{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pypair.association import binary_binary, continuous_continuous, binary_continuous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preparing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reading and filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bd_all = pd.read_spss('17_abril.sav')\n",
    "\n",
    "# Filter the dataset to work only with alcohol patients\n",
    "bd = bd_all[bd_all['Alcohol_DxCIE'] == 'Sí']\n",
    "\n",
    "# Filter the dataset to work only with 'Situacion_tratamiento' == 'Abandono' or 'Alta'\n",
    "bd = bd[(bd['Situacion_tratamiento'] == 'Abandono') | (bd['Situacion_tratamiento'] == 'Alta terapéutica')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Defining sets of patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-pandemic\n",
    "conj_pre = bd[bd['Pandemia_inicio_fin_tratamiento'] == 'Inicio y fin prepandemia']\n",
    "# Pre-pandemic abandono\n",
    "pre_abandono = conj_pre[conj_pre['Situacion_tratamiento'] == 'Abandono']\n",
    "# Pre-pandemic alta\n",
    "pre_alta = conj_pre[conj_pre['Situacion_tratamiento'] == 'Alta terapéutica']\n",
    "\n",
    "# Post-pandemic\n",
    "# Merging last two classes to balance sets\n",
    "conj_post = bd[(bd['Pandemia_inicio_fin_tratamiento'] == 'Inicio prepandemia y fin en pandemia') | \n",
    "               (bd['Pandemia_inicio_fin_tratamiento'] == 'inicio y fin en pandemia')]\n",
    "# Post-pandemic abandono\n",
    "post_abandono = conj_post[conj_post['Situacion_tratamiento'] == 'Abandono']\n",
    "# Post-pandemic alta\n",
    "post_alta = conj_post[conj_post['Situacion_tratamiento'] == 'Alta terapéutica']\n",
    "\n",
    "# Concatenate the two data frames and add a new column to distinguish between them. Useful for plots\n",
    "conj_post['Group'] = 'Post'\n",
    "conj_pre['Group'] = 'Pre'\n",
    "combined_pre_post = pd.concat([conj_post, conj_pre])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Inspecting the dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"PRE\")\n",
    "print(conj_pre.info())\n",
    "print (\"-------------------------------\")\n",
    "print(\"PRE-ABANDONO\")\n",
    "print(pre_abandono.info())\n",
    "print (\"-------------------------------\")\n",
    "print(\"PRE-ALTA\")\n",
    "print(pre_alta.info())\n",
    "print (\"-------------------------------\")\n",
    "\n",
    "print(\"\\n\\n\\n\")\n",
    "\n",
    "print (\"POST\")\n",
    "print(conj_post.info())\n",
    "print (\"-------------------------------\")\n",
    "print(\"POST-ABANDONO\")\n",
    "print(post_abandono.info())\n",
    "print (\"-------------------------------\")\n",
    "print(\"POST-ALTA\")\n",
    "print(post_alta.info())\n",
    "print (\"-------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Replacing unknown values with the mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9.0 represents unknown according to Variables.docx \n",
    "print(bd['Social_inclusion'].unique())\n",
    "mode_soc_inc = bd['Social_inclusion'].mode()[0]\n",
    "# print(mode_soc_inc)\n",
    "bd['Social_inclusion'] = bd['Social_inclusion'].replace('9.0', mode_soc_inc)\n",
    "print(bd['Social_inclusion'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bd['Alterations_early_childhood_develop'].unique())\n",
    "mode_alt = bd['Alterations_early_childhood_develop'].mode()[0]\n",
    "bd['Alterations_early_childhood_develop'] = bd['Alterations_early_childhood_develop'].replace('9', mode_alt)\n",
    "print(bd['Alterations_early_childhood_develop'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bd['Risk_stigma'].unique())\n",
    "mode_stigma = bd['Risk_stigma'].mode()[0]\n",
    "bd['Risk_stigma'] = bd['Risk_stigma'].replace(99.0, mode_stigma)\n",
    "print(bd['Risk_stigma'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bd['NumHijos'].unique())\n",
    "mode_hijos = bd['NumHijos'].mode()[0]\n",
    "bd['NumHijos'] = bd['NumHijos'].replace(99.0, mode_hijos)\n",
    "print(bd['NumHijos'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Quantifying Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Total missing values Age: {bd['Age'].isnull().sum()}\")\n",
    "print(f\"Total missing values Años_consumo_droga: {bd['Años_consumo_droga'].isnull().sum()}\")\n",
    "print(f\"Total missing values Risk_stigma: {bd['Risk_stigma'].isnull().sum()}\")\n",
    "print(f\"Total missing values NumHijos: {bd['NumHijos'].isnull().sum()}\")\n",
    "\n",
    "print(\"\\tCONJUNTO PREPANDEMIA\")\n",
    "print(f\"\\t\\tMissing values Age: {conj_pre['Age'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values Años_consumo_droga: {conj_pre['Años_consumo_droga'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values Risk_stigma: {conj_pre['Risk_stigma'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values NumHijos: {conj_pre['NumHijos'].isnull().sum()}\")\n",
    "\n",
    "print(\"\\tCONJUNTO POSTPANDEMIA\")\n",
    "print(f\"\\t\\tMissing values Age: {conj_post['Age'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values Años_consumo_droga: {conj_post['Años_consumo_droga'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values Risk_stigma: {conj_post['Risk_stigma'].isnull().sum()}\")\n",
    "print(f\"\\t\\tMissing values NumHijos: {conj_post['NumHijos'].isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution of variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Classifying variables into numerical and discrete/categorical "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_atts = ['Education', 'Social_protection', 'Job_insecurity', 'Housing',\n",
    "        'Alterations_early_childhood_develop', 'Social_inclusion',\n",
    "        'Risk_stigma', 'Sex', 'NumHijos', 'Smoking', 'Biological_vulnerability',\n",
    "        'Opiaceos_DxCIE', 'Cannabis_DXCIE', 'BZD_DxCIE', 'Cocaina_DxCIE',\n",
    "        'Alucinogenos_DXCIE', 'Tabaco_DXCIE', 'FrecuenciaConsumo30Dias',\n",
    "        'OtrosDx_Psiquiatrico', 'Tx_previos', 'Readmisiones_estudios', 'Nreadmision'\n",
    "        ]\n",
    "\n",
    "num_atts = ['Structural_conflic', 'Adherencia_tto_recalc', 'Age', 'Años_consumo_droga', 'Tiempo_tx']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution of discrete attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Count plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(len(disc_atts), 1, figsize=(15, 5*len(disc_atts)))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=1.25)\n",
    "\n",
    "for i, disc_att in enumerate(disc_atts):\n",
    "    ax = sns.countplot(x=disc_att, data=combined_pre_post, hue=combined_pre_post[['Situacion_tratamiento', 'Group']].apply(tuple, axis=1),\n",
    "                       hue_order=[('Abandono', 'Pre'),('Alta terapéutica', 'Pre'), ('Abandono', 'Post'), ('Alta terapéutica', 'Post')],\n",
    "                       ax=axs[i])\n",
    "    ax.set_title(disc_att, fontsize=16, fontweight='bold')\n",
    "    ax.get_legend().set_title(\"Groups\")\n",
    "    \n",
    "    # Adding count annotations\n",
    "    for p in ax.patches:\n",
    "        if p.get_label() == '_nolegend_':\n",
    "            ax.annotate(format(p.get_height(), '.0f'), \n",
    "                        (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                        ha = 'center', va = 'center', \n",
    "                        xytext = (0, 9), \n",
    "                        textcoords = 'offset points')\n",
    "\n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format with DPI=600 in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/countplots.svg', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Normalized count plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot countplot \n",
    "def plot_count_perc_norm(i: int, group:int, disc_att:str) -> None:\n",
    "    \"\"\"\n",
    "        group: 1 (all), 2 (pre), 3 (post) \n",
    "    \"\"\"\n",
    "\n",
    "    # Define data to work with based on group\n",
    "    if group == 1:\n",
    "        df = bd \n",
    "    elif group == 2:\n",
    "        df = conj_pre\n",
    "    elif group == 3:\n",
    "        df = conj_post\n",
    "\n",
    "    # GOAL: find percentage of each possible category within the total of its situacion_tto subset\n",
    "    # Group data by 'Situacion_tratamiento' and 'Education' and count occurrences\n",
    "    grouped_counts = df.groupby(['Situacion_tratamiento', disc_att]).size().reset_index(name='count')\n",
    "    # Calculate total count for each 'Situacion_tratamiento' group\n",
    "    total_counts = df.groupby('Situacion_tratamiento')[disc_att].count()\n",
    "    # Divide each count by its corresponding total count and calculate percentage\n",
    "    grouped_counts['percentage'] = grouped_counts.apply(lambda row: row['count'] / total_counts[row['Situacion_tratamiento']] * 100, axis=1)\n",
    "    \n",
    "    # Follow the same order in plot as in computations\n",
    "    col_order = grouped_counts[grouped_counts['Situacion_tratamiento'] == 'Abandono'][disc_att].tolist()\n",
    "\n",
    "    # Create countplot and split each bar into two based on the value of sit_tto\n",
    "    ax = sns.countplot(x=disc_att, hue='Situacion_tratamiento', data=df, order=col_order, ax=axs[i, group-2])\n",
    "\n",
    "    # Adjust y-axis to represent percentages out of the total count\n",
    "    ax.set_ylim(0, 100)\n",
    "\n",
    "    percentages = grouped_counts['percentage']\n",
    "    for i, p in enumerate(ax.patches):\n",
    "        # Skip going over the legend values\n",
    "        if p.get_label() == \"_nolegend_\":\n",
    "            # Set height to corresponding percentage and annotate result\n",
    "            height = percentages[i]\n",
    "            p.set_height(height)\n",
    "            ax.annotate(f'{height:.2f}%', (p.get_x() + p.get_width() / 2., height),\n",
    "                        ha='center', va='bottom', fontsize=6, color='black', xytext=(0, 5),\n",
    "                        textcoords='offset points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(len(disc_atts), 2, figsize=(15, 7*len(disc_atts)))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=1.5)\n",
