18-network-ecomplexity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Economic Complexity and Product Complexity\n",
    "\n",
    "Economies grow by upgrading the products they produce and export. The technology, capital, institutions, and skills needed to make newer products are more easily adapted from some products than from others. Here, we study this network of relatedness between products, or “product space,” finding that more-sophisticated products are located in a densely connected core whereas less-sophisticated products occupy a less-connected periphery. Empirically, countries move through the product space by developing goods close to those they currently produce. Most countries can reach the core only by traversing empirically infrequent distances, which may help explain why poor countries have trouble developing more competitive exports and fail to converge to the income levels of rich countries.\n",
    "\n",
    "\n",
    "- Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yıldırım, M. (2013). The Atlas of Economic Complexity: Mapping Paths to Prosperity (Part 1). Retrieved from https://growthlab.cid.harvard.edu/files/growthlab/files/atlas_2013_part1.pdf\n",
    "- Hidalgo, C. A., Klinger, B., Barabasi, A.-L., & Hausmann, R. (2007). The Product Space Conditions the Development of Nations. Science, 317(5837), 482–487. http://doi.org/10.1126/science.1144581\n",
    "\n",
    "https://github.com/cid-harvard/py-ecomplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:03.012143Z",
     "start_time": "2019-07-19T06:17:01.380636Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import pandas as pd\n",
    "import scipy.sparse.linalg \n",
    "import scipy\n",
    "from scipy import sparse\n",
    "from ECI2 import *\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 digit sitc trade data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T09:01:29.635614Z",
     "start_time": "2019-07-20T09:01:10.790408Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/anaconda3/lib/python3.7/site-packages/ecomplexity/ComplexityData.py:54: UserWarning: Duplicate values exist, keeping the first occurrence\n",
      "  'Duplicate values exist, keeping the first occurrence')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa1cceb5c0>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from ecomplexity import ecomplexity\n",
    "\n",
    "# Import trade data from CID Atlas\n",
    "data = pd.read_csv('../data/country_partner_sitcproduct4digit_year_2017.zip', compression=\"zip\", low_memory=False)\n",
    "data.head()\n",
    "\n",
    "# Calculate eci's\n",
    "data = data[['year','location_code','sitc_product_code','export_value','sitc_eci']]\n",
    "data = data[data.year==data.year.max()]\n",
    "trade_cols = {'time':'year', 'loc':'location_code', 'prod':'sitc_product_code', 'val':'export_value'}\n",
    "cdata = ecomplexity(data, trade_cols)\n",
    "cdata = cdata[['location_code','eci','sitc_eci']].drop_duplicates(['location_code'])\n",
    "\n",
    "# Compare against Atlas\n",
    "sns.scatterplot('sitc_eci','eci', data=cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T12:48:07.065622Z",
     "start_time": "2019-07-20T12:48:06.857281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa1cc3a550>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare against Atlas\n",
    "sns.scatterplot('eci','sitc_eci', data=cdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T09:04:52.576710Z",
     "start_time": "2019-07-20T09:04:52.548106Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_code</th>\n",
       "      <th>eci</th>\n",
       "      <th>sitc_eci</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>0.835738</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4631</th>\n",
       "      <td>AFG</td>\n",
       "      <td>-1.378222</td>\n",
       "      <td>-1.237445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9680</th>\n",
       "      <td>AGO</td>\n",
       "      <td>-1.764049</td>\n",
       "      <td>-1.822037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18791</th>\n",
       "      <td>AIA</td>\n",
       "      <td>1.165619</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19869</th>\n",
       "      <td>ALB</td>\n",
       "      <td>-0.688075</td>\n",
       "      <td>-0.288839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25952</th>\n",
       "      <td>AND</td>\n",
       "      <td>0.328331</td>\n",
       "      <td>1.066444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28707</th>\n",
       "      <td>ANS</td>\n",
       "      <td>0.606416</td>\n",
       "      <td>0.734075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30727</th>\n",
       "      <td>ARE</td>\n",
       "      <td>0.460639</td>\n",
       "      <td>0.057493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38576</th>\n",
       "      <td>ARG</td>\n",
       "      <td>-0.386276</td>\n",
       "      <td>-0.156561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44106</th>\n",
       "      <td>ARM</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>-0.416436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      location_code       eci  sitc_eci\n",
       "0               ABW  0.835738       NaN\n",
       "4631            AFG -1.378222 -1.237445\n",
       "9680            AGO -1.764049 -1.822037\n",
       "18791           AIA  1.165619       NaN\n",
       "19869           ALB -0.688075 -0.288839\n",
       "25952           AND  0.328331  1.066444\n",
       "28707           ANS  0.606416  0.734075\n",
       "30727           ARE  0.460639  0.057493\n",
       "38576           ARG -0.386276 -0.156561\n",
       "44106           ARM  0.000319 -0.416436"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T09:06:33.485924Z",
     "start_time": "2019-07-20T09:06:33.351243Z"
    }
   },
   "outputs": [],
   "source": [
    "# To download gdp data from wordbank \n",
    "# https://data.worldbank.org/indicator/ny.gdp.pcap.cd?most_recent_year_desc=true\n",
    "df_gdp = pd.read_csv('./GDP/API_NY.GDP.PCAP.CD_DS2_en_csv_v2_41089.csv', sep = '\\t')\n",
    "year =2017\n",
    "df_g = df_gdp[['Country Code', str(year)]]\n",
    "gdp_dict =dict(zip(df_g['Country Code'],df_g[str(year)]))\n",
    "cdata['gdp'] = [gdp_dict[name] if name in gdp_dict else 0.0 for name in cdata['location_code']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T09:43:10.289206Z",
     "start_time": "2019-07-20T09:43:09.924783Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare against Atlas\n",
    "df = cdata[cdata['gdp'] >0]\n",
    "sns.scatterplot('eci','gdp', data=df, label = 'eci computed with ecomplexity')\n",
    "sns.scatterplot('sitc_eci','gdp', data=df, label = 'sitc_eci given by Hidalgo')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.xlabel('ECI', fontsize = 15)\n",
    "plt.ylabel('GDP', fontsize = 15)\n",
    "plt.title('2017');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T09:09:34.310208Z",
     "start_time": "2019-07-20T09:09:34.015915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare against Atlas\n",
    "df = cdata[cdata['gdp'] >0]\n",
    "sns.scatterplot('sitc_eci','gdp', data=df)\n",
    "plt.yscale('log');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:42:22.574804Z",
     "start_time": "2019-07-20T08:42:11.386470Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/country_partner_sitcproduct4digit_year_2017.zip',compression=\"zip\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:42:30.066044Z",
     "start_time": "2019-07-20T08:42:30.039788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4672972"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:42:36.465055Z",
     "start_time": "2019-07-20T08:42:36.424173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location_id</th>\n",
       "      <th>partner_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>year</th>\n",
       "      <th>export_value</th>\n",
       "      <th>import_value</th>\n",
       "      <th>sitc_eci</th>\n",
       "      <th>sitc_coi</th>\n",
       "      <th>location_code</th>\n",
       "      <th>location_name_short_en</th>\n",
       "      <th>partner_code</th>\n",
       "      <th>partner_name_short_en</th>\n",
       "      <th>sitc_product_code</th>\n",
       "      <th>sitc_product_name_short_en</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>696</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>11321</td>\n",
       "      <td>0.633293</td>\n",
       "      <td>-0.044210</td>\n",
       "      <td>ABW</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0484</td>\n",
       "      <td>Bakery products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>696</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>9772031</td>\n",
       "      <td>-1.237445</td>\n",
       "      <td>-0.921007</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0484</td>\n",
       "      <td>Bakery products</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>696</td>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>1013881</td>\n",
       "      <td>-1.822037</td>\n",
       "      <td>-1.232933</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Angola</td>\n",
       "      <td>ARE</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>0484</td>\n",
       "      <td>Bakery products</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   location_id  partner_id  product_id  year  export_value  import_value  \\\n",
       "0            0           7         696  2017             0         11321   \n",
       "1            1           7         696  2017             0       9772031   \n",
       "2            2           7         696  2017             0       1013881   \n",
       "\n",
       "   sitc_eci  sitc_coi location_code location_name_short_en partner_code  \\\n",
       "0  0.633293 -0.044210           ABW                  Aruba          ARE   \n",
       "1 -1.237445 -0.921007           AFG            Afghanistan          ARE   \n",
       "2 -1.822037 -1.232933           AGO                 Angola          ARE   \n",
       "\n",
       "  partner_name_short_en sitc_product_code sitc_product_name_short_en  \n",
       "0  United Arab Emirates              0484            Bakery products  \n",
       "1  United Arab Emirates              0484            Bakery products  \n",
       "2  United Arab Emirates              0484            Bakery products  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:44:43.779084Z",
     "start_time": "2019-07-20T08:44:36.070457Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.drop_duplicates()\n",
    "len(df)\n",
    "df_year = df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:53:21.975822Z",
     "start_time": "2019-07-20T08:53:21.950663Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4672972"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:45:21.308476Z",
     "start_time": "2019-07-20T08:45:21.259345Z"
    }
   },
   "outputs": [],
   "source": [
    "df_gdp = pd.read_csv('./GDP/API_NY.GDP.PCAP.CD_DS2_en_csv_v2_41089.csv', sep = '\\t')\n",
    "year =2017\n",
    "df_g = df_gdp[['Country Code', str(year)]]\n",
    "gdp_dict =dict(zip(df_g['Country Code'],df_g[str(year)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:45:55.520248Z",
     "start_time": "2019-07-20T08:45:55.144771Z"
    }
   },
   "outputs": [],
   "source": [
    "eci_hidago = df_year[['sitc_eci','location_code']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:46:04.401118Z",
     "start_time": "2019-07-20T08:46:04.376325Z"
    }
   },
   "outputs": [],
   "source": [
    "eci_hidago_dict =dict(zip(eci_hidago['location_code'],eci_hidago['sitc_eci']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:46:13.959972Z",
     "start_time": "2019-07-20T08:46:11.984066Z"
    }
   },
   "outputs": [],
   "source": [
    "x = list(df_year[\"location_code\"])\n",
    "y = list(df_year[\"sitc_product_code\"])\n",
    "data = np.array(list(df_year[\"export_value\"]))\n",
    "sx = list(set(x))\n",
    "sy =list(set(y))\n",
    "dx = dict(zip(sx,range(len(sx))))\n",
    "dy = dict(zip(sy,range(len(sy))))\n",
    "x1 = [dx[xi] for xi in x]\n",
    "y1 = [dy[yi] for yi in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:56:58.670208Z",
     "start_time": "2019-07-20T08:56:58.644633Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(470, 4672972, 4672972)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y1), len(x), len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:46:22.342163Z",
     "start_time": "2019-07-20T08:46:21.487592Z"
    }
   },
   "outputs": [],
   "source": [
    "# 构建出口矩阵\n",
    "M = sparse.csc_matrix((data,(x1,y1)),(len(dx),len(dy)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:57:09.988820Z",
     "start_time": "2019-07-20T08:57:09.963867Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<235x769 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 154175 stored elements in Compressed Sparse Column format>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:46:32.311668Z",
     "start_time": "2019-07-20T08:46:31.975218Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==Time 0.21==\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/anaconda3/lib/python3.7/site-packages/scipy/sparse/compressed.py:282: SparseEfficiencyWarning: Comparing a sparse matrix with a scalar greater than zero using < is inefficient, try using >= instead.\n",
      "  warn(bad_scalar_msg, SparseEfficiencyWarning)\n"
     ]
    }
   ],
   "source": [
    "#计算RCA\n",
    "R = RCA_sparse(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:46:46.567913Z",
     "start_time": "2019-07-20T08:46:46.542839Z"
    }
   },
   "outputs": [],
   "source": [
    "R=R.toarray()\n",
    "R[np.isnan(R)]=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:46:58.503090Z",
     "start_time": "2019-07-20T08:46:57.534350Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==Time 0.51==\n"
     ]
    }
   ],
   "source": [
    "Mat_Dict = Get_eci_pci(np.matrix(R))\n",
    "eci,pci = Mat_Dict['eci'],Mat_Dict['pci']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:47:10.377125Z",
     "start_time": "2019-07-20T08:47:10.198005Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==Time 0.09==\n"
     ]
    }
   ],
   "source": [
    "#利用稀疏矩阵计算：\n",
    "Dict = Get_eci_pci_sparse(sparse.csc_matrix(R))\n",
    "eci_sparse,pci_sparse = Dict['eci'],Dict['pci']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:47:45.131931Z",
     "start_time": "2019-07-20T08:47:34.635670Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/anaconda3/lib/python3.7/site-packages/ecomplexity/ComplexityData.py:54: UserWarning: Duplicate values exist, keeping the first occurrence\n",
      "  'Duplicate values exist, keeping the first occurrence')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017\n"
     ]
    }
   ],
   "source": [
    "# 利用ecomplexity计算出来的结果\n",
    "from ecomplexity import ecomplexity\n",
    "from ecomplexity import proximity\n",
    "time = [2017]*len(x)\n",
    "trade_cols = {'time':'year', 'loc':'name', 'prod':'skill', 'val':'value'}\n",
    "SData=pd.DataFrame({\"year\":time,'name':x,\"skill\":y,\"value\":data})\n",
    "cdata = ecomplexity(SData, trade_cols)#使用过滤函数\n",
    "res=pd.DataFrame({'name':list(cdata.name),\"eci\":list(cdata.eci)})\n",
    "res = res.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:48:37.948298Z",
     "start_time": "2019-07-20T08:48:28.555824Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/anaconda3/lib/python3.7/site-packages/ecomplexity/ComplexityData.py:54: UserWarning: Duplicate values exist, keeping the first occurrence\n",
      "  'Duplicate values exist, keeping the first occurrence')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017\n"
     ]
    }
   ],
   "source": [
    "trade_cols = {'time':'year', 'loc':'location_code', 'prod':'sitc_product_code', 'val':'export_value'}\n",
    "cdata2 = ecomplexity(df_year, trade_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:48:44.173008Z",
     "start_time": "2019-07-20T08:48:42.339374Z"
    }
   },
   "outputs": [],
   "source": [
    "res2=pd.DataFrame({'name':list(cdata2.location_code),\"eci\":list(cdata2.eci)})\n",
    "res2 = res2.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:48:55.686309Z",
     "start_time": "2019-07-20T08:48:55.635557Z"
    }
   },
   "outputs": [],
   "source": [
    "y =[eci[dx[name]] for name in res.name]\n",
    "y1 = [eci_sparse[dx[name]] for name in res.name]\n",
    "y2 = [eci_hidago_dict[name] for name in res.name]\n",
    "gdp = [gdp_dict[name] if name in gdp_dict else 0.0 for name in res.name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:49:04.868286Z",
     "start_time": "2019-07-20T08:49:04.842533Z"
    }
   },
   "outputs": [],
   "source": [
    "total=pd.DataFrame({'name':list(res.name),\n",
    "                    'gdp':gdp,\n",
    "                    'eci_hidalgo_rep':y2,\n",
    "                    \"eci_ecomplexity_cal\":list(res.eci),\n",
    "                    'eci_mat':y,\n",
    "                    'eci_sparse':y1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T08:49:13.312382Z",
