{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Bubble Plot\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Import primary modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Dataset: Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website.\n",
    "\n",
    "The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. In this notebook, we will focus on the Canadian Immigration data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Let's download and import our primary Canadian Immigration dataset using *pandas* `read_excel()` You may need to run the following command.\n",
    "```\n",
    "!conda install -c anaconda xlrd --yes\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Download the dataset and read it into a *pandas* dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data downloaded and read into a dataframe!\n"
     ]
    }
   ],
   "source": [
    "df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
    "                       sheet_name='Canada by Citizenship', skiprows=range(20), skipfooter=2 )\n",
    "print('Data downloaded and read into a dataframe!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. You don't need to understand it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>American Samoa</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>United States of America</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
       "      <th>Viet Nam</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>368</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9378</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>103</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1981</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>10030</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>1829</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1982</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>626</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9074</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>2162</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1983</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7100</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>3404</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1984</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>237</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6661</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>142</td>\n",
       "      <td>7583</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Year  Afghanistan  Albania  Algeria  American Samoa  Andorra  Angola  \\\n",
       "0        1980           16        1       80               0        0       1   \n",
       "1        1981           39        0       67               1        0       3   \n",
       "2        1982           39        0       71               0        0       6   \n",
       "3        1983           47        0       69               0        0       6   \n",
       "4        1984           71        0       63               0        0       4   \n",
       "\n",
       "Country  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "0                          0        368        0  ...   \n",
       "1                          0        426        0  ...   \n",
       "2                          0        626        0  ...   \n",
       "3                          0        241        0  ...   \n",
       "4                         42        237        0  ...   \n",
       "\n",
       "Country  United States of America  Uruguay  Uzbekistan  Vanuatu  \\\n",
       "0                            9378      128           0        0   \n",
       "1                           10030      132           0        0   \n",
       "2                            9074      146           0        0   \n",
       "3                            7100      105           0        0   \n",
       "4                            6661       90           0        0   \n",
       "\n",
       "Country  Venezuela (Bolivarian Republic of)  Viet Nam  Western Sahara  Yemen  \\\n",
       "0                                       103      1191               0      1   \n",
       "1                                       117      1829               0      2   \n",
       "2                                       174      2162               0      1   \n",
       "3                                       124      3404               0      6   \n",
       "4                                       142      7583               0      0   \n",
       "\n",
       "Country  Zambia  Zimbabwe  \n",
       "0            11        72  \n",
       "1            17       114  \n",
       "2            11       102  \n",
       "3             7        44  \n",
       "4            16        32  \n",
       "\n",
       "[5 rows x 196 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_can.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)\n",
    "df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
    "df_can.columns = list(map(str, df_can.columns))\n",
    "df_can.set_index('Country', inplace=True)\n",
    "df_can['Total'] = df_can.sum(axis=1)\n",
    "years = list(map(str, range(1980, 2014)))\n",
    "df_can_t = df_can[years].transpose() \n",
    "df_can_t.index = map(int, df_can_t.index)\n",
    "df_can_t.index.name = 'Year'\n",
    "df_can_t.reset_index(inplace=True)\n",
    "df_can_t.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "A `bubble plot` is a variation of the `scatter plot` that displays three dimensions of data (x, y, z). The datapoints are replaced with bubbles, and the size of the bubble is determined by the third variable 'z', also known as the weight. In `maplotlib`, we can pass in an array or scalar to the keyword `s` to `plot()`, that contains the weight of each point."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 1: Normalize the data pertaining to China and India.\n",
    "\n",
    "There are several methods of normalizations in statistics, each with its own use. In this case, we will use [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling) to bring all values into the range [0,1]. The general formula is:\n",
    "\n",
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/Mod3Fig3FeatureScaling.png\" align=\"center\">\n",
    "\n",
    "where *`X`* is an original value, *`X'`* is the normalized value. The formula sets the max value in the dataset to 1, and sets the min value to 0. The rest of the datapoints are scaled to a value between 0-1 accordingly.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# normalize India data\n",
    "norm_india = (df_can_t['India'] - df_can_t['India'].min()) / (df_can_t['India'].max() - df_can_t['India'].min())\n",
    "# normalize china data\n",
    "norm_china = (df_can_t['China'] - df_can_t['China'].min()) / (df_can_t['China'].max() - df_can_t['China'].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Step 2: Generate the bubble plots.\n",
    "\n",
    "- To plot two different scatter plots in one plot, we can include the axes one plot into the other by passing it via the `ax` parameter. \n",
    "- We will also pass in the weights using the `s` parameter. Given that the normalized weights are between 0-1, they won't be visible on the plot. Therefore we will:\n",
    "    - multiply weights by 2000 to scale it up on the graph, and,\n",
    "    - add 10 to compensate for the min value (which has a 0 weight and therefore scale with x2000)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18f352b9348>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iE5+gqKiIu+66iwkTJuDxeDjjjDM6DdlLV099OPL5IwO0I1/r6/y60935dNeHo4MBXdc7LkiP3OfI9739OdXPogPdnV/7ubW31d8LyFtvvZWf/OQn3HbbbZx44olkZ2dz++2389e//rXHfQ4ePMiFF17I1VdfzQ9/+EOKioo4fPgw55xzTpfP9ug+A536PJAL3qOD3t709Hn09Z3o7b3uz/mnY6DfNej+e9XdcwP5DRypoKCARx99lEQiQW1tLaWlpdxzzz0ATJkyBYD/+q//Yv78+fzgBz/o2O9Pf/oTEydO5NVXX+Wcc85h3LhxvPzyy13ar6mp6bEASGlpKZs3b+54nE6RoJ/97GfcfPPNPPPMM12KaIwbN47q6upOz7U/7qkPvSkpKem4KbZw4UJKS0t56aWXOP/88/vdlhAicyQDJoQ4Jk2fPh2Px8OaNWs6Pb927do+921oaGD79u185zvf4bzzzmPOnDn4fL4ud+I9Hk+fd4vnzp3LmjVrOl3cbtmyhZaWFubOnduPMxq4uXPnsm3btk5zcGpqaigvLx+2PvQmNzeXsrKyTtXegC6Pj/baa69x/vnnc91117F48WKmT5/eZa7T0TZs2EA0GuXnP/85p59+OrNmzeozo9SduXPnsn379o45YZDK/rS0tPS631VXXcXu3bv5v//7v25fb2pq6ndf+iNT59+TuXPnUl9fz/bt2zuei8fjrF+/fsS/ax6Pp2Mds4cffpjly5d3ZJ3D4XCXyqXtwW/7DYLTTz+dffv2dfqOvffeexw6dKjHbK1pmkyfPr3j35gxY3rt4w9/+ENuueUWnnvuuW4rGJ5++um89NJLnYLS559/nkAgwOLFi9N4F3rW3mY8Hh9UO0KIwZMATAhxTAoGg1x//fX827/9G88++yzl5eV8//vf57333uszc5Gfn09xcTH33Xcf5eXlrFmzhiuvvLJLmfcpU6bw6quvUllZ2aXAQLv/9//+H62trXzpS19i69atvPHGG1x99dWcccYZnHnmmRk739587nOfo7i4mMsvv5yNGzfy9ttvc8UVV1BWVjZq1g7753/+Z37xi1/w0EMPsWvXLm699dZusw1HmjVrFitXruTVV1+lvLycf/u3f2PdunW97jNjxgw0TePWW29l3759PPXUU/z7v/97v/v7uc99juzsbK666iq2bNnC2rVrufbaa7tdCuBIn/nMZ/jCF77AF7/4RX7wgx+wZs0aDh48yKpVq7jmmmv48Y9/3O++9Eemzr8nZ599Nqeccgqf+9znePPNN9m6dStf+MIXiMVifO1rX8vYcdqFQiE2b97M5s2bSSQSVFdXs3nz5k7rd23YsIHHHnuMPXv2sGbNGj7zmc+wefNm7rjjjo5tLrnkEp5//nluv/129uzZw1tvvcWXvvQlSktLO9ZZO+ecczjxxBO56qqrWL9+PevWrePqq6/mtNNOY8WKFYM+lxtuuIGf/vSnPPTQQ8yaNYvq6mqqq6s7BfVf+9rXaGlp4Stf+Qrbtm3jmWee4Qc/+AHf+MY3OmUj29+TxsbGTu9RuyeeeILf//73bN26lQMHDvDyyy9z2WWXMX78eM4+++xBn4sQYnAkABNCHLP+53/+h09+8pN87nOf45RTTqGpqYkvfelLXSqiHU3X9Y4LtgULFvClL32JG264gXHjxnXa7tZbb+Xtt99mypQpneZvHWns2LG8+OKLHD58mJNPPplPfOITzJs3r6Oq3HDw+/28+OKLeL1eli9fzooVKwgGgzz//PPdDrMbCf/0T//EN7/5TW688UYWLVrEmjVr+OEPf9jrPj/4wQ9YsWIFF198MUuXLqWpqYlvfvObve6zYMECfvnLX3LvvfcyZ84cfvazn/Hzn/+83/0NBAIda0WdcsopfP7zn+fGG2/sM8MB8OCDD/LrX/+aV199lfPOO4/Zs2fz9a9/nbFjx/Iv//Iv/e5Lf2Tq/HuiaRpPPfUUJ5xwAh//+Mc5+eSTqa6u5qWXXuo0BzFT3nrrLRYvXszixYupqqrirrvuYvHixZ0WAo/H49xyyy3MmzeP888/n3g8zurVqzvNQbv66qu5++67+d3vfseCBQu48MIL8fl8vPDCCx3DBnVd59lnn2XixIl89KMf5WMf+xjTpk3j6aefHtBw1KP94he/IBaL8alPfYpx48Z1/Punf/qnjm0mTJjAiy++yHvvvcdJJ53EP/zDP/AP//AP/Od//menttrfk7/85S+sW7eu43E7r9fL3XffzRlnnMGsWbO4/vrrWbBgAatXryY7O3vQ5yKEGBxN9XegvxBCjGJnn302+fn5wxoACSGEEEKkS4pwCCGOWe+++y4bN25k6dKlJBIJHnroIV599VWee+65ke6aEEIIIUS3JAATQhyzNE3jV7/6Fd/85jdxXZcTTjiBJ598kgsuuGCkuyaEEEII0S0ZgiiEEEIIIYQQw0SKcAghhBBCCCHEMJEATAghhBBCCCGGiQRgQgghhBBCCDFMjvsiHJWVlSPdheNeUVFRj4vciuEhn8HoIJ/D6CCfw8iTzyBzHAdeftnL+vUe/H7oz5JmgUCASCSS1rauC/E4LFuW4Kyz4uhyiz9j5Pcw8kpLSzPa3nEfgAkhhBBCfBjFYvDAA0EaGnQCgaE9lq6D3w+rV3vYu9fkC18IM0rWgRdi1JH7E0IIIYQQHzLRqMavfx2kuVnH5xu+4/r9UFenc//9QWKx4TuuEMcSyYANAaUUbck2DrUeYnfzbiJ2BFe56JpOwAwwPW86E3MmkmVlofVnLIAQQgghRB/icbj//gDRqI5lDf/xPR5obdV54IEg110XHpE+CDGaSQCWQTE7xpqqNbxT/w4t8RZQ4Lf86NoHiUZXuWyp24KmaeR4c1hQtICl45biM4fx9pQQQgghPpSUgj/9KUA4PDLBVzvLguZmjUceCfD5z0f6NfdMiA87CcAywHEdXjjwAlvqtuAqF5/pI2gFu91W13SCntRrtmuzunI1a6vWsqh4EedOOhdDN4az60IIIYT4EFm71qKiwsDvH+megGVp7NtnsHmzxeLFyZHujhCjhgRgvXAch9j7A5h7GipYGa7kyb1PErEjeA0vBgbJZPp/ZAwMULC+cj1ba7fyqamfojSY2Uoro5lSCsdxADAMCT6FEEKIgWpp0XjlFd+oCL7aBQLwwgs+Zs60CQbVSHdHiFFBArAeOI5DNBolGAz2GHxtqdvCM3uewW/6yTKzBnU8y7JQSvHwnoe5eNrFLCheMKj2jiWGYdDS0oLf75cgTAghhBgApeDRRwOjcr6VpsHjj/v54hfTK2kvxIedVEHsQSwW6zP4+suevxCwAhkrpKFpGgErwDN7nuGduncy0uaxQNM0gsFgR7ZRCCGEEP2zZ49BZaXBaLyPaZpw4IBBRYVcdgoBEoD1qqfAqiJUkcp8WUOT4/dbfp7e8zQVoYohaX80kmqQQgghxMC9/rp3VA/x8/th5UrvSHdDiFFBArAe9BQQOK7D4+WP4zeHdoC13/TzxK4ncFxnSI8zmkgQJoQQQvRfW5vG4cPGqK40qOtw4IBJNDqKOynEMJEArJ9ePPAiYTs85MGCpmmEkiFePPjikB5HCCGEEMe2lSu9o3LuV3fefNMz0l0QYsRJANYPMTvG5rrNeI3hSaF7DS+bazcTs0dmbtTq1aspKyujsrKy28dCCCGEGHn79xvHRADm9UJ5udR/E0ICsH5YU7UGV7nDekxXuayrXtfv/W644QYuv/zyjPZlyZIlbNq0iZKSkoy2K4QQQoiBSSSgtfXYuZxradFxh/dSSohR59j5xY4wpRTv1L+Dz/QN63F9po/NdZtRauQn1no8HsaMGYOuy9dGCCGEGA1qagz6sfzoiEskNBoa5DpCHN/kF5CmtmQbLfGWETl2a7yVUDI04P3bs2F/+MMfOOWUU5g1axbXXHMNDQ0Nnbb77W9/y0knncS0adP43Oc+R0VF5yqMRw9BVErxL