mw-lifecycle-analysis/text_analysis/case2/.ipynb_checkpoints/040425_phab_comments-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba9e5acd-e17d-4318-9272-04c9f6706186",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4f0b3f0-5255-46f1-822f-e455087ba315",
   "metadata": {},
   "outputs": [],
   "source": [
    "phab_path = \"/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/case2/0512_https_phab_comments.csv\"\n",
    "phab_df = pd.read_csv(phab_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac5e624b-08a4-4ede-bc96-cfc26c3edac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def http_relevant(text):\n",
    "    if pd.isnull(text):\n",
    "        return False\n",
    "    # expanded dictionary for relevancy\n",
    "    # http, login, SSL, TLS, certificate \n",
    "    for word in text.split():\n",
    "        if \"://\" not in word.lower():\n",
    "            #http\n",
    "            if \"http\" in word.lower():\n",
    "                return True\n",
    "            #login\n",
    "            if \"login\" in word.lower():\n",
    "                return True\n",
    "            #ssl\n",
    "            if \"ssl\" in word.lower():\n",
    "                return True\n",
    "            #tls\n",
    "            if \"tls\" in word.lower():\n",
    "                return True\n",
    "            #cert\n",
    "            if word.lower().startswith(\"cert\") and not word.lower().startswith(\"certain\"):\n",
    "                return True\n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5925c49-ea1d-4813-98aa-eae10d5879ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_migrated(comment_text):\n",
    "    if pd.isnull(comment_text):\n",
    "        return False\n",
    "    text = comment_text.strip()\n",
    "    if text.startswith(\"Originally from: http://sourceforge.net\"):\n",
    "        return True \n",
    "    return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d449164e-1d28-4580-9eb1-f0f69978f114",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/3758790231.py:41: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
      "/tmp/ipykernel_59130/3758790231.py:44: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n"
     ]
    }
   ],
   "source": [
    "#find gerrit phab PHID: PHID-USER-idceizaw6elwiwm5xshb\n",
    "phab_df['isGerrit'] = phab_df['AuthorPHID'] == 'PHID-USER-idceizaw6elwiwm5xshb'\n",
    "\n",
    "#cleaning df\n",
    "phab_df['id'] = phab_df.index + 1\n",
    "#may have to build out the reply_to column \n",
    "phab_df['reply_to'] = phab_df.groupby('TaskPHID')['id'].shift()\n",
    "phab_df['reply_to'] = phab_df['reply_to'].where(pd.notnull(phab_df['reply_to']), None)\n",
    "\n",
    "phab_df = phab_df.rename(columns={\n",
    "    'AuthorPHID': 'speaker',\n",
    "    'TaskPHID': 'conversation_id',\n",
    "    'WMFaffil':'meta.affil',\n",
    "    'isGerrit': 'meta.gerrit'\n",
    "})\n",
    "\n",
    "# after 9-3-2011 before 11-27-2013\n",
    "phab_df['timestamp'] = pd.to_datetime(phab_df['date_created'], unit='s', origin='unix', utc=True)\n",
    "filtered_phab_df = phab_df[(phab_df['date_created'] <  1385596799) & (phab_df['date_created'] > 1315008000)]\n",
    "#filtered_phab_df = phab_df[(phab_df['date_created'] < 1381691276) & (phab_df['date_created'] > 1379975444)]\n",
    "\n",
    "#removing headless conversations\n",
    "task_phab_df = filtered_phab_df[filtered_phab_df['comment_type']==\"task_description\"]\n",
    "headed_task_phids = task_phab_df['conversation_id'].unique()\n",
    "filtered_phab_df = filtered_phab_df[filtered_phab_df['conversation_id'].isin(headed_task_phids)]\n",
    "\n",
    "#removing gerrit comments \n",
    "mid_comment_phab_df = filtered_phab_df[filtered_phab_df['meta.gerrit'] != True]\n",
    "\n",
    "# filter out the sourceforge migration \n",
    "# Originally from: http://sourceforge.net in the task task_summary\n",
    "migrated_conversation_ids = task_phab_df[task_phab_df['comment_text'].apply(is_migrated)]['conversation_id'].unique()\n",
    "\n",
    "#cut down to only the data that is relevant (mentions http)\n",
    "relevant_conversation_ids = task_phab_df[\n",
    "    task_phab_df['comment_text'].apply(http_relevant) |\n",
    "    task_phab_df['task_title'].apply(http_relevant)\n",
    "]['conversation_id'].unique()\n",
    "\n",
    "task_phab_df['is_relevant'] = task_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "mid_comment_phab_df['is_relevant'] = mid_comment_phab_df['conversation_id'].isin(relevant_conversation_ids)\n",
    "\n",
    "task_phab_df['is_migrated'] = task_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
    "mid_comment_phab_df['is_migrated'] = mid_comment_phab_df['conversation_id'].isin(migrated_conversation_ids)\n",
    "\n",
    "comment_phab_df = mid_comment_phab_df[(mid_comment_phab_df['is_relevant'] == True) & (mid_comment_phab_df['is_migrated'] != True)]\n",
    "task_phab_df = task_phab_df[(task_phab_df['is_relevant'] == True) & (task_phab_df['is_migrated'] != True)]\n",
    "#comment_phab_df = mid_comment_phab_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "942344db-c8f5-4ed6-a757-c97f8454f18b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique conversation_ids: 1021\n",
      "Unique ids: 6282\n",
      "Unique speakers: 293\n"
     ]
    }
   ],
   "source": [
    "unique_conversation_ids = len(comment_phab_df['conversation_id'].unique())\n",
    "unique_ids = len(comment_phab_df['id'].unique())\n",
    "unique_speakers = len(comment_phab_df['speaker'].unique())\n",
    "\n",
    "print(f\"Unique conversation_ids: {unique_conversation_ids}\")\n",
    "print(f\"Unique ids: {unique_ids}\")\n",
    "print(f\"Unique speakers: {unique_speakers}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d226d781-b002-4842-a3ae-92d4851a5878",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def preprocess_text(text):\n",
    "    text = str(text)\n",
    "    text = text.replace('*', ' ')\n",
    "    text = text.replace('-', ' ')\n",
    "    text = re.sub(r'http\\S+', '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ae40d24-bbe8-49c3-a3a9-70bde1b4d559",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/2783900859.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['processed_text'] = comment_phab_df['comment_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8eddf40-1fe2-4fce-be74-b32552b40c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment_phab_df['processed_resolved_text'] = comment_phab_df['resolved_text'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8469b16-4ae6-4b06-bf1b-1f2f6c736cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")\n",
    "\n",
    "def extract_dependency_tree(text):\n",
    "    doc = nlp(text)\n",
    "    dependency_trees = []\n",
    "    \n",
    "    for sentence in doc.sents:\n",
    "        for token in sentence:\n",
    "            token_info = (\n",
    "                token.text, \n",
    "                token.lemma_, \n",
    "                token.dep_, \n",
    "                token.head.text, \n",
    "                list(token.ancestors), \n",
    "                list(token.subtree), \n",
    "                list(token.children)\n",
    "            )\n",
    "            dependency_trees.append(token_info)\n",
    "        \n",
    "    return dependency_trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b9a12f9-71bf-4bc9-bcfd-c73aab4be920",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/2805711855.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df['dependency_tree'] = comment_phab_df['processed_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "337a528a-5667-4e1f-ac9a-37caabc03a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "#comment_phab_df['resolved_dependency_tree'] = comment_phab_df['processed_resolved_text'].apply(extract_dependency_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "e3364ab1-1879-4b89-8b3b-6ab5449fccfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114      After last update via SVN bot does not work, s...\n",
       "156      Timestamp has been changed since 27th septembe...\n",
       "176      **Author:** `happy.melon.wiki`\\n\\n**Descriptio...\n",
       "246      Steps to reproduce\\n1) Login to translatewiki....\n",
       "370      Recently, several refs are not accessible thro...\n",
       "                               ...                        \n",
       "45008    We have reports that since the HTTPS enabling,...\n",
       "45245    ssh is quite painful over a slow and/or lossy ...\n",
       "45299    The problem:\\nEspecially during lightning depl...\n",
       "45372    There are many pages for which VisualEditor is...\n",
       "46077    **Author:** `ka.hing.chan`\\n\\n**Description:**...\n",
       "Name: comment_text, Length: 382, dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_phab_df['comment_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a3f5d40b-f56e-4e31-a7f9-40b7ddb4d2a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get VAD scores\n",
    "import numpy as np\n",
    "#https://saifmohammad.com/WebPages/nrc-vad.html\n",
    "column_headings = ['Word', 'Valence', 'Arousal', 'Domination']\n",
    "vad_lexicon = pd.read_csv('NRC-VAD-Lexicon.txt', delimiter='\\t', header=None, names=column_headings)\n",
    "vad_dict = vad_lexicon.set_index('Word').T.to_dict()\n",
    "\n",
    "def vad_scoring(dependency_tree):\n",
    "    valence = []\n",
    "    arousal = []\n",
    "    dominance = []\n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            valence.append(vad_dict[lemma]['Valence'])\n",
    "            arousal.append(vad_dict[lemma]['Arousal'])\n",
    "            dominance.append(vad_dict[lemma]['Domination'])\n",
    "\n",
    "    # Compute average scores across the comment\n",
    "    avg_valence = np.mean(valence) if valence else 0\n",
    "    avg_arousal = np.mean(arousal) if arousal else 0\n",
    "    avg_dominance = np.mean(dominance) if dominance else 0\n",
    "\n",
    "    return [avg_valence, avg_arousal, avg_dominance]\n",
    "\n",
    "def dominance_prevail(dependency_tree):\n",
    "    dominant_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Domination'] >= 0.75:\n",
    "                dominant_words += 1\n",
    "            if vad_dict[lemma]['Domination'] <= 0.25:\n",
    "                dominant_words += 1\n",
    "    return dominant_words\n",
    "\n",
    "def arousal_prevail(dependency_tree):\n",
    "    arousal_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Arousal'] >= 0.75:\n",
    "                arousal_words += 1\n",
    "            if vad_dict[lemma]['Arousal'] <= 0.25:\n",
    "                arousal_words += 1\n",
    "    return arousal_words\n",
    "\n",
    "def valence_prevail(dependency_tree):\n",
    "    valence_words = 0 \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in dependency_tree:\n",
    "        if lemma in vad_dict:\n",
    "            if vad_dict[lemma]['Valence'] >= 0.75:\n",
    "                valence_words += 1\n",
    "            if vad_dict[lemma]['Valence'] <= 0.25:\n",
    "                valence_words += 1\n",
    "    return valence_words\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "828fb57a-e152-42ef-9c60-660648898532",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/2858732056.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
      "/tmp/ipykernel_59130/2858732056.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
      "/tmp/ipykernel_59130/2858732056.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
      "/tmp/ipykernel_59130/2858732056.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)\n"
     ]
    }
   ],
   "source": [
    "#establishing per-comment VAD scores \n",
    "comment_phab_df['avg_vad_scores'] = comment_phab_df['dependency_tree'].apply(vad_scoring)\n",
    "comment_phab_df['dominant_wc'] = comment_phab_df['dependency_tree'].apply(dominance_prevail)\n",
    "comment_phab_df['arousal_wc'] = comment_phab_df['dependency_tree'].apply(arousal_prevail)\n",
    "comment_phab_df['valence_wc'] = comment_phab_df['dependency_tree'].apply(valence_prevail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "27e47f6f-0257-4b70-b222-e91ef888c900",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/335308388.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
      "/tmp/ipykernel_59130/335308388.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
      "/tmp/ipykernel_59130/335308388.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n"
     ]
    }
   ],
   "source": [
    "comment_phab_df[['average_v_score', 'average_a_score', 'average_d_score']] = pd.DataFrame(comment_phab_df['avg_vad_scores'].tolist(), index=comment_phab_df.index)\n",
    "comment_phab_df = comment_phab_df.drop(columns=['avg_vad_scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "184ccbe6-0a7a-41b8-9b02-bc439ff975d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expand the dependency parser \n",
    "\n",
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots|scripts|gadgets)\\b'\n",
    "pattern = r'\\b(http|https)\\b'\n",
    "\n",
    "dependency_relations = []\n",
    "resolved_dependency_relations = []\n",
    "\n",
    "for index, row in comment_phab_df.iterrows():\n",
    "    text = row['comment_text']\n",
    "    timestamp = row['timestamp']\n",
    "    comment_id = row['id']\n",
    "    conversation_id = row['conversation_id']\n",
    "    WMFaffil = row['meta.affil']\n",
    "    \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['dependency_tree']:\n",
    "        if re.search(pattern, token, re.IGNORECASE):\n",
    "            dependency_relations.append({\n",
    "                'comment_id': comment_id,\n",
    "                'timestamp': timestamp,\n",
    "                'wmfAffil':WMFaffil,\n",
    "                'token': token,\n",
    "                'dependency': dep,\n",
    "                'head': head,\n",
    "                'depth': len(list(ancestors)), \n",
    "                'children': len(list(children)) \n",
    "            })\n",
    "    '''        \n",
    "    for token, lemma, dep, head, ancestors, subtree, children in row['resolved_dependency_tree']:\n",
    "        if re.search(pattern, token, re.IGNORECASE):\n",
    "            resolved_dependency_relations.append({\n",
    "                'comment_id': comment_id,\n",
    "                'timestamp': timestamp,\n",
    "                'wmfAffil':WMFaffil,\n",
    "                'token': token,\n",
    "                'dependency': dep,\n",
    "                'head': head,\n",
    "                'depth': len(list(ancestors)), \n",
    "                'children': len(list(children)) \n",
    "            })\n",
    "    '''\n",
    "#resolved_dependency_relations_df = pd.DataFrame(resolved_dependency_relations)            \n",
    "dependency_relations_df = pd.DataFrame(dependency_relations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "82498686-14f4-40c8-9e33-27b31f115b47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#now analysis/plotting \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.gridspec import GridSpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "82cd9dde-0d14-4de5-8482-5a39de8d2869",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_59130/2457686520.py:7: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n"
     ]
    },
    {
     "data": {
      "image/png": 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dpv+dmpqKG264Adu3b8fx48cN173lllsqnb+9fv16uN1u3HfffVFzpiMzYJFj9Hg8yM/Px/nnnw9FUbB9+/YKHwNQG6xxHIfc3FzD9nvvvReKomDt2rWG7YMHD0a3bt0qvd/KmM1msKz6dUKSJJw6dQopKSno3LkzfvzxR/16TqcTf/75J77//vtK73PXrl0YPHgwcnJysGHDBqSnp+uXaZnszz77LGoaQXXFcxyvWbMG5557Ls4++2zD9a677jrDfa1fvx4ulwt///vfDe9JjuNwzjnnGN7PNpsNS5cuxS+//IJBgwZh9erVWLRokf5+VBQFK1aswNixY6EoiuH+Ro4ciaKiIv01XrNmDZo3b44rr7zScP/lZaXLGjhwIH788Ue9YuOrr77C6NGj0bt3b73SZ/PmzWAYBhdccEFc9xlLSkqKYT60IAg4++yz4/7MOOecc7B+/fqonyeffLLaY6qqhx56CFlZWcjOzsaQIUOwf/9+zJ8/H5dffjkA9b387bffYtq0aQDUSpebbroJzZs3x1133RVXyT8hpGGh8nJCyGnvgQcewFtvvYXHH38czzzzTNTlv/32GxRFQceOHWPenud5/feBAwfi4Ycfhs/nw+bNm9G8eXP06dMHvXr1wubNmzF8+HB89dVXuPrqq+MaW05ODl555RXIsoz9+/fj0UcfRV5eniH4qsr4Yj23oqIiNG3aNOblkfPGx48fj5UrV2Lr1q04//zzsX//fmzbtg1PP/204TZbtmzBQw89hK1bt0YFNUVFRYby3chgXZOenm6YT15dK1asQGpqKnieR8uWLdG+ffuY1ys7Bi1AKyws1OdOVuU5dejQIaoctFOnTgDUqQHZ2dn69rZt21b6PLTS08qWsTty5AgefPBBfPTRR1GvXzyls4cPH0aLFi3gcDgM27VS/sOHDxu2xzP2eMiyjGeeeQYvvPACDh48CEmS9MsipwLMmDEDGzZswNlnn40OHTpgxIgRuPbaa2POex07diyaNWuGzz77LKozetu2bXHPPfdg4cKFWLZsGQYOHIhx48bhH//4R7VKy4H4juPDhw8b5jJrOnfubPj7t99+A1A6z7essvN5BwwYgNtvvx3PP/88Ro4ciUmTJumX5eXlweVyYcmSJViyZEnM+9Pe49rc97LHbtnxlWfgwIEIhULYunUrWrVqhZMnT2LgwIHYvXu3Ieju1q0bMjIy4rrPWFq2bBk1xvT0dPz8889x3b5Jkya46KKLorabTHX3lfbWW2/FVVddBZZl4XQ69b4IkdLS0rBgwQIsWLAAhw8fxv/+9z88+eSTeO6555CWloZHHnmkzsZLCEk+CroJIac9rZPykiVLcN9990VdLssyGIbB2rVrY2YlI7/UX3DBBRBFEVu3bsXmzZv17NfAgQOxefNm7N27F3l5eYasWEXsdrvhC+KAAQPQp08f/Pvf/8bixYurPL5Yz61p06ZYtmxZzMuzsrL038eOHQubzYb33nsP559/Pt577z2wLKs3OgPUAPHCCy9Ely5dsHDhQrRq1QqCIGDNmjVYtGhRVNOo8rK8SpnGXdUxaNAgvXt5RSobQ1WfU1XEms9bHZIkYfjw4SgoKMCMGTPQpUsX2O12/PXXX5g4cWKtrOueqLE/9thjmDVrFiZNmoS5c+ciIyMDLMvi7rvvNoy7a9eu2LdvHz755BN8+umnWLFiBV544QU8+OCDmD17tuE+r7jiCrzxxhtYtmwZ/vnPf0Y95lNPPYWJEydi1apVWLduHXJzczFv3jx88803ekO7qkjkcaw957feestwgkZTNjgMBAJ6Y8H9+/fD6/Xq1Rzaff3jH//AhAkTYj7emWeeWeUxxtKvXz9YLBZ8+eWXaN26NZo2bYpOnTph4MCBeOGFFxAIBLB582ZDFUh11OZnRl3p2LFjzMC/PG3atMGkSZNw2WWXoV27dli2bBkF3YQ0MhR0E0IahAceeAD//e9/Dc2zNO3bt4eiKGjbtq2esSzP2WefDUEQsHnzZmzevFkvDxw0aBBeeeUV/O9//9P/ro4zzzwT//jHP/Dyyy9j6tSpaN26dZXGF+u5bdiwAQMGDKg0iLLb7bjkkkuwfPlyLFy4EO+++y4GDhxoaED38ccfIxAI4KOPPjJk/yoqca9MspsIVfU5/f7771AUxTDuX3/9FQCq1WxMy9Dv2rULHTp0iHmdnTt34tdff8Ubb7yBG264Qd++fv36qOuW93q2adMGGzZsgNvtNmS79+7dq19eG95//30MHToUr776qmG7y+WKOmlit9sxfvx4jB8/HsFgEJdffjkeffRRzJw501D98cQTT8BkMuGOO+6Aw+GI2WitZ8+e6NmzJx544AF8/fXXGDBgAF566aVaC2batGmjZ7Ej7du3z/C3tr+bNm0aV2D20EMP4ZdffsGTTz6JGTNm4L777tNPyGVlZcHhcECSpErvq02bNti1a1fUsVt2fOXRyrw3b96M1q1bG044BgIBLFu2DCdOnKj0sy/Z7/f6LD09He3bt8euXbuSPRRCSB2jOd2EkAahffv2ejBbdt7t5ZdfDo7jMHv27KhsiqIoOHXqlP63tqTX//3f/+HIkSOGL54+nw+LFy9G+/bt0bx582qPdfr06RBFEQsXLqzy+Mq6+uqrIUkS5s6dG3VZKBQyLH8GqCXmR48exX/+8x/89NNPGD9+vOFyLQsVOY6ioiK8/vrrVXqOkex2OwBEjaWuVPU5HT16FB9++KH+d3FxMd5880307t07ZuayMiNGjIDD4cC8efPg9/sNl2ljijVGRVFiTpco7/UcPXo0JEnCc889Z9i+aNEiMAwTV2fo6uA4Luq4Xb58Of766y/DtrLHsSAI6NatGxRFgSiKhssYhsGSJUtw5ZVXYsKECYalrIqLixEKhQzX79mzJ1iWrdW5sqNHj8Y333yD7777Tt+Wl5cXVWUycuRIpKam4rHHHot6XtptNN9++y2efPJJ3H333bj33nsxbdo0PPfcc/jiiy8AqK/tFVdcgRUrVsQM1CLva/To0Th69Cjef/99fZvX6y23LD2WgQMH4ttvv8XGjRv1z74mTZqga9eu+gnNyqp8kv1+rw9++umnmB3WDx8+jD179sRd8k8IaTgo000IaTDuv/9+vPXWW9i3b5++zA+gBuSPPPIIZs6ciUOHDuHSSy+Fw+HAwYMH8eGHH+LWW2/F1KlT9esPHDgQjz/+ONLS0tCzZ08Aataqc+fO2LdvHyZOnFijcXbr1g2jR4/Gf/7zH8yaNavK44s0ePBg/POf/8S8efOwY8cOjBgxAjzP47fffsPy5cvxzDPPGBorjR49Gg6HA1OnTtW/0EcaMWIEBEHA2LFj8c9//hMlJSV45ZVX0LRp03Ib1VWmd+/e4DgO8+fPR1FREcxms75mdl2o6nPq1KkTbrrpJnz//fdo1qwZXnvtNZw4caLaJx5SU1OxaNEi3Hzzzejfvz+uvfZapKen46effoLX68Ubb7yBLl26oH379pg6dSr++usvpKamYsWKFTHnxvft2xcAkJubi5EjR4LjOFxzzTUYO3Yshg4divvvvx+HDh1Cr169sG7dOqxatQp33313uXPia+qSSy7BnDlzcOONN+L888/Hzp07sWzZsqh14UeMGIHs7GwMGDAAzZo1wy+//ILnnnsOY8aMiZqHDqiNDf/73//i0ksvxdVXX401a9Zg2LBh+PzzzzF58mRcddVV6NSpE0KhEN56662Yx3MiTZ8+HW+99RZGjRqFf/3rX/qSYW3atDHMR05NTcWLL76I66+/Hn369ME111yDrKwsHDlyBKtXr8aAAQPw3HPPwe/3Y8KECejYsSMeffRRAMDs2bPx8ccf48Ybb8TOnTtht9vx+OOPY+PGjTjnnHNwyy23oFu3bigoKMCPP/6IDRs2oKCgAIDa1O+5557DDTfcgG3btqF58+Z46623ohoPVmTgwIF49NFH8ccffxiC60GDBuHll19GTk5OpeX77du3h9PpxEsvvQSHwwG73Y5zzjknYT0ETgfr16/HQw89hHHjxuHcc89FSkoKDhw4gNdeew2BQAAPP/xwsodICKlrddQlnRBCEiZyybCyJkyYoACIuTTRihUrlAsuuECx2+2K3W5XunTpotx5553Kvn37DNdbvXq1AkC5+OKLDdtvvvlmBYDy6quvxjXOskuXRdq0aZMCQHnooYeqNL6yS4ZplixZovTt21exWq2Kw+FQevbsqUyfPl05evRo1HWvu+46BYBy0UUXxRzbRx99pJx55pmKxWJRcnJylPnz5yuvvfaaYVkkRVGX74lc+izyeUcuIaQoivLKK68o7dq1UziOq3T5oshloypS3vXKLtlVnef02WefKWeeeaZiNpuVLl26RC1DVNExGOvxtTGcf/75itVqVVJTU5Wzzz5b+b//+z/98j179igXXXSRkpKSojRp0kS55ZZblJ9++ilq+aVQKKTcddddSlZWlsIwjGF5I7fbrUyZMkVp0aKFwvO80rFjR+WJJ54wLE2mKOqSYXfeeWeFr295Yi0Zdu+99yrNmzdXrFarMmDAAGXr1q1Rx8HLL7+sDBo0SMnMzFTMZrPSvn17Zdq0aUpRUZF+nVj71Ov1KoMHD1ZSUlKUb775Rjlw4IAyadIkpX379orFYlEyMjKUoUOHKhs2bKh07OUtGRbvcfzzzz8rgwcPViwWi3LGGWcoc+fOVV599dWY+3vjxo3KyJEjlbS0NMVisSjt27dXJk6cqPzwww+KoijKlClTFI7jDEuQKYqi/PDDD4rJZFJuv/12fduJEyeUO++8U2nVqpXC87ySnZ2tXHjhhcqSJUsMtz18+LAybtw4xWazKU2aNFH+9a9/6csHxrNkX3FxscJxnOJwOAxL4f33v/9VACjXX399XK/TqlWrlG7duikmk8lw/Jb3mVje51pZ5e0rRVFfb9TRkmFPPPFEhfdx4MAB5cEHH1TOPfdcpWnTporJZFKysrKUMWPGKJ9//nmlYyCENDyMopxGnSsIIYQQQgghhJDTCM3pJoQQQgghhBBCagkF3YQQQgghhBBCSC2hoJsQQgghhBBCCKklFHQTQgghhBBCCCG1hIJuQgghhBBCCCGkllDQTQghhBBCCCGE1BJTMh983rx5+OCDD7B3715YrVacf/75mD9/Pjp37qxfZ8iQIfjiiy8Mt/vnP/+Jl156Ka7HkGUZR48ehcPhAMMwCR0/IYQQQgghhJDGSVEUuN1utGjRAixbfj47qet0jxo1Ctdccw369++PUCiEf//739i1axf27NkDu90OQA26O3XqhDlz5ui3s9lsSE1Njesx/vzzT7Rq1apWxk8IIYQQQgghpHH7448/0LJly3IvT2qm+9NPPzX8vXTpUjRt2hTbtm3DoEGD9O02mw3Z2dnVegyHwwFAfSHiDdQbO1EUsW7dOowYMQI8zyd7OI0S7YPko32QfLQPko/2Qf1A+yF56LVPPtoHyUf7oHzFxcVo1aqVHnOWJ6lBd1lFRUUAgIyMDMP2ZcuW4b///S+ys7MxduxYzJo1CzabLeZ9BAIBBAIB/W+32w0AsFqtsFqttTTyhsVkMsFms8FqtdIbK0loHyQf7YPko32QfLQP6gfaD8lDr33y0T5IPtoH5RNFEQAqncac1PLySLIsY9y4cXC5XPjqq6/07UuWLEGbNm3QokUL/Pzzz5gxYwbOPvtsfPDBBzHv5+GHH8bs2bOjtr/99tvlBuqEEEIIIYQQQkhVeL1eXHvttSgqKqqwqrreBN2333471q5di6+++qrCevjPP/8cF154IX7//Xe0b98+6vKymW4t5Z+fn0/l5XESRRHr16/H8OHD6WxWktA+SD7aB8lH+yD5aB/UD7Qfkode++SjfZB8tA/KV1xcjCZNmlQadNeL8vLJkyfjk08+wZdffllhwA0A55xzDgCUG3SbzWaYzeao7TzP00FSRfSaJR/tg+SjfZB8tA+Sj/ZB/UD7IXnotU8+2gfJR/sgWryvR1KDbkVRcNddd+HDDz/Epk2b0LZt20pvs2PHDgBA8+bNa3l0hBBCapMsywgGg+VeLooiTCYT/H4/JEmqw5ERDe2D+kHbD7IsJ3sohBBCqiGpQfedd96Jt99+G6tWrYLD4cDx48cBAGlpabBardi/fz/efvttjB49GpmZmfj5558xZcoUDBo0CGeeeWYyh04IIaQGgsEgDh48WGEQoSgKsrOz8ccff1TaoITUDtoH9YO2Hw4ePIh27dpBEIRkD4kQQkgVJDXofvHFFwGoa3FHev311zFx4kQIgoANGzbg6aefhsfjQatWrXDFFVfggQceSMJoCSGEJIKiKDh27Bg4jkOrVq3AsmzM68myjJKSEqSkpJR7HVK7aB/UD7Isw+12o7i4GMeOHUPr1q3pJAghhJxGkl5eXpFWrVrhiy++qKPREEIIqQuhUAherxctWrSocFUJrfzcYrFQwJcktA/qB20/ZGVl4dixYwiFQjSvkhBCTiP0PyghhJA6pc0NphJZQqpGC7Rpfj0hhJxeKOgmhBCSFFQeS0jV0HuGEEJOTxR0E0IIIYQQQgghtYSCbkIIIaSeYhgGK1euTPYwCCGEEFIDFHQTQgghlWAYpsKfhx9+uNzbHjp0CAzDYMeOHXU2XkIIIYTUH0ntXk4IIYRUl8fjAc/zcLlccDqdEEURdru9Vh7r2LFj+u/vvvsuHnzwQezbt0/flpKSUiuPSwghhJDTH2W6CSGEnHb8fj8WLFiAZs2a6T8LFiyA3++vlcfLzs7Wf9LS0sAwjP5306ZNsXDhQrRs2RJmsxm9e/fGp59+qt+2bdu2AICzzjoLDMNgyJAhAIDvv/8ew4cPR5MmTZCWlobBgwfjxx9/rJXxE0IIISR5KNNNCCEkqRRFgdfrjdouyzI8Hg84jjOsES1JEp566inMmTNH3+ZyufS/7733XnAcF9dj22y2GneEfuaZZ/DUU0/h5ZdfxllnnYXXXnsN48aNw+7du9GxY0d89913OPvss7FhwwZ0795dXyrN7XZjwoQJePbZZ6EoCp566imMHj0av/32GxwOR43GRAghhJD6g4JuEtPgYReBYTkU+UTYBQ7BkASbmU/2sAghDZDX6427PLtJkyY4dOgQFi9eHPPyxYsXY/r06cjJyUF+fn6l91dSUlLjkvQnn3wSM2bMwDXXXAMAmD9/PjZu3Iinn34azz//PLKysgAAmZmZyM7O1m83bNgww/0sWbIETqcTX3zxBS655JIajYkQQggh9QcF3SSKwnD43+9erP/1KLxBGTaBxfBOToztmQnBRDMSCCHJk52djZMnT8LlcsW83OVyIS8vD9nZ2XEF3TVVXFyMo0ePYsCAAYbtAwYMwE8//VThbU+cOIEHHngAmzZtwsmTJyFJErxeL44cOVKbQyaEEEJIHaOgmxh4AyLW7nFh1a6C0m1BWf/74m5OyngTQhLKZrOhpKQkarssyyguLkZqaqqhvJzneTidzpiBt9PpRIsWLfDNN9/E/djJMmHCBJw6dQrPPPMM2rRpA7PZjPPOOw/BYDBpYyKEEEJI4lHakhgIJg7rf3XFvGz9ry4IpvjmSRJCSLwYhoHdbo/7RxRF5Obmxryv3NxcvYt5PD81nc+dmpqKFi1aYMuWLYbtW7ZsQbdu3QBAn8MtSVLUdXJzczF69Gh0794dZrO5TrLzhBBCCKlblOkmBp6gBG9QjnmZNyjDE5SQZqVzNYSQ5LHb7Zg5cyYAdQ63tmRYbm4uZs6cCYvFUqfjmTZtGh566CG0b98evXv3xuuvv44dO3Zg2bJlAICmTZvCarXi008/RcuWLWGxWJCWloaOHTvirbfeQr9+/VBcXIxp06bBarXW6dgJIYQQUvsoeiIGdoGDTYh9WNgEFnaBMt2EkOSzWCyYPn06Tpw4gZMnT+LEiROYPn16nQfcgJpdv+eee3DvvfeiZ8+e+PTTT/HRRx+hY8eOAACTyYTFixfj5ZdfRosWLfC3v/0NAPDqq6+isLAQffr0wfXXX4/c3Fw0bdq0zsdPCCGEkNpFmW5iEAxJGN7JaZjTrRneyYlgSIKJo3M1hJDk07qOa93BtTLu2jZx4kRMnDhR/5tlWTz00EN46KGHyr3NzTffjJtvvtmw7ayzzsL3339v2HbllVca/lYUpeYDJoQQQkhSUdBNDGxmHmN7ZgJQ53BT93JCCCGEEEIIqT4KukkURpEwoI0Fl/Rsj2K/BKeVgxiSKOAmhBBCCCGEkCqioJvE9PKb7+Bki6FIs3K4+Aw/BvbpmuwhEUIIIYQQQshph4JuElNIUuAOSHAHJBSleZM9HEIIIYQQQgg5LVG9MIlJlEqXDfMHxSSOhBBCCCGEEEJOXxR0k5hCcmnH3IAYSuJICCGEEEIIIeT0RUE3iSlUmuiGP0hBNyGEEEIIIYRUBwXdJKbITHdQlJI4EkIIIYQQQgg5fVHQTWKKzHQHQxR0E0IIIYQQQkh1UNBNYoroo4YAZboJIeS0t2TJErRq1Qosy+Lpp5+Oue3hhx9G79699dvceOONuO666/S/hwwZgrvvvrtuBx62adMmMAwDl8tV54996NAhMAyDHTt21PljE0IIOf1R0E1ikkqryyFSppsQ0si99NJLcDgcCIVKe1yUlJSA53kMGTLEcF0tONy/fz8AICcnBwzD4J133om63+7du4NhGCxdulTfpl0/8qdly5Y1Gn9xcTEmT56MGTNm4K+//sKtt94ac9vUqVPxv//9r9z7+eCDDzB37ty4HzeZgXJ1TZw4EZdeemmyh0EIIaQBoaCbxCQpjP57MLLWnBBC6gkpGIAcCkEscUMOhSAFA7X2WEOHDkVJSQl++OEHfdvmzZuRnZ2Nb7/9Fn6/X9++ceNGtG7dGu3bt9e3tWrVCq+//rrhPr/55hscP34cdrs96vHmzJmDY8eO6T/bt2+v0fiPHDkCURQxZswYNG/eHDabLea2lJQUZGZmlns/GRkZcDgcNRoLKRUMBpM9BEIIIXWAgm4SkyHTLVHQTQipX2RRxNFN67Ft7n3YNuc+bJt7H45uWg9ZFGvl8Tp37ozmzZtj06ZN+rZNmzbhb3/7G9q2bYtvvvnGsH3o0KGG21933XX44osv8Mcff+jbXnvtNVx33XUwmUxRj+dwOJCdna3/ZGVlVTi+GTNmoFOnTrDZbGjXrh1mzZoFMfxaLF26FD179gQAtGvXTs+sl9126NChqPLyssqWl7/11lvo16+fPt5rr70WJ0+eBKCWZGuvQ3p6OhiGwcSJEwEAsixj3rx5aNu2LaxWK3r16oX333/f8Fhr1qxBp06dYLVaMXToUBw6dKjC1wAAFi5ciJ49e8Jut6NVq1a44447UFJSol8e6/k9/fTTyMnJ0S9/4403sGrVKr3KIHKfHzhwAEOHDoXNZkOvXr2wdetWw32tWLEC3bt3h9lsRk5ODp566inD5Tk5OZg7dy5uuOEGpKam4tZbb0UwGMTkyZPRvHlzWCwWtGnTBvPmzav0uRJCCDl9UNBNYlIiDg0KugkhtUlRFEjBQMwfORiM2hby+/DXxs/w14a1kHw+AIDk8+GvDWvx18bPEPL7yr2/sj+KolQyulJDhw7Fxo0b9b83btyIIUOGYPDgwfp2n8+Hb7/9NirobtasGUaOHIk33ngDAOD1evHuu+9i0qRJNX35AKhB+tKlS7Fnzx4888wzeOWVV7Bo0SIAwPjx47FhwwYAwHfffYdjx47hqquuitrWqlWrKj+uKIqYO3cufvrpJ6xcuRKHDh3SA+tWrVphxYoVAIB9+/bh2LFjeOaZZwAA8+bNw5tvvomXXnoJu3fvxpQpU/CPf/wDX3zxBQDgjz/+wOWXX46xY8dix44duPnmm3HfffdVOh6WZbF48WLs3r0bb7zxBj7//HNMnz497uczdepUXH311Rg1apReZXD++efrl99///2YOnUqduzYgU6dOuHvf/+7PuVg27ZtuPrqq3HNNddg586dePjhhzFr1izD1AEAePLJJ9GrVy9s374ds2bNwuLFi/HRRx/hvffew759+7Bs2TL9JAAhhJCGIfr0OiEAJDDgwr9HLh9GCCGJJotBfP/APXFd12RPwVkz5+D4lk0xLz++ZRNaDBmO7fMeRMhTEvM6kfo/shCcYI7rsYcOHYq7774boVAIPp8P27dvx+DBgyGKIl566SUAwNatWxEIBKKCbgCYNGkS7r33Xtx///14//330b59+3KzyjNmzMADDzyg//3YY48hNze33LFFXjcnJwdTp07FO++8g+nTp8Nqteol41lZWcjOzgaAmNuqKvKkQbt27bB48WL0798fJSUlSElJQUZGBgCgadOmcDqdAIBAIIDHHnsMGzZswHnnnaff9quvvsLLL7+MwYMH48UXX0T79u31THHnzp2xc+dOzJ8/v8LxRGbhc3Jy8Mgjj+C2227DCy+8ENfzSUlJgdVqRSAQiPmaTJ06FWPGjAEAzJ49G927d8fvv/+OLl26YOHChbjwwgsxa9YsAECnTp2wZ88ePPHEE/qJCAAYNmwY7r33Xv3vI0eOoGPHjrjgggvAMAzatGkT11gJIYScPijTTWKSUTqnOyRR0E0IqR94RyrEEree4S5L8vkQKikB70hN+GMPGTIEHo8H33//PTZv3oxOnTohKysLgwcP1ud1b9q0Ce3atUPr1q2jbj9mzBiUlJTgyy+/xGuvvVZhlnvatGnYsWOH/nPDDTcAAG677TakpKToP5p3330XAwYMQHZ2NlJSUvDAAw/gyJEjCX8Nytq2bRvGjh2L1q1bw+FwYPDgwQBQ4WP//vvv8Hq9GD58uOG5vPnmm3rzuV9++QXnnHOO4XZagF6RDRs24MILL8QZZ5wBh8OB66+/HqdOnYLX663Bsyx15pln6r83b94cAPRy+l9++QUDBgwwXH/AgAH47bffIEmlDUn79etnuM7EiROxY8cOdO7cGbm5uVi3bl1CxkoIIaT+oEw3iSmyvJwy3YSQ2sTyAvo/sjBquyzLcBe74Uh1gGVLP5MYlgNntcYMvDmrFXxqGnpMnhr3Y8erQ4cOaNmyJTZu3IjCwkI9wGzRogVatWqFr7/+Ghs3bsSwYcNi3t5kMuH666/HQw89hG+//RYffvhhuY/VpEkTdOjQIWr7nDlzMHWq8blt3boV1113HWbPno2RI0ciLS0N77zzTtR84kTzeDwYOXIkRo4ciWXLliErKwtHjhzByJEjK2wQps2xXr16Nc444wzDZWZzfFUHsRw6dAiXXHIJbr/9djz66KPIyMjAV199hZtuugnBYBA2mw0sy0ZNKRCr0AeA53n9d4ZRT07LctWmYJVtnNenTx8cPHgQa9euxYYNG3D11VfjoosuiprjTggh5PRFQTeJSWFKv+DSlG5CSG1iGCZmiTcjy2CFADjBbAi6pWAA2QOG4K8Na6Nukz1gCBRZirtkvKqGDh2KTZs2obCwENOmTdO3Dxo0CGvXrsV3332H22+/vdzbT5o0CU8++STGjx+P9PT0Kj9+06ZN0bRpU8O2r7/+Gm3atMH999+vbzt8+HCV77uq9u7di1OnTuHxxx/X54NHdncHAEFQT2pEZnq7desGs9mMI0eO6CcuyuratSs++ugjw7bIZnWxbNu2DbIs46mnntKPl/fee89wnaysLBw/fhyKouhBc9m1twVBMIw3Xl27dsWWLVsM27Zs2YJOnTqB47hybqVKTU3F+PHjMX78eFx55ZUYNWoUCgoK9PJ8QgghpzcKuklMkUF3iBLdhJB6hBPMOGPoSADqHG7J5wNntSJ7wBCcMXQk2IhsZKINHToUd955J0RRNASMgwcPxuTJkxEMBmPO59Z07doV+fn5sNlsCRtTx44dceTIEbzzzjvo378/Vq9eXWEWPVFat24NQRDw7LPP4rbbbsOuXbui1vBu06YNGIbBJ598gtGjR8NqtcLhcGDq1KmYMmUKZFnGBRdcgKKiImzZsgWpqamYMGECbrvtNjz11FOYNm0abr75Zmzbti2qIVlZHTp0gCiKePbZZzF27Fhs2bJFn2uvGTJkCPLy8rBgwQJceeWV+PTTT7F27VqkppZOR8jJycFnn32Gffv2ITMzE2lpaXG9Hvfeey/69++PuXPnYvz48di6dSuee+65SueTL1y4EM2bN8dZZ50FlmWxfPlyZGdn63PgCSGEnP5oTjeJSWFKz8pTdTkhpL5heR4thgxH31mPo++Dj6PvrMfRYsjwWg24ATXo9vl86NChA5o1a6ZvHzx4MNxut760WEUyMzNhtVoTNqZx48ZhypQpmDx5Mnr37o2vv/5ab+ZVm7KysrB06VIsX74c3bp1w+OPP44nn3zScJ0zzjgDs2fPxn333YdmzZph8uTJAIC5c+di1qxZmDdvHrp27YpRo0Zh9erVaNu2LQA1oF+xYgVWrlyJXr164aWXXsJjjz1W4Xh69eqFhQsXYv78+ejRoweWLVsWtfRW165d8cILL+D5559Hr1698N1330WV699yyy3o3Lkz+vXrh6ysrKjsdXn69OmD9957D++88w569OiBBx98EHPmzDE0UYvF4XBgwYIF6NevH/r3749Dhw5hzZo1huoOQgghpzdGqcp6Kaeh4uJipKWloaioyHAmm5RPFEVc/dj7cLTrAwBQju7EWzOuTPKoGhdRFLFmzRqMHj3aMIeQ1B3aB7XH7/fj4MGDaNu2LSwWS7nXk2UZxcXFSE1NpQAkSWgf1A/afhAEAYcPH670vUMSh/4vSD7aB8lH+6B88caa9D8oiY2NyHQncRiEEEIIIYQQcjqjoJvEZgi66TAhhBBCCCGEkOqgaIrExpb22FMi1uwmhBBCCCGEEBI/CrpJTExEpluhw4QQQgghhBBCqoWiKRJbZKabocOEEEIIIYQQQqqDoikSE8tFBt1cBdckhBBCCCGEEFIeCrpJTAwXsRwAS0E3IYQQQgghhFQHBd0kJsZEQTchhBBCCCGE1BQF3SQmloJuQgghhBBCCKkxCrpJTGxEeTkT0VSNEEJI/bFkyRK0atUKLMvi6aefjrnt4YcfRu/evfXbTJw4EZdeeqn+95AhQ3D33XfX6bg1mzZtAsMwcLlcdf7Yhw4dAsMw2LFjR50/NiGk/vJ4PAgGgzh58iSCwSA8Hk+yh0QaAAq6SUyRmW6Go6CbEEImTpwIhmHw+OOPG7avXLkSDMPU+XiKi4sxefJkzJgxA3/99RduvfXWmNumTp2K//3vf+XezwcffIC5c+fG/bjJDJSrq+yJBkIIicXv92PBggVo1qyZ/rNgwQL4/f5kD42c5ijoJlHEUMjQvTwy600IIfVFQJQRkhQU+0MISQoColzrj2mxWDB//nwUFhbW+mNV5siRIxBFEWPGjEHz5s1hs9libktJSUFmZma595ORkQGHw1GHI2/YgsFgsodACKkGj8eDefPmYc6cOfpJRZfLhTlz5mDevHmU8SY1QkE3ieLxBgx/MybKdBNC6hdRkrF6TwEmv/87Ji/fj8nv/47VewogSrUbeF900UXIzs7GvHnzKrzeihUr0L17d5jNZuTk5OCpp54yXJ6Tk4PHHnsMkyZNgsPhQOvWrbFkyRLDdWbMmIFOnTrBZrOhXbt2mDVrFkRRBAAsXboUPXv2BAC0a9cODMPE3Hbo0KGo8vKyypaXv/XWW+jXrx8cDgdatGiBm2++GSdPngSglmQPHToUAJCeng6GYTBx4kQAgCzLmDdvHtq2bQur1YpevXrh/fffNzzWmjVr0KlTJ1itVgwdOhSHDh2q8HUEgIULF6Jnz56w2+1o1aoV7rjjDpSUlOiXx3p+Tz/9NHJycvTL33jjDaxatQoMw4BhGGzatEm/7oEDBzB06FDYbDb06tULW7duNdxXPPty7ty5uOGGG5Camopbb70VwWAQkydPRvPmzWGxWNCmTZtKjxlCSHLxPI/FixfHvGzx4sXgeUpCkeqjoJtE8ZQpoWFNQpJGQghpDBRFzVJH/YRkBEKK+m/Edl9Qwse7CrDy51PwBtUg2xuUsfLnU/h4VwF8QSn2/cX4URSlSmPlOA6PPfYYnn32Wfz5558xr7Nt2zZcffXVuOaaa7Bz5048/PDDmDVrFpYuXWq43lNPPYV+/fph+/btuOOOO3D77bdj3759+uUOhwNLly7Fnj178Mwzz+CVV17BokWLAADjx4/Hhg0bAADfffcdjh07hquuuipqW6tWrar0/ABAFEXMnTsXP/30Ez744AMcOXIEN954IwCgVatWWLFiBQBg3759OHbsGJ555hkAwLx58/Dmm2/ipZdewu7duzFlyhT84x//wBdffAEA+OOPP3D55Zdj7Nix2LFjB26++Wbcd999lY6HZVksXrwYu3fvxhtvvIHPP/8c06dPj/v5TJ06FVdffTVGjRqFY8eO4dixYzj//PP1y++//35MnToVO3bsQKdOnfD3v/8doVAIQPz78sknn0SvXr2wfft2zJo1C4sXL8ZHH32E9957D/v27cOyZcv0kwCEkPrJ5XKVO23G5XKhqKiobgdEGhRKYZIoXp+xNI5lOYhiCDxPhwshJPGCIQW3vPNbBdc4qf/mMHNYeHk7rNsbu7x73d5CjOmegXs+OAB3QKr0sV+5piPMfNXmY1922WXo3bs3HnroIbz66qtRly9cuBAXXnghZs2aBQDo1KkT9uzZgyeeeELPCgPA6NGjcccddwBQs9qLFi3Cxo0b0blzZwDAAw88oF83JycHU6dOxTvvvIPp06fDarXqJeNZWVnIzs4GgJjbqmrSpEmGx50/fz6GDRuGkpISpKSkICMjAwDQtGlTOJ1OAEAgEMBjjz2GDRs24LzzzgOgZtu/+uorvPzyyxg8eDBefPFFtG/fXs8Ud+7cGTt37sT8+fMrHE9kFj4nJwePPPIIbrvtNrzwwgtxPZ+UlBRYrVYEAoGYr8nUqVMxZswYAMDs2bPRvXt3/P777+jSpUvc+3LYsGG499579b+PHDmCjh074oILLgDDMGjTpk1cYyWEJI/T6YTT6YwZeDudTqSlpdX9oEiDQZluEsUbCMS1jRBC6lqalUOxP6RnuMvyBmUU+yWkWWt3qcP58+fjjTfewC+//BJ12S+//IIBAwYYtg0YMAC//fYbJKn0RMCZZ56p/84wDLKzs/UybgB49913MWDAAGRnZyMlJQUPPPAAjhw5UgvPxmjbtm0YO3YsWrdujbS0NFxyySUAUOFj//777/B6vRg+fDhSUlL0nzfffBP79+8HoL4u55xzjuF2WoBekQ0bNuDCCy/EGWecAYfDgeuvvx6nTp2C1+utwbMsFbkfmjdvDgD6foh3X/br189wnYkTJ2LHjh3o3LkzcnNzsW7duoSMlRBSe0RRRG5ubszLcnNz9ek9hFQHpS5JFJ8/CMCEUNAHk2AFAHh8AaSl2JM7MEJIgySYGLxyTceo7bIio7jYjdRUB1im9BwxxzKwCWzMwNsmsEi3mvDQqPgyi4Kpel3HBw0ahJEjR2LmzJmGjGdVlJ0fyDAMZFl9Tlu3bsV1112H2bNnY+TIkUhLS8M777wTNZ840TweD0aOHImRI0di2bJlyMzMxN69e3HFFVdU2CBMm2O9evVqnHHGGYbLzGZztcdz6NAhXHLJJbj99tvx6KOPIiMjA1999RVuuukmBINB2Gw2sCwbNU2gKl+OI/eD1oVe2w/xstuN/z/26dMHBw8exNq1a7FhwwZcffXVuOiii6LmuBNC6g+73Y6ZM2cCUOdwu1wuOJ1O5ObmYubMmbBYLEkeITmdUdBNoviDIgATpIBfD7rVQJwQQhKPYZiYJd6yDJhNDMwmFixbGnQHRBkjuqRj5c+nom4zoks6JFmBma/9Qq7HH38cvXv31svBNV27dsWWLVsM27Zs2YJOnTqB4+LLwH/99ddo06YN7r//fn3b4cOHaz7oSuzduxenTp3C448/jlatWkGWZWzevNlwHUFQ+3xEZnq7desGs9mMI0eOYPDgwTHvu2vXrvjoo48M27755psKx7Nt2zbIsoynnnpKPwbee+89w3WysrJw/PhxKIqiB81l194WBMEw3njVZF+mpqZi/PjxGD9+PK688kqMGjUKBQUFenk+IaT+sVgsmD59OqZPn468vDw0bdoUiqJQwE1qjIJuEsUfVBvIKJIISQyC4wV4/VReTgipH8w8i7E91MBl3d5CeIMybAKLEV3SMbZHBniubmZO9ezZE9ddd11Ut9t7770X/fv3x9y5czF+/Hhs3boVzz33XNxzkAGgY8eOOHLkCN555x30798fq1evxocffpjopxCldevWEAQBzz77LG677Tb8/PPPePLJJw3XadOmDRiGwSeffILRo0fDarXC4XBg6tSpmDJlCmRZxgUXXICioiJs2bIFqampmDBhAm677TY89dRTmDZtGm6++WZs27YtqiFZWR06dIAoinj22WcxduxYbNmyBS+99JLhOkOGDEFeXh4WLFiAK6+8Ep9++inWrl2L1NRU/To5OTn47LPPsG/fPmRmZsY9N7O6+3LhwoVo3rw5zjrrLLAsi+XLlyM7O1ufA08Iqb9MJhNatmyJ7OxsdO3aNepEHyHVQXO6SRR/QC3LU+QQZEn9Xc1+E0JI/cBzLMZ0y8BzV3bAc1e1x3NXdsCYbnUXcGvmzJkTVYrcp08fvPfee3jnnXfQo0cPPPjgg5gzZ06VytDHjRuHKVOmYPLkyejduze+/vprvZlXbcrKysLSpUuxfPlydOvWDQsWLMCcOXMM1znjjDMwe/Zs3HfffWjWrBkmT54MAJg7dy5mzZqFefPmoWvXrhg1ahRWr16Ntm3bAlAD+hUrVmDlypXo1asXXnrpJTz22GMVjqdXr15YuHAh5s+fjx49emDZsmVRS2917doVL7zwAp5//nn06tUL3333HaZOnWq4zi233ILOnTujX79+yMrKispel6e6+9LhcGDBggXo168f+vfvj0OHDmHNmjWGig1CSP2Un5+P/Px87Nq1CydOnEj2cEgDwShVXS/lNFNcXIy0tDQUFRUZznqT8r31yZdYX9gMvrwj4KypEFKcuLmbhEF9uyV7aI2GKIpYs2YNRo8eTetCJgntg9rj9/tx8OBBtG3btsKSPVmWUVxcjNTUVApWkoT2Qf2g7QdBEHD48OFK3zskcej/guSr632wY8cOnHXWWQCAs88+G99++22tP2Z9R++D8sUba9L/oCSKX1TLy6FIUGT1d8p0E0IIIYSQhi4/P1//3e/3J3EkpCGhoJtECYTndEOWoEgh4zZCCCGEEEIaqLy8PP33AC2ZSxKEgm4SJSCqHV6ZiEy3jzLdhBBCCCGkgYsMuinTTRKFgm4SRZS0oFsGZPX3oEiZbkIIIYQQ0rBReTmpDRR0kyjBkNqJNzLo9lN5OSEkwRp4H09CEo7eM4TUPsp0k9pA63STKGJIBgSAhQxJUQNwynQTQhKF4zgAQDAYhNVqTfJoCDl9iKI61Ut7D5G64XA4kj0EUofy8/PRpEkTZGdno7CwMNnDIQ0EBd0kSlBSA20WChhFzXRr87wJIaSmTCYTbDYb8vLywPN8uUtRybKMYDAIv99Py1UlCe2D+kGWZQQCARQXF8Nms8Fkoq9vdcHj8YDneXTt2hWKosDj8cButyd7WKSW3X3vNLz51n9RWOJHeooFAVGGmafPP1Iz9KlNooha0M0oaok5gKBEQTchJDEYhkHz5s1x8OBBHD58uNzrKYoCn88Hq9UKhmHqcIREQ/ugftD2g91uR/PmzWlf1AG/348FCxZg8eLFcLlccDqdyM3NxcyZM2mN9AZMlGQcM+dgyqoj8AZl2AQWI7qkY2yPDPAcBd6k+ijoJlFCkjpnjGMANhx0i+F53oQQkgiCIKBjx44IBoPlXkcURXz55ZcYNGgQeJ6vw9ERDe2D+kEURXzxxRcYPnw4BEFI9nAaPI/HgwULFmDOnDn6NpfLpf89ffp0yng3QAFRxuo9BfhoV2lJuTcoY+XPpwAAY7plUMabVBsF3SSKKJcG3Uy4aUswRJluQkhisSxbYcaI4ziEQiFYLBYK+JKE9kH9wHEcJEmiEv86wvM8Fi9eHPOyxYsX4/7776/jEZG6wLEM1u2NPYd73d5CjOuRWccjIg0JfXqTKHI4qc0x6rxuoDT7TQghhBDSkLlcLrhcrnIvKyoqqtsBkTrhFSV4g7ErO71BGV7qb0RqgIJuEiUUjq9NHMAx4Uy3ROXlhBBCCGn4nE4nnE5nuZelpaXV7YBInbDxHGxC7NDIJrCw8bRqAKk+CrpJFC2+5llGP0Ao000IIYSQxkAUReTm5sa8LDc3V1+6jTQskqxgRGdnzMtGdEmHJNN3YVJ9NKebRJHAgAFg4lhwrAwJQIg+aAghhBDSCNjtdsycORMAqHt5I2LmWYzqmgZZlrHht+LS7uWdndS9nNQYBd0kiqww4ADwHAMuvCoJnd0jhBBCSGNhsVgwffp0TJ8+HXl5ecjOzoYkSRRwN3CrP1oFa0oqnrl8OIoDEhwCA5eriAJuUmN0BJEoMtRIm+dYmMJHCK0YRgghhJDGxG63o2PHjhg7dizefPNNWiasEfj9999x9bU34J4PD2DRxj9x3bx38dP2H5I9LNIAUKabRJEZNdIWTBxMbAgAQFO6CSGEENKYSJKEY8eO4dixY9SxvJHIy8uD1ZkFd0CCOyChJKjA7/cne1ikAaBMN4miQAu6Wb2choJuQgghhDQmkcFWMBhM4khIXcnPz4fN2VT/m+PNFHSThKCgm0RRtEw3z8EUntQtK0wyh0QIIYQQUqd8Pp/+OwXdjUNeXh6s6RFBt2ChoJskBAXdJBqjrkNo5k16plub500IIYQQ0hhQprvxyc/Ph9WQ6RYo6CYJQUE3icZGBN0mCroJIYQQ0vhQprvxycvLg82Zpf9NmW6SKBR0k2is2l/PzHMQwpluhQ4VQgghhDQikcGWKIpJHAmpK1Hl5TSnmyQIRVIkCsOpQbdF4CHwatabMt2EEEIIaUwo0924eL1eeL1eWCMy3SaBgm6SGBR0kyilQbcJgonTNiZxRIQQQgghdYvmdDcu+fn5AGDoXs7yZvgo6CYJkNSge968eejfvz8cDgeaNm2KSy+9FPv27TNcx+/3484770RmZiZSUlJwxRVX4MSJE0kacePAhMvLrWYBZl79XWHp/AwhhBBCGg+fz4cmTZqgR48eMJvNyR4OqWX5+flgWA6W1Ex9G8tyCARpagGpuaRGUl988QXuvPNOfPPNN1i/fj1EUcSIESPg8Xj060yZMgUff/wxli9fji+++AJHjx7F5ZdfnsRRN3yMiQegBt1COOimTDchhBBCGpNuPXvhyB9/4rONX2HRM4sREOVkD6nB8Xg8CAaDOHnyJILBoCEGqGt5eXmwpGWCYVnDpEp/MJSwx6hPzzcRY/IGRIQkGUU+9V9vgE5QlCepQfenn36KiRMnonv37ujVqxeWLl2KI0eOYNu2bQCAoqIivPrqq1i4cCGGDRuGvn374vXXX8fXX3+Nb775JplDb9BYkwAAsFoEmMNzurWO5oQQQgghDZ0oyfjuJIcpq47gvrXHcfeHh7B6TwFEiQLvRPH7/ViwYAGaNWum/yxYsCBpc6jz8/P10nKn1VQ6zgQF3fXt+dZ0TMGQjLV7XJj8/n7c9f4BTH5/P9bucSEYovdILKbKr1J3ioqKAAAZGRkAgG3btkEURVx00UX6dbp06YLWrVtj69atOPfcc5MyzoYsJEngwplum0WARVB/10rOCSGEEEIasoAoY/WeAqzaWaBv8wZlrPz5FABgTLcMmHmadlcTHo8HCxYswJw5c/RtLpdL/3v69Omw2+11Oqa8vDy9iVq6zQSXNwCF4RAISTW+7/r4fGsyJm9AxNo9LqzaZXyPaH9f3M0Jm5mvxdGffupNJCXLMu6++24MGDAAPXr0AAAcP34cgiDA6XQartusWTMcP3485v0EAgEEAgH97+LiYgDqUg+03EPl3J7STp0mjoWJCxfYcCZ6/eqQ9lrTa548tA+Sj/ZB8tE+qB9oP9QtjjVh3d7CmJet21uIcT0yaV/UEM/zWLx4cczLFi9ejPvvvz/quK/t1/z48eOwhjPdqRYWLGRI4OAPhmr82FV5vnWlJvtAMHFY/6sr5m3X/+rC385sPO+ReJ9nvQm677zzTuzatQtfffVVje5n3rx5mD17dtT2devWwWaz1ei+GwNXiQ/AWQCA77Z+jSN5xUDqADAshzVr1iR3cI3Q+vXrkz2ERo/2QfLRPkg+2gf1A+2H2udwOND77AHwBmOXyHqDMty+ILZ/twVut7uOR9cwOBwOdO3aFS6XK+blLpcLp06dwi+//GJ4jWv7+N++fTts6Z0BAEUn/4IsCoDA40TeqRp9B67u861NNdkH2dnZ6HJmnwrfI56ghL0//1hukrQh8Xq9cV2vXgTdkydPxieffIIvv/wSLVu21LdnZ2cjGAzC5XIZst0nTpxAdnZ2zPuaOXMm7rnnHv3v4uJitGrVCiNGjEBqamqtPYeG4sCfx/G/r9SDZ/TFI/Hzb39g1251nvfo0aOTPLrGQxRFrF+/HsOHDwfPU3lOMtA+SD7aB8lH+6B+oP1QtxjWBJvAxgwqbAILh1XAwIEDkzCyhkNRFDidzphBn9PpRGZmpv4a19Xx//rrr+vl5b06t8PxXcfgA5CS6qzxd+CqPN+6UpN9wLBche8Ru8ChT58+tf0U6gWtqroySQ26FUXBXXfdhQ8//BCbNm1C27ZtDZf37dsXPM/jf//7H6644goAwL59+3DkyBGcd955Me/TbDbHXNaB53n6jyoOIUkBAEghEVaLBQ67DUAALGei1y8J6LhNPtoHyUf7IPloH9QPtB/qRkCUMaJLuj6HO9KILumQZAVm2g814vF4kJuba5hPrMnNzYUoilHziWv7+C8oKIC1vVpenpkiwMQygAyEFKbGj1ud51vbarIPvAERwzs5DXO6NcM7OREMSY1mTne8x0ZSg+4777wTb7/9NlatWgWHw6GXIKSlpcFqtSItLQ033XQT7rnnHmRkZCA1NRV33XUXzjvvPGqiVku8gSAAQA6p/9osAoAAWJMAWZbB0nrdhBBCCGnAzDyLsT0yoMgK1v/qgjcowyawGNElHWN7ZIDn6LtQTdntdsycOROAOn9Yq2rNzc3FzJkzYbFY6nxMeXl5aJ8e7l5uM4FnAchAIlaK056voih49tln68Xzrck+sJl5jO2prmce+R4Z3smJsT0zIZjoPVJWUoPuF198EQAwZMgQw/bXX38dEydOBAAsWrQILMviiiuuQCAQwMiRI/HCCy/U8UgbD58/CICDHFKbAljM6vJhDMtCDEkwC/QmIoQQQkjDxnMsOvB5GHN5F7gDEhwCA4ZhKeBOIIvFgunTp2P69OnIy8tDs2bNIMtyUgJQQA26e0QsGcazajPhkKwk5P4tFguuuOIKzJgxA3l5eWjevDlCoVDSnq82pgkTJuj7IDs7G5IkxTUmwcSiWzMBl/Rsj2K/BKeVgxiSKOAuR9LLyytjsVjw/PPP4/nnn6+DERFfIAjACiUcdNutpaX6Hp8fZqFxlIoQQgghpHF7ZtFTsAyfAaedx0+fvIbVL89N9pAaHLvdjrZt2yIlJQUXXXQRFi1alJRxSJKEwqJiWBzqssXpVhP48Ao+ksIk7HFee+01vP3228jOzsbatWsNvayS5csvv8S0adOQnZ2Nu+66C7feemvct33gP2uR3aUf0qwcruygoE/XdrU40tNbvWikRuoPX0AEYIUshwAAVktp0O3zB4G0JA2MEEIIIaQOBQIiQqKCElcQhW5f5TcgVaYoCg4fPgxFUdCrV6+kjaOgoAAWRwYYlgXHACkWTs/Yykhc5tbtdiM/Px/5+fkoKiqqF0G3x+PRx3TkyJEq3VZiebgDEtwBCcfyPbU0woaB8v/EwB9UM9xaptvMm6DI6mQWX3i+NyGEEEJIQ+cLlq6/qzCJy3aSUn6/X6989fv9SRtHfn4+rOH53GlWE1iGqbWgO9bvyRS55FV+fn7VbsyXlqEXuuNbOquxoqCbGPiDaoZbCWe6WZaFLKn/6Xj9gaSNixBCCCGkLvlFSf9dYbgkjqThKikp0X/3+ZJXTZCXl6cvF+a0qoXAZl7d540p6M7Ly4v7dqFQCJy5tLu5q4SqQSpCQTcxCGr/wUghfZvWVM0fEGPdhBBCCCGkwQmGSttWK/SVuVZ4PKUlyckMuvPz82ELN1FLt5UJuhkurj5U8aiPQXfkPqhK0H3q1CkI9lT9b7ePKmIrQp8gxCAgGjPdAKBomW4qLyeEEEJIIxEMRWa66StzbYjMdCezvDwvL89QXg4AVkH9l+PNCIVC5d62Kupj0F3d8vITefngLaWZbrePknMVoU8QYqAF3YgIumVJ/U8nEKQ3EyGEEEIah4jqcrAmHpIklX9lUi31JdOdl5dXmunWg251xR6OFxJ2QqC+B91VyXT/deKU8X6CiTkx0VBR0E0M9FIqOeI/lnCm20fl5YQQQghpJIJSaXk5a+IRDFLFX6LVl0x3fn5+6ZzucHm5Rct0C5aEjS3y+Ub+nkyRQXdBQUHcJ5dOnHIZ/vaJiSnBb6hoyTBiEAxJAAcwSsQ8pnAArmfBCSGEEEIauIgp3eBMAoLBIKxWa/IG1AB5PB40adIE2dnZ4Hk+aePIy8uDtasx023m1dwkx5sTmunWnm99qZyI3AfHjx9HYWEhmjRpUuntTha6AVvp34H68XTqLQq6iUEwJANmAErEOydcak5BNyGEEEIai8igO9GZbo/HA57n4XK54HQ6IYoi7HZ75TdsYHr16Ycjf/yJwhI/0h1WBERZD3brUn5+PtLCc7qdVrWBGs+Gg24hMUG3KIpY8eEqDBs6BIUlfmSm2hP2fGtyPM184EGc1etMdR+kWOJeIrjA7TUE3aJCBdQVoVeHGIjhUqrITDfCvwdorgYhhBBCGomQUro2NxvOdCeC3+/HggUL0KxZM/1nwYIFSS2vTgZRkvFDHo8pq47gvrXHMWXlYazeU6B/F61LeacKYEnNBFBaXs6b1P3P8YkpL1cYFr5mZ+nP918fHkzI863J8SRKMo4KbUr3waoj2HjQH9eYXCXGpYRDoGX1KkKZbmIQktX5GExkpjv8e2QXT0IIIYSQhixyfeZEZbo9Hg8WLFiAOXPm6NtcLpf+9/Tp0xtFxjsgyli9pwCrdhXo27xBGSt/VptzjemWUacZb3dADTJZKEgxq8GjwGlBd80z3QFRxurdhfhod6G+LRHPtybHk7YPPtplHNOqnQVgGKbSMbn9IjioqxwxHA+ZTd70gNMBZbqJQUjSgu7SM1za71ReTgghhJDGIBQKAVxpbopLUNDN8zwWL14c87LFixcndV5zXeJYBuv2Fsa8bN3eQnAsE/Oy2qAoCrySGhI5zAxYRn1snistLw8EAuXePh4cy2DdvsQ/35ocTzXdB95guDo2EG4IZzLHMeLGi4JuYqBlulmUdiDUgu5gqO7LfQghhBBC6prf74eJt+h/J6q83OVyweVylXtZUVFRjR/jdOAVJT1oi7osKMMr1l11pcfjAWdzAgDSbaVBaiIz3bX1fGtyPNV0TP7wxTZWfV8wPDUZrAgF3cRAi6tZRCyTAS3opvJyQgghhDR8Pp8PLC/ofyeqvNzpdMLpdJZ7WVpaWo0f43Rg4znYhNhhiE1gYePrbn5w5HJhmSml2Vo+HHSbEtBIrbaeb02Op5qOKRSepdwkPAeeM1PQXREKuolBuLrckOnWfhcp000IIYSQRsDv98MklGa6uQRlukVRRG5ubszLcnNzIYpijR/jdCDJCkZ0SY952Ygu6ZDkulvzOS8vDzan1rm8dEqBEC4vZxOQ6ZZkBcM7O2NeVpPnW5PjqSb7QFEUyJx6guKMzBQAAG+xQ6SpqOWiRmrEQAu6OSY66A4loZskIYQQQkhd8/l84PjSrGeiMt12ux3Tp0+HLMt47rnn9CWecnNzMXPmTFgslsrvpAEw8yzG9siALMvY8GsRvEEZNoHFiC7pGNsjQ59PXRfy8/NhDS8Xlm4rDY30TDdvgd9/skaPYeZZXNwlFYqiJPT52u12zJw5E4A6h7sqx5OZZ3FJ9wzIkowNv5WO6aJOaZWOqaSkBCarGmy3PyMLW/OKAQAnC4pwRrPMaj2Xho6CbmIgKQxYAJG9E1hGgQJAlOrurCMhhBBCSLL4fD5wQmnQzfGJWzLs/fffR9++ffHnn38iPz8fzZs3hyiKjSbg1vAci5ahP/D05b3hDshwCAxKPJ46DbgBNdNtTVPLyyMz3fqSYQlap/vDFe/jjM698cwVXVHsl5DCK2BZU42fr8ViwaRJkzB9+nTk5eUhOzsbkiTFdTwFfB44vYfxzBV9w2MCvv3uO/Bc0wpvl5eXB8Gulq63yEyFFDwJTrAg3+WmoLscVF5ODGSEP2AiysvDJ/oQkinTTQghhJCGz+/3g4tspMbxNe5grdmwYQMuu+wy5OTk4JprroEgCI1imbBY7pt2L/717h4s2vgnLpu2GAd+21fnY8jLy9Mz3c6ITLfAJq6RGgAcOXIE9z71Bu754AAWbfwT3Xr3B6Mkphx78+bNyMnJwdixY7F27dq4jye3241bZ8zVxzRk/G2Y8I9rK71dfn4+BHsqAMBm5hAKeNTtLnf1n0QDR5luYqCtSRl50o1jgBAo000IIYSQxqFspjtR5eUA8MMPPwBQA5f8/HxIkgSOq7vGYfVJiccDnyLgT1cQJUE5IcFtVeXn58PW8nwAgNNauh94k/pl2GS2wueq+bjcbjd4ix3ugAR3QEKRT4Tb7UZmZs0zw4WFhfrxVFBQUPkNwrxeL0yCVR+TV+aRl5dX6e3y8vJgtrcAANgFFnJQfX1OFXuq9wQaAcp0EwMlnOk2RQbd4d8p5iaEEEJIY6BmuiOC7gSVl7vdbuzbZ8zmlrfkU2PgF0urKDnBDJ/PV+djOJl/CpbUDABAuqGRWulcS58/Mfuet5RmoAWrA253YjLDhYWl622XlJTEfTuv1wvOXFrRYXakw+fzweOpOHg+kZcPk9kGAEgROCCkVoEUldT9/jtdUNBNDBRGPcNnipjTbQqX11DzckIIIYQ0BmUbqXFcYjLd27dvh6IoaNmyJVJS1EZUVclMNjTGoNualEx3QUl4nWlFRoo5ItMdEXT7gzXvKl9SUgJTRNDNW+0JC7ojj6HKAuZIWqZbY0trAkDN/lfkRIG6/reiKLAKLFhJfQ2LPHW//04XFHQTA4XRystLP2i0rHcdrt5ACCGEEJI06pJhEUG3YEYgAUH3tm3bAAD9+vVDerq6XFNklrKxCUYkdEyCJSmZ7iK/BACwsCEwTOT3XwZQ1C+/vmDN516XzXTzVkeVstIVqW6m2+PxwBSxvrYjU53bXlmJeZ5LfQxWCoJlGHBQXx+3r3EseVcdFHQTIzac6Y44u2cK15dTeTkhhBBCGgOfzwc2ItMNAIEEZDu1+dx9+/aloBuAKJd+30xUw7Kq8krqd18Hb9zOMAwYRQ3IA6JU48dxu90wWWz634KtdsrLq57pLi0vt6aq88sry3QXutWTIyao7wmeUc+eeAIUdJeHgm5ixKpzWfiITLf2u6QwMW9CCCGEENKQ+Hw+QzACAIEEZDsp011KFEWAE/S/TebkZLoDUKNtp42PukwPuhMwxzI6051SP+Z0R5SXCynqMVlZprvIo87hFlgl/K+63SfSXNTyUNBNjMKZ7sg1A7VMt9ZkjRBCCCGkISvbSA0AAmLNgu7i4mL8+uuvACjTDYRLm62lQSjH133QLYoiYFbn1melRq9rrS2hG0xQprsugu6qZLrLlpdzFvW1qCzTXRJQXw9buAmUJdx/zp+YFdAaJAq6iQETznRHlpcL4UndMgXdhBBCCGkEyjZSA4BgDYNurYlaq1atkJWVhdatW6NHjx4JW//7dFNSUmIIQjnBUufl5QUFBbA61XnM2enRa1uz4bLpoJSYTHfkSYZ6U14e0b2cMQngBEulmW4to50SjrZtgvpvUKZYoTy0TjcxYDi1tEaIyHTzJjX7LdM5GkIIIYQ0An6/H1yZzGewhiXGWmn53/72NwREGQuefAqnijzITLMjIMow843re5bH4zEE3YlupOYNiBBMHDxBCXaBQzAkwWY2lpDn5eXBlq4G3Rl2Ieo+TFAQQM33PRDOdJtL53Tz9WXJMCHDsM3sSK806A7I6rGaZldPTGnBd4hihXJR0E0MWJP6YcRHLNRtNnGAVNrZnBBCCCGkIVMz3cYgLBiqWYnxDz/8gC5dumDe/Cewek8B1u0thDcowyawGNElHWN7ZBim9zV0ZZfQSmSmOxiSsXaPC+t/demv8fBOToztmalXcAJqGbU1LQsA4LRGh0Xa7khAdTn8ogSGjUhqWe1wuysObuO6X7/fcLKi6o3UWhi2WRwZFZaXi6IIJTwXP8Ohlqan2syACEhM9Lx4oqKgmxjoQXfEh77AU9BNCCGEkMbD7/eDE8pmumsWeW3btg0Ln16Mz351Y9XO0nWVvUEZK38+BQAY0y2j0WS81Ux3aeaX4wV4fTUPur0BEWv3uLBql/E11v6+uJtTz3jn5eXBmt4KQOygm2cByIBYw3Vzg8EgFNZ4EkewOVDiqvmSYWV7AlR5yTCHzbDNUkmmOz8/H0KKEwCQkaqeNEm1mYEi6ME4idY43tUkLrIs6/OXBJ7Tt5v58IcQw8W6GSGEEEJIg+KN6F6uyGqwLdagxLioqAgFBQUYNnQI1u9zxbzOur2F4NjGMye2bHk5APgT0CFeMHFY/6sr5mXrf3VBMJV+nz2ZfwoWh9rQzmmLDrpN4f0RquGyuWWXCwMSV15eNuiu7pxuLd9mdqRXmOnOz8+HYEsFADjM6muWHg7cGZO53Ns1dhR0E50/Ym09c8QHkh6AsxR0E0IIIaThi/xOhJCafRVr0Exr+/btyM7ORpE3AG8w9v14gzK8iahjPk2ULS8HEhN0e4JSha+xJ1j6Gh8vDGeF5RBShOiwSDAlZtncsp3LAa28PPFBd9XndKtBd6Zdzf5bHBkVZrrz8vIg2NWg225WY4PMNLXrOWu2lXu7xo6CbqLzRnTPNFOmmxBCCCGNlD8i+GVC6vejmgTd27Ztw/Hjx+FMscIWI7gDAJvAwsY3nu9aHo8HvLVM0F3DDvEAYBe4Cl9ju1D6Gue71X3LSX4wTHRgrU23TETQrZ1g0IoZhARnurOy1LnpVZ/Trc7LbhIOus2OdBQWFiIUir0v8vPzYQ4H3drrnOlUg26TxQ5ZprW6Y6Ggm+i8vtKgO7L0xiKob0KGoxYAhBBCCGn4AlrGVVEAWf09JFW/xviHH35Afn4+Dh0+ghFd0mNeZ0SXdEg1nDt8Oim7ZBgABGtaxw117v3wTs6Ylw3v5DTMzS/yq79bIMa8vllfNrdmIZPb7dZPMGhzx9V1uhM3p7tVK3VueiAQKDdgLityne5MuzouiyMDiqKgoKAg5m3y8vIg2NIAQD+B0TRD/ZvlTChyxx/0NyYUdBOd168G3bIUgskQdIeDbZaCbkIIIYQ0fIFwVptjZDCK+ntNmmlpy4WdOnkcY7pn4G89M/UsoU1gcemZmRjbo/E0UQPCAV+ZoDuQgKW5bGYeY3tm4m89Mgyv8d96ZGBsz0zDsmEeUU072/nY+1ar/ExI0B1+rhnh4JZhWXgCwRrdLxAddAPxZ7u9Xl9E0K2+Lo4MdQm18krMI8vLtZL8jNQUyOHeBycLi6v6FBoFiqKIzutX3/hyyHi2z2pWOxGylOkmhBBCSCMQDJeXc1AAyJCAamehi4qK8NtvvwEAunTpgl9OeJGTacYzV7RHkVeEQwBCstKolgsDYme6a9olXCOYWPTIFnBJz/Yo9ktItXA4UegxLBcGAAGogWaszuWAFnQrUGqYeIp8rg6zCSwUyGDgE2v+fLWMdHZ2NjiOgyRJ8Hg8SEtLq/S2vmDpd36tvNyerpapl9dMLe9UIUzt1UDdFp7TzbIsQn4PBFsq8grd6JxT7afTYFEURXRq0M1AloxBt1peLlN5OSGEEEIaBS34M7GArKi/VzcJ++OPPwIAcnJykJmZiXXbTmLtnkI4zBx4fx6W3TsOW7ZsgaNTp4SM/XTh8XhgSjM23kpkH7lH31wHR9uz4GB8cCtWnDy8D8unjTFcJ2QKz2d2xO66bRVMAMQaL5sbOafbyjMwmwBfCAjKNe9Wr2W609PTYbfbUVxcHHczNX9EU7km4Qy82ZEBoPxMd35RCcwAoCiwRlRmyAEvYEvFqaKal8w3RI3rlBqpkC9c4qKUCbqtlnCm20QL3hNCCCGk4dPmFptYgIUabVd3SrdWWt63b18AwKFT6nQ+d0DCcS+L/Pz8qA7UjUHkkmEcEz6xkcAp7RJYuAMSPH/tQ5HHD2t2B/zv25365YqigLWoZdLZ6Skx76O0r5EASar+GQG1vFw9wWDhWVjDGffaCLqB+MvLtXJ+jlGQGs72m8LLgZWX6XaVqN38TYwENqL5nCL6w5f7qvoUGgUKuolOWx5DkYzNF7Tyco43U0dCQgghhDR4WlabZxmwWkBYzdLnH374AQDQr18/yIqCwwV+/TKTRQ32GmPQHVlybeO01zhx65RL4TCnRboVysl9AIC3v9yrX+52u2FJawIAaJnljHkf1nBfI04wIxCxyk9VRWa6LSYWtnADMpa31Oh+AWPQnZKiHk/xZrqD4d4FPAs4tFJxwQaGYcvNdBf71CSdhTO+HxhJ3V7koaA7Fgq6iS4gVhx0q9ep+VIOhBBCCCH1mbZEFM8xYKEGF3I1l42KzHSfdIvwirK+bBTLmcDyQqMMuj0RTbzs4a+aEhIXdMuMGjDbzDxG9lCbg3nT2qGgSF2mKy8vD1anuj3baY95H1q1J8eb4ff7Y14nHpGN1Cw8C7sl3MHcVvNlw7RjJyMjo8qZbq26XOAYpISDbjAMhJS0cjPdnoB6I1uZpn+sosYIxd6aN4driCjoJjq/tjyGbAys7dbSeS4eX/U/cAghhBBCTgdambNgYvWguzrl5S6XC7///jsAoE+fPjh0Sv0elZNh0a8jWB2NM+j2l05ndIS7YEsJDE0UVi0NT7EKuHbU+fAVHAVvTcGLH3wBADh+Mh/mFCcAwGmL3bfIEu5ebhIsNQ+6rZGZbvXxErFWd3Uz3YqiQAxXdAgmBhzLwB7eDxZHRsxMt6IoCK+yhlSrcdqpCeoFkfuVlKKgm+hKg27jnBWrpTTo9vnp7BUhhBBCGjZtiSizidXnGyvV+NqsNVFr27YtMjMzcTBcWt420wJLOFPI2xpn0O0P1/AzUGA3q0GownAV3aRqTFrQbYaJ45BjUl/jHfnq6/7HSfVvORTUg82yhHBHeY4XEprp1pYyU9fqTlzQXZVMtyiKYE1qJt8cXirYYVH/NTvSYwbdRUVF4MxqYJ9mtxguE1j1feIJJrAbXgNCQTfR6aXjZYJus8Dra+9pa3kTQgghhDREoiiC4dSAzcxzeim4XI3SZ20+d2kTtdKgWyvP5a0p+rJPjYk/nGblGVmfO81wPEKhxExlZExq0ijNrpaw3zbufEghEdbs9li39SecKFSzwYrfDYaJvW95Tt3O1TDTXVJSUjqnm2f1rt+8reZBt3bsRGa64wm6vV4vuHB5vzU8x1yb121xZMQsL8/Pz9fX6C6b6Q7H6/AnYBm0hoiCbqLTgm5GiW6WpoTX7taarRFCCCGENER+vx+coAZsZt6EcNxVrWWjtPncWhO1QwVq8iIn06wHXoI1pVFmugPhen2BAyx6w7KaBbeRWF7dh6nhoLt9q2zgpNpI7Z2vfkVesfo4rOgt9z4EUzjoTuCcbmtE0C1YHXE3PYvF5/PpjdgiM93x3KfH44FJUF8bM68F3dqyYbEz3ZFBt10wViVYw9n7ACW6Y6Kgm+iC4cURGUQH3XI46NaWFSOEEEIIaYh8Ph+4cMBmETi9vFyuxtfmyCZqJ9wifKIMnmNwRppZD1Iaa3m5GO5UbjYxsJnVrGlN505H0gLKtBSrvm3Mmc0BAL60djjuVr/bmlH+d1ueTVzQbdKWDDMlrrxcO25YloXD4ahSebnX69Ub2ZnDZfRaebnFkY78/HwoijFrrQbdaQAAu9n4ftCmCIgUXsZErwrRBbW5NUr0KSo53NGcgm5CCCGENGQ+nw+coM5X5TkWpnDgVdVMt8vlgtVqRZMmTdC3b1+9tLx1uhkcy8AWzi7ylsaZ6Q4p6utp5TmYw5lfTrDA56v5klNeXwBseE53elrpGtzXjDwPvlN/gbemANnd0NIpoIXTUt7dgDeVjiuRc7qt2r5PUNDtdDrBsiw6dOiAJk2axJXp9nq9MIWPcy2jr5WXmx0ZCAQCUfeTn58PczjotpXJdKeEO71LiN2UrrGjV4XoxPBafVqXzkhKuKN5IEhLhhFCCCGk4fL7/XqmW+AYcFqsXYUmXx6PBxaLBR999BGaNlWXpTp4Ss0+ts1UAx29xNiWgsLj+xM0+tODLMuQwkt6WQUOPJfYoPtUUWkgm55aGnSzLIvBbXic368FujfviGJ/CGmWNvAGRD3bHkmf082bEQhUvwzcEHSbIsrLbQ643UXVvt/CwkJ06dIFz7+0BCFJwQ03347b77gT+379rdLber1e/eSS2WTMdNud6vrl+fn5cDgc+m3UTHcb9Tplgu40uxnwADIb/ToSCrpJBC3THTPo1jPdNKebEEIIIQ1XZHk5zzF6phtsfJluv9+PBQsWYPHixXC5XHA6ncjNzQV/3o0ASoPu0hLjxlde7vV69SW07GYTzOFMa6LKy11u9QSHJAZhNQuGy24ZOwCf7DyFJV8fhzcowyawGN7JibE9MyGYjPtY0BupmeH3x163Oh7ukpLSJcMM3csdcLv/rPb9iqKIr7Z+g40HA5j8/u/687moU1N0l2T9ZEYsHo8HJrNa8m4uk+lOyWgGQF3LvG3btvpt8vPzITTpqV6nTMd3Z4oV8AAwmUGiUXk50WmZbm3ukoGW6RYp6CaEEEJIw+X3+/Wy28igm+F4yHJ035tIHo8H8+bNw5w5c+ByuQCoZeZz5s7FbyfUTKm2RrclItvZ2IJuj8ejZ37tZj7hmW6XW22OJgWNAbw3IOKTnaewalcBvEF1X3qDMlbtKsDHO0/BWya5VLpkWPXndCuKAl9Epaihe7nVXqPy8rYdOmHjgQA+2lVoeD4f7SrEx7sKEBDLP17VOd3h8vIyc7qtaRkAENXBPHJOt81szHSnO9QAnhWsINEo6Ca6kKwG21ysVRPCS4b5qbycEEIIIQ2Yz+cDy6vZUSEi6GY5HmIlyQee57F48eKo7anNcqCwPASOQYs09b71Od1WO0pKSiq974ZEXUJLDdKsAgczl5iGZZpijxq4y6LxvgQTh/W/umLeZv2vLggmYyCplZebBAt81RxXMBgEY1KDWwbqMaXN6VbLy6sfdLfIboYNv8UuT1+3txAcW/4yd+qc7nAjNa28PBxIC3YnAER1MK+oe3kTp1qGzoX3KzGioJvoQuGlG2JWooSbq2kdzgkhhBBCGiKfz2fMdOsBoYBgsOKGsi6XS89wR8po2x0A0NzB6YGQNaLEGECjynZHZrotJha8Vl5utiYk0+32qgGyEgoYHzco6RnhsrxBGZ6g8XuuEJGJ8vmr10y4bBM1hmEM+74mQXexL1jh8/FW8L1dLS9Xg269kZpFnXnMWtRjMiroLnDp7w17mfLyrHQ1GDcJVmq8HAMF3UQXntIdM9PNhDPd2lrehBBCCCENUWQjtcjyctbEVxp0O51OOJ3OqO2ZbXsAANpnlWYBtRJjW2o6gEYcdPNsQsq4I7m94WBbMlYP2AVOn09dlk1go7K3kXOivdUMJCOXC9P3udatnRdQ4q3+SQaHRajw+WjVFLHEbKQWznQznAkmsy2qvLzYp74GDBR9eoSmSTjoBoC8guo3h2uoKOgmulB4KrcpZqZbjciDIcp0E0IIIaThUpcMCwfdLKtnulmu8qBbFEXk5uZGbc/MUTPdLVNLgyAt8LKEy3UbU9CtlpdHBN0JznR7wllpRjYG3cGQhOGdnDFvM7yTM+p7LsdC/w7sC1Qv8VQ20w2o2X19rNW8XwDYtWcPLuqYGvOyEV3SIckx+jSFRa7TrWX0zSZGL6m3ONKjMt3FPjE8fgYsY8zSWc0CQgF1350sLK7Gs2nYKOgmOu19GavTodbRXKSgmxBCCCENmJrpLl2/WPsOxMZRXm632zFz5kw8+OCDesbbmZ6O7E69AQBdmqfp17UK2vzZxhd0R2a6rWUy3YkJutXgkFWM31ttZh5je2bibz0y9AyxTWDxtx4ZGNszM2rZMIZhIvoaVW/OfdnlwgCAZRmYoN5veeXh8XjlpRcwtJ0l6vmM656OsT0y9PXPY4mV6WYYJmKt7nRDplsURYgILylWTnZdCqhd40+5ql8y31DRkmFEJykMGJQ2jYjEQMt0V/+DgRBCCCGkvlOXDFNLvnm2NOjm4igvBwCLxYK7774b06dPR15eHpiUJpi19i8IHIPmaaXLV+kdrC3qOtKNKeguiVhCy2xiI5bmSsySYb5gCDABHKKTRYKJxcXdnPjbmZnwBCXYBQ7BkBS1XJiGUSQo4OGvZl8jt9sNk9WY6QYAgVMQkgB/BR3GK7Nz505ccN65WLn+K1zSsz2K/RJSeGDjxk3gueEV3tbj8cCUZpzTDagdzAu8IZgdGYZMd3FxMcw29aSRwxp7LW45qJ4wKQx3jyelKOgmOhkMOFSS6ZYo6CaEEEJIw+X3+0vLyzlGX0qVNVWe6dZ4PB506tQJrVq1wrPvbwQAtMkwG7pJa+XlbLjEtzEF3R6PByZLNgCtvFx9LUyCBb6imme6feEA2RRrGVxAz2inWcOPW8F61qwiQQIQqEHQXTbTDQAWjoFXAgJy+R3GK1NYWIi9e/di5feHcEQsRJqVw4Ylc/DD6jcgSZKaqS+H1+uFKcvYvRwonddtcaQj/+QefXtxcXG5nct1IfX94Sqp+T5saCjoJjot6I51pk8LuinTTQghhJCGzOfzgbOpQbehvDzOTDegBu75+fkIBoM4dEpt6tU202K4jtbBmjGZwTAsCgoKEvUU6r2SkhLw5nBzsYhMt8lsrfbSXJEC4UZFFVRXx42FrAbd1ZxiGWtONxCudAgColL9QWonakKcBe4SCe6AhOJASF0b3OeDzVb+8l2GdbpjBN1mRwaO7TZmuisLullZfX8UewMxL2/MaE430cko/2wfGz5TGJIp6CaEEEJIw2VspBZZXh5/plsrkbZYLDhUoP6ek1Em6I4IwHhrSqPLdPPWyO7lkUtz1TxgC4aD7vISslXB1TDxFLkmeWTQbTeruc8QOChK+Q3PyqMoin6iJoDScm8twC8pKanw9mrQrY7LzEWWl6vjsqSmw+Vy6evHFxUVQbCnhcceO4TU5qm7fY1nzfl4UdBNdAqjHg6CKfoTSnsvilLVPxQIIYQQQk4XZZcMY5nSTHcgEF9AqAfdVpsedJfNdPMcCz5cbt4Yg+7I7uV8RKbVF6x5016tN5m5nHnaVaFNL6jud+DITHfkiZaU8Lxok9ke93EVyefz6SeBvKGIDHqKGhh7PJ4Kb+/xePRGarEy3RZHBgDg1KlTAMJzurWgu5yzGSZGfeFr0pG9oaKgm+gUJlxOEmNNPy3ormjpAUIIIYSQ053P54Mp3L2c59hql5cDgLNFOwRCCswmBs1ThajraSXmvK1xBd3uEg9M4YDPwrPqWuj60lw1z5KGFPWLq7WCdarjxbFa0F2925c3p9thUY8H3pYCt7vq3b6144XjOBQHSrPwVocaGFee6fbp+8CY6VZfM0dmMwDQm6lFlpfbygm6w/E6fDVoDtdQUdBNSoUz3WY+eqq/9hlBU7oJIYQQ0pD5IhqpCRGZ7uqUl6e36QoAaJ1uActGN7XSMp+C1dGogu7ITKhVW0YrvLyXX6x5llQKhziWBNSXa429Q9VMPLndbj2rb+YjGumFI1Te6qhR0N0kuwX8odKxaeu+V5bp9kUsgWZopBYOuu3OJgCgLxumBt1apjt2CGkNv1gBWmE4CgXdpBQbLicRYgXd6puIqssJIYQQ0pD5IzKtJq76jdQAIKVFJwDRpeUafdkwW+MKur3h15hRZJjCWVY2vDxtIAEZHklbT9oce2mrqtCW0q3usNxutz5/3RoR3NrCWXjBWrNMd9OW7QzbzeGgu7JMd+QSaHzkkmERjdSAMpluW7iRmjn2yQwtAx6sQUf2hoqCblKKVYNtc4ygW1viglYMI4QQQkhDFhmMCBFBN8cLCASqFnTbstsDANpmmmNeTwtSGtucbq38OHIdbU4LuhNQmqywarBtt9Q86NaavIWqmXiqsHs5al5entGijWG7FuBXlunWTm5wjAKWiS4vN1kdAMpmuivuXp4Sfr1DSEAHuwaGgm6iYzg12LYK0R9Q2lk+ynQTQgghpCELhMubGcjg2NLycgAIBOObb+z3+5GV1RTtOnaEw8whp5JMt2BNaVRLhvnDzdL4iNe2pg3LDDj1u2yKNfbJjqrgw6v6SNUMm9Ty8uju5fp8/hqWl6c1bWkcb7gjeWVBt97hvczT0jLdjGADw5lizukur7w8NbzUnsTQqtRl0StCdFrQLfAmlD2Pq33gyKByEUIIIbXHGxAhmDh4ghLsAodgSIItASWihMRLy7TqZeUoDQLjnW98/sDBOPzHHygRgVSLCeWtCBVZXu71ehEMBiEI0Q3XGhqt71dkwpRnAShAMAFBN2NSX0OHLfbJjqrQ5jvLSvW+A5eUlCAlRqbbpu37apaXaydpUjKyAQBpFg5FfglcOOiurLxcDJeA85zxeaUIHBgACgBzilMPuouKimC2Vdy9PNVuAYIATDU/2dHQUNBNdEz4rKDVzFPQTQghpM4FQzLW7nFh/a8ueIMybAKL4Z2cGNsz07CkDSG1KRieS2fSGqihapluUZKxvcCCBd8c1o/jEV3SMbZHhv59SmPTsp2WFABq9rJZs2YJeR71mRbwmSMiEZ4FICWmaS8bXvIt1V7zoFsIz72WmeqVTLvdbrQMZ7oj53RHVjmUlJys8v1qmW5LWhOIALJTBRT5fWDDHckrynQrigIx3Biu7LJqLMvAbuZQEpBgSUlHfn4+ZFmGNyDqDQbLy3RnOGxAIcDwNX/dGxr6H4zoWJMWdEefYdXWT5TpkCGEEFILvAERH+88hVW7CuANL7LrDcpYtasAH+88pTdeIqS2aZlWLRZhGEBRtPnGFWe6A6KMj3cVRB3HK38+hY93FUTNV9aWtEpxZgJAo5nXLYazxpFLaGkZ10SsNqUFnk6Hvcb3ZUlA0B1zTrc2n99Ws/JyPiUdAPQl6ZhwlrmiTHcwGNRPTJj56O/2ejO11Azk5eXB5XLBZFVPDLGM8XlEykwLX0ewVvn5NHQUQREdG8502yzRJSFa0K1QppsQQkgtEEwc1v/qinnZ+l9dEEzUmIfUDS3TakgAyuoc5GAlizVzLIN1e2MHzuv2FuqNaTVattOepnaKbixBtxQutrVFNO/VqlmkapZxa0QxBFM46HM6bDW6L6B0VR+lukF3SUnMdbprWl6uHSuMRZ1nna2tAx8ura8o0+31evXXyBpjqeDUcDM1c4oT+fn5yM/P10vLbQIHhom9j5o41aDbZLFDlqn7ciQKugkA9QOKDc/ptlmiM93m8JcdhaFDhhBCSOJ5gpKeGSzLG5ThCdLCr6RuaI28+IgAmdGC7lDFx6FXrPg49pYJ2rVmWpYUNaBpDEG3oiiQw422bBH15VqZc3Ublmlc7tJgMyOcea0Jq3ZigK36rFxFUeCL+OyK1UhNsKag2F3x/OtYtGNF5tUTC1qmGwwHlhcqzHR7PB6YzGrQHSvTnRLOdFvCme78/PxKm6gBQNMM9ThmWQ6nXFU/kdCQUQRFAAAen1//3R6j06OgB92UaSCEEJJ4doHT57eWZRPYchv3EJJoWqbV0EdAiS/otvEVH8fa2syl11evq62t3BiC7mAwCC5c/p0SsaSXWS/jrll4UlCsBpuKLMNhq3mZszXcyJHhTFDK64hXDr/fr5e6s4yxaZk1Ith1e/1Rt62MdqwEGfV7u57pBsBb7JVmurV9UHZONxCZ6VbndOfl5VW6XBgApKbYIIfUqUAnC4uq8nQaPAq6CQDA4wvov9ut0c0P9LW7KegmhBBSC4IhCcM7OWNeNryTs9Jgh5BE0dZjjgy6mfAa0pUdh5KsYESX9JiXjeiSDkk2Bm3anG4+PF+2MSwbVlJSAlO43DpySS9t7nRNEzxFbi8AIBT0gWVrHupovY44wYJgML512jWG+dwm1lCWzXMsmPDJnBJ/fF3xIxUWFoK32PXKgAybSV9TnDfbKi8v1zLdpuhS8chMtyiKOHjwIAR7uHO5ufzXlGVZhPzq455yVT1735BR93ICAPD6S4NuIcbcDrO2LQEfXoQQQkhZNjOPsT3VZlLUvZwki6Io+tJQ5og+AoyiQAEgVtJa28yzGNsjA4osY/2vRZV2L9dKjLnw/NrGkOn2eDzgrWogao8oL1fLuMVqNyzTFHl8AMyQg1XPHsdiD0+75HgBfr8fZnP8y2FFnmCI1XzMBAkiOHgCVQ+6CwoKYE1vCkDNmpt5FhaeRVCSYLLaKywvjwy6BS56XI5wptvubAIA2Lt3b1yZbgCQgz4ATpwqqnid8MaGgm4CAPD61TN3khiIeVbQUoP5LIQQQkg8BBOLc1pbcUnPTBT7JTitHMSQRAE3qTOiKEZ0dS4NLljIkFG6nFhFeI5FDnsSz1zRDcV+CelWEyRZiQq4gdLycoSXWGo0QXesbt5mHoAIjjcjFArBZKred85iLegOBSq9bjy078Acb4Hf70daWlrct43MdFtjBN08I0ME4A9VrWxdURQUFhYio0NbAIDTqo7RwrMo9kuVlpd7PB69kVqsz1ete3lKhrp83d69e2HO7gtAbaRW4dhC6smOwhJvVZ5Sg1fl/8X++OMP/Pnnn/rf3333He6++24sWbIkoQMjdUvLdGvzMMoy8+H5LCyVlxNCCKk9b378Be754AAWbfwTe34/ApuZr/xGhCSIz+fT1yK2RHTWZsNrdYfiCLoB4PEnF+GeDw7g6c//gIljYjarAkoDMYVTs6mNIeguL/urvdc5wQKfz1ft+3d7w8F2qGql4OXRglJOMMPvr1r2XA261UZnsTLd4di2ykG31+uFKIqwObMARATdJm3d98oz3aVzuqPLyx0W9f6sqWpX/V9++aW0vLyCRmoAwEjq615UkphKg4aiykH3tddei40bNwIAjh8/juHDh+O7777D/fffjzlz5iR8gKRu+INqsF1u0B3+j4fhKNNNCCGk9viCIbgDEv50BZFP3W9JHfP7/TCFs84WQ6ZbDYq0zuaVCYZkuAMSjroqDh61tZrBsOAES6MIusvLdNv0Mu6aBd0lWp8iOfZ32qrSKhQ4vrpBt/pcYzUss4QD3nIa3pdLO07smc0BAOk29TjSTuKY4mikZjKX30hNy3Tz9tKu+oItvvJyTlFL5d3+xJz0aCiqHHTv2rULZ599NgDgvffeQ48ePfD1119j2bJlWLp0aaLHR+qIL6B+MClS7A8orYkEw1HGgRBCSO0JRCyppM7NJKTu+Hw+vbw8shycZcKZbjn+oBsAOKbi61tMDLTeWoLV0SiC7pJy1q3WMsoms6XKwW0kT/g7LStXfZ50LFpzMpNQ9XG53W49qx+rvFzbJipVqyTVjpP0Zi0BGMvLAYCvZE63umSYmoEXuFiZbnU8nMWhb9PndJsrHqsp3HTQ40/MSY+GospBtyiKegOBDRs2YNy4cQCALl264NixY4kdHakzWqZbKecDyhKxXAIhhBBSW/wRjar0MlFC6ojf74cpXF4uRJTdanFJJX3UdMFwRryyoJthGD3w4m0pjSLo9ng8MMUoudaCv5pmur3hTuAcErPigTYuNtxIrSrcbrfeNC5Webm2ZJrEVO37tXacOJqomW6nrUx5uTmOTHe4vLyiOd1gOf0ESbzl5QKrHvPeIK04EanKQXf37t3x0ksvYfPmzVi/fj1GjRoFADh69CgyMzMTPkBSN/zhs4KQYgfdtnCmmzUJMS8nhBBCEiGyfLfER0E3qVs+nw9cjEy3FnSXXfKrPFpGPMZ02Sha0C1YHY1myTC9uVhkppur/tzpSH5RC7qrNk+6PNpxYOKrl+mOldXXaEG3zPJVWgNcC7qt6Wqjs7KZbpOl8iXDtI75seZ0m02sfrLBHJ7XbY6ze3l4OjiqsQpag1bloHv+/Pl4+eWXMWTIEPz9739Hr169AAAfffSRXnZOTj/aBxSU2GelLNoahSYeslzFiSeEEEJInCKTI1SeSOpaZCO1yLJbLf4OxTmnW8uIl9M/zcCmlwQ3nkx3rDndWmWBSbDWKNPtF9UXn2cTFHSbtAx81U8GVLZkWKpNPdZMlpQqPWft5IzZoQbE6WUbqVntEEWx3HXFDUuGlbM6RGq4xNySoq47Xzqnu+KDWutTEKBEt0GVa4WHDBmC/Px8FBcXIz09Xd9+6623wmazJXRwpO4EguGgW479DrFbS9ck9PqDSLFZ6mJYhBBCGpnIJr6+IAXdpG75/f6ITHdp0K3FJZISR+oaQCh8PT7GfNmytCCFtzng8/kQCASqtBb06abE4wWXHu4Qzyc+0x0InxiJ54RHPAQ2HHQLZvh81cl0pwCIPac7zW4BUALBlgK32x13LKWdnGGtasm3Xl6uncAJB/oejweCEF2l6vF4YEoPN1KLsZQdAKSYOeR7QnqmWysvt1UypzvFbMJJAKJCSz1GqtarUVhYiP3792Pbtm04deoUACAnJwdNmzZN6OBI3QmEM92MEjuLbbWUvmF9fir3I4QQUjtCEUGNl1IlpI6p5eVqMGIMutXf40x0Q1tZLK6gWysvt6lNqxp6trs4oldDrDndphouGaZVy8Qqm64OPiIT7K3id+DKysu1AJa3plTY+KyswsJC8NYUMOFpn2XLy7Vjqbz7rKy8HABSw3XiFkcGTIIVHK8+VmXl5dWdp97QVSno3r17NwYNGoRmzZrhnHPOwdlnn42mTZti2LBh2LdvX22NkdQBrcsmU055uS3ijKsvQEsAEEIIqR0SSr/QBUIUdJO65ff7I8rLS78ma0G3jPgCOSn8Fbu80t1IWtCdmqGuudzQg26tuzhkSX9dgcj1sGsWdGul/bGC3OqIPHFS1eqbyO7lscrLS6cWOOB2x79EYmFhIazpTfX70Jb90o4lS3j+dXnzuitrpAaomW4AMDuceudyjild5qw8ava+dO15oor7aDx+/DgGDx6MvLw8LFy4EGvWrMHq1avxxBNP4NixYxg4cCBOnjxZpQf/8ssvMXbsWLRo0QIMw2DlypWGyydOnAiGYQw/WuM2kljB8BcbppymEzxvghxuslbVs3yEEEJIvOSIryaBUGLmZBISL2MjtYhMdzgAjz/oDjehMlW+FJQtPEfWkd4EQCMIurXu4oqx05bevbwaS3NFCoU/Q6xCYjKtJpaBEp5+6a1inwlDpjvmkmHq8aGVl8ersLAQtjT1JI1WWg6UnmjQmp6Vl+lWlwzT5nTHPqa1ZcPSm56Bbr36AlCPVYap+D2Q7lDvFyaaihop7qNx0aJFaNOmDbZs2QKLpfRFHDVqFG6//XZccMEFWLRoEebNmxf3g3s8HvTq1QuTJk3C5ZdfHvM6o0aNwuuvv67/3ZDnuCRTMCQBJoBF+U3SZEkEy5n0Nb1rgzcgQjBx8AQl2AUOwZAEm5nWBieEkMZCYUu/mtCKM6SuqZludQ5uZCM1LQCPN+hWmHCWkK886NYCL3uaOne2oQfdflGCCdFLevGmxJSXS+BgAmA1J7C8WQ4BLFetTLfVWn55uVWoeaZbKy0HIsrLraVzumPxer1I18rLy5nTnRrOdF934y3okjoBr+8MxZyXXlZGagpwDGCFioNuj8cDnufhcrngdDohiiLsdnul93+6ijvTvX79esyYMcMQcGusViumTZuGzz77rEoPfvHFF+ORRx7BZZddVu51zGYzsrOz9Z/I5m0kccRwLQ5bwfIKckj9oPH5a6e8PBiSsXaPC5Pf34+73j+Aye/vx9o9Lr30nRBCSCPAlZ5oFenjn9QxNdOtlsVGzuXVlo1S4vjqLEkSED55ZKlk/itQWhJsTVEbVTX0oNsnqsG2iTG+wbXgj+VMNaqqVMJzie0JTNow4Uy3P1i1dbCM5eXRJ2wiO9dXOeh2xgi6tRMXlkqC7ogu/eXN6bZw6v75cPU63HDTrQCAovzjlVYhZKalGMYQi9/vx4IFC9CsWTP9Z8GCBTWqcKjv4g66Dxw4gD59+pR7eb9+/XDgwIGEDCrSpk2b0LRpU3Tu3Bm333673riNJJa2LirHlB90K+Hycn8tdJP1BkR8vPMUVu0qgDeovsm9QRmrdhXg452n4K3F7DohhJB6JCLoDlWv3ysh1ebz+UrnunKR843V4FmJI9MdCARKAxq+8myrVl5uDneHbuhrdWv9EcuuPMVHBH/eGiR4FE59zVOsiZtTzCjad+Cqld+UlJSAN6sdyWOXl2uZ6RQUVyHoLigogM2plpen26Iz3abwY5ZXXu6LOHkQa063x+PBZ5+sBABwlhQINvXYPLBvD+bNm1fhGuBZGeH537wZJd7oINrj8WDevHmYM2cOXC4XAMDlcmHOnDmV3vfpLO66C7fbjdTU1HIvdzgcVeq6F49Ro0bh8ssvR9u2bbF//378+9//xsUXX4ytW7eC42KfOQwEAggESs+OFRcXAwBEUYQoUuBWHm1ONwtFf53Kvl5a0F3i9SX8tRRMHNb/6op52fpfXfjbmZmNav+Vtw9I3aF9kHy0D5IvGfuANZV+UZbA0v4HvRfqksfjAZemBt2MIkF7ybUKXIXlKt0Pbrdb74BuNlV+DPPhhIcpXBKcn5/foPe1FrcKnGJ8noqi/jAMSnyBqOM+7teEU094WM2mhL2OjCJDgdpMuCr36Xa7wYf3qwly1G21bD9r4lF4yh33fRcWFqJZuLzcITD67fT7C584Ki4ujnmfvkBExl4OQRSNJ5N4nseH7y7DgLsHwexI1+eIB73FWPzqYtx///3ljtWZYtV//+tEPtq1bBZ134sXL45528WLK77v+ijesVZpsoPb7Y5ZXg6oO1VREtvw5JprrtF/79mzJ84880y0b98emzZtwoUXXhjzNvPmzcPs2bOjtq9bt47WEa9A3qkC8JmAx12M9evXA4D+r0YOqcsPbPtxB5Siowl77OzsbHQ5s4+e4S7LG5ThCUrY+/OPOH78eMIe93RQdh+Qukf7IPloHyRfXe4Dlj9D/z0oAWvWrKmzx67v6L1Q+3bv3g1u8HAAwLdfb8FeTk3kHPvzD8DaCbLCVHpMFhQU6M3Yjhw6iDXHv6/w+kfFFAAtEVLUhNL27dsb9HHvCTcj8xa7op6nIrUDYxKw77f9UZfFe/wzpkwAwP59v2CNPz8BIwbEQBo4ixNH/jpapX1TUOjSs85fbfocZtaYKVfPM3QCw7DYsWtPXPetKAoKCgr08vKDe3/Gmv1qltwnmwB0AGNSj79vv/0WWVlZUfdR7FHnzDNyCJ+uXWu4zOFwoGvXrsg/egSAumSYEJ76EPQUweVy4dSpU/jll1/KLYkX/a3AW+xYs+5/aJftjLpvLcNdVjz3Xd94vd64rhd30K0oCjp16lTh5ZV1s6updu3aoUmTJvj999/LDbpnzpyJe+65R/+7uLgYrVq1wogRIyrM1Dd2a377ED4A6WmpGD58ONavX4/hw4eD50vL/P773BcAgI6du2D0iHMT+vgMy8EmsDEDb5vAwi5wFU5vaGhEUYy5D0jdoX2QfLQPki8Z+2D5f0uXIDUJVoweHfv/+8aE3gt15/PPP8eJcJZw2JBByLCowV7HDu3xx18Aw/EYPXp0hfdx4MABvLJFTU5069IRo7udXeH19530Yevnx2B1OAEATqez0sc4nT254msAwBnNsjB69GDDZR/8dw8UAM2an6G/BlU9/t95/WcAwOALzsc5PTsmZMwfv/4dggDSmzSNe98oiqIvHQcAl1w8Iua67R/+9xcoJjOyWrSK675LSkogSRJs4aB76Pn90TFLPWZ9oow1Kw6BYTlwvBk5OTkx73PKrMcAABaei3m5oij6CQLB5oA1Ve2sH/AUwel0IjMzEwMHDix3jMte/ha8xY4OXbpj1Pm9ou7b6XTGDLzjue/6RquqrkzcQffGjRurPZhE+fPPP3Hq1Ck0b9683OuYzeaYHc55nqf/qCqgLW0hmFj9dYp6zWS1FEWU5IS/lt6AiOGdnFi1K3oe0/BOzkbbxZyO2+SjfZB8tA+Sr672gS8QBGuKeByTQPs+Ar0Xal8gENCz1FYzD+3ltpp5ADLAcpXuA1mW9fuw8KZKr++wqsGNEp5aUVRU1KD3swQ1o59ijX5/s5AgAQhKStRl8Rz/siyDCy+FlZWRlrDX0cQqCEJdAzze+/R6veDCHcJZRj2GYiUoOSWEEMzwifF9v9am81rDc7qbOMz67ThTadUxb7HD5/PFvM+QHP7ezzExL/d4PLjt5ok4IEtgWQ6OZq0BAEFPMXJzcyvtNK4E1Ux6sdcfdf8ejwe5ubmYM2dO1O3iue/6Jt7jIe6ge/DgwZVfqYpKSkrw+++/638fPHgQO3bsQEZGBjIyMjB79mxcccUVyM7Oxv79+zF9+nR06NABI0eOTPhYGjspnGA2xTgDp5PVK2nzvxPJZuYxtqdaDrT+Vxe8QRk2gcXwTk6M7ZkZs8kDIYSQhqWw2NgbRiuRJKSu+PwBvXu52khNDWLMgglAEAxnqrS6U112TD124/n+ojXTkhn1y3tD716usOrztFuigxUOMiQAAbF63zXdXh9YVg3qnY7EBW5ajzet8XBcY3G79Xn6Vr789a1NjIQQAG8gvudcWFgI3ubQ19mO7F7OMgzMJgaBkAKTxRazKZmiKBDl8HFdzhJgdrsd/545E7f93z7ILAdHdg4AYNSFQzBz8vhypxvrJLURXlFJdCM1u92OGTNmQJZlPPfcc/qSYbm5uZg5c2bl932aijvojjd1XpUS7h9++AFDhw7V/9bKwidMmIAXX3wRP//8M9544w24XC60aNECI0aMwNy5c2mt7lqgfYbwbPn/iTCK+mEQEKu2XEK8BBOLXi3MuKRnexT7JTitHMSQRAE3IYQ0EsUlxrlxXCXrvBKSaP6ACO1bpsCxQHgtabULeRAsxyMUClWY3fL7/XojtVjlxGVZw8uKSWDBcKYGHXRLkgQm/NrE6i7OhU9yBKsQ3EYqLC4NMjNSU6p1H7FoX0WrGnTz+nJh5X+XFRgFfgC+OJfILSws1EvLbQIb9T3ZYmIRCEngLfaYQXcgEACrN/orf0k7i8WCZukpOOYOwZ6RDQC4fNzouIJiVlbn7Rd7Yy/9tn79evTt2xd//vkn8vLy0KxZM8iy3GADbqAKQbfT6azwrJ521k+S4j8zNWTIkAqbr1V13W9SfRIYcABMXPkfCowSznRX8+xjPJ5a9hnYlmchzcphZAs/BvftWmuPRQghpH4pLvEZ/uYEC2RZBsvSyVdSN/xiSA+6eY6B9rXWLKhBNmviEQwG4wi6zfp9VMYaEZDx1pQGvWSYx+PRA9FUW3SApcWPQSm+ALSsIrd64k4K+sHHsVxbvLSklKTE37/KsFxYBQkkswmAUrqUWmXUNbrDy4VZo5+jhWdR5Jdgstpjrizl9Xr1LHll68in2Xgcc5cm25z2+BKfJqi3cftjd/b+/PPPsXjxYvTq1QuSJCE7O7vBN4o8reZ0k9ojgwWHisugGGjl5dX7IIxHSAYCAQnugIRDTD4SP6mBEEJIfeX2+gEIkIJ+cIIFLGeC1x9ESowv54TUhkD4Ow4DGSxbGnRbwkE3xwsIBoMVzjk1lJdXkMzQmFgGAscgKCkQrCkoLExMx+36yOPx6EtoOWwxejCxajKuuvmdQrcHgAlSMLqsuSZ4jgEUIFSFoDuyvLyiTLfVxAIiIMrxnVyM7FzuLCfoBlBuptvj8ZQG3RWMCwAcZmNQbiu7uHo5eEaBDMAbiF0du23bNgDA5Zdfjoceegi//vorgsEgBCFxa6vXN0md003qD0VvpFb+GS8G2gdh7WW6I+P5PFdi130nhBBSvxV71KBb9BbppeUFRW4Kukmd0eYSa2XOGjOvfj9iOTXTXZGqZroBwCqwCPok8NYUlOT9Cb/f3yBLbUtKSmCylM5zLsvEMoAMiNXM77g9fgApkMUEB90mBhCrlumOLC+P9Vw1NkENukOoOOusKSwshC28RrfTFiPoNpUG3eVmuoXSdeQr4rAYx2Tn4xujmQN8AHyh6IpmSZKwfft2AMCVV16JRYsWweVyYffu3TjrrLPiuv/TUdz1WsXFxXH9kNOTEj4UKsp0s+FMt1iLme7IqTKFntjzQAghhDRMHr/6ua+Ifkii+rvLHZ2pIaS2iOGyZo4xBgt8OGPN8XEG3eGgRogz6LaFgxmzXe2N1FDndUeWl8fKsmqvV4xYLS7FXnWKihKqeB9VlVaxIKN6QXdFGWWtoZzExtcFWy0vr36mO7KremWVGClm4/3bzfGFjtpJhliJ7r1798Lr9SIlJQWdO3dG3759Aai9vhqyuINup9OJ9PT0cn+0y8npSWHUQ8FcwfwXTs9012LQHXFIFvtqp2EbIYSQ+snjD39RlkKQAuqXZ5fbW8EtCEksbQpd2RyEKTynlzUJcWa61TLZuDPd4SDF2URtWNVQg+6SkhLwlvLnOWvJH0mpXh+HEp/2GRJ7LnF1aZUOMhNfphcIl5eHg+6KMsqOcEM5hY2vtLqyOd3asRTPnG6zqeLjMzWivJyBEvdJJHv4dkE5+vpaaflZZ50FjuP0oFvb3lBVa063oigYPXo0/vOf/+CMM86olYGRuqWEl1eoKOgOV/xUqXNjVUnhueUA4BFr73EIIYTUP1rQzSghyCE1013s8VV0E0ISSitrLruaixYzaY3UKuL3+/Xy3XjmdAOlgVJapprBbKhBt8fjgcmSBiB29lcLTquSUY6kBd2MktjEjUULuuPPV6qZbmvl5eVpdgsAP8CbK12ODgiXl7ePo7zcbIO73Dnd8R2fKRHl5QIjVTq20tuFs/dM9Pi0jHa/fv0M/zb0THe153RzHIdzzz0X7dq1S/igSBKEz9yp61DGxoWD7pBce8GwEvFh5o+zoQQhhJCGwRcIASaAVSRI4fJQdZ43IXVDm0FXNkNdmunmEQhUPP3N7/eDs4eXDKskk6jRGlSlZjQB0HCDbjXT3QJAOUG3nlGuXudxb0AEePUzJJEs4e/HWpIqHoby8goy3c4UKwA/eEsKvF5vhU36gLLl5dHj0dbeNlVQXm4Sqp7p5pn4X9NUmxnwxy6Z1zLaWoZb+3fnzp0NupkaRTVExaofJuYKGiRo///UZtAtR3yYhZiG+aYjhBASm19vYiUDkhp06+WihNQBLeguW0bLhYNurpbKy7UA1J6WCQANdtkwt8ervzbWGIGoNRzcykz1QhRfUP0MMeH/2fvzMLnO8s4b/5619q03tXbJWizLkncgBoNt8IIX2diQkAwzwzBJSCZvogB54wv9WPLGM0PmZyYsGpYQsjIkL8kQlgQwWHgDYgJIeBOSLGvfeu+uvers7x/P85yq6j5VdU7t3Tqf6/JldXVVnafP+nyf+76/d3dEN5svuyGfz0NkLcMaRLqJ6AbkcBS5XK7p97pNL5capJfbngMejNRkzn15aTJKTQCF2rm8ruu2iRoT25s3b0YqlYKqqjh8+LDrbSw3fNHtAwDgBHLRspYYTrDrsos+akDVyqYlrjzXTh8fHx+f+lSLbs4k6aF2nbePTw8w6hjLMvHsKr1cUcGLZD7lNr2cGamFYkkAKzfSXZ254iREK+LWnanYYtg9ROQ6GyAKsfmxB9FdnV7e0EiNRpOlkDvRnSkqdvlCool7ealUgrGo6xBJLyeLAXKTSHcsICAWELAuKSMZdJ/yn4yS7+ek2rn8sWPHUCqVEI1GsX37dvIejsOb3/xmACs7xbytrvFu8/p9Bh8mukOB+jc5tsrbRR81QKicknww1sUN+fj4+PgMGkqViRVvkoliQemsIZLP5UWhUIAkSUin00gmk9A0rW76rmVZdkuoxZl/Au9edJfUyjnrpWUYAASbuJcXFQ2yKKCgGojIAlTdQLjB3G3QyJUUIArA0Ox9Wg35WxSAF13VNy+G3UOatJ/2TDgoAyiBEyTX48rlcpDGm9d0227joZhjZHoxZZMc76BgOS7qVLuXAySyHYtV5tTESM1dy7CQzOMTD1+BbFlHPLgBumnBzdk2nIgCsCBQcc9gqeUPPfQQNAMQLAtFzcDf/b9fwX868H387Cc/dvHtyxPXovvhhx+u+blcLuO3f/u3l9y4vva1r3VmZD49heOZ6K6f0i3SB0cXs8vtcQCAFIlDNwyIgvv6GR8fHx+f5YtKjTplAVCo6C6pficLn9Yol8t47LHHsH//flt07927F/v27XPsga2qKkSZ9NdeLLqlqvRyRWkS6a46Z92K7jATSmEijpxEt6qbePxIGgeOp1FUTYRlHnduT2LP7uGmacKDQqFMFyRM58U0Im4V8FIAmqZ5ru9VaFBX7vDUkcyPS+ClAHRdhyQ1l565XA7ipuY13ezYu0kvtywLKnU5TwSd/8hgVU03QBaelohueRUAINAgE0MzTDx+ZAFPHFuwz7e7dqSwZ9eQ3UKvHqOpOIAMpFAUmq5DEsn8/uDBg9ixYwc++/kv4NtH5mu++45t1+P9f3BLw+9dzrgW3YlEoubnf//v/33HB+PTPziaBhUK1r+5sQdOF83LwYmV7fO8gEvT89iwerR7G/Tx8fHxGRg0e8LMQdItmKjUaPr4eKFQKOCxxx7Do48+ar+WTqftnx955JElgaNSqQRBIqI7uKibS3VUtqw2zr5gopuzDPAuI7V2m6cAGdNi0V1UNDx+JI1vHq7UehdV0/75np3JZRHxZqJbMJ0X0yJ0HirKQZTLZc+im7nPN0rnboVIiI0rAEVRXIvuMGsZ1mA8IbpCIAbCyGQvNfzOfD6PYJyY7Q1FnMfAauVZz/fF0fNisWhHoOullyuaiW8fmcc3XpqrfE417Z/v2znU8G8aG0oAyAAAZheyWD06BIBEuv/nJz6FJ17N45sv157L//yLNPnuXUG7tn8l4fov+uu//utujsOnz/BU7IYbRrrba+PgBib+Gecm53zR7ePj43OZwCbMsshD0gwoqKSL+vh4QZIk7N+/3/F3+/fvx4c+9KElr5dKJdtgakmku+rHZqJboScyZ7k/d5nw4uj2F4tuWRRw4Hja8bMHjqfx4DXDrrfVT4oq821wXkwLU9EtyEGUSiXE43FP369bHARUWnx1CrYYIEgBlMtlRKPRpp/J5XJIUNHtZBrHqE49X8gVG37nwsICQiniXD4UDTi+hy04yKFKpLuaQqEAcaixkZrAc3jimHOJwxPHFvDArsbnWywSgqGpECQZM1R067qOCxcu4M2334b3f/Oc4+e+/2oWD16zMuf9npaBzpw5gy9+8Yv47Gc/i1/84hfdGpNPj9ENAwIVu8EGoltus3eiG9gKs6GR1K3JuXTXtuXj4+PjM1gwE6ugyNvpoX6g26cV0uk00ul03d9lMpklr1e7ji8WI0JVxFppIrpVvcqF3yUsxRgCmQctFt0F1UBRdf6+omqisEwulDJdkBDrOGGzdGdRJuLWKwaoIV2HI6UBkXyvIAVdj8utkZrIc7Boi8RMkxaJCwsLCNN2YU7O5UAlqi4FnUU3qelu3DKsqDU+34pa8/NNL5PtzqZJpP3o0aNIJBJIF8oNvzu3QjtWuD4jn376adx///0olUrkg6KIv/qrv/LTzFcApSpn2EjIedUMgF2/YXbJ9F43DFt0q7k5hIZWY2qhuaGEj4+Pj8/KwAAPAWTCXFQN5FCJfvv4eCGZTCKZTDoK72QyuaRsEqiNdC+uxeZ5DpZpgOMFKFpjnwFVNyEB4OG+Ho9FOw3qbbO4ZVhEFhCWeUexEpZ5RDpdxNwlFMNCEIBcZyrJ+poLcsjWHF4wObIfIh1Ota+My/1igNuWYQAAXQFEGZlC4x7w1e3Ckg7O5UAlqs627ZhezjI66tRmh6XG51vYRSaBqZUApDCfJds/dOgQJicnkYqGG5/LgeVxLnvFtXr6yEc+gjvvvBMXL17E3NwcfvM3fxOPPPJIN8fm0yMKpcrNIxKq36ZLpheY1SXRXT0OTskCAOZy3m+4Pj4+Pj7LEzZhDgVEOz1Ut/xOKT7e0TQNe/fudfzd3r17oWlLo9Uk0k3Tbp3ECDX3U5pE+ZghoJe2Vcy9XLfIeb840q3qBu7cnnT87J3bk3Z0fdBRTXI911sjYCLQi7itxqKtxiINPIpagQWeWHq5G/KFSkS5mejmqbFcvkmLxPn5eYSaRLrZtniZbNsxvZyOq15Nt2FauGtHyvF3d+1IwXDjqqyRBYR0nuyvgwcPYnZ2Fq+ePFn3u+/YFse//fRnzb97GeI60n348GE899xzWL16NQDg4x//OL7whS9gbm4Ow8PLo47Ex5lCqbKqFgrKsEznsIIsCoAGWFx3RHc2XxHYAYvcdNKFlZli4uPj4+OzFIsj05JwQEI4QKKJOlZm1MOnu0QiEezbtw8AXLuXEyM1ItYcXcdt0d040q1R0S3w7kU3ixwyUbpYdIcDEvbsJvPt5exertG/r54IZRFlUQq2FOmGQER3LFw/c7MVZMFbpNuyLJSr/Cga1XQDgAAdOoCC0vjcWlhYQDi1CQCQbCK6OZ6HIAcdI91DMksvdx5XQOKxZxcxP2vFvRwAOLM2ZZ61Czt3+iTuumMbLAs48Ertd9+6QcLtt/42fvrTnzpeo8sZ16I7m81iZGTE/jkcDiMUCiGTyfiie5lTpq0vDF2DKAjQ+iS6WaTbNA3EJAsFAPk6NR8+Pj4+PisQgUxLoqEAiooGGJXot4+PV4LBIH75l38ZjzzyCGZmZjA2NgbLsupO5svlMkSZRbodRLdFRLfaJNLNtJbk0Ie6Hiy93AJp9aSUCyiVSgiFQvZ7nn36SVy/81rcv3sLsmUD8aAAwzCWjeAGKr4NoTrpyba4DQRRKnkvMeREIra7JrpFGcXSUj+AxZAUbnLsRL7SdrceEkzoAEpN6mnmFxYQSr0GQP308uo6bSkYWVrTXSpjtI53Qc2YBB737RzCA7uGUdQMBEUOhmm5EtwAIFhkASFXUqHrOl544QUAwNC6rfhv3z2HX75hBP/rHVuRKemIBQUsFHXcf/etePnll/Hyyy/jNa95javtLBc8uQx873vfq6mBMU0TTz75JA4fPmy/9sADD3RudD49oUhFt6k3jioHZBEoAejSBChXpKJbVRALCCgAKOl+WqGPj4/PZYNAJoKRYADRoAYUAItfea1jfHrHhz/8Yfzrv/4rxsfHEQ6H8ZOf/KTue0sl0ocZcBYjzI28WSq3bpEItxcDbVnkwHOAaQHBSBz5coHU71aJ7o985COY4kdx7/s+iURIwOyFU/jz//IW9xsZAAw7m8X5umb7XZSCKJdnPX8/O36JaLjFETpTLTSrM0TrkcvlbCOzetHkamTBQglAWWucHTGXKUBYRf7GZMj5BOM5DkGRQ1m3IAUjSyLd5apFo0CTxQBmyhYyLXz/wAHccccdzf4UG4k61BcUHUeOHEG5XEY8HsdpLYlL2TSefTWDG9fH8PKlAr76wixWxSVbZx46dOjyFt3vfve7l7z2W7/1W/a/OY6DYSyPmhKfCsRITYClN3bjtNtn8N0R3eQmJsLUFaSiAUwCUL2doj4+Pj4+yxieRl+i4QBKigoUAPCD33vYZ3A5dOgQZmdnMTs7i1TKuY6UUSqVIMpE0Dill3OWAQvEKK0RhkXdo11GBAEyhw5JPAqqieFVa5Cfm8DCwgLWrFkDAHjxxRfxk5/8BNe+7XeQUwzkFAOl0vLLBqzUXDtf19Vp3MWi9/RyFl1ORENN3umN6vOhWPYmukMueoYHBQ4ZAEqTQ7pQJNFj3lAanl8BiUdZNyAGw0si3SWVpbBbzmUUdVBVbyWfsgCoIG3iWGr5Da95LX50KgcAuJ16FFy/Poov/XQKJ2YM7H79W/C9733Pfv9KwvXdwDTNpv/5gnt5UqKRbstsXEcSlOkNsktRB7ZyaOoaRuJkhdIUOpse5OPj4+MzuPB2amgQsTBJ8+Uk/zng0xpTU1O4cOGC/fPCwoKjgRqDGKk1EN3UjVxrEulmKdSOZmwNYCnXqbHV9ngZf/7nfw4A2HnTG+zX+EBno7m9wKLZLLE6RmfV+6zQxFRsMYqq2TX5yVikxRE6I/CcPU9mGaKNyOVyEF20C2MwIz3NavzenErOQdlqPIagWGkbtlh0s0UjiSOLPd0iRNPcy7qFgwcPAgCuvv3tyCsGhsIirllD9k8yJOKG9aTveWT7LQBgv38l4bkIJJvN1v3diRMn2hqMT38oKeQBZBmNRXdAImKbE7oU6aY3V8vQMJaKkW0FOnvT9PHx8fEZTEzTtNvYJGNhO1LFhLiPj1dYtGz79u22uFjciquaRi3DAICn6eWa0TgcaYJ8NuBCbFUTpsIrOTxWM9ZCoYAvf/nLAIC1W3fZ7xeDUU/f328sy7IX0eIR5+ta9hhRrmYhW0mjHkrEWhhhY1hGaLHcODMUIG26JLftwgBEaF/xZsaRRVp2GRYaL/yw6LrokF7O3PW9lD+0AhuDanL2tShuvAkAcOvWBIQqz4PbtiUBAJfMIQhyEIcPH27JvX6Q8Sy677vvPsed8Morr+C2227rxJh8ekxZdSe6QwGyesgJ3Yl0M/EPU8faMZICJobiXdmWj4+Pj89gUVY08PT5Eo+G7ZpMIdDZNFGfywc20X/ta1+LoSHixDwzM1P3/dWRbqcoNU9bgDF38nqYVDgFRG+qhomUWIr0YWaR7q985SvIZrPYsmULNLESjBDkIDL5wtIvGlDK5bKdch2POF/XPM/Z89Gi0lzcVsP6QZuGjnCHW4bRLwZQmTc3IpfLQaR/a9BFTXc0RLJJzSblNGXQTAG5cYSaCX0ptDTSzRzkvaSWt0KUlhCoJocXXngB8dWbMW/FwHFEdFdz9eowxqISygaw+453Qtd1vPTSS10dX6/xLLqj0Sgefvhh6HpFoB09ehS33XYb3v72t3d0cD69ocxqO5qklwdoejnXpfo6luYOQ8f6VcQRXwyEkM56d6/08fHx8VleVEepktEIUnEyYRWkALQmLZp8fJxgKao33XST3YFndra+ORdpGVY/vZyn6eV6s0g37fISqNeMug4s0h1NkTkQE91f+MIXAAC/8d7ftmt6GRMzta3FBpl8Pg8pSBbTkg1qrjk6Hy15FN2ZXBEAYCgl8HznHd3ZuMpq83LaXC4HKeS+pjseImLaalJWaYhkv6XCjefi1enl1ZFuy7Jsd303iwHtEKN/U76kQlEU7Lr7PwAArlsbxVCkdvw8x+HWbUSI77jj1wBgxdV1e97bX/va15DJZPCud70LlmXh8OHDuO222/Brv/Zr+PSnP92NMfp0Gbv1hdX4JhIMkAuE71Kkm4l/zjIwmorD0IgIPzfp3b3Sx8fHx2d5kSmQCbOpawiHAkjFK6mz8/7iq08LsEn7jTfeiNFREj1uGumWWaR7qegWWKTbrB/ptizL9r4JSt7mS0ychWMk229hYQHPP/88fvazn0GSJNz/jnfBAhAUOagF0rZqcq55+6pBoVAoQKRlg/XcywGAo62mKoZf7sjkifGaoXlLS3cLG1fZxSJgtZGam/TyeIR5WDTpTR0g98WRWGNxbke6F9V0k3OcbCPQ5fxyu4RACoKXZGx6Pelw9ebtCcf3v2lLAgIHSCObkFp/pS+6Q6EQvv3tb+OVV17Br/zKr+Atb3kL/uN//I/4xCc+0Y3x+fQAhd48OLOx6GapOrzYnUh3tejmeR5akfgHXJhePqu4Pj4+Pj6tkV00YY6EAjDpc2nBF90+HpmcnMTFixfBcRyuu+46O9LdSHRXR7qd08vJ/40GoltVVdvMKxTwNl9iRmrBKCmtW1hYsA3UHn74YRgBUqc8GpNhKkRIzaZznrbRT/L5vB39bRRl5WgQqNykH/pisrT1rKV3S3STELHiYlxeW4alYiSCLcghsnDjgGVZECNJAMB4qrHnUb2a7mKxCJGW7IQ8ZmJ4JRUjWQ1yKIaNr7kbQjCK4YiI3audx54IibhxAznHt93+KyvOTM2V6M5mszX/8TyPf/iHf8BPfvITvP3tb8dHPvIR+3c+yw9bdFuN06VYTTcvStC74FSvUDdQHmQclkJuEpPz/nnl4+Pjs9LJ0QmzSUU3z/MwFCLE0zRt1MfHLSxKdtVVVyEajdqR7qbp5Q2M1NhLjWq6qyOJIdlbpJull0thIjzOnz+Pv/u7vwNAWvRO50i69VhUgqWR62Uuu3yujUKhADHY3FyM9yBuq2H3EOjeXM/dwuanbkW36KFl2BAtp5FCsSU12NXfGUoQk701I87RYkbATi+vbRlWKBTstmpePQe8Yv9N4Ri23f5OAKSWm+fr15LfTlPMN7/+ARx79RRKJe9t4wYVV6I7mUwilUrV/Ldz505cuHABf/Znf4ZUKmW/x2f5wVoHcGgsusPBSipLyWMbBzcoGtm+QMfB6+TmOZtZPg8UHx8fH5/WsEV31YTZpMIik/efAz7eYFGyG2+8EQBcRbqJkRoJMEjiUmEgUrHQyEeNfAcV3R4j3UyIioEIRkZGcObMGQQCAWzfvh233XYbpvNUdMck8Aa5Thbyy8fhOZPLQxDJ/m0kutk8UGnSD30xrAsOZ3qrBXcLE93N+rQDtTXdbmqnUzEmUKPI5ZyzF+bn57FqfBzrkjJWJxtHuoNVke5q0U0i3TS93OEc7yTDiShiAQFb16/C1mteBw7ArVuTDT9z1TgxVJPDMbz+wXfj/PnzXR1jL3G1BPf00093exw+fUTVDUCo9J+sR7XoLpYVxOo4T7YKi3SzmikJ5Ka5nB4oPj4+Pj6tkS/RlFCjWnST1+wIlk/fKRQKkCQJ6XQayWQSmqYhEnHX3rOdz3qlup4bgMtId9kWhZJDejnTTo181GqEu8c+3WEqlIbGxnHmzBlMT09jbGwMZ8+eBcdxdqR7NCpBpHOkbLE7Ud1ukKmazzUSomwe6EbcVlMsawAP8E08ilpF4CxoqLTcakQ+n4cYWAfAbcswEnWWQlFks1msXr16yXsSw6vwqV8JIFvWkQgJKCoawnUWdkKSs5FasViESCPdcpeN1DatHccnNkvIlnXEgyLOzJWQCjeWnjzHYc+uIcSCAq7+tU+jrBrQDROqbtT9W5cLrkT3rbfe2u1x+PQRVTeBQGUFrx6hqvYLXnsnuoGla7H0raBgQQWQLfuutT4+Pj4rnSLLoDKqolRUgGeL3anR9PFGuVzGY489hv3799vCee/evdi3bx+CwcYGUO18thWY6L7pJtIX2JWRmmaAjcTRSI1GulkfbsfvqEovd/qORrAa29PnJ7Bu3euW7KeZPLkexmIyArwFHUBeWT5zpGxVNovQIMWYacFmrdkWU1B0IATw6I7oFu3yApeR7tXujdSYSOZ5wdE4UtVNPHUijwPHL6ComgjLPO7cnsSe3cOO4tl2Lw85Rbqp6O5iyzBVN/HEK1kcOJ6uGe/m4VBTsf9Lm+P41stz+PPnJl39rcsFzzbU3/3udxGNRnHLLbcAAD772c/ii1/8Inbu3InPfvazfor5MoTdPIQmkW5REGDqGnhRqrT36ug4aISbXk9RmcM8gGJ3soR8fHx8fAaIQpnc7LmqKBVHBXihtHyieSuVQqGAxx57DI8++qj9Wjqdtn9+5JFH6kat2/lsK0xMTODSpUvgeR7XXXcdALhqGVZWNVt0O9V0SwIHGG7Sy+u3HWsESxm/ODWLdDoNoLKfLADTV74LAKnpDolADkBR8yZM+0muqAABAE2Mztg8UPMW6CbGayFAbBJEahXRw7hyuRykK9yLblnkYJkGOF7AfKa2pruoaHj8SBrfPDxfeU017Z/v2ZlcEgUOLop0W5YFjuNoTTdLL++OgG1lvJ347KDjeW//4R/+oW2Y9vLLL+MDH/gA7r33Xpw+fRof+MAHOj5An+6jm7UR5kaYBm2X4LF3ohvYTYzdAxK0v59iLd9VLR8fHx8fd7D2QNWpoUyAF7rwzPHxhiRJ2L9/v+Pv9u/fD0mqPxFu57OtUG2ixsS8m0i3UuVxw3MONd00XdxsMH1WFAWiXN8BvRFROu+RQtElv/vCX38ZqmGBAzASkRChLbcUo7t1uZ0kz2qujcbXM1us8C66yQdcaNyWYN/rJuu9xkjNhbjlOA6GQrwrMoVa8zBZFHDgeNrxcweOpyE7GKJV13SbpglFIQsdNZHuLonuVsbbic8OOp739unTp7Fz504AwD/90z9hz549+NjHPobPfvazePzxxzs+QJ/uo7O0bhdng6mTG2WxC0Zq7CbGbgJDMbISp3NyvY/4+Pj4+KwQWNtIoSo1lP276IvuvpNOp+3oq9PvMpn6/aLb+WwrLDZRA2qN1Oq1ZGKu1PUy/yRbdDdJL5fqO6A3Qi+TtGIn0W0FiKvzUESEKHCIh8hChYblI0JYNgtvNU6JZ2nPhuVt/7Fa6251wmLHU3eRXFDTMsztKgDNAEgXaj0sCqqBouqs9IuqiYK6NJ2+uqYbgF3XXS26A11KL29lvJ347KDjWXTLsoxikazEfP/738ddd90FABgaGvJbhi1TvES6LeoIqaidnwCxmytbGR5LkoeOJYU7vi0fHx8fn8GiTFdeRa4yo2VpouVlPNFaKSSTSSSTybq/SyTqtzBq57OtsNhEDahEujVNq+sOrRpLz8Fq2PzEalrTTdPLPbpDjw2R/SCHYkt/t+lK8v8oCUQkImQbBr98Um1L9DoWmhidsbRn76Kbfb47YpIdfzfjqhbdbiLdAMDR8oLcInO8iCzY7eQWE5Z524StmkBVTTcAu667UChAZJ4DXYp0tzLeTnx20PG8t2+55RZ84AMfwH/9r/8VP/3pT3HfffcBAI4fP45169Z1fIA+3UdflNbdCIuml3ejptugDzF2ExgfjgMAhODSFV8fHx8fn5UFaxtZPV9mz6WyRxdjn86jaRr27t3r+Lu9e/dC0+ovxrfz2VZgkW5mogYA4XAYoRCJ8NVLMWfeMvXmQxL9hcXVnzDV1nR7m2azRSZBDoAXa8X0m+97GABpFwYAQzEakBA7b0LXLUr2Nd5EdEtEWJmcN4GlUzHcrf7TbH7aqLyAUS263dR0AwBPA1v5cu31oOoG7tyedPzMnduTpAvRIoKLIt1MdBeLxao+3d1ZnGhlvJ347KDjWXR/5jOfgSiK+OpXv4rPf/7zWLt2LQDg8ccfx1vf+taOD9Cn+zBDELGBkyTDMsjJrmidd8tkN7EgvdmuXzUMAJDCcWhd2J6Pj4+Pz+DglBoaoP9W3ORz+nSVSCSCffv24cMf/rAdtU4mk/joRz+Kffv2NTRCi0Qi+OAHP9jSZ71y6dIlTE5O1pioMZq1DaPZ5XXnQ6ye1Gowfa4W3V7doUcSlSDDyDiZX7P9dNUNNwMg7cIAYJRmA/Ly8skGZNdxMw0atEW3N5mi0+MSDnRHdNuLAS5S+vOFop3x4FZ0i2AeFrVzXs6ycNdVKTy4e9iOAodlHg/uGsKe3cOOxmIsus5SyWvTy7sb6Q4HJOzZPYwHdw25Hm8nPjvoeHYv37BhA771rW8tef2Tn/xkRwbk03uY6Ha1ImtSI7UupJcz0c1uauvHRwBkwfE8LkzPYfPaVR3fpo+Pj4/PYMBMk6rrYGWBQwHeDZV8usPk5CRuvPFGXLhwATMzM7aIddPy66c//WnNZ1evXg1d1zveLoyllu/cuRPhcK0gHR0dxblz5+pGutl8qJ5Ylun8BHx90VWdXu5VdPM8h4DIQdEtHHzhMIJmEYlEApqm4ZM/mgNQiXSvGkoAUCCGojBNEzw/+KazqkmERzNNHJSJPLE4bzKFieGQ3B1hxlK2my0GmKaJ6ooYt6Jb4kwYqGQEML7w9acxGd6Od94wjM+8YwsKqoGILEDVjbrCmW2T4wWIcqgm0i0GyHXRrZpugAj6e3Ym8eA1w67G26nPDjJtjb5cLiObzdb857P8YLUprgw/TBbp7nx6h8WTm2uIrmIFZAlqgZxT5yfnOr49Hx8fH5/BgWWQV7exYREvzVw+Ds0rmR//+Md46KGH8MY3vhF79uzBli1bwDm4fDvx7LPP4qGHHsKmTZuwZ88evPrqqx2NcDOcTNQYzdqGsXNQqjO5tyPdDdKe20kvB4AwPed1CBgdHYUsy4hEIpjJkWDHGI10j48kAQCCFEA2X/S8nX7AruNgE/EUYqKb9yi66fujwe6IbrYY0GjRBaAp3EEibEXeXSYpUFmMKC+aYj93roxLWRV/9U9PQBR4JEISRIFvGPUNiJztPCCGInakuxctwxjhgOR6vJ387KDieW8XCgX87u/+LsbGxhCJRJBKpWr+81l+MBfOeg+ZGqj5hdqNdG/6ELNvagCMMhHdE3OddTb18fHx8RksjEUlRgAQornmRgPjKp/ewQTtzTffjNnZWUxNTeHFF1/09NnZ2VkcPnwYk5OTXRmjk4kao1nbMHYO1hMjAam56CqWFfACeZ/XSDcAhGhKbXW0U9VNLJTIvGs0RozURlNxu43rpdkFz9vpB7p9jTcR3UxgCR4TcgXyuUgo4HlsbnAbga8xUZPcp7oHRdYqrXLe/NtLxxFcswOWaeI37rmp3keXwHGc7ZouBSO1kW7bSM2/r/YSz6L7kUcewVNPPYXPf/7zCAQC+Iu/+Av88R//MdasWYMvfelL3RijT5dhad1uet9xFnkIdCPSzW6u4UBVizCN9CqcXsh3fns+Pj4+PgMDEzyhqoXXEO1FbCyjtkgrGSZob7rpJlvUstfcfpZFt+tFm9vBsixHEzVGo0i3ZVlLytwWw0Q31yACW6pqb+e1ZRhQafVUqspPnslr9u+iVJTzPA+9ROZGU8skMMGu43CgsWiNBMk8kBPkuu3dHBHI52Lh7ohut4sBrZioAZUFF82qnH9/832yqKVNHsO1V27yMNpKXbcUjCyq6SZR+OWerr3c8Ly3/+Vf/gWf+9zn8Pa3vx2iKOKNb3wjPvzhD+NjH/sY/u7v/q4bY/TpMpbtGu5edHfDPZCjK5ThYEV0iyZxSZ/LlTq+PR8fHx+fwYE5FVeL7ih9Hpge00x9Oo9pmvj5z38OgAhaJmqZyG3ExMQELl26BJ7nceuttwKoH21uh0uXLmFqago8z+Paa69d8vtGke7qWuy6ottFenGnRHexKtI9TUX3WEyqSec3FJJWPpNeHoEJi0Wim6R/s0i1IAehqu675fA0rT8RCbU4wsawoFCjRRdgkej2EE2OskVGGknPF8tIh9YDAG7bGvc83oBEti0Gw44tw7qdXu5Ti+e9PT8/jyuuuAIAEI/HMT8/D4C0EvvBD37Q2dH59ARWm+RmxYuj7SzULkS6OXGp6A7QthKZUueN23x8fHx8BgdWvxmumpCziBeWUS/ilcqrr76KXC6HUCiEq666ylOkm73nqquuwsaNGwF0R3Sz7Vx99dVLTNSASqS7ruiWmNu0s6hikU5elGAYzvOgskrL70zddb17NWFaUlGdXr64nttGJwGJ+WzB83b6gcWT6zlaNc9zgoluUQ6gXC67/n5BImIyHu2O6GbHnxMbjz+Xy0EMeY90R0NskZFs58+/8SzkaArlzAzes+dNnscbFJ3TywXqXt5NIzWfpXgW3VdccQVOnz4NANixYwf+8R//EQCJgLM2ED7LC9ZvMlDnIVMNT0W3ZnTeSpYXyU02WlWLE6bPl7ziW9f6+Pj4rGjoRJNNPAEgHqHO1k0muT7dhwnaa6+9FqIo2qL7yJEj9oS+HtXmZs3adrVDIxM1oHHLsFKpZIvuepHuoFwR3fUisArNBOSs1oITlfTypZHu0UWimzfIGNJ598K0n3B0/8abpH8z0yxBDqFUcpfpqBuGbRCWinXeoA+oLAbwUqDuogtAI900hduL6Lb3C50P/9sFBQAwbkwi0IIje0hySC8vKRDo/dRPL+8tnvf2e97zHts044Mf/CA++9nPIhgM4v3vfz/+8A//sOMD9OkBNE0qILsR3aS2RtW7IbrJTaDaACNGU21Khr8a5+Pj47OS4ezU06pnQJhMolnaqE//WFwrvWbNGqxevRqmaTY1U6uuBW8UbW6XRiZqQPP0cttgqk4EkIluQZTri26aCci3KbqLVRmF0zmyrbFY7eKTBBJVzy6TbECeRaIjjdvEsbRnL5HubL4IjrZNS8a7JLrpvUmQZCiKUvd9+Xweop1e7l5qJWmEnpOCeO6FVxBcfSVM08Bv3vualsbLBL8YqkS6y1VGyL6RWm/xXCT1/ve/3/73HXfcgWPHjuHQoUPYunUrrrnmmo4OzqdH0JS+eulUNW+lolvvcKRb03QIEjXAqKrFSUUDmACgeT9VfXx8fHyWESwKxoQ2ACRjYQCGPVn36R9OgvbGG2/Et771LRw6dAivf/3rXX323LlzADof6W5mogY0NlIrlUr24k69WuyAh0g3my95JUzNtMrV6eV55/RymbegA8iVu9BRpsNomgYhQOZ3qdjS1P9qWARWkIKuI90LVSn2yS5FuqNhlvYeRLlcdixhAGiku4X08iF6vxPkEP72yZeAsWugT76C3dseamm8QQcjtTI16ONgQXLZysynM3iOdH/pS1+qWd3ZuHEjHn74YezYscN3L1+u0Eh30EUPPHZ96mZrD5N6FEqVlcxoqDK5Gk2QG5op+BMuHx8fn5UMEzzV9ZjJKO11K4dgmn6ZUb9YbKLGeNObSJ1pIzO1S5cuYWJiAjzP47rrrsPq1auxa9cu6HpnheLU1BTe8IY3YOfOnY4magAwNjYGAMhkMktEM4l0k3OwXtqtZIvB+pFuVSfzIwGtna+sxVSRppdbllVJL4/VztPC1CirpHd2TtYNCoWCbS6WbCa66aKHGPAgunNEdOtKCaLQnW4HIbboIgUaRrp1Xcea9ZvIZzyI7lQ8jFhAwBXrxmCNbAUAvHlbsuXxBh1ahik0aCbyaMlzwKd1Wkovz2SWtibI5XJ4z3ve05FB+fQW5sIYdFEvwhZ/NaOzN/hsoXJTjUcrN+NVqRgAgAt0Z9XSx8fHZ9ApKhp0w0SmRP5fVJZHKqkXTNO06zHjVdlOLGLF8Tyy+WJfxjao9PK8OH78OPL5PK677jpsvfIq6IaFbFnH77//A/iXbz+OhYX6faJZlPu+++6DIAXx+je8Ed97+kd44vtPQtE6s5BSVDSMjI7hr778D3jxpZdtUz4AUDTTHm88kcK/fOe72LFjB+bm5mq+o7qmu16km2lxXqgf6WaeN616VNk13XTfpEsGNMMCzwHDkdp5WoSarinLoASv2tE71qSPtixUR7rdppeTeaShdq++nZ0XolQ/7b1QKOA973kPHnz4lwF4KzNYt2oEn3j4CvzBHRvxv951LT5w22q8Z88bWx6vnV4eDNuRbla14GEtwKdDeM7ZtSzLcWXkwoULSCQSHRmUT29hdXQhF5FugQc0AEaHI915elM1da3GLGLt2BBwVoUUScA0TfC8f5fw8fG5fFB1E48fSePA8TSKqomwzOPO7Uns2T28okxwimUVPM26SlQtvFaniS7kCkjGoz0f2yDS6/Pi0KFD2LFjB57+wQ/xnSMLeOLYgr3dO7Zdj7/50v9GsVh0TLc9ePAgduzYgS///Vfw7SPzNZ+9a0cKe3YNQRJaH3OjfcFxWLLNO7Zdjx/9+N8wOzWJ1atX299TKpXshZ96oluicxBelOpGOjXDQhCAyLc2T2K9mktUHU3nibgfjkgQF6UDx0MSLcEb/D722XwRPO1SE5Ibj5fVGnM8j4LLSDcJ3kiwtC6KbrFy/IsOiwHlchmPPfYY9u/fj6ve8Qi2vulh/ORff4iHr3kQwWDjjE1VN/HEscyS83jn6tbveTXu5ZMk0s3WuQJ+PXfPcS26r7/+enAcB47j8Ja3vAWiWPmoYRg4ffo03vrWt3ZlkD7dhd0E3US62bO80z5q+QK5eRl67crx+vFhABMQpADms3mMJL33KfTx8fFZjhQVDY8fSeObh+crr6mm/fM9O5O2y+9yZyFb6TOcqBLakiRCV0sQ5RAWsgVsXtuP0Q0W/TgvDh48iP/5iU/hyROlJdv951+kASTxxg2Go+g+dOgQ/ucnPoUDrxaWfPYbL5Fo8307hxBoIfTWaF+8dlMMPzuXt7dRGe8CgCTesG645rtIyzDq6lxnEYDNgRqllzNR0+raR5gZqdH0ctYubLFzOQAko0GgDBj84Lv7z2fzAIiAbGYuVr3/CyV3fbqzxTKAOCzDfV9vr8hVix75Yq3oLhQKeOyxx/Doo48CgB3V/+63/xnJ9BE88sgjiEScsza7dU0HF7mXm6Zpz99X0qLtcsH1Hn/b296GBx98EJZl4e6778aDDz5o//erv/qr+MIXvoAvf/nL3RyrT5fg7f7Yzd1h2Sprh7PLkS+RFWNTq71ZDsWjdqrQuYnOtxfx8fHxGVRkUcCB42nH3x04noYsDn50yy0sddzQNYQCtQLCUMgzIOOnlwPoz3lx4sQJvPn22+pu9/uvZpFKLF0UtywLp0+fbvjZJ44tQGjR0KnevogFBIzFZDxxzDnt/fuvZjE8lKp5jaSXN450i1QMNkovZ6KmVZOqxenl03VM1ABmvAW7xdQgk6FtzQy1DL7JvhF4wKIeDoVy/drpamxxbnSvzKL6vFg8LkmSsH//fvtnMUiOjV4uYP/+/ZCk+qK5W9d0Jb2c1HSXy2XbzC5Ypy2eT/dwHen+oz/6IwDApk2b8M53vrNpmoTP8sA0TbuGKRxsvlLaLdFdLCsAJFiLbpY8z0MrZiHIQVyaWcANV3V2uz4+Pj6DSkE17GjXYoqqiYJqIBFaGdEK5uthOtRjWjqZ3Gby7tJMVzq9Pi8Mw8D09DQW8uWG280UFYwmaqeVly5dAs/zSDf5bFEzEBe8dympty8SIQHZst5wm/mShqFYRXiUy2U7vbxeyzC7pluUoChZx/cYFvlsPeHeDJZ6XWSiO+dsogYAI8koMAPwgcbGZINAlkaGTRfp3xzHwTJUcHwQxbI7EZ0vE9HNm91zcud5DqahgRckFEq1ojudTiOdTts/s0i3Vi4gnU4jk8nYLesW061rOlSdXl4ooFgsQrRFt98VqNd4PoLvfve7fcG9gihXGa+4iXSzh0iHS7pRoDdLp7QgSyF1KJPzuc5u1MfHx2eAiciC3T5oMWGZt02UVgI5NiHXl0a1LFp2lC+5i3itdHp9Xhw/fhynTp1CKhpquN1ocKkoPHToECYnJ5GKNf5suMWoW719kSkZiAfFxvspWCs6SKSbBB/q1ZhX11SX6kS6mehuNX2XpZeXNROmZWGG1nSvii0NjKwaItkFYig+8O7+7BqHwzXuiEHEc1Fxly5eUsj7ebTWH90tlk7mzYuNC5PJJJLJpP1ztehOJpMNfa+6dU3b6eUhkl5eKBQgyqGa3/n0Dn+PX+YUq4xAIk3cJIFKapWBzhowlNjNy1i6Qskb5EY9m/VTC318fC4fVN3AnduTjr+7c3sSqt7dyWUvsUW35jDBNnzRXU2vz4uDBw9idnYWh55/HnftSDm+545tcTz51NO2Q/Lizx499krdz961I9WyOWu9fZFTDEzn1IbjPXrslZrXqo3U6ka6q14v13GLN+jUOtCi6A5Via+yZlbahTmkl68eIX+fIEpI5wpLfj9I5EtsnudORHMmE93uItdFlbyv1VZtbmEZmaVy7d+haRr27t1r/8z6dOulAvbu3QtNqx+x79Y1vTi9vFgsNj3HfbqHL7ovc4qlatHdPIOBrdyaHT51bNHtkBYkgby2kPcnXD4+PpcP4YCEPbuH8eCuITsKEpZ5PLhrCHt2D68YEzUAyNv1mEsn5GzynXdpqLTS6fV5wVp+/eDpJ/HWq1J4cPdwzXbfds0w3rIljD/8wPvwwgsvOH726C9ext07ln72wV1D2LOrNRM1oPG+WBWTsWfXEN52zdJt3r45iC/99V/WfBcxUqMtw+o4O1fXnpdVZzFocSQqKbcYvZd4DizQni7pyJSI4HKq6R5OxmDSYMXETP22bYMAy2h020KLs8jfpWjuRHeZpuNLXJd7ltP7UUmtFdGRSAT79u3DRz/6USSTSYg00v0b7/mP2LdvX10TNaB717TtXh4IQ1EUZLNZiAEqun0jtZ7jJ/Rf5hSpEYRp6JBc1HdIAnmIWB2OdLOHF+dwMw4JFhQAuXL36nR8fHx8BhFZ5HHPziTu3z2MbNlAIihAN4wVN2EqsqiRw8KrQNNFi3VEzuWILPK4Y3uscl6EBOh6d84LJpzXrt+AP33qAu69egj/6x1bkCsbiAQEXEgr+MiH/hDHjh3DwYMHccsttwAgJmoHDx4EAKzasAX/7Xvn8I7rR/Dpt29BtmwgKll49cRJSIJznatbZJHHdWsCuH/3FhQUA/GgALVqX9y3kwj7dIn8bmJiAre8/k017cIAEukWU6xPt/N+5DkOlqGDE0QoqnPk0uLIZ1utmeU4DmFJQE4xcG6ezNEiMo9IYKmI53keeikPOZrE1HwWV7e0xd5QVA0gAgiWu+uYt0yYAEqqO5GuUge7rvuD0UWOkkMEPhgM4jd/8zfxyCOP4He/fgGGBfzOb/2Gq7Jcdq9/8JphFFQDEbn2PG4FO9JNo+4zMzMQaf1/q5kYPq3TsuhWVRWnT5/Gli1batqH+Swv2ETHdOn2KIs8YAFWhyPdZU0HeHKTXUw0wEMBUPTnWz4+Ppch4YCE3/ziv2J0fA3mz5/An/3Onf0eUsdhUTDOUXST54LTJPdy5pVT5/G3RzkkQgLC86/gw+/ufNtWwzDw85//HAAQ3XwjXj1dxt/+ZAqfeHgLMmUdH/n2WaiGibXjpJcbE+gAcPHiRUxPT0MQBKTDG3HxdAbf+cUCvnV4Dopu4Rsf+y28Zvtq/O3f/m3b4/zvX/oeQhuvx1Z+Au9/xxvtUjgACEg8pnMqPvXMRZQ0E/dET+HYsWNL3KSrI92NUm8tk4ruOhFYiydz4nbcoUMyj5xi4OwCEd1OqeUMQ8kD0SRm0oPte8P6josu0795ENHtNrVaoW8LdDltmgWHynWO//e+9z3s+9CH8daPPwOAdOFxC4toM9M0sY3+9UBVTTcV2tPT0xBpernfp7v3eD6axWIRv/7rv45wOIyrr74a586dAwD83u/9Hv7H//gfHR+gT3exRbfuUnTThwhbye0UCr0ZOxlgJMPkJqRYK8c0yMfHx8cLGQW4kFYxV1iZKdYlux5z6TOApYuW9cE2iuo1mUIJOcXAhbSKiS4Zjb7yyisoFouIRCI4XiTRsjdtTULkOWwaCiIWFKDoFlI73wgAdmS7+t9X79qFfz1D6o3fvD0BWeBxIa2iBBkzMzMdGWfJJJHhTNo5xToRFOn1oyM+NAIAS7btpmUYAMAk52hd0U3Ty0NtpPmztmFn54nXwZiDiZoNdQOfH3DfG0Un17HEu0v/Zottisvrnn59y6UKbmHp8WzeuphTp07VdFrop2EZcy/neB5iIEwWwaiRWr1e9D7dw/Me37dvH1588UU888wzNekSd9xxB/7hH/6ho4Pz6T4lhbmGu410M9HdWQFcEd1Lb8ZDUXKD0PnmLc18fHx8ViLM/Ab8yswsK9vPgKUTbDZnVTRfdFdju0ED6Fb1FRPOr7n1bhydKoEDcNtW4sLMcRxu35YEAFziVwEgIj2XIwsALOp9w53vwGxBR0Tm8ZoNMSRD5BwOJcc6Jrp1gcwTxlPOdbMBibeFrBwbBgDMzs7CsipzDtIyjEW6G0yPzfqiS9d12wG9HdEdtkV380i3YJL5W7rQvBVXP2FZ4m6NuAW62Ka57FGrW2SfhbqcX87uUWqdxYBTp07ZzuWSwLXcg74TyCIHjm5eCkZoennI/p1Pb/Esur/xjW/gM5/5DG655RZwXOWAXX311Th58mRHB+fTfZj7puXgGu5EgNUo8R2OdNObl+BggDGWoqk50uD3ofTx8fHpNKZp2nV4EJt3mViOMEEtOjwD2OSwThvby5ZslchSze5MoJlw3nbbrwAArlkbwUiVALzlijgkgcNEzsTOX7oDlmXh+eefB1AR7PGrbqPvTUAWeSTDZB4RTo1hdna2I+PkgqR11voxZ7dyALbY50MxAEQgZzIZ+/elchk87RXeMNJNI52qQ6S7OkU9FGg9UMCEY47mTI859OhmiNRsNlMc7CwYdo4GXYo9ttimuhTdzDU+HOjuwiTfJAJfLbqDfa6b5jjOHoMYipD08gBLL/cj3b3G8x6fmZnB2NjYktcLhUKNCPdZHihai6K7w5FutpLpdC9ePUxW1YVgrKPb9PHx8VkOzGfz4OhCJy+tzIwfNoF1ysRkk0Y/0F1Loaplkd6lZjQHDx4EL0owxolFF4tsMyIBAa/dSJ7N19z7nwAQoW5ZFg4dOoRQagxpiaRz37adPMsrke7RjkS6NU2HHCXj2ry2vilbior9oi4gGiWL+dXbL1UZ9TWq6eas+pFOEi0noqYdF/nQop7NTs7ljABN1y4o7mqf+wU7R91Gotm9wO11b3J0MafLXR0EmpGp1RHdJ0+etI3LQgPQC9uu6w5S0U3Ty7td++6zFM9nw0033YRvf/vb9s9MaP/FX/wFbr755s6NzKcn2C0vHMxrnAiym5nQ2ZVEjfbodFp4Wz9OUsHkSNxOh/fx8fG5XFjIVHofC1KojyPpHiya5Si66YssfdSHkK8S3QbXeaFhGAZeeOEFbLjxTmiQkAqLuHbt0vTtN1MhLm+4DlIoikOHDuHChQuYmZnB9tt+GRY4XDkWwtoEiQCnqtLLC4UCSqXSku/0wplL0+AFEZZpYsu68brvS4aI2Fso6RgZIQsB1ZH26nTxRpHuiuheKnKrI92BdozUJPeiOySRsRa1LrfKahODBmvcRqJZ2zbDZRaHxZN9FAl2d2FSpIscThH4TCaDubm5SqR7EEQ3i3TbNd1+y7B+4Vk5fexjH8M999yDI0eOQNd1fPrTn8aRI0fw3HPP4dlnn+3GGH26SEV0u1shZZFursN1hWzB0Gl1ed3YMCxzHhzP48LkLLZtXNPRbfv4+PgMMnPVolsOQjcMiMLKMpbUGjwDWOTK6FI0d7lSUnSAVhtYQueFxrFjx1AsFrHjzn8HALh1a8KxPnXraBBrEzIuZoDNr38ABw8exMGDB8FxPHa85dcA1EbIq9PLASJ8169f3/I4T16YBhCCml9AQK4vTlmEPV3UMTo6ijNnztREupUqX4FGmZscTFgAtCaiu2GKehPCVWJN4IChSP2/KxoQkAWgdKnEoFNYdGHIregOCGyxzeUGBPL9sXB3S3CYVtXNpQM7ffo0AGB4jLSjG4QUbjvSHYpgZuZVjNOa7kEY2+WG5z1+yy234IUXXoCu69i9ezeeeOIJjI2N4cc//jFuvPHGbozRp4vY7psOrbqcYDVKXIcj3Ux0Oz2kJEmEVswCAC5Mz3d0uz4+Pj6DTjpXcSXmeB7Z/GC7FLcCewY4TQTZJJ2lj/oQqtOhOal5H2CvHDx4EPHxzRjdfhM4johuJziOw+3bkwCA7be/E8ePH8fTTz+NNde8EXJ8BNGAgJs2VtomMfFbLbrb4fw0cSw3S5mG72NiP10ionvxtlm6uOBg6FoNM3x1MviqTi9vR3RXp5cPR6WGZlzxEJmX6RjshTi2MBR1GYlmkVjTZTkjRxc7YuHOXwvViPRYOKW9M2+r1es2ABiQ9HLROb3cN1LrPS09wbZs2YIvfvGLnR6LTx9gopv1HWxGkK4id1p0GxYHDvXTXYxyDogmMTHb+KHq4+Pjs9JI54sAKmnlC9kChhIry+NCBw8RQNDB2jgaCgC5Sv9jH0K5atbPy50vOzh06BC23U4M1K5dE8Fwg2jrG66I4x9/PoPUhisxvOVafPnLX8a17/7vAIjZWrUbOBPdYiAMkToqt8PEQh6QAMFo7N5tR7qr0surt22L7iYtrXiYMABoRp2abuq70KguvBnVdc+NUssBIBkJAGXAGPAOLxw1gYy7jEQHJAHQ3We48FR0J6PdNd1liymGtfT4njp1CgAwunodgAFJL6+q6dY0DQIzUvNbhvUcz08w1pe7Hhs2bGh5MD69R9UMgK/UKDWD1HRr4MXO1o8Z4CCivujmNFLzNZMpdHS7Pj4+PoNOtqDU/JxZgZFuk5ksyUunJdGQDOQAdCGFejlTXVMqBiMwTRN8BzuLhKMxXH/D26EAdiS7HhFZwOs2xfDDk1nc8o7fgnT+Jxh53ZuR15aarwVp+66SZiLcgbZhczkFGAJCXGNvGlZLvlAn0s3WMJoFAN1GutvpgxyWeMQCAhIhAeuSjc/7oXgYKAMQuxvhbZdkIoaxpIzRlLsFw6AsACX3LWpZBDcR7a7vBTuuTqbqTHRv3LgJSMpIBvuffcBq/kVaZ+63DOsfnkX3pk2bGta6GMZguyf61KLqJiA790Z1IhyUAWjghc6KbjbhCkrOp6RoEcOYuVx7his+/aNQKECSJKTTaSSTSWiahkiEPASKigZZFFBQDURkAapudN2B1MdnuZAr1Yru6nTzlQJLIXUS3SxdlBN90V1NtVk1L4hI5zqTAcHux4986P9BJCDg2GQBu9c497+u5q4dKdy4Poqrf+29yCv/GdGAiFOzJaxOLD1uyZCIkqYi1IG2YVmFqJ9YoLGIsNPLizq2OES6mYhulhbOMr11c+m8SVEUCFKlR3OrbB0N4RMPX4FsWUciJELRTATqRE1HkzFgGuADg9tWtaho+Ox/ei2yZR3JkIiiojV9xodoZqWbDJdiSbGDQalEtMm724MFh5wi8Jqm4Vvf+S7uuOMOZBUDiWDjY9cLqmu6gcrihF/T3Xs8i27Wf5GhaRqef/55fOITn8B//+//vWMD8+kNLD2Kb1LDxAgGZAAF8IIITdMh1RHJXmETLqfUQgAIcCSdK1vSOrI9n95SLpfx2GOPYf/+/bbo3rt3Lz70oQ8BvIjHj6Rx4HgaRdVEWOZx5/Yk9uwe9t01fXwAFMoaUKVbsoWVt/jYyHk4QdNF+S7ULS9nFncsmlnIti26Vd10vB9fNR6B3KCuGABWx2UcPJvDnz83WfPZLSOhJffyZEjERFbtSNuwsiVAAjAcbbwow9LLVcPC0ChxOa/ediND12oE2kveqWNUuVyGKA+5+p56aIaJZ09mcODYgr0f79qRwp5dQ5AcoudjQ3EAZUihWMezHTpBvXOq2TPeXoBzIbrnMjn736l4d0V3QOQBoxIsqubjf/pJPHmyhN//2ilXx64XVNd0A4Bou5f7ke5e41kxXXvttUteu+mmm7BmzRp8/OMfx8MPP9yRgfn0BlbD5FZ0R4KVWpyioiDRIdHN0ofqRbrDEskuzKuD3RLDZymFQgGPPfYYHn30Ufu1dDqNRx99FHvf9wd45oyCbx6uGOQVVdP++Z6dST/i7XPZU1T1WtFdbFy7uiyhE2tn0R0CUIYQWJnt0lqF1cEzZhZyuHJT699XVDQ8fiTd0v3Y62dTYdo+Ktl+pFsXQpAAjKcai62AyCMs8ShqJsKpVQBq08uZS3YzccS0tO6QX1wqlSFIzEjNu8hSNBPfPjKPb740Z79WVE18g/58386hJVHTNWNDAC6BFyXMZ/MYScY9b7dbtHNOhdm9wIXoTudI6aGha7bhb7cISAJgLE17L2sGnjxZwj//YsF+rdmx6wUs0i0GwuAE0c4I8Gu6e0/H9viVV16Jn/3sZ536Op8ewSLdbOW2GaEq0V0qd7BnNk9TC+vcfBMh8nrJ8FfmlhuSJGH//v1LXr/yyiuRiEdx4Hja8XMHjqchi/2vh/Lx6TcltbZsq1Dq4L13QGCp45HQUpOlIRq5EkQJxUWp9pczxiK36urWcq0gi0LL92Ovn2VR51Cq/ZpuPkRE5vqxVNP3shTzQHwYQG2k26B94Jul3TL3asOhZVSprICjkWaphUiiwHN44tiC4++eOLbg6GKeikVg6iQLcGLG+bP9op1zii3AuSkryeRJ9o+hdD8LKEgj8BZfO3aB5/D9V53Nfusdu15gR7pDEbueG/DTy/uB5z2ezWZr/stkMjh27Bg+/OEPY9u2bd0Yo08XYX0G3d4LwlVRiEInJz80tbCe6E5GyERMhx/1XG6k02mk0+klr+/cuRN5xUBRdfYTKKomCqrvEeHjU17UqDbfyQXPAYE5Gzu1+6lOF13IticsVxLWohZq6Xx7gqOgtn4/9vpZW3QnRtuKdGuaDilKxPYVa0ebvp9tVwgnAVQi3aZpwqJiOSA1XuxlAUIn9+qiUrk2W6npLmqN92NRW3oMeJ6HViLp1dPzWc/b7CbtnFNMdPOiDMtqHBhi5pKm1v0sIFt0L7r+8mXN87HrBdXu5ayem4MFP9DdezznBieTySVGapZlYf369fjKV77SsYH59AaWHuV2QZbneRi6BkGUUFY6N/FjLciq09erGU2EgQxgDrg7p89SkskkksnkEuF95MgRRAMCwjLv+KAKyzwidWr8fXwuJxbPS0tKY5fm5YhA2/3EHZyHw6GA/dxJ5wpYu2q418MbSKxFrTszbdb6R+TW78deP8sizuHUGM63Eek+dXEKPC/ANA1csXZV0/en6HYtifgE5HI5KIoCwzDstPBmotuOdDvowJJC/Bcsy4TUQmQzLDXej+E6YzOVIhAbwkw65/j7ftHOORUNBwEUIMhBKErjIE+uWAYQgaV3PxPGrjVfdP1FZLGlY9dt7PTyYMSu5xZ5NDTF9ukOntc5nn76aTz11FP2f8888wyOHDmCkydP4uabb+7GGH26CDMC8ZJlYtE0pmInRTdNHwo71PMBwKohkj7GBbprkOHTeTRNw969e5e8/sorryCTzePOOq1o7tyehKr7kW4fH21RRK2krizRbZqm3WYpEXGu2zZUIigXVqBze8vQFmpqkUQ3s8X2nsmqbrR8P/b6Wda+q10jtZMXpgEAWn4Bstw8E45FusuQIAhEBM3OzlIDNHIOBh0c9KsRaQTbxFLRUlKo2atptCRqDNPCXTuc0+Tv2pFyTGkHAOgkwjufHazro51zKkpLTUQ5iHK5cQQ7V6Ri2+i+2S6rGecW1Zq/euIk7tjmXE/f8Nh1mVB1pJu1C/Oj3H3Bc6T71ltv7cY4fPoEy1oUPaRBmfSmVlY6d3PjG9TzAcC6sRRwWoEUSQykO6dPfSKRCD74wQ/CNE185jOfqXEvj0WCeOvOCMBxOPDKgu9e7uPjgA4BMgDT0MELIkqacwrjcqVQqtTBxqPObY9MjUyqV6Jze6vwNDvAKKSBcBz5cnvP5HBAwv27hlu6H4cDEvbsJhkIblyqqyPd8/PzJNIseI8Enp9eABCGWXKXVs1Ed7pkYGRkBFNTU5iZmcHo6CgEicxDAk28RCSeB0zAdEgvZwtinNnawlhA4rFnF3E/f8KlezkACCZZcEkXBsvzwOt5UfNZGoQR5CBKpcbXfYGW3HBm90V3uE6t+V9+8c/woY/+EThuqOZvHRj38lDENqP051b9wbPo/ud//mfX733ggQe8fr1Pj2ELb6KHNCjLIA+TstpB0S3Vr+cDgA3jIwAuQhAlzCxksWo42bFt+3Sfqakp3Hjjjbhw4QKKxSISiQQ0TYNmCfiTA+fx0LXD+PTbtyBbNpAICtANw38o+PhQWEtFrZBGID5id51YKVTXaSdjzv2gLSq6cyvRub1FWAs1XiPRzWIHPDD+9Ks/xN233ID9b9+CkmYgIgtQdXf3Y1nkcc/OJB68ZhgFtfFnmfgVA2EIcggLCwsYob2zvTC1UABkQDDcnRcsvTxd0jE6OmqL7mg0amdbNGulJAlUdDtEshWNim6r9WMhCTzu2zmEB3YNo6gZCEsCDNNqKNpEkO1l2sx26AayyOP6NQHcv3sL8gp5xrs5p5jRl+hKdGuACPBt7He3kFpz1XYBZ/zgBz/A4zf/Er564Dncv3sLiqqJWKD5ses2lfTysJ3N4Zuo9QfPovttb3sbOI5bYmqw+DWO42AYfmrooGNYHDh4a21hdTjSrWk6BHrzitQR3cl4FLpSghgI4dzkrC+6lxlHjx7FQw89hDe96U149tlnAQCyLON7RxdwbkHB116cwz9994fA+FUwJo7i///ee/s8Yh+fwYH1sDZLOSA+AmWFPVqZCZKhqQjUSxGmzx07jfQyxzRNO2oVABFaZa299NVMvoCXsmEce+YS7ludxjvveB0AQPQwP2DtnxIhvuFnq9t3hVKkbVgrons2rwBDQIhzF1lOhMgCVrqo29ubnZ3F6tWrbV+BZvMhSeQBDbAcKjTL1CyLt9pbGGOtpeK0brhZNmJQsKACA2s++if/+7uQN9yAzdwE/u9ffqOrc0qiwpAXJRRLjRdVSqoOiICA7i9IEu8hFbwYgGVZdhnBqVOnsLCwgC8+NwUNc9h76xqkwmFPmaTdwI50BypGasE+tC7zaaGm+4knnsB1112Hxx9/3HYlfvzxx3HDDTfge9/7HkzThGmavuBeJrCaJC9RRcskx1bROiO6c8XKCma8Tj0fAOg0fezi9GC1xPBpzqlTpwAAqVSlVs2yLDz9ahoAcPu2BGCouJBWcWFhsGrSfHz6Dk1jFAxyr9T7UxrYNXIFMqE2GjgPs7TRwgp0bm+FQkkBT1sWRWmWq9Km3vj8Pz0DORxHeWESb7/9pjZH2Bw7xTzZetuwnEIuhnjAnbBhteQLNNINkLZhpVIJgsxEd+PvYvMlp5pulYnuHoi/akI0Ol9qc+GlW5QNDjnF8HSc5arjkG8iusu05EbkeiC6w+Q8EeQAVJXcjxYWFrCwsAAxGMGCQv7W1fHu9gt3i+1eHopADJLynVAT3wKf7uB5r7/vfe/Dn/3Zn+GWW26xX7v77rsRDofx3ve+F0ePHu3oAH26iwkOArxFumGy9PLOmPlU1+jFwvVFt6UUAADTC37LmOUGE91btmyxXzs+XcKljApZ4PD6zXG8fCiECwagcc51/T4+lyssjTjI6TAB6NbKilKQZ0AIplZfUPMWed4U2qxbXinMZSou1UNhCXkAWpvnxaFpIDQOrONmIbZQX+2VZEjEpYyKULL1tmElS4QMYDjqTuCwtHbNsDAyvg5AxUiNRbrlJqJbYjXf3NJ9pFJL816L7mhAQAaAYg6mI7UKCQFU2r+6oXrxo1BqvNhWpiU3vQjgRkPkfixIAZTLZQQCAXuOs/W618MCMBQWEQ8NhrCtjmoH48QvwC/f6w+e9/rJkyeRTCaXvJ5IJHDmzJkODMmnl5j0FPB0AZrk5sZql9qlQGv0DF2DJNW/SQkGSSucHTB3Tp/mnDx5EgBwxRVX2K+xKPfNm+MIywLWjSYAAJbvUO/jUwNPUwKjMm1V5P3RPdCwKFajdj8sbXSlObe3SibHUvIVJKiQMbzHUWye+ulhhMa3wtQ1/JcHetOJxu7VnWo90m0I5NoYT8VcvV8WeUSodXNibC2Aqkg3XdxqFoRgRmsWt/R9msEirr2NOMfDZNFBb+Mc6CamQM7RkbizUaITPMfZC3H5UuOyEpWm//Siyygz/OUFEQWaqclE96ZrXw8A2Dw8OO1tZYEDsx8IxklJhV/T3R887/XXvOY1+MAHPoCpqSn7tampKfzhH/4hXvva13Z0cD7dhz00ZC/9A6lRhdKhyQ8zxmHutPWQQLY3aO6cPs1hDyQmunOKgZ+dJRkLt28jYnvTGvIwkKIpmObKMory8WkV3TAgBYm5WCpMa7u5wZxYt0qeRbEatPthaaOlAa1Z7TV2HbxSQoJ6oZhC85ZZ9fj7H5AsRXP6FWzbuKb9AbogVZVe3mqkmw+RFk0bVjm32XKCif3o8GoA1S3D3EW67fkS7xTpJv/vtW9WMkLOAYNv/RzoJpxM7mHjQ84ttephGeTeUGxSVsJaY/dCTFYHqfJ0/srmOCNX7AYwWKKb4ziE6JhDCeIk3+wc9+kOns/Ov/qrv8LExAQ2bNiArVu3YuvWrdiwYQMuXryIv/zLv+zGGH26iEXTowINIsyLYa6cneqhXKArmKbe+KYaEslKZm6luQitcCzLWiK6//VkBpppYUMqYD+ctq0fBwCIcgjT85n+DNbHZ8BYyFTKaUYTJKpnDejEulXsCXWDNkvMUFpZYc7trZLJkwibqStIROkEX2itNCedzaMQ2wwAuPvqsY6Mzw3tRroVVYMcJWJ7yzr342bbDSTIQi+LdPOSu5pue77kkF6u05YwvfapGmIRZGlwxF41YoSI7dWjSU+fY8a9RaXx/FCn7dtCXgJILVKT9l4m81c2x5GH1wMANg0N1nFgKebBhB/p7ieel8u3bt2Kl156CQcOHMCxY8cAAFdddRXuuOMO28HPZxlBI93eRDeZ9HSqbQ2ZcMmwmojuqMyjDKDoZxcuK2ZmZlAoFMBxHDZt2kQN1IiofvP2pH3fSMajUItZyOE4Xj03ifER95ELH5+VCqvdNU0DY8koMA2gjYjmIFKgnTD4BqI7IAAaAMUYTKOoXkMyxKKwNAVD8SQwVSlD8MrnvvYspPBWlOYn8K5/98aOjrMRSeokHkqMYubsIc+fP31xChzPwzQNbFrjQXTTCLsYTgIgke5SqWS3U2rayoqZUDlEutm0SPLQhrUTjCZjwBQgBJxb7vWThUzOds3eOO7Rod4W3RoQqV+3b4AHDyDYg/xynuNgaCoESbaDRidPnoQUjECTaebe8GB50zDRHaKiu1lbPJ/u0FKOGsdxuOuuu3DXXXd1ejw+vYY+NIIenAw5kElPxyLdZQVA1O7/XY9kRMYsAMVaWamVKx1Wz71u3ToEAgEcmypiIqsiIHK4eVNtHZ5RzADhOM5OzqF3Uz8fn8FlIUsMJA2lZHd34KTBmtC1S0nVAQngUf+ZwoSQ5ic6AQByrMbV1DCSjAFQIQTd18tW8/wMMVDbKMz3xECNwcRvKDWG2UPe08tPXpgGEIGaW2joB7MY5mBu0ZTnmZkZaqRGTKbcRbp1cMLSbbKIa7Pv6DTjwwkARYihGEzTBM8PTiTz7AQ5toamYjjprvaewTIrlSYXvgEBEoBwj1y5LV0FJNnupnDq1CmkNu4EAAxHRMSDgzVPZW3DmJGaH+nuD67Oiv379+O9730vgsEg9u/f3/C9e/fu7cjAfHoET04BL6KbuXJqHYp0l1i/b7OxK+1wLIQpS0B8/eqObNenNyxOLX/hQh6xgIAb10cRWrQqzatEYFyay8HHxwdYyBUBhGGoJSSjpMURv8JEd1k1AKlxj90QjdRolh+hASqt0zhTp6J7DqIcQklREQq4b1V08OhpjG28EplCGb/dIwM1BhO/4eQYzreQXn5+egFABFY56+lzLL1cF0hke25uDoVCAYJEatmbCeagLAHQwfFL502GLbp7K2pWj6YAFMELIubSOYwOJXq6/UZcmiHHSStmPC8GcHa3nMai26LHIhrqTZsus6rWXNM0nDt3DtvvvA3AYNVzMyqRbvIMCfiR7r7gSml98pOfxLve9S4Eg0F88pOfrPs+juM8ie4f/OAH+PjHP45Dhw5hYmICX//61/G2t73N/r1lWfijP/ojfPGLX0Q6ncYb3vAGfP7zn8e2bdtcb8OnMWylNiC5T1fkaaSbuXS2S0V0N76pvuPO1yMZDSJb3gDdMKHqBsKBlZVmuRI5deoUduzYgU/u/wx0w8JbrkzioWtHHA2RZJAH2Uy2tOR3y4miokEWBRRUAxFZ8M9Vn5YhtbthWFoZiWgIQMlO1VwpKDRrqpHjM+sr28y5vVvX3qBd04WyRrIDLANjQwkAcwCAmfkMNqwebfp59vds3rAOn9gm4OR0DlvX9FaoMfErBkJ0cckbU+kCIAOi7u15wSLsRYMs+hqGgcnJSQhjuwA0N5kiorsETpSWRJVZR5heRxIT0bCd8jwxu9Cy6G50nhcKBUiShHQ6jWQyCU3TEIk0T2efmM8CiMBSvLd7ZUGeZpFuCERsR0M9WpCkmZnFsopz587BMAyMbbkGwODVcwOARI0oeZHes5pklvp0B1ei+/Tp047/bpdCoYBrr70W//k//2c8/PDDS37/2GOPYf/+/fjbv/1bbN68GR/5yEdw991348iRIwgGB++kXo4w0R30MHnotOgu09ZjLI3ICVU38aPTBRw4fhFF1URY5nHn9iT27B72+w0OOMViET/68b/hmdMqvvDVE/bxu2tHCnt2DdVEBKISUACQLi3fHFJVN/H4kTQOHE/756pP22SpOy4MFalEFEAJvCihWFIQ7tUEs8sotN1Po8sjFBABCzBRP/25W9feIF7TJTs7wEBAlqCXixCDYUwvZJuK7np/z9Yxs6d/jyzyCApA2QAKuvftzuVVYAgICd6eFyzCnlVMxONxZLNZnD9/HsJaZqTWeCxsvsSLEjRNQyBQuQ5t0d0DQ69qeJ6HXs5BkIYxNe8t8s9odJ5bhmbPyZno3rt3L/bt29d0Pj5H27zyDVoC1kOABQOVhbl6cCIR3bFwb7QBM3grqZqdzTe2lYruAYt0l8tlnHjlKBDfbL/25BPfxVu2v9PXUj2mr0UH99xzD+655x7H31mWhU996lP48Ic/jAcffBAA8KUvfQmrVq3CN77xDfzqr/5qL4e6YuGoIY+XdDSBAwwAWocMbcqqAQhkxd6JoqLh8SNpfPPwfOU11bR/vmdn0o8iDjD3P/gQnj6l4J9/sWC/VlRNfOMlEpm5b+cQAjT1KRWWUABQNJanOPXPVZ9OkyuSiSpnaEjFowBIGu5CNr9iRLdKnyVyg8s+GpSBEmA6pPQC3bv2BvWaLtPIn0SzAwyViO65dOPSnEH7e5JhEZM5HXwojmKxiHDYfV16ViF/ezzgLVWWGbilizpGR0dt0b32TdRIrUmkO0T3jyDKUFW1RnRbPOsI01vRDQCmUgRiw5hNe48oNzovOA7YEUrj0UcftX+XTld+fuSRRxpGvOfzChAFJDQuIXRCgAkDlXtEPVjJTTzSGxHJ0czMsqLj1Mw5SKEogsOk7/vmocG5LxcKBTz22GP4+VngyrdURPfXv/qPiOVONT12Pp3Fs+g2DAN/8zd/gyeffBLT09NL+uk+9dRTHRnY6dOnMTk5iTvuuMN+LZFI4HWvex1+/OMf1xXdiqJAUSqradksWfHTNA2a5v2CX+mwVBNJ4Oz9s/j/Sz7DWVR0Gx3ZpyVFA8LEFd3p+2RRwIHjacfPHjiexoPXDK+4Y9vsGCwnbrj+Orz/G2cdf/fEsQU8sKty/EZiMi6YgMrJff/bWzkGl+O52k1W0nXQKrmSCgQB3tIh8hwMXYMgSpieT2PMY8/bVujFMWALuCJffzshWQBKAASpp8+JQbmmFx+HklZJydc0DaZKUqxn07mG4xmUv4eRoqI7lBzDpUuXsHHjRtefLZsiZACpsLfnRUSi2XqmhdE1G3Dy5EmcP38eGyQSfOCs2rnN4n0vUGdyXpRQKBRqooWsDWv1nKpn6CQrZqbJOeBEo/PiiVfSuOfBjRgZGVnST33//v340Ic+1HB7mZIGRIEg7zzHa4TAsfRy6iVUb15KneejwR7NHWiteaGsYObVVzHETNTCIoKCNTDPLEmSsH//fmy+69drXteVkqtjV43/PK6P233iWXT//u//Pv7mb/4G9913H3bt2tW1NmGTk5MAgFWrVtW8vmrVKvt3TvzJn/wJ/viP/3jJ60888YSnFdTLBV7YCgB4/tDPMHn6WM3vDhw44PiZfDaDwDgwN5/Gd77znbbHcP7SBLD1KqilwpLvGx8fx45rbkBRdU5lL6omCqqBYy/9vOF5sVypdwyWC8FgELtv/KWGxy9XUvH8T/8VuVwOC5OTwNgWWHKkI+dWJ3B7DC73c7WbLPfroB3OnL8EbNsBjd4fDWU1BDGBJ5/5Ac4dH+rZOLp5DLKFEiIAMvOzda/7M6emgZHVsHipZ8+JQbym2XGYW8giOAoUsgvkvCiTudihFw8jqs07fnYQ/55CaTWABELJUXzzm9/E1q1bXX9W5WXIALKzlzw/L2RuG1RLAB8iC1cXL16EQIXbD555CiF+aeYd2/c5QwKwBbwo4fHHH8fQUOU6NKnovnjuDL7znVOextQueoksvBx+5SS+852y68+5OS/ShRLGx8eXiO50Oo25uTkcPXoUuZxzlsVMOo/wKFDKzns+ToVsAXwSmEtnAIw63od03YAoXw0AePH5g7h06qinbbSCphiQAZw4fRa/eO45DG8mfgBBdR7f+c7hrm/fDbFYDFdddRXS6TR0pdYzQVdLro6dE5fz87gexaI7TwrPovsrX/kK/vEf/xH33nuv50H1gn379uEDH/iA/XM2m8X69etx1113IR7vflRgOaHpOv7pq+cAAHe+5c12HZimaThw4ADuvPNOSA4Ga989+XUUAETjyY6cB0+f+QbmAUTDQcfv43gBYZl3fCCEZR4RWcANN9zQ9jgGiWbHYLlw/PhxpKLBhscvFpLxxjeSBmGrjpzC514CArFhvPWtb+hr25NWjsHleK52k5VyHbTD909/AxmQtMl7770Xf/dn/wYA2H7VLtzzhuu6vv1eHIOvHP1nAMCGdavrPlNCPzuMUycBQQ7h3ntvWfL7bl17g3JNLz4OXzv2dVgAxkeHce+99+Lvj30bADC6ej3uvffuut8zKH8Po/jiHM4dzSCcGsOVV16Ju++uP/bF/P3FnwEA3vz6m3DXzdd62u6/PX4eFzMatuy8Fs898U1YHA+epobfc9cdCFd11li872fzGp741nkIooxbbrkFmzZtst+7/wd/BQDYdfVVuPcm91H7TvB/jn0DAJAYXuV5btbsvEhGQo4LMclkEsPDw/Yz3Al2fV+xftzzuJ6+dADzACKxJAA43odmFjL45vdIudqD992LZLz76dL/+OrjAIBV42twsFTC0HVE9L9u50bcu/O6rm/fLZZlEdO7cqHmdV0tuzp21fjP4/qwrOpmeBbdsix7WolslfHxcQDA1NQUVq+utIiamprCddddV/dzgUCgpr6GIUmSf5IsolCqpOEn4tEl+6fePmMmIya4juxTo6qvpdP3FRUNd25P1tQaMe7cnuy7i2w3We7n7ZkzZ3Dm3AXcse36mppuxl07UjBMy3bPv2rLOuClCxDkIOayBawZ7V0krx5ejsHlfK52k+V+HbQDM/kPiuT+aFEzoqKi9XSfdPMYsJTcSKD+NkZSCQAaBDnY0+fEoF3T7Djo4CGAuLpLkgSR9jgvqkbD4zRof89whMzXQskxLCwsuD7HFFWDHEkBAK7ctNbzuZkKS7iY0ZAYJXW4YlUbvlBQdjRTY/s+FKikl1uWZW/bNE3bJycWDvX8nhUUABVAUTM9b7vReXHXlUmcPnt2SZQbIG2Cm7mY67wMCcBoIux5XMwF3gCdJzrchwol0vXEMk0Mp+I9Waxn7Q1Vw8KpU6dw+78jonvraGSgnlWFQgF79+7F3z/zi5rXdaXo6tg5cTk/j+vhdn94PjP/4A/+AJ/+9KdhWZ0x0arH5s2bMT4+jieffNJ+LZvN4ic/+Qluvrm3vSRXKtWiO+ywUFEP1sOyQz5qtkFGPdPUcEDCnt3DeHDXEMLUaScs83hw1xD27B72RcwAc+rUKfzfH3gf7toew4O7h2uO39uuGcaeXRUTNQBIRCNQCxkAwIlzyy8F2z9XfToNE912CyLqmssM1lYCLCU3HKx/fSSipDxMDISgG0tTf7t17Q3qNW1QF/dwgMROWEuggtK4FdCg/T2sfVcoOeoo6upx8sIkOJ6HaejYtHbM+3apg3kwSTL8hCrRLfGNyyYFOgfiBRFlRbVfVxQFgky+JxzsTb/oasISGVephW5Qjc6L+3cNY8umDfjoRz+KZDIJgES4P/rRj2Lfvn3NRZtErt2xZNTzuCqiu75cWciSKK6ulnqWHce89jL5Akqaifj4JgDAxgFzLo9EIti3bx8euL/WtPq/vPc33B07n47iOdL9ox/9CE8//TQef/xxXH311UvU/de+9jXX35XP53HixAn759OnT+OFF17A0NAQNmzYgPe97334b//tv2Hbtm12y7A1a9bU9PL2aZ1iuTJp89IyTKQ3NdPqTD2/TrOZGj3oZJHHPTuTeOCaYaRLBuJBAZqm+y2YBpyTJ0/i2LFj+PTf/QvuvfcefPrtW6DoJsKSAMO0HKMJRjEDRBI4MzmHN/VhzO0iizxuWBvE/bu3IFs2kAwJ0HTDP1d9WkKzOPCAne7KmUR0F8pqg08tLyzqSB5u0EUjFY8AIAty6VwBI8ml5WKyyGPLSKDj154s8rh+TeV740EBhtHfa9peqKCiWxaAMoCS1ryVpyzyuGkduUflFQOJIOnH3I+/hzmJh5NjmJn5RZN3Vzh1YRpAFGp+AaLg3Sk8RcW+SKPlrJ6bh9nUq6h6rlItusvlsi3eI30Q3dGAiDQA1WxtbibywOaRAD69ewtyZQOxoIDprELOCzGARx55BB/84AcxNTWFVatWwTRNVy2nhFAMADA+7L13eEASaKvA+udmplACEICpuq9jbxeRGgpPz85jaBOJco9EJMQCvXetb0YwGMSvPPwgPvvcnP3a//Xbv+W3C+sDnkV3MpnEQw891JGNHzx4ELfffrv9M6vFfve7342/+Zu/wSOPPIJCoYD3vve9SKfTuOWWW/Dd737XP1E6RJFO2gxN8bQ6yCLdJjokuq3a761HOCDBNE386eOvIGcFcefoAt52+2s6Mgaf7sD6V+bEIXz6mUt4YPcQ3nEdiSyIdY43rxFDiktz7o09Bo2P/913wa29HomQgLesKuAtr93V7yH5LFN0CJBREVestWKxSURzWUFTchsJlXg0DMs0wfE8FjJ5R9GdL5bxp09dRDwkIREScO86Bbdcv6MjQ/yT//09yBvINZ0pGXjf68LYtnFNR767FdhCRTRI07NFDmUAisuW1fv/4QmUx67FuHEJH/m1N0Fs0pu6W7Ce2aHUGGaOu490n59OA4jCKrfWk5pFurkAib6yCLWA5il8YpXoLikV1+JyuQyRfk9Q7n1H3niIXEdai92Az03O4lPPLCAWEPDO61L4hxcWEA0I+B8PbALHcYhEIvjc5z6Hz3/+83j961+PL3zhC02/U1E1SFR0r1817HlMAYnkzJuoL2azTHS30Ae8VUQeUADMLWQwvJmI7s3Dg9MqbDHJWBhARXSnelD37rMUz1fmX//1X3ds47fddlvDNHWO4/Doo4/W9Ab06Rws0m3q3ltLAJ0T3QaN5ARcrLLzPI8zp16FvGYnDr16CW+7velHfPoIE916eBTQgTWJ5g+lAMhi0Gyud6vWnaaoAaZiIKcYmJFbmxT6+ACAyZHHdIT1Bqa1hEVl5bRtYXWw0QZ9x0VBgKGWIQbDSOednWJffvUsOJ5Hjl57Z4RZ3HJ9Z8ZYNHgo9HsB4Jmfv9JX0Q2BLFBEQ+T/kYCIBbiPcuYUE2XFgDkz1a0RuiLBIs5yELNNeoxXM5UuAAFANFp7TjDRrQkhAJX0cpFvLrqr1yfKam2km/WLlvuwiJGMBoEiYPKtRdlffvU8gChmpifxmk2b8b8PzWEia+CV6RJ2rCIp4qlUCocPH8bwsDsBfWFqDhwN6qxrQXSHZCK6Wf9zJ+xSG7132T9sulpUNAzTSPemAUstrya0aH7tZ971B3+vX8aUVTJp8yq6JXqxWh0S3Uy8ByR3aTmjATLpOZdeOemVKxHLIgYjHC8gbZKH0eah5g+lKK10yJRchmwGEMWqnMsrqfbWp/dYPDNmoqKA1u6WXaQRLxeYUIlFGt8fDI20RMrmS46/P3JmoubnmXTB8X2toHFkjFopDwA4fG6u0du7DicSYRWPsP7E5DzRG0QEqynTRIl+p8PKAg8JZDDpovvsjbk8ef47tfZyA6slVyzyf5Ze7kaLcBxnz5vKamXM5XIZokS+RxK70063EUMseimFWvr88Qsz5B/FeYRkATdvItkkT1X1716zhiw0Xbp0ydV3Xpgi14layCAge/cLYBkDLLPDiTzzJzJ7txDJMjMFScYQbRe2eYBFd7V3jsDVZmv49A7XojuVSmFoaGjJf5s3b8bdd9/t921bhrC0KMtoNdLdmTUbljbkVnTvXEdqsIqi9/ogn94xOTmJUqmE1Lqt0E0gKPFYFW/+0E1FyHuKxvJdE9SrIg0rqfbWpw8sElds7lTWV5LoJn9bPNJYLJgauZZIDedSzkxlan6ez3cuW8aiacj8wmkAwHS5v/enxfssRiPeJudO2Kgg70tG+p8SG5FIdLmgu9+nOZV8Jh5o7TiwtPY8nf6wSHczEzWGZRKxzYIXAK3ppunlzcrlusFYiqRxC4FwS5+/OE8WqYIWEbG3b08CAA6eyyNHV2mY6J6YmFj6BQ5MzJFr0ii3Vi5m+zzw9c/rAp3L8mbvSm7Y8Q0lRhFfRVrDbXIRVOgX1ZFuP8rdP1ynl3/qU59yfD2dTuPQoUO4//778dWvfhV79uzp1Nh8ugx7WFgeb1SySPtScB0S3fR7gpK70/HWG7bjx/9aRGB4HdLZPJJx746YPt2HpZZvvYHYoW0aCoBvYlADAKuSYZxXK5GlZUlVpKFQXjlpwD69h4mrZJScUzTbEqre3Q4ivcI0TYgB8rc1E92gNZv1skemcypQ9RWZUmeuPdM0IUfJYu+1ayI4DMCMrurId7cKi8ySWk0gEQ0C84AlukstNgRyfx2JtybQOkkyKCCtAWUPFY9lS4QMYCTW2nMiQQ3cdBOIJEcqotutWDbIvElZFOkWJHad9l7YEKOyAsRQFKZpenbyniuZQBIYCpHPbR4OYtNQAGfmFfzwZBb3Xj1kt/DN5/PI5XKIxWINv3M6nQcwDE51XihrBnPT50QJgPP1XFJ0IAAI6F12HCuHHNtOetrHRAPRATRRYwSrIt2BPmRh+BBc3+He/e53N/z9ddddhz/5kz/xRfcyosxqAk1vN6qAJDatsfEC69Hq1nhkx6a1UL77bwjEhvH0waN46M2+mdogwkT32qtuBOA+9WrtSAIHLwEILN/FFCFYMXkqqSvI8Mqn5wgymcQz45uAyCMPQDVXhujOFSuT8WQzcx+6QJwvOYvurC5AAklllSMJFNTO7KOJmQVb5P7qHTfh//fEDALxERw7fQE7Nq/ryDa8oKiaLRKTMbLPhmJhYB7gJHf3WU4mYnvVUGPR1AuGY0GcyakwxQh0XYcoNp8LGCK5LsZTrT0nJIFHNCAgrxhYs/lKlOkihNsooEXnTYpWub+XSmWIgSH6/b0XNqtHUwAK4AURMwtZrBpOevp8CQEEAawdqizE3L4tib/+yRSefjWNe3amEI1GEYvFkMvlcOnSJVx55ZUNv3MuWwKCgGC1lvEVoT4PnCCjruhWmeju3T2RZXwG46ROfZBTywFyPvIcYFp+pLufdGzP33///Th27Finvs6nB5TZw8JrpJulgXMdWtWjtTohl23LeJ4HlyPmL4devdiZMfh0nJMnTwIAYmu3A3CferV5DXE3l2JDMM3ll0KrqBrkSJXo1pZvbbpPf9ENA2KQTICHaEZPiN5/dWtlTJxYj10ASEQaR11Z+mi97BFdogIsQ54L5RZbJy3m5AXyvFELWWxYPQpljnz/s88f78j3e2Uhm7f/naLnBTs/BNld5FoMkXvUmtFkZwfXAqsSZMyh5Cjm5+ddfYYPkfKyDatSLW83RaPdo+s2Q6SLGG4MXQEAtIuAWnV/LymVxSC5D6I7EY3A0MgYJmfT3r8gRPbl1rUVw7Nf2hxHUOQwldNwdIoskHlJMc/QpuEBrrXnIBPdvFQ/g4P5W0guTPA6RXBROeSONYNd7shxnB3tDvTh3PQhdOyprSgKZLn3fQl9WsdOi2ol0g0AHYp0Q/AmugFgLMjM1PzU3UGFmaghRtIw3Tp7bttA0tcEKYCL0+4mYIPE+cnatjfKCkkD9uk9c1VuziO0XjNI+3UbK0R0Z6kTuaEpkJqUGPFg7dKW3vdN04QUIwt27PmgcZ2Zk5yZJGZQRjENAAhp5P+/ON+f+1M6RxYqTEO3RckoPT/EYKTpYmUmX7AXczaMj3RxpO4YipLjFE6NYXa2eduwkqIiECMR5S3rWk/zZ2ZqQ6s32pkMbr1lOBbp1ivzp2r/jn5EugFAp0Z/U/OZJu+sJV8sIxAn58I1WzfYr4ckHjdvJgs0T1NDNZZi7sZMLa+S/RPx7qEGoNLRQJACdbsdKQZ5XerhLTGw6F416JFuAAjSBSU/0t0/Orbn//Iv/xLXXXddp77OpwewtCjO8hZNDDIHygZukl7gaOuTRj1aF3P1evLALUmDvbp4OXPq1Ckk1m6BxQkISTxWxdw9dWORENQCmTCwCNNy4vxUravxSqm99V14PUYAAM5/SURBVOk9cxkiuk3TQCxM0mnDtAzH6FSmUZ/JFojZmaE2d/kXabu0krp0ofjUhSmIQdLLe/cGEqmzxM5MhC/NkePAa0Tsrk+Se9l0uT/HgGUHGGrZrtsdGybPQo7naxZrnDh7ibhUG7qG0dTSfue9xu7VnRzFzMxM0/efOj8JgHRe2Ugzo1qBtQ2Ljay20/XdesuweVNNpJuKbssy++YObapkEWs2nW/yzlpeOk7a7enlIjavHav5nW2odj6HbEn35GBepoaosWBr88VYmFzDghyEqjqnqLPbQS9rlUOLyiEH2USNEWKRbl909w3XV8EHPvABx9czmQx+/vOf4/jx4/jBD37QsYH5dB9VNxELCNiwdsjT5wL0ZsN1KNLNerRGGvRoXcyt12/Hv/6ogMDwOsxnchhK9L8uzaeWU6dOYXjbzQDcm6gxjGIaiCRwdnL5Rbon57IAKul56vLLkPcZEOYzBQAcjHLRFlfRkAwUG7fQWU5kCyUAYZhac6dxibegoza6yHj5xAUAcSjZWWzcPoQfngD4DvlCzGbLQAII0JrSG7etwfmLgNUnMzWyz0Iw1Mo+i4VDMHUNvChhej6D0aH6C9KXZtIAQtAKGc9mW90gaYvuMXei++IMgCjU/AJEofV5CNtuODWGeIrMgwIuvWVAF4C0qnOxqGiAAMDQwHl43nUUnZwT8znnXvb1OHp2AsBqaNnpJefEpqEgrhgO4tRcGT85m8UNN9yAJ554wpXo1iAiACAVbc3wLkq7NohyEIriLLpj0QgiSRkRq3fzwFBAREwVkAgJKGTTiAywiRojFRbBcUAiOPhjXam4fmo///zzjq/H43Hceeed+NrXvobNmzd3bGA+3edX77kFq5IR5MoboBsmVN2wnSIbwdLAmVhuF1arEwm6vynv2LwOyneeQyA+jGcOHcXDb35tR8ZyuVNUNMiigIJqICILrs+JJd9TLGJiYgKvvfNqAO5Tyxm8RiYME3PZno25UzC3VoZu+fVTlwudPhcz+SKACIwq599oKAAUAQgro5yLmKKFYRnNjZYkHlR0L80eOXFpFkAcKC1g7ehG4EQJUjjekovzYjJlA0gANAsab75pJ752/hwC8eG+mKllCmUAIVh6JTuA53no5QLkaBIzTSLdpI1TCJbiLRraLViadzg5hpnZE03ff356AUAUVottqBgput2N23fh7ttuwb8czULigUKhgEiksakfzyLdRmVVtaRoQBieS/Y6iUB7VS94bJd3ZioDhFZD1J1729+3MwVB4HD16ghueO/v4Hf+r9/F4SNHm36vSbNNWnXJr54XOi22FRUNH//NtyJT1pEIbURR0Xry/L/j5uvxjmgQ2bKORLB3220VRTPx+7etpeMVoWhmTe9un97gWnQ//fTT3RyHT49RdRM/O1/GgScnUVRNhGUed25PYs/uYTSTCCy9nBc6E2kRaIsTtqLpFi4/BcSHcejVS3j4zR0ZymWNqpt4/EgaB46nl5wTXmuATp8mvWzHtl4LANjsMfWKRZRmc41TTjs55k4xnysDVX+u0bkqHp8BphvnYiZfAhCpEVexcBCYAziXraEGnQJzIjea+3PIAocSKumk1VycLwIpIAQFG1aPAjgHXpQwOZfGmlFv2VyLKRo8BABDYdrbOh6FMncBodGNeObnr/RcdNvu7XrtQgVZnEliIds4yjlLf8/rnetj3g5JamgmyAFMTTavRZ5MF4AgIJrtjZ9Fus9NzuE7/+dL2HbXf8Jf/+UXcW6diX379iEYrP/c4mDCQm2ku6zqVHT3r2OFDLLtnMd2eazdXlx0XjC4dl0U/3J4Hn/+XGXOeMf2NbjOMCE1aI/GyWTxYnyotTKGakO60iIvh349/1XdxI9OF3Dg+MWBmXc0QjNMfPvIPJ44tmCP964dKezZNdTw2Pl0npWRn+bjiaKi4fEjaXzzcCV1t6ia9s/3XJVs+PlwMABAAS+2v6qnqJr9PdGQN2E2FjSQBnDeN1Nrm6bnxM6kp1XcU6dOgRNEJNZR53KPke6oDOQBpMv1IwadHnOnyJRUIAho5QKkYAQG/FSulU63zsVskYqKKnEVp4uTnLiM+9hXwUzROKO5UAlKPDIANIeSjfmSCaSA4RCPWCRkX3/nJmbbFt0qH0AIwKpkJfoZ0og4PHJhoa3vbgUmujlz0bOPiuiFfGPRvZBXgAgg1WnB1GskgQevl2GKQcxmm/dzXihoQBAI8+2J2wBH/v65gmqfU8VcFo8++gkAwCOPPFI34s3DggFAMypZF6wjDNfHSHdAABQABaeVqQawdnurYksX8xSNiLZvvlzxKymqJv758AJ4nsd9O4cco6amaUKKkDKHdWOtXYMCz8E0dPCCCFWvXPj9ev4P6ryjHuzYfeOl2mPHfq537Hy6g7+nL0NkUcAB6kK5mAPH05AlAdFo/Vq4IEsv74Doru7RGo+EPH1213qSwluSkm2P43Kn6TkhehOOJ0+eRHLtVvCijLDEY8yliRqDRZSKRv1bVKfH3CnytDewniMuvBY/OA9gn+7QrXMxVyJim68SV4koSdMUAoNv3OMG1v6Ls9yIbtouzWHqUubI/lg3TESSUSSlKROz3lycneBk8jxcO1qpk96YIuKkH2ZqRXuf1QorjqboZ5qkFmfLZF8HhcExeZRBxr5QbH4eZOk9Nt5mbepIlJwzoeQoROpezlpu7d+/H5JU/97N057QelV6uUJN1ZjLfj8ISSQyXPK4HsHa7W1ctdQLQOA5PHHMeXHpiWMLEOqYxs2lc7ZB3frxYcf3uMGkx0SpEt39ev4P6ryjHq0eO5/u4Ivuy5CCaqBYx92pqJooqEZD0R0KkMkGzwtQ1PZWynOFqlpFj6L71htIFDUwvBbzmfZquy533JwTXjh16hSGN5F67o1DQU8makAloqTx9YVFp8fcKcoG+VtFjdZLdsj7wGdw6da5yARp9SQ+FSfXhiiHoGn9S2PtFGXaupJHc8fBSIA6tztlj4RJj+Fta4mbtUV9IaYWmvtCNIK0IiNRus1VTtk3bCUOzlZsVdMWXZ2mSPeZsEjcCXThIlduXB9foKI1Kg/OhDsikjHlXEwpFIvcU0daNOdiWAqZNwiijPDQOABAp+Z06XQamUz9BRuBI+PVzMrChS26PXaE6SRReo0oHnrUV7fb27lp9ZLfF7XG97ei5nx/O0fbZ+pqqS2zW5Nm+lSL7n49/wd13lGPVo+dT3fwRfdlSEQWEJadD31Y5hGRBeTz9Q1WwlUu46Vy8zYvjWCi29BUzy6kV25aCyU7C54X8PTBI22N43LHzTnhhVOnTmFoMxHdm4e9T4xYRIlr4D7c6TF3Cg3UjV8gD7OVUnvrU59unYtFhYgosUqQpmKVdFfWr3k5U6aTPsGF6A4HybVlLWqXlskXEEwQ0bB723ryfQZ5Ns25SFduxMXpeTtat21DRZDcftNVME0Dgdgwjp252NY2vMJapomL9hn7mS3W1EOhPd7jocFZEIwHyJjKZvNrxRDJAv3q4fbcqkeGUlDyJAoYHSV1+SzSnUwmkUjUd4BnAUK9Kr2cpT/zXP8yCOJh8rzRPVSP1rTb27Zxye/DUuP7W7hOb3Pikg/oxfaCIhb1e6hOL+/X839Q5x31aPXY+XQHX3Rfhqi6gTtp38XF3Lk9CVUzGovuQEVEFeu0cHALM9Fh6UNe4fOkj/OhVyfaGsflTtNzwsE1tBEk0r0LgPd6bqASUZKiQ3WjSJ0ec6dgbq3D1BmXl1ZG7a1Pfbp1LpZooanEVybx8SiZHAMrS3SLLoRKlC74Li7ZeOn4OQCAVspjw/gIgIqh1EKxvWfUCdoTWi1kEA1X7mXMTA0Anv358ba24RUW8VtciinT86ReZIuhc0SYDce8ZZd1k2FqDa8LzcfEh4gYXj+WamubmqaBVT4tFt179+6FptVfvGD+XnpVpFulAtzNAlK3SNGUedNDdwPSbg9QMjM15zjDMC3ctcN5X995ZRKG6XztTs6TLBNLafM+RSPdas0CR3+e/4M676hHo2N3145U3WPn0x180X0ZEg5I2LN7GA/uGrJXwMIyjwd3DWHP7mE0W/gKVplEFEvtRbqZIYzpol2ME2NB8nC7kBkMQ5jlSrNzwosxiGmaOHP2HFLrrwQAbG5BdLOIkiDJuDA15/ieTo65k7Do/Lph8n9eXhm1tz716da5qNBJZnULWJ7nodMWYgvZ5S+66wlIJ5joxqLskWNniTDWczN2ezBqTI1cAzNGN5ybIpFQo5he8ruw3h8zNdYybXFQLUj/5rJDS7VqLIkI29FE47ZYvYSVFFmBKCyr/viLJQWBGBERW9e31yc9Eolg20byrGE13QFRwEc/+lHs27evYdswZlJdpQNtU7V+GlgPs2MquV9QIe32AJTTjr8PSDz27BrC264Zrrm/PXB1EvfsSNQ14ppjLvlGmy751Jiu2rQuHJBw79VDeHD3cE+f/4M676hHvWP3tmuGsWeXb6LWa3z38ssUWeSxZSSA+3dvQUk1EA2QnrKyyENrUuPB8zwMTYUgySi1GekullUAAVh6a6J514Zh/DAPlOT2Vrx9yDlxw9og7t+9BXmFnBMvXyrguRdfwW03XuX6eyYmJhAc3QBBDhATtaj3h1A0HISaT0OOJnHywhRtAbSUQ0dPYtPIOD69ewuyZQPJkACNnsf9wDRNSGHSGmXL2hH87HSl9laS/NvtSkYWebxuQwj37x5GtmwgERSgG+2di6w8MLhoYmSqZSAYQabQXur0IMAeN26yHONhZ+f2s9MZILwGUlWP4VhQQAneDaUWMzGXBfgx8NpSR/ANSRlnAcwovU3RZE7bAbG2bjcsC8gBUJqsMwhBsiC4eqR++nSvWTuSAE7OIRAfQT6fRyzmnDp+4jzJajN0DetpVkM7DEcCACqi8M//7HO4dlxu2C4MAESaX17lowbdBAT0V3SPJWPAJUAIuF9QqW63Vw9JIC7le3YNIV0yEA/weOKJA/h/n7+IX//1X3f8zEJBAWKVrJNWYSaLi7sW/NE//ATvvG039r99C0qagYhcmcd2E1nkcc/OJB68ZhgFtXfbbRV27B7YNYyiZiAsCTBMy28X1gf8WeBlzP/4zisYTqXwW9cHcM229RA9XICmTkV3E8OWZhTp5y0XPVqduO2GK/HDH+QQHFqL2XQWI8nWekH6ED72pe9C3nA9NnMTuDCXw3xkC3DpsCfRXW2itmk4CM6jiRrDKGaAaBJnJ+frvueJnx7FSdlELCAgERLwppEc3nrzNS1trxPMLGQhSCQKt2vLOuA0GXs6V8Do0OBMcH26w+f+z/eRGdqFREhA6eJRfPK3723r+zSLgwAgtEiRmrRvd3YliG5bQDZ//iSiJHonLMoemcppQBiIS5VZeTIsYxqA2uY0ZyZXBhJAkFv6jLpx2xqcvQBYsXGYpmlH2buNZnLgQKKy1TCjOa1Bm0JN0yGFaXr2qtYdpTvNqlQEwBzCyVHMzs7WFd2nL80CiEHLzXv2gXGC9QhnhANSwwg3g7k+G6g833SLiG6pj47Qq4YTAPIQQ1HohuFqH1W322tEQOJxeq6ELz43iVwuh8+86x68733vq/v+XNkAYkCoTZd8nrr0V5V046mfHsYkhvHJ75/Fo3eO4op1JOvByzy2HVhEO0H3Wa+22yosoh0XyD1CFAbHRPFyYrDPEp+uUVJUyOE4coqBeMR7zalFe6qW23QvL7HPu+jR6sS2jWtQzsyC43k89TPfTK1dcoaInGKgkF3A7TvGAADGyHZcrJPi7cSpU6cwvJnUc7eSWs7gdRJZmpivb8JyZo6Ij5xi4EJaxYWp+gK9F5yn+0kvF7Fu1bBdezufre+R4LNyWChq9rk4nWlfEDOXbiambGiNY67YXnnPIKBbZPLnSnTHaLs0OVjj9ZA3yH4aj1eeZSNxItANvj1PhUyZbMcpYef21+yEaegIxIZw9NSFtrbjBdYyLbQovzwWIgt+Bld/oeHizDw4ujiwdoBEd4rWA4SSY5ienqn7vgvUnIs5j7dLMly7rySXYoS9r7oklolCt9/RDVhPep4XMOvSuX9xu71GjEQkXEiryBgBCFIAExP1/XQKdFoXDbQnNZgbfHWk++9/cBQAYE6/YgtuH59BxxfdlykXaCsHyzSxlt6kvWCZ5G7abnp5SaGi22q97k4oEDO1F05OtjUWH8CgvTo3rUphz5tuQGn6DAQ5iM9+/Yeuv+PkyZMYYpHuodYnvCyyNJOtXw+W50k0hInbhXx/I38Xp0ltp17K1tTeZnJLU1N9Vh55rSrq1YFEMpOKp2hokSkS7dudb9NTYxBgkcKgi/zyoTj1SRBE24QTAHSJ3Ac2rUrar42lyGucHG5rfEWDTJOGIkuNqRLRSMVM7fnemamZdOq2uH40ESHiabHRXDXnJ8nCoFrI2u0/B4EEjTgLkoxLM/UXTycXSAmBZLZZJ0xhYp8hu4xYsvRys2oazc7lfqYZxyIhGLTt2cRs2t2HFrXba0Q0INjlLpHRtbh06VLd96rUiT4Zbu88EzjyfGf7Op3NoxDbDAC4++qxtr7bx6eX+KL7MuU8jQhqxUxLtaaVSHd7tTrs85zZ+vesCpEb8vnM8u9Z2090w4CcIA+wq69YA57nsTNOJravlqKue9GeOn2mLRM1BossZRXn7eaLZcjDxHG2PHOGvLdNp+J2maJReUslItvUyORnJdTe+jRHQUXsmEL7rvUW7fG+WHTz9H5ZUJa/gSSL5gfl5s+hVLzSQnCBZo+YpgkpsbTH8JqRJABACLXXVkrjyT1sVdJZvIcNEk08ejHd1na8YFJRHQnWiuskda6GWP++e4kKMaPcmUhxp5AEHmaZHNNLc/UjtAtFcs6H+M44hC+JdIsuI91UWJtW5f0GnVL3u7ZXp/txer5+n3GGU7u9RnAcZ/u0xMY2NBTdOr0HDsXaMxNlnQ1YVsznvvYspHAMpfkJvOue17f13T4+vcQX3Zcpk3PkZmyUW0t7ZZFuReuM6ObbaLGxawMxUynLybbGcrlz4twESds0dFy9hTx8f+fhN0EvFxEaWY+vPvlTV99zMaNBkGRI0DHagokag0WWWKRpMc8eOgJBlKDm04iY5DzOlfu78DJHI9qsRzBrhbcSam99mmPJlfRMTuqAaz01DGMRTIYAkhlUUpb/QiPruR1x4fobkCW7pRNzbn/13AREOQTTNLB7e6XHMKtXlkJR5IutR0VZN4K1VMQvZmOK3KdmlB5a5PAsA6J2YcfOBJDrO1fPpOkz38EYrt9wGjmmMw16q+dUIsASwc5MX5OhVtPLyfatKs8SFol1UyrRTUyFHNvZTPP5nVO7vWaM0T5rsdH1mJiYqO82Tx3UWdZJq7BDwjIJnqfVBxvFztT1+/j0Cl90X6ZM0wcvp7UoBmgLh3ZrulmP1nZE9203kKhqcGgtZlys7Po4c/gkSZNUMzN22uGq4STkhRMAgH/5ubuaxSxIRGg8wrVsogZUIkss0rSYn71CxmNlJ+z2QM3603abhTwRBDLodbGCam99miOGk/a/mUN0O7Ae74lobZRVAJnkltrMNBoEWCp0OOhugY6lzmbyRFg43bcAYPVoCibtinH20nRLYzNNE1KUlF9dUSf19sZtawFUzNR6Al2MiYVrRfdIkpxzYrB+Sv1cjjzzRbO/WUFOyCa5T84X6s8ryhY5T0bajJ4yEi2ml8vUxK46vZwtIAXcWPF3EY4aLbKWXY1warfXDLaYHh1bh2KxiGzWOTOBZZmsqbNg5Ra2O01OwPd/8jJC41th6Br+ywN+lNtneeGL7suUuRyZuIhWiw9eKrrVJu3FmqFS5xE2iWyFrRtWo5yeJmZqB30ztVY5cZHU+nHl2oWLX755CwDAHN2OcxP1DW4AIJ/PQxom0aYrV7e3ur12lDjsskjTYs7Mk4nFiKwhQg2F2mzJ2zY52quH9ctdSbW3Po2Zz+QghSvnvBSOQWszE0ig9cipeK3BkcST+2V5cQ+d5Qh1040E3dV9Ls4eOXGJmjyW0jXv43keWpGIgYszrfXRvjA1Z3cj2LJ+3PE9t910FUxdQyCWwuET51rajld4OqZEpDaiPZIi3TsEKVA3up+h6dkBrs83SwdCAhlTVqk/HzAlck2sHmp/UQsgtdnxYEUku410sxRyi1squoN9bg8p0HldutD8uXN2mjzvq9vtNWNVjJx/qTWkrtopxZyZ9QLAulXefYOqYR0TLQj4P8+RIACmX6l7Tfr4DCq+6L5MyZbYg7fFSRs1Pms3vVylTS4Frr2WEkKRiEHfTK11LqXJJDbM1S7E3POG61CaOg1BCuDz3/hRw+84ffo0hjcTE7Uda5JtjeeKNaS+XIoNO0aQCtRE7aq1ScRCzVvl9IIivRxi1K11JdXe+jTmxDly72HpzwAwOZdu+fs0TYcYIKJqOFErMNgkVNHbu28OApxIJvCxsLvIpWW3SyOi8uI8ieYtvm8BgKmQjK6JBjXCjThxnph0qvk0onXGl4hGoMxfBAD88MUTLW3HKwJN241Ha0X3aFXLzKk6516OemSEW6/86Rpxet8smfXv40KI/I0b2hRy1VSnmMtuRTcT1lzVWGnaf1Du786VaPlJrtT8uTOVI++Ji+7ngizSnRjfBMBZdDOzXtM0WjLrrYYtcBhCAKUkCQLcs9t3LPdZfvii+zIlT+uiInJr6b9DkSDWJWUE5PZcKTWDjKPdEqhVIfI9BbRvXnS5skADI8OR2gkPz/PYlSIT2hPleMMUyompaey+5hrEAgI2t+FcDlQiS4Io4Rx9gDNyhRIC1ETtjdduQ4KmWRpcfyc7qkX2XYK6tbLa22Kfa819us8Z5gqdnYNeJkKwHdE9l6kYXY1UiSkACFCzpz5XU3QE3k6VdpkubLDsERrNK5N7/0h4qVDjdHJTm824j+JVc44ajhqlxmVLESOLWECAynX/+WOapt2nPBVblAEhidBKZKFhLu1slFY2yLkTW9yGbgAYisiIBQRcsXmT4+/zxTKGh0ewLilje5VpXrskQyJiAQHrkrLrSLfdI51fKroXu8r3moAAxAICxseau5Hb7fYS7s9dZqQWTJFntFPbMGbWqxezLZn1VhMQecQCAq68/nUYSiZQmruEX7375ra+08enHwzeXfcypKhokEUBBdVARBag6kbXb9plk4MAIB70fgoUFQ373/tmZMs6kqGNKCpay+NloltqU3S/5YYtWLtxDa5evQ2Zktaz/VgoFCBJEtLpNJLJJDRNQyTSvNflIFLmgwgBWD+8NG3vdx66FY89dQm/9o7XwrDICvrifVxUNNx+++24vqQjHhRh6O2lL0bDQaj5BcjRFE6en8KmNZXWIM8eOgJejELNL2D3ttfihVfPAwuAJfZ30UXnA5AAjMRIBIqVTZTbzAhxS6N7ST/uM/3cbqu0ek1fnMkASIFT89AFEWIwjOn51iKsQEU0mbqGyCLDrCBdpdT7LLo7cb7xMhXdkfrmX9VwrGSjTCLeZS6EIJx7DEsWee98vjUjtYn5HMCvAt/EdOyP3/NWjKciyJU3QDfMrp7j2XzJ7rOdjC/9mw21BCkUrWuipUJEAEAqOngL1P/urtdgOBaqux81E/jEw1cgW9aRColtzT2qefjaYaxNBpAt6+DAQdFMBJpMSuxId5Xo5kQyllCf728f/A93Y91wFHllY9PzUZdikFDbbq8ZwxEJHAdAlBFKjDpGuifm0gBGWzbrreY333Y7VqUiyJbJ3OLi/JhvoOazLPFFd59RdROPH0njwPE0iqqJsMzjzu1J7Nk93NW2ExokCPD+4O30eNmk0e3qcj3u+qVr8K3D8/jz5yZ7th/L5TIee+wx7N+/356g7927F/v27UMw2BmTl17CR0gK2JXrl/a9HB1K4P/ZE8V3jy447mMAXTmPjWIGiKbsiBPjZ69cBIJXwspOgud5jCQiwEL7PXnbhm5/1RBJfZd4Czp6U3vb6NoEunN82hlTv9vqONHONT2dKQJRQLIUqNSgstUIKwDMZwsASK/3xQZHQephoPcxWa0T55tuGBCp03bcpejmaWlTkTq3c/S+tW3d0qhekDehAciWWlv0msmWgSQQ5Oqn6aq6iYMXyjjwVG+ePfPZSgQ7GVsqui2VnHsLOedzzxTIeTwS7/O9chGqbuLHZ4o4cPyS435UdRNPvZrr+L1EM0w8f6GAx568YH/vXTtS2LNryHYodyIg16aXW5Zll0qEXfoTdANVN/HCJQUff2aq6X6q126vGaLAYTgsYbagIbpqvaPonkkXAIy2btZb9ff8zOH6WjdkDuQzxMenEb7o7iNFRcPjR9L45uGKoCiqpv3zPTuTXVstt2gfz9GE+6hsN8bLShLdOob2alzNKBQKeOyxx/Doo4/ar6XTafvnRx55ZFlFvOczOQRiZLJ8zfYNS35fVDR8t84+fu2mGH56Nt+V/c/r5IE9MV+7Wn52QQFWA6MBMpleNZwAzugQO+AY3Q5ikIjt1cNJACSDQwegGN2tvW10DXTz+LQ6pm5ut1XavabTJR2IAhHRgkbrjhdyrbdlSueKAKIw1aVRWrbfjD55GHTqfMtXtdJbbBZXD7tkQ9GRzuYRiJP71nVV7cIYEZlDGkBBa+36y9L653qdD/txjqfpOaUrJedon0HT7utE97kA2c/jQ+0ZXXaSpvvx6hQe/8VCx/ezopn49pF5fPPluZrv/cZL5Of7dg7VjXjbZmn0GGiaBoGK7kioP6Lb6/lY225vs6dtjcWI6GZtwxYznysDoTbMerH8niE+Ps3wl4n6iCwKOHA87fi7A8fTdkuKbsBTcTI+FG/yzgrdGK9hcfS7Wz8V+7EfJUnC/v37HX+3f/9+SNLyehC8ePwsAEAtZLHGwfSk3j6OBQSMxWQceMXZHbjd/c8iTLO52glkQaiYqAEVkSsGQm315G2HYkmx3avXjxMhYNfedtkouF/Hp5UxdXu7rdLuNZ3XyLFOhQQINK3ZjXtwPbJFIkgtben5zHpaW1x/1s07db5VR2PjUXeRV5FjJRsGXmD3rWIWa2lf7moSIbKfymZrz5eiQT43FHEWUf04x1mrNMNhMQYAeJp+79Sm0DRNSNRRes1oquNja5VG+/HHZ3OQhe7sZ4Hn8MQx53P1iWMLEPj6GXgs0s0L9Bwrl+1a+0iwP6n7Xs/Heu323FBpG+Yc6c5QE7cg33qW13J7hvj4NMMX3X2koBp1+woXVROFLs3UTdOEGCbtmNaOuX/wdmO8Bj0Fm9VP9XpczUin00in03V/l8ksr37hx84Sl14jP+v4+3r7OBESkC3rXdv/MapzMuXK9+cKJQSGmInaVgDAqpEkLGrwNjFTm4reK85PUbdWQ8dqOqHtVe1tv45PK2Pq9nZbpd1rWuHIpHU0HoJM2zHlyq271meZYDeWRoqiNJJmCf0R3Z0637J5srBgqGXXNZoV53YTr5yj963cnON7h6JEBOktGixqPPn8eMo5Ct+Pc5yJaafFGAAQQbJ/nM69dK5gC8MNq0c6PrZWabQfZYHr2n4uao2/t9igJSpzKOeoeVq5XIYgEbHdr/Ryr/upXrs9N4zRh3N0dJ2j6GZmvWGp9dLB5fYM8fFphi+6+0hEFhCWnQ9BWObt3sOdZno+A4Eafnh58HZjvCYT3W2sWPZjPyaTSSSTybq/SyQSHd9mNzk3QwyfZMO5DrDePs6UDMSDYtf2f4pGmEpVkapnDh4BL0pQcgvYtZWkwouCAI0atkzO9WfB4wKtO9eKWVtA9Kr2tl/Hp5UxdXu7rdLuNW1JRJitGY6Dtf2tN2F0Q466c7O2c9XEqFs/hP5M7jt1vrFe2/Witk4E6MdVAzg73fi+NZokx8QS3dWLL4aj5SJrR5yPfT/O8RzN5LEcFmMAQKaZAAVlqSA5O0EWBg21jKF4f0txqmm0H1XD6tp+DkuNvzcs1f/eIM024UQJlmXRSDe5LvtVa+x1P1Xa7XnPyGEO5rGxDbh06RIsq7aEo2wSsd2KWS9juT1DfHya4YvuPqLqBu7cnnT83Z3bk1DbdH+uxzn64NXLRSSi7uuOuzFeiyOnYFBu/cbcj/2oaRr27t3r+Lu9e/dC05ZXX+aZApnYJ2Tn2sd6+zinGJjOqbhrh3PGRLv7n0WYWMQJAH52nKTEcbnJGoMps0wm3jN1WuV0m8n5DB1Hpf68V7W3/To+rYyp29ttlXavaTGSBABsWj1iTxRb9O8CABRopFKAk+gm1wMv9SeNtVPnGxPdpu6+7jNABY1mAtN5so8SkvPixuphIpb5FrweTNOEFCWlNlesdW691I9znLVKY63TFsM6gZUd0msuzZBUaq2YXWLO108a7cebN8agGt3Zz4Zp1T1X79qRgmHW9wJg6diCKEHX9ZpIdzseNe3g9XystNvzPv8ajZG/Pzq2DuVyeUkmkAby7GvHJX+5PUN8fJrhG6n1kXBAsp1ee+nue2k2DSAOveStnU03xstqEttpsdGP/RiJRLBv3z4AWBHu5XlDhAxgPOE87kb7eFVMxv1XD8EyrY7v/3WjSfzkPMAFK94DZxfUGhM1Bku3nGvDMbodpheoW6teMYeKBCRA7X7tbb+OT6tjGkT38nau6dl0FlKICLttG8YRDRzDHADVasMgUtWBICDCIY07GgZggZf6c5/p1PmWLykAIrA8iO4gjT5qFoeyITS8b60dSwHHC5DCceKU7qHN0LnJWTsjbOt6Z2fnfpzjRUUDOOcMCICUtBQBKA6/npzPAojAUvpzj6yHm/3Yjf0ckHjs2UUWVp44tuDJvZyll/OiDEVRUCqVIdLU/Xa7sbSK1/OxUbu9ZrBIdzg5BkEO4tKlSzWZQq2Y9S5muT1DfHya4YvuPiOLPO7ZmcT9u4eRLRtIhgRoutHVm8nUQg5AHJbq3VmXjfeBa4aRLhmIBwVomt76eHmWhtue8Zgs8rhje9zej6kQ6Qvbzf0YDAbxe7/3e3jkkUcwMzODsbExWJa17AQ3ABgyEbVbVtev8WfH/sFrhmt677J93Oh3rbJ5zQhwXoEUTdmT5qIQRwgVEzUGb1DH6BZ78rbLfJ64tbLewAB1sVV7U3srizy2jgRw/+4tKKkGooGlx6f6utX1Nq5bD2O6elzG/bu3IFs2kAgK0I3uXpftEAwG8cADD9jX9OjoqP16I06cmwRAsodGhxJIhGXAAvQ2HrGszZyT3UUyFgZQgBAIwTTNvkQtZZHHljbPt0K5cdTWiRBNKTUgwJBJ+vem8aTjezeMjwIogBdETMwsYP24+3Kqk+enAMhQ8wsIh66s+77F98WgJODlSwX8+KVXcesN9T/XKkWFLMbwcI7yRQIC5gGo1lLhN5MpEsFu9Oce2Yhmz5dmv28VSeBx384hPLBrGEXNQFgSYJhWQ8ENACFat82LElRVRaFUBsAi3f0R3UDtfsqUyXV5ejrnuJ8atdtrRjRAUr+LqmnXde/cudP+fStmvU5067j7+PQDX3QPAOGAhPd+/kmMrLsC27mL+E/339LV7c1lS4AACC0+eMMBCaZp4k+/dwI5Q8KvbtXwxhuuam0w1PkzEmzf7TsSFPF7//AKUrEg/sPVEq66Yl3b39mM06dP495778X4+DiGhobw7LPPdn2bnUY3DMgJ0pt75+a1Dd/L0qUTIfLAE6smJo1+1ypb168GfnIGgijh/OQshhJRBIbJcb31+u0172UGQlkH195ekClpQAgIVLm1xsMBIANA6E0a8J98+xUMD6Xw3utkXLt9w5Ljo2k6/vSJU8jpAt65RcWbbtzZ4Ns6wx9/6UkMbb4GiZCAmakpfPE3fqnr22yH3/3d38WJEycwPj6OyclJHD9+vGn7v7OTcwCGoBVI+m4qFgKygNXGcS/TfooBh+BsKh4FUADPCyiUFMRc9rjuJKZp4uPfP4dkJIj33xxFcv34kvMtXyjhE9+/iKwK/NZ1Iq7ZvqnmO4pUdHOme9EdDkiACZicYN+3dl3hfN8KhwLQSnlIoSjOT855Et3npuYBjMMoNveIqL73/f7nv4OF6BZwEy91RXSXNAMIAoJDBgQAxIKspGXp9C5dUEgveYeShUGg2TOkG88YoGLkGqeLo6IL0RygLcMqorvy3JH6LAjZfnrx6Cv4p1MSFjI5fPLhzVhFu3wAaNpuzw1jUQln5hXExmrbhrVq1lsP8uzScOyln+OGG27w24T5LFv8paIBIZfN4EJaxdmp7jsvL1BXXLmNBy/P8zh55ixyioGjZyZb/h6Oim6v7SqcEAUBc3MzuJBW8crZpX0ju8Hs7CxmZ2dx+PBh/OIXv+jJNjvNK6cvQpBkmLqGnT1YqPBCOBSAkiNC5sT5KTxz8Ch4QYSSm1+yqCJzZBKaK/dnQlmgbq2RKrfWSu1t9w2vMvkC5EgcOcXAaMq5hlWSRJw68SpyioEfHznT9TEBgM4HkFMMXEirUAIpnJ90dsgfBHRdxwsvvIDZ2VkcO3YMs7OzOHnyZNPPXZwlwoxTST3/MO05zcmti2Ea6EbQIdRdbYI1l+mPhwFLv84pBsarJvPVRCMhnDh5EjnFwA9fPLHk9wWFiG3Ocl+byZzbER6GIAVgGjqu3rK+7vtZGdXEXNr1NgBgYp4cS6GqXMQNt11Jooba8DZc6kInhTJ11JY453pjZrJn8EuFSZbeG0NCa33LfSqwSDjPCygpip21YZkm+hjoruG2G67E9MUzEINhfP7rP6j5XbN2e26o1zasVbPeZkxOtj7X9PEZBHzRPSCwFjOZYvcNuHLU1TTUZp6DpJO6sLMzrbtF8/TGHAl1JhJolckElE2Cu83MzIz977m5OShKf6Ks7fAy7dWpZGYQaDPNvxuYJXIsz08v4ODxiwCWmqgBlfO5qPVnQmm7tVZdWAnae7gXtbckHRbQ1VJdEQQACZ6IiBNTParrlGr7Lz918GhvttsCR44cQblcRiwWw0033QQAOHXqVNPPTWfIPpUtMvEeS5GUSqEFAy+GRtODQw4OvZIk2o7fmZz3MqFOwM43Nb+AaLj++R0xiOg9cmFpP+SySkQgXydq6wQT3cEEmcw3vW/RMqqpeW+LE7M5sn8DnLdn8oO33YjSzFmIcgif+9oPmn/AIwrNgKhnrJ2kbdKcnO3Z9CIa8Kd+7SJV9fAul1UUFSq6DRUcNxiqm+d5bA2S8/4XGRmmWbnOjp0lArZeuz03jFEztdhorehu1azXx2el4995B4QInafnlC439EXFUbfdB2+cOsZO51pfKOBFIrY7Jbolk0yUJhd6IyhmZ2ujdstxJfbUBJkM88pg9hZnkaaJuRzOpcnEZjSwNDIWpnm4Dp1yeoJG+zSz3sAAq72FXXvbTU5fIgtAWj7dsMZ36yoyCcqYvUlJFqjBWGn6DADg5TMzDd7dXw4dOgQAuOGGG7Bt2zYA7kT3QpHcVCMiEUSrR5MAACkUhaa1lnnBHO8jdVIpmehO5/pjinWOZmUZTQw5N6bIvX1WXfp3lGmfXSezuHrEw7XnLV9ON3y/YJJ7xmzOW8Q6UyZjisneBBTP89gRJdt6pRDp+HXPMiACdcKpqVj9LAvFIudUMjx4i6vLDaFq/5cUDWWatWEZg5W6/zsPvRGGpiA0thnf+dHz9uvnZogYr9duzw1jVZHu6vRyYtYLz2a9Pj4rHV90Dwgx2ti1bHR/hVSxiMJPhttLeR2Pk8lU3mi9HRJLu401iJR4IUTraecL7t1w26E60g2gZrV3uTCRJhPECD+Ybc5YpGk2r6AokAjiznVL68TitJZR63J7rnowt9aRKrfWFE0D5nkBuaK3Sb9XLtCME05pHNF7/dWbAQDi0LqWBaFbSooKOUyO2ShH03wLg5vaykT3TTfdhCuuuAKAO9Fd0Ml9O0mzHMZHKufnxOzSCK8bTOp4Hw05CyRTJ1k12UJ/TLFY+jWvNY60v+ZKUm/NxVcvEaCsrZVYJ1XaiVik9lnR7L4V4Mg5nil6eyaUTDI9Gop4F6j/5aE3QldLCI1uwNef/pnnzzfCFt1ODnsAhmhpg+Agug17YbD3HgArDYEjqeQAuc+VVHoeejAF7AXrx0fAzxwHAHz13yr3smbt9twwGmO9umsj3cSsFy2Z9fr4rGR80T0gpCJEwKo98LYzePLgHY619+DdtCoJANClWEufLykqeGpcEgt3ZhKQCJJTOt+j595KEN0LNCN+JNwfsdqMOI00zZfMionaDduXvC9BryGD608Uhw8Qgb16uOLWWl17u5DtbkRyKk0mOJLVuMThl3Zvg14uQgyE8K8vvtLVMV2g9dumaeCXrhwHAGihztX4dZqDBw8CAG688UZbdLup6VapmBlLkPtYKCBDo33jJ2nUxysWTQ+O1ckCsjQquru8mFMPln4dbJJ+feuNO2HqGuRoEi+/eq7mdyoT3R5mIiySyxhuct8Kt5hFpvFE3I8nvafHrhkdgjT3KgDgmwfPNXm3N3Tahi5UJ798lJY2OGVZWFSIj9XxfPBxD8dxdlS7rGoosR5tHvwJesWDN5Lnpjq0DZN0ETBv0AXCOu323GBHukfW4VJVpHsuS+5JrZr1+visVHzRPSAMx0kaqin0oN2UTCYRq4ZaE8uMnZtI71I5MdZSCl2+UJksRiOd+btHYuR7ylZvhNfi9PLqFKvlgsKTidiG0fbOh26RopGmcmQNNVGbw45NS92K2TXEIs69xDRNSDSiu27VkP26JIkwqDjqdu1tmhZshpuYJEmSCH2B1PH/25GzXR3TeZqCrBWyuOO1VwMAgqlxnJsYvBRzTdPw4osvAiCie8uWLQDcRbotek9dU7XgYlDRPb3QYoolLb2J17s3UsfvfKk3WT2LcZt+HYuEoMyT8+2HL75a8zuaXQ6HsvW6JGK1HgHrRxoLyBgtO/Hqr8gFybFcS0sFvPLwa0lGiTF6ZUfNAw2O/D0h2XmBvtqheiZde+4J9G9aPZzo2HguZ0yzIrrLdIGDq9M/vZ88/ObXojRzDmIghM9RQzXWbm9znXZ7bhiOSOABCHIACwUNlkWePZ0w6/XxWYn4ontAYL0MuUD3TSfEELnZrhlJtvU9u7ZugGkaEOQgXj3nXWxmq0R3pyLdq4fIBMxcZN7ULVike82aNQCWZ6SbjxDn0u3rx/o8EmdYpCkQJxFSLjflWLM8kiDHnpN7c+yrmZxL26aApDdwBUMh5/lCl2tvWXZHMtRcwSR5EoE4Md3dMTHHaFPJY83oEEpz5PoYRDM1ZqIWj8exdetWO9J97tw5aFrjaK4YTgIANq2uuACz1MrZTGv7mJnvJaPO5zOb3Nu9rnsMS79OuagPjhgk3fToxXTN6zpLlfYQ6k7Fa5+RVza5byXtLDL3C7G6YUCKkhKBK9Z672EMAHvedANK02cgSAF8/hs/auk7nLCo6K7XZjMaDsLQaB171YKPomqQI2SesX68Nbdqn1osmkquqDoU6irPW9335fEKz/PYHiH3oaPZYE2b0KvrtNtzg8BzGKKGRHJyFdLpNIDOmfX6+Kw0fNE9ILBehlI43lXDpXyxDIkaG61rsU0EIxwKQM0Q0fnyifMtjQUADE1paPzkhY10MiGEerOSzyLd11xzDYDlJ7pn5jMIxMi5d22LvTq7zfpFfT7Hgs7pe2N04UoM9d4tlbm1aqX8EidnVnub63LtrQKSjjwab76AtXUVuQdkrO7Wds6kyUSP08jCQ0AhTrkvnx28SHe1iRrP8xgfH0cwGIRpmjh7tn5GwMx8xr6nbl0/br/OGeS4z7eY4SAEyHm0WGQyeJrGWij3p4bUTr9ONb/eNg05m6kxh3YvojsWDsE0K/eAZvetEZoBY3jomX6etkMDgK0bVrv+XDU8z2NnnJwDr5aiHXuuW7QVWCRY35NFp1kWs+m8/RqLtlumiXVjvujuCPQ8LFeLbg+mgL3kd972RhhqGaGxTfj8V59y1W7PDauog3m0ysGcmfWyLBMfHx+CL7oHhPVUAAtysKtutOcmyWTXNHSsHl1qRuWZUhoAcOKS936khRKZkLD0205wxTqyeitHk7ao7yYzMzMYGRnBXXfdhZGRkWWXXm736swv1KQlDhKb1owgFhCwLikjFhAcTdQAYJxmbohyCLlCb+tcJ2iLOqO01MSsV7W3LLtj7UjzMoHX7yKpr9LQOihq90TbHK37FWkrrbUx8siZ6I/hdkNYPTdrFcZxHG688UYAjVPMT5wnHQu0Uh6jQ5XFPsGiBl4tmDoqqgaR1t8OJZzTp0WQSX5J7U8NKUu/Xuci/fo1V5KaUi6xpkZ8GnQKEvSQX87zPCK8jnVJGUGzWGNa5wSrX/aSAXOCtkNTcgsIBVo3HP2dh98EXSkhNLIe33nupZa/pwZW6x+uv4hgquReM5+tiO4L07TUo5iFJPkhyI5Aa7oVTbf9CQZVdK9dNQxhlhiqvZQNY11SRkjPtt0mdMw2U1tni25m1pvwXfJ9fGrw77wDwnAyBkM7C0EK4OzELIYS3amvvTi98P+1d95hclXn/f/eMn1md3Zne1HvDYFEbxJGAglTTMCY4IaJk0Acgvkljgk2YMexje3YARdsQ4iNbcAdjClGgIQRoqkg1HvfXmZmd/otvz/OvbO72ull793d9/M8PGjnztx77nlPe895CwAbEuEgRKH4XUgnF4cCoK0//9OcwUgUgB2qVLpF/5SGGshSFwTRgkMnOnDG3Gklu/fpSJKEX/zqKVy2cgUikop/vP0OvPPu5rI9bzjhWAJWUUAoLsNlFRCXZDjTpBbKxP4TXQBaIQ/mv2kyVsxobcR3p4sIRiVU2EVEYi0pv1dXXQlV6QTH82jr7sNcV+Fmc/nS2R8EUJE6WusY+d4KLi8AYEoOFiznLZmNH27eDovdhU3b92Hl2YvKUqZgJAE4ALuWVWDpjAa0dwOSCYOpbdmyBfPmzcPn/uXzkGQV4YSMV15bj1deeRV9fen7x9H2XgA+SGH/iM+tnAwVwEABJ9G9gaHNmxpvRcrviJwKBUA0UZzSXchYkq/59aXLFuDJgwdhdVVi+/5jOHMe2/RRNKXbkcfCPxxL4OGbF2njwVSEY4mM5W2uqwJOyhCduVs/nejqB9AAJVJcGsV6nxfzHAF8+JKZWNToQiCSKGq8BgDOwpTtjBk/NOsa/+DQxjPbGPRBjuaXr5zIgGZKHk9IiMvMn1nIIxL/WHPbqsXw1DZhYePsnPtPNnSl2107lDZMD9arW5kQBMEgpdsk8DyPRDgIobIWbd39yUVJqWnvDQCogxIdzPrdXKhxCugC4I/mP9GEo3Ewpbt0ygjP80gM9EKoasDhU91lVbpllUOk/kx8/tnjCMcVOK08Lp89GwlZgUUonxFJXFLw4m4/1u33J5+7ao4XVy/2wZpPGGBouTo9gE0xZ2qPuKTgpT2BFO9qHfWuoiAgERmE1VWBzt4A5qYItlYueoMRQAQEZbTVhu57W06l2x8cTKbmmj2lIcu3WV3J/adgaZyDd3YfK5vSPRhn44LTwsyIVy6fjxdf7IK9qgFH27owrckccQQSiQSi0Sg2vvU2NhxN4BtvHhzWp8/EVStSK74A0NYbBOADFx95fG8XgAiAcCL/k69eP1OMZCkxyl1Bx8IDMQAxqfCTtULHkuHm1zNbsrc3t9OOeO9JOOqn443tB5Lzm+6f7LTlthQppLxTGmoAdEK0OeAPDsJbkTnwGgC09w4AYgMEqfhx8QsfvQjP7+rHTzd1FD1eAwCvKd3pfP0BgNNykweGubR0BwYB+JKuHkQJ0Mb2mCQjIeef/m6sOX/JHDy3o7dkbREAat2a5UX9FLS1HWEfaoElKUo+QYyEzMtNhBpjinBHX4HRbnNAD+rDSaWZeFt8bHCN8Pn7hoa1AEBqifNa6vV4qsBUPbkQSyj4864+/GlXP8JxNtmG4wr+tKsfz+3sQ6yAhXYuhGMJPLejF8/u7Bvx3Gd39uG5Hb0Ix/Kry+4QWzRUWs23UCjkXeUYa9/d/rE9zenXcgCnitaq+97mK5t8OKCZOEuxCOqqczvR8wpaMLXu8m24RBWmbFfYh/JXR3pZJOv1JgqmtmvXLnz9m9/C+sMxPLuj97Q+7cdf9g2k7dNdATaWWtWRmyouK5te842aDQyll5Nj6WWjW2TH5ML6bjFjyXDza2ealGan41ZYn9zbNnR6rPsnOzP4Jxdb3tqqCsiaNdXxHKOI9wyyzTM7V1z05XAsged39o1qU4WO1wCSbgeVGXJt664NA8M2+nqDI109iOLhkifdcjJ/ushnjuZvFKVeO+gk04bVDpmXlypYL0FMNEjpNhG8ZhLWEyzfIrhP87G0qKVRAGa3MNNC3lmd5Zujiei+pEppfRItCntHPW9xORB4Duv2+VNee3lvP4QyTbxWUcC6/amfu26/H1YxP5eBkMImzCZveQNqFUJB76qd4vQGxvbkfiDK2nAqFzZhDHxvj+mB3EL9OQclnJMMplY+E8CEFjG62j2kmNliLE/sjmOlS6NULLt27cJlK1fglQOpzYlf2R9I26f9WtQgt2Wk8uvWNhriav7TbL8WfE1JpI9LoQcfSyiFjTXFjCXM/Bp5mV9P87E20Ds8mJqQPSiYTqHl5XkeiRArZ1t36t+fTq7p0LJR6vE6FpeSWRKq0vj6A4BFG3MGY0ObBgFNAbdx5vQ5Ho9wmv92XJKTkfgtJl1Vl7ot6ujm5Y7KGrR19owI1susTAiC0DHp8DA5sYApof2DpQssdjoBbYFo40sz8S6ZPYXdr8IHfzA/k/WItrNa6ryWToG9W18BAYxyJZyQk7vFo67FFYSL9LNMRyie+bmhPBU7xcbMZmc05r9pUm4KeVdeixjdP8aB1PRore4U0Votmrlhsb63mTjZzZQKLpZ7H7xgEUuJZaluLlswNdXCNnNqKociXDd7WB11mMij4fDhw+gfjBbUp0MSU8y8p+XHqdQCXUlc/v6SAa39qhmCTDq0o25JLUwxLGYsae9lp9ZCHhZTZ89lUZK5yuZkMDVO1IOCZfBPLkF5desn5l6VnYjC6rbaVXgQNaD04/Xw08iqDGbyuhXE8I2+IVePvB5JZEA/6U7IypDSLZjzpLvUbVHHaRVg0Sy8esNyMkq+Ikuop5NughgBKd0mwiGwSTFYiD1ijoQS7BkuS2kmhtaGGsTDzBx+276jef02EmfvyamlVUYq7axZD5TRis5pEeC0pu4+TisPp6U8qTJc1szPdeURBTiRkIbl6mwqSflKSSHvKmqTfzA8tiaUcS08RpVz9CJdP/koxvc2G7qJs0XNfcPunMWzkIgMQrQ68Ob7+8pSLt7GlO1G35BP9Jkz6wEAkrOw/Mfl4I033kCV256xvTnSHGHFOSbzusqR1iJVHva3mkeqKp3BsCZHOX07dlhZm5NR2FhTzFiim1/buNw3ay45az5kieWKfl+bK/hcgoKVoLy8xCwGcrUik/JIh5aJUo7XADCouWTJUiJjVHWHyOb34UuJqDzS1YMoHg5sPZWQlGRQwEJ9o8tNqdvicLzaEDcgCcOi5AdKEqyXICYS5hwdJim6D2C4jGlXYwp7RqWjdBOvPMBy7+473plfWbSTI67EKTZqPGzBFEP5tvRlRcWqOd6U11bN8UJWyuMjHZfkjM+NS7lvYOw+fBKCaIGciGP+9NQRwY2kkHe18azeB8q4cZUKmWerDl+KHNlDvrfle36/Nmi4xNzbnSgIkP2nAABv706fh7pQFEVJRoxuGpaecOXyBQAAu7ceR07lN2aUg3g8jtdffx2vrd+A1fNSp5+6fHYF+v2pT0lVKztxbK4Z6Uvv0/Jrc9b8XTcGNOWKV9JPBnrwMYUrbGFbzFiim19X5GF+zYKpnQAAvLH9IABA0JTuClf2OiqmvGKeVmScnfmk5pIOLROlHK+BIWsZOZbZwkBXruLDXA90V48qd/YNDiI39PRgCVmBDFbXZlW6S90Wh6Pn6o4JTrRrsXSUqAnzQhKEwZhzdJikeDW7r5havt3BBFf6idcqs8H1eHd+wat0pVsosdLdWM0WTHre4nJgs/BYPceNaxZWJRc4TiuPaxZ6ccVcN2xlcuxy2iy4ckE1rl3sG/HcaxdV4+rFvrxSf+w6zBSueLDLlHlbnTYLrl7sw7WLqnN+V/0QJ5IY28Bweg7g+qrRUa6HfG/L9/zBhGbibM9v7KgSmBJyqAzB1Lr6AskI11Mah3z76n1eRHq0YGpb9pb8ufmya9cuxONxfPWB+3D1otF965pFVVg53Y7N77yV8vcWF1PUpzaMTNVWX83agmDL/7Q0pKUZ0+MBpMJtZwqryhXWdwvpXzqFml+7FWbmva8tAEmWIVjZPFTpyT5WF1Ne3YosEMm+oy3JMqx5pEMrV5lToZuLZ/L1BwCXdl9p2BJPEVld11ZSGqdSwesn3bIKRbM4sRXoG11uSt0Wh9Naw9ZcjuomHG3rBlC6YL0EMZEw30p7ElPtseNwHJC54vzIMqGK7ESh1luc2dxwKi0KBgF0DeZ3RK/vrJZa6Z7aUA34AcHpLel9T+eB+76EK669EQ/9zTkIRmVU2AS8/PLL+NpvNuCb3/xmWZ55vL0bP3irHzed3Yjv3zAT/oiMCrsASZLy3mE/3NEPiDUQYubN22oVeaxZ4MW1S3wj8gine1eXVcAgynuqnApBi9baWDM6cnixvre5EIMFdgC1lfmdqs5p8GC7AgRR+oX4cS24mxQNo9I9cryxxfsBtGCnCYKpbdmyBQDg8XhgEXgsaXLiw4uqWZ+2C9ix/zAuuuBqfOITn8DatWtH/La7LwDRzupu1mmp2hpqvAD6YXG4EYsnYMsnF3VcBuyAmGFsdDusQAiAWPh8sfOD7ZheOxMPLZ6JYFRGpUOAlKF/6Ui8HRYA9d782s30GjsOAuhNWBEcHNroyZT+ajj5jgc6biuHPuRmRXasrRu8aIGqKJjRmj0dWq5l/vBiH4JRGV6HgEQOZU6FftKtSplP7D0OKyAD8rANGU7b/NE3g4jiEThABiDJClSOydNWJteyUlBo/8lGo9cOIABPXSuOtXUBM0sXrJcgJhKkdJuIOq8b6ALUAswRc4W3s4m3oYQTb0MlW0gNyvk1p5jEdolLHXdkVmsDsLcXVlclBsPRtHlui2X79u3449sHsOZfvotKh4BARMZjt96ESy44pyzPA4AfPbMRHe4F+Oav38ST/3I5/vvlwxiQBNw0M45Lli3I617t/ghQA7h4c0+O+g58pYMtDMQMOdA9Dgs6MeRjPRaMiNbaODpaa7G+t7mganlRm3359esLFk3H9g8Aa3VL3ophNtp6/AAqIEVGp0BsrRBwCkB72PigQ5s3bwYALFu2DADwo43tiEsqZtXYcbAnCqFzB/bu3YtDhw6N+u2B4yxVWyIyiBrvyLpvrKkCwKJ8t/f055WTPKqZRWRys6xw2YEegBMKV7pffGU99jU64bEJqHQIUDr24JufXZv1d7r5dWtdanP8dJwztxUHDwNcZRP6AkNB/9w5mJfr5DMe6FQ4LOgDEMshkvyhk50A7IiH/Bn9pvPBabPgXx55Aa6W+XD27cOXPnVlQfeJanMmsqTZrHTbgQCgam1DURRYNFePljxlRqSH14JkSoqazDlvZqUbKKz/ZCOZNqyuFR0nt8M3s3TBegliIkHm5SZCz2koaAuaUiPJ8tDEW1+6aNXTGrwAANmaX7kTMhuUS+0C1VxXDVmL+nvgeHtpbz6Mnp4eOLx1GIjJOOmPYyAmw13Xivb28jxTURQcirKF/aKqOCwWEYcP7sdATMamXUfzvp8/zhSeWvfE2XurcrENFqWAiNGFcqytiz1TSqDB5x11vVjf21wQNauOKaeZOGfj7EUsmJpgtWPjttKaenf2MwsKNT7at2/pTHaCKJsgmJp+0r1s2TIEoxJ6QxIGYjLm1jsxEJMheFm8g8OHD4/67VH9ND/UP+qazWpBIsIUyw7NzzFX9A3JzEo3U1R5a+GbitsPHAeA5Bh2vC+7SWgx5teXnDUfciIOq6sC7+ximxhSPFL2gEs+LaidlIMV2fHO/NOh5YIUDeGkP46jebphDSeuBWPkMvj6A4BXz+GtmZT3+gcgWNi7Uxqn0qEfGEgKoPJsnNc3WScTdZpPt7umOZkhx11kuj2CmIiQ0m0imuuYImx1VZYlhU97dz94gU0IUxpKt9hdMJ1FvrZW1kGSc7fr1X1cS+3+zPM8EoNs4XRE8y8qB93d3XBWjTy98tS1oq2trSzP+826t+GoaUEiGsIdH7kUAFDJM9++Q135By2JC8ykc0pNeTZ5jKBaC2SmivlHjC6UU92srSXCwZQ5spO+t3x5NgL6AgOwOJkMZ0/Jzxy2nMHUerRc6YI82hT2suULoCoK7N46HDrRUdLn5kM8HscHH3wAAFi+fDmO9rKyNlRYsKCB9Y9BnlkxpFK6T/WyU3wukdonXo6xftndn5+ipWf2sWfYkfRqftBCEUr30U5NsdRTH4np01DpFGN+7XTYEO9j/vzv7GdyV+KZ/ZNLQa2Wsk5PYZeJjn62UZJPOrRcqNJitoTlwic8vV3wWTJ+VGtB/ATNau6YlsZJikXgzZBqjMgPvXvKigroSncRvtHjlWqnCCgyBIsVnqZZAChKPkGkgpRuE9FS74Oq5S/Vcx2WkpOdLMp4IjJYUpPrxbOmQJElCBYb9h05lfPv9AxKYhnyWqqan/Kp7tKeViTvr6raSffIzQt3XSv6+/sRiZQ+iMif32fKvL3/EGqrmcXC7Hq2gAoo+bskCC52Kjp3Sn2JSmg8NZWsPnjb2AULatcULyVNjmy3QztdE8qzGDuomThL0TBqUwRyy4YeTO1wT2nbrF9L22bF6EjytdWViPaxsWLDVuOCqe3cuRPxeBxVVVWYPn06jvYxBXBatR0tXhtEnkNM4eGubcGJEycQi43cQOgJsjqzqalTe6lxdr07kLptpENS9UBH6U+AvR5NsbLYCtqk7e3tRQRsQ6jeoVkdVdQmc2ing5lfo2Dza4+qnf7HNNNnqfzp/fRYC4I9u8LZM8BkbOdKmwGhXoujkuAL3xDUU8Vni4NS62WbcKLdBUVRcKqLbQymcvUgCkfg2dpFVjlwWtDIyah08zwHUWIbjNVTmZtbtYei5BPE6ZDSbSKYOSJTFvVch6WkTTNxlCKlDZxls1oQC7AT5Z2Hc1e6Zc09rRwpNiwKWzh1+suTtmJgYADxeBwOLzvpbq5kC0hvwzQAQEdHaU/vjrZ1Qa2dCwC48YJZyc/PX8ieJ1a3IJHIfZHY0dMPq9sLADhj7tSSldNo6rTNCNExdqf3PQHWxjgp9YldhWbyzpXp9P1oh7aZFupPedKejbmNrK5KHUxtQItmly47oT3uBwDsPN5b0ufmw3DTco7jcKSXyXC6zw5R4NBaxWTWNG8ZVFXFsWMjrQH6I+wd3ZbU0fI57ZS/fyC/DQ3d/99lT7+Arxp2YtkfzE+pB4CtW7fCXctM58+aVg1VUWCxu3CsPbN1ULHm19N9rD+IvikAAHUMlO4WzYrM4qzMOk4GY0yh9ZTYPFZPP8bZCh+b9GCMIpdZ6a7RNt940YLgYBhduqtHjNI4lRIxqXQDnLap6rSXLxCumXFxrB/rbgw1laUL1ksQEwVSuk2GrCnE7T2lP6Ht0szmEC99eiA+ysp7uD33zQJ9AWEtQSCP03EKbFHSV6ak5z09zBLBVc1OiefUsZPm6pYZAFByE/NHntkIwWJFpPMw1l50ZvLz8xbPRiIagmhz4M3t+3K+3/b9THmIDfSNCgA1nmnU4yJYbAgMjs0Cs2+QKWqWNKedSd9bS3mUbt2ag0vhO50LFyxibdbqa0UkVjoFSHPtg8eW+rS2tZJp4x0GBlM7PYjacKUbAKZVM5lNW3I+gNEm5iFJS9WWZmdB1CL4BsK55YfWUbSo0+4MC3iXwwZFZpVciNK9efNmeOpaAQBTfE7EgmxM++DAiYy/K9b8+px57JmiHjA0S1CwUtCi+TFzPI+TXZk3efR0aD53aZUn3f/d4q7Oak2QDt0CIptLls/rSVrNdfcH0RNM7+pBFI6udCvgwGtZBFz2sXNtMhPVjpGNstE3OpMHQUx2SOk2GVyiMHPEXEhOvErpJ16ntsvZ1p/7QkzRmp+9DNE+9XzFg2U6ROnuZqdB+km3rnS7atjJUSmVbkVRcDjuBQAsrh55SmOxiJD7NZ/cPIKp7T/Byq+ESm9RYSQ1VRVQFHb62N49OrhVOQhq2qWdT33aOeR7W56sBF2a77RVLaxfL184E4nwAASLraTB1GIqUxwrnalPa80QTG1EELWIhL6wBA7AVO2Ee5qmfPumLwQwWulOcOx7dWlyH1u1E8nBaH6myqp2auZxpl/A8zwPWfOH9g/kv5G6ZcsWuDWlu85jBSKsvxw4mfmku1jz64vPZMHUdDiltGbcqXDYrIiHmWn1yc7MY54kMJk3VJX2pG6m5v8uWKxJV6980fNuZwqwB7BYDVJ0KJ6AP8RkZknh6kEUjkVzjZMUFbx2wutyTE6lu8E70py8maLkE8QoSOk2GYJ2WtYbLH1wmUw+lsVS42KrgP481v260m3LtoIopDwVbAKIoTz+Vd3d3bA4PRC008vZtUyhEj0+cIJYUqX7qb9sgsPXjERkEHdcf+mo616BtZWD3bkvvI/3MIsKm1J633MjEQUBkh4xurc8/vynE0owZduVxhx1yPfWWpYAif2a0u8SUyv92RAFAXKAtdd39hwvWblkXjMzrEitkF529lAwtYNlzDKQjlgsNiKI2hHNn7uhwprMra6feFuqmSn06WnDVCsz8W6uTX2qo8cSCsXzO9nUXRF014R06Ep3YDD/frxl2/twVTcCYCl/HGCD96n+zONIsebXLJja0Gk6lyUoWKnQrcjaskSS5+zM8kc3By8Vbqcd8UH2bN0vPl90CwhbDi5ZshZPoDcwiGBUd/UobIwgUmPRrPQkWYVoYX11sirdU+tGjoGlDNZLEBMFUrpNhp1jk2MgUvrF+aC2WEpz8FQUrTVs8Rnjcz/N0/Na2i2lj3LZWM385hRLefyKenp64NROuV1WHrVuC6wCB47j4fI1ljRt2IF+FR6bAEfgcEpT8GQwNTV3n1xZsKPFa8X0monndyVrJzw9/tLGLkhHVGHDaKUjdccq1vc2G6GEbuJc+OZVtRiDxyaAd5TQ1UDLHV5XlTp4VY23AtG+U/DYBOw7kf501e0uT7TlvXv3Yu7cuZg1axamTZs2zLR8aNHcUsmCqamiDe661hEn3YqiwKKlzprelDoNk1OzA47mqVfqacC87sx9WpWYohwM57dJ29vbi96QBI7nYRU4VNiFpHloXyTzBkEpzK89aggem4AWrxU1YxVwSYsw3+1P3wclWYbPV4MWrxVzpzaVvAiy5gd/rKMwCyM97aAjB+swVbOa6x8MI6zts7uttOQrJRaRyUFS1GQWAfckVbrntDAl22MT0ORUShqslyAmChTT32Q4LcAAgME8T0ZyIZuPZTHMaanD5sMA78o9/7fKawsIa+l3AaY2VAP9gKDlLy413d3dcGjpwrwOERzHodZtwalAHJ66KTmddIdjCVhFAaG4DJdVQFyS4RwW+fTSyy4HeAH/dM25qLCLCIZSLwLPXzgdO3YClupmxOIJ2LLUZziWwDf+7koEoxK8jqkIxxIjnjvu0XxNe4Olj12Q8nGcBSKAanfqRYbue8sLIvqCg2ioKa3ZXYyzwg6gtqJw8/UvfWoNaiudGIhOgSQro9piIejB7Jo0P/tU/OfHzsH0WjdCMTnlcxMycOmKlayP8LmXK1vfCoVCmDt3Lv70pz+hvr4e4XA4mS5sWvWQHEWBw5QqGw73RuGbtnCE0t3VF4BoY3U+O03qLI/dgh4ACTU/ZUfQou9XVWTZFNOCkA1G8nMt2LJlS9Kfu85jAcdxaK5yolcFIsi8WJYEOywozvz6gVuvRL3XhWBUQqV9bMYgUdGsyDIEtQtFJfzPjXMQjEqocoglLxevKf7tfYVtCOppB+05WIdxWtsIDEYRVwVYAHhdkzPIV7mwijyQYNHL9QBi5ThEGA+0VDlx14omLGxk/bpU8whBTCQm5+hgYirsIgYAROXSBxeKQ4QVgNdZ+ol36dypePJwF2yeanT3BZIprTKi5bW020rfDGe3NgB7emB1VWAgFIHHVVp/2p6eHjgq2c6uHkSpzsOUbndtS1alOy4peHG3H+v2+xGOK3Baeaya48XVi32wijxUTsCrB8NYt7/ttOv2UdHez1syGz/cvB0Wuwubtu/DyrMXFfzciQAvs8Vm/2D58/8CgCqytlWXRgnRfW95hxuBAnxvsz5fs+ZorikscE1cUvDO8QjW7W8vWZsYDEdhcbAT6ikNqU+B45KCXR0x/M9fO1M+Ny4peHFP/m01WxuPRqP41re+hYcffhh+vx9erxd33nkn/Is/AWDIj1tnmk9Tuqcvwr4/vwFVVcFxHA6e6ATAIREeSJv7uNJlBWS2MZMrkVg86bbiq8x8ys8pzCIqX6V78+bNQ/7c2on17JYafHACgMOb+ZlFml/HJQXvnYhi3asdYzoG2TgZCoBAmuCacUnBK/uDZR0b7VwCKoCegQLHJj1CtjX7nMlrQfyC4Rgk3gYLAJ+nPHElJitWUQASAIZlprBMkHk0X3iew5GeKH66aWz7NUGMJ6gnmAyvmw3eiTL4IstagJiaNEF/iqGhpgrxEDOd++BAjn6h2q69qwwpNhprqyAn2EL0QBn8Rbu7u+HUT7qdbAFUqy1ePXWtGc3Lw7EEntvRi2d39iGsWTSE4wqe3dmH53b0IhyXMl+PjVw0ioIwFExt98iURnk9N1b+KMJjgajFLAiGy5+KCAB4LfdvQ4aNpmJ8b7MhurwAgGmNvrx/W642cbxDC9QnS6hPcdJd6j6Q631D0Ti+8Y1v4Ktf/Sr8fj8AwO/341v/80P0R2QWRK36NKVb+7t62kKEQqFkEMVjHSzatxT2p62HKjdTclQhd5PTvmFuEdkyC+j+0OFofnLasmUL3LVM6a71sHF4yWzmt26rqMFAKHU7TSQkWDWT+hktdXk9EzB2DNKGaQzERluRjVW53Nq0HogWZsnGCWyOcTuyz5kiWNsIxSTAwub8+jSuHkRhWDXzcs46NGZYBeOyMRjFZFlbEESxkNJtMmq13IaKWHp/GM7KJt46b3lyGMuDLCLrvuO5BYnhRLYCcdhKr3TzPI/EAPObO9KWORpvIfT09CQjl1fpJ93aisqdxbzcKgpYt9+f8tpbxwZgFdJfX7ffn5zoh1OlBVM7lCGYWqbnprvveMSmRREfjJU/Uq8ky7A4tZO/+vSuFYX63majxx9MnijPSmPinIlytYlTXSwSdiIcgCiMvkc5+kC2+67b74fNIuJXv/rVqGvV01h08nqPBY7T8jHpwdRqZywGMBTB/JQWjDBTqrZaL5ONPvbmQm+A3VdOxOHM4h8qaIpVOM+2ztKFsUwL+rg1tbEWiWgIHM9jR5qN02Pt3eAFEaqiYGaLedpbLni0qHaprMjGqlzVmnl3WC5s6cVpFhC5+A1btXEwFJOGuXpQROlSYtV86y3axqsiyxD4yad0T5a1BUEUCyndJqO+ik2OnK30Aa5ELUhSc4mjsupYZbb41CNjZ4PXTOXKFngkxgLmnCpDzvMRJ93DzMsBwFPXAr/fj3A4tQIcisvJ3eDTsQpcxuvhuIJQfHRUplyCqRVy3/GInjI5nCh/pN727n7wAntga31qM2oASd/bgRIr3QePdwAAEtFQbi4dp1GuNtGuRY5XoqkV0nL0gWz31X/ncIw2sfVNZy4ZzZ7RC+Zmrw0WnoPocMNTNyWpdPcE2WmwDelPceqq2Jgr2HMfz/uCrM7kWHZXBBHsXSOJ3OXU3d2N48ePw13HTrb1cYvneUhBtkG5+2hqSx096nZ8sD9r7IhUGDkGVWkKbzyFV91Ylavey8bnBF/YprqudHtyCFKlh24JJVRYnGxdkWljkMgfm+a/rW986purk43JsrYgiGIhpdtktNSxSdHirISilC6YWmAwBNHOJvyWhvzNUHPBa2VKTvdgbqcuvL5rX6YolxYtb3Gnv/R+tN3d3XB4NZ9u3bw8qXQzs810JuYuqwBnmiiycVnNeN1p5eFKEUTngkUzAAwFU8v3uenuOx7R3yNWhrgIp3Oig1l3JMIDGU8ldd/bULS0Ju9H2zUT51BhOcnL1SZ6Akxx5KTUZsrl6APZ7qv/LhIZXSY9D/esutHmtyLPoVXL2109fWEybZg/whaSbkv6zZ0GzbTeYnchEstN9nrObSWRfYNGP5SP5qF067nJK+s1pXtYFHKLxDYqj3Wl3qg83snamRIJ5vy84Rg5BtXoVmTC6PlmrMql+8FztsLMvAUtLVW2VHIAkhYbgzKbnxRFRlMdKd2lxKafdGubGoo8Oc2oJ8vagiCKhZRukzG1kZ2WCRYrekuY8ui4tjiXpQQafN6S3Xc4DZVsIRBSsp+AKIoCXov2Wa68lk6BbVr0h0o/EaYyL691sfe2ODywur1ple64JGPVHG/Ka+dP9SAup7++ao4XcWn0AvucxbOQiAxCtDrw5vv78n5uuvuORyq0QIEJlH+ib+thSogUzdxXC/W9zfr8Xqb8ZDJxzkS52kSfFijKoqYLWlX6PpDtvqvmeBFLSLjllltGXdPNy1sqUrcZPbiab/qi5El3SNvUqcqQg7FxmDlvR09uGyOBEKu7XE7N9LVsPgdJW7Zsgc1TBcHmBAfA5x46+fWIbMzsHEi9QdDRz5RyQS4sNoGRY1BDdXorsrEq14xmtlFr8fgK2lTX01J5PdktJ1xagFLZyqwtEqFgSlcPonB0aw9ey8SCSap0T5a1BUEUCyndJsNb4YYUZwsaPVBPKdB9LKVwADxfHrHPaGQLTNmW3Wc8GkskJ6pSRxbXqdTyFg+WeB6Mx+MIBALJPN36SbdV5FGl/dtT15rWr9tps+DqxT5cu6g6uTvstPK4dlE1rl7sg9MqZr6eIgWHKAiQ/ZmDqTltFly5oBrXLvblfN/xiNfFNnFkvvzv06UpIYhntqYQhgc1KuXzA2yssKqFnaBnbYsFtomAlp/QxqdWLMrRB3K5r8tuxT333IP77rsPXq8XAFDfOgOu6gZwAOY2eVPed3q1Fk18WNqwBMc+q6tMP35ZLCISEdZGOnr8ab83nKQLQg4LeJvIFP9EHvob8+dmp9xVThFWYWg+qK9gG1YDUmrlrHeQbQQ4uMLacbnaWy401bL5yeKsGKXw2i0CVs+vKvvYOKu1EQAgiBYcz3N+T0hSMkWd15M9RoDbwcpsrWTzlJJlY5DIH/vpUeTl8scRMSNG9muCGE9QyjATIoWDEK0OnOrqx/IFpblnR18QgANKrLATsVxYNKMFf+mJwVpZj0RCgiVDvsqB8NBJicdZHqW7tsKOLgAxlDZQW29vL6zOCghWtujWlXuABSXqD0vw1KZXugGmoM+uteLDi2ciHJfhsbFcwnpqDU6V8aFZTly7xDci13Cm1BtVQgwRpA+m1uMP4sFXTuFj57bg+zew5+Zy3/FGtccJxACUIRjh6fQMRAALICqZTyV139t8zIBzoT8sAW7AJRbuv24VeaxZ4MU1S3zwR2RU2AXIcnFtIqT507ss6U389eema+NWkcea+ey6PyLDYxfQ1hfKWq4/btiCWXPn4aHFMxFNyLBbBOxoC+FIbxRz652w2+34whe+gHvvvReBQADHQhY8/EYnGiutsFtS31sPplY9bSE2/pIp3apmItxSm9mXXo4OwuJwo6s/N6VnMJoAOIBXclG62diTj9K9ZcsWuFvOADDkz60zvd6LowFAsqQ2fw7GmFw9tsJdN7LJvVxMaawB0A7Baod/IITqyqHN4T+89i42h+rx0bNq8IMbZpatXE6HDfHBfljdVTh8sgvTmnKPAO8fGJq7q7OkkgOACocNiCCZP5qTxiaF4mSCnXQP63zK5FS6AeP6NUGMJ6g3mBBVC6CT6yItF7p1H8tE6VMW6SyY0QJFSkAQLdhz5GTG7+opaVRFKZt5eaNmTqhYSpsirbu7Gw4tiJrLyo84KapNRjDPnDZsMBzFg+tO4O4/HEZ3dw9EgR+1G/z6a69AVWRUOiwpr5/OnAb2vkGkft8f/eF1dEZ4fPP370LgkPN9xxs1WsRo3lb61HinE9DSklm5zMr0kO9t6eI0AEBI0k2ci9s/ddos2HngGL63/iTufHpX0W0iprAXrnRkLpfTxtpgurZoEYDXN6zHMxvew91/OIwv/PLtrGa5v9/Shoc2tOFrP38JlQ4L/rSjBw+/3oaX9gyZd7tcLlitVtTW1uJEgC2Up/vSb9I0eW0QecDq9CCYYH7hFi111rSm2ozlUbUxtycwmPF7OiHNBUG3jsiEQ7Mvl3J0pejq6sKJEyeScSf08Upn/jR2EmupqEtZzxGFPcfnLm4jM5vcy0F1hTuZRvJY+8hT5me3nERbMI6Hnnqp7OWSw8xf/pgWDyJX/EG2LlAVJaeNaq975HfEAq1hiPQ4TgsmqLsRTVaM6NcEMZ4gpduE8DLbke4Jli4AWL9mFmjJEGm3WCwWEbEAi36763D6U15gyIRSlmJlM3efpgWME7Q8xqWip6cnaVp+urJTNyyYWqaT7r9u3QNBtKC3rx+zWutLUq4LF7NgatbqlpRBm3b0sbLOsAbLVudmoF6L4i3aPSUNRpgKPedvNp13yPe2tBHV4xxTfmorircWaazx4qQ/johqTZunOVcSHOsH1Z7irQ0GBwdxxbJZ6A8OwlE7BX9c/17a7779wX7Ym+ZBVRTccBEzE7pgOmsP204OMsuA0zjax8aiadXpyyryXDJ/t2/6Iry79QOIVlbns6dkTp3Fab7Z/QO51WlEc9C2cNnbrr6oVXKcyvUgas2zmA973WnK85LZU6EoMkSbA4dPjk79KAnsnRvGYb5nnueRCDGFt617aAPmeHs3lNo5AIAbzp1R9nIIWnDB9r7cNmF0+rWTbikeyWn8rq4YueloT+PqQRSO3Xa60k11TBBEeibuynscYwVbGPpDpUs/EYyye9r58qZR4mNsUXO4vS/j90IR9m5KonybALO0xbDVWYHAYOnM6oefdHtPO8nTF7HuLOblm/cxSwA1cKpkCvDyhTORiAxCsNqxcdveEdde2LgNjvoZkBNx3H7dhSV5nlnRI0YLFiuCg6WPXD8crVvBY8t80qj73pY6c4pqZQGVmmsqir5XXXUlZIn1x+PtxeW2V0WmnOkRo4uloaYKlr4DAIBnN6fOIQ0AP3tlOwAg0bEXZ8ydBoCl/JpT54CiAn89ODoq95FepnRP92W2uNGV8uppC7HxvfcBAPFwEJXuzO8oauO5bhWRDT39VxpL9xG47JrSnWP8Al3prmmdDWC0ebnTYUNc2zj94OCJUb/n7KydtdaNz3zPqhZwkLlbMR55ZiMEiw2RriO46uIzy14GG8f6WM9AfubeQW0jTInn9rvTTdAzuXoQheGwjdy04nOwTiEIYvJCSrcJcQhMMQ5GSzeAh+Kaj6W1vBOvi2cLijZ/5lOdUIQtHBSpfCZvDT5vMijdAS2fcSkYkS7sNKVbTxvmrmvJqHTrC32ftXSbDsODqb27Z6Ri8ttNBwEAXPe+vPwIxyO1VRVQtIA2bTlGjC6UOJi8q9yZFTbd91ZSS9v/RJdm4tyYIUd4jvA8D0kzfT3ZlXnTLOu9tLzUjb78c4en4/pzpgMA5Nq5OJEiCNVgOAq/g5lNXzpz5CbEytmsHBsO+qEoQxuP/rAEf0QGxwFTqzKfyicjmE9biK279rOyhPxZy23VTqwHcwyiF9OG/Sz7OAAAt+6aw+fmXrB582ZWJi24Vp07hbIeZW3g4KmR5s8JSYLVw9qbHoV7vMFrfs3dAS0tm6LgYJS55SysjI+JBZBHm4MD0fxORXXrMCXHXNC1VSP7QEUWVw8if05XugWU91CDIIjxDSndJsRtY2JJYQlZMDFV97Esr49NrYutFAPxzMqFnjpJLWOKDZ7nkRj0AwCOthV3cjecjObl2iLWVd2Ijq70zxzk2UJvXlPplBIAqBbZguxwz9CmR2evH7GqWQCAq89sLunzzAjP85AiLB5CZ2/qfMOlQha0qNZZogkP+d6Wbsjt7gvAoim3s7KYOOeKHmixvbewPMwAIMkyLE7WrptLeCJ69SVnIdJ1FILFhkee2Tjq+k+f2QCruwrRQDc+c80lI66dPcUDl5VHb0jCjvYhq5cjmml5U4UVtixHy/pJePX0hTh4gple84nslhS6rhOO56Zk6W7/6YK6Dcfj1JRuMbe4GFu2bAFvsSKh5aqu9YyeD5xgY0hb/0jroCMnu8DzAhRFxvTm0rjEjDWW06zIfvvKO3DUtEKKhnH7Ry7J9NOSUa2llowo+Y0FA2FN2c5xo7quauTcUuUqT+yUyYzTfvpJN5mXEwSRHlK6TYhXyzMcV0uXU1PSfD99JfCxzERrDVMmY3xmJSQc0xYQZc5rycWY8nWqp3Al4nTYSXdq8/IKuwCrwIHjechWD0Kh0Wbtg+EorL4WAMDFZ8wuWbmA4cHUhkxeH/njXyHanYj0nMCNl59b0ueZFVkLRtjtz89vMl84K2vn9dWZ0+QN+d6Wrk8fPMGsNxKRQdR4izcvB4YCLfYECnfHaO/uBy+wfjGloXQnojzPY0EFGzcORNyj/PXfPsmUkQa5I5k/V8cq8rhoBlNCXts/tBGjW5xMyxBETae50gZOlWF1uOFsngcAsCG7AqSn0MnVcCmh6il3sp9MVmrpFnlL9sBmnZ2dOHnyJDy1LQA42C18SrcIn4M9vy8y8tTuiOZykBjsH1W/44UhKzKmfD+3jVkGWfsPot7nHZMy1HvZ2Jzg85uLQ1GtreU4ZzodNsjDTNFrvePPD9/s2E876aZA3QRBZIKGCBPi04IiSXzpdqZVC7tnrbc0PpbpmNPKlFHB7cv4vYhualnmFBsWlS3SO/2l8+3t6elJa17OcdxQMLXalpQRzF/fshuCaEE8FMAZc6aWrFwAcNHimQAAq48FU1MUBbsCbGEwyz4woQOojSDBFpt9wfKlyAMAMccT3SHf29KZeB5tZ+a/Ug4mzrmiB1rsGyw8nsTJTlauRGQQbmdpN/nuuP4SSNEwHDWt+N2r7yY/3/T+Ptgb50JRZHx27dkpf7tiDpPV+6cG0Rdi73lU9+fOEERNR+A5+KxsvGpZugIAkMo6+3Q8muxz3USVtY0Zly17W6lw60p39vLr/tzzzzofALPK4bjRFkktPqacRbmR9zzR5QcAKJHSbWCONUkrsgRwtK0Lau1cAMCN588cszK01HoBAJw980bd6ehR7Xk19zlTGpYitCHLxiCRPxZh5Hwqkts8QRAZmCQr8PFFcke6hKmuBDu7Z2OZd/N1JdLq9qIjgz9tJMYWEFyZlW6ndrLRHy7diXp3dzeceiC1FGGrk8HU0kQw37xfD6LWVnIlePnCmUiEByBYbHhj6x4WQK1uOuREDHd85OKSPsvM8Ao7FfIPli83rT84CNHGlJ7W+sybTEnfW6F0J4Rtmgk4lyjdxoIeaHEgWni/bOvxA0DSxL+U1Pu8sPaz+ATPbR1KS/jzVz9gz+zYh8WzU29kNVfaMLfOAVUFXtcCqunm5ZnShQ1HV871TbcqV3Z5Vmom4DKXm+z1jRm3I/vptdfN5gjR6oAsZz5K1/25Zy1mmxKnpwvTmd2ixQdwVo/4vFMzNxfk8ZvvWXeviqkCHnnmTQgWKyKdR3DVxWeNWRl0f3iLuxpSFpkNJxJnfZLPIy2VEh9yM2qpq87wTaIQBJ6DqgzJI5fghwRBTF5oiDAhTVr0ZdFRmp3pREKC6GTmp6X0sUxFvc+L+CBTtj84kD7KsL6AKHdeS6+DnRoNJkq3Bc1OujWf7hTBaWqTacOmpDzpPtLLThFrShhETYfnecgBpui/u/cEfvf2YfZ59360NhQfbGu8YMkzYnQhHNeCecmJ+KigRaeT9L0VistvPJyuAFtQW0uYf9etBXkKJQoPCNTVr5n0x8tjZaCfSiq1c3C8vRsDoQgCLqZoXzbbm/G3K+ew668fDKA3lEBAC6I2pTo3q6KFU0ZurtR7s2+M6qmb1Bz9rvU24snBSqBKi1DN8XzWSP36SXf9dGYaf3rkcp0ls6cAAOyVNSOyPvSFWDtzcOXdKC0nPo9uRWbF4Tjrs4uryuvidDqzprBc6IJoSRkQMB16KjkhH7/hYUHXpjSOz+B3ZkeRhvqDRaCjboIg0mNqpfuBBx4Ax3Ej/ps3b57RxSo7U7T80hanJ2W+5Xw51d0HnmfK51hMvPJgHzw2AYOx9IuDWELbtS9z4BE9f7EeZboU+EMxCJoPZaVjtMlovR7BvDZ1BPOQFkRtfrO3ZGUajk+MwWMTINsqYW+eDwC4dllLWZ5lVqzaie1grHybOm3dfnhsAlrdalaLhSHf29K5jPgjrA+5LaWLmFuhnQRG8wzyNJyeIFP+BKU8Gx5XXXwWIp1HIFhs+NWr2/Dnt3bBV12NqL8Lt16d2Zrj7CluuG0C+sIS/rKnDy1eK+bU2mHL0Rlzdj1Tcj02AS1eK2a1ZM8EoKdN4yy55VLnNOW80pVd6fZ6htyFsrlShEIh1NTUwFHNlL6UkcsBtNT7kAgzK4Xt+44lPw9qGTAqbONXsaj1uuCxCZg1ex7qmqYiEQ2NWQA1HYfNitgA25jWA/LlQkxic6Ulj7SfvJyAxyagyaXC48qt/RH5ochD45yVnLoJgsiA6XNILFy4EK+88kryb1E0fZGLpqmuGorSDZ4XcLy9G3OnFRdx+kRHLwAeifDAqBQX5eCrt1yEGXVuhGIyJFlBXJKTgaR0YgkZsJRf6W6u8WBnD6BY0/uyh2MJWEUBobgMl1VIWV4dVVURlrVAR5bRPl3AkNkmMy8fGWV5IBSBrUxB1HTu/dQa1FY6EYxOQYVdxN72ASxuLs+zzIrTwiEIICKVL4XLGQtmY+W5DgxEpbTtXIf53soQcvC9zZWQxIFHamuLQvF57DgmDQVeLAR/OA5UArYynoheOb8CZy5pwsLG2RiISrjeLuJkbx0slsx1YRF4XLWwCo0VVixsdGHVPAmVdhGxhJI1ejkANFVacdeKJixsdCEYleB1TEU4lkgrdwCoq64AjsoQ7bkFstL9syvd2ZUkURAgxSIQbQ74B1Ir3aFQCKIo4tFHH0VdXR2+uq4DgIRaT2oZ8zwPaaAHFqcHe4914vwlbOyIKiKsAHxZ0uOZmcsvOBMfcdgQjEqosIs42j2A2urSZpDIBSUSADxVONGVe0rDmMzGMmseet39n1iJmXVuBHMYo4jCUIe5CJDSTRBEJkyvwYqiiIaG0qTDGS+IgoBEKAibpwonO/uKVrrbewMAqsriY3k6cUnB7s4YHnqjE+G4AqeVx6o5Xly92DdiQopru/blzms5pd4H9ACi05u2vC/u9mPdfn/G8uoEAgFYPcwSocqZevGi+3R76lrRvmekefnrW3aDF92ID/Zj8exziniz1MQlBe8cj2Dd/vYR7zO/Mb/F2njHZRUQBBCTy3MqF5cUbDoaxrr9bTm1G+Z7OwDR7oQkyxCF4qOYxzkr7ABqK0t3glXrdQE9Q4EXC0G3cClnWuBPXHEentvRi59u6hhR/60+JevC9/K5Vfjzzr4Rv109rwpXL6pOuYk2HFlRcaQnOuq56eQOAA0+L4BelkEgFs+68SnYmDl6dUVuSrocj0K0ORAYjIy6Fo1G8a1vfQsPP/ww/H4/vF4vrvmfjQAvpj3pBgCLzBT4Y91Dkd5lkbWJhqrxGQU7Lil4/eAg1u0/OUJ202uzt5lSI0jMGqS9N/c5OaHpdtYco3XFJQV7OmN4OMtcTBTH8LSnVK8EQWTC9CPEgQMH0NTUhBkzZuCWW27B8ePp/YQnEkqM+UV29BWfZ7irX5vYc8gpWwzhWALP7ejFszv7kjlpw3EFz+7sw3M7ehGODU1OcZldL/ccNVvLX2xxeuAPjkwflU95dYanC6tOE0Spxi0CUGGxu9DRN3JR9d4+LYhasKPkQdQKeZ+Jim4mnShhii6dQupZ970FkNX3NldUK7tnc03pTur0QIu8rXDFSrN6T5mOqhQU085jCQXP7+rDszt6R/z2mQ968dzOPsQS6S1vCn1uw7DglW1dfZnfLTLkulJdmZsMVC1SfzA0UukOhUL4xje+ga9+9avw+/0AgKgqQuVFQFXg4NKb/1da2Pt1DQy9E+9gPtBT6ssbF6QcmG1stGtWID15ZAlIqEzZzsUVwmzvO6EZFkjNnsXShiCIyY2pR4hzzz0XP/vZzzB37ly0t7fjK1/5Ci6++GLs3LkTHk/qIGOxWAyx2NBEFgyyCL+JRAKJxPiZaLiEvhMeLLrcPYEwYAcEOZbTvfTv5Ptcqyhg3X5/ymvr9vtx7RJf8p66fxoPtaxyqfI4k+aXew6fxPKFQ6lh8imvTkdHB5xa5OIKG5+27E5eRlgR0R8d+X5H+2JAE+CzxDO+dyEyKOR9JioehwVIADJvKfid08mgkHp22YdON7v7/DkFycqGxcWUn5Zab8nkWl/lARCFxVWBSDRa0Il8DCJsACocYtHlSiWDYtq5wIt4eW9qk96X9/bjmkXpf1vMcxPhAVicHpzs7EnG7EhFZ++QUu51O3KqP1XzKfUPhEZ832Kx4OGHHx7xXU9tKwAg3NcJh21m2vvXeaw4AmBAEtjcKcmwuFl7m1JfPe7GEbONjS6LigEAgYic83MllYMFgF3ksv7GbO873sk0H4+IXi5klw1RGIWuS4nSQTJIT651Ymqle82aNcl/L1myBOeeey6mTp2K3/zmN7jttttS/uYb3/gGvvKVr4z6/OWXX4bTWboUXOVGCrNT2T0Hj+KFF14o6l4HT7SDnz0PiXAgr3utW7cu5+82NDRg3pKzkrvqpxOOKwjFZez9YCs6OjrQ2x+AvRYIBf1Fv1824gNuiLYWPP/K6+g6tq+g8uq8++67cGjpwnpOHcULL2xO+XshXg+IVRiUxRHvN8C54QZgjfbm9N65yqDQ95modJzoABqmQeGtRbev4TIopp6l2FSINgdeWvcaptV7iyrTQDgG0X4GAODovp3oOb6/qPvpxBMSgEXgeQFP/vaPqKnIf8xMqEzp7u88VbK+rcugmPr3eDxYes6FGX87EIlj27tvYmBgpIVKsf0rEa2ExenBa3/dBP+pQ2nf83hXALCfCzkRw2uvvpL2e8ORY+wEdPvOPahSA8l3nT9/fvKEW8ddx+JJBDuPobd3Lvbs2TPqXQEg2t8ONM5DQnRh3bp16AmGwYsCFEXGnu2bcWBXeawYyoEZx8ZooAeoYcEQc+0j0YQCC4Dero6MvzHj+04UUs3HcmJoQ/XEsSN44YXyu/FNZvJZlxLlgWQwmnA4NwtGUyvdp+P1ejFnzhwcPHgw7Xfuuece3H333cm/g8EgWltbsXr1alRUZE7rYyae2fdHyACc3lqsXbu2qHs9t/+PiAOorXTldK9EIoF169Zh1apVsFhyD7rC8QKcVj7lZO+08nBZBZx1FsuHqr9fra+q6PfLxq++9xIAoKphCtauvbyg8up0dXXBMcDMOZcvnovL0vhln9jYhndPRmGrasAll1wCt9uNgVAEv+1j7hGfvG5V2nzCQGEyKOR9JipW3w48dYSl3Vu7dmVB90gng0Lr+clHt0C0OTB/0RKsPHtRQWXS2bR9H7AHSEQG8dGPXVfUvU7n94+/D6uzAjPnLcKFS/PPFvGrY5sAABcsX4q1K5YXVZZUMiimnXO8mPG3HocVF1+cOgJ6Mc/95UOvAgCaps7E2rXpo2Vv2LwL7x0E5Fg453HxV3uZAtbQ3Iq1a69Ifq6qKrxe7wjF262ddMcCXfD5fGnftW7qITyyA7BXNeBDH5qJ7/3v00A1EB/oxzV/e3VO5TITZhsbT0TX4904IDi9WLt2VU6/+eU+No/Nnjkt62/M9r7jnUzz8a8ODm2OLVm4AGsvL27MI1JT6LqUKB0kg/ToVtXZGFdK9+DgIA4dOoRPfOITab9js9lgs42OrmqxWMZVI3FbeQQAhBMoutwxhQcHoNIh5nWvfOssHEtg1Rwvnt052m9x1RzviMipsu6fZhHKLheLytwNegdjI56VT3l1+vr64KyaBgCodtvSln1KjRvvnozCXduKnp4eVFVV4c3tH4AX3YgN9GPpvHNy8unORwaFvM9EpbmuGjgSg+jwQBCEovznT5dBofWs+96GolLRbf5UdwBADaRQf8n7jxwZBJwV6AmECrq3qPv+NtSUrGzDZVBMO48lFKyeV4VnPugddW31vCrIigpbmjIX81xeZmNQMBLPWCeDkQQAB5R4NOe6E8DMW2OSOuI3oVAId955J7761a8mP/PUsxzcS+dMQSKRgMuVOqvD0vkzoLx/AILVjiNtPegPJ4BqQI0Gx9U8qmO2sXFqgw/vHgc4uyf3+hTY9yqcjqy/Mdv7ThRSzcecOrSx4XamXxMQpWG8reUnIiSD0eRaH6YOpPav//qveP3113H06FFs2rQJH/nIRyAIAm6++Waji1Z2PHa2HxJVio++HNf2Vqpc5U314rRZcPViH65dVA2nFirbaeVx7aJqXL3YN2KSl5F7UJhicYksQnp/eKTPhdNmwVWLqnHtYl/W8up0d3fDUcl8ujOlatLThnnqWpO5ut/bz4KocQOlD6IG5Ff/E52mumoAgCBa0qZSKpRTHT1YNb8qr3YDDPneDkSixZehl+2qcvHSvhuAZMDFzv7BLF8cTWAwBNHOTNKnNNaUtFg6xbRzm4XH1Yuqcd2SkbK7bokPVy+qzpg2rJjnimCBs4LhzLnLA1owNFXOPce5AD1Q1sgUbS6XC/fccw++/OUvw+v1AgC8jdMAADdcdXlahRtguaTjgW4AwK4jpzAQY2OoKI+OkD4eMNvYOKOZzSFWdxWkYSmnMiKw+dvtzJ7Oz2zvO5EZrnSPRUpWgiDGL6Y+6T558iRuvvlm9Pb2ora2FhdddBHefvtt1NbWGl20slPttuGkCiSKyJerowgsaJOvAP/MfLGKPNYs8OKaJT74IzIq7AJkWR6VSkMBDwGA1VJ+30CvXUAHgMHE6A2MB3+9ER++9Cw8/DczMRCT4bYJaO8LpU390d3TA8dC1v68zvTdp25Yru72dpY27Fh/HGgEam3ly1+s1/+1S3wj8o5PtlQm1RVuKFICvGhBe3c/qitTB14shIf/+CZc05fh48uqce3imbnXs5ZaZjCce8TidPQEI4AHsKH0AU1ERbMMCeavYB1v7wEAyFICdWXMf1xMO7cIPK5aUI1rFvkQTshwWgTIipo1XVgxz7VyCmQAA9HMfX9Abxt5KN0WToUM5vN7Ona7HVdccQX+/d//HYFAAN/cFIY/IqO5KvtcwEUDABpxuK0PYVmAAMDB56ggmhAzjY0zWhugvnkIvGjBsbZuzGzNnhaV16LaV+QYhNFM7zuRGa50Dw+YSRAEcTqmVrqffvppo4tgGDWVLsAPqGLxUY45GzvRaKgeG592p82CE509eOTdAQQiMu5dWQ1nbfWI7yiakcVYpNiorXSgQwViGLm739HTj33RShza0Iabpoexv2MQB5VGDB7dhp9/4caU9+oNhFEtsonVa8+gdHvYs1zVDTjZtg0AEBYq4AAwv9lb/EtlQD/FqHSwOhZzUCYmGjzPIxEZhM1Thc6+IBaW6L6RWBxdlkbYgnG88sbbWHTzFTnXM68whStUgnQ9/REZ8ABuS+nz3Fs5GSoAf5ZT2VSc6uoH4IAUDpTFmmM4xbRz/US7QhC13+ZuUVTIc+0iEAIQzpCSDAAGo3GAH2oruWARABksL3MqbrjhBkiShBf+sg7+CMu1XefJ4bSUi0EG0OaPIMZZ4ARQYStP3vuxwixjo8NmRTzUD5vHh0MnO3NUutlaoMLlyPk5ZnnfiQyPYUq3o7zWhARBjG9oBDYpjT6mIBeTL1dHdLJTvsZab9H3ypXW+hocPHIcAzEZ6zfvHnVd5dgJt91a/pPuZq0uVetIc8of/uGvEG0ORLqPY80FZ8CmxjAQkxEW0m9OBGLspMfKSRkX6m6bAE47rTrZO4DAYAg2H4scfOmZc4p6HyI3FM30uttfumiyj//pr7BV1CA20Ie/v25FXr/lNd/bSKx4S4eQxNpelbP0ZqK6AcdgLLOCmIqOPmb2rsTKYPY+jnFp41wsy0FxWGsbInI/UbZp41BMHr0B09bWho6ODvT19aGqaToAwGnh4bJmn/p9LtYQ/DEOsshOxn1uUipKhRJhfeVEV+oUdiO+qygQbEzZrvKkdwsgxh4OQ/2OlG6CIDJBSrdJaaplOVEtzgooSv6LX52+wABEK5uspzaUx8cyHWKEmZpuP9w16prKswWdw1p+37Jpmm+pqOU1BtgiZu8gq5c5rhB4nsfFS2cDAGy+FgQGUysNIc1E3ZOl2BzHwaYw89zuQQkbNu8GL4iIDfRh/oyWot6HyBEtcFlfMLdUDrnwxmHm5+yLnoQzzwVWOt/bQkhw7Nl1lbmfeuVKhZ0piBE5/1PN7gDrN1xifPr+lguPFv8hrmbeZNTbhsjlPubrcTEkdbS8tmzZAgBYsGABBiT27FqPBRyXXbatPrbhG+Md4Oxs47ahqvhNYIIhSKyPtPdlj50QisTA80x+Xs/4SX06GRC4IaXbk6PpP0EQkxNSuk3KtCaWC5oXLejo9Rd8n2O6j2UiDp+3dH6tudCobcifGkyxgNSUbqe9/Er3zNZ6AIDF4UZfgJ16PrthCxy1UyHHo7jjOpY2Z+HMVsQG+sALIjZs3pPyXnFN2fFmCKKmU2llk3EwwWPzfhZMrVxB1IjRCAoz4+4fLD5wGQBs23sEloa5AIBbrzgz799btMVZKt/bfFGtTPlpqim937QecDFegPdR/yDzSbaUwdd8PFPpZHUqc5nHO71t5HAQncShnaInUgTd3Lx5MwBg2bJl6B5gMtHjTWRjdguLXcG7qiE6vQCAKfVVGX5B5IOdYxssPQPZYzz0BoasdaoqaePDTAzvqm5n6TdBCYKYONDq36R4XA4kouzU6GTH6PQ2udLWzUzXEmPgY3k6S6axjYO43Tf6ouZLORbRPmurKiBF2WnnweMdAIBnNh9lxejZj+Z6Vj6e58ENsOtbDrSNuk8sFkue+NRUZN/R1he3Ud6B435mal5rG7+BiMYbyYjRkfx9k1Px2IubwfE8Ym17cPbCWXn/Xo8ZGEvje5sriqLA4mbKz/Sm0luv6AEXFSF/U8mgFijMzpfe13w8U63VqSpmrlPd/Nwm5m5l4LCysVThRp+i6yfdy5cvR9cg6we5+HMDwNK5U1lZKnyweVhMjpkt9TmXi8iMx8pkHMzBjSMwwOYvKR6FRTR1KJ5Jx/CTbgdFhScIIgOkdJsYOcJ2t091+wu+h+5jqcbyT/9TLCuXzQMAOKqbcKpz5MYBJ7CF31hE++R5HokQ23w40t6Dtu4+JHzMlPy6s6eO+K6uFB/rH3360N3dDYeXbSTUVWY38WvRzDNh9yb9xBe00EnRWGHTFL9QNkfaHIjE4uixNgMALpxWmHmn7nsbL7I43f1BiJp/5+wcAjDlix5wUQ/AmA+hOKtzt3V8B9wqNTXa6SRnzdx29LZhzyPCtEuzFjpd6VZVdcRJd5d20l2b40l3U2014iEtNR3PQ5ElTGuuy7lcRGZ8bjb3RZTscU36tbSHcrw0VjtE6dBj0ylyggLVEQSRERohTIyqBYLq7A8WfI8ezZ+Vl4pPU5QvUxprEe1nJ8frt4w01+ZFtvBz2scm8Iiez7itJ4gf/eGvEK0ORLqP4boVy0d8T1eKw+Jos92enh44q7Qc3RnShenMbGKnQ+6G6UNB1M6iIGpjhdPCFL9wovhT18eeeR22Ch9iA734bJ4B1HR039tircsPaNYaifAAvBWlNzXVAy6KzvyzHcRULSq4g058hlOnbWSI9szykrQp2ZFHgEl941LlR25gtrW1obOzE4Ig4IwzzkD3YH7m5QAgD/Yk/x0f7IcolD/w5WShoYptaklCdqupgRBTtpU4xUowG3o8VTlRGosqgiAmLqR0mxhBLjxfro7RPpYWPZjakZHB1PSco+4xCjxiVVk9dAbC2Bdii5157sgok/tL9GBq1c2jgqkNP+muysGne2otW2h76qdoQdR6MW9ac3EvQuSMy6ZFjE7h65ovG4+ytlATO1WwS4SuSMlFDrvHOlifksL+ou6TDj3gomh1wB/Mz0JG4ljd+DwUUGg4jT4vAEC0ORCOpN8AlcHaiCuPWBduh9YehZG/GR5Eze5woEtXurNFgRyGVR4aA9VI4Zu/xGha69gGL2fPvrk1ENaU7sTYb54TmRF5Nr+oEindBEFkhpRuE2PVfFL7C8iXqyNY7GjxWtFUZUzE00YXm5DahgVTUxQFgubb6B6jFBsuUYXHJoCraEBdy3RI8Qhu/8jFo743f0YLYgO94AUR698bmeqsp6cHDi876c4lkJrPZYGqyPDYBLR4rajGAAVRG0MqNGVEQuGnc263G/tPdKBm+iKoioLbrlxW8L1039tile6BqIQWrxU+W3n8pn1eT/LURg/EmCuqhZm913oprdFw6mu8ULUsFG3dfWm/V1nhQYvXikZf7gHy9LzNnDhyM0g3LV++fDmCERn1Hgsq7QKqXbkr3ZVWNTl+1Tpp7Col05vZXGJ1V0GSM/ucDGgbNaoBFmtEZgSeg8cmYJqPgqgRBJEZishhYhwiEAEwWKBPajiWwP2fvgKBqIRK+1SEYwk4xzjQx5LptXilD4jbhwI+haNxcJry6XGNzUT1xU9ciaZqF4LRKaiwizjSNYCm2tELWxZMrRPw+LDlQBuuWzl0rau7Gw7vIgCANwfzclHg8K+XtWJeowtBA2UwWfG6bEAUkPnCTqYTMnDpipUIRGV893oBBzoGsKil8GjhLrsFSAAKl7nthGMJWEUBobgMl1VAXJKTbSYcS+AzV19S1j7N8zwS4QCEylqc6u7HGXOn5fxbQTOf1k92CYYoCEhEB2F1VqC9x49ZUxpHfSccS+D7n12JYFSC15G7bHWlm7eOtC7YsmUL5s2bh//48v1w2Xh8fmUzKuwiZFlNns5l498/fiUaqmj8KgczWxqgKgfBCyKOnupK2SZ0QlG2CaZKlBXAbNx50yptbSFBkpUR4zVBEMRwSOk2MR6bgAiAcAHzbFxS8OJuP9bt9yMcV+C08lg1x4urF/tgzSNIT7GsXDYfr6zrhaO6Eac6e9Fc70MwNJQ3ucJV/hP4uKRg66koHlzfMaIuZtQpKeui1iYjACQjjut09Q9AqGIKXGUOJ91xScHBnige2dRhqAwmK9UVTiAKwJK/qXNcUvDintH9Z05D6jaTCy67FUgAKp9+QZap3wIYsz7NAi/WJgMx5kIiISX9wJvrKGDg6SjREOCsQLd/YNS1YsbrKo8TQASCdWgDU1VVBAIBbHzrbWw4Gsd3fncoed/V86pw9aJqWLIEfYpLCjafjGLdazR+lQOb1YL4YD9sFT4cOtmZRelOABYAMpkwm4l0awvqIwRBpIJGBRPjdTEFL998ueFYAs/t6MWzO/sQjjOTxnBcwbM7+/Dcjl6EY2O3W97aUINIXzsA4NXNzFw7pPmnqYoCe5l3hAupi4WtejA174jPu4Os3LwUyXpSZCYZTFZqtbz0vDU/U+dyyc6j5WqGmLrNZ3pu50Acz+3sG7P2pAde7AmEs3xziFPdfeB5Zso/pbG2pOWZCKgJFpujNzAyVkSx7c3rYe1bEC1Jf/FTp07hnnu/jPWHY3h2x8j7PvNBL57b2YdYhoh+NH6NDYrmJ3+iy5/xexEtrD2nSOUuEpEj1EcIgsgXUrpNjM/DTi5kPj+/Z6soYN1+f8pr6/b7YRXHNgKtNcrShX1wtBvAUFAYOREru49zIXVxyZkswrjN1zwikJQ/ouUg5rIvfMwmg8lIg+YXa3G4oSi5hwwvl+w8WtBALk2u5nTP9dgE1HmsWLevv+RlSoceeLF/MPcURSc6WD9PhAcKDjY3keG1U8rT67TY9lY1LIJ9X5Cdou/YsQOXrVyBVw4EUv7m5b39EDJsHNL4NTYIMtuIae8bbf0wnGiCKd28Skq3WaA+QhBEvpDSbWLqq9lJXbbcrqcTisvJndfTCccVhIpNFJwnTW62uGsf1PIma6cxyhhE+yykLuZNa0Ys2AueF7B+81Cqs1CCvYdLzK7AmU0Gk5HGWmaxwIsW9OURhbtcstN9b4U05u7pnlvpEBCMSmPanuwC66vBaO6L/PZepuBJkcwKxGRF1AJjBsMjg2EV296cDhtkzde3P8hO0Q8dOoT+wWjG+4YT6e9L49fY4NA2cHsHMwdIi0lMFqR0mwfqIwRB5Asp3SZGj2ArODx5/c5lFeC0phat08rDlUcO2FJwxnSWZithZ36pY6l0F1IXPM+DG+wEAGw5cCr5eRTMLDiXyOVmk8FkxOtxJZWR9u7Up8SpKJfsmO8tIFhTK93pnhuIyKiwi2Pantzas/KJJ9HVrynbidxN0icTVo4t0AdjIxWnUrQ3WcvfHBhk/9+4cSOq3PaM93Va0t+Xxq+xwWNjG7nBWOZMBLr+JiB3ix2ivFAfIQgiX0jpNjFTtHy5Focbg+HczTzjkoxVc7wpr62a40VcGtsd2JXL5gMA7NWNONHRg3BMi8Qql9/nqdC6qLOzz4/7h8ooC+yk0ufObu5vNhlMRnieT566duUREKxcstN9b/lhvre5PHcgJqNrII7V81IHJytHe6p0sA2mqJr7wrFvgI1RokJpjVKh79WF4yMVrFK0NyXO6j44GIaqqnjttdfw2voNWD039X1Xz6uCrKRX9Gj8Ght8buaGEVEy9zP9QFXkSOk2C9RHCILIF1K6TUx9jReKzE5Fjnd05/w7p82CqxZV49rFvuROrNPK49pF1bh6sW/M01k01/sQ6WsDALy2eQ/CWvoTjIHS7bRZcPViH65dVJ1XXSxsrQYARCxeACy3OGdnFgcNVdkDcxX6XKK0KDF26poqYnQ6yiW74b63vYHR5cn03HqPFR9eWD1m7clXwU7jZS73+/rDrF9bOVpspsJpY4pVVD79cwvWLixuvFY1q6FgOIqTJ0+iu7sbX/zCv+KqRb5R971uiQ9XL6qGzZJ++qfxa2xoqGJjgiRkTp0pqexEXOQyn4gTYwf1EYIg8oVShpkYURCQCAdg8/hwsrMPC2a05vzb/3xqI65feRYe/puZiCSG8v0alcaCBVNrwo6j3ZjTpJ3YjVEkVqvIY80CL65d4huR+zhTXVx65hy8uTEEm68ZfYEBqFIcDi+LyNxck1uu5kKeS5QYiZ0A9gbzM3m2ijxm1djw4cUzEY7L8NiKl53ueyuIFvgHQmhtqBn1HfZcKz68eCYicRnu0547Vu2pzusGugDVkns8icEYO4XLIYX9pKTCbkEXgARGn2re/+t3cdOKRYWP11qQtoFwDFu2bAEACIKADQcCmOaz4aG/mYmYpMBpESAratZ0YQCNX2NBa10VcATg7BUZvydDgAAgjTUzYRDURwiCyAcaGUyOEmUBoPLJl7v3yEkcU2rw0IY2bN1zGJUOC0SBN3TntdnNmlpbSEU0wU64OWXsTsScNlYHudbFvOktiAV7ksHUenp64PAy3/TaitzzPuf7XKK0CApTRvx5ROEGgB5/EN957RTu/sNhBP39JZOdHGM+t/6B1JsAiqLgWy8fxd1/OIzOru5Rzx2r9tRQQDyJiMxO4yrs5MuYCq/mlnK69cBfNr2PDlTju68cQ1tXT0Gy1VNJhaJxbN68GQCw7Oxz8PyuXjy0oQ0720KosIsQBS7jCffpOG0WqIqMvR9sharINH6VmJktbE6xur1IJNJvQisc61NWIXOqSmLsoTmeIIhcIaXb5HDaSV23P5Tlm0P8+Ll3wAsiou37cd7iWeUqWl6cMYMtLiRHDSJxtrjgVHOboXKDXQCArQfb0NXVDUclO5nMJZAaYQ4sYG0sGMnPlWHD5t3geB5dne1oqk3tS10ISkLzvQ1FUl7ff6wNgtWOQDiGKY2jT8LHitZ6FvTQ4vAgFs+t7uKa4ZTXlV+Kw8lCtRZITxVHbtr9+s0D7B9dezGtqa6ge+tRrUPRRPKke/rZq+CPyKiwCzij2Z3p51np6Ogo6vdEamY010NRZPCCiKNtXWm/p/JMkaPYXARBEOMXUrpNjkVlC96+HE/qEgkJbRwzg17eaB7lcOXyBQAAe1UDugPsXXiTR2Kt14KpnQhIONXdD160QFUVVJDSPW6waamv8k3fsvUgUzKkvpMlLU/S9zaUuj/vPMSeFw90G5rrukVTujmex8nO3px+owhMmaypyC/F4WShppIpvsNTQHb3BRDxzgQAXLWkseB761GtI3EpedIdrWFj7sUzKyHSCakpsVhEJAZZZoVDJ9Mr3RCY0m0js2WCIIhxC43gJsfGs8VUMJKb//PPn98Iu7ce8VAAt1+/oowly4+m2mpEelkwtZMhtgA0u9K9sJUpHhGLF+19LPCVGh2EyNMCdrzgEJmsIon8AhCdCLD+5pQCpS2Q5ns7mCJ6OQAcbOsDAHDREj83T2xWC+IhVoZTXX05/YazsQCDDdWZ/VMnK3VavYiOoUCMj/zxr7DYXYj0nsTHrji/4HvrUa07uvvQ09ODyvopOBFim4MrZucWg4IwBiXCXMeOd6ZPa8iJbAPOnodrAEEQBGEuaAQ3OW4rUxpCOSoNr+5jC+SK0DF4XJkjoo411ph2YlbZAgAQYO5IrJeeNQcAYKtuwoke5lvPS6nNgglz4tYiRseU/DZKolYvAKDRVdo2Otz3NhVt/czX28mVP4d9NuQo22hq6/Hn9H3Ryfy/G2u9ZSrR+KahxgsAEK0ODGjuBTv62QnmDGsQPF/4dKzrYsdOso3N8264HSqARY1O1HuMs5ggsiPIzOqlwz+Y9ju8hVmROKxkZUUQBDFeIaXb5Oj5cmNKdlHtOngcQsM8AMAnL1tc1nIVQouHvYPVpZ34mLz1zZ3WjGiABVM7FGRKm001XhkicqfCyRQOKY9EDd19AdirmwEAcxpLe0o43Pc2Ff2a1bnPZbzzJhdnimEu8ST6AgMQrWyTb2qKqOwEUFddCVVhJ9Jt3X14/o2tcNRPh5yI4/brLizq3npUa/9ACJwgom7pKgDAytneou5LlB8Hx8aE3oH0+e0FKyndBEEQ4x2Tqz1ElRbxNpFDvtyfPv8ueF5AtH0/LjpzXrmLljdnTK8f8bfZlW4AEEKdAABb80IAgFM0d/A3YiRel5Zvms89ouz6LXvA8Tyi/i7Ue7PnZM+H4b63qYjyrLytvuICX5UCQdtg6h3Ibt1xooNZsciJOHze3COeTyZEQUAiwk4zO3sD+N1bhwAAXPe+ggOo6egRyUWbE61nXgbY3Ki0Cziz1fh2RGSmwsY2dIOx1FY1sXgCgoWtA1w2UroJgiDGK+NA7Znc1HnZokkVM5uKJxIS2nmm1J7TZM6J+bKzF4z4ezy4p9U7mJJkr2D+3ZWUDmlc4avUlGZL7q4W2w62AwCEUIbARgWi+95GEqk3b3hnNQBgbmtxSlgpsHGsjP5w9ujlJzW/70Q4UJSZ9ERHjjGl++CpbsSqWGaJDy9tKvq+dm0HU7Q7MXvlTQCAS2ZVUvyJcYDPwxTqqJp6bukPDpmdO+3kKkAQBDFeodWRyanXgu/w9swnFv/33Buwe+sQH/TjH69fORZFy5uGmipEek8l/7YI5m9+ejA1nWoXLXrGE7XappVgyz2i9skgUzb1DZdSom80xaTRp1p9gQHYtM2dJXOmlPzZ+eLSjANC8ez10NHHgkGpsdxTG05KtJRxL+/pg2h3ItJzAh9ddV7Rt3VoJ6DeljloWnwhOAArZlEAtfFAgzZGSULqjcE+TelWpASsIm36EgRBjFfMr/VMclrrq+GxCZjRXAdJTm/afCJigccmoDJ8HG6nPe33jMYW64PHJqDFa00qRGZmxVlzASBZ5ukNpcvZTJSfel8lPDYB05tqoSi5KdFRK5PxgtbqkpfHmkHp3r7/GAAgHgqiqbb0z84Xj50pchE5+2lpXAZavFb4HDSlZIKXY2w8X7AUHpuAWfaBklgGOK0iPDYBixcvgscmYFGTE7UUQG1cMKW+Ch6bgJlTm1NeDwyE2fxTIaCiglw3CIIgxivmtEMmkrQ01OG714sIRiUAHMKxBJy2If/UcCwBiyjgzmvPQYVdxGAk9cRtFv7z1ivQVO1CMCqh0jF11PuYjdlTm3D3iiDmN7oRjErwOkTTl5kYYkpjfbL/KCoQzSK7zl4/7NXM3PfSM+dg7wdbS1oem4XHIIBECv1/3/FOAC2QB3tK+sxCqXLb0AYgkWWaCMcSuO2aSxCISqi0m79PG8kDn7gMM+rYWFJhFxGNt5bkvjevvRg1Fc7kff0hCvg4Xjh/6TysPNeKYFSCJCuIS/KI/jNr2hR8d6ENwaiEKsdcxBMyLNS9CIIgxh2kdJuYuKTgL3sDWLffj3BcgdPKY9UcL65e7INV5BGXFLy425/iug1WE0Ypi0sKtp6K4sH1HSnfx4zEJQWHemL48abOcVNmgpGt/6Ri/ebd4Hgfov4uzGg5D3s/KG2ZdN9bSR19enysKwi4AatsDhPtmgonEAQUMb3lTPoxiPrH6cQlBbs7Y3jojdKOJXFJwdvHIli3v/20+1pJBiYnLil4Zf9Axjn+r4cHsW7/SepfBEEQ4xxSuk1KOJbAi7v9eHZn39BncSX59+p5Xry8N/31NQu8pjptyvY+ZisvMD7LTDAKld37hzsBtw9CuLss5dJ9b+UUnj3dIQlwA5VWc+Svb6iuAIIAZ00dwZ36R+6Uq65IBuOXiTbHEwRBEJmhrVKTYhUFrNvvT3ntrWMDcFjFtNfX7febLuBKpvcxY3mB8VlmglGo7PQgao3O8ii+TquudI9+/qDMrjVUmiMmQ0sd8yu3uCpT+sNT/8idctUVyWD8MtHmeIIgCCIzpHSblFBcRjhN1GCrwGW8Ho4rCMXNlU96vJUXGJ9lJhiFyi5mY0HUFk2tKUu53A4W3EpNkTdctrIgSTMbzRGsr7WBRVIXLDb0+gdGXaf+kTvlqiuSwfhlos3xBEEQRGZI6TYpLqsApzW1eOKymvG608rDZTXXLvh4Ky8wPstMMAqRXWevHw5fCwBg5bJ5ZSmX28Fy8kIYqXRLsgxrJcvNvWC6OYIhVld6IMUjAIATnb2jrlP/yJ1y1RXJYPwy0eZ4giAIIjOkdJuUuCRj1RxvymvnT/UgLqe/vmqOF3HJXLvgmd7HjOUFxmeZCUYhsnvtvd0AgKi/E9Ob68tSLl3p5sSR6Zz2HTkFwWKDIiWwYEZLWZ5dCFKYnXCf6uofdY36R+6Uq65IBuOXiTbHEwRBEJmhQGomxWmz4OrFzLwzXWTTbNfNRC7vYzbGY5kJRiGye/9wJ+DxQSxTEDUAqHQ7AAC8xTbi852HTwGoRizYDZt1Udmeny9qjEVS7+wfbV5O/SN3ylVXJIPxy0Sb4wmCIIjMkNJtYqwijzULvLh2iQ+huAyXVUBckpOTbbbrZmO8lRcYn2UmGKfLzm4RsKMthA1b9mD1uQtHff/UgAzeAzQ4y1emSrcTQAK8dWSwtENtfYBQDT4aKN/DC4CXowCAnkDqNGZWkcf8Ois+vHgmQjEZFXbqH+ko11hCY9T4pZA5PpaQSLYEQRDjEFK6TY6eEqTSwSZZUeDzum42xlt5gfFZZoIxXHZ3//gF9LhmQmlLrXTHbNVwAFhcpiBqAFBV4QLgh2h1IJGQYLGwIbjdHwF8gItPlO3ZhWCFBADoD8XTfueB37+P2oZmXD89jg+dvYD6RwbKNZbQGDV+yXWOd4oyXt+wHpdccsnYFpAgCIIoCTQzEwQxKbhiSRMAQK2bhyOnOkdc6+jph8PHApitXD6/bGWo8gzlvPYPDJ0e98fY/2uc5gqO5BBY6rSBqJTy+tG2LtirGjAQk7FweuNYFo0gJh2Dg4NGF4EgCIIoEFK6CYKYFFxxwVJEOg9BEC145Jk3R1xLBlHr78C0prqylaHC7YSq5bzuCw4toGM88/WeUusp27MLwW1jU0Qotc6N9Zv3AAAivSfRUGOOVGcEQRAEQRBmg5RugiAmDWf4mMJ7JFEFSR6K/rv9CDv5FiM9ZX0+z/PJNFyBgfDQ5y4WMGlOa/kU/kLwOlmU9bia+gR+xzFWX7bY6OjmBEEQBEEQBIOUboIgJg13XH8pEpFBOHxNePovbyU/PzXAlPFGV7pflg4lwYKT+QeZ0t3dF4DNw06Jl8yeUv4C5EG1hwV8k3hbyusd2r5BS4W5zOIJgiAIgiDMBCndBEFMGqorPXAGDgMAXtwx5Ncds7GT5sVTa8teBiXBHLgHwkz5/uDAcQBAfNBvOhPtuirN3N3iSHldcrL6WjqjPHnNCYIgCIIgJgKkdBMEMan42MVzAQBc3VwcPN6Otu4+OHwsyNplZQyilkRikcAHwkz53nucKf/yYG/5n50njb5KAIDgGO1rfuRUJ+xepmyvXL5gTMtFEARBEAQxniClmyCIScWq85Yg0nEQvGjBj//0FtZvHgqiNqWx/CfdUFhasMEIU7qPdw8AAGxKOO1PjKK1gVkAWJ0ViMRGpg1bv2UvACDScxL1Pu9YF40gCIIgCGLcQEo3QRCTjjM13fqYXI0jXSG0eK2oE8dG6eUVFgo8FGPKd48WGrzSqo7J8/OhubYaisICzp3sGBlkLhlELU5B1AiCIAiCIDIhGl0AgiCIseaO6y/Ff718HDdfvwwLG10IRiVU2qciHEvAabOU9dkCmBIb1nJfhxQLrACavKn9po3EYhEhhYOwuqtworMPs6c2Ja91hDlYvEArBVEjCMLEyPEYOF6AHI1AsDugKjIEa+rgkGZhPJaZMBZqM+aHlG6CICYd3go3HrhmLl7a3Y+fbupAOK7AaeWxao4XVy/2wSqWzwhIADvRjiaY0i3bKgAAMxqry/bMYpCjg4C7Ch29gZGfO2thAbB0ZoMxBSMIgsiCkkigbcM6dLy5AXIkAsHhQMOFK9C88grwlvJusBbKeCwzYSzUZsYHpHQTBDHpCMcSeGm3H8/u7Bv6LK4k/16zwAtLmfRuC69CAhBJKEgkJFgrWW7uhTOaMv/QILgEyyve5R9MfnboRAfs3jqoioLLKIgaQRAmRI7H0LZhHU698uLQZ5FI8u+mFatMdxI4HstMGAu1mfED+XQTBDHpsIoC1u33p7y2br8fVrF8JtO6Mh+XVew5chKCaIEsJTB/ekvZnlkMosoCqPUNRpOfbdjKgqhF+06htrrSkHIRBEFkguMFdLy5IeW1jjc3gOPN5xozHstMGAu1mfEDKd0EQUw6QnEZ4biS8lo4riAUl8v2bJvIAQDiMrDz0Cn270AnLBZzGh7ZOFZPgXAi+dmOYyy9mT3uN6JIBEEQWZGjEciRSOprkQjkaOprRjIey0wYC7WZ8QMp3QRBTDpcVgFOa+rhz2nl4bKWb2fYrvmLSwpwuINF/hZiA2V7XrG4rGyTIJQYiq7eGWGftVaac6OAIIjJjRyNQrDZIDhSB6gUHA4IdvMFrxTsjoxl5m02hDs7xrhUhJnJ1mbM2M4nK6R0EwQx6YhLMlbN8aa8tmqOF3GpfCfddk2hl8Cj3c92oF18ItNPDKXCzhTriMwlP5NdzA+dgqgRBGE2+nZux/b//k/49+9FwwWXpvxOw4UroCrlG+cLRVVkNFy4IuW1hgsuRWDfHnzw3a9h/y8fQ7ijbWwLR5iO3u1bENi/Z9y188kKHVMQBDHpcNosuHqxDwDz4U4VvTyRKI8irKckkyHAH1chAKh1m3cornLbcFIFEmDlPni8HfbKWhZE7WwKokYQhDmI9ffh6LO/Qf/uHQCAjk2vY+4n/x7guJFRnS+41LRRnQWrDY2XfAhQVXRsen1EJOqmFatx4sVnAVVF3wfb0PfBNlQvPhMtl6+Bs7HZ6KITY4iqKDj5yos49coLsNfVY9Ed/zq6nVP0ctNh3pUeQRBEGbGKPNYs8OLaJT6E4jJcVgFxSS5rujAAcNksQBxQORFx3goHgCk1nrI+sxhqK12AH1BFOwBgw9Z9ABoR7TuFGu98Q8tGEAShyjLaN67HyXXPQ4nHwQkCmi5dheYPXQHeYkXTilVovuxKyNEIeKsVgf17EOnqgKu51eiij0JVFOx/4lE0XHApln3pG5Bj0WE5l62Ydu2NqDvnApx85UX07diW/K968VI0X74WLlK+JzxyPIZDv/4F+nZsAwBUzVsEwW5PtnMpHIJgtyN4+CA4gYKomQlSugmCmLTop86VDqZoi0L5PW5cDitTugULBKcXADB3Sn3Zn1soDdUVgB/g7W4AwM7jPUBVIwVRIwjCcAaPH8XhPzyFcNtJAIBn+kxMv/5mOOsbk9/R0yXxbg+OPPsbdL75OmqWnYtZN33SkDJnInBgD4IH9yF06gSWfem/YHHrG7JDy3VnYzPmfOLvEG4/hZOvvoi+He8n/6tetBTNl6+Bq8mc2TCI4oj5+7HvZz9BuO0EOEHA9OtvRt3Z5wMYaueiw4nt3/0vxHq7MffW21E1f5GRRSaGQUo3QRDEGFLhtAEBgHdUwOr2AgCWzJ5ibKEy0FxXBRyOQnRWQlEUdEZ4WKooiBpBEMYhRSI48dKf0Pn2G4CqQnS6MOWq61C77DxwfPrN05ozz0bnm6+jd/tWTLv6BohO5xiWOjudb78JAKhddi54izXjd52NzZjz8b9DuKNt6OR75/vo2/k+qhadgZbL18DVZL7TfKIwBo4fwf6f/xSJgSBElxtzPvlZVEyfNep7vMWCqvmL0LFxPbre2UhKt4mgVRNBEMQY4nEyM22bpxoAEBvoQ231XCOLlJEpjTUAWD7xrr4AZFc9LADOmkVB1AiCGFtUVUXfB1tx9E+/Q2IgCACoWXYupl71kWGnwulxt06Ds7EZ4fZT6N76DhovWlnuIudMPOBH/x7mj1537oU5/87Z0IQ5H78N4Y42nHr1JfR+sBX9O7ejf+d2VC3UlG8TmtITudOz9V0c+t2voEoSk/en/xH2al/a79efexE6Nq5H/56diPn7YfNWjWFpiXSQ0k0QBDGGVLqdAIbSbymhPuMKkwOVbhekaBii3Yk3t++HvbIGiiJjxXIKokYQxNgR7e3BkWd+jcC+3QAAe00dpl//MVTOyn3TkuM41J17EY4+82t0vb0RDReuAMdx2X84BnS99xagKPBMmznCPD5XnA1NmH3LZ9B8+RqmfG/fgv5d29G/azuqFi5By4fWwNViXqsqYjSqouDEX55D2/qXAQBVCxZj1sc+DcFuz/g7R30DPNNnYeDIQXS/twktq64ai+ISWSClmyAIYgzxepwAQsm/bUrEuMLkiBQJQrQ7sWHHCcDnRaz3FGq8pHQTBFF+FFlG+19fxcl1L0CVEuAEEc2XrUbTytXgxfwjM9eceTaOP/9HRLo6MHD0UEoT3bFGVRR0vctMy+vOu6ioeznrGzH7b29Fy4fW4OSrL2rK9wfo3/UBquYvRvOqtXCT8m165FgUB5/+Ofp3fQAAaFq5Gq1XXJ3RfWI49eddhIEjB9H17iY0X3YlBVUzAaR0EwRBjCFVFW4MV7qrbMaVJVfUeBgA0CG54ADgSPgNLQ9BEJODgaOHcPj3TyHS2Q4AqJg1B9M/8jE4agsPPik6HPAtXY7u9zah6+2NplC6/ft2I+7vh+h0wbf4zJLc01HfwJRv7eS75/3N6N+zA/17dsA7fxFaLl8Ld+vUkjyLKC2x/l4WMK39FDhRxIwbbkHtWefkdY/qRUshOl2IB/zw79uNqgWLy1RaIldI6SYIghhDqivcADqTfzdVOYwrTI4IchQAYK+dBgBo9VLeT4IgyocUDuH4C8+g691NAADR5cbUD1+PmrPOKYk5eP15F6H7vU3o3bENU6+5ARaXu+h7FkPXOxsBMP/0UudVdtQ1YNbNn0bzh9bg1GsvomfbZvj37IR/z0545y1CyypSvs1E8MhB7H/iUUihQVjcHsz51D/AM3V63vfhLRbULjsX7W+8hs53NpLSbQJI6SYIghhDLBYRciIGwcKOuGc0VhtcouxYIQFA0qztrFn5+xsSBEFkQ1VV9Gx7D8ee+z2k0CAAoO6cCzBl7XUQna6SPcfVMgXOplaE206gZ8u7aLzkspLdO19i/n7079kJAKjPI4Bavjjq6jHrY5ry/epL6Nn2Hvx7d8K/dye88xayk+8p08r2fCI7XZvfwpHfPwVVluFsasXcT/9DUUHQ6s69CO1vvAb/3l0UUM0EkNJNEAQxxsixSFLpXjSj2eDSZMchArrnuaLIWElB1AiCKDGR7k4c+eOvETy4DwAzj55+/c1lMf/mOA71512II394Gp3vbETDxSsNC6jW/d4mQFXhmTEbjrryZ4Vw1NZj1sc+pZ18v4Sere/Cv3cX/Ht3wTt3AZpXrYVnSv4nq0ThqIqC4y88g/a/vgoAqF68FDNv+mQy93ahOOrqUTFjNoKHD6Dr3TfRuvrDpSguUSCkdBMEQYwxHquKWq8VfQMRzJ1ufqXbbeMh2gRUOgR0t59EdSUp3QRBlAZFSqBt/TqcWv8XqJIETrSg5fI1aLzkQ+DF8i1TfUuX49if/4BodycGjx6BZ/qMtN/1eLKnIysEVZYRPHIIostd1lPuVDhq6zDrpk+i+bIrmfK97T349+2Gf99uVM5dgJbL1xZk1kzkhxSN4OCT/wf/3l0AgObL16Dl8rU5B0zLRt15FyF4+AD6dm5Hy4fWFBVQzWrNnDueyAwp3QRRBHI8Bo4XIEcjEOwOqIpc9M7kRIXqihGOJfCDT52DYFRChV1EXJIhmjyq6L/ctBr1VS4EoxIq7VMRjiXgtI1fv+5MbXGitdOJ9j6E8RTTpk7/bWJwAId//xQC+5jCUTlnPqZ/5CbYfbXlfAUAgGh3oPHiDzFT85YWJAYHUo4HAi/g/LPOhMBx7O8C3/X0emLXecy44W9ZjnFVzXC38qEr3y0fuhKnXvsLure+i8C+3Qjs243KOfPRsmotPFNnlG0sMeMYVco2nlruQ9cHTxxFtK8HnGjBzI9+AjVLl5X0XaoXnYG5n7kDFTNmIREahOh0ZS1TqjlRjUWx+vLLocoyUKK4A2aUfTkhpZsgCkRJJNC2YR063twAORKB4HCg4cIVaF55RckDoYx3qK4YcUnBi7v9WLffj3BcgdPKY9UcL65e7INVLM2udqmJSwreOxnFutc6xk2ZM5GpLQKYUO2U+h1RaoppUyl/e8GlmHXTJ7D/F4+i/vxL4TvjrDE1825auQpt61/God88UdLxIFs9mbFv2mvqMPOjn0CzrnxveQeB/XsQ8/dh0R3/D+0b15e8vGash5K38Wxyv+BSLLz9bsSDAbgay2D5pgKDx47g4FP/l3dbBMo3J5pR9uWGlG6CKAA5HkPbhnU49cqLQ59FIsm/a5efh3DbqcLvL0uw9XbBv/sDCML47qbOphZ0b36rbHVVLkotA7FpCtYdk/Dszr7kZ+G4kvx71VQRUtvxop9TSowuc6llkKktVi8+E307to27dpqOUvW7iTQWjWfMIIdi2lTa3776EsBxmPvp2yE6xjaTQ3Ief/WlkWUqcjzIVk8NF65Ax5sb0l5vWrHK0NM+u68WM2/8uGZ2/hdULViM9jdeS1lPQOFjYz7taazaf1naeDa5a32gacWqkr/PUBvPvy2Wc07MVldG94FyQTMoQRQAxwvoeHNDymsdb25A04pVOPz7J5PRVwvBC+DQ3vcL/r0ZEF1unHnPV8teV+XCi9LIQHS5ccZ//BfW7T+S8vq6/X5cs2g6tpuoHsxSZi9KJ4N0bVF0uWH31Yzbdno6pe53Xoz/sWgi4IVxciimTeXy2+bLrixxibOTbh4vZjzI9q49295D82VXmq4uUmH31WDmjbdAkSQc+s0TKb9T6NhYSHvyorztv5xt3Ci5Z1qrZipTOedEs44HYwEp3QRRAHI0AjkSSX0tEoEUDqFyznzE+noKur+qqujv70dVVZVhEVVLga26BlIoVNa6KhellIGtugahaALhuJLyejiuIBRNmKoezFDmUssgXVu0eCqQGBwYl+00FaXsdxNlLBrvGC2HYtpULr+VoxHw7vIEK0tHunm8mPEg27vyFiukSNh0dZGJcqx38m1PY9H+y9nGjZJ7JtllKlM550SzjgdjASndBFEAgt0BweFIOWgIDgcs7grMvvnTBd8/kUjghRdewPlr18Iyzn1bFEkqa12Vi1LLQJKZP3QqJdZp5eF22uA1WT0YXeZSyyBdW0wMBGFxe8ZlO01HqfrdRBqLxjNmkEMxbSrbbwX72JqWA+nn8WLHg0zvqiTiEB1O09VFJsq13smnPY1V+y9XGzdK7plkl6lM5Z4TzTgejAXjLwoOQZgAVZHRcOGKlNcaLlwBVZHHtkAmhuqKEZdkrJrjTXlt1Rwv4pL56mE8ljkT6dqiFBpEtLdnQrVT6ndEqSmmTZmxPZZrPMj0rjVnng1FSpiuLjJRLtmNpzYBjE+5F1qmcs+JZpT9WEAn3QRRAILVlozsOJkiLxYC1RXDabPg6sU+ABg30cvHY5kzkaktOmrqJlQ7pX5HlJpi2pQZ22O5xoNc3tVsdZGJcsluvLWJ8Sj3YspUzjnRjLIfCzhVNSgx4BgRDAZRWVmJQCCAiooKo4szLtDNeNaSOWFWypVjcCLKYLzlYyyXDMKxBKyigFBchssqIC7Jps95bVSZyyUDytOd+/tMxLFoPGImOZQzh7ER5DIexEKDsLncZc3XbIa6yISRebrHuv2PZZ7usZB7MWUafo232aHKMkS7fUzKNV7IVdekk26CKAJ9cBgK+EBdKh1UVwxdWa10sFNiUTD/afF4LHMmMrXFidZOJ9r7EMZTTJsyY3vMNh4kEgm8tWUrLr74YljyUAiyvasZ6yIT5SqvGeuhnG3ciPctpkxJ5dtmx8uvvILLL798zMo10RjfKyeCIAiCIAiCKCMDAwNGF4EgDCcejxtdhHENKd0EQRAEQRAEQRAEUSZI6SYIgiAIgiAIgiCIMkFKN0EQBEEQBEEQBEGUCVK6CYIgCIIgCIIgCKJMkNJNEARBEARBEARBEGWClG6CIAiCIAiCIAiCKBOkdBMEQRAEQRAEQRBEmRgXSvcPf/hDTJs2DXa7Heeeey7effddo4tEEARBEARBEARBEFkxvdL961//GnfffTfuv/9+bN26FWeccQauuOIKdHV1GV00giAIgiAIgiAIgsiI6ZXu7373u/jsZz+LW2+9FQsWLMCPf/xjOJ1OPP7440YXjSAIgiAIgiAIgiAyIhpdgEzE43Fs2bIF99xzT/Iznudx+eWX46233kr5m1gshlgslvw7GAwCABKJBBKJRHkLPEHQ64nqyzhIBsZDMjAekoHxkAzMAcnBOKjujYdkYDwkg/TkWiecqqpqmctSMG1tbWhubsamTZtw/vnnJz//whe+gNdffx3vvPPOqN888MAD+MpXvjLq8yeffBJOp7Os5SUIgiAIgiAIgiAmB+FwGH/7t3+LQCCAioqKtN8z9Ul3Idxzzz24++67k38Hg0G0trZi9erVGSuCGCKRSGDdunVYtWoVLBaL0cWZlJAMjIdkYDwkA+MhGZgDkoNxUN0bD8nAeEgG6dGtqrNhaqW7pqYGgiCgs7NzxOednZ1oaGhI+RubzQabzZb8Wz/Ij0Qi1EhyJJFIIBwOIxKJQJIko4szKSEZGA/JwHhIBsZDMjAHJAfjoLo3HpKB8ZAM0hOJRAAM6ZzpMLXSbbVasWzZMrz66qu47rrrAACKouDVV1/F5z73uZzuMTAwAABobW0tVzEJgiAIgiAIgiCIScrAwAAqKyvTXje10g0Ad999Nz71qU9h+fLlOOecc/A///M/CIVCuPXWW3P6fVNTE06cOAGPxwOO48pc2omBbpJ/4sQJMsk3CJKB8ZAMjIdkYDwkA3NAcjAOqnvjIRkYD8kgPaqqYmBgAE1NTRm/Z3ql+6abbkJ3dzfuu+8+dHR0YOnSpXjppZdQX1+f0+95nkdLS0uZSzkxqaiooI5lMCQD4yEZGA/JwHhIBuaA5GAcVPfGQzIwHpJBajKdcOuYXukGgM997nM5m5MTBEEQBEEQBEEQhFngjS4AQRAEQRAEQRAEQUxUSOkmRmGz2XD//fePiAJPjC0kA+MhGRgPycB4SAbmgORgHFT3xkMyMB6SQfFwarb45gRBEARBEARBEARBFASddBMEQRAEQRAEQRBEmSClmyAIgiAIgiAIgiDKBCndBEEQBEEQBEEQBFEmSOkmCIIgCIIgCIIgiDJBSjdBEAQx4YjFYkYXgSAIgjAYmgsIs0BKN1EyVFUFBcM3ByQHYxkcHEQkEjG6GJOWffv24b777oMkSUYXhSAMh+YD46C5wFhoLiDMBCndREnYs2cP7rjjDlxzzTW4//778frrrxtdpElJX18fAIDjOFpoGcT+/ftx0UUX4Te/+Q0ttgzggw8+wNKlS/Htb38br7zyitHFmbTs378f99xzD2677Tb84Ac/wMGDB40u0qSD5gNjobnAWGguMAc0FwxBSjdRNHv37sX555+Prq4uNDU14Ze//CW++MUv4qtf/arRRZtU7N69G/X19bjrrrsA0ELLKP7v//4PH3zwAe69914888wzZNo2hmzfvh3nnXcePvOZz+DGG2/Ek08+iUgkQv1gjNm9ezfOOeccbNu2Df39/fiP//gP/NM//RMef/xxo4s2aaD5wHhoLjAOmgvMAc0FIyGlmygKWZbx4x//GFdffTV+//vf4yc/+Qn++te/4tJLL8Vvf/tbfPGLXzS6iJOCtrY23HrrrViyZAkee+wxfP7znwdACy0jWLlyJf7jP/4DH/vYx3Drrbfit7/9rdFFmhRs3boVF198Me6++2788Ic/xLnnnovnnnsOHR0d1A/GkHg8jq9//eu46aab8NJLL+EPf/gD3nvvPXi9Xjz22GP44Q9/aHQRJzw0H5gDmguMgeYCc0BzwWhI6SaKQhAEHD9+HMFgMPlZc3Mz7r77bvzN3/wN1q1bhx//+McGlnDio6oq1q9fj6lTp+L73/8+Hn30UTzyyCO4++67AdBCywieffZZfOc738HHP/5x/MM//AP+/Oc/4/bbb8cPfvADo4s2IfH7/bjooovw93//9/ja174GAPinf/onzJgxA//5n/8JVVXBcZzBpZwcWK1WdHR0gOfZ8kJVVcydOxcPPvgg5syZg9/85jd4/vnnDS7lxIXmA3NBc8HYQnOBeaC5YDSi0QUgxi/64LVs2TJs2LABx48fx5QpUwAAdXV1+OxnP4t9+/bhmWeewa233gqbzWZwiScmHMfh4osvhsfjwQUXXIALLrgAqqriM5/5DFRVxfe+973kQosmm/Jz3nnnobq6GtFoFI899hhcLhc+8pGPwO12Y926dUYXb0Li9XqxadMmLF26FAAbm0RRxOrVq/H888+jt7cXNTU11AfKjKqqkCQJLS0t6OvrQywWg8VigaqqmDZtGr70pS/h4x//OJ588klcddVVRhd3QkLzgXmguWDsobnAHNBckBo66SYKRh+wVq9ejbfffhuPPPJI0mdJVVU0NzfjC1/4Al5++WVs27bNyKJOeKZMmYKrr746+fdNN92E//u//xtxwiHLMn71q19h586dRhVzUlBRUYFQKIRNmzYBAMLhMOx2O6LRKI4ePYpoNGpwCScmS5cuHXGCJwgC7rzzThw+fBiPPvooANAiq8xwHAeLxYJPfvKT+N3vfof//d//Bc/z4DgOsixj1qxZ+PrXv46nn34ae/bsMbq4ExaaD8wBzQXGQHOB8dBckBo66SbyIhAIgOd5eDye5KB29tln46c//Sk+/vGPw+Fw4K677kJFRQUAoKqqCgsXLoTD4TCy2BOOEydOYM+ePeju7saqVavg9XphtVohSRJEUYQgCLjxxhsBALfeeisAtsh65JFHJnXkyFKSTgayLGPOnDmQZRl33nknXnjhBezcuRPf/va38dGPfhS/+c1vcMMNNxhd/HFPpvoXBAGyLKOxsRF///d/j+effx4f//jH0draanSxJxzhcBgWiyV5iqGqKi677DJ84xvfwD//8z/D4XDg1ltvhSAIAIDKykrMnTsXTqfT4JJPHGg+MBaaC4yF5gJzQHNBDqgEkSO7du1Szz77bPV//ud/1EAgoKqqqsqyrCqKoqqqqj766KMqx3HqZz/7WfWFF15Qjx8/rn7xi19UW1tb1ba2NiOLPqHYvn27Wl9fr5511lmq1WpVFy5cqP7bv/2b2t/fr6qqqkqSlPyuJEnqL37xC5XjOLWqqkp97733DCr1xCKdDHp7e1VVVdX/+q//UjmOUxsbG0fU+V133aXu2bPHqGJPGPLpAy+++KLq8XjU5557zqDSTlx27dqlXnbZZepzzz2nJhIJVVXV5HwQDofV++67T+U4Tr3//vvV999/X+3v71e/+MUvqrNnz1a7urqMLPqEgeYDY6G5wFhoLjAHNBfkBindRE4cO3ZMXbJkiVpdXa0uXLhQ/clPfpJUvPWOpaqq+sILL6hnn3222tDQoM6dO1edNm2aunXrVqOKPeHw+/3qWWedpf6///f/1N7eXjUSiaj33HOPesEFF6jXXnttcqLXJxpZltXbbrtNraioUHfv3m1k0ScMmWRwzTXXqP39/er27dvVT3/60+q2bdtUVR058RPFkW8fUFVVXb16tXrppZeO2CQkiuPo0aPqvHnzVEEQ1GnTpqkvv/xycrE1nMcff1ytr69Xm5ub1fnz56tNTU00J5QImg+MheYCY6G5wBzQXJA7pHQTWZFlWX3sscfUtWvXqvv27VNvu+02ddasWSMUb1mWVVmWVVVV1Y6ODnXnzp3q22+/rXZ0dBhZ9AnHkSNH1BkzZqgbNmxIfhaLxdTHH39cPf/889VbbrlFDQaDqqqyzZAXXnhBnT59Op1olJBMMjj33HPVT37yk2osFks56RDFk08f0Mek3//+9+rBgwcNKe9EJJFIqN/73vfU6667Tj127Jh65ZVXqg0NDWkXW4cOHVI3bNigvvjii+qJEycMKPHEhOYDY6G5wFhoLjAemgvyg5RuIif279+v/ulPf0r+PVzx9vv9qqqqtHM4BnR3d6uLFi1Sv//976uqOmRlIMuy+sMf/lA966yz1CeeeCL5/Y6ODrW9vd2Qsk5Usslg6dKl6i9+8YsR14jSkU8foFOl8qAoirpx40b197//ffKzK664Qm1sbFRffvllNR6PJ79HfaB80HxgLDQXGAvNBcZDc0F+cKpKCRuJwvi7v/s7vP766/i3f/s3fOxjH0NFRQV+8Ytf4Nprr00GUiNKSyKRwM0334z29nY8+eSTmDp16ojrV1xxBSwWC/785z8bVMKJD8nAWKj+zYGiKMn8qzpXXnkltm/fjieeeAKXXXYZBEHAn//8Z6xcuRIul8ugkk5cqC8YC9W/sVD9mwOaC3KHUoYReSPLMgDgsccew6WXXopvf/vbePLJJ/F3f/d3+PznP4/+/n6DSzgxUVUVFosFP/rRj3Do0CHceeed6OrqGpEa4+qrr0ZPTw+lIikTJANjofo3D8MXWZIkAQBeeuklLF26FJ/61Kfw8ssv4x/+4R/w+c9/HsFg0KhiTlioLxgL1b+xUP2bB5oLcoeUbiInFEVJ/lsQBCQSCQBM8V6xYgXuuOMO/PrXv8bLL788areRKA0cxyEej6Ourg4vvfQS3nnnHXz84x/H5s2bkxsh77//Pnw+36hdR6I0kAyMherfPAxf3IqimJwTXnzxRSxduhRXXXUVfvWrX+Hpp59GY2OjUcWcsFBfMBaqf2Oh+jcPNBfkDpmXE1nRcx12d3fjwIEDuOCCC0Z8ftddd+EXv/gF3njjDSxYsMDg0k5c9Pru7e1FPB5HJBLBmjVr4Ha7IUkSZsyYgVdffRUbN27EkiVLjC7uhIRkYCxU/+ZAl0N/fz96enowe/ZsAEjmhf785z+PJ554guaEEqKqKjiOS/5NfWFsofo3Fqp/c5BODjQX5AZt/xCjGL4Po3eoY8eOYenSpfjggw+S1wRBwFNPPYWHH34YL7/8MnWoMqGqKiRJgiAIOHr0KJYsWYJXX30VM2bMwHvvvYe77roLq1atwtlnn4333nuPJpgyQDIwFqp/czBcDseOHcOiRYuwZcuW5HVRFPHYY4/hoYceojmhRMTjcQBALBYDQH1hrKH6Nxaqf3OQSQ40F+QOnXQTAICTJ0+is7MTy5YtS3lt/vz5uOWWW/DII4+M2OWKxWLo6upCa2vrWBZ3wnLw4EH85je/wb59+3DxxRfj8ssvx7Rp0wAwOSxevBg33ngjfvKTn0BVVTKbKgMkA2Oh+jcH+chh+Jxw8OBBiKKY/C5ROHv37sWDDz6ItrY2NDQ04N/+7d+waNEiANQXxgKqf2Oh+jcH+ciB5oLMUAslsG/fPixfvhy33347Nm7cOOp6e3s77rjjDvzoRz8a0aEAwGazkcJdInbu3IkLLrgA27dvx4EDB/DTn/4UDz74IAYHB6GqKjZv3ozbbrstObDRBFN6SAbGQvVvDvKVw3BmzZpFi6wSsGPHDlxwwQWw2WyYOXMmOjs78d3vfheJRAKyLOPdd9/FZz7zGfz4xz+mvlAGqP6NherfHOQrh+HQXJCCMqUiI8YJ7e3t6ooVK9QLL7xQXbNmjbp69Wr1r3/9a/K6LMsGlm7ycPz4cXXBggXqF7/4xeRnP/zhD9UZM2aop06dUlVVTeY7JMoDycBYqP7NAcnBeA4fPqzOnDlTvffee5OfPfDAA+pnPvMZVVVVNRKJqKpKuYfLBdW/sVD9mwOSQ+mhraFJzsmTJyEIAr71rW/hzjvvhCAI+NrXvoY33ngDAGj3cAxQVRXr16/HnDlz8I//+I/JSPG33XYbAGDXrl0AAIvFYlgZJzokA2Oh+jcHJAdz8N577+HCCy/EnXfemfwsGAxi8+bNOOecc7Bq1Sr85S9/gSAII2KwEKWB6t9YqP7NAcmh9IhGF4AwluXLl+O///u/ccYZZwBgqcF+8IMf4Gtf+xruvfdeXHLJJcnPeZ5P/p8oHRzHoaamBldeeWUy3ZqqqkgkEojFYggEAqN+o54WQZIoDpKBsVD9mwOSgzlYvXo1zjjjDNTV1QEAvv3tb+NHP/oRvvnNb8Jut2P79u245pprsHnzZixevNjg0k48qP6NherfHJAcSg8p3ZOU4cqzrnADwNq1a8FxHB5++GF8/etfB8dxuPjii3H33Xfj5ptvxrnnnmtUkSckenT4tWvXJj/TF7FutxsNDQ2wWq3Ja0888QTOO+88zJkzx4jiTkhIBsZC9W8OSA7Go8vA6/XC6/UCYFGDDxw4gD/96U9YtWoVAODIkSN49tln8f7779Nit4RQ/RsL1b85IDmUDzqynGT4/X4ASJ5aD0c3D1mzZg3+5V/+BTzP4+tf/zo+8pGP4OGHHx6x4CKKQ5eDIAiQJGnEteGnRjzPJ+Vy77334nOf+xydKpUIkoGxUP2bA5KD8QyXgSzLyc9VVYXVasWPf/xjrFq1KjlncxyHxsZGCmJaIqj+jYXq3xyQHMoPKd2TiD179uCss87CfffdB2C04s1xXHJRdeWVV+KOO+7AO++8gw0bNmDbtm0488wzDSn3RON0OYiiOGoDBACi0Sh6e3uhqioefPBBfPe738X69esxe/bssS7yhINkYCxU/+aA5GA8p8tAEIQRi9rh/9et0x599FFIkoR58+YZUOKJBdW/sVD9mwOSwxhR7khthDk4fvy4unTpUnX27NnqokWL1K985SvJa6dHKFcURZVlWb3rrrtUj8ej7tixY6yLO2HJRw6yLKsXXXSRunDhQtXpdKrvvffeWBd3QkIyMBaqf3NAcjCefGSgqqq6d+9e9fOf/7xaVVWlvv/++2NZ1AkJ1b+xUP2bA5LD2EE+3ZMAVVXx1FNPoampCXfddRfefPNNPPXUUwCA++67DzzPJ304ALabtXPnTvz2t7/F+vXrsWjRIiOLP2HIVw6KokBRFLS1teGtt97CkiVLjCz+hIBkYCxU/+aA5GA8+cpg165d+OUvf4mNGzdiw4YNJIMiofo3Fqp/c0ByGGMMU/eJMaW9vV392c9+pqqqqnZ2dqr333+/Om/ePPWBBx5Ifuf0Ha1AIDCmZZwM5CuHxx9/XD1w4MCYl3MiQzIwFqp/c0ByMJ58ZbBz5061s7NzzMs5UaH6Nxaqf3NAchg7SOmepLS1taXsWM8884waj8dVVWVm5kR5SSeH3//+9waWanJBMjAWqn9zQHIwHpKBsVD9GwvVvzkgOZQPMi+foLS3t+PEiRPo7+/H5ZdfPsJMUI84+Pd///cAgKeffhqqqiIQCOChhx7CyZMn0dTURJFpS0Ap5EAUB8nAWKj+zQHJwXhIBsZC9W8sVP/mgORgIIap+0TZ2L59uzp16lR1zpw5amVlpTpv3jz1ySefVHt7e1VVZWYi+il2W1ubet9996kcx6lVVVXq5s2bjSz6hILkYDwkA2Oh+jcHJAfjIRkYC9W/sVD9mwOSg7FQyrAJRnd3N2666SbccsstePHFF7F7926cccYZ+M///E88/PDD6O7uTob7B4DGxkYcOXIEHo8HGzduxLJlywws/cSB5GA8JANjofo3ByQH4yEZGAvVv7FQ/ZsDkoMJMFrrJ0rLrl271GnTpo3akfr3f/93dfHixeq3vvUtNRQKJT9/7LHHVK/Xq27dunWsizqhITkYD8nAWKj+zQHJwXhIBsZC9W8sVP/mgORgPHTSPcFIJBKQJAnhcBgAEIlEAADf/OY3sXLlSjzyyCM4ePBg8vsf/vCHsXXrVpx55pmGlHeiQnIwHpKBsVD9mwOSg/GQDIyF6t9YqP7NAcnBeDhVVVWjC0GUlnPOOQdutxuvvfYaACAWi8FmswEAzj77bMyaNQtPPfXUiNx7ROkhORgPycBYqP7NAcnBeEgGxkL1byxU/+aA5GAsdNI9zgmFQhgYGEAwGEx+9pOf/AS7du3C3/7t3wIAbDYbJEkCAFxyySUIhUIAQB2qhJAcjIdkYCxU/+aA5GA8JANjofo3Fqp/c0ByMB+kdI9jdu/ejeuvvx6XXnop5s+fj1/96lcAgPnz5+Ohhx7CunXrcOONNyKRSCSDI3R1dcHlckGSJJCRQ2kgORgPycBYqP7NAcnBeEgGxkL1byxU/+aA5GBOKE/3OGX37t245JJL8MlPfhLLly/Hli1bcOutt2LBggU488wzcc0118DlcuGOO+7AkiVLMG/ePFitVjz//PN4++23IYok+lJAcjAekoGxUP2bA5KD8ZAMjIXq31io/s0BycG8kE/3OKSvrw8333wz5s2bh4ceeij5+cqVK7F48WI8/PDDyc8GBgbwta99DX19fbDb7bj99tuxYMECI4o94SA5GA/JwFio/s0BycF4SAbGQvVvLFT/5oDkYG5oO2Mckkgk4Pf7ccMNNwAAFEUBz/OYPn06+vr6AACqqkJVVXg8Hjz44IMjvkeUBpKD8ZAMjIXq3xyQHIyHZGAsVP/GQvVvDkgO5oZqeBxSX1+PX/7yl7j44osBALIsAwCam5uTnYbjOPA8PyKAAsdxY1/YCQzJwXhIBsZC9W8OSA7GQzIwFqp/Y6H6NwckB3NDSvc4Zfbs2QDY7pTFYgHAdq+6urqS3/nGN76Bxx57LBmZkDpV6SE5GA/JwFio/s0BycF4SAbGQvVvLFT/5oDkYF7IvHycw/M8VFVNdhh9J+u+++7D1772NWzbto2CIowBJAfjIRkYC9W/OSA5GA/JwFio/o2F6t8ckBzMB510TwD0WHiiKKK1tRXf+c538K1vfQubN2/GGWecYXDpJg8kB+MhGRgL1b85IDkYD8nAWKj+jYXq3xyQHMwFbXFMAPTdK4vFgkcffRQVFRXYuHEjzjrrLINLNrkgORgPycBYqP7NAcnBeEgGxkL1byxU/+aA5GAu6KR7AnHFFVcAADZt2oTly5cbXJrJC8nBeEgGxkL1bw5IDsZDMjAWqn9jofo3ByQHc0B5uicYoVAILpfL6GJMekgOxkMyMBaqf3NAcjAekoGxUP0bC9W/OSA5GA8p3QRBEARBEARBEARRJsi8nCAIgiAIgiAIgiDKBCndBEEQBEEQBEEQBFEmSOkmCIIgCIIgCIIgiDJBSjdBEARBEARBEARBlAlSugmCIAiCIAiCIAiiTJDSTRAEQRAEQRAEQRBlgpRugiAIgjApn/70p3HdddcZXQyCIAiCIIpANLoABEEQBDEZ4Tgu4/X7778fDz30EFRVHaMSpebTn/40/H4/nnnmGUPLQRAEQRDjFVK6CYIgCMIA2tvbk//+9a9/jfvuuw/79u1LfuZ2u+F2u40oGkEQBEEQJYTMywmCIAjCABoaGpL/VVZWguO4EZ+53e5R5uUrVqzAP//zP+Ouu+5CVVUV6uvr8eijjyIUCuHWW2+Fx+PBrFmz8OKLL4541s6dO7FmzRq43W7U19fjE5/4BHp6epLXf/e732Hx4sVwOBzw+Xy4/PLLEQqF8MADD+DnP/85nn32WXAcB47jsGHDBgDAv//7v2POnDlwOp2YMWMGvvzlLyORSCTv+cADD2Dp0qV4/PHHMWXKFLjdbtxxxx2QZRnf+ta30NDQgLq6OvzXf/3XiLJyHIdHHnkEa9asgcPhwIwZM/C73/2u9AIgCIIgiDGClG6CIAiCGEf8/Oc/R01NDd5991388z//M26//XbceOONuOCCC7B161asXr0an/jEJxAOhwEAfr8fl112Gc4880xs3rwZL730Ejo7O/HRj34UADtxv/nmm/GZz3wGe/bswYYNG3D99ddDVVX867/+Kz760Y/iyiuvRHt7O9rb23HBBRcAADweD372s59h9+7deOihh/Doo4/ie9/73oiyHjp0CC+++CJeeuklPPXUU/jf//1fXHXVVTh58iRef/11PPjgg/jSl76Ed955Z8TvvvzlL+Nv/uZvsH37dtxyyy342Mc+hj179oxB7RIEQRBE6eFUo53FCIIgCGKS87Of/Qx33XUX/H7/iM9P96desWIFZFnGG2+8AQCQZRmVlZW4/vrr8cQTTwAAOjo60NjYiLfeegvnnXcevva1r+GNN97AX/7yl+R9T548idbWVuzbtw+Dg4NYtmwZjh49iqlTp44qW64+3d/5znfw9NNPY/PmzQDYSfe3v/1tdHR0wOPxAACuvPJK7Nu3D4cOHQLPs33/efPm4dOf/jS++MUvAmAn3f/4j/+IRx55JHnv8847D2eddRZ+9KMf5VijBEEQBGEeyKebIAiCIMYRS5YsSf5bEAT4fD4sXrw4+Vl9fT0AoKurCwCwfft2rF+/PqV/+KFDh7B69Wp86EMfwuLFi3HFFVdg9erVuOGGG1BVVZWxHL/+9a/x8MMP49ChQxgcHIQkSaioqBjxnWnTpiUVbr1sgiAkFW79M72sOueff/6ov99///2M5SEIgiAIs0Lm5QRBEAQxjrBYLCP+5jhuxGd6VHRFUQAAg4ODuPrqq/H++++P+O/AgQO45JJLIAgC1q1bhxdffBELFizA97//fcydOxdHjhxJW4a33noLt9xyC9auXYs///nP2LZtG+69917E4/G8yqp/ppeVIAiCICYipHQTBEEQxATmrLPOwq5duzBt2jTMmjVrxH8ulwsAU3wvvPBCfOUrX8G2bdtgtVrxxz/+EQBgtVohy/KIe27atAlTp07Fvffei+XLl2P27Nk4duxYycr89ttvj/p7/vz5Jbs/QRAEQYwlpHQTBEEQxATmn/7pn9DX14ebb74Z7733Hg4dOoS//OUvuPXWWyHLMt555x18/etfx+bNm3H8+HH84Q9/QHd3d1LJnTZtGj744APs27cPPT09SCQSmD17No4fP46nn34ahw4dwsMPP5xU0kvBb3/7Wzz++OPYv38/7r//frz77rv43Oc+V7L7EwRBEMRYQko3QRAEQUxgmpqa8Oabb0KWZaxevRqLFy/GXXfdBa/XC57nUVFRgb/+9a9Yu3Yt5syZgy996Uv47//+b6xZswYA8NnPfhZz587F8uXLUVtbizfffBPXXHMNPv/5z+Nzn/scli5dik2bNuHLX/5yycr8la98BU8//TSWLFmCJ554Ak899RQWLFhQsvsTBEEQxFhC0csJgiAIgjANHMfhj3/844j85ARBEAQxnqGTboIgCIIgCIIgCIIoE6R0EwRBEARBEARBEESZoDzdBEEQBEGYBvJ6IwiCICYadNJNEARBEARBEARBEGWClG6CIAiCIAiCIAiCKBOkdBMEQRAEQRAEQRBEmSClmyAIgiAIgiAIgiDKBCndBEEQBEEQBEEQBFEmSOkmCIIgCIIgCIIgiDJBSjdBEARBEARBEARBlAlSugmCIAiCIAiCIAiiTJDSTRAEQRAEQRAEQRBl4v8DrS0Adlm+pecAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "#task_phab_df = phab_df[phab_df['comment_type']==\"task_description\"]\n",