     "start_time": "2019-07-20T08:49:13.282445Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gdp</th>\n",
       "      <th>eci_hidalgo_rep</th>\n",
       "      <th>eci_ecomplexity_cal</th>\n",
       "      <th>eci_mat</th>\n",
       "      <th>eci_sparse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>25630.266490</td>\n",
       "      <td>0.633293</td>\n",
       "      <td>0.835738</td>\n",
       "      <td>0.012386</td>\n",
       "      <td>0.012386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>556.302138</td>\n",
       "      <td>-1.237445</td>\n",
       "      <td>-1.378222</td>\n",
       "      <td>-1.617771</td>\n",
       "      <td>-1.617771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGO</td>\n",
       "      <td>4095.812942</td>\n",
       "      <td>-1.822037</td>\n",
       "      <td>-1.764049</td>\n",
       "      <td>-0.796055</td>\n",
       "      <td>-0.796055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AIA</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.940984</td>\n",
       "      <td>1.165619</td>\n",
       "      <td>0.277133</td>\n",
       "      <td>0.277133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>4532.889198</td>\n",
       "      <td>-0.288839</td>\n",
       "      <td>-0.688075</td>\n",
       "      <td>0.153267</td>\n",
       "      <td>0.153267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name           gdp  eci_hidalgo_rep  eci_ecomplexity_cal   eci_mat  \\\n",
       "0  ABW  25630.266490         0.633293             0.835738  0.012386   \n",
       "1  AFG    556.302138        -1.237445            -1.378222 -1.617771   \n",
       "2  AGO   4095.812942        -1.822037            -1.764049 -0.796055   \n",
       "3  AIA      0.000000         0.940984             1.165619  0.277133   \n",
       "4  ALB   4532.889198        -0.288839            -0.688075  0.153267   \n",
       "\n",
       "   eci_sparse  \n",
       "0    0.012386  \n",
       "1   -1.617771  \n",
       "2   -0.796055  \n",
       "3    0.277133  \n",
       "4    0.153267  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-20T09:32:38.145761Z",
     "start_time": "2019-07-20T09:32:36.836553Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lE1OVaYIg7IAVstzQ2BzyDGsJZlZK4K49h1U3ANM8bpzyBxKnyrSSjz/+GBdccAH+9Kc/AeioQt3Q0IAbb7wRADBp0iTccMMNeOONNzBr1iwRQxKE7YgWaqz7zSoZhAzUM72hSoPE2pQzmqshOp9N6pWshr89iCpfnapxNFPogSCIxMRK1WdA265JYc1aVaa1rtcruiXlExMEQTgBrZBnLUeLJJglUaxcj0k/W7HVsUaIID5y5AgmTJgQ+jkvLw9VVVWhnwsLC3HxxRdj/fr1JIiJhEGUUJPnZ3T1epDqceNEkz/sfn9ZuQfPVLD7t8kN0ox5GzQ917z5HpkWcjukZ2pobEZmxWHVvnRyAoEglpbvAKD+PrWKghEEkRywqkED/O2KWKLW7XZh1eMTI44rRaxyDmpQtWiCIOIBNUeMRKu/XXMTUK/4arxtAAoRxF6vF0FZsHh2djaamppQV1eH7OyOFzVgwADU1NSIGA4AsHHjRmH3IgizWBVqynDn401+pHlScPs1FwHoEIcLlm8Ju0avCrSe51ovvFp+H7X5Gq2WzeqdrOR4kz8m1a4Jgogv5F5aNYwIUFYuL2/+Lk8rJaoWTRBEPCCtrZTrTIlAIMhsg2m020cs2ntaQYggHjBgADZt2oRgMAiXy4X+/fsjGAxi586dIUHc0NCAQICvDDdBJAus/Iyl5Tu+rf6sLly1DBGP51rukW5qaUO7YgFXXJjDJXTlglXuFTaLVq4KQRDJB49H1ogAVQpaM6F8kudj0p2rmd5mXqyGgBMEQVhhdEE2c+0mFTtlCWbeVDylHXeiw0OIIJ4yZQruvvtuXHfddbjnnnswYMAA5OTk4PHHH0eXLl1w6NAhrFu3DgMHDhQxHEEkDCxjolZ4ivdaQNtzrfyMd9dOq7gCwO95NoqT+tQRBBFbeIpgGS0AKCqUz6i3WSl++/XxhhXtMhMCThAEYRWtyEItwcxTRLbKV6dqJ5UOD7s9yEIEcUlJCT777DO8+OKL2L17N/Ly8nDHHXfgtttuw0033QQASElJwS9/+UsRwxFEwmClh5uo/m28Yd8sYXqosVlVLBuFVYHaSX3qCIKILTzeX7vaFRnxNqvlP7MqWFMOMkEQsUQvstBsEdkqXx0WrdjK/FxaV/L0Q442QgQxANx11124/vrrQ31+x40bhxUrVmDt2rVIS0tDcXEx8vLyRA1HEAkBy8ikety6XuJYtx3SajOl58WVeie/X3NA9bmKC3OQn9ub2isRRBJgJExYq6iLE0KMJW+z1HO9bOW2UCi1fH5G2j1RDjJBEGpE04uqF1kIAM+V10S0ANWa65MrtmraM8nhodfeKRYIE8QA0KdPHzQ1nW5OP3jwYJw6dQpDhgyBx+MRORSRxMQirCJWoRusXTlAOwRZLcfXCGaejyXeh+dlYf2mfQgyjF6mvHdy6VDdsZ1cdIEgCGsYrRTNCksuLsxxnBeV9Ww8RQUJgiC0sNOLKq3bjja1h63ptFhaviOiPo2S4XlZALQjEGOFMEH8xhtv4IknnsDMmTNxww03AADa29tx/fXXo2vXrnj44YcxZswYUcMRSUosDEKsjY7WrtzS8h1hHtUMrwezSgYJrWwtfz6ALUjVxLvUTkltBzDNk4Lxw7rh56WXcj8vQRCJDctTygoTFlEEK1YY8QITBEEYwS4vqtmCWDy1cDbX1mM2tCMQY4UQQVxZWYl7770XWVlZ6NevX+h4e3s7brnlFrz66quYM2cOli5diksuuUTEkESSEguDYIfRUfOaAsApf3hlduXPPPdRzpm3srWa0VOK2RnzNqh6sd1uF+ZMHYIM1HPPV1nx2olVCAmCsIaZVkXx0s+SQp0JgogWdnhReQtimUWau9kcZZEIEcR//vOfceaZZ+Ktt95Cjx49QsdTU1Mxe/ZsTJs2DRMmTEBZWRkJYsISsTAIrHs1NDZj4h2rhIfysjy2qR63IWHO69k2Utlaz+ix7hUIBLGsYhcuyeuMgoLIeapVLDQzPkEQ8QUrJ9hopWheYtnWSCvf2eh9CIIg5IjwohpNl5O6iKiht+5mFUqVI82dp11otBEiiD///HOUlpaGiWE5PXr0wBVXXIGVK1eKGI5IYmIRVqFV+TkI8Z5LlseWlT/MMkI8nu0qXx1cbhcz39fIeID2u2pobMabHzXjnZp1oTBvpWi3Or5d7Nu3DxUVFaitrcXx48fRs2dPDB06FFdeeSV69+5t9/QIwpGUrdzGFIzRqBRtNF/ZKqx8ZzP3cQpk6wjCGVj1oppJl9Nb/2kxq2QQFmkU1eqU4gqbu93pdEIEcadOndDY2Kh5TlNTEzp1ElrDi0hCYhFWoTaGEpGeS6OCT2mEWB5X5f0lY8jK92VVttYyetOL8vHUq1vR1s4W2Meb/CGja6Y9k9PaLi1evBhLlixBW1tb2PE1a9ZgwYIFuPfeezFlyhSbZkcQzkQpTuVEq0CW0XxlqyjznY3itPxosnUE4RyselHNpMtpOT2U62417/Ovr74wohaORNBhGSZCFOqQIUNQWVmJffv24ZxzInc2Dxw4gMrKSgwaNEjEcEQSE4uwCuUYrN9ZUZ5LlsHJ8HrCjBQQKf55PK5aZe0BwOUCgKCqwdLbbBhdkM00dnIko8tTZMHI+LHmrbfewuLFi5GdnY2bbroJgwcPRp8+fXD8+HFs3boVZWVl+N3vfoe+ffvi4osvtnu6BOEYWOLU7XZFTQCayVe2ipTvrLUBAHQ896rHJ0ZtHlYhW0cQzsOKF9VMuhzLQaTsdMLyPs+ZOgTLHyrGjHkbIta57d+m1jklJU6IIJ45cyY++OADTJs2Dddddx2GDh2Krl274uTJk9i+fTteeuklHD9+HLNmzRIxHJHkxCKsQj6G2i8yIM5zyfJ6zyrp2ECSvL+ZsmJbM+ZtwKHGZrh0ctbkgpJlDINBoFWlWBdvResTnCLXqBh2u10howw4o7DWX//6V5xxxhl49dVX0atXr9DxXr16IScnBz/4wQ8wefJkLFmyhBaJBCGDR5yWrdyGio/3I7h8vxBvaazzleXMLh2qKYidXoCLbB1BJBZa3l41DjU2hzmIGhqb4XJ1rBk319Yj31cX9rlWyp4T2irpIUQQDx8+HI888gjmzZuHhQsXwuU6/ccmGAwiPT0djzzyCAoLC0UMRxAxJdph2npe79EF2fD5fCgoKIjYhdPKBVb2ijNqDDundeISoUbvy0OK2+XIatN79+5FaWlp2AJRTmZmJn70ox+hvLw8xjMjCHPEquiUnjjlzfc1Ml9WTq8TcnTlojyWhb94IVtHEImFWopbpxQX0tM6aabLSesurfxjvZS9rowCW05KiROW1Dtp0iRcfvnl+Oc//4na2locO3YMXq8X559/PsaOHYvu3buLGoogYkqswrRZ96vy1eG58q9xbPkqXY+wRIbXgxfmjgs7xpMbLYd3587ofXlQNnN3SrXpzMxMHDlyRPOcEydOoGfPnjGaEUGYJ5ZFp/TEKU++r9H52t3DWKvqtPTcN//hXdTVnwwdj3bhL17I1hFE4qHM2w0GgUuGnIXK6v2aTh+WB3jhK1s0c4H79ExHla8OTS1tEZ8pi2rZjdAqV926dcOECRMwYcIE3XNffPFFLFu2DJWVlSKnQBBRwa7qd0Y8wnqoCfuW1jZmKLO0c6dXpl8ZUhMtnBBac/3112P+/Pm44oor8KMf/Sjic5/Ph7///e+46667bJgdQRgjlkWn1MRpvz5erN+0jzu02Mx87ephrFVROzurCwBgwh2rmNdHq/AXL2TrCCKxWFaxK8LZ0B4IYnNtPeZMHRJW5yXV4w47TyvljoUkqpeW74gYFwDS0zokqJQCaEerJTm2lX0+fvw4Dhw4YNfwRAJjtM+akzFTlRnoyOtlvQdlIYSFy7eoFg4bnpfF3dtYft8qX51qAa1OKS4Eg5HeXzlmq13HirS0NJx//vm49dZbcdFFF2H48OHo27cvWltbsWPHDmzYsAFdu3bFvn37MH/+/NB1LpcLd999t40zJ4hIYl10Si5O9YpOSchDi+0okmUGnmfT+9zuZyJbRxCJhV4e7ylZLZnjTX4sWL4FS8t3YFbJIFOpcXOmDgndSw2pA4ne+jJWUB8kIqHgFXBORSlizXpcu3o93EKWVf15c209NtfW6/Y2ViKJ445Q7xoca2oPCXIAzL50brcrZECj3VrLLHPnzg39v8/ng8/nizinsbERL774YtgxWiQSTsTOolMsb68Seb6vnfM1gt6zyUOkWdj9TGTrCCKxYK0p+/RMZzpfJNE6ZtjZEWHVWmT2TMfogmzMmLeBeY5UOFWOnelxJIiJhEKv0h0PdnmY1cQ8C7fbhWAgiK5eD5pb28KKJKR5UgCA+z2wqkRrhSjzhC+PLshGBupxHFlYVrELC5dvQZ+e6bhy5Dmq+Spzpg4Jm5sTvfzLli2zewoEIQw7i07peUDV8n379fGqikm1+dpZqEqEd9fuwl9k6wgivlBbuwKn11JdvZ6wgqXAaWfDwuVbmPdt9beHwqqfK6/B0SZ9UTw8LwuA9lqRZSftSo8jQUwkFCwRyetptdPDbCQ8Wr64UzOCLON2qLE54nxW9b8gTgtvJbzhy9v3nMTa6vD3WVm9H2OGnY3NtfURhtspuSQsRowYYfcUiCQi2qLOzqJTLG+vywWsfmISgI7nn3Tnal2BKXlkzRbfEo1WMS0esrO62F5lmmwdQdiLEeeM2tr1yRVb4XIh5DBRrvPcbhfGDDsbowuydWvASC2YMlCPZyoO666pN9fWYzbYXukMrwed0zpFtaWpUUgQEwmF1ZA6ER5msxjZFaus3o/83N4A1D2pLOOmFkqd4nahU4orzMssofYujYQvV9YcU32fm2vrw6pgx1uoe1tbG/71r3+htrYWR48exW9/+1t89tln8Hq9yM523nyJ+CNWoi7WRafkIl+NVDe4RLAc5buJZbGwsE2LFV/hypHnMD3vPBQX5tguhuWQrSOI2KO1JgIi131qa1etei1Ah92U1pJ63ULkInV6UT6eXLFV8/4Njc2YMW+D6jo0zZOCWSWDADgrPc6tfwpBxA9Wi67Y2TyctSumJuZb/e1YWr4Di1+vQUNjM4I4bTCrfHWYXpQfCp2WYIVStweCSE/rhEyNXTm32wUXOvJClKHNLKp8dczQGuX71NqIcBqbNm3C2LFj8Ytf/AKLFi3CX/7yFwBARUUFrrzySjz//PPCxjpx4gSuuuoq7N+/X9g9ifhAS9TFK5LI17LHre3mQ46ldxOr4lvK55EL8+LCHMP3c7tdjhLDZOsIwh5YayLWus9svRm5w2fO1CHI8HoizlGK1NEF2fB21venqs1JvoaUxszsmW54fRkNSBATCQVL1GmJPYkqXx1cDE9yLEI4WCKWtYg73uTX9GarGRpWvvCJJj9emDsOLD96MBDE6gWT8MLccdxiWL6bGXE/dIRHV/nqANi7EWGEXbt2YdasWWhubsZNN92EceNOe7mHDBmCPn364IknnsDGjRstj1VTU4Np06bhyy+/tHwvIv6Il4rKRoi2mJfeDSsiSHShKj1PtNHx7M4blkO2jiDsg7X2Ya37rNg2aazRBdlY/lAx7rjmIl2RylpLapHZMz1iDTm6IBsvzB1naH0ZLUgQEwkFS1TqhWBIAs5qiLAVWCKWR8zLkRs3paFhCXvpuN7nvPDkQ8s92qLGjTZ//OMfkZaWhjfffBO33XYbzj///NBnl19+OV5//XV079495EmxwmuvvYb77rsPWVlZlu9FxB+xEnWxJNpiXno3LGEpWnDqbVpoPW9xYU5ovm63y3Gh0mTrCMIetJwzLAKBYMTalxflOotHpJpZmznNwaGEcoiJhEL6xTVaoVhLwCkblLMQUZ1a2SdYYtErW8C7ltQyVGp5InLBr/c5D1W+Ou7wHSkESA2ntFqS4/P5cOWVV6Jfv36qn2dlZaGoqAgVFRWWx3r44Yct34OIX+ysAB0trBab0qNfHy8Ae4uFSUy6czXzMyk02kkCWAnZOoKIPXrOmVSPW7UIaqYsl9hI+LRynaW3jt2+5ySeqVDPDdbDaQ4OJSSIiYSDJSq10Nq5kvqwSfdWg1Xhb2n5Dpxo8luumuxyoSPzMRL3AAAgAElEQVTOWAc9Eam3YWB2Q0FCL1RaDTXjnuH1YFbJIMcV1GptbYXX69U8JyUlBa2trTGakTo7d+60dXw91HqaxgOxnPeIXKC+wQvf7iYEgx02oKC/FyNy2w3Nw0nv+qLz0lH9eZPqZy4X0LurG4eOB3TvM+w7Xnx5sCXi3Lr6k3ig7B1cNaIXRuQCI3LlYq4dD5S9E/E+rxrRy8ojMdES/oFAEBN/syqq41slUW2dk34fREDP43yMPNOzKw+g1R9pA10uYPywbgCANZ8cgV9WBNWT4sIleZ2RgXrcUtQbL757EHvqFRWlXUCax43mUwG4XEAwCHT3pmDMkG7IQD18vnps33My7N4Njc3446tbsWfPHgzO7RLxuRGkOTr5+xUiiPft24dzzjG2a92vXz8MGzZMxPAEYRlWaXgJeW6u2g4aq8KfJPasVE1eVrEL7fprxNAOod79lRsGVb66iHZH8grQRufK2zpKi85pnRwnhgGgf//++Ne//oVAIAC3OzJywO/344MPPkBubq4NszvNwIEDkZaWZuscWPh8PhQUFNg9DcPYMW+rwzntXRcU6LeSeqDsHWz5ollTUG75gm2rqz9vwn2zfxRxvGzltjAxHgx2nJuVmWneU7vcfAEoIeMboLW11ZB4TERb57TfB6vQ8zgfI89U5atD8ymGTQkCPy+9FACQm8v24pat3BYhhgFgUP/emDf7Ys3xn6nYECF2/e1BlG86gtzcXLxfu0tVDGt1dwkGgjFvo2nU1kkIEcTjxo3DhRdeiEmTJqGoqAjdu3fXvWby5MmYPHmyiOEJwjJ6JeeB0z181Urh84hA3vZNSsHNE5riAkyJWNHtjkTliEgl+53Wi3jq1Kl44IEHcPfdd+P//b//F/bZ4cOH8eCDD2Lv3r249957bZohQTgbvVDhq0b0wn2zOxaQE+5YpXqOXti1dJ1ccEejFZOIEPBotIISAdk6gogtWl015OHGWlGQLDu344tvdMdnrd8CgaDmOpdlA6VirPGCEEE8efJkvPPOO7j//vvx8MMPY/To0Zg0aRIuu+wyeDyRJbwJwmnIQ4VZArRPz3RmKXzehZGeYFQTqDyYzc0Q3XeZV8ADHR7tYydaVcODAGf2Ip42bRq2bt2K1atXY82aNSHPxA9/+EP873//QyAQwNixY/HTn/7U5pkSRPyj11dez+bK2yBFo2q3lX7D8vEn3bk6JNz1POixgmwdQcQWrfUhbz0VLTunlx+stX5r9beHQq15cXrOsBIhgnj+/Pl44IEHUFlZidWrV6OqqgrvvvsuunXrhuLiYkycOBEXXnihiKEIImpIu25KUQqczs1duHyL6rVShT89T7GegTATcmyl+JTodkfTi/KxgPGO5Ejl96/53TqmIAasifNo8dhjj+Hyyy/HypUr8e9//xttbW04ceIECgoKMHnyZPz4xz8WOp6ItiYEEY/oFRbjFaPrN+3TFdeAfji3ErXiXf36eFFXf5JrXhKScN+x+1DYtXJBb4coJltHELGDJUgzvB7NNZBc6GqhFw2oFylpRAw7sSiqHsKKaqWmpqKoqAhFRUU4evQo1q1bh4qKCrz++utYsWIFsrOzMXHiREyaNAnZ2c5Z3BLhWK2ULKLScjTvx4NWYSmWB5mnwl+nFJeugdAyaBleD040+dH128bpPMW6zO4Imt3ZG12QjaXlO1QLZcmRnpOnl52TSvX//e9/x9ChQ0O2jiCI6KFXLZpXEAcCQRQX5miK67KV28I+5xWjUgi4PFfw5j+8a1gUA2BeY0dYNdk6gogtrC4fs0oGMa9Rc+Co0Tk1BS2ntKMBpf8uWrHVUuQMbz0bpxGVKtPdu3fHtGnTUFpainfffRePP/449u3bh8WLF+OZZ57BqFGjMGfOnIRLno93rOaTis5HFX0/I7ByNLTaEknXzJinXpI+naNQlFbIyil/ALdfc5Hhis9GdwSt7uzNKhmka6DlfY/1QqydFHZz3333YejQoViyZIndUyGIhCJMRC7fj+ysLnj2rrGaOccskatEanMEsMW1yBzjZ+8aq+ptlo9vhGj3b1aDbB1BxBYzXT70ogol21PBsJOSw0HuPOnq9aCppQ3tJuyOFP0XjwgXxO3t7fjggw+wdu1aVFZWoqmpCZ06dcK4ceNQVFSEXbt24Y033sDPfvYzPPjggygtLRU9BcIkVvNJReejir6fCKRx5V5QZZ9ilsDT85oC2iErRp+d5/1ZbbOkhl4+tl7fY9a5TqC1tRXnnXee3dMgiIRCzaNaV38SN//hXTx711jmdUqRy0ISo1riWnSOMWus2aVDMenO1YbuKw/rjhVk6wgi9hhtG8qKoHMBYQWt3q85oLoG7er1RDhPjjf50SnFFYpK7NMzHS2tbbprWKet14wiTBBXV1dj7dq1WL9+PY4cOYJgMIihQ4di0qRJGD9+PLp16+ifVVRUhGuuuQZFRUV4+umnSRA7CNYvVkNjMybesUpXLInORxV9P5GckuW9KvsU8+SqsZDeLSsP18iz874/M32b9ZDu6fP5cBxZ3H2PjYSD28GUKVOwevVqlJSU4Pzzz7d7OgSRELBChXnCjpXC02xRqmjkGLNg5UZnZ3VRfWZJ0McSsnUE4Xyspr2d+tZJonRKtLUH0TmtE5Y/VAxAPTQ7xe2Ct3Mnx67XjCJEEP/whz/E119/jWAwiLPOOgs/+clPUFJSgnPPPVf1/DPOOAM5OTn43//+J2J4QhBa4atB6Icsi85HFX0/Ueh5Xq16GrRylY08O8/7Y+UYi8zdlhcrW1axCwuXb8Gyil1hYebxYkSljb2SkhLk5OSgX79+6Ny5c8R5LpcLTz/9dKynRxjAKdV8rZAIzyAavbZOLPQKeJnNMZZQXi9H/t055TslW0cQ0cfMWksZ3twpxYU2WX/gTikutLS2YeIdq0JOBpZ3t9UfYK775c4TaU7PldfgWFN7QghgJUIE8ZEjR1BSUoKSkhKMHDmS65obbrgBZ5xxhojhCUHw9OLVCtsVnY8ajfxWEeh5XjMZQjTTgJgV8ex691DLMV6wfEuEd1pE7rad+eAiefbZZ0P/v2fPHuzZs0f1PJcr9iGOBD9WxY0TSIRniDVaYjOaOcZaRbaKC3PCrjcr6EVDto4goouZdZFaeHOK2xVWdLWp5XR4M0+qHis6xuV2RUSHZqA+Yes/CRHEr7/+Ovr27YuuXbsyz2loaMDnn3+OwsJCAMCECRNEDE0IRBm+yvJnsgSh6HzUaOS3ikDP8ypCzCqfvZs3BTNLhnCX3pfe1ZypQ5jvz0iLJ578ZbXxM779zIn54GaorKy0ewqEAEQWULKLRHgGCVaocHZWF2Fj8GwgRCPHuGzlNs3Qb6d+X2TrCCK6mFkXqV3THjgd3jxj3gYuESyH1TZUsm1yoZ4RcXXiIEQQX3XVVZgzZw5uueUW5jnLli3Dyy+/jC1b9HuUEvYhD19lVUvWCtsVHf7qxHBaPcE7uiAbu/YcDvM0jBl2NnelbjUB29HSQ1uMqu00zpk6hFnxz2guttb5rPHHD+uGggJn54MboV+/foavefHFF7Fs2TJaYDoI0QWU7CAWzxCr8N1n7xob4UWVqkyLgrWBsO6jvVj30V7d5zNTG0IrTFrCqf/myNYRRHTRqtszY94GVUeG3lrKzJpK3jb0UGMzXCq2ThLqtxT1Nnz/eMGUIP7Xv/6F3bt3h34OBoPYtm0bli1bpnq+3+/HunXrkJKSYm6WhC04NWTZbvQ811W+OlRW7w8ZlEAgiMrq/cjP7a3rYWWFz+jtypnZaeRpeaQ83+j4lTXH8PNS5+aDx4Ljx4/jwIEDdk+DkGGl8J1TiPYzxCIkWym4s7O6YH/DSQSDwFeHmlC2cpuwsfSEp97zsXKM+/XxhqpGu1d8FZYLzNsSKlEgW0cQ/GitwaTjyjBq1jUutwtVvjp09XoMeYg7pbgwPC+88ClPTnEiYkoQd+vWDY8++iiCwSCCwSBcLhc++OADvP/++5rXXXvttaYmKWfNmjUoKytDW1sbrrvuOvz0pz+1fE9CHaeGLDsBLc+12fBgreukXTmWB9mMB5YnZ1xCbyOENc7RpnbmWLS5QtiFXgGleCBazyAXqWqICvFVE9xyD7FoAc7aQFDCej61HON+fbzMObM80kri6d8cQRDi4F2DydePrGsCgSCeenWr4d7BKW4XKqv3hzliWCS6A8OUIB40aBDKysrwzTffIBgM4p577sHYsWMxZsyYiHNdLhc6deqEvn37Yvjw4ZYme/DgQSxatAhvvvkmUlNTcfXVV2PkyJH4zne+Y+m+BBsnhixHC95qf3rnaYXBaKEnarU8yGY8sHr9giUyOTZCWON396ZEjEWbK4Td6BVQigei8QyxDPHlFYyiBDhrA0GJ1vMpc4wn3bla9Ty9vsjKexIEkXyorYv0vLPSNYtWbI2wMfJK07y0ylqIanHagVFveIx4wXQO8WWXXRb6/82bNzMFsUg+/PBDjBo1Cj169AAAXHHFFVi/fj3mzJkT1XGJxIe32h/PeVpGrcpXxxTZLrcLQZVFVJ+e6di+5yTKN0UaQGnn0KwHVr7hYaXVEmv8MUO6qY5FEHbjlGq+VhD9DDwiVVSIL69gFCXAlRsILIw8n1YeN49HOpHCpQmCMI5yXcRTu2d0QTYWLo9+PabMnukR68G/rNyDZyrU85vjHSFFtebPny/iNrrU19cjMzMz9HNWVha2b98ek7GJxIY3zJnnvOlF+RGti+TXK42HJLLVFk9pnhQMz8vCmk/Yi7hDjc1CPLBWBCtr/IwE3k0kiESDR3xeOfIcIcW2eEOYRYpG+QYCyxtuJIRZK4+bxyPdr4+XeyyCIBIfVkh0S2tbmEPFaA0YNVLcLng7d1LNOc7smR5RkLXKV4c1nxyBvz2y+nQiiGJTgnjOnDkoLi5GcXFx6GcerDZwDwQCYT3vpPxlXnbu3Gl67Fjg8/nsnoIp4nHeyjlrFTaQn8tznlYBLOX9AOC58q9Vc0hcLmD8sG6orNkfMkBqdPOmwOfzIQNQVACsh88XO0GqNj4Qn/8+CCIZ4RGpSpFnNteXN4RZEqiiK16LCDnXyuOeXToUO3Yf0my59NWhJuMTJwgiYZGE5dLyHWFC9XiTH4tfr8GuPYexubZedS3aKcWFYBDcecS3XX0hAOhGF0rRg2pjxmP7TBamBPG7776LvLy8sJ95sNrA/YwzzkB1dXXo54aGBmRlZXFfP3DgQKSlpVmaQ7ToaKsT22bXVkJkJeyYt1XU5pxZcVj1lz2zZ3rYuaLPq/LV4WjTfvWJBoGfl16Ktz5axXyWNE9Hf2Ktlkx24vR/H62trY7fKCOIWMESeKw+wXKM5vqyilRJVablAjVaFa+thpzriepn7xqrmZft1JZLBEHYx+iCbCyr2BXhuW31tzNtiVTrBTgdqdfV68Epf3tEnnCaJwVzpg6JiH5kdU3RK/yVKNWnTQniyspKdOvWLeznWPD9738fTz/9NL755hukp6djw4YNeOihh2IydqLBmzObLPDm4E4vyseTK8Ir+aW4Xarn8ey6Se9cDSlnhBUa43a7MGfqEABg9qyLJiI2VAiCcA4sgceTW2xG3KkJUrVNNNb4ogpuWUF6Btbm3+zSocy8ZcohJghCDSMiUxnerJaWp7VWM9o1RUmiVJ82JYiVDdt5G7gfPnzYzHAh+vbti1//+teYPn06/H4/SktLMXjwYEv3TFbMtgZKVIzk4CoDHdQCH3jup2Vo5OJ5elE+/vjq1rCwaWmHD4DqxoYUVhMtsUobKgSRmKiJ1Gj00zUSAq1VvCoeSIQ2XwRBxA4jOcJ64tlKfRi9eydS+0whRbVmzpyJ+fPnhxW8UrJq1So88sgj2LRpk6WxJkyYgAkTJli6B6Hf4icZ4TEayyp2RZS2b2sPqm4k6N1P613Lw1lGF2Rjz549eL+2JULgzpi3QXVjQ774ioZYpQ0VgkgeeHKLjYg7rRDoEbn848eLhzUR2nwRBBE7eHsUA9H10Hb1elSLbgF8LTnjCSGC+IMPPsCECRNw3333oaioKOyzhoYG/P73v0dVVZVj83eTETN9awmxGwms7yCzZ3qEgRmc2wU/L73U9LiixSptqPDTr18/DBs2zO5pEIRptApgmRF3WiHQI3IjI86c6mGVe7ldLqBoTwrzPSRCmy89yNYRhBhGF2Rj157DYZtog87rhdq9Rwy314wGGV5PRBXqeEeIIH7sscfw8MMP4/bbb0dlZSXuu+8+ZGRkoLy8HPPnz8fRo0dRWFiIBx54QMRwhADM9q1NdkRuJIj4DkSG1ajByj1J9g2V/fv3o7y8HJ999hmam5vRo0cPfPe730VxcTGys8M3HSZPnozJkyfbNFOCsI5oD6fREOhoeljNVq9WermDQQgp9OU0yNYRhFh46q9U+epQWb0/ZBMDgSBq9x7BmGFnRzUdTskJhneYdTyeESKIJ06ciMLCQvzud7/D22+/jc2bN6N///746KOP0LNnT/zhD3/ApEmTRAxFCEJE39pkRORGAs93IC93n1lxOOLzaIbVaOUJJ/OGyiuvvIKHH34YbW1tEZ8tXrwY9957L66++mobZkYQ0YPl4RTZDkkZAi261ZISK9WrnVzoSxRk6wjCOnIB3NXrQXNrWyj1jpXSxkpL21xbr+mZZYlts0VQk8n5IUQQA0BmZiaWLFmC++67D6+++ioOHjyIHj164LXXXsPZZ58tahhCIFYS7ZMV0RsJWt+BliCVz6Gr14NUjxsnmvzo0zMdw/OyUFm937JYXVq+g5knLBnkZNtQ+fDDD/Hggw+iT58++MUvfoGCggJkZWXh2LFj2Lx5M5555hk8+OCD6N+/P4YPH273dAkiqrAEpXRMTcSWrdzGvF9HCHS75r0Bax5YuchmwSNq473Qlx5k6wjCOtv3nMTa6tPrOLV8XLWUNiNpaaw+wfIiq/I1oZG6MlrOj0TrNCJMEDc0NOCRRx7B+vXrkZ6ejgsuuADV1dWYNm0a7rnnnojcYoKIV2K1kcDaIVxavgOn/IEwA5vmScHt11wUmld+bm/ThqrKVxfRFF6OZJCTcUPlueeeQ0ZGBl555ZWwjb5evXrh3HPPxahRozBlyhQ8//zztEgkYkK0vaha6LVjkgvk4sKcUAsiFlL7Iq17W/HAavUEVs5bD1GFvuz8/rQgW0ckI6JFXmXNMa4IPqXQ5fXM6tm0Vn+76gYgb10ZpROomzcFM0vYHU7k18QbQgTx3/72Nzz55JM4ceIERo0ahXnz5uHss8/Gm2++iUcffRS333473nrrLdx///0466yzRAxJEAkPa4eQZ4fRrFjlacKeiKEyvGzfvh0/+tGPmFEv2dnZGDNmDP7xj3/EeGZEMhItLyovRryh0ry0rrn5D+9if8NJBJfvFzKmUmzyXssjakUU+rL7+9OCbB2RbESjneTRJn0xDESuq1ie2eF5WZgxb0MoOpDluJDDsntadWVYGwMd/dbZHU7iudOIW8RN5s2bh2AwiPvvvx9//etfQwb0xz/+Md5++21cdtlleO+99zB+/HgRwxFEUmBUeIqo8MzThD0Z8oRZ+P1+eL1ezXO8Xi9aWlpiNCMimdHyosYCo97Q9Zv2aV5TV38SQR3NyjumJDblRWl44RG1s0uHorgwJzQflwshLzgvdn9/WpCtI5INrXaSZnFxmCu1lLbRBdmYM3UIMnumw4WO7iNjhp2Nyur9aGhsRhDqzhEjsNaY0saANI60MVDlqwudk4idRoR4iH/wgx/goYceUvX+ZmVlYcmSJaGK0wThZMyEy0Qrj4K1Q5jqcasaQhGeWz1jluH1xO3unwjOO+88vP/++2hpaUHnzp0jPm9ubsZ7772H3FyVZqoEIRi781i12jGpIWJevB5YK6KSV9TKC411eE46/p8VBs3rsXZCHjLZOiKRUVu3RUPk8WzwjRl2Nte66v2aA1zh12p0SnGFCnkB2nVltDYGbinqDSAxi20J8RA///zzuqHQJSUlWLt2rYjhCCIq8OyKWb2myleHGfM2YOIdqzBj3gbNe8t3CIGOHcI5U4dgVskgpHlSws4VVeFZy5ileVIwq2SQ5THimalTp2Lfvn249dZb8dVXX4V99vnnn+Pmm2/G/v37UVpaatMMiWSC5S016rk1i9JLGk3cbpchD2w0RWXZym2YdOdqTLhjFSbduTqsUJiaZ3rdR3tx8x/e5fZYx+r704JsHZGosNZtXb0e1fOtiLzu3hTNzwOBICqr90esBdXmaMUjHAx2ODQkb/OcqUOYIpxnY2B6UX7U1qF2IayoFgA0NjaioqICtbW1OHr0KJ566in4fD4EAgEMHz4cffr0ETkcQQhFa1eMZTiMXGMmP0XKBe7wPhREjB0LrzTQYUhnlQxKau8wAEybNg2bNm3C3//+d4wdOxZ9+/ZFRkYG6uvrcezYMQSDQYwbNw4//elP7Z4qkQSIyGO1irIdE2/hKl7cbhdWPT7R1HVmRXHZym1M4c3K+61v8KKggO2Zrqs/yT1+LL8/FmTriESFtW5L9biR5kkRWlF5zJBuWLXpCNo1bJHampEnfc0I7YEgOqd1wvKHinXP5fH+JmLrVmGCeM2aNfj973+PlpYWBINBuL4NnK+qqsJzzz2HadOm4fe//72o4QhCOGbCZYxcY0Zws4hWhWcRRk7+h6ObNwUzURfXRlKOy+XCk08+iVWrVuGtt95CbW0tDh06hC5dumDEiBGYPHkySkpK7J4mkSRIos1JVYolgczT