{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MF reloaded 1\n",
      "EMS reloaded\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from statsmodels.api import Logit\n",
    "import patsy\n",
    "\n",
    "from joblib import load, dump\n",
    "\n",
    "import model_functions as mf\n",
    "import eval_measures as ems\n",
    "\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_context(\"paper\")\n",
    "sns.set_style(\"ticks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 56) (41618369, 56)\n",
      "CPU times: user 1min 18s, sys: 45.7 s, total: 2min 3s\n",
      "Wall time: 2min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with pd.HDFStore('out/Training_2002_2005.h5') as cstore:\n",
    "    df_first = cstore['first_author']\n",
    "    df_last = cstore['last_author']\n",
    "    \n",
    "print df_first.shape, df_last.shape\n",
    "df_first.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'source_id', u'source_year', u'source_j', u'source_n_mesh',\n",
       "       u'source_n_mesh_ex', u'source_is_eng', u'source_country',\n",
       "       u'source_is_journal', u'source_is_review', u'source_is_case_rep',\n",
       "       u'source_is_let_ed_com', u'source_T_novelty', u'source_V_novelty',\n",
       "       u'source_PT_novelty', u'source_PV_novelty', u'source_ncites',\n",
       "       u'source_n_authors', u'sink_id', u'sink_year', u'sink_j',\n",
       "       u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng', u'sink_is_journal',\n",
       "       u'sink_is_review', u'sink_is_case_rep', u'sink_is_let_ed_com',\n",
       "       u'sink_T_novelty', u'sink_V_novelty', u'sink_PT_novelty',\n",
       "       u'sink_PV_novelty', u'sink_n_authors', u'year_span', u'journal_same',\n",
       "       u'mesh_sim', u'title_sim', u'lang_sim', u'affiliation_sim',\n",
       "       u'pubtype_sim', u'cite_sim', u'author_sim', u'gender_sim', u'eth_sim',\n",
       "       u'n_common_authors', u'auid', u'gender', u'eth1', u'eth2', u'pos',\n",
       "       u'pos_nice', u'sink_last_ncites', u'sink_prev_ncites',\n",
       "       u'auth_last_npapers', u'auth_prev_papers', u'jj_sim', u'is_self_cite'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load author years data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.46 s, sys: 1.6 s, total: 11.1 s\n",
      "Wall time: 11.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_authors = pd.read_csv(\"data/AuthorityFirstLastYears.txt\", sep=\"\\t\").rename(\n",
    "    columns={\"au_id\": \"auid\"})\n",
    "df_authors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auid</th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9731334_2</td>\n",
       "      <td>1997</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2155715_1</td>\n",
       "      <td>1990</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7867892_1</td>\n",
       "      <td>1994</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14609221_2</td>\n",
       "      <td>2003</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8101337_1</td>\n",
       "      <td>1993</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         auid  first_year  last_year\n",
       "0   9731334_2        1997       2009\n",
       "1   2155715_1        1990       2009\n",
       "2   7867892_1        1994       2009\n",
       "3  14609221_2        2003       2009\n",
       "4   8101337_1        1993       2007"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "auid          object\n",
       "first_year     int64\n",
       "last_year      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_year</th>\n",
       "      <th>last_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9300182</td>\n",
       "      <td>9300182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1989</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1865</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1980</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1994</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2003</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9999</td>\n",
       "      <td>2099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       first_year  last_year\n",
       "count     9300182    9300182\n",
       "mean         1989       1994\n",
       "std            16         15\n",
       "min          1865          0\n",
       "25%          1980       1986\n",
       "50%          1994       2000\n",
       "75%          2003       2007\n",
       "max          9999       2099"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors.describe().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3, 3), (3858, 3))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors[df_authors.first_year == 9999].shape, df_authors[df_authors.first_year <= 1900].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load author expertise data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 3s, sys: 7.96 s, total: 1min 11s\n",
      "Wall time: 1min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_expertise = pd.read_csv(\"data/AuthorExpertise.txt\", sep=\"\\t\")\n",
    "df_expertise.columns, df_expertise.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index([u'PMID', u'auid', u'match_len', u'match_prop', u'overall_coverage_len',\n",
       "        u'overall_coverage_prop'],\n",
       "       dtype='object'), (58761322, 6))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_expertise.columns, df_expertise.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PMID                       int64\n",
       "auid                      object\n",
       "match_len                  int64\n",
       "match_prop               float64\n",
       "overall_coverage_len       int64\n",
       "overall_coverage_prop    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_expertise.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 56)\n",
      "(41618369, 58)\n",
      "CPU times: user 1min 17s, sys: 42.8 s, total: 1min 59s\n",
      "Wall time: 1min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print df_first.shape\n",
    "df_first = df_first.merge(df_authors, how=\"left\", on=\"auid\")\n",
    "print df_first.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_first[\"au_age\"] = df_first[\"source_year\"] - df_first[\"first_year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'source_id', u'source_year', u'source_j', u'source_n_mesh',\n",
       "       u'source_n_mesh_ex', u'source_is_eng', u'source_country',\n",
       "       u'source_is_journal', u'source_is_review', u'source_is_case_rep',\n",
       "       u'source_is_let_ed_com', u'source_T_novelty', u'source_V_novelty',\n",
       "       u'source_PT_novelty', u'source_PV_novelty', u'source_ncites',\n",
       "       u'source_n_authors', u'sink_id', u'sink_year', u'sink_j',\n",
       "       u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng', u'sink_is_journal',\n",
       "       u'sink_is_review', u'sink_is_case_rep', u'sink_is_let_ed_com',\n",
       "       u'sink_T_novelty', u'sink_V_novelty', u'sink_PT_novelty',\n",
       "       u'sink_PV_novelty', u'sink_n_authors', u'year_span', u'journal_same',\n",
       "       u'mesh_sim', u'title_sim', u'lang_sim', u'affiliation_sim',\n",
       "       u'pubtype_sim', u'cite_sim', u'author_sim', u'gender_sim', u'eth_sim',\n",
       "       u'n_common_authors', u'auid', u'gender', u'eth1', u'eth2', u'pos',\n",
       "       u'pos_nice', u'sink_last_ncites', u'sink_prev_ncites',\n",
       "       u'auth_last_npapers', u'auth_prev_papers', u'jj_sim', u'is_self_cite',\n",
       "       u'first_year', u'last_year', u'au_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 59)\n",
      "(41619240, 64)\n",
      "CPU times: user 1min 9s, sys: 20.4 s, total: 1min 30s\n",
      "Wall time: 1min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print df_first.shape\n",
    "df_first = df_first.merge(df_expertise, how=\"left\",\n",
    "                          left_on=[\"source_id\",\"auid\"],\n",
    "                          right_on=[\"PMID\",\"auid\"],)\n",
    "print df_first.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'source_id', u'source_year', u'source_j', u'source_n_mesh',\n",
       "       u'source_n_mesh_ex', u'source_is_eng', u'source_country',\n",
       "       u'source_is_journal', u'source_is_review', u'source_is_case_rep',\n",
       "       u'source_is_let_ed_com', u'source_T_novelty', u'source_V_novelty',\n",
       "       u'source_PT_novelty', u'source_PV_novelty', u'source_ncites',\n",
       "       u'source_n_authors', u'sink_id', u'sink_year', u'sink_j',\n",
       "       u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng', u'sink_is_journal',\n",
       "       u'sink_is_review', u'sink_is_case_rep', u'sink_is_let_ed_com',\n",
       "       u'sink_T_novelty', u'sink_V_novelty', u'sink_PT_novelty',\n",
       "       u'sink_PV_novelty', u'sink_n_authors', u'year_span', u'journal_same',\n",
       "       u'mesh_sim', u'title_sim', u'lang_sim', u'affiliation_sim',\n",
       "       u'pubtype_sim', u'cite_sim', u'author_sim', u'gender_sim', u'eth_sim',\n",
       "       u'n_common_authors', u'auid', u'gender', u'eth1', u'eth2', u'pos',\n",
       "       u'pos_nice', u'sink_last_ncites', u'sink_prev_ncites',\n",
       "       u'auth_last_npapers', u'auth_prev_papers', u'jj_sim', u'is_self_cite',\n",
       "       u'first_year', u'last_year', u'au_age', u'PMID', u'match_len',\n",
       "       u'match_prop', u'overall_coverage_len', u'overall_coverage_prop'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_first.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41619240, 63)\n",
      "CPU times: user 36.4 s, sys: 37.5 s, total: 1min 13s\n",
      "Wall time: 1min 13s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_first = df_first.drop(\"PMID\", axis=1)\n",
    "print df_first.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 56)\n",
      "(41618369, 58)\n",
      "CPU times: user 1min 27s, sys: 39.6 s, total: 2min 7s\n",
      "Wall time: 2min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print df_last.shape\n",
    "df_last = df_last.merge(df_authors, how=\"left\", on=\"auid\")\n",
    "print df_last.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_last[\"au_age\"] = df_last[\"source_year\"] - df_last[\"first_year\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41618369, 59)\n",
      "(41619267, 64)\n",
      "(41619267, 63)\n",
      "CPU times: user 1min 54s, sys: 1min 7s, total: 3min 1s\n",
      "Wall time: 3min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print df_last.shape\n",
    "df_last = df_last.merge(df_expertise, how=\"left\",\n",
    "                          left_on=[\"source_id\",\"auid\"],\n",
    "                          right_on=[\"PMID\",\"auid\"],)\n",
    "print df_last.shape\n",
    "df_last = df_last.drop(\"PMID\", axis=1)\n",
    "print df_last.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Modelling considerations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "TOP_15_COUNTRIES = [\"USA\", \"UNKNOWN\", \"UK\", \"JAPAN\", \"GERMANY\", \"FRANCE\", \"ITALY\",\n",
    "                    \"CANADA\", \"CHINA\", \"AUSTRALIA\", \"SPAIN\", \"NETHERLANDS\",\n",
    "                    \"SWEDEN\", \"INDIA\", \"OTHER\"]\n",
    "TOP_15_ETHNICITIES = [\"ENGLISH\", \"GERMAN\", \"HISPANIC\", \"CHINESE\",\n",
    "                      \"JAPANESE\", \"SLAV\", \"FRENCH\", \"ITALIAN\", \"INDIAN\",\n",
    "                      \"NORDIC\", \"ARAB\", \"DUTCH\", \"KOREAN\", \"UNKNOWN\", \"OTHER\"]\n",
    "GENDERS = [\"-\", \"F\", \"M\"]\n",
    "\n",
    "def prepare_data(df):\n",
    "    df[\"eth_weight\"] = 0.5 # Partial weight to multi ethnicity\n",
    "    df.ix[df.eth2 == \"UNKNOWN\", \"eth_weight\"] = 1 # Full weight to single ethnicity\n",
    "    df.ix[df.source_country == \"-\", \"source_country\"] = \"UNKNOWN\" # Set - to unknown\n",
    "    df.source_country = df.source_country.astype(\"category\", categories=TOP_15_COUNTRIES, ordered=False).fillna(\"OTHER\")\n",
    "    df.ix[df.eth1.isin(\n",
    "        [\"UNKNOWN\", \"TOOSHORT\", \"ERROR\"]),\n",
    "      \"eth1\"] = \"UNKNOWN\" # Set unknown ethnicities\n",
    "    df.ix[df.eth2.isin(\n",
    "            [\"UNKNOWN\", \"TOOSHORT\", \"ERROR\"]),\n",
    "          \"eth2\"] = \"UNKNOWN\" # Set unknown ethnicities\n",
    "    df.eth1 = df.eth1.astype(\"category\", categories=TOP_15_ETHNICITIES, ordered=False).fillna(\"OTHER\")\n",
    "    df.eth2 = df.eth2.astype(\"category\", categories=TOP_15_ETHNICITIES, ordered=False).fillna(\"OTHER\")\n",
    "    df.gender = df.gender.astype(\"category\", categories=GENDERS, ordered=False).fillna(\"-\")\n",
    "    df[[u'source_is_eng', u'source_is_journal', u'source_is_review',\n",
    "    u'source_is_case_rep', u'source_is_let_ed_com',\n",
    "    u'sink_is_eng', u'sink_is_journal', u'sink_is_review', u'sink_is_case_rep',\n",
    "    u'sink_is_let_ed_com', u'journal_same', u'affiliation_sim']] = df[[u'source_is_eng', u'source_is_journal', u'source_is_review',\n",
    "    u'source_is_case_rep', u'source_is_let_ed_com',\n",
    "    u'sink_is_eng', u'sink_is_journal', u'sink_is_review', u'sink_is_case_rep',\n",
    "    u'sink_is_let_ed_com', u'journal_same', u'affiliation_sim']].astype(\"bool\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prepare_data(df_first)\n",
    "prepare_data(df_last)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tables of gender self citation for age and prior papers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filtered_data(df):\n",
    "    # Base version 1\n",
    "    df_filtered = df[(df.gender != \"-\")\n",
    "                & (df.source_ncites >= 10)\n",
    "                & (df.source_ncites <=60)]\n",
    "    print df_filtered.shape, df.shape\n",
    "    print \"Filter dataset is %.2f%% of the original data.\" % (df_filtered.shape[0] * 100./df.shape[0])\n",
    "    return df_filtered\n",
    "\n",
    "def aggregate_function(x, median_col=\"auth_prev_papers\", span=1):\n",
    "    median = x[median_col].median()\n",
    "    x = x[(x[median_col] >= (median - span))\n",
    "         & (x[median_col] <= (median + span))]\n",
    "    t = x.groupby(\"gender\")[\"is_self_cite\"].agg([np.mean, len])\n",
    "    t[\"median\"] = median\n",
    "    return t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26148675, 64) (41619240, 64)\n",
      "Filter dataset is 62.83% of the original data.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>au_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(4, 6]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.039301</td>\n",
       "      <td>0.045931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1075897.0</td>\n",
       "      <td>1850463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, 8]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044391</td>\n",
       "      <td>0.053354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>797698.0</td>\n",
       "      <td>1502238.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8, 12]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.053800</td>\n",
       "      <td>0.063481</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1037551.0</td>\n",
       "      <td>2141894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068604</td>\n",
       "      <td>0.080691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>881774.0</td>\n",
       "      <td>2023373.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 22]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.080206</td>\n",
       "      <td>0.096689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>361556.0</td>\n",
       "      <td>927872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(22, 27]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.088466</td>\n",
       "      <td>0.109285</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292644.0</td>\n",
       "      <td>863976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(27, 33]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.096430</td>\n",
       "      <td>0.125897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189672.0</td>\n",
       "      <td>682763.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean                     len                      \n",
       "gender      -         F         M   -          F          M\n",
       "au_age                                                     \n",
       "(4, 6]    NaN  0.039301  0.045931 NaN  1075897.0  1850463.0\n",
       "(6, 8]    NaN  0.044391  0.053354 NaN   797698.0  1502238.0\n",
       "(8, 12]   NaN  0.053800  0.063481 NaN  1037551.0  2141894.0\n",
       "(12, 18]  NaN  0.068604  0.080691 NaN   881774.0  2023373.0\n",
       "(18, 22]  NaN  0.080206  0.096689 NaN   361556.0   927872.0\n",
       "(22, 27]  NaN  0.088466  0.109285 NaN   292644.0   863976.0\n",
       "(27, 33]  NaN  0.096430  0.125897 NaN   189672.0   682763.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.5 s, sys: 10.9 s, total: 25.4 s\n",
      "Wall time: 25.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_t_first = filtered_data(df_first)\n",
    "display(df_t_first.pivot_table(index=pd.cut(df_t_first.au_age,\n",
    "                                    bins=[4,6,8,12,18,22,27,33]),\n",
    "                      columns=\"gender\", values=\"is_self_cite\", aggfunc=[np.mean, len]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "-         NaN\n",
       "F    3.962280\n",
       "M    5.787288\n",
       "Name: is_self_cite, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first[[\"gender\", \"is_self_cite\"]].groupby(\"gender\").is_self_cite.mean()* 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27526723, 64) (41619267, 64)\n",
      "Filter dataset is 66.14% of the original data.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"3\" halign=\"left\">len</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>-</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>au_age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(4, 6]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043577</td>\n",
       "      <td>0.054559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>245198.0</td>\n",
       "      <td>670691.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(6, 8]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054254</td>\n",
       "      <td>0.068265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>288087.0</td>\n",
       "      <td>913149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(8, 12]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068714</td>\n",
       "      <td>0.080418</td>\n",
       "      <td>NaN</td>\n",
       "      <td>713097.0</td>\n",
       "      <td>2403210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(12, 18]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.084920</td>\n",
       "      <td>0.095047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1148708.0</td>\n",
       "      <td>4332862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 22]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.097655</td>\n",
       "      <td>0.105987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>666520.0</td>\n",
       "      <td>2911318.0</td>\n",
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       "    <tr>\n",
       "      <th>(22, 27]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.109483</td>\n",
       "      <td>0.113665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>646299.0</td>\n",
       "      <td>3328608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(27, 33]</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.119224</td>\n",
       "      <td>0.123024</td>\n",
       "      <td>NaN</td>\n",
       "      <td>474820.0</td>\n",
       "      <td>3166102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mean                     len                      \n",
       "gender      -         F         M   -          F          M\n",
       "au_age                                                     \n",
       "(4, 6]    NaN  0.043577  0.054559 NaN   245198.0   670691.0\n",
       "(6, 8]    NaN  0.054254  0.068265 NaN   288087.0   913149.0\n",
       "(8, 12]   NaN  0.068714  0.080418 NaN   713097.0  2403210.0\n",
       "(12, 18]  NaN  0.084920  0.095047 NaN  1148708.0  4332862.0\n",
       "(18, 22]  NaN  0.097655  0.105987 NaN   666520.0  2911318.0\n",
       "(22, 27]  NaN  0.109483  0.113665 NaN   646299.0  3328608.0\n",
       "(27, 33]  NaN  0.119224  0.123024 NaN   474820.0  3166102.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.5 s, sys: 12 s, total: 29.5 s\n",
      "Wall time: 29.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_t_last = filtered_data(df_last)\n",
    "display(df_t_last.pivot_table(index=pd.cut(df_t_last.au_age,\n",
    "                                    bins=[4,6,8,12,18,22,27,33]),\n",
    "                      columns=\"gender\", values=\"is_self_cite\", aggfunc=[np.mean, len]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "-         NaN\n",
       "F    7.834364\n",
       "M    9.931546\n",
       "Name: is_self_cite, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_last[[\"gender\", \"is_self_cite\"]].groupby(\"gender\").is_self_cite.mean()* 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot author age and self-citation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_lower_quantile(x):\n",
    "    return pd.Series.quantile(x, q=0.05)\n",
    "    \n",
    "def get_upper_quantile(x):\n",
    "    return pd.Series.quantile(x, q=0.95)\n",
    "\n",
    "\n",
    "def mean_confidence_interval(data, confidence=0.95):\n",
    "    from scipy import stats\n",
    "    a = data*1.0\n",
    "    n = len(a)\n",
    "    m, se = a.mean(), stats.sem(a)\n",
    "    h = se * stats.t._ppf((1+confidence)/2., n-1)\n",
    "    return pd.Series([m, m-h, m+h], index=[\"mean\", \"ci_l\", \"ci_u\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Journal names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 27s, sys: 3.24 s, total: 1min 30s\n",
      "Wall time: 1min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_journals = pd.read_csv(\"data/FullArticlesData.txt\", sep=\"\\t\", usecols=[\"PMID\", \"journal\"])\n",
    "df_journals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PMID</th>\n",
       "      <th>journal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26151966</td>\n",
       "      <td>J Hum Lact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26151965</td>\n",
       "      <td>J Hum Lact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26151955</td>\n",
       "      <td>EuroIntervention</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26151954</td>\n",
       "      <td>EuroIntervention</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26151953</td>\n",
       "      <td>EuroIntervention</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PMID           journal\n",
       "0  26151966        J Hum Lact\n",
       "1  26151965        J Hum Lact\n",
       "2  26151955  EuroIntervention\n",
       "3  26151954  EuroIntervention\n",
       "4  26151953  EuroIntervention"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_journals.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "J Biol Chem                   171068\n",
       "Science                       167415\n",
       "PLoS One                      133591\n",
       "Lancet                        129945\n",
       "Proc Natl Acad Sci U S A      121705\n",
       "Nature                        104418\n",
       "Br Med J                       97226\n",
       "Biochim Biophys Acta           96039\n",
       "Biochem Biophys Res Commun     78341\n",
       "Phys Rev Lett                  76322\n",
       "N Engl J Med                   72020\n",
       "JAMA                           66849\n",
       "BMJ                            65858\n",
       "Biochemistry                   62430\n",
       "J Immunol                      62245\n",
       "Brain Res                      56834\n",
       "Am J Physiol                   54726\n",
       "Biochem J                      54355\n",
       "J Bacteriol                    51716\n",
       "J Am Chem Soc                  50057\n",
       "Cancer Res                     48966\n",
       "Ann N Y Acad Sci               47684\n",
       "J Urol                         47368\n",
       "Phys Rev B Condens Matter      46890\n",
       "FEBS Lett                      46770\n",
       "Appl Opt                       43386\n",
       "Blood                          43160\n",
       "J Virol                        42269\n",
       "Med J Aust                     41119\n",
       "Ugeskr Laeger                  40687\n",
       "Name: journal, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_journals.journal.value_counts().head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Journal categories\n",
    "\n",
    "* MEDICINE - NEMJ, JAMA, LANCET\n",
    "* BIOLOGY - CELL, Journal of Bio Chem\n",
    "* Bioinformatics - PLoS Com Bio, BMC BioInfo\n",
    "* EPIDEMIOLOGY - MMWR. Morbidity and Mortality Weekly Report, Emerging Infectious Diseases, International Journal of Epidemiology\n",
    "* DENTISTRY - Journal of Endodontics, Journal of Clinical Periodontology, Journal of Dental Research\n",
    "* GENERIC - Proc Natl Acad Sci U S A, Nature, Science, PLoS One"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'BIOLOGY': {'Adv Exp Med Biol',\n",
       "  'Biochem J',\n",
       "  'Biochemistry',\n",
       "  'Biochim Biophys Acta',\n",
       "  'Bioinformatics',\n",
       "  'Cell',\n",
       "  'FEBS Lett',\n",
       "  'J Bacteriol',\n",
       "  'J Biol Chem',\n",
       "  'J Virol',\n",
       "  'Mol Cell',\n",
       "  'Nucleic Acids Res'},\n",
       " 'GENERIC': {'Ann N Y Acad Sci',\n",
       "  'Nature',\n",
       "  'Proc Natl Acad Sci U S A',\n",
       "  'Science'},\n",
       " 'MEDICINE': {'BMJ', 'JAMA', 'Lancet', 'N Engl J Med'},\n",
       " 'SPECIAL_MEDICINE': {'Blood',\n",
       "  'Brain Res',\n",
       "  'Cancer Res',\n",
       "  'Circulation',\n",
       "  'Clin Cancer Res',\n",
       "  'Gut',\n",
       "  'J Am Coll Cardiol',\n",
       "  'J Clin Oncol',\n",
       "  'J Immunol',\n",
       "  'J Urol'}}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JOURNAL_NAMES  = dict(\n",
    "    GENERIC=set(['Proc Natl Acad Sci U S A', 'Nature', 'Science',\n",
    "                 'Ann N Y Acad Sci',]), # General Science\n",
    "    MEDICINE = set(['JAMA', 'Lancet', 'N Engl J Med',\n",
    "                    'BMJ', \n",
    "                    #'Med J Aust', 'Ugeskr Laeger'\n",
    "                   ]), # General Medicine\n",
    "    BIOLOGY = set(['J Biol Chem', 'Cell',  'Adv Exp Med Biol',\n",
    "                   'Mol Cell', 'Biochim Biophys Acta', 'Biochemistry',\n",
    "                   'Biochem J', 'FEBS Lett', 'J Bacteriol', 'J Virol',\n",
    "                   'Bioinformatics', 'Nucleic Acids Res']), # Biology\n",
    "    #BIOINFORMATICS=set([]),\n",
    "    SPECIAL_MEDICINE=set(['Cancer Res', 'Clin Cancer Res', 'J Clin Oncol',\n",