    "task_phab_df = task_phab_df[task_phab_df['is_relevant'] == True]\n",
    "task_phab_df['first_comment'] = task_phab_df.groupby('speaker')['timestamp'].rank(method='first') <= 5\n",
    "#task_phab_df = task_phab_df[(task_phab_df['date_created'] < 1383264000) & (task_phab_df['date_created'] > 1351728000)]\n",
    "\n",
    "'''\n",
    "task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "unique_taskPHIDs = task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "wmf_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] == True)]\n",
    "wmf_tasks = wmf_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "other_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] != True)]\n",
    "other_tasks = other_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "unaff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] != True)]\n",
    "unaff_new_tasks = unaff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "aff_new_tasks_phab_df = task_phab_df[(task_phab_df['first_comment'] == True) & (task_phab_df['meta.affil'] == True)]\n",
    "aff_new_tasks = aff_new_tasks_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "sns.lineplot(x=unique_taskPHIDs.index, y=unique_taskPHIDs.values, color='black', label='Total', marker='o')\n",
    "sns.lineplot(x=wmf_tasks.index, y=wmf_tasks.values, color='#c7756a', label='WMF-affiliated authors', marker='o')\n",
    "sns.lineplot(x=other_tasks.index, y=other_tasks.values, color='#5da2d8', label='Nonaffiliated authors', marker='o')\n",
    "#sns.lineplot(x=aff_new_tasks.index, y=aff_new_tasks.values, color='#c7756a',linestyle=\"dotted\", label=\"WMF-affiliated new authors\", marker='x')\n",
    "#sns.lineplot(x=unaff_new_tasks.index, y=unaff_new_tasks.values, color='#5da2d8', linestyle=\"dotted\", label=\"Nonaffiliated new authors\", marker='x')\n",
    "\n",
    "plt.title('New Relevant Phabricator Tasks Indexed with HTTPS')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.ylabel('Unique taskPHIDs')\n",
    "plt.xticks(rotation=45)\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "'''\n",
    "task_phab_df['week'] = task_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "unique_taskPHIDs = task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "wmf_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] == True)]\n",
    "wmf_tasks = wmf_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "other_task_phab_df = task_phab_df[(task_phab_df['meta.affil'] != True)]\n",
    "other_tasks = other_task_phab_df.groupby('week')['conversation_id'].nunique()\n",
    "\n",
    "#plt.savefig('031825_new_tasks_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "9a9b08a7-6c95-4971-b259-8e713c58fbe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_13098/3506948764.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
      "/tmp/ipykernel_13098/3506948764.py:17: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.\n",
      "  unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
      "/tmp/ipykernel_13098/3506948764.py:18: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
      "/tmp/ipykernel_13098/3506948764.py:20: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#task_phab_df = phab_df[phab_df['comment_type'] == \"task_description\"]\n",
    "unaff_tasks_phab_df = task_phab_df[task_phab_df['meta.affil'] != True]\n",
    "# Rank speaker's task values within each group\n",
    "unaff_tasks_phab_df['speakers_task'] = unaff_tasks_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
    "\n",
    "# Filter dates 08-01-2013 to 09-30-2013\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df[(unaff_tasks_phab_df['date_created'] < 1380499200) & (unaff_tasks_phab_df['date_created'] > 1375315200)]\n",
    "# Bin the speakers based on the number of tasks they created\n",
    "bins = [0, 6, 26, 51, float('inf')]\n",
    "labels = ['0-5', '6-25', '26-50', '51+']\n",
    "min_speakers_task = unaff_tasks_phab_df.groupby('speaker')['speakers_task'].min().reset_index()\n",
    "min_speakers_task = min_speakers_task.rename(columns={'speakers_task': 'min_speakers_task'})\n",
    "unaff_tasks_phab_df = unaff_tasks_phab_df.merge(min_speakers_task, on='speaker', how='left')\n",
    "unaff_tasks_phab_df['task_bins'] = pd.cut(unaff_tasks_phab_df['min_speakers_task'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "# Calculate the weekly breakdown of binned speakers_task values\n",
    "unaff_tasks_phab_df['week'] = unaff_tasks_phab_df['timestamp'].dt.to_period('W').dt.start_time\n",
    "weekly_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).size().unstack(fill_value=0)\n",
    "\n",
    "speaker_breakdown = unaff_tasks_phab_df.groupby(['week', 'task_bins']).nunique()['speaker'].unstack(fill_value=0)\n",
    "\n",
    "# Reshape the DataFrame for use with Seaborn\n",
    "weekly_breakdown = weekly_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='count')\n",
    "speaker_breakdown = speaker_breakdown.reset_index().melt(id_vars='week', value_vars=labels, var_name='task_bins', value_name='speakers')\n",
    "\n",
    "# Plot the stacked bar plot using Seaborn\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(data=weekly_breakdown, x='week', y='count', hue='task_bins', palette='colorblind')\n",
    "#sns.barplot(data=speaker_breakdown, x='week', y='speakers', hue='task_bins', palette='colorblind')\n",
    "plt.title(\"08-01-2013 to 09-30-2013 Weekly Unaffiliated Task Creation by Contributor Tenure\")\n",
    "plt.xlabel('Week')\n",
    "plt.ylabel('Tasks')\n",
    "plt.legend(title=\"Contributor had created # tasks between 06-01-2013 and 08-01-2013:\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('031625_weekly_tasks_by_history.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b7cfad77-d48a-4708-91f3-89ae1179b90c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_13098/2708736932.py:27: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
      "/tmp/ipykernel_13098/2708736932.py:28: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
      "/tmp/ipykernel_13098/2708736932.py:35: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
      "/tmp/ipykernel_13098/2708736932.py:36: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of comments for each date group:\n",
      "date_group\n",
      "Before announcement                      565\n",
      "After announcement, before deployment    297\n",
      "After deployment                         976\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group:\n",
      "date_group\n",
      "Before announcement                      100\n",
      "After announcement, before deployment     71\n",
      "After deployment                         143\n",
      "Name: speaker, dtype: int64\n",
      "\n",
      "Number of comments for each date group and engaged commenter subgroup:\n",
      "date_group                             est_commenter  meta.affil\n",
      "Before announcement                    False          False         549\n",
      "                                                      True           16\n",
      "After announcement, before deployment  False          False         284\n",
      "                                                      True           13\n",
      "After deployment                       False          False         953\n",
      "                                                      True           23\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each date group and engaged commenter subgroup:\n",
      "date_group                             est_commenter  meta.affil\n",
      "Before announcement                    False          False          99\n",
      "                                                      True            6\n",
      "After announcement, before deployment  False          False          70\n",
      "                                                      True            8\n",
      "After deployment                       False          False         139\n",
      "                                                      True           14\n",
      "Name: speaker, dtype: int64\n",
      "\n",
      "Number of comments for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         1786\n",
      "               True            52\n",
      "dtype: int64\n",
      "\n",
      "Number of speakers for each engaged commenter subgroup, and WMF affiliation:\n",
      "est_commenter  meta.affil\n",
      "False          False         184\n",
      "               True           23\n",
      "Name: speaker, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nplot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'new_commenter\\', scatter=False, legend=False, palette=palette)\\nplot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\\nplot1.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\\nplot1.add_legend(title=\"Comment publication timestamp:\")\\nfig1 = plot1.fig\\n# Plot for arousal_wc\\nplot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\\nplot2.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot2.add_legend(title=\"Comment publication timestamp:\")\\n#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\\n\\nplot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row=\\'engaged_commenter\\', scatter=False, legend=False, palette=palette)\\nplot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\\nplot3.set_titles(row_template=\"Author\\'s 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\\nplot3.add_legend(title=\"Comment publication timestamp:\")\\n'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1333.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [\n",
    "    pd.Timestamp('1900-01-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2013-08-01 00:00:01+00:00'),\n",
    "    pd.Timestamp('2013-08-28 00:00:01+00:00'),\n",
    "    pd.Timestamp('2100-08-28 00:00:01+00:00')\n",
    "]\n",
    "labels = ['Before announcement', 'After announcement, before deployment', 'After deployment']\n",
    "\n",
    "#creating variables of interest\n",
    "affective_comment_phab_df = comment_phab_df\n",
    "affective_comment_phab_df['date_group'] = pd.cut(affective_comment_phab_df['timestamp'], bins=bins, labels=labels, right=False)\n",
    "affective_comment_phab_df['speakers_comment'] = affective_comment_phab_df.groupby('speaker')['timestamp'].rank(method='first').astype(int)\n",
    "#all comments prior to june 1 2013\n",
    "subset_comment_phab_df = affective_comment_phab_df[affective_comment_phab_df['date_created'] <= 1370044800]\n",
    "#getting counts \n",
    "comment_counts = subset_comment_phab_df.groupby('speaker')['speakers_comment'].max().reset_index()\n",
    "comment_counts = comment_counts.rename(columns={'speakers_comment': 'pre_june_2013_comments'})\n",
    "#merge back \n",
    "affective_comment_phab_df = affective_comment_phab_df.merge(comment_counts, on='speaker', how='left')\n",
    "affective_comment_phab_df['pre_june_2013_comments'] = affective_comment_phab_df['pre_june_2013_comments'].fillna(0)\n",
    "\n",
    "affective_comment_phab_df['new_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] <= 10\n",
    "affective_comment_phab_df['est_commenter'] = affective_comment_phab_df['pre_june_2013_comments'] > 50\n",
    "\n",
    "palette = ['#31449c', '#4a7c85', '#c5db68']\n",
    "\n",
    "comment_counts = affective_comment_phab_df.groupby('date_group').size()\n",
    "speaker_counts = affective_comment_phab_df.groupby('date_group')['speaker'].nunique()\n",
    "\n",
    "print(\"Number of comments for each date group:\")\n",
    "print(comment_counts)\n",
    "print(\"\\nNumber of speakers for each date group:\")\n",
    "print(speaker_counts)\n",
    "\n",
    "comment_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil']).size()\n",
    "speaker_counts_engaged = affective_comment_phab_df.groupby(['date_group', 'est_commenter', 'meta.affil'])['speaker'].nunique()\n",
    "\n",
    "print(\"\\nNumber of comments for each date group and engaged commenter subgroup:\")\n",
    "print(comment_counts_engaged)\n",
    "print(\"\\nNumber of speakers for each date group and engaged commenter subgroup:\")\n",
    "print(speaker_counts_engaged)\n",
    "\n",
    "comment_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil']).size()\n",
    "speaker_counts_wmf = affective_comment_phab_df.groupby(['est_commenter', 'meta.affil'])['speaker'].nunique()\n",
    "\n",
    "print(\"\\nNumber of comments for each engaged commenter subgroup, and WMF affiliation:\")\n",
    "print(comment_counts_wmf)\n",
    "print(\"\\nNumber of speakers for each engaged commenter subgroup, and WMF affiliation:\")\n",
    "print(speaker_counts_wmf)\n",
    "\n",
    "#comment_phab_df['before_after'] = comment_phab_df['timestamp'] > pd.Timestamp('2013-07-01 00:00:01+00:00')\n",
    "#fig, axes = plt.subplots(2, 1, figsize=(10, 12), sharex=True)\n",
    "affective_comment_phab_df['polarized_wc'] = affective_comment_phab_df['dominant_wc'] + affective_comment_phab_df['valence_wc'] + affective_comment_phab_df['arousal_wc'] \n",
    "plot1 = sns.lmplot(data=affective_comment_phab_df, x=\"date_created\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", row='est_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot1.set_axis_labels(\"Timestamp\", \"Count of Polarized Words\")\n",
    "plot1.set_titles(row_template=\"Established Author: {row_name}\", col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot1.add_legend(title=\"Comment publication timestamp:\")\n",
    "fig1 = plot1.fig\n",
    "'''\n",
    "plot1 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"dominant_wc\", hue=\"date_group\", col=\"meta.affil\", row='new_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot1.set_axis_labels(\"Timestamp\", \"Count of Dominance Polarized Words\")\n",
    "plot1.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot1.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot1.add_legend(title=\"Comment publication timestamp:\")\n",
    "fig1 = plot1.fig\n",
    "# Plot for arousal_wc\n",
    "plot2 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"arousal_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Timestamp\", \"Count of Arousal Polarized Words\")\n",
    "plot2.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")\n",
    "#plot2.add_legend(title=\"Before/After 07/01/2013 Wide Release\")\n",
    "\n",
    "plot3 = sns.lmplot(data=comment_phab_df, x=\"date_created\", y=\"valence_wc\", hue=\"date_group\", col=\"meta.affil\", row='engaged_commenter', scatter=False, legend=False, palette=palette)\n",
    "plot3.set_axis_labels(\"Timestamp\", \"Count of Valence Polarized Words\")\n",
    "plot3.set_titles(row_template=\"Author's 100+ Comment: {row_name}\",col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot3.add_legend(title=\"Comment publication timestamp:\")\n",
    "'''\n",
    "# Show plots\n",
    "#fig1.savefig('031725_engaged_commenter_D_scoring_fig.png')\n",
    "#plot2.fig.savefig('031725_engaged_commenter_A_scoring_fig.png')\n",
    "#plot3.fig.savefig('031725_engaged_commenter_V_scoring_fig.png')\n",
    "#plt.savefig('031625_engaged_commenter_VAD_scoring_fig.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5a91a59a-0d1c-48b3-93dd-b9df76ca68e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14ca72b957f0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1333.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2 = sns.lmplot(data=affective_comment_phab_df, x=\"speakers_comment\", y=\"polarized_wc\", hue=\"date_group\", col=\"meta.affil\", scatter=False, legend=False, palette=palette)\n",
    "plot2.set_axis_labels(\"Index of Speaker's Comment\", \"Count of Polarized Words\")\n",
    "plot2.set_titles(col_template=\"WMF Affiliation: {col_name}\")\n",
    "plot2.fig.subplots_adjust(top=0.9)  # Adjust subplots to make room for the title\n",
    "plot2.add_legend(title=\"Comment publication timestamp:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d2d67d38-f005-4c94-be3c-39eb6b22686f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19468/1559616732.py:4: UserWarning: This pattern is interpreted as a regular expression, and has match groups. To actually get the groups, use str.extract.\n",
      "  filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'resolved_dependency_relations_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[20], line 5\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(WMF|Foundation)\\b'\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#pattern = r'\\b(bots)\\b'\u001b[39;00m\n\u001b[1;32m      4\u001b[0m filtered_dependencies \u001b[38;5;241m=\u001b[39m dependency_relations_df[dependency_relations_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(pattern, regex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)]\n\u001b[0;32m----> 5\u001b[0m resolved_filtered_dependencies \u001b[38;5;241m=\u001b[39m \u001b[43mresolved_dependency_relations_df\u001b[49m[resolved_dependency_relations_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mcontains(pattern, regex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)]\n\u001b[1;32m      7\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m      8\u001b[0m gs \u001b[38;5;241m=\u001b[39m GridSpec(\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, height_ratios\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m6\u001b[39m, \u001b[38;5;241m6\u001b[39m])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'resolved_dependency_relations_df' is not defined"
     ]
    }
   ],
   "source": [
    "#pattern = r'\\b(ve|VE|visualeditor|VisualEditor)\\b'\n",
    "#pattern = r'\\b(WMF|Foundation)\\b'\n",
    "#pattern = r'\\b(bots)\\b'\n",
    "filtered_dependencies = dependency_relations_df[dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "resolved_filtered_dependencies = resolved_dependency_relations_df[resolved_dependency_relations_df['token'].str.contains(pattern, regex=True)]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "gs = GridSpec(2, 1, height_ratios=[6, 6])\n",
    "\n",
    "# Main plot: Token depth by timestamp\n",
    "'''\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.scatterplot(data=filtered_dependencies, x='timestamp', y='dependency', hue='wmfAffil', style='dependency', markers=True, s=100, ax=ax0)\n",
    "ax0.set_title('VE Depth by Timestamp w/o URLS')\n",
    "ax0.set_xlabel('')\n",
    "ax0.set_ylabel('Dependency Type')\n",
    "ax0.legend().set_visible(False)\n",
    "'''\n",
    "# Calculate the median depth over time\n",
    "filtered_dependencies['week'] = filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "median_depth = filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "wmf_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] == True]\n",
    "wmf_median_depth = wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "other_filtered_dependencies = filtered_dependencies[filtered_dependencies['wmfAffil'] != True]\n",
    "other_median_depth = other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax0 = plt.subplot(gs[0])\n",
    "sns.lineplot(data=median_depth, x='week', y='depth', ax=ax0, color='black', label='Median Depth', marker='o')\n",
    "sns.lineplot(data=wmf_median_depth, x='week', y='depth', ax=ax0, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.lineplot(data=other_median_depth, x='week', y='depth', ax=ax0, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax0.set_title('Median Depth of \"VE\" in Phabricator Sentence Dependency Trees')\n",
    "ax0.set_ylabel('Median Depth')\n",
    "ax0.set_xlabel('')\n",
    "\n",
    "# Calculate the median depth over time\n",
    "resolved_filtered_dependencies['week'] = resolved_filtered_dependencies['timestamp'].dt.to_period('W').dt.start_time\n",
    "resolved_median_depth = resolved_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_wmf_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] == True]\n",
    "resolved_wmf_median_depth = resolved_wmf_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "resolved_other_filtered_dependencies = resolved_filtered_dependencies[resolved_filtered_dependencies['wmfAffil'] != True]\n",
    "resolved_other_median_depth = resolved_other_filtered_dependencies.groupby('week')['depth'].median().reset_index()\n",
    "\n",
    "# Plot the median depth over time\n",
    "ax1 = plt.subplot(gs[1])\n",
    "sns.lineplot(data=resolved_median_depth, x='week', y='depth', ax=ax1, color='black', label='Median Depth', marker='o')\n",
    "sns.lineplot(data=resolved_wmf_median_depth, x='week', y='depth', ax=ax1, color='#c7756a', label='WMF-affiliated authors', marker='x')\n",
    "sns.lineplot(data=resolved_other_median_depth, x='week', y='depth', ax=ax1, color='#5da2d8', label='Nonaffiliated authors', marker='x')\n",
    "ax1.set_title('Median Depth of \"VE\" in Coreference-resolved Phabricator Sentence Dependency Trees')\n",
    "ax1.set_ylabel('Median Depth')\n",
    "ax1.set_xlabel('')\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.show()\n",
    "\n",
    "#plt.savefig('031625_VE_depth_fig.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}