3kjC7XahXx+vqmg0Kw6NhnPL0SpsxRK8vt1NAMx5piXx7oTvT4JsHZGosNZtJ5r8uP2aiyLWP4D5isqDc7vgnZoTut5d5Zx4w7RT3C50SnGh1R/QPZf3nlqtloD60LFE6zQiRBBv2rQJd911F7KzszFz5kzU1NTgjTfeAACMGTMG7733Hl555RUMHjyYDChhG/JebZkVhyOEnpmcCCPXsIxRQ2NzqGpgtHbZjOxuWjFySi/40ab2uC/Fr8TlcqGkpIRsGeEIlB5apyCf16Q7VzNbFMm9v2Urt6Hi470IBtV7GBsdH4CpKtPSdWpjs+4h5QoaHcesBzwWkK0jEhGtdZva+sdqReUTHKHOyjUja44ZXg86p3WKWMvJ13guhg3iDf3Wcoz4fPU6V8cvQgRxWVkZevXqhddeew3du3fHwYMHQ58NHToUL7/8MiZMmIDly5cnlWFNtKbV8QxPuLL2rpg6Rq5hGThpPqx5WSUarQRYiPSCEwQRG6LdC5c3tHt26VCMyG2PSA8xi5VwbpaoZQleqZqsUc+0E8KjCSLWqK2PM2I0ttG1Hmvdxutx1Vr7scZmzZGVuiYX8mrtM3nye+XfCSufOpERIoh37NiByZMno3v37qqfd+3aFWPHjkV5ebmI4eKCWIoQQh8eoWYmXNjINaz8XCWiBWQsRWoiluKXk5eXF0oHYeF2u9G5c2eceeaZGDVqFG6++Wb06tUrRjMkCGPEoheukdDutz/5Bg+uWG17CDGrsBVL8Bb072hRJM1VTxQ7KTxaDbJ1RLRgrY/HD+sGQXthmhhZt2kVPuX1uGqt/TIZY1tJXzNzbZWvDk+9ujVUiVoe4i3XL7HatLADIYI4ENCPXT916hTa2tpEDBcXkKfMWfAKNTPhwrzXyI2U1m4hwN6RNEMsRWoiluKXM2XKFHz88cf46quv0L17dwwYMABZWVk4ceIEdu7ciUOHDqFr167IycnBoUOH8Le//Q0bN27EypUraaFIOBKtXrgixZpaaLfSM92RR9wU+pxXnPN6uI20YmJ5bnkE7+zSocz8aSeHSMshW0dEC9b6uLLmGH4eo6LlvOs2rR7EvBWVjQpwXiGrda7SY7ysYhcWLt/CvOfS8h1hbZmUKNsuJSJC2i4NGDAAVVVVOHXqlOrnJ06cwD//+U/k5eWJGC4uSHRPWbzBEmQut0tzB1A0owuy8cLccaFWSlqImhfr2aMhUhOxFL+csWPH4sCBA7jhhhvwj3/8A8uWLcMTTzyBJUuW4L333sOtt96KkydP4rbbbsPGjRuxcOFCHDx4EGVlZXZPnSBUsasXrlp7IlYlZi0hy2pzJG+FJJ3H+0xG2jtJVH/ehJv/8G6oHZOTewzzQLaOiBasdfDRJnFVlUWhtWY34jyR1n63X3MRAGDh8i0RrTeNtPEsW7kNC5Zv0T2X9548LZ0SXb8IEcTXXXcd9u/fj1mzZuHTTz8NCeNAIIAdO3Zg1qxZOHjwIK655hoRw8UFsRQhhD5qQg3oWJwsWL4lYvEUbXgMi9bOpBFiKVLlvZNd6OjBp9XvLt54+umnMXLkSNx5553wer1hn7ndbtx8880oKCjAokWLAADFxcUYPXo0/vGPf9gxXYLQxa5exka8tVoiUsvDLWEkd9jtdumKYa3WSnqC1wk9hnkgW0dEC9Y6WK9nrx2w5srj1FCiJ061IkuV91GzZ2rn8t6Th0TXL0IEcVFREWbPno1NmzahtLQUf/7znwEAgwcPxv/93/9hy5YtuPbaazFhwgQRw8UFie4pizckocZajKz7aG9MPMVVvjrMmLcBPD4CUbtxSpGq15RdxHgvzB2H1Qsm4dclZyaMGAaA3bt344ILLtA853vf+x4+++yz0M/9+/dHQ0OD4bHWrFmD4uJijBs3Di+//LLh6wmCB1ZocLSLPRmtxGz0PvLjIkKlecYUdX8nQLaOiBas9fGYId2iMp607pp4x6oIr6weItfyeuKUN7JUS8zytm9SHs/QKaKVDPpFWB/iX/3qV7j88suxcuVK/Pvf/8bx48fh9XoxYMAATJ48GSNHjhQ1VFyQiE2r453RBdlYuHwL8/No53erVf7TwspunFpuyQtzx5m+H9FB7969sXXrVs1ztm/fHlZg8MiRI+jWzdgf+oMHD2LRokV48803kZqaiquvvrYmL/IAACAASURBVBojR47Ed77zHVPzJggWdvUyNtKeSBKRN//h3bCw6i5pbI+SXETz9kLmfW6jrZWM3t8JkK0jogVrfZwB8S19rBa41VvLG8n51ROnvDVYtJwl8nOrfHVwuV0IcrRgmlUyCE+u2Ip2lXPlhb+o7RIngwcPxuDBg0XeMq5JtKbViYBW+fto50eo7Q6ysLIbRxXOo0dRURFeeOEFPPzww/jVr36Frl27hj7z+/1YvHgxtmzZEkoPqaurw4YNGzB0qLFF8IcffohRo0ahR48eAIArrrgC69evx5w5c8Q9DEF8C6uXMU+xKrMtm1jVmvtkuPHNyWDE/ZRiGABOtrLtqdwTqyVg1yyYpDtX3rmrES9FtJSQrSOiidr6OBpiS0SBW9ZaXlmZuaGxGU+9ujV0jRLW+jOIjl7Hw/OyUFm9P2K+w/OyuO4DnC70Ja0D1eye2vqSnHiCBTFBOJ3pRflYwPASRzs/giW4XQBuv+YiIZUFAapwHk1uueUWbN68GS+99BLefPNN5Obmok+fPjh58iRqa2tx/PhxfO9738Ntt92G5uZmXHHFFQCAGTNmGBqnvr4emZmZoZ+zsrKwfft2oc9CEFrwtGOy0rKJ5ZmW+hBLQnvdR3uZFZt57g9oC9iyldsMe22Vc9dCK0Q62v2frUC2jog31NZG0Sxwq1aZua09iKXlOzC6IBtVvjo8V/41ji1fhT4905mCF+gQ05XV+5HVs3PExl9l9X7k5/YOrd9YbZyKC3NC5ywt36E6jtvtYqbMJbsTz5QgHjFihKnBXC4XNm3aZOpaghDB6IJs/POTWlR/3hR2PBb5EVrhMLyGiMf7SxXOo4fX68XLL7+Ml156CW+99RZ27twZ+iwnJwc33ngjrr/+eqSmpuKrr77CpZdeip/85CeGbWYgEAjrARoMBnV7gsqRz8uJ+Hw+u6dgCqfM++1PvoFvdxOCQcDl6uiBe9UI9VY3Zudc8fF+xvG9GJHbYX/WfaR+zrqPTp+jxYhcYERuP9mRjmseKHsnzEYbFcMuV/hzj8gF1n2kfq78eYwgzf3tT76J+HsiMew7XozIbVf9DpTXSZsJ9Q0NzO8yliSqrXPK77Ao4uF5tu85icqaYzja1I7u3o5c4cG5XVTPNfs82/ecxJpPjsAv89b+8dWt6OxxodkfaT+6eVMsvztWZebjTX78ZeV7EfN555N9GJKbjv8eaFWtqN3qb1ettN/qb8dz5TWhkPIMAOOHdVN5px22Zvuek8y5BQJBZKDekjc+Hv7NmcGUIJaHzhDJgZE8Cadz1YheuGxEXsyfR21Xz6gQ5/H+JnovYLvxeDyYMWMGZsyYgVOnTqGxsRFdunSJsIv9+vXDkiVLTI1xxhlnoLq6OvRzQ0MDsrKyNK4IZ+DAgUhLSzM1drTx+XwoKCiwexqGccq8y1ZuCxNSwWBHy5+szMwI76LenLU8lMHl6mI3GMTpezLOAWD6XSnFsBnOzuwSOT7P85jgwRWrVY+73S7cN/tHhq/b8kUz7pst/t9Za2urYfGYaLbOKb/DooiH56ny1WFt9el1z9GmdqytPobc3FyVkGnzz/NMxYaQ+JTwtwfROc2DNAQi1l0zS4agwMKar6MwF9v+vV/bojqfvYeD+NtDV2HiHau4iqtKHG1qx4LygzjR5A95m1PTWuBqakZqWipyc3NDz/NMxQbmfTJ7plv6NxMP/+bM2DrApCDeuHGjmcuIOEXLK5lh58QsYEdoiIgcDR7vrwjhTfCRmpqKvn37Cr/v97//fTz99NP45ptvkJ6ejg0bNuChhx4SPg4Rf2i1GVIK4rc/+QYPrlitKnj1wp1ZebfRbBukFPtm+epQ5D2MPg9vOLNWletJd6q/e73rnAjZOsIMsUrhYq2NTjT5I1LShudlYVnFLixcvsW0Q0Sr0nOG12O6gJYWkte3obE5zHYrIwW1ogHV1oGJ5PCyQlRyiFtbW3H8+HH06NEDnTpRmnK8o2XQbinqbdOsYo8Ro8E616oQ5/H+8ghvMoB8TJ482dR1LpcLb775pulx+/bti1//+teYPn06/H4/SktLqWAhAYBfSCnFpVLw6glrVt6t1bZBWkLTSHskLdTekZHnsZIbrTYPtevt2HDQgmwdEQ1ilcLFm5Km5uB56tWtWFq+I+R95VkPac1/VskgLKvYpblWY+UBm0W+ycB6FxleT8RzURHW0whTqy0tLXjuueewZs0a7Nt3+o9afn4+SktLcfXVV8PtFtL2mIgxlJNqzGhE08AMz8tSXdS1tLahylcXur+W8CYDyM+uXcab1wMwlAPHYsKECUnVu90J2FnkiHdsXiGlJ3j1hDVPO6biwhxVe5SdpZ4fqCc09byj2Vld8NWhptB8WOeriUoj7aV4vfBlK7dpzlfr+mhtOJiFbB0RDXg28aUN+obGZmRWHDa1Qc8bGafm4GlrD4Z5X7UqRes9l1x0suYjPW+rvz1kx8y0clMirclZ72JWyaCIa6gI62mECOKjR4/i2muvxeeff4709HTk5+cjMzMTx44dQ21tLR566CG88847WLp0KTwe7ebPhPOgnFRjRkOEgVF6cC/J64zjqENltXrOyvEmP7eoJQPIT21trd1TIGKEKK9gtMfmFVJ6gldrASYJPT3xOLt0KHbsPhRRCKau/qRq9WY9oak1p+ysLmHjaC0eWaKS1V5KCY8XXvmd8SC/3q7+zyzI1hHRQE+oWtmgV66Txgw7G5tr6zUj33gcOfJK0Uafa1bJoDDB63J11CmQevkC4UJZHkViFWlNbiQ9jxxepxEiiP/4xz/iv//9L6699lrcdtttYQUXWlpa8MQTT+Bvf/sbli5diltuuUXEkEQM0TZokZXqEjEcl2Uc1DYKrBoYtT8Qaz5pQeca9TL6EryilgwgQURiJDfXzrF5hZSeJ1mrFZHyuJZAV8vXZc1dT2iy5lRcmKMZTi33sogQlTxeeDPh3UrPNa9AJ4h4RU+cmd2gV1snVVbvZ7YUkuDN3WVVadZ7rl17DofZsGDw9Hp5dEE2ZszbICxMWo7SG86bnkcOr9MIEcTvvvsuhg8fjrlz50Z81rlzZ8ydOxc7d+5EeXk5CeI4RMugKUu3J2o4rpYRlYcqa53La2DU/kD424Pw6xhogE/UkgG0xqlTp1BdXY3Dhw+jvf309xQMBtHW1obGxka8//77eOmll2ycJWEUO4scGR2bR0jpeZJnlw417OE0I3JFoXW/VY9PDP1/2cptmsWseODxwmvNR+nNVrs+HiBbR4hAS5yZ3aA3K6RF5u4qn6vKV6dqN+TzEuV4GPKd3jhwuMmy44mKsJ5GWMj0kCFDNM8ZOnQoVqxYIWI4wgZ4d5sSNRx3elE+FizfovqZ8tmsGhgrBpNH1E4vyseTK7aiXbagS3G7ktIAGqWurg7XXXcdvv76a83zqF5C/GFnkaNojD27dCjqGxqw5Ytmpjg0mrfGmqMo8cvyumoJd/k7EhX2zuOF1/rOnr1rrK356CIgW0fEArMb9GaFtNLBw7JcaZ7T/655ox61Kk9Lz2imurSSDK8H82ZfbOkeEiK6nyQKQgTxoEGD8OGHH2o2VN+2bRvy82nBnejEWzgur6EbXZDNFMTKZ7NqYLSKNZzyB5g7m0ZEt/LXNAhgafkOS20IkoGnnnoKBw4cwGWXXYbhw4fjhRdeQH5+PkaNGoXdu3fj7bffRo8ePbBq1Sq7p0oYxM4iR9Ea+6oRvTR72mqFTauhJtC17qHMI9YT/maEtfSOtHJ6zYS963nheTzw8SSAlZCtI2KBWQeClUg3uYPnmt+tUw2PTvWkADAW9ai3zq3y1XF5qFPcLrhcHbnMSljFsaxgRxtSJyJka2/u3Ln46quv8Mtf/jKswjQAnDhxAg8++CA+//xzPPDAAyKGIxwMyxg5MRxXMnQN3+4SSoauo+F6JJkGnm10QTZemDsOqxdMwgtzxxkyNtOL8pH2rTGW8KS4MKtkEOZMHYLMnulwoUMgZ3g9cH07N73cGYllFbsiDG0g0FFlkec9JDMff/wxLrjgAvzpT3/CzJkzUVhYCL/fjxtvvBGPPvoo/vznP+Obb77BG2+8YfdUCYPMLh2K4sKckDhzu10oLsyJiaixa2zWuMWFOarnqwl0rTkqPb4sgS8dN+oRl96RXoGraIS9K9+dy4WY/XuJBWTriFgwuiA7tK4B+NcyauskoGP9MmPeBu71ywlGKpp0XCvqUYneOleKJpQ/rxpXjDwHv/rJhaFzJBtjZJ1HGEeIh/jBBx9Et27dUFlZiY0bNyIzMxN9+/ZFS0sLvvzyS/j9fnTq1AnXXntt2HUulwubNm0SMQXCIcRTPoLR8G4rz2ak0Jiah/mSvM5hLZWswOOtT4Qw92hw5MiRsPYgeXl5qKqqCv1cWFiIiy++GOvXr8esWbNsmCFhBatePSthsnZ5FLXGlZ5F/rN0DQ9KIaoXimzUY83bvzhaYe/yd+fz+VBQkBhiGCBbR7ARXThV8lB2/A6xI1qU1wBQ7fdrpHaNnqfZSNSjnvdXukaa06IVW1U36zbX1mN26VBaf8UYIYJYyjE588wzQ8cOHToEAOjTp4+IIQgbsSrmnBp+azS82+yzmSk0pgxh8fl8qvc18555c1icGuZuJ16vF8Hg6T9g2dnZaGpqQl1dHbKzO979gAEDUFNTY9cUCYuYFbV2tm2KBtKceZ7JSA60mgCXv3Pl9TzeXb1z5J7peM/tjRVk6wg17C6cqrbuURPFrf52LFi+BcsqdmmujfQcHUZCs/WErnSN9A5ZdstqjjFhDiGCeOPGjSJuQzgQEWLOqZjJQTHzbNEoNGbljxJvlUUnhrnbzYABA7Bp06ZQvYT+/fsjGAxi586doUViQ0MDAoGAzTMlzKAlakfkal9rZ9smo/CKQt5nspIDzQp3lsKPJ9yhn6OqJZzlYcwiNi2SRVCTrSPUiGXhVKX4HZ6Xhcrq/RHrHq21jN7aSM/RYTQyULpOq3Cp2jtUe3ar7zMRW6BGEyGCmEhcErVqNGDM0FkxLNEoNGble1H+AUhLTUHLqUjjPDwvy/T8EpUpU6bg7rvvxnXXXYd77rkHAwYMQE5ODh5//HF06dIFhw4dwrp16zBw4EC7p0qYQEsAjsjtp3mtnW2bjGBEFPI+E29vZDW0Kkvr9R6W0OpfLJ+DVt9lq1EAehsm8QbZOkKNWBVOVdv0Z7U00osk0VsbaTk6zEYGKguXyn/meVfK+Rpdg9rtyY9HhAni1tZWfPLJJ/jqq69w6tQp5nnTp08XNSQRA+KtarQReA2dVcMSjb6/Vr8X+R+AGfM2oOVU5HWba+sx2/QME5OSkhJ89tlnePHFF7F7927k5eXhjjvuwG233YabbroJAJCSkoJf/vKXNs+UMIMVUWtn2yYjiPBk84ZC86D1brU+k3uf1cK7o4GVDZN4g2wdoQZrPeNyuzDxjlWa6ygjgo7HiyoRCASR5knR9RSb9boajQxUK1za1h4MiVyetDX5Ws7MGjSRnVnRQoggrq2txS9+8QscPHgQAMLyTuS4XC4SxHFGNMSck+AxdFYNSzQKjYn8XhJ50yMa3HXXXbj++uuRlpYGABg3bhxWrFiBtWvXIi0tDcXFxcjLy7N5loQZrIhaO9s2GYFX9Jet3Ma8h8hnMtPHmLeas+gc7niJAhAF2TpCCSvlSvodUBNrZgSdkfVHpkYusZxYeUhZc5CO86StyddyZtagtK4zjhBB/Mgjj+B///sfJk+ejCFDhoSMJxH/xFPV6GghwhsLiC00JvJ7SfRNj2jQt2/fsJ8HDx6MwYMH2zQbQhTaolbbW2ElbDiW8Ip+rXBlkc9kpheylXxnK8RLFIBIyNYRcpTrGZfK74RSrJkRdLzFP6V1j+TcUIpvI2Pqwevl1rMTWhWy5c8kYWYNSus64wgRxJ9++imKioowf/58EbfT5cknn6RQnRgRT1Wjo4UIwyK60JjI74U2PdgcOHDA9LVnnXWWwJkQsUBL1KpVele73mkCWAmvJztWXk+j4c5GvbSBQBCT7lyt+TzFhTlcxbKsbJg4HbJ1BC/y9cxERtE7uVgzI+hY65Ixw87G5tp6NDQ2w+12hfUEls9rwfIthsfUQs3LvWD5Fuzac9hU7QX5XPWEtpk1KK3rjCNEEHu9XmRmZoq4lSbHjx/H/PnzsXbtWsycOTPq4xEdxEvV6GjhVMMi3xFdVrELCzlaDLDuAyT3pgeLH/7wh3Apq2NwsmvXLsGzIWJBPIhaK/B6smPpDZ1dOlS17ZIarPG1Qq/17rtj9yHU1Z8MO18t3NrqhomTIVtHmIFHrJnt6AGor0vydUKwRxdkM72vZj2krJzmdR/tRX5u77D1UibjeTO8HsyYtyHiefTW2GbWoLSuM44QQTxx4kSsX78ev/71r5GeHj13fGVlJc4991z8/Oc/j9oYyQ6VaY9kdEE2du05HLYIGjPsbEe8F1GVBJN904OF2iKxpqYGhw4dwnnnnYcLL7wQ3bt3R1NTE3bs2IFPP/0UOTk5uOyyy2yaMUHowyP6eT3JotoQ8XqkWfnLRkOv5cjFsBy1cOtE3TAhW0eYgUesmXUqsNYlPCHYoh0ZWp5lZRi22tgpbheaW9twvMkPIHKtprX2NituaV1nDCGC+Fe/+hV2796NiRMn4ic/+Qn69euH1NRU1XPHjBljepySkhIAwNNPP236HgSbRCjTHg1BX+WrQ2X1/tCCLRAIorJ6f8SuoB3zpUqC0eXZZ58N+/mdd97Bxo0b8fvf/x7XXHNNxPlr167Fb37zGyo0Q0Qdo0LU6Pk8nmS9Fk5GxuQprqVVTEttvlbDvhO1WJYaZOsIM+iJNWmNI2+PJBXBAhDmMb0krzMKCrTHq/LVMXOL5aJVtIdUK6dZKZaVOcKZPdPRIhPDEvJwb721N4nb6CNEEB88eBD79u1DXV0dFixYoHqO1NydJ7SmoqIiIh/5vPPOw1//+ldL89y5c6el66ON3SFXz5V/rSquniuvQQbqmdfZPW+J7XtOYs0nR+BvP13t8I+vbsWePXswOLdL2Lk8c96+5yQqa47haJN6cQa99yJyvqw5a1UzdMr34pR5iODpp5/GpZdeqrpABIDx48ejoqICf/rTn/DjH/84xrMjkgUjvYTNnC+h5w3VKmQFwNCYvL2EtVDOVy93WI9ELpalB9k6gheWWFM6WaT2SJIYVorANZ+0IDeX3RpJuh8LZTi0Wp7uwuVbTInj6UX5zLxktTBsaWyfz4eCggLNXGtybDgDIYL4/vvvxxdffIELL7wQF154Ibxer6X7FRUVoaioSMTUwhg4cKBjK2BLvzR2cmy5+i/ssaZ25tycMG+JZyo2hMSlhL89iPdrW/Dz0ktDx3jmXOWrw9pq7bL4Wu/FynzfqTmB92tbwnY1M1CvOlZmxWFVUZzZM90R34uT/n2o0draamijbO/evSgsLNQ858wzz8QHH3xgdWoEwcRoReVoVWDWytllhS+v+2iv6pjSsYqP9yIYhJAq3VbCqKXrjSAqfNwJkK0jrKIl9KT/l+OX9erlvZ+EVji0iOhHKXVOaU94w7C18qhZ4dhWeicTxhEiiLdu3YqLL74Yzz33nIjbETYR72XaRfZd42kKb/W9sOZ1vMkfkWcyflg31VAipxb8SlTOPPNMfPjhh/D7/fB4PBGfnzx5ElVVVcjJybFhdkSyoFc86uY/vHs6L3b5fsP34UWr8JaZe88uHYoRudY2GvXIzuqimjOcndUFXx1qMi1m3/7kG1R/3hT6mdcL71TI1sUfTqsBY2ZNZvazOVOHGBLScg8s73ubXToU+bm9Tb1jrbWaVv9kNeHutO85UXCLuElaWhoGDBgg4laEjUwvykeaJyXsWDyJK5ZANSNc9US0iPfCO69Wfzsqa46pfja6IBtzpg5BZs90uNDhGdb6w0BYo7S0FP/9739xyy234N///jcCgQAAoL29HT6fDzfeeCP279+P6dOn2zxTIpHRqrQcJoZN3ocXrQJXdqMME5cY1L8PigtzQs/udrtQXJiDZ+8ai1WPT8SaBZOw6vGJhkWsb3eT6nGtfs5OhmxdfCF5QRsamxHE6c30Kl+dbXPSWpOZWa+xPsvsma655tES5kbf2+iCbLwwdxxWL5iEF+aOM+RhZq3V1NbeEnKPOuDM7zlREOIhHjNmDN577z3cdtttqjuJoqH+w9Eh3su0D8/LMh3OokSrgEKmoPeitmPIQspjZu0Mxst3FO/MmDEDn332GdasWYP3338fKSkpSEtLQ0tLS2jBOGPGDEyZMsXmmRKJjFYFaCMhwlaFq1bhLSuhyiLQChM3I3j1CDIc4vFamItsXXzhxDxUvQg25WeeFJfmes1sRJxW9GMs3xtrrWakd7ITv+dEQYgg/s1vfoPp06fjZz/7Ga6++mrk5OQw2y9RRUJnE6/iSqoErcRseySW4TXifWWJV/nxrl4PUj1unGjyow+jEiEAdPemJEQV8HjH7Xbj8ccfx5QpU7B27Vp89tlnOHr0KLp3744LLrgAkydPxuDBg+2eJqEgkXI7AfNCVAplFvkOWIW3igtzmEWyYoHZnsRmcbnURXG8FuYiWxdfiEwZEwWPk0X+2SV5nTXXMmadNlpCeiGHCNVDRAgzb+9kJ37PiYIQQfyDH/wAQEcoTU0NuwIcQA3ciejAyvndXFuP2Srn6xkwq95ylnjdtecwKqv3h44fb/IjzZOC26+5KCSW1Qz3mCHdaGfQQYwaNQqjRo2yexoEB2YrLDsdM/1wVz0+UfccUZsHPG2boolWfrNReN5JQX9vWA6xhBPCx61Ati4+MFMDJha5qFpOFuVnPB0pzDhttNZzPCJUC+XfFyuOCh4PeLzX+nEyQgTxhAkTIhq6E0QsMbJrtn3PybAK0iwDZsVbzhKv0qJKeVwStSzDnYF6vPVRo+pYVIkwetTW1iIzMxO9e/cO/cwLRcM4g2hVWHYqWkWj9BDZV1i6xq53rBVWbgTeDZWrRvRCVmZm3EYikK2Lb4yGEydbxBlrPcd6b8PzssJ6JKttFlT56lRtjFlHBY8jhgqpRg8hgvjRRx8VcRuCMI2RXbPKmmNR97SyBDorXE/ZUF45D5+vXjOvOZH/kNlJSUkJ5syZgzlz5oR+5t38o2gYZxDr0Fm7efausRGFtbKzuuDZu8aGnacmbkX2FbYbUR5qIxsqdm4AWIVsXXxjNKqNIs46UHtvZ/X2cnl95cWulJgNYdZzxMR7rR8nI0QQy/H7/fjiiy/Q0tKCHj164KyzzopJoS0idsjDbLp5UzAT9nsnjeyaSQWqlIjMwWCJV1YYH0+4i1YRrmT8QxYLJk+ejPz80/+GjCwSCWcgMnQ2XpDEL6sPuJbXU41AIGjZ025HHrcIgZosGypk6+IfI1FtlIt6Gvl7q/LVqRa3Ultjab2rrt7o6Z54rfXjdIQJ4mPHjuGxxx7DmjVrcOrUqdBxr9eL4uJi3HnnnejWrZuo4QibUIbZHG1qd4R30siuWXdviqooFpmDwRLoY4adHZZDLB3nCXcxUomQEMP8+fPDfqZomPhDVOhsosBqR6SFVl9hHmFoNY/bzqJoybKhQrYuudCLqnNSr9tYzsWI11craq+ppQ1VvjpkCJ0dEU2ECOITJ05g2rRp2L17N/r27YtBgwYhKysLR48exZYtW/D6669j27ZteO2115jVp4n4wMlhNry7ZmOGdMPa6mNRzcHQEuhmG7tL9zVaBMJJf9gIItbYXdzJSZgRw4C26OURhla8y3YXRaMNFSIR0YqqU8sv/v/s3XtcVNX+P/7XgIAgXj9ej6EH8gAaCjkpah5TwQuaYh07WXkp05Qi/ZZZWj4yO16yq5pFWql5RSMTTVESw6NHSxkV84aZpmimKOQFiOv+/eFvRmaYPdc9s/eeeT3/kj2b2Wtm8D3rvdda7/XB2kM4ee662+Omu9c6WxpYMO1jWZq1V1UtYGXGSbyQ8H+St5FcQ5KEOCUlBb/++ivGjx+PF198Ef7+/obHBEHAwoUL8dlnn+GLL77gHsIqIZZEecI0m06h9RAaGipbdUWx47YmrvZMD/e2whnucPXqVWRkZCA/Px8lJSUQzOy1otFoMHfuXBlaR+a4Ym2nGrdyEktM9Xx8NGjdNMhsUS4xA2PbWH0vLI0uJ07dbPR7XUNta/O2/eexbf95l7/33nxDhbHOc1m6aT92dqbZJG/b/vNoH/p/dvcdTLeaBGDYarJLZHMcPHVVdAmeuwdhLI36mvax7Jm1Z6l/x0ELZZAkIc7MzERMTAymTJlS6zGNRoP/9//+H3788Uds27aNCbEKWEqi1FLy3ZZtlZQUcOxJXO2ZHq7kEX01OnXqFEaOHIni4mKznUM9dhI9m9yjlo6yNr3ZnmRYnxgC1ott2TLlWv97VwuCUHPZs7U2u+O9V3OxLEcx1nk+sX6QpQEOe/oO2bp8LN30M26VVBiO1fx3QVGpUewwtwTP3YMwYqO+g7q3FR3gsDZrz1L/DgAHLRRCkoT48uXLiI+Pt3jO/fffj7Vr10pxOXIxS0mUGkq+q3FU1N7E1daE3hNG9JXkvffew+3btzF69Gj06dMHjRs3lrtJJAO1buVkKTEd1L2t1RFkvS0fJBr+nTh1s9lzar4XYtOOzdH9encv35S0Izb9jun1yHmMdd7L0iiprX0H036YrUz7Pe4ehHGkirPlfvFVi/07/b/NPabU/qqnkiQhbtiwIfLz8y2ec+HCBQQHB0txOXIxS0mUabBoEOSLccOiLf7HFRutddU0ETWOiroqcVXLiL5aHDlyBHFxcXj99dflbgrJSK2Vh8US00Hd2yJpeIxNSavpmmFb3gtz047Ffq/mYKStCbqldpBjGOu81+iE9qLTgG3tO5jrh9mqZr/H2iCM6Sh0/SA/PDeso1N9PXtnEFpKonW6qw717zho4X6SNJQlRAAAIABJREFUJMTdu3dHRkYG/ve//+HBBx+s9fju3bvxww8/YNCgQVJcjlzMWhJVM1jc2dbDcjJsrjiDabCVchTX3uCjhPUbrkpc1TCiryYajQahoaHWT/QyalxP6wxLCV1K2hGnXrsr30tr62EtvS4902JStlZhNp12rF87bI7+PbQnyfW0qs9yY6xTP0f7Nr21ITh57nqtG2T29B2cSehq9nssJZvZunwsSD2Mqhpx4lZJBRauP2z0u5ZI1f+zlERb699x0EIZfKR4kuTkZPj7+2PChAmYPn060tLSsGPHDqxatQqTJ09GUlISAgMD8cILL0hxOXKx0QntEeDna3TM0STKnruENaeQiMnW5WPs7EwMnZKOsbMzka2rPTNBLJCYO65P2AuKSiHgbmJu7nldaXRCe9TxNe7Q1fHVOJ249taGIPmxaDRrHAgNgGaNA5H8mOURfRLXtWtXHDx4UO5mKIp+Pa3pWlB7pruqjaUKw/aMappyx3uZNDwG6e8NxZYPEpH+3lCjJNXS6/Lx0RhGkmsS+x19wSyxttvyHtqT5LLqs7QY69TN2b5N0vAYTHmys8N9B0cTOnN9zd7aEIxOaI+mjQNxragUKzNOGhLZKjM3zSqrBKt9ScB9/T9LfWop+9vkHElGiP/+979jxYoVePXVV/Htt99i06ZNAGAoxNC2bVu88847vNuoEo6soRBj711CS+fbujbYnlFRJU2vNq1bYqGOiV2UVkBMzV555RWMGDECb731FpKSktCiRQuXXm/BggXw9fVVdDFCta6ndYal6cXOTN2V+73UX8P0tZlLhE1/Rz/qXFPNglc1z6lZkMsc/fOITfEOaV4Pl66VeM2MBDkw1qmbFH0bZ/oOYsWpAvx84O/ni9slFQgO8kN5RRXKKqoBAIH+Pnh+eO2kW6zvZ2mwpaCoFNm6fIvtd1f/z5Y+tdyzFEmihBgAoqOjkZGRgUOHDuHUqVO4ffs26tWrh/bt20Or1UKj4XQmNZEqibJUnEHsfDG2Bi97EnqlFJ0yd6dTv48dA6N8unbtWuvYX3/9hfXr12P9+vXw9/dHQEBArXM0Gg1++uknh69769YtzJs3D1u3bsW4ceMcfh53UOt6WnPsma5s61Rheyj1vbRWxVk/HVpsGrRpUmuaKJvSv4dSbHnkbdP5HcVY51nk7ttY64fdTXKrDb9TWWUcO/SjwOb6kGUVVVaXeFhbhufO98hSn5qDFsogWUIMAHl5eWjcuDFGjhxpOLZkyRLUrVsXUVFRUl6KVMLSxuWmrE0TsSd42RpgHFm764o1x3J/eZF55goBuqM4YFZWFv7+97/jmWeecfm1nOWKxFAO9m6lJDZ66czUXSW8l2KJ6rb9560mklIl7jXfQ2e2PFLr9lhyYKxTNnP9jvoWzldCQU1L/TBzAxwVVXcHAWypUl1dLcDXR2N22jRgfbRXCe8RKYckCXFlZSVmzJiB9PR0vPDCC0hOTgYAlJWVYcGCBViwYAHGjBmDadOmSXE5UpGadwktjRQ3syGxdEXwGp3QvlZRBl8f8bW7rtrSiYFZmXbt2iXLdYcNGwYA+Pjjj2W5vj1ckRjKwd7pylKMXpoS2we4ddMgh5/TnWwpymXKdPpz57BAyZJVuaegqwljnXKJ9TsGP9DAaM/umpReUNPaIIAt9Wf0/Uaxithi17E08qyk94jcS5KEeNWqVdi0aRN69uyJuLg4w3F/f3+sXr0an3/+Ob766iv87W9/w+jRo6W4JKlIzbuEzoyuuirAm87mtzS735k1J5Zeu9K/vMhxixcvxqeffooTJ07UeiwjIwPz5s0zOhYWFoYVK1Y4dc1jx4459fv26BoKXC0Igu7XEgjCnf8/2nuD0DW0CjqdzuzviB2Xk6Xpyvr2mra7ayjQNbR1jSPir9kWFwtqJ8P6444+r5TvtbXn6hwWiJwzJRbPMaW/AfBAuyA83LWJTdexlS2fqZSU+HftTmqIdWr8jL7YdNlsvyMr9yY6hZp/PfUBDH6gAbJyb+JGSRUaBvkiLroB6uMqdLqrFq939Fxxrd/rFFrP6XNrahDkixsltRPeBkG+0Ol0Ni21+2dkXdSH5deif76a7d1y4E9UVNWODfa8R/ZQ49+cJZ72evQkSYi/+eYbdOzYEV988YXRcY1GA61Wi86dO+PRRx9FamoqE2Iv58xaCSmLfemtzDhZa91KZZX42l1HpzZbG1l2xWsj5RBEKqQlJCQgISFB8utFRUWZXe/nKmKjFObc2arNjl9wE5/US6LTlbVarVvaLay9aP64AIeu7VCbRdoAWG+DVlt7mrKtcs6UoHmzZugaWiXZ+2ztM5WSUv+u9crKytxyo0zJsU7pn5GYm2vTzR6/UWL5/4pWCzwz3L5rZevysTXnbl/lRkkVtubcRGhoqNliV7aea2ocak+J9vPVYNywaGi1IWiWcd1iUlw/yA/PDO8FABbP1T+f3icZmWaT4WaNA7FsRn+LbXaEWv/mxKjh9Tga6yRJiC9evIinnnpK9HGNRoNu3bph9erVUlyO3EgJe/TWJHXxAXsTXEenNtsysszCCkTyUcLUb1fub2yrQd3bmn0fBnVva9PvJw2PMVtx2hbbf7pgMuLuHCV8pkTOEut3NAzyNXO2cyz1VfSP6/uDf5VVOjxjztwgwD8j61qcNacX4OeL54Z1NPwsdu6g7m1rtYP1WkiMJAlxw4YNcfbsWYvnXLp0CfXrWyoBoBxKSwLl4qr1so62xRWfidgXTXCQn9nzHZ3azCBMpGyuWBNsrsKxpWuIJXCA9UrPjrbH9PmkeB8cLa4ldTVtV3ymRO4m1u+Ii27g9HOb9q3ERlpNtzqyNHpra7/GdBCg5lRc0/oz+puF5urN2DPDjvVaSIwkCXHPnj3x7bffYufOnYiPj6/1+N69e5GVleWS6TJSU1ISKDel7NHrys9kdEJ7LFx/uNa06ZK/Ks3uYefo1GYGYXIE9+R0L2cqGpuyVOHY3LGa1xZLih0pBlUzCbZ07ZqcfR8cKa6l/z2pSfmZkusw1okT63dYWz9rjbm+lRgfH41Nu4UA0vVr7Jk1Z8u52bp8/FVWWes467UQIFFC/Pzzz2Pnzp2YNGkSevTogZiYGAQHB+P27dv4+eefsXfvXgQHB2PSpElSXM6llJIEKoE7RzUtjQC78jPprQ3B0k0/41ZJhdFxS3sAOzK1mUWziNSnZjKp0QAJ53xtTq7EKhyLnat/3qThMaIJsb1J5ncHCq0WuXJFxWWxkW79tGux13dnBN22Trcp7jdMnkqsf+Rs4SdbKjkDd/oqtibDdXw1+KusEkOnpCtqhqXYNk71g/zw3LCOimgjyUuShLh169ZYtWoVZs2ahb1792Lv3r1Gj8fExODtt99GmzbKX7fDqa13uWtU09oIsKs/k9smybC553d2yjaLZhGpi+kIryDYN23ZnuTV9Fyp9iPW/Wq94rPU05QB61OVk4bHiCawjlQw5X7D5Kks9Y+cXYRoqQ/VrHGgUV/F2taZGtxZalbyV6VhgKGgqBQfrD2ED9YesmlrTVcSS/7rBtRhP4wASJQQA0B4eDjWrFmDK1eu4NSpU7h58yaCgoIQHh6OkBD1/LFxautd7hrVtDYC7OrPxNrzSzVlm0WziNTD2T1s7Zk2bJroSlUMSqTgr8VrS8XaVGVrj9sz4sv9hkntxG66W+ofvZDwf05dU6zvI1Zx2dJ+vy8/2RkrM07Wmm2n5+rlh9YGLTjYRdb4SP2ELVq0wEMPPYS4uDjExcWpKhkG7iSBAX7Glfu8dWprb20Ikh+LRrPGgdDgTpBMfixa8mBmLVC5+jOx9vxiX0gfpR7G0CnpGDs7E9m6fEnaQkTKYGkPW1vYk7yanps0PAaDurc1JKs+PhoM6t7WJcld66ZBkj+ns/Qjvvr3Wj/im5J2xOz5zn5WRHLS33QvKCqFgLvJY7Yu36WJnD19q97aEES3E0/A9e23pGa1ailZev/0xAZQvHGwi8yTbIRYEASkpqZi48aNOHXqFKqqqnDixAmsXr0ax48fx5QpU9C0aVOpLucynNpqzB2jmtZGaF39mVh7frEvHn