Method X ransomware.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate results from TTPs from an Excel sheet\n",
    "This notebook prioritizes security controls based on a given excel sheet of TTPs. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Determine relevant threats  and actors\n",
    "### 1.1. Read sheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "SECTOR = 'Government'\n",
    "SECTORS = ['Government', 'Education', 'Healthcare']\n",
    "\n",
    "CONTROLS = \"CIS_v8\"\n",
    "\n",
    "YEAR = datetime.date.today().year\n",
    "YEAR = 2023\n",
    "INTERVAL = 2\n",
    "TTP_THRESHOLD = 0\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# ttp_df = pd.read_excel('data/TTPS Ransomware from Kaspersky.xlsx',usecols=[1,2])\n",
    "ttp_df = pd.read_excel('data/TTPS Ransomware Group-IB.xlsx')\n",
    "ttp_df['ttp_weight'] = 1\n",
    "ttp_df.columns = ['mitre_attack_ttps', 'ttp_weight']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mitre_attack_ttps</th>\n",
       "      <th>ttp_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>T1133</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>T1190</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>T1566</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>T1189</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T1200</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>T1567</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>T1020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>T1490</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>T1485</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>T1486</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mitre_attack_ttps  ttp_weight\n",
       "0              T1133           1\n",
       "1              T1190           1\n",
       "2              T1566           1\n",
       "3              T1189           1\n",
       "4              T1200           1\n",
       "..               ...         ...\n",
       "92             T1567           1\n",
       "93             T1020           1\n",
       "94             T1490           1\n",
       "95             T1485           1\n",
       "96             T1486           1\n",
       "\n",
       "[97 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttp_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Rank actors and TTPs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2. Rank TTPs\n",
    "#### 2.3.1 Explode dataframe per TTP\n",
    "Explode the dataframe per TTP. Now a weighting calculation can be done per TTP, based on the information provided. Information includes the actor results (and kill chain phase).\n",
    "\n",
    "For now: the actor weights are summed per TTP to obtain a ranking per TTP. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.2. Group by TTP and sum actor weights\n",
    "Sum the actor weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   ttp_weight\n",
      "mitre_attack_ttps            \n",
      "T1133                       1\n",
      "T1069.002                   1\n",
      "T1534                       1\n",
      "T1550                       1\n",
      "T1570                       1\n",
      "...                       ...\n",
      "T1222                       1\n",
      "T1140                       1\n",
      "T1197                       1\n",
      "T1053                       1\n",
      "T1486                       1\n",
      "\n",
      "[97 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "ttps_ranked = ttp_df\n",
    "ttps_ranked.set_index('mitre_attack_ttps',inplace=True)\n",
    "print(ttps_ranked.sort_values(by='ttp_weight',ascending=False))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.3 Enrich TTP information\n",
    "Information from the ATT&CK data is used via the attackcti package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[taxii2client.v20] [WARNING ] [2023-06-22 16:51:34,413] TAXII Server Response did not include 'Content-Range' header - results could be incomplete.\n",
      "[taxii2client.v20] [WARNING ] [2023-06-22 16:51:34,431] TAXII Server Response with different amount of objects! Setting per_request=750\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "T1053    3\n",
       "T1133    2\n",
       "T1072    2\n",
       "T1197    2\n",
       "T1210    1\n",
       "        ..\n",
       "T1027    1\n",
       "T1036    1\n",
       "T1070    1\n",
       "T1562    1\n",
       "T1486    1\n",
       "Name: mitre_attack_ttps, Length: 92, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from attackcti import attack_client\n",
    "from pandas import json_normalize\n",
    "import sys\n",
    "\n",
    "mitre_columns = ['technique_id','technique','tactic','technique_detection']\n",
    "\n",
    "def get_attck_dataframe(matrix = 'enterprise'):\n",
    "    if  (matrix.lower() == 'enterprise'):\n",
    "        # Instantiating attack_client class\n",
    "        lift = attack_client()\n",
    "        # Getting techniques for windows platform - enterprise matrix\n",
    "        attck = lift.get_enterprise_techniques(stix_format = False)\n",
    "        # Generating a dataframe with information collected\n",
    "        attck = json_normalize(attck)\n",
    "        return attck\n",
    "    else:\n",
    "        sys.exit('ERROR: Only Enterprise available!!')\n",
    "\n",
    "attck = get_attck_dataframe()\n",
    "attck = attck[mitre_columns]\n",
    "\n",
    "ttps_enriched = pd.merge(ttps_ranked,attck,how='left',left_index=True,right_on='technique_id')\n",
    "ttps_enriched.index = ttps_ranked.index\n",
    "ttps_enriched['technique_name_id'] = ttps_enriched['technique'] + \" (\" + ttps_enriched['technique_id'] + \")\"\n",
    "ttps_enriched = ttps_enriched.drop(['technique_id'],axis=1)\n",
    "ttps_enriched.index.value_counts()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top 10 techniques for actors targeting Government sector'}, ylabel='TTP Name and ATT&CK ID'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ttps_enriched.nlargest(10,columns='ttp_weight').plot.barh(y='ttp_weight',x='technique_name_id',xlabel=\"TTP Name and ATT&CK ID\" ,title=\"Top 10 techniques for actors targeting {} sector\".format(SECTOR))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only take the top percentage of the TTPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taking 100%; 97 entries\n"
     ]
    }
   ],
   "source": [
    "PERCENTAGE = 1\n",
    "\n",
    "def take_percentage(percentage,df):\n",
    "    top_percent = int(len(df) * PERCENTAGE)\n",
    "    print(\"Taking {}%; {} entries\".format(percentage*100,top_percent))\n",
    "    return df.nlargest(top_percent,columns=\"ttp_weight\")\n",
    "\n",
    "def take_threshold(threshold, df):\n",
    "    return df.loc[df['ttp_weight']>threshold]\n",
    "\n",
    "\n",
    "ttps_enriched = take_percentage(PERCENTAGE,ttps_enriched)\n",
    "ttps_enriched.sort_values(by=\"ttp_weight\",inplace=True,ascending=False)\n",
    "# ttps_enriched = take_threshold(TTP_THRESHOLD,ttps_enriched)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "Within the tested Government sector, the top 10 techniques used are shown above. \n",
    "The top technique is '[Obfuscated Files or Information](https://attack.mitre.org/techniques/T1027/)', where adversaries obfuscate the contents of an executable or file by encrypting, encoding, compressing or otherwise obscuring them on the system or in transit. This is common behavior to evade defenses. \n",
    "\n",
    "The second technique is '[Tool]'(https://attack.mitre.org/techniques/T1588/002/), is described as \"Adversaries may buy, steal or download software tools that can be used during targeting.\" This is a very generic technique, since the use of the use of tools is common among hackers and there exists a wide variety of tools, hence why it is listed in such a high position."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Control prioritization"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Control Mapping\n",
    "Source of the mapping: [Security Control Framework Mappings to ATT&CK](https://github.com/center-for-threat-informed-defense/attack-control-framework-mappings).\n",
    "\n",
    "This maps controls from NIST 800-53 to MITRE ATT&CK. The following versions are considered:\n",
    "- MITRE ATT&CK v10.1\n",
    "- NIST 800-53 Revision 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load ATT&CK to NIST 800-53 mapping\n",
    "def get_nist_sp800_53_attck_mapping():\n",
    "    nist_attck_mapping = pd.read_excel(\"data/mappings/nist800-53-r5-mappings-attckv12.xlsx\")\n",
    "    nist_attck_mapping = nist_attck_mapping.drop([\"Mapping Type\",\"Technique Name\"],axis=1)\n",
    "    return nist_attck_mapping\n",
    "\n",
    "def get_nist_csf_attck_mapping():\n",
    "    nist_attck_mapping = pd.read_excel(\"data/mappings/NIST_CSF_ATTCKv12.xlsx\")\n",
    "    return nist_attck_mapping\n",
    "\n",
    "def get_cis_attck_mapping():\n",
    "    cis_attck_mapping = pd.read_excel(\"data/mappings/CIS_Controls_v8_ATTCKv82_condensed.xlsx\")\n",
    "    cis_attck_mapping = cis_attck_mapping.drop([\"CIS Controls v8\",\"CIS Controls\",\"Asset Type\"],axis=1)\n",
    "    cis_attck_mapping['Technique ID'] = cis_attck_mapping['Technique ID'].str.split(', ')\n",
    "    cis_attck_mapping.drop('Description',axis=1)\n",
    "    return cis_attck_mapping.explode('Technique ID')\n",
    "\n",
    "def get_iso_attck_mapping():\n",
    "    iso_attck_mapping = pd.read_excel(\"data/mappings/ISO_27001_2022_ATTCKv82.xlsx\")\n",
    "    return iso_attck_mapping\n",
    "\n",
    "def get_nist_iso_2013_attck_mapping():\n",
    "    iso_attck_mapping = pd.read_excel(\"data/mappings/ISO_27001_2013_ATTCKv10.1_FROM_NIST.xlsx\")\n",
    "    return iso_attck_mapping\n",
    "\n",
    "def get_cis_iso_2013_attck_mapping():\n",
    "    iso_attck_mapping = pd.read_excel(\"data/mappings/ISO_27001_2013_ATTCKv82.xlsx\")\n",
    "    return iso_attck_mapping\n",
    "\n",
    "def get_cis7_iso_2013_attck_mapping():\n",
    "    iso_attck_mapping = pd.read_excel(\"data/mappings/ISO_27001_2013_ATTCKv82_FROM_CIS7.xlsx\")\n",
    "    return iso_attck_mapping"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the TTPs with the controls based on the selected framework. Remove controls which are expired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of TTPS: 70\n",
      "Number of controls: 91\n"
     ]
    }
   ],
   "source": [
    "ttp_control_map = get_iso_attck_mapping()\n",
    "if CONTROLS == \"ISO_2013_CIS\":\n",
    "    ttp_control_map = get_cis_iso_2013_attck_mapping()\n",
    "elif CONTROLS == \"ISO_2013_CIS7\":\n",
    "    ttp_control_map = get_cis7_iso_2013_attck_mapping()\n",
    "elif CONTROLS == \"ISO_2013_NIST\":\n",
    "    ttp_control_map = get_nist_iso_2013_attck_mapping()\n",
    "elif CONTROLS == \"ISO_2022\":\n",
    "    ttp_control_map = get_iso_attck_mapping()\n",
    "elif CONTROLS == \"NIST_SP_800_53\":\n",
    "    ttp_control_map = get_nist_sp800_53_attck_mapping()\n",
    "elif CONTROLS == \"NIST_CSF\":\n",
    "    ttp_control_map = get_nist_csf_attck_mapping()\n",
    "elif CONTROLS == \"CIS_v8\":\n",
    "    ttp_control_map = get_cis_attck_mapping()\n",
    "\n",
    "ttp_control_merge = ttp_control_map.merge(ttps_enriched, left_on=\"Technique ID\", right_on=\"mitre_attack_ttps\",how=\"inner\")\n",
    "ttp_control_merge = ttp_control_merge.loc[ttp_control_merge[\"Control ID\"] != '/']\n",
    "ttp_control_merge = ttp_control_merge.loc[ttp_control_merge[\"Control Name\"] != 'Vervallen']\n",
    "\n",
    "ttp_control_merge.sort_values(by='ttp_weight',inplace=True,ascending=False)\n",
    "print(\"Number of TTPS:\",ttp_control_merge['Technique ID'].value_counts().index.size)\n",
    "print(\"Number of controls:\",ttp_control_merge['Control ID'].value_counts().index.size)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get TTPs incl number of controls that mitigate them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distribution of TTP weights for actors targeting Government sector'}, ylabel='Frequency'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ttp_control_merge[['Technique ID','ttp_weight']].drop_duplicates().plot(kind='hist',bins=30,title=\"Distribution of TTP weights for actors targeting {} sector\".format(SECTOR))\n",
    "# ttp_control_merge['ttp_weight'].plot(kind='hist',bins=30,title=\"Distribution of TTP weights for actors targeting {} sector\".format(SECTOR),xlabel=\"TTP weights\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "The TTP weights are a normalised summation of the actor weights. This means, the higher the TTP weight, the more (high weighted) actors share this TTP.\n",
