AEJMC-attendee-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aggregate-bacteria",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "toxic-vampire",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r'D:\\EMAT_22110\\Projects\\AEJMC21 Attendees List.txt', encoding = 'utf-8') as f:\n",
    "    attendee_list = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "joined-toolbox",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_io.TextIOWrapper name='D:\\\\EMAT_22110\\\\Projects\\\\AEJMC21 Attendees List.txt' mode='r' encoding='cp1252'>\n"
     ]
    }
   ],
   "source": [
    "print(attendee_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "lesser-quebec",
   "metadata": {},
   "outputs": [],
   "source": [
    "attendee_soup = BeautifulSoup(attendee_list, 'html.parser')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "czech-greene",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [x.text.strip() for x in attendee_soup.find_all(name='a', attrs={'data-cvent-id':'attendee-list-item-name'})]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "seasonal-establishment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aaron Atkins',\n",
       " 'Aaron Chimbel',\n",
       " 'Abby Hendricks',\n",
       " 'Abdul Wahab Gibrilu',\n",
       " 'Abdulaziz Alowais',\n",
       " 'Abhijit Mazumdar',\n",
       " 'Abhinav Aima',\n",
       " 'Adina Schneeweis',\n",
       " 'Adriana Mucedola',\n",
       " 'Adriane Grumbein']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "weighted-brass",
   "metadata": {},
   "outputs": [],
   "source": [
    "firsts = []\n",
    "lasts = []\n",
    "for i in names:\n",
    "    splits = i.split(' ')\n",
    "    firsts.append(splits[0])\n",
    "    lasts.append(splits[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "complex-substitute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aaron',\n",
       " 'Aaron',\n",
       " 'Abby',\n",
       " 'Abdul',\n",
       " 'Abdulaziz',\n",
       " 'Abhijit',\n",
       " 'Abhinav',\n",
       " 'Adina',\n",
       " 'Adriana',\n",
       " 'Adriane']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "firsts[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "apart-franchise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1966"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(firsts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "difficult-inspector",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Atkins',\n",
       " 'Chimbel',\n",
       " 'Hendricks',\n",
       " 'Gibrilu',\n",
       " 'Alowais',\n",
       " 'Mazumdar',\n",
       " 'Aima',\n",
       " 'Schneeweis',\n",
       " 'Mucedola',\n",
       " 'Grumbein']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasts[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ideal-lotus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1966"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lasts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "interested-dialogue",
   "metadata": {},
   "outputs": [],
   "source": [
    "companies = [x.text.strip() for x in attendee_soup.find_all(name='div', attrs={'data-cvent-id':'attendee-list-item-company'})]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "skilled-madison",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Weber State University',\n",
       " 'St. Bonaventure University',\n",
       " 'UT Austin',\n",
       " 'The Chinese University of Hong Kong',\n",
       " 'The University of Alabama',\n",
       " 'Park University',\n",
       " 'Penn State New Kensington',\n",
       " 'Oakland University',\n",
       " 'Syracuse University',\n",
       " 'University of Kentucky']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "russian-soccer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1966"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "smooth-plaintiff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1966"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(companies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "excellent-balance",
   "metadata": {},
   "outputs": [],
   "source": [
    "at_df = pd.DataFrame({'name': names, 'first': firsts, 'last': lasts, 'university': companies})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "worth-coast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>university</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aaron Atkins</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Atkins</td>\n",
       "      <td>Weber State University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Aaron Chimbel</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Chimbel</td>\n",
       "      <td>St. Bonaventure University</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abby Hendricks</td>\n",
       "      <td>Abby</td>\n",
       "      <td>Hendricks</td>\n",
       "      <td>UT Austin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abdul Wahab Gibrilu</td>\n",
       "      <td>Abdul</td>\n",
       "      <td>Gibrilu</td>\n",
       "      <td>The Chinese University of Hong Kong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abdulaziz Alowais</td>\n",
       "      <td>Abdulaziz</td>\n",
       "      <td>Alowais</td>\n",
       "      <td>The University of Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</th>\n",
       "      <td>Zhuling Liu</td>\n",
       "      <td>Zhuling</td>\n",
       "      <td>Liu</td>\n",
       "      <td>University at Buffalo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962</th>\n",
       "      <td>Zifei Chen</td>\n",
       "      <td>Zifei</td>\n",
       "      <td>Chen</td>\n",
       "      <td>University of San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1963</th>\n",
       "      <td>Ziwei Zhang</td>\n",
       "      <td>Ziwei</td>\n",
       "      <td>Zhang</td>\n",
       "      <td>The Chinese University of Hong Kong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964</th>\n",
       "      <td>ZIYANG GONG</td>\n",
       "      <td>ZIYANG</td>\n",
       "      <td>GONG</td>\n",
       "      <td>University of Illinois at Urbana-Champaign</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1965</th>\n",
       "      <td>Ziyuan Zhou</td>\n",
       "      <td>Ziyuan</td>\n",
       "      <td>Zhou</td>\n",
       "      <td>Bentley University</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1966 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name      first       last  \\\n",
       "0            Aaron Atkins      Aaron     Atkins   \n",
       "1           Aaron Chimbel      Aaron    Chimbel   \n",
       "2          Abby Hendricks       Abby  Hendricks   \n",
       "3     Abdul Wahab Gibrilu      Abdul    Gibrilu   \n",
       "4       Abdulaziz Alowais  Abdulaziz    Alowais   \n",
       "...                   ...        ...        ...   \n",
       "1961          Zhuling Liu    Zhuling        Liu   \n",
       "1962           Zifei Chen      Zifei       Chen   \n",
       "1963          Ziwei Zhang      Ziwei      Zhang   \n",
       "1964          ZIYANG GONG     ZIYANG       GONG   \n",
       "1965          Ziyuan Zhou     Ziyuan       Zhou   \n",
       "\n",
       "                                      university  \n",
       "0                         Weber State University  \n",
       "1                     St. Bonaventure University  \n",
       "2                                      UT Austin  \n",
       "3            The Chinese University of Hong Kong  \n",
