polynomial regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build and Interpret a Polynomial Regression Model\n",
    "In this notebook we will illustrate the polynomial regression model with the ozone dataset [link of the course](https://openclassrooms.com/en/courses/5873596-design-effective-statistical-models-to-understand-your-data/6233046-build-and-interpret-a-polynomial-regression-model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ozone</th>\n",
       "      <th>Solar.R</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.3</td>\n",
       "      <td>56</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ozone  Solar.R  Wind  Temp  Month  Day\n",
       "0   41.0    190.0   7.4    67      5    1\n",
       "1   36.0    118.0   8.0    72      5    2\n",
       "2   12.0    149.0  12.6    74      5    3\n",
       "3   18.0    313.0  11.5    62      5    4\n",
       "4    NaN      NaN  14.3    56      5    5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"data/ozone.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns=[\"Month\",\"Day\"])\n",
    "df=df.rename(columns={\"Solar.R\":\"Solar\"})\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(111, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ozone  Solar  Wind  Temp\n",
       "0   41.0  190.0   7.4    67\n",
       "1   36.0  118.0   8.0    72\n",
       "2   12.0  149.0  12.6    74\n",
       "3   18.0  313.0  11.5    62\n",
       "6   23.0  299.0   8.6    65"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1b111fa2108>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/5IooZa1hIuIBMA1jwZJ7lVKjdjbMCjNnzlQ7dmTX0yff+FQJWpM/7956QbmbQIyxJYLLqtzWIkbBdqnBjVb2sbuNDs6CUpEya/W+m2VBCfjcOHQkVDaZIAVTkXJLyofRmLFxcSdaG33oHQxj+YM77R4LCj5ZLmkIZ2GseqUHwGdEBEqpzYU2gBBCdKxUSzPzE9+2cq7lMsWFZFqptnLITsDKPTW67onvmwI+26vsOTlDDyHVTmr/m9rakNbn+4aPKd9AfvNDqbCahvABAFMAvIpjwZcKY64ohBBSNLIpuIX6iZfbgk7SKYbvv90PRpQbQsqH1f7n9DiiRKz6gM8EMFcptVIp9fex17V2NowQQoyw6iduhtMzrdQihd7TUkC5IaR8WO1/lTCW6FhVwPcAOMHOhhBCSCpGgXlGAXm5BEBWkoWkVsjnnmYLxC12oC7lhpDSo/fjkXDEUv8zG0vcLpQ8aD8bVn3AJwDoEpFXAMQzmyulLrSlVYSQmsdsyXFqawP8HhfWXHQGxvncGAlH4c+h2pluIdEH8xmTmnDtuVMRjUXM06+39Fjx/U8k23K0He4iqXIDONeyRkg1kNiPVy/osNT/UscSr8eFoaMRXHjn9rSxAEBZYzqsKuA329kIQghJxWzJccvyOVh6zytpA7HVIBvdQnL15h1obfDj+vnT4kVc6NdbPnLx4f5wOGQoG4+tPAdtjXVFCdRNJVFuEmWFqScJsYfEfrzhhX1Yu2h6vAJxpv6XOJb0DoaS5ovEsaJvKFzWmA5LCrhS6nciMhHA2bFNryilejIdQwghhWC25D8a1Sy7AphlrUi0kFy66eWKiJgnxzg6aiwbR0fHKt+FI1G0NvixekEHmgJeDARHseGFfQW5i+RqpSeE5Ic+bo+EI1i9oAMbXtiHXfsHcNvTe7F6QQc+eUIjAj6Ppf5nNo8cHdWK/pCeK1azoFwCYB2AFzCW+/BfRGSVUupRG9tGCKlhzJb8vW6XpaXIbG4IrY1+HOgfoV9vBeIWMZQBd2wuDvjcaSsb6y6ejoCvMHcRpqAkxF6Mxu21i6bjtqf3Ytf+Aax5sisnJdlsHnELyj72W3Wc/CGAs5VSlymllmIsJ/hq+5pFCKl1zIJpWut92LikM2vAnpWoebsj5stVsbPSyXbdAj431l2cXIk0UcGOaCqufANj937Vo7sRSTgP7w0h5SO1/0UiGnoHQzh4OJg2bt+wdTdWzJuSl9uX2TwS8JU/W4pVH3BXistJH6wr74QQkjNGS/7NAS/e6h3CT3/7JlYv6EBLvQ8nN9XB7XLh4OFgkluAlawVdvr1Mm90fmiawrt9w3ivbyQeZPuxlnGY3FIfv25NAR8mjq9LCsSdOL4OTYGx+zYaMXZTGo1o8e/gvSGkPBj1vw2LO/Gz597EVZ/7uGHf/eQJY3OBmduJmbuhmesYgLLHdFhVwH8jIk8D+GXs/aUAfm1PkwghZIzUJf/ewWPBd8909WDGpCbTIEorWSuK4ddrNvDbEQhYCwwEwzh05ChWP74nyX2kaZwXx9cfq4o5uaUejXVew/uW7d7z3hBSPoz634oHd2L1gg4MBEcN+27A5zHtm9keqM1cx8od02HJiq2UWgVgI4DpAM4EsEkpdX0+Xygi00Tk1YTXERH5HyJys4gcSNj+lXzOTwipXlKt2ivmTUlzNbh68w4cPByEgsLmK2dldVXRB+eTm8ehtdGfs/K999AgFq7fjrlrn8fC9dux99AgNE0xb3SeBMNRQ/eRYDj5umW6b9nyilu5N3RRIcQezPpfU8Abz3aS2Hc3LO5Ec8Brer58i2SljiEAStrnrQZhrgZwn1LqsYRty5RSm3L9QqXUXgBnxc7hBnAAwDYAVwC4Qyl1W67nJITUBqmWzaaA13Ag7+4P4vuPvIa7l87EE9fMRTBsj4UjkyWVeaPzI6Ipw3sazWEyzLayke3e0EWFEPsw638DwdF4tpM1F52BSccHsK93GD977k3csnC6qQW8GMaOcvR5q37cfw/gaRH5QsK2FUX4/nMB7FNKvVeEcxFCqpxUy+ZIOGoYSDMQHI0rw1ENeVm3rZBp4C+0Ymet4nWJ4T315HjvCrGQs+w8IfZh1P82LO7E1p37AQC9QyH4PC6semQ3lj+wE8909WRUposRTF+OPm/VB/wAgIsAPCIijyql1mEsHWGhfAPH/MoB4BoRWQpgB4DvK6X6i/AdhJAqoqXBh4eu/izcIqj3u9MCafSUVcAxZbh3MGSLn18mS2ot540284u3Qp3PjTsuORPXbXktfk/vuORM1BWYQjC1TVNbG0zvDd2HCLEPl0swtbUBW5bPwWhUg9ftQmu9D7csnI4fLYhiX89QPO0gkF2ZTg2mP7+jDT+6oCM+9heSL9zOPm9VAYdS6r9F5K8B3CUijwAIZDsmEyLiA3AhgH+IbboLwBoAKvb3dgBXGhy3DMAyADjllFMKaQIhJYNyWziZStNvWzkXwVHjgTuqKSxcn16GuBiKcKYsKoUooU4gX5ktdCm3KeBDS4M/KcNJS4M/nuEkH3JtUzndhypdbsoNx1rno2kKb/UOGfZHABgORdA7FAIAS2NqorFD0zR8OBzGt/7tDzmNP+Xo86JUdr86Efk3AP8fxpTjfRhTjH+glPp43l8schGA7yqlzjf4bDKAJ5VSZ2Q6x8yZM9WOHTuyftfkG5/Ks5Wl4d1bLyh3E4gxtsx6VuWWJNM7GIor0jqJJeiNlKyNSzrx09++iWe6egyPKQZGkwKAcvkQl11ms90nKxRbCc21TeXyAa9h3/Oyyy0pHVbG8nzH1HzHnzz6XsEym9ECLiIeAP8M4G8BzMCYz3g7gPsATCvwu7+JBPcTETlRKXUw9nYhgD0Fnp8QUkVkWyI0cvnQNC1J+U49phgYpbhKTJeof2etpLkrxlJusStO5tqmcrkPMT0iqQWsjOX5jqn5jj/l6PPZgjDXATgewKlKqU6l1AwAUwAcF/ssL0RkHIDzADyWsPknIvJfIrIbwBcAXJfv+Qkh1YeVQJv0wDtXWaqd1bIPsd3VRfMhnzYVkp4yX2pZbkjtkE9/tNo3Chl/St3nsyngCwBcrZQa1DcopY4A+DsAeftNKKVGlFItSqnDCduWKKU+rZSarpS6MMEaTggheWUVKVcmEicqoaXCidlfnNgmI2pZbkjtkE9/tNo3KqWvA1l8wEXkTaXUabl+ViroA05shn6JJSAXf998fIPLEdRWRl9eR8isEwMJc21TjclNuXGE3JLMFLNP5NMfrfaNEvVde33AAXSJyFKl1OakbxVZDODPhX45IaS2yTaoGg2kufrCFuJPnO9AXsspCIH8rnmxJ81CZKdcinCtyw1xLlb7RCSioWcoFE8v2Nbgh8eT7myR6xiRS98odgyJXWRTwL8L4DERuRLAToxlQTkbYykIF9rcNkJIlZMp6Kyl3ldUJchOi4sRlTIJOIFiK7yFni/fYMhiPERQbogTsdInIhENfz40iBUP7oz3uw2LO3H6xEZ4PK6C+0e19Y2MPuBKqQNKqc8C+CcA7wL4bwD/pJSapZQ6UIL2EUKqmEyBNcWsTKYrZAvXb8fctc9j4frt2HtoEFqG8uashlg6in2tCz1fPsGQ+cgYIZWClT7RMxSKK9/65yse3ImeoRD7hwGWStErpf5TKfUvSqmfKaWes7tRhJDaIFNgTTEzQuSjkOX7/Zqm0DsYwoH+EfQOhmp6grFKvgqv2XUuVHbyCYbkAxupZqz0idGoZtjvIlENBw8H2T9SsKSAE0Iqi0pRAjNFrBczI0Q+CpnP48b5HW3YuKQTDy+bjY1LOnF+R1vG76eVJz9yvdeapvBu3zD2HDiM7v4g9hw4jHf7huPXOV/Z0fuNpmnYuKQzp0wKTCFIqo3EeURBYfOVs5L6xMYlndA0LT7HeN3GaV/39Q6juz/I/pGC5VL0hJDKoJIyKWQKrMlU5j1X8ikz3Bzw4tpzT0vzZ2wOeE2PYSGV/Mj1Xg8Ewzh05ChWP74nvv+6i6ejaZwXx9f785Kd1H5zfkcbHvrOZ+F2iSV/1XKWryek2JjNI09cMxfBcBRRTeHHT3Xhma6e+GefmFCPDYs7k8bMdRdPx09+sxcr5k1h/0jBUil6p8I0hMRmKjI1VjFKgTuFYmXGyOehJJ/reKB/BHPXPp+2ffsNX8DJzeNybnceVKTMArnd6wP9I7h008tp9+bhZbPj1zlX2Sm031TSg68DqVi5rVYy9QcApp81B7zoGQohEtXgcgn+/qFd2LV/ADMmNeEHX5qGG7burpb+YXsaQkJIhVFNS+HFinrPJ71bvm4rtPLkRy73OqqU4b2JJtiTcpWdQvsNUwiSaiJbfzD7zOPx46SmMTeU3sEQeodCAIBd+wdw29N7seaiMzClrQEBL/sHfcAJqTJYTc+YXMsM53MdK6kKWyVT5zW+N3Xe/Ke0YvSbcpSvJ8QOMvWHfKtS9g6FcMJxdWhvCrB/gAo4IVUHlcDikM91TLSCbr/hC9i2cm4lL7E6lgn1fsN7M6E+/9US9htCjpGpP1jtKxwPM0MfcAdAH3DHUrF+iZVSst3pVOA1qViZzRU77k0pqnE6XH7KRc3IbSWRSX4Lle0q6Bv0ASeEpJOr/ysDyIyptspr1YQd96aY52SfIpVOpv5QSF9h3xijbC4oIvKuiPyXiLwqIjti244XkWdF5K3Y3+ZytY+QWoJFRPKnUnKu1xrlvi/sU4QY0zccxh3P7sXqBR14eNlsrF7QgTue3VtzfaPcFvAvKKU+THh/I4DnlFK3isiNsfc3lKdphNQO1ZQ5pRhYXR6lJceZ5Htfirkszj5FSDJ6/xqNRnHZOacmpSRcu2g6NE0rdxNLitOCMC8CcH/s//sB/G0Z20JIzcDMKccwq2YZiWhpFlVaOZ1DosX7gyNHc74vxa5iyj5FqplcV5gS+9dwKBpXvoGx/nnD1t1JaURrgXIq4ArAMyKyU0SWxbZNVEodBIDY37bUg0RkmYjsEJEdvb29JWwuIfnjdLk1i2pvDnhrzr3CSKm+49m92NuTrpxVs5XT6TKbSKry/P5AEK0Nfmxc0omHl83GxiWdaG3wZ7wvxX6YYlaV8lBJclup5POwmti/hkKRtHGztcEPpVRNzTXldEGZq5R6X0TaADwrIn+2cpBSahOATcBYhLOdDSSkWDhdbo2KiDQHvHird6hi3StycSNJ3E/TtLTJYVHnJCx/YGeacrZl+ZyqLbzjdJlNJNGntCngxfH1Ptx8YQe++9CupJLYAZ/5fUl9mJoxqQkr5k3BSDiC3kHk7I5iR2GeKsgcYTuVJLdOJFHGvB4XPC5BMJwsb2YPq5mqxib2r57BUNK4OWNSE66fPy1e3bbS5pp8KZsCrpR6P/a3R0S2AZgF4JCInKiUOigiJwLoKVf7CKk1UqPaewdDOQ+ypULTFD4cDuHoaBRuEQR8bjQFklNkWfEBNtpv45JOnN/Rhme6jg0/LfU+Q0u3W4C7l85M+x5aOUuLpmlpPqXrLp6O1gY/uvuD6O4PYtWju/HYynNMz5FYxdSsbPbU1gb0B0dNFWAjBbmYWVXe7RvGe30jGOdzYyQcxcdaxmFyS31VKymkdBiNh+suno6f/GYveodC8TE0HImitcEff+AdCI5iwwv7LFcJ3vDCPtz5rRnoHx7FOJ8bLQ1+XH7vK46ca+ykLC4oIlIvIo36/wDOB7AHwBMALovtdhmAx8vRPkKIc4PI9Enia+tfxOd/8gIu3fQy9n4wiHf7huPLllbdCT4cTn/IWP7ATqxe8Kkk14Hj632G/rwul4uFJhxAVCHNp3TVo7uxYt6U+D7d/UGMRsyDvBJdRlbMm5J2vqs378D7h4Omy+7F9iFPZSAYxqEjR7H68T24dNPLWP34Hhw6chQDQcYbkOJgNB7q/ShxDPW6Xbh+/jSsebILl256GWue7ML186fB6zZXKVNdskYjWlyWB0bCjpxr7KZcPuATAfxvEXkNwCsAnlJK/QbArQDOE5G3AJwXe08IKQNODSIzUq5XPbob7/WNxBVsqw8PR0eN91NKJaXI2vDCPqxdNN3Qn5flx8uPUsrwPjYFvPH32WQ30WXk9BMaDc/XMxgyfaizO6I7TzkAACAASURBVCA3GI5i1aPpDxnBcHUrKaR0mI2Hej/Sx9BIVDOUxUjU/AE3sX/d+a0ZuG7La/Hj+4bDjpxr7KYsLihKqbcBnGmwvQ/AuaVvUeWRT3VPVtwkuaBbLJzmXmGmXI/zueMKduJyp47RgO4WMdzP5RKsebIraftAMIwty+dAKUX/W4dhdr9HYsqpVdnVH6Z6B2F4vlRlOvGhzu4Vo6jJQ0atZY6oNpzk1282Hg4ER+P/+zxuBEfTgyi7+4OIZJFFvX8d6B9JOl43cKS6fJV7rrEbp6UhJIQ4hESLhZPcK7wel6G1ZCQWKARYz0AR8Lmx7uJky/ZYsJ4r7fjrzpuGE8bXFdXSXe5iMdWC0f3esLgTDX4PHl42G2suOgN+j/Xpzuh8G5d0YuvO/Un7tTcHICI40D8CiSkvqZ8Xy4pX5zVekarzchqvVOx2W8oVs/Fwwwv7ksbQTLJoZUxLXV3dtX8A97/4DrYsn+OoucZuRKnKHfBnzpypduzYkXW/fKzFpSQfyzQt4CXBlt5vVW5LjZMsMZn4aDiEfT1D8SXMeMBdox8fn9CQFIiZ7fdkCmwDYOv1MAsUnTjen5Z1IAdqSmYTiUQ09AyFEIlqcLsEm198Bxv//3fjn7c3B3IK6kqVH6OsQBsWd+Jnz72JZ7p6cH5HG6499zSseHCnLZkcqrzoU03Kbe9gCAvXb0+zOJcr+DB1PFQAPnZ8AJpC0nikaQp7PxjE1Q8kyOKSmZja1mApc5ZNY1+pKbhx5a6ESQhxAJU0uQfDUfzzr/6MW7/2aZxwXB3cIvjgyFE0+j1JbU3N6mKEyyWY3FKPxjqvoaKdq7KmaRqiCpbcVMx8htdcdAauuO+Pjr4HTkPTVNrEf/vXz8S80yfCJWIpS0MqRvKTmFZQRHDzE3vi2XL0v/ddMQt9QyGMhKM5Wd2ttKfYaQ3tolIe5stNMdyW8rnWZsdkGw8T8XtdWHPRGXHDhd/rwpHQqKXMWamy7PW4MHQ0ggvv3O74+aeYUAEnhOSV11XHqqW5WBOyz+NG71AIi3/+SnybbjXKByuKeib0h5c7nt2blgov0ySSyZdd/78WUnEVAyP5/f4jryU9zNxxyZnwul040D9SFKVQKYXewWSf8Ge6enDV5z6OSze9DKD41sxCZbUUVNLDfLmxGqtiRj7XOtsxVmSsbziMW3/9BhZ1TsI4uBGOarj112/gpq9+yvIDReL39A6GsPQepiEkhNQg+VpirPgwFtvP0UkVBjVN4YMjRzEcimDVl07H/S++YzkLhlmWGT3gST9HtafiKgZWHmau2/IaXn//SN4ymCrHl256Gf/zK6djxqSm+D68f/Zng6kmCh3LjNIGXr15Bz4cDpkeU4z7o+fdT0xDeNk5p0IEuPfys9P6RLYHCqemvLUbKuCEkLxTDloZzIs9ITslOFRXyC7Z+BIu3vASrrjvj7jsnFOTJh+zSUTTFNwuYOPiTsOAJ51aSMVVDKw+zKSuLuQig0bKznVbXsMNXz49/n1G908P0qyVINtaVaaskhik2DccxtTWhrzHMrO0gUdHzdMBFuP+GOXdv2HrbgyHolj9+B5cP38aZkxqsvxA4dSUt3ZDFxRCSN4pB60M5nZMyE5Yijd6sLhh626sXtCB5Q/sBGA8iSQuAbc2+LHmojNw6oR6jPO5MRSKoHcoFD+2FlJxFQMj+dUr+OkUap02U3ZOOq4O22/4QtyPNfH+bVjcGfcTrxVXjELdKqqZYrvnmKUNdGc4VTHuj1ne/aFQJJ4T/OFlsy27ejk15a3dUAEnhBgGeDXVefDBkaMYjWrwul1oa/DDkxJUZmUwd/qEnOifHvC5EdEURiNa1snD7MFCnzTMJpFExb27Pxj3Ud62ci4mt9RXRJCd0zAL6kpUho0UcqOHI7NYBVNlJ/a5QHBK87ikIM37t7+NRZ2TcNXnPo6B4CjueHYvblk4vagPj04LeKx2ZaqQ611IrI0RetpAvSjOsTSq5mNrtvtj5feZjek9g6H47wLGgth1i3+m81VSgHExoQJOCAGQbFWORDT8+dBgUkq1DYs7cfrExiQl3Mpka+eEXKjykWqNvn7+tKTJLJN1ymwSOjFmETVrD5fo7SdVGTZSyFNlMJt10kjZWf/tz+Af/+P1JAv3xPFjfcgtwBc/eQK+/8ixdJlrF02Hppm7B+SKEwMeq1mZKvR6Z+v7mcYzo8+aAj5MHF+XlI1k4vg6NAXMx9ZM98fq72sOeLFhcWfS/LB20XTc9vTYA67+cJvL9XLCqmapYR7wGoJ5wHOmJnPTAsD7A0FcsvGlNOVyy/I5OKkp2VfPKF9yf3A0aXAHip9TuxjKR2Ie3o1LOtOqX2bKYpHv95vl/n3imrk4dCRUqDJVkzJr5V5ke1gzuy/6UnpzwIv/7h+J50huafDjJ795I55+UN9fz7xy7+VnY/Xjeyz1oXxxWh7pAqgIuS30emc6vqXeZyrDADJ+Vqyx1erv6x0M4YfbdmNR5yS0NfpxXMCLW3/9RpqrVd9wuFrk04iCZZZBmISQNEajmqGlJhJNt97plouTm8ehpd6Ht3qH0jKeAIjvU6wqksUI7tQtUjMmNeG0tgbc/vUzsXFJZzyQMpNl2uUSTG1twJblc/C7VfOwZfkcTG1tyPrbzDIfRDTF7BE5kBjM9sGRo1mvXaKcGsmgmXWyuz+Iheu3463eIZzSPA5nnHxczMInScq3vv+k48eU9skt49Da4E/7vJhGL66mlJZCr3emrCeZxrNMn2WTayvofWkkHMHqBR1ZA8nDkSie6erB8gd2YuH6F7HhhX1Y9aXT8cKqebjvillo8LuLcr2qHbqgEELS8Lpdhu4VHnfmZ/Zi+zhmsloWY3D3edw4v6MNl51zKpbE8tAmLqf2DoVMfdWNir9YsVibLQEfPBzkZGWRVIv3oyvmFEUWjGR+IDhqKMe9gyHD/fd/dMyvX/c737V/IP55IbEPqf3B6zHup06Jr6g2Co1nyeT+kW08s2tsMFo90se/XfsHDH+fPm4u6pyEk46rg6aAK+77Y5Ifer3f4/j4n3JTcgu4iEwSkedF5A0ReV1E/q/Y9ptF5ICIvBp7faXUbSOEjNHW4MeGlBR5GxZ3oq0hsxJdTItHtvzhxUhd1VLvw48u6DBMqXXtuVMz+qoXYoE3slrVaiqufEi99n3D4aLIQqp1cu2iY2kFU+XYaP91F0/Hz557K77/qkfH5Ej/vJDYB6P+MHQ04pic+LVAMWoQmFmsM/V/O8cGs2xOK+ZNMf19zQEvrj33NKx5sgvvHz6K7z70p6TjVz26G8Fw1FE1G5xIOSzgEQDfV0r9SUQaAewUkWdjn92hlLqtDG2qCfLxhaffeG3i8bhw+sRGbFk+B5GoBo9JFpRUimnxyGZNL0Zwp8slcLsErQ1+rF7QgaaAN162fEpbA9qbAvHgpFRLfLGXV6s9e0QxSb32G17Yh7WLpqdVIc1VFnTrZHA0ikOHjyIUieLGL5+OgeAotu7cj4DPnZTRQc/hrN/zax7aFbd2A2PyMKWtPmNQrlWM+sPSe17BE9fMrcqARydiZ4BpS70Pm6+cFY8xGAlH8bGWcXEZtjI25BOUbjaOffKExrhveuo5+oOj8QDMpoDX8Pioqu6A3GJQcgVcKXUQwMHY/4Mi8gaAk0vdDkKcjqYpDATDCIajiCqFOq8bE+qL4z9tBY/HlTVYzCgAs1gTRTYFN9fB3ew7Az53WvaTdRdPR4PfnTEzwMTxfssPG1Z+Lycr66Q+6O3aP4D7X3wHW5bPgVIqLouZgoPNgoUBwCVA23g/bnmqKx5Ydt8VZ+PQ4RCufsDY5ah3MBTPsqLT3hyAx+Uqit+3WX8IhqM4uXlcwecn1sgnW4eV7CaapiE4Go0H7urypX9nappNj0tw8HAwSXatBoUntkeypNc0IlEWB4KjhsfXeV15X69aoaw+4CIyGcAMAH8AMBfANSKyFMAOjFnJ+8vXOkLKh6YpvNs3jENHjlpOi5fPdxQrhV9i+xKtgkbntZo9xIo13ergnuk7I5qKX2Pg2BLqYyvPAWBuiX9s5TmWHzaYiqu4GK0WXHfeNJwwvs70oWnD4k787Lk34wp16vvNV85CKKKl+cL2Doaxa/8A9n8UTMpqkigHbY11hm0qZiEe+tNWJpn6P3BMaV69oCMpC1Pqip8+NmQyCFiJv0k9/vyOtrSUgusuno5rHtqF3qFQ1rHZaPVpw+JOHJ8hFSIZo2xpCEWkAcDvANyilHpMRCYC+BCAArAGwIlKqSsNjlsGYBkAnHLKKZ3vvfde1u9iGsL8qXEXlKKZHnOV297BEPYcOGyYxqwYKZyKncIvl/ZZPS5bG3N5gDD6zvM72nDzhWdgNKrhr9e9kHbM9hu+gJObx+FA/wiueWgXVsybkuSicue3ZuDE4wJZ21DiVHFlk9lSk+n+m13z1Cqlie/N0gbq+zy8bDYu3fRyWjt+v2oe3DEf/kSruojEle/E8xUSkGzUHz4xoR69w+GMBbMqgKqV20z9H0D8MzP50sehbOd7eNlszF37vOnxen8JjkZw6HAIoUgUXrcLA8FRHOwfxrkdJ0JTCm/3DuNnz72VFDhsRYm/8cufxOHgKHoGQ9i6c3/RC045kIJltiwWcBHxAtgK4BdKqccAQCl1KOHzuwE8aXSsUmoTgE3AWI5P+1tLSOHkKrfhSBTjfG5LPsb5WLLNrLqJS/jZzpOvD7TV44pRMCL1O2dMasKKeVPiuWtvfmIPFnVOMrQsej0u9A6G4HGJoYtKnc9lyWJdqam4nD7WZrr2Zte8KeCNv29t8OO0tgY8vGw2BoKjaBpn7MuqHzMSjhrKyb7e4XjWk81XzkJD3di0qpRC72A47XyF3He/x5VUdKVpnAd7e4ayFsyqJZwmt+FI1DDGJDW7yUBwNJ5ZRN9v6879aSscZrIdVTBdITEqOHbjY/8Vl5n13/4M1jz5Oq763MdxxX1/TDu3UaGglgYfnrhmLoaORnBgIIj3B4LwxrJk9Q6GHT++OYGSK+AiIgB+DuANpdT/m7D9xJh/OAAsBLCn1G0jxCn4PG7TCT9xQM7Xkm02iL8/EMTFG16yZG3Od0k8l+PMlKxc0x0mphtMXCpdu2g6Ht91wDCAb+hoBEvveQW3fu3T8clK/65Vj+7GI8vnZPyd+fxeUhwypRQEgBmTmnD9/GlJqScfuGqW6THtzQFMOj6AOy45E9dteS3pQUwvb9/a4MehI0ex9J7daZ/nkobQ7IG6bziMpbH26vz2e38dV76BMdlc8eDOohb7qUUKdc9LxCzGJOBzI6odU5qf6zqEa744FSt/8aekh6nmhIdGwFy267wuU5e4xPFy9YKONJe7lb/4E1Yv6DD1585U1fLE4/w4MIAkhV7/fSQz5XhEngtgCYAvpqQc/ImI/JeI7AbwBQDXlaFthDiClnofPtYyDusunp4xhVOuqfD0ggv6+RJpbw7Ej0s8j1k6QD3g0qx9iYVSegdD8fSBxUhNlatVOVO6wXM7JuK2p/di9YIO/H7VPGxbORcTx/vjyo7X7TL8rlGDokRm3232e82uESkMo2t+3xVn47iAFw8vm411Xz8T925/J0kW/p9fvYH13/5M0jHrv/0ZTG8/DttWzsWEBh98MQv0w8tm44GrZiUp1yvmTTGMJciUhjD1/kciGt7tG8aeA4fR3R/EngOH8W7fMDRNGcq8S4zzQxsVzCKZSbwX3f0j+OG23YbpT82OMeu/ZjEmEU3FM5/ce/nZ+Lt5U+LKt77figd3oj/20KhjNp5MqPfHVwy33/AFbFs5N25ASZSdpoAXrQ1+bFzSiYeXzcbGJZ1obfCjKeCN+3MbjVVmc81IWDP9fSQz5ciC8r9h7Dvzq1K3hRAnoltf/B4XprQ2YMuy2YgooM7rSsuCkosimroMue7i6UlWmbu+/RkMhSKYMakJu/YPxM+Tydqcr4tIodk+jKxA53e0QURwoH8k7Zwul8DlEsNr1dbox679A1jzZFfcgj42CWeO8s9WlEjH7PcC1rMWkOykWi0/MaE+nkazzutG31AIP3jkmPX69q+fGQ+wBIBnunrw91+cmuQqcNPjr+On3zgLHp8LDT4vGuu8+Gh4TCHyuFxJWU/M0rGZpSE06iP3XXE2+ofDSZkw1l08HU3jvIYyr5m4HViVTTKGWTEaXT6sBDOa9d/RiHFV4dHI2ENSKKJh9eN7cPvXzzTcLxiOoHcQcdnJNn6mtjHR4NLdH4SmlKFFXlMKu/YP4Lan92LNRWdgSms9Aj5P1kJBkaiGcz7egqs//3G4XYKopnD379+O/z5iDnspIQ4i1dr8tbtexOGjEbQ3BdDWWJemmOVSoCFRkd61fwA/+c1e3Pb1M/H8D+ZhzUVn4P9+/HWsenQ3fvClaZgxqSl+nkxKvllRiWyW+ULLJ6dagc7vaMO1556GSza+ZGq1csXSbaVeqwa/J806mXhdjaxCVooSJWL0ewsp5EOSSe03P9y2G3t7hnDJxpfw+XUv4O0Ph3H1A8muGt9/5DV8//zT4udobw7gw6Ewlj+wE5duehnLH9iJ3qEQ3vhg0LAUvd/jSlqh0l3GEmlvHktDaCTnRvd//0fBuIuLvi1TURO/R/IqmEWSyVSMRn+fatSw2n8zjdGJ59Af9FP30+UvcTyzMn4m9olrHtoVl9WoiUV+zDsY6B0KobXRj5OOC1gqFFTndWPxnI/hivv+iC/e/jtccd8fsXjOx1DvpwtKNliKnhAHYTaoP3HNXEQ1pFk8cinekqpI79o/gMPB0bhVUOeGrbux5qIzcMJxdfGlx1x9mO0OPHS5BFNbG7Bl+RyMRjV4XIJ//I/XM/qEuwVpvt5rF43l+04tOJF4XfUc0w9e9VkoKEQ1YEKDFx6PqyBfUSvXqJi+qNVMar9Z1DkpyTd6QoPP8FqfeFzyUrs/obS7Lh+3Pb3XUJ40TWEoFIkHRbpEcNfiTvxdQkDk7V8/E26T22V0/1MDr/Wg4UhMDoxSfJ44XmUsmEUZyk62oF2j8c7omNYGP8KRKA70j8RzdWuaho1LOrH8gZ1pY/TBw8H4OYzS+d3+9TNx66//bCh/2UjsE939QWz70wHcd8UseN3GK4EnHVeH36+aZ1p0zWyuiWoqzXVm5S/+hC3L56CJ6ekzQgWcEAdhNqgfHDiK5QkT+8YlnZhQ74PL5cqad1vHaAm7pd5YMUmsAmk28DYHvElVARO/1yxQSGTMPaVQBUDTFN7qHTJdMtZ/R6Iy63K5cP+L7yS5GNz/4ju4ZeH0+ING4m+ZNrERDy+bHbdOXffwq/Fzb7/hCxhfV1gqx2zBmcVIFVkrpPabVHcQt0mxEb/XleQeAmCsEmY4gjc+GMRtT+9NkieBwvsDwXjKv4DvmJISVQrH13tw3xWz4JIx95CoNlY0RSe1AMr5HW1JaQoTA69nTGrCD740LS04OPX+u1xiGnBJGbJGpqBdM6NG6jF6YO+lm16OX+v13/4MBMBwOIpHls+GppA0ViaeI9H9Y9LxAez/KAi/95jsJI5nuRYymzGpCRfNOBmX3/sKVi/oMPytAZ8no3Jv5vqyP8FdL7GtjEPIDl1QCHEQRst81547Na58A2OD2/IHduLV7sPxpfGWel9Wdw6jJey2Rr/hsmLA607yn04N7pna2oC3eoeSAjPf+OAIDh0OoncwZBiguXbRdNz8xB7sPTSISEQrKPgw25Kx/p2JVquWeh+uO28a1jzZhUs3vYw1T3bhuvOmoTngNQwy1e/H9x95Dcsf2JmWyaJQF5Jswah0UbFOar9JXc7/4MjRtIDmdRdPxzifO6nf6Ev7AZ8Ha57sSiorv/yvJuPgkRAu2fgS/nrdC7hk40voHQzj1/91EJduehlKKbzffxSX3/sKvnj773D5va/gSDAChTHZTnWTuWTjS7j23NNwfkdbvE0faxmHu5eMycSKeVPSgoZzvf+UIWsY9cWNSzpxViwA1+iBJfWYa8+daphd5P3DR/GDR17Du30jCPjcSWN06jl6h0LweVxY9chuXHHfH+P1B/Q2JWYjSR2vNE0lBYVKgstdoiwZudTdtbgTTXXZ7bFGri9et8vY9YpxCFmhBZwQB2FkbT51Qr3p8mjq0mQmy4iRBcNq6fjUdIC9g6G0iX35Azvj1dz0iphbls/B+wNB9A2H49bEroODeOg7n8W3/u0PeVvlzJaM9XYb/Q4zC06mINPU+3F+Rxt+dEFH3BLV2uBPakcuVqrU9gR8bkQ0FS8vrWnGwVvMr5tO6n3aunN/UnW/e7e/g+vnn56UQ3vi+Do0BdKrlur37JHls/HeR0EIxizTUyc24Bsx6yZwTMG69/KzsWVnN05qCuDbMZnWP79uy2vYsmw2UA98OJzeZ/SUgTd9VaVZ4UfCkYLvf6XmoC81+QSGp7rBAcYZafRxetWju/HwstnQAsnjgr6Cabbq0hTwWspG8tjKczB0NIL3+kbiLlF6H0hcEdIt7asXdGBqWwPe6hnCvzz3Jm6+8Iy8Ule2NfjTKmkyDsEaVMAJcRBGE4GCMl0eBY5FyvcMqnjuajPF1iivdj4ZSTL5TCYqsEopXLzhpbT9egZDGf21s2G2ZHxSU8Aw44SO0e/PFmSqXx9N0/DhcDjpwcEsz7PVpf9M5aU3LulMc1HI5ntfq5g9XKa+Py7gM5Vzo2IliZkifvGdzxrKic/jwsPLZsffp36up2M7OmqSRUJTOOX4Y86y+mqQx2XsNuPNocCON8GnPd9z1ApWimolkuoGd+/lZ2cdp6PKfFzoA5JK0evHtzcHkmJUzMarYHjsoeqXr7yHZ7p60N4cwL9+awYevGoWVEq2HD3rU2Il2B9dkJvLSOLDantzXcY4BGIMrxAhDiN1mW9Cvd/QnWPDC/vi79/4YBBfW/8iDh05itaY5cHqcnM+GUnMIuITJxtd0THaL7VNuVrlzNw3Thhfl3NmlWyZZPTr43K54oFUepvN8jznuvRvtP/yB3biRxd0mLqokGRS5djjcWV8nyofiffAKKf3aFQZysnbvcO4dNPLeLt32PBzd+x73CZZeBKDNBPdC37627cM85L7zKI6DfC4xND1xlNk/+9aymev/9aDh4NJffZnz72Vdq1Tx2mvy2U6LpiNaSemZCPRH6oSaW8OYP9HI1h6zyu47JxTMWNSE7r7g/juQ7vgdrnwvS2vpbmdpLYtF5eRVDeYBf+yHYeDo2hvHoeTmgJUvi1CCzghFYDf48Ivr56Nj4bDaKzz4NZfv4Fd+wdiVo7P4OYnXo8rhLd+7dNwuwQnjK9DVCmMRqJFCXxMxMhVRs8YARxTYI3227ikEz/97ZtJ58vVsluMXOKZfouRomtmeZrS1pBmdc916d9sf7dLivIbSXZSi5Wk3o9Nv9uHe684G90fBeNuLMfXe3HzE10AjilgRhUPgbGKiJk+B5IfAs7tmIg7//OtpKDhO//zLdz01U9Zzi4RDEfxk9/sTTrHT36zF3d+awZQX4SLhtoK9Ez8ral5u/XUrvpqSFRT+PFTXfFx+u6lM00LJ6Wuthm5pOl9X3+oSpUj/Vw3bN0dt2x39wfT8nt/oq0BmlK4JaFtVl1GEq3euVQiJsZQASfE4eglqDdfOQsX/ev2eGqyqz73cQwER+ESJPkMfqxlHLr7g0lltjNNiPmkKUudLIwmG/08Rq4B1503DV0HBzMqvNnIdcnY6m8xuwZmbi8BrzutHbmWn8+0Pye00pB4D4yKLw0EwxiNFU3R5fb2r58Z/1xXwB64ahZ6joTS/MybAj5MHF+X0Q899SHgma6eJBckAFi94FM5/abeoVDczQCw9rCby5iQKYbC6bKb69hnlLc7UUbGgijdcbeyWxZOx01fTY43yTQuZHJJ08dws4eqG798OoD09ImaUvG2nXBcHU5uCkDTFG6+8Az86ALrLiOZHj7072VsQW5QASfE4eiTclQbWwLftX8gPqG2NwewekFHfN/25gBCkfRCC2YTYiHWq0QF2GiyKbbfuZ1YUeZzybmey7757E+KT+I92PDCvjQr448u6Ij7/wPHivkk+tH2DoXgjynyRv1gcks9Guu8pnKf7SEg0aUl199kVa5yHRMqNdAzn7Ev8bca5e1OvLZGY4rV+5HpocbsoUp3/9P/1y3bzeO8aSt0mVJXmpHt4YPxKblDBZwQh6NPynf//m3c9e3P4O9iRQ/09FH/8tyYO0d7cwAbF3di8Oio5QmxWNarXK3RxbJel5Jc3F5ydZEppksNyY/Ue1Dvd8czXHjdLmhKGfar1Mw7J4xPr1ib+B2Z5D7bQ8DaRdNNC/tY+U25Wnn135hpTMh1tccp5DP2+TxunN/RhkWdk9AU8GI0quGOS87CxOPqEPBay5xi5X5keqg58bhAmhKvB4Prc8Dx9V5sWT4nbtkuRkGcXB4+iDWogJOMTL7xqZyPeffWC2xoSe2iT8p3PLsXXo8kFfqo97vw44Wfxk1f1eDzuOF2Aa/tP2x5QqxU61W5yOXBoRYeSqqNTC4AD33ns3ll3sn1+3UFLTgaxaHDR3Hr1z4Nr9uVVDgqn99klVzHhEpdvcln7GsOeHHtuaelpdw7aXyd5cBDK/cj00NNqhKvV9y881szbH1wNysaNKWtwdLDB0nHcQq4iMwH8FMAbgD/ppS6tcxNIqSkGPklTpvYiJsvPAOXbHwpbVDetnIuTm4eFz/2Yy3j0ixnZhNipVqvCLETI+voj5/qMiwpnsninQ+JDwHDoQiu2/xqSRXbXMeESl29yfQ7zXzD+4OjceUbOJbLvdj+7tkeagyV+CIF1Vptk+5TrldMJrnjKAVcRNwA/hXAeQC6AfxRRJ5QSnWVt2WElIZMfonKZAk8udz6mJ9p0zgvHl42G1EF1HldmFBvnJavUq1XhNiJkXX0ma4erLnojJIpmuVSbPMZEypx9cbsdw03SQAAIABJREFUd+qVcY3G4FKtGDrxocaJbap0HKWAA5gF4C9KqbcBQET+HcBFAKiAk5ogW/CNFcuUyyU4vt5vySLCQZWQdPQy3ql9LaqAiSVUNMuh2NbKmJBPZdxSrhg68aHGiW2qZJyWLf1kAPsT3nfHthFSE2SysJgVaijUWp1PIR5Cqhm3wLBwSS4BkJVMrYwJRr+zHGMwqU2cZgE36uVJJbVEZBmAZQBwyimnlKJNJEfyCdwEqjt406rc5hJ8U62WKeIManmsdblcuP/Fd5JyLecTAElKT6FyyzGYlAqnWcC7AUxKeN8O4P3EHZRSm5RSM5VSM1tbW0vaOELyxarcZrOw1IplipSfWh5rW+p9uO68aVjzZBcu3fQy1jzZhevOm0ZLZwVQqNxyDCalwmkW8D8CmCoipwI4AOAbAL5V3iYRUjpoYSGk/LAf1i6896RUOEoBV0pFROQaAE9jLA3hPUqp18vcLEJKCgNdCCk/7Ie1C+89KQWOUsABQCn1KwC/Knc7CCGEEEIIsQPHKeCkdsk3eDNXqjnYkxBCCCHOx2lBmIQQQgghhFQ1VMAJIYQQQggpIVTACSGEEEIIKSFUwAkhhBBCCCkhopTKvpdDEZFeAO9l2W0CgA9L0Jx8cXL7nNw2wP72faiUml/sk2aR21q/5oXi9PYB9raxHDJrJ066n05qC1Bd7akUuXXCNWcbyv/9AFCnlDqjkBNUtAJuBRHZoZSaWe52mOHk9jm5bYDz25cPTv9NbF/hVEIbnYKTrpWT2gKwPeXACb+RbSj/9xerDXRBIYQQQgghpIRQASeEEEIIIaSE1IICvqncDciCk9vn5LYBzm9fPjj9N7F9hVMJbXQKTrpWTmoLwPaUAyf8Rrah/N8PFKENVe8DTgghhBBCiJOoBQs4IYQQQgghjoEKOCGEEEIIISWECjghhBBCCCElxDYFXETqROQVEXlNRF4XkX+Mbb9PRN4RkVdjr7Ni20VEfiYifxGR3SLyGbvaRgghhBBCSLmw0wIeAvBFpdSZAM4CMF9EZsc+W6WUOiv2ejW27csApsZeywDcle0L5s+frwDwxZddL1ug3PJl48sWKLN82fyyBcotXza+CsY2BVyNMRR76429MjX6IgCbY8e9DKBJRE7M9B0ffljuSqSE5A7lllQalFlSiVBuiZOx1QdcRNwi8iqAHgDPKqX+EPvolpibyR0i4o9tOxnA/oTDu2PbCCGEEEIIqRpsVcCVUlGl1FkA2gHMEpEzAPwDgNMBnA3geAA3xHYXo1OkbhCRZSKyQ0R29Pb22tRyQooL5ZZUGpRZUolQbkmlUJIsKEqpAQAvAJivlDoYczMJAbgXwKzYbt0AJiUc1g7gfYNzbVJKzVRKzWxtbbW55YQUB8otqTQos6QSodySSsHOLCitItIU+z8A4G8A/Fn36xYRAfC3APbEDnkCwNJYNpTZAA4rpQ7a1T5CCCGEEELKgcfGc58I4H4RcWNM0d+ilHpSRP5TRFox5nLyKoAVsf1/BeArAP4CYATAFTa2jRBCCCGEkLJgmwKulNoNYIbB9i+a7K8AfNeu9mRC0xT6hsMIR6LwedxoqffB5TJySSeEVCuTb3wq52PevfUCG1pCKhXOJUSHskCyYacFvCLQNIW9hwZx9eYd6O4Por05gLuXzsS0iY3sLIQQQizBuYToUBaIFWq+FH3fcDjeSQCguz+IqzfvQN9wuMwtI4QQUilwLiE6lAVihZpXwMORaLyT6HT3BxGORMvUIkIIIZUG5xKiQ1kgVqh5BdzncaO9OZC0rb05AJ/HXaYWEUIIqTQ4lxAdygKxQs37gLfU+3D30plpvlot9b6Mx2UKsGDwBSGE1BYt9T788urPIhRRcAmgKcDvkaxzCak+7NArqp1a/O01rYDrN3x8nQdbls+BWwCXy5X1xmcKsADA4AtCCKkxNE3hcDCCFQ/ujI/9GxZ34sTxqqxjfy0qNuXG5RJMm9iIbSvnWr7uxQjcrNR7XatBqzXrgqLf8IXrt2Pu2udxycaX8NHIqCWBzRRgweALQgipPXqGQnHlGxgb+1c8uBM9Q6GytSl1nlu4fjv2HhqEpqmytalWcLkErY1+nNw8Dq2N/oL0CitU8r2uVb2pZhXwQm54pgALBl8QQkjtMRrVDMf+SFQrU4tqV7GpRArVHSr5Xteq3lSzLiiZbni2ZRw9wCLx+MQAi/bmAFob/FgxbwqaAl6MhKMI+Bh8QQgh1UTiXOFxCc7vaMMzXT3xz9ubA/C4y2fnqlXFplQU0+Ujm16RDafc63yuSaG/vVKpWQXc7IZ7Pa6svkjZAiw2XzkLh44cxapHdyd93hSoDH8sQgghmTHyW71rcScA4Jmunvj71jIGYdaqYlMKiu23nG/gpo4T7nW+16TQ316pyFgF+Mpk5syZaseOHXkdayYoE8f7ceGd29OEeNvKuWht9Ccdb/aU1zN4FF9b/2LWcxDHY8vTUiFyS+yjSkrRU2ZLRO9gCAvXp88VD109G5Gohqim8OiO/8Z3Pv+Jso37FRTcVnFya3b/C5nnC7GoO+FeF3JNKjCAtODG1awF3CxK+eDhoKVlHD3AAkgXnNGIsS8gl/0IIaQ6MFvyPzgQxKWbXgYAzJjUhKXnRHGgf6QsSkU+2TiINfJ1+cikaCbqFbnihHtdiBtMIb+9UqlZBRwwvuG5LuMYPXU+9J3Pln0piBBCiH2YzRUDwVEAY8r39fOn4dJNL5fV+lyLik0pyMflw24rdbnvtRPcYCoJ26JDRKRORF4RkddE5HUR+cfY9lNF5A8i8paIPCwivth2f+z9X2KfT7arbZnQfZH0KlbZfJGMIo9//FQXNi7ptHwOQgghlYXRXLFhcSe27twPALj23KnxOCCgsrJSkOzkqisAlZ2pxAr5XJNaxk4LeAjAF5VSQyLiBfC/ReTXAL4H4A6l1L+LyAYAVwG4K/a3Xyn1CRH5BoC1AC61sX2GGC3jNAe8pktGRksuz3T1YM1FZ3DZjxBCHEqhPqdmc8UtC6fjpq9GEVWKrohVTC4uH7qsjYQjVS0TTnCDqSRsU8DVWHTnUOytN/ZSAL4I4Fux7fcDuBljCvhFsf8B4FEAd4qIqDJEiab6d2daMjJbcnG5XFz2I4QQB1IsVwCjJX/9fe9giMvxVY4Vl49EWVu9oKPqZaLcbjCVhK0JSkXELSKvAugB8CyAfQAGlFKR2C7dAE6O/X8ygP0AEPv8MIAWO9tnhWxLRk5dctE0hd7BEA70j6B3MFQR1bAIIaQU9A2Hcceze7F6QQceXjYbqxd04I5n9xbVFcCpcwMpLYk6xIYX9mHtoumUCVBHAWwOwlRKRQGcJSJNALYB+KTRbrG/RmaHtDsiIssALAOAU045pUgtNSdbVK8Tl1yckI6IJFNquSWkUKpZZjVNw2XnnIobth6r1bB20XRoWvGqVjpxbqgFnCa3iTrErv0DuO3psQe/T57QiIDPU5MyQR1ljJKU6FJKDQB4AcBsAE0ioiv+7QDej/3fDWASAMQ+Pw7ARwbn2qSUmqmUmtna2lpQu6w8gekuJomkLhnpSy4nN49Da6O/JAKUqe3VHuhRiRRTbgkpBdUss1GFuPINjI2RN2zdDU2hqFa5fOYGWgYLw2lya6RD+NwumzKfVwb56ijV1jdss4CLSCuAUaXUgIgEAPwNxgIrnwdwMYB/B3AZgMdjhzwRe/9S7PP/tNP/2+oTmBMrNGVru1NK0hJCiBNRJgGSoYiGr298qWxWOVoGq49EHaK1wY/r509Lq5Jda/c3Hx2lGvuGnRbwEwE8LyK7AfwRwLNKqScB3ADgeyLyF4z5eP88tv/PAbTEtn8PwI02ts3yE1jiMuL2G76AbSvnluyGmz3tZWu7Fas9IYRUMoVYw8zGyHc+HC7ryiFXL6uPRB3izm/NqLnUlEb9NB8dpRr7hp1ZUHYDmGGw/W0Aswy2HwXwdbvak0ouT2C5RPUWq5xqpqe9bG13otWeEEKKRaHWMKMxcuPiTvzof+1J2q/UK4fVsnpZgWXFbUXXIQ70jxT9/jr5Wpv106mtDTnrKNXSNxKp2UqYdlRsKuYSidnT3raVc7O2ncE/hJBqJtP4aMVYYjRGul1A71Aoab9SrxxWQyXBanQVKBbFvr9Ov9aZ+mmuOko19I1UShKE6UTsSBFVzCWSTE97VtpejsBQQggpBVasYdlcVFLHyKZA+dMGVkPqwmp0FSgWxb6/Tg9mzNRPc9VRqqFvpFKzFnA7rMTFXCLJ9LRn1nZgLIKfVm9CSCWTbVk9mzUsH8ugE1YOndCGQqlGV4FiUez76/Rgxnyt1mb9v9L7Rio1q4ADxywg+s0+eDiY903VnyAfXTEHfcNhbHhhH3btH0B7cwAi5qVpzQQpmx93ql+605eiCCHEClbGsmzjY2KhnaaAFwPBUdzx7F7csnB6RheVbPE+qeN2c8CL/uBoURWCSq8kWI2uAsUk0/3N1Z87n2tdqPtWLu3O1E/NjsnW/yu5b6RS0wo4kJ/iajQIv9U7lHSOtYum4/4X38Fl55wKt+T+nbk+7RWjUzk5mIMQUhuYjWVPXDMXUQ3x8Wlqa4Pp+GhHoR2jcXvD4k787Lk38UxXD9qbA9h85Sw01HkwGtFqdgxlEoD8yEcXyedaF3uFQtMU3u0bxnt9Ixjnc2MkHMXHWsZhckt9xtV6s9+ary5TifpLzSvgud5so06ycUknfvrbN9OKOtx7+dlY9/SfccvC6Xl9Zy5Pe4V2qnJa0Cux4xBCCsOs3xuNZa0NfhwcOIrlD+60ND6ZFdrZsnxO3u01GrdXPLgTqxd04JmuHrQ2+HHoyFEsvae2czxXo6tAKchH8cznWhd7hWIgGMahI0ex+vE9cblfd/F0NI3z4vh643Zn+q1Od6spJjUbhKmT6802EpzlD+zEos5Jaec4HBzFdedNS3satcNHrtDc3+UKnNE7zsL12zF37fNYuH479h4arPgKV4QQczL1e6Ox7Npzp8aVb+DY+PTBkaOGgWRmhXYKqe1mNm43BbwAgBXzptRcjmczmAQgd/LVC8odzBgMR9PkftWjuxEMR037eabfWks5wmteAc/1ZpsJTqrwtjcHcFJTwPAJzI5COYV2qnIFzlRqxyGE5E+mfm80lp06od5wfHp/IGj44G7HGGt2zoHgKACgKeBl8CHJm1IV0Ct2ccGoycNuVJn3cxEx/a356DKVGvhbswq4noYnHInioe98Fud3tAHIfrPNOklboz9NYE4YX2co1Hak0ym0U5l1CKMA0mJSqR2HEJI/2dKTpY5l4/zG467+oJ764G7HGGt0zg2LO7F1534AwEg4WnQFqlTp4kj5KYbMWpWXYq5Q1HmN+2ad12Xaz90C09+ajy5TLv2lUGrSB9zMj3vNRWfA5XJl9KEyC3o46biAJT8s3e/x+HFebFk+B0opR0TPuwVYu2h6WtBSagBpsWHEPCG1h5ViYqlZnlLH3bWLpuO2p/fG92lt8CMcieJA/0jWIM18MPK3bQ54ccvC6bjpq1EEfO6iBh9Wql8ryY9CfedLLS+6LqM0hY1LOrH8geT4jAn1fvQNhw37ucvlyvhbc9VlyqW/FIoU4hNXbmbOnKl27NiR83G9gyEsXL89TShSgx0iEQ09QyFEohrcLoHHJXC5XHmnnnLygNo7GMIPt+3Gos5J8bRdW3fuz5q2q1CcfE0A2NKAfOWW2MvkG5/K+Zh3b73AhpYUREXIbKHZp0QENz+xB8909QAAZkxqwvXzp8V9UYsxjuQTHF7MgPJc56nRqAav24W2Bj88nopb3K4IuXUyuczhhabTTO2/53e04UcXdMDtkpxSCpbjtxeRgn9ATVrAMy1/6m4p9X43uvuPYkVC1L2eWvC686YVvbx8uXNbttT7cN1500qeOooR84TUHvn0+0SrmKYpXHfeNHQdHER3fxDXnjvVMAAy37E1X8WhmHmKrbjnRSIa/nxoMGme2rC4E6dPbKxEJZwkkOvDnNXUm1bSaWaT9VRd5pmuHnQdHDTM5FaK+b1c+kuh1KQCbrb8GdVU3OLw2+/9dXxQA46lsVq9oCPvgd2qv3Mpij2kYkdHsTqAVFtyfUJIZgq1FKeOV0aBYKkuKblasI2MJY+tPAdtjXW5/+A8sOKe1zMUSpunVjy4E1uWz8FJTYG0c5LKIJ8HQKupN7Ol07Ty8BqORNHa4E8qdLXhhX0IR6KGfbuQ+d3KWFGphjzbFHARmQRgM4ATAGgANimlfioiNwO4GkBvbNf/qZT6VeyYfwBwFYAogGuVUk/b0TYjP+6NSzrx46e64kLp/j/svXuYFOWZ9/99+jT0zKAzjjOoMIphETPLjiGDKPJuQpaVdVc3LAvqJguKJgIhLtls1sP7ZrniXsTfTySu17p5ETARRddEI/rT1RzI+oY3u3hkJCFkDDEkGkBlxmFG59BMH+r5/dFdTR+ququq69Td3891zQVdXVXP01X3c9ddz3MfAtBNOWUlSFBRZDZQoJRCtePt1Cp2GsI+dy0hhHiEXbohV18NjEzk6VbVJeXa7S+bcnFR+3XP1Rdp6v+TieLZRKdqGOjFGwUDyL5UJFKKZj+TKesFh4j3WFktN5p6s1w6TfWzlo2jyrsiJe655iLc9YM3snbJ5uXdaGoI2vrcN6MrqnEiz8k1qiSAr0gpPwrgUgBfFEJ0Zb67V0r5scyfanx3AfgbAH8I4AoAW4QQjkTiaUXZntkUyfMnFNCOqh2OJUwHCapCdMezB7FpWXfJKGe9t1M1z3i1pOljekFCiBZO6IbCDBJ6Liml2sjtl6rnc5nWGs0L6nK6hkHhc+qpdZehIRTAp795qr1gQPs5FQrS/aSasZIdzGgaw3LpNPWOy5X3P757D6578FVcf9n5mNPZks39PR5XbB3btW5HODZKpZTvSilfz/x/BMAbAKaWOGQJgO9KKSeklL8D8BsA85zqX3EankBWKNcunIH/5/t9RcbypmXd2NV7xLRvkSpEu/v68Y0fHcJdf/1HeOEfPonvrr4UU07Lf2Or5O3UTzC9ICFECyd0Q6GxOqNDO294qTZy+7V1z+Ei/b95eTeikVNGiRvGQe5zSkDgugdfzWvvkRd/h/tX9BSlRuxorq6ZQJKPlZzgRtMYlkunqXeclrzftusA1i6ckf2styKjjjuzaTVr3Y5wxQdcCDEdwBwArwBYAOBmIcR1APYhPUs+hLRx/nLOYUdR2mC3ldzlvpZoGLv7+jEwEseGq7rQMbkBzQ0hNDcEcefS7rwIXyPLj4VCJITA9Tte1VxS0fP7K/d26jeYXpAQooVTuqGUS4qRNsKhQPaY/UeG8Y0fHcLGJbNxblsjfj84jimnTUJL9JRR4rZxoNXetv96Czd9YgaeWDMfyZSCUPVmQSE56LkflZr4M+oHXS6dZjgUQCgg8O4HsbxzlJscnNYaRSig72ZrxfWs1u0Ix0epEKIZwC4Afy+l/BDA/QBmAPgYgHcB3KPuqnF40euREGK1EGKfEGLfwMCAxiHWyBXKaa1RTGuNYv+RYax5pBdLt7yIGx56DaFgMJu03szyY+7b7NqFM4oCJcoVkDDyduo3nCiEUc04JbeEOIVTMuuGbrDSRiggsHn5qVnvgdEJTAoH0BwJYvbU0zG9rSnPUFAN9lymtUYRdsj41ZsVlRA4pyWKc9uacE5LtO6N71rQtVYL6xktsFO4XygUQPvkBpx9ehSDo/E8NyfVrinlupLrA6437qysGNW6HeFoHnAhRBjAcwB+JKX8F43vpwN4Tko5OxOACSnl/5v57kcA7pBSvqR3fqdyfBp5UzOao7XwfPdcfRGu3f4yCtl726cwtbUxu7/bWVCcwMkAJZdgbto6gnnA9XEiD7jTusFsG8eGxnHzY/uxduGMvMwO3/zsnKxuzuXE2AQOvTeSl3t88/JuzDprMs5ost8FpMYD26tCbmudUnZNW1OkSP62/O3HIQC8PxrHeW2NmN7WBACa4+7Y0DgWbPpJUZu5to8WPrYj/JsHXKRrgH4bwBu5xrcQ4mwp5buZj0sBHMz8/1kAjwkh/gXAOQBmAnjVqf7lonWDc5doopEgkorMLsm0RsOIJ1O45+qLskp6/5Fh3eVH9W322ZsXIBZP4cm18zE4Fs8eV7ikohXN63QaHyfwW1SyjwcyITWN3anJgOICNO1NEQyfTFpuIxIKYmB0Amse6c1u08pSpf4OAHj69WN5qdju/uEhfPOzc4Am7TYq0UHVmmqtXvG6kJOVvo7Hk9i8vBspRSIcDOSlFyyUv3Am0DeWSKGtuQEdzadm3LXGnVV3EiftCK9tAid9wBcAWAngF0KIn2W2/S8AnxFCfAxp95K3AKwBACnlL4UQTwDoQzqDyhellI572peaVWif3GAoLaBaEnlgdEJz+VFRJIZjcbw7fBJrdAr7OLWkUuOzJobhdaheamRmum5xYuwVFqBZ3NWBv1t0Ab7waK/lNtqaIth54zy8PTiOxkgQ4/EUzmtrzOpmrd+xeXk37v7hIew/MgygtEFBHVQ/WK306oV85Lbb3tyAW6+Yhduf+kWejKvBx6oxrCgSbw2O5Y2Vk22pIjetXKz4tTuJH8ajk1lQ/ltKKaSU3bkpB6WUK6WUf5TZ/umc2XBIKe+UUs6QUs6SUv7Aqb7lUs4vyUhawNt2HcD6RTOxeXk3Rk8m8/zA1Zv88yMfZI3v3OPu+PRsR294rafxMQqvAyHe4MTYKyxAs6ynM2t8V9LGRFLBhmcO4trtL2PDMwcxkTyVT1vrd9zyZFr3A+X9Uyu9Dk6nPST2YeVee/WMym137cIZRek7b3nyAJIFMjYUm8DxD0/mjZXjH57EUGxCtx2rfu1O4QeboL6jNVA6kl1RJGKJpKG0gJ1nRHH3Dw/hugdfzbuB6k1ujAR1k+Q7KYC1nsbHKLwOhHiDE2OvMN2ZWiCtkjbKPZD1fseM9ib831sW4ok18zGzvVlXn1d6HfxgMBBjWLnXTj+j9FIA5rarN44SyfzCTrG4ommox+KlC0AZDRJ1Az/YBHVvgOtF9oZDARw6PoLD/WOGktYfHhjT9ANXb7JeYQen0+no/T4hhOFcnLWAlbyqhJDKcWLshYP5GUjs0K/lHsh6v+PwwBg+uXkPrtn2Et4cGNXVp5VeBz8YDMQYVu61k8+oUqsnue0aHUcpRbvqptO2hNk84qXwg01Q9wa4XpqbUEDgpp37cN8LbxYVZChMC7hpWTe27jmc/Zx7A9WbrFXYwQ3/J720hnc8e7CuljFrPZ0RIX7FibHX0dyArTkFaHb1HikqSGO2jXIP5NZoOK9N1T/2vhfeBFB+RrrS6+AHg4EYw8q9dvIZVWr1JLfdrXsO56Xi1OtDJKiTgtPBCqx2u2D5wSZwNA2h09iVYkgr7d/g2ATG4ymkpMTweAKJlIKASCeZnzJ5UjYtYEqR+PrzfdmAzEIn/sIAh/WLZuL8M5vQ2BDEmU3uLMHk/j4hBO549iB29/Vnv9dLn1juXGrUMJAe4IqiICUBKaUvI/QtRDwzNZYPcCsIs0aCPT2R2XJjy45sA4XnaJkUwsBYPFuARisLipk+lAvKGhiZwFefPoBlPZ1oiYbRMbkB//DEz7MBmAAwp7MlnQUFsHwd9PbxQ9CYg9ScrjUq87n7NTUEMR5Xspl97CqqVC4FYG4f1KxviaSSLcoTi+f/hsIgaHVi78Ipk0v2txI9YCb1s9F2K9RL/k1DWE3kprlRo3uPf3gyL7/rPVdfhG//929x59LubNJ6dX+1gpTWDfRD6qjc33dsaDzP+AaML2PqPQAaQgHc9YM3cP1l52eLDPnx4eC3tIiE1AJGDMNKx55R47M9bD0DSTldHU+msLuvP6s/t63swcDoqaCzOZ0tuPWKWbh2+8uWr0O5fnr9LCHGMSLzWhlIcu0Ou56h5VIAavW1lCyGQgFcOGWyqQqslb5AWnXBKteulzZB3bugFDI4Fsfbg+NFAQZf+d7P8dUruxAMIG/Jw0hQQSWBB3b6PAGVLWPqLWO9PTiOZT2dJSt8EkJqEzeCAyttw+jxpXR1JBTE4q4ObFvZg8dXX4rTJoVw7zUXZfXp+kUzi54bZq9DuX76KYiNVE65DCR2jSMz7haqzXF0eBzvfXAS7c0Nmv0JhQKmKrBWOoat2i5+Dl7mDHgB8WRKN2PJibE4/vZbr7g2s+vEkmMluTj13kAbI0E0QvuaMUCIkNrGjeDAStuwo4+t0TDWL7ogb9n9oRsuxlNfuAyJlIKU1A5MM9MGAy3rCyMZSOy490ZXT7RsDrXOSalig0aoVLat2i5+HlOcAS8gEkonldd60+ofmXD17cnIm5vZGfJKcnHqvYGOx1OeZXkhhHiLG8GBlbZROHu9bWUPFnd1mOrjUCyRl3v86FAMq3a8BiEEprY2IhTQDkxLF4U2BgMt6wsrGUisYmT1RMvmuG3XAaxdOKPi/ohMDJ3KnM4W7Fh1MZIZG8Yp28XPY4oGeAFtTRGc19ZYFAl8z9UXZTOdWHl7suJKUu7NrVxUsF6bVpcx9ZaxzmtrxK7eI55keSGEeIsb2QSstJGr/wIBifWLLsDG5/pw7faXsfG5PqxfdAFac+o5lDp+YGSirD4OChTpwE3LuhE0sVjph8wMxD2sZCBxEj0Zn9nRjB2rLsbOG+dZ7k/u+FDjJTY8cxCf3LzHUdvFz2OKLigFBAIC09ua0NIYxuOrL0VKkXjng5O46we/MlRuWAurriTlAif0ZsifXrcAbU0R291X9JaxAODOpd1QFAVPrJnv2ywohBD7cSM40GwbhTp3x6qLseGZg3m6cu2jvboZFLR09mOfv6RMIFsAD7/4O2y4qgst0TCGYwk8/OLvcOfSbsd+J6luCu93NBLEU+suQyKpeHLv9WyON/tHsfG5Pjxw3VzL584dHxd0NGPlg6+6Yrv4eUxxBlxx6UOhAAAgAElEQVSDQEDgjKb0W9a01kacHg1no92tvD1ZDQIo9+ZWakbGqcCDQECgrSmCSCiYbQcA2ic3YMrpUZzTEmWAECF1hhvBgWbaKNR/enE9eiuZWvrz68/3YdtK/VzjbU0RfPnyWXmz7F++fJbpmTYGWtYXuff7jKYGdEye5Nm917I51DonldoQueNDdefNxYjtYjUphV/HFGfAy2DH25PVIIBybZeaIXcq8KDGc9ESQmqAQv2XSCmaujKsk7lBS3/u7uvHxiWzdfWxn2faCDFCrgzH4km88d5INgATqMyGKBwfZm0XRVFqzvagAW6ASnNFlnMlsdp2blRwbpEfCYloxHqbpSjl9sIc26TeqJHiPTVHoc4NBgQ2L+/Oy7G8eXk3QjoPbjVoUy26MxxLYFfvEQQCgZJ6zuu8woRUiirDAyPAxuf6bLUh1HMritTNaDI4Fte0XVISjtsedhQMM4NjBrgQohPATgBnAVAAbJdS/qsQ4gwAjwOYDuAtANdIKYdEOlT8XwH8BYBxAKuklK871T8tnLr4laT+K4X6RvnszQvw7vBJrMlJj/XAdXOx88Z5uC7jZ2VXm35O6UMIIUCxzg0HA3jitSPYsepiBAMCKUXigZ/+Fl/605lAU/HxWikHt67oKRm0SUgtYcZuMWs7lVot0mtX2pDmsxRerO47OQOeBPAVKeXrQojJAHqFED8GsArAC1LKu4QQtwO4HcBtAP4cwMzM3yUA7s/86wpOXnwnlyYDAYGUgqzxDZx6M3xq3WW2t1nJbD4hhLhBoc4NBwNY+vGpuOGh1/JmwKMRbb2llXKwVNAmIbVGJbnDjdhOeqtFeu3qzYzbZXt4sbrvWBCmlPJddQZbSjkC4A0AUwEsAfBwZreHAfxV5v9LAOyUaV4G0CKEONup/hXidLUkJ4MAcmel53S2YNvKHtxz9UVIJBW0NUVsbdPPKX0IIbWBHRWAc3UuBIqqDN7y5AEkdc7LlT5CrOcOr9R20mq3nO1Rqc7wYsy74gMuhJgOYA6AVwBMkVK+C6SNdCFER2a3qQCO5Bx2NLPtXTf6WM0KV52Vbm9uwD/+2axsSXgnllAYaEQIcRInViMTSUVTvyeSiub+XOkjxBhu2U6lbA87dIYXY97xNIRCiGYAuwD8vZTyw1K7amwreoURQqwWQuwTQuwbGBiwpY+KIiGEwJNr52Pbyh7M6WwBcKqKmdYblZm3LTtmc0qhvhmuXzQza3wD9s/iq/g1pY+fcUJuCXESr2TW6oxaKT1rthqe1ZU+u3V94fmSScXRZ0ktUKu6tlLZKnV8Jee2WmnSSpt6tocds/BerO47OgMuhAgjbXz/u5Tyqczm40KIszOz32cD6M9sPwqgM+fwaQDeKTynlHI7gO0AMHfu3Iq1j9ab06Zl3Xj4xd9h/aILcMezB7G7rz/vjQqAobctRZEYjsVxYiyOIydiaIyky9yf19aI6W1Nts9KNzUYz3XrdrRvvWO33BLiNG7LrKqTxuNJbLiqC1v3HDac/kxvBmzKaQ2IxdMFTswEwpcqOqZWxSzUm3bP3Gudb+uKHtz3wq+LnknU3aeoNl1r5FlcqWyVOh7It2cWd3Xgn67sQjAgDNkGVpJM2D1WCmfh53S2YO3CGRiPJzEwAkP2jRer+0LK0vIphAgAWC6lfMLUidNZTR4GcEJK+fc52zcDGMwJwjxDSnmrEOJKADcjnQXlEgD3SSnnlWpj7ty5ct++fWa6VcTAyASWbtlbtOzw+OpL8c//8Uvs7uvP2/70ugUAoHlMrrO+KmCjJ5NIKkpR+qtZZ03GGU32Ovbr/ZanvnAZhBBZoWqNhvHmwGhN5dN0CEcuhh1yW09YSfXnZxxOQ1iVMqs3EaLmIC7Ur+oxquEihMA1214q0n0bl8zGDQ+9hmmtUey8cR6aJ4UsVxksZzTo6V+rQVx659twVRfWPNJb8fl9RlXKbaUYNUQrla1SxwOn7Jk5nS2W3FjNTujp9cdqJe3c8+n9BvVl3EbDuuITlHVBkVIqSBvGZlkAYCWAPxFC/Czz9xcA7gJwuRDiTQCXZz4DwPcB/BbAbwA8AGCdhTZNo+e/lFJknvGtbo8nU4Z8ntQlkTObI5rBP7G4/b7lWkso915zET48mcDSLXuxYNNPsHTLXrzzQczRgFNCCDGD1hLybbsOYO3CGZopAFXDRdVr7wzHNHVyYybLydGhGK578FUICMuuc+WWue32hdU7X0vOdaiWOCWijVHXiUplq9Txud+tXTjDkhurWbdUvf68MxzL2imHjo8YdoXJtX30fsPPj3xg6dxOYtQH/MdCiH8UQnQKIc5Q/0odIKX8bymlkFJ2Syk/lvn7vpRyUEq5SEo5M/Pvicz+Ukr5RSnlDCnlH0kpXXlt1fNfCgUDun5NRnyeVAFL6eSuTNl073P9qAbH4jj79AZsXDIbj6++FBuu6kJSkVi147U8YSxVBpYQQirFrH+n3gN5ZkczNlzVhfte+DWGYonsd4WGi5qiLJdprVEM5xxTqY4rZwRZ9YXVQ+98ub+JgaHVjVHDulLZKnV87nct0bArtoFef1RD3+ykYK77yIVnTS77Mu6XCUejBviNAL4I4KcAejN//l3XMYGe431Hc4OuQ74RZ31VwN774KSmoE0KVx7/WjgLtHTLXhwbOon7XngT125/GWse6UVAiCJh1HtYUZETQipFSy+Vm3HSeyC/2T+KNY/0Yndff54RUGi4bN1zGJuWdefp5M3Lu7F1z+G881Wi48oZQXYHcWmdb+uKHuzqPWLL+Yn3GDWsK5WtUsfnfjccS7hiG2j1Z9Oy/PFq1vBXZ+HVKuC5TGuNIpE6lfHILxOOZX3A/Yxd/l16/kul/JrK+TypD6F7f3wIn/sfH8FXvvdz2/2t9fyoVL9HANi2sqeonOzirg586U8vwJpHem3vU41Rl36JfoM+4KbwXGat+Kua9QHXamNxVwfu+PRsSCkRDgUwejJZVAm4Eh1nxF/X7uD2wvO1RsMYiiVqMXjec7n1AjPBiJXKlhF7RlEUvD8Wd8U2KIzhUBNeqFiNbzgxNoFD740Uxd0BwGceeKWicxdQ8QUxnAVFCDEbQBeASeo2KeXOSjvgB0pVZCrcXijEZ58e1RRMdUnkzqXdUBTFcnCBVpvq8XrLV+ef2ZTNZ7mr9wi2rujJK6n85ctnYWZ7M3N5E0Jsx4q/aiAgMLO9GU+smY9kSoEEcOfzfVnju3C2Tyvzwpcvn4WzTpuU1WNnRNN6N5FSEA4G0NFcWcpUI1kS9J4llbRZGHhKagczmTcqla1Sx+d+1z55kqH+WJm41GtTUSS+fPks9L07UpRJxeyLRyyewt0/PIQNV3WhJRrGcCyBu394CF+98qMA/LVyZMgAF0J8DcBCpA3w7yNdNv6/AdSEAW4Us6lz7FDGpdrUSxzf2BA8VYI5FMBEIoWNS2Zn0yA2hAK2PygIIQSwVtBCUWReZiY1FdrX/vIPdQ3dUoZL4fnsmsnzUm86UaCIeI/fnsVG+qMnizPbmy2Nu1JpP83KfCQUxMDoRDZTEJDWP+e0RLH3tk/5asLRkAuKEOIXAC4CsF9KeZEQYgqAb0kp/9LpDpbC7eUlu9NMVdpmW1OkrHB60ecawtNlUSuuFw67N3gCXVBM4flSvhVD0a0UftWs92rxN+XgudwS45RKI6iVDtTNcezii6prLigxKaUihEgKIU5DunjORyptvNrwolx9qTaNLF950WdCSP1ipaCFWyn8qlnv1eJvItWJniwmU4rn49iLgjpWMZqKY58QogXp/Ny9AF4H8KpjvfIpdqeZsqPNcvk3jfbZ7hLKhJD6xWxeYDt0a64OE0JgcVdHRefzG148fwjRwkr6ZjvbUc+nZ7eY1T9eYcgAl1Kuk1IOSym3Il0853op5Q3Ods1/2J1myo02jRxvJW0YIYTYRaV6rlCHXbPtJaxfdEHWCPdT4JVVvHj+EKKFlfTNdrajBmdWu91S0gdcCPHxUgdLKV+3vUcm8MK/y+40U260We74GvctrAT6gPsA+oCbomp9aSvRc3aXtvYrXjx/XKJq5bZeqTQLSqXt+MBucdwH/J4S30kAf1JpB6oNLyKWnUw/BNC3kBDiPZXoOT0dJqXE1NZGO7rnC/yWMYPUL2bSNzvRTi3YLSUNcCnlp9zqCCmNkzMfVtKGEUKIX3BCh9XwbDMhlvDTmKgFu8WQD7gQIiyEWC+EeDLzd7MQIux050gap32d6FtICKlm7NZhteBfSoid+G1M1ILdYjQN4f0AwgC2ZD6vzGz7vBOdIvkMjsWzOS2B9DLLTTv32ebrVE1pewghpBC7dZjTOpeQasNvY6IW7BajBvjFUsqLcj7/HyHEz0sdIIR4EMBVAPqllLMz2+4AcBOAgcxu/0tK+f3Md/8TwOcApACsl1L+yPCvqHHc8HWibyEhpJqxU4fVgn8pIXbixzFR7XaLUQM8JYSYIaU8DABCiI8gbSiX4iEA30Rxufp7pZTfyN0ghOgC8DcA/hDAOQD+UwhxgZTS8TtrxKfJa7+nWvB1IoQQLzGjx53SuV4/S0h9Yae8lRoTlGtrGDXAbwHwEyHEbzOfpwMomQdcSvlTIcR0g+dfAuC7UsoJAL8TQvwGwDwALxk83hJGSpa6WNZUF9XXqbAP1eTrRAghXmFWjzuhc/3wLCH1g93ypjcmWqNhyrVFSgZhCiEuFkKcJaV8AcBMAE8B+BDAbgAlXVBKcLMQ4oAQ4kEhRGtm21QAR3L2OZrZptWn1UKIfUKIfQMDA1q7GEbPp2lwLG5qH6fJ9XXae9un8PS6BRTuKsNOuSXEDWpJZs3qcSd0rh+eJfVALcltJdgtb3pjYiiWoFxbpFwWlG0A1Kt4CYDbATwM4DiA7Rbaux/ADAAfA/AuTuUZ19JqmqG1UsrtUsq5Usq57e3tFrpwCiM+TX7xe6qW0qpEGzvllhA3qCWZtaLH7da5fnmW1Dq1JLeV4IS8aY0JyrV1yhngQSnlicz/rwWwXUq5S0q5AcAfmG1MSnlcSpmSUioAHkDazQRIz3h35uw6DcA7Zs9vFtWnKZdCPz8j+6goisTAyASODY1jYGSCKasIIcQHmNHjpahEx9vVB0KM4LS8qWMhJSV2rLoYczpbHGmnlilrgAshVD/xRQD+T853Rv3Hswghzs75uBTAwcz/nwXwN0KIBiHE+Ui7u7xq9vxmUX2aFnd1YNvKHjy5dj4e+/wlaI2Gi/Ypl2vSbzkyCSGEpLEjZ3ClOr4W8haT6qGUvFU6WZg7Fj5x9x5seOYgbr1iFuZ0tlCuTVDOiP4OgP8rhHgfQAzAfwGAEOIPAHxQ6kAhxHcALARwphDiKICvAVgohPgY0u4lbwFYAwBSyl8KIZ4A0AcgCeCLbmRACQQEZrY340t/egHWPNKrGUBgNNek33JkEkIISWNHzuD3xyY0dfxT6y5Dx+RJrvSBEKPoyRuAioMmteydW548gMdXX0q5NkG5UvR3CiFeAHA2gN1SSvU1KQDg78oc+xmNzd8u1RaAO0t3136GYoms8Q1oG85Gck3SD4oQQvxLpTmDTya0dfzJhOJaHwgxg5a8DYxov0iamSzUs3cAUL5NUNaNREr5ssa2XzvTHffREqT25gbEkykcGxo3/DbHXN2EEOJfKs1VHBRCU8cHOdFHqoh4MoX25gZsuKoLLdEwhmMJbN1z2NRkIe0deyjnA17zFAYqzOlswa1XzMK121/W9fPT8p+ifx8hhPgTK/7bhXp+UiSAzcu783T85uXdiEZodJDqIRoJ4tYrZmHjc324dvvL2PhcH269YpYpOdayd7at7IGiKExAYQLTgZS1RmFy+fWLZuKWJw/oLs+USm5P/z5CCPEfZmN0tPT8zhvnYcppk7BxyWw0RoIYj6cw5bRJaIlykoVUD0lFFtk4tzx5AE+tu8zwOQr9y1OKxNef78Puvn4W4jFB3c+AFyaXn9HRVNKXu1Rye+bqJoQQ/2E2RkdLz1/34KtoaQxj9tTTMa01itlTT8f0tibqeVJVJJKK5lhIJI3HMgCn/MsjoSA++61XsLuvP3suFuIxRt3PgBf6BUrIkr5NDLYktcb02583fcxbd13pQE8IcQazPqt6ej4WT2Fqa6NuO5X6mRNSCUbkz27/bdpE1qnrGXAtv8DRk8mSvtylktuzEA8hhPgPszE6VoqYsBYE8QLV7jj+QQxvvPdhWfmzO16NBaasU9cGuN4y45TTGrIuKU+vW5Dny6QnvK3RMJUvIYT4lIZQABuXzMbjqy/FxiWz0RDSf/xZMVJKuScS4gS5L30/O/qBZkrlQvkrdLsttHHMwgQU1qlrFxSjy4zqG6a6rDOzvbko2JKFeAghxD/kLscLIXDXD97I+qkCaUNBTz9bKZrDpXjiNrl2R0s07In8scCUderaAFeXTtqbG7B24Qy0RMMYj6fy0vGUynqSK2BUvoQQ4g+09PamZd0YGIlj/5FhAOX1s9miOZFQEIu7OrCspzObX3lX7xEuxRPbUV8ux+NJbLiqC1v3HMZwLGHIt9uoTWMGFpiyRl27oLQ1RbDzxnl5OTE3PHMQxz885b9tdFmRflCEEOIPtPT2bbsOYO3CGdl97NbPrdEw1i+6IC+/8vpFF6A1GratDUJy3U4+uXkPNj7Xh3/8s1l4oe84Ni3rLusKQlcp/1DXBnggINA8KaSZ91sVRqMz2/SDIoQQf6Cnt1V97IR+HoolsPbRfB/ctY/2YiiWsK0NQvReLhd1TcHDL/4Oj33+kpK+3Vyt9w917YIC6OfEVIXRaMoe+kERQog/0NPb57REsfe2Tzmin2nYEDfQk7OPnjUZdy7tLivXLCPvH+p6Bhwo7zpiZmabhXgIIcR79PT2WadNckw/0w2RuIGenEUjIUNyzdV6/+DoDLgQ4kEAVwHol1LOzmw7A8DjAKYDeAvANVLKISGEAPCvAP4CwDiAVVLK153sH1Bcir5QGDmzTQgh1YUXervcs4QQO6hUzmjT+AenXVAeAvBNADtztt0O4AUp5V1CiNszn28D8OcAZmb+LgFwf+ZfR9ESxtZouKiaFCN8CSGkenA7M4Ndhg2raXqPn++BHXLGrCX+wFEDXEr5UyHE9ILNSwAszPz/YQB7kDbAlwDYKaWUAF4WQrQIIc6WUr7rZB+BfGF0IkWPip8HNSGE1BJe6NtKDRsnnz/EGNVwD2rRgK5H+8gLH/ApqlGd+bcjs30qgCM5+x3NbMtDCLFaCLFPCLFvYGDA9s45laKHZYrrG6fllhC7qWaZrVZ9yxRxlVOp3PIeuE+1jtdK8VMQptarTtHVl1Jul1LOlVLObW9vt70TZiPZk0kF7wzH8PbgGN4ZjiGZVDT346Cub5yWW0Lspppl1i/6Vq2ifGxoHAMjE2UNCiuZVMy2UetUKrfl7gGvtzmMXC+/jFe38SIN4XHVtUQIcTYAtTbwUQCdOftNA/CO250zk6InmVTwq+Mj2dyv01qj2LqiBxdOmYxQKP/dhimqSC0x/fbnTR/z1l1XOtCT6oPXznn8oG+tuDKYTRFXDe4S1Uape8DrbQ5WEi+NFzPgzwK4PvP/6wE8k7P9OpHmUgAfuOH/XYiZFD39oxOahRf6RyeK9mWKKkIIcQc/6Fsrs3pmU8TV68yhk5S6B7ze5mAl8dI4nYbwO0gHXJ4phDgK4GsA7gLwhBDicwB+D+DqzO7fRzoF4W+QTkN4g5N908NMhHEipV3EJ5kqdkNhiipCCHEHP+hbK7N6ZjNc1OvMoZOUuge83uYwW0m83uwjp7OgfEbnq0Ua+0oAX3SyP0YxGmEcDgY0l6rCwQAGRiaKBq+RdIdcxiKEEPMUZlGY2d7saa7jSCiIxV0dWNbTiZZoGMOxBHb1Hik7q2cmwwWrGjqD3j3g9T6