    "We can see that the distribution of TTP weights is a right-skewed distribution. This is expected, since this means that the number of TTPs that are shared by these actors is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8nUybNk1RlKTrOV++fIq7u7vWdfPvv/8qtra2SqVKlVIdV79+/bTqXLlypQIoK1asyHIcp0+fTrf/k+s7fvx4qs+6dOmi6Ovrq+9r1aql9bskJX19faVLly7q+/nz5yv6+voKoFhYWKjXRM2aNZWOHTtmOfZkjx8/VoyMjBQ/Pz+t8sjISMXAwEBp1aqVWpb883L69OlstfH239B32S75nE+ZMkWJi4tTXr16pYSEhChly5ZVAGXr1q3qtuPGjVP7CFAKFy6sdO3aVTl//ny24lYURZERGkIIIYT4n7Nv3z5MTExo0qSJVnny8Nu3v/X29vYmb9686ntdXV2aN2/OzZs3+fvvv9NtZ/v27RgaGqpTOtKLJWXbyZo2bYqJiUmqWEqXLk3BggXV94aGhjg7O6vDdbPC0tKSGjVqpIrDzc0t1bzqgIAAFEVJtchbSuXKlWP79u0MHTqUAwcO8PLlyyzHAtC6dWut95UqVaJQoULs379fLevTpw8PHjxg3bp1QNJQ+AULFlC3bt0sLVCp0Wjw9/fXKitVqlS2+i2tOv38/NT3enp6ODk5YW9vr7XugZWVFba2tmm2ldmxHzt2jJiYGNq3b098fLz6SkxMxNfXl9OnTxMbG6tVR+PGjbMU/759+/D29sbBwUGrPCAggBcvXmRpJEZ6/Pz8tIaGFy9eHCDVugzJ5ckjlt7XiRMneP36Nc2aNdMqr1ChQprXybp166hcuTKmpqbo6emRK1cuFi9erDVUPyN//vkn1atXJ1++fFrnJ3mtkYMHDwKwf/9+zMzM+Pbbb7X2T7kg5ucwf/58cuXKhb6+PsWLF+fYsWOMGzeO7t27A0k/2+fPn6d79+7s3LmTZ8+efZQ4wsLCuHfvHm3bttW6bkxNTWncuDEnTpzgxYsXWvu8/bPTrFkz9PT0tH5vfAjpTbF6uzyjqVgpP+vWrRsxMTFcvXqVf/75h2+//Zbly5dz4cIFpk2bRkxMDK1bt8bGxoaiRYtmOMoKkkYQvXz5MtXfEAcHB2rUqJHmKK7PaciQIeTKlQtDQ0O++uorIiMj+fnnn7V+l44cOZLIyEiWLFnCd999h6mpKQsXLuSrr77K8lTGZJLQEEIIIcT/nOjoaOzs7FL9B9TW1hY9Pb1UQ1zfHkacsiyj4bAPHz4kX758Gc75jY6ORk9PL9XChhqNBjs7u1T1W1tbp6rDwMAgW0mEtOYmR0dHp1meL18+9fP0zJkzhyFDhrBp0yaqV6+OlZUVDRo0UIc6Zya9/k3ZpqenJ9988w0//fQTkHQjGRERkel0mGTGxsappqUYGBhkuPbGu9Spr6+PlZVVqm319fXTbCuzY//nn3+ApOkeuXLl0npNmTIFRVHUxfaSZfWJH+9zzjPzdh8kPwkhvfL3OQ8pJcecMgGZ7O2yjRs30qxZM/Lnz8+KFSs4fvw4p0+fJjAwMMvx/PPPP/zxxx+pzk2JEiUAePTokRpXWjGldf4/pWbNmnH69GnOnDlDWFgY0dHRWtMQhg0bxvTp0zlx4gR16tTB2tpafcLHh5TRugn58uUjMTGRx48fa5W/3Xd6enpYW1u//xSF/y/5d21a9cXExGhdy+m1Gxsby5s3b1Jd9yYmJri6umJgYEB0dDQDBgxg1qxZWFpa0qdPH2JiYrh58yarV69m4MCBGSZpMuu7D9UfH0qfPn04ffo0ISEh3Lp1i6ioKLp06ZJqu7x589KhQwcWLlzIhQsXOHjwIPr6+vTp0ydb7clTToQQQgjxP8fa2pqTJ0+iKIpWUuPBgwfEx8eneiJAWgv3JZellWBIZmNjw5EjR0hMTEw3qWFtbU18fDwPHz7USmooisL9+/cpW7Zsto4tK9L6JtHa2pqoqKhU5ffu3QPI8GkKJiYmjB07lrFjx/LPP/+oozX8/f25du1apvGk179OTk5aZb1796Zp06acPXuWefPm4ezsTK1atTKtP6uSkxNvr1uSfFP6MWR27Mn9Pnfu3HSfMPD2jXJWH/X4Puf8vyr55zE5EZTS/fv3tUZprFixgsKFC7NmzRqtPnv7/GckT548lCpViokTJ6b5eXJyyNramlOnTqUZ0+dkY2ODl5dXup/r6enRv39/+vfvz5MnT9izZw/ff/89Pj4+3Llz54M9HSn5vKV3Pero6GBpaalVfv/+ffLnz6++j4+PJzo6OsPfydlRsmRJAC5evKg1eiC5LPlzAHd3d1avXs39+/e1Ei0XL17UqistAwYM4KuvvqJly5ZA0si+oKAgLCwsKFu2LLVr12bbtm2pnviRLLO++6/9HBcoUCDDay49VapUoXbt2mzatIkHDx5ga2ubpf1khIYQQggh/ud4e3vz/PlzNm3apFWevBCgt7e3VvnevXu1bpASEhJYs2YNRYsW1XoU4dvq1KnDq1evtJ6OkVYskHRzldKGDRuIjY1NFUtWJC8ol51RG97e3ly5coWzZ89qlS9btgyNRpPuf6bfljdvXgICAmjZsiVhYWGphomnZeXKlVrvjx07xu3bt7WeJAFJiwAWLFiQAQMGsGfPHrp3757lm/esSL7ZvXDhglb5li1bPlgbb8vs2CtXrkzu3Lm5cuUKXl5eab6SRzlkl7e3N/v27VMTGMmWLVuGsbHxf+Yxx9m5nsuXL4+BgQFr1qzRKj9x4kSqKT8ajQZ9fX2ta+j+/fupnnKSHENa7derV49Lly5RtGjRNM9NckKjevXq/Pvvv6mupd9++y3TY/qvyJ07N02aNKFHjx7ExMRoPVnmfbm4uJA/f35+++03FEVRy2NjY9mwYYP65JOU3v7ZWbt2LfHx8al+b7yr/PnzU65cOVasWEFCQoJafuLECcLCwrQWkK5fvz4ajSbV04yCg4MxMjLC19c3zTb279/PunXr1EVmISmZnXIa2fPnz7X65G0VK1bEyMgo1d+Qv//+W51WlpP8888/aT5ZKyEhgRs3bmBsbEzu3LmzXJ+M0BBCCCHE/5x27drx008/0b59eyIiInB3d+fIkSNMmjQJPz8/atasqbV9njx5qFGjBiNHjsTExIT58+dz7dq1TB/d2rJlS4KCgujatSthYWFUr16dxMRETp48SfHixWnRogW1atXCx8eHIUOG8OzZMypXrsyFCxcYPXo0np6etG3bNtvHl/xt4C+//IKZmRmGhoYULlw4w28u+/Xrx7Jly6hbty7jxo2jUKFCbN26lfnz59OtWzecnZ3T3bd8+fLUq1ePUqVKYWlpydWrV1m+fHmaNyFpOXPmDJ06daJp06bcuXOH4cOHkz9/fnUefzJdXV169OjBkCFDMDEx+eCPHLSzs6NmzZpMnjwZS0tLChUqxN69e9m4ceMHbSelzI7d1NSUuXPn0r59e2JiYmjSpAm2trY8fPiQ8+fP8/DhQxYsWPBObY8ePVpdA2LUqFFYWVmxcuVKtm7dytSpU7GwsPiQh/rOzMzMKFSoEJs3b8bb2xsrKyvy5MmT5poYVlZW9O/fXz2HDRs25O+//2bs2LHY29trjZSqV68eGzdupHv37jRp0oQ7d+4wfvx47O3tU02Xcnd358CBA/zxxx/Y29tjZmaGi4sL48aNY/fu3VSqVInevXvj4uLCq1eviIiIYNu2bSxcuJACBQrQrl07Zs6cSbt27Zg4cSLFihVj27Zt7Ny582N333vx9/enZMmSeHl5qY9znTVrFoUKFaJYsWJA0joh3t7ejBo1ilGjRr1TOzo6OkydOpXWrVtTr149vvvuO16/fs20adN48uSJ+jjklDZu3Iienh61atXi8uXLjBw5Eg8Pj1Trp6Ql+QlVyY9IPnPmjPoI1ZRrK02ZMoVatWrRtGlTunfvzoMHDxg6dCglS5bUekRriRIl6NixI6NHj0ZXV5eyZcuya9cufvnlFyZMmJDmNLTXr1/z3XffMWbMGAoXLqyW+/j4MG7cOMzNzblx4wZ79+5l8ODB6R5L7ty5GTlyJN9//z3t2rWjZcuWREdHM3bsWAwNDRk9enSm/fFfsnz5cn7++WdatWpF2bJlsbCw4O+//+bXX3/l8uXLjBo1KntJ3GwvIyqEEEII8R+T1srr0dHRSteuXRV7e3tFT09PKVSokDJs2DDl1atXWtvx/5/uMH/+fKVo0aJKrly5FFdXV2XlypVZavvly5fKqFGjlGLFiin6+vqKtbW1UqNGDeXYsWNa2wwZMkQpVKiQkitXLsXe3l7p1q2b8vjxY626ChUqpNStWzdVG28/FUJRkp4qUbhwYUVXV1drFf+qVasqJUqUSDPW27dvK61atVKsra2VXLlyKS4uLsq0adO0njqQ3CcpnzgxdOhQxcvLS7G0tFQMDAyUIkWKKP369VMePXqUYd8kr7i/a9cupW3btkru3LnV1fpv3LiR5j4REREKoHTt2jXDulNKb+X95KclpBQVFaU0adJEsbKyUiwsLJQ2bdooZ86cSfMpJ2nVmV7/vn3usnvsBw8eVOrWratYWVkpuXLlUvLnz6/UrVtXWbduXarjyezpFyldvHhR8ff3VywsLBR9fX3Fw8MjzSc+JP8cZObtp1ckS36CTMp4FSXtpy6kdT3v2bNH8fT0VAwMDBRAfeLI2085URRFSUxMVCZMmKAUKFBA0dfXV0qVKqX8+eefioeHR6qnyvzwww+Ko6OjYmBgoBQvXlxZtGhRmtdFaGioUrlyZcXY2DjV01IePnyo9O7dWylcuLCSK1cuxcrKSvnqq6+U4cOHK8+fP1e3+/vvv5XGjRsrpqamipmZmdK4cWPl2LFjn/UpJ5md0xkzZiiVKlVS8uTJo+jr6ysFCxZUOnbsqERERKjbJJ/btJ5Ck570rpNNmzYp5cuXVwwNDRUTExPF29tbOXr0aJrHFRISovj7+6v92bJlS+Wff/7JUvv8/6dnpPV6265du5QKFSoohoaGipWVldKuXbs023nz5o0yevRopWDBgoq+vr7i7OyszJkzJ90YRowYoXh4eChxcXFa5Q8ePFCaNGmiWFhYKA4ODsqsWbOydEy//vqrUqpUKUVfX1+xsLBQ6tevrz5lJ9l/4Sknb5/zt125ckUZMGCA4uXlpdjY2Ch6enqKpaWlUrVqVWX58uXZiltRFEWjKBmMbxFCCCGE+B+n0Wjo0aMH8+bN+9yhCJLWkujduzeXLl1SF14UIjPh4eG4uroyevRovv/++88djngPY8aMYezYsTx8+PA/tz6E+O+RKSdCCCGEEOKzO3fuHOHh4YwbN4769etLMkOk6/z586xatYpKlSphbm5OWFgYU6dOxdzcnI4dO37u8IQQn5AkNIQQQgghxGfXsGFD7t+/zzfffMPChQs/dzjiP8zExIQzZ86wePFinjx5goWFBdWqVWPixIlpPjpVCPG/S6acCCGEEEIIIYQQIseRx7YKIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5FFQYUQQgiR4yUmJnLv3j3MzMzQaDSfOxwhhBBCZIGiKPz777/ky5cPHZ3sj7eQhIYQQgghcrx79+7h4ODwucMQQgghxDu4c+cOBQoUyPZ+ktAQQgghRI5nZmYGJP2HyNzc/DNHI4QQQoisePbsGQ4ODurf8eyShIYQQgghcrzkaSbm5uaS0BBCCCFymHedLiqLggohhBBCCCGEECLHkYSGEEIIIYQQQgghchxJaAghhBBCCCGEECLHkYSGEEIIIYQQQgghchxJaAghhBBCCCGEECLHkYSGEEIIIYQQQgghchx5bKsQQggh/meUHL0THQPjzx2GEEII8T8l4oe6nzuENMkIDSGEEEIIIYQQQuQ4ktAQQgghhBBCCCFEjiMJDSGEEEIIIYQQQuQ4ktAQmYqOjsbW1paIiIjPHcpnExwcTO7cuTPcZsyYMZQuXVp9HxAQQIMGDbJUf3a2/dgiIiLQaDSEhoZ+sjYPHDiARqPhyZMnH72tt8+TyBl++eUXHBwc0NHRYdasWTniPH6On6X/Ba9fv6ZgwYKEhIR87lCEEEII8R8nCQ2RqcmTJ+Pv74+jo2Oqz6KjoylQoECWbkZ/+eUXqlWrhrm5+TvfvCbf+Kb1un//fqb7f8qboNmzZxMcHPxJ2vrUqlWrhkaj4Ycffkj1mZ+fHxqNhjFjxmS5vkqVKhEVFYWFhUWW93nXJNDAgQPZu3dvtvd7XwkJCUyePBlXV1eMjIywsrKiQoUKBAUFffJYPpQ3b94wdepUPDw8MDY2Jk+ePFSuXJmgoCDi4uI+WDvPnj2jZ8+eDBkyhLt379KlS5fPdh7Tk9b16ODgQFRUFCVLlvyobY8ZM0b9Pairq4uDgwOdOnXi4cOHH7Xdj8XAwICBAwcyZMiQzx2KEEIIIf7j5CknIkMvX75k8eLFbNu2Lc3PO3bsSKlSpbh7926mdb148QJfX198fX0ZNmzYe8UVFhaGubm5Vpmtre171fmhZefmPCdycHAgKCiIoUOHqmX37t1j37592NvbZ6sufX197OzsPnSIaTI1NcXU1PSTtJXSmDFj+OWXX5g3bx5eXl48e/aMM2fO8Pjx408eS0pv3rxBX1//nfbz8fHh/PnzjB8/nsqVK2Nubs6JEyeYPn06np6eHyx5GBkZSVxcHHXr1tW6tj7FeYyLiyNXrlzvtK+uru4nu65LlCjBnj17SEhI4Ny5c3Ts2JG7d++yffv2VNsmJCSg0WjQ0fnvfqfRunVrBg0axNWrVylevPjnDkcIIYQQ/1H/3f/NiP+E7du3o6enR8WKFVN9tmDBAp48ecLAgQOzVFffvn0ZOnQoFSpUeO+4bG1tsbOz03ol/+f8wIEDlCtXDhMTE3Lnzk3lypW5ffs2wcHBjB07lvPnz6vfZiaPoPjxxx9xd3fHxMQEBwcHunfvzvPnz1O1u2nTJpydnTE0NKRWrVrcuXMn3Rjf/sZ2/fr1uLu7Y2RkhLW1NTVr1iQ2NlZrn+nTp2Nvb4+1tTU9evTI8FvuW7duUb9+ffLmzYupqSlly5Zlz549Wts4OjoyadIkAgMDMTMzo2DBgvzyyy9a25w6dQpPT08MDQ3x8vLi3Llz6baZUr169YiOjubo0aNqWXBwMLVr106VXFqxYgVeXl6YmZlhZ2dHq1atePDggfr521NOkqf47Ny5k+LFi2Nqaoqvry9RUVFAUnJg6dKlbN68WT2XBw4cAGDIkCE4OztjbGxMkSJFGDlypFY/pjc16EP3/dv++OMPunfvTtOmTSlcuDAeHh507NiR/v37q9soisLUqVMpUqQIRkZGeHh4sH79eq16Ll++TN26dTE3N8fMzIxvvvmGW7duAUkjZ/r27au1fYMGDQgICFDfOzo6MmHCBAICArCwsKBz584AHDt2jCpVqmBkZISDgwO9e/dOdX2mNGvWLA4dOsTevXvp0aMHpUuXpkiRIrRq1YqTJ09SrFgxIGn6QO/evbG1tcXQ0JCvv/6a06dPq/Ukn/u9e/fi5eWFsbExlSpVIiwsDEi6Ftzd3QEoUqQIGo2GiIiIVOcxPj6e3r17kzt3bqytrRkyZAjt27fX+hl0dHRk1qxZWsdRunRprdFEGo2GhQsXUr9+fUxMTJgwYQIJCQl07NiRwoULY2RkhIuLC7Nnz1b3Se96TGvKycGDBylXrhwGBgbY29szdOhQ4uPj1c+rVatG7969GTx4MFZWVtjZ2WVptJOenh52dnbkz5+fevXq0bt3b3bt2sXLly/Vn6c///wTNzc3DAwMuH37No8fP6Zdu3ZYWlpibGxMnTp1uHHjhla9R48epWrVqhgbG2NpaYmPj4+ahMvsen38+DGtW7fGxsYGIyMjihUrpo5IevPmDT179sTe3h5DQ0McHR2ZPHmyuq+1tTWVKlVi1apVmR67EEIIIb5cktAQGTp06BBeXl6pyq9cucK4ceNYtmzZf+pbvvj4eBo0aEDVqlW5cOECx48fp0uXLmg0Gpo3b86AAQMoUaIEUVFRREVF0bx5cwB0dHSYM2cOly5dYunSpezbt4/Bgwdr1f3ixQsmTpzI0qVLOXr0KM+ePaNFixZZiisqKoqWLVsSGBjI1atXOXDgAI0aNUJRFHWb/fv3c+vWLfbv38/SpUsJDg7OcMrK8+fP8fPzY8+ePZw7dw4fHx/8/f2JjIzU2m7GjBlqoqJ79+5069aNa9euARAbG0u9evVwcXEhJCSEMWPGZDlBpa+vT+vWrbWmTAQHBxMYGJhq2zdv3jB+/HjOnz/Ppk2bCA8P17rJTsuLFy+YPn06y5cv59ChQ0RGRqqxDRw4kGbNmqlJjqioKCpVqgSAmZkZwcHBXLlyhdmzZ7No0SJmzpyZYVsfq+9TsrOzY9++fRlOAxgxYgRBQUEsWLCAy5cv069fP9q0acPBgwcBuHv3LlWqVMHQ0JB9+/YREhJCYGCg1g1xVkybNo2SJUsSEhLCyJEjuXjxIj4+PjRq1IgLFy6wZs0ajhw5Qs+ePdOtY+XKldSsWRNPT89Un+XKlQsTExMABg8ezIYNG1i6dClnz57FyckJHx8fYmJitPYZPnw4M2bM4MyZM+jp6anXUfPmzdVk0alTp4iKisLBwSFVm1OmTGHlypUEBQWpP5+bNm3KVr8kGz16NPXr1+fixYsEBgaSmJhIgQIFWLt2LVeuXGHUqFF8//33rF27Fsj4ekzp7t27+Pn5UbZsWc6fP8+CBQtYvHgxEyZM0Npu6dKlmJiYcPLkSaZOncq4cePYvXt3to7ByMiIxMRE9dp48eIFkydP5tdff+Xy5cvY2toSEBDAmTNn2LJlC8ePH0dRFPz8/NRkXmhoKN7e3pQoUYLjx49z5MgR/P39SUhIADK/XkeOHMmVK1fYvn07V69eZcGCBeTJkweAOXPmsGXLFtauXUtYWBgrVqxINa2xXLlyHD58ON1jfP36Nc+ePdN6CSGEEOLLIlNORIYiIiLIly+fVtnr169p2bIl06ZNo2DBgvz111+fPK4CBQpovc+fPz9hYWE8e/aMp0+fUq9ePYoWLQqgNVzZ1NRU/SYzpZTfahcuXJjx48fTrVs35s+fr5bHxcUxb948ypcvDyTddBQvXpxTp05Rrly5DOONiooiPj6eRo0aUahQIQD1W+dklpaWzJs3D11dXVxdXalbty579+5Vv0F/m4eHBx4eHur7CRMm8Pvvv7NlyxatG1E/Pz+6d+8OJI1emDlzJgcOHMDV1ZWVK1eSkJDAkiVLMDY2pkSJEvz9999069Ytw+NJ1rFjR77++mtmz55NSEgIT58+pW7duqm+UU6Z5ChSpAhz5syhXLlyPH/+PN1pA3FxcSxcuFA9jz179mTcuHFA0nk0MjLi9evXqc7liBEj1H87OjoyYMAA1qxZkypBldLH6vuUfvzxR5o0aYKdnR0lSpSgUqVK1K9fnzp16gBJyaUff/yRffv2qSOiihQpwpEjR/j555+pWrUqP/30ExYWFqxevVqdBuHs7JzucaWnRo0aWomrdu3a0apVK/XnoFixYsyZM4eqVauyYMECDA0NU9Vx48YNqlWrlmE7sbGxLFiwgODgYPU4Fy1axO7du1m8eDGDBg1St504cSJVq1YFYOjQodStW5dXr16pI5oAbGxs0p3CMXfuXIYNG0bDhg0BmDdvXrpT5TLTqlWrVIm5sWPHqv8uXLgwx44dY+3atTRr1izD6zGl+fPn4+DgwLx589BoNLi6unLv3j2GDBnCqFGj1ORwqVKlGD16NJB0LubNm8fevXupVatWluK/du0aCxYsoFy5cpiZmQFJP0/z589Xr9sbN26wZcsWjh49qiZfVq5ciYODA5s2baJp06ZMnToVLy8vrd+DJUqUALJ2vUZGRuLp6akmxVMmLCIjIylWrBhff/01Go1G/b2YUv78+TNcjHry5Mla50UIIYQQX57/zlfr4j/p5cuXqW5mhg0bRvHixWnTps1nigoOHz5MaGio+tq5cycAVlZWBAQEqN+Yz549W52mkJH9+/dTq1Yt8ufPj5mZGe3atSM6OlpryL2enp7WaBVXV1dy587N1atXM63fw8MDb29v3N3dadq0KYsWLUq1dkKJEiXQ1dVV39vb22tNy3hbbGwsgwcPxs3Njdy5c2Nqasq1a9dSjRIoVaqU+m+NRoOdnZ1a79WrV9UFHZOlNb0oPaVKlaJYsWKsX7+eJUuW0LZt2zTXGzh37hz169enUKFCmJmZqTfCGY1oMDY2VpMZkHl/JFu/fj1ff/01dnZ2mJqaMnLkyAzbgY/X9ym5ublx6dIlTpw4QYcOHfjnn3/w9/enU6dOQNKop1evXlGrVi11nQ9TU1OWLVumTikJDQ3lm2++eec1HZK9PeoqJCSE4OBgrXZ9fHxITEwkPDw8zToURUGj0WTYzq1bt4iLi6Ny5cpqWa5cuShXrlyqn5uU12nyOhlZOd8AT58+5Z9//tFKLOrq6vLVV19laf+3pTUqbeHChXh5eWFjY4OpqSmLFi3K9Lp629WrV6lYsaJWv1WuXJnnz5/z999/q2Up+wKydu1fvHhRTay4ubnh4ODAypUr1c/19fW16r169Sp6enpqghaSpnm4uLio5yZ5hEZasnK9duvWjdWrV1O6dGkGDx7MsWPH1P0DAgIIDQ3FxcVFnR7zNiMjI168eJHuMQ8bNoynT5+qr4ymAAohhBDif5OM0BAZypMnT6ob73379nHx4kV1rnTytIk8efIwfPjwT/KNWeHChdN9jGpQUBC9e/dmx44drFmzhhEjRrB79+501+64ffs2fn5+dO3alfHjx2NlZcWRI0fo2LFjqnUU0rqBy+ymDpJurnbv3s2xY8fYtWsXc+fOZfjw4Zw8eZLChQsDpLpJ1Wg0JCYmplvnoEGD2LlzJ9OnT8fJyQkjIyOaNGnCmzdvtLbLqN6UU17eVWBgID/99BNXrlzh1KlTqT6PjY2ldu3a1K5dmxUrVmBjY0NkZCQ+Pj6pYs0s7sziPXHiBC1atGDs2LH4+PiooxlmzJiR4X4fq+/fpqOjQ9myZSlbtiz9+vVjxYoVtG3bluHDh6vtbd26lfz582vtZ2BgACTd4GVW/9t9lNZaIMnTQZIlJiby3Xff0bt371TbFixYMM22nJ2dM03mJcfy9s9IWsmQlOcg+bOMzkFa0monpXftn7Vr19KvXz9mzJhBxYoVMTMzY9q0aZw8eTJb8aV13Gn1UXavRwAXFxe2bNmCrq4u+fLlU6+ZZEZGRlptpPezlDLGjK63rFyvderU4fbt22zdupU9e/bg7e1Njx49mD59OmXKlCE8PJzt27ezZ88emjVrRs2aNbXW4IiJicHGxibdGAwMDFIdpxBCCCG+LDJCQ2TI09OTK1euaJVt2LCB8+fPq6Mjfv31VyBp1ESPHj0+R5ipeHp6MmzYMI4dO0bJkiX57bffgKRvKZPnfyc7c+YM8fHxzJgxgwoVKuDs7My9e/dS1RkfH8+ZM2fU92FhYTx58gRXV9csxaTRaKhcuTJjx47l3Llz6Ovr8/vvv7/zMR4+fJiAgAAaNmyIu7s7dnZ2GQ7PToubmxvnz5/n5cuXatmJEyeyVUerVq24ePEiJUuWxM3NLdXn165d49GjR/zwww988803uLq6Zvmb94ykdS6PHj1KoUKFGD58OF5eXhQrVozbt2+/d1tv+xB9D6j9FRsbqy7WGBkZiZOTk9Yrec2IUqVKcfjw4XQXLLWxsdEakZSQkMClS5cyjaNMmTJcvnw5VbtOTk7pPgGlVatW6hoib4uPjyc2Nlbd/8iRI+pncXFxnDlz5oM+ucLCwoK8efNqJdSSn/aR0tv98+zZs3RHoKR0+PBhKlWqRPfu3fH09MTJyUkdhZAsrevxbW5ubhw7dkwrmXDs2DHMzMxSJQWyS19fHycnJwoXLpylm3w3Nzfi4+O1kjLR0dFcv35dPTelSpVK99G4WbleIanPAwICWLFiBbNmzdJalNjc3JzmzZuzaNEi1qxZw4YNG7TWVrl06VKaa7QIIYQQQiSThIbIkI+PD5cvX9YapVG0aFFKliypvpJHGBQvXlx9usXdu3dxdXXVusG4f/8+oaGh3Lx5E0gaIh0aGppqccCsePDgAffv39d6xcXFER4ezrBhwzh+/Di3b99m165dWv9Bd3R0JDw8nNDQUB49esTr168pWrQo8fHxzJ07l7/++ovly5ezcOHCVG3mypWLXr16cfLkSc6ePUuHDh2oUKFCputnAJw8eZJJkyZx5swZIiMj2bhxIw8fPnyvmzonJyc2btxIaGgo58+fp1WrVtn+RrtVq1bo6OjQsWNHrly5wrZt25g+fXq26rC0tCQqKirdG5+CBQuir6+v9u+WLVsYP358ttpIi6OjIxcuXCAsLIxHjx4RFxeHk5MTkZGRrF69mlu3bjFnzpz3Shql5136vkmTJsycOZOTJ09y+/ZtDhw4QI8ePXB2dsbV1RUzMzMGDhxIv379WLp0Kbdu3eLcuXP89NNPLF26FEhaRyR5MdozZ85w48YNli9frj4RpEaNGmzdupWtW7dy7do1unfvrj45JiNDhgzh+PHj9OjRg9DQUHV9hV69eqW7T9++falcuTLe3t789NNPnD9/nr/++ou1a9dSvnx5bty4gYmJCd26dWPQoEHs2LGDK1eu0LlzZ168eEHHjh2z3uFZ0KtXLyZPnszmzZsJCwujT58+PH78WGtUQo0aNVi+fDmHDx/m0qVLtG/fXmuqUXqcnJw4c+YMO3fu5Pr164wcOVLrSS2Q9vX4tu7du3Pnzh169erFtWvX2Lx5M6NHj6Z///6ffHHlYsWKUb9+fTp37syRI0c4f/48bdq0IX/+/NSvXx9ImtJx+vRpunfvzoULF9S1OR49epSl63XUqFFs3ryZmzdvcvnyZf7880/1d97MmTNZvXo1165d4/r166xbtw47OzutkXeHDx+mdu3an7RfhBBCCJGzSEJDZMjd3R0vLy91Nf+siouLIywsTGv+88KFC/H09FQXWqxSpQqenp5s2bJF3aZatWqZPv0CkoZX29vba71CQkIwNjbm2rVrNG7cGGdnZ7p06ULPnj357rvvAGjcuDG+vr5Ur14dGxsbVq1aRenSpfnxxx+ZMmUKJUuWZOXKlVqPD0xmbGzMkCFDaNWqFRUrVsTIyIjVq1dnqT/Mzc05dOgQfn5+ODs7M2LECGbMmKEulPguZs6ciaWlJZUqVcLf3x8fHx/KlCmTrTpMTU35448/uHLlCp6engwfPpwpU6ZkO5bcuXOnGqafzMbGhuDgYNatW4ebmxs//PBDtpMmaencuTMuLi7qugZHjx6lfv369OvXj549e1K6dGmOHTvGyJEj37utt71L3/v4+PDHH3/g7++Ps7Mz7du3x9XVlV27dqGnlzT7b/z48YwaNYrJkydTvHhxdZ/kpKG1tTX79u3j+fPnVK1ala+++opFixapUxQCAwNp37497dq1o2rVqhQuXJjq1atnejylSpXi4MGD3Lhxg2+++QZPT09GjhyprmWRFgMDA3bv3s3gwYP5+eefqVChAmXLlmXOnDn07t2bkiVLAvDDDz/QuHFj2rZtS5kyZbh58yY7d+7E0tIyS32dVUOGDKFly5a0a9eOihUrquuApFwDaNiwYVSpUoV69erh5+dHgwYNtNZpSU/Xrl1p1KgRzZs3p3z58kRHR6sL7SZL63p8W/78+dm2bRunTp3Cw8ODrl270rFjR62FbD+loKAgvvrqK+rVq0fFihVRFIVt27ZpLTi7a9cuzp8/T7ly5ahYsSKbN2/O8vWqr6/PsGHDKFWqFFWqVEFXV1f9nWlqasqUKVPw8vKibNmyREREsG3bNjWxc/z4cZ4+fUqTJk0+Q88IIYQQIqfQKB9iEr34n7Zt2zYGDhzIpUuXPvq3iI6OjowZMyZLSQ0hhEhPYmIixYsXp1mzZh9kRJD4tJo2bYqnpyfff/99lvd59uwZFhYWOPRdi46BceY7CCGEECLLIn6o+1HqTf77/fTpU8zNzbO9vywKKjLl5+fHjRs3uHv3rtbc6A/t2rVr6hNGhBAiO5KnmFWtWpXXr18zb948wsPDadWq1ecOTWTT69ev8fDwoF+/fp87FCGEEEL8x8kIDSGEEDnenTt3aNGiBZcuXUJRFEqWLMkPP/xAlSpVPndo4hORERpCCCHEx/NfHaEhCQ0hhBBC5Hjv+x8iIYQQQnx67/v3WxYFFUIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOJLQEEIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOJLQEEIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOJLQEEIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOJLQEEIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOJLQEEIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOJLQEEIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOJLQEEIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOJLQEEIIIYQQQgghRI4jCQ0hhBBCCCGEEELkOHqfOwAhhBBCiA+l5Oid6BgYf+4whBBCiP+0iB/qfu4QPggZoSGEEEIIIYQQQogcRxIaQgghhBBCCCGEyHEkoSGEEEIIIYQQQogcRxIaQnyBoqOjsbW1JSIi4nOH8j/L0dGRWbNmqe81Gg2bNm36bPF8Kb70fq5WrRp9+/Z9rzoiIiLQaDSEhoYCcODAATQaDU+ePHnv+LJq4MCB9O7d+5O1J4QQQoicSRIaQnyBJk+ejL+/P46OjmpZnz59+OqrrzAwMKB06dJp7rdz504qVKiAmZkZNjY2NG7cmPDw8Azb+vbbbylYsCCGhobY29vTtm1b7t27l614g4OD0Wg06itv3rz4+/tz+fLlbNXzOUVFRVGnTp0sbz9mzJh0z0N2pew7MzMzvLy82Lhx4wepO6uCg4PJnTv3B6svvf7Jbj9nV7Vq1bT68+1Xyp+p7AoICKBBgwZZ2i6ttm/evMnGjRsZP378O8eQlkqVKhEVFYWFhcUHrTcjgwcPJigoKNPfL0IIIYT4sklCQ4gvzMuXL1m8eDGdOnXSKlcUhcDAQJo3b57mfn/99Rf169enRo0ahIaGsnPnTh49ekSjRo0ybK969eqsXbuWsLAwNmzYwK1bt2jSpEm24zY3NycqKop79+6xdetWYmNjqVu3Lm/evMl2XZ+DnZ0dBgYGn639oKAgoqKiOH36NB4eHjRt2pTjx49/tnjS877n82P388aNG4mKiiIqKopTp04BsGfPHrXs9OnTH63tlHx9fdU2k1+FCxfGysoKMzOzD9qWvr4+dnZ2aDSaD1pvRmxtbalduzYLFy78ZG0KIYQQIueRhIYQX5jt27ejp6dHxYoVtcrnzJlDjx49KFKkSJr7nT17loSEBCZMmEDRokUpU6YMAwcO5Pz588TFxaXbXr9+/ahQoQKFChWiUqVKDB06lBMnTmS4T1o0Gg12dnbY29vj5eVFv379uH37NmFhYeo2x44do0qVKhgZGeHg4EDv3r2JjY1VP3d0dGTChAm0a9cOU1NTChUqxObNm3n48CH169fH1NQUd3d3zpw5o9V2ZvU+ePAAf39/jIyMKFy4MCtXrkwz/pRTIYYMGYKzszPGxsYUKVKEkSNHqn0SHBzM2LFjOX/+vPrte3BwMABPnz6lS5cu2NraYm5uTo0aNTh//nym/Zc7d27s7OxwdXVl4cKFGBoasmXLFgDu3r1L8+bNsbS0xNramvr162tNR0oeOTB9+nTs7e2xtramR48eWufwzZs3DB48mPz582NiYkL58uU5cOAAkDRloUOHDjx9+lQ9njFjxmidk4CAACwsLOjcufN79c/b/Xzx4kVq1KiBkZER1tbWdOnShefPn2fr2FKysrLCzs4OOzs7bGxsALC2tlbLHj58iJ+fH6ampuTNm5e2bdvy6NEjdf/169fj7u6uxlOzZk1iY2MZM2YMS5cuZfPmzeoxJfdfWgwMDNQ2k1+6urqpppw4OjoyadIkAgMDMTMzo2DBgvzyyy9adZ06dQpPT08MDQ3x8vLi3LlzWp+/PeUkebTNzp07KV68OKampmqCJVl8fDy9e/cmd+7cWFtbM2TIENq3b681AiW9vkj27bffsmrVqnT7QAghhBBCEhpCfGEOHTqEl5dXtvfz8vJCV1eXoKAgEhISePr0KcuXL6d27drkypUrS3XExMSwcuVKKlWqlOV90vLkyRN+++03ALWeixcv4uPjQ6NGjbhw4QJr1qzhyJEj9OzZU2vfmTNnUrlyZc6dO0fdunVp27Yt7dq1o02bNpw9exYnJyfatWuHoihZrjcgIICIiAj27dvH+vXrmT9/Pg8ePMjwGMzMzAgODubKlSvMnj2bRYsWMXPmTACaN2/OgAEDKFGihPrte/PmzVEUhbp163L//n22bdtGSEgIZcqUwdvbm5iYmCz3X65cudDT0yMuLo4XL15QvXp1TE1NOXToEEeOHFFvUFOOlti/fz+3bt1i//79LF26lODgYDWJANChQweOHj3K6tWruXDhAk2bNsXX15cbN25QqVIlZs2apY6yiYqKYuDAgeq+06ZNo2TJkoSEhDBy5Mh37p+3vXjxAl9fXywtLTl9+jTr1q1jz549qa6JzI4tq6KioqhatSqlS5fmzJkz7Nixg3/++YdmzZqpn7ds2ZLAwECuXr3KgQMHaNSoEYqiMHDgQJo1a6Y18qJSpUrZjiEtM2bMUBMV3bt3p1u3bly7dg2A2NhY6tWrh4uLCyEhIYwZM0br3KTnxYsXTJ8+neXLl3Po0CEiIyO19psyZQorV64kKCiIo0eP8uzZM61EU0Z9kaxcuXLcuXOH27dvpxnD69evefbsmdZLCCGEEF8Wvc8dgBDi04qIiCBfvnzZ3s/R0ZFdu3bRtGlTvvvuOxISEqhYsSLbtm3LdN8hQ4Ywb948Xrx4QYUKFfjzzz+z3f7Tp08xNTVFURRevHgBJH2D6+rqCiTdFLdq1Ur9drpYsWLMmTOHqlWrsmDBAgwNDQHw8/Pju+++A2DUqFEsWLCAsmXL0rRpUzXWihUr8s8//2BnZ5dpvZGRkWzfvp0TJ05Qvnx5ABYvXkzx4sUzPJ4RI0ao/3Z0dGTAgAGsWbOGwYMHY2RkhKmpKXp6etjZ2anb7du3j4sXL/LgwQN1WsX06dPZtGkT69evp0uXLpn24+vXr5k2bRrPnj3D29ub1atXo6Ojw6+//qpOKQgKCiJ37twcOHCA2rVrA2Bpacm8efPQ1dXF1dWVunXrsnfvXjp37sytW7dYtWoVf//9t3ptDRw4kB07dhAUFMSkSZOwsLBQR9m8rUaNGqluot+lf962cuVKXr58ybJlyzAxMQFg3rx5+Pv7M2XKFPLmzZvpsWXHggULKFOmDJMmTVLLlixZgoODA9evX+f58+fEx8fTqFEjChUqBIC7u7u6rZGREa9fv87wmJL9+eefmJqaqu/r1KnDunXr0tzWz8+P7t27A0nX98yZMzlw4ACurq6sXLmShIQElixZgrGxMSVKlODvv/+mW7duGbYfFxfHwoULKVq0KAA9e/Zk3Lhx6udz585l2LBhNGzYEEjq95S/K6KiojLsC4D8+fMDSb+zkrdJafLkyYwdOzbDOIUQQgjxv00SGkJ8YV6+fKne3GfH/fv36dSpE+3bt6dly5b8+++/jBo1iiZNmrB79+4M59cPGjSIjh07cvv2bcaOHUu7du34888/szUn38zMjLNnzxIfH8/BgweZNm2a1vz6kJAQbt68qTXdQ1EUEhMTCQ8PVxMMpUqVUj9PvqFNeSOVXPbgwQPs7Owyrff69evo6elpjXpxdXXNdAHM9evXM2vWLG7evKne6Jqbm2e4T0hICM+fP8fa2lqr/OXLl9y6dSvDfVu2bImuri4vX77EwsKC6dOnU6dOHXr06MHNmzdTrbvw6tUrrTpLlCiBrq6u+t7e3p6LFy8CSdORFEXB2dlZq47Xr1+nijUtaY0Yepf+edvVq1fx8PBQkxkAlStXJjExkbCwMPVcZ3Rs2RESEsL+/fu1Eg3Jbt26Re3atfH29sbd3R0fHx9q165NkyZNsLS0zHZb1atXZ8GCBer7lMf4tpTXfHJSKXkEUXIfGRsbq9u8PR0tLcbGxmoyA5L6LLnOp0+f8s8//1CuXDn1c11dXb766isSExMB8PDwyLQvjIyMANQE5tuGDRtG//791ffPnj3DwcEh09iFEEII8b9DEhpCfGHy5MnD48ePs73fTz/9hLm5OVOnTlXLVqxYgYODAydPnqRChQoZtpknTx6cnZ0pXrw4Dg4OnDhxIks3Tsl0dHRwcnICkhIG9+/fp3nz5hw6dAiAxMREvvvuuzQf9ViwYEH13ymnuiQnVNIqS77xyqze5DU8spOcOXHiBC1atGDs2LH4+PhgYWHB6tWrmTFjRob7JSYmYm9vn+baCpklUGbOnEnNmjUxNzfH1tZWq86vvvoqzXU/kteIAFJNEdJoNFp9pKurS0hIiFZiAEjz5v5tb9+Mv2v/vE1RlHTPS8ryjI4tOxITE9XRH2+zt7dHV1eX3bt3c+zYMXbt2sXcuXMZPnw4J0+epHDhwtlqy8TERP15yExGx5dyikd2pFXn23W93fcpP89KXyRPo0p5HaZkYGDwWRfaFUIIIcTnJwkNIb4wnp6erFixItv7vXjxItXNavL77Nz8Jd/UvH79OtsxpNSvXz9+/PFHfv/9dxo2bEiZMmW4fPlylm/ysiqzeosXL058fDxnzpxRv5EOCwtTF1BMy9GjRylUqBDDhw9Xy95eJ0BfX5+EhIRUsdy/fx89Pb1sPx7Uzs4uzWMoU6YMa9asURcZfReenp4kJCTw4MEDvvnmmzS3Set40vOu/fM2Nzc3li5dSmxsrJo0OXr0KDo6OqlGk3wIZcqUYcOGDTg6OqKnl/afV41GQ+XKlalcuTKjRo2iUKFC/P777/Tv3z9bffShuLm5sXz5cl6+fKmOiDhx4sR71WlhYUHevHk5deqUej0kJCRw7tw5rUftZtQXAJcuXSJXrlyUKFHiveIRQgghxP8uWRRUiC+Mj48Ply9fTjVK4+bNm4SGhnL//n1evnxJaGgooaGh6sKQdevW5fTp04wbN44bN25w9uxZOnToQKFChfD09ASSnpbg6urK3bt31ffz5s0jNDSU27dvs3//flq1akXRokWzNTojLebm5nTq1InRo0ejKApDhgzh+PHj9OjRg9DQUG7cuMGWLVvo1avXe7WTWb0uLi74+vrSuXNnTp48SUhICJ06dVJvDtPi5OREZGQkq1ev5tatW8yZM4fff/9daxtHR0fCw8MJDQ3l0aNHvH79mpo1a1KxYkUaNGjAzp07iYiI4NixY4wYMSLVk1myqnXr1uTJk4f69etz+PBhwsPDOXjwIH369OHvv//OUh3Ozs60bt2adu3asXHjRsLDwzl9+jRTpkxR101wdHTk+fPn7N27l0ePHqU7jeB9+ietYzM0NKR9+/ZcunSJ/fv306tXL9q2batON/mQevToQUxMDC1btuTUqVP89ddf7Nq1i8DAQBISEjh58iSTJk3izJkzREZGsnHjRh4+fKhOh3J0dOTChQuEhYXx6NGjbD8J6F20atUKHR0dOnbsyJUrV9i2bRvTp09/73p79erF5MmT2bx5M2FhYfTp04fHjx+rozYy6wuAw4cP880332T4sySEEEKIL5skNIT4wri7u+Pl5cXatWu1yjt16oSnpyc///wz169fx9PTE09PT+7duwckLdz422+/sWnTJjw9PfH19cXAwIAdO3ZozXUPCwtTb8SMjIzYuHEj3t7euLi4EBgYSMmSJTl48KDWUHFHR0f1MZ7Z0adPH65evcq6desoVaoUBw8e5MaNG3zzzTd4enoycuRI7O3t37GnkmSl3qCgIBwcHKhatSqNGjVSH6uanvr169OvXz969uxJ6dKlOXbsmPp0j2SNGzfG19eX6tWrY2Njw6pVq9BoNGzbto0qVaoQGBiIs7MzLVq0ICIi4p1v0I2NjTl06BAFCxakUaNGFC9enMDAQF6+fJmtERtBQUG0a9eOAQMG4OLiwrfffsvJkyfVNQ0qVapE165dad68OTY2NlpTlz5U/6R1bDt37iQmJoayZcvSpEkTvL29mTdvXpaPKzvy5cvH0aNHSUhIwMfHh5IlS9KnTx8sLCzQ0dHB3NycQ4cO4efnh7OzMyNGjGDGjBnUqVMHgM6dO+Pi4oKXlxc2NjYcPXr0o8SZkqmpKX/88QdXrlzB09OT4cOHpzllJruGDBlCy5YtadeuHRUrVsTU1BQfHx91/Z7M+gJg1apV2V6YVQghhBBfFo3yrhNohRA51rZt2xg4cCCXLl1CR+fz5jVfvnyJlZUV27Zto3r16p81FiHEx5GYmEjx4sVp1qwZ48ePz3T7rVu3MmjQIC5cuJDu9J23PXv2DAsLCxz6rkXHwDjzHYQQQogvWMQPdT93CMD//f1++vTpO01/ljU0hPgC+fn5cePGDe7evfvZnwpw8OBBatSoIckMIf6H3L59m127dlG1alVev37NvHnzCA8Pp1WrVlnaPzY2lqCgoCwnM4QQQgjxZZIRGkIIIYT4oO7cuUOLFi24dOkSiqJQsmRJfvjhB6pUqfLR2pQRGkIIIUTW/a+M0JCEhhBCCCFyvPf9D5EQQgghPr33/fsti4IKIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcSWgIIYQQQgghhBAix5GEhhBCCCGEEEIIIXIcvc8dgBBCCCHEh1Jy9E50DIw/dxhCCCFyuIgf6n7uEEQWyAgNIYQQQgghhBBC5DiS0BBCCCGEEEIIIUSOIwkNIYQQQgghhBBC5DiS0BDiCxQdHY2trS0RERGfO5TPJjg4mNy5c2e4zZgxYyhdurT6PiAggAYNGmSp/uxsK3KmatWq0bdv33Q/l2vg3Tx48AAbGxvu3r37uUMRQgghxH+cJDSE+AJNnjwZf39/HB0dtcqDg4MpVaoUhoaG2NnZ0bNnz3TriImJoVevXri4uGBsbEzBggXp3bs3T58+zVYsBw4cQKPRpPm6f/9+pvu/nXT4mGbPnk1wcPAnaetjOHbsGLq6uvj6+n7uUN5LymvE1NQUDw+P/+R5+VTXS0BAgNofuXLlokiRIgwcOJDY2NiP3vbHYGtrS9u2bRk9evTnDkUIIYQQ/3HylBMhvjAvX75k8eLFbNu2Tav8xx9/ZMaMGUybNo3y5cvz6tUr/vrrr3TruXfvHvfu3WP69Om4ublx+/Ztunbtyr1791i/fn224woLC8Pc3FyrzNbWNtv1fEwWFhafO4T3smTJEnr16sWvv/5KZGQkBQsW/NwhvbOgoCB8fX2JjY1lzZo1dOjQAXt7e3x8fD53aKpPeb34+voSFBREXFwchw8fplOnTsTGxrJgwYJU28bFxZErV65PFtu76NChA+XKlWPatGlYWlp+7nCEEEII8R8lIzSE+MJs374dPT09KlasqJY9fvyYESNGsGzZMlq1akXRokUpUaIE/v7+6dZTsmRJNmzYgL+/P0WLFqVGjRpMnDiRP/74g/j4+GzHZWtri52dndZLRyfpV9SBAwcoV64cJiYm5M6dm8qVK3P79m2Cg4MZO3Ys58+fV7+hTv5G/Mcff8Td3R0TExMcHBzo3r07z58/T9Xupk2bcHZ2xtDQkFq1anHnzp10Y3x7CsH69etxd3fHyMgIa2tratasmepb8enTp2Nvb4+1tTU9evQgLi4u3fpv3bpF/fr1yZs3L6amppQtW5Y9e/ZobePo6MikSZMIDAzEzMyMggUL8ssvv2TWvcTGxrJ27Vq6detGvXr10hw5sGXLFry8vDA0NCRPnjw0atRI/ez169cMHjwYBwcHDAwMKFasGIsXL1Y/v3LlCn5+fpiampI3b17atm3Lo0ePstRX6Z3fjOTOnRs7OzuKFi3K999/j5WVFbt27VI/f/r0KV26dMHW1hZzc3Nq1KjB+fPngaTkmUaj4dq1a1p1/vjjjzg6OqIoCgAHDx6kXLlyGBgYYG9vz9ChQzO8tnfs2IGFhQXLli0DUl8v1apVo3fv3gwePBgrKyvs7OwYM2aMVh3Xrl3j66+/xtDQEDc3N/bs2YNGo2HTpk0Z9oeBgQF2dnY4ODjQqlUrWrdure6TPIppyZIlFClSBAMDAxRFITIykvr162Nqaoq5uTnNmjXjn3/+0ao3o2vizZs3DB48mPz582NiYkL58uU5cOCA+vnt27fx9/fH0tISExMTSpQooSZSHz9+TOvWrbGxscHIyIhixYoRFBSk7uvu7o6dnR2///57hscthBBCiC+bJDSE+MIcOnQILy8vrbLdu3eTmJjI3bt3KV68OAUKFKBZs2YZ3tyn5enTp5ibm6On9+EGf8XHx9OgQQOqVq3KhQsXOH78OF26dEGj0dC8eXMGDBhAiRIliIqKIioqiubNmwOgo6PDnDlzuHTpEkuXLmXfvn0MHjxYq+4XL14wceJEli5dytGjR3n27BktWrTIUlxRUVG0bNmSwMBArl69yoEDB2jUqJF6Mwywf/9+bt26xf79+1m6dCnBwcEZTkF4/vw5fn5+7Nmzh3PnzuHj44O/vz+RkZFa282YMQMvLy/OnTtH9+7d6datW6qb87etWbMGFxcXXFxcaNOmDUFBQVqxbt26lUaNGlG3bl3OnTvH3r17ta6Tdu3asXr1aubMmcPVq1dZuHAhpqamal9UrVqV0qVLc+bMGXbs2ME///xDs2bNMu2rjM5vViQkJLB27VpiYmLUUQeKolC3bl3u37/Ptm3bCAkJoUyZMnh7exMTE4OLiwtfffUVK1eu1Krrt99+o1WrVmg0Gu7evYufnx9ly5bl/PnzLFiwgMWLFzNhwoQ041i9ejXNmjVj2bJltGvXLt14ly5diomJCSdPnmTq1KmMGzeO3bt3A5CYmEiDBg0wNjbm5MmT/PLLLwwfPjxL/fA2IyMjreTZzZs3Wbt2LRs2bCA0NBSABg0aEBMTw8GDB9m9eze3bt1Sf34g82uiQ4cOHD16lNWrV3PhwgWaNm2Kr68vN27cAKBHjx68fv2aQ4cOcfHiRaZMmaJeMyNHjuTKlSts376dq1evsmDBAvLkyaN1DOXKlePw4cPpHuPr16959uyZ1ksIIYQQXxaZciLEFyYiIoJ8+fJplf31118kJiYyadIkZs+ejYWFBSNGjKBWrVpcuHABfX39TOuNjo5m/PjxfPfdd+8UV4ECBbTe58+fn7CwMJ49e8bTp0+pV68eRYsWBaB48eLqdqampujp6WFnZ6e1f8rFGgsXLsz48ePp1q0b8+fPV8vj4uKYN28e5cuXB5JuNosXL86pU6coV65chvFGRUURHx9Po0aNKFSoEJD0rXJKlpaWzJs3D11dXVxdXalbty579+6lc+fOadbp4eGBh4eH+n7ChAn8/vvvbNmyRWs9Ez8/P7p37w7AkCFDmDlzJgcOHMDV1TXdeBcvXkybNm2ApOkJz58/Z+/evdSsWROAiRMn0qJFC8aOHasVD8D169dZu3Ytu3fvVrcvUqSIut2CBQsoU6YMkyZNUsuWLFmCg4MD169f5/nz5+n2VUxMTIbnNz0tW7ZEV1eXV69ekZCQgJWVFZ06dQKSEkkXL17kwYMHGBgYAEkjZTZt2sT69evp0qULrVu3Zt68eYwfP149xpCQEHV0xfz583FwcGDevHloNBpcXV25d+8eQ4YMYdSoUerooeRtv//+ezZv3kz16tUzjLtUqVLq2hDFihVj3rx57N27l1q1arFr1y5u3brFgQMH1Ot54sSJ1KpVK9P+SOnUqVP89ttveHt7q2Vv3rxh+fLl2NjYAElJzAsXLhAeHo6DgwMAy5cvp0SJEpw+fZqyZctmeE3cunWLVatW8ffff6u/TwYOHMiOHTsICgpi0qRJREZG0rhxY/Vcp7xmIiMj8fT0VBMkb6/nA0m/A86dO5fucU6ePFkrNiGEEEJ8eWSEhhBfmJcvX2JoaKhVlpiYSFxcHHPmzMHHx4cKFSqwatUqbty4wf79+zOt89mzZ9StWxc3N7d3Xsjv8OHDhIaGqq+dO3cCYGVlRUBAgDpaYfbs2URFRWVa3/79+6lVqxb58+fHzMyMdu3aER0drTUlRE9PT+sbZ1dXV3Lnzs3Vq1czrd/DwwNvb2/c3d1p2rQpixYt4vHjx1rblChRAl1dXfW9vb09Dx48SLfO2NhYBg8ejJubG7lz58bU1JRr166lGqFRqlQp9d8ajQY7O7sM6w0LC+PUqVPq6BM9PT2aN2/OkiVL1G1CQ0O1boBTCg0NRVdXl6pVq6b5eUhICPv378fU1FR9JSdXbt26lWFfvev5nTlzJqGhoezevZvSpUszc+ZMnJyc1HieP3+OtbW1Vkzh4eHcunULgBYtWnD79m1OnDgBwMqVKyldujRubm4AXL16lYoVK2qNFKlcuTLPnz/n77//Vss2bNhA37592bVrV6bJDNA+d6B9TYSFheHg4KCVnMsssZbszz//xNTUFENDQypWrEiVKlWYO3eu+nmhQoXUZEby8Tk4OKjJDEC97pKv/4yuibNnz6IoCs7Ozlp9fPDgQbWPe/fuzYQJE6hcuTKjR4/mwoUL6v7dunVj9erVlC5dmsGDB3Ps2LFUbRgZGfHixYt0j3nYsGE8ffpUfWV3RJkQQgghcj5JaAjxhcmTJ0+qG297e3sA9WYOwMbGhjx58qS6mX7bv//+i6+vL6ampvz+++/vvNhg4cKFcXJyUl8pv7ENCgri+PHjVKpUiTVr1uDs7KzeiKbl9u3b+Pn5qet8hISE8NNPPwGkWsMirakNWZnuoKury+7du9m+fTtubm7MnTsXFxcXwsPD1W3e7guNRkNiYmK6dQ4aNIgNGzYwceJENcHj7u7OmzdvtLbLbr2LFy8mPj6e/Pnzo6enh56eHgsWLGDjxo3qtWBkZJTu/hl9BkkJMX9/f62EVGhoKDdu3KBKlSqZ9lV2zy+AnZ0dTk5OVK9enXXr1tGjRw+uXLmixmNvb58qnrCwMAYNGgQkXfPVq1fnt99+A2DVqlXqCBZImrby9nWQPEUnZXnp0qWxsbFJNYUnPRmdu7TazKrq1aurx/jq1Ss2btyotaiuiYlJqmNJq62U5Rmd98TERHR1dQkJCdHq46tXrzJ79mwAOnXqxF9//UXbtm25ePEiXl5eapKlTp063L59m759+3Lv3j28vb0ZOHCgVhsxMTFaSZi3GRgYYG5urvUSQgghxJdFEhpCfGE8PT3VG79klStXBpK+IU4WExPDo0eP1CkCaXn27Bm1a9dGX1+fLVu2pBr58SF5enoybNgwjh07RsmSJdUbUX19fRISErS2PXPmDPHx8cyYMYMKFSrg7OzMvXv3UtUZHx/PmTNn1PdhYWE8efIkw6kbKWk0GipXrszYsWM5d+4c+vr677WI4eHDhwkICKBhw4bqoogRERHvXB8kHeOyZcuYMWOG1o3n+fPnKVSokLqORKlSpdi7d2+adbi7u5OYmMjBgwfT/LxMmTJcvnwZR0dHraSUk5OTeiOdWV+ld36zwsnJicaNGzNs2DA1nvv376Onp5cqnpTrNLRu3Zo1a9Zw/Phxbt26pbV+ipubG8eOHdNKUhw7dgwzMzPy58+vlhUtWpT9+/ezefNmevXqleWY0+Lq6kpkZKTWwpynT5/O0r4mJiY4OTlRqFChLCUV3dzciIyM1BrVcOXKFZ4+fapO+cnomvD09CQhIYEHDx6k6uOUI0wcHBzo2rUrGzduZMCAASxatEj9zMbGhoCAAFasWMGsWbNSLW576dIlPD09s3T8QgghhPgySUJDiC+Mj48Ply9f1hql4ezsTP369enTpw/Hjh3j0qVLtG/fHldXV3UY/d27d3F1deXUqVNA0siM2rVrExsby+LFi3n27Bn379/n/v37qRIMWfHgwQN1/+RXXFwc4eHhDBs2jOPHj3P79m127drF9evX1ZsuR0dHwsPDCQ0N5dGjR7x+/ZqiRYsSHx/P3Llz+euvv1i+fDkLFy5M1WauXLno1asXJ0+e5OzZs3To0IEKFSpkaZj/yZMnmTRpEmfOnCEyMpKNGzfy8OHDLK3/kB4nJyc2btyoJhxatWqV4ciLrPjzzz95/PgxHTt2pGTJklqvJk2aqE8qGT16NKtWrWL06NFcvXqVixcvMnXqVCCpj9u3b09gYCCbNm0iPDycAwcOsHbtWiBp8ceYmBhatmzJqVOn+Ouvv9i1axeBgYEkJCRk2FeZnd+sGjBgAH/88QdnzpyhZs2aVKxYkQYNGrBz504iIiI4duwYI0aM0EpgNWrUiGfPntGtWzeqV6+ulajo3r07d+7coVevXly7do3NmzczevRo+vfvr7V+BiT9/Ozfv1+dfvKuatWqRdGiRWnfvj0XLlzg6NGj6qKg7zpyIz01a9akVKlStG7dmrNnz3Lq1CnatWtH1apV1WlYGV0Tzs7OtG7dmnbt2rFx40bCw8M5ffo0U6ZMUZ9k0rdvX3bu3El4eDhnz55l37596nkdNWoUmzdv5ubNm1y+fJk///xT65y/ePGCkJAQateu/UGPWwghhBD/WyShIcQXxt3dHS8vL/VmNNmyZcsoX748devWpWrVquTKlYsdO3ao3/bGxcURFhamzmkPCQnh5MmTXLx4EScnJ+zt7dVXym99q1WrRkBAQKZxubi4aNVhb29PSEgIxsbGXLt2jcaNG+Ps7EyXLl3o2bOnuvho48aN8fX1pXr16tjY2LBq1SpKly7Njz/+yJQpUyhZsiQrV65k8uTJqdo0NjZmyJAhtGrViooVK2JkZMTq1auz1I/m5uYcOnQIPz8/nJ2dGTFiBDNmzKBOnTpZ2j8tM2fOxNLSkkqVKuHv74+Pjw9lypR55/ogabpJzZo1sbCwSPVZ48aNCQ0N5ezZs1SrVo1169axZcsWSpcuTY0aNTh58qS67YIFC2jSpAndu3fH1dWVzp07q+uR5MuXj6NHj5KQkICPjw8lS5akT58+WFhYoKOjk2FfZXZ+s8rd3Z2aNWsyatQoNBoN27Zto0qVKgQGBuLs7EyLFi2IiIggb9686j7m5ub4+/tz/vx5WrdurVVf/vz52bZtG6dOncLDw4OuXbvSsWNHRowYkWb7Li4u7Nu3j1WrVjFgwIBsxZ5MV1eXTZs28fz5c8qWLUunTp3U9j706KfkR8FaWlpSpUoVatasSZEiRVizZo26TWbXRFBQEO3atWPAgAG4uLjw7bffcvLkSXVdjoSEBHr06EHx4sXx9fXFxcVFXZRXX1+fYcOGUapUKXVaUsqfvc2bN1OwYEG++eabD3rcQgghhPjfolGyMulXCPE/Zdu2bQwcOJBLly6l+rb5Q3N0dGTMmDFZSmoIIbQdPXqUr7/+mps3b6pPgfkSlCtXjr59+9KqVass7/Ps2TMsLCxw6LsWHQPjjxidEEKIL0HED3U/dwhfhOS/30+fPn2n9bDksa1CfIH8/Py4ceMGd+/e1XrKwYd27do19QkjQojM/f7775iamlKsWDFu3rxJnz59qFy58heVzHjw4AFNmjShZcuWnzsUIYQQQvzHSUJDiC9Unz59Pnobrq6uXLx48aO3I8T/in///ZfBgwdz584d8uTJQ82aNZkxY8bnDuuTsrW1ZfDgwZ87DCGEEELkADLlRAghhBA53vsOWRVCCCHEp/e+f7/fafL88uXLqVy5Mvny5eP27dsAzJo1i82bN79LdUIIIYQQQgghhBDZku2ExoIFC+jfvz9+fn48efJEfTxj7ty5mTVr1oeOTwghhBBCCCGEECKVbCc05s6dy6JFixg+fDi6urpquZeXl8yVF0IIIYQQQgghxCeR7YRGeHg4np6eqcoNDAyIjY39IEEJIYQQQgghhBBCZCTbCY3ChQsTGhqaqnz79u24ubl9iJiEEEIIIYQQQgghMpTtx7YOGjSIHj168OrVKxRF4dSpU6xatYrJkyfz66+/fowYhRBCCCGEEEIIIbRkO6HRoUMH4uPjGTx4MC9evKBVq1bkz5+f2bNn06JFi48RoxBCCCGEEEIIIYQWjaIoyrvu/OjRIxITE7G1tf2QMQkhhBBCZMv7PsdeCCGEEJ/e+/79zvYIjZTy5MnzPrsLIYQQQgghhBBCvJNsJzSio6MZNWoU+/fv58GDByQmJmp9HhMT88GCE0IIIYQQQgghhEhLthMabdq04datW3Ts2JG8efOi0Wg+RlxCCCGEEEIIIYQQ6cp2QuPIkSMcOXIEDw+PjxGPEEIIIYQQQgghRKZ0sruDq6srL1++/BixCCGEEEIIIYQQQmRJthMa8+fPZ/jw4Rw8eJDo6GiePXum9RJCCCGEEEIIIYT42LI95SR37tw8ffqUGjVqaJUrioJGoyEhIeGDBSeEEEIIIYQQQgiRlmwnNFq3bo2+vj6//fabLAoqhBBCCCGEEEKIzyLbCY1Lly5x7tw5XFxcPkY8QgghhBBCCCGEEJnK9hoaXl5e3Llz52PEIoQQQgghhBBCCJEl2R6h0atXL/r06cOgQYNwd3cnV65cWp+XKlXqgwUnhBBCCJEdJUfvRMfA+HOHIYQQIoeL+KHu5w5BZEG2ExrNmzcHIDAwUC3TaDSyKKgQQgghhBBCCCE+mWwnNMLDwz9GHEIIIYQQQgghhBBZlu2ERqFChT5GHEIIIYQQQgghhBBZlu1FQZNduXKFHTt2sGXLFq2XECLniY6OxtbWloiIiM8dymcTHBxM7ty5M9xmzJgxlC5dWn0fEBBAgwYNslR/drYV/zuqVatG3759P3cYOc7FixcpUKAAsbGxnzsUIYQQQvyHZTuh8ddff+Hh4UHJkiWpW7cuDRo