//yLyxbtoxp06axdOlSfvKTnxCPxwfcVyGEEEKkr7zcxDe894YHxTQVu3ePwlr5QgwjCcDSdKj1EIxQEkopxaG2Q4NqY8uWLaxevZrf//73/OEPf2Dbtm38+7//e8frL7zwAj/60Y/4h3/4B1588UU++clP8h//8R999quoqIg777yTlStXcsstt/Doo4/yy1/+clB9FUIIIUR6amv1Y2L+VzuvFyoqJAATxzeZCZmm3c27h2zdr774LT+7m3czp3DOgNuwLIvbb78drzdVQOQLX/gCv/nNbzpe/9WvfsVFF13E9ddfD8C0adPYtWsX9957b49t6rrOt7/97Y7HEyZMYP/+/Tz44IN861vfGnBfhRBCCJEe2z62yrprGiSTx1afhcg0CcDSFLEj6NrIJAx1TSecDA+qjRkzZnQEXwAlJSXU1dV1PN61axeXXHJJp31OOeWUXgMwgD/+8Y88/PDDHDp0iEgkguM4uDK7VgghhBgWx+J/co/FPguRSTIEMU3DXf0w08e3jhqfoGnaoAt7/OUvf+H73/8+n/zkJ3nooYd44YUXuOGGG0geS7OBhRBCiGPYaF58uSdSy0sc7+QnkKaRyn4N1/FnzJjBW2+91em5DRs29LrPunXrmDdvHtdffz0LFixg6tSpHD58eCi7KYQQQogj6PrIV0nuL0OmgInjnARgaQqYgRHLgrnKJWgFh/QY119/Pc888wz3338/e/fu5ZFHHuGJJ57odZ9p06bx3nvv8cILL7B//37uv/9+nnvuuSHtpxBCCCE+kJOjjqkhfbYNeXnHUIeFGAISgKVpet50osnoiBw7mowyPW/6kB7jggsu4Ic//CG/+tWv+NjHPsaf//xnvve97/W6z1VXXcWll17KTTfdxHnnncemTZv453/+5yHtpxBCCCE+MH26TSQy0r1IXyQCM2bYI90NIUaUpkbDCr8jqH1Nq6NFIhECgUDH49ZEKz/f+PMhz0R125dkhBtOvIFsT/awH3s4mKaJbdtd3nMxfIqKiqivrx/pbhz35HMYHeRzGHnyGaSvrU3j5z/PJhDI/OVcIBAgkuHoLhqFb32r7Zhau2ykye9h5JWWlma0PamCmKZsK5tcby622/ddG6UgEfHRUptP46ExJGNelNLQNIXli1MwoZbcsY14/PG0Js/meHPIsrIycBZCCCGE+DDJylIEAi5wbFTjyMpyJfgSxz0JwNKkaRoLihawunI1PrP7vxx2wuTQu9Oo3TOeaMiPpjRMbwLtiAmyytWo2TURdIUvGGXMtMNMmL8H09N9YBezY5xRdgbasVjmSAghhBBDStOgqMilrs4Y9RURXRdKS2X+lxAyB6wflo5b2m01QtfR2LV6Hmv+72Mcencajm3g8SWw/PFOwReApissfxzLm8CxDQ69O401//cxdq2Zi+t0/cupazqnlpw6ZOckhBBCiGPbmWfGCYdHefRFav7XihXxke6GECNOArB+8Jk+FhYvJO588MejrS6X9Y+fTdWuiRim02Mmqyemx8YwHarKJ7H+8bNpq8vteC3uxFk0ZlGPGTchhBBCiClTHPLzR39macwYVzJgQiABWL+dN+k8gmYQpRTVu8rY9OwZOLaBaQ2uoo9p2Ti2waZnz6BmdxlKKbKsLM6deG6Gei6EEEKIDyNNg0WLEsTjo7euWjQKS5YkRrobQowKEoD1k6EbfGbmZzi4o5jyNxdhepMZG3OtaWB6k+x8YxEHdxZx6YxLMXRZrVAIIYQQvTv11MSoXeBYKfD7FYsXJ0e6K0KMChKADUTLBNh+OZhDtC6YGYVtV6SOI4QQQgjRB68XLrwwRiQy+uaCRaPwyU9GMaX0mxCABGD95jjw+OMBJhYUMzN/Jkk3s3dzkm6SWfmzmFhQzBNP+HGcjDYvhBBCiA+p+fNtpk5NkhxFiaZEAk44IcnMmXJBI0Q7CcD66cUXvYTDqeGCJcESFo9ZjK7pOGpwf1gc5WBoBovHLGZscCyaBqGQxosvejPUcyGEEEJ82H3601E0TaFGwXQwpcCy4KKLYiPdFSFGFQnA+iEWg82bPXi9H6T3czw5nFJyCiWBEhzlpLVQ85Fs18ZRDiXBEk4uOZkcT07Ha16vxubNHmJD9HfrM5/5DN/61rd63ebWW2/l9NNPH5oOCCGEECKj/H747GejQ3btkC6lIB6HK64I45V7yUJ0IgFYP6xZ48XtpnqqrunMyJ/B0nFLmZg9EV3TSbpJkk4SRedbUApF0kmSdJPoms7EnIksHbeUGXkzul9jzIV16wb2l6uxsZH/+I//4Mwzz2Tq1KksWLCAT3/60zz22GPYdnqB4le/+lX+8pe/DOj4QgghhBh+U6c6XHpplEhkZI6vVOqm9ZVXRhg/XsrOC3E0mQ6ZJqXgnXcsfL0syWXqJpNzJzMpZxIJN0FLvIWmWBMJN4FSCk3T8OgeCnwF5Hhz8OgetD5KKPp8sHmzxfLl8X5VW6ysrOSSSy7BNE2+9a1vMW/ePEzT5K233uLee+9l9uzZabUTDAYJBoPpH1gIIYQQI272bJvLLovw2GMBfD4yVrG5L+3B1+c/H2bKFAm+hOiOZMDS1Nam0dKS3tulaRpew8uYwBhmFcxiftF8FhQvYH7RfGYVzKI4UIzX8PYZfLVrbdUJhfr3l/O73/0uiUSC559/nk9/+tPMnDmTqVOnctlll/G3v/2NqVOndmx7++23s2jRIubOncsNN9xA5IhbZkcPQWx//MILL7B8+XKmT5/OZz7zGfbv39+xTXNzM9/4xjc4+eSTmTZtGmeeeSb33HMPajQMSBdCCCGOE7NmOVxzTRjTVMOyRlg8Dh6Py1e+IsGXEL2RACxNhw4ZwMgEEEqp94+fnqamJl555RW+9KUvkZOT0+V1y7IIBAIA/PWvf6W5uZnHH3+cO++8kxdeeIG777671/Zramr4/e9/z5133snTTz9Na2sr//zP/9zxeiKR4IQTTuC3v/0tr776KjfccAO33norjz76aNrnIIQQQojBKytz+X//L8SCBTaRCENSnEMpiETgpJMSfP3rYcaOleBLiN7IEMQ07d5t4vePzLH9/tTx58xJb97W/v37cV2XmTNn9rltWVkZt9xyCwDTp0/noosuYtWqVb0W50gkEtxxxx0UFhYC8PWvf52vf/3rxGIxfD4fY8aM4etf/3rH9hMnTmTLli08+eSTXH755WmdgxBCCCEywzThE5+IsXBhgqef9tPQYBAIKPRB3oZ33dQaX8XFLp/7XJTSUgm8hEiHBGBpikS0Qf+hGihdh3A4/SGI7UP90hniOGfOnE6PS0pKeO2113rdZ+zYsR3BV/s+SikaGhooKyvDdV3uvvtunn76aaqqqojH49i2TVlZWdrnIIQQQojMmjDB5etfD3P4sM7KlV4OHUpdBva3SmEslro2mTTJZsWKOGVlEngJ0R/DGoC5rst3vvMdCgoK+M53vkMoFOL222+nrq6O4uJibrzxRrKysgB48skneeWVV9B1nWuuuYZFixYBsHfvXu666y4SiQSLFy/mmmuuQdM0kskkd955J3v37iU7O5sbbriBMWPGZLDvGWtqyI8/ZcoUdF1n586dXHDBBb1u6/F4Oj3WNA23j4N1t0+qj6n97r33Xu68805uvvlm5s+fTzAY5L777uPvf/97+ichhBBCiIzTtFQgdvXVUSIRjQ0bPBw6pNPUpNPaqpNMpv67ruupoYXhsIZS4PEocnJc8vJcJk1yWLIkMWIjg4Q41g1rAPbcc89RVlZGNBoF4KmnnmL+/PlccsklPPXUUzz11FNcddVVHD58mNWrV3PbbbfR1NTEj3/8Y37xi1+g6zr33Xcf119/PTNmzOAnP/kJmzdvZvHixbzyyisEg0F++ctf8uabb/LHP/6RG2+8MWN9H6ns10COn5+fz0c+8hEeeOABrr322i7zwJLJJMlkMsM9/MDatWs566yzuPLKKzue27dv35AdTwghhBD9FwgoVqyIdzxOJKC21qCtDeJxnbw8L9FomJwcxZgxLpY1gp0V4kNk2MKKhoYGNm7cyEc/+tGO5zZs2MCKFSsAWLFiBRs2bOh4ftmyZViWxZgxYygpKWH37t00NTURjUaZOXMmmqaxfPnyjn3eeustzjrrLABOO+00tm7dmtGqe4GAGrEsmOtCMNi/c/nJT36CaZpccMEFPPnkk5SXl7Nv3z6eeOIJLrjgAvbu3TtEvYVp06axZs0a3nzzTfbs2cP//M//sGnTpiE7nhBCCCEGz+OB8eMdZs92WLQoyZIlitmzHcrKJPgSIpOGLQP2wAMPcNVVV3VkvwBaWlrIz88HUlmb1tZWILWA8IwZMzq2KygooLGxEcMwOs09KiwspLGxsWOf9tcMwyAQCNDW1tZtFcCBmD7dZssWi5FYEisaTR2/P8rKynjhhRe48847ufXWW6msrCQrK4sZM2bwta99jRNOOGGIegs33HADFRUVXHvttZimycUXX8y1117LE088MWTHFEIIIYQQ4lgwLAHY22+/TW5uLlOnTmXbtm19bt9T5qq3jFZ3r3VXhOLll1/m5ZdfBuC///u/KSoq6ra9mpoaTPODt2fKFA1d10dkKKKup45/ZH/SMXbsWH784x/z4x//uNvXn3rqqS7Pfetb3+pUAfHb3/423/72t3t8DLBs2TJqamo6HhcUFPCb3/ymS9vf+973euyraZp4vd4ePw8xtEzTlPd+FJDPYXSQz2HkyWcwOsjnMDrI5/DhMywB2M6dO3nrrbfYtGkTiUSCaDTKHXfcQW5uLk1NTeTn59PU1NSRrSosLKShoaFj/8bGRgoKCro839DQQEFBQad9CgsLcRyHSCTSUdDjSOeccw7nnHNOx+P6+vpu+xyPxzGMD9be8vshO9vBtodpKfkjZGcrfL4kdv+SYMcM0zSxbZt4PN7j5yGGVlFRkbz3o4B8DqODfA4jTz6D0UE+h9FBPoeRV1pamtH2hiWf87nPfY577rmHu+66ixtuuIF58+bxzW9+kyVLlrBq1SoAVq1axcknnwzAkiVLWL16NclkktraWqqqqpg+fTr5+fn4/X7Ky8tRSvHaa6+xZMkSAE466SRWrlwJpIpAzJ07N60y7OnSNFiwIEkslrEm0xKLwaJFSTJ4KkIIIYQQQogRMqLrgF1yySXcfvvtvPLKKxQVFXHTTTcBMGHCBJYuXcpNN92Erutcd9116O+P/fvyl7/M3XffTSKRYNGiRSxevBiAs88+mzvvvJNvfOMbZGVlccMNN2S8v0uXxlm71tP3hhmk63DqqfG+NxRCCCGEEEKMeprKZKnAY1BlZWW3z0ciEQKBQJfnn3vOy+bNFl7v0Kek4nHF4sVJLrjgwx2AtQ9B7Ok9F0NPhjeMDvI5jA7yOYw8+QxGB/kcRgf5HEbeMTkE8cPkvPPiBIOpxQmHklKQlaU499wPd/AlhBBCCCHE8UQCsB70lBg0DPjMZyIcUU1/SESjcOmlUY6oA/Khd5wnY4UQQgghxHFAArBe9BQQlJW5XHxxdMiCsGgULr44SlnZCK38PAIk+BJCCCGEEMcDCcB64PP5CIfDPQYGCxbYfPKTMSKRzA1HVAoiEbjooigLFnxIa853QylFOBzG5/ONdFeEEEIIIYQYUiNaBXE0MwwDv99PJBIBul/UecYMuPLKEE8+mU04rOP1Dvx48ThkZblceWUbpaUO7x/2Q08phc/nw+/3d1p3TQghhBBCiA8jCcB6YRgGwWCw122mT4ebbkrw4oteNm/24LrQn0ROLJYqNX/KKQnOPTeBYQwiijtGSXUfIYQQQghxvJAALAMMAy64IM5HPhJn7VovW7ZYtLbqKKXw+1MBVjvXTc3x0jSNnByXM85Icuqp8X4FbUIIIYQ4fiWTUFurs2uXRUWFTmurjuOkrjF0HUwTcnNdJkywmT7dobjYPa6Kegkx2kkAlkE+H5x1VpwVK+KEQhoHDxrs2WMSDmsdfxSDQcX06TYTJjhkZSm6GdkohBBCiAxxXdA0RtV/b5UCxwHbTvXLNFPXCL31USk4dEhn5Uovhw+bJBIaXq/C4+m6bTwOoZDOvn0mL7+s4fMpJk+2OeusOCUlx0+BLyFGKwnAhoCmQXa2Yu5cm7lzj59iGkIIIcRIiUZh/36D3bst6up0mpp0EonUDVBovwnqkp+vKClxmDnTZvx4Z1gyQ+Gwxq5dJnv2GDQ2GjQ1aSQSH0RbmgY+nyI/36WoyGXGjCRTpzp4vanA6623LNat89LYqBMIKLxe8Hp7rwCmaXTa7tAhk1//2qS42GX58niX65NwMkxFqIJdzbuIJCO4yiW7IptwOEyWJ4sZeTMoyyrDb/oz/wYJcZyRAEwIIYQQxySl4PBhnVWrvBw8aJJMQiDwwdD/o7NDyaRObS1UVBi88YaHrCzFCSfYrFgRJycns8uhKJUKCF97zcOhQyauqxEIpEa+GAb4u8QxGk1NBg0NBps2WXi9UFJi09RkEA5r+P2pUTQDlQpAIRLR+fOfA2zclGDuWZt5p2UtjbFGwnYYV7n4TT+GlopKA26ASCSC4zqsr16PrulkW9kU+Ao4ddypzMyb2W2RMiFE7yQAE0IIIcQxZ+dOgxdf9NHUpOP3t2d70tvXslL/QGPbNovNmy0mTnS4+OIoeXmDD8Tee8/gpZf8NDenMlapYCu9dtsDpYoKndWrAygFWVmKWbNssrMH37ekk6QqcZCNG+p5ZH2cOad7GTtdETADPe5j6AZZehYAjnKojdTyyM5HyPZks6BoActKl0lmTIh+kABMCCGEEMeMeByeftrPjh0mwWAqWBmM9mCsttbg7ruzWL48xumnJwc0ZywWS/WtvNwkEBhYxkopKC83qKkx3g8SIZHQ2LjRoqzMYepUp1Nxr/TbVexv3c+htkNomoZhGngx2fHaiURbs5h8YnnabWmaRtAK4iqXddXrWFe9jjPLzuSM0jMkIyZEGiQAE0IIIcQxYfdugyefDOA4gw+8jqbrqQzaypU+tm/3cOWVkX5lnFJ98+M4GoGek0m9Ugp27DCpr9cxj7pCsyyoqjJoaNCZN8/uV3AXSUbY1rCNqB3F1Ds3bPkSHNgyA8c2mHbKe/3us/f95XNWHV7F9obtXDbzMvJ9+f1uR4jjyQDuoQghhBBCDK/Nmy0efjiAptGRGRoKfj80N+vcc08WDQ3pZXO2bEn1Tde1QfVt1y6Dujq9x8IghgGOk8qGtbam17eDbQd5q+YtEm6iS/DVzvImqdg+lYNbpg+06/hNP62JVn71zq/YUL1hwO0IcTyQAEwIIYQQo9rmzRbPPusjEBiecvLtAdB992XR1NT7AbdssXjmGd+As17t6uo0qqqMLpmvnvq3eXPvQZhSit3Nu9nfsh9TN9Ho/TxMT5L9G0+grT63v13voGs6XsPL8/ufZ9WhVQNuR4gPOwnAhBBCCDFqlZcbPPusr5uqgUNL11OBzm9+EyQa7T542bPH4C9/GXzwZdtQXm71K3tmmqngLxrt/vU9LXuoCFX0mPXqjuFJsO2VJbjO4KLcgBXg9crXWXlo5aDaEeLDSgIwIYQQQowarpsKSJT6oKjFcAdf7XQ9NeTvsce6diAehyefDODzDf44O3aYHeuV9bd/27ZZqKOmgx1sPUhlqBJL7994SE2DRNTLng1z+t+Zo/hNP69XvC7DEYXohhThEEIIIcSwUwqqqnTKyy0OH9ZpadFpbdVxXfD5dGKxbMrLTWIxjWBQUVDgUlDgpl1qPlNMEw4cMNmyxWLhwmTH808/7SOZ7LrWWH+1tmrU1+sDakfTIBLROHhQZ9KkVAQXSoTY37q/X5mvI5mWTeV7U5g4f8+gM3sBK8BLB15iet50KcwhxBEkABNCCCHEsInFYM0aL++8Y9HUpOP1qo7go/1/AwGNujqN5uZUNcCWFo36egNQZGcrJkywKSpSwzIfLNUfxfPPe5kxwyYQUJSXG+zYYWWkEuP+/cagCne0B4hjxiTw+RTbGrcNOPhqZxg2BzbNJP/c3YNqB8AyLB4pf4Tr518vJeqFeJ8MQRRCCCHEkEsk4Mknfdx2WzarV3uwbY3sbNVt5kcp2LvX7FSQwrIUlgWxmMb27RZr13qorNS7DL8bOhovvphKv7344uDnfQEkk9DSog86kDQM2LXLZG/LXuJ2fND90k2X+gMlg54LBqnCHPWRet6ofGPQbQnxYSEBmBBCCCGG1K5dBr/8ZTY7dlh4vfQ5b6qxEWKxni9RLCsVpO3alRoamEhkuMPdME0oLzfZv1+nsXHwQRPAoUNGRgJITYOGJsXBlqpBZ7/a2XGLql2lGWnLb/l5reI1IslIRtoT4lgnAZgQQgghhoTjwBNP+Hj44QBKpT9fat8+MM2+IxPLglBIY906D9XVQ39Jk0ho/PGPgYwVBWls7Lrg8kC1xFsI1xZnpjHA9CWp3jUuY+3p6JIFE+J9EoAJIYQQIuOSSfj97wOUl5sEg+mv35VIQHOzlvb27eXid+40OXhwaC9rTBM2bfKgZ+AwqSqPmZkTpVDEVZhI88DX8OpOtDUD4yzf5zE8bK3fiqsGUO5RiA8ZCcCEEEIIkVG2DQ8+GKSmRsfj6V+Q0dg4sHldlgX79pkcOjR0lzYtLRrhsIZtD76taFTDcQbfDqQqH7rKxUmY2InM1VeLhX0ZbS+UDLGzcWfG2hPiWCUBmBBCCCEyRil49FE/tbUaltX/DE9joz7gqoCWlSreUVMzNJc3DQ06hpEa9jhYTU2ZqwgYSobQNR0FxMOZWzTNdXTaGnIy1l7ADLC2am3G2hPiWCUBmBBCCCEy5u23LfbuNfF6BxZgxGLpDz/sjmWlinMkk31v21+RiIZlpQKxwQqFtIzN/0q6qZM1DIdoawZq479PNxzCDZkb1qhpGo3xRtTwla4UYlSSAEwIIYQQGREOa7z0km/ARSqUSg3Ny4Tt2wexuFYPYjEdXYdQaPCXT647uECzne3aH8yr0iAZH+TK0EfQdYVjZ3bJ2EgyQigZymibQhxrJAATQgghREY89ph/UAUqlCIj86J0PVXII9OVEdvnfmViDlimRO0oGh9EcsrJ5DkrXDeziye7yuVw6HBG2xTiWJPZ2xpCCCGEOC5VVuocOGCQlTXwNtwMFsizLNi/32Ts2ERGMk1HjprL1NpdSqVfHbInMSeGrn0QdCmVuYBJKR3Tk9loM2AF2N20m9kFszPa7rEu4SSoidSwq3kXVeEqWuItHdnNYDBIIpogaAUZExjDjPwZjM8aj9/M3Hw/MbwkABNCCCHEoK1a5SWQuarlGRGPazQ3a+Tnj745R36/i+MMfh2wLmXdM5iwcm0Df3Y4cw0CuqYTsWVBZgClFHtb9vJaxWtUhipJuAn8hh/L6Dx81nZtYk6MmBOjNlLLhpoN6JpOka+Ik8eezKIxizK2ALcYHvJpCSGEEGJQYrFUCXivd3DtZGJ9rSNZluLAAZP8/MFX5NC0D7JVmehnfr5i3z4NGFxwqI7aXxtke53a0l1yipsz1l47R2Wo/v4xynEd3qx8k021m2hJtBAwA/hMHz58fe5r6AZZeirNHLEjPH/geV459Aqz8mdx7uRzj7msWPt6eMlkKgNumuDzqYwVqBmtPuSnJ4QQQoihtn69J2PD8jJ94dXamrq4G2hp+yNZlsJxNCxr8CcbDCp0PQNv2lFN6GbmghuPP4knEB90O0dWPdQ0rUvQeDypDlfzaPmjtCXb8Bk+gtbgqla2B1zvNb3HjqYdnD/5fBYWL8xEV4dEMgk7dpjs2mXS2GjQ2KgRj2sdfz90PfUvJ8eloMCltNRlwYIEBQUfru+MBGBCCCGEGJT9+018fd+875OmpYbmZbLIheumCnIUFw/+As7vV7S0aOTkDL4tw0jd6bftwY0Z1I6YRKaUhsc/+ICpnT+nf0MFlVJE7AiNsUZa4i1E7SgJJ4GLi1IKTdPQ0SkOFGNoBhOzJzI9bzrF/uJO5/Fh5LgOLx98mfU16/EbfnxGBn4wR7D01B2GZ/Y8wzv17/CZGZ8ZVdmwxkaNVau87NplEo9r+HypQMsw6HbocjyuU1Wlc+AAvP66l5ISm2XL4sye7WQ8Uz4SJAATQgghxKA0Nmbu4tnvV7S2Zqw5LAsaGw2Kiwcf1WVlKerrNfLzM1MtJDvbpaHBGNQFpX5EQWvXNgjkZqbEu50wyS+tTm9b1+ZQ2yFqIjXE7FRREEMz0DQNTdMwMDrmpilUqhJi22H2NO/hpQMvkefNY0HxApaOW4rPzGxgMhrEnTgPbn+QumgdAXNoJ0oGrACVoUp+uemXXDvvWor8RUN6vL5EIhpPPOFj/34Ljyc1tLA/c0U9HvB4FC0tBn/+c4BAQHHBBTHmzBlFpUgH4EMQQwohhBBipMRimVkXq11hoZvRRZQ1LXURmAmFhalMTjCYmeFQkyY5gy677zW9HYU4NF3hCcQy0LPU+zZp4f5et0k4CbbWb2VN1RoOtR3CVS4ew4Opmz1mtGzXJs+bh6Zp+EwfWZ4sbGWzunI1t228jUd2PkI4mdnCHyMpZse4/937aYw1Zjzr1RNTN9E1nfvevY/KUOWwHPNoSsGmTRZ33JFFZaVJIDC4eV2a1h64afz5z34efthPLDNf9REhAZgQQgghBqy21showJSfrzCMzLUHqUn+mZCVpcjLczPWv0Ag1eZg5s/5TX/HnCrLF0c3Bp+dUy7kjavH8nafZVBKURWqYn31eprjzRia0a8qfHnevC7P+UwfXsPL/tb93LH5Dt6uebvT3LFjUcJJ8Lttv6Mt2dYxRHC4aJqGpVv8/r3f0xBtGNZj2zb86U9+nn3Wh2Vlfl6n3w8HDpjccUcWBw4cm6HMsdlrIYQQQowK4TDoeuaGIJomFBaqjK4Jlqm2YjFYvjye0YBz/HhnUHPe2rMdrm2QM6YpI32y414mLd7Z7WtJJ8mWui3sat6Frumd1iDrjlLgOgZu0oPrGBiYeI2ey2WauolH9/Dcvud48L0Hj+ls2BO7nqA53jzswVc7TdMwNZMHtz9I0s3gl7YXiQT89rdBDh40hnRZCstK/d35wx+C7NyZ4Ts2w0DmgAkhhBBiwJJJPSMLHR9p2jSorMxcWfpMBWDZ2Yorr4xyxx1ZGamqCDBmjMvBg4pEQhvQ+6ihYeomjukQzG8bdH9cRydvXD05xS1A5yvoaDLKxrqNKKV6zHgl2nKJVkwnXj+OZDgXJ5INSkMpHVCYps6GMTn4syPkjGmkaHIVwbyuQVbAClATruHuLXdz7dxrKfQXDvrchtO7de+yu3k3AWtkF8fTNI24E+eve//KJdMvGdJj2TY88ECQxkYdj2dIDwWkhiX6fPD44wGuvDLC1KnHzvIGkgETQgghxIAZxuCG0HUnKytVpj1T7WYiQIzHFfPnJ8nNVUyc6GQsqNM0mDs3Oai5YH49C19uM5qWibL2GnPOfrvL05FkhLdr3wZFl6yXcnVC+0+g8qUrqXr+CzRvP5lY41jchA/NTKJZCXRPDKwoOX4vdtxDa10eB7bMYMMTZ/PWkyuo2jkB1+n8QbUHeb9+99fDPoxuMKJ2lOf2PzfiwVc7j+Hhnfp32Neyb0iP88QTfhoatIzdnEiX1wsPPxygpeXYqaQpAZgQQgghBszvV0MyV2fWLHvQBSraDTaTplRqvtZZZ6VKvF98cTSjwxADAZgwYeBDEfP8WeSX1Q+6H8m4xbRTt2L5Ep2ej9kxNtVtQkM7quw9tO6ez+Fnr6HhrY/ixALovgi6lew26NU1nSxPahFhTQPTcvD44ySiXsrfXMCaRz7G4W2TOwXeuqZj6Ra/2fobWuItgz7H4fDkridH3VpnATPAk7ufxHaHpnrg1q2ptb08nvSDIMd1aIm3UBmq5GDrQQ60HuBw22HqonXEnXjaf1c0LTUk8dFHAxm/GTRUZAiiEEIIIQasuNjFMDS6rAg8SNnZinHjHKqqjEFP4vd6B9e3aBS++MVIRz/y8hQrVsR49VUf/gwttTR5skNjo04kovWryEcyCQsWOFS6eTTHm9EYWBbATpoUTqhl3KxDnZ5XSvFu/bugOq85ZkcD1K25kETzGHQrju7tvSSdQhE0gz32z/KlIto96+ZSu7eMOWe/hS+YCng1TUND4w/v/YGvLfxan/PORlJLvIU9LXtGTfarnaZpROwIm2s3s6RkSUbbjkbhr3/t+7fgKpeaSA11kbqONeIc5aBresd3S6n3b+hoqQyo3/STZWUxKWdSr3MHDQNqanTWrrVYunR45rsNxuj9BgshhBBi1MvKUgQCGayYcYRp0xy83sEPRfT5Bt5APK5YtCjJxImdz3HZsiRjxrgZy9JpGixalMTrTb8AieOk5pDl5yum5EwZcHbDSZpkFzYz5yNvdXltf+t+ona0U/AVOjCTyue/SLItH91Kb+FnpRS53tw+t7N8SSItWWx4/Gxq9pR2PK9rOk3xJlYeXpnW8UbKykMr8RjDMAFqAPymn/U16wfdTjwOLS0ajY0azc0ajz3mR6meA/+4E2dn407WVK2hvKmcUDKEoxwM3ehYtsDQjI5qmpZhYekWGhoxO0ZtpJa1VWvZVLuJhmhDj5kxvx9efdVHODz6hyJKBkwIIYQQA6ZpkJfn0tyc+Upkug7z5tls3Gih6wOby5VMahQUDCxATCahoCC18OvRNA2uvDLCPfdk4bqZKRhiGHDiiUk2bbKIxXrPhLluKrCcOTMVdAWsAGMDY6mL1mFo6X8WdtIkp7iJBeetQzc6X9iGE2EOhQ51KrjRunseTVuWo3vSC7wglf3K9mSnXape1xXoDjteX0wy7mH8nP1AKoBYXbmaBUULRnyB4e7Yrs3Opp39Ksk/3OqidVSFqxgXHJf2PrW1Ops3W9TUGDQ0aITDOo6TGoJq2/D22x58vtRwZL9fUVTkUFSkcHHY1bSLmkgNhmZ0DCftr/Zqm1E7ytaGrfgMH3MK55Dtye66rQ6vvebhggvS/36OBMmACSGEEGJQxo4dXCn13gSDigULkh0XfP2lVGrtrv5KJiE72+Waa8I9DoHMzlZce20Ix8lcpUXTTAVheXk9L0jtumBZisWLk52CtBn5M9IOvpSCZMzDmMmVLDx/bbfrh71b9y4GH7TXtm82TVtW9Cv4gtQFdL4vv1/7AFieJHvXzaVy54SO57yGlyd2PdHvtobDu/XvEndH94V/wAzw2uHX+tzOcVILKd97b5B77gmycaOH2loDx9Hx+SAYTBXLqa838HhSNyRiMY3GRp3t2y1efd3lb+v3U93ahKVbGRs2aukWjnLYWLuR3c27OxYh73jdgu3brYwuYzEUJAATQgghxKCcfnqCRKLv7QYqNzcVbLhu/4OwYFD1e55WIpEK2r785TDenqedAKk1y66/PoSmqYwFoYYB8+fbzJ6dOucjLyYdJzWn7aSTkl0CQ0MzmF0wu881nxzbQNcVC85bwwkrNqPpXd/U1kQroXioY+hhrGEsjRvPTlUz7AdXuRT7iwc8N830Jtm9eiHN1akATtd0aiI1VLRVDKi9obSjcQd+I0OTAodI+/vXm3ffNbn99iyefdZHKKQTDNJtZUOlUguxH3kTQNOgzWmkJlJDqDGHqu0zqD9QgnIzOyzQ0i2qwlVsqN5AzO78nQyHNbZtG5m119IlAZgQQgghBiU/P1UwYygrkGVnK5YsSeD1ph/oJJNQVpb+JC2lIBKB2bNtrruu7+CrXX6+4utfDzNunEMkkvbh+jRmjOLUUxPk5bnYdmoh6OJilxNP7Bp8dfTFl09psLTb+WB2wsSxDYonV3LKZ/9OflnPpd0PtB7oGEqnHIP6tReiWf0PvnK8Ob0WT0iH4Unw3sqTcJ3UZavf9LOqYtWg2hwKzfHmTnPlRqu2RBtxp2umLhrV+MMf/Dz1VABN0/pcSLm1VSOR6Hy+ddE62hJtqcIauotuOESas6nYPpVYW2YLkxiaga1s3q55m0jygx9eIAAbN0oAJoQQQogPudNPj2c0+OiO3w8nnZRk8mQ7rSGJpgklJemNRUokFJqmuPrqMJdcEuv3WkZ+v+ILX4hw0UUxbDtz2TDLghkzkixdGueKK8LMnp0kEqHX9qfnTafQX4jt2ri2TiLqxTAdJi7czdIrXuKE5VswzJ7fl6Sb7BRMNG5ejpvw9msOnqtcsqws8r39H3p4NE0DO+5h15r5QCqLs78lVRxktHBch+Z480h3Iy0JN0FNuHMW7OBBnV/8IovDh00CgfTupDQ06BhHzBusi9YRSUa6DDfUdBeloGb3eBoPFw/+BI5s+/2lETbWbuz4PmgaNDeP7kB49M4SFEIIIcQxY9Ysh9xcRTKpZWTh455oGkyc6FJcnGD3bpOmptTF3tEZIdtOZb96K47RnvEKBlOVDs85Jz7oRWQXLkwyY4bNiy96KS83SSS0AZWqb+9bVpZiyZIkH/lIqm+uC/v3G+zcaVJba9DUpBEK6e8HoxqaptA0jYUls9mb2EDEt4vSqfVkj2lO+3OpDFV2VJqLNxXRtm8uhjf9YMdVLkErSKG/sP8n3gPDsqkun0DprH1kF7eiUGyo3sDy8cszdozBqI/Vk3ASo7YC4pH8pp9dzbuYmDMRgN27DR55JIDX279CN6HQB4VimmJN3QZfR9JNh7b6fFzHoGhS9WBOoQtN09hUu4lTSk7B1E1aW3WiUTK2TESmSQAmhBBCiEHTdfjMZyL87nfBYbno8ftT86SSSTh82KCmRiceT109mmZqntSUKV2HHyaTqXWLLCtVwv288xLMmZPs19pbfQkEFJdcEiOZTBUyePttDw0NOslkanhUT8MHEwmIxzW8XkVxscOFF8Y54YTOQaSuw9SpDlOnfnBuySTYtobjpOaPWZbCNEGp2Ty3fx8bayoBP6Q5D6sl3tIx/LB527J+zftylUuOJ2dARTf6YnoS7Ns4mwXnrcNn+jjYdjDjxxio5njzqFt8uSeWbnVk6w4c0HnkkQA+X//biUZT36e4E6c10ZpWoQ3dcAg3ZaPpisIJvc9F6w8NDVe57GjcwbyieTgOVFYaTJuWoXUiMkwCMCGEEEJkRFmZy4knJti0ycLnG54hQJYFU6Y4TJ7skEhAS4tOdbXG4sVJfD461ukyjFSma+xYhxkzbMaNcwZ00dnfvp1ySpJTTkkSi0FVlcGuXanMVTSaCpg0LdU3v9+lrMxlxozU+mL9ycRZViroOpqmaXx8yseZXTCbJ3c/mXaGpr2ogZPwEKstQ7f6rrCilELXdUoCJYOe89UTTYeWqkKScQvLm6Qp1jQkxxmIuBMf1QtEH81xHSIRjT/9KZD2XMejJRIauqGoi9b169x1wyXUkIvHFye7uHlgB++uXU2nIdZAXbSOHE8xlZW6BGBCCCGE+PA777w4u3ebfa5jlWmaBl4v5OS4fPSjCc49d3SVA/f5UoFid1m5oTY1dyrfWPQNnt33LO/Wv0vADPR4wey4DnEnjqEbNL+3GNLI6rTP9yrwFwy42mG6lKY49M40pp68g7ZE26gZ9qeUGvJzz7THH/ej6wMbMqxU6l9jrBHXdftdfEQ3HJoqiwnkhTCszK1hYekW5U3lnFSUTyIxegPi0dszIYQQQhxzDAO+9KUImpa5tbHSFYvB9Ok2H/tY5oIv27WpDFWyvno9r1e8ziuHXuH1itfZVLuJhmhDl3WIRiuP4eHT0z/NV+d/lQnZE0g4iS7luwEidgSHVJAYqZqMbnVf0t5VLgqFz/RRmlVKob9wWAIQ03JoOFgCQNyNUxepG/JjpsNreI+Z7wLA4fISDhwwehwOmw5XOYQT4YFXftQUdftKB96BHrjK5UDrgVG9FphkwIQQQgiRUTk5iuuuC/Gb32R1zEsaatEoTJtm89nPRgdVBEQpxd6WvayvXk9DtJHahiQNVbmEakrA8ZCaRxVH97SQU7qBonFhxuQFKPYXs7xsOUWBokyd0pAYGxzLFbOuIGbHWF+9nnfr36Ul3kLCTeA1vCTdJEoplKtjh7NBSw0/VChc5aJrOqZmkuPJIceTMyJl1+NhP66joaERtsPDfvzuZHuyj5k5YImEYt/aWcwqGHgbmgZtydZ0pxX20IYiHvYRbsohmN868IaOYmgGdeEGPN6yjLWZaRKACSGEECLjCgoUX/1qiAceCNDaqg/ZfCulUsHXwoVJPvnJ2ICDr5gdY131OjbVbmb/7gD12+cRaZ2PHU9NxjK9iU5tu65G/a6Z7NcVli9OsLCRtQv+wLQJfk4bdxrzCudh6MM4BrOffKaP5eOXs3z8cmzXpjZSy+7m3ayrXkfADKBCY9GdILpXoaPjNbz4TT+WYY34UDs7aRJpyULPDnW7ntVIGBMYMyqGQqbjwPYScrTB3ShQShFVbYP+LuimQ2tNfkYDMIBowqXF2gVMzmi7mSIBmBBCCCGGRHa24h//MczLL3tZv96D39+/Mtd9SSRSBSguvzzCzJkDm1ullOKtmrd4ce+r7N88g6Z9Z5GI+N4PuBSWr/sCFLqu8Pjj77eh0VZbRNNfL+BAXoh3TtjC9LmvcunMTzMxe+KAz2+4mLpJaVYppVmlFAeKiSQjNNUvwM0tBj1rpLvXhW44NFUUk39CFYY2OoJcS7fI8eSMmoCwN7W7pjO5MDioNuqj9WhmDJUYfMnTRNRLMubp8bc2EJZhcEBbhQRgQgghhDjuGEaqMMfChUkef9xPY6NBIKAGFYjZdir4OuGEJBddFBtwFbdQIsRjux5j264o+984DzvhwbDsAV0IarrC8iew4x4Orz+Fpt2t1C9/hFOnzOKCKRdg6YNcYGyYeA0vLi7hphxMj52xBaUzybBsQg255Ck1ZBUXByLfl091OLPrW2Vaa10uTlshVvHgvo+10Vp8fi/hWKo65WBohktzVSHFU6oG19ARPL4kbVoljuuMyky0BGBCCCGEGHIlJS5f/3qYvXsNXnvNy+HDxvvl09Pbv32ooderOOEEm7POipObO/A5N+VN5Ty648/sX3MStXsmYnoTGavGZnqTRNsCbHvm4zQvepe9LXfxpTlfIs+Xl5H2h1KeNw8dPTXHapQW9dO0VNZR07QhWW9soCbnTGZfyz785ihd/Reo3DGJ7ODgL/+jySj+3CRt9fkY+uB+N5qmiEcy+575syMk3Dj1sXrGBsZmtO1MkABMCCGEEMNC02DaNIdp0yK0tWmsXeuhstKgqUkjFNJJJjWUSi0sHImAYWh4PIq8PJf8fJdZs2zmz08OqnIbwLv17/LnHc+y86WziTTlZHToUztdV6A7HN64kERbHvfY93LdvGspDhRn/FiZlO/NH9UBxJF8ho987+gJwE4acxKrDq8a6W70qrnRw8zc8YNqQylFzInhyzLQjcwsq+AkTVxHRzcGX7rQTlgUTqrCY3jY1bRLAjAhhBBCCEjNDzuyXHw0Ck1NqSAsJ8dDOBzB61UUFLhpV1GMO3GqQlWUN5dTG6klbsdxcNDQ0DWdgBlA0zQ2VW3lwMsXEWvNyugaRN0xvUlqd08E1+B+fsPXFnx1VGfC2rNKuqFQo7Son1Kg6y553rwRqcLYE5/pY2ruVA61HRq1izJr0ULycwYXtEbtKLZr4zF0/Dlhoq2BQQ9DVK5GIuLDlx0ZXEOApjuUzd6Prns41HZo0O0NBQnAhBBCCDHi/H7w+1N3v4uKoL4+vTvrSTfJxtqNvF39Nk3xJpJuEp/p63bOVUVbBWur1tHw+uXYLU34PC3kaXn4zCEq0fg+02NTu2c8lj/Gb43f8o3F3xjVc8LyffkE8ptpPDgJ9O7XARtJTtIkUNBCgW8QddSHyFnjz+Led+4lyzP6ipe0tSnytAmDDlqjdrTj/+eOqyfcPAVDH1wmTDcdIi1Zgw7AXFejoKwOw3IArVNfR5PRGZ4LIYQQQvQikozw5O4nuX3j7bx04CWiThSf6SPbk91tcKOUYlvjNhJ7luI0lWGYDkk3SU2khsOhw7TEW4a0v6Y3yeF3Z1Bf4+eve/86pMcarAVFC/CXHMR1RudlousY+MceZH7R/JHuShclwRLGZY0blYsyq2SAsYFxg27HVnZHEOfxJfEGYoPOlmqawrEHXyzDSVhMPmnHB49VZoZIZtro/GUJIYQQQvRgU80m7th0B+VN5RiakdacpX2t+2hrMWnefhq654Ohj7qmo5SiOd5MRaiChJP5+WDtTG+CXSuXsrnmXfa27B2y4wzWjLwZjCnSsDyj8+LVsGyKi2BW/qyR7kq3Pjvjs0P6PRqISDLCmeM+gjGAwW9KKcLJMIfaDrGtfhvvNbxHVbiKilBF6jdTtJFwIkrMjmG79sAXpFaDy8w5SZOSmQcJ5o2Oxbl7IwGYEEIIIY4JkWSEB7c/yF/3/RXLsDD19C4mI8kIh1oP07TuYjSj+yF1uqbjKpeqcBVNsaZMdruDpkEi5qVq48k8tfspbHcU1ngnNQ9s4Zj5mMG2ke5Kt8xgKwvHzhtV87+OlO/LZ/n45aNm+Jvt2kzKmcT84rn92i9mx9jRuIPVVavZUL2BfS37aEm0YLs2rnI7/umeKEb+YWJJm1AyRGu8lVAyNKzfb6VSNzhmLN3a6fnROhdvdPZKCCGEEOIIjbFG7tpyF9XhavxW/6r07W3Zi9NYRrKpGE3v/e68rum0JlqpDlcP/E5+L0zLpnbPBNqicTbXbs54+5myrHQZRZOqsBOjq1yAnTApGF/J6aWnj3RXenVG6RkU+4tHfAicen9s4GdmfAbLSg3160tLvIWNtRtZV70uteAyGh7D03HDo7t1tXzFFRieOBo6mqbhKpdwMkxrojXtxak1feDDNu24hzkfeatLFUWP4Rlwm0NJAjAhhBBCjGoN0QZ+/c6vAdLOerWzXZvmeDMt752K5o2ltY+u6STcxJAFYa5j0LhrNhtqNmS87Uzxm34uPi8PR42uLJ2rbM7/iH/Ul8rXNI2rZl+VWlNtBOeDxZwYn535WQJWgNxct9d191zlUt5Uzua6zcTsGJZudZtB8hge9KNCCE2D4KTU3Kv2+WDtGcqYHaMt0dbr++A6Bt5ger/Po9lxi2mnbCN3bHOn55NuclSWoAcJwIQQQggxirXGW/nt1t9i6MaAhhNVhCqwEx7iDWVp3f1vp6F1FOnINNOTpHrnJGoitVSHqzPefqZcOv/jjBnfinJHx1A/5ULR+FYumvWxke5KWoJWkC/P+zJKqREJwqJ2lEunX8rU3KkAGAbk5XXfj9ZEK+ur11MTrumzQqeG1m0WTDeTZE1JDQE8siiHpmkoFG2JNmJOD0GW0gZUATEZt5i4qJzx8/Z1eS1mx5iZN7PfbQ4HCcCEEEIIMSoppXh458MoTQ14LkdNpIbovoUdQ7H6Q0MjbsdpjjcP6Ni9ibYG0cJjeK3itYy3nSmmbvKNz04jNrDERMbFYvCNz0ztdxZ0JOX58rh+wfXomj5sc6KUUkTtKJfNvIzZhbM7vZaf73apWFgXrWNz7WZc5XYbWHWnp8/A8CTImvIumu6gnM6Bu6alfk8Ru2ugpRkOlif9JQ+USgVf007ZzuTFu7rdxtItSoIlabc5nCQAE0IIIcSo9EblG9RF6zC0gZWntl2bmB0jVj8O3RrYela6ptMSbyHpZnY9LM1waKoYS320PqPtZtriaSUsO1Ub8SAsFofTTtY5cfrgy6gPt1xvLv+48B8pyyob8sIcMSeG1/DylXlfYWZ+1+zP1Kl2p8+yLlLH9obt/Q5q/YYfl+6zaYYnSfa0d7ByG1G20SUblnSShJOdKxVa3iSkmaG2kyamx+HEi15j/Nyuma92ud7cIV/jb6COnVsIQgghhDhuNMWaeL3i9UHN9Qknw7jKxQ7lDaovuqZTG6mlLKtsUO0cybBsWmoKaY7vwHbtUZ3V+c5Vc/iH3fuoD7dgjUA/bdehJC+H7149ZdiPnSl+089Vs69iS90Wnt//PNA5i+Q6Oi3VBTQcGkuoMYdYWwAnaaKUhuUxcVUMXzCGPydEwcQa8kvrU0HL+5RSxJwYp5ScwkcnfLTHTNbixUlefTUVlDTFmniv8b0BLQqe7cmmOdHc4+uargiU7sPOrSVSMQPXttAMB01LBWG2axOxIwTMAK5jkFXY0OcxHVtHOQals/cx9eT30I2eA7aYHePksSf3+7yGy+j9tQshhBDiuPXM3mcGdGF4pPpoPZryYEez0c3BrctkuzatiVZyPDmDaqedpkGsNUjCSVAbqaU0qzQj7Q4F09T4969N5V9+epiIWzeswaLt2gRVMT/66vheC0gcKxYWL2Rm/kye3fssu5t3Ewv5qNwyn4ZDJSSjHkxvsqOSn6YrNBS6rnCSOtG2AJGWIDW7J6AZDnklDYxftB1vQQ3js8u4cMqFfQ6583hg6tQku/dqvNf43oA/S03T8Jt+onYUjZ7nCJrBMNkztpBoKSDRWIIbC4DhdGTCknoSy9DIKux5IfRkzMLyJRk7rYJJi8vxBvquqqhrOqeOO3VA5zYcJAATQgghxKjSGm/lYNtBAmZgUO1EkhG0eA44xqCveHRNpy3RlrEADCAR8+AxPOxr2TeqAzCA8WXwnWsmc+vvTZrdykEHx+lIukny9FJuuno8E8aPrmqMg+E3/Xx8/GU8ssZk7bZ6mpJVJFUY3ZdE7yMg0nSF4YuSdJO01udw6O+XsHDSOM7/rKIkmF6hj7PPTvDMmgoczRnw8F6APE9e6jfWx3psmqbw5jXgzWvAjvlINJbixH2ohJ9w1KaowCEZs9A03i/4omFYNt5gBH9OhKLJlYydVtFrxutIjnKYmjsVr+Ed8LkNNQnAhBBCCJG2ZBJqa3XKyy0qKnTicR3HSWV0DEORm+sybZrDpEk2eXmKgayV++LeFzNygW9joxwfqpc79P1qz7WJO/GMXdgpR8fQDELJUEbaG2oLF9rceNUEHnyigIORHSgGXhylN65y0dCYGpjLFy7NYtGizM6/UwqamjQOHjRpadFIJDQsC7KyXCZNcigsdNGHsErC5s0Wzz/vQ9Ng5tjxKFVGxI7QGGukJd5C1I4Sd+IdlRM1V8NRDh7dg9/0k+XJotBXSLYnG13TUUn4zW9gyZIE554bx+gjpmoyduLkH0JrHAvGwKszWoaF1/CScBO9ZsGOZPpimKV7gdTn4MT8jD3jdWYWzMB1NUzLxhOIEcxvwzAH1reEk+CciecMaN/hIgGYEEIIIXqlFOzfb/Daa14OHzZIJjW8XoWnmzVOGxsNtm/3oJQiK0sxe3aS5csTBINp3r12HbbXbc9MhiXDS3jpmk5zvDljawtp2gdrjh0rFi9O4vMFeOLJUyhv3klTsh5Lt9K+AO+NQpF0k+RbRczIncWln7KZMyczwVcsBmvXeikvN2lu1olGQdc1LCt1k0ApsG1w3dQwvbw8l0mT7H59d/viOPDYY3527TIJHJHc1TSNoBUkaAWZkD2h43mlFC4uWYEsotGei3doGgQCsGmTh927Ta65JkJWVvd9Vkrx/P7nWfBRlw2PDf57XBwopiJUMaB9VdJL4UmvYo/dRGCsIsuTNej+RO0oy8Yto9BfOOi2hpIEYEIIIYTo0ZYtFqtWeWlu1gkEFD4f+Hw9X5CmLgZTryulsWWLh7ff9jBpks3FF8fIyen9YrYyXElbog0zQ5commGjZTASSzgZDJZ0F4XC1I6ty7HZs21umuTw2GOz2bY7TE1yPy3xFtAY0JA2RzmgUlXrxlqTmTMtyGc/G81I4FNXp/Pyy1727TNRCny+1HpYWVlwdIR+5ByzcFjv+O5OmODwkY/EmDhx4Nki24aHHgpQXa13Cr56o2kaBkafQ/zaeb0Qjercc0+Qr3wlTG5u1/fvYNtBGmONZPmymHbau5S/sRjTO/DvtKEZ5HvzaYw19isbqlwdb2E12VO3gWaxv3U/84rmDbgfkMqc5nvzOWvCWYNqZzgcW794IYQQQgyLSETj8cf9HDhgEAgw4Ithy0r9q6oyufPOLM4+O8appyZ7HJpY3lSOz/RhO4Of86NrOoYvAkbm5g+5ys1Y1UKPL4HrunjN0TtXpSeBgOILX4iwebOHV16ZT2ubQ51zkMZYAzEnhuM6GLrRbUDmKKfjdZ/hY4x3LGOsSeRmG6xYEefEEyMDGrra6RgOvPSSlw0bPPh8qeCkv9q/u3V1Bg88EGTu3CSf/GSs28xvb1wX/vSnVPDl8QztotaGAa6rcf/9Qb761XCX3+2qilUErSAAJTMqaDhUQuPBMRgeZ8DHzPZkE06G0x6KqBRoQPFpf3v/c9ZoijcN+ncVd+JcM/eaIRkWm2kSgAkhhBCikx07TJ5+2gdoad+t74thpP69/LKP7dstPve5CL5uluipClfhMTzYDD5o8hpeQnoIM9CKmxh4OfujxZwYWfrghkspBf6cMDEnxsTsiRnq2fDStNSQxIULk7z3nsHq1ZOoqpoGmotrhmmKNdKWbMNVqUyfhoau6WRb2eR7C9CdICidkhKbZcsSzJkTzcjcq+pqnUcf9dPWln62qa/zDAahvNzil780ueSSKNOmfRCwKAWhkEZtnSIUTWXzAn6DwnyN/HzFSy95OXTI6Pb7PhR0HRxH449/DPCVr4Q7gtlIMsKh1kOd1saac9ZG3nnxVFpqCjCtgf/mxgTGUBWuwlFOr0GYUoBrUvLRhzF80SOeV1SEKpiUM2lAx4/aUT49/dMU+YsGtP9wkwBMCCGEEB02b7Z49lkf/szFK534/VBba3DPPUH+4R8iHcMV2zXFmjB9mbk8KfAVUB2pxgq2EYv7B51VgVRWLWpHybIGF4A5CYvckgYM3cjo+mIjQddh7lyHuXMjNDdr7Nplsm+fj8bG8bS26iSPmMZlWZCT41JQ4DJpUpxZs1LFWjJl926DRx4J4PWS8YDH40kNq/3TnwKsOCvM7tpatu5po7bBoS3sEk86aCiUpsDVsUwNS/lpODieMYUJ5kzzk5M18KqD/WEYqWI5q1dbnH566gPY1bwrNdzzCJqumP+xdWx/5WQaK4oxPQNfsHxccFyvQZhydTRg7Nn/h5Xd2uk1UzdpjDUOKACL2lE+PuXjzCmcM6C+jwQJwIQQQggBwNatJn/5iy9jWa+eWBYkEjr33Rfg+uvDnS6Uo3aUbLIzcpxcby4aGt6iCqK149GszMzfaq9ON6g2XJ2C8bVkW9mDWmx6tMnLU5x8cpKTTz5ykeDUkEDDICNBcE927zb4v/8L