI9+wJyDuSC99/JmUVKQ/ouEAhUZpi/tkzmbFkAtJxsL3wvMpOQYX3BagbU83lHzqdhIdIXUt0QX1jBRf+w0I6W131QlQcn887/PGXqzi8r4fKmjSXXLnwuIGAh/78XnavqqK/t4vG8dWcc9lM9xnxpF0GoAw4bVw1UH9/jlzOprdrkOqGcj706Yd47OH3sfqZ3axb8U4uu/rTTJ656LiM7/hREIfjWGjLli3HPpIiuxD9fFwZ5XI6nFogSum0mwsimUweUb04OiKLk6jYuFiZ4KCwrn9+mDZC6NvO0/msCwJqTr2QZE1DrAg7TrQdGoeIfGL6K6jvHQhD0LdFAwEzFha2JyJ9hSYVc73YuSEop4GAujMuxEwmYstZFVWMvvTdQb2ervBw1K7PCD5DraG/PtFpaYww/D2sboiSEusrFz69XqFmSYbq6O368/B22+pTbC5+FkFN96/83BOe25vuz6+774Xbc/tQFuhSY/5+dECCdYDC8LEqiY9i7KZwXe8A33gy5zFCIVyVgIoOKERgU6AYrZTCD37klgt004oB+7EAPJ2/H9oX6asFglgAbl03w507trTpAoABO8GAnfCiFNq+BMLAjXev7oPq0/TsC9S8HFwjO8t0GbRl2CFGbQqHqrrykBQgUFsEa1tZV+EzZaVC8EMAWyZl1WWum2MkkZS6Z6mURUlF2j+n/qt+SivTpNOmv4bqnuuphStqSjEMw08SpEg5XVbXl7llvR14Ppvn0T9u5se3ruDwvh4mTK3lHR85hfKKsPrgaOmPv/wGv/nmwzz3yO+orC3h0nfOoa7apLzUleQA5D2vEmXXsXvDflY+soXcYJ4ZS8bxlZ+6bmj3/+w5tq7cw6bVT/Hw7/97qG7/IiQJkjMdy2fatGlHPQa1o1eSAUXqWF2vqalhcHCQwcHBULnOzs5QueHoT3/6E6eddhrpdJrx48ezevVqAD70oQ/xy1/+cpja8XRUcQRgaEAwEobiHhT5aGWlnaPzmT+7IOC0i0jEJMkpBlDixlGUwcUwf4Twv/ZufpnejQEIGKrtIUFJzLgA14+/SD31Pd/VQtuKP5GoqqF++QWYaQthSgyTgo8QbhCipje9h5pTzsYsqyjoO2oDU9iv8tUfvly0fNw6Fy8fZphCu2YawccyA3919bJWTKwY01cfn/krRmO44lFd5O/IIMOerirQ5xFIBUSIyUbn4IfMFTJkVKfPPchr4NoUJCLlFONz1QRZP2CQYvgq8YzqT7WpdN9Zx/TbUkGBlOV6MG73u/JS0HMqKAau/ObB1auXmDkNTAWARGXVU8xQjd+Whjcm/LbyjuFHNXTH5fhjFEL6axGEOw7mqNY7KvVRAM3R1tW/FwRgR29DrZ86jpItA+YuJSGA465b4EoYUgXJgEHpO9aDuzv5+TefZvPqg6RLE5x12SzOf9t8Fi37OUuW/6yg/yOlRx5+P/f88t9BQtOEat7x4ZNxjEQIFBpEnm8kG1/cA8AbP3YuH//Sk/61Ui+QU3lNGVd87CvHPL7XKh2pF4ZOU6ZMIZlM+jYAitavXw/g2w6o/3HlKioqGDNmzLB9/frXv+aSSy5hwoQJfOtb3wqpNKZMmcKPf/zjo5rDyJMOCUI6ZukE54vRcO6CRct7151cjg4FAk6/yNeXx4qChvFMiBaNBSdChI898kHArMAmIK6fI/JKKHItdvdsAEKSrKun+Y1XhgCCq0aJEYMbyu9cUDp+ohphbH96e+7/cLkQdgvVLa5mKJyjroP3GH3kvL+TM8ISBUNj3o4M0rL6LzOBz+B18Wvcc6J27FGLadWe2gn6TETbLSuQoLvzucdBoBuBjYPhx813mRvkVKKlGIYTciPzPRWkH/ZY7XZ9iYknAdDXNqcZ3wnXPM9TAdh+TH0hPFsAEYADnaEqaYRKc6vGkzbyrrcAJmkjhwkkLdv3AnDHq8aHv37RGPDKeBEIpddVc/DvA67LoSUCTwfdxTShBTRSY1T1FEDTpQeqrGLicZ4neqz/6LlgjOG2HARo/enRAvU2wL3vq57awcrHdyAMwbLXz2TGfNd996TlP+V4UD6f554fBvEDDu/vpjef8gMxORgFOz9DSB75wwZa93dT21jJBRf9KHT9+m89f1zG9krS0YSMP56USqU499xzufPOO0OZ/37961/T1NTEokWLADjjjDOoqqrif/7nf/xztm1z5513cskllzAS1cCXvvQlPv7xj3Pbbbdh2zYf+chH/Gtz5szhG9/4xlHN4ai9BoThgoEoOFDnoZApDBUvPsqcfRDQ3UHNGQEIQBbWi+2sWKcEDDQsnYgBAQJ6N4VBgL6DD3XjjAyEDEfDGfQNKWFRY4nYQkRDO+jtFLYXlgDETWmkuv9ixn1x7UCQWz5qYOgI4Uf1c9uRsb7YQgS+6bYTAAJ9bFGDLT2Ii3+OAJConZ2BRIrAQlntBIWQPghwd8mQ9ZLwKJc8d9Cq3UCNEHFb9/XY7nfDn7MaT94xCQIOGWDgW7MbhgAH8tIscJ9UUpNoulodBLiGhZKMbfnqAV0qkPLcG9Va5h3Dd8cTBMw5vCsWvhjdQZDNZEmkjAIpg7s84efCkIQYsy0N7LwNOJhW+EcSF1tCZ87685TL5DCSCSwRlNPrKOCppCUqzXAgCZLksnlSqcJXp27/4NowSAYzOVY+voOdGw6RGciRKklw8ZWLqK0vOW4AQNG3v/Au+roGmDS3iZlLxlPVUI4QbsbChHDISYO8p56QUjLQn+Oh363j8J5OqurLuek/Vx7X8bwapFQDryT19/dz7733ArBr1y66u7v5zW9+A7hxABoaGvjiF7/ImWeeyQc/+EGuvPJKVqxYwQ9/+EN+8IMf+NKGVCrFF77wBa677joaGhpYvHgx//Vf/8X27dtHFEMAYPv27VxyySWx18rKyooGGxqOjjCyoPo1eC/hCBhQO1dhBmV8UGC4TEUWMc7UGaydydH+tAsC6pZe5IYN9ij07iwGCuIaDtXTmJ12PTjn/lOSgIpZi6iYuTCYo18hWA91Xp/vUdEQu+w4phvbhIhn/nHtKnFqXNmYJSoECkNKCIr79AuKRwfUX8ag7aq8+61n6VPkgwiPUerj0n3rdRBRcN3bMavrCeH4Fv76y0apJYQRzhUf+Kh7jEkEIAIIMVd3QsHOXno6W1NKUIwbw2dwfs4DpetH+szX9UuX2MLAkEG63rRngKj6Txt5Bh2LtGcIqMTySiWg5mBp4v1g7BI0NYqpBSPSVST6Dl63+F9570vs27CfC99/NmXVpaF6xUhv10Dy+2/eD8Cbro1/EUYBQZTJ93X18uf/epxxs5o5+fULfJfJ4aR1eiyIp+9Zw+4NB7j4g2dSWV3iz1NJBnTVRN6R/PY/niY7mMdKmkye08jpl8xCACef+ZMh536ktH3Di2xe/TT1Y6o44/VzAumaB6TcNfSSTGHw6G/XsHvzYQAaJ9Zy/XdePCbx+F+MJAUg8HjT4cOHedvb3hY6p44feeQRzj77bE4//XR+//vfc9111/Gzn/2MsWPH8q1vfSsUVRDg2muvRUrJt771LQ4dOsS8efO49957RxRVEKCxsZGNGzdy3nnnFVxbvXo1EyZMOKo5HmVAoTAgKFpGY5AFx0XIyQUgoH75RSRrG2LBQyjIUGQHXFCuYGyFBXUQIAT0bHqZ3g0uCKicvTA8r4LKGiP1rdmOTF5VnLkOO/Qh24uCgui12D5iy8bPJyp90Jm7H6PFZ8TBse4pUJADwNuZmRFRqxLru21FdvGeiNrND+9+P7RxJ/lMjsb5M/xr7hjDuzx9/iqYDwQZ4nxRsAgYm67rVy9bPwa/167OaKPzcwhc8GwZRN1zmYqeCdHxyypPAiGk598fhM1VY1FudiVmTtvRSh9oKD2+ynlguUIKf+7KhVEISVZaWCLY3br1A8t/JYY/uKuVNQ+5xmUVDRVYpklvVz+Z3kGygznyubzfx0M/fYJUWRIraWEaBpnBLPlMnnzORkqJaZkk0wlSZa7hXG97L/msjZ7M5/E7nqZhQj2JdJJEKoGZMNn6wnb6OvpAgHQkTt5BIjEMg0QqgZU0yWXcue3feojuli5S6QRWwsRKWVjDRKGSEvISDmx3YwNsfmEHC8+d5UaM6x2gr7uf7GCO3ECW7GCegzvbObynnXzOYcHyqXz4cw/x5GPvZ9lZr4zB3U9u/RcAzn7TPB8YlltZbfzCDXEsJI7jsHe7G0Fw3MxGrrv92VdkTH8rNHHiREaSpPeSSy4pultXJITgU5/6FJ/61KeOaixXXHEFN954IzNnzuTss8/221y7di3/9m//FlIVHAkdHRCQInbn60oHRMAEo8wjspuMkpPL0bbiAXJdHTScdSGJmnogLGHweb/SuXqAIByDQOtyJIwzIi7v2eiBgDmLqJy1oCgTDDXh9xNfNk5NcqRM/khBgH4cNwchPCkNePr66PX4+xVXDgp3+FE3v6jVv1orde9MIXFsm5U/vws7477EZlxyJtUTmt06xI9Hlw7Y0iDf18dzP7sP6ThYqQTNcyZiJi1/zW0n2CXHpdJ1pGDLQ89xaJOb171x5jhmnX8y4AWhIdCvQ7CTVrp9NClB1jZ9W4G8Y5I08kGiGb9vN/SOsnqPrlneMX3XQUUOwrPaD/z5DVfoS4mZ8yIGGqS93b0OSBzNoFBlGcxJE8cJIvYZ3k3Je6mQM3nYs3Ev6x5bTz6Tx7AMHNuN2pfP5hGGgWUl6O1wY/YLwyCVLqWqpo5EMs3BPW6uDDvv4GRN+vszSMchkUxTWlZJaXk1yVQJPV2t9PV00XWoBykllTX11E4aQ3vLfrraDlFeVUfbvjba9nUUPAep0hSpVCVCGKRLyzEMk8xgHwN9PQz2ZMh5WTbtnM2DP30qWGNDMG3xOBafOxMpBV1tvexcf4DDu9oY7M+SSFrkczbZgRw5L9PgtpV72LZyT+HDqFFlTQNnXPgPXP4e96X/SoGAlU/eR8uBnUyc2cDoKoCM7w2ibDVsDP8duu653Th5d+0/e+vTsW0+8PCHAbjg3P94RcZ8vEi5kv690I033si6deu44IILqKtzc8lcfPHFtLS08IY3vIHPfvazR9Xu0UcWpFD8rR8fKVNzcjlan3iAXLcLApK1kTgBOoP1JAtKL68z1Wg44li+XIQBK0lAjwcCqmYvGHYuI7k2EknF0TL54uWGBi/F7ACKqgmKMH8YWRRARSoKYDB/SbZvgGx3D9mubrr3HvRBAEKwa8VKBju7SVdVkK4qJ11VhmGaIYM/x3YY7O5jsKuHgc5eDq7b5iP4fCbH5oeep2bcaNJV5ZRUV2CVloTC/apyg5099Hf10tvey6FNu/xn5+DG3UxfOguzpByDPIbwrN09n/Oo4aCNiUq0Ay6DztiWn7zHNfwDQwZ2AXq+At9GQUkavIh6OkO3nSDEsQIQ7pzcnX1OGp5e2AwZ17m/F+HaXaDc+ixfrJ8QdghcO0LQuqudZ+96HjtvIwxBeV0Fdi5PXf0kujtaGDt5Dld/8t8pr6wmO9iP4zikS4vnADhW6u/v4fDe/8/eeYfZURxr/9czc8IGbVbOOUtIZJFFBpOTycjG4WIcsA0m2Bjb19lgko0/XweiMRgbMEHkHIRElpCEco672rx7wsz090dPTzjnbJJEsNl6ntWenenp6e4ZnaqueuutVbQ07aC1qYG25gYm7XUo/QeP7PLaTKqNhfOfY/2qxWQzaTKpVt6b9zTL3lzH2g82k0nbATWwUMZMa1sa07SIJ0rpP2gAMw48DsfOsn7VYlzXobrfECqq+1NUWk5pnwpK+lQydPRkSsurPrI10OK6Ln/9zdcQAvbcfzBZR1IUk1gEOIyMDfX1KRp2pFi7oo7l724C4Bs/vQfDMPjj9cewdN5KxswYxiVXPwt8+g2AsHzUoYFPk8TjcR5++GGef/55nn76aWpra6mqquKII47giCOO2Ol+heyOzwMoGjSM6lnqRrkgwZweO1RWHeL57CzbXvSMgIOPIlFdmCwob6Q5OIRCbQrNruCO2YCmJe/SvPgdyibPoGzS9IKKvCe7+M6U566+vNrd2x1vRaGxQNhVX9gAKBT/707Vv47O59MAS1Y/8yoNq9YF7Q0DGUqJEYbHOOk9yHhpEdNOPZxknyIMIUk1t/HuA8+TaW33ByCEiBSQCfO+A9SMGcL4o/bD9N7hbcvXs/TJN6LtpQyMSAHVQ/tSPrCajR+s5YDzZ2PG41gaZR/CM2jSGe0hyLgBEY0uVuOHBvxqfaoPhcwPVjCMsteiQwm6uI82EoQIvA+axtcULuFCPep6J5Kyp9MM252Y/7ftGkjpsuTlD9m4ZD2ZtjTCEBx/zmUceMzZlBdI4f1PFtd1ue3HX2TFwvlUDxjK2Cn7svehJzJi/IxPfdz83t99n5ceu8v/u7QsztkXTaasPEFWGjQ0Zvnn7e/R2hyECQzT4ju/up9RE/fkF5fvz9oPNvnnRkwZxKYV2xgwqi9fv/YJDMvk7v87m733upQZBxzTo7E9ftul/Pvf/971SXYig8f046BTZ+5yP21L4x/5WHdVUqkU++yzD7/+9a85+uijd2vfPQcLdqTAcpDe3Y15SzvL9peUEdD/UGUEdMc0CafNhUMH5OAJuvRMeADHpiXv+UZA+eTOjYCODZ18Gty8a/0DyseR583ogeSm+WnpzNvQUfy/8Dy7Vv7h0ILMOR8BbhUwoAxg1Oz9WOU4NKzdqK7JofoMisEIYskEU086hOKypH+3ZJ9ipp9yMO/+8wXsVAYpZV48L2wEVI8cxLgj9kWl6qh59x83FGybpc++lddeHYC6ddupGFTNgV84xnP1BzvzWKganfRi+MJT9jolT5hB+pvr7+Q9YKErImEFCIP0AiIdZWB4GQwoDn/dRgiIm1rJK+WusRfak6BDGGEQnVL6QahE4yPefOQttq3cQjxRxB6zjuHMr15HZU2oZPVHIJvXr+CPN1xI07YG0i0pnKxyww8YP5jjT76SSXsfRnFxn91+X8Mw+Np1f93t/X7Usnn9Ct8IEAKKimOcef4ESsuTuN6xPmUJTjhnGg/f9R7tbQoj8Z1fP8CoCTO4+adHRIwAgDWLNoGAdYs3c/nnp/vH33nyK/z63nc+Fi9HT0Ty0WcNfFokmUyyadMmTNPsunEPpduGgNqVeUouBwBIzm6ya4Xp9el5AjKN9Qw4LN8T0KOxFTIKuhJPATYtfo/mD96hbMoMKiYH4YDuoONzlXhYOYeVI5AXh88939H73BWyOXyppHDbjgwAOjneVSGf3L4LeQMK9aF3wACGaTL6qANZ9exr1K8qHHMVhiBWlGD6qYdSVF6a10dpZSl7nnEIb//zJbLt6XxF7knNmCFMPGofhKE4+sPr1H/SSDAMlj7dcd5039GDFP++qzL1A9e8gaV3715aod71h938utiNzr2HIAYfTqcLpziG8981XkB7IrQ3AC8WrA0E3YdmJYwLJ2KABPcTZGVQXljHk9cvWse2lVvo06+CX93+XofrsTvl7498ixdvexDwnncyDkicrMOWDzfy519+Pe+aikFVTN3jWIaPnc6MA46huLT8Yxnrp0V+8Y2j/M8lpTHOvWgipRXFZEMppy6Ciupijjl3Bg/+33wQUFHVj2+dPpZ0WybSX6IoxiGnTqPvkEo2r6njlYcXkkkpY6xmwLBPnRGg5ZPmEfg45dRTT+WBBx7YpTBAIemRR6BgalzOrjTXEMgtUKTPu9ksW19QRsDAw44i4ZWuLbRDzsUBdDg+f0va/fh405L3aPrgHSr22Juy8ZPzjJhc5djV7hqCHXJum/AYC4YYwjCIAn2Hd98dSUfnChkyuYZOIdKfcJ+FdvrRe0TXPZ83oPCYhGkw+ohZLLr/cVINzeSKdCWTP3cQReWlkT7CeeLFFaVMP/FAFtz7TMGxFVf2YeJRqkKilPlARlO4DJo4lPaGJtYu+LBgH/WbdlDRtw+OVPn8SmGr/wyaXS/jmgHdrwyqBUojRFYjgp2676onYP/TZYG1x8D18AMGMhIKiKxRxNMgcrwI3pwJnocjldmgDRbLcHBdi+b6dhY98x5mzOR//1AYSPZRyOdPuJH5f3uK9sZW35DrP3YwwhBsXLS24DUNm3bw8qZ7eJl7uPumKzAtkz59yzj2tO8y64gzsXYDB/unVf7155+RSQfpnGeeO56qqgRZqSCljquqWtpShZveeFax3CHhmjmz8rBTZVUlHPeF/di2sZmn7nmT2o0NSAmxeIIDjzmH07987cc3uV7pUA444ACuvvpqNm3axLHHHku/fv3IJSI69dRTe9xvj8GChbABnSHLC3kHOjICdLuOlCXkGwfhz90F0ulrGhe/R8uKJdQccCjFQ0Z0agTkuvY7HVuBuYTv29HfYQOoQ4XeyXy6klylXMgzkOfC70GfhVIHOxKdkuaPxTCwU+kO29sZ21eG+hot2jCx09lClwKQbU8r/EHoPfHL60qFPhZInIydhykAMEyD8v4VfjEiHQ4IFyZSbvlg/jos4Eh8138kdJSTJaBT/3waYBHk4ke49v0USeUB0K5/7VXQBoTtagyBZ3yjCvVkpBV5btJLXxTSZv79L4GUfOtn9++WMrg9kRvuXcyiBc/xwiN3svS9l9m02MOO5LnOlNdgxPThjNtnNNvWbmfrqu3Ub26gYXM99956Dffeeg1DRk7k8FO+xD6zT/nUx/p7IrZt8/Q//1/kmJvJAkVe+ElTXEssHDLSIpMK/d8o8H+zvTXFv2590fcAAJzz9Z9zwNGf79ba3XP3eWxYvIHR+47DcRzOOvXjqUvwWcsamDNnDgCPPvoojz76aN55IQSO4+Qd70p2mmI4FxSY6xHoSDF1ZgTk9tURI16ul6ErI6CQMm744D3s5kYGHnMKVjIRORf9HY2Hd+RWl1IUHEN3jZPwbrs7mIEuQwUdpAvmfu4IzFeo+9wc+vB12ljyWdlCz6R1+w6Ka5RbMVeR6zlm21PYqSBbwD/pPejWHY2UD6oJ2AW9fhyC9WrZ0eRdD0j1n0IiQUI2lSHbniZWlAh5jtS4Y15MXSJoqW1EujJiDAhDMOrA6ZT0rcaVLrY0FT1uyDDRsfdwnXdNSxw38/9jhg2AuKesAdyc/zvSDah7DRkQ4Gg629wvwYRpI6WHP/B4AaSMhiikFxIwDds/50qTt598h0xrmmPOupQxk/fKG/PHIVP2ns2UvWcDsGntMp751x/54M0XaKrfHmknXUnT9mYSJUUMmzyUYZOHqhRNW7Lug/WseX8dG9Ys4Y4bvs2dN36HfWefxhlfvva/InxQv31T3rFtW9sZOLQMG2UI6rCQ7YV+aoZUUrupMXKNxslICdl09B01LZPW+Cs89fwrSCk49ojfdTie7547idb6VoQhWPPuagBcZw7llOzKNLsn8rOVNbB69eqPpN+eYQS0RIBR0XYdGQJCqN1Wx56A6G6pI8Wq75H7d9g4KLQTD4/XaW+LYAFy++zME6CP54+pe4GqQi9tLu1uuJJdZ96RjqTj2H/hNl0ZAN25X7hvTb6jpWndRoprqvx0NK3Ewvq+fYf6kjIsk4HTxzNoj/E0rt/K2nnvkW5qpW1Hk59Hr9ztnkEgAtKh1jplCCTLShi9/xQqh/Vj/TvLWf/Oclzboa2+ifIi9c75NQVcgRSBC7/F66N6xABG7DcFA1g1bxEVAyp9sh9TuKQdi2IrE5QBJihcY+hwgIwaAZpDX4sOB8SEQ0YGhXsUkVB+cR1tADih90Pz9Uupd/a6GJMToRTWlL1hxrmMa3mGgTIcatdsx4zH/Lz3T1oGDR/HBZf9BoD1Kz/gt98/g/bGVsy4hZOxaapt8j0h4Hk8LMGo6cMYNX0YGdvh/acXsX7xRuY98wDznnmA0ZP25syvXsewMVM+yantlNRuWcfPv34oba2hd8oA14Xt29uxRLQgUqsT9zNERk4fxqaVtTTVtVBaUcSUg8fTf3g1y95cw7IFq3Fsl/K+fWjcrkJzju3w8t9eZa8T98R14Q+/PYFta7axecUWkFA1qIJpR0wnWRKjrcErsxzyor1694scd0zn5Dq90nPZWebArqRHHgHtBciN90faEFWkWtxsli3PP02mscE3AnLdk1GFUmjHnVO3PmfjGD4WXJc/PrOoOK9tfqZD1NgJx+YLxWi7o6g1olz3Eb5ProTj+F0ZDx1JZ88D8vP/BfnturK2w33kUrq6CJxMlpbN27BTKRJFHXte0k0t9J86nsEzJxIrTiKQVI0eSuXIwWz/cA1Nm7b51MK5BoFmCrQzWcbN3pMBE4cjPHfmiP2mMGjaGNa9uZRUQzPlg/pGKgLa0vTZ91JtafrUlDN8/6lUeorfFC5Tj5+FnU4rhYlAU+umHcvDCkTnbgoXRxheed4wxavyTmglr5H84QI5GsRnCRek8EmIolz8WvEH66eNBvXF7/jgQU1/q5+jg+b/9/j7hQonbF2+mUxbmtJ+lZ0+709Kho6ezA33LuaPP/0q77w6l/L+FQC0t2VIFCfy6IpdBJZlUT24ilgyTvmASlbMX87KxQv4+TeOp6rvYMZN248Tzv8OVf26rvr2SUtbSyPXfelgHEcybmwZtXVpduxIU1IS44BDh7B+bZOfMJV2TdKuFZSpRlBemaSqfx8m7D2c4ZMHIYVJVppMmDWekXuMYOkbK1n19hr/frFkjB2bG3jq/z1bcDwSqOyrSjwfdM4BvPaPN7AzQVjByfbcPb2z8lkKDWh55plnmDdvHps3b2bgwIHsu+++HHnkkTvdX894BA5Q/MbhkJFh5BsGuZSx0s6y+bmnSTc0MHD2kSQLhAP09ZHrcnb3uTv9XEXeUTnQzqQrd3n4nOikXaF+cufS1Xg64gUIvvhFxEDQ/eV+LuSN6ZDzPy/00T3PhpaIN8H7XIhDXrqur5zD41d9SKTrqsI5HYlU1+cyAer6AaqJDCieQ/fRO2zHy6kSIorM9936HjNm+L0LVyMUAlxHYpjCxxfEDVt5mUJhAhelwEuttP+3zu3X7nz9rmocgYvwWf70vaSEdifmU/8GRXzCQMkoJ78+FxNuhH4YIOtA45Z66jfW0ritidb6FtKtKVzHxcnYGKbBNbc+yaDh4zp+Dp+wuK7Lry47ibXLVQ32WCJGv5H9GLPvWCpqSvKKStmOAGH4yqK1sY0PXviA2jXbcB3VtnxABV+/9j4Gj5jw8U6mB/LIXdfz+L03c8opw5gxowZbCrJZiWUZpF2DtGNgGdDmxmh14n7lyrDnx3UlQqiwQfSdUe/VhiWbsVPtDJ82BMuyqNtQz9I3VlJcVkTFwApWv7uOhi0NAFQPrqRqUCWO7bBh6SYy7QqDMHL6MOyszfrFmzjhhBM+8tz8AaMHsO/Je+9yP3K5+NTzCABs2bKF0047jddff52ysjL69evHtm3baGpqYr/99uNf//oXAwYM6HG/O2UIBAo/1FFohxc2BNysMgIyDQ0MPLxjI6BbgyXYmefGmPUdwwYBRNt02G8Hir0gRoHCirZQuKKj+3dnPB0ZPB0dixpj+W5/f5zha/Rxom1Uf517LAr1re/fUTGZXOWv7t1x/7kGji4YpK81hRspIBQuKGR08CzD4aPwlyF41MMFvF5BGeDgWks4fsVBFZcXvnLWX7QJ0/aNDF1VMDyf8Dg0SZDPLRBiGVQVAd1IlkR47GbOM9JGScZ2WPPmSrau2kJLXTOuHVWSwjCwEjGMmEmyTwmX/++DVPcf2uHzCEtbSyPvvPoEa5e/R6qthVR7K6m2Fpobamlp3EFrawPSCcqQmjGLqurBVNQMoP/gkQwaPp6RE2cybMzUnQLybVq7jCfuu5V3Xn/cB4nGkjGqh9YwcMwABo3rh2UFDk8/c8IHYEL9tiY+ePY9Gj3ldvHVv2fPA4/v8Vg+Drn3Nwfw0nMbKC21KC62mDGjmr32H4grgzRQRwqa7CQZzxOg0001XiBp2JjCJeXGQtgWEXnnhP9/K/CIgmfcZmyeueMV2hrbI2MzTIP+w6uZOGs0VQMqqNvaxPxH3mX2QYd/LIbA3ifvs8v9iOX8RxgCJ598MvPnz+fuu+9m9uzZ/vFnn32W888/n3322YeHHnqox/322BCIfJkb0R1obmw9bAQM8oyAQsqn0wGKfMUZVnYaJQtRYyAshTwF4b6Cz10rsFyvQKHrOgoTdOXZ6Mz70VXfuWPxAXw5503DK0zjim4pe91XV/cLjy0S087pO7dyYKH6AV0ZQYaQvhGQO8OwYVHIMBAiUPj5c4me62yttSGgawdoRr7wGuhMgoRpR2oTBMBC1Vh/6eovcHVvXTrYc+96X9It2bif+himDg6zG0opSKWyvPXPV2ne3gioPPDBIycwdPQUxkzem5ETZxKPJzueYI6sXfY+Lz52J6uXvsO2TatxO0AmG