1ny5sLrxF5KmenLVsrkKV/LrGRT3cVg7p0rURx62/tHfGVaoo5kTuYjmZa2r7IlTPk7O1bzU7qKbrPeFlFlU2zYlwxImtLnRnWcSFrJEmIKysr8fzzz2PPnj2oU6cO6tWrhxs3bgC4s0fxt99+C51Oh9TUVDRp0kSKS7oUp7a6ly2BytWfidjzZ+vyofHRQLAS5L218BqRp5Ji2rKlAlkAkP7eUIfaZiuNxvq0adPq1/qft+0/77JRaWvsHfHlfsOkFvZUdr5WVIqXn+xsoX9kX1EtsWnF9vRbkobHIGP/eZj7n1hdLVgtwOWKEVlbRtE52EXWSJIQL1u2DP/973/xzDPPYNKkSfjiiy/w6aefAgBeeeUV1K9fHx9//DGWLFmC6dOnS3FJUpBsXb5RpWZ7q/YpKVDV/MIIDvJDaVmlzdPuuBaFzImMjMSwYcPkbgbZybQwlEYDJHSzP0F0x+il2Aiv9t4gq1WmLZGrMJW97xn3G1YGxjrrbK3sDNxJHi31j+ypMi3l9pVio9Y+PhrEPXAPDp66avZxV43I2jqKzsEuskSShHjTpk3o3LkzXnvtNQCARnP3S6tOnTp44YUXcPDgQWRnZzMh9jBHzxVj8wHjfXxvlVRgQephALYHWiUEKtMvDLHiEGK4FsXzZGVlISwsDKGhoYafbRUXFwcAiI+PN7s/OylfzWnLOp0OWq39CZarRy8tVVh+uGsTNG/WzOw+xLaSozCVI+8Z9xt2DmOde9h647xm8ihF/0jK7SvNzeoD7sSerJyLhlozzu7O4Ux7OB2a7CVJQpyfn281CEZFReHw4cNSXI4UJCv3plEyrGdpH19HuCOw2nPn1nRakDPB111fGmS/F154AcnJyUhOTjb8XPOGnzmCIECj0eDkSemLh5D6uHr00tJ6266hrY0SxZojybaSozAVR3zdj7HOPcRGM+sH+aFuQB2X9QOkLM6lb9dHqYdrxYeaSba7BjqUNMvQFdhHdA9JEuIGDRrg0qVLFs+5cOEC6td3dtc0UpobJeIJZEFRqSSbs0s51ccSW78Y9PvpSRGg3PXayDHJycno2rWr4WdbOomkDu4sJGXP6KW97bJnva0thb5MyVWYiiO+7sVY5x5io5n2LDNzhC3bS9rTp+mtDcGHItsw2ZNk17xugyBfjEO+Q++DEmYZugL7iO4jSULcvXt3ZGZm4uTJk2jfvvYo2ZEjR7Br1y4MHDhQisuRgjQM8rWYFNcsgQ849h9Yyqk+loh9YdSkHwmWKvi667WRY/SjJXovvviiTC0hKVmaZixnEuZIu+xdbys2oiyGham8A2Ode8g1mmlpWrGjSZezFbBNr3ujpIrJngn2Ed1Hkn2IJ02aBH9/fzzxxBOYPXs2cnPv/EF/++23ePvttzF69Gj4+/sjKSlJisuRgsRFN0AdX+t3kZ3Zf85dG6qb25PP10eD+kF+LtuHmZvFq09JSQlWrFiB//3vf0bHn332WSxbtgyVlZUytYxsJZYUWtrrVkxK2hEkTt2MIVPSkTh1s92/b0u7LCWxYgmr2HFLU6DdtfcxqQNjnWv01oZg2Yz+2PxBIpbN6O+SxCZbl4+xszMxdEo6xs7OBAAkPxaNZo0Da/VnLCVdlpjrMwFAl8jmNrXR0et6E/YR3UeSEeI2bdrgq6++wrRp07B69WrD8ddffx2CIOCee+7B/Pnzce+990pxOVKQTqH1EBoaalRlWoyj/4Gl2odPPzWnoKgUzTKuG+7KmlaW9vfzwe2SCrfcuXXlHoMkvcLCQowZMwZnzpzBuHHj8OCDDwIASktLcejQIezbtw87duzAl19+ieDgYJlbS2IsJYXWRmQtrcN1dqTZ3u2Gal7H3DRrnU5X63xLI8rumqastH2PqTbGOvUyN+K7cP1hBAbUMdu3sTXpMjetOu6Be2otwcjKuYj2of9nte/EZM869hHdR5KEGADuu+8+bNmyBbm5uTh27Bhu3bqFoKAgREREoEuXLvDxkWQwmhTIdPrw2NmZkv4HlqKCoNiUoJPnriMr56JRZekAP1+8/GRnt0xHYXVEdVm8eDF++eUXvPzyyxg5cqTheGBgIA4cOICvvvoK77//PhYuXIg33nhDxpaSJWJJoZ5YZWXTKc32/r6j7bK2jteeRFbuPXuVOl2djDHWqZe5kdfKKsEwaGE6JdqWpEusD+XvV7tvb+uUXiZ71rGP6D6SZ6nR0dF46qmnMHHiRIwePRqxsbFmk+HFixejQ4cOUl+eFMDcNBpn/gP31oaITvWxldjUnO0/XZB1yo4Ur43cZ/fu3YiLi8Nzzz2HoKAgo8f8/Pwwbtw4PPTQQ9ixY4dMLSRbWEv+xJJlW9ffOlqZ2d7pz45IGh4j69RoR6aFk/sx1qmXLSOsNfs5tvTZxPpQYjMDbWmD1H1FT8Q+ovtINkLsCEFw/3YO5HquKBrhbBErseAs1nF155QdT62O6ImuX7+ONm0sJydhYWG11tyRslirtiw2ImtroutoZWZXbzdkOlVZjjXCjkwLJ/djrFMf/ZRmW/8n6fs5tvTZ7O0T2TLKa3rdBkG+GDeMyZ4p9hHdQ9aEmDyX0v4Di03NEZuiyCk7ZE6rVq3Mrsus6ejRo2jZsqWbWkSOspQUi43IWptqbe33bW2XK5JUpUxVdnRaOLkXY526mE5ptkXNfo61Ppul/ZPLK6odntJb87o6nQ5aBfUbybuoamGvTqfD8OHDkZiYiDFjxljd+5hIT2xqzsDYNpyyQzZLSEjAzz//jPnz56O8vNzoscrKSixcuBCHDh3iFnMqYe/0YWuJrpIrMytlqrI7poWT8xjr1MXclGa9+kF+8DW54WRvP0esD/XcsI6c0kseQVUjxFOnTsWnn36KyMhIpKWlYfbs2UhJSZG7WaQCNafmFBSVolmNKUHtQ//P7XsCkjo999xz2LVrF5YvX460tDRERkYiODgYxcXFOHXqFG7evImIiAhuMaci9ozIunpKsys5OlVZ6orQan4PvQljnbqITWnWAFj7n0G1dtMAgA/XHsLKjJM29XmsTatmn4nUTjUJcXl5OSZPnozIyEgAQEREhNEWT0TmmNsmoD6uQqvVGs5R2vRuUq66desiNTUVS5YswdatW3Hw4EHDY61atcITTzyBCRMm1CpCQ57DXVsT2ctc4to19O7jjkxVdtU0a6W+h3QXY53ymPZn/hlZF/qujLWKzfp+jli1aP05lrCvRJ5MNQmxv78/EhMTAQDV1dVYvHgx4uPj7XqOY8eOuaJpkrG2Xkep3NHuo+eKkZV7EzdKqtAwyBdx0Q3QKbSe1d/ZcuBPVFTd6QQWFJVi0frDGNK1EQD1vdf8+1CGunXrYvLkyRg3bhzy8vJw4cIFxMfHo7KyEo0aNZK7eeSFxBLXqwVBhg6zI9stWZpmzYTW8zHWKYe5RHbLgb8QGpqP3toQm7fnEasWbcs2SUSeTJEJcUZGBubNm2d0LCwsDCtWrEB5eTmmTZuGyspKTJgwwa7njYqKQkBAgJRNlcydYgJa6ycqjDvana3Lx9acu4H+RkkVtubcRGhoqMUA/klGpiEZ1quoEpCVexPPDO/l0jZLjX8frlFWVmb3jbJr165hzpw5+P7771FVVQWNRoNhw4YhJSUFGzduxLx58/DAAw+4qMVEtYklrrpfSwz/dmSqMitCezfGOuUwl8hWVAmGRNbW3T3Epla7c2cNIiVSZEKckJCAhISEWseLi4uRlJSERo0aISUlBX5+fjK0jtzN0TuaYgH+RontVRjVztyUcd4FdlxhYSEef/xxXLp0CZ07d0ZZWRlOnDgBAAgMDMTvv/+O8ePHIzU1FRERETK3lryFWIJqurOhvVOVpa4ILfV6ZHIdxjplsSWRNZ3SnK3Lx9jZmUbf/9amVhN5K1VVmZ46dSratm2LBQsWwN/fX+7mkJs4ekdTLMA3DPI1e9zT6KdYFRSVQsDdtULZuny5m6ZaixYtwuXLl5GSkoK1a9eiT58+hseefvppLFu2DJWVlSz2R24llqBqnNzJSMqK0Ppp3foEWz+tOyXtiFNtJNdgrFMWsf6M2HGx7/8ukc25swaRGapJiE+cOIGsrCwcOnQIjzzyCBITEzF+/Hi5m0VuYO8XgZ7YNgFx0Q0ka5uSWRpZJ8fs2rUL/fr1M+oc1hQbG4v+/fvjyBF28j1VStoRJE7djCFT0pE4dbMiEjqxBFV7r3MFj+zdlsoSpWz7RLZhrFMWc/0ZP1+NaCIr9v2/bf95+Pv5oH6QH7dJIqpBtinTkZGRGDZsmM3nd+jQAXl5eS5sESmVrcUiTImtqamPqy5tr1JwrZD0ioqKEBJiuePQokULFBYWuqlF5E6uqrrsLLH1wV1DnV8eIlVFaK5HVhfGOmUx15/5Z2Rd0UTW0vf8rZIKBPj54uUnOzMRJvr/OZQQZ2VlISwsDKGhoYafbRUXFwcAiI+Pt7tKNHknW4tFiP2u6Xk6nXckxFwrJL2WLVsa1tGJOXr0KFq2bOmmFpE7KbnqsrnE1VyFd7nW8Uq9Hplci7FOeUz7M5Z2cBD7/tczrcPCeiPk7RxKiF944QUkJycjOTnZ8LPGymIlQRCg0Whw8iSna5L9uP+d/RwdWSdxAwYMwJdffonU1FSMGDGi1uPLly+HTqfDM888I0PryNXUPsop5wi3I9s+kXwY69TN3Pe/Kf0osjN7ExN5CocS4uTkZHTt2tXwsy0JMZFSeeqdUWdG1sm8iRMnYvfu3Zg1axbWrFmD6upqAMC0adNw/PhxnDlzBm3atMHEiRNlbim5gqOjnEqpriw2wu2OpNiRbZ9IPp4W67J1+fhi02XcXJvuFd+FNb//xUaK9bPFuDcxkRMJcU0vvviiJI0hcjdPvzPKkXVpBQcHY926dfjggw+Qnp6OkpI7+7xu2rQJ/v7+SExMxKuvvooGDbyjcJu3cWSUU0nrji2NZLsrKWYCrA6eFOs8/XtejP61LUg9jCqT//t1ahTkYr0RIgmLapWUlGDDhg34xz/+gQcffNBw/Nlnn8WDDz6I0aNHo04dRW57TF6Md0bJXsHBwZg5cyZmzJiBc+fO4ebNmwgKCkJYWBi3g/NwjoxyKmndsdgIt54S1kKTcnhKrPPm7/mVGSdrJcMAEBhQx/DaWW+ESKKEuLCwEGPGjMGZM2cwbtw4Q0JcWlqKQ4cOYd++fdixYwe+/PJLBAcHS3FJIknwzig5ytfXF+3atZO7GeRm9o5yKmndsdgIt55a1kKTe6k91nnz97zYa7xdUmH4N+uNEEm0D/HixYvxyy+/4KWXXkJSUpLheGBgIA4cOIApU6YgNzcXCxculOJyRJIRuwMaHOSHsbMzMXRKOsbOzkS2Lt/NLSMiTyC2vliO6sr6fYXFsOIzeSKx73lvGAG15bX31oYg+bFoNGscyL2JyWtJkhDv3r0bcXFxeO655xAUFGT0mJ+fH8aNG4eHHnoIO3bskOJyRJIxt9l9HV8NSv6qREFRKQTcXW/EpJiI7CW2vliu6sqWkmJWfCZPZO573ltGQG197b21IVg2oz82f5CIZTP6MxkmryNJQnz9+nW0aWP5izQsLIwbuJPimLszGhhQp9aaG/16IyIie+gTUP3oq4+PBoO6t5V1ra4S20TkKvrv+YZBvl43AsrRXyLbSLKGuFWrVhY3CAe4gTspl2kl5qFT0s2e5w3rjUgZdDod5s2bh4qKCjRq1Ahz585F69at5W4WOUiJ1ZWV2CbyPu6Kdb21IaiPq9BqtZI/t9Jxtwki6yQZIU5ISMDPP/+M+fPno7y83OixyspKLFy4EIcOHcLAgQOluByRS3nzeiNShqlTp2L27NlIT0/HkCFDMHv2bLmbREQkOcY6IlICSUaIn3vuOezatQvLly9HWloaIiMjERwcjOLiYpw6dQo3b95ERESEUcEtIqVixUWSU3l5OSZPnozIyEgAQEREBFavXi1zq4iIpMVYR0RKIUlCXLduXaSmpmLJkiXYunUrDh48aHisVatWeOKJJzBhwoRaBbeIlEg/tWhlxklcKypF08aBGJ3QnlOOyC38/f2RmJgIAKiursbixYsRHx8vc6uIiKTFWEdESiFJQgzcSYonT56McePGIS8vDxcuXEB8fDwqKyvRqFEjqS5D5BZcc0PukJGRgXnz5hkdCwsLw4oVK1BeXo5p06ahsrISEyZMsOt5jx07JmUzJWet5oRSqbHdbLP7qLXd7qCUWOdpnxFfj/J52mvytNejJ1lCfO3aNcyZMwfff/89qqqqoNFoMGzYMKSkpGDjxo2YN28eHnjgAakuR0SkegkJCUhISKh1vLi4GElJSWjUqBFSUlLg5+dn1/NGRUUhICBAqmZKSqfTqbKwjRrbzTa7j9LbXVZWJuuNMiXEOqV/Rvbi61E+T3tNang9jsY6SYpqFRYW4vHHH0dGRgY6deqEDh06QBDubFsTGBiI33//HePHj0deXp4UlyMi8mhTp05F27ZtsWDBAvj7+8vdHCIil2CsIyIlkGSEeNGiRbh8+TJSUlLQp08fLF68GCdOnAAAPP3002jfvj3GjRuHlJQULFiwQIpLEhF5pBMnTiArKwvt2rXDI488AgBo3rw5Pv/8c5lbRu6UknYE23+6gOpqAT4+GgyMbcNtksijqDXWZevyWWOEyMNIkhDv2rUL/fr1Q58+fcw+Hhsbi/79+3vsvHMiIql06NCBs2m8XEraEWzbf97wc3W1YPiZSTF5CjXGumxdvtEuFAVFpVj8dS4AMCkmUjFJpkwXFRUhJMRyIGjRogUKCwuluBwREZHH2v7TBbuOE5F7rMw4abQlIwCUVVRhZcZJmVpERFKQJCFu2bKlYYq0mKNHj6Jly5Y+t3XhAAAgAElEQVRSXI6IiMhjVVcLdh0nIve4VlRq13EiUgdJEuIBAwZg//79SE1NNfv48uXLodPpuL8cERGRFT4+GruOE5F7NG0caNdxIlIHSdYQT5w4Ebt378asWbOwZs0aVFdXAwCmTZuG48eP48yZM2jTpg0mTpwoxeXIi7GYBRF5uoGxbYzWENc8TkTyGZ3Q3mgNMQAE+PlidEJ7GVtFRM6SZIQ4ODgY69atw4gRI3Dp0iX8+uuvEAQBmzZtwvnz55GYmIh169ahQYMGUlyOvJS+mEVBUSkE3C1mka3Ll7tpRESSSRoeg0Hd2xpGhH18NBjUvS0LahHJrLc2BMmPRaNZ40BoADRrHIjkx6J5Y55I5SQZIQbuJMUzZ87EjBkzcO7cOdy8eRNBQUEICwvj3nIkCUvFLPhlRESeJGl4DBNgIgXqrQ1hn4PIw0iWEOv5+vqiXbt2Uj8tEYtZEBERERGRpCSZMk3kDixmQUREREREUmJCTKoxOqE9Avx8jY6xmAURERERETlK8inTRK6iX7PDKtNERERERCQFJsSkKixmQUREREREUuGUaSIiIiIiIvJKHCEmIvJSKWlHsP2nC6iuFuDjo8HA2Dbc6oeIyMtk6/K5HI28GhNiIiIvlJJ2BNv2nzf8XF0tGH5mUkxE5B2ydflY/HUuyiqqAAAFRaVY/HUuADApJq/BKdNERF5o+08X7DpORESeZ2XGSUMyrFdWUYWVGSdlahGR+zEhJiLyQtXVgl3HiYjI81wrKrXrOJEnYkJMROSFfHw0dh0nIiLP07RxoF3HiTwRE2IiIi80MLaNXceJiMjzjE5ojwA/X6NjAX6+GJ3QXqYWEbkfi2oREXkhfeEsVpkmIvJe+sJZrDJN3owJMRGRl0oaHsMEmIjIy/XWhjABJq+mqoQ4JycHc+fORUVFBVq3bo358+ejYcOGcjeLHMR974hIKbgnMxF5O/bLyFupag3x9OnT8e6772LLli1o164dvvzyS7mbRA7S73tXUFQKAXf3vcvW5cvdNCLyMvo9mfUVtvV7MqekHZG5ZURE7sF+GXkzVSXE27ZtQ7t27VBRUYErV66gQYMGcjeJHMR974hIKbgnMxF5O/bLyJupKiH28/NDXl4eHnroIfz0008YPHiw3E0iB3HfOyJSCu7JTETejv0y8maKXEOckZGBefPmGR0LCwvDihUrEBERgX379iE1NRUvvfQSUlNTbX7eY8eOSd1USel0Ormb4BBH2t0gyBc3SqrMHnfH+6DG91qNbQbU227yHj4+GrPJL/dkJiJv0bRxIArMJL/cj5i8gSIT4oSEBCQkJBgdKysrw86dOxEfHw8AGDp0KObPn2/X80ZFRSEgIECydkpJp9NBq9XK3Qy7OdrucbizVqXm9JwAP1+MGxYNrYsLOKjxvVZjmwHlt7usrEzxN8rI9QbGtsG2/efNHici8gajE9qb7ZdxP2LyBopMiM2pU6cOZs2ahZYtWyIqKgoZGRno3Lmz3M0iB3HfOyJSCu7JTETejv0y8maqSYh9fX3x0Ucf4c0330RVVRVatGiBOXPmyN0scgL3vSMipeCezETk7dgvI2+lmoQYAB544AFs3LhR7mYQERERERGRB1BVlWkiIiIiIiIiqTAhJiIiIiIiIq/EhJiIiIiIiIi8EhNiIiIiIiIi8kpMiImIiIiIiMgrqarKtKMEQQAAlJeXy9wSy8rKyuRugkPU2G622X2U3G59TNDHCLVjrHMtNbabbXYfJbebse4OJX9GjuDrUT5Pe01Kfz2OxjqN4CnR0YJbt27h9OnTcjeDiBQqPDwc9evXl7sZTmOsIyJLGOuIyBvYG+u8IiGurq5GcXEx/Pz8oNFo5G4OESmEIAioqKhAvXr14OOj/hUkjHVEZA5jHRF5A0djnVckxERERERERESm1H+bkIiIiIiIiMgBTIiJiIiIiIjIKzEhJiIiIiIiIq/EhJiIiIiIiIi8EhNiIiIiIiIi8kpMiImIiIiIiMgrMSEmIiIiIiIir8SEWEFycnLw6KOPYsiQIZg4cSJu3Lghd5Os0ul0GD58OBITEzFmzBhcunRJ7ibZbMGCBfj444/lboZVW7ZswaBBg9C/f3+sWbNG7ubY7Pbt23j44Ydx8eJFuZtCCsNY516Mda7FWKcOaow71qg5LlmilpglRq2xzBKPj3MCKUZ8fLzwyy+/CIIgCO+9957wwQcfyNwi6/r06SOcPHlSEARB+Prrr4WJEyfK3CLrbt68KUyfPl3o1KmTsGjRIrmbY9Eff/wh9OnTRygqKhKKi4uFIUOGGP5GlOzIkSPCww8/LNx3331Cfn6+3M0hhWGscw/GOtdjrFMPNcYda9QYlyxRU8wSo9ZYZok3xDmOECvItm3b0K5dO1RUVODKlSto0KCB3E2yqLy8HJMnT0ZkZCQAICIiApcvX5a5VdZlZWXh73//O5555hm5m2LVvn370K1bNzRq1AhBQUEYMGAAtm/fLnezrNqwYQNmzpyJ5s2by90UUiDGOvdgrHM9xjr1UFvcsUatcckSNcUsMWqNZZZ4Q5yrI3cD6C4/Pz/k5eXhmWeeQZ06dfDyyy/L3SSL/P39kZiYCACorq7G4sWLER8fL3OrrBs2bBgAqGI6ztWrV9GsWTPDz82bN8fRo0dlbJFt5syZI3cTSMEY69yDsc71GOvUQ21xxxq1xiVL1BSzxKg1llniDXGOCbEMMjIyMG/ePKNjYWFhWLFiBSIiIrBv3z6kpqbipZdeQmpqqkytNGapzeXl5Zg2bRoqKysxYcIEmVpYm6U2q0V1dTU0Go3hZ0EQjH4mUjLGOvdgrCO6S41xxxo1xiVLPCFmiWEsUycmxDJISEhAQkKC0bGysjLs3LnTcHdv6NChmD9/vhzNM8tcmwGguLgYSUlJaNSoEVJSUuDn5ydD68wTa7OatGzZEjk5OYafCwoKPHrKCnkWxjr3YKwjukuNcccaNcYlSzwhZolhLFMnriFWiDp16mDWrFk4duwYgDt3zzp37ixzq6ybOnUq2rZtiwULFsDf31/u5nicHj16YP/+/SgsLERpaSkyMzPRq1cvuZtF5DDGOjKHsY5cSa1xxxrGJeVhLFMnjhArhK+vLz766CO8+eabqKqqQosWLRQ/Z//EiRPIyspCu3bt8MgjjwC4s1bi888/l7llnqNFixZ46aWXMHr0aFRUVGD48OHo1KmT3M0ichhjHZnDWEeupMa4Yw3jkjIxlqmTRhAEQe5GEBEREREREbkbp0wTERERERGRV2JCTERERERERF6JCTERERERERF5JSbERERERERE5JWYEBMREREREZFXYkJMivHxxx8jIiICO3fulLspREQuw1hHRM5iHLFP37598cADD7j0GhcvXkRERASef/55o+NHjx7F3r17XXptV4mIiEBiYqLczXA5JsSkGF27dkVycjJCQ0PlbgoRkcsw1hGRsxhHlKdBgwZITk7G4MGDDceys7Px+OOP48yZMzK2jKypI3cDiPRiY2MRGxsrdzOIiFyKsY6InMU4ojwNGjTAiy++aHSssLAQ1dXVMrWIbMURYiIiIiIiIvJKTIjJKbdv38b777+P+Ph4REVF4Z///CdmzpyJ69evG51XWlqKRYsWYeDAgYiOjkbfvn0xa9YsFBYWGs5xdj3MtWvX8Prrr6Nfv37o2LEjevbsialTp+L8+fNG5/Xt2xdPPPEETp06hVGjRiE6Ohq9evXC22+/jT///LPW8+p0OiQnJ6Nnz56IiopCly5d8Mwzz+DHH380Om/UqFHo27cvdu/ejb59+yI6OhqTJ08GAJw/fx6TJ09Gnz59EBUVhb59++Ktt95CQUFBresdP34czz//PGJjY9GpUyckJiZi3bp1EATBofeFiJzHWHcXYx2RY5QUR4A7/19feeUV9OjRA1FRUUhISMCSJUtQUVFR69yLFy/ijTfeQK9evRAdHY3Bgwdj2bJltc49d+6c0XPGx8fj3Xffxa1bt4zOmzZtGjp06ICioiLMmDED3bp1w/33349nn30WFy5cQHl5Od577z307NkTnTt3xqhRo3Dq1Cmj57A3xpmTkZGBESNG4P7770fnzp0xZsyYWjFv7NixiIiIwMqVK42Ob968GREREXj22WchCEKtNcTTpk3D9OnTAQDz5s1DREQEzpw5g86dO6N3795mY9306dMRERGB3377zab2mzp48CAmTJiA2NhYaLVajBgxwuzfyKZNmzBq1Ch06dIFUVFR6NmzJ6ZMmYL8/HyHrqt2GoHfPOSgW7du4cknn8Tp06fRvXt3dOjQARcvXsT333+PVq1aITU1Fc2bN0dpaSmeeOIJnDx5Eh07doRWq8WFCxewa9cuhIeHY926dQgODsbHH3+MxYsX45NPPkF8fLxdbSkrK8OIESNw+vRp9OvXDyEhIbhw4QK+//57NGzYEBkZGWjUqBGAOwFUo9Hg1q1baN26Nbp3747c3Fzk5OTgH//4BzZs2ICgoCAAwM6dOzFp0iQ0adIE8fHxqFevHn755Rf897//ha+vL9LS0tC+fXsAdzqJx44dAwDExcUhODgY9957LwYPHox//etfKCoqwoABA9C8eXPk5eVh9+7dCAsLw+bNm+Hn5wcA2L17N5KTk+Hn54f+/fujSZMm2LNnD06fPo1///vf+M9//iPVx0dENmKsY6wjcpaS4ghw54bUmDFj8Ndff6F///7429/+hpycHBw+fBj//Oc/sWTJEvj6+gIATp8+jVGjRuHGjRvo3bs3QkNDceDAARw7dgzDhg3D/PnzAQC5ubl4+umn8ddff6FPnz4ICQnBkSNHcOTIEYSFhWHdunWG+DRt2jSkp6ejXbt2qK6uRq9evXD69Gns3bsXYWFhaNu2LU6fPo2+ffuioKAA27dvR4sWLbBjxw4EBgYCsC/G9e3bFzdv3kROTo7hPVi4cCE+/fRTtG7dGr1794ZGo8H27dtRWFiId955x1BM6tKlSxgyZAiAOwl0ixYtcPXqVQwZMgSCIGDLli1o0aIFLl68iLi4OMTFxeHTTz/Fzp07sXHjRmRlZaFnz56IiYnBmDFjMG/ePGzcuBGrV69Gly5dDO0pKytDjx490K5dO6xfv97uzzQ9PR3Tp09H3bp1ERcXh8aNG2PHjh34448/MGfOHAwfPhwAMH/+fCxbtgyRkZGIjY2FRqPBwYMHcfz4cbRq1Qrbt29H3bp1AdwpqhUZGYn09HS726MqApGD3nrrLSE8PFxYvXq10fGdO3cK4eHhwqRJkwRBEIQFCxYI4eHhwpw5c4Tq6mrDeZ999pkQHh4uLFu2TBAEQVi0aJEQHh4ufP/993a3ZdeuXUJ4eLiwcOFCo+NffPFFrTb26dNHCA8PFyZOnChUVlYajv/nP/8RwsPDhUWLFhmODRgwQOjatatQUFBg9LxLly4VwsPDhQ8++MBwbOTIkUJ4eLgwb948o3NXrVolhIeHC2lpaUbHZ82aJYSHhws//PCDIAiCUFJSInTr1k3o1q2bkJ+fbzivqqpKePHFF4Xw8HAhOzvbzneGiJzFWMdYR+QsJcWR6upq4eGHHxY6duwo/Pzzz0aPzZ07t1Y7n3zySSEiIkLYsWOH0XOMHTtWCA8PF44dOyZUVlYK/fv3Fzp06CDs3r3b6Dnfe+89ITw8XJg+fbrh2GuvvSaEh4cLjz32mFBWVmY4/vjjjwvh4eFC3759hVu3bhmOT5s2rVZssCfG9enTR9BqtYafc3NzhYiICGHkyJFCSUmJ4XhhYaHQr18/ITo6Wrh+/brh+Nq1a40+pwkTJgjh4eHCd999ZzgnPz9fCA8PF5KSkgzHvvnmGyE8PFxYvny54dj+/fuF8PBw4c033zR6n7Zt22b2b8QWf/75p6DVaoVu3boJZ8+eNRy/fv260LNnT6Fr165CeXm58McffwiRkZHCU089ZfSeCYIgjB8/XggPDxf27NljOBYeHi4MHTrU7vaoDadMk0MqKyuxadMm/OMf/8BTTz1l9FhcXBw6d+6M77//Hrdv38bWrVsRHByMKVOmQKPRGM4bOXIkxo0bh3bt2jndHn3BghMnTuCvv/4yHH/yySeRnZ2NJ5980uh8jUaDV1991XD3EwAmT56MoKAgbNmyxfCcU6ZMwbvvvoumTZsa/b6+kIXpNCcAGDBggNm25ebmoqqqynD8pZdewt69e9G7d28AwK5du1BYWIhx48bhnnvuMZzn4+ODKVOmAAC++eYbG94NIpIKYx1jHZGzlBZHcnNzcfr0aQwfPhxRUVFGj02ePBl+fn7YuHEjAOCPP/5ATk4OevTogf79+xvO02g0ePnll5GcnAx/f38cPnwYv/32GwYPHoxevXoZPeekSZPQokULbNmyBeXl5UaPPfHEE/D39zf8fP/99wMAHn/8cQQHBxuOd+rUCcCd0dqabIlx5qSlpUEQBLz66quGEWcAaNy4McaPH4/S0lJkZGQYjo8YMQLdunXD9u3b8eabb+KHH37A4MGDjSpK2yo2NhatW7fG9u3bjaac62fRDBo0yO7n3L17N27duoUxY8YYVR5v0qQJpk+fjnHjxqGkpAT+/v5499138cYbbxi9ZwAMo9Xm4r2nY5Vpcsi5c+dQUlKCqqoqfPzxx7UeLysrQ1VVFfLy8nD+/Hl06dIFAQEBRufUq1cPU6dOlaQ9PXr0QEhICH744Qc8+OCD6NGjB3r16oXevXujVatWtc5v1qxZra0K6tevj9DQUBw/fhylpaUIDAxEv379ANwJwL/88gsuXLiAM2fO4KeffgIAs5UDW7dubfTzgAED8Mknn2D9+vXIzMxEz5490atXLzz00ENo1qyZ4Tz9FMTjx4+bfU99fX1rrZ8hItdirGOsI3KW0uLI8ePHAQAXLlww25569eohLy8PgiAgLy8PABATE1PrvPvuuw/33XcfAGDVqlUAYDQFWM/f3x8dO3bEzp07cfbsWURGRhoea9OmjdG5+inONW+WATC8H6YJta0xzpT+PcjMzER2drbRY3/88QcA4OTJk4ZjGo0Gs2fPxtChQ7F+/Xo0b94cM2fOrPW8ttBoNBgyZAg+++wz/O9//0Pv3r3x559/Ys+ePejVqxcaN25s93PqY6a5z8k0wR4yZAiqq6tx+vRp/Prrr8jPz0deXh727dsHwHy893RMiMkhN2/eBACcPXsWixcvFj3vwoULAGB0l88VAgMDsWHDBqSkpCAjIwOZmZnIzMyEj48P+vXrh7ffftuwbgUAWrRoYfZ59KMjt27dQmBgIPLy8jB79mwcOHAAAODn54d7770XUVFR+O2338wWRNCvu6h5rbS0NKSkpCArKwtbtmzBli1b4Ofnh0cffRRvvPEGAgICDAUntm7dKvo6b9y4Yd8bQ0ROYaxjrCNyltLiiL49e/bswZ49e0TPKy4uNvxftNam27dvWzyvefPmAO4UDKtJnwCbqjlqbImtMc6UPg4tXbpU9LlN41BISAg6dOiAnJwc3HvvvWjYsKFNbTRn2LBh+Oyzz/Ddd9+hd+/eyMjIQEVFhWHdsr30n6ktfzuZmZn44IMPDIW7goKCEBUVhcjISOzbt88rCxsyISaH1KtXDwCQmJiId999V/Q8fcApLi42+3hJSYloMLRXkyZN8MYbb+D1119HXl4e9uzZg/T0dOzYsQM+Pj5YsGCB4dyysjKzz6EPKI0aNcLt27cxduxY3Lp1C6+99hp69OiBsLAw+Pv7Izc3F999953NbQsJCcHcuXNRVVWFY8eOYc+ePdi4cSPWr1+P+vXrY+rUqYb3YcWKFejevbsT7wQRSYWxjrGOyFlKiyP656hZaMnauebaVF1djfLyctStW9fwGq9evWr2eWrGHCnZEuPMCQoKgq+vL3Jzcw3F/qzZuHEjcnJy0KhRI+zfvx8bN27Eo48+6lC7Q0NDERMTg6ysLJSXlyMjIwMNGzZEnz59HHo+S59TeXk5fHx8UKdOHeTm5mLy5Mlo2bIlPvzwQ3Ts2BEhISHQaDRYunSpYZTY23ANMTkkNDQU/v7+OH78uNk7SStWrMCnn36KyspKtGrVCidPnqw1zaW8vBwPPvggxo4d63R7Dh48iNmzZ+PChQvQaDSIjIzE+PHj8fXXXyMoKMioqiBwZ/qS6RYApaWlyMvLQ4cOHeDv748ff/wR165dw1NPPYWxY8ciMjLScMfy119/BQCb7qJlZWXhrbfewu3bt+Hr64vo6GgkJydjzZo1AO5sdQLcqeQH3J1OWNOff/6JOXPmeH6VPyKFYaxjrCNyltLiiKX/gxUVFXjnnXcMU6D15x49erTWuYcPH0ZMTAxSUlIMVej1/89rqq6uhk6nQ1BQUK2lFs6yJcaZExERgaqqKqNp0XqHDx/G+++/bxRPr1y5gnnz5qFp06ZIS0tDkyZNMG/ePFy5csVi+2quAzeVmJiIkpIS7Ny5E4cOHcLAgQNtHhk3FR4eDsD85/Tll18iOjoaBw4cwNatW1FdXY2ZM2di8ODBaNOmjaGNZ8+eBWBbvPc0TIjJIQEBARg0aBDOnDmD5cuXGz32008/4d1338U333yDhg0bYujQobh16xY++eQTo/NWrlyJkpISSUYICgoKsGrVKixbtszo+LVr11BWVlYrAFdUVODDDz80/KcXBAEffPABSkpK8K9//cvwGoHaxQV+//13w5SnyspKq207e/Ys1q1bh3Xr1hkd1xeG+Nvf/gYA6NevH4KDg/HFF1/g3LlzRue+9957WLlypWE6FRG5B2MdYx2Rs5QWR7p06YJ77rkHaWlpOHz4sNFjS5cuxfLlyw1rbENCQnD//fdj7969RtOrq6ur8fnnn0MQBDz44IPQarVo27YtMjMzsXv3bqPnXLRoES5fvoyEhASHEz4xtsQ4cx555BEAwNy5cw3TvYE7U7/feustfP7550bFAWfOnImbN2/i9ddfR0hICF577TXcvHnT6jriOnXqGNppavDgwfDz88P777/v1HRpAIiPj0dQUBBWrVplVHjszz//xPr161GvXj3ExMQY4v21a9eMfn///v2G2UC2xHtPwynT5LDXXnsNhw8fxvz585GVlYVOnTrhypUryMzMRJ06dTB37lz4+PhgwoQJyM7OxmeffYaDBw8iOjoaZ8+eRXZ2Njp27IgxY8Y43Zb4+Hjcf//9WLduHU6fPo2YmBjcvn0bO3bsAAC8+OKLRuf7+fkhPT0dJ0+eRHR0NHJzc3H48GHExsZixIgRAACtVovWrVsjPT0dRUVFiIyMxOXLl5GVlYWAgABoNBqbNn7/97//jQ0bNuD999/HgQMHEBERgevXr2P79u0ICgrCc889BwBo0KABZs+ejVdeeQWPPPII4uPj0bx5cxw4cAA///wzOnbsKMmdYSKyD2MdYx2Rs5QUR3x9fTF//nyMHz8eI0eORFxcHEJCQnDs2DH8+OOPuOeee/Dyyy8bzp81axZGjhyJCRMmID4+Hq1bt8aPP/6IEydOYPTo0YYK0O+88w6effZZTJw4EX369EGbNm1w+PBhHDlyBPfeey9effVVp9tuypYYZ063bt0watQorFq1CoMHD8ZDDz0Ef39/7Ny5E5cvX8aIESMMVfY3bdqEH374AT179jRUlR42bJjh+KZNmzBs2DCz19GvcV63bh1u3LiBUaNGGY41bNgQffv2xY4dOxASEgKtVuvw+9CoUSO8+eabmD59Oh555BHExcWhXr162L59OwoKCrB48WL4+/tj0KBBWL58OWbNmoWDBw+iWbNmyMvLw969e9G4cWNcv37dpnjvaThCTA5r0qQJNmzYgLFjx+LKlStYtWoVcnJy0LdvX2zYsMEQSOrVq4e1a9fi2WefxR9//IGVK1fi+PHjGDlyJJYvXy7J3UJ/f38sWbIE48ePR2FhIdasWYPt27cjOjoaq1atQs+ePY3Or1u3LtauXQvgTpAqKChAcnIyvvjiC8PdvKCgICxfvhz9+/fH8ePHsXr1apw4cQJDhw7F5s2bERkZiZycHNG1PnoNGzbE6tWr8cQTT+C3337DV199hezsbPTq1QsbNmwwqraYkJCA1atXo1u3btizZw9Wr16N4uJiPP/881ixYoVhjQ4RuQ9jHWMdkbOUFEcA4IEHHsDXX3+NgQMHIicnBytXrsTvv/+OUaNGGaoo60VERODrr79GQkICDhw4gFWrVqG0tBTTp0/H9OnTDed17twZaWlpGDRoEA4fPow1a9bgzz//RFJSEr7++mvJ1w8DtsU4MTNmzMC7776LVq1aYfPmzfj222/RtGlTzJ071zDyW1BQgLlz56Ju3bp46623jH5/1qxZCAgIwNy5c1FQUGD2