    "\n",
    "for i, disc_att in enumerate(disc_atts):\n",
    "\n",
    "    # # 1: ALL    \n",
    "    # plot_count_perc_norm(i, 1, disc_att)\n",
    "    # axs[i, 0].set_title(\"\\nALL\")\n",
    "    # axs[i, 0].set_xlabel(disc_att, fontweight='bold')\n",
    "    # axs[i, 0].set_ylabel(\"% of total within its Sit_tto group\")\n",
    "    # axs[i, 0].tick_params(axis='x', rotation=90)\n",
    "    \n",
    "    # 2: PRE\n",
    "    plot_count_perc_norm(i, 2, disc_att)\n",
    "    axs[i, 0].set_title(\"\\nPRE\")\n",
    "    axs[i, 0].set_xlabel(disc_att, fontweight='bold')\n",
    "    axs[i, 0].set_ylabel(\"% of total within its Sit_tto group\")\n",
    "    axs[i, 0].tick_params(axis='x', rotation=90)\n",
    "\n",
    "    # 3: POST\n",
    "    plot_count_perc_norm(i, 3, disc_att)\n",
    "    axs[i, 1].set_title(\"\\nPOST\")\n",
    "    axs[i, 1].set_xlabel(disc_att, fontweight='bold')\n",
    "    axs[i, 1].set_ylabel(\"% of total within its Sit_tto group\")\n",
    "    axs[i, 1].tick_params(axis='x', rotation=90)\n",
    "\n",
    "    \n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format with DPI=600 in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/norm_countplots.svg', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution of numeric attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(bd[num_atts].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(len(num_atts), 1, figsize=(12, 5*len(num_atts)))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=1.5)\n",
    "\n",
    "for i, num_att in enumerate(num_atts):\n",
    "    plt.subplot(len(num_atts), 1, i+1)\n",
    "    sns.boxplot(\n",
    "        data=combined_pre_post,\n",
    "        x = num_att,\n",
    "        y = 'Group',\n",
    "        hue='Situacion_tratamiento',\n",
    "    )\n",
    "\n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format with DPI=600 in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/boxplots.svg', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(len(num_atts), 3, figsize=(15, 6*len(num_atts)))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=1.5)\n",
    "\n",
    "for i, num_att in enumerate(num_atts):\n",
    "\n",
    "    # 1: All alcohol patients\n",
    "    sns.histplot(data=bd,x=num_att,bins=15, hue='Situacion_tratamiento', stat='probability', common_norm=False, kde=True,\n",
    "                 line_kws={'lw': 5}, alpha = 0.4, ax=axs[i, 0])\n",
    "    axs[i, 0].set_title(f\"\\nDistr. of {num_att}  - ALL\")\n",
    "\n",
    "    # 2: PRE\n",
    "    sns.histplot(data=conj_pre,x=num_att,bins=15, hue='Situacion_tratamiento', stat='probability', common_norm=False, kde=True, \n",
    "                 line_kws={'lw': 5}, alpha = 0.4, ax=axs[i, 1])\n",
    "    axs[i, 1].set_title(f\"\\nDistr. of {num_att}  - PRE\")\n",
    "\n",
    "    # Subplot 3: POST\n",
    "    sns.histplot(data=conj_post,x=num_att,bins=15, hue='Situacion_tratamiento', stat='probability', common_norm=False, kde=True, \n",
    "                 line_kws={'lw': 5}, alpha = 0.4, ax=axs[i, 2])\n",
    "    axs[i, 2].set_title(f\"\\nDistr. of {num_att}  - POST\")\n",
    "\n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format with DPI=600 in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/histograms.svg', dpi=600, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Turning binary variables into 0/1 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Alterations_early_childhood_develop'\n",
    "alterations_mapping = {\n",
    "    'No alterations (first exposure at 11 or more years)' : 0,\n",
    "    'Alterations (first exposure before 11 years old)': 1,\n",
    "}\n",
    "\n",
    "bd['Alterations_early_childhood_develop_REDEF'] = bd['Alterations_early_childhood_develop'].map(alterations_mapping)\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# Social protection\n",
    "bd['Social_protection_REDEF'] = bd['Social_protection'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Risk_stigma'\n",
    "bd['Risk_stigma_REDEF'] = bd['Risk_stigma'].map({'No':0, 'Yes':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Sex'\n",
    "bd['Sex_REDEF'] = bd['Sex'].map({'Hombre':0, 'Mujer':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Smoking'\n",
    "bd['Smoking_REDEF'] = bd['Smoking'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Biological_vulnerability'\n",
    "bd['Biological_vulnerability_REDEF'] = bd['Biological_vulnerability'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Droga_DxCIE'\n",
    "bd['Opiaceos_DxCIE_REDEF'] = bd['Opiaceos_DxCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['Cannabis_DXCIE_REDEF'] = bd['Cannabis_DXCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['BZD_DxCIE_REDEF'] = bd['BZD_DxCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['Cocaina_DxCIE_REDEF'] = bd['Cocaina_DxCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['Alucinogenos_DXCIE_REDEF'] = bd['Alucinogenos_DXCIE'].map({'No': 0, 'Sí': 1})\n",
    "bd['Tabaco_DXCIE_REDEF'] = bd['Tabaco_DXCIE'].map({'No': 0, 'Sí': 1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'OtrosDx_Psiquiatrico'\n",
    "bd['OtrosDx_Psiquiatrico_REDEF'] = bd['OtrosDx_Psiquiatrico'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Tx_previos'\n",
    "bd['Tx_previos_REDEF'] = bd['Tx_previos'].map({'No':0, 'Sí':1})\n",
    "\n",
    "# --------------------------------------------------------------------------\n",
    "\n",
    "# 'Situacion_tratamiento'\n",
    "bd['Situacion_tratamiento_REDEF'] = bd['Situacion_tratamiento'].map({'Abandono':0, 'Alta terapéutica':1})\n",
    "\n",
    "# --------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Defining groups of variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_vars = ['Education', 'Social_protection', 'Job_insecurity', 'Housing', 'Alterations_early_childhood_develop', \n",
    "            'Social_inclusion', 'Risk_stigma', 'Structural_conflic']\n",
    "ind_vars = ['Age', 'Sex', 'NumHijos', 'Smoking', 'Biological_vulnerability', 'Opiaceos_DxCIE', \n",
    "            'Cannabis_DXCIE', 'BZD_DxCIE', 'Cocaina_DxCIE', 'Alucinogenos_DXCIE', 'Tabaco_DXCIE', \n",
    "            'FrecuenciaConsumo30Dias', 'Años_consumo_droga','OtrosDx_Psiquiatrico', 'Tx_previos', 'Adherencia_tto_recalc'] \n",
    "target_var = 'Situacion_tratamiento'\n",
    "\n",
    "# Incluir alcohol?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Columns that are already numeric and we don't need to redefine \n",
    "no_redef_cols = ['Structural_conflic', 'Age', 'NumHijos', 'Años_consumo_droga', 'Adherencia_tto_recalc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# res_vars = ['Tiempo_tx', 'Readmisiones_estudios', 'Periodos_COVID', 'Pandemia_inicio_fin_tratamiento', \n",
    "#            'Nreadmision', 'Readmisiones_PRECOVID', 'Readmisiones_COVID']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### One-hot encode categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify columns to one hot encode; empty list otherwise\n",
    "one_hot_vars = ['Education', 'Job_insecurity', 'Housing', 'Social_inclusion', 'FrecuenciaConsumo30Dias']\n",
    "\n",
    "one_hots_vars_prefix = {\n",
    "    'Education': 'Ed',\n",
    "    'Job_insecurity': 'JobIn',\n",
    "    'Housing': 'Hous', \n",
    "    'Social_inclusion': 'SocInc',\n",
    "    'FrecuenciaConsumo30Dias': 'Frec30',\n",
    "}\n",
    "\n",
    "one_hot_cols_dic = {}\n",
    "\n",
    "for one_hot_var in one_hot_vars:\n",
    "    # Create one hot encoding version of attribute and concatenate new columns to main df\n",
    "    encoded_var = pd.get_dummies(bd[one_hot_var], prefix=one_hots_vars_prefix[one_hot_var])\n",
    "    bd = pd.concat([bd, encoded_var], axis=1)\n",
    "    one_hot_cols_dic[one_hot_var] = encoded_var.columns.tolist()\n",
    "\n",
    "# print(one_hot_cols_dic['FrecuenciaConsumo30Dias'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Defining final version of columns of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "soc_vars_enc = []\n",
    "for soc_var in social_vars:\n",
    "    # If no need to redefine, append directly\n",
    "    if soc_var in no_redef_cols:\n",
    "        soc_vars_enc.append(soc_var)\n",
    "    # If need to redefine\n",
    "    else:\n",
    "        # Check if it was one-hot encoded\n",
    "        if soc_var in one_hot_vars:\n",
    "            # Append all one hot columns\n",
    "            soc_vars_enc = soc_vars_enc + one_hot_cols_dic[soc_var]\n",
    "        # If not, use redefined version through mapping\n",
    "        else:\n",
    "            soc_vars_enc.append(soc_var + '_REDEF')\n",
    "\n",
    "ind_vars_enc = []\n",
    "for ind_var in ind_vars:\n",
    "    # If no need to redefine, append directly\n",
    "    if ind_var in no_redef_cols:\n",
    "        ind_vars_enc.append(ind_var)\n",
    "    # If need to redefine\n",
    "    else:\n",
    "        # Check if it was one-hot encoded\n",
    "        if ind_var in one_hot_vars:\n",
    "            # Append all one hot columns\n",
    "            ind_vars_enc = ind_vars_enc + one_hot_cols_dic[ind_var]\n",
    "        # If not, use redefined version through mapping\n",
    "        else:\n",
    "            ind_vars_enc.append(ind_var + '_REDEF')\n",
    "\n",
    "# Final version of columns we need to use for correlation analysis\n",
    "corr_cols = soc_vars_enc + ind_vars_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ed_Not Complete primary school', 'Ed_Primary education', 'Ed_Secondary Education', 'Ed_Secondary more technical education', 'Ed_Tertiary', 'Ed_Unknowledge', 'Social_protection_REDEF', 'JobIn_Non-stable', 'JobIn_Stable', 'JobIn_Unemployed', 'JobIn_unkwnodledge', 'Hous_Institutional', 'Hous_Stable', 'Hous_Unstable', 'Hous_unknowledge', 'Alterations_early_childhood_develop_REDEF', 'SocInc_Live with families or friends', 'SocInc_live alone', 'SocInc_live in institutions', 'Risk_stigma_REDEF', 'Structural_conflic', 'Age', 'Sex_REDEF', 'NumHijos', 'Smoking_REDEF', 'Biological_vulnerability_REDEF', 'Opiaceos_DxCIE_REDEF', 'Cannabis_DXCIE_REDEF', 'BZD_DxCIE_REDEF', 'Cocaina_DxCIE_REDEF', 'Alucinogenos_DXCIE_REDEF', 'Tabaco_DXCIE_REDEF', 'Frec30_1 día/semana', 'Frec30_2-3 días\\u200e/semana', 'Frec30_4-6 días/semana', 'Frec30_Desconocido', 'Frec30_Menos de 1 día\\u200e/semana', 'Frec30_No consumio', 'Frec30_Todos los días', 'Años_consumo_droga', 'OtrosDx_Psiquiatrico_REDEF', 'Tx_previos_REDEF', 'Adherencia_tto_recalc']\n"