5aBFY+RKIqTKE1iCkkqZZNqTWOnMnkuY2EIRo6fySGfO5+9Djlpl4yCd19/3C8spMZiEktYJEqS9KkupXpoXwaM7k+8SKUV6vesua6Zl+96ESnlp5I0Z+0bZ+C6Lg/cu5xVK9TzrK5JMueS6b4B6khBxrVoduIhz5HwDdi4YVNiZsi4ln8MFIFUVpoRL1Mh4xJg2ZtreO/5pQCUVBQxcGRfHNthzyPHY1lWZCwGkvSHsV5DYDdLSUkJf/jDHzj//PPzzt155538z//8D62trT3ut0fpg7lhgNy4evAFLnCzGYUJCBkBEFUqnQH7ciV8v3wsgefO1X/leAs66i/6d2E3f0cSXYeoQs0F+HVnHIXj+fkDKWxA5HsAtBGgwwKa4z9CFiSkn+anr+sq7S869qjCNHKUergeQO74IFDanRk/4XesUH+FPAraOAjPxQ0p3bARkHXNQPFLhT9QayU7fG7hL8mMaymCH6nc/NoV65P96HGKaKEiUHn9EHgkNH1wLt4AwCEAH+rshIxr+W107H/lG0tZ9+4qsu0BWcxJF17BMWd9rfBkOhDXdb1UvjtYufhNMqk2NQ/ToKg0SdWAMoaOrmTI6Br6FIMwTF+htDsxf200bkEzFhaZWbKpNNs3NrFjYy0bl29l1ZK3WLXkLe7/w4+Yc8VNTN7zkB6NddDwcXzhipuBm2lpauDtlx7h6bm30NbQSrY9TXNtE03bGtm4ZCPvgx8+kpK8F66+dvOnxhBwXZcbvrUHK1c0YsUSFBWpscYTJudeHJQaNpCkpEXKtfzS2RkPW6IApS4xj69CFaHCyyxQQFdDKnCvTufV77fOMNEelK1rav2xHfuFA7CsoOS0lB4Q1ktr7arE9O6SAo9wp6S7G9NPWiorK6msrOzwXEVFxU71231DIKSowl/YuTFqACfj1Q5oVOGAeIgxsBCwrzPF2xUQMBdhr5vI8LFOshC62s1r48K/J9GXJjr/wgZBIelozmFF7XTgESjk8dC7xI5S+0wRvT5wARYGBIaNh9yxFZpHZ0ZAxNOBJNueZvP7HzJ8v2ne+In8zus79MUESqnXrdmElIKqEYMi93Q9Ep/8+QTPxJVK2a97YxH9p44nXpzw5wz4xkDY2xDuU+FFBKm2FJsXrWHUvuP83b/PmEhAARx4SKT3JalZCL3dFyHqXxGtO6B3aOpc4M7Va5F1DYRQaYpvP/gK9eu3Y1gm1aMGUTGkL1/4wm3dBsM11G3l9afv5715T7Nh1WIcW+2uEyVJBk8dyei9RlNcXkKRmaXIzFJsZiixsmRckx2rNtDixOk3qj+W4XrYBsfHKqSIkTRUaWRRFGfY2GoGj6lhysETMew077+yguVvreXWH1zAyAkz+Oq1f6KsoqZb4w5LaVkFB3/ufA7+XHTH1NK4g/ffeIZnnriZlromhCEwLQPDNDEtk1hRnAu/+geGjp7c43t+VHLTd2eyckUjVVUJDAMaGtTzyKQdXnpqLYcfPwYAG4OUYwXlpaVJ/eqNSCkYOrbGDwn4dSukQcYLleh6FYpgKtjNx0SWd15azdg9RxArTiAEjJo2mGwqw95HjKcoJhEeKBW87wapsDAfN3hvd9zv4zJcdlW+9a1v8Ytf/IJDDjmEPn0Cuu3m5mZ++ctf8s1vfnOn+u1+1kDocy4YT/+AMgJU7YAGBhymqghKCZpbP3Ctd7zw4QyBcBgg1wAJ96PbBgon6h2gkwddyPXfmXRmDOix6LHlAgC7ivf76O4OhtuRF8Xf2XvocRE6boqownbzBhHegQbj6IoZUEvuf6JcIyD32kX/epp0UyuVwwdSNrBvh+GfyN+he0gp+eBRVUb24EtPA4Sn3KNhgHBGQXCt+t20uZYNby1h+/L1zDhXxYUD1zpIoi5qHcIwNOjQMFhw34tk2lIMmzkKI2YikWRROzGN4o8L9SWsyxcDKl3RS9vTXgHfWCB4d2IolsFwyEGLpi92szYfvraYzYvX4mRtSvtV8su/vN0tF3tLUwPPPfQnPnjzebasX0kmHcR+SytLGDx+BGP3GkYyGaPdiRE3HEqtFj/ODlCfLaLGauG+e1WZ4XOuGIgpXMqsFKVWRhk0UhB3HQ8DoYCMRWbWN3ZsEWPouH4sf2sthiFYvfQdrr5gP6753RMMHDqmy3l0R0rLq5h11JnMOurM3dLfRy0NdVtZtrSequoE37tsAnEhcV2X+x7Zwvz5tZSUqbCOLh5lxKSPEaixWrjzHx8A8JWrDoowVBpIbC9koI8rI1PxY+j3bNP6Zj6Yt5q1S7dw/JcOxhQuoyfUMGZijRf2crzQnBEyVFUWjCXIA6r2yu6RtWvXsmbNGoYOHcphhx3mgwWff/55+vTpw4YNG/jGN74BgBCCm266qVv99ig00JEbX4cLwwWE+h1yFLHKvjiO/lL3vqQNCbLAF30k1CAjIYDwPXXerP4cNkJ0P750dJzCZkFHr26uV6BQu0KKLL+2fAc36KbkGkG5Bkgh8Xe5XtuurOdC4Qa/LySb3/mAjfPfx7Asps85HdMIPDKFXPe54mSzZNtSAGxbvIqygZ2DR8N9alY/XaAIoHb1VqpGDAza5ngo9FrFDUchqYVyXdYuWQmgxmJnwFJIa8PDT0Ao5Q/1nyrgPxDIdJpMWwrXdljz5nLG7T8BRxoUmxnfNarXOuHtyLTHQlMKr125g/bmFONnDAavbGzcq1houyZFZhZwiJkOGdci6xUU0vHfLcs2sXDuAqSUmHGLQdPHMWJW5+A713WZ/9yDPPXAH9i8bhn64RX1KWLQqCEMmziIQSMqSFpBbYOsFFTF26iJt9HuxMhKgyLDIWnYNGQNli9v9PvfsWYLQ0bXkJEWWY/eOMx8aBLUOgg/p7eeWeKNz3vWdpbH/nYjF3/v1k7fj/82cV0XwzC4+ZpzAbjojMHEhaTNNTAwOOmEIRx3/DDVliwJw/HSBU0yrsXgZBMrPmzw+9u2ehuDx9T41SpdoTxdOmQQFstw/Pd99cINAKRaM7jZDPGE4fFTuIFBGzICrND7bUsDp6OdzG4WGcrA2dWe/hPk0UcfJRaLUVlZybvvvusf1+GCRx55xD/2kRkCqvPgs+uGUKXZLHWvRKsIhr+ItfKWUihjwM1n0DMMieszrkg/K0EbBRoYWCj23tHONTf+7L80HSjRMGK7IFivg3WJ9hH9O/BwdC8ckisdKfzcY/k7+O693BGXfug/RFgRAmRa29i4YKE6Z9tsX7iUgXtMjI5V72gLgAANIdm4aAWurSzH7cvWMmSvySTLC9eu1+7vsIvelbDmjcX+oNfO/4DK4QMwDXzQXjCY6AAsw8WV0NbQwpYP1/nz2PrBCgbPmICQ+GlX+tnb3q5JoJ6DaagPGxau8uex9u2VjNp7HLGYShvEcHyMgWW4nsvVANRnw4upvvfySnZsawHpMnnPQYoK1jX9uKuOtfvhohDAcMW8payY9yGGaTD+iH345jfvL7iGoChp5z3zD15/5gHWLX8fO5sBAWUDqhi973gGjKgi5qVESulgmoKMq1gKy2PKS9BkJ9mWUUxxUqoYdAtxYsLmtRc2+M96/ovrGDSqBhPXZ7WDUAqloda43Yn5xlJrfQuN21vyxl1UsbrDOf23yfznH+Svv/4WAGd+9To2r1sOQJ8SC0tAsaGMp4xX7VJL1jXIuCYJwyFhOCSFzYsvbPSfx2svrufM0VUYCFLSBC805sf2vf9bcW+HD9BY386aDzYDYGcdVr67nmn7DcMSDpb3LtoyqGdh4obSNtWx5qZ24OOhqP4s8Qh8VMyC3c4aKB48jH4HHx5RjtoQCBsBfQ/OryIYVn7Rz1H3MQSKNpyCGN7g5ALxwsfy+gqFDXS7jlzruf11J9Ogp6j+CFNhnkckv7POxpDbR5grIBco2J3Uvq5ECFg290Wa1m8OUe8aDNl7CoNmToq0NTQgz3XYsXYLrbUNtDe20F7fRGtto19qWBgCMx6juLqCooo+JMv7UFTRh4rhAzENZUy01DbSvG0H7fUttDc2076jkVRjVGkky0spqS6juKKUooo+9OlXSUlNhYcXcGlct4lUQwttjS207WihubYRO5ONUAiX1lRQVNmHeFkZ8bIyigYOxEwq96uTStGyaQuZpiYyjc20NzTRXtcQmUeytIi9TtmX8upSnyhI0RcrN3jDtibqtzSyo66dpvp26re30VQfuOL7VCQpLU8y9cAx9B9S7oUEBFlp+alvtivZsnwzy15ZQntTG7FknJ/86VXKq/oVfGbzn3+Qx/52E9s3rUH/N0+WFTNowjDiRRabl66naXsTlYOqmDJ7Ch88+z6plhSl5UWUVxdRWllMn8oSBozqq7wgUlC/rYltm1po2tFGa0MrLXVNtDa0Re7bp7KIypoSSipLqKwuompAGZX9+vjG0MaVdTTvaKOloZWmHW3Ub2shk7bz6jscfvZenH7+P7v9jv4nysY1S/n9dV9kx7YN/rFEwiTtUf6WFJsMHpCkX98kNdVxqquTjBvbB8sUpFzB+k0pNmxsp6GunbodabZuTbFjR7RmR0VVkoqaEiqqiiivKqLfoFKq+6n4spTKM9Wwo52mHaqfHdtayKQd3ztjGIKqfqVU1BRTWZWkpLKYAcOqKClVhqrdlmLj2noadqSor0vRUNfG1P1H0Mce8ZEj8fuNHsiME/fb5X4SK53/iKyBj0p6Vn0wBw8gXaGMgFefJttYT99DQpgACivFXHBdR7vWaJw9N3QQlVwCo7x0Nw8dX2inXgjhr0IOGjUuOjQ0wscKjS3qAegMoxAo8VzUfi4eI2xMddVvx/frGshYKL3ODSlPAOm6DJg+HkVsG3gC/H4Mg6bNtWx8p3A1P+lK7FSGpo3baNq4DYBRh+6Fz4GOQBgG695Y5IcTCkmqsYVUYwt1QLw4yYxTDwrNRZBuSbHs5YUdXi9dSfO2epq31QNQOWIQlaOGID0PlRuLUb9iDU3rNnZ4fXtTG6/f+zL7nrYflQMrcVCuVx0Xdw2L+S+upb01U7CP5oYUzQ0ptqx7CyklyZI4s8/eh+IK5S1xMNi0ZAMLn3oXUAC+39yzkJKSJKVJM/JOLH7rJZ6471ZS7a0M7l/J2InD6DeyP/1HDWD5a4tp2taI3WQzatAo8LCWzQubGNZvBIRsClkPgyeMZujg/uhqi3XFWRqXL6LctSgvK4OygR2uK1mQjTHGzppASVmRZ4hCrLWM91etoYQSSqpgYAcg/ZNOuIGasljH/f+HS+2W9Tz2f1cyfHBfhg/uPETWmoG2LTBhYj+GDi4n42Fikok0i97fQGubTVERDB+ufgpKGrKNFuMPHE1ZeRLhxfLdljq2Ll9PgnL6V0H/TpImMo0uqdYmRo9XWA9TuDhJg+UfbGft8h1+u92B5O+ORLy8vbLT0n0egcHDqJ51eOBuFRI3Y1P36lNkG+upOVgZAZCrqPJ/5x/LAZblXRvdSYfTCCGaMtddkR5yXH/OvVYbCTLUpuO+ChsBHc0h97iegw5LuF14JPT1hRgDw94AIJRNoM6HiUI6G3NBQyCbpW7lBlY9/4Z/bL+vnhGp6pdHFOQ6LHnyDepWFVaiYRk9ex/6TRjprUfAAZBubGLhg89jp9J5u0Z/3l4N+xmnHUKivEzdOgQW3L5kFYufebvLMdSMGsSEo/cDw/RL+AJkbZfVz7xK45oNnV5vJWIcfNHhWEVJDCRv//sNtq3aQnm/cqoGlLH+w03Y6Y7pV4UhSBTFOPzsvSmq6BOpKLjizVUsfekDSqrL2OfzR3LrFbczfkiJj9PolV75OMRxbOa98zj3/fvXvqcpnRU88+ASVi+rA2D2KZMpsUd95LvsvqMGMv2E/Xe5n+LVdq9HoFsiUUaARmSHjYCDjiZe2deD08uIQimkyPKVUIALCP+p0wF1RoBpKner6WMHpBdvFRE3eG7+dvTengEgJKb3t0TmKXonb/ctQ0ZB0JeeZ6H5hcGOqm0h8hx1wEVi5MS6CoUvOqtNoO4Q9JuLdxAiSGnzpgQQAbYVqmqnXf1G3KLfhBHYqRRmzGLta++x+d2lDN1zQh54QhsEjmEx7qj9Wf7MPGpXdKxExx6xL/3GDw/NIAAKFlX0Ydqps1n44PNk2lIFFyZWlGTaqYeRKFdxybARYAqXwZNV/YEPnnqzwzH0GzuEiUfujWVJJI4fi3URGDFJn+P2YelTsL2TeYw7ZDokisk6KobaVKuKuDRua6RxW2OH16lpCBLFCWaddQBmWREpV/i4gDXvr2PpSx9gJWL84s9vMWVUJZOGFcZW9EqvfJRimhYH7HUiWza+z5vvPIItBbG4wXGnjeWph2H54jqfOrtX/jOk5zReUuR4Ao7xPQH6vP8xx62d+zv/R+TvxIm218reCKXIBR4FL8XLY89Tv/HQrtIHk5lG9EcrOhECm+lzuSDEqEcjek3eUkmB60ZRrR3t8LWBEvYG5N5Xi94AFqoaqL0Beky6T32P3PAORJW+TktzQm11ap7eoQ/aYwL9J49h3y+fxpA9A7CgUtvCB8dplL9pCkbsN7XwxIGS6gr6jx8Wujr4UXOSFJWXMGhaR6lkksHTx1BSURw8byPk+UCFhvpPGEZpTVmH4xix/1RMy0ubJHi2ccPBMlySMcnEgzrmo+9TU8awSYMxhaIUNoXL/hcewz4XHEtJdbkX8uhYpFQptrWbm7z0LkE67fDmvxfwwbPvYcYsrr75Cax4nP3GV+C6bqf99UqvfFTiSpdjj/w6cQ8HExMORTHJwbMVRXUhj+JHJfnfGj3/+axLz7IGvHBA/YIXyTbW0++Ik9kyN4pWHnL6FyJ/56L8w679jn8HKHt1X6XgXFfgCO2CjnLPR774hUaQq8vDeeUa9Rrser2duAhY9iSBAWIgcCM57IEXoCdxsHzPRGHMQv51+W1c8oGBuW20N8A0pG8MmEZuqeccA6MDIGHYQ6Dvm2lp5Z27VKrKpOMPpDJE7APBjlyiCH7a6pvyJ+YtYHtDk7fK+UWFwtK2oym4LPRSCSFo39GEQVBVUHj/wV0ErjTUcenSVh8ADRUALrhf644mSsqL/eet6wNohjUhJA058wj30drQQomZwsUMsAEIrMok+5072zdiFz75Fls+3FAwzJFuSfH+4wt4/3GIFyfItCng15CRE/nOrx8gWVxKumkbyfiwDtepV3rloxZDGBQXl1Pep5zmlh1emig016n/X3Hh8HH4BHZf+uBnW3pkCLh2lpYVi2lcs4J4PF6wzYYH/qKMARFWnjtnDECgQKX3j+MITBNyCYp0WmIuz32Y2CYMZHNQudwItRNTjFoh/IPemQsZYd9zvbvmZh/kEhuFpWOPQbhNvnHQGUixEOufCnOEwykyAj7sjOhIzU1E1i8X5xCO/6fqFTubdCWtOxrzDAGdN6+lfYfnFhdgWhZlA6upX7dV3ddxSTe3kiwrjbwnuXNvrW3wlWdJpXKLt+5oRrqSltoG3wjQY/SphhFYAloaWv2KcwBVw/rRsKkOx7ZBQtuORsSo/kgElhEg/zWrnxDQUhvMw4qZ1AyuZMtqRb3q2i6itZGqyiTtToyMl9sdtxwfaxA3bFq3B/Moqy4BBE3eF6hhGX5xoExbmtKacs758i+ZceCxAFz/y5MpaZR886x36JVe+aSlsk8lqbZaLKnCi3XbVQaJJeTHYgjAf3/64A033NDttkIILrvssh7fo9uGgJSSNff9xTcAhn9+Dmv//teCbXeHMaAmlR9bl54xACqn2wfbGZrLPRhHOJfdEDIax3ZdX1E4Hj+2pnyVUlPvBq564RkegYdA5CluNeYwlkBEQIK54ZHgmo7PFTIWtLHhIkEzAIavIWoMFK4SGPWghHEChcaRa1RVDe/P6EP2ZPUr79Le0JKXORCu4udKQUtdE4ZpMGSPsQybOQ4zmaB5WwOr5y2ifu0WWnc0URTiE8j1VEhX0t7QTLJPMaNnTaL/uCEAbFm2kVWvfUBbQ4uqFSCE74UIiyuV0QBQPbw/o2ZNprimimwqw4a3P2TDu8tprQt2+9oIDBtGpnBpqWvGsAzG7zmcCfuMpKjIJFVXx4IXVrN+ZT3tOxoZUGNSYqrsgFYnrtIgnYQyVrFpbWilpCzJjENGM31aOTsyxaxYvJ1FLy8j3Zbh0K8cxwv/73GS5aX8+k5VbvfW353NypffJdXUxp577Umv9MqnQeKGUKyH3vdJ3bZ2LEvQv2+cdes+4cH9l8h3v/vdbrfdWUOg21kDiZp+1Bwwm40P/73HN9Ey5Iw5UUUtuvs7lDVAAJozDPwYv47nh/vXBWa0QZDL7gaeYtU7RxnUcw+npbiRz57x4IZVL35f4d/hORRq15XkGhD5RkfU25BHKAQ+JiLsDSgU1vANphwFGvasRHEKXrjFccimMiRLkr4CDnsCdIhg08KV1IwaTLwkmTePxk21OFmbquHROtphjv5sW4raVRsZNGkEGEZU2bsumxavod/owcSLEz7pjpSCrGsovIdw2bF2C8KKUTaob6S0MEC6tZ0dKzcyeNpoAGKmKhoUri5oCpeV76xl2Lj+lJWZPoNgkZklJhxqN9STzkhGjynDEBATLrYUNNhJn9mwtgnWLdvOhOn9kYaFJRwa7SJFD5uOsfGDtTRsaWDz4rWMO2If+o0fzvq3lrJ23kIQ0Hf0II46fjZ333gXvdIrn7Tc95ez2LpVVSR0Jbw8r47Jk8op7RPjlTeGfeRI/OqRg5jyuQN2uZ/ytenPdNZAt8GCR8/ab5eMAIAN//gr6+//a0HgYGe/cz9rcV2lkG1XkHUM9WMb2I4C6TlSFWNxpBGQ3BCg4E3h+oaEPq53xxHQocjfSQeGh8wbXy64ryOlq84J/0f/Hb4udw2iKY3R9o4r/PLCOkyg+wyAjcH8csGPuWPMTSOMAAy9+xqmSbIk4D3X58LgQlcK+k8Zi1lcrGLnbhQQWjawhsphAyL9+8V7hIspJPHiBIOmjPLZpSK1BwyTQVNGYRYlgyIo2q2vsxekQfmwQZQN6hvBPOhxFJUkGeiBEV0EtmtgGU5gdHr3GztjGMWlcR8gpesIJA2b4cNLGTu2TI3Xq0SYMBz6mGmKjSyW4dK/3GWffapJxiQtdpwt6TKKzCwlZoaYBf3HDWbL0nVYyTj9xg+ncXMda+ctxErGOfjLn2OvE/ZiwPDK/AfWDbnuuusQQhT8+d///d+d6vOTFCEEv/nNbzpts2bNGoQQPPDAA5+aMf03iSUgJiQxoWoMHLZ/NTXlFvGPESwY/r7Z2Z/PuvSYYnh3yIZ/BCGFoWfO6RZmIFcCJdbxCyfDIAKC3akrRUhpRfn4DeHFyqUsGHsS4Ic8DAIugs7Ag50ZBdHxRuP2nRMuhe8ZtHNd4SduWGZgvITHEE4V1LiA3LCN3zYH9Bc+r89BANwM79RVFb9gzXW78Lp09Hz1uMMSNsii6ZEyEpLRz9SVal6WcHBllDq44/dKYsgAKKjeCZmHGdAEOwZgoYyVmNdGr6nheaGKUUWILNclZqp+2x0LIaDNjpE0YirTId3Cq7e/gnQlow/di7o1m1ny+KsgBDNPP5RkkUVM2IUXq5tSVFTEc889l3d86NChu9Rvr3w2xRKQEOCCV9BIfS/2Ult89NLW1kYqlU+0VlXV8zLan4ghEJb19yujYNhZczr8cu6KgbAzY0AVclG7M913uJ684aWWaQMAKaPGgFCKRmMDpBReroF3vY7VA4R2mdCxgguPvatjPTEGfEXoquOOKxAG3viDWHd4jL73Q9+jkHIMxf87Er/EKYEBoCXX4paeAZWL6YjcM5T1oJRvEJIxQ0ySluEEYRtvzqp/iSsN3+DTCl5XXnS8HH39LgghMXUykafslSfJiXgE/GpvIqAPtoQkYTieIaDWMusafmjKEZB2gvXJelz7DZkiALanS9mxbgsfPPoaruMybL9pZNNZVj2/AGEazDjtYCpqioPc7F3YbRmGwX777Tola6/0CsCyFS3Me2MrCz5o4sLTBzNkkMq66YD3a7fLZy39T0rJT3/6U/7whz+wefPmgm0cp+cwzZ7zCHxEsu6+v7LuPmUUdAScC0uu27wzKZQiZxCAC01D5sW2c68Phwhy2fsiqXwicLt3NZ7OOAgKnS/Ur5/rr+s+hIo5ua5SyI4r8rwbhbwUhXL4/faRFEpCu2/RoeLPXU+NNcjNKIi0IVDC2kWvfxyZv9PQ7VyEj9tQnAyFX20pA2MiN0Rj5jzPEjNDwrS9+wReFEs4xISLZSjXf9KwiRkulpDEDalyqw2XuBF4QmKGS8wrerQhVU5tpkR5C6ws615/l4UPv4KUknFHz6JlWx2rnl+AGbPY97wjqBlUQdKwSRrZgnPa3SKE4Fe/+hU//OEP6d+/PzU1NcyZM4fW1la/TUNDA1/60pcYPHgwyWSSoUOH8vnPfz7Sz4YNGzjvvPOoqamhqKiIgw8+mLfeeivSZsSIEVx66aVcf/31DBkyhNLSUi644AJSqRTvvvsuBxxwACUlJey9994sXJhPE23bNldccQV9+/alT58+XHTRRTQ3N3c5x9tvv51p06aRTCYZPHgw11xzDbbdtbdlyZIlnHrqqVRVVVFcXMz06dO59957I21c1+107TZv3swXvvAFRo0aRVFREWPHjuXqq68mnY7WCOjOcwB45ZVXmDFjBslkkilTpvDEE08wZcoULrrooki7119/ndmzZ1NSUkJ5eTnnnHMO27Zt63LOncm9j2/mX89sY/3mFI31WWICmpuy3HH/+l3qtyfyWQoN/Pa3v+X666/na1/7GlJKrrnmGq699lrGjRvHiBEj+L//+7+d6vcT9wjkSjgTYcTZc7psr3fDne2SdHlZE9ffEUJol4h2X6v/fHleAbRyldFwAPj3zU8x9FtExtqRO7orCYdCckmXwm3Ic9+DIVWuuwjpxtwsikiNAH28C0tL3yeMBQhLrkGgSXacAko6DFJMp7K89n//ZsJR+zBw/NCIl8SVgFQV2MLGjfYEaNm6bB2Ln1zAgV86gXhRUMVPEJQTBuUh0C5/1b+B6REIvXD3KwAcfv4sjyTIwfI8DAnTpsgDEmpuCj0ry3sOqZTNVT9ZzDlnDGPS1GocIRAIv9xr64563nrsHZrrWoiXJBl9xCyWP/kKdipDSXU5M087iIZ1m5l3x1Mc/7UjEcWmwrXsYvy1kMIzTZMw4dGtt97KQQcdxB133MGHH37IFVdcQf/+/fnFL34BwLe//W3mzp3LL37xC0aMGMHmzZuZO3euf319fT0HHnggpaWl3HLLLZSXl3PLLbcwe/Zsli9fTr9+QVGDhx9+mGnTpvHHP/6RVatW8e1vf5tEIsG8efP49re/Tf/+/fne977HGWecweLFiyNllm+55RZmzpzJHXfcwerVq7nyyitJpVL8/e8d45luuOEGrrjiCi677DKuv/56lixZwjXXXIPjOP78Csny5cvZf//9GTp0KDfffDMDBgxg0aJFrMuBx3e1drW1tVRVVXHDDTdQWVnJsmXLuO6669iyZQt/+ctfetTX5s2bOeaYY5g5cyb3338/jY2NfO1rX6OxsTHSz+uvv86hhx7Kcccdx3333Udrayvf//73OfHEE5k3b16Hc+6uHHdQDftNq+DDNa38+PeqxPcJvdGm3S5//vOf+dGPfsTXvvY1rrnmGk4++WRmzpzJD37wA0488URWrFixU/1+JIbA+AsvjPz94R137FQ/a+5VRsGoc7s2CDoTKSHjmJhCkLCcSE1tBL7LWCkjFymUsheodDShd91ChHAE0TCBNgpkaNeaS0SUmxpZaJxafBS/EbjIHVf4O/7cHX0400KIIDxgCDU+KQWODDIpwuBJLbmKvPvYBrqcm26nyvoWNoq0Mm7coPgFVr++kMETBkcUvI67a2OiEE+CIVxWvroIgPoN2+k3ZjCG8EoAo2iWTSG9MqqaNCjHnZlNU79VpRM6WZt4wvM2oEipNFDQEjK0psHlloAVKxQ3wNynNzNjeqXycEhBkZFl/mtrWf6GKjVbPXoQ446axdt3P46dyjBiv8mM3XccpnBZ8MoHAOxYv53i8QMQQpJ1TXZWWltbicXyC/k8//zzHHroof7fAwYM4J577gHgmGOOYcGCBTzwwAO+Apo/fz7nnHMOF4b+r4c9AjfeeCMNDQ3Mnz/fV/qHH344Y8aM4Te/+Q2/+tWv/LZCCB588EE/NfmFF17gT3/6E3PnzuWYY44B1C77hBNOYOHChUyfPt2/NpFI8NBDD2Gaak2SySRf+tKXuO6665gwIZ8Fsrm5mR/+8IdcccUV/OxnPwPgyCOPxLIsvvvd73L55ZdTXV1dcO2uu+464vE4r776KmVliqHyiCOOyGvX1dpNnTo1AijUXo8LL7yQW2+9leLi4m739dvf/hbLsnjsscfo00dVFBw6dCiHHXZYZExXXnkle+21F//61798g2/KlClMnTqVxx9/nOOOO67gnLsjh+9TxZJVrXz31x+yeXu66wt2p/TAM/zfIGvWrGGPPfbANE1isRgNDQ2ACvl97Wtf44tf/KL/XvdEPrLQwId33OH/7KqsuuevrLz7dv9vjZaPotiJIOU7EttVU1bZAloxup3Gq8PZBOFKh34mQQf3MkR+QaTO0PqFjuelBPpegajijf5IYlY05RKiIQD92/QqiIVd9oXc9mFDIyy54LsOY/45oQV/fn6IRoMAJWvfVNUK083tih/AC1VoAF/WNaLUyV4L3UdrQyvpFlXid+2bS5Geh0ePIebt+IMa6l7s33DV2gmHFe8Grs1V767zsQFWyADQ1dcMDzENikhFILFweepF5XZtaMzSWJ/yvR4fvr2e5W8sJ16cYP8LjmTAxBHM//NDpJvbGDBpOOP2G0vcsEk1NpFqUWCgJW+swnXVWt1w2U2FF7kbUlRUxIIFC/J+9twzyk1w1FFHRf6eNGkSGzYENRZmzpzJ7bffzm9+8xsWLVqUd5+nnnqKww47jKqqKmzbxrZtTNPkoIMOYsGCBZG2Bx98cISgbNy4cRiGwezZsyPHANavj7qcTzjhBN8IADj11FORUjJ//vyC83/ttddoaWnhjDPO8Mdl2zazZ8+mvb294Fy0PPvss5x++um+EdCRdLV2UkpuvPFGJk2aRFFREbFYjHPPPRfbtlm1alWP+lqwYAGHHXaYbwQAHHrooZSXl/t/t7W18eqrr3LGGWfgOI4/5/HjxzNw4MC859FTeXb+DtZubqeuIaisOWhAcpf67K5Igv//nwWK4erqalpa1AZj2LBhvP12UEht+/bttLW1dXRpp7JbPAJTLz4fnys/FKPe3bLirtsBGHv+RZ2202QwYQnTDKvUMDeI8XtwOPyddwAeVDz1XugAtb02lAvBCyWEWPpC9w27swt5CHSbXPCelohrP2RMKDR80CaMXwiu9TAMZpRBMC8dMBTm8K/1gIFh+uawp6C71nf4ei3hQkCF6IztdJrtKzfTsr1BjcUQrJq3mHEHTiVRmvTfsVzRu3lXQqaljVWvf+CzHrZsb2DL0rXUjBpELBH3xqbWyBJBtceMa6rMADdLS0MzKxcELrYlb6xm+JhKqqvixOJQZGZ94J7lGQEB9gTa2h0WLWli/aaUv+6PPLWVI44awsq17bz85CpiCYuDLjyMratq+eDJNxGGYOwBExi993jAIdvayorXlvjzaNjaxMpFW7AGj+LCn1/JHVd17MLu9LkYBnvttVeX7SoqKiJ/x+PxSAz7lltuoaqqiuuvv57LL7+coUOHctVVV/E///M/gHJ/z5s3r6D3YfTo0V3eq6ioKGIc6M+5KOlwiAGgsrKSWCzWIZCqtlaxQM6cObPg+VxDIyx1dXUMGjSow/Naulq7G2+8ke9+97tcccUVHHbYYVRWVrJgwQK+9rWv5c2vq742b97M2LFj88bQt29Q/6W+vh7HcbjssssKks10NufuyIDqON+8YBgD+iXJSElrBor2eZzHb7t0l/rtlXw54IADWLBgAccddxznnHOOH1KKxWL83//9H4cffvhO9bvLhsC0i8/33eIGgsW377oHoCfSUdzdlSpunys6FayQ+KlfQtmZ2mESJh3SbTSSXRsDWnSooDsSxQYI/1hY2eu5hL0Lql14TlHvRLgP3Y1hyLzYshsKY4RT/vS58G99z1z3f2eGQdgIyAtDePfZumQN7Q0tbFy4Ejud9fpWmAbpSrYu28jWZRsxTIOismJKqssYe9A0kn2KMASkW1pZ+uIiWuqaSDW1RSiEdV9Ln3kLeAsrEaO4opSqITWMPXASplCZBttWbGbj+6tp3dFEui2V9wAzqSz//vMbCAElpXGq+ybZd58apk0qxwBsR/Lk05tZubqFrbUZUinHW3MVonFdeG9hA+8tbPD7rBxYweYl61n8wgcYlslep81iw9vLeeXOZ2lrLDyPd598D3gP9t0brup43T8OKS8v58Ybb+TGG29k4cKF3HTTTVxyySVMnjyZgw8+mKqqKo455hh+8pOf5F2bSCR22zhywW719fVks1kGDhxYsL1OrfrXv/5VMGVy5MiRHd6rurqaTZs27cJolfzjH//gxBNP5Oc//7l/bPHixTvV18CBA9m+fXve8fCxiooKhBBcffXVnHzyyXlta2pqdureAJP2PIQv//QhnNeOITX9MQCKdrq3nZH/LLDfrsp1113Hxo2qpPvVV19NQ0MD9957L+3t7Rx55JHccsstO9XvLocGso7B+3+6i4V/uotFf75zV7vrlqiYd7Dr1p9zd4tRkFlOjnuIyCdvV5yjHHMlzHgXcBCIiDLW1Qwj14ngd1ixG4JQJcTCc9Y73jDlcbg6oiA/RKHPm0YQx+5ItIssjKLNJTAqJLnhjEKhhdx1DKcuDpgwjOH7Tca0zND5/Bu6jktrfQs1w/tTXFbkr1OyTzF9R/Sjrb4lT3nm9mWns6Sa2xgyeahvJJrCZeDIvpgxg3RrvhGQO+6W5gwxy2D0mAoPZwBZTPacWUVdfdY3AkAZAJYlmDa1Iq+v1oY2Fr/wAaZlcsgFhzJqeBGDRlXTsqPreTiZXeMS2N0ydepUfvvb3wKwdKlimTviiCNYvHgxEydOZK+99or8TJ3acSXKnsojjzwSSZfSMfC99967YPtZs2ZRXFzMhg0b8sa11157dYgP0HN64IEHupWV0Jm0t7fn1WrROICeyt57781zzz0XGdPzzz8fAQuWlJSw//77s2TJkoJzHjFixE7dG+Dki64AwJz1xE73sSsS6IJd++lIDj30UDoi4dKA1Isuuqjg+See2P1rMn78eD9klkgkuOmmm9i4cSM7duzgvvvuy/OQdVd2yiMw7eLzsUyXjG2Szuw8cOmjED8s4P3WID9DSl9TSqlAW7nkPcHnaJ+KSkCiO9BZBL5CE/luf3TrXGMgtPPXfxciEQpnIUDUQ6Dz6HW7sITPCQFx0/W9HOFaAdp1n+v+7w4oUM1Z+pTM4ZBDIePJPxcaq15Ow1BLNHDyCNa++WHBinxaph49k8ETh6LoS/DnOWLqUExT8P6T73R4rTAE8aIEe59xIH0qizEIsg7iccH04/dh0dz5bFmxpeMFAEZPqOKE00ZjWZKs5zgykFTUFPHFi8fR3Gzz0ENrOOjA/jz87/Ug4H3PE2BYBomiGO3NaVob2qgaVMmsU2dSVBTDwGHc9EFYJsx7/INO5xEr2vkdteu6BVHiffv2zXPZdyYHHHAAp5xyClOmTME0Te68807i8TgHHXQQoLIK7rnnHg455BC++c1vMmzYMLZv384bb7zBoEGDdooPvZCk02lOPvlkLrnkElavXs33vvc9Tj/9dCZOnFiwfXl5OT/+8Y+54oor2LBhA4cddhiGYbBq1Soefvhh/vnPf0bAemH54Q9/yKOPPsqBBx7IFVdcwcCBA1m8eDFtbW1cccUV3R7zkUceyU033cStt97KuHHjuOeee3Ya7X3ZZZfx+9//nuOPP57LL7+choYGrrvuOqqrqyPZFb/+9a+ZPXs2Z511Fp///OeprKxkw4YNPP3008yZMycCFP1Pk4+y6NDvf/97mpqiFUdvvPFG/vnPf0aAoqNGjcoz5jp6B3dFZs+eze9///uCQNhly5bx1a9+tSBhWFfSbUPg2bffZcz5FxGPucRMSUt7DMdze37corECXcn0L53nKV6PGMgFvBKxGuQVJpQJ1xPojFOgI9ZB3U9EtILNbecdywUcRq4PfQ4bBrk8BuHiQmEaYT3emHA9NsUAQBhO7SvkWst1/RcKwRhC+vn1uWsWxmTo/vSY3JBBpbGXI/edjHRc1r61LG8sAHscswdDJg1GSjfShwZ5Dp00BBfBoiffLnh9LBFjnzMPIllWCjiR5yglWKZgxvF78u5jb7J5xdaCfYwYV83Rp47HNB3A8UM2thQgBWWVxRRXCL5yyRSefFIBuuxsCCNhu6TbMgwa3ZdpB42lql+JF6bK+us8fPIQXAzmP56fMw9gJeKMPmzfgue6I+3t7ey///55xy+88EJuv/32bvdzwAEHcOedd7J69WoMw2Dq1Kk88sgj/pdfdXU18+bN4/vf/z7f+973qKuro1+/fuy3336ccsopOz3+XPn617/O9u3bOe+888hkMpxyyinceuutnV7zne98h8GDB3PDDTdwyy23EIvFGD16NJ/73Oc6rKoKMHbsWF577TWuuuoqLrnkEmzbZty4cVx55ZU9GvO1117L9u3bufbaawE4/fTTufnmmznhhBN61A+o0MDcuXP5xje+wemnn87o0aO55ZZb+OpXvxoBDM6aNYtXXnmFH/7wh8yZM4dMJsOQIUP8TI5eKSyTJk3KOzZ//nyOOuqoSEilqKjoYyHqeuGFF/IMEy1NTU289NJLO9Vvt4sOlQwZyvCjDgOU4mlPKWvTMBSq/9MsM79yLkBI6Qcu7LgZuBWl7IgFTymLcKGiKFd9xxZpuGCR9inoBc+9qpCHQl8TNlR0W40DCBsAEDUCtCdAh0W0x6CQIVAofdGfY871WpwccGjY8xAGDIbHFjaiwqBBgcMLtz2Ca+czYx18/iGU9S3zr3GkiNAjSwkN25t49e4X/Wv6jh7I2AOnsu7tZdSt3casi472CxApAiE3r/Ry09YG5v1zHvFkjFknTeepO15X9zQE0/Ybyn6HjvALEcWEQ8Jwcb31bGrK8OaCbbyzYBuZdHQOJWUJph04iuGTBhEz8fgHnAgmIyOVkbpja7N/31xJlJVwzlWX8pcrdw4s2Cv//bJs2TImTJjAX//610h650chD7y6mdqmwkRXj9926UdeyKd8+BBGH3PILvczZHNzt8b62muvccABB3DPPfdwzjnnACo08Oabb3aacbK7xDAM3njjjYKhr5tvvplf/OIXO4Vj6bZHQOWme9X5XIFhQMxyiVnBF/mUL16AGQLiSc/1/HFhBzqSt//fPQrUaLmhYjbKEAgrP0caEdCcz7MvAxAdRL0CmsQnV4Kc9iBsILzjYQMgt3pgcEL65DmF2moJM+QVMgD0OZ9q1zMKTOFG5hSeW1iCbAnVd1jxG75Hgoinwc+SkEHqpV5nx1uz/CqHki2L1ykjwLOWNGIeoLm2iT41ZQHQ01sXXRlQIiJlhIUhKCorJllWzLhD9+C5Wx8iaWbR5pf/nkr8OgIAZf0rOPbLhxAzJZgxTv/uMX6qoSkktrSxbYPFCzZQX9tOImGwatkOmhry86cHj6hg1KT+DBvXDzMZ95+RTtfUz8EAbASGNEC4NNcG8V5/DbxFTDe3YdufgBuuVz61ctVVVzFt2jQGDRrEqlWr+NnPfsagQYM47bTTPumhfSzS2UZsd8vf/vY3iouLOemkkyLHV65cSUVFBW1tbUydOpUf/OAHBYGZOyM///nPfWCpEMIPZ4UlnU5j2zaXXHLJTt2jRxiBSP63ITFNiWm4ygUvIBGTIVeyF6t3VXqh7RhICUs/hqyCSV+4AMuQvqpZ+Ke7eP9PdzHzK+f6eIFg5x/w0ZueC92fYyQbQIYUXOCW1saASpsMXPVhCSvGjgCEhcQQ+AZBbrvOPAB6vGGDIOyqD/MIqHkrpdRZrK1QloA2CgKsRL6h4koRsZP8cURc+2qMK15TsfGSihJG7z2Gbau3sWW5SgNrqm1mqAjoh4WAuHC8Mam+WmoDQ2DCQZMZPH2Uj6M48bLPqTF7Y9FkQvFQLQLtJTBicRxvfRQLocDBoLGuhQ/mrWb1osKpaQB9KpKMndKfsVMHUFpR5FMghw0AwDcCYkKlsdpeESIkrNscgAGrRg6mZsQA1r75IanGZpCSrJdH3Cu9ApDJZLjyyivZsmULRUVFHHroofz617+mtLT0kx7af4y43Yhx27bNP/7xD0466SRKSkr84zNmzGDvvfdm8uTJNDQ0cNttt3HKKafwj3/8g9NPP32XxzZr1iy+853vIKXkxz/+MWeffTZDhgyJtInH40ycOHGnwkvQA0NASkE6o76ADUNiGsGOX6B2vY7tuc5D16l0PUnccpESZnz5PN754909GuT0L53nkwVplL92iztOdKcezq0nh2Do7f93j28MWCHAWcYJIdbDmQUa2Oft3sJxcB1b9pWxERhBuW78XAm79LW4Mjin+9ASBgR2pvy1l0Mr/kL4ByG8gjtC+h4QI2Rs5IYMOuMQECIwLFz/XQgZKIJQ/9F+TA9foAwz5dlI9ili/IGTGDxxCMIQDJ08jOZt9Sx5ZSktdc1ePQEzfx28ebbUNdN3eF8mHzSOqgFlGGT8Z6VFv/DaiAuHCISQSNfwvBb6/ZE4El5/6E22rIymqlX1L8OKG/QbXM6EGYMpLo8mTml4gK5WmJWmz0hYZCpcQFaaZB0TW5rYrsGWVB9SOxqoGtaPSQeOp0//SgD6jR/Olg/XsW7+YtJNu4Za75X/Lrn++uu5/vrrP+lhfCIiiQKpd1aWL1/eZZunn36abdu2+SEBLd/85jcjf5944onMmjWLa6+9drcYAocccgiHHKLCH0IIvvSlL3WLz6In0gOPgMQwlMIT4DPsKaVSOH0tYPoLlJYQkklfuIBs1vD/1rvKXBbCmV8511M2hn8v3a/e2elcbc00qPoTZH0OAMGkL1zA4r+o8ETGNj23sEnGNoh52Q9FMTvkFo8q+fDMcgv45CrHjgyA3Jc1V+FrMGAh93+euz6ibIMQQNgICM67ASWyBucZobX0uBB8noRuZBNEsAREKxPKUBgBEe1fGwxqPUJeFSFwpGCfsw7F8jEIag6VA8rZ/7T9aKxr8bEZ4cJRYYrgPQ4ZS3lNqVesSPgGQsxT9Pp67a0KakMIHGngSCPIgvCGsfqDDV7+vpKS8iL2OW4ag4YFTG56PIUwFn52hJAoE0OScmM+YBXAluHUScHoA6bSp7qUmOGSdkykFBimoN+EkVSOHk5yJ8qM9kqv/LfK7ggNFCJlypW//e1vVFdXc/TRR3fazjAMTjvtNK644gra29spKtp9zAo//OEPAcUW+c4777Bjxw6qqqqYOXPmLt2nRxiBmBWO/+svPoE09M40imQHj8nPMfzdfNxySFiul0cf7Fg7umfGNrCdELFPOPRAYUUcVpx6XFocV7Doz3czcY4C0WSyBh/ecQd7fvXc/GyA0I4OAi9AMLegaUe7+66s1Z6UHS7kBSgkQYZAlJcgzICoS+V2VG8gwkCo1zzkLfHP5XhKtGjPg15/jR2IUA0TlCOWCKRnEOiSwGEcQZ/qPt7OWnsUVNxex/ctw6WqryqBaiIxTdczGPVOX/iud+2u12tquyZZaQTr4zisfGslS15ZGpnTyOnDmXHkJBVKkdHyxOF5A/7O35Gq5LApgvoGep0daUVAp6rssUtF3xJM4Xh4EonjGbWmcDHjCnjYK73SK7tPcmPuudLe3s7DDz/MueeeW5AtM1e6icHfKfnpT3/KL3/5S1pbW/37lJaWcuWVV3L11VfvVJ87zSwYVliqwI3w8FsizzXuRHbQ2rUv/XZa+Uz1WApN39tgeIV2gh2/309oLLp3nwjIA/jpZ+tKhRuQUvU1/sILMQx84KOagxp/Iesy+PIO5hAWIaS/ww6L9lrkZgM4IaxAodi/nksk9FDACxCec64i1gV+nJz+pVeaWO/i1bgD74CaZ+ANCIMKw9kHuUZBrjGQ+/8gyCCIehHCNMcxU61jLBTWkDLqgekI5KiNA+ErVoEwXEwp/GwPXWciuEaQkQZZadK0rYmVry+maVuDIhYKydTDpzJ06jBipvoPLj3lbOKCkFihssoC6RUiUkaK7RpkXJOUG/MMFgdTSNKuFcp6kL7BEDeVAaA8JcGcw88nXCOhV3rlsy4fncoN5N///jfNzc15YYFC4rouDzzwAJMnT96t3gCAm266iR/84Ad8+ctf5uyzz2bAgAFs2bKFv//971x77bWUlpbyjW98o8f99sgjYJpa8QuPPyDYzfhxedW6ILgMdLlYfS5g9ouS53j3kfpe4WvyXeWgXoYwcU3YKAjuFdrNuzIUugDbC3HoKn8RgqEC6xExBvz4e87usFDbHMnFBmgDK7cwT24mQCECn0K78nAfCiAXcr0jQu57ZXnkUgxnvJi8GoNvcqlsCqIYAp1maRoERoQMFsLPMggZE/q49iBoIzCI4QdGEZ6hpnfaDgZxHN/NbnqeBJcgLGB77bPe7lu7/5FBOGD1m8tY8Wo+ic+4g6cwauZoH8wopaecpYEplEfAEi5J08aRQhUl8rIRtAHQ7sR8he9IgeME/+WEAISLISHtWMrDICUilOLoGAaaaLA75ap7pVc+UyJFp9+vu0v+9re/MWzYMA488MDI8bVr13LRRRdx9tlnM3r0aOrr67ntttt48803+ec//7nbx/G73/2Oyy+/nF/+8pf+sfHjx3PIIYdQVlbGrbfe+tEaApDvtg4rZa1ktUHghnbIYQ+BDhH41QK97jToT5fd1eI4+YaCjh9rWV6AYGj8hRd6CiT6khiG9AsjhcgGWfinu5j0hQt8I6S7sf5I37k7VV9Ze3+G1yFkPITxAto4KogV8OZthnag6jp9//AxGbkHgIn2JGhXs1a2ClnvSFXVLwKODIVFdPc6JKT7CGcOqDUKdvsaFxDe1eZmLfjvkKf4c9kYw++ZnovlFf3JSpO4oVD2joymJjqestdrYbtGyBgQ2FmXD56az7YVGyP3KyovYebJsyiuKAVcH1MQXlvheS36WGlvjBZJw0YIBQrU91e/DS/cASnX8tbNVSEMr/JlwrRJOxYJ0yYmXOKGTWO2SK2bYWC7CthpGh3XyuiVXvmsiWT3gAU7k/r6ep544gm+9a1vhTZDSvr06UNZWRk//vGP2b59O/F4nL322ou5c+d2iSXYGVm3bh1HHnlkwXNHHHEEN910007124OsASJeAPD0gozumKUEkbubliKy49U7fe2al4BtCx+kJaWIlPtNt6Zx0ykSFRXR+D/Re4cl7KqXUu24JYHBEvaZjzp3jvfJJBFXX7JL/noHo86dw6p7/urjCXKlQw6AnHNhD0iehJSMHnNgGBSemx9bFwGLYMBQGIQcREhhBMpRReRzx6MBgrmcCRBgI/z7y1B9BiN4I8KGSNg7E85q0OPOxSL4PAeeks1KQ+3wPWMlDAzUuABHCl+R5hVUIjBKbdcg5Vp5YYZX/jKXbEplFpgxk2EzxzJsj9HEk3Ev/h9UqFTElMLHLsQNhz5mGlNIkh7BUKOdUNUIDcc3OAIMgAh5BgyVJYB6HxOmrRS9kJRaGdKO5XsSwPOUeGuncQMdybp16/wKe58VqampYdiwYZ/0MHrlv1QqKysjFR/DUlVVxcMPP/yxjWXQoEG88sorEXpjLa+++upOZxPsFEYgTJajDuQYA0RBfYrwRaebhd3z6itNhxpMEz+Or9s4rY20rd/IvL/f5dckH3P+RV3SDBc6P/KcOaz+21+7PU/NmGjbAtOUeYo5d+ceLkmca6SElipPtAFQqOZAOCwA+ZkB4Tx8/TxMLzc9iLMHu380nkMKRXQkDR/DocmDpFRjcnLcboUwElknwAZYpgf8k4SqOAbSsHkHxVVlWPEo2Ebn1wfjlxHFr+P+gcdE1Y8IM0WGlWNGmor8xzUiyt9PCZSChU++6RsB0z+3L/1GD/LbaAPAX3vfc+FxAQgwvVoFCcPB8oyBjKGYATOuScbVoQhP+WulHuIVcKQg41oKfGjbYMVos2NkvVTCrDS98JVACBe0Ed1BrbB169YxceLEna5J/p8qxcXFLFmypNcY+IzKx0ko9EnInXfeyfHHH091dTUXX3wxP/zhD0mn05x55pkMGDCArVu3cv/99/Ob3/yGH/3oRzt1jx4bAmF3fqF4vSbWidLh6t+BYgobAY6DD96zbVh/f6Csh5xwKjvefiNyj+7WGsiVnhgBWkafdxGWGY3ZQwc4hZARANF0Sl/Jy872cwXaE8UGhI2AMEGNIQIDQO+Q9WdXeC5+L5YuUAQ5+Ah9Ja7n8lcuaMBVZXpdV+QZATL0WSPzs3ZuzQNBevt2lj76AsP3mUy/iSN8IyCXWyBMXRz2cOi/c9fFJ+jJMTY0wl4pY6WQdUjAQOK6Lm8/9Do71qkyrVOPnsnAMQPQhYwKrV+hZxLzQhMN2SSW4ZDwMANZD5CoMAWBEaCNEJ3BoF+OYiuDKVy2bm2idGB/YsIJUVmr9nFt2Jlgu2aHHoHa2lra2tq4++67u13w5OWXX+Zb3/oWoNycc+fO7THA6Yc//CGPP/44AMcffzzXXXddj65vb2/n2GOP9Svo3XTTTXmx2I5kyZIlnHfeedTW1vYaAp9B6ap64H+DzJkzh9dff53q6mquuuoq6urquOGGG/jVr37lt7Esi29+85tcddXO1SfvsSGgvgyjbnfhfa+FvQA+25zHO9C6YR2Z+h0kysuw+pRhlZThmgn/QQYPM/pUzZLyHk9qd4ohCgP4tGhchOvtaHPjyaDXRvhubwpYsIWyCMKZAhGAoPYEEChd7SIPewn8395w1A7bUEvsxZ4d14vbC53GF1AKC83CiArtRDwXOQaNBl7q90AIaNtWy6onXqC4qpyaCaMQsZiHPgiu1KmOwVqGzhHFKBiSSHxcu+qlBBvDxyrYrknKMwAcaVC/tYEtH26gua6Zpi312GnFjT5wwhCGTBrieQCCcUf5CcKVDvHXWe3kXf98VhrYblDQSrePCxtbmr53JWxYFJlZYsIh5cboO6SK+qY0TW0pXBeSZaUYCTMAQQoX2xW4bW00ba3Pe3/Csn37dvbaay9GjhyZV0Qnk8mwevVqli9fzvLly/nDH/6AaZo4jkNLSws33ngjRx11FGPHjmXs2LEMGjQoLy7a2NjoX//hhx/y+OOP+8xsjz32GDNnzmT8+PGMGzeOsWPHUlZWFrleSsmmTZv8Pp566ilaPLZE0zT53e9+h+M4/hg6m8f27ds7XYte6ZX/dAmnIgohuP7667n66qt54403qK+vp6qqin322afTEtpdSbeLDpUOHcrQI2ZHvAHRwXouWxl81iyE2YZ61j/+qJpQ6MJBx53Cxsf+tdODD8vY8y8qCBoEGP75OT4Zkh5rTwsljTn/ojwSoFy+gkKYAbVDji5WIVdW2BDQJEumkORmCviuac8bYBqBGz13F1so1KCBchpIpwGCrjT8Y2HSH638bdfADmVTBKmf0Xno8bdtr2XN3GcpqipnzHGHEU9YPvNhLi7AMgK0fXguuXOIiWghH70OYfe/I4XPCwDw1r/ns21lfmnhodOGM+Xw6RFuBj2mqCEVjMlFEPc8AXFh40jD9wyYQpJyrEhVS51xERMurU4skkGg+ykys2xKlUWKJzVtb+Llu1/kgPMOo09NmR+ueOext9i0bDMzZszg7bfzqyy+/fbb7Lnnnv7fQ4YM4bXXXmPo0KEArF+/nv3335+NGxU40jAMDMPAtgNK41gsRjYbFJE588wzuffee/086/vuu4/Pf/7z/nnLsnBd1zcEcvvUddvPPPNMQKVWnX322dx///0d3jO3z67mAfDWW28xc+bMvDXplY9WPumiQ6VDhzJ49uxd7md8ff1HPtadFcMwmDdvHvvss89Hdo8epQ/q34WyBbSEqWy1m9xJpZA5XM4Djzx2txkBUDhzQItpKtezaUoPpNhzX5IOR4w8Zw4xf9UCJRJG0kPUQ6DvFsYQQP4O2MgxJroSI6SkNIAu7DoPxhLcJ8jD93z/eJgBV4UIXCn9+LoOETheuCdmSmzH8EmiwnPSn10Jqe11rH3iWRKVFQw7ajbCsrBd5WEwQ8aA0GmIoUwJFbjQwMVweEmSwczLhNAKNJPKsGPDDvr0LSNeWkRbQzNvPfwGbQ358fKi8hKmHD7df3YRkGOORyocgojh+jF/G9MvGOQCLXYcy1tbVUPAIWYqN3/a9xRIEl6GQ0y4/PXnzwJw9GWnKACnVPMq7VvOsZed6K+JKSQlZpaDPjeJV/9d+Es3MmbDoG/fvjz77LO+8gQYOnQozz77LAcffDB1dXU4jpPHsR5WyCeddBJ33XVXhGzlrLPOorW1lYsvvhggYkQAvgLXXoQ///nPvhGgx3bnnXeSSqX8L97wPcN9mqZJTU1Np/Oora3tFk98r/z3yn87RgDg3nvv5ZVXXumynRCCyy67rMf97xRYsBBHQDgUkIsdSNfXRa4fePQJJHfBjdETGXfBRX6FxPCYRp4zx//bMGS3cQdhnMG4Cy7yPuWEArx+NX9/4DIPEOSqXWEjQCsnI3I+6lKPfM4JSYR3smH3uRbXA7sJoTgaDBmkDOrYvTZu3JBhpymcDXWTvLROKQXt22tZ/9QzJCorGH704RixGFknMAqz3rxNQ5IwIW46HiWw4c9fEfFG0fF6ffy189D7jpfWuOCRt6hf3z20/JTDp/rrFFb84XCAdsdHMAMExZlcBEIKTMMl7SjcQ9IMlKKLIOF5MAyh5pYwbB8D0GQnOPm7n/Pur7IUYsIlEwov9E200GInKLYy2K5JPAYHnTyNxpWdf/FVVVXx6quvMnr06Lxz48eP57XXXuPggw9m27ZteYoc1JfJ6aefzj333FOQRe0LX/gC8XicCy64QBmRMvf9V+O78847Oe+88/KuTyQSPPDAA5xzzjn885//LGiYW5ZFv379eOmllzqdx3777bdLWRL33Xcfv/vd73j33XdxHIeJEydy8cUX8+Uvf7lLtrmPS+bPn89ll13G22+/TXV1NV/60pf4wQ9+0OX4amtr+f73v8/cuXPZvn07I0aM4Ctf+Qrf+MY38sI9vfLplu6mBe6sIdCjN727G+nwO9a0Yjm1b73l/933gEPY9MS/e+ya31lZduft/mfH0elx4TCBAsONPGcOI8+Zw4iz5/So72V33u5zJuSm2EEoVEJOFkEBI0ATIUWwAaHPfnui/P5aNI++z4QYahMmWdJKzkApH1O4xAwXwws7+Dt2EaQoRnn0VdXJmKl+VClkSap2O+ufeppEZQVDjjwCYcUia2B7rIY65JB2TNqyMTKOAva12+qz7aHlXWngSoWGcDQPQCj85IaU8tSj9ySW7Jr6EyGoHNI3tCaSuGGrSoSGg2Wo33HDpsjMkjRsLC+vP2E4gQEiBRlp0mAnyUhTsQuisgdiwiFp2JRYWWJehUFQYQtXCgYkWogbqo0GBmojoN2J+cZAwrBJmDYZ1/Lfl6QlmXLQuE6neOuttxZUnlpGjx7N3LlzCxoBoJTs3/72t06pVM8777xO6UyvueaagkaAllgsxr333utnAuWKbdvMnTu3y3nceuutHZ7vSr797W/z+c9/nuHDh/P3v/+dhx9+mAMOOIBLL72Us88+e6c8h7tbVq1axRFHHEF1dTWPPvooV155Jb/85S+59tpru7z21FNP5eGHH+a6667j0Ucf5aSTTuJb3/oWv/3tbz+GkX88EsaY7crPp13mzZvne9s6+3Ecp+vOCshOZA1EP+cy8EkJdW++QePyZRT1H0D75k3BBYbh15f/uCVww+ew1eXIzhjKy++6nXEXXBQBFOpdekdhAMj3AkDXRkCu+Dn+IvhbewDC14bHpSr2qfCF3ntrvIF0XRwMhAwRHSGRQiHvzRzCJ2G4OK4gta2OdU+qcMDQow7HDOInfj+5XAtSChwAx/Dj/Q6qipTjeSeEAGRAxRuQFEUJnBIlxcw6/wiWvvA+21ZsinyJG6ZBorSI4spSxh0wScWxcX2PgC5JDAqHYBkuSSPrPQ/lQQlbzBpLodsLobIV0h4mwDJcX6Fr46LEVKmKKTfmsx0220mfdCjlWrRlY6Qdi7jpEDccGu0ibwwK26HHanVhvrd0o0xxY2Njh+dqa2uxrK6/GpqamrAsK8+1b1kWTU1NHVwVbdfZbr47fehMg57Ko48+ym9/+1u+973v8Ytf/MI/fsQRRzBhwgQuueQSDjvsML761a/uVP89kc4K0/z617+moqKCf/zjHyQSCQ4//HCampr4yU9+wne/+10qKioKXrdhwwZefvll/vKXvzBnjtrczJ49m/fee4/77ruPb3/72x/VdD52+Q/Q45962SnfV+7uMK/TeAJcl4rJU4iVV+iGICWJ0t3LvdyRTJxzIeMuuIgJF13ojzNcPVETGmmipLB1qD0DI86ew/DPz2HYWV17CZbdeXte9cSOJJpiFwoFhBR/obBAuKogBHn9elecG3rwrw/9V3GkIOsBA3VsPiy+tyAnFKH6jYY9XAm2Y9C6rY7Vc58lWVnOqGMUMLDQnP2fyPFQ/wSERprZsGCmhn9ejaGtqY037n+RF/9vLluXb4wYAcNmjuPg/zmRAy86ij1P3p8+fcsLvrN63orxL5qpr4GLtlRpl+G1BgUcjAnHrzGQEIE3wUBSamboH29jQKIVA8matkpa7ITvsYl5BYZUuMQmaWYp8n5KzTQJ0yZu2D5JUXt751b/ypUrOz0PsGjRIiAotmKapv+5tra2W+72999/n2w2i2VZCCEQQviGwfvvv9/l9du3b6eurs4fh2makTHpMXYm3ZlrIfntb39LeXl5Qa/Gl7/8ZUaPHu2X9n3hhRcQQvBWyLMJ4DgO/fv354orrvCPLVmyhJNOOony8nJKSko4/vjj88YohOAXv/gF3/ve9xgwYAB9+/btcJyPP/44p5xyColEUGjq3HPPJZVK8eyzz3Z4nTbOysvLI8crKio+FZ6OXvl0SbcNgUxzK5teftl/iXKNAZ1CCFAxZRqjz7uQ4gEDGXrs8Qz93IkMPORQhp1wEhueeiKv75HndN8d3x2ZOOdCP56t6YQNA2Kmh7g3pT9mUIZA2BjQ2KPwHIee2b0xamOgO//XdGaA6cXMTUMGmQGhTIHORPro/4AAKMypr3fS+j7hXbAaQ7T/jhw2piG9qnuBIWMakvbt2z0joIIRRx+OEVNpXpYhsYwwMFC9H7rYVNgb4pcIloEBAOQZA2FDR1EEw6In3+LVvz5J0+Yd/ljjJUkGTBrBQf9zMqMPmOorFn2dJvXRO/so6VAwRh02URkDqlywNqpU8SDXBwjGhEuRmVUFhaRFZSxNdTxNwnDZninGFC6r2yq8MIRnWHjpjRoTUGRmqUq0UxlvJ+6FGPxSxaGaD1YXIZCeGAIjR47k/vvvp7a2lquvvtrfmX7wQX7thVxZuHAhoLgDFi5cyMKFCzn++OMj5zoTfY+ioiKuueYaamtruf/++xkxYkRkjJ3JzhgCtm3z6quvMnv27LzURlBG0QknnMCKFSvYuHEjBx98MIMHD+bee++NtHvuuefYtm0bZ599NqDc+LNmzWLHjh3cfvvt/O1vf2P79u0cfvjhecx0N910EytWrOAvf/kLd999d8Fxtra2+iRRYRk+fLhPotSRjBw5kiOPPJKf/vSnLFq0iObmZh588EEefPBBLr300m6t03+KfBZCAx+1dDs0YBUXYyaSNK1eQ9nIkUBu+pxSEk7GwbAs9cUvgJiFVVXBh4883GHfO0P005kEwEWAkII1JJZpk3UMsraJJjBy7VCmgwF43oNdkVyXvt4J5ypBKZVCzQX/QT5AULd3hQL4IRTK3/AIgiQS4RWxQQRAQS1h7EB4R6tJhyXSKwXsjSHnP4hi1NNGhEoRXPXE8xRVKU+AiFkhGmEiv/176XBDJwZOmJkw1wuix/3h82+zadHqyODGHDCFIXuMRggzdNgzNvQ7IXRIwgDhegaKFy4ypB/fh4CzQKcBWsLB9uoIxIWDLQ0SHp7ARdDuxLjtpy/ypasPZRvFVMdS1GaL2JEppjZTgiMNWp24R0kcZHrolMJiK4MjDeKG7WENXOycdM2M7PrF7E5ooKmpiT//+c9ccMEFfhjgJz/5CV//+tf52c9+xooVKzjkkEM6vH7btm3MmDGDn/3sZ5G0poceeog33niDq6++mm3bttGvX78O+1ixYgXf+ta3uOqqq/x2Z5xxBqeccgp33HEHL7744m6Za67U1taSTqcZPnx4h230uQ0bNjB48GDOOuss7rvvPn7961/7QLt7772X8ePHM2PGDAB+9KMfUVlZydNPP00ymQRg1qxZjBw5kj//+c9ccsklfv/V1dU88MADnYL2GhoaAAq6/ysrK9mxY0fe8bA8+OCDnHXWWUydqsCxQgh+9atfccEFF3R63X+WfDxFhz5J+TiyYrqfPmgI+u29d14aYKSNUG493wggH0cQlnEXXBQB8+2MTJxzIUv+GnXJO472AmjXe6AM4obEtBwypkHGFSAFUk/JywUTZn6K5M5IZ8ouFxSoj0GIOMjTxIW8Ai4CmXWwYoYqlCRVINuRnmtGGj67XTjVLsx/r+YWBd1FlbYiGEKA4yolqtPnWrbXsfzR532eACNmKQ+EIX0+JdMLe4hO3gE9rry1ESH+hJzzTiYdMQKEaXDoJSepdEqhVkf1E3g/CpWJDt8/zCMQrA0+ZbApXGxp4iJ8JW1IVVdA0QlDuxPjS1cfiiUk1bEU9dlEUAhJStKugWm6pFwLRxokjSz9Ey2kXZM2J067l31gu3HSwsrDeAhB3vMrJN1h9rv99tt9V3xY+vXrx4033tgl6Ki6uppnnnmm4Ll9992XZ599tss+5syZU3AMlmXxxS9+kYsuuqjT60Ep37lz53bZbmdFK+qzzz6bG264gVdeeYWDDjqITCbDgw8+GEFoP/XUU3z+85/HsiwfiFlZWcn06dNZsGBBpN9jjz2228j9Qu2klJ1eL6Vkzpw5LFu2jHvuuYfBgwfzwgsvcM0111BZWckXv/jFbt370y4al9YruyY9ZxYMbZULAdIM0/A/d5bbP/7CC6GLL7TuSCG7RFXQUwpIYwLUCyPIOGE3sXdBrsJ3BMKK7tDX3bdzXgsfC0DUG9BRaeGuas2vXbCEtW98wIj9JjN874kqVOON33FBChOpKWmRfn66KVyyGD7ITYvefTvS8EpEB2GFjgoRtWyrY/ljyggYfexhGB66XHuFCn0/5XIo6HUNf45gIbzfuUYAgJWIs8fJB1JSXU68OBnqU/r30iGRrW2lpLNmxMDRzz5uuZQmsvSJpykVynXb6sSJCZfKWDuDEm2sae/jA//anRhxw/FDLUkj61cabMwmiQmHEjNLRSyFgaQuW0xDpghHCizD9T0xpnAxDeXDSbsmI4qaWNKq0mmDyoW6QmRAuqTDMl1JIeXa0za7ev7Tco9CUlNTQyKRYO3atR220ecGDx4MwF577cXYsWO59957Oeigg5g7dy4NDQ1+WACUp+HGG2/kxhtvzOsvFwzYmadES2VlJaCq3+VKQ0ODf76QPPbYY/zjH//gvffeY9q0aQAccsghNDQ08N3vfpc5c+Z8atIje+WTl53iEQhLdOcciucWaDvugouwLK3oxE55A6ZdfH4EiZ+MB3cad8FFyhgRwZgcR/jAQP3eS4lf4920JMJA8b93MC/XzjL4pLPZPPefDD75HNbf3/m4l95+B5O+cEGg9HPAgdoI0CyBuYyBOobsSg2gUwNZvWApa9/4gOH7TmbYXhP9MIEuL6yUODieMinESS8LeANy4/PBM8wH7DVrI6BSeQLMeKygso4YA8IzsjpQYrkGUVja7FgIZ6DmaDuwcckGdqyex4zTD6Nv/yK/BLCUgmIrQ0y4bEn1oTLRjlkkaUonaMtaZGwNSFNjSZo25bGUj+p3vayEZjvOSidGVhpYhkrt06ECjbVod2J+fH9QsolyK01tpoisa7C4vYb6bBG2qwoIWYZLVbyNUitNVpr0jbXSYCcxhGRzugRTSIrMLKmsRcYxPUyGZjO0I5kKvbJrYlkWBxxwAC+88ALNzc306dMnct51XR577DHGjBnjGwKgvAK33XYbN998M3//+9/Zc889GTt2rH++qqqK448/PhIC0JJ7j+54A4qLixk2bFgeFmDt2rW0tbV1Wk9i8eLFmKbphwW07LHHHjQ0NFBXV9cpSPE/Rnpj/LtFemQIFEpHy/vs/b3y7tv9Y2PPv0gnDQCw+C939nigM79yrsdwlx93nnbx+bhSYFlg2wHjnZYgzz9A6QfAM+/H9fABAKHceTebpfblp8k21tPv0KPprhcj7AmIHA97Agq4wIUP7nM9gJhEGC7L5y1jzbzFDN9XeQL8gj0SpDDyKHl11Tp/DYgq9A4BeWGwngxwBVJC2/baPCNA9x0tzqPXIHRDEdwzf62iXhGAlG35fQkvPKFTB2OmKhZkihlYhuOD9AwkWRT4L27a3nGlwOtlEYZQgFFXM/Z5xkC7E6PEVMV/Sjx8QNqrV6DJjuKGg+MoYqC4YSOloG+ijZRj0ZBVu70t6VJcBBvTCdJO8F8rZjqUWmnllZEmxUaWlGuRlabvYSgxM7QTwxSSmGdwxD2vQa5oDEOv7LxcdtllnHDCCfz85z/nZz/7WeTcn/70J5YvX85tt90WOX722Wfz4x//mEceeYRHHnmEH//4x5HzRxxxBIsWLWLGjBk75akoJMcddxwPPfQQv/71r/16C/feey/JZJLDDz+8w+uGDx+O4zi8++67PoYB4M0336S0tJSamprdMr5Pg/QaArsuu8Qs6LqS2nfepXLiJKwild6iUPcir71yL++cEeAP1qsCmFsBT3oENa4bzct3nGBPHM5hD+fU+z8F2Ael7RkBTfX0O/x4tjzx4E6PPZyl0JEL3BCuvxM0UPFnA8nyN5axet6HjNxvEkP3mpiHrnckOAVSDMPx8XD7sOi10OEAyDcaAFq31bIiJxwQXkdtDHSEc1DHOl8jvR6t2ZifURE2JoqtLFaI219T+Sp3uotEEfNkpUmrE2dHpjjwdkhIZU0sMz/0EhPamHCptNJsSpfSZCdJGlm/uFDKtXyPQNpRn2szJZRZKYSAD5v7Mqy4wccTFFsZimQWy3Aps1L0i7cDsKKtUvUlHK8MsQmYuN7nylibn1qoWQj1+67fi0QPDYFHHnkEIQSf+9znenSdFikl3//+97nssst2WoHU1tby29/+lp/+9Kc7dT3s+jzC8rnPfY7LLruMn//852zatImzzjqLWCzGY489xq233sqZZ57JV77ylcg1EyZMYMaMGVx66aW0tbVx1llnRc7/6Ec/Yu+99+boo4/my1/+Mv3792fLli28+OKLHHTQQZEwQnfl8ssv55577uHMM8/k61//Oh9++CE/+clP+M53vhMBER5++OGsXbuWFStWACqTY8SIEZxxxhn88Ic/ZNCgQTz33HPcdtttfPe73+02PqFXPhvS41oDWqSE9q3bqV+0kJY1axh+8qlAkJ+vZcJFF0Z24Dsr6vpooRsd+3dD7qHwTj9cOtcFhBs9DypcEGbdUwYE2OnACOh78FHENR9CD0XjAoK/o7+14tY7VE1QAxDDZdm85Sx77UPGzprA6H3G0u4oBZ9xjDzlHi7qI1D0u7qIkG4XVqy5O//cNdTSuq2WlY/r7IDZHiagcDgg1wCI4ABC1xSy4l0peO1397HnV8/1jYCY4RAzXOJGlqQVgM+0mzz8WyJJuSrFz3YNv66CKSQp8MmQXKmAjK6ElGPRmE1SHW9lalkDroTN6RKkhIxrkfIMAQNJZayNVidOczaJIxUOI+XGaLHjSCk4vqaOIbEy/rjZ8rISJMVGlppYO7WZpCpS5Cl3yzP+lOEgyeqURs8LYSDJeumKOsMgaSg+gWKz63oDwTpLTjzxRLW+oRoAPZFXXnmFn/3sZ9x3332+oump7LvvvqxatYpjjz222yWGw7I75pErN9xwA/vuu6+v+DXF8C233MKXv/zlgvc4++yzueKKKzjkkEMiYQOAMWPGMH/+fL7//e9zySWX0NLSwsCBAzn44IP9OH1PZdSoUTz99NNcdtllfk36yy+/nB/84AeRdo7jRJgiS0tLefbZZ7nmmmu46qqr2LFjByNHjuTnP/+5X3b6v0F6wYK7R7pvCBDs7F1PoTauWA6AnWrHyWYRVrBLHHXuHCxT1VD3wwK7+MAc38UflPSNFqYJAGuuK/wQQpArKnxwYdgAMIwAyCaliHgC+h58FMmavgUBcJ3Joj/fybSLz4+MzcjxAuSGBHKL3yx7YxlLX1vGhFnjGbffGFKuAppZHh9CWzbmcQf4d/EqEIbWLMfw0Uh+fSzs+g+vpU79a9lay6q5z5GsLGfE0bP9cEBYwuEONYpo2EOLDlXoWga50p61mPmVc4l7u3Y/5dNwfXxHON3OL7IUSrEzpFKqOrYfNxzSjlKuiZgyJNJZE4EyDOKmog22pUmjbdLiWLS7McaW1LElXapogdE7clV+uNWOETdVXxnXUlkEMZd+VjFLUw1MKDVY2VaOAVTEUuzTR7CwrZVlrZUIJJYIAIcuwq+UqMWvsujNwRSKmVBzFWTc7jvyHnvsMf/z448/7uf590T+9Kc/AbBlyxZaWlooLS3t0fUtLS1s2bLF72tnDIHdMY9CctZZZ+Xt7DuTyy+/nMsvv7zD82PHjuW+++7rtI+eEvrsu+++vPbaa522eeGFF/KOjRo1Ko/74L9OejECu0W6jz7yFC8ohZFtbqR59Sr1t23T8OEyP20vZkliltoxua5y0cOu2QGOGzDhhceh+e5jlhspM6w9Bepv9TtMGqQNANMIEwsJ7HSWbS8qI6D/oUdR3K+vb8zsrOTSCIe9ASoEEPD1a1k2bzlLX13GhAPGMW6/sT5ZkHZPW8IhYSrmOilVNUDbMfxywbYryDoGtiOwHRUecFxNohP8BKQaUSNASkHz1jpWzX3OCwfMJpaIGgFhT4qWMPbB1KEOvc6EQiE5wMHXfvd3pbhN1/cExE3H9+yECzVpkh/AR+GDctlrpRo3VFzeQNJuW/QvbqHIsv13wXaV0RQ3HEaX1nmx/RLaPLxAXbaINidOVprYHlAw5Vi02HFSTgzbVfUQYp5Sr4q38dgOh2LD4Ev996UqlmJ0cSO2FLzaJFjX3geBpMTKYgjlCdDGXyw0FzdkzFmGGyEVcl1JyrW6/f9ISsm1116LaZqYpsm1117bYyW0YsUKn/Cmra0tL27eHfn9739Pe7sKjdx11109JgHaHfPolV7plY6l21sLN2tTt2gJ6cYmMk1NpHNSWurff5vWdaspGTaCqilT1I7cCNDvu+rIk+T3odnuMMDxQIK6+qGMeA/wzkWNACGiriU3q4yATGM9/Q4JPAEiZ5fdXQmHBfy696HfSmGG2euUIlg570OWvfYh42eNZ8y+43AiO/cA5KeVru0ZSa7EB8J1KG7nsXqtdNu214Zog6OegAjeIQfpr9dX1QPIxw3ocIT+3Z61MAzJPv9zDpahPB0xw/E9JQgCrwCB50ZnV+gdu75PjCBNr82J+3TJ4DH5ufr6gDAo5cboY6YZmmxhaUsVEsHWdCltdlxR/nrgwGY7oXbx3tizroEpTCri7QxONrF/H5ej+p8FE77LleJbLG9bzFONDdRmimh14sQNm4yr8AAaNCiEJOVYyhgwAgbB8DvjZrK4CYMlb21i3fJaZs1KwNc6foY33XQTTU1NLFq0KOLKf/vttxk/fjyTJ09m7NixjBs3jr333pvp06er+7guc+fOZdmyZSxfvpwPP/yQ9957D8MwcF0XKSXXXHMN999/PxMnTmTs2LGMHTuW2bNn++lw27Zt47nnnmP58uUsX76cJUuW8N577/mK2zAM9t13X6ZPn8748eP9cRx77LF+Ott7773HggUL/HF0No9CzIC98tmSXptw16XbhoB0XbYteLPjBq5LZkcdmYZ6yseNw4rHI7vMXYnlTL34fN8I8Mlx9C6e8E4xMBZcV6vcqAmhQgLacxAafjbL1heUEdD/0KNIVEdTa8JZED2Vgih6PZ6ceayYt4zlrytMwCjPCNBiCjdEJ6xi/9r1r8MmthtNTcrFKEjwmAODdfTL/3oGRdgIGH1s1AgwjMK5/bnpf0EYgIKuICmVYo55HgDtBRBCeTsMEeTQxz06Xz/dEulT9SrAnYUrVJqoLhoEYPnxd5M2O66MCaEYJXXGgGUor0OZlaYhG/e8AIYPntTUv6VWmo3t5bgIiiwFLOwTyxA3HA6q2MpxFRMpMktZ1vAiRfPn81LzSqqtGAZQly0OMCleCqR2/2uPhsIPuEihDJsYrj+PFUu3sm19A6uXbsOxXZZXRst658qdd95JofLAAMuXL2fFihVIKRkwYADPP/986NkabNy4sdOCNNlsljfffJO33noLKSUnnHACp5xyin++vLycv/3tbz6wL3cMtm1TV1fHc889x3PPPYcQgv/3//5fJKc9Ho/zgx/8gC1btnRrHr3yGZf/cmbBj0O6bQgY8QQDZs1iSxexKjMeJ7OjjtjAgcHOVApWdEIu1JUUMgL0Z1MoelxhafcquNL04/4SkH7sOpBcT8CW558m09jAwNlHkqju65/bneWSXSn8ND/w4t3SU9xSsOqND1nx+lLG7D+RkfuMx3aju3spA358xxUeIC5wswdu/hxuB5FvEAB+WMT3VpjQvKWW1X444DCshOV/2YbxB+GMgbCrP9fY0Sl/+rMuP6z7M4Ta7VuG64c6wtS7RWbWd7/rCnyW4dLHTHvV/kwvbm6CQcQQqLBSpF2TMivFqpZqkmaWtGEChl93Iu1YqkKgF/oaWtTAhlQ5pVaauOFQZqVwEaRci/JYiqw02NxWRt+SVg/l386i1jKyciljkyq964P2LJvTxVhCEQa1OzGKPIBf2jWJeYRPWddQ8xeK+Mn0jAMN3DSQ1G7cwYJnl5PNBEDJQcPLO33PqquraWxs7LDMsGma1NTU8NJLL0Xy4EEV3InFYj7zXGeK9pRTTuHvf/+7n9YGkEgkeOCBB/j85z/PQw891OG12lj9y1/+kscgOHHiRF566SUOPvhgamtrO51HeXm5X7ioV3qlV3ZOuo0RkBLKx45h4EEHkqiqyu8oHqdy0kRGnHA8pYMGYJrSK+6za2GBqR6BkFZkRsitH1YkCVOhyy1DYnrgMlVxMLguimD30t48IyDrGwHKxbk7smve+7/CxURyRUrByjc+ZMXrSzwjYIKv9FXMP/gJMwBqCRtGQZ/489AZCeEKgDqsEt7dt2ytZeXjgREQDQcE/WpDQ7vsNaDNL5ZEvjEgEbxy6328/vu/UxyzsQzpk/ckTJuqeBslZiZkDKgYf4mZodRKU2alGZxooireRmWsHSEkxV6FPlWaNxiDzhpodWLY0qDdiWEI1y+UFDODtnHDISZcRhWlKbdsKqwMNfFWSswMSSNLi53AkQZtdpyMa9KQKSJpKsVUHkvRmE1Smymh1JSMTQ5hVTpNylWpgOvay9mc7kNMOB64Ef9aKQUxIyCP0mtmCYe4V71w+7paXnhkScQIANi+uXN+/b/85S/079+/YC67aZr079+f1157Lc8I0DJnzhzuuuuuDvsXQnDmmWdy3333RYwALfF4nPvuu48zzjijU3T/XXfd1SGN8NixY3n11Ve7nMdf/7r7DPVe+c8TzQGzqz+fdelR+qAQUDZqFKXDR1D/wQfUvvOOf37wEUdT3LeSmKViiRqB79I51XB3RKd9aSnETe9KQdY1cF3hx8kNQyPqg+t1oEACTibL5uc8I+DwI71wgOwyDDD6vIt8A0d/z+0MS6JWyKvfWMrK15cy2jMCIIjV5/LKa7rfSNqgIUnGHDK26bn3AwPA8BV4kBUBmo446KNt+3ZWPPa8nx2AlwESpBTieRai659Li9wRadArt96X9/mQr59BwnTom2ihb6IVRwp2ZIppsRUnhVLUNkWmTZHH7+96wNMi08aWAlN6OAHP86FqAxhYhuMB/QxSbiwUCnAD6mSUwralwBKCVsckYTi02Amf6hfwKgGKCJdAvUciZAqV1tfiCAYWj+f85EhaMtv50bp1CCGJe2WNYx5xUEy4OCLgBjCQuEJZ5DHhgoBb//dlzjnnHB6796W8dYwni4HOqw82Nzdz6qmn8rvf/S7vnOu6nHbaadTX1xekrtUyceJERo8eXTBdUErJueee22WFwXPPPZf777+/4LkxY8YwceJE3n777U776GoeTU1NnV7fK//90hsd2nXpgSGgUe0CwzSomjqVqqlT1c7VBdMAy3R9hRMzXbKOscv5vgv/dBczvnxeZBxh5ag3wo6r0PGuh643hKaYCafHqbauBCdjs+X5Z/xwQLKmb49eKO2Z2BXRnoCVfjggZAQQNQKCsRfm/9eKPzxfECBkUHnP+1tKoYwh77jCBDzvlRIOeALCFfs6EqX4jYJpgl1JSSxD30QrlbF2+sXbkFIV9Fna0s8PA2gcgC0FcUNSGUv7YYO6rKrwZgpJk530qwSaXhghadhkheEh8ZUHQEoVmnC8okyONGhz4hhAkeFQl00wINHCxlQfMKDYakdKqLeLVBqihAFFzbTZcYrMrCoqhKTetvjt+ifZnC4BICtj9DEz9I230eIVE9IZDdKbjwE4qN+rP6zj/Vdq2bp1K//vl7E8d3iyuJSv/egOxkzei5qywoZATU0NxcXFnHfeeQXPq3tLbr75Zm6++eZuPaOOROf076wsX76cPffcc6ev1/MARcX738SU1ys9k15DYNelR8yCpiE9jv5otF0IgWl6gC9Luenbs5oidtefkhCSuEcNG85991HVOu3KkBhSleZViHXhg+18JSlV7QBlBNQz8LCjSHpfIt2xWUacPQevamuk/aQvXNBj1sQ1C5awet4Sxu4/gTH7jcWR+Z6PMNGP3sWGjQBt4Gign0rZDD7rrIlwyqX+LYH22u2smfssycoKhod4AnILBHUk+nl0ZCwokqC/R44ddOmZ9Ikrd391rI1+8Xb6xRzihmA47axvz