Gl26dMFTTz2FGzduYM2aNYZlKHoDBw4EAAwdOtSRl2/kkUcewbJly9C+fXvs2LEDGzZswD333IOlS5cadhJo3749li5divvuuw87d+7Ehg0bcO3aNUyaNAnp6enw8fGpNcLvDTSCN04UJ6/Wt29f3Lx5s9ZaOyIiT8JYR0SezBNi3IcffoglS5YgMzMTbdu2lbs5XosjxERERERERG70xx9/IC0tDbGxsUyGZcY1xKRIO3fuNFv5T4zpujkiIjVgrCMiZzGOqMvmzZuxbNky/Pbbb/jrr7/Mfh4bN240Ko5lSf369fH0009L3ErvwoSYFGnnzp349ttvbT6fwZ2I1IixjoicxTiiLi1btsTly5cRHByM119/HV26dKl1zrfffosDBw7Y9HytW7dmQuwkriEmIiIiIiIir8Q1xEREREREROSVmBATERERERGRV2JCTERERERERF7JK4pqVVdXo7i4GH5+ftBoNHI3h4gUQhAEVFRUoF69evDxUf/9QcY6IjKHsY6IvIGjsc4rEuLi4mKcPn1a7mYQkUKFh4ejfv36cjfDaYx1RGQJYx0ReQN7Y51XJMR+fn4A7rw5/v7+MkaLk2QAACAASURBVLdG3LFjxxAVFSV3MxzCtsuDbXdOeXk5Tp8+bYgRaudIrFPC5yAlvh5l87TXA6jjNTHWyU8NfyeWsP3yU/trcEf7HY11XpEQ66fT+Pv7IyAgQObWWKb09lnCtsuDbXeep0y5czTWKeVzkApfj7J52usB1POavD3WyU1NbTWH7Zef2l+Du9pvb6xT/0ISIiIiIiIiIgcwISYiIiIiIiKvxISYiIiIiIiIvJKq1hCfPXsWr7zyCsLCwhAVFYWnn35a7iYREUmOsY6IvAFjHREpgapGiHU6HVq2bIm6devi/vvvl7s5REQuwVhHRN6AsY6IlEBVI8RarRZxcXEIDg5GUlISvvzyS7mbREQukJJ2BNt/uoDqagE+PhoMjG2DpOExcjfLbRjriJQlW5ePlRknca2oFE0bB2J0Qnv01obI3SzVY6wjcoy395OkpqoR4qNHj6KiogL+/v6oU0dVuTwR2Sgl7Qi27T+P6moBAFBdLWDb/vNISTsic8vch7GOSDmydflY/HUuCopKIQAoKCrF4q9zka3Ll7tpqsdYR2Q/9pOkp6roExYWhnfeeQfBwcH497//LXdziMgFtv90QfS4t9z9ZKwjUo6VGSdRVlFldKysogorM05ylNhJjHVE9mM/SXqKSIhv376NESNG4LPPPsM999wDANiyZQtSUlJQWVmJMWPG4KmnnkKnTp3w0UcfydxaInIl/R1PW4+rCWMdkfpcKyq16zgx1hG5kif3k+Qie0Kcm5uLGTNm4LfffjMcu3LlCj766CNs3LgR/v7+GDFiBGJjY9GuXTunrnXs2DEnW+t6Op1O7iY4jG2Xh6e1XaMBBDMxXaNR92tVcqxT8/tqDl+Psqnt9TQI8sWNkiqzx/WvRW2vyZWUHOvkpva/E7ZffjqdTtX9JKW2T/aEeMOGDZg5cyZeffVVw7F9+/ahW7duaNSoEQBgwIAB2L59O5KTk526VlRUFAICApx6DlfS6XTQarVyN8MhbLs8PLHtCed8sW3/+drHu7WFVivtVKCysjK3daiUGuvU/DdkDl+Psqnx9YzDnTXENadNB/j5YtywaGi1Iap4TYx18lPD34klbL/89K/Bnf0kKbnjM3A01smeEM+ZM6fWsatXr6JZs2aGn5s3b46jR4+6s1lEJBP9+hdPq57IWEekTvp1wqwybRvGOiLX8tR+kpxkT4jNqa6uhkajMfwsCILRz0Tk2ZKGx3hFYGesI1KH3toQJsBOYKwjkpa39JPcRZHbLrVs2RIFBQWGnwsKCtC8eXMZW0REJD3GOiLyBox1RKRkihwh7tGjBz7++GMUFhYiMDAQmZmZ+M9//iN3s4jczpM3Xv/uQCHeTt3ska/NVox1RPLJ1uW7fBq0O66hBox1pHae3B+Ti5LeU0UmxC1atMBLL72E0aNHo6KiAsOHD0enTp3kbhaRW+k3XtfTb7wOQPVBOCXtCHLOlBh+9qTXZg/GOiJ5ZOuMC2UVFJVi8de5ACBZwuqOa6gFYx2pmSf3x+SitPdUMQnxrl27jH4eMmQIhgwZIlNriOTnyRuve/Jrs4axjkh+KzNOGlWNBoCyiiqszDgpWbLqjmsoGWMdeQpv7rO4itLeU8UkxERkzJM3XrfltSlpKg0ReZZrRaV2Hbfk6LlifJKRWWtatJTXICL52NofY7/Fdkrr4zIhJlIoHx+N2cDg46P+ypzWXpvSptIQkWdp2jgQBWYS06aNA+16nmxdPrYc+BMVVXfiWc1p0VJdg4jkZUt/jP0W+yitj6vIKtNE3iwl7QgSp24WvUs2MLaNm1skPbHXoD9uaSoNEblGti4fY2dnYuiUdIydnYlsXb7cTXKZ0QntEeDna3QswM8XoxPa2/U8KzNOGpJhPf20aKmuQUTuo++DDZmSjsSpm5GSdsRqnwVgv8Vetryn7sQRYiIFMb3DWJMnTb9JGh6DqwUFOHS2tNbUopS0I4qbSkPk6bytAJT+NTlTATpbl292BBi4My1aimsQkfuIjfIO6t4Wg7q3tTgdWg39FiVN6dZfVyntYUJMpCBidxJ9fDRIf2+om1vjWg93bYKZSVqjY5ZuCACeMV2cSIm8sQBUb22Iw69NfwNBjH5atDPXICL3sjTKm/7eUIvJmtKmAJtS4pTupOExihnk4ZRpIgVRwx1GV7I2tcgTposTKRELQNnH3A0EPU6LJlInZ/pgSpsCbIpTui1jQkykIGJ3EpVyh9HVLH3pDOreVjF3Eok8jVihJxaAMs/SjYLkx6I5KkykQs70wZKGx2BQ97aGc318NIrqt3j7gIs1nDJNpCADY9uYnTJs6Q6jktaEOMvSlCO1viYiNRid0N5oDTGg/JHObF2+bOtzxSpIN2scyGSYSAJy9G0c6YPVpKQpwKaUPqVbbkyIiRTE3iID3x0oRM6ZEsPPSlgT4gxbv4w86SYAkRKorQCU3EXAHLmBYJrAd4lsjoOnrqri/SZyJ7nWuyqt0JOUxPpX1dUChkxJd+i1elJfjAkxkcLYc4dR92uJ2ePbf7qg2qBkjRILQxB5AjUVgJK7CFjNGwgFRaVoZiWhNZfA14xjnl7Vm8gelta7uvp7XsmjvM4wTfZN2duX8rS+GBNiIhUTRJZ+qHVNiC1fgnJ+URKRMiihCJj+BoJOp4NWe6divtg0bktFuPQ8vao3ka243tU+to7U1kz2E6duNvt+2tqX8rS+GBNiUj1PmrJhL43GfFKs1jUhtnwJ8ouSiMTW8DpSBEyqtciWpnHbmqizqjcR17uK+e5AId5O3WzU3wXg0Eits30pT+uLMSEmVfO0KRv20t4bZLSGWE8pZf6tMb2ZIcbbvwSJvJVYsipVETAp1yJbmsYtlsCbYlVv8mS2DmA4W9zKE6WkHRGtGWOOtZFaZ286eNpNCybEpGqeNmXDXg93bYLmzZopaoTc1i88cwXBxHjzlyCRt7IlWXV2ZFfKtciWpnG//GTnWgm8KaVX9SZyhj0DGJ5c3MpR9u4XbG2k1p6bDub6dZ5204IJMamap03ZcISSCkDY84UnVhAMuHvn0dyXoKfdlSQi86wlq1IUAZNyLbKlady9tSE4ee666IiOtaJcRGpn7wCGkvo2SmBvv9Zan8jWmw5i/bpB3dtiUPe2HnPTggkxqRqTI2Wx5wtPrCAYAKS/N1T0MU+7K0lE5rmjcJaUa5GtTeM+eOqq2d9r1jgQy2b0t/t6RGrCAQzniPV3xdjSJ7LlpoOlfl36e0NVmwCbYkJMqsbkSFns+cITKwgG3K1+WPOOY80pOzWp/a4kEZknZbIqRqq1yEDtrZh8fDSGEW0AomuIWUiLvAEHMJzTumkQ8q8WWz1P6j6Rt9zIYEJMqsZ1JspizxeeWEEw4G6g1U/N+fnXa2a/CEKa18Onr8U72WoiUiIpk1UxUq1FNn0+07XPC9cfFv0djY8GQ6ekO31tIiXjAIbjUtKO2JQMD+reVvL+r6WR6ZS0Ix7T32ZCTKrHdSbKYcsXnqWRXrGgK/ZFkH+12KMCMhHdZW3EVarEUYq1yDWZW/tcWSU+mqKPe85UuCZSOg5g2E+svyTGFQVlxfp1ADxqVxcmxEQkGWtfeKbFGfT0dzWHTEm3+5reUlGcyBuJjbg6mjhKteewJbZsrySmrKIKSzf9zISYPJI3D2DYugNHzfMtbatkjiumMScNjxGdpQfY3gdLSTuCjB8vQlh7UZE3Q5gQE5GkLH3hWSu6ZW/RCMDz1rEQkTGptkaScs9hSxyJYzXdKqlAti6fSTGRh7BnBw49e7dZqnktqRPNS9fEdwWxJdY58vrdjQkxEbmctWk/+uO2Fo0gInWzZ6RWqmrTUu45bIkUN+mkbhMRWWfvKK6t7N1yCnA8jrgi0bTUFluKojny+t3NR+4GEJFn098ZtCWgWroLSUSeQT9SW1BUCgF3R2qzdflmzxerKm1vtWl3bOME3NlGyZz6QX42V9Rl5Wki9zLtq+hHMVPSjjj93I5UarYUK3x8NAhpXk/0cUdHlx1piy1F0dRQqZojxETkUrYE5upqwbDVEhF5NntHam2pNm3LiLOU2zjVvF6DIF+Mw90pzmLtfW5YR3y49pBNzy/l1lJEdIelEWBXjmI6suWUWDGrmpWkxequmLuWM1tXirUlpHk9m94bNWy5xYSYyEO4aqqPs2xNcp1Jhllpmkg97B2ptVZtGrCt6JZU2ziZrkW+UVJldD1LWznpX4Mlvj4aSbeWIiLguwOFRls9mq5jdeUopiNbTpkrUto57M6NMmsDCKaJpqUCXbas53W2QrgattxiQkzkAZRcsMDZAjPA3f2Gxe6GKmkdChFZ5shIraVq0/5+PjaNOEu157AtI9xiWzmNTmiPBamHUWUhJgbVrSPaJndUySbyRLpfzS/JslbUU4pRTEcTStMipbNSvrep8rRpomnLTD1r/ShnKoTrfy/jx/MQBNtGpd09yMOEmMgDKLlggaU97Gz16WvxFh/nVGsi9XB0pFYsETU9pmduxFmKPYedWYvcWxuCpZt+xq2SCtFzbos85q4q2USeSBDpJuj7D64exZRiyymxpF5PLHG0pY/k6n5U0vAYdA2tglartXquHIM8TIiJPMD/x96ZB1ZRXv3/exMCEggKmmBLI8YlEcsSiYZFtIAIsoRgGytqxYpWjaVapFhcWm3FulDc8C3WV339WQVUaglYgggal4oiF0FU3FkCVROEyhKWkMzvDzqXe+fO8sy+3O/nH83cuTNnZi5nznnOppfqUzl1oa/p01oro/JKoRHJq7NhqEMhhOhjNVJrttGUW3W4dmuRtRxeo+N41SWbkCgSi6k7xbL9YDct2IuIpp7NtGhmpeZnIpl6QbKj/Ajy0CEmJALoKbsgpE+rrYw2NDam1PNokbw6G4Y6FEKIMVYitVqOqBpu1uHarUXWuw6943jVJZuQKFJ2Yq6qzZFsP1iN4noV0TRy6rUQydQLkh3lR1dqjl0iJAKIKDKn2/DbZUx5F8N9krspAodeLKMGdE8o/6ysWNo+hJBoMmFkD7TLyRbaV68O1y6Dywox6YI+yO/cHjEAR+ZmY9IFfYTPp3Udebk5usdxavwUIZnImPIurtkPehFNJyk7MVd1u5ENKNtOWoh2i/YKLQffzSg2I8SERABlqo8aQayzHTWgu2Yrf626YSfqcAgh4UMt1Vor0mqUluyELLI88XgcZSacb6sp4051ySYkU3HLfvDK7hpT3gUF+fmWUrPlaw/qRJJk/MgGpENMSESQlZ1WO/4g1YfImKnZCYMSJ4S4izLVeuL0pY7NFvYCZZfoGy7uKxxZdqpLNiHEWbzsb2LXqVfaXXIUO0j2lN16bivQISbC0CEJB2GrsxVR7kEeK0UIsY/VcUJhipo60SXaiS7ZhEQNv+3TMNldYbGnvM4GZA0xEUL+BySvgMn/gGbPX+OzZERJFOtsvarPIYR4j+woNu7YCwmHHcW6eL3hd5X1vPmd25uq5/USvS7RhBBrBME+DZPdRXtKHUaIiRBBnnNL0olanW2Qx0oRQuxhd5xQWKKmTnSJthpJJySqBMU+DYvd5WefmRdXbscf5y0MZKYpHWIiRJgaNRF/cDNlKehjpQgh1gniOCE3HE+784udSLkmJGrQPjWHl/XOycyevyZl7FXQbDc6xEQIv/4BWcXvepKwYvW+uV2TIjJDb/GKTVi8YhOfNwkFjPQdxq6jaBflszjjlAIsX7XF0PGUv9e4Yy/ya781fIZ2653tRtIJiSJ6C+YVU2oSNkF5kceCmcBNm1V57G7H5KK+YU/afm7XOwclkq8FHWIiBBsGRAM9pWvnvrmt6ETGSsnweZOgw0hfKn42xlJ7Fmrvuv3NLXh0wbqE49wxNwdN+w6i5b/6SOQZ2u0SHcRIOiF+Y7RgLtsEDY25KCvzUDBBtGwvJxb41Y5d37AHhQUdsHVbk6dBo6BH8ukQEyH8aIFulaCvQvmFkcNr5755oeiMxkopyfTnTYILI32p+DlOSO1ZaLGrqRm7/jvfeJfKnGORZ2in3tnvSDohQaS6qtQwgwwA4l80Ge7jB3rNrOwu8Gsde+u2JtTMGGv6eHYIeqYpHWIiDBsGBIPkKG8sBozckC30XIwcXjv3zUtFJ5I+DUTneZPowUhfOn41xnL6nrv5DMM0YooQL9FLm5aRAmYSJNtyRlhd4A+SPRz0TFM6xCRyBH0Vyg7KKK8kHa6dNWrxb6QYRe6bVsq1l4pONH06Cs+bRBNG+lLxs55a61nYOZ5b+BlJJyTIiCyUx2yaBFbqfLW+o7TljLDqwAbJHq6uKkVDYyNWf7k3kJmmdIhJ5Aj6KpQd9FJrjNJqjBSj0X0TqTH2KqU+OVtB68UShedNogkjfYfxu55a7Vm0yY7hYIt5A9SLZxiWEVOEeInIQnnZibmWj2+lx4red8zO/LXqwAbNHh5T3gW3VQewkBt0iEkECVO9s1mMVgn10mqMFKPRfTNKufYrpT7Kz5tEE0b6DuN3PbXas9i3/6BqjbCSNtkxtG/XBruampGfwc+QkCCgXChX2gTlRWK9AtSw0mNF7ztmI75WHVjaR+LQISaRJCz1zmYxqpNJ/kzthTBqQHddxah335yqRUmRa95WR5RzVJ83iS6M9B3Cy3pqrdRs5bMYO6VG8xixGAAJKd+Px+MoC2L7WkIyFKVNMHv+Gvxh7hZIc7ZYcgqt2D9639HL2Duv33GOOrBm7aNMHVtKh5iQEGFUJyOn1Wil6owa0N1yZ0EnalE4EosQkoxX9dRmUrP16oqzYjH8+qLTuJhBSEhwwu6wYv8YOb1aGXt+LvBnso2W5bcAhBBxqqtKMWpAd83P5bQavVQdq2il7JhJ5XFDLkJIeJkwsgfa5WSnbHOjFlcvNVtEJpmWVkn1O4SQYOKE3WHF/tH7jmzLyQ51VlbMsDGqF2SyjcYIMSEhQ149nD1/DWrf3gRJQlpaixut9p2oRQnSCABCiP94VU9tJjVbPvfMOatNHYsQEjycsDus2D9G3wliqVcm22h0iAkJKdVVpSgvalGtXXOq1b5aLYmdYe5W5MrUehZCMgUv6qm10qBjWTHUxevTzj+4rBBP1a5X/Y4EYOL0pWyiRUgI0Ou9Ujl1oSkH16ztoVbLbOacXhOkMU1eQ4eYkAjiRKt9N2pJROTSG1afSfUshBDnUBuvBBzSKVq1xBNG9sCDz76nOoIpuQY5zyWZCSH20eu9ItsZXtgWYajPNWs7RilgwRpiQiKIE/UpbtSSVFeVorCgQ8q2woIOaTOFRcZLEUKIKIPLCnHO6T9Q/UyrlhgAJB1VpPc9QkgwkO2h2H+DnHrRTjdtC7s2ldwpu2JKDSqnLsTs+WucFA+AOdtRaa/JDr4bcnkBI8SEOETQVsrs1qe4UUsye/4a1DfsSdlW37AHs+evQXVVqfCLIRPqWQjJNLTGIjl17OWrtmh+rlYX/FTterQY6BrWExPiPnbtK2WJWYXGaDU3bQs7NpWX0WVR29HKbOYgQ4eYEAdwWlkFwbl2o5bESIGKvowyoZ6FkEzCzFgkK6h1mU5GbcyT1uglo+8RQpzDDWdQr67YLezYVFadTzdtyag14GLKNCEO4GR6cVDSUJwYs6RET4GauT47MhBCgoeZsUhW0Ivkqo15qovXGx7TjfFQhJBUvBwjCcA1W8uOTWXF+XTbltRy5MMasKBDTIgDOLlSFpQ5cG7MydNToCLXF5RZfYQQZzEzFskKWpHcrKwYJl3QJy0KbeSI53dur/o9QoizuDlGUg23bC07NpUV59NtW9KNoImfMGWaEAdwMr04SGkoTs/J0+tgqNUFEgAWzaxMpP4sXrEJS97Z7HuNNiHEObTGIomkJIvUHqt1mW6Xk63p1Oo54jEAT9w63FAuQoh9vB4F5KatZdWmsjI5RM+WdGL0k5XZzEGGDjEhDuDEmCOZKM+B01OgWqOWsrJimjVE677Yhr/8dphn8hNC3EHLYTVKSRatPZb/X+k4A4dmCiudaS0HHWDdMCFe4qR9lUyQbC2jWl/5/2vf3gRJgpDzqVcn7dS4KaeDJn5Ch5gQB3BypczsrN6seVsTn4VhpU5WoPF4PNHxEdC/bq0Un+QO1YSQ8KLlsBqlJBvVHj9Vux6NO/YmjMP8zu1xw8V9MbisUNeZ1ppBnJ0VY90wIR7iViTSyNZSOqndjsnF1m1NjttYok3DlJ2yjTDKvEsmrJ2hnYQOMVElCF2Ow4bISpnIfTVS/nrKU21bWJ6b3nXrKXUqckKiweCywoSj+lTtetw3ZzWeql2fcIzVUqO1Uptl51Z2duWISLLTq+dMyynRjy5Yh11NzQCAvNwcXDWuF+uGCfEYI/vKis2qZ3Oo2VnJIyPt2FhKWbWiuHZtG7XrC1JJXtCgQ0zS8HLeWVQQUcbX3rNMWKHqKX8zDRHC5izK162sF9aDipyQYGFnnrDy/SM7sOs3fIvlq7akRXM75uYkHNZksrJimmOWZKfXqJGX7KATQoKLns267ottKXZX4bIdKWVWWraWqJ1lduxRt2Ny0+xALZywbZTXJ9cOK4lCSZ5d6BCTNKI2bNttRBYQZs9fk6IEkzF7X80oSbecRTOrsWZXbtXupx5U5IT4g5rjC8DyPOG6eL1qNsj+5hbVHgP7m1vQNicL7XKy02qP9WYOy3JlZcUgqegX1ggTEgxE7Actm1VNl9Q37MG19yxT7T2SfC5RRMYeJe+rZQeq4YZt41Y9dhSgQ0zSYEqFOUQWEPRWG83eVzMD5d1QqGYyCPT2LS9KP67ZlxFARU6IH2jV37bNydJMQxapB9ZCSy/sampGXm4O2uZkYXdTc8Ixl2uH9VA7JmcLExIMRG0NszaDmlOqPJcoVsYeieKGbRO1ztBOQoeYpBGkznthQGQBQU9hm72vZholuKFQzWQQ6K3c1r4NjNyQrVqvo8WoAd2pyAnxAKO0Z636W63IrMg8Yb199BYCdzU1o11OdqJZlszMOasNzykfW2qVUq7TTto3IcQ+oraGmSCB2XMZYWXskRbydbht25jpDJ1J/YToEJM0mFJhDpEFBD2Fbfa+aq3wqW1zQ3GZySDQeyFI0uGUJpGXUVZWLFIt/gkJKiKjjEQc3GRE0pD1Rh2d1++4lBpiJcoo9OCywpSGWHpIrRIWzqxM/C06yokQ4h6itoayLtfJc8lY6TJtxlEfNaB74GybTOsnRIeYpMGUCnOILCBo7VNY0MHW7Dfl6CIvnpGZDAKRF4JomrRy4SCTVi4J8RK97suyQ6jlvObl5uBAc6vpecKA+ixi4LCx2KPoaN1UaKWTftW4XqrHU6J01kWu3whGmAmxh6itsXVbk6njFhZ0MHWumhljTR1fRs/uc2t8k5M2Uab1E6JDTFRhJE4ckQWEKC0ymMkgEEnvNhoHoHavMm3lkhAvMeq+DKg7r+1ysnHVuF4AzM8TBoxnEctdnydOX6rqFCsd2+TjaTnRas66yPXrwQgzIfYRtTXMpCYXFnRQbajlRmakHbvPiWakdm2iTOsnRIeYEAcQWUCIyiKDGSVvNEMYOOzwau1npqtkVFcuCfESrehvssMp4rxaQWTUkZYzrhaFlo+lFinWmisscv16OBFhJiTTEbU1jDLR5AwTZUadlXNZuQYrc4pFm5HKuGETZVo/ITrEhBDTmFHyowZ013WK5ZeO1j5qCj3TVi4J8RJRh9OvOb1GzrgSNQcVAI5o10b1O2YcbjXsRpgJIYcQsTX0FtTN1OYGJWih59yWF3VT/cwNmyjT+gnRISaEuIpy5VUmFgNG9jd+WWmtUGbSyiUhXmLW4fQDM864WQfV7vXbjTATQsSJUkkaYM25dcMmitp9NYIOMSHEddRWXg+lLxmPTlBT6Jm2ckmI1/gV/XUDKw6qneu3G2EmhJgjKNFdJ7Di3LplE0XpvhpBh5gQIoTbXZ3NKPRMW7kkhGhj1NHZawc1DBF2Qogxfkyz0LeF1Dvm0yayDx1iQogmyS+DZNzo6mxWoWfSyiUhRB2Rjs6DywqxfsO3KbrlnNN/4KqDGqUIOyGZiF/TLORjGzUkVfsebSLr0CEmhKiifBmo4XRXZyp0QogZRDo618XrsXzVlsTCXmurhOWrtqBH0dF0WgkhqgRtmsXiFZvQ0JgLjUbZxCahc4hbWlrw85//HDfeeCN69erltziERBatl0EyTnV1Fk1L8iN9yS+o60imYJTyrIdIwyw7Y5DsyEbEoK4jQUIrM07Gi2kWWvZX/IumtG2ZZBe5Segc4kceeQQFBQV+i0GI78yevwa1b2+BNGeLK0pQROk70dVZKy1p3RfbsHVbU0LJdzsmF/UNe9L2A9xNX/IL6jqSCYikPCv3T3ZQO+bmYFdTc9p+yQ2ztJzmxh17MXH6Uk1n16xsxBrUdcRJ7DiIIplxZuweq7Jo2V+SYrNfad1RJFQO8UsvvYSTTz4Zra2tfotCiBBurdx5oQSNht0DznR11loJVTq/yX8rvx81xU9dRzIFM9Hbung9Hnz2PRxsOaSXGnfsRVZWDNlZMbQk6SplwyytLtPyMeT/Kp1dLdkeXbCODrFDUNcRJ9GzjcqLjL8vkhmnZfco7T07i/ha9ldM4YsHLa07zITOIe7YsSM++OADbN68GTNmzPBbpEgQ9XQLv67PTafVCyWoN+zeyftoN/3Ii/Qlr6GuI2FHNNXYzIzgRxesSzjDMq2tEo5om40uHdpi24696JibAwC4b85qPFW7HhNG9lDtMq2G0hHXkm1XUzPq4vV0ih2Aui76eGmD6dlG5UXdDL9vZE8UFnTQLOdS2nt2FvGVzrTM0R2zhOSNol3kNqFyiO+77z4AwKxZszB48GB/hYkIQUm3CHMkVQs3nVYvlKBXbfxFItFG348aXum6ung9HlvwFXbOqWF9JHEMvVTjPMW+ZmYEq6VGA8C+Ay14/q7hmueddEEfTLqgT4qDrhUxK8c3wgAAIABJREFUTnaC9fYTqT8mxtCui3ZQwmsbzG3bqL5hD2bPX5Mmu0hk2YwsW7el1woDwLe7UzMprMwsJupkGe/iPrt378aYMWOwZcuWxLZFixZh1KhRGD58OJ555pmU/X/1q1+x8YJD6DltXiErzOQOoItXbMLs+WtsH9vP63NTMWspO6eVYHVVKWpmjMWimZWomTHWlReY3bRrJ9K2vSJIuk52Hr5raoGEw85DXbzelfORzEEvDVrJhJE90C4nO2Wb1RnBRunXT9w6HAtnVuKJW4cjX8XhBlIdcT0ZtKLHSuri9Zg4fSnGTqnBxOlLM+bfV5B0XZBx0/4JAl7bYF7YRmqym7HrRGQRrSHWsn+6HZOLyqkLUTGlBpVTF0bm9+QmvkeI165di1tvvRUbN25MbPvmm29w//3344UXXkDbtm0xfvx49OvXDyeddJKtc33wwQc2pXWfeDzu6fn0nDazsliVvfbtLRrbN6G8SD/FzQjR63Pjvsdi6cpL3m73fH1PaI9Vn6evIPY9ob2j1/Liyu2If9EESTokd9mJuRhT3sWx48fjcZQXAYtXmP+uLE95UYvn/26sEDRd99iCr1Sdh8cWrEUeGmyd32/C8HswQ/L1vL9hD5av3YnvmlpwZG42zunTCb2LOvgoXTpG9brJ15MHYPTpndKuKQ8NiMfFf4fxeFz3vMrfxFmnHIFFK/ehOSkFOyc7hrNOOSKxbx6A9jkx7G1OV+SdcrMN3yHvb9iDRSv/kzhH4469eOjZ97Bhw4bAPTMnCZquCxLK34mb9o8bmNWtdmxMK/aHnm0EOPNuUJNdy97TktFIDlH7sbwIaGjMTblPR3fMUq1dbmhsdNR+s0pQ38++O8TPPfccbrvtNtx4442JbW+99Rb69++Po446CgAwYsQILFmyBJMmTbJ1rp49e6Jdu3a2juEm8XgcZR4PGMuat1Uz3cKMLHZkl+aovxAkCbbvh8j1uXXfR27IVq3BHdm/O8rK7EVay8rkLtObIEnWa3r1UrVmz1+T8mKRJGDV500oyM93JFKcfN9HadwrNbKyYqiZMdb2+QFg//79nhlUQdN1O+fUqG9vavFcDzmJH3rUTZKvpy5ej3+uOpwS/F1TC/65aieKiooClb6bX/utqnMqR2WVz6esDLi8yvi4eQu+UU2bzsvNQVlZme551c5ZVGRc53wtCtLqj9vlZOPKcX1Q9t99lc9IPmZMJZ2xuUXCGx/vw+VVZxtfsINksq4LCmq6yU37x2ms6FarNqZV+0O2jdTsGhH5teQ1kl3L3iss6JAyLcPITpNl13Kuy07MVdVlyVROXaj63dVf7sVt1f7+prx4P1vVdb47xHfeeWfatoaGBuTn5yf+LigowPvvv++lWBmDVuMkL9NQ3ayBcPv69BxKt2twq6tKUV5k3Xkxqu3xsnuh8l7pEaYU6WSCpuvM1G6SYGBlnq4fc3TVmlgdToO2nn1w1bheeGDeeykdpbOzYrhqXC+B86YzuKzQ8F4kd5w2uofKGmZJQ5clp1tHcc5x0HRdkAlLDajVMY9WbTA79kd1VallG0WvmWjyPmrnlOWzau/pjXySjyeSNaAXlVerfyaH8N0hVqO1tRWxpN7ikiSl/E2cw6vGSXq46bS6eX0izSLsKGa3MXrheN29UL5XlVMXap5Dq8NjWPFT15l1Hoj/mOnIDPg3R1fPiTSTBm3muCKf2zmvyDHUFizUkBedMmnOMe06dYIQlDDCTmMsqzaYX92T9RbnjWS3a+9p2WTJWXEi6cZ6jUo5o1ibQDrExx57LFatWpX4u7GxkUPbXcRvp82LSKob1xf2+W9GLxw3V671Vpv1Vmi1Oi+GFT91nWxwP7ZgLXY2tUQmOhVlzEb1rUSUnULUiXT6uEafuxmRFWm0lbzo5Ofz8RradeoEIShhhF1bx4oN5mfk3Cmb0Wz3cKcWAYyi3GGxUb0mkA7xwIEDMWvWLGzfvh3t27fH0qVLcccdd/gtFnERv51yKwR9/puRMjZ64bi1cm202lxdVaqpzINyb53Cb103uKwQeWgIXK0aOey4Ne7Yi/zabzXn6epF9c1GlKOO2xFZrQWLrKwYpFYpzQHPpOfjt64LMkG3f/ywdczYH0EcW2Ulqu7UIoB8/Eyxo5wikA5x165dMXnyZEyYMAHNzc2oqqpC7969/RaLkBScUl5uKHMRZWz0wjFSqlYRWW0OS12VXajriBpm5unqRTidqhOPSp3rowvWuRqR1VqwmHRBH9XjZ1IdP3VdePHjfSwaOXdjznGyTZaMGfvMSlTdySCE3AcmE+wopwiMQ/zKK6+k/F1RUYGKigqfpCHEGCeUl1tD60WUsdVULbvyiaw2h6GuyirUdcQIvVTaJ24dLuy8OVEnHpU617p4vWqHasC5iKzZGuao1/FT10UDv97HIpFzp0vX9BpbmbHPrETVnU6fj7Id5QaWHOLdu3ejrq4OH3/8MXbt2oXOnTujT58+OPPMM9G2bVunZSQkkDihvNyqQxZVxkYvHDfkE1ltDkpdFXUd8QOnUmmdaDIVlTrXp2rXa37mZETWTO20W03ArEBdR7SQ37t2xzy6gdPp3Fo2j3Ifo2u3GlV3Mn0+KHZUWDDtEP/jH//A3XffjZ07d0JKGpQVi8Vw7LHH4o477sCgQYMcFZKQoGJXeblVm2OkjEVSgmbPX+OKfKKrln7XVVHXBZuopPGq4WQqrdXmVsk1zGo4WefqxbPUk9fPiKxbzcfMQF2X2YiUbdkd8+gGs+ev0fxMz/HUu14R20Zkn6BEZ/22o8KEKYe4rq4ON998M/Ly8nDNNdegd+/eOOaYY7Br1y6sXr0aTz/9NK699lo8/fTTrA0hRACRVUQrNcZ6ylgkJWjdF9tQ37BHV26rBHm1WYa6LtjopfHm+SmYQ/idSqu8v2rYjapqOdxupWRrLTLk5eb47pD6CXVdZuNW2Zbb6NkxgLbjaXS9eiOLzMDobPgw5RA/8sgjOPLII/H888+jsDD1BTJw4ECMHj0aP/3pT/Hwww/j0UcfdVRQQqKI0Sqi1ZeVnjKunLrQUC49ZzhZPi2MnPggrjYnQ10XbPTSeH858mifpHKO5FTaxh17ke9xBNxonq6Rc24U8TVyuN1IydZaZLhqXC/HzhFGqOsyGzfHR7rZ/VkvtXnUgO6a5zG6XqORRTKz568RmsFMBzg8mHKIP/nkE4wdOzZNacoUFRVh+PDhWLp0qSPCkegTxHb5XmK0imjnZaWljO2ufuq9bADrTnyQfgvUdcEmaONq3Ej5lVNp4/G45wtHevfRyDkXacJl5HAbyWAFL+p1w5jGT12X2bhVtuV25FmkOZWZ78nblTaZFkGa5Rsk2ynMmHKI8/Ly0NraqrtPLBZDu3btbAlFMoOwpuo4jd4qohsvK7spQW50fQzab4G6LtgEaVxNVLowJ6N1f/M7t8cTtw7X/a5IEy4RZ1f5LJ1wNt2s1w3r74C6LrPRswdEoqBauBl5BsSbViU7izGdSi9lU09ZxoopNar7B2WWb9BspzCTZWbnn/70p1i0aBHef/991c+//PJLLFmyBD/96U8dEY5EGz2FSQ6hVatrp4bXblMHvUYWgDUnXuuZL16xyfB8bkBdF2wmjOyBdjnZKdv8Glej5wD6TV28HhOnL8XYKTWYOH0p6uL1Qt+zc39FovdGCxfKc8nOZuOOvZBw2NkUvR41rN4bLYL8O9CDui6z0bMH5Pfy7PlrUDl1IW6fswWVUxcKvZPdijzLaMmdvF12FuVzSjqn1jqeEzaYfP8qptQI3z9RaEc7h6kIcWlpKU444QRcfPHFGD16NE4//XR07doV+/fvx7p16/Dss88iJycHnTp1wlNPPZXy3QkTJjgqOAk/bivMKOBGp0LRlCAtjFZ4rYwb0JMjebXTq9Qg6rpgo5f+Go83eCpL0NK3ZexELO2kF4tE79XqeWXUUrIfXbDO0dFPbkRzg/o7MIK6LrOprirVbbJpNQJpdeyQKCJNq/RqgWX5jOwIuzaYX6njtKPNY8ohvvLKKxP/X1NTg5qaGsT+m4OQ3Kr/nnvuSWvdT8VJlLitMKOAmtLvdkwulryzGYtXbEIsBozckG1asWqlaVdOXWioSI0+t/ICMUrjllc7vUoNoq4LPkEYVwMEK307Gbvzg63eX5EO2SIOt9HYJ8C6s+nGbOWg/g6MoK4jeraY1dRnJxfztRbC9crNjKKwNTPGCp3bbrfooKSOE2NMOcR33XWXW3KQDCQoc9rUCFKTgmSlr1xtlCR41qhCRmSwPGDuBWLU2THZ+VXiRnML6joiyoSRPfDgs+/hYMvhfzttsmO+zrYF/ItYikaX9RxukbFPgHVn04174/eoLKtQ1xE9W0wveqyHU2OHrEZY9VKGzTqLdrpFW43gitqgQbajw4Yph/j88893Sw6SgQR1TluQmxTo1dpaedEo771Iw63kkVBaz87sC0TeV2TcgRI3UoOo64gZlLVpkgSs3/Ctrx2HvYpY1sXr8eiCddjV1Azg0Fzfq8b1Mmy+pYdIF2oAlp1NN+6NF12s3YC6juhhJwJp1g4wmi2cjNFCuJ5d4KWzaOX+6dmg5UWp+wbVjg4jphxiQpwmiHPa3E5xsYOekhfpCJnsxCqPu3jFJhQWdNCcQZysaN1YNLDqFDM1iPjJU7Xr0aL499SiyGjwo+OwFxHLunh9WnR8V1MzZs5ZjZlzVluenywSqY3FrN9Lt+5NUNL4CTGDns3jVQTSjDMM2FsI99KOs3L/9J5HeVG3tO1BtKPDiK5DbLU+JBaL4f/9v/9n6buE+Mns+WsC3aRAL4Jr5LCLvHC2bmvCqAHdVVcbZWda7xh2Fw2sNPxy4sV8xRVXICvLVNN9ANR1RDzN1m6Nqh56I4ncjFg+Vbs+xRlWYnUhQCuCm4xex1gjku9N4469yMqKpXSEjrJTS11HlOjZPG5GILUW6EUwirBqMWpAd9PnskN1VSnWfbEtJdBQWNDB1qxk4g66DvHKlSv1v9ymDfLy8rB3717s27cPANCuXTvOqyOhxMhhDEIk0k5Nj0gbfvkFqFTWoqu3Tihs+fxGDb6cfDGvWrVK93PqusxEZPatiPMm40b9rlHHZDedO5HrsbIQoNeFWibfZuq3LE8YZwfbgbouuPjVu8QorVd+J8fjcZSVlTlyTrMRYSVWIqyA92Vvs+evScu6q2/Yo5vRx0ZZ/qDrEL/77rspf//73//GFVdcgRNOOAG/+c1v0LNnz8RK42effYaZM2di/fr1ePLJJ10TmBC3MHIYux2T68h57Lz09EYkGClLOw2zRGfaOamwtZz/UQO6O/5Se/PNN1MMPuo6IjqaR8R5k3Gj47AbHZNFEV0MkB3nung9HlvwFXbOqdGNWCsjuEqcSv328975BXVdMPGzd4kfjZmszslVTtpQs6GCFEm1UoKn/zyM3zPEGroOcV5eXsrfDz/8MDp27IhHH30URxxxRMpnJ598MmbNmoUf//jHmD59Oh5//HHnpSXERYyU6NZtTbbPYealp+U4jxrQ3dLLy0zDLCVepi/LeNksIi8vL8VIpK4jos6SWmryGacUYPmqLZ50HHarm7RIdFytw7Yax3Rub3r2b3J0W0QWK4R1drAdqOuCiZ+9S/xozGTFaZXTndVsqHVfbMPWbU26x435EGC1kv6s9zzi8bgrchKTTbX+9a9/oaqqKk1pyuTk5GDgwIF47rnnHBGOEC8xchidWHUUfemJOM5mX1566dZGxzC6N269QP1qFkFdR8w4S2qpyT2Kjvak47AbHZNFnVf5/5O7TCuRFwLsRGPdSv0O6+xgJ6GuCwZ+1416/a4VWaBP3le2LyqnLlTdR6sZaDJlJzqT5WcGq+nPbJTlPaYc4vbt2+Orr77S3efzzz9PiywTEgaMZuE6kQ4s+tIzcpyt1PTYWQX2Mn05CFDXEbvOklcdh93omGzGeVVep1Y09745q1XP5Wc0Nqyzg52Eui4YZFrdqFWbwmoDrvP6HYfyIu/TjTknODyYcogHDBiAxYsXo6amBpWVlWmfP/nkk3jrrbcwfvx4xwQkxC1SUpLnbcV5/Y7TTEcGnFFgoi89O6vFTs4Hlsm0WXfUdSQszpJeN2mrqcZ2Uom1FgKCGI0N6+xgJ6GuCwaZ4jhpdZYWsSn0ukdrsWjm4d+0H+nGmWY7hRlTDvHkyZPx9ttvY9q0aXjsscfQs2dPdOjQAbt378Z7772HzZs3o3v37rj++uvdkpcQR9BKSR41oDsWzax0rduj6EvP6mqxG405lPciqlHhZKjrSNidJbM1u8l0zM1RTYHumJtjWZ6gLjBk+uxg6rpgkAmOk1ZnaT2bwq3RTF7C9OdwYMoh7tatG/7+97/jvvvuw8svv4zPPvss8VleXh7Gjx+PyZMno1OnTo4LSoiTiKYkO43oS0/LcW5tlVA5daHmi9Lpxhx+dr70E+o6AoTDWdJyfNvmZAWqg7J8zscWrMXOphZXFhjcar4VZajrgkPUHSez9onoaKbCgg6qNcR+R9f9GqNFrGHKIQaArl274p577sH06dOxefNm7Ny5E506dUL37t3Rpo3pwxHiCyIpyW4pM5GXnvy5llMsby8vSv9MDeV20Wvzs/Ol31DXERH8dsK06n21xkCJpD3v1miQpbU9CKgtDMycsxrrN3wbeV1lF+q6aBB0B8xsKZjonOK//HaY6Wt3+15lajAhzJjSdBMmTDDcJzs7G0cccQS+973voX///hg+fLhl4QhxC6OUZC1lJqdVO6HQjBTyui+26X5/yTubUV7ULU1+o1RrM4ra786XfkFdF32ccGTtpCXbRZZfZA5wMiI1u27U+yr1jt69svJs1BYGgENGdY+ioxkp1oC6zn+ccM68cMBeXLkdf5y30LKcZkrBzNQLW3GG3b5XmRxMCCumHOKvvvoKO3fuxHfffXfoy23aoEuXLtizZw/27ElPV5g7dy7OOusszJ49G9nZ2c5ITIgDGNXy6g2NV1OcTivk2fPXGI4RUHuxiNQoa626Ll6xKU3mTOt8KUNdF22ccmTtjBKyg1J+NfJyc3CgudVSza7T9b518XpVvaN2r9SezQPz3sOjC9Zhd1OzpoOsF/n2K008DFDX+YtTzpnbDtjs+Wuw6vOmxN9W5BTtoSKaKi1j9v554axmajAhzGSZ2fmRRx4BAJSVlWHu3Ll4//338frrryMej+PFF1/Ej370I3Tp0gWLFi3CsmXLcOGFF+KNN97Ak08+6YbshFimuqoUowZ0Tzh2ymZRRkorWaHKylv+jqyQ9VY49RSy3udKXly5PeVvo+syi1YNTmurZKnjo8zs+Wvwh7lbUDGlBpVTF9o6lhtQ10UbPUfWDHa6MdtBKxoq0y4nG1eN64VJF/RBfuf2iAHI79weky7oI+QYDi4rNPxuXbweE6cvxdgpNZg4fSnq4vWax3t0wTrNz5T3Su3aWlol7GpqhoTDixfK8+lFr+VzmJE5U6Cu8xcjW0AUtx0wJ+QUsU/MOsNaLHlnM2bPX4PKqQsTdoZsL+ndK6dsEa2gQdSDCWHGVIT4nnvuwdFHH40nn3wSOTmp3SZPOukkzJo1C+effz7uv/9+/OUvf8Htt9+Ojz/+GAsXLsQVV1zhqOCE2EVvlq/R0Pjkz6ysNhq9vERfYvEvmtK2OdmYQ6+W2WqKURhqa6jroo1Tjqxfo4T05MxXRFCtRkb1GoqZjbCrdayWUd4rkWegFlmeMLIHZmrMOj6mc3tf09uDDHWdvzjlyLqdzeWUnEb2idmFAC2S7Qr571WfNxk6vE7ZIpkyRitKmIoQv/vuuxgyZEia0pRp27YtzjzzTKxYsSKx7bTTTkN9PVdhiXMoV/3ciC4aKa3kl4yVF4XR6qHoS0zyIPumuqpUUx4rLy+nVsTdhLouHFiN+Gk5rGYd2Qkje6BdTmraqBejhLTkzO/cHk/cOtx1B8+pCDuAtHsl+gyUjvPgskKMGtA9bT/5eTgpc5SgrvMXpyKJWjaLXQdMtre0cDri6XZKsYid4YQt4nS2HnEfUw5xbm4utmzZorvP119/naJYW1tbNRUtIWaYPX8NKqbUmE5PtoKszLRIfslYeaEZvbzMvMTMLgpoXZfe9TqZjhWG2hrquuAjR/wad+xNSaV9f4N+7T3gnCMrklrsBn454jJmI+x5GvOL2+Vkpd0rtWtTQ81xrq4qxZSL+6o+D7fS2+vi9bh/wVehTcOmrvMXpxxZNxwwZTmYE3IaoWc3FRZ0sH18uc+L0T5OUF1VipoZY7FoZiVqZoz1xBn2ImAUVUw5xP369cOyZcvw8ssvq37+2muvYfny5TjjjDMAAM3NzXj99ddRVFSkuj8hohjVlbgRXayuKsWimZWGLxkrLzSjl5fycz3MLgqYfXHqHdfK6nAYamuo64KPVsRv+dqdht910pEdXFaIJ24djoUzKz2Jzsrn9MMRlzEbYb9qXC9kK/59Z2fFMOmCdJ0zuKwQ55z+A93z6zn/8vO44eK+AID75qzGxOlL0VHDKbeT3i4vynzX1KJb3xxkqOv8xUlH1mkHTM+uciviqWU3jRrQHX/57bC0e2XWSZabnhrtoyQMjqaVfjbkMKZqiCdPnowVK1bguuuuw+mnn45evXohPz8fu3fvxvvvv4+33noLHTp0wA033ICDBw9i7Nix2LhxI6ZPn+6W/CRDMHJ43YwuatW8JHeWTkZ0HIFRLY3yc63zyZipfRGtMzZaiLCyOhyG2hrquuCjFdn7rkm72VQyejWyQcXvmccyZrtQyzLKY6KUdc5K3v24QfPcRt8F1Guc22THkJ0VQ0uS/hSJquvdc7+6jDsJdZ3/ONn3w0n07KqaGWNdOad8H7SmdijvlV46txrJx9KybYy6Xgex7wnAUU92MeUQFxYW4tlnn8Wf/vQnvP7663j33XcTn8ViMQwcOBC33norioqKsHnzZnzzzTeYOHEifvKTnzguOMksjBxer6OLWo6imzUi8ougYkqN5j5GStrseCi9hQg7q9gAUPv2JkiS+AKCl1DXBR+thlZH5kZzFIzTTaHsONfJDqHI95PPdWRutuG5tBY7YgCeuNV4Bq6ao3qwRUJebg6OaNdG+JqN7rlfXcadhLqOaKHXXHT2/DWu2zpGzJ6/Rtc2HDWge1oQYfGKTVjyzmZ0OyZX9TuFBR3Szh0WRzMM5WhBxpRDDADHHXccHnnkEezYsQMffvghduzYgY4dO+LUU09F165dE/sVFhZi9Wr1jo+EmMWo67PX0UU/FaTRvVCbJwxYW+XUO4+d66yuKkV5UUtad+8gQV0XbLSilOf06eSjVO7hZDTSCedaNMKuPNd3TS2G57LbvVvLId3d1Iw5d4wSOgZgfM/96jLuNNR1RA2tbC7Af2fQKHstKyuWcKzVbJ/6BvVeE1u3pU/uCIuj6Xan8ahjqoY4mc6dO2PQoEGoqKjAkCFDUpQmcGhlkRCn0HN4/ejc56eCtOr8W+nuHIZ6X7ehrgsmWnW0vYvsN14JIk5GI73suGzlXHabhjnVRdzonvvd3MxpqOtIMnp2ld/OoFEZXbKdZKbHjBmHMmh2kFudxjMF0xFiQvzAqK7EDi+u3I4/zluYOG63Y3KxdVuT7nn8XIkzqn/RwooTr7VCrJVuRIiXqEUp4/HU+tOg1N3axclopJaj17hjL8ZOqXH0Phk5lUY1ulaem9kaZy2M7rksz2ML1mJnU0uof18kfJgtgbKCF7aOleswcsjl1Ojz+h1nynlXu64w9D0B3LWTMwE6xEQYL5SvHm40npg9fw1WfX44RUaZSqOVVuy3gqyuKjXtEFt5sVVXlWLdF9vS0ovqG/YI1RD5/ZshmY3Tdbd+oubkAcC+/QdRF683dT1ajh6AlG7JgP37pOdUGj0fq+e2U+OcvK+IYz24rBB5aAh0+QeJHl41enLb1rF6HUalY8pjiaJ2XU47mm7aRUFt0BYGLKdMk8wiqu3cRVNplPsFYei62XnCVtNp1GpqAON7F9XfDAkPXqYGu42cIq6c6burqdn0qB+RWb9O3Se9tGI3n4/oOCytedbyIoOf460I0cJKCVQQsXodTjjkhQUdhG04p0Za0S4KLowQEyHC0mXPLKKpNGr7+b0SV11ViobGRqz+cq/QSqPVVU6r9dJR/c2Q8BCFLsDJDC4rxFO167GrqTllu9nmWsoIqta/ZCfuk/JcnXKzceW4Q07lfXPUGzR5+XyMGmeFcTwXiT5e9TFx+z1u9TrU7BmjjtNBmGxBuyi40CEmQoSly55ZRNJu5P2CyJjyLijIz068FGRlq+cUm1W6VmuIovqbIeEhKl2Ak3HKyU929CZOX+rqfUo+1//Nfx1P1a7HfXNWI5YVg6SiD7x8PlFbNCGZgVd9TNx+j9u5DrWZxFpyLXlnM8pOzMVt1edaF9YBaBcFFzrERIiotnPXGyug3M8IP+plX1y5Pa0G2uk6Iqs1RHqLDRVTahCLASM3ZHNVlLiGmeZKYWm+5YaT71QTKiPq4vVYtPI/aG45pBfUnGGj8zr9nKK4aEKijd78Xaf7mLht+zlZo6xnz7W2Slj1eZMj85Pt2HpRtaWjAGuIiRBRbedeXVWK00/KTakjMVNXIuNXXUj8C2v1vWawWi9t9NuQJLB2hriKaA2oXh1p0HBj1I9XtbJP1a5POMPJZGXFhM7rxnOK2ugkEm305u+60cfEbdvPyX4symOpYdc2smvrRdWWjgKMEBMhotzOfUx5F9xWba9DqF91IZJGlo3T6TdWUq2VvxktWDtD3ESkBtSojjRI2B1JpHdct69VKw1ZapWwcGal4ffdeE5u3U9C3EDL1sjKirnyHvXC9nOyH4t8rIopNaqf27WN7Np6Ubalww4dYiKM302kgoQyZcavupBYTNspdiI1yC7Jvxm9F1QQZCWZS9DqSJPTgjvlZuNKpI5VsuOlQ7SjAAAgAElEQVS8+pkabjc9WW9+8sTpSy1fCxtnkbDgh60hv8fj8XjaeDG3SsXsHFcvWms3NdmJ+09bOpjQISbEJGpz87Rwuy6k7MTclBriZNyYSegWYZKVRI8g1ZEqZ/N+19Ti2Exgv+cyTxjZAw89+15K2rSZ9GS9+clmryUsNeOEJBOkGlS3ZiHbOa5eSjlwODXZqsMdpPtPnIU1xISYxEwNitt1IWPKu2jOHQaCM5NQpL4mKLKSzCNIdaRuzub1ey7z4LJCVJQfZblW2Wh+sui1hKlmnJBkglSD6tYsZDvH1dvn9JNyUV1VaqsOOEj3nzgLI8SEmMQoIux1XUh1ValuZ8UgIPIiC4qsJPMIUh2pm+nbQUgN713UAZdXnW3pu8nPSStSLHItYaoZJySZINWgupW+bee4evuMKe8CwF4dcJDuP3EWOsSEmEQvZaZmxljXzquW4lNeZCxTEAjzrGeSGQSljtSp9G21lOAgpYZbRX5OdmYnB2FhgBCrBKEG1c06XTv2jMh37TryQbj/xHmYMk2ISfxImdFK8Xlx5XbfZDKDyIssKLIS4idOpG9rpQSfcUpBYFLD7WLnPmk5zWFaGCDEL0TrdK1ix54R+a6WPcJF+cyGEWISatzqcKiHHykzWik+8hzioKfxnNfvOM0XaNBkJcQMTjdnUqZvd8rNxpXjzM0E1koJfvfjBky6oE8gUsPtYifNfcLIHinNxYDwLgwQ4hSi9pReCZQTs5Dt2DN6343H4wC07REuymc2dIiJpzjpwLrV4VAEN1Nm1O6RVipP8silIKfxqL2k+p7QHrdVn+uzZIRYR61r88w5q/HognW4alwvS46m0sE+65QjTB9HLyU4KKnhTmD1WoJUM05IEDCyp2bPX4Pat7dAmrNF9zhOzxOWZVvyzmYsXrFJyG40soWCHkAg/kCHmHiG0w6s3QHpXiOyGKB3j9SIhSjDR/mSkldrreBHZgCJBk5GdNUisQCwq6nZ0jgjNQd70cp9KCqqN3UcrVrhWFYMdXFzx4oCWs880+4DCR9eveuMOjvr2SEybqQc2x3BpFyEl8comwkg0N7IDFhDTDzD6Rb9fgyot4pom3+z96LsxFzHZAwLdkYmkMzG6XE7ek2YrIwzUnOwm1sk08fRGk/U2ipl3HghjlgiYcXLd52ePSVql7iRcmzVblS7d6s+bzJ972hvZA50iIlnOO3AhqkxgqhSN7oX8rVlZcUwakD3xBiBTMKt2Yck+jg9h9eoCZPZrsVOdT8eXFaISRf0UdWFXs4dDgJ+z14mxCpevuv07CkRu8SJ2mE1rNqNTt072huZA1OmiWc4PRpItDFCENJdRJW60ctHOdZJTjsOwjV6RZgyA0iwcHrcjlpzpmSMHGZlKm/H3Bzsamo2fRw1BpcV4r45q1U/i+J4Ia20aI5YImHFy3ednj0l2xZK3Bo1mWzPaGFkNzp179x4Bplkr4UJRoiJZzg9Gqi6qhSjBnRPi5omK5agpLuIRrP17oXWMYJyjV4RpswAEiycHrcjR2LzcnPSPjPqWqyWyrt3/0FkK37HOdkxy92PM2W8kF5adKbcAxI9vHzX6dlTXo51VNozWhid26l75/QzyDR7LUwwQkw8w43OfkaNEbTSWhav2IQl72zGef2OQ3mR5dMLIxrNrq4qxbovtqG+YY/hvjJhay5mF45MIMmodWWWG6cocWPcjtycyWyzLrVU3oMtEvJyc3BEuza2ukzLZMp4Ib206Ey5ByR6aL3rWlslVE5d6HhkUcuekrfVvr0JkuTuqESjVGTRcztlJzhtb2SavRYm6BATT/F6NJDeKqO8MtfQmKtpQDuFmcWAv/x2mKmUmkxLIebIBCJjtiuzm+N2zHYt1krZ3d3UjDl3jEr8bacbe6aMFzIaMwVE/x6Q6KF81yXj5ZhJ+RzlRS0oc9lY0rNbFs2sFD6O1qhHs/fKaXsj0+y1MEGHmEQakYYQ8S+ahI5lt+7DzGKAmX2drs0OA0GeuUy8Q68rs5bDE5RxO1qjkZxO5Q3K9TqJ2drrKN4DkhnI77rKqQtV3/NRiyxatWe07DMnRj06aW9kor0WFugQk0ijle6SjCSwMOf0DGUn0UurkutSRB15NnsgYSJsDZOSHbmOuTnIzoqhJck4spPK6+R8ZS+wKq9aVkB2VgxtsmM42OLMvSQkaAQxsqi0iwBY7jZt1EhLL0U5yPaZEpZ8BRc6xCTS6KUcycQEFub8rPswclLl/1dTssptei+KML1UCAG8i7KaRc3ZA5DiyO1qakab7BjycnOwu6nZtBOrdK737j+YcAjlplIAAukUqzm1ovKqZQW0tKbXXgd9QYAQM3gdWVSzO5L7rag5w8Ahm2Pxik2mFtS1jgWI1QyHqS6XJV/BhQ4xiTxyuouW0i07MdfwGH6tzoo6qdVVpYZjCpJRe1GE6aVCCKDeNMpOV2Yn0HL22uZkqTbROqJdm5SaYSvnUEsXlptKBdEp1GuCZSSvaO01IVHCy8iilt2R3G/FqPmVmQV1rWOJjnUKYvRcD5Z8BRM6xCRj0FqZKy9SnyGajF91H2acVDPKX23fsL1UCFFrmGSnK7MTaDl7WrOKraR3q53DqWN7gZ1Ud62sgI65OZg4fSkjxCSSeBlZ1LI7kvutWF18V4s827U9WJdLnIAOMcko1FbmRBot+FH3MXv+GlMvCpEGYsn7Ks8lui8hQULZMMlOV2YnMOuEWknvFj2HmWN7WYNsJ9VdLSsgOyuGvfsPJiLlQU8ZJ8QKXkUWteyI5H4rovZG8j56GW9qiNoerMslTkCHmBAB3F6dVa6ats/Jwp792hEgtReFSAOx5H2T0Ut/4kuFEHG0nL283BwcaG51ZB6u1jmSETm27AQrj+WUQ6nmZOfB3nxktayAfUnOsEyQU8YJCTJ6zu7s+WtQXVVqyt6QMUqzViJqe7AulzgBHWJCBHFrdVZt1VTPGQbUXxTVVaVY98U21Dfs0fye1otCb6WXLxVCxNFy9q4a1wuAM/NwtaKkuUe0EW7QpaxDVmLXodSqpR59eidcXmVvNrAyK2DslBrV/YKaMk5IkNFzduUUaL1mnkpkJ9pMBpubYy0JUYMOsctwjA0xwuyqqd5Yg7/8dpil3xxrcAhxBrUIZrKz50TE0ugcIojUITfu2IuJ05dacty1aqmXr92Jy6ucnQ0c1G7jhISR6qpSTUe3tVVKzETOyoql2CMVGgtTshNtlGYt2kSLEDegQ+wiHGNDRDDTtCorK2b427GyUsoaHEKcw0lnz61ziEZPraZPax3/uybjZmBmsZOCTQhJR895lbeL2rTy/kZp1mzgSfyEDrGLcIxN9BCNvpqJ0ppphtXaKqFiSo3qXEA7sAaHEP9IruWV9UF+Us2tG4jUIcvI6dOAeFRa6/hH5mZbF1oDJyLmhJDDmKkRXrxik2Gmm5w2Le+vhh8ZacziJDJ0iF2EY2yihWjE32xmgJXmFGpzAe3CGhxCvEdZayu/H5Jrbp36N56MWlRVD1keZU0woB451orantOnkwPSp+NFVJ6QTEFtkVzPdjWya5U2UBAy0pjFSZLJ8luAKKO12sW6zHCiF/G3sp9MdVUpRg3onvhdZGXFUFjQIeVvLZLnAhJCwodeLa9cc+sGg8sKMemCPsjv3B4xAPmd22PKxX2Rr1F3m5UVU60JliPHIsefdEEf9C7q4PCVEELcoLqqFDUzxmLRzErUzBiLmE3TVbaB1Gwevd4obmHWViPRhhFiF4lKXSZTSg4hGvG3khlgFJ3ValYhMdmAkMBhZqavUS2vGzW3MlpRVbXIrpbTrie/2vHj8QaL0hJCjHDTXis7MRerPre+CJ9sAwUhI41ZnCQZRohdJCirYHaQU0qUTRRmz1/js2TeIxrxdyMzQOu7dldsCSHOIqdAN+7YCwmHU4vr4vWq+xt1Qnaj5lYPrciuVuSYnZwJCQZu22tjyruk2bRmCFp2JLM4STKhihB/9tlnmDVrFnJzc1FRUYEzzzzTb5EMCcIqmB3YGOwwohF/NzIDtI5ZdmKu5WOS4BJGXUcOoTVuSGumr14tr5s1t3qYiRyzkzOxA3Wdc3hhryltWmUdrh5By46MShYncYZQOcRNTU24+eabkZ2djfvuu4+K0wOYUnIY0U7MbnRs1jpmeZF76ZTEP6jrwotWCrHW9uQOyepdpoORYsxOzsQNqOucww97Tcs2UdsWtCAKp2uQZELlEPfp0wcbN27EtGnTMGHCBL/FyQi0OgtmakqJaMTfjcwAtWPG43FHz0GCAXVdeNEaN6SXWqzXITlINbfs5EychrrOOfyy17TsnTA4lmHP4iTOESqH+IMPPsDxxx+PefPmYeLEiRg1apTfIkUeppS4D5uWESXUdcFDtFGW1rghphYTkg51nXM4aa+p2SXlRU5ISUgwCZVDvH//ftxyyy3o2LEjfvSjH/ktTkagl1KSrDBjMWDkhmw6cibhHDyiBnVdsFDOCtabwauWWnzGKQV4qnY97puzmqnGhCRBXeccVlKA1RxfAKp2SUNjrisz0QkJAoFwiHfv3o3x48fjkUcewQ9+8AMAwKJFizB79mwcPHgQl112GS655BKUlZWhjP8aPUctpUTpyElS+uB1YgyblmUW1HXhxGyjrOTUYjPONCFRgbrOH8ykAOstyKsR/8L6yCVCgo7vDvHatWtx6623YuPGjYlt33zzDe6//3688MILaNu2LcaPH49+/frhpJNOsnWuDz74wKa07hOWmtDat7dobN8UykZPft13vSYYojKF5TejRphlN0uQdV3UnoPT16NWEyxvNzrXYwu+UnWmH1uwVrhhFp9P8IniNVklyLrOb4L0O9Gy47SQpGDJb4Wwyw+E/xqCKr/vDvFzzz2H2267DTfeeGNi21tvvYX+/fvjqKOOAgCMGDECS5YswaRJk2ydq2fPnmjXrp2tY7hJPB4PzUqpNEddkUoSQnMNMn7e96x5WzWbYIjIpCd70GuTg/B7379/v2cGVVB1XRCeg5O4cT35td+qOsX5ndsbnmvnnBr17U0ttv+NBxW9euswXo8RYbgm6jr/CdrvRMuO0yIWC599BwTfFjJD0H5DZvFCfqu6LssFWUxx55134vTTT0/Z1tDQgPz8/MTfBQUF+Oabb7wWjejAgebOoNXswm7TMjkVSna25VSo2fPX2DousQ51XXiZMLIH2uVkp2wTbZSl1V1ar+t0mJFTxBt37IWEwynidfF6v0UjHkFdFw7M2mtlJ+a6JIl70BYiovjuEKvR2tqKWOzwP1RJklL+Jv7jliOXaVRXlWLUgO6JF1NWVgyjBnS3vXqpV5tMggN1XTgYXFaISRf0QX7n9ojhUGR40gV9hGqA7TjTYUSv3ppkLtR1wUPLXhs1oLuqXTKmvIuX4jkCbSEiiu8p02oce+yxWLVqVeLvxsZGFBQU+CgRUaLsZhiLASP723fkzGA3DSbl+/O2WurG6MT1ujEHT682mQSHKOk60bFEbn3f7eNancGr1nU6yl2mt2nUW2ttJ5lBlHSdFmFIzVXKWFjQAVu3NanKrJTdidpPr+8RbSEiSiAd4oEDB2LWrFnYvn072rdvj6VLl+KOO+7wWyyiINmRO1QX4K0zbGdckdH3lUq72zG5qG/YY/l8XpOVFdOsTSbBISq6zm4nZbc6MTt9XKvOtVVnOowc07m9ar11VFPEiRhR0XVahGGEopqM9Q17HMlKk4+v5+z6cY9oCxFRApky3bVrV0yePBkTJkzAuHHjMGbMGPTu3dtvsUiAsJsGo/d9tZqTZGfYyvm8hint4SAqus5umqxbabZOHpe1sWJkWoo4ESMquk6LMKTmuimjSK2uH/eIthARJTAR4ldeeSXl74qKClRUVPgkDQk6ZtNglCuXet83o5yDmnajTGkPavpWJhJFXWc3TdatNFut7zfu2IuxU2pMRXnNzCJ2K/3bT0SvKdNSxIk2UdR1WgQ9NXf2/DWOyagWCdaym5IjwH7cI9pCRJTAOMRBIgx1IJmMXndAtTQYtTQdve+bUc5BTrtxozaZEDXspsm6lWardVwAKVFeQD2FOtkJ1NIKSqfbrfRvPzF7TZmUIk6igx3bL8ipuUobSIkZGfXSnrWQP/frHsm2UNhHFhF3CWTKtJ+wRXvw0YvgqqXBmIn4ntfvOFPKmWk3hNhPk3UrzVbtuEq0UqiVKdJaKJ32KHZZjuI1EZKMXdsvyKm5RjaQGRmtpjcveWdzoO8RIXSIFYShDiTT0Yvgqq3mGkWE5f/KjSXMKOfFKzahcupCLpiQjMbOWCInvi96XC2So7x18XpMnL4UM+esTnMClag57VHsshzFayIkGbu2n1sjFJ1AzwYyK6PV9Ga5HK2woEMg7xEhTJlWEPQ6EGI+7UZv/5oZY9PSaGTlbJQGJBPEbpKEeI3dNFm30myTjztx+lLd1GxlarAWsf9+R602NopdlqN4TYQk44TtF9QyJT0byKy8esc6r99xunaT012tCXESOsQKglwH4ide1lUbnUtL6eql45jZHzDvFAOHVpKp5AkJLhNG9khzeJOjvGqpwUqysmKYPP40Tefd6BxhJIrXRDILI7siyrafiA0kauPpHUvUbqKtRIIIU6YVsMYhHS/rqkXOZTY1yWoqk9b3tGAWASHBxig1WyQFuLVV0h235Fb6t59E8ZpI5iBiV0TZ9jOygczYeEbHUn6uBm0lEkQYIVbAFu3p6NXWqN0XO9Fk0XOZTU2ymsqk9j352pTYXUlmd3NC3Cc5hVruIn3fnNU4pnN7dMzNwa6mZsNjaI1bUjuHGmEcy8TO0SSsiNgVQbL93LAF9GwgszaekT0lf145daGurUSbhwQJOsQqBLUOxC/M1NboteQXuadhqOHWShlqbZVQMaXGkmK3e98IIeZQGyWUbWJRq3HHXkycvtS0MxvFsUyEBBlRuyIItp8ftoATdpeac6uXXk2bhwQNpkwTQ/SaVSmx26nRzLn8wiglyEpKObubE+ItavXCLSYX3mRnVit9WvS8HGFEiHuEwa6Q8cMWsHt/tFKuAWimV9PmIUGDDjExxExtjd2VxrDU8VRXlaJmxlgsmlmp+dIwo9jDEBknJEro1QsbzS5OxqwzyxFGhHhLWOwKwB9bwO79MUq5lm2lmhljE9Ff2jwkaNAhJoaYaUpld6UxyLP8tHBCsYdpBZuQKKA1MkhuGJXcQGrUgO7I1xkxZMaZ1TovRxgR4g5hsiv8sAXs3h8rNhBtHhI0WENMhBCtrbEy4ggId3MFJ8Y1WL1vhBBr6I0SUmsgVQ39OcaijbI4wogQ7wlCfbAIXtkCajZXzYyxlo5lxQaizUOCBiPExFGsrDR6OdbJDZxIxwrTCjYhUcDKKKEJI3ukpVO3y8nGGacU4OHn16Jxx15I0K8t5ggjQogWXtgCTttcVmwg2jwkaDBCTBzH7Eqs2Zb/QcOpcQ1hWcEmJCqYHSUk76uMBOs1ylI7PkcYEUK0cNsWcNrmsmoD0eYhQYIOMfGdKDRXoGInJDNQc2bvm7NadV82yiKEBA03bC7aQCTs0CEmvuNEDa4WYa5NJoSEg2M6t9esLTaLshb5rFOOQFmZE1ISQoi7NpcZaJ+RIEGHmLiOkdJzq7kCB78TQpIRbXxlFqcaZdXF61OO07hjLxat3IeionqmWBNCDBFxMoPQ0Ir2GQkadIiJq4goPSv1JyJK36vaZK5yEhJ81JzNh59fCwCazqaoA61VW2zWiVWrRW5ukTRrke3ITAiJFlr21uIVm1IaVjnV98QOTtlntL+IU9AhJq4iqvTM1J+Irix6UZvMVU5CwoHZxldmHWgnGmVp1RyL1iJbcfoJIdFAy94CoBqI8NNGccI+o/1FnIQOMXEVN5xSUSfbbJ2M6Epj8n56MlIhExIctJzKxh17MXH60rSIqlkH2gns1iJbkZkRZUKigZFdJUeLlfaNH1FWJ+qYwz6hhAQLOsTEVdxo3iDqZJupkxFdaVTuZ1ZGQog/aDmbABLbkyOqdqO1VlCrRc7JjgnXIpuVmRFlQqKDlr2lJNm+AeBLlNWJOuYoTCghwSHLbwFItLEysN0ILWdaud3M4He9lUaR/URlJIT4w4SRPdAuJ9twPzmiqhWVtdI5WpTBZYWYdEEf5HdujxiA/M7tUVF+lLBzalZmvYgyISRcmLWrlryzWdj2cRoz9pkWorYgISIwQkxcxY3mDWZWFkXrZERXGkVXHr3s1kgIMUat8ZVWxHjbjr244eK+jnSOtiJnsgMcj8eFv2u227UfUXBCiDvIto5IFhugb894EWW1W8cchG7ZJDrQISau43TzBjecbNHUbqOUJHY5JCS4KJ3NidOXatbsOtU52kvMyuzk/GRCiP/I9pZIrxPZvgnCTGIrBKFbNokOdIhJKHHayRZdadTaz2yqDyHEf4wiqk50jvYaMzI7NT+ZEBIskm0krd4nsn0T5iir392ySXSgQ0wIDinVdV9sQ33DnsS2woIOaYqWK5KERAc/osBB6uocxig4IcQcWnaLvC0Z2jQkU6FDTAgOraAmO8MAUN+wB7Pnr1F1ivmyICQaeBkFDmJX5zBGwQkh5lDaLXpRY9o3JBOhQ0wIvJtn58e8P0JIMNDq6nz/vPcAcNQRIcQbnLR5aNeQKECHmBB4M89OdNYxISSaaHVvbm2VfI8UE0IyB6dsHto1JCpwDjEh8GaenV/z/gghwUCvezPn/xJCvMIpm4d2DYkKdIgJgXZHRSc7LXoRhSaEBJcJI3ugXU625uec/0sI8QKnbB7aNSQq0CEmBIdSe0YN6J5YHc3Kijk+SsmLKDQhJLgMLivEpAv6aP6b5/xfQogXOGXz0K4hUYE1xIT8F7e7R4vOOiaERBe5RpjzfwkhfuKEzUO7hkQFOsSEeARnGBNCAM7/JYREA9o1JCrQISbEQzjDmBACcP4vISQa0K4hUYAOMVGFc+UIIcQf6uL1iehxp9xsXIl6Os+EEGFowxFiDjrEJA3OlSOEEH+oi9en1Bd/19TCGcWEEGFowxFiHnaZJmlwrhwhhPjDU7XrU5ptAZxRTAgRhzYcIeahQ0zS4Fw5QgjxB61ZxJxRTAgRgTYcIeahQ0zS4Fw5QgjxB61ZxJxRTAgRgTYcIeahQ0zS0Jofx7lyhBDiLhNG9kC7nOyUbZxRTAgRhTYcIeZhUy2SBufKEUKIPyhnFHfKzcaV4/qwoRYhRAjacISYhw4xUYVz5QghxB+SZxTH43GU0RkmhJiANhwh5mDKNCGEEEIIIYSQjIQOMSGEEEIIIYSQjIQOMSGEEEIIIYSQjIQOMSGEEEIIIYSQjIQOMSGEEEIIIYSQjCQjukxLkgQAOHDggM+SGLN//36/RbAMZfcHym4dWSfIOiLsWNV1fj8Hp+H1BJuoXQ8Q/GuirgsGQf+dGEH5/Sfs1+C2/FZ1XUyKinbUYdeuXfj000/9FoMQElCKi4uRl5fntxi2oa4jhOhBXUcIyQTM6rqMcIhbW1uxZ88e5OTkIBaL+S0OISQgSJKE5uZmdOjQAVlZ4a8goa4jhKhBXUcIyQSs6rqMcIgJIYQQQgghhBAl4V8mJIQQQgghhBBCLECHmBBCCCGEEEJIRkKHmBBCCCGEEEJIRkKHmBBCCCGEEEJIRkKHmBBCCCGEEEJIRkKHmBBCCCGEEEJIRkKHOECsWrUKP/7xj1FRUYFrrrkG3333nd8iCROPx1FVVYXKykpcdtll2Lp1q98imeaBBx7ArFmz/BZDiEWLFmHUqFEYPnw4nnnmGb/FMc3u3bsxZswYbNmyxW9RMp4w6x01oqCLtAiTjlIj7HpLDeoyIkpUdFMY9VAUdE+Ydc3DDz+M0aNHY/To0bj33nv9FkcdiQSGYcOGSZ999pkkSZI0Y8YMaebMmT5LJM6QIUOk9evXS5IkSc8//7x0zTXX+CyRODt37pRuuukmqXfv3tJDDz3ktziGfP3119KQIUOkHTt2SHv27JEqKioSv5swsGbNGmnMmDHSD3/4Q6m+vt5vcTKeMOsdNcKsi7QIm45SI+x6Sw3qMmKGsOumsOqhKOieMOuaf/3rX9KFF14o7d+/Xzpw4IA0YcIEaenSpX6LlQYjxAFi8eLFOOmkk9Dc3IxvvvkGnTp18lskIQ4cOIDrr78ep5xyCgCgpKQEX331lc9SibN8+XIcf/zxuPzyy/0WRYi33noL/fv3x1FHHYXc3FyMGDECS5Ys8VssYZ577jncdtttKCgo8FsUgvDqHTXCrou0CJuOUiPseksN6jIiShR0U1j1UBR0T5h1TX5+PqZNm4a2bdsiJycHJ554Iv7973/7LVYabfwWgBwmJycHn3zyCS6//HK0adMGN9xwg98iCdG2bVtUVlYCAFpbW/Hwww9j2LBhPkslzrhx4wAgNClADQ0NyM/PT/xdUFCA999/30eJzHHnnXf6LQJJIqx6R42w6yItwqaj1Ai73lKDuoyIEgXdFFY9FAXdE2Zdc/LJJyf+f+PGjaitrcXcuXN9lEgdOsQ+UFtbi7vuuitl2wknnIAnn3wSJSUleOuttzBv3jxMnjwZ8+bN80lKdfRkP3DgAKZNm4aDBw/i6quv9klCbfRkDxOtra2IxWKJvyVJSvmbEDXCrHfUCLMu0iIqOkoN6i2SKYRdN0VND1H3BIPPPvsMV199NW688UYcf/zxfouTBh1iHxg5ciRGjhyZsm3//v1YtmxZYsVw7NixuOeee/wQTxc12QFgz549qK6uxlFHHYXZs2cjJyfHB+n00ZI9bBx77LFYtWpV4u/GxsZQptEQbwmz3lEjzLpIi6joKDWot0imEHbdFDU9RN3jP/F4HNdddx1uvvlmjB492m9xVGENcUBo06YN/vCHP+CDDz4AcGiFrm/fvj5LJc7UqVPRvXt3PPDAA2jbtq3f4kSagQMHYsWKFdi+fTv27t2LpUuX4isAyzgAACAASURBVOyzz/ZbLBJCwq531KAuCibUWyTToW7yB+oef/nqq6/wy1/+En/+858D6wwDjBAHhuzsbNx///34/e9/j5aWFnTt2jU0NQMfffQRli9fjpNOOgnnn38+gEM1Gv/7v//rs2TRpGvXrpg8eTImTJiA5uZmVFVVoXfv3n6LRUJImPWOGtRFwYV6i2Qy1E3+Qd3jL48//jj279+Pu+++O7Ft/PjxuOiii3yUKp2YJEmS30IQQgghhBBCCCFew5RpQgghhBBCCCEZCR1iQgghhBBCCCEZCR1iQgghhBBCCCEZCR1iQgghhBBCCCEZCR1iQgghhBBCCCEZCR1iEhhmzZqFkpISLFu2zG9RCCEhhXrEHEOHDsXpp5/u6jm2bNmCkpISXHvttSnb33//fbz55puuntstSkpKUFlZ6bcYhDiCXb1pRo9MmzYNJSUlWL9+veG+1157LUpKSrBlyxZLcvHfKRGFDjEJDOXl5Zg0aRKKior8FoUQElKoR4JHp06dMGnSJIwePTqxra6uDhdeeCE+//xzHyUjhADe6s1hw4Zh0qRJOOaYY1w/FyGitPFbAEJk+vXrh379+vktBiEkxFCPBI9OnTrhV7/6Vcq27du3o7W11SeJCCHJeKk3hw0bhmHDhnlyLkJEYYSYEEIIIYQQQkhGQoeY2GL37t3485//jGHDhqFnz54466yzcNttt+Hbb79N2W/v3r146KGHcN5556FPnz4YOnQo/vCHP2D79u2JfezWsGzbtg0333wzzj33XPTq1QuDBg3C1KlTsWnTppT9hg4diosuuggff/wxLr30UvTp0wdnn302/vjHP+I///lP2nHj8TgmTZqEQYMGoWfPnjjjjDNw+eWX4+23307Z79JLL8XQoUPx2muvYejQoejTpw+uv/56AMCmTZtw/fXXY8iQIejZsyeGDh2K22+/HY2NjWnn+/DDD3HttdeiX