    "                'J Am Coll Cardiol', 'Gut', 'Circulation', 'Blood',\n",
    "                'J Immunol', 'Brain Res', #'Am J Physiol',\n",
    "                'J Urol', \n",
    "               ]) # Speciality Medicine\n",
    ")\n",
    "\n",
    "JOURNAL_NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lancet          129945\n",
       "N Engl J Med     72020\n",
       "JAMA             66849\n",
       "BMJ              65858\n",
       "Name: journal, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_journals[df_journals.journal.isin(JOURNAL_NAMES[\"MEDICINE\"])].journal.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PMID</th>\n",
       "      <th>journal</th>\n",
       "      <th>JOURNAL_TYPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26151285</td>\n",
       "      <td>JAMA</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26151284</td>\n",
       "      <td>JAMA</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26151282</td>\n",
       "      <td>JAMA</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26151281</td>\n",
       "      <td>JAMA</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26151280</td>\n",
       "      <td>JAMA</td>\n",
       "      <td>MEDICINE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PMID journal JOURNAL_TYPE\n",
       "0  26151285    JAMA     MEDICINE\n",
       "1  26151284    JAMA     MEDICINE\n",
       "2  26151282    JAMA     MEDICINE\n",
       "3  26151281    JAMA     MEDICINE\n",
       "4  26151280    JAMA     MEDICINE"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df_journals[df_journals.journal.isin(v)\n",
    "                      ][[\"PMID\", \"journal\"]].assign(JOURNAL_TYPE=k).reset_index(drop=True)\n",
    "          for k,v in JOURNAL_NAMES.items()]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot full models for journals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'source_id', u'source_year', u'source_j', u'source_n_mesh',\n",
       "       u'source_n_mesh_ex', u'source_is_eng', u'source_country',\n",
       "       u'source_is_journal', u'source_is_review', u'source_is_case_rep',\n",
       "       u'source_is_let_ed_com', u'source_T_novelty', u'source_V_novelty',\n",
       "       u'source_PT_novelty', u'source_PV_novelty', u'source_ncites',\n",
       "       u'source_n_authors', u'sink_id', u'sink_year', u'sink_j',\n",
       "       u'sink_n_mesh', u'sink_n_mesh_ex', u'sink_is_eng', u'sink_is_journal',\n",
       "       u'sink_is_review', u'sink_is_case_rep', u'sink_is_let_ed_com',\n",
       "       u'sink_T_novelty', u'sink_V_novelty', u'sink_PT_novelty',\n",
       "       u'sink_PV_novelty', u'sink_n_authors', u'year_span', u'journal_same',\n",
       "       u'mesh_sim', u'title_sim', u'lang_sim', u'affiliation_sim',\n",
       "       u'pubtype_sim', u'cite_sim', u'author_sim', u'gender_sim', u'eth_sim',\n",
       "       u'n_common_authors', u'auid', u'gender', u'eth1', u'eth2', u'pos',\n",
       "       u'pos_nice', u'sink_last_ncites', u'sink_prev_ncites',\n",
       "       u'auth_last_npapers', u'auth_prev_papers', u'jj_sim', u'is_self_cite',\n",
       "       u'first_year', u'last_year', u'au_age', u'match_len', u'match_prop',\n",
       "       u'overall_coverage_len', u'overall_coverage_prop', u'eth_weight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_first.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indiv journal analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "journal_model_formula = (\"is_self_cite ~ \"\n",
    "            \"I(auth_prev_papers == 0)\"\n",
    "           \"+ I(auth_prev_papers == 1)\"\n",
    "           \"+ np.log10(auth_prev_papers + 1) + I(np.log10(auth_prev_papers + 1)**2)\"\n",
    "           \"+ C(gender, levels=['M', 'F'])\"\n",
    "           \"+ C(source_country, levels=TOP_15_COUNTRIES)\"\n",
    "           \"+ mf.MC(eth1, eth2, weights=eth_weight, levels=TOP_15_ETHNICITIES)\"\n",
    "           #\"+ I(source_ncites == 1)\"\n",
    "           \"+ np.log10(source_ncites)\"\n",
    "           \"+ I(np.log10(source_ncites)**2) \"#\"+ I(np.log10(source_ncites)**3)\"\n",
    "           #\"+ I(source_n_authors > 20)\"\n",
    "           #              \" + np.log10(np.clip(source_n_authors, 0, 20))\"\n",
    "           #\"+ I(np.log10(np.clip(source_n_authors, 0, 20)) ** 2)\"\n",
    "           \"+ np.log10(source_n_mesh_ex + 1) + \"#\"I(source_n_mesh_ex == 0)\" \n",
    "           \"+ np.log10(sink_n_mesh_ex + 1) + I(sink_n_mesh_ex == 0)\"\n",
    "           \"+ I(year_span < 0) + I(year_span == 0)\"\n",
    "                         \" + mf.score_log_1(year_span) + I(mf.score_log_1(year_span)**2)\"\n",
    "           \"+ I(sink_prev_ncites == 0) \"\n",
    "                         \"+ np.log10(sink_prev_ncites + 1) + I(np.log10(sink_prev_ncites + 1)**2)\"\n",
    "           \"+ I(jj_sim == 0) + np.log10(jj_sim + 1) + I(np.log10(jj_sim + 1)**2) + journal_same\"\n",
    "           \"+ source_is_eng + source_is_journal + source_is_review + source_is_case_rep + source_is_let_ed_com\"\n",
    "           \"+ sink_is_eng + sink_is_journal + sink_is_review + sink_is_case_rep + sink_is_let_ed_com\"\n",
    "           \"+ np.log10(np.nan_to_num(source_V_novelty) + 1)\"\n",
    "           \"+ np.log10(np.nan_to_num(sink_V_novelty) + 1) + I(np.log10(np.nan_to_num(sink_V_novelty) + 1)**2)\"\n",
    "            \"+ I(match_prop == 0) + I(match_prop == 1) + match_prop + I(match_prop**2)\"\n",
    "          )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Bambi analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from bambi import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bm_model = Model(df_t_first.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bm_model.add((\"is_self_cite ~ I(auth_prev_papers == 0)\"\n",
    "           \"+ I(auth_prev_papers == 1)\"\n",
    "           \"+ np.log10(auth_prev_papers + 1) + I(np.log10(auth_prev_papers + 1)**2)\"),\n",
    "            random=[\"1|I(gender=='F')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_7f124d399a4ec8f0e8e9a79b4c866b8d NOW.\n"
     ]
    }
   ],
   "source": [
    "bm_model.build(backend=\"stan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "[Errno 12] Cannot allocate memory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-f635c68e9830>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbm_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchains\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/bambi/models.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, fixed, random, priors, family, link, run, categorical, backend, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m                               \"sampling begins.\" % self._backend_name)\n\u001b[1;32m    274\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/bambi/backends/stan.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, samples, chains, **kwargs)\u001b[0m\n\u001b[1;32m    301\u001b[0m         '''\n\u001b[1;32m    302\u001b[0m         self.fit = self.stan_model.sampling(data=self.X, iter=samples,\n\u001b[0;32m--> 303\u001b[0;31m                                             chains=chains, **kwargs)\n\u001b[0m\u001b[1;32m    304\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/pystan/model.pyc\u001b[0m in \u001b[0;36msampling\u001b[0;34m(self, data, pars, chains, iter, warmup, thin, seed, init, sample_file, diagnostic_file, verbose, algorithm, control, n_jobs, **kwargs)\u001b[0m\n\u001b[1;32m    718\u001b[0m         \u001b[0mcall_sampler_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mizip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    719\u001b[0m         \u001b[0mcall_sampler_star\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_sampler_star\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 720\u001b[0;31m         \u001b[0mret_and_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_map_parallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_sampler_star\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcall_sampler_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    721\u001b[0m         \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msmpl\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmpl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mret_and_samples\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/pystan/model.pyc\u001b[0m in \u001b[0;36m_map_parallel\u001b[0;34m(function, args, n_jobs)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn_jobs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0mn_jobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0mpool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m         \u001b[0mmap_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/multiprocessing/__init__.pyc\u001b[0m in \u001b[0;36mPool\u001b[0;34m(processes, initializer, initargs, maxtasksperchild)\u001b[0m\n\u001b[1;32m    230\u001b[0m     '''\n\u001b[1;32m    231\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitializer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxtasksperchild\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mRawValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypecode_or_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/multiprocessing/pool.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, processes, initializer, initargs, maxtasksperchild)\u001b[0m\n\u001b[1;32m    157\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_processes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocesses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_repopulate_pool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m         self._worker_handler = threading.Thread(\n",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/multiprocessing/pool.pyc\u001b[0m in \u001b[0;36m_repopulate_pool\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    221\u001b[0m             \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Process'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PoolWorker'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m             \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdaemon\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m             \u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    224\u001b[0m             \u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'added worker'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/multiprocessing/process.pyc\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mforking\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_popen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m         \u001b[0m_current_process\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_children\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/multiprocessing/forking.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m    119\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpid\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m'random'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 12] Cannot allocate memory"
     ]
    }
   ],
   "source": [
    "results = bm_model.fit(samples=5000, chains=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fdf0e636b10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fde5f84ea50>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fde5ff5d250>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fde5ffc9190>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fde5f368d90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fde736da210>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fde73714c50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fde5f78b9d0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fde3d874910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fde5f860490>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fde3d7b1d90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fde3d93ee90>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdc29cf21d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fdc29c7a210>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fde3d699910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fda9e826bd0>]], dtype=object)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8nazN2Wfx2ifb8Oue/IDreUQJkQb3ljqLdT1ztNAJAFgtgqGCGkyIbrjROymB\nchRpPiFF3cfNLyeLEFh4Y5O+ptaNud/uw7tf7a5zfPQ3Qn2fJepwN4FiIRcbzMliMC4Qy9Yfw8GT\npfjrXf0v6jwsI1KTonFlVmss33Cc/RAzGg2LxYJhw4bh1VdfxQcffIDJkyejoKAA7733HgYMGICi\noiLMmjULu3fvxqhRo/Dvf/8btbW1WLp0Kd566616HXNIr3SMGtQOPM9pnKw/vroKL3ywqc7tpRCU\nLPV3S23gVquMMb2hPumlnzDxHz/qtjE23D0igUTk/CRCCIj3eN+uO4Zjuf5l3KlBRvv97Mspxtdr\njwTMu5IIQXrLaIwc2Bad2iSYrmfGrIW/4+3FuyBJBG5RgkXgNeoKhSrq+k/8lCxCwHOy8R5Ksr+R\nkU/vDwftvdKXcOd5Tmlc7VHdL73TYhE42KyhmWn7j5f4Ofdm4gN9VtQhgPRaUiVL36CZ1xS+kA9w\n8mwl9h4rQjBQ4z1H9zzpw+bcHgkRNv+oCJvF62TVEeqn7sNms/CGeYWN4WTpn9VAxUOqHXolS+1k\n+edkWXRFPmgxGJd38mDbgQK/FgZ66HdAXe2YPr+N2VrgYoXF9TAYF4CCkmp8sfIQbhnWCb0zkxt7\nOA3CbSMyMeWdDdiSffaSCYVkXHzce++9fssef/xxAMDLL7+sWa7/+3zgOc4vXDAnV9vagKoY+mUA\ncCK/ErmFVQFD5tTOi1lhArdHxJHTZaipdaNv11aKcUkIUSrHeUQJN09ZhgVTx6BlQpSy7ZZsuRy6\nrMppnYOzRdXIzGihORZVYahSNmfJLhSU1GBI73TT85CdDB4Cz/lNyBhdHz3l9lrvuhJEUc5D4TlA\nAvDsHwZgaJ82+PPMtcrY9cqBvjKdKMpOj80qhBQuaNRImBr2HMdpjGd6GSvsTuQV2cHznBKOFajB\nrCDw6JCegINB9hMrKKnG8//5FQAwZ8oIbN1f4B2P8frUwVPfZxpyRj/TXxMjJeuJf/0CQA7nqwt6\nqILSGs1yo8k5vZLlcovK+JyhKlmG4YIXl5JFv2cWgYNemCPE/7vz/H9+xXezbtE889PnbQl4n2jf\nP42S5T1WQ+XgXcowJYvBuADMW5aN2Ggb7rm+e2MPpcHo2i4RPTsmYdn6nMYeCoNxwVErWabl2g0M\nPWrgVjvcdYbMmSlZal5fsB1T3tmAlz7crFm+eNVhLPjxgGbZOZ2h++ZCOfQtkipZqs+MikLQ4g7U\n4TArLqEfsedFAAAgAElEQVQ/B4HnIAi8n9Gmd1wIIZi3LBsFJb5wRY9H3sbtkeARJQgCB95ruFot\nsvMWaRNMDUJDJYvnYbPwISlZDgMl5a0vfgdgnpP10GursfdYMXhO7WSZO84WgUNWl+An5dSFM9Z4\nQwEBc0PeKO+KXkuPKGHWwh14Z7G2v5TaSQhF2diXU4w/vrpKUWf03xGjfemdrGqHW9lOH8aop7rG\npwJZrTwk4n/MxnCy9O0wAzkuVJGkVDvcsFkFWC2Cf3VBiRjmZLk9YshNj+VxqhRL77GYkhU6zMli\nMBqYHQfPYev+Avzx5ssuuTBBPbcO74yDJ0tx6FRwM68MxqWC2slSzzgDUKbvjYzdUGaH1SFCnhBU\nFwDILfIveW2mGkXYBBCizSUyUs48uvONjZZ/38oCOFk0XM7Cc34hTy6dylRud2LZhhyl8AGgcgxU\n4YIcT50s2Si3CL5CB3p10eWRsHxjjlICXBQJBF520Fwh5GQFKpVvlpNFizUIPAeLRR7zFysP4k9v\nrAFgFC7IIzbKvKhIdk4xJv/zZ+XePPfer8pn6tL2kiT3aVOrO4DvGVKPlRrqbo+EDbvz/KpYqsP6\nQnl2//vtPhSVOfwqKAbalz7fzu5wK2Oty3FQO2E0j0v/vFXYL3zhC38ly/yZ0ytZdocb0REWQxVY\nItpiFRSXW/KbWNBj5DxpwkIl898vRmCYk8VgNCBuj4iPvtuHyzq1xNX92tS9wUXO5ZelI71lDL5j\nahajmcFznNJYmM7WR+gqugVSsoJBbYiGOqtsr3GjV+eWiI3yTfSYGU2RNkEufKEam5GSRc+HOhz0\nfNVK1oIf9uOO575HWWUtDp0qxc9bTsFe4wIvcH55MvpQPmocqq8RPW+PR4LHI8FikcMFASglrDVO\nlsE5fvRdtqI6KUpWCDlZm/flY+GKg6af85x8baihqr+WPM8pYz2WW6EUFjEKF6Sl+Y1YvPowSipq\n5R5duvNU9y0UJYIlqw9j0ksrDMMT1cvqKuGuPk4ofbJOFVQC8Kkz+v0bPYv6nKzqWrfyLNSVk6V2\nwmiBFf3zVlntwuZ9+bh5yrIL5kD4OVkBHNVqnZJld7gRFWGBIPg7WYQQQyfL7RHrbN5sNGGj3hf9\nDoU6scNgThaD0aB8tz4HBaU1eOS23s2i9KnAc7jl6k7YvDdfE+LDYFzqqJUsmpujn4k3Sr4Pxbir\nr4ELAJU1LqS3jMGtwzsryySJQBQl/LonT+MEyEoW0eaAqVSegpJqiKKkjEepXuj9+7/f7cNve+Xq\ncet25sLlkVBSUYv/7TgDACgsc8DC836z+N/8ckxTsGDj7jy/86CGntt7fAvvCxekif+CwCnGq/q8\n1A4LLfYgShJ4jgtJyXp9wfbAK3iVLGqoEkI0916dk6WGOj3UORN4DlEBnCyqjLk9kl+BihKVYiSK\nEnYdkQtTqA1ltyhvoy3SAb/11KjvmX7SIJCTSm8DLdKiVy2NJiD0kxTqcMG6lCy1ekOLZXhECZ1a\nJ4DnOYwZ3B6V1S4s9TbfvlChcHxIOVm6cMEaN6IivUqWvk+WZOxkzflqN0rqaCRs1KKB1+RkeZ2s\nMPa4ay4wJ4vBaCCKyx1YsuYIbhraER1bh15F62Jl1KB2iIq04vtfWXNiRvNB62TJYVn63kRGoUGB\nKvF5REljTFJD1GrhQ55Vtte4EBttQ0qir9CFKBF8/b+jmPnZDpzIr1SWR9oskHThgg7vOIrKHHj4\n9TX4cdMJxTClTpbaUJ3//X4AvspmHlFS1ouKEAxn41duPolZn/tKon/2k1ct8q6250iR4oSJIlGU\nLBomaLWolCyJKlm+/ceoVLzEeLlfkCTKuSwCz4dNzSCEoNblUcJFCdHee57j/CrBAYDHIyqhlBSj\n/lYUJRfO7fFTK4rKtEoWxW2gZKmfQeqUmjnx6nU9IsGmvb5S7PrwNv0+AZ/Kq3dojSYgrLprpHay\n9E6afHy30rtN/b2xWn1FRiRCMGZweyTGRaCy2qUoY4Gq/NWX7Jxiv4kCfZX1UMMFoyIssmruV13Q\nuE/WzkOFWLTqcMBx1vVbooQLssIXIcOcLAajgfj4+/2ItAmYdN2lW+zCiKgIC66/oj1Wbz2lmZVm\nMC5lBI7DN78cw+LVhxUD0q+SoIGRQnOUjJj2380Y//cffdurezqFaBRW1bgRF21F9/ZJmmMfPS2X\n0laPwWrhZSVLZRxTg2/HIbkEtNMt+uXHqMPdCr1FNai64fZIcKiMRoHnDQ3bXYcL5XM1+OzTn3yF\nOzyipORk2WwGTpa3QIb6HNROFjXWRUILcWgrAr739W7MWuhz+EIhJ7cCv+09qygo6kbFgHytjfLh\n3CKB1eLLMQP81RwKIQSnCuSGuy63pDhZk2/q6beuKBGlwiC9R2VVtSgoke+R2jmm4zSrXql+hr9d\nfwwzPvWpemZ5auoWA9TJ8qvyaOBsGDpZxNzgn/Hpdjz573UAtM2K6X4+/fEATp6tBKcq2V9XsZrz\n4e/vb1KKyQDyOfspWSaOy7Ez5Zq8OkA+/6gIC3iB9xuvmZIFADUB8gcB42tf6/Tg6Bm5Gim95o+9\nGVwvu0uN88ndY04Wg9EA7D1WhI278zD5pp6aHIjmws3DOsHllrBqC2tOzGge0BnqL1YeUoxWfbqV\nUbiNKBGlZ5Le2dp7TFt0gKpNETaLMvus7y9kRrXDjdgoK1qnxOLPd2Qpxz5XKof16sOliC6PKPdc\nFXJyy1HpNTiiIiy+nCyvEW00I05LgG/YnecrNiHJRUFKK50axUU+rvz/SlWRht1Hi3DzlGV+uVmi\nKBuW1BGhRqbAcz4lS7WNuvCQXVXlzleIw7fuz1tOYYNKhXC6Rbg9YkgVCCMjBGUMerWI4/xDBt0e\nERaBx+CeacoyMyeLOliAHBpHw+O6tk/0W9eo9H9Zpc9w1IQL0tAwE+Nfve7hU2Waz4pMwtKKVct9\nSpb2OhqF66mvT0ykBXaVkmU0yUC/HzsOnsOeI76+XdTZXbtdDlcVOA4Wb04g/UqanW990auivx86\nhzuf/8GvKMyKzSdR6/SgrErrUG036Gdld7gQHWEBB2DzvrOaz8xysgBzhZFi5FAv33gcT7+9AU63\nGLCZelNj//ESFJbV1L1ikJwtrsaUdzbUe3vWJ4vBCDMeUcKH3+5Dt3aJGDmwXWMPp1FomRCFYf3a\n4Ptfj+OWqzuZ/vgzGJcKRk1a9caJmZJlETiMH9UFv/yeG/AYe44WwSLwiI+2KUZhKBPwNPRs8GVp\n+OCbvZAIUWa5PaIEgecQH2NDu9Q4ZB8v0Yx/15Ei7DqyHneO7KKsT8dAK7kZGarUKF65+aRmGb1e\nf3tvo982BSXVePj1NX7L1X3H5ONLsFl5JfdNUbIsvKKuqR2MaFV+k73GjdMFlSirdELgOfCCHC5I\nHSA9dz7/g9+yuoj0Fm4ghOjK7xPveDmofTaPR4LVwuOvd/fHw7f2BgBkpBr3G1PPrjvdHtjc8rkb\nOWWi6DsnpXCIyklRK3h0mHpnkj7eEiGIi7b5VSoEgBfe34RFr97oN7GodrLsJk6W0XdDrWTFRFk1\n4YKBcqimz9tiuh8A4HgOvFdJVZSxMOcbnTlXpfl7wy7//EIA+HHTCfy46QQAbZ8xp8uDVknRiiIM\nyM9NhE1QWi/sP16Cyzq1BCD/1lgMwgUB+VpH2ARMHN0Vi1cf8ftc7xC2TY1Txm+maDYFRFFCYZkD\nCbE2bNiVh1NnK/GD91peMyADV/fLQIf0eLRMiKx3TjxV8+oLc7IYjDDz46YTOHOuCm89ebVpGFBz\n4NarO2Pd77nYuDsP1wxo29jDYTAaFN6gfxA14KgJY1jCXSLgOU6T02WG0y1i1KC2OF1QpRjB+n45\ngaCTHfR3SRQJiCo8zGLhcfs1mXC6RL9wQQpNxnd7JE3JekJI0CqPpJp1NyrpffRMuem2yS2iUFzu\nkHOyRAnRkRY/JcvC8z4nVO1kRfiM/zPnqvCXWb8AAFonxyh5UGb9huqDOidLk8vkNVzl8fqumVuU\nnSyrhUeLODlnLDbKij/d2hsff5+t2bdanVCHC0ZYBdgsvKaJ8OlzVdh/vEQ+Bi3brnJqjJQsvXGt\nfmb0Toua3UcKkdYyBolxESipqEXXdokahSvP20pAn5Nl9N3QKFlRVlTXegKGC5qhz2sj3okNUSL1\n2l8wHNI1kM4trDJZ0xi7V3ku1C1XXxN1FU91oRUjoiIsiI22weUW/SYS9Aq02lkL9J2WJIIKuxM1\nTg9aJkTCKvAQLsCEKiEEh0+XYc220/h5yylERQiodckqsM0qwOUW8cvvuVi/Kw+SRPDEhL4YM7i9\ncj4WgUe53YlKuwtL1x1Du9Q43OGdPNJz8myl4fJgYU4WgxFGyipr8eXPhzBmcHt0aesfttGc6JzR\nAv27tcLXa49geL+MZu1wMi591M+3Ei6oW+eJf/2CL/55A+Jj5N5HN09ZBp4D2rSKhcDV7WS5PSJs\nVsFbojywcWhUMIEWW6AOoST5Gg5vP3AOTq+h4uIkSJK/EgcA+d5y4x5vM2C6H7dHgls3Fr2BlhQf\ngdJKp9fIk5cJBs6lujLc1AcH45/ztyp/t0mJQXG5A26vkmWx8ErTWqUqn8AZhmyqlSx9byjqWHkk\nAsG81kRo0OusU7JoqJt/uKBkaCgLgv81cjh9+a5Olwibt7iDzSr4/db+qgp7dHtElFQ4NErQrsNF\nGNA9VRkr3acaJYdNkhtAX92vDbbsO6tx5gBg5mdyDlJslBV2hxvfvzVOo2QpYw4iXFCvZB05VYbj\n+RWm65uhdwolQuRm2BJRvj/hrJy352gR/vP/9ih/i6KEqprQ8pOpk6VHPQGgvoaEBJ4cEEUJEd5n\nxOWRNIqn/lqq8wXdASpufvPLUV9xGsgFbV568Ar07hx8A+1AZOcU4/dDhbj/pp4oq6xFSUUt9uUU\n45tfjqLC7lNSHU4R99/UE3eO7AKPKGH6vC248coOyCuqxqc/HsC7X+2GvcaFtTvO4My5KsRGWf3u\nR6RNwK978/3yr4rKHLiiVzpO1DMdLWQnKycnB//5z3+QlJQEq9UKi8UCj8eDkpISPP/880hKSqp7\nJwzGJcqCHw+A5zjce0OPxh5Kk2DitV3x3Hu/4rd9+biqz6XfJ4zRfNE6WV6D2MBL2XesGEP7tFaM\nbonITg9vUG1Pj8stwSrw3nwSufLg1P9u9lvvT7f2xvzl2X7LaW8mOtssSb68q2/XyaWsrRYeHAdT\nJUsJI/KWUKeqydb9BX4hYIW6fKs5U67BH6atBCE+pcTILFT35GqVFK35jOZVUSfPwvO4bUQm9hwt\nVoxSi8DD7jWitDlZxiaPHL7oLekuSkCAin6hoDwSOiWLhuepKwz++8vfsWlPPlqnxBrsh/NWe/Qp\nEGoly+kWYXNRJ4uHPjJKfT1Xbz2NFZtP4jFvXh4ALNuQgwfG9oQg8MrzoJ/Bp8eXJLlQSNd2iRrn\nTY+66FGF3YnEuAhNPlLI4YKRVmTnlCh/hxLGpp9wkAgU5ZI6vCfyKtE62f/a14d1urDfZ97daFgN\nMRDVNW5NoRaK2gnfe6wIIwfKUSKEEKUtAQCMGdweq7b6cqJrXSIirPLz73SJgZ0s1TH0xUwW/LAf\n993YEzzPKZUceU6+pg6niOUbcoJysggheOrt9co+xl3dGf26tkKbVrHYsCsXyzbkKI7U/uMlOKhS\nBnt1bom/3tUFGa1icSy3HJv25OP6K9or1+eVR65U1h3QvRX+/v4mfPKDXDTn3ht6oNblQevkGCS3\niEJCbASmz9uCud/uAwDccGUH5XcSkJ/BQZmRWDSnzlMypF5K1gsvvIDk5GQ88MADaN26NV577TVs\n2bIFixcvxmOPPVa/kTAYFzkHT5TifzvO4M93ZCEhNqKxh9Mk6NmxJbIyk7Fk9RFc2bs1U7MYlyzq\ncEGa32HkNMVGW+H2iPjpt5PKMoHnIfB8naF/bo8Iq1UuWe72SDh4olRjfDww9jJcflkq9uWUKPlF\napRwQe9Qc/LKUVqpnbm1WnivUe+/PQCl4pnbI0EUJcRGW1Fa6dRUUaPsO1bsddi0xwd84WJGbmVl\ntW+Wmqp+lKgI2WyZ+t/N6NQ6ARYLjz5dUvDtmzcr6+QX25FXZMfuI4Ua54Zuq6dX52QlRMronumv\nQ58uydhztNhvPT2ciZIVo3IGKXuPFaNb+ySMH+UftkR/N4vKHWiVKDudNbUexEZZUVPrhtMtIsIt\nG81yA1/t76xabdp9VC4IUW7X5lTlFtnRPi1euV7q5+ray9th9bbTkIh8fQRe7isWbD6gyy0iOtKq\ncbJEiaDW6UFuoR1JCZGGSpL6+uj7hYWSQ2XTK1kSURwJ2l9sxmfbNTlR9eXwqVKs2X5as+xYgPBX\nNfTaAoC91q1pt0BRq0xrt5/BE+P7QhB4uak2xykOD1U2lX2LkpK76HSJQAzwyff7cVXf1ppcytSk\naM0xynWFOr755RiuGdgW7dPiUVBSjZED2+LR27Ow83AhVm89VWeRDUB2un8/dA45uRW45epOOFdS\ng+/W5+C79TnKOkP7tEaH9HgUlztgr3Hj2svbYdSgdkiMi9BMRKS1jAk4gduxdQKevrs/Xvl4K8YO\n7YgJo7v6rfPJS2Mwb3k2JJHgkduz/D7PzQ2cKxuIkJ2szp07gxCCjz/+GAMGDFBeCqmpqSgqKjLd\nbsmSJViyZIlmmcvlnzjJYFyMiBLB3G/3olPrBFx3RYfGHk6T4q5ru+GFDzZh6/4CDOmd3tjDYVzi\nLFiwALm5uaiqqsIjjzyCTp06AZAN5WnTpiEuLg6lpaWYPn06bDYbXC4X7r33XkyaNAnjxtXfyNIo\nWbRhrsFsuygRfLc+RxNmw/Pw9r6pW8myWQRYLBxqakXN+i9Mvlz5fh08IRvI+kIUFgsNp5MNsG+8\njVjVWAUBHMd5qwuaj8XjkeCRCGKibH6OGqW4woHEuEgl70qt3NBQQqNz/mLlIeXf6oqAgNbwlku4\n+0/cUMWjtLJWo2SZNfbt17WVUh3SSFHR38e4aJvfOkYoQhbRKmq0rYf6XKodbgzqmYp+3Vr57Yca\nvQ++uhqfTrsOSfGRqKl1IzrKCo8oweUW4XTLxm2E1T/cUN2o+Kw33FPfryn3nNbJojwwtifiom2y\nkyXJziLP85rZ/rrwiERz7ceP6oKv1x5F9vESTJ+3BQmxNjxzzwC/7dTXJ1rnINMS/cFA+6hRaLEZ\nAH7V/s6Xpet83ykaMhkscvEZeazVDmMlS5/zVFnjAvGqSDzvze0U/dtCSMRXFMXlkfOylq47hqXr\njuHVR2XlZ/ZfhyMjNRavqMJz9VUPKSUVDhzPr8QVvdIRFWHB0KzW2HHgHE4V1J3D9OmPB7B622lE\n2ATcfW03iBLBZTvO4PCpMmzam4/bRmRi8k09wzYpO6hnKibf1BP9u/t/twB5MuThcb3Dciw9IWeo\nuVwuTJ8+HVlZWbjjjjtw7pxcZjI/Px9t2ph7kxMnTsTSpUs1/82dO7f+I2cwmhA/bzmJ43kVePT2\nLMP+J82ZXp1bomfHJCxZczjoctMMRn2ora3Fpk2b8OKLL+Lxxx/H/Pnzlc+2bNmC1NRUPPvssxg4\ncCBWrFgBAHjnnXdw5ZVXmu0yaNRKFo33N3Ky3G7RLwSH9/ZpqjMny1tNL9JmgcPp0VSFU+djUGdG\n32vKp2SZ/0ZZLDx4XjbK1OMZe1VHv7FIIgnYosLl1uZ+aJQsg0a4RuhVCDWiaJzDdPeYbgC8TZU1\nhS+MnawIm6AYr3qFhBDil3cUqEGwGo2S5b0XM/5yFfp0SQGgLTBQ6xINjWpA68Bv218AQO59FB1h\nQXSkBcXlDsxfvh8C718WHoDf+AH/XBua36O/He3S4pXj0/OgSlYwyPl6ouba33tDD0RFWJSQxAq7\ny1ABsVp4TH94CD54bqSfs23YJ87ksdarOpIutC6clJTXYljfNvjylRvw0oODQ9pW/XvhdHkU5UmN\nRWdfVNhdmP6RXE2R53zfbZ7jMP/Fa/HGY0OVdel1cLpETT4XnViIi7Eh0mbRPG9GTmiNw4O//ns9\nBJ7D8P4ZyvKoSIumCbQZuYV2DOmdjs+mXYfYaBsSYiNw24hM/PWufvjzHVm4P4wOFiB/D+8Y2QUd\nWyeEbZ/BErKS9emnnyI3Nxdr164FAMTExGDmzJkoLS3F888/H/YBMhhNnQq7Ewt/OoiRA9uiR0eW\nk6iH4zhMGtMdL374G37bexZD+7Ru7CExLiEWLFiAzZvlvCSHw4G4OLnktT66ori4GGlpacpnhw4d\nwrJly9C/f39UVFT471hFqJEYi1cfBmDsZBkZvALP16lkEULgdouwWgR5hrzGrS3eoDJKLF4DUp9r\noe4jZQYNFySSrGT17pyMlx4cjKgIC1rERuBzr8rkESV4JAktoyNN9+V2ixoDV33cYHNU9KWX1RM1\nNbUepUy6mluu7oxFqw5DFLV5ZRaLsXMUYRWUvCW9kuURiUYJAoCWCebnrB27b8z0Xqmvgb7cekyk\niZPFqZ0x2Yh1ukRE2gRwXASWbzyuOibnl5OlHz9gXnhCfb16d07GgO6tsG6nHC7lU7I4WIN0NF0e\nUSk9rh5jfIwNVaqw0DdUTY0B4I5rMmGzCor6QHuOUc6cq8LCFQdx5HQZ2qfF46FxvUzHoFcepTqK\nRJwPeUV2DOqZirhoG3p0SMLf7x/kd25mqKv81bpEw2eb1427osqpUZuoc8JzHFolRivhpQANJZWf\nnVqn7/7Te0+dfrMKhpTj+RUotzsx/eEhSGsZoyyPtAmahuNmnC2pRlZmsp/jHBlhwY1XdjTZ6uIk\nZCfr4YcfxsMPP9wQY2EwLkoWrjgIiRBMvqlnYw+lydKnawr6d2uFT388gMsvSwt6FpTBqIvJkydj\n8uTJAACPx6PkBeujK9LT07F161bNZ+vWrUNGRgYOHjwInucxfPhwtGjRwu8YEydOxMSJEzXLcnNz\nMWrUKOVvoyIRtJeT+iO3R/RzHBQlS7Vima60uVxuWlZ2Yr09g9ROlMbJstCeSNox0e8dz3OaXCn9\nmDlvoQWJEERGCEou04TRXRUnS8nJCqBk/bDpBDLb+l9PwL+yXn0otzvRJiXGbzk1FmucbhCVn2kW\n4hZhE3z9l3RKlsst+jnGCbERWDpzLF76cLNSGj0QBD7FTj1Dr1euYqKMTTJe45xKyrhsVgGRBuoc\nXbtlQiRKKmr9lFNANuLVfPz9flgETuPEdmnbAhzHKc+WKPkKXwT7G+50yQ2cY6O0jk5cjHGvLcqw\nvtrIKKMqd//vf0chSQS7jxThD9d3N07wA5AQa4PVwiuTHkQKz/OnZ+fhQtgdbqWABsdxuDKrNaIi\nLIb3QI+vIibxOllGSpZ23AtXHlTuj9Mt+pws3URKVmay4tQ73R6N4jRvmVwkx2gSxuge0WWd2miV\noagIi9IzT83xvAp8sfIQJl7bFVU1LpRXOU37v11qMEuHwTgPjp4pw6qtpzDpuu5IjA9udrO5Mnls\nTxSUVmPFbycaeyiMSxSLxYJhw4bh1VdfxQcffIDJkyejoKAA7733HgYMGICioiLMmjULu3fvxqhR\nozBz5kw88cQTGD16NK699lpDBytYzMLe9Mas08BYlBPWOU0/pfnL92vWocqP1Sog1tsM1qGajVYb\nVTT0LVBpZqOQwcGXpaFv1xTw3uqCxJtMT+E4Dn29oW4eb3XB2Drykxy1xjkp4arAamSsUWPxva/3\noKC0WlmudgzUpx9hFRSFQBQJvlrja9j60Gur/UrRc5yc56PvL6RHaRQr+ZQs9X3SOx5m4YKaktqq\nfD+rhUeLAEWWaJ5TtYG6oA/rqna4MXvRLs1zHBtt1YyZKlkCzwWdk+Vyy02r9U5NfLRNU+BEzaTr\nuqNzhva7WG2Q26Qe6/pd2uIELz04WCmawvOcRn2UwwX9n3+1g1nr8uD+6T/j5inLcP/0lViy+jB+\n+f2MXLDD5UF5lRMlFQ488MoqLFkjK9fTvJU+W+scf6MCFkbQ7+vvh+SCLUZOlj4n6/ApX7Ncp0v0\nhQuqzu+zadfhpQcHK2qiyy1pfpfOllRr9q1W+Yxyyiq86hZ9PijUmVRfx/IqJ16cuwnbDhTgmTkb\n8PJHWxAXbcXgy9JMr8OlBOuTxWDUE1Ei+OCbvWibGoebhl5aEndD0LF1AkYNbIfFqw9j5KB2AWfA\nGYz6cu+99/ote/zxxwEAL7/8suE2t99++3kf10jJAoBap0fzWWlFrZJXQ6modmrUAp7n/MqNU+Pa\nauERG22FKBGU231qlzrHhBq0+nBFdeEJgfcPTxw/qgsibRZwvK/whd4Xe+XRKzF93ha4PbLxrDYE\nv39rHP746ioUqUq3m4VAxkRZ0btzMvbl1F2lj5LZtgVuHd4Zq7f5qrd1SI/3W09tQKtzfdSGvsBz\nitIXYRNgqaU5WQQLV/iKktgdbr/ryHm1oqF9WuPw6TIYMXJgW9x8VSd8vfaoogrqx6a/x/rwKQqv\ncbJElFbWKoWE6GfD+2XgthGdvQOUlwXKHdP3waJonCzvb7S6r5ooypX5glay3B65KqZ3fXoP4qJt\nKCyrMdzGqHhH1/aJwCbzCTq9wzage6ry7PIch5YJUSgoqVHOw0jJEiW5f9tPm07AZuVRWlmLO0d2\nwdb9BViy5gjcHgn//nInOE6+j4/cloXicgc+X3FIcz3Tk7VOVmpSNE4X1N2MmD5n0+fJOVYRBuGC\n6jy+Nx8fhr+9txHF3oqftCEvoP3e0glg+rzL4YL+jjfdt/q3hLZCePCWXkpbiDK7E9GRFr9rGBVh\nUXrm0Wcv+3gxqmrcuPHKDqisdiErMxnD+rYxrfR5qdE8zpLBaABW/HYCR8+UY8ZfrmqQ0INLkT/c\n0B0bdufhqzVH8MebL2vs4TAYYcNMyXI4PeAA3DmyC1b8dkLJ11JTVOZQZo+pMZ6q6w9Fw8RsFl4J\n+1e3oQEAACAASURBVNmrKiOuCRf07kuf96T+nTJKLKdV2NThgvrQRnk9XnYeJcmvgIDe1wyUZ1bj\n9J8lXzbrFrjcIsa/8KOy7LU/X4nYKJsSnjRpTDd8ueowRg5sa/jbazRmwBdG6VvH62RZBeX661Ur\nwN8hobu/dXhntE2NU4xiQHbYnC4RrRKjlWtMiK/prfq669UkM3VIvc2W7LPYebhQXt/CY8zg9oiL\ntmHy2J5+1yKQI+R0i95+a9r7o75dUZE6JcvbO03gOdPKkw+M7an0JAK8SpaHwGLh8eAtlyk9lOJi\nrDh82rhyndGzOaJ/BggBZi/aabhNYZkD5apGsgLPKc4wz3NoGa9TsgxyspZvyMGW7AJN+fq7x3TD\n/d5UgBWbT6KorAaSRPDNL8c0uXBfrz0Km1XAH2++zM9ZTtJFufTq3BICz8FmFbD9wDlluV55Vuex\n0Xsl8BxeenAwPB4JGanavl70ngKAYPAdoL8bx/Mr8Obn/i0X6POTmZGAjd4eaFU1LnCcdiyb9uQb\nTpJSx+mrtUcw7urOOHK6DMfzKhATacGjt2eZfi8vZZiTxWDUg5IKBz776SDGDG6vhIQw6qZlQhTu\nuCYTX605ghH9M/xiuhmMixUzZ6KqxgVRImiTEgubVTAM3XI4PYpaQJvhUmfL4q06+OLcTQAAm0VA\nakvZAdt7zOdkqQ1Tm9dZeu/r3ZrjWHVKjh4lZ8v7kSQRw7BCi8DDLUpwuv1LqF8zIANfrz2q/E2r\n6ukNTUCuUqaH5zm/PKOszBTN3+nePjmhVnK1CqriC6rlETZBuX5GVe70TpbaGdU7BLFRVqUoBUUd\nBmqmsgHGzgUghxtS8op84Y8WgcdlnVr6vYPoXgJdH6dLhCDw8Ijac9OUvPeeAx3XjE+34+DJUvTr\nmmLap0ofzuZyi3CLspJ16/BMZXl8tA2lFcE7WRzHBQyNXLn5pP9ClZKVpA4XVPWjok4xAI1zeMOV\nHdA6OUajBt4wpIPy7yOnyxUVdvm/boFHJKZObff2Sfh5yynl7zceu0r596Mz1ij31O2RNE6++hni\nvZMCgsDj8p5yqB0hBDaroEym1Lo8yvXnDK6hRZCf119+P2M4TnpNbriyo3ItqmpcsFkFv6qGRmGE\nWV1S0KNDEpasPoIlq30ht5ne3L7mCHOyGIx68N/v9sFm5TF5LCt2ESrjR3XBr3vyMeerXXjr/672\neykzGBcjZkoWLYEcFWEJWJGNKkISkZuX0oIKEpEN1XxvfyOrlUdayxj06JCkmXFXG9SZbVsoao8a\niyYnydzJop/R8Cmj9c6cq0K1w+0XrnfvDT3QMT1BmSkXJYLPpl2nGKuP3tYbHbyllI2UrGBQF/AI\nBYuFQ//urZCZ0QLfqfoZ2ayCMouvzv2JibSgulZbJOD+m3pi5MC2pseIjrSgpAKIjbZpFSCDnKwu\n7Vpgqyp01Mwp0jcOrgt6z9S/rR1bx+NEvq+HkdPlgYXnoK8dp2ne7A1npOOiz1tSQqSiEunRV2es\nrHHJSpbudz4uxmZYaRMwVmHk8zFeri5qoYauLedk+fKiZCVLHk+UzaJxogf2SMWEUV3rrBQ8cXRX\n7MspRoRN7itntZg/i9cMbIuaWjc+8haY0I7d95vgESVNzzl19UnqNKknNTiOQ0qLSMVJc7p8SpbR\n5AjHcXIlTZPKnvR7rw7lK6moRXyMze+7ZpQiERtlxYy/XIUDJ0pQUFINgMORM2W4olfz7Y/JrBsG\nI0S27S/Ab3vP4qFbegXdlJLhw2oR8H8T++J4XoWmcSODcTFj5mTREsgRNiFgzydqxIiihKfeXo/f\nDxUq+1WrZG29hR70RqvaoLYIPO6+rrvfOuq/6Ux0T5UxGaySlZ4cgwq7C3HRNvTOlEu8v/P0CABe\nQ86mNhwJEuMjlaION13VSVFe/nJnH9Pr8c8/DdH0+FHjG2eITpYg912694YemqQVi8ArjkSNqlDH\nYK9xSNXH9565BneO7GKqWLz37DUY1keuihcbZfU5sgSqEu6+be8c2RUPq0qPmzmNZr6kUeNkNWqn\nbcokbbNfp1tWsmhPMYq6aAEtIa6+zhmtYjHpuu7olZlsPCadwvXr7jxNThYl0LuTN5l40z/PtJhF\nRitt2Bw9J21OllbJovvSl4Yf1rdNUK1Y+nRNwZLXbsS8F66tc12B53DL1Z0NP9Pm20maXClJApJb\nRKFDerzyDOjDc5Pifc5jVmayYU6WGkKI0sdPzfUqlQ6Qn+W23nBEm1XwmwDo08X4/vM8h16dkzH6\n8vYYfXk7PHZHH/Q3aLDdXGBKFoMRAg6nB3O/3Yu+XVI0TfgYodG9fRJuGSb3srmiV7piODIYFytm\nhS+W/iKHzkVFWJQwPjWP3tYb7dLiFadnz9Eiv3VorsZz9w1UjFOiq1dtZIhHRQioqvEZvUb5S+pZ\na5+BRpUsyVDxuuvabrh9RCYEntOEL1HUioNkElYGAEN6t8b3b41DXpEdj85Yq/msXwDDzFqHIWmG\n0fn/+Y4sAL4xq8M5U1rIBuy50hpYLXzA36k7R3ZB+7R4/G+7HIoVE2XxOauEGBa+EHgOyS2iNH8b\nMXJgW0RHWTFD12/J7JmjGo7aOdI3tqWqx6TrumORSvFU52jRZ0PtCLzx2FVoESeH7V3drw027MrT\n7Ld7B62DUlTuMKwuGNDJClLJev2xobDwPD796QBO5FfCZhXwzYyxqjVUOVkm4YL6XlS0ImEwREda\nEaBNXFCob7lHlDSqacuESHz84rUgBLj7pZ8A+F8DWvZ/SO903DS0o1IUxuxZMlL8AOBBXY50+7R4\nXNYpGWfO2RFh5f2cu4Zq5nypwa4SgxECX/58CBVVTvz5zuaZxBlO/nBDd6S0iMKMz7YH1UOEwWjK\nmOVmFnor7UXaBMMZ8sG90tE7M1kxnmZ9/rvfOtTJUhtOevvaKOxWX51Mnz8FaI0uw3BBEyvBZhWC\nCvXVF1Ywok1KbEglnesbLmikQNE8H2o01qjCBWnp7bPFdrRKjDI8Xrq3GWsPr3NR7VXCoiN9SpZc\n+EIyHLP6PWJ2PoLAY2iWfxN3M/XUFy7o25/emaj15mQFwsjJilOV7TYKGezdORl/v3+Q8re9xm2s\nZMWo9qPbjZmDQHtE9eiQhJuGdkRaUgxaJUUrTZz1Jc8VJcsgXFBRsnTbhOJkhQP1/f9lR67SJPjd\nZ65Bq6RoJe+Prqd3bmj+Yly0TQ5b1E2U6FE/MepzN/pu0GfbZhX8ns1Qv3vNFeZkMRhBsv94CZZt\nyMFdY7opzQYZ9SfSZsELky9HYWkNZi/aqQlTYTAuNv5vQj9k6UKo3v/bSOXfkREW3Dysk992QoAc\nCgp1hALNHhsZpvpFRoaX2yMpoXz6cEFRJCaZN4FRhzcO7xec4v/8/YOw5LUbQzpOqOGCRuvT62Yx\nULJoD7CCkhqkJvk3PQbk0MmvXr8Jl3udRGq8x8fYlGu3dX+Bkn+kv0+a3mUhGq5mxSeM9qc2qDmO\nVqILbAJSA1593dSOmf5y0r/V/b5KKmpRXesJqGT96/+uxkcvjFa+P2bXgeYUprSIwqO3Zynr0VL4\neoeJNjSOsAqawiuE+L53+jL3F9rJUl/b9bty4fAqWQm6cdBLop8ooY6wzartcWX6LHnfs9dd0R7/\nfWG0stjI4U5PlgvsVNd6mJNVT1i4IIMRBA6nB28v3omubRNx+4jMujdgBEX79Hg8dXd/vPHpdny1\n9ggmju5W90YMRhOE5zl0bZeIvceKcd0V7dEyPhJtU+PQs2MSDpwoRYRVUMKs1NCZZ2psjx7UDimJ\nUboQLn8VRK9i6MPBAG0FsGsvb2c4brdHRGyUFdUOt0G4oHEJ97qguULjR3XBPdcH13TYIvBBt8Kg\n8zEhG3oGq1Pjku5r2YYcAED/7q0UA/hscTWu6G2evK8Oubzvxh7IzGiBtJYxcHoLDPy6J99bCMB/\nzJom0iGej5mPpShZKqdc7UxYBF5TJMEMWl3QdFyqxX+6tTeu6iurbepnsapGLtoh6sqTq50Zm1VA\nWssYJaTPVNFTFRLRjNPrZOm/A3+8+TIM75eh9K2yWXi4PJLSUJkeW82FzrNWO6bVDjfOlcq9vPQV\nNunF1jtD0dTJ8oYi0+9QXRMQt4/IRGJc4FjHNK+SVVZZ6xemaFachKGFOVkMRhAs+GE/SiudmPbQ\nFawaXpi5Mqs1Jozuii9WHkJqYjRGDDCv3MVgNGXuub47Rl/eDm1SfEr3s38YiG9+OYrE+EgIPIdn\n/zBAExKoN/IjbYKmqhigVrLU4YKyofnnO7IQHWk1NJjUJcLNwhldHgnP3z8Iq7eeUhwqX/NZKWS1\nSN5OHltUhCVkxyGU/Qc7tluHd0a39ololRjt9xm97nqV8Nk/DMQBb4XHcrvTr2+ZGdGRVoz2OrTq\nUy+trPUeRx8uqB5LaO8W85wsuj9jlcwicHB7zJXRawZk4Fxpjd+zqUe9NKNVrPIM6p9fADinazys\ndkypgkqdc7MCMdSB0J92dISsnOnDYzmOQ2bbFsrffxnfB7MX7UKfLilKGKs+TE7fIDqc3HdjD78i\nHfQ7N3ZoRyxZcwR5hXYA/teQ3ir980OvI3U0fU5W4LEYTcroaZcWj4E9UtG1XaLfd42vw0FnyDAn\ni8Gog52HC/HTbyfx8K29kNGKFWhoCO65rjuKyuSwQYkgYIlkBqOpYhF4jYMFyNXBHrktS/n76n4Z\nGieLGkVUrYqMsPgZQEpOlsqwoXbmkN7ppjPSVEUDAiTCuyVkZrRAZobPGKV5WGYl3OuChi7FNpAq\nQPuE9TapcKeH4zhc5a3651sm/9+naGiN7SibNg8l1cBBC+a4FDP1TW281mUYt4iLUKpVAtAUc9Ac\nl6oePIc2KTGa3loA4PDm/VhMnJm+XVtpfoPNDHL1+akVIaP1J47uarotDfOjyp8+f4xipmTR4g91\nqSsjB7bDoJ5piIm0gkAOZb3n+u74482X4eHX1/iNK9yMH9XVbxl9Hug1szvcmr5tFDouvdpL10v2\nPgt1hQvSKxflp5T5I/Acpj10BQBgl7cBtnJcpmQFBZuSZzACUFZVi9mLdiIrMxljh/rnUzDCA89z\neHJiP4wY0BazF+3EZz8dME3qZjAuJWjIVq2LGpj+SpbH46/cUCUrysQgBYBpD12Bsd5+NmZGl7r5\nKUUdLlif3IuBPVLx1N39MGZw+5C3DYa0ljFYOnMsBvZIDWp9t0lfIACaKnPUsYiwyUU91NfbKNSz\nLoycLL2zq76+dRn4c6aM0Iz7IVX5d+2BfevM+MswvPn4MMPVrCZRGfp7HoxBrn5mqZNEzzUpPkJT\neEIPnSSgim1dTp0+fzfKq2SJQeT1xnn7lwk8h2f+MADpyTH1urfhgj5j9JrZa1yG32mlhLtOQaJq\ndQvvNaTX3LTwhfcS6VW/uvALF2Q5WUHBnCwGwwRJInh70S5IEsHTk/qzRM8GRhB4/PWufrjvxh74\neu1RvPHpNiWen8G4VKHGkNOb8B5QyVKFd00c3Q2xUVa/nBI10ZFWtPSWCDczuu4a091vGTX8RLF+\nOVkcx2HkwHYNaohZDcrhm+EWzQtEqK8pVfN8VfVU69UjPEq9BS25rw8JDEURSIyLVMIWB/ZINVV8\nKALPoUVchFLVUl/UQX9OiiHPhe5kqZVA6nDRkvcpdaiA9N3q8jr8Zk4WvXT6XDQa4mep5/NW13Vs\nSOilpt9ju8Pt17tLXs+nTqqhfbVivcVG6LWsy14J9bupDy1l9lBwsHBBBsOE79bnYOfhQkx76IqA\ns3CM8MFxHMaP6oq2qXH495c78eiMtXhg7GUYNagtK5nPCIoFCxYgNzcXVVVVeOSRR9Cpk6xAE0Iw\nbdo0xMXFobS0FNOnT0dhYSE++eQTWK1W9Pz/7N15fBN1+gfwz+Rqep80PSgFCnJDuUVRubbrgegq\nWhVrEXVxOdbFC34uCqzgCsrigYqAiLi6VAHFk0MUQaDlECgFylGkpZSWnjQ90lzz+yNNaNr0Tps0\n+bxfL140k0nyZNpk5pnn+32mb19MmjTJYXH3jAoEAMT27GSZ/G5mmZNV46B4RL8w/G9x4934zGf9\nax/MPz9lKIL8lDaH3NW8GLErfOy0DVWyamxT88G6rRbxLUkYa247c2JQ+2la2oa+oSYh5mes/dzv\nPDcGhdc0eO7tPTYf56k0NV+o3ba/viSk5vuzNVzQKIp457kx9Q5nHTUg3DJEEDANXTW9Xn1JljmB\nqBV3dRLYWDLXkKfu6e+Q+dbmz2XN4YK2tnfhNdOcvppdEgHgwephmD2r556Zn6++P6uIEG/kFJTb\nvrMJcVpuM8lqEiZZRDac+qMQG344hXtujWnykBSynxv7h+ODuePw0Tcn8XbSUexIycQjf+6FQT07\nMdmiemk0Guzbtw9r1qzBpUuXsGrVKixZsgQAkJycDJVKhZkzZ2Lz5s348ccfkZqaiqioKOTl5aFz\nZ8deXDxK5Ytvl98DAFbzbgDb3QWbyjw8qPbHpqGLqQsS83DBljW+cAa3De6MS3lqXMi5ZmmfbkvN\nbWo+WLckKTVbl7fg4qs1v6vUFVqr6x1ZXr+Z29ecZDVUWTM/Ze3XCvb3RLC/Jzw9pJZ5WTV5KWUo\nr9TVicnWNZQA6+tk1e5eCJiuZ9Utwr/eOF+aOsLqtjnh8pDbPjQN8PHAY3f2wYTh1p0yzb+3QT2b\nNkfPlkm3xrT4sa1h3tTmxLKsQgf/TnXnMkoEwCiizgnfQD8lpt93fc5nY5Ws12bcjMv5Zc2Ok8MF\nW4ZJFlEthdcq8e9PDuGGLoFIvKuvo8NxW8H+nngxYRgmjOiCT74/hZc/PIDwEG+MGxZl1ZaX3Nv6\n9etx4MABAEBlZSV8fU1DlFQqFfLz8y3rFRQUICwszHJfeno6Ll68iEceeQRdu3bF7Nmz8f7779t8\njaSkJCQlJVkt02pbP5T15SdG2lzuWavD2bbkiwBadmBjrmQ15+SEZbhgB65kPf/oUOQVVeDJJTuh\nbyDJqrlNLZ3lhLoHqi0ZLlibrXmm9V3suT5NqWQ15vkpw/DqupQ6XfpsXXy4IfVVsgDgo3/+CYF+\nDbcIr808P9DWcDnT6wk2m0dEqXyx8oWx6KLqeI2pzCc0zNuvvFILpUfdxPTd58eivFJfZ3lt5t9d\nfZ93c6LdXLxOVsswySKqQasz4LX1ByERBMxLHF7vGTxqP0N6hWLwDZ1w/Fw+dh2+hE0/n8Nn29LR\nKdAT/boHo3eXQESH+yE63K/dr3FCjjd16lRMnToVAKDX6zFjxgwAQE5ODiIjr3eUCw8PR0pKitV9\nISGmM99SqRQyWf27w/j4eMTHx1sty87Oxvjx41sU88vTRkJnMGJE3zCb99duI33oVB6Alh3YGOup\nZDXEvK7BKHbYShZwvTqga2KS5eNp+v6orG4m0Nrhgk3R/EqWtPr/+vdNXVR+KCrNr/f+2l0F178S\nhyqdASs+/71FMQGAR63ujKFNbHlfk7niWN9wwYZEh/k1+zHOwDJcUG4eYmn7/Xdp4vu7PlzQvn+v\nDV1Em+rHJIuomiiKeH/zcVy4XIrXZ95cZ+wzOY4gCIi9IRSxN4SiQqPDsbP5OHmhEGkZhdhz9LLl\nDHGQnxLRYb7oEuaHLmG+6BLmi+gwvyZN3KaOTyaT4ZZbbsHixYuhVqsxY8YM5ObmYtOmTZg5cya+\n++47vPHGGyguLsbChQvRo0cPvPvuuwgODsaECRPaLc4R/WwnV2beSrnN5a2pZDXnoMvSXbCFjS+c\nhTkR0drooGhWcx5OtwjTgayPV3UTgZrDBdtovk5zD4bNfwOVmvqrGvMShzc4JKz2dajMlY3y6ues\n3SCjKVpTWTMzz8myx3N1FNe7C9boztiK/VVbJVmsZLUMjzyIqm3ccQa7Dl3CPx4ajF7RQY4Oh+rh\npZTjpoERuGlgBADTEJPsq2XIvFKKi1dKkZWnRnLaFWzdkwHANJY9SuWLG7oEYkCPEAy+IdShLXup\nbSUkJNRZNmvWLADAwoULrZb37NkTb731VnuE1SxenvUlWc0/+DRahgs2/THXG18YO+xwQcDUsMHX\nS477x/asc19ooCcu5ZVZtTGXSiV49/mxlkqivStZsx+MrbOsuQerfxnTA6nnC3Dpqrredbw95bih\nS2C995uTz9qt0IP9lbiUp7Y0UagtPNh6iHbNBNweybh5uGBHTuybq3Z3QaB13Q6b2l3Q7I3Zttv7\n1xYRYn39v5Z8F7kjJllEALYnZ+LzHWcw5fbeGF9rUi05N7lMim4R/nUmWGuq9Lh0VY2M7Gs4m1WM\nM1nF2HkwCwAQ09kfNw+MwK2DO1taIhM5i9qVBrMWHehbhgs2v5JlFDv2RUclEgGfv2q7A+P8x0fi\n7KUSdAq0np/SNfz6sKya29seZ+5tXTesuc87qGcnAGhRhzgz8wF97TlZcxOGoVhdZbNq97/Fd9Zp\nkW7+0zBfi621Fv/t5joXvXV15t9/zeGfDVUpG2M+QdBQ9bam3l2bdkJZLpPgy9fuwgMvfQ+gY38v\ntKdmJ1lqtRqrV69GWloaPv74Yyxfvhx6vR6FhYWYN28egoJYAaCOJSXtCt7ffBx/vjG6zlXpqeNS\nesjQMyoQPaMCcfuorgBMTU2OnsnHodO52LjjDDb8cBp9uwVh4s3dMWpguFsNUyHnVV9C1JLmCy2r\nZNWsUDT7JTuEiE4+iOjk0+A61t0F22ZDNHf7ymUSjB4UgVtiIxtfuYHnsMXHSwGfeua1+tiorj44\n/gaUqKswbVK/FsdSU4/OAZZrlbkLW7//wtLKFj/f5HE9kZWrRq8GKpktVXMYoz0awbiDZidZOp0O\n06dPx6xZs5CVlYWioiIsWbIEycnJ2Lhxo2XSMVFHkJx2BUs3HMKIvir87b6BbjVMwR0F+3tiwogu\nmDCiCyo0OiSn5WJHSiaW/fcwgv2VuOfWGNx5czfLJGQiZ9K6Fu7NqWS17jVdhfVwQeeYkwUAcx8b\n3qrXbM6FnBsSGuSF+dNsd8ikpgmpng9XM/FtzRxifx8PLPrrqFbH1RhWspqm2b/JmpWqgoICqFSm\nawjVbpdbW1u1wCVqqf2pOVj26WGM6BeGFx4d5pALEZLjeCnlGDcsCuOGRSEjuwTf7L2A9d+fwte/\nnseD429A3I1d2V2SHGbWA4NQWq7Fhh9OW5a15MDmeuOLpj/G3nNtOqqaSZashWfub+gSgLNZJfXe\n74gubYp65mRR+0u8qy8G9exk1Vb9mfjBDoyoaVjJappWzckKDw9HXp6ptWztdrm12bsFLlFr/HQw\nE+9+eRyj+ofj+UeHcpiYm4vpHIA5Dw/BgxNuwP+2n8GHX5/Ad/v+wFP3DsCQXqGODo/c0J9v7Ir0\nzCKrZS05EWT+bmvOY91huGBT1NwOLa3oLfnbzXjg/76v935HJLGWxhft/spUm0IurdNt1N/H+Rsz\nsZLVNM1Oso4dO4bt27cjMzMTGzZsgJ+fH5YuXYqioiLMmzevLWIkshujUcR/t53Gl7vO4U8jumDm\n5EGsYJFFZCcfPP/oUPxlTAxWf30CC1YfwKgB4Zj+lwEtuoAjUWvUPpBpSdXj/nE9oZBL0a9bcJMf\nU/MCue58MGV9MeKW7Sca6xTniOGYcg6HplbidbKaptlJVmxsLGJjYzF37ty2iIeozVRodHjni2PY\ndzwHj93ZB5PH9XTroTBUv5jOAXh95mj8+ns2Pvr2JGYs+xmPT+yHuJHRbj1HhdqXPZIsTw8ZHmxm\nQx8JhwsCaJ/GF45IYs1t6/80om63Q6Km4H6wadjCndzC+ewSLPv0MIpLNZj72DCMHtTyzkzkHgRB\nwJihURjaR4WPvknDe5uO49ej2Zj9QGyjXcmI7KH2gUx7nT22GibnxsdSknao6Hl7tv9hmEQi4Nvl\n97T765LrYCWraThOilya3mDE5p/P4YV39sBTIcNbz45hgkXN4uulwD8eGoJFfx2Fq8WVmP3mL/j6\n1/MwGDmjgdpW7eP69jp7XHNSu1tXsux0nayR/cIwvK/K5n2t6SRH5CisZDUNP93ksk79UYj3Nx1H\nVp4ad9/SHVPv6mu31rXkfob0CsXK58diw/en8NE3J7E/9QqeeWgwIlnVojZS+0CmvRKempcwcOMc\ny25t2xtqc+7OSSx1XPy7bRomWeRysnJL8b8dZ/Db8Rz06OyP5c/cip5R9r8wH7kfTw8Zpt83EDcN\nisA7SUfx9zd/QcKdfXD3LTEcPkF256imE4oaSZY7n5ji2XpqT+OGRaFbhJ+jwyA7YpJFLuNsVjG2\n7snA3mOXEezviVkPDMKEEdE8+CW7GxATgnefG4tPfrhe1fp7fCw6h/o6OjRyIY46yK+ZZCkVbpxk\ncddB7WjOw0McHUKjJALAkfJNxySLOrSySh2ST1zBtgMXcSarGCEBnph+7wDE3Rjt1mdgqe0pPWSY\n/peBuGmgqar1zPLdePSOPph0K6taZB++XgqHvK5Cfn2YnIc7J1nt9Dmecf9AXrOKOgSJRAKjwejo\nMDoMJlnU4VRodEg5mYvfjuXg9zNXoTcYMSAmBP+XOBwj+4XxulfUrmpWtdZ9exL7U3MwY/IgdIvw\nd3Robmn9+vXIzs6GWq3G9OnT0b17dwCAKIpYsGABfH19UVRUhEWLFuHo0aPYuXMnFAoFVCoVEhMT\nHRy9NT9vBda/Eoep/9rRrq+rkLGSBbRfB7U7burWLq9D1FoSiQAYHB1Fx8EkizqE8kodDp3Kxb7U\nHBxJvwqd3ogbugQg8a4+uGlgBEIDvRwdIrmxmlWt9748hn+s+BV33dwNj/y5N3w85Y4Oz21oNBrs\n27cPa9aswaVLl7Bq1SosWbIEAJCcnAyVSoWZM2di8+bN+PHHH5GWlobExER07twZjz/+eL1JVlJS\nEpKSkqyWabXaNn8/gGPmRNWs4Hg0cjFdV+bOF2ImsmXm5EHYuifD0WF0GO777UlOr7Rci4MnsK17\njQAAIABJREFUr2Bf6hUcO5sPvcGIHlEBmPLn3hgdGwlVEBMrci4DYkLw7vNjsXXPBSTtPIPdR7Ix\neVwP3HlzNyjd+GC1La1fvx4HDhwAAFRWVsLX1zQvTqVSIT8/37JeQUEBwsLCLPelp6dj0qRJWLhw\nIby9vREXF1fva8THxyM+Pt5qWXZ2NsaPH2/vt1OHo5svuHMly9HbnsjZjBsWhXHDohwdRofBvT45\nlQqNDnuPXcZvx3KQmlEAo1FEn65BSLyrD0YNiGBiRU5PLpNi8rieGDu0M/634ww2/HAaX/2agQfH\n34DbR3GuoL1NnToVU6dOBQDo9XrMmDEDAJCTk4PIyOvXxAsPD0dKSorVfWvWrMEHH3wAhUKBhIQE\nPPLII+0ef2McPb/PnU8OsE01EbWG+357ktMQRRFnMouxPTkTe49fhlZnQP/uIfjrPf1x44BwBPt7\nOjpEomYzdbiMxf1je+J/O9KxZusJbPr5HO68qSviboxGoK/S0SG6HJlMhltuuQWLFy+GWq3GjBkz\nkJubi02bNmHmzJn47rvv8MYbb6C4uBgLFy6EKIr4z3/+Aw8PDwwePNjR4dvk6GqKO1eyiIhag0kW\nOUxpuRa/HLmEHSmZyMpVIyTAE/eP6YEJI6LRKZCJFbmG8BBvPPvIUDww/gZ8tfs8vvjpLDbuPIvR\nsRG466Zu6BUdyDPmdpSQkFBn2axZswAACxcutFp+55134s4772yPsFrM0fOC3Lm7IAA8+8gQXM4v\nc3QYRNQBMcmidmU0ijiRUYAdKZnYn3oFoihiRL8wTLu7H2JvCHX40BiithKl8sXf4wdj6sR+2JmS\niR/2/4HdR7IRGuSFWwZFYPSgSMR09mfCRVYcXcny9HDvw4SxQzn/hIhaxr2/PandFJVqsOtQFnam\nZOFKYTkiQrzx6O29MW54FIdNkVvx81bg/nE9ce+YHkg9l4/fjudgR0omNv9yHqogLwzpHYrBN4Ri\nYI8QeLMzodtzVI71TPxgaLR6eCn5N0hE1BJMsqjN6PRGHD6dh12HsnDodB6kEgE3D4zA7Adj0T8m\nmGfsya1JJQIG9wrF4F6h+Nv9A3H8XD4OnLiCI+lX8eP+i5BIBPTqEohe0YG4oUsgenUJRKdAT35u\n3Iyjft8TRnRxyOsSEbkKJllkV6Io4nx2CX4+fAm//n4Z6gotukX44YlJ/TB2aBR8vRSODpHI6cik\nEgztrcLQ3iqIoogrheU4eiYfqefz8duxy/j6V9N1SQJ8PdCrSyC6RvghWuWHLmG+iOjkA7mMF+Am\nIiJyJkyyqNV0eiNOZBTg4MlcpJzMRUFJJQJ8PTB+uOl6Ct0i/B0dIlGHIQgCIkJ8EBHig7tu7gYA\nKLxWibNZJTibVYyzWcX4cf9FlJabLoYrlQgIDfKCKtALoUFeCA3yRLCfEn7eHvDzVsDPWwFPpQwK\nmRQKuQQyqYTVMCIiojbGJIuarbxShzNZxUi/WITTF4twJrMYlVV6hPgrMaJfGEb2D8egHiGQSnl2\nncgegv09MWqAJ0YNCLcsK1FXISuvFFm5auQWViCvqBznL5VgX2oOyit1DT6fQiaBTCaBAEAEIIrm\ne0TLz+bloihiZL8wzH1seBu8MyIiItfEJIssjEYRWp0BlVo9Ssu0KFFXoaSsCkWlGuQUlCMnvww5\n+WUouKYBAHh7ytE7OhD3j+2BoX1UiIlkZzSi9hLg64EA304Y2KNTnfu0OgPUFVqUlmtxrawKlVUG\n6PQGaHVG6PQGVFX/LwgCzJ9Y00dXgPkjLAiw3B/TOaCd3hUREZFrYJLVwegNRmiq9KisMkCj1Zv+\nVZkSI6vlVXpUVumh0Rqq/zf9XKU1oEpngLbGvyqdEVVaA/QGo83XVMgkiOjkg/AQb4wZGoXITj7o\nFR2IyE4+Dm8vTER1KeRSBPt78kLeREREDsIkq40ZjCIqq/So0OhQqdGjQqNHRZXO9L9Gj8oaP2u0\npsSoqkZiZJ001Z8I1aSQSaD0kEHpIYOnQlr9vwweCil8/RVQyCXwkEuhsPyzvq1USOHv44FAXw/4\n+3jASyljhYqIiIiIqIlanWSdPXsWa9euhZ+fH7p164YpU6bYI65mEUXTPAKxxs/muQXG6jvEWutB\nFCHCNESu+ibE6seI1ZMS9AZTgqSp0qOiqkZ1qPp2ZfU/U7JkSqRqJ08araHB2D0UUnh5yODpIYOn\nUgalwvSzr7cCoYFeUHpI4elhWq70kMJTUZ08ecigNCdQ5p8Vpv85F4qIiIiIyHFanWStXbsWc+bM\nQXh4OJ588kk88MADUCjar013hUaHJ5f8BHWFtt1eUyLAlBR5yOCplMNLKYOXhww+nqbEyFMpg5dH\n9fLqnz3NPyvl8PIw/ezpIWNCREREjQry40XbiYg6klYnWYWFhQgLCwMA+Pv7o6ysDEFBQXXWS0pK\nQlJSktUyrbb1iZFSIcOsBwahtFxrmqQtAAJw/efqydwS00IIACTVK9Wc2F1zPaH6fplEYqoSVVeT\nPKuH4ClkbIFMRETt482/34JOgV6ODoOIiJqh1UlWWFgYcnNzER4ejpKSEgQGBtpcLz4+HvHx8VbL\nsrOzMX78+Fa9vkQi4KaBEa16DiIicg3r169HdnY21Go1pk+fju7du1vu27JlC9auXYt169YhLCwM\nycnJ2Lp1K+RyOUaPHo24uDgHRl6/XtF1T1wSEZFza3WSNW3aNKxYsQJ+fn6Ii4tjhYeIiBxCo9Fg\n3759WLNmDS5duoRVq1ZhyZIllvuHDx+OgwcPWm6vWbMGH3zwARQKBRITE+tNstpqJAYREbmuVidZ\nMTExWLZsWYseazCYmkLk5ua2NgwiInJS5u9483e+Pa1fvx4HDhwAAFRWVsLX1xcAoFKpkJ+fb7Vu\nVFSU1W2j0WiZQyyR1D8/1tZIjMzMTMTFxXH/RUTkwlqz/3JoC3fzDtARHQmJiKh95efnIzo62q7P\nOXXqVEydOhUAoNfrMWPGDABATk4OIiMjG3ysh4cHtFot5HJ5g0mWLdx/ERG5j40bN2Lu3LnNeowg\nmvuVO4BGo0FaWho6deoEqVRa5/6nn34aq1atckBkzovbpC5uk7q4TeriNqmrvbaJwWBAfn4++vfv\nD6Wybbvkffrpp8jMzIRarcaMGTPg4eGBTZs24emnn8by5cuxd+9eDBkyBBMnToREIsHmzZshl8tx\n2223NWuOsEajwT333IPVq1fb3H85k470t99RYmWc9sU47aujxAk4f6zm/dfixYvx9ddfN+uxDq1k\nKZVKDBs2rN77FQoFOnfu3I4ROT9uk7q4TeriNqmL26Su9twm9q5g1SchIaHOslmzZgEA5s6dW+dM\nZEP7oIYolUp4e3u32/tqjY70t99RYmWc9sU47aujxAl0jFijo6ObPdoBAHiRJiIiIiIiIjtikkVE\nRERERGRHTLKIiIiIiIjsSLpw4cKFjg6iIf3793d0CE6H26QubpO6uE3q4japi9ukdTrK9usocQId\nJ1bGaV+M0746SpxAx4m1uXE6tLsgERERERGRq+FwQSIiIiIiIjtikkVERERERGRHTLKIiIiIiIjs\niEkWERERERGRHTHJIiIiIiIisiOZowMAgPXr1yM7OxtqtRrTp09H9+7dLfdt2bIFa9euxbp16xAW\nFobk5GRs3boVcrkco0ePRlxcnAMjbzv1bRNRFLFgwQL4+vqiqKgIixYtwtGjR7Fz504oFAqoVCok\nJiY6OPq20ZxtcvXqVXz88ceQy+Xo27cvJk2a5ODo20ZztolCoYBWq0VCQgIeeeQR3HPPPQ6Ovm00\nZ5ukpqZi48aN8Pb2RpcuXfDEE084OHr7Onv2LNauXQs/Pz9069YNU6ZMAQB8//33SElJgU6nw+TJ\nk9G3b18sXLgQISEhqKysxCuvvOLgyJ1bfdvV0dRqNVavXo20tDR8/PHHWL58OfR6PQoLCzFv3jwU\nFBQ4RdwZGRl47733EBQUBLlcDplM5pRxpqenY9WqVQgJCYGnpycAOGWcgOn7bfbs2ejbty8qKyud\nNs4tW7bg+++/R/fu3eHv74+qqiqnjPXatWt49913LcdVBQUFThnn1q1bkZaWBgDYs2cP4uLinDLO\nc+fO4b///S8CAwNhNBohiqJTxnno0CFs3rwZAQEBiImJQVZWVuviFB2ssrJSfPLJJ0VRFMWsrCzx\npZdesro/KytLnDt3rnjlyhVRFEVx2rRpYlVVlSiKovjYY4+1b7DtpKFtsn//fnHlypWiKIripk2b\nxK+//lpcvHixmJWVJRqNRjExMdERIbe55m6Tf/3rX+LHH38svv766+KRI0ccEnNba+42EUVRXLZs\nmfjWW29Zbrua5m6T48ePi2VlZaIomr5bXM0LL7wg5uTkiKIoik888YTlu/PRRx8VRdG0vf7617+K\nX375pfjVV1+JoiiKb7/9tnjo0CHHBNxB1LddHa2wsFBUq9ViYmKimJmZafn7P3DggPjee+85Tdzn\nz58X8/PzRVEUxalTpzptnOfOnRMLCwtFo9EoPvroo04bpyiK4kcffSR++OGH4rvvvuvUcW7evFmc\nPXu2+Prrr4tffPGF08a6Zs0a8Z133hH/85//iOvXr3faOM2+++478cMPP3TaOD/++GPxwIEDoiiK\nTv1ZevXVV8Vz585Z4mhtnA6pZK1fvx4HDhwAAFRWVsLX1xcAoFKpkJ+fb7VuVFSU1W2j0QiFQgEA\nkEhcZ7RjU7dJQUEBwsLCLPelp6dj0qRJWLhwIby9vV2qsteabXLx4kU88sgj6Nq1K2bPno3333+/\n/d9AG2jNNtm6dSuGDBmCa9eutX/gbag12+See+6BXq/Hm2++6XJVLAAoLCy0vGd/f3+UlZUhKCgI\nMpnpq1+pVEKr1aKgoACxsbEAbH8Pk7X6tquj1YyhoKAAKpUKwPXfqbPEHRMTA1EUsW7dOgwdOhRG\no9Ep4+zRowdOnTqFf/7znxg5cqTTxpmcnAylUomYmBgcOnTIaX/vADBu3DiMGzcOAQEBeOyxx9Ct\nWzenjDUrKwsTJkzArbfeimnTptX5fnSWOAGgqqoKu3btwqOPPgqNRuOUccbFxWHevHnYunWrJT5n\njDMxMREbNmxAUFAQDhw4gKlTp7YqTockWVOnTrUErtfrMWPGDABATk4OIiMjG3ysh4cHtFot5HK5\nSyVZTd0m4eHhSElJsbpvzZo1+OCDD6BQKCxDwVxBa7ZJSEgIAEAqlVoOKF1Ba7bJ7t270blzZ5w+\nfRoSiQS33XYbAgIC2v092FtrtklpaSn+/e9/Y9q0aejZs2e7x97WwsLCkJubi/DwcJSUlCAwMBDA\n9RNUFRUV8Pb2Rnh4OHJzcwGYtk2fPn0cFnNHUN92dSbh4eHIy8sDcP3vXavVOkXcWq0Wr732GiZO\nnIjIyEisXLnSKeNMTU1Fjx498MEHHyAxMRERERFOGedPP/0Ef39/pKam4vLlyxAEwSnjBEzJi/l7\n2RybM8ZqPoYAAIPB4LSfJQDYtWsXxowZ49Sf+U8++QSvvvoqoqOjkZCQYLW/caY4jUYjpk+fjuDg\nYOzfvx8lJSWtilMQRVFsj8Ab8umnnyIzMxNqtRozZsyAh4cHNm3ahKeffhrLly/H3r17MWTIEEyc\nOBESiQSbN2+GXC7HbbfdhvHjxzs6/DZR3zaZOXMmFi1aBG9vbxQXF2PhwoX46aefkJqaCg8PD4ii\niGeffdbR4beJ5myTzMxMvPfeewgODsagQYNcdk5Wc7aJuQK8ZcsWSKVSl52T1Zxt8tprr+HatWsI\nCwuDTCbDc8895+jw7SojIwMffvgh/Pz80LNnT6SmpmLJkiXYtm0b9u/fD51Oh4ceegi9evXCwoUL\nERQUBK1Wi/nz5zs6dKdWe7vGx8c7OiQAwLFjx7B9+3Zs27YNt99+u2V5UVER5s2bh6KiIqeIe82a\nNUhJSbGc2DAYDJBKpU4XZ0pKCjZv3gwvLy8YDAYEBwejqqrK6eKsGe+RI0eg1WqdNs60tDSsXr0a\nkZGR8Pb2hl6vd8pY8/Ly8O9//xsqlQphYWG4du2aU8YJAK+99hoefPBB9OjRA2+99ZZTxpmSkoJt\n27YhMDAQhYWFCAwMdMo4MzIy8OabbyIgIADjx49HWlpaq+J0iiSLiIiIiIjIVbjOeDsiIiIiIiIn\nwCSLiIiIiIjIjphkERERERER2RGTLCIiIiIiIjtikkVERERERGRHTLKIiIiIiIjsiEkWERERERGR\nHTHJIiIiIiIisiMmWURERERERHbEJIuIiIiIiMiOmGQRERERERHZEZMsckm///47brrpJuzfvx8A\nkJCQAABISUnB888/b1lv3bp12LRpk11fu/Zr2MuWLVss/8aOHYuEhATLv4MHDyIhIQEXLlzA7Nmz\nW/U62dnZePjhh+0UNRERNQf3Xy3H/Rc5E5mjAyCyt4KCAnz44YcYPHhwg+tdvXoVP/30Ez7//PN2\nisx+Jk2ahDlz5tRZ3r17d3Tt2hU7duxAXFycAyIjIqKW4v6L+y9yHUyyyOX4+flh5cqVePnllxtc\nb9OmTfjLX/4CAEhPT8fcuXPh7++Pnj17IiAgALNnz8bSpUuRlpYGvV6PZ599FsOHD8fEiRNx++23\n47fffoO3tzfWrFmDvLw8/OMf/4BCoUDXrl0tr7F+/Xps374doigiMTERd9xxBxISEiyvIZFIkJKS\nYhXXRx99hCeeeMJq2U033YRHH30UALBz506b72fp0qUAgAcffBAvvfRSvTsptVqNZ555BjqdDgaD\nAcuWLYO/vz9mz54NnU6Hvn37NrjdiIiobXD/xf0XuQ4mWeRyFApFk9ZLTk7GokWLAADvvfceXnzx\nRYwaNQpTp07F8OHDkZycjPLycnz66acoKirC9OnT8eWXX6KiogKDBw/GrFmzkJCQgDNnzuCbb77B\nAw88gMmTJ+ONN95AZWUlMjMzsX//fvzvf/9DVVUVHnjgAfzpT38CAAwcOBD33nsvAGDWrFl1Yvv0\n00+b/b4jIiIAAFFRUcjJyYFOp4NcLq+z3v79+xEdHY0FCxYgPT0dBQUF+PXXXzF48GA888wz+Pzz\nz5GWltbs1yciotbh/ov7L3IdnJNFbuvq1asIDQ0FAGRkZCA2NhYSiQS33XYbACA1NRUpKSlISEjA\nM888g7KyMstjhw0bBgBQqVRQq9W4cOECBg0aBAAYPnw4ACAtLQ1nz55FQkICnnzySeh0OhQXFwMA\n+vfv36rYv/nmG6sx7RcuXLC6PzAwEEVFRTYfGxsbi5SUFCxevBhlZWWIjY1FRkZGnfiJiMg5cf/F\n/Rc5P1ayiACIogiJxHTOQRAEAKYzig8//DCmTp1aZ32pVGr1WFEULbeNRqPl8RMmTMD8+fPrPN58\nhm7lypVNHm7xt7/9zXK7vjHtTaFSqfD1119j3759ePvtt3HvvffajJ+IiJwf91/cf5FzYpJFbis0\nNBT5+fnw9vZGly5dcPLkSQwdOhR79+7FkCFD0L9/f6xcuRKJiYkoKirC559/Xm/no65du+LkyZPo\n2bMnDh06BADo27cv3nnnHWi1WoiiiBUrVmDevHlWj5s1a5bdhlvUVFxcjKCgIJv3mTtWjR07Fr6+\nvti2bZsl/jFjxuDw4cOtem0iImpb3H9x/0XOj0kWuZzDhw/j7bffxoULF3Dq1CnExMTYXG/EiBE4\nfPgwunbtiunTp+OVV16BSqVCVFQUJBIJhg0bhr59++Khhx6C0Whs8MxbQkIC5syZg++++w7R0dEA\ngMjISEyePBmPPPIIACAxMdH+b9aGy5cvIyIiwuZ4dgDo0qULXnjhBXzwwQeQSCR4+eWXERISgtmz\nZ+Pw4cOcOExE5CDcf3H/Ra5DEGvWWYlcVEJCQp2za3l5eXj22Wfx2Wef4ejRo/Dz80NMTAyWLFmC\nAQMGYNKkSQ6KtnVWrFiBPn364Pbbb3d0KERE1ErcfxF1TKxkkdtSqVQYN24ctmzZgh49euDFF1+E\nUqlEYGAgXnjhBUeH1yJ//PEHMjIyMGfOHOzevRsfffRRnXUWLFiAHj16OCA6IiKyB+6/iJwfK1lE\nRERERER2xBbuREREREREduTQ4YIajQZpaWno1KmTVUtRIiJyHQaDAfn5+ejfvz+USqWjw7EL7r+I\niFxfa/ZfDk2y0tLSMGXKFEeGQERE7eSzzz6zXAi1o+P+i4jIfbRk/+XQJKtTp04ATIGHhYU5MhQi\nImojubm5mDJliuU73xVw/0VE5Ppas/9qdpKVkZGB9957D0FBQZDL5ZDJZNDr9SgsLMS8efPqvYCc\nLeYhFmFhYejcuXNzQyEiog7ElYbVcf9FROQ+WrL/alHji5deegnz589Heno6ioqKMHfuXNx3333Y\nuHFjS56OiIiIiIjIZTS7khUTEwNRFLFu3ToMHToURqMRgOmaDfn5+fU+LikpCUlJSVbLtFptc1++\njsoqPeau3Ive0UGYPK4nQoO8Wv2cRERE9vL067swcXQ3TBzd3dGhEBFRO2l2kqXVavHaa69h4sSJ\niIyMxMqVKwEAOTk5iIyMrPdx8fHxiI+Pt1qWnZ2N8ePHNzcEK0qFFDcNjMB3v13AsbP5WP6PW+Hr\npWjVcxIREdnL5fwyrP76BJMsIiI30uzhgp988gmys7Oxa9cubNiwAd7e3li6dCm++eYb3H///W0R\nY4MEQcBDf+qFZbNvQWmFFss/OwJeX5mIiJyJRBAcHQIREbWjZleynnrqKTz11FNtEUurRIT44O8P\nxuLfnxzC0bP5GNIr1NEhERERAQCYYxERuZcWNb5wVqMGhKNHVAA+357OahYRETkNgVkWEZFbcakk\nSxAETPlzb5zJLMbxc/U34SAiImpPTLKIiNyLSyVZADC0dygiO3ljR0qWo0MhIiI3Zx5VIXW5vS0R\nETXE5b72BUHAhBHRSE67AnVF61vEExERtZTRaEqyWMkiInIvLpdkAcC4YVEwGEX8+nu2o0MhIiI3\nZmCSRUTkllwyyQryU2JIr1DsOXrZ0aEQEZEbMydZEuZYRERuxSWTLAC4eWA4Tl8sQuG1SkeHQkRE\nbspgMAJgJYuIyN24bJI1ol84JBIBySeuODoUIiJyU9crWUyyiIjcicsmWX7eCgyICcZ+JllEROQg\n1+dkOTgQIiJqVy6bZAHAqAERSMsoQBm7DBIRUTspr9ShrFIHANDXGi546FQuXnhnD3ILyx0WHxER\ntT2XTrKG9VHBKALHeGFiIiJqJw/N/wEPz/8BwPUW7ubGF3uPXUZ6ZjGyctWOCo+IiNqBSydZqiAv\ndA71we/pVx0dChERuSHLcMFa7QXNy4mIyDW5dJIFAEN6h+JI+lWIIndoRETUvurrLmjkPomIyKW5\nfJI1tLcKRaUaXLxS6uhQiIjIzdS+TpY5teKJPyIi1yZzdABtrX/3YCjkUhxJv4puEf6ODoeIiNrZ\n2bNnsXbtWvj5+aFbt26YMmUKAODkyZP46quvAABjxozB6NGj7f7aBoO5u6B1lmXkcEEiIpfm8pUs\nhVyKATHBnJdFROSm1q5dizlz5mD+/Pn45ZdfoNWaOs5u2LABnTt3hlQqRXh4eJu8tsFoGi5oqWSZ\nkyzmWERELs3lK1mAacjgR9+koUKjg5dS7uhwiIioHRUWFiIsLAwA4O/vj7KyMgQFBeHUqVOYP38+\nJBIJXnnlFSxfvtzm45OSkpCUlGS1zJyoNUZfu5JVjZUsIiLX1uwkS61WY/Xq1UhLS8PHH3+Me+65\nByNGjAAA3H333Rg4cKDdg2ytob1DsfprEcfPFWDUgLY5W0lERM4pLCwMubm5CA8PR0lJCQIDAwEA\nwcHBEAQBnp6eMBgM9T4+Pj4e8fHxVsuys7Mxfvz4Ouuar4tlZmnhLmGSRUTkTpo9XFCn02H69OmW\nSbtSqRS+vr4wGAxtNtyitSI6+SA82BtH0vMcHQoREbWzadOmYcWKFVi8eDHi4uIwf/58AMCMGTOw\nYMECLFiwoE4S1VIarXWyZh4uaE6xxOpJWWx8QUTk2ppdyQoKCrK6vWLFCkRHRyMjIwMffvihZedV\nW2uGW9jD4F6dcPh0HkRRrDNsg4iIXFdMTAyWLVtmuW1OqEaMGGEZiWEvVVq95WdRFPHFT+cAsIU7\nEZG7adWcLJ1Oh6ysLERHR8Pb2xsajabedZsz3KItDOkVih/2X0ROQTkiO/m0y2sSEZF7MXcTBICs\nPDVOZBQAuD5ckI0viIjcQ7OTrGPHjmH79u3IzMzEf/7zH6jVauzZswdlZWV48skn2yJGuxjQIwRS\niYDf068yySIiojZRs0JVqdHXud88fJBzsoiIXFuzk6zY2FjExsZi7ty5bRFPm/FSytG7axCOnr2K\nu2/p7uhwiIjIBdUcBaipMXTQ31sB4HpyxSSLiMi1ufx1smoa0isUJ84XQKc3Nr4yERFRM9VsaFFZ\nZWqC0adr0PVhgsa66xERketxqyRrcK9O0GgNSL9Y5OhQiIjIBdUcLmhuguGllFmW1/6fiIhck1sl\nWTGRAfD1UuD3M1cdHQoREbkg6+GCpkqW0kNWo5JlHi7Y3pEREVF7cqskSyIRMPiGTkyyiIioTdSs\nUGm0BihkEsgkkusVLCMrWURE7sCtkiwAGNwrFBcuX0OJusrRoRARkYupmTtVafXwUMggSExzsPKK\nKnD2UjEANr4gInJ1bphkdQIAHD3LahYREdmXWKuSpfSQQiIIMBpFPPvWr6iobuvOxhdERK7N7ZKs\nYH9PdI/0x8GTuY4OhYiIXEzNClWFRgelwpRkiSJQWq61uR4REbket0uyAGBkvzAcSb8Knd7g6FCI\niMiF1CxQ7T2Wg97RQRCEunOwmGMREbk2t02yKqv0OJFR6OhQiIjIhdRMptQVWvTpGgSJRLCRZDHL\nIiJyZW6ZZHWP9EdIgCdS0q44OhQiInIhtedaCYIAQRAsc7HMOFyQiMi1uWWSJQgCRvYObrN9AAAg\nAElEQVQLw8GTuZx8TEREdlN7lyKRCBAEQF1jPpZpPe57iIhcmVsmWYBpyGDBNQ0yLl9zdChEROQi\nag8DlAiAVBBQVqmzWm5gJYuIyKW5bZLVPyYEXkoZUtLYZZCIiOyjdoFKEAQIEqHR9YiIyLW4bZIl\nl0kwtLcKKSc5L4uIiOyjTiWrerhgY+sREZFrcdskCzANGfwjpxS5heWODoWIiFxA7blWEkGAxEaW\nxcYXRESuza2TrOF9VVDIpdhz9LKjQyEiojZy9uxZvPjii1i8eDE+++wzq/uKi4sRFxeHw4cP2+W1\nRKP1bYnENGSwNlayiIhcm1snWV5KOUb2C8Pu37PZ6YmIyEWtXbsWc+bMwfz58/HLL79AqzV1+hNF\nEW+99RZuvfVWu71W7eRJEATYmJLFShYRkYuTNfcBarUaq1evRlpaGj7++GMsX74cer0ehYWFmDdv\nHoKCgtoizjZz2+BI7D12GRevlKJbhL+jwyEiIjsrLCxEWFgYAMDf3x9lZWUICgrC2rVrMXnyZOze\nvbvBxyclJSEpKclqmTlRq61OC3dBgISNL4iI3E6zkyydTofp06dj1qxZyMrKQlFREZYsWYLk5GRs\n3LgRM2bMaIs428yQ3ir4eMrx8+FLeGISkywiIlcTFhaG3NxchIeHo6SkBIGBgaiqqsKpU6eg0Whw\n8OBB5OTkYMiQIZBI6g7wiI+PR3x8vNWy7OxsjB8/vs66thtfcE4WEZG7aXaSVbNSVVBQAJVKBQBQ\nqVTIz8+v93HNORPYnuQyCcYOi8LPhy/hsTv7Qi5z6xGUREQuZ9q0aVixYgX8/PwQFxeH+fPnY8mS\nJVixYgUA4N1338WoUaNsJljNVXvouSCA3QWJiNxQs5OsmsLDw5GXlwcAyMnJQWRkZL3rNudMYHuL\nGxmNb/deQMrJKxg9qP73QEREHU9MTAyWLVtmuV17XzR79my7vZbN4YJsfEFE5Haafdru2LFjWLp0\nKTIzM7Fhwwb4+flh6dKl+Oabb3D//fe3RYxtrmu4H3p1CcT2A5mODoWIiDowmy3ca8zJuve2GHgo\npBwuSETk4ppdyYqNjUVsbCzmzp3bFvE4zJ03d8WK/x1FZm4posP8HB0OERF1QLVzJ0FyfbigTCrg\niUn9ceHyNTa+ICJycZyAVO2W2EgE+Hrg270XHB0KERF1ULYqWZ4epvOZUqlplysIbHxBROTqmGRV\nk8ukuPOmbvjl8CVcK6tydDhERNQB1a5QCYIAPy8FAECnN12pWCIInJNFROTimGTVcOdNXSFIBGzd\nk+HoUIiIqIOo0hksFazayZNUIsCnOskyV68EidBgJau4VIOXV+1HZZW+jSImIqK2xiSrBn8fD9wx\nqiu+++0C1BWOby9PRETOb/K87/DmZ0cA2G7h7uslt1rWWCVrW3Imjp3Lx/Fz9V8WhYiInBuTrFru\nG9MDBoOILb+cd3QoRETk5DTV1aY9Ry8DsNH4QhDg662wWiaVCA02vjBfrovztoiIOi4mWbUE+ilx\nz20x2LonA1eLKhwdDhERObH8a5VWt+s0vpAI8PVS1FlmMBjrfU7zdbU4b4uIqONikmXD5HE94a2U\n45PvTzk6FCIicmL5xdeTLFEUbXYX9PG0Hi4ol0qgayDJklZfV4uVLCKijotJlg1eSjkevaM39hy7\njPSLRY4Oh4iInFRZhc7ys95ghLFW7iSRCBAEwWqZTCaxdBq0RbBUsuwXJxERtS8mWfWYMCIaXcP9\nsHZrGs8mEhGRTZWa6x0Aq7QGm40vapM3Mcmq/VxERNRxMMmqh1Qi4MlJ/XEmqxi7DmU5OhwiInJC\n5VXXK1lVOkOd6pPERpalkEuh0xvqfU42viAi6vhkjg7AmQ26oRPGDOmMj749iaF9VAjyUzo6JCIi\nciIVmhpJlo1KlqR6ftU/Hx+B0nLTpUHkUgku5ZUhv7gSlVU6RKl8rYYUSgXOySIi6uhYyWrEk/f0\nh1QiYNWWVEeHQkRETubw6TxLo4qaFyU2M+dON/YPR9zIaACm4YIAMG3xDsx84xeczy6xeow5MWN3\nQSKijotJViP8fTzw1L0DcODEFexPzXF0OERE5ETKKnQwVFecqrRNGy4ol1vvessrdVa3JewuSETU\n4THJaoLbBkdiWB8VVm1JRVmF1tHhEBGRk+jZJRC3De4MwHYly2aSJZVa3TYYa1e/mGQREXV0TLKa\nQBAEzLh/EDRaA9Z9e9LR4RARkZOYmzAMj9/dF4C58YXthKkmmcy0zNfLdP2s2smUOVFjjkVE1HEx\nyWqiToGeSLyrL3YezMKxs1cdHQ4RETkJTw9TD6nSMi1qT6OS2NrLVq9z39ieAOpWssxJV+3lRETU\ncbC7YDPcMaorfjt+Ge98cQwrnx8LL6Xc0SEREVEjzp49i7Vr18LPzw/dunXDlClTAADbt2/H7t27\nAQCjRo3CpEmTWvT8Xko5QgI88f2+C7htSGer+2wNF9QbTMmTh9w0bLC+JEtvqP9aWkRE5NzskmRt\n2bIF33//Pbp37w5/f3/MmjXLHk/rdCQSAc/ED8bsN3/Bum9PYtYDsY4OiYiIGrF27VrMmTMH4eHh\nePLJJ/HAAw9AoVAgMDAQr732GiorKzF37tx6k6ykpCQkJSVZLdNqrefnRnbyxvFzBega7m+13NzE\noiaD0ZQ8KaqTLKPBOskyVJfDDEyyiIg6LLtVsry9vSGTyRAeHm6vp3RKYcHemDqxH1ZtScVNAyMw\npFeoo0MiIqIGFBYWIiwsDADg7++PsrIyBAUFYcSIEaioqMDSpUsxc+bMeh8fHx+P+Ph4q2XZ2dkY\nP3685fa0u/vjmf/sRsG1Sqv1bM3JMleyFNVdBs1Jl5n5po5JFhFRh2WXJGvcuHEYN24cAgIC8Nxz\nz2HMmDEIDg62WqcpZwI7ijtGdcX+1By8m3QUK18YB29PDhskInJWYWFhyM3NRXh4OEpKShAYGAgA\nuHLlCt5++2384x//sCRhLdUp0BOAaV5WTTYKWZYKlaKx4YJ6JllERB2VXRpfZGVlwWAwAAA8PT2h\n1+vrrBMfH48tW7ZY/Vu1apU9Xr7dSSQC/h4/GOUaHT76Js3R4RARUQOmTZuGFStWYPHixYiLi8P8\n+fMBAK+88grkcjk++eQTrFmzplWv4a2UQyIA18qrrJbbGi44bngUQoO80K+b6WRk7e6C5g6FegMb\nXxARdVR2qWRJJBIsWrQIkZGRCAsLg0qlssfTOjVVkBcev7s/3t90HDcNjMCwPq7/nomIOqKYmBgs\nW7bMcts89K+1iVVNEokAHy8FrtWqZNkaLhgR4oOP/vmnersImocPsvEFEVHHZZckq3///njnnXfs\n8VQdyu03RmP/8Ry8+8UxvPfCWPh4KRwdEhEROYivlwKl5bWGC9oaL1jjPolga7ig6X8mWUREHRev\nk9UKgiBgdnwsKqv0+PCrE44Oh4iIHGjcsKg6yxrIsUz3SyQ2Gl+Yki4d52QREXVYTLJaKTTQC3+9\ndwB2/56N3UcuOTocIiJykAcn3FBnma3hgjVJpUIDc7KYZBERdVRMsuxg/PAo3BIbifc3pyK3sNzR\n4RARkZOwdTHimqQSAQaD7e6CtZcTEVHHwSTLDgRBwIzJg+DrJcebnx3hBSSJiAgA0EiOBYkgWCpX\nZpYW7m6+LxFFEf/bno6CksrGVyYicjJMsuzEx1OO56YMxbmsYnzyw2lHh0NERE6gKcMF63YXrJ6T\n5eZJVoVGj893nMGyTw87OpQmS79YxBEtRASASZZd9e0WjMS7+uGr3eex9+hlR4dDRETtbMzQzs1a\n3+ZwQZEXIwYArc50/c3TF4s6RBOQvKIKvPDuXqz88pijQyEiJ8Aky87+MiYGt8RG4u0vjuKPnGuO\nDoeIiNrRsw8PwbzE4U1ev6Hugh15uOCnP57Gi+/ubdV7qKpOsgDg/KUSe4TVpopLNQCA7KtlDo6E\niJwBkyw7EwQBf38wFuHB3ljy8UEUqzWODomIiNqJIAgQLD83vr5UYqO7oAskWV/8dBanLxZBXeu6\nYQ++9B3Wbk1r0nNoayRZlVV6u8bXFjRaU4y1f5+uRF2hxbWyqhY/Xqc34MtdZzl3ndwCk6w2oPSQ\n4Z+Pj0CVzoAFqw+grFLn6JCIiKidmOdheSnlja4rldSdk3V9uGDHO1g/eDIX2VfVlts1q1FGo4jK\nKgO27slo0nNpawwRrKhy/v1oldb0Xmv/Pl1J4qLteHTBthY//qdDl7Dhh9PYl5pjx6jqdyQ9D4mL\ntjcr8TUYRZzIKMDv6Vdx+HReh6ii2qKu0CJh4TZcylM3vnITuONcQ1EUkd+KxjsyO8ZCNYQFe+Nf\nfx2F/3t/H/61Nhn/+usoKD24uYmIXJ25guWlbPw73+Z1sjpw44tX16VAJr1ewjMnHhevlOK3Y6a5\nyt5N2C6AdSWrQmPfSta+1BwYjSJuiY2023Nq3CDJstfcuHI7/z7r88VPZ1FUqkGxWoNgf88mPSb5\nxBW8vuGQ5bZEAOY8PAR+Ph7w8ZRDLpOgW4R/W4VsN2cyi1GirsLPhy8h8a6+rXquc5eK8exbe/DS\n1BEYNSDcThG2n9zCcuw8mAWDwYjxw7sgSuULURRRrtHDx1OOj75JQ4VGjz5dg6xGIPz6ezYOHjvT\n4tflUX8b6hbhj4VP3YiXV+3Hq+tS8M/HRzTpzCYREXVc5mtjeTXhxJpEqL+7oKOHC5ZX6qBUSCGV\nNm/Qi75GI48qnQGiKOKdpKM4V10RCPRTNul57JlknbxQiI07zmD6fQPQOdQXr39iOohukySrAyXH\nb3x6GCP7h+HWwc1r2NJSHnIpgOvJd0sZjCI27jiD3MJyzHxgEJQK2581z+rPYG5hRaNJ1hv/PYzL\n+WUIDfRCsL8SS2fdAoPRiLkrf8Pyz3+3WrdXl0DcfUt3RIb6ICLE2+rYTm8wQioR6u0smpZRAEEQ\n0K97cHPecrOZE+KmDFtuTG5hBQBUV6nbP8k69UchcvLL4OulQL/uwZDLpfCQS6HTG/HJ96cgkwro\nHOqDIH9PfLv3AoL8lBg3LAr9ugfjh/1/YPVXJ2AURXgr5dj8y3kM76tCWYUOpy8WoUuYL7JyTdW+\nHSmZVq8rkQjoHxOMP35uWdxMstpY7+ggvPLEjXh1XQr+7719+Oe0EQgN9HJ0WERE1FYslaymDBeU\n1B0uaLkYsX0P1s1DX5q6D3po/g8YNywKA2JCMG5YFCQS0xu7WlSBYH9lk5KvKq0B+1JzLAkWAAT4\nejTp9bW66++/tXOyDp3KxbFz+Th6Jh+dQ31trqPTGyCXSVv8GlU6ffX/psSysfb9zmDPscvYc+wy\nBvQIgUQQUHhNg+6RjVdpdHoj5LLmzzgRq4fCmrdVS+09dhkbd5oqDH8a2QWRnXzwxU9n8dS9AyCr\n8XdpHkF0paC80aTmyOk8lGv0yMi+hlEDwqEKMn1O1vzfBFRU6VFeqUOVzoBfjlxCclou3vzsCADA\n00OKYX3CMG5YFIL9lfi/9/ehZ1QAXp42Egp53b+n/3t/HwDg2+X3WGI7f6kEtwy2X8IPAFm5pQAA\n0Q6FVfPvrbGLq9vDZ9vS8cWusxAAyGQSSAShzuffWylD98gAXLqqRom67hxBTw8Z9h67jH8+PgIf\nfnUCXcP8MDdxGAJ8PLDtQCZ2Hc5CWYUW3SL80DMqEEN7q5BwRx8A1htLEATkXsnBt2ta9l6YZLWD\nAT1C8PrM0Xj1o2TMWfErnokfjBH9whwdFhERtQHzgYhnE4bFSaRCnWSqscYXGq0em34+h7Agb3h6\nyHDzoAir+4vVGuj0xjrJ1He//YHVX5/Ay9NGNroPMs/j+PnwJfx8+BKkUgFjh0bhalEFnliyE9P/\nMgATR3e3eoytoWRVOgMu51t322vqQZ95PpevlwIVmvrnZBkMRmTmqq2Sg6SfzqC0TIun7h0A4HqS\ndvZScb2vNXned3j6voG46+ZuTQuw9nNUV2dE0fR6zj5ypWZy//OhS9hz9DIu5FzDJwv+jKBGqo3X\nyqoQEtC04Xc1mat9lU2oTFZW6aGQSyGVWB/Yl6irsPyzIwgL9kJBiQYpabn49Wg2rpVpcefN3RAd\n5gfA1FJ/33HT3K/aTcjyiiqQkV2CmwZe/+zojSJuG9wZ/WOCMbL/9c+H0kMGpYfMsk16dA5A4p19\nsfLLY6is0iM5LRcHTlzB3mPXL91z4nwBNu48g8fubHyY3iur9yO3sAKjYyMgCKbhw6f+KET/mJBG\nH1sfg8GI/25LBwCU1mo+05h3ko7icn4Zls66xbLM/HszV5fLK3W4Vl6FiBAfVGh0loph+sVi/Ho0\nGykncxHg64FOAZ6YPK4nPD1k6BzqA0EQ8Nvxy9h9JBteSln1Z1ZASIAS3/32B9IvFsFgFDFqQDh6\nRwehtNyUQMmkEkwe3xOHT+fhcn4ZrhSUQ6szQhXkhbHDOqNft2AYRRHbDmSia4QfqrQGLFqbjPmr\n9gMA/vqXAYgI8QEA3De2B+4b26PF27Y5mGS1k+6R/njr2TH4z+e/49V1Kbh5YASeurd/k8cIExFR\nx+LvrWh0HVuNLwzVWYiunsYXaRmFSNp51nL7s3/dAb8ar/W313ehXKPHN29Owr7UHIzsFwa5TGoZ\nCvPuF8ew5qUJDc4TnrHMenzM6YtFGDs0CgfSrgAASmx0mDN316upSmtApUaP0EBPXC02TSCv2Qyj\nIeYDugBfBY6fy6+3OvT5jjP44qezVtvhvz+aDjAT7uwDpUJmaUC1+0g2Ho7rVec5rlWfDf9q9/lW\nJ1kAcK1Ma9ck69jZq3hv03G889xYywFta9WsDkgkAi5UX3YmcdF2S5XF7PFXd6BruJ/ldrFa06Ik\nq6r6b6R24l3bvtQcvP7JIdwaG4kXEoZZ3bdm6wlTzIKAnlEB+GbvBct9pWXXE4qjZ65afi6rsE7S\nF61NxqU8NRY8eSOG9VEhr6gCVVoDBvYMQdzI6Ebfh0IuxbOPDIUoirhSWI4AHw9s+eU8MnNLET+h\nF7anZGLLL+cxdmgUolS+2Jeag/3HczD7wdg6z2XuwFlaroW/jwd+2P8HPvzqBN6acxtiOgc0Gost\n5s8aUDfBbIjRKGLnwSyr26+s3o8rBaamF5/vOAO5XIqvdp9HabkWPaICcOHyNXQN80NecQXKK3UQ\nBCBK5Yvzl0pw/lIJDpwwfWdEqXwR7K9E6rl8dO8cgItXtNiXesXyOQ8N9ITBKMLTQ4qn7xtoM9Ef\nPaj+ap8UwN23XD/x899Ft6NEXYXKKj16RQc2eRvYE5OsduTv44GFT92I3b9nY+3WNPxt6c+4fVRX\nTBzdjUMIiYhcRFH19ZIG9mj8TLStFu5lFaaDrvoqWbW7XR0+nYtxw7pYbpubCpzIKMDSDYcBAPeN\n6YHM6uFDJWVV2PTzOTx6Rx+bz2+ralRQ/Zq51Qdbti6UbKs6UaXTQ12hg6+3Ai8kDMOHW1ItB9oN\nKSipxP5U08HZbYM747/b0jHllR/xxt9vRWQn0xlpg8GIskod0i8Wmd6XWmOVbAKmuSRdw/1QXqPL\n7/R/76rzeurqbZ5XVGFZZjCKdaooDdHUSLKKSjUID/Fu8mMbsy/1CnILK7Dn6GXsSLmIMUOiLAeU\nBqOIgydzcWP/sGYNUay5TUrLtfBWyix/O7Xfe0FJpeVvAIDVEK0/cq4hSuVrNUyvPuZtdPBUHjJz\nSy1Vp9qOnM4DAKRmFNRJrs3JrN4oIuGOPnhr4++WpOKlD/bhmzcnQRAEq1bz5t9v7fe+aG0ytr4x\nCU8u2QkA8G5mYiwIgqVCUvPzFBLgiW0HLiLlZC6iVL5Y+/UJFFzTWP2NmCnkUpRr9MjJL4e/jwdy\nqj9j5pjVFVr4ejV8wiYl7QpWbUlF4l19MWZoFFZ/bUpE+8cEo0RdBZ3eCJm0/nli57NLUFSqwdH0\n64mpVmfAqT8KcfxcgdW6n3x/Cr5eCkwY3gW7Dmehc6gPDEZTVWnUgHBMvLkbfLwUKKvUQVOlR7Fa\ng9N/FOHw6TwoPWR4YPwNePjPvS1/X2WVOhw6lYvhfVQQBAF6gxH+Pk0bUtwQfx8PuzxPazDJameC\nYBpyMbS3Cl/uOovtyRexdU8GRvRVYWS/MAzto0Kgb9MmBRMRkfMZPej/2Tvv+CjKrY//tqaSkISQ\nRggh9BZ6E0UFEVHQKyCvIqIgXgXL5SoXpHotIHi9oIiIIIiKwlUQRBSkdxI6hBBCEkjvvWydmfeP\n2ZmdbWks7AbO9wOf7M7OzHOm7M75Pec85wlHWaUOD/aJrHNdhVyOwjIN0vMqcCO7HIN6hCO7oApq\nlQIanRG5RdU2znphKS8E+nYOQW5RFc5cLbAQWQLzVp8QX287lAK1SoG+nVvixKVcG6cTgBjtuWGK\naEgRerIFgVdZYyvENHbE04XkQpRV6tDMW41OUYGIbR9skVZlzdEL2QgN8sY7nx2BoD3HDeuAYxdz\ncDO3AuevFYgia832y/jzxE1x29JKHVpbZUHmFlWjRXMvVGsMeKBXBI6ct2w7JbMM7SKbW5yPiQv/\nxPRxsfjtSCrkchnG3N8WgKzOqmolFXx0p6hMI0YPCkpqUFKhRceogFsao6U0OaR5xdVIzihDckaZ\nKLL2nLqJ1Vsv4aPXBqNHu+A693Xyci6++yPRYtLk5IxS6AwMOkYF4Fp6KXIKq5CZX4klG09j0/uP\n2eyj1CSyqjUGvPnpITwxJBp//1uPOtvW6hmolHK08PfCF/+7gKWv34/KGj6CIxVTxeX8+Sur1KGk\nwrIyoCDmDAYG3du1wDfzR0CrM2L83F38NlU6BDTzRE5RNdpFNkczLxVSs8vxv33J6NUxGO0jA+Dj\npRI7Q/4rKWpRn4qg9aF5Mw+0DffHxl2JyCqoRKXpuxWfmCeuU60x4JvfEsTOlBWbz+GrOcPE9OH8\nEg2emvUbGJbDd4setSgYc/xSDn7ak4SPZwyBr7cam/clo6hci6+3X8aDfSJxxiRSO0QGYG98Bp6e\nvRPTx8XisUFt7No7c/lhm2U5RdVIybL9LQCAr+YMg5+PGtPHxTocm+frpYKvlwotmnuhfWQAxjwQ\n43C9h+rxW9kUIZHlIvx81Jg6phueHdER++IzcPBcFj7bcgEAEBnii7bhzREd7oeQIG8E+XkhyN8T\nAX6ejRpo6ipYloORYWEw8v/Nrxn+L8PCaPrMwLCQAfBQK6A2VY3xUAt/lfBQKWrthSEIgnAXvD1V\neGZ4h3qtq1DIcDm5CK9/chAAEPj7FZRU6NC1bRCupBVj1soj+OHfZif36o0SnL1agM5tArHo5YHY\ncSQV63YkYNJjnRHk72lXPAn06hCMWc/3xewvjiI+MR//94gWGr0Rft5qXMsoxXtrTwGAXTGRVVCF\n5xb8IYorYayEFK2d4hQHz2YBAHp3bAmAH99i3ZtfozUgPbcSxy5l47cjaRafBQd4QSGXYeU7D+GV\nxfuQV1wDg5FBcbnWQmABvKC6mVuB9pHmFKtPfjgj9uK3b21OGXrjmZ5Y+b8LmLniMHZ++qSFaKyo\n1ovVBwE+PRMAvn9vJJo388AvB67jtyOp2LjoUfGZlFtUjeMXs/H8Y53xw+4krN95RYwiAsDYh9rh\nxSe6AgByiqrg46mCv68HSiu1qKjWY8Xm83j/lUFixEKjM8JgZOHtqQTHcSg1RWXSrARwUZkGq7de\nAgBk5FWiTZg/yqt0CGvhg6s3SuDlqUQ7U8qZkWGx9eB1MZVSyqWUIrRo7oW3JvTC9GUHsP90BrYe\nTAEA7D+dYbGuj6cSpZVaMAwrpv0du5iDHu2C4eWhQIfWAVApFWAYFgqFDBXVenz7eyJeGNUFWr0R\nEcG+eHRgFNbtSMBXv17CnyduYvk/hmLF5nN4uG9rFFdocO5aAXq0a4FLKUVIySwTRVZ8Yp44x1YX\nSSELaerrC+/twYYFI3D1Rgl6dghGQloxMvMrkZZdjuuZpZj9Qj8LX+rw+SzxtbNEFgCEBHkjLacc\n+09n2v3810MpYmqet6cSOUXVSEgrRrkp5fHAmQwxlfjTH88iKswPQ3pEoFObAKzZdgmllTo8u+BP\nPD+yE4ymQiSVNQbMWXUMAPD6+J7Q6o3ib8K2g9cxqFsYlm8+h2eGdRALgQiRYGve+M9BtI3wR6Cf\nB0oq+PuvRXMvPNK/tRgxbko+qSuQcdyt1R1JTk7GunXr4Ofnh+joaEycOLHe22ZlZWHYsGHYv38/\nWrW6M+VD3ZnSCi3OJhUgOaMUadnluJFbYVHCFuBFiKeaFx6eaoVYJlQug+mvDDIZ7IoR4VKLF1xy\n5TnTG+FusLgprLaT3jEsy0kEE2MhpqRlfJ2BTAaoFHKolHKoVAr+r+m9QiGHXMbnlQvnQS43nw+5\n3PzeYplpPYv39pYJ76X7Ni1TyGSQSdoSr4lcBhlgvj4214r/a339YGpHJrPcFhw/SSnHcWA5/npy\nLD9+gxOWs7D8XPKa3xZW60mX8euKbQjvWU5cl38Ni9cy8Tgkf2E6Bsk5kNkct3R9yTmS3FuMYIfE\nFtZimdkOhUIGhVwGhUIOhVwGpUIOhcL0Vy6DQi6H0rSOcJ/IZDIo5DBfL5nMdD1N7ZvaYSxes/xf\nhm+fMb3mwMH0D9KfVeGl9HiFe0Z6Twk2iPdCLa+F82jxWnIfCveRcO04jk//Ea85a7qGkvvG+rpL\nX8e08kf3WxiE7erfekfPqV27diEuLg4GgwHjxo1Dnz596r1PZx3Tb0dTsXZ7Ah7uG4n7e0bg/LUC\nU/QkBj/9lYS98RloG+GPob0ioNExYkW1aU92w5gHYlCjNeDZBX/apBz+87neyC6owpZ95rFbM8bF\nYuSgNricUoRFa0+KhSrUKoXNs0bK/T0jkFdcjeuZZaII4DgObSP80adTCJo345cdOpuF88mFAHhR\nITjpD/SKwOAe4bivRzh+PZSC9TuvIKaVPyJDmqGFvxd+OXDdbrtLXx+CLtFmR777+LUAACAASURB\nVHrR1ydxTjLORmBwjzDEJeTVOT/VpMc649C5TLQNb46+nVuKZbkHdQ+DkWFxOjG/1u39fNQY2rsV\ndprGAD06MAp+PmpUaww4eiEbCoUca+cOx+qtl3AhuUB0TL09lWIJemm56JaB3iiQpCc+fl80PFQK\n6AwM9sZniNfEU60QhamPl0pMdRvQNRRXb5bYLWwQEugtpj52iwlC1+ggnL6aj7Rs28hEkL8nisu1\n+GbeIwgO8MIzc3fZTWtbMv0++Hqrsfync0jLLodSIWvQc164zzpGBWDSY53FogSOeGJINA6fy0ZM\nK39EBPvC39cDP+7hBWL3mBZYNG2gWBIe4CND/1xxGDlF1eLxL5g6AKu3XrJIdRR4dGAU+nYOwcGz\nmYgI9sXP+69j+cyhoii9VQ6ezbSIksllAMsBUaHNkJ5nOUHw+68MworN5yGXAUXltY+herhvJA6c\nyYSXh9JiXN3EkZ2QXViFQ6aOjfdfGYTico3YgW9NbHv+N72kQguOA+6LDRfHenqoFWJa5ruT+0Fv\nYNCrY0uXp9+5glv5rb9lyb5u3TrMnDkTYWFhePnllzF+/Hio1ba5o1u2bMGWLVsslun1Dat4crcT\n4OeJ4f1bY3h/Pu2D4zhUVOtRXK5FcbkGJRVa1GiN0BkYaHVG6PSMycE2O0SiA8xxorNqT3AJi8wu\nre1cCtLtHH0mk0EidnjhoxSEkOm/4/cKi2UcB+iNDPQGBjq96b/B9N/0WoiEGU3CToiSCQ4wZ3J6\npY6j2RGXLGM5cAazyJBuz3JW4sLKwXcsQOw7qpai59bukcZgI+bkjgWe3J7gsHbmrUQkYDo2QHTe\nAfO54CTHLtyfghDhWMlryeeApcCVy3lb5HI7y01pNILYYVhe4DMM/1eIqIrLJcKjwefS1J5C0rZC\nLhfPG2Cq3m06b9L3sBYwNmLX+p6CjeN8p7C+zgO6ht6SyHI1jp5Tmzdvxvfffw+tVou33noLa9as\nsbv97Xx+jR7SFn07hSDclALXt3OI+NmLT3TF5dQipGWXIz23AiqlHG3D/fHC453RswMfGfL2VOGl\nJ7piy95rqNIY0DLAC7Nf6If2kc0hk8kQFeoHnYHBwO5h4rxd3du1wAd/H4x98Rm4llEKhmExpGcE\nnhgSjWbeamh1RjAshw/Wx+FaeikeG9wGnaICUKM1wt/XA+euFeCvU+nIK6nGht+viPbKZbxg+b9H\nOqJthD8GdA1DoL+nWAobAPp3DUVOUTUYhkVCWjEu6XlR1sxbhS9mPQw/HzV2HE6Fn4/aQmABwP89\n0hE3c8tF8bL4tfsgkwHtWwfgx91JOHg2E706tsSBM3zkoF1kc9RoDBjauxUMRhaj7osWI4wMy+F8\nciEOnMkUB+aHtfDBjLGxAICLKYUY3D0cpxPz4Outxt74dNzIqTDNweMBluXn1OE4Xjx4eygx/6X+\n8FQrMfPZ3gCAgtIapGWXo3tMC0xfth8lFTpwpk6Lh/tEIqugCglp/JxJGXmV2HX8Bnw8lfDz9UBw\nc09kF1bD21OJyJBmuJbOV0WUjqNKSi9Bj3Yt8NyjnZBXXI2DZ7PQqU0AQgN9cOBMpiiyElKLkZBa\nDF8vfrxR23B/THuqG1q15AsyDIkNh5FhxWjRE0Pa4urNEkwZ3RU7j6Xh0NksvPxkN7Ha3UtPdMHW\ngynoEh2E89cKIJMBPdoF48zVPIfpZc+P7AS5XIaEtGL07xKKmFbNERHsg1YtmyHuCp9C9+lbD2D9\nzit4qE8rRIX5oVXLZggL8sH6nVdwwSTevT2ViA73xzvP97EQWAAvQNe8O5wvgrInCWMfaod+nUMQ\nPHUAPt9yHmEtfFFepUNxuQZeHkoMiQ1Hzw4tMbBbGAxGFm3C/BBTj/L19eWhPpHo2DoAV9KK4d/M\nA4UlNdhxJA0TR3bC6q2X4OWhxKzn+4JhWXSMCsTbE3tjw84r6BIdhFH3RePctQJ0jwlCh9YBSEgt\nRptwP8xYdgAHzmRieL/WeGFUZxy/lIPth1ORX1KDzlGBGPtQO2TkViItpxzR4f6ICvPDoO5hGNwj\nHOm5FTidmIf2kQHIKaoCx/GpwaFBPpgwvAMGdAvDuIfbi3OObdqdhNJKrUUFRqJh3HIka+rUqVi3\nbh1kMhnefvttzJs3D4GBgfXa1tW9mwThCuxGmliz4LMXfWJZTox6yOwJHzGKYSueCPtYnGerqBjH\ncaKYUyhkkMvlLjuf9qNLthFFQagx0mOwE5WVRsYso19mge1sXP1b7+g59dJLL2HDhg0AYPG6PtzJ\nY6rPnEscx6GyxmBT+OF2t11RrRdTuRmWEx3524XwW1ibTToDI0aw66K0UgsPlQI1WiN8vVUOJ7Xl\nOA46A2PxeY3WALVKUa+CD8I4G0dzizEsB4OBgcqqZLlwvIVlGgT5eaKkQgcvDwWUSrlDWy2Or0Ir\nRjz8fT1uy/dbSkW1HnIZHwlRyOWo0RmhlMtqrWRZF3oDIz736nOuAZhSFd07la0xc6kJc7BZX/um\nMi9bU8SlkazQ0FDk5eUhLCwMZWVlCAhwTZlEgmgq8BEiAKAfRFdicR0aP//obUdmEnpubKLb4+g5\nJZfzTlhNTQ18fJxXCc7Z1Md5kslkThdY9Wn7drRZG/URCdYRjtoQCk3VVW5dJpPZOLYNKdFel8Ov\nkMugsCNEhOMVKhAHBzSsbLpQLOFWRE5DsL4fnCG67U3oWxfuLrCAxnXaObq3SWC5J7f8rZsyZQqW\nL18OPz8/jBgxgi40QRAE4VZYP6fmz5+Pjz76COPHj8fChQthMBgwbdo0V5tJEARB3EXcssiKiYnB\nsmXLGrUtw/CD6vLy8upYkyAIgmiqCL/xwm/+ncb6OTVhwgQAwMiRIzFy5MhG7ZOeXwRBEHc/t/L8\ncmkJ98JCfiBjQyoSEgRBEE2TwsJCREVFudoMp0DPL4IgiHuHzZs3Y/bs2Q3axqUiq1u3bti0aROC\ng4OhUNzaiINXX30VX331lZMsuzM0RZuBpml3U7QZaJp2N0WbgaZpd1OxmWEYFBYWolu3bq42xWl0\n69YNbdq0wddff33Lz6/bTVO5T4CmYyvZ6VzITufSVOwE3N9W4fn14YcfNnhbl4osT09P9O3b1yn7\nUqvVTa5CYVO0GWiadjdFm4GmaXdTtBlomnY3JZvvlgiWgKenJ3x8fJrEcTWl+6Sp2Ep2Ohey07k0\nFTuBpmFrVFSUWCipIbh/+RWCIAiCIAiCIIgmBIksgiAIgiAIgiAIJ0IiiyAIgiAIgiAIwoko3nvv\nvfdcbYSzaIqDqpuizUDTtLsp2gw0Tbubos1A07S7Kdp8N9FUzn9TsRNoOraSnc6F7HQuTcVOoOnY\n2lA7ZRzHcbfJFoIgCIIgCIIgiHsOShckCIIgCIIgCIJwIiSyCIIgCIIgCIIgnAiJLIIgCIIgCIIg\nCCdCIosgCIIgCIIgCMKJKF1tQENJTk7GunXr4Ofnh+joaEycOBEAsGvXLsTFxcFgMGDcuHHo06eP\niy21xJHdqampWLFiBTp37ozp06e72EpLHNm8Z88eHDp0CAAwaNAgjBkzxoVW2uLI7u3btyMuLg4A\ncP/992PUqFGuNNMCRzYDQGlpKSZMmIDFixejb9++LrTSFkd2z5kzBwqFAt7e3ujRowdGjx7tYkvN\nOLL5ypUr+PXXXwEADz74IIYMGeJKM21wZPfKlStRUVEBrVaL8vJyfP755y629O6ntu+rK6msrMTX\nX3+NhIQEbNiwAZ9++imMRiOKi4sxZ84cFBUVuYXdqampWLVqFQIDA6FSqaBUKt3SzqSkJHz11Vdo\n0aIFvLy8AMAt7QQAjuPwxhtvoEuXLtBoNG5r57Zt27Br1y60bdsW/v7+0Ol0bmlreXk5Vq5cCbVa\njZCQEBQVFbmlnTt27EBCQgIA4MiRIxgxYoRb2nn9+nX88MMPCAgIAMuy4DjOLe08ffo0tm7diubN\nmyMmJgYZGRm3ZifXxJg1axaXk5PDcRzHTZ06ldPpdBzHcdzzzz/PcRzHaTQa7pVXXnGZfY5wZHdm\nZiZ34sQJbtWqVa40zy6ObI6Li+NYluWqq6u5119/3ZUm2sWR3adPn+ZYluWKioq4mTNnutJEGxzZ\nzLIst3DhQu6DDz7gTp8+7UoT7eLI7tmzZ3Mff/wxt2jRIu78+fOuNNEGRzb/61//4jZs2MAtXryY\nS0lJcaWJdnFkt8CSJUu4jIwMV5h2z1HXtXAVxcXFXGVlJTd58mQuPT2dmzt3LsdxHHfy5Elu1apV\nbmN3SkoKV1hYyHEcx7344otua+f169e54uJijmVZ7vnnn3dbOzmO47755htuzZo13MqVK93azq1b\nt3JvvPEG9/HHH3P/+9//3NbWtWvXcp9//jn33//+l/v222/d1k6B33//nVuzZo3b2rlhwwbu5MmT\nHMdxbv1d+uCDD7jr16+LdtyqnU0uXbC4uBihoaEAAH9/f1RVVQEAlEo+KOfp6Qm9Xu8y+xzhyO5W\nrVpBLnfPy+DI5v79+0Oj0WDp0qWYMWOGK020iyO7+/btiz///BNvvfUWxo8f70oTbXBk87p16zBu\n3Dj4+/u70jyHOLL7zTffxMyZMzF37lx8+eWXrjTRBkc2JyYmYuzYsXjzzTfdzmbAsd0A3+Pu4+OD\nyMhIV5l3T1HbtXAlgYGB8PX1BQAUFRUhJCQEABASEoLCwkK3sTsmJgZBQUFYv349+vTp47Z2tmvX\nDnl5eZg+fToGDBjgtnaeOnUKnp6eiI2NFe1zRzsB4OGHH8b777+P2bNnY+fOnaLv5m62ZmRkIDY2\nFjNnzsThw4fd+pzqdDrs378fffv2dVs7R4wYgS+//BLvvvuuaJ872jl58mRs2bIFq1evxsmTJ9G8\nefNbstM9vftaCA0NRV5eHgCgrKwMAQEBACAKlZqaGvj4+LjMPkc4studcWRzbm4u3n//fbz22mvo\n1KmTK020iyO7T548iVGjRmH9+vVYs2aNK020wZ7NOp0OiYmJOHToEOLj47F161awLOtiSy1xdK6v\nXbsGtVoNtVoNzs2m4nNkc1BQEGQyGby8vMAwjCtNtEttvyE//fST23Uc3M00hd/zsLAw5OfnAwBy\ncnIQERHhNnbr9Xr8+9//Ro8ePTB27Fi3tfPSpUto06YNVq9ejdOnTyM3N9ct7dy3bx+Ki4vx66+/\nIi4uDmfOnHFLOwFevAi/rxEREWKnuLvZ2qJFC/E1wzBue48CwP79+/Hggw+69Xd+48aN+OCDD7Bk\nyRIAEG1yNztZlsXf//53vPrqq+jduzfKyspuyc4mNxlxamoq1qxZAz8/P7Rv3x6XLl3CRx99hN27\nd+PEiRMwGAz4v//7P7FHx11wZPdvv/2GAwcOID8/H4888gimTJnialNFHNk8bdo0hIaGwtfXF4GB\ngZg2bZqrTbXAkd3ffvstMjIyUFNTg65du2LSpEmuNlXEkc0CK1euxKBBg9xuTJYju3/55RecPn0a\nXl5eGDx4MEaMGOFqU0Uc2RwfH48tW7bA29sbo0aNwqBBg1xtqgW13SNTp07FN99842IL7x2sr8WE\nCRNcbRIA4MKFC9izZw92796NkSNHistLSkowZ84clJSUuIXda9euRVxcHNq3bw+Ad2IVCoXb2RkX\nF4etW7fC29sbDMMgKCgIOp3O7eyU2nv27Fno9Xq3tTMhIQFff/01IiIi4OPjA6PR6Ja25ufnY8mS\nJQgJCUFoaCjKy8vd0k4AWLx4MZ555hm0a9cOK1ascEs74+LisHv3bgQEBKC4uFjsSHY3O1NTU/Gf\n//wHzZs3x7Bhw5CQkHBLdjY5kUUQBEEQBEEQBOHONLl0QYIgCIIgCIIgCHeGRBZBEARBEARBEIQT\nIZFFEARBEARBEAThREhkEQRBEARBEARBOBESWQRBEARBEARBEE6ERBZBEARBEARBEIQTIZFFEARB\nEARBEAThREhkEQRBEARBEARBOBESWQRBEARBEARBEE6ERBZBEARBEARBEIQTIZFFEARBEARBEATh\nREhkEQRBEARBEARBOBESWcQ9y5EjR/Dzzz87fb+TJk1Ceno6UlNT8eijj2L79u313vajjz5Cfn4+\nsrOzceXKFafb9sADDzh9nwRBEMSdhZ5fBOH+KF1tAEG4itv9g3358mWMGDECTz31VL23mTdvHgBg\n69atYFkWXbt2vV3mEQRBEE0Uen4RhPtDIou4Z9m2bRuSkpKQkpICg8EAhmGwbNkytGrVyu76//73\nv5GUlASGYTBjxgwMHToU3377Lfbs2QOO4zB58mQ89thjAIDy8nJ89dVX0Ol0iI6OxtNPP22zv9LS\nUrzzzjvQaDSIjIzEkiVLMHnyZMybNw+rVq2CSqVCVFQUOI7DZ599BrlcjtjYWMyaNcuufQaDAW+/\n/TZKSkpgNBoxb948dO7cGTNnzkRhYSH69evnvJNHEARBuAx6fhGE+0Mii7inOXr0KAYOHIhFixYh\nKSkJRUVFdh9SpaWliI+Px65du1BdXY0DBw4gPT0dJ06cwE8//QSdTofx48fjkUceAQD4+/vjlVde\nQXp6ut0HFABs2LABjz/+OJ5++ml88sknSEpKAgB4eXnhb3/7G0JDQ9GvXz+MHTsW33//PXx8fPDm\nm2/i6tWr6Ny5s83+rl27Bq1Wix9++AG5ublITk7GsWPH4OXlhc2bN+Pw4cPYsWOHE88eQRAE4Sro\n+UUQ7g2NySLuaXr27Im4uDh8+OGHqKqqQs+ePe2uFxAQgJCQELz55ps4cuQIRo0ahYSEBCQnJ2PS\npEl4+eWXYTAYUFpaWu+2r169iu7duwMAZs2ahS5dutisU1RUhJs3b+LVV1/FpEmTkJGRgezsbLv7\na9u2LYqKivDuu+8iOTkZQ4cORWpqKmJjYwGAegIJgiDuIuj5RRDuDUWyiHuali1bYvv27Th+/Dg+\n++wzPPXUUxg7dqzdddevX48LFy7gl19+waFDhzB8+HAMHz4c8+fPb1TbcrkcLMvWuo5arUarVq3w\n/fff17k/b29v/Pzzz4iPj8emTZtw8eJFeHt7i5/X1RZBEATRdKDnF0G4NxTJIu5p4uPjcebMGTz0\n0EN46623cPXqVbvrZWVl4ccff0TPnj2xaNEi3LhxA126dEFcXBz0ej10Oh0+/vjjBrXdtWtXnD17\nFgCwcuVKnDlzRvxMJpPBaDTC398fer0emZmZAIDPP/8cJSUldvd35coV7NmzB4MGDcL8+fORmJiI\nNm3aiFWeTp8+DZlM1iAbCYIgCPeEnl8E4d5QJIu4p4mIiMDKlSuxevVqyOVyLFiwwO56LVu2RFxc\nHHbu3AmFQoGXXnoJERERGDduHJ577jkAwOTJkxvU9uTJkzFr1iz88ccfCA8Px4wZM8TPYmNjMW/e\nPISGhuL999/HP//5TygUCvTo0QOBgYF299eqVSt8+umn+PHHHyGTyfDmm28iNjYWv/zyCyZNmoRe\nvXpBrVY3yEaCIAjCPaHnF0G4NzKO4zhXG0EQBEEQBEEQBHG3QJEsgpCQn5+Pd955x2b5M888g9Gj\nRzdqn6tXr8aJEycslqlUKqxfv75R+0tISMDSpUttlk+fPh2DBg1q1D4JgiCIpg09vwjCvaBIFkEQ\nBEEQBEEQhBOhwhcEQRAEQRAEQRBOxKXpglqtFgkJCQgODoZCoXClKQRBEMRtgmEYFBYWolu3bvD0\n9HS1OU6Bnl8EQRB3P7fy/HKpyEpISMDEiRNdaQJBEARxh9i0aRP69u3rajOcAj2/CIIg7h0a8/xy\nqcgKDg4GwBseGhrqSlMIgiCI20ReXh4mTpwo/ubfDdDziyAI4u7nVp5fLhVZQopFaGgoWrVq5UpT\nCIIgiNvM7UirS05Oxrp16+Dn54fo6GgxurRnzx4cOnQIADBo0CCMGDECCxcuRGBgIPLy8rBkyRJ4\neXmhtLQUEyZMwOLFixvUS0nPL4IgiHuHxjy/qPAFQTgBjuNQVqlDdmEV0rLLcT2zFHnF1WAY1tWm\nEcRdzbp16zBz5kzMnz8fBw8ehF6vBwAEBARg8eLFWLBgAfbu3QuNRoMpU6Zgzpw5CA8PR3p6OjiO\nw4oVK/DAAw+4+CgIoulSXK4Bw1KhaoKwhubJIohGotEZcfxiNuKu5OFKWgkqa/Q263ioFWgf2Ryd\nogJxf88ItI3wd4GlBHH3UlxcLKbr+fv7o6qqCoGBgejfvz9qamqwdOlSzJgxAwEBAQgICMDx48fB\ncRw6deqEtWvXYty4cWLEyxFbtmzBli1bLJYJYo4gbieb915DWJAPhvZ2z2hpeZUOL77/F6Y92Q1j\nHohxtTkE4VaQyCKIBpKZX4nth1Nx9EIWNDoGnaICMGpwG7QJ94O/jwfUKjnkchkqqvVIyy5HckYp\n9py6iV8OXEd0uB8eHdgGIwa0hkpJFckI4lYJDQ1FXl4ewsLCUFZWhoCAAABAbm4uPvvsM/zjH/8Q\nRdiGDRvg6emJ2bNnQ6fTITExEVqtFvHx8cjJyUHv3r0hl9smeEyYMAETJkywWJaVlYVhw4bd/gMk\n7mk27U4CADzQKwIymczF1tiSnFEKAMgvqXGxJQThfpDIIoh6UlyuwU9/XcPeuHQ0b+aJMffHYHj/\n1ggN8nG4TZ9OIQAAg5HFmav52H86A2t+vYStB6/juREd8VCfSCgUlLVLEI1lypQpWL58Ofz8/DBi\nxAjMnz8fH330ERYuXIjQ0FBs3LgRgYGBGDBgALZt24YhQ4Zg6dKlGDNmDJYvXw4AWLlyJQYNGmRX\nYBGEO1BRrYe/r4erzbAhM78KABDgd3dMzUAQzoREFkHUActy+PPEDWzYlQiVQo4Xn+iKx++LhlpV\n/0iUSinHoO5hGNQ9DOl5Fdi0OwmfbbmA346mYca4WHSMCryNR0AQdy8xMTFYtmyZ+F6IOK1du9Zm\n3Z07d9rdxxtvvHF7jCMIJ5FTWO2WIovl+LFYOj3jtH3uOZWOL36+gG1LR0OlpI4PoulCdy9B1EJB\naQ0Wfn0CX/16GQ/2boW18x7B3x5s1yCBZU1UqB/mvtgfn7xxPwBg1sqj+HLrRdRoDc4ymyAIgnAy\n+09noMrO2Ns7QX6pe6bjcSaRpdUbnbbP/aczAABGKhxFNHEokkUQDriYXIil35+GSqnAe9MGiql/\nzqJTm0As/8dQ7DyWhh92J+FsUgH++WxvdG0b5NR2CIIgiFtj9Ns7AABDYsMx+4V+d6xdmQzgOKC0\nQnvH2mwIJo0FrRMjWQYjY9p3wyoWzlx+CEN7t8JTQ9s5zRaCuBXqFFmpqalYtWoVAgMDoVKpcOLE\nCfTv3x8AMHr0aHh6etqdo4Qgmiocx+H3Yzew7rcEdGsbhNkv9IOfj/q2tKVQyPHU0Hbo3zUUy388\nh3e/PIanH2yHiSM7uVVhDI7jUFimQW5RNQxGFgzDQqGQw99XjciWzeDpQf01BEHc/VRp7lzGAcdx\noogpcTORxbIcdh2/AZ2BF0RanfMiWUaGP+iGloVPySpHSla5KLLiEnJRozPioT6RTrONIBpCvTyj\nuXPnokWLFpg6dSoUCgWaNWuGsrIyhIWF4ZNPPsHMmTMRFhaGl19+GePHj4dafXscUoK43bAsh7Xb\nL+P34zfwxJBoTB3TDco7UJgivIUvPp4xBFsPpuDHPUk4d60A/3yuD9qE+d32tu3BcRzSsstx5mo+\nziYV4GZuOTQ6+z2VchkQ0dIXHVoHYHD3cPTsEHxL6ZQEQRBuyx2cDoqViIyScvcSWanZZfh6+2Xx\nvcaJIkuIZLG3OPfWhxviAYBEFuEy6hRZMTEx4DgO69evx+jRo9GrVy9ERUUhNTUVa9ascThHiTU0\nzwjh7jAMixVbzuPQ2Sy8+rfueHxI2zvavkIhxzPDO6B3p5b4749nMXP5YUwc2Ql/e7AdFPI7U7pX\nozNi/+kM/H4sDdmF1fBUKxDbPhjPDO+I1iHNEB7sAw+VEkqFDAaGRUmFFjdzKpCWXY5LKYXYfzoT\nXh5KDIkNx5NDYxAV6hqRSBD3Gpn5lZi+7AA+efN+dGrChXS0OiM81Aq3LFd+p2El6XJlVToXWmKL\ndeejMwtfGEyRrFsVWQThauoUWXq9HosXL8YTTzyB2NhYnDp1ClFRUfDx8YFWq3U4R4k1NM8I4c4Y\njAyWfX8G8Yn5+OdzvV3a89WuVXMsn/kgfvjzKr77IxHxV/Lwj2d7IbyF721rM6+4Gr8fu4G98enQ\n6owY0C0MrzzVA93bBdWattgywFt06DiOQ0ZeJY5ezMZfp9KxNz4DvTu2xLMjOqJTm6br9BFEUyAt\nuxwAcPVGSZMVWTdzK/DGfw5iwiMd8PzIzq42x+UI6XJKhdypkSIp8Yl5aBPmh5YB3g3ajmEsBZDG\niYUvjKZIVkPTBQnC3ahTZG3cuBFZWVnYv38/9u/fj7KyMhw5cgRVVVV4+eWXAcBijhLqfSKaGgzD\n4pMfzuLM1QLMndwPA7qFudokeKgUmDqmG/p3DcWKzefx5qeH8OwjHTHmgbZOG6vFcRwSUovx29FU\nxF/Jg5enCo8ObIPH74tGSGDDHrgAIJPJEBXmh6gwP0wY3gFHL2Rj68EUzFp5FIO6h+GFUZ3RqmUz\np9hOEIQld4M7KhR3SM0qd7EljuHu4JkWIjm+XirUaG9NxBy7mI0Wzb1sBPgH38Shhb8nNix8tEH7\ns67850xBZDDy+6ZIFtHUqVNkTZs2DdOmTat1HekcJQTRlGBZDiu2nEfclTy86yYCS0r3mBZY+faD\n2LgrEd/9kYg9p9Lx0uguGNgtrNEdGlqdEUcuZGPn0TTczK1ARLAv/v50DzzUJxJeTipgoVIq8HDf\n1hjaOxIHz2Ri0+6reP2Tg3j6oXaY8EhHeNCYLeIuhuM43MipQNsI/zvedn1/FrR6I9Kyy3HxehHG\n3N8WPl6q22tYPWAbWE3ubkfQGD5eqkZFsjbvvYYW/p4Y3j8KS787AwDYRDGgggAAIABJREFU+emT\nNusVNWK8l8G6vLqTrl1RmQaVNXxxkbvpfjh8LgsDuoXCU01Fou4l6GoT9ywcx+GrbZdw+FwW3n6u\nDwa6mcAS8PZU4bWxsXhscDTW7biMxd+eRlRoMzw1tB2G9o6oV2SLZTlczyzFwbNZOHg2EzVaI3p3\naokXn+iCXh1aQn6bxnwp5DIM798a9/eKwM/7k7H1wHUcu5iDN8b3RPd2LW5LmwThav6KS8cXP1/E\n528/iOjwOy+06sP4d3eJr2Mi/NG/a6gLreFx18iFtJT4nfT7pZGskgpNg7fftDsJADC8f1St+28M\nRqOlyKptVyzL4ZMfzqBzdCDG3B9T634TbxSLr++WdMGcoir8Z9NZjHmgLaY92d3V5ohodEb8c8Vh\nzHupP2WZ3CZIZBH3LD/vv44/T97E6+NjMbR3K1ebUydtwvzwwd8H41JKEX49lILPtpzHuh2X0aN9\nMHp2CEZUqB8CmnnAz0cNg5FFfkkNMvIrkZZdjriEXBSVa+Hvq8Zjg9rg0YFtENbC547Z7qFS4PmR\nnfFAzwh88fNFzPvquKlUfWeolDQnOnF3kVfMTxxbWqFDdLiLjbFi0nu78eyIjhbL3CViIDj97jbq\nwFW+vnA++EgWA5blnNohpjc2vliFzUTBtZyj9LwKHLuYg4TU4jpFlq+3uTq1u4ruhiIUBamquXPl\n/+tDel4FsgqqsPNoGl4bG+tqc+5KSGQR9ySHzmXh+z+vYsLwDnh0YBtXm1NvZDIZYtsHI7Z9MNLz\nKnD8Yg7OXyvAml8vO3wghQZ5o3/XUAzuEY5ubYOguAMl6R3ROtQPS2YMwbaD17FpdxIuXi/Evyb1\nu6OCjyBuNwoF7wgbWbaONZ1IPYRSQWkNyip1+PVQitWmDXNmD53NxNaDKQjy98SCqQOdVv3UXcSe\nNewtXsek9BJ0iAxosEASrouQyqnVG+HtWb+0TsZaBJnYdSwNw/q3hqdaeUsVAa1FVm3XTkh1VCrq\nPn69wWzT3RLJEo7jTlUJri/C8ICa21RUhSCRRdyDJKQW4bPN5/Fgn1aYOLKTq81pNFGhfogK9cNz\nj3ZCjdaAwlINyip1qKjWQ6mUo0VzT7ecKFghl2H8sA6IbR+MT344g5krDuNfz/dF704tXW0aQTgF\noby1I0e3MaRklkGhkInphxzHgWE5O/P4OXbkUjLLAADR4f5itI3fV8NsuZRShJu5FbiZWwGN1gBf\nbzVKK7V44b09+OSN+xtdTfROatKGYGQa7+xn5ldi1udH8fKT3fDkA7VHcaxhrUSWRld/kZWcUWZ3\n+Ve/XobOwOLph9pZiCytztigZ4XRaHlOahPqQjvl1XpcSy9Bx1qqXxoM5pugsZEshmEtOhMZlnMo\ncP72r9/Qp1MI5k8Z0Ki26oOQWnm70vIbi/DbcehsFp5+sJ3bpjY3ZShPh7inKCitwcffnUbHqAC8\n+UzPu6YaprenClFhfojtEIz7e0VgUPcwtI8McDuBJaVD6wAs/8dQdIhsjn+vO4ltB683uEedINwR\nwaG7FedcisHIYuaKw3j3y+Pisj9O3MTf/rVTnLi1Pi0JBQW8PS1/Fxr6tZOuL/TS38ypAACcvJzb\nsJ1JENMFaxGKDYVhOew4kipWrGvsPgSsz1VppbZWMa0zRWbScysa3a6vSWQ1pMLg5n3XAADNvG1F\nmZfp+mslZdd3n0pvkG3WhS+kp6W8SocDZzLF98I5MBhZvPP50doFmSSS1ViRZX2tDbWkRRoZDnFX\n8hrVTn0RRKa7iSzp+d11/IYLLbl7IZFF3DPoDAyWfBsPlVKBdyf3c1opdKLx+HqrsWjaIDw1tB02\n/J6IL36+6NTef4JwBc6OZAn7qdaYx3RcSeMLBOQWVdd7P8L21nMcNTRNT7q+8FpMiapHSpgjmNvQ\nyRKXkIt1OxKw++RNm89u5JRj7Y7LddtVy3V84b09+Hq7430I94K2Eal50jFZABpUYVBw7EODbFOx\n/X34cU9SQePj2bAOOet0Qalw+nTTWSz/6Zx43qyPvbTS8cTK0nFiTCNDm9YC8FYEtjMQxKy7pQtK\nv8eCALyQXIDicg1OXs5Fckapq0y7a3Dfbm6CcCIcx+HLXy4iPa8SS18fAn9fD1ebRJhQyGV4aXRX\nRLT0xapfLqK4XIPZL/RzWjl5grjTiGOynBTJsieCIoL5ycmT0kvROtTPYewnJasMHMehfWQAqjR6\nAJbONdDwMVnS9QUhIDjUCnnj+25vR+ELwcG2F81Y9v0ZZBVU4eUx3WrNapD2+BsZ1ib97HxyocNt\njaLQaPi4F5t0wQZEsgrL+GqE9gSGsF9njsmS3kIVNfx9ZjCl7Vm3k11QhUA/T7v71VtEshpnm/Ux\n6w32j/NOZU4IIrMx34207HKolHJEhji/+p/08IWUxgVrTiIypBky8ysB2C/5T9QfimQR9wR/HL+B\nA2cyMX1sLNpHBrjaHMIOIwZEYeHUAUi8UYy5q4+j0vSgJoimhhjJuo2DjAL8+I6in/YkgWEdT5E7\nc/lh/HPFEQDmSJatyGpY29L1hUM0moRIfYobAEBhqQafbT5vESW609Xk6pvWKf386s0SvPPZYYvP\na4t0CWKkMYKGtU4XrCWSVVKhxZ8nb4rbFZdpIJfLYDCyNgUkhEimNMJUV5GJ04l5GP32DlGsWpdw\nt1fmXlhHZ7C0u7TS8bxceumYrEaKINt0QfvX548TNxu1/4YinOfGdB689d9DmL7sgM1yg5FFXnH9\no9j2kF4zA8OKvw+3ul/CDIks4q7nSlox1u5IwBP3RWN4/9auNoeohT6dQrD4tSHIL67GvNXHUV7l\nOK2EINwVocfaeZEs22WCf1RUrsW19BJxuUzGO9nxiXkorTA7s3+cuCE6ldY9+w3t0Zc6v4KQZBkh\nXbB+bsV3fyRi3+kM5EjSHevjVFfV6BGXUL9xXwzD4j+bzjr8XLhOtY3ZAWzFckpWOQDzeRMEyoXk\nAuyLtxzbJAgNe6l+LMtZpIDa+xyQpgs6Xveb3xLw5S8XcSmlEHojA4bl0NzXA0aGtYk6CfZKhV9d\nqa17TGO2Siv43+TaIlmC4hfEjbXArO17YVldsHGdFNb3t6NI1lfbLjVq/w1FEJnOrJ65eutFTFu8\nDycv5+JsUn6j9iHt1DAYWRSZop/17Sgh6obycYi7mvIqHZZ9zxe6mPpkN1ebQ9SDdpHN8dFr92H+\nVycwb/VxfPjqfWjejNI7CfskJydj3bp18PPzQ3R0NCZOnAgA2LNnDw4dOgQAGDRoEEaMGIGFCxci\nMDAQeXl5WLJkCY4cOWKxzpgxYxptx09/XcO5pHwsmTEEgs5w1pgseyJIuqy0Qmfh5KZkleGDb+Iw\nuId5gvXVW80OpbXTW58A0sXkQtzIrcBTQ2MsqhwIjqNQrr6+407s+Zv1Kdn935/O4XRiPrYvG12n\noKuqRcAAgNzkTNY1ZsdRhM2cKsn/XbDmJADLyX/FSJYdR3/nsTSs25GAnxc/brdIkdCsl1oJuVxW\na7qgEO2at/oEfvpwFL+dhxIandEm6sQwLDbvvSaKD4VcVue5F9K3NXojarQGnE0qsPi8vFqPGq0B\n3p4q8Z5wLLLqLhQCND6y6UhUugrh+J05Nkw4/4u/jQfgOK2P4zhUaQxoJpl/zPwZ/9ffVw2DgRVT\nTOVyOYDGp5ISZiiSRdy1sCyH//50DgYjh39N6mun1DHhrkSH+2Px9PtQXqXHgjUnKHWQcMi6desw\nc+ZMzJ8/HwcPHoRez98rAQEBWLx4MRYsWIC9e/dCo9FgypQpmDNnDsLDw5Genm6zjiO2bNmCp59+\n2uL/q6++arHO5r3XkJReKo5lAPge+/jEPFy87njMjj1+PZSCeauPi06ZPWeT48zpR9VaSzEhOHVl\nDgoM2Pbs1+2Ezl9zAt/8lsDbw3EQtNTfl+xHQmqRZExWPUWWnTaF46ytw7+wlHcE7YkWa+qqxqcw\nnUC9gU+pc1RYwpGTLiyuLeIiRG3sXcOkm3wEsqhcY3//pm3kcl7kCOmCsz4/gj9OWFaDkxa4ECJz\nXp5KGIys7RglI4tNu5Pw8/7r8PZUQqWU1xl1FUSWVmfEFz9fxKWUIovPqzUGvPyR5XfIkcCsTWQZ\njCy8PPiiVLWJo8oaPfacuok/T960Wc/6eB211zLQGwAQ6Hd7O/GE76O12L0VaotqSrmUUoTnFvyJ\ni3bGDQpiWK1SwGBkRBHv6kIhdxN1RrJSU1OxatUqBAYGQqVSQalUwmg0ori4GHPmzEFRUZHdXkSC\ncDXbD6fgXFIBFk4dgCB/L1ebQzSQqFA/fPjqYLz75TG8t/YkPvj74HrPEUPcOxQXFyM0NBQA4O/v\nj6qqKgQGBqJ///6oqanB0qVLMWPGDAQEBCAgIADHjx8Hx3Ho1ImfI0+6jiMmTJiACRMmWCzLysrC\nsGHDxPfSqIbg0x2/lC3OV9SQAeTbDqagrEqHnKIqRIX62RUdHMfPkSWXy1CtMYjFfNbtSMCDvVsB\nsBxo36qlL7IKqtC7U0tkFVRZ7KuhWVkcx6cFsiZnbOexNPTuyM9zV+/Jzk3HlJpVhunLDuDrd4eL\n59A6raq8SgeVUg5vT5UoaHR6ps7fA2vxaY1QUc1gZPDF/y5g3+kMu9fJkZNunS5oD8FhtXcNheIP\nxeVatGppW9jALLJk8PZUiiIwKb0USemlGDU4WlxXOt6nSijV76GEkWFs7Jc60b7ealRrDHWm5gmR\nNo3OKKaVWSNMEWDdjk0kqxYnXm9gTBE4xmEkS6sz4p3Pjoipps28VRgSG2GxjxrJtbeupikQEuCN\ngpIaMR3zdmEuvnLr4sVgZJGZXwmNrn6RJuFeOHw+C7EdggHw4r59ZHPx/lUrFTAwrFjd806Pjbyb\nqdev4dy5czF//nwkJSWhpKQEs2fPxtNPP43Nmzc77EUkCFeSlF6C7/64iqeGxqBfl1BXm0M0kqgw\nP7w3bRAy8yvx4fr4evVeE/cWoaGhyMvj57kpKytDQABf2CY3Nxfvv/8+XnvtNVFQbdiwARkZGZg9\ne7bDdW4VhmVF58XRhLB1ITjMZgfdTiQLgEwmg4+n0kZMHDqXBcAyqqRUyPHY4DboEBkAjdX6jRmT\nJd23EAni22nYeI5jF3MA8CmOrAMn7/lFu/H3j/dbfFafkui1jXfibRXGZLHYdzrD4XqOI1l1iyyz\nwLFdR0iDdiRahP3LZTJ4qhW1lnCXnjMh2uepVsJg5GxElvS9r5cKSkUD0gV1RnGeLUdw1umCBsai\nRHxd6YKeaqXNMUn5YXeSKLDCWvjgdKLlmCSDkcWir0/abe/AmUxUVJuqH5oiftJiG7cDoaS8M0TW\nqx/vw1v/PWTbhoN9W3dYlFRoMWvlUWzakyQKfw+1AgYje1uqe97r1CmyYmJiEBQUhPXr16NPnz4I\nCQkBAISEhKCwsNBuL6I96pNuQRDOoKpGj0++P4O2Ef54YVQXV5tD3CIdWgdgwdSBSEovwfKfzlEv\nG2HBlClTsHz5cnz44YcYMWIE5s+fDwBYuHAhVCoVNm7ciLVr1+LSpUvYtm0bMjIysHTpUly9etVm\nncYiTb8zMpzdAe61OZbWCALGYHL+7O2P4zjIZICHWokj57Jhz4mXzllVpTHA10sFlVJu46w7+krN\n/+q4wyIT0oiV3mCOlijkMvwVl25RdMMeQpNCpTlfL5XDSBZgTn1kRJFlX3CcSyrATdPEv7WJLI7j\nkF9aw9tfR+ELR785gpnWY++S0ktER144L/Z2oVLy59CeyLqeWSreV3K5DAq53CIaZj2xrVQkFZbx\nx+XloYSRsU0X/FOSatjMW8WPyaozXZBP4avRGuucXkPYk9Cu0cgizDTlAGA7j5UUvYERo2b20gDH\nzt6Jk6Z7MjjACx0iA5BTaOl3GhgWSenmOZ6Ea1BcrsHyn87hs83n+bZqKe/vTOqKZH3ywxnM/+o4\nCkpq8NNf1xx2erAsh4JS+4L86dk7xQIYOYVVWPPrJRiM5u+lcL8I1zkjr1I8vx4qS5Flj6T0Eurk\nbAR1pgvq9XosXrwYTzzxBCIiIvDFF18AAHJychAREQG9Xo+8vDyEhYVZ9CJaU590C4K4VTiOw8qf\nL6BKY8BHr90nPsSIpk33mBZ4+7k+WPr9aWzclYiXRnd1tUmEmxATE4Nly5aJ74XnjD3RtHPnTov3\ntyKspEjH/rAsZ9dZqazRI6CZ/bmBrBEcIsH5t58uCMhl5smIC+04X9J0wWqTyGI58zihsBY+pu1t\nG6jRGnDxehEy8iot5ugxMrwzJo1Y6Q2M6LwxLPDFzxcQ6OeBjYtGOjxGwZEUxJNapTCLrFqcPbEy\nngOHb9FaPoKhVMjQJTrI4X4OnctCQYlJZNURyXAYaRJSRK3snfX5UUSH++Hztx8yp8bZOSThOhSW\nabBkYzzOJOZjYLcwvDa2B/654gg6tub9KblcxleN5Mz3ltJKZBWUaiCT8feFcC8IESdrUS111H29\n1FAo5HWmCwpRvxqtURzLFhbkg1w75b7FSBbD4FRCLo5cyEYvU6oaAJxJzMczwzrYnZtMb2RFEWct\ntqs1BuiNrHjdDAYWEcE+iE/MtbgfElKLLbYzMhxyCqvEaKgwxteZaXz20BkYMAwrirj4xDyUV+ng\n7+sBluUwc/lhTHuqG46czwYArN1xGacS8jByYBQC7MwjVtdk1Hmm34JFa08ir7gGowZH23QACB0v\nOgMjXicPlQJVGr3D6ocMw2LW50cxqHsY5r7YvwFngKjTA924cSOysrKwf/9+fPfdd/Dx8cHSpUvx\n22+/YezYsTa9iLVN6EcQt5s9p9Jx4lIuXh/f0+5M90TT5b7YcLz0RFdsO5RiM+ibIFyJNEplZFi7\nzoo0UsBxHLIKKm3WETCXFq89kgXJFMT20r2kkSw+zUsFueQZ/dITXUz7t7VBGLdVWqkTnVOAF1Qc\nZyngpOmCwrkoqah9+gVzJEtnssEcAayt1LUgMnR1jEkxMpxNcQYpGXnm819XYZ0lG0/bXS44qRxn\ne/5v5FTgXFKBJJJl757gPysq0+BGdgX0RhY3csvFSE96Hh+Rk8tkkMlk4DhzFEgaSYy/kofdJ2+a\nrotMrBInpvjVUgCkeTOPWiNZG3cl4tWP92HNr5cB8BHEGp0R/bqEYFj/SAfnhf9rNHJYu53fTqVU\n4Jv5jwDgx5Q5KgajNzDwVPNRM2uxbS0En3+sM/p1DYVGx+DQ2Uxx+a+HUiy3Y1hkFdpmWRmNLOQy\nc0TL2bzxyUFMmPcHjEbzceQUVmPqh38hPa8CaTnl+O6Pq+JnHir+em0/nArANo23rjGGwj1RaYqi\nshwnCnnBNxf2KXyPAaHwhfk7LPXiTyXk4uBZPv345OX6TZ1AmKkzkjVt2jRMmzbN4ecBAQEWvYgE\n4SrS8yqwdvtljBgQhft7RtS9AdHkeGpoDPKKq7Fm2yUEN/ei8XaEy7mUUghPX7OTxrCc3UISL33w\nF96bNhB9OoXg8LksfPrjOXz+9oOIDve3WVeMZBlqj2TJZMBbE3rhsy3n7Yos65QylUIOnaRrVYhO\n2EtPyi2yPyGpTs/wY7IkAk7osQcaXrZeFJIMV6/qgmwt6YINGVsmPTdnrjZ8nqHD57LQy1TsA+Dn\nLbJm0dqTeOWp7gDsp4sK16yoTCOWwGdZThTCwudyuQxyGbD75E3sPnkTgOXYNyE9EuBFk3lMlinF\nr5ZKdIF+nlDI5WL7AMRS7ADwy4HrNjbXaA1oGeBtIbSlCJfBYGREx18uB1oGeIvrVDsQfnoDIxZx\nqa1qYIvmXnh0YBQAoJm32kLU+3qpLMr3G1kOcsmuzGPG+OIpNVqDKf3WeUECjuPEKJ+BYcRy+tcy\nSlFQqsGfpjnrpELSvxlfZn3boRS8NLqrzT1T1xhD4btnYCTinzHfV4BlJNhcXVBukS4oPe17TqUj\nu8D+MCCibiiXirgr0BkYfPL9GYQEeWPaUzQf1t2KTCbDK091R5/OIVj2/RmkZZe72iTiHufzLRew\n/Kfz4nuGYR06+8KErummKEqpgxLrCqv5m2orfBHWgo/Y20uxs3YZFQqZRSRLoZBDLrMvahyNeRLS\nsqRTYvBjP4RIVj2FjtVqjCQVrrZ0QcF8e+mC1lXsLJqz2qXUn5Y6r/UVaj/uSbJYV+jtt8ZYS9ED\n4bNqrdEsUlnOPNZLEFmmSJYU6fmXftS8mQcup/IRPLWKF1m5RTUOj8PHSwWFQiZOJn38Ug4mzPsD\nBaX2tzEyLD8uy1NpcS9ZIqQLsqIYtD6tKgdVKPUGFl4OCl9Ixz56mI6NPwYlqjR89EatlCM4wLKa\nMGMVXRbHjDEsvD2VYO1EIhtLel4F4hPzkJBmTlk0GFl4mAQvYxXZlNqltBKt1mmsdYksI8vhi58v\nSDpnOFFwCVFAoTm9JF1QbTUmS2qTkWGRX2K/w4WoGxJZxF3BN78lIKeoGrOe7ytWJiLuThQKOWY9\n3xfhLXzx4YY4lFfVnpZEEHcSxsGYLMAcPRGcG0cuqkJSWhyopfAFYBP1sFzH8r1SLrdw1pUKIQ3N\ndltHgkWIZFmOyWJFJ66usT0C1sfEspy5hHQtQkduckTtVRe0LiFeG1KBIFQ45Nuu3/bBAV4W19lR\n8QSpyLqQXCBGogBzCqmRYUVxyrCStElpJMsqKqmwEFnmz9RKs/gQhEhOUe2RCKVcDqOprcOmypQa\nrdHuOD+jkUVhaQ1a+HtZRDOlmCNZrBjtsrkXHYosBp4O5smSpvVZiiyVWKpcoZDZfP+MDCeO4+KN\nMdvnqMhGY0jOKMXrnxzEB9/EiemQLZp78SLLZK/RKrIk/e4J6YByOf+dtC7IYk9khbXwwbalo+Hl\noQTDsGJHjtCGVLxL29UZWPFeF6sL2qnuyac/N+ZsEACJLOIu4OTlHPx54iamjO5qN/WGuPvw8lBi\n7kv9odMzWPb9mQZVbiMIZzJ9XKzFe8ZBdUHAPPmt8HFd2Ul6o2XvsxQ+XVAmOrr2xI21HQqFpbOu\nMIkue/Y6KiyhMzDgWM5yTJaRMVeSq2cky/o7yzBsvSJZopNoR2QJ0Qx7HL2YjZyiKmh1RhiMrI1o\nsd6/lECrIgTBAV7IK66xjI44MDkugZ9ewGBksWDNSaz6xZxWKI5jM42HUZoEgrUNcpnMJmokFbnS\nT54Z3kF8LUSy8uwUpwCATlEBGNYvEnKFTHTGhUp9WYVVmPLhXzbbVFTrUa01IiTQ22EkS1hsNLJQ\nmopPWd9jDjINxXmyALNIEKJqFpEstVlkqRRyHDiTadqv3Katm7kVWL/zivhemAjbYGTFlMqGprla\no9Ub8fZnR8T3W/YmA4BY3VGwV5ycWuxQMO9DSHFkWY6fRNoqklVjp/CFSimHSimHUiGz+e6xHGdO\nQ2UsRZ3eqvCFo+qCNDHxrUEii2jSFJTW4PMtFzCgaygevy+67g2Iu4aQQG/MfqEvEtKKsUHyACWI\nO4mHJHIA8GLHkcgSHHvhcyPD8aXESyxTswRnR0gXsuf8CCXcxUiWHXFjHaFSmtIDBRSmqnX2zNXp\nLec2EtDqjabJiC0dbMEZq81ZzSmqwsSFf6KoTGMjkqQRwNoiWfYKSXAch5IKrcMqgACQmlWOWZ8f\nxfi5u/DuqmMOBa69toP8LUWWr5fKonBAbVzLKLWwW4pwrowMC4ZhoVIq7EZC5XJbQS6dp0wayZJW\n1BUc+xoH45+ee7QTPNVKKOXmebIER996LjUBoSBKSJB33ZEshhWrIFrfi47EuM7ACxKhmuIXP1/E\n1A/38qlvEtGhVpmPM7vQLCKVdiJZGXkVFu+TM8owds7vJpFlmZpYUFpTa1Eae2TmV2L8u7sA8JGr\nZt5q8TODkYVREskSKgTa61ColkRiNTqjRSTr0NlMC5EJAI/0b413JvYBwEc2rb97/Jgsy7FYjKST\nQmjbQ6UwRVNt71FtHRUNidohkUU0WRiGxX9/PAcPtQJvTuhFlS3vQXq0C8bUMV3x29E07K9lMlGC\nuF2o1ZYiy8jYL3wB2Bai0BkYXE4twssf7bWYu0hwhAxG89gKazjwAktwdO0LMdv2rW0QqtZZozMw\n8PZSie8f6d+at43hwIGzGD/CcZI5kWoRWUk3S1FRrcfvx9JsSn/zaXKm/bF8MYqLybYV6IR2LiQX\nIvEGP+5l++FUTP73HvxhKibgCEFAXMsodRiFsXcerVPJ1CqFRTXExiKk6AnpgmqV3G4klC/hXlu6\noHm5NA3PQ2VZpdIa4V5QKOQoqdBalAh3VHGvoppPz/bzVjs8h+JcZjqjaKf1qXIkxg1GBmqlAnIZ\nX/FQmCRap2csRIdQiQ+wrA6pkMssRBcAMZVQiiBYxLFSLIcvf+EF3WtLDyDdVEyE4zj89Nc1fLg+\nDgu+OoETl/jU0n3x6dh64Dr2xadj20FzNcMPXx2M1bMfNh+PgbGIZO04IlQO5D+Xfl+qtAZxPY3O\naCGOP/3xHF8NUfL9nTG+p5i9o5TLbK4ZX13QMo3XUXVBfpntNaG5sW4NGrxCNFn+ty8ZiTeK8dFr\n98HPR133BsRdyeghbZGaVY5Vv1xEZEgzdGhtf64+grgdqJS2k8I6Kp6gsIpk6Q0MKk3+YKqkiItZ\nZNWSLshygKz2MVnWzrowBkt43TEqwFT4wv6YLG/JpLNjH26PvfEZYmEGyygGJwrCrQdT4Ahfb160\npWaVi+N9XnqiCzb8nmiRJsdyHP697hQA4Mt/PWyxD+GcnLmajzNX87Hz0ydx9WaJuKxWJMepqGe6\n4KGzmTYFdjxUCrBs7VUQpbQM9LYcE2TCHMniAHBQqxSo0RjqmS5oX2RJj0sQIo7Gi4kiSy7DheRC\nvPrxPnHMmzRq0q6VP1Ky+HOgM5hLyDs6h8JxVWuNYlqj9b1odBCd1RsYqFUKyOWWaazVWoPlmCxJ\n58ZDfVqJhUcUdsZ6VdRSol8ossGwHI5cyBYju+t+S0B2YZV4n/by1wBzAAAgAElEQVRsH4ziCi2W\nfn+GPx4r+x8b1AbPP9ZZ9EW+XTgCR85nY8PvV2BgGHioPCzWZyUCWzzGGgMCm3kit7ga0xbvs7HV\nwLBQKeViFFh6/hUKuU2xGo6VpCcK07WZzJaO/RPOZUOLyRB1Q5EsokmSkFqEzXuv4ZnhHdA9poWr\nzSFciEwmw4xxsWgT5ofF38ajtFLrapOIewiVvXTBOgpfCAPv9QZGdLKkTrTgpAqOrt3CF+AnIxb2\naW9MlvVmfDVBfv1ObQIhk9kWvsgprEJ6XgV0krEx/HGax9bwhS8s3Yf6jN1gxMl3ecHx8YwheHxI\nW5P9nHgM0uOdvuyA+Hr02zvsRsqk506IuNlDelkcZT4IbSdnlGL02zvw6Y/nbNYRI1mmHY57uL3D\nNgEgOszP7nKbCJmSH09kvVxI65SiclD4QhrpUNcVyZKZRRbAz21mLoxgdq69PMwRTeGelMtso6LW\nx1WtMUgiWVYiy261RT6a6aGS28zdVaM1wiCxSZoWOfPZ3ujcJtDiWKRU1SKyBIGh1RtRrTHgjfE9\nMeaBtriQXIjCUg3GD2uPBVMH4INXB2PZ60MwZXRXDIkNt9hHdLgfHhvcxqKzN8jfC75eKnAcoNEx\nNgW5pCnDAtVaA/x9HXcYG4ysw6qMSoXMRhCxnLTwBf83NbtM/FxaXRAAdHYqikrvA0eimnAMRbKI\nJkdljR6fbjqLjlGBePaRjq42h3AD1CoF5r7YHzNXHMbHG0/jo9fuc1i9iiCciVplJbJqKXwhOMOC\n41JepUe1wpTKJPFf6hPJ4tuQFr6ox5gsuVwsOCAzNciPfTGv84/lh6DRMRjQNdSyuIDJqWUY20gW\nx9lPE1yyMR7/mtTPJoInOIMeKoX5M7Z+hS/sIRUg1kUq7KFUyB2LLFPbpxIcT7yqVsnBceaIZV2/\nNV5WY9uMDAulQm5zzlRKhen82qYLWgua5s3MkRHpJ1JHWCg64UhkCetKI0TCutLUMW+J/YLIUihk\nDp1uQThUawxiWqlNuqCdToEj5/lolEqpgEppeX6qtQYxigZYR/Jk4nt7c3fVVnVSmA9MGM8X6O9p\nEWWb8EhHcTyVr7caTz4QAwB4Z2IfvvS7Kbpk734SvjMaSRqggFlkmY9JqzNaXFdrDEbWQlxK4SNZ\nliKLs0gX5NuTTjex+S++OIeQVmqvYqdUuMlkwIXkAkSGNEOQv5fNuoQt/8/eeYdHUW5//Duzu+lt\nN21DCCGEEnoJxUiRJioKqICIgGAEVBAFvQhXEAtFwhUQhIsQECkqeK947XJ/XkBUmqKINOkJMSSk\nkJC+2d35/bGZyczszO4m2ZANnM/z8JCd8s6ZsjvnvKeRFkI0KqxWDqt2/IYykwV/G5eoGBpA3J6E\nhfhi7uM98GfadSqEQdw0vLTSsC1Hniy5l2DbN6ex+ctTAGSeLL7whdnesyPAVXmyqvazKha+kH7W\naOxze+SerLIKm2ynLuVJ8l545dVaZVzIf3uVFPkDx69KvAi8jPz563SsJNyRv2w1N7Kqz8mRksrj\n76tVrW7niqGn02hs4Y28kaV1PMMfIMptA6oVV7lh7K3TSK4Dj1K4oOSZUMnJ4vdRy69iBQ9Wtfef\nN37EyrXYoykYWQqGn3yMfb9mCPlSvLgP3mUzUuSFLziOw9s7bAaAl85mZIlzsErLzJKwR/vcQtv/\nasU41ODPjQ8LNAT5oFd7I+7u2QyT7m8nKRUvPZ7NyPTSaVQNdt7LXVJuthtHCBcU3RuT2YpIgz98\nve2PybIMKkXVGuVoNaydJ8tktgp5iMfO5iCvUFoY5mKmLQSU/54rhQZarFx1tUgLh1fWH8Tsd35Q\nlIGwhzRUolHx7z3ncPhkFp4f0xURBj/nOxC3Fe1bhCJ5mK0Qxr5flZuDEoQ7Efcl8vHSYNs3Z1Tz\nknglUUmZYVDteeLDpJQKX3y67zw4vugCwwjGj1mphLtC2BmvePOKk7wZMe+ZKyqVzr4LlRGrPFni\nEuIc1L0l4pl6vg8W75HQadmqog6Q5GTVtGeRWMcNDnBuZBUWm/Dht2cU1ymV1paj07JVXgzbRjqN\nsiLOIzf8eCNTXvxBHobIo9WyduGC4m1YlXDB6n5ryveG3+1qbnWhCN74MVUqG1n8YTUso+g1AqT3\nj8+X4z2MycPaVx1HKpPY2+St00Cr1UgMkJLySol3Tc2Lxi8PC3bu0QSqz+1Gic0Y9PfVwRDkg+fG\ndMVIJ2GgztCJKiDqA+U5Wbb/5ddBq2HwxtQ7Jcsmj+gALcvAbHEcLijPyXot9SD2//aX8HnNv36X\n7wagOqxUrchF6xhpnrNS/zRCGTKyiEbD0TPZ2P7taYwe1ApJHaMaWhzCQxnWtwXu6toU73x8DJcy\nC53vQBB1QCeaodZqNHZllsXwHgWlsshf/nQJc9f+CMAWOgdAKFktNoLe++IkrlUpOQxT3W9IqYS7\n3FjRinKyqpF6ssS6q4/IyOKVV0uVgSf2ZImrC9rJIJKLPy/+GvEGKscBe49mCIaDvKS9IyxWTmJY\nqHke5JSolDWXN21VQqtlYbVyePatvQCgGsLFExIgVfh5I9ts4STGgk7HguOqDRPAdj80CtUFxfKJ\n14k9OaxgZKkUvlDwwPBKv1jh9vG2zyyxhTAqDisxHnkv3rRRnQRZlUIl+V5YgE3p99KyMImq6d0o\nrkCpqKy8qieLN7JCfPHF8hHKAorgQzn5UDkvrWvPjyuIDaJwvTS8jjfm5SF6Wg0rObc2zfQ2A6uq\nd5ZquCBr78lytaKjUPhCpchFv67ReHJ4e8V1hGOcGllFRUVYvnw5nnjiCQDAiBEjsHjxYixevBjH\njx/H2bNn8dJLL2HRokX44IMP6l1g4vYkK68Eb20/iq6tIzDu3rYNLQ7hwTAMg2dHd0aTMH+8+f7P\nQrgEQdQH4ollrUKo0oInewl/80aT2ozxqUs25bo6XFC58MXkxf+HQyeuVoUsVXmyFBQo+TJ5M2J+\nm8/2X5BUO+O9DnpRfpPgybJawXHSEu7gOFSqKHCz36lu0Co3BMUK4+nL+SgorlAcwxFlFWZVT05t\ncKVXl04rbXirFsLFEyIrZvDfw2lIy7qBM5fzJd5CPvT03V3Hq49VFY7mMFxQhPi+8AaH2qnw12ru\n4z3Qq73RNq7V3pOl5DViVTxZtjwgDv27NQVge546xIcKuU+A7XsifxbE1Rf5cMFKs1WYAPjjYh4K\nS6Sl2sXwOYa88e+rYBgCQGJChOQzvx0/8eHMYK4J4rHCQ6SRN2qVMDWyiZAR/eKh0diMelOlRVU+\nrYZ1Wm5dLaxRKHxRqTzxoNUqTc4QruD0aaqsrMRTTz0lPOgajQaBgYGwWCyIiorCxo0bMWvWLMyf\nPx979+6FyaRexYUgakO5yYzFm4/A31eHv41PpAo3hFN8vLX4+6QeuFFSgRUfHq1xjgdBuIpaZTce\nsVLEexSUEszFyHOyOAX75Up2MRjRMZWMLLnho2Htw87KKswoKq3E8fM5goJsCLKFNombqgq5X1U5\nQxpZuKBStTjAVrGOR24YyBXGopKa6w/7f8vAnl+uVMtZVyNLCNlUr5ao1bAS75/OSR5QgJ/UyPr3\nnnN49h97UVxWKemFJC+iAlQbXvx9izT4oU/nJpJebOLfNyVPlhr8+t6dm+DhAS0l68SheUp5bhqW\nVRyfF6WZMdA2jtlq987WKHqyqkPQeCOLb3wd6KfDn5fzUSgywu2U/qqPfE6akvcNAJrLKj3ybQr4\nUDtxk+O6UiryWMs9WWpoNYzkHjJstUFZYbLYVTPl0SiEC8pRay3hrXPsyZJ71wjXcVpd0GAwSD6v\nXLkSsbGxuHDhAtavX4+8vDwYjbYZkODgYBQXF9vtAwA7d+7Ezp07JcvIICOcwXEc1nz8OzJzS/DW\nc30lL32CcESTsAC8MC4RCzcdxs7vzmLsEKpESdQPLMtIqs2JEStFvNFUYbKgd+cmMBr8FPO3LLKk\neCWvhb+P1taMWDCy7Lc5f6VA8lnJkyWWjT8ur6iKPXNCuKCVAzhpmJstXFDdcPzjQi5CArwVm/qK\nuS5Sou/vHYevfrqkOibPuk+OSz7XdRLOarX1/CosVtdPdFpW4h1yVl3QVe+IUqga//zw962ZMRBs\nVX4Ox3FgGGk/KWlOluPjig0V+TnwXo11cwYi7WqR/b4qhS8O/mFr1ssr7lYrZyeHTsPCbOGEohh+\nPjpczS0W1nvpWOi0GpRVGaBxTYJx/HwugkUhhfICF/wn/v7zoa7r5gzEMynVbQDkXkd5uKA7q9LG\nGm0G3eQRHVwqyMIfX3xfGIYRvHPlJouqfFqF6oJy1Dya/PdQbX95pcfGRnZ+KfILyxHfNFhxIqM+\nqVEJ98rKSqSnpyM2Nhb+/v4oLy+H0WhEVlYWoqKiUFBQAL1euRHomDFjMGbMGMmyjIwMDBo0qPbS\nE7c8n/9wEd//loEXxyUKnc0JwlV6tjPi0bvb4KP/nkGrmBB0bxvZ0CIRbubs2bPYuHEjgoKCEBcX\nh3HjxgEAdu/ejX379gEAkpKSMGTIECxYsAAGgwFZWVl48803sWfPHhw+fBiVlZUYNWoUEhMTayXD\ne/PvBsswmLF8r906sYJdVjWzXWEyIzo8AA/3b2lnZHGiwgfHz+fil9PZ2Pb1abtxzVXNiDUqnqwu\nrcNx7GyOZJlyTlbVca3V5Z6jIwJwJu06osMDhPXV4YK8J0tWXVDByON5+Z8/AQCmPthRWMYw9gaR\nOGTMGFq7wkbuMLKWvP+zw8bGckVXzbugtr0aOgUvCu9Z4e8by9hC7U5fzse3By/jvjvjpJ4skUGj\nZAQtfCoJr288DLNF6mGSG4K8JyvQz0uxYh/LABqFZyll6y9V52JfNEWQUcPAYrHisVe+AQB0bhWG\n38/lVp9zVQl3/vvSPCoIx8/n4kJGdY6t/DnmPcr8OfHXvGlEIHYuHoox8762ySW7F74iT5aj0v61\nIdLgJ+SFOfMy8cgnQliGESY7yk1mBzlZjGKfKzFqIaa8QayWC+nod8OT4TgOPxz7C//YfhSArfhI\nTGQgJj3QDq1ilG0Vd+PUyDp27Bh2796NtLQ0rFixAkVFRdi/fz+Ki4sxefJkADbvVlBQEIYMGeLW\nB5S4vfntz2t474uTGN6vhRDfTRA1ZeyQNjh35Tre+uAoVs68C1Fh/g0tEuFG+JD1qKgoTJ48GaNH\nj4aXlxf0ej2WLFmCsrIyzJkzB3379kVycjISEhKwbNkypKWlYceOHdi2bRvKy8vx/PPPY/369bWS\nge8Zo+RNEisnJVX5geUmS1UFNXuFSR7a+vrGQ4rHNFVapM2IRUbWS+O74+fTWXb7iMtu21WrqwoV\nBIDENpEYOaAVYiID7fa3WG0eO/HuZosVeQXOK46JlTydVr30NQAYQ2v3Pa17uKB6vgyPPPdOKRdP\nvj4kwNtpzpmyJ0saLsiyjJB7c+hEFu67M07iIVTyPorx8dbC11uDolKr5FrZebKqvBosyygaiQzD\ngHVw3uLWBnI55IUvxAYWUB0ueL2qtHykyOBmqqph2p2bUMKdlfwPQJIPJp8cqDayLG4NFZTjqvGv\n1bCSbVmmWubTl/PRrU2E6n7OPFlqyHt4KY3dGMMFz2cUCAaWt5cGfj46HD+fixfetuWJtm4WgsVP\n94aPtxaZucUABzQRTSy5A6dGVpcuXdClSxfMmTNHdZtly5a5VSiCuJRZiDe3/IzOLcPwxANU1Yao\nPSzL4MVxiXjh7e+xaPNh/GNGX8lLl2jcqIWs9+zZE6WlpUhJScH06dOh1+uh1+vx008/geM4JCQk\nQKu1vQJ9fHwchq+7Gu6ulMcjtiN4I6ui0gIfL42i8upq+XKbvaIcLsiwyknuGo19ThaP1Votv1bL\n2BlYgO27ZLXaSrjLx3Emt5dOIyl24CyELlSlBHdYsA9yC8sV1wHu8WRpNYyiwcwj91w5K3yh1bBI\nebYPfvj9L2z/Rlo6fsqIDkj97AQA5XygK9m2MDp56X3x35Jy7mIFXalgBcOAt0jEEwDy+1Eh6oel\ndk0deTfEBqNcDmfX11ZdUCPkq4kbTAf46lBUWmlfXbDqf8GTpSKz/DvHb//rmWsIDqi/dARXPUFy\ng4ZhGfBX0lGfLI2GUQ0H5FHzZDkLofPSsY0qF/6rHy9iw3/+EPIDU57tg3ZxoeA4DkfPXMPyD46i\nuKwSZ9ML8Pd/2iq6ns8oRKCfDttfv09y/Y+czMIfp9JqLUuNwgUJ4mZw7XopXks9hKhQf8yd2MOt\nMdLE7UmgnxdeSe6F2e/8gH9sP4r5yb0a1UuDUEctZP3q1atYtWoVZs6cKRhhmzdvho+PjzBpyFaF\nVpWWlsLfX91zUpdwd/ELu7S8EmaLFZVmK7y9tIrPYE16RLGscuELpWp0gE3xFJR1SNdbReGCak3e\nNSwjNCOuafhQpMFPouR5OTFMfLyU1ZNlM/ohedF/Vfer6Yy7IchbWpzDykGn1cBsUQ+9khe6cPaO\n0mpYRBj88Mig1nZGVlx0dRi8I88eIwkXtEqWia+rRqG6oHQcqVdM7Rz4ypYswzhU7NUQG4yuFL6Q\n7CsLF/T30YFlbF7GAF8vZSOLDxeskknNyya/d+LrpdaDyh24+lxqNdLvLivzGKr3yVKXPSTQGwVF\nFYpGGMPYvos92kXi51PKHtwIvZ/QR6wxsPtwGqwckNQxCqMHtRJCAxmGQfe2kdjy6j3IKSjD00v/\nh/MZhWjb3IC2zQ04fTkfW78+BX9R8/CtX59GZWm+2qGcQtor4VEUFlfgtdRD0GgYvDrlDvI4EG6j\nmTEIs8d3x69nsvH+lycbWhzCTSQnJ2PlypVYtGgRhgwZgvnz5wMAFixYAJ1Ohy1btiA1NRXHjx/H\nrl27kJ6ejpSUFJw+fRqjR4/GggULsHDhQkyZMqXOsijl5ogVJisHoUKaWoiOo6p29jBVBpW0PDrL\n2HuaAPvy0JLjcpzQ60pNYWNZWz6QlbPNsNeE6HB/WEQl8XQKs+erX+yPzq3CAKj3u3IUmmfL86qZ\nWrPqhQGSz7Pe/l5Q7tXQyu6z2AukdFm0Qsif/cpmkYFo3SzEqZwMy4/PCIY4P5z43osNGsXS6wzj\nmpFVKQoXVLmmjgxtsWEmLy3upWUd9pNjWUZiZOm0rCCfv6/N+FZvRizNYbNbXzVO+xaheHJ4B4mh\nWNMm2DXBWSoNn4OoYVlphUhG6klU+90ID1GuXjh2SBuhV5lSYR6Gsf2GvPiYej6qMdS/0eRkVZot\nuJR5A/clNcfLk3oq5l556TSSfNM3p/XGG1OT0DQiALsPpWHX3vPCv7pCnizCYyguq8SCDQdRWFyB\npdP7SEIECMIddG8bieThHbDxsxOIiQzEkF6xDS0SUUfi4+MlIeu8xyk1NdVu2y+++ELyuW3btrj3\n3nvdJotSuJdcN/nyR1vFPF8VZWn/sb9cPp5YWa6Ue7IUlFBxTpbMkQWrlROUTLXZcrEnS35ej97d\nBrsPXcb1IuW8I7OFg1UcLqhwjLgmwUIYmZoyKfeyiT1RGrbmYU2uVn0To9Myss/VMnl7ae2MNEcy\naTQs2jYPxdn0AtVtAFHhC1ZkZMHek+U0XJBlhP0k1QVl3io+VE/DMqoeK4fnJVp3KfOGZF1IoI/q\nc8JjK+FuEWTTaFjAbEWAr1fVeUjllTcjVjMKeGOtZdMQPHhXvKQsvDOZ3E3zqCBcvmq7Nn7eNkNI\n3pOKYaQeQ32gsl6kFl6bnlUkGOFiG6tbQgR+PXNNmBRwFDKo0zaOnKzS8kr8bfUPAICmkc5zq1a/\n2B9B/l7QaGzP17o59pEJHMfh633H8MIehQFcgIwswiMoLa/Ea6kHkZ1fiiXP9FbMByAIdzC8bwtc\nyS7CP//9O6JC/dGxZVhDi0TcIigVLmBZBkum9UZRiQlvbvkZx87ZKv6pPXfrPjkulJ92lsherXiz\nEg8YA+WZc5ZlVHOyOI4TSsarKdW8gm8rfGFvaDjyA1itHCwiLS/AVzlKgfdwuOLJWjdnILx0Gjy5\n6P8AANNHdb4pyqBOI8vJEhl+Pl4aOyPLUf6ZhmUQ5G8zHNTOGZDmZPH3+sipLAx78TM80DtOeR+1\nnCxFT5Z0W96rqVb4Qm18HrExLPfOhoX44vSlPLt9OrUMQ58u0QCkhplWwwry+fvpqo4t3be6GbF9\n7poY3pjkZW/IsPF3/jYAw178DED1pIJWVhpf3vTZz0eLf7400M7rNiAxBsfP5+LH3zMly0cNbIUl\nW44AkObu8ZMc/O+Es+Itdf1epV29gb1Hr6BHOyPatwit01hy3th0CKcu5sHXR4fcgjIkJkTgnjua\nO93PlYrVDMOgc6vwWstG4YJEg1NuMmPhe4eRnlWEN6YmoUU0lWon6g+GYfDUQ53QvkUoFm8+LMwk\nEkRdUfJksQyDjvFhQnhKRnYRerU3qoZCxzUJwkeLhqJLa/UXe4SssamGlRe+qDam5B4hobqgbEyr\ntbpvkZpSrZEVvmghUlJsvaPUzSyzxSpR8uJUfud5+dRm1sXKXliIL/QiT9Tgns2cKs3OlElXkBuh\nUiPLfu5aLceNl+eh/i3x/JiuDpVPsddSrmDnVFV27FtloAjHVcvJQvVYPEqeRVvYad09WfIiF2HB\nPvgrx1bQQ5z/0jwqCPclNQcADOoRI5GNN0R541weFlrtyVIPzQSq7z8rup4NCf/s8Aa2ViuvLshI\nntmwYF/ERAbaNVX28dZizuM97L43LWNCBE+WOGxTHsLqLJzRlXDBsgozPt13Hll5JXbr/vW/c/hk\n73l89N8zCns6x2yxorS80i7MtKjUhJ9PZaOswow72hvx+pQkvDYlyeGExc2EjCyiQSk3mbFk8xGc\nu1KAVyffgdbNbk7vAuL2Rqdl8fKknogw+OHVDQdU+4MQRE1QyjPilThxqegwlfwJwBbGpNWweH5M\nV8X1EXpfoby52JMlLiTAigpf8ErcK0/2kuwjx8px+HtVPytVzwXDe7Jsf//jub54rKrJt07Lwuog\nncxi5SRGVryKkfXS+O6Y8mAHVeVXLJuGZe3y4JwpzXd1a4pnR3dxuA0AJwaPupGlFOboqCgBy7LQ\naVmbgSgzZiIMfrjnjtiq7arD4Cwyo6WgqALR4f54aUJ3u7GVZBcMNka8rVixly5TCx91dK3Fnk55\nkYvQYF/B8GomiloRj3fPHc0xckBLYf/wEFvOEj85oXZsp54sWYl3RwbwzcCvqhky/9xonHiyendu\n4nA8eQsIAJif3BOA1Mji76mrp++oyAnPsbM5eO+Lk/j4u7OS5ZcyC/H9bxkAgGvXlVs9lJvMOJOW\nL0z0iLFYOUx98zuMmfc1xr7yDfKrSvtn5hRjWlWj6bdf6I+nHu6EbgnKJe4bCjKyiAajpKwSr6Ue\nwslL+Zj/RE+3u5AJwhH+vjq8NiUJWq0Gr244IInNJ4jaMGpAK7tlvELu413t4VDLnwCqPTgBfsrl\npPmiALbBbf9pWEYaLigqfFFdTbB6f7FcPGq9ll55spegvLMatqoZsS0ny0unEXKabDI59mSJjxHf\nVOoF61QVPhlh8MPwvvGSfcVeDY1MAZXjzMjy99EJhosjHBUgsauWJ/rsIzOyZo3t6tDjI14l3+6d\nF/sLBqFQXZBlJAVEACC3sEwlNFBN9uqxeMTPQ3CAd9V6x4aIoyIjYo8DH3rIExZS/fy3ia2eWJWf\n/2P3JGDaqM5oER2MB/rYQiKDqzxa8lNzNSeLla1v6Cqz/OQLb2RZrJzsvlQbOAmxeqfPt1VhpqNV\njB6tYkKE3meAuP+a8njyZuCuebJsLSrEuW0cx2Hx5iPw8dKgV3sjruaWKBY92fb1acxe/QOWvH/E\nbt1Pv/+FnOtleLh/S5gqLTh6OhuvbjiIuWt/RHFZJV55spdLoX8NARlZRINQWFyBl9f9hMuZhVj4\nVBK6tPas2Qfi9sAQ5IM3piahqLQSb2w6hHInVcUIwhG9OzfBF8tHSJbxyomvyMjilVglnJUD17CM\nYIiJPQ7yEu7yktbVYUHK42bmVIf4iGXo2c4ohKHlFZTh33vOoajUJIxnqlKgbeGC6nJbZEaWONxp\nV8owLH6mt+q+bZtXT8CJlUJ1I0IdJU/Tpvl34x8z+kqWOSoxLr+GYsW3W0KkfGvJp9enJGHp9D6i\nsaShYZJxFc6VYezDBfMKyxWVYCUFWuzJUrtW/PPJPwZqIZZKCn+HeNu9EntNlDxZPOJ8F/l4XjoN\n7ktqDoZh0KtDFL5YPkISXiimOrfIsfHAe3rkxhYArJk9QHGf+kTwZFV9p81mq0K4oDSPzBFqBRI1\nLCMNF3RynWrTOqeswjb+jZJqIysrrxTZ+aV4cVwihlRNbkxe/H8oLa8UtrmSXYTPf7gIADhxIQ9p\nVSH8py/lY9yCb/CP7Ufh76vD2HvaINBPh9UfH8Pv53KQmBCJFTP7oWc7Y41lvVmQkUXcdHKul2Hu\n2h+RV1iGJdNsTeIIoqGIDg/Aq5PvQHpWERZtPoxyExlaRN1467lqhZ2f7BcXP+DzS5RwpkhJChGI\nZuPFyhXLVJdrVlXcZYf5bP8F4W81zwWv3JeWmwVFPbCqEEFIgI+TnCxbuGBUmD/Wzx2kWO5eDTXP\nn1pxD0coGVkRej+J4g/Ye18cHUMsxkN3xeOZkZ0U1wG2qm5qURvyQicsa2+AsYxyqXFXS9fbqgvy\nsilfK/75rPb2qOfoybm/dxyaGQPRLs4gLJPnCYnvZ4voYPh6awTZ6kq1J0t5vUUwsiA5ZoCvDrHG\nIOWd6hG5J6vSYpUa12x1Cfe6XB+NhlX0ZKkN6axZuBJ8wZez6QX4M83WW+rKtSIAQHx0CBLbRGDO\n491xo8SE9744iWNnrwEAfjpuK9bxjxl9odOyeHH1fnAch28PXYaGZTBrbFe8N/9u+HhpMfPRbrjn\njlhMHtEBzz/a1WM9WDxkZBE3lbSrNzBn7Q8oN1mwdHofKnTrtbIAACAASURBVHJBeAStm+kxP7kX\nTl++jkXvkaFF1I02sQZEGGzhNkoehkCVUEBAqriumzMQk+5vJ1kvbjSslrzPMIzdjL1SbyQ1HBlL\nPLwM/bvFYMGTvdC1TbjqLDpgU24tVg4+Xho0CXdeXlmMoxw2Oc6MDbUmx/LdHHmyHHmcNBpGUma7\nJmox79WolkkUNibOyVK40HL5o8KUm2szjHQsJUKqPFnmquPUxJPVJCwAa2cPlIS7vj3rLsk24qIv\ngX46PD7U9ozXtMeZGPmpqPVxE74Xsh1qY1S4A/5atG1uM0qjwwMUwgUd9/4S8/yYLkLorRiNzNvt\nLFxQJ6vC6MpvQrmor9mi947gvS9O4r+H0sAwgD7IGxoNiz6dozG8Xzx2H0rDK+sP4qsfL+K3P6+h\nfYtQJDQ34KH+LVFhsmDE7M/x/a8ZSOoYhYHdmwnXqWd7I54d3QUP9GnhVB5PgIws4qbx65lreGnN\nD/D11iLl2T5oGkFl2gnPoXOrcCx40mZoLdxEhhbhHpSUGFc9WU0jApHYVhp+xjKMoFDzPgl5aA/L\nMELonlwRludoiQkJ9EZosI9QWMMR4hyhHu2MVZ+rFbEFVYU2eCwWKywWa61yYPROelmtfrE/Nvx9\nMAAXwgVdqFoIOPFkye6pPORPMlQNmrjGNQlG6suD7fLpxMOwLAOrggEoln/t7AF467l+qrIzCvuI\nCQqwPZ+858NRtUm7ZQoGmaOWLDqtRhjHlWdDTdXnz4p/7tUuO29kyQ26hlLa+V6NPdoZ8e+lDyCu\nSbDkeWJE1QVdmSAZ3DNWMfRWfm2FUvaiYy16+k7MHm9rSsx7mrWs9LryVJrt86rKKsxoGhGA16cm\nISrMH5/uO4/DJ7MQ4OsleYaSh7XHjkVDEWnww7uf/oFTl/IF726PdpHC8R67JwEjFfJcGxNO+2QV\nFRVhw4YNOHHiBDZv3ozly5fDbDYjLy8Pc+fORW5uLjZu3IigoCDExcVh3LhxN0NuopHx9YFLWP/p\nH+jSKhwvTeiuGldNEA1J51bheHVyL7y+8TAWbjqMV57spTrzTRCuoKTsuerJAuzDeVjWvqFwy6Yh\nuJorKpvMVDeo1QhGleOcLACwWDgM6h6jvoEIpXHEDpaO8dLZdHNVwYyaeCsYxqZsOfMyiEOGnCmi\n8sIUwn6yE6o0W/GvN+/H0TPXsHTLz9JtHYQLsiwj8aLU1KQ0hvqDYRhwHKcYLsgwjOBhEiO+rs0c\nhL2xtrrsDmWQ5wyqhY+qNbx2BS8tK+Tzyb2ttUL+PVEZrHkT27URhzPK8yhvJkkdoxweX8OKPNd1\nDBcUU90nq3pZ51bhQvl1/j7Km1TzFJdWQh8k/S6VVZjh461Fx/gwzE/uheUfHsWvZ64pTlj6++qw\nbs4gIU/Mv8qLG6G3RQDEGgPxyODWNT1Nj8Ppr11lZSWeeuopcByH9PR05OfnY86cOXj44YexY8cO\nbNy4EbNmzcL8+fOxd+9emEz25ReJ2xeLlUPqZ39g3SfHce8dsVjwZC8ysAiPplPLcLw2+Q78mX4d\nr244iGKFkrIE4QxedxErnUPvbI6WMSEIFHmy5Mn2ciVV7gljxUpX1f9ihZFfXu3JkgqkVl0QACpM\nZpcT3pX2F892ixVCPx+tUPiiJopidfEOFgMSm7rU48qRkj+4RzPVJtDy/Z5+uBN8vLTCTL5ULvV9\nxeGcStu6gtIpiD2HY6vK5kv2cfG6MgxgNPg53EZuZKldd6Vr7erzs/algVj09J2q49QU+Qjy53Pe\nEz3xSnIvxBqD8J9/DEdCcwMaijaxeiE80BkMwwjeO1fCBdUoKCqXfNaoFNMQCoII3kXbdvJwQaVS\n62UVZvhV5ZgF+Xth7uM9EBXmj/tVmmXrtCwCfHUI8NUJ9yskwBv9ukZj5thuNTo/T8XpFK3BUP0g\n5ObmIjLS5sqLjIxETk4O8vLyYDTaKnsEBwejuLhYsg/Pzp07sXPnTskyMshubcoqzHhr+1H8cjoL\nUx7sgGF9WjhteEcQnkDHlmF4Y2oSFm46jJfW/IjXptwhzLARRE0Q/+Y9M7Kz3fpYYxBCAr1RUFX2\nWK70KHm25NtEypRmmwdIlpMl7K/+G2wyW1VnruUo6cViRYxhGKydPQBaLYvvf/0LX/90CdYaGlks\nA1irZJ41thuef9S54iW+Xhv+PhhT3/xO+Pz8o8q9xwD7Hmd3dmoinIedXE4MYfHH2vR+lIdeAqL8\nOwYYemcc1n1yHIDNwD51KV/1vm577V7MXLkPRaW2Rq4sw2DuxB64kFGgevxgWTir2ntb6Ziu3l9j\nqH91v7d6KKMuH/KODlHC3w1dtl0tlFMJhqnO1RuQ2LTWx7x8tUjyWSurOsojL7LBFyWRO0+Lyyoh\np9xkkUR++HprhTBeV2FZBrPHd3e+YSOhRnEwUVFRyM7OBgBkZmYiOjoaJpMJWVlZiIqKQkFBAfR6\n5R+UMWPGYMyYMZJlGRkZGDRoUC1FJzyZzJxiLH7/CK7ll2Jeci+PLrFJEEq0iwtFyrN98GrqIbz0\nzg94bUqSpOw0QbhCTRVIZ54sDcvahQ/JJwBYhqkOF2SlYUHO5HGnJ4sPW9NqGKFPVk0UXN7Y0LC2\nkvQuOLIk56dW/EEJb50G00Z1xj///btkeYCffeSFfU6W8vqB3WNcym+T48iwk98/vk9ZXBPl36aQ\nQG+8v+AezF37I05ezAPLMgj083LYNsVRiwElmcQYgtR7wKmPU/fyAPw142SfGzssw8DHS1vnkEZ5\nIZfqEvbqx50yooMoJ1RqZX3+w0UkxBokz0BRiQnRETUranOr4/TJPnbsGFJSUpCWloatW7ciKCgI\nKSkp+PzzzzFy5EgkJydj5cqVWLRoEYYMGXLLPNhE7TlyKgsvvP09Ks1WvPWcZ/cwIAhHNDMG4a3n\n+iLQzwtz1vyA38/lNLRIRCPDpVeiSpid2mf5snC9tPoen8sEOC7SoIQrIXn8MeSIPVliETWsrSJe\njT1ZtShdXRcd5L6k5nbL2sWF4tXJdwil6pXkYRkGY+5uLflcF5R2r+5zJl3J5+INcJJLx98bV66P\nvMqhGnKD+cnh7WvVX0njBr3RWbhgY6U+vHyAePJFOj7/FdawDIb3i0d0VSVQeeGLn37PxLkr1yXL\nCoorhMqUhA2n36QuXbqgS5cumDNnjuJ6vV6PZcuWuV0wovFhtXLY+X9/4sP//oke7SLxwmOJCKD8\nK6KRExrsi6XT+2DJ+0ewYMNBPDm8PYW+Ei5T09AkR0UVAJsRJG+kKm50bNun2pMlVCIUlHTHx6+L\nJ8tLpxF65YjXazQsyirMOPjHVXRLcL3xfF30S38XDQVX6N42EmI13s5wZRmMv7ctxt2TUPW5bsdT\nzsmSHrtl02CczyiEqapwgKs9nly5po6egceHthX6edkbALW7YXW9XuJDVxuTbhjTzQT46pBo16za\nMfV1HvzvkryEirz1A08zY3WFyEVP3Yn56w8g/4Y0z6uguELwrBI2qGwW4RZKyiqx4sNfceRUFsYO\naYNH725TbzMwBHGz8ffV4fWpSdj0+Qmk/ucELmQUYtqozqrloAmCxxVjnBOpOvJZfbvSy5rqcEF+\nU7viGAwDrio6iC80pFMo2ayEq0aW0s/7tFGdsfyDo/ZjijbW1cDToXbtAv28FBPveR4Z3Bp9Ojdx\n+Tg1RS08UO5tcqW3kBK9OkRh39EMxWPwl3LZjL4wWzjkFpTh3JUCl/s8ueJl07AM/vnSQDslGgBG\nD6r22HnSRBMjM/A8SDSBjxYNrfE+dfWK8gxIbIrfz+Ug/4Yt95M3ynUyzzX/e9FD1jqiaUQg7ura\nFN//loHgQG+wDHC9Ko8UsOXgV5gsLoea3i6QkUXUmbSsG1iy+QgKiivwypOUf0Xcmmg1LJ56qBNa\nNg3B2n//jvSsG/j7pJ5UEINwiCtKklgX12qlhrtckdVpWaclr8WFL0YPao07OzURqplVG2bK+zoy\nsl5J7oXdh9Jw5FQWlLwW/bs1xZavTiG3oEyyXFw+2tXCGjYZlYVcM3uAUChEiQn3tXU69t8n9lBs\n6qsui+3/u3s2UwgXhOxz3RTj58d0xdQHO8qOLw2d1Gk10GltPagc9aHiEfpHuTD5qdGwaBLu59K4\nUhlrtLlb4Y/N54SFBbvewNqTcZeR9cJjtv5Xw178DACQ0FyPCfe1xR0dpPqav68Ou1IeEPpkiRH/\ndgQHeONafimy80uxdMsRtI2z9bly1tPudoOMLKJOfP9rBtb86xjC9X5YOfMuNAmnpEfi1mZQj2Zo\nZgzEks1HMHPFPjw/pit6iSpXEYSYmuhID/VviYHdpRXE5EqWzZNVNbZKeBbDMLBUadW+3loMvTNO\nWOfMueLICOrZ3ogbJRU4cipLCEeU8/RDHbHju7N2MvPUxJM16f522P7tabvlhiCfWhVYEMNXD1Si\nVUyI3TL+dKc82BHX8ksl65Q8iYB641xnaDWsXS81edhnbXFlb1fz8jyKKpG7tYlA62Z6u7YGjZX6\nighiGEa1D5WSgWXbqfpPQ7APPtl7Hr7eWpzPKMT5jEIYQ/2EpsKEDTKyiFphqrRg42cn8M3By+jb\nJRrPju4MPx/KvyJuD1rF6PH2C/2xaudvWLT5CO7vHYcnhrWn8MEG4OzZs9i4cSOCgoIQFxeHcePG\nAQB2796Nffv2AQCSkpIwfPhwbN68Gdu2bcOePXsAAF999RVOnjyJiooKJCYmYujQmofzOMOlcMEq\nbXzi0LZ2TUPtcrJq6MlSU9LUDDRnCjavgFWarYrre3WIspt08BXlR9WkMMK9Sc1xr0IxivpkV8ow\nh3lLDKqvaUiAN6IjAuzugys5Rm2bG5CeXeR8wyrq2pC2XZwBpy/nu7S/uxT73p2auL1cOl9Fsbms\nmiJ/FA64pRR9Twx7BIDx97bF6xsPYfu3Z4RlPdsZ4UXvQAlkZBE15mpuCZZu/RnpWUV4ZmQn3JfU\n3KNiswniZhAc4I1Xknvhyx8v4b0vTuLEhVzMntDd5QR0wj1s3LgRs2bNQlRUFCZPnozRo0fDy8sL\ner0eS5YsQVlZGebMmYPhw4dj8ODB+P7774V9d+/ejbfeegtlZWV45ZVX3Gpk1aoJrYJCKldSdRqx\nkaV8EHEzYvn+Tj1ZTowgXomqNFscDyRCXISiJuGCDYFabpNwqZnqv9vHh2Lu4z3stnUlxGvZjL41\nkksIF6zlq3bC0HZ4oE8Ll4zc2lQIVGLuRPtr4whX9IiEWAN2Lh5qN6lb1zw4T8Vd4YLugK806Out\nRfe2kejUMgzHz+dieN8WyMwtwaAezRpYQs+DjCyiRhw4nolVO39DoJ8X/jGjL1oqhFUQxO0CwzAY\n1rcFOsSHYtm2XzBr5fd47J4EPHRXvJ1Hgqgf8vLyYDTa8gqCg4NRXFwMg8GAnj17orS0FCkpKZg+\nfToAICZGWub6wQcfxMyZM1FZWYmxY8eqHmPnzp3YuXOnZJnJpF54AQD6donGv/53zsWzUC+vrZST\nJRTHEK3q2c5YlStlMwLUqoT5+9pe+93ahCtK4tzIsq03W1xXZsUKcUM3gq0t4pL4QoELlW0ZsVvF\nTQjNiGt5/TQsg7AQ1/KUanuP6npnXTWQbqeoGXeHC8YaA5GW5boHVczoQa3RrU2EkIf82pQ7cPnq\nDbRsGkIT7SqQkUW4RLnJjC1fnsKXP13CHR2MeP7RblSenSCqiGsSjJWz7sL2b85g69encOB4Jp4f\n0xWx1Ly43jEajcjKykJUVBQKCgqg1+sBAFevXsWqVaswc+ZMwQiT8/HHH2PdunXgOA7JyckYOHCg\n4nZjxozBmDFjJMsyMjIwaNAgVbkm3NcWY4e0cekcHOmWciVLp9XYlXAHgFee7CUktTMOPFl+Pjp8\nvOR++Hgph/U4y5nyqgoXlDc3dYS/6F3R2FUxSSimA0+i+4/r2HvpTpQmiDq1DPPoZuy3qo7v7vu9\n+JneyJblFLqKhmXQuple+KzTatAqRu9gD4KMLMIp5zMKsPyDo8jOL8WUER0wrC/1CCIIOT5eWkwe\n0QG9OzXBqp2/YubKfXh0SBuMHNDKbeE3hD3JyclYuXIlgoKCMGTIEMyfPx+LFy/GggULYDQasWXL\nFhgMBowcORKpqalIS0tDSkoKHnzwQSQlJWHlypUwm83o37+/W+ViGEY9gbxG40g/i/tkqcGy1X2y\n+KajYuR9tSTrnPSX4j1ZajlZSvg5OF5jofo+VBuwahZjfRQrqA4XrP93r1ZB/sXP9Ha+Y0NWFwQf\nLthwMtQH7n6UggO8qcz6TaTx//IR9YbFyuGTPefw4e4ziIkMxMqZd9HMPEE4oW2cAateHIAPvz2D\nD789gwPHr2L6qM6SGUDCfcTHx2PZsmXCZ97jlJqaarftnDlzMGfOHOFzmzaueZoaErucLK1G1DNJ\nrbqg+v7O0Ac6rtpXm5wsv1sg6qE6XNC5J6s+JiGFcMGbYMg0xh6X/CW/5XKyGuG9IKqh6VVCkUuZ\nhZjzzg/Y/u1pjOgXjxUz+5GBRRAu4q3T4Ilh7bFsRl9YLFa8uGo/Vu34DdeL7Jt7EoSj3Fa5wq7V\ninKC1DwpjMiTVcNy3CFO+tzwhSHMZteVWS8PKHbRuVWYewZinHuy6sPZdDW3BAAQGerv/sFl1MRI\nfH/BEDz9UEfnG7r5uGrcWiaWZzV8JmoOebIICWUVZny4+ww+/+EiokL9seSZ3ugQ76aXE0HcZrSJ\nNWDVC/3xzcHL2P7tGRz4IxNjh7RxucoXcXvw8qSeyLmunCch95ZIqwuqj8mpFL5whlp1PR7+ua20\nuO7JYhgGw/u1wOf7L970kLKN8+7GjZKKOueOiGuN8IUXmqtUEhX6ZLlR4+eN5oTmntX/KTTYF95e\ndVMl/auuZ5C/l5Mt1RGMkVvMyiIbq3FT42/Grl278NVXX6FFixYIDg5GRUUFzGYz8vLyMHfuXBgM\nnvUDQLgGx3E4+MdVbPz8BAqKKvDYkDZ4eEBLt+QUEMTtjEbD4oE+LdC3SzS2f3sG731xErsPpeHJ\n4R2QmBBBM5UEfL21aKaisMsfj7gmwbhyrahqnSsl3F0z5nu1N+LXP6853Y43wmqSk8WP//n+izdd\nCY40+CHS4FfncfjryTBAuN4Xa/42ADGRgYrb1sdXeuL97dCva1MPLThVVR2zlhZ0l9bhmPdET/Rs\np1ygxhWqbaxbw8piGcDKNd5qnISNWk0/+Pv7Q6vVIjIyEseOHcPixYtx6NAh7NixA9OmTXO3jEQ9\nc/JiHt7/8iTOpF1H19bhWPT0nWgSFtDQYhHELUVwgDemj+qMe++IxYb//IHXNx5C51ZhmPRAe7Rs\nSq0QCGXEhtT7C4YgNNgXmTnFVevU97E6aUbM8+a03jBbrOjUMtwl9ZSfeDPX0Mi6VTy3/P242eHz\nfj66W6rJrhiGYXCHrIF1TWnfIhS7D6U5zSlsLGg1LExma50m4VpRi50Gp8ZG1sCBAzFw4ECEhITg\n8ccfR1xcHAAgMjISOTk5qvvVps8IUb9cvnoD274+jSOnshDXJAivT0lC1zbhNLNOEPVIfNMQLJ3e\nB4dOZGHLVycxa+X36N+tKcbdmwDjTci3IBovocG2PkdCuKCK54Blq8PLnE2E1zQc3N9XhxbRwRg7\nJKFG+3lSU9Xa0MjFr1fEXr6GYkBiDHq2M0raBTRmNBoWqIORtXPxUKehv0T9U2MjKz09HdHR0QCA\n6OhowVDKzMwUlitRmz4jRP1w8mIePtl7Dj+fykaEwQ8vPtYN/bo2pSo2BHGTYBgGSR2j0KNdJP57\nOA0f7f4TPxz7C4N7NsPoQa3dEt5E3Lo4+60W98lyt3GjYRmseqG/W8dsDNQmv+pWCV1rLNwqBhZg\na9UA1L6a5O3UsNmTqbGRxbIsXn/9dURHRyM6OhpmsxkpKSnIz8/H3Llz60NGwg2Ullfi+18zsPtw\nGi5kFKJpRACee6QL+ic2pbwrgmggtBoWQ++Mw4DEGHz90yXs2nce3x1Jx6AezTB6UCvybBGK8IaT\nVab5a1gGFisHhgGeeKAdtn59Wii5ThDOSEyIwNEzzvPy5JAp6X748FqKLGrc1NjI6tChA1avXl0f\nshBuxmLlcPpSHvb8cgX7j/2FykoLuiVEYv4TbdCjnZE8VwThIfh6azFyYCsM7R0nMrbS0KtDFEb0\ni0e7OAO9bAkBtd9uQ7APcq6XgWUYtIk1uNZAlnCJmnz9Gut3dd4TvWCqdL1qJFF/aG6RHMbbHSrh\nfovBG1Y//p6JA8czcb2oAmHBPni4f0sM7tkMEXoKQyIIT4U3tu7vHYc9R6/g8/0XMHftj2gRHYxh\nfeLQu3M0fL3pZ/t2hzeyrFapD8EQZDOyPBE+BLZ7u8gGlqR23GI9bhXRadla5fFEVukVFObsPppH\nBSG3wDO/y4Tr0Nv6FuB6UTl++/Majp65ht/+zEFRqQmGIG/07twEfTpHo21zA3mtCKIR4eOtxdA7\n43DvHc3x65/X8Nn+C1i18xjWf/oH7uzUBIN7NEP7FqH0vb5NUcuz6tXeiD/TrnvkLLg+yAdfLB/R\n0GIQ9UDn1uFYM3sAYlXaEBA1Z/b4RKRnFTW0GEQdISOrEWKxWHEm7TqOnsnGr39ew4WMQgBAiybB\nuDcpFokJkWRYEcQtAMsy6N42Et3bRiIrrwR7f7mC//1yBXt+uYKwYB/0aGdEz/ZGdGwZBm/Kvblt\nEDxZMvfKqIGt0L9bjIf2Umrc1CoC8DbwfvGQgeVe/Hx0Htd4mqg5ZGQ1AiwWKy5mFuLkxTycuJCH\nExdyUVJuRqCfDl1bR2BYnxbo2iYChqBboz8EQRD2GEP9MfaeBIy5uw1OXsrDgd8zceRUFr45eBne\nXhp0jA9DhxahaN8iFPFNg6mgzS0Mb2TJQ9gYhkG43rcBJCLEBPt7AQC6JUQ0sCQEQTQkZGR5IJVm\nC86mF+DkxTycvJiH05fzUFZhgU7LonUzPYb3i0diQgRaxuipGzhB3GawLIOO8WHoGB+GqQ91RHpW\nEY6cysKxszn48L9/wlRpgYZlEBMZiOZNgtCiSTBiIgMRafBDpMGPqs3dAvDhgtztkCjUCAnw88Ku\nlGHUp4ggbnPIyPIArt8ox7mMApxNu46Tl/JwNu06TGYrfLw0aNvcgJEDW6FDizC0igkhBYkgCAGG\nYRAbFYTYqCCMHtQalWYrLmQU4HxGAS5l3sDFzEIc+D0TJrNV2McQ5IOwEB8E+XsjOMALQf7eCPDV\nwcdLA28vDbx1Gnh7aeGt00CjYcCyDJqGB0BPnnKPgZ9bIxPLcyEDiyAIMrJuMjdKTDh/pQDnMq7b\n/r9SgLzCcgBAoJ8O7eJCMf6+traQn+hgj0xgJgjCM9FpWSQ0N0hi+S0WK3ILy5GVV4KsvFJk55cg\nr7AcN0pMuJJdhBsleSgpq0S5yYJKkTEmpkN8KN6c1udmnQbhBJasrJvO+HsTsPnLkw0tBkEQjQgy\nsuqJcpMZGdeKkZ5VhCvZRUjPKsLlrBu4ll8KwFaquWXTENzVtSlaxoSgVUwIIg1+jba/BkEQnolG\nwwqhgp1bOd7WYuVQYTKjwmRBRaUFVisHi5VDaDB5sRqSpx/uhFYxIcJntcIXRP1x351xuO/OuIYW\ngyCIRgQZWXWgvMKM7PxS2wxxfimy80txNbcEGdeKkJ1fKiQlhwb7ICYyEHd0MKJV0xC0jAlBk7AA\nqv5HEIRHoWEZ+Pno4OdD1ek8ift7S5V7oYQ72VgEQRAeCxlZIjiOQ1mFGcVllSgurURxmQlFJZXI\nv1Fu/6+wHMVllcK+2qrZYmOoH3q1j0JMZCBijYFoGhlI5XQJgiDqibNnz2Ljxo0ICgpCXFwcxo0b\nBwDYvXs39u3bBwBISkrC8OHDsXnzZmzbtg179uwBAJw8eRKffvopAKB///7o06dxhESSJ4sgCMLz\nafRGltlixa6951FQXAHOysHCceA4m8FktXKwVn22WjlUWqwwVVqEUBj+f35ZWYUZFqv9S0vDMtAH\n+SA0yAeGYB90jA+DPsgboUG+iArzR6TBD4YgH/JMEQRB3GQ2btyIWbNmISoqCpMnT8bo0aPh5eUF\nvV6PJUuWoKysDHPmzMHw4cMxePBgfP/998K+W7duRdu2bXH16lVERUU14FnUDHrXEARBeD51NrLU\nZhFvFmaLFb+czkZxmQkMw4Ct+sewEP5mWQYMY/M2eXtpEOTvBS+dqJJW1d8+XloE+ukQ4OeFAF8d\nAvx0CPTzQqCfF73UCIIgPJC8vDwYjUYAQHBwMIqLi2EwGNCzZ0+UlpYiJSUF06dPBwDExMRI9j11\n6hTmz58PlmWxYMECLF++XPEYO3fuxM6dOyXLTCZTPZyNazQJ80dckyAM6hHjfGOCIAiiQaizkaU2\ni3iz8PHSYtmMvjfteARBEITnYDQakZWVhaioKBQUFECv1wMArl69ilWrVmHmzJmCESYnNDQUDMPA\n19cXFotF9RhjxozBmDFjJMsyMjIwaNAg951IDQgN9sXqFwc0yLEJgiAI16izkaU2iyjH02YCCYIg\niMZPcnIyVq5ciaCgIAwZMgTz58/H4sWLsWDBAhiNRmzZsgUGgwEjR45Eamoq0tLSkJKSggcffBDT\npk3Dq6++Cj8/PzsjiiAIgiDqQp2NLLVZRDmeNhNIEARBNH7i4+OxbNky4TP/nklNTbXbds6cOZgz\nZ45kWc+ePetXQIIgCOK2pM5GlnwWsSZ9nvjwjKysrLqKQRAEQXgo/G+8o5C8xga9vwiCIG596vL+\nqrORJZ9FrAk5OTkAcNOLZRAEQRA3n5ycHMTGxja0GG6B3l8EQRC3Dzt27LCLhHBGg5Zw79ChAz74\n4AOEh4dDo9HUebynn34a7777rhskq39I1vqjMclLstYfjUneW11Wi8WCnJwcdOjQoZ6kuvl06NAB\nzZs3x4YNG9zy/qpPbvXnqyEgOd0LyeleGoucgOfL80xj1QAAIABJREFUyr+/Fi1aVON9G9TI8vHx\nQffu3d02npeXF5o2beq28eoTkrX+aEzykqz1R2OS93aQ9VbxYPH4+PjA39+/UZzX7fB83WxITvdC\ncrqXxiIn0DhkjY2NBcuyNd6v5nsQBEEQBEEQBEEQqpCRRRAEQRAEQRAE4UbIyCIIgiAIgiAIgnAj\nmtdee+21hhbCnTSmxGqStf5oTPKSrPVHY5KXZG2cNJZr0VjkBBqPrCSneyE53UtjkRNoPLLWVE6G\n4ziunmQhCIIgCIIgCIK47aBwQYIgCIIgCIIgCDdCRhZBEARBEARBEIQbISOLIAiCIAiCIAjCjZCR\nRRAEQRAEQRAE4Ua0DS2AK5w9exYbN25EUFAQ4uLiMG7cOADA7t27sW/fPgBAUlIShg8fjs2bN2Pb\ntm3Ys2cPAODkyZP49NNPAQD9+/dHnz59PFbWd955Bzdu3EB5eTkKCwuxevVqj5X1q6++wsmTJ1FR\nUYHExEQMHTq0XmWtq7zbtm1DWloaTCYTRo4cic6dO3uErEOGDMGCBQtgMBiQlZWFN998E3v27MHh\nw4dRWVmJUaNGITEx0WNlzczMxNtvv422bdti2rRp9SqnO+Tdv3+/3bPiqbLu3r0bhw8fBgD07du3\n3r9jdZHV19cX169fx5gxY7BkyRJ07969XmVtaNSuVUNTVFSEDRs24MSJE9i8eTOWL18Os9mMvLw8\nzJ07F7m5uR4h94ULF7B27VoYDAbodDpotVqPlPPMmTN49913ERYWBl9fXwDwSDkBgOM4zJgxA+3a\ntUNZWZnHyrlr1y589dVXaNGiBYKDg1FRUeGRshYWFuKdd96Bl5cXIiMjkZub65FyfvbZZzhx4gQA\nYP/+/RgyZIhHynnu3Dls374der0eVqsVHMd5pJw///wzPvnkE4SEhCA+Ph7p6el1k5NrBMyePZvL\nzMzkOI7jnnzySa6iooLjOI47fPgwZ7VauZKSEu7ZZ5/lOI7j0tPTuYkTJwr7vvTSS9zmzZu5JUuW\ncOfPn/doWXnefPNNLj093aNlnTFjBldRUcEVFBRwM2bMqHdZ6yrvpEmTOIvFwpWVld0UeV2VNT8/\nnzt9+jTHcRyXkpLCnT59mhs/fjzHcRxXVlbGTZ061aNlvXLlCnfgwAFu7dq19S6nO+RVelY8Vdaf\nf/6Zs1qtXG5uLjdr1iyPltVqtXILFizgFi5cyP3888/1LmtDo3atGpq8vDyuqKiImzhxIpeWlsa9\n/PLLHMdx3MGDB7m1a9d6jNznz5/ncnJyOI6z/TZ7qpznzp3j8vLyOKvVyo0fP95j5eQ4jtu0aRO3\nfv167p133vFoOT/55BNuxowZ3NKlS7mPP/7YY2VNTU3lVq9eza1YsYJ7//33PVZOni+//JJbv369\nx8q5efNm7uDBgxzHcR79XVq4cCF37tw5QY66ytkoPFl5eXkwGo0AgODgYBQXF8NgMKBnz54oLS1F\nSkoKpk+fDgCIiYmR7Hvq1CnMnz8fLMtiwYIFWL58ucfKCthmzvz9/RXXeZKsDz74IGbOnInKykqM\nHTu23mWtq7yTJ0/GsmXLEB4ejpKSEo+RVa/XQ6/X46effgLHcUhISIBWa/ta+vj4wGQyebSsAPDX\nX3/Vu4zulFf+rHiyrF9//TU+/PBDj5c1NTUVo0aNEjxetzpq16qhEcuQm5uLyMhIAEBkZCRycnI8\nRu74+HhwHIf33nsPiYmJsFqtHilny5YtcerUKcybNw+9evXyWDkPHToEHx8fxMfH4+eff/bY+w4A\nAwcOxMCBAxESEoLHH38ccXFxHilreno6Bg8ejH79+iE5ORldunTxSDkBoKKiAv/73/8wfvx4lJeX\ne6ScQ4YMwdy5c/HZZ58J8nminBMnTsTWrVthMBhw8OBBTJo0qU5yNgojy2g0IisrC1FRUSgoKIBe\nrwcAXL16FatWrcLMmTOFk5YTGhoKhmHg6+sLi8Xi0bICwEcffYRnnnmm3uWsq6wff/wx1q1bB47j\nkJycjIEDB3q0vH5+fnjppZeQlZWFP//806Nk3bx5M3x8fDBnzhwAAMvaUiVLS0vh7+/v0bI2BHWR\n19XvoSfIevDgQQwdOhSDBw/G1KlTkZSU5JGyVlRU4NSpUygvL8eRI0eQmZmJbt26Cc/xrYjatfIk\noqKikJ2dDQDIzMxEdHQ0TCaTR8htMpmwZMkSPPDAA4iOjsaaNWs8Us7jx4+jZcuWWLduHSZOnIgm\nTZp4pJzfffcdgoODcfz4cfz1119gGMYj5QRsxkt0dDQACLJ5oqxhYWHC3xaLxWO/SwDwv//9D/37\n9/fo7/yWLVuwcOFCxMbGYsKECcjKyvJIOa1WK5566imEhobiwIEDKCgoqJOcjaIZ8YULF7B+/XoE\nBQWhVatWOH78OBYvXowpU6bAaDQiICAABoMBI0eORGpqKr799lvce++9ePDBB1FYWIidO3fCz88P\nQ4cOrXdFpS6ytmnTBk8++SQ2bdpUrzK6Q9ZDhw4hLy8PZrMZERERgrXvqfKeO3cO+/btA8dxePbZ\nZ4XZs4aWtVevXpg3b56QKzh8+HCkpaXhwIEDqKysxKOPPlrv+WN1kfXcuXPYs2cPsrOzcffddyM5\nObleZa2rvCtWrJBsM2XKFI+V9fDhw0hPT0dpaSnat2+PCRMmeKysbdu2BWDLK01KSrrlc7Lk12rM\nmDENLRIA4NixY9i9e7fw28eTn5+PuXPnIj8/3yPkTk1NxeHDh9GqVSsANiVWo9F4nJyHDx/GJ598\nAj8/P1gsFoSGhqKiosLj5BTLe/ToUZhMJo+V88SJE9iwYQOio6Ph7+8Ps9nskbJmZ2fjzTffRGRk\nJIxGIwoLCz1STgBYsmQJHnnkEbRs2RJvv/22R8p5+PBhfPvtt9Dr9cjLy4Ner/dIOS9cuIC33noL\nISEhGDRoEE6cOFEnORuFkUUQBEEQBEEQBNFYuHXjOQiCIAiCIAiCIBoAMrIIgiAIgiAIgiDcCBlZ\nBEEQBEEQBEEQboSMLIIgCIIgCIIgCDdCRhZBEARBEARBEIQbISOLIAiCIAiCIAjCjZCRRRAEQRAE\nQRAE4UbIyCIIgiAIgiAIgnAjZGQRBEEQBEEQBEG4ETKyCIIgCIIgCIIg3AgZWQRBEARBEARBEG6E\njCzCI8nIyMDYsWNrvN+vv/6KO++8EwcOHBCW7d+/H6NGjcLo0aOxa9cuYfnvv/+Ol19+uUbj//XX\nXzh58iQAYODAgTCbzTWW8Xblo48+Qvv27YVrtmbNGnz99dcNLBVBEIR7offXrQe9v4jaQEYWccuQ\nm5uL9evXo2vXrsIyjuOwbNkyrF+/Hlu3bsV7772HkpISAMCSJUvwt7/9rUbHOHToEE6dOuVWuW8H\nvvvuO1y5cgURERHCsqeeegqbNm1CeXl5A0pGEATR8ND7y3Oh9xdRW7QNLQBx67Nr1y78+uuvuHbt\nGtLS0vDyyy/jrrvuwqBBg5CUlIRTp06hb9++mDVrluL+n376KT766CMwDIOHHnoIjz76KH788Uek\npKQgIiICERER6NGjBx544AGsWbMGr7zyirBveno6wsLCEBoaCgDo1KkT/vjjD2i1WjRr1gwGgwFF\nRUWYOXMmTCYTzGYzli5ditjYWPTr1w/79+8HAIwdOxavvvoq1q5dC51Oh9jYWADApk2bsHfvXvj5\n+SE1NRUajcZO/oyMDLz44oswGo24fPkyHnnkEYwbNw7Dhg1D165dkZCQgIEDB2LevHkwm80IDAzE\n0qVLMXv2bDzzzDPo1KkTrly5gnnz5mHr1q2K10jpWu7fvx9r166FVqtFbGwslixZgl27duGHH37A\njRs3cO3aNSxcuBBdunTB+++/j927d4PjOEycOBH33XcfJkyYgFatWiEkJAQDBw7EwoULodPpYDAY\nsHz5cuh0OrvzXLNmDZYuXWonX1JSEgYPHoxvv/1WWKbT6XDXXXfhm2++wUMPPeToESIIgmgQ6P1F\n7y96fxG1hTxZxE3h0qVLePfdd/HGG29g586dAGw/apMmTcLHH3+M//73v8jLy7Pbr7i4GBs2bMD2\n7duxbds2bNu2DUVFRVixYgVWr16NdevW4bfffgMAeHl52f1w5ubmwmAwCJ9DQ0ORk5ODQ4cOoXv3\n7sI248ePx7Zt2zBhwgR8+OGHiucQEBCAhx56CJMnT0bPnj0BAB07dsSOHTsAwOEM4Z9//onXXnsN\nH374ITZu3Aiz2YzS0lIMGzYMjz32GFauXIkZM2Zgy5Yt6NWrF/79739j0KBBwkty7969uPvuu1XH\nV7qWxcXFWLVqFT744ANcu3YNZ86cAQBkZmZi48aNWLBgAdatW4e0tDQcOHAAH330EbZs2YJ169YJ\nIRGdOnXCc889h08//RSTJk3C9u3bMWLECBQUFKjKooS/v7/i8sTERBw5cqRGYxEEQdxM6P1F7y8l\n6P1FOIM8WcRNoWvXrmBZFkajEUVFRQAAvV6Pli1bAgASEhKQlpYmzNjxpKWloUWLFvDy8gIAtGnT\nBpcuXUJ+fj7i4uIAAHfeeafLcnAcBwC4du0a2rdvD8D24lqzZg1SU1NRXFyMdu3auTxeYmIiACAi\nIgLFxcWq28XHx0Ov1wMAwsPDkZeXB47j0KFDBwC2+PqMjAywLAuTyYQePXpg2LBhmD59Op599ll8\n//33WLRoker4StdSr9dj9uzZ4DgOFy9eRGFhIQCgW7duYBgGnTp1wuXLl3HixAmcPXsWEyZMAABU\nVlbi+vXrACDIN2DAACxatAgXL17EAw88gPDwcOHY+fn5eP7551FRUYG//voLEyZMQN++fTF16lSn\n1y8yMhLZ2dlOtyMIgmgo6P1F7y8l6P1FOIOMLOKmoBSGwL8w+L8ZhlHcV7xcKVFXbT/A9vLIyckR\nPmdnZ6Nv376SbbZu3Yq4uDgsX74c+/btk4QEODouID0v8fnIUTtXfubSy8sL//znPxEcHGw3/pUr\nV1BaWoqoqKgajf/qq69i06ZNiImJwXPPPae6j5eXFwYPHoz58+fbbcPL16dPH+zYsQP/93//h2nT\npmH16tWIj48HABgMBmzbts1huAVBEERjhd5f9P4iiNpA4YJEg1FYWIjLly/DYrHg7NmzaN68ud02\nsbGxuHDhAkwmE0wmEy5evIgWLVogMDAQV69ehclkwqFDh1SPERMTg7y8POTk5KCkpATHjx9Hp06d\nJC+v69evIyYmBgCwZ88eVFZWAgCsVitMJhMKCgpw6dIlALYXYm0qMl26dAk3btxASUkJ8vPz7WY8\nO3bsiL179wIAvvzyS/zyyy8AgP79++Odd95B//79HY6vdC2Li4sRGRmJ3NxcHD9+XDiv33//HRzH\n4Y8//kB8fDzatWuHw4cPw2QyoaKiQvEls337dpSXl2P06NG4//77ceHChRpfAyWys7MRGRnplrEI\ngiBuFvT+qobeXwShDHmyiAYjPDwcmzdvxsmTJ3HvvfdCr9dj165dMBgMQuhAQEAAnnrqKTz++OPg\nOA6TJ09GQEAAZsyYgSlTpiA6OhoJCQlgWRa//PILVq1ahYsXL+LUqVOIj4/HypUrMW/ePEybNg0M\nw2DGjBnw9fVFr1698K9//QuPPPIIRowYgb///e/4z3/+g/Hjx+ONN97A4cOHMXLkSIwePRpt2rRB\n27ZtAQCdO3fGvHnzYDQaa3SucXFxeOONN3D+/HlMnTrVbmZ0xowZmDt3Lv71r3/B398fK1asAGBL\nCF65ciW++OKLGl/LRx55BI8++ihatGiB5ORkLF26FMnJydDr9Zg2bRoyMjKwZMkSREdHY9SoUXjs\nsccAABMnTrQbPyYmBtOmTUNgYCACAwMxadIku22aNm2qOgu4fft27N69Gzk5OZg0aRL69euHqVOn\n4rfffkOPHj1cuYQEQRAeA72/qqH3F0GowBFEA9G3b99a77t//34uKyuL4ziOe+aZZ7hffvmlRvtb\nrVZu9OjRXH5+fq1lcJUrV65wjz76aL0ew9Vr+cknn3ArVqyoV1lcpbKyknv44Ye50tLShhaFIAii\nRtD7y33Q+4u4VSFPFtEosVgsePrpp+Hj44NWrVoJCbyuwjAMXn75Zbz11ltYvHixW2Q6ceIEUlJS\n7JaPGDHCLeNnZ2f/P3vnHSZFlbXxt6o6Tc6ZOOQcRRABFUVUkF0VUVEXA2uAXdOaUdQVFV10jSvK\nGtHPcdEVlCWKknMeJcMMDMMwTE4dq+r7o/pW36qu7ukeZpgZuL/n8ZHpUHW7urr7vHXOeY/hXJSb\nb765UbZ/rpk7dy7uvvtuRERENPdSGAwG45zBfr98sN8vxvkMJ8tBuh0ZDAaDwWAwGAwGgxEWzPiC\nwWAwGAwGg8FgMBoRJrIYDAaDwWAwGAwGoxFp1p4sh8OB3NxcpKSkGM6hYDAYDEbrRxRFnDlzBr17\n94bNZmvSfR08eBDz5s1DbGwsOnbsiMmTJwMAFi9ejM2bN8PtduOmm25CWloa7r//fgwbNgwAMG3a\nNMTHx4e8H/b7xWAwGOc/Z/P71awiKzc3V/0BZDAYDMb5zVdffYXBgwc36T7mzZuHRx55BBkZGbj3\n3nsxceJEWCwWfPPNN/jyyy/hcDjw0EMP4bnnnoPFYkFkZCQAICYmJqz9sN8vBoPBuHBoyO9Xs4qs\nlJQUAMrCw53bwGAwGIzWQVFRESZPnqx+5zclpaWl6u9JXFwcampqkJiYCJNJ+bmz2WxwuVxITU3F\ne++9h8zMTHz99df4+eefMWbMGMNt5uTkICcnR3Ob0+kEwH6/GAwG43zmbH6/mlVkkRKL9PR0tGnT\npjmXwmAwGIwm5lyU1aWnp6OoqAgZGRmoqKhAQkICAIDnlRbkuro6REVFobS0FFVVVcjMzERUVBQc\nDkfAbU6aNAmTJk3S3FZQUIDRo0ez3y8Gg8G4AGjI7xebk8VgGOD2iNi27zQKimtgs5jQo0MiOrcN\nvV+DwWA0D3fffTfeeustxMbGYsyYMZgxYwZmzZqFiRMn4vnnn4fb7cbUqVMRFRWFV199FW3btkVF\nRQWee+655l464zyjus6FXQfPYET/rOZeCoPBaAaYyGIwdPyy/QQ++fE3VFQ7EWE1weUWIUoyenZM\nxH1/7IvsrLjmXiKDwQhAp06d8Prrr6t/kwzU2LFjMXbsWM1j33nnnXO6NsaFxZtf78C2facxrE8G\nTAIzc2YwLjSYyGIwvMiyjH8v+g0L1xzB8H6ZuHVMN7RLi4FHlLBt32nMX7ofj729Bvf9sQ/GDuvQ\n3MtlMBgMRgumrFIpQZXlZl4Ig8FoFpjIYjC8fLPiIBauOYJ7J/TG9SOywXEcAMBsEjCsTyYGdU/D\nRz/sxfsLdqOyxolJV3Vr5hUzGAwGo6XikSQAygU8BoNx4cFEFoMBYFPuKXy9bD9uu7o7JozsZPgY\ni1nAtJv6ITHWhvlL9yPSZsb4EdnneKUMBoPBaA14PIrIkpjIYjAuSJjIYlzwlFU58E7OLlzUMw23\nXNU16GM5jsNtV3dHrcONjxfuRXK8DcP6ZJ6jlTIYDAajteARFZEFprEYjAsS1onJuOD56L97wfPA\nX28eoJYI1sc943tjWJ8M/GP+duw7VtbEK2QwGAxGa8MjKuqKZbIYjAuTsDNZR44cwfvvv4/ExESY\nzWaYTCZ4PB6UlpbiqaeeQmJiYlOsk8FoEvYeLsH6PYV4+JYBiI+xhvw8nufw6G2D8NyHGzDrs814\n8+FRSE2IbMKVMhgMBqM1QTJZTGMxGBcmDcpkPfPMM5gxYwb279+PsrIyPPnkk7jhhhvwzTffNPb6\nGIwmQ5JkzFuYiy5t43H5oLZhP99qFvDMlCGwmAXM+mQLHE5PE6ySwWAwGK0Rn8hiKovBuBAJO5PV\nqVMnyLKMTz75BIMGDYLkdc9JS0vDmTNnAj4vJycHOTk5mttcLle4u2cwGo0NewtxtLASrz44HDwf\nWpmgnvgYK2bcdTGeeG8t/pmzE0/eMTjkkkMGg8FgnL8Q4wtaYsmyjFqHB9ER5uZZFOO8pbLGCZPA\nI4qdWy2GsEWWy+XCK6+8gnHjxiErKwvvvfceAKCwsBBZWYGnmk+aNEkdCkkoKCjA6NGjw10Cg3HW\niJKMr5cdQP8uKejdKfmstpWdFYeHbxmA2V9sw7cZB5m1O4PBYDDgkbw9WZJPZi3blI/3F+zG/BfH\nIi469BJ1BqM+bp+5FJnJUZj79JXNvRSGl7BF1ueff46CggL8/PPPAICoqCjMnj0bZWVleOqppxp9\ngQxGU7Ap9xROnK7G9In9GmV7l/bLQt5VVZi/dD/aZ8RiaO+MRtkug8FgMFonRFzR1YJ7j5QAAKrr\nXExkMRoN0XuuFZbUNvNKGDRhi6ypU6di6tSpTbEWBuOcsXD1EfTokIieHZMabZu3jemO/FNVePPr\n7XjjLyPRPiO20bbNYDAYjNYJ3ZPFgfPe1lyrYbRm9h4pQYeMWMREWjS3ny5l4qolwizcGRccB4+X\nY19eGSaMMh463FB4nsMjtw5ESkIk/v7JZpRXOxp1+wwGg8FofdAW7qxll3E2PPPBejz7r/V+t+ed\nqgIARFjZ+NuWBBNZjAuOhWuOIDUhAkN7pTf6tiNtZjx398VwukU8P3cjauqYuQuDwWAwFIjIYo6D\njHBxe0QAwLHCKr/78ouqAQAWMwvrWxLs3WBcUJRU2LF+dyHGj8iGIDTN6Z+eFIWX/jwMJRV2PPuv\nDSyjxWAwGBcwXhNmAFDdZ0PRWA6XBzsPFDfRqhitjRq7O+B9xWV1ymPq3EzAtyCYyGJcUCxefwwW\nM4+rhrRv0v10zIzDKw8OR1m1A0++uw75Rf5XnhgMBuNcs+NAMc6U25t7Gec9hSU16r9lg3JBqZ5A\n+Ey5Hf/6bg+e/2gj7GwGY6NRa3ejtLJ1nv+1lMgiM9gIolfJi5IMp0s8p+tiBIaJLMYFg9MtYtmm\nPFw5pP05mSPRMTMOb/xlBMxmHo+/swbLNuWzK0wMBqNZmfnRRjzx7prmXsZ5T1mlr4JBMjC+EMXA\nvwV1Djfufnk5Vm07oTxWYr8bjcWDr6/ClJeWN/cyGgSdyaqscWruo0MLdr60HJjIYlwwbNxTiOo6\nN64Z1uGc7TM9KQpv/GUEhvXJxHv/2YWnP1iP/XllTGwxGIxmo6SSlTA3NYECXZLJEiXJ8H4AcHt0\nWQox8GMZ4VFW1XrPfTqTVV6tFVn0LLb6sqSMcwcTWYwLhmWb89ErOwlt02LO6X4jbWY8cutAvDB1\nKKpqnXj83bV46M1f8ePao636C5/BYDDCRZZlfPDdbhwuqGjupTQpYoCgl/RkeYJksvRB8ldL9+PT\nH39r5BUyWhu1QTJZ9DkjsUxWi4F5PTIuCE6eqUHukVI8cuvAZlvDoO5p6N8lBdsPFGP5pnzMW5SL\nj37Yi4zkKPTqmITuHRLRvUMC2qbGgOeZzy+D0ZooKq1FakLkefHZ/XX7CdTa3bju0uxG37bLI2HJ\nhjws2ZCHH+dMaPTtNwS3R8Scr3fgyovaYXCPtEbZJh3o0poplEyWvpRwycY8AMBd43s1ytpaE5Ik\nnxefKYLLLcLpFv3mXIX6XII+28lEVsuEiSzGBcHyTfmIijBjeL/MZl2HIPAY0jMdQ3qmo7zagb2H\nS5B7tBS/HS3Fz9uOQ5aBKJsJ3donYlCPVAzvm4mkuIhmXTODwQhOaaUdU19Zial/6I3rRzTu/L3G\nJJRejYpqJ+Z8vQMAmkRk1TkCO6Q1FzsPnMH63YXgOa5JRJZR0BusJ0tvanAu+f1YKZ58bx3mvzgW\ncdHWZlsHAGzffxovfLwJ/55xFVITIpt1LY3FzI83IvdIaYMuMIhBzilayLNywZYDE1mM8x63R8LP\n247j8oFtYDULzb0clYQYG0YOaIORA9oAUEoBDhwvx4G8Mvx2rBSfLPoNH/+Qi54dE3H9iE4Y2icD\nwnl0RY/BOF84VVILACgqrWvyfVXXuRBhNcHUgBEUZM5OsIG4z83dAABITWiaizt1jpbnlFdjV+YZ\nRjeiIRKdqaJ7cHnvwQ8meJvTuODHtUcBKL1LgURWebUDOw8U44rB7Zp0Let3FwJQSuOIyCoorsYD\ns1fh389ehdTE+oXXidPV+Pu/N+OBG/tiQLfUJl1vKOQeKQUAzFuYix4dEsO68Bus70qSZHCcIraC\nCXjGuYWJLMZ5z5bfilBZ48KYoU1r2362REWYMbBbKgZ6fwiqal3Y8tsprNhyHK99sRUdMmJxz/W9\n0L9r8/9QMBgtlYMHD2LevHmIjY1Fx44dMXnyZADA4sWLsXnzZrjdbtx0003o2bMnXnjhBSQnJ8Nu\nt+P5559v0P5kWcam3CIAQEJM01/5v+25JRjZPwuP3zE47Od6PPVnSIi9dWxU+OVMoUBnsmRZVnuU\nmprisjp8uXQf2qfH4qYrumjuszu9ZViNuBRaKGlCXlIuGCRb1ZxGFwXFivV8sAt6s7/Yht+OluLy\nQW2b7P0rrbTjtHf2E31xdO3OkwCAwwUVIYms//56GKdKa7E/r6xFiCzCwjVHsHDNkbAyWsEyWZIs\nQ+B5eESJZbJaEMz4gnHes2xTHrq2i0fHzLjmXkpYxEZZcOWQ9pg9fQRmT78UEVYTnpu7Ee9+uwsO\nV8u7GsxgtATmzZuHRx55BDNmzMAvv/wCl0vJUnzzzTd46aWXMHPmTHz00UdYvHgxhg0bhscffxzx\n8fHYtm1bg/a353AJFq45AgCIjTo35VVrdp1s0PPcIQTvJHhrqmRKnd333eUKQfSFyvjHFmL+kn1+\nt4uSDLdHxPLN+fh1ewEWrDrk9xin9/s0FBFKqHO48fHCvX69Mep+ReOeLJLJ8rTQTFaF11Ah2Bqq\n65TPFDlXSivtOHG6+qz3/dCbv6r9Zw/M/hl7Dpco+6GWQpwxQy1lPHRCMVgJNsi3tRBUZEkyzCbl\n3CqvcjJTrRYCy2QxzmtOl9Vh16EzmHZTv+ZUwkcUAAAgAElEQVReylnRs2MSXpt2KZZszMMni3Jx\n9GQFZtx9MevXYjB0lJaWIj09HQAQFxeHmpoaJCYmwmRSfu5sNhtcLhdKSkrQv39/AEBaWhrOnDkT\ncJs5OTnIycnR3EbEW4XGSrlhwfGyTfkhXQg629EPRBAEyz2QxzTVmIk6py/YrbO7G7WEO2flQdx+\nTQ/Nba98ugVbfi/CVUOU0raMJP/sB8lkBRJMRvy07hgWrTmK3tlJGNbHv+RLk8nSzMlSkFpoTxZZ\nX7CSM/JyPJIMQYA6d+psjUyOnqzEBwt245phHXzZRWgzeyXeTGuo5g4kq1PlFYbNDc9zDTamoEsC\n9dkqWYa3hFjEE++tBXD27wfj7GEii3Fes2JLPqxmASP6ZzX3Us4anudw3fCO6NYuAS9/uhmPvLUa\nz941BN3aJzb30hiMFkN6ejqKioqQkZGBiooKJCQkAAB4XincqKurQ1RUFDIyMlBUpJT5FRYWokeP\nHgG3OWnSJEyaNElzW0FBAUaPHg2H04MIqwCnS9QE1lNfWYGRA9rgjmsCbxdQAvD3/rML8TFWfPnC\n2KCPDWb7HQpqpiZAiZcsy3CLEnhOm31pTOierFqHGwmxtrCeL0oyOCBkx7ktvyvv8ZkKJTg3Ooak\nMiAckeVzCTQ+UAGNL7zP23GgGLzAYXhff4G293BpyOtobDi1ZyzYsVBejyhKQCP3OXOc/zGlBYXD\n6c06hihEybGvrm0ZIsts4uF0ifU/kOKf3+zAoO5pECUZZoGHyyMZZrIa0qfJaFrYO8I4bxFFCSu3\nHMfIAW0QaWu8hubmpnPbeLz58CikJERgxocbkHukpLmXxGC0GO6++2689dZbePnllzFmzBjMmDED\nADBx4kQ8//zz+Pvf/46pU6fi6quvxqZNm/D666+jtrYWffv2bdD+6pweRNrM4Hlec/W/qLQO3648\nWO/zSVlPhKX+a57EuAIACs/UhL3W+kSEKMmQZcBiFpqsr8Pu9Imshphg/OHxRfj7J5sD3l9jd2PD\nnkL173Rv5ooYDuiPQVWtCz+sPmJ4XzBI2V+grESgniwiYlZuPY7XPt/q9zy3R8KnPzXfTCy+HvEI\nAER/NUVZIwffeT5hZCfv/vznjIW6b5JFrNKJrKbK1NaHxRR+2P3z1hN4/cttECUJJu/z9RpTkmUI\nTGS1OMLOZFVXV+Ojjz5Cbm4uPv30U0yYMAFDhgwBAIwfP77BP1QMRmOzfX8xSisduLqFG140hMRY\nG2bdPxwv/nsTXpi3CTPvHYo+nZKbe1kMRrPTqVMnvP766+rfJAM1duxYjB2rzRS99tprZ72/xeuP\noW3bNhAErkFBJ3EmTAnBzc9Jzck5VliFzJTosPZVn4gg91vMQshBqMPpgc2qDSV+WncU/bumoE2q\n/+B3OgOhmGwkhLQfmm37Tmv+ptf6t7fX4OSZGnw/ezzMJh7RkRagtE7drz4Dkl9Upf7b5Qk9w0Ay\naYFElhTAXbC+/NvhE74hzVE2E2rPtRtjSCJGua9Jyho5Ts242iyC31q4EIxDaMjboO/J8oi+HqZz\niVknssIxf5FEX7ZK//mUZCXLxWhZhP2OuN1u3HfffeobLAgCYmJiIIoiMjIyGn2BDEZDWbopD9mZ\ncejSNr65l9Ik2KwmzLxnKLq2TcCL8zbht6PNV2LCYFzIFJXWQeA5TWAdKuQKe0wIbn5ut2/7wcu5\njCFBcaCQjgw7tZgFhLL56joXJj6zGCu35PvW6JEw97978cDsVYbPobN9r3y21S/DEA4lFXbM/Hgj\njp6sVG876c3wEUGqf616YRBBCcSwMlm8sRgprbRDlmVdT5bv/vqk6+L1x9R/z7j74pDXY4QoySgo\nDs+QgsT7gXrGfjtaipNnlAsDTTX0lhiiWL0ii95PKMYhhPJqB0Tvwde/t83l4GgyacsrwzmEouwT\nWUY9WYKgPdvXNtAgh9F4hC2yEhMTER3tu3r21ltv4a9//SsmT56MuXPnBnxeTk4ObrjhBs1/999/\nf8NWzWDUw5lyO7bvO42rh7U/ZxbBzYHNasLz91yMzm3i8fInm8P+QWUwGI2DwAfOZImSrCmTo6lP\n+NDQmZaGZM1U44sAOyNrsZr5kDJZxPRj/Z5TftsIhEeSNMIm0HEJhbW7TmLH/mLsOFDsdx9xDKRf\nq80i+K2PDrbDcRckm6UdEk+V1GLKS8vx5ZJ9Wic46ljqg3v6OBeX1WH1zgL1b8tZ9jst+PkgHpi9\nCpU1zvof7IVTRYzxsdhLlaefbY+g4f7hO09VkSWHn8kqrbTjzheWodhrA+/WZSmby1xEb41fn1DV\n9/YJAmdonmHUk/X6l9vU+57+YF3AC7GSJGPqKyuwKfeU4f2MhnNWuUW3243jx48DAKKiouBwBLaM\nnDRpEr7//nvNfx9++OHZ7J7BCMiKLfkwmwVcNrBNcy+lybFZTZhx1xDERVvx4rxNYf2gMhiMsyfS\nZoLA82pgrRcoyzfl4aE5vxo+N5xgz0VnsnQB7q87ClRr7UCEWi5oNZtCMr6oqVNKsGqpUqz6gl9J\nlDWOgmfTG0OOXXWdvz331t9Pe00yfEGt1SL4CSkiFBJjrWGVC5Jj5aBEIjn+G/ee0gTBMrVLfUxN\nmyDoS9rO1nnxcIFSekiXmdaHvidLlmXMX7IPJV7jEFokNCSbCihBfaD3neN8gsjm7VOsrHbhH/O3\no7LGqb6f9Qm8gtO+nkWTwPud+00hEENB79cS7Bg+8e5avDhvk++xogye48BzBiJLlmESjK+eOFwe\n5B4pxbvf7jS83yNKKCqtw5tf74BHlPDKZ1tQWBJ+zyfDn7BF1q5duzB79mzk5+fjzTffxLJlyzBr\n1iy8/fbbuOuuu5pijQxGWIiihBWb8zGyf9Z5ZXgRjOhIC16YOhR2pwd//2RzWD+qDAaj4cyePgIf\nPjUaPM+pwkcf0JVVOXGqtNZQgJBgb93uQvy89XjQfQXKZLk9IuZ8tR1vfr0j6PNJ8CrLvqHD2vtJ\nTxYfkvEFERV0Nuqht1YHfHxxWZ3X9tsXDIZTokcH5rIsq4FmRbXvAm+qt7ft/QW7sWxTniZFaDUL\ncOuCaxLkdsyMC2stpLTSQYkkX6aQ0xlfBJ5vRAsrfbaFZHIaCjlcXBhTllVjCdFnGJGz8iDemK9k\nRehsSTCb92BMfGYxXvtCa/pB3luPKKvln6Qna96ivVi9swAHj5er72d9pbnl1DlhtQiaCxRAwwXi\n2aKvrAmUyXJ7JOzLK9NkaZWBw95Mlr4nK4i7IIkHSP+nHnKxwu704HhRNTbuPRWSaQ+jfsI2vujf\nvz/69++PJ598sinWw2CcNdsPFKOk0oGxwzo091LOKelJUZhx98V45oP1+GDBbjx8y4DzulSSwWgJ\nJMXZkBBj8xpfKMGKS1eWR4LnqjoXEmK0luV0JmvFluMYfVG7gPuie7LoIJMEkMEcB3NWHsD8JfsB\nKAJtykvL8fEzVyI9Kcq3/TCNL0g/lYlq5iflWXqOF1Vh2hu/IDUxUpMNCSuTR4mgWodHFTIb9ypl\nTl3axuNP1/XEjA83AFCyTDynzWTphS4RuTarKajIOlxQgZT4CHUIrssgk0WyZErpKG18ofzf7vT4\nBfe1djeS4yM0ayGEWy5od3ogyzIibeYGZwjVOV7eY0scIPNOKQYhtED2K72UZL9yOD2SJMPlFrFh\nj7Y0jdYab8zfDsCXySurUqozOM4nF+vLRJVT8+usZgG1drfmmOhF17lC/5scqOyXlLvqHysIHATe\nX5zJsgxBMD5fSLY0UGUivS1SAhxPDXtWxFcVGxfTAJgVCeO8Y9nGfHTMjD1vDS+C0b19Ih68sR9W\nbTuB/1EN1AwGo2kRqD4Jt/fKseCdzeX2BqNVNf7lfHSgaq/HSY7OUNPBGclw1Tr8y+YI367wvzKt\nLy8kYlCxcA+6FM3zQ7H9PpBfDgAoKa/TWE3TwqamzhXUCIMurauqdar7I9mk267ujtQE37Dh2Cir\n5vlWswBRkjVBJRFdERaTXyaJ5pG3VuPxd9aqf/syWb73jLzPPMdpjCMkrxHGzc8sxs9bTwAAbh3T\nTXlNbjoTpstkhSmyJj27GLfPXIod+4vxpxeXYfNvRer+Q4WIgDqHG298uU11XyRikpzTgP/7HUww\ny7KMGrtbHSasxyijY9ONNfCIkipY6ytLpc8jkhGkz7VH3vo16PObCr34DZQNNKpGEUVJLRf0myUm\nIaC7oKueyhZasJIMYHyM77Pz+pfb8Dfq3GeEDhNZjPOKM+V2bNtXhKuHdrhgszhXDmmHccM74uOF\nuWyGFoNxjqCNL0iWg1z1J8FdZa1/v6TH4wtw6jOBcAcqF/Rela+scWH8YwuxaO0Rv+e2SfO3U9cH\naqqrW4iZLNKLRYK0miA9YadKlVIlq0WAwHPI8trPk8BcFCXc+twSzPhwveZ5e4+UqEEiHSxW1bj8\nAm2TwGlK7MwmXmN8YfUG7XQ2iaw9whY8k0VeQ1FpLf713W71vaKfQ/7N87o5WbKsyTz2yk5ShxAf\nL6rGVu/A5LPJZLk9ImRZWcPMjzdqMjnhOOmR43X8dDXW7DqJVz/bAsB37LTlgtrtBjt+v2wvwK0z\n/qfOK9NjJAStVu3r94iS+rj63AVpwUfEqluXCa0LclGiqdCLqkBli0YDiz2iDIHnjcsFZdnPXZBQ\nX9aOXkNxuSKC6fd53zHlPWsqN8nzGSayGOcVKy8gw4tg3DOhN7p3SMTsL7apDcsMBqPp4CnjC5ea\nyfI26Xt8IkiPJpNlUCJEQ0q3lP4vqlxQlwFZvikfeuiytrvG9QLgH/DRPVmyLKOsyoE9h88EXA+5\n2k5eQ7AsFLnP7hQh8Byev+dizT5JSdixQt/cqlq7G898sB6f/KgM56VFVmWN008kCgKv9vGQdXG6\nckF6n4AvwLSahZDMEJZuzMP/NuRhq3dWl5t6HzyqyNIGwbKsPdY8x6nzkt7O2YmX/r3Zuy7t+xjI\nyMCIYIOd6eP0n58P4v+WHwj4WNVd0PtayFPJcdUaX+jPn8AZky1eIblsU57h440CeH0mz+2R1H3W\n1w+mEVnetes/JySr2JgUl9UFFbV618aA5YIG2Se3RwQvcOA4zs+YRpLr78kCjI8zfSy/Xrbfuy+t\nIAWaz5GxNcNEFuO8QZRkLPcaXkRFXBiGF4EwCTyevHMwBIHDK59tqbdcgMFgnB0CJXzcVG8OQJcL\n+mey6ICsvkzWmQo7YiItiLSadMYX2uCnUxv/UmmS2ejRIRFDeqUB8A+43G5tueC3Kw/i2X9twPQ3\nVmlEGoFcbScB+fo9hX6PkWUZbo+oEWCCwKt9XB71mPl/R5FMWaFu9hUAVNa6IEky2qRGo326kqUz\n8bwm+6OILN/2SNBOiynyb4vZv1/LiBRv/xTpXXF7JBw8Xo7xjy1EeY1SasVxnCZwlWVZk3kReE7T\nx0bQv4/hVGPUJ7Jq7W5s+a0IX/xvnxpIG0F26dBlUkjpntaO3lik66muc2G7V5TS5gv0PozEhl+5\noEdSz9n6jCvotRhlsgDgWGElGhNRlHDPrBV459tdAR+jd7e85+UVhn2MDqeRyJIgcMYW7nKIIsvo\nO8bIrt9IUIVjDMNQYCKLcd6wOffUBWl4EYiEGBuemTIExwqr8K/v9pyVVTKDwQiOYnxB3AWVoIYM\nrPWVC/pneuhMSH3lOMVldUhNjIBJ4LU9WbqLKKu2ncA9s1aof4uiBLvTg2k39cPs6ZeqfTX6QPVU\naS3MJh6JsTbIsqwKlvyiamzY6zMq2HGgGHanx5fJ8q7FaA7P7C+34eZnFqvGCYAiMkj/iNsj4fCJ\nCuQX+c/4I8575JWepIw9HE6ParRAerwEgdMEmh6PpHHWI9kYEkB+9MNe/LpdyWaQfq36cOkCTY9H\nwi/blG0cO6m8Rp7j8P2vh9XHSLJWTPO8L5Ol2RaZmdaASvdg/XiiKOHd/+zC3z/ZXO92yM+EQ5dV\n5Qz67vSBeKA5Y8eLqlVBRZcx0gG/cU+WLpMl+kRWfVlHjcgi7zt1W0ykRXM+NQZnvFUjq7YFzpAZ\niRfDOW9ufzHk9kjg+QAW7gHcBT2ipPl+qLX7nydGWUGj95JlssKHiSzGecOitUfRvX0CurZLaO6l\ntBi6tkvAtJv6YuXW4/hpHTPCYDCaCtr4wqXLZJGApcIwk0WVkdXjzHamwo6U+AiNXTy9Pxr66rja\na2URwHGcui69qDhWWIX26TEwCTz0F7ePeGcu1drdmPnRRvx7Ua5fJkt/ldzu9GD97kJ4RFmTwRAo\nkeERJTzyz9V4xdv7Q1Nj14rSFVt8FvceUTGT4Hnf6yFB5pyHRgIA3KKsybyQviKy3h/XHsX2/cXq\nsRGDzG8i6Mu4XG5Rfd9Uq+xSrVU2bTcPKILFbPLvtyLCoF+XlKBrIOw9UoIHZv8MjyippimvPjjc\nr5JDlOSQy8bJ69e/Tl+ZnuR3G+HHdUfhcHlQXefSiLSySn8zBUDbd2QosqzaTNa/vtuDA8fL/dZh\nBC0sSCbL5ZEg8Bwmj+2Oay7pgKJSYyfMhlJQ7BNtHlGCR5SwatsJ7cBrA0ETE2Xxu82oJ8vtkXzD\niP16smDYk3X0ZKUmY2gkxo0uLpCLP/RnmmWywidsC3cGoyVypKACvx0txRO3D27upbQ4rhzSHkdO\nVmLeoly0z4hB386h/YAzGIzQoYcREyMKkmEJ1V2Q16UwyqsciI2yqNuprnMhLTFeYxdP7y8QJDgi\nwoYEY/rgqvBMDdqkxYDjtMJA4Dm1n4yUpVVU+3qiyFr0JYU1BkOClf3zqiAqrXQYPgbwv+pud3gw\nvG8mdh86A1GSIIpK0ExEFnldXdslIMpmUowSqNfoKxeU/OzuybERJf+hrrTw0ge/bqrviwgL/XGQ\nZe2x3nuk1DCT5faKgGenDEFplXJc3np4FB75p/HssY/+uxcFxTWorHGqJg6ZXkMRmmDDf40eq3+d\nFirLR78OvdD5ad0xJMba8MX/9iE7Kw5vP3oZAKC0ygGLiUdCjFUtswS02TIj44tA5W9A8KzK/KX7\nsG63r3RV7clyixAlGXFRFtTY3XC6Rfzt7TWqcPtxzgSUVtpRVetCx8y4gNsPxD+/8c2pu/35JXB5\nJLg9EpLjbervrtG69b1nxWV1OFHsn2Uj54dRuaAkyYbn1GNvr9EcR/3A60BrIt8Zh09UBH0cIzgs\nk8U4L1i09iiS42wY1jejuZfSIrnn+t7onZ2E1z7fhtMB5tgwGIyGQ2eXSIO9n/EF5S54+EQFDhdU\naMoF6UyWLMu488VlmLcoV72tzuFBhNWkyZrR++ve3j+Ln3+qSg2USIke2Y8oytifV4bxjy1EaaUd\nJZUOJVPGcZBlGaIkoW1aNAZ1T0ON3QVRktXA2GoR6s1k6UvOCJU1TrUniQ7i9BCRtuvgGYx/bCH2\n5ZUhKc4Gk8DD481SCTyvilA6mDSZeIg6kUXKz+577Wfc99rPvscKnCqsjK7q09vQZ3hyj5Ri4RrF\nzXF/Xrn3dWsfI3st3Akut2hot+0RJZhNPGxWk+q+2LltPKZP7Oe3DgAo9GYHa+xu1Zwg0mbyGz2s\nZOi0t1VUO3Hbc//zK5mjZ3oRkuJsqjtisEwW4BPGZKAwAJRVOZAYZ1NFAHltjnoyWcFmbgUr7czR\njSsgIoa8dzzPw2zi4RElVWARHv3navx1zq8hlY5Kkoy1O0+iuKwOtXY3KmtcuGxgG/TKTkK79FhV\nqNTaqVlqBkKFPqZOt4h7Zq3A54t/93ucyyOC5zgIBhbushx4Rhm9T+NywcClgfR5QLK+jNBhIovR\n6imvdmDNzpO4dnjHoFe+LmRMAo8n7hiMCKuAWZ9uNmxiZzAYDYceQKtmsvQ9WVQm67G3V+ORt1Zr\neh/oGIkEhFt/P63eZnd4EGkzQ+A5TdkR2d8LU4f5OatO/8cvmPnxRgCA2Uwc4pTvSUmSsXbXSQBA\nwekalFU5kBQXAY7jIMnK/TzHITrSjO37i/GHxxepfVdWs6D2jbi9azEqFzSizuFRj82vOwoMHwMY\nX3VXMnscFq05AodTNCwXJP92ixJESl2kJUb6bQ9QMmtqn5pRwCkFzmTR6OeOEZRMlna79LoJbo9k\n+BtGMpx0tocM9QUUMepwecBxxrO1REnyy2TlHi1BdZ0bKzZrnSjJ8TpEid/EWJt6vomS7JuLZlD6\nZiROaupciI60qMe4b+dkANpjafS8YOWzoYggAikTJeYrFjMPs8D79R1t23dadbl8J2dnwO3tO1aG\ng8fL8duxUrw+fxvumbUC97y8HABw85Vd8dq0S/H6X0aomTxyXhiJXUBbQkhn+vQo5YI8eN4/8ydJ\nssZM5dJ+mZr7yUWEWZ9u8TvH6WPZt3MysrPi1O8s2pHxw+/3BFwbwxgWkTJaPUs35IHnOVw9tENz\nL6VFExdtxbN3XYyTZ2rxzre7mBEGg9GIaOdk+a6YA1S5IJXJInENfTWfHvRKemzoPog6hxsRVhNO\nl9Vh4ZojyFlxQLM/i5nXzInSo5YLqj1ZkiqEquoUt77kOBt43lsuKCuBbnSkr8fnoPfKP53JEtWr\n3loBos9kvfHXEep+Q3HOMxJZdqcHpZUO1NjdWL+nEIJAiyzfNk3eIJrOkKQnR+HaSzpg3PCOmm2a\neJ9hBgl4jxVWYuLTP6GqVjuPa7lOlISCJMuGgkRf3uXxSIYlX6QMkn4tRWW+vq+n3l+HA/nlsJgF\nw+OqZP2M11ZWpS3XNMooRUeYNeWCxBClzun//tC9UM/N3QBAOf/NAq+ea53bKu6X9RlfBMMoI1Re\n5cAW7wBmmrTESFgtAl77fCsAINJqUjKdun1+9N+9AICUhAhs3Fvod/7tOFCMO2YuxRPvrcVjb69R\nHROvG94RtQ4PMpKj0CbVV66ZnRUHm0XAu9/uwi/bT+BMuXEViVuTHfT9u22atvQzaLmgrLhrEpLj\nI/DlC2Pxh1GdvK/Jd4FBb8BDn5vZWXEwCzzcHuW7ob4ZW4zgMJHFaNU4nB78tP4YrhjcFrEGzaMM\nLdlZcXh40gCs3XUS3/58sP4nMBiMkBAEPqjxBc9zqPbajtPQ5Tt0fFynG3brESW4PBIibSZVCOw6\ndAayLOPEacWZzyTwQa/+kwBeLReUZFXkrd5RAJ5TAmA6kyXwHCyUSQMxC7BZTHC6RUR5A3C3R/QL\nfO06W/ForyGDPoOgN0QAFJFXU+dChG4gbbv0WM3ftLsgLTBMgpLto4+3iefxwI39MPmaHtptUMeN\nCKqf1h2DwyXiVEmNXzAeaQuvnZ3ub7trXC81w6EXVG5RMrR2J5kseh3lVdqMx6ptJ9T3Sa+z9ueV\nacr3AN97s2bnSciyDIfTg+37TxtefEuM9ZULekRJFVnv/We3sj7qnKum+vB2HVRmrBHxSAREgvf9\ndgbpyZo4uovfOmiMROvMjzcaOihaTDxSKZERYTMZO/FJEiaO7oJ7xveG3SniTy8sxWc//YbnPtyA\nB19fhZkfbUSN3Y0rBrcFAHz3y2H075qC+2/oi5xZ12LOQyP9RC75fL359Q5MfWWlsh6zoCntpUU8\nfb52yND2hbk9SuaW4zgUFNcgZ6Vv3pkkazNZPMchPsaqmqhoyov9TE2U/Q/omoJJV3WDycRj1bYT\nuPmZxc0ysPl8goksRqtm6aZ81NjduPHyzs29lFbDiAFZuPnKrpi/ZH9Qq1kGgxE6dCaLzJuiLd0T\nY6yQZP+SMtpBjw6iSXDjEZVtkav+dIDPgcPKLcfx3S+KXTjHcThOWaHrRQ/pA1IzWaKsbnfP4RJ0\nahOvlAsCak8Wz3OaPqRTJUoPD8lkRXnXY2RyoXcys5qVx7p1AfInM67S/D3n6+246amfsONAMRJi\nbOrtX7xwNS4fpC2HFHheLQOk41ulb0vSBO8kI6Qv0zPqyVq9s0Ddvv44xkSGdkGPBNISZXzRu1MS\nsrOU4JkWWco8McmwV4uIGDpQNirFtJrJc7Wvb/v+036PJc8XJRkrthzHlJeW4YWPN/mVQ7427VK0\nSY2GKCrmGSs2H/fbFm3YYlTu5vYo4tHjUdYfHWGBwHOqACkuq1MzpAAw7tKOuPPann7bMVo/jdG8\nKUD5XDx8ywD170ir2VBkKUYqPIb1ycCLU4ehXXoMftl+AhU1TvTplARAeZ/uv6Gv+pw//6GPsk2b\nOeTz4rHbBuLhWweqf3sC9LmRmWwE2sJ918EzmL9kP4q8TpayLMNEndfknOnizRqWVvrcJfU9g+Si\nzfSJ/REdYdacg8EGjDPqh7kLMlotLreI//56CJcNbIP0pKjmXk6r4vax3VFSYcc7OTsRH2PFwG6p\nzb0kBqNVw/McjhVW4t+LchEXrVypJ0GxW5SRFB+BkkoHKmuc6v2ANpMlGgTRbm9gSlz9Iihba44D\ntvyulEclxSli5IbLOqt9U/qAWV8uWFRWi0MnytX9JcbavNvl1OwLz3G45apu6J2dhP9bfkCdd8Vz\nHFweyfta7IZ22HTDP6CUMwK+K/fZWXG4bGAbPzvzX7crAqeotA5t02LU22nBReB5DneP74XBPdI0\n95u8xga0MOENygoBb0+WWi4oobzKoR47p1v0y5q0SY2u10CoY2YsnrtnKCY/vwSgerLorI+Jet0v\nfLwJOw4UI9vA1U7toaMEI8lE9emUjL1HSgAgYKmoUcmX3eUT7T+uPaoaZ+jHAfTKTkJeYSU8koxj\nhVV+5YWAIl5J647RmAKPt1yQHIMImwk2i6AG+4++vVrTr0iXzQZCn5kDtL1zNDzPqY6TtQ4PImym\ngO6OJpNSjjeweyoGdtf+LibHR6BTm3hEWE3ITI5CTJRFc34a8e7fLsfT76/TlB6aBF4j9OnMrsao\nRWdhT5cLEvYeLkF6UpSSdabEEXlMXLQVEVYBE0Z2xjfe8mJ9Tzb5PJKLEHRGjImss4NlshitlpVb\nj6O82ombrgheVsDwh+M4/OXm/ujXJWmQLWwAACAASURBVAWvfb5FcxWRwWCET02dG8Xldvyw+oh6\nNV8dnOoRkRynXJXW90PU2j3o0ykZIwdkacqGiKgymzjv30qQFqELvBxOEYmxNvzjr8psqCG90jHj\nriEA/J3wiJghAVjOioOa4DbBK7J4Dt5yQeWxsVEWXNI3E3HRviv1oiRDFCXVaOHnbccRE2lRSwIB\nZdgvnTEgIoCIybcfvQx/vMy/CiGLsiGvrifIE3gOFrOAwT3SNLermSy6XFB1V9SGPiaep/rUZE1A\nbFQGmZEUhR/nTEBvb3bDiKS4CNUgQqLcBeng2kyJvQP5Zbikbwam39zPb1tkuXTWYu4PignB3+8b\nhsljuwOAWsanx8isg4i0tqkxarkpoJyrfvv3lsLSAmrUgDaqW2PImSyvWLVZBFgtJrVnr1I32iCY\nqyCglJcWltT4z/IKYDFONkfe9whvT5Yeu1PU9DXpmTi6q3pB8r3Hr1A/c8HokBGLDpnaEleTwGuO\nGS0O6QstfsOYPSIEntNkbIu8Yp/0T5LPNv2Yb18Zh8lju+ORWwd4X6dOZHn3ST4ftACtNBDNjNBh\nIovRKnF7JCxYdQjD+2bWeyWJYYxJ4PHUny5Cm9QYzPxoI44V+l8ZZDAYodGRCqTI4NdTpbVYt/uk\n2hQPKLOy6MClosaJ9KRIdGufoHEZIyJLLbHzXu2mA2m3R4LLI6Jvl2QkU6VFRMx8+tNvmjWS4CmQ\n6USit1eG4zlA9lmkE+gMnChJ8Iiyuq/jRdXIzor1y6bQAsViMIDXiKyUaHz8zJUAAjv2EQL1oCnG\nF9phxCR41z9FEHzGF6IoaYJQl9sn1HplK6IqwqZ9T9ISI9EhQxtIp8RHqMdZpowvNCKLOh61Dg+G\n9ExHl7b+NvwkIH/0n2tQXuVArcOjChNB4JHszWJyfubtCsQFkoa8xsyUKE1gb5QMImumBVTntvGG\njzV6v/SZLJvFm8nyGqUk68rijIbq0iTF2SDLimuhdj+BM1n064i0BujJEiVNNigYRpmwQNgs2gsj\ngsBpXmPATJZF0GRdSbkgXZpLvmskr+sjOVcEg8/4wG7KhQiHy6PJSOrPTZOuXFDfF9nS2Zx7Ss3a\nNTdhi6zq6mrMmTMHd911FwBgzpw5mD17Np544gmUlZU1+gIZDCNWbTuBM+V23Hxl1+ZeSqsmwmrC\nS38ehtSESDw3dwOOF1U195IYjFbJXeN74Z+PjAIAnKnwlZLN/mIbnC4R6UlR4DhlVtbtM5dqnpuV\nEg2B51VzAcDnzEeCMf1AYUDJVLncop94IWKIlN0R6gsME6hyQclb4kZf2KdFliQpPVtEVOUXVSE1\nITJAmK8QzJSDLlnu2j4e6UlRuGxQGzx550VB1xwo66EYX2gzWYJ6lZ/TCC2LWdAMaNaILCqTRYQU\nySaS7NlHT1+plIX96SK1Pzgh1qZmE4iJCABNEK+3Wyflmn6vkXrOhr2n/IxDEmMVkVLjzXbq42uj\n/iWfyPIfXqyHBPq0O57FzMPtEb0ulIGdASXJ22tm4tVg3mYVYKMyWZnJ2nJ/o/Nk5r1D1Qwn6X16\nf8Fu/OnFZYYljC9MHYqUBOW4ELFLzmWrRfDNjNPtylyPwGsI+uyz3qDGQ33u6dJUm9WkEWgeUYbZ\nJKDcK3ZTEiKwbtdJuD2KRb8mk2VwDElmbO2uQvzpxWXqhVWyf3KexVBuopU1TkRYzQgXjyhh0doj\n+Ex3oacpOFZYidNldfCIEjbsKcTLn27BV0v3B53Bd64IW2S53W7cd999kGUZx48fR1lZGZ588knc\ncMMN+OabbwI+LycnBzfccIPmv/vvv/+sFs+4MHE4Pfh62X5c0jejQVPZGVqiIy146b5hiIu2YsaH\nG9SeCwaDETomgVdFCrm6TBMbpTTG60ujAOCKwW1hEjhNMC5Sc4kApVQI0IkslwiXR4JFJ546ZMT6\nlRrpn2tEepJiIEHPQaLLmuhyQSWw812lr3N4kJYUqc4kCpcZdw/B249ehszkKIwZ0h4A8NhtgzCs\nT/AB84H6d0wCD5dH1IosOktBva6MpCifE6RfJktU3wNisNGpjWImcPPorvj2levUwPaSvpmI9gqA\nSJvJlzH0mogo6/XtV5+xSYwzFln0e1BYUqOZXUQ/r9ab2fEXWf4lgKQfKj3A7DDN/r3HuLjcd15b\nTDxkGX4Ojno8ogSPqMz/IsfAahZU45Q9h89ozFoAGGaZBvdIw6AeihAnTsJbfz+NsioH3vx6u58D\npM3iG8pM3p+/3DwA/bokg+M49bOgz7yGmskKB/1n0WziNectMQQBtH13NougZk3p24g74ICuqXB5\nJOQeKYEkyeA4DmT5vEEmS7H4V+aBAVDFmj6TlZHsE95VtS5ERYT+mXZ7JBwuqMCKzfn4+IdcfPfL\nYcz97x64PRLyT1U1aEan3ekJWLYoyzL+OudXTH9jFdbsLMCrXpt+QJlFOPuLrVi5JR/b95+G2yOe\nc7fEsL8NExMT1X+XlJQgLU25kpOWloYzZ84EfN6kSZMwadIkzW0FBQUYPXp0uEtgXOAsXHMElTVO\n/Om64O5DjNCJi7bi5fsuwfMfbcRT76/Dc3dfrJbGMBitic8++wwFBQWorq7Gfffdh+zsbADKj/HM\nmTMRExODsrIyvPjii5g7dy4OHz6M1NRUtG/fHrfffvtZ7ZsEf2VV/gFBVIQZsVEWfL1sv+b2pDgb\nEmJtvmG4kgSeF9SAlARAJJNFB6AujwiTxKtDhgkcx2Hy2O749yJ9uaBx2U+EVYDdKaoGQkQciJKs\nueIeSV3RJv0wdL/P5QPbYtSANvhgwW7sPKiNB7q19y+D068tOysOc5++Mujj9AQqLUtJiMRvR0sN\nywX1xEVbqHJB2a9ckGSy+nRKxsfPXKkeJ57n/LIUZA82i6DJZBERQB9PfQJIX1ZGoF9DUUmdZnYa\n4MuA1eos8x+7bSDmfL3DcJt2hwc8p52fRLhrXC/YnR61B4wcN3pGGDmX3B7RT+DQuD2Smski5Xwc\npxw3h8uDZ/+1we85gd4nkrGNpVz8urVPwO5DJVi5RTu/LDUhUlWbRHAM7pGmZh/J+221mDQiVG+K\n0hjoDSwE3SBqjyjhQH4Z0hKjNHOyBIH3O79oUXj72O5YvjkfJ05Xw+WRwHOc+lqNsoE8z8FmEdTz\nW1A/59pMFp1ZrKp1ITk+Ah0yYpF3qgpHCirUiwx6CoqrMePDDSitVDKL7dNjYLOY8NO6Y1i/uxDl\n1U4M7Z2OxyYPQsHpGkRHmkMyLZv50UbsyytDVko04mOsuH5ENjpmxiEjOQrvL1DGCDhcojrnbPrE\nfhjYLQ1fLduHdbsLsW53oWZ7w/pkIMKqlKxOGdfL7xg3Jme15YyMDJw+rSjiwsJCZGVlNcqiGIxA\nlFc78N0vh3Dt8I7ITK6/zIEROgmxNrw67VK8/MlmPD93A564YzAu7h38KjKD0ZJwOBxYv349Pv74\nY5w4cQIffvghZs2aBQDYtGkT0tLSMG3aNHz33XdYsmQJACA6OhqyLAf9/crJyUFOTo7mNpfLPyNl\ndAWeEB1hwR9GdcZ7/9mluf2inune55JMigyzySeu6PlEgH8mSzLJfpksAJgwshMWrjmqyaoZBZDx\nMVZcNrANflh9RLWMJvGZR5Q0pYgWs28/bq9j3aAeqYiPseKe63sh0qaIsHEjsjUi67vXxmmCPjJn\nqDEIFJBnZ8Zi+aY8TWbNSJCNGtAGd1zTQy05EyVlZhTHKdt2USJCELh6g0IyWNZqMakBrxzA+OKq\nIe2wL8/XZhHotZAMWvv0GBSX16mZjNvGdAOgLe+i6ZmdZDi4FlCyAyaToLpS0phMHCZf3j3ousi5\nkHukNORM1oSRnbBg1SEASsZ2U67/4OBA+wOgnucx1EzMf/x1JJ7+YB027j2l3mY28UhJiPBlsgw2\nR4wv9GYhwT7DDUWfyTKZtO6CJZV2/O2dtWibFq1awgOKONQLAFqIx0ZbERNpwccLc72P9wmlQJW5\nFrOgikqHywO706POOyNrSqWym2T49BWD22Luf/fi4bdW45MZY1BSYcf8pftw4xVdMKBrCo6frsaP\na4+itNKB28Z0Q0WNEyP6ZyEu2ooHX1+F8moneJ7DptwiTHx6sbq/R28biEibGd3aJ6hloLIsqxdx\nyqoc2JdXhtTESAzsnopftp3Aq59vRWpiJKbd1A/LNuWjQ0YsLu6VDlGS0b9rijob7OFbBuKKwW3x\n89YT6Oq1sv/wv3uxce8pdG4bj8MnKlDn8OCxyYOCv4FnQdgia9euXVi2bBny8/PxxRdfIDY2FrNn\nz0ZZWRmeeuqpplgjg6Hyf8sOgOc4TGK9WE1CdIQZL/55GN74chtmfbYFd17bEzde3jlgozyD0dx8\n9tln2LhxIwDAbrcjJkYxwtFXV5SUlCA9PV29b//+/bjlllsQGRmJyMhI3HvvvRg1apSf8xwQXiXG\nzHuH4sV5m/xuj44w4+qh7fHdqkM45c0Q3HN9L4y7VMm0+TJZsuH/fT1ZvoDN6RYhy4DZ7L9mjuMQ\nE2lWRZZSKmT8OZ4yrhduvLyL31BfjyhpAsQrL2qH9KQozFuYqwb6sVEW/OXm/prt0QEkx2kD2R/n\nTDBcQ0MJ1OeVnhQFSdb2IxkZbwzrk4HoSIvq+kjKBW0WE3hOyWQZmVYEghyXCIsQwPjC915ddXF7\nCAKHt/5vZ9Dtt02LwaJ/XI8vl+zDL9sL1H0M9ZZS6t9XYoAheA1MAMV2vqC4Rn2M3eWBWeAM+8D0\npglGwoOch0bDf2ncHgker7vgHdf0UCtQCr2fASOMPoOA7zwiYp7Qs2MSlm3KU/8ms+TIyzA6R8gF\nB6vus9MU5YJpupJMs64ni/ROniqp1WQFjTKl9OdR4DkkxdlUsxFRlikHTeNziZ6B5XCJ6rgHGr3w\nNutEYWFJDV7+ZDMcLhF7DpcgNTFSnVGWnhSJW6/urnn+wjeux8kzNTCbeLz77S7sOVyCIT3Tcbig\nHG/M3w5AueBwca90tE2LwS/bC/yMK56dMgTZWXH446jOWLklH18vP4CXP9mM+BgrnrvnYs2waZq+\nnVPQt3OK+nenNvGIi7YiIzkKSzbm4YMFu7H19yJkJEfh1WmXBswmN5Swt9a/f3/0798fTz75ZKMu\nhMGoj/xTVVi2OR93XtND04DNaFysZgFPTxmC+Uv24fPFv+NYYSX+cnP/Rv/yYTAagylTpmDKlCkA\nAI/HgwcffBCAf3VFRkYGNm/erLnv0KFDGDhwoNKjYTZDDtLAHyqDe6Thb5MH4R9fbdfcTkoJ6f6G\nrJRoNShSjRdESfN/j6gXWTz6dk7GnsMlarAdyLWPDrTiowMMSpWVYC0+xvedqpYLitpyQUHg0a9L\nCgTBN6DYqCfKqB+kqQgkTPTfV8P6ZCCKspfX9+vQA5rrnB5EWAVIsjJY+tcdShAcSpaDvE82i0lX\nLqidRaSugzpWwYxBSIldSYUdny/eB0Cb1Xxo0gDVwZIg8LzqAHjHNT00/SpKJos3tH3Xmybo15WR\nHKXJagbD7ZHgFiW/fsBSg75F37q1Iv2ygcoAavL+cRyQGGvFTVcoF1vTEiM1vY6krJWITaOLC0Qk\n6s1HmqJcsFOWtrxOEHh1VhYtqmKjLJq/rWYhqMgClGHDpI+6zuFR1x/oM0hfpKGHEo8c4PuupMcw\nAMr3Cy0+t/xeBIdLxPUjs7FozVF0bhOHgd1SsXRjnqHY4XlOdYF++f5LsHDNUYzon4kIqwk1dje+\nWrofK7Ycx4otx5GdFYcIqwk9OiTiqiHtIHtfI+m/T0mIwM1XdUNUhBl2pwcjBmQFFFhGdO/ga3m6\nfFAb/PDrYdidHhwuqMTEpxf7CWKTwOOG4ckhb18Pi5oYrQJJkvHuf3YhIykS40dkN/dyznsEnsOf\nruuJDhmxeCdnJ/JOVeGJOwajfXps/U9mMJoJk8mEESNG4OWXX0Z1dTUefPBBFBUVYcGCBZg2bRp+\n+uknvPHGGygvL8cLL7yAjRs34umnn0ZycjJGjhwJQWgcq+JRA9ugR4dE7DhQrPYMkL6M6Aif2KGD\ndvJvUhZIgi19uaBJ4DHrgeH4dUcB5ny1HU6XGHA+Eh1cBrowJcNfWNLlgkbBGs9xqsAzCkrpoLyx\nQtZAAiRQ5sFG2U5Pua4nbgwwT1EdwOrdTnm1AzkrDiI7Kw41dS443SKWbswLui8a2mrfl8nyNxdQ\nXxd1fOvLlJEMDhk+TIvrK4e0C/jaAJ8jH8Hh9PgJDKM10eviOWDhP5RM5P780Nyk3R5RyWTpjl2/\nrinYsb/Y8Dn0uhf9w5f5JCLL4fLg85lj1dv1gbEqTIJkssgteqOWpigX7Nw2Hk//6SK8++0u1Njd\nMAmKC+APb1yPR/+5Goe8LnhOl8+o5fZruqNnx0QIPKcphdSv9+7xvbBiy3EAymBzky4brYc4OgLA\ne//ZhQdv7AuOU0xmCPrnms3aTNaiNUeRkRyFqRP64I5resBmMUGWZQzunoou7YL3XnIchz+M6qT+\nHWkzY2jvDKzadgKAMmSa5zncOqYbrrq4veE2BJ7D9SM7Gd4XDjaLCXOfvhKyLGPWp1tQY3eju653\ndNehM/hyyb4G74OJLEarYMnGPBzIL8crDw4PGFAwGp9RA9ugXXoM3pi/DY++tRr3/qEPxg5tz8oH\nGS2WO+64w++26dOnAwBeeOEFze2jRo3CqFGjmmQdqYmRGDusgyqyCLRbGB3QkUCQiCsiqojjIJmR\nQ4KdWKovxagnC9D2ZYST/aczWUaZKqVXSdssH2RjIe83EB8/c6WfCxwh0BV7+vHBMkTqXCHvY0hA\nJYoSrBYT6sJ0Q1MzjmZePf6anizd8eJDzGQB/lbgRmWiAHzigtp2pM6lzu70aDJ7mqfrRZZX9NC/\nvYGypy/fdwk27C3E/zbkAdAaX9A8M2UI3vq/HVi/uxDTJ/bD6h0nVfEYSGxGeUWmXWfwoe+TS/O6\nZAbrySLHmgwJJzRFuSCgOE9+8uNvXpHlf3GlQ0Ys8ouq1M/92KEdwHEcundIxBczr8adLy4D4J/J\nio5UynXf/XaXkskyBS8X1JuUfPDdHsPH3/fHPli59TiOFFTCoisXBIDR3r5KkjHmOK7BPdzD+mTg\ns+fH4ODxcrzy2VbIsowh3l7VcwHHcZhx98WG9+3YX4xn3j7W4G0zkcVo8ZRW2vH54t9x1ZB26NOp\n4WlbRsPomBmHNx8ahXmLcvHBgt3YnHsK027qr84gYTAYoWM2EFaALyOkt24n/9YHqrTICmTNTrvQ\n0Y+nueWqbn63keDcLWrnZBEEgVfLBY0yWfSsn0duHWi433AIZjZBD8ilocsFg4kXEjyS4JoImVq7\nGz2zk3AgvxzxMVZUVDvVob/BoK326Z4sycDCXb+2+kSWXigFykSRrdD7ioowIyrCjFq7YmGtGKwo\nr3naTf1w8Hi5mhHR6wwitGmRpT/nHprUH/26pCIlIQIWs6CKrILiGtX4Qr/2nh0SsX53Ibq2S1Bt\nxfXrpiHvjX7uVyr1W/S3yYMwyOsgGKwnKzk+Ao/eNhCDuqdh9U7fPDlzE4kswCeojC6uJMRYkXcK\n6qBhIcD3hMUs4IbLOqOWsiLv3UlxAq51+ARcoFMp1IrocZdmo9bhVkSWWfC72ELK/xqLpLgIDOsT\ngfcfvxySDL/h3s3FgG4pSE2IRENlFhNZjBaNLMv413d7YDULuGt8r+ZezgWLzWrC9In9MbhHGj5Y\nsBvT/7EKd43rhTEXt683MGAwLmQ+fGq0JnBLjvcFhHQPmKArF5Q0IsubDRCMRZbTrbX0JtCzZfR9\nFgRivEHDeXcjipLh51vgObiD9GSR4aaTruqq9tM0JkT0/PGyzhjcI9XwMfQVf6NsF7nJl6XhwXG+\nIPevkwbgxOlqrNtdiMzkKIzsnxVSloMITCJCOE5n4a43qaD+DDTzixChKxULZMmvbo8SwBFWExJj\nrarIAnzB/thhHTCwW6oqsgJmsihhlZEchT9e1hmL1hyBKMno0jZBvfBGi0HSm2hUhjfu0mx0zIpD\nx8w49O6UrLoNBjK+IFlgvcjiOA7ZmXGwWgSM0pxvgXuyAODyQW39nBEDjQRoDMg5YaKOIxGUZMYe\nMbHQzFPT2b3rY6HEGOW5g7qlYklZHoDAgj2cvlOr2aSum9cdF1M9M/caSrsW1pLAcRwu7pWOzd81\n7PlMZDFaNMs25WPzb0V48s7BfjXljHPP0N4Z6JWdhHkLc/H+gt1YueU47r+hLzq3NZ6bwWBc6GSl\naEdN3Da2Ow6eKFesr6mAx6RzFyRiC1AEl0eUNIENPSuovNphuG86yxNIZBmhugtKsnFPFs/B5SY9\nYoEzWfWJhobyzqOXoaLGGXQYPZ3lMQ44OQC+ckibxYThfTOxbnchYiLNGNAtFcXldZAkxU66vkHO\nhHsm9EZKQgTSE5XsmywDeaeq0DEzFjznvxY+QDBthL5/rj6TBlqsREWYkRBjw4nTPodB+jXR4kK/\nRiLoKyjRbhJ43D2+F9buOomSCrvG8c9o7pCR8QrPc2p1yvUjsrH19yLsPlQSdiYLAN56xL/sV81k\nBSlZ1b/WpujJIiTF2ZB3qgomnbEHoGSyAGUulX5dJIPYtV08+nf1v6hgs5qw8I3rwfMclnlnmQV6\nzURk9emUrJZnBoI4L5pNgt97Ymqiz3ZLJDWx4VU7F85RYrQ68ouq8PEPe3HVkHa4tB+bwdZSiIm0\n4JFbB2LWA5egzunBo2+vxvsLdqs/DgwGIzBWs4Bu3uZw+iq6oM7JkvDV0v1quZVym3+5IN1zdPVQ\n4wbxntRA8agAs5SMUI0vPJJh9kbgKXdBg/vJ62oKpzZAueofTGCRddVnZw1ohU1vXTk6uS+YSYSe\nxFgbpozrpdnn0o152HvYOKANpycrNkrbV1dfbyy9OZPA+w2ENuoN0q8JADKTo3HZoDa47499/fZB\n3ms6e6UvawSA9OTg88U4jkOc9/UF6rtOjo/Axb3Sccc1Pfzu43kuoIANxXpf/5ym4NHbBuGJ2wdr\nSwG9xzrae9GEDPcWdCLrhzeux5yHRgV8LWTdZHuBzg3ylXPdpR3rXS/9Puj325QZv5bGoB4N7w9j\nIovRInG6Rbz+5TakJkZqhvMxWg59O6fgnccuw93je2H1jgLc9+pKfP/LITX4YjAYxnRrr9gI06WD\nJNNUU+fymxFjVC7IcRx+nDMBP86ZgIQY416h5+8Zqjp50a6GgFLK99CkAYbPU40vJCmgaYDPXdA/\njIj2Cjr69TUHZpM28DSCDqpJH0i1t2SQZILsTg/MZ2m4dPB4ueowGWj/9ZGdFWeYsdFDXi7Hcbjm\nkg7q7ZPH9sBTd16kZqbo984o8Kd57LZBGDusg9/tJBtLZ6+MMln1DXEGoP526M0d1DXyikFBqCVl\nZB31ea/855Xr1AsVwQYrny2xURaMGKC9YEzef9o5EQjs8Fgfqtiqp1wwjio3vuGyzoaPJRdyZNnf\nAKcpM34tjUDGQqFw4RwlRqtBlmXM/X4PTpXU4ok7Bhv+MDFaBiaBxx9GdcaHT43G8H6Z+Px/+/Dn\nV1Zi6cY8TbkTg8HwMaxPBua/OBZtUn3N46Qno9zAyEGSZJRW2sO+yh5hNakGENG6TNbtY3sY2n4D\nvgDPo5uTRRAokWUU/PXrkoKX77+kSfqxwsHuVNbYPiNwkz69fr05CLnPI8oNDrR6eOfyuNwSJhjY\nTodrvti5TSil2b6N3v/Hvvjh9fEAlNczvF8m7hqnDASmA3m6hC2c84y8x/p5aklxNrRPV457vy7J\nIWUCXarIapzffOJGWN/rsVlN6vpos5hzAbmgQS6yON0iOIOy0lAR6hNZ3v/TbqN3XuufGQSCZ7Ka\nKkt9vsFEFqPF8eO6o1jh7fWprySE0TJIjLVh+sT++OCJK9ArOwnvL9iNB19fhTU7C5r0yiCD0VrR\nW6orgkjA0ZOVfo91ukSs212IguIav/vqY/yIbIwd1gEDuqaE/BwSewc2vuBVC/dAV7T7dUlp9lEP\n117SAYN7pKnlmTQ+4wtt3xINXRLV0EzWOG9ZVkWN01BoNMXg5iu89tqAEmzrSzqJYQbd4yVosqSh\n7+uucb3w/exxfrd/9vzVGH2RIuIHh1huRc4pesbZ2UDKFkM5Dwd2V3qdwhls2xgQ8RKtZrLEsMob\n9fjKBo3vl72/x/QFhUCGLlZynsjwM75oKqv78w2WImC0KHYeKMa/F+bi+hHZGBNgEB2j5ZKVEo0n\n7hiMGy/vjC+W7MMb87fjmxUHcOPlXTBqYJsLqsSAwQgXnufww+ojfrfXeecC0UM8QyU2yoJpN/UL\n6zmq8YUoBZyTpf67BV/RfuDG+l83/Vr0vUT0fdZAM6nqgRZuRr1GDRFZT94ZfDD8ndf2wO1juwe8\nn/T20UZzQgMzWUovlLEoItUMibGhzWhzNnYmy3vsQ3HUG9Q9TTWPOJeQ/ZFzz+kSA7orhrO9QK+D\nXPOMDsFIjHy2lXLBc2cQcj7BjhKjxXDidDVmf7kNfbuk4G5m196q6dQmHi9OHYbXpl2K1IRI/POb\nnfjzqyvx49qjmonzDAbDx+WD2qJ9eowmEwH4+jRI6VlToykXNOrJooRVaw+2tEJKKxboq/X12aUH\nol6R1YCg/tJ+WUHnFHGcf/aKxsgpMZx5XaFCXABp58FguOrpyQoXst86e2i/Oc0xjkQ/O8vh8vjN\nKQuH+owviOAMJVumznlDw3vELnRYJovRIig8U4MZH65HYqwNT94xmKWizxN6ZSehV/YwHD1ZiQWr\nDmHewr3IWXkA40dk47rh2WHZSjMY5zv336C4t9XUubBq2wn1dnKFP