tc_forecast_infra.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b341954a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting TC forecast (the connection seems to be down now, but it worked earlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb8882f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from climada_petals.hazard import TCForecast\n",
    "import logging\n",
    "import datetime\n",
    "import os\n",
    "import numpy as np\n",
    "#how to current current tc forecast, the server seems to be down for now\n",
    "\n",
    "logging.getLogger('climada.hazard.tc_tracks_forecast').setLevel(logging.WARNING)\n",
    "print(\"Processing ECMWF forecast. Date: \" + str(datetime.datetime.now()))\n",
    "\n",
    "forecast = TCForecast()\n",
    "forecast.fetch_ecmwf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "454a4592",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#this way you can fetch data for past dates\n",
    "forecast = TCForecast()\n",
    "\n",
    "target_dir = {}\n",
    "date_list = [\"20230222120000\"]\n",
    "for date in date_list:\n",
    "    target_dir[date] = os.path.join(\"/Users/szelie/Library/CloudStorage/OneDrive-ETHZurich/data/forecast_data\", date)\n",
    "    if not os.path.exists(target_dir[date]):\n",
    "        os.makedirs(target_dir[date])\n",
    "        print(f\"Created directory: {target_dir[date]}\")\n",
    "    else:\n",
    "        print(f\"Directory already exists: {target_dir[date]}\")\n",
    "    forecast.fetch_bufr_ftp(target_dir=target_dir[date],remote_dir=date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b37518b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing: 100%|█| 23/23 [00:\n"
     ]
    }
   ],
   "source": [
    "# open an event in the tc track forecast class, you need to be able to run the cell above first\n",
    "tc_fcast = TCForecast()\n",
    "tc_fcast.fetch_ecmwf(\"/Users/szelie/Library/CloudStorage/OneDrive-ETHZurich/data/forecast_data/20230311120000/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f26ad52",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create a climada tc object\n",
    "from climada.hazard.trop_cyclone import TropCyclone \n",
    "tc = TropCyclone.from_tracks(tc_fcast)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "379f06fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['07S', '70B', '70C', '70E', '70L', '70P', '70S', '70U', '70W',\n",
       "       '71L', '71P', '71S', '71U', '71W', '72P', '72S', '72U', '72W',\n",
       "       '73P', '73S', '74P', '75P', '76P'], dtype='<U3')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we have several events, and we woul focus only on one at a time\n",
    "np.unique(tc.event_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "616e2dcf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tc.centroids.set_region_id()\n",
    "tc_moz = tc.select(reg_id=508) # selecting centroids only over mozambique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd838716",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<GeoAxes: title={'center': 'TC max intensity at each point'}>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OTg7fffdd7LV6+N+ioddqc6gp7DN8+HC6dOnS4Lo1yUJDr4Ea+fn5bN26NXZO3377bezvXNc5ffTRRwD06NGDyZMnH9c5HG7w4MH1TjGTkJBwXO8jx9JQkaqZM2fGHpwsWLDgiGWff/45AP379489tKnLddddV2ubpvjZz35W77Ka/5fFxcWUlJQ0+RhCiPZHplcQoo04/On2sSoftoSGWj4Of5rd0Ho1rSCHJ62HC4VC/Otf/+LVV19l/fr1hMPhevdVWFhY7zKLxcJrr73Gqaeeit/vj90IX3311Vx66aX1btecUlJSSE5Ornd5UlISUP+1aIycnJxYa9GDDz7Igw8+2Kjtalrf6tKnT596l9XEDLXjvvLKK3n44YcJBoOMHDmSSZMmccEFFzBmzBj69+/fKi2oAFlZWZx77rl88sknvPjii9x5551HLK9pyXK5XPz0pz9t8nHKy8t54oknePPNN9m8eXOsdacuDb1Wm8Pq1auB6hadxl7n+l4DixYt4qmnnuKLL75oMGk4+pzC4XCsGuaYMWMaFUN9GnoNQvP83wGw2Wyxhx91SUlJoXv37uzcufOISp/BYJBt27YBxFpI6zNkyBAsFgvhcLjeSqyNcTz/LxtqbRZCdCzSoidEG5GSkhL7Pj8/v9WP73A46l12eFe8xqx3eMtcjcLCQkaMGMFvfvMbVq1a1WCSB9WtKA3p168fv/3tb2M/Z2Rk8OSTTza4TXNyOp0NLq+5Fg0lCMfS1NdBTfe6ujQU9+F/56P/hn379uW///0viYmJRCIRPvjgA371q18xcOBAUlNTmT59eqPL1Z+oa6+9FoCtW7fyzTffxH5fVVXFm2++CcBPfvIT3G53k/a/c+dOBg0axJ/+9Cc2bdp0zL/hsV6rJ6opr4O6XgN/+tOfGD9+PG+//fYxW4aOPqeioqJYif8TbTFv7P+dut5HjkdSUlKDreAAqampQPX51SguLo59f6xu9BaLJfbA5/B9HK+m/r8UQnRs0qInRBuRmZlJp06dOHToEOvXr0fTtBYb62SEW2+9NTbu7rLLLuPqq69m0KBBsTm4FEVB1/XYOdfcVNantLT0iK57+fn5bNiwoc7xOG3V4Td1999/f6NbK1uq6+9ll13GOeecw5tvvsmnn37KkiVLOHToEAUFBbz66qu8+uqrzJw5kxdeeKFFX7sXXXQRqamp5Ofn8+KLLzJq1CgA5s2bR1lZGXBi3TZnzJhBTk4OiqJwzTXXMHXqVPr27UtKSgo2mw2o7lbYo0cP4Niv1RNV8zq48MILefjhh5u0j88//5y//OUvAPTq1Yvbb7+dMWPGkJ2djcvliiVEM2fO5D//+U+D59RarbfxoDHn2tJ/fyGEqI8kekK0IWPHjuXtt9+moqKCRYsWceaZZxodUrMoKytjzpw5APz85z+vc2wVHPkk/VhuvPFG9uzZA1R3ey0vL2f69OmsX7++3mIVbc3hXUNDoVC945pak8fj4brrrouNTdq8eTPvvfceTz/9NPv37+fll19myJAh3HrrrS0Wg8ViYcaMGTz66KO8+eabPPnkkzidzli3zZ49e3LGGWc0ad/ff/99rOjF3XffHUuOjnY8r9UTlZyczP79+ykuLm7ya+C5554DIDExkW+++YZOnTrVuV5955WUlISqqmiaxv79+5sUQ2srKioiGo022KpX01p6ePfIxMTE2PcNdYMGiEQisZa8w/chhBCtof00BwjRARzeCtGa3RBb2rZt22JdNRsq+tLYIh9vvPEGr7/+OgC/+tWv+M9//gNUt7K0ZILR2nr06BEbHzl//nyDo6lbv379uPPOO1m2bFmsJbEmqW+sprQQXXPNNUD1mKV58+aRk5PDwoULgRNrzdu0aVPs+xN5rTZnq1dN4aEVK1ZQWlrapH3UnNeZZ55Zb5Kn6zpr1qypc5nFYoklmc1RDbM1BIPBBsfNFRQUxAq+HJ5A22y2WEGY5cuXN3iMtWvXxt7bGhoP2JI6UgurEOJIkugJ0YZccMEFsQqX7733Hm+88Uajt62vlSweHD4er6HxTLNnzz7mvvbt28cNN9wAVBeGeeyxx7j44otjZc5ffPFF3nnnnROMuOXVTEURDAbrXcdkMnH++ecD1Tf5ixYtapXYmiI7OztWqOd4pxuouRbQ8PU4XN++fWPddF988UVeeumlWNffhiotHktjXqu6rvPss882uJ+mnFN9aipchsNhnnjiiSbto+a8Gvr/99FHH9U7ZQVUd5mF6jGMNdVg413NQ6D6ltV0u5wwYcIRy8455xwAvvvuO5YtW1bvPmpaSg/fprU15r1ECNE+SaInRBuiKAqvvvpqrODJjBkzmD17doNjQA4ePMi1117LjBkzWivM49arV6/YU+f6EtLnnnuOefPmNbgfXdeZOXMmJSUlsTnFaooYPPnkk7ExU9dff/0xu1wZraagRc20EPW56667Yl3Ppk+fzpYtW+pdV9M0Xn/9dfbt29d8gf7g3XffbbCAx969e2PTGnTv3v249n14cY9jXY/D1bTqffnll/zzn/8Eqm+2s7Kyjuv4hzt8rrn6koR77rnnmC16TT2nusycOTM2L90DDzzAe++91+D633zzDYsXLz7idzXntWTJEnbv3l1rm927d8ceoNTn5ptvjrXaXn/99WzevLnedVviNdgU//znP+tM1LZu3coDDzwAQHp6emyuuho33HBDbJzp9ddfX2dL6meffcbzzz8PwIgRIzj11FObOfrGaex7iRCi/ZExekK0MYMGDWLOnDlMnToVv9/PL3/5S2bNmsVPf/pTTjvtNFJSUqioqCAnJ4f//e9/vPvuu4ZMx3A8UlJSOO+88/jkk0/48MMPueCCC7j++uvJzs5m//79vPbaa7zxxhuMHDmywfm/Hn/8cb744gsA7r33XoYNGxZb5na7eeWVVzjjjDMoKCjgF7/4BR9//HGLn1tTjRo1il27dsUmfR89enTsybzH44lVAxw0aBCPPvoot912G3v37mXo0KFcc801nHvuuWRmZhIMBsnJyWHp0qXMnTuX3NxcNm7ceELJTl2eeOIJpk2bxgUXXMBZZ51Fv3798Hq9FBcXs2rVKp5++ulYa9GxEoajDRkyBLvdTiAQ4J577sFisdC1a9fYjXbnzp3rrPZ6xRVXcOutt1JeXh4ba1XTsttUQ4cOpX///nz33XfMmjWL0tJSrrzyStLT09m5cyfPP/88n376KaNGjTqi4ufRagrEANx2223cfffdZGRkxB54dOvWDbO5cR/RNpuNOXPmMH78eILBIJdccgmXXHIJP/nJT+jZsyeKopCXl8fq1at55513WL9+PU8//fQR4xSnT5/Ohx9+SEVFBWeccQZ33XUXQ4YMIRKJ8OWXX/KPf/wDv9/P0KFD6+2+mZ6ezr/+9S9mzJhBXl4ep512Gtdddx3nnXce6enpVFRU8O233/L++++zZcsWwxOPTp064XA4mDBhAv/3f//Heeedh8lkYsmSJTz88MOx8YhPPvlkbJL2GgMHDuT//u//+Pvf/87GjRsZOnQov//97xk6dGhsSpenn36aaDSKzWZrVG+EljJq1CgWLlzIypUreeSRR5g0aVIsIXc4HHTu3Nmw2IQQLUwXQsQFQAf0cePGNWr9tWvX6iNHjoxt19BXWlqaPnv27OOOaeHChbF9LFy4sN717r333th6DRk3bly957h79249Kyur3nPo37+/vm/fvtjP99577xHbr1+/XrfZbDqgjxo1So9EInXGcPfdd8f2MWvWrGNdglqOdU1mzpypA3rXrl0b3M+xrtnatWtj53P018yZM2utP3v2bN3pdB7ztWC1WvVt27Yd1znV2LVrV2y9F1988YhlNX/bhr5MJpP+0EMPHdd+a/zud7+rd78NxXzdddfF1ktKStIDgUC96zbWqlWrdJ/PV288Y8aM0Tds2HDMc7riiivq3ceuXbuOO66lS5fq2dnZjXpPePnll2ttP2PGjHrXt9vt+n//+99Gvb5feukl3eFwNHj8urbv2rVrva/vwzX0PtIYh5/DsmXL9KSkpDpjVBRFf/jhh+vdTzQa1W+88cYGz9Pn8+mff/55ndsf63Xf2PfVF198scHXzb59++o9x6ZeQyFE2yBdN4Voo0455RS++eYb5s+fz80338zJJ59MamoqZrMZr9dLv379mD59OnPmzGHPnj2xKojxqmvXrqxZs4bbbruNnj17YrVaSUxM5NRTT+WRRx5h5cqV9T55DgaD/PznPycYDMZa7uqrpHfvvffGulDdfvvtse6E8eaUU05h6dKlTJ06lS5dutRqUTjaddddx65du7j//vsZPXo0KSkpmM1mXC4XvXv35rLLLuPf//43ubm59OrVq9njnTNnDq+99hpXXXUVp5xyCunp6ZjNZtxuNwMHDuSmm25i7dq13HXXXU3a/yOPPMKzzz7L2LFjGzX/WY3p06fHvv/Zz34Wm/7gRJx66qmsXbuWa6+9luzsbCwWCykpKYwePZpZs2axcOHCRlV2ffXVV/nb3/7G6aefjtfrPeGiGSNGjGDbtm38+9//5oILLiAzMxOr1Yrdbic7O5uJEyfy4IMP8v3339fZlfvll1+OTUfhdrux2+306NGDa6+9lpUrVzJ16tRGxTFz5kx27NjB3XffzamnnorP58NqtdKlSxfGjBnDgw8+GCuMY7Thw4ezZs0abrzxRnr06IHNZiMlJYUpU6awaNEi7rzzznq3VVWVWbNmsXjxYqZNm0aXLl2w2Wx4PB5OOeUU7r77brZt21ZrfF9r69y5MytWrOCaa66hZ8+eR4wPFUK0b4quywQvQggh2qd//etf3HjjjQCsWbMmVqFSdFxXXXUVL7/8Ml27dq1zPKIQQrQX0qInhBCi3XrppZeA6hZSSfKEEEJ0JJLoCSGEaJcWL17MihUrgOMvACOEEEK0dVJ1UwghRLuRk5NDMBhk06ZN/Pa3vwUgMzMzrqcXEUIIIVqCJHpCCCHajXHjxpGTkxP7WVEU/t//+39SgEIIIUSHI103hRBCtDsej4dRo0bx0UcfcckllxgdjhBCCNHqpOqmEEIIIYQQQrQz0nWzjQoEAoRCIaPDEEIIIYQQrahmfsxjaQv3io09F9E0kui1QYFAgO7du5OXl2d0KEIIIYQQohWlp6eza9euBhOkQCBA965u8vKjrRjZ8WvMuYimk0SvDQqFQuTl5bF37148Ho/R4cQlXdcpLS3F6/WiKIrR4bR78Xy9v/vuOxYsWMCuXbu49tpr6d+/v9EhnZC6rvXs2bP5/vvvefzxxw2Orv2J59d2eyPXunXJ9W5eO3bs4JlnniEajXLVVVcxaNCg2DJd11mwYAEff/wxF198MWeccUaTj1NWVkZ2djahUKjB5CgUCpGXHyVndTc8CfFZkqOsXKPrqbuPeS6i6STRa8M8Ho8kevXQdR1d1/F4PPIB1gri+XqPGDECi8XC888/T2lpaZv/P1PXte7SpQs7d+6koKCAHj16GBxh+xLPr+32Rq5165LrXb9z1J8c1/qdznDj7WdHQeHQ537ue/6JI5Y70i30n9EF/6YIFzx7Qateb0+CiifB1GrHE/ElPlN8IYRoRkOGDEFVVVauXGl0KC3inHPOAeC///2vwZEIIUTH4OpqpfMUL72uS8HX30G4TGPXq0X4c8K1V/4hr1OtClu3biUQCLRusKLDkhY9IUS7F4lEANrth2tiYiIDBgxg06ZN/PrXv2b8+PFceumlRoclhBDtjm+wg+TTXahmBV3XCZdGKd0UoGRjVb3bBA5GKN1chbunjSeffBK73c5ll13G6NGjWzxeDR0NrcWP0xQaUvi/pUmLnhCi3Xv66afRNI1JkyYZHUqL+eUvf8npp59ONBplyZIlRocjhBDtiwqdp3hJGelCj+oUrvaz4/kCct4objDJq1G6KcCuV4q455576N+/P3PnzqWysrIVAhcdmbToCSHaraqqKh5//HFyc3Pp06dPqzw9NYrZbOaqq65i9+7dFBcXGx2OEEK0iuMdT9cUqhW6Tk3C5FCp3Bdm/yelNKmRTIeMjAzOPfdc1qxZQ35+Pt26dWvucIWIkRY9IUS7tGHDBu68805yc3MZOnQot956q9EhtYrevXsTDod56aWXjA5FCCHaPNUG3X6WjMmhUrjcz/6PmpjkHcblcgHg9/ubIUIh6ictekKIdqWgoIBZs2Zx8OBBzGYz11xzDaeeeqrRYbWaqVOnsmPHDlasWMGAAQMYNmyY0SEJIUSbpNqh25XJqFaFgm/8jeqi2Rher5eEhASWLFlCWloaiYmJmEwtUxkzqmtE43QoXFSPz7GD7YkkekKIdqOwsJA///nPRCIRBg4cyIwZM3C73UaH1apUVeX3v/89v/3tb3n33Xcl0RNCiCZQbT8meflfVVC2qfmKeZlMJi6//HJee+01/vSnP6GqKmlpaZx33nnyni2alSR6Qoh2obKykgcffJBIJMKvfvUrBg8ebHRIhrFarXTu3Jn9+/cbHYoQQhyhNcbUNYfsS5Oqk7zF5ZRtDjb7/ocNG0Z6ejqLFi2ioqKCPXv28OKLL5KVlUVGRkazH090TJLoCSHavEgkwl/+8hcCgQBTp07t0EkeVF+P/Px8zGZ5ixdCiOOhWiHjPC9Wr4mSbytbJMnLycnh9ddfZ+/evbWWFRQUSKInmo3cBQgh2rynnnqK0tJSzjvvPM444wyjwzHc4sWLCQaDDB061OhQhBCibTBD1kU+7KlmFEXBvzfEoa+av1jKV199xZw5c8jMzOSaa66JFdAqKysjMTERr9fbrMernkcvPgfpxWtc7YkkekKINq2yspLt27eTnZ3N5MmTjQ4nLowcOZJ33nmHDRs2cODAAXk6LIQQDbB1MpN1kRfFohA4GKZwhZ+q/ZFmPYZihtRxbl5//XXGjh3L5ZdfjsViiS1PSkpq1uMJAZLoCSHauPfffx9AkrzDOBwOfvGLX/Dss8/y/PPP88c//tHokIQQbVBbGU93InwnO0gZ4QIdDn5ZTvmW5u+qaUlQSRufQGXUz8yZMxk+fHizH0OIukiiJ4Ros4qKivjqq69wuVwMGDDA6HDiyu7duwGIRqPGBiKEEHEq80Ivzs4WogGdve8WEylt/nL/JodC1qU+zC6V/A/LGf5s6yZ5GtqJTvvXYuI3svZDEj0hRJuzc+dO5s6dS05ODrquc9VVVxkdUtz55ptvsFgs3HXXXUaHIoQQccWcoJJ9iQ+TQ6XqQJjcD098EvT6qDYVk01FtaikT/CwcOFCxo0bh6qqLXNAIQ4jiZ4QIm5VVFSwYMECDh48SLdu3RgwYAAHDhzgxRdfRNd1UlJS+MUvfkG3bt2MDjXuOJ1OwuEwVqvV6FCEECJuuHvZSD8rARQoXOmneE3zTIJen3Dpj70qwhVR3nrrLRYtWsSkSZM47bTTWmyidCFAEj0hRJw5dOgQr7zyCnv37iUY/HGsxLp163j33XeB6slm77jjDrp06WJQlPEvFArJDYQQHUBTx9EpqkJKt0QKdheja+2/+qHZrZJ2VgKODAt6RCf3w1ICB5u34EqddDj0VTmhFJU9i4t5dsff+fjjj3n55Zf5+OOPmTRpEqeffrq08IkWIYmeECJuFBQU8Oc//5loNIrP5+Okk05i/Pjx9O7dm61bt7J161ZUVWXixInY7Xajw41rZWVlkggLITo8Vw8rKcNcWHzVD74C+RFyPyhBb4Ucr0bpd0Es3cIAdO3alRtuuIE9e/bwySef8J///If9+/dz6aWXtsixo7pOVI/PRD5e42pPJNETQsSNJ598kmg0yi233EK/fv2OWNa/f3/69+9vUGRtS25uLrqu06NHD6NDEUKI1meClOEuPH3tmKwquqZTuS9MwTflhIrjowBIly5d+OUvf8m8efP4/PPPOfnkk+nZs6fRYYl2RhI9IYRhIpEIs2bNorCwEE3TKCoqYvTo0bWSPHF81q9fD8DJJ59scCRCCNGKTJA50YMz24qiKkSDGsXr/BSsqGyxYitNVVVVxZIlS/j666/p2bMnWVlZRock2iFJ9IQQhnn77bfZsmVLbCzZqaeeypVXXmlwVG1fVVV1cYHk5GSDIxFCHK4jzEtnFFdXC+lne1DMCqGSKIXL/fh3h4wOqxa/38/nn3/O4sWLCYfDDB8+nEsvvRSbzWZ0aKIdkkRPCGGYDRs2AHD++efTuXNnBg8ebHBEQggh2hQVMs7x4OpmBa3lJj1vDma3yt/+9jfKysoYO3YsZ511Fj6fr0WPqaGjEZ9j4eI1rvZEEj0hhGE6d+5MUVERH3zwAQBnn312iw1I70gCgQAAFovF4EiEEKLlODLNZJzrRbUqBA9FyP2wBC3+GvEAUC0KmRd4ALj77rtJSUkxOCLREUiiJ4QwzA033MD27dvRNI3Zs2ezePFiSfSawbp167BarXg8HqNDEUKIZqfaIPUMD+4e1urpC76uoPTbgNFhNSjxVCchWyU333yzJHmi1UiiJ4QwVK9evYDqqpqrVq2irKxMEpQTsG7dOvx+P2PHjjU6FCHaPBlTFx9UK7h72nB1sWFLMWN2V885FyqJsu+DUrTKOKu0chSrz4Qry8r+9/Lp1KmT0eGIDkQSPSFEXOjTpw+rVq1i+/btDB061Ohw2qRIJMJrr72GoihcdtllRocjhBBN4uhswd3DhiPNgsWropgVFEVB13W0sE7gYJiCZX4Cea04Gd4JSOhtIxrQKN/R+v1KNXSicToWTsbotTxJ9IQQcSEjIwOAvLw8gyNpu5588kn8fj8XXXQRVqvV6HCEEKJOqlPF09OG2a1gcpgwOVRMNgVzgorJrqIoCgB6VCdcoRE8FKJid4iKXUGIGhx8E0TKo6gWBdWkGB2K6GAk0RNCxIXMzEwACgoKDI6kbVq8eDE7duygX79+TJo0yehwhBCiFrNHJfM8L9ZE04/JnP5Dq44G0ZBO1YEwlftClG8LEimP7y6ZjeXfG0YxKTgyLYTDYUwmE6qqGh2W6AAk0RNCxAW73Y6iKOTn5xsdSpu0cuVKFEXhpptuMjoUIQwh4+nim7uXjfSzEkCByn1hSr+tJFgYJVLRPpK5xnB2NvO73/0Ol8vFWWedxVlnnWV0SKKdk0RPCBE3EhMT2bt3r9FhtEllZWVYLBZ5SiyEiDudznDj7WdHj+jse7+U4KG2MbauuTgyqm+33SfZ6dSpEx6Ph7lz5zJmzJgW72Yv8+h1bHJHIISIG0OHDiUcDrNixQqjQ2lzMjMzCYVCVFRUGB2KEEJUM0OXnyTi6+8gXBpl1yuFHS7JAwiXVyc0JpvK9OnTOe200wBi3VeFaCmS6Akh4saFF16IyWTirbfeQtM6Tnee5lAzncL8+fMNjkQIIcCabKLHjGSsSSbKtgbIeaM4biczb2mBvDB5X5SR898isrOz+fbbb0lNTcVslo51omXJK0wIETesVisTJkzgs88+47XXXmP69OlGh9Rm9O/fH7PZzOrVq7nkkkuMDkeIYzp6TJ2iKqR0S6RgdzG6Jl262jJPfxupYxIAyP+ynLItQYMjMl6oKEq4XCM/P59169Zx+eWXS4ueaHGS6Akh4srFF1/MunXrWLp0KdnZ2YwfP97okNqMzMxM9u3bZ3QYQogOLP3sBNw9bWghnX3vFhMqlt4ZNRQVXnrpJZKSkhgxYkSrHDOq60T1+HxwEq9xtSfSdVMIEXduv/12HA4Hc+bM4euvvzY6nDYjJSUFTdMoLi42OhQhRAejWqHrlUkk9LITLIiw8z+FkuQdToW0sxPIzc3lF7/4BXa73eiIRAcgiZ4QIu643W7uv/9+rFYrb775JpFIxxu83xTnnHMOAE888QShUAcdDCOEaHX2dDPdp6dg8aiUbKxk77ySNjmxeUtKPs2JK9vK9ddfT7du3YwOR3QQ0nVTCBGX3G43P/3pT3nllVd45ZVXuPrqq40OKe517dqV0aNH8/XXX/OHP/yBu+66i+TkZKPDEu2AzFHXcag2SB2TgLOLlWhAp2JHgMIVlfWun3iKg+ThLtDgwGdl+HfJQ6ajubKtuLrY2Pr2IX7z7H0AzNfeMjYo0SFIoieEiFsjR47k008/ZeXKlVx00UWkpKQYHVLcmzZtGunp6cybN4/77ruPO+64gy5duhgdlhDCINYUM57eNuxpFpTD+3Ep4E1MwF6sgQ6KScHsUlGt1QVCogEds0slaagL3yAHhSsqKdlY9eN+k02kn+XBmmQiGtDYO6+kQ01+fjwsiSpaRKdiZ+snwdoPX/EoXuNqTyTRE0LEtalTp/L000/zxRdfcMUVVxgdTpswYcIEMjIymDVrFi+88AL33Xef0SEJIQyQMcmDq4sVRVHQNR39qDtrS4IJq/7jrWC0SiNwMEzRmkoCedVd5n2DHSQPc9JptJuUES6iQQ3VrKBYqhNCf06IA5+VyV17A4IFUVSzgi3JTKAgbHQ4ogORRE8IEdc2b94MwJAhQwyOpG3p378//fr147vvvuM3v/kNF110ERMmTDA6LCFEK1DtkH1xIlafmaqDYfK/LKtVGKV6OgvtmNNZlGyoomRDFYmnOPD0saNaFaJBncodQYpWV0orXiMEC6uTZluKSRI90aok0RNCxLVly5ZhsVg46aSTjA6lzbn++ut56623WLVqFfPmzWPgwIGkpaUZHZZoITKOTjiyLKSekYAlobqPZunmKvIXVTTLvovXVVG8rurYK4ojqFaFtLPcRMMalbmS5InWJVU3hRBx6+uvv6aiooLRo0cbHUqbZLVamTZtGjfffDMAy5cvNzgiIURLSTzFQecLvFjcKoH8CLkfljZbkieaxp5upstPErGnWihcXknE3/qtn1H0uP4SLUta9IQQcSkQCPDmm29isVi49NJLjQ6nTevevTsAO3fuNDgSIdoes1uN++6JKSOc+E52Eq3SyHmzCC1odEQdnAJJQ50kneokcDDCwS/K8XXyGh2V6IAk0RNCxKV//vOfRCIRrrnmGsxmeas6Eaqq0qlTJ7Zt28bBgwel+6YQdbD4VNw97NhTzVh9JlSbgsmuoigKobIoe+cWoRk8c4DZo5I6xo010Uy0SqPqQAh7uhV7qplIhcbuN4pk/ro4kDHRg6urlaLVlRStqURRFOhkdFSiI5K7JyFE3Fm9ejXbt2+nV69enHrqqUaH0y5Mnz6dxx9/nAcffJCf//znnH766UaH1KHJeDrj2dPNONItmBwKVp8ZZ011Sl1Hj+hoIZ1olYaiKlg9JnpclULBcj/BQ2GSh7mwpVjQozoRv0awKMKhJeUtlgimT0jAmW1FtVVXutRCOmaXGXuqBV3XqToQJvfDUql8GQccnS24u9s48Fnpj9MpKMbFE9Wrv+JRvMbVnkiiJ4SIK6FQiP/85z+YzWZuvPFGo8NpN3r16sWECRNYsGABc+fOlURPdFie/jY6jXSjWn4sU6DrOpEKjbwFZbFpBQ7n6mol7awEOo10x9YPl2soKli8JqxJJhJ62Di4qJzyrc3Yb1KFblcmYXarRAM6/l0hDi2vIFJandFZvCrRMGiVkuHFC98gB8GCiCFz5glxNEn0hBBx5d///jfhcJgZM2Zgt9uNDqddWbJkCQA/+Ym0JomOx5FpJn2CB7PLhBbWKFrrx58TIlKhEanUGmwN8+eE2PlyIb6BDlSbQsnGSrTAj8tdXS2kn+0h7cwEHBmWZiuCkjXZh9mtUrKhkoKllbWWh0slwYsHqqV6XkFbsglXVyv5i6UIjogPkugJIeLG+vXr+f777+nevTsjRowwOpx2Z+DAgaxZswaTyUQoFCI/P5/MzExUVQowi/at0xluvP3soEPJpioOLWnCjbhWPadcXfw5YXa8VEiXyxLx9LWjR3UOfeVvcryKGTpf6MORbqEiJ1hnkifig7e/ndQzEmI/Vx0IU74l0MAWQrQeSfSEEHFj7ty5qKrKTTfdZHQo7dKMGTNYv349L774Ipqmoes6JpOJrl27cs4553DyyScbHWLcOtfy0wYnlRbxzdvPTsSvsWdOCxZUicKeOcV0nZqId4ADPao3KUFL6G0j9YwEFBP494Y48ElZCwQrmkuwoLqrbzSoESqJkvd5GXocNbRqxO/QzXiNqz2Rx7hCiLhQUFCA3+9HVVWcTqfR4bRLVquV888/n2g0isViYfz48SQlJbFz506eeeYZ7rzzTrZu3Wp0mEI0K1dXC4qiULo50CpVM3PmFBMp1/ANdpIy2tXo7RQzZF3sJe3MBNDhwGdl7P+otAUjFc0hkB+hfFsAk03FkWYh+7JErMkmo8MSApAWPSFEHCgtLeXee+9F13VOOeUUo8Np1yZNmsSIESNISEiITVsRCASYN28eS5cu5cknn2TmzJlSrEW0G76Tnei6Tsn6Vur+qMHuN4rodmUSiYOcePvaKd1URcm3gXrn43P3spE2vroVryo3TO4npTJNQhtSsMKPxWui5NsqfAMddL7Qx955xXE//6Jo/yTRE0IY7rvvvkPXdS677DImTJhgdDjtXmJi4hE/2+12pk2bxtlnn81DDz3EG2+8IYmeaB9UcKRZCJdG0WsX02w5Gux+rYjEUxwkDnWSeIqrOuGMQKgkQuXeECXfBohWaqSMdOIbXL3swGdl+HdJtca2JlKusfftEgAq94TIvjSRzPO8hEqj6BG92YrzCHG8JNETQhhm586dPPfccwwePNjoUASQlpbGqFGjWLRoEf/73/8499xzjQ6pSRozR51q54iqifVRVIWUbonHXlHEpfQJCaBC0VpjipkUr6uieF0VjkwzCb0dODIs2JLN2DtZSBziJFwaxeI1yWTn7Ug0oJP3RRmZ53lRLGD1min4uumFeU6UhkLUyIn8GqDFaVztiSR6QgjDvPDCC5SUlLB48WIAvF6vwRGJSy65hJUrV/Lee+8RiUS44IILjA6pWZndKl0uT8RkV9E1nWhAo3JfmEPflDcq8RNti9llAh3KtzTj3HZNULU/QtX+8tjPjkwzqWckYPWZ0cI6e+ZKkteeBPIi7HypEFuKmS6XJ2JJlDF7whiS6AkhDBMIBPB4PDidTkwmE6eddprRIXV4VquVhx56iN///vd8+eWX7SvRUyH7skRUm0L59gBmt4rVZybhJBsJJ9nQo4ACeljn0NIKw5MDcWJUO9hTzUT88TdOqmp/hJw3inFkWQiXRNHkpdYumezVLVZaUIfG1+URotlIoieEMIzZbEbTNP70pz8ZHYo4jNVqpX///qxdu5aFCxdy5plnGh1Ss8ia4sNkVyhY5qdk/Y/zodnTzSSd5qq+KdPB6jOTNj4BLaBRubc1B3aJ5pQx0QsK5C2I3+kJqvaFjQ5BtCBrcnWLbbgsCklGRyM6Ikn0hBCGsVqtVFTIIPV4NG3aNLZs2cJbb71FYmJii1ZDbcyYuqZSreDsYiPpFCe2FDMVu4JHJHlQ3c1q/4c/lrFXnSo9pieRMiqBPW8Wt1hsogWp4MiwEMiPEMiTZF0Yw5ZsJlho7OtP06u/4lG8xtWeyDx6QgjDWK1WIhG5CYtHTqeTP//5z6iqyty5c40O57ilnZlAz2tT6HF1Chlne7AmmyjfHuDA/47dumNxqSiKQtV+qX7YVrm6WVEUhfJtMvBSGCceEj3RsUmiJ4QwjN1uR9Pib/yMqOZ0OjnllFMoKioiNzfX6HAaLXm4E08fO1pQo3xrkLyFZex8uYC8z8uPvTEQrdTQdR1nlhVPPyspI1yknuFCtbdw4KLZhAoj6LqOI8NidCiigzI5FKyJJoL50j1XGEcSPSGEYYLBIIoi5ZXj2dixYwHYtGmTwZE0nhas7g9UviPIwYXllG8JHldFzYi/OkE0u1VSx3pwdLaQ0MdOj5kppJ+dIJ+cbUC4VCNaqeHKthodiuig3D1toEPFbukZIIwjY/SEEIaIRCIcOHCATp06GR2KaIDLVV0q7rm7X+Pv5z1rcDTHZk02kXy6Cz2q49/T9BusgwvLKV7nx55moSQSpaKinNRxbtw9bfTsaqN0UyUl3waIVEiLdDyyJpsw2VWiQfn7iNan2hQST3FSsTuIFtRRVOMeaEbjeB69eI2rPZFETwhhiHfffRdN05g4caLRoYh2JPO86rkY98wtIlR8Yjf5oWKNcGkIWzcXgfwIOW8Wk9DHRqfRbhJPceE72UmkQmPfeyWS8MWRpGFOkoY6QYe8z+O34qZov9LGJ6CYFAqWGjdRuhAgiZ4QwiDLli1DURSys7PZsmULwWCQvn37YrVKV6sTcfDgQZYuXcq2bdvQNA1FUVAUBVVVY/8e/X1DXzVVURN62DBZQNdA1wG9+ns0vfpnDXRdR9f0H5f98G/N79D0H34G9B+/1zUgqoGqoFoUFLV6nrHjZU83Y3arVOwInnCSV5/yLUHKtwSxZ5jxDXDg7mmj69QkyrYGOPRNBcRp3QV3TxuJQxxEAzrB/DDFGyob7M6a0MdGygg35dsCFHwT/zerihlSRrnxnGRHtShEqqLkflhKqFBmIRetz5ltpWRDpTwAEoaTRE8IYYj09HR27tzJQw89FPudyWTiD3/4AxkZGQZG1nZ9/PHHfPjhhwCxRA6qE7DD/z36+8awJZuxJbfeR0YgP0zJpirMThVvfweKCfZ/UlZdwe6weydPHxvuXnbsncyotupuQAUrWz4xCRyIkHegHNe2IGlnJeDr78Dd3cqul4ta/NjHK6GPjbTxCbGfXVlWEk9xUvJtFcHCCGa3CYtbxexWMdlVzE4Vk7P6tePt74jrRM+coJJ2ZgKODAuKUp3gFa2ponht1bE3FqKFVOwM4hvkpHJfmKr9UoxFGEcSPSGEIW6//XY2b97Mtm3bcDgcaJrG+++/z9NPP31E8icab/78+djtdm6++WZ69OhxYvPTqaCooJgABVSTCgpgAkUBxaRUjztRflhPBVSlepmqVBcs+eF7Ra3ehqPXUY7cDkAL6/gGOLB1MpN+pgf4oUVQgS6XJVb/rOsECyNYPSZUq4qu60SrNPy7wxSuqCBS2npP0f05IXa+WEjahAQSetkwe1QiZfHzFN+e/sPk72Gd3a8WokXAmWUh/SwPiYOdR6yr17SyRnT8u0MECiKkDHORfLqTwhWVBp1B/dy9bKSflQAKhIqiHFpaIROQi7iQv7gck0Mlc5KXnDlFRP3GTRgnY/Q6Nkn0hBCG6devH/369Yv9vHPnTjZu3EggEMBul1r2x0vXdTweDz169DjxndV0q4zU/Nh6yUvJ+iosXhVrkhl08O8OkXiKg5QRbgBCxVFsSWb0iE7BigqK11XRiuHVqWJXEM9JdhzpFsrLgsYG8wOzW6XzhT7QYO+8YrQfatNU7gmz86XC6q6uLhOh0iihkkid3U59A+wkDnFSuT8cV0lUpzPcePvZ0SM6+z4oJZgfp31mRYekR6BoTSXZU3zYU8z4/fHzf0d0LJLoCSHiRlZWFhs3bqSgoICsrCyjw2lz3G435eWNmysu3oVLqwuh1CheV0WkUsPsUuOyW17wYPW8ba4uVsq3xkeil32Jr7rL68elhOto5QzkRTjWoMI9c0voNi2JzpO85H5S2vrJnglcXa1YE80oKljcJpxdrJgdKqGSCHvf/jGBFSJemBNUMs5OIJAfxp8TAmm5EgaRRE8IETe83uqKiUVFRZLoNUFycjLFxcVGh9Fi4iWBqkvEr4EGtpT4+FhNHefG7DJRtMZP5d6mJ2fRSo3cD0rofJGPzhd4KVzhb5VEW7VC2lkeXF2tR8y1qes6ehSK1vjjsjupEACdRrpBVdj/cSm69kMXdSEMEB+fSEIIASQlJQG062SlpZyj/oS0sxJIOMl2YmPzRJOoThXFpFB1wPguWq6uFjx97YRKIs2SDAXyIuS8XkT2ZT5ShrvxDXZQtLqS0u8Czd5l1jvQjnt0YqwYTKg4Ssm3lQTyoxDViPg1acETcS9cEcVpsaKYFapLDhtH0xU0PT5bFOM1rvZEEj0hRNxITKwutlFaWmpwJG2TapUPTaNolRq6rje6Mqk1USVjkg+zo/6/WbhMY8+84kYnU55+NhIHO7H4TOhR2Pd+8/0/ivg1dr1aRKfRbjx97KSOSaDTaDdaUCdYHKFyX5jyrQEi5UcGa05QyTjHg8VnompfmAOf1T+vXdJpDrx9HZQUBAnkRyhcXtGkaTaEMFrhCj/eAQ5cXa2UbmpgHhMhWpgkekKIuJGcnAxAWZlMctwUqk0x+uFxhxbIj2BPNdP1Z0mYHQqKWUEL6RStriRcHiWhpw1HpgWTXUVRFXRdJ1warfNvppgVbMlmkoY6KVrVcKucNVEl+5LE6gqkmk5VbpgDX5SjVTZzc5sGh5ZUcGhJBZ4BdtxdrdiSzTjSLTgzrKQMc8W6VupRHdX8Q/VVIFql4e5hI/syH3vfLYE6prfzDXISDWrsfKmoutKqEG2UHoGoX8PsMhkdiujgJNETQsSNmkqbNZN0i+Nj9ZjQQnKDbJT9n5SQfXEiZqdKxK8RrtBwpFnoNKq6Wqiu62ghncDBMMHiKKUbKxuc2L3X9Skknuwg4o9StvnH8YnWRBVnlg13TxsWT/XcdwAFyytarVBN2aYAZYe1VDgyzbi727D4zJidKopFIVwaJVwWpXCVn3CJRvrZCST0stPr2hSilRqVuWGChyIoVoWEXjZUi0LZFmn9EO1DpKq6eJQQRpJETwgRV1RVxe+P3wma48GNp/2egt3FtVo9TA6VYIF0dTOKFoCcN44aX6pWT+qOqlCxK3RcrWwFy/wkD3eRNs5Dp9E6aDqKRYkVJ6mZPzBUFOHQN8Z2c6zaHznm8fM+L6d8WxDfIAe2VDMJJ9nw9K5+uKPrOuU7g0S319HUJ0QbFDwUwd3dismhoNVRR+rwsdTztbdaLA6ZR69jk0RPCBFXTCYTlZVSTe94JfS2oagK/j1SqSKuaBzRGnc8SjZUUbKhiuThTtzdbKBAuCxKsCBC1YEwlblhw+cPPF7+nNAP5ebB7FKxeFW0cPVNsaIqpHRLNDhCIZpHycYq3N2tZE32sfedEqPDER2UJHpCiLhisVgIBuO3jH68ShrqRNd0itZIktzeFC6vpHB5+/u7RvzVVTSFaI/CpVH2f1xGl58k4ki3Gh2O6KCk87AQIq6YzWbCYeNL1Lcl5gQVi9dEZW6ozbXwCCFEexUuq+6KrEqeJwwiLXpCiLgSCoVwu91GhxE3jp4Tr67ubTXFPg59LUVshBAiXmjR6nHUihkwqFd9FJVonLbryIjclheff3khRIekaRrBYFASvePk6mIlUqERLpHmPCGEiBsaaGEds0Nut4UxpEVPCBE3Vq1aha7rDB482OhQ2ozEoQ4Uk0LRWqlUKoQQ8aYyN4TvFCdKvo0Kv5mqgzI0QbQeecQghIgbX3zxBYqicNZZZxkdSpug2iD5VBfRkEbZd1LARggh4k3BMj8o4BvgIOsSH2Z3695667qCFqdfui7TK7Q0adETQsSN0tJSnE4nVmvHHLl+9Hi8Y+l8oQ9UOPBJWcsEJIQQ4oSES6KUrK8icYKPsu+riFRIF3vReqRFTwgRN9LS0vD7/ezZs8foUOKed4ANW4oZ/+4QVfukK5AQQsQrb387/j1B8hdLF3vRuiTRE0LEjRkzZgDw7rvvGhtInFNtkDLCjRbWOTBfWvOEECJemRNUzC4TlXvlgZxofdJ1UwgRN5KSkjCZTJSVSfLSkNSxCVQpfg58VCrz5gkhRDzTf/jXoOFoURSiRh38GOI1rvZEEj0hRNzYunUr0WiU3r17Gx1Kizve8Xg1fIPtWH1mDq0LUrU/0sxRCSGEaE6RCo1wRRRbp4459lwYS7puCiHixtKlSwE499xzDY4kPpmcKsmnV1fZPPi5TI4uhBBtgX9nEFdXK4pZWrBE65JETwgRN/bu3YvJZMLr9RodSlzqfJEXFDj0TR1Jngpml7ylCyFEvCnZEEA1KSSdYjc6FNHBSNdNIUTcKCoqIiEhwegw4lLiEAe2RDNl2wJoBZbY71NGOPH0c6Baq58Uh4qi7P9fKZEyGbwnhBDxIFKpUbo5gO8UJxV7wgTyWq8wS1RXierx+RAwqh97HXFiJNETQsSFAwcOEAgE6NOnj9GhNLumjserYXarJA9zEQ1o5C+sIKVbIgDp53hI6GkjGtCo3BMCFZxZVrpekcSO5wpi2yb0thHxa5RvC0rxFiGEMEDZlgA2q0b6hAT2zClGC0uWI1qeJHpCiLgwZ84cAC677DKDI4k/nS+s7rKZ+3HpEb93drYQDWnsfKkw9jtPHxtpZ3pIPt1J5b4QnS/0oajVrX1p43Uq94XZ/2kpRFv1FIQQomPToWxzFWlnerCmmAkckOkWRMuLz7ZcIUSHEgqF2Lp1K+np6aSkpBgdTlxJOs2J1Wem7PsAwfwjq2xW5oYwWVVSx//Y3bVsSxA9quPubiN1bPXv939SwsGFZYQKozizLGRf7GvNUxBCiA5PdSikjHJTtT9E8KAkeaJ1SIueEMJw77zzDrquM2XKFKNDiSvmBJWkoU4ilVHyF9UuwJI3vxxrohlPHxvhkgjF66owOVVQIVwWxdnFSlVuGH9O9U1F2ZYgWRf7sKeZqx/zSTdOIYRoFa4sK4opzP5Py9Bb8b1XQ0GL03YdDem+2tIk0RNCGG7ZsmU4nU5OPvlko0M5ISc6Fu9onS/y/dBls/4J5PfMLab79GSST3dRmRsmdawbRVFQbdXdNQ99XX7E+pGKKKSZJckTQohWpNpVwpUaWkiSmxNRVFTExx9/zMqVK1m9ejX79++noKCAYDBIUlISgwYNYsqUKcycORO3213nPl566SWuvvrqRh3vxRdf5KqrrmrGM2hdkugJIQy1evVqgsGgzJ13lOTTnVg9Jko2VREqaGBidA1y3y+my0+SyL7Uh6IoVOwMYk83owV1QsVHZnT2NAu6jM8TQohWZXGpVFXIm++J+uKLL5g+fXqdy/Ly8sjLy2P+/Pn89a9/Zd68eQwbNqyVI4wvkugJIQy1detWAMaPH29sIHHE7FVJHOIk4o9yaMmxJ0YPFWvsfaeEpFOdBAsiFK2qpOe1KYRLjrypcHaxYHarVOwMtVToQggh6mBNMRPYJmPzmkPnzp0544wzGDVqFNnZ2WRmZhIIBNizZw+vvfYan376KXv37mXixIls2rSJzMzMevf1v//9r8HlWVlZLXEKrUYSPSGEoYqKigBkkvTDdJ5UfS1yPyxp9DbBQxEOfPpjF08trGPxmcAERCHhJBtp4xPQo5C/pP6uoEIIIZqX1WfCbFepOtBA74wWEkUhitLqx22MpsR18cUXc/nll9e7fNq0aTz11FPceuutlJSU8Oijj/L444/Xu37v3r3p1q3bccfRVkiiJ4QwlKa1zcFizT0er4bZq2LxmqjcG6rV7fJ4HPq6gvQJCfS6JgV0UEwKWkQn98MStEAzBiyEEKJBtk4mAAL50qJ3oszmY6cuN954I3fffTcVFRUsXry4FaKKX5LoCSEMZbPZgOopFqxWq8HRGC91dPXg8fyvj91lsyEV24PkAYmDHaBC4ECYQ0v9UoRFCCFa2w/1V6rnNJViLC3NbDZjt9upqKggGAwaHY6h4rPeqhCiwxg6dChQXdlKgLOzlXCZRqT0xDOyiu1B9r5dwt65JRz6WpI8IYQwQuX+CFpEJ2mow+hQOoQFCxZQUFAAQN++fQ2OxliS6AkhDHXaaafRuXNn1q9fz7/+9S+jwzGUaq3uYlmZK8VShBCivYhWapRuqsI7yIEtpXU700V1Na6/mkt5eTmbN2/mz3/+M5deemns97/+9a8b3O6qq64iLS0Nq9VKSkoKI0aM4I9//CO5ubnNFpuRpOumEMJwd911F48++igbN27k5ZdfZubMmUaHdISWGo93NGty9VtyuFRKcAshRHtSvj2IKwUS+tgINjRljmi0Rx99lDvuuKPOZSaTiccee4yxY8c2uI9FixbFvi8sLKSwsJDly5fz2GOP8cQTT/DLX/6yWWNubdKiJ4QwnKqq3H777XTq1Inly5dTUXFi49PaKou7esC+FpA+lkII0a7ooEV0FCU+K2Aaqays7IivEx1XN378eDZs2MCtt95a7zo9evTg9ttvZ968eaxYsYIVK1bwxhtv8JOf/ARFUQgEAvzqV79i9uzZJxSL0STRE0LEBVVVmTZtGgDvv/++wdEYw58TRNd1XD1sRocihBCiGZkTVGyJZqoOSOXNo2VnZ+P1emNfDz/8cKO2u/rqq9m4cSMbN25k+fLlvPzyy0yYMIEvv/ySqVOnsnz58jq3u+SSS9i+fTt///vfufTSSxk2bBjDhg3jpz/9KXPmzOH999/HYrEAcNttt5GXl9ds59raJNETQsSN3r17Y7fbWb9+vdGhGEILQbRKw5FuMToUIYQQJ0AxQcoIFymjXdjTLCSd4iRcEcW/u3WrQGoocf0FsHfvXkpLS2Nfd911V6POLTk5mYEDBzJw4EBOP/10ZsyYweeff87DDz/Mxo0bGT9+PJ999lmt7bxeb4MtqxdeeCH33nsvAJWVlTz//PNNuPLxQRI9IURc6dq1K+Xl5UQixoxhOEf9Sa2v1lS1P4xqVTA55e1ZCCHaKnuahcRTnCQOcpI1xYs12cyhRRXodQzBNurzJl54PJ4jvmqmXWqqO++8kxEjRhAIBLjuuuuadD9x3XXXxZLBw8fxtTVyJyGEiCunnHIKAKtWrTI2EIOUfFuFoij4BtmNDkUIIURT/dBgpIWr583z5wSpzJVum61l8