79+6N27NyorKzF37lxIkmTpvhASdIKkR4BD/15/85vfYODAgejZsydGjhyJv/71r2hubk7bd8uWLbjllltw9tlno0+fPhg9ejSeeOKJtH03bNiQcsxhw4bh3nvvxa5du1L2mzZtGk499VTs2LEDt956K/r374/TTjsNV1xxBTZv3owDBw5gxowZGDRoEPr27YtLL70UH3/8ccoxzOo4NWprazF+/Hicdtpp6Nu3Ly677LI0nTdx4kSUlJTgqaeeStm+cOFClJSU4IorroAkSWk1xNOmTcNNN90EALjrrrtQUlKCzz//HH379sXgwYNVdd1NN92EkpISbNy4UUh+Je+++y6uvvpq9OvXD2VlZRg/frzqb2TBggW49NJLccYZZ6Bnz54YNGgQpkyZgvr6ekvnJcQtgqY3AeCLL77Atddei7KyMvTt2xdXXHFFWq2wWg1xS0sLHnvsMYwYMQK9e/dGRUUFli5dqnkeO/9O9+zZgxkzZmDo0KHo3bs3fvzjH+OVV17BLbfcgpKSkpR9W1tbMWfOHIwbNw69e/dGWVkZLr/8cvzrX/8yeWcOI+vDBx98ENOnT0dpaSn69euH2tpaAIAkSZg7dy7OP/989O7dG2eccQauueYafPTRRynHeeedd1BSUoLnn38ezzzzDIYNG4Y+ffpg7NixeOGFFyzLl6kwZZpYZteuXbj44ovx6aefYsCAARg+fDi2bNmC5557Dm+88QbmzZuHgoIC7N27FxdddBHWr1+PXr16Yfz48di8eTPmzJmDVatWYe7cuejYsaMtWfbv349f/OIX+PTTT3HuuefivPPOw+bNm/HPf/4Tb775Jmpra3HUUUcl9m9oaMCECRPQrVs3XHLJJVi7di2eeeYZrFy5Es899xxyc3MBAMuWLcN1112HLl26YNiwYejQoQM+++wzvP7661i5ciXmz5+PHj16JI67Y8cO/PrXv8Y555yDjh074sQTT8T27dvx85//HDt27MCIESNQUFCATz75BHPnzsU777yDhQsXIicnBwDw2muvYdKkScjJycHw4cPRpUsXvPHGG7j99tvx0Ucf4Y477rB1nwgJGkHSI8ChBanLLrsM+/btw/Dhw/H9738fq1atwn333Yd3330Xf/3rX5GdnQ0A+PTTT3HppZfiu+++w+DBg1FUVISVK1finnvuwSeffIJ77rkHALB27Vr8/Oc/x759+zBkyBAUFhZizZo1ePzxx/Hqq69i7ty5KfpJkiRMmDABra2tOP/88/Hpp5/izTffxNVXX43u3bvj008/xXnnnYfGxkYsWbIEV111FV566SW0b98+cQxRHafGgw8+iL/85S/o1q0bzj//fMRiMSxZsgSXX3457r777kSTmjvuuAMVFRV44IEHMGLECHTt2hUNDQ248847ceSRR+JPf/oTYrFY2vGHDRuGnTt3Yvny5Rg0aBBKS0tRUFCAESNG4IUXXsCqVatwxhlnJPbfv38/li5ditLSUhx//PGmn2lNTQ1uuukmHHHEETjnnHPQuXNnvPTSS/jlL3+JO++8E1VVVQCAe+65B0888QROOeWUxHW/++67ePHFFxGPx7FkyRIcccQRps9PiNMETW8CwL59+zB+/Hgcf/zxuPDCC7Fhwwa88soreO+991BbW4uuXbtqfnfatGlYuHAhTjrpJFx44YXYuHEjrrvuOhx99NFp+9r5d3rgwAFcfvnlWLt2LU477TSMGDEiEYT4/ve/n7Jva2srJk+ejCVLlqCwsBA/+clP0NTUhOXLl+OKK67A7373O1xyySWW79dzzz0HALjooovw5ZdforS0FADw29/+FjU1NTj55JMxfvx47N27N7FA+de//hUDBgxIOc7cuXPx8ccfY+TIkTjyyCOxbNky3HTTTdi6dWtaqQrRQSLEIrfffrtUXFwsPf300ynbly1bJhUXF0vXXXedJEmS9MADD0jFxcXSnXfeKbW2tib2e+SRR6Ti4mLpiSeekCRJkh566CGpuLhYevnll03L8sorr0jFxcXSgw8+mLL9scceS5NxyJAhUnFxsXTNNddIBw8eTGy/4447pOLiYumhhx5KbBsxYoRUXl4uNTY2phz30UcflYqLi6WZM2cmtv3sZz+TiouLpbvuuitl37/97W9ScXGxNH/+/JTtf/jDH6Ti4mLp1VdflSRJkpqamqT+/ftL/fv3l+rr6xP7tbS0SL/61a+k4uJiqa6uzuSdISTYBEmPtLa2SmPGjJF69eolrVu3LuWzP/3pT2lyXnzxxVJJSYn00ksvpRxj4sSJUnFxsfTBBx9IBw8elIYPHy6deuqp0muvvZZyzBkzZkjFxcXSTTfdlNj229/+ViouLpYuuOACaf/+/YntF154oVRcXCwNHTpU2rVrV2L7tGnT0nSDGR03ZMgQqaysLPH32rVrpZKSEulnP/uZ1NTUlNi+fft26dxzz5X69Okjffvtt4ntc+bMSXlOV199tVRcXCy9+OKLiX3q6+ul4uJiqbq6OrHt73//u1RcXCz93//9X2LbihUrpOLiYun3v/99yn1avHix6m9EhP/85z9SWVmZ1L9/f+nLL79MbP/222+lQYMGSeXl5dKBAwekr7/+WjrllFOkSy65JOWeSZIk/eIXv5CKi4ulN954I7GtuLhYGjt2rGl5CHGCIOlNSTqsc373u9+lbJ8+fXrav3NZx3300UeSJB3+dz9x4sQUnff0009LxcXFUnFxccImsvvv9PHHH5eKi4ulP/7xjyn34+67706cS+Yf//hHQq49e/Yktm/evFk688wzpVNPPVXavHmz6Xsl68OSkhJp/fr1KZ/Juu6GG26QmpubU85ZXl4unXXWWYl79Pbbbydkrq2tTey7bds26ZxzzpFOPfVUacOGDably1SYMk0scfDgQSxYsAAnn3xy2grZOeecg759++Lll1/G7t278c9//hMdO3bElClTUqIFP/vZz3DllVfipJNOsi2P3Jzlo48+wr59+xLbL774YtTV1eHiiy9O2T8Wi+HGG29MRHoA4Prrr0dubi4WLVqUOOaUKVNw7733pnVDlJtPKFOTAGDEiBGqsq1duxYtLS2J7ZMnT8abb76JwYMHAwBeeeUVbN++HVdeeSV+8IMfJPbLysrClClTAAB///vfBe4GIeEgaHpk7dq1+PTTT1FVVYWePXumfHb99dcjJycnkYr29ddfY9WqVRg4cCCGDx+e2C8Wi+GGG27ApEmT0LZtW7z33nvYuHEjRo8ejbPPPjvlmNdddx26du2KRYsW4cCBAymfXXTRRWjbtm3i79NOOw0AcOGFF6ZEdHr37g0A2Lp1a8r3RXScGvPnz4ckSbjxxhtTIs6dO3fGL37xi0S0Qmb8+PHo378/lixZgt///vd49dVXMXr06JSO0qL069cP3bp1w5IlS1JSzuUsmlGjRpk+5muvvYZdu3bhsssuS+mg26VLF9x000248sor0dTUhLZt2+Lee+/FLbfcknLPACSi1Wr6nhCvCZreTKa6ujrl76FDhwKAbirzP//5TwDAr3/96xSdd8kll+CEE05I2dfuv9N//OMfyM3Nxa9//euU+zFp0iQceeSRafsCwO23356SUVNYWIjq6urEc7BK9+7dccopp6Rsmz9/PgDglltuQZs2h5N4CwsLMX78eHzzzTd46623Ur5z2mmn4bzzzkv8ffTRR+Pqq6/GwYMHU3Q10Ycp08QSGzZsQFNTE1paWjBr1qy0z/fv34+WlhZ88skn2LRpE8444wy0a9cuZZ8OHTpg6tSpjsgzcOBAFBYW4tVXX8WZZ56JgQMH4uyzz8bgwYPxve99L23//Pz8tPECeXl5KCoqwocffoi9e/eiffv2OPfccwEcMjY/++wzbN68GZ9//jneeecdAFDtktqtW7eUv0eMGIH/+Z//wbPPPoulS5di0KBBOPvss/GjH/0I+fn5if0++OADAIdSNtXuaXZ2dlqtICFhJmh65MMPPwQAbN68WVWeDh064JNPPoEkSfjkk08AIJHmlswPf/hD/PCHPwQA/O1vfwOAlBRgmbZt26JXr15YtmwZvvzyyxTj6LjjjkvZVzbIkhfLACTuh9KhFtVxSuR7sHTpUtTV1aV89vXXXwNASu1fLBbD9OnTMXbsWDz77LMoKCjAbbfdlnZcEWKxGCoqKvDII/+/vTsPqLJO1Dj+HBBQwDRvol5DcxmgUjGdIquxMlygXPNOmtWULaZD423MsWxu6uSSZmWmklbKuOQSlWQKIphep0yFEQoXnNzQMtdUFgPhnPuHlzMi23vY3sM5389/5+Xn4RHjjYff8r6vr7/+Wvfdd5/Onz+vbdu2qWfPnrr++usdfs/ie2ZZ/07XFuz+/fvLarXqwIEDOnjwoI4dO6bMzEz7D6Ccig1n4Gz3zWLe3t6lft4q3gqSl5dX7p/bv3+/PD09S2w/K3bbbbfp0KFD9tfXX399lb9P8/PzdeDAAd16661q3LhxiY/5+fkpODhYO3fuLJGrRYsWCgwMLPVe3bt3t4+pqmvv5dKV+6+Pj49WrFhR6mOHDx+WdOX+WzyRIqnM08GLf1HKz4zGUYhRJRcvXpQkHTp0SPPmzSt3XFZWliTV2B6V8jRq1Ehr1qxRdHS04uPjlZiYqMTERHl4eKh3797629/+VmKPXnl7WYpngrOzs9WoUSNlZmZq6tSp9pukl5eXOnTooE6dOunIkSNlHv5y7d6VFi1aKDY2VtHR0UpOTta6deu0bt06eXl5aciQIXr11Vfl4+NjP1yn+LelZblw4YJjXxjAiTnbfaQ4z7Zt27Rt27Zyx+Xm5tq/FyvLlJOTU+G4gIAASVcOvrlaeXt8r55BqYjRe9y1iu9DixYtKve9r70PBQYG6pZbblFKSoo6dOhQaqbFEYMGDdL777+vL7/8Uvfdd5/i4+N1+fJl+75lRxX/mxr5bycxMVFvvfWW/eAuX19fderUSSEhIfrmm2842BBOwdnum8WuLd1Xq+h75+LFi/Lx8SkxI1qsrHtJVb9Piw8UvHoi4mrF9+JiOTk55T4ruXjs1SsSHVXW1ys7O1uFhYUV/rtee/+9Nrf0779j8f9/UDkKMarEz89PkjRw4EDNmjWr3HHFP1zl5uaW+fG8vLwKD3dxRLNmzfTqq69q4sSJyszM1LZt2xQXF6eNGzfKw8NDc+bMsY/Nz88v8z2K/0fTtGlT5eTkaOTIkcrOztaECRN01113qX379vL29lZ6erq+/PJLw9kCAwM1ffp0FRUVKSMjQ9u2bdNnn32m1atXq3Hjxho/frz96xATE1Pq0ATAFTnbfaT4Pa4+aKmysWVlslqtKigoUMOGDe1/x1OnTpX5Plffc2qSkXtcWXx9feXp6an09HT7YX+VKT4Iq2nTptq+fbs+++wzDRkypEq527Vrp65duyo5OVkFBQWKj49XkyZNdP/991fp/Sr6dyooKJCHh4caNGig9PR0jR07Vi1bttTbb7+tzp07KzAwUBaLRYsWLSq1TBEwi7PdN6vruuuuU1ZWli5fvlzqnnPtzHJ1vk+Lv27llcRrv05+fn7l3reLS2lN37d9fX3l5+dXanVORcq619fW/1dcGXuIUSXt2rWTt7e39uzZU+Zv42JiYrRgwQIVFhaqVatW2rdvX6klfQUFBbr77rs1cuTIaufZtWuXpk6dqqysLFksFoWEhOjZZ5/VJ598Il9fX6WkpJQYf/jw4VKPO7l06ZIyMzN1yy23yNvbW99++63OnDmjESNGaOTIkQoJCbHPzhw8eFBSxb/1LJacnKzJkycrJydHnp6eCg0NVVRUlH1JTGpqqiTZj/svXjp9tfPnz2vatGmKi4tz8CsDOC9nu49U9D14+fJlvfHGG/Yl0MVjv/vuu1Jjd+/era5duyo6Otq+DLD4+/xqVqtVqamp8vX1LbXVorqM3OPKEhwcrKKiolKPSpGu/L1mz55d4n568uRJzZgxQzfccINiY2PVrFkzzZgxQydPnqwwX1mnTxcbOHCg8vLylJSUpH/+85/q16+f4ZnxawUFBUkq+9/po48+UmhoqHbu3Kn169fLarVq0qRJevDBB9WmTRt7xuIlm8wQwxk4232zum699VZZrValp6eX+ti19+LqfJ/6+/vrpptu0v79+0t9PYonK64WEhKiixcv6sCBA6Xeq/geWNN7sIODg/Xzzz+X+UjOr776Su+8806pZdDff/99qbFpaWmSpNDQ0BrN58ooxKgSHx8fRUZG6ocfftCSJUtKfGzHjh2aNWuWPv30UzVp0kQDBgxQdna25s+fX2Lc0qVLlZeXVyOzoadPn9ayZcu0ePHiEtfPnDmj/Pz8Uj9sXr58WW+//bb9xmmz2fTWW28pLy9PDz/8sP3vKJU+oOGnn36yL2cpLCysNNuhQ4e0cuVKrVy5ssT14kNwio/67927t/z9/fXhhx/a94oUe/PNN7V06VL7EijAFTjbfeT222/XjTfeqNjYWO3evbvExxYtWqQlS5bY99gGBgbqtttu0z/+8Y8Sy6utVqs++OAD2Ww23X333erevbvatm2rxMREbd26tcR7zp07VydOnFBERESVC195jNzjyjJ48GBJ0vTp00vMpOTk5Gjy5Mn64IMPShwOOGnSJF28eFETJ05UYGCgJkyYoIsXL1a6j7h4eWRZz3Z+8MEH5eXlpdmzZ1drubR05RFPvr6+WrZsWYmDx86fP6/Vq1fLz89PXbt2td/vz5w5U+LPb9++3b4ayMj9HqhtznbfrK7iRyfNnj27xD1n/fr1pUpqdb9PhwwZopycnFJ7rxcuXFiqhBavcpk2bVqJmepjx45p/vz58vLyqtLhgRUZPHiwbDabXn/99RKl/dSpU5o8ebIWLVpUalZ/06ZNJX5Jefr0aUVHR8vX11cRERE1ms+VsWQaVTZhwgTt3r1bM2fOVHJysrp06aKTJ08qMTFRDRo00PTp0+Xh4aFRo0Zpy5Ytev/997Vr1y6Fhobq0KFD2rJlizp37qw//OEP1c4SHh6u2267TStXrtSBAwfUtWtX5eTkaOPGjZJU6llsXl5eiouL0759+xQaGqr09HTt3r1bYWFhGjZsmKQrhya0bt1acXFx+uWXXxQSEqITJ04oOTlZPj4+slgs9j0pFfn973+vNWvWaPbs2dq5c6eCg4N19uxZJSQkyNfXV88995ykK8uGpk6dqpdeekmDBw9WeHi4AgICtHPnTn3//ffq3LmzU/w2F6hJznQf8fT01MyZM/Xss8/qscce0wMPPKDAwEBlZGTo22+/1Y033qg///nP9vFTpkzRY489plGjRik8PFytW7fWt99+q7179+qJJ56wH2zyxhtv6Omnn9bzzz+v+++/X23atNHu3buVlpamDh066C9/+Uu1s1/LyD2uLHfeeacef/xxLVu2TA8++KDuvfdeeXt7KykpSSdOnNCwYcPsh7isXbtWX331le655x77D4aDBg2yX1+7dq0GDRpU5ucp3uO8cuVKXbhwQY8//rj9WpMmTdSrVy9t3LhRgYGB9gNsqqJp06Z67bXX9Morr2jw4MF64IEH5Ofnp4SEBJ0+fVrz5s2Tt7e3IiMjtWTJEk2ZMkW7du1S8+bNlZmZqX/84x+6/vrrdfbsWUP3e6AuONN9s7pCQ0M1cuRIffTRRxo0aJDuu+8+/fzzz0pKSlKbNm1KTARU9/v0ySefVEJCghYtWqTU1FR16dJFe/fuVUpKiq677roShXzgwIHavHmzNm7cqAEDBqhnz5725xBnZ2frf/7nf0odflhdQ4YMsX/OzMxM/e53v7OfFn3+/HmNGzeu1Of08/PTk08+qX79+snf319JSUk6c+aMXn/99XL3S6M0ZohRZc2aNdOaNWs0cuRInTx5UsuWLVNKSop69eqlNWvW2H9o8vPz08cff6ynn35aP//8s5YuXao9e/boscce05IlS2pkZsTb21sLFy7Us88+q3PnzmnFihVKSEhQaGioli1bpnvuuafE+IYNG+rjjz+WdOUHstOnTysqKkoffvihfebC19dXS5YsUZ8+fbRnzx4tX75ce/fu1YABA/TFF18oJCREKSkp5e7PKdakSRMtX75cw4cP15EjR/T3v/9dW7ZsUc+ePbVmzZoSJ8tGRERo+fLluvPOO7Vt2zYtX75cubm5GjNmjGJiYux7YABX4Uz3EUn67W9/q08++UT9+vVTSkqKli5dqp9++kmPP/64/RTlYsHBwfrkk08UERGhnTt3atmyZbp06ZJeeeUVvfLKK/Zx3bp1U2xsrCIjI7V7926tWLFC58+f1+jRo/XJJ5/Uyj4vI/e48vz1r3/VrFmz1KpVK33xxRf6/PPPdcMNN2j69On2md/Tp09r+vTpatiwoSZPnlziz0+ZMkU+Pj6aPn16mUv/pCuz8SNGjNCFCxe0YsUK+zaUYsWPERkwYEBV/volDB48WIsXL9bNN9+sjRs3as2aNbrxxhu1aNEi+5MEbr75Zi1atEi33nqrkpKStGbNGp05c0Z/+tOfFBcXJw8Pj1Iz/IBZnO2+WV1/+ctfNHXqVPsBqQcOHNDUqVNLnKYsVf/71MfHRzExMXr00UeVlZWl5cuXKycnR4sWLdJNN91U4lBUi8WiOXPm6K9//av8/PwUGxurr776Sl27dlVMTEypR17VBIvForlz5+rVV19Vo0aN9Mknnyg+Pl4dO3bU/Pnz7RMoVxswYIAmTJiglJQUrV27VoGBgVq4cKH+67/+q8bzuTKLjU0xcDO9evXSxYsXS+0rBgBX4Ar3uLffflsLFy5UYmKi2rZta3YcAC7g+PHjatasWZmHid1///1q1KiRNmzYYEIyx+3YsUNPPPGEnnjiCb366qtmx6n3mCEGAABO4+eff1ZsbKzCwsIowwBqzOuvv67u3bvr2LFjJa5v2LBBP/30U5nP9IV7YA8xnFJSUlKZp5yW59o9wgDAfaR++eKLL7R48WIdOXJEv/76a5n/Hp999lmJw7Eq0rhxYz355JM1nBJwba5833zkkUe0detWDR06VH369FHTpk118OBBbdmyRS1btlRUVJTD78k9yTVQiOGUkpKS9PnnnxseX59uyADqBveR+qVly5Y6ceKE/P39NXHiRN1+++2lxnz++efauXOnofdr3bo1P3wCDnLl+2avXr0UExOjxYsX66uvvtKFCxfUvHlzDR8+XGPGjNF//Md/OPye3JNcA3uIAQAAAABuiT3EAAAAAAC3RCEGAAAAALglCjEAAAAAwC1xqBYAuAir1arc3Fx5eXnJYrGYHQeAk7DZbLp8+bL8/Pzk4cFcCABcjUIMAC4iNzdXBw4cMDsGACcVFBSkxo0bmx0DAJwKhRgAXISXl5ekKz/0ent7m5ymbBkZGerUqZPZMRxWH3OTue44e+6CggIdOHDAfo8AAPwbhRgAXETxMmlvb2/5+PiYnKZ8zpytIvUxN5nrTn3IzVYKACiNjSQAAAAAALdEIQYAAAAAuCUKMQAAAADALVGIAQAAAABuiUIMAAAAAHBLFGIAAAAAgFvisUsAAAD1QHRsmhJ2ZMlqtcnDw6J+YW00emhXs2MBQL1GIQYAAHBy0bFp2rD9qP211Wqzv6YUA0DVsWQaAADAySXsyHLoOgDAGAoxAACAk7NabQ5dBwAYQyEGAABwch4eFoeuAwCMoRADAAA4uX5hbRy6DgAwhkO1AAAAnFzxwVmcMg0ANYtCDAAA4AQqe6zS6KFdKcAAUMMoxAAAACbjsUoAYA72EAMAAJiMxyoBgDmYIQYAADDJ1cuky8JjlQCgdlGIAQAATNB/XFylY3isEgDULpZMAwAA1DEjZVjisUoAUNuYIQYAAKhDRmeGeawSANQ+CjEAAEAdMVqG494cUAdpAAAUYgAAgFp27WOVKsIyaQCoOxRiAACAWjRmZpKOnco1PJ5l0gBQdzhUCwAAoJY4WoYje7StxTQAgGsxQwwAKhn7YwAAIABJREFUAFALjJ4kXSyyR1tmhwGgjlGIAQAAapijZXjdWwNrKQkAoCIsmQYAAKhBjpThwAA/yjAAmIhCDAAAUEMGvWS8DHtapAUTwmsxDQCgMhRiAACAGjDopTgV2YyPXzubmWEAMBuFGAAAoJocKcN+Pp4skwYAJ0EhBgAAqIYZq48bLsOeFmnV9IdqNxAAwDAKMQA4qZycHD300EM6fvy42VEAlGHMzCT1Hxen/CJj4/18PFkmDQBOhkIMAE4oPT1dw4cP15EjR8yOAqAMY2Ym6dipXMPjI3u0ZWYYAJwQhRgAnNCaNWs0adIkBQQEmB0FQBkcLcOjh3atxTQAgKpqYHYAAEBp06ZNMzsCgHIMm/iloXGeFk6SBgBnZ7HZbA48IAAAUJd69eqlpUuX6sYbb6x0bH5+vjIyMuogFeC+Zqw+bmjPsI+n9MojlX/f1qVOnTrJx8fH7BgA4FSYIQYAF+PMP/Smpqaqe/fuZsdwWH3MTeaaFR2bpg3bjxoa6+fj6VT7hfllGQCUjz3EAAAAFXCkDPNYJQCoXyjEAAAA5XC0DLNnGADqF5ZMA4AT27x5s9kRALflyKOVnG2ZNADAGGaIAQAArhEdm2a4DPt4skwaAOorZogBAACu4ugBWuMfblXLiQAAtYUZYgAAgP/nSBmO7NGWmWEAqOeYIQYAAPh/CTuyKh0T2aOtRg/tWgdpAAC1jUIMAADcmiOHZwUG+FGGAcCFUIgBAIDbGjbxS+XmFxkay8wwALge9hADAAC3FB2bRhkGADfHDDEAAHA7Rg/P8vCwqF9YG8owALgoCjEAAHArjpThuDcH1EEiAIBZKMQAAMBtOHKAVr+wNrWcBgBgNgoxAABwC/3HxRkey55hAHAPFGIAAODSjC6RLrburYG1mAYA4Ew4ZRoAALgsR8twZI+2tZgGAOBsKMQAAMAlOVqG/Xw8WSYNAG6GQgwAAFyOo2U4MMBPq6Y/VIuJAADOiD3EAADA5bBnGABgBDPEAADApQyb+KXhsZRhAHBvzBADAACX4Mgzhv18PFkiDQCgEAMAgPpv2MQvlZtfZGgss8IAgGIsmQYAAPWaI2U4MMCvltMAAOoTCjEAAKi3HCnDnhZpwYTwWk4EAKhPWDINAADqpUEvxanIZmwse4YBAGWhEAMAgHrHkTIc2aOtRg/tWruBAAD1EoUYAADUG9GxaYafMcysMACgMuwhBgAA9QJlGABQ05ghBgAATs+Rw7MowwAAo5ghBgAATs3Rk6QpwwAAoyjEAADAaTlahtfOHljLiQAAroRCDAAAnNKgl+IMl+HAAD/KMADAYewhBgAATofHKgEA6gKFGAAAOJX+4+IMjw0M8KMMAwCqjCXTAADAaQx6yXgZ9vPx1IIJ4bWYBgDg6ijEAADAKUz5+LjhZdKBAX6cJg0AqDYKMQA4oXXr1ikyMlJ9+vTRihUrzI4D1Kro2DT1Hxcng11YgQF+zAwDAGoEe4gBwMmcPHlS77zzjj777DN5e3tr2LBhCgsLU8eOHc2OBtS4MTOTdOxUruHxlGEAQE1ihhgAnMw333yjO++8U02bNpWvr6/69u2rhIQEs2MBNS46Ns2hMhzZoy1lGABQo5ghBgAnc+rUKTVv3tz+OiAgQN99953hP5+RkVEbsWpMamqq2RGqpD7mdubMM1Yfl8FHDMsiadKjN0oqctq/k7PmAgBUjEIMAE7GarXKYrHYX9tsthKvK9OpUyf5+PjURrRqS01NVffu3c2O4bD6mNuZMzvyWCVPi7R29sBaTFN9zvy1lqT8/Hyn/0UZAJiFJdMA4GRatmyp06dP21+fPn1aAQEBJiYCas6YmUkOjXf2MgwAqN8oxADgZO666y5t375d586d06VLl5SYmKiePXuaHQuoNkf3DK97izIMAKhdLJkGACfTokULvfjii3riiSd0+fJlDR06VF26dDE7FlAtwyZ+qVyDm4Yje7TV6KFdazkRAAAUYgBwSv3791f//v3NjgHUCEf2DFOGAQB1iSXTAACg1gyb+KXhsT6eogwDAOoUM8QAAKDGRcemKWFHlqxWm6Hxfj6eGv9wq1pOBQBAScwQAwCAGhUdm6YN248aLsOeFmnV9IdqORUAAKVRiAHAoCeeeEJr166tcMyyZcvUr1+/OkoEOKcN2486NJ5HKwEAzMKSaQAox6+//qrCwkJJks1m086dO3XbbbcpJyenzPEFBQX6+uuv9dNPP9VlTMBpFM8MG+Xn48nMMADAVBRiACjHp59+qqlTp5a4tmjRIi1atKjCPxcaGlqbsQCn5EgZpggDAJwFhRgAyjF8+HDt2rVLZ8+elSSlpKSoVatWat26damxFotFXl5eCggI0OjRo+s6KmA6o2V43VssjwYAOA8KMQCUw8PDQ3PmzLG/DgkJ0ZAhQxQVFWViKsC5jJmZpGOncg2NjezRtpbTAADgGAoxABi0f/9+syMATqX/uDjDY/18PHnGMADA6VCIAcBBR48e1dmzZ2W1WmWzXXmsjM1mU2Fhoc6fP6+tW7dq5syZJqcEategl4yX4cAAPy2YEF6LaQAAqBoKMQAYdO7cOY0aNUoZGRmVjqUQw9UVGXvEsCJ7tGVmGADgtCjEAGDQu+++q++//15BQUHq3r271q1bpw4dOujmm2/WwYMHtWvXLt1www368MMPzY4K1Jro2DQl7MgyNJYyDABwdhRiADBo27ZtateundauXSsPDw+dPXtWv/76qyZPnixJWrt2rV5++WWlpaUpJCTE3LBALXDk0UqUYQBAfeBhdgAAqC9OnTqlu+++Wx4eV26dN998s9LT0+0fHzRokG6//XatXbvWrIhArYiOTVP/cXGUYQCAy2GGGAAMatiwoXx8fOyv27Rpo4sXL+rkyZNq0aKFJKlLly4UYrgUR2aFPS3S2tk8ZxgAUH9QiAHAoA4dOpSYEW7Xrp1sNpv27t1rL8Q5OTm6dOmSWRGBGmW0DHt4WBT35oA6SAQAQM1iyTQAGBQZGamUlBRNmDBBx48fV3BwsAICAjR37lwdPHhQO3bs0Pr163XTTTeZHRWoNkeWSPcLa1PLaQAAqB0UYgAwaMSIEerTp4/i4uKUkpIiT09PjRkzRvv27dNDDz2kJ598Ujk5OXr22WfNjgpUS/9xxp8xzH5hAEB9xpJpADCoQYMGmjt3rtLT09WyZUtJ0rBhw9SkSROtX79ePj4+GjBggO69916TkwJVN2ZmkuGxlGEAQH1HIQYAB4WGhpZ4HRERoYiICJPSADVn0EtxKrIZG0sZBgC4AgoxADgoPz9fP/74owoKCsodw3OIUd84UobXvcVJ0gAA10AhBgCDfvnlF7322mtKSqp8Sem+ffvqIBFQM6Jj0xyaGQYAwFVQiAHAoOnTp2vTpk1q27atbr311hLPJAbqI0eeMSyxTBoA4HooxABg0Ndff63bbrtNK1askIcHh/SjfnO0DLNMGgDgiijEAGBQQUGBunXrRhlGvTdmZpKOnco1PJ5l0gAAV0UhBgCD7rnnHqWmppodA6iWYRO/VG5+keHxLJMGALgyCjEAGPTKK69o+PDh+vOf/6wnn3xSN954o7y9vcsc6+/vX8fpgMqNmZlkuAxThAEA7oBCDAAGNWnSRJ07d1Z8fLzi4+PLHWexWLR37946TAZUzpHHKgUG+FGGAQBugUIMAAZNnz5diYmJatiwoTp06KBGjRqZHQkwxJEy7OfjqQUTwms3EAAAToJCDAAGJSYmqmPHjvr444/VuHFjs+MAhjjyjOHAAD/KMADArVCIAcCg/Px89ezZkzKMesOR06TZMwwAcEcUYgAwqFu3btq/f7/ZMQBDZqw+LqOHSVOGAQDuiodpAoBBEyZMUFpamt544w2dOHGi1j/fnDlz9N5779X654FriY5NU/9xcYbLMAdoAQDcGTPEAGDQG2+8oWbNmunvf/+7/v73v6tBgwZlHqxlsVi0Y8eOKn+e7OxszZgxQ+vXr9czzzxTnchwM9Gxadqw/ajh8cwMAwDcHYUYAAw6cuSIJKlVq1a1+nmSk5N100036amnnqrVzwPXY7QMU4QBALiCQgwABm3evLlOPs+gQYMkieXSMCw6Nk0JO7IMjWWJNAAA/0YhBoBaNG/ePC1YsEB79+4t9bH4+HjNmDGjxLX27dsrJiamWp8zIyOjWn++tqWmppodoUqcNfeXO88p5Yc8Q2N9PKWnw6932r+L5Lxf58rU19wA4O4oxABQy2y2sh8CGxERoYiIiBr/fJ06dZKPj0+Nv29NSE1NVffu3c2O4TBnzj354zhD4+rDMmln/jpXxNlz5+fnO/0vygDALBRiAADqIUcO0KoPZRgAADNQiAEAqGfGzEzSsVO5lY7z8LCoX1gbyjAAAOWgEAOAk3rhhRfMjgAnZLQM/7ajryaN7l0HiQAAqL88zA4AAACMiY5NM1SGJemhO5rVchoAAOo/CjEAAPWE0UcrRfZoW8tJAABwDRRiAADqCau17BPLr8ZzhgEAMI49xAAA1BMeHpYKSzGnSQMA4BgKMQAATig6Nk0JO7Jktdrsp0X3C2tT5qOWAgP8tGBCuAkpAQCo31gyDQCAkxkzM0kbth+1zwZbrTZ7EY7s0VYeHhZJV2aMI3u0pQwDAFBFzBADQC0KCQnRoEGDzI6BeqSik6QTdmQp7s0BLIsGAKCGUIgBoBzJyclq37692rVrZ39t1AMPPCBJCg8PV3g4s3cwprJnDBs5VAsAABhHIQaAcvzxj39UVFSUoqKi7K8tFkuFf8Zms8lisWjfvn11EREupLIyLMm+VBoAANQMCjEAlCMqKkp33HGH/bWRQgxURUXLpK/WL6xNHaQBAMB9UIgBoBzFM8PFXnjhBZOSwJVFx6aVeXJ0Wdg7DABAzeKUaQBwQF5enmJiYvT111+XuP70009r8eLFKiwsNCkZ6quEHVmGxkX2aFvLSQAAcD8UYgAw6Ny5c3rkkUc0c+ZMffvtt/brly5d0j//+U+9+eabGjFihHJyckxMifrGyEFZkT3aMjsMAEAtoBADgEHz5s3Tv/71L7344osaPXq0/XqjRo20c+dOjRs3Tunp6Xr33XdNTIn6prKDsijDAADUHgoxABi0detWPfDAA3ruuefk6+tb4mNeXl565plndO+992rjxo0mJUR9VNFBWZRhAABqF4dqAYBBZ8+eVZs2FZ/y2759+1L7i4Fi0bFpStiRJavVJg8Pi/qFtbEX3vKuAwCA2kMhBgCDWrVqpdTU1ArHfPfdd2rZsmUdJUJ9cu1p0larzf569NCuFGAAAEzAkmkAMCgiIkLff/+9Zs6cqYKCghIfKyws1Lvvvqt//vOf6tevn0kJ4YzGzExS/3Fx5T5ayegp0wAAoOYxQwwABj333HPavHmzlixZotjYWIWEhMjf31+5ubnav3+/Ll68qODg4BIHbsG9jZmZpGOnciscY+SUaQAAUDsoxABgUMOGDbVq1SotXLhQ69ev165du+wfa9WqlYYPH65Ro0aVOnAL7quyMixVfso0AACoPRRiAHBAw4YNNXbsWD3zzDPKzMxUVlaWwsPDVVhYqKZNm5odD04kOjbN0LiKTpkGAAC1iz3EAOCAM2fO6MUXX1RYWJhGjBihiRMnyt/fXytXrlTv3r2VkpJidkQ4gWsP0CqLh4eFxyoBAGAyZogBwKBz587pkUce0Y8//qhu3bopPz9fe/fulSQ1atRIP/30k5599lmtWrVKwcHBJqeFmSo7KCswwE8LJoTXURoAAFAeZogBwKC5c+fqxIkTio6O1scff6z777/f/rEnn3xSixcvVmFhoaKjo01MCbNEx6Zp4Pgv1H9cXIUHZVGGAQBwHhRiADBo8+bN6t27d4kifLWwsDD16dNHaWnG9o7CdRQvka7sxGgPDwtlGAAAJ0IhBgCDfvnlFwUGBlY4pkWLFjp37lwdJYKzMPosYQ7QAgDAubCHGAAMatmypX3PcHm+++47tWzZso4SwUxGDs4q5uFhUb+wNhygBQCAk6EQA4BBffv21UcffaRVq1Zp2LBhpT6+ZMkSpaam6qmnnjIhHerSmJlJhp8xHPfmgDpIBAAAqoJCDAAGPf/889q6daumTJmiFStWyGq1SpJefvll7dmzRz/88IPatGmj559/3uSkqE3RsWmGyrDEEmkAAJwde4gBwKDi5w0PGzZMP/74ow4ePCibzaa1a9fq6NGjGjhwoFauXKnrrrvO7KioJUaXSfOMYQAA6gdmiAHAAf7+/po0aZL++te/6vDhw7p48aJ8fX3Vvn17eXt7mx0PtciRMswyaQAA6gcKMQBUgaenpzp27Gh2DNQhowdosUwaAID6gyXTAADUEJZJAwBQvzBDDABOJjU1VTNmzNDly5fVtGlTTZ8+Xa1btzY7FipAEQYAoH6iEAOAkxk/frwWLFigkJAQxcbGaurUqYqOjjY7ltuJjk1Two4sWa02WSwVj6UMAwBQP7FkGgCcSEFBgcaOHauQkBBJUnBwsE6cOGFyKvdTfICW1WqTJNls5Y+N7NG2jlIBAICaZrHZKvrfPADALFarVaNHj1bnzp0VFRVV6fj8/HxlZGTUQTLX9eXOc0o9mFdhAbZYrhRki0Xq3sFXD93RrO4CAtXQqVMn+fj4mB0DAJwKS6YBwCTx8fGaMWNGiWvt27dXTEyMCgoK9PLLL6uwsFCjRo1y6H2d+Yfe1NRUde/e3ewYZYqOTVPKD3mVjvti9sA6SFN9zvy1Lk99zCw5f25+WQYA5aMQA4BJIiIiFBERUep6bm6uRo8eraZNmyo6OlpeXl4mpHM/Rp8xDAAAXAd7iAHAyYwfP15t27bVnDlz5O3tbXYctzBs4peGxvGMYQAAXAszxADgRPbu3avk5GR17NhRgwcPliQFBATogw8+MDmZayo+PKsyFosUcSePVgIAwNVQiAHAidxyyy3KzMw0O4ZbMFqGI3u01R3titS9O2UYAABXw5JpAIDbGTMzyVAZlnjGMAAAroxCDABwK2NmJunYqVxDYwMD/Go5DQAAMBOFGADgVoyWYT8fTy2YEF7LaQAAgJnYQwwAwDUie3CAFgAA7oBCDADA/6MIAwDgXijEAACXFh2bpoQdWbJabfLwsMjPx1O5+UWlxgUG+FGGAQBwM+whBgC4rOJHK1mtNkmS1WpTbn6R/Hw8S4wLDPBjvzAAAG6IGWIAgMtK2JFV5vVLl61a99bAOk4DAACcDTPEAACXVTwzbPQ6AABwLxRiAIDL8vCwOHQdAAC4FwoxAMBl9Qtr49B1AADgXthDDABwWcWnRl99ynS/sDacJg0AACRRiAEALm700K4UYAAAUCaWTAMAAAAA3BKFGAAAAADglijEAAAAAAC3RCEGAAAAALglCjEAAAAAwC1RiAEAAAAAbolCDAAAAABwSxRiAAAAAIBbohADAAAAANwShRgAAAAA4JYoxAAAAAAAt0QhBgAAAAC4JQoxAAAAAMAtUYgBAAAAAG6JQgwAAAAAcEsUYgAAAACAW6IQAwAAAADcEoUYAAAAAOCWKMQAAAAAALdEIQYAJ5OSkqIhQ4aof//+ev7553XhwgWzIwEAALgkCjEAOJlXXnlFs2bN0rp169SxY0d99NFHZkcCAABwSQ3MDgAAKGnDhg3y8vLS5cuXdfLkSQUHB5sdCQAAwCUxQwwATsbLy0uZmZm69957tWPHDj344INmRwIAAHBJFpvNZjM7BAC4o/j4eM2YMaPEtfbt2ysmJsb+etWqVVq7dq1WrVpV6fvl5+crIyOjpmMCcBGdOnWSj4+P2TEAwKlQiAHAieTn52vbtm0KDw+XJOXl5enuu+/W7t27Df3ZjIwMp/6hNzU1Vd27dzc7hsPqY24y1x1nz10f7g0AYBaWTAOAE2nQoIGmTJlin+mNj49Xt27dTE4FAADgmjhUCwCciKenp9555x299tprKioqUosWLTRt2jSzYwEAALgkCjEAOJnf/va3+uyzz8yOAQAA4PIoxACAaomOTVPCjixZrTZ5eFjUL6yNRg/tanYsAACASlGIAQBVFh2bpg3bj9pfW602+2tKMQAAcHYcqgUAqLKEHVkOXQcAAHAmFGIAQJVZrWU/ua+86wAAAM6EQgwAqDIPD4tD1wEAAJwJhRgAUGX9wto4dB0AAMCZcKgWAKDKig/O4pRpAABQH1GIAQDVMnpoVwowAACol1gyDQAAAABwSxRiAAAAAIBbohADAAAAANwShRgAAAAA4JYoxAAAAAAAt0QhBgAAAAC4JQoxAAAAAMAtUYgBAAAAAG6JQgwAAAAAcEsNzA4AADBHdGyaEnZkyWq1ycPDon5hbTR6aFezYwEAANQZCjEAuKHo2DRt2H7U/tpqtdlfU4oBAIC7YMk0ALihhB1ZDl0HAABwRRRiAHBDVqvNoesAAACuiEIMAG7Iw8Pi0HUAAABXRCEGADfUL6yNQ9cBAABcEYdqAYAbKj44i1OmAQCAO6MQA4CbGj20KwUYAAC4NZZMAwAAAADcEoUYAAAAAOCWKMQAAAAAALdEIQYAAAAAuCUKMQAAAADALXHKNAC4CJvNJkkqKCgwOUnF8vPzzY5QJfUxN5nrjjPnLr4nFN8jAAD/ZrFxdwQAl5Cdna0DBw6YHQOAkwoKClLjxo3NjgEAToVCDAAuwmq1Kjc3V15eXrJYLGbHAeAkbDabLl++LD8/P3l4sFsOAK5GIQYAAAAAuCV+TQgAAAAAcEsUYgAAAACAW6IQAwAAAADcEoUYAAAAAOCWKMQAAAAAALdEIQYAAAAAuCUKMQAAAADALVGIAQB1KiUlRUOGDFH//v31/PPP68KFC2ZHqlRqaqqGDh2qgQMH6g9/+IN+/PFHsyMZNmfOHL333ntmx6jUunXrFBkZqT59+mjFihVmxzEsJydHDz30kI4fP252FABAFVCIAQB16pVXXtGsWbO0bt06dezYUR999JHZkSo1fvx4TZ06VXFxcerfv7+mTp1qdqRKZWdna+LEiVqyZInZUSp18uRJvfPOO/r444+1du1arV69Wj/88IPZsSqVnp6u4cOH68iRI2ZHAQBUEYUYAFCnNmzYoI4dO+ry5cs6efKkrrvuOrMjVaigoEBjx45VSEiIJCk4OFgnTpwwOVXlkpOTddNNN+mpp54yO0qlvvnmG915551q2rSpfH191bdvXyUkJJgdq1Jr1qzRpEmTFBAQYHYUAEAVNTA7AADAvXh5eSkzM1NPPfWUGjRooD//+c9mR6qQt7e3Bg4cKEmyWq2aN2+ewsPDTU5VuUGDBklSvVguferUKTVv3tz+OiAgQN99952JiYyZNm2a2REAANVEIQYA1Ir4+HjNmDGjxLX27dsrJiZGwcHB+uabb7Rq1Sq9+OKLWrVqlUkpS6ooc0FBgV5++WUVFhZq1KhRJiUsraLM9YXVapXFYrG/ttlsJV4DAFBbKMQAgFoRERGhiIiIEtfy8/OVlJRkn2EdMGCAZs6caUa8MpWVWZJyc3M1evRoNW3aVNHR0fLy8jIhXdnKy1yftGzZUikpKfbXp0+fZhkyAKBOsIcYAFBnGjRooClTpigjI0PSldnNbt26mZyqcuPHj1fbtm01Z84ceXt7mx3H5dx1113avn27zp07p0uXLikxMVE9e/Y0OxYAwA0wQwwAqDOenp5655139Nprr6moqEgtWrRw+n2Ye/fuVXJysjp27KjBgwdLurLH9YMPPjA5meto0aKFXnzxRT3xxBO6fPmyhg4dqi5dupgdCwDgBiw2m81mdggAAAAAAOoaS6YBAAAAAG6JQgwAAAAAcEsUYgAAAACAW6IQAwAAAADcEoUYAAAAAOCWKMQAANSh9957T8HBwUpKSjI7CgAAbo9CDABAHbrjjjsUFRWldu3amR0FAAC3x3OIAQAAAABuiRliAAAAAIBbohADAFCJnJwczZ49W+Hh4erUqZN+97vfadKkSTp79myJcZcuXdLcuXPVr18/hYaGqlevXpoyZYrOnTtnH1PdPcRnzpzRxIkT1bt3b3Xu3Fn33HOPxo8fr6NHj5YY16tXLw0fPlz79+/X448/rtDQUPXs2VN/+9vfdP78+VLvm5qaqqioKN1zzz3q1KmTbr/9dj311FP69ttvS4x7/PHH1atXL23dulW9evVSaGioxo4dK0k6evSoxo4dq/vvv1+dOnVSr169NHnyZJ0+fbrU59uzZ4/GjBmjsLAwdenSRQMHDtTKlSvFwjUAQF1iyTQAABXIzs7Wo48+qgMHDqhHjx665ZZbdPz4cW3atEmtWrXSqlWrFBAQoEuXLmn48OHat2+fOnfurO7duysrK0ubN29WUFCQVq5cKX9/f7333nuaN2+e5s+fr/DwcIey5Ofna9iwYTpw4IB69+6twMBAZWVladOmTWrSpIni4+PVtGlTSVcKscViUXZ2tlq3bq0ePXooPT1dKSkp+s1vfqM1a9bI19dXkpSUlKQ//elPatasmcLDw+Xn56d//etf+t///V95enoqNjZWN998s6QrhTgjI0OS9MADD8jf318dOnTQgw8+qIcffli//PKL+vbtq4CAAGVmZmrr1q1q3769vvjiC3l5eUmStm7dqqioKHl5ealPnz5q1qyZtm3bpgMHDuj3v/+9Xn/99Zr65wMAoGI2AABQrsmTJ9uCgoJsy5cvL3E9KSnJFhQUZPvTn/5ks9lstjlz5tiCgoJs06ZNs1mtVvu4999/3xYUFGRbvHixzWaz2ebOnWsLCgqybdq0yeEsmzdvtgUFBdnefffdEtc//PDDUhnvv/9+W1BQkO3555+3FRYW2q+//vrrtqCgINvcuXPt1/r27Wu74447bKdPny7xvosWLbIFBQXZ3nrrLfu1xx57zBYUFGSbMWNGibHLli2zBQUF2WJjY0tcnzJlii0oKMj21Vdf2Ww2my0vL89255132u68804E3p2hAAAHM0lEQVTbsWPH7OOKiopsL7zwgi0oKMi2ZcsWB78yAABUDUumAQAoR2FhodauXavf/OY3GjFiRImPPfDAA+rWrZs2bdqknJwcrV+/Xv7+/ho3bpwsFot93GOPPaZnnnlGHTt2rHYeq9UqSdq7d69+/fVX+/VHH31UW7Zs0aOPPlpivMVi0V/+8hd5enrar40dO1a+vr5at26d/T3HjRunWbNm6YYbbijx58PCwiSp1NJwSerbt2+Z2dLT01VUVGS//uKLL+of//iH7rvvPknS5s2bde7cOT3zzDO68cYb7eM8PDw0btw4SdKnn35q4KsBAED1NTA7AAAAzurw4cPKy8tTUVGR3nvvvVIfz8/PV1FRkTIzM3X06FHdfvvt8vHxKTHGz89P48ePr5E8d911lwIDA/XVV1/p7rvv1l133aWePXvqvvvuU6tWrUqNb968eanHOzVu3Fjt2rXTnj17dOnSJTVq1Ei9e/eWJP3444/617/+paysLP3www/asWOHpH+X3au1bt26xOu+fftq/vz5Wr16tRITE3XPPfeoZ8+euvfee9W8eXP7uOLl1nv27Cnza+rp6an9+/c7+JUBAKBqKMQAAJTj4sWLkqRDhw5p3rx55Y7LysqSJPn7+9dqnkaNGmnNmjWKjo5WfHy8EhMTlZiYKA8PD/Xu3Vt/+9vf7HuIJalFixZlvk/xTHB2drYaNWqkzMxMTZ06VTt37pQkeXl5qUOHDurUqZOOHDlS5kFXDRs2LPG6RYsWio2NVXR0tJKTk7Vu3TqtW7dOXl5eGjJkiF599VX5+PgoOztbkrR+/fpy/54XLlxw7AsDAEAVUYgBACiHn5+fJGngwIGaNWtWueOKS15ubm6ZH8/Ly7MfYFVdzZo106uvvqqJEycqMzNT27ZtU1xcnDZu3CgPDw/NmTPHPjY/P7/M9ygu+k2bNlVOTo5Gjhyp7OxsTZgwQXfddZfat28vb29vpaen68svvzScLTAwUNOnT1dRUZEyMjK0bds2ffbZZ1q9erUaN26s8ePH278OMTEx6tGjRzW+EgAAVB97iAEAKEe7du3k7e2tPXv2lDlLGhMTowULFqiwsFCtWrXSvn37VFBQUGJMQUGB7r77bo0cObLaeXbt2qWpU6cqKytLFotFISEhevbZZ/XJJ5/I19dXKSkpJcYfPnzYXtaLXbp0SZmZmbrlllvk7e2tb7/9VmfOnNGIESM0cuRIhYSEyNvbW5J08OBBSTL0KKTk5GRNnjxZOTk58vT0VGhoqKKiorRixQpJVx7rJEnBwcGS/r10+mrnz5/XtGnTFBcX5+BXBgCAqqEQAwBQDh8fH0VGRuqHH37QkiVLSnxsx44dmjVrlj799FM1adJEAwYMUHZ2tubPn19i3NKlS5WXl1cjs6GnT5/WsmXLtHjx4hLXz5w5o/z8/FL7ei9fvqy3337bXmhtNpveeust5eXl6eGHH7b/HaXSB2f99NNP9mXihYWFlWY7dOiQVq5cqZUrV5a4/uOPP0qS/vM//1OS1Lt3b/n7++vDDz/U4cOHS4x98803tXTpUvsSdAAAahtLpgEAqMCECRO0e/duzZw5U8nJyerSpYtOnjypxMRENWjQQNOnT5eHh4dGjRqlLVu26P3339euXbsUGhqqQ4cOacuWLercubP+8Ic/VDtLeHi4brvtNq1cuVIHDhxQ165dlZOTo40bN0qSXnjhhRLjvby8FBcXp3379ik0NFTp6enavXu3wsLCNGzYMElS9+7d1bp1a8XFxemXX35RSEiITpw4oeTkZPn4+Mhisej8+fOVZvv973+vNWvWaPbs2dq5c6eCg4N19uxZJSQkyNfXV88995wk6brrrtPUqVP10ksvafDgwQoPD1dAQIB27typ77//Xp07d66R2XQAAIyw2IysgwIAwI2dP39eCxcu1KZNm/Tzzz+rWbNm6tq1q8aMGaOQkBD7uJycHC1YsEAJCQk6efKkrr/+evXt21f//d//rcaNG0uS3nvvPc2bN0/z589XeHi4w1kuXLigDz74QElJSTpx4oR8fHzUtWtXjRo1St27d7eP69Wrly5evKjly5dr8uTJ2rt3r5o3b65BgwZp1KhR9mXRknT06FHNnj1bqampunTpklq1aqXbb79df/zjH/Xcc8/p8OHD+uabb+Tn56fHH39cO3fu1K5du3TdddeVyHby5ElFR0fr66+/1s8//yx/f3/dcccdioqK0m9+85sSY3fv3q2FCxdq9+7dunTpklq3bq1+/frp6aefrvXDyQAAKEYhBgDABRUX4mv3FQMAgH9jDzEAAAAAwC2xhxgAAJMkJSVp3759hsdfu0cYAABUD4UYAACTJCUl6fPPPzc8nkIMAEDNYg8xAAAAAMAtsYcYAAAAAOCWKMQAAAAAALdEIQYAAAAAuCUKMQAAAADALVGIAQAAAABuiUIMAAAAAHBL/wdMxfYbeC5A+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x1080 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,15))\n",
    "\n",
    "plt.subplot(338)\n",
    "plot('eci_sparse', 'eci_mat')\n",
    "plt.subplot(331)\n",
    "plot('eci_sparse', 'eci_ecomplexity_cal')\n",
    "plt.subplot(332)\n",
    "plot('eci_sparse', 'eci_hidalgo_rep')\n",
    "plt.subplot(333)\n",
    "plot('eci_ecomplexity_cal', 'eci_hidalgo_rep')\n",
    "plt.subplot(334)\n",
    "plotGDP('eci_sparse', 'gdp')\n",
    "plt.subplot(335)\n",
    "plotGDP('eci_ecomplexity_cal', 'gdp')\n",
    "plt.subplot(336)\n",
    "plotGDP('eci_hidalgo_rep', 'gdp')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## sitc digit-2 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('country_sitcproduct2digit_year.csv',low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:04.157869Z",
     "start_time": "2019-07-19T06:17:04.099342Z"
    }
   },
   "outputs": [],
   "source": [
    "year =2017\n",
    "df_year = df[df['year']==year]\n",
    "df_year = df_year.drop_duplicates()\n",
    "df_year = df_year.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:37:57.147344Z",
     "start_time": "2019-07-19T06:37:57.111325Z"
    }
   },
   "outputs": [],
   "source": [
    "df_gdp = pd.read_csv('./GDP/API_NY.GDP.PCAP.CD_DS2_en_csv_v2_41089.csv', sep = '\\t')\n",
    "df_g = df_gdp[['Country Code', str(year)]]\n",
    "gdp_dict =dict(zip(df_g['Country Code'],df_g[str(year)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:10.947251Z",
     "start_time": "2019-07-19T06:17:10.923358Z"
    }
   },
   "outputs": [],
   "source": [
    "eci_hidago = df_year[['sitc_eci','location_code']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:11.653280Z",
     "start_time": "2019-07-19T06:17:11.633477Z"
    }
   },
   "outputs": [],
   "source": [
    "eci_hidago_dict =dict(zip(eci_hidago['location_code'],eci_hidago['sitc_eci']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:12.332737Z",
     "start_time": "2019-07-19T06:17:12.304161Z"
    }
   },
   "outputs": [],
   "source": [
    "x = list(df_year[\"location_code\"])\n",
    "y = list(df_year[\"sitc_product_code\"])\n",
    "data = np.array(list(df_year[\"export_value\"]))\n",
    "sx = list(set(x))\n",
    "sy =list(set(y))\n",
    "dx = dict(zip(sx,range(len(sx))))\n",
    "dy = dict(zip(sy,range(len(sy))))\n",
    "x1 = [dx[xi] for xi in x]\n",
    "y1 = [dy[yi] for yi in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:13.505436Z",
     "start_time": "2019-07-19T06:17:13.481576Z"
    }
   },
   "outputs": [],
   "source": [
    "# 构建出口矩阵\n",
    "M = sparse.csc_matrix((data,(x1,y1)),(len(dx),len(dy)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:14.786383Z",
     "start_time": "2019-07-19T06:17:14.729120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==Time 0.03==\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/anaconda3/lib/python3.7/site-packages/scipy/sparse/base.py:597: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  return np.true_divide(self.todense(), other)\n",
      "/Users/datalab/anaconda3/lib/python3.7/site-packages/scipy/sparse/base.py:597: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return np.true_divide(self.todense(), other)\n",
      "/Users/datalab/anaconda3/lib/python3.7/site-packages/scipy/sparse/compressed.py:208: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  res = self._with_data(op(self.data, other), copy=True)\n",
      "/Users/datalab/anaconda3/lib/python3.7/site-packages/scipy/sparse/compressed.py:282: SparseEfficiencyWarning: Comparing a sparse matrix with a scalar greater than zero using < is inefficient, try using >= instead.\n",
      "  warn(bad_scalar_msg, SparseEfficiencyWarning)\n"
     ]
    }
   ],
   "source": [
    "#计算RCA\n",
    "R = RCA_sparse(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:17.780629Z",
     "start_time": "2019-07-19T06:17:17.760716Z"
    }
   },
   "outputs": [],
   "source": [
    "R=R.toarray()\n",
    "R[np.isnan(R)]=0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:18.751186Z",
     "start_time": "2019-07-19T06:17:18.713118Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==Time 0.01==\n"
     ]
    }
   ],
   "source": [
    "Mat_Dict = Get_eci_pci(np.matrix(R))\n",
    "eci,pci = Mat_Dict['eci'],Mat_Dict['pci']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:20.768240Z",
     "start_time": "2019-07-19T06:17:20.736815Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==Time 0.01==\n"
     ]
    }
   ],
   "source": [
    "#利用稀疏矩阵计算：\n",
    "Dict = Get_eci_pci_sparse(sparse.csc_matrix(R))\n",
    "eci_sparse,pci_sparse = Dict['eci'],Dict['pci']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:21.726213Z",
     "start_time": "2019-07-19T06:17:21.584916Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017\n"
     ]
    }
   ],
   "source": [
    "# 利用ecomplexity计算出来的结果\n",
    "from ecomplexity import ecomplexity\n",
    "from ecomplexity import proximity\n",
    "time = [2017]*len(x)\n",
    "trade_cols = {'time':'year', 'loc':'name', 'prod':'skill', 'val':'value'}\n",
    "SData=pd.DataFrame({\"year\":time,'name':x,\"skill\":y,\"value\":data})\n",
    "cdata = ecomplexity(SData, trade_cols)#使用过滤函数\n",
    "res=pd.DataFrame({'name':list(cdata.name),\"eci\":list(cdata.eci)})\n",
    "res = res.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:22.830528Z",
     "start_time": "2019-07-19T06:17:22.681061Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017\n"
     ]
    }
   ],
   "source": [
    "df_year = df[df['year']==year]\n",
    "trade_cols = {'time':'year', 'loc':'location_code', 'prod':'sitc_product_code', 'val':'export_value'}\n",
    "cdata2 = ecomplexity(df_year, trade_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:17:23.472063Z",
     "start_time": "2019-07-19T06:17:23.443861Z"
    }
   },
   "outputs": [],
   "source": [
    "res2=pd.DataFrame({'name':list(cdata2.location_code),\"eci\":list(cdata2.eci)})\n",
    "res2 = res2.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:38:13.881586Z",
     "start_time": "2019-07-19T06:38:13.855129Z"
    }
   },
   "outputs": [],
   "source": [
    "y =[eci[dx[name]] for name in res.name]\n",
    "y1 = [eci_sparse[dx[name]] for name in res.name]\n",
    "y2 = [eci_hidago_dict[name] for name in res.name]\n",
    "gdp = [gdp_dict[name] if name in gdp_dict else 0.0 for name in res.name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:38:14.680038Z",
     "start_time": "2019-07-19T06:38:14.653246Z"
    }
   },
   "outputs": [],
   "source": [
    "total=pd.DataFrame({'name':list(res.name),\n",
    "                    'gdp':gdp,\n",
    "                    'eci_hidalgo_rep':y2,\n",
    "                    \"eci_ecomplexity_cal\":list(res.eci),\n",
    "                    'eci_mat':y,\n",
    "                    'eci_sparse':y1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:38:16.116888Z",
     "start_time": "2019-07-19T06:38:16.085669Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>gdp</th>\n",
       "      <th>eci_hidalgo_rep</th>\n",
       "      <th>eci_ecomplexity_cal</th>\n",
       "      <th>eci_mat</th>\n",
       "      <th>eci_sparse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>25630.266490</td>\n",
       "      <td>0.633293</td>\n",
       "      <td>-0.428741</td>\n",
       "      <td>-0.430580</td>\n",
       "      <td>-0.430580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>556.302138</td>\n",
       "      <td>-1.237445</td>\n",
       "      <td>-1.080686</td>\n",
       "      <td>-1.083838</td>\n",
       "      <td>-1.083838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGO</td>\n",
       "      <td>4095.812942</td>\n",
       "      <td>-1.822037</td>\n",
       "      <td>-1.478833</td>\n",
       "      <td>-1.482787</td>\n",
       "      <td>-1.482787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AIA</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.940984</td>\n",
       "      <td>1.044546</td>\n",
       "      <td>1.045672</td>\n",
       "      <td>1.045672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB</td>\n",
       "      <td>4532.889198</td>\n",
       "      <td>-0.288839</td>\n",
       "      <td>-0.195837</td>\n",
       "      <td>-0.197208</td>\n",
       "      <td>-0.197208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name           gdp  eci_hidalgo_rep  eci_ecomplexity_cal   eci_mat  \\\n",
       "0  ABW  25630.266490         0.633293            -0.428741 -0.430580   \n",
       "1  AFG    556.302138        -1.237445            -1.080686 -1.083838   \n",
       "2  AGO   4095.812942        -1.822037            -1.478833 -1.482787   \n",
       "3  AIA      0.000000         0.940984             1.044546  1.045672   \n",
       "4  ALB   4532.889198        -0.288839            -0.195837 -0.197208   \n",
       "\n",
       "   eci_sparse  \n",
       "0   -0.430580  \n",
       "1   -1.083838  \n",
       "2   -1.482787  \n",
       "3    1.045672  \n",
       "4   -0.197208  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:40:58.534905Z",
     "start_time": "2019-07-19T06:40:58.506626Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.set(style=\"whitegrid\")\n",
    "#for key in [ \"eci_hidalgo_rep\",\"eci_ecomplexity_cal\",\"eci_mat\",\"eci_sparse\"]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:45:12.639790Z",
     "start_time": "2019-07-19T06:45:12.612899Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot(key1, key2):\n",
    "    # [ \"eci_hidalgo_rep\",\"eci_ecomplexity_cal\",\"eci_mat\",\"eci_sparse\"]\n",
    "    plt.scatter(total[key1], total[key2])\n",
    "    plt.xlabel(key1,fontsize=20)\n",
    "    plt.ylabel(key2,fontsize=20)\n",
    "    #plt.show()\n",
    "    \n",
    "def plotGDP(key1, key2):\n",
    "    df = total[total['gdp'] >0]\n",
    "    plt.scatter(df[key1], df[key2])\n",
    "    plt.xlabel(key1,fontsize=20)\n",
    "    plt.ylabel(key2,fontsize=20)\n",
    "    plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T06:47:18.689964Z",
     "start_time": "2019-07-19T06:47:17.618034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "\n",
    "plt.subplot(231)\n",
    "plot('eci_sparse', 'eci_mat')\n",
    "plt.subplot(234)\n",
    "plot('eci_sparse', 'eci_ecomplexity_cal')\n",
    "plt.subplot(232)\n",
    "plot('eci_ecomplexity_cal', 'eci_hidalgo_rep')\n",
    "plt.subplot(235)\n",
    "plot('eci_sparse', 'eci_hidalgo_rep')\n",
    "plt.subplot(233)\n",
    "plotGDP('eci_sparse', 'gdp')\n",
    "plt.subplot(236)\n",
    "plotGDP('eci_hidalgo_rep', 'gdp')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-19T09:01:18.716019Z",
     "start_time": "2019-07-19T09:01:18.156971Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plotGDP('eci_hidalgo_rep', 'gdp')\n",
    "plt.subplot(122)\n",
    "plotGDP('eci_ecomplexity_cal', 'gdp')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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