4PMN3XFq6hNs2dXGky9DTlmIgnFtaGiYXug8ktRFuVD53ngcJ87eyih7DkXJzdaZOz3I5FLfkL4XkLrZsWqVj7lzU0HunpY93S7toBuKueesZ99bJ3B46zQMT89DhHvTHoRVh6tJqiQ6H6Qz3aQXT14tY5Y9lxoW3I2YHevX8VzlEnfiXDr9UmYXzu5/h0fQ6B8kKYQQQoghV1+v89RT/iEPvtoZRqpgwJ/+FEgNS3qfrTI3X8uje7B06/2J/pnLsig1+LaCeSH8OSHyfHmD79Aop2lgmkMbfNXW6jzyyNAFX+FYgr++Wc3L65pobE1iem1CNeOItfacCW2rz8NOpta3MnQd03IJxWze3NTKU6/WUN/SfSCSSZYFzz6busPRGG3ssaCHpsHUk3dw0iWrsHxJ7PjAcjS6plOaVUqOlZMaeurqKNsif/4blHzk8R6DL4CEm0i7yE17FvprC752zAVfIAGYEEIIcdxzXXjkEf+wzVFpZ1lQWWmwbt3QLOqraRp53jywoviKK1BqiFMOabLjHsbN2k/EjrC4ePFId+eY57rw6KN+vN7MB19KKbbuq+eZV5tobU2tF6a/fxDdcKg/UILrdr2cVioVgOnGUUP+AMtSJBLwwhsh1myrxc1AQN8TXYcDB0yiUY3GWGOf2wfzQ5z8qZVMPmkHhmmTiHkYSPey9TGU+MoIjjvMmHN/S86Md/r8bJRStCZae93Gdm1idowzS8/kqwu+OurLzfdEhiAKIYQQx7mVK700NelDNu+rN34/vPKKjzlzbHJyMr+w7+ScydRF68ibs46qv1/e6x34dKVbFrwnhmkz7oQDGGaQeYWDK70t4O9/99LSomf8BoLtOqzcUkF1hbdTifojKVen4UAJxVMqOz0fD/uw4x50s4fqghqYpmLvfqhrOszHTh2D3xqaapi6Dq+9bpLMTWLofc9B03TFxAV7mTB/L83VBRzcNItQUw52zEJpYHkTnYIppcCxDdykieFx8GWFmTB/N6UnHEA3bSrD46gIVRC1o5ia2ePvx9ANonb3651F7Sge3cPM/Jl8ZPxHjvnMsQRgQgghxHEsHNZYs8YzIsFXO9OEZ57xcdVV0YzPhwpYAbKsLGL5NXgLq0i25aHpg8s4DCZItBMmZXP24epx5hbMT+uCWPSssVFj7VpPxofO2q7NSxsP0lSbhWX1su6d7hJpziLaFsCf/UFw31pTiGb0XdrdNCHc6uNva2o477Qigp7MjwH2eFLzO9Xp/fveaxrkj2skf9walIJ42E9zdQFNFWOw4yZK6WiaQjNcsgtbKBhfQzA/hG4cOUdSoyyrjLKsMtoSbexv3U8oESLhJlAoLM3qCMg0NBzXQSlF3ImTdJP4TB/53nyWly1n0ZhFmVmgfRSQAEwIIYQ4jr3+uicjpb8HwzBg/36TcDg1ZDBCZufGTMiewHuN71F02nNUvfBF0AdejMNVLl5jYJkKpcAbjDJ1yQ7irsPysuUD7odI+fvfvQNa46s3jnJ49Z19NNXkYph9By266dBcVYQ/+2DHc4mYJ+3hkLrhEg/5eHlDDeefWjok68KFQjoq6mOg9W00DXxZUUqmV1AyvWJAbWR7splfNB9ILWjeEm+hMdZI3ImjUCSdJIX+QiZmT2R89nim502n0Ff4obxJIQGYEEIIcZxyXdi2zer3wrJDwTDgtdc8jJk9ht3h3Rltu9hfTKWnkpAWIm/eapreOQPd07+Ka+0UasBZOidhsfD8NcTcCMtKl5HtyUy1xw+zplgTB1oPsKdlD42xRiLJCI5yUuuK2V7WrTmPoM9LvjeffF8+PsM3qCGiSinePrSD2spizDSCr3aJiA87YWJ6bFxHw0laXeZ/9UY3FOHmLF7fuYuz58zJ+FBc5Wq01eWTnd2c0XYHymN4KA4UUxwo7nguZsc4e8LZLC1dOoI9Gx4SgAkhhBDHqZ07DcJhjWBwpHuSKkzw9js2H1tUSG3daizHwtJT/wY750rTNOYUzmFd1Tqyp28hWj2JeH0pmtn/NY90TR/QMCg7YTFxUTmBghYCZj5njT+r320cL5Juko21G3m7+m3qY/VoaPhNf5fvwYHt44klHWythYZoA65yCVgBSoIllAXLBpQ5qQxVsbc8B6Ofw1Q13aW5uoiiidUkol6Uq0E/D2+YLnUHi9gxZg9zimf0b+c+BAIa1VWTYGpzRtvNpKSbZFxw3Eh3Y1hIACaEEEIcp7Zvt4at7PzRHOVQG6mlIdpA1I4Sc2LEIyat7/ydnYl3cB0XTdOwdAuf6SNoBikJlpDjyRlQQOYxPEzLm8bu5t2MWfZXqld9imRLIZrZv7L3Awq+4hbjZh1g8uJdRO0418y9JuMZjg8Dx3V46eBLvFP3Dgk3gd/0E7R6vjvQcLCkY+FgU09d0tquzf6W/RxsPUixv5jp+dMxtPQioYSTYNPOFlSi6Kh5TH3TNEU8lMqMxsN+NH1ga8Vp6Lz3nkFZTgu53twBtdEdwwA9NrorBpq6SUmwZKS7MSwkABNCCCGOU42NxpAvBnu0qB1lf+t+GqONOMrpuHA2NANTt6B5CqVjD1LdWt0RpMTsGFE7SnWkGr/hZ1zWOEqDpf3OcIwLjqM10UptpJaSFU9S88ZFxBtK0NNcoNlVLrme/l0UJ2MeyubuZdop24naUS6cfCFF/qJ+tXE8qAhV8Hj544TtMF7Di1/ve5hntLX7uwft36naaC2NsUZmF8xOq2re9ob3CDeM6Xfw1c5OmKl1rxxjwOvOaboiEcpma005yyYsGXT2F1JzD5NJMJwCQs1eLEvD8sXRjaErfz8Q2VY2PnOY18IYIcMSgCUSCW6++WZs28ZxHE477TQuu+wyQqEQt99+O3V1dRQXF3PjjTeSlZVa0O7JJ5/klVdeQdd1rrnmGhYtWgTA3r17ueuuu0gkEixevJhrrrkGTdNIJpPceeed7N27l+zsbG644QbGjBkzHKcnhBBCHHOUgqYmDWOY5rfbrs3Opp3UR+sxNANd0zG1zpchljdB4+ExzJo1lYqWik5ZIo1UNsxWNvta9nGg9QBTc6cyLjgu7YtUTdOYlT8Lx3VoiDUw9synadpyBm1756NZsT6DUUM3CFjppQxdRwOlM+P0LZTOOkTUjvKRCR/hxLEnprX/8UIpxUsHXmJ99Xp8pi/tAieJiJdkzIvl6zl4NjQDhWJL/RZKAiXMzJ/Z6bviKrfjX8JJUFlj49pWz6Xj+zoXV08NPxzQ3h/QNEVdZQ71RfWd5kil3Q+Vqg5ZU2MQjWrEYhq2Dcqaxa7DrXhMC1138WVF8WVHyC+rY9zMgxjWwM47E5RSFAQKRuz4w21YAjDLsrj55pvx+XzYts0Pf/hDFi1axPr165k/fz6XXHIJTz31FE899RRXXXUVhw8fZvXq1dx22200NTXx4x//mF/84hfous59993H9ddfz4wZM/jJT37C5s2bWbx4Ma+88grBYJBf/vKXvPnmm/zxj3/kxhtvHI7TE0IIIY45oZBGPK4TCAz9XfCGaAM7m3biKKfXIXyaDrGQnyxvFkErSNyOdxtctWc4djfvpiZSw9zCuXiM9CqJtM8H29G0g7pIHQWLXyM46T3q116IHQuiW/Fu91MossysPttXKrXQcu7YBuac/TaWL04kGeWCKRewZOyStPrYF1e5NMYa2dOyh/0t+2mJt5B0k6nCFGh4DA953jym5U1jcs5k8r35GcmkZJpSij/v/jPvNb6H3+pfYZPW+lxUNwsgd8fSLWoiNYQSIQr8BYSSIaJ2lLgTx1UuGhoNsQbqD08HpxlD6Ri6gaVbaQ9fhFQ1w2hrIDX8cBA/K01XxFvyONh6qF8BWDIJhw8b1NToxGKpRaM1LfXPsiAQ9BEOKDQtNWwzGfeQiHloqixm39uzKCirY/JJOwjmhQfe+QGK2lEWFi8c9uOOlGEJwDRNw/f+6niO4+A4DpqmsWHDBn70ox8BsGLFCn70ox9x1VVXsWHDBpYtW4ZlWYwZM4aSkhJ2795NcXEx0WiUmTNnArB8+XI2bNjA4sWLeeutt/jsZz8LwGmnncZvf/tblFKj8g+OEEIIMdKSSXDdoQ2+lFLsat5FVbgq7YvZ9ovqGXkz2FK3pUuW7EimbhJJRlhXvY55hfPI9+Wn1S9N0zgh/wRyPDnsbdmLlV9D6fkP0bJzMeH9c0mGc9A90U4ZMQ2t12Fs7ZXvsgpamLDsXYonV5F0E+ial6tnX83k3Mlp9a03rYlWVh5ayc6mnUSSEQzd6LbqX8yJ0RJvYUfjDhzlkOvNZW7hXM4oPSPtDN5Q6xR8DaCqZDzsT2udLYBwMkxropVD9iF8bb6OQg+GZnRkyeJJGxUPoBkujnKwHZuYHcPQDLyGN60AX9NcnKSJP7cN5Rpg9G9+4ZGcpElji020MEqA3j8zpaCiQmffPhOlUmuLHV3ZVCnwejS8/gIaog0d2WVNo2MeXVNVEfV//ghFkys54cwtw5oRC1gB5hbMHbbjjbRhmwPmui7f/va3qa6u5rzzzmPGjBm0tLSQn5/6Y5mfn09raysAjY2NzJjxQfWXgoICGhsbMQyDwsIPJhAWFhbS2NjYsU/7a4ZhEAgEaGtrIycnZ7hOUQghhDiGDO0NSqUUOxp3UBet61fhivaQMNebS0mwhJpITa+Bm6ZpGBi8W/8ucwrnpD2/StNSC8QW+YvY3rCdtkQbebPfJveEt4nVldK642SSrYU4cT+OAyV5OaA00FIXs66j4yQ86KaNNxAjb1wzk0/cQSA3glKKqBNlQdECLpxy4aAXj22INvDM3mc4HDrcURkyy9N7Nk7TtI5gy1Uub9e8zYbqDUzOnczFUy/uc/+h9vdDfx9w8AXgOgZaH/OsInakY66hrumYuknCSdAQbaDQ/8H1ZNSOkoh6wTXg/flfGhqapqFQRO0oUSeKz+hjiKQGSml4g/EBzwFrpxsu8bYcDocOU5jbc/GMWCxVTCcU0jB7uaq3bY2CApec7MnURmq7LQKj6wrdl6Dx0FjWPfZRZp25icIJdYM6j3Qk3STzi46vRcmHLQDTdZ2f/vSnhMNhfvazn3Hw4MEet1Wq+y9tT8/39Fp32a+XX36Zl19+GYD//u//pqhIJsKONNM05XMYYfIZjA7yOYwOx8vnYJoQCOgEApkPxJRSbK/fTrPdjN/bvwtsn9dC13UCgQAL/At489CbOK7T54gWC4vy1nKyAlkU+NOfSxIgwLKsZVSFqjjQcoBQIkR2aS05Zc8BkIh4CEbmUBA9FTseQykNTXfxBuMUjq8np7gV05PKFCSdJEkXJudO5oJpFzApb1K/zv1ornJ5ed/LrDywEq/hpShn4N/L9ixKk9PE/Tvv58LpF7J0/NJe39eh+i1UtlWypXkLhTkDr8oXCFqYpoVlde2/q1xqw7WpLKFhoPNBsGFgEHEj5Gl5HQUfEskERIrQzO6/Z+3Pxd04trLJ8mR1G8Cksk8GPr+Ox+vS7zr0RzLATWST1Fo6fg9Ha22FzZs1NA38ffzMlIJx4wx8Ph+FoUIidgSth5swlgVK6exceTrTTt7FlBP3Dfw80hBNRvn0gk+T7T1+1sUb9iqIwWCQOXPmsHnzZnJzc2lqaiI/P5+mpqaObFVhYSENDQ0d+zQ2NlJQUNDl+YaGBgoKCjrtU1hYiOM4RCKRjoIeRzrnnHM455xzOh7X19cP1amKNBUVFcnnMMLkMxgd5HMYHY6XzyGZhGQym0gk821Xhio52HwQS7dIOumvtaUUeLUwrusSeb9jM3NmsqluEwZGWtMKNhzewGklp2EZ/cs65Rl55BXkEUqE2N+6n+Z4Mwk3gcfwMG+Whqlv7LKPq1waIxGMmEHADDCrYBYrylakskv24K4xWuOt/HHHH2mINeA3/cQY2MLRPXnknUd4c++bXDHrih6zUEPxW3Bch3u33ItyFJHkwL98Sm8mEXdB7/z9ijtxaiI1QCqL5TjdD6Oraq2iLLsMDY1YMoYd9YPm0su9/lT/lUNztJmAFeiS2VSuDnqMZDKJbsVIRL2DqjIaj+i0RFs6/R7atbVpbNpkYRipYYQ9nGaH1DYJIhGYlj2Nt6rf6jvjZCTZsWYy0WiCyYt3DfxEehG1oywbt4x4W5x4W/fzL0eD0tLSjLY3LItQtLa2Eg6nJvQlEgneffddysrKWLJkCatWrQJg1apVnHzyyQAsWbKE1atXk0wmqa2tpaqqiunTp5Ofn4/f76e8vBylFK+99hpLlqQmtJ500kmsXLkSgLVr1zJ37lyZ/yWEEEL0wLIgJ2dg5bZ7k3AS7GnZM6Bhd45tkDumqdNz2Z5s5hXOw8HpdSRMOw2NbY3b+n3sdlmeLOYVzeO0ktNYNm4Z/3ryvzI9bzr5vnxyPDlkWVnkenIp9BUyK38Wl826jBsW38BNJ93Ex6d8PCND+xqiDdz7zr2EkqEBD9HrS8AMUBup5d537iWcHL6iCysPr6Q12Troa7Ts4uYuQxBjdozqSHVq+GAfQ2xdXBqjqWkstmOnXdADUhmxSDJCwu1cgdF1dPw5kY7+KWdwQ+qUq5FwEiSczseJRmHz5g+Cr764LuTnux3b+k0/E3MmYrt9z1GzvEkObJ5JxXsTB3IKvfdLueR78zlrwlkZb3u0G5YMWFNTE3fddReu66KUYunSpZx00knMnDmT22+/nVdeeYWioiJuuukmACZMmMDSpUu56aab0HWd6667Dl1P/TC+/OUvc/fdd5NIJFi0aBGLFy8G4Oyzz+bOO+/kG9/4BllZWdxwww3DcWpCCCHEMSs/36W6OnP3YpVSbGvY1mnIV3+4SZOC8TVdni/wFTCvcB5bG7ZiqN4zYbqm0xpvpTpcPeBFXZNukoAV4MvzvjzsRSuaY83cv/V+dE0f8sWa2+dE3ffufVy/4PohC/baOa7DprpN+IzBr/Xk8cexfHHa5zLGnTg10Zq0v3saGmE7TL7Kx1EOSvXvvdY0jWgyimZpHTcbNN3F409lcQK5IXTLTs0bHCCFhqtcYnYM8/1LdqVg2zYLXU8v+AJwHI3JkzsHWxOzJ1IXrSPuxPsMVi1vkr3r51E4oRZfVuYysXHn+F2UXFPp3E76EKusrBzpLhz3jpfhPqOZfAajg3wOo8Px9Dm89pqHN97w4k1v6aU+1UXq2N64fcBFJ5ykwelXvUBWtq/LkCuAtkQb2xq2kXATvVZHhFTZ+KXjlvb74i6SjDA1dyqXzrh02BeFTTgJ7txyJ47rDOtFqe3a5HhyuH7B9Z2Om+nfwpbaLTy992mCVrBf+7mORrQ1i8bDxbTV56HcVMBwYMsMNA282W3U2nv7PdzPVS653lxa4i1EDszBTfQ/2FZKke3JTgXMhkvZnA/mSzVWFNFWlzfgBY81TVE8+z1On3Q6HjdV1vDAAYMDB4xeC24cKenaKCPMuBMO4ripcYq6puM3/WRZWbzX+B6alkbG0NXwZ0c48aLXM7J4eyQZ4eNTPn7MrIuX6SGIwz4HTAghhBCjw5IlSV5/PUPRF3Co7dCAgy+lIGdME7rR87DIbE82p5Scwp7mPVSFqzC0nrNhjutQHa6mNCu9C6ekm0RD41PTP8W8onkDOofB+svevxC342mvaZYppm7SEGvg1UOv8tGJHx2y46ytXpt28KVcjdp946jYOo1IaxZ2wkQ3HAzL7hQANB0uJuIEcLR8DE8cb0ENntz6tKoQ6ppOW6INSGWvBkLTNMLJMFlWDt5gtNNreSWNhBryBtRuqk+pc2jPlcTj9Bl8KRThZJi2RBu2a5NMaoyZsZ/6aLJTkOUoB8d1cJXbMc8wz5vXY+Cv64pwYw4V2yczfu7+AZ8TpOZ9nTPxnGMm+BoKEoAJIYQQx6lAQDFhgk1trTnou9pRO0pbsm3AAZgd8zL5xJ19bqdrOjPyZ1ASLKG8uZxQIoShGV0uHE3dpCJU0WcAFrNj6JrO9LzpfGLqJ4Z8GF5P9jTvYXvD9hFbp8tv+llTtYYFRQv6tfhvusLJMLWR2j7Pz3U09m88gepd40nGvJjeBLrh4PF3rTKRU9xEfVU2roqhaxrK9hCtmkKsZgJWbgO+sYfQtN4DK8d1UCh0XwQ7kt0R9PSHq1xiiSSl4zpnC3XDpWhCNXUHStHTXLPsSKY3NfdL13VwYN8+A72HxKirXJriTUSSEVzloms6ytXJLWolK9vm6GUnDM3AMFJz1MYGxlIZrqQ10Yrf9JPvze/2JoDpTXJ421TK5uwf0N8LpVIl/TO5KPmx6vgbdCmEEEKIDmedFScSGfyYogOtB9JaaLknvpwwOUcV4OhNtiebk8acxCljT6HQX4hSirgb7xhmBal1oEKJUKf92i8CQ8kQlm5xZtmZ3HTSTXx25mdHLPhyXIen9jw1Ysdv5zN8PLbrsbSKnfTXobZDuKr3YKitPpf1T5zN4e1TAA3Ll+j1Ql8zXGxvNUcGF5phgwaJ5mLadi/AjvS9XpqrXMysJnAGmpfQsD0N6FaiyyuB/BD+nBCu27/fmHJ1vIHUzQGv4cV1oaGh+wAskoxQEaroKKbSfjNCNxwKJtT2eSxTNxmfNZ6AGSBqR6kKV9EYa0TR9XsQawvSXJX+Mg/t4nYcUze5Zu41x33wBZIBE0IIIY5rEya4jBvn0NSkYwyiaFtronXA85aSMQ/TT9s6oLvqfsvP7ILZuMollAjRFG+iJd5C3ImjlKIqUsV0z3QMDAJWgAJfAVNypjA1byr53vxhqZislKIiVMHOpp1UhatoijURsSM4ykFDozZay4HWA+R4csjx5FDkKyLo6d88qUzQNI36aD2H2g4xMSezVe92Ne3Cb3UfYCoF+94+gcPvTsPwJDGtvqvzQSqr5imuIblvEdpRGSZNd1GuTvjAbDz5tfhLDnTbhq7puLh4fVHQ+5+lAlCuibf4MC1xRb4vv8vrRZOqqNwxGdfWSfcnkqqoGMbUTbyGl8PVcWw7Vb20YxvlUh+tJ2pH0TW90xBD19EZN+tgn4tVt9M1nTGBMUTsCA3RBloTrUTtKMX+4k7ZMMsX58DmWeSXrknvPN4vIrJ4zGLOn3w+pi6hBwwwAEskEui6jpnuDEAhhBBCjEqaBpddFuGuu7IGHIC5yiXuxAeUAXNtnfyyWsZMHVxRLF3TyfHmkOPN6fT8GP8Yrp5z9aDaHqioHWVN5Rq21G+hJd6Cz/B1rE9m6mZHZbvqUKp0eluijeZ4M/ta9xEwA5QGSxmXNW5QmcX+CpgBVlas5As5X8hou3XRum7PQykof2MhNXvKML3prxkHqbXSLJ+Dt6CaeGNJl2F+mgYYDommMbhJD8Hxu3ucG6bpLro3ipvs39pdytWxsprwZLURtjXy6RqA6YZi3KyDVO2YhOtoaQVhhmXjCcTxm/5UYFyvdwm+KsOVHcMNj+Q6OmOnH+6oyNgfATOAL8tHS7yFUDJERaiCkkBJR/CsaRBuyEWp3qswxp3UsafkTOEjEz4y4IqkH1ZpRVC///3vWbZsGdOnT2fjxo3ceuutaJrGDTfc0LEOlxBCCCGOTbm5io98JMbf/+7DP4BRcOFkGMd1OuaU9Iemw+yzui5ynCmN8cYha7sntmvz/P7nebf+XRQKr+El25Pd7bbhRJhwMtwRmBmagaEZ2K7N3pa9HGg9wPjs8UzMnjgs2TpN0zjUemhQiyR3J2pHu31+1+r5qeDLk17Wq13SSZJwExiagW/MIey2fFyn+7mMmuFgh/KIVE4jULa7y+uGZuAoB29hFZGK6Whm+n3RNJdA2V4gNZQ0kox0O8/NMB3GnbCf6vKJOEmr16IfytXIKmgDzSVgptqKRo/IbimXylAlCtUp66WUBkqjZMYhvMGBl4vXNZ18Xz75vnwiyQiN8Ubyye/4jqpEgFhboGPNM6UUCTdB0kliGRZ53jxOGnMSp447dcSH1Y5WaQVgb7zxBpdffjkAjz/+ON/4xjcIBAI8+OCDEoAJIYQQHwKnnZZk/36TffsMvN7+Xeg3xZow9P4HX3bcZM5HN2B5+3fx3R+hRIi4E8drZK7aY28Oth3kz7v+TCQZwWv2fczDocM9vnftw7UOtB6gLlrH3MK5w3ZBu7luMxPHZW4YoqO6Du+r2jmBqvJJWN6uc6f6EkqGOjI/mgb+8bsI75sHPRS70AyHZGsBce84vEVVnV5rf5/1nEb0miRHF6zoiXIMAuN3oekflHcP2+EeC40YpkvpCQdoPDyGUEMumuF0GzAqpZNb0oDt2JQGS7FtiMc1TDNV5bAqXIWLe9SQQwNvIEbx1AoMc2BDKbsTsAIErAC2azOncA6OcqhuasPbMofScamstaEbjAuOY3rudIoDxTLMMA1pvUPxeByv10tbWxs1NTWcdtppAMfNOilCCCHEh11qKGKUP/whQEWF3q8gLO7E+z3/y05YzDpzM0UT6/rb1X5xlUvCSQx5AKaU4pVDr7C6cjV+059W8AWpQiF9vXembhJ34myo3sDM/JlDPpzLa3g50Nr9nKlMScY87F43b0DBF0DCTXQKQExflMD4ciKHZ3aZD9ZOM2xideOxcjsXzNDQ8Bpe4k4cT14tsYZxvS6HAKAcE3/Jfqzs5s7n5fY+jFLTFYUTawgWtlC/rxQnaXYKxJSr4c8JY1g2Xt1PjjeHSIT3i3goGmONOK7TkQ11HQNNdykYX0N2UUuvxx4MUzfZ07yHk0tOZox/LFPdSXz6hMwtyny8SeuvZWlpKa+//jrPP/88CxYsAKC1tRWPZ3jXqRBCCCHE0DEMuOqqCFOmOES7HzHWLVe5fS7k2k65YMctZp/1NmOnVwywp+lzlYvtDl2GDVLB17P7nmVN1RoCViDtoYLtFRnToaFh6iblTeUcbjs8mO72fSxNoymefkXKdBw5/0sp2P7KSX2WiO9Nd5+pld1CYHw5yjHosZCj7hA+NLPTU5qmkefNw1Uu3uIKDCvR4/5KfRB8efK7VhhM97vmC8Yom7uXosmVWN4krm2kFpjWUkU7bDeV/QKw7dR3Je7ECSVCaOg4toFuuOSNq2fCvL1DGny1S7gJ9rakFry27aEfDvthllYAdt111/HCCy+wbdu2jqGIW7Zs6QjGhBBCCPHhYBhwxRVRPvrRGPE4OGmMZtI0rduS1UezEyYef4Iln1pJ8eTqDPS2b7quD2h4ZH88f+B5ttRu6ffwwKSb7DNjcjRTN9nTsoeqUFXfGw9CS7ylU0n/wTpyWF5rbR5NVUXoxsDK3Sulegx0rOwWgpO3ohkOyu16matp4MaCJNs+KJahazoew4NlWKnhjBN2Qjf7KldHA4ITdnYbfEEq4E/3M9U0COaHGDfrIKWz9+HPDTF+7h4sXwI36SVHKyUUglBIYdtQH63D47MJ5IUomXGIsjn7yB3bOOBFpPvL0AwqQhWEEiHc4Tnkh1ZaQxCLior4j//4j07PnXnmmcyfP39IOiWEEEKIkaNpsHRpkjlzbB57LEBFhUEgoHpcBNbSLZRSPWZ+nKSBUhoT5u9h8uLyAS12OxgDXRw6He/UvcNb1W8NaAHlhJPApf9XspZuUd5cTrYnmyxP7+tcDZTjOsTszA0xK/YXUxepw9ANDmyaheXtf4W+dn29Z6Y/Sva