uTJAOzZs4cVK1Yc9/apqamkpKQAtOkKnJLoCSHiyvDhwwFYvXq1wZEYI5AXQY/quLvLOD0hhGirqnLD7HihgB3PF1C02k9CT7t0y29FNUkaQE5OTpP2oettf3L7JiV6Bw8e5P333+fuu+/m7LPPjvV1VRSF++67r1H7CAQCvPfee9xyyy0MHz6cpKQkLBYLycnJjBw5kvvuu6/ZBj9269YtFl9DX926dTvmvl599VVOOeUU7HY72dnZ3H777ZSVlR1zu23btvG73/2O008/PXauKSkp9O/fn4kTJ3Lffffx5ZdfEggEmuGMhWi77HY7LpeLrVu3Egp1zPnkgoURLB6T0WEIIYQ4AVqoOlEoWl1FoCBC8ghn68eASjROv7QWbG86vBXO7XYf9/b5+fkUFhYCkJmZ2WxxtbYmzaOXnp5+QgfdsGEDY8aMoby8vNayoqIili1bxrJly/jHP/7Bs88+yxVXXHFCx2suf/7zn2ODMwH27dvHY489xhdffMGSJUtwuVx1bvfggw9y33331eojXDNfx+bNm5k/fz73338/v//973nkkUda9DyEiHcXX3wxr732Gv/85z+5+eabMZvNVFVVsXjxYlatWkVeXh42m41BgwYxYsQIfD4faWlpx32ceB0PUb4jiD3VgquHFf/OjpnsCiFEe1KxM4C9rwWTXSEaaPstRfFM0zTefvvt2M8DBgw47n3Mnj071qI3bty4ZouttZ3whOk9evQgKyuLxYsXN3qbsrKyWJI3evRoLrzwQk477TSSk5M5dOgQb7/9Ns899xxlZWX87Gc/IyEhgUmTJp1oqEyZMoUHHnig3uVWq7XeZd999x33338/drs91pK5Z88e/vSnP7F27Vr+8pe/1Jmg/e1vf+OPf/wjUF3l55e//CXjxo0jIyODUCjEvn37WL58OR988AHff//9CZ+jEO3B6NGj+frrr9m6dSu33norLpcrNreeoiikpqZSXl7O8uXLY+WTTz755DY/sWmN0k1VpIxw4e1nl0RPCCHagUiljhUwuVSigToqsohGeeGFF5g+fXps+oOjaZrG7373OzZu3AhU30/06NEjtnz37t0UFxczZMiQeo/x4Ycf8pe//AWo7mV09dVXN+MZtK4mJXp/+tOfGD58OMOHDyc5OZkvv/ySM888s9Hbq6rKFVdcwb333kv//v1rLZ84cSKTJk3ikksuIRqNcsstt7Bt27YTnmTS5/MxcODAJm371ltvoWkaf/vb37jlllsAGDFiBKNHj6Z3797MmTOnVqJXWFgY68qalZXFN998Q3Z29hHrDB8+nMsuu4y//e1vrFixItZMLERHd/vtt7N8+XIWLVpEQUEBffr0YcyYMQwZMgRVre7usW/fPjZt2sQHH3zA7t27jQ24GekRiFZq2NNkPIcQQrQH7m5WQv5KQsWtm+RFdZWoHp8lOaJNGAP329/+lj/84Q9cfvnljBo1im7duuFyuSgpKWHt2rW8/PLLrFu3DoCEhARmzZp1xPa7d+/mzDPPZOTIkVx00UWccsoppKamous6O3fuZO7cucydOzfWmvfoo4/SuXPnEz5XozQp0bv//vtP6KCjRo1i1KhRDa4zZcoULr30UubNm8eOHTtYu3YtQ4cOPaHjnoiavr5HJ7SdO3emb9++fPvtt7W2+d///kdVVRVQXer16CTvaKeffnozRStE26eqKiNHjmTkyJH1rpOVlcW+ffvQNA2v19uK0bW8ytwwCSfZMLtVIhWa0eEIIYQ4AY4MC4VLAyBv5yfs4MGDzJo1q1YSd7g+ffrwyiuvcPLJJ9e5fOnSpSxdurTe7Z1OJ//4xz+4/vrrTzheI51w182WdOaZZzJv3jwAduzYYWiil5qaClTPpXF4q2BeXh5btmypc9zinj17Yt/36tWr5YMUogOJRCI88sgj7N+/H7PZfMyuFfE6Hq8+JRur8PS24xvsoOAbv9HhCCGEOAGRSg1XDxuoChaPiYpdQcq3te7k6e3BsmXL+Pzzz1m4cCFbt27l4MGDFBcX43Q6ycjIYMiQIVxyySVcfPHFdQ7JOvXUU3n11VdZunQpq1at4sCBAxQUFBCJREhMTGTAgAFMmDCBa6+9Nnbv35bFdaIXDP74H8BkMrYC3cUXX8yDDz7IHXfcQVlZGWeeeSb79u3j3nvvxe/386tf/arWNoe/wDZv3sy5557bmiEL0W4VFRXx/PPPs3//fgYMGMB1113X4Bjbtih4KIIW0XF1tUmiJ4QQbVzx6kqUbA3fyQ4URSEa0iTRa4K+ffvSt29fbr755iZtn5CQwLRp05g2bVozRxaf4jrRO3wm+n79+p3w/hYvXszgwYPZsWMHuq6TlpbG6aefzpVXXsmUKVMaHAN42mmnceutt/Lkk0/yhz/84YhlAwcO5E9/+lOtbQ4f6Pnggw9y5pln1tuELIRovKeeeor8/HxSUlK44YYbYmP22ptgYQR7aly/TQshhGiESEDDZlVRFIWIP0rxmspWOa7WwtMYnAgNqT7a0uLzLw+sX7+ejz76CKgui9ocid6uXbvYuHEjlZWVVFVVsXv3bubMmcMll1zC2LFjj5hzoy5PPPEE//znP+nfvz8Wi4X09HRuueUWlixZgsfjqbX+uHHjGDRoEAAFBQUMGTKE8ePH88ADD/DZZ59RUlJywuckREdUXl5OUlISf/7zn9ttkgdQvi2AoigknCSTpwshRFvm7WvH7FI5ML+MnDeLCZfJYD3R8uLyUXEwGOTaa68lGq2uTPTQQw+d0P6sViuTJ09m4sSJDBw4EK/XS0lJCUuXLuVf//oXe/fu5euvv+acc85h6dKlDRZ1uOGGG7jhhhsadVxVVXn77beZNGkS27dvR9d1Fi1aFGupVBSFgQMHMmnSJK655hp69+59QucpRHtXXFzMu+++S1VVFRkZGfWu19bG49WndHOATqPdePrapYuPEEK0USa7grOLlX3LSqjYIe/lovXEZaJ38803s2rVKgBmzpzJ5MmTT2h/K1aswOfz1fr9+PHjufnmm7n88sv57LPP2Lx5M/fffz+PP/74CR3vcL169WLdunXMmjWL5557jm3btsWW6brOxo0b2bhxI4899hi33norf/3rXzGb4/LPIoShPvroo1grv8/na/OVsBolChG/hr2TvCcIIURblXpWAnpUp2R9ldGhiA4m7vo8Pfzwwzz33HMADBs2rMHSqY1VV5JXIyEhgTlz5pCcnAzA7NmzCYWad4Jil8vF7373O7Zu3cqWLVt44YUXuOmmmxg6dGhsXGA0GuXxxx/n2muvbdZjC9EebN68mY8++giPx8Pvf/97HnrooTq7S7dHlXtDKBYFizfu3q6FEEIcg6efHVeWlaJVlUSDrT8mLaorcf0lWlZcPSZ+5plnYoVO+vbty8cff4zL5Wrx43q9XqZOncqsWbPw+/2sWrXqmPP8NVXv3r3p3bt3rBR8Tk4O9913Hy+99BIAL7/8Mtdeey1jxow55r50XY9N6CiOVHNt5Pq0jpa83oWFhTzzzDOoqspdd92Fx+M55nEUtf18eJRuCuDt5yDxZCeHvvKjqAqK0r7OMZ7J9W49cq1bl1zv1uHsbCFQECaYrzbqWh/r803ua8TxiJtE77///S833ngjAF27dmX+/PmkpKS02vH79+8f+/5YRVmaU9euXXnxxRepqqrizTffBOCtt95qVKJXWloq/+Hroes6FRUVAA1WUxXN40SvdyAQYMeOHRw4cIDExESGDBmCqqrs2LGDd955B7vdzkUXXYSu65SWlsa2u/G039e5v5RuiU07kTjldibgHOhC32dFURV8mQmgKOia/P9vaXK9W49c69Yl17t12DUryT1cKMkWXDusaGEdPQyBggh6pPZ1P/wzri5lZWUtFapoh+Ii0Xv//feZMWMGmqaRkZHBggULyMrKatUYjE6Yrrvuuliit3379kZt4/V6O0z3teNV8/f0er2S6LWCE7neCxYs4O233z7idx988AE2m43y8nJUVWXmzJmcdtpptbYt2F3c9KDbENvOKPY0CwV7ilFQQNcpyCmRm7NWoKhyvVuLXOvWJde7dRTshrJCO1mj0zD1jGD+4TMyvCtM3vzyWus3VBAQ5OG1OD6GJ3oLFizgiiuuIBKJkJyczPz58+nZs2erx/Hdd9/Fvs/MzGz143fu3Dn2fWPLxSuKIv/hG1BzfeQatY6mXO+ioiLefvttHA4Hl156Kb1792blypUsWLCAaDTKgAEDmDlzJm63u87tO8rNSdmWAI50Kwk9rZRvC6Hr1efeUc7faHK9W49c69Yl17t1FG8MYCovq344qeqknenB4lbrvO7H+gw93nuaKCrR+CvJAUBU5tFrcYYmet988w1TpkwhGAzi8Xj43//+x4ABA1o9jtLS0lhrmtPprLPloKWtXr069n337t1b/fhCGOGVV14B4De/+Q3Z2dkAnH/++Zx//vlGhhV3yrYEST1DJ6GPnfJtzVssSgghROvRI6CHdWTkjWgNhiV669at44ILLsDv9+Nyufj444859dRTj3s/48ePj81Lt2vXLrp163bE8k8//ZRx48bhcDjq3L6iooIrrriCwsJCAK655hpstuaZnHj27NmsWbOGO+64o8FWyj179nD33XfHfp4yZUqzHF8II7z22mts3LiRCRMmMHDgQObOnUtubi6JiYncdNNNuN1uKioqmDdvHlu2bCEjIyOW5DWkvcyN1yQaRCpkmgUhhGgX1I7TI0UYq0l3DV999dUR48i+//772Pfr1q2LVZCscdVVVx3x844dOzj33HMpKSkB4IEHHsDr9fLtt9/We8ysrKwGp0mozyOPPMK0adO49NJLGTNmDD179sTtdlNaWso333zDv//9b/bs2QNAnz59uO+++477GPUJhUI888wzPPPMM4wePZqJEydy6qmnkpaWhqqq5ObmsnDhQp577jnKy6v7aU+ePJkJEyY0WwxCtKZ//OMfbN++HUVReOedd3jnnXeA6pbynJwcHn/8caZMmcJzzz1HNBrF5/PFijCJhvn3hPANcGD1xWcXHCGEEI0jI0pEa2lSovfcc8/x8ssv17nsvffe47333jvid0cnekuWLCE/Pz/282233XbMY7744ou19tNYRUVFPPfcc7H5+epyxhln8Prrr5OUlNSkY9QlNTUVq9VKKBTi66+/5uuvv25w/Z/97GcNxihEPFu+fDnbt2+nT58+TJ06lYcffhibzcYtt9xCVlYW//rXv9i4cSPPPPMMZrOZX/3qVwwePNjosNuMko2V+AY48A5yoO81OhohhBBNFS6L4upmw9PPTvm2AHqk5Y6l6SqaHp8PCDXpv9ri2n0/oEcffZQFCxawdOlStmzZQkFBASUlJTidTjIzMxk+fDhXXnklEydObPaiHVdccQXnnnsun332GYsWLWLNmjXs3LmT4uLqSoEej4devXoxcuRIpk2b1qSuq0LEA03TWLJkSSyxU1WVJ5544oh1Zs6cyb333kswGOT6669n4MCBxgTbRoVLNLSwhjPLil8SPSGEaLNKNlbh7mEjbVwCKcNdFK70U7opYHRYoh1SdKPnFRDHraysDK/XS2lpqUyvUI+a+dZkeoXW8d5777F06VLOOussJk6ceML769Dj8RrQebIXR4aF0gUaBTuLZYxHK1BUhZRuiRTsluvd0uRaty653q3n6Gut2hW6XZmEyfZjS1vOm0WEiqPM195qcF+NvQesWe+FNUNwJpia7VyaU2V5lF8MXSv3sy0oPttyhRBtRiQS4YsvvsBms8n40hZWtiWAoii4ulmMDkUIIUQTWdwmTDaV8h1BACL+KJEKzeCoRHvU7rtuCiFa1ssvv0w4HOaMM85o9ByQomnKtwVJG6/j6tI8lYGFEEK0vnBpFF3X8e8JUrjCTzSgoYVbplVV5tHr2OLzLy+EaBM2b97M6tWrSU9PlzGmrUGDiF/D4onPbjhCCCGOTQvrhIqjJA114u1vx+qV93TRMqRFTwjRJJFIhGeffRZVVfn1r3/d5P3IeLzjE6nUUE0y7lQIIdqyqv1hfAMdWAaZ8PSxs/v1IrSQtHCJ5iUtekKIJvnPf/5DIBDg8ssvx+v1Gh1OhxEpj8o7txBCtHGHvqlg12uF7H69CJNdxdXVanRIoh2SFj0hxHELBAKsXr2apKQkxo8fjxTvbT3h0iiKomD1qQSLokaHI4QQoik0iJRXF2CpOhAmdawb1dL8vTU0IKrHZy8QKT/T8uS5sBDiuC1ZsgRd15kyZYrRoXQ4wcLq5M6WKs/phBCiPTgwv4yqgxGSh7uMDkW0M3KnIIQ4bvv37wfg5JNPPq7tZDzeiQseigBgS5LB+0II0RaZXCoZEz0ULvNTdSBMtEpDj+pEA9I7RjQvSfSEEMctISEBgOLiYtLS0gyOpmOJVGjouo7ZI2/fQgjRFlk9JhxpFrKm+KjMDWF2qli8Jg7MLzM6NNHOyJ2CEOK4JSYmAlBQUCCJnhE0MLul570QQrQlzs4WkoY5Yz+HSqNoIZ2q0jD5Syqo2h9u9mNqqGhxOlIrXuNqTyTRE0Ict+TkZKA60ROtLxrWMTvkA1IIIdoS90k27KkWIpUaBSv8lKyvRJeaWqIFSaInhDhuNa14ubm59a4j4/Fajh7SUK3xWUVNCCFE3QqXV2JJMGFLMVO+NVBnklfz2Tlfe6uVoxPtkTwSFkIct06dOuF0Ovnqq6+YNWsWoVDI6JA6lGiVjmKWRE8IIdqSaJVG/lflqBYFS4IU1BItTxI9IcRxU1WVP/7xj2RnZ7Np0ybuuece/H6/0WF1GGF/9Vx6qsyvK4QQcc2ebiZ1nAvfIAcmu4Kriw0trBMoiLTK8aO6GtdfomXJFRZCNInP5+Ouu+5iypQpVFRU8OyzzxodUocRKaueZtbWyWJwJEIIIepidqtkTvKQfXEi9nQL7p42Ms734B1gp3x7AD0sUymIlidj9IQQJ+Tcc8/l5Qf+i2d0gIu7/ZzKvdKNs6UFSyI4AVuKmarc5q/SJoQQoumc2RbSz/aghXQOzC/DvyuE+Tw79i4WtIhO0ZpKo0MUHYQkekKIE1ayMUCX0eDuapFErxWES6LouorVJ2M8hBAintjTzGSc56Vyb4iDX5SjhXQUVaFsS5BoXhUVu0NEyrVWi0dDQSM+x3THa1ztiXTdFEKcMEdG9TOjwKHWGXPQ0ekRQAeLVxI9IYSIJ4lDnISKIxz4rAwtVN090+xU8fSzV4+tlkJaohVJi54Q4oR5BzvQdR3/bmnNay16VMfslGd1QggRT0xWhXBxFA5rtEs9042vnwM9JUjSMBdVeWFUi4LJrpC/pEI+O0WLkURPCNFodc2NZ3ardJuWRLAggibDxVpNNKhjskuiJ4QQ8aTqYATfQAeOjABVB6o/FCNVGhF/lJz/FuPobMbd044W0rCnWvD2d0iiJ1qMJHpCiBOSeoYbgOK1Mr1Ca4oGNMyJ8hYuhBDxpGhNJfZOZjIv9HJwQRkVO0MUr6wkc1AKiSfbKVhRScXO6sQu41wPiqllu3LG8zQG8RpXeyJXWAhxQpxZVsJlUcJlUiq6NUXKNXkHF0KIOKOHdXI/LqViZ5D0czz4BjtAUdAiOib3keOqbSlmQsUytl20HHkcLIRoMnOCiqIqVO4JATajw+lQwqURFMWG2asSKW29Cm5CCCGOQYODC8qJ+DU6jXLDKEBRKFrx47QK9nQLlgQT/j3SbVO0HEn0hBB1qms83tEcGdUTdgfyI9gl0WtVoeIoAPYUCxWlQYOjEUIIcbTCZX7KtwSw+sy4LBqRyh8fyvkG2AmVRGQuVNGiJNETQjSZrdMP0yociGBPNTiYDib4w1QWtmQTFTsMDkYIIUSdQsVRwqUajm4/Dm8wORTcPWwULG/5se1RVKJx2s8/XuNqT+QKCyGazJZoRtf1I55SitYRKtHQdV3m0hNCiDbG1dUGCpRtCRgdimjnJNETQjSZOcGEFpYiLIbRwOyWRE8IIdoSi9dEpEJDC8rnp2hZ0nVTCNGo8Xh1MTkUolXyQWUULSKTpgshRFtjdqrSE0a0Ckn0hBBNppoVQlVSGtoo0YCOydGyczAJIYRoXuHyKM4u1lY5lqYraHp8fk7Ea1ztiTwKFkI0ma6BapW3EaNE/FFUs3xQCiFEW1K1P4zZoWJNlq73omXJHZoQoski5VGsXpO8kxgkUq6BAsi9ghBCtBmBvDBaRMeZ2TqteqLjkq6bQnQwTR2PV5eidZWkn+khdYwLbU+z7VY0khbRURQF1Qxa1OhohBBCNIauVffIMLvkKaloWZLoCSGarHxLkKShURL62KkqV2C30RF1LGaHiq7raDJfuhBCtCmqRW2VqtVaHM+jp8VpXO2JXGEhxAnJ/agERVFw97QZHUqHY3IoIEVPhRCibVHAZFek8qZocdKiJ4RoEtUKWVMSsSZWDxALHAwbHFHHo9pUdOmyKYQQbYrJrqCoCtEqSfREy5JET4h2rDnH4x0t+7IkLB6V4KEIZd9XYQ24WuxYom6qVUGPSpOeEEK0JSZHdYe6qLToiRYmiZ4Q4rilTUjA6jVRsrGSQ1/7UVSFlG6S6LU2xYQkekII0cbYkqpvv8PlLd8lQ9NVND0+R2rFa1ztiVxhIcRxUa2Q0MtGqDTKoa/9RofTsWlUT68ghBCiTbAmm0ge7qIqL0y0Sh7UiZYlLXpCiOPSaUwCAPmLyw2OROgaqBbJ9IQQoi2wdTKTNdmLYlIwOVRUm4IWlGRPtBxJ9IRoJ1pyPN7h3N2tRCs1qnKl+IrR9KgONkn0hBCiLfANtqOYqt+zVbOCya6iBaWilmg5kugJIRrN5FRRzAqVu0JGhyKobtFTJM8TQog2IXgwgr1TlMrcEN5+jlY5ZhSFaJz28Y/XuNoTGaMnhGg0T187iqJQtjVgdCgCiPg1FLOC2S1v5UIIEe9Kvg2w+/UiyrbIZ6hoHXJ3IIRoNFdXK7qmU7VPum3Gg4JvqsdJpp7hNjgSIYQQx8uRaUGRvnWiBcnLS4g2qLXG4x3NlmQi4pd5f+JFqFgjXBrFmWWtfjePGB2REEKIY9F/+BhNG5eAFtSo2Fl7OMThn/PztbdaKzTRzkiLnhCiUWrG51UdkNa8eFKwtAJFVeg0Slr1hBCiLQjmRyjfXt19M1zRsg9Pa+bRi9cv0bLkCgshGsXTT8bnxSN/TphIZRRvHzvWRHlLF0KIeOfItODqZqNiV5BgvnTFEC1H7gqEEI3i629Hj8r4vHiUt6AcFOhyeRLmBHlbF0KIeGVyKGSe56HqQJi8+WVGhyPaORmjJ0QcM2os3tFcXa2YnCpl30trXjyqyg2T+2EJnS/ykX2xj12vFBkdkhBCiDp4+thBUcibXxYbqydES5FETwhRL98gB+6eVuxpFtDh0NcVRock6lG1P0LJhkoST3aRONRB8Zoqo0MSQghxFJNLJVwWRQvprXK8KPE7X51MFd/ypI+PEKJOWZf46DTajT3NQiA/wu43itBlKEFcK1haSTSokXyqS97dhRAiDqlmBS3cOkmeEHIrIISokz3VTOBQmO3PFrDvnRIiZdLHpC3IX1KBYlJIGeEyOhQhhBBHsSSYiFbJ56loHdJ1U4g4ES/j8Q4XLo2CfB61KRXbg2hnaPgGOfD2d4AOKNVfCj98fxgtrBPxa5RvC1C8vkr+3kII0UKsiSYcGRYOfdN6wyDieRqDeI2rPZFETwhRi2qr/le6l7RNu14rpNOoBGwp5urETtPR9R8m6dVB1/TqhM4EFrcJq9dEynA3yae7KN8W5OAX5QafgRBCtD++kx0oJoVggYyDEK1DEj0hRDUVfAMd2FPN2NMtKIqCf0/I6KhEE2hBOLjw+JI1dy8bycNceHrbcXezsu+DUoKH5GZECCGaS9GKSqw+Exnnedk7r5hIC0+WLoS0mQohsKWa6XlVMp1GuXH3tGF2qJRtCeDfJYleR1GxPUjOf4s4uLgMxayQfYkPV1er0WEJIUS7EanUyP24FD2s0/kCr8x7KlqctOgJYQCjx+OljHTi7mknXB4lVBjBO8ARmz6hZJOM0+rIyr4LEjgYIfuSRDLO85D/VQVlm2T+RCGEaA5aQCf3o1KyJnvpPi2Zko1VLTp1UVRXicbpWLh4jas9kSssRAdj62TGN9iJyabiSLfgG+hEC+nkzCmiZKMkeQJChVFy/luEFtJJHeMmZYTT6JCEEKLdCJdG2fd+KQDWJJPB0Yj2TFr0hOhgXN2sKIrCvo+KCRREsHpNhApl2lJxpIhfY9crhXT9aRK+k52Y3SbyPpciLUIIcaIsXhOdL/ASroge93hqIY6HtOgJ0cFU7q0ed5dwkh0iSJIn6qVHYPdrRQQPRUjoZSfrYp/RIQkhRJtm8ahkXexDj+rse7dECrKIFiUtekK0MKPH4x0tkBchGtTw9reT0NNGpEoDHaJVGgXL/FJpUdSy9+0SMs714O5uo+uVSeTMKQJ5PiCEEMctZaQbPaKz970StEDLT2Gko6AdPYFqnNDjNK72RFr0hOiA9r1fQuW+MLqmY3GrWBJUHJkWsi/1kX6Ox+jwRBw68L8yijdWYvGodP95MqpTPj6EEOJ4OdItlG0JtEqSJ4S06AnRAYUKo+z/qPSI36lOlawLvbh7WEnoY6N8S9Cg6ES8KvjaT6RcI2Wki+5XJrL37WJCxdLtSAghGitUFsWWLLffonXII1khBABapcaeecXoYZ20cQmYXfL2IGor2VDFgfllKCaFLpcn4ciUGxYhhGissu8DuLpZsfik2qZoefIJLUQzirfxeMctCrmflJI12UfnyT5y/ltkdEQiDvl3htj3Xkn16+QiHwcXllO+VVqAhRDiWMq3BEg+zUniKQ7yv2y5+fNqyDx6HZtcYSHEEQIHIoSKoli9JsxueYsQdQscjLD7zaLqFuAzE3B0thgdkhBCxD1dg+L1VXhOskvPGdHi5BUmhIhJ6GOj+/QkbMlmokGNSKWMvxL1i5Rp5MwpBg06n+/F7JGPFCGEOJbSzQF0wNXdZnQoop2TT2UhBACJpzhIG5+Aya5SurmKXa8UguR54hgiFRr7PiwBFbpclogiAwKEEKJBelgneCiCPVXeMEXLkleYEE3U5sfjHcXdo/rJ4vbnCyTBE8clcCBC/uJyUs9IoMsVSeS8LmM7hRCiIeGyKJaEli/IoukKmh6f89XFa1ztibToCSEAqMoLoygKWZN9UklRHLeyzUFKNlZh9ZjofJHX6HCEECKuhcuiWKS7u2hhcjcnhACgYIUfR7oFe5qZrMmJ6LpONKDj3x2kaFUlEb8084mGFXzjx5poxpVtpdMYF4e+8hsdkhBCxKVwWRSzy4RiAj1qdDSivZJHCUKIahHY+3YJO18ponC1n6r9YRQVPH3tdPt5Er6THUZHKNqA/R+VEiqJ4B3gwDPAbnQ4QggRl8Ll1Q9Pza3QfVN0XNKiJ8QxtLexeMeiVWoUrayM/WzrZCbzfC8pI1xEAxrlW2S+NNGwnLnF9JieTOoYN+HSKFX7wkaHJIQQcSVSVt2MZ/GYCJe0XJNeFJVonLbrxGtc7YlcYSFEg4KHIuT8txAtrJM2LgFbJ3k+JI4hAnve+mHahUlezF75qBFCiMNF/BpaVMeSIO+PouXIq0sIcUxaCPa9UwxA1mQvqkz9I47h6GkXVKvREQkhRHwJF0dxZMqbo2g5kugJIRolVKxx4PMyFLNCl8uSjA5HtAGBAxHyvyxHtSh0+Ym8ZoQQ4nAlm6pw97Bi8co4PdEypA+WEIfpaOPxjpd/Z4iKHUHcPW2YPSqRMqnEKRpWtiWINclE4skuOk/2kvt+qdEhCSFEXCjfHiRtXAL2NDPh0pYZpyfz6HVs0qInhDgupd8HUBQFd3fpvykap2BpJf49QZyZVjqd4TY6HCGEiAt6WCdUHCFpqBOTU27JRfOTV5UQ4rhU7Q+jazpJpzrJnOTBnn7sjgHp53joeW2KTNHQge3/uIxQcQRvPzu+QfI6EEIIgP2fVg+JyJjoMToU0Q5J103RYUk3zSbSoHh9JYmnOHF2seLMtrLr5cJ6V7cmqrh7WFEUhZQRLgIHwwTyIq0YsIgXNdMupIxyESqJULlXpl0QQnRs4dIoB78oJ2uyD3uGhcCB2u+LNfcr87W3Wjs80cZJi54Q4rgVLq9k+zMFlH5bhaIqmF11v5W4e9nIPN8HwJ55xaBD+lny1LLDilZPu6BHIXOSF4tMuyCEEFTlVSd3Vk/zF2XRUOP6S7QsucJCiCYzOVR0XSdUcmRRlvQJCfS6PoWMsz2Y3Sql3wUIHopQvj2IxWPC3UvG93VUEb9G7ocloEC2TLsghBCgQcQfxeKR23LRvOQVJYRoMpNDBf3I32VM8pBwkp1wucahryvY/nwBh5ZUAHDwy3L0qE7qWLe8+3RggbwIB2XaBSGEiAnkR7CnW4wOQ7QzMkZPdAgyHq9lqJYjSyO7ullxdbFSdTDMvndKam+gQeFqPymnu+l2ZSJ73ipGC7VOrCK+lG8JYks04TvZSdbFPva9W2J0SEIIYZiqvDDJp7mqH4I248xFUV0hGqfTGMRrXO2JPFMXQjRZVV4YRVXwDrCBCmnj3ehR2PdByZErmiHxVCddr0wieZgLAEuCmR5XpVSX25d3og6pYFkl/pwQjnQLqeMTjA5HCCEMEyqMoFrqH/MuRFNIi54QoskKlvvx9LGTMsqNQ3cRUCs5uLgMIlQndyc78fS2Y/GoKIqCHtWpOhCmZGMVWlgnbXwCvv4OPL3t5C8qp3xb0OhTEq3swKdldLkiEU8fG6HiCCXrq4wOSQghWp3+QyueYpJWLtF8JNETQjRdFPa+U0zXK5KweE0Ubw9hcpjoemVSncmdf9eR/TR3v1pEQh8bqWPcpJ2VgK2TmYJv/AadjDDKnrnFdJ+eTMoIF+GSCP4cmXZBCNGx2NMtaFGdcFnU6FBEOyKJnmh3zlF/gqIqpHRLpGB3MbqmH3sj0WThCo2SjZW4hrixp1pwpFl/TO42VOHf3fAgvPItQSp2BOn2syR8Ax2S6HVEWvW0C91+lkTGuV72vFVEqLgZB6kIIUQ8UsHdzYqnjx1ntpXS7wLNOj4PQNMVtDgdCxevcbUnkugJIY5fHd0yTQ6Vqu1hStYfO7k7mh6BwpV+0sZ58A60U/ptoIUCF/EqWqmx7/0Ssi/2kX1pIrteLUSTnrxCiHYscbCDlBFuAA4uKqfse/nsE81LEj0hROM0MOaudGOAEiVKwe6yJreghkqqu6vI+ISOK5gfIe+LctInJNDlJ0nsfrXI6JCEEKLFlH4XwJFhwZltJZAXrjVdkRAnShI9IUT9Giqocli3TEVVcHRzndChrIkmAMIlkRMOW7RdFduDFCeZSBziJOsSX93TdAghRDughXTyFpTT8xcp2FMthIplfJ5oXpLoiTZN5sdrAY1M7pqb1Vud6AXlg67DK1xRiTXRjLu7jeThTgqXVxodkhBCNBvFBO4eNqw+E85sK1pEpyqvZYpQ6bqKpsfnlA16nMbVnkiiJ4QwLLk7XE3XTWeWhbLvZHBWR3fgf2V0n5lM4mAnhasqQfJ/IUQ74OpqpdMYN5YEE+GKKKHCCPs/LiVcKm9yjVFUVMTHH3/MypUrWb16Nfv376egoIBgMEhSUhKDBg1iypQpzJw5E7fbfcz9ffrpp8yePZsVK1Zw6NAhOnXqxOmnn87111/Peeed1wpn1LIk0ROio4qD5O5wZVuCpI7RSRnhlkRPAJC/pJzMiV7SxiVw8Ityo8MRQogTlnZWApFyjd0fFtWb3CkqqDaFaJUM2jvaF198wfTp0+tclpeXR15eHvPnz+evf/0r8+bNY9iwYXWuq+s6v/rVr5g9e/YRv8/NzeWdd97hnXfe4frrr+ff//43itJ2awdIoidER3KMScyPnueuVWlwaGkFqWMSSDszgYML5ca+o/PvDBEui5LQy8ahr8ulCqcQos2r2BXE1dWGFqo9j4LFo+Lp78DTx47JrlC4spLiNdJ1/WidO3fmjDPOYNSoUWRnZ5OZmUkgEGDPnj289tprfPrpp+zdu5eJEyeyadMmMjMza+3jj3/8YyzJGzJkCL/73e/o2bMnO3bs4G9/+xtr165l9uzZdOrUiQceeKC1T7HZSKIn2gwZj9dEcdZy15DSbwP4BjpI6G0jGtRkTj1B3sIysib7SJ/gYf/HZUaHI4QQJ6RwmR93VxvZlyQSLotSuKqSYH6Y5JEuEgc5iQY0yrYE0DVIOd1FuDRKxY6mP+WKohAlPlukmhLXxRdfzOWXX17v8mnTpvHUU09x6623UlJSwqOPPsrjjz9+xDrbt2/nb3/7GwCnnXYaixcvxuFwADBs2DAmT57MuHHjWLVqFX/961+5+uqr6dmz53HHGg8k0ROiPapJ7k6yYfGa4qvl7hj2vl1MlyuSSBzsxNvXzq7XC9FkaqEOK3AgQqgoijPbitmtEqmQidSFEG1XNKBjcqiYHGDxmKqnJNIdOLKsHPqmgtJNVeg/9Oi0dzLj7Wc/oUSvvTGbj5263Hjjjdx9991UVFSwePHiWsv/8Y9/EIlUV/h++umnY0leDafTydNPP83IkSOJRCI88cQTPP30081zAq1Myt0I0V6YIfFUJ12nJtLrmhRShrmwJJioyguz/3+lbH+2gNz3S+M6yQPQQrD71SIOLa1AsShknO01OiRhsLzPSwFIP8djcCRCCHGCFNDCOlpYJxrSsKVYsKdZKF5bScmGH5M8gIhfk7llm8BsNmO32wEIBo9MknVd57333gOgb9++jBgxos59jBgxgj59+gDw7rvvouttc7yktOgJ0Za14Za7YylZX0VCLxuOzhasiSqhYmnJ6ahCxRpVB8I4MixYU8yECmSuRSFEG6XDjhcKsCSodPtZMlirf62FaycSqkWpbpJRkMnUj8OCBQsoKCgAqpO5w+3atYvc3FwAxo0b1+B+xo0bx5YtW9i3bx+7d++me/fuLRNwC5JET8QdGYt3DO04uTvagc/L6DY1ic6TE9n1cqHR4QgD5X1eTvfpSWRd6GXnfwpB8n4hRFulg2L+saWuYmeQss21xyiUbw+QMdFL1mQf+94radKhNB00PT5bBbVmTF7Ly8vZt28fb731Fo899ljs97/+9a+PWG/z5s2x749OAo92+PLNmzdLoieEaCH1JXd5PxRUaUfJ3eEipRqFy/2kjHDT6Qw3hxZXGB2SMEi0UqNgqZ+UkS6yL/ax9+0So0MSQogmCxVF2f7sIRSzghasO+Px54SIBjSiwfb9ZKus7MhCWzabDZvNdsztHn30Ue644446l5lMJh577DHGjh17xO/37t0b+z4rK6vB/WdnZ9e5XVsiiZ4Q8aqDJndHK15XhW+QA28fO4e+qpCWnA6sZEMV9jQzCT3tkvgLIdo8PQp6tP5mLXuaBZNdpXBl+65AfXhCBXDvvfdy3333NXl/48ePZ9asWfTv37/WsvLyH6duOtaE6i6XK/Z9RUXb/LyRRE+IeCLJXZ3yv64gc6KX1LFu8he1zTdb0Tzy5pdjS66uRBc4GKZ8i1SjE0K0TyZHdc3E+iZWby/27t2Lx/Njsa3GtOYBXH311Zx33nkAVFZW8v333/Of//yHBQsWMHXqVJ599lmGDx9+xDaBwI9dZK1Wa4P7PzyOqqqqRsUUbyTRE4bq6OPxrMkmXF2s2NMs2JLNmN2qJHd18O8MEa6I4uljJ/+rCmjfn3niGPbMLabHjGTSxiUQLIgQKpQXhBDiR+a01Nj3ZWN6ABC1/ThOTT2s8In3mz0ARHL3t1J0dUsc4kAL6ZR9H4hV3nRkWgAw2VQikaZ1Z9F0FU2PzyL7NXF5PJ4jEr3GSk5OJjk5Ofbz6aefzowZM3jkkUe46667GD9+PO+99x4TJ06MrVNTjRMgFGr4/urwip1HT8HQVkiiJ0QrsqWaSRzswNG5ujuGolR/8Oi6jh6W5K4hh76qIONcD2lnJHBwYfmxNxDtlh6Bve+V0OWyRLKn+Nj5n0J0KcQphGirFEgZXt2N0NXFysEvy0kZ4cbTx04gP1xnRU5RvzvvvJP33nuPZcuWcd1117Fjx47Y/HsJCQmx9Y7VHdPv/7HL7LG6ecYrSfSEaAW+QQ4ShzgwO03ouo4W1KnaHyZwMIx/b4jAAblLPRb/7hCRCo2Ek2wcXFIOcsk6tFBhlIOLykkbn0CXyxPJeaPY6JCEEKJpdAgWRFAsCo7OVnrMTEEL6+QtKKN8m3RPb4rJkyezbNky9uzZw4oVKxg1ahRwZAGWffv2NbiPwwuwHD2OsK2QRE+IlmKCTqPceHrbUS0KWkSndHMVRasqifilokhTHPqqnIzzvHQ+z0vuh6VGh9OsPAPsJJ3iJFQcIe+LMrTalbbFUcq3BLGnWvANcJB+TgJ586WlVwjRNgULI1h8JnI/LMGRaaEqN0ykQu4VmiolJSX2fU5OTizRO7xAy/fff9/gPg5f3q9fv2aOsHVIoidaTYcYj2eGxME/FFPxVRdTiVRpFK32U7yubQ7kjSf+nHD1xNmdLSQPd1K4vNLokJrMmW0hcYgTq9eEroPZpYIGZreVHjNSODC/TLrwNsKhJRXYO5lx97DhGxyhZIP8PxNC1FbU78cxeikb4q8rZLAwgruHjUiFdkSRKdWi4OlvJ6GHjaeeeoo+ffoc1341FDTidB69FoyrZlJ0OLLbZffu3cnMzGT//v0sWrSowX0sXrwYgM6dO9OtW7cWibOlSaInxAlSbdWVMt297FgSfiymEsiPULy2Ev9uuVlvTrkfltLtyiSShrjw9HVQsqGS4rVt4+be1slM0mlOnJ2tqGYl1o1XUaFyb4j9n5ZhTzXT+UIf6Wd72PWfAjTptXNMe98toceMZFJGugjkhwnkSb9eIUTbEiyIoFoULF4T4ZIoJrtSPbXQQAeqWaFiV/WHwdy5cw2ONP5pmsbbb78d+3nAgAGx7xVFYcqUKfzrX//i+++/Z9myZYwYMaLWPpYtWxZr0ZsyZUqspkJbI4meEE1gcqokDnHg7m7D7KpO7rSITtWBMCUbpZhKi9Jg92tFdBrjwtPHQcpwN0lDnOx9ryQuqi+avSpJQ1yY7AqKSUExgWJSsPpMmGwquq4T8WuUbA1QvK4S7aiXSiAvQt78MjLO85B5vo9975QYch5tigZ75hXTbWoSnS/0sevVAun6KoRoU2rm07MlmzE7VTIneQEo/a6Kkg1VRPwav/7s12zatInnnnvOyFAN9cILLzB9+nQsFkudyzVN43e/+x0bN24EYPTo0fTo0eOIdX7zm9/w7LPPEolEuOWWW1i8ePERVTWrqqq45ZZbADCbzfzmN79pmZNpBZLoCdFIZo9K0hAnrq5WTI4fkruwRuXeEMUbqqjaFzY6xA7l0Fd+Dn3lxzfYQcoIF1lTfOx8odCweFQrZF+aiMVrOnLBDz2EokGNkk1VFK2uJFrZ8LgLf06ouotqhgVrokqoWMZpHEukXOPAZ9UJcpfLk9j9apHRIQkhRKM4sy10vsAHgNVnwjnIQbAwwv5PStGCR3YzbatFQZrLb3/7W/7whz9w+eWXM2rUKLp164bL5aKkpIS1a9fy8ssvs27dOqC6wuasWbNq7aN3797cfvvtPPLII6xatYrRo0fz+9//np49e7Jjxw7++te/snbtWgDuuOMOTjrppNY8xWYliZ5oEe1lPJ412UTSECfOLCvqD3PwaCEd/64QResqCeZLFzGjlWyoQrEopAxz4R1op/RbY5pyMs7zYvGaqNwT4tA3FYRLTyw5O/hFOd2mJZF2poe9b5c0T5DtnD8nRPHaSpKGusi80Mv+dlawRwjRSOqP88bpP/S40w97Bqcf9TzOaCZ7dbyV+0JU7g2RPMxF3oKyWkleU0R1hagen90OmxrXwYMHmTVrVp1JXI0+ffrwyiuvcPLJJ9e5/MEHHyQ/P58XXniBtWvXMnXq1FrrXHPNNTzwwANNijFeSKInxFHs6WYST3HiyLSgWn5I7oI65duDFK/xS+tKHCpeXUnSKQ46jXKjR3XKNtc/sM3WyYy3v52KXUEq9zTcCpsy0klCLzsm52GTzWqghXWiVRrh8iihkigRv4Yjw0LwUIT9n5Q1yzlFKjRp1WuCwhWV2NMsuLKsJA1zUrSy7RbsEUJ0DKq1+l6jMjeELdWMHtXx58gQkLosW7aMzz//nIULF7J161YOHjxIcXExTqeTjIwMhgwZwiWXXMLFF1+M1Wqtdz+qqvL8889z2WWXMXv2bFauXElBQQEpKSkMGzaMX/7yl0yaNKkVz6xlSKInBODsYsE3yIkjvTq503WdaKVG6fYgxWsriZTLTXa8y/2olM4X+kgb5yFlhEbegrJaiZx3oJ1Oo90oioKnr52yLUHyv6xdkt/V1ULamR5MdpVoSKNqf5hIpYaigtltwuxSMbtNWHwmXF1+nPQ+f0nDk68er5pWvYzzfOT8V7oiNlbuB6V0n55E0lAnwfww/hzpVi2EiF/e/tXjw0q/C5BxrofK3DBaqHZr3v79+1mzZk1rhxdX+vbtS9++fbn55pubZX/nn38+559/frPsKx5Joic6LNUOaWd6cGVZUUzVyV2kQqN0c5CidVVoxxhHJeJLIC/CjucKSD7dSeLJTjInedkzpyjWEqaYq+c11EI6+94vJuNsD96+dhyZFva+XYQWAEemmZSRbmwpZtCgYIWf4jUNtwiZ3Sr2NAuBQ2EiZc37molUaJR+F8A3wEHGeR4OfNo8rYUdQc5bRXT/eQoZE73s/m+RzEclhIhbJRurSBufgDPLiiPdQv7iIx8aqlaF9LM9PPDAA4RC0tInGk8SPXHC2tp4PNUKaeM9uLpXN+mHyzTKtwcoWV+7AqJoewpXVFK6JUC3qUeOb/MNdqCoCgcXlREqjJLzZjGdxrjwDnDQY2YK6KCo1Ql/5b4QB/5Xht6IIZiRCo2KipabA+HQkgpsyWbc3WykTUggf2Hzthq2V1oAcj8qIWuyjy6XJ7LzP4UguZ4QHVd9w93ioGx+tKr6zcnd3YquQcXuIz9TPH3tuLpYCRZFUNx17aF+mq6i6eqxVzRAvMbVnkiiJzoOM6SPS8Dd0wYKhEujHPyyXObcaocipRqhwkh1y5wKaODubkPXdPw7f8zmD33lp3xHEN8AByanSrAgQtFqf9zNXbfv3RKyL/fhOcmOalaIbDM6orYhcCBCwTI/KSNcZF/sk6I2Qoi4ZE2srg7j7mGjcIUfLXBkVlq5N0SoOILZpVK8xW9EiKKNkkRPtH8qpI514+ljr07wyjXyF5VTlSvjdtqzonVVZJztIXGIk+LVlahWFS1S+5Fu4ECEvAO1x+nFm71zS8i6xIe7uw1LmoOC3cVGh9QmlKyvwp5qIaGnjU5j3Rxq5nGUQghxomqqblbsCFK8vqrW8lBxdS8UgIgu9y6i8STRE+2Xyg+TattRTArhiiiHlpRLYYYOomJ7EG28TuIgB8Wr20flxX3vlNB5spf0vh4ST7ZTtLb2DYGoLW9+GbbkRLz97QTyw5RvibMmWyFEh1b6fYCIX6NkY/O/p2soaHE6vYJGfMbVnkiiJ45LWxiPZ3arJJ/uInOIjwp/OZHKKPlfVRzRZU90DEVrK0kZ5qLbz5MwO1Ui/rY/SGv/h2Wk9kwicahTEr3jsGduMd1nJJM2PgE0KN8myZ4QHYnzgNER1C9cEqWkpHHv54oZkBEnopEk0RPtgxmShzpJ6G3H7KruAqGFNQ5+WUbZ93JD11EVr67E7FCqS1crtMjTUiOUbQ+idlZxZFmo2ict1I2hRyDnjWK6Tk0k7awEUJGWPSFEm+PobIUco6MQbYUkeqLNcnS2kNDLhiPDgsVrQlEUtEj1JKNFqyr/P3v3HR9Hde///zUz27SrLlm23DtgbMBgXDAdDKGXAIEQCISES3J/+abc3PQEEpJcQkJCbh6EG0IoKYSAQycUQ8Bg4wLYBIwNuNtyV9f2nZnz+2PbCMnq1q5Wn+fjYRhPPbuypDk7530+lJbatG2Tp3jD3YFlIQ4sK6zweqLFwjsGXEUyY1lvWGGbbQ83MfHKiuSTPaSzJ4QYWqzowaYPFaIj6eiJoUFPzkZVMtmLt8aFK6Cjacmp8JUF0f0mTe+ECW1Nduw0XYPSHLdZiENES/fvpJ/Xa3bYZttDDUz8dBUjTykhsjeB2TL0h/QKIYaH2L7ejeJQaHmbhVN52q5CIh09cVC5zOPpXiidXkRgggdvtQvdq2U6dnZCEd1nEtoZp+3DqBRCFsNO0Sg3pooS2iZPo/rCjkLd402Mv6KS6rnF7F0iheiFKCQqlv3ZWPqvD5P/P9i+g9AeIXJFOnoiL7jKdMoO8+Ef68FTbqC5sx07K2IT3pkguC2WnElRRmOKYc5T6SIRU3lX728oiTfZKFPhrTRy3RQhhBDikJCOnsgJ3ygXJdN9+GvduEsNNCP5+F7ZCjNoE90ep21LLDMUUwiR5SrSie+3ct2MIU9ZoLlk6JAQQojCJB09ccj5x7kpnuylaJQbV7GOZmjJDB1gm4p4i0Vkd5y2jTGi+2TOYCG6JX2TAaEshSYP9IQQBcxWeVxHL0/bVUikoycGNIun+3VKp3nxj/PgrTIwfO0nTTFDNlbEJLwrQeuHUcxWydcJ0VtmyMJbIT+++8u2wPDKjYYQhcZqbMp1E4TIC3KnIPqlaIyb4inJp3XJIZhks3VRRWR3gvDO5NO6QihWLUQ+CG1PoI/XKD+6iOZ/F0ZtwFxQpkKTEhVCCCEKlHT0RK/4RrmomO3HN8KFUZR8WgdgWwqzzSKy1yS0NUpouxRxFuJQaf0gSmmtRtVxfuno9YMyVbZUhRBCCFFgpKMnuuUq06meE8A/wYPh0ZMlDmKKyJ4E4boEbR9JiQMhBlvzuxEqjwtQPNVLcJNMv9kXdkJJ3lEIUdBspWOr/PxEK1/bVUikozfM9DSPp3uh8rgAJVO9GKmhTVbUpnldmIa3QtjRQ9lKIUR3GtdGqDjWT8XRRdLR6yM7Lh09IcTQscR+lNbWVsrKynLdFDFE5H1Xet++fTz11FN873vf48wzz6SsrAxNS9ZYu+WWW3p0jgceeCBzTHd/HnjggS7PtXv3bq677jpGjBiB3+/nlFNO4aWXXuq2DYlEgr/97W9ceumlTJw4Eb/fj9/vZ8KECcydO5frrruOBx54gK1bt/boNR0SOlQcU8SEqyqZfF01FUf50V0awc0xtv+9ka0PNnJgmXTyhMgLNiRabTzlMm1kX9lxKZUshBg6GhsbeeaZZ3LdDDGE5P0TvVGjRuW6CRm7d+9m3rx51NXVZda99tprnH322Tz44IN85jOf6fS4jRs3ctlll/Huu+922LZjxw527NjBm2++yYMPPghAJBLB5/MdmhfRiZJpXsqPKsJb7UpOpGIlJ1FpfDtEZLeUOxAiX8WbTNylnlw3Y8iy4jaapqH7kA+whBB5zV1m8NOf/pRYTEZwiJ7L+46e0+TJkxk7diyvvfZan8/xwgsvMHr06INuHzt27EG3fe1rX6Ouro4FCxbw3e9+l4qKCv7xj39w5513ctNNN3HOOedQVVXV7pjGxkZOP/30TOfwlFNO4TOf+QwzZszA7/fT1NTEunXreOWVV3j++eeJRAZnYgVfrYvK4wL4a91oRnKWzFi9SfO7Edo2yg8RIYYCK5LsqKADEpPtNTuWfKLnKtKJR+UNFELkju7R0AywIh1HGhg+jdqzSykuLua///u/ueuuu3p8XqmjN7zlfUfvhz/8IfPmzWPevHlUVVXx6quvctppp/X5fNOnT2fixIm9Pi4Wi/Hkk08ybtw4lixZQiAQAGDhwoXYts1vfvMbnn32Wa699tp2x912222ZTt7NN9/c6XDT0047jS9/+cu0tbVx//33YxgDMxTr43k8d5nOqDNL8I/PTqqSaLNp3RCh6d8RuVEUQgwr6Y6e4TegSX4ACiFyp+aUEkqmeNn2cCOJZiuz3vBpjLmgHMOr8aUvfYmioqIctlIMNXnf0fvRj36U6yYA0NDQQCwWY+7cuZlOXtoZZ5zBb37zG3bt2tXhuCeeeAKAkSNH8sMf/rDLa5SUlPD//t//G7A2A2BA9byPTaoSsWn6MEzj25K3E2IoU+kPfuVD0T4xI8nOXfmRRbjLDcLb4zKDsBAiJxLNJuClcraffa+0AWAUaYw5vxyjSKfuqWZGPjiS1tbW3DZUDCl539HLFxUVFbhcLt566y3C4TB+vz+z7dVXXwU6zxPu2LEDgEmTJqHrgzf3TTgcZtSZJRRP9qLpGlbcJrg5RsObIRItciMjREGRjl6fBLfFsBM2xZO9FE/2Ep9lsv3hplw3SwgxDMVTT/FKD/MRqzfxVBqUTPFiJxR1TzW3e8onRE9JR6+HioqKOPvss3n22Wc566yz+M53vkNFRQVPPPEEd955J36/n3PPPbfDcR6Ph1gsxqZNmzBNE5fr0L7lra2t/PWvf2XdunUUT/FiBm0OvBEktDV+SK8rhBh8ykxlzHy6PInqCxM2399A9fwAFUf5CW6RfLIQIjciexOZ5RELi4k1mjSvi9L8bhjb7PsMwTYadp5+Gpiv7Sokw66jd91117FhwwaampooLS1l6tSpnHnmmXzxi19kzJgxXR575513snLlSpYvX87555+fWa9pGv/7v//LyJEjOxwze/ZsXnvtNerr6/nqV7/Kr371KzyegZ8l75yST1Fzagn+sW4AEi0W+5cFidQlujlSCDFURfYmqDgKiid5aX5vcCZyKjg2BCZ6Ubai4a1wrlsjhBimzDab+lUhqucFULbCXWJQPquIiqOL8Pl9fPe73811E8UQlPd19Aba0qVL2b9/P4lEgoaGBlatWsVPf/pTpk6dyu9///suj506dSqrV6/miiuuoLy8HJ/Px4IFC3jmmWe44YYbOj3my1/+cmb5rrvuYvz48XzhC1/g/vvvZ/369SjVvzpOBw4c4Fe/+hUTP1OJf6ybeJNF3ZPNbH+4STp5QhS40JY4ylaUzRi8ciyFxlNl4C7RCW2Py4RUQoicalobZsejTex/PUjD6lDyz5shNE3jH//4R66bJ4agYfNEb/LkyVx66aUsWLCAcePGAbBlyxb+8Y9/sHjxYqLRKDfddBOapnHjjTd2eZ6///3vPb7uZZddxg9/+ENuvfVWlFLs27ePe++9l3vvvReA8vJyTj75ZD71qU9x+eWX43a7e3zuO++8k7179wIQqzfZ92ob8QYZwy3EcBLaHicw0UPpEV5aN8jQw94acWIxAAeWBXPcEiGEgFiDSayhfQ3jr//uCv70pz+xe/duiouLc9QyMRQNiyd6l1xyCZs2beIXv/gFl156KccffzzHH388n/rUp3jkkUd46qmnMh2sr33ta5nO00D50Y9+xMqVK/nkJz+J1+ttt625uZmnnnqKq6++miOPPJK33nqrx+fdsWMHkyZN4gc/+AE7/9EsnTwhhqE9S1qxE4oRC0ty3ZQhR/dA0Sg38QYLMySP84QQuTfy1BJqTimmeKoXoyhZW6+srAyA1atX9/p86Tp6+fpHHFrD4ole+hvkYM4//3xuvvlmvv/97xMOh/njH//I9773vQFtw9y5c1m8eDGhUIg33niDVatW8fbbb/Paa6/R2NgIwMaNGzn11FNZuXIlM2fO7PacW/7UwA77TZZ85000Xb5ZnDRdQ9OQ92WQyPs9eDp7r5vfjVA1J4B/nIfILhmy3VMjTkiWyqlfGTrov135tz145L0eXPJ+D56evteB8R5KD/cRbzEpOyJZL88wDH77298C8NZbb/WrlrQYfoZFR68nvvCFL/CDH/wApRRLly4d8I5eWiAQYNGiRSxatAgA0zR54okn+OpXv8quXbsIhUJ89atf5aWXXur2XFVjy3HrAz+xSyHQdI3y0SWgaSi7fzlI0T15vwdPZ+919WEBikrclJbZBNzy/vfU6GPLsU2bgMcmMLHzfeTf9uCR93pwyfs9eHr6XvvHuiktLWbPyhZueeq7bN++nWg0yqhRo3jzzTepqamhpaVlEFsuhjrp6KXU1NRQXV3NgQMHOi18fqi4XC4uu+wypk2bxty5c4nH4/zrX/+ioaGBqqqqLo9t2N6MS+t5pm840XQNlKJ+e7P8AhsE8n4Pno+/176RLsqKdcJ1Qfatb85184aM8pk+gmGDhlVBmrZFD7qf/NsePPJeDy55vwdPj9/rHaBPSGBXJxgzZky72eBnzJgBIAXTRa9IR8+hvzNg9sfRRx/N3LlzWbZsGUoptmzZ0m1HT9kKpckP54NRKvUeyS+wQSHv9+BxvtejzykFBbuebpH3vhfKjy7CNhWNa7svSyH/tgePvNeDS97vwdOj99pODiUfdUYpK1euZMGCBR120bTeDbXN5yxcvrarkAyLyVh6Yv/+/TQ0NAAwevTonLTB+cmNrsuXRgjRtbKZPnSPTv3KkBRM74Wi0S4Mv05ws8xSKoTIL20bY7RsiPDwww9TV1eX6+aIIU56Eyn33HNP5oneKaecMujXV0qxdu1aIPlpzYQJEwa9DUKIoUUzkp+GJoIy425vVC9IlVRY3pbjlgghRJZmgLvMILrXxDRNHnrooVw3SQxxBT90c9u2bTQ1NTF79uyD7vPMM89w6623AuDz+bj++usH7Ponn3wyX/ziF7nsssu6rJF366238tFHHwGwcOFCqqurB6wNQojC1PJ+hOr5ASpn+wltiee6OUOC4dfxVruI7ktgy1smhMgDRWPclM8qIjDek5mZc8KECQMyw6YM3Rze8r6jt2zZMjZt2pT5+wcffJBZfuedd3jggQfa7X/ddde1+/u2bds47bTTWLBgARdccAHHHHMMNTU1mRzc4sWLWbx4ceZp3i9/+ct2Qyj7a/369Xz605/my1/+MhdddBEnnngiU6ZMoaysjGAwyHvvvcdf/vIXli9fDoDH4+GXv/zlgF1fCFG4vFXJH+FKRm32WM1Jyad5+1+XAulCiMGnezXcJQZW1MYM2nhHuBhzXhmxBpP6FSFiDSbxFovftf0u100VBSDvO3r33nsvDz74YKfbnnzySZ588sl26z7e0UtbsWIFK1asOOh1/H4/v/71r7nxxhv73NbOjB07loaGBhoaGrjvvvu47777DrpvbW0t999/P/PmzRvQNgghClPxFC+aprH3ZZmFrUd0CEzwkGiziTfIcFchxODyj/NQe3ZJ5qmdGbZBKWINJjsfbwYbltiP5raRoqDkfUevv4477jj+8pe/sGLFCt566y327NlDfX09pmlSUVHBkUceyRlnnMHnP/95ampqBvz677zzDmvXrmXJkiW88cYbbNiwgT179hAKhfD7/YwcOZJZs2Zx3nnn8alPfYqSkpIBb4MQojApMzkSQXfL8JeeqJrjR9M1GlaHct0UIcQwVDbLR6zRZP/SIK4iHV+tm6JaN/uXtoGMzBCHQN539B544IEOwzN7o6SkhKuvvpqrr7564BrVS7Nnz+4yIyiEEH0R3WcC4BvllidUPVB6mA87bhPcJLNtCiEGmQa+GhdNa8LEDpjEgNCOOIGJHvzjPbjLLMI7Bj44LBm94S3vO3pCCCE6F9mTvCnwVbuQwZvdM/w6kd2JXDdDCDEMeapcGJ7kMM20UYtKKZnixU4odLdGvMVi/fr1meLoQvSXlFcQQoghyo4nC/B6KoxcN2VI0DRN6g0KIXLCV518tjLmvHImXFXJyNNLKJniZf/rbWz+Yz3b/96IGbT4wx/+QGNjY45bKwqFdPSEEGIIsxMKV7F09HpCKYWrWH7tCSEGX+uHUXY+3szel1uJ1MUpne4DkgXSAeJNFnueb8Xn8/Hcc8/lsqmigMjQTSGEGMKssC0dvR6yInamJIUQQgwqBbEDJtF9iraNMRrXhnGXGthxldnFTihmz57NunXrBvKy2ORnFk51v4voJ/loUwghhrBEm4UmfZceie4z0b2avF9CiJwzg3anmeEJEyZQX19PJBLJQatEoZGOnhBCDGGxBhNN03CXyY/z7rRtjqFpGiWH+XLdFCGEyFhiP5r509jYiMfjweeTn1Oi/+TOQAghhrDogeQMbkWj3TluSf4LbomhlKJkkjfXTRFCiA4ef/xxnn76aY444gg0LT+HW4qhRQawDGGByV5iW2UGOSGGMyNVLN2KSdqhW3Yqp1ctv/qEEPnH5Ur+bDr33HMH7JxSR294kyd6Q9io00qY9NlKmUVOiGGs7MgilFJEdg98od1CJDk9IUS+qqioAMCyrBy3RBQK6SEMYS0fRDB8OuMvq8h1U4QQOVA11493hIvwjjh2NNetGRrSOb3SwyX/IoTIHWcuD2DNmjX87W9/48QTT2TChAk5bp0oFNLRG8Lql4doWBXC8OlUzfPnujlCiEHiLteZcFUFlccGsMI2u19ozXWThox0Tq9YcnpCiDzx4Ycfcv/99zNnzhyuvPJKyeeJASODV4a4pnciVBzrp/TwIhpWhXPdHCHEIeYu0xl1eimtra20bIiwf2kw100aWuxk7UHJ6Qkh8sWSJUsYOXIk1157Lbo+sM9gJKM3vMkTvQIQ2ZXA8Gny1RRiGBh1VilosPPxFunk9VFkr4nu0dA9uW6JEGK4U0oRCoUYMWIEhmHkujmiwEjXoABE9yfQNA3fCPmEWohC5ykziDdZxBvMXDdlyGrbFE3l9Ipy3RQhxDDjzOUppXj22WfZvn07J5xwQo5bJgqRdPQKgBVLlljQvfIIXIhC5hvlQtM1YvWJXDdlSAttjaOUIjBJHukJIXLn8ccf55///CcXXXQRs2bNynVzRAGSR0AFwI4l/697pN8uRKEqn1VE9fwASinaPorlujlDnhmy8VbKr0AhRG5Eo1Fefvllzj33XM4+++xDdh3J6A1v0jMoAPJET4jC5ql2UX1CANtU7P5nK3ZUiqP3l7IA+ZEphMgR0zRRSjFixIhcN0UUMOnoFYBEs4VSCv9oGYYkRKFxl+mMOa8MFOx4tInILhm2ORB0NyhTOsxCiNx46aWXcLvdTJs2LddNEQVMOnoFwAzaJFptiid78NXKUCQhCkXRWDcTrqzE8Gk0vB3GDNq5blLB0FwatvSZhRA5snbtWiZMmEBFRUWumyIKmHT0CsTOxxsBqDwukOOWCCEGyqgzSsGG7Q830vS21MkcSJqhYcel4yyEyJ1Nmzaxbt26Q3qNdEYvX/+IQ0s6egWieIIXTdOIy2x8QhQM3a0RbzJJtEiHZKBpOlgxGbophBh8zmze1KlTc9waUchknN8Q9mTLnygtLcW2bb74hS9hm4r61fKpvxAFRZNPPA8VOyodaCHEoaOlCqA/HXyQjz76iGnTptHU1MQ//vEP1q9fz3XXXUdRkdTzFIeOdPQKwJo1azC8Oo1rQiD3LUIUDDuhMHzS0Rtoug80TcMMyw9MIcSht2zZMpYsWUIwGExOnuf387nPfY45c+bkummiwElHrwCsWrUKpRQNa+RpnhCFxAxZeCvkx/RAc5cm31MrIh09IcSht2fPHsrLyzn33HPxeDwcffTRg/YkTykNladZuHxtVyGRO4gCEAgkJ2ApO9xHy7pojlsjhBgoiWYLb5UrmaaWPsmAcRUn4+kyi6kQ4lArmeZhxYoVfOITn+Ckk05Ck+H4YhBJR68AXHbZZax4bSUjFhajLEXrhliumySEGADxFgtN0/BUGMQbrFw3p2C4S5K5mURQ3lMh8p3u9/f6GDucuxFOrtpRAGguxeW3HMebb77J/PnzWbhwYc7aJIYvmXWzABQXF7Pt4QbshKLmpBJ0b65bJIQYCIY3+cmvKR2SAeXyp97XVnmiJ4QYeIZXMeZUm/fee4/rrruOz3zmM+i63HKLwSf/6gqEHYU9z7eg6RpV84pz3RwhxABwFRsopbDlIf2AMoqS76sZko6eEGKgKWrm2mg6fPe732Xu3Lk5bY2Nltd/xKElHb0Coft1yo9ODm8onuDJcWuEEAOhqNaNLbXeBpxRJDcXQohDo3SywlsB+9/Uqa6uznVzxDAnGb0h7Fvf+hZjxowhEokw+ZpKNE1DKUX9qlCumyaE6KfAZA+GV6f5/Uium1JwDK8uk9sIkee0ObMA2D+nJLvO+X2rOq4bsaohubDuw0PcuiSjJNs2xtdieGxGz4kyd+5crrr7qkFpgxBdkY7eEObxeDhw4AC6rhPZncBdZmB4Ndo+knFeQgxlug9qzyjFNhUNq4K5bk7B0T0aypInpUKIgVU9JYzXW8RFF12U66Zk2ErDztMyBvnarkIiHb0h7NZbb6W0tBSARfrljL+iIvlJtRBiSBt3cQXosOefLdjxXLem8OhuDduUjp4QYuD4K+MEqhNcdtk1+PswU6gQh4L0CgqJrZBcqxBD24iTivGUu2jZECW8M5Hr5hQkzaVhm7luhRCiUGguqJoSIdzo4rjjjst1c4TIkCd6BUQppKMnxBBWNNZN2Qwf8RaLA6/JkM1DRTM07JiUrBAiX7hG1wIQn1abWdc2IVkrqnVK9ul7u4yenbzhKd2aXWWWFyX3W3B0x4usfC+7rPoe0tVnz8g2weMCFCPGtuIvSfCD238gBdFFXpGOXgFRtvTzhBiSXFAxs4iq4wNgw87HG3PdooKm6WDHZeimEKK/FFWj2ygbEebyyz+dl7NsKqWh8jQLl6/tKiTS0SskSoZuCjGk6DD6E6X4x3nQtGRubO+SVuxorhtW4DSwIjLtphCiPxRVtW1UjAxxoK6UE088MdcNEqID6egVkH6MRBBCDDJXQGf8FRXoHo14o0XjmjDBzTJj7qGm+0DTNOnoCSH6QVE5NkzFqDAH6kpoORDIdYOE6JR09AqIdPSEGBp0H4z/VAW6W2P/6220rpcO3mBxlyR/7ZnS0RNiwGgud6+PsU6clVnefoIPgMgox/elSg6v1uzsUCXnfY5mJ7e3Tsmua52UnO3S05JdV7MmOXWx15295bXjPZvO2DUiOxQzfNwkAIyETXVlMxVVYS699FLOPPPMHp1LiFyQjl4hkVk3hRgSxn+yMtnJe7WN1g+lkzeYXMXJyabNkHT0hBC9V1XZQnVVK/sPlA+JTp7U0RvepKNXQOSJnhD5r2i0C1exTtvGmHTycsBdbABgtsmsm0KI3hlR1syIihb215fT2Fya6+YI0S2po1dIpKMnRN4rn5UcWnRgWVuOWzI8Gf7kJ8iJNvmBKYToueqyZmoqmjlQX0Zjk3TyxNAgT/QKxBL7Uf7whz+wdu3aXDdFCNEFd5mBssDuWUREDDCX30ApJUM3hRhA2qxpmeVdZ5QDoJyPEhzVTNK18NoOTzjWmckFRx4v/eF1+1ye45ypfTvL7cUrsut2neoBoHTcnMy6ESvrM8vW+x91fD3zjgKgeWLyg7mRviZGFjWyt7Wcxx75WYf9hchX0tErIC6XfDmFyHeGT8OOSycjVwxf6kZSvgRCiB6o8TYxuqiRfa3lHAhWdH9AnpE6esObDN0sILouX04h8p2yQdPll1uu6D5dOnlCiB6p8TYxxt/InkgF+4dgJ08I6RkUkMwTPXmwJ0TeUgmF5pKOXq4YHg1lq+53FEIMa+U1Qcb4G9kbqWBvVDp5YmiSLkEBMYzkbHK6Aenh7kKI/BLcGqPy2ACByR5CWySoN9hcxYYUSxdigLgmTwRg98LyzLrIceHkguq8/p1KZes02/GsIfXhi3Jk9DSr4zDrdrOLpyfOdT6ySB/j+CxNaclzt0zNrgyNydbHq5mUbHsikD6Rorq0hWqjjb3RcnbHK0DXeP2JbyDEUCMdvQKS6ei5dOyY3MgIkY8a3g5TcYyf2jNLaVkf4cCKUPaGRRxS7jIdzQWhOulgCyE6KnGFGeOvp8hIsMuuZF+snKFeoFjlcR09yegdejJ0s4DI0E0hhgALdj7ejBWzKZ/pZ+oN1VSfEMh1q4aFsiOL0DSNlvXRXDdFCJFnRnibmVqyB8s2eM8cR51dxVDv5AkhHb0CknmiJ19VIfJa7IDJ1gcb2f1CC1bEpuIoP6PPL8t1swpeYJwH21TE62VsuxCivRpfCwBbQyMJ48txa4QYGPLsp4Ckn+hpLh2ZVk6I/BfaGmfr1kbGnF9GYKyH0eeWsvufrbluVsHSXBrKkolYhOiPyMXzMst1ZySfeAXGtmTWeVI5O7tdRi+7bFl6al32e1Fl8niOJ2i61uHYTG4PRwSws9sdzXGezGL2eqY/udJlW3hmtjIiEsITS34AVFrVRjhaBcDKv/5XJycXYuiQjl4BcbvdAJTN8GGG7ezPOQ00newPPi21qGnZZZJ/T67nY+tBS+378W12XBHeFSe4JYYd60VjXTDqlBKMgI4VUZghi9COOJG6RPfHClFgdj3TwpgLywiM91L7iVL2PC+dvUPBDNn4AvJrTwgBmlLM272TgJm97zDRCKje3MzkPwWoPP18K0+bVVDkN14Bqa5OziJVdkRRv86jevkToWSqj5qTFWbQpu6ZZsyWzp8m6l4onuzFiipqzyxNDhxWZDqSFUf5UUphhW1iDSbBbTGCm2LYMm+CGAZ2PdXC2IvLKZ7opfasUva8KJ29gRarT1A0yo3u17HDMupBiOHMsG28VvIpXkLT2aOXMd5qot4oznHLhBg40tErILNnz+ZHP/oR1834MtgKZac+xbEBlVxWdurmJv0/O7Xc13seFxRP9FIy2UtgkoeJV1bSuCZM45vhdruVTPMy8rSSTKFoZSv2LGnNTC/vKtYpmebFP86Dt9KFf5yHwHgvNScprIhN45owLetkAgVR2OqeaGbsJeUUT/Yy6swS9r7UlusmFRSjyEAphYpLJ0+I4c40DN4eOYbqSJidxeXM3b2TZq2IA3oJnlw3TogBIh29AjNixAjiDYM4V7sJwU3JJ2+eCp0xF5RTdVwAd4nBvn8lb1IDkzyMPL0ElVA0vhemeIKHhjXhdjXEzKBN09oITWsjyRU6FE/yUjIl2fmrObEE/zgPe56TpxyisNU93sy4T5ZTMtVHotWiYXW4+4NEt3QfFE/0YIVtlMzFIkSP6EfPyCxvu6Q8ueCsUVeUHPbo92R/n1sqmcEzrezMcJajZp6uJ0cN2c7cnp7O7TkyeJncnmOGuXb18VKr2uX2UiOSHLdB6aiJch6c2q854CNkFeEPx/CrBNv0ERimRtWDbyb3+wNCDGnS0RMDJt5ks/VPjYy/rILS6T58I91gK9zlBsqC7Y82YbbZHZ72dcqG4OYYwc3JsfJjLymneIKXiuP8NL0tN76isO38RzOTrqmkYrafto1R4k3yBKo/PFUGYy8oBx32LwvmujlCiDxTaibvNdoKcLZNGw0tT8tE2HnarkIiE/GLAbdjcRNtm6O4i3XcZQaR3Qm2/qUes63vN6t1jzdjRmyqjvXLxxNiWNj1zxY0TaP0sMK78RhMI04MMP6yCnSvRv0bIUJbJfQrhMgybIvx4SaCeLE0I9fNEWJAyS2zOCT2LmkDBjZftH9pG7VnlzL6LJmCXhS+eIOFUgpPpfyY7gtPhc6Y88txBQwSbRZ1TzdjtsqTUSFElsc0ObZlDz7bZJ0+NtfNEWLAyR1EAVpiPwrAIv3yHLdkYIW2xYnuN/GP8zD24jL2LmnDDMmNmyhM/vFuNE0jsk8CZd3xVOiUH+WnqNaNMsFO2PhGJcvNNL0Ton6lDPcWoqdcUyZllrefU55ZHn/yjuR2Pft710zl5xJW9kmYmcnoZdclHDm7dHbPub3T3F7q6ZptZa/nzNlpqfCdctTMS+f12s8drqX+m12rmRpuy2L+7jp0E/7tHUvFg5spTW1/Pv4QQhQC6eiJIaXu8WbGXFRG0Sg3E6+uZMejjZJfEgWpfGYRSila3hs+nZSaU4oJTPBgeHWUpYg3Wxx4I0h0b8fObslhXkoP8+Eb4UJ368nZNC1SdUMNEi0Wu59rIXGQci9CiOFtcnMjLstmrXc8Md1NRa4bdIgopaFUfmbh8rVdhUQ6emLI2fVkC75RLsZeVE7tOeW0ro/Q9E4k180SYkD5RrmxwvawqCPpLtMZd2kFhlfHitrEGk0Mr453RPL7fP/rbbSujzHi5GJKp/vQjOSn+Uol63e2fhSh+d2wdOqEED1SlEgwrrWFzRWVxKLuXDdHiENGOnpiSIruNYnsTeCv9VA9v5jAJC/hujhWxCa0M47V1rui70LkE1exju7WaE3NOlvIika7GHNeOehwYHmQ5veyH9rofp1Jn65kxPxiwjsSlB3hQ5mKcJ1JcFuM1g3RvtcAFUIMW1ObGogbBttLy/FKiV5RwKSjJ4asXU+2UDzFy6gzSiga6aZoZPJTOaUU4V1xrM05bqAQfeQblcrn7U7kuimHVOlhXmpOLQEb6p5s7jBE0w7b1K8MMmJhMZM+UwXA/hXJp3tCiENPOSahLPcke0QuPVukLm4nbyNNRwYvbicPijsyeDEre7tp6sn1CSP7KU3CTK5z1t5Lx/E0PTu8z9ay2+3Uvs7Bf+mPeDtbl15bGolRGwryob8GV6vB2L9vBeC5RimaJwqPdPTEkBbcHGPT5hi6X8dwg7vMoGpOAP8YD6VHlOKaZhHdm8CKKBIhCytsY7bZMomLyGue8tSNUNDqZs+hq2qun4rZfuyEYsfipoPOiNmyLkpkT4LKYwMkWkzp5Akh+kxTiiP21hPS3ez1lHZ/QAGwlYaWp1k4O0/bVUikoycKgh22sYFEi014RzNFtW6KLghQVOvGP9rTYX+lUp/xKVAWKEsR2ZvgwLIgZlA6gSK3WjdGqTzOT9XxAXY91ZLr5gy46gV+yo/yY4ZsdjzS2G0OMd5gsXeJlFQRQvTPlPomKiJR3ikZ2262TiEKlXT0REGK7jPZ+1Ib9TuaMPwa7mIdV8DAFdAxijSMIgPDq6F7dXSPhuHVCEzwEJhQScOqkEzuInLKbLGJ1ZsU1brRfWAXUIakdIY32ckL2mz7a2OumyOEGAZcls2E+lamHmhiU3UFLVZRrpskxKCQjp4obDaYrXZqWFjX9cg8FTpjLqygal4Ab7WLvS8NbMF3IXpj/2tBxl1azshTStnzQmE8zdJ9UHNSCXZcsf3v0skT4lDR5s7KLG+9qAQApTt3SCbW4j43u2JxFJAeRWeNzD5iv+DItzqcO+E4kZkK8TkzeOncXlTPrnPW3ounMnqG4xg9laTTtGyuL/3AzdScDe/IJru9OJJgbH2QsmAcXSk0pdBt8McSGDbU+cvY6api0sN7M8c8V/e/XZ5/qFMq+Scf9bVda9as4fnnn+f1119n3bp17N+/H7fbzejRo1m4cCE33HADJ554YpfneOCBB7j++ut7dL3777+f6667rm+NzTHp6AmREm+y2frnBsZdUk7JVB+2Cftflc6eyI3YAZNEi0VgggfNBaoA6qaPPDWZidn9z5aCeD1CiPxQFDWZVtfCmAMh4i6dxmIflq5haxoKjX1lfvbZFcQNue0d6k455RRee+21Duvj8TgbN25k48aNPPDAA1xzzTXce++9eDwd4zvDifyLF8LJhp3/aGb8FRWUHubFjtvUvxHKdavEMHVgeZDR55Yx8rQS9i4Z4h86GBCY4CHRYhHdJ708IUT/uUybaVubGbc/SNyls358BY0lPkqDcfxxk5jLIOjx0FhShHef3PIWgl27dgEwevRoLr/8ck466STGjx+PZVmsWLGCO+64g127dvHnP/8Z0zR56KGHuj3nCy+8wOjRow+6fezYsQPW/sEm/+qF6MSOR5qYcFUl5bOKsBOKxjfDuW6SGIbCOxPEmyyKJ3sZe7GBGbKwYwq/y01zo06ieejMyllUmywZ0bK+gAKHQoic8UdMjl1fjzdusbm2FI9lM31XC26rCQXE3AYe00JX8NKR45Fb3sJw+OGH87Of/YxPfvKTGIbRbtv8+fO55pprWLhwIR999BF/+9vfuOmmmzj55JO7POf06dOZOHHiIWx17si/eiEOYvvfG5l0dSWVx/rRNGhYLZ09Mfh2LG5i/GUV+Ea6ABeaplFaWox7uk281WL7w41Domh4UW2yzmW4wGsDCjHYNFfye8tamM3l7TzTl1k+4Yx1ALgd9e9cmgUKKs0y2tzNoCl0zU7tl/2Bojt+uOhaMlBlOorrJVL18zx69il9xHJ3WOfSs8Pnopqr3fkANC2d0ev4+pw5LmUkd3DHbeb/ez8Jt86/D6tk9gcNWLrO9pHFNJT5aPH5MF06paEYJ67fQ3GDouaxpsx5nvvo5x0vVKCU0lB5WsagL+165plnutxeXV3NHXfcwQUXXADA4sWLu+3oFTLp6AlxMDZs+1sjE66spPLYAIFJXva90kZsvww7E4PITj5hTjOKNMYcrxP3Rime7GXClZVsfyj/JzbxlLtQShGvl+8fIUT/uCwbj6kwbJtJdW1EvC5WHFNDQqVua1PF1EsiyYllopLNG1ZOPfXUzPLmzZtz15A80PVURkIMc8qEbX9pJLglhqfcYNwl5Yy/rBzdm+uWieHKjkGkLsG+l4O0fhjDU2oQmJj/YfN4s4mmaQQm5X9bhRD5LVLk4u1ZlRi2ojhicqDSh2W0v6Wtao0wc3sjB4oCxF3S0RtO4vHszLEfH9453EhHT4ge2PNiK1v+3Ei4Lo6nysWEK6ty3SQh2L+sDaUUVXMCuW5KtxrXhFG2ompu/rdVCJH/msuSHxq5LJuYu/3NvMu0OG7jfhqLvfy7elQumidyaOnSpZnlI444otv9r7vuOkaOHInH46G6upr58+fz/e9/PzPxy1AmH3EI0UN22Gb3s61UzfNTOTtAyXQvbR/Fct0sMZyZEN1n4hvpyvvC6q6i5OeKuis/syJCDCXqxGMyy1suTBb/LpuRHcJdrmdn6Y2mMnO2Mw9lAApMpZFQGqDhTm2ynKE4Rw07nWTGz6U5sn5GctnrqJfiTm1POLJ8zjyeK5UFdK7r9DV2U2NN6cnXYxo6peE4mpGsmwcwYl8Cl614v3okEx6LAPD8Oz/u+oQFaihk9Fpb29eK9Xq9eL19Gzpl2za33XZb5u+XX355t8c4O4YNDQ00NDSwatUq7rjjDu68807+4z/+o09tyQfyRE+IXmp4K4xSCv9YGYImcq9+ZRCA6vklOW5JFwwYf1kFaLBv6RAvEyGEyAuWSycYcBH1GIw6EKEoku1slkZjRF0GUbe7izOIfDFu3DjKysoyf/7nf/6nz+f69a9/zerVqwG45JJLmDNnzkH3nTx5Mt/4xjf4xz/+werVq1m9ejUPP/wwl19+OZqmEY1Guemmm7jnnnv63J5ckyd6QvSWBShwlw3vcd8iP0T3mlhhm9LpXhrXhDBb828Kzup5AQyfzoHlQSJ1MuumEGJg7Bzr54gPW4m5deatPcB7UytpLPPisSy6eWAo8sjOnTspLS3N/L2vT/OWLl3Kt7/9bQBqamq4++67D7rvJZdcwmc/+1m0j031evzxx/OpT32KZ555hksvvZREIsHXvvY1LrzwQkaNGnrDgOWJnhB9YIZsfCNclM8qQvd1v78Qh9Lu51tBgwlXVFI0Jv8+wfaP8aAsRfN7kVw3RQhRQHaN9rOv2oc7YWPrGnM21HPWyl2Ma2lld2kej3IQ7ZSWlrb705eO3vvvv88ll1yCaZr4fD4effRRRo4cedD9y8rKOnTynM4//3xuvvlmAMLhMH/84x973aZ8IE/0CtgS+9HM8iK9+zHKouf2vtTKmAvKGbGwmBELi1G2wo4rzJBNvNkkdsDENhWaoaG5NHQDNJeW/LuR+ruhoRmAoaHpYIVtYvUmkT0JovtkCnrRc7EDJrufa2H02WWMOb+MlvcjHFgWynWzsnRQtny8LkRvGOXlADRcnJ1MIl6cujF1fEyvPMmn+EXu7NNyZ8285njy00iPY53PMNEU+JSXoOZDadm6d+3zdo4RAkb60h1r6zml6/DpzpBdN3eb6fygM0eoUnleG8fNuAnlDQkMS1G632T36CLeObyKE9/eRyBisqcsQH1xEcXLDOIfuJlEjCVvfL/rixc4W2loeZrRsweoXVu3buWss86iqakJwzB4+OGHB6R23he+8AV+8IMfoJRi6dKlfO973xuA1g4u6egJ0QfRfSab762nZLoX3yg33koDd4mBu8zAU2lQMqXrx3zq4ylzBWhQMtWX2a4siO5P0LA6RHSvdPxE18I7Emz5ayPjLiqnfKYfZUP9G3nU2SM/bzSEEENHSYvJkW9nJ+6oaIwTLAoRiJjUl3vZMKqKqMfFlJgMER8udu/ezZlnnsnu3bvRNI377ruPiy66aEDOXVNTQ3V1NQcOHBiyM3BKR0+Ifmj7KNZx5k0dfCNdaIaGMhV2QmGbCjsBKm6jDtZn06FolCvZcaxy460yKKp1M+7iCqyoTetHURrfCmHHD3K8GPbssM32vzUy6ZpKyo8syo+OngHuEgMrkn/ZQSHEEKIU3kj2qWTd2CLG1kVQCt6eUcWBqiLsFrmtHU7q6+tZtGgRW7ZsAeC3v/0t11577YBeo8MH80OMfEcIMdBsiO7pwxM4GyK7TSK7TSCZZdJ9UD2vmOIpXiqO8lM+q4h4g0nD22FCW6XHJzrX8FaIkaeUUn1CIKedPU+VwZjzytAMaPp3OGftEEIMfRPWRRi1LUZTlZsDY7zsrvKzt7aIem8RdJG1EoWppaWFs88+m/Xr1wNw22238Z//+Z8Deo39+/fT0NAAwOjRowf03INFOnpC5DE7CvuXBtm/NEjRWDdVxwfw1bgYfXYZdsImuDVO/coQVlielois1g0xqufZVBzlp2Saj/qVQdo+HNyajyNPK6FkejJQ3/BmmJZ1eVzkT4gB5BqTvCG0a8p7dsBBfnwnypLfPy2Ts50YbUayPIkzG1ekJ5ebQkWZdW6Xs9Zd8gI+V/YDSJ8rgabAr3lotVMZvVRNvGJX9meFrXf80FJ35PYyeTxnbi+97KjBZ5Btrzd1Tmc+y+dK7uvM41kquc60dFCKmh0x9td4ee+oCgASMRdRnwc7mr2VnfB08tov/+s7Hdo9XCnVfU3CXOlru8LhMOeddx5r1qwB4Hvf+x7f+ta3BrBlSffcc0/mid4pp5wy4OcfDNLRE2KIiNQlqKtrBgOqjvNTeriPkmleSqZ5SbTaNL0bpvV9uZkWSVv+3ED1vABlM4oYdVop1fMs9i8LEtpyaJ8EF412UXt2GYZXJ95ssuvZFsw2+SBCCNF3lfsS6Dbsr5Fproe7eDzOJZdcwvLlywH4yle+wk9+8pNenWPbtm00NTUxe/bsg+7zzDPPcOuttwLg8/m4/vrr+97oHJKOnhBDjQUNq8M0rA7jqXZRPS+Af7SbkSeVUHNCMaG6OA0rg8Sb5OZ6WLOSk7HUrwwx4oQAZUcUUbuolJYNUQ68FjwklxxxcjFlR/jAhgMrgjT/W8opCCH6QSlGbY8yaX2I+lEe9o2Ujt5wd9VVV/Hiiy8CcPrpp3PDDTewbt26g+4fCASYNGlSu3Xbtm3jtNNOY8GCBVxwwQUcc8wx1NTUoJRiy5YtLF68mMWLF2ee5v3yl79kzJgxh+5FHULS0RNiCIvXm+x+tgWA8llFlM8qIjDeQ2B8JVbYpnl9lKa14YMODRLDgA0HloU48EaICZdXUHaEj+CmaCoLOrCKJ3hAwZY/1WMP7khRIUSBKWqwGPVenECDzZ7xXrbOCIApWbzh7rHHHsss/+tf/+Koo47qcv9TTjmFV199tdNtK1asYMWKFQc91u/38+tf/5obb7yxT23NB9LRE6JANL8Xofm9CK5iner5AQITvVQfH6DqOH+mTMOhuLkXQ4QNOx9vYvJ11Yw8rZRtf20c8Eu0bYpScXQAX42b8E6Z3lwMb/vnl2eWm+Ykvx/aZZLSywepJWYEkse43Nkn8JaZzK1Zjv1U+njHeaKO66TnKXF5HBk9TzKjV+zyckBTKA0CnuSnM6adzdb5Xdmh3gkjud5ZW89OTSPt0p0t6siZ6ysykueM2dlb0GDCA0BrzIcRV4x7J0LF7ijBUoM1sytoqvKCCQlHHs+OJwv7jXk2296lz36jy3YMR8mMXn52kHOVHTzuuOP4y1/+wooVK3jrrbfYs2cP9fX1mKZJRUUFRx55JGeccQaf//znqampyU0jB4h09IQoMGbQZu9LbUAbxVO9VB7rxzfSzdgLK7BiNm0bozSsljINw5Edh9aPYpQd7qPiOD9Nbw/sTJiNb4cpP8pP1dwA4Z3NA3puIUSBUwp/o8nEtyIYCfjo6GIOjPYQTXUChYCBKXdQUlLC1VdfzdVXXz0ALcpv0tETooAFN8UIboqhe6FqbjElU72Uz/RTdmQRwS0x9i5py3UTxSDb/2obgfFuqub4cRfr7F86cHk9Ow6RXQmKxrgJTPYc8olfhBAFIAJV75gEdtm4gwnCZTqbFvppdntz3TIhhjy9+12EEEOdHYMDrwfZcn8DdU81EW+yKJnio3xWUfcHi4Kz9aFGEs0WZUcUMe6T5QP6m2DX8y0oG0aeXCK/YYQQXdJaNIqe9lCy1SZco7N5QREfnRIgHpAfHkIMBHmiJ8QwE9ltsuORJqbeWE3xFC/N78nMiMOOCdv/3sSoM0sonuJl/KXl7FjcPGDnblkXpuLoAFO/UI0ZtGl+NyL/zsSQ4Zo8MbO8/YqORZL1VPzUGUtzRNCIViX/XzynPrOuMpWRMq1sB8ZKZeGcI9GcWSq7k1yV3sl+6bCfc0Cb5qhH19lIt2jcjQZEbBchPCgNEqn2RE13Zj+fKztsstybLN/jN7JP6tN171wq+7qcGb60hGP7gVgJKEjUuRm1KkHCo/HRyQFMn04o5oFEto0AZszIXi+WvW0d9XJyefnir3d8gSJDKS2PM3r52a5CIh09IYYpZYPulh+yw9nel9qodWkEJnjwVBnEG7qeUKGn6leEidVblEz3UVTrZsTCYlwlOvVvhAbk/EKIIUqBey8Uvwue/QkiVRp7FroxdXmCJ8ShIB09IYYpK2rjKpFfrsPdgeVBAhMqqZ4bYPdzrQN23raNMdo2Jmfxm3RNJeWzitBc2iGr4SeEyGMK2GOgrfFQuQ8SlbD7RBehWj05LajEeYU4JOQuT4hhKronge7WcBX3/seAq0Rn9LmlTP5cFdUnBA5B68RgMdts4o0W/vEeas8pxXUIsjF1TzdjBm3KZxQx8oySAT+/ECI/KQVql4H2bBH6c36wNJpOhcZzIDTayNZ+EEIcEvJEb5hYYj8KwCL98hy3ROSLpvciFE/1MvGqSkLb4/hGujCKUjf5KvnEL9ZgEt6ZoHVzDDtsgwE1JxZTergPAE3T8NW4u7iKGArqnm5i7IUVBMZ7CHymkpYN0QF98pZottn210bGfbKckqle4g0mTe9IZk8cOvqxRyYXOutIHGR69tbpyQ8h9l8Yy6w7beq/AWhL+DLrwqkMW9TK3kIlrGyOzFTp7F3Ha1tG9oOUeOqYznJ7AMpKHt8ue6en/uask5de1S7r5/xL8jzpGnyZ44C47SKGC+Voh+nu/MOe1tSFTFd2u8/uWEfPo5ugQN+j43rHhbHPIFEJwVMhNtqgNeGDOITi2Vk1w6k8XjSW/X2SSOXxVDz73lYvz77nK/8i2byeULT/N5RP8rVdhUQ6ekIMU7H9JruebWH02WUUT/ZiJ2wi+xJgg+7RcJca+Md5CIz3MmJhcebGQdM04q0Wze+GqTmxhNAOGXMz1NlR2PFIE54qg9qzyyifUURkV4Lg5lj3B/fCzn80M/XGagKTvNLRE6JA6buzHTy72iZ2ZozGGq88vRMiB6SjJ8QwFqlLsPmBegJjPYS2d9Jh0yEw3oN/nAfDp6Pp0LoxSmhLnFFnlqCUomV9GJBf4IUg3mCx/e+NTP18NSVTvQPe0YPUJEAu+fciRKHRglC02sC9053p4Nlj7dSjQ/meFyIXpKMnxHBn0XknD8CG0LY4oW0dtxfVurHjCjsKmqR9C4cFVsQmMNHD5M9V0bohQv2K8ICdXtkKTTp6QhQOGzzrdXz/1lFuiJ8Sx5pkyed/eULKKwxv0tETQvSeDoZfJ7IrkeuWiENg5z+aGbGwGF+tm4qjA3hHuNn1VMuAnFtZoBnd7ydETxmVFQDs+uwRmXW+Mw902C89cvAgET1OGPUWAAnlqNuWuhF1ZtBcenK7S2XrxTkzeumbV03reCFDzx5TlFpO6NljY6bzPKk2OHJ7ndXWS3eoNEfiqf0oSdVuPwBdU2iAodkY2ChHe53Hxh3tsexkTtH5WovawpSuAqNVkTjcJnaMhe5VgE5rPJtrbHXk8dpiyfWReDaPF4snb0cT0extqZ3K5lWsye5X+eCqbOPuRQjRDenoCSF6rXSaF03TaNsczXVTxCFghmz2vJgstTD6vDIC4zxUL/APyJM9O65w+eVTXCGGMiOmGPlenPLtEK+G2HkmdpVMrSFEvpEBV0KIXiue5kMpReuHA5/hEvll97MtJNosyo/yD0gpjejeBJpLo2i0fM4oxJCjFOVbTaa8GKFkt0XrXGg6G+nkCZGn5DetEKLXfCNcWGEb7O73FUPfjn80Mv6TlVQc5adkipdYg4lS4EqV47BNRePb4R4N5T2wvI2SaV5qTi1l+0ONh7rpQogB4g1aTFgTobjJonm8wb5ZHsorZPbcvCf1FYY16egJIXqlZJoXw6vTvEl+wQ8XdhS2/bWRkaeXUDwpOQtrckNqBx3GnF9G87vhbod32nEIbolRPMWLt8ZFbL95aBsvCkbwyvmZ5dCojgOSwrXZu8YFI3Z12K5ryX+wtup8MFPMdnXYnkgtm3bHYKkzL2c7AnDp9X0ZMmXojjtfVzIXaNvZdYlUZq6z+2PlaIMzH6hnsnfZdYau0BS4NBs3FkoDPXVtZ44w3R5/o8m0lSEsr8aBMzTiIxXFxJI18xzXAGiOFwHtc3nOmnmhWPLnRzyevQU1Y8nXZcey73Pp+8ls3qi/vp9Z93zi4U5euRDiYKSjJ4TolREnFmObigPLB66gthga9v2rjX2drNd9MOGKSspn9SzH1/BmiJKpPkqmeqWjJ0Se0xOKSW+HiZdo7D7Zjc8v37NCDBWS0RNC9Njo88swvDqNb4dk2KbIsKPQ9E4ETdconuLtcl/dA6PPKwfAisq4HSHyVWl9Am/QYvy7EVxxxd75bmyPTKQkxFAiT/SEED0y+txSAmM9hHfFaVorwzZFe8HNMUacUIxvpKvLQus1J5fgLtFpei9M05qBq88nhBhYR6xIjtpQwLbjijCL5dO9ISmP6+iRr+0qINLRE0J0a+zF5RSNchPeHWfX0wNTT00UFjuRvAnUuvm97S4zwIb65aFBaJXIZ8YR09v9XdNAr/Vj+Ko7rXXnvCcMHp/9sGnB5K0d9m0zOz5Z1jupa6d3knbrLG/XLo/XxToAK1X3TmnOunYHf3rtzMQ5aalvpoTj3HpqX2dtvY9vSy4783jp782OGT03Jl7NRGnZ711Dtyk+kBye2TZKp36Gm2gl+FLncTmu40rlHtO5PICWVJ28UNyTWRd2LMdiyVtPM5a9BVXx5OsJbM7WzBv7SPLr+lyTFMwToq+koyeE6NL4KyrwVroIbo2x54XWXDdH5CtX6ua2u9GYMlpTiLxVtSPGpHeynei6Ezygy1MXIYYq6egJITqnw4QrK3GX6LR8EGX/q225bpHIY97y1OyEbV0P71KWknS4EHmqeme8/Qrp5AkxpElHTwjRqcBED55Sg/DuuHTyRLdKpvlQShHZe/Baer5RLnyj3NhxeawnRD768IRi/GGT6q0JqrbHuz9A5D2lejDSIkfytV2FRDp6QohOhbbHUZbCWyU/JkTXfCNdlE73YUVsYgcOPvW6K2CgaRqaDoHJHkJb5EZyOIuNKQVg62XJR7waMKXGZg/udpm39EQSimyu02Vnt6druTlr3Tlzdmnd1cLL7Oeoo2d2koUzM7X1stssx3L65tVSzrp2Ha/TWWaw3XY61r/Lns9RJy+Vx9PbrXPk6Ay73fkg+f5oJN9bGw0FGCjQNKwAlO1L0DbSaJfH8+jJun7pXB5ks3ltjjp56eVILJu3a1czL5pcVvHs18O3O7luwqN7Muueq/vfDq9bCNE7MoBGCNE5CxreDmF4daoX+HPdGpGHXKU6Yy4sY+zF5aDB/te7rq0Y3ByjflUQTYfaRaVUnxAYnIYKIXrEiNtMWBXFHVHsO7LrUilCiPwnH9ULIQ6qaU2E8iOLKJ/lp3FNGPvgs+aLYaZspo8RC4sBiNWb7Hm+FTPU/fTrTWsjNL0XYeoN1QTGe6h/Q2bfFGLQKEVVXYLSAyaemE25D0oTQRTgidr4ghZosG2Bj1iJThFWrlsshOgH6egJIbq09+VWxlxQTu3ZZex6SkorCPCOcDFiYTF2XFH3RBPxpt7V16qeG0DTNFrejx6iFgohPs4bspj4boTSepO2CoOET8f0apAAFITKDRrHuGmdZGB5ZRKWQqHyuI5evrarkEhHb5hZYj+aWV6kX57DloihIrLbJLovQVGtG98oF9G9B89gieGhbEayTtaOxU3dzrLZmeJJXuy4TfN7ke53FkOCMfMwAHYtqurxMcFjkx39q2etSq5QGr54FSFPE85/VXYqZWIdpG5dQiWzXqYjO5bI5Ogcub1Uhq19Ls+Rx0sd02nNPEfmL24ZqfY4c3mOOnqp9e2ydSp9NeXYr+NNrvOcCdPInKe4OcERa9rYO9HHrik+DCN7Ho+RfOrmzAm2ywcmoGZHlHEfRkh4dN6fW0LLCA+agtFaEXtJ1ij0upI/272GiSvVznY181LLwUS2Jl5bIjm8s13NvGhy2ZnLs2KOfGRq2dOQ3T7pH40APLfx9g7viRCi76SjJ4To1u7nW5h8bTW1i0rZ+ufGXDdH5JinMvmrI10kvbeUDZ3MlSGEOAjLpeGN2Uz4MIwnarFjVlG2wrlSGHGFO6Jwx2zcMYUeU7hjCn+rSVm9iWbD3ok+th/mx3bJN58Qw4V09IQQ3bKj0Px+hIpZfsqPLqL53/IkZjhrWBVkzAXljL2wgh2PNPX6+Oi+BCXTvOi+5L8tIUTXIsUutk/1M2FTmNrtMcrrE9iGluzYxRUfn5jT0iHh1Yn5dXYc7qdplJtIkdzyCZFr+/btY+/evYRCIdxuN+Xl5YwbNw6fz3dIriff9UKIHqlfHqL0MB9VxwdoXhdBMvrDV2S3SXBzjJKpPiqOKaLpnd51/IPbYpRO91E8yUvrBpnhR4iPK24yqd4eZuORxbjjNjW7o4zeHqGl0sWuqUVU742hNEh43agiSHg14m6DhFcj4dNJ6FrmiV9mWKnULBuelJb8k4/ytV0D6I033uC5555j6dKlrF27lnA43Ol+kyZNYt68eZx11lmcf/75VFX1fBh8V6SjJ4TosX2vtlG7qJTaM0rZ82JrrpsjcmjvS20EJngoP9rf645eOtdn+DvWNRN5Ssvmv1yTJ3TYrFKdisiobG/ixvOW9O4aSsNtRXG5wu0ycencmnNdwpG9s1I5u5jtqNWW2p7Qs+1Ob3fm9uKqY5WpdvXoUsvpXJ7z2gnHOsvueMOq2tXRUx1eQ3q7M8tnaDb+ZpMjlydLlVQ2xXCHFEqH1loXO472YXk0dozKlrxJ5/FMy/FaOhlV3aE9CnTNxtBslAZGKoPnNrKf4jlr5kXN5PvXEs8+eQinsnntauallq24M5eXbZseSq6f9GS2HMvz/761Y4OFGKL27dvH73//e+6//3527NiRWa+6qBC/ZcsWtm7dysMPP4zL5eITn/gEX/rSlzj77LP71Rbp6Akheiy0JU68ySIwyYOrTMds6VtGSxQGZQN27x8T+EYmf/UkmuWxsBjeNFsxbU0Qf6vF5tkBNE0xfVWy5Ei0TCNcZRAqN2gd7cLyaO0mXBFC5Jfdu3fzs5/9jD/+8Y/E4/FMx84wDI488kiOO+44ampqqKyspKKigkgkQmNjI01NTXz00Ue89dZb1NfXk0gkePrpp3nmmWeYMWMGN998M5dddlmf2iQdPSFEr+x+oYWJV1Yy8uQSdj0t5RaGs3iTRdEoN5M+W4kZtIk3WyRaLVRCYcVtVALshMKO26BreCtduMsMSqd7UbYiuFWGbYrhrexAgsq9CeJejRnL20CDYLnBR/OKKQokgOxsoEKI/PXjH/+YX/ziF4TDYZRSjBo1iiuuuIJLL72U448/nqKioh6dZ+vWrfzrX//ioYceYunSpbz//vt86lOfYt68edxzzz3MnDmzV+2Sjp4QolfMFjt5gz/aje4BO57rFolcqXuimdqzS/GNdOGtdOGtdqFp3WculFI0vBnudHiZEMOBZikq9iaYsCGZ19lyTAB3zMawFA1jPTIzphgwSiX/5KN8bVdf3HLLLQB84hOf4Otf/zqnn346ut77D2kmTZrEDTfcwA033MDevXt54IEH+NWvfsXKlSt57LHHpKMnhDj0DrwRZOz55VQvKGb/0mD3B4iCtecFR1bTBZ4SHd2jo3s0NLeG4dXQXTpKKeKNJrEGE1se5A0J5plzMstbr86urxzRBny8Hl1SqZYtv1JsJKdUNXrao1cauhHDa0TaHZHO4KXr5QHYjhuo9Hqfnt0etZM5MdNxjNtKnjWmZW99nHm89JOzuJXdbqc+uHDWk7NVxyHH7Qo/28l9nZ95+NzJp3NlwRglW2xKttsYcYhUauw+woM9xiKmaZjKwJ2a6SqWakdn9fGceTy7m+Gc6a+T87W6DBtNgWEoXKmMXjqP53ytzqeJbfFkzbxgzJtZF03l8RLOmnnpbF7U0a5EdnnSk8kfAC+u+EGX7RZiKLngggv44Q9/yHHHHTdg5xw1ahTf/va3+cpXvsLdd99NIBDo9TmkoyeE6LVIXQIzbFE8xSsdPZFlQrzJRh7ViUJihBQldRZtU/X2vbeeHh+3qdyRoGKbSVGzjemF1sk6zRNcxEt1yd0JUQCefPLJQ3buoqIivv71r/fpWOnoCSH6JLI7QfEUL7oHlJnr1gghxMDyvwNFMRvbC6XrFK4wNB3dw5lilaKk3qR6R5yKPQk0BW2jDJpnugjXaqBrmNLBE0IcYtLRE0L0SXBbnJKpPoqneGn7UIJ6QojCUvQh6Ins38s32N129Dxhm4rtcap3xvFGFJFinV2H+whN0jB9OkWuRJfHCzHgFPlbQzFf21VApKMnhOiT4NYYSikC46WjJ8RQ0HT9AgDipY7hh12MRDSz5dI468h3MssLyzYe9BjdMWzX0Hp2F2dlCnpr6FoMW4+QoGOHypnRa7/s6rDOrSVzbumsnrNtuqM2nHPZWYcPIHgxlD6aPD5eBXoMPHo2n+dJ1ZvTTEXZgRD+LeDdC7YLwhM0Dkx0EavSQLPRbB03FjHTUesvlX9rl8HrYR6vs7p9Tp3V8NP09vX6tNT/Db19HT1n9jIYz+bxQqmaedF2NfOSr8fupGae5qidN/657Pv28ivf6bLtQgw3+/bt45lnnqG+vp5JkyZxwQUX9HiWzu5IR08I0TcWKFPhKZei10KIwqP80Hi+ovwl8DRoWB+77zIiivL3bQLbbYwExGqgeQEEx+ool0ZchmYKMext2LCBm2++GU3T+P3vf095eXm77U899RSf/vSniUQimXXjxo3jqaee4qijjur39eWnkBCiz5QNmiHTgAshCpNVDsHjk8tG9j4Md4tizPMmgZ02bdN09l8AjYs0IpM1lJRGEEKkPPHEEyxevJh9+/Z16OTt37+fz3zmM5nae+k/O3bs4IILLiAUCvX7+tLRE0L0mbIVmjzQE0IUKC0GgTXZv7vrFa6gYtRSE8sHdee4aDrKwCqVzp3IT0ppef2n0L388stomsb555/fYdvvfvc7gsEgLpeLX/3qV/z73//m9ttvR9d16urquPfee/t9fRm6KYToMzsBhq/wf1ALMVTpHk9mOVaR/F496vL1mXXnVb0LdF/rznJ8Lpze161lp9vtLI+nd3NO++OfNSsNS4+DHsNylDGw0/XtOsnlJc+TnODEmcdLb3dm8AwtndHLtlXvJkeodA0tdV3braj+V3JmC9sL9afq6EUKHUXYzF7btDtm77rK45nt1jledyd5vK5q5jlfifOnsp7K5rkMx3uhq2QdPc1RRy+V0XPm8oKx7L+faDxVMy/meO9jqa9JLPu10eLJq495Nduipf/85kHbLUQh27FjBwBHH310h22PPfYYmqZx7bXX8tWvfhWAWbNmsXHjRv7whz/w5JNP8pWvfKVf1+/TE719+/bx1FNP8b3vfY8zzzyTsrIyNE1D07RMZfiubNu2LbN/T/9MnDixL00FYOLEiQN2jb/85S8cc8wx+Hw+xo0bxze+8Q1aW1u7PW7jxo1885vfZO7cuVRWVuJ2u6murmbGjBmcddZZ3HLLLbz66qtEo9E+v04hBlu80UR3a7hKZXCAEKKAKND36BS9paNFIF6jaDoXIkdAaAbUnwuWXz7kEkJ0bf/+/QCMGDGi3fr6+nref/99AD796U+323bhhRcCZLb3R5+e6I0aNarfF+6tww47bNCv+XE//vGPufnmmzN/r6ur44477uBf//oXr7/++kEr1v/0pz/llltuwTTbFxtraGigoaGBDRs2sGTJEn70ox/xrW99i9tuu+2Qvg4hBsqBFUECEyoZe1E58XU6bMt1i4QQoo9MoMHAtdOFsdlAD+pYJYrIsTahw3TQIXw0mOnhZl0/sBQif0gZg5xJT7Ly8Qc5y5cvRymF1+vlxBNPbLettrYWgObm5n5fv99DNydPnszYsWN57bXXenzMmDFjeO+997rd73/+53946KGHAPjsZz/b5zamXXTRRfzkJz856HaPY4jLx61fv54f/ehH+Hy+zJPMHTt28MMf/pC1a9dy6623dtpBu/322/n+978PQFlZGf/xH//BKaecQm1tLfF4nLq6OlatWsXTTz/NBx980O/XKMRgMltsGt4KUzXHz8jTS4j7whxY3v/wsBBCHErKBrvRhbXPi33AQB1woTUaaEpDcyusSRaJqQnCVUZyHKR06oQQfVBZWcn+/fvZsWMH8+fPz6x/+eWXATj++OM79D/SD4YO9gCpN/rU0fvhD3/IvHnzmDdvHlVVVbz66qucdtppPT7e7XYzc+bMLvexLItXX30VgJKSEi6++OK+NLWd8vLybq97MI8++ii2bXP77bfz5S9/GYD58+ezcOFCpk+fziOPPNKho9fQ0JAZyjp27FjeeOMNxo0b126fefPm8clPfpLbb7+d1atX09DQ0Kf29cUS+1EAFumXD9o1ReFpejtM2wcxjryhmLIji6SjJ0SOxc85PrNcd012JMmv5j4ItM/WuUnWNzO03vdkDMdjgs7yeN2d00rl1tJZPaU0EnoU3QhjOpIl6XygM5fXWV5Pd7QnqlLLjiYoBaEPigltKMWqd4Opgaag3IYRFrFpYFUrwqUu0A3AyOTnnDm6uG10WNefPJ7dTUbP7m7CivTLdpZHdGQP0/XxnF8PQ7fRFOh6to5eayxZODEcz+YNI46MnpnK4bWrmRdNLuvx7MVrViX/v+wf3+i63UIMA0cffTRLlizhoYce4oorrgCST/ceffRRNE3rtP+0fft2YGBGUPapo/ejH/2o3xfuzksvvcTu3bsBuOyyy/D7/Yf8ml3ZtWsXQIcvyJgxYzj88MNZt25dh2NeeOGFzCPbb3/72x06eR83d+7cAWqtEIPLitjYURkbIoTIP8qGtg9LqF9didXqxj0hiuf4NoyaBMFyN6T6NYlEqkK8dfBzCSFEb1x55ZW8+OKLPP3001x55ZWcdNJJPPLII+zbtw9d17nqqqs6HLNqVfLTksmTJ/f7+nk76+af/vSnzPJADNvsr5qaGgCWLl3a7qng3r17+fDDDzvtdadn2gGYOnXqoW+kEDlSVOvCXWbQtNnsfmchhBgk