Fkawl5dx5/DRevaDuS9HrYUS4OpobsHVFT5EPeCyRwnXbXtL0eXIj3SEhhNQTVvSp02nPWqNhrF90QdHzoTUatq2Nep059Ape7zRa423byh6c2RRBIBDIjiW969UaDdP+QZ0Z4OUUbu73KUXi68/3YXdfv65whEIBXDhlMp5YMx/JlIJQMICGsMCSb75Y5PP09LoFeW/Uer5RhfsRQggpjZY+vffHh3DHp2dDSlmk792YABmKJTRjhOzU8U5VNayXnMxmfyerSKYpHG/tzQ3o/3ACDaEAjpyI4by2Rkxva9K9XrR/0tSNAV5O4Wp9v2lZNwZG4th/ZFhXOEKhAM5pORU8cGxo3JDPE33JCCHEHgr16ZzOFlx/2fm4Rmcl0g0DwC0db3dRlnpZnbX6O2uxCI5ZcmV7TmcL/vHPZuG2XQey13Hz8m60NIZxRlOD5vWi/ZOmbgzwcgpX6/vbdh3Ahqu6sOaRXsPCYdRHjL5kxAmspLgjpNop1KdrF87IGgRAsb53wwCoVh1fL7OT9fI7nSBXtrXG2i1PHsDjqy8Fmsofr1INY8Nu/FSIx1HKKVy97885fRK2rezBk2vnQwhRNum+0TRSZtNNEUII0aZQn7Y1RUrqe6tpz8wUYalWHV8vs5P18juNYLa4UK5st0TDmtcxVeIU1To27KZuZsDLvXFpfb+4qwOKBDY+12d4icqojxh9yQghxB4K9akQoqS+txJMZ9ZloVp1fL3MTtbL7yyHFVecQtnWuo6Twvrzu9U6NuymbmbAy71xaX3/1Su78MXHXjeddF/1eZra2oj2yQ0lhdjIfoQQQkqTq0/POm1SSX2fawDsve1TeHrdgrK+v1aKsFSjjq+X2cl6+Z3lsFpcSJXts0+Pal7HM5tKu/FU49iwm7qZAS/3xqU1g5LUKbRTj0tUhBBSLRiZYTMbTOeEy4Ifs43Uy+xk4e8MhwIIBQTe/SBWs79Zi0rlul7kxQnqxgAHyitcNW+luhyz4aouLlERQkgVYne2CrtdFvycbaReMn2ov9PP98Jp7JDrepEXu6kbFxSj5C7HbN1zGJuWddf9EhUhhNQ7drssWF36J/ZTz/eCrjjeUVcz4EbIXY7Zf2QY3/jRIWy4qgsfPWsyopEQl1YIsQhTJJJqxu6ldmbh8A/1fC/oQuIdnAEvoDA91f4jw9j4XB+ikVDdBgoQQgixN3DMaipEYj/1fi8YEOkNnhngQoi3hBC/EEL8TAixL7PtDCHEj4UQb2b+bXW7X1yOIYQQ4jR81vgH3gviBV67oHxKSvl+zufbAbwgpbxLCHF75vNtbnbID8sxfoyMJ4QQP1HtetIPzxqShvdCm2ofY37HawO8kCUAFmb+/zCAPbDZAE8mFfSPTiCRUhAOBtDR3IBQKH8hwMuI3nqOxiaEECN4pSftNkjKPWtoALmH3zJ5eH3v9cbYlNMaEItTHu3ASx9wCWC3EKJXCLE6s22KlPJdAMj822Fng8mkgl8dH8E1217CJzfvwTXbXsKvjo8gmVTsbKYi6jkamxBCjOCFnlQNkqVb9mLBpp9g6Za9OHR8pGzZ7mppj/gHP9x7vTH28yMfUB5twksDfIGU8uMA/hzAF4UQnzBykBBitRBinxBi38DAgKkG+0cnsPbR3jyBWvtoL46PnMTAyIQvBKmeo7FrmUrklhAv8LPMuqEnFUViYGQCx4bGMTAygffHJlw1+jkZYw035bZQRuyyIfxw7/XGWGMk6Fmfag3PDHAp5TuZf/sBPA1gHoDjQoizASDzb7/GcdullHOllHPb29tNtZnQqWw5kVR88zZX79HYtUolckuIF/hZZp3Wk1ozkOMT7k6OcDLGGm7JrZOz1H6493pjbDiW8KxPtYYnBrgQokkIMVn9P4DFAA4CeBbA9ZndrgfwjJ3thoMBTYFKKdI3b3OMxiaEkNI4rSe1ZiB/9/6Yq5MjnIzxN07OUvvh3muNsc3Lu7F1z2HP+lRreBWEOQXA00IItQ+PSSl/KIR4DcATQojPAfg9gKvtbLSjuQFbV/Rk3VCmtUaxdUUPRicSmNPZgv1HhrNvc14FQDAamxBCSuOUnlT1/ng8iQ1XdWHrnsPYf2QYAHDfC29i24oerMl5fjg5OdLWFMHOG+fh7cFxNEaCGI+ncF5bIydjfIKZWWqz9oRq/BYGQGrde6dslcIxFg4FMHoyiYHRCQCcHLQDTwxwKeVvAVyksX0QwCKn2g2FArhwymR8b818nEwqeOv9MWz4/w5iYHQCm5Z14+EXf4dIKOh5JhK/RWMTQojfsFtPaun9Tcu68Y0fHcL+I8MYGJ3A2S2TXJ0cmUgq2PDMwbznEPEH6ix1rhGuNSNsxZ4w+oLptK1SOMbObJKcHLSRuquEGQoFEAoGsPLbr+CGh17D/iPDODoUw227DuCfruxCW1PEFwEQhBBC3ENL79+26wDWLpyRNWxaohHXKgbyOeRvjLpBWb2PRqpTui0jrJhpL37LA+4KektHwYBAICB8EQBBCCHEPfT0/kfPSs9Euj3bx+eQvzE6S+3kfaSMVDd1NwMOFAc4zOlswY5VFyMl0ymFohHvAyAIIYS4h17gWzQSys72OZV2zkx/+BzyD0ZmhJ28j1bP7aYcE33qZgY8N1AhGgli5w3z8PaJcZzZHIEEsO7fX8/zodp54zxc9+CrrgTbEEII8RatwLdtK3ugKAoGRibQGg3jzYFRxysDqs8qRVGwbWUP1jziTtAnMYaTAZVW+vDY5y/B15/vw+6+fkPntstv3OtKnbWAkLJ633zmzp0r9+3bV3a/QoFb88fT8Zcfm4a1j/Ziw1Vd2PhcX1EgxVPrLoOAoHDVN47ccKNyO/32551onlQhb911pdFdPZXZaifXqEgpMs+weezzl+Cz33ql6Fmxccls3PDQa7YEwBU+qxZ3deCfruxCMCBq/TlUFXJr1Xi101jV6sO2lT04symCQCBQ9twDIxNYumVvkRw/vW6B4aBmrxNV+ISKf2hduKAUBiosn3tuNhVhSzSM9uYGbFvZg8dXX5B9e5MAACAASURBVIptK3vQ3tyARFJhsAEhhNQRqktBJBTEZ7/1Cnb3pWvBHR2KoX9kwvHKgIXPqt19/fjst15BJBS0/Byiu4F9lAt61LvWdgYvavVhzSO9CAQChs5th984A4TtoS5cUAoFLhgQ2c+KlLj1ilm45ckD2Te5zcu7EY3Qz44QQuoRLSNlcCyumXbOzsqAdgfVuTVTWS/uCKXuj1vXulIZMZo+0ck+kDR1MQNeGKiQUmT2c0qRWeMbSAvRLU8eQJKzBIQQUpdoBbft6j2CbSt7HK0MaHfAnhszlU6WZPcb4ZB2Ne1wKODarHClMmJHFVkGCNtDXcyAFwZBPLnv97h/RQ++8GgvwsGA5ptcIqlkPyeTCvpHJ5BIKQgHA+hobkAoVBfvLoQQUjeUCm778uWzMLO92VRlQK8D9tyYqdQzPM34FFcLoYDA5uXdRSvmoYBALG7tWluRkUoqpNpRRdaJwNJ6pC4M8FyBi8WTeOO9Eex54zh2rLoYk8Kll2OSSQW/Oj5SVL7+wimTaYQTQkiNoBfctnHJ7LzgNqOVAZ2sgGgUO9wNylFP7gixeAp3//AQNlzVhZZoGMOxBO7+4SF887NzLF1rq24rlVZIrbSKrN1yWq/UtAWZGxAxOBZHW1ME0UgIG5/rwz3/+SYuv/enWP+d/di8vFt3OaZ/dCJrfANpxbL20V70Z2Y9CCGEVD96wW2l3BFLBdc5WQHRKHa4G5SjFt0R9IIpI6EgBkYnsOaRXly7/WWseaQXA6MTWQPU7LW2IiN+CYBkVczKqdkZcL03y5ntzXlLJwOjE5hy2iQ8te4yJJIKhBAICmQN9kRK0Xy7T6YUnZYJIYS4TaWBgHozuUeHYvjK935uOqDODzPDbsxU1po7QqlZ6VK/1cq1tiIjZo6pl+DYaqVmDfBSfmlagwSA5qBriYY1l5VCwZpePCCEkKrBjgwUei4Ew7GEJb9mN9w/jFCpu4GR89eSO0I5n/ZSv9XstbYiI0aPYa5u/+M7K1IIcYUQ4pAQ4jdCiNutnqfUW6LW0oneoIuEBbauyI9837qiBx3NtRVcQggh1Yody/JaLgSblp3KcmJ29toN9w+/UEvuCOVmmL12ETJ6jF9cVYg+vpoBF0IEAfxvAJcDOArgNSHEs1LKPrPnMvtmqTfoTsYVXDhlMp5YMx/JlIIQs6AQQoivsMPdIy9YP5HC4f5RfONHh7D/yDAA87PXtTYzXC+4uXJhRUaMHuMHFyhSGr9ZkfMA/EZK+VspZRzAdwEssXIis2+WpQJJQqEAzmmJ4ty2JpzTEqXxTQghPsKuQEB1dnNaSxRnnT6pZIpBM+erhZnhesHtlQsrMmLkmFoMjq01fDUDDmAqgCM5n48CuMTKicy+WdZaIAkhhNQLdutvzl7XL7Vy72nT+B+/GeBaEp6XA0oIsRrAagA499xzS57MTEBErQw64k/MyC0hfqCaZNYJ/e108CJxBjvkthbuPW0a/+M3X4qjADpzPk8D8E7uDlLK7VLKuVLKue3t7bY2zuVC4hROyi0hTlBtMkv9TYDqk1sn4ZjwN34zwF8DMFMIcb4QIgLgbwA863GfCCGEEEIIsQ1fuaBIKZNCiJsB/AhAEMCDUspfetwtQgghhBBCbMNXBjgASCm/D+D7XveDEEIIIYQQJ/CbCwohhBBCCCE1DQ1wQgghhBBCXERIKcvv5VOEEAMA3i6z25kA3nehO1bxc//83DfA+f69L6W8wu6TlpHber/mleL3/gHO9tELmXUSP91PP/UFqK3+VIvc+uGasw/etw8Ak6SUsys5QVUb4EYQQuyTUs71uh96+Ll/fu4b4P/+WcHvv4n9q5xq6KNf8NO18lNfAPbHC/zwG9kH79u3qw90QSGEEEIIIcRFaIATQgghhBDiIvVggG/3ugNl8HP//Nw3wP/9s4LffxP7VznV0Ee/4Kdr5ae+AOyPF/jhN7IP3rcP2NCHmvcBJ4QQQgghxE/Uwww4IYQQQgghvoEGOCGEEEIIIS5CA5wQQgghhBAXoQFOCCGEEEKIi1S1AX7FFVdIAPzjn1N/jkC55Z+Df45AmeWfw3+OQLnln4N/FVPVBvj773tdiZQQ81BuSbVBmSXVCOWW+JmqNsAJIYQQQgipNmiAE0IIIYQQ4iI0wAkhhBBCCHERGuCEEEIIIYS4CA1wQgghhBBCXIQGOCGEEEIIIS4S8roDtYaiSAyOxRFPphAJBdHWFEEgILzuFiHEBTj+a4/ptz9v+pi37rrSgZ4QUgx1TvVCA9xGFEXi0PER3LRzH44OxTCtNYoHrpuLWVMmc0AQUuNw/BNC3IQ6p7qhC4qNDI7FswMBAI4OxXDTzn0YHIt73DNCiNNw/BNC3IQ6p7qhAW4j8WQqOxBUjg7FEE+mPOoRIcQtOP4JIW5CnVPd1KUBrigSAyMTODY0joGRCSiKtOW8kVAQ01qjedumtUYRCQVtOT+pX5ySWWIfHP+E1D5+0sXUOdWN6wa4EKJTCPETIcQbQohfCiG+lNl+hhDix0KINzP/tjrRvuoztXTLXizY9BMs3bIXh46P2DKI2poieOC6udkBofpjtTVFKj43qV+clFliHxz/hNQ2ftPF1DnVjZDSXcERQpwN4Gwp5etCiMkAegH8FYBVAE5IKe8SQtwOoFVKeVupc82dO1fu27fPVPsDIxNYumVv3rLNtNYonl63AO2TG0z+mmIYkVxTOHLjzMqt0zJL7MMH498XMltLMAuKK1SF3PpRF/tA59QrFV9k17OgSCnfBfBu5v8jQog3AEwFsATAwsxuDwPYA6CkAW4Fp32mAgFBo4jYCv38qgeOf0JqFz/qYuqc6sVTH3AhxHQAcwC8AmBKxjhXjfQOnWNWCyH2CSH2DQwMmG6TPlPECyqRW8os8YJKdS0hXuCk3FIXEzvxzAAXQjQD2AXg76WUHxo9Tkq5XUo5V0o5t7293XS79JkiXlCJ3FJmiRdUqmsJ8QIn5Za6mNiJJ4V4hBBhpI3vf5dSPpXZfFwIcbaU8t2Mn3i/E20HAgKzpkzG0+sW0GeKVAWUWUII8R7qYmInrhvgQggB4NsA3pBS/kvOV88CuB7AXZl/n3GqD/SZItUGZZYQQryHupjYhRcz4AsArATwCyHEzzLb/hfShvcTQojPAfg9gKs96BshhBBCCCGO4kUWlP+GfvqWRW72hRBCCCGEELepy0qYhBBCCCGEeAUNcEIIIYQQQlyEBjghhBBCCCEuQgOcEEIIIYQQF/EkD7hXKIrE4Fic+TsJyYHjghBSj1D3ES+pGwNcUSQOHR/BTTv34ehQLFvBataUyRxwpG7huCCE1CPUfcRr6sYFZXAsnh1oAHB0KIabdu7D4Fjc454R4h0cF4SQeoS6j3hN3Rjg8WQqO9BUjg7FEE+mPOoRId7DcUEIqUeo+4jX1I0BHgkFMa01mrdtWmsUkVDQox4R4j0cF4SQeoS6j3hN3RjgbU0RPHDd3OyAU/292poiHveMEO/guCCE1CPUfcRr6iYIMxAQmDVlMp5et4ARz4Rk4LgghNQj1H3Ea+rGAAfSA659coPX3SDEV3BckGpk+u3Pmz7mrbuudKAnpFqh7iNeUjcuKIQQQgghhPgBGuCEEEIIIYS4CA1wQgghhBBCXIQGOCGEEEIIIS5CA5wQQgghhBAXoQFOCCGEEEKIi9RVGsJCFEVicCwORVGQkoCUkrlAiS9RZZX5amsX3mNCqhuvx7DX7RNz1K0BrigSh46P4N4fH8L1l52P23YdwNGhWLYa1qwpkym4xBeosnrTzn2U0RqF95iQ6sbrMex1+8Q8deuCMjgWx00792FZT2fW+AaAo0Mx3LRzHwbH4h73kJA0qqxSRmsX3mNCqhuvx7DX7RPz1K0BHk+mcHQohpZoOCuwKkeHYognUx71jJB8VFnNhTJaW/AeE1LdeD2GvW6fmKduDfBIKIhprVEMxxKY1hrN+25aaxSRUNCjnhGSjyqruVBGawveY0KqG6/HsNftE/PUrQHe1hTBA9fNxa7eI9i0rDsruKrfVFtTxOMeEpJGlVXKaO3Ce0xIdeP1GPa6fWKeug3CDAQEZk2ZjDuXdkNRFDyxZj6zoBBfosrq0+sWMLq9RuE9JqS68XoMe90+MU/dGuBAWmDbJzd43Q1CykJZrX14jwmpbrwew163T8xR1wZ4OZJJBf2jE0ikFISDAXQ0NyAUyvfaYd5NUqtQtk9hRBcQQkg1UKjbW6NhDMUSjuh6Pkf0oQGuQzKp4FfHR7D20d5sTs2tK3pw4ZTJ2Qcv826SWoWyfQojuoAQQqoBLd2+dUUP7nvh19jd12+rrudzpDR8eujQPzqRfeAC6XQ+ax/tRf/oRHYf5t0ktQpl+xRGdAEhhFQDWrp97aO9WNbTmf1sl67nc6Q0nhjgQogHhRD9QoiDOdvuEEIcE0L8LPP3F170TSWRUjRzaiZTSvYz826SWoWyfQojuoAQQqoBPd3eEg3nfbZD1/M5UhqvZsAfAnCFxvZ7pZQfy/x93+U+5REOBjRzaoaCpy4Z826SWoWyfQojuoAQQqoBPd0+HEvkfbZD1/M5UhpPniBSyp8COOFF20bpaG7A1hU9eTk1t67oQXtTBAMjEzg2NI5gAIbybiqKRP/ISfz+xBiODY3jxNgEFEW6/psIMYpfcsoqisyOt4GR/HFT6js72lC3R4IC92vogo5mZhsghFQXWrp964oe7Oo9kv1sRNcnEikcGxrH24NpuyaRKJ7V9stzxK/4LQjzZiHEdQD2AfiKlHLIq44EAgKnR0N46IZ5CAhAkUBTQwC/eX8sL6Bg543z8NS6y5BIKpoRvlpBCJuXd2PKaZMwva2JgQjEl/ghp2ypAB4AtgT36LUxs70Zbw6MZrev+ePp+O7qS6EoEiFmQSGEVCmFuj0cCmAikcJn5p2Hz/2Pj2A8nkJDGd2WSKTwq/5RfCEnMP3+FT24sKMZ4fCp2W0/PEf8jJDSm5lYIcR0AM9JKWdnPk8B8D4ACWAjgLOllDdqHLcawGoAOPfcc3vefvttR/o3MDKBpVv25vkv7Vh1MTY8czBv27TWKJ5et0A396bWeaa1RrFxyWzMnno6c3b6G9u0hFtyW0vojZ2n1y0AAN3vzIwpvTaeWDMf12x7qeLze0DdyOz02583fcxbd13p23bqnLqRW79RSs/q6bpjQ+O4dvvLRcc8vvpSTG1tdLzPPqFimfXNFI6U8riUMiWlVAA8AGCezn7bpZRzpZRz29vbHeuPVvBAYyRoOqBALwihMRJkIEId4Zbc1hKlAnjsCu7RO09SJ/CynsYsZZZUI5Rbc1jRpUlFautNutaawjcuKEKIs6WU72Y+LgVwsNT+VlGTwiuKgqQikVIkJmWWTBKpU24kavBArpCNx1NF28oFFGidZ1prFOPxFAMRSMVoFTkAUBOFD/TGjjpuFnd1YFlPJ1qiYQzHEtjVe8T0mNJrI5QJvCzcHo0EMTAyUfXXlhBSG+Q+A8KhAEIBgVi8WD/pFcQpp2e12goGhLbepC40hScGuBDiOwAWAjhTCHEUwNcALBRCfAxpF5S3AKyxu13V3/PeHx/C9Zedj9t2HUB7cwNuvWIWbnnyQJEP6APXzc3zDT2vrbFoW7mAAjUIQcsHnIEIpBL0/JcbQgFc9+CrVV/4QGvsqONNUSTWL7qgqDhOa04qrUra6GhuKNq+88Z5OP7hBItKkLLQbYW4gV6M2d0/PISB0QlDMTOl9KxeW+3NDdi8vDvPbrqfgemm8cwH3A7mzp0r9+3bZ3h/1ddpw1Vd2PhcH44OxbBtZU/2/yqq/1NbU8SW2UVFkXh/bAInEwqCAohGgmiJcuasCnDkBpmVWz1KxRfc8NBreduqwHdZE71ZGyt+i2bbKNwuIfHXW170u1+4r2XWTvzsA26FOjfA60Zu7URPD264qgtrHuk1HDNjpFx8YVtzOluwftFMfKS9CaGAQEdzQ14AZh1Qscz6xgXFDVRfp5ZoOCtEuf9XUf2fAgGh+WA1+7ANBAQ6Jk+y3nFCNCgVX1C4rVp9l/XGoJ0FHvTaKNx+bGi87v3CCSH+oVxRnVz9VEp36enAUm3tPzKMGx56DXtv+1Q9BV7aim+CMN1A9XUajiWyeSlz/6+ifn57cAzvDMeQTPqv4p1dOZBJdZF734UQmrI7Hk8Vbas03sBv8ma1wEMlv8Nsm367ZoQQY7g5dp3QSWpRHVU/2VEQp/AcczpbsGPVxUhJ6Zp+qzWdWlcGuOrr9Ppbg9jytx9P+43uOYzNy7vzEsXfv6IH//wfv8QnN+/BNdtewq+Oj/jKCFd9sZZu2YsFm36CpVv24tDxkaoXRlKawvt+x7MHi4pFPXDdXJzX1mhr4QM/ypuVAg+V/g4zbfrxmhFCyuPm2HVCJ21e3o2tew7n6Sc7CuLknmNOZwtuvWIWNjxzEJ+4e48r+q0WdWpd+YAD6Zv43ocnccezB7MZFBQpIYTAOS1RBAXwz//xS+zu688eo+YFPqclWuLM7mGn/yspia/8ErXu++KuDtzx6dmQUjqWBcWv8mbEbzEXO36H0TY9vGa+klknoQ94TeEbuXVz7Nqtk6xkQTFD7jm08oA7qd98+ByiD7hZAgEBKSV29/XnGdkAsPe2TyGpyKLtal5gv2Cn/yupHrTu++6+fnztL2WRD56dCsmv8mbEbzEXO36H0Tb9es3qHbeMaVK9uDl2HdNJTQb3M4l6Di/iYWpRp9aVC4pKKX+ocCb/b+F3oaB/LpUd/lyk+vDqvteKvLn5O2rlmhFSb1BPlMeLflfrtSqFf6xKFynlD9XR3FDkV7vVZ/kt7fDnItWHHffdShBLrcibm7+jVq4ZIfWCqhvjyRQe+/wlWNzVAYB6Qgsv+l2t16oUdecDrlIq9+9bg2N4e3AcjZEgxuMpnNfWiOltTb7K222HPxcpi2/8ElUque96hXuMFJKpFXlz83d4dM18J7NOUWvuJPQBtx+jcqulG7et7MGZTREEAoFa1BMV40W/fXat6ANuFT1/qMGxeLaKoIofAs4KscOfi1Qfldz3wbF49gEDpP3nbtq5z5Bs14q8ufk7auWaEVLraOnGNY/0uvLcr1Y94UW/q/Va6VGXLiilqEVHf0IAyjYhhGhB3Ui8gAZ4AbXo6E8IQNkmhBAtqBuJF9S9Aa4oEifG0kFpvz8xBikldt44r6Yc/QkB/BXE4reKZn7rDyHEPZzQjX7QKX7oA9HHkg+4EOLfAOjeSSnless9chE14PL4hydxy5MHTgWmrZyLZ29eoJnMnpBqJRAQmDVlMp5et8DTIJZKgkHroT+EEHexWzf6Qaf4oQ+kNFZnwPcB6AUwCcDHAbyZ+fsYgKpxmhoci+PtwfGs8Q1kAtMe2YeUAkxtbUT75AYKK6kZ1CAWL2VbLxh0cCzuel/82B9CiPvYqRv9oFP80AdSGksz4FLKhwFACLEKwKeklInM560AdtvWO4eJJ1NojAQZfEGIi/gt4Mlv/SGEVDd+0Cl+6AMpTaVpCM8BMBnAiczn5sy2qiASSuf5ntYaLUo7mBt8USr3pKJIvD82gZOJFIJCIBoJoiVqbOnKZzktic9wWz4K22uNhjEUS9jevhrwVGrMuYkX/eHYJ6Q6MTJ2S+mUetWzhVAHVm6A3wVgvxDiJ5nPnwRwR4XndI22pgjOa2vE5uXd+T7gOcEXpfyoABR9t3l5N6acNqls4R76Z5FSuC0fWu1tXdGD+174NXb39dvavhrwVPjbvAp0drs/HPuEVCdGx66eTmmNhvOOX9zVgfWLLsDaR3tt1wV+07O5UAemqbgSphDiLACXZD6+IqV8r+JeGcSO6myKIjEciyMWTyElgUnhAM5sOuX/NTAygaVb9moW5gGg+d3GJbMxe+rpJRPGlzpvLSWar3I8q87mtnzotbfhqi6seaTX9vb9NvvhZn8cvreshFmlsBKm/dgtt2bGrpZOGRyL5x2/bWUPNj7X55ie95ueVakR+8cXlTCDAAYy57pACHGBlPKnNpzXFQIBgTOaGoAm7e/L+VFpfdcYCZb1s6J/FimF2/Kh115LNOxI+36raOZmfzj2CalOzIxdLZ1SeHxLNOyoLvCbnlWhDkxTkQEuhNgE4FoAvwSgZDZLAFVjgCuKxFBsArG4gpQiEQkGcGZTBCPxJGLxFJKKxI5VF+O+F97E/iPDAIDFXR0QQiCRUoq+m9YaRUAICCFwbGhc18fLqH+WX99gibO47b+n195wLKHbfq5shkMBhALC1dSdZsdG/mqXxKRwMG+1y40+APrXWgiBRCKFgbE4EikF4WAA7U0RDJ9McvwT4gP0xi6QntUtjA8r1A2Fxw/HEnmf53S2YP2imUgqEu8Mx9DR3IBQqDhZnZ7e8cpeMNquuh+QtqOW9XSiJRrGcCyBXb1H8uymetB1lc6A/xWAWVLKCTs64zZ6ecAfuuFifBBL4Evf/Vmeb/fdPzyE9skRrF90Aa7Z9lLRdwOjE/jfn52DYCCQ/V7Px2tme3NZ/yz6SdUvbvvvabWn+oADxYUptGQzdxw4Ladmx4Zuzv8K+ml1fGpd603LurH74DvoOf9MfCFHV9y/ogfP/ewotv3XWxz/hHiM1tjdvLwbNz+2P0/vAcXxYVrP/V29R7B1RQ/WPtqL9uYG3HrFrDz9tHVFDy6cMjnPCNfTOzPbm/HmwKjr9oJRPZi732UfacPNfzIT6/799Txd9/De39aVrqvIB1wI8QMAV0spR+3rknEq9e8aGJnAwWMfYMMzB/PeaHesurho27TWKB5ffSmEEFnjOve7Rz43D5FgAAEhcHXO96V8vFSfML23xhrxk6pmPPVL9HMWlHI+407LqdmxoTfWK+lnJeNTUSTe+/Ak3hmOYXAsjq17DuPu5d244aHXis63Y9XFuPzenxo9f1X40toBfcBriqqRW1VPxhIpHO4fLVoBLxUfpvXcV/XsRDKFv9n+ctExT6yZj3NaotltenrniTXzNW0Tp+0Fo3owdz89u8ipmCOH8NwHfBzAz4QQLwDIzoJXSyVMvTzgernBAUBKqfld/4cTmNYahVLwfSkfr3L+WfSTqm/c9t/Tak+v/XI+407Lqdmx4UTO/0rGZyAgIKXE8q0vZbcFA0LzfMGcly6Of0K8RdWTx4bGccNDr+V9Vy4+TO+53z65AW8Pjmkek0wpedv09E4ypXhiLxjVg7n76dlFTsUc+RWrlTBVngWwEcCLSFfGVP+qgtw84LlobVP9X1UfrsLvxjO+r4Xfqz5eWucy0j+rxxLiJHqyqfqMOy2nZseG3livpJ+Vjs/C41OK1DxfSpF5nzn+CfGeUuPfim4IBwOax4SC+Waa3rlDOsc7rS+M/tbc/fTsolIxR7VIRQa4lPJhrT+7OuckiiIhIXHBlGZs+duPZ4VhWmsUnWdE8W+fmZO3bdvKHrRGw1kfsGmtUczpbMGOVRfj4Rvn4Q86mvO+X9zVgW0re7J+XLnnMurLm9uW2WNJbaIoEgMjEzg2NI6BkQkoSmVpRK2iJZubl3dj657Drsip2bGRm/O/cFwripK9lrnX98TYBPpHTupe60rHZ1tTBDtvnIcdqy7G46svxdhEEjtuuDjvfPev6MGT+35v6fyEEOcoNf6t6IaO5oYiW2Hrih50NOfPluudu6O5oWSbRp8dpfbT+k6rP4V6tbDfW/ccLtLFW1f0YFfvEcPXqxaw5AMuhHhCSnmNEOIXSGc9yUNK2W1H58ph1b+rMGhgcVcHvnplV/o7KTEpHEAiJZFMSZwYi2NwLI5dvUfw5ctnZQMshmITeHd4QjOBvqJIHOofwZpHerPn/6cruxAMCNO+vMyC4im+8kv0W1BuNWdBUWTanezrz/flFRpqCAVw3YOvagZEFV5rNbDz7cFxNEbSM+zntTWWLcKV25+i+7lyLtqaIziZSCFkLQuKr2TWSegDXlNUpdyWq5JtVj8dGx7HRFIiIABFAg0hgaktjUXBjHp6B4BudhSzgZKF+wHagaXqd2q7KaVYr6rt5F6TaCSIpCKRSCqOVgR1kIo7Z9UA/58A/hPAEIBE4fdSyrcr7ZgRrA6ucgFkO1ZdDAAlA7asFOjxeUABKcZXDwUG5dqH3rXcuGQ2bnjoNUMFMiq9Hw7dT1/JrJPQAK8p6kZu9bASzFhqP7vPDZS3a+rsGeVZEGYbgH8FcCGAA0j7gO8F8JKU8kSlnXKacgFkjZFgdlvhPmpQgJUCPbUeUECchUG59qF3LdWxb6RARqX3g/eTEKJiJZix1H5OnNtpnVhvWDLApZT/CABCiAiAuQAuA3AjgAeEEMNSyi77umgviiIhhMCTa+dn038BwPpFM3HW6ZPw4y9/ArFECoOjcUxrTSfIn9PZgrULZ2T9kZKZJRP1e5XFXR3p7xWJ766+FJt+8CvsPzKcTa6fkrIoWb8X0K2lOlFlrr25AWsXzkBLNIzxeHoprxAj99gtOfBLAZzcY4QQeeNXHaNtzQ1p/0Upi8Z3YVBQJBTULCZhNgizVBuEkPqgUJ8oMm2rFNoNVvRG4TGFBX9UVzcAWPPH07F87rkIBgRSisST+36fPbfWOVJSon/kJEKBdF+1ihPapdNqzXapNA/46QDmA1iQ+bcFwC+klDfY073SmF1e0vJvuufqizApHMAXH9ufl/z+9GgIR4di2LH3d7j+svNx26785PizOprxm/fH8vzICxPL33vNRfjevqNY+vGpthX/qBS/+RH7HF8tixotJmPkHrslB34pgKMV96GOVy1/by29UNhGMqngV8dH8uJAtApn2Pk7DOArmXWSWnNBsUINua3UjdzqkatPSsWgAPq+2Eb0n9a571/Rg3974df4o3NOx8KPTikqBnZhRzOCwUDJc+QWYnOiKJsPbRfPfMC3A/hDACMAXgHwMoCXpZRDBo9/EMBVAPqllLMz284A8DiA6QDeO3M19gAAIABJREFUAnBNufOZHVzl/D5zt31vzXwEAwLxlKKbHP+s0yZlE/LHkwpW7Xi1aL/HV1+KazWO98onqs58tCrFdw+F/pGT+OstL1bsh+eWHPilAI7WMYu7OnDLn12IUEBg5YPFY/d7a+YjGBTZICEnCmU5MKPjO5l1ChrgNMDL4Ue51cNIoRpVt1SyAqhX8GfDVV34yJlNmsXA1GJAue1q2TW5hdgeX32prbPUPrRdKv5RVtMQngugAcB7AI4BOApg2MTxDwG4omDb7QBekFLOBPBC5rOtlPP7zN2WSClIpBTEk9rJ7ZMpJZtQPyiAwdEJzf1SinbhHq98ouijVd0kdOTRrB+eW3LglwI4Wsfs7uvHibE4+ke0x64iJTomT8LU1ka0Ty52mbHjGqo6RK8NQkh9YKRQjapbrOgN9Rg9m6QlGtYtBqYWA8otIlQqjk79zk6dVou2iyUDXEp5BYCLAXwjs+krAF4TQuwWQvyzgeN/CqAwWHMJADWH+MMA/spK30pRqohO4bZQMIBIKAhFQjfpfe559Yp8eJUcXw8W96lujNw/u/axq79+KIBTqnCQ1WJZHEuEELswUqjGDt2iV/BnOJbQLQZmtBiQk4XYalHfWi7EI9McBPB9AD9AOgvKDABfsnjKKVLKdzPnfhdAh9W+qRQW1QgEJLYVJLrfvrIH086I5m27f0UPGiMBtEbDaAgJ3F9QqGdr5ns1GX1rNKxZ5CM3Ob5amOfJtfPx2OcvQWtOyVWzv0Urib7RJPss7uN/St3L1mg4W+AJ0C62EAygSM4L77GTcpDb/2AAumPDbIEII33PPVf/yEmcGDvVD73CQbt6j5QslqUoMnue358YQ//ISd0CFBxLhFQ/RovR5OqYSgujlStUY0S3GNGlWgV/7s8UwXngp78tKky4dUUPgiLtApJMqsV1lKLnkNOF2NzQt24XurPqA74e6cwnC5DOA74XwEuZf38hpVQMnGM6gOdyfMCHpZQtOd8PSSlbNY5bDWA1AJx77rk9b7+tnXJcL+igvbkB6xfNxPlnNiEYENj43C/REo1g9SdnIBxMR/1u3XMYL/52EA9cNxd/cGYT3h4ax5ETsWzS+84zorj7h7/KSzQ/s70ZH04kMpkegEnhQDbTQzKp5BXmMRs8UC74wGxwQq1FEjuIbRfFitzqBd/c++NDWNbTibamCDomN+Cc06MIhQJFMq/KeWODdtYRJ+RAq/87b5yH0xtDOBlXisaG1cAarb6r1yf3XLnBQDtvnIfmSSEkkkpR4SC9IhDlgkgB7cIXHuK6zHoFfcDpA655ogrl1mwxGjsDDnP1WkAAb5+IQQCGinyZ0aXJpIL+0QkkU0pRwa+mhiDG4wqSKQUSwJ05RXW2rujBfS/8Grv7+vMKDCoSeO+Dk1CkNF2QzOr1sVvfWngWeRaE+S/I5P5WZ60tnGM68g3wQwAWSinfFUKcDWCPlHJWqXOUCrAwEtCgFXypBhGon59YMx/XbHtJN9hA/WxHEnw9yh3vw+CEWsH1wKBKCiH4QQ7M9sHOPpcrsGXlvE4EkTpM3QSz0QCnAV4OK3JrRQdXomPM9sEpO8Po+YzaSD7Vj7pYuH7eFOKRUv5DpQ1r8CyA6wHclfn3mUpOZiSgQSv4siXHNUQNxiwVbKB+tiMJvtXjazE4oV6ppBCCH+TAbB/s7HO5AltWzutEECkhxL9Y0cGV6BgrfbDrGCt9KLR9kjo2UrXpRy+en5Z9wCtBCPEdpF1WZgkhjgohPoe04X25EOJNAJdnPlvGSECDVvClGkQwp7MFO1ZdjGBAZP23H199Kbat7MHiro6i/dRk+Vo+Q3rBAwAM+XOHQ6UDOWsxOKFeKbyXufIlhMgWe1KZ1hpFNBLEwMhEtgjCnM6WvO/dlAOzsmin7FoNDNLy+1O3BQLAOS1RS31025+QEFI5pXSSHTrGiN94Ob2opVvstgPK/Vb1cyAg8J//8Elc0zOtbLt+1ole2FGeGOBSys9IKc+WUoallNOklN+WUg5KKRdJKWdm/q2opH3ZgIaVc9GpEXz5+luDmNPZgluvmIUNzxzEff/5Jm7+k5nY+Fwfrt3+MjY+14e/W3RB0X6fuHsPlm7Zi0PHR4qESit4YPPybtz82P6iY1Q/pKVb9mLBpp9g6Za9GD2ZLBl8wGCw2iH3XhbK1zXbXsL6RRdkjXDVv/r4h+mls0/cvQcbnjmIW6+YhTmdLZ7IgVlZtFN29cZZqcAgrfF26PgI3hocw1efPoDfDoxj43O/xKZl5gKi9M7rpwcOIaSYUjrJDh3z11texKH3RjSf/0b6oKdbWqNhW+0ArT5szQRr5v72v3tsP1bteBUr5p+Ha3qmmda1ftGJXthRFVXC9Jpy/l25DvvRSBBJRWaLagQDwO27DhSVkb7j07MhpcwmmdfzH39izfy8/XK/0/IZUvsSS6RwuH+0qFRrOT/eZ29egJQC3eADBlY6gid+ieWKHaiyFwkFISE1C/PYXQTBDGZl0U7ZzT1XYaCl1nlLFeeKp5Ts2J/T2YK1C2egrSmCc1qiOOu0SSX76KE/vm98aZ2GPuD0AS9HJVWH9XSSXTqmnN+4Xh9K6Za2poitdkBhH9Rg9Vg8icMDY0V2TKnnjh9ilMph8lnkjQ94tZCbNL6QY0Pj2N3Xj919/Xnbv/aX6ReScv7j6ouLUZ8htS/HhsbzAj8Lj9HzQ4rFU5ja2mjpt5LqIldW9GRPlQW9fYD/n72zj5OjKvP97/TbpGcmMEOYwUgCKBuiIzsCw1tgr4vmGlFBFhNB16CAmkSuxHWvvHg1C9zo/RjirivrhklcBQLiggksCJ+FuFyz7g0gMEZiCESMBicBMsMwA/PSmZ7uOveP7mqqu6u66/2l+/f9fOaTdE1X1VPnPOfUM1W/8zwIzB+s+qKbvqt7rDbj79cqztWKt7TfOwfHSguPdlz3/ro3tTDo8Qkh9qg1J7k1x9TTjRvZUGtucTsO0Dte1+wWvDSS041j8oo0PH8U5kS/46hAJChhwKzOq1ZCfDcLglDPTSoJU0GdRqVWcS4nxTDYL4QQwP2iNWGYW4yK+VQW7NESBrvDRtMG4PV0XpuvPBO3XX4G3n7krKrE9LX0YPU0Q/X2cUuHFObFDsQcZnzBTd1aM/qMUfsdP6cVWwcGLWu/K4+rLcD148+fBQHjxdqEkMbDjm7c6vH8XuujV8xHW7BHb36rtHtJTzfu/vxZyObyTTsnNrQGvB5Gep/KhOxLerrxtY+8G2NTMxibmilLMm9Hv1pvH6eaWLvFTUgVgesSzfiCGxrqZvYZo6I+I5NZKEqheJCqubfStnoFuNYt7cUdj/8RX/ngQq/aNnCf9QtqwKkBr0cY/FYt5vXSyBRaU3FIAMcflYYi4Wi+DnrNl7aYT2XBHqN7h2q3oih4bTJruzhhSAimEE9Y8GpweVHQw0+isNghIjTsTaES+oz71JpH1j60x6u2bRqfZQDOALweYfDbRp9bw1A0KCAc+2zTSlBq4UVBDz+JwmIHEi7oM+5Tax5h2xLSHDT63BqGokFRpaGzoFhFfT0SiwH/8bd/ifHDMxgan0b/9n0YnphG9+wWbLyskAfTKMm836+F9M6pLnao/OvSjcUOiiIxlskik80jLyVmJeM4uq0lSq+NiA5OfKaWlMtMKi+3xor2mEIIxAUQi8VKqbPMpg1zC6M2VRd3NvPiI0KaBS/vx4C9udTN+TeViGNJT3dVSudaMVJeSvx05SJM5/JIxmM19/HK7jDAALyIqoH97s/34rPnvAPXbd1V0iatX9aL1lQcf3vvsxiemEb/8j50asqxavf3U0NrdM4FXe34wWdOr9rudJGGqmU79OZhXLNlV5S1W6QCdYGMVZ+p5YMvDk/ojgcAro8VPTvWLe3FL/cewoWnzMOquwbKxvPNj+zF8MS0p76r16aqBpyFsghpDuzOrWawE3e4Hat0zErg6sUn4YuaOfbW5X3omFUeXmrP29XegmvPX4jr7/ttaR+9uMpLu8MANeBFVE2Sqs+s/Gv12x//cyz/4VOlz5VapSA0TX4m5FfPt/vgG1jzwO6oa7fM0rC6RD3sPF0w8sF7Vy7CJRuf0PUTAK6PFSM7brv8DFxx+9OBrecweirv4ZObpvFZasCpAa9HWPzWqye3YdBfvzyW0Z3r7125CG/veCvtoPa8RgUOI6YbZyEet1A1SUaFd5Ka/JZ6WqUgNE1+JuRXz9eaivt+ncQf7PiMkQ/O5JWafuK2DxnZEY+JQNdzsEAWIcSreSAM+mujuT6XVwzPaxRnNZtunIswi6g6LaPiG2rSfPVzpVYpiCTzfp8zlYhjKptnMn1SwsgHjQo12C1gZdeOvCJdLYJBCCFhwYtigFYxW5THbIFDv+wOAwzAi6g6Lb3iG/3L+3BUawr3rDgbt11+BjZfeWaVfkuv8Mbdnz8LyThKhU1yOaVU6GRo/DBenzRX9MSoQEq9hPza/V4ey+DQGxlHCe/ntKVw/JxWrF9mrzgJCSe1CvDUK87TmU7i7s+fhS2rFmHjZX1Y0tONH3zmdHS3t9QsdGW2kITR+Su3d6aTVcdct7QXW575E26tKBhhpQiGmeJEiiIxNH4Yf3p9EgdHp/D6ZHMWlSCEWMdJATTbxQAvq9jnMvv3cL2iPBt1ivJobe3fvg/fveS9ZftsvvJMSMiq+Ej9v6Io2HhZX0PFHtSAF9Emyz+6PYV0KoFkXCCdjGNiOofP/OipusJ/vcIb6oKvrtkprF58kuXFYPUWHpgtJuRWEZDyLCjArGSskbOgNLQuEajtX0DtxZJ6+268rA8Lu2cjkYg5zoJidYHngq72UrYTVW89nVPw4yf347QT5mBOWwrds1twRDqByen6Wkwzi370vrN+WS+OOWJWqViXzzS8z6pQA04NeD3C6Lda3FhYaFVfnssp2P/6JAZfz6A1VXirPf+oNE44qg2JhPVnspWFhqayeRzVlsSND+6pim20hXjGp3MlG2JCIJ2KG8ZH2jjqGx/tQTwmwpAFhYV43F6EaWUxWaWmq1bhDQC6iw7qLQazu/AgoCIgjUbD3xRq+RdQe7Gk14tinI5Jp/aZ2d/oO2svOhknH3tkEGOs4X1WhQE4A/B6hNFvtQSxsNDsokmz2ClcWLmP0aJM7TFCWAiRizDdwu5iMjPH0C74qvU7K8est/CARUCIGer5V63feb0oxuj4OZNj0ql9ZvY3+k5rKs4xRgipSRALC80umjSLncKFlfsYLcrUHiMqhRCtwAC8iFGyfHWBQeX2dCqO4fFp3QI4Xe0tWHXeiehIJzGVzUOREm8ezukmq6+3GMxuEn+rRUAaLcF9lPGzL4z8RAiBRFzU9D2vCkxoizXcdvkZuOWxF7FzcKx0/IRmTJ46vwOrzjsRc9pSEKLwilNtK7P2GbW30f5A4QlOraJXU8VCP4QQYoSVOdStgjtGMY26aNLseXI5BUMT05DF/fViDaPrqbxuNS6pdQy940U9buEizCKd6WTVQoL+5X3o0lnksPnKM3HozcIrlHPX/QIXb9iBvYfG0ZlOYvOVZ+La8xdi7UN7cOmmJ7Hmgd2ICYFXRidx9eKTStvXPrQHX/rAAjy251DNxQR2FlkY7bduaS+2DgxW7a/q0CqvhwvJ/MfvvjDykxsf3I2Jw7mavmfXN2uhvf733bwdax7YjWvPX4hT53eUjq8u8FzS042vfqgw1pb1P4FLNj5R1lZm7KvV3nr7r1/Wiy/dvbNszOt95/g5rZFeHEQI8R6zc6id+4LRPl1tKd1Yp7u9xfR5cjkFLxwaxyUbn8DVd++sSsxQb6F75XVvHRisskl7DL3jNULcQg14keHxaXz9/l1VT6i/dXFvVVEbCYmPb3hcV7dl9Lt7VpyNSzc9WbX9zs+diZZEHG87YlbNxWB2/sozWwQkhAnuw4LvusQg+kJRJF598zBeHstgZDKL/u37sHNwDPM603jwS+cir8DQ99x+AmF0/fesOLtqkfGrbx6uqwWvZ1+99lb3z8zksW9oouppvFr06rXJaRyeURAXQDoVR0c6sCcxTaOlpQacGvB6hNFvKzEzh7pdcKczncTQxDRyeQWJeAzd7S1IJGKmz1OpIz91fgdWL16AE7vaMCsVRyImkMnWvidUXndnOllaRJ9MxErH0P5fe7wQxC3UgLtFNpfHtj1D2LZnqGz7DRdWF7U5ODplWTebU6Tu9qE3pzGvM13zZm03ib/Z/RoxwX1UCaIvYjEBKSWW9T9Rdd5MNo9jO1tr7uvmZGd0/QDKzqPaXK+t6tlXr73V/Q+OTuGK25/W/V4sJtA9e5aFqySEkAJm5lC3C+4kEi26Cy7NnqdSR75zcAxX3P40fnnNeW/NhW01L0n3uqvaoc3g/xZsDTOUoBSxkuS91netFiYJg1a0ERPcR5Wg+iIsPuDWOHT7fGFpH0JI8+FXwR2z+5gtvuMljTAnU4JSxEo+Tr183/VyJ+vlLjbKF2xWOmIWdbHETF5BUvO6qda1a/M5NzG+vxZ1Iy+sehwr0hC3zusUs3ao+ehfGTuMlXdVj0MrOXTN+L7u95b3YW7HrCq5ScALgyL3Kp9SEvtQglKbKEhQzGBnfrayjzY/92uT2bLYZuNlfTi6LVUWg6gacG3e7v7lfXjXMdUxg1fzodHcXWlrLRsc2sY84G4OLivFQb77871Y2je/VNzj7UemS45Xq7PraUXdLqBjdqDkcgpefiODofFpjExmsXVg0FHBngYhkJuC0wnLbjAdlhXl9ezQXl9XewtWL16AdxzdhtaWuOWiUIoicXBsCjN5idcnszV9Xx2/U9N5/PG1Sdzy2Iu6hSYC/kMmcoEMA3D7MACvTaME4IB7WVDqFTxb0tNdKnaTVyS++fAebNszVDWXqQ/2KnXktY7t9nyovT4jWwHzD0Ut2sYA3O/BFVTxEbsFdMwm3Q/BgoYwEsmbQqP3pZvXNzw+jd0H38CaB3abOl69c4eg7SPnswzA7cMAvDaNFIB7hRtFCK0e2+350E5BOReuj4sw/Sao4iN2C+iYTbrfCAsaSIFG70s3ry+by6M1FTd9vHrnbvS2J4Q0Fk4Lntk5ttvzoZ2Ccm5cn1MYgFvEq+Ij9Y5vVECnHvWS7mvPq1coyMz5wiJdIAW89tGgcXJ9lb6aTMQwlc2bPp723HpFgBq97QkhjYXRnJUwiB3sLHL3ej6sF794dX1OaeoVdnbwovhIveMbFdAxQ3d7i2HSfS2d6SRWVxQKWr34JHQWy78a0QjJ8BsNr300aOxen56vThzO4fg5rVWFJOoVxjIqAqRXmKeR2p4Q0lgYzadGBXvqxQRmju32fFgrfjGyQS3oFuRcTQ24Dbx+4utVFpRaiyXsarVCoHn1ksjqEhv9rYSd6zPy1Qe/VNAJZrJ55CUwKxmruZizXhGgysJdzIJSG2rA7UMNeG2oATeH3nw6Mpk1LE5o5d7ux73IbEG1sGVBoQTFBm4XH/H6+IlETDfpvha7Wi1qXsOJ1z4aNHauz8hXS8WG6hSO0J67VhGgRm97QkhjoTdn1SpO6PTYbmO2oFoQttWCEhQCwH5S+0ZIhk+aAzd9lX5PCGlkojTHRclWLaF7Ai6E2A9gHEAeQE5KeXqwFkUHtTjJ4WweM4pEIiZ0c43roeqkKnNi1tND2d2PELtUvjbsTCcxmpmp+xrRTV/VO9bG5X2Ixwr2NZLchxDSfPh9b9fO68lEDImYQCZrThoypy2FzVeeiZdGptCaimMqm8fxc1pDH4eELgAv8n4p5WtBGxElFEVi/8gkRiam8ZV7n61bbbOSWExg4TGzcf9V51rSQ9ndjxA76BWNWL34pLJCU0bFFNz0VfVY9111Tqkwzzf+bXdVYR5CCIkift7b9Qr2rF/Wi5sf2Wt6Tp3OKaV6Dup9IOxQgtIgjExm8dLIVCn4BgoaqGu27MJLI1MYmczWPYaqhzq2sxVds81XFLS7HyFWGZnMliZpAFjaN78UfAMFn//C5mcM/d1NX43FBAQElv/wV7ji9qexc3Cs7vkJISQq+HVvr5zX1dhl1XknmppT9faPwjwcxgBcAtgmhBgQQqwI2pioUKugSGsqzkWRpCGoXGyjFqjS4uciYC5CJoQQZ9QqQKj+v9acGtV5OIwSlHOllC8LIboB/FwI8YKU8pfqL4tB+QoAOO6444Ky0VfMpMpJJeKGBUWmijoqq8ck7tGMfusGeoVz5nWm0dXeUiiC057CbZefgVseexE7B8cA+Lv4ppEL79jxWaYU9Be77d1A6Qur4FwbLHZii1oFCNX/15pTK/c/dX4HVi9egLyUGB6fDm18E+o84EKIGwFMSCm/o/f7ZsjxqaeN0tNDWdGAmz0mYW7aINHz081XngkAOPTmYVyzZZdtvaCXNgY8lgL1WQbg0SCEATjn2gbA7nzoVAOu3b+rvQXXnr+w7P7g0Zzs+GChCsCFEG0AYlLK8eL/fw7gf0spH9H7fjMMLiuFbrRZUHKKRNwgC0qDF89xE94UAsTIT3+6chE+oVME554VZwfyNidkb5MYgJO6MAAnXuAktnCSBaVy/0s3PelHfNNwhXiOAXC/EAIo2Ha3UfDdLFjRNsViAke1tdQtKBJVvRRpLgz9NK/obgcQyB+QQRdzIISQMOAkttCdR00WR9Puf3B0KjLxTagCcCnlHwC8N2g7woQXGlO/dKtmnwyG7AkiCQlGfpqMx3zVXev5JwD6LCGEaDCas4UQODg6ZWuutBofRGldThizoBANajJ8tcqTG8nwvThmJaom6+INO3Duul/g4g07sPfQOBRF2voeaT6M/LS7vcVz/1Ux8s/9I5P0WUII0aA3Z/cv78OND+62NVfaiQ/8iG/cIlQacKs0i77LiyfEXj91NqsFC7kenbrEgDHyU7/emhj559qLTsYVtz9dto0+Sw14VKAGnHiFdm4WQuDGB3dj256h0u+tzJV24wOf7g8NpwEnOnihMfVat2pWC0Y9OqmFkZ/6pbs28s/WVLxqG32WENLsaOfmg6NTZcE3YG2utBsfRGVdDiUoxBNUHZYWPR2W2e8REgRG/jmVzVdto88SQshbOL2/N3p8wACceIJZHZafei1FKSTlPzg6heHxaWp2SQkj3zDyz+PntEZCY0gIIV5g5n7q9P4eJT23HShBIZ4QiwksPGY27r/q3Jo6LLPfc0oIC6aQkFDPN/T8E4DnPksIIWHE7P3U6f3dr/ggKPgEnHiGqsM6trMVXbNbDAeN2e85YWQyW5osgIKO7Aubn8HIZNb1c5FoUc839PzTD58lhJAwYuV+6nSubOS5lgE4aQq42JMYQd8ghBDzcM50BwbgpClo9MUcxD70DUIIMQ/nTHdgAB5xuLDQHI2+mKNZ8MLf6RuEEGKesM+ZUYmLuAgzwnBhoXkafTFHM+CVv9M3CCHEPGGeM6MUF/EJeIThwkJrNPJijmbAS3+nbxBCiHnCOmdGKS5iAB5huBCCNBP0d0IIIbWI0n2CAXiECdtCiKjorkg0ccvf6aeEENKYGN0nhBChm/OpAY8w6kKISq1TEAshoqS7ItHEDX+nnxJijxOuf9jyPvu//VEPLCHEGL37RP/yPtz44G5s2zMUqjmfAXiECdNCCCPd1f1XnYuu2S2+20MaDzf8nX5KCCGNS+V9QghRCr6BcM35DMAjjroQImiipLsi0cWpv9NPCSGksdHeJw6OTpWCb5WwzPnUgBNXCJsenRA96KeEENI8hHnOZwBOXCHsifkJAeinhBDSTIR5zqcEhbhCmPTohBhBPyWEkOYhzHM+A3DiGmHRoxNSC/opaXbsZDQhJKqEdc6nBIUQQgghhBAfYQBOCCGEEEKIjzAAJ4QQQgghxEcYgBNCCCGEEOIjDMAJIYQQQgjxESGlDNoG2wghhgG8VOdrRwN4zQdz7BJm+8JsG+C9fa9JKc93+6B1/LbZ29wpYbcP8NbGIHzWS8LUn2GyBWgse6Lit2Foc9oQ/PkBYJaU8mQnB4h0AG4GIcQzUsrTg7bDiDDbF2bbgPDbZ4ewXxPtc04UbAwLYWqrMNkC0J4gCMM10obgz++WDZSgEEIIIYQQ4iMMwAkhhBBCCPGRZgjANwVtQB3CbF+YbQPCb58dwn5NtM85UbAxLISprcJkC0B7giAM10gbgj8/4IINDa8BJ4QQQgghJEw0wxNwQgghhBBCQgMDcEIIIYQQQnyEATghhBBCCCE+wgCcEEIIIYQQH4l0AH7++edLAPzhj1c/nkC/5Y+HP55An+WPxz+eQL/lj4c/jol0AP7aa0FXIiXEOvRbEjXosySK0G9JmIl0AE4IIYQQQkjUYABOCCGEEEKIjzAAJ4QQQgghxEcYgBNCCCGEEOIjDMAJIYQQQgjxkUTQBhB3UBSJkckssrk8Uok45rSlEIuJoM0ixFc4Dohb0JcIIV7CALwBUBSJvYfG8YXNz+DAaAbzOtP4wWdOx8JjZvOGQZoGjgPiFvQlQsxxwvUPW95n/7c/6oEl0YMSlAZgZDJbulEAwIHRDL6w+RmMTGYDtowQ/+A4IG5BXyKEeA0D8AYgm8uXbhQqB0YzyObyAVlEiP9wHBC3oC8RQryGAXgDkErEMa8zXbZtXmcaqUQcQOF16vD4NA6OTmF4fBqK4koVVUJCRb1xYATHRzTxst/s+hIhhJiFAXgDMKcthR985vTSDUPVK85pS5W0jBdv2IFz1/0CF2/Ygb2HxhlkkIaj1jgwguMjmnjdb3Z8iRBCrMBFmA1ALCaw8JjZuP+qc6tW7A+PT+tqGe+/6lx0zW4J2HJC3KPWODDCSOvL8RFuvO43O75ECCFWYADeIMRiQvfGQy0jaSaMxoERHB/RxI9+s+pLhBBiBUpQGhxqGQkxhuMjmrDfCCFRhwF4g0MtIyHGcHxEE/YbISTqUILS4FDLSIgxHB/RhP1GCIk6DMCJa7DX91MnAAAgAElEQVR0M/ELJ76mty+1vs7g2CeEEGswAG9w/CqpzNLNxC+c+Br91H2CaFP2IyEk6lAD3uD4VVKZpZuJXzjxNfqp+wTRpuxHQkjUYQDe4PiVZo3p3IhfOPE1+qn7BNGm7EdCSNShBCXi1NNequm6tDereZ1ppFNxDI9Pu6bZNDoP04IRN1EUCSEEHl79F0jFY5iYzmFofBpbBwZN+Rr91H302nRJTzeEEDg4OmV6frGiI2c/EkKiDp+ARxgz5Zj10nVtvvJMHHpz2tUyzkwLRrxG9fcbH9yNsakZXHH707h4w+NY+9AerF58EjrTybrHoJ+6T2WbLunpxurFJ+GSjU+Ynl+slpZnPxJCoo6Q0n7QFTSnn366fOaZZ4I2IzCGxwtBdOVToMpyzJVPliQkPr7h8br7WaUBMyF4Ynyz+61dVH9fc0EP1j60x7b/NqCfagnEZ7VtKoTAJRufsNQ/Zucyo3M2YD82G5xrI8oJ1z9seZ/93/6oB5b4jmOfpQQlwhjpIDMz+apXv9qb2MHRKUP95MtjGczkFSTjMXS3tyCRMP+ShKWbiZeo/t6RTur7fTaH4XHUDcSc+qka+CmKgrwEpJRNHwBq27TW/GKE4Vym6VMAZQG3mTcehBASVihBiTBG5Zj3DU3UfI1rtF9Okbhk4xP4y/XbccnGJ/DCoXHkcorn10GIGVS/HcvM6Prv86+OuyKnqoUqlfj6/bvw++FJSzKLZsFOmXijfbR9un9kskyi8kKxH9j+hJAowgA8wujpINcv68Utj70IwDg1l95+G5f34VsPv/Va/8BoBqvuGsDQxLSPV0SIMarfbh0YxLqlvWX+u25pL/q37/M8HZ2a/m5p33xct3UX0+DpYEefrbdPZZ++NDJVNT8t7Ztf+sz2J4RECUpQIkxlOWYA+NLdO7FzcKz0Hb1Xv3plnGfyeWzbM1T2vQOjGeTyfAJOwoHqt9+6uBeKouDelYuQyyt4/tVxfOfRvSW/9zIdXT0ZDNPg2SsTr90nk83p9mlrqvwJutoP2s9sf0JIVGAAHnG02svh8WkMVzyxNnr1W6mDHXrzMG67/Ay0puIYy8ygf/s+DE9MIxHnSxLiL7UW11X67fD4tO6CTK/S0VXKYJgGTx87Ont1n+Fx6PbpVLY8uFb7QcVO6kNCCAkKRlcNhN3UXIoiMTKRxZoHduPSTU9i7UN7cO35C3H7FWegu52LKol/hD0dXS0ZDNPguYNRnx4/p7VsW//yPmwdGARgL/UhIYQECdMQNhh2UnMZpQC774vnoPuIWV6bHGaYGstnopCOLuRZUBrCZ/X6FKjOgjKambGd+pCEiobw22aEaQjtE4gERQjxZQBfQOECfiCl/EchxFEA7gFwAoD9AC6RUo4GYV8UMAo6zLz6rdzXKAXYjIH+m/l3iVfYKTFe6fOKIutWeXXiw2blFRwn9jFq48ptVlIfutEffvQp/YaQ5sD3AFwIcTIKwfeZALIAHhFCPFzc9piU8ttCiOsBXA/gOr/tiwLqa/ovbH4GB0YzpVe0C4+Zbarcc+W+d3/+LNN6VifnJqQeTkuMm/FPP3yY48Rf6vmNG/1BvyGEuEkQGvB3A3hSSjklpcwB+E8AFwO4CMAdxe/cAeCvArAtEqip0OykQNPb95sP78HGy/pM6VmdnJuQejjVdJvxTz98mOPEX+r5jRv9Qb8hhLhJEBKU3QC+JYSYAyAD4CMAngFwjJTyFQCQUr4ihOjW21kIsQLACgA47rjj/LE4ZNh5TV9r3217hrD2opNNpQ1zcu5mhn5rDjsp7LSY8U8/fLgRxkmUfLae37jRH/SbaBAlvyXNje9PwKWUzwNYB+DnAB4B8CyAnIX9N0kpT5dSnt7V1eWRleHGaqU5VRN7cHQKQgjdfWOxGLpmt+DYzlZ0zW4xDHisnFt73uHx6abOSEC/NY+q/z22sxVz2lIYmcya9iEz/ulk/Jj1YzvVIMOGlz7rxdyg9RttatZa856V/vCjTxvBb4KGcy2JCoGkIZRS/lBKeZqU8n0AXgfwIoBDQoi5AFD8d6jWMZoZK6/pK9O63fjgbvQvNyc3cXJuq+nkCKnEjg+Z8U8n48esH/udHjFK+DE3uD3vAf70Kf2GkOYhkDSEQohuKeWQEOI4ANsALALwvwCMaBZhHiWlvLbWcZo5xZDZlfJ6ad2W9HTjxo+dbDt9mplz20knF0KYGitA7PqQGf90Mn7M+nFA2SxC77N+zA1ezHsAs6B4SOj9lujDNIT2CaoS5taiBnwGwP+QUo4KIb4N4F4hxOcA/AnAJwKyLRKYTYVmpPm+4UKJYztbPTs3tYzEKXZ9yIx/Ohk/Zv3YTjXIZiAoLbXTeQ/wp0/pN4Q0B4EE4FLK/6azbQTA4gDMaRgqn5x0ppMQQmDLqkUYmcyif/s+7Bwc80VT6DSdHCFu+JDTp4lmbGjSJ5a28aNNU4k4lvR0Y2nffHSkkxjLzGDrwCDnH0JIaAjqCThxmcr8sWpp5lV3DZTyya5b2os7Hv8jvvLBhZ5rClUtY2U+W2oZiVmc+pAbOZXr2cC8zdbxo00708mq+a9/eR8600kvL40QQkzDUvQNQqXmceNlfVj70J6qp0z3rlyEtx0xy5fgoAGeDFKXGDBOfMgtrXEtG0K41iESPut1m4awX0htIuG3pBpqwO3DJ+AeYHRz8TIg1WoeT53fgRO72nR1llJK34JgahmjT9B/ROn5kFmb3NIa1/Jjt/XMQbe3X9Rr0672Fqy5oKckH+nfvs9yzm6nx7BDs/QfIcQ5DMBdxuj16YKudrw4POHZq2pVV9nV3oKvfmghBl/PUINNHBFGeYUVm/xYh+DmOcLY3kGQTsVx7fkLcc2WXaV2WL+sF+mU+TZ14xhWYf8RQqwQSB7wRsaolPDQxLSnJYZVXeXqxQtw3dZduOWxF7FuaS/zyRLbhLEsthWbopa3OYztHQQ5RZYCZ6DQDtds2YWchTzhbhzDKuw/QogV+ATcZYxeSc/kFU9Tb6mlmNta4jgwmsGB0Qy+8+je0ivYeZ1pzD0yzScxxDRhTCVpxSanZe3N4OY5wtjeQTCT058rZ3KKr8ewCvuPEGIFBuAuo/dKeklPNxIxofuqWgI4ODqFdCqOjrTzwhDtLYmSFGXVeSeiI53EVDaPdCrO4JvUpNKXkolY6GRM2vF16vwOrDrvRMxpS0GIwhqLSh+PxUSpnH02l8fIZNZXXa4VTTBTdxZwox2MjiGEwMHRqVKa1tHMjGt/nOmlPvz1/pGyc1ITTghRYQDuMpUpttR0gDf97DmsW9qL67aWaxKvvnsnhiemsX5ZL445YhZOmNNmeoI20hz+5AtnlV65arfbCfBJc6DnS5uvPDN0qSTV8fXdn+/FZ895R9l40tPbeq3LrXV8AJbOzdSdBdxoh850Ev3L+8rSEN66vA83Prgb2/YMldIS3vLY70qfnfqFXurDynNSE04IUWEaQg/QPvUSQuCSjU9UPbHraE3hmp8+i52DYwAKT2fWXnQyTj72SMeptu5duah0Tu12puCyTNOkxjLypQe/dC7yCkKV1UFRJF5987ApH/c6HV2t4wOwfG4Xsmg0hM86bYfh8Wl8/f5dpafRR7WlsP7RF7Btz1DpO/M601hzQQ9W3jlQ+uzEL4x8wc1zNDAN4bfNCNMQ2odPwD1Am2Lr4OhUaULeOThWmojvWXF2KfgGClrB1lS8Si9Y60ZkpDnMeag3Z5qtxsTIlzLZvKPS3V4QiwlIKU35uNF1TWVzGBqXOLqtRdd/3Up1aHUcMnVnAaftkM3lsW3PUCngvmfF2WXBN1Doiw5NYR61b4bHp23Nb0a+oHcOQghhFhSPUbWIWuZ1pjGVzetuqyzHvPfQOC7esAPnrvsFLt6wA3sPjUMpruQ3OnYiHtPd7lRLWs8eEl2MfCms+mOz9hp974VXx/HxDY/r+q8VP69lR9TatJGobPuxzIxuX8zklbLPeUXant/UNROV5xjLzJR9Zv8TQgAG4J6jl6bsu5e8F0e3p8q2rV/Wi+PntJbpHOultTJKgdbd3uJJ+jWm2Wpc/EjZ5yZm7dX73rqlvejfvs/Qf91KdRi1Nm0kKtt+68AgNnz6tKo5t6UYDM/rTGPjZX345sN7bM9viZjA+mXlqV83fPo0bB0YLH1m/xNCVChB8ZjKNGUA8KW7dwIAvv3xP8fbjpyFuBBoTcXRNbu8RHy919u1UqB5kX6NabYaFz9S9rmJWXvV792z4mxM5xS8ODSB7zy6tyT/siJbsZPqMEpt2kjozbvf+48Xyypj3vzIXnzvU6dix3XvRyoRh6IoujIVs/NbJpvHzY/sLTvHXU+8hBsufA9uuPA97H9CSBkMwB1iRiuq1TMOj09jeGIaB0YzWP7DpwC8tTCncj8zaeCMtJJeaEm9SpNGXXk4qPQZRZG29bB+YNbHYzGBVCKO3x2awNqH9tT1XzN+Xstnw95uYcHpuK+3f+W8+/gfRnDvwIHS7+d1ppFOxsu+42R+SyXiGJ6YLq3zUfe/JvEuT3X9nD8JiSaUoDjAjiba7GtpRZGYOJyreqUZ5CtML16pU1ceThqtX+a0pXD8nFZT46men9dqm0ZrN69w2k5W9zczdzmd34KQHNHfCIkuTEPoALspzsw8sVCPXVlQ573zj8RRbcFlSXD7aYvXaeIc0rSpsULeL7ZQFImxTBaZbB55CcxKxmxlQXE79aDLRMJnnfqXnf3NzF1eP5V3mwYap5HwW1IN0xDahxIUB9TSitaaiM28OlePfWA0g/7t+8qC8CNaFCQSwby8cFvaQl15OAlLv7gZ0MRiovDHa5u573bNbimd/5U3MqXzu5l6sFnlA3pt2NXegmwub6pqpBn/1GvbenOX1fnNzjncJCzjlBBiHQbgDjDSiiYTMcfV99Rjd7W34KsfWlhW8a9/eR/edczswIJwN2H57XAShn7xuoql3fMfc0RLzbYx225BX1+QVPrXqfM7cO35C3HppidNtUU9//SjbcPQf2EYp4QQe0Q/ggsQI81fIiYcp+tTj7168YJS8K0ea9VdAxiamHb/ggKAqdrCSRj6Jei0l0bnzynSldSDQV9fkFS20+rFC3DNll2m26JeO/vRtmHovzCMU0KIPfgE3AFGKcheeSPj+LWgeuzWVNyw2mUjELX0d81CGPol6NfrRuefySmupB4M+vqCpNK/8iYrmxrtb7ZKsJttG4b+C8M4JYTYgwG4hlxOwdDENGbyCpLxGFpTMUxO55FMxJCICWSKlSo7ZiUwPJktfa+7vQWxWKqkFRVCYElPd1lOWTuvBWMxgWRcPxVhIt44Ly9YfjtYtDpWIQTiAojFYrb1rG4tdrP6et1tPXWt86s+q84Zg6NTpbkgkYiZardmlw9Upgm02ha15o1UIo4lPd1Y2je/lJP71/tHAAAvjUwiGY+hqy2FscM5Q3/R8ycAZWOlcp5f0tMNIYQpHbtbcP4kJJowAC+Syyl44dA4Vt01UNLzbfj0abjriZfw+B9GsH5ZL25+ZC+6Zqdw9eKT8EXN9/qX96E1FcdnfvRU2TYA2LZnyNFrwe72FvQv7yuzq395H7rbOeES5+jpWNct7cUdj/8RX/ngQst6VjO6WLPaWfX1euX39MaRF3rceufXmzOsrM+wcn2Njttt0ZlOYvXik0p9s6SnG1cvPqlMY37r8j7802O/K5ujVX/R86fNV56J6ZxStk07zy/p6cbqxSfhko1PNJ2mnxBiHaYhLPLyWKY0carM60zjtsvPwAe/+0vM60xjzQU9AKBbzGPtRSfjitufLtt278pFkFI6fhKiPmXL5RUkNE/ZiOc0fGosozRmay7owdqH9lhOZ2YmLZqV1Glmn2p7lY6t1vmN5ox7Vy7C2zvSjo9vk8j6rJttUekPGy/r052311zQUyqco/UXPX+67fIzsOaB3br9LaWEEELXHyKYEjAIIuu3zQ7TENqHT8CLzOQVXT1fvHgDODCaQUc6Wfp/5fdaU/GqbVJKHNvZ6ti2RCJm+oZOiBWMdKwd6aQtPasZXazVUu9mghev9Li1zm80Z1hZn0H5wFu42RaV/qD6sxbtnK5+Vv1Fz5+M1uOo8/zB0anANeGEkOjQtAF45dMWI611vlhRbF5nGmOZGbSl9HWbU9k8Tp3fgVXnnYju2S2YPSuJWKzwJKYzncTrmSwOz+SREAKJeAwzecWzYhBEn0ZoV790zke1pbCkp9uyHtnoeOlUvFSe3coaCbPX60RPncspGJ6YRjavIB4TSKdi6EwXAsG3CvdIzErEkYi/tRakGdZnuIUfY0+7hicRE2V9M5aZ0e2rscxM2edZqRheHssgp0j8x9/+JTb9575S+fqpbL5KV751YLDkY25p+mu11cxMvvA2VJFIxAS621uQTDbHmgFCGo2mDMD19H0/+cJZVVrrDZ8+DT/45R8wrzON9ct6cf+vD+LTZx+H9ct6Symz5nWm8b1PnoK2ljiuPX9h2fZ1S3vxy72HcOEp88qOq+rJhyembetjiTUaoV390jmvW9qL9Y++gNWLT0Kn5gmh3eNtvvJMHHpz2lA7a6T3tXK9djXEejru9ct6cczsHJIJgQOjmbIxrR27enMG12dU48fYq+zHJT3duHV5X2mtztaBwbLPWg04gJKfvjI2XXUPAIDH/zCCP+tuK9OVq/2tjhE3dOy12iqfV/DC0ETVNbyru51BOCERpCk14EZ60YeuPhdTWaWktdbLgnLppierysPPSsaQVySuv++3uhryK25/2lB76EQfS8xjs11DpUv0Uuf86puH8fJYBiOTWfRv34edg2O2j135BE9C4uMbHjfUzho9EbV6vXaeshrpuNdedDLmH9WKy297qubYrZwzQrA+I1Q+C/gzp+n145Kebtxw4XsAoCx7ldpXlVlQcnkFn9DxhXtWnF3Tj7XX4fRJf622yubypUWklfa5IXUMmND5LTEHNeD2CeQJuBDiKwA+D0AC+C2AKwDMBfCvAI4C8GsAl0kpXa9ooCgSmZmcrlZvcjpfmsjUiRQABAQ60ilksoX83gdGM6WFOwBwz4qzEYsLQw15Le2hWX2soiilV/hRlU8ESRhy9jrF7DVYDQJiMQEpJZb1P1H32Gao1PIaaWNV7ayRvVb7zI6G2EjH3ZqKoyUhsOaCnpLcQP3DRDt2tXMG0cePsafXj9v2DOEbH+3BcXPaStsq19J0aZ4cvzQyWXYMVVKYUyRSAGZy+r6SmcmXpR108keFUVtlsjlI6K8/yinRfYhGSDPjOAAXQvQC+AsUgukdUspddb5/LIDVAHqklBkhxL0APgngIwC+K6X8VyFEP4DPAbjVqX1a1Nd7r75x2FYZY6MS1KqO0EhDXm8frUZQT0e4pKcbr01msfLOgTJ7oiSfCJpGyLls5hrsvu73sn1qHbuWvX70mZGOWwJ4I5MrZc7Qpmc0GrtEnyD70YoeX3uMU+d34KsfWliqQjyvM427P3+W7jn2DU3gitufdmVeNmqr518dR8roGnkPIBXYeSpN/MfRu1IhxNcB/ATAsQDmAbhbCPE1E7smAKSFEAkArQBeAfABAFuKv78DwF85sU0PtXTwLY+9iHVLezGv01oZY70S1P3L+7B1YBD92/dh/bLyY65b2ostz/wJ/cv7yravX9aL/u37dDWCeqWFv/HRnlLwrbWnGUpWu0UjlGw2cw12y2N72T61jl3LXj/6TM2zXzk+jz8qXdL6qnZdt3UXrv/wuw3HLtEnqH60qsfXHmPVeSeWgm+g0P/ffHgPNl5W7Su3PPZi6TtO52W9tlq3tHC/uOWxF6vuMbdyzQEhkcWRBlwI8TyAPinlVPFzK4ABKeW76+z3ZQDfApABsA3AlwE8KaX8s+Lv5wP4dynlyTr7rgCwAgCOO+64vpdeesm0vQdHp3Duul8AeOv1Ykc6iXmdacw9Ml16aqH9npYd170fc49Ml70u70wnMZqZQTaXRzoVR06RmMkpZRUF38qCoiAhYDkLSjaXN7SHr7/NY0Of6dqjJSd+q6XeNdTy3Xq+4mWmCqNj17PXr+wZw8XsGbFiFpTDWUXXrv+69v2ICYRZBhY6nwX8zYLiRI+vzaTyl+u3V/3+V1/7AGKxWEk+86W7d2Ln4FjZd5zOy2pbZbI5PP/qeEn6BBTuW//016ci33hZUELpt1El7E/AqQEv4FSC8lLFMRIA/lBrByFEJ4CLALwDwBiAnwL4sM5Xdf8ykFJuArAJKCywsGKs9vXezsGxskWQ2puBmRLUWsxo/o5uaykrYawN+OuRNkh9GJbX31FJ7xdkzmUnfqul3jWovlu5UDidqu8rXraP0bHryROc2FSvlLi6LZGIYW6FNnhY0S+NPisZb5rF0G75LOCOb1X2p/bhh9qXTuslqDUXhsendVMOxmKxsgXzwxPTZfu7MS+rbTU8Xl30bXhiGi2J5vFBO7jpt4R4idMAfArAc0KIR1EImJcA+H9CiH8AACnl3+rs898B/FFKOQwAQoj7AJwDoEMIkZBS5lCQs7zs0LYqzKaJcrssshVdrtF3N195Zlmp+7C8/m6E9H6NxJy2VDHt3+Gy9Hk/+Mzp6EiH7w8jr8qxG/llSyJWNY7cTGtIvKGyP9Wy79qUgG7OO5Wl7CtTDgLe+wh9kJDGxqkE5XO1fi+l/KHOPmcB+BGAM1CQoNwO4BkA7wOwVbMIc5eUckOt49tJMWT2aa2XZZEB4zRcRt+976pzICBC95S5wdMmRjI11tD44brp0sKEF29QjPxy7UUn44rbny7b5mZawxAQSZ+th9nS8m75uNl5zWsfiagP2qEh/TYoKEHxhWAlKHoBtol9fiWE2IJCqsEcgJ0ovC56GMC/CiG+Wdxm+dhmMPsqVPs9o0nQ7OSoppbS6s7HMjNQlOqS1UZpqGZySij13o2Q3q/RMEqXFtY+sSNPqDf29Pyyq70FJ8xpxT0rzi5LK6hN8anN+d/gAU+kMFNavqu9BdlceUpAu31XKx3sy2MZzOQVJIu5xL0kSNkcIcRbHAXgQojzAawFcHzxWAKAlFIeVWs/KeUNAG6o2PwHAGc6sccLjF5lL+hqx4vDE3WlF4oikVcklvR047PnvKMsrdXGy/rQNXuWaf15GImavc1Ao/eJGdlTZRucOr8D156/EJdp5CdqpdrKFJ+1KtWSYKjsz8rS8mr/qoVqnEpSjNLBDk9ky2Qpty7vw0O/OYCN/7Wf8jtCTNLExXvKcFqy7fsAVqKQhrALwNHFfxsGozRpQxPTptK9jUxm8c2H9+D6D7+7Kq3VyjsHqr4ftXR5UbO3GWj0PjGTarGyDVYvXlDSxKv7XLd1Fy475x1VKT6v2bILq847kek+Q0Rlf24dGCxLO6jXv076Tm8Mff2jPVWpKb941wCWnX6cK+ckhDQXThdhHgDwGylltZaiQTB6FZkzqKBX+Zo/m8tj254hfPG8PzP1/VhMYOExs0ulh8P+Gjxq9jYDjd4nZmRPlW2Ql1J3n7yiv92oUi0JBj2f7kwn6/av3b7TO18mq19BOa4ZV/QXQohZnAbg1wL4mRBiO4BSPiYp5S0OjxsKcrnC3xV6ryLjMYEtqxZhZDJb0pLqveZXX2UOjeunNdOTBURN9xc1e5sBM30S1QVeRhKbZEXOZ20bDBuMP6MKijP5t8a+WrEzim3VSFT6tKIpwZ6I6fejE9lV5fleHlN0z5HX2OG11Mtsak36JiHhx6kE5SYAeQAdKEhP1J/Ik8speOHQOG762XNlVTPV9FeXbnoSy/qfwNqH9uCrH1qIJT3dNVMabh0YrFl9kxA/UXXUF2/YgXPX/QIXb9iBvYfGy4KasKInD1i/rBcTh3OG9hvJcrrbW3SP1T4rURrTnelkZNuqUan03xsf3F1VCdPt+bWrLYVbK85x6/I+bHnmT56dU4vemN0/MknfJCSiOE1DOCCl7HPRHkt4mWLo5bEMLtn4RFn2kjltKRxzxCx86gdPVj0FuXflIrztiFm6Tx7UpxaKoiAvASkln1REg4ZNjRX19JGvT07j2cE30JqKlzKaDE9M17Tf6Cm20bHUMT0ymY1SWzWsz2rR898lPd248WMneza/Do9P419++XssO/04xGMCeUVix4tDWHLy232Z0/Wu+bbLz8CaB3ZHxTdr0RR+6xdhT0NohxAuwgy8EuZjQogPSCn/r1NDgkRbflhNLaVIiX/61Klob0ng8EweL79xGN96+Hl899JTdHWAUsqaqQkjNhmSEOOGHMKt9JG1bNH+TgiBuABisZgrQUommy/L561Sy36jcWh0LHVMZ3N5dLW3YM0FPaUUov3b95WdixIVf9Hz3217hnDDhbKUrjWXU/Dqm4fL5vWxwznbfZTN5fHU/jGcdsKckh888Oyr+OB75vqSIlbvmltT8bq+SQgJJ04D8C8A+KoQYgpAFibTEIYJVWqirm5f0tONqxefhC9qUk2tW9qLrQODuPb8hZCQhlpDVoUkXuOWj7mRqrCWLQCqfrduaS/uePyP+MoHFzoeE26mWqx3rHQqjmvPX1hWWXT9sl6kU/G67cBx7w31+szMvG61j2alYrp+MCvlVMlpDr1rjglR0zcJIeHF6cxxNIAkgCMR0TSEQxPTZamllvbNL03SwFvpypb2zcc1W3ZhbCqL9cv0tdxm0qMR4gS3fMyNVIW1bNH7nTqO3BgTbqZarHesnCKrUtxds2UXckWdLce9/9TrMzPzutU+ys7o+0F2xh+9td41v+3IWTV9kxASXpxWwswLIT4J4J1Syv8jhJgH4BgAA65Y5wMzFekE9SqsqWnJCq/oJW5+ZC/uWXE2AJS9ymRVSOI1bvmYG6kK69lSexw5GxNuplqsdyyjyqIzxSxJHPf+U7fPTM7rVvooa5B6Vs2Y4zV611yrcjIhJNw4rYT5fRSegL8PwP8BMAWgH8AZzk3zh2Q8hii9Q1kAACAASURBVCU93VjaNx8d6SSOakvpvtpUK6+NZWYwPDGNVCJepSdt9AqEJHjc9DGnaxPq2VJrHIVhTJjVbde7To77YKjlv5XpJSsrZwLW+8goZWU8JnBwdMoT7b+ej2qv2Si9Jn2PkPDjVIJyjpRyJYDDACClfB1ApPLqdbWlcPXik7D2oT24dNOTWP/oC1WpplQN+IZPn4Zf7x8xfNXd6BUISfCEycdq2aL3O3UcuWGv0zSKVvav1+Zh6hNSoLu9pSwt4daBwap53WofVR5TTUN408+e8yQFoBkfpe8REl2cpiH8FYBFAJ6RUp4mhJgD4D+klKe6ZWAt3EgxZJTO6oYL34PXJrJoTcVLWVC2Dgzixo+dbJhuEGA2hAYjlKmxwuRjQWVBcZpG0er+9do8RH0SSp8NAjW7VS6vIOFCFpTKY8ZjAjf97Dls2zNU+r2bKQDN+miIfM8J9FsXYRpCXwgmDaEQIiGlzAH4ZwBbAXQJIW4CcAkKxXkig1E6q298tAcX/fOOqu/fcKGsObkx5SDxmjD5WC1bvLTTqe7a6v71riVMfUIKJBIxvL0jXbatK+lMmqE95sHRqbLgG3BX+2/WR+l70cFOYBzCwJO4hF0JylMAIKXcDOAbAL4DYBTAJ6SU/+qSbb6QTMRKr+9UVF2f3nZq64jXKIrE8Pg0Do5OYXh8uiGr2jm9RlV3rcXK+HS6Pwkffo8bOz5kxUb6KCGNjd0AvPQIWEr5nJTye1LKf5RS7nbJLl9QFImJw7mqtILrlvZi8+N/9Ly0MSGVRLlEvFncuEan2ldqZxuLIMaNVR+yaiN9lJDGxm4WlC4hxN8a/VJK+Q82j+srI5NZfOZHT6GrvQW3XX4G3sjMYGQyi+88uhc7B8fwx5Ep3LtyEUvHE98wyikdwdLShrhxjU7TELqZxpAETxDjxqoPWbWRPkpIY2M3AI8DaIdHCyf8QFEkZnJ53HHlmUjFY1CkxLL+J8q+o5Y2nntkGiOTWbzyRoaTIPGUqOaUtrIQzM1c5k6CKyv7e7mgVO8cnGes4cW4MdMflT6kSkz09glqbNOvCAkndgPwV6SU/9tVS3xEr3T05ivP1M2nmk7FWWaa+EYUc0pbLcUetWvUu751S3txx+N/xFc+uNCVuYDl7J3htk/Z6Y96+1i10Q2foF8REl4ca8CjiN6rwG//+/P4578+rUpvl1Mky0wT34ii7tNqKfaoXaPe9V23dReW9s13bS5gOXtnuO1Tdvqj3j5WbXTDJ+hXhIQXu0/AF7tqhc8YpR68+gMLsOaCHrz7bbORTiUwpy2FV97IRFISQKJJFHWfdlL6Rekaja5PLW/uxlwQVelRWHDbp+z0R719rNrohk/QrwgJL7YC8GLFy8iSSsTLys+PZWawdWAQL79xGGsf2lO2KMbKa0NFkRjLZJHJ5pGXErOScRzd1hLawIKEE7va5KACWTWVp5XX/1HKXWw0B6jlzbXXabc/oibLCSP1fMpK39jpDzP7WPF7o/uUFZ8Iyq/CMC8REnaclqKPJJ3pJFZrys+vfWgPvvSBBbpl5s2+NlQUif0jk9j76jgu3fQk3nfzdnx8w+MNl0KOhIcwpCw0SuUZZkmJVfTmgHVLe7F1YLDsOp30R9RkOVHDjxSAbveh3n1q9eKT0JlOmj5GEH4VhnmJkCjgqBR90NgtM2tU4vfelYt0y8yb+Wt+eHwauw++gTUP7LZdHpuEjlCXR3Zajt0NVBu62luw6rwT0ZFOYiqbx3vnH4mj2hrH581kQXHaHy49NQy1zwaFnb6x0x9uPvl1a3z7/TTapt1N4bd+VcJkKXpfCKYUfdTJ5vLoam/Bmgt60JFOQpESeUViJq9gZDJra4LK5vJoTcWptyOuUnnz7EwnMZqZKflUV3tLmc/56W+KIpHN5fH3n3gvxjIz6N++DzsHxwAAO657P9Dmixm+YEY64FRvGyVZTtQw2zdWg1W98em3zfXw26+oOyfEHE0ZgKdTcVx7/kJcs2UXutpbcO35C3H9fb/VTdNkNo1TKhHHVDZPHSdxDT3f61/eh1se+x227RnCvM401i/rxc2P7C0Fvn75m1Fqvu88uhfDE9NN6fPUcYcXM31jNWVf5feX9HRj9eKTsOquAVdS/kXVn6JqNyF+05Qa8Jwicc2WXTgwmsGq804s/R+oTtNkNo3TnLYUjp/T2tBaWOIver636q4BLO2bX/p8zZZdWL14AQB//c0oNd/qxQua1uep4w4vZvrGasq+yu8v7ZtfCr7N7O+GzWEkqnYT4jeBPAEXQiwEcI9m0zsB/B2AzcXtJwDYD+ASKeWo2+efySmlSVJNJaZF+7rM7Ou0WEzghDlt6GhN4p4VZyMvgVnJGLOgENvUSn+n/Xxidzt2XPd+X7MNGNl2Ync75nWkm9Lno5ZesZkw0zdWpROV3693L/HC5jASVbvDSiPque3gl37eTwIJwKWUewGcAgBCiDiAgwDuB3A9gMeklN8WQlxf/Hyd2+fXviJTU4kZvS4zep2WTsV1Sw4f1dbSUNpXEhy10t9pP6eTcd+1w4bjIhn39UYbtnRn1HEHRz1fqNc3VqUTld+vdy+xQ1T9Kap2E+InYZCgLAawT0r5EoCLANxR3H4HgL/y4oTaV2T92/fVlI3ovU7bfOWZOPTmNNMsEU/R873+5X3YOjBY+hzUq90wvGZmujOi4oYvWPXpyu9vHRhE//I+Si8IIaYIPA2hEOJHAH4tpfy+EGJMStmh+d2olLLTaF8nKYa0T0vSqThyisRMTtF9clL5ZEVC4uMbHme6wcYn8NRYtbKgBP3EN+inz2FIwxhCAvfZIAgqZV+Yx2fEaAq/pZzEXzyWoEQ7DaEQIgXgYwC+ZmGfFQBWAMBxxx1n+ZyVE+bcI9/Sq6q/e+WNTNnkWfk67eDoFNMsEUvY9Vu9V7k+5pS2bJuf+JXuLOg/NILC6VzrJ16k7NPrdwBV2+yMT+IdUfJb0twEnYbwwyg8/T5U/HxICDFXSvmKEGIugKHKHaSUmwBsAgp/3Vo5Wa00UwBMp6BimiViFSd+axWr6dSiih/jsFnaUg8/fdYpbvuCXr9vvvJMTOeUpvSFKBElvyXNTdAa8E8B+Inm84MAPlv8/2cBPODmyWqlmbKSgioM+ldCjLCaTi2q+DEOm6Uto47bvqDX7y+NTNEXCCGuEdgTcCFEK4APAlip2fxtAPcKIT4H4E8APuHmOeu9pjT7CpNplkiYaZZKdH6Mw2Zpy6jjti/o9TsrHRMt1HMTpwQWgEsppwDMqdg2gkJWFE8wek2ZTMSQy0tsWbUII5NZ9G/fBwBYvXgB8lJiaPwwEjGBTLZcC0hIGGkmiZRVHbqRnttoe722dFsf3qx6czdwc02CXr8bVToWQuDg6BRSiTg6ZiUwPJnFTF5BMh5Dd3sLEomgXzQTQsJI0BpwX1FfU1Zq+CYO5/CZHz1V2vb9vz4VMzkFX7n32dI2teT38MQ0fvCZ09GSiJXtQy0gCQtGft7sfzga6bkXdLXjxeEJXW1vrbZ0Wx/ezHrzsKHX73/W3Yb+5X1lpeZvXd6HO3b8ARv/az9W/rcTcMEp8/BFze/7l/fhXcfMZhBOCKki8DSETrCTYqjyCVM8Bnzs++Xpq267/AyseWB31ZOONRf0YOWdA5jXmcbai07GFbc/Xfb7Jk9/1ohENjUWn6RWY5Sq7t6Vi3DJxicMU9gZtaXbaRBdOl5kfTZs6KWf/cb9v8XSvvnoSCcxlpnB1oFBLO2bj5V3DuDnX3kfrrj9aV3/entHOsAriQSR81tKUMIP0xCGDDMpBY20fmoJ8AOjGbSm4lW/pxaQhIWgUwSGESM9dy6v1NT2GrWl2/pw6s3Dhd69YtueIWzbU56c63N/8U4AQDwmDP2LEEIqafr3YqrWT4sEqrZpS4DP60xjKpuv+n2lxlZRJIbHp3FwdArD49Os0EcsQf8xxk7b6I31eZ1pJOIx3e31NPNGx7OrtXf7eM2O2+PHqH/U+0Jekbq/j8eEJRs47glpDpo+AK9MX7WkpxtHt6eqytOvX9aL/u37Srq+P+tuq5nyimWyiRPoP8bYbZvOdLKqVHj/8j502Uxh53bqO6Y3dQ8vxo9e//Qv78PWgUEAwJZn/oRbK/zr1uV9uOlnz5m2geOekOah6TTgemi1fkIIXLLxCXS1t2DVeSeiI52EBDD3yFl49Y3DJd3fNy/+cwgIQ40ty2Q3BIHpEuk/xthtm+HxaXz9/l1VGt5vXdyLOW0pW5r5EGZBiZyW1gu8Gj/1Ss+rWVByeQXxmMBNP3uuTLJSz4YmHveR81tqwMMPNeAhRq8s/StvZHBgtPCz8s6B0nfvWXE2Lt30ZOnzDRcqOLazVfc4c9pS1HMSR9B/jLHbNtlcXlfDe8OFedua+Vr71QqmjX5H7b472PWRen8A6fVP5Wd1waWqGbdiA8c9Ic1D0wbgRim/5rSndHO9qjo/9bM2D7DecY45oqVpcjET92mmXN5Wsds2frZprZSCAJhu0GPs9LXbaSCTiZhh3Qk37SaERJOm1YAblZhOxERNnV+lLtPoODlFUs9JbEM9sDF228bPNq1Vwp7l7b3HTl+73S+JmNBdS5SoEcxz3BPSPDTtE3CjV32ZbL6qpHFnOolvXdyLGy6sfi1pdJyZnMJy9cQ2fpRZjyp228bPNq0nJaDMwFvs9LXb8o9MNo+bH9mLNRf0lNYc3PzIXnz/r08F2tyzmxASTZo2AK/1qi8WE2WLsl7PSMOnFvWOQz0nsYtZ/2nGojteaLbdxGheAAAhBGUGPlCvryvHTTIRw5Ke7qpFuk7SSg5PTJetJTLTz7xvENIcNG0AbrXEdGUpelUXyLLfJEhYvjyc6M0L65f14kt370TX7FRVSXPOGf6iN25+8oWzsHrxSWX90r+8D53FAmxW4b2BkGCxm6nG4+wpJZo6DaHVEtPaUvTatFDN+ASySQh9aqwmTlsWetR5ITOTx76hCdzy2IvYOTgGoFBv4MaPnQwppdtzRuh9NgzojZvbLj8Dax7Y7epY4r3BNJHzW6YhbFxMBuBMQ+gE9VVfLqdgaGIag6NTSMZjiAt9jaa2FL1WF8hXhiQomLbMHbwIlNR54eDoFK64/emy323bM4QbLpSlVKZ+2kX0x01rKu54LOn1l/beoFa5ZH8SQpo6AAeAXE7BC4fGq147LunpriqgoC1FT70mCQNMW+Ycr2U8dvuI8iLv0OuTqWze0Viq11/sT0KIlqZNQ6gyNDFdCr6BwhOPVXcN4Osf7TEsRU8dHwkLTFvmHK/TAtrtI6Yr9A69Pjl+TqujsVSvv9ifhBAtTf8EfCav6L52FEApFVQyEUMiJvD9vz6Vrw1JqGDaMud4LeOx20eUF3mHUZ8AsD2W6vUX+5MQoqXpA/BkXD/1VCIeq9Z1G+RuJcRrammBuQbBGX7IeOz0EeVF3mLUJ3bHUr3+Cqo/uY6AkHDS9BKUrrYUrl58EtY+tAeXbnoSax/ag6sXn4QuvsInIUHVjl68YQfOXfcLXLxhB/YeGoeiRDeDUZgIq4wnrHYRfer1VxD9ybmDkPDS1GkIAaZxIzUJRWos+qj3hPUpoQ27QuGzzUq9/vLbzyI0d0TOb5mGsHFhGkKfoC6PhB36qPeEVcYTVruIPvX6y+/+5NxBSHhpagmKoshSWWgt8zrTEELg4OgUhsen+bqOBIqqHdVipB1V8wzTd6MB+yu82OmbsPWnlbmDEOIvTRuAq9q4Gx/cjXVLe8t0ef3L+3Djg7upmSOhwKx2lHrPaMH+Ci92+iaM/cl1BISEl6bVgGu1cafO78Cq807EnLYU5h45Czf97LmqIjwh1MwR7wmNLtGMdjRCek8Cz/orND4bZez0TVjHX1jXN1QQOb+lBrxxoQbcZbSTUDIRQ155K//3zsExrLxzAADwy2vOKwu+gdqauYhMbqRBSMQEZvIKXnkjU+Vvbus96dveQn1ueMnm8uhqb8GaC3pK6Wn7t++r2Td6/dnV3oJsLo+Do1OBjSE/dOecKwixTlME4HolgDd8+jTdcvOJeMx0rlaWFiZ+oPrZd3++F5895x24busuXX9zM88wfdt7mOc7vKRTcVx7/kJcs+WtsbZ+WS/SKeO+qezPU+d34NrzF+LSTU829BhqhLmCT7NJEDSFBlyvBPBVP/41rv/wu6u0cd3tLaY1cywtTPxA9bOlffNLwTdQ7W9u6j3p295DfW54ySmyFHwDBf+/Zssu5GrouSv7c/XiBVXHaMQxxLmCEHsE8gRcCNEB4F8AnAxAArgSwF4A9wA4AcB+AJdIKUfdOJ/Rq943MjOlV4zzOtOYe2TaUtlovkImfqD6WUc6WdPf3CxLT9/2Hjf7i7jLTE7R9f+ZnGK4T2V/5qVsijHEuYIQewT1BPx7AB6RUr4LwHsBPA/gegCPSSkXAHis+NkVjFIxDY1PY+WdA/ifP30WqUS8qrT3sZ2t6JrdYnhDZIon4geqn41lZur6m1nfNXvOWuciznGrv4i72PV/bX+mk4mmGEOcKwixh+8BuBDiCADvA/BDAJBSZqWUYwAuAnBH8Wt3APgrt86p96p3/bJe9G/f5+i1L18hEz9Q/WzrwGBVykyv/I2+TZoZN/y/WcZQs1wnIW7jexpCIcQpADYB2IPC0+8BAF8GcFBK2aH53qiUsrPWsaykGKrMgpKICWSyzl/7cvV3QxOa1FiqnymKgrwEpJSe+xt9O5KExmejjhv+3yxjyIXrDNRvuQiTaGnkNIQJAKcBuFpK+SshxPdgQW4ihFgBYAUAHHfccTW/W3dSaLO5nwaWiiZmcMNv/fYz7TmbJZAgb2HFZ4k+dsZtFMdamO6D9FsSFYIIwA8AOCCl/FXx8xYUAvBDQoi5UspXhBBzAQzp7Syl3ITCE3Scfvrpho/v7aZGaoSUSiR8eO23XhJGm4j3mPXZRiQon+dYc04z+y2JFr5rwKWUrwIYFEIsLG5ajIIc5UEAny1u+yyAB5ycx25qJKZUIkESRv8Lo02EeElQPs+xRkjzEFQhnqsB/FgIkQLwBwBXoPDHwL1CiM8B+BOATzg5gd3USEypRIIkjP4XRpsI8ZKgfJ5jjZDmIZAAXEr5GwCn6/xqsVvnsFtlLpWIY0lPN5b2zS+VIN46MMiUSsQXvK6OaEdfyoqNpNnwy+crx2M6xbFGSLPQsJUw7aZG6kwnsXrxSVj70B5cuulJrH1oD1YvPgmd6aQfZpMmx8uUXqq+9OINO3Duul/g4g07sPfQOJQa1f28tomQMOKHz+uNx0NvTmPzlWdyrBHSBPiehtBN6qUYsvO0b3h8Ghdv2FH1BOL+q84NzSpv4huBpMbyKguCE9+OYmaGJoVpCF3Ca583Go/3XXUOBESzjTWmISShoZHTEPpGZWokRZEYHp+uObFRg0eCxquUXka+rShK3XERpjRjhPiB1z5vNB5ncgqO7Wz17Lxm4B/chHhPQwfgWsymd6LelTQqer69pKcbr01msfLOAaY9I8RHwnqvYSpEQvyhYTXglZhN70S9K2lU9Hz7Gx/tKQXfANOeEeIXYb3XMBUiIf7QNE/AzUpLYjGBhcfMxv1XncvXb6Sh0PNtSq4ICYaw3ms4JxDiD00TgFt53Ue9K2lUKn17eHw6lK/BCWkGwnivCas0hpBGo2kkKGF93UdIkHBcEEK0cE4gxB+a5gl4WF/3ERIkHBeEEC2cEwjxh6YJwIFwvu4jJGg4LgghWjgnEOI9TSNBIYQQQgghJAwwACeEEEIIIcRHGIATQgghhBDiIwzACSGEEEII8REG4IQQQgghhPgIA3BCCCGEEEJ8REgpg7bBNkKIYQAv1fna0QBe88Ecu4TZvjDbBnhv32tSyvPdPmgdv232NndK2O0DvLUxCJ/1kjD1Z5hsARrLnqj4bRjanDYEf34AmCWlPNnJASIdgJtBCPGMlPL0oO0wIsz2hdk2IPz22SHs10T7nBMFG8NCmNoqTLYAtCcIwnCNtCH487tlAyUohBBCCCGE+AgDcEIIIYQQQnykGQLwTUEbUIcw2xdm24Dw22eHsF8T7XNOFGwMC2FqqzDZAtCeIAjDNdKG4M8PuGBDw2vACSGEEEIICRPN8AScEEIIIYSQ0NDQAbgQ4nwhxF4hxO+FENeHwJ79QojfCiF+I4R4prjtKCHEz4UQLxb/7fTRnh8JIYaEELs123TtEQVuKbblLiHEaQHYdqMQ4mCx/X4jhPiI5ndfK9q2VwjxIS9t84Kw+GqYfaJ4zvlCiF8IIZ4XQjwnhPhymGwUQswSQjwlhHi2aN9Nxe3vEEL8qmjfPUKIVHF7S/Hz74u/P8FL+6KC3lzp8/lNj4MA7TGcDz22xdIYjCpCiA4hxBYhxAvFa13k5zUKIRZq+vY3Qog3hRB/43c7CyG+Uuzn3UKInxTnON35zEMbvlw8/3NCiL8pbvO0HXy5F0opG/IHQBzAPgDvBJAC8CyAnoBt2g/g6IptNwO4vvj/6wGs89Ge9wE4DcDuevYA+AiAfwcgAJwN4FcB2HYjgK/qfLen2L8tAN5R7Pd40D5o4VpD46th9oniOecCOK34/9kAflfs/1DYWDxPe/H/SQC/Kp73XgCfLG7vB/DF4v+vAtBf/P8nAdwTtD+G4UdvrvT5/KbHQYD26M6HPthiaQxG9QfAHQA+X/x/CkBHUNdYvEe8CuB4P20AcCyAPwJIFz/fC+Byo/nMIxtOBrAbQCuABID/ALDA63awMgfYvc8E7uQedtoiAI9qPn8NwNcCtqnqpgJgL4C5xf/PBbDXZ5tOqHAwXXsAbATwKb3v+Wib7g2nsm8BPApgUZB9bfE6Q+WrYfYJHVsfAPDBMNpYvGH8GsBZKBSNSFT2t9ZXizeX11Bcm9PMP3pzZQA2mBoHAdqjOx8G0E41x2AUfwAcgULgKSq2B3KNAJYA2OG3DSgE4IMAjirOTw8B+JDRfOaRDZ8A8C+az2sAXOtHO3h9L2xkCYrqOCoHituCRALYJoQYEEKsKG47Rkr5CgAU/+0OzLra9oSlPb9UfMXzI80rp7DYZpew2x9KnyjKNU5F4SlzaGwUQsSFEL8BMATg5yi83RiTUuZ0bCjZV/z9GwDmeGlfRNCbK4MmbHM1oD8f+obJMRhF3glgGMBtQoidQoh/EUK0Ibhr/CSAnxT/75sNUsqDAL4D4E8AXkFhfhqA8XzmBbsBvE8IMUcI0YrC0+b5CKYvXL3PNHIALnS2BZ3y5Vwp5WkAPgzgfwgh3hewPVYIQ3veCuBEAKegMBn8fXF7GGxzQlTtD8xuIUQ7gK0A/kZK+Watr+ps89RGKWVeSnkKgHkAzgTw7ho2RLXvvSbKc6VfGM2HvmBhDEaRBAryg1ullKcCmERBcuA7RX31xwD8NIBzdwK4CAVp59sBtKEwJivxbM6SUj4PYB0KDzMeQUGimau5k//YmscbOQA/gMJfSSrzALwckC0AACnly8V/hwDcj8LN+ZAQYi4AFP8dCs5CoIY9gbenlPJQMbhRAPwAhfYLhW0OCbv9ofIJIUQShRv/j6WU94XRRgCQUo4B2I6CJrBDCJHQsaFkX/H3RwJ43Q/7wozBXBk0oZqra8yHnmNxDEaRAwAOSCl/Vfy8BYWAPIhr/DCAX0spDxU/+2nDfwfwRynlsJRyBsB9AM6B8XzmCVLKH0opT5NSvg+F+fFFBNMXrt5nGjkAfxrAguJq3RQKr3AeDMoYIUSbEGK2+n8UNF27izZ9tvi1z6KgpwsSI3seBPCZ4mrfswG8ob6K8QvV8YtcjEL7qbZ9UhQySrwDhQUaT/lpm0NC5as6hMYnhBACwA8BPC+l/Iew2SiE6BJCdBT/n0bhBvY8gF8AWGZgn2r3MgD/VxZFhM1KjbkyaEI1V9eYD70+r9UxGDmklK8CGBRCLCxuWgxgD4K5xk/hLfkJfLbhTwDOFkK0FvtdbQej+cwThBDdxX+PA/BxFNojiL5w9z7jlXA+DD8oaIV+h4IG8+sB2/JOFF6dPAvgOdUeFPSej6HwF91jAI7y0aafoPDqcgaFv+A+Z2QPCq9Y/rnYlr8FcHoAtt1ZPPeuosPP1Xz/60Xb9gL4cNC+F1VfDbNPFM/5Fyi82tsF4DfFn4+ExUYAvQB2Fu3bDeDvitvficIfhb9H4VVyS3H7rOLn3xd//86gfTHoH6O50mcbTI+DAO0xnA89tsXSGIzqDwrSnmeK1/lvADr9vkYUFnKPADhSs81vG24C8EJxPrsThWxjuvOZhzb8FwqB/7MAFvvRDlbmALv3GVbCJIQQQgghxEcaWYJCCCGEEEJI6GAATgghhBBCiI8wACeEEEIIIcRHGIATQgghhBDiIwzACSGEEEII8REG4BFECDFPCPGAEOJFIcQ+IcT3ivmjCYkEQoivCyGeK5bR/o0Q4qwa390uhDjdT/sI0SKE+K4Q4m80nx8VQvyL5vPfCyH+lxBii8XjXi6E+L6bthJSLNv+m+LPq0KIg5rPjBVCAgPwiFFMhn8fgH+TUi4AcBKAdgDfCtQwQkwihFgE4AIAp0kpe1EoVjPo4vHjbh2LkCKPo1ABEEKIGICjAbxH8/tzADwmpVymsy8hviKlHJFSniKlPAVAP4Dvqp+llNmg7SMFGIBHjw8AOCylvA0ApJR5AF8BcKUQ4keav3KHhRA3FCszrRdC7BZC/FYIcSkACCHOKz5Z3CKEeEEI8eNicA8hRJ8Q4j+FEAPFJz1zDa0hxDpzAbwmpZwGACnla1LKl4UQi4UQO4t++iMhREvljkKIW4UQzxSfnt+k2b5fCPF3Qoj/B+AT/l0KaRJ2oBiAoxB47wYwLoToLPrpuwGMCiF2A6Un2/cJIR4pvqm8WT2QEOIKIcTvhBD/CeBcn6+DNDlCiM8KIZ4qxgkbhBAxIURCCDFWjBV+Xbzvn1WMA/4ghPhIcd/PCyHup2TBJQAABAdJREFUL/5+rxDiG0FfT5RhAB493gNgQLtBSvkmCiVj/7H4F+9FKFTPuh2Fsq2nAHgvCk8a12sC6lMB/A2AHhQqW50rhEgC+CcAy6SUfQB+BD5dJ/+/vfsL7aqM4zj+/uAEk6W7SCJiZVQU5GKYBg7Lggq6lP4sgoogpKiLoOiiEKybuimkQOhGiiAbQnQVpBNWJKJFacOo6A9RaFJoi6I/Tj9dPM+2nz/m1mQ7Mf28rg7POefhGTw75/v7nu85z+zaAXTXIGSLpHWSFlHma7/tHqADeGSSc5+xvYqy4uQ6Sde27PvL9lrbb831HxDnFtuHgNG6FHYfsAfYC6wBVlFWS2zPLPYC/UAP0C+pu157n6UE3rdSrr0RjZC0AlgP9NVYoQO4p+5eCuywvZIylzdRlp6/C3iupZvr6zkrgXsl9TYz+rNPx/89gJgxUZYBnrS9BjLbgcdsf1/rFrfVTPmRmnVZDfwG7LP9I4Ck/cBy4FdgBbCzJsQXUJZjjZgVtn+XdB1wA3AzMAA8D3xn+6t62OvAo8DmttPvlrSBcu26iBLAfFb3Dcz12OOcNpYF7wNeAi6u2yOUEpV2u2yPAEj6HLiUUroyZPvn2j5AKSOMaMItlPv/x/X+fh4T5X9/2t5Zt4eBEdujkoYpscGY92wfA5D0DrAW2N/A2M86CcDnn4PAHa0NkpYA3cA3lHqvt20Pju2eoq+/W7ZPUOaDgIO218zaiCPa1B+EQ8BQvcA/MN05ki4DngRW2z4m6TVgUcshf8zBUCPGjNWB91BKUH4AnqAkM7ZOcvxk11eYPIES0QQBW21vPKVR6uDUJzgnmZi/Jzk1Vmyfv5nPZyglKPPPLmCxpPth/IWzFymP7x8Ezrf9QsvxH1Aefy6QtAy4Edg3Rf9fAsvqi3JIWijpmimOj5gRSVdJurKlqRc4AiyXdEVtuw94v+3UJZQge0TShcDtcz7YiAm7KS8PH7V9wvZRoItShrLnP/axF7ipfqViIXlfIZo1SHmKeAGMfy3lkhn2cZukLkmLKeWuu2d7kOeKZMDnGduWtB7YImkj5UfUu8DTwBfA8VpOAiUb/irlBnGA8kv1Kds/Sbr6NP3/I+lO4GVJSylzZDMl8x4xGzqBVyR1AaPA18AGYBuwvWZjPqLM33G2D0j6lDIXvyUX/mjWMKWE5M22tk7bv0jqnK4D24clbaIE7IeBTyhlfhFzzvZwfXl9UOVrPseBh4FDM+jmQ8r/wOXAG7ZTfnKGZOfpQUREREScnqSHgBW2H5/24JhWSlAiIiIiIhqUDHhERERERIOSAY+IiIiIaFAC8IiIiIiIBiUAj4iIiIhoUALwiIiIiIgGJQCPiIiIiGhQAvCIiIiIiAb9C2smvCBbxkznAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These scatter plots are all far from being nice and linear. In particular, the curvature in the scatter plot Ozone vs. Wind is an indication of nonlinearity between the variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets interest us to the influence of the wind in the amount of Ozone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3751520559966308"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "M1=smf.ols(formula=\"Ozone ~ Wind\",data=df).fit()\n",
    "M1.rsquared"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The R squared is low 37% so we can trust to this reliability of this model\n",
    "\n",
    "Let's add the powers of the wind in a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(2,6):\n",
    "    df[\"Wind_{}\".format(i)]=df.Wind**i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ozone</th>\n",
       "      <th>Solar</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Wind_2</th>\n",
       "      <th>Wind_3</th>\n",
       "      <th>Wind_4</th>\n",
       "      <th>Wind_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>67</td>\n",
       "      <td>54.76</td>\n",
       "      <td>405.224</td>\n",
       "      <td>2998.6576</td>\n",
       "      <td>2.219007e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>72</td>\n",
       "      <td>64.00</td>\n",
       "      <td>512.000</td>\n",
       "      <td>4096.0000</td>\n",
       "      <td>3.276800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>74</td>\n",
       "      <td>158.76</td>\n",
       "      <td>2000.376</td>\n",
       "      <td>25204.7376</td>\n",
       "      <td>3.175797e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>62</td>\n",
       "      <td>132.25</td>\n",
       "      <td>1520.875</td>\n",