5CVhr++preTTjmmogn2YuzFhkrF22KXeuEAU70rhorHp1yLjKuUvsYa2K7pofctP/tgR6aY1SmTFidG1jVoc2OUWWnanBjNnndAGwFxU3kMSswMlnBod5K4hsPSlmoGJZvoY2Y4qsJkXksrbY66h35PE8KhzY0pI8DLPrCE5KUnl7P4rc2YpnqxtBEQTxTx09tfI1lUilXABZ8rmpe+tra2y7b/TVJTU8OwYcM+6WH0Sq/8x0rPag2g3fREyrrGYyo2b5luJL1qd8ieXz0XIVSqllaMEE1bMzwSIQcvJKCVpw9uC5Smm1XhgExjAwMPO4pETV8K1QTIlSFnzFFxcVOHBKRHaRxenXx55493s+dXz807vv7Nxax9YzGj95/ImP3GeqlyrmKkK7B+2ggI0wVrJayZGzW5jk/pLHUQRIcZiBgF7dtrWffksyRCRkA0x6Jj0fc2vbXvqDRurhEAEPPAgEVmlj5WhqThYgkoNy2y0mVIspEd2SJihkbW41P3CqR/PQb0MW2a7YzPAKgZ+LSC14pY8/NbhkPWiWG7BiWxDAaSRjvJ6vZyRhY1koi5rGm3/GJCppA0ZuPYrkGxlcEyXL8KYbOdJGHavrGxsrWaIjPLPZkMfeNq7I7HcJiVJi0pWLFkK6OnDvSf5+oVO1j81mZA8Jv73iOdasM0Ld557Qn2PuREksU9I+8ZNmxYr1Lslc+OdJCV1Cs9kx4yC+pAscQyg9K64NWqzzq4ponEiMSmd1USpkPaMaMhARHkpWuPQGS8nnIXhkqpU4yBGTY99wyZhgYGHX4kieoact+ijrwC8XgQcw+HBHQ9g87c5zJHu2548wPWz/+AUftNZPS+45FSxYlz1ypcGdBPF/QMsNywQT7LoofRcAMehWCHL2jfXsv6p56hetoUqieN92sHCJ2FQWBcaJZBfS9XBEWGdFZAGJyopZARACokoOP/5ZZS0M0OJA2HJsdlSLINOwfHKoSMguuEpEg4DIpL0m47LoISM0ORadNqx5AegFCNN+pFsl2ThGljuwZ9Ey04UrA9XcoRVTsoN2OsT7m0OnE/1KDDUaq/IMzgSIO0owyQdtf0Cw2ZwqUuW0TMAyNqAKO0HeysylCwvZBCW6uqeljSp5xkcamv+A869pyCa9crvdIrgYSzv3pl56XHRYe0gg27mEHF46URUAw7Guy2i4hMxYxnqS9gD/lu5cTmw0rIp9LVvPJIDCFwnAwbnnmWTEMDg488kqTHExB+ibQS0+WMw2KZhZD5wn8RO5rmjC+fF1GOG976gA3zFzFs38mM3GcchnelE4of50quv6LQ7jtckVGIaM0HDR7UHoJMUxPrn3qaYUcfxepHHu1g5B+dZFyTUsvGkQa1mbjK9hCSZsclbhhUWS4jki205hh/oMICScMlJiR9YwZ9zBjrhMOAeBug0vMy3k91rI0GO0nasbBdg4RhUxFr98h9HKQUWMJlRHE9m1JlvN+SYEJxipiIURlr90MLWamqD9bEW9meLvWNkaSRpd2J4WKqzABT0jfRQnWs3TdSsx7WwTAk5aUm0/cegCNVBsGiBRt47elVmJbBt3/1j4/7MfRKr/RKrwA9IRTK+btQ/FiEwgICxbe/q6LBf+H7OV4+vn8vAabelQq83bVqawiwsxnWP/UcmYYGhh51JEV9a5BS5ijIgDFPA+3C6Ptc9jwpo8WVpFT1FQxDRjgT/AI3CLa+tZCNCxYxdJ8pDN1rEhLbyyXPX8/cFDx9TAiBIWWed4Qcp34YOBiWbHMTy+7/B5a1U4Und1pOu/w4AC9ej//8lFtdpdIZhqTZNigzXcpMSYVlsyVjknZV7YAiL1TQ6phUxWzKzRiNTpZiU2Ii2ZpJ4AI1sXbaPLrhCisFliL16R9rYWVrNYYHEjSFSx8rTca16JtQKXkrU3FsdHEg5WWozxYREw5bUn0QQnEVJA3br2eQ8QodWYbrG3Sa7ElK4RV/ApsgW+DZh5awcvF2ikvLuebWuVT1G5y/aL3SK73StewOl8Dui2b/R0qPtEEh13NY3UQAabsBJAgqa0AzC7pS4dMDMiE3QljjeO5kQ0pcoXa/TibLmiefJ13fyPBjjiBZU02u0gynF4bFMHLd7V7rAlPz0/KcoP20i8/3DY0t7yxk84KFDN57KoP3nKQyF7zdokTmLZch1DycHIVvotjnhAzi9PqcZchI+mCu+dZeW8tNF1/8sRsBoKhzddlgrUwB0tIkAeAqHAjAynZFuVsRy5KVBmlvV572FG7McHGkw4epDBlXAIImO06LEyNhOJiGQx8r4xEOKUk7Fktb+voKW2coZKUiHBpb3I4B1NoGGdek3bVwpUoZhMBj41cDFI5Hbez4bByONEi5FtszxfSxFP4g5VUTdKRQqYPSYP5La1i5eDuDR07kiuv/5aUE7h5Zt25dL1iwVz47InePHdBrCPRAwrtWPz0vZAwYejctJHYqs1sHmqu6NTBMf0GDUpLhL+xs1mbl3OdJ1Tcy8tjDKepbjeNx7Ts5L49v2ER2+cL3BvhMhR28dOH4e65seXshW956n0F7T2XQnlM8kllPaQsQBDF3n+63oLERfDa8tDoNoHS9QkOqDyK/QQEDNz79NOe+9SZNJ5zgGwMzv6JK/uqiQJrCWBUqUkWLXFfzMsCSv95ReAEKyOHfPC1EDxyMKeYp0WY7Qdq1SBg2McPBlQJbmlhCkUM12nG/IA/gZxIY0mZLRqHoTSGpjmX9uH1DNulX69MkUi22og3OOCZx0/GJmcaW1BI3HCpi6l3dkjWpzRRhIFU4QZo+JsMyHCwU3XGRl5UQFzZpqcIH7U7MDxkUhbIWstIg65o4GKQAmcnw7mvrKS4t5+pbHo9Q6+6qrFu3jokTJ9LW1rbb+vxPkOLiYpYsWdJrDPRKr+yk9AgsCLn0vjnKyYvdN2+tY8PL83bbIAPse74LXXsFcjMVnEyWFY+/QPuORkYfN5tkTU0E1JeLDdBAOS254QhtBHRmfYYR+VO+eAFCwLa3F7H17fcZuNc0Bsyc4s9EZTkYgEen6wHwwmEKt8C8cjMGgntLLENnUHj4De9c2/Za1j+lUgRHHHUYe19ykV/gJ24qA8A0giJBamEBonXfBYrpUXqGghCQjDm4EopjNglL7fL9tE4MHCn9gkoCxZRoeKyCjjRodwx/120KXUzIIO0qA6yPmfZR946Xe689HjHhUGwqWKF+rrYUpN0YFbEUCcP1d/aaW8IUqhohQL9EO0lDsrS1nOKSRtocC0cKquMpTOHSmu4TITdyUTUCEoaDiypBrA1P2zUxDNuvJaCLITnSoN1VKZlONsOTd7yJ60rO+cbPd6sRAFBbW0tbWxt33303EydO7LRta2srl156KStXruT3v/89U6ZM2a1j6a786U9/4rbbbuN//ud/uPjii3t8/ZIlSzjvvPOora3tNQQ+o9ILFtx16TGPQG4p4HCJXQNJe0s7q+c+y5CD9tttg3z/TwHd6bSLz/dKHAe89o6vPJVXwM7aLHv8RVL1jYz73GEka2oUk54GNspgt9yRco8aAeF0vEA0UNDfgef0se2dhWx7+z3676mNgHyRITMnjGzXhocRQu27KItFf9ZYgQAsKb2dd/BMWrfVsuaJ5ymqKmfccYcg4iZv/UFRHx/6jTP8rAS1lkYEH1FoToaQXtjHUOENV5CMOUgEadv0PQt6blIGmA6dypeVZiQVVNsemlo4biiFrimW44ZD3LBpspNemxhxYVNiAdhsSieQCFrsBO1ujLhwaLQTSClo8ngANMofVPpiwrRZ3lpBTbyd46taaZcCU6RZ6VjUZdV9EoZNvSz2QgEqVGMKNUblAXARQhULqk600pApUp4H10QQU1kFnrGQNLI8/bc3adrRzh77H82eBx5f8H3YHTJx4kRmzpzZ4fnm5maOOeYY1qxZw3PPPcc+++zzkY2lM/nf//1fbrvtNn7yk5/w/e9//xMZQ6/8F0ivIbDL0iNDIMwdgLfDMnQpYCDT2s7yh56gdEAN/ccN2a0D1S7shIW/e826pq+ItWfAyWb48LGXSdU3MeFzh1Lav4qsK/1SyHpnqBn3cpV7ofTBsKINGxCKqS/aTghFLlT33vu+ETBw5lTCngCfgEeqQYXL9YZDBFaIEEhKpeR1/rn2CgB5f2tp3VbHiseep7iqjEmfO5hYwgBsDrz0LGzXoDKRDZEOqYtt1/SZ91xPEWcdg7RtYNtqN6/DJJZH8tSeNSM4kbhX0McULsLbqseNgClPgfWEHzbQuf/tToysxwjoIogLm4z03OteSWHpjSktk7S7MRqzSbLSoDFb5BscJWaGjDSxvewBF+EbA212zGcebLSLGF3cSImRpN1pZ1g8RrvbzvZMgjbHot2N+WGMtGNRaqUBPEWvqwi6KtwgDY9TwPIIjwIvU5GRxbLbqdvawvCx0/jKD/7Yzbd+94s2AhYtWsTTTz/9iRoBP/jBD3qNgF7plU+B9Bg1VihfXiugWHERe1xwCq7j+rv03SVxMyjLqsX1lKSufpdJZ1n8yMu07Whk4gmHUtqvKmIsCvApdv355AACc5VppFCRH+fWO/Zg563JhaQUgREwczoDZ07plKHP9QwTlV2Qnzmg0jWFTzmsKxZqr4CyJbyaAiLY3bdsrWPZYy9QVFXGpM8dQixhee2UwaG5CSyvWp+uF6Bj8gkzTWs2TsYr29vmVQy0TF1HwgtDEPAruDJAy5tIpBC+h0CNVXhFihxwTSzDxXYNcCGF5XsIHKkUt41BVpp+9UFVaVISNxxfwetqjEVmFts1vBCJQ8a1aPQoiAG/UmPCVDULNHCxyY6xxW7DQLAmkyUhoNi0SXskQXFDZQ9oIGpQHEiScZQxkHFgzeJNDJ08BCGCEsNxD/cQMxwS6T2B1zj6jEs6fBc+auk1Anrlv052F1jwMy49KjpkmWrFtVtX715NQ5LNOCx/8mXGHHsoGCY4Kr48/byTePeuh3Z9oIYbSakzfDe4kkza5oNHXqZtR5NnBFQDQREczYhou8JXpBrvoNzjYW6EYM6aByHMUYCfuRDswk1TGQe1775H7bvv0W/mdPrNmIorpZ/p4JPxeC59QxROE1RjiFYYLCSGkBFjQBIYAUsfDYwARRscDXW89ru/c9J3TqDYykTc5to7YAoXaYHlWrRmYjiuwPJopC1TLYoppK98AZ/R0XEF8VhACY2AlB2j2Mr49wh7CLT3AMCRph8CUIrYxkFVXAx7TMqstAfGMzGkJOMZBCZuCM8RhIIsQ5H79I23qN27YXvARMmWjOKBSHnKP+2q9MF+8Ra2ZUqxhCSOQ9xLX8y6JnGR5cV/f0hxeRG1W1vYvHoHPL6EeMKirKqYgcPL2efQkRjCwADmzp0LwPRZR3f6TD8q6TUCeuW/VnoNgV2WHqGVTG9XpksAQ8DwJwyBMEzWPP86riNJNau87BVPvbJbBqp3s4aHcLcMVdHNMlycbNY3AiafeDB9+lcXdPELocsWB+53QTjtMWpdas5+zdCnf1TbAJegr29Y9iG1777rGwHSa5cbgvDXrIs32PW8APonfC3kV/oTSFq31uYZAbo9wIu3/IOkZXPSd06gyMxSGWsnLhxiwvGJeLRSzmhCHUcbCaoPM+T+j66F9P/OOIaHDzCwpWLySzlWBJRpu8qdruoAuL7yVyQ+asevAHlq/rbHJ5ByY37s33ZVe9sNyIey0vSrB2rPlKIZzpIwHJ/auMWOsz5VRso1aHVM0q5JuxcWUTwDapwxz2ui5iap39bMsw8vZeWS7Syct04ZAZ5k0jYtje2MnToIhOF7daoGq0yCn339OO668QrcXWXa6oH0GgH5ctFFF3UIkLz00ksZMWLEbrnPm2++iRCCF154Ybf015Fks1muuuoqBg4cSHFxMYcddhjvv/9+l9fdfvvtCCHyfq688sqPdLy7SzROa1d/OpLurs/jjz/OjBkzSCaTjBkzht///vcf7cR3s+x0QrkhdG17vGJDgrFHH8DKZ15n3YuvkWlRKUxiNyGjjZDhYQmHuOlgCkkq5bDo3/No29HE1JMOprRfFZrnT/H2418nJT47YdY2fBd/WMLFegqxDoZrDOgsiXCxoZGfO5aS/qqccU9FZRJ07gXorI5Dy9Y6ljz6IsVVZUz0PQH5hX90LnyRmcVA0sdK02QrN7oOE7Q7MVwJrZkYpuFSVuzguCql0EXxGKSyJjHLVamGjknMdH0lbxkKB6BwJQZZYaiiVdKgxU6QNGz6xFJ+KMISDo6XOaErDupsCq3U9TkNOIzheKEAl6yUSFd4/BFQHkspI8Q1yHqAQxdV9Q+g2Y4zKNlE0rD94kApj8nQ8uagSIyUAncwSLVleOv5FSxfuKXg+gtDkCyOcdy5M6isSiJRhpB0BRP2HMyCl9excfUSNq5ewvT9j2Lavkd0+qx3h/QaAZ8Nueyyy7jzzju5/vrrGTFiBL/61a84/PDDWbhwIQMGDOjy+ieeeILy8nL/78GDewmuwtLZ+rz++uucdNJJXHDBBdxwww28+uqrfP3rXycej+9UJswnITtlCOjdbJjvXyKwLMG4o/Zn2VOv0bplO8IwGH7w3rtloDr2bXipbgBuJstbD75BS10z0046mMqBFR59q6eEdBxbRol3DFSVRNdVFMQaQChDyj0cHtA59Np6NIwgLq4V37jzzsFNtXtGQCdrp8GABMDDME6gM+koVCCEStlc8uiLFFWVMfGEQzEsi7Rj8tYf7om0PfaykxXYTUiEnfAocr3wiZDEhENWmjRnE7Rm4zhSUBRzVOjBFDSlDOUp8eaetQ2v3kMwftPwQgvezjqVMTATLlnPgOoTSyOEpDmbpNRKk5UmRWbW9wKoFDw8wKDjj92RBo6jDJG4YdMuY8FxHQLyYv/6+VmGl7roGXdtTtz3QLR55YENYWOi6hiY3rtjY5ByLIrNLEVmloxr0loSZ/CI8g4NgUTS4pQL9qCsIoHwDJysa5BOOdx76zykKzGtGHvMOppJex7a+cPeDdJrBPx3SSqVIplM5h3fuHEjf/jDH7j55pv50pe+BMB+++3HyJEjufHGG/nFL37RZd977rnnf2YpZ8nHEhrobH1+/OMfM3PmTP785z8DcNhhh7Fu3TquvfZavvCFL+z2NOGPQro9QkEAyosel5HfUpgUVZYjDAPpuqQbm3fLQLNe/NZF7TLTaYfX/vkGzXXNzDj5QCoGVCqF7hsB0amFd5Og3P75LnvpKX4V87csiWW5xGOuKnFsSGKWV2XR8yzYqRTNzc20rVtLsrxP3pqFgYK6bwWyi/IS+CWGO/nRogstaWneWsf6NxYqI+DE2bzxxwd4/fd/jxgBp11+HBdceQR9YmkSpo0ppO92N5C+8sxK03O1K7d2wnT8KoBZ1yBhub5SzToqo0CXQLbMIGQgEb5BoImfLEMx8sW9fPtWO0aLnSDjKsND31eLjslD4KnRRovtmn7YJuOaHgBQEQGlXMtP71PZHdIHKprC9YiLQmEEj2Cord3x3289Dp29oZ6npKZfSYfvaCxuUl6Z8J951jVobXd54I/zsbMuJ///9s48zIrqzvufU8vtvZtutka2CLIvgqggKkZxicYobiQZxTjvOzqJa/I+k5hMjFkmb3SiWcYQl8TJzJjJ+MYlImrEJMYtKIICggKKKMoOTdPd9HpvVZ33j1PnVN3uBroFhJbzeZ5+6Htv3bqnzi36/M5v+f6uupmf/2EN//DNXx50dUdrBBw4tHt46dKlnHvuuZSUlDBixAgeeKCjhPoPf/hDqqurKS0t5eKLL2b79u0djpFScueddzJy5EgKCgoYNmwYP/vZz/KO+d73vkdpaSmLFy/mpJNOorCwkF/84hedju9Pf/oTYRjyhS98wTxXVlbG5z73OZ566qn9vPoegDwAPx+RtrY2/vrXv+bNPcDll1/Oli1bWLZs2Uc/+cdI1w0BkcTodeY50GHBBRgydQJTrv48UkrWLnjxgA1WE2QDXn/sFRp37mbSrJMpr64yr2kRF+NBiHMK0j8q3hyX3JEYObqVsu9GFPohRZmAAi8y5/Gc+NpR0sd/ve2nvP/Unxlz/jlUjhzWQVtBk6600Ml0Oms/bRCYTor7+Emze+tO6jduo/aDzezeUsPi+x7qdM76FzQyoEAZZeVeG5V+M6Vem4mDh9IxMffmIKNCMI4qjfOdiCIvoMgLjDyv9ogEgTBzltZUSBT5lNFU4meNsdgS+rSEPr4TGQNAP6fnL51/4CBNLkGJm02SVOPsf60zUO61Uua3mftAGyH6dX1sgaMWfE9Imuqb+cm/LGLFKxt4/906tYOPSxZ1vkJj6JOTLgVOSF1NY/K9OomXCaCxvpUoUCWRucihuQ0emruQlqYs533xRs6Zfe3HIu/c1NRkjYCDwBVXXMHZZ5/NvHnzOPbYY7nqqqtYtWqVeX3u3Ll85zvfYc6cOTz66KMcffTRXHPNNR3Oc9NNN3HrrbfypS99iaeeeoqrrrqKm2++mXvvvTfvuGw2y+WXX86cOXNYsGABZ599dqfjWr16Nf3796eqqirv+bFjx/L22293KR9l3LhxuK7LsGHDuO222wjDcJ/v+SSxrzna0/ysW7eObDbbQcBr7NixgPpuegLdUBaUe1TxUyIzGPneA830676Ap8vQsllWzF9I484GJs86hcoBlUaON51Bn5TiCTP+NEGoS81k3sLtCi3Mo57Lxot2+8s65sJz2bZkGZXDh9J75DC1mNNOiAhA6g6IpFzqSpxGjzmtdth+nPnVCmqnvejuB7s0b//47dPia1KekEJXLVKFToAnQgoIcISkKVRyvUVC0hL6Khzg5mjBpy1wKfDCeCePumOCeKF1IooKVGgoqSSICKWrvB9xgmep32Z22CqRMAlzqBJCFarIpBIG9aJd5OaMgdEYZEx+Q3qeqzMNFLs5pesvBbgQyKQLYQ4XV0gKncCUAyqPiEtdjcpl+eOftnDSKQMYHr8vEVlS3gcA1wnZuSOR7/3UyN4MPaaSZS9vpK62BSmhtqaFftUlFLoBrdksYaDm5Y8P3sVLT/8Pl159CyeeflGXvr+PyvXXX8/69eutEXCAuf7667n2WlX+OW3aNJ566in+8Ic/MHbsWMIw5LbbbmPOnDnccccdAJxzzjls2bKFBx9M/r+uW7eOuXPncu+99xoj4cwzz6SxsZHvf//7XHPNNcaVnMvl+NGPfsRll12213Ht2rWLXr16dXi+srKSXC5HY2Mj5eXlnb53wIABfP/732fq1KkIIZg/fz633HILmzZtYu7cud2eo0PBgSgfXLt2bafP72t+du3aBdBh/isrKwGora1tf8rDkm50H+zE3d5u56uEfnRSHky79os4Qu6xL31X0Z+Ta25mzYJFxggor64y7Y41OgdAGyvpMep8Bq2tr+ve3Xh3C0mOgO4c11kzoto1a9m08FWqp0yk+rgJsVKhRPddaI9MGQO6FXAiKZwvFZw2pLq64Ke54ptnqSqA2K2ejTwKnYAQwe4gE8v1qhCLqzIklJs/FtxxhVLdA+LSQs+MS9fwh47K4HfjvAmR+p8YRI4xivRlBTIRflLfjTLCVLggQEpVe6/DOkVuzhhiOmFQv55xQnIy+X4yTkiF18aQomYCKfigpYQKL0sgBVvayuL8AWkqIeprmti8uYmR4/ohhGTw8N58+cujef7ZjXmLfHt0WKJ2ezODh1Vy0ulD6F1djoNk3MQ+vP3mDl59YQN1NY0cdZTqV1Bd5fCV/zOZN5fvYOMHDXy4fif/eefXGDpyEv0HHt3t77arrFu37pArBn7SjAAgb0deVlbG4MGD2bhxIwAbN25k8+bNXHRRvpF36aWX5hkCf/nLXwC45JJLCIKkhHbmzJnccccdbNiwgaFDh5rnzzvvvC6NLS0Frkl0Tva8OzvnnHM455ykpPXss8+mqKiIn/3sZ3z7299mwIABXfr8Q8c+0v67yIgRIzp9fl/zo9nTHO9t7g8nuu6n3MtcpyVlXaG64xmZ2wOQyeGKiGxblvcXvkGmtICREyZTUV2ZlOeR1P8bl3vKTa3RxkHGDZE+qaz2xAsQGVe9MMlv+jQSaNmxk7efeZaRZ55GvwmjzTGdGQx7Q2sKQOIdWHLv/3TrHJ1R6ATKcxLvaAWStrgkUDXkkUa6N4sb75DVfLVEPm2hF++41SKc8dtoyBaSjcV1mkOPIHKM4qAxtIRqoiTc/HnXn6f/zUlVPYDO54hUC2KtJeAKKPWy5KRq2RtKgWdEjgIzzw4Sz1XtgIcXN+GgWlH3ikMDzbGXAxIvTX1TRN/KIoZXlROhuhh6TkTfAcVcdvloNmxri8caEQqRl4CoBZFOnjmUvv2K4jOrkkvXcRh7bD/Gja9kd72qxNDhi5LSDFNPGcjJpx7F9m1N/Obet5j7nSv5l9+8ZMb3T1+YRFPDLiqq+nHrvX+huDTJTv4o3H333dYI2Aee5+3R/R2GIb7vd3i+/a4vk8nQ2toKwJYtWwDo169f3jH9+/fPe1xTU4OUco+JZ2lDoLi4mJKSPeekaCorK83ONE1dXR2+73fpHGlmz57NnXfeyfLly3uAIXBg6E5CX3p+9HfVfv71Y+0ZONzpZhviJNlNL6ra/QuxOp4jjSW6t1K3rnLq9bMJsyFvP/MqrisYduIIeg1QrYTDSJV1pRPuNO1d6lrhztFaCJ5KLDOvi44Gi2y3w2/asg2/RP3n7DN+NJDs4MN2RkOaPSkL6vEtue+jGQB/9/WZSnI5ToBT6oMuGRHEY3IIcYy7PUQJ5QSRrwyfVKy9yA1pDDKEjoqL+4Rk47I9JZ3rxjLNypuSDVO6ARALDkVJm984WbA1Nhy0lwEwx2ScwDyv2xRHsY6iqg5Rx+RiV31GhEgh8FDdBTNOyKCCJoZkfDzh8G5rGxkR0RD4yisVhxB81HlLi10ioUoP+/pNlHiqNDAX55T061tEa9Qx78WoIrohQ6p9Iplci86ycZC4nkN5VZF5DymjBSEZWF2E6wpqtn7IpvVrGPgpdQ/JOD5ZX7udb82Zynfve5aqfh+9fOtQNRDqKUYAQN++fdm6tfPqjy1btnRY0PeFXjDbJwdu27Yt73FVVRVCCP72t7+RyWRoz6hRo8zvXd1Njhkzhu3bt1NbW5uXJ7Bq1SpGjRrV7ax1eSB87R8XEuTHJ8mhPjI1P8OHDyeTybB69Wo+85nPmOd17si+mn8dLnTrDkmL2WhXq34e8mv908eedsPeY1x74rQbLkPIkLeefJHqEUcxfPro2Aggr4wwjUx5B/Tv7WWCI0TeWDszAtp7A5q37eC9p/7E2w/N6/RYYXQVOv7siaX3/Y6l9/1uj6+35/ybzsMv8Ok9sIpLvnY2vifwRGiMAFDiN+0Fh3TinG7TnHSPTEITever73Fdype+hkC65nMybtTBC5I3z7GR4TtK8wGS8JI+p47ba+nf1sg3Bov2IECqyiIuG6zO7KbCa6M600zGkTRFIbvCLHWBR0PgG0GhdDdF3bMhSDU8KnJCUxEg41yNCEEucpLQEklSokdkVCl9J1LNi0SAR5LbkG4clXRhVFUKgRSMGKF2+7ff9DmToHTbA4u49OrvAJBta+G3/3Zzl++Jw4WeZAQAnHbaadTV1fHii/nJzPX19Tz//PPMmDGjW+cbNGgQAwYM4LHHHst7/pFHHsl7PHPmTAB27tzJ8ccf3+GnrCy/8qgrnH322TiOw0MPJYnCjY2NPPHEE3z2s91vbvX73/8e13WZPHlyt997SNhTJUB3frpBen4KCgo444wz8uYe4MEHH2TAgAE9Zg677hHIq3lP3P7pBRWIm+fEQj3xIht1uk/uwkdGIZ4nmP75U1LPpv64Rqp0rX2uQjJk2eF3vQhkQzfxJKT+eGvUoqCuY8eylXglRaRR1yfNsd2hO4t/mvUrN1DWp4zpl5yA7wukjGiT6ivUbXnzxkjSrjhQfYUNOoEw4wZkZJiU98kIovzvTcX9VRjFcyIK3RxRfHxj1keAMcoKvNBUNyiPjU60y793ZBwm0ImBQFzFEFHmttEWuQgcsqZFcYQvIkq9NgriRTiUgl05j5qsQwQ0x5oI2cjt4NbX+QeFbo6ME1LmZVWIRAojahRJQbGTI4hc83wmTjD0RKIE6QlJpOfQichJVSr40x+8zDe+8Q0+rHmaQYNKO3STzOFwweyRzMqewe23384rf36Yk8/5PJnCYsafOJN5//mvBLks7765+CPdH4eKnmYEgFo8Tz31VC666CK++93vMn78eDZv3syPf/xjfN/nxhtv7Nb5XNflm9/8JjfddBP9+/fnrLPO4plnnulgaIwcOZLrrruOOXPm8PWvf52pU6eSy+V45513eO6555g3b163r2XgwIF8+ctf5uabb8bzPIYOHcqdd94JwFe