0X1e9r1cQ7zBS9HkEKXn7ide7ngi5ZgtU4hClM9lDPK1XQPp2muv5b777mPZsmU8+uijPProo5lt119/PYcffniHY9KzcZ5++un9vn5eTpXX1tbGE088AcCECRM4+eSTc9sgyAwd/e///m/+53/+h5UrV7J48WLOOOMMQqEQl1/ecfijc8zthg0bBqupQgy6UWeVgoI9z7XkuilCCIFtauxdNoKdj45F0xXjrthJ9bkH8FQnct00IcQwous6zz33HF//+tcZO3YsLpeLcePG8YMf/IC77767w/5PP/0027ZtA2DRokX9vn5ePtFbvHgx4XAYSPaENW1gevyvvfYaRx11FJs3b0YpxciRI5k7dy5XXXUVF110UZfXmTNnDl/5ylf4zW9+w3e/+91222bOnMkPf/jDDsfMnj07s/zTn/6U0047rdM6GkIMZdXz/RheneZ1EexYrlsjRGHY99UTsn/pYlS0swxcOnoXGpNd9/8d/VJm2a8lv0E9WnZsYjqv58zb+VLruqsx1x2jk4ZbOOvjae3WKaUR1eO49RimY790li9K9umbW8s+lYvayexZIrWubbefjS9MxGx1U3R8G0XHhAgbLpqjpQBEHPX2IlZ2OWwmP5yNW9lzp/N2neXx0tuc69pt72Eez7nOmcdT9sHvSZTz666l/+/I5Tlq5qXr6Bl6+9yepiCa8BDSvCgtm82LOjJ6iWj2NlGls3mxbHv11HLlu9n2rPzr1w/abiGGo0AgwC9/+Ut++ctfdrvviSeeyNatW4Hkw67+yssnes5hm9dee+2AnXfr1q289957hMNhIpEI27Zt45FHHuGSSy7hpJNOyuTwDubOO+/kd7/7HTNmzMDtdjNq1Ci+/OUv8/rrr1NaWtph/1NOOYVZs2YByXoZs2fP5tRTT+UnP/kJL7744oBMmypETulQPsuPGbFp+0h6eUKI3LESOttfHcP6v0/D8NqMu2oH/uNCOPqEQghxyAxE3buKigomTJgwIJ08yMMnejt27GDp0qUAnHDCCQOSbfN4PFx44YWcddZZzJw5k7KyMpqbm1mxYgV33303O3fuZPny5SxatIgVK1ZQVlZ20HN98Ytf5Itf/GKPrqvrOo899hjnnHMOmzZtQinF0qVLM69P0zRmzpzJOeecww033MD06dP7/VqFGEwjTylBMzTql7fho//TAAshRG+EW7zs2VqNrzTG+pemEA+6GX/ybjyzImg6hMyDl00SYlhQ5G8dvXxtVx8dddRRTJw4kfPOO48LLriAU089Fbc7tzngvOvo/eUvf0GlxiQM1NO81atXU15e3mH9qaeeyv/3//1/XHbZZbz44ots2LCBH/3oR/zqV78akOtCchKWd955h7vuuot7772XjRs3ZrYppXjvvfd47733uOOOO/jKV77Cz3/+c1yuvPuyCNGB7oGSaV4SbRbBLXF8E6WjJ4QYHJapse6lqex8rzazrmRMkMMv2YyvIk6jWZzD1gkhhiOlFFu3buWuu+7irrvuori4mEWLFnHBBRdw7rnnMmLEiEFvU971KP785z8D4PV6+dSnPjUg5+ysk5dWUlLCI488wpQpU2hoaOCee+7htttu67J4em8FAgG++c1v8s1vfpOPPvqI5cuX8/bbb7NixQrWrl2LUgrLsvjVr35FQ0MDDzzwwIBdW4hDQffCuE9Wggb7XmnNdXOEGNLsk5N57m1fzH68/e7Jd2aW2+zkBCJtjk+/2+zkr+9muyizLmR7e3Q9Zx29gJY8t8+R2/Omsl7GQSJiBulsXbZBVmrReUx6nbOaXruM3sfWKTSUZuHRzHYf9MdTYy/DVvL3cjTk5o3Hjya438+4+btpaSgmMDaMfbjJPq0UYtBqFaWOyX6aHjKT70+0XS4vu9xVfbzO8njtauf1I4/nzOLZliNR01lGMp3hc2xLZ/P0TmrnOZed6+KmC01BJO4ihAelQSye/DeViGVvDe1Ydtyrlsrj6fFsG8s+Sran4oGV2Tb+sWOzhRgOtm/fztNPP83TTz/Nq6++SltbG4899hiPP/44mqYxd+5czj//fC644IJMtOtQy6uO3urVq/nggw8AuPDCC7vsoA2ksrIyrrzySu666y5CoRBvvfUWJ5xwQvcH9sH06dOZPn06119/PZD8R3HLLbdkOncPPvggn//85znxxBO7PZdSKvP0sz80vfCmt9V0DU0rzNeWD8ZeWIa7RKd1fYToXkve70Ek7/XgGoz3OzOZhmOdc9rx9LLzx312Xcf9DkbRyTGOTlZ2Xfv/H/Q87drb8ZhO1zmvo9qvS08Dr5TW6TFKaezbXMm/l0zHUjpHXf4RJbVhGs3kaIJGs9jReK2ziwMfm8Cmk+X+rDvo9o/9/+PLXa3rTqfn7kF7NZU8RgNQ3bexq+3O74+BuC8pNOn7tYF4b+T9zV/jxo3jS1/6El/60pcIh8O8+OKLPPPMMzz77LPs27ePlStXsmrVKn7wgx8wbty4TKfvtNNOG9AHTE551dE7VJOw9MSMGTMyy91NyjKQJkyYwP33308kEuHvf/87AI8++miPOnotLS0D8g1fPbGi3+fIN5quUT66BDQNZcsPxYFWUVOK7tVo2WVTPdEr7/cgkvd6cA3G+22PSBbqTjjuxNvCYzPLYTv5BC7quHxCJZ+02M6neKrrGwVF8jymHs+si6ee7mla9mmPTddP9PTU7b3dwyd6znfN6qRDmX6WqJRGIlbTbtuBPWVs/nA0vkCMtpCfA9sqGTmmhdr5+/H4SyBagjv19K7Iyj7dtK3k++Kys7c57tQTwZiVXedzLJudPL1LP4GzHJ3j9CygzhkyLcfcdlYnnefsOse50+sc75nSunmilz5nJ0/0nLOcuh1V310quexR2XW20kFBMb5UuyCeuiU09exTPGU42uNOPdFzvK7ikuRy+cTyzLqWFimz83FKKYLBIEC/Z5Jvbe3tKJpMdz4P5Wu7+s/v93PxxRdnyrOtXr2aZ555hqeffpp///vf7Nixg7vvvpu7774bv9+fGeJ53nnnZWp3D4S86eglEolMR6empoZPfOITg3r9XH9C8oUvfCHz+jdt2tSjY8rKyjqd7bO36rc19fsc+UbTNVCK+u3NcjN8COjvxyk5zEdrcwvxZlve70Ek7/XgGoz32x4XBWCX4/dQib/OsUNyeKXl7OilOjC6Y+gm3Qzd1FLDNF16doZcT2qdNxdDN1X7dUpp2Gh4iupQwN5dFbzwxEz8JVGUbZAwLY44cTVjj9xHk12SOW8i9UQv4sjlBTNDN7O3OSEt+f7ENMfQTUfJhvQQ0XbDMOli6KajU9Zue6Yj3PU62/G6s++JczL0LoZuOrbpKt3Ry77THrLDc92pr63PMWTX0pLdtSgu9hBBAbHULWHC0Tm2HaUmtEQnQzfbku0xtzVn13Uxod1wlb7HLCsr63dHb6BKjonBNXfuXObOncuPf/xj6urqMp2+V155hVAoxBNPPMGTTz6JpmnMmTMn87Svv2XZ8qaj9+yzz1JfXw/Apz/96UGfkGT9+vWZ5dGjRw/qtQHGjMkWPtL1nlW90DRtQL7hC/VmUankayvU15dLDWvDlBzmY+TpJexY3AzI+z2Y5L0eXIfi/d713Ww84LpPvwjAf1Zkfw+5yN5gu1K/EjTH9e3Uz36X40mcV0922tyOTltnteyc9dbS8TC73VMlldovu87n6NSkn+gY7ToyyWMSKtueaGpdzNF5iSrDcUz7J2NKaVjoJBJu1q6eygerJlA+MsiRl27EcNs0msXYwA6zmkYrO/lT0Ew+lQrb2Sea6Txeuzp56Yyfmb2/iDo6gr3N41nObF0/8njK2RNuN363k+GnmZp5jnWp+niaI4OnGY6OYGp7OoMIEIm70RR4Ei4iyo0CzFQez447OnfR7GvQUh28wI7sxWv+8h4AzyceRnQtfc8mHT0xduxYbrrpJm666SYikQhLlizJDPHcs2cPq1ev5s033+Tmm29mzJgxXHDBBdx00019yvXlTUfPOWzzs5/97KBeu6WlJfM0ze/3M2fOnEG9PsDbb7+dWZ40adKgX1+I3jBbbELb4wQmeJhyQxWtH0RhX65bJYQYypSCXVuqefO1aYSDXqYeu5MZC7fS6irq/mAhhBiCioqKuPDCC7nwwgsBeOuttzJP+9auXUtdXR3/93//x8iRI/vU0cuLgumNjY08++yzAMyaNYtjjjmmx8eeeuqpmU9Itm3b1mH7888/TyQSOejxwWCQK664goaGBgBuuOEGvN6ezVzWnXvuuYebbrqJzZs3d7nfjh07+N73vpf5+0UXXTQg1xfiUNrzfCv1b4RQdrJo+riLynGV5sWPFCHEEHNgdxnP/3U+a147nKqRrZz1uZUcffom3F6r+4OFEAen8vyPaGfOnDnccsstvP3229TV1XH33Xdz7rnn4vf7+3S+Pj3RW7ZsWbscWXqmTIB33nmnQ3mA6667rsvzPfzww8TjyXD4QD/Nu+2227j66qu59NJLOfHEE5kyZQrFxcW0tLTwxhtv8H//93/s2LEDgMMOO4xbbrllwK4dj8f5/e9/z+9//3sWLlzIWWedxXHHHcfIkSPRdZ1du3bxyiuvcO+999LW1gYkZxs944wzBqwNQhxKze9FaH4vQsk0LyUXlzDm/DK2/aUx180SQgwRoVYfby09jC3vj6FiRCsLzlpH7ZT3CXYzsYwQQhS60aNH8x//8R/8x3/8R5/P0aeO3r333suDDz7Y6bYnn3ySJ598st267jp66WGbhmFw9dVX96VJXWpsbOTee+/l3nvvPeg+J598Mg899BCVlZUDdt2amho8Hg/xeJzly5ezfPnyLvf/9Kc/3WUbD5Ul9qOZ5UX65YN+fTH0BTfHie5L4AroyXECdreHCDEstVyzAIBxi7Zn1t1U/j4AuiOXZzgycd5Urs1NIrMuPbGG3UleR3dOyuHI6+mZ2RkdNdbSM2w6J/fIXKNjLi+5r9ZhnTu1r3MmzvQ5o45sWHq2UKXggzcn8Pbrh+HymMw5ez0TZ+6hOTKFukQFjXY2g5cufh61s3m7oJUdeRNJZe+CVrZzmK6VFzad65K3PDFHRq8/ebyDZfR6msfLbG+Xy+tk2bFdy+QnHV+v9NdVd7z3jrxeeo6fcDz7XsTjLjTAjLtI2K7kw5VUBi9dLw/aT7xStC/ZkDF/35JZ91zLfQgh8lfOM3obN25k1apVACxatIhRo0YN6Pl/+ctf8vLLL7NixQo+/PBD6uvraW5uxu/3M3r0aObNm8dVV13FWWedNeAB1yuuuIKzzz6bF198kaVLl7JmzRq2bNlCU1NylsvS0lKmTp3KggULuPrqqznuuOMG9PpCDCbbTN1NSE5cCNEFpeDtVw/j/dWTmXn8FqafsB231+q2DqAQQgxl77zzDq+//jpbt26lra0Ny+p6aLqmafzxj3/s1zX71NF74IEHOgzP7Ktp06b1q7TBq6++2uX2OXPm5GRylbSysjIuv/xyLr9cnpSJwuYb4aa1LQISqRFCfIxSsGbZdFxei/11FWz/aBTzz1zHzDnbaLN9uW6eEIUrn7Nw+dquAbZu3To+//nP8+abb/b4GKVU7jp6QgjhFJjkwfDqtL0VzXVThBB56MN/j2ft8ukA+PwxTr14DVMP353jVgkhxKG1ceNGTjrpJFpbWzMPtkpLSykrK+txObX+kI6eEKLfKo4pQtmK+lXhXDdFiCHLmXnTHZNiG6lYQcBxU2DbVnrH7DGpmwgPHXN5B+NJ5fW8jv18qetZjtE2zuLopPJ4Xi17CxFWyfzgTjOZwYtF3by7ejKhtiLiERc7N4/k8GO2M+OELfgCMXQddpnlADRaqULnSiNslhFPJIip7LnbrOQTv4ijTl7Qkb1LZ/Q6y+OFzWyuL5ZaZ9kdc3nQ+zye7Rhq2tM8nnO/zJe7s1yeY7szj5dOmOiOPJ6eyuPpjlye7jhRKJZ8X+JxRzYxlszoYWkoK7m3Fk1+7fR4to2e1uzy+EeTHfPndv0WIUTP/OAHP6ClpQVd1/nWt77FTTfdxPjx4wft+tLRE0L0m8uvY8WVTMIihABg5csz2PT+GKprW/B6Esw9bT1HztlKXJPbDiHE8PHKK6+gaRrf+MY3+NnPfjbo15efuEKIftPcGsocJoPthRBdqt9bypb1ozlm4SaOWbip3SyfwyWTI0TeUFryTz7K13YNoGAwCMAll1ySk+tLdWMhhBBC9Iltw5YdFURjLg7UF/Pm0sN46s8LKa8KMnPelu5PIIQQBWzChAk5vb480RNC9JsyFTIiS4j2XIdNBeDDL1Zn1nnGJj/dLXHWXUs95rIP8rgrndfzO7/J9GQdPct2jpdOrjO6eWzWrmZe6gN1o5MP1qOOjF7CkR/0pTKAdabG9k01PL94HslHdRqGy+KoeVs4ZsHGzM+EA+kMHtBglWSWw6nMXZtVlFyhNGzLS9Ty0WY76+Qlc3Yhq+M6yGbz0rk8gEgiuT3mWJfO45ntMnp9z+Ols3jJdXTY3i6Plz6+s5p5neTyOm5IbU7l8LR2NfM61tFL5/Igm80zo9n3QsVTtQ1tAy2RXDZiqfqIjozexH80ZJaf2/SLDu0RQnTtwgsv5Be/+AXLli1j3rx5g359uTUTQvSbskDTC38IhhCivZamAIbLYsGidfgDMUZPqMflTnZGLBk0JIQY5r72ta/xpz/9idtvv53LL798UCdiARm6KYQYALpbQyUkfCPEcKIU7NpWTUlZmMOP3sn4qfsznTwhRH5QKr//FLqRI0fy7LPP4vF4mDdvHvfddx8tLS2Ddn15oieE6JeSw7zoXg0rNgx+YgshMtaumMaOzaNYdPFbuW6KEELkrdmzZ7N06VLmz5/PF77wBW688UZGjBhBUVFRl8dpmsbmzZv7dW3p6Akh+sQ7wkXtWaW4inWUqWh6T2roCWEUZ/No2y6vAeC601/JrLus7G0A/Fr2yVc0ld9qX0cvu+zVknkz2zF7ZbrGnU9LZK+dys4lupnJzjnKurs8X5rznGHb4P01E3jztcM59sQPmXD43sy2BiuQWU7Xx4srI7OuzS5ynCeZIwum6uShNDTLS8gsImg76+QlX3/YzGb0nHm8dK08Zx4vmsrodZbHs7vJ6PU0j+fM4HWax2tXMy/1Prermae13wYfy+ilVrWrmZdcdtZHTNfPi8SyuUVnzTwrlnr/Y9mvgx5L/gszACOuJROWieTFJz3Rltnv+Xd/0rFBQohe+fvf/86NN95IMBhEKYVSin379nV7nKb1PxIjHT0hRJ+MOb8M3a0R3BRj39Ig1eMrct0kIcQhZtsay5bM5P01k5g5ZwuzF27MdZOEEF1R5G9Zk3xt1wB67bXXuPrqq7FTE2dNnDiRo446irKyMnT90CfopKMnhOi10iO8GF6d+tVBmtZEZCIWIYaBWNTFC0/MoW77CE46+10Om70j100SQoi89tOf/hTbtqmoqOBvf/sbZ5111qBeXzp6QoheK5nqQylF0zuRXDdFCNELkbCbeNRDeWWI3owKam328/Qj8wmHvJx7xUrGTGzA6mycoRBCiIy1a9eiaRq33nrroHfyQDp6Qog+cJUY2AkFMsGeEN2yHR2ihEoO1Ym1D2ul/m8e5PjkdreWzVh5U0Xqqh3rYip5fFhZmXWh1KnjcYPH/raQPbsqAaisbGP+wg+ZOWtnh+slUtdrS2XrbFvjn48dj2nqXHztMsorQ8ntti9zTLPtByBqZ3NioVQtPOd+YUf2Lmj62q9TGobpo1UrIuI4T9hK1ckzO+byAOKpbJ5zu5nK1HWWx3Ouc+bxbCtd/66HeTxnFtK5PXUeHNm6zL6d1Mw7WIc7nc1zZvTSy7pjXTyRfN1xR0bPimf/XRBLvh4tmn1delxDAzRDQ09l9MY/n/zg7sWVP+y8QUKIXovFYgAsWLAgJ9eX8gpCiN6zVa+eBgghcutfzx+d6eQBNDaW8M+n5/D+unHdHrvu7YnU7yvjjAvXZDp5QoghQmn5/afATZ06FWBQSyo4SUdPCNFrkT0JNJdG0WgZFCDEUNDYUALAVdcu5ciZO3C5LKZM3cOLzx2DZR38ZivY6mP1a0cw49htjBzTPEitFUKIwnDVVVehlOKpp57KyfWloyeE6LX6FUEAKucEutlTCJEPrrjmdf7rO48zZmwjp5y+DtvWUGgkEi5aW/0HPe71F2fh9pjMPfmDQWytEEIUhv/3//4fc+bM4Xe/+x3PPffcoF9fPo4X7SyxH80sL9Ivz2FLRD6z46BMhcsvnxUJcTDpUnnO3FpUdfZrN52py+aubEdez53a7lbZ7ze31vF7L117z+/I7enpc7tt0qGwktIE06ftY8vWEQDsq6uioiI7JDOdzdv8QS3bNtay6OK3ML0apkq+jnT2Lqyyde0yeTzLmcfrbF02oxdK1cXL5PEUeCw3Qc3bPqOXrpPnyOBF22X0ku1NmNnX3VUez3Y8wexPHk85zqM588p6+/3AUUbP+fBU62xu+a7zeOn6eZajrl8iVTPPinXM5QFoqWU9lr24kcro1Www0RoSKAUvv/rdTtojhOiPvXv3cu+993LjjTdywQUX8OlPf5rLL7+c6dOnd1swHWD8+PH9ur509IQQvVb7iVJ0t07sQDzXTRFC9MHkiQf4aONIJk/ex1NPH8eGD2tZ9Il3KC5OThwQj7lY/tJMxk/Zx6TD9hDG3c0ZhRD5SFMH+UwhD+RruwbSxIkTM4XPlVL89a9/5a9//WuPjtU0DdPsfJKunpKP44UQveIf56Z4opd4k8neV9py3RwhRB+MHdOEbeucesoGLrzwbXbvquShP5/M7l0VAKx+7XBiUTcnnvWeTLwkhBD9oJRCKdVuuad/+kue6AkheiXRZqGUQnNpUl5BiCGqojw5VLOlxc9Rs3Yyakw999x9Nn958FSmzqhj0/qxzD/tfUrKpFamEEL01f3335/T60tHTwjRK4lmm1i9ibdafnwI8XHBM4/MLHc2c3i6yLiz2Hg0lYmzHJ+cJBxZLXcqPOZxjHPSU7XyDMfjtnRGz6bzT4HTZ4/aNsobw++P8s66cXhKw5TUtHHxZ5bxxF9OZNP6scyct5mJc3bRmsnjZbN1rXYyV5LO4EE2h9fZuki72nnZ5UiqPl44tU5T4Dc9BJWXkJUdKhpL1cmLJbI/c9K5PMhm83qax1OOD6j6k8fTHMc6a+Zl8nrO86S/dp09HXXu1knNPOe69OtKxLLvhZ2umRfNvida3PHvIpXNM6LZdTVrLDQN7vnj9ZSVlWWGlgkhBtZnP/vZnF5f7tSEEL3mKjawosNgcL0QBUrT4NQz1vHCc7PZ/FEtPn+Mk896lzknfoBRZHH4sTuIK6P7Ewkh8puCg3z2k3v52q4CIh09IUSvaRrYpvyEFmIomzlrJ1Nn7GL/3nJWr5rGi08cz+FHbef4BRty3TQhhBADQCZjEUL0mhmypbSCEAXAMBS1Y5o46+K3mDB1L5s2jMFKyPe2EEIUAnmiJ4TotdYPooxYWEzJYV7aPozlujlC5ITr8GkAfPT56sy6L57zQmbZpyWS/9cTmXVWqhZeyJF5y+yvZ6fR9jnq6KXzejHHDGzuVOZLd6wzUjGrhONhuzMLaKv0OkcNtlR7GlsC1G0dwaz5W7A8OpbSabU71sQDaEutDznWBdMZPcuRx7NSdfIc60KO5Wgqh5euk6cp0Ew3QdtDxOpYJy/uyOg5a+b1No/nzOX1KY+XPqezP9zunOmDHdu7isBpHXN5kK2fpzm2J6LJ9yCTywNI1c9z1snrbLlyQ/bNWPbYN1BK0dLS0kXDhBA9demll3LzzTdz9NFHD/i5o9Eo//d//4ff7+fGG2/s1bHysZ0Qotea34+gbEX5zO6LfQoh8t+a1w7D4zOZOW9LrpsihBhISsvvPwXiiSee4Nhjj+Wiiy7itddeG5Bz7t+/n1/84hdMmjSJ//qv/2Lv3r29Pod09IQQvWdDvNHCWyWDAoQY6rZvqmHz+2OYfdJHuD1WrpsjhBBDzve+9z28Xi9PP/00p512GuPHj+eb3/wmK1euJB6P9/g8u3bt4k9/+hOf+MQnGDt2LN/+9rfZt28fJ554Ip/85Cd73S65SxNC9Enz+jAjTy6l9EgfbRtk+KYQQ9HeXRW8/NSxjJu6j+lH78x1c4QQYki69dZbufHGG7n11lt58MEHqaur44477uCOO+7A7XYza9YsZs+eTU1NDRUVFVRUVBCJRGhqaqKpqYmNGzfy5ptvsn//foBMsfQjjzyS//mf/+H888/vU7ukoyeE6JPW9TFqTlSUzyiSjp4YNpo/uyCz3HBM8v+2L5t9muzZn1l2a8mnYwG94/eHR8s+OUvn6NJ5uaTsr2cPyX0Tjq3xVBDMp2WvbaduDKyDBML01Op4arjU/t3l/PPv86moaeWY8z6kJVUfr7MMXpudHaYdTtXFS+fyIJvNS+fygEzOLmxm10Wt7OtKZ/PSdfI0Ba6Em5DmJWZnM2jxRHLZdNTO61cez1k7r4d5POc6lc7ROa7X45p5Dp3VydP0jtvTuTwAO10/L5bdUU/VzHPm8pw180q3Js+z8q//1XWDhBD9Mm7cOO655x5uueUW7rrrLh588EF2795NPB7n7bffZs2aNV0en+7cud1uLrzwQm688UYWLVrUrzZJR08I0WexRhNvpfwYEWKoUQr+9ewxlFSGOO3yt4m75PtYiIIkdfQG3ejRo/npT3/KT37yE5YtW8Yrr7zC66+/zsqVKwmFQp0eM23aNE466SROOukkzj33XEaMGDEgbZGf7EKIPks0S05PiKFg7+5yNn9US5E/Ru3kejQUTfWlnHbZW7g9FnFbvo+FEGIgaZqW6bylNTU1ceDAARobG/H5fIwYMYIRI0bg8XSciXkgyE92IUSfWREbTdOkpp4QeeyD98fy3JPH4fUliEWTNxOGy8LjTTBiTHNuGyeEEMNIOp83WKSjJ4Tos8AEL8pSmGG7+52FyGOu2lGZ5djhYzLLmgZ2tY/N/2mjgC99Ilsnr7MMXrRdfbzkTGsJ5ah5lopOGY6Al0HHbJ3tyHzFNcfxKR6VvHa77zytYygsEnbz8vNHU1YepqU5AMCxp3+AmTCoGt1C2OMhbHva5/GsZB4vncWDbG4PIJzK4XWWxwu1y+O1r5OXXJe97Yim1sdTNfE0BUWmiyiuTO08ADO13bayHyj1J4/niDV2m8frrCZeZrtz2NlBSvN14Mjjpec9b5/RyzYunc1Tzpp50eRBetyR0Utl8wxHFNS/J7tc/sCK5MJ9XbRLCFGQpKMnhOiTwCQPrhKd0NaeTxsshBhcry45injMTTyW7FT5/DEOm7Mjs71N+Q52qBCiEEhGb1iT8VZCiD6pObkEbNj7r9ZcN0UIcRA7tmUD/V5fnIuuXZbD1gghhBhM0tETQvSJpoFtKpSZ65YIIQ7m2i+8zLTDd2G4LM6+YjUl5ZFcN0kIIcQgkaGb4qCW2I8CsEi/PMctEfmoZUOEytkBRp5Wwv6lwVw3R4gBs3dBMmd25LkfoSkoM0v5XNV60BQxO5s3s1NFzwwOklFNf5Tq2OzTEx12S2f03Fr2UxNd6zimqbPrxB2f1xqpGkzOenwrV09n4wdjOOWitXhGxWi2i9rVxEtn89qcNfE6XZfN66VzeBHHexEyk9vTuTxw1MkzO+bygEwOL5ZI1dEDii2DGC4SiWwuLZ2tc2bw+pXHO1hGT3Vcl6mZ5/hypL80XWbxPi61rzOPlz6Rs3ae5cjj2allrV3NvFRGz1Enz0iNnvc2Zc8z8i/vZpafT/0uF0IMP9LRE0L0ScOqMMUTvZQe5sNdphNbl+sWCSGcPlo3hn+/MY1jT/6QiYfvJaQOzfTdQog8Jhm9YU2Gbgoh+mz735sI7YzhG+lmzHlluEvlR4oQ+eCDf49j6T+PYeqsncycvyXXzRFCCJEDclcmhOiX3c+20rAqhO7RGHdZBS7p7AmRM5al8fqLM1n63DEcdtQO5p/9fmdVF4QQYshas2YNP/vZzzjnnHMYN24cXq+X4uJipk+fzvXXX8+yZb2bdOr555/n0ksvZezYsXi9XsaOHcull17K888/f4heweCRoZtCiH5rfjfKAX8Q7ywYd1E5W//cmOsmCdEtddLszPKGq7LDGv+/U5K/3G2lg9LwxqoIa3Y2nPUxluMzU2c9OiuVlUvo2dxVQrlS67K/ftPZPLej3p4nVaPPSXfW3kuFz9K5PADb1njl8dns3jKCeWevY9oxdcn6d3b7tnWW0Qt2ksdrt85y5vGSx3SWx3PWyYtk6uRl18VMx3uRWk7XydMAyzIwlQvb7CSP5wzF9SeP54zJWZ3UzHN8VtVlzbyD/HvosB/ZrJ8zj6cbycY5X6vtyOhpPayZ5won/z/mb5sy655rvb/rtgkxRJ1yyim89tprHdbH43E2btzIxo0beeCBB7jmmmu499578XgOPmRdKcVNN93EPffc0279rl27ePzxx3n88ce58cYb+b//+z+0IfqJmXT0hBADIrrXxCwxCUz04CrTMVukiLoQg0UpWPnCkezaMoLTPrmG0ZPrc90kIUQ+UFovZw4aRH1o165duwAYPXo0l19+OSeddBLjx4/HsixWrFjBHXfcwa5du/jzn/+MaZo89NBDBz3X97///Uwnb/bs2Xzzm99kypQpbN68mdtvv521a9dyzz33MGLECH7yk5/07TXmmIyxEkIMCP84N4HxHlBghaSTJ8RgemfZNDa+O46F57wnnTwhRME6/PDD+fvf/86OHTu48847+eQnP8nxxx/P/Pnz+drXvsY777zD9OnTAfjb3/7W6dM/gE2bNnH77bcDMGfOHJYvX86VV17J8ccfz5VXXsmyZcuYM2cOAD//+c/ZvHlzn9p75ZVX8uKLL6JUbmaekY6eEKLfymf6qJobQNmw658tUltPiEH04dpxvPvGVI495UOmzNyd6+YIIcQh88wzz3DFFVdgGEan26urq7njjjsyf1+8eHGn+/3617/GNJM3K7/97W8pKipqt93v9/Pb3/4WANM0ufPOO/vU3kceeYRzzjmHCRMm8IMf/KDPHca+kqGbQoh+8Y10UbUggB1XbPlzAyou8yWLweOaNiWzbFYV9+gYK5D81bf5quxnnV8/IRu6t1PZOkOzAA2XZuPWrA6ZrPR+ztp67aROb9nZ6yS05M1JVGWP8WnJ2nq6I2TmzOils3keR509N8ntezZWs2rJkUw7bgfjj99Nq+1rd+6wIzPYZiczd84cYTqHF7Y8jnWpOnmOdSHHcjqbF3bUxIumcnid1cmLJ7K3GglHRs80k++Lnfq/RvK9smwdpTpm8Jy5vP7k8ZzHOrerTuoedlozr6ejzRw18zLZPMe/IdtKrlSODF67mnmpZSOWvaCeqpnnjHCOX5zs3D+3564eNkwMJ5rqPk6aK4eqXaeeempmubOOlVKKJ598Ekg+IZw/f36n55k/fz6HHXYYH374IU888QT/+7//2+usXkVFBU1NTdTV1fGzn/2Mn/3sZ5x44onccMMNXHbZZfj9/l6dr7fkiZ4Qol9GnVmKpmnsfz0I8iRPiEFTX1fGyqdnMnb6fmaf/pHMrimEECQnZknr7Mnf1q1bM1m/U045pctzpbfX1dWxbdu2Xrdlz549mad6uq6jlGLZsmVcf/311NbW8oUvfIE33nij1+ftKenoCSH6JVaf7N0Fxh/kqYYQYsA17Stm+WNHU1nbyrzzpISCEEKkLV26NLN8xBFHdNi+YcOGzPLhhx/e5bmc253H9ZTH4+Gyyy7j2WefZceOHfzsZz9j+vTpKKVoa2vjvvvu46STTuLwww/n9ttvZ8+ePb2+RlekoyeE6Jc9L7Rimwr/uINPYSyEGDj7t1aw9KHjKC6PcOIl/8ZwyeRHQoihq7W1td2fWCzW/UEHYds2t912W+bvl19+eYd9du7cmVkeO3Zsl+cbN25cp8f1RW1tLd/+9rfZsGEDy5cv54YbbqCkpASlFB99vjw7HgAA0lBJREFU9BHf+c53mDBhAueffz6PP/54JkPYH5LRE0L0S+kRXjQDzFa52RS5teOcAAC1J+zKrNNSIRCXnv336U0tz3QEtGzV8XPPZGZOQ3dk9Jz7GamglHWQ0FYiVRcvXZcOwEhd06snMuvcqfO0y+A5QljpDJ+Fzq73a1i/ZDLVE5o55oIPiHpcRO3kr3I79dlta7uaeB0zemHLmdHrmMcLppbb18lzZvSS14s483iZjF7HPJ4zl2eZ2ffPTqQyalY2owcaytKztfNwZOoc6/qUx7M/tj+gDEeOLr1vpzXz6LnUS1TOjJ7RMYykYqn6gc5cXjx7ISOaqpmXHYWWyeZNeDw7s+pzm37Ri8aJYUfR/t90Pkm1y9mZArj55pu55ZZb+nTKX//616xevRqASy65JDNzplNbW1tmubi462x3IBDILAeDwT61qTMLFixgwYIF/O///i+LFy/m/vvvZ+nSpZimyXPPPcdzzz1HVVUVn/nMZ7j++uuZNWtWn64jHT0hRJ8UT/UyYkEAw69jJxQNa0O5bpIQBcmMGax/Zgqtu4uxEgZjj9rLkWduQpcxOUKIArBz505KS0szf/d6vV3sfXBLly7l29/+NgA1NTXcfffdne4XjUYzy10VVP94WyKRSJ/a1ZWioiKuueYarrnmGpYuXcpVV13Fvn37AKivr+c3v/kNv/nNbzjhhBP4zne+w7nnntur80tHTwjRK4FJHqrnF+Mu1cGGto9i7H89SPX4ilw3TYiC1LK7mKbtZUxZsJOqCc2Uj2lDl0yeEKJAlJaWtuvo9cX777/PJZdcgmma+Hw+Hn30UUaOHNnpvj5fdtSDc+KWzjiHkX68BMNAiEajPPbYY9x///288sorKKUyNfcmT57Mrl27iMViLF++nAsuuICLL76Yhx56qMedYfk8UAjRI7VnlTL1xmpGn12Gu0QnuDnGpj/Ws++VtnbDqIQQA8OK6yQiBqEDRRgeiykn7KRibJtMvCKEEA5bt27lrLPOoqmpCcMwePjhhzn55JMPun9JSUlmubvhmKFQdrRSd8M8e2PVqlXcdNNN1NbWcs011/Dyyy9j2zaBQIDPfe5zLF++nE2bNrF3717uuuuuzAQuTzzxBD//+c97fB15oie6tcR+NLO8SO8YahWFr/YTpRRP9BJvsWj9IELL+xHsrj8EE2JAtF2VrW8Uqk1lug7yEWU6vzS97EBmXbk7DLTPvOmp3J5znTNHl6lnpwClYdtuorbnoEWfnLk9y7Fsp4JdVicNdl4v3Q5nbs+vx9n92iga30s+KS8ZEySm3JlMS7qNCZX9NR5N1fNznjudy4NsNi/oyOhFUjm8kJld12mdPMtxndT6uCN7F0tl89rXyUsud5bLS25IZ/QcPVddQzO1TvN4zmxdX/J4mS+rownta+ql9nV8nVUPO9XOPF66fp7W7sueXJfO5QHo0eQOzlye7qiZZ6QeJOiO+RjGvpjMFj3/3k971jAhCtzu3bs588wz2b17N5qmcd9993HRRRd1eYxzApa6urou93VOwPLxLGFv7d27lz//+c/cf//9fPjhhwCZp3fz5s3j85//PFdeeWW7XGBZWRlf/OIXufHGG7nqqqtYvHgxf/3rX/nhD3/Yo2tKR08I0S3/GDeJNovtf2vMdVOEKHjBj4pp3OGleUM5AO5AnFHH7c9to4QQIs/U19ezaNEitmzZAsBvf/tbrr322m6PmzFjRmb5gw8+6HJf5/bOSjV0xzRNnnrqKe6//35eeOEFLMvKdO6qq6v5zGc+w+c///l2beqMYRh84xvfYPHixb2q5ycdPSFEjygZninEoNi/pAZsDV91FHdpgiMu2Pyxp0NCCDG8tbS0cPbZZ7N+/XoAbrvtNv7zP/+zR8dOmjSJ0aNHs3v37nY19zrz2muvATBmzBgmTpzY63bW1tbS2Jj8kFwpha7rLFq0iBtuuIGLL74Yt7vnNYirqqoAelV2QTp6QohuJdpsPGVG9zsKIfrNWx3D7TeZeFFyyJB08oQQIiscDnPeeeexZs0aAL73ve/xrW99q8fHa5rGRRddxN13380HH3zAypUrmT9/fof9Vq5cmXmid9FFF6H1ISDd0NAAwPjx47n++uu5/vrrGT9+fK/PA1BZWcnNN9/cq2OkoyeE6FbrB1FGnFBM6WFeWj/seyFTIdJch0/rcvv+hSMACJ2TrXd0xMjklNMePZutczmWPXrHTznTebTuMnp6J/k7AzuZ0bM8hE3fQTN61kHmNTNTdfQsR9DL6OQc3lSdPF2zCb5fTGy/j4oz92bq8CUy5+l4HdtR4K3NSs4I1y6j56ipl8njdZLR67ROXiL7SXPMkdHrLI8X7ySPZyWS69J18gBUwpGZy2Tr0hX0AEMDU+s0j6dZHfN2QJd5POd+mbfPme9z1tTrS828NMeXJl2bT3Pm9lLZPN1ZMy+VxzMcuTzdkX1OL9e+kZ3S/cXVPcvlCJGmcdAfXTnXl2+1eDzOJZdcwvLlywH4yle+wk9+8pNen+erX/0qf/jDHzBNky9/+cu89tpr7WbVjEQifPnLXwbA5XLx1a9+tQ+thcsuu4zPf/7zLFq0qE8dRaeKigrp6AkhBl7zugjV8wNUHBuQjp4Qh4jZatC0tIrAzFZKj2jNdXOEECLvXHXVVbz44osAnH766dxwww2sW7fuoPsHAgEmTZrUYf306dP5xje+wW233cZbb73FwoUL+da3vsWUKVPYvHkzP//5z1m7di0A//3f/820aV1/OHkwjzzySJ+OGyjS0RNCdM+G4JYYxVO8BCZ6CG2TKTeFGGita8rQPTblJzRJCQUhhOjEY489lln+17/+xVFHHdXl/qeccgqvvvpqp9t++tOfsn//fu677z7Wrl3LlVde2WGfG264oU9PDNM+97nPoWkaP/nJT6itre3RMQcOHOBb3/oWmqbxxz/+sc/XBqmjJ4Toob2vJuvl1Zw0cHVkhBBJdlAntL6EkmNa0T15Os5KCCEKiK7r/PGPf+TZZ5/loosuYvTo0Xg8HkaPHs1FF13EP//5T+699150ve/dpQceeIAHHniApqamHh/T2tqaOa6/5ImeEKJnTGjZEKF8pp+Sw7y0yRBO0Q/KyD6y2nFhNQDhqdk6chjJp8ZuR74rnR0z7ew6l+6o25Zadub2XHYypKU7wlpureM6Z0bPcIa4lIayfLSZRaCpdsfYnWTmLEfqxO6kCFv6Os5r6IZN7J0AmlvhnRkmYbtIOPKD6fp4zjamc3sxO5ujS2fzgpazdl52e7pWXtSxLl0rz1knL5Kpk+fM5WXf53Q2z3Sss9IZvXZ18lKv37HOmbP7eLZOAzS05D6d5fEcebvu8niZfZ1fonQNQmduz/kl6ulT1NTXIZ3FS540u5yZPMeRx9NiHWvmGVEttS57GsOxPGJt8i8vv/rdHjZMiE4orecFIQdbH9qVLk0wkM4991zOPffcAT9vPpAnekKIHjuwLISyFeVHFnW/sxDDXQ/uR9ROF4kPvSQ2FBE4qk2e5gkhxDAXjUYB8Hg83ezZPXmiJ4TolUTQxlMhpRaE6NJeA/2ZAPblQSjtvPOmdhvYLwRIPxv3HxUcvPYJIYTIS+kZRUeNGtXvc0lHTwjRO7ZC5eswECHyRWNywIy2xos6NdrpLmpv9lew54Qgulee5gkhxFD24x//uNP1v/vd76ipqeny2FgsxubNm3nqqafQNI0TTjih3+2Rjp4Qold0j4Yy5YZU9J5+3MzMcsOsksxyOo42YlRzZp3LSAapDEegKl3XzbnOY2SzbOnsnq5lf7W5Uvu62uXx0hk9R07OmZlzFp1SoFseWjVvjzJc6VyeS9MpArTNbiKzFKo8VbsvlR90aRbadjcaYMyMwpHxTO4OaLfcVRYwaDryeKmMXjqLB9k6eQBhK7k96sjepTN6cUdGL9pZnbxEdruVyk0683h2qn6ectTR08zUG2Y683QHr4+nkcyT6AkN1UkeT2u3Lrucyfq1j1a2u0a7Y/qSy3PInNtZO8+xrKVqBWo9rJlnOD4HqPwo++/51ed7XgBaiINS9GgYeU7ka7v64ZZbbulQL08pxd13393jcyil8Pl8fPOb3+x3eySjJ4ToOR2MIp14s9X9vkIMY1aNozO6tpOhznHQGlITmKzzQUSekgshRCFQSmX+aJqGpmnt1h3sj9frZeLEiVx99dWsWLGCo48+ut9tkSd6Qogeqzrej6ZptH7Q+VA0IUSSKgWrwsZo0nFtN7A+sjGnOx5DecA+NwxNOvoKL/YWDxwt31dCCDGU2bbd7u+6rqNpGuvWrWPGjBmD3h7p6Akh2tG9EBjvxVttoGywwgo7buOpdlF+ZBFW1KbtIymtIER3zHEKPagwJ9l433Bj7LVQhynUKDs5ZLDWgmIb3vJive+Do1tz3WQhhBADaPz48WiaNiAzaPaFdPSEEAB4a1zUnFiMd4Srw/jyNCtmU/dkz4t+iuFL9/szy/GFyU8xd56ZzYt5pmU7NUaqBlncctRlS4WeXI48npVaNhwBroSdPcZIhbCcx6Tzdq5Oaua1y+gdZFlT4DE9hPC2L/mkwLsTXC0QmQR28cffAbDG2Xjf1YhOhESZhfdDHdcWH+YIG3uiiVavo28zwAD92Gi7XF7Yyubs3KkQo+0IlKXzdhEre/MQTGfw2tXJy25P18pL18mDbK08Z24vnc1LtKuT58jjpbJ5ylHjUKXWac48XnrZmZProj6eBslhTqbWeR7vIBm99JerXU4ufc1OMkB9mkvKUTMvcx1n7Tzn60rVytMdebx0zTxnnTw99XlZ6c7si3n98W/0oXFCdEEyejm1bdu2nF5fOnpCDHMl071UHR/AVZy8e4nuN2l5P0JkTwI0cJca6G6NRKtFvEGyeUIABN6F4veSy0YLtJ7YcR+rIllU22iE2AxF7HALz27wbNBxve1GlSqseQl8h4fQ3B2PF0IIIfpDOnpCDFOByR5GnlqC4dFRliK4Jcb+19uwPxYTMlvtzk8gxDDlX5/s5AVnQeB9MCsPsqMGVgkYrRqgQANzrMIca1GkJVKPsECTspRCCCEOAenoCTFM1SwsRndrNK4N0fBmuN2wKCFE5zy7oWQNhI6E8Ixkh88u6nxfLQZGGyQmdjI+Sea8FkKIgnH66acDyeHnL7/8cof1ffHxc/WFdPREryyxHwVgkX55jlsi+kvZYMcVDavCuW6KKCTzk9NB7zgjkFmVSJXMU47sm+nI49l2KjOnZzNNdir/ZDnWGXqyd2Q4AlrOOGm6vp7RSUbPWSdP62VGz2e5CCoPWlxR9UaC8CiNvUe6QEG1kSARNggmkq/Ho2eHN/u2JGsERqoNEqkmpdvhzAwm9OSxbkf20HYEyWKpX9W2o3cYNDtm9NJ5vKjVsU4eQCy1PuaoiZfORXZWJ89MOL5Gjowe6WVnHi9dO86ZVUvXx+uk5l1yffvtGqDpoJuAc5R4ut6e3XEdkO00O7OA6dxef2vm6Z2cR+94cmceL73cLqMXa/9/gKIDyfOseOi/et8wIXpIU+1rTOaTfG1XX7z66qsAHeY4ePXVVzPlFXoqvf/B5kvoDenoCTFMmSELX0CCQUL0iFKMeNNEs/9/9u48TKryzBv/9zm1V1d19UYDTbMvsomiyKoCssUVUdEkRmPGjKMxTMy8SZxER03UvMbR0cSfWRxUTHw1CbjgElHUAC4oIiCyiOwNNND03l17nfP8/qilT1HVe3XX0t/PdfXF4ax3VS9Vdz3PfW6gaqoxlmGqFsDgl3H7GZsBUz1gOR5e5doIVF+JLiUaRESU+S688MKkiVlr63sLEz2iPirYoMHaHxBGQIbSHQ1R5hBBidJNIZgbJE6ebwLsgPOQBscxiROzjFBtLS/aQaeA86AGaCEYvYC1RoOpObwtehNNxYfwaBMTPSKinBQd0evo+t7CRI+oj2o+6Ef+GVaUXeLCsdca0h0OUcYo2qEir1JCaIDjsApHngbH5yoahytwl8cX11VNNWLAxyE4jmgIWQX8AwTqywQCxYBi02A6BcAK1uQREVGvY6JH1EdpwfB0M3uZGeZiA1snUKcZi4sBAM3nj4qtC9rDw1beQS0/T8IZDP+r6zum78smI+v1dWnJ6vaiJWFxNXi6cypapJebrvDDkKQeL1mNXkswEnkVATSXGOCsUlG4S0N+voaDw0yoPNMMGTxtWM4E+C9qqWXT9/Azalo44RMyVnMW3R7SNX0LaIkvxdHeeQCgRfb1ai1Trb2RXnlJ++QFE+vyAMAf6ZWn748XiCzrvx9qpDYvvk+evj9eYo2eEorW6OkeRLT2TkvSO0+3HD1GIHwHUhFC8p55+hmy+sQ5WQ2fOO3fTog7t5JkXeSccXV5vpYdonV4iq5nniFyN2Nzc8u6guc2hhee7XyMRB3GPnp9GhM9oj7IVKBg0GUuSE2idquHSR71SeYmDQO+8kNowMkzzPAVGGBp0mDySRw7y4S6IUZoJoF8hxWVDq1rjbaJiIjasX//flRXV2PYsGHo379/ys7LRI+oD7KVmSCEwIn3G9G019/+AUS5RpMY+ZEHQgM0AzDiYy+OT7Sg6FAQITPQNMAAaRAQErBpzPCIiKjzTp06hZUrw3esv/766+FyueK279u3D9dddx22bdsGIHzHzSuvvBLLly9HQUFBt6/PqgGiPqj5gB9SSjhHW9IdClFamHwSZq+EyS8RsggEbQJDPvfB7NFwaLoN0sDkjohygMzwrxz30ksv4Yc//CGeeOKJhCTP7/fj4osvxrZt2yClhJQSmqbhlVdewZVXXpmS63NEj6gP0nzhHnrmQv4JoNZFa/D05ICWdZVzSwAA9WcFY+tE9ONDXe2cjPRTk/paLf32yLJ+nRato1MSj1F1tWOKkthTL1k9nohbB3iNEqoBMKhAY4EJFeNtMAUkghYBRQCIPCQhgYA0wC+MCVM3DUpirZ++zjAUeTL02+0ifOKQ1lInF4wrLgsL6F6e1cg5PaGWD2ai9XjJ+uT5dDV6AV2/wkCk9k7fwzBaj6fp6u1kpAZP6ur2RFyNnoj7F9DV2SWpx4ury2ujP54AoKiRPnpJeubp6+Tiyiuj++q+P12aZisSrxP92dRfUPFHvq9J+uTplxXdOiVyZ+N+f9kWW/d2pC8tEeWud955B0IIXH311QnbVqxYgf3790MIgSuuuALz5s3Du+++i9dffx0ffPABVq5ciaVLu9e3miN6RH1UQlsXBbD2N6Jgkg354y1QrGkJi6jHWTwqxn/aCIMKVA224MgZdkARCFqVJL8YREREXbNnzx4AwNSpUxO2vfjiiwCAiy66CK+++iqWLVuG1atXY/78+ZBSxrZ3Bz/OJ+qj/DUh2AaaMfLmEkAJj8Tom3qWXiBR+7kHtZs9aYySKIWkxMBDPgz92oOgWcGuaU409guPfom+MIeIiIh61alTpwAAZWVlceu9Xi82btwIIQRuueWWuG3/8i//gnfffReff/55t6/PRI+ojzr6egNKL3TC1t8IqQKBBhX+mhB8J4NQzAL95zpReJaNiR7lBFtzCKO2u+GsD+HEMCsOn2EHTO0fR0SUzYQ8bapzBsnUuFKpvr4eAKAo8ZMoP/30UwSDQSiKgvnz58dtGz58OACgqqqq29dnokfUV2lA1bqmVjc3Dw0g/wwL8kaY4T4QaHU/yl3+ScMAAFXnttSGxdU+RV63hKqr74q+cOvrsqJ1dvq6PH1PtEhdl75GL1pTF9+/LFq3p6uJ098RU8QfCwCKJjHksBvDDzTDazNg63mFaCw0ARIQLaWFyXvqRS4ZgBE+mCBFfE2gOdIcL6iredN0NYXRcxp0RWqhSK+/kKLvoxepk9P3EdQVnPkiPfN8oZaX7GhtXkDXJ8+XrE9eUFfrF6ltVIMt19aS1eNFa/B0+ym62rqkNXpt1ePpntqkffR0NXoIRfroJemZl7QuL3Zg9+rywteJXCBZz7yA7rmI1OYZkvTJA3T1erp4B724FwDwVvNzXQiSiLKVw+FAQ0MDTpw4Ebd+3bp1AIAJEyagsLAwbpvJFP77bjR2P01jjR4RJVX9SROkCgxckI/8CSzYo+zjaApiyqYaDD/QjCND7dg8vRgNBeb2DyQiIkqBsWPHAgDWrFkTt/6ll16CEAKzZ89OOCaaFKainx4TPSJKSvMBB5+vhhaU6Dc9L93hEHWKzR3COZ/XQgDYfF4xDox2QmPLBCIi6kWXXnoppJR46qmn8Ic//AE7d+7Ez372M+zcuRMAsGTJkoRjtmzZAgAoLy/v9vU5dZOIWqX5AE9FAI6RFsAAQG33EKK0U1QNE3fWI2hW8PmUIqhGhTdbIaK+SYouzmvuBZkaVwr98Ic/xO9//3scP34cP/zhD+O2zZgxA3Pnzk045vXXX4cQAhdccEG3r89Ej4ja5K9T4RQC1n5G+E6E0h1On6OcMyG2HHKYIQSg9bNCG+yLq3OLawvQwddO2U4rgWjTcE130xLvgJbiqFj9nJpYq6WfL9Jm3R4QK76Suhd9icTeetHHKPXFWrrTGDUNZ++qQUmdH6oCbDqrH/wwhuu+9JeLHp9s3WnCLfUMCMAACcCoGxWM9tHTfwCifwyxc7aUzCEUedKitXqRyAEA+kpYfb1etB7PH9LX40Vr9FpO7o/U44V0fQZDQV39YJJ6PJmkHi9pDZ7uV1+Jbu9gPV6yurxwQKfth5Y+ero2gwn99oDT3h92471iXA2oEl2nqxWNPFaDT1d7GflGxfXO09XoRb/tg149Flv31onfdz1IIspaLpcL7777Lm644YbYSB0AXHDBBUnbJ3zxxRf47LPPIITAggULun19JnpE1CbbACOklPBVMcmjzDbmQAMKGgP4eng+qousaHKYOJJHRERpNW7cOGzevBkHDx7EiRMnMHDgQAwbNqzV/Z999lkAwMyZM7t9bSZ6RNQmS7ERqk/G3+WOKMM43AEMOe5GXb4Zh8odkEruTwkiIqLsMXz48FjrhNacddZZOOuss1J2Td6MhYjaZLAp8NdwNI8y24R99QCAwsYAChv8be9MRNRXyAz/oh7FET3qkrXaytjyAmVpGiOhnpQ33AwhBDyH2UcvE9SNs4XLkYaaUekJT0qM1SrF9QNLXKe2tMJrqYlrp86p5Ty6miV937roq7RuXeyYZL3T9B8txtVbJfbZE9F6PE13omgNVdzJgfzmAAobwz+jFf3zUJNni9WetZwvsa6vnRLF2K6qMEAaws+3pjsoWgun752n6gono/3z9D36orV5AWFIWKfn0/XH80eW/bqeeNHavGR98pLV5YU3RJb1tXfBSL2dvs4ysl0J6urSdJ/1dLoer5Uaveh5kpRMJv/56YG6PH09Xqxfn+75MUR75uk+O1D8SFinf1xl71QDAN468EjXgyQiSgEmekTUKucIC6SUaPzam+5QiJIaUO3GWftq0JBnxrYxRfDYTN1KAoiIiFItGAzijTfewIcffoiDBw+iqakJqtr2rcyFEHjvvfe6dV0mekTUKmt/E2RQQuNMOMpAg041Y9L+WlSW2PHlyGJIQ/vHEBER9aZ169bhpptuwpEjR2LrpGx93qoQAlLK2MyW7mCiR0StMjoU+KtZn0eZZ8iJJkw8VIeKUgd2jCgEhOAdNomITiNk3Oz7jJKpcaXSzp07cckll8Dv90NKCbPZjNGjR6OoqAiK0vO3SmGiR0TJKeFeZ7wRS3qJQMvzX/pRDYQAig/aYTruabWPXqA0DwBQNbmlME+L+2svTj8kaa2fiNTHxfUaiyvRE4nr2qrb09+5VX+dNrbH9fqLvCsodPsx8VAdAGD/ABcQqVeTSYu94kONi7G1D0ujmw0SAuEaPE2E00h9/79ojZ5B943Q9O9cIiOMqq7OMKi1Puyo750X7ZMHJK/HC