       "      <td>17490.0625</td>\n",
       "      <td>2.011357e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.0</td>\n",
       "      <td>299.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "      <td>73.96</td>\n",
       "      <td>636.056</td>\n",
       "      <td>5470.0816</td>\n",
       "      <td>4.704270e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>63</td>\n",
       "      <td>275.56</td>\n",
       "      <td>4574.296</td>\n",
       "      <td>75933.3136</td>\n",
       "      <td>1.260493e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>30.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>6.9</td>\n",
       "      <td>70</td>\n",
       "      <td>47.61</td>\n",
       "      <td>328.509</td>\n",
       "      <td>2266.7121</td>\n",
       "      <td>1.564031e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>14.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>75</td>\n",
       "      <td>204.49</td>\n",
       "      <td>2924.207</td>\n",
       "      <td>41816.1601</td>\n",
       "      <td>5.979711e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>18.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>76</td>\n",
       "      <td>64.00</td>\n",
       "      <td>512.000</td>\n",
       "      <td>4096.0000</td>\n",
       "      <td>3.276800e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>20.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>68</td>\n",
       "      <td>132.25</td>\n",
       "      <td>1520.875</td>\n",
       "      <td>17490.0625</td>\n",
       "      <td>2.011357e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ozone  Solar  Wind  Temp  Wind_2    Wind_3      Wind_4        Wind_5\n",
       "0     41.0  190.0   7.4    67   54.76   405.224   2998.6576  2.219007e+04\n",
       "1     36.0  118.0   8.0    72   64.00   512.000   4096.0000  3.276800e+04\n",
       "2     12.0  149.0  12.6    74  158.76  2000.376  25204.7376  3.175797e+05\n",
       "3     18.0  313.0  11.5    62  132.25  1520.875  17490.0625  2.011357e+05\n",
       "6     23.0  299.0   8.6    65   73.96   636.056   5470.0816  4.704270e+04\n",
       "..     ...    ...   ...   ...     ...       ...         ...           ...\n",
       "147   14.0   20.0  16.6    63  275.56  4574.296  75933.3136  1.260493e+06\n",
       "148   30.0  193.0   6.9    70   47.61   328.509   2266.7121  1.564031e+04\n",
       "150   14.0  191.0  14.3    75  204.49  2924.207  41816.1601  5.979711e+05\n",
       "151   18.0  131.0   8.0    76   64.00   512.000   4096.0000  3.276800e+04\n",
       "152   20.0  223.0  11.5    68  132.25  1520.875  17490.0625  2.011357e+05\n",
       "\n",
       "[111 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Ozone</td>      <th>  R-squared:         </th> <td>   0.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   65.44</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>9.09e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:06:32</td>     <th>  Log-Likelihood:    </th> <td> -519.94</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   111</td>      <th>  AIC:               </th> <td>   1044.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   109</td>      <th>  BIC:               </th> <td>   1049.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   99.0413</td> <td>    7.472</td> <td>   13.254</td> <td> 0.000</td> <td>   84.231</td> <td>  113.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wind</th>      <td>   -5.7288</td> <td>    0.708</td> <td>   -8.090</td> <td> 0.000</td> <td>   -7.132</td> <td>   -4.325</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.375</td> <th>  Durbin-Watson:     </th> <td>   1.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.009</td> <th>  Jarque-Bera (JB):  </th> <td>   9.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.708</td> <th>  Prob(JB):          </th> <td> 0.00862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.228</td> <th>  Cond. No.          </th> <td>    31.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Ozone   R-squared:                       0.375\n",
       "Model:                            OLS   Adj. R-squared:                  0.369\n",
       "Method:                 Least Squares   F-statistic:                     65.44\n",
       "Date:                Sun, 14 Jun 2020   Prob (F-statistic):           9.09e-13\n",
       "Time:                        12:06:32   Log-Likelihood:                -519.94\n",
       "No. Observations:                 111   AIC:                             1044.\n",
       "Df Residuals:                     109   BIC:                             1049.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     99.0413      7.472     13.254      0.000      84.231     113.851\n",
       "Wind          -5.7288      0.708     -8.090      0.000      -7.132      -4.325\n",
       "==============================================================================\n",
       "Omnibus:                        9.375   Durbin-Watson:                   1.625\n",
       "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                9.508\n",
       "Skew:                           0.708   Prob(JB):                      0.00862\n",
       "Kurtosis:                       3.228   Cond. No.                         31.7\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M1 = smf.ols('Ozone ~ Wind ',data=df).fit()\n",
    "M2 = smf.ols('Ozone ~ Wind + Wind_2' ,data=df).fit()\n",
    "M3 = smf.ols('Ozone ~ Wind + Wind_2+ Wind_3',data=df).fit()\n",
    "M4 = smf.ols('Ozone ~ Wind + Wind_2+ Wind_3 + Wind_4',data=df).fit()\n",
    "M1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Ozone</td>      <th>  R-squared:         </th> <td>   0.505</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   55.20</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>3.06e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:05:16</td>     <th>  Log-Likelihood:    </th> <td> -506.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   111</td>      <th>  AIC:               </th> <td>   1020.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   108</td>      <th>  BIC:               </th> <td>   1028.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  166.7663</td> <td>   14.344</td> <td>   11.626</td> <td> 0.000</td> <td>  138.334</td> <td>  195.198</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wind</th>      <td>  -19.9586</td> <td>    2.741</td> <td>   -7.281</td> <td> 0.000</td> <td>  -25.392</td> <td>  -14.525</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wind_2</th>    <td>    0.6621</td> <td>    0.124</td> <td>    5.335</td> <td> 0.000</td> <td>    0.416</td> <td>    0.908</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.370</td> <th>  Durbin-Watson:     </th> <td>   1.615</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.025</td> <th>  Jarque-Bera (JB):  </th> <td>   7.308</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.626</td> <th>  Prob(JB):          </th> <td>  0.0259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.105</td> <th>  Cond. No.          </th> <td>    888.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Ozone   R-squared:                       0.505\n",
       "Model:                            OLS   Adj. R-squared:                  0.496\n",
       "Method:                 Least Squares   F-statistic:                     55.20\n",
       "Date:                Sun, 14 Jun 2020   Prob (F-statistic):           3.06e-17\n",
       "Time:                        12:05:16   Log-Likelihood:                -506.96\n",
       "No. Observations:                 111   AIC:                             1020.\n",
       "Df Residuals:                     108   BIC:                             1028.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    166.7663     14.344     11.626      0.000     138.334     195.198\n",
       "Wind         -19.9586      2.741     -7.281      0.000     -25.392     -14.525\n",
       "Wind_2         0.6621      0.124      5.335      0.000       0.416       0.908\n",
       "==============================================================================\n",
       "Omnibus:                        7.370   Durbin-Watson:                   1.615\n",
       "Prob(Omnibus):                  0.025   Jarque-Bera (JB):                7.308\n",
       "Skew:                           0.626   Prob(JB):                       0.0259\n",
       "Kurtosis:                       3.105   Cond. No.                         888.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Ozone</td>      <th>  R-squared:         </th> <td>   0.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.502</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   37.98</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 14 Jun 2020</td> <th>  Prob (F-statistic):</th> <td>8.56e-17</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:05:24</td>     <th>  Log-Likelihood:    </th> <td> -505.79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   111</td>      <th>  AIC:               </th> <td>   1020.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   107</td>      <th>  BIC:               </th> <td>   1030.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  201.5751</td> <td>   27.191</td> <td>    7.413</td> <td> 0.000</td> <td>  147.672</td> <td>  255.478</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wind</th>      <td>  -31.8274</td> <td>    8.351</td> <td>   -3.811</td> <td> 0.000</td> <td>  -48.383</td> <td>  -15.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wind_2</th>    <td>    1.8401</td> <td>    0.793</td> <td>    2.320</td> <td> 0.022</td> <td>    0.268</td> <td>    3.412</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Wind_3</th>    <td>   -0.0349</td> <td>    0.023</td> <td>   -1.504</td> <td> 0.136</td> <td>   -0.081</td> <td>    0.011</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 8.294</td> <th>  Durbin-Watson:     </th> <td>   1.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.016</td> <th>  Jarque-Bera (JB):  </th> <td>   8.233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.659</td> <th>  Prob(JB):          </th> <td>  0.0163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.205</td> <th>  Cond. No.          </th> <td>2.60e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.6e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Ozone   R-squared:                       0.516\n",
       "Model:                            OLS   Adj. R-squared:                  0.502\n",
       "Method:                 Least Squares   F-statistic:                     37.98\n",
       "Date:                Sun, 14 Jun 2020   Prob (F-statistic):           8.56e-17\n",
       "Time:                        12:05:24   Log-Likelihood:                -505.79\n",
       "No. Observations:                 111   AIC:                             1020.\n",
       "Df Residuals:                     107   BIC:                             1030.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    201.5751     27.191      7.413      0.000     147.672     255.478\n",
       "Wind         -31.8274      8.351     -3.811      0.000     -48.383     -15.272\n",
       "Wind_2         1.8401      0.793      2.320      0.022       0.268       3.412\n",
       "Wind_3        -0.0349      0.023     -1.504      0.136      -0.081       0.011\n",
       "==============================================================================\n",
       "Omnibus:                        8.294   Durbin-Watson:                   1.569\n",
       "Prob(Omnibus):                  0.016   Jarque-Bera (JB):                8.233\n",
       "Skew:                           0.659   Prob(JB):                       0.0163\n",
       "Kurtosis:                       3.205   Cond. No.                     2.60e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.6e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M3.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "These results show that adding new powers of the original variable does increase the performance of the model (R-squared increases, log-likelihood decreases). However, the p-values for the coefficients show that the coefficients M1 and M2 are reliable; but for M3, the p-value associated with Wind3 is well above 0.05 and can't be trusted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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