0d4jVTCDeOLbrXCsjSbx+HFZ2KsvX/v0o8hVRFa7C9MbxFh8mXjchFcgpwDGxchrxl+7rmPPVY//6WO+sO7ruMmPpVuZ8ZCORZITzx1/MzOwIjqMRiXj58a3VeHhvSL/L6TB1k70te5mtzx7Rfhzr0sqA/dM//VO3z994440Z7YwQQgghRo/cXMV114W59toQ48bZxOMQ7+a6Oc+b1+Wuv3JT83w0TVE6ez9Lr3iZKSftHPbgK2gGh6xwRdSO8rf9fxtQ8AXvBxIDfDss3WJb47YhWTQZQGmq39m53szMn0nUiZKMWzRXF6a9Hla3fUvjnDVN4S85SNbUrZj+EMrVUc4HRTucWBZu0oOr3I7vh8fwkOPJwVUuvqJqzJx63IQH0xchOPk9AuN39xl8AWllg49kJ01ySpqYvWITtmszMWciJ49fQG6uoqDAZfx4qE8eHvHgC1JDYZuibfiyBh5AizQzYN190SORCHpPt8KEEEII8aGgaanFmq+6KkokovH22xaHDxs0Nuq0tOg4Duh2Dm7Cj62bGJ4E/pwI/uwwxVMr+P/s3XmY3FWd6P/3d6m9el/TnXT2zr4vkEBICBFQARFZRFEBHRdcRr135vrc8afjvdfHmbkPogioo4jjqAyCoMhV1gBhlUASYjayr52kl+q11u9yfn80aQjp7tTaWz4vHp4nXfWt8/1Unf5W16fOOZ9TMb5lyJOudyv1lxas7d/v+X1Oj0933dxAknaS/Z37mVo6Nad2+qXIemPt/owPj0dHp2lX7pUVMynHb/rjmA2968ISbbXYPWW4KR/K0YifaMBbuw8vQRxbx3UMgm4tUddCC7ZTdd6T4Or0HJiD7kt/NDCTfrWTHkrrW5h7yUbQXFzX5brp1512jOtvIal34mPo9oMbjJ300FX8V2D5cIcyag2agH3hC18AejdePvXvU3p6erjgggsKF5kQQgghRpRgULFq1TvV42wbYjEN24afbXsJV0/i8acy2si2kFzlUuWvKkjbh7oOsb9jPyFvKOs2dE3PKQkzdZNjPcdoKGro26Mpb7Te9lNkV6XwvYKeIDXBGvadLM94z68zQ8v8NdMMh0D1Mag+hlLgWj5QivKZ2xlf5GKYCm8wQWltG1qwjc1tr4PqTfZ8VceIbL649+cBKiy+WzqJq3LBsU0mLNjTOy1Xg4Sd5KZZN50xovrCkQ2Ei/1Y8exGWvPNY2q0ebYgCVj2Bk3AvvzlL6OU4nvf+x5f/vKXT7uvtLSUurq6ggYnhBBCiJHLNKG4uHd0q6G6iKM9nWh5HDXJVcyKMau8MGtVnj/2fNZTD0/xGb6cZxNpmsah7kNMK52WUzvv5dN9BMwAMfK3IfPKupX8pb0nvfUvg9C13sIq2U6/1DQwvElczWLueccYX/TehM7H4qrFbGrehFKKcMMeArWHaX31chInJ6D54gN+yaCjD7oPllJgJ70ES3uYs/YlgqVRlFIk7CTXNV7HxOKJZzymOdZMuKyCtmgIfRhHk0/xBhPEtDZs15Y9v7I06Ks2e/ZsAO699158vqHZwFAIIYQQo8+F9Rdy3/b7CHmyHxHKt2JfcUGm58WsGEe6juA3/Tm1Y+omPt3X7ybF6TI0g5ZYC1NLpmY0Ne9syv3leW0PoLFkNir+FuQ4AgZgaiaWym2NmkqGqfQ0QD9FPQKeAEtqlrC5pXddluFNUnPRH4k319G1czmJtjrQHHTz9Odi6Ea/I3Suo+FYHsLlnYw/bzvVU5rQ9N5qjq5yuWnWTf0mXwBt8TYmzG+neV89eiA/I5LZciyDqinHSDkpWmItjAuPG9Z4Rqu00lbDMHj66ac5ePAgicTpc2C/9KUvFSQwIYQQQowe48PjKfeXk3RGxuL8pJPkvNrz8p5EALzY9GLe1kf5TT9RK5pTGyk3RWu8lapgfqZbusqlPFCel7bezbYMqgN1nLT25zxy4jE8Z2zGnAmlFEEjjEoFwN//6+83/SyvXc7e9r2ciJ3A1EwC1U0Eqv+AkwjQ+dYSUpFarJ7eNZDKMTAMDynDh3I1dN3F9Fn4i2KEyrpoWLCXYMk754pZMRqKG7hu+nUDjqZGrSg9qR7C5Rqh8i5S8dyS/lwpV2figj0ow8Pezr2SgGUprd/+u+66i0OHDrFkyRJKSkoKHZMQQgghRhlN01hSvYRnjjxTsKqDGVG9U94K4Uj3EbyGNy9tFXuL6Ux25pSQeHQPrYn8JWBxO8700ul5aevdbLs3Ue/sbsJyrZzWv4U8od5NibNMsHVdp8QsxXUGf7yhGcwon0FNqIadbTuxXAtDMzD8ccoXvAj0Tit0EkESXWHq/dOoDtRiemz8RTH8RbEzpivGrTgew8OVU65kYfXCQc/fnerGcR1MTOpnH2D3S/Px+PNXnTITyoWS2ja8gRRKeWmLtw1LHGNBWlf7m2++yV133UUoNHKmFQghhBBiZFleu5zXTrzW++F6GCtxJJwEy2qXFSQRVErRkezIW3vjw+M50n0k53biVjwP0fQKmIGCrJ0zDNB1mFsxl9dPvp5T0uk3/Ji6mdWeW65yqQ3Woqe0tDeDLvWVcv6482mJt3C0+yhdVhemZvYWUtHADMQI+aPMrJuMoZ25ybirXOJ2nHJ/OSvHrWRp7dK0yso7yulb61Y7/ShH/jYNK+kZlkI3jm0yfcXfgN4vXHKZOnuuS3sjZssanmxbCCGEEKODoRtc13gdv9j2CwKe4RkFU0pR7ClmXcO6grTfY/UQs2I5F+A4xWN4KPWV0p3qzilpjTvxQTfDTpft2swqn1WQ4gpeb+9m3j5PkInFEznUdSin84Q9YTqSHRlNB3WVS5G3CJ/hw9YVhif9z7eaplEdrKY6WE3cinO05yg9Vg8JJ0HSTlLsLe4dlUNDoTB1k4AZoNxfTrm/nOU1y6kL12XeR28frumK2Ws3sumPqzF9Q/u53E6ZTJi3j2BpbtNlRa+0fusvuugi/u///b+8//3vp7S09LT75s6dW4i4hBBCCDEK1YXrWFyzmC0tW/AZQ1/AK+EkuGnWTXndw+rdmmPNOG5+v/mfWDyRzc2bc5rWaLs2lmvlPDUy5aRYPX51Tm0MxOOBoiKXVEqnoaiBhJPgZPRk1klYkbeIzlRn2se7yiXoCVLu713fZvqTeLMsahHwBJhe9s40zY5EBzfOuJGQN4RSCo/hIewJU+wtzikp9uge9HfVjQyX91A/dz/Htk3G9OVezCQdrqsRKI4xadHuvtuUUiNiY+jRKq3f+McffxyA+++//7TbNU3jrrvuyn9UQgghhBi1Lpt4GU09TbTGW/O/P9Ug4naciydcTG2otmDnSNiJnEvHv1ext5hiXzExK5Z14qiUynlKmOVYNJY1UuIr3Hr/sjKXkyd1NE2jsbQRIOskTNf0vjV0Z3vdTiVfVYF31skFivJTYj/lpFhYvZDZlbPz0t67lfpKMXUT5bwzVXLKkl1E24rpbC7HMAs7DVC5vWX75132ymkbqsftOA1FuW+qfa5K67f97rvvLnQcQgghhBgjDN3gk7M/yb3b76Uj0ZG3ghWDidtxVoxbwQV1FxT0PA75/8CraRqzy2ez8eTGHBrJPQ5TN7l62tW5NzSIujqXo0d7R8M0TWNG2Qz8hp9DXYcGLOE+mFJfKTErNmjy6SqXYm8xZf6yd25zNYKlPVk/j1OUUngNL1dOuTLntvrjNbyU+ktpt9r7btN0xdz3vcbWJ86jq6Uc01OYkTDlamgaLLryBfyh06ubKtSAZfPF2Y2c3RKFEEIIMWZ4DS+fnvNpqgJVJOzE2R+QJaUUUSvK6vGruaThkoKd5xSv7s2q8MPZ+E0/k4snY7vZfZjW3v4vWzErxhVTrij4tNGlS1PY73mKE4snsrSmtyhFNs+/KljVb58oetfEjQuNOy35AnBSHsbP2Zfxud4rZsf48LQPF/RLhv62BNANxYLL/0plwwnsZP7PbadMTF+KxVc/R6D4zJFCv+mn1Fea9/OeK9IaAYvFYjz44IPs2LGD7u7u03Ye//GPf1yw4IQQQggxenkNL7fMuYVnjjzDaydew2/481odMeWk8BpePjbzY0wrnZa3dgdT7C0uWNv14XoiiQidqU4MzcjosRpa1mty4nacBVULmFk+M6vHZ6K4WFFf79DWZpxWyS/oCbKsZhmHug7RFO0tU5/u8/HoHsoD5UTiEXRNx1UuuqZT5Cmi1F/ab2IaKusiVJbbCFjcjnNh3YVMKZmSUztnM6V0Ctubtp+x8bemK2ZfvImWg8fY/dJCXEfDMHP7ckApsFMe6mcdYMqynf1WiVRKFWSj7nNJWiNgP//5zzlw4ADXXnstPT093HrrrVRWVvLBD36w0PEJIYQQYhQzdINLJ17KZ+Z+Bp/pI2blvu7GVS5RK0pjWSNfXvjlIUu+AKqD1QUb7dA0jbmVcwl5Qhmv5/IZPgw9s6QNepOIaaXTCjaFrj8XXJAk1s+vgaZpTCqZxIpxK5hZPhOf4cNyLVJOCsXA5eIVCp/uo8hbhIZGRaCC8UXjKfOX9Zt8WQkP9XP25/Qc4nacxdWLWduwNqd20rFywspBk52qSSc577pnqJjQjGOb2KnM19O5joaV8OIPx1l81Qamnb9jwBL9cTvO8trlGZ9DvCOtHtq6dSt33HEHRUVF6LrOsmXLmDp1Kv/6r//KFVdcUegYhRBCCDHK1YZquW3+bWxt3cqrJ16lOdpM0BPMqOhE0kmCgkklk1gzfg114boCRtw/Uzcp8ZaQcAozrVLXdBZWLWRry1a6Ul1pF6fIZs+zuB1nVvksrpl2zZCOZkyf7lBV5dLTo9NfPRNN06gJ1lATrCHlpOhKdRFJRIhaUSzX6puJpWm9o35BM0h5oJwSbwmt8Vb2dOwZcDqmUuALJ6iZeiyr2JVSxOwYK+tWFmyrg/c6NT31aM/RAa8X02sz++JN2CmTI9um0Lx3AomoH+XqeHyp0wpowDsjXbg6ps+ifHwzkxbvIlhy9i9IAmaAOeVz8vLczlVpXdVKKYLB3v0u/H4/0WiU0tJSTpw4c6M5IYQQQoj+GLrBoupFLKpexMnoSTYc20BLvIWOZAcpJ4WpmxiagcfxkHSSWG7vXkcBI0B5oJwpJVNYMW5FQTZYzkSZv4ymnqaCJS26prOgagH7OvfR1NOEoRmDnuvU3lbpcpVLykmxZvwaLqi7YMinkmka3HBDjB//OIzfP/ixXsNLZaCSykBlWm3Xheso8haxvW07lmudMZXTSXmYf9mraW/A/G4pJ4VX93LjjBtPK0E/FNZMWMNPt/70rP1sem0mL97N5MW7ScV8dDaX0XakhmRPENfRQWlohoPHl6K0rpWyulYCRbEzErSBWI7F/Kr5WY22ineklYBNnDiRHTt2MG/ePGbOnMm9996L3+9n3LhxhY5PCCGEEGNQTaiG6xqvA3r3sGqJtXC05yhxO06wKEi8J06Jr4SGogZKvCUjar3J0pql/FfHfxHyhAp2Dk3TmFY6jZpgDTsiO0jayQFHwxzlpD0aGLNj1ARquL7xekr9pXmMODPl5YpVq5Js2OAjkOd8ushbxPLa5aclsLqmY6dM6mYdpKiyK6P2HOWQsBPMrpjNVVOuGpKqnu81LjSOaaXTONZzLO1RUW8wSdWkE1RNyt+Aia7pXDzh4ry1d65Kqwc/97nP9Q333nrrrfz2t78lGo3ypS99qaDBCSGEEGLsM3WTceFxjAv3frFbWVlJa2vrMEc1sOml0yn2Fue871Y6irxFLKtZxrHuYxyPHSduxTF187SEtNhbPGj1Qle5xKwYNaEaLh5/MYuqF42IhHbVqhRHj5ocPKjj8+U3Hl3TmV46nQnhCRzsOsjJ7g785S1MWbYj7TYSdgJDM5hSOoW149dSGUxvFK5QPjL9I9y5+c5hO3/cjnPNtGuGfQR6LDhrAua6Ls899xzXXHMNAMXFxXz+858veGBCCCGEECORpmnMr5zPqydeLXjZduhNJiYUT2B80Xi6Ul0c6jpEzI71TtN0LKaVTkOp3pLrpzZkjtu9iVqRt4hxwXGsHr+amlBNwWPNxKmpiL/5TZAjR4yzTkfMht/0MyU8g2WTbBa8/2X2dFfTkeygM9lJyk31xoHWW+RD9U55LPIWUe7vnfK6tGbpsIx49SdgBnj/pPfzx31/JOgJDum5LddiaslUZlfkf7Ppc9FZEzBd13niiSe47rrrhiIeIYQQQogRb0Xditw2Ts6CpmmU+EqYXzUfgJSdIuEkOH/c+SScBK5yMTSDoCfItJJpjAuPG/GjFYYBN90U45FH/OzY4SGY57wiFoOpUx2uvz6BaS7mPBYDvQlFa7yVmBXrK3nvN/1UBiqHJKnO1vyq+RzoOsDfWv82ZH3ruA4BM8CHp314SM53LkhrCuLq1at56qmnuOyyywodjxBCCCHEiBcwA1w+6XIe3ffokI9GnOLgcNuC20bcyFamdB0+8pEEs2bZPPaYH9fV8GS3pVkf2+6t9HfFFQkWLrR474xLj+5hXGh01jK4aspVJJ0ku9t3FzwJs10br+7l7+b+3Rn7kInspZWA7d27l8cff5xHH32UioqK0+YNf+c73ylYcEIIIYQQI9WCqgVsbd1KU09T2oUR8iXhJFheu3zUJ1/vNnu2zeTJPfzpT3727u3NwHwZDkalUuC6MHmyzdVXJwgGM692ONJpmsZ106/j0f2PsrVla8G+AEjaSUp8Jdw699YRP5I62qT1bnHJJZdwySWXFDoWIYQQQohR5drp13LXlrtwlZvRnma5sFyLcn/5kO1DNZQCAbj++gTxeIJXXvGxdauH7m4NpXrve+++Ya4L8biGpinCYcXixRYXXpgiFBp7ide7aZrGh6Z+iEnFk3j84OMAefsSQClF3I6zqHoRl0+6fMi/XDgXpPWKrlmzpsBhCCGEEEKMPgEzwK1zbuXf//bveHRPwasL2q5NwAxwy+xbhizhGw6BAKxdm+Tii5N0d2scOWKwZ49JZ6eO83bxScOAoiKXadNsGhocSkrUGVMNx7oFVQtoLGvk93t+z77OfQTNzDY3f7dTm0yX+Er46IyPMr5ofJ6jFaeklYCtX7++39s9Hg8VFRVMnz4dT66TdYUQQohRylUuJ6In2Ne5jyPdR+hMdeK4DgqFjk7QE6QqUMX00uk0FDfIdJ4xpiJQwWfnfZZ7t90LcMbmv/mSclIUe4u5de6t58x6HE2D4mLFnDk2c+bYwx3OiBQwA9w06yaaepp4/ujzHOg6gFIq7d8Rx3VIOAlqg7VcNuky5lTMGdPJ/UiQVgK2YcMGdu/eTUlJCRUVFbS1tdHZ2cnUqVNpbm4G4B//8R+ZOnVqQYMVQgghRpKoFWXDsQ3sbNtJV6oLr+HFq3vPGAVJOAla4i280fwGpmYyoWgCq+pXMal40ojYj0nkriJQwRfmf4EH9jzAiZ4TBDz5S7JPjUzMLJ/J1VOvHjFl0cXIUheu48aZN5KwE2w8uZFDXYdoT7TTnerGci1c5YLWW3b/VLXMMl8Z1cFqVoxbQUWgYrifwjlDU6d2WB7Ez3/+c+rq6vjABz7Qd9vjjz/OsWPHuPXWW3n44YfZtGkT3/3udwsabCE0NTUNdwjnvJG+4ea5QPpgZJB+GBnS6YeUk+JP+//EzshOTM3EY2Q2C0QpRcyKUeov5Zpp18hUn/cYzdeCUoq/nvgr64+sx9CMnNfPpJwUHt3DVVOvorGsMU9Rpmc098NYkms/pJwUHckObNdGoTB1k5AZIuwN5zHKsa2uri6v7aU1vvjSSy9x+eWXn3bbpZdeyosvvoimaVx11VUcPXo0r4EJIYQQI9Ge9j38aPOP+kpAZ5p8Qe8C+pA3RMpJcd/2+3hs/2PYrkyvGgs0TeP8cefz5YVfZkbZDBy3d1PkTCiliFpRdE1nSc0Svrzoy0OefImxw2t4qQ5WUxeuoz5cT02wRpKvYZbW1zIlJSW88cYbLFu2rO+2TZs2UVxcDIBlWZimVEgRQggxdimleOrwU/z1+F8JmIG8TAPTNI2gJ8jWlq0c6DzAp+d+etj2lBL5VeQt4uppV2O5FpubN7O1ZSuRZIS4FUfXdHyGr2+djaMcknYSTdMImAEq/BV8YNwHmFk+U9biCDEGpZU13XLLLXz/+9+noaGhbw3Y4cOH+frXvw7Anj17zhghE0IIIcaSPx/8M5ubNxckQfKZPuJ2nJ/87Sd8bt7nCHlCeT+HGB4e3cPy2uUsr12OUoqOZAeHug5xPHYcy7HQNA2v7mV8eDwNxQ2EPWFZFyjEGJfWGjCArq4utmzZQiQSoaysjMWLF1NUVFTo+ApO1oANP5ljPvykD0YG6YeRob9+WH94PS8ff7ng1Qsd18Fn+vj8/M/jMzLcgXYMkWthZJB+GBmkH4ZfvteApT1vsLi4mIsuuiivJxdCCCFGumPdx3ip6aUhmRpo6AYxK8aj+x7lusbrCn4+IYQQQ2/ABOy73/0u//RP/wTAt771rQGHw7/zne8UJjIhhBBimDmuw4N7HhzSfbu8hpedkZ3sad/D9LLpQ3ZeIYQQQ2PABGz16tV9/167du2QBCOEEEKMJE8dfoqoFR3yTW+DZpBH9z/K3y/6+5zLmAshhBhZBnxXv/DCC/v+vWbNmqGIRQghhBgxbNdma8vWIU++oLc6YsyO8WbLmyypWTLk5xdCCFE4aX+ttnPnTg4cOEAikTjt9muuuSbvQQkhhBDDbUvzFlJuioA+dNMP3y1oBnntxGuSgAkhxBiTVgL2i1/8gldeeYWZM2fi9b6z74mUSRVCCDFWbTy5cUjXfvWnOdbMyehJakI1wxqHEEKI/EkrAXvhhRe4/fbbKS8vL3Q8QgghxLBLOkkiiciwTD98N5/pY2vrVt4Xet+wxiGEECJ/0tpevbKyEo/HU+hYhBBCiBHhZPQklmsNdxh4dA8nYyeHOwwhhBB5lNYI2Oc//3l++tOfcsEFF1BSUnLafbNnzy5IYEIIIcRw2dOxZ9hHv05pT7QPdwhCCCHyKK0EbP/+/WzevJmdO3eetgYM4Mc//nFBAhNCCCGGS0usBY8+fDM/HNehK9VFJBGhM9mJx/CglEJDw2N4KPWVMrVkKpNKJlHqKx22OIUQQmQurQTs/vvv53/8j//B/PnzCx2PEEIIMewcnGE5bzQV5WD3QSKJCI7rYOomrnLpTnWja2+vGrB7R8V2tO3AxaUqUMXymuUsrF4oe4YJIcQokNY7tc/nk6mGQgghRIEk7AQ7IjvoTnZj6AaGZmAYBgCucs84XtM0gp4gAHE7zuOHHueZI89wyYRLWFKzRKoUCyHECJZWEY4bbriBX/7yl3R0dOC67mn/CyGEEGONqQ3NSJJSiqPdR9l4ciMxK4bH8Lwz0vU2TdPQGDyhCpgBTN3k8UOP88sdv6Qn1VPIsIUQQuQgrb8wp9Z5PfXUU2fc98ADD+Q3IiGEEGKYVQYqOdB1oKDrwJRS7GjbQWuiddDzeHVv2iNaATNAS6yFu7bcxSdnf5K6cF2+whVCCJEnaSVgd911V6HjEEIIIUaMxrJGXjj2Ah5vYRIwpRTbWrfRkew4a5KX6WbQhm6gK537tt/HLXNukSRMCCFGmLQSsKqqqkLHIYQQQowYNcGago5+vdX+Fu3J9rMWzXCVS9gbzrh9TdPwGT7+Y8d/8KWFX6LIW5RtqEIIIfIsrQQsFovx5z//mYMHD5JIJE6775vf/GZBAhNCCDE2KaVGfJEIr+GlIlBB1Irmve1IPMLJ2Mm0EjzbtakOVGd1Hk3TMDSD3+3+HbfOuXXEv+ZCCHGuSCsB+/73v4/ruixfvvyMfcCEEEKI/iilaOppYmvrVprjzXQkO4hZMVxcNDR8uo9SfykV/gpmls+ksazxjAIUw2lp9VL+cugvBM1g3tp0XIdd7bvSLvIR8oayGgE7xdANjkeP89qJ1zhv3HlZtyOEECJ/0voLsGfPHu69915MU/YXEUIIMTjLtdh4YiObmjfRlmjDb/j7ptp5jdO/xOtIdhBJRHiz5U3CnjCzK2Zz0fiLCHlCwxH6aRZWL2T9kfV5bXN/534c18HQjbMea7kWk0sm53zOgBnguaPPsah60RmvvxBCiKGX1leNM2fO5NixY4WORQghxCi3u303d266k/VH1pN0koQ94bOuc9I1vXeUR4M3W97kh5t+yAvHXkApNURR98/UTRZWLSTpJPPSnqtcWuItaSVfAB7dQ02wJi/nthyLN06+kZe2hBBC5CatIa3bbruN733ve0ybNo3S0tLT7rv22msLEZcQQohRxHIt/rD3D+yK7CJgBjKu3HeKx+hdF/X80efZ3radG2fcSImvJJ+hZuSShkvY2b4Ty7FyXkPVHGvGcq201n5ZrsW8ynl5m5IZ8AR4o/kNzh93vqwFE0KIYZbWO/v9999PW1sbnZ2dHD9+vO//EydOFDo+IYQQI1zCTvDzbT9nb8degp5gXj7gB8wA3alufrL1J7TEWvIQZXYM3eDa6deSsBNnP/gsmqJNaa39cpRDVaCKcn95zud8t7ZEG22Jtry2KYQQInNpjYC9/PLL/PCHP6SsrKzQ8QghhBhFUk6K+7bfR2eqM+/ri3RNR0Pj3m338tn5n817QpKu+nA9F9ZfyIvHXiTgyW5kTylFwk6cNTlVSuHVvcwom5HVeQbiOjqxtnIeeryDksQEUikNpUDXobjYZdo0m4YGh+Li4Z32KYQQ54K0ErCamhoMI70560IIIc4dD+15iI5kR8GKO2iahqEb/GrHr/jSwi+ddT1ZoayZsIa4HeeN5jeyml6ZclOk3BRefeDXyVUupm6yuHpx2uvEzibWGeTgppm0H60mlfTQFPKxcNzpr+HJkwZbt3rQNI3KSodly1IsXGghdbeEEKIw0np7XbVqFf/2b//G5ZdffsYasLlz5xYiLiGEECPcttZt7O/Yn/WoULp0TSdmxfjLwb9w5ZQrC3quwbx/8vvxGB5ebnqZoJnZVMvuZPeg9zvKwWf4WFS9KC8bQFtJDzufW0z7sSoMj4VuuPiCSWz9zBEuXYdQCEARi+k8/rif9et9XHppkoULrZxjEUIIcbq0ErAnnngC6F0L9m6apnHXXXflPyohhBAjWtyO8+eDfy548nWKz/SxpXkLC6sWMqFowpCcsz/rGtYxpWQKj+x9hJSTSnvkL+km0QdYdm25FnWhOqaWTs1L0Y3m/XXseXk+ygWPP3XafY7rnPXxgQCAxp/+5OfNNz1cf32cQECmJgohRL6klYDdfffdhY5DCCHEKLLh6Ibe/ayGcHp6wAzw5KEn+fTcTw/ZOfszpWQKX174ZR478Bg723aia/pZEzFXuWfcZrkWATPA3Mq5FHuL8xLbwU2NHNrSiMefItdcLhiEEycM7rknxGc+E6WkRJIwIYTIh/zUtxVCCHHOcJXL9rbtQ76pr6ZpNPU00ZnsHNLz9sdreLlm2jV8bfHXWFKzBF3T6U51k7AT/e5fZmgGrnJJOSkc5RD2hFlYtZBlNcvyl3xtnt6XfA0k0wqVpgmuq/Hv/x4iGpXy9UIIkQ+DjoB961vfOuub9Xe+8528BiSEEGJkeyvyFj1WDyFPaMjP7TW8PHfkOT