4bC60K6GrxoPZ4WarmGjKvHS6zRi/XE0/fJCyauS1Z719F6PKEm2Q9oqcNL8l5Hf0zLz0Lifp0hlfh/T792NDbFr6vRC0TXtewX7Zmn6GIc8HFjbHnNzge7FygR5YwHH3wQPp8PBoMBDzzwAG6//XY4HI5euz4TPSJKyuiMvGkMsK8CZRaPRXdTEiPnaxIRUWZat24dhBD4j//4D9x55529fn0mekSUlHOUFQDgrQymORKiFkJKTDpYg5Ai8MkZA6Aa2CWIiIgyU21tLQDgyiuvTMv1+QpJREkVnmWDFtTgPsxEjzKElDjrUDVKmrzYPKoUDXmW9o8hIurL0t0nr4/30evfvz8AwGQytbNnz+CIHhElGPiNfBjMCqo+bEp3KH2eumNP3P+FIqC5C6EeqovvMSdaPrcTs88GADgqW7Y7Klt2jdY6BfJbapHcg+K3AYCM1KPFFczr/xNdbKduT8ho3z79St0hbfRJEwKAlBhxqgGD6prg8AexdWgpam15EMHT4on23ktWy5Vkv9YIJb7PoISABgUSgKLru6ZGPivV3z1N368vuqzqrqdGeuZpSYIMhBL75AFAINIXL6Rbp0bWabp6Oxmp0ZO6uj2h64UX7Q+n75mnBOO3AS0981qrretsPV7cz4/+xyfJt0FokXMkqx/timT9JZXk7y6VJD3zostxNXqR56xke0vbmbc/u7cbQRJRrrrooovw5z//Gdu2bcOUKVN6/foc0SOiOCUz85A31AxfVRANO3zpDocIBR4/xp6ohdtiwqbhA3CioPcK2YmIiLrqJz/5CaxWKx555BG43e5evz4TPSKKKTzHhoIzbQg2aTjycn26wyECpMSoqjp4TQZsGdofNU57uiMiIiLqkAkTJuC5555DRUUF5s+fj927d/fq9Tl1k4jCFKD4vDyoPonDf6tNdzREAIBRVfXo1+TF5mED2p1ySUREp8ngPnp9oUbvX/7lXwAA48aNw6effoqJEydi0qRJOOOMM2C3t/3BpRACTz/9dLeuz0SPiAAAiin8R6X5gA9Q29+fMoxsKZhS1m0BADjbOcRYXBxbbj5/FACgarKuXUGy/mW6ZCvWl8yoe7WOhqGbLxKtzRNJavDC65PU8AHo1+TG6JN12FtSiOpoTd5pxybrs5c0H2wvSdSf0xR+ELGyRwlILdxHT1VbHpgSqz3U1eXp6r8MkRpKVbcumOR3K1ri5w8l9skDgFBkWdX3zIv2xNPFg1j/u8Q+efr1+rq9aD2eou+ZF4kxruddF+rxYsfr6/KS9cyLHhvdVwKt9jjsqCQ/u7GfV906g0/3XASi63TbA/HbAKBwb/g/767/RTeDJKJct2LFCojYa6CAlBLbt2/H9u3b2zxOSslEj4hSR/OH/7AYrBw1ofSzB4I4q7IKVQ479pcUpjscIiKiThsyZEgs0UsHJnpEBABQ7AqEEFB9fWAuBWU0U0jFOUeOI2A0YHtZKadsEhFRVjp06FBar89Ej4gAAPaycI8X30n2zaP0MYVUTK2ohFnV8OnQMoQMhvYPIiKi5DK5X12mxpVDmOhRt63VVgIAFihL0xwJdYe5MPyG2n+KiV5fZK8I90wcVtHOjkpLkVXtxHAVYP0ZSfrjSZm4rpUaveh/zKqKKZXHYQmp2FxWBq9igQghUVx9X+L6rvRd09cZxvrMRWvrBCBluEZP6nrQxXrh6QrTDLpiNlULb1c0/TGJiWu0Hi+kq7cLBVv205L0x5Oxejtdz7wkffKE7tc5Wq+n6NZF6+z0NXpoowZPv5y0Hk+/X2R7sro8AFAi+3arT14rWnrm6VZGrmPwJ9blhdfH/6tfdlS2PEH/fPvO1AVKRNSDmOgREQBA9YbfdZVMdyDQqEIxKaj/wo1AndbOkUTdZ1RVnFtZCWswiM8GDUKzxZLukIiIiFJOSona2lp4PB6UlZXB0IMzV9hHj4gAAA27fAg2q8gbakHhmXa4xloxZGkR/0pQjzOqKqZUVsIWDGIzkzwiotSRGf7VR6iqimeffRYXXngh7HY7SktLMXz4cOzZsyduvzfeeAM/+9nP8OCDD6bkuhzRI6IwDTj0fG34piwA7INNGDA3H67xVjTs8LV7OFFXGDQN51ZWwh4ZyWtikkdERDmkqqoKV155JT799FNI2XZ2O3z4cFxxxRUQQuDSSy/F2Wef3a1rM9EjojiaJzxV0304XJxiyufNMHJVqKam5T/65TYYR42ILQsZrtFzHm7Z3jw4/K+USer2TmNWQzj71HHkBYPYXDoIzYo1rqYs4dBYrV/LC2WyHn8dvUmnvi5P6uroonVmLfV4AlDCy5qujk7IxL59mu4/mhbeN9RG7zxA3yev5XdN1dXeIRpHsnq8JH3ylCR98sLbW1+XtCdeknq7uPXt1O0l7Zmnfy6S1W52g4zr3Zi4LlqbqK/BU/T1eJF6PaPucy1LffiBffDqT1MTJBH1Kaqq4vLLL8dnn30Gg8GAa665BhdccAF++MMfJt1/woQJmDFjBj755BO88sor3U70OCmLiJIy5oXfdGrBPjS3gnpNgc+L6ccrYAsG8XnpIDRZrOkOiYiIKKWee+45fPbZZzCZTPjHP/6BF198ET/4wQ/aPOaKK66AlBIffvhht6/PET0iSspcGP7zEGxIMhxB1FVSYmhjPUbXV6PBYsX2koHwG/lSRETUE4SMH2nPJJkaVyq9+OKLEELgtttuw4IFCzp0zOTJkwEgoX6vK/jqSkRJmVyRdgu1TPQymgAsJUaoPg2hpsy+Q6pRUzGx+iRKvW4czC/EvoJiSDZDJyKiHLV9+3YAwOWXX97hY/r16wcAqOlgSUVbmOgRUVLmfAOklAjUJmtkRplAmAQGLymApSj8pzzQoKJxjw/12z2QPfRtC+07EFt2RZYNTmdsnfP8sQCAqnNMsXWqBXAGfJhUdxxGTcMXhQNxyuaACIXLs2L91trL+WI1XS07xtfodfA8kf54+iRT6Ov+YjVqInxKRbTUo+mvHT1EXxumu3i0z55UE6skVF1PvGifPE1Xoyf1tXfR2jzdOkWNbktWo4eE/fTr9b0JozVzyerx9OuS1eMpyWoPO1qXh9T1z4teM+7akWX9Y43W4Bl8ieuAlto9o7flZ2Hji/8nNUESUZ9UX18PoCV564hgMPzHOhVtF1ijR0RJGayRd2GZPUjUpzlHWmApMsJ92I9gswqzy4CSqXkou9iV7tBaSIlB7nqcV30UQcWATSVDUG11pDsqIiKiHldUVAQAqK6u7vAxu3fvBtC55LA1TPSIKKlAowohBIz5/DORiaylRvSfEx5Js5Wbofll7MY59kFmmDLh+yYlRnhqMK7hFI7Z87G5uBw+o6n944iIiHLAhAkTAAAbNmzo8DHPP/88hBA477zzun39DHgnQESZSDGF/zwYLPwzkYlMrsit+z0aKlbWoWJlHQ4+XwPVHx6CtZWZ0xkeICVGemow1FeHPfkl2FNQCin4s0RERH3HlVdeCSklnnzySVRVVbW7/x//+Ee89957AICrr76629dnjR4RJZU/xoKQW4X/VHyxl6lAQf85+TDlK6jf4UPdFk+aIuzbmvYG0Lj3VNzUWs0v0fS1DwVn2lEyMw+KWaB+u7fng9E3hVMEAImR3moM8dXj67x+qDQXwBCIr8lKVp6VtGdeknVx5+lozzx9rztTpAedpotb10dPnt4nThOQUoEEYvV94WtHavB034SW3nv6nnr6ur5IPz5dT7xoDZ9M0icPaKnDE7pzt9TbJfbM09elxdXrJeuZ10Y9nn5dXD1esuncbdXm9UBdXtw5lcR10WsrSWrw9H309MtRm1b8R0pCJCL613/9VzzyyCM4cuQILrroIjz99NOYNm1awn579uzBww8/jBUrVkAIgYkTJ+Laa6/t9vWZ6BFRcgLwVcUneeYSI4YsKQAUQKpA8Xl2+E4E4DvBO3OmRZI33J4jQRScCageDf1mOhBya2jen+TdbI+RGFBQi2JvI/Y4+uGYraBP3EKbiIjodBaLBatXr8acOXOwa9cuzJw5E4MGDYptv/HGG1FVVYVjx44BAKSUKC4uxksvvRT7MLE7OI+GiJKSIYm8IWYMmO9E8VQ7Bsx3hpM8ARx7vR6VaxoghIClmDVXmcRdEYCnMgBhEHAfCaD0QgeMeb33p35AQS2KnS1JHhERpZHM8K8+4KyzzsJnn32G6dOnQ0qJo0ePxrZt3boVR48ehZQSUkpMnToVn376KUaNGpWSa3NEj4iSqlzTiIGL8uEYaYEQAlJKaH6JyjUN8J0IoWiKHQDgOxVs50zU206+34QhSwshBKAFJfrPdeLYGw09ek1hBIomm+ByNqKythjH+hX06PWIiIiyxahRo/Dxxx/jgw8+wGuvvYbPP/8cVVVVUFUVxcXFmDx5Mq644ooON1XvKCZ6RJSU91gQB56pAYyAMU+B5tOg6WYA2spMkFLCdyIEobDpdSYJNWuoWt+EgQtdaNrrg3O0FSXT81DzuQcymLqPUIUBsA00wXGGBc6hAsIIHGnqh1o1P64/WfRT22Q91tq/SOJyst55CetPp3u1E6fX4EFXl6fbHlsndfVxuqcvej39z7/U1fppocQHHFun760XrcHT1+XF1eMlbo/V6CXpk6e0UqMnktTetVmPl+Q50etoz7werctrJY5o7Z2ir8dL0kdPr9/zW8MLf+x6iEREbbngggtwwQUX9Nr1mOgRUdtCQKgh8V2ejLyhMxcbEKxjs71M03wggIbdXjhHWdGw24uCSTa4JtjgPuxH014/3IcD7Z+kDYY8BeWXuWAuNCLklWjYJ9F0QEPdtPwUPQIiIiLqDtboEVGXVH3QBEhg8JJC2AaxTi8TnfqoGaFmFXlDzHAfDiBQF4K5yIiyi10YMM8JYejaeY15CsqvKIAwClSsqkPF6yrqdmgI8QasREQZRcjM/sp1iqLAaDRi165dHT5m//79seO6ff1un4GI+qRQo4Zj/2iAEEDZJfkYsMCJvKFp7t1GcWQIOPZmA9wVAZiLDLCWmuCtDOL42kbkDbdg0OUFMNg6N6/O6FRQvrgAQgBHX6uHvzrU/kFERER9lJRdy2i7epwep25SyqzVVsaWFyhL0xgJ9Rbv0SAOPFeNAQtdyB9rwICFTlRtABp39+bt/Kkt4Xq9ZgBAwSQbSmbkoWmvD0dfq0fZN1wYck0hTrzbBO/xtm+qI0wC5kIDBi7Ih9TCd14NNYen7Aq7veV61nDiqK+NiqaSmm4EUf9Jbps1XElq8EQrNXrJaNFXOX29WWTasdTV1iXrqSd1ffQQrT3TBSuiPfX0PfhEsno93bpYvZ2uZ16SPnkirv9d5DnV19tF+8Tp14US1yndqMeLe050ZJKa3Nhz2onvTWe1WpcXuU5cf7xIHV6yPnr6n73S1V/Hlt9y/zk1gRIRpQDbKxBR2mkB4Pg/GnHktXoAgHO0Nb0BUavqd3jhOxFC2cUuyKBExUt1CDZrKJ3jbPUYg1Vg8FUFGHVzCYZcVQjVo+HYay1JHhEREaVOdXU1ACAvL6/b58roRG/Lli349a9/jYsvvhiDBw+GxWKBw+HAmDFj8L3vfQ8ffvhhp863Zs0aXHXVVSgvL4fFYkF5eTmuuuoqrFmzpkPHV1ZW4qabbkK/fv1gt9sxe/ZsvPvuu+0eFwwG8eKLL+Kqq67CsGHDYLfbYbfbMXToUEydOhU33XQTVqxYgYMHD3bq8RBlEpNDgRCCU/kymQZUvtWAkFtD/3lOqG4NdVs9MLsMMCTptSeMQNklLpgLDAg2qqjd4saR1fUIuZnkERFljXT3yuvDPfSiOjo653a78cQTTwAARo4c2e3rZuzUzdmzZ2PDhg0J6wOBAPbu3Yu9e/dixYoVuOGGG7B8+XKYza3XBkkpceutt+Kpp56KW3/s2DG88soreOWVV3DLLbfgj3/8Y6vfiMrKSkybNi2uyeGGDRuwaNEiPPfcc/jOd76T9Li9e/fimmuuwfbt2xO2VVRUoKKiAp999hmee+45AIDX64XVyhERyj6OERZo0o/6Hd50h0Jt0AISzYf8cI2zAUAsMbf2M8LtbpnnZnQo6D/XCXOBAUdXN8BfwwSeiIioLSNGjEi6fuHChTCZ2r5xnd/vR1VVFTRNgxACl19+ebfjydhE79ixYwCAsrIyLF26FBdccAGGDBkCVVWxceNGPProozh27Bj+8pe/IBQK4YUXXmj1XHfffXcsyZs8eTJ+9rOfYeTIkdi/fz8efvhhbN26FU899RT69euHBx54IOk5fvzjH+Po0aOYMWMGfvGLX6CwsBAvvfQSHn/8cdx66624+OKLUVxcHHdMbW0tLrroolhyOHv2bHznO9/B+PHjYbfbUVdXhx07duCf//wn1qxZA6+Xb5ApexlsCjSEb9JCmc02wIRAbThxCzVr8FeHUHSOHYF6FcEGFQVn2lB8Xh5Uv4bKNY3tJ3m2lg+nojV6Bl2dWLRuy9DOj0bSWj19jV6SdUnPo68FjAxUxvXJi6xTdD3fNH39l66WTUT/jdbP6XvvmaL7t3w0LZJNlNH3xwsl6ZkXrcGL63+n76kXXad7DMnq8dT4bdHHcPr29urxYuv0ZYtJ7tCqqC07xGoTe6ClZrQeL64uT7ccrcfT1+hFa/OMup550cfY772WD2zfOvmHFEZKRH3doUOHEtZJKWN5TUdNnz4dP/vZz7odT8YmemPHjsWvf/1rXH311TAY4l9hpk+fjhtuuAGzZs3C119/jRdffBG33norLrzwwoTz7Nu3Dw8//DAAYMqUKdiwYQNstvAn2eeddx6uuOIKzJ49G5s3b8ZvfvMbfO9730sYKvX7/Vi9ejUGDx6MtWvXxubMzpo1C5qm4be//S3efPNN3HjjjXHHPfTQQ7Ek795778V9992XEN/cuXOxbNkyNDU14dlnn014rETZQjGIPjcVIxvZy02wDzLj+NsNsXVVG5ow8BsuDPtmEUJeDQarQP0OL2o3eaClsME6ERFRLvvud78b9//nnnsOQghcccUVKCgoaPU4IQSsVisGDhyImTNn4qKLLkrJzVgyNtF744032txeUlKCRx99NDasuWrVqqSJ3mOPPYZQKPzx5hNPPBFL8qLsdjueeOIJzJgxA6FQCI8//nhsbmxUTU0N/H4/pk6dmlAYOW/ePPz2t79Nmqm/+uqrAID+/fvjnnvuafPxOJ1O/Pu//3ub+xBlMoNdgeTNNjOSKV+BfYgZQgiUTM+D51gAzQdbpmn6qkI49HwN8oaZYSszo3GPD/5TnKpJRJT1MrkeLlPj6oZnn3027v/R0qwHH3wQ48eP7/V4MjbR64g5c+bElvfv35+wXUqJ1atXAwiPEE6fPj3peaZPn44zzjgDe/bswauvvorf/e53cVl0YWEhjEYjNm/eDI/HA7vuVuLr1q0DAAwYMCDhvBUVFQCA4cOHQ1Ey+r43RN0ijOEm2r6jbd+in9Ij/wwris5t+ZBKqghPsdO3ONCA5gMBNB8IJBxPREREnXfvvfcCAEpLS9Ny/axO9AKBljckyaY8Hjx4MDbSNnv27DbPNXv2bOzZswdHjx7FoUOHMHz48Ng2m82GRYsW4c0338TChQvx85//HIWFhXj11Vfx+OOPw26345JLLkk4p9lsht/vx759+xAKhVLS4Z4oExVMskEIgYbtvvZ3pl7nPRmCFpLwVQWhujU4R1thyjcg2KC2f3AHaHZLbDlaR6Wvl4r1smuvtq69Gr3EtnTxxyuJ26P1bUL/WZuWeL24HnbR/niqCNfoGQAR7a2n6AvXIvvr+ujp699iiXSw5eKxOrpgkhq9JPV2+vUilLhOv1+sbk//be1oPZ5+v8hm2crnk9HavF7vmae7Rlx/vGiNXrKeebo6wn4bw7csf+vg/6QwUiKi1kUTvXTJ6mGm9evXx5bHjRuXsH337t2x5bFjx7Z5Lv12/XFRjz/+OIqLi/HRRx/hsssuw6xZs/Df//3fkFLid7/7Hfr3759wzOTJkwGE+2HccccdcYkpUS5xjrZAqhLuCv6MZyJPRQD7l1fj2GsNCDSoUH0ago2pSfKIiIgoM2XtEJOmaXjooYdi/1+6dGnCPkeOHIktl5eXt3m+wYMHJz0uatSoUdi0aRN+/vOf45133oHP58PkyZNx9913Jx3NA4Bly5bFWkQ8+eSTWLVqFS6//HLMnDkT06ZNw7hx41JSaEmUbgaLAtXHu21mA5PLEL7jZg7WRhARUTwh4++gm0kyNa6eoqoqdu3ahYMHD6KpqQmq2v4Hrqff6LGzsjbRe+yxx7Bp0yYAwJIlSzBlypSEfZqammLLDoejzfPpb7LS3NycdJ8RI0bgb3/7W4djvOaaa3DPPffg/vvvh5QSJ0+exPLly7F8+XIAQEFBAS688EJcd911WLp0abv9NYgylTDE32KeMpfq1WAt5d8aIiKi3uDxePCrX/0KTz/9NGprazt8nBCibyZ669evx3/+538CCBc3/uEPyfvg+Hwt9UJtNVQHAIulpcYklf3sfvnLX+LSSy/Fww8/jDfeeAN+f0vhSn19PV577TW89tpruO+++/DCCy8kTViJMp4Q0HiTxqzgPxVC4SQ7FLOAFkhNcq7ZWhJHQ+Sc8Z/UdmzmQns1eslOoz8mWgsYV3sXrUHTfXAarflKVrenPwYSLTV6seNbTh7rzdfKz36sZ56+/i/W/65lXbR/XlwNXpJ6vGTb9fvF6hHjHkvb9XhKkg+Uk9Xm6fdrr1ayW0T4K1nPPP3j19eARpfj+uiFwo+76MvG2Lq3dv06tbESEbXD4/Fgzpw5+PzzzyFl738gnnWJ3s6dO7FkyRKEQiFYrVasXLkyaX0cAFitLU1826uP0ydgp7dg6K6pU6di1apVcLvd+Pjjj/Hpp5/i888/x4YNG2KZ/d69ezFnzhx88sknmDhxYofOK6VMyw9NRwglvVNShSIgRPrj6CuEAsiQ5PPdC7r7sx1sCL/LN7uM7TdC73BQiYs9eJ+OdsPoQr7Y5r4C7Z+zo/F0dl1vnScTCLT9fJ++7+nLSdfpb+aToa+X6RJ9D8Hnpeel8rnm9yu7/Pd//zc2b94MAJg0aRJuv/12nHPOOSgqKuqVO/JnVaJ38OBBLFy4EHV1dTAYDPjrX/+atHdelNPpjC23Nh0zyu12x5bbm+bZVXl5eViwYAEWLFgAAAiFQnj11Vdxxx134NixY3C73bjjjjvw7rvvduh8DQ0NGfsLXzKsMK3XF4pAQZkTEIJTCntBfn4+TJoNJcMK+Xz3sO7+bOcNNyM/Pw8FAzSozhR9r0pbPlSzuMKzJ/RvujVjL43oRW6+LHU3YY4ua/p1xvh/gdPu8mhsuaukANDPZAqPMp127tg5leTPY7QGW+iOiS4Lg25ELzoSl2R0DtCN+KmJ25Uk6+JGU5ON6Om2tzWi19pdNyWSbI988BD3PYyeR59stXM3zZLI6LCq/95Enz/dSKRBN/tYiUzYMbT8GEJERvRc/Vs+uG1oaEh4LH2ZlDL23oj3C+hZqXyuGxsb298p7uLI3JrsTI0rhVauXAkhBC644AKsXbu218u0sibRq6ysxPz581FZWQkhBJ555hksXry4zWP0N2A5evRom/vqb8CivzFLTzIajbjmmmswevRoTJ06FYFAAO+//z5qampQXFzc7vEulwv5+fm9EGnnVR+qS+v1hSIAKVF9uJ6JRw8pmGRF3ojw3TabmpvgdIS/73y+e1Z3fraFEXDOKcLRz6tQtbPtD786pX/LNPn6gvC7d30yoZp7d+qmpk8SkqzTTPHbgNMSOFN8ogcAx4KBxEQvkhC2muhl+dTNVhM9kbhdRhO9JFMukyZ3Sa4jIsccbw5A1b0Xin2f9IleB6duBk+2lGK4XK7kD6iPin5Q7HK5mOj1sFQ+1/xeZZeDBw8CAH7yk5+k5V4cWZHoVVdXY8GCBThw4AAA4IknnuhQcaK+A/1XX33V5r767claNfSks846C1OnTsWHH34IKSUOHDjQoURPCJGxv/CZ8GZfynAcmRBLrik6z46ic+ynNdyWfL57SVd/toUUUAwCgZpQSr9PqrXlpcTgjyRJuqmlSjsf20YTh2R/ztrr1Sb1o4W62rqEdfrkJ5LcxCUd+nNHEzOh+7+aZL/Y9fRDVrrrRJK5aMIH6JI2/UhctCdekuROv29cn71ko3yR76lIVm8I3chhku+9/vsV26o7Nu4hitP20y0n+04n2y/uGP23UAmPlGq6703s8esTubg+euEz6Z+f/IPhBG/NZ/cliYiiou8jMvW9RC5J1XPN71V2sdls8Pl87d79v6dkfB+9hoYGLFq0CLt27QIAPPTQQ7j99ts7dOzw4cNRVlYGIL7nXjLRNgiDBg3CsGHDuh5wFw0aNCi23Btzdom6wznSAqkC+56qjn0dfZXTojKdFpTwHAvCNpB33SQi6gui7RUy9SvXRe+70d7Mwp6S0RmFx+PBpZdeii1btgAA7rrrLtx5550dPl4IEZve+dVXX+GTTz5Jut8nn3wSG9FbvHhxr39aIqXE1q1bAYRjHjp0aK9en6izTE4DQs1suJ2VBCD5rSMiIupxt9xyC6SUeP7559Ny/YxN9AKBAJYsWYKPPvoIAPCjH/0IDzzwQKfPc8cdd8BoDE8rWrZsWULrBK/Xi2XLlgEI18zdcccd3Qtc58ILL8SLL76IYDDY5n73338/vv76awDArFmzUFJSkrIYiFLFOtCIYd8pwqhbSiAMAnXbPekOibpAdWswFxky63aLREREOejb3/42rr32WqxcuRKPPPJIr18/Y2v0vvWtb+Gdd94BAFx00UW4+eabsWPHjlb3z8vLw/DhwxPWjxkzBj/5yU/w0EMPYfPmzZg1axbuvPNOjBw5Evv378dvfvOb2GjaT3/6U4wePTplj2HXrl349re/jWXLlmHx4sU4//zzMXLkSLhcLjQ3N+PLL7/E888/H0tmzWZzWn4IesJabWVseYGyNI2RUKqUXeyCYhTwV4fQ8JUXjbv87R9EGad+hxeDzyhA/hgLGvd073uoRPqTBqwtnxlG66U0/ezQ9qbniCR3bIxtS1ylv4Omvhde0p55In4boOt/p69l03/sGa1li9yMRWgAtEi9nb6+TU0MLu5umbEaPd26ZDdRSVajl+yYuLq91uvxlCR1e6fHFqW/uczp52mvPjJVTr8rp1Tip3Qlu9lKtBYUAAyRej37yZYn6N0Nd/VEqEREnbJhwwbccsstOHLkCO68806sWrUK3/rWt3DGGWfAbre3e3xb3QU6ImMTvZdffjm2/P7772PSpElt7j979mysW7cu6bYHH3wQVVVVeOaZZ7B161Z885vfTNjn5ptv7tKIYVvKy8tRU1ODmpoaPPPMM3jmmWda3XfgwIF49tlnMW3atJTGQJQqiknAfSiA42938tbOlFH81SH4T4VgLzd3O9EjIqIMx/YKaTVnzpy4krDPPvsMn332WYeOFUIgFOpev9uMTfRSSVEUPP3007j66qvx1FNP4bPPPkN1dTVKSkpw3nnn4d/+7d9w8cUXp/y627Ztw9atW7F27Vp8/PHH2L17N44fPw632w273Y7+/fvjzDPPxKWXXorrrrsuru8fUSax9jdCCAFfdYoabFOPE0ZAtvLtch8JoGCCLflGIiIiSpl09rzO2ESvJ56USy65BJdccknKz9uWyZMnY/Lkyb16TaJUsw8JT9HzVgba2ZMyxYibSiBViZpNbtjLzQg2qKjd6oHml7D2M0EL9oGPUomIiNLon//8Z1qvn7GJHhFlDtdYK6Qq4TvOEb1soRgFYBToN8sB7/EgXOOtyB9rhbsigLwhZhxf2/0puCJaX6DrwWaIJpC6uiuhtV3gJUXiMcmadccaa+umwURr1YBIM3mc1jsusqy1U8umtXJrsmiNnkhS13f6+YD4RujJ+t+JJPV2Sht98sLrZdz5WnsMsT56SfbTS/bc6mv5YjVzPXwH6mh9YLJ44urxfNF1LTEaddvNTeEH/M+3O35XbiKi3jB79uy0Xp+JHhG1KX+CFcY8A+q+5F02s8mpj5rRb5YDniMBVG1ohtQkis6xI2+YBc0H/Wjez/o8IqKcxxq9Po2JHhG1yTnSAiklqj9ypzsU6oT6L72QqkTReXkY+q0iNOz0ovZzD07x+0hERNQnZGwfPSLKDJ6KAIQQcE20pjsU6qSGXT4cfqEWdVs9yB9nxbDri+Eaz+8jERFRX8ARPSJqk/toECUAjHZ+LpSNtKBE7eceNOz0YsRNJSg404aGXb6UnFvkhWv0DL6WojDNEK7rUuIa3CXOz4nv0daxPnqqObEGL9lysp55yfaT+nX6HnPR9UqSGj01sQYvrneevj9esnq8ZH30ku4nE4/pYD1eXK8/Ham03vcvrk6uJ3vm6a7TUgvYss4QCNfnKbqZxcZIb0Z9jZ7++duw+qc9ECkRUef86le/Svk577nnnm4dz0SPiNqUPzp8x80m1nRlNceI8BTcqg+a0x0KERH1EiGTftaWETI1rq6677774m4WlgpM9IioRzlGWKGpEoEatf2dKWMVTLKheZ8f3spg+zsTERFRp6WyPVwqkkYmekTUqoKzbDA6FDTv42heNjMXGWAuMOLUhxzNIyIi6gnp7pmXDBM9IkrKPsSEkul5UH0SJ/7ZlO5wqBuco6xQ/Ro8qR7Ns9sAAIpfN9prijRHa2dOTnyNXpJ9I/VkmrFlx2hdllR0vfO0xJ56QleLlqz/XbJaPuhr3cRph2hI2o8udh59z7u4/netb0+2n74uz6DvvdfBeryO98zTbe/ZVnkJMSSrBTQEWv5rEIDRByi6erxYbZ7u0+2P//YfPRMsEVEXpbtnXjJM9IgogalAQdk3XJAqcGRVXdIm0ZQd7EPMKDzLhrovvfw+EhH1Neyj16fxNnpEFEcxA4OvKgQEcOz1eoTczA6ylWIRGLggH+6KAGo2sX8eERFRX8JEj4jiDFlaBMUkcHJdE3wnQ+0fQBnL2s8IxSSgBSQE/9oTERH1KZy6ST1urbYSALBAWZrmSKg95Ve6YHQoqNvmQdMe3oAlW1lLjZAS8BwNom67B4WT7PAeD6Lxq9T0z4uStnDrDSXQhTuyxvVTSywU0yKvTnHt+CKDy0qoZX+ZZHtcLVtku2yvRk9PtBwr0NJL7/RjotfR97/rSj1e0nX6Pntt1OMl65mXrF+eft9kPQx7QvQ6SXvnoeUxGiJ/agQABRIGn4zrmRc95pPnWZdHRNQZTPSICABQOtsB2wAzmg/5UfOpJ93hUDeUznbCUmyE72QQ5mIjAg0hNB9i4k5E1OewRq9P42QeIkLBJBvyx1rhrw3h+JrGdIdD3dS0LzxyF/JqqN3sRsXKOmg+vqISEVH2O3nyJF577TXcddddmD9/PlwuF4QQEELgvvvu69A5VqxYETumva8VK1b06OPpSRzRI+rjjA4FJTPCbRQqXqpLdziUAo17/Cg6Nw/BBhV127zpDoeIiChlBgwYkO4QsgYTPaI+bsCCfADAsdfq4nqJUfZSPRpqNrnRb6YDlmJjrD4v5XdQlZH+bsGWHxwlUvPVbk+3JLVh0qBbFymu0wyJPfHi6uS0xJ56cetUoT9dWJJaPv0UomicQo2U66kttXf6ln8iVlvXsi7Zsr6GL1qHl3RdXF1ekno8Vf+4IstK8nrF048FdPVxPdk7T3fuaDz6uBTdcx6tzYvW48X66Pll9EcLAOBa/UXPxEpEWW/EiBEoLy/Hhg0bunyOt99+G2VlZa1uLy8v7/K5042JHlEfZik1wloaTgQCdWyjkEvqt3sRcmvIH2tF4Vk25J9hxaEXatMdFhER9SIh4z+gyiRdjeuee+7BtGnTMG3aNBQXF2PdunWYO3dul+MYM2YMhg0b1uXjMxkTPaI+bMC88Gje8Xeb0hwJ9YTm/X407/fDVmZC+RUFMBcbEKjhsC0REWWvX/7yl+kOIWvwZixEfVTeCDNM+Qqa9/mheTial8uCDeHkzuQwpDkSIiIi6i0c0SPqo0ovcAIacGIdR/NymgIUTbFDSgl/baj9/TtBeAPhBatJtzJSpKUmzskRrdToRfu+Sd0O0Ro1oestF92srztL1lNPtNMzL9pTT18mqJ9CpOkWRGSbEgqX8enr+qI1dfreeYYkPfUMwcT+eEpAty7yXMX36Eusx2u37jG6v66+r7d65iWLJ9n3w6Br5WiM1ObFavQEICzh/V3v7Y3t95b7zz0WLxFRLuOIHlEfVHCWDUabgrrtHt6AJYcZ7AoGX1kA1zgbhBAommyHuYijekREfYbM8K8McNNNN6F///4wm80oKSnB9OnTcffdd+PYsWPpDq3bmOgR9TFGp4LiqXlQAxobo+e4ETcWw1oaHm3znQoib5gFJdPy0hwVERFR5li/fj2qqqoQDAZRU1ODTz/9FA8++CBGjRqFP/3pT+kOr1s4dZMo1yiAtdQIc7ERMiDhOxVEsF6D0aXAXm5Gv+l5EApw4h02Rs91ql9DyK3hxNpGBOpU9LvAgYIJNgxZWojj7zTGaveIiIjSpbEx/v2IxWKBxWLp8euOGDECV111FWbMmIHBgwcDAA4cOICXXnoJq1atgs/nw6233gohBG655ZYej6cnMNEjyiGDLnPBNsgEcVotjpQytk5KieNrG+E+HEx2CsohB56tift/0z4/XOOtsBQbUXSOHSfXNXV66oww6urxguGfobift2gfvSQFZVK/n34+iSk8nVTo++hF69KUZH3ydJfT1+tFjpGKvvde5Dyqbl2yuSyJDwEIRWr0QuG+dxKn9bpro08e0FKvp18Xrc3T95OL1eDp6/Ja6RUYpX+Mpx8TX5eXsFuPSNYzLyquLs8nE5aFrmbSseMk/Mea8Y9Tf+ypUIn6lGxorxBNsqLuvfde3HfffT167SVLluC73/1uwvul8847D9dddx3eeOMNXHXVVQgGg/jxj3+MK664IisbtTPRI8oRJbPyYC83w3cqiOaDfgRqQhAmBZYiA0z5BoTcKrzHQ/AcDUCm9p4clCV8x4M4sKIGzlEW9DvfAesAE2o3u9G015/u0IiIqI86cuQI8vPzY//vjdE8l8vV5vbLLrsM9957L+6++254PB48/fTTuOuuu3o8rlRjjR5RDsgbbkbBRBsCjSqOvFSPui1euA8H0bzPj5pNHpx4twnVGz1wH2KS19dpfomGnT4cebke/uoQBszLR8lM1u0REVF65Ofnx331RqLXEf/6r/8aG/Fbv359mqPpGiZ6RDmg6Fw7IIEjq2rTHQplCf+pEE6sbUTVB00onGSHc3RmvLASERFlgtLSUpSUlABA1t6Bk1M3iXKAucCIYKMKLZDuSCjbNOz0wVZmRtG59g5N4VQcutG/YLSRnK7/XZLasVjRm74WwqRr86Ak9syT0XW6+rTodn2NXXzvufD5lXZ6uUXr46S+d54unGjdiFDDJW6KpqvRi6vHi/ybpE8eABii9Xj6Gr1g6/V4cY+1g3V5cf0Ko5sNvVOYFxdPko+NjZHavGifPAAwehMfY/6u8AdUQgBPfPSzdqdUEVEnZFAbgwSZGpeOlFkQZBs4okeU5RS7AmEAvMd5cxXqmub9PpgLjLD2N6JkZh5sZab2DyIiIsphVVVVqKkJ39SsrKwszdF0DRM9oixnzlcghOCt8qnLgs3hIaXBSwpRMMGGQZe5INhXnYiI+rCnnnoqNqI3e/bsNEfTNUz0iLKcwRr+NVb9ibezJ+oI/6kQ3If9cB/2I9isQaqAwa5AMffSvfmJiIh6yaFDh7B169Y293njjTdw//33AwCsViu+973v9UZoKccaPeo1a7WVseUFytI0RpJjovPHk9XvEHWEBCrfasTgJQUwu8JDecOvLw5v0iQ8R4M4vqYBUgMQ0hWhRfroQbTzsxf52RSGlmFCqa8ji/XHa1mnhMLLmr6PXqyWD7p1+p550X+T9d7T1VlEe+rp6/v0DyFaeihb+ugZ/NE+evp6u2isut55ujrZ6Hol0HKhturx9HV7ejJZzV3k917oGuXJ3voTELmOvmde9NoGXZmnMVKbZ9D1ztM/V3mHmwAAb+36dfgcUqKhoaEHAibqw3KwRu/DDz/Evn37Yv//6quvYsvbtm3DihUr4va/6aab4v5/6NAhzJ07FzNmzMDll1+Os88+G6WlpZBS4sCBA1i1ahVWrVoVG8175JFHMGjQoK4Fm2ZM9IiynPdE+N2mpZi/ztQ9Bnv4nXv1J83wV4egmAXMRUYUTrZj0BUFOL62Mc0REhFRX7d8+XI899xzSbetXr0aq1evjlt3eqIXtXHjRmzcuLHV69jtdjz22GO45ZZbuhxruvGdIVGW0/zhURdzAYuqqHsq32qAwarAW6m7sc+BANwVAQxcmI8hVxXg6D8C0NhfnYiIstS5556L559/Hhs3bsTmzZtx/PhxVFdXIxQKobCwEBMmTMC8efPw/e9/H6WlpekOt1uY6BHlAC0kYXQw0aPuCdSqABJv6uOvCuHoK/UYfkMx7IMMaD7AG/8QEVF6rFixImF6Zmc4nU5cf/31uP7661MXVIZiokeUA1SvhMHGGj3qOSG3BqlKmBwaZCBSiBYpbBP6ArdkPYeMhrj9AUCE9I3tIsVeunq8aB89JKtl09XyxfWji/bMU3W1fkr0fPqAEutaFV3uKqN1dNE+esFwT7xwjZ4u7EhPPH0fPYNuWUS3q+3U4yXpmZe05jZJy7zerssDdM+lvo1eZBDYqKvHiy4rwZbvtbW6pYjx7c9/mfIwiSieQNyvakbJ1LhyCe+6SZQDQm4VipF/MqkHKYAwCBROsqL0Qlu6oyEiIqJ2MNEjygGBOhVCEVCs6Y6EcpYGnPq4Gd7KEPKGsaE6ERFRpmOiR5QD/DXheVO2/uY0R0K5zFsZhKWfAd5jofZ3JiIiorRijR5RDvCdDL/xtpQa4T4caGdvoq5xjrFAqhLH36mHVAElWhSn648X7bOn75kXo+jWKboaPTVSIGfQ98QLL0f76QEtPfWEvreerrZOGKI983TrtGidnK7fXLT8T1/0ptsuIqsNQQkR+dcQiPTRCyT2hIuvy2u5eLQ2L1k9nr6fnIzVEbYy/TpaM6ivYWyvd2GKxfXMU6LfG11tYuROrEZvy+OPPi8G3bq1H97Vg1ESUYIc7KNHHccRPaIcEKhRIaWEpZCf3VDP8VeHYLAqsJdz5JiIiCjTMdEjyhFSA4z5/JWmntO0z4/mA34MXJQP13gWhBIREWUyviskyhGaX4PRzl9p6kEacHxtIxp2elF6oRN5wziCTERElKn4Kk2UI1SPBhOnblJPk0D1J24UnGmHbYBA09cBKLrasmhtntTN7hTRZnaarqBOa/lQQqiR5SS99WRcPV6k5s2g70uX2FNP6D7viPbUE0JXDCKj63T7ycS6P0NQQghdHz0Z3xMuWR89Rf8YktTjxXrmxZXYtdczT0ZWpbMuTxdP5Hk2+lvWGXxaZJ2+jjD87z/fubPHYiSitgnZUnecaTI1rlzCj/+JcoQEm49S78gbZgEABBpUlF6Qh7yhSW68QkRERGnFj/+JcoTJYYDq09rfkaibbAPCLx39puch0KDCNd4A95EQTvzTl+bIiIiIKIqJHlEOMDoVKBYBbyX7m1HPUyPT806ua0bjHj+cZ9gxYI4N9kEGeI+nOTgiIiICwESP0mStthIAsEBZmuZIcsOA+fkAgKoPmtIcCfUFdVs9qN/hheYLJ3z+k34ANjiGKvBUhPs4xvXRM0RGmkO6Gr1kPfV0ffSghdfpa/Cive6EruYtWU+9uBq9WM833amNib319DVxhkBLbZ0Q4f8b/FqkRk9XjxeIxKjq6vaS1OMJTd/YL3zNZH3w4soI44r0ers2LxKjvrhDF44xWc+8yGwCaWyJdcPqn/ZYjETUQeyj16cx0SPKctb+RlhLjfAeDyJYz6mb1POkCkhdE3AZSbDMBazVIyIiyhRM9IiymGIFBl1WAEjgxLsczaNepgB5Q82AVKD6NLiPBNMdEREREUUw0SPKYkOuKYIwhnubqR6O5lHv6jcjDwVn2gGER/gadvnAlxUiIqLMwFdkoiw16AoXTA4Dare44T4QSHc41AdZ+pkAAO4KP2o+9SDUFIKI1GgJtaUeT0b65MX66QFASFc0Z4hsV/XbIx9c6I6J9tQTmq4uL245cm19SVx0iqm+5C1SgxbXO08fTjC+Rk8J6mv0dPV4kRo9xMXQsj167bh6vCSzW6Px6nsG9nZdnr43S6w2T7fO6JMJywZdjV60vpJ1eUQZiLVwfRb76BFloZKZebCXmcNvsDd50h0O9VGnPm6GGtBgzDPAX8M7vhIREWUSJnpEWcY5xoKCM20INKqo/EdjusOhPsxfFULzfj+0ED8uJiIiyjRM9IiyiLnEiP5znJBBiSOratMdDvVR5kJD7NXDYFUAlocSERFlHNboEWUJxQIMXuwCABx5pQ4ay/IoDQom2dBvpgP+2hACdSocwy1x/RtlpDZPBlumcsZ66hl0GaG+t1y0Xk9fwxftqRfS1bxF1+n63yXrqSf07foiu0qpKziLLupq9Ay6/ngismwIapE+ehoUvxqu0Qvo4on2+lP1vfMSe+YlrbeTyf7Ty3V5uktGe+fp1+nr8gxxy1rcfgCw/nXW5hFlIiHje3RmkkyNK5dwRI8oS4TvsClw4r0mBOo4hELp4RxpQcitQvNLGB0Kqj5sQsNOX7rDIiIiotNwRI8oCwy6zAWT04C6bW407/enOxzqw/y1IVj72+CtDKJ2iweBOrX9g4iIiKjXcUSPKMMJI2AbZILUJGo28w6blF5VHzTj5Lom2IeaMeSaQghTGqYbEhFRx8gM/6IexRE9ogwnQ4CnIgD7EDOG31CMk+83wn04mO6wqK/SgMavfLANMCF/rDWuzg0AIMPTiqWujx6CkZ9XQ8tni3E99aLLcT3oIusUfS1fOKmUumPj+uhF6vWEvt4sWsOne7VTQtFrtByrr71Ton30giqEAERAhSFSoydCiXWGcTV6OtKYrDYvEqN+VW/3zNNf2xCNoWWdIRCO0ehNUpenY/nnlz0aGxERdQ9H9IiyQOVbjaj93AODRUH/i/LTHQ4RZCRhkbr2ec4xFgh+fEhERJQRmOgRZYmGXT5IKRFq4o1YKP08x8KjdAVn2gAA1gFGDLgoH67xtnSGRURERBH87JUoS6geDTIkYSowAAYAvAcGpZHvRDjR6zfLAX9NCNYBJgCAFmDRBRFRpmB7hb6NiR5RFjm5vhkD5jlRtjAflW81pjsc6sNCzRqOvFqPsm/ko9/5jliruGBjpI+erkZPqJFCMF1vPUR76wEttXkh3fZoHZ5BVzwWqdsT+l51+p56sT56uncP0UVF33svfLyi652nBHU1eoFw7CLSR08JaBCBECBPO3f0Mepr7JTEiTJCV8cotci+hoTdeo2+Z160Nk/R9SOM9s8zeJN/mmTdvB8AsMb7lx6KkIiIUoFTN4mySPM+P/zVIdiHmGEdwM9pKH3MhQaUXuiAwapA9WgwF4Z/HkNuTi0mIiLKBEz0iLLMsbcaAQkMXMibslD69J/nhKXIiKoPmlDzmTu2PtTMOcVERESZgIkeUZbRPBrqtnlgtBtQMsOe7nCoj6r/0ouQW0XhZHusPi/kViGZ5xERZY5098ljH7204twvSqu12srY8gJlaRojySIKYCsLv7EuONOO+u0+TpejXte0xw/NL1H2DRecoyzhdfv9LTvIlp/JWL2eqvtsUV/DF61rUxK3C/0x0Z56IV2NWZKeeoqu151mjGzX1+BF6uz0vfNEXI1euFZQhMI1eiKoJq3Ri/W/a63e7vQeg0B8PV8vk5GnQurijfYUNPhbHn+0Z57Q/VmxfnU8tvxW9VM9FiMREaUOR/SIsszQawth7R9O9IQiMPjqgvQGRH2WiNwoxVISqc9j6w8iIqKMwUSPKIsMuaYAJpcBDTu8qPqwCQCgmNI3QkB9m2IO/+w1Hwgg5FZhsPElhYiIKFNw6iZRlihfUgBLiQmNX/tw6qPwzS8advmg8LeY0sRbGUT1JjfqtnlQvrgABjs/dCAiyiTso9e38S0iURYYdLkLtv4mNB/w4+T7TS0bNEALpC8u6tuCDSrqtngAAKpHg9GefERPxurtdMVhyXrqxfXHi9TwhXTnjPbU09XtCV09nr5XXmy7iNbt6XrmRWrzREhXJ+jXLUf6+QlVRmr0QhABNXzjAP012qu3i4aWxp55+hij/fP0tXfGSD1etC4vvD38XFmONsTWvVXxeA8GSUREPYHzbIgyXNnF+bAPMsN9JIDj77BJOmWmkEfj1E0iIqIMwldlogw2YIETeUMt8BwPoPLNhvYPIEqTYIMKc4ExVrdHRERE6cVEjyhD9Z/rhHOkFb6qII6tZpJHma1pnx9CAZxjLOkOhYiIotLdJ4999NKKNXpEGajf+XlwjrHAXxPCkZfr0x0OUbtUj4bmQwG4xtvQsMMXvzHSU0/qeufF9dSL1OsJfR+96LJBVzsW7WGn6IrMNN0IohpdbjmPgmg9nr5PXqRmMKirywvoagbVyL6aBggAITXcE08C4RVt0PfOS1fPPN1lZZKPc42+xJ55iq6PoLEu/P17a/f/7Zn4iIioV3BEjyjDlEy3wzXBhmCDioqVdekOh6jDGnZ6YSkywlZmSncoRJRjfD4fdu7cme4wiLIKEz2iDFJ0nh0FZ9kRatZw+O9M8ii7eCuDCNSF4JpgS3coRJRjduzYgU8//RSqfmYAEbWJUzeJMkTh2TYUnWOH6tFw6K+1LbdmJ8oiddu86D/XiYadJngrg+kOh4hyxJQpUzBp0iQYDOnsV5KFMrkWLlPjyiFM9IgygGuiFcXT8qB6NRx8sRbgB5aUpRr3+JA/1orSCxyoWFkXLc8DEF+jF9dTL9YzT/eDH30zp//0PtpTz6Cvy9PX9UXeNei68CqRejt9DV60Nk/4dXV5cT38IsuxnnntvBvJhLo8Hanr9Sd14STrmRetV9TX6L299Zc9HCFR15jN5nSHQJRVOHWTKM3yx1nQb5YDml/i8F9rgVD7xxBlsqoNTTDlG1B4tj3doRAREfVZTPSI0sgxyoLSC53QAhKH/loDLZDuiIi6L1Cnou4LLwrPscPk6v40K2Hk/B4iIqLOYqJHlCZ5w80YMM8JGZQ49Lc6aL72jyHKFrVb3FA9GkovcHTrPIWjgxi2wA/nYA51ExF1lpCZ/UU9izV6RGlgH2zCwIX5kCHg8N/roHl45xXKLTIEVH3QjEGXupB/hgWNe/zQF+wl7akXV8OnwmiTcI0I/z9vgIqmysSeeHE99SL3fhH6mrlQshq9yLK+JlDTLcfq7NoejYxeR7bXW6+XSGM4Dn1dniHQ8lwYvZF6vIDu+xCpd1z70d29ECEREfUmjugR9TJbmRFlF7sgVeDwqlqEmpnkUW7yHAmgYbcX/S50wjnG0unjbf3D/zYdUWDgPRiIiIg6hSN6RL3I2t+IQZcVABI48lItQg1M8ii3nfqgGYpRYMBF+VB9DfBUdLwQ1doP8NcLSN6FloiIqNM4okfUS8zFBpRfUQAAOPJqPQJ1TPIo90kNOPFeE7wngig8qzON1CVs/QBfjYBiBlR/j4VIRJS7ZIZ/UY/iiB5ljLXaSgDAAmVpmiNJPVOBgiFXFQICOPpaPfyneGMJ6lvqv/Bg4CIXLP2M8J8KQYZamqmLJD3zTHkGGCwKvFVAwRkSqk+3XdF9Rqmr1xPRVzRdDVqsNi+o+50LJanR05/T0EbNnSY7tl8vkbqwo7V5hmBLjAZfy2M0RGr0NGNL3O+9//OeDZCIiNKGI3pEPcyYr2DINUWAAI692QDfCSZ51Pc0Hwog0KB2eFTPVgpoqoS/TsBgllDZeoSIiKhTOKJH1IOMeQqGLi2EMADH1zTAeyzY/kFEuUgC9V960G+mA4q5GVqg7Tk7tlIBf3V46qfRBqi+9I+eERFlGyFl/J2IM0imxpVLOKJH1EMUKzDkukIIo8DxtY1wH2aSR31boFaFUAQM1naSNhG+EYu3SsLsAhQT4K9lokdERNQZHNEj6gGKGRj2rWIoJoGT65rgPsB5Z0TRUTzFpADQ9XKL1usZwp89mgsEFJOAr0qFtVSFpgK+ahUwRmr5DLoaPE1/nsg6fe1dIPK7p6/Ri9b6GdrukxdHyvCNA0RmJJxSicShtMSjRGrzDL6W58Tga3nc0hh+ft9b94teiJCIiNKNI3pEKSaMwNBvFUMxC1R90ISmPbxdIBEAyOiNTNrJr0z54ZemQIOErQTw10CfFxIREVEHcESPKJUMwLBvFcFgFaj+xI3GXUzyiKIUc3j0qb36PFO+AjUgoQUBawlQ/3VvREdElIMyuY1BpsaVQziiR5QqCjDsm0Uw2BXUbPag/gtvuiMiyiiKOfyS026i5xQINklYCgUUE+A71RvRERER5RaO6BGlyNBrC2F0KKjb5kHd5550h0OUcVpG9OLnYcpIzZxQw+tNToFQk4S1n4QWkvBXR+rjorV1qu4zSl3NnEBku19XExuM1P/pavnieua1RZOAQMZMG5W6xxrtmafv62eM9MlTfPr+gC3HvLuetXlERH0JR/SIUmDItYUwuQxo2OFFzadM8oiSUUwCUpOxm6a0tZ8akLD1V+CrBqf3EBERdQFH9Ii6afDVBbAUGdGw24tTH7nTHQ5RxhIGASkRHiVrI3kLNmnIG2yEYgLqdzHLIyLqKiHDX5koU+PKJRzRI+qG8itdsPYzoWmfD1Xrm9MdDlFGC9SGoBgEzAVt33az9nM/ArUaPEc1NFf0UnBEREQ5hiN6RF1UdpkLtgFmNB/y48S7TekOhyjj+U6FIKWEtb8JgTo1YXu0n16gFjj+Xri2TJjNse0iWpun6hNF3TzQaP+8QEuNXqz+r7M984DwyGMGiNbmySQfzUbr8gDA4A1F9msJfO2Hd/VscERElLE4okfUBQO/kY+8cjPcRwM4vqYx3eEQZQUZlPCdCCH/DGu6QyEiIsp5TPSIOmnAfCccwyzwngig8o2GdIdDlFVqt3pgG2iCrcyU7lCIiHKfzPAv6lFM9Ig6oXSOE85RVviqgjj6KpM8os7yVATgOxVE0bn2dIdCRESU01ijRxlnrbYytrxAWZrGSOL1Oz8P+WdY4K8N4cjL9ekOhyhr1W7xoGyRC5ZSI/xVLTV20Xo6ABChyHp9bV20Nk+3n35Z+v2RdbrGd9Hec+2V6En9R8sZUpwXIQ2J8URr8wy+xF4Vyue7ezwmIiLKfBzRI+qAkul2uCbYEGxQUfH3unSHQ5TV3AcDUL0a8oaY29+ZiIiIuoQjekTtKDzXjoKz7Ag1azjMJI8oJQINKkzOTtwJk4iIOo199Po2jugRtaHgLBuKp9ihejQc+mstoLV/DBG1T/VpUCyZNUWSiIgol3BEj6gVrolWlEzPg+rTcOjFWiCx7RcRdZHmlzC5Wh/Ri9XrhXQ1aJF6PRFqSRBlUN9HL7IsRMIxyS+i+zhZZFjSqSTGY/C3fNJk8AYTD/n6CABgje//9VxcRESUNTiiR5RE/hkW9JvlgOaXOPxiLWTi/Q6IqBtUrwaDLcOSKyIiohzCRI/oNI5RFpTOcUILShz6aw20QLojIso9gboQTPkGKCYme0REPSbdffLYRy+tmOgR6eQNM2PAPCdkSOLwX+ug+dIdEVFu8p4MQQgBSykrCIiIiHoCX2GJImzlJgxclA+pAof/VgfVwzuvEPWUYL0K1a/B2s8I77HEerNojZ5I0jNP329PBnTHGsKfXYq26vKA03rmZQ6pq8uTuppBJZDYM0+o4cegnKiNrXur9n97OkQiIsoiHNEjAmAdYMSgS1yQGlCxshahZiZ5RD0t2KTCyBYLREREPYIjetTnWfoZUX5FASCBIy/XIdjAJI+oN6huDcY8ft5