       "4                      The University of Alabama  \n",
       "...                                          ...  \n",
       "1961                       University at Buffalo  \n",
       "1962                 University of San Francisco  \n",
       "1963         The Chinese University of Hong Kong  \n",
       "1964  University of Illinois at Urbana-Champaign  \n",
       "1965                          Bentley University  \n",
       "\n",
       "[1966 rows x 4 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "severe-frank",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "David        20\n",
       "John         19\n",
       "Michael      17\n",
       "Jennifer     15\n",
       "Brian        11\n",
       "             ..\n",
       "Dzmitry       1\n",
       "Martha        1\n",
       "Frankline     1\n",
       "Indah         1\n",
       "Gisele        1\n",
       "Name: first, Length: 1291, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df['first'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "funded-reading",
   "metadata": {},
   "outputs": [],
   "source": [
    "first_counts = at_df['first'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "mineral-listing",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_firsts = first_counts.loc[first_counts > 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "neither-remains",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1hAGcJEmSJPWEAZwkSZIk9YQBnCRJkiT1hAGcJEmSJPWEAZwkSZIk9cQ9BnAR8dmIuDoizh567X0RcV5EnBURR0XECnP53ksi4rcRcUZEnDbBekuSJEnSQmdeRuA+D+w65bVjgU0yczPg98Ab7ub7H52ZW2TmrG5VlCRJkiTBPARwmXk8cP2U136Umbe3/54MrDkNdZMkSZIkDZnEGrjnAd+fy9cS+FFEnB4RB95dIRFxYEScFhGnXXPNNROoliRJkiTdt4wVwEXEG4HbgS/P5S07ZOZWwG7ASyNix7mVlZmHZ+aszJw1Y8aMcaolSZIkSfdJnQO4iHgOsCfwzMzMOb0nM69of18NHAVs2/XzJEmSJGlh1ymAi4hdgdcDT8zMv8/lPfePiGUH/wYeB5w9p/dKkiRJku7ZvGwj8FXgJGCDiLgsIg4ADgWWBY5tWwQc1t77wIg4pn3rqsCJEXEm8Cvg6Mz8wbT8FJIkSZK0EFjsnt6QmfvN4eXPzOW9VwC7t39fDGw+Vu0kSZIkSXeaRBZKSZIkSdK9wABOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSesIATpIkSZJ6wgBOkiRJknrCAE6SJEmSemKx+V0BLXhmHnz0PL/3kkP2mJZyRy1bkiRJWhg4AidJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPXGPAVxEfDYiro6Is4dee0BEHBsRF7S/V5zL9+4aEedHxIURcfAkKy5JkiRJC5t5GYH7PLDrlNcOBo7LzPWB49r/7yIiFgU+DuwGbATsFxEbjVVbSZIkSVqI3WMAl5nHA9dPeXkv4Avt318AnjSHb90WuDAzL87M24Cvte+TJEmSJHWwWMfvWzUzrwTIzCsjYpU5vGcN4E9D/78M2G5uBUbEgcCBAGuvvXbHamlhNfPgo+f5vZccsscCUfYo5Y5atiRJku6bpjOJSczhtZzbmzPz8MyclZmzZsyYMY3VkiRJkqR+6hrAXRURqwO0v6+ew3suA9Ya+v+awBUdP0+SJEmSFnpdA7jvAM9p/34O8O05vOdUYP2IWDcilgD2bd8nSZIkSepgXrYR+CpwErBBRFwWEQcAhwC7RMQFwC7t/0TEAyPiGIDMvB04CPgh8Dvg65l5zvT8GJIkSZJ033ePSUwyc7+5fOkxc3jvFcDuQ/8/Bjimc+0kSZIkSXeaziQmkiRJkqQJMoCTJEmSpJ4wgJMkSZKknjCAkyRJkqSeMICTJEmSpJ4wgJMkSZKknjCAkyRJkqSeMICTJEmSpJ4wgJMkSZKknjCAkyRJkqSeMICTJEmSpJ4wgJMkSZKknjCAkyRJkqSeMICTJEmSpJ4wgJMkSZKknjCAkyRJkqSeMICTJEmSpJ4wgJMkSZKknjCAkyRJkqSeMICTJEmSpJ5YbH5XQNLkzTz46Hl+7yWH7DEt5U5n2aOUO51lT+fxkCRJmhNH4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJxab3xWQJP27mQcfPc/vveSQPRaIsu/rdZ7OsheU4yFJWvA5AidJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPWEAJ0mSJEk9YQAnSZIkST1hACdJkiRJPWEAJ0mSJEk9YQAnSZIkST3ROYCLiA0i4oyhP3+LiFdOec+jIuKGoff819g1liRJkqSF1GJdvzEzzwe2AIiIRYHLgaPm8NYTMnPPrp8jSZIkSSqTmkL5GOCizLx0QuVJkiRJkqaYVAC3L/DVuXztYRFxZkR8PyI2nlsBEXFgRJwWEaddc801E6qWJEmSJN13jB3ARcQSwBOB/53Dl38NrJOZmwMfA741t3Iy8/DMnJWZs2bMmDFutSRJkiTpPmcSI3C7Ab/OzKumfiEz/5aZN7V/HwMsHhErT+AzJUmSJGmhM4kAbj/mMn0yIlaLiGj/3rZ93nUT+ExJkiRJWuh0zkIJEBFLA7sALxx67UUAmXkYsA/w4oi4HfgHsG9m5jifKUmSJEkLq7ECuMz8O7DSlNcOG/r3ocCh43yGJEmSJKlMKgulJEmSJGmaGcBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8YwEmSJElSTxjASZIkSVJPjLUPnCRJuu+YefDRI73/kkP2mJayRylXkhY2jsBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8YwEmSJElSTxjASZIkSVJPGMBJkiRJUk8sNr8rIEmSNI6ZBx89z++95JA95nu501n2KOVOZ9kej+7lTmfZ9/U6T2fZC8rxAEfgJEmSJKk3DOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJ8YK4CLikoj4bUScERGnzeHrEREfjYgLI+KsiNhqnM+TJEmSpIXZYhMo49GZee1cvrYbsH77sx3wifa3JEmSJGlE0z2Fci/gi1lOBlaIiNWn+TMlSZIk6T5p3AAugR9FxOkRceAcvr4G8Keh/1/WXpMkSZIkjWjcKZQ7ZOYVEbEKcGxEnJeZxw99PebwPTmngloAeCDA2muvPWa1JEmSJOm+Z6wRuMy8ov19NXAUsO2Ut1wGrDX0/zWBK+ZS1uGZOSszZ82YMWOcakmSJEnSfVLnAC4i7h8Ryw7+DTwOOHvK274DPLtlo9weuCEzr+xcW0mSJElaiI0zhXJV4KiIGJTzlcz8QUS8CCAzDwOOAXYHLgT+Djx3vOpKkiRJ0sKrcwCXmRcDm8/h9cOG/p3AS7t+hiRJkiRptuneRkCSJEmSNCEGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUEwZwkiRJktQTBnCSJEmS1BMGcJIkSZLUE50DuIhYKyJ+GhG/i4hzIuIVc3jPoyLihog4o/35r/GqK0mSJEkLr8XG+N7bgddk5q8jYlng9Ig4NjPPnfK+EzJzzzE+R5IkSZLEGCNwmXllZv66/ftG4HfAGpOqmCRJkiTpriayBi4iZgJbAqfM4csPi4gzI+L7EbHx3ZRxYEScFhGnXXPNNZOoliRJkiTdp4wdwEXEMsA3gFdm5t+mfPnXwDqZuTnwMeBbcysnMw/PzFmZOWvGjBnjVkuSJEmS7nPGCuAiYnEqePtyZn5z6tcz82+ZeVP79zHA4hGx8jifKUmSJEkLq3GyUAbwGeB3mfnBubxntfY+ImLb9nnXdf1MSZIkSVqYjZOFcgfgP4DfRsQZ7bX/B6wNkJmHAfsAL46I24F/APtmZo7xmZIkSZK00OocwGXmiUDcw3sOBQ7t+hmSJEmSpNkmkoVSkiRJkjT9DOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEmSJKknDOAkSZIkqSfGCuAiYteIOD8iLoyIg+fw9YiIj7avnxURW43zeZIkSZK0MOscwEXEosDHgd2AjYD9ImKjKW/bDVi//TkQ+ETXz5MkSZKkhd04I3DbAhdm5sWZeRvwNWCvKe/ZC/hilpOBFSJi9TE+U5IkSZIWWpGZ3b4xYh9g18x8fvv/fwDbZeZBQ+/5HnBIZp7Y/n8c8PrMPG0O5R1IjdIBbACcP49VWRm4ttMPMf/Kts79L9s697/sPtZ5Osu2zv0vu491ns6yrXP/y+5jnaezbOvc/7JHLXedzJwx9cXFxqhAzOG1qdHgvLynXsw8HDh85EpEnJaZs0b9vvlZtnXuf9nWuf9l97HO01m2de5/2X2s83SWbZ37X3Yf6zydZVvn/pc9qXLHmUJ5GbDW0P/XBK7o8B5JkiRJ0jwYJ4A7FVg/ItaNiCWAfYHvTHnPd4Bnt2yU2wM3ZOaVY3ymJEmSJC20Ok+hzMzbI+Ig4IfAosBnM/OciHhR+/phwDHA7sCFwN+B545f5X8z8rTLBaBs69z/sq1z/8vuY52ns2zr3P+y+1jn6SzbOve/7D7WeTrLts79L3si5XZOYiJJkiRJuneNtZG3JEmSJOneYwAnSZIkST1hAHcPWgKWOW2HIElzFRGLeO+Q7nu8ruesNZcm0q70GEt37z4RwE36Qo+IZSNiBkA2kyx/OgyOQUT8R0Q8cH7XZ15FxHL38ufFpBrWfXzA9LHO0226Omky844+3Dvmpl0nvXpGTNfvchqeMUtExOKTLHO6RcQD7qXPGWd/2rsrd2IdKn2+rqGOxTSUuXhrLt0xifIyMyNiyYhYehLlzcl0Pw/7cj+6L+nrselyf+rVw3lYROweERvBXW+mXS+YiFi0/f004B3AFyPi2xHx1vZZS06o3utHxMcj4rkRsU1ELDOJctvNLoADMnNa9tqLiPtHxCYRscoEi90/In4eER+IiP0iYt0Jln0XEbHE4AEzOGfGeZC1Y778vR2EjmPSDY+hjoNVpuvGGRHPHDzEp2NUa7iTZpygZfB9ETEzIt4cEb+OiPdFxD4RscbgHjMpEbHyJMtrZS4aERvDnQHoRBpjEbFlRKw+ibKmlHuXc2G6Otzatb7+uOUMBSevAp7cXhucNzHJcyQi7jehcgbH+FkRseccXp+050VtTTQREbFCRGw8bofK0O9pq4h4+dA9aaLXdStzWhuhk7iuh+79q0XEi4EvRcQX27HZfMyyl4uI5wKHAC9pry07bp2Hyl8kIhYdBImTKneqST1b5lRuRDx08P9Jny/t+G8abSBjQRYRq0bEI6J1ME1XG2e6RMRK0K3Dd1p6uu4l+wPbRsRlwCXA94HjMvPPHcsb3ND+C/go8GngQcBWwMHAP4FjIyLGPEGWpDYzfwjwcOBfEXElcG7787tRb65DdVoLODcidgF+npm3jVHPQdmLZua/IuLNwBbAVcBNEXENcDXwozH39jua2mZiQ2AX4BkRcQtwDnAWcHRm3jpG/RdrW17sRgWL2wA/Af4X+HH72eb5dzp0PB4JPB+4FbgwIv4EXAlckJmXd63v0OesRh2TvwE3tLJvzczbO5QV7Yb/KOCJ1B6OpwKXdClvavFAUns+7gLcOPS598/Mm8cqvIKUlwI7R8S7M/PCccqbQ/kbUQ3pM4Afjnk8Bjf6dwA/An4PPBLYA3go8ALgM2OUP3z+PRfYMyIeA/wC+A1wHHByZv5jxDIXycw7ImIT4GnAIyJiVeCPwPeAYzPz9x3ru3xm3gC8tv0ZviZXB67OzH91LHtQ782Ajanr5Wrg19R99C9dyp1D+ZtT5/YTIuI3mfnKqFkOfxn1WA+dX2cBT4+I72bmPyJiKSqoO7p9rWudlwOeTR2LayPiQuo8vD4zL+hS5tC98R/AuyJiJ+Bd4x7fOYmItYAnZubhUSOUdwDjjmTPAt4eEZ8HvpaZfxv8bkcpZOj9i1Hnw83AZ7qev3MzdL9eD9gI2Bw4groeo0vwNXTf2B54BXB/6lw7EbgoM2/pUNVFgH9R9+dtgfcDawBbU+f2SZn52i71BP6DumeuAgxGfh8WEffLzG93qOvU8p8KvKwFVMdHxOnUdXLhuM+s9jmLAJtSz9sfZeYpk+oMa+UvB7wtIo4GvpmZN02gzMH97t1U+/TPwM0RcTV1X/1+Zl4zQnmD83h54GHUXtG/AU7KzF+NW99BnYFPAOsBK0XE5cAJwPHA9yYRzLWfYQ1gVeB+wLXAlZn5t451HhyXBwL7AE9sHXu/B44CTszMG++2kEFZ09BZea+JiBWAzwHLtT/rAddQjfO3dAiElqYaX69rN7tFgCWA5YFrJ3Wjbp+zCrACdZOaCazc/v5kZh7bsdznAW+kbvTfpi6WS6iTbaxgLiLOAV7d/rsGsDqwDvDuzPzDOGW38gNYnDoe61MNsq2A52fmtRMo//dU/W8AngLsDGwCPC4zfzxCOYOb3DeA3wEXUMdhVWAl4AuZ+f0x67oI8FXqIbsj1UC/lQoKPp+Zf+9Q5rrAN6kH9oOBNakb0RnAa8c9tyPiJcCDM/PVUb3nWwAHZ+be45Q7VP7Lgb2AI4EjMvPvXRphrazBDXTdVt5vgB2AtYE/AD+m7gGdHrgRcQp1fn0NeE5mXh8RXwT+JzNP7lrvKZ/xe6phdzzwdepBsAWwbWaeOWJZg8bd+6iH9mJUZ9DlwGuAozLzBR3quATwHOr39jDgGcCpmXl9+/qhwJsnEGidSHUgHEBdIxsDtwEHjRPwDx2XQ4HzqOvm2sx8R9R+p9dk5jdGKO/+wKqZeXH7/4eoa3JF4MXAX4BXZeZVY9T1JdR9bXFge6pRsBXwwcz82KjlzuFzgnpGrgZ8FjilBeNjdWwO1f8pwN7U83sinTVRI2R7Uo3pEzPzcxMoc33gndS9/22Zecy4Zc7hM44GzqSCjZ2pRvSjgOM7dNIM7nmnUZ1IS1L3vHWp9s1zMvOXHct8K9UZemK75hcHZgD/zMzLR7nfDT1fvw0cRDX6b8rMT7Tr8LrMfMsE7v1nUIFsUsd0M6rt8ZLMPH7UcofKH5zHB1D34w2BR1MDAOcCH87ML3Ytf8pnbUMNZMygOtq+N7i3jlHmIlTn+YuoYGUN6lpfG3hrjtBZP3QsPgDcRHVGrNvKnQG8JzM/0LGeg9/jo4G3Z+YjW4fPo6nrfNPM3KlL2XP4rNWo58ttVJvvRuo5eQXw1VHbTkPH5WCqc+JKqp33J2qv7B9m5j7zVFhm9u4PsHj7+/nAIUOvzwC+TDXwRilvkfb3HsBFVE/SStP8MwwauctQN9AHAY8BVuxY3iAYX6mV83HgB1Rv9MPHLHM94GNTvrYssPGYx2BQ/pLA46ievI3ba4sCy41Z/nJUw3Ez4Ntz+Pr9gMU6ln3kHI7HI6kGWtf6Ds7DxwD/ByxFBW+bAcdSPWCjlrlmK+dZwIemnH+7Ug3GrvXdlbpZLk8Fm++kGusfBn4IPG2c39/Q5yzV/t65HY9Xj1ne4DjvDxw2/DlUY+mdY5S9IjV6f38qEHw8sDTwc+D+Y9Z7cL1sRo22LQuc3l5bHvjk4GfrWO4vqI6ZI6iODajG+pYd67t4ux+9E/hlOy5HA19s96ffTOBYbNGujUWpzoiVgHcDHxm8ZwKf8StqdPV/gZ3aa98Fnj58Ps1DeY+hguJftmvkbdSo1oeATSZ0Th/Zzo83UdPpoZ6Jzx6z/BWBLanG3DbA2VRQ+8Rxyp3D57wG+CvVYfVT4OXAg0Y5zndT9nrtmjwWeOyYZQ3aIHu0Mp8xoZ9/cM5tSY3cAPy6/f0AKqBbtGPZy1CB1tTXdwCW7VjmYsAx1KylTm2XuZT7NmrGwm+Hfv/HAw8bPk4dy14C+NjU84nqVF9yzHov2v7+OrAd8HbqmbgJcBrwmjHLH1zns6j27nrA+6hO++8Am49Z7kZUZ8/w15ajQ1uPuz5XVqM6q7agOh6/COwwgfNkFeA/gQdO6tybwzF5JnBo+/fmwH7UM+YtY5b7IyrA/xiwT3vtHaMcl75OoRz0vDyEuikNpuVcExEnACONUOTsnpzzgK9QD6jToqZn/pbqNek0hWjYUO/SVtQ0l32A8zLzsW0Kza9yHodO51x8rEc11E/LzJe2FzcCLh2z6jsAL42IdahG2PGZ+VfgnDF7XgdTMN5B9VAdRI0gnkM11s+hphCOU+8XU6Nkt0fE+6nf77VUT97NrUd5ngz1+qwDbBUR36WG70/JzOuooftxDI7jjlSD/4nAmZl5VkS8lzomo9qHCuL+BaweNfXz3FbfHwA/GON3uD7V43UA1Tu8PfAGqjHzSrjL9N6RRa1NeBP1u3ssdT5cB7wuIp5BNUrP7VJ2swhwR+tN/3O79v4X+N+u9c7Mv0TEC9p58gmqk2l/4O/tfOs8+jZUnzuAT1GjvtdGTQ9eE1itS9mtrotRgduNVON5rYhYkQpoP9mlvsDtmXldu7d9jhpV3owayXog8NaO5Q4fi/Wp3uftqKlg10XEN6kgf6zpJe24LEIFQC+nGjE/b6MMa1MPYOb1mGfmcRGxKdVI2qnV+Y9UJ9ON7Zz7bce6DupwI3UtrkMFWVDPyE7TJ4fsT41sbk/dmz5F/R7f1u4pb8kOMwOmyuqR/0DU1OmnUgHShyNi81GOzdC9+gFUR9NW1PH4GfA64KCoacJfy3noQR96dj+easBd2X7uW6gg5s1tROQ1Xa9vuMt5vTJwckQ8lQraoBq/l81LfafUfXAvW6f99wPUfe6SzPxzZv5ilOfgFCtQyyq2otpM51NLFL6Xmed1LBPgcOr3dCOwW7vH/TEzT4Ju65yG7r0rUdfIhVHTak+hZgZcPcZxGBj87teinlfvoDpez46Ik6iZUZ0NnVvvpzr9j6Bm1SxCnZdd1/MNjuejgFe2tuRnqLbeDXRo67Xrb1nqWF9Fjeb9IzPPj1r31fnZPVSX51JtnJ0j4jfUjLOLWr3HXkLUPIQaJSNrdsuZwFej49rJod/hCcBlVOfrIBfGLtRzcp70fQrlKlTvw6XUQ3xtag3bSzNzrMZ0e0g/nJqL/dl2kxtr6tOUIeWzqZv/dllrKl5N9WC9ecQyBw+WJwBPp254a2bmEyLi4cCNXRsFQ5+xPDW6tBM1r3sV6qH14sz8xThlt/JPy8xZEfF1qvfn5Ij4CfD+HGNqSmuUbkI1OtZpf26mbibXAD/IzPM7lDuDCq62YfbFdxvw9cw8smt9h8rfmPo9PogKaAcjOUdk5khrqCLicVQjd3Xqd3ct1Wi8ktnHYKTpOENl35/qBRwc282onrblgOup0b2/dCj3flnrgoLq6foZNbVxLSr4vKI1bJ6e8zrVYM6f82mqMX0GNY3yUur4/DYz/zliWYPrcE9qZOnyFrjsSp1v57Xgbuzpk+3zBg3U/YDdqNHkY3Iy08M2phoI11HToJ47Znn/Bzwv25qB1tk21trLoXvpCtRo561U59KN1PV4Wma+c4zy7wfc0o7x6lQv6WbUA/dm4IzMfHOXQD8i1qSm4NxBXZdbUOswPzfO/TQqIdZy1Pn2GOB/2udEZu4wRrkzqB7z52bmnyNiRg6thWmN9l2z41T6KcHWNlRg+9vMPKprnYfKfiYVIF9KHevvUve+jakpdB/IEdZURa2HfBxwMnA+df/7Y2be2IKB8zPz3WPWOajf42upBuq3qNGbXagR9w92LHcnajToAdS1fXP788PMPKNLPYfP/dZO2Bl4IbUW/GXj3O/afeip1Cj1xdS6/qu7dq4N3TO+RnVonkg9rx5MTe37ZGZ+oUtdp3xOUM/ai6hzbFUqmPuvzFxzAuUvBjyJCthmUNP4PjFuua3s5al278OodY2rULMbnpuZv+5Y5jrUNfdCYIP276dn5mYTqOuvgQOpdtgaVMfgA4FXZA0yjC2q4/951EyMk6hr/7SsTvBxyl08M/8ZEVtTcctVwLqZucs8l9G3AK71li9LPUDviIgHU7/AWdR8/x9l5jdHKG/w8FiJWkS+InWzuAg4OyecNKF95neoqSJvoRr934mIz1GLOw8fsaxBQoBPUzf6jYGls+aJvw5YPjPf1PWmN5fPXIMaJfpJdlivMaWsGcBh1FSDz2bm1u3131JDyeOMwA0+48CsRfHLUj2Fm1FB3X+P0ksYtbj3bdQ0iZvba0tTx/zh1GjZz8at75TP3ItaFB7UFMhOc9xbb9HS1NScjan1lktk5ss6lje4bnambu7/0XrNV6JGg9bLzE91LPu1VGPlh9RUwT9MPQ9ab/dLM3P/Lp/RyliauvE/lnpYrUSttzhwjDIvBB6RQ8mUIuJBmXlR1zKnlL8Idd94f2s03p+a1nFGdlsbOfg9LkmtDQ3gG1TDZqlRro+5lP8AajrwL6n1oeOOBNEC49dQQf251PlxQ1RWtudTAfm3s/tshkHD/3tU4+tGaibAetTz4dbMPGXE8gbHeSY1ZXKfbCMp0bYTGLXTYKjsQefB/2vlvKu9vg7VML28y3Gf0imxU2a+bui1xalG8OLAyzLz/V3q3j5n0LD+T+q+tBFwQguQd6NGncbqhJzL576FGih4+4jftz41on75lNffDPy+aydeO6arZuZl7f+LULMbHkQ1So8GvjNGh9vOVGfY8lQDfT1q3ddhWSNEXToj9qTaAjOpqa+fysxTh74+SoKwwTWyHRW8/oFac3TSuPehKZ/zOWrt7WWt02NF6hhfODj2Hcvdl7rPXZ0tKUz7nb4TuJ0Kvud5zewcyh9uqy5Jdc5sQK0tvpmaxnrLOB2ELThcg3oO/LH9eweqo/evI5QzuE88GLi0BSorUctkbqJGJX/XsY6DsjcGnpqZb22vL009t1YaPgfH1eo9SCq3GdUhvizwhHHa1FEDLLdTz6ttqPbZCTlCQqE+TqGcSZ2sO7cA5X+p4fbXDw7mvPb6TLm5PJMKAn9I9VDtBuwTEadm5kcm/DMcRvUszcrMZ0XEltRI0X+NWlDO7slenOql25+axgYVVAwWzA6yBc6ToZvFA6lpmftRUyNOBn6ZmfM8zHsPn3FN67l8O3Bdezi+jBqx6By8tRvzFlTA8lLg8Nag+znw84g4c8TgbRHq4XxLRNwQEddTmU+/QQWyY90wpjSWZmXmW1sP0w1Uz+NxY5R5P+rG8xBq+P8w6sYxTorgwfTXTaiMlmQlm7k2KsHGOCPg36UW9G4OvBlYOiKupUbJTqN6Ts+hptiMZOiYLEf1pD+MSsTzpRZsjLyH4tC1sjlwRdYoxZKZeWv7nK9SAWJnQ/e0RwE75+yF/DdHxFnUA+Dno5Y7dP87gmosvYk6zr8DdoyIS7s2GJuVqN/VtsAqEXET1QP78+yeiWxVaoRiS2B34O9RSZZ+TU3LvKNLMDvF4i0oPIRaM3MB1al3HnWfGmSzm1eD++8uVPKTf0UbaabuU48EOo2sDFmE+t0BkJmXMv70eaggcN3hjoihYPPWiPjwmOUPntVPoaZkf5TZP8dzqDVrvx01wIiaRfNMahTrAqqz9IdDbzmCGnG/p3IG1/faVPB9I7BiRPyT6jT+JRVcvZ9aCtDV9tSU9jOpe+r3qPbNbeOez+0+9N/U8pLXZ5vZ0p6TN8Po0xJbw/ZN1LrLb1PPl9dGxPsHz8NRymzHeGlqyvYHqAbtRsBbIuL5OUKysbup8+rUPenFEfFxKtj6E/W8Gddi1LnxwdY4P5Z6lh08TlA1MHQs96M6dbeigqF/UCOf/8rM941a7tAzcRbVYbAF1aZ5Y0TcTl03XTO2Hk4tjTmXuvedBPysa2fVFM8D9mtts8Nb8H1x+zOWoWv+/lTMsSIVaH2fOn+W7RK8DZX7Yeq58iJgm6wZfhtQbap51scA7oTWiH4odePYiJonv0hE3Eo1xn5ztyU07UDuSJ1Ua1JD3L+CO29Os6jMYPMcFM7rz0DdrJeIiAuoRunn2o1knrUG5xqtd/KD1ENkI2p+7sbtZ/oxdNr3ZdBA/y9qHeCvqYfr44CNI+KVmfnREcu8i6EL4FgqgcRzqMX9x1LTf8axHNVT8nyqkfEy6uF1BtWYfCK1KHpe63oH8LmoveSWj1r/sB+VjGFmRPwyM7usURsYzL1/MtUgAHgXNRXjpIi4IDP/2LHsT1ENmL2okYSkpk++jZqfPrKhxuvKwHOjRlKPA87Jml7Vaf55e4BfSuvJjho13ZA6DptQ599+rTdwnEbNF6jF90+jAsKjqCnCI2cgm3IjvzQiZmbmJe3/j6HNn5/QPeQR1P0Kqhf2H9Q5sxcdArhWr5lUz/9TI+LxmXlua/C9i3rodpY18vOyqNGgjake3c3a313LvBR4fVQWr3Wp+9SK1L1qUarR8LUx6/359s8PU73zW1PB4t7UVJdXjVjk4PpeixqpYCgw3o1aS9S1rndEZVp8OrU+9FPUbIxfjNNDPHSuBvUs+VB7Xl1M3U9+mpn/zDGnw7bn8GJU0LYxlTTn+e3LDwL+3yjlDV1n+1CNvPdQU9A/3EYEjs3M3XP0UfEnUPfOV1HH48G0KZ+Z+T3qWuwsM09o190j2me9i+pMPi9qRspns+Na/NYZukMbJXpV1Prtb2fmTe3Yz/PvcOj47gr8KTM/1Mo4hboOX0IFzfNsqEPkEa3MLw197YfUUpaxAzhqevX11O/t08Dv27E9PsecHZCZR7Rg4hDqWbUT1TG9QkTcCDwpx5x21xxHrV+/hGrvLUMFSV3bB4N70yupDuk7qCnpUAMCf6XW+s9bYbODlA2p2WDnRsTe1HZctwNHRcS7ut6bhu5Lh1FthV2oTvnrqS2pXpXdtxO788egrvW3Uh3dTwd2z8wLo9YHjrz0ptU9o6b975iZW7Wg+ZwWKH6calPO81YNvQvgWvC2CJVt57Nw55qCmVSPxDxvYt1uOi8EXk+to9gkIj5IjTBdR43GDT53Ynt4UKNl783M/4oa4fpHdkulfSBwQdQ+cn+lbnJPoy66BPbPmmbVZfrk4OfdmGoYPYKa9nNx1BSEcZJH3Kn1iD2Guvnsk932o/k3WeukjqQCudupY74J1XicSWVsGqWegwfMvhGxStaUoRPa1+5PBRjj1HcQEG0KfDpqes91VAPkY9RUl5Fu0K1htyR1k38J1fh8IXXerEdrSHbVGo2nUQ/EtaipHLRRlv/X8Xf5OGqtyvFRqZi/Ri0wH3SsrJyZf+14Tg8fkzWpB/hzqSxZUNtMnEUd95Fl5plR64F+ERFnUzf5VajGNMx+UHYpe3A9/gR4fkRsOdRRtTsVjHa1JZUwYRdm916u2z533J7/HahOlPOA97aX78eIPY1Tyhxci08EnpKZV7Zr8OdURrJLxqzzcKB9IdXLfSx1H1kPWLs9iOf5HBy6vj8BfCwi3kSNAK1PnfMjjyZPLT9q9P7h1L36XcCMiDg9M581TtmtnkdQ58kG1HrXragskRORtQzga9TIy2IRcSDVcXNVtm0XOlzvD6Y6Rr9HjWYNRqI2aP+ep7WYQ597HvDX9qz+CzUq+AOq02Ck6YJzEhFPotbQ/ZzWGROVSGhn6tm+CTXi17X8JTPza63d826qvfPfo46GDF0bvwcyIrbPzJPbZ9xKa/zHCKPUQ++7GlgmIp5DjdRcSk1fu/+oZQ6LWjP9fepZNQguN6Gmfz6Vau90DuCGfvePpZJrPQs4JmoK5dpUp0Tn4G0oKFqeeqZsQ035/FbXMgeGjudK1HNkX2qLEKj7yWdaHeapA3LoGlgPODtqf9G9qbbpLdQ12Xl98tDnXED9zj7a6rcd9UwYe9nNUKfYEzNzgzZgNJix9TlqZH+kIG7oHNkWOCVqxs5fWkyzOjX1c56DN+hhANc8EHhR1F4876EW4V4WlQxjlEbjHdR0w1Woh8Wu1BDyK6I2qj4lM7tmYJujiHgb1YDZPCL+DJxOZbT6fo6+GeNDqWkLL6UWsn6KOWR269jQHUxnuJiaYrECsGlEXEo9wM8YtcyBmD1kvyU1XeKX1D49b4+IG4CzcsRkLnOStVfYp6gbyR+oYG5t6qK/YcSyBje584HdI+JYqjH2w6z1cCePW9/mo1QvzObAszLzT1Gb23YtfwsqKFmUSkhxfuukeE+OueF4OybfgTunmK7TPm+lroF4Zn6rPfQ2oqbKfQBYPiLOo6YVfaG9b5z1nOtSI8oPohpkV7eOlGVH7ZVv18gTskYLN83aI+yTVO/rllSv2u9bncfeRzJrr6UnUJky/0Wd1xdTU3W6lnlUGyH7IHBk1L43B9MxKBxqbGzTyjmJ2s/xPa3HcfMcMRnPlPr+K2p63JXArhHxf1nTo3/a7q9de6Kn/hzvpu6xV1NB3OXUsT6u1WNe1/asRd3zL2jB5iHUKM6x1KjT+7Nl1+tQx8H6sc2oc/mr1AyMoDrfVulY7nAwsjb1zD05K5NmUGm7JzEN6k6Z+bOIeDE1lXJNqiPlvXOozz2VM2hkLk6dHxdTgfh1WSNRp7byRh053JQaTX4w1ZFyEbUv4D/a544TvC1PdbItFhF/oTpIT6ESIn2DGhnpUu7gWftiaqbI9lQgcxb1nNkjIl6SI67pBMjMUyPiXOD97bn9AGpE5OODt4xQz29Qsx++SI1abQ88LiIeRY2ADNZYdkpeQj2TboqIt1PTXC+hEsl9NTM/3s7pcQxGbDagkl1UZesauaj9GcdgRtTLqIB2R2pt8beiEpVdnpnndC28BfXfpjqLtwaujpqZsR51rncZxDiB6nj4ATWN9PdRe42eOEY9B+fzZlTgvRb1uzyXapO+dYL3pY2AX0Vljf9Xu3cvRk2vH3kEbtDpR7XlNqUSMP6kffmNdJhB07sArt14LwMeHRHPpXqfF6EysI3UaGwn5B/bn9OiNo9cj1qguC31ABh76tNQg2ZdqofmLdS0p69QD/IL6dYA+y8q4HwmlWp9O+okPoXaO2asBmNW7/tz2kn3KWpk72lA5Bibaw8dy0dQ03xeFTUddBUqALj/OPUeusi3Yvao5O+ytmu4ndqHo9OxycxTWu/wU6hsaX+nblATkTUN4wbgfS1424UKaLv2Kt0MHEo1IE+LmsqwCbWQvZOh4/swavRgO2pt3WCvurFGUdsN+EzqvKb1fu3A7OmlnTORNX+k1nidAFwSEVtQI51dGtFrADe3oOc/W2Pxj1Tj4Me0TIYd6wnc5XhvQmWqfT01hXAzaurjsWOW/0lqnd6PqNHZx1KB8tFdi6QaMztT18ZpVEcT1KjIXrRe3a4y87aIOIxKdrB61KjqutRshnmehTGXsu9oHRJP4q4b2q5OTdf/FbXWZV5tRq212j4qOcp3qE3G/zL0bOj0jBm6j72WmiJ3PnW8T6CeAWfP9Zvv3iLAvyLiI9RI/TOBxSPicqrzo3PSkmFDP/8W1H3kNOpZc/XwPXrUayhqbdfgHncANTPnwtYBeewIwfegfttTIxPvptoGb6HO8XOpTINjyUrV/rioqegPoxrRLwaWjIjrgONy9ARnMXRObUDb7Jma5nh4Zl7fngevj4injRLQtuM7M2ut9uFUwL08NYX+ivYzzdP53BrFJ1OduG+lApVbqY6vw6mtHnKUMqdYAfi/9jknUp0Ra1MN9EUi4oocc0RoqF4zqCUFKzN7SUHnttKQQfl7ZeY27ZgPskK+mAq+OgdwWSPgn6V+h2tTHW+bUYnT/javz9t2jJ/aOpJWycx9464j3StRS5+6GgTab6E6Ih5PdRw8nppK+mqqHTIJF1HnyyHAWe35ewCzj/vI2jH8W0ScRo1u/mdEPJF6To487b93WSihMvsMouyoNNrvoC72A3OEdWRDDaNB1qMrqNGZY7NGKpbICewlMfQ5B1I9BkcC78rMJ0bE/lTGvpESmAyVuQ41d/kz1EL4jaiRkH9l5pM71nfQC7Y6tbfSFe317akL6MIccah3SvkfoRq4S1MZ3Q4b/rmo83LsKVYx9+0aVhjleA89xJejel+ui1rQ/nJqncWvMvNpE6jvx6kNwo+PGhFalHqYLZIjZscaqvNbqIf1la3x+BrqBvelMRp3g884o5X3IWp0bEfq/N4xuyeooDXGd6NGP46jeqHHWmfTyh0ck8OpkfuHUtM+H0oFud/t8rBt5+xgy4B1qAbCMtQi5aNzhKy4cyl/cH58jGoQHDb0s6xFBYkjXY9D378hlY11n6GvPSA7Zjud8hkHU2n3n0j1dB8VtXj78uyw2H4un/EgKiAMag3Sj3KMzMFD99WNqFHDVw99bTlgrRyxpztqv7Grqani21AdVctTnT9/Az6fk9lndAVqnfKeVLDxR2DbHC8b5xlUIH401YH3QirAf2ZmjpxYZC6fsSaVrOMGqgGc1HP4V8BHRm20Tw2Go0YSHt5+jqszc57X1A2dD8+l1vN8fOhr21Nr0L8xgU7eO6cGRo3GLZWZV0Wtt3k09Zw8YsQyX091Xn6J2pft366Ldmzen/O4HcvQvejF1M/+pnYPOYDKFtk5y+LQZ2xGNcj3pDp0z6em9B3e5RhHzTi5ITPfNvTaclSnzJZUW2kS2/8sSnXwrEc9B5dtXxpnScFw+UtSo5tfpWZLbNnOzd9QU/06J2KJmtGweFZSrLWoIP/S1rEwSjnrULkjjqPWD/+RShBzGfV7vGSc62TocwZbT/2caoP9kwqAXpmTzVi6MtXO25aa8XEx1aHQabpt66DZO4dm9kXE/bNlNR9VH0fg7gfsHzUVZ0UqLe6PgRcwYuKEdvIPZz1ailqT8MOI2D8nlBJ+6IS9juqxW4/Z2cHWpgKZUR0QlX3uKqoBcDZt49YWXMxo/+7ygH0jdcG9kcry82eq0f9lan3gbV0f3K2x+yeqUfcQ4P7tQfhdaiFx58BwYCj4W59a6PoW4OvttU3pNtIC1VA8qN1Ij6fOl5toWRi7Gqrvw4F3RM2HPoYaifwA1Qs5apmDgPNhgwdXZn4Z+HKMsQ/XUMN/A6pn/gTqAXhARDyZ6jmepyRCcyh70Ig5gHp430L1/C8WEX8EPpOZX7+7Mu5Oq/eyVCN8MK2l6yjToM7R7iOzBoFaexgOslz+fuh9XRdtD86PHZk9RXpRak3WW6g5+aNeN4PvX51aK/VS6hq/dBLBW/MBqvdyB+DYiPhvalrfx8YtOCIeTSV6OJ5aY3hljpctc2DwO9qJ2tB2Xapz7ITsvqHtVVFbH+xBdYwNttpYk+o8+Os4FW7XOVmJfY6ikgR8Bti3S/A2dI1vTd2rbwHu1zo130d1sP22feY40wZXyloX9AhqC5YXtdcfRB2rme3aGiUV/SDg2pqaJfFYalbKZzLzKy04GuV6HLxnK2DPdm3/KDPPydnrvoZHujoZCt7eTj1XHh01OnYp1dnWpSP5S9Tvb0fqOb4UNeX6WCrL4O+okeGXjlLV9veuwLvb8Xwr1fn9uIg4v0vHYEQEdV7dkZlnUVM839faC4+nMmH/ltnrlUfxMeC7EXFEzs6i+jdqJGQbZm+UPpachiUFU8q/NSpj92updsfTIuIxwMXjBG/N61t5S1Ftj+9Ss1RGnb58aUT8iXr+/YBaBvFgamBhT2pEa6wM5q19e0rU+tBbqMDzmtbxOMngbTMqwP828BFgyew4u2OobbM3LV9CROxBLZn5ZUS8PLvsmTtm59m9Liq74oHUSbY2NdXjl9Rc9CtHKGfQk/Q4ah+bJwx9bbAQ9dkTrvui1FB4UBf6o6gA9E054kaaUVNOzqcSBOxNLc69iDoWp3e9oIce3qtQIz9/bgHWPtSoyLrUNK5b77agef+8raipSo+iAttzMvPxEyp7D+oCfFZmPjRqzd3hVA/IyMcnaqT2T9RDbFVqauMdMcZI7dDx3gz4WGbuFBGfoNZGHgt8JUfchHdKI+Y/qXPsK9TN7rZxe8zbZzyWuhH9sNX7MVEbxb5sXntz51Dm4FgcQWVc+0l7fV0qC9QfWyNs5IXsQ2VvQaXTPh/4PDUy8pccY95866X7OhVQ/Iua234r1es69gh++4ylqd7XX1Mbug+y414AbJ0dp9hGzWDYnerM+xMVCP6V6rGf5/vpPdR7L6oR9jsqJfUfOpY1PBX9m1Rj7iHUmpBrqQbeq0c9N+byWStSMxp2os7zlemwoe1QnbelNpd95tTP6fLgnlL2M1odL2D29N1nAw/IzIO7lN3KfyC1TvRS6pr5X6oh9vh2vY876vQS6j66BDUN6n3UNT6JWS+DqZhnU0Hco6m9v07o0pnS7ncPo4Lv1ajr5TzgneN2HEw5rz9Ppeb/eGZuFpUN+x3UudP5uLSA4qHUNNWdqU6VnXN2ttxRy/sJ1RB/HDXa8kkqqc3BmXnyOB1WQ/W9s/M7atbEcdl9j71XULNwntGCxb2o0eSVqI6Oi++2gHuoa859ScEvJxG8Tfm8zamlIf+iOvC/kZXsZdRyBvXegmofbEetvX8qFaCvkpkrjljmv/3e2710deo58/PsuOVSVMf5P1udl8pK/nEQFXz+idp66nldyh76jOHtnPaiRt6uyMzdWhttkVHb6q3cQbzxLWpfwGupLZK+TR3ro7OSLY2kVyNw7eQ4JyLm+JAesafgnrIeLd3K7JT1aE51o473FtRi6j1bw+/W7NBLOjiJWmP3e9SDdQsqGHpt1Jz2Lr0Fg/UrT6VO3ue0nsaTW7mdR29afQcbjz+b6jU/ljanuPX+PKhr2XNwHDWPe9GoDZZ/RQUGXYK3JamG7d7UxrLfihoN/sc4D9ah8/VPVAr6n1CZyD4dEY+g7dEzYpmDRtX6VAPpGdTN+TfUCMJpOcKmnHP5jB9HxMnU1IVTo5LFrMwYvZlDx+K9wDOjUi+f3Rr8hwy9b+Trcajs1aiH3jrU1IjLqCRCP8nRE5gMGrB7UFONb47aTHllanrcAYyQfvluPidydkKeg6j1ritRo50/GzV4i1q/8hJq0fSRmfnVqFGPLZjdyBspS+tQ2YMH1QbUusKtqPvTO7JN4RqjcTe4N+1ApbB/dStvCapRuuEk7tUALaj6DrN71NeiRopGbegN6jyD2jvsOdTC9esy8+9dg7dWx8Ex/Ae1Jm8T6nf4PCrT7hvm+I33VOGIx1O9+hfQsjpHxDepnv8zqQYI0D2ranM9tT5pGeq8+0/g/Ii4on3uL0fpWBkKhDannquDaUq/iIiTqMDo8aOce+2+v0K7311KdQjeQqUuf0Jmvmley7q7j2H2mtHjqGfBYErtRlTSn64dhItQz8CV2+d8PzM/FxHL5OhJ04YdTAURt2VN6V6RSmxzMoydZGpOa93eToctb6KmiF6TmR+JiA+1628v6rn4lRyaEjuBun6CWlLwFGq63SeBtSJi3CUFg/N6Y6qT8GTgP8fpPGm2iBpU2Aw4qrVDT2x/iEomNmq7Ots5txi1H+ft7R73l9bR1Knzrnk98IGovYKvi4g/Z+ahEXEctU55Env5DW/ndAR1LNZtr+1Lbd1zxqiFDj2Xfkfd59agpod/O2pp1X1/CmU7OZ5HpXJfjRpmP5LKBDjSyEJMY9ajKZ8zGJ15HjUd7FJq6P4m6iF1EmMsiqR+h7dk5tHA0e2CfHDH4G3YalRylbsYJ3hrBifyy6hpr4OH5G1UT2nX6Y20soZvNptRvWDvpdacLJEj7g8yFMDvSz20V6F6Xr9FZRL9W2Z23lIhInZtdbyRCii2ppLQQK05GblXZiAzvwZ8rV0rD6dGFPanRrBH7gVrv6cjqUDwt1SShEsi4v1UYHtGe31kQz1fm1Nz5+9PTZH+Z0RcBfw2M4/qUvawzPwBtVnuslSDdxuq13TkB+zQA/R+VDrt/0fdO55HHY9HwV0Cva51Hizg/2Wr96OoNOZfoNuUohnUaPc2wP2i9kH6DXBi1nqepbPj9gFDD6qvtPr9ptX3lVEpyz87RuNu8H3LUhtLP4JKTnQdNV3nB+P0/A+dgzOpxune1PT8X1HJlkae+jP0e9+YCup3p4KVSyLiaioA7xzEtc8YTJtcgpqWuSzVCTRyz3+7V3wf+HVEXEaN6J1CHd/vDB/bcYPldn8a9NAvTU31ewgVoN/OiHsyDtXtDqozbJOcPZ3vflTgNc/XY7uvrUN18K5P9ZbvRF0/Z1DrZsfu5B2qyy+opDlfYXYHyp50yE43VKfnU43+lVqdD4yIT+ddNzTvUudfcdd75mbUVO6x73dz+byR1oAPeQzw0agsxmtQCWee2eVanpOh4GriSwoGWvkzqeN7BrVf7hoRcRE1u+Y1HY/3U6ng5K/ABlF75f6CGh26Pitr5yjTl9cefB9TljNFJSI7oGuHRxsAOb51kg7a5hdFxOlU+/ninMzU/8HPuhR1v34GlSwQqkN85PMmaoub5agOiP9uZa6Ylbl6JvDQzPxpl8r2IoAbukgeSg17/wf1kPkLlUr1johYIedxWl9Mf9ajOw31nD2Z6pE5lxplWpPaumDkxv9QL/dzqV67LaNSip9E9eCNk1J8kIHtfsCLo9aa/KqVfVqOn3p+sJFhZhtFHPzeIuIN1Aji2KL2qluCOsbrUlNuX9mhqMEF/Uxq1OJAZvciPYsKyDsFcFEjeBtS2f8G6ebPYHbw/DpqhKtr2RtRDaKbqYbuWMk0qN7nJ1LTIdam1lVAXUsnUJtvj7sO6XHUA+/d1LSwNagG7+ow3lqyNvK0DdVZcxNtGmWOuSE9dVNfjxqJe0lm/iNqD7sPjlnunaL2MdqT2uT2DVF7OnWdxnx5Zr6o3T/2ohqpy1GNu9up/fH+r0Mdl6DuG4sBfx46rl+Lmo73zaiU/52mew793lehMgTvD/wpah/Ma4AfjHn+DXpf30Fdk7+nOj32AB4aES/IDtsftMbHb6h1FFtQi/w3YvaIy+gVnR1sbkrNlHgMtc3BaVRSpVs6Xiv/pH7/y1AdS7dQAdUzgH9ExCk5gQQ0Q8+wpajpjetTI5P/RwVzG7Rnxcg/Q1ZylROB78fsrJkzqPWBMO8jh0tR96CXtDKy1fE/shJN3a993liBbLRZKZl5XkT8mFoztGVE/J46D7vcnwbtlpdTGxD/MWpt9VOoHAK/zu6Jj7aiAs3rqHbBqbTzDsZvM01Su14/E5Xlehb1HP9EG/X4DZV2vvO+YUPn5lpUgP9gamSZ9vdDc7zp+YMp1jtRHaaDdaJLUYH5FmMc7zdR96HNqJko21PPxyuBy1ugP0pH3i5UsPwH6jr5BhV0JXUdjdwRMWR5anT+flnZxB9CHZMdqOnil1PLLMYydCw/SO0XuB5wbUS8nEpO1iXD5ROoWS1nUcucvkONIC5B3V9f2bW+vVgDN/SweiXVK38c8LzM3D8qs95K4zTCYsJZj4bKXR54G9V7uQ+1Q/yNQ19fg5pKM1JP6dCN9LfUVIaftHrvQp3Eb8rK+DZOY3dpqvG8CdV7vDGVGatTZsspZQ/WO1xGZTC8g7rAP5aZW02g/LWokattsxb+LkEle7gJOKTLMYmIT1NJGT4JPDkrE+XPgNdnhz105lD+C6mG6QOpBvCtVMD8+RHLGTSM9qd6+2e0L11JDf8fnZmf7VC/4Yf3M6kRw+Ophv/W1FS534zRwzaYWvtS6pr42tDXVgVub8e8y/qVwTE5kDYqRgXJf6ammh2Tme/oWO9lgDVzyuLpiHgnld2x8/qHoWO+LdXR9D3g5VnrOTelMo91Tn8dESdQI+EXUyOe+1IPxPdkh3UKrcf5JVRjbn3qgf1VaorfesBhOeJ6zrl8zlJUoLgldV+aSY2uv2zcslv5p1DB1deoKeTXR8SXgP/JzJPmdYQhaprrytRI3vaZudfQ1xah1uR03f9t8Ez8P+o63IOaQrQWlTL+zZn5pY5lL0p1VD2bapx/lgrstgL+npPJujio/5HUvWQpqjG5EtVT/+OuZQ99xpJUI29DqjF5+Sj3jtbR+GKq0/hwKiHWycAmObm1rVtQ18bR1Hn8B6rDcSUqK+dFXe8href/KGqtzak5ez3Z+cBOOeJslPa9S1OjNIdQSVJ+S/3efkqtJescDN1bWofK2lRn5JdyzOUEQ+UuQ10nb6N+fytTyXneOkaZr6DaA4tTbYNDqaUnw+3ISWSCXT4zb4jKCLsLNR39PzuW9QBqNP051H30VGoG0EFd2h6tzIOo4PIsqnP7POrauL2179bOMbIPt8/YkqGBhaj8GPtTgy2nUOvCu4yGb0e1O9ahpozfQl3n51PLOU7L+3IWyqEHxc3UgVyfWte0FNV4HHm6YLuIpyvr0cD9qAfTS6lA6Jyoxbg/zMxTs+NoVmvULU3Nzz219Tqf1P68ffh9o5QblbL4mdTN7U3MXgdxVLs5rd6lvnOo//VRKf7/HxXIBRW8fGSccqOmIy5BNQiOaa8tkpU188dU8PbuEcpblVpL8Vdqqu3/UDfRx0YNfcc4wVvUfPylgR9nW6/RPnMDqnHapedu8Dvfl+rZeT41OnEBNaLXaWrt0Ll0BhVwP5u6Rv4naz3gmh3rOyj/9tbgOpgaUZ9FjSSfkplXzaEeIxXf/n4StY7neVRD7JfU9IiTRy0wZk9RejZ1TrypBVXPA87NzDd2qOe/fQxV951ox4J6iEE1Sh/eueCairk49RC8kUpn/6GofQe7Pgh/TCWVmkUl/HhW+zdUgPieMeo7CGaXohbab0Cdi4dR0+1m3M23j/I5K9Ky+VL3721aoLs29YwYZYRhNepZ8gLgjKhR1BupZ8pLqePfKYAbqsNDM3OfqGQ0b6MCuME6u07aef2JiPgy1Qn0eKoz85yo2SuTmJlyR3sGr5uZ2w5ej1rC8LKIOD1HmFo61EnzYGq2y4OZnSH4x1Qa+VFH8v5KZVr8HDWS9U0qicJEgrfmNqoDZSeqM/NX1GjfmdQIzjjH+VbqufX/gC+2391GVCr3P4/S8I/Za98fQ41c/YTabmmP1rh+ZB+CN7jzGXIpY2bDjWlcUjDkBqrtuAp1bb+M2pPsUur8ODM7jPANXS+DTK2PbT/P/zH+qOT11BKTbw0Fy4+l42b0zVFUJ/Tm1CjyIsA1UbkNLmWM+92QnYFdo9beD0a/n0/lOUjoFiy3NuIp7fuXowYrdmb2kpyFZg3cp9oJcTbVkPkYNUXsKx3KSobWt8Vdsx59v/Umrzlmff8MvKg1Oh5I3aS3pxpKawEfzcwPjFLm0Am0HZUV670R8VGqYX4DFXB0vekfQV0o11KNjg2jMiv9Bnhtdtz7Yi5+3z5jGyqIOZXZUw+6Wo8aAbhf+7MWcOhQoD/qguX/pBpcb82aMnMw1TjYghp5euGY9V2MCtR2jZoC+zuqcferzBxp/cfA0O/+Fmr9yizgGZl5RVTimK5pcBeh0vXeGhF/pTK8bUNNT3ljdsigNFT2elR2pwujpklvRwWgHwZWjohfZOa+XcsfOibXA5dQN9BPZaVFX4zRU/DD7HvH46lG3nLUthtXAFtFxKbZUq1PoN5/oc63d1PryqBGyn42Rtk3RsRXqE6lL1KNjftRo4md1mS1QPAb7c8gSNyeSv7xBGrEs6tBMPtpqjNiLyroTOr39zY6JDmYqv3sB7TnzKepqWLPpUaebh5x5OmCVu+HUz3G21BTEx9LNYTHSoARtebkmNa5dgc1pe1yanH8yJ2Dref8SdQ5vDo1fXQ1aopmAu/NCe7HSI04nR2VNOWX7fw5jUraMUrwNrxv6Kuo5/aXqWtmC2qtz8/o0GBvv+8/R20afyPw6BawHDGJkZscWj8dlTV5FjV6sTt17F9HBaCj1HnQufQm6uc+gmos/pO6B7558FbmcY3/0O99TarT4VG0NYWtzMF2KRNf/7YAm/YlBZn5+faMmkX9/naggphHUdmTD+pY9OB39P+o6YIHUe2nV1Lrld81iWt9KFgeeer5QHu2bkLdI77RjsdDqPvpZtS9tFN7aYrPUyPh61KzDQ6g2oDXRG2l9a5Rn41DHY/LUZ2uT6MyMf93+/o6wx3Uo+rLFMqVqTn4S1MXxknUQu0dqcXlk1i8OPUz16Q2/Rwnde/gl/ckapPLq9rrS1Prem7Kjhu4tgBwD+oE3pB6uFxJ9ZSe3qG81YBvZ+Z2UemML6AaXv+gApUbqGk54+4f9gDqprcv1TA9nRqRHLenavizlqFudE9ofz+AGgl95CjHux2H7wJ7ZctQ2AKZZampqifmeAlMlqQadDOoUeWNqd/lSlTP7LNyhDnoUeuMVqbOg4dQjaEXUw3I44EXZ+bGHev6BCr73GnUA2qr9hkXUZkMO095ah0Qx2bmdyNiE+rBd9vQz7RmZv4qum0fsCrVE30z1ft8PvW7ez417efJmbnpGHX/CTXV7vHU/PhPUo2m1+cE0mkPfc7rgf2oHvUtqfPmVdkhDfhwnVoHzSOoHsErqMb/WWPUc3mq4f8UZu8F9JUW/IybWnxJatT00dRz4GlU7+V61PScsdbnts9YgpqOeVP7/8OphtPFWVN4R26gDt37lqCm0TyIGnW5YMzjsRR177iCClweQ3VQPCAzR14P0s6xd1OB8NuowGFlKjC8Arh5Eufy0Oc9lxoB/yPVcRhUgPDHrKyB83ysI2L1zLwyIt4MfDkzL24dCCsMlXn5BM7BDajA/hU5wnYS81DuElRd/5mztwh5KDVd7oaOZX6GemZf0a6dB2THrUGikr79nEpZf3l77RBqZP1RVHD/pYUlgBu6pqdlSUH7jEE24zcAN2bme4e+tiG1n+mPxih/aWpWxOsH7Yx2T/kdtfxk7D15J6E9o15KddTdSE0/PJdK1PTXGCPp1j187qLUTI/NgY0y88338C1zLCNrpPMd1P1tFhWzvD0idqe2Phg1s/Hs8hf0AK71zr2QSjSyNNXIXYaabvfuSfQSTKeo/dSOz8yHDr22NHXxnT9m2YN1BItSx+WJ1DzdM0Z9UEVl99wtM5/aHlLvzsy929fWozKQbTJGXQcn8iuoi+JCas3hldSo1rE5tEak42fcZe+YoddnADtm5shD+PHv+8fsRgVFqwD7jXPxDX3G/ajz+/7UOrVB0DJSKveIOJTas+Rd7f+bUtPYDqKmGHw8Kwtjlzo+h5pCuiSVQOi/c4zUyFPKPgN4Utb0kx9Qo72DTemfSHV+dHqYRE3VvTGH1iG0ht3+1DSrI7LjvjStrG2pYGXVrDW5K1AjqA/pWuZcPmdpaiRhQ2p61U/G6rmrReCrUw+Vm4AzxrmXDl3fB7R6fr6VuzdwVVbilXHXTT2Meha8irpfzIpaR/yezHxW13KnfMabqcDwzuCTls5+xIBiOMnILGoE5HJqpsHJWUklJhLct89bmRo9Wwr4alZmzlHLWILZowrPp4K2b1MbV/+yvWciDfShBvDKVO/6rrRNj6kphGcA35qXgKN18lzW/vyT2pvyddlxXcm81n0C5QzOkVlU1us7qNlFV1NB7RldO1NaB/QvqCD83TnG+qCoEY9XUefwNlRn2I+o7IV/pkYULulafp/F7G0ank0F4P+Tmae14//PMe/RD6NmbR1ATd/9OvUs+31UPohzsrZhGrnO7bzbg+pw/B7VkXIbbRuSzHx2lw7T6dDaXZtQM6oe0v5emhpcuIXqVBh50GLKZwyeX2tQbbyNqQ7en2aHbb6G697ucydSz5VDqUGWH0TEV6lr56i7L+VuZOYC/Yfq7X8iNX1r8NpmVIN0n/ldv7up9yLt772prJYAy7a/N6YColHLHATcq1HTAT9FrSF7NXVzXXWM+j6L2WsMLwC+MPS13akEBJM4Ht+jGgnvp6b2QS2IfvSY5S/a/t6ZWhfzQyrIfynVQ7jSiOXNBO7f/v0hal3JN6kRgIMmcH4M6vtkKtHD9YNjACzWscxfUFmpoKbXntmOwdupYHMS5/UK7Xr8DjWCegK1TcNyHct7KJVEBKrRfOaU8+Ukque4a33XpRpFD5rD155FJSQY53gsSTVqlmz/fwTwhuGfoWO5i7W/Z1HJAn5G7TG0w9B7YsQyB/ePdalp0T+lHuAfppKkPHfUMofKHr6+9xh8HjX6/R3gqRM499Zpx3pNahTkKVTD47tjljuo+xZUz+6y1Mj9h6mpYX8Zo8xvUOunftjq/CNqStF/jFtnag3dIlNefy+w8rjHupW1UjvG36A6mLaZRLlD58YDqTU9Lxpcn9Sz8WXtnFlvxDI3bN97DvUsO4d6Fmw3qXpP8s/QOfJ+arrj2lTnx5vaNf+iMcp+ALWe+Dhq7ebJ1FTKx0yg3ltRmVpPpIK4X7XfYef7Xd/+tOtvcM+f2c69Z1PP3D0n9BlrUh2Nf6TaIB+m2koHU0H+ZmOW/yBq2v+3mT2qdToTaN9M+Fg/v/291tB9b32qw+rdVMKVSX3Wce04v4N6Pv6eGmx4xBhlLk0F3/ejOgMHr/8aWH2s+s7vX849/OCrURlaaL+0JZndCHlqu8ktM7/rOZe6D+q5W6vnykNfey3VUzNqmYMb/supUZAnU70z/0U1xF4/gXo/oNX5m+3m/BNqCs0zJ3Rc3kc1kL5MZXijXShbjFnu4Nj8jJpK9Etq35QTqfUnjxyxvAOoRsDpVC/jHUwoCJryOb+jMqueTjUgg2owjXRzpqY6/Xro/9+gMm+uSE0dPJXaaHWi5zjVqH4ZtdltlzL+H9VrviX1sPrU0Nc2BU4YfNYY9XwFNY1vUOcnUQ3qXzFiI3G4LlTCko9TQctDqcQdqwH3m+AxPpOaPr4r9bC9kGrgjXw8hq6RA6iEPrR6700F+WPdP9qxfQc1vWz49Z9RU3Imee7tSgX37wM2HbOsrajA8ADgnXP4+uJdz0Fq+w5aXdeipjN/ElijY13XZi7PPKoRecUkj/Pw73ZC5QzOwV2oYPY9VOP059R+Vl3KHHR2PG9wH6IaertTU47/q7226HQcm3GPKdURu92Ur92fMTquppS1HNWx9F7ac3zUY9Gu7X8L0Npx3o16nu8wbl378odannEWlaH1QKqj4Cftd/nYCX7O6lSugBWozukXUDNqdp5Q+Su359ZgO493UsH+jUwg2J9A/dam1oGuQG2U/lFqXdpjgVUm9BnRfv4lqWUxw19bgXo+dh4caeU8heq4u4JabvEyKnndWHVfoKdQtml9j8s5JDBoQ9THZOZm937NRhMR76GCrROotKGbU9N+Rlp4OTT0/UZqCtVJ7fXlqB6Jm7ISM0xqiscgg9AuwP9mx7n4U8ocJM7Zmprb/UdgVmZ2zqg3VPYSVIN/u6gsZlu3179Hrf/6U4cyh/eP2YsaufgNY2ZqamWvS01L3LvVe1Z7/SzqgT7PC6CjUpYfQt2I/katQ3pS1lqQFaibxay7KWK+aL+z7al1ac+gbqaHUQ2Cx1EdH2/oMp0jKlPoNVnrrz5ETcvaC1iVCuhGTWozXPYgC+xLqUxkm1Br7d5HrTvpfK20sgf79304M/eY8vXfUA/wv4xY7uD+8R4qKcfbpnx97Ckzbbryj6hex/Ooh9a6mfmEMctdklrz/FhqXcjRjDntc6jsdzO0oS3VedV5Q9uhclekzo8PUusjd6Gm/JySmdvc3ffeTZkHUI2YPzBlr6WoLJdPmdPzckExNFXpM1Tn7Cfa6xtRm9x+NjOPHuV4R8SDqOfIydSeZ1cNTV1aC7hsEs/D6dCmz3+Pun98iVpHdXJmjpWQJyK2ofbLPR04PWdvaD4RU5crRGXXPi4zj5zk5yyopnNJQSt/Paqj+9XAWTmU7C5qU/nbM/MPHcodXBerUUHEWtT2UE+LobVkUXsG/iXH2AZnEqKy0r6Gmi55ETVKOIOq92CAZ6REgHP4jJ2oIPxX1P3/R+3PLTnmdPHh+1ibKr0LNbvjPOrZ3nWDemDBz0J5G/CE1mD5BRVEDPZh2JK2ceSCpjXMt6UaAutn5uujUhHvQV3wB2SHPViGTqanAXu3hsf3WyBx+tD7JvKwauVcSk396aw1SLegRoJ+npkXRMSvgXdRCQg672U1xepUBtGVgMuiFhhfSQ1Tjxy8wZ0pcX8E/GgooH0i1fM4rqSC+rdSwQURsS+VPGek7FVZCRbeQo1irUDtBThYn/cCOm42Pt2ykpUc3/68tAXMj6KOyZOoYw3zmC1tisdQG4ueR+1p+AqqB/qrXes7FOTsQE2BOAW4MDP/FpXC/FET6Oh4OLXO8kJqS4UPUz29f6HWXt44avAGd0ndvhjw4oh4BJXm+gRqnWGnLJFD1/em1HTGLaP21tyIGgEeZyH/YM3V06iZCx+nAvDXAKtGxBmZ+equ5TcT3dC2dUpsQj0DzqJGmL9Fpei+jEo00knedWPiHamscd+JiDv3Wupa9r1hqINgUYayhmbmuYOgoJmnDInt3HsG1Ri9P/DciDgeODUi7qCNslMNwAXVq6nR042ojqxXRMRZmfmqUQoZ6qB5GDXN7hLq2f2nFih+JsdIqjFsDg3btzOBLLB9kZlfAL7QOkd3pLaReSQ1Xf8k4B1jdvCuTD37nggsErWH6SVZSUteRc0gGTmAo9ot/6L2JV6KahcMEps9JiI2yMz3Z8dkN5OWmT+LiL9RSe+2p47LT6i1natTs8PGdTF1n96Mmla6KZUJ+zdReyZenB3XwbVgeX1qujjUuur3jttROrBAj8ANxL9vDHgaNYr1isz88vys25xELVxfitqf6K3UCXIu1di7PDtuSNzKXpQamXgs1UgYnMQ/zcy33933zi8R8TXq4fpHqvf83VSQ8SjqxD4mx8joNfTgWq41pIOaTvMG6tj8JDPfMN5PMT0iYgeq53klqgd5OSqr47c6lneXUZTWk/YCqle3c8aq+WGCI8kTH0WNyhD3Aurhd2FmHhS1GfuOmfnMcUazWqN0u1b2utRD5UoqeFsRODQzv9ml7Fb+ILvWGtQU2D2pNYw7dyxv+Pq+leqYWYZagL8G8IOu1/dQj/GrgT9n5leiNihenlp78q8cYy/GOXze8jnmhrYR8QWqoXF+q+f7qE22n0fdj77fJQC/m88b3mvp/ybQgTDtorLNfpNqOP2Q6pz4L2qtycjXZETsRk1NWpQ675amnrlXZ+bzYwHOjhgR9xt02MXsRDKRmSPtTzk0uvkW6n5xKdWh9IaIOAK4NjNfuSAfiz6b0sE71gbhLeDenGorfZV6BqxIraPaAXh4lw63ofvpN6kOsX2B6zLzk1EJv27JzDfG7H3/FhhRibd2o9q851P30XG2ppla/sbUNbMlNZ10U6pj5SXZcVunFrx9nZrdcQHVkfSX9jmfH7d904sAbtjQRTKxaX3TJSol68OY3QhbiRr+PTwzfzhm2atSU6xWpKbLPSAzD13Qbs4tgPguLckKNa1jR2rD7Quo3unXjzNlZOimdBx1ThzWXl+VWrt2/aR6PMYVd9388zwq8L66jRauSgWbt07w8xalRn3/Me7N4r5g3IdsVDrt46npTmtTQdxVVO/dIsCHMvOESV2HbVRiEWo0cX3q2nlnZp45YjnrU4viH0k9QG6iGvxJdaZck5nP7VC/u7u+L6RmUYx1fbfPeTxtQ2lq8/HOm8ZPKfffNrSlrpdOG9oOHY/HMft4rJqZB0yivn021NG2OdWguYNay/5YKonTezLzt6N03LTrOQajy4Pvi8pMuTa1VUPnDtPpFhH7UyOIM6gR/a9Tz4BxNmf+GpUY5alUAPuBiPhvasrxNxa0NoLuWXuOr011DF2TY2b9jIinUWt/nwrsmjUr6hfUOtSJbX8zZh03p9Z9X0qtBU9q6c3u1BKDP2Tmg8b8jMWoY/pAKiPkg6d8fUNqBG6kNtkgAI6I/aj8Dq9vnb4bUbNVbs7M94xTd+hhALega0HbV6kpcadTiSWuaF9bi+pV+dWoDZqhIOV5VO/82tT0mQtoGyxP7IeYoKi9fh6fbV1G1NzuH7TXukwBmNvnPIAKii6h9rL6Znv9v6kNGBeIKTQRsT2VYOVUarrFilQj9yTqAX5udkgBrukXs9NpP44a0budCob+SM1n77yZ+dBnDK7zGVSQ+VJqDcsLxix3kEb8UGr07Qzq/nE8tZbvl9n2Ohyx3Gm7voeOxdrUepNNqJ7XC6lr/YzMPG1Cn3E6tVbta8ze0PYq6t4xzz3RczgeawKfy8xd2sjq2Osq+qoFtzdTwfEXcmj2TNS09791DczbfX6wL+UfqdG3K6jG1wJ1vIfOuYcCX6CC/O+3Py+k7isrjPJMj9p/cX9mb8FwGzWK8ALqmtmXOi8vWBAa55q7oY6OHajOje2oAOZYav3szeME4UPn39uoc+QqqnPw+7kAzVSKiGdR6/pvodpLx1FZIVeigqA/Zea4y3vWpJZq7El1ZL6U6lj6PZXg63XZYU1x1Jr7xamZKBdm5jumfH0iWzQYwE3YlAb6OdQUmn9RF+AvqEWXN3Use5A44SVUT90m1I36A8Abxxmyny7tIvwk1ej6ITX8fVEO7cs1ZvmDm92zqExb76Oymh1ETTf4dGY+chKfNa6hG+fcNv/cktr8c+QNI3Xvi1rntRvVI7gFddP/NJVJs+vDdXi/xHVamY/KzH2j1kcul5mHdyh3Vyqb1/nA2zPzzy2o23Cc0azpvL6Hru0DgQ0y8zVR++7tQE2/viozD5zA50xsQ9spx+MH1KjnBZO63/VZRDyZapDuS2WfPRP4a2aeFxHHUsd/nqfaDt1Pt6MC/HdRHZtrU42xGzLzlZP9KcY3dF6/kurAO47ae2v/iHgmteXNR0cs8z+oDp9nDQd+bQTgIVS74+iJ/RCadlH7o76Gas+cSs1sWAvYKceYNh4Rr6U2iP9yu3aCSmrVac/B6dQ6MnemEpack5k/nnD5i1Mdmm+m2mGXU8fjYmqw5frMfNGIZS5KdZzMohLK7UvFAr+h1pufkR1zMky1oCcx6ZX2QDk5KtvM1Ab6ltSc4zMYcVF//HvihF9x18QJj1gQgzeAzDwCOKKNkO1Encyvi4hHUzelt486TWkuNgZOzcyLIuIUZu9r95sJlD0RQ72eZ1C9PM+m6vk/mfmt1hs0kalhmh7DU7baA+8s4D1tquPjqN70s6nOmi4G58hOwNuA/ahOG6h7yDwn07hLobVx6PlUx8ZrI+L31PSqf47TI38vXd/rUSOdZGV6+xXwofbw7WyoF/vRVJbgxSNieEPbEzLzmlF6S+dwPJ5OHY9HUcfjvyd0v+uj46i1aetSjaNHAn+LiGupwH/Ue/Ug0cnG1CjnN+78Qo1urdT+vUCNOA117gz2XV0fWLR1GmxNjRyOai9qQ+Nb270om69GxOuptYFawA11SmxATTM+gVrne0DrANmaagN2KXuQ6Xt3KskNg0AwIvaLiNsy87xxf4ZJaM/ZJVrn2ZHt/vkfEfFS4BhqXfXYQVDrvDy7zZy4jcpdsSmVgOrL1MyoUT2S2dt2/JGa9v9gasrqy6jr/snj1h0M4CZquhroQ42HPwE3UCfwIKvgY6gG48SGZadDVjbHo4Cj2sW5FvXQGSub49DD8OvU6ALUNLGPUI3px41T/iS1B+vi7SH7V2rPmG2orHJvnMQUPE2vdo3f2RiMu6bT/kFE7E2NunQtf5Ap8iSq13HHnL3FxvbUHjhdy/5Da8y9mOpxvCki1qF6HcdasD4d1/fQsViCylC6E3WvG2TNHCtT2tC94zxqj6FtqQb1P9qfz7X3jXxPna77XV+1YPlvEXES8IvMvCRquu3aVHKeI1rDdZRga/C+LYAntoD+h5l57nBDdEEK3oZl5qfauXE29Rz4GDVa9pUOxa1ETR+FFtjG7OQoW9Myn44z9U7Tb+hcXYvaYPvB1PpQ2t8P7TJjImr6/xpUYreNgQ+2Ue8fUe3Vd1BZ0hcUWwDPjpp2fQ3VIfoLaj+1D1PTsJ8ziQ+K2oZrMyrwuolqR35hjEGRp1GjbIMRzb8C50Vl0/87LeP4JDiFcoKmNNBnUlP4tqGGwTs30ONeTpzQFxGxJZXZ7T+pecw/GzTq2rSoQ6n93xaI9YER8QRqy4TTqIyTWzF73caRk54eoHtf66S5Omt7hHHKWZZKhrEjlYL+eqpH8tljV5I7H1ovoEb1/l9m/nES5U6HmHDWzLl8xsrUKN/iVOKpx1KdYxtT+ykeN6nPWhgNTRv8HBWsHdem9a4LfC3HyMoZEY+lfmcrUZ0ei1HTX9+Z83kfqzlp59ozqNHIE6jOmmWpa/0XLfgftcz9qWPwypyy3ruNvD9ilKnAmv8iYhmqw/9t1Lm9MnBml+nYUUmglqWCoMdTWUrfQI02nUNNsR13O5aJaVM7H0nVc2sqycgl1IjWltQz9o1jfsZgucILqSn5Sd0//kw9Z36Qmf/dodzTgf0y8/cRsURm3jY0svoZar/ELp00//5ZBnCTMx0N9LgXEif00dAF8QDqxvZtaprIX6ib1Pep1Pmd9u+YDjHNm3+q3+Ku+6kdnZmXRW1wvBl1bh+ZE0zt3KZsPY7KhLrAXCcwfVkzh8rv1Ya29wVRKfLPzcwHtylRb6ZmqvyG6uAcaWQhKqPvClkbd69PNexuoTKi7piZm0+y/pPQGtIvpNbFL011ECxDzRh5d9frOypB2sdbuT+j9hu8hRq13zAz91nQppLqruKuGap/SyXAu6QF/HtTIze/nRqgz2PZX2nlvb89Z55NTWNeiVqr+7kF/dxoAyRLTOpePNSpdAy1vOl5VLv6l9SWJh8dtc0etf3DJ6lOqh8Nvb541nKFXwNPz8wLJvEzOIVysh5AbRewEZUWeOwGeruhv6/9mZo44cttLctYiRP6qDW+NqT2tvk9sGELdnekjs1nqOQuH5mP1byLnP7NP9Vvn2X2fmoPiYh3U/Pll6KCmc2pzLYT0R6E35lUeRP2/fb3odT99AyqQ+x4KlnKL8csv1cb2t5HbABcFLVP6rOpmSPfi4izc/S99t5P9ZIv04K3b1NrDmdQ58qh7X0L2rKCt1HJVr4/CFjbM/3D1Kjh/3UpNDP/FLUVxtOo5GabUFn0jqZG2rXg25JKRLM6NdNqv5phy8nUSO3vuwRvzWZUxwHAi6jp3IdTI3x7URtjL7CzMODOKe+3TGqm2VAZ11Oje5tS7ejzW1ty5BHrzPxHRBxJTVH9IPDzzLyoBW+PARadVPAGjsBNi6EG+vOpHuSxGuhtnnxMPWnjrokT/iczuyZO6J223uEn1MV3AbWw97QWzA3es6A9vP9N+91OZPNP9VfcC/sl9klMU9bMofJ7u6Ft3wxNVbo/9Tt9HrWJ7X+16UuPzMxnjXK/johDqRHZl1D3/qSeB6/KzOOH1n8tMNpo7/cyc1Z7fi0C3NbOw6dSz4AXZ8cs1UOfswQ1mmxnYE8M3Y/mlqF6KypD9UgJ8FrZKwGfp/YGvIR6zrwlM49qXz8DeEwuRNsXRe0RfCvVQboR9Zx5OtVm/ynw5MzctEO5g9/j3sCu1Cj4CsBM4A9Ux83XJvEzgAHctJuOBnrcNXECEXE4Na/2yHHL7pM25eDhVMafLakR0KupnsyTM/P7d/Pt0gIj7qX9EvskItalsmYmtZ3C/pn58ElOBYsebGjbdxHxROB7VMfEdTm0PjQi3gz8LjP/b8QAbgUqGc8LqZGEw6iRik1yzPWn0yVqD9fH5Rz2lYpaO3tMZm5279dMC4rWttuMGqFegeqYP62dH//MzKs6lvsUauRtaeDPmfmU9vqWwKGZucPCdK9rnXQ35tB6wqh15/tTiWOOyMxTx/yM1ajf5QOoYPGs7LDX6t1+xkLy+7pPiwklTuiT9jA8e3iKakSsQm218Fhq3crIvVXS/BDTvF9iX7WpLC+mEhXdRPVqXj6JUbGh3tIFekPbPmuB1nOpqeyHUZmULwIupTrbrqY28O7UEGmNpJdTHXkzMnPje/iW+WboGv89tU77fzPz5+1rTwD2ysznz8cqaj6JaUqAN+UzHtT+eXVm3tjWQL+lfe5r+zBjaVJa5+B3qWvuoilfexaVRfLs+VK5ERjAqZei0lG/IDPPjog3UJnMFsqRCt13xF33U9uTSog0yf0SeymmIWtm9GhD275rI8p7Uwmc1qOS8vydSqzw6Y5lDpIQrE1NO3s0tabziAV5Knq7xnek0qDvTF3jmwOvyMwvz8+6af6I+ZChup2HGwKXZOblC9MIHEBEvALYLjOf0WbK7UV1Fq4I7JuZF8/XCs4DAzj1Tut1/VZmbt+y/pwJbJptu4CI+BLwusz88/yspzSO9lAZ7B+20K+PjAllzYzZG9q+Azh4yij+ftRakwViQ9u+G6whbAl5jszMM9powwbU+vBrMvOoSSQmiNr8+NNUINRps+N729ASi12oEbkb5nOVNB+EGarvNW2E85rMvDkiPkQlPdqLmuL9lcz8+Hys3kgM4NQ7bfrkbpn51IjYBHhrZu7TvrYe9SDcer5WUtICp03JfDKzN7Q9FRje0PZsYA8DuMlqa91OyMyfTfPnLFSjCLpvmXQCPP27iDgA+ChwHrW36CrAMzPzq/O1Yh24jYD66DZg94g4mdrHZDil+IbUNARJmuoxVFKUPbjrhrYvpza0/bbB22S1KY4vAnaKiKQSTN06HZ9l8KY+a7MsvgN8Z0oCvEXmZ73uSzLzM8Bn2hTSWdT0609ExIHUHnxv7Uuw7AiceqtdgNtRa2N2pvbIci2BpDmK+8CGtn3SGqGLA0+i7tW7UFtj/An49MKWOVnSgmc6ssXfGwzgdJ/gWgJJ9yQizgYe1rKwvZp/39D2DZNIkKK5i9rz8MnAtZn59YUp+50kTYoBnCTpPs8NbeePlsDkQdQWGX8EzqX2frtmvlZMknrMNXCSpPu8zLwuIj4PvIna0PaCoeBtS+Dm9h4TYUxIRGxDzYp4NzVDYgNqKuWN1LpDSVIHBnCSpIVCZn6jjbRBbSI92J7gaVS2N6iEAU7pG0NE7EwliNkC+EJmfmPoaw+l1hyaNVKSOjKAkyQtNDLzoikvLQ18j5pWCTDWfmQC4BHUyNvfgAdFxKLADzLzvOEsnwZvktSNa+AkSdJEtL2sXkWtd7uY2tdqHWrU7Vbgz1SymJvnVx0lqe8cgZMkSZOyJ/AQ4G2ZeUdEnAAsRyUyeTpwtcGbJI3HzQElSdKkPAn4bgveIjNvz8zrM/NU4Hpqbdxg6xdJUgcGcJIkaVJWorYMAFgkIhZpiWKgNk2/pf3bAE6SOjKAkyRJk/IF4ICIuF9m/isz78jMQdC2BfBzgMw0WYwkdWQSE0mSNBERsRbwcWorhp8Bl1GjbtsDG2bmPm4fIEnjMYCTJEkTExGLU3vrbQKsDDwUOBr4ZGb+xQBOksZjACdJkiYuIpYAlsrMv83vukjSfYkBnCRJkiT1hElMJEmSJKknDOAkSZIkqScM4CRJkiSpJwzgJEn3ORGREfGBof+/NiLeOh+rJEnSRBjASZLui24F9o6Iled3RSRJmiQDOEnSfdHtwOHAq6Z+ISKeEBGnRMRvIuLHEbFqe/2tEfGFiPhRRFwSEXtHxHsj4rcR8YO2vxkRsXVE/DwiTo+IH0bE6u31l0fEuRFxVkR87d78YSVJCw8DOEnSfdXHgWdGxPJTXj8R2D4ztwS+Bvzn0NceBOwB7AUcAfw0MzcF/gHs0YK4jwH7ZObWwGeBd7bvPRjYMjM3A140TT+TJGkht9j8roAkSdMhM/8WEV8EXk4FYANrAke2kbMlgD8Mfe37mfnPiPgtsCjwg/b6b4GZwAbAJsCxEUF7z5XtPWcBX46IbwHfmoYfSZIkR+AkSfdpHwYOAO4/9NrHgEPbyNoLgaWGvnYrQGbeAfwzM7O9fgfV6RnAOZm5RfuzaWY+rr1nD2rUb2vg9Iiwk1SSNHEGcJKk+6zMvB74OhXEDSwPXN7+/ZwRizwfmBERDwOIiMUjYuOIWARYKzN/Sk3JXAFYZpy6S5I0JwZwkqT7ug8Aw9ko3wr8b0ScAFw7SkGZeRuwD/CeiDgTOAN4ODWV8og29fI3wIcy869j11ySpCli9uwQSZIkSdKCzBE4SZIkSeoJAzhJkiRJ6gkDOEmSJEnqCQM4SZIkSeoJAzhJkiRJ6gkDOEmSJEnqCQM4SZIkSeqJ/w+IUUoY3KkQ3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_firsts_df = pd.DataFrame({'Names':top_firsts.index, 'Count':top_firsts})\n",
    "ax = top_firsts_df.plot.bar(x='Names', y='Count', rot=75, figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "increasing-rapid",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Donna Halper       2\n",
       "Amara Aguilar      2\n",
       "Angeline Taylor    2\n",
       "David Dowling      2\n",
       "Shafiqur Rahman    2\n",
       "                  ..\n",
       "Carlina DiRusso    1\n",
       "Naeemah Clark      1\n",
       "milan ismangil     1\n",
       "Edmund Lee         1\n",
       "Pam Parry          1\n",