0oEGDBjRs2JCGDRt+jBiFEB/Z5MmT8ff3x9HRUas8ODiYUqVKYWhoiJ2dHT179sywnl9++YVq1aphbm6ORqPhyZMn2Y7lwIEDaDSaNF/379/PdP+3kw4f0+zZswkODv4kbX0Mx44dQ1dXF19f388dynvbv38/9erVw8bGBkNDQ4oWLUrz5s05dOjQJ2k/+bp9+5rfuHEj48eP/+jtV6tWTf05MTAwwNnZmUmTJuXYda3c3d0pV64cM2fO/NyhCCGEEOI/LNsJjT59+lC4cGH++ecfjI2NuXz5MocOHcLLy4sDBw58hBCFEB/Ty5cvWbx4MZ06ddIq//HHHxk+fDhDhw7l8uXL7N27Fx8fnwzrevHiBb6+vnz//ffvHVdYWBhRUVFaL1tb2/eu90OysLDIdFTHf9mSJUvo1asXR44cITIy8nOH887mz5+Pt7c31tbWrFmzhqtXr7J8+XIqVapEv3790t0vISGBxMTEjxqblZUVZmZmH7WNZJ07dyYqKoqwsDB69+7NiBEjmD59eprbvnnz5pPE9D46dOjAggULcmxSRgghhBAfX7YTGsePH2fcuHHY2Nigo6ODjo4OX3/9NZMnT6Z3794fI0YhxEe0fft29PT0qFixolr2+PFjRowYwbJly2jVqhVFixalRIkS+Pv7Z1hX3759GTp0KBUqVHjvuGxtbbGzs9N66egk/co6cOAA5cqVw8TEhNy5c1O5cmVu375NcHAwY8eO5fz58+q31ckjKH788Ufc3d0xMTHBwcGB7t278/z581Ttbtq0CWdnZwwNDalVqxZ37txJN8a3p5GsX78ed3d3jIyMsLa2pmbNmqmGzE+fPh17e3usra3p0aMHcXFx6dZ/69Yt6tevT968eTE1NaVs2bLs2bNHaxtHR0cmTZpEYGAgZmZmFCxYkF9++SWz7iU2Npa1a9fSrVs36tWrl+ZIky1btuDl5YWhoSF58uShUaNG6mevX79m8ODBODg4YGBgQLFixVi8eLH6+ZUrV/Dz88PU1JS8efPStm1bHj16lKW+Su/8piUyMpK+ffvSt29fli5dSo0aNShcuDCVKlWiT58+nDlzRt02eVrRn3/+iZubGwYGBty+fZvTp09Tq1Yt8uTJg4WFBVWrVuXs2bNa7Wg0Gn799VcaNmyIsbExxYoVU6dZRkREUL16dQAsLS3RaDQEBAQAqaecZOV8HTt2jNKlS2NoaIiXlxebNm1Co9EQGhqaztlMYmxsjJ2dHY6OjvTs2RNvb282bdoE/N+1OnnyZPLly4ezszOQNLWjRo0a6nno0qVLqp+LJUuWUKJECQwMDLC3t9caqfX06VO6dOmCra0t5ubm1KhRg/Pnz6ufnz9/nurVq2NmZoa5uTlfffWVek5u376Nv78/lpaWmJiYUKJECbZt26bu6+PjQ3R0NAcPHszwuIUQQgjx5cp2QiMhIQFTU1MA8uTJw71794CkxULDwsI+bHRCiI8ueYRVSrt37yYxMZG7d+9SvHhxChQoQLNmzTK8uf9U4uPjadCgAVWrVuXChQscP36cLl26oNFoaN68OQMGDKBEiRLqqI7kR03r6OgwZ84cLl26xNKlS9m3bx+DBw/WqvvFixdMnDiRpUuXcvToUZ49e0aLFi2yFFdUVBQtW7YkMDCQq1evcuDAARo1aoSiKOo2+/fv59atW+zfv5+lS5cSHByc4ZSV58+f4+fnx549ezh37hw+Pj74+/unGk0xY8YMvLy8OHfuHN27d6dbt25cu3Ytw3jXrFmDi4sLLi4utGnThqCgIK1Yt27dSqNGjahbty7nzp1j7969WtdJu3btWL16NXPmzOHq1assXLhQ/dsQFRVF1apVKV26NGfOnGHHjh38888/NGvWLNO+yuj8pmXDhg3ExcWlOpfJ3t7vxYsXTJ48mV9//ZXLly9ja2vLv//+S/v27Tl8+DAnTpygWLFi+Pn58e+//2rtO3bsWJo1a8aFCxfw8/OjdevWxMTE4ODgwIYNG4D/G1k0e/bsdPs+o/P177//4u/vj7u7O2fPnmX8+PEMGTIk3boyYmRkpJUw27t3L1evXmX37t38+eef6ogqS0tLTp8+zbp169izZ49WwmLBggX06NGDLl26cPHiRbZs2YKTkxMAiqJQt25d7t+/z7Zt2wgJCaFMmTJ4e3sTExMDQOvWrSlQoACnT58mJCSEoUOHkitXLgB69OjB69evOXToEBcvXmTKlCnqNQSgr6+Ph4cHhw8fTvP4Xr9+zbNnz7ReQgghhPiyZPspJyVLluTChQsUKVKE8uXLM3XqVPT19fnll18oUqTIx4hRCPERRUREkC9fPq2yv/76i8TERCZNmsTs2bOxsLBgxIgR1KpViwsXLqCvr//R4ypQoIDW+/z58xMWFsazZ894+vQp9erVo2jRogAUL15c3c7U1BQ9PT3s7Oy09k/5LXnhwoUZP3483bp1Y/78+Wp5XFwc8+bNo3z58gAsXbqU4sWLc+rUKcqVK5dhvFFRUcTHx9OoUSP1aVDu7u5a21haWjJv3jx0dXVxdXWlbt267N27l86dO6dZp4eHBx4eHur7CRMm8Pvvv7Nlyxatm04/Pz+6d+8OwJAhQ5g5cyYHDhzA1dU13XgXL15MmzZtAPD19eX58+fs3buXmjVrAjBx4kRatGjB2LFjteIBuH79OmvXrmX37t3q9il//y9YsIAyZcowadIktWzJkiU4ODhw/fp1nj9/nm5fxcTEZHh+33b9+nXMzc21zveGDRto3769+v748eNq/XFxccyfP1+rX2vUqKFV588//4ylpSUHDx6kXr16anlAQAAtW7YEYNKkScydO5dTp07h6+uLlZUVkDSyKLNpSBmdr5UrV6LRaFi0aBGGhoa4ublx9+7ddK+RtCQmJrJr1y527typdd2bmJjw66+/qj+/ixYt4uXLlyxbtgwTExMA5s2bh7+/P1OmTCFv3rxMmDCBAQMG0KdPH7WesmXLAkkJuosXL/LgwQMMDAyApBFImzZtYv369XTp0oXIyEgGDRqkXovFihVT64mMjKRx48bquUnr/xD58+dPd7HiyZMna12fQgghhPjyZHuExogRI9Q5xxMmTOD27dt88803bNu2jTlz5nzwAIUQH9fLly8xNDTUKktMTCQuLo45c+bg4+NDhQoVWLVqFTdu3GD//v2fJK7Dhw8TGhqqvnbu3AkkrUkQEBCgjlaYPXs2UVFRmda3f/9+atWqRf78+TEzM6Ndu3ZER0drTQnR09PTGoXg6upK7ty5uXr1aqb1e3h44O3tjbu7O02bNmXRokU8fvxYa5sSJUqgq6urvre3t+fBgwfp1hkbG8vgwYNxc3Mjd+7cmJqacu3atVQjNEqVKqX+W6PRYGdnl2G9YWFhnDp1Sh19oqenR/PmzVmyZIm6TWhoKN7e3mnuHxoaiq6uLlWrVk3z85CQEPbv34+pqan6Sr6hvXXrVoZ99S7n9+1RGD4+PoSGhrJ161ZiY2O11mDQ19fX6i+ABw8e0LVrV5ydnbGwsMDCwoLnz59n2M8mJiaYmZll2M/pyeh8hYWFqQvxJsssmZZs/vz5mJqaYmhoyLfffkubNm0YPXq0+rm7u7tWMvLq1at4eHioyQyAypUrk5iYSFhYGA8ePODevXvpXgchISE8f/4ca2trrXMdHh7OrVu3AOjfvz+dOnWiZs2a/PDDD2o5QO/evZkwYQKVK1dm9OjRXLhwIVUbRkZGvHjxIs32hw0bxtOnT9XXf2EEmRBCCCE+rWwnNHx8fNR51EWKFOHKlSs8evSIBw8epPqWSwjx35cnT55UN9729vYAuLm5qWU2NjbkyZPnky0eWbhwYZycnNRXyiewBAUFcfz4cSpVqsSaNWtwdnbmxIkT6dZ1+/Zt/Pz8KFmyJBs2bCAkJISffvoJINUaFmlNbUhvukNKurq67N69m+3bt+Pm5sbcuXNxcXEhPDxc3SZ5qH3KejNalHLQoEFs2LCBiRMnqgked3f3VAs6ZrfexYsXEx8fT/78+dHT00NPT48FCxawceNG9VowMjJKd/+MPoOkhJi/v79WQio0NJQbN25QpUqVTPsqO+e3WLFiPH36VOsJOKampjg5OamjP96O/e3zGRAQQEhICLNmzeLYsWOEhoZibW393v2cnozqURQlVXwppwJlpHXr1oSGhnLr1i11sV9jY2P185SJi/TaShlTVs6zvb19qvMcFhbGoEGDgKSnDl2+fJm6deuyb98+3Nzc+P333wHo1KkTf/31F23btuXixYt4eXkxd+5crTZiYmKwsbFJs30DAwPMzc21XkIIIYT4smQ7oZEWKyurLP2HXwjx3+Pp6cmVK1e0yipXrgygtS5OTEwMjx49SvMm8XPw9PRk2LBhHDt2jJIlS/Lbb78BSd/Av/1UhDNnzhAfH8+MGTOoUKECzs7O6vo/KcXHx2stIhkWFsaTJ08ynLqRkkajoXLlyowdO5Zz586hr6+v3ry9i8OHDxMQEEDDhg1xd3fHzs4u3eH3WRUfH8+yZcuYMWOG1k3o+fPnKVSoECtXrgSSRhHs3bs3zTrc3d1JTExMd7HGMmXKcPnyZRwdHbWSUk5OTupNdWZ9ld75fVuTJk3IlSsXU6ZMeec+OXz4ML1798bPz09d/DLlAqZZkTzy4X2fyOHq6sqFCxd4/fq1WpbymsyIhYUFTk5OODg4aI0ESo+bmxuhoaFao5SOHj2Kjo4Ozs7OmJmZ4ejomO51UKZMGe7fv4+enl6q85wnTx51O2dnZ/r168euXbto1KgRQUFB6mcODg507dqVjRs3MmDAABYtWqTVxqVLl/D09MzS8QshhBDiy5PlNTQCAwMz3Uaj0Witci+E+O/z8fFh2LBhPH78GEtLSyDpBqR+/fr06dOHX375BXNzc4YNG4arq6v6NIe7d+/i7e3NsmXL1CHx9+/f5/79+9y8eRNIeoJC8pMcktcYyKoHDx7w6tUrrTJra2v+/vtvfvnlF7799lvy5ctHWFgY169fp127dkDSUyTCw8MJDQ2lQIECmJmZUbRoUeLj45k7dy7+/v4cPXqUhQsXpmozV65c9OrVizlz5pArVy569uxJhQoVsjTk/+TJk+zdu5fatWtja2vLyZMnefjwYYbrP2TGycmJjRs34u/vj0ajYeTIke/9mNE///yTx48f07FjRywsLLQ+a9KkCYsXL6Znz56MHj0ab29vihYtSosWLYiPj2f79u0MHjwYR0dH2rdvT2BgIHPmzMHDw4Pbt2/z4MEDmjVrRo8ePVi0aBEtW7Zk0KBB5MmTh5s3b7J69WoWLVrEmTNn0u2r8PDwDM/v2woWLMiMGTPo06cPMTExBAQEULhwYWJiYlixYgVApjf3Tk5OLF++HC8vL549e8agQYMyHZ3wtkKFCqHRaPjzzz/x8/PDyMhIa4HLrGrVqhXDhw+nS5cuDB06lMjISPXRqx/6i4PWrVszevRo2rdvz5gxY3j48CG9evWibdu25M2bF0gaYdG1a1dsbW2pU6cO//77L0ePHqVXr17UrFmTihUr0qBBA6ZMmYKLiwv37t1j27ZtNGjQgBIlSjBo0CCaNGlC4cKF+fvvvzl9+jSNGzcGkta1qVOnDs7Ozjx+/Jh9+/Zp/bxERERw9+5ddZ0WIYQQQoi3ZXmExuPHj9N9PXr0iNWrV2e4Wr8Q4r/J3d0dLy8v1q5dq1W+bNkyypcvT926dalatSq5cuVix44d6nD5uLg4wsLCtOa3L1y4EE9PT3UBwypVquDp6ak+3hKSHmOZ/EjLjLi4uGBvb6/1CgkJwdjYmGvXrtG4cWOcnZ3p0qULPXv25LvvvgOgcePG+Pr6Ur16dWxsbFi1ahWlS5fmxx9/ZMqUKZQsWZKVK1cyefLkVG0aGxszZMgQWrVqRcWKFTEyMmL16tVZ6kdzc3MOHTqEn58fzs7OjBgxghkzZlCnTp0s7Z+WmTNnYmlpSaVKlfD398fHx4cyZcq8c32QNN2kZs2aqZIZkNR3oaGhnD17lmrVqrFu3Tq2bNlC6dKlqVGjBidPnlS3XbBgAU2aNKF79+64urrSuXNn9Zv+fPnycfToURISEvDx8aFkyZL06dMHCwsLdHR0MuyrzM5vWnr16sWuXbt4+PAhTZo0UZ9SEh4ezo4dO1Itzvq2JUuW8PjxYzw9PWnbti29e/fG1tY2W/2aP39+xo4dy9ChQ8mbN6/Woq3ZYW5uzh9//EFoaCilS5dm+PDhjBo1CiDVWjfvy9jYmJ07dxITE0PZsmVp0qQJ3t7ezJs3T92mffv2zJo1i/nz51OiRAnq1avHjRs3gKQEy7Zt26hSpQqBgYE4OzvTokULIiIiyJs3L7q6ukRHR9OuXTucnZ1p1qwZderUURfyTEhIoEePHhQvXhxfX19cXFy0FuldtWoVtWvX/s+MChNCCCHEf49Gyerk3HRs3ryZ77//nnv37jFkyBCGDh36oWITQnwi27ZtY+DAgVy6dAkdnQ8yEy1djo6OjBkzJktJDSEErFy5kg4dOvD06dNsjxzJqV6/fk2xYsVYtWqVOgUuM8+ePcPCwgKHvmvRMTDOfAchhBAiAxE/1P3cIXwRkv9+P3369J3Ww8r2Y1uTHT16lCFDhnDu3Dl69uzJ0KFD1eHqQoicxc/Pjxs3bnD37l0cHBw+WjvXrl1TnzAihEjbsmXLKFKkCPnz5+f8+fMMGTKEZs2afTHJDEhayHf48OFZTmYIIYQQ4suU7YTG5cuXGTp0KDt27KBdu3asXr2aAgUKfIzYhBCfUJ8+fT56G66urly8ePGjtyNETnb//n1GjRrF/fv3sbe3p2nTpkycOPFzh/VJOTs74+zs/LnDEEIIIcR/XJannNy5c4dRo0axYsUK6tWrx8SJE7Ue6SiEEEII8bm875BVIYQQQnx6n2zKiYuLCxqNhgEDBlCpUiVu3rypPskgpW+//TbbQQghhBBCCCGEEEJkR5ZHaGRloUCNRkNCQsJ7ByWEEEIIkR0yQkMIIYTIeT7ZCI3ExMRsVy6EEEIIIYQQQgjxMXzc5zMKIYQQQgghhBBCfASS0BBCCCGEEEIIIUSOIwkNIYQQQgghhBBC5DiS0BBCCCGEEEIIIUSOIwkNIYQQQgghhBBC5DiS0BBCCCGEEEIIIUSOk6XHtlpaWqLRaLJUYUxMzHsFJIQQQgghhBBCCJGZLCU0Zs2a9ZHDEEIIIYQQQgghhMi6LCU02rdv/7HjEEIIIYQQQgghhMiyLCU03paQkMCmTZu4evUqGo0GNzc3vv32W3R1dT90fEIIIYQQQgghhBCpZDuhcfPmTfz8/Lh79y4uLi4oisL169dxcHBg69atFC1a9GPEKYQQQgghhBBCCKHK9lNOevfuTdGiRblz5w5nz57l3LlzREZGUrhwYXr37v0xYhRCCCGEEEIIIYTQku0RGgcPHuTEiRNYWVmpZdbW1vzwww9Urlz5gwYnhBBCCCGEEEIIkZZsj9AwMDDg33//TVX+/Plz9PX1P0hQQgghhBBCCCGEEBnJdkKjXr16dOnShZMnT6IoCoqicOLECbp27cq33377MWIUQgghhBBCCCGE0JLtKSdz5syhffv2VKxYkVy5cgEQHx/Pt99+y+zZsz94gEIIIYQQWVVy9E50DIw/dxhCCCFyuIgf6n7uEEQWZCuhoSgKT58+ZdWqVdy7d4+rV6+iKApubm44OTl9rBiFEEIIIYQQQgghtGQ7oVGsWDEuX75MsWLFJIkhhBBCCCGEEEKIzyJba2jo6OhQrFgxoqOjP1Y8QgghhBBCCCGEEJnK9qKgU6dOZdCgQVy6dOljxCNEjhUdHY2trS0RERGfO5TP7ujRo7i7u5MrVy4aNGjwucP5aCIiItBoNISGhr5XPdWqVaNv377qe0dHR2bNmvVedeZ0wcHB5M6d+3OH8Z+h0WjYtGnT5w7jk3j9+jUFCxYkJCTkc4cihBBCiP+4bCc02rRpw6lTp/Dw8MDIyAgrKyutlxBfqsmTJ+Pv74+jo6Na1qdPH7766isMDAwoXbp0mvvt3LmTChUqYGZmho2NDY0bNyY8PDzDthwdHdFoNFqvoUOHZive4OBgrf3t7e1p1qxZpm1nRf/+/SldujTh4eEEBwe/d33v68CBA1rHamNjQ506dTh//vx71evg4EBUVBQlS5b8QJEmOX36NF26dHnn/bObEEnunydPnrxzm59DynNqZmaGl5cXGzdu/KQxfOjEy5gxY9L8XREVFUWdOnU+WDtvq1atWqrfKSlfKX+vZVdAQEC2EpsGBgYMHDiQIUOGvHObQgghhPgyZPspJzNnzkSj0XyMWITIsV6+fMnixYvZtm2bVrmiKAQGBnLy5EkuXLiQar+//vqL+vXr079/f1auXMnTp0/p168fjRo14ty5cxm2OW7cODp37qy+NzU1zXbc5ubmhIWFoSgK165d47vvvuPbb78lNDQUXV3dbNcXFxdHrly5uHXrFl27dqVAgQLZriPZmzdv0NfXf+f90xIWFoa5uTmRkZH07t0bX19frl27hoWFxTvHZ2dn90FjBLCxsfngdX4KiqKQkJCAnl62/7S8s6CgIHx9fXny5AnTpk2jadOmHDlyhIoVK36yGLLifa/nj3GdpbRx40bevHkDwJ07dyhXrhx79uyhRIkSAO/0++B9tG7dmkGDBnH16lWKFy/+SdsWQgghRM6R7REaAQEBtG/fPt2XEF+i7du3o6enl+omas6cOfTo0YMiRYqkud/Zs2dJSEhgwoQJFC1alDJlyjBw4EDOnz9PXFxchm2amZlhZ2envt4loaHRaLCzs8Pe3p7q1aszevRoLl26xM2bNwH4448/+OqrrzA0NKRIkSKMHTuW+Ph4rf0XLlxI/fr1MTExoVOnTmg0GqKjowkMDESj0agjNA4ePEi5cuUwMDDA3t6eoUOHatVVrVo1evbsSf/+/cmTJw+1atVSRw7s3LkTT09PjIyMqFGjBg8ePGD79u0UL14cc3NzWrZsyYsXLzI9XltbW+zs7ChXrhwzZszg/v37nDhxAoBjx45RpUoVjIyMcHBwoHfv3sTGxqr7Ojo6MmHCBAICArCwsKBz585pTjnJ7DhjY2Np164dpqam2NvbM2PGjFRxvj3C4smTJ3Tp0oW8efNiaGhIyZIl+fPPPzM93mQajYZff/2Vhg0bYmxsTLFixdiyZQuQNG2mevXqAFhaWqLRaAgICACSEhRTp06lSJEiGBkZ4eHhwfr169V6U54fLy8vDAwMOHz4MNWqVaN3794MHjwYKysr7OzsGDNmjFZMP/74I+7u7piYmODg4ED37t15/vx5lo8pWe7cubGzs8PV1ZWFCxdiaGioHtvdu3dp3rw5lpaWWFtbU79+fa0pYckjB6ZPn469vT3W1tb06NFD62fvzZs3DB48mPz582NiYkL58uU5cOCAevwdOnTg6dOn6kiG5ONM63oBGDJkCM7OzhgbG1OkSBFGjhypthccHMzYsWM5f/68Wl/yz8/bU04uXrxIjRo1MDIywtrami5dumj1X1aOLaXk82RnZ6cm1KytrdWyhw8f4ufnh6mpKXnz5qVt27Y8evRI3X/9+vW4u7ur8dSsWZPY2FjGjBnD0qVL2bx5s3pMBw4c4M2bN/Ts2RN7e3sMDQ1xdHRk8uTJan3W1tZUqlSJVatWZeNqEEIIIcSXJtsJDV1dXR48eJCqPDo6+pN/gyPEf8WhQ4fw8vLK9n5eXl7o6uoSFBREQkICT58+Zfny5dSuXZtcuXJluO+UKVOwtramdOnSTJw4Uf129X0YGRkBSSMtdu7cSZs2bejduzdXrlzh559/Jjg4mIkTJ2rtM3r0aOrXr8/FixcZN24cUVFRmJubM2vWLKKiomjevDl3797Fz8+PsmXLcv78eRYsWMDixYuZMGGCVl1Lly5FT0+Po0eP8vPPP6vlY8aMYd68eRw7dow7d+7QrFkzZs2axW+//cbWrVvZvXs3c+fOfedjvXjxIj4+PjRq1IgLFy6wZs0ajhw5Qs+ePbX2mTZtGiVLliQkJISRI0emqjMrxzlo0CD279/P77//zq5duzhw4ECGawUkJiZSp04djh07xooVK7hy5Qo//PBDtn/fjh07lmbNmnHhwgX8/Pxo3bo1MTExODg4sGHDBiBpBEtUVBSzZ88GYMSIEQQFBbFgwQIuX75Mv379aNOmDQcPHtSqe/DgwUyePJmrV69SqlQpIOlcmpiYcPLkSaZOncq4cePYvXu3uo+Ojg5z5szh0qVLLF26lH379jF48OBsHdPbcuXKhZ6eHnFxcbx48YLq1atjamrKoUOHOHLkCKampvj6+mr9rOzfv59bt26xf/9+li5dSnBwsNY0qQ4dOnD06FFWr17NhQsXaNq0Kb6+vty4cYNKlSoxa9YszM3NiYqKIioqioEDB6r7pnW9mJmZERwczJUrV5g9ezaLFi1i5syZADRv3pwBAwZQokQJtb7mzZunOs4XL17g6+uLpaUlp0+fZt26dezZsyfV9ZrZsWVVVFQUVatWpXTp0pw5c4YdO3bwzz//0KxZM/Xzli1bEhgYyNWrVzlw4ACNGjVCURQGDhxIs2bN8PX1VY+pUqVKzJkzhy1btrB27VrCwsJYsWJFqmkt5cqV4/Dhw+nG9fr1a549e6b1EkIIIcSXJdvjghVFSbP89evXH3x4uBA5RUREBPny5cv2fo6OjuzatYumTZvy3XffkZCQQMWKFVNNXXlbnz59KFOmDJaWlpw6dYphw4YRHh7Or7/++q6HwN9//820adMoUKAAzs7OdO/enaFDh6ojr4oUKcL48eMZPHgwo0ePVvdr1aoVgYGBWnVpNBosLCzUYfLz58/HwcGBefPmodFocHV15d69ewwZMoRRo0aho5OUW3VycmLq1KlqPffv3wdgwoQJVK5cGYCOHTsybNgwbt26pY58adKkCfv378/ynPvo6GjGjh2LmZkZ5cqVY+DAgbRq1UpdmLNYsWLMmTOHqlWrsmDBAgwNDQGoUaOG1g3r2wvAZnacL168YPHixSxbtoxatWoBSTf+GU3N2bNnD6dOneLq1as4OzsDpDviJyMBAQG0bNkSgEmTJjF37lxOnTqFr6+vuv6Rra2tuh5EbGwsP/74I/v27VNHHhUpUoQjR47w888/U7VqVbXucePGqceTrFSpUup1UqxYMebNm8fevXvV7VIuglq4cGHGjx9Pt27dmD9/fraPDZL+Bk2bNo1nz57h7e3N6tWr0dHR4ddff1WnSQYFBZE7d24OHDhA7dq1+X/s3XlcTun/+PHXXWlPRZslkhSlVGIsM9lFthl7CGMbM5axNoytMnayjuVjItuIsQ3GOigpQkRIkiVjMvZ9r35/+HW+bq03GWPm/Xw87seDc859netc93Xu7vM+7+s68CorZd68eWhra1OxYkWaNWvGnj176N27NykpKaxevZo//vhDOb+HDRvGjh07WLp0KRMnTsTU1FTJdHrTm/0FXgWJstjZ2TF06FDWrFlDQEAABgYGGBsbo6Ojk+cQk1WrVvHkyROWL1+OkZERAPPmzaNFixZMmTIFa2vrfI9NEwsWLMDT05OJEycqy5YsWYKtrS3nzp3j4cOHvHz5ktatW1O2bFkAXF1dlW0NDAx49uyZ2jGlpqZSoUIFPv30U1QqlfK+15UqVSrPSZYnTZpEUFCQRscihBBCiH+XAgc05syZA/xf6vLr6e3p6ens37+fihUrFn4NhfgIPHnyRLno1cS1a9fo1asX3bp1w8/PjwcPHjB27Fjatm3L7t27c52vZvDgwcq/3dzcMDc3p23btkrWRkHdu3cPY2NjMjMzefz4MZ6enmzYsAFdXV3i4uI4cuSIWkZGeno6T58+5fHjxxgaGgIUKDMlMTGRmjVrqh1P7dq1efjwIX/88QdlypTJs6ysu/4A1tbWSrr+68sOHz6cbz2yAgePHj2iQoUK/PLLL1hZWREXF8f58+dZtWqVsm1mZiYZGRlcvHhRGcOf37Hmd5x37tzh+fPnakOTihUrhpOTU65lxsfHK0Gmd/F6GxoZGWFiYpJjtl2WM2fO8PTp02yBiufPn+Ph4aG2LKd2eX1/ACVKlFDb3759+5g4cSJnzpzh/v37vHz5kqdPn/Lo0SPlIr0g/Pz80NbW5smTJ5iamjJ9+nSaNm1Kv379OH/+PCYmJmrbP336lJSUFOX/Li4uatkuJUqUICEhAXg1JCwzMzNb2z979qxA51lO7bJu3TpmzZrF+fPnlUBA0aJFC3y88KqfValSRa2dateuTUZGBklJSUpAI69j00RcXBz79u3LcVhbSkoKjRs3pkGDBri6uuLj40Pjxo1p27Yt5ubmuZbZvXt3GjVqhJOTE02aNKF58+ZKkCmLgYFBnkPJRo4cyZAhQ5T/379/H1tbW42PTwghhBAfrwIHNLJSYjMzM1m4cKHajyRdXV3s7OxYuHBh4ddQiI+AhYUFd+7c0fh9P/74I0WLFlXLSli5ciW2trbExsZSo0aNApWTtd358+c1CmiYmJhw7NgxtLS0sLa2VrtAysjIICgoiNatW2d73+vBm4JcfGZmZmYLzmRle72+PLeyXh9+o1Kpsg3HUalUZGRk5FuPqKgoihYtiqWlpdpFZEZGBl999RUDBw7M9p6sYEte9cuS33HmluGWl6yhMe9K0zbLWvfbb79RqlQptXV6enpq/8+pXfLa3+XLl/H19aVv376MHz+eYsWKceDAAXr27Jnv3DFvmjlzJg0bNqRo0aJYWVmp1b9q1apqQaosr0+6mlc9MzIy0NbWJi4uLtsQn4LMWfNmuxw6dIiOHTsSFBSEj48PpqamhIeH5ziPSl5y6mev1z/L254nb8rIyFCyP95UokQJtLW12b17NzExMezatYu5c+cyatQoYmNjKVeuXI5lenp6cvHiRbZv387vv/9O+/btadiwodocLbdv385zglw9Pb1sfVEIIYQQ/y0FDmhkPcqxXr16bNiwIc87L0L813h4eLBy5UqN3/f48eNsF0pZ/9fkwiPriSglSpTQaP9aWlo4ODjkuM7T05OkpKRc12vC2dmZ9evXq12IxcTEYGJiku1i+X0qV65cjo/Y9PT05PTp0+98rPkdp7m5OUWKFOHQoUNKoOTOnTucO3dObQjH69zc3Pjjjz84d+7cO2dp5CZruGB6errasejp6ZGamppr3d7W0aNHefnyJTNmzFCGG61du/atyrKxscnxc/P09GTNmjVYWVlpnAGRxcPDg/T0dK5fv85nn32W4za6urpq7ZaX6OhoypYty6hRo5Rlly9f1rg8Z2dnli1bppbNEh0djZaW1nvpI56enqxfvx47O7tcn2CjUqmoXbs2tWvXZuzYsZQtW5aNGzcyZMiQXI+paNGidOjQgQ4dOtC2bVuaNGnC7du3lSFQp06dypYNJIQQQgjxOo0nBd23b58EM4R4g4+PD6dPn86WpXH+/Hni4+O5du0aT548IT4+nvj4eGVSwmbNmnHkyBGCg4NJTk7m2LFjfPnll5QtW1b5IX/48GEqVqzI1atXATh48CAzZ84kPj6eixcvsnbtWuVxq69nE7yrsWPHsnz5cgIDAzl9+jSJiYmsWbNGbQ6Agvrmm2+4cuUKAwYM4OzZs/z666+MGzeOIUOGKBe0H9J3333HwYMH6devH/Hx8SQnJ7N582YGDBigUTn5HaexsTE9e/Zk+PDh7Nmzh1OnTtG9e/c826BOnTp4e3vTpk0bdu/erdzV3rFjx7setqJs2bKoVCq2bt3KjRs3ePjwISYmJgwbNozBgwezbNkyUlJSOH78OD/++CPLli17p/2VL1+ely9fMnfuXC5cuMCKFSsKPcOvc+fOWFhY0KpVK6Kiorh48SKRkZF8++23/PHHHwUqw9HRkc6dO9O1a1c2bNjAxYsXOXLkCFOmTFHmubGzs+Phw4fs2bOHmzdv5jlEwsHBgdTUVMLDw0lJSWHOnDls3LhRbRs7OzsuXrxIfHw8N2/e5NmzZzkem76+Pt26dePUqVPs27ePAQMG4O/vrww3KUz9+vXj9u3b+Pn5cfjwYS5cuMCuXbvo0aMH6enpxMbGMnHiRI4ePUpqaiobNmzgxo0bylAtOzs7Tp48SVJSEjdv3uTFixfMnDmT8PBwzp49y7lz5/jll1+wsbFRCzhGRUVlG4YihBBCCPE6ja8k0tPTCQ0NpVOnTjRs2JD69eurvYT4L3J1dcXLyyvbXeZevXrh4eHBokWLOHfuHB4eHnh4ePDnn38CryYN/Pnnn9m0aRMeHh40adIEPT09duzYoQw1ePz4MUlJSUoqvp6eHmvWrKFu3bo4OzszduxYevfune3xhnZ2dtkelakJHx8ftm7dyu7du6lWrRo1atQgJCQkx8n78lOqVCm2bdvG4cOHqVKlCn379qVnz55vFRx5H9zc3IiMjCQ5OZnPPvsMDw8PxowZo3HGS0GOc9q0aXh7e9OyZUsaNmzIp59+StWqVfMsd/369VSrVg0/Pz+cnZ0JCAgocFZAQesdFBTEiBEjsLa2Vp6WMX78eMaOHcukSZOoVKkSPj4+bNmyJddhBAXl7u5OSEgIU6ZMoXLlyqxatUrtkZ2FwdDQkP3791OmTBlat25NpUqV6NGjB0+ePNEoY2Pp0qV07dqVoUOH4uTkRMuWLYmNjVXmaqhVqxZ9+/alQ4cOWFpaqg0fe1OrVq0YPHgw/fv3x93dnZiYmGxPy2nTpg1NmjShXr16WFpa5vjYUkNDQ3bu3Mnt27epVq0abdu2pUGDBsybN6/Ax6WJkiVLEh0dTXp6Oj4+PlSuXJlvv/0WU1NTtLS0KFq0KPv378fX1xdHR0dGjx7NjBkzaNq0KQC9e/fGyckJLy8vLC0tiY6OxtjYmClTpuDl5UW1atW4dOkS27ZtU4J7Bw8e5N69e7Rt2/a9HJMQQggh/h1UmRoO6u7fvz9hYWE0a9aMEiVKZBvHmzXXhhD/Ndu2bWPYsGGcOnXqg2cdPHnyhGLFirFt2zbq1av3QesihBCaateuHR4eHnz//fcFfs/9+/cxNTXFdtBatPQM32PthBBC/BdcmtzsQ1fhPyHr7/e9e/feapiwxo9tDQ8PZ+3atfj6+mq8MyH+zXx9fUlOTubq1asffKb9yMhI6tevL8EMIcRH59mzZ1SpUkXtaU5CCCGEEDnROEOjZMmSREREvLfJ6YQQQgghNCUZGkIIIQqTZGj8Pd41Q0PjgMaMGTO4cOEC8+bNy/WxcUIIIYQQf6d3/UEkhBBCiL/f3z7k5MCBA+zbt4/t27fj4uKS7Tn3GzZs0LgSQgghhBBCCCGEEJrQOKBhZmbGF1988T7qIoQQQgghhBBCCFEgGgc0li5d+j7qIYQQQgghhBBCCFFgGgc0sty4cYOkpCRUKhWOjo5YWloWZr2EEEIIIYQQQgghcqWl6RsePXpEjx49KFGiBN7e3nz22WeULFmSnj178vjx4/dRRyGEEEIIIYQQQgg1Ggc0hgwZQmRkJFu2bOHu3bvcvXuXX3/9lcjISIYOHfo+6iiEEEIIIYQQQgihRuPHtlpYWLBu3Trq1q2rtnzfvn20b9+eGzduFGb9hBBCCCHyJY9tFUIIIT4+7/r3W+MMjcePH2NtbZ1tuZWVlQw5EUIIIYQQQgghxN9C44BGzZo1GTduHE+fPlWWPXnyhKCgIGrWrFmolRNCCCGEEEIIIYTIicZPOZk9ezZNmjShdOnSVKlSBZVKRXx8PPr6+uzcufN91FEIIYQQQgghhBBCjcZzaMCrjIyVK1dy9uxZMjMzcXZ2pnPnzhgYGLyPOgohhBBC5Enm0BBCCCE+Pu/691vjDA0AAwMDevfu/TZvFUIIIYQQQgghhHhnBZ5DIy4ujnr16nH//v1s6+7du0e9evU4ceJEoVZOCCGEEEIIIYQQIicFDmjMmDGD+vXr55gGYmpqSqNGjZg2bVqhVk4IIYQQQgghhBAiJwUOaMTGxtKqVatc17do0YKYmJhCqZQQQgghhBBCCCFEXgoc0Lh69SomJia5rjc2NiYtLa1QKiWEEEIIIYQQQgiRlwIHNCwtLUlKSsp1/dmzZ7GwsCiUSgkhhBBCCCGEEELkpcABjYYNGzJhwoQc12VmZjJx4kQaNmxYaBUTQgghhBBCCCGEyI0qMzMzsyAbpqSkULVqVZycnBg6dChOTk6oVCoSExOZMWMG586d4+jRozg4OLzvOgshhBBCqMl6jr3toLVo6Rl+6OoIIYT4B7o0udmHroJ4Q9bf73v37uX4AJL86BR0w/Lly/P777/TvXt3OnbsiEqlAl5lZzg7O7N7924JZgghhBBCCCGEEOJvUeCABoCXlxenTp0iPj6e5ORkMjMzcXR0xN3d/T1VTwghhBBCCCGEECI7jQIaWdzd3SWIIYQQQgghhBBCiA+mwJOCFkRwcDD79+8vzCL/tW7duoWVlRWXLl360FUR72jTpk04ODigra3NoEGDCq1clUrFpk2bCq08IXISERGBSqXi7t27H7oq/0l169Yt1O+N3ERHR+Pq6kqRIkX4/PPP3/v+3sWzZ88oU6YMcXFxH7oqQgghhPiHK9SAxtKlS2nSpAktWrQozGL/lSZNmkSLFi2ws7PLtu7WrVuULl0634uMS5cuoVKpcnz98ssvBa5L1gWNubk5T58+VVt3+PBhpcx/i8DAQOWYdHR0sLCwwNvbm1mzZvHs2TONy/vqq69o27YtV65cYfz48e+hxv/3WcfHxxf4PY0bN0ZbW5tDhw69lzq9b3/Xhd7byO28Cw8PL3AZ3bt3/0dcWNaqVYu0tDRMTU3f+76cnJzQ1dXl6tWr731fWd7m3PknSU9PZ9KkSVSsWBEDAwOKFStGjRo1WLp0qUblDBkyBHd3dy5evEhYWBiBgYH/2ExLPT09hg0bxnffffehqyKEEEKIf7hCDWhcvHiR27dv079//8Is9l/nyZMnhIaG0qtXrxzX9+zZEzc3t3zLsbW1JS0tTe0VFBSEkZERTZs21bheJiYmbNy4UW3ZkiVLKFOmjMZl/dO5uLiQlpZGamoq+/bto127dkyaNIlatWrx4MGDApfz8OFDrl+/jo+PDyVLlsTExOQ91rrgUlNTOXjwIP379yc0NPRDV+dfaenSpdnOvw8RoHj+/Plbv/fFixfo6upiY2Pz3oOWBw4c4OnTp7Rr146wsLD3uq9/k8DAQGbNmsX48eM5c+YM+/bto3fv3ty5c0ejclJSUqhfvz6lS5fGzMzs/VS2AF68eFGg7Tp37kxUVBSJiYnvuUZCCCGE+JgVakADQF9fHx8fn8Iu9l9l+/bt6OjoULNmzWzrFixYwN27dxk2bFi+5Whra2NjY6P22rhxIx06dMDY2FjjenXr1o0lS5Yo/3/y5Anh4eF069ZNbbtbt27h5+dH6dKlMTQ0xNXVldWrV6ttU7duXQYOHEhAQADFihXDxsaGwMBAtW1CQkJwdXXFyMgIW1tbvvnmGx4+fKi2zeLFi7G1tcXQ0JAvvviCkJCQbD/Gt2zZQtWqVdHX18fe3p6goCBevnyZ57Hq6OhgY2NDyZIlcXV1ZcCAAURGRnLq1CmmTJmibPf8+XMCAgIoVaoURkZGfPLJJ0RERACvMluyAhj169dHpVIRERFRoPaxs7Nj1qxZasvc3d2ztVGWcuXKAeDh4YFKpaJu3bp5Ht/SpUtp3rw5X3/9NWvWrOHRo0dq6+/evUufPn2wtrZGX1+fypUrs3XrVmV9dHQ0derUwdDQEHNzc3x8fJQLqGfPnjFw4ECsrKzQ19fn008/5ciRI8p7w8LCsn1GmzZtUrtgzro7vGLFCuzs7DA1NaVjx45KMKl79+5ERkYye/ZsJfvh0qVL3Llzh86dO2NpaYmBgQEVKlTI8051fnXNyk7as2cPXl5eGBoaUqtWLZKSkvJsXwAzM7Ns55++vr5aG+zcuZNKlSphbGxMkyZNSEtLU45/2bJl/Prrr8rxZfWrq1ev0qFDB8zNzSlevDitWrVSG5qWldkxadIkSpYsiaOjI/CqT40fP55OnTphbGxMyZIlmTt3rlqdVSoVCxcupFWrVhgZGfHDDz9kG3Jy+fJlWrRogbm5OUZGRri4uLBt2zaljDNnzuDr64uxsTHW1tb4+/tz8+bNfNsrNDSUTp064e/vz5IlS3jzieHz58+nQoUK6OvrY21tTdu2bZV169atw9XVFQMDA4oXL07Dhg3V+vTSpUupVKkS+vr6VKxYkfnz5yvrcjt3IiIiqF69OkZGRpiZmVG7dm0uX76ca/2/++47HB0dMTQ0xN7enjFjxqhdnOfXpwEePXpE165dMTY2pkSJEsyYMSPfdtuyZQvffPMN7dq1o1y5clSpUoWePXsyZMgQZZu8+nlWhsqtW7fo0aMHKpWKsLAwgoKCOHHihNL/wsLCGDp0qFqG5axZs1CpVPz222/KMicnJxYtWgTAkSNHaNSoERYWFpiamlKnTh2OHTumVv+c+lzWceX1vV28eHFq1aqV7btTCCGEEOJ1Ggc0kpOTmT59Ov3792fAgAGEhIRw4cKF91G3f639+/fj5eWVbfmZM2cIDg5m+fLlaGlpHmuKi4sjPj6enj17vlW9/P39iYqKIjU1FYD169djZ2eHp6en2nZPnz6latWqbN26lVOnTtGnTx/8/f2JjY1V227ZsmUYGRkRGxvL1KlTCQ4OZvfu3cp6LS0t5syZw6lTp1i2bBl79+4lICBAWR8dHU3fvn359ttviY+Pp1GjRkyYMEFtHzt37qRLly4MHDiQM2fOsGjRIsLCwrJtVxAVK1akadOmbNiwQVn25ZdfEh0dTXh4OCdPnqRdu3Y0adKE5ORktQvf9evXk5aWRq1atQrcPpo4fPgwAL///jtpaWlqdXxTZmYmS5cupUuXLlSsWBFHR0fWrl2rrM/IyKBp06bExMSwcuVKzpw5w+TJk9HW1gYgPj6eBg0a4OLiwsGDBzlw4AAtWrQgPT0dgICAANavX8+yZcs4duwYDg4O+Pj4cPv2bY2OKSUlhU2bNrF161a2bt1KZGQkkydPBmD27NnUrFmT3r17K9kPtra2jBkzhjNnzrB9+3YSExNZsGABFhYWue6joHUdNWoUM2bM4OjRo+jo6NCjRw+NjiUnjx8/Zvr06axYsYL9+/eTmpqqBCqHDRtG+/btlSBHVt95/Pgx9erVw9jYmP3793PgwAElGPJ6JsaePXtITExk9+7daoGoadOm4ebmxrFjxxg5ciSDBw9WO+cAxo0bR6tWrUhISMjxOPv168ezZ8/Yv38/CQkJTJkyRQmQpqWlUadOHdzd3Tl69Cg7duzgr7/+on379nm2xYMHD/jll1/o0qULjRo14tGjR0oAB+Do0aMMHDiQ4OBgkpKS2LFjB97e3so+/fz86NGjB4mJiURERNC6dWslILJ48WJGjRrFhAkTSExMZOLEiYwZM4Zly5YBOZ87L1++5PPPP6dOnTqcPHmSgwcP0qdPnzyzVExMTAgLC+PMmTPMnj2bxYsXM3PmTLVt8urTAMOHD2ffvn1s3LiRXbt2ERERke88ETY2Nuzdu5cbN27kuk1e/Twrk69o0aLMmjWLtLQ0OnTowNChQ5VMtaxldevWJSoqioyMDAAiIyOxsLAgMjISgGvXrnHu3Dnq1KmjfK7dunUjKiqKQ4cOUaFCBXx9fbNlub3Z5wr6vV29enWioqJyPe5nz55x//59tZcQQggh/ls0esrJpEmTGDt2LBkZGVhZWZGZmcmNGzcYMWIEEydOLFBWgXh1x6xkyZJqy549e4afnx/Tpk2jTJkybxUkCg0NpVKlStSqVeut6mVlZUXTpk0JCwtj7NixLFmyJMcLnlKlSql91gMGDGDHjh388ssvfPLJJ8pyNzc3xo0bB0CFChWYN28ee/bsoVGjRgBq8yOUK1eO8ePH8/XXXyt3V+fOnUvTpk2VfTk6OhITE6N2ATdhwgRGjBihZJHY29szfvx4AgIClH1romLFiuzatQt4dXGyevVq/vjjD+XzGjZsGDt27GDp0qVMnDgRKysrACULRZP20YSlpSXw6q5l1n5y8/vvv/P48WMlU6pLly6Ehoby5ZdfKusPHz5MYmKicnff3t5eef/UqVPx8vJSu8vt4uICvLrDvGDBAsLCwpRhTYsXL2b37t2EhoYyfPjwAh9TRkYGYWFhSpaLv78/e/bsYcKECZiamqKrq4uhoaHa8aampuLh4aEEBHOagyaLJnWdMGGCcpE2YsQImjVrxtOnT5WMi5z4+fkpQaAsJ0+eVNryxYsXLFy4kPLlywPQv39/goODATA2NsbAwIBnz56pHd/KlSvR0tLip59+Ui6uly5dipmZGRERETRu3BgAIyMjfvrpJ3R1ddX2X7t2bUaMGAG8Ol+io6OZOXOmcs4BdOrUSe28vnjxoloZqamptGnTBldXV0C9byxYsABPT08mTpyoLFuyZAm2tracO3dO6U9vCg8Pp0KFCko/6tixI6GhodSrV0/Zp5GREc2bN8fExISyZcvi4eEBvApovHz5ktatW1O2bFkApW4A48ePZ8aMGbRu3Rp49V2SdZHcrVu3HM+d27dvc+/ePZo3b658PpUqVcqx7llGjx6t/NvOzo6hQ4eyZs0atSBsXn364cOHhIaGsnz5cuXzWLZsGaVLl85zvyEhIbRt2xYbGxtcXFyoVasWrVq1Uvp0Qfp51pAiU1NTpQ2MjY2VTLUs3t7ePHjwgOPHj+Pp6UlUVBTDhg1TAqj79u3D2tqaihUrAq8y0163aNEizM3NiYyMpHnz5sryN/ucv79/gb63S5UqlefE2ZMmTSIoKCjP9hNCCCHEv1uB0wD27dvH6NGjGTVqFDdv3iQtLY1r164pAY0RI0bIE04K6MmTJ9kulEaOHEmlSpXo0qXLW5f5888/v3V2RpYePXoQFhbGhQsXOHjwIJ07d862TXp6OhMmTMDNzY3ixYtjbGzMrl27lMyOLG/OA1KiRAmuX7+u/H/fvn00atSIUqVKYWJiQteuXbl165aSSp6UlET16tXVynjz/3FxcQQHB2NsbKy8su7qP378WOPjz8zMVC4kjx07RmZmJo6OjmrlR0ZGkpKSkmsZBW2f9yU0NJQOHTqgo/MqXunn50dsbKySTRIfH0/p0qVzvfjMytDISUpKCi9evKB27drKsiJFilC9enWNx7rb2dmpzTnyZv/Iyddff014eDju7u4EBAQQExOT67aa1PX1vlqiRAmAfOsyc+ZM4uPj1V62trbKekNDQ+ViuaDHFxcXx/nz5zExMVH6W7FixXj69Klan3N1dc0WzACyDWOrWbNmtmPNKTvsdQMHDuSHH36gdu3ajBs3jpMnT6rVb9++fWrnQ9bFbV7nRGhoqNp3W5cuXdiwYYMyzKVRo0aULVsWe3t7/P39WbVqlXL+VqlShQYNGuDq6kq7du1YvHixMvzpxo0bXLlyhZ49e6rV6YcffsizPsWKFaN79+74+PjQokULZs+erQwHys26dev49NNPsbGxwdjYmDFjxmQ7p/Pq0ykpKTx//lztMypWrBhOTk557tfZ2ZlTp05x6NAhvvzyS/766y9atGihzMFUmOekqakp7u7uREREkJCQgJaWFl999RUnTpzgwYMHREREKIE/eHWO9O3bF0dHR0xNTTE1NeXhw4fZ2uXNPlfQ720DA4M8v8dHjhzJvXv3lNeVK1c0Ol4hhBBCfPwKHNBYuHAhvXr1IjAwEHNzc2V5sWLFCA4OpkePHixYsOC9VPLfxsLCItuEbnv37uWXX35BR0cHHR0d5YLSwsKiQJkG69at4/Hjx3Tt2vWd6ubr68vTp0/p2bMnLVq0oHjx4tm2mTFjBjNnziQgIIC9e/cSHx+Pj49PtskJixQpovZ/lUqlpDJfvnwZX19fKleuzPr164mLi+PHH38E/m/SuNeDC1neHHefkZFBUFCQ2kVlQkICycnJed5dz01iYqIy5j4jIwNtbW1lKE/WKzExkdmzZ+daRkHaR0tLK9uxFHSyvLzcvn2bTZs2MX/+fKUvlSpVipcvXyrzoxgYGORZRl7rs+qc0+eStaygx5ZX/8hN06ZNuXz5MoMGDeLPP/+kQYMGuWaGFaSuOdUla11+dbGxscHBwUHt9Xo5OR3fm+3ypoyMDKpWrZotUHLu3Dk6deqkbGdkZJRnOW/u93X5vbdXr15cuHABf39/EhIS8PLyUubiyMjIoEWLFtnql5ycrAwRedOZM2eIjY0lICBA6ZM1atTgyZMnyvwIJiYmHDt2jNWrV1OiRAnGjh1LlSpVuHv3Ltra2uzevZvt27fj7OzM3LlzcXJy4uLFi8pntHjxYrX6ZAUA8rJ06VIOHjxIrVq1WLNmDY6Ojrm+59ChQ3Ts2JGmTZuydetWjh8/zqhRozT6zsvvs8+LlpYW1apVY/DgwWzcuJGwsDBCQ0O5ePGiRv28IOrWrUtERASRkZHUqVMHc3NzXFxciI6OJiIiQm3+nu7duxMXF8esWbOIiYkhPj6e4sWLZ2uXN/tcQb+3b9++rWTY5ERPT4+iRYuqvYQQQgjx31LggMbhw4fx9/fPdb2/v/9H+3jIv5uHhwdnzpxRW7Z+/XpOnDih/Lj76aefAIiKiqJfv375lhkaGkrLli3z/PFXENra2vj7+xMREZHrPAJRUVG0atWKLl26UKVKFezt7UlOTtZoP0ePHuXly5fMmDGDGjVq4OjoyJ9//qm2TcWKFZXx76+/73Wenp4kJSVlu7B0cHDQeB6Ss2fPsmPHDtq0aQO8+pzS09O5fv16trLzGvZRkPaxtLRUuyN8//79bKn/r8u6G581j0VuVq1aRenSpdX6Unx8PLNmzWLZsmW8fPkSNzc3/vjjD86dO5djGW5ubuzZsyfHdQ4ODujq6nLgwAFl2YsXLzh69KiSsm9pacmDBw/UJm18m0dm6urq5ni8lpaWdO/enZUrVzJr1iz+97//vXVdP6Scjs/T05Pk5GSsrKyy9bmCPFb1ze/gQ4cOKRkUmrC1taVv375s2LCBoUOHsnjxYqV+p0+fxs7OLlv9cguUhIaG4u3tna1PBgQEqD2BR0dHh4YNGzJ16lROnjzJpUuX2Lt3L/DqYr127doEBQVx/PhxdHV12bhxI9bW1pQqVYoLFy5kq09WYDKvc8fDw4ORI0cSExND5cqV+fnnn3M8hujoaMqWLcuoUaPw8vKiQoUKeU4gmpOsgNfrn9GdO3dyPQ/z4uzsDLwabvK2/Ty38ytrHo29e/cqwYs6deoQHh6uNn8GvPquGzhwIL6+vri4uKCnp1egCWIL+r196tQpZeiREEIIIUROCjyHxl9//ZXnePVy5cpx7dq1wqjTv56Pjw8jR47kzp07SrbL66npgPKjsFKlSsoTI65evUqDBg1Yvny52tCL8+fPs3//frUnEbyL8ePHM3z48ByzM+DVD/P169cTExODubk5ISEhXLt2TaOLxPLly/Py5Uvmzp1LixYtiI6OZuHChWrbDBgwAG9vb0JCQmjRogV79+5l+/btancdx44dS/PmzbG1taVdu3ZoaWlx8uRJEhISlNn0c/Ly5UuuXbtGRkYGt27dIiIigh9++AF3d3dlbgVHR0c6d+5M165dmTFjBh4eHty8eZO9e/fi6uqKr6/vW7dP/fr1CQsLU54mMWbMmGzzMbzOysoKAwMDduzYQenSpdHX18/xAjc0NJS2bdtSuXJlteVly5blu+++47fffqNVq1Z4e3vTpk0bQkJCcHBw4OzZs6hUKpo0acLIkSNxdXXlm2++oW/fvujq6iqPtrWwsODrr79m+PDhFCtWjDJlyjB16lQeP36sDHf65JNPMDQ05Pvvv2fAgAEcPnz4rR7TaWdnR2xsLJcuXVKGXgQGBlK1alVcXFx49uwZW7duzbXfGRkZ5VvXd3H37t1s33kmJiYFzp6ws7Nj586dJCUlUbx4cUxNTencuTPTpk2jVatWBAcHU7p0aVJTU9mwYQPDhw/Pd76F6Ohopk6dyueff87u3bv55Zdf1J5QURCDBg2iadOmODo6cufOHfbu3au0cb9+/Vi8eDF+fn4MHz4cCwsLzp8/T3h4OIsXL87Wh1+8eMGKFSsIDg7O1id79erF1KlTOXHiBFeuXOHChQt4e3tjbm7Otm3byMjIwMnJidjYWPbs2UPjxo2xsrIiNjaWGzduKHUKDAxk4MCBFC1alKZNm/Ls2TOOHj3KnTt3GDJkSI7nzu3bt/nf//5Hy5YtKVmyJElJSZw7dy7XDDcHBwdSU1MJDw+nWrVq/Pbbb9kecZ0fY2NjevbsqXy3WltbM2rUqHwDr23btqV27drUqlULGxsbLl68yMiRI3F0dKRixYro6Oi8VT+3s7Pj4sWLyhA0ExMT9PT0lHk0tmzZonyH1q1blzZt2mBpaakEU7LaZcWKFXh5eXH//n2GDx+ebwYYFPx7OyoqivHjx+dbnhBCCCH+uwp8C/vp06c5jtnOUqRIkWxppiJnrq6ueHl5qT15oiBevHhBUlJStjHFS5YsoVSpUsqEgW+qW7cu3bt3L/B+dHV1sbCwyDVdecyYMXh6euLj40PdunWxsbHh888/L3D58OoRpSEhIUyZMoXKlSuzatUqJk2apLZN7dq1WbhwISEhIVSpUoUdO3YwePBgtZRkHx8ftm7dyu7du6lWrRo1atQgJCREmTwwN6dPn6ZEiRKUKVOGunXrsnbtWkaOHElUVJTaI2+XLl1K165dGTp0KE5OTrRs2ZLY2Fi1uRLepn1GjhyJt7c3zZs3x9fXl88//zxbUOt1Ojo6zJkzh0WLFlGyZElatWqVbZu4uDhOnDihZJi8zsTEhMaNGyt3xNevX0+1atXw8/PD2dmZgIAA5W6to6Mju3bt4sSJE1SvXp2aNWvy66+/KnNyTJ48mTZt2uDv74+npyfnz59n586dSnCuWLFirFy5km3btimPrM3tcbR5GTZsGNra2jg7O2NpaUlqaiq6urqMHDkSNzc3vL290dbWJjw8PNcy8qvru/jyyy8pUaKE2uvNx6TmpXfv3jg5OeHl5YWlpSXR0dEYGhqyf/9+ypQpQ+vWralUqRI9evTgyZMnBUqnHzp0KHFxcXh4eCiTZWr6GO309HT69etHpUqVaNKkCU5OTsoEsSVLliQ6Opr09HR8fHyoXLky3377LaampjlemG/evJlbt27xxRdfZFtXoUIFXF1dCQ0NxczMjA0bNlC/fn0qVarEwoULWb16NS4uLhQtWpT9+/fj6+uLo6Mjo0ePZsaMGcoEmL169eKnn34iLCwMV1dX6tSpQ1hYmJKhkdO5Y2hoyNmzZ2nTpg2Ojo706dOH/v3789VXX+XYJq1atWLw4MH0798fd3d3YmJiGDNmjEbtCq+eQuPt7U3Lli1p2LAhn376KVWrVs3zPT4+PmzZsoUWLVrg6OhIt27dlMmLC3pO5qRNmzY0adKEevXqYWlpqQz/MTU1xcPDg2LFiinBi88++4yMjAy17Ax49bfnzp07eHh44O/vrzw6Nj8F+d4+ePAg9+7dU3t8rxBCCCHEm1SZBRzYq6WlxQ8//KB2sfe6Bw8eMHbs2HxT4sUr27ZtY9iwYZw6deqtHtGqCTs7OwIDAzUKavxT9e7dm7Nnz+b5KD8h/ovs7OwYNGiQ2tODhPhYtWvXDg8PD77//vsCv+f+/fuYmppiO2gtWnqG77F2QgghPlaXJjf70FUQb8j6+33v3r23mg+rwENOypQpo4yjzmsbUTC+vr4kJydz9erVPO/2v6uzZ88qTxD5GE2fPp1GjRphZGTE9u3bWbZsmdrjRIUQQvy7PHv2jCpVqjB48OAPXRUhhBBC/MMVOKCR17Pgxdv59ttv3/s+KlasSEJCwnvfz/ty+PBhpk6dyoMHD7C3t2fOnDnK4wqFEEL8++jp6TF69OgPXQ0hhBBCfAQKPORECCGEEOKf6l1TVoUQQgjx9/vbhpzMmTOnQNsNHDhQ40oIIYQQQgghhBBCaKLAGRpZM8bnWZhKxYULF965UkIIIYQQmpAMDSGEEOLj87dlaFy8eFHjwoUQQgghhBBCCCHeh/f7vFAhhBBCCCGEEEKI90ACGkIIIYQQQgghhPjoSEBDCCGEEEIIIYQQHx0JaAghhBBCCCGEEOKjIwENIYQQQgghhBBCfHQK9JST+/fvF7hAeVSaEEIIIYQQQggh3rcCBTTMzMxQqVR5bpOZmYlKpSI9Pb1QKiaEEEIIIYQQQgiRmwIFNPbt2/e+6yGEEEIIIYQQQghRYAUKaNSpU+d910MIIYQQQgghhBCiwAoU0HjT3bt3CQ0NJTExEZVKhbOzMz169MDU1LSw6yeEEEIIIYQQQgiRjcZPOTl69Cjly5dn5syZ3L59m5s3bxISEkL58uU5duzY+6ijEEIIIYQQQgghhBpVZmZmpiZv+Oyzz3BwcGDx4sXo6LxK8Hj58iW9evXiwoUL7N+//71UVAghhBAiN/fv38fU1JR79+7JE9eEEEKIj8S7/v3WOKBhYGDA8ePHqVixotryM2fO4OXlxePHjzWuhBBCCCHEu5CAhhBCCPHxede/3xoPOSlatCipqanZll+5cgUTExONKyCEEEIIIYQQQgihKY0DGh06dKBnz56sWbOGK1eu8McffxAeHk6vXr3w8/N7H3UUQgghhBBCCCGEUKPxU06mT5+OSqWia9euvHz5EoAiRYrw9ddfM3ny5EKvoBBCCCFEQVUetxMtPcMPXQ0hhBD/QJcmN/vQVRCFTKOARnp6OgcPHmTcuHFMmjSJlJQUMjMzcXBwwNBQfjwIIYQQQgghhBDi76FRQENbWxsfHx8SExMpVqwYrq6u76teQgghhBBCCCGEELnSeA4NV1dXLly48D7qIoQQQgghhBBCCFEgGgc0JkyYwLBhw9i6dStpaWncv39f7SWEEP9Gt27dwsrKikuXLn3oqmike/fufP7554VWnkqlYtOmTbmuv3TpEiqVivj4+ELbp/jvadu2LSEhIR+6GkIIIYT4h9M4oNGkSRNOnDhBy5YtKV26NObm5pibm2NmZoa5ufn7qKMQQnxwkyZNokWLFtjZ2SnLVCpVttfChQvzLOerr76ifPnyGBgYYGlpSatWrTh79qxGdYmIiFD2p6WlhampKR4eHgQEBJCWlqa27ezZswkLC9Oo/A9Nk+P7GNWtW5dBgwYVePuff/4ZbW1t+vbt+/4q9R6FhYVhZmam0XvGjh3LhAkT5EaJEEIIIfKk8VNO9u3b9z7qIYQQ/1hPnjwhNDSUbdu2ZVu3dOlSmjRpovzf1NQ0z7KqVq1K586dKVOmDLdv3yYwMJDGjRtz8eJFtLW1NapXUlISRYsW5f79+xw7doypU6cSGhpKRESEMsdRfvX5JyvI8b2NFy9eUKRIkUKs6fu1ZMkSAgICWLBgASEhIf+JSbjd3Nyws7Nj1apVfP311x+6OkIIIYT4h9I4Q6NcuXJ4e3tTp04dtZe3tzflypV7H3UUQogPavv27ejo6FCzZs1s68zMzLCxsVFeBgYGeZbVp08fvL29sbOzw9PTkx9++IErV6681VAWKysrbGxscHR0pGPHjkRHR2Npaal2AfjmkJN169bh6uqKgYEBxYsXp2HDhjx69AiAI0eO0KhRIywsLDA1NaVOnTocO3Ys237T0tJo2rQpBgYGlCtXjl9++SXPep45cwZfX1+MjY2xtrbG39+fmzdvFsrxZWRkEBwcTOnSpdHT08Pd3Z0dO3Yo67OGwKxdu5a6deuir6/PypUrlXaZOHEi1tbWmJmZERQUxMuXLxk+fDjFihWjdOnSLFmyRK1O3333HY6OjhgaGmJvb8+YMWN48eKFsj4wMBB3d3dWrFiBnZ0dpqamdOzYkQcPHiifR2RkJLNnz1ayUPL67C9dukRMTAwjRoygYsWKrFu3Lts2S5YswcXFBT09PUqUKEH//v2VdXfv3qVPnz5YW1ujr69P5cqV2bp1q7J+/fr1ynvt7OyYMWOGWtk5DTEyMzNTsn6y2nfDhg3Uq1cPQ0NDqlSpwsGDB4FX