IRNRT2Eevb+MrLPVp5kIFg68sAAAcWV2PQA17KBD1lhATPSIioh7DET3qs4wuBYOvLgIEcOyNevir2EOBqDeF3BryhrU9dVNfjxftk6fvndduPV6Upvvo2JBhd/qM1uPp6/JCLTMLFG/48errFZVmPwDgrSO/7YUAiYgoGzHRoz7J6FAw9JoiCANQ+Y8GeCuZ5BH1tpBHg9GuAAJJb7NtsAqUXuiAIU+gYacf7iO9HiIREVHW4pwZ6nMUu4Kh1xZCGIHj7zTCcyTxjn9E1PMUs4AW0JIneXYFpRc6kTfUBAGB/rPzIPjRJBFR56S7Tx776KUVEz3qUxQLMOy6QgiTwIn3muA+yG7oROlicigItnKH20GXuOAYYUHVB27U7/BBGAQEX7GIiIg6jJ+PUp+hmIFh3yqGYhao2tCE5n3+dIdE1KcZHYakrUwUk4AxX0HtVg8av/KicLIdql9DqDH8O9vhurwMpu+TJyM1g0JXR2jw6GYahBKfo7f2PNRzwRERUU7g56PUNxiBod8sgmIRqP7YjcbdTPKI0s3oUBBqTrzTbelsBwCgcbc3sobze4iIiDqLiR7lPgUY/q0iGGwKaj51o/5Lb/vHEFGPMzoMCVM37YPNcI6yomp9M4KN4W2hJg0GiwLFJGAuNMA13hy+gQsREbUr2ksv076o53HqJuU2BRh2bREMdgW1Wzyo28YkjygTCCNgtCWO6Bms4QzO7GqZnhlsCu9jdCoYuNAJs8sAz9FQLBEkIiKiREz0KKOt1VbGlhcoSzt9/JClBTA6FdRv96D2M08qQyOibjDmhRO502v0DNbwRJPCc+xo3OtDqEmLjfqZ8g2xBDDkzfIkT9ENScrwR9sGr64/YKBlWZrCj/ntzff1SmhERJQbOHWTctaA+U6Y8g1o2OVD9UYmeUSZxOgIv/xER+ui6/rNCtfnKUaBfjPCy6pbg1Ql8sdYYvtKdkUhImqflJn9RT2KiR7lpPIlLpgLjGja68epD5rTHQ4RncbkMEBKCdXd+sicwdbyEhVs1mAbGJ6EEqhPvIELERERxePUTco5gxa7YO1ngvuoH1XrmOQRZSKjU4Hq0SB1eZ5jhAVSlTi5oQmO4Za4D2lCzSrMLjMAINiU5dM2iYiIegETPeqSmpoaBAIBDBw4MN2hxCm71AX7QDOaD/sR2sNP/YkylTFPSajPKzjThqZ9fjTtCX/phXTJXciTxYmeIfF2odGeeXF1ebpegazNIyKirmCiR61qbm5GdXU1ampqUFdXh/r6ejQ0NGD//v2or68HABiNRjgcDuTl5cHlcqGkpARnnnkmxo0bB0Xp3ZnBA7+Rj7zBZniOBnDi7SaUDCvs1esTUceZnImtFUxOA2pPJK+nDeruzqm6WddBRNQRmdzKIFPjyiVdSvS2bNmCNWvW4IMPPsCOHTtQVVUFk8mEsrIyzJo1CzfffDPOP//8Ns/h8/nw9ttv491338WmTZuwd+9eNDU1IT8/H2PGjMGiRYtw6623YsCAAV16YHrDhg3D4cOH291v6NChOHToUJv7PP/883jkkUfw1VdfoV+/frjuuutwzz33ID8/v83j9u7di//93//FunXrsG/fPjQ1NcHlcqG0tBTl5eWYOXMm5syZg+nTp8NqtXbm4bVK0zRs3rwZW7duRXNzM2w2G0pKSlBaWoqysjLYbDYoioKBAwdCURRomoa//vWv2Lx5M3w+X6vnNZlMmDRpElwuF/bu3YvGxkY0Nzfj2LFjAIANGzYAABwOB8rKyjB27Ficd955KC4uTno+r9cLVVXhcDi69DgVKzDo0gJY+5ngPRHAsTcaIBQ22SLKZEaHAn9NIG6dbKMwX/O3bAu1UddHRNSbNE1DU1MTLJbwzaJS9R6OKBU6nejNnj079kZeLxAIYO/evdi7dy9WrFiBG264AcuXL4fZbE7Yd/v27Tj//PPR1NSUsK22thaffPIJPvnkEzz22GP43//9X1x77bWdDbNH/OpXv8K9994b+//Ro0fx6KOP4v3338cHH3yAvLy8pMc9+OCDuO+++xAKheLW19TUoKamBrt378batWvxy1/+EnfeeSceeuihbsXZ0NCA1atX4/PPP0cwGJkSJESrb6KMRiOmTZuGbdu2we12Iy8vD2eeeSZcLhcKCgpQWFiIoqIilJSUoLCwsM2RupqaGmzevBm7d+9GZWUlvv76a3z99dd47bXXYDabMXnyZCxZsgQOhwPr1q3D22+/Hfs5mDNnTqe/166J1vCd+RSg+YAfx99p7NTxRJQeRochYeomJCBa+fPirgjANUGFucAAqfJjYCJKv6+//hrLly9Hc3NLPfH/9//9f70+o4moNZ1O9KKjNmVlZVi6dCkuuOACDBkyBKqqYuPGjXj00Udx7Ngx/OUvf0EoFMILL7yQcI7GxsbYm/tZs2bhsssuw5QpU1BcXIxTp07h5ZdfxvLly9HY2Ihvf/vbcDqduPjii7v5UIHFixfjgQceaHV7sqQ0ateuXfjlL38Jq9WKu+66C/Pnz0dFRQXuuecebN26Fffff3/SBO3hhx/G3XffDQBwuVz4t3/7N8yePRsDBw5EIBDA0aNH8emnn+L111/HV1991anH85vf/AYjR46Epmnw+Xyorq5GQ0MD/P5wbYvT6cQ3vvENzJs3L/bY6urqcOTIEZw4cQLBYBA+nw8ffPABPvroIyiKgksuuQSXXXZZp+LQKy4uxqJFi7Bo0SIA4U+6vvrqK2zduhVffPEFPv30U3z66aex/Y1GI84++2xUVFRg3bp1Hf5eK1ag/PJCWIqNUP0ajr/TCO8x3m+dKBsoVgHFKOJaKwBAoE6FpV/yl6Vgg4rDf6vHqO8XQTFl14i9TPKmT/G2jGYKX+RDQGPLfm9v/WWPx0VE3bN27VrY7fa4RC8QCHBUjzJGpxO9sWPH4te//jWuvvpqGHTF4gAwffp03HDDDZg1axa+/vprvPjii7j11ltx4YUXxu2nKAquvfZa3HvvvRg/fnzCNRYuXIiLL74YS5YsgaqqWLZsGfbu3QshuvfiXlBQgIkTJ3bp2JUrV0LTNDz88MNYtmwZgPDjnTVrFsaMGYO///3vCYleTU0N7rvvPgBAeXk5Pv74YwwePDhun2nTpuHqq6/Gww8/jE2bNqGmpqbDMVVXV6Ouri72f7PZDKfTiXHjxuGiiy7CqFGjEo4pLCxEYWEhJk2aFFu3ZMkSHD16FOXl5Sn/FEpRFIwfPx7jx4/H9ddfj7179+KDDz5AU1MTJkyYgIsuugiKoiAQCOC//uu/8Prrr2P9+vVYuHAhvvzyS/j9ftxwww1xN30pPNuG4ql5gACa9vtwYm3iyDARZS5jpG2CelrTc8/RAJyjLMkOiQl5NBjy+Gk5EaWfw+HAnj174ta1NWiQFjLylYkyNa4c0ulE74033mhze0lJCR599FFcfvnlAIBVq1YlJHozZ87EzJkz2zzP4sWLcdVVV+Gll17C/v37sXXrVpxzzjmdDTdloiOZc+fOjVs/aNAgjB07Fjt27Eg45u2334bX6wUA/Od//mdCkne6qVOndiqm//7v/4bD4eh2cqYoCoYMGdKtc3TU6NGjMXr06IT1ZrMZ999/P1544QV89tlnWLVqVWzbr3/9azz44IMwOhQMuizcH0/1aah8uwG+46GEcxFRhov8yZKnzdz0HAmg8Cw7LKVG+KuS/26HPBqMdiZ6RJR+CxYswPbt22OlOXfffTenbVJG6ZG7bs6ZMye2vH///i6fZ+7cuXjppZdi50lnoldaWgoAWL9+fdyo4IkTJ7Bnz56kN42pqKiILScbXUuFXPqDYjabcdNNN+G6667Dtm3bMH78eBw9ehRPPvkkfvWrX2HYt4sAATTu8eHkPzmKR5StBJLPzvBUBuGvDaH0fAeOvFKf9NPekFuDkSN6RJQBysrKcN9996G6uhoDBgyAzWZLd0hEcXok0QsEWmoPTp/e2RnRWrPunicVrrzySjz44IP46U9/isbGRsydOxdHjx7FvffeC7fbjVtvvTXhGP3w/e7du2N1a9Q2m82GGTNmAAjXNY4cORL79++HFpA49mYD/Kc4ikeUzUKecG2eKd8Q//usAac+akb55QVwjbeiYWfi3X+FQPZM90mSj0Z75SnelppiGanNW/PF/b0SFhGljtPphNPpTHcYREn1yMei69evjy2PGzcu7eeJ2rBhAyZNmoS8vDzY7XYMHz4c1113HV599dU2b+sNAFOmTMGPfvQjeL1e/OIXv8CMGTOwdOlS7Nq1CxMnTsQ999yTcMzkyZNjyw8++CC++OKLbj+GvujUqVMwGo04sKKGSR5RDlC9Ev7aEIqn2GEbZIqtN9gE+s91QgtJaIHkf5MtxUYEGthegYioI4SW2V/Us1Ke6GmaFndTkqVLl3bpPF988QXefPNNAMCECRNSkugdPHgQX375JTweD7xeLw4dOoS///3vWLJkCS644IJYHV5rHn/8cfz+97/H+PHjYTKZMGDAACxbtgwffPBB0j56s2fPxplnngkgfOOUyZMnY86cOXjggQfwzjvvxJqOU9tKSkrC8997ZPyZiNLh+NuNkBpQfnkBhlxbCEs/I1zjbDCYBQ7/rRZNe/0Jx5iLDDDlG+Cu4B12iYiI2pPyt86PPfYYNm3aBCB8N8cpU6Z0+hx+vx/f//73oarh6T2//vWvuxWT2WzGFVdcgYULF2LixIlwuVyor6/Hxo0b8Yc//AFHjhzBRx99hAULFmDjxo1wuVytnuu2227Dbbfd1qHrKoqCl19+GRdffDH27dsHKSXWr18fG6kUQmDixIm4+OKLcfPNN2PMmDHdepy5at68eThw4ACKzrajdrMn3eEQUQoEG1RUrKyDdYARpec7MWCeE+7DAag+iVBT8o95HSPMUP0avLwJExERUbtSOqK3fv16/Od//ieA8M1L/vCHP3TpPD/84Q+xefNmAMB3v/tdXHHFFd2Ka9OmTVi9ejVuv/12zJ49G2effTbmzJmDn//859i5cycWLlwIIFxH98tfprZ30ahRo7Bt2zb85je/SbjbpJQSX375JR5++GGMHz8e/+f//J+EpuoUngJrNBoxeuFgrNVWpjscIkoh34kQTrzfCFO+AflnWKFYWm+j4xhmhvtwEMjk6T5CJHyJoBr7UjwBKJ4ApBCxrzXbH8Ca7a33eCUiIuqKlCV6O3fuxJIlSxAKhWC1WrFy5Ur079+/0+f5v//3/2L58uUAgPPOOw9PPvlkt2MrKChodZvT6cTf//53FBcXAwCeeuqpuJvJpEJeXh5+9rOf4euvv8aePXvwzDPP4Pbbb8c555wT6w2oqir+53/+B9///vdTeu1cMXLkSNTW1sY1JSWi3BCoVVH3hRcGqwKDJfnLksllgKXYiOaDqf37TESU02SGf3XByZMn8dprr+Guu+7C/Pnz4XK5IISAECLWv7oz1qxZg6uuugrl5eWwWCwoLy/HVVddhTVr1nQtwAySkqmbBw8exMKFC1FXVweDwYC//vWvCb3zOuJPf/oTfvGLXwAIN2b/xz/+gby8vFSE2CaXy4VvfvObePLJJ+F2u7F58+Z2+/x11ZgxYzBmzBh873vfAwAcPnwY9913H1asWAEAeO655/D9738f559/frvnklK2exOZXLFo0SLs2bMHb731FoTS+if+UUIR4Q/TO7AvdR+f796Tq8917WceQJMI1KhJH5tzhBlaUMJ7LAihtNyFWeh3FZ1bh2TrTtsuhO7fNvZr7zwJ8erW9ZW/4+2Jvqbx+egdfL57Tyqfa36/kLSlWVdIKXHrrbfiqaeeilt/7NgxvPLKK3jllVdwyy234I9//GNsYCbbdDvRq6ysxPz581FZWQkhBJ555hksXry40+d58cUX8YMf/AAAMHToUKxduxYlJSXdDa/Dxo8fH1tu76YsqTR06FA8++yz8Hq9+Nvf/gYAWLlyZYcSvYaGhj7zCz9gwAAUFRXhq6++Qsmwwnb3F4pAQZkTEAJS6xvPUTrx+e49Of1c1wBWANZhiR/w9T/HCdWroXiwhNC12xH6XqLRZYN+XXhfYUiyX7JjASCaaCoGCAG4+tkgFAEpk+8nDbo3ALokFNH1uu+TCITvMip1bxoaGhoSHm9fJKWMzdrI1jdV2YTPd+9J5XPd2NiYipByxogRI1BeXo4NGzZ0+ti77747luRNnjwZP/vZz2ItvR5++GFs3boVTz31FPr164cHHsjO6fXdSvSqq6uxYMECHDhwAADwxBNP4MYbb+z0eV577TXceOON0DQNAwcOxHvvvYfy8vLuhNZp6U6Y/vVf/zWW6O3bt69Dx7hcrqR3+8xV5eXl2L59O+pPNSDkbrtIRygCkBLVh+tz781wBuLz3Xv64nNttCsoMBlw8oMmNB0Ktp7oRZI5/XYYDEnWKXHbAMQncNHtkURPAqg94UlM9CL76RM9aUx8WVU8ujuIRo55c8eDrTzaviv6OhydhkU9i89370nlc83vFXDPPfdg2rRpmDZtGoqLi7Fu3TrMnTu3U+fYt28fHn74YQDhFmobNmyINbw/77zzcMUVV2D27NnYvHkzfvOb3+B73/seRo4cmfLH0tO6nOg1NDRg0aJF2LVrFwDgoYcewu23397p87z33nu49tprEQqFUFxcjLVr16bliYw+DgAoKyvr9esPGjQotqwoHSudjM5H7isuueQSbN++HYXn2FC1vv1aPSkBqck+82Y43fh8956+9lzbh5kgVYnmw8HwYxa6x63/EyhP+7cr65Jtl5Hn/PSaksiy/nPCZJ8ZyiTH9KW/3Z0RfV3j89M7+Hz3nlQ91509Xpz2JzOTdDWuVNw48bHHHovdAPGJJ56IJXlRdrsdTzzxBGbMmIFQKITHH38cTzzxRLev29u6dDMWj8eDSy+9FFu2bAEA3HXXXbjzzjs7fZ6PP/4Yixcvht/vR35+Pt5++21MmDChKyF1S0NDQ2w0zW63d6klRHd9/vnnseXhw4f3+vWzwZAhQ2Cz2eAYbkl3KETUi/KGWuCpDLbaRJ2IiKijpJRYvXo1gPA9QaZPn550v+nTp+OMM84AALz66qtpn/3XFZ1O9AKBAJYsWYKPPvoIAPCjH/2oS/NWt23bhksvvRRutxt5eXn4xz/+gXPPPbfT55kzZ07sE5JDhw4lbF+zZg28Xm+rxzc3N+Paa69FTU0NAODmm2+GxZKaROKpp57Crbfeiv3797e5X0VFBe66667Y/7tS49hXTJw4EYpFwFSQ0s4gRJTBDDaBYKOa7jCIiCgHHDx4MHY/jtmzZ7e5b3T70aNHk+YZma7TUze/9a1v4Z133gEAXHTRRbj55puxY8eOVvfPy8tLGKHav38/Fi1ahPr6egDAAw88AJfL1eZ5ysvL22yT0JqHHnoI119/Pa666iqcf/75GDlyJBwOBxoaGvDxxx/jj3/8IyoqKgAAZ5xxRpduy9qaQCCAP/3pT/jTn/6EWbNmYeHChTj33HPRv39/KIqCY8eO4Z///CeWL1+OpqYmAMAVV1yBefPmpSyGXHPJJZdg06ZNKJ6ShxPvNqU7HCLqBUIRkGoGf5Kqm24vdJ/4Cm9iKwh136HeiIiIiFqxe/fu2PLYsWPb3Fe/fffu3Vk3667Tid7LL78cW37//fcxadKkNvefPXs21q1bF7fugw8+QFVVVez/P/7xj9u97rPPPoubbrqpU7FG1dbWYvny5bH+fMlceOGFeOGFF1BUVNSlayRTWloKs9mMQCCAjz76KDYK2ppvf/vbbcZIQP/+/aF6NdiHmNMdChH1EmEQkBzQIyLqvFiBcQZKU1xHjhyJLbd388fBgwcnPS5bpKSPXiZ75JFH8N5772Hjxo3Ys2cPqqurUV9fD7vdjrKyMkybNg3f+ta3sHDhwpQXI1977bVYtGgR3nnnHaxfvx5btmzBgQMHUFdXBwDIz8/HqFGjMGPGDFx//fVdmrraFzUfDKBggg2WUiP8VaF0h0NEPUwYkNkjekRE1GWnt4ywWCwpK6NKJjqLDgAcDkeb++r7eUdbZGSTTid6qShEvOmmm7o8One600cLTzdlypS03FwlyuVyYenSpVi6dGnaYsg1NZ974BpvRfG5dlS+xX4yRLlOGARk2x1ViIgoS+lHzQDg3nvvTWkp1el8Pl9s2Wxue4aYPuFs654fmSrnR/Qo97zd/Df84he/gNnsRiWY6BHlOqFk5oheXKP0CKHvmRfdr7o2tvxO4IUejYmIKNscOXIkri90T47mAYDVao0tBwKJtdR6fn/L3/TTWzBkA966kLLSeeedh2AwCPtgU7pDIaIeFq7Ry7xEj4go00X76GXqFxAuZdJ/9XSi53Q6Y8vtTcd0u92x5fameWYiJnqUlRYtWgQAKJxsT3MkRNTjZPjOm0RERN2lvwHL0aNH29xXfwOW06eYZgMmepSV7HY7SkpKYOvPET2iXBdsUmFyGtIdBhER5YDx48fHlr/66qs299VvHzduXI/F1FNYo0dZa8aMGXj99dfhGGVB877Euhgiyg3BBhUmV4YkeklGFuPq8vQ3LPOF16+p/d+ejoqIiDpo+PDhKCsrQ2VlJdavX9/mvhs2bAAADBo0CMOGDeuF6FKLI3qUtebNmwcpJQrOzL7iWCLquIxK9IiIsonM8K80EEJg8eLFAMIjdp988knS/T755JPYiN7ixYtT3oatNzDRo6xlNpsRqFNh7ceBaaJcFmhQYXIqfMUiIqKUuOOOO2A0ht8/Llu2LKF1gtfrxbJlywAARqMRd9xxR2+HmBJ8h0xZrX6nF/0vcCJ/vAWNuzh9kygXBRtUCEXA5FQQbGBDPSKivuzDDz/Evn37Yv/X19Ft27YNK1asiNs/We/uMWPG4Cc/+QkeeughbN68GbNmzcKdd96JkSNHYv/+/fjNb36DrVu3AgB++tOfYvTo0T3yWHoaEz3Kao07fSid5UDBBDsTPaIcFWxQAQAmlyF9iV50yo5u6o7wBcMLoVDLfqaWG0S9dfR3vREZEVGr9G0MMk1X41q+fDmee+65pNtWr16N1atXx61LlugBwIMPPoiqqio888wz2Lp1K775zW8m7HPzzTfjgQce6FqgGYATYSjr+atDMBcZ+NNMlKNCbg1aSMLMOj0iIkoRRVHw9NNP480338TixYtRVlYGs9mMsrIyLF68GP/4xz+wfPlyKEr2vsHkiB5lvbrtXgycn4/CSTbUbfO2fwARZR3ekIWIiABgxYoVCdMzu+OSSy7BJZdckrLzZRImepT1mvf5IedK5I+1MtEjylHBBhWm/DR+qhqdshkItqyLTtk0tryUvvX1b3oxKCIiotYx0aOc4D0ZhG2gCcIIyFD7+xNRdgk0qnCOsKQ7DCKi7CJlfH/PTJKpceWQ7J10SqRTt80DIQSKzrGnOxQi6gHBBhVGhwLBVy0iIqIO4Usm5QRPRRBaSMI52pruUIioB0RbLBjzW+r0hKmNA4iIiPo4Tt2krLZWWwkAWKAshbcyAPtgMxQrIANpDoyIUipQH26xYCk2wGAFQs0ahl/vglQljr7pQaCmB9ouKAIQCH8kGgwBEkBQV6MXaaXw1r7/Tv21iYiIuomJHuWM2i0e5A2xoPjcPFRv9KQ7HCJKIdWjwXsiiP6zHVBMul52BgHbQEPPJHpERFkuF/voUcdx6iblDN+JENSABsdI3rCBKBfVf+mLJXm1W3xoOhAeug/UMckjIiI6HRM9yimeIwEYbAqMDv5oE+Wa5oMB1O/w4cgrTajd4oMMSYQ8Gnwn1XSHRkRElHE4dZNySu1mNxwjLCiaYod2KN3REFFKSeDUR24IgwGmAgXOUWZUb/KnvqVKtGeeJsM1ehqAQCBco2dsuRnMWwceSfGFiYiIUofDHpRTAnUaNL9E3lBzukMhoh5UPMWKkFtD455g+zsTEfVVMsO/qEcx0aOc03woAMUsYCrgjzdRLrKWGuAYZkbt577waBsREREl4Dthyjm1m90AANdYW5ojIaJUU0wCpbPt8J0KoWk/R/OIiIhawxo9yjmhZg2qR4OzP3+8iXJNyUw7DDYFlWuaUjvtR4jEdV5vuEbPb4xtf+vQYym8KBERUc/hiB7lpOYDfigmBdYBTPaIcoUxT0H+GAtqP/ci1MQ5m0RE7Yn20cvUL+pZTPQoJ9Vu8UBKicJz7OkOhYhSxDXBCi0o0fh1IN2hEBERZTwmepSTND+gejTYBpjSHQoRpYAwAq5xFjR+5YdkaR4REVG7OK+NcsJabWVseYGyFADQXBGAMkggb5gZ7kMcASDKZs7RVihmgfqdPoQL51IgWV0eAHh94X+NxvClFAPePPw4RGv7ExERZSAmepSzmvb4kF+moPAsGxM9oixXcKYN7sMBhJo0CIOh/QOIiAjQZPgrE2VqXDmEUzcpZ8kQEGxUYS3l9E2ibOYcY4GlyIi67b50h0JERJQ1mOhRTmvc7YMwCDjHWNIdChF1gWIWKJnhQNNeH3wnQukOh4iIKGtw6ibltPqdPhRPy4NjuAVNX/vTHQ4RdZJrvBWKSeDURnfqTpqs1s6v+/ughD8Dfevo7yClRENDQ+quTURE1EuY6FFuUwEZkjAX8UedKBvZyszwHA1A9WiA4CQUIqJOkZGvTJSpceUQvmpSHyAgWfBLlJUMFgHVy+boREREncVEj3KeMIBvFImyVMijweTkXTaJiIg6i/PZKPeJcPN0Iso+vpNBFJ2bF/5YMtUD835d2xVDy+eebx1/MsUXIiIi6n0c0aOcZnQqEEIg2KSmOxQi6gLP0SAUk4C1lJ9LEhF1lgAgZIZ+pfvJ6QOY6FFOsxSG3xwy0SPKToG6cEsFTt8kIiLqHCZ6lNNMrvCPeLCeiR5RNhLG8Ge+krOviYiIOoVzYSjnrNVWQkqJ60ffCpMznOj5a9lomSgbRVujBLr7O6y0TBKSwSAAQOjaNag1td07PxFRJpIy/JWJMjWuHMIRPcppBocBUkpovnRHQkRdYe1nhFQlAg0clSciIuoMJnqU0wx2BeCUL6Ks5RhpgftIgL/HREREncREj3KawapAUzk1gCgbmfIV2Pqb0LTXn+5QiIiIsg5r9CinKSYBGWSiR5SNnKOt0AIa3Ie7kehFavOkmjj1U4Za6v7eCf6169cgIspQ0VYGmShT48olHNGjnKaYBFQ//5IQZSPnaAuaDwYgeS8lIiKiTmOiRzlNKIDqZXEPUbax9DPCXGBE017eSYmIiKgrmOhRbhNM9IiykXO0BSGPBs+xYLpDISIiykqs0aPcJgGjw5DuKIioMwTgHGlB034f0N2Z11rkBLp+TcIYful7q+6pbp6ciCjDSXT/72hPydS4cghH9CinaQEZa5pORNnBPsgEY56Bd9skIiLqBr4DppzmrwvBYFdgLuSPOlG2cI62ItAQgr+Kd2EhIiLqKr77pZxW9c9mAED/eflpjoSIOipvmJmjeURERN3EGj3KWb/f/Bu4XC78/ve/x86dO1E6x4mqdU3pDouI2mCwCRgsCvzVKRrNi/bKM5tiq9469cfUnJuIKMMJKSFkZhbDZWpcuYQjepTzbrvtNvhrQ3CNtaL/XGe6wyGiNhid4ZsnhZp4t1wiIqLuYKJHOU9RFFT8vQ7+mhDyz7Ci/0VM9ogylSmS6AWb1DRHQkRElN2Y6FGfUbGyDv7qEPLHMNkjylRGhwI1oEELcEoPERFRd7BGj/qUilV1GHxNAfLHWAEBnHyPNXtEmcTkNHR72qZUW0YDhSlcm7emmj3ziKgP0iJfmShT48ohHNGjPufIqnr4TgWRP5o1e0SZxuRUOG2TiIgoBZjoUZ905KV6BOpCcI6xwFxsSHc4RBRhdHR/RI+IiIiY6FEfduzNBgDAgPnssUeUKYwOBSE3R/SIiIi6izV61GeFmjV4KgKwDzGH31w2cxSBKO0EILv6q6iGDxS6nnlr6panICgiouzEPnp9G0f0qE+r2eyBEAKFZ9nSHQoRERERUcow0aM+zX8qBC2oIW+YJd2hEPVJRqcCxygLrP2NECyXJSIiShlO3aQ+z3cyBNsgE4QRkKF0R0PUhwhg2LeKIBQBAPBWBtIcEBERUe5gokd9Xv1OL+zlZhScaUPdVm+6wyHqOyTQuMcH1zgbpCphHWjqfH2e/gBj+CVtTcMzqYuRiCibychXJsrUuHIIp25Sn+c+GIDUJJyjrekOhajPqVrfjOZDfgiDQKhJg2IQUH28MRIREVF3MdEjAuCvDcFcwAIhonTwnQgCAEz54d/BQA3bKxAREXUXEz0iAE1f+yEUAecZvCkLUW8zWFteilS/Bn8Ni2WJiIi6izV61Ces1VbGlhcoSxO21+/womR6HorOyUPTHn9vhkbU51lKjWg+4If3RBDuwx24IYu+Lk+09Mx7u/m5HoiOiCiLSRn+ykSZGlcO4YgeEQBogPtwAKZ8BZZSfv5B1FuEAlhLTfAeD6J+uxfBhvhpm/njrCiZlQfHSAsMdpGmKImIiLIPEz2iiJPrGwEApRc40hwJUd9hKjBAMQr4TgWTbi+YYEPhmXYMXJCP4d8uhL3clHQ/IiIiisdEjyhC84VvCmEpMcLo5K8GUW8w2MK/a6on+Z02fdVB+KqCOPBcNXynQig829ab4REREWUtzlEj0jm5vglDrytC6WwnKt9oSHc4RDlPhsI1GsIgAIGEvkpGu4KQW4PqlfCdDCFvqBnC0HKH3Le9f+nFaImIsouQ4a9MlKlx5RImekQ6wXoNgVoV9kEmWPoZ4T/Fu/8R9aTofVX6zXLANtCEQIOKqg1N8J0I/+4Z8wzwHg9P6wy5NRgdStKEkIiIiOJxfhrRaSrfagAkUH5FAczF7K1H1JOskZsf2cvNqN/lhRaQKL+8APljrQAAY56CkDt8gxbviRAUo0DROWyDQkRE1B4mekSnCTVrOL62EcIADLm6EEVT7OkOiShnFU62I9AQHr1r2uvH0dfq0bjHh/5znBh2fREMViV2J05/dQhaUIZH9IiIqH3R9gqZ+kU9ilM3iZJwHwyg4qU6DLrUheIpeXCOsuDY6w0IuZPfMIKIOk+xCJgcBpz4ZyNKpjngOsOKqqpmVG1oRtM+P5yjLVC9GpoPhnvr2QeZoJgEHvp/92Lo0KFpjp6IiCizcUSPqBWBGhUH/1yLht1emFwGDLu+CAVn8o5/RKmi+SVCbhVmlxENO71wjrVCsYaH67yVQVStb0bNJk+sHs8xwoxgk4ohQ4akMWoiIqLswESPqB1V65tx5JV6aEGJkpl5cI5hfRBRqgSbNFj6GeEcY0GoWYVUW9lRAI5hZjQfCEAIzt0kIiJqDxM9og7wV4Vw6P/VABpQPCUv3eEQ5QSDVcA2wIS8wWYoRoFjbzRABpPXbNjKTDDYFDQfCPRylERE2Utomf1FPYuJHvU5a7WVWKut7PRxWgAINKixBs9E1D3OMdbY8rE3GxBqSvKqLxRAKHCOtKK4uBivVT7fixESERFlL96MhagTZEhCMM8jSolgvQpfVRCnPm5GoLa1OZsABJA33IzJkydz2iYREVEHMdEj6gQtxFu7E6WKuyIAd0X7UzFtA40w2hScc845vRAVERFRbmCiR9QJkokeUa9zjLAg2KSypQIRUWdlcr+6TI0rh3ASGlEnaK3cKIKIeogAHMN5t00iIqLOYqJH1AlaQIbfbPI3h6hXWEoMMNqVWNN0IiIi6hi+XSXqhOiInmLlrw5RbzA5DACAQH0bN2shIiKiBKzRI+qEaKJntAABT5qDIeoDQt5wywVzgSHNkRARZSEZ+cpEmRpXDuGwBFEnaH6O6BH1ikj/vNVH/oLS0lIseWBeuiMiIiLKKny3StQJmj88umBgokfUK4QQKC0txYkTJ9IdChERUVbhu1WiTlAjI3oGK+/+R9QbVq9ejR07duDCCy9MdyhERERZhTV6RJ3gqwoBACzFRgD+9AZDlOsE8MEHH2D+/Pk4//zz0x0NEVHWEVJCZGi/ukyNK5cw0aM+a622Mra8QFnaoWNUjwapSZiL+KtD1JPWqn/Dhx9+iBdeeAFTpkxJdzhERERZh1M3iTpJ9WmwlprSHQZRTquursbLL7+M6dOnY8iQIekOh4iIKOsw0SPqpJrPPVCMAsVT7ekOhSg3KcDy5cvhcDiwdGnHRtuJiIgoHhM9ok5q3OmD6tNQMImJHlFPKJmWh2PHjuHmm2+GzWZLdzhERNlLysz+oh7FRI+oC6o3NUMxCpRMZ7JHlCprtZX4ny/uQeFZdlx55ZUYOnRoukMiIiLKWkz0iLqgcZcfqk+DayITPaJUqaurw5///GdMnDgRF110UbrDISIiympM9Ii6KDaqN4PJHlG3CeDZZ5+FyWTCjTfeCCHYq5KIiKg7mOgRdVFsVG8CEz2i7io61479+/fjX/7lX+BwONIdDhFRbpAAtAz9Yolej2MzMKJuqP60Gf1n56NkZh6qP3anOxyirKHvY7lnzx787ne/w6WXXopRo0alMSoiIqLcwRE9om5o3O1HyKvBNcHG3yaiLmhqasKKFSswevRofOMb30h3OERERDmDb02Juqn6k2YoBoGS6XnpDoUoq2iahueeew6qquKmm26CovAliYiIKFX4qkrUTU17/Ah5VRRwVI+oU9577z3s2rUL3/3ud1FQUJDucIiIco6QMqO/qGexRo8ILfVCC5SlXTq++hM3BszNR+HZdtRt8aQyNKKcoa/LO3jwIFavXo358+djwoQJaYyKiIgoN3H8gSgFmvb4IVWJ/DGWdIdClPHcbjeeeeYZDBkyBIsXL053OERERDmJI3pEKeI7FYS1vwkwAFDTHQ1RZnK73fjd734Hn8+HH/3oRzAYDOkOiYgod0kAmTpFMkPDyiUc0SNKkfovfRBCoPBMW7pDIcpI0SSvtrYWP/rRj1BSUpLukIiIiHIWR/SIUqR5vx/yIon8M6yo2+ZNdzhEGWOttjIhySsvL093WERERDmNI3pEKRRsUmF08NeKSI9JHhERUe/jiB5RCql+CZNTpDsMooyhmAWTPCKidJEyg2v0MjSuHMKhB6IU0vwaf6uIIhSzwKDLXUzyiIiI0oBvSYlSSPXx0ykiAHjT9/8w975zUDysgEkeERFRGjDRI0oh1adBCME6Perz9uzZg4qKCvzgBz9gkkdERJQGrNEjSiGDRYGUEiGvlu5QiNKqrq4OQggMGTIk3aEQEfVdGoBMvXUA3yr1OA47EKWQKV8JNwBlw3Tqw0z5Ct555x2MGjWKDdGJiIjShCN6RDprtZWx5QXK0k4fb7AboIVYp0d919OHHsWTTz4Jo9GIG2+8Md3hEBER9Vkc0SNKIYNVQPMz0aO+yTbIhMceewzFxcX4yU9+guLi4nSHREREOUYI0aGvOXPmpDvUtOOIHlEKCUVAC3LSOfU9jpEWDLjIiZEjR+L73/8+rFZrukMiIurzhJQQGdqvLlPjyiVM9IhSin+0qG8x5SsoPMeO/DOsaNrrx22/v411eURE1ONuu+02/OAHP2h1e15eXi9Gk5mY6BGlmMjUu1sRpZCpwICljy7C5s2bkZ+fj4ULF2L27NlQFFYEEBFRzystLcXEiRPTHUZGY6JHREQdZi42oOicPDhGmLFv3z5ce+21mDlzJkwmU7pDIyIiIh0mekSpJJG5/WqIusHSz4iic+xwDLcg2KiiakMznvjiCRiNfBkhIspYUoa/MlGmxpVD+ApNlEL8k0W5xtrfiKJz85A3xIxAfQgn3m9E0z4/oIFJHhERUQbjqzRRqrFIj3KArcyEonPtsA8yw18bwvG1jWg+4MdadWX7BxMREfWwlStX4sUXX0RFRQWMRiMGDBiAmTNn4qabbsLcuXPTHV5GYKJHREQx9nITis7Ng22gCb5TQVS+3QD3wUC6wyIiIoqza9euuP/v27cP+/btw5///GdceeWVWLFiBVwuV5qiywxM9IhSiTV6lKXyhppRdK4d1lITvCeDOPaPBngqmOAREWW1LKjRa2xsjFttsVhgsVhaPcxut+OKK67AvHnzMHbsWDgcDpw6dQrr16/HH//4R9TU1ODVV1/F4sWLsXbt2j59szAmekREfZUAHMPNKDonD5YSI7yVARx7ox6eo8F0R0ZERH3E4MGD4/5/77334r777mt1/2PHjqGgoCBh/YIFC7Bs2TJcfPHF2Lp1K9avX48//OEP+Pd///cUR5w9mOgRpRhL9CjTKSaB/LFWuCbaYHYZ4DkawNHV9fAej0/w1mqsxyMiop515MgR5Ofnx/7f1mgegKRJXlT//v2xatUqjBs3DoFAAE888QQTPSJKkQydHUEEAEangoIzbcgfa4ViEGg+4MeJ9xrhrwqlOzQiIuqj8vPz4xK97hoxYgQWLFiAN998E/v27UNlZSXKyspSdv5swkSPiCjH2QaaUDDJhrxhZmh+iYYdXtTv9EF1a+kOjYiIelIW1Oj1hPHjx+PNN98EEJ7qyUSPiFKDUzcpAwgFcIyyoGCSDdYSE/y1IVRtaEbTXh8kB/CIiCiHyUxNbntZRid6W7ZswZo1a/DBBx9gx44dqKqqgslkQllZGWbNmoWbb74Z559/fpvnWLFiBb73ve916HrPPvssbrrppla3V1ZW4he/+AXefPNNuN1unHfeefiv//ovzJ8/v83zBoNBrFq1CitXrsSWLVtQVVUFAOjXrx/69++P8ePHY86cOZg9ezaGDx/eoVip50XrkxYoSzt8DP+uUNoIwFxkgG2AKfxVbobRpsB92I9jn7R/gxXW4xERUa7Qt17oq6N5QAYnerNnz8aGDRsS1gcCAezduxd79+7FihUrcMMNN2D58uUwm809Gk9lZSWmTZuGo0ePxtZt2LABixYtwnPPPYfvfOc7SY/bu3cvrrnmGmzfvj1hW0VFBSoqKvDZZ5/hueeeAwB4vV5YrdaeeRDUK3gzFuoNwghY+4WTOusAE6ylRhgsCqQq4TsVQuMeHxp3+xBsUNMdKhERUa85cOAA1q5dCyBcrzdo0KA0R5Q+GZvoHTt2DEA4C1+6dCkuuOACDBkyBKqqYuPGjXj00Udx7Ngx/OUvf0EoFMILL7zQ7jnffvvtNrP68vLyVrf9+Mc/xtGjRzFjxgz84he/QGFhIV566SU8/vjjuPXWW3HxxRejuLg47pja2lpcdNFFseRw9uzZ+M53voPx48fDbrejrq4OO3bswD//+U+sWbMGXq+3I08NZTIO6VEPsg4wwTnSjP7jnHApCoQioPo1+E4EUbfNA++JEPxVQUjmdkREBAAaMrekpAtl4q+//jouvvhiGI3JU5iTJ0/immuuQTAYnsVy++23dyfCrJexid7YsWPx61//GldffTUMBkPctunTp+OGG27ArFmz8PXXX+PFF1/ErbfeigsvvLDNc44ZMwbDhg3rdCx+vx+rV6/G4MGDsXbtWuTl5QEAZs2aBU3T8Nvf/hZvvvkmbrzxxrjjHnrooViS11pPkLlz52LZsmVoamrCs88+m/BYiahvM9gE8s+wIn+sFeYCI4JNKoKNKk7taIb3eBCBOmZ1RETUNyxbtgzBYBBXX301ZsyYgWHDhsFms6G6uhrr1q3Dn/70J1RXVwMAzj//fCZ66Q6gNW+88Uab20tKSvDoo4/i8ssvBwCsWrWq3USvq2pqauD3+zF16tRYkhc1b948/Pa3v42NQOq9+uqrAMI9Pe655542r+F0Ovt0n4+ckqmfnFH2EIB9sBmusVbkDTVDSqD5gB9VG5rhOxFCyTCg8ZAfUuveCDLr8oiIKNtUVlbiiSeewBNPPNHqPldffTWWL1/ebk++XJexiV5HzJkzJ7a8f//+HrtOYWEhjEYjNm/eDI/HA7vdHtu2bt06AMCAAQMSjquoqAAADB8+HIqi9Fh8lEEkmOhRl5mLDHCMtCD/DCtMDgN81UGc+rgZTXv90ALhpE4o/AEjIqK+6bnnnsP69euxceNGHDhwANXV1WhsbITD4cDgwYMxc+ZMfPe738WMGTPSHWpGyOpELxAIxJZ7csqjzWbDokWL8Oabb2LhwoX4+c9/jsLCQrz66qt4/PHHYbfbcckllyQcZzab4ff7sW/fPoRCoVbnExNR32UuNCD/DCucoy0w5hmg+jU07fOjcbcP/mr2QSAioq4TUkJk6P0DuhLX7NmzMXv27B6IJjdldeaxfv362PK4cePa3f+mm27C7t27UVdXh/z8fIwaNQrz58/Hbbfd1u4deR5//HF88skn+Oijj3DZZZfF1gsh8Lvf/Q79+/dPOGby5MnYsGEDqqurcccdd+B//ud/evzuoJQBeNtNaodiEnCMCo/c2QaYoHo1NO71wX0oAN9J3kyFiIiIui9rEz1N0/DQQw/F/r90afu9zvSJYU1NDWpqavDpp5/i0UcfxeOPP45/+7d/a/XYUaNGYdOmTfj5z3+Od955Bz6fD5MnT8bdd9+ddDQPCBeMRltEPPnkk1i1ahUuv/xyzJw5E9OmTcO4ceMgmBTkFCk5c5OSEIDRocBcaIRzpAWOERYIA+A5GsDxtxvQfDjQpbuPdQbr8YiIiPqWrE30HnvsMWzatAkAsGTJEkyZMqXVfUeMGIGrrroKM2bMwODBgwGEe2y89NJLWLVqFXw+H2699VYIIXDLLbe0eZ6//e1vHY7xmmuuwT333IP7778fUkqcPHkSy5cvx/LlywEABQUFuPDCC3Hddddh6dKlMJlMHT43ZS7m7gQAln7hpM4+xAyTywDFEP7BCDSoqN3iQdPXPoTcPZzdERFR3yZl5rZ+ytS4coiQMvue5fXr12P+/PkIhUIoLS3F9u3bk06dBICGhgbk5+e3OnL2xhtv4KqrrkIwGITdbsf+/fuT3lilOzZt2oSHH34Yb7zxBvx+f9J9Ro8ejRdeeKHNhDWqsbERLpcr9tgokZQSDQ0NcLlc3R41XaC0P1ocNez6IigmgQMrarp1zWwjFIGSYYWoPlTX7TtBZjNLsRGOURY4R1pgyjcg5NHgPuSHvyaEYIOKQIOKUFP3kruuPtcc0euaVP4tobbxue5dfL57Tyqf646+B4zuN3/0j2E0ZOadJ0OqH+/ufYzvZ3tQ1o3o7dy5E0uWLEEoFILVasXKlStbTfIAwOVytXm+yy67DPfeey/uvvtueDwePP3007jrrrtSGvPUqVOxatUquN1ufPzxx/j000/x+eefY8OGDaitrQUA7N27F3PmzMEnn3yCiRMndui8UkpkYZ7eK6LPTSqen67c5bCv3RlRKAJC9L3HDYRvpuIcaUbeKAvM+Uao3nByV7UhAO/xYPhOrDrdfY66+lzzb0XXpPJvCbWNz3Xv4vPde1L5XPP7RZ2RVSN6Bw8exPnnn4/KykoYDAa89NJLWLx4cbfPW1VVhQEDBkBKiQULFuCdd95JQbTtC4VCePXVV3HHHXfE+vDNmzcP7777bpvHRT+lOXz4MD8BaYWUEs3NzXA4HCn9pPIHU+5sc3vZxfkQJoFjrzWk7JrZQCgCBWVO1Fc29YkRPaNTgX2wGXnlJpjyjVADGrzHAvAcDcJ3KtSj9Xadea5/v/k3PRdIH9FTf0soEZ/r3sXnu/ek8rlubGzE0KFDOaJHHZI1I3qVlZWYP38+KisrIYTAM888k5IkDwBKS0tRUlKCU6dOJW183lOMRiOuueYajB49GlOnTkUgEMD777+PmpoaFBcXt3u8y+XiL0Yrop9fpHpKSvWhuja359UBikW0u1+uEYoApET14fqcTfRM+QY4RprhGGmBpUhA9ftx8usmNO8PwHOs52+mEtWZ57q9GQ3Uvp76W0KJ+Fz3Lj7fvSeVz3Wnj9ckIDL0dTlH3y9kkqxI9Kqrq7FgwQIcOHAAAPDEE0/gxhtvTOk10jmwedZZZ2Hq1Kn48MMPIaXEgQMHOpToCSH4x7kN0ecnlc9Re2+sZQf3y0VShh93Lj12U74Cx0gLHCMtsJaYoAU0NB8OoGaTG54jgbS1Qejoc82/D6nRE39LKDk+172Lz3fvSdVzze8VdUbGJ3oNDQ1YtGgRdu3aBQB46KGHcPvtt6f0GlVVVaipCd88o6ysLKXn7ih9Hz9FUdISA6WABPsrZDmjUwm3QBhpgbWfCVpQwn3Yj9rPPeHkjj3MiYiIKAtkdKLn8Xhw6aWXYsuWLQCAu+66C3fe2XaNVFc89dRTsRG92bNnp/z87ZFSYuvWrQDCn9QMHTq012Mg6suMjvDInXOkBdbSSHJX4UfdVg/cFdmR3PGumkRERKSXsYleIBDAkiVL8NFHHwEAfvSjH+GBBx7o1DkOHTqEuro6TJ48udV93njjDdx///0AAKvViu9973tdD/o0F154IW677TZcc801bfbIu//++/H1118DAGbNmoWSkpKUxUC9THJIL1sY81qmZdr6m6CFJDwVARz/ohHuw/6sSO6IiIjaxD56fVrGJnrf+ta3Yne/vOiii3DzzTdjx44dre6fl5eH4cOHx607dOgQ5s6dixkzZuDyyy/H2WefjdLS0lgd3KpVq7Bq1arYaN4jjzwSN4Wyu3bt2oVvf/vbWLZsGRYvXozzzz8fI0eOhMvlQnNzM7788ks8//zzsWTWbDbjkUceSdn1KT2Y5mU2Sz8jis6xwzHcEk7ujgRw/N1GuA8HIIN80SEiIqLckLGJ3ssvvxxbfv/99zFp0qQ29589ezbWrVuXdNvGjRuxcePGVo+12+147LHHcMstt3Qp1taUl5ejpqYGNTU1eOaZZ/DMM8+0uu/AgQPx7LPPYtq0aSmNgdKAmV5Gsg40oegcO/IGmxGoD+HkuiY07/dDY3JHREREOShjE71UOPfcc/H8889j48aN2Lx5M44fP47q6mqEQiEUFhZiwoQJmDdvHr7//e+jtLQ05dfftm0btm7dirVr1+Ljjz/G/9/enYc3VeZ7AP9mX5pu0BZKWyg7KIgsgywVRARBr+hwUUeRRbkuV50RRVDRUZRBBReY64yKC+CMjiCLK4oIlH0rUqEiUJbSQilLoWvSJE3Pe//oNNPSPTnJSZrv53n6EHq2X877EvI773b48GHk5eXBarXCbDajTZs26N27N2699VbcfffdCA8Plz0G8jPmDAHHnFiZ4Jna6eG45ELeT8UoPekI6rJ6d998TolOREREDQrYRE+O5Q7Cw8MxceJETJw4UYaIPNO3b98GxwhSyxLEuUOLE5asR6t+ZhjjdLBfKMfZH4pgzXYqHRYREZEfBfAYPX5r8rmATfSIgpIA2HdTObpIDSyd9AjvaoShlRa2s07kflcI25lypUMjIiIi8ismekRy4qSbfmdorUVYJz0sHQ0wtNL+e2kEJy5sK4U9jwkeERERhSYmekTNUH2tslHqOxWMJLTpojSI7GmEpaMBuggNKuwSrNlOXNpjhe2ME6JC6Qh94ydpJYQQKCoqUjoUIiIiCnBM9IjkFLD94FsGXZQGrfubYeliQEWZQGmWA6UnHSjLKwckpaMjIiIKMFxHL6Qx0SOSET+z5KENUwMqwFVamb3pIjVoNcCM8C4GuEolXNxWiuIjdggmd0RERER1YqJHJDeO0fOYKUGHVn3NMCfqYb9YjnMbStCq/78TPBsTPCIiIqKmYqJHJCfBPM8Tar0K8WMiYG6nd/9OF6lBh7ujKxO8HaUoPhw6CV71saBEREREnmCiRyQjdt1sOq1FjcirTDDGaaFvpYXWrK6xXZQLXNxjrWzBa6GTqxAREfmUJBCw69VJARpXC8JEj4gUETMoDOFdjACAosNlsHQ0QGNUw5rjQGmWEyWZTPCIiIiIPMVEj0hOfDjVZLbccneiV5hRhgtbShWOiIiIiKjlYKJHJCsO0msKjVkNQ0zlx0/RoTI4L4du0x3H4xEREZEvMNEjkhHH6DXMnKRH5FVGhHXQQ3IJXNxVisKMMqXDIiIiapmEhICdySxQ42pBmOgRyYmJXp30rTRoMzwcxjY6OPJduLijFCXHHJCcvGFEREREvsBEj0hG+mgtpHImL1VUGqBV/zBE9zHBWVSBM18XoiyvXOmwiIiIiFo8JnpEHqoaWzVKfScAQBelhsaoQskxh5JhBQxTgg5xw8Khtahx6WcbCn6xAeyl4caxeURERORLTPSIZNJ6QBgA4NI+q8KRKEttUCF2sAURPYywnXXi7PdFKC8K3clWiIiIFCNE4E4gEKhxtSBM9IhkYojTQZQLuIpDt9kqvLMerYdYoFID5zeXoPiIXemQiIiIiEISEz0imWiNKrjKQvPplNaiRmyKBeVmDUqO23FxRykqQvReEBEREQUCJnpEclGrIDlDrIuiCojqbULr34VBZ1Ij57tilJ7iGMUrcTweERER+RsTPSKZqFQIqeUVDK21iBtugSFWi+JDZSi5IGDNcSodFhEREVWRBAL2y4kUoHG1IEz0iGQiuQQ0JrXSYficSgu0GhCG6GtMcBZU4MyXhXDkVyAm2aB0aERERET0b0z0iGRSYZOgi9QAarTYZQTMiZVLJmjMalxKs6HgQOWSCSq1SunQiIiIiKgaJnpEMslPs6Ld6EjEDApD/s6WtcSC2qBC7BALIrobYct1Inctl0xoDMflERGR4ri8QkhjokckE+tJJ6RygbBkQ4tK9DRGFRLGRUFrVuN8agmKj3LJBCIiIqJAx0SPSEblRRXQRWmUDkM2ar0KCf8VBY1RjdNfFrIVj4iIiChItPyZI4j8yFnkgkqDFvMvq+3IcGgtauR+yySPiIiIKJiwRY9IRo58F8I7G2GM08J+zqV0OF5p1d8Mc3s9zn5fBGcBk7yGcDweEREFJIHAHQsXoGG1JC2k3YEoMJSdKwcAmNrqFI7EO+YkHVoNMOPyPhtsp8uVDoeIiIiImomJHpGMXLbKdRXU+uBdbkBrUaPtyAjYcpy4/LNN6XCIiIiIyANM9IhkFNXLBAAoLwnero6xQy2QygXObSpROhQiIiIi8hDH6BF56SdpJSRJwhtvvIHs7Gy4bBUoPupQOiyPGON1sHQ0IO+nYkgOdp6vC8fjERFR0OA6eiGNiR6RDD7//HNkZ2ejNMuBvB+LlQ7HYzEDw2C/UI7SE8GZqBIRERFRJXbdJPKSy+XC7t27ERkZGdRJnsashileh8KMMqVDISIiIiIvMdEj8tKGDRtQUVGBm2++WelQvBKWpIMQAtbTTqVDISIiIiIvsesmBZ3CwkLs2rULmZmZcDqd0Ol06NevH4YMGQKt1v9VOjU1FVqtFsOGDcM8/N3v15eLOUkPxwUXJDv7zF+J4/KIiCgoSRIASeko6iYFaFwtCBM9Chr79+/HmjVrcPnyZffv1Go1JElCZmYmVq1ahdtuuw2jRo3yW0zHjh1DSUkJBg4cCLU6eBvI1XoVzEl6dtskIiIiaiGY6FHAkyQJH3/8MdLT06FWq9G7d29cf/31uOqqq6BWq+FyubB9+3Z88803+PLLL7Fp0yY88MADiI2N9Xlsa9asAQBMmDDB59fypdbXhQEqoPiwXelQiIiIiEgGTPQooNlsNsyfPx8XL15EUlISZsyYAb1eX2MfrVaLG264AcOGDcPKlSuxdetWLFy4EFdddRWmTJmC8PBwn8RWWlqK7OxstG/fHhaLxSfX8BdjjBbWU064rOxGQURERNQSMNGjgJWdnY2FCxfC6XRi6NChmDhxYoP7q9Vq3H333RgzZgw+/PBDnDlzBs8++yyGDx+OCRMmyN61ctu2bQCAW265Rdbz+ptKp4KxjQ4qjUrpUAICx+MREVGLwXX0QhoTPQpIW7duxYoVKwAAkydPxqBBg5p8bGRkJGbMmIGDBw/i888/x+bNm7F7925MnDgR/fv3ly3Go0ePAgB69eol2zn9TRepQdubKls8DTH8OCAiIiJqKYJ39ghqsZYtW4bly5fDYDBg9uzZzUryquvQoQNee+01jBs3DuXl5fj4448xd+5cnD9/XpY4q1oIg3kSlna3REKtD974iYiIiKhufIRPAcNut2PBggU4d+4c4uPjMXPmTBiNRq/PO2bMGNx4441YunQpDhw4gJdffhn9+vXD5MmTa433a474+HgcOXIEubm5SEhI8DpOf9OY1dBHanB2XRHsF11gx00iIiKiloOJHgWE3NxcvPXWW7Db7bjuuuswZcoUWc+v1+vx8MMPIy8vD4sXL8b+/ftx8OBB3H777Rg5cqRH50xJSUFqairWr1+P+++/H8B/xneNUt8pW+y+oo/SAADKiytQEeKTsHBcHhERtUgcoxfS2GeLFLdr1y68+uqrcDgc+MMf/iB7klddfHw85syZg8mTJ0Oj0WD16tWYPXs2Tp486dG5zGYzfv31Vx9E6nvhXQxw2SQ4CyqUDoWIiIiIZMZEjxT12Wef4Z///Cd0Oh1mzZqFYcOG+eW6gwYNwhtvvIGUlBQUFRXhzTffxKJFi1BaWtqs81xzzTUoKyvD6dOnfRSpb7QaYEbkVSYU/GID+ECNiIiIqMVhokeK+fDDD7Fjxw7Exsbi1VdfRYcOHfx6fa1Wi3vvvRfz5s1DcnIyMjMz8eyzz2LNmjWQpKZ1ZaxaWuGHH37wZajyUQNtRoSj9YAw5O8pReHBMqUjIiIiIiIf4Bg9UsSpU6eQnp6O+Ph4PP/884rOXBkVFYVZs2bht99+wyeffIINGzZg586dmDRpEvr06dPgsTExMQgPD8eRI0f8FK134kdFwNxej3MbilFy3KF0OIrgeDwiIgoZkkDAdt2RAjSuFoQteqSIqhawxx9/PGCWJ7jqqqswf/583HLLLXA4HFi8eDHmzZuH/Pz8Bo/r06cP7Ha7R+P85GZqp0P0tSZE9jJBH635zwY1EJtigaWjAefWh26SR0RERBQqAuMbNoWcEydOwGKxIDo6WulQavmv//ovLFiwAFdffTVyc3Px4osvYtmyZXC5XLX2PX/+PM6ePQsAWLx4MZxOp7/DBQCotJVr4iWOi0LMIAviUizocHcrtB0VAUtnAxLHRSGypxEXtpbAmi1vjIYYLdRaLs5AREREFEjYdZP8rqCgADabDf369VM6lHqZTCY89thjyMnJwYcffoi9e/ciPT0d1113HQYMGICsrCzs3bsXeXl5AACLxYKSkhLMnj0bkyZN8nu8kT1NCGtfc01AIQkY22gR3tkAZ6ELp78uhONC7WTVIyogopsR0deaoI/WIn93KVAsz6mJiIiIyHtM9Mjvvv76awDAiBEjFI6kce3bt8fcuXOxfft2rF69Gtu3b8f27dsBACqVCt26dcPdd9+N+Ph4bNiwAV9++SUWL16MTlNbw37RBVuOE8WZZZB83FPSFK+DEAIqlQpn1xVBH6VBzCAL8ndbUZbrREWZfP3gDXFaxF1vgTFWB9sZJ/TRgGQPzH72HI9HREShTAgJQgTmWrmBGldLwkSP/MrlcuHnn39GVFQUOnfurHQ4TZaSkoKUlBTk5uYiPT0dHTt2RPfu3aHV/uef0E033YSUlBR88cUX2J66E+ZEHcKS9IgZEgZnYQVyVhT4LD77xXJYOhkAAM6CClhPOWFO1CPmujBc2guUZjkgZGjMM8RokXhbFJwFLpz+sgBqgxrmRD3Kzrmgb+X9+YmIiIhIHkz0yK+++eYbVFRU4LbbblM6FI8kJCQgISGh3u1GoxGTJ0/GP6d+C6iBsGQ94kdFQK3z7Ri2woNl0EdpIZULuEoqF0A/v6UEbYaHo+3ICLhsEs6uK/Kq66ZKC7QbW/lenAUVcF6uQGyKCc5CF8qLKwAmekREREQBg4ke+Y0kSdi6dStMJhMGDx6sdDi+JwFqnQoqlQqFGb5dr05UAOdTS2r8zlUiIfe7ImjD1YgfFYG2I8ORvbzA41mWVWoVtGGVM3lGdDdCF66Gsa0Ol/bZvA2fiIiIfEGIwF3GQARoXC0IZ90kv0lNTYXT6cTIkSOVDsXnfpJW4idpJWYueQwA4LIq1w/dVSLhwvZS6CO1MCfpGz+gHpJTwGWtbC28lGaFsY0O5SUSin4NvEXXq+4/ERERUahiix75zY8//gitVosxY8YoHYrfdO3aFQBgjNOiVMG16xwXXLCfL0fs4DDkXnJ5nHie/qoQ+kgN7BddgAooL3JBcgqo1FxegYiIiCiQsEWP/GL//v0oLS3FoEGDAmaBdH+IiYmBqBAI86IlTS7nUkug0qrQ/s5oaMM8KwPhEgjvakTHya3RekAYoq4xyxwlEREREckhdL5xk2IkScLy5cuhVqsxYcIEpcPxO3u+C7oojeL/2soLK3B6TQHUBhXMic1PPFUaIOG2KJjb63FprxUlx+xsySMiIgpkQgT2D/kUu26Sz61ZswalpaUYM2YM9HrlW7b8rTjTDlObcFg6GlB6QrnumwBQUSZQXlgBY5wWxUebd6w5QQ9DKy2yV16G81IF4sdEuMfsKYXj8IiIiIjqxhY98qnLly8jNTUVkZGRGDdunNLhKKL0hB1CCIS1D4wktzTbCUsXA1TNfMxjiNPCVSbBeakyudNHa+EsUDbRIyIiIqK6MdEjn3r33XchhMDDDz+sdCiKkewABKAND4x/bkWHyqDWqxDe1dis4wwxWjguVq7Dp9IAunA1Ez0iIiKiAMWum+Qzu3btwtmzZ9GnTx8kJycrHY5i1HoAqsplDgKBq0SCLceJVn3NiOhhhKtUgjXHgZJMR71r7Km0lV03L++vXDNPF6mBSq2Cs9DzBdiJiIjIxyQJUAXG949aRIDG1YIw0SOfcDqdWL58OXQ6He6//36lw1FU69+FQaVSoeS4XelQ3EpPOtFmhAEumwStSYW2IyLQ6loXzm8pgf1c7eTNkmyAWvef96CPrvzo8GeLHsfjERERETUdEz3yicWLF6O8vByTJ08OyQlYqgvvbkSFXYLtdLnSobgVH7OjwiHBmu0ERGW3zNgUCxLHRSFvXTGsOc4a+5sSdHDku9ytkvooDVxlEiQHZ8wiIiIiCkSBMWiIWpSvvvoKhw8fRnJyMgYNGqR0OIpKS0uDRq9G0eEypUOpSQKsp5zurpqOfBfOfF0I6ykn2o6KgCG25jMgQ2stHPn/aenTR2vgLGC3TSIiIqJAxUSPZLVjxw6sX78eUVFReOqpp5QOR3Fr166FkAQupdmUDqVxAji3sRjOIhfirrfU2iaqrXejj9KinBOxEBERBTal18njOnqKYqJHsjl8+DA+++wzGI1GvPDCC9BqQ7tncFFRES5cuIAePXvgJ9fKoBhjJiqAizusMMbpENnL5P69yyZBF6mp/IsK0EVp4Cz0T6L3kxQc946IiIgokIT2N3GSTV5eHt59911oNBo888wzMJvNSoekuF9//RUAMHjwYIUjaR57XjkKMmyIGRwGqVxC6QkHtGFqGON0UOtVUOtUUGtVcBaxRY+IiIgoUDHRI6+VlpZiwYIFkCQJ06dPR5s2bZQOKSCcPn0aANC1a1eFI2m+S7usUGsrZ+PEiMrfOQtdUKkBtV4FAJAcnBaZiIiIKFAx0SOvuFwu/OUvf4HD4cCkSZOCMqnxlcjISADApUuXEB0drXA0zSMk4MKWUpRmOWFopUHJcQdcpZWJnTb834mei33riYiIApmQJIgAXUdPcB09n2OiRx6TJAnz589HcXExxo4dG3RdFH0tKSkJAJCTk4MuXbooHI1nbDlO2HJq/k6trUz0hA8m3eRYPCIiIiJ5cDIW8tjixYuRm5uL3/3ud7jtttuUDifgJCcnA6gcv9iSqLRs0SMiIiIKdEz0yCOrVq1CRkYGOnbsiPvvv1/pcAKSxWKBSqXCxYsXlQ5FVmpd5Z+inIkeERERUaBi101qti1btmDTpk1o1aoVnnzySaXDCWg6nQ6FhYVKhyErtugREREFCSEABOj/11xHz+eY6FGzHDp0CCtWrIDJZMLzzz8f8mvlNcZsNqO0tFTpMGSl1qogJAHINIaa4/KIiIiI5Meum9QsS5YsgUajwXPPPQeTydT4ASGubdu2sNlscDqdSociG5VWxdY8IiIiogDHRI+a7NKlSygrK0O/fv0QExOjdDhBYeDAgQCAdevWKRyJfNRaFcfnEREREQU4JnrUZKmpqQCA4cOHKxxJ8Bg4cCBMJhM2bNjQYlr1VDoVJB8srUBEREQyk0Rg/5BPcYAVNVlGRga0Wi06deqkdChBQ61W484778Q//vEP/Otf/3KPRxulvlPhyDyn1gLCw66bHI9HRERE5B9s0aMmcblcyM/PR2JiotKhBJ1BgwahVatWSEtLQ1FRkdLheI1j9IiIiIgCHxM9ahKbzQYhBNq3b690KEFp6tSpEELg448/VjoUr6m1Ko9b9IiIiIjIP9h1k5pEra58JiBJMs2pH2K6dOmCDh064Pjx4ygoKFA6HK+otCpInIyFiIgo8AkB2dZDkhvX0fM5JnrUJFXr5Qn+o/TYiBEjsGzZMhw9elTpULyi1qlQYW/8Pw2OxyMiIiJSDrtuUpNUJXoVFRUKRxK8OnToAADIzc1VOBLvqLQqCM66SURERBTQmOhRk1Qleuy66bnY2FgAwPnz5xWOxDsagwoV5awHRERERIGMXTepWZjoeU6tVkOr1eLy5ctKh+IxlRrQRWrgPMiWXSIiokAnJAGhCsxhNxwO5HtM9KhZmOh5x2QyoaSkROkwPKaL1kClVsF5ue6+mz+Wr4BKpfJzVERERER0JXbdpGZhoucdjUYT1OMcDa0rnw05LgfveyAiIiIKBWzRo2ZhM7t3hBBB3eJlaK2Fs6gCgssrEBERBT4hIXCXVwjQuFoQtuhRs7BFzztCCPeahMEoLNkA+7lypcMgIiIiokawRY+ahYmedyRJglqtrrHG3Cj1nQpG1HSGOC30kRqc31w5xrD6exBCoKioSKnQiIiIiOgKwdu0QIpgouedYG7R0xgr43YVc3weERERKS8nJwdPP/00evbsibCwMLRq1QoDBw7Em2++CZvNpnR4imOLHjWZSqViouelYB7jWF5UmeDpIjVwWVkPiIiIAl1LXl5h7dq1mDhxYo0eRTabDWlpaUhLS8NHH32E77//Hp06dfI21KAVnE0LpBgmet4J5ha98pIKCCGgi9QoHQoRERGFsAMHDuCuu+5CUVERLBYL5s2bh507d2Ljxo148MEHAQBHjx7FrbfeitLSUoWjVU5wfuMkxTDR806wzrr5k7QSa22fQaVSYdaSx2uMzyMiIiLyp+nTp8Nms0Gr1WL9+vWYPXs2Bg8ejBtvvBEffPABFixYAAA4cuQI3n77bYWjVQ4TPWoyrVYLq9WqdBhBLZhb9KqeiFksFoUjISIiolCVlpaGzZs3AwCmTZuGwYMH19pnxowZ6NmzJwBg0aJFKC8PzRnDg/MbJykiLCwMxcXFSocR1II50atK8pnoERERBQkhBfaPB7766iv36/vvv7/OfdRqNSZPngwAKCgocCeGoSY4v3GSIqKjo1FWVlbjd5IkYffu3ezS2UTB2nUTYIseERERKW/btm0AKhsg+vfvX+9+w4cPd7/evn27z+MKRJx1k5qsbdu2yMrKQmlpKSwWCyRJwttvv42TJ08iMzPT/eSE6ldXi17VeLdAW0/vynF4hYWFAJjoERERkXIOHz4MAOjSpQu02vpTmR49etQ6JtQw0Qti/u5GmZCQAKfTidWrVyMsLAxbtmxxt/Dt2bMHd9xxh1/jaYgQAsXFxVCpVH5tQZMkCdnZ2e4WTpVKBaPRiLZt20KtVsPpdMLpdNZZdi4RWP3Hr4xx586diIuLg8PhgMPhqLFNqfsdiniv/Yv32394r/2L99t/5LzXzf3u50I5EJirK1TGhtrvyWAwwGAw1HmM3W5Hfn4+ACAxMbHB80dHRyMsLAxWqxWnT5+WIeLgoxLBvLBXiHI4HDAajUqHQURERER+VtXDqqHvgna7HR07dsS5c+f8GFnzWSyWWssfvPTSS5gzZ06d+1+8eBFxcXEAgLvvvhvLly9v8Pxt2rTBhQsX0KtXL2RkZMgSczBhi14QMhgMsNvttVpViIiIiKhl0+v1jT7wNxqNyMrKgtPp9FNUnqlr7oL6WvOAygS2il6vb/T8Vee6co6JUMFEL0g11KxNRERERKHNaDS2uB5g1d9PU5LYqkYRk8nks5gCGWfdJCIiIiKigBceHu5+fWWXz7qE+tJQTPSIiIiIiCjgGY1GxMTEAADOnDnT4L4FBQXuRC8pKcnnsQUiJnpERERERBQUevbsCQA4fvw4XC5XvfsdOXKk1jGhhokeEREREREFhZSUFACV3TJ//vnnevfbsmWL+/XQoUN9HlcgYqJHRERERERBofq6zUuXLq1zH0mS8I9//AMAEBUVhREjRvgjtIDDRI+a7PLly/j000/xxBNPICUlBZ06dUJERAQMBgPi4+MxevRo/P3vf29wcKwkSTh06BCWLFmChx9+GH369IFWq3UvInrq1CnZ4k1OTnaft6Gf5OTkRs/16aef4tprr4XRaERSUhKefvrpBhctnTp1apOuXf1HjvcuRxkdPHgQixYtwr333os+ffqgXbt2MBgMsFgs6NatGyZPnoyNGzc2KZ6zZ89i6tSpiI2NhdlsxvDhw7Fhw4Z69z916lSz79vUqVObe5tqkeO+1cdms6FTp07Nqm+NYd2Wp4w2b96MBx54AF27doXFYkFERAS6du2K8ePH4913323wXKFStz2J25s6zrrtXd0+ceIEnnjiCVx99dUIDw9HWFgYevTogSeeeAKZmZlNiidY6nZDZs2aVeN6mzdvbvSYdevWYfz48UhMTITBYEBiYiLGjx+PdevWyRJTKNZtXxk4cCCuv/56AMDHH3+MXbt21drnrbfewuHDhwEATzzxBHQ6nV9jDBiCqIlWrlwpADT6k5SUJPbu3VvnOZYtW9bgsVlZWbLF26FDhybF26FDhwbP8/LLL9d5XN++fUVpaWmdx0yZMqVJ15b7vctRRrfeemuTzjF+/Hhhs9nqjSU3N1ckJibWOk6tVot//vOfdR6TlZXV7Ps2ZcqUgLhv9ZkxY0az6ltTsG57V0bFxcXirrvuavRc6enpdR4fSnXbk7hHjx7tcbys257X7ffff1/odLp6jzeZTGLp0qUNxhJMdbs+v/zyi9BqtTWul5qaWu/+kiSJhx56qMF4H3roISFJkldxhWLd9qX9+/cLk8kkAAiLxSJeffVVsWvXLrFp06Ya5dmtWzdRXFysdLiK4Tp61CwJCQkYNmwYhgwZgqSkJLRr1w52ux05OTn47LPPsG7dOpw+fRqjR4/GoUOH0K5duxrHCyHcrw0GA6699lrk5+fjxIkTPov59ttvx1/+8pd6tze04OZvv/2Gl19+GUajEc8//zxuuukm5OTk4MUXX0R6ejrmzp2L119/vcHr//jjj7XuQ10SEhIa3acpvC0jrVaLfv36Yfjw4bj22msRHx+P1q1b4+LFizh48CDef/99nDx5EmvWrMGUKVPwxRdf1BnHk08+iTNnzmDw4MGYPXs2oqOjsXr1aixatAiPPPIIxo4di9atW9f7PhortyrR0dHNu0H18Pa+1SU9PR2LFi2C0WiETqdDSUmJLLFWYd1ufhlZrVaMHTsWO3bsAACMGTMG99xzD7p16wZJkpCdnY20tDSsXLmy3jhCqW4nJCQgIyOj0Wu89tpr+Ne//gUAmDJlitcxs243r24vX74cjzzyCIDKbmpPP/00RowYAZ1Oh3379mH+/PnIzs7G//zP/6BNmzYYO3ZsnXEEW92+kiRJePDBB+FyuRAXF4cLFy40eswLL7yADz74AADQt29fzJo1C507d8aJEyewYMECpKen44MPPkBsbGyT3ltjQq1u+0rfvn2xYsUK3HfffSguLsbs2bNr7dOtWzesXbu2xpIMIUfpTJOCR3l5eaP7/PWvf3U/RXnyySdrbd+zZ4945513xJ49e4TD4RBC1HzS5IsWPW+eHM6ZM0cAEP/3f/9X4/dnzpwRZrNZdOzYsc7jfPWeGiNHGTV2jrKyMjFkyBD3OQ4cOFBrH7vdLgwGg0hKSqr1hPGJJ54QAMQnn3xS67jqT4Z9+cT3SnLctyu5XC7Rv39/AUC88sor7vooZ4se63ZNTSmj//3f/3W3UDTUuiFJUp3XZN2uzeVyiXbt2gkAIjw8XFitVk9CFUKwbtenoTKyWq0iLi7Off8PHTpU6/iLFy+Kzp07CwCiY8eO7v9/qwu2ul2XhQsXCgCiR48e4rnnnmu0Re/YsWPu1r8BAwbU6qVitVrFgAEDBACh1WrF8ePHPY4tFOu2P5w6dUo8+eSTolu3bsJsNouoqCgxYMAAMX/+fK8+i1oKjtGjJtNqG28AfvTRR92LUm7durXW9oEDB+Lxxx/HwIEDG3xqFShyc3MBoNYg3oSEBPTo0cO9PVDIUUaNncNoNOJPf/qT++91nePSpUtwOBwYOHAgwsLCamwbOXIkAATUvZPjvl3pr3/9K37++Wd0794dzzzzjNcxyi0U6/Yvv/yC999/HwDw1FNPNThOSKVS1XlN1u3aNmzYgLNnzwIAJkyYALPZ3OxzyCnU6vYPP/zgbrmaPn06rrrqqlrHx8TEuFt6srKysGLFilr7BFvdvtLp06fx5z//GQDw3nvvNek7xsKFC93T87/zzjswmUw1tpvNZrzzzjsAAJfLhUWLFskbdDMFW932hw4dOuDtt9/G0aNHYbVaUVBQgLS0NMyaNUvxz6JAwESPZKXVamE0GgEADodD4Wi8FxcXB6DmFL0AcO7cORw9ehRt27ZVIiyvyFFGVV846jtHdHQ0tFot9u3bB5vNVmNb1aD4YLt3zblv2dnZePHFFwE0/QuHv4Vi3X7vvfcghIDBYMCzzz7r0TVCvW7XpWpmO0CebpveCrW6nZaW5n49ZsyYes9Rfdvq1atrbQ/2uv3oo4+itLQUU6ZMwQ033NDo/kIIfP311wCAHj16YNCgQXXuN2jQIHTv3h0A8NVXX9UYguJvLbFuk28x0SNZbdy4Efn5+QAqPziDXdUUvjNnzsRrr72G3bt3Y9WqVRg5ciSsVivuvPNOZQP0gBxltHz5cvfrus5hMplw8803Izs7G6NHj8batWuxc+dOzJo1C4sWLYLZbMYtt9zi2RtQSHPu26OPPgqr1YpJkybVevIaKEKtbgshsGrVKgDAjTfe6B5n5HK5kJOTg+zs7CYlOaFet69UUlKCr776CkDlk/Vhw4bJHV6zhVrdvnz5svt1VSJQF4vF4m7hqKvlNpjr9hdffIHvvvsOrVq1whtvvNGkY7KystwtYMOHD29w36rtZ86cUXQ2ypZYt8nHlO05Si1BcXGx+O2338TLL78sIiIi3P3At27d2qTjfT1Gr2PHjqJ3797CbDYLk8kkkpOTxV133SW+/PLLJs2iVTU24cqfXr16iaKiIr++J095W0aSJIlz586JTZs2ifHjx7uP7969e73jS44dOyZat25d676pVCrx0Ucf1XlMII31EMKz+/b5558LACI6OlqcP3/e/XtfjNFj3W56GWVmZrq3vfLKK+LixYvioYceEuHh4e7fGwwGcfPNNzf67yJU63ZdlixZ4j72z3/+s9dxsW7/R1PLaPr06e5t+/btq/d8DodDqFQq9755eXm19gnGul1QUCDatm0rAIgPP/zQ/fuXXnqpwTF63333nXv7woULG7zG22+/7d537dq1HsXJuk1K4Kyb5JE333wTM2fOrHObRqPBW2+95V7jRGlZWVk1/n7q1CmcOnUKX3zxBYYOHYoVK1Y0OLvUokWL0L17d/ztb3/DsWPH0Lp1a9x555145ZVXEBER0ej1MzMzG13/KCIiAu3bt2/aG2oiOcqoV69eOHToUJ3bOnTogNWrV9c7vqRLly7Yu3cvnnvuOaxfvx52ux19+/bFCy+80KSnwoWFhfj1118b3a9Lly7uLk1y8Oa+FRQUYPr06QCA119/vcGn63Jg3a6tvjL67bff3K+FELjmmmuQl5dXYx+Hw4Eff/wR69evx4IFC/D000/XeY1QrNv1qd5tc/LkyV7FVx3rdm31lVH1Fr5t27ahf//+dR6/ffv2Gt0Oc3JyanX1C8a6PWvWLJw7dw5DhgzBtGnTmnzc6dOn3a8TExMb3DcpKanO4zwRqnWbFKJ0pknB6Y033qjzidINN9xQ54xfDfHVk6auXbuKcePGib/97W9i8+bNIj09XaSmpopXX31VJCUlua/Zs2dPUVhYKNt1hWj+mjW33367rNcXQp4yuvrqq2sdr9FoxJw5c+p9cugNT9Zjqm+tM095c9+mTZsmAIjBgwfXejIrZ4se63bzy6h6y5PBYBAAxJgxY8TevXuF3W4XFy5cEO+9956IjIx07/ftt9/KFnOw1+26ZGdnu1uIhgwZIkuMrNvNL6OcnBz3zJGJiYni0qVLtfZxOp1i6NChNc65YcMGWWJWsm5v27ZNqFQqodVqxcGDB2tsa6xFb8GCBe7tP/zwQ4PX+f777937vvnmmx7FGup1m5TBRI88kp+fLzIyMkRGRobYs2eP+OSTT8TIkSMFANG7d2+xe/fuJp/LV4leQUFBvduKi4vF6NGj3df1ZErxhgTCh6ocZXTs2DGRkZEhDhw4IFJTU8W8efNEQkKC0Ol04r777hMlJSWyxhwIX4Y9vW9btmxxf+Goa8kJORM91u3ml9E777xTI65Ro0YJl8tVa79t27YJtVotAIirr77a60WSqwRz3a7PvHnz3LG+//77ssTIuu1ZGT3++OPuuHr06CG++eYbUVxcLMrKysS2bdvE8OHDBYAaC6rL9SBDqbrtcDhEz549BQAxc+bMWtsbS/ReeeUV9/aNGzc2eK2NGze69507d65H8YZ63SZlMNEjWb322msCgDAajeLHH39s0jFK9R0vLCx0j0UICwurc10hTwVyf3hPyqi6goICcd111wkA4pprrhHFxcWyxab0WI+GNHTf7Ha76N69uwAgZsyYUefxciZ6jWHdrl1GH374YY0vMvv376/3PBMmTHDvV1fS7olgrdsN6dGjhwAqW0gb+hIrJ9btusvIbreLW265pcEv7z179hSPPvqo+++bN2+WJTal6nZVIte+ffta6/5V315foufvFr3GhGrdJt/irJskq2effRaDBg2C3W7Hgw8+6F6fJhBFRkbiD3/4AwDAarVi3759CkfkH96WUVRUFJYtWwYAOHjwIF577TUfRBl4Grpv8+bNw9GjR5GUlIQ5c+YoF+S/sW7XLqPw8HD369jYWPTt27fe89x8883u16Fw7zz5TNi7dy+OHDkCABg3bhyioqJ8HGUl1u26y8hgMODbb7/FRx99hL59+0KlUrm3tW7dGjNmzEBaWlqNmWWjo6P9Fr/cjhw54v6/55133qm17l9TVP9MaGzMmtVqdb+uvryQnEK1bpNvcTIWkt24ceOwe/du5OTkYO/evRgyZIjSIdWr+sKyobTQqLdl1KNHD3Tr1g2ZmZlYtWoVXn31VR9FGljqu2/z588HANx000347rvv6jy26ouC1Wp1L08RFxeHG2+80Sexsm7XLKPqkyk0Z+KFqoWoW7rmfib4ahKWpmDdrruM1Go1pk2bhmnTpqGkpATnz5+HyWRCfHw81OrK5/oZGRkAKtfl69q1q9/fg1wWLlwIp9OJTp06wWaz1Vjyp0r1SWE2bdqEc+fOAQBuu+02hIWF1fgcOHPmTIPXqz4BS/XPB7mFat0m32GiR7KLiYlxv87Ozg7oRE8ouPCpkuQoo5iYGGRmZiI7O1vO0AJafffN6XQCAJYuXYqlS5c2eI78/Hzcc889ACrXZvJVose6XbOMqn+BqqioaPAc1bfXN6tsS9Ocz4Ty8nKsWLECQOXDioYW6fYF1u3Gyyg8PLxGixVQ2Wp14MABAEDv3r1hMpl8E6gfVLVMnjx50v152pC5c+e6X2dlZSEsLKzGZ0JV63R9qm/v2bNnc8NtslCt2+Q77LpJsqv+FMpXXRzkUn3K9Xbt2ikYiX/JUUZV5wj0MpYT63bgq6+MoqKi0Lt3bwCV05lLklTvOU6cOOF+3dA05y1Jc+r22rVr3Yt333vvvX5Phlm3Pfv8WbNmjTtBuuuuu2SLK1h17NjRXX+2bNnS4L5VC8wnJCQgOTnZZzGFat0m32GiR7KSJAlr1qxx//3qq69WMJqGFRUVuZ9Km81mDBgwQOGI/EOOMtq3b5+7Ja9Xr16yxRbIGrpvonJiqwZ/OnToAKBy/cGq323evNknsbJuV7qybo8fPx4AUFxcjE2bNtV7nurnCJT1QH2puZ8J1bttTpkyxWdx1YV1u1JzP7cdDoe7VctkMmHq1Klyhud3y5Yta/Qz96WXXnLvn5qa6v59VaKmUqlw++23A6hssdu9e3ed19q9e7e7Re/222+vMf5RTqFat8m3mOhRky1ZsgTl5eX1bpckCbNmzXKPARg6dCg6derkk1huuOEGqFQqqFQqnDp1qtb2devWoaysrN7jS0tLcdddd+HSpUsAgGnTpsFgMPgkVn/ytowOHjyIXbt2NXiNvLy8Gl/uJk2a5GXUymPdDnxylNFjjz3m7s721FNPobi4uNZ5Pv30U3cCfuuttzY6ni/QyV23L1++jLVr1wKo7P537bXXNjkW1u26yVFG+fn59d47h8OBSZMm4fjx4wCAOXPm1FooPVRNnz7d3SL9xz/+sdY9LCsrwx//+EcAld24p0+fXud5WLcpUIXG4AOSxVNPPYXZs2djwoQJGDJkCJKTkxEWFobCwkKkp6fjk08+wS+//AKgcnzA3//+9zrPUzVjY5Wq/3wAYNWqVTXGIXTp0gUpKSnNjvX111/HxIkTMX78eKSkpKBz586wWCwoKirCzp078f777yMnJwcA0L17d5/OlJiZmdnojF5A5QQR3s5c520ZnTx5Er///e8xcOBAjBs3Dn379kWbNm0AAGfPnsXmzZuxZMkSFBYWAgBGjBiBBx54wKuY61NYWFhjMH199Ho9unXr5tW15Krb/sC67XkZxcbG4vXXX8djjz2GjIwMDBw4EM888wyuueYaFBcXY82aNXjvvfcAABEREVi4cKFXMdcnmOv28uXL3WNS5W7NY932vIw2b96MBx98EPfddx9uuukmJCUlwWazYd++fXjvvfeQmZkJALjjjjvw1FNPeRVvQ/xZt+XQrVs3PP3003j99dexb98+DB06FM888ww6d+6MEydOYP78+UhPTwcAzJw50+MJbEK1blMA8NtCDhT0IiMjm7TQZvfu3cXevXvrPU9TzlH1U9+aPFWLv6KeNWGqb2/oZ9iwYeLMmTMy3aH/aO7ipADE0qVLvb6ut2X05ZdfNjne++67r861i7zhycK7cqxLJ1fdbkhT19Fj3a6bnGU0d+5codFo6j1HbGys2LFjh9cxV9dS6nbVGpoajUbk5eU1KxbW7brJUUYrV65s8FiVSiUeeeQR4XQ6vY73SkrV7cY0to5elYqKCvHAAw80GO+0adNERUVFvedg3aZAxRY9arLdu3djw4YNSE1NRWZmJs6fP4+CggKYzWbEx8ejb9+++P3vf4877rgDer1e0VjffPNNbNy4Ebt27cLRo0eRn5+PwsJCmM1mtGvXDtdddx3uuecejB492mf97ZXgbRmNHTsWGzduxKZNm7B9+3acOXMG58+fh8PhQGRkJLp06YIhQ4Zg0qRJzeqyFehYtwOfnGX0wgsvYMyYMXj33XeRmpqKvLw8GAwGdO3aFePGjcOf/vSnFvMkW877duzYMezZswcAMGrUKNm7/7Fue15G119/Pd544w1s3LgRR44cwYULF6BWq5GQkIAbb7wRDzzwAMd81UOtVuPjjz/Gf//3f+ODDz5AWloa8vPzERMTg9/97nd4+OGHMXbsWK+uEap1m5SnEoJzuRIREREREbUknIyFiIiIiIiohWGiR0RERERE1MIw0SMiIiIiImphmOgRERERERG1MEz0iIiIiIiIWhgmekRERERERC0MEz0iIiIiIqIWhokeERERERFRC8NEj4iIiIiIqIVhokdERERERNTCMNEjIiIiIiJqYZjoERERERERtTBM9IiIiIiIiFoYJnpEREREREQtDBM9IiIiIiKiFub/AUWBaBA8gFV7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 900x1300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tc_moz.plot_intensity(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1ef1b32d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S',\n",
       "       '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S',\n",
       "       '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S',\n",
       "       '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S',\n",
       "       '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S', '70S',\n",
       "       '70S', '70S', '70S', '70S', '70S', '70S', '07S', '07S', '07S',\n",
       "       '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S',\n",
       "       '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S',\n",
       "       '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S',\n",
       "       '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S',\n",
       "       '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S', '07S',\n",
       "       '07S', '07S', '07S', '07S', '72S', '71S'], dtype='<U3')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(tc_moz.event_name)[np.asarray(tc_moz.intensity.sum(axis=1)>0).reshape(-1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ecb730cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "tc_moz = tc_moz.select(event_id=list(tc.event_id[np.array(tc.event_name)=='70S']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bf1921ee",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
       "       52])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tc_moz.event_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07be4b90",
   "metadata": {},
   "source": [
    "# Getting infrastructure data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8a83a95b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import shapely\n",
    "import os\n",
    "import pickle\n",
    "import numpy as np\n",
    "\n",
    "from scipy.sparse import csr_matrix\n",
    "from datetime import datetime\n",
    "\n",
    "# on climada_petals branch feature/networks until merged!\n",
    "from climada_petals.engine.networks import nw_utils as nwu\n",
    "from climada_petals.entity.exposures.openstreetmap import osm_dataloader as osm\n",
    "from climada_petals.util.constants import DICT_GEOFABRIK\n",
    "\n",
    "# on climada_python develop branch\n",
    "from climada.util import coordinates as u_coords\n",
    "from climada.entity.exposures.base import Exposures\n",
    "from climada.entity.impact_funcs import ImpactFunc, ImpactFuncSet\n",
    "from climada.engine import Impact\n",
    "from climada.hazard.base import Hazard\n",
    "from climada.util import lines_polys_handler as u_lp\n",
    "from climada.util.api_client import Client\n",
    "\n",
    "from climada_petals.entity.exposures.openstreetmap import osm_dataloader as osm\n",
    "from climada_petals.util.constants import DICT_GEOFABRIK\n",
    "\n",
    "from climada.util import coordinates as u_coords\n",
    "from climada.hazard.base import Hazard\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "066748e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# general paths & constants\n",
    "PATH_DATA = '/Users/szelie/Library/CloudStorage/OneDrive-ETHZurich/data/infrastructure/'\n",
    "PATH_DATA_OSM = '/Users/szelie/climada/data/openstreetmap/' # path to search for osm.pbf files and to download to if not existing\n",
    "PATH_DATA_HVMV = PATH_DATA +'power_global/grid.gpkg' # path of this file from gridfinder. needs to be downloaded.\n",
    "PATH_DATA_PP = PATH_DATA +'power_global/global_power_plant_database.csv' # path of this file from WRI global power plant db. needs to be downloaded.\n",
    "#PATH_DATA_CT = PATH_DATA +'cell_towers/opencellid_global_1km_int.tif' # path of this file from worldbank open data gridded celltowers. needs to be downloaded.\n",
    "PATH_DATA_POP = PATH_DATA + 'population/' # path to search for population files and to download to if not existing\n",
    "PATH_SAVE = '/Users/szelie/Library/CloudStorage/OneDrive-ETHZurich/data/infrastructure/mozambique_results'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "961cb617",
   "metadata": {},
   "outputs": [],
   "source": [
    "cntry = 'Mozambique'\n",
    "iso3 = u_coords.country_to_iso(cntry)\n",
    "\n",
    "try:\n",
    "    path_osm_cntry = PATH_DATA_OSM+DICT_GEOFABRIK[iso3][-1]+'-latest.osm.pbf'\n",
    "except KeyError:\n",
    "    path_osm_cntry = PATH_DATA_OSM+f'{cntry}'+'-latest.osm.pbf'\n",
    "path_worldpop_cntry = PATH_DATA_POP + f'{iso3.lower()}_ppp_2020_1km_Aggregated_UNadj.tif'\n",
    "path_el_consump_cntry = PATH_DATA + f'power/Electricity consumption per capita - {cntry}.csv'\n",
    "path_elgen_cntry = PATH_DATA + f'power/Electricity generation by source - {cntry}.csv'\n",
    "path_el_imp_exp_cntry = PATH_DATA + f'power/Electricity imports vs. exports - {cntry}.csv'\n",
    "path_save_cntry = PATH_SAVE + f'{iso3}/'\n",
    "if not os.path.isdir(path_save_cntry):\n",
    "    os.mkdir(path_save_cntry)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f5bd97f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# COUNTRY SHAPE    \n",
    "__, cntry_shape = u_coords.get_admin1_info([cntry])\n",
    "cntry_shape = shapely.ops.unary_union([shp for shp in cntry_shape[iso3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "42108203",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# POWER LINES\n",
    "gdf_powerlines = gpd.read_file(PATH_DATA_HVMV, mask=cntry_shape)\n",
    "gdf_powerlines['osm_id'] = 'n/a'\n",
    "gdf_powerlines['ci_type'] = 'power_line' \n",
    "gdf_powerlines = gdf_powerlines[['osm_id', 'geometry', 'ci_type']]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "aaa41e76",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# POWER PLANTS\n",
    "# try WRI pp database, then OSM\n",
    "gdf_pp_world = gpd.read_file(PATH_DATA_PP, crs='EPSG:4326')\n",
    "gdf_pp = gdf_pp_world[gdf_pp_world.country==f'{iso3}'][\n",
    "    ['estimated_generation_gwh_2017','latitude','longitude',\n",
    "     'name']]\n",
    "del gdf_pp_world\n",
    "if not gdf_pp.empty:\n",
    "    gdf_pp = gpd.GeoDataFrame(\n",
    "        gdf_pp, geometry=gpd.points_from_xy(gdf_pp.longitude, gdf_pp.latitude))\n",
    "else:\n",
    "    gdf_pp = CntryFileQuery.retrieve_cis('power')\n",
    "    if len(gdf_pp[gdf_pp.power=='plant'])>1:\n",
    "        gdf_pp = gdf_pp[gdf_pp.power=='plant']\n",
    "    else:\n",
    "        # last 'resort': take generators frmom OSM\n",
    "        gdf_pp = gdf_pp[gdf_pp.power=='generator']\n",
    "    gdf_pp['geometry'] = gdf_pp.geometry.apply(lambda geom: geom.centroid)\n",
    "    gdf_pp = gdf_pp[['name', 'power', 'geometry']]\n",
    "gdf_pp['ci_type'] = 'power_plant'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "860447d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "extract points: 100%|█| 28120/\n",
      "extract multipolygons: 100%|█|\n",
      "/Users/szelie/python_projects/climada_petals/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py:434: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  gdf = gdf.append(\n",
      "extract lines: 100%|█| 309/309\n",
      "/Users/szelie/python_projects/climada_petals/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py:437: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  gdf = gdf.append(\n"
     ]
    }
   ],
   "source": [
    "# SUBSTATIONS\n",
    "CntryFileQuery = osm.OSMFileQuery(path_osm_cntry)\n",
    "gdf_power = CntryFileQuery.retrieve_cis('power')\n",
    "gdf_power = gdf_power[[power_type in ['generator','plant','substation','switch', 'transformer'] \n",
    "               for power_type in gdf_power.power]]\n",
    "gdf_power['geometry'] = gdf_power.apply(lambda row: row.geometry.convex_hull.centroid, axis=1)\n",
    "gdf_power=gdf_power.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "525c2222",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "extract points: 100%|█| 791/79\n",
      "extract multipolygons: 100%|█|\n",
      "/Users/szelie/python_projects/climada_petals/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py:420: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  gdf = gdf.append(\n"
     ]
    }
   ],
   "source": [
    "#hospitals\n",
    "gdf_health = CntryFileQuery.retrieve_cis('healthcare') \n",
    "gdf_health['geometry'] = gdf_health.geometry.apply(lambda geom: geom.centroid)\n",
    "gdf_health = gdf_health[['name', 'geometry']]\n",
    "gdf_health = gdf_health[gdf_health.geometry.within(cntry_shape)]\n",
    "gdf_health['ci_type'] = 'health'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "afacd523",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "extract points: 100%|█| 226/22\n",
      "extract multipolygons: 100%|█|\n",
      "/Users/szelie/python_projects/climada_petals/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py:420: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  gdf = gdf.append(\n"
     ]
    }
   ],
   "source": [
    "# EDUC. FACILITIES\n",
    "# from osm\n",
    "gdf_educ = CntryFileQuery.retrieve_cis('education')\n",
    "gdf_educ['geometry'] = gdf_educ.geometry.apply(lambda geom: geom.centroid)\n",
    "gdf_educ = gdf_educ[['name', 'geometry']]\n",
    "gdf_educ = gdf_educ[gdf_educ.geometry.within(cntry_shape)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "6e456df7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f5/lms0b90j1kx7kk4v552qkjqw0000gp/T/ipykernel_92019/3940183040.py:8: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
      "  geo_mask = [mp for mp in cntry_shape]\n"
     ]
    },
    {
     "ename": "RasterioIOError",