       "Name: name, Length: 1956, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df['name'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "violent-powder",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df['name'].value_counts() > 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "muslim-sarah",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>university</th>\n",
       "      <th>lower_uni</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>Donna Halper</td>\n",
       "      <td>Donna</td>\n",
       "      <td>Halper</td>\n",
       "      <td>Lesley University</td>\n",
       "      <td>lesley university</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>Donna Halper</td>\n",
       "      <td>Donna</td>\n",
       "      <td>Halper</td>\n",
       "      <td>Lesley University, Cambridge MA</td>\n",
       "      <td>lesley university, cambridge ma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  first    last                       university  \\\n",
       "428  Donna Halper  Donna  Halper                Lesley University   \n",
       "429  Donna Halper  Donna  Halper  Lesley University, Cambridge MA   \n",
       "\n",
       "                           lower_uni  \n",
       "428                lesley university  \n",
       "429  lesley university, cambridge ma  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df[at_df['name'] == 'Donna Halper']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "grateful-duration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>university</th>\n",
       "      <th>lower_uni</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Amara Aguilar</td>\n",
       "      <td>Amara</td>\n",
       "      <td>Aguilar</td>\n",
       "      <td>University of Southern Caliifornia</td>\n",
       "      <td>university of southern caliifornia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Amara Aguilar</td>\n",
       "      <td>Amara</td>\n",
       "      <td>Aguilar</td>\n",
       "      <td>University of Southern Caliifornia</td>\n",
       "      <td>university of southern caliifornia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  first     last                          university  \\\n",
       "63  Amara Aguilar  Amara  Aguilar  University of Southern Caliifornia   \n",
       "64  Amara Aguilar  Amara  Aguilar  University of Southern Caliifornia   \n",
       "\n",
       "                             lower_uni  \n",
       "63  university of southern caliifornia  \n",
       "64  university of southern caliifornia  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df[at_df['name'] == 'Amara Aguilar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "qualified-enlargement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lee              29\n",
       "Kim              29\n",
       "Yang             17\n",
       "Zhang            17\n",
       "Wang             14\n",
       "                 ..\n",
       "Himelboim         1\n",
       "Glasser           1\n",
       "Stephenson        1\n",
       "Scheinfeld        1\n",
       "Mudiyanselage     1\n",
       "Name: last, Length: 1534, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df['last'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "afraid-summary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "University of Florida                           50\n",
       "University of Maryland                          31\n",
       "University of South Carolina                    29\n",
       "Michigan State University                       27\n",
       "Syracuse University                             25\n",
       "                                                ..\n",
       "doctoral student, Ohio University                1\n",
       "University Of NEW Mexico                         1\n",
       "school of communication, Shenzhen University     1\n",
       "Assistant Professor, Syracuse University         1\n",
       "Research Associate, Cardiff University           1\n",
       "Name: university, Length: 905, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df['university'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "public-carolina",
   "metadata": {},
   "outputs": [],
   "source": [
    "uni_counts = at_df['university'].value_counts()\n",
    "top_unis = uni_counts.loc[uni_counts > 13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "bound-vancouver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_uni_df = pd.DataFrame({'Universities':top_unis.index, 'Count':top_unis})\n",
    "ax = top_uni_df.plot.bar(x='Universities', y='Count', rot=75, figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "comprehensive-salem",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PhD Student, School of Journalism and Media (UT Austin)                                   1\n",
       "BYU                                                                                       1\n",
       "Doha Institute for Graduate Studies                                                       1\n",
       "University of Florida College of Journalism and Communications                            1\n",
       "The University of Texas                                                                   1\n",
       "Interim Executive Director/ Conference Manager, AEJMC                                     1\n",
       "Mediaplus Consultancy                                                                     1\n",
       "Winthrop University                                                                       1\n",
       "Christopher T. Assaf                                                                      1\n",
       "University of Baltimore School of Law                                                     1\n",
       "Syracuse U                                                                                1\n",
       "Craig Newmark Graduate School of Journalism at CUNY                                       1\n",
       "Department of Environmental Science & Policy, George Mason University                     1\n",
       "George Washington University                                                              1\n",
       "SC DHEC                                                                                   1\n",
       "Ivane Javakhishvili Tbilisi State University (TSU)                                        1\n",
       "U of Colorado-Boulder                                                                     1\n",
       "Associate Professor; Director, Institute for International Journalism, Ohio University    1\n",
       "University of Missori                                                                     1\n",
       "Loyola University                                                                         1\n",
       "Azusa Pacific University                                                                  1\n",
       "Lesley University                                                                         1\n",
       "Huntsman Cancer Institute, University of Utah                                             1\n",
       "University of iowa                                                                        1\n",
       "AEJMC                                                                                     1\n",
       "Retired                                                                                   1\n",
       "Darden School of Business, University of Virginia                                         1\n",