1Qb8bOFjqeMAk864GqtwRZJ\nbtACwG8+FO3w1sBjH0Vt3ygb1hwVlRaqDMyIyEbqg85MVsYXpCeFVoLn9o4FsDaSyGrnFaKNtb2m\nYOqE3khPikQb71odzrPLZBFM/gOzwQAAIABJREFUgXqyqDd9zkMjg5Ygk3NTlmW/XrXWfnHlXMFE\nFqPZKSqtxbP/Wo8Iqwmz7r8kpDQ2o3WRnRWHJ+4YjMJruuP7Xw7jm+UH8d2qw7j2kg64fmQnJMYa\nu6MxGBci+v4fhyuwk19TUJ+tOMnOCDzXogPYYDw2eSBq7W7ExwQuZaNFVkN7smhXPaNMVnP0rZF5\na/q5WwTakOVsGH1RW/TMTlTFVn2QiwnWRioXHH1RW2SlRKP7OcoAN4SEWBvuvLanKn6cLs9Z9enV\neIcZBzqvB/dIx5bflaHPXdsloKtBvyKBbKNDRqyfazATWaHBRBajWSk8U4PnPtoIQeDx8v3DVYct\nxvlJZnI0pk/sj1vHdMMPq49g8fpjWLjmCC7tn4UJIzuF6JzFYJzf6LMmTm+5oOlcZbKo3Rj1ZN15\nTQ+M6JeFuGhLo/XPnGtCmb1Iv/ZQ52TpMZt4DOmpBLZGIqs5MoEWs39PFqCU6TlcIuIMBgc3BI7j\nQhZYAII6VjZ0/z06tlyBRcNxHEwCD6dbPKtKnkrvvMpA4xOevHMwah3ukLaVmRyNuU+NRkZyFHKP\nlGruY+6CodE6vx0Z5wUHj5fjxXmbEBNpxkt/vqTZZ6owzh1JcRG45/remHRlVyzfnI8f1x3Dr9sL\n0Cs7CdePyMbFvTNabRkSg3G26LMbZEDpOSsXBJ3J8r/fZjW1muA1XOY8NFItY6azeGdz7M1eUWMk\n1JqjXDBQf9m7f7schWdqm80V8uqh7fHdL4ebZd8tAbOJh8MlIjaq4dnhyhplBER8tHEmy2IWAg57\nNiIzRRHJvTsl4ZkpQ/Da51sgycxdMFQaRWR9//33WLx4MbKzsxEXF4fp06c3xmYZ5zFbfi/C619u\nQ4f0WDx3z8V+dsaMC4PoSAtuuLwLJozshI25p7Bw9RG8+vlWpCZE4Moh7XHlRe2QksDEN+PC5lyX\nC2p6sprZhv1cQ5dP0c6J4QSmekhm0qi0sjnMFiyqu6A2lZWeFBXS0OCmYsq4Xpgy7sI1vTKbeNid\nnrM6Jy7qkY7VOwsaXQRxHIdhfTLA8xwkUWaZrBBptExWVFQUTCYTMjIyDO/PyclBTk6O5jaXy9VY\nu2e0EkRJxv8t34+cFQcxpGc6Hr99EBs2zIAg8Li0XxYu7ZeFA/llWLIxDwtWHcI3y/djYPc0jLm4\nHS7qmc7qwBkXJMVldQDOpfHF+eMeeDY0hvEF4MscGQnWZu3JYiMMWxTk8206C+OLh28dgAdu7NtY\nSzKAAyBf0N8L4dAo0e0VV1yBK664AvHx8Xjsscdw2WWXISkpSfOYSZMmYdKkSZrbCgoKMHr06MZY\nAqMVUFHtxJtfb8euQ2dw29XdMenKrs1yFY/RsunWPhHd2idi6oQ+WLPrJJZvzscrn21FfLQVowa2\nweiL2rIh1YwLhm7tEvC/DccAAOZmML5oyXOwmhq6J6sxMll6hzZlH82RyWqdZiXnO+R9iY5suOuu\nSeBhimi67wmjId6MwPw/e/cdH1WZNnz8NzWTMum9EFKAAKGDAmIDRWy4tkUFkUVcfLGs7D4uWB6F\nR8W24tqlKCrqiiuuuqKgqCjSUVroCSENEtIzKZNkZs77x2QmGRIgIW2SXN8PfJLMnDPnPtPOuc51\n39fdJkFWZmYmUVH2QaSenp5YLDIPjnC1cXcOb3++F0WBBbPGOGdXF+JMvD11XD2mN1eP6c2xnFK+\n357Bjzuz+PKXNOIifRk/MoZLh0VLsRTRrY0ZFMHhzGKgcwpf9OQr1po2GpM15cp+1FhsJMY0LuzT\nGZks3Rm6C4rOVVphH0/lzsMnHO9WGTPdPG0SZKnVahYuXEhUVBTh4eGEhYW1xcOKbuBUcSXvfJXC\n5r0nGZ0czpybh8hJsWix+Cg/Zt84mJnXJ7PzYB4//ZbF+2sOsOLrAwzvF8r4ETFcmBzeqqvNQriT\nRXMuIsjXwImCCudtnZLJaoM5e7qqthqT5W/04IE/Dm3yvs44V3UGWR2/aXEWnVVwpEW6QhvdSJsE\nWcnJybz66qtt8VCim6g01/LZj0f58uc0vAw6Hp42gouHRnWNLxHhtnRaNWMGRTBmUASl5dX8ujuH\nH3/L4oUPd+Jt0DJuaBTjR8bQv3egvNdElzYoIRiAwlKz87bOmIy4Jw9wd+ku2E7PfWdlsob2DWHq\npKQO37Y4swX3jObhVzdSUlch0B3JYbVlpOKAaFOV5lrWbErnPxvSMNdY+MOlCdwyvg9ehvPvYyxE\nU/x8PLh2XDzXjosnK8/Ejzuz+Om3LNZtzSAiyJvLR0Rz+ciYTq2WJURrOcp/Q8d13Ws4Zqcnj71o\n2CWqvZ6Hzuh2pVKpeGr22A7frjg7xzyRvcLaZjLo9hAR5M3xk2Wd3YwuQ4Is0SbKq2r578ZjfPWL\nPbiaMKoXf5zQl9BAr85umugBYsKM3HXtAKZd3Z99qfn8uDOL1RtS+fi7wwyMD2L8yBjGDYmUYF90\nOQ0Dno7KZHl71n9OOqqLojvqiKIfknEXDlqNmjf/Pt6tpy35v9ljyDxp6uxmdBkSZIlWycozsWZT\nOj/syMRqU5h4YSw3X97Hrb8kRPelUasY2jeUoX1Duddcy5Z9J/lxZxavfbqbJZ/vZdzQKK4bF0ef\nmIBzP5jokt577z2ys7MxmUzMnj2b+Ph4532ff/45y5cv59133yU8PJytW7fy5ZdfotPpGDduHBMn\nTuzEljdN3yCT1VHVWBsGWT25umBHPN9SYVc0FOPGWSyAAKOBAKOMq28uCbJEi9lsCjsP5fH1xmPs\nOpKPr7eeyZckcM3Y3gT5SXAl3IOXQceEUb2YMKoXp4oq+em3LNZuOc6PO7Po28ufay+KZ9yQSCmW\n0Y2YzWY2bdrEsmXLyMrK4u233+aZZ55x3j9q1Ci2b9/u/HvZsmW89dZb6PV67rrrLvcMshpksjoq\n62FoMGluz64u2P777nhJOypLKYToOBJkiWYrr6pl/fZMvtmUzsnCChKi/XjotmFcPDRKTlSFWwsN\n9GLKlf24ZXwfth/I5etf03n5X7/zzlcpXDU6lkljehMaIF1bu6L33nuPLVu2AFBVVYXRaL8SHBYW\nRn5+vsuyMTExLn/bbDb0ej1gr5J7JqtWrWLVqlUut9XU1LS67c3RcExWR1G5VBfsuZmWjtj3ID9P\nbrgkgQsGSlVmIbobCbLEWSmKQsqxQr7blsHmPSew2hTGDo5k7u3DSeodIP3JRZei0agZMyiSMYMi\nycwt45vNx/n612Os/vEoFwwM59qL4hjSJ0Te113IjBkzmDFjBgAWi4U5c+YAcOLECef8jWfi4eFB\nTU0NOp3urEHWlClTmDJlistt2dnZTJgwoXWNbwaPTr6A1bMzWe3/PaBRq5h1Q3K7b0cI0fEkyBJN\nKi4z88POLL7flsGJggrCg7z445V9uWJUL+kSKLqFXuG+3HvTYKZf05+fdmaxZnM6/7tkC9GhPlx7\nURzjR8ZIoYwuRqvVcvHFF/P0009jMpmYM2cOubm5fPbZZ9x777289NJLpKSk8Oabb3Ldddcxa9Ys\nnnzySXQ6HXfccUdnN79JOm3nBFl6nYaaWmuPHpPVk/ddCNF6EmQJp9LyarbsO8mve3LYl1qAWq1m\n7OAI5twyhEEJwTJAV3RLXgYd146L55qL4tibWsCaTeks+2IfH3xzgHFDorh0WDTJCUE9upR1V3Ln\nnXc2uu3+++8HYN68ecybN8/lvpEjR3ZIu85XZ81Tpdeqqam19vBMln3fPT3kVEkI0XLyzdGDKYpC\nRq6J3UdO8duhU+xNLUBRFJLjg5l902DGDYnC11vf2c0UokOoVCqG9AlhSJ8Q8ourWLv1OBt+y+L7\n7Zn4+3gwdnAEYwdHMiAuSAapiw7TWV1XHe/xnjwmy3Fh0TExtBBCtIQEWT1Ida2V9BOlpGaVcDiz\nmL1H8ykqq0arUTMgLpB7bkjmosGRBPhKeU7Rs4UEeHLn1f2ZNimJw5nFbNydw6+7T/DN5uN4emgY\nnBjC8KRQhvYJISLYW8ZwiW5HWxdk9eRMFsCLD1xM7wjfzm6GEKILkiCrm1EUhbKKGvKKKjmRX05O\nfgU5+eVk5ZnIzDNhsylo1CpiI3y5ZFg0w/qGMiA+EINe3gpCnE6lUpEUG0hSbCB3X5/MsZxSfjuc\nx++HTrHkP/uw2RT8fTxI6h1A/95BDIgLJCHar9PG0QjRVhyTEPf0cUlJvQM7uwlCiC5Kzqy7EJvN\nHkAVm8wUlpopKKmioKSK/AY/C0uqqLHYnOv4+3gQGeJNnxh/Jo3pTZ8Yf3pH+ErJdSFaSK1WkRjj\nT2KMP1Ou6Ed5VS0H0gs5mF7EweNFfLT2IDUWGzqtmthwI3GRfsRH+REX6UdcpK8U0RDnbeqkJPrH\nduzJvk4yWUII0SoSZHUSRVEw11gxVdZQXlnr/FleVYOpspbyyhrKKmooKa+muMxMUVk1JeXV2GyK\n8zFUKggwehDs70mwvydxkX4E+3sS4u9JSIAnkcHe+HjJmCoh2oOPp44LBoRzwYBwAGotNtJySjh0\nvJj0E6UczSrhx51ZWOs+sxFB3vSO9CU61Kfuv5GoEB+8PSX4Emd325X9Onyb9WOyJMgSQojzIUFW\nK9VabFSaa6moqguUqmqdQVL9T/vtDYMpU2WN8+TrdN4GLT5eeny8dAQYDcRF+jE8yUCg0YMAXwMB\nRgMBvh4E+XnKAHwh3IROq3Z2LXSotVjJyivnWE4p6SdKOX6yjB92ZFFUZnYuE+jrQXSokYhg77oL\nJF6EBHgSGuBFkJ9BMgmiUzi6vHZWdUMhhOjqWh1kHTlyhOXLl+Pr60tcXBxTp05ti3a1iM2mYFMU\nFEXBalNQFHumyKbY77P/rmCz2e+3WGzUWGzU1Frt/y02ah0/LVaqa22Yqy1UmGupNFuorPtZUVVL\nZbWFyqr62xt2zWtIrVZh9NLh46m3//TSEx7kRWKMP0ZP+9+O241eOoxeerw9dfh46qRUtBDdhE6r\nIT7K3m2woUpzLdmnysk+ZSL7lH3M5JHMYjbtOUF5Va1zObUKAn0NhAR4EehnwM9bj6+3B34+evy8\nPfD11uPro8fboMPgocWg16DTqltViKP+u9OG1apgsSkY9BoJ9noYrXNMlrzuQghxPlodZC1fvpy5\nc+cSERHBrFmzuPXWW9HrO66LWqW5llnPfI+psvbcC7eAQa/By6DDy6DF26DD06DF6KUnLNALL4MO\nb4MWz7qfXp72n0YvPca6DJSnh1YqjgkhmuRl0NG3VwB9ewU0uq/SXEt+SRX5xVV1PyvJL66iqMxM\nzqlySsurKas4cyZcrVZh0GvQ6zTYK1CrUKnsRTxUKlABCmC12i88Waw2rHUXoGw2GxZr48cd2jeE\np2aPbdPnQLg3f6NHZzdBCCG6tFYHWYWFhYSH28ck+Pn5UV5eTmBg4wG6q1atYtWqVS631dTUtHbz\nGPRaHvjjMMora1CpVKjVoFap6n5X1f2O83e1WoVep0avtV/x1evqf+rrfmo1apl4VwjRKbwMOmLD\ndcSGn7lstKIoVJgtlJVXU1peQ2V1LeYaK+Zqi/Nnda3VntVHoe4fimLP9KtU9rE2Go0Kjdr+X61W\no9XU/65Rq5z3x0X6nbEtonuac/NghvYNkbkShRDiPLU6yAoPDyc3N5eIiAhKSkoICGh8ZRZgypQp\nTJkyxeW27OxsJkyY0Krtq9UqxgyKaNVjCCFEV6JSqfCp614cGdLZrRHdkY+XnokXxnZ2M4QQostq\ndZA1c+ZMXn75ZXx9fZk4caJ0kRNCCCGEEEL0aK0OshISEnjhhRfOa12r1QpAbm5ua5shhBDCTTm+\n4x3f+d2BHL+EEKL7a83xq1NLuOfn5wN0SkVCIYQQHSs/P5/Y2O7RBU2OX0II0XN88sknzJs3r0Xr\ndGqQlZyczEcffURISAgajaZZ69x77728/fbb7dyyjiP7495kf9xbd9qf7rQv4Lo/VquV/Px8kpOT\nO7lVbSc5OZnevXuzdOnSZh+/OktXem91lbZKO9uWtLNtdZV2gvu31XH8evrpp1u8bqcGWQaDgZEj\nR7ZoHb1eT3R0dDu1qOPJ/rg32R/31p32pzvtCzTen+6SwXIwGAx4e3t3if3qSu+trtJWaWfbkna2\nra7STugabY2NjUWtbvmcgTLLoBBCCCGEEEK0IQmyhBBCCCGEEKINSZAlhBBCCCGEEG1Is2DBggWd\n3YiW6k6Dp0H2x93J/ri37rQ/3WlfoPvtT1O6yj52lXZC12mrtLNtSTvbVldpJ3Sdtra0nSpFUZR2\naosQQgghhBBC9DjSXVAIIYQQQggh2pAEWUIIIYQQQgjRhiTIEkIIIYQQQog2JEGWEEIIIYQQQrQh\nCbKEEEIIIYQQog1pO7sBp3vvvffIzs7GZDIxe/Zs4uPjnfd9/vnnLF++nHfffZfw8HB27tzJJ598\ngre3N7169eLuu+/msssu48orrwRg2rRpxMbGdtauAC3bn61bt/Lll1+i0+kYN24cV155JU8++SRG\no5GioiIWLlyIXq/vxL058/4oitKorZ988glZWVkoikJKSgqffPJJl3l9mtqfJUuWkJqaSmhoKLGx\nsUydOrVLvz579+5128/PkSNHWL58Ob6+vsTFxTF16lQA1qxZw7Zt26itreWWW25hwIABLFiwgODg\nYKqqqnjiiScaLTNixIhO2YeGmrs/AwcO5IknniAwMJDc3FyeffZZFi5ciEajwcvLi8GDB3P99dd3\niX0JCwvj3nvvZcyYMQDcd999bNq0ye1em/Nxpuegs5lMJpYuXUpKSgorVqzgpZdewmKxUFhYyPz5\n8ykoKHCLdqelpfHGG28QGBiITqdDq9W6ZTsPHTrE22+/TXBwMJ6engBu2U6wf8c/8MADDBgwgKqq\nKrdt5+eff86aNWuIj4/Hz8+P6upqt2xraWkpr732Gnq9nrCwMAoKCtyynV9++SUpKSkA/PLLL0yc\nONEt23n06FE+/PBDAgICsNlsKIrilu3csWMHq1evxt/fn4SEBDIzM1vXTsWNVFVVKbNmzVIURVEy\nMzOVRx991OX+zMxMZd68ecrJkycVRVGUPXv2KOXl5YqiKMrMmTMVRVGUCRMmKP/85z+VRYsWOe/r\nLC3dn5kzZyrV1dWKoijK9OnTlc2bNyuvv/66oiiK8tlnnylffPFFB7a+sbPtz9naunTpUmXXrl2K\nonSd16ep/Xn11VeVRx99VHnqqaeUH3/8scu/Pu78+Xn44YeVEydOKIqiKHfffbfzczFt2jRFUez7\n+uc//1n597//rfznP/9RFEVRXnnlFWXHjh2NlnEHzd2foqIi5eDBg4qiKMrzzz+vHDx4UJk3b57y\n3HPPKU8++aTzc9SZmrsvWVlZyo033qgsXrxYWbx4sWKxWNzytTkfZ3oOOlthYaFiMpmUu+66S8nI\nyHB+B2zZskV544033KbdqampSn5+vqIoijJjxgy3befRo0eVwsJCxWazKdOmTXPbdiqKorzzzjvK\nkiVLlNdee82t27l69WrlgQceUJ577jnl008/ddu2Llu2THn11VeVxYsXK++9957bttPh66+/VpYs\nWeK27VyxYoWyZcsWRVEUt/4sPfXUU8rRo0ed7WhtOzs9k/Xee++xZcsWAKqqqjAajQCEhYWRn5/v\nsmxMTIzL34MHD8ZisfCPf/yDu+++G4B33nmH2NhYfv75Zz799FP+9Kc/dcBe1GvN/thsNmcmRK1W\nU1BQQHh4uHP9Q4cOtXfzG2nu/pyprfn5+WRnZ3PPPfcAXef1aWp/brvtNry8vPDy8mLWrFn84Q9/\n6NKvzw033OB2nx+HwsJCZ3v9/PwoLy8nMDAQrdb+lWUwGKipqaGgoIChQ4c69ys/P7/RMu6gufsT\nEBBAQEAAmzZtQlEUkpKSePDBBwkODgbg/vvvZ+nSpZ22H9D8fQkNDeX1118nMjKSjz/+mB9++MEt\nX5vzcabnoLM1bENBQQFhYWFA/WfDXdqdkJCAoii8++67jBgxApvN5pbtTExM5MCBAzz22GNceOGF\nbtvOrVu3YjAYSEhIYMeOHW77ugOMHz+e8ePH4+/vz/Tp04mLi3PLtmZmZnLFFVdwySWXMHPmzEbH\nGXdpJ0B1dTU//PAD06ZNw2w2u2U7J06cyPz58/nyyy+d7XPHdt5111188MEHBAYGsmXLFmbMmNGq\ndnZ6kDVjxgznTlgsFubMmQPAiRMniIqKOuu6ZWVlPPvss8ycOZM+ffpQWlpKQUEBsbGxeHt7O99s\nHak1++Ph4UFNTQ06nQ61Wk1ERATbtm1r9vrtobn7c6a2/uc//2Hy5MkAXer1aWp/jh49yvDhw1Gp\nVOh0OsLCwtixY0eT63eU1uyPO35+HMLDw8nNzSUiIoKSkhICAgIA+8UHgMrKSry9vYmIiCA3N9e5\nX/3792+0jDto7v4ArFixAoPBwLx58wA4fPgwkZGRgL1LUGdr7r4UFhZSVlZGZGSk8/3kjq/N+TjT\nc+BOIiIiyMvLA+o/8zU1NW7R7pqaGhYtWsR1111HVFQUr7/+ulu2c+/evSQmJvLWW29x1113OT+H\n7tbO9evX4+fnx969e8nJyUGlUrllO8EevDiOTY62uWNbHRe2AKxWq9t+lgB++OEHLrvsMrf+zL//\n/vs89dRTxMbGcuedd7oct92pnTabjdmzZxMUFMTmzZspKSlpVTtVijsctRtYuXIlGRkZmEwm5syZ\ng4eHB5999hn33nsvL730Ehs3bmT48OFcd911fPPNN5SWlhIeHo5Wq+XBBx/kkUceISQkhJKSEv72\nt7+5fFDcfX/UajWrV69Gp9Nx6aWXMn78eBYuXIi3tzfFxcUsWLCg08f8nGl/7rvvvibbev/99/Pi\niy/i6elJbW1tl3l9mtqfLVu28MUXXxAcHExcXBy33357l359Fi1a5Lafn7S0NJYsWYKvry99+vRh\n7969PPPMM6xdu5bNmzdTW1vLbbfdRr9+/ViwYAGBgYHU1NTw+OOPN1pmyJAhnbIP57M/KpWKxx57\njHHjxgEwefJk9u/fz44dO/D09GTs2LFMnDixS+xLXFwcjz/+ODExMZSUlPC///u/bNiwwe1em/Nx\n+nMwZcqUzm4SALt372bdunWsXbuWSZMmOW8vKipi/vz5FBUVuUW7ly1bxrZt2+jTpw9gP4nVaDRu\n185t27axevVqvLy8sFqtBAUFUV1d7XbtbNje3377jZqaGrdtZ0pKCkuXLiUqKgpvb28sFotbtjUv\nL49nn32WsLAwwsPDKS0tdct2AixatIg//vGPJCYm8s9//tMt27lt2zbWrl1LQEAAhYWFBAQEuGU7\n09LS+Mc//oG/vz8TJkwgJSWlVe10uyBLCCGEEEIIIboyKeEuhBBCCCGEEG1IgiwhhBBCCCGEaEMS\nZAkhhBBCCCFEG5IgSwghhBBCCCHakARZQgghhBBCCNGGJMgSQgghhBBCiDYkQZYQQgghhBBCtCEJ\nsoQQQgghhBCiDUmQJYQQQgghhBBtSIIsIYQQQgghhGhDEmQJIYQQQgghRBuSIEuIFsjOzub2229v\n8j5FUVi/fn2HtCMnJ4f9+/d3yLaEEEJ0fXL8EqJjSZAlRBvJzs5m7dq1HbKtrVu3cuDAgQ7ZlhBC\niO5Njl9CtD1tZzdAiK7ozjvvZPTo0WzdupWqqiree+89nnvuOXbt2sX777/PVVddxWOPPYbFYsFo\nNPLcc8+xf/9+VqxYQXl5Oc8++yxbt25l1apVaDQaHn74YUaOHMnzzz9PSkoKFouFv/71r4waNYor\nr7ySa665hi1bthAfH89jjz3GG2+8gU6nIzY2lgsuuKCznw4hhBBdhBy/hOgYEmQJcZ5CQ0NZuXIl\n8+fPZ+vWrUyfPh1PT0/uuusu5s2bxwMPPMDQoUNZuXIln332Gf379yc9PZ01a9ZgNpt5//33+eKL\nL8jJyWHFihVYLBYqKipYuXIlRUVFzJ49m3//+99YrVaGDx/O3Llzueuuuzh+/Dg33ngj4eHhcoAS\nQgjRYnL8EqL9SZAlxHkaPnw4AGFhYZhMJoxGo/O+PXv2kJ2djVqtpqamhlGjRgGQlJSEVqvl2LFj\nxMfHo9VqiY2NZcGCBSxdupRt27Zx5513AlBeXu58PMf6gwYN4vjx4x20h0IIIbojOX4J0f4kyBLi\nPGm19R8fRVFc7tPr9bz55pv4+fk5b9u2bRs6nQ4AlUrV5Dq33347M2bMaLQtx7KKoqBSqdpqF4QQ\nQvRAcvwSov1J4Qsh2oharcZqtQL2K3Y//fQTAF9//TU7d+50WTYuLo5jx45RU1NDYWEhc+fOJTk5\nmQ0bNqAoCoWFhbz22mvO5ffs2QPAvn37SEhIQKVSYbFYOmjPhBBCdGdy/BKi7UmQJUQbiY+PZ9eu\nXbz++us88MADfPHFF0ydOpWvvvqKpKQkl2V9fHy4++67ufPOO7nvvvu44447GDlyJAMGDOC2227j\n3nvvZcSIEc7lt27dyh133EF0dDT9+/dnyJAhvPXWW84DoRBCCHG+5PglRNtTKafnfIUQbmX8+PF8\n9913Lt07hBBCCHcnxy/Rk0kmSwghhBBCCCHakGSyhBBCCCGEEKINSSZLCCGEEEIIIdpQp3aSNZvN\npKSkEBISgkaj6cymCCGEaCdWq5X8/HySk5MxGAyd3Zw2IccvIYTo/lpz/OrUICslJYWpU6d2ZhOE\nEEJ0kI8++oiRI0d2djPahBy/hBCi5zif41enBlkhISGAveHh4eGd2RQhhBDtJDc3l6lTpzq/87sD\nOX4JIUT315rjV6cGWY4uFuHh4URHR3dmU4QQQrSzzupWd+TIEZYvX46vry9xcXHODNS6devYsGED\nAGPGjGHy5MnNfkw5fgkhRM9xPsevFgdZJpOJpUuXkpKSwooVK7jhhhu44IILALj++usZPHhwixsh\nhBBCtJfly5czd+5cIiK/lEDTAAAgAElEQVQimDVrFrfeeit6vZ6AgAAWLVpEVVUV8+bNa1GQJYQQ\nQpxNi4Os2tpaZs+ezf333w/YIzuj0UhJSQkRERFt3kAhhHD4flsGm/edRK1Scc8fkgkP8u7sJoku\noLCw0Nmlz8/Pj/LycgIDA7nggguorKzk+eef57777jvj+qtWrWLVqlUut9XU1Jx1m4qi8PwHO4kM\n8Wb6NQNavxNCCCG6lBYHWYGBgS5/v/zyy8TGxpKWlsaSJUt4/PHHm1zvfA5SQgjh8M3mdN5avZdB\nCcHkFVXw0OINPPP/LiIh2r+zmybcXHh4OLm5uURERFBSUkJAQAAAJ0+e5JVXXuGhhx4667iqKVOm\nMGXKFJfbsrOzmTBhwhnXsVgVNu09ASBBlhBC9ECtGpNVW1tLZmYmsbGxeHt7Yzabz7js+RykhBAC\nIDW7hLc/38t1F8Xx5xsHUVFVy/w3fuW1f+/mpQcvQaORKf/Emc2cOZOXX34ZX19fJk6cyOOPP84z\nzzzDE088QXh4OO+//z6BgYHcc889bbZNq9XWZo8lhBCi62lxkLV7927WrVtHRkYGixcvxmQy8csv\nv1BeXs6sWbPao41CiB7u43WHCA/05u4bklGpVPh46bnvlqH8/fWNrNmczuSLEzq7icKNJSQk8MIL\nLzj/dlzwW7ZsWbtt02JT2u2xhRBCuL8WB1lDhw5l6NChzJs3rz3aI4QQLo5kFrPjQB5zbx+GtkHG\nqn9cIJePiObzn1K5Zmycy31CdDaLRTJZQgjRk8lZiRDCrf3312OEBXpx6bDGZbInX5JAYamZbSm5\nndAyIc7MapMgSwghejIJsoQQbquq2sKWfSeZMDKmyXFXidH+9O8dyNebjnVC64Q4M4tVugsKIURP\nJkGWEMJtbU05SXWNlctGxJxxmWvG9iYlrZCTBRUd2DIhzk4KXwghRM8mQZYQwm1t+C2bpNgAIoLP\nPB/WhckR6LVqft2T04EtE+LsLBJkCSFEjyZBlhDCLVWaa9mbms/FQ6POupynh5YR/cOccxIJ4Q6k\nu6AQQvRsEmQJIdzS7iP5WKwKIweEnXPZcUMiScsulS6Dwm1IJksIIXo2CbKEEG5p58E8okK8iQz2\nOeeyowaEo9Oq2bLvZAe0TIhzs0omSwghejQJsoQQbsdmU9h5MI8R/c+dxQJ7l8FBCcH8diivnVsm\nRPNYpIS7EEL0aBJkCSHcTvqJUopN1YxMal6QBTAiKZQD6YVUmmvbsWVCNE/D6oI2W8uzWvuPScVM\nIYToyrSd3QAhhDjd3tQCdFo1A+ODmr3OiP5hLPsyhb2pBYxOjmjH1glxbg0LX1htCmq1qkXrz3/j\nVwD++9INbdqu5vqfV36hd6Qv9986tFO2L4ToOSrNtRSUVHGyoIKDx4tIyylFURSqqi3U1NoI9DPg\n+Ab19NAS6GtAr9OQFBuAp0FLrzBfjN561CpQqVr2XQtQU2tl094TmCprnLep6rZYVHD+PWQkyBJC\nuJ29qQX07x2IXqdp9jqRwd6EB3mx82CeBFmi0zUsfGG12dB1oY4jiqJwOLOYw5nFEmQJIc6qptaK\nTqtGpVJRbDLj6+2BRq3CXGNh3dYMjmaWEB7sRUKUH+knyrApClqNmuMnyyguM2OusXIsp9T5eJ4e\nWgYnBqPVqAny80SjVlFWUR/8FJaaOX6yjBJTNZ/9eNSlLTqtmtAAT0IDvBg5IIyR/cPQatTotRr8\nfPTkFVVSUl7N+u2ZlFXUUGKqpryqhrzCSmosNrSaht/T9gtl1RVF5/3cSJAlhHArFquN/ccKuHl8\nnxatp1KpGNYvlN2H89upZUI0X8PCF+fTXbChI5nFhAR4EmA0nNf6s59dz8VDo/AyaNFq1Uy+OOGs\ny5eYqp2/m6stGDzkVEEIYT8+b0vJ5dP1R8gtqiA0wIvM3DJ8fTwoK6/GpoDRS4dOq6bSbMFcY8Xf\n6MHOg1YqzBY89Bq8DTqsNhsGvZak2EBUarhseDSJ0f4E+3sSGuiFphmZf4vVRlaeCUWxDzGwWBVy\nCyvIzDWRmVfGsi9SWPZFinN5D72G6horAF4GLX17BRDkZyAxxh+jl56R/UPpExPQaDvZ2dlMWP/s\neT1f8s0phHArqVklVFVbGZIY0uJ1BycG8+3m4+QXVxES4NkOrROieVwzWa0Lsv72yi8E+xlY8cRV\nLV5XURROFFSwav0R522TRvc+a5Y4v6TK+fu3W45z42WJLd5ud3GqqBK9ToO/0aOzm9Kpfv49mwBf\nDwafx/dye1AUhaNZJSiKQmK0PxpN18kUn05RFFZ+e5CYMCOXj4jp7Oac1de/pvPOVymoVXDV6N6U\nV9VyYXI4NbU2dFo1Ri89ZRXVaNRq9Do1o5MjiAkzUmuxkVtYQUiAJwZ924QeWo2auEg/AOKj/Brd\nn1tYwdaUXApLq9Bq1HgZtIQHehMa6ElMmBEvg65N2nHWNrb7FoQQogX2pObj6aEhMca/xesmxwcD\nsC+tgPEj3ftgJbo3q63pwhfbD+TSJ9qfAN/mZaWqa+1XXgtKzefVjgqzpdFtpsoagvzOfBGiqtri\nsmxPZbHauPuZ7/H38WDlwknn9Rg7D+YR6Gto8iQQ7CfYz6/cSXigFzOuG9ia5rbIoYwierXgRPMf\nH/0GtO8Ywaw8EylpBVw9Nu6cy67bmsEbn+0BYGB8EAtmjW5xxvX4yTJiwozNypq0p7yiSv79g73b\n29C+IeedsW5PFquNT74/TImpGm+Dln89fU2Lxj7ptGpiwozt2MLGwoO8+cOlZ8/at7euG/oLIbql\n/WmF9O8ddFrf6ObxN3oQE2YkJa2gHVomRPOdXvgC7GMXnnpnG//8ZFezH8dRAMOxfkvlF1c2uq26\nbgxEXlHj+8DeRRDA21OHuabl2+wuHNUdS8qrKS2vPsfSdiWmalatP4yi2F/zhcu38pfFG3jxw53U\nWho/lwUlZjbtOcHqn1LbruHnkJZdwsOvbuTJpVs6bJvnoigKf/3nz7y5em+zJvJOyykl2N+TaZOS\n2H+skFsfXcMH3xxo9vaKTWYe+MdPfPbDkXMv3M4aVhGdvmAdL67ced6PlZpdwhc/p7VFsxo97qrv\nj7BuawbRocbzKi7RE0mQJYRwG1arjUMZRQyICzzvxxicGMw+CbJEJ3PpLlgXcO04aK9SZW3BHFqp\nWSXO3wsadONrrsImMmDVtVb+sngDs575vsl1HIFVgNHDGXC1VMOpFEpM1V1yaoV/fvK78/fU7JKz\nLFnv43WH+PDbQ2TmmVzK+P+yK4cD6Y0H0HfG8/L74VMAHMooZtkX+865fFPBoUNqdgnfbk5v8r7y\nqlrWb88gK8/U6L7/bjzGd9synH//67vDzvddbuGZpy4oNpmx2RSKy8zEhPow5cp+zvsc2aDmKC6z\nB81Hs0rIyjNhrjm/93lzfbr+CA+/+kujzHBNrdXZlTc00AuAX3bnnPd2PlhzgHe+SnH53mho464c\nnn53m3PbjosBLeGhb35Bqp5OgiwhhNtIP1FGVbWVAS0o3X66QQnB5BZWcqqJK/hCdJTTqwtm5Zl4\n7v0dgL0bS1Oqqi0uJ+YON19uHxOVkVvWrG0risLhjCLKq2pdqnI5VJ8jO+U44fTz8Wgyk7Vl30ke\nf3sTiz/+rcnxZsdySpny2DfsTbUXoblzwVoeWvxzs9p+Ju98lcI7X6W43Hbfiz+e9ar93tR8Xvxw\nJ8dyShs9DwfTi7j+b182eUJvtSn8uDOLI5n2E1WNWsWJ/ObNWeZZ12WtsMTs7Oo59/bheHvq2LT3\nRKPlG3bNPFcGx2ZT+P3QKRZ//BvLv0xp8r0C8N22DG555GvA3t3TccJttdqoqrZQYqom0Nc+xmzX\nkVPO9YrLzE22obhBIZQ9R/KdleA+/ymVuS/XZ5/M1RZO1WVHfz90itsf/4ZXVu3mzdV7nOsrisJ7\nX+9n6Rf7eO3T3ew5an+PNKwSl3OqvMn9KquoYfqCdXyzOZ2S8uomu9weyihi+/7cJtdvqKjMfvHB\nalOY88KPvLpq9znXOV/FJjMrvz3IoYxi3v1qv8t963dksv9YIX17+bP80SuYdUMyalV9F+PjJ8vI\nPmXvRrm57v1Ta7G5BL42m8IH3xzgVHElPl56ADKbCGwBXvhwJ9v253IgvZCb53/N5P/5ipf/9XuT\nyzbUMIvueO7EucmYLCGE2ziQXohWo6Jvr8YVfprLMbdWSloB40f2aqumCdEiDU+ebTbFpbuZukFX\nm2KTGbVKhdFLz/97/gcqzbUM6eNaXODqsXF8tfEYK789xOjkCIrKzGSfKifjZBmTL2k85uBwRjEP\nv7aRqBAfcvIbn7A2DLK+2ZxOVq6JsCAvhvUNpVe4kapqK3qdBi+D1mU/HH7dncOeo/Zs8bRJ/Smv\nqsXopXcWm3EEg5/9cJSoEB8ATp4lO3EuuYUVzmDqoiGRJMUGUlFVS2auiXe+SjnjuIvFH/9OYamZ\nX3bZMwOfPH0N3p72MUgH0gsB2HM03xn0niyo4M/PrueKUb1YvyMTgHtuSGbNpnS+/CWN68bFnbOb\nVKCf/cS/sLSK6lpfADw9NFx1YSxfbTzGvTcOdpkzrWFmo6jMTGiAV6PHzCuq5Nn3t3PBgHD+9d1h\n5+2x4UauvDC20fL/+u4w1TVWamqtPPbWJtJPlPGn6wbw31/TKS2vZkxyBBHBPowZFMmaTenkFlYQ\n7O/J9IXruGp0bKOy/cUNTqofX7KZXuFGnpw1mhVf1wcM+1ILWPntQY5mlbDo/13Ek8vsXREnXhjL\nd9syyDhZxobfsxuV3H787c3cMbEftZb64K5hUNdQWl028VhOKSWmagbG2b/r+/cO5OBxe5bw4Vc3\nAvD589eTklZA1ikT14+Lb/S6OQKFsgr7tjbuzmHu7cPQads+S3OqQbfc9TsyuXvyQGcwdDijGICF\nfx6LSqUi0NeATYEKs/0z9cA/fnJ5rI/+72r+/tpGcvLLuW5cHNdeFMfXv6azZlM6eUWVzmCoqYsr\nDc17vb4b8o87s5h7+3CX+yvNtaTllLL7SD5/vKKvy3dGU9lx0TQJsoQQbmN/eiF9YgLwaMH8WKfz\nN3rQK9zIvtRCCbJEp6moqg9OrDaFmrqTyEBfD5euSdMXrAPg5YcudZ68bE1xvRIf6GvggT8OZfHH\nv5OZa+Ktz/ey/5g9SBgzKNKlkmZuYQVL6rqAnR5geXvqqKiq5edd2c7b3lq9t8ES+3l2zkVU11gw\n6DV46rUUmVxPqPKLq8gvqSIi2JuTBRXkl1Q5x439+9lrsVoVZ7e4XUfyue9F15PEhtJPlBIa4OUM\nfMB+ccSg1xIV6kPGyTKSegeybmsGWo0ai9VGalYJSbGB3LPI3tXRz0ff5GNbrbZGJ+sPLt7Aglmj\niQkzOl+Pt1bv5UR+BX+6fqAz8+YIsAB8fTyIDjWy/UAu67ZmkJZTyrRJSfy65wTeBi1L/rOPDxdO\ncla3cwTQWafKGVwXLOt19kI+FqvNefIM9uxcw0xcVp6pySAr/UQpadn2/wAXDAjnVHElOw7msTe1\ngMxcE/feNJj+dd2sdXVtKTFVc/ykPeBd8XX9eKWC0ioCjAZ8vOzP+z2L1jvv27g7hyF9QjBXWxjZ\nP4zcwkpKK1yfx4qqWtIbzGsE8ESD8V2PvrXJ+fu0SUn8vCubnQfzWLOp6W6FHzcIHME18Cwtr8bg\noSW3sML5uqjVKgpKqpzd6xb+eQxHs4p57K3NzvV+/j2Lf313mFPFVQzpE0JmrgkUuHhYFFAfKDiy\nlQA3zfuaDxdOws+nbStJZubas0qXjYhmw2/ZZOaZ6NsrgLuf/p6iMjO3TuiDT91nwNfb/t4oq6hx\nvk8a+nZLuvNz/fWv6Xz9a/1zatBrnftVVnHuMYSXDY9m+4FcKs0W9hzJZ0hf+/u1tLyaaU+udS7n\n661n+Zf2LLJOq2bapKQWPwc9lQRZQgi3oCgKB44VMWFU66sCDk4IZvvB85+lXYjWqqiqH2tjtSlY\n6k7qfbz0TXbB+6ZuXMuHCyex7IsUl0BIp1UzJjkCtVpFSlqBS1Zs5tPfcfPliUy8MJYdB/OcJ0Ng\nP4na8Hv94ziCrO+31wcRDpcOi+bnXdm8uXovo/qHYfDQ4qHXYK6xkn6ilKfe3YZWrXZmpMYOjuBk\nQQU7DtQHhLc+soZgf0+XsWMNn4eGFEXhwZc2oFareP6+cQT4Gti2/6TLvDYASx6ZwPYDuVw+IpqU\ntEJOFlZQXGbGVGl/3CDfpqsklpRXN5qf7FRRJe+vOcDjMy90FgSx2hQ+35DKn64f2GQXKz9vPQ9O\nGcq0J9c6q9mt3XLcZZkDx4sYlGCvbOrIyPxnQyqOhJVep0FflyHJOFlGct2y3zZ4HLVaxSffHaao\n1NwoO1XZoELkHy5N4O7Jyfxr3SGX4OTvr2/k5YcuJTHGH43GEejZ5xAKD/Iit7A+m3IgvYhrL4qj\nrLxxtqPSbOGF0wovTL4k3uXv8qpask6VY9BrqLHYzjoPnL/Rg94RvhzLKXXJjP5lylA2/J5NTn4F\nt0/sx/rtmc5s1I4DeVx/cTwGvdblZN/hl105WG0KQ+uCAk8PLf17u3Yxf6VB978SU7Vzn8YNjUSl\nUpFbWIGnR+NMbVGZuc2CrJpaK/cs+p6iuvFf990yhJ9/zyYz10Sgr4GiMjOJ0X7cPrF+XJkjyDqa\nVULAadMGqNUq0nNcuwwH+no4H9/TQ+sMUAtLzVitNjQaNYcyigjx98TjtNLpl4+IYXRyBM99sIPH\nl2xm2aNXEB7k7cz8Ony09pDz94+furrNSrD3BDImSwjhFk4UVFBSXu3s7tcayYnBnCqqPGP1NCHa\nypkGjldU1TrH51itNmrqxlAYvfTOsToNfb89E5XKfpLlOEluyOChpU+MP2u3ZlBptnDrhPrJulf/\nlMrs535wBlieHhr+fudI/jZ1hHOZSWN688j0UWfcD8eJdFaeic83pFJRVYuHXkNqVgkPvrSB/OIq\nly5/EUHeGPQaVv+UiqeHxtnFrLnFOUrrTvBtNoWHX9vIrGe+dwZYV42OdQ6uP3CskLyiSqJDjYQH\nefHVL8d4+LWN+PnouXhoFMdOlHLj3//rkhHaeTCPVz+1n2Tfe9NgRiSFOu/btj+X597fQX5xFaOT\nw7nvliGoVPZxTF/9cozhSaG8/vDlzhNfnVbtctLdVObs0Tc3cf3fvuT5D3ZwoqA+e+ioGOih0zjX\ne+TNTWxNOYnVanMG3gA3XppAkamad75K4cedmfzPq7847yuvsj9XYwZFOKemGDUw3Hn/TXXzmC1Y\nvoWdB/OclVkXLNsKwOwbBzdq8/B+oVw77tyl0gHWbqkvUKFW2bubfvDNARJj/Pnyxckuyw7r69rV\nVaVS0b93IL/sznHpZubr48HT917Eiv+dyMQLY3no9mHO+w4eL+KBf/zU5Hg/nVZNVbWF8CAvIoO9\nXW4/k4aZscff3syi97az40Cuy/vC4UwXBc7HnqP5zgCoX2wABr19nqbMPBP5xfbPydzbh7t0UXRM\nrfDSR7+5ZAQBYkJ9KGlwgeXluZfy/pOT+NdTVwP294mprpvgjzuzWP5VCqeKK3n41Y3MeeFHXqor\nwz9/+ihGJIXSNzaA0cnhzjGf9yxaj82m8O2W44QHeXH1mN5cPiLaJRDVt0N3yu5MwlEhhFvYf6wQ\nlcrev761kusCtX2pBYRdIF0GRft5esV23ngsqtGUAxVVtfh66+3FLGwKNbX2E2qjl86ZhTmdothP\nSh3z9kwYFcOwvvUngrdd2Y+Fy+0nzoFnmWfrsRkXOrv+/Om6AYCKmy5PdAkIrx7Tm2lX96e8soat\nKSdJiPbn9Ycv5/667n0JUX4uE/Dee9NgLhkWxdotx/ngm4PotBqSegey+0g+V43uzVWjY9macpKd\nZ8kgV5prURR79ugvi+3beekvl/C3V+oDirGDI7j/1qHcf+tQ/rJ4A69+utuZjbltYj/2HM0nr6iS\nOTcPpryqlo27c7BYbfz8exaXj4gmv6TK+RyBPZt37UVxvPyv3zmcUUROfgWb9p4gKsSbEUlhBPt7\noijwWl1QZvTUExvuS1iAFwa9hv51434+efoaPPQaamqt7DlawOGMIlb/lEqgr4EBcYH8uucEv+45\ngb4uoCptkCWyB1n1z+XPv2cTH+XnEkTMuG4gI/uH8cibm3j5X7ucz9djb20iNbsUPx89j864wLl8\nYrQ/j9w1it1H8pl+7QB2HsojM9fksu8OEcHeJMUGcCijmLfnTyCvsJJh/UJQqVSM7B/GzoN5GL30\nvPH3y5n97A9UVVu46bJEPt9gDxIbFj24akxvvt18HEWBy4Y37nVw5QWx/PWOEWg1KufYs1sn9HUG\nwXqd/TlUnzZG6vQu4rmFleSdNo5veL9Q4iJ9Wf1TKsP6hTYaZ/XigxezfX8uN13ehz8vWu8MrhyF\nNPr28mdvan3l2ehQIxcNieRUUSX33DCIv7++sU2DrKNZJfh46vjnXy9zFhrpFW7kvxuPsTXlJECj\n7qG+3noCjB4Um6qd3UMdYsKMpJ8oxdtTx82XJ5IYbZ9L0sdLz/CkUPalFlBsqibYz0BBqZmvf013\nTtZbabaw82AeFwwI56IhkVw0JNL5uFMn9XdeENiacpKsPBNP3H0howaE897XroU61J08p1hXI0GW\nEMIt7D9WSGy4r3NAcGv4+djHZR1IL+QKCbJEO8o4Wcbr/97NQ7e5DhwvN9di9NaTV1TJ8ZNlOOIb\no5f+jNkeRwEHx4lMcnwQlw6Pdt4/ODHY+XuvcCMTRsXww44svvrHZB59axMpaYXce+MgZ4AFcNPl\n9Rmvhielc24ZAthP6hzLxIb78t4TEzHXWJ1BXE2tjU/XH6FfbABGLz0XDYnkg28Ootepeei2YRzN\nKmF0cgQA/zN1BDsP5jknrj3dE0u3OAf6A1w0OJKEKD/uv3UIAb4GRiaFYWsQCE4a05s367ro9Y7w\nJTLEh4hgezGPgfFBLpmR1OzSRl3LokN9nOO95t4+nE/XH2HltwcByMmvYNIYTyKCXSs9OrZv8NC6\nPHeOx9Fq1IwZFOFcrm8vf+ZNH8Ws0ir+d8lmsvLK8Td6MO3qeD781t7NykOvweilp1e4kcxckzMg\nO92AuCBnt02AKY9947yvtImufWMHRzJ2sP1k+a5rB7BoxXZn4BYf5UdYoBdb9p0k0NfAjOsG8s5X\nKUQEeTuLkUD92J1HZowiwGjAoNdQVW1hQFwgk8ZcgZ+PnrdW72XckEiG9QtFq1Gz82Ae+cVVxEX6\nOp8Ti9XGk7NGMyKpcfDj663nPy9cz/60QnpH+vLVxmONirvomxiHm5FbhloF0WH25+3KC3uh1CX/\nmroYlxQbSFKs/fb/+/MY9qcXsur7I3xY193toduGM+eFHwH7Z+nqsb2d73PHVAVNTd59vjJzTcRF\n2l8Hh/goP7btz3VmspqaQPmvdwxn1fojpKQVutweE2bkt0OnMNdYnGO4HPx9PPj9kL1S5CMzLsBq\nVfj76xtZf1rX4MkXu3b9BHsWcP70UTz3wQ6erauA2q/uefT1btvxaT2NBFlCCLdwML2IYf1Czr1g\nMw2MC2J3XXlgIdpLYrQ/R0+bkya/uIr0nFIuHR5NalYJr326m2lXJ6FRq/D00JKaXUqtxebSxal3\nhC93T052eRy12jU7ptdpeOb/jUVRYHBiCIMSgnngj8NQqVQ8dNtw9h8r4NJh0ZyLv/HMJ06O7koO\n0yYlcdnwaGLCjABEBvvw8txL6RVmRK/TuCzv7alj9KCIprfp4+ESYA1ODGb+Xfbui1eN7l2/z9Sf\noAfVnQDHR/kRWRcY3POHZN77+gBRoUZn4YPp1/Tng2/swdNFgyO5bWI/8osriQqtDyagvry6Q4i/\nF1EhPgzrG8KuI/bviruuHXDG56ahCwaEccMlCYzsb880Bvl5khjtT1ZeOTqNyqVogV6nQa1W8cbD\n412KXQxODGbe9FEU1xUXUatV/OW2ofy6J6fJrnJnb084X7w4mWM5pWw/kMuY5AhiI3yx2RTUahUD\n44NY/NCljdYrqgtUe9W9vl4GLcWmaozeemcA2rDbKdhP9vOLq4iue36XzJ9AhbnWmTVpilajdgb/\nd17dv9H9DTNZH/3f1cx65nvSckqxKfaulAAXDrSPS3yYEVw0JOqsz0dijD+JMf58sSEVE/agLCbM\nyKt/u4xiUzXD+7l2FfTQ21+jtpy3LK+ogt4Rrs/JkD4hLhUimzK0byiDEoL5w9//63J7WKCXs+ue\nj6frxcgpV/R1zvnVK9yIXqshwOjBwbrxgoMSgigurya5wYWahiJD6i82jB8Z4xwbdqbCMqJ5Whxk\nmUwmli5dSkpKCitWrOCll17CYrFQWFjI/PnzCQxsfVcfIUTPUlRm5mRhBdPi265q0cD4IL7dcpzi\nMnOT86mInuPIkSMsX74cX19f4uLimDp1KgBr1qxh27Zt1NbWcssttzBixIhzPFJjoweF8++N+VTX\nWp0nipl5ZVhtCjddnkhSbABvrt5LYakZvU7tLBf+654cLh8R4yyzfmPduBqor1DXVNecwYn1FyJU\nKhWO4VthgV6EBZ47a7vwnjHOgKk5VCpVo+Ud3ZSa4qHT8Lc7hvPSx/a5d26+PJEKs4Ufd2Y5l7ll\nfB+mX9P4RPt0jmCwYXA0IimMEUlhAGj0Wv770g2AvRrbBQPDnQUoekf4NrEv9Y9bVW1xnvTfeFki\nu47ks/ihS1yyDmej02qYdYNrUGysOzHVaTUYPV2DLAdHV9AZ1w5g8iXx6LQa5wmtY90P/+9qbn+8\nPosV7O9JiH/TBT5OFx/lR3xU/Yn9ubp3XXNRHB98c9DZnXHckChWrT/i0qbTPXTbMPYcycfLYM+m\nhDbzOTsbxwUHdd24xEBfAyfr5ibz9tQxZlB997ZLmnEhwWHUwHC+3Xzc+XdcpB9NjURTqVR4G7Qc\nyynl+20ZhAR4kuS4AOcAACAASURBVBDt32SFv3MpKKli95F8UrNLuTDZ9aLDgLhAFt4zhl1HTjHo\nDAEPgEajZskjE5j97A/MuXkwHnoNFwyoH4cXFuT6nEeG+PDm38ejKIozk3hhcgRrtxxn3NBIrhl7\n9vF3cZF+LJpzEX17uVb3PdsFGXFuLQ6yamtrmT17Nvfffz+ZmZkUFRXxzDPPsHXrVj755BPmzJnT\nHu0UQnRj++u6RbRF0QsHx2PtTy9k3Dmueorubfny5cydO5eIiAhmzZrFrbfeil6v55NPPmHlypWY\nzWb+8pe/sGTJkhY/dkyoLzbbKTJzy+gTY5/fzdHjzdNDS0JdQFJiqkar0XD9xfGs2ZTOq6t2MyY5\nAkVRuOGSBGdBA6gPBjTtMP5heBOD/dvaZSNisNoUjmaVMP2aAaz+6ahzXM/T945t1FXsTBwn/s15\nHk7PAjbFUQXvrmsGcOnwaOeJ/bB+oax+7romu6y1hJdHXZdCrdpZHh0ajzdybPNMczL5eOp45a+X\nkZlnosRUzQ2XNJ7nqa3cOqEvt07o6/z79quSGJQQTHTomQPxAKOBy0a0vgpsQ479c+TvAn0NziIi\np2cgW+IPlybw7ebjTc73djofLz3fb890Vt/Ua9WMGxqFn48HvSOMqFT2SalrLVasNgWbojB2UCQD\n6krn22wK9734IzkNJq6OPy27p1KpGJ4U2qzPYWSwj/MigsMdVyWxff9J+sQ0faGj4ftk5vUDuXRY\nFAPimndcdVygaGhY31Be+etlfPDNAX47dKqJtcTZtPid2zBTVVBQQFiY/YpSWFgY+fln7pqzatUq\nVq1a5XJbTc3ZJ0sTQvQM+44VEBHs3airUmsE+3sSGujF/jQJsnq6wsJCwsPtV4H9/PwoLy8nMDAQ\nrdZ+CDQYDGc9Hp3t+OXokrZw+VY+XGiv8uUoMKFWqfAy2LdRWl6NXmc/qR8/MoaV3x4kJ78cq01B\ne1o1QUcmqz2CrI4yYVQvJoyyZ9YcQUZUiI/LuLJzcVxFbxiAtoYjaPM3ejSqRtfaAAvqgwGdRk1w\ng8xTw2398Yq+hAV5O8czncnpGamOolGrXMb0dTTHBYoAowdpOfZuuE2NW2qusLrCEo45xM7m4Wkj\nOFVcRUKUH3lFlXz5SxqZeSZy9+dSXlcQw8dTh8Vqw1xjxcdTx1e/HAPsGcPIYG9ngPXiAxdTbDIz\nakDYebe9KbdP7OdS8v1sPD20zukCzpdarSI+yo8n7h5NyzqwCmjlmKyIiAjy8uyVhE6cOEFU1JlP\nZKZMmcKUKVNcbsvOzmbChAmtaYIQohtISStwVgRsS8nxQexPLzz3gqJbCw8PJzc3l4iICEpKSggI\nsGecHGOeKisr8fb2PuP6Zzt+GepKjTcsTOA4UVSpwLuuS1WJqdpZ/viSYVGs/PYgpRU1WOvGzLio\n+7O7VPJyBDDD+oa0KCPjodM0upLfGpcMi8LbU9dk6e624Aio1WqVcwxZ42V0XD2md7tsv6u7blyc\ns0tcoJ/BOT9YazJZGo2a5Y9dedZqnA59YgKc2ejwIG9nxtVmU6ixWKm12JyfZ5XKXiXz1z0nqKq2\nUFpezdotx7lqdCz3/GFQk9nLrqy7fBd1tBa/c3fv3s26devIyMjggw8+wNfXl+eff56ioiLmz5/f\nHm0UQnRjJaZqsvLKuWV833Mv3EID4oL46bcsyqtqG1VjEj3HzJkzefnll/H19WXixIk8/vjjPPPM\nM9x666088cQT1NbWcs8995z/418/kI/X1U/Y6ag8p1Kp8Kp735WWVxNYl6l1jHcpK6/GZrOhOa3A\nRXfIZDU0rF8ovSN8GdG/ba/qt5SjZHl7aTgvmkat4opRvUiM7vhsVFfVcD6vyRcnoNOqST9R5sxG\nna/mjrM7E7VahUGvxXDa8CytRsVlDap/3nZl8zJMoudocZA1dOhQhg4dyrx589qjPUKIHmb/MXum\nKTmh7TNZA+MDURQ4mF7IqAaDhkXPkpCQwAsvvOD825GVmjRpEpMmTWr14+u0amobTCzbMJOl16rR\nqFVUmC2EB9uDKU8PLXqtmpLyGixWpdHkwypnkHXmCVa7krBAL177n8s7uxntzpHJqrXa3wt/uW3Y\n2RYXZxES4Mn0a5pX6VEId9U9vsGFEF1WSloBoYFejSZlbAtRIT74+3g4Azkh2oNOq7YPhK8rrNBw\nTJZKpXJWYXN0F1SpVPj6eHCibkzW6Rmr9ix8IdqPI5PVMOAWQvRcEmQJITrVvrQCBrVDFgvsJ7MD\n4gM5kF7ULo8vBNQXNnBkMJTTRoh71mU4+vSqrwg2rG8IP+zMquta5noodo5bkhirS3EU1mht9zQh\nRPcgQZYQotOUlleTkWsiOb51FZDOZmBcEEeziqmuKyEtRFvTaewZKkcGQ6mrw+UYLG6pu71hKeWB\n8UHU1Fox11gbdRd0JLCU06M14dZiwoy8+ODF/O2Ols+3JoTofiTIEkJ0mgPp7Tcey2FgfBAWq8KR\njOJ224bo2bSOTJbFHsgrdb3FHBkpRyGMhhO8NixL3bi7YN2cQRJjdTlJsYF4S5EdIQQSZAkhOlFK\nWiHB/p7t2r2md6QfXgatlHIX7cbZXbAuY2Vzjsmy32+12v82ejUIsvT1JZ7PNCZLgiwhhOi6JMgS\nQnSalLRCkhOCWjR3Tktp1CqSegeyP02CLNE+dBr7odTRLfD0bn6OoMvoVZ/hMOgbZLI0TY/JskmU\nJYQQXZYEWUKITlFaXk36yVIGt3JG+uZIjg/iUEYRVqtU/RJtT3taJssRGjnmu3JUHfRpcSZLgiwh\nhOiqJMgSQnSKXUfyURT7RKXtbUBcEOYaK2k5pe2+LdHzNK4uWDcZsdo1I+Whqw+sPM82JquurKCE\nWEII0XVJkCWE6BS7Dp+iV7iRYH/Pdt9W317+6LRqUqTLoGgHjiDL0V3QVpcwdcROT949mqvH9nZZ\nx8Mlk+V6KL58RDR+PnqSYgPbp8FCCCHanQRZQogOpygKu4+cYljf9s9iAei0Gvr3DmRfWkGHbE/0\nLKcXvqjv5mePsgYlBjPn5iEu67hksk4r4R4Z4sOHC692qUYohBCia5EgSwjR4Y6fLKOorJrhHdBV\n0GFwYjD7jxVgkXFZoo3ptPXzZFmsNqpqLEB9JqspHvozdxcUQgjR9UmQJYTocDsO5OGh1zCwHefH\nOt3gxBCqqq2kZpV02DZFz6BrME/W0+9uY9kXKQBnrZrZMLA6vbqgEEKIrk++2YUQHW7b/pMM6xvi\nUgigvfXp5Y+nh4Y9qfkdtk3RMziCrBqLjd8OnXLefq6ZCYL9DACoJZMlhBDdjgRZQogOVVhaxZHM\nEi4cGNGh29Vq1AyMD2bvURmXJdqWoxz7Cyt3utx+rvnfokONgHQXFEKI7kiCLCFEh9p+IA+1CkYN\nCOvwbQ/pE8KB9CKqqi0dvm3RfWk16kbZqObETQ9OGcbNlyfSr1dAO7VM/H/27jy+iTr9A/gnR5O0\npTe9aKG05bZyH4pyCFjRBRHlcEWgogjL4k9ddGWVRZFL8AABV44CFeSoBwouylHkWmhLgUJbbgr0\noAe9mzbNOfP7I5lpkib0Spu0fd6vFy+ayWTmmTTNfJ95vt/vEEKIvVCSRQhpVvEpOegZ6gOPdtJm\n3/eAHn7Q6hiaZZDYlEAggLNEVGNZbXy9nBE17hHIjGYaJIQQ0jpQkkUIaTalchUu3y7EsL5Bdtl/\nsF87+Hk548K1fLvsn7RexrMFArWPxyKEENK6UZJFCGk2Z1JyAABP9O5gl/0LBAL07+GPC9cfGN3L\niJDG4ya/4NSlkkUIIaT1oj4KhJBmcyo5G326tIenW/N3FeQM6OGHQ/H3kP2gAh393Rq0jQclCqyL\nTcaVO8Xw9XTGP6b1R48Qb9sGSloUxixppySLEELaNqpkEUKaRW5hJa7eLcaI/sF2jaNvV19InERI\nSMtt0OsflCjw7pqTyH5QgenP9oB7OwkWbTyLq3eLbBwpaUkYRp9k9e3qC4C6CxJCSFtHSRYhpFkc\nPZcBZ6nYbl0FOTKpGAN6+OGsoetifWh1DFbvPA+JWIi1747Ei091xbI5QxES4IZvfroMnY5pgohJ\nS8AlWa4uTgDqNrsgIYSQ1ouSLEJIk9PpGBxLysSI/sEOMZPa0EcDcTu7DPnFinq97pcTt3ErqxTv\nTx/Id3mUScWYM7E3MvPkOBR/z/bBkhZBYbgtQKCPKwDqLkgIIW0dJVmEkCaXdC0fxeUqPDMkxN6h\nAAAG9QqAWCSoVzWrqKwKP8TdxF+eCEWvUB+T57p18sJTA4KxN+4mtFTNapMWzhiE/5vSF97uMgCU\nZBFCSFtHSRYhpMn9ejId3Tt5ITzYw96hAABcnZ0wsKc/jiVl1nmWwZj/XoXESYRXIrtbfP6FEV1Q\nKlchMS3PlqGSFmJgT388PSSEvykxpViEENK22STJ2rdvH15//XUsX74cGzZssMUmCSGtxM3MEly5\nU4QXRoY71NX9yCEhyMiT40ZmSa3rXs8oxomL2Xj12Z5o5yKxuE5YkAe6h3jhj/i7tg6VtCDcWCxH\n+qwTQghpfjarZLm6ukIsFiMwMNBWmySEtAK/nkyHn7cLHo9wrO+G/j380d5DhiMJGQ9dj2FYbPk1\nFZ0D3RFZS3fHZx/vjMu3CpFbWGnLUEkLIqQZLwghhMBG98kaNWoURo0aBU9PTyxYsAAjR46Ej4/p\nmIXY2FjExsaaLFOr1bbYPSHEQT0oVuBMSg5eH/8IRCLH6p0sEgoQ+Vhn/HTsJl59tic/lsbciYvZ\nuJlZiuV/GwpRLQ3oob074D8/XcaZlBxMGtW1KcIm9RQTE4Ps7GzI5XLMmTMHYWFhAACWZfHJJ59A\nKpUiLy8PixYtgp+fX6P3J6QKFiGEENiokpWZmQmdTgcAcHZ2hlarrbHO1KlTsW/fPpN/GzdutMXu\nCSEO6sDpO3CWiDBmcCd7h2LR+CdD4SQW4oe4mxafr1Jp8d3Bq3j80UD07uJb6/acpWL0b+D08MT2\nlEolzpw5g0WLFmH+/PnYunUr/5xarcbEiRPx4YcfYsiQIUhNTbXJPqu7CdZtrB8hhJDWySaVLKFQ\niCVLliAoKAgBAQHw9/e3xWYJIS2YXKHGkcR7eG5oKFxkTvYOx6J2LhJMHNkFe4/ewPhhYQjybWfy\n/PbfrqC8Uo3Xxj1S520O7d0BX+2+iAfFCvh5u9g6ZFKLmJgYxMfHAwCqqqrg5uYGAPD390dBQQG/\nnlQqRd++fXHlyhVcv34dU6ZMsbrN+vTE4LoL1nE+FUIIIa2UTZKsiIgIrFu3zhabIoS0EvtPpUOn\nY/H88HB7h/JQzw8Px7GkLHz2XRI+f2sYfx+vuHOZ+CP+Hua+2BuB7V3rvL3B3PTwqbl4YYRjH3tr\nFBUVhaioKACAVqvFvHnzAAA5OTkICgoyWffAgQPIzs7GkiVLIBRa79gxdepUTJ061WRZdnY2Ro8e\nXWNdrkcp5ViEENK2OdYgCUJIqyBXqHHg1B2MHdrZ6lgnR+EsFeNfUYOQW1SJf317BqeSsxG9Pw1f\nxyZj1MCOeG5o53ptz9XZCX27UZdBRyAWizFs2DAsW7YM3377LaKiopCXl4cNGzYgLy8P69evh1wu\nx+eff46EhASb7JOf+IJKWYQQ0qbZpJJFCCHG9p9Mh07HYNJTLWPyh9AOHvj0zcfx7c8p+Pz7CxCL\nhHj12R6YPKpbg6biHvpoINb/eAlFZVXw8XBugohJXU2fPr3Gsvnz5wMAjh49avP98d0Fbb5lQggh\nLQklWYQQm5Ir1Dhw+g6eHRoKLwevYhnrFeqDtf8YiZJyJTzdpBA3YjbEIRGB2PDTZSSk5uIvT4bZ\nMEri6LiknApZhBDStlF3QUKITXFVrJee6mLvUOpNJBSgvadzoxIsAHB3laB3eHucTc21UWSkpRDS\n7IKEEEJASRYhxIZaahWrKQztHYi09EKUVajsHQppRiKaXZAQQggoySKE2NCvJ9OhY9gWWcWytcci\nAsECSLySZ+9QSDMS0OyChBBCQEkWIcRGyivV+O30HTw3tHObr2IBgJe7DD07e9Msg20M3SeLEEII\nQEkWIcRG9p/SV7FeHElVLM7jj3bA5VsFqKzS2DsU0kwaMhslIYSQ1oeSLEJIo1EVy7KhjwZCq2OR\ndJW6DLYVIgHdJ4sQQgglWYQQG9h3/Ja+ikVjsUz4ebugS0dPmmWwDaH7ZBFCCAEoySKENFJRWRV+\nO30HE4aHwcuNqljmhj4aiAvXH0Cp0to7FNIMqJBFCCEEoCSLENJIe47cgMRJhBef6mrvUBzS0N4d\noNbocPHGA3uHQpoBV8miWhYhhLRtlGQRQhosK1+Oo4kZmDy6G9o5O9k7HIcU5NsOIQFuOHXpvr1D\nIc2AZhckhBACUJJFCGmEnX9cg7eHM8Y9GWrvUBzaqIEdkZiWB7lCbe9QSBMTCmhMFiGEEEqyCCEN\ndOVOEeJTczHtme6QOInsHY5DGzmgIxiWxamL2fYOhTQxIQ3KIoQQAkqyCCENoNUx+Pbny+jS0RNP\nDexk73Acnre7DP27++FoUiZYany3anyOZd8wCCGE2BklWYSQevvlxG1k5svx95f6QCSkm6/WxdjH\nQpCeXYard4vtHQppQjQmixBCCEBJFiGknu7cL8Puw9cxYXg4unT0tHc4LcagXgEI8nXFLydu2zsU\n0oSEdNGBEEIIKMkihNRDZZUGn39/HsF+bpj+bE97h9OiCIUCvDCiC85dzUNWvtze4ZAmwo/JIoQQ\n0qZRkkUIqROtjsHqnedRXK7EP6cPpMkuGmDUwI7w8XDG9v9esXcopIlQJYsQQghASRYhpA5UGh1W\nxiTh8q0CfDBjEDr6u9k7pBZJ4iTCa+N6IelqPi5ep5sTt0ZUySKEEAJQkkUIqUVWvhwfbDiNSzcf\n4KPXBqN/dz97h9SiDesbhEfCfLD+x0sor6T7ZrU2lGMRQggBKMkihFjAsizu3C/DNz9dxv99eRxV\nSi1W/v1JDOoVYO/QWjyBQIB//LU/lCotvtx9ARotY++QiA2JRXRaJYQQAojtHQAhxL6Uai0y8+TI\nyC3HvbxyZOSWIyNXjtIKFdxcJHjlmR4Y/2QYZFL6urAVP28XvPfqACzblogVMefwwfSB9P62Eu6u\nEnuHQAghxAHQWZ2QBmBZFiq1DlUqLarUWlQptVByj1VaqNQ6iEQCiIVCiMUCiERCiIVCODkJIXUS\nQeIkgsTkZxEkYiEETdjXSK3RIaewEtkP5LiXW47MPP3/eUWV/D19/L1d0DnQHZGPhaBHiBf6dvOD\nk5iuzDeFAT388dFrQ7DyuyS89eVxvP58BAb1CnCY+45xn/HySjXauTjBReZk75BaBJoQhhBCCGCD\nJOvmzZuIjo6Gu7s7QkNDMW3aNFvEVWc6hsXp5GyUK9QQCgQQABAIBRAIBNC3VfT/CwQCCAz/13ws\nAATgl1c/FkAoFEAkrPm/SCjkf26ohrSnG9MIZw0taUs3yeSfM1lo8h+/junr6vL6Bu7X6PUaLQOV\nWgeVWgelWp/QqDT6n1VqHTRaBlqd4Z+WhZZhwDCmO+TeOv2nBID+12x4TsAtgpZhoNYwUGv021Vp\ndNBodVBpGGg0OigNMTTFzUYlYiGfdEmdRJBKRHCWiiGTiCAz+t9ZIoZMKobUSQixSAixWMh3U9Jo\n9LFWVKlRKlehqFyJnIIK5Bcr+JjdXSXoHOiOgT39ERLgjpBAN3Tyd6OGdDMb2NMfX/9jBDb8eBnL\nt59De09n9Ovmi04BbvD1coGPhwxOIiFEIiH//cOyLFhW/7fDAgALMEZ/2wzLAqz+78bSekq1DkrD\nxYAqlRZyhQbllSqUV6r1/yrU/GO1oStjRLgPVs570l5vEyGEENLiNDrJio6OxrvvvovAwEC88cYb\nmDx5MiSS5usuodbo8N3v11BWoQLLsmBYANz/pNUSiwSGJEQMqURfBRKLhYbKkRBikT5ZFggE1Y1M\nwCTxM0+SWEPDVCwSQiIWwc1VAqmTCE5c4mP4n090pGLIJGK4SMWQSQ3JkFT/WOIkAsOwhsSPhY5h\noNHq/6k1Oj6JU2l1+v/Vhv813PP6JFKl0TeIuYZxqVyFKpVWn2iqdFBptNDq9PvhjkcoFEAiFqKd\niwSeblJ4uUkx5JFABPu1Q5BfOwT7tYNnO2mTVs1I3QX7uWHlvCdwI6MEJy5mIzW9ECcuZjfLWC2h\nUAA3Fye4u0rg7iqFu6sE/t4uJo/dXSUIC/Jo8lhaG/rzIoSQtq3RSVZRURECAvSD4T08PFBRUQFv\nb+8a68XGxiI2NtZkmVrd+Jm1nKVibP93pMXnzJMurrHNMiwY7mowt54hK2OMrhLrGP1yhmWh05n+\nrGMY6Bj9OjbVBMkhC9akemP0n/5ns9aA8UPudfwyk+fq/vqH7ddSY8S4sgQATmIhpBJ9UiWTiGhw\nuRnuM8yCBt63RAKBAD06e6NHZ/13J8uyKK1QoaRcBa1OX5XVf98wEKC6Cq9/LfjquwDVy82Xca+R\nOukvCDjLxE3eRbWt2rP0WXuHQAghxM4anWQFBAQgLy8PgYGBKC0thZeXl8X1pk6diqlTp5osy87O\nxujRoxsbglUCgQAiQ5dB6iVPWjOBQACRiBrLrYVAIICXmwxebjJ7h9LixcTEIDs7G3K5HHPmzEFY\nWJjJ83/88Qc2bNiAgwcP2myf7Vxo8gtCCGnrGp1kzZo1C2vWrIG7uzsiIyPpqighhBCHoFQqcebM\nGWzZsgVZWVnYuHEjli9fzj9/9+5dXL9+Hb6+vnaMkhBCSGvU6CQrPDwcq1evbtBrdTodACAvL6+x\nYRBCCHFQ3Hc8953flGJiYhAfHw8AqKqqgpubGwDA398fBQUF/HpVVVXYtm0bFi9ejNmzZz90m5a6\nu6tUKgB0/iKEkNasMecvu07hzp3wmntGQkIIIc2voKAAISEhTbqPqKgoREVFAQC0Wi3mzZsHAMjJ\nyUFQUBC/3tmzZyGRSLBx40ZkZWXhyJEjiIy0PL7XUnf38+fPY9q0aXT+IoSQNmDv3r344IMP6vUa\nAWtpXu5molQqkZaWBl9fX4hEpqOm5s6di40bN9opsuZHx9v6tbVjpuNt3epzvDqdDgUFBYiIiIBM\n1rzjzHbu3ImMjAzI5XLMmzcPUqkUP/30E+bPn8+vExUVhZiYmHptV6lUYsKECdi8eXON85ejaUmf\nzZYSK8VpWxSnbbWUOAHHj5U7fy1btgy//vprvV5r10qWTCbDwIEDLT4nkUgQHBzczBHZDx1v69fW\njpmOt3Wr7/E2dQXLmunTp9dYZpxgAah3ggXoz1+urq52O676aEmfzZYSK8VpWxSnbbWUOIGWEWtI\nSAiEwvrP3ExzPRNCCCGEEEKIDVGSRQghhBBCCCE2REkWIYQQQgghhNiQ6JNPPvnE3kFYExERYe8Q\nmhUdb+vX1o6Zjrd1a2vHa0lLeQ9aSpxAy4mV4rQtitO2WkqcQMuJtb5x2nV2QUIIIYQQQghpbai7\nICGEEEIIIYTYECVZhBBCCCGEEGJDlGQRQgghhBBCiA1RkkUIIYQQQgghNiS2dwCcmJgYZGdnQy6X\nY86cOQgLCzN5/o8//sCGDRtw8OBBO0Voe9aOWafTYfHixXB2dkZhYSEWLVqE9u3b2znaxrN2vCzL\n4pNPPoFUKkVeXh4WLVoEPz8/O0fbeA/7TO/btw/R0dHYtm0bAgIC7Bhl4928eRPR0dFwd3dHaGgo\npk2bBgA4ePAgEhMTodFoMGnSJAwYMMDOkdqOtWNOT0/H2rVr0bNnT8ybN8/OUdqOteM9fPgwTpw4\nAQB4/PHH8fzzz9sxyuZj7f2wN7lcjs2bNyMtLQ3bt2/Hl19+Ca1Wi6KiIixcuBCFhYUOEXd6ejq+\n+eYbeHt7w8nJCWKx2CHjvH79OjZu3Ij27dvD2dkZABwyTkB/Hn3rrbfQq1cvVFVVOWyc+/btw8GD\nBxEWFgYPDw+oVCqHjLWsrAzr16+HRCKBv78/CgsLHTLO/fv3Iy0tDQBw6tQpREZGOmSct27dwvff\nfw8vLy8wDAOWZR0yzqSkJPz888/w9PREeHg4MjMzGxcn6wCqqqrYN954g2VZls3MzGQ//PBDk+fv\n3LnDfvXVV+zMmTPtEF3TeNgxV1ZWsqmpqSzLsuzmzZvZU6dO2SVGW3rY8SqVSjY5OZllWZb9/vvv\n2bi4OLvEaEu1faYzMzPZDz74gM3NzbVHeDb1/vvvszk5OSzLsuzrr7/OqlQqlmVZ9tVXX2VZVv9e\nvPnmm3aLrylYO+asrCz27Nmz7DfffGPP8GzO2vEmJiayDMOwlZWV7Pz58+0ZYrOy9n7YW1FRESuX\ny9mZM2eyGRkZ/PdOfHw8+8033zhM3Ldv32YLCgpYlmXZqKgoh43z1q1bbFFREcswDPvqq686bJws\ny7Jbt25lN23axK5fv96h4/z555/Zt956i/3ss8/YH374wWFj3bJlC7tu3Tr2q6++YmNiYhw2Ts5/\n//tfdtOmTQ4b5/bt29n4+HiWZVmH/ltaunQpe+vWLT6OxsZpt0pWTEwM4uPjAQBVVVVwc3MDAPj7\n+6OgoIBfr6qqCtu2bcPixYsxe/Zsu8RqK3U9ZhcXF0RERGDp0qW4c+eOw1wlra+6Hq9UKkXfvn1x\n5coVXL9+HVOmTLFLvI1V1+MFgI4dOzZ7fE2lqKiIr8Z5eHigoqIC3t7eEIv1Xy8ymQxqtdqeIdqc\ntWMODg7G/fv37Ryd7Vk73sGDB0OhUGDVqlX4+9//bucom4+198PejGMoLCyEv78/gOrvIEeJOzw8\nHCzLYtu2bRgwYAAYhnHIOLt06YKrV6/io48+wpAhQxw2zoSEBMhkMoSHhyMpKclhf+8AMGrUKIwa\nNQqenp6YMWMGQkNDHTLWzMxMjBkzBsOHD8esWbPQt29fh4wTAFQqFY4dO4ZXX30VSqXSIeOMjIzE\nwoULsX//1zccxgAAIABJREFUfj4+R4xz5syZ2LFjB7y9vREfH4+oqKhGxWm3JCsqKooPXqvV8l1r\ncnJyEBQUxK939uxZSCQSbNy4EVlZWThy5AgiIyPtEXKj1fWYi4uLUVBQgH//+9/4/fff8csvv7TI\nRKuuxwsABw4cQHZ2NpYsWQKhsGUOFazP8bYmAQEByMvLQ2BgIEpLS+Hl5QUA/O9RoVDA1dXVniHa\nnLVjbq2sHW9ubi6+/vprvPPOOy2+22t9tITff2BgIPLz8wFUfwep1WqHiFutVmPFihUYN24cgoKC\nsGHDBoeMMyUlBV26dMG3336LmTNnokOHDg4ZZ1xcHDw8PJCSkoL79+9DIBA4ZJyAPnnhzodcbI4Y\nq/EQDZ1O57B/SwBw7NgxjBw50qH/5r/77jssXboUISEhmD59OvLy8hwyToZhMGfOHPj4+ODs2bMo\nLS1tVJwOczPinTt3IiMjA3K5HPPmzYNUKsVPP/2E+fPn8+tERUUhJibGfkHamLVjfv3117F48WL4\n+PggKysLCxYsqDFGrSWydryTJk3C9OnTMWbMGADAiBEj8Nhjj9k52sazdrxz587Fl19+idOnT6N/\n//4YN24cBg8ebO9wGyw9PR2bNm2Cu7s7unbtipSUFCxfvhyHDh3C2bNnodFo8PLLL6NPnz72DtVm\nrB3zgQMH8OeffyI/Px9PP/00Zs2aZe9QbcLa8c6ePRsBAQFo164dvL29W3xvg7oyfz+mTp1q75AA\nAJcuXcLhw4dx6NAhjB07ll9eXFyMhQsXori42CHi3rJlCxITE9G1a1cA+kasSCRyuDgTExPx888/\nw8XFBTqdDj4+PlCpVA4Xp3G8Fy5cgFqtdtg409LSsHnzZgQFBcHV1RVardYhY83Pz8fKlSvh7++P\ngIAAlJWVOWScALBixQpMmTIFXbp0wdq1ax0yzsTERBw6dAheXl4oKiqCl5eXQ8aZnp6OL774Ap6e\nnhg9ejTS0tIaFafDJFmEEEIIIYQQ0hq0zH5ZhBBCCCGEEOKgKMkihBBCCCGEEBuiJIsQQgghhBBC\nbIiSLEIIIYQQQgixIUqyCCGEEEIIIcSGKMkihBBCCCGEEBuiJIsQQgghhBBCbIiSLEIIIYQQQgix\nIUqyCCGEEEIIIcSGKMkihBBCCCGEEBuiJIsQQgghhBBCbIiSLEIIIYQQQgixIUqySJ1lZ2fjr3/9\nK//48OHDWLduXYO2lZiYiPfee6/er4uLi0O/fv2QkZHBL9u3bx8mTZqEyZMn49SpU42K7/r168jK\nygIADB8+vN7xtVUsy2Lt2rUYMWIEv2zRokW4ePGixfXXr1+P7Oxs/vHChQstrrd161ZMnjwZkyZN\nQlxcHIqLizFr1iyo1WrbHgAhpFWj8xexhs5fpKmI7R0AaZlUKhU2btyI2NjYZtvn1atXceTIEXTr\n1o1fVlFRgW3btuHHH3+EQqFAVFQUhg8f3uD4jhw5goEDB6Jjx462Dr9V27VrF7y9vcGyLL/svffe\nw9y5c7Fnzx4IBAJ++UcffYTMzExkZmZixIgROHLkCLKzs7Fo0SKMHz8eQ4YMAQBkZGTg0KFDiI2N\nhVwux0svvYS4uDhERkZi165deO2115r9OAkhLR+dv4gxOn+RpkJJFmmQQ4cOYcSIEZBIJFi/fj3K\ny8tx584d5OTk4PPPP4enpycWLFiAgIAA3Lt3D1OmTMG0adMsbmvTpk04fvw4GIbBnDlzMHr0aPzy\nyy/Ytm0bgoODIRQKMWPGDERERGD16tWYPn06/9qUlBT06dMHzs7OcHZ2hre3N7KysnDx4kU+vvv3\n7+ODDz6AUKgv3H799deorKzE+++/jz179gDQX/WLiYlBbGws4uLisHbtWgDAqlWrcO7cOXTt2hWf\nffaZxfgTExOxdetWCIVC3L9/H2+//TbGjBmDv/zlLwgNDcWECRPQqVMnLFu2DAKBAEFBQVi6dCkm\nTZqErVu3wsfHB/Hx8Thw4ABWrlxZY/vZ2dkW38tffvkFe/fuhUAgwOOPP463334b69evR05ODnJz\nc1FeXo41a9YgJCQEq1atQlpaGrRaLf7xj39g0KBBGD9+PPr164cePXogODgY69atg0QiQdeuXbFk\nyRKLx3nu3Dm89dZbNZ6bOHEiXF1dER0dzS/z9PRESEgIkpKSMHjwYH75xx9/jNdffx2hoaEYN24c\nhg4diqioKAwcOJA/QQFAx44dERMTA6FQCDc3NyiVSgDACy+8gIkTJ9JJihDSIHT+qkbnLzp/kaZD\n3QVJgyQkJGDgwIH845KSEmzduhWzZ8/G/v37AQA3btzAJ598gt27dyM6OhparbbGdjIzM3H8+HHs\n3r0bW7ZswerVq6HT6bB+/Xp8//33WLlyJZKSkgAArq6uNV5fWFgIb29v/rGPjw8KCgpM4isqKsI7\n77yDHTt24Mknn8Rvv/1m8ZjCwsIwbNgwLFy4EGFhYSgsLMTkyZPx888/Izk5GcXFxVbfj5s3b2Ld\nunXYuHEjvv76awDA3bt3sXDhQjz99NNYvnw5vvjiC+zYsQMeHh44ceIEnnrqKb57yIkTJ/D0009b\n3b6l91KlUmH79u3Yu3cvDh8+DLlcDgCoqqpCTEwMZsyYge+++w4JCQmorKzEzp078c0332D16tUA\nAIVCgfHjx+OVV17B7t27sXjxYuzevRv9+vXjTwh1Zel3AwADBgzAuXPnTJadO3cOc+fOhVKpBMMw\n+PPPP/Hll1/i9u3bJusJhUJ+u7/88guGDRsGAJDJZPDw8EBOTk69YiSEEIDOX+bo/EXnL9I0qJJF\nGuTBgwfw8/PjHw8YMAAAEBAQwJ9UwsPD4eXlBQDw9fVFUVER/P39TbZz/fp19O7dG0KhEB4eHvD0\n9ERpaSk8PT3h4eEBAOjXr1+d4+LK/cbxtW/fHkuXLsXatWtRUFCAcePG1WlbXl5eCAsLAwD4+fmh\noqLC5IRorHfv3pBIJAgKCuJPFh4eHggODgYApKWl8X34FQoFgoODMXr0aERHR2PixIk4d+4c/vGP\nf1iNxdJ76e7ujjfeeAMikQj5+fkoKysDAAwaNAgA8Oijj+LAgQNISUlBYmIifwW1oqKCf68iIiIA\nAJGRkVi0aBFeeOEFjBs3DjKZjN/3jRs3sGzZMpSXl0Mul+PcuXOYOHEiXnzxxVrfQ39/f6Smppos\ne/LJJwEATzzxBABg0qRJAGB1jMOZM2ewZ88ebN++3WS7eXl56NChQ60xEEKIMTp/maLzl2V0/iKN\nRUkWsQmxuPqjxJ0ojPs3syxr0q/ZmPFyrVYLlmX5rhHmz5vz9fVFfHw8/zg/P9/k5AnoB6lGRkZi\n4sSJ2LVrF4qLi2ts09JVSpFIZPLY+Hgehtu2k5MTv8zNzQ07d+6ssW56ejrS09PRsWNHSKVSq9s0\nfy+1Wi0+++wz7N+/H15eXnjppZcsrisQCCCRSPDXv/4VUVFRNbbLxfjiiy/iiSeewKFDhzBjxgzs\n2bOHPyl2794dO3fufGh3i6aSkpKCL774Alu3boWbm1uz7ZcQ0nbQ+avm8dD5q/Ho/EWouyBpED8/\nPxQUFDx0nbt376K8vByVlZUoLi6Gj49PjXW6d++OS5cugWEYlJaWQqFQwNPTE0VFRVAoFCgrK8Ol\nS5es7qN3795ITk6GQqHgr4YFBwebxFdSUoKOHTtCq9Xi5MmT0Gg0aNeuHYqKigAA9+7dQ0lJCQD9\nl7qlE1Ztrly5Ao1Gg/v37/NXMI2Fhobi/PnzAIAdO3YgPT0dgP4K6qZNmxAZGfnQ7Zu/ly4uLpBK\npfDy8sLt27eRkZEBjUYDAPz7lZqairCwMERERODEiRNgWRZFRUVYv359je3/5z//gaurK2bOnIkB\nAwbYrCtDfn5+jau/daXVarFkyRJs2LChxhXY/Px8BAQE2CJEQkgbQ+cvU3T+sozOX6SxqJJFGmTI\nkCE4f/48XzK3JDQ0FJ9++ilu376NN998EyKRCJs3b8bQoUP5dUJCQjB69GhMmzYNLMviww8/hFgs\nxqxZs/Dyyy+jc+fOePTRRyESiXDo0CHs2rUL165dw3vvvYe+ffvio48+wty5czFz5kwIhUIsWrSo\nRnxTp07F4sWL0aFDB0yZMgXLly/HhAkT0LdvX0yePBl9+vRB586dAQD9+/fHJ598wvdLr6uOHTti\nwYIFuHv3rsVuEx9++CE+/vhjCIVCBAQE8FMJjx49GvPnz8fixYsfun3z99LLywv9+/fHlClT8Oij\nj+KVV17BihUr0Lt3b7Asizlz5qCgoADr1q1DcHAwevXqhZdffhkMw+Ddd9+tsX1/f3/MmDEDbm5u\nCAoKQs+ePWusM2TIEJOBvcbWrFmDixcvori4GNOnT+e7YyQnJ+OFF16oy1tYQ3x8PLKzs02mx/3q\nq6/g7u6O0tJS6mpBCGkQOn+ZovMXnb9IE2EJaYCqqip24sSJrFqttvh8VlYW+/LLLzd4+7///jsr\nl8tZhmHYiRMnsrm5uTaNz5YSEhLYBQsWNNn26/Nerlu3jv3hhx+aLJb6KC0tZadMmcIyDGPT7cbG\nxrLR0dE23SYhpO2g81c1On9ZRucvYgtUySINIpPJ8Oabb2Ljxo1N0se5oqICr776KqRSKUaNGlXv\n0npTxHfixAls3bq1xvKxY8faZPtpaWlYtWpVjeUTJkywyfab2xdffIF//vOfDx2TUF/FxcU4dOgQ\nNm7caLNtEkLaFjp/VaPzl2V0/iK2IGDZOo6GJIQQQgghhBBSq3pXssrKyrB+/XpIJBL4+/ujsLAQ\nWq0WRUVFWLhwodUpQgkhhJCmdvPmTURHR8Pd3R2hoaH8TWQPHjyIxMREaDQaTJo0Cb169cLKlSvh\n7++PzMxMLFmyxGTqZ0IIIaQx6p1k/fjjj/Dw8OBnsCkuLsby5cuRkJCAvXv3Yt68eXXellKpRFpa\nGnx9fWtMN0oIIaR10Ol0KCgoQERERJMnMtHR0Xj33XcRGBiIN954A5MnT4ZEIsHevXuxc+dOKJVK\nvP3223jnnXfQvn17/P3vf8eyZcuQmZmJbt261Xk/dP4ihJDWrzHnr3onWZmZmRgzZgyGDx+OWbNm\noW/fvgD0s7s8bErU2NhYxMbGmiyrrKzEvXv36hsCIYSQFmjXrl0YOHBgk+6jqKiIHwPj4eHB34SV\nuxeSTCaDWq1Gt27d8PXXX2Pp0qXIyspCeHi41W3S+YsQQtq2hpy/6p1ktW/fnv9Zp9MhPz8fAJCT\nk4OgoCCrr5s6dSqmTp1qsiwjIwORkZHYtWsX3TOAEEJaqby8PEybNg2+vr5Nvq+AgADk5eUhMDAQ\npaWl/E1JuRvEKhQKuLq64sSJExg9ejQmT56MmJgY/rEldP4ihJC2qTHnr3onWVOnTsXKlStx5swZ\njBw5EmVlZVi1ahWKi4tN7gdQF1wXi4CAAAQHB9c3FEIIIS1Ic3SrmzVrFtasWQN3d3dERkZi0aJF\nWL58OSZPnozFixdDo9Fg9uzZ6NChA9asWYP8/HxkZmbiL3/5S732Q+cvQghpOxpy/qp3kuXv74+1\na9fWe0eEEPsoq1BB6iSCTEp3bCCtX3h4OFavXs0/5ipQY8eOrTFd9YoVK5o1tpaEZVkwDAuRSGjv\nUAghpEWiVhchrVCVSov9p9Jx9FwmHhQrAAAhAW4YPywcTw/uBKHQdvf+IIS0PocTMvDNT5fx48q/\nQCahpgIhhNQXfXMS0srczSnDsm2JKC5X4qkBHdG7qy+0Wh0S0vKw4cdLSEjLxYJX+qOdi8TeoRJC\nHNSZyzkAALWGgYy+KgghpN4oySKkFUm9XYglWxPQob0rls4dig7t2/HPjRkcgnNX8/DV7otYEp2A\nT+cMhTN1ISSEWKDRMQD03QYJIYTUH3W2JqSVuJ5RjE+3JqB7Jy+snj/MJMHiDO4VgCWzH8O93HJ8\n8f0FakARQizSGpIs7n9CCCH1Q0kWIa1AYWkVlm8/h86B7vj3rCEPneSie4g33ps2AOeu5uG303ea\nMUpCSEvBJVc6HV2IIYSQhqAki5AWTqtj8Nl3SRAJBfgwanCdZhEcEhGI8cPCsP2/V5H9QN4MURJC\nWhKt1pBkMZRkEUJIQ1CSRUgLt/fIDdzKLsUH0wfBy11W59fNeK4nvD1k2LQvlboNEkJMUHdBQghp\nHEqyCGnBrmcU48djN/HymG7oGepdr9fKJGK8OSECl24V4GxqbhNFSAhpiTSGboJUySKEkIahJIuQ\nFkqrY7Dhh0vo3MEDU8Z0a9A2hkQEom9XX3z/xzXo6Io1IcRAq9Xp/6fvBUIIaRBKsghpoX45cRtZ\n+XK8NbkvRKKG/ylPf64nsh9U4PiFbBtGRwhpyTRaQyWLkixCCGkQSrIIaYFyCyux98gNjBsWhi4d\nPRu1rW6dvDDkkQD8cOwmdQ0ihAAAtDp9JYu+EwghpGEoySKkhWFZFv/5+TLc20nx6tieNtnmS091\nRW5hJc5dybPJ9gghtvOgRIGTF5u30lylMiRZbXwK92mL/8D/Lt9v0n2cScmBRksVQ0JaG0qyCGlh\nTl7MxqWbBfjbi73hXIfp2uuiZ6g3uod4Yf+pdJtsjxBiO68vO4ovdl2wy77b8pgsHcOivFKNnb9f\na7J95BRU4LPvkvD9H023D0KIfVCSRUgLUl6pxpb9aRjaOxCDHwmw6bZfGBGOK3eKcDOzxKbbJYS0\nLCqNjv+5LXcX5MajVam0TbYPoVAAALiXW95k+yCE2AclWYS0INt/uwKtjsGbLzxq820/HhEIP28X\n/HqSqlmEOArjbmTNlfCUyVX8z225ksUdu1LddEkWd4vCorKqJtsHIcQ+KMkipIVIvV2IuKRMzHiu\nF3w8nG2+fZFIiAnDwnAmJQcPihU23z4hpP5KjRIejVb3kDVtp1yh5n9uy5UsrWE8Gjc+rSlwN4Iv\nKlM22T5Iy1BSrsTB/90B04b/5lobSrIIaQGUai3W/3gJ3UO88OzjnZtsP2MGd4JMIsLvZ+822T4I\nIbU7m5KD9OxSlMirG9/aZpocwbiR15ancG+OKh6XxCrVzZNAA/rEbtWOJKRnlzbbPknt4pIysfGX\nVJxKptuptBaUZBHSAnz/x3UUllbh7an9+D78TcFF5oSR/YNxLCmLZrsixI5WfpeEd9acREl5dZKl\naaaEh6uuANXVnLbIVkltiVyJr/cmW/xOZfj3uvneZ42Wwf8u52DjvpRm2yepHVe1zi1y3J4kZy7n\n4MP/nLF3GC0GJVmEOLhrd4tx4HQ6/hrZHR393Zp8f2Mf74zSChXOXaXp3AmxtxKT7oLNlWRV/0yV\nrMb74ehNxCVl4lZWzUmFuPeabcZcVm34HAkETXfBjtRfvqGbvkKpafA2/jyfiX9vPGurkGr4OvYi\nUtMLm2z7rQ0lWYQ4MJVGh69jkxEe7IkXR3Zpln2GdvBA146eOJKQ0Sz7I4RYZ1zJarbugkYt/rY9\nJss27zdXgRSLaja5uK6ZbDNmWRrD7JFN2SuC1F+hYfKTyqqGJ1lr9iTj0q0CW4WEB8WKZv1stjaU\nZBHiwLb8mor8YgXemdoPIgsn6KbyzGOdkXzzAX9ljRBiH7uP3OB/pkpW8zLuKtmY9567obPQQuWI\nT7IavPX646botxQPsR+14fdS0Ygki0vkbZEY5RZW4vXlR3H0XKbRUoHNtt8W2OZOpoQQmzuSmIHD\nCRmYN6kPQgLdm3Xfw/sFYeuBVBxNzMCrz/Zs1n0T0hg3b95EdHQ03N3dERoaimnTpgEADh48iMTE\nRGg0GkyaNAkDBgzAunXroNFooFKp8P7778PJycnO0T9ccyVZxpUsLVWyAAAVVWp4uckatZ2Hjclq\nzjYrF4eQLrM7FO730pjughInIbQ6BhotA4mTqFHxFBuq6Hdzymo8p9UxcBI3bvttAf2JEeKAbmWV\nYOO+FDw9uBPGPhbS7Pt3looxvF8wjp7LaNNXsknLEx0djXfffReLFi3C8ePHoVbrpyPfu3cvPv30\nU3z88cfYvHkzUlJSkJmZCWdnZ/j7+zt0gvVknw4Amu+eVcZXqS39/Sem5eLfG89izZ6LrW666Vc/\n/gPLtiUCME2KKhQNb/hy3QXVFqbgZ+xQEeAqJuZjsq7cKcLPf95q9ng4Ko2uTd8+hPu8Naa7oMSQ\n+NTlBtoaLVOnipRxxZP7kSbGqhtKsghxMEVlVVgRk4SQADfMfbG33QYnP/NYCIrLVbhw44Fd9k9I\nQxQVFSEgIAAA4OHhgYqKCgCAWKzvuCGTyaBWq5GVlYWAgADMmzcPCoUCV65csbrN2NhYvPjiiyb/\n5s6d22THYJ7YDOqlP55m6y5otBtLswsmpOXh0q0C/Hk+C5WNuOpuT5dvFeCihe+2sgo1Eq/oJ/3R\nMdVvRHmlusa6dcWNpbP0+2Obua16L7e8urug2Zisf/3nf4g5eNVuF9Y+33kery8/arPtKZQabD2Q\n1mISAj7JUjb85tdOTvpmvaIO23jxg9+w7Tfr33vcBQCuDVJW0fyT8LR01F2QEAdSXqnG4s3xYBgW\n/5o5uNHl/sboEuyJkAA3xJ3LxGBDI48QRxcQEIC8vDwEBgaitLQUXl5eAAChoW+UQqGAq6srfHx8\n+Ne4u7ujqqrK6janTp2KqVOnmizLzs7G6NGjbRr7/lPpcHeV4LGIQJPlzlL994A9ugtaqrSYTIzR\nQqd4X2SYge23LydYXUerrT42VQPvY1VWoeKTNks3k27OSlZGXjne+uI4hvUNAlBzTJZAIADLssgp\nrGyWmWzNXb1bbNPtHTh9B7+eTEdEmA+GmP1NOYqSciX2n0rHjOd6QaNlIBIKGpXkOhnGZNVWyeIq\n0L+eTMfrz0dYXsnw0RQI9Mn5W18c559qrqp6S0dJFiEOIr9YgY83x6O8Uo3P/v4E/Lxd7BqPQCDA\nmMGd8N3BqyirUMGjndSu8RBSF7NmzcKaNWvg7u6OyMhILFq0CMuXL8fkyZOxePFiaDQazJ49G717\n98aBAwewatUqKBQKTJ8+3d6hI3p/GgCgS0dPk+Uyif5UbamRXhsdw+LyrQL07+5X59cYt/stdQc0\nTqyMqz3NpaxChfJKdZMnAsYNSa76U1+lRlf/1RoLY7KasbtlUal+jE32AzmAmkmWk1gIlVqH/GKF\nXZIsqZMQcujfd0szMdaX0pBo2PNiZW2i96fh1KX7iHwsBBotA5lE1KgZPbljrW1cl7oOn2fjSlZB\niWk3Tqpk1U2DkiyWZfHWW2+hV69eqKqqglarRVFRERYuXAhvb29bx0hIqxefmoP1P1yCs8wJq996\nEsF+zX+Cs2Rk/46I+e9VnLyYjeeHh9s7HEJqFR4ejtWrV/OPuQrU2LFjMXbsWJN1V6xY0ayx1caz\nnRSlFSrczio1We4s1Z+q0++X1fuK/MEzd7Dl1zR8+fZwdOvkVafX1DaFu3FiZY+bFb/91QkUlSkf\nWoWqK5ZlrXbJNr75c10apZYYV8DsUclSKDUokasQ5NuOTxQFhhnizCe+4GK1V3dBLkFQqnVo59z4\nJIs7XkdOsnQmN/5m4O4qaVSS5SSuWyWrLhcNuIsMQgEglZi+h5Rk1U2DPsXbt29H7969wTAMiouL\n8cEHH+DFF1/E3r17bR0fIa1aRl45lm1LxIqYJPTs7IOv3h7uMAkWAHi6STGwpz/ikjJrX5kQ0mjP\nDwursUxmSLL2GE3nXhcsy6K4TF+9kCvqPqaIGwwvFAosVloYlgU3nMceDfKiMmXtK9WR8Vgr82M1\nvi9ZQ5Ms48auxdkFG9Cgvn6vmJ/5rTafbk3E3M+OAajZsLaWXNYn8cstrMRhK/dUTL7xAOMX7EdR\nmfWuuMb4JKsOkzbUBZc0Spxqb+qWVaiw4cdLzd8NzvBWawxVTplE1KguuHWdmKIu3V+NtyERU5LV\nEPWuZCUkJEAmkyE8PBxJSUnw9/cHAPj7+6OgwPoN0GJjYxEbG2uyjJv1iZC2QqHU4H5BBa7dLUZC\nWh5S0wvR3kOG96YNwPB+QXab5OJhxgzuhOXbzyE9uxThwZ61v4AQ0iBVai18vZzx+KOBiE/N5Ze7\nyvQzH9b35rHrYi/xF0jqc3Wca2OLRULLlSwdC4mTCEq1zqFvVhx79AaKy5UPnUCoUqnhu0KbJyHG\nFTt1AxuVxgmDpW00pJL1/vrTaO8hw/bFz/DLvt6bjG6dPPHs0FCTda/cKareP3d8hrfCuLug8Sxz\n9ekBunjzWeQVKfCM2Sy4V+8W4azhM/zn+Sy8MKILX2WxRspXsmyUZBmOty5v8Q9xN3E4IQMj+wcj\nIry9TfZfH9wEMjKJ2GSCifriknZrn6szl3Nw+vJ9THumB7/MWjW3eqp/QY3tNaTrcltU7yQrLi4O\nHh4eSElJwf379/lfTE5ODoKCgqy+rrkGDhPSnFiWRWa+HNfuFiOvqBJ5RQqUVqig1uig0TJ8A0Sn\nY1AiV6JKpf9iEgkFeDS8Pd55uR+G9wty6PtNDOzpD892UsQlZVKSRUgjsSyLyioN2rlITJbrGBYq\ntQ4yiZhPqjhOYiHGDwtDstlseDrD/XC4Spc54wp0fSpOXINKLLJcydIxLJzE+iTLkQfAf3/oOgBg\n3JNhVscYGVcNzBv3F6/rLxxLnEQNr2QZVQyi96dhglm364b2Fiw0q+bFJWUiLimzRpJlzPwYTCY4\nMfo9mzeolWotdv5xDdOf7cmPD+Rew01tb9xQZxgWH2z4H7/ejt+vwc/LBSP6Bz/0mKorWbZpwHPH\nW5dqIZcA26trITcboLSRY7K4BNnarJWf7UgCAEwa1ZVfptWxcBJbSrKqfw/mMVElq27qnWQtWrQI\nAJCYmIgLFy5ArVZj1apVKC4uxsKFC20eICGO6HZ2KX4/cxcXrj9AcbkSAgHQ3tMZAd6u8HGXQeIk\ngsRJCJFh8K5QIICXmxQ+HjIE+Liicwd3k5OVIxOLhBg5IBjHkjIxa/wjDp0QEuLofjx2Czv/uIZR\nAztLWPfnAAAgAElEQVTinZf78Q1TruLhLBXD1dk0yRKLBIZZx0wbOmv2JONkcrbVsUmuMjE/HXT9\nKllckiW0OLEFw7L8JAX2nF1w474UvDb+Eb4CYk4qEUGl1uFGRrH1JMvwvqg0Osz45DC/PD27FCeT\nswHou3A1NMmqretbfboL6hgWPx27Wet6t7NLcSr5PmaNf8TktdzEGyWGrobGCbLx+DPzmM6m5OLA\nqTvw93bB88Oqk8Q3VhxFheGeTjqGhVik/yxb6ppa20QMLMvy1ZwqW1WyDAluXaqF3O+3tmpbU+Hu\njWVt4ovC0ir4eMhq7e3CHauulmM27i6o1ugsHjeXSAkEAjC6tplksSyLhLTc2le0osGtvCFDhmDI\nkCEN3jEhLdHFGw/w07Fb+m5+ns4Y1jcIfbv5IiLMx+rV5NZgzKBO+PVkOhKv5OHJPtYr1oSQh8vK\n18/s9uf5LIwZ1AmPdtF3TeKqKM4yMfp288X+U+n8a0QiIZzEQmjNEh4uCcgtrERge9ca+2rnIuGT\nLG09GkVcG89aJYvRsXAyJDbmMTWng2fuIrC9a43qkLmHTc7BVfjMk6FPtybA18sZQx4JQEJqboNn\nFzxxMfuhz9cn+U27XchX5x5m6dZEFJcrTZIsjUbHjw8rkeu7oxl/JoyTZfOkxEWmP7eZ35C5oKR6\nrJVao+NnBLTU3U1TS8Xzv/+7izv3ywA0bExWWYUK+47fxoy/9ILI0K1WVZ9KVgN/v43FGgZlcUmo\nTCqu8ZlQqrV4bekR/DWyO14x6uZnCfe7q+2YjW94rNboalzYAaoTKZZla/yd1/b7bC0uXH/Az/ra\nEHQzYkLqoLhciU+3JuDjzfFQqrX456sDEf3hGLwxIQIDe/q36gQLAEIC3dG1oyfiztEEGITUxdHE\nDIxfsB9HEjPwQ1x19cG4AaU26o7DdRdylogxsKc/xgzqxD8nFgkhEgpNGsXGY2juF1RYjMHNpbrh\npKzHfZ5MK1mWuwtKDFe+LVWy7hdUYPyC/biRYdv7HllibRIMlmWru4s95Kq+tSRHKBRCp2PQzlli\n6C7YsEYlNyYqPNhDH4vZ/tiG9hesJ7WWwd6jphOncMeu1uhMGt3mMXGzW+YVVVrtdnorsxR5RZUA\n9Dd0Nldbkp92p5D/uT6fVc7OP65h34nbyMwr55fV5ffP4RIKe1VmK426CzIMa/I74GLbc+RGrckT\nPyarlvWMby1g7QKCxugm2m21u+DJ5Gz4ejk3+PWUZBFSi3NX8/DWF8dxK7MU/3x1IL58eziG9Qvi\nuwK2FWMGd0LyjQd1nimKkLbs5+O3AAAbfryEnX9cQ2Gp/u/G2n2luO47MsONh9sZJUhikQBikcCk\nIsMlZYD1RpJx1956JVkMt1+h1dkFubErlo7nXq6+oXv5VmGN5xpiRcw57Pj9quVYrTSgNVqGH+/0\nsAYn16g2b0Q6iYTQaBk4iYX6JKuOA/0vXtfPqGdejRn3hH6slHmDvz4TX1hb13xcHNejzLShXjN+\njZaBVscg6tPDeH350er9WEkEj1/I5q/qm3/mFm06i9kr4nD+Wj5+P3u35r4sJGelchVKypWY/vEh\nJKbl8csbMmNllbJm9au28UmcvKJKvtujvSqzqen6vxVuGIHx37px4nc72/T2DizLIjW9EOMX7Efq\n7UL+c1zb52rTL6n8z9aqeHziybA1kk/jpLw1y8gtR4+Qht+aqnVffiekEXQMi5j/XsGvJ9PRv4cf\n3nm5H7zcZPYOy26G9w1C9P40/Hk+C5NHd7N3OIQ4JI1Why370yDnJwTQLy8uV6K9p7PVBj/XiOem\nSua6XgmFAggEAohFQr4xnZCWi7uGrlWA9UaS8WyEqnqMc+EaaCKrswsyfJJlqSseV+Wqa2JSG26m\nxRnP9aoZi5X30zgJeFiSxTWqzRuRIpGAT7Kk9Zj44s/zWQCAgtIqfhzYs493htBwUyr92KXq9Y0T\nAIZhHzqDpPm9jy7fLECfbr5WE2jj98bSfZO0OgZf703mP6vVrzNfr3o7yTf1k6+UWJlCfkl0gslj\n7jOv1db8Hby5Ms5KXPWrJmXly3Hq0n0AwD/Xn8byvz2Bbp28aoxPyimowPlr+TXu+Th7RRz/c1NW\nssoqVLh44wGeGtARLMvi+0PXkVekv8nvxev691VmuB/Vix/8hq0fPQ0/bxeTCxkqQ9Xx6LkMPD8s\nHJduFuDjLfEAgPT7pXxCXFuF1PjznJVfgU4B7jXW4ZIsrZap8Te0/odLGNTTH17utmsTKVVaiMVC\nm9yIur4qFGr8bfWfNbq6sizQ/0nfBm+XkixCLFBpdPhy1wUkpOXitXGP4IUR4fWePrm1aeciweMR\ngTiWlIlJo7o65HTzhNjbuav5+OPsvRrLS7lxMEaNOO6msEB1g4YbgM6NK+EaHCKRkL/Cv3z7OZNt\n1yUBMG+In7+Wj5JyJZ7sGwSpkwgaHcNPIME10JxEQpMr4im3CyAUCKBjWDhLue6CTI3kgEsUj53L\nxKiBHdGhfbta42soa1UP4/fkod0FddzVetPtiM0rWXXsLshVIrkxdgKBvrs19/vU6RjAaKIO4wkK\nGJaFEHVPshZtOovfvpxgNYE2TrIsVR40WgaJV/JqLK9RbTPajrurfrr7ulZGpU5CaHSsxUqatRvm\n1mWc2t9WHcOzQzvj+WHh+OXEbX65Uq3Dt/tSsOadETXi/yQ6AbmFlSZJlnlcDblvWW2y8uW4cP0B\nLlzLx6VbBRjWNwgMw5p0I+Z4GyUtuUWVyCmswL83xfPLVGodvth1Aeev5aNfNz/kFFZ3FW7nXH0j\n47ocxyNhPrhypwif7UjC7qXPws1sxlPuM6zVMRZnEb18qwAjB3SsdT/mHpQocPlmATzdpBjUKwCA\nfmKPN1fGQSYR49M3H0eXjs07k/FnO5JQKldh9oQIk0lAhEIhwn0b/pmgJIsQM3KFGsu2JeJ2Vin+\nNXMQHn+0g71DchijB3fCqc33cf1eCXqGNryETkhrdS+n3OLyErkSxy9k4fy1fH5ZQakCOh0DkaFB\nDxglVXySpf/fSSSA1lrVxkKDt1SuMrlHkvm4La7icCYlB906eWHPkRs48MXz+pnEDLsRiUxnFfvo\n27P8z0Me0TeOrt0rxqdbE7HhvacQEujOvw7QTzP+r2/OYNu/IyFA/e/zVRdWK1lG78nZlFyEdvBA\nn641r0hba5RyyaSTWIgOvq5IuprP/64ehuvuxU1DzrKAUFD9npjvx/ixeZXLnLWkxGoly6hhXGnW\nnc7NRYIqldZi1SC3sBK/nryNF0Z0AWDaHdHdVd8Qr2tlT99lVVejOnX9nvXxeoxRwrvl11R0D/HC\n8H7BUKq0mPzhQXz8xmPIflCBLb+m4flh4XxXPw53g2nzqg73mdAxLP/3pTB7X6x1522MA6fv4FD8\nPf6xRsvA2jVK88rQIbMbPavUOv5vuaJKg+JyJbzcpCiRq8CwLD/2zVKSZV7dCglw478jsvLl6BXq\nY/I8912m1VWPyXrpqS6YMDwcM5YcxuZfUzGsX7DRe6mBQCDgx/CZYxgWN7NKsC72Ej8B0IJpA+Du\nKsG5K3nQaBlotGr8c8NpfPF/wyGViLDr0HVUVmnwzGMhkCvUcHV2QnGZEnuP3kClUotxT4aiV6gP\nHosI5OOoj+wHchw8cxeXbxVieN+gGlVOQH+7qYaiJIsQIxUKNRZtPIuCEgWWzX2CEgkzfbr6or2H\nDHFJmfTeEGJBfnGlxeUlchV2mc0Mt+HHy0jPLsO8SX34hixfyTI0fo0rWQzDWmw8WRqT9XVssslj\n44TL2IXrD3DB0FVJpdHfp8tk4gsrVSCuu+DtLP0YkdT0Qj7JMn6JXKHGC+8fQO8u7bH8b09Y3FZj\nWLtifzL5Pv/ztXvFWLTxrMWp7jNy5Xg0vH2NZI3rRugkFmJgT38cTshAaYUKPh4PHwTPdfeqUmv5\n91EgEFRXssz2Y1yFMq9ymeOSrLAgD34mPqDmbHxcU9N4X+ZTqHu6SVFWobJ4fySuMjRheDgEhmST\nw800WNckK7eoEoE+rjUqIe+vP231NcYJ2YHTd4DTwPB+wbhraPQbT6nNsixKypVwd5XwyVWpXIX8\nYgWffFZ/Rqon+nCWilEqV0GhMu8qaftKlvl7ZW3KdMBsCnkW0JhVUJVqrdFsj2q+G3KJXKWfBZCr\nzFr4uzWfrKJLsCfmT+6LDT9eQn6xokaSlX5f/7et07F84jttbE84iYWYOLILfjlxGxm55QgL0k/q\n8trSI5CIRdi5ZKzFY4tPy8Vn3+nv0zV1TDccTsjAl7su8M8P7xeEKWO64Z2vTuLtr06YvPai2T0C\ne4V6QyIW4cCpOzhw6g7emBCBCcPDodMxKCitQoBPzdlWq98HHQ6euYtTyfdxy/D9FR7sgbem9LX6\nmoaiJIsQg8oqDRZvjseDYgWW/+0J/ouDVBMJBRg1qBN+O30HsydEtPpZFQmpL2uz3VlrlP4Rfw/l\nlWo80UdfMa/ZXdD0f/Mr7Z5uUotd2czHFsgVapMbxlpSpdTWSLKsJTESJ32cXBcjrjukeYwioQAa\nACm36z8JxvgF+/H8sLCHrmOtUbz7cO1TnQPA9v9eQXxqDv4+ubqBJRDoK1mA/j3gfid1GSvEfScq\nqjR8RVCfZFWPyeJk5cvxn59T+McPHTumY7Dj92sAgHkv9cZ766qTFGuVrPxiBf9zZZVpIublJkVe\nUSU83aRW98nd+4qriHXr5InjF7IR2sEDIRbG8FiiUusgNoxvqytrv9NMQ/XDuPvpiYvZyHpQgcgh\nIXxyqNbo8IbxRB5mCYdao4NGy2D6J4cwepBpd7emSLLME0yNlrFadTFezqJmN0uVRgcXqX5SHLlC\ng4KSKrT3dMatrFIwDGtUma25bfPvIBdnJzzRuwO+O3gFv5+5i6cMXf9YlsX6Hy7xY/U0RpUsLr5X\nIrvjwKl0pKYX8m0lhVILBbT46c9b8GwnRfLNB3hhRDi6dvTCsaRMrN2bDE83Kd54PgKDevnj+eHh\nqFCo+VtUeLlJIRAIsOlfo5GVL4dOx0IqESG3sBIPShR45ZkeuH6vGEq1DgN7+qO4XInEK3k4lZyN\n/126j+eHheHbfSk4nJCBHR8/w1cFNVodVu04j6x8OQLbu0IgEJj0KFj77giEB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6dRypCTpUN7Qg1uPE6pQ8V5c8f+32pYQAWtplEuh46W1HRbejGYlAGhh6dRdkW9dk3bdw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\nf+6/bkWAv56EqGC3rEsIIYRrOF1kmc1mysvLsVgs1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fda9e4361d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results[100:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/content/smishra8/SOFTWARE/anaconda2/lib/python2.7/site-packages/bambi/results.py:346: UserWarning: Multiple MCMC chains are required in order to compute convergence diagnostics.\n",
      "  warnings.warn('Multiple MCMC chains are required in order '\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd0.95_lower</th>\n",
       "      <th>hpd0.95_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1|I(gender == 'F')[False]</th>\n",
       "      <td>-0.001151</td>\n",
       "      <td>0.037598</td>\n",
       "      <td>-0.056117</td>\n",
       "      <td>0.049701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|I(gender == 'F')[True]</th>\n",
       "      <td>-0.003557</td>\n",
       "      <td>0.037211</td>\n",
       "      <td>-0.060585</td>\n",
       "      <td>0.041850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1|I(gender=='F')_sd</th>\n",
       "      <td>0.038701</td>\n",
       "      <td>0.078857</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.103080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(auth_prev_papers == 0)</th>\n",
       "      <td>-0.038119</td>\n",
       "      <td>0.014020</td>\n",
       "      <td>-0.064268</td>\n",
       "      <td>-0.007719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(auth_prev_papers == 1)</th>\n",
       "      <td>-0.020105</td>\n",
       "      <td>0.010725</td>\n",
       "      <td>-0.036769</td>\n",
       "      <td>0.001919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I(np.log10(auth_prev_papers + 1) ** 2)</th>\n",
       "      <td>0.044541</td>\n",
       "      <td>0.008407</td>\n",
       "      <td>0.026558</td>\n",
       "      <td>0.059693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>0.043910</td>\n",
       "      <td>0.040540</td>\n",
       "      <td>-0.002829</td>\n",
       "      <td>0.103333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is_self_cite_sd</th>\n",
       "      <td>0.220345</td>\n",
       "      <td>0.001362</td>\n",
       "      <td>0.217944</td>\n",
       "      <td>0.223128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>np.log10(auth_prev_papers + 1)</th>\n",
       "      <td>-0.039475</td>\n",
       "      <td>0.022008</td>\n",
       "      <td>-0.077879</td>\n",
       "      <td>0.007790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            mean        sd  hpd0.95_lower  \\\n",
       "1|I(gender == 'F')[False]              -0.001151  0.037598      -0.056117   \n",
       "1|I(gender == 'F')[True]               -0.003557  0.037211      -0.060585   \n",
       "1|I(gender=='F')_sd                     0.038701  0.078857       0.000014   \n",
       "I(auth_prev_papers == 0)               -0.038119  0.014020      -0.064268   \n",
       "I(auth_prev_papers == 1)               -0.020105  0.010725      -0.036769   \n",
       "I(np.log10(auth_prev_papers + 1) ** 2)  0.044541  0.008407       0.026558   \n",
       "Intercept                               0.043910  0.040540      -0.002829   \n",
       "is_self_cite_sd                         0.220345  0.001362       0.217944   \n",
       "np.log10(auth_prev_papers + 1)         -0.039475  0.022008      -0.077879   \n",
       "\n",
       "                                        hpd0.95_upper  \n",
       "1|I(gender == 'F')[False]                    0.049701  \n",
       "1|I(gender == 'F')[True]                     0.041850  \n",
       "1|I(gender=='F')_sd                          0.103080  \n",
       "I(auth_prev_papers == 0)                    -0.007719  \n",
       "I(auth_prev_papers == 1)                     0.001919  \n",
       "I(np.log10(auth_prev_papers + 1) ** 2)       0.059693  \n",
       "Intercept                                    0.103333  \n",
       "is_self_cite_sd                              0.223128  \n",
       "np.log10(auth_prev_papers + 1)               0.007790  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[100:].summary(ranefs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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