     "evalue": "/cluster/work/climate/evelynm/nw_inputs/opencellid_global_1km_int.tif: No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m                      Traceback (most recent call last)",
      "File \u001b[0;32mrasterio/_base.pyx:302\u001b[0m, in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mrasterio/_base.pyx:213\u001b[0m, in \u001b[0;36mrasterio._base.open_dataset\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mrasterio/_err.pyx:217\u001b[0m, in \u001b[0;36mrasterio._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m: /cluster/work/climate/evelynm/nw_inputs/opencellid_global_1km_int.tif: No such file or directory",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mRasterioIOError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[151], line 9\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m      8\u001b[0m         geo_mask \u001b[38;5;241m=\u001b[39m [mp \u001b[38;5;28;01mfor\u001b[39;00m mp \u001b[38;5;129;01min\u001b[39;00m cntry_shape]\n\u001b[0;32m----> 9\u001b[0m     meta_ct, arr_ct \u001b[38;5;241m=\u001b[39m \u001b[43mu_coords\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_raster\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPATH_DATA_CT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msrc_crs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mepsg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m4326\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m                                           \u001b[49m\u001b[43mgeometry\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgeo_mask\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     u_coords\u001b[38;5;241m.\u001b[39mwrite_raster(path_ct_cntry, arr_ct, meta_ct)\n\u001b[1;32m     12\u001b[0m gdf_cells \u001b[38;5;241m=\u001b[39m nwu\u001b[38;5;241m.\u001b[39mload_resampled_raster(path_ct_cntry, \u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m5\u001b[39m)\n",
      "File \u001b[0;32m~/python_projects/climada_python/climada/util/coordinates.py:1848\u001b[0m, in \u001b[0;36mread_raster\u001b[0;34m(file_name, band, src_crs, window, geometry, dst_crs, transform, width, height, resampling)\u001b[0m\n\u001b[1;32m   1845\u001b[0m     file_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/vsigzip/\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(file_name)\n\u001b[1;32m   1847\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m rasterio\u001b[38;5;241m.\u001b[39mEnv():\n\u001b[0;32m-> 1848\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mrasterio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m src:\n\u001b[1;32m   1849\u001b[0m         dst_meta \u001b[38;5;241m=\u001b[39m src\u001b[38;5;241m.\u001b[39mmeta\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m   1851\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m dst_crs \u001b[38;5;129;01mor\u001b[39;00m transform:\n",
      "File \u001b[0;32m~/miniconda3/envs/climada_env/lib/python3.8/site-packages/rasterio/env.py:442\u001b[0m, in \u001b[0;36mensure_env_with_credentials.<locals>.wrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    439\u001b[0m     session \u001b[38;5;241m=\u001b[39m DummySession()\n\u001b[1;32m    441\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m env_ctor(session\u001b[38;5;241m=\u001b[39msession):\n\u001b[0;32m--> 442\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/climada_env/lib/python3.8/site-packages/rasterio/__init__.py:277\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)\u001b[0m\n\u001b[1;32m    274\u001b[0m path \u001b[38;5;241m=\u001b[39m _parse_path(raw_dataset_path)\n\u001b[1;32m    276\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m--> 277\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m \u001b[43mDatasetReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdriver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdriver\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msharing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    278\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m    279\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m get_writer_for_path(path, driver\u001b[38;5;241m=\u001b[39mdriver)(\n\u001b[1;32m    280\u001b[0m         path, mode, driver\u001b[38;5;241m=\u001b[39mdriver, sharing\u001b[38;5;241m=\u001b[39msharing, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[1;32m    281\u001b[0m     )\n",
      "File \u001b[0;32mrasterio/_base.pyx:304\u001b[0m, in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mRasterioIOError\u001b[0m: /cluster/work/climate/evelynm/nw_inputs/opencellid_global_1km_int.tif: No such file or directory"
     ]
    }
   ],
   "source": [
    "# # TELECOM, ignore for now\n",
    "# # cells from rasterized opencellID (via WB)\n",
    "# path_ct_cntry = path_save_cntry+'celltowers.tif'\n",
    "# if not Path(path_ct_cntry).is_file():\n",
    "#     if cntry_shape.type=='Polygon':\n",
    "#         geo_mask = [cntry_shape]\n",
    "#     else:\n",
    "#         geo_mask = [mp for mp in cntry_shape]\n",
    "#     meta_ct, arr_ct = u_coords.read_raster(PATH_DATA_CT, src_crs={'epsg':'4326'},\n",
    "#                                            geometry=geo_mask)\n",
    "#     u_coords.write_raster(path_ct_cntry, arr_ct, meta_ct)\n",
    "# gdf_cells = nwu.load_resampled_raster(path_ct_cntry, 1/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c9fd5603",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "extract points: 0it [00:01, ?it/s]\n",
      "extract multipolygons: 100%|█|\n",
      "/Users/szelie/python_projects/climada_petals/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py:434: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  gdf = gdf.append(\n",
      "extract lines: 100%|█| 276465/\n",
      "/Users/szelie/python_projects/climada_petals/climada_petals/entity/exposures/openstreetmap/osm_dataloader.py:437: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  gdf = gdf.append(\n"
     ]
    }
   ],
   "source": [
    "# ROADS\n",
    "# from osm; by default, take all types of roads\n",
    "gdf_roads = CntryFileQuery.retrieve_cis('road') # for large countries: take main_road only and uncomment later ones\n",
    "frac_unclass = gdf_roads.groupby('highway').size()['unclassified']/len(gdf_roads)\n",
    "\n",
    "if frac_unclass < 0.2:\n",
    "    # if unclassified road fraction is quite small, take only main roads\n",
    "    gdf_roads = gdf_roads[(gdf_roads.highway != 'residential') & \n",
    "                          (gdf_roads.highway != 'unclassified')]\n",
    "\n",
    "gdf_roads = gdf_roads[gdf_roads.geometry.type=='LineString']\n",
    "gdf_roads = gdf_roads[['osm_id','highway', 'geometry']]\n",
    "gdf_roads = gdf_roads[gdf_roads.within(cntry_shape)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f43eb168",
   "metadata": {},
   "outputs": [],
   "source": [
    "# give ci_type attribute\n",
    "gdf_pp['ci_type'] = 'power_plant'\n",
    "gdf_power['ci_type'] = 'power_component'\n",
    "gdf_powerlines['ci_type'] = 'power_line'\n",
    "gdf_health['ci_type'] = 'health'\n",
    "gdf_educ['ci_type'] = 'education'\n",
    "# gdf_cells['ci_type'] = 'celltower'\n",
    "gdf_roads['ci_type'] = 'road'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b95ca989",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save locally to save time\n",
    "for gdf in [gdf_pp, gdf_power, gdf_powerlines, gdf_health, gdf_educ, gdf_roads]:\n",
    "    gdf.to_feather(path_save_cntry+f'{gdf.ci_type.iloc[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "48129a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-load:\n",
    "gdf_pp = gpd.read_feather(path_save_cntry+'power_plant')\n",
    "gdf_power = gpd.read_feather(path_save_cntry+'power_component')\n",
    "gdf_powerlines= gpd.read_feather(path_save_cntry+ 'power_line')\n",
    "gdf_health= gpd.read_feather(path_save_cntry+ 'health')\n",
    "gdf_educ= gpd.read_feather(path_save_cntry+'education')\n",
    "#gdf_cells= gpd.read_feather(path_save_cntry+'celltower')\n",
    "gdf_roads= gpd.read_feather(path_save_cntry+'road')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dd05ee0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def exposure_from_points(gdf, impf_dict):\n",
    "    # TODO: check why 'FL' tag not taken when read in raster flood file\n",
    "    exp_pnt = Exposures(gdf)\n",
    "    exp_pnt.gdf[f'impf_FL'] = getattr(ImpFuncsCIFloodMoz(), impf_dict['FL']).id\n",
    "    exp_pnt.gdf[f'impf_TC'] = getattr(ImpFuncsCIWindMoz(), impf_dict['TC']).id\n",
    "    exp_pnt.gdf['value'] = 1\n",
    "    exp_pnt.set_lat_lon()\n",
    "    exp_pnt.check()\n",
    "    return exp_pnt\n",
    "      \n",
    "def exposure_from_lines(gdf, impf_dict, res, \n",
    "                        disagg_met=u_lp.DisaggMethod.FIX, disagg_val=None):\n",
    "    exp_line = Exposures(gdf)\n",
    "    if not disagg_val:\n",
    "        disagg_val = res\n",
    "    exp_pnt = u_lp.exp_geom_to_pnt(exp_line, res=res, to_meters=True, \n",
    "                                   disagg_met=disagg_met, disagg_val=disagg_val)  \n",
    "    exp_pnt.gdf[f'impf_FL'] = getattr(ImpFuncsCIFloodMoz(), impf_dict['FL']).id\n",
    "    exp_pnt.gdf[f'impf_TC'] = getattr(ImpFuncsCIWindMoz(), impf_dict['TC']).id\n",
    "    exp_pnt.set_lat_lon()\n",
    "    exp_pnt.check() \n",
    "    return exp_pnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9f4716f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# some impact functions we have used in the past, we will probably have to reconsider\n",
    "class ImpFuncsCIFloodMoz():\n",
    "\n",
    "    def __init__(self):\n",
    "        self.tag = 'FL'\n",
    "        self.road = self.step_impf()\n",
    "        self.power_line = self.no_impf()\n",
    "        self.power_plant = self.no_impf()\n",
    "        self.power_component = self.step_impf()\n",
    "        self.health_level1 = self.health_level_1_impf()\n",
    "        self.health_level_2_4 = self.health_level_2_4_impf()\n",
    "        self.education = self.health_level_1_impf()\n",
    "        self.celltower = self.no_impf()\n",
    "\n",
    "    def health_level_1_impf(self):\n",
    "        step_impf = ImpactFunc()\n",
    "        step_impf.id = 1\n",
    "        step_impf.haz_type = 'FL'\n",
    "        step_impf.name = 'Step function flood'\n",
    "        step_impf.intensity_unit = ''\n",
    "        step_impf.intensity = np.array([0, 1])\n",
    "        step_impf.mdd = np.array([0, 0.81])\n",
    "        step_impf.paa = np.sort(np.linspace(1, 1, num=2))\n",
    "        step_impf.check()\n",
    "        return step_impf\n",
    "\n",
    "    def health_level_2_4_impf(self):\n",
    "        step_impf = ImpactFunc()\n",
    "        step_impf.id = 2\n",
    "        step_impf.haz_type = 'FL'\n",
    "        step_impf.name = 'Step function flood'\n",
    "        step_impf.intensity_unit = ''\n",
    "        step_impf.intensity = np.array([0, 1])\n",
    "        step_impf.mdd = np.array([0, 0.65])\n",
    "        step_impf.paa = np.sort(np.linspace(1, 1, num=2))\n",
    "        step_impf.check()\n",
    "        return step_impf\n",
    "\n",
    "    def step_impf(self):\n",
    "        step_impf = ImpactFunc()\n",
    "        step_impf.id = 3\n",
    "        step_impf.haz_type = 'FL'\n",
    "        step_impf.name = 'Step function flood'\n",
    "        step_impf.intensity_unit = ''\n",
    "        step_impf.intensity = np.array([0, 0.95, 0.955, 1])\n",
    "        step_impf.mdd = np.array([0, 0, 1, 1])\n",
    "        step_impf.paa = np.sort(np.linspace(1, 1, num=4))\n",
    "        step_impf.check()\n",
    "        return step_impf\n",
    "    \n",
    "    def no_impf(self):\n",
    "        no_impf = ImpactFunc() \n",
    "        no_impf.id = 4\n",
    "        no_impf.haz_type = 'FL'\n",
    "        no_impf.name = 'No impact function flood'\n",
    "        no_impf.intensity_unit = ''\n",
    "        no_impf.intensity = np.array([0, 1])\n",
    "        no_impf.mdd =       np.array([0, 0])\n",
    "        no_impf.paa =       np.sort(np.linspace(1, 1, num=2))\n",
    "        no_impf.check()\n",
    "        return no_impf\n",
    "    \n",
    "    \n",
    "class ImpFuncsCIWindMoz():\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.tag = 'TC'\n",
    "        self.road = self.road_impf()\n",
    "        self.power_line = self.pl_impf()\n",
    "        self.power_tower = self.pt_impf()\n",
    "        self.power_component = self.no_impf()\n",
    "        self.power_plant = self.no_impf()\n",
    "        self.celltower = self.tele_impf()\n",
    "        self.people = self.people_impf()\n",
    "        self.education = self.health_level_1_4_impf()\n",
    "        self.health = self.health_level_1_4_impf()\n",
    "\n",
    "    def health_level_1_4_impf(self):\n",
    "        impf_health_1_4 = ImpactFunc()\n",
    "        impf_health_1_4.id = 2\n",
    "        impf_health_1_4.haz_type = 'FL'\n",
    "        impf_health_1_4.name = 'Step function flood'\n",
    "        impf_health_1_4.intensity_unit = 'm/s'\n",
    "        impf_health_1_4.intensity = np.array([12, 18, 20, 40, 50, 60, 70, 90])\n",
    "        impf_health_1_4.mdd = np.array([0, 0.03, 0.3, 0.5, 0.6, 0.61, 0.61, 0.61])\n",
    "        impf_health_1_4.paa = np.ones(impf_health_1_4.intensity.shape)\n",
    "        impf_health_1_4.check()\n",
    "        return impf_health_1_4\n",
    "        \n",
    "    def road_impf(self):\n",
    "        # Road adapted from Koks et al. 2019 (tree blowdown on road > 42 m/s)\n",
    "        impf_road = ImpactFunc() \n",
    "        impf_road.id = 2\n",
    "        impf_road.haz_type = 'TC'\n",
    "        impf_road.name = 'Loss func. for roads from tree blowdown'\n",
    "        impf_road.intensity_unit = 'm/s'\n",
    "        #impf_road.intensity = np.array([0, 30, 35, 42, 48, 120])\n",
    "        impf_road.intensity = np.array([0, 20, 40, 42, 44, 120])\n",
    "        impf_road.mdd =       np.array([0, 0,   0, 50, 100, 100]) / 100\n",
    "        impf_road.paa = np.sort(np.linspace(1, 1, num=6))\n",
    "        impf_road.check()\n",
    "        return impf_road\n",
    "    \n",
    "    def p_fail_powertower(self, v_eval, L=97.2, x0=77.8, k=0.3):\n",
    "        \"\"\"\n",
    "        adapted from https://arxiv.org/abs/2107.06072 Fragility curves for power transmission towers in\n",
    "        Odisha, India, based on observed damage during 2019 Cyclone Fani; based on functionality disruption curve\n",
    "        \"\"\"\n",
    "        p_fail = []\n",
    "        for v in v_eval:\n",
    "            p_fail.append(L/(1 + np.exp(-k * (v-x0)))/100)\n",
    "                          \n",
    "        return p_fail\n",
    "    \n",
    "    def pt_impf(self):\n",
    "        # Power tower impact function \n",
    "        v_eval = np.linspace(0, 120, num=120)\n",
    "        p_fail_powertower = self.p_fail_powertower(v_eval, L=97.2, x0=77.8, k=0.3)\n",
    "        impf_pt = ImpactFunc() \n",
    "        impf_pt.id = 8\n",
    "        impf_pt.haz_type = 'TC'\n",
    "        impf_pt.name = 'Disruption func. for power towers from strong winds'\n",
    "        impf_pt.intensity_unit = 'm/s'\n",
    "        impf_pt.intensity = np.array(v_eval)\n",
    "        impf_pt.mdd = np.array(p_fail_powertower)\n",
    "        impf_pt.paa = np.sort(np.linspace(1, 1, num=120))\n",
    "        impf_pt.check()\n",
    "        return impf_pt\n",
    "        \n",
    "    def no_impf(self):\n",
    "        impf_none = ImpactFunc() \n",
    "        impf_none.id = 6\n",
    "        impf_none.haz_type = 'TC'\n",
    "        impf_none.name = 'No-impact func'\n",
    "        impf_none.intensity_unit = 'm/s'\n",
    "        impf_none.intensity = np.array([0,  140])  \n",
    "        impf_none.mdd =       np.array([0, 0 ])         \n",
    "        impf_none.paa = np.sort(np.linspace(1, 1, num=2))\n",
    "        impf_none.check()\n",
    "        return impf_none\n",
    "\n",
    "    def tele_impf(self):\n",
    "        # adapted from newspaper articles (\"cell towers to withstand up to 110 mph\")\n",
    "        impf_tele = ImpactFunc() \n",
    "        impf_tele.id = 3\n",
    "        impf_tele.haz_type = 'TC'\n",
    "        impf_tele.name = 'Loss func. cell tower'\n",
    "        impf_tele.intensity_unit = 'm/s'\n",
    "        impf_tele.intensity = np.array([0,  80, 100, 120, 260]) / 2.237\n",
    "        impf_tele.mdd =       np.array([0,  0, 100,  100,  100]) / 100\n",
    "        impf_tele.paa = np.sort(np.linspace(1, 1, num=5))\n",
    "        impf_tele.check()\n",
    "        return impf_tele\n",
    "   \n",
    "    def p_fail_pl(self, v_eval, v_crit=30, v_coll=60):\n",
    "        \"\"\"\n",
    "        adapted from  https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7801854\n",
    "        and Vulnerability Assessment for Power Transmission Lines under Typhoon \n",
    "        Weather Based on a Cascading Failure State Transition Diagram\n",
    "        \"\"\"\n",
    "        p_fail = []\n",
    "        for v in v_eval:\n",
    "            p = 0\n",
    "            if (v > v_crit) & (v < v_coll):\n",
    "                p = np.exp(0.6931*(v-v_crit)/v_crit)-1\n",
    "            elif v > v_coll:\n",
    "                p = 1\n",
    "            p_fail.append(p)\n",
    "        return p_fail\n",
    "    \n",
    "    def pl_impf(self, v_crit=30, v_coll=60):\n",
    "        # Power line\n",
    "        v_eval = np.linspace(0, 120, num=120)\n",
    "        p_fail_powerlines = self.p_fail_pl(v_eval, v_crit=v_crit, v_coll=v_coll)\n",
    "        impf_prob = ImpactFunc() \n",
    "        impf_prob.id = 1\n",
    "        impf_prob.tag = 'PL_Prob'\n",
    "        impf_prob.haz_type = 'TC'\n",
    "        impf_prob.name = 'power line failure prob'\n",
    "        impf_prob.intensity_unit = 'm/s'\n",
    "        impf_prob.intensity = np.array(v_eval)\n",
    "        impf_prob.mdd = np.array(p_fail_powerlines)\n",
    "        impf_prob.paa = np.sort(np.linspace(1, 1, num=120))\n",
    "        impf_prob.check()\n",
    "        return impf_prob\n",
    "    \n",
    "    def people_impf(self):\n",
    "        # Mapping of wind field >= hurricane scale 1 (33 m/s)\n",
    "        impf_ppl = ImpactFunc() \n",
    "        impf_ppl.id = 7\n",
    "        impf_ppl.haz_type = 'TC'\n",
    "        impf_ppl.name = 'People - Windfield Mapping >= TC'\n",
    "        impf_ppl.intensity_unit = 'm/s'\n",
    "        impf_ppl.intensity = np.array([0, 32, 33, 80, 100, 120, 140, 160]) \n",
    "        impf_ppl.mdd = np.array([0, 0,   100,  100,   100,  100,  100,  100]) / 100\n",
    "        impf_ppl.paa = np.sort(np.linspace(1, 1, num=8))\n",
    "        impf_ppl.check()\n",
    "        return impf_ppl\n",
    "\n",
    "    def binary_impact_from_prob(self, probs_fail, seed=47):\n",
    "        np.random.seed = seed\n",
    "        rand = np.random.random(probs_fail.size)\n",
    "        return np.array([1 if p_fail > rnd else 0 for p_fail, rnd in \n",
    "                         zip(probs_fail, rand)])       \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fb4d6de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_health = exposure_from_points(gdf_health, {'FL': 'health_level1', 'TC': 'health'})\n",
    "exp_educ = exposure_from_points(gdf_educ, {'FL': 'education', 'TC': 'education'})\n",
    "#exp_celltowers = exposure_from_points(gdf_cells, {'FL': 'celltower', 'TC': 'celltower'})\n",
    "exp_pplant = exposure_from_points(gdf_pp, {'FL': 'power_plant', 'TC': 'power_plant'})\n",
    "exp_pcomps = exposure_from_points(gdf_power, {'FL': 'power_component', 'TC': 'power_component'})\n",
    "exp_pline = exposure_from_lines(gdf_powerlines, {'FL': 'power_line', 'TC': 'power_line'}, res=500)\n",
    "exp_ptower = exposure_from_lines(gdf_powerlines, {'FL': 'power_line', 'TC': 'power_tower'}, res=500, disagg_val=1)\n",
    "exp_road = exposure_from_lines(gdf_roads, {'FL': 'road', 'TC': 'road'}, res=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9bfd7346",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_impfset(imp_class):\n",
    "    impfset = ImpactFuncSet()\n",
    "    for attribute in set(imp_class.__dict__.keys()).difference({'tag'}):\n",
    "        impfset.append(getattr(imp_class, attribute))\n",
    "    return impfset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "77b0c9d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "impf_set_tc = make_impfset(ImpFuncsCIWindMoz())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc938e9",
   "metadata": {},
   "source": [
    "# Impact calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4cdf7cbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-05-15 08:29:54,350 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n",
      "2023-05-15 08:29:54,367 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n",
      "2023-05-15 08:29:54,376 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n",
      "2023-05-15 08:29:54,390 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n"
     ]
    }
   ],
   "source": [
    "# Wind-field impacts - Points\n",
    "imp_tc_health = Impact()\n",
    "imp_tc_health.calc(exp_health, impf_set_tc, tc_moz)\n",
    "imp_tc_educ = Impact()\n",
    "imp_tc_educ.calc(exp_educ, impf_set_tc, tc_moz)\n",
    "# imp_tc_cell = Impact()\n",
    "# imp_tc_cell.calc(exp_celltowers, impf_set_tc, tc_moz)\n",
    "imp_tc_pcomp = Impact()\n",
    "imp_tc_pcomp.calc(exp_pcomps, impf_set_tc, tc_moz)\n",
    "imp_tc_pplant = Impact()\n",
    "imp_tc_pplant.calc(exp_pplant, impf_set_tc, tc_moz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0341d2e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-05-15 08:29:54,403 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n",
      "2023-05-15 08:29:54,568 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 34489 coordinates.\n",
      "2023-05-15 08:29:54,588 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n",
      "2023-05-15 08:29:54,752 - climada.util.coordinates - WARNING - Distance to closest centroid is greater than 100km for 34489 coordinates.\n",
      "2023-05-15 08:29:54,760 - climada.hazard.base - WARNING - Impact function id=8 has mdr(0) != 0.The mean damage ratio must thus be computed for all values ofhazard intensity including 0 which can be very time consuming.\n"
     ]
    }
   ],
   "source": [
    "# Wind-field impacts - Lines\n",
    "\n",
    "# Power lines (lines)\n",
    "imp_tc_pline = Impact()\n",
    "orig_res = exp_pline.gdf.value.values[0] #this works only if all values the same!\n",
    "exp_pline.gdf['value'] = 1\n",
    "imp_tc_pline.calc(exp_pline, impf_set_tc, tc_moz, save_mat=True)\n",
    "imp_tc_pline.imp_mat.data = ImpFuncsCIWindMoz().binary_impact_from_prob(imp_tc_pline.imp_mat.data)*orig_res\n",
    "exp_pline.gdf['value'] = orig_res\n",
    "# Power lines (towers)\n",
    "imp_tc_ptower = Impact()\n",
    "imp_tc_ptower.calc(exp_ptower, impf_set_tc, tc_moz, save_mat=True)\n",
    "imp_tc_ptower.imp_mat.data = ImpFuncsCIWindMoz().binary_impact_from_prob(imp_tc_ptower.imp_mat.data)*orig_res\n",
    "# Total Power lines\n",
    "imp_tc_pline.imp_mat+=imp_tc_ptower.imp_mat\n",
    "imp_tc_pline = u_lp.impact_pnt_agg(imp_tc_pline,  exp_pline.gdf, u_lp.AggMethod.SUM) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "593e6278",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-05-15 08:29:56,658 - climada.engine.impact - WARNING - The use of Impact().calc() is deprecated. Use ImpactCalc().impact() instead.\n"
     ]
    }
   ],
   "source": [
    "imp_tc_road = Impact()\n",
    "imp_tc_road.calc(exp_road, impf_set_tc, tc_moz, save_mat=True)\n",
    "imp_tc_road = u_lp.impact_pnt_agg(imp_tc_road,  exp_road.gdf, u_lp.AggMethod.SUM)"
   ]
  }
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