407UNDfu7+BD1BLp14Kesa1tESb2Ffxz4Odx2mM9WJ7dooFKZmMr5oPK3xVsYXjafIW3TWdV62ZdC8v472o9UkukMk4z6Uo4OmMD02/qI4ofJOaqYepaiyi8ixSg5tmoEnMHDyBWS1vkzXwXE0fvWrIJ//fBTZRkwIIXIzaAK2du3aoYpDCCHEKLG1dStBMzgs5zZ1k2PRY8Ny7sHomk5NsIaaYA0r61aecX/KSfF/X/+/+E3/oO1EI0Uc2DSDjqYqXMfA8Kb6Eh7t7QIajm0SbS+iu6WEY9um4ivqofN4JaGKrkHbVqis+80woK1NZ8MGL6tXD57kCSGEGNygCdiaNWuGKAwhhBCjRSQRyctmy9lqT7Zju/awlaTPhtfwUuwtJuX2n7y4js6eV+ZxYvcETK+Fbjro5uAFM3TTRTeTNO+bQHdrCZ3NFVRPbsIb7H/TaMuxKA9kv5daIAAvvuhjwQKL0lJZDyaEENmSNWBCCCHS5ip32NdgWY5FS6xlWGPIRk2wpt9iHD1tRfz1wbU076t/u3hG+smN6+hE28OYXhvlahzf3UD7sap+j9U1nVJfabbhA71rwp591pdTG0IIca6TBEwIIUTaolYUyx3evaEMzaA53jysMWTjovEXEbNip93W2VzK5sdW4To6hsfOuM2u5lLgndFI3XDoaiml5eC4M44t8ZXkvMeYacKePSaWbA8mhBBZkwRMCCFE2k4VlhhOmqaRdJLDGkM2akO11IRq+n6OtofY+vgKdNPOurBFtL0Y3Th9VE03XOIdYdoO1/bdlnJSTCyemN1J3iOV0ti2LffNooUQ4lw1YAL2T//0T33/fvDBB4ckGCGEECNbPjYKzgdDG7r9x/LpwroLiVkxXEdj29PL0Q0n6+TLdTScVP+JkGa49ESKiXWEUShCnhAl3pIcIn+H3987CiaEECI7A/4lbWpqIpXqXSz82GOPDVlAQgghRi6f4Rv2JMx2bYq8RcMaQ7bmVs5lYvFE9m5sJBkN5FTSPRX34boD94VuOLQdqcWyFHMq5uStcIqmQXv7yEjEhRBiNBrwK6xly5bx93//91RXV5NKpfj2t7/d73GyD5gQQpw7/KafsCeMowav0FdIhmZQF6obtvPn6rLa63hwxwG8/szXfL1bojuIpg/eD46j0NtmE5yY320DOjslARNCiGwNmIDddttt7Nq1i+bmZvbu3cvFF188lHEJIYQYoUp9pbQl2obt/EFPkLA3PGznz9Xrr5Qxq9Lkra5tOZXSd2xz0IqJCoXf40WP1eA4FkYeZ21aFiiFbMoshBBZGPSdf+bMmcycORPbtmVPMCGEEADUhes4ETuRc0W9bCilKPdnv5fVULAsaG3V6e7WcJzeNVNVVS7hsMJxYMcOk5ricjBn8FbkrYLsZ6aUwmN4qAnV4NgaTU06EyacWQJfCCHE0EvrXX/t2rVs27aNDRs20N7eTllZGRdddBFz584tdHxCCCFGmBXjVvDXE38dlgQsZse4rPayIT/v2UQiGs895+P4cYPOTp1UqndoSNPAdRWmCcGgAhSHDhlMmuRSE6zB0Ax2RXYBmRc4MQwXpTQ07fRRMFe5BMwAVcEqNDRME1pajLwmYKYpo19CCJGttBKwZ555hvvvv5+1a9cyffp0Wltb+eEPf8gNN9zAunXrCh2jEEKIEaTIW8T48Hja4m15K+yQrpAZYk7FnCE952CamnQee8zPyZMGPh8YBvh84PP1NzVQY98+k0OHDE6cgLo6h4aGSs6rPY/tke10JjszSmp94SjqRDm8vQ7s1PYAlYFKQp7QaccmEvntp+JiGU0TQohspZWAPfroo3zzm99k0qRJfbetXLmS22+/XRIwIYQ4B62uX82vd/36jA/6hZS0kyytXTrsVRgBHAeefNLH6697CQQgmGaNi2hUw+/vXT916JBBc7POnDmwsGohJ6InONx9mLgVxzRMNAZPmnzBJJqucJWLpmkEzAAV/op+Xx/LglQKvN5snu3plILycknAhBAiW2klYN3d3YwfP/602+rq6ujp6SlIUEIIIUa2KaVTmFoylSM9R4ZkKqJSiqAnyNoJawt+rrNJJOCXvwwRiehpJ16nnJqaCL3T+FIpjddf9zJ7tkVtZS21oVq6kl0c6j5E1IqSdJIopTB0Aw0NRW/CpVCYhonXB0GjlGJP8aCjka6rkUhoeL25b6KdSMCkSTZRK8qR7iMc6T5CwklgaAbF3mKmlEyhOlhdkLVtQggxFqT17jhz5kx+9atf8fGPfxyfz0cikeC3v/0tjY2NhY5PCCHECHXN9Gu4c/OdQ3KuuB3n5jk3D/uH+mQS7r03RHe3js+X+ePVe/IfTetNxLZv9zBnjkVlpaLYV8w83zwALNeiO9VNT6oHRznomo7X8FLiLSFgBtgXNzl+3EhrPdZ7z52NhJ1gX9dhfOpBnnyjE0c5BMwAuqajUNiuzdOHn8ZreCn3l7OsZhkLqhYMe78JIcRIktY74t/93d/xgx/8gJtvvplwOExPTw+NjY38/d//fVonaW1t5e6776ajowNN01i3bh0f+MAH6Onp4Y477qClpYWqqiq+9rWvEQ73lhZ+5JFHWL9+Pbquc8stt7Bw4UIA9u/fz913300qlWLRokXccsstaJqGZVncdddd7N+/n6KiIr761a9SXV2d3asihBDirAJmgA9N/RAP7XmIgBko2Hnidpzz685nQtGEgp0jHUrB/fcH6e7W8WQ56DdQouTxwI4dHpYuTZ02qubRPZT7ywes/DhxokNTUzr15VVumz47KXZEdtAR76K8tgPdYxHkzOE/wzDwGb2ZadSK8ueDf2b9kfWcP+58Lqy7cMjXDAohxEiUVgJWVlbGd77zHdra2vqqIFZUVKR9EsMw+MQnPsGUKVOIx+N84xvfYP78+Tz33HPMmzePq6++mj/84Q/84Q9/4KabbuLo0aO8/PLLfP/736e9vZ3//b//Nz/84Q/RdZ2f/exnfO5zn2P69Ol873vfY8uWLSxatIj169cTCoX40Y9+xEsvvcRvfvMbvva1r2X9wgghhDi7meUz+cCkD/Dng38uSBKWsBMsqFzAugnDv974jTc8HDliZDzt8N1MU502DfHdDKM3CVuyxEo7WfJ4etdjdXTo6IMsjdN1spp+qJSiKdrE/s796OjoTpCpy15L+/FBs/fFev7o82xr3cb1jddTEUj/84MQQoxFGc0JqKioyCjxOqWsrIyysjIAAoEA9fX1RCIRNm7cyD//8z8DsHr1av75n/+Zm266iY0bN7Jy5Uo8Hg/V1dXU1tayd+9eqqqqiMfjfVMfL7roIjZu3MiiRYt4/fXXue666wA4//zz+cUvfoFSSr5tE0KIAltcsxiP4eGP+/6Iz/DlpUiGUoqYHWN57XIum3hZ33t5c7SZbZFtNPU00ZHswHIsAAzdoMhbRFWgipnlM5lSMiWvxTpiMY2nnvLnlHwBhEKKnh76TZY0rfc8hw8bTJzopN3mjBk2r702eHUN0yTjKZNKKXZGdtISb8Gje7Atk3GzDhAqy3z9d8AM0GP18JOtP+HGmTcypWRKxm0IIcRYMeSTspubmzlw4ADTpk2js7OzLzErKyujq6sLgEgkwvTp0/seU15eTiQSwTCM0xLAiooKIpFI32NO3WcYBsFgkO7uboqLi4fqqQkhxDlrXuU8xofH88DuB2iNt+Y0GpZyUngNLx+f+XGmlk7FcR02n9zMxpMbaY4349W9eI3TEw7XdYkkIrTEWnij+Q2KPEXMrpjNRfUXEfQMnDU5rkNropV9Hfs4Hj2Oq1yKjxdDEhrLGhkXGoff9LNhgzcv+15VVrocO2YMWI3QNKGpyaChwcloFGzaNJtduzx4PP2PcgUCmU1BVEqxI7KDtngbHt2DcsEbSDJ12Y70G3kPXdPxGT5+u+u3fHzmx5lcMjnrtoQQYjQb0gQskUhw++23c/PNNxMc5GtENcBK4YFuH+i+/ka/nn76aZ5++mkA/uVf/oXKysqzhS0KzDRN6YdhJn0wMoz2fqikkn+q/yeeO/QcLx55kZ5UDyFPKO2ZCHErjqmbLK5ezIdmfAiv4eVI1xF+/bdf05nsJOQNUe09+9reIooAeKvnLXbt2sUHpn2AFeNX9MWhlOJw52Ge3P8kh7sOk7STeAwPPsOHpmlEuiKk7BRbOrZg6ibl/kr2vvlR6orH5zyq5vPB7t3aoNMFLQt6ekxqatJvd/Lk3gIhR49qZ6xPs22orVUEg+kvXNsb2Uun3UnAF0ApUI7BeVe/TLAo92mmQRXkD4f/wDdWfoMiX1G/x4z2a2GskH4YGaQfxp6zJmCu67Jjxw5mzpyJaWafr9m2ze23386qVas477zzACgpKelbU9be3t43WlVRUUFbW1vfYyORCOXl5Wfc3tbWRnl5+WmPqaiowHEcYrFYX0GPd1u3bt1pe5e1trZm/ZxEflRWVko/DDPpg5FhrPTDvPA85sycw67ILl5uepm2RFtfmXKv4UWnt2Ke5Vp9o12lvlLOrzqfpTVL8RpeOiOdPHXoKV478Rp+04+macSsWFbxPLD1AV7a/xI3zriRzmQnD+19iNZ4K0EziK7pGBi4jkucOADBYJBUIoWJCQ7sOaCx5cA+dgR3Mal4EvXh+pymtxcVmUQig6/Z2rtXUVRkZdTuhAkQjxucPGnw7j/XjgNVVSliab58MSvG3ta9mLpJyrJQrsHCD24Ab3fabZyN4zr8+JUfc8ucW/p9LcfKtTDaST+MDNIPw6+uri6v7Z01o9J1nX/7t3/jV7/6VdYnUUrxk5/8hPr6eq644oq+25cuXcrzzz/P1VdfzfPPP8+yZcv6br/zzju54ooraG9v5/jx40ybNg1d1wkEAuzevZvp06ezYcMGLr/8cgCWLFnCc889R2NjI6+++ipz5syR9V9CCDFMdE1ndsVsZlfMxlUu7Yl2DnQeoDneTMpJYeomJb4SppZMPWPPKKUUD+99mJ2RnQQ8eRhxMYOciJ7gHzf8I2WBMoo8RYQ9Z35BN5C2Q7X4Ai66prOvcx/N8WbmlM/BZ2ZRhx6YNMmhuVkfdFPkRCLzv1+aBo2NDoGA4tAhsy/BKy11067aqJRie9t2DN3ATnkIFPcwd91rBIrjGcczGEM3aIo28UbzGyytWZrXtoUQYqRLa0hr1qxZ7N69O+t9v9566y02bNhAQ0MD//AP/wDAjTfeyNVXX80dd9zB+vXrqays5Otf/zoAEyZMYMWKFXz9619H13U+/elPo7/9l+Qzn/kM99xzD6lUioULF7Jo0SIA1q5dy1133cWXv/xlwuEwX/3qV7OKVQghRH7pmk5FoCLt6neP7X+MXZFdeauq6CqXnZGddCQ6OBk/yZKaJRikU7q9V7SjCF3vnebu0T3E7TgbT25kUfUiQp5QxvEEg4qqKpdIRMcYIIxUqvf/wZK0/mgaNDS4VFWl2L7dQ1eXzvTpdtqP70h20BVP4NV9TFywm4aFe/Ky9q0/ATPAy00vs6R6iXxhKoQ4p2hqsIVVb/v5z3/OSy+9xNKlS6moqDjtjfKGG24oaICF1tTUNNwhnPNkaH34SR+MDNIPsDOyk4d2PzRo4YxMKKXY2rqVrlQXhmbgKIfKQCWzymcN+JhgMEjsXXPt/vrgWhzrzO8rXeWypGZJVomi48Bf/+pFqf73BksmYfFii5KS7HdPjsVgzpwU3d0GR44YuC4EAv1XYEylIJnU2J98g9CE3UxcuA+PL7MpkNnosXr4xKxPnFEVUa6FkUH6YWSQfhh+Qz4FESCVSvVNDzxVdVAIIYTIp4Sd4E/7/5S35AtgX+c+OpOdfVMcDc2gOdZMbbCWMn9ZWm24Tv+LtTRN482WN1leuzzj4hyGATNnWmzb5qG/5dWapmHlkP+kUoqJE10+/OFkX3n7AwcM9u0zaW3V+/Yi0zQIh10mTHCon9TD/cceJODNbmplNkJmiJeaXpKy9EKIc0paCdhtt91W6DiEEEKc45489CSu62IMNC8vQ92pbo71HMOjn74AyqN72N2xm+U1y9Oa+qZpiv7GoTQ0LNdiX+c+ppdO7+eIwZWXK2bMsHjrrf6TsMGKdAwmlVJUVSk+9rFY3+haMKiYM8dmzpyBpyPu6TiE02QBQ5eAaZpGW7zt7AcKIcQYkvbb+9GjR3nooYe49957gd6pe4cOHSpYYEIIIc4dtmuzK7ILj5F+qfTBnNpE2NT6/54xbsXpTHWm1ZZhDrwpsqEZNPU0EbWiWcVZU6OYPdvCccB1T78v0/Vf0DvtcOJEh5tvjvab1A1md/tuAkZ+1t1lojvVTdzOb5EPIYQYydJKwF555RW+/e1vE4lE2LBhAwDxeDynyohCCCHEKVuat5ByU3lrryPVQcyKDTjC5TW8HOpK70tEf1GMwVZLm7rJwa6DWUTZq7JScd55KUIhhWWBUmAYimAw/fVfltX7/5VXJvjYx+IZJ18AbfE2DD0/o4+ZsJVNc6x5yM8rhBDDJa236N/97nf8f//f/8ekSZN45ZVXAJg4cSIHDx4sZGxCCCHOEdsj2/NW9RDgcNfhM6YevldPqgel1FmnIRbXtNHeVInp7X/6noZGJBHBdu3TyulnwuuFhQstmpt1jhwxSCYZNOmD3vvjcfD5FLNm2bzvfcmMkrb3st30qyXmk4YmI2BCiHNKWn8pOjs7mThx4mm3aZomZWOFEELkRUeyI29tucrtq3o4GMu1SDiJsyZ+NVObOLhp4KqJ0DvlsTnWTF04+0pZmgY1NS4VFS7jxtmUlipaWnTa23USCa0vITOM3r29SktdZsywWbDAymrEa6TQ0Oh/lZ0QQoxNab1lT5kyhQ0bNrB69eq+21566SWmTZtWsMCEEEKcGxJ2gu5Ud95GwKKpKI5yzpqAaZpGR7LjrOf1h+MUVXQQ7w4OuCeWqZu0J9tzSsBOSaV6pxKWlfUmJUr1lqV3HA1dB69XDbh/WC6GY/ohgIub19FPIYQY6dJKwG655Rb+z//5P6xfv55kMsl3v/tdmpqa+OY3v1no+IQQQoxxUSuK+94KFDmIJCNpbbRsaEbaxTPGz93LzueW4vEPvE4tH9PolIJx45y+5At6R8b8fiCHUaKoFWVry1YOdx8mkoiQclIoFIZmEPaGqQpUYbt2TtMos2XqJtWB6iE9pxBCDKe03mXr6+v5wQ9+wBtvvMGSJUuoqKhgyZIl+Hv/IgghhBBZc5SDS/4SsB6rJ63RHE3TcFV6562afILDWztJ9gTR9P4ToZSTexGRRKJ39CtfDnUd4rmjz3Gk+wgAfsN/2vIBRzlEEhFaYi0c7T7Kwe6DjA+PZ2LxxCFLxMKecF73fhNCiJEu7XdXn8/HzJkziUQilJeXS/IlhBAiLwzNyHgj48Gos1WveNdxZ5umeIqmwZxLNrLx4bWYev/FKnJdx5RIwPLlKWpqck9GE3aCP+77I7vadxEyQ2ed4mfoBnXhOg51H6Ip2sSJ6AkayxqpClblHMtglFKUB8oLeg4hhBhp0krAWltbufPOO9mzZw+hUIhoNMq0adP4yle+QlVVYd+chRBCjG1F3qK0E6F0aKRXIMpRDmFvOO12A0UJpi7bzt6/zsXjs7I+b38sC6qqXNatS2bdxilHu49y/677e5+fJ/3n5zE8hL1h4lYcTdPYEdlBZbyS2eWzC1Z0K2bHOH/c+QVpWwghRqq0vnK8++67mTJlCvfddx8///nPue+++5g6dSp33313oeMTQggxxnkNL0Xeory15zE8aU8tLPGWZNR2/exDTF78FlbyzBL32SaRqRQUF7t86lNR9BwHAg91HeI/dvwHmqZltal1Q1EDtuod4fPoHiKJCFtbt6Y9qpipIm8RjaWNBWlbCCFGqrTe6vfv389NN93UN+3Q7/dz0003sX///oIGJ4QQ4txQ7i/P24f8cl95XxIxGFMz8Rm+jNtvWLCXaef/DdsyUe47I0PZVPKLxaC+3uEzn4niyzyU07Qn2vn1rl/jM3xZj1hV+Cvwm/6+vjA0g65UFzsjO3MLrh9xO855tefJljZCiHNOWgnY9OnT2bt372m37du3j8ZG+dZKCCFE7hZWLSRmx/LSVrGvGP0sf94UihJfSdYf/utnHWbZNevxF8Ww4l4c181oOmMq1Tvt8IorEnziEzG83qzC6KOU4oHdD+DVvTklNJqmMbt8No5y+m4zNIOWeAstsZbcgnwXV7lUBapYMW5F3toUQojRYsA1YA888EDfv2tqavje977H4sWLqaiooK2tjc2bN3PhhRcOSZBCCCHGttkVs3ny0JN52ZDXa3jxmT4c1xnwGMu1mFQ8KafzBIoSLL7qBVoP1bJ3ywSKWEwiAT4f/e4XZtsQj0NJiWLJEotVq1IEAvkZ9Xux6UVaY60EPLnvp1XkLaIuXEdTT1NfJUSP7mF3x27K/GV5qY6YclJc33i9jH4JIc5JA76LtrW1nfbzeeedB0BXVxcej4fly5eTSuVeclcIIYTQNZ25lXN5/cTr+Mwc5+IB44LjONh9EFPr/89cyAxlNGI1EE2DqkknmDy9hxsaVrNjR4LDh03a23V6/0Rq6LqiuFhRXe0wY4bNpElOzmu93s1xHV49/mpekq9TppZMJWbF6Eh19L2GrnI50n2EySWTc2o7Zse4bvp1lPnL8hGqEEKMOgMmYLfddttQxiGEEOIct3bCWna07cB27ZxHRurD9RzuPtzvfbZrM79yfk7tv1vUirJuwjpKSxUrV1qsXHlmhcRC2ta6jbgdJ+QJ5a1NTdOYWzmXHW07iCQimLqJoRmciJ1gUvGkrPpHKUXCSXDt9GuZWT4zb7EKIcRok/Z3cMlkkkOHDvHWW2+d9r8QQgiRD6Zu8pHpHyFux3Nuy9ANJhRNwHZPL8ZhK5v6cH1eRr/gnbVM86rm5aW9bGxs3pjX5OsUXdOZUzGntzKia6OUImkn6Uh2ZNxWwkngNbx8Zu5nmFU+K++xCiHEaJLWRO7nn3+eX/ziF5imifc9K4V//OMfFyQwIYQQ554JRRNYVb+KF4+9mPOUuoaiBlpiLSSdJJqm4SqXIk9RzlPo3i3lpLh+zvV53Ug6E0opIvEIhp6/fdTeTdM0JpVMoiZYw472HaSSKZrjzWlPH0zYCQzdYGn1UtY1rCtYnEIIMZqklYD9+te/5r/9t//G/Pn5m7IhhBBC9GfNhDXYyubVptzWNWmaxpyKObze/DrKUYS9YeZXzc9bshS34lzScAkVgYq8tJeN9mQ7CSdBSM//CNi7BTwBllQvoSfVQ1uiDcd1iDtxdHQCZqBvSqLt2sTtOIZmUOYvY3ntcpbXLs+q3L8QQoxVaSVgpmkye/bsQscihBBCALCuYR3F3mKeOvwUHt2T9SbHftNPY2kjJ2MnmVMxJ+t23ituxVlRt4IVdcNbRr051pz2ptP5EPaGqQhU8MUFX6Q92c7BroMc7jrcV7Y+5AkxvXQ6deG6rPZFE0KIc0FaCdgNN9zAr371K6699lqKi4sLHZMQQgjB8trlzCibwe92/46mniZCnlBGxR/idpyAGeDv5v0dJd4Sfr3r1ySdJF4j+023XOWSdJJc3HAxF9RdkHU7+ZJ0knlLKtPlKhdN0yj3l1PuL2dx9eIhPb8QQox2aSVgdXV1/O53v+OJJ54447537xcmhBBC5FOJr4TPzP0MB7sO8sKxFzjSfQTbtQl6gmdMJTxVZc9RDtWBalbXr2Zh9cK+fau+vOjL/PnAn3mz5U0CZiDjqYgxK0aFv4Jb5txCZaAyb88xF7qm52XvNCGEEEMnrQTsRz/6ERdddBErV648owiHEEIIUUiapjG5ZDKTSyYTs2Ic6j7EnvY9tCXasFwLpRSGZlDsK2Zy8WSmlk6l3F9+Rjse3cOHpn6IZTXLePbosxzqOoSrXPyGf8CRNdu1SdgJqkPVrBm/hkXVi4at4EZ/yn3lfdP/hoqs5xJCiNyklYD19PRwww03yI71QgghhlXQE2RW+aycSpnXhev4+MyPE7fjbGnewqGuQ7Sn2ulOdaOUwnEdTN2kzFdGTbCGBVULqA3V5vFZ5E9VsAqvPnRfjCqlZANlIYTIUVoJ2Jo1a9iwYQOrV68udDxCCCHEkAiYgX4LaVRWVtLa2jpMUWXGa3gp9hWTclJDcr64HWdK8ZQhOZcQQoxVaSVge/fu5fHHH+fhhx+mtLT0tPu+853vFCIuIYQQQqRhUtEktke249E9BT+XoRnMqZxT8PMIIcRYllYCdskll3DJJZcUOhYhhBBCZGj1hNVsad1S8ATMVS4NxQ1SXl4IIXKU9hREIYQQQow8xd5iGooaOBk7WdCS9HE7zsXjLy5Y+0IIca5IKwFbv379gPetXbs2b8EIIYQQInMfnvph7nrzLgyjMAlYykkxp2IO9UX1BWlfCCHOJWklYC+88MJpP3d0dHDixAlmzpwpCZgQQggxzIp9xaydsJanDj9F0AzmtW2lFB7dw5VTrsxru0IIca5KKwH79re/fcZt69ev59ixY3kPSAghhBCZO6/2PJp6mtgR2ZG3dVpKKZJOklvn3orXkH1AhRAiH7LeTXLNmjWDTk0UQgghxNDRNI0PT/sws8tnE7NjObfnKIeUm+LmOTczLjQuDxEKIYSANEfAXNc97edUKsWGDRsIhUIFCUoIIYQQmTuVhNWfqOeZI89g6mZWhTnidpzqYDU3NN5Aia+kAJEKIcS5K60E7MYbbzzjtvLycj73uc/lPSAhhBBCZE/TNM4bdx6zymfxyL5HONx9GI/uOWuZeqUUMTtG2BNm7YS1rBi3Ak3ThihqIYQ4d6SVgN11112n/ezz+SguLi5IQEIIIYTIXbGvmE/N/hTdqW42HNvAwc6DdKW6SDpJNDQ0TcNVLrqmEzSDlPvLubz2cmZXzEbXsl6hIIQQ4izSSsCqqqoKHYcQQggx5KJRjZ07TQ4cMOjqMrBtKCrSUSpAXZ3L7NkWNTXu2RsawYq8RXxw8gcBsFyL5lgz3alubNfGb/qpClRR7C2W0S4hhBgigyZg3/nOdwZ9sKZpfOtb38prQEIIIbKjFHR2ajQ36yQSGl6vorpaUVbmIp+tT3fsmM4zz/g4csREKQ2/X/W9Rq6rEYuZHDkCGzZ4qahwWbYsxbJlFvooHxjy6B7qw7KXlxBCDKdBE7BVq1b1e3skEuEvf/kLyWSyIEEJIYRIj1Jw4IDBCy/4OHmyN/FyXQ1dB9cFTVMEAlBR4bByZZIZM5xRn0TkwrLgz3/28+abHoJB8PsBVL/Hejy9/yeTOk895eeNN7zccEOMior+jxdCCCHSMWgC9t5Nlru7u3nkkUd45plnWLlyJddee21BgxNCCDGwpiadhx8O0NZmEAopDAN6i9OemSC0txs8+GCQoiLFhz4UZ8oUZ8jjHW7RqMa99waJRjUyLeIbCEA0qnPPPWGuvTbOrFl2YYIUQggx5qW1BiwWi/Hoo4/yxBNPsHjxYv71X/+V2traQscmhBCiH0rB00/7ePVVL4EAhMNnH5HRtN7kzHE0fv3rIPPnW1x1VeKcGQ2LxzV+9rMQqZSGN8v9hHUdgkF46KEA110XY+bMcy+JFUIIkbtBE7