2TZffvkl9+7dU443MDAQgPnz51OhQgX09fWxtrambdu2avtp2bIlq1evzrVthBBCCCHeKqBx48aNbMtv374tAQ0hxL/S/v378fLyynFd//79sbCwoFq1aixcuJCMjIwCl/vo0SOWLl1KuXLlsLW1fed6GhgY0LdvX6Kjo7l+/Xq29Wlpafj5+dGjRw8SExOJiIigdevWZGZmAvDgwQO6detGVFQUhw4dokKFCvj6+ioX41nGjBlDmzZtOHHiBF26dMHPz4/ExMQc65SWlkadOnVwd3fn6NGj7Nixg7/++ov27dsXyvHNnj2bGTNmMH36dE6ePImPjw8tW7YkOTlZ7b3fffcdAwcOJDExER8fHwD27t3Ln3/+yf79+wkJCSEwMJDmzZtjbm5ObGwsffv2pW/fvly5ckUpx8TEhLCwMM6cOcPs2bNZvHgxM2fOVNtXSkoKmzZtYuvWrWzdupXIyEgmT56s1LdmzZr07t2btLQ00tLS8vzslyxZQrNmzTA1NaVLly6EhoaqrV+wYAH9+vWjT58+JCQksHnzZhwcHIBXwZ6mTZsSExPDypUrOXPmDJMnT1YygeLi4mjfvj0dO3YkISGBwMBAxowZ81ZDlEaNGsWwYcOIj4/H0dERPz8/Xr58Sa1atZg1axZFixZVjnfYsGEcPXqUgQMHEhwcTFJSEjt27MDb21utzOrVq3P48GGePXuW4z6fPXsm83gJIYQQ/3EaDznJzMxEpVJlW/7w4UP09fULpVJCCPFPcunSJUqWLJlt+fjx42nQoAEGBgbs2bOHoUOHcvPmTUaPHp1nefPnzycgIIBHjx5RsWJFdu/eja6ubqHUtWLFikqdrays1NalpaXx8uVLWrduTdmyZQHUhm7Ur19fbftFixZhbm5OZGQkzZs3V5a3a9eOXr16Aa/aYPfu3cydO5f58+dnq8+CBQvw9PRk4sSJyrIlS5Zga2vLuXPncHR0fKfjmz59Ot999x0dO3YEYMqUKezbt49Zs2bx448/Ku8bNGgQrVu3ViurWLFizJkzBy0tLZycnJg6dSqPHz/m+++/B2DkyJFMnjyZ6OhopfzXP1s7OzuGDh3KmjVrCAgIUJZnZGQQFhaGiYkJAP7+/uzZs4cJEyZgamqKrq4uhoaG2NjY5HmsWeXMnTsXgI4dOzJkyBDOnz+vBC1++OEHhg4dyrfffqu8r1q1agD8/vvvHD58mMTERKWd7e3tle1CQkJo0KABY8aMAcDR0ZEzZ84wbdo0unfvnmfd3jRs2DCaNWsGQFBQEC4uLpw/f56KFStiamqKSqVSO97U1FSMjIxo3rw5JiYmlC1bFg8PD7UyS5UqxbNnz7h27ZrSX183adIkgoKCNKqnEEIIIf5dChzQGDJkCPAq/XTMmDFqY3jT09OJjY3F3d290CsohBAf2pMnT3IM2L5+cZv1/RccHJxvQKNz5840atSItLQ0pk+fTvv27YmOji6UoHBWtkVOgecqVarQoEEDXF1d8fHxoXHjxrRt21aZ0Pn69euMHTuWvXv38tdff5Gens7jx49JTU1VK+fNoTc1a9bM9akmcXFx7Nu3D2Nj42zrUlJSNA5ovH589+/f588//6R27dpq29SuXZsTJ06oLcspw8bFxQUtrf9LVLS2tqZy5crK/7W1tSlevLhatsu6deuYNWsW58+f5+HDh7x8+ZKiRYuqlWtnZ6cEMwBKlCiRY8ZMfnbt2sWjR49o2rQpABYWFjRu3JglS5YwceJErl+/zp9//kmDBg1yfH98fDylS5fOtY0TExNp1aqV2rLatWsza9Ys0tPTNZrTxc3NTfl3iRIlgFf9KSsA9aZGjRpRtmxZ7O3tadKkCU2aNOGLL75Q+22RNXzr8ePHOZYxcuRI5bcJwP379wsl00kIIYQQH48CBzSOHz8OvPoxmZCQoHY3UVdXlypVqjBs2LDCr6EQQnxgFhYW3LlzJ9/tatSowf379/nrr7+wtrbOdTtTU1NMTU2pUKECNWrUwNzcnI0bN+Ln5/fOdc0a+vH601iyaGtrs3v3bmJiYti1axdz585l1KhRxMbGUq5cObp3786NGzeYNWsWZcuWRU9Pj5o1a/L8+fN895tTAAVeZRm0aNGCKVOmZFuXdeGriZyO781955RJaGRklK2sNycGValUOS7LGkZ06NAhOnbsSFBQED4+PpiamhIeHp5t3om8ytDEkiVLuH37ttpFfkZGBsePH2f8+PH5zteS3/qc2ikrYJRFpVJlW/b6nCFZXj/mrDLzOmYTExOOHTtGREQEu3btYuzYsQQGBnLkyBHliSi3b98GwNLSMscy9PT00NPTy3UfQgghhPj3K3BAI+vpJl9++SWzZ8/OdkdKCCH+rTw8PFi5cmW+2x0/fhx9fX2NH1GZmZmZ6zwBmnjy5An/+9//8Pb2zvUiUKVSUbt2bWrXrs3YsWMpW7YsGzduZMiQIURFRTF//nx8fX0BuHLlSo6Tdx46dIiuXbuq/f/N4QJZPD09Wb9+PXZ2dujoaDzKMd/jK1myJAcOHFCbfyEmJobq1au/075yEh0dTdmyZRk1apSy7PLlyxqXo6urS3p6ep7b3Lp1i19//ZXw8HBcXFyU5RkZGXz22Wds376d5s2bY2dnx549e6hXr162Mtzc3Pjjjz9yHdrj7OzMgQMH1JbFxMTg6OioZGdYWlqqPSo3OTk514wJTY9XR0eHhg0b0rBhQ8aNG4eZmRl79+5VhgadOnWK0qVLY2FhodH+hBBCCPHfofGvy6VLl76PegghxD+Wj48PI0eO5M6dO8rwjC1btnDt2jVq1qyJgYEB+/btY9SoUfTp00e5a3z16lUaNGjA8uXLqV69OhcuXGDNmjU0btwYS0tLrl69ypQpUzAwMFCCCJq4fv06T58+5cGDB8TFxTF16lRu3rzJhg0bctw+NjaWPXv20LhxY6ysrIiNjeXGjRtUqlQJAAcHB1asWIGXlxf3799n+PDhOd7l/+WXX/Dy8uLTTz9l1apVHD58ONtklVn69evH4sWL8fPzY/jw4VhYWHD+/HnCw8NZvHhxnsMaCnJ8w4cPZ9y4cZQvXx53d3eWLl1KfHw8q1at0qQpC8TBwYHU1FTCw8OpVq0av/32Gxs3btS4HDs7O2JjY7l06RLGxsYUK1ZMbegLwIoVKyhevDjt2rXLtq558+aEhobSvHlzAgMD6du3L1ZWVjRt2pQHDx4QHR3NgAEDlCeQtWnThpCQEBwcHDh79iwqlYomTZowdOhQqlWrxvjx4+nQoQMHDx5k3rx5anOh1K9fn3nz5lGjRg0yMjL47rvvNH7krZ2dHQ8fPmTPnj1UqVIFQ0ND9u7dy4ULF/D29sbc3Jxt27aRkZGBk5OT8r6oqCgaN26scfsKIYQQ4r9D46ecPHr0iDFjxlCrVi0cHBywt7dXewkhxL+Nq6srXl5erF27VllWpEgR5s+fT82aNXFzc2P27NkEBwerDT948eIFSUlJyh1tfX19oqKi8PX1xcHBgfbt22NkZERMTIzaBJ5169Yt0KSMTk5OlCxZkqpVqzJ58mQaNmzIqVOncHZ2znH7okWLsn//fnx9fXF0dGT06NHMmDFDmaNhyZIl3LlzBw8PD/z9/Rk4cGC2iUXh1aSP4eHhuLm5sWzZMlatWpXrPkuWLEl0dDTp6en4+PhQuXJlvv32W0xNTbNdqL/N8Q0cOJChQ4cydOhQXF1d2bFjB5s3b6ZChQr5tp+mWrVqxeDBg+nfvz/u7u7ExMQoE2pqYtiwYWhra+Ps7IylpWW2OUrg1WfxxRdf5NhGbdq0YevWrfz1119069aNWbNmMX/+fFxcXGjevLnaE17Wr19PtWrV8PPzw9nZmYCAACVbwtPTk7Vr1xIeHk7lypUZO3YswcHBan1vxowZ2Nra4u3tTadOnRg2bJjaEJiCqFWrFn379qVDhw5YWloydepUzMzM2LBhA/Xr16dSpUosXLiQ1atXK9koT58+ZePGjfTu3VujfQkhhBDiv0WV+ebg2Hz4+fkRGRmJv78/JUqUyDb+9vWZ1oUQ4t9i27ZtDBs2jFOnTuV7If6u7OzsCAwM1PhJE0L8W/z444/8+uuv7Nq1q8DvuX//PqamptgOWouWnmZBFyGEEP8NlyY3+9BVEG/I+vt97969t5rWQuMhJ9u3b+e3337LNqu8EEL8m/n6+pKcnMzVq1ff65MUzp49i4mJidocFUL81xQpUkR5XK0QQgghRG40DmiYm5tTrFix91EXIYT4R/s7MtAqVqxIQkLCe9+PEP9kffr0+dBVEEIIIcRHQOMhJytXruTXX39l2bJlGo+jFUIIIYR4H941ZVUIIYQQf7+/fcjJjBkzSElJwdraGjs7u2yznR87dkzjSgghhBBCCCGEEEJoQuOAxueff/4eqiGEEEIIIYQQQghRcBoPORFCCCGE+KeRISdCCCHEx+dvH3KSJS4ujsTERFQqFc7Oznh4eLxtUUIIIYQQQgghhBAa0Tigcf36dTp27EhERARmZmZkZmZy79496tWrR3h4OJaWlu+jnkIIIYQQQgghhBAKLU3fMGDAAO7fv8/p06e5ffs2d+7c4dSpU9y/f5+BAwe+jzoKIYQQQgghhBBCqNF4Dg1TU1N+//13qlWrprb88OHDNG7cmLt37xZm/YQQQggh8iVzaAghhBAfn3f9+61xhkZGRka2R7UCFClShIyMDI0rIIQQQgghhBBCCKEpjQMa9evX59tvv+XPP/9Ull29epXBgwfToEGDQq2cEEIIIYQQQgghRE40DmjMmzePBw8eYGdnR/ny5XFwcKBcuXI8ePCAuXPnvo86CiGEEEIIIYQQQqjR+Ckntra2HDt2jN27d3P27FkyMzNxdnamYcOG76N+QgghhBBCCCGEENloPCmoEEIIIcQ/jUwKKoQQQnx8/rZJQffu3YuzszP379/Ptu7evXu4uLgQFRWlcQWEEEIIIYQQQgghNFXggMasWbPo3bt3jlETU1NTvvrqK0JCQgq1ckIIIYQQQgghhBA5KXBA48SJEzRp0iTX9Y0bNyYuLq5QKiWEEEIIIYQQQgiRlwIHNP766y+KFCmS63odHR1u3LhRKJUSQgghhBBCCCGEyEuBAxqlSpUiISEh1/UnT56kRIkShVIpIYQQQgghhBBCiLwUOKDh6+vL2LFjefr0abZ1T548Ydy4cTRv3rxQKyeEEEIIIYQQQgiRkwI/tvWvv/7C09MTbW1t+vfvj5OTEyqVisTERH788UfS09M5duwY1tbW77vOQgghhBBqsh77ZjtoLVp6hh+6OkIIId7SpcnNPnQVxN/oXR/bqlPQDa2trYmJieHrr79m5MiRZMVBVCoVPj4+zJ8/X4IZQgghhBBCCCGE+FsUOKABULZsWbZt28adO3c4f/48mZmZVKhQAXNz8/dVPyGEEEIIIYQQQohsNApoZDE3N6datWqFXRchhBBCCCGEEEKIAinwpKBCfOxu3bqFlZUVly5d+tBVKXRnz56lRo0a6Ovr4+7uzqVLl1CpVMTHx3/oqr2ViIgIVCoVd+/e/dv2GRYWhpmZ2d+yr+7du/P555//LfvKj0qlYtOmTR+6Gu/F3/mZinfzej+8fv06lpaWXL169cNWSgghhBD/eBLQEP8ZkyZNokWLFtjZ2aktDwsLw83NDX19fWxsbOjfv3+e5Vy7dg1/f39sbGwwMjLC09OTdevWaVSXrAt2lUqFlpYWpqameHh4EBAQQFpamqaHxrhx4zAyMiIpKYk9e/Zga2tLWloalStX1risj4WdnR0qlYrw8PBs61xcXFCpVISFhRW4vA4dOnDu3DmN6lC3bl0GDRqk0XsAZs+erVHdchIYGKjWh0qWLEnnzp25cuXKO5WrqazgWdZLV1cXBwcHfvjhBwo45/Q/Qp8+fdDW1s6xP31Mnj9/ztSpU6lSpQqGhoZYWFhQu3Ztli5dyosXLwptP4GBgbi7uxdaea+zsrLC39+fcePGvZfyhRBCCPHvIQEN8Z/w5MkTQkND6dWrl9rykJAQRo0axYgRIzh9+jR79uzBx8cnz7L8/f1JSkpi8+bNJCQk0Lp1azp06MDx48c1rldSUhJ//vknR44c4bvvvuP333+ncuXKJCQkaFROSkoKn376KWXLlqV48eJoa2tjY2ODjs5bjSr7aNja2rJ06VK1ZYcOHeLatWsYGRlpVJaBgQFWVlaFWb1cmZqaFkrmgIuLC2lpafzxxx+sWbOGhIQE2rdv/+4VfAu///47aWlpJCcnExQUxIQJE1iyZMkHqYumHj9+zJo1axg+fDihoaEfujpv7fnz5/j4+DB58mT69OlDTEwMhw8fpl+/fsydO5fTp0//7XV62yDKl19+yapVq7hz504h10gIIYQQ/yYS0BD/Cdu3b0dHR4eaNWsqy+7cucPo0aNZvnw5nTp1onz58ri4uNCiRYs8yzp48CADBgygevXq2NvbM3r0aMzMzDh27JjG9bKyssLGxgZHR0c6duxIdHQ0lpaWfP3112rbLV26lEqVKqGvr0/FihWZP3++sk6lUhEXF0dwcDAqlYrAwMBsQ06yMkL27NmDl5cXhoaG1KpVi6SkJLX9bNmyhapVq6Kvr4+9vT1BQUG8fPky1/ofOXKERo0aYWFhgampKXXq1MnWDiqVip9++okvvvgCQ0NDKlSowObNm9W22bZtG46OjhgYGFCvXr0CDwvq3LkzkZGRalkJS5YsoXPnztmCOSEhIbi6umJkZIStrS3ffPMNDx8+VNa/OTwh6w70ihUrsLOzw9TUlI4dO/LgwQPg1bCRyMhIZs+erWQnXLp0ifT0dHr27Em5cuUwMDDAycmJ2bNnq9XlzSEndevWZeDAgQQEBFCsWDFsbGwIDAzM9/h1dHSwsbGhZMmSfPbZZ/Tu3ZtDhw5x//59ZZsFCxZQvnx5dHV1cXJyYsWKFXmWefXqVTp06IC5uTnFixenVatWBfo8ihcvjo2NDWXLlqVz587UqlVLrS8UpK8EBgZSpkwZ9PT0KFmyJAMHDlTWPX/+nICAAEqVKoWRkRGffPIJERERau8PCwujTJkyGBoa8sUXX3Dr1q186w3wyy+/4OzszMiRI4mOjs52vM+ePSMgIABbW1v09PSoUKGCWuDj9OnTNGvWjKJFi2JiYsJnn31GSkqKsj6v8/f58+f079+fEiVKoK+vj52dHZMmTSpQm7xp1qxZ7N+/nz179tCvXz/c3d2xt7enU6dOxMbGUqFCBeV4Bg4ciJWVFfr6+nz66accOXJEKSe/74uwsDCCgoI4ceKE0vezMo5UKhULFy6kVatWGBkZ8cMPPwCa90NXV1dsbGzYuHFjntsJIYQQ4r9NAhriP2H//v14eXmpLdu9ezcZGRlcvXqVSpUqUbp0adq3b59vyv6nn37KmjVruH37NhkZGYSHh/Ps2TPq1q37zvU0MDCgb9++REdHc/36dQAWL17MqFGjmDBhAomJiUycOJExY8awbNkyANLS0nBxcWHo0KGkpaUxbNiwXMsfNWoUM2bM4OjRo+jo6NCjRw9l3c6dO+nSpQsDBw7kzJkzLFq0iLCwMCZMmJBreQ8ePKBbt25ERUVx6NAhKlSogK+vr3LRnyUoKIj27dtz8uRJfH196dy5M7dv3wbgypUrtG7dGl9fX+Lj4+nVqxcjRowoUHtZW1vj4+OjtEXWnfbXjyuLlpYWc+bM4dSpUyxbtoy9e/cSEBCQZ/kpKSls2rSJrVu3snXrViIjI5k8eTLwathIzZo16d27N2lpaaSlpWFra0tGRgalS5dm7dq1nDlzhrFjx/L999+zdu3aPPe1bNkyjIyMiI2NZerUqQQHB7N79+4CtQO8Ggq1YcMGtLW10dbWBmDjxo18++23DB06lFOnTvHVV1/x5Zdfsm/fvhzLePz4MfXq1cPY2Jj9+/dz4MABjI2NadKkCc+fPy9wXY4ePcqxY8f45JNPlGX59ZV169Yxc+ZMFi1aRHJyMps2bcLV1VV5/5dffkl0dDTh4eGcPHmSdu3a0aRJE5KTkwGIjY2lR48efPPNN8THx1OvXj3lYjo/oaGhdOnSBVNTU3x9fbNl/XTt2pXw8HDmzJlDYmIiCxcuxNjYGHgVAPL29kZfX5+9e/cSFxdHjx49lEBgfufvnDlz2Lx5M2vXriUpKYmVK1cqw+Lya5M3rVq1ioYNG+Lh4ZFtXZEiRZSspYCAANavX8+yZcs4duwYDg4O+Pj4KOdklty+Lzp06MDQoUOVDKG0tDQ6dOigvG/cuHG0atWKhIQEevTooXE/zFK9enWioqJyXf/s2TPu37+v9hJCCCHEf8u/Ox9diP/v0qVLlCxZUm3ZhQsXyMjIYOLEicyePRtTU1NGjx5No0aNOHnyJLq6ujmWtWbNGjp06EDx4sXR0dHB0NCQjRs3Ur58+UKpa8WKFZU6W1lZMX78eGbMmEHr1q0BKFeunBJw6NatmzK0xNjYGBsbGwBu3ryZY9kTJkygTp06AIwYMYJmzZrx9OlT9PX1mTBhAiNGjKBbt24A2NvbM378eAICAnIdy16/fn21/y9atAhzc3MiIyNp3ry5srx79+74+fkBMHHiRObOncvhw4dp0qQJCxYswN7enpkzZ6JSqXByciIhIYEpU6YUqL169OjB0KFDGTVqFOvWraN8+fI5ju1/fa6LcuXKMX78eL7++mu1u+VvysjIICwsDBMTE+DVcKM9e/YwYcIETE1N0dXVxdDQUGl3AG1tbYKCgtT2FRMTw9q1a/McDuLm5qa0c4UKFZg3bx579uyhUaNGub4nISEBY2NjMjIyePLkCQADBw5ULlynT59O9+7d+eabbwAYMmQIhw4dYvr06dSrVy9beeHh4WhpafHTTz+hUqmAV9kFZmZmRERE0Lhx41zrUqtWLbS0tHj+/DkvXrygT58+dO3aVVmfX19JTU3FxsaGhg0bUqRIEcqUKUP16tWBV4Gl1atX88cffyjn8bBhw9ixYwdLly5VzmEfHx8lGObo6EhMTAw7duzItc4AycnJHDp0iA0bNgAoQb1x48ahpaXFuXPnWLt2Lbt376Zhw4bAq3Mjy48//oipqSnh4eEUKVJE2XeW/M7f1NRUKlSowKeffopKpaJs2bLKe/Nqk9yOJb/A6qNHj1iwYAFhYWE0bdoUeBV02b17N6GhoQwfPlzZNrfvCwMDA4yNjZUMoTd16tRJLajYqVMnjfphllKlSuU5lG/SpElq55oQQggh/nskQ0P8Jzx58gR9fX21ZRkZGbx48YI5c+bg4+NDjRo1WL16NcnJyXneORw9ejR37tzh999/5+jRowwZMoR27dppPO9FbrImUlSpVNy4cYMrV67Qs2dPjI2NldcPP/ygltJeUG5ubsq/S5QoAaBkgmQNW3l9P1nZB48fP86xvOvXr9O3b18cHR0xNTXF1NSUhw8fkpqamut+jYyMMDExUfabmJhIjRo1lAtoQG1oUH6aNWvGw4cP2b9/P0uWLMkxOwNg3759NGrUiFKlSmFiYkLXrl25desWjx49yrVsOzs7JZgBr9osq955WbhwIV5eXlhaWmJsbMzixYuztcmbXm+jgu7LycmJ+Ph4jhw5woQJE3B3d1fLqElMTKR27dpq76lduzaJiYk5lhcXF8f58+cxMTFR+kCxYsV4+vQpKSkpREVFqfWPVatWKe9ds2YN8fHxnDhxgjVr1vDrr7+qZdrk11fatWvHkydPsLe3p3fv3mzcuFHJcjh27BiZmZk4Ojqq7T8yMlI5DxITE7P1m4L0o9DQUHx8fLCwsADA19eXR48e8fvvvwMQHx+Ptra2cmH/pvj4eD777DMlmPG6gpy/3bt3Jz4+HicnJwYOHMiuXbuU9+fVJjnJzMxUO49ykpKSwosXL9T6RZEiRahevXq2fpHX90Ve3syG07QfZjEwMMj1uwdg5MiR3Lt3T3n93RPiCiGEEOLDkwwN8Z9gYWGRbXK5rB/ozs7OyjJLS0ssLCxyvfhMSUlh3rx5nDp1ChcXFwCqVKlCVFQUP/74IwsXLnznumb9yLezsyMjIwN4dQf19fR9QBlWoInXL7qyLnyy9pGRkUFQUJByJ/l1bwaDsnTv3p0bN24wa9YsypYti56eHjVr1sw2POHNiz2VSqXs912fhKGjo6M8ESE2NjbHMfeXL1/G19eXvn37Mn78eIoVK8aBAwfo2bNnnpMW5lXv3Kxdu5bBgwczY8YMatasiYmJCdOmTSM2NjbP973NvrKeKAKvJghNTk7m66+/Vpuf4M0L3LwuejMyMqhatapaoCKLpaUlurq6ao8Ctra2VuapsLW1VepSqVIlLly4wJgxYwgMDERfXz/fvmJra0tSUhK7d+/m999/55tvvmHatGlERkaSkZGBtrY2cXFx2fp91tCPt+lH6enpLF++nGvXrqnNuZKenk5oaCiNGzfGwMAgzzLyWl+Q89fT05OLFy+yfft2fv/9d9q3b0/Dhg1Zt25dnm2SUwDF0dEx3yDB6wHTN5e/uSyv74u85DQhryb9MMvt27extLTMdb2enh56enr51kcIIYQQ/14S0BD/CR4eHqxcuVJtWdYdw6SkJEqXLg28+gF98+ZNtbTv12XdLdTSUk9u0tbWLtAP/fw8efKE//3vf3h7eys/5EuVKsWFCxfo3LnzO5efF09PT5KSkpSL0oKIiopi/vz5+Pr6Aq/mw8htuEtunJ2d2bRpk9qyQ4cOaVRGjx49mD59ujKZ5ZuOHj3Ky5cvmTFjhvLZ5TenRUHo6uqSnp6utiwqKopatWop6fXAW2XTvI0xY8bg6OjI4MGD8fT0pFKlShw4cEBt6EdMTAyVKlXK8f2enp6sWbMGKysrihYtmuM2b/aP3Cbe1NbW5uXLlzx//hx9ff0C9RUDAwNatmxJy5Yt6devHxUrViQhIQEPDw/S09O5fv06n332WY77c3Z2ztZv8utH27Zt48GDBxw/flwtUHL27Fk6d+7MrVu3cHV1JSMjg8jISGXIyevc3NxYtmwZL168yBZksLa2LtD5W7RoUTp06ECHDh1o27YtTZo04fbt2xQrVizXNvH09MxWTqdOnfj+++85fvx4tnk0Xr58ybNnz3BwcEBXV5cDBw7QqVMn4NWTSI4eParRI4hz6vu50bQfZjl16lShzE0khBBCiH8vCWiI/wQfHx9GjhzJnTt3lAteR0dHWrVqxbfffsv//vc/ihYtysiRI6lYsaIyrvvq1as0aNCA5cuXU716dSpWrIiDgwNfffUV06dPp3jx4mzatIndu3ezdetWjet1/fp1nj59yoMHD4iLi2Pq1KncvHlTGc8Pr55yMHDgQIoWLUrTpk159uwZR48e5c6dOwwZMqRwGggYO3YszZs3x9bWlnbt2qGlpcXJkydJSEjIdXJFBwcHVqxYgZeXF/fv32f48OH53tF+U9++fZkxYwZDhgzhq6++Ii4uTnliQkFVqlSJmzdvYmhomOP68uXL8/LlS+bOnUuLFi2Ijo4ulGwaOzs7YmNjuXTpkjI8w8HBgeXLl7Nz507KlSvHihUrOHLkCOXKlXvn/eXH3t6eVq1aMXbsWLZu3crw4cNp3749np6eNGjQgC1btrBhwwZlOMWbOnfuzLRp02jVqhXBwcGULl2a1NRUNmzYwPDhw5XAX05u3brFtWvXePnyJQkJCcyePZt69eopgZH8+kpYWBjp6el88sknGBoasmLFCgwMDJRHEXfu3JmuXbsyY8YMPDw8uHnzJnv37sXV1RVfX18GDhxIrVq1mDp1Kp9//jm7du3Kd/6M0NBQmjVrRpUqVdSWu7i4MGjQIFauXMm3335Lt27d6NGjB3PmzKFKlSpcvnyZ69ev0759e/r378/cuXPp2LEjI0eOxNTUlEOHDlG9enWcnJzyPX9nzpxJiRIlcHd3R0tLi19++QUbGxvMzMzybJOcDBo0iN9++40GDRowfvx4Pv30U0xMTDh69ChTpkwhNDQUd3d3vv76a4YPH06xYsUoU6YMU6dO5fHjx/Ts2TPP9nqdnZ0dFy9eJD4+ntKlS2NiYpJrtoSm/RBeBY/j4uKYOHFigeskhBBCiP8emUND/Ce4urri5eWV7a788uXL+eSTT2jWrBl16tShSJEi7NixQ7nT+uLFC5KSkpTMjCJFirBt2zYsLS1p0aIFbm5uLF++nGXLlil3nuHVYzi7d++eb72cnJwoWbIkVatWZfLkyTRs2JBTp06pDYPp1asXP/30E2FhYbi6ulKnTh3CwsIK/QLZx8eHrVu3snv3bqpVq0aNGjUICQnJ9eIJXj0i9c6dO3h4eODv7688ClITZcqUYf369WzZsoUqVaqwcOHCt7qIKV68eK7BFHd3d0JCQpgyZQqVK1dm1apVao/GfFvDhg1DW1sbZ2dnLC0tSU1NpW/fvrRu3ZoOHTrwySefcOvWLbVsjfdt6NCh/Pbbb8TGxvL5558ze/Zspk2bhouLC4sWLWLp0qW53vU2NDRk//79lClThtatW1OpUiV69OjBkydPcs3YyNKwYUNKlCiBnZ0dffr0wdfXlzVr1ijr8+srZmZmLF68mNq1a+Pm5saePXvYsmULxYsXB15NTtq1a1eGDh2Kk5MTLVu2JDY2FltbWwBq1KjBTz/9xNy5c3F3d2fXrl2MHj061/r+9ddf/Pbbb7Rp0ybbOpVKRevWrZVHsy5YsIC2bdvyzTffULFiRXr37q3MvVK8eHH27t3Lw4cPqVOnDlWrVmXx4sXKd0h+56+xsTFTpkzBy8uLatWqcenSJbZt24aWlla+bfImPT09du/eTUBAAIsWLaJGjRpUq1aNOXPmMHDgQCpXrgzA5MmTadOmDf7+/nh6enL+/Hl27tyZY3ZTbtq0aUOTJk2oV68elpaWrF69OtdtNe2HAL/++itlypTJNSNHCCGEEAJAlfmuA9iF+Ehs27aNYcOGcerUqWxDRgqbnZ0dgYGBBQpqCCGEUFe9enUGDRqkDIspiPv372NqaortoLVo6eWcrSWEEOKf79LkZh+6CuJvlPX3+969e/neQMuJDDkR/xm+vr4kJydz9epV5a7u+3D27FnlKRpCCCE0c/36ddq2bas86lkIIYQQIjeSoSGEEEKIj55kaAghxL+DZGj8t0iGhhBCCCHE/3cqyOetfhAJIYQQ4uMjk4IKIYQQQgghhBDioyMBDSGEEEIIIYQQQnx0JKAhhBBCCCGEEEKIj44ENIQQQgghhBBCCPHRkYCGEEIIIYQQQgghPjoS0BBCCCGEEEIIIcRHRwIaQgghhBBCCCGE+OhIQEMIIYQQQgghhBAfHQloCCGEEEIIIYQQ4qMjAQ0hhBBCCCGEEEJ8dCSgIYQQQgghhBBCiI+OBDSEEEIIIYQQQgjx0ZGAhhBCCCGEEEIIIT46EtAQQgghhBBCCCHER0cCGkIIIYQQQgghhPjoSEBDCCGEEEIIIYQQHx0JaAghhBBCCCGEEOKjIwENIYQQQgghhBBCfHR0PnQFhBBCCCEKS+VxO9HSM/zQ1RBCiH+9S5ObfegqCCEZGkIIIYQQQgghhPj4SEBDCCGEEEIIIYQQHx0JaAghhBBCCCGEEOKj88EDGrdu3cLKyopLly596Kr844SFhWFmZvahq6GoW7cugwYNeqcy3uaYCmO//zaatkn37t35/PPP3/r9Im92dnbMmjVL+b9KpWLTpk0frD55ebOuhaWwvq80bbt/2vekKBwJCQmULl2aR48efeiqCCGEEOIf7IMHNCZNmkSLFi2ws7NTlqlUqmyvhQsXFqi8zMxMmjZt+lYXFBEREWr7LF68OPXr1yc6OlqjcvLy5oVlXjp06MC5c+cKXLamFyoTJ05EW1ubyZMnF/g970rTYwLYsGED48ePL9C2hX2h/r4u/rJ0795d6W9FihTB3t6eYcOG5fsjXpM2AZg9ezZhYWHvWNu/V2BgIO7u7oVS1uvntZGRERUqVKB79+7ExcUVSvlvSktLo2nTpoVW3vvuhzn5448/0NXVpWLFin/rfjVtO02+Uwo7+FGYfTQ/Tk5O6OrqcvXq1b9lf4VN0z7s6upK9erVmTlz5vurlBBCCCE+eh80oPHkyRNCQ0Pp1atXtnVLly4lLS1NeXXr1q1AZc6aNQuVSvVO9UpKSiItLY2IiAgsLS1p1qwZ169ff6cyNfXixQsMDAywsrJ6b/tYunQpAQEBLFmy5L3t401vc0zFihXDxMSk0OqQmZnJy5cvC628gnj+/Hmu65o0aUJaWhoXLlzghx9+YP78+QwbNizHbV+8eAFo3iampqb/+bvYWd8pp0+f5scff+Thw4d88sknLF++vND3ZWNjg56eXqGX+3cKCwujffv2PH78uFCDuvnRtO3ex/dkXufr+5B1XufmwIEDPH36lHbt2n10gcl38eWXX7JgwQLS09M/dFWEEEII8Q/1QQMa27dvR0dHh5o1a2ZbZ2Zmho2NjfIyMDDIt7wTJ04QEhLyzhfoVlZW2NjY4OrqyujRo7l37x6xsbHK+jNnzuDr64uxsTHW1tb4+/tz8+ZNZf26detwdXXFwMCA4sWL07BhQx49ekRgYCDLli3j119/Ve4WR0REcOnSJVQqFWvXrqVu3bro6+uzcuXKHO8mbt68GS8vL/T19bGwsKB169bAq8yEy5cvM3jwYKXsvERGRvLkyROCg4N59OgR+/fvV1v/6NEjunbtirGxMSVKlGDGjBnZyrCzs+OHH35Qtitbtiy//vorN27coFWrVhgbG+Pq6srRo0eV97x5TFl3OFesWIGdnR2mpqZ07NiRBw8eKNu8mXUxf/58KlSogL6+PtbW1rRt2xZ4le0QGRnJ7NmzlTa4dOmSknmzc+dOvLy80NPTIyoqipSUFFq1aoW1tTXGxsZUq1aN33//XW2/ubXp+vXrcXFxQU9PDzs7u2ztk9U23bt3x9TUlN69e+f6Wejp6WFjY4OtrS2dOnWic+fOSnZRVvssWbIEe3t79PT0yMzMVGuTkSNHUqNGjWzlurm5MW7cOKVt8soMev78OQEBAZQqVQojIyM++eQTIiIi1LZZvHgxtra2GBoa8sUXXxASEpKtf27ZsoWqVauir6+Pvb09QUFBasGjwMBAypQpg56eHiVLlmTgwIE51icsLIygoCBOnDihtH3WhVxqaqrSv4oWLUr79u3566+/cj22LFnfKXZ2djRu3Jh169bRuXNn+vfvz507d5TtYmJi8Pb2xsDAAFtbWwYOHKiWMXP9+nVatGiBgYEB5cqVY9WqVdn29WaG2B9//EHHjh0pVqwYRkZGeHl5Kd8p79IPC6OuOcnMzGTp0qX4+/vTqVMnQkNDs20TFhZGmTJllP5w69YttfWv990yZcpgbGzM119/TXp6OlOnTsXGxgYrKysmTJiQa9tlfTdu2LCBevXqYWhoSJUqVTh48KBaPV7vhydOnKBevXqYmJhQtGhRqlatytGjR4mIiODLL7/k3r17SjsGBgYCuZ+v3333HY6OjhgaGmJvb8+YMWOU4MO79NHczuvchIaG0qlTJ/z9/VmyZEm2bfPqX5D73w2AO3fu0LVrV8zNzTE0NKRp06YkJydnq+vrZs2apZZVmfX9Mn36dEqUKEHx4sXp16+f0la59eHLly/TokULzM3NMTIywsXFhW3btinl+vj4cOvWLSIjI3NtGyGEEEL8t33QgMb+/fvx8vLKcV3//v2xsLCgWrVqLFy4kIyMjDzLevz4MX5+fsybNw8bG5tCqd/jx49ZunQpAEWKFAFepUPXqVMHd3d3jh49yo4dO/jrr79o3769st7Pz48ePXqQmJhIREQErVu3JjMzk2HDhtG+fXvljnxaWhq1atVS9vfdd98xcOBAEhMT8fHxyVaf3377jdatW9OsWTOOHz/Onj17lPbbsGEDpUuXJjg4WCk7L6Ghofj5+VGkSBH8/PyyXbAMHz6cffv2sXHjRnbt2kVERESO6fkzZ86kdu3aHD9+nGbNmuHv70/Xrl3p0qULx44dw8HBga5du+b5Yz0lJYVNmzaxdetWtm7dSmRkZK7DYI4ePcrAgQMJDg4mKSmJHTt24O3tDbwaVlGzZk169+6ttIGtra3y3oCAACZNmkRiYiJubm48fPgQX19ffv/9d44fP46Pjw8tWrQgNTU1zzaNi4ujffv2dOzYkYSEBAIDAxkzZky2O6fTpk2jcuXKxMXFMWbMmDw/j9cZGBio3bE9f/48a9euZf369cTHx2fbvnPnzsTGxpKSkqIsO336NAkJCXTu3LlA+/zyyy+Jjo4mPDyckydP0q5dO5o0aaJc2ERHR9O3b1++/fZb4uPjadSoUbYL0Z07d9KlSxcGDhzImTNnWLRoEWFhYcp269atY+bMmSxatIjk5GQ2bdqEq6trjvXp0KEDQ4cOxcXFRWn7Dh06kJmZyeeff87t27eJjIxk9+7dpKSk0KFDhwId55sGDx7MgwcP2L17N/Bq3L6Pjw+tW7fm5MmTrFmzhgMHDtC/f3/lPd27d+fSpUvs3buXdevWMX/+/DwzuB4+fEidOnX4888/2bx5MydOnCAgIED5Tnvbfvg+6ppl3759PH78mIYNG+Lv78/atWvVgoyxsbH06NGDb775hvj4eOrVq8cPP/yQrZyUlBS2b9/Ojh07WL16NUuWLKFZs2b88ccfREZGMmXKFEaPHs2hQ4fyrM+oUaMYNmwY8fHxODo64ufnl2uWVefOnSldujRHjhwhLi6OESNGUKRIEWrVqsWsWbMoWrSo0o6vZ0LldL6amJgQFhbGmTNnmD17NosXL1aGQLxrH83vvM7y4MEDfvnlF7p06UKjRo149OiRWrAxv/6V198NeNVHjh49yubNmzl48CCZmZn4+vrmmzXypn379pGSksK+fftYtmwZYWFhyndibn24X79+PHv2jP3795OQkMCUKVMwNjZWytTV1aVKlSpERUXluM9nz55x//59tZcQQggh/lt0PuTOL126RMmSJbMtHz9+PA0aNMDAwIA9e/YwdOhQbt68yejRo3Mta/DgwdSqVYtWrVq9c71Kly4NvApoZGZmUrVqVRo0aADAggUL8PT0ZOLEicr2S5YswdbWlnPnzvHw4UNevnxJ69atKVu2LIDaRZuBgQHPnj3LMegyaNAgtTtnb5owYQIdO3YkKChIWValShXg1RAEbW1tTExM8g3o3L9/n/Xr1xMTEwNAly5dqF27NnPnzqVo0aI8fPiQ0NBQli9fTqNGjQBYtmyZ0i6v8/X15auvvgJg7NixLFiwgGrVqtGuXTvgVZCmZs2a/PXXX7nWKyMjg7CwMGUIhb+/P3v27Ml2wQyv7nwaGRnRvHlzTExMKFu2LB4eHsCrYRW6uroYGhrmuK/g4GDleACKFy+utB/ADz/8wMaNG9m8eTP9+/fPtU1DQkJo0KCBctHj6OjImTNnmDZtGt27d1e2q1+/fq5DR3Jz+PBhfv75Z6W/wavsiRUrVmBpaZnjeypXroybmxs///yzUqdVq1ZRrVo1HB0d891nSkoKq1ev5o8//lDOx2HDhrFjxw6WLl3KxIkTmTt3Lk2bNlWOx9HRkZiYGLZu3aqUM2HCBEaMGKEMD7O3t2f8+PEEBAQwbtw4UlNTsbGxoWHDhhQpUoQyZcpQvXr1HOtkYGCAsbExOjo6am2/e/duTp48ycWLF5Vg1YoVK3BxceHIkSNUq1Yt3+N9Xdb8EFmTEk+bNo1OnTop2S8VKlRgzpw51KlThwULFpCamsr27ds5dOgQn3zyCfAqOFipUqVc9/Hzzz9z48YNjhw5QrFixQBwcHBQ1lepUuWt+uH7qGuW0NBQOnbsiLa2Ni4uLjg4OLBmzRpleODs2bPx8fFhxIgRwP/1hx07dqiVk5GRwZIlSzAxMcHZ2Zl69eqRlJTEtm3b0NLSwsnJiSlTphAREZFjllGWYcOG0axZMwCCgoJwcXHh/PnzOc7vkZqayvDhw5V1FSpUUNaZmpqiUqly/H7I6Xx9/W+OnZ0dQ4cOZc2aNQQEBLxzH83vvM4SHh5OhQoVcHFxAaBjx46EhoZSr149IP/+ldffjeTkZDZv3kx0dLQSXF+1ahW2trZs2rRJ+R4vCHNzc+bNm4e2tjYVK1akWbNm7Nmzh969e+fah1NTU2nTpo3yN9Le3j5buaVKlcp10vBJkyapHZcQQggh/ns++Bwa+vr62ZaPHj2amjVr4u7uztChQwkODmbatGm5lrN582b27t1baJPmRUVFcezYMVavXk3ZsmUJCwtTMjTi4uLYt28fxsbGyivrh3NKSgpVqlShQYMGuLq60q5dOxYvXqyWzp6X3LJVssTHx6td6L6tn3/+GXt7e+VHrbu7O/b29oSHhwOvjuP58+dqQ4GKFSuGk5NTtrLc3NyUf1tbWwPqAZysZXndFbazs1ObD6JEiRK5bt+oUSPKli2Lvb09/v7+rFq1isePH+d7zJC9fR89ekRAQADOzs6YmZlhbGzM2bNnlTvjuUlMTKR27dpqy2rXrk1ycrLaWO/8Ps8sW7duxdjYGH19fWrWrIm3tzdz585V1pctWzbfi57OnTsrwwkyMzNZvXp1gbMzjh07RmZmJo6Ojmr9OjIyUsn6SEpKyhZ8ePP/cXFxBAcHq5WRlS3z+PFj2rVrx5MnT7C3t6d3795s3LhR47lMEhMTsbW1Vcu8yfr8EhMTNSoLUDKHslLg4+LiCAsLUzsGHx8fMjIyuHjxIomJiejo6Kh9thUrVsxzfpL4+Hg8PDyUi803vW0/fB91Bbh79y4bNmygS5cuyrIuXbqoDeVLTEzMNlQwp6GDb57b1tbWODs7o6WlpbYsv6yR179nSpQoAeT+nTJkyBB69epFw4YNmTx5slrmUl5yOl/XrVvHp59+io2NDcbGxowZM6ZA3w8F6aMFOa/hVXDpzc9iw4YN3L17F8i/f+X1dyOrj2QFvOBVoNfJyUnj88nFxQVtbW3l/3l9j2cZOHAgP/zwA7Vr12bcuHGcPHky2zYGBga5fsePHDmSe/fuKa8rV65oVGchhBBCfPw+aEDDwsKiQBf7NWrU4P79+7mOk9+7dy8pKSmYmZmho6ODjs6rxJM2bdpQt25djetVrlw5HB0d6dChA0FBQXzxxRc8e/YMeHXHsUWLFsTHx6u9kpOT8fb2Rltbm927d7N9+3acnZ2ZO3cuTk5OXLx4Md/9GhkZ5bm+IPOIFMSSJUs4ffq00lY6OjqcPn1aGXaS1/CQN2UFeuD/LgpzWpbXkKHXt896T27bm5iYKMGmEiVKMHbsWKpUqaL8uM/Lm+07fPhw1q9fz4QJE4iKiiI+Ph5XV9d8JwTMzMzMNkdJTm2W3+eZpV69esTHx5OUlMTTp0/ZsGGD2iSHBSmnU6dOnDt3jmPHjhETE8OVK1fo2LFjgfafkZGBtrY2cXFxan06MTGR2bNnK8eX3zFnZGQQFBSkVkZCQgLJycno6+tja2tLUlISP/74IwYGBnzzzTd4e3trlNqeUz3yWp6frIu2cuXKKcfw1VdfqR3DiRMnSE5Opnz58tkCIAWR33n7tv3wfdQVXgU8nz59yieffKJ8P3z33XccPHiQM2fOAAX/jsjp3NbkfM+pnPy+UwIDAzl9+jTNmjVj7969ODs7s3Hjxnzr+uZ5dujQITp27EjTpk3ZunUrx48fZ9SoUW/1/ZDT8oKc12fOnCE2NpaAgADls6hRowZPnjxh9erVQP79K6/1uX2Or9dVS0sr23Y5nbNv87n26tWLCxcu4O/vT0JCAl5eXmrBXIDbt2/nGvjR09OjaNGiai8hhBBC/Ld80ICGh4eH8gM5L8ePH0dfXz/XO4sjRozg5MmTaj/s4dX8DllzYLwtf39/MjIymD9/PgCenp6cPn0aOzs7HBwc1F5ZP1BVKhW1a9cmKCiI48ePo6urq/yg1tXVfesZ293c3NizZ0+u6wtSdkJCgjJB3uvttX//fo4cOcKpU6dwcHCgSJEiauPa79y5o/HjVt8XHR0dGjZsyNSpUzl58qQyRwBo1r5RUVF0796dL774AldXV2xsbLKlNudUnrOzMwcOHFBbFhMTg6Ojo9odyoIyMjLCwcGBsmXLZrsoKKjSpUvj7e3NqlWrWLVqFQ0bNlSyY/Lj4eFBeno6169fz9ans9LDK1asyOHDh9Xe9/pkr/Dq3EhKSspWhoODg3JH3sDAgJYtWzJnzhwiIiI4ePAgCQkJOdYrt7ZPTU1VuxN75swZ7t27V6ChFG/KmlOhYcOGyjGcPn06x2PQ1dWlUqVKvHz5Uu3Yk5KS8gyoubm5ER8fz+3bt3Nc/7b98H3UFV5lBAwdOjRboKRevXpKloazs3O2eS/ymwfj7+To6MjgwYPZtWsXrVu3Vv4OaPL9EB0dTdmyZRk1ahReXl5UqFCBy5cvq23zvvtoaGgo3t7enDhxQu3zCAgIUALQ+fWvvP5uODs78/LlS7UJRG/dusW5c+eUulpaWnLt2jW1oEZec37kJre2t7W1pW/fvmzYsIGhQ4eyePFitfWnTp1ShhUKIYQQQrzpgwY0fHx8OH36tFqWxpYtW1i8eDGnTp0iJSWFn376iVGjRtGnTx/lUX5Xr15Vu8CysbGhcuXKai+AMmXKKHde35aWlhaDBg1i8uTJPH78mH79+nH79m38/Pw4fPgwFy5cYNeuXfTo0YP09HRiY2OZOHEiR48eJTU1lQ0bNnDjxg3lx6GdnR0nT54kKSmJmzdvanR3ety4caxevZpx48aRmJhIQkICU6dOVdbb2dmxf/9+rl69qvbUldeFhoZSvXp1vL291drr008/pWbNmoSGhmJsbEzPnj0ZPnw4e/bs4dSpU3Tv3l0tTfxD2bp1K3PmzCE+Pp7Lly+zfPlyMjIylOEwdnZ2xMbGcunSJW7evJnnHUIHBwc2bNigXLB16tQp2/Y5tenQoUPZs2cP48eP59y5cyxbtox58+ZpPF9GYevcuTPh4eHKBIIF5ejoSOfOnenatSsbNmzg4sWLHDlyhClTpihPHBgwYADbtm0jJCSE5ORkFi1axPbt29XuOI8dO5bly5crd8gTExNZs2aNMg9BWFgYoaGhnDp1igsXLrBixQoMDAyUuWbeZGdnx8WLF4mPj+fmzZs8e/aMhg0b4ubmRufOnTl27BiHDx+ma9eu1KlTJ98hPnfv3uXatWtcvnyZ3bt307ZtW37++WcWLFigBEuzMhH69eunZF5t3ryZAQMGAODk5ESTJk3o3bs3sbGxxMXF0atXrzzvgvv5+WFjY8Pnn39OdHQ0Fy5cYP369cqTOt62H76PusbHx3Ps2DF69eqV7TvVz8+P5cuX8+LFCwYOHMiOHTuYOnUq586dY968ednmz/gQnjx5Qv/+/YmIiODy5ctER0dz5MgRte/fhw8fsmfPHm7evJnncDUHBwdSU1MJDw8nJSWFOXPmZMv0KOw++roXL16wYsUK/Pz8sn0WvXr1Ii4ujhMnTuTbv/L6u1GhQgVatWpF7969OXDgACdOnKBLly6UKlVKmY+qbt263Lhxg6lTp5KSksKPP/7I9u3bNfpcstrqzT48aNAgdu7cycWLFzl27Bh79+5VC/pcunSJq1evKgFHIYQQQog3fdArVFdXV7y8vFi7dq2yrEiRIsyfP5+aNWvi5ubG7NmzCQ4OVnss5osXL0hKSirw3AlZ6tatqzZpY0H16NGDFy9eMG/ePEqWLEl0dDTp6en4+PhQuXJlvv32W0xNTdHS0qJo0aLs378fX19fHB0dGT16NDNmzKBp06YA9O7dGycnJ7y8vLC0tCQ6Olqj+v/yyy9s3rwZd3d36tevr3ZnLTg4mEuXLlG+fPkcU3SfP3/OypUradOmTY7lt2nThpUrV/L8+XOmTZuGt7c3LVu2pGHDhnz66adUrVpVw5YrfGZmZmzYsIH69etTqVIlFi5cyOrVq5UJ84YNG4a2tjbOzs5YWlrmOd595syZmJubU6tWLVq0aIGPjw+enp5q2+TUpp6enqxdu5bw8HAqV67M2LFjCQ4Ofqu+VZjatWvHrVu3ePz4cZ6PaM3J0qVL6dq1K0OHDsXJyYmWLVsSGxurzANQu3ZtFi5cSEhICFWqVGHHjh0MHjxYbQ4cHx8ftm7dyu7du6lWrRo1atQgJCRECViYmZmxePFiateurdw13rJlC8WLF8+xTm3atKFJkybUq1cPS0tLVq9erTzS09zcHG9vbxo2bIi9vT1r1qzJ9xi//PJLSpQoQcWKFfn6668xNjbm8OHDdOrUSdnGzc2NyMhIkpOT+eyzz/Dw8GDMmDHKvA1ZbWVra0udOnVo3bo1ffr0URsi9CZdXV127dqFlZUVvr6+uLq6MnnyZCWb52374fuoa2hoKM7OzjlOtpn15I4tW7ZQo0YNfvrpJ+bOnYu7uzu7du3Kc9Lmv4u2tja3bt2ia9euODo60r59e5o2bapMHFmrVi369u1Lhw4dsLS0VAsIv6lVq1YMHjyY/v374+7uTkxMTLanFRV2H33d5s2buXXrFl988UW2dRUqVMDV1ZXQ0NB8+1d+fzeWLl1K1apVad68OTVr1iQzM5Nt27Yp2WKVKlVi/vz5/Pjjj1SpUoXDhw+/VfA2pz6cnp5Ov379qFSpEk2aNMHJyUnJhgRYvXo1jRs3zjXoKYQQQgihytRkwoT3YNu2bQwbNoxTp0699wwAOzs7AgMDP/iFpxD/Br179+bs2bO5PlJRCCHe1rNnz6hQoQKrV6/ONglzbu7fv4+pqSm2g9aipWf4nmsohBDi0uRmH7oK4l8g6+/3vXv33mo+rA/62FZ49djP5ORkrl69qjYrfGE7e/YsJiYmdO3a9b3tQ4h/s+nTp9OoUSOMjIzYvn07y5YtU7ubKoQQheXy5cuMGjWqwMEMIYQQQvw3ffAMDSHEx6F9+/ZERETw4MED7O3tGTBgAH379v3Q1RJCCEAyNIQQ4u8mGRqiMLxrhoYENIQQQgjx0XvXH0RCCCGE+Pu969/vD//YCiGEEEIIIYQQQggNSUBDCCGEEEIIIYQQHx0JaAghhBBCCCGEEOKjIwENIYQQQgghhBBCfHQkoCGEEEIIIYQQQoiPjgQ0hBBCCCGEEEII8dGRgIYQQgghhBBCCCE+OhLQEEIIIYQQQgghxEdHAhpCCCGEEEIIIYT46EhAQwghhBBCCCGEEB8dCWgIIYQQQgghhBDioyMBDSGEEEIIIYQQQnx0dD50BYQQQgghhBBC/Delp6fz4sWLD10N8Z4UKVIEbW3t91a+BDSEEEIIIYQQQvytMjMzuXbtGnfv3v3QVRHvmZmZGTY2NqhUqkIvWwIaQgghhBBCCCH+VlnBDCsrKwwNDd/Lxa74sDIzM3n8+DHXr18HoESJEoW+DwloCCGEEEIIIYT426SnpyvBjOLFi3/o6oj3yMDAAIDr169jZWVV6MNPZFJQIYQQQgghhBB/m6w5MwwNDT9wTcTfIetzfh9zpUhAQwghhBBCCCHE306Gmfw3vM/PWYacCCGEEOJfo/K4nWjpyR0/IQrLpcnNPnQVhBAiV5KhIYQQQgghhBBC/IupVCo2bdpU4O0jIiJQqVT/+KfQSIaGEEIIIYQQQoh/BLsRv/2t+9M0C6lu3bq4u7sza9Ys4NWFf7169bhz5w5mZmaFX8FCkpaWhrm5eaGWGRgYyKZNm4iPjy/UcjUhAQ0hhBBCCCGEEOJfzMbG5kNX4b2QISdCFMCtW7ewsrLi0qVLH7oqfytNU9P+y7p3787nn3/+oavxr/VPbd+6desyaNAg5f92dnbKHRvx9rZu3YqHhwcZGRkfuipCCCGEonv37kRGRjJ79mxUKhUqlYp69eoBYG5ujkqlonv37sCr3wj9+/enf//+mJmZUbx4cUaPHk1mZma++5k7dy6urq7K/zdt2oRKpeLHH39Ulvn4+DBy5Ejl/1u2bKFq1aro6+tjb29PUFAQL1++VNa/+bs+JiYGd3d39PX18fLyUvbxZrZFXFwcXl5eGBoaUqtWLZKSkgAICwsjKCiIEydOKG0RFhZW0KYsNBLQEKIAJk2aRIsWLbCzs8u27tatW5QuXVqjMWaZmZk0bdr0rQIGH8t4tr/bP/1CMutzMzc35+nTp2rrDh8+rPwh+C8LDAxU2uH11++//87s2bM/yB/Jj8Xx48dp3rw5VlZW6OvrY2dnR4cOHbh58+aHrtpbad68OSqVip9//vlDV0UIIYRQzJ49m5o1a9K7d2/S0tL4448/WLduHQBJSUmkpaUxe/ZsZftly5aho6NDbGwsc+bMYebMmfz000/57qdu3bqcPn1a+TseGRmJhYUFkZGRALx8+ZKYmBjq1KkDwM6dO+nSpQsDBw7kzJkzLFq0iLCwMCZMmJBj+Q8ePKBFixa4urpy7Ngxxo8fz3fffZfjtqNGjWLGjBkcPXoUHR0devToAUCHDh0YOnQoLi4upKWlkZaWRocOHQrYkoVHAhpC5OPJkyeEhobSq1evHNf37NkTNzc3jcqcNWvWf/7i9b/KxMSEjRs3qi1bsmQJZcqU+UA1+md5/Y9i1svb2xtTU9M8x6U+f/7876vkP8z169dp2LAhFhYW7Ny5k8TERJYsWUKJEiV4/PjxW5f7vto0MzNT7Y5Rbr788kvmzp37XuoghBBCvA1TU1N0dXUxNDTExsaGUqVKUbx4cQCsrKywsbHB1NRU2d7W1paZM2fi5ORE586dGTBgADNnzsx3P5UrV6Z48eJKACMiIoKhQ4cq/z9y5AhPnz7l008/BWDChAmMGDGCbt26YW9vT6NGjRg/fjyLFi3KsfxVq1ahUqlYvHgxzs7ONG3alOHDh+e47YQJE6hTpw7Ozs6MGDGCmJgYnj59ioGBAcbGxujo6GBjY4ONjQ0GBgYFb8xCIgENIfKxfft2dHR0qFmzZrZ1CxYs4O7duwwbNqzA5Z04cYKQkBCWLFlSKPULCwvDzMyMrVu34uTkhKGhIW3btuXRo0csW7YMOzs7zM3NGTBgAOnp6cr77OzsGD9+PJ06dcLY2JiSJUvme/Fw9epVOnTogLm5OcWLF6dVq1Zqw3CyhgVMnDgRa2trzMzMlHS34cOHU6xYMUqXLp3t2Ata7vTp0ylRogTFixenX79+vHjxAngVxb58+TKDBw/ON9MhJCQEV1dXjIyMsLW15ZtvvuHhw4fZ2nPnzp1UqlQJY2NjmjRpQlpamrJNeno6Q4YMUdIHAwICCpQ+CNCtWze143/y5Anh4eF069ZNbbtbt27h5+dH6dKlMTQ0xNXVldWrV6ttU7duXQYOHEhAQADFihXDxsaGwMBAjY4XYPHixdja2mJoaMgXX3xBSEhItuBBfmmMbzpy5AiNGjXCwsICU1NT6tSpw7Fjx/Jtn9f/KGa9dHV1sw05yUrjV35KYgAAHANJREFUHDJkCBYWFjRq1AiAM2fO4Ovri7GxMdbW1vj7+yt3N7Zs2YKZmZkyjCE+Ph6VSqX2B/yrr77Cz8+vwJ+BpjIyMggODqZ06dLo6enh7u7Ojh07lPWXLl1CpVKxYcMG6tWrh6GhIVWqVOHgwYO5lhkTE8P9+/f56aef8PDwoFy5ctSvX59Zs2apBcryapvc2tTPz4+OHTuq7e/FixdYWFiwdOlS4FWAYurUqdjb22NgYECVKlWUu1Xwf9lJO3fuxMvLCz09PaKiojhx4gT16tXDxMSEokWLUrVqVY4ePaq8r2XLlhw+fJgLFy68fYMLIYQQH1CNGjXUfpfWrFmT5ORktd/kOVGpVHh7exMREcHdu3c5ffo0ffv2JT09ncTERCIiIvD09MTY2Bh4NSwkODgYY2Nj5ZWVRZLTzY2kpCTc3NzQ19dXllWvXj3Hurx+47ZEiRLAq5sp/xQS0BAiH/v378fLyyvb8jNnzhAcHMzy5cvR0irYqfT48WP8/PyYN29eoU7M8/jxY+bMmUN4eDg7duwgIiKC1q1bs23bNrZt28aKFSv43//+p3aRATBt2jTc3Nw4duwYI0eOZPDgwezevTvXfdSrVw9jY2P279/PgQMHlIv91+/k7t27lz///JP9+/cTEhJCYGAgzZs3x9zcnNjYWPr27Uvfvn25cuWKRuXu27ePlJQU9u3bx7JlywgLC1OGIGzYsIHSpUsTHBys3NXPjZaWFnPmzOHUqVMsW7aMvXv3EhAQkO1Yp0+fzooVK9i/fz+pqalqQasZM2awZMkSQkNDOXDgALdv386WdZEbf39/oqKiSE1NBWD9+vXY2dnh6emptt3Tp0+pWrUqW7du5dSpU/Tp0wd/f39iY2PVtlu2bBlGRkbExsYydepUgoOD1T7D/I43Ojqavn378u233xIfH0+jRo2ypSdqmsYIr1IZu3XrRlRUFIcOHaJChQr4+vry4MGDArVTQWSlcUZHR7No0SLS0tKoU6cO7u7uHD16lB07dvDXX3/Rvn17ALy9vXnw4AHHjx8HsqdvwquL76z0zYJ+BpqYPXs2M2bMYPr06Zw8eRIfHx9atmxJcnKy2najRo1i2LBhxMfH4+joiJ+fX64BJBsbG16+fMnGjRtzDazl1zZZ3mzTzp07s3nzZrUg2M6dO3n06BFt2rQBYPTo0SxdupQFCxZw+vRpBg8eTJcuXdTaFSAgIIBJkyaRmJiIm5sbnTt3pnTp0hw5coS4uDhGjBhBkSJFlO3Lli2LlZUVUVFROR7Ts2fPuH//vtpLCCGE+LeoW7cuERERREVFUaVKFczMzPD29iYyMpKIiAjq1q2rbJuRkUFQUBDx8fHKKyEhgeTkZLWgRZbMzMxsNwBz+w3x+t/mrPf8k+a4kqecCJGPS5cuUbJkSbVlz549w8/Pj2nTplGmTJkC30EcPHgwtWrVolWrVoVaxxcvXrBgwQLKly8PQNu2bVmxYgV//fUXxsbGODs7U69ePfbt26c2tq127dqMGDECAEdHR6Kjo5k5c6Zyt/t14eHhaGlp8dNPPylfZkuXLsXMzIyIiAgaN24MQLFixZgzZw5aWlo4OTkxdepUHj9+zPfffw/AyJEjmTx5MtHR0XTs2LHA5ZqbmzNv3jy0tbWpWLEizZo1Y8+ePfTu3ZtixYqhra2NiYlJvoGi1ydwLFeuHOPHj+frr79m/vz5au25cOFCpT379+9PcHCwsn7WrFmMHDlSuaBbuHAhO3fuzO9jAl6lIzZt2pSwsDDGjh3LkiVLlLGIrytVqpRaEGXAgAHs2LGDX375hU8++URZ7ubmxrhx4wCoUKEC8+bNY8+ePcpnmN/xzp07l6ZNmyr7cnR0JCYmhq1btyrvez2NEcDe3p7x48cTEBCg7PtN9evXV/v/okWLMDc3JzIykubNm+faPgkJCcrdBgBnZ2cOHz6c47YODg5MnTpV+f/YsWPx9PRk4sSJyrIlS5Zga2vLuXPncHR0xN3dnYiICKpWrUpERASDBw8mKCiIBw8e8OjRI86dO6f8QCjoZ6CJ6dOn89133ylZD1OmTGHfvn3MmjVLbaKvYcOG0azZq8fIBQUF4eLiwvnz56lYsWK2MmvUqMH3339Pp06d6Nu3L9WrV6d+/fp07doVa2tr4FU2WX5tk1Obli9fHiMjIzZu3Ii/vz8AP//8My1atKBo0aI8evSIkJAQ9u7dq2Sx2dvbc+DAARYtWqQEhwCCg4PVvltSU1MZPny4ckwVKlTIdmylSpXKdTLmSZMmERQUlFtTCyGEEO+Frq6uWoaFrq4uQI5ZF4cOHcr2/woVKqCtrZ3vfurWrcu3337LunXrlN8mderU4ffffycmJoZvv/1W2dbT05OkpCQcHBwKdAwVK1Zk1apVPHv2DD09PQC1LMmCerMtPgTJ0BAiH0+ePMkW2Rw5ciSVKlWiS5cuBS5n8+bN7N27971MXGloaKhcfANYW1tjZ2endmFobW2dLT3szWE0NWvWJDExMcd9xMXFcf78eUxMTJRUtmLFivH06VNSUlKU7VxcXNQyVqytrdVmadbW1qZ48eJKXTQp9/Uv/xIlSrxVutu+ffto1KgRpUqVwsTEhK5du3Lr1i0ePXqkbPNme76+r3v37pGWlqbWdjo6Ojlm8eSmR48ehIWFceHCBQ4ePEjnzp2zbZOens6ECRNwc3OjePHiGBsbs2vXLiWzI8ub87e82S75HW9SUlK2FMM3/69pGiO8SkXs27cvjo6OmJqaYmpqysOHD7PV/01OTk5qdxfWr1+f67ZvtnlcXBz79u1Tq2fWxXJWX8q625GZmUlUVBStWrWicuXKHDhwgH379mFtba28p6CfQUHdv3+fP//8k9q1a6str127drbzTtP0zgkTJnDt2jUWLlyIs7MzCxcupGLFiiQkJBS4bSB7mxYpUoR27dqxatUqAB49esSvv/6q9NkzZ87w9OlTGjVqpFb28uXL1crNqewhQ4bQq1cvGjZsyOTJk7NtD2BgYJBrHxs5ciT37t1TXllZX0IIIcT7ZGdnR2xsLJcuXeLmzZuULVsWlUrF1q1buXHjhlpW45UrVxgyZAhJSUmsXr2auXPnqgUi8pI1j8aqVauUgEbdunXZtGkTT548UebPgFc3dZYvX05gYCCnT58mMTGRNWvWMHr06BzL7tSpExkZGfTp04fExER27tzJ9OnTATSa58/Ozo6LFy8SHx/PzZs3efbsWYHfW1gkQ0OIfFhYWHDnzh21ZXv37iUhIUEZwpGVomVhYcGoUaNyvGu4d+9eUlJSss1N0KZNGz777DMiIiLeuo6vp4LBqy+inJYVJD0sty+xjIwMqlatqlzYvM7S0vKt6/Iu5Wqa7nb58mV8fX3p27cv48ePp1ixYhw4cICePXsq83Hktq+CzpFREL6+vnz11Vf07NmTFi1aKJNJvW7GjBnMnDmTWbNmKXNgDBo0KNtEjXm1S0GOtyAph1lpjK1bt85Wz5zSGOHVvCc3btxg1qxZlC1bFj09PWrWrJnvRJO6uroFvrtgZGSUrZ4tWrRgypQp2bbNCgrUrVuX0NBQTpw4gZaWFs7OztSpU4fIyEju3LmjllFQ0M9AUzm195vL3ia9s3jx4rRr14527doxadIkPDw8mD59OsuWLStQ20D2NgXo3LkzderU4fr16+zevRt9fX2a/r/27j0oqvKNA/h3uSyKwY4ICCggogQEAoomlEKaEDaOqaMmKBgzloUKCE2MDULqhDJKZl7IcXLwUmBjmhdwZCZF1LGApIgUKVcxlTC8gIYa+P7+cHbHBVZ2cWE9+/t+ZnbGPWf37PPwyC77nPe8b1SURkyHDx/GoEGDNJ6nOuOj7diZmZmIjo7G4cOHUVRUhIyMDOTn52PatGnqx9y8eVPjfaD98du/BhERUU9LTU1FXFwcfH190dLSAqVSiU8++QRpaWl45513EBsbq74sOjY2Fi0tLRgzZgzMzc2xePFivPvuuzq9jkwmQ1hYGPbv349x48YBeHzCQ6FQYOjQobC1tVU/NjIyEocOHcKKFSuQnZ0NS0tLeHt7a13UwNbWFgcPHsT777+PwMBA+Pv7Y/ny5YiOjtb6t11nZsyYoZ736/bt29i+fbt62drewoYGUReCgoKwa9cujW179+5FS0uL+n5ZWRni4+NRWlqqcWb/SWlpaR3eVPz9/fHZZ59hypQphg9cB50Ng+tsSDvweChbQUEBHB0dNd5An5WhjqvLkLfy8nK0trZi3bp16lEke/bs0et1FAoFnJ2dcebMGYwfPx7A46WzKioqOsyDoY25uTnmzZuH7OxsFBUVdfoY1egB1SigR48eoba2Fj4+PjrHqku+3t7eHS7paD/kUN9hjKr4N2/ejMmTJwN4fIaip5cQHTlypHpOEguLzj/eVPNorF+/HmFhYeo/FrKysnDr1i2NsyaGqMGTbG1t4eLigpMnT6r/7wCPJ/XUNhFXd8nlcnh6eqpH4ujys9EmNDQUrq6uKCgoQFFREWbOnKkeXuvr6wsrKyvU1dVpNIN05eXlBS8vLyQnJ2POnDnYvn27uqGhGqUVFBSk93GJiEi6Lq1+09ghPJWXl1eHybrT09ORnp7e4bGWlpZYv349tmzZ0q3Xaj//nUwmQ2NjY6ePjYyMRGRkpNZjtT9hFRoail9++UV9f/fu3bC0tFRPKB4eHt7hOYGBgRrbrKysOsTY23jJCVEXIiMjUV1drTFKw9PTE35+fuqbh4cHAMDHxweOjo4AHq/c8eSXRScnJ43n+Pn5AQDc3NzUz+9tp06dQnZ2Ni5cuIBNmzbh22+/1ToMLiYmBvb29pg6dSpKS0uhVCpRUlKCxMRE/PXXX92OwVDHHTJkCE6cOIGrV69q/eLs6emJ1tZWfPHFF7h48SJ27tyJ3NxcvWNOTEzE6tWrsW/fPpw/fx4ffPABbt++rdcxVq5ciRs3bmj94Bk2bBiKi4tx+vRpnDt3Du+99x7q6+v1eg1d8l28eDEKCwuRk5OD2tpafPnllygqKtIYMaDvMEZV/Dt37sS5c+fw448/IiYmpseX8kpISMDNmzcxZ84c9eoYR48eRXx8vLrZpVAoEBgYiF27dqmHb44fPx4///yzxvwZqhyetQbtffjhh1izZg0KCgpQU1ODtLQ0VFZW6jz8tDOHDh3C3LlzcejQIVy4cAE1NTVYu3YtCgsL1fP16PKz0UYmkyE6Ohq5ubkoLi7WuNTOxsYGqampSE5ORl5eHv7880+cPXsWmzZtQl5entZjtrS0YNGiRTh+/DguX76MU6dOoaysTKNZdObMGfXIHiIiIjKsHTt24OTJk1Aqldi/fz8++ugjzJo1yyhLrz4LNjSIuuDv74/g4GC9z+T/999/qKmp0Xr9tzbh4eG9NlQrJSUFFRUVCAoKwsqVK7Fu3TqtX7Ctra1x4sQJuLm5Yfr06fDx8UF8fDxaWlqeaWSFoY67YsUKXLp0CZ6enlqHqAcGBiInJwdr1qyBn58fdu/ejaysLL1jTklJQWxsLObPn4+QkBDY2NhoDJPXhVwuh729vdZLfNLT0zFy5EhERkYiPDwcTk5OGsuW6kKXfF955RXk5uYiJycHAQEBOHLkCJKTkzWGG6qGMRYXF2P06NEYO3YscnJy4O7urvW1v/rqK9y6dQtBQUGYN28elixZom729RQXFxecOnUKbW1tiIyMhJ+fHxITE6FQKDTmdXnttdfQ1tambl70798fvr6+cHBw0PhCbYgatLdkyRKkpKQgJSUF/v7+OHLkCA4cONDphJi68vX1hbW1NVJSUhAYGIixY8diz5492LZtm3oiT11/NtrExMTg999/x6BBgzrMAbJy5UosX74cWVlZ8PHxQWRkJA4ePPjURq25uTkaGxsRGxsLLy8vzJo1C1FRURqX633zzTeIiYmBtbV1N38yREREz6fS0lKNuafa33pDfX095s6dCx8fHyQnJ2PmzJnYunVrr7y2IcmEIS8MJzJRhYWFSE1NxW+//abzEq3dNWTIEGRmZvZ4U2PIkCFISkrSWAWDCAAWLFiA8+fPa10uk6in3bhxA97e3igvL9d5BFtTUxMUCgVck/bAzIpNECJDed6H/5M03b9/H0qlEh4eHnrN2WAqWlpacPXqVa379bnMVwqeVm/V5/edO3e6dZKUc2gQ6WDy5Mmora3F1atX4erq2mOvc/78efVKFES9Ze3atZg0aRL69euHoqIi5OXlaSxjS9TblEolNm/ebLTL8YiIiHpS3759Ta5pYSxsaBDp6FmucdfVk8ssEvWWn376CdnZ2WhubsbQoUOxYcMGrbNiE/WGMWPGGHyiVCIiIjI9bGgQ/Z+6dOmSsUOg54S+88MQPc9++yTSoCsxERFRz+lqSXIyDT1ZZzY0iIiIiIiIqNfI5XKYmZnh2rVrcHBwgFwu1zpROkmXEAIPHz7EjRs3YGZmpl723ZDY0CAiIiIiIqJeY2ZmBg8PD1y/fh3Xrl0zdjjUw6ytreHm5tYjiyuwoUFERERERES9Si6Xw83NDa2trWhrazN2ONRDzM3NYWFh0WMjcNjQICIiIiIiol4nk8lgaWkJS0tLY4dCEmX4MR9ERERERERERD2MDQ0iIiIiIiIikhw2NIiIiIiIiIhIcjiHBhEREUmeEAIA0NTUZORIiIiISFeqz23V57i+2NAgIiIiyWtsbAQAuLq6GjkSIiIi0ldzczMUCoXez2NDg4iIiCTPzs4OAFBXV9etP4ied01NTXB1dcWVK1dga2tr7HB6hKnnyPykzdTzA0w/R+b3fBJCoLm5GS4uLt16PhsaREREJHlmZo+nBVMoFJL6Q05ftra2Jp0fYPo5Mj9pM/X8ANPPkfk9f57lRAQnBSUiIiIiIiIiyWFDg4iIiIiIiIgkhw0NIiIikjwrKytkZGTAysrK2KH0CFPPDzD9HJmftJl6foDp58j8TJNMdHd9FCIiIiIiIiIiI+EIDSIiIiIiIiKSHDY0iIiIiIiIiEhy2NAgIiIiIiIiIslhQ4OIiIiIiIiIJIcNDSIiIiIiIiKSHDY0iIiISPI2b94MDw8P9OnTB6NGjUJpaamxQzKIzMxMyGQyjZuTk5Oxw+q2EydOYMqUKXBxcYFMJsP+/fs19gshkJmZCRcXF/Tt2xfh4eGorq42TrDd1FWO8+fP71DTsWPHGidYPWVlZWH06NGwsbGBo6Mj3nrrLdTU1Gg8Rso11CU/KdcPALZs2YIRI0bA1tYWtra2CAkJQVFRkXq/lOsHdJ2f1OvXXlZWFmQyGZKSktTbpF5DfbGhQURERJJWUFCApKQkfPzxxzh79izGjRuHqKgo1NXVGTs0g3jppZdw/fp19a2qqsrYIXXbvXv3EBAQgI0bN3a6Pzs7Gzk5Odi4cSPKysrg5OSESZMmobm5uZcj7b6ucgSAN954Q6OmhYWFvRhh95WUlCAhIQFnzpxBcXExWltbERERgXv37qkfI+Ua6pIfIN36AcDgwYOxevVqlJeXo7y8HBMmTMDUqVPVX3ilXD+g6/wAadfvSWVlZdi6dStGjBihsV3qNdSbICIiIpKwMWPGiIULF2ps8/b2FmlpaUaKyHAyMjJEQECAscPoEQDEvn371PcfPXoknJycxOrVq9Xb7t+/LxQKhcjNzTVChM+ufY5CCBEXFyemTp1qlHgMraGhQQAQJSUlQgjTq2H7/IQwrfqp9O/fX2zbts3k6qeiyk8I06lfc3OzGD58uCguLhZhYWEiMTFRCGF6v4O64AgNIiIikqyHDx+ioqICERERGtsjIiJw+vRpI0VlWLW1tXBxcYGHhwfefvttXLx40dgh9QilUon6+nqNWlpZWSEsLMxkaqly/PhxODo6wsvLCwsWLEBDQ4OxQ+qWO3fuAADs7OwAmF4N2+enYir1a2trQ35+Pu7du4eQkBCTq1/7/FRMoX4JCQl488038frrr2tsN7Ua6sLC2AEQERERddc///yDtrY2DBw4UGP7wIEDUV9fb6SoDOfll1/Gjh074OXlhb///hurVq1CaGgoqqurMWDAAGOHZ1CqenVWy8uXLxsjpB4RFRWFmTNnwt3dHUqlEunp6ZgwYQIqKipgZWVl7PB0JoTA0qVL8eqrr8LPzw+AadWws/wA06hfVVUVQkJCcP/+fbzwwgvYt28ffH191V94pV4/bfkBplG//Px8VFRUoLy8vMM+U/od1BUbGkRERCR5MplM474QosM2KYqKilL/29/fHyEhIfD09EReXh6WLl1qxMh6jqnWUmX27Nnqf/v5+SE4OBju7u44fPgwpk+fbsTI9LNo0SL8+uuvOHnyZId9plBDbfmZQv1efPFFVFZW4vbt29i7dy/i4uJQUlKi3i/1+mnLz9fXV/L1u3LlChITE3H06FH06dNH6+OkXkN98JITIiIikix7e3uYm5t3GI3R0NDQ4QyVKejXrx/8/f1RW1tr7FAMTrV6y/9LLVWcnZ3h7u4uqZouXrwYBw4cwLFjxzB48GD1dlOpobb8OiPF+snlcgwbNgzBwcHIyspCQEAAPv/8c5Opn7b8OiO1+lVUVKChoQGjRo2ChYUFLCwsUFJSgg0bNsDCwkJdJ6nXUB9saBAREZFkyeVyjBo1CsXFxRrbi4uLERoaaqSoes6DBw9w7tw5ODs7GzsUg/Pw8ICTk5NGLR8+fIiSkhKTrKVKY2Mjrly5IomaCiGwaNEifPfdd/jhhx/g4eGhsV/qNewqv85IqX7aCCHw4MEDyddPG1V+nZFa/SZOnIiqqipUVlaqb8HBwYiJiUFlZSWGDh1qkjV8Gl5yQkRERJK2dOlSzJs3D8HBwQgJCcHWrVtRV1eHhQsXGju0Z5aamoopU6bAzc0NDQ0NWLVqFZqamhAXF2fs0Lrl7t27+OOPP9T3lUolKisrYWdnBzc3NyQlJeHTTz/F8OHDMXz4cHz66aewtrZGdHS0EaPWz9NytLOzQ2ZmJmbMmAFnZ2dcunQJy5Ytg729PaZNm2bEqHWTkJCAr7/+Gt9//z1sbGzUZ4EVCgX69u0LmUwm6Rp2ld/du3clXT8AWLZsGaKiouDq6orm5mbk5+fj+PHjOHLkiOTrBzw9P1Oon42NjcacLsDjkXsDBgxQb5d6DfVmpNVViIiIiAxm06ZNwt3dXcjlcjFy5EiNZRalbPbs2cLZ2VlYWloKFxcXMX36dFFdXW3ssLrt2LFjAkCHW1xcnBDi8ZKDGRkZwsnJSVhZWYnx48eLqqoq4watp6fl+O+//4qIiAjh4OAgLC0thZubm4iLixN1dXXGDlsnneUFQGzfvl39GCnXsKv8pF4/IYSIj49Xv1c6ODiIiRMniqNHj6r3S7l+Qjw9P1OoX2eeXLZVCOnXUF8yIYTozQYKEREREREREdGz4hwaRERERERERCQ5bGgQERERERERkeSwoUFEREREREREksOGBhERERERERFJDhsaRERERERERCQ5bGgQERERERERkeSwoUFEREREREREksOGBhERERERERFJDhsaRERERERERCQ5bGgQERERERERkeSwoUFEREREREREkvM/kby739lWz7EAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ttp_control_merge[['Control ID','Control Name']].value_counts().nlargest(10).plot(kind='barh',title=\"Number of selected TTPs mitigated per control\")\n",
    "\n",
    "ttp_control_ranked = ttp_control_merge.groupby(['Control ID','Control Name']).sum()\n",
    "ttp_control_ranked.sort_values(by='ttp_weight',ascending=False, inplace=True)\n",
    "\n",
    "# ttp_control_ranked.nlargest(10,columns='ttp_weight').plot(kind='barh',title=\"Top controls by number of mitigated TTPs. No TTP threshold\")\n",
    "ttp_control_ranked.nlargest(10,columns='ttp_weight').sort_values('ttp_weight').plot(kind='barh',title=\"Top controls by number of mitigated TTPs. Top {}% of TTPS\".format(PERCENTAGE*100))\n",
    "ttp_control_ranked.reset_index(level=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control Name</th>\n",
       "      <th>ttp_weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Control ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.1</th>\n",
       "      <td>Establish and Maintain a Secure Configuration ...</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.3</th>\n",
       "      <td>Remediate Penetration Test Findings</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18.5</th>\n",
       "      <td>Perform Periodic Internal Penetration Tests</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.1</th>\n",
       "      <td>Establish an Access Granting Process</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6.2</th>\n",
       "      <td>Establish an Access Revoking Process</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8.9</th>\n",
       "      <td>Centralize Audit Logs</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2</th>\n",
       "      <td>Address Unauthorized Assets</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.7</th>\n",
       "      <td>Deploy and Maintain Email Server Anti-Malware ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12.1</th>\n",
       "      <td>Ensure Network Infrastructure is Up-to-Date</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9.1</th>\n",
       "      <td>Ensure Use of Only Fully Supported Browsers an...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Control Name  ttp_weight\n",
       "Control ID                                                               \n",
       "4.1         Establish and Maintain a Secure Configuration ...          42\n",
       "18.3                      Remediate Penetration Test Findings          41\n",
       "18.5              Perform Periodic Internal Penetration Tests          36\n",
       "6.1                      Establish an Access Granting Process          36\n",
       "6.2                      Establish an Access Revoking Process          36\n",
       "...                                                       ...         ...\n",
       "8.9                                     Centralize Audit Logs           1\n",
       "1.2                               Address Unauthorized Assets           1\n",
       "9.7         Deploy and Maintain Email Server Anti-Malware ...           1\n",
       "12.1              Ensure Network Infrastructure is Up-to-Date           1\n",
       "9.1         Ensure Use of Only Fully Supported Browsers an...           1\n",
       "\n",
       "[95 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ttp_control_ranked"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations\n",
    "There are some controls that map to a lot of TTPs. For the complete results for the ‘Government’ sector, there are 215 TTPs and 106 controls. Merging the two gives a resulting set of 2545 mappings. Since there is no measure of control effectiveness to a TTP in this data, they are sorted based on the TTPs they control and their respective weights.\n",
    "\n",
    "There are two ways to approach the control mapping, either a generic approach or a targeted approach.\n",
    "- Targeted: Take the most prominent TTPs, those with the highest weights, and display the controls for this. \n",
    "- Generic: Take the complete, or near-complete, set of TTPs used by the actors. Rank the controls based on the number of TTPs they control and their respective weights. The downside of this is that controls that map to a large number of TTPs will probably always end up on top. This means that the initial TTP ranking matters less.\n",
    "\n",
    "When simply counting the number of TTPs that a control covers, only the general ones come to the top. Even if the TTP weights are considered. One possibility to solve this, is to only take the top x% of TTPs into consideration."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get TTPs mitigated by said control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Control ID</th>\n",
       "      <th>Control Name</th>\n",
       "      <th>Description</th>\n",
       "      <th>Technique ID</th>\n",
       "      <th>ttp_weight</th>\n",
       "      <th>technique</th>\n",
       "      <th>tactic</th>\n",
       "      <th>technique_detection</th>\n",
       "      <th>technique_name_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Control ID, Control Name, Description, Technique ID, ttp_weight, technique, tactic, technique_detection, technique_name_id]\n",
       "Index: []"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "control_id = \"A.13.1.1\"\n",
    "ttp_control_merge.loc[ttp_control_merge['Control ID'] == control_id]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Port to ISO 27002:2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iso_2022_2013 = pd.read_excel(\"data/mappings/ISO_27001_2022_2013.xlsx\")\n",
    "\n",
    "# ttp_control_ranked_2013 = ttp_control_ranked.merge(iso_2022_2013,left_index=True,right_on='Control-nr. 2022',how=\"left\")\n",
    "# ttp_control_ranked_2013.drop(['Control-nr. 2022'],axis=1,inplace=True)\n",
    "# ttp_control_ranked_2013 = ttp_control_ranked_2013.drop_duplicates()\n",
    "# ttp_control_ranked_2013 = ttp_control_ranked_2013.groupby(['Control-nr. 2013', 'Control-Name 2013']).sum()\n",
    "\n",
    "# ttp_control_ranked_2013.sort_values(by='ttp_weight',ascending=False)\n",
    "# ttp_control_ranked_2013.to_excel(\"Ranked controls to ISO 2013.xlsx\")\n",
    "\n",
    "# ttp_control_ranked_2013 = ttp_control_ranked_2013.drop_duplicates()\n",
    "# ttp_control_ranked_2013.to_excel(\"Ranked controls to ISO 2013 RM duplicates.xlsx\")\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Export results\n",
    "Export the results to an excel with three sheets:\n",
    "- Actors\n",
    "- TTPs\n",
    "- Controls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = pd.ExcelWriter(\"results/{} Result Sheet ransomware TTPs.xlsx\".format(CONTROLS),engine=\"xlsxwriter\")\n",
    "\n",
    "ttp_control_ranked.to_excel(writer,sheet_name=\"Controls\")\n",
    "ttp_control_merge.to_excel(writer,sheet_name=\"TTP and Control mitigations\",index=False)\n",
    "ttps_enriched.to_excel(writer,sheet_name=\"TTPs\")\n",
    "\n",
    "writer.close()"
   ]
  }
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