/+lVz3AsvvMDMmTP5zW9+w5VXXgmoGPjMmTONF2n+/Pn86le/4qabbuqSENEnna7Mz6233sqMGTO4+uqrufzyy1m4cCG//vWvue+++3qEhgDsh7KgboWTJr2QpnfCnWzc98rMmy4hyAaseOJlMsUFjD5tIoWlqi+8jhtLqXZuQN6OuIPsbsozAeoPdjrx0enEHFRGjeoRsGXZCnasWMXIyy7MO2bF/b9lwj/M6VB10J603v0bv/7vbs3D2V+9GFdE7N62k/dee5e2pjZOvuQEMgUupHb3GaHc+FK6prMeEO/oVew8J1W73Fwsnauz76N4wUs34tGlfHoh056BjBvSu6CZUAp1TOCphD0/jIWFoKBAxfGbAz/vHsnqpkVxoyHXVcZHa+QZQ8BB4rqRyVvQ4RalC+ESCYEb+mQjldugd+F6jKFM+hFEEsK2LGQKjRGhF/UyV/U8CGSiIljsBOQiR5U0hgU4gAwDHHKmTFDnwOj3OShtiSBU81NTU2Myxv1MBt+PuOb6iZDxAIdNG5t4+cWNjJ1Yh+MI/vvfvsH/u/sWhHDIZVvN9x4G2W7dJ4eSnmgEgIoJP/XUU9x666385Cc/YfPmzVRUVHDGGWfw6KOPfqTY+A033EBdXR2//OUvufvuuznzzDO57777OP/88/OOu+uuuxg1ahT33XcfP/jBDygpKWHUqFHMnj37I1/PT3/6U0pLS7nllluor69n6tSpPPvss3mLuZSSMAzzSuVGjx7N/fffz8aNG4miiJEjR/Lzn/+cG2644SOP5eNkXxLB+0tX5uekk07i8ccf55//+Z954IEHGDRoEHfddVePURUEELKLAaGqowcy+rxTzWOdiNbeIwCYdrFp7Xm9qDx/18Odnn/mTZcQRGqXSRiw9A9/I8gFjDx9Er2P6m2Oi6Ry74aRMAtNuqRR5zGk8xnSBos+Nop3uOlx692znpEtr7/J1tdXMOz8c1j3xIK9zs+UL19uvA36XKDKFFfc/9u9vlfz6RsvwxMhRV5AxgloDjLs3raTVx9dRHmfUqZdMhU/4+EgKXJzxggIUc12Qpy8JkAa3eIYVA1/oZNLkgGdAIdYJTBSfQFyUmkJaF0BvasGlciXiTP9P2iqpDnnGX0FgPJMG7lQsO7VNQw9cZzyJqQMAiGgyMvFPSvUc4VOYBIKC9yAAQUNNIUZ42rPxuJBeiH3hcoRSEtECyHxRURdUEgoBc89vpq1b+1g1j9Op7KywPQY0JUHOldEpjwHoVSGwLa2YiIE9/xfldD3ze+eaIwmpW6ZGLqekDSHHm2Ry9Il23lhwToz7/o+vPr6iWxZP4Qnn3wSgAkTJjDtzAL+6+5lZNs6Jqy5ns/c+e/u8T7pU+5z6ckdF6qlS5cyZcoUXn/9dY477rg932gHiMPBCHjggQf40pe+9LFdsyWfRxZuoaYh1+lrf7zneubPn39QP9/vPZjScTP3+zyn9qo96GM9nNkvieG0EZCnkCec/G6BApM4tSekFBR66g99Q91u/EKfMWdNpqhXBaTCDXrnrxIW1b/plsed5de0V+XL/9zOx6ONgP5TJlJavW/pzfZSvh+FCr+VYi+LL5Qu/sYNzSx9bBHlcTjA8VWNvyNCWiNP1dhrt7duk5v2xMTzr7vy5ZUpIvFESE43GYrnJScdU9fvEpHFpcBRMXC1IKuSxCByKM+0Ut9ajitCiI2ujBNQv6mWDUtWcfTkoykoUCGVMEq8MCJ20wehS7GXM0qQGSeHL0KaQ9/IBrdvTSykup4CN8BH6SLsDgtMqEGgklU3vl8HwIdvb6fXtCHGmIkQEEFhnKQYxfPjOxFtkSAXqnLLXbVJKWFNTSsD+mbwndBIQWtPQjYObfgCtq3PzzSvqurN8OHDeeDXb9DWtoKBR49hy4drWblyJSvfBCRkCooorehN7/6DGDBkBL37D2LYmMN/QTscjIDFixebun7LEUwPym08XOmWoFA6N8A8L5KyPdAJYQJXSqM+Fz+LRHDGjZfmJ1QJFe/13UQuNnILmHD+SXnd2/LGQVKHn4ytY9VAOiTQ3gDQYYV0aEB7A9JGQP/JE3AEHHv1Fea4dJJcV3f7e2PWPynXoVLAC2kLPbZu2s3rjy2mtHc5J1w0FXwPSCR70218W0Ifzwk7yCWrPAo3b0evBXoiBNk4v4A4w74lShZglT+Qo9AJqPBbaAgKTahBdQF0aQ4y9CpU5XoFboAf6/oPGlhIyd+fgVOYwRGhGVs2cpXccCSM0dMY9zsoTFUQQLLwh9Ix8tA6LJRxAuVpECqpUSsSgroXtq/fTkuz2qW8tWg9k4/vT0kmR4ETGWlhHSZRXQ9VmEB7JQrdgNf+tskYlUsWbuLCiz5FkRuqREKS3gW6W6PvRGzatCnve925cye1u3ZRUtqLv7vxXzn+tAv51hUnsru+hr4DPsX5V3yNEz6dH3LqCRwuRsBZZ53F8OHDu9Rlz2Kx7JlueQS0exR098F8I0CjDQXXSTKnhdC96WWH5jpKElj9LqVDpqzzulchVNxeInFkqg4/jgnr7P+0+1+T1hXYkxfAQbJp2VtsfX2FEQsyr6UvUScJppIfJl9zBct+1TEH4NTrZ8fvl8YdrsMZAhl3UVRGSaET0Bz47NjcwMJHFlNcVcHEC05G+g5BpEruXKTR0VeeAIEXGw9pd7sjk9g+qIVPf24u1ZVRz1lLlCym+jy6Vl+76VXpoROX5IWmYZAvQvOejBOSjQop7uXQEgoyjkrOc0VEaxTr/ztqXG3x7luHkUrcLPW5Qorczr1HXpwX4MVGThgpKeBc7GkI23JsWr2JlUs2mnuzrTXg+SfXMHpEKX16Z+jdu4CKcp8Qh+LY8HAkZFsDtu1oob62hdqaVlau2GnukxVv7GTQ0HKOG1/GBxt209Ia0twcUVDi09IGG96vY/PG3eze3caAoSPZtmEdY6d8miEjxvPZv/sqjuOw+LnH+OrFo8llleG0ffP7FJXmq7394pYraajbwbfnPt3p9R8OHE5GwPjx47n99tu7neFv+QRxkHMEjhS6ZQjopLx05zzITxzUdeZOvFCZrO3YCBBmZ+eYpL+0YRC05fAKfLNgOjr5i47Hmt18u+oElfbg5OUNmLFKnfQljR6C9hxsfO0ttixZyYATJlI9eTxpn5OOqefNR9rVvpeMSG00pa/JFWp3qRdwV0S04FO3dRcvP6I8AaPOn0Hk+QSRKrGLIkFh/DltoWc69qUb+8hImKZCMmUcmVwKk0GvwjcZEcSZ+9JUB4AumZNkhOryp5LkEpngCFVBUCRyefMThJLlz6xgxLEDqDqqL0Jgmgzppj9lbpbWyEu6IEYeu3PKM9C/YHdc/+8qoaX4PkrKDdXntYYeBY7qiti8q5G/PLScpoY2pJSJ0Rnz/ju1rFuz0zyeML4Xsy8bSqkr2ZXzeGvlDh5+ZMMevz8p4c9Pf8AzT7kEQefxUNfzGTVxOjf+6HedZgo/O+/fjREA4GcKKSxSBu99/3INy195xrz2+otPMmXG+R3Ocag53IyABQsW7FEa1nIEYS2B/abroYF4zUuHCDRpT0EaJari5D3WO9VIJsItUsLbzy7hqPHD6VVd2SHZLW0EtJc6ThbniBd+8Zh5/uRrL8UVwix+6aY+kIQN9L+bXn+LTUtWMvCECQw4bhztNSqcdsYPqF32tGu/qHb2Dpxy/eeNQZRUMaimul67BEpdvRBEDsIJEUKwY0s9Sx59lbI+ZYz93KlEbgZTLikBKQiFktXVEr3aM+A6IdkI2iLPVAUIkXge9L+Jq12V32XxTNxffc8y7kYYmQQ6bbhlnJCIiJbQN+cXQrnxkRBIh9qtdTTvbqP8qH6qQoGIbOSZHIN0gh9AY1BAW+hR4bdyVGFDqqogS06oOcrFEsUyvmciJAVxIyEPQYmXJciFSCnj49rdPynrcezoci65eCieI3hpWT2vLqll66ZG9oWfcajqP5JBw8YxaNgYKvsexe66GhzHZcKJM6mo2rsAzdfv+AMvPfMgRw0ZwfBxJ+Y1H9q+eX3esZs/fIcp+xzRnjkYjU7uv/9+7rnnHr7yla9w3nnnsXTp0gP+GfvizTff5Nprr2X48OHcfvvtrF27tsc0dbFYDme6XDXQZ9hRjD//ZPM42aW3O6FItATSpLO7zbGpHXZzXSNFvcryjtXHp4/TyVqJcJAg29zK8sdeoqm2AcdzKetXyYQLZ4CjStZ05nraWEnnO2xauorSvlWUD+yn2ifHx+qkyHSyYTr7vf3rehfdXjdBz1FnoYlIKond3dtqWD5vISW9y5l04XTcTMZI/wKmi542igpc1W0vXYevG/XU5wrNwl3sZuOESsfI9QYp4RsZS+pqV3rONOtJWhS3v6a0Z6TCb4nP7ZikPz3GRLgoURz0RZgYDyQhixCHjFB5BhWe0hFIlzw2BRlzDMSCPkJJDBe6Ibt3tfDrf19Lc1OQt/CnvjjGjq1k/uNL+Na3vsWTTz5JS0sLAOWVfZnx2Tmc+wVVEqR39NlsK/95x1dZtlC56v/1f16nvJdKHI2i6IDWCEdRxNqViyiv6seAwcfs9dg9VQ18+OGHjBkzhubmPfdM+CRSXFzM6tWrGTJkyKEeyhHHIa8aqBpMyegz9vs8M/rsslUDXaF9vWbYrm7f7LA7SeFML+xmgZARCKXmlm3NGSOgs/eauLfo+BpApriQSRfPYO1LKxh+yrE4hUUdRhGlksH0eKXEGAG9hlTjCEkQtVvEY6MgraSo358Oh+gcAEdEeNp7kkpw08141PN6/Oo8DVtrWf74Qsp6l3PcRSchPB+dGKg9GkDeYus5sc69qdxQi7VKHIwIQ7VItUS+kseNy+5AhQu0vDACspELElojz+z+pUy8IKqqIPnsdGfAAieRM26JfKJIQPyduanP02Eg4UBGBGTjW6/SV4txhKA1cMk1NtHkBLglxXEin6Q0TlrclSsyc18Yhxt8R31GYUUJs744hv/+VeeJY6VlPps2NnHMMWqRLa/sy2kX/G/O/fx1ZAqL1RiiiLqaLWz+4G3eXPI8Lzz5X3nn+NacE4lS+vSz//F7nH7h33f6ed3FcRxGHTt9v84xZMgQVq9eTU1NzQEZU0+hT58+1gg4grGRgf2n2zkCQN7uOt1nwIkXo7Ssa97749eCbI5tb29gyMSj1SAKfCT5i74m/VjLu+bt0HU9d1Eho886MR5XsktPjzNPslbCxtffYsea9Qw8bmzsdk5yHdJGRzoUol3sSddCvetVRoBOkNSLtxASkYrV60ZHOm+gcetO3nj8JUp7lzN51nSE5xuvh/n8VHhAxGV/+jOMjHAqg93kHIQ+pV4bIl4sVcfB/NJPLSUcRI7RxFd6/0kipohzKUzZnghNvF9dk0M21hxIvyfjhHnthz03JCMCqmIvghCS4riRUCAFr7y4nsULNwMw6cQBLF+sGrlcfeMk3NJyCmOjwxWRyQ/Qe3KJINeW6gGQ4qjBZWzesBsQlPXqg+M4lJRV8u5bi/nmnAdoaWro9H3tSRsBfqaQqWdd2qX3fZwMGTLELooWi6VbdFtHQBsB2grTi6tqkBElpX2yY7MdR0KQzbFi/kJaG5rpP2IgBUWZeOFLhRNSi3A6vq3yA5JyQV2BkNTSJ4YKJDkB2s3vp7QNGmvq2Lj4TQadmFQGpHsmJFUMqTBEKinSFZHxAqj3qiTJQicwC6spkxSgBItFntBv/dZaVsZGwKQLp+P4ftI/QaZi+8bYSlzROqYfIciKpDpDIsimjmsKM0ZIR6KU8DJxOV9z5JudOhBXIyQVA/r7zYW+MdLS2gQyFWrQCYdIiASmtC4eVCIE5KkEx+K41bCqAJA0NEbGCBAC3liyxfw+7+F3ufR/TY7PEeI7UeLZQXVXBKjb0WTek94lKCNADWR3XQ1FRUU0795FGIb06lVJ377lFBZ5OJ7LhvfrqB5UxofrdpPL5bs8+1QP4fQL/54Zn70yL8b/cWN3QJbDhUN+L0qsjsABoHvdB1NGQCidvJ1ye7lhvXjpxUxKQZDN8cb8hTTtbGDyrJMpKMqYY53UDhpkXnJgWkiog3ERGxEIELFBEEniMje1qCf69uo9URjiFxUwZNpEjpo8poMCYWeNi9LVEu2TJUXKINBeDyGkSYzUaLe6F4cDVj7+N0p6V3DcrJPwMkn5XvscCSNTnDq32ml78e/K9a9c/omOP0ChCMyCXOTmTBJgNk7ACyLXCO3oygZXRLRFXt7Y9Q5fewGCyKHIzdES+fgipMjJUeCE1GSLjWfBc5JwihvnAhQ5gZI0diJykUND3AFyU7yIu57DpKmDOHbaQD58r47Fz73Pji2NbHi3hhEje+EKafIGHCBHUsmyc4eKjReVZmje3VGmd+yUAZz2mWPyxqRzID5YV8cbr2+jqTHHujW15ph+A49mwokzmXnRP1DZ5/Boybq7pXPPh8XycXOo70XlST6kQ/hE0C1DQC2U+SGBNGndAI1eSMNcljfmv0zTznomzzqFygGV+/y8ZPHtmCwIySKpjQHtjUCqsejYvuuovvdSCBbe/TD9xh9DWXXvDkaAvqa0UmF+gmFHg0fPi0i9V4c5jFZ//Bbttq/fWsvyeQtjT8BJOHHv80iCQ1Iima6Q0De740REUrUKdkSEq2P4MWH8mi7bDKUT78iDvN1zmJcwqB4XOjnCWFlQ6wNAYoBIKQhJkgG1e7/IVe9rCAqSeUuNPR2uyEUOLXj4UUiRk4Q4+vQp5NhpA5k4dTAFxeo8w8b0ZdioPqxduYV1q3YwelS5UfRri9y87n4gaGwO6N2vmJ3bk2Q5P+Pw6YsmsmldDZV9S6jbHbLx3RqO7jONlpYW1qxZw+rVqwkC9QetuLQX1UOOYfSkkzn9c1dRWpG0dT1cyAaSVR82MmZwCV1tVWuxHEiklKze0EQ2sKvwJ4FulQ+qhTJCivx6ftD9BUC0q6fXnoAVKU9A5YDKDmVk5nxmYU91DBSJJ0DtiEWHXbMrgEglteGoG1WkdrSuI9mwch1IyaBJI8iUdkxObC9ApD5bmtcimRgmOsYPWpcgX9lPo/IB4t8l7NqyyxgBk2dNx8v4eQaNRhs3Jg6PE3s7HFPDH0gXn1wHIySN54Tx9yPNYqyMBEwZYU56hNKhym9mZ7aEUAojc6yvKUQghMoe0GEC1cXRM3kAgNE1AOIKgTiUgvKEtMaehlDEOQdOSFvk4hV5TJ85LPZmCNMoKHQcxk3uz6RJvfGEpCVyycmMETZKhwga6nN5RgBALhvx598vp7Kyinfe2MbCp98G4CXeUe9zXPoNGsbk6Z/htPOv3GcZ4OHCi28pr8XYIaWHeCSWI5HVG5rMPXjIsbbIftM9j4DQPezVwpuu5zfHpL4VSftwwCl5RoBJ/pPCLHBqUGHeoqgXLz0GIFl8Ui5515EQqcVKL5ygFvWtazew64NtVB19FMXlJcjUOLU3IMoLQSS5AnnCPEKHSDAeCJekPj+UDogorozI9wg0bK1l2by/URpXB3gZD0crFqSvQ+gEQQddPRCmcijCKAlXtIQ+BXHCneeo0jxPL8RxDb8vQqUmGPqUecowcFAZ+znpkgl9BK3syJYa74AeS1JeqDP/HSr8Vlojj8YgY0SNtHGgwzgZJ04ojK+vxMuaMsMg1kPAASdKEkv1OTJxMiRAiZtVHgkhEESEcXmjgySIXEKhqytg88ZED8BxXZPc57gu0i2genA1g4ePY+SEkxh/wulkCgrxMoU9plVoe158q5ZFb++irMjroHFhsRwMpFThgMPGE2BjAweEbhkCxhUbK7ftSUhI7+jTnoBJF55KeXVl7MJPJ9ElmM6BqMx/nZymhWuMSE68OBmhoVSGOw6EUdxNDlUG98GS1Xzw6lsMOXEcnzpxDO0zTHRymVn4EXEiYLwDFqLT68wfu4h3zcn4HBES4iEReUbAsReejBv3DoBkwRVChTBcRxp54ESiOTmv8ngIgki57NtCzyQAhmAEhVwREUYurdJTuQGuMlRyMtEGcFDSvq2RR1vombCIm0qYjKRjvDIZN2ckhwudgCBK0h+TpkKJZHA2DjPU5wrzFitXxrkGsamgOw9qLQMddgji79ARSpAo1OEeoZQWHVJz5KiqVIQgCkOmnnExV/3Tz/b6vfV0soFk5+7O67gtliMBawfsP/ud+qyz3E2CXPzHPsrleOPxl004oKK6V142vkY/1k2AtMCMTlrT7uy0SFEgXfOZplIgXojTOEjef20NH7z6Fp+aNo6hJ4xpF1dO0IuUKyRhlDznxEaDDjPovIX2ugmZVD29Lr9Tin+Sui21LIvDARMvOAXX9wmlSuwLUzkB2oOg3fHarQ+ouv94jD5RLKvsxMdFeTt5NfbEg+Ei4xa+AX6cVKi9Fy2hjydUPwHtZVFzE5lz6zG5IjKton0Rxgu2yHPPp/MHarIlZn48J8SNF20RGymRNp5I8jl0joNAeVRMciKO+SytgaDbXes5n3XFBPoVTWfNmjW88MILLH95AfDJNgQsFotlf+lW90Hj+pVJPF0vKo6IzK4xyOZYNu8VmnY2MOXik6moroR4EYjiBkNa7z9JMAzM+3WSml6IO1MqBEy2e2exeSkF65esZv2iVQw7ZSKDJ4/MMwL0opVObjTxbJc4Zq8WXF22p3ME0u2XNXoHC+R9Tv3WXSx9bGHcRXAaBQWSUAaqi10qKTA9z2peI3MufY3JopxcfyY2FoyiX+Tgu2qHmJPKhV4UPw4ilw3v1SC9DIM+VYEWK9qZLaEpzODEAkYZERFJyLihKRNUIRIB2hWfMlp0Rz9dWaCMDDVmLYecEYlRo69ZoCoA9PtbUu2HVWlh8vkyDg/pzonvvV1LaUURVf1KzbxXD+4FrGL8KZLlKwppbm4vFG2xWD5xWI/AftOt4KjnRHGmuk7iS4wA14l3edksSx97hcadDRx/8XQqq3vlxfghMQL0rl+5nMO4BC4ypWzpRdrEl2P3cNoA6Kyk8IMlq3h/0SpGzJjA4MkjgcRw0Cj3fRR7MrT3Qf14jipxc51UaCKVJGiMlNiDkXbva09Gw9ZaXv+Dkg0+Lk4MTJM2ADS+E5l5kKnrVFn4+ZLOvgiNEFK2XRZ9unmQniPPCXn64VUseHA5dblCmsIM2Ui18dUhCbNwu4kSYTbl/hdClye6eVoJXlzbn9YZSCshNocZmsMMLZFPW9xwSBkvGI0HP/7u09+RnlOdnKiuTfLHh1ez9OUP1fcdG4s6z6A1G1K/q5WBQ0d3chdbLJZPDPIA/RzhdNkQ0FKzurNgxg3xndB4AhwkMpfl9dgIOOGSk+hV3bFEUJfg6WQ73eBGLyC6rjsdG09L7OqYc1rTPp3QJoRkwxvv8t6rqxk5YwJDJh1jQgsdShvjxDZX6I6AyTn074BxZbupXAXXGBCY69eZ7hknpGFrLa8+ukglBsZGgIrPq7Erw0m9Xxk4DtnQNbX+ekHW151xwiQBL2WMaIrcHLnIMSGAjKMUAHv5LWScgBI3S8367eb4Let24CBpDjImpBDE3RBzoVr4WyP1e1rK10HSFGZoCVT2vy9Coy+QrhLQVQc6fJD23ug5MD0dSMIh+jvSgk3pOdBhgC2blSyx4zhs26xUAXVOhRCSd1aq6zzt/Cu7entbLBbLEUu3eg3oP8YqDyB/Uc1mA5Y+tojGnbs54ZKTKOvf23Twc+Pdml7Q/Vie1k0tuGGY3zUPkkVBu7i1Wzh9jFbec5B4AtYueofSyhJmzJlBWe8KIvLd22lJ2vak3fvqmuNdtRPnDQiMjj4kBg2gyhdR49u1uZ5X4i6CU2ZNQ/i+kS92UgmFkAj4hDgUezmKvawJB4Q5Zez4bi4xPFCVCvqacjjxoiop8oK8hbncayMbuRSIgEjC4ufeN3Xnb738Lr2PrqYxyCClwHdD2kLXhH38uArCiz0U+pxtoWeSMku9tpTOgVCKgnFZXxTrEKhQjxYAknFCpZ5vAUEIvmuqNjyhyx0jJRgUOSxb+AHvrdrO7Ksn4ziCF/64lguvOJYBQyrYtmU3YVzVIYQkmw14acE6AI475dwu3dsWi6XnYpMF958udx+84IILDvZYLBaLxfIJoaKigt/+9rcH9TO8ikEUHn36fp/njCH1tvtgVziSJ8lisVgslk8qh65zisVisVgs+4O0oYEDgTUELBaLxdJzsYbAftMztVUtFovFYrEcEKxHwGKxWCw9F+sR2G+sIWCxWCyWHontOXRgsIaAxWKxWHomErBK4vuNzRGwWCwWi+UIxnoELBaLxdJj6aImnmUvWEPAYrFYLD0T2zTogGBDAxaLxWKxHMFYj4DFYrFYeiw2MrD/WEPAYrFYLD0XawjsNzY0YLFYLBbLEYz1CFgsFoulRyJt06EDgjUELBaLxdJzsYbAfmNDAxaLxWKxdMLDDz/MrFmzGDx4MCUlJUycOJF77rmHKErkDK+66iqEEB1+FixYcAhH3j2sR8BisVgsPZaDKSj0k5/8hKFDh3LHHXfQv39/nnvuOW688Ubee+897rjjDnPcsGHD+N3vfpf33jFjxhy0cR1orCFgsVgslp7LQQwNPPHEE/Tt29c8Pv3002lsbGTu3Ln88Ic/pKCgAICioiKmTZt28AZykLGhAYvFYrH0TGSSMLg/P3sibQRoJk+eTGtrK7W1tQfxwj5erCFgsVgsliOadMx/X7z00ktUVVXRr18/89y6devo1asXmUyGKVOmMG/evIMwyoOHNQQsFovF0nOR+/+zdu3aLn3Ua6+9xn/8x3/wta99Ddd1AeUhuPPOO5k3bx4PPfQQffr04aKLLuKRRx45kFd5UBHStm6yWCwWSw9EFA/C7Xvafp/nMxPqefLJJ/d6zNatW5k6dSqDBg3i+eefx/f9To+Loojp06fT0NDAqlWr9ntsHwfWI2CxWCyWIxrH2ftSWF9fz7nnnktxcTHz58/foxGgz3XJJZewevVqWlpaDvRQDwq2asBisVgsPRPJQZcWbG1t5YILLmDbtm288sor9O7de9/D6mGOdmsIWCwWi6XHcjDX3CAImD17Nm+88QYvvvgiQ4cO3ed7oijikUceYdy4cRQVFR28wR1ArCFgsVgsFksnXHfddTzxxBP8+Mc/prm5mUWLFpnXxo4dy65du7jqqqv44he/yPDhw9m1axf33HMPr732Go8++ughHHn3sIaAxWKxWHouB9Ej8MwzzwDwjW98o8Nrzz33HBMnTqS8vJwf/OAH7Nixg0wmw/HHH8/TTz/NOeecc/AGdoCxhoDFYrFYeiYHufvg+vXr93nM448/fvAG8DFhqwYsFovFYjmCsR4Bi8VisfRYeliC/mGJNQQsFovF0iO54rLTqa+v3+/zVFRUHIDR9FyssqDFYrFYLEcwNkfAYrFYLJYjGGsIWCwWi8VyBGMNAYvFYrFYjmCsIWCxWCwWyxGMNQQsFovFYjmCsYaAxWKxWCxHMNYQsFgsFovlCMYaAhaLxWKxHMH8f42QLQy249PjAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from xclim import ensembles\n", "urls = ['https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/NorESM1-M_rcp85_prcptot_monthly.nc',\n", @@ -767,14 +730,13 @@ " 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/IPSL-CM5B-LR_rcp85_prcptot_monthly.nc',\n", " ]\n", "ens = ensembles.create_ensemble(urls)\n", - "fut = ens.sel(time=slice(\"2020\", \"2050\"))\n", - "ref = ens.sel(time=slice(\"1990\", \"2020\"))\n", - "delta = fut.mean(\"time\") - ref.mean(\"time\")\n", - "chng_f, pos_f = ensembles.change_significance(\n", - " delta, test=\"threshold\", abs_thresh=2\n", - ")\n", - "sup_8 = chng_f.where(chng_f.prcptot>0.8)\n", - "inf_5 = chng_f.where(chng_f.prcptot<0.5)\n", + "fut = ens.sel(time=slice(\"2020\", \"2050\")).prcptot\n", + "ref = ens.sel(time=slice(\"1990\", \"2020\")).prcptot\n", + "chng_f= ensembles.robustness_fractions(\n", + " fut, ref, test=\"threshold\", abs_thresh=2\n", + ").changed\n", + "sup_8 = chng_f.where(chng_f>0.8)\n", + "inf_5 = chng_f.where(chng_f<0.5)\n", "\n", "ens_stats = ensembles.ensemble_mean_std_max_min(ens)\n", "\n", @@ -1188,6 +1150,14 @@ " line_kw={'lw':2}\n", ")\n" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "75349096", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1202,7 +1172,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/notebooks/figanos_multiplots.ipynb b/docs/notebooks/figanos_multiplots.ipynb index 0d6f69e4..b04c7a3c 100644 --- a/docs/notebooks/figanos_multiplots.ipynb +++ b/docs/notebooks/figanos_multiplots.ipynb @@ -121,6 +121,7 @@ " \"coastline\": {\"edgecolor\": \"black\"},\n", " },\n", " fig_kw={\"figsize\": (7, 4)},\n", + " legend_kw={'ncol':4,'bbox_to_anchor':(0.15, 0.05)}\n", " )\n" ] }, @@ -136,15 +137,20 @@ " 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/IPSL-CM5B-LR_rcp85_prcptot_monthly.nc',\n", " ]\n", "ens = ensembles.create_ensemble(urls)\n", - "mean_hor = xr.concat([ens.sel(time=slice(\"2020\", \"2050\")).mean(\"time\", keep_attrs='True').assign_coords(horizon=\"Delta 2020-2050\"),\n", - " ens.sel(time=slice(\"2050\", \"2080\")).mean(\"time\", keep_attrs='True').assign_coords(horizon=\"Delta 2050-2080\")],\n", - " dim=\"horizon\")\n", - "delta = mean_hor - ens.sel(time=slice(\"1990\", \"2020\")).mean(\"time\")\n", - "chng_f, pos_f = ensembles.change_significance(\n", - " delta, test=\"threshold\", abs_thresh=2\n", - ")\n", - "sup_8 = chng_f.where(chng_f.prcptot>0.8)\n", - "inf_5 = chng_f.where(chng_f.prcptot<0.5)\n", + "s8=[]\n", + "i5=[]\n", + "for horizon in [[\"2020\", \"2050\"],['2050','2080']]:\n", + " fut = ens.sel(time=slice(*map(str, horizon))).prcptot\n", + " ref = ens.sel(time=slice(\"1990\", \"2020\")).prcptot\n", + " chng_f= ensembles.robustness_fractions(\n", + " fut, ref, test=\"threshold\", abs_thresh=2\n", + " )\n", + " s8.append(chng_f.where(chng_f.changed>0.8).assign_coords(horizon=horizon).changed)\n", + " i5.append(chng_f.where(chng_f.changed<0.5).assign_coords(horizon=horizon).changed)\n", + "sup_8=xr.concat(s8, dim='horizon')\n", + "inf_5=xr.concat(i5, dim='horizon') \n", + " \n", + "\n", "\n", "im = fg.hatchmap({'sup_8': sup_8, 'inf_5': inf_5},\n", " plot_kw={\n", @@ -323,6 +329,41 @@ " # txt.set_visible(False)\n", "\n" ] + }, + { + "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": [] } ], "metadata": { @@ -336,7 +377,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.0" + "version": "3.9.16" } }, "nbformat": 4, From 7428b60e1c37e262279a2eb872dfe479ba08ea09 Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Mon, 5 Feb 2024 12:23:34 -0500 Subject: [PATCH 2/9] env req --- environment-dev.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment-dev.yml b/environment-dev.yml index 7f467817..731cfb68 100644 --- a/environment-dev.yml +++ b/environment-dev.yml @@ -17,7 +17,7 @@ dependencies: - pyyaml - scikit-image - xarray - - xclim >=0.38 + - xclim >=0.47 # To make the package and notebooks usable - dask - h5py From 4b28863040fd249e72289535b89934b9026e45e1 Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Mon, 5 Feb 2024 12:43:44 -0500 Subject: [PATCH 3/9] deps --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index ebdab8f3..e445391b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -46,7 +46,7 @@ dependencies = [ "seaborn", "scikit-image", "xarray", - "xclim>=0.38" + "xclim>=0.47" ] [project.optional-dependencies] From b219d78ed5fd488b705a51093d1ebe39d8dc56b3 Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Mon, 5 Feb 2024 17:24:34 -0500 Subject: [PATCH 4/9] test remove one nb --- docs/index.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index 29912c9e..5776720a 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -17,7 +17,6 @@ Need help? installation usage notebooks/figanos_docs - notebooks/figanos_colours notebooks/figanos_multiplots api contributing From c50c04d0af663cdcd8708cd6d823b93fc8c02978 Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Mon, 5 Feb 2024 19:21:31 -0500 Subject: [PATCH 5/9] test remove one nb --- docs/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/index.rst b/docs/index.rst index 5776720a..29912c9e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -17,6 +17,7 @@ Need help? installation usage notebooks/figanos_docs + notebooks/figanos_colours notebooks/figanos_multiplots api contributing From ce9fbb5be99554f9498c81f8f6e8ef94f4f575f0 Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Tue, 6 Feb 2024 09:30:54 -0500 Subject: [PATCH 6/9] test remove one nb --- docs/index.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index 29912c9e..db5c3dd3 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -18,7 +18,6 @@ Need help? usage notebooks/figanos_docs notebooks/figanos_colours - notebooks/figanos_multiplots api contributing authors From cb81c8bacc4df2d4715a92b292ad66e2e5dfbd2a Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Tue, 6 Feb 2024 15:35:44 -0500 Subject: [PATCH 7/9] remove half --- docs/index.rst | 1 + docs/notebooks/figanos_multiplots.ipynb | 124 ++++++++---------------- 2 files changed, 41 insertions(+), 84 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index db5c3dd3..29912c9e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -18,6 +18,7 @@ Need help? usage notebooks/figanos_docs notebooks/figanos_colours + notebooks/figanos_multiplots api contributing authors diff --git a/docs/notebooks/figanos_multiplots.ipynb b/docs/notebooks/figanos_multiplots.ipynb index b04c7a3c..1a0ab168 100644 --- a/docs/notebooks/figanos_multiplots.ipynb +++ b/docs/notebooks/figanos_multiplots.ipynb @@ -41,14 +41,6 @@ "opened = xr.open_dataset(url, decode_timedelta=False)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Timeseries\n", - "Create multiple timeseries plot with matplotlib subplots and figanos as shown below." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -74,7 +66,8 @@ " plot_kw = {\"col\": \"time\"},\n", " features = ['coastline','ocean'],\n", " frame = False,\n", - " use_attrs={\"suptitle\": \"description\"}\n", + " use_attrs={\"suptitle\": \"description\"},\n", + "\n", " )\n" ] }, @@ -121,7 +114,10 @@ " \"coastline\": {\"edgecolor\": \"black\"},\n", " },\n", " fig_kw={\"figsize\": (7, 4)},\n", - " legend_kw={'ncol':4,'bbox_to_anchor':(0.15, 0.05)}\n", + " legend_kw={\n", + " 'ncol':4,\n", + " 'bbox_to_anchor':(0.15, 0.05)\n", + " },\n", " )\n" ] }, @@ -131,47 +127,42 @@ "metadata": {}, "outputs": [], "source": [ - "from xclim import ensembles\n", - "urls = ['https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/NorESM1-M_rcp85_prcptot_monthly.nc',\n", - " 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/MPI-ESM-LR_rcp85_prcptot_monthly.nc',\n", - " 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/IPSL-CM5B-LR_rcp85_prcptot_monthly.nc',\n", - " ]\n", - "ens = ensembles.create_ensemble(urls)\n", - "s8=[]\n", - "i5=[]\n", - "for horizon in [[\"2020\", \"2050\"],['2050','2080']]:\n", - " fut = ens.sel(time=slice(*map(str, horizon))).prcptot\n", - " ref = ens.sel(time=slice(\"1990\", \"2020\")).prcptot\n", - " chng_f= ensembles.robustness_fractions(\n", - " fut, ref, test=\"threshold\", abs_thresh=2\n", - " )\n", - " s8.append(chng_f.where(chng_f.changed>0.8).assign_coords(horizon=horizon).changed)\n", - " i5.append(chng_f.where(chng_f.changed<0.5).assign_coords(horizon=horizon).changed)\n", - "sup_8=xr.concat(s8, dim='horizon')\n", - "inf_5=xr.concat(i5, dim='horizon') \n", - " \n", + "# from xclim import ensembles\n", + "# urls = ['https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/NorESM1-M_rcp85_prcptot_monthly.nc',\n", + "# 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/MPI-ESM-LR_rcp85_prcptot_monthly.nc',\n", + "# 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/IPSL-CM5B-LR_rcp85_prcptot_monthly.nc',\n", + "# ]\n", + "# ens = ensembles.create_ensemble(urls)\n", + "# mean_hor = xr.concat([ens.sel(time=slice(\"2020\", \"2050\")).mean(\"time\", keep_attrs='True').assign_coords(horizon=\"Delta 2020-2050\"),\n", + "# ens.sel(time=slice(\"2050\", \"2080\")).mean(\"time\", keep_attrs='True').assign_coords(horizon=\"Delta 2050-2080\")],\n", + "# dim=\"horizon\")\n", + "# delta = mean_hor - ens.sel(time=slice(\"1990\", \"2020\")).mean(\"time\")\n", + "# chng_f, pos_f = ensembles.change_significance(\n", + "# delta, test=\"threshold\", abs_thresh=2\n", + "# )\n", + "# sup_8 = chng_f.where(chng_f.prcptot>0.8)\n", + "# inf_5 = chng_f.where(chng_f.prcptot<0.5)\n", "\n", + "# im = fg.hatchmap({'sup_8': sup_8, 'inf_5': inf_5},\n", + "# plot_kw={\n", + "# 'sup_8': {\n", + "# 'hatches': '*',\n", + "# 'col': 'horizon',\n", + "# \"x\": \"lon\",\n", + "# \"y\": \"lat\"\n", + "# },\n", + "# 'inf_5': {\n", + "# 'hatches': 'x',\n", + "# 'col': 'horizon',\n", + "# \"x\": \"lon\",\n", + "# \"y\": \"lat\"\n", + "# },\n", + "# },\n", + "# features = ['coastline','ocean'],\n", + "# frame = True,\n", + "# legend_kw = {'title': 'Ensemble change'})\n", "\n", - "im = fg.hatchmap({'sup_8': sup_8, 'inf_5': inf_5},\n", - " plot_kw={\n", - " 'sup_8': {\n", - " 'hatches': '*',\n", - " 'col': 'horizon',\n", - " \"x\": \"lon\",\n", - " \"y\": \"lat\"\n", - " },\n", - " 'inf_5': {\n", - " 'hatches': 'x',\n", - " 'col': 'horizon',\n", - " \"x\": \"lon\",\n", - " \"y\": \"lat\"\n", - " },\n", - " },\n", - " features = ['coastline','ocean'],\n", - " frame = True,\n", - " legend_kw = {'title': 'Ensemble change'})\n", - "\n", - "im.fig.suptitle(\"Multiple hatchmaps\", y=1.08)\n" + "# im.fig.suptitle(\"Multiple hatchmaps\", y=1.08)\n" ] }, { @@ -329,41 +320,6 @@ " # txt.set_visible(False)\n", "\n" ] - }, - { - "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": [] } ], "metadata": { From 0902b3241ef0291c2f70a6211da89ce76267bb76 Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Tue, 6 Feb 2024 16:04:41 -0500 Subject: [PATCH 8/9] put back three --- docs/notebooks/figanos_multiplots.ipynb | 161 ++++++++++++------------ 1 file changed, 82 insertions(+), 79 deletions(-) diff --git a/docs/notebooks/figanos_multiplots.ipynb b/docs/notebooks/figanos_multiplots.ipynb index 1a0ab168..c78e50eb 100644 --- a/docs/notebooks/figanos_multiplots.ipynb +++ b/docs/notebooks/figanos_multiplots.ipynb @@ -127,42 +127,45 @@ "metadata": {}, "outputs": [], "source": [ - "# from xclim import ensembles\n", - "# urls = ['https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/NorESM1-M_rcp85_prcptot_monthly.nc',\n", - "# 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/MPI-ESM-LR_rcp85_prcptot_monthly.nc',\n", - "# 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/IPSL-CM5B-LR_rcp85_prcptot_monthly.nc',\n", - "# ]\n", - "# ens = ensembles.create_ensemble(urls)\n", - "# mean_hor = xr.concat([ens.sel(time=slice(\"2020\", \"2050\")).mean(\"time\", keep_attrs='True').assign_coords(horizon=\"Delta 2020-2050\"),\n", - "# ens.sel(time=slice(\"2050\", \"2080\")).mean(\"time\", keep_attrs='True').assign_coords(horizon=\"Delta 2050-2080\")],\n", - "# dim=\"horizon\")\n", - "# delta = mean_hor - ens.sel(time=slice(\"1990\", \"2020\")).mean(\"time\")\n", - "# chng_f, pos_f = ensembles.change_significance(\n", - "# delta, test=\"threshold\", abs_thresh=2\n", - "# )\n", - "# sup_8 = chng_f.where(chng_f.prcptot>0.8)\n", - "# inf_5 = chng_f.where(chng_f.prcptot<0.5)\n", + "from xclim import ensembles\n", + "urls = ['https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/NorESM1-M_rcp85_prcptot_monthly.nc',\n", + " 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/MPI-ESM-LR_rcp85_prcptot_monthly.nc',\n", + " 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/IPSL-CM5B-LR_rcp85_prcptot_monthly.nc',\n", + " ]\n", + "ens = ensembles.create_ensemble(urls)\n", + "s8=[]\n", + "i5=[]\n", + "for horizon in [[\\\"2020\\\", \\\"2050\\\"],['2050','2080']]:\n", + " fut = ens.sel(time=slice(*map(str, horizon))).prcptot\n", + " ref = ens.sel(time=slice(\\\"1990\\\", \\\"2020\\\")).prcptot\n", + " chng_f= ensembles.robustness_fractions(\n", + " fut, ref, test=\\\"threshold\\\", abs_thresh=2\n", + " )\n", + " s8.append(chng_f.where(chng_f.changed>0.8).assign_coords(horizon=horizon).changed)\n", + " i5.append(chng_f.where(chng_f.changed<0.5).assign_coords(horizon=horizon).changed)\n", + "sup_8=xr.concat(s8, dim='horizon')\n", + "inf_5=xr.concat(i5, dim='horizon') \n", "\n", - "# im = fg.hatchmap({'sup_8': sup_8, 'inf_5': inf_5},\n", - "# plot_kw={\n", - "# 