BUKsX/+3//j8cee4zZs2fzv/7X/2LChAlDFZsQQoj3UAr++Ec/27b1TqHL1KlEbPt2Dz09Oh/7WGzMJ2FKwQMPBEgmNcy0vnYcXDAIDz8c5Ctf6Ukr+RVCCCHebdA/RV/84hdxXZerrrqKqVOn0tnZSWdn52nHzJ07t6ABCiGEeMezz/rYts1DIDDwMa5y+8qLD1RO3O+HQ4cMHn3Uz9VXJwoU7cjwxhsejh41Bn3NMmWa8OCDAW6+OSYFToQQQmRk0ATM+/Y8jSeffLLf+zVNO2OPMCGEEIVx8qTOyy97z0gkHOVwPHqc1ngrCTtBykn13ec1vPgNP2X+MurD9Zj6O2/7fj/87W8e5s2zmDr19Ol0Sik6U50c7j5MW7wNgCJPEZNLJlPmLxs1+0S5Ljz/vC+vyReAYcDhw73l68/F9XRCCCGyN2gCdvfddw9VHEIIIc7i4YcDb1ft6+Uql/2d+zkZPYmjnL7k6t1JlqtcYnaM7q5uDncfpipQxfTS6Ri6AfQWl/jjHwN89as96Dp0Jjt57shz7OnYQ8yOAeDVe7+Ms1wLF5eAEaChuIE149dQGxrZ64F37DCzKrqRjlBI8cILPqZMieW/cSGEEGNWHmbDCyGEKLRjx3RaWvS+RKI71c32tu2knBSmbmJqg7+dn0q4WuItRBIRZpbNpDxQjqZBT4/Gtu0ah0N/YlPzJnyGD1M3CXlOz1o8hqfv30e6j/DTrT9lWuk0rpl+DQEzz0NMebJxozertXLp0DQ4dswgmQSfrzDnEEIIMfaMjjkkQghxjtuw4Z1pdJF4hM3Nm3GVe9poVzoMrTcR29a2jRPREwDo3gT/9vvX2NqylZAnlFabuqYT9oY50nOEOzffycHOgxnFMVTa2/WCrtFKJuHkSaNwJxBCCDHmSAImhBCjwMmTOroOPaketrVtyzjxei9TN9ndvpumnibeaH6dnvYgXt1/9ge+h0f3YGgGv971a/Z37s8ppnyLRjWi0cJWyAgEYM8emUwihBAifZKACSHECBePQ3e3jqtctrdtzzn5OsXQDF489iIKhWN5iHdlt1BK0zT8hp//euu/6En15CW2fIjFNBynsAmYxwOdnVIGUQghRPrkazshhBjhOjp0bFvjQNcBUm6qbxphriLJCLayiSQilBCgJ1JMsDS7BErTNEzN5ME9D3Lz7JvRCjTvz7LgzTc97Nlj0tGhE49rKAVer6K0VFFX53DeeSnCYYVSDEmJeCVbgQkhhMiAJGBCCDHCOY6G47qciJ3IW/JluzY9qR4MzSBuxyn2ONhWbm0busGR7iPs6dhDY1ljXuI8JZWCJ57ws3Onh1Sqt4T+u5OrVEqjubm3KMYrr3iZMMHhoosS6HphsyPHgWBQMjAhhBDpkymIQggxwhmGoj3Ziu3aeWuzI9lx2ihVdzKK6cl9P6ugGeTFYy/m3M677dtn8KMfhdm2zYNh9K67Gmhky+Ppvb+52eA3vwlx4oRR0BGqeBymT89fvwghhBj7JAETQogRrqzMpcNuwaN7zn5wmuJ2HI3eLEZDI+EkCVd05tyupmk0RZuIWfnZG+v11z3cf38QpTS83vQfp+u9iVhLi86OHWbBkjBdh/p6tzCNCyGEGJMkARNCiBHO7wfl7cpbe7Zr46rTkwbXiOIPR/PW/pHuIzm3s3OnyeOP+wcd8TqbujqHkyd13nor/zPulYKaGpdAQKYgCiGESJ8kYEIIMQp4Sptx3fxUlEg6SRTvJA1KgVHUgqNZeWk/6Amyp2NPTm3E4/Doo/6+vc+yVVfn4vX2lvFva8tvRY54HFasSOW1TSGEEGOfJGBCCDEKjF+4HSeZnymIlmuhv+vtX6UCFDVuROVpnp6hGTlPQXz00QBK5Z4wGQaMG+egafDWWx7cPM0WVApKSlzmzMlP0iqEEOLcIQmYEEKMAsUVUcKVXag8LzdSCoxAD/5xBwpWOj5T0ajGnj0mnjwteZsyxcHnU1gWnDiRnz978Thcf30cXf6KCiGEyJD86RBCiFGgyFvE7DWv49i5ZyWmZvZNQXQtP5XnPY7X8GBq+Vkn5SoXr5FBxYz3ePFFL0Z+qu0DvevH5s610fXeMvW5isc11qxJUlsrxTeEEEJkThIwIYQYBcp8ZfiLo0xa9BZ2KrckzG/6UShcy0vR5G34K08QMAN5GwGL2TGmlU7L+vFHjpgZVTxMRyikWLjQIhrVSOWwbCsahZUrk6xaJWu/hBBCZEcSMCGEGAXmVM4hbsdpWLCXcTMO5ZSEmbqJZgUJjDtA+aLnsV2bMl9Z3mLV0GgobsjqsUpBJFKYqZBFRYq5cy1MUxGPZ/ZYywLbVlx7bZy1a5MFiU8IIcS5If91eYUQQqQlakU50HmAvR17idkxisJF2AmbqaVTmVI8hbA33HfsnPI5POV5CoDpK7bhCyY4uLkRw7TRMvgqTSmwk15qZu7DM+svaJqOhsb4ovF5e17VwWqKvcVZPTaRgFRKy7n64UBCIcWaNUkcB/76Vx+dnTrBoBqwzH083ru58/TpFh/4QKJgcQkhhDh3SAImhBBD7Ej3EdYfWc+R7iM4yiFoBtE1nU7VSU+0h7+1/g1d06kP17Nm/Boml0zG0A0WVS3ileOv4Df9NCzYS+XE4+x8bgk9bcWYPgtNH7iKoVJgJb0EimLMv/Sv6CUn2HjSBmVQGajE1PPz5yBqRVk7YW3Wj3ddrWCbJkPvejDbhhUrLM47z+LAAYPNmz1EIgZdXRq2reG64Pe7lJW5TJlis3ixlfcpkUIIIc5dkoAJIcQQsVyLPx/4M1tbtxIwAgTMM4dTdE0n5AkB0BZv49c7f83sitlcOeVK1kxYw/bIdpJ2Ek3TCJZGWXL1Brpbizm4eQY9bSVYMR+O806hCcN08fiShMq7aVi4m9LayNujPSHK/eV0pjppLG3My/NTSlHhr2BB1YKs2/B4VEErCzqORjDY+29N662QOGWKc9oxlZU+Wlvzsym1EEII8V6SgAkhxBBI2Anu234fHckOgmYwrcdomkbQE2R3+25+uvWnfGbeZ7hh+g38bPvP8Om+vqIZRZVdzHvfRgCspId4VwjHMjBMF384hjfY/5qlySWTSTgJ9EzmMA4i7sS5ec7NObXn9UI47GLbhcnClFJMmOCc/UAhhBCiQKQIhxBCFJirXP5jx3/QmerMqjy71/ASs2P8YtsvKA+U87EZHyPpJPvdONnjsyiu6qCsro3i6vZ+ky+lFHE7zkemf4Tb5t82YFuZiNtxPjjpg1QGKnNqB6C0tHBzEAMBKCuT8vFCCCGGjyRgQogxz1UubfE2DnUd4kj3ESKJSM4JRybWH1lPa7wVj55b5cLOZCdPHHqCySWT+czcz+A1vCSdzCrypZwUmqZx08ybmFc5j4biBm6adROWa2G7dsZxKaWI2TE+MOkDLK5ZnPHj+9NbLj4vTZ3GdaG+3h6w4IYQQggxFGQKohBiTHKVy1uRt3j5+Mu0xltJOAkc10FDQ9d0Ap4AVYEqVtWtYmrp1LztgfVenclOXjn+StrTDgfjM31sOrmJ82vPpyZUwxcXfJH1R9ez+eRmkm5y0HPE7Tge3cPcirlcPvny05LBicUT+cqir/Dgngc53HmYoCeY1usRs2KU+8u5ec7NeRn5OmXuXIunnvIB+e2TeBzWrJES8kIIIYaXJGBCiDHnRPQED+55kPZEO0EziKmbhPXwGce1xdv4zVu/oTpQzfWN11MRqMh7LM8deQ6vnr8Sej7TxzNHnuH6xusxdIP3NbyPi8dfzJstb7IzspP2RDvdVjeuctE0jZAZosxfxvTS6SytWTrgFMiQJ8SnZn2KfR37eKHpBY71HMNRDgEjgKH3FvVwlUvciqNpGtXBatZNWMe8qnl5W0N2imHAihVJnn3Wn7ey744DEyY41NfL9EMhhBDDSxIwIcSYsvHERh4/9DgBI9BXTXAgmqYR9oTpsXr48dYfc9WUq5hfNT9vsSil2N2xO28l3gEMzeBA5wFc5fYlPqZusqRmCUtqlvSd11EOuqZnlBxpmsa0smlMK5tG3I5ztOcoe9r30JPqAcBv+plaMpWG4gaKvEV5e079WbnSYts2L52dOoZx9uPPxnHguusy3H1ZCCGEKABJwIQQY8brJ1/niUNPZDzdT9d0AmaAR/c/iqmbzK6YnZd4OlOdxO34WRPBTCXsBG3xNqqCVf3er2kappbb23vADDC9dDrTS6fn1E62NA0+/vEYP/5xCNfVcipNH4v1Jl/h8NCt+xNCCCEGIkU4hBBjQiQe4cmDT/a7t1a6AmaAP+7/I1ErPxUgjnQfyUs772XoBvu79hek7ZEkHFZ89rNRTFORSmWePLkuJBJw7bVxZs7MvMCIEEIIUQiSgAkhxoSH9j6Ex8i+yuApOjq/3/P7PEQEXcmuvE4/PMWje+hOdee93ZGopETxpS/1MHOmTU9Pb1J1NkpBNNq7n9htt/Uwa5YkX0IIIUYOmYIohBj1jkePczx6nLDnzEIbmTJ1k0Ndh+hMdlLiK8mpLV3TC1LuXqHyXvjivTo7NbZvNzl82CQW0wANn8+lrs5l7lyLqqqhK2bh8cCHP5zg/PNTPPecjwMHTJLJ3j29PG/n3I7TW+VQ16G21uGyy1LMnSsl54UQQow8koAJIUa9F46+kJcy76d4DS8bjm7gyqlX5tTOuNA4LNfCjz9PkfVK2knqQnV5bfOUY8d0Hn/cT1OTga6Dz8e7khidw4dhwwYv1dUua9Ykh3R0adw4lxtvjJNKwYkTBrt3m3R0aCjVm4xNm2YxYYJLKCRrvYQQQoxckoAJIUa95nhzXkeETN3keOx4zu2MC48ryBREXdMZHx6f1zZdF/7yFx+bN3vx+yE4QD7r9fb+H4vp/P73AaZOtbnmmjg+X17DGZTXCw0NDg0NztCdVAghhMgTWQMmhBjVbNemK9mV93Y7Eh05t+EzfHndoPiUMl8ZYW/u0y1PcV349a+DbNniJRAg7Wl7gQAcOmTy7/8eIpHIWzhCCCHEmCYJmBBiVEs5KRzyPxJiuRauyn2d07KaZcTsWB4i6hW34yyqWZS39gD+8Ac/R4/q+LOYKenxQDSq86tfhSjAcjchhBBizJEETAgxqmmahkb+Ky2crV2lFI7rnLXIxsKqhRR5ivJSjEMpRcAMsLx2ec5tnbJ7t8H27R58vuxfQ48Hmpt1XnzRm7e4hBBCiLFK1oAJIUY1v+HHa+T/g3/QDKK9Zy7eyehJNhzbQGu8lY5kB65y0TWdEl8J1YFqLhp/EdXB6tMeY+gGH5n+EX65/ZcEPbkVConZMT4+8+N49NzL7UNvufYnnggQyH7rtD6BALz4oo/zzkvhlTxMCCGEGJAkYEKIEcd2bVpiLRzuOUzCShDyhJhYPJGKQMUZxTY0TaPcV05nqjOvMZT5y/r+3ZXs4qE9D3Gk+whBTxBd009L+uJ2nP1d+9mxdQcTwhO4rvG609ZoTSiawMUTLubZo89mXa0xbsdZVb+KqaVTs39S73HokEF7u563qoGuC6+95uHCC628tCeEEEKMRZKACSFGjLZ4G88ceYb9nftJ2klM3UTXdBzl4LgOQU+QxtJGLmm45LQEZ1rpNF5qegm/mZ9y7zE7xpzyOQDsbt/Ng7sfxGN4Bi18YWgGIU+IlngLP9zyQ66ffj3Ty6b33X9h/YUYmsEzR57Bb/jPGF0biFKKuBNnTf0aVo1fldsTe4+NGz0Eg/lbuOX3w65dXknAhBBCiEHIGjAhxLBTSrH+8Hp+vPXHHOw6iEfvTXb8Zu/0woAZIOwNo2s6u9p38aMtP2LjiY19jz9/3PlpJzTp8Bt+FlYvZH/nfn63+3f4TT+GZqT1WEM38Bt+Htj9AAc6D5x234q6FXxu3ucIeoL0pHoGXRemlCJqRfEZPj4959N5T74AIhEj7xsVRyKaFOMQQgghBiEJmBBiWCmleGD3A7x8/GUCZuCsiY6pm3gNL48ffJzHDz4OgN/0s7RmKQk791rocTvOynErcZTDw3sexm9kN6rmN/z8fs/vSTmp026vClbxhflf4JOzP0lNsAaAmBWjO9VNV7KLmBUDBVWBKj4242N8aeGXqAsXZtPlnp78Fy9JpTQSif7bdV2IRHSOH9dpadGxh24PZyGEEGLEkCmIQohh9ZeDf2Ffxz4CZmaVIIKeIK+feJ1SXynnjzufdQ3r2NO+h6gdTXu06r1s16Y2VMvKupU8uu9RLNfKusCHpmmknBRPHHyCK6deecZ9k0smM7lkMgA9qR6idpSysjKS3UnCnnBeR/QG4rqgF+BrOMc5/d/btnnYuNFLW5tOIgFKaWgaeDyK0lKX2bNtzj8/mVUZfCGEEGK0kREwIcSwOdZ9jE3Nm7JeuxXwBFh/ZD2dyU50TedTsz+FoRk4bub7glmuRcAM8PGZH8dWNm+1v5VzdUWv4WVnZCeWO/iaqLA3TE2whrqiOoq8RUOSfAF4vfmfK6hp77R7+LDOnXeG+eMf/XR26pgmhMNQVKQIhxU+H8TjOi+95OUHPyhi40aPTF8UQggx5kkCJoQYNk8ceiLrKX6nmJrJU4eeAnoTmc/P/zxl/jLidjztNmJWjHGhcXx23mcJmAF2R3Zn9PjBxJ04ezv25qWtfCstzX+2Ew67eL3w4osefvnLMI6jEQoN/hi/v3cvsccf9/PggwHc3Pe/FkIIIUYsScCEEMOiK9XFsZ5jOY/2GLrBvs59faNMIU+Iz8z9DJc0XIKu6UStaL/FLlzl0mP1YOomV0y+gk/O+mTfSNyejj0579l1StAMsrd9ZCZgdXUOVh4LFioFFRUuGzd6ePZZP6GQyqjIRzAIe/aYPPKIzEUUQggxdskaMCHEsNjTvidvU+3idpzmaDP1RfVA7xqrFeNWcH7t+ezu2M221m20J9pJOL1FOgJmgAp/BfOr5jO5ePIZcXSnus/YbyxbuqbnfY+yfFm5MsVf/+rFk599nYlGNebPt3jssQDBLPNXvx927vSwY4fN7NlSpUMIIcTYMyQJ2D333MOmTZsoKSnh9ttvB6Cnp4c77riDlpYWqqqq+NrXvkY43LvHziOPPML69evRdZ1bbrmFhQsXArB//37uvvtuUqkUixYt4pZbbkHTNCzL4q677mL//v0UFRXx1a9+lerq6qF4akKILB3sOpjz9MNTfIaPfZ37+hKwUzRNY0bZDGaUzcioPUV+p+YNVm5+OIVCiqlTbQ4fNjFz/GugFFRWOmzalHtCFwjAX/4SYMaMbozs6qkIIYQQI9aQTEFcs2YN//N//s/TbvvDH/7AvHnzuPPOO5k3bx5/+MMfADh69Cgvv/wy3//+9/mnf/on7r33Xty3FwT87Gc/43Of+xx33nknJ06cYMuWLQCsX7+eUCjEj370Iz74wQ/ym9/8ZiielhAiByk3lbcRMEM36LF68tIW9Ja1z1fSpJTK2wbRhXD11QnIQ8KZSMCllyY4fNjIS2XFWKy3eqIQQggx1gxJAjZ79uy+0a1TNm7cyOrVqwFYvXo1Gzdu7Lt95cqVeDweqqurqa2tZe/evbS3txOPx2lsbETTNC666KK+x7z++uusWbMGgPPPP59t27aN2G+chRC9DM3I23XqKhef4ctLWwATiyf2TVfMVcJJMLl4cl7aKoRAQPGhDyWI51BzJB7XWL06yeHDJt7cCke+Ky7YskUSMCGEEGPPsK0B6+zspKysDICysjK6uroAiEQiTJ8+ve+48vJyIpEIhmFQUVHRd3tFRQWRSKTvMafuMwyDYDBId3c3xcXFZ5z36aef5umnnwbgX/7lX6isrCzMExRpM01T+mGYDUcfzOyeSVOyCZ+Ze+LUlexi0cRFVFbk5zlcVHQRLza/mJ9CHBZc2HghYW/4rIcO17Vw4YUQCsHvfqfj92tpj2ApBdGo4v3vV1x2mZef/1ynpCR/JfSTSUVlZX6KoWRC3pOGn/TByCD9MDJIP4w9I64Ix0DfiA/2TXl/9w00tWndunWsW7eu7+fW1tYMIxT5VllZKf0wzIajD2r1Wjp7OtNKTM7Gtm0CViCvz6HSrKQ52pxTMQ5XudQEa0h0JUhw9hG14bwWJkyAT31K53e/C9DcrBMKMWgFw3gcgkHFtdfGmTzZobUVjh8PEYvlb2JFIgHNzd0F2Sx6MPKeNPykD0YG6YeRQfph+NXV1eW1vWFLwEpKSmhvb6esrIz29va+0aqKigra2tr6jotEIpSXl59xe1tbG+Xl5ac9pqKiAsdxiMViZ0x5FCNH0kmy8cRGDnYfxHIsSotKcZMuF9ZdSE2oZrjDE0OkKlhFRaCCpJPMqR2lFOPD4wmYgTxF1uuKyVfw460/zqndlJPig5M/mMeoCquy0uULX4iya5fByy/7aGkxSCbfycKUAp9PUVbmsnp1ioULrZyLd5yN6zLkCZgQQghRSMOWgC1dupTnn3+eq6++mueff55ly5b13X7nnXdyxRVX0N7ezvHjx5k2bRq6rhMIBNi9ezfTp09nw4YNXH755QAsWbKE5557jsbGRl599VXmzJmTt8X9In86Eh08efhJ9nfux3GdvsIECT1BT7SHba3bqAnVsGLcCuZXzpc+HOM0TeOCcRfw2IHHcprqF3firG1Ym8fIelUEKlhdv5rnjz2fVRIWt+OsHr+aikDF2Q8eQTQNZs1ymDUrhuNAW5tOJKKhFBQXK6qr3QGrHHq9vcUz8sU0kSqIQgghxpwhScB+8IMfsGPHDrq7u/n85z/P9ddfz9VXX80dd9zB+vXrqays5Otf/zoAEyZMYMWKFXz9619H13U+/elPo7/99ednPvMZ7rnnHlKpFAsXLmTRokUArF27lrvuuosvf/nLhMNhvvrVrw7F0xIZONR1iN/u+i2GbuDRPXj00z/B6ZpO2BsmakV5dN+j7O/Yz4emfSit6V+Wa7GjdQcn4ydJOSmKPEXMqphFdVC2IhjpFlYvZEvrFppjzZh65m9HSTvJvIp5TCiaUIDo4ML6C+mxe3j9xOsZJYkxK8ay2mWsql9VkLiGimFAdbVLurt6lJc7RCJ63kasysrcjDZyFkIIIUYDTZ3j5QKbmpqGO4Qx73j0OPduuxe/4e93VCsYDBJ7z9fmcTvO3Iq5XD3t6gHbbU+089zR59jdvpuUm8Kre9E1Hdu1sVyLmmANy2uXM79yPoYuX6MPZjjnl8ftOP/+t38nYScySsJSTorqYDWfmv2prJK3TLzZ8iaPH3wcRzmDVltMOkkMzeDySZezoGpBxucZ7fP89+41+O1vg4RCubdlWbBgQYr3vz+3KarZGO39MBZIH4wM0g8jg/TD8Bsza8DEuUEpxf1v3T9g8jWQgBlga+tWppdNZ07FnDPuf7P5TR7d/yhew4upm6d9APcaXryGlx6rhz/t/xMbT27kk7M+OaL3YjqXBcwAn533We7fdT/HoscImmcfaYpZMRrLGvnI9I8UPPkCWFC1gMayRl5qeontbdvpSnXhuA4aGi4upm5S7C1mYdVCVtatzPt6tNFi6lSH4mKF4+Q+bGXbsGpVKg9RCSGEECOLJGCioHZFdtGT6iHkyfwr8aAZ5OWml89IwN5sfpM/7v9jWm2GPCEiiQj3bruXz87/7BlTH8XIEDAD3DLnFt5ofoOXm16mPdGOz/Sd1l8pJ0XKTVEVqOIDkz/ArPJZQx7juoZ1rGtYR8yK0RxvJm7HCZgBaoI152zS9W6aBqtXJ3nsMT/BHKrHp1Iwe7ZFOHxOT9AQQggxRkkCJgrq5eMvpzWi0R9N0zgePU5bvK2vkEF7op0/HfhTRgmdR/fQlerikb2PcH3j9VnFIgpP0zSW1ixlSfUSjnYfZWf7TlriLbhu7wjTuNA4ZpfPpjo0/Gv7gp4gkzyThjuMEWnhQou//c1DU5MxYLGOwSgFXq/iiivysxG2EEIIMdJIAiYKJm7HOR49ntPIgN/w81LTS1w19SoAnjv6XFajWF7Dy572PcSsWH421xUFo2kaE4onMKG4MIU1RhrbhuPHDTo7NUpLwXUNxo1zskpeRgJNg49+NMa994bo6NDxetN/rOuC48Df/V00o8cJIYQQo4kkYKJgolYU13VzasPQDaJWFOitdrinfU/Wa350TefFphe5dOKlAx5zMnqS544+x/HocVJuCl3TCZgB5lfOZ3nt8kELMAiRiY4Ojeee87F7t0k0qmEYGoGAQU9PkGAQpk2zuPjiJOXlo28antcLn/50lIcfDrBnj5nWdMR4HCoqXD760RhlZaPvOQshhBDpkgRMFIyjHBS5f5BylAPAjrYdJN0kQT27ESyv4WVXZFe/CVhHooP/2v1fnIyeJOAJYGgGhtZbOTFhJ9hwdAMvHnuRxdWLuXTipbJHmciaUvDYY362bPHg9fbuddW7D70iGHxn0+F9+zzs2OFh9myLD384Meo2I/Z64aMfjbNrl8Hzz/s5cULHMMDvp6+0fCoFqZRGaanLxRcnWbnSkrLzQgghxjxJwETB+AxfXhIVr947F+lk7GTfv7OVsM9cV9Icbebe7fdi6iZhb7jfx52qoLjx5Ebak+3c0HiDJGEiY0rBf/1XgH37zj4qZBgQDMJbb3n4z//U+cQnYqMuCQOYOdNh5swokYjGW2+ZHDliYtu9iWZNjcuMGRbjxsl+X0IIIc4dkoCJgin2FhP2hHMaBYtaUaaXTQd6q+ClszHzYE6Npp0St+P8x87/wKN70kqoAmaAvR17+cvBv/CByR/IKRZx7nnsMT/795sEMlgW6fPBsWMGjzzi5yMfGb2FKcrLFStWWKxYYQ13KEIIIcSwGoXfp4rRQtd0ZlfMJuVkv5fPqfVX0JvQ2a6dU0zvLeDxwrEXsF074z3KtrRsIW7Hc4pFnFu6uzXefNODP4vt6Hw+2L7dSyQiw0RCCCHEaCcJmCioVfWrzhh1SpflWswsm4mh967Fmlk+E8vN/ttzpRSVgcq+n13lsr1tO14ju2mNrzS9knUs4tzz/PM+zBzmHPh8imeflSIwQgghxGgnCZgoqJAnxJLqJRmPFimlMDSDSxou6butOlhNTbAm61hidoxV9av6ft7XsY+uVFdWbfkMH39r+1vWsYhzi+PAzp1mTqXlTRP27vVgyQw+IYQQYlSTBEwU3OWTLqexrDHtJMxVLpZrcfPsm8/Ys+v82vP7ytJnqsRXwpSSKX0/H+s5hk/PfkQhakVRSspli7NradHp6cn97TYWgxMnjDxEJIQQQojhIgmYKDhN07hu+nUsrVlKykmRdJL9Hucqlx6rh6AnyBfmf4GqYNUZx8yrmse40LiMpyLGrTiXTbzstLVeKTeVUyVDpRSuOvs+Z12pLna372Zr61YOdh3MeR2bGH3i8fys3dJ1ja4uWQcmhBBCjGZSBVEMCU3TuHzS5awev5qXm15ma+tWeqwelFKolCLlpGgoamD1+NWMD48fMDHSNZ1PzPoEv9j+CzqTnWmt34pbcS6fdDkzy2eednvYE8Z27aw3djZ1s299Wn/2tO9hw7ENHIsew3VddE3HUQ4hM0RjWSMXT7iYEl9JVucWo4+m5We0VMq1CyGEEKObJGBiSAXMAJc0XMLaCWuJ23ESToKayhpiXbEzKhQOxG/6+bt5f8cjex9hb/teNE07IxFTShGzY5T4Sri28dozki+A2RWzeebwM1k9D6UU1cHqAe/70/4/sbl5MyFPiJAZOuOYt9rfYnvbdm5ovIFpZdOyimGkUUrRHG+mPdGOoRlUBCoo95cPd1gjQjCoAA1y3JjccRRFRTLtVQghhBjNJAETw0LTNIKeIEFPkBJ/CVZPZlMKPbqH6xuvJ27HefHYi+yM7CRhJ3CUg0f3UBmoZFX9KqaUTBlwNK3UV8q40Dg6kh0ZT0WM2TGuqruq3/v+cvAvbG3dOuCmzvD26Jlm8MDuB7h5zs3Uh+szOv9IYrkWrx5/lc3Nm4kkIn23a5pGbbCW82vPZ37V/HN64+qqKpfiYhelcnsNwmHFuHHZVRUVQgghxMggCZgY1QJmgPdNfB/vm/i+rB5/Qd0F/G7P7wh7Bk6W3kspRZG3iOml08+4LxKP8PrJ1wl5zhz1ei9N0/AZPh7d9yhfWPCFjOIeKeJ2nF9s+wUdyQ78pp8ib9Fp9/dYPfxx/x/Z1raNj8746KBTNscyXYfZsy02b/bizW7XA2wbZs60cyplL4QQQojhJ0U4xDltZvlMZpfPJuEk0jpeKUXCSXDd9Ov6HdF59uiz+Iz0KytqmkZzrJmT0ZNpP2aksFyLX2z7BT1WD35z4N2FQ54Qh7oP8eCeB4cwupFn1aoUTg6DV6kUrFnTfwEbIYQQQowe8l2qGNUsCzZt8rBli5dYTMNxwONRjBvnsmZNkurqwasUaprGR6Z/hIf2PMSuyK5BR64c5WA5Fh9t/Cjji8afcb9Sin0d+zIu6hH0BHnh2Atc23htRo8bbq+deI32ZDsBM3DWY32Gj7cib3Gs+xj1RaN3umUuQiHFsmUpXn/di3/gfLVfiQQsXGhRUiLrv4QQQojRThIwMSopBc8+6+P11z2kUhqBt3MATQPb1jh4UOcnPzGpqXG59toYFRUDf3DVNZ3rpl/H1tatvHr8VU7ETuA3/Bha73S5hJ3AY3iYWjKVSxouocxf1m87CSdByk0R0M+ekLz3/N1Wd0aPGW5KKTad3JRW8nVK0BPkuWPP8fGZHy9gZCPbpZcm6enR2LnT0/c7ezbxOEydanPFFemN0gohhBBiZJMETIw6SsHvf+9n167eD7H9fZA1DAiFoKtL56c/DfOpT0Wprx94NEzTNBZULWBB1QJaYi1sbtlM1IpiaAbjQuNYULUgrZL3Kssqd9k+bri0xluJJCKDFhp5L13TOdx1GMd1ztm1YJoG11yT4MknFa+/7kXXGXBNmGX1rvtauNDigx9MSPl5IYQQYoyQBEyMOk884etLvs7m1Afc//zPILfdFqW4+OyJTlWwiksnXppxXD7Dl3Yp/XdTShEwMhs1G26dqc6skkbbtUk6SYJ6sABRjQ6aBpddlmTVqhQvvOBl+3YPXV06SiksS5FIaITDLvPmWaxenSIcHl3JuRBCCCEGJwmYGFXicY1Nm7xpT9+C3g+8mqbx9NM+rrmmcNO4dE2noaiBYz3H0LX069vErBjnjzu/YHEVgqEZWSVgmqad0+Xo3y0YVFx2WZL3vS9Ja6tOPK5RVuYjHu+hstLFODcHCYUQQogxT6ogilHlpZeyq+FtmrB3r4mV2XZjGVtdv5q4Hc/oMWX+MiYVTypMQAVS7i/vWyOXCa/hzahK5LlA16G62mXiRIcpU6CmRpIvIYQQYiyTBEyMKtu3e/Bl+fk9ldLYsiXzKYKZqC+qZ1rpNFJOKq3j43acdQ3rRt2oUImvhNpQLUqlPwpmuzaNpY0ZjQ4KIYQQQow18klIjBqOAz092Scqfj8cOVL4oYXrG69nXGjcoCNhSinidpzLJl7GrIpZBY+pEC6ou4CYHUv7+ISTYM2ENYULSAghhBBiFJAETIwaltVbATFbp0rUF5qpm3xy9idZPX41pm4StaJYroWrXJJOkpgdoyJQwU0zb2JZ7bKCx1Mos8tnM6NsRlqbWMesGBePv5hSX2nhAxNCCCGEGMGkCIcYNbxecirFrRR4vUNTUU7XdFbVr+LCugs50HWA/Z37SdgJSnwlLKhaQLG3eEjiKCRN07iu8Toe3vMw29q2EfKEzpheaLkWKSfFxRMuZlX9qmGKVAghhBBi5JAETIwaug5lZS7xeHYDt7EYzJhR4Coc76FpGlNKpjClZMqQnneo6JrOtY3XcmH0Qp47+hwHuw5iuRYaGj7Dx5zyOayesHpMJJxCCCGEEPkgCZgYVZYtS/HEE/6MytCfUlSkmDHDyX9QgtpQLR+d8dG+aZanErDRVlxECCGEEKLQJAETo8qiRRbr1/uAzD7YJ5OwbJmFPspXPTquw7a2bRzqOoSu6SyqWkR9Uf1wh9VH13QC5ujaVFoIIYQQYihJAiZGFdOEyy5L8thj6Y+COQ4UFbmsXp0sbHAFtrl5M08ffpq4HSdoBnFx2dS8iQp/BR+d8VEqAhXDHaIQQgghhDiLUT4eIM5FCxdaXHJJgnj87FURLQv8fpdbb43hzW4P5xFhc/NmHtv/GLqmE/KE0DQNQzMIeULE7Bg/+9vP6Ex2DneYQgghhBDiLCQBE6PSihUW118fo7jYJRrtHeV6t3gcHEcxc6bF5z8fJRQamuqHheAql/WH1xP0BPu9X9d0dF3niYNPDHFkQgghhBAiUzIFUQw5peDoUZ3XXvMSj2uUluqUlXlYtszKaJSqsdGhsTFKa6vOhg1eurt1HKe31PyMGTaLFlmYY+A3fGdkJ1E7SsgTGvAYQzPY37WflJPCa4zioT4hhBBCiDFuDHw8FaPJW28ZPPWUn0hEJxDoLS3f3a3x5pt+XnjBz6xZFh/8YCKjxKmy0uWaa86+GfBodajrEH7Tf9bjkk6SHquHcqN8CKISQgghhBDZkCmIYshs3uzhd78LkkzqhEKcVpHQ7wePB7ZvN7nvvhC2PXxxjjQe3YOr3LMep6GdsRGyEEIIIYQYWeTTmhgSJ07oPPaYn2D/y5j6+Hwara0ajzxy9hGfc8WiqkWknNRZjyvxllDiLRmCiIQQQgghRLYkARNDYv16H/40cyqvV2P3bg/RqGziC1AZrKQ2VDvoKFjcjrOoepFsfCyEEEIIMcJJAiYKLh6HQ4fMjDZB1nV44QUpJnHKRxs/Cgps98y5mXE7zqTiSVxYf+EwRCaEEEIIITIhCZgouKYmg0Qis5EZrxeOHZMaMacU+4r54sIv0ljWiOM6xOwYMSuGR/ewevxqPjbzY7L+SwghhBBiFJBPuKLg4nEto9GvU6QQx+mCniAfnvZhbNcmakX7NmKWaYdCCCGEEKOHJGCi4IqKFK6b+UbIHs/o3Ty5kEzdpMQnxTaGk1K92yckkxp+v6KoSH5XhRBCCJEeScBEwdXXO4RCCkh/pCYeh/PPtwoXlBBZcBx47TUPb7zhpb3dwHV71yuWlTksXZpi2TILwxjuKIUQQggxksmiEVFwpgmNjXZGUwo9Hli2TBIwMXJYFtx3X4hnnvGTTOoEg4pwWBEMKpJJnaef9vPLX4aw5NdWCCGEEIOQBEwMibVrk7hu79Sts4nHYcmSFF4pgihGkAceCNDSohMI9H9/IAAtLRoPPjjAAUIIIYQQSAImhkhxseITn4iSSoE78HZWxGIwZ47FJZckhy44Ic6itVVn/37zrF8KeDwae/eatLXJW6sQQggh+iefEsSQGT/e5XOf66G+3iaV6q2OaNuQSkE0Cj6fy+WXJ7j66gRS2E+MJM8+6017I3G/H557ToZvhRBCCNE/KcIhhlRFheJjH4sTj8P27R7a23WqqvxUVUWpq3Ml8RpiSileOf4KrUdbmR2azbSyacMd0ogUiRhpF9cwjN7jhRBCCCH6IwmYGBaBACxd2lutoLJS0do6yLxEUTA7Izt56vBTVBVXsfXYVr6++OsEPcHhDmvESWft4rs5TmHiEEIIIcToJ1MQhTiHtcZbMTUTTdOwXZu4HR/ukEakUMhNOwlTCsJh+UJBCCGEEP2TBEyIc9jy2uWUB8pJ2knmVc6j3F8+3CGNSBdckCIaTW9+bCymceGFqQJHJIQQQojRSqYgCnEO85t+vjD/C1RUVNDW1jbc4YxYkyc7VFQ4xOM6+iBfW7kuVFQ4TJwocxCFEEII0T8ZAROiH11dGgcOGBw7ZmS0gfRopUn1k0FpGnziEzFg4PVdvbcrPvGJmBSTEUIIIcSAZARMiHdpbtZ57DH/24mXhlKKoiLFvHkWl16aHHT0Q4xtJSWK227r4U9/8nPggIllaZimwnHANGHKFJsrrkgQCmVYsUMIIYQQ5xRJwIR428mTOj//eQifD4JBgFMfpDU2bfLS2mrwsY/FJAk7h4VCio9+NE4iAbt3e+juhqIiRWOjnfY+YUIIIYQ4t0kCJsTbHn44gM9Hv9PHfD7Yt89g2zYP8+dbQx+cGFH8fuT3QAghhBBZke/yhaB36mFzsz7o2p1QCP76V+/QBSWEEEIIIcYcScCEAA4fNjCMwY/RNOjokOoKQgghhBAiezIFUQjAMEApjXfWffVPqtuNXpZF3yhnTY171oRbCCGEEKIQJAETApg+3UbTBk++lIKaGtnfabRRCp580sebb3qIRnsH/cNhl2XLUqxenZKkWgghhBBDShIwIYBwWDFpks2xYybmAFdFNAqrVyeHNrBzlGXBs8/62LHDQyKhEQopFi5MccEFqYyrUD76qJ+//c1DIADFxe9UtnzxRR+WpfG+90mfCiGEEGLoyBowId72kY/ECQRcku/5PK5Ub/J10UVJGhrc4QnuHGLbcN99IV5/3YPjaHg8kEppPP+8jwceCKAy2Garp0dj69be5Ou9AgHYuNFLIpG/2IUQQgghzkYSMCHeFgjA5z4XZenSFLquiMchkYDSUoePfjTGxRenhjvEc8LGjR6am3V8vtPnBgYCvXtvHTiQ/uKtTZs8g671sm2NXbs82YYqhBBCCJExmYIoxLv4fHDppUkuvTSJbYOuIxsvD7G//c3b74gV9G6E/OqrXqZMiafVViKhDZqAGYaSETAhhBBCDCn5aCnEAExTkq/hYA2yv7GmgWWlXzVjxgybRGLg4x0Hpk2TwipCCCGEGDry8VIIMaKUlrq4Ayy1syyork4/YWpocKisdPptz7ahvt6hslLW9QkhhBBi6EgCJoQYUS6+OEk83v+olW3DqlXpr8XTNPjEJ2J4PIpotLegyqmiKuGwy8c+lt5URiGEEEKIfJE1YEKIvFEKOjs1TLO3tH826upc3v/+OE8+6UfTetflJRK967Wuuy6ecbvFxYqvfKWHHTtMtm/3oGmwYEGKxkZH9gATQgghxJCTBEyIIaIUY/oDfySi8dvfBolEdDQNJkxwuPHGGD5f5m0tW2Yxd67Nq696iUQ0xo1zWLrUwuvNLjZdh7lzbebOtbNrQAghhBAiTyQBE6LANm3y8PzzPmIxjepqh+uui1Namt3oUL4pBdu3mySTGlVVek77nP3XfwWJx3VCod6fT5wwePRRP9ddl12ZwUBAcfHFskmyEEIIIcYWWQMmRAG1tOj86U8BXFfD74eODoPf/CY43GEBvcnXAw8EePjhAC++qPPLX4Z4+eXs9sTq6dFoa9NPqxrp8cDhw/IdjxBCCCHEu0kCJkQBvfaah0DgndEuXYdIRKenZ/jnIjY16ezc6SEUAq8XQiF4/nn/gBUIB2MYqt+S/abkX0IIIYQQp5EETIgCKi93z9jXyjDA4xn+KYg9PdoZa9Icp7fSYKYCAZg61SaVeud5xeO9xS6EEEIIIcQ7JAETooAWL7YIBBSp1Dvlz+fPt7IqTHHKgQM6P/1piP/8z8CA5drTMX68i9erUG/nTI4DFRVu1oUurr02zty5Nobh4vW6rF6dZPVqScCEEEIIId5NJggJUUA+H3zhCz288IKPSERnzhybuXOtsz9wEA8/HEQpjY4Oncce82Vd5CIUUtx0U5Q//jGAUorKSocbbsh+XyzThCuvzC4WIYQQQohzhSRgQhRYIACXXpq/an6uq6HrvSXtHSe3tWQNDS5f/nKUysoAra2xPEUohBBCCCEGIlMQhRhlrrwyjs+nKC93+OAHZcRJCCGEEGI0kREwIUaZmTNtZs7sGe4whBBCCCFEFmQETAghhBBCCCGGiCRgQgghhBBCCDFEJAETQgghhBBCiCEiCZgQQgghhBBCDBFJwIQQQgghhBBiiEgCJoQQQgghhBBDRBIwIYQQQgghhBgikoAJIYQQQgghxBAZUxsxb9myhfvuuw/Xdbnkkku4+uqrhzskIYQQQgghhOgzZkbAXNfl3nvv5X/+z//JHXfcwUsvvcTRo0eHOywhhBBCCCGE6DNmErC9e/dSW1tLTU0NpmmycuVKNm7cONxhCSGEEEIIIUSfMZOARSIRKioq+n6uqKggEokMY0RCCCGEEEIIcboxswZMKXXGbZqmnXHb008/zdNPPw3Av/zLv1BZWVnw2MTgTNOUfhhm0gcjg/TDyCD9MPykD0YG6YeRQfph7BkzCVhFRQVtbW19P7e1tVFWVnbGcevWrWPdunV9P7e2tg5JfGJglZWV0g/DTPpgZJB+GBmkH4af9MHIIP0wMkg/DL+6urq8tjdmpiBOnTqV48eP09zcjG3bvPzyyyxdunS4wxJCCCGEEEKIPmNmBMwwDG699Va++93v4rouF198MRMmTBjusIQQQgghhBCiz5hJwAAWL17M4sWLhzsMIYQQQgghhOiXpvqrXiGEEEIIIYQQIu/GzBqwbHzjG98Y7hAE0g8jgfTByCD9MDJIPww/6YORQfphZJB+GH757oNzOgETQgghhBBCiKEkCZgQQgghhBBCDJFzOgF7935gYvhIPww/6YORQfphZJB+GH7SByOD9MPIIP0w/PLdB1KEQwghhBBCCCGGyDk9AiaEEEIIIYQQQ2lM7QN2zz33sGnTJkpKSrj99tsBOHjwID/72c9IJBJUVVXxla98hWAwyAsvvMCjjz7a99jDhw/zr//6r0yaNIl//ud/pr29Ha/XC8A3v/lNSkpKhuU5jUaZ9INt2/zkJz/hwIEDuK7LRRddxIc//GEA9u/fz913300qlWLRokXccsstaJo2nE9tVMlXP8j1kL1M++Df//3f2bdvH7quc/PNNzNnzhxAroVc5asf5FrIXmtrK3fffTcdHR1omsa6dev4wAc+QE9PD3fccQctLS1UVVXxta99jXA4DMAjjzzC+vXr0XWdW265hYULFwJyPeQin/0g10P2Mu2H7u5uvv/977N3717WrFnDpz/96b625HrITj77IKtrQY0h27dvV/v27VNf//rX+277xje+obZv366UUuqZZ55R999//xmPO3TokPriF7/Y9/O3v/1ttXfv3sIHPEZl0g8vvPCCuuOOO5RSSiUSCXXbbbepkydP9j3mrbfeUq7rqu9+97tq06ZNQ/tERrl89YNcD9nLpA/+8pe/qLvvvlsppVRHR4f6x3/8R+U4Tt9j5FrIXr76Qa6F7EUiEbVv3z6llFKxWEx95StfUUeOHFH/+Z//qR555BGllFKPPPKI+s///E+llFJHjhxR//2//3eVSqXUyZMn1Ze+9CW5HvIgn/0g10P2Mu2HeDyudu7cqZ544gn185///LS25HrITj77IJtrYUxNQZw9e3bfNzanNDU1MWvWLADmz5/PX//61zMe9+KLL3LBBRcMSYzngkz7IZFI4DgOqVQK0zQJBoO0t7cTj8dpbGxE0zQuuugiNm7cOKTPY7TLRz+I3GTSB0ePHmXu3LkAlJSUEAqF2L9/v1wLeZCPfhC5KSsrY8qUKQAEAgHq6+uJRCJs3LiR1atXA7B69eq+3+2NGzeycuVKPB4P1dXV1NbWsnfvXrkecpSvfhC5ybQf/H4/M2fO7BthOUWuh+zlqw+yNaYSsP5MmDCB119/HYBXX32Vtra2M4555ZVXzkjA7rnnHv7hH/6Bhx56CCV1SnI2UD+cf/75+P1+PvvZz3Lbbbdx5ZVXEg6HiUQiVFRU9D2+oqKCSCQyLLGPJZn2wylyPeTPQH0wadIkXn/9dRzHobm5mf3799Pa2irXQoFk2g+nyLWQu+bmZg4cOMC0adPo7OykrKwM6P1A1NXVBXDG7315eTmRSESuhzzKpR9Okeshd+n0w0DkesiPXPrglEyvhTG1Bqw/X/jCF7jvvvt46KGHWLp0KaZ5+lPes2cPXq+XhoaGvtu+8pWvUF5eTjwe5/bbb2fDhg192bDIzkD9sHfvXnRd56c//SnRaJRvfetbzJs3T97ICyTTfqipqZHrIc8G6oOLL76Yo0eP8o1vfIOqqipmzJiBYRhyLRRIpv0A8rchHxKJBLfffjs333zzoKPsA/3ey/WQH7n2A8j1kA/p9sNA5HrIXa59ANldC2M+Aauvr+eb3/wm0DvlZNOmTafd/9JLL50x+lVeXg70DkleeOGF7N27V95UcjRQP7z44ossXLgQ0zQpKSlhxowZ7Nu3j1mzZp02WtnW1tbXLyJ7mfZDTU2NXA95NlAfGIbBzTff3HfcN7/5TcaNG0coFJJroQAy7QeQvw25sm2b22+/nVWrVnHeeecBvdM829vbKSsro729neLiYqD3m/x3/95HIhHKy8vPuF2uh8zlox9ArodcZdIPA5HrITf56API7loY81MQOzs7AXBdl4cffpj3ve99ffe5rsurr756WgLmOE7fcKNt27zxxhtMmDBhaIMegwbqh8rKSrZt24ZSikQiwZ49e6ivr6esrIxAIMDu3btRSrFhwwaWLl06nE9hTMi0H+R6yL+B+iCZTJJIJADYunUrhmEwfvx4uRYKJNN+kGshN0opfvKTn1BfX88VV1zRd/vSpUt5/vnnAXj++edZtmxZ3+0vv/wylmXR3NzM8ePHmTZtmlwPOcpXP8j1kJtM+2Egcj1kL199kO21MKY2Yv7BD37Ajh076O7upqSkhOuvv55EIsETTzwBwPLly/nYxz7WV55z+/bt/Pa3v+W73/1uXxuJRIJvf/vbOI6D67rMmzePT33qU+j6mM9V8yaTfkgkEtxzzz0cPXoUpRQXX3wx7j5dnwAAA7NJREFUV111FQD79u3jnnvuIZVKsXDhQm699VYprZqBfPSDXA+5yaQPmpub+e53v4uu65SXl/P5z3+eqqoqQK6FXOWjH+RayM2uXbv41re+RUNDQ9/v7o033sj06dO54447aG1tpbKykq9//et9608ffvhhnn322b7tABYtWgTI9ZCLfPWDXA+5yaYfvvjFLxKLxbBtm1AoxDe/+U3Gjx8v10OW8tUHlZWVWV0LYyoBE0IIIYQQQoiRTL6qEEIIIYQQQoghIgmYEEIIIYQQQgwRScCEEEIIIYQQYohIAiaEEEIIIYQQQ0QSMCGEEEIIIYQYIpKACSGEEEIIIcQQkQRMCCHEmHXnnXdyzz33nHbbjh07uPXWW2lvbx+mqIQQQpzLJAETQggxZt1yyy1s3ryZrVu3ApBKpfjpT3/KJz/5ScrKynJu33GcnNsQQghxbpGNmIUQQoxpr7zyCr/+9f/fzv28whrFcRx/P2Mom4dCLGRhrKgnxmwsZqnsbCZrkrIYW/MfKOqJSfkDZCkLZW1FUoPFTNhZyG7KwobHj7tTuqt7u/cR3q/t93T6nuXnnG9nhziO2dvb4+bmhlKpxPb2Nre3t/T09DA7O8vIyAgAh4eH7O/v02w2CcOQ6elpJicnAWg0GmxubjI1NcXBwQFRFLG0tPSZx5MkfTHZz25AkqT/aWJiguPjY6rVKtfX16yurlKpVCiXy4yOjlKv14njmI2NDcIwpKOjg0qlQm9vL5eXl6ysrJDL5RgcHATg/v6eh4cHtra28A5TkvSnHEGUJH178/Pz1Ot1SqUSR0dHjI2Nkc/nyWQyRFFELpfj7OwMgHw+T19fH0EQMDw8TBRFXF1dve8VBAEzMzO0trbS1tb2WUeSJH1RvoBJkr69zs5OwjCkv7+f09NTTk5OqNVq7/WXl5f3EcTz83N2d3e5u7vj7e2Nx8dHBgYG3teGYWjwkiT9NQOYJOlH6erqolgssri4+FstSRLiOKZcLlMoFMhms6ytrX1YEwRBWq1Kkr4hRxAlST9KsVikVqtxcXHB6+srT09PNBoNms0mz8/PJElCGIa0tLR8+EFRkqR/wRcwSdKP0t3dzfLyMjs7O1SrVTKZDENDQywsLNDe3s7c3Bzr6+skScL4+DiFQuGzW5YkfSN+Qy9JkiRJKXEEUZIkSZJSYgCTJEmSpJQYwCRJkiQpJQYwSZIkSUqJAUySJEmSUmIAkyRJkqSUGMAkSZIkKSUGMEmSJElKiQFMkiRJklLyCybYlzppPRkpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax0 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='India',\n",
    "                    figsize=(14, 8),\n",
    "                    alpha=0.5,                  # transparency\n",
    "                    color='green',\n",
    "                    s=norm_india * 2000 + 10,  # pass in weights \n",
    "                    xlim=(1975, 2015)\n",
    "                   )\n",
    "ax1 = df_can_t.plot(kind='scatter',\n",
    "                    x='Year',\n",
    "                    y='China',\n",
    "                    alpha=0.5,\n",
    "                    color=\"blue\",\n",
    "                    s=norm_china * 2000 + 10,\n",
    "                    ax = ax0\n",
    "                   )\n",
    "ax0.set_ylabel('Number of Immigrants')\n",
    "ax0.set_title('Immigration from India and China from 1980 - 2013')\n",
    "ax0.legend(['India', 'China'], loc='upper left', fontsize='x-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Thanks for reading :)\n",
    "Created by [Alex Aklson](https://www.linkedin.com/in/aklson/) and modified by [Tarun kamboj](https://www.linkedin.com/in/kambojtarun/)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}