       "Nancy Green & Associates                                                                  1\n",
       "Eastern Illinois University                                                               1\n",
       "Auburn University at Montgomery                                                           1\n",
       "Assistant Professor, Shippensburg University                                              1\n",
       "UW-Eau Claire (emeritus)                                                                  1\n",
       "Carleton University                                                                       1\n",
       "The Chinese Unversity of Hong Kong                                                        1\n",
       "School of Media and Communication，Shanghai Jiao Tong University                           1\n",
       "Medill School of Journalism, Northwestern University                                      1\n",
       "Associate Professor and Chair, Digital Media and Journalism, SUNY New Paltz               1\n",
       "School of Journalism and Communication/Chinese University of Hong Kong                    1\n",
       "Mercer University                                                                         1\n",
       "Messiah University                                                                        1\n",
       "New York Institute of Technology                                                          1\n",
       "Associate Professor, Temple University                                                    1\n",
       "Marquette University (professor emeritus)                                                 1\n",
       "Loyola University New Orleans                                                             1\n",
       "UofSC                                                                                     1\n",
       "doctoral student, Ohio University                                                         1\n",
       "University Of NEW Mexico                                                                  1\n",
       "school of communication, Shenzhen University                                              1\n",
       "Assistant Professor, Syracuse University                                                  1\n",
       "Name: university, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "at_df['university'].value_counts()[-50:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "global-clearing",
   "metadata": {},
   "outputs": [],
   "source": [
    "at_df['lower_uni'] = [x.lower() for x in at_df['university']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "monthly-equilibrium",
   "metadata": {},
   "outputs": [],
   "source": [
    "uni_freqs = at_df['lower_uni'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "twelve-calvin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "museum of public relations                                               1\n",
       "g d goenka university                                                    1\n",
       "iium                                                                     1\n",
       "rider university                                                         1\n",
       "univeresity of tennessee, knoxville                                      1\n",
       "school of journalism and mass communication                              1\n",
       "joan ganz cooney center at sesame workshop                               1\n",
       "school of journalism, the university of arizona                          1\n",
       "penn state beaver                                                        1\n",
       "graduate school of journalism, university of california, berkeley usa    1\n",
       "austonia                                                                 1\n",
       "school of journalism and communication, the chinese u of hong kong       1\n",
       "uw-eau claire (emeritus)                                                 1\n",
       "washington and lee university                                            1\n",
       "american university in cairo                                             1\n",
       "ku leuven                                                                1\n",
       "st. bonaventure university                                               1\n",
       "cha university                                                           1\n",
       "university of pittsburgh                                                 1\n",
       "westfield state university                                               1\n",
       "ryerson university                                                       1\n",
       "harding university                                                       1\n",
       "dart center for trauma and journalism/ university of tulsa               1\n",
       "university of missouri - st. louis                                       1\n",
       "thammasat university                                                     1\n",
       "colorado mesa university                                                 1\n",
       "california state university - northridge                                 1\n",
       "north dakota state university                                            1\n",
       "georgetown university                                                    1\n",
       "morningside university                                                   1\n",
       "university of texas at el paso                                           1\n",
       "unc hussman school of journalism                                         1\n",
       "s. i. newhouse school of public communications, syracuse university      1\n",
       "usc                                                                      1\n",
       "university of illinois springfield                                       1\n",
       "hsjmc university of minnesota                                            1\n",
       "disney                                                                   1\n",
       "university of missouri-kansas city                                       1\n",
       "suffolk university                                                       1\n",
       "auburn university school of communication and journalism                 1\n",
       "university of indiana bloomington                                        1\n",
       "zhejiang university                                                      1\n",
       "eastern connecticut state university                                     1\n",
       "angelo state university                                                  1\n",
       "roger williams university                                                1\n",
       "drexel universty                                                         1\n",
       "wxlv abc 45 news                                                         1\n",
       "the washington post                                                      1\n",
       "california polytechnic state university in san luis obispo               1\n",
       "regis university                                                         1\n",
       "Name: lower_uni, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni_freqs.tail(50)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
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 },
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