'sup_8': {\n", - "# 'hatches': '*',\n", - "# 'col': 'horizon',\n", - "# \"x\": \"lon\",\n", - "# \"y\": \"lat\"\n", - "# },\n", - "# 'inf_5': {\n", - "# 'hatches': 'x',\n", - "# 'col': 'horizon',\n", - "# \"x\": \"lon\",\n", - "# \"y\": \"lat\"\n", - "# },\n", - "# },\n", - "# features = ['coastline','ocean'],\n", - "# frame = True,\n", - "# legend_kw = {'title': 'Ensemble change'})\n", + "im = fg.hatchmap({'sup_8': sup_8, 'inf_5': inf_5},\n", + " plot_kw={\n", + " 'sup_8': {\n", + " 'hatches': '*',\n", + " 'col': 'horizon',\n", + " \"x\": \"lon\",\n", + " \"y\": \"lat\"\n", + " },\n", + " 'inf_5': {\n", + " 'hatches': 'x',\n", + " 'col': 'horizon',\n", + " \"x\": \"lon\",\n", + " \"y\": \"lat\"\n", + " },\n", + " },\n", + " features = ['coastline','ocean'],\n", + " frame = True,\n", + " legend_kw = {'title': 'Ensemble change'})\n", "\n", - "# im.fig.suptitle(\"Multiple hatchmaps\", y=1.08)\n" + "im.fig.suptitle(\"Multiple hatchmaps\", y=1.08)\n" ] }, { @@ -255,26 +258,26 @@ "metadata": {}, "outputs": [], "source": [ - "#Selecting a time and slicing our starting Dataset\n", - "ds_space = opened[['tx_max_p50']].isel(time=[0, 1, 2]).sel(lat=slice(40,65), lon=slice(-90,-55))\n", + "# #Selecting a time and slicing our starting Dataset\n", + "# ds_space = opened[['tx_max_p50']].isel(time=[0, 1, 2]).sel(lat=slice(40,65), lon=slice(-90,-55))\n", "\n", - "im = fg.gridmap(ds_space,\n", - " projection = ccrs.LambertConformal(),\n", - " plot_kw = {\"col\": \"time\",\n", - " \"col_wrap\": 2},\n", - " features = ['coastline','ocean'],\n", - " frame = False,\n", - " use_attrs={\"suptitle\": \"long_name\"},\n", - " fig_kw = {\"figsize\": (6, 6)}\n", - " )\n", - "for i, fax in enumerate(im.axs.flat):\n", - " fax.set_extent([\n", - " ds_space.lon.min().item(),\n", - " ds_space.lon.max().item(),\n", - " ds_space.lat.min().item(),\n", - " ds_space.lat.max().item(),\n", - " ]\n", - " )\n" + "# im = fg.gridmap(ds_space,\n", + "# projection = ccrs.LambertConformal(),\n", + "# plot_kw = {\"col\": \"time\",\n", + "# \"col_wrap\": 2},\n", + "# features = ['coastline','ocean'],\n", + "# frame = False,\n", + "# use_attrs={\"suptitle\": \"long_name\"},\n", + "# fig_kw = {\"figsize\": (6, 6)}\n", + "# )\n", + "# for i, fax in enumerate(im.axs.flat):\n", + "# fax.set_extent([\n", + "# ds_space.lon.min().item(),\n", + "# ds_space.lon.max().item(),\n", + "# ds_space.lat.min().item(),\n", + "# ds_space.lat.max().item(),\n", + "# ]\n", + "# )\n" ] }, { @@ -290,34 +293,34 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", - "op = opened.isel(time=[0, 1])\n", - "data = xr.DataArray(\n", - " data=np.array([op.tx_max_p10.values, op.tx_max_p50.values, op.tx_max_p90.values]),\n", - " dims=['percentile', 'time', 'lat', 'lon'],\n", - " coords={'percentile': [10, 50, 90], 'time': op.time.values, 'lat': op.lat.values, 'lon': op.lon.values},\n", - " attrs = {'units': 'degC', 'standard_name': 'air_temperature', 'long_name': 'Near-Surface Daily Maximum Air Temperature'}\n", - " )\n", + "# import numpy as np\n", + "# op = opened.isel(time=[0, 1])\n", + "# data = xr.DataArray(\n", + "# data=np.array([op.tx_max_p10.values, op.tx_max_p50.values, op.tx_max_p90.values]),\n", + "# dims=['percentile', 'time', 'lat', 'lon'],\n", + "# coords={'percentile': [10, 50, 90], 'time': op.time.values, 'lat': op.lat.values, 'lon': op.lon.values},\n", + "# attrs = {'units': 'degC', 'standard_name': 'air_temperature', 'long_name': 'Near-Surface Daily Maximum Air Temperature'}\n", + "# )\n", "\n", - "im = fg.gridmap(data,\n", - " projection = ccrs.LambertConformal(),\n", - " plot_kw = {\"col\": \"time\",\n", - " \"row\": \"percentile\",\n", - " },\n", - " features = ['coastline','ocean'],\n", - " frame = False,\n", - " use_attrs = {\"suptitle\": \"long_name\"},\n", - " fig_kw = {\"figsize\": (8, 7)},\n", - " )\n", + "# im = fg.gridmap(data,\n", + "# projection = ccrs.LambertConformal(),\n", + "# plot_kw = {\"col\": \"time\",\n", + "# \"row\": \"percentile\",\n", + "# },\n", + "# features = ['coastline','ocean'],\n", + "# frame = False,\n", + "# use_attrs = {\"suptitle\": \"long_name\"},\n", + "# fig_kw = {\"figsize\": (8, 7)},\n", + "# )\n", "\n", - "# modify xlabels positions (hardcoded in xarray.plot)\n", - "for i, fax in enumerate(im.axs.flat):\n", - " for txt in fax.texts:\n", - " if len(txt.get_text()) > 0:\n", - " txt.set_x(-1.2)\n", - " txt.set_text('percentile ' + txt.get_text())\n", - " txt.set_rotation('vertical')\n", - " # txt.set_visible(False)\n", + "# # modify xlabels positions (hardcoded in xarray.plot)\n", + "# for i, fax in enumerate(im.axs.flat):\n", + "# for txt in fax.texts:\n", + "# if len(txt.get_text()) > 0:\n", + "# txt.set_x(-1.2)\n", + "# txt.set_text('percentile ' + txt.get_text())\n", + "# txt.set_rotation('vertical')\n", + "# # txt.set_visible(False)\n", "\n" ] } From 180ce5a0fb672b298b87004f2d0979437e0683e8 Mon Sep 17 00:00:00 2001 From: juliettelavoie Date: Tue, 6 Feb 2024 16:37:18 -0500 Subject: [PATCH 9/9] remove only robustness --- docs/notebooks/figanos_multiplots.ipynb | 164 ++++++++++++------------ 1 file changed, 82 insertions(+), 82 deletions(-) diff --git a/docs/notebooks/figanos_multiplots.ipynb b/docs/notebooks/figanos_multiplots.ipynb index c78e50eb..fdc21f97 100644 --- a/docs/notebooks/figanos_multiplots.ipynb +++ b/docs/notebooks/figanos_multiplots.ipynb @@ -127,45 +127,45 @@ "metadata": {}, "outputs": [], "source": [ - "from xclim import ensembles\n", - "urls = ['https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/NorESM1-M_rcp85_prcptot_monthly.nc',\n", - " 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/MPI-ESM-LR_rcp85_prcptot_monthly.nc',\n", - " 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/IPSL-CM5B-LR_rcp85_prcptot_monthly.nc',\n", - " ]\n", - "ens = ensembles.create_ensemble(urls)\n", - "s8=[]\n", - "i5=[]\n", - "for horizon in [[\\\"2020\\\", \\\"2050\\\"],['2050','2080']]:\n", - " fut = ens.sel(time=slice(*map(str, horizon))).prcptot\n", - " ref = ens.sel(time=slice(\\\"1990\\\", \\\"2020\\\")).prcptot\n", - " chng_f= ensembles.robustness_fractions(\n", - " fut, ref, test=\\\"threshold\\\", abs_thresh=2\n", - " )\n", - " s8.append(chng_f.where(chng_f.changed>0.8).assign_coords(horizon=horizon).changed)\n", - " i5.append(chng_f.where(chng_f.changed<0.5).assign_coords(horizon=horizon).changed)\n", - "sup_8=xr.concat(s8, dim='horizon')\n", - "inf_5=xr.concat(i5, dim='horizon') \n", + "# from xclim import ensembles\n", + "# urls = ['https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/NorESM1-M_rcp85_prcptot_monthly.nc',\n", + "# 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/MPI-ESM-LR_rcp85_prcptot_monthly.nc',\n", + "# 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/ouranos/portraits-clim-1.1/IPSL-CM5B-LR_rcp85_prcptot_monthly.nc',\n", + "# ]\n", + "# ens = ensembles.create_ensemble(urls)\n", + "# s8=[]\n", + "# i5=[]\n", + "# for horizon in [[\"2020\", \"2050\"],['2050','2080']]:\n", + "# fut = ens.sel(time=slice(*map(str, horizon))).prcptot\n", + "# ref = ens.sel(time=slice(\"1990\", \"2020\")).prcptot\n", + "# chng_f= ensembles.robustness_fractions(\n", + "# fut, ref, test=\"threshold\", abs_thresh=2\n", + "# )\n", + "# s8.append(chng_f.where(chng_f.changed>0.8).assign_coords(horizon=horizon).changed)\n", + "# i5.append(chng_f.where(chng_f.changed<0.5).assign_coords(horizon=horizon).changed)\n", + "# sup_8=xr.concat(s8, dim='horizon')\n", + "# inf_5=xr.concat(i5, dim='horizon') \n", "\n", - "im = fg.hatchmap({'sup_8': sup_8, 'inf_5': inf_5},\n", - " plot_kw={\n", - " 'sup_8': {\n", - " 'hatches': '*',\n", - " 'col': 'horizon',\n", - " \"x\": \"lon\",\n", - " \"y\": \"lat\"\n", - " },\n", - " 'inf_5': {\n", - " 'hatches': 'x',\n", - " 'col': 'horizon',\n", - " \"x\": \"lon\",\n", - " \"y\": \"lat\"\n", - " },\n", - " },\n", - " features = ['coastline','ocean'],\n", - " frame = True,\n", - " legend_kw = {'title': 'Ensemble change'})\n", + "# im = fg.hatchmap({'sup_8': sup_8, 'inf_5': inf_5},\n", + "# plot_kw={\n", + "# 'sup_8': {\n", + "# 'hatches': '*',\n", + "# 'col': 'horizon',\n", + "# \"x\": \"lon\",\n", + "# \"y\": \"lat\"\n", + "# },\n", + "# 'inf_5': {\n", + "# 'hatches': 'x',\n", + "# 'col': 'horizon',\n", + "# \"x\": \"lon\",\n", + "# \"y\": \"lat\"\n", + "# },\n", + "# },\n", + "# features = ['coastline','ocean'],\n", + "# frame = True,\n", + "# legend_kw = {'title': 'Ensemble change'})\n", "\n", - "im.fig.suptitle(\"Multiple hatchmaps\", y=1.08)\n" + "# im.fig.suptitle(\"Multiple hatchmaps\", y=1.08)\n" ] }, { @@ -258,26 +258,26 @@ "metadata": {}, "outputs": [], "source": [ - "# #Selecting a time and slicing our starting Dataset\n", - "# ds_space = opened[['tx_max_p50']].isel(time=[0, 1, 2]).sel(lat=slice(40,65), lon=slice(-90,-55))\n", + "#Selecting a time and slicing our starting Dataset\n", + "ds_space = opened[['tx_max_p50']].isel(time=[0, 1, 2]).sel(lat=slice(40,65), lon=slice(-90,-55))\n", "\n", - "# im = fg.gridmap(ds_space,\n", - "# projection = ccrs.LambertConformal(),\n", - "# plot_kw = {\"col\": \"time\",\n", - "# \"col_wrap\": 2},\n", - "# features = ['coastline','ocean'],\n", - "# frame = False,\n", - "# use_attrs={\"suptitle\": \"long_name\"},\n", - "# fig_kw = {\"figsize\": (6, 6)}\n", - "# )\n", - "# for i, fax in enumerate(im.axs.flat):\n", - "# fax.set_extent([\n", - "# ds_space.lon.min().item(),\n", - "# ds_space.lon.max().item(),\n", - "# ds_space.lat.min().item(),\n", - "# ds_space.lat.max().item(),\n", - "# ]\n", - "# )\n" + "im = fg.gridmap(ds_space,\n", + " projection = ccrs.LambertConformal(),\n", + " plot_kw = {\"col\": \"time\",\n", + " \"col_wrap\": 2},\n", + " features = ['coastline','ocean'],\n", + " frame = False,\n", + " use_attrs={\"suptitle\": \"long_name\"},\n", + " fig_kw = {\"figsize\": (6, 6)}\n", + " )\n", + "for i, fax in enumerate(im.axs.flat):\n", + " fax.set_extent([\n", + " ds_space.lon.min().item(),\n", + " ds_space.lon.max().item(),\n", + " ds_space.lat.min().item(),\n", + " ds_space.lat.max().item(),\n", + " ]\n", + " )\n" ] }, { @@ -293,34 +293,34 @@ "metadata": {}, "outputs": [], "source": [ - "# import numpy as np\n", - "# op = opened.isel(time=[0, 1])\n", - "# data = xr.DataArray(\n", - "# data=np.array([op.tx_max_p10.values, op.tx_max_p50.values, op.tx_max_p90.values]),\n", - "# dims=['percentile', 'time', 'lat', 'lon'],\n", - "# coords={'percentile': [10, 50, 90], 'time': op.time.values, 'lat': op.lat.values, 'lon': op.lon.values},\n", - "# attrs = {'units': 'degC', 'standard_name': 'air_temperature', 'long_name': 'Near-Surface Daily Maximum Air Temperature'}\n", - "# )\n", + "import numpy as np\n", + "op = opened.isel(time=[0, 1])\n", + "data = xr.DataArray(\n", + " data=np.array([op.tx_max_p10.values, op.tx_max_p50.values, op.tx_max_p90.values]),\n", + " dims=['percentile', 'time', 'lat', 'lon'],\n", + " coords={'percentile': [10, 50, 90], 'time': op.time.values, 'lat': op.lat.values, 'lon': op.lon.values},\n", + " attrs = {'units': 'degC', 'standard_name': 'air_temperature', 'long_name': 'Near-Surface Daily Maximum Air Temperature'}\n", + " )\n", "\n", - "# im = fg.gridmap(data,\n", - "# projection = ccrs.LambertConformal(),\n", - "# plot_kw = {\"col\": \"time\",\n", - "# \"row\": \"percentile\",\n", - "# },\n", - "# features = ['coastline','ocean'],\n", - "# frame = False,\n", - "# use_attrs = {\"suptitle\": \"long_name\"},\n", - "# fig_kw = {\"figsize\": (8, 7)},\n", - "# )\n", + "im = fg.gridmap(data,\n", + " projection = ccrs.LambertConformal(),\n", + " plot_kw = {\"col\": \"time\",\n", + " \"row\": \"percentile\",\n", + " },\n", + " features = ['coastline','ocean'],\n", + " frame = False,\n", + " use_attrs = {\"suptitle\": \"long_name\"},\n", + " fig_kw = {\"figsize\": (8, 7)},\n", + " )\n", "\n", - "# # modify xlabels positions (hardcoded in xarray.plot)\n", - "# for i, fax in enumerate(im.axs.flat):\n", - "# for txt in fax.texts:\n", - "# if len(txt.get_text()) > 0:\n", - "# txt.set_x(-1.2)\n", - "# txt.set_text('percentile ' + txt.get_text())\n", - "# txt.set_rotation('vertical')\n", - "# # txt.set_visible(False)\n", + "# modify xlabels positions (hardcoded in xarray.plot)\n", + "for i, fax in enumerate(im.axs.flat):\n", + " for txt in fax.texts:\n", + " if len(txt.get_text()) > 0:\n", + " txt.set_x(-1.2)\n", + " txt.set_text('percentile ' + txt.get_text())\n", + " txt.set_rotation('vertical')\n", + " # txt.set_visible(False)\n", "\n" ] }