     ]
    }
   ],
   "source": [
    "print(corr_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-pandemic\n",
    "conj_pre = bd[bd['Pandemia_inicio_fin_tratamiento'] == 'Inicio y fin prepandemia']\n",
    "# Pre-pandemic abandono\n",
    "pre_abandono = conj_pre[conj_pre['Situacion_tratamiento'] == 'Abandono']\n",
    "# Pre-pandemic alta\n",
    "pre_alta = conj_pre[conj_pre['Situacion_tratamiento'] == 'Alta terapéutica']\n",
    "\n",
    "# Post-pandemic\n",
    "# Merging last two classes to balance sets\n",
    "conj_post = bd[(bd['Pandemia_inicio_fin_tratamiento'] == 'Inicio prepandemia y fin en pandemia') | \n",
    "               (bd['Pandemia_inicio_fin_tratamiento'] == 'inicio y fin en pandemia')]\n",
    "# Post-pandemic abandono\n",
    "post_abandono = conj_post[conj_post['Situacion_tratamiento'] == 'Abandono']\n",
    "# Post-pandemic alta\n",
    "post_alta = conj_post[conj_post['Situacion_tratamiento'] == 'Alta terapéutica']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_vars = [col for col in corr_cols if len(bd[col].unique()) == 2] + ['Situacion_tratamiento_REDEF', 'Risk_stigma_REDEF']\n",
    "cont_vars = ['Structural_conflic', 'Age', 'NumHijos', 'Años_consumo_droga', 'Adherencia_tto_recalc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_corr_matrix(df, cols):\n",
    "    \n",
    "    # Initialize nxn matrix to zeroes\n",
    "    n = len(cols)\n",
    "    corr_matrix = np.zeros((n,n))\n",
    "\n",
    "    for i, var_i in enumerate(cols):\n",
    "        for j, var_j in enumerate(cols):\n",
    "            # Fill lower triangle of matrix\n",
    "            if i > j:\n",
    "                # Binary with binary correlation: tetrachoric\n",
    "                if var_i in binary_vars and var_j in binary_vars:\n",
    "                    corr = binary_binary(df[var_i], df[var_j], measure='tetrachoric')\n",
    "                # Continuous with continuous correlation: \n",
    "                elif var_i in cont_vars and var_j in cont_vars:\n",
    "                    # Returning nan sometimes:\n",
    "                    # corr_tuple = continuous_continuous(df[var_i], df[var_j], measure = 'spearman')\n",
    "                    # corr = corr_tuple[0]\n",
    "                    corr = df[var_i].corr(df[var_j], method='spearman')\n",
    "                # Binary vs Continuous correlation:\n",
    "                else:\n",
    "                    if var_i in binary_vars:\n",
    "                        bin_var = var_i\n",
    "                        cont_var = var_j\n",
    "                    else:\n",
    "                        bin_var = var_j\n",
    "                        cont_var = var_i\n",
    "                    corr = binary_continuous(df[bin_var], df[cont_var], measure='biserial')\n",
    "                # Assign value to matrix\n",
    "                corr_matrix[i][j] = corr \n",
    "                      \n",
    "    return corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.14765034990105885\n"
     ]
    }
   ],
   "source": [
    "print(binary_continuous(bd['Situacion_tratamiento_REDEF'], bd['Age'], measure='biserial'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_heatmap(sit_tto: int, group:int) -> None:\n",
    "    \"\"\"\n",
    "        sit_tto: 1 (include it as another var), 2 (only abandono), 3 (only alta)\n",
    "        group: 1 (all alcohol patients), 2 (pre), 3 (post)\n",
    "    \"\"\"\n",
    "\n",
    "    # Define columns based on sit_tto arg\n",
    "    if sit_tto == 1:\n",
    "        # Include target as another variable\n",
    "        cols = [target_var + '_REDEF'] + corr_cols\n",
    "    else:\n",
    "        cols = corr_cols\n",
    "        \n",
    "    # Title plot and select datat based on group and sit_tto\n",
    "    if group == 1:\n",
    "        plot_title = \"Correl Matrix - ALL\"\n",
    "        if sit_tto == 1:\n",
    "            bd_ca = bd[cols]\n",
    "        elif sit_tto == 2:\n",
    "            bd_ca = bd[bd['Situacion_tratamiento'] == 'Abandono'][cols]\n",
    "        elif sit_tto == 3:\n",
    "            bd_ca = bd[bd['Situacion_tratamiento'] == 'Alta terapéutica'][cols]\n",
    "    elif group == 2:\n",
    "        plot_title = \"Correl Matrix - PRE\"\n",
    "        if sit_tto == 1:    \n",
    "            bd_ca = conj_pre[cols]\n",
    "        elif sit_tto == 2:\n",
    "            bd_ca = pre_abandono[cols]\n",
    "        elif sit_tto == 3:\n",
    "            bd_ca = pre_alta[cols]\n",
    "    elif group == 3:\n",
    "        plot_title = \"Correl Matrix - POST\"\n",
    "        if sit_tto == 1:    \n",
    "            bd_ca = conj_post[cols]\n",
    "        elif sit_tto == 2:\n",
    "            bd_ca = post_abandono[cols]\n",
    "        elif sit_tto == 3:\n",
    "            bd_ca = post_alta[cols]\n",
    "            \n",
    "    # Complete title\n",
    "    if sit_tto == 2:\n",
    "        plot_title += \" - ABANDONO\"\n",
    "    elif sit_tto == 3:\n",
    "        plot_title += \" - ALTA\"\n",
    "\n",
    "    corr_matrix = get_corr_matrix(bd_ca, cols)\n",
    "\n",
    "    # Create a mask for the upper triangle\n",
    "    mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
    "\n",
    "    # Create heatmap correlation matrix\n",
    "    dataplot = sns.heatmap(corr_matrix, mask=mask, xticklabels=cols, yticklabels=cols, cmap=\"coolwarm\", vmin=-1, vmax=1, annot=True, fmt=\".2f\", annot_kws={\"size\": 4})\n",
    "\n",
    "    # Group ind vs social vars by color and modify tick label names\n",
    "    for tick_label in dataplot.axes.xaxis.get_ticklabels():\n",
    "        if tick_label.get_text() in ind_vars_enc:\n",
    "            tick_label.set_color('green')\n",
    "        elif tick_label.get_text() in soc_vars_enc:\n",
    "            tick_label.set_color('purple')  \n",
    "    for tick_label in dataplot.axes.yaxis.get_ticklabels():\n",
    "        if tick_label.get_text() in ind_vars_enc:\n",
    "            tick_label.set_color('green')\n",
    "        elif tick_label.get_text() in soc_vars_enc:\n",
    "            tick_label.set_color('purple') \n",
    "\n",
    "    # Increase the size of xtick labels\n",
    "    # dataplot.tick_params(axis='x', labelsize=12)\n",
    "\n",
    "    # Increase the size of ytick labels\n",
    "    # dataplot.tick_params(axis='y', labelsize=12)\n",
    "\n",
    "    # Add legend and place it in lower left \n",
    "    plt.legend(handles=[\n",
    "        plt.Line2D([0], [0], marker='o', color='w', label='Social Factors', markerfacecolor='purple', markersize=10),\n",
    "        plt.Line2D([0], [0], marker='o', color='w', label='Individual Factors', markerfacecolor='green', markersize=10)\n",
    "    ], bbox_to_anchor=(-0.1, -0.1), fontsize = 20)\n",
    "\n",
    "    plt.title(\"\\n\\n\" + plot_title, fontdict={'fontsize': 30, 'fontweight': 'bold'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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KrGqdq7H1w63EnYnDLcANc0vDsivfbr7Ksu5l725PhWYVSDifQML5BG6dvMXaAWuxcbGh+6LuVGpVyaj50+LSsHMrXJKYnZatrGhIp9Mxp/ocfdFadmo28+rNK1TwN/b2WKOz2k9vz68dfuX7mt+Tm5nLmn5rSLiQgG0ZW6XnekHWHy/9wbdu35KTkcPilotJjU3FO8ibNpPaKM2q0bMG6wato/309voSrasHr7L1w634vai2jK/t5LYse2EZcWfi0OZqOTj7IHFn4ojZH8PA3QOV5VRqWUnZXI/CFOd8AZ1Why6vcElN8tVkSjmoLZRJjEqkXL1yhcbNS5mTk5Zj1NwajYaslCwsrC3yS3f+f/FkVnKWwX6lHNXdppov1aRCswqk3EjRl8gBVG5bGb+eas95/1f82TZ2Gy//8TJo8h+36H3RbP1gK7X711aaValVJT6M/5Cs5CxsXGz04w3eaoClraWyHFMWkxZo9007FrVYxBzfOVRsXhGAK3uvkJWcxYAd6goxG3o2ZMP5DQwPHA78U062IGwBQV5BSjJKolgL8guG0nPyy1C3RW6jf53+AJS2KU1ylvGlmiVR/mMKGo2GlKwUrC2s0el0aNCQmp1a6D5zLGV84aVOp2PguoH6UrDM3EyGbRiGnaXh68qaPmuMznoWy+RMeQ5OaGn4oXR33+5Gz/lvfMv4ci7+HJWcK1HHvQ7zjs6jknMl5h6Zi4eD2nLXNj5tWH9uPfU86jGo7iBGBY9i1dlVHLl+hBdrvKg0q2eNnmyP2k6gVyDDGw3ntT9fY+GxhUQnRTOq8ah/n+ARRd6JpKtv10Lj3Xy78cl2NX/xypSP1bR20+j8W2e2RW7Tv06FXg0lJjmGjf02Ks1657l3CCwfSHRSNM9XeV7/3KrsUpmvW6v9a4Yu1i6kZKUAUN6hPKdunSLAPYDEzET9a5kq43eOx9bSFoDsvGwm7ZmEk7WTwT4zOswwKmNR90VGff3j6OvflxGbRuBg5UCLii0A2H1lNyM3j+QV/1dMfjxCCCEKK1OmDObm5ty8edNg/ObNm5QrV/hnMADlypV7pP2FEOJxPM76VODbb79l6tSpbNu2jdq11f7sUwghHnV9unTpEpcvX6Zr139+FqTVagGwsLDg3LlzVKlSpXgPWgjxzHuc904eHh5YWlpibv7Pvw3y8/MjNjaW7OxsrKysivw6IYR4FI+zPn3++ee8/vrrvPHGGwAEBASQlpbGW2+9xaeffoqZmVmRXyeEEMXtfj8bd3R0xMbG5j5fVfLkcwAhxJNKPgcQQjyp5HMAIcSTSj4LEEI8qeSzACHEs0TVZwFSkCeEEEI8JSpUqMD8+fP118uVK8cvv/xisI9Go1FWkHdx80Ve3/a6vhwPoGzNsnT6vhO/tP/lAV/56KydrZUXFz0JWX+9+RcAuyfuLrRNo9EwPm+88szUm6mc+OUExxcd507kHWr0qEG/v/tRuV1lstOy2T1xN2sHrOX9K+8blePZ0JPzG84TODwwf+D/966ELQjDK8jLuBvx/3VfVPyFJAUcvRwZFj6MUytOcfPETbJTs6k3pB4BrwZgaaOufArA2sma17e+TnRItD7Lo74HldtVVpoD0GVuF7Z8sIXV/Vajzcn/QMjMwox6Q+rx/P89rzSrQrMKDD0+lJCpIbgFuHFpyyU86nswJHQI7gHuSrPulZOeQ1J0EnnZeQbj7rXV5ZrinC9QpX0VDsw6QNefuuqzslOz2TVhF9U6VVOa5ezjTOzxWJwrOhuMX9x8kTJ+xv21LX3J5V3X59WbZ3C9ONZC+3L22JezNxgrKIhUqe3ktmx4dwMzvWeizdPyfc3v0eXpCOgXQIvPWijPMzM3MyjHA3Cu5Kw0Q6fTsbTtUsws8n/gmZOew/KuyzG3Mix4HRo2VFlm2ZplefvE2xyac4ib4TexsLGgTv86NHqvETal1f1Dx8ltJ/PCshc4E3eGXG0usw/O5kzcGfbH7Gf3wMKv04+jJIq1AJpVaMbo4NE09W7KoWuHWPnSSgDOJ5zHy9H49akkyn9MUWpUULp29/V68+oZXNdoNOSNzyvqyx/JgLqGZY+v1X7N6Dnv51kskzPlOTihlen/atvIwJHcSL2Rn99yAh2XdWTZyWVYmVuxuPtipVk/df0JrS7/PeG7jd7F1daV/TH76Va9G0MbqlvbAaa2m6q/3Me/DxWcKhB6NZRqpasVWWRnLG9Hb7ZHbqdq6aoG49sit+Ht5K0kw5SPVctKLTk//DzfH/qeiIQIAF70e5F3nnsHTwdPpVkADTwb0MCzgcFY5+qdlee0qNiCrZFbCXAP4OWaLzNy80h2RO1ga+RW2vq0VZpzLuGc/noT7yZEJkYa7KOiPPTe9d0UvmrzFZeTLhsUlGp1WvrX6c/ktpNNfjxCCCEKs7KyokGDBmzfvp0ePXoA+f9Qe/v27bz33ntFfk1QUBDbt2/n/fff149t3bqVoCA1he5CCAGPtz4BTJs2jUmTJhEcHEzDhg1NdLRCiP+SR12fatSowcmTJw3GPvvsM1JSUpg9ezbe3mp+FiSE+G97nPdOTZs25bfffkOr1ep/weT8+fN4eHjIL8QJIZR5nPUpPT290C++FfwCr05X+A94CiGEqQQFBbFxo+EfiHsafjYunwMIIZ5U8jmAEOJJJZ8DCCGeVPJZgBDiSSWfBQghniWqPgvQ6GQ1E0IIIUQRpjhMYdDeQZSra9gmfuPYDRa3XMzHyR+X0JGJ+1nedTkXgy/iWt2V+m/Up07/OoXKhdJupfFtuW+ZoDWu/CM6JJplLywj4LUAwheH02BoA+LOxBGzP4aBuwfi2UB9aYN4fNlp2dy5dAcAlyouWNk9Gz9wTYtLY92gdVzcdLHI7SqL10x5zidfTebXDr+i0+m4feE2ng09SbiQgG0ZWwbtGYSdm52yrLAFYez6Yhftp7dn/ZD1dFvQjduXbhMyJYRuC7rh/4r/Y899efflh9qvUstKj50BsPLFlQ+9b581fYzKKkpSdBK3Tt0iOzWbcvXK4VrNVXlG6s1Utn6wlcjtkaTdSoN7votXda7v+nLXQ+3XakIrJXmmdun2JaaGTCX8Zjip2anU96jP2KZjCXAPKOlDM0p0UjTvbHiHmOQYRjQawZD6QwAYtXkUebo8vnvhuxI+wkfTanErNJoHlxZp0LBjwA6jcnZffrhixJaVWhqVY2pLji95qP1KotTpaXAr7RZudm733Z6rzSXsRhiNyjcqtmNIz0knIj6CCk4VKGNrXFntf8mPh3/k/eD3GVx3ME28mwCwL2Yfi48vZnbH2coLAOHZeqyuJl9l/bn1RCdFk52XbbDN2ELSu93OuE1mbiaeDp5odVqm7ZvG/pj9VCtdjc9afIaLjYuyLFM4dO0QDTwaYG5mXuT2rNws1p1bR+9avZVnn084T3hsODaWNgS4BVDRuaLyDCGEEI9v5cqVDBgwgHnz5tGoUSNmzZrF77//TkREBO7u7vTv35/y5cszZcoUAPbv30/Lli2ZOnUqnTt3ZsWKFUyePJmwsDD8/R//ZzNCCHGvR12fvvnmG8aPH89vv/1G06ZN9fPY29tjb29/vxghhHhkj7o+3WvgwIEkJiaydu1a0x64EOKZ9qhrU0xMDLVq1WLAgAEMHz6cCxcuMHjwYEaMGMGnn35awrdGCPEsedT16YsvvmDGjBn89NNPBAYGcvHiRd5++20aNGjAypUP/29uhBDi36SmpnLxYv6/5axXrx4zZsygdevWlC5dmgoVKvDxxx9z7do1li5dCkBUVBT+/v68++67DB48mB07djBixAg2bNhAhw4dSvKm/Cv5HEAI8aSSzwGEEE8q+RxACPGkks8ChBBPKvksQAjxpCqpzwIsiuXWCCGEEOKp59PGh80jN9NreS8cPB0ASL6WTPCoYCq3rVwsmWlxaSScSwDA1dcVu7LqSqBKMstUbN1sGbh7IN5B9/9rOLZlbRkZNdLorArNKjD0+FBCpobgFuDGpS2X8KjvwZDQIbgHuBs9P8C1Q9fwaOCBmblZkdtzs3I5t+4ctXrXeqz5z60/99D7+nbzfayMAge/O/jQ+waOCDQqqyipN1JJuZFCxRYVsbSxRKfT/Wv50KPKSs4qeoMGLEpZYG5VdGmEMYLfDyYzMZM3Dr7B4laL6fNnH9JuprHn6z20n95eaZYpzvkCjl6ODAsfxqkVp7h54ibZqdnUG1KPgFcDsLSxVJpV/436WNhYsPOzneSk57C632ocPB3oOLujUeV4YHzx3cOydrLWX9bpdET8GUEpp1J4NswvLbxx9AaZiZn4vehXLPlOFZxwquBULHMXWDdwHUnRSbT4vAUOHg6g9umrZ6riu5snbuLm74bGTMPNEzcfuK97bXXPryqlqzC/23xl892rpIq1KjhV4O9+fxcan9lxppL5TV3+s2vgLiXz/Jsnrfju386fh2XK4jtTnfOmPAc9pntwY8wN/e0K+DGAjf024u2U//46IT2BoIVB5I3PMzrrfmwtbanvUb/Y5k/MTOTQtUPcSruFVqc12Na/Tn+lWRcSLrDz8s4is8a3VFdkDPD2c29Tzr4c00On8/uZ3wHwK+PHypdW0r1Gd6VZBVQ/VidunsDfzR8zjRknbp544L613Wsry90euZ1uK7pR2aUyEfER+Lv5cznxMjqdTvm5WNqmtP6ymcaMcc3GKZ3f1IIWBhmsGY5THDk+7DiVXfJ/ZpKYmUjf1X2LpSCvumt1qrtWVz6vEEIINfr06UNcXBzjx48nNjaWunXrsnnzZtzd87/HjY6ONvhLnU2aNOG3337js88+45NPPqFatWqsXbtWfilOCKHco65PP/74I9nZ2bz00ksG80yYMIEvvvjClIcuhHjGPer6JIQQpvCoa5O3tzfBwcGMGjWK2rVrU758eUaOHMnYsWNL6iYIIZ5Rj7o+ffbZZ2g0Gj777DOuXbtG2bJl6dq1K5MmTSqpmyCEeEYdOXKE1q1b66+PHj0agAEDBrB48WJu3LhBdHS0fruPjw8bNmxg1KhRzJ49Gy8vLxYsWPDEl+OBfA4ghHhyyecAQognlXwOIIR4UslnAUKIJ5V8FiCEeFKV1GcBGp1Op1NzE4QQQghRnDp16sTy5ctxcsov4Zk6dSrDhg3D2dkZgISEBJo3b86ZM2eU5CXFJLGi2wpunb6Fk7eTfszN342+6/vi6OWoJAcgOy2bTcM3Eb40HJ02/62JmbkZtfvXptP/OmFpq64QypRZAJd3Xyb021DizsYBULZmWZp82ISKzSsqzcnLyWNZx2V0ntsZ12quSucuKRPNJzLmxhjs3PLLC6c4TmHY8WG4VHYBIPVmKjM8ZzA+7/HKNb40+/Kh9tNoNI+dUWC2z+yH21EDIyONLzAskJ6Qzqreq4jaGYVGo2H4heG4VHZh3eB1WLtY02G6un9I9KXZlw8s3XP0cqTOwDq0mtAKjZmadq/pHtN5Zd0rlG9UnimOU3jryFu4Vnfl3Ppz7Ju2j8Ehg5Xk/FfkpOeQnZqtf84Z6/Tvp6nRo4a+HDH5ajIOng76xz8nPYdDcw7R9KOmD5rmkWwdu5WM2xl0mdtFX66pzdOy4Z0NlHIsRfv/U1ecqM3TcnzxcaK2R5F2K03/mlJgwA51JVVTHKYwaO8gytUtp2zOoqTdSnvg46/N1XIj7AblG5U3KudLsy/5IPYD7Nzs9GtHUT+aULH+FkjOSi5yXIOGUhalsDK3MjrDfKL5A4u1bqbexHOGZ7EUa+Vp81gbsZaz8WcBqFW2Ft18u923UOxR3Hu77i3/Kc7bVZx+P/07PWr00D/2V5Ov4ungiZkmf+1Iz0lnzqE5fNT0I6OzbCfZcuX9K5S1KwtA5986s6DrAjwcPAC196Epy+RMdc6b8hw0+9KM2A9i9VkOUxwIHxZukOUx3QPtBO2Dpnkkedo8Fh9fzPao7UUWye0YsENZ1l/n/uLVNa+Smp2KYylHg/duGjTcHntbWdb8o/N5e8PblLEtQzn7coWywoaGKcsyleJ+rO4+/8y+NHvg66PKNbfR/Ea8UPUFvmz9pf6cd7Nz49U1r9KxSkfefu5tZVmmeD0GqPl9TUIGh+gL+d7Z8A4TW0+kjG0ZIL/gs9KsSqR/mm5UjqnWjNHBox963xkdZhiVJYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQTwqLkj4AIYQQQjyc4OBgsrKy9NcnT55M79699QV5ubm5nDt3Tlmek7cTb4W9ReS2SOIj4gEo61eWyu0qK8soEDw6mCu7r9D3r75UaFoBgOiQaDaN2ETwmGC6/Njlqcw68esJ1g1ah9+LfgSOCAQgZl8MS9supcfiHgT0C1CWZW5pzs0TN5XN92+ykrOK3qABi1IW+kIsYxQqgyii1tmYrucJ2gmP/bWPamSUutK7RxE8KhgzSzNGRY/ie7/v9eO1+tRiy+gtMF1dVo/FPdjx6Q7qDKyjL8+6duga4UvCafFZC9Li0gj9NhSLUhY0/6S5kszstH/K3GxcbEiLS8O1uituAW7cCLth9Pz3Pc+LUMqxlFFZ59Y//Prt283XqKz7sbS1VFoSurrvaoOSy+9rfm9QcpmVksX2j7crLcg7/vNxBoUM0pfjQX4JatDoIH5u8rPSgrzNIzdzfPFxqneujpu/G6jpfSySo7ejUevdw5ruMd3gMfsx4Ef6beynL8pNT0hnYdBCo0vrRkaNxLasrf6yKThPdX5giaeXoxcD6wxkQqsJ+oK0R3XvY3Q58TI52pwH7qPCxdsX6bSsE9dSruHrmr8+TAmZgrejNxv6baBK6SpGzX/vMeuKeEFWebtMVWrUd3Vfg9K1mt/XNChdS8lK4ePtHyspyMvMzTS43/Zc2UNGbobBPqruw6CFQQ8sk0vMTKTv6r5KCvJMdc6b+hz8Nw9aSx7HyM0jWXx8MZ2rd8bfzR9NMb6gjNkyhsH1BjO57WRsLW2LLQfg671fM6nNJMY2e3b+Gl5xP1ZRI6Moa1tWf9lUzsafZXmv5QBYmFmQkZOBvZU9E1tNpPuK7koL8kzxegwQER9BrjZXf/3XE7/yQZMP9K8lOp2OzNzMx57/UahYM47FHnu4rOJ8QyqEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEECYmBXlCCCHEU6JQMYQJSiA0Gg1Vnq9CleeNK1b5N2dXn6X3qt5UalVJP1atUzUsbCxY1XuV0tI6U2btnbSXdtPaETQqSD8WOCKQ0Bmh7Plqj9KCPICA1wI4tvAY7aa2UzpvUaY6T33gL/o7ejlSZ2AdWk1ohcas+H5JX3VBiakVPI+L63Zc2nKJ14Jfw9HL0WDctZoriVcSlWaFLwmn/fT21OpdSz/m29UX9wB3js47Sv/t/XGq4MTeSXuVFeSV8S1D/Ll4nCs5414nP8e5kjNH5h7BwcPB6Pn/7TyH/MdQo9EYXRi2oseKh9pPRdbd5tWbV2Sxm0ajwcLagtJVS1NnYB18Wvs88twPU3KpmjZXS3xEPGV8yxiMx0fEo9OqPYBTK07x8u8vU61TNaXzFqXjrI5sH7edLvO64FzJudhy7n3MEi8nos3RPnCfx+Fc0bnIy8VpcY/FfLrjUwbWGUij8o0AOHTtEEvCl/BZi8+IS4vj29BvKWVRik+af1Jsx1Ec6/2ITSOoUroKB944oC+VS0hP4LU/X2PE5hFs6LdBeea9VN4uU5UaPUzpmimpug+f9TK5ks4pDitOreD3l3+nU7VOxZ51LeUaIwJHFHs5HsCdjDu8XOvlYs1w+cbloQvBbo+9bXRecT9WFZ0rFnm5uNlZ2pGdlw2Ah70Hl+5copZb/nv6+PR4pVkl9Xpc1Fr4NK0bOwfsLOlDEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIk5OCPCGEEELc1+Xdlwn9NpS4s3EAlK1ZliYfNqFic7W/rJ+TnoOdu12hcTs3O3LSc57arDuRd/Dt6lto3LebL9s/2a40C/KLoY78fITIbZF4NPDAys7KYHuHGR2UZfVY3IMdn+6gzsA6lG9UHoBrh64RviScFp+1IC0ujdBvQ7EoZaGsDK24RW6P5MDMA8SfzS+BKONXhsbvN6Zyu8rKs8IWhnFg5gFuX8gv6ihdrTSN329M/TfqK83JScvB0tay0HjG7QwsSqn9ViBmfwyd53YuNF6uXjliQmMAqNCsAknRScoyA0cGknojFYCWE1qyrOMyTi47ibmVOd0Xdzd6/gE7Bxg9x8OaoJ1gsqy7VelYhSM/HsE9wB3PRp4AXD98nZsnblJ3YF3izsTxS7tf6L2mNzW61yiRY3wUdQfVZf2Q9dy5dEe/Nl09eJV9U/dRd1BdpVnmVuaUrlpa6Zz3s6rPKnLSc/iuyndY2lpiZmlmsH3s7bEmOQ5QUyZzbv25h97Xt1vh19HHsSR8CdPbT6d3rd76sa6+XQlwD2De0Xls77+dCk4VmLR3UrEW5BWH3Vd2c2DIP+V4AK62rkxtO5WmPzctwSNT42kvNXoSyf13fxqNhpSsFKwtrPNLcNGQmp1KclYygP7/KlmZW1G1dFXl8xalQ5UOHLl+hMou6t/f3uvlmi+z5dIWhjUcVmwZszrM0l9OyEjg6z1f06FqB4K88gvKQ6+GEnwxmM9bfK4kz5SPFcD1lOuERIdwK+0WWp1hYe2IwBHKchp7NSYkOgS/sn50qtaJMVvGcPLmSdZErKGxV2NlOfBsvh6fiTtDbGoskF9AGhEfQWp2/vcoqgsGhRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDiv0QK8oQQQoinhEajKVRmUZzlFid+PcG6Qevwe9GPwBGBAESHRLO07VJ6LO5BQL8AZVneQd7smrCLnkt7YmGd//YkJyOH3V/uxivIS1mOqbMcvR2J3B5ZqEApclskTt5OSrMA4k7F4VHfA4Db528bblR8qoQvCaf99PbU6l1LP+bb1Rf3AHeOzjtK/+39cargxN5Je40qyIs7E0dqbH65gE6nIz4inuzUbADS49ONuxF3OfzDYTaP3EzNl2oSODL/fL964CrLOi2jw8wONHq3kbKsneN3EjojlEbDG+Ed5A1ATGgMwaOCSYpOovXE1sqyKjSvQPjScNp81SZ/QAM6rY590/ZRqXUlZTmQf74fW3iMdlPbGYwfW3hMf75nJGRg42KjLLP2a7X1lz0bePL+lfeJj4jHqYITtmVsjZ6/UstKRs/xpEuPTydoTBAtP29pML7n6z0kXknk9S2vs3PCTvZ8teepKMhr/2177MvZEzo9lJQbKQA4eDjQ5MMmBI0JUpoVNCaIA7MP0GlOp2Ivm+owS13B6ZNgRY8VD7WfRqNhfN54JZn7Y/Yzt/PcQuP1ytUjNCYUgGYVmhGdFP3YGSVRrAVQyrwUKdkphcZTs1OxMrcq4isenZT/GEej0aC5682YBsPrTytTnvOmOgd1Oh3V51Q3uF5vXj2D66rX/DFBY5h9YDZzOs0p9teTztU68+HWDzkTd4YAtwAszQ2LlLv5dlOWVbV0VT7f+TkHrh4oMktFwduAuv+UGff6vRcTW0/kvUbvGWTMOTSHbZHbGBU0yug8Uz5Wi48vZujfQ7Eyt8LVxtUgT4NGaUHejA4z9M+nL1t9SWp2KitPr6SaazVmtJ+hLAdM83oMhdddoNjW3bZL26LT/VPm2uW3LvpjKI41A+DI9SP8fvp3opOiyc7LNti2ps8a5XlCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCFESpCBPCCGEeErodDoGDhxIqVKlAMjMzGTYsGHY2dkBkJWVpTRv76S9tJvWjqBR/xQKBY4IJHRGKHu+2qO0IK/j7I782uFXZnjNoFydcgDEhsdiYW3Ba8GvKcsxdVbQmCA2j9hM7PFYvJv8/yK0fTEcX3ycjrM7Ks0CGLBzwL/vpEjM/hg6z+1caLxcvXLEhMYAUKFZBZKik4zKWdp2qUHZwG9dfgPUlw3snbw3vwjvvX+K8AJHBOLd1JuQySFKC/KO/HiErvO7EtD3n+eQbzdf3Gu7s2n4JqUFec9Pe56lbZdy48gN8rLz2PbRNm6dvkXG7QwG7xusLAfyi8n+ePkPLm66iOdzngBcP3Kd+Ih4eq/qDcC1w9eo1afWg6YxiqWtpb4ksjhk3Mng2MJjxJ2NA6BszbLUG1QPm9LqSv8KRG6P5MDMA8SfzS/9KeNXhsbvN6Zyu8pKc878foa3jr5VaNz/FX9+avAT3eZ3I6BvAAdmHHis+S8GX8TayRrIL2eM3B6J/Sl7ADITMx//wO9DY6ah6UdNafpRU7KS818XSzmWUp4DEBMSQ9TOKC5uuohbLTfMLM0MtvdZ00dZVt0BdZXN9SAajYaslCwsrC3y114NZKdm6+/Lgv8ba4J2gpJ5HoW3ozcLjy1karupBuMLjy3E2yn/NTohIwEXG5fHziiJYi2ALtW78NZfb7Gw20Ialc9/vTp47SDDNgxTVnZlyvIfU5YaBV8Mxsk6v8RVq9OyPXI7p+xPAZCYmagsp+DcKLgdqdmp1JtXDzNN/rqhQ/egL39kz2KZnKnOwZ0DdiqZ51GExISwM2onmy5uopZbLSzNDIvkVJZdvfnXmwBM3D2x0DaNRkPe+DxlWT+F/YS9lT27r+xm95XdhlmKC94g//n8TbtvCo13rNqRcdvGKckw5WP1+c7PGd9iPB83/1i/VqgSHhtObffa+udNZZd/3l/aWdkxt0vhAjtVTPF6DPnrT9ulbbEwy//4IyMng67Lu+qLY3O1uUbNXyBqZJSSeR7FilMr6P9nfzpU7cCWS1toX6U95xPOczP1Jj39epr8eIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQorhIQZ4QQgjxlBgwwLD87LXXCpe59e/fX1nencg7+Hb1LTTu282X7Z9sV5YD4ObvxvALwzmx7ATxEfmlHf59/Ql4NQBLG8t/+eonN+u5t5/Dvpw9odNDOfP7GSC/5OqllS9Ro3sNpVmm5ujtyLGFx2g3tZ3B+LGFx3Dyzi+ayUjIwMbl8YvDRkaNNOoYH0VmYiZVO1YtNF6lfRW2jd2mNCsvJw/Php6Fxj0beKLN1SrNcvN3473z73FoziGsHKzITs3G70U/nnv3ORw8HJRm+Xbz5d2Idzn601ESziUAUPWFqryy9hWcKzkD+c8JlbR5Wo4vPk7U9ijSbqWh0xoWDA3Yoa408sqeKyzvupxSTqX0j9+h7w6xZ+Ie+v7Vl4otKirLOvzDYTaP3EzNl2oSODIQgKsHrrKs07L8IkeFhY0W1hbE7I+hdNXSBuMx+2OwsM7/dlGn1ekvP6q1A9YaXP976N8G14ujLKxAcRXjFbB2tsavp1+xZtxNm6clYm2EvjSxbK2y+HbzxcxcXWmOTqdjTvU5Btfn1ZtncF3FY/ZN6W8YfmE4tq62rBu8jo6zO1LKoXgfr2/bf8vLf7zMpoubeM4zfy06cv0IZ+PPsrr3agAOXztMn1qPX2xYEsVaAN+98B0D1g4gaGEQlub572Vytbl08+3G7I6zjZ7f1OU/pio1Ahiw1vB1YujfQw2uq1qjFnVfpGSeh/WslcmZ8hxsWamlybIKOFs7m6zUSjtB7XvNBzH12uFq68q6iHWMaTLGYHxdxDpcbV2VZJjysUrPSecV/1eUl+MBbIvcxoRdE1jx0gqsLayVz/8g93s9joiPYFXvVYDxr8cAE1oalvF29+1eaJ9efr2MygCo6Kzue4CHNXnvZGZ2mMm7jd7FYYoDszvOxsfZh6F/D8XDvvjKwoUQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQwtQ0urt/Y1YIIYQQ4v/7rup3NPmwCQ2HNjQYPzL3CKHTQxl+YXgJHZl4kOtHrnP699MkRSeRl51nsK3PGuNKBu52bv05/nj5D8rUKIPnc5767PiIeHqv6k31LtU5/ONhbl+4TYcZHZTlFpfV/VZTrl45mn7Y1GB8/7f7uX7kOi+teElZ1sbhGzG3NC90v2z5YAs5GTl0/r6zsqxn3cb3NnJ88XGqd66OvYc93NP303FmR2VZPwb8iFeQF51/7KwvJNPmadnwzgau7r/K2yffVpY1w2sGzcY1o9F7hkV4h74/RMjkEEZfG60sa8/Xe9g7eS/136xP+efKA3Dt8DWOLThGs0+a0eLTFoTODOXixou8vvV1ZbnFJfVmKls/2Erk9kjSbqXBPd/tjs8bXzIHZqTbF2+zrNMyUq6l4OqbX/KTcC4BR29H+m3oR+kqpf9lhodzefflh9qvUstKRuVMtp/M2yfexqWyCxPNJzImdgx2Ze2MmvNhXE68zNwjczmfcB4AX1dfhjYcSmp2Kv5u/sWeX9wuJFwgIj4CAL+yflQtXbj49Wnw5a4vH2q/Ca0m/PtO/0FXEq881H4lUer0NEnKTGJr5FYuJ15GgwYfFx/aVW6HYynHkj40cR+Ljy/mjfVv8EK1Fwgsn18wfPDaQTZf3Mz8rvMZWHdgyR7gI/po60eUtinNuGbjlM+t0+n4dMen7Lmyh5DBIbh844Lm3jfSd7k99rbS/Kg7Ucw7Oq/Q63El50pKc0zpQsIF1p1bZ7Bm9KjRg8oulZVn2U224/Q7p6nkXAnXaa7sGrCLAPcAzsadpc3SNtwYc0N5phBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBClAQpyBNCCCGeQjqdjoSEBDQaDa6ursWScfjHwwS/H0zdwXXxbuINQMy+GI4vPk7H2R0LFec9qnPrz1H1haqYW5pzbv25B+7r2833qckqSadWnOLP/n9StUNVLm25RJX2VUg4n0DqzVT8evrRfVF3pXl3ou5w9KejJJxLAMDV15WGQxviXMlZaU7ChQTOrTtH4uVE0ICLjws1etTApbKLUfMe/O6g/nJWchb7v91PhaYV8AryAuDqgavE7IshaEwQLT5rYVRW8Ohg/WVtrpbji4/jVMEJr8b5WdcOXiMpOona/WvT6X+djMq626xKs6g3uB51B9XFydtJ2bwFbp64+dD7utd2V54/rcw0ei7tSbVO1ZTPfa9JNpMYenwoZXzLGIzHn4tnXt15fJrxqbKsyfaTGXZ8GKWrGpaeJVxIYF69eXyS+omyLIATy05weM5h4s/FA1DGtwyNhjcioF8AADkZOWg0GiysLZTmFodlLywjKTqJ5957DgcPh0KliTW611CemRaXZrAOFkfR27JOy0AHLy57EZvSNgCkJ6Tz52t/ojHT0G9DP+WZxemX538h9WYqng08Ob7kOP59/LGwKfr86v6z2teuAslZySw/uZyfj//MketHyBuf9+9f9JCe1WItU5b/PKt0Oh1Hbxw1uA/rlauHRnP/YqqnganOeVOdg7+e+JX3Nr5HclaywbiTtRNzO8+lj7+60umSkJadxu4ru4lOiiY7L9tg24jAEUqzriZfZf259UVmzegwQ2kWwMGrB/nu0HecjTsL5JeFjmg0gkCvQOVZxS1Pm0eX5V3IyMkgwC0AS3NLg+0q7r91EevoXqM7i48vfmBB3oC6A4zOKknx6fH6daOScyVcbdX+DGXK3imM3zUerU6Lm50bOp2OuPQ4zDXmTG47mQ+afKA0z2uGF5te3USAewC1f6zNx80+pm9AX0JjQum4rCNJ45KU5gkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghREl58hsOhBBCCKEXGxvLRx99xPr160lJSQHA0dGRnj17MmXKFNzd1RVQPff2c9iXsyd0eihnfj8DQBm/Mry08iUlBUMreqzgg9gPsHOzY0WPFffdT6PRMD5v/FOT9U3pbxh+fji2ZWz5xuWbQuVMdxt7e6xRWffaO3kvHWZ2oNG7jZjiMIWOszvi7OPM30P/xt7DXmkW5BfVtZvSTvm8d9s7ZS+7xu9Cp9Vh52aHTqcjPS6dbeO20XZyW5p80OSx5z4w84DBdRsXG+LOxBF3Jk4/Zu1szbGfjxldkBd7LNbgumcDTwDuXLoDgG0ZW2zL2BJ3Oq7Q1xqj8fuNOb74OLsn7santQ/1htSjRs8aWJRS823A3Lpz0Wg06HQ6g3Kfgg7uu8eMfW4VxdzKvFCJXHHxqO9B/Nn4wgV5Z+Nxr6O2/M+3my9n/zxL0w+bGoyfW3eO6l2qK80CqP1qbWq/Wvu+2y1tLO+77WFE7Yji7JqzJF5ORKPR4OzjTM2XalKxRUWj5i1KdEg0g/YOolzdcsrnvld2Wjabhm8ifGk4Om3+OW9mbqYvurS0Ne5+u9uV3VcYcmCIvhwPwNbVlrZT2/Jz05+V5RTITMokcmukQTFp5XaVKeVYSsn8PX/tyYGZB7hz6Q4ajYbMpEwsMk3z44k9V/aw8NhCVp9ZjaeDJy/6vcicF+Yom99UxVqjg0fzVeuvsLOyY3Tw6Afua29lT62ytXip5kuYm5k/Vt79yn/GbRtXLOU/BYq71AhgR9QO1pxdk5+j0eDj7MNLNV+iRUXjXvvvtTNqJ0PWD+FK0hWD10kfZx9+7v6z8rxnrUzOVOdg2I0wBq0bxKsBrzKq8ShqlKmBDh1n4s4w68AsXv/zdWqUqUGdcnWU5BVYdWYVv5/+vcgiubChYcpyjt04RqffOpGek05adhqlbUoTnx6PraUtbnZuSgvytkdup9uKblR2qUxEfAT+bv5cTryMTqejvkd9ZTl3C/QKZJnXsmKZu4CpHqspIVMIvhiMbxlfuGX4vvpBZXaPonuN/BLagXUHKpnvYSVmJrIwbCFn4/OLDGuVrcXgeoNxslZb6H361mne3vA2+2L2GYy3rNiSHzv/mH/fGmln1E4+2/kZn7f4nJGBI3GxyS9xv51xm1kHZjFu2zgalW+kdI1vUbEFWyO3EuAewMs1X2bk5pHsiNrB1sittPVpqyxHCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFKmkZX8Fu5QgghhHiiJScnU7duXVJTU3n11VepUaMGOp2OM2fOsHz5clxcXAgLC8PeXn0Rmnh4x5ccx/8VfyxKWXB88fEHFuTVHVBXafZku8m8c/odnCs5M811GgN2DcA9wJ24s3EsbbOUMTfGGDX/zRM3H3pf99rGF4ZF7Yzil3a/0OLzFgSODMTGJb8YKuN2BgdmHWDv5L0M2DGgWEq2njU3wm5wfPFxTi0/hTZPS0C/AOoNrodHfQ+j5k28kqi/HHssli0fbKHJh03wDvIGICY0htDpoTw/7Xlq9DC+WPNe+6fv507kHTrN6WRQGqLK3ed83Nk4tn20jUbDG+HV2AuAqweucvj7w7Sd2hb/Pv5GZR387qD+clZyFvu/3U+FphXwCvonK2ZfDEFjgowubDSlv4f9zdGfjmLjYoNrdVd0Oh23L9wmMzGThu80pNP/OinN+77m97y47EU86hl3bj+Mv4b+RdS2KF6Y8wIVmlYA8gv6No3YROXnK9Plxy7Ksr4p/Q39/u6HdxNvg/HofdEs77pcaeHqiV9PsPG9jWQlZxmMWztZ03luZ6PP9XvN9pnNm0fexNbVVum8d4tNjWXx8cUsPLaQ5KxketfszdyjcwkfFk7NsjWV5YTdCCNwQeB9i7VWnFrB4TcPKynWar2kNX/2+RNna2daL2n9wH2zcrM4HXeaHjV6sKTHkkfO2hm1k3a/tLtv+c/kvZPZMWCH0vIfU5QaAQz7exg/Hf0JFxsXqrtWR6fTceH2BRIzE3mn4Tv8r9P/lORcvH2ROnPrEFg+kJGBIw3Oje8OfseR60c48fYJZeV19yuTM9eYKy+TM8U5b8pzcNC6QaRmp/LHy38Uuf2l31/CsZQjP3dXV0763cHv+HTHpwysM5Cfwn5iUN1BXLpzicPXDvPuc+8yqe0kZVmtFreiumt15naZi9NUJ8KHhWNpZslrf77GyMCRvOj3orKsRvMb8ULVF/iy9Zc4THEgfFg4bnZuvLrmVTpW6cjbz71tdEZyVjKOpRz1lx+kYD9jmPKxcvnGhZkdZhZbed2/3V93U3HfFThy/Qgdfu2AjYUNjco3AuDw9cNk5GSw5fUtysoTY1Nj8f/Bn7J2ZRnWYJjB+jQ/bD4J6QmceucUbnZuRuX0WdUH51LOzOs6r8jtb/31FinZKSzvtdyonLvdzrhNZm4mng6eaHVapu2bxv6Y/VQrXY3PWnymXyOFEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOJpJwV5QgghxFPiq6++YunSpezfv5+yZcsabLt16xZNmzZl0KBBfPLJJ0pz87LzSLuVhk5r+JbBqYKTsozwpeHU6lMLi1IWhbJPrThFnf7GF8mURJapzfCawaubXsU9wJ0fa/9Is4+bEdA3gJjQGJZ1XMa4pHFGzf+l2ZdoNBp0Op1BGVnB28m7x8bnjTcqC2BVn1WUci5F13ldi9z+11t/kZ2STa/lvYzO+q/Iy8nj8A+H2TZ2G9ocLW4BbgSOCKTuoLpGF8zNbzSfVl+0olqnagbjFzZeYOfnO3nr6FtGzV+UlT1XErUzCpvSNrjVcsPM0sxge581fYya/+5z/kE0Go3R5/xsn9kPt6MGRkaONCrrG5dvHljeeTdjitfO/nmW1a+spsu8LtQZUEd/jum0Oo4vPs6Gtzfw8h8v49tNTdEVwKUtlwidHkqXeV1wruSsbN6iTCszjd6relOpVSWD8aidUazqvYoP4z5UlvVn/z+5EXaDbgu7Ub5ReQCuHbzGX2/+hUcDD3os7qEk50bYDRYELiDg1QAaj2pMmRplQAdxZ+I4MOsAp1ac4s3Db1KuTjkleabQdXlX9lzZQ+dqnXk14FU6Vu2IuZk5ll9ZKi/IK4lirYd15PoR2i5tS9K4pEf+WlOX/5iq1OjPs3/yyupXmNdlHgPqDNCvUVqdlsXHF/P2hrf54+U/6Obbzejb9N7G9zgbf5bt/bcX2qbT6Wj3SztqlqmppJDvWSyTM+U5WP1/1fmh8w+0q9yuyO3bIrfxzoZ3OD/8vNFZBWrMqcGElhPoG9BXXyRX2aUy43eO53bGbeZ0mqMsy3mqMwffOIhvGV+cpzoTOiQUv7J+HLx6kAFrBxDxXoSyLIcpDhwfepwqpavg8o0LIYNCqOVWi/DYcLqv6M7l9y8bnWE+0ZwbY27gZueG2ZdmRb6fLfjeJW98ntF5pnysyn1bjr2D9lLNtdq/7/wY7nd/FUXFfVeg+aLmVC1dlfld52Nhlv89ea42lzfWv0HknUj2DNqjJGfs1rFsi9rGvsH7sLawNtiWkZNBs0XNaF+5PVPaTTEqx2e2D7/0/IVmFZoVuX3vlb30X9ufqJFRRuUIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcR/kcW/7yKEEEKIJ8GGDRv45JNPCpXjAbi5ufHxxx8zf/58ZQV5CRcSWD94PTH7YwzGCwoGVBSgFVg3aB1VO1bFws3wrUlWShbrBq1TWlpnyqyJ5hMZc2MMdm52BuPpCel86/at0vsQoGKLikRujcQ9wJ2aL9dk88jNRO2IInJrJD5tfYyef2TUP6Vcscdi2fLBFpp82ATvIG8AYkJjCJ0eyvPTnjc6C+DaoWv0/KXnfbfXfr02a/uvVZIFkHw1mXPrz5EUnURetmEJRIcZHZTlAFw/cp3Tv58uMsvYUrei5OXkEfFnBMcXHefS1kt4Nfai3pB6JF9NZvsn24ncFkmv34wrGrx18hbOPs6Fxp19nIk7E2fU3Pdj7WyNX0+/YpkbDM/54mbKrA6z/jmfMxIy2PP1Hqp2qIpXkBcAV0OvcjH4Ii0+N6486fii4zQe3Zi6A+sajGvMNNQbXI/4c/EcW3hMaUHeqj6ryEnP4bsq32Fpa1moNNGYwr975aTnYOduV2jczs2OnPQcZTkAL3z3AmsHrGVh0ELMLc0B0OZq8e3mS8fZHZXlHPrfIWr0qFGocM+jvgc9l/YkJz2Hg7MP0v3n7soyASK3RxK1ParIQl5jszZd2MSIwBG83fDtYisZKrAveh8/dP7hvtuHNRzGOxveKdZjuJ/a7rVZ2mPpY33toWuH+KXnL/fd/nrt1+m/tv/jHlohM0NnUtG5YqFSo45VO/J2w7dptqgZM0NnGl1qtOj4IkY3Hs3AugMNxs00ZgyuN5hz8edYeGyhkoK8XZd3MaVt0cer0Wh4P/B9Pt7+sdE5AHOPzuWNem/wRasvDMZL25RmYuuJxKbG8uORH5UU5JnqnDflOXg95TrVXavfd3t11+pcS7mmJKtAdFI0TbybAGBjYUNKVgqQf7saL2ystHTN0twSM03+a6ObnRvRSdH4lfXDydqJmOSYf/nqR2NnaUd2XjYAHvYeXLpziVputQCIT49XkrGj/w5K25QGYOeAnUrmfBBTPlYjA0fyv0P/47sXvlM2593uvr8uJ15m3PZxDKwzkCDvIABCY0JZEr7kvmvX4zpy/YhBOR6AhZkFHzX9iIY/NVSWszVyK+OajStUjgdgY2nDh00+ZNq+aUa/ltxMvUkl50r33e7j4kNsaqxRGffaeGEj5hpzOlQ1/B51y6Ut5GnzeKHaC0rzhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCipEhBnhBCCPGUOH/+PE2aNLnv9iZNmvDBBx8oy1s3cB1mFmb0/bsvDh4OoFE2dSE6na7I+ZOvJlPKqdTTnVWEvKw8zK3MlWYBdJrTidzMXABafNoCc0tzYvbH4NfLjxafGV+C4lzRWX/5j5f/4IXvXqBap3/Khtxru+Pk7cTOz3dSo0cNo/NSb6biXMn5vttdfFxIjU01Ogfyy5lWdFuBS2UX4iPicfN3I/FyIjqdDo/6HkoyCpxacYo/+/9J1Q5VubTlElXaVyHhfAKpN1OVl73dCLvBsUXHOLX8FBozDXX616HDzA6UqVFGv49fTz/mPzff6KwyfmUImRJCtwXd9Od3XnYeIVNCKONX5l+++vF0X6S2pOted5/zz5K6A+rqL//e63daT2xNo/ca6ccCRwRyaM4hIrdFEjQq6LFzboTdeODa4/eiH7/3+v2x5y/K3eV/xc07yJtdE3bRc2lPLKzzv7XOychh95e79WWDqlg7W/PKuldIuJBAfER+qVBZv7KUrlpaaU70vmg6/9D5vtsbDmvIhnc2KM3c9eUu9kzcg2dDT+w97NFo1L7hCBkcwsKwhTT4qQF+Zf14vfbrvOL/itKMAqYs1hodPJqvWn+FnZUdo4NHP3DfGR1mYGVuRfcaj7dmmrr8x1SlRmE3wvisxWf33f6i34v0+t248tgC0UnRBLgH3He7v5s/V5KuKMl6FsvkTHkOpuekF3nuFShlXorM3EwlWQXK2ZfjdsZtKjpXpIJTBQ5cPUCdcnWISoy67/cTj6teuXocvn6Yaq7VaFmxJeN3jSc+PZ5fTvyCv5u/0qzGXo0JiQ7Br6wfnap1YsyWMZy8eZI1EWto7NVYSUbLSi2LvFxcTPlYHbp+iB1RO/j7/N/UcquFpZmlwfY1fdYYNf/d99fEpROZ0X4GfQP66se6+XYjwD2An47+xIC6A4zKuptjKUeik6KpUcbw+8WYpBgcSjkoy4m8E0l9j/r33d7QsyGRdyKNzsnMzcTK3Oq+2y3NLPVFkaqM2zaOqe2mFhrX6rSM2z5OCvKEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEM8MKcgTQgghnhLJyck4Ozvfd7uzszPJycnK8mKPx/LW0bcMirRUm1dvHmhAo9GwtO1SzCzM9Nt0eTruRN2haseqT13Wwe8OAvlZYQvCsLL/5xfmtXlaovdEF8v9alPaRn9ZY6ah2bhmyjMK3Dp5C2cf50Ljzj7OxJ2JU5KRm5n7wCJBM0sz8rLzlGRt/3g7QR8E0frL1kxxmELv1b2xc7NjzatrqNKxipKMAnsn76XDzA40ercRUxym0HF2R5x9nPl76N/Ye9grzZr/3HwqP1+Zzj92pkaPGphbFr4/nX2c8X/F+EKULnO7sLzrcmZ4zcC9tjsAN0/cRKPR0Pevvv/y1cZJi0sj4VwCAK6+rtiVtSu2rLgzcSRFJxU693y7+SrNSb6azLn154rM6jBDXQncxeCLtPumXaHxqh2rsm3cNqPmTo9Px9HL8b7bHb0cyUjIMCrjXneX/xW3jrM78muHX5nhNYNydcoBEBsei4W1Ba8Fv1Ysma7VXHGt5loscwOkXE/Btfr953et7krKtRSlmUfnHqX74u7Ueb2O0nkLNPZqTGOvxszqOIuVp1fy87GfGR08Gq1Oy9ZLW/F29FZWyGPKYq1jscfI0eboL9+PRkHDsanLf0xVahSfHo+X4/3LLL0cvUjISDA6ByA1OxVbS9v7bre1tCU9J11J1rNYJmfqczD4YjBO1k5FbkvMTFSWU6CNTxvWn1tPPY96DKo7iFHBo1h1dhVHrh/hxRovKs2a3HYyKVn56/iktpPo/2d/3t7wNtVcq/Fzt5+VZs3oMIPU7Pwy6y9bfUlqdiorT6+kmms1ZrSfoTQLYNGxRdhb2fNyrZcNxv84/QfpOelKSt5M+Vg5Wzvzop/aOe8nNCaUuZ3nFhpv6NmQN9a/oTSrT60+DFk/hG+f/5Ym3vl/AGBfzD4+3Pohff3Vfc+Qkp2CY6n7vw91sHLQn5/GWhC2AHuror+PK3i+qXTh9gVqlq1ZaLxGmRpcvH1ReZ4QQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQpQUKcgTQgghnhI6nQ4zM7P7btdoNOh0OmV5ZWuWJT1eTVHH/fj2yC+Uij0eS5UOVQyK5MytzHGu5EzNXoV/8ftJzzow8wCQ/5gdmXsEM/N/HreCrM5zOyvJykrOopRjKf3lBynYT4UyfmUImRJCtwXd9CV2edl5hEwJoYyfuvK/ewsG75aV8uDb+yjiz8bTa3kvAMwszMjJyMHK3opWE1uxovsKnnv7OWVZdy7doXrn6kD++ZCdlo1Go6HxqMYsbbOU1l+2VpY1InIEzhWdH7iPlZ0V3Rd1NzqrfKPyjIgcwcllJ4mPiAegVp9aBPQLwMru/qU2xshOy2bT8E2ELw1Hp81f/8zMzajdvzad/tcJS1tLZVl3Iu+wsudKbp68abDeajT55VPj88Yry4rcHsmKbitwqexCfEQ8bv5uJF5ORKfT4VHfQ1kOgK2rLRHrImgyponBeMS6CGxd71/m9DDysvMws7z/65aZhbqSy7vdvnSb44uOc+fSHTrO7oidmx0XNl3AqYITbrXclOW4+bsx/MJwTiw7oT/n/fv6E/BqAJY2xp97waODaf1Va6zsrAgeHfzAfVWVJuak52Bhff8fE5iXMic3M1dJVoG87Dy8m3grnbModlZ2DK43mMH1BnMu/hwLjy1k6r6pjNs+jucrP8/6vuuV5JiqWGvngJ1FXi4upiz/MVWpUXZeNpZm93+uWphZKC1dOxN35r7FdPHp8cpyntUyOVOegwPWPrhIreC1X5Wfuv6EVqcF4N1G7+Jq68r+mP10q96NoQ2HKs1q6NlQf9nNzo3Nr21WOv/dKrtU1l+2s7JjbpfCBWwqTQmZwrwu8wqNu9m58dbfbykpyDPlY7Wo+yKl8z2It5M388PmM+35aQbjC8IW4O2k9jX62/bfokFD/7X9ydXmv6ewNLPk7YZvM7XdVKVZKVkp9y3xTM5KRofxPz+p4FSB+WHz/3UflZxKORF5J7JQGerF2xexsyy+onAhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhDA1KcgTQgghnhI6nY7q1avft5BBRTne3QVr7b5px9aPttJ2clvcAtwwtzQ32FdF2VqrCa0AcK7kjH8f/weW8jxNWSOjRgKwpPUSeq/pjY2LTbFlfePyDWNujMHOzY6pzlOLPD90Oh0ajUZpgVeXuV1Y3nU5M7xm4F7bHYCbJ/KLw/r+1VdJhlMFJ8Lmh/3rPipY2lnqi7rsPey5c+mOvkhLdVGktYu1vtzPobwDt07dwj3AnczETHLSc5RmFZTjXT96nfiz+eU7ZWuWVV6yVsDKzooGbzUolrmLEjw6mCu7r9D3r75UaJpfPBEdEs2mEZsIHhNMlx+7KMvaPHIzzj7O9N/en9k+s3nj0BtkJGSwZcwWnv/2eWU5ANs/3k7QB0G0/rI1Uxym0Ht1b+zc7Fjz6hqqdKyiNKvVl61Y/8Z6ruy6QvnA8gBcO3iNi5sv0nV+V6Pn3/n5zvsWFao+3wEu777MsheWUaFpBa7suUKbSW2wc7PjZvhNji08Ru9VvZXmWdpa0uDN4jnnY4/Fos3R6i+bysXgi1g7FV0mk5mYqTyv3hv1OPnbSVp+3lL53PfjW8aXac9PY0rbKfx1/i9+PvazsrlNXaxlCiVR/mOKUiOAz3d+jq1l0WWg6TlqX//bLm1b5Pv1gtJVlefGs1YmZ8pzUDtBq2SeR2GmMcNM80+h7Cv+r/CK/ysmP47ilJqdqi+WK/CgIszHEZ0UjY+LT6Hxis4ViU6KVpLxrD5WMzvMpNfvvdh0cROB5QMBOHTtEBduX2B179XKcvK0eRy4eoAvWn3BlHZTuHT7EgBVSle571r8uHQ6HdXnVH/gdhXr0+X3Lxs9x6Pq7tud9ze/z599/qRK6fzvDS7evsiYLWPo5tvN5McjhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDFRQryhBBCiKfEokWLij3j3oI1nU7H0rZLDfYpjrK1ugPqKpvrScoasPPBhSEq9N/RH5vSNibLK1C+UXlGRI7g5LKTxEfkF6/V6lOLgH4BWNlZKcl4//L7SuZ5GF6NvYgOiaasX1mqdarGljFbuHnyJhFrIvBq7KU0q2KLikRujcQ9wJ2aL9dk88jNRO2IInJrJD5tC5d6GCPtVhqr+qzi8u7LWDvnFw1lJmbi09qHXit6YVfWTmle+C/hHJ13lDuRdxgSOgTnis6EzgzFpbILNbrXUJoFcHb1WXqv6k2lVpX0Y9U6VcPCxoJVvVcpLciLCY1hwI4B2JaxRWOmQWOmoUKzCrSd0pbNIzYz9NhQZVnxZ+PptbwXAGYWZuRk5GBlb0Wria1Y0X0Fz739nLKsugPrUsavDIe+O8TZNWcBKOtXlkEhg/AKNO7cr9iiIgnnEv51H5W2j9tOm6/bEDQ6iCkOU/TjPm18ODTnkNHzn1t/7qH39e3ma1TW3Wu6Kdf3tQPWPnC76oK33Mxcwn4KI2pbFG61CxfydpjRQWne3czNzOlRowc9avRQMp+pi7VeXPniQ+23ps8ao3JMXf5jqlKjFhVbcC7hwc/pFhVbGJ0DEDUySsk8D+NZLJMriQIqU8vMzeTEzRPcSrtVqEhOZeFVQnoC43eOZ+flnUVm3R57W1lW1J0o3tv0Hrsu7yIz95+C1YLncN74PGVZAG52bpy4eYJKzpUMxsNjw3G1cVWWY6rHyme2Dxruv9ZFjoxUltWpWicuDL/Aj4d/5Gx8/vvBrtW7MqzhMLydvJXlmJuZ0/6X9px99yw+Lj4EuAcom/teOwfsLLa5S9q056fRcVlHanxfAy/H/PfrV5Ov0rxCc75t/20JH50QQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQqgjBXlCCCHEU2LAgOIvxzFlAc/dtHlaDsw8wOnfT5MUnURetmFZwtjbY5/KLIDkq8mcW3+uyCwVpT+VWlbSX3bxccHR27FQaYxOpyM5JtnorHtZ2VnR4K0GyuctCR1mdCA7NRuAVl+2Ijs1m9MrT+NazZX2M9orzeo0pxO5mbkAtPi0BeaW5sTsj8Gvlx8tPlNTxFNg0/BNZKdm887pdyjrVxaAuDNxrB2wls0jNutL2FQ4/ONhdo3fReD7gez9ei+6PB0ANi42HJx1sFgK8nLSc7BzL1zyZ+dmR056jtIsXZ4OK4f88kfbMrakXE+hjG8ZnCo6EX8uXmmWpZ2lfr2w97DnzqU7uNVyAyA9Pl1pFoBXoBdey9QWQQIM3DVQ+Zz/5ubJm7z4W+HSMDs3OyX33YoeKx5qP9VFsusGr6Pj7I6UcihlMJ6dls2m4Zvo/nN3JTkTtBOUzPMobp24Rbm65QCIOxVnuFFtF98zx8nayeD6byd/o2v1rjiUciihI1LDVKVGuwbuMkkOQEVntWWgD/JfKJMrTu9seIdpz0/D3soegOUnl9PNtxt2VvnvNxIzE+m3uh8bX92oLHPzxc30/7M/8emF30+oLpJ7/c/XuXj7IkPqDcHd3v2BBWzGeu3P19DpdPzc7edizwLo69+XEZtG4GDloC+33H1lNyM3j+QV/1eUZJjysXo/8H2D6znaHI7FHmPzxc182ORDZTkFvBy9mNR2kvJ57+Xv5k/knUh8XNQWg9+rZaWWxTp/gU7LOrG813L9a/LUkKkMazgMZ2tnIL+Usvmi5px594yyTCdrJ/YP3s/WyK2Ex4ZjY2lDbffaykpdhRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDiSSEFeUIIIcRT4tChQzRo0ABzc/Mit2dlZbFu3Tp69+792Bl3l62Z0u4vdxO2IIygMUHs/GwnzT9tTuLlRCLWRtByvNpfbDdlVuT2SFZ0W4FLZRfiI+Jx83cj8XIiOp0Oj/oeSrMAZvvMZsyNMdi5GRaGZdzOYLbPbKVlTQDhv4RzdN5R7kTeYUjoEJwrOhM6MxSXyi5KytCWdVpGr+W9sHayBiBkaggNhzXE2jn/enpCOouaL+LdM+8aneVS2UV/2crOii5zuxg95/3YlLbRX9aYaWg2rlmxZV3cfJHXt72uL8cDKFuzLJ2+78Qv7X9RmnXof4foOr8rNXrUYN/Uffpxz4aebPlgi9KsAt5B3uyasIueS3tiYZ3/rU1ORg67v9yNV5Dawjc3fzduht/ExceF8oHl2T9tP+ZW5oT9FGZw/qjg1diL6JBoyvqVpVqnamwZs4WbJ28SsSYCr8bqi+xuX7rN8UXHuRN5h46zOmLnZseFTRdwquCkL+Z7Wlg7W5N6IxUXH8PH5MaxGziWdzR6/pIokAMIXxJOu6ntChXk5WbkEr40XFlBXkkoqXLe4mDqYq1F3RcZXF91ZhXTnp9GZZfKSuYvYOryH1OVGpnStH3TGN5oODaW+e8B9kXvo6FnQ0pZ5D+nU7JSGLttLD90/qEkD/ORmeqcN+U5OO/oPL5o9YX+Ng39eyiBXoFUtsp/XmXlZhF8KdjonLsN3zScl2u+zPiW43G3d1c69732Ru8lZFAIdcrVKdYcgPDYcI6+dRTfMr7FngXwVZuvuJx0mbZL22Jhlv++UKvT0r9Ofya3nawkw5SP1cjGI4sc//7Q9xy5caRYs4vT122+5oOtH/BV669o4NFAv14UcCxl/Ps1gN9P/06PGj2wMs8vuL6afBVPB0/MNGYApOekM+fQHD5q+pFROcGXgsnKy9Jfn7x3Mr1r9davT7naXM4lnDMqoygajYb2VdrTomILSpmXKlSSL4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQzwKzkj4AIYQQQjycoKAgEhIS9NcdHR2JjIzUX09MTKRv377K8o4tOsbpP04XGj/9x2mOLzmuLAfg5LKTdJ3flSZjmmBmYYZ/X3+6LehGy/EtuXrg6lObtf3j7QR9EMTbJ9/GwtqC3qt7MypmFJVaVqLmyzWVZgHodDoo4vfis1Oz9eVhqhz+8TBbRm+h6gtVybyTiS5PB4CNiw0HZx1UknEp+BJ5WXn663sn7yXjdob+ujZXS8K5hKK+9JHNrjyb9IT0QuOZiZnMrjxbSUaBieYTSbuVVmg8PSGdieYTlWbptDrMLQuXappZmqHT6pRmJUYlUq5euULj5qXMyUnLUZpVoOPsjsTsi2GG1wyWtl3K0rZLmek9k5j9MXSc3VFpVvPPmuvvs9YTW3Mn6g6Lmi/iwsYLyrM6zOiAV2B+EV6rL1vh09aH0ytP41zJmW4LuynNurz7Mj8G/Mi1g9c4u/os2anZANwMv8muCbuMmvv7mt8bPGc3vLOB9Ph/nmdpt9KYZDvJqIx7+b/iz7ax20iNTQVN/nMgel80Wz/YSu3+tZVmmUJWchaZSZnodDqyUrLISv7nv4w7GVzYeKFQKaoxNryzQX8OAJxcfpLstH+uZyZmsqzTMmV590q+mkzy1eRim7+4zTs6j/Scf87xoX8P5WbaTf314ijWMoWiyn9uZ9zWX1dd/vP76d/JzvvnvLuafBWtTqu/np6TzrR904zOqfl9TYPb8c6Gd4hPj9dfv5V2C9tJtkbnAHy8/WNSslP0119Y9gLXUq7pr6fnpDPv6DwlWZ2WdSIpM0l/fWrIVBIzE/XXE9ITqPm9mvehpjrnTXkO6nSG7490qH2/VJSbqTcZHTS62AvXAGqUqUFGbsa/76jAc+WfIyY5xiRZAFbmVqx8aSUR70Ww7MVlrOmzhksjLvFz95/1JWnGMuVjdT8vVHuB1WdWl1i+sTot60R4bDjdlnfDa6YXLt+44PKNC85TnXH5Rl3pdN/VfQ3Wvprf1+Ry4mX99ZSsFD7e/rHROSWxZmh1Wr7a/RXlZ5THfrI9UYlRAHy+43MWhi0s9nwhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhDAVtU0pQgghhCg2hX7xWlf4F6+LGntcIVNC6DKvS6FxOzc7/n7rb+oOqKssKzU2FfeA/JIBK3srspLyCzCqd6nOzs93KssxdVb82Xh6Le8FgJmFGTkZOVjZW9FqYitWdF/Bc28/pyQneHR+8YhGo2Hn5zuxtLXUb9Pmabl28Brl6hYuLjPGof8douv8rtToUYN9U/fpxz0berLlgy1KMgqdz8XYNZB4OVFf8ne33KxcUq6lFPEVj+9+z9O8rDzMrQqX2RnDp40Pm0duptfyXjh4OgCQfC2Z4FHBVG5bWWmWs48zscdjca7obDB+cfNFyviVUZpVwM3fjeEXhnNi2QniI/JLjfz7+hPwagCWNpb/8tWPpmqHqvrLpauW5r2I98i4nYG1izUaTRHNlEZwqfxPOYiVnRVd5hZei1XZPpjzVKsAAQAASURBVG47bb5uQ9DoIKY4TNGP+7Tx4dCcQ0bNHR8Rjzb3n1KrE7+eoMkHTbAtk184pdPpyM3MNSrjXm0nt2XDuxuY6T0TbZ6W72t+jy5PR0C/AFp81kJpFkB2WjZXdl8hKTqJvOw8g22BIwKNnn+q81Q0Gg0ajYY51ecU3kGTX6KoytF5R2n1RSus7POLhP4e+jdegV5YVc6/npuVy6XgS8ryIL/EcM/XewidHqov57NysCJoTBAtPm2Bxkzt86s4lURJjimY+nb1Xd2XG2Nu4GbnBuSXGh0fdpzKLvmvWwWlRh81/cionIj4CHK1/6xBv574lQ+afEAZ2/zXLJ1OR2ZuplEZBUx5HxZVJte7Vm+crZ2Bp7NM7ll9bhV4qeZL7Lq8iyqlqxR71g+dfmDc9nGMbzEefzd/LM0N3y85lnJUlrWg6wKGbRjGteRrRWbVdi+e4trqrtWpVroagPL3aKZ8rO5n1ZlVlLYpXWL5xto5QO333PfzLK8bX+/5miXhS5jWbhpv/vWmftzfzZ9ZB2cxpP6QEjw6IYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYRQRwryhBBCiGeIygKApOgkXHxcCo07V3QmKTpJWQ6Ao5cjKTdScKrghEsVFy5tuYRHfQ+uHb6GeSm1hWGmzLK0s9QXJtl72HPn0h3cauWXvaTHpyvLiT0WC+SXANw6ecugZM3cyhz3Ou40+aCJsjyAxKhEytUrXLpnXsqcnLQcpVnF6dz6fwpiLgZfxNrJWn9dm6clansUzpWclWQd/O4gkP88DVsQpi+gKsiK3hNNmRpqi+RemPMCK7qtYFalWTh5OwGQFJOEm78bL/76otKsoNFBbHx3I7mZueh0Oq4dusbJ5ScJmRJCtwXdlGbdzdLWkgZvNii2+QusG7yOjrM7UsqhlH7MprQN2WnZbBq+ie4/d1eWNbvybN48/Ca2rrYG45mJmcyrP4+RkSOVZd08eZMXfyt8Lti52Sldp4AiSy5VF9eYW5nTbX43Wn7eklunbpGdmk25euVwreaqNAfgxrEb/NbpN3LSc8hOy8amtA3p8elY2lpi52anpCBvwM4BoIMlbZbQe3VvbErb6LeZW5njXNFZX36pgimLSQts/3Q7xxYeo+3UtlRoWgGA6JBodn2xi9zMXNpOalv8ByGeKCVValRUjuo1yhSe5VKoZ9WcTnN4+Y+X2Ru9lwC3gEJFciMCRyjLcrZ2JjkrmTZL2xiM63Q6NBoNeePz7vOVjy4uPY5Lty8xaN0g/ZhGoymWrAJLw5fyf/v/jwsJF4D8srwPm3zI63VeVzK/KR+revPqoeGfNUiHjtjUWOLS4vih8w/KckytZaWWJX0ISmk0GoPHCSh0XbWl4Uv5qctPtK3clmEbhunH65SrQ0R8RLFmCyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEKUlBnhBCCCGKZOdmx80TNwuVg8WGx2LjalP0Fz2mGj1rELU9Cq9ALxoNb8Sfr/3JsYXHSIpOovGoxk9tlldjL6JDoinrV5ZqnaqxZcwWbp68ScSaCLwaeynLGbBzAADrBq2j43eGBV7FxdnHmdjjsThXdDYYv7j5ImX81JS8aTQaCnULKO4aWNFjhT5r7YC1BtvMLc1xruRM++ntlWQdmHkAyC8gOTL3CGbmZv9kWeVndZ7bWUlWASdvJ94Ke4vIbZHER8QDUNavLJXbVVaaA1D/jfpY2Fiw87Od5KTnsLrfahw8Heg4uyP+r/gryzm3/hxVX6iKuaW5QcFhUXy7+SrLDV8STrup7Qo9v3IzcglfGq60IC/xciK6vMKFRrlZuaRcS1GWA2DtbE3qjdRChag3jt3Asbyj0ixTcqrghFMFp2LNCB4VTPWu1ekytwtTnabyxoE3MLM048/X/iRwpPHleACVWlYCYGTUSJwqOD2VZV3/JnxJON0WdDN4vrrXdsehvAMb39koBXkPsP7ceoPrWp2W7ZHbOWV/ymC8m69xJaUlUf4jxN1MfQ6O3zkeW8v8ktrsvGwm7ZmEk3X+a0p6juLyWGD5yeVsubQFawtrdl3eZbDWa9AoLV17dc2rWJpZ8tuLv+Fu716s9+PgdYOp51GP5b2WF3sWwIzQGXy+83Pee+49mrZpCkBIdAjDNgwjPj2eUUGjjM4w5WPVw7eHwXUzjRll7crSqlIrapSpoSwHICMnAx06/Xl/JfEKf0b8Sc2yNWlfxfjvhU7cPPHQ+9Z2r210ninpdDoGrhtIKfP87xEyczMZtmEYdpZ2AGTlZSnPvJZyjaqlqxYa1+q05OQ9PWX1QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjxb6QgTwghhHiKnDlzhtjYWCD/F7EjIiJITU0FID4+XmmWf19/No3YhJWDFRVbVATgyu4rbB65WWnZFUC7qe3+ye3jj3NFZ2L2x1C6Wml8u6oruDJ1VocZHchOzQag1ZetyE7N5vTK07hWc6X9DDWlawXycvII/yWcoDFBuPm7KZ27KEGjg9j47kZyM3PR6XRcO3SNk8tPEjIlhG4LjCviKaDT6Vg3cB3mpcwByM3MZcOwDVjaWQKQl5VndMYE7QQAZvvM5s3Db2JbxtboOe9nZNRIAJa0XkLvNb2xcVFbNHk/Go2GKs9XocrzVYo9q/artan9am1y0nPITs3Gzs1OecaKHiv4IPYD7Nzs9AWHRdFoNIzPG290XlZyFjqdDp1OR1ZKFhbW/3wLpc3TcmHjBWW38+7Cv4vBF7F2sjbIitoeVai01Fj+r/izbew2Xv7jZdCATqsjel80Wz/YSu3+xhWUmKLkEiB4dDCtv2qNlZ0VwaODH7hvhxkdlOXGHo+ly7wuaMw0aMw15GblUrZyWdpNa8faAWvxe9FPWVbSlSSSriTdd3vB6/TTKON2BmVqFC5WLVOjDBm3M0rgiIxjymKtHit6FBob+vdQg+sajYa88ca9XpZE+Y8pmLp0bUHYAuyt7AHI1eay+Phiytjmn/spWerKT5/FMjlTnoMtKrbgXMI/r8dNvJsQmRhZaB+VPt3xKV+2+pJxzcZhpjH79y8wwqlbpzg29Bi+ZdR+31OUK0lXWN93fZFFXsXhf4f+x4+df6R/nf76sW6+3ahVthZf7P5CSUGeKR+rCa0mFOv8d+u+ojsv+r3IsIbDSMxMJHBBIJbmlsSnxzOj/Qzefu5to+avO7cuGo0GnU73r2W/xr5m3S34YrB+Pbq3RDYxM1FJxoC6Awyuv1b7tUL73H1OqlCzbE32Ru+lorPh+79VZ1ZRz6Oe0iwhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhChJUpAnhBBCPEXatm2LTqfTX+/SpQvAQ/+y+aNo81Ubki4nsbTtUsws8n/5X6fVUad/HdpObqsspyhejb3wauxVrBmmyHKp7KK/bGVnRZe5XYolB8Dc0hynCk5o87TFlnG3+m/Ux8LGgp2f7SQnPYfV/Vbj4OlAx9kdlRUo1h1Q1+B67dcKl3XV6V9HSVZBed3dMhMzsXa2LmJv4wzYaViioM3VkpuZi5W9lZL5D3538KH3DRwRqCTzXpa2lljaWhbL3AWlhvdeLi5Tnafmlw1pNMypPqfwDpr8AkwVCgr/NBoNawesNdhmbmmOcyVn2k9XW67ZdnJbNry7gZneM9Hmafm+5vfo8nQE9AugxWfGFQDpdDqD15CcjByWd12OuVV+6aU2V816FXssFm1O/lw3wm7c/7VQcT+UuaU5GrP8Se3c7EiKTqKsX1msnaxJjklWmrW41eJCY3ffThVlkAV2jt+pf/7mZeexZ9IefVljTnqOspwC5eqU49CcQ7zw3QsG44fmHMK9jrvyvOJk6mIt7QTTvOaXRPmPKUqNdDodbZe2xcIs/0djGTkZdF3eFSvz/NfjXG2ukhyACk4VmB82X3+9nH05fjnxS6F9VHgWy+RMeQ7uGrhLyTyPIjsvmz61+hR74RpAQ8+GxCTHmKQgr41PG8Jjw01WkHcj5QZNvJsUGm/i3YQbKTeUZJjysQq7EYalmSUB7gEArItYx6Lji6hZtiZftPpCv1apyprZYSaQX7Lmbu/OsaHHWH1mNeN3jTe6IC9qZJT+8rHYY3yw5QM+bPIhQd5BAITGhDI9dDrTnp9mVM69Bqw1XDuKKpE11qLui4ye41GNbzGeAWsHcC35GlqdljVn13Au/hxLTyzl775/m/x4hBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCiuGh0d7fsCCGEEOKJdeXKlYfar2LFikpzEy4kEHs8FksbS9wC3HCu6Kx0foC9U/Zi725PvcH1DMaP/XyMtLg0mo1t9lRmXTt8DZ1Wh1egYQHf1YNXMTM3w7Ohp7IsgLCFYUSsiaDnLz2xKW2jdO4HyUnPITs1Gzs3O5NlqhbyTQjOlZzx75Nf7vfHy39wZvUZHDwc6LexH+XqlDM649xf58hIyKDuwLr6sT2T9rDnqz1oc7X4tPHhpZUvYeNi3GM322f2w+2ogZGRhYsBH8W8evMeunRsaNjQf9/pEYUvDadWn1pYlDLs/c7LzuPUilNKChQv774MOljSZgm9V/c2eG6ZW5njXNEZB08Ho3PuNttnNm8efhPbMrZK532QpOgkbp26RXZqNuXqlcO1mqvRc+76ctdD7ddqQiujs0rCL+1/oe7AugT0C2D9m+u5deIWjUY04sQvJ8i8k8kbB99QlpWZlGlwXZuj5caxG+z8fCdtJrWhctvKSnIWt1r8UGUx9xZ9GuPy7sv81vk3nCo44RWU/3p5NfQqyTHJ9NvYj4rN1b6vEU8+sy//vXhKo9GQNz7PqJwvd335UPtNaFX8ZawqDVo36KH2K4lSp2fFketHaOjZUNl8ozaPoqxdWT5p/omyOe/nj9N/8MXuL/iwyYcEuAVgaW5YaFzbvXAh9eP66ehPfL3nawbXG1xkVjffbsqyAPx/8KdfQL9C9+PXe75m5emVnHz7pNEZpnysnpv/HOOajqNXzV5E3omk5vc1edHvRQ5fP0znap2Z1XGWsizbSbZEvBdBBacK9P6jN7XK1mJCqwnEJMXgO8eX9E/TlWU1mt+IL1p9QadqnQzGN17YyOc7P+foW0eVZT0pVp1ZxUs1X1I6594re5m4ZyLhseGkZqdS36M+41uOp30VtWXaQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghRkqQgTwghhBAPRZun5dbJWzhVdDK6wOtesyrNotdvvfBu4m0wfvXgVVa/spqRUcaVeJVU1vxG82n6UVNqvlTTYPzsmrPs+2af0vIkyC8ru33xNnk5eThXdMbSzrCEojgKykramVVnCt2/j2O2z2xeXPYi3k28ubT1Eqt6r+KllS9x+vfTJEUn8fqW143OWNJ6CX4v+dHo3UYAxOyPYVHzRbSa2IqyfmXZ8ekOqr5QlQ4zOhidZSoPW4AGxVOCNtF8ImNujClUzpiekM63bt8yPm+8sqzEK4k4VXB6qPKw4pCZmIm1s3WJZD8t8nLymGQziWHHh+Hm71bsedePXCcrJQuf1j6k3Urjz/5/ErM/BtdqrnT7uZuSYs1/c3n3ZbaM3sJbR98q9qzilHwtmcM/HCYhIgGAMn5leO6d55SXTz4JVBdrFbiecp2Q6BBupd1Cq9MabBsROEJ53r2Ko/znv+Za8jXKO5Yv6cNQrrjO+XupPgdTs1Mx15hjY/nP9z3HY4/z+c7P2Xhho9EFjXcbsWkES8OXUqdcHWq71S5UJDejwwxlWUUVUGo0GnQ6nZLiyX/LujtTZRbA6jOr6bOqD+0qt6Opd1MA9sXsY3vUdn5/6Xd6+vU0OsOUj5XTVCfC3gqjSukqfBPyDTsu7yD4tWD2Re/jldWvEDMqRllW7R9r80b9N+hZoyf+P/qz+dXNBHkHcfT6UTr/1pnYD2KVZdlMsiHsrTD8yvoZjJ+NO0v9n+qT8WmGsqx/k5GTYfAcf1y52lwi4iOwMreiumt1/fi6iHWM3zWeiPgIsj7LMjqnIGvy3skMrjcYL0evf/8CIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYR4ilmU9AEIIYQQQo01a9bwxRdfcOLECSXzbX5/M24BbtQfUh9tnpbFLRcTsz8GS1tL+v3dj0qtKinJAUiNTcXew77QuF1ZO1JupCjLMXVW3Jk4POp7FBovV68ccWfilGYB+PbwVT7n3ebVmwcP2Q2mqoxPm6slPiIecytzXKu76scj1kWwa/wu4iPilRTkpcam4ujtCMD5v89Ts3dNqrSvgnMlZxYELjB6foBbp2/RfkZ7/fUzq85Q+fnKtPi0BQAW1hZsHrm52AryCnqxVRa8FUfp3aPQ6XRFnpPJV5Mp5VRKSUZ6fDrZadk4V3TWj906fYvQb0PJTsumRo8aBPQLUJJVIOSbEJwrOePfxx+AP17+gzOrz+Dg4UC/jf2MLl4LHh380PsWZ2FjbmYuh+YcoskHTZTMZ25pjlMFJ7R52n/fWQHPhp76y3Zudry2+TWT5N7N3t2e+HPxJs28fuS6wW1XwbG8I20ntVU6Z0kyZbEWwOLjixn691CszK1wtXE1WOc1aJQU5D1M+Y8pC/JUlRo9SGZuJnMOzeGDJh8Ua05saiyT9kxi4bGFpH+aXqxZBZ7GMjlTnYMxSTH0XtWbQ9cOYa4x571G7/F1m68Z9vcwVp5eSc8aPdk/eL/ROXc7eesk9TzqAXAq7pTBNs3Dvvl+SFEjo5TO9yDaCaZ5PS7Qq2YvDr5xkJkHZrL23FoA/Mr4ceiNQ/r711imfKx0Op2+8HRb1Da6VOsCgLeTN/Hpal/7x7ccT7/V/RgVPIo2Pm0I8g4CYMulLcruuwJ+ZfyYEjKFBd0WYGVuBUB2XjZTQqbgV8bvX75ajazcLOYcmsP/7f8/o8v/Tt06RZffuhCTnF9Y2N23Oz92/pHeq3pz6tYp3qz/Jhv6bVBx2ABYmFkwbd80+tfpr2xOIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYR4UklBnhBCCPEUmTdvHlu3bsXKyoqRI0cSGBjIjh07GDNmDOfPn6d/f3W/JH1m1Rlqv1YbgPN/nedO5B3ei3iP8F/C2fHpDgbvG6wsy8nbiZh9Mbj4uBiMR++LxsHTQVmOqbMsSlmQejMVl8qGWak3UjGzMFOaBcVfVlbcBXz3unXqFr91+Y3kmOT8/O6+dP6xM6t6r+LWqVvUf7M+/Tb0U5Jl7WJNckwyTt5OXNp8idZftwb+fzGForKt7JRsbF1t9dejQ6Kp+fI/5X5la5Ul5brakkaAsIVhHJh5gNsXbgNQulppGr/fmPpv1FeeZSoFZY0ajYalbZcaPJ90eTruRN2haseqSrI2Dd+Evac9HabnF8Wl3UpjUfNFOHg6ULpKadYOXIs2T0ud1+soyQM4OvcoLy57EYBLWy8RuS2S1za/xunfT7P1w628vuV1o+aPPfaQRSAKel7S4tK4dvAa5lbm+LT1wczcjLycPA7/cJiQKSFoc7XKCvIAmn/anB2f7KDnLz2xKV285VmmdPPETYPrOp2O1BuphEwNoVxd4woTi5Kdmo3GXIOljaV+LPZ4LDs/38mFjRcYnzdeWdaxRcewsrei1su1DMZP/3GanPQc6g6oqyyruJVEsRbA5zs/Z3yL8Xzc/GPMNOrfX5i6/OdBVJYaAcSlxXHw2kGszK1o69MWczNzcvJy+OHwD0wJmUKuNldJQd6djDu8s/Edtl7aipW5FeOajeO9Ru/xxa4v+Hb/t9R2r82i7ouMzinwrJXJmfIc/HDrh2TmZjK742zWnF3D7IOz2Ru9l8DygVwacQkvRy8lOXfbOWCn8jnvp6JzRZNl3S0zNxNrC+tiz2ng2YBfX/y12OY35WPV0LMhX+/9mnY+7dh9eTc/dv4RgKg7UbjbuSvNeqnmSzSr0IwbKTeoU+6f97RtK7elp19PpVlzu8yl6/KueM3worZ7/s8bTtw8gUaj4a++fynLycrN4otdX7A1Mn/d/ajpR/So0YNFxxbx6Y5PMTczZ1TjUUbnjN02lqqlqzKn0xyWn1rO8pPLORt/liH1hrD51c3FUubatnJbdl/eTaW6lZTPLYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQTxIpyBNCCCGeElOnTmX8+PHUrl2biIgI1q1bx6effsr//vc/Ro4cydChQ3Fxcfn3iR5Senw69uXsAbiw8QK1etfCtbor9QbX4+Dsg8pyAOq/WZ/N728mLycPnzY+AERtj2LrR1sJGhP01GZVaV+F7R9v55V1r2DtlF8IkZmYyfZPtlP5+cpKswpkJmZyZtUZbl+6TdMPm2JT2oYbYTewc7fDsbyjUXMXdwHfvbaN3UbpqqXpNKcTp5af4uTyk8SfjafekHq8uvlVg+ImY/m96MfqfqtxreZKekI61V6oBuQXiZWuWlpJhkN5B+LOxuFUwYns1Gxuht+kw8wO+u0ZCRlY2qq7TQA7x+8kdEYojYY3wjvIG4CY0BiCRwWTFJ1E64mtjZr/G5dvHrpEbeztsUZl3a2grDH2eCxVOlTByt5Kv83cyhznSs7U7FXzfl/+SK4euEr3xd3118OXhmNT2oZhx4dhZmHG/m/3c/j7w0oL8lJjU3H0zn++nv/7PDV716RK+yo4V3JmQeACo+cfsHOA0XM8jOiQaH7r8htZyVloNBo8G3rSfVF3VvRYgZmFGa2+aEWdAeruN4DDcw5z++JtpntOx7miM5Z2hs+poWFDleaZyty6c9FoNOh0OoNxr8ZedP+5+32+6tElxSSxqvcqrh26hsZcQ6P3GtHm6zb8PexvTq88TY2eNRi8X11BLkDIlBC6zOtSaNzOzY6/3/r7qSrIK4liLYD0nHRe8X+lWMrxwPTlP6YqNQqJDqHLb11IzkpGo9HQ0LMhi7ovoseKHliYWfBFqy8YUEfNejlu2zj2x+xnYN2BBF8KZlTwKDZf3IyZxowdA3bQ2Kuxkhx4NsvkTHkO7rmyhzV91tDYqzG9a/Wm3LfleDXgVd5v/L6yjPu5ePsil25fokXFFthY2qDT6dBoFLTV3uOX8F+Ye3QuUXeiCB0SSkXnisw6MAsfZx+611D3mpKnzWPy3snMPTqXm6k3OT/8PJVdKvP5js+p5FyJIfWHKMsqoNVpuXj7IrfSbqHVGZZct6jYQlmOKR6rWR1n8eqaV1kbsZZPm39K1dL55c+rzqyiibe6cuEC5ezLUc6+HFeTrwLg5ehFo/KNlOc0Kt+IyBGRLDu5jIj4CAD61OpDv4B+2FnZKcsZv3M8847Oo13lduyP2c/Lf7zMoLqDOHD1ADM6zODlmi9jbmZudM7ha4fZ8voW6parS/MKzVl+cjmfNPuE1+sYV2j9IC9UfYFx28dx8tZJGng0KHS/dfPtVmzZQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghhSlKQJ4QQQjwlFi1axPz58xkwYAB79+6lZcuW7N+/n4sXL2Jnp+4XyQvYu9sTdyYOew97Lm6+SOcfOwOQk56DmbnaApYmHzYhPSGdje9sJC87DwALawuajm1K84+bP7VZz3/7PItbLGZWxVl41PMA8gu97Nzt6PlLT6VZADdP3GRpu6VYO1mTeDmRBm82wKa0DWfXnCUpOomeS9Vl7hy/k0qtK+Ed5I2FdfG8pbx2+Bqvb3mdcnXLUaF5BU4uP0mzT5opLSIr0GFmB5wrOZMUk0S7ae30hWspN1J47p3nlGTUfLkmwe8Hk/ZJGhc3XsS+nD1ejf8pjrl+5Dquvq5Ksgoc+fEIXed3JaBvgH7Mt5sv7rXd2TR8k9EFeR1mGRb87fl6D1U7VMUrKP92XQ29ysXgi7T4XF0hCfxT1uhcyRn/Pv7Fdg5CflmdcyVn/fWoHVH4veiHmUX+OujbzZeQKSFKM61drEmOScbJ24lLmy/R+uv8x0mn06HN0/7LVz+aqB1ReDf1xqKU+vtwx2c7qNapGs0/ac7xJccJnR7Kyp4raTu5LTVfUlNgeK+C8sRnzciokQbXNWYa7MraKT/3t364ldzMXDrO7sjZNWc5OPsg0XujKR9YnhGXRuDoZVzRalGSopNw8Slc8Otc0Zmk6CTlecWppIq1htQbwh9n/mBcs3HFMr+py39MVWr02Y7P6FStE580/4Qlx5cwPXQ6PVf2ZHLbybxU8yUFt+Qfmy5uYnGPxbTxacN7ie9ReXZl6pary+S2k5XmwLNZJmfKc/Bm2k18nPNLtN3s3LC1tOWFqi8oz7lbQnoCvVf1ZmfUTjQaDReGX6CyS2WGrB+Ci7UL0ztMV5b14+EfGb9rPO8Hvs+kvZPI0+V/P+Rs7cysg7OUFuRN2juJJeFLmNZuGm/+9aZ+3N/Nn1kHZykvyDtw9QD9VvfjStKVQoWyGo2GvPF5RmeY8rGq7V6bk2+fLDT+f+3/D3ON8Wvg3bQ6LV/v+ZrpodNJzU4FwMHKgTFBY/i0xafKC1jtrOx4q8FbD9yn82+dWdB1AR4OHo+V8ceZP1jacyndfLtx6tYpav9Ym1xtLuHDwpWWGcanx+Pp4AmAk7UTdlZ2SktPi/LOhncAmBE6o9A2Vee6EEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEI8CaQgTwghhHhKREdH06ZNGwCaN2+OpaUlX375ZbGU4wHUHVSXP3r/gYOHAxqNhsrtKgNw7eA1ytQoozRLo9Hw/DfP0/LzlsSdjcPSxpLS1UoXS2GTKbMcyzsy7MQwTi47SWx4LJY2ltQdVBf/vv6YW6otNQAIHh1M3YF1eX7a80xxmKIfr9apGqv7rVaadTX0KqEzQtHmain/XHkqtqxIpVaV8G7qjaWNpZKM9Ph0HDwdALB2ssbKzsqgUE4lc0tzmnzQpNB40KggZRktx7ck5VoKm0dsxr6cPT1/7WlQNnlq+Smqd62uLA8gLycPz4aehcY9G3iizTW+aK3ugLr6y7/3+p3WE1vT6L1G+rHAEYEcmnPo/7F3n2FRXe3bh39D71XAQrOiCAo2xIYFu2KJ0dhbLDFRHzXGVI0mRk1ijUZNs9eosXeDWKKS2HulWMACiiCKCPN+4A9xBBuzBqPvfX7JzNp71rVndpk9HgdXuLT9ktLPMq98QzG3M+fBnQfglfX8auRVAnsH/ruCBh6lPVKaWa5tOVZ2WolzaWdSE1Ip3bQ0APGH43Eq5aQ0a0nYkn/P47peeIeoO49vHL9B8x+b4+LrQr0x9dg/aT+h34ZStlVZBVv+rwPTDlC5b2VMLEwI7BmInbsdGiN1xSf/BTERMZTvUD7Xd1XGwwxOLD1BxW5qikNjdsXQYVUH3Ku7U759eb4v/D3+nf2p/j/DFb1Yu1pz/dh1nSJKgPij8Vg6qyvyKgivolgLYFyDcbRY0oLNFzbj7+qPqbHu+Tupce4CnZdR0OU/BVVqdPzGcX5s/iO+Lr6MqTeGSfsn8W3ot0oLwrJdS75GuULlAPB28MbCxIIuFbooz4E3s0yuoI/Bx8vAjDRGmBmbGSwLYMiWIZgamRI7JJZyM8rljHco34GhW4cyEXWlaz9E/sDPLX+mddnWjN87Pme8StEqfLj1Q2U5APOPzuenFj/RoEQD+m/onzNesXBFztw6ozQLoP/6/lQpWoUNnTZQxLYIGtTfCxTkvsr2MOMhN+7dIFOre+/uae+pLOOzHZ/x6+FfGd9gPDU9awKwJ3YPX+78kgePHjC2wVhlWS9qV8wu7j+6n+/XX7l7hcpFKgNZpYzmJuYMqT5E6fcIZP37QnJaMhYmFmi1WjRouP/oPnfT7uqsZ2eurmQ4c5TawmwhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhPivkoI8IYQQ4jWRlpaGhYVFznMzMzOcnNQWJT2u7pd1cfVzJelyEuXf/reUR2OsoebHNQ2SaWZjRrGqxQwy96vKMrM2o3LfygbPAbj29zVazG6Ra9y2mC0p8SlKs7pu60rmo0yuHLhCzK4YYiJiODDtABlpGRStWpRee3rpnaHRaEhLTsPEwgStVptVRHb/EWl303TWM7cz1zvrcePsxtH/SH8cSzgqndfU0pQ289s8dXn38O46z2P3xlK0SlG9yhsrdK3APzP/ofGkxjrjB386iH9n/3zPm5cLWy4QOiE013ipJqXY/vF2pVnZRhuNfmbJxciMkXpnuFd358C0A4T9HMbpVad5mPyQ4vWL5yxPOJeAvYe93jmPazy5MQ7eDiRdTiL021DMbLKKeZLjkqk6oKrSrBG3R3A18ioxEf93Hk85QMbDrGJF73re1P+6fr7nvn/7PlaFrICs49/UyhRXP1dVm55jy9At+L3jh4mFCVOLT2VY3DCsXQ1THvs0jx48wsTCcD+v1/RcQ6kmpTBx1c1IS05jTc81ygry7l2/h0NxByCruM7UypRSTUspmftp/Dr6sWnQJsxszfCqk9VEGRMRw+bBm/F7x8+g2YZQ0MVaAOP2jGPLhS34FPKBG+hcF1UURBV0+U9BlRrdvn+bQlZZpc+WppZYmVrh52qYY06LFhOjf89fYyNjLE0MUwD5JpbJFeQxqNVqKTO9TM65k/IwhcDZgTrvEyBxRKLeWdm2XtzKli5bcLfTLYIu7VyamDsxynIAou5EEVg4MNe4ubE599LvKc26mnyVUk65v0MytZmkZ6QrzQI4n3ieFe1X5JmpSkHuq3MJ5+i9tjd/Xf5LZ1yr1aLRaMgYmaEsa97RefwS9gthPmE5YxXcKlDMthgDNg54JQV5+srQZuhcj0yMTLAxs1Gek33NePx54OxAneeq99eL8p/pz8ZOG/Gw9yjwbCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEUEEK8oQQQojXyBdffIGVVVbZ0MOHD/n666+xt9ctZpo0aZKyPN92vrnGAroHKJs/27x683hWf0v3P7s/feF/OOvo/KPPXK6q0CibsblxrvI4yCrwsnZRXxZlZGKEZ01PrF2ssXSyxNzWnDOrz3DrzC0l82u1WqaXma7zfHbgbJ3nGo1GSQmabrDa6fJrUdNFSor6Dv96mItbL+JePavI4+qBqyTFJlGhWwW2DN2Ss96TJXovy8rZijNrzlBjWA2d8TNrzmDlbKXX3E/TYVUHnfM5Mz2TuMNxHJ13lLqj6yrJqPdVPeY3mM/YhWPJfJRJrU9rYen4b6nRyaUn8QrxUpKVzdjUmBof1sg1HjwkWGlOdpZnTU88a3pS+9Pa3Dh5g7+++4vji45zZf8VvQryAG6euplT0KnVakk4m0D6Pd1SHLcKbnpl2Ba15dTKU5RuVhqtVsvdK3d59OBRnuvae6orM9Rmatk1dhcHZx0k5XoKA88NxLGEI39+8ScO3g5U6l1JXdb/lYQ+6e6Vu5jbqy0J1RhpdB4bmxkrnf9J9b+qT1J0EvMbzMfIJKuASpuppWK3ijT4poFBs1V7FcVaABP3TeS3Vr/RI6CH0nmzFXT5T0GVGgGcunmK+JR4IOt9nE04m6skrIJbBb1ztFotDeY3yCnJu59+n5ZLWuYqkzvU75DeWW9imVxBHoNzWs3Re46XdS/9Hlamue+VEu8nYm6i9hpf3KE4R+KP4OWge++y+cJmyhUqpzTL18WX3bG7c2WtOLWCwCK5S/r0FVQsiAuJFwxakFeQ+6rnmp6YGJmwvuN6itgWUVJ4+jSJ9xMpW6hsrvGyhcqSeF/td1ZB0Wq19FjTA3PjrP3y4NED+m/oj7Wp7u/iVR1W6ZUT3j1cr9cbUvSdaNIz1ZdRCiGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCFEQZGCPCGEEOI1UadOHc6ePZvzvEaNGly6dElnHY3GcH80b0huAbrlSJnpmcQfiefGiRtU7K62RK4gszYP3qzzPCM9g/TUdIzNjDG1MlVWkJcUm4Sdux0+YT7sGrOLdsvbZS3QZC3bPmI75d5SW3hx8KeDRO+MJiYihkdpj/Cq7YVXXS9qf15b77KrbN3D1ZUVvpYUFPXdPHGTIpWKAHD74m0ArApZYVXIipsnbv67ooJLR93RdVn77lpidsZQLKgYkFXGd2HzBVr+3FL/gDyUbZ27yMO3nS+u5V05ueykkoIytwpuvH/6fWL3xmJT2Ab3IHed5eXfKY+Lr4veOU9KOJ9AdHg0927cQ5upezCEjAxRl3Mugeid0bnO54bfN8S7rrfe889vMD+r3O3/LG6xGMj6vlJVclnn8zpsGriJTR9sQqPR8HPVn3OtY4hCzV1f7+LovKOEfhvKuj7rcsZd/Vw5MOWAkuNvduBs0GR9Xo8XyAFoM7TcjrpNqSbqioByikn/75rwMOUhswNn65TmAYxIHKEs09jMmHbL2lHv63rEH4nH1NIUV39XHLwclGUUlFdRrAVgbmxOTY+aBpu/oMt/CqrUCKDB/AY616gWi1sAutcoFaVro0JG6Txv5dNK7zmf5k0skyvIY7B7wMvdfy45voQwnzCszfJfRl3bszbzj87nq/pfAaBBQ6Y2k2/3fks973r5njcvQ4OH8v7G93nw6AFarZbIq5EsOb6EcXvG8UvYL0qzRtYZSffV3bl69yqZ2kxWnV7F2VtnmX9sPus7rleaBTCw2kCGbR1GfEo8/q7+mBqb6ixXUXZZkPvqSPwRDvY9mGdxnWoVC1dkeuR0pjWdpjM+PXI6Fd3U/kYuKE+ey10qdDFIToj3y92Xj98znv5V+uNg4WCQ7RFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiDeJRvv4XwILIYQQQvyH7PxyJw9THtLo+0ZvTFbC+QQ2vLeBGsNrUKqxmlKjMcZjGBY3DGNzY35v9zvX/rlGWnIatkVtSYlPwSPYg04bO2FmbaYkD2C00WisXawJHhZM1QFVMbNRN3d+7Rm/hyr9q2DhYKHXPOvfW0/9r+pjVchK0ZblzzjbcfQ/2h/HEo6vdDtexpUDV4icFsnN01nley7lXKg2qFquUjlDu33pNjMrzOTTlE8LNBdgpv9MOm3shL2Hfb7nOPjzQTa8twGrQlbYFLbRLT/VQL9D/RRsaZbsczlocBBlWpTB1d9VWdnqnZg7L7SeiiK0tOQ0kmKSmFlhJt22d8PS2TLP9QpXLKx3VrZppabRYnYLSjQooXO+3jpzi1+Df2XEbf1L5HaO3glAxOgIgocF61xrjc2McfB2wPctX4zNjPXOAjgy78gLrRfQPUBJ3ssYZzeO/kder2vi86go1gIYt3sccSlxuYqNXhV9y396run5QuvpW84WcyfmhdbzcvDSKyc/9sbupUrRKpibmL/0ayOiI15ovZctdVJB1TH/PAVZQGU3zo4j/Y9QwrFEvuc4ceMEDeY3oFKRSvwZ9SdhPmGcvHGSxPuJ7O21l5JOJRVuMSw6togvI77kYuJFAIraFmV03dH0rtRbaQ7A7pjdjNk1hqPxR0l5mEKlIpUYGTKSRiXV/+YyGm2Ua0x12WVB7quqP1dlcuPJ1PKspWzOp4mIjqD54uZ42nsS7B4MwL4r+7h89zIbO22ktldtg2/Dk2zH2XK0/1G9zq2XceXuFYraFsVIk/s4UknFNeNFFfRnKIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQqpm86g0QQgghhGHY2dlx5MgRSpR4ff8YukKXCvxc7ecCKcgrqCzn0s6Ejg9lVZdVfHDmAyVzZvcdW9hb0HVbV2L3xHL92HUepjykSKUilAhVfwx0WNWBmF0xnFh6gp2jdlI4sDDedb3xruuNZy1PTK1MlWc+z+5vdlO+fXm9C/JazGyhaIv+/+Me5I77ooItw3tS+v10Dkw7gF0xu1eSfyf6DpnpmXrNsfvr3dQfW59aIwxfiBI0KIiYXTFEjIng3PpzSs/jly2+2zBgA/XG1MtXOaW5rTmufq60mtMKj5oemJgb/qdu8tVknEo55RrXZmrJSNe/hAeg7qi6ADh4O+DXwQ8TC8O+r5ctvju+5Dg+YT5KC1if6g2s9u+3vh9B7kGUMNPvezryWiR/Rv3J+nPrKe9aHlMj3XN3VYdVes3/sr7Z/Q3ty7fPdznZyxbf5bfU6GWL7wZsGMCYemMoZFXopV6XH00XNc13gdLLFt8VZJmcqmP+efQ9Bl+GVsHFyc/Vj3MfnGN65HRszWxJeZhC23Jteb/q+xSxLaJgK3V1rtCZzhU6k5qeSsrDFFytXZVnZKvtVZttXbcZbP7HRQ2OMnhGQe6rCaET+GjbR3zT4Bv8Xf0xNda9ttuZq7vXDfEO4dzAc8yInMGZhDMAtC3XlgFVB1DUtqiynP8y3xm+BVJcp+KaIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ/7+QgjwhhBDiDZVdmvY6u7zvssHLgF5FlpGJEcnXktVOqvn3oWctTzxreaqd/wllW5elbOuyADxIekDs7lhO/X6KxS0WozHS8PmDzw2anyc9D/lLOy4RtSOKezfuoc3UnazVb630m/wVefTgEQd+OEB0eHSe76vfoX5K8zIzMjmz+gy3Tt8CwKW8Cz5hPhgZv1xp0Yua4DhB59hHC2nJaZhamdJ2YVuDZBaE+7fvU/7t8gWS1WRKEwAe3HlAzO4YYiJi+POzP7lx8gZFAovQa2+vAtkOgGMLj1Hjwxr5KsjL9rIFb/pw8XUhdndsriLAUytOUSRQbUlOQb6vl7G+33rcg9wxK1EABXlvIFUlOQ4WDrQt99+55hV0+U9BlRotPLaQD2t8WCAFeQX5Gb5uZXL/pRyV7C3s+azOZwbPuZ9+Hy1arEytsDK14ua9m0zZPwVfF18alVRbFH456TIajQZ3u6zy5MirkSw+vhhfF1/6Vu6rNAtevvQyvwpqX4XODwWgwfwGOuNarRaNRkPGSDVlvNmK2hZlbIOxSufUx6e1PsXJMncRsaG8jtcNIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYR400lBnhBCCCHyNDdkLoG9A/F92xdTS1ODZi1ru0x3QAvJcclc++cadb6o89pmnV17VjdKqyUlLoXI6ZF41lRbYBf+RTimVs/eT40nNVaamZqQSkxEDNE7o4neGc3NkzexcLTAq3bBlFOotHP0TnaN2UXRKkWxKWKDRqN5/osMTcEmrO29lotbL1KuXTmKVSumZM6nSbyQyOLmi7l75S7OPs4A7Bm3BzsPOzpt6IRTSfUFF40nN9Z5TxojDdYu1hQLKoalo6XyvILi+7YvF7depEr/KgWWmZmRSWZ6Jo/SHvHowSMy0jK4dfZWgeUDepdcFrQ6I+uwuvtq7l69izZTy+lVp7l19hbH5h+j4/qOes8/wWkCA88NxKqQVe4yyCeMSByhd16+vGb77E01p9WcV70Jr5SUrunnTX1fr5PNFzZjY2ZDLc9aAMyInMHPh37G18WXGc1m4GjpqCyr1dJWtC3Xlv5V+nPnwR2q/VINM2MzbqXeYlKjSbxX9T1lWZ1WdaJvpb50rdiV+JR4QueH4ufqx6Lji4hPiWdkyEhlWdkuJl5kyv4pnL51GgBfF18GBw2mpFNJJfMX5L4K7x6ubK68HLt+7IXXreBWQWn2i+ynT2p/ojRTCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHE60cK8oQQQgiRp8KBhdn64VY2DdyEb3tfKvWuhHt1d4NkWdhb6A4YgbOPM/XG1KNkIzVlBq8ia2nrpTrPNRoNVi5WFK9fnEYTGynNunH8BsZmxk9fQXEx2kz/mdw8fRNLR0u86nhRqU8lvEO8cavgpjaogBycdZBWc1tRsWvFV70p/1LQV3Nu/Tk6beykvJAxL5sGbcKxhCO99/XG0imrnC41IZU/uvzB5kGb6bShk/LM4vWLY+dhl2ehYVJsEvae9sozC4JTKSfCvwjnyv4ruPq7Ymyqe24HDQpSlrVp0KasgstTT5zPdb1x9XdVlvMmKtuqLB3XdWTXmF2YWpsSPjKcIpWK0HFdR0o21P/7pPHkxpjZmuU8NmTBpRBCiFdn+LbhTAidAMDx68cZunUow4KHER4dztCtQ5WWYB6KO8TkxpMBWHFqBYVtCnO432FWnlrJyJ0jlRbknbhxgmrFqgGw/ORy/N382dtrL1svbqX/+v7KC/K2XNhC2NIwAgoHUNOjJgB7L++l/I/lWddxHQ1LNtQ7oyD3VYh3iLK58hIwKwCNRoNW++wfHRqNhoyRGcpyC2I/iSyzW8zGzfr1/H0uhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBAgBXlCCCGEeIomU5rQ6PtGnF17lqPzjjKnzhycSjkR2CuQCl0rYONmoyyr1ZxWyub6L2WNyhxVYFkd/uiAtat1geVV7l8Z7xBvXP3ejAKtjIcZeNTweNWboeOT5E/0nsO2mC3mtuYKtub5YiJi6L3/33I8ACtnKxqMb8BvNX8zSObU4lMZFjcs17GfmpDK1OJTGZmhtniloBz66RBmNmbERMQQExGju1CjtiAvJS6Fyn0rZxXivSHnc0Hyqu1F121dDTJ3QPeAfx/3CHjqev/fkILApyo+tTiaZ3xAlwZfKsCtEUK8rKjbUfi6+AKw8vRKWpZpyTcNvuFQ3CGaLWqmNCs1PRVbc1sAtl7cStuybTHSGFHdvToxd2Ke8+qXk56RjrlJ1n3o9kvbCSsTBkDZQmWJS4lTmgXw8Y6PGVJ9CONDx+uOb/+YEdtHKCleK8h9BXDnwR1+PfQrp2+dBqC8S3l6BfbC3kL/EuiowVF6z5EfBbGf/n8QER3B9/u+5/TNrGPD18WX4TWGU9urds46nfzVF5QLIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIURBkoI8IYQQ4g2l0ejfJGNkYkS5tuUo17Yc927c4+BPBwn/Ipwdn+6gdLPSBA0Konj94gq2VuhDxb5+WdXer5bzWKvVvrLtUCXw3UCOLz5OyBchBs9KuZ7Ctg+3cWnHJe7duAda3eUqS90aTWzE9hHbaT6rOQ5eDsrmzYuxuTEPkx/mGn+Y8hBjM2ODZGYfe3llmli8vj91BkcNLrCst39/u8CyDC0jPYMZZWfQcX1HXMq5GDxv7btrqdClAt51vQ2eNcZ4zFPLIL93/f61LYN8KXmf7gL4X9D/dJ6nZ6ZzOP4wmy9sZniN4a9mo4RenlV4KP5bvOy9MDUy1WsOM2MzUtNTgawiuW4VuwHgZOnE3bS7em/j40o5lWL1mdW0KduGLRe3MKT6EABu3LuBnbmd0qzyruWZ9c8smpduzrZL2/iq3lcAXEu+hrOls9IsgNM3T7O83fJc470CezFl/xQlGQW5r/659g+NFzbG0sSSasWyfntN2j+JsbvHsrXrVioVqaTX/F4OXio286UVxH7Kj4K67tb2rI2lieXzV3yGhccW0nNNT9qWa8ugoEEA7L28lwbzGzC39VwpxhNCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCvDFe39YIIYQQQjzT04qj8uNq5FUOzznMyaUnsXa1pmKPiiRfTWZxi8VUHVCVRt83euk5JzhO4EX/Bn1E4oiXnv9VZW0ZuuWF1208qbFeWdlU7uuXcXT+Uf767i8SzicA4FzGmRrDa1Cxa8VXsj2etT0xsczf7e2jB4849NMhorZH4VrBFWNT3UI3VfsKYE2PNSTFJlHnizrYFrF94WMzP4pWKcqjB4+YVmIaplamGJka6SzX93h/XJkWZVjXdx1hv4ZRrFoxAK4euMqG/hvwCfNRlgP/nmcajYbwkeGYWv1bTJOZkcnVA1cpHFBYaWZeHtx5gIWDhc5Yi9ktsHazfsor/psSLyayf8p+bp2+BYCLrwtBg4NwKun0irfs5RibGvPowaMCy0u9mcrCJguxdrGm/DvlqdC5gsGOu6dd5zPSMgxWQPlf03lTZ2yL2b7qzVBKRbEWwODqeZdqzoicwT9x/+g9/8tSUf7zMt7EMjntG9oIqeqYfx59jsHIq5FULlIZY6O8r61pj9JYc3YN7cu3B+DEgBP53s5stTxrMXTLUGp61CTyaiTL2i0D4FzCOdzt3PWe/3EjQ0bSaWUnhmwZQoPiDQj2CAZg68WtBBYJVJo1IXQCbZa14bu/vqN7xe5ULJz1G2Ht2bU5hW8quVi7cCT+CKWdS+uMH4k/gqu1q5KMgtxXQ7YMIcwnjJ9b/oyJUdZvnEeZj3h37bv8b/P/2NVzl9K8glIQ+yk/9L3uHoo7hKmRKf5u/gCsObOGOUfm4Oviy5d1v8TM2AyAjZ036r2tY3eP5dvQbxkSPCRnbFDQICbtm8RXu76SgjwhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxBtDCvKEEEKI11h2YY5Gk7sYZNOmTRQrVizfc9+7cY+jC45yZM4REs8nUqZlGd5a8hYlG5fMyQvoEcDCJgvzVZDXeMq/hWP3E+6z6+tdlGpcCvfgrGKBK/uucGHLBep8USff7+FVZMUfjtd5HncojsxHmTj7OAOQcC4BI2MjilQuondWtlZzWmFub/7C6y9uvpiWv7TMKmfLp32T9hH+RThVP6hK/Zr1AYjdE8uG/htIvZVK8JDgfM+dF22mlsQLidy7cQ9tpm55gVcdLwA6b+yc7/lvHLuRU2x188RN3YWKe3di98TSc3fPAilwW9lxJXev3qX+N/WxcbMxaBlf02lNWd19Nb8G/5pTMJj5KBOfMB+aTG2iNCv7PNNqtdw4fkOnIMzYzBi3im7U+LCG0sw9E/bg4O2AXwc/AH5v/zunV57GprANnTZ2onDFrP3p38k/3xnL2i7Lc9zC3gKnMk5UercS1i5qy/cubLnA0rClFA4ojEdNDwAu773Mj+V/pOO6jpRsWFJp3rP4d/HH3O7Fr2V5qfp+VfZO2EvYL2EYmRg9/wV6eGfNO9y/fZ9Tv5/i+OLj7J+0n0JlC+Hf2R//Tv44eDvonXFg2gEg63v+0C+HMLMxy1mWmZFJ7K5YCpUtpHdOftl72ecq3nwR+SmT9azl+dI5r0p4VDj1itfLc9nsf2bTr0o/QE2x1rM0Ld2UT3Z8wpxWc5TMdzftbp7jGjSYm5grLf95GQVVJtfFvwt25nb5eu2NezeeWfz0KPMRh+IO5ZSGJX+SnK+c/HgdyuSWn1xO67Ktc46xK3evUNS2KEaarOtPanoq0yOn81HNjwD9jsHgX4OJGxaXs7/sxtlxpP8RSjiWAODOgzt0XNkx5z2pML3ZdAZsGMCK0yuY2XwmxeyyfsNtOr+JJqXU3kO1821HLc9axCXH5RTWATQo0YA25dooyUhNT8XK1Iq63nW5NfwWd9Pu4mjpmLO8b+W+WJlaKcl6XJ9Kfei7vi+Xbl+ihkfWfeDey3uZsHcCQ6sPVZJRkPvqn2v/6JTjAZgYmfBRzY+o8lMVpVkFqSD209NcuXsFIM8yw1MDTlHUtmi+5+63vh8f1/wYfzd/Lt2+xDsr36FN2Tb8fup3UtNTmdJkSr7nftKl25do6dMy13iYTxif7vhUWY4QQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQrxqGm12s44QQgghXhvz58/nu+++4/z58wCUKVOG4cOH07VrV2UZX5l9hVNJJwJ6BRDQIyDPUqa0u2ksbbWU7uHd9cpa/tZyvOt5U+2DajrjkdMjubT9Eu+sfkev+V9V1r5J+4jeGU3rea2xdMwqHrl/+z5req7Bs7YnNYapLfB6UeNsx9H/aH8cSzg+f+WnmFp8KnVH16Vit4o640fmHSHiywgGRw3Wcyv/dWX/FVZ2WklSTBJP3rpqNBpGZoxUllUQZvjOoO2ithQJVFeS+DRjrcbSe1/vnPK2gpBwPoFbZ24B4FLOBadSTgbLWtNzDU2mNtG7VO1FTC0+lbaL2uJRw4OL2y6yov0K2i1rx8nlJ0mKTaLrVv2vv2t6rslz/MGdB8QfjefBnQf03NUTV7+nlyy9rNmBsynZuCSh40N1xrd/vJ2LWy/S71A/JTn3b9/n8K+HuXk6q4DSpZwLgb0CsXTKXynT0yxrs4xLOy5hZmOGm78bptamOss7rOqgNO9xd6/c5fiS4xz57QgJ5xMY+Uj/a9PU4lMBuBNzBzt3O4yM/y2jMzYzxsHbgbpj6uIelLvo5b9sXr15L7aiBrr/qd89xqtg/rU5g6oN4psG32BqnHUM3kq9Rc81PdkTu4fbI24XyHZ8u/dbfvz7R6L/F61kPqPRRnmWMmdzt3OnR8UejKo7Kqe4TB9zDs+hg1+H5xZpXU66TFHbok8taXuab/d+y8BqA7E0zboO7Y3dS5WiVTA3yfpOSU5LZsT2EfzY/Mf8vYHHGI8x1ild85/pz8ZOG/GwzyomvZ5ynaKTipIxMkPvrJctk9PHk+/ryTI5Ve+roHIg6ziP/zA+J8t2nC1H+x/VySoysQiZozL1zvoz6k9CvEJe+tjND8/JnoT5hBHmE0b94vV1StdUsxprRf3i9QnzCaOVTyvcbNwMlvU4rVbLlP1TmLhvIteSrwFQ1LYow2sMZ1DQoGdev56nIPdVNrfv3VjQZgGNSuqW0W+5sIVuq7tx/cPrBbYtKhlyP+UlU5vJ17u+ZuK+iaQ8TAHA1syWYcHD+KzOZ0q+rwDsx9tzqO8hSjqVZMKeCfwZ/Sdbumxhb+xe3ln5DpeHXFaSA1BqWimG1xieU7ibbdY/s5i4byLnB55XliWEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEK+S4f4aUgghhBAGMWnSJL744gs++OADatasCcCePXvo378/t27dYsiQIXpnaLVauu3oRtEqRTG1NH3qeuZ25nqX4wFc2HKB0AmhucZLNSnF9o+36z3/q8raN3EfXbZ2ySnHA7B0tKT+1/VZ0GjBKyvIUyE5LhmPGh65xj1qeJAcl6w0a33/9RStUpROGzphW8QW1HYm5HL3yl0A7NztDDJ/kylN2PHxDlrMboGDt4NBMrIVKluIR/cfGTTjSc6lnXEu7VwgWa3mtCqQHICU+BTsPLKOiXPrz+Hb3peSjUri4O3AL0G/KMl41vvRZmpZ22ctOz7ZQcd1HZXkAdw8fZN2y9vlGg/sFcj+KfuVZMTsimFJ2BLM7cwpWqUoAJE/RLLrq110XNcRrzpeSnIALBws8H3LV9l8LyojPYNr/1zj6oGr3Im+g42bjZJ5s8tG59WbR/tV7XW+TwwpMyOT/ZP35xRAZjzULZ0akThCr/lV3Dv8l4V3D6fbH93Ydmkbi99aTNTtKHqv7Y1PIR+O9DuiPC9wdiCax74ctWiJT4nn5r2bSsrdss1tPZfP/vyMHhV7UK1YVtFw5NVI5h2dx+d1PufmvZt8v+97zE3M+bT2p3rnfbzjYwZvHszbvm/Tu1Jvanjkfd+UXTL3sj7Z8Qk9AnrkFOQ1XdRUp3QtNT2V2QdnK/kMnyz4jb4TTXpm+jPXya+OKzvqlMn5zvDVeV/Jacl8suMTJQV5T26zltzvQcX7epGcgqSqwOvdte9y58EdmpRqQuuyrWlaqim25rZK5n7SgjYLWHt2Le9vfJ+b927SuFRjwsqE0bxMcxwsHJRmnfngDGvOrGH5yeUM2jSIioUrElYmq5zP381fadbjNBoNQ4KHMCR4CMlpWb9HVH2eBbmvsnUo34Hea3vzfcPvc65/ey/vZfi24XT0U3cvWNAMuZ/y8tmOz/j18K+MbzCemp7/9+8nsXv4cueXPHj0gLENxirJ0Wq1ZGqzijO3R22nRekWQNZ31K3UW0oysg0LHsagzYM4En9E59iYe2QuU5tMVZolhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBCvkhTkCSGEEK+ZH374gZkzZ9KtW7ecsbCwMMqXL8+XX36ppCAPLcxvMJ8BJwcUSNGVlbMVZ9acyVUYd2bNGaycrV7brLS7aaTeTM01fu/mPR4mP1SaVdCcSjlxcvlJan9aW2f85LKTyo+ZxPOJtF/RHqdSTkrnfZw2U8uur3exb+I+HqZk7RszWzOChwVT57M6aIzUtfKt6LCC9NR0ppWchqmVKUamRjrL9S2eelzo+FC2DttK/bH1cfV3xdjUWGe5uZ25sqzMjEyOzD1C1I4o7t24hzZTt7ym+59qCrGWtV1G67mtMbczZ1nbZc9ct8OqDkoyASwcLbh7+S72HvZc3HyRel/XA/6vDCMjU1nO02iMNAQNCmJR00VK57V2sSb+SHyu8zb+SDzWrtZKMja+v5Hy7cvTfGZzjIyzjvfMjEw2DNjAxvc38t7x95TkQMGWJgJEhUdxfPFxTq88jTZTS7m25ei4viPF6xdXmvNkoVxmRiY3jt/A3sveIKV5EaMjOPTLIYKHBRP+eTi1P6vNneg7nFl9hpCRIcrz3jQ1PGpwpP8R+q/vT6XZlcjUZvJVva/4qOZHyoq1HtfKp5VOQZ6RxggXaxfqetelbKGyynLmHZ3HxEYTaV++fc5YS5+W+Lv5M/vgbHZ024GnvSdjd49VUpB3dehV1p1dx9yjc6k7ty4lHEvQM6An3QO6U9imsN7zv6mla2/q+3oTXRp8iWPXj7H27Fq+/+t7uq/uTi3PWoSVCaNV2VZ42nsqywrxDiHEO4SJjSdy8sZJ1p5dyw+RP9B7bVb5ZJhPVoFddpGiPjztPRkYNJCBQQNJepDExvMbWXN2Dd/99R1Olk45WSFeIRgbGT9/whcUdTuKR5mPKO1cWqdw7XzCeUyNTfF28M733AW5r7J93+h7NGjotrobjzKzCq9NjUx5r8p7jA8drzTrctJlNBoN7nbuQFb56eLji/F18aVv5b5Ksx5n6JJByPru+iXsF8J8wnLGKrhVoJhtMQZsHKCsIK9K0Sp8vftrQouHEhEdwczmM4Gs49LN2k1JRrb3qr5HYZvCTNw3keWnlgNQrlA5lrVbRquyBXsvLIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQhiQFeUIIIcRrJi4ujho1auQar1GjBnFxcUoyNEYanEs7cz/hPpRWMuUz1R1dl7XvriVmZwzFgooBcPXAVS5svkDLn1u+tlll25RlTc81NJrYiGLVsrKuHLjCtuHbKNe2nNKsglZ3dF1WdFhBzK4YPGp6AHB572WidkTRbnk7pVnFgoqReCHRoAV5Oz7bweFfD9NgfAM8a2YVXMTuiWXnlzt59OARDcY2UJbVeEpjZXM9z8ImC4GswsvHabVaNBoNIzNGKsvaPHgzR+YeoUzzMrj6uYKB+nAs7C1y5ja3My+w4p1ybcuxstNKnEs7k5qQSummWRfH+MPxBj02H2dmbUZ6arrSOSv1qcT6vuu5fek2HjX+PZf3TthL9aHVlWQkXkjk7RVv55TjARgZGxE8NJjZ82cryXjSvZv3SDibAICzjzPWLmrK/h43qdgk7ifep1STUrT8qSVlWpbBxNwwP7E3/28zrv6uVOpdicyMTObWmcvlfZcxtTKl0/pOeNf1Vpp3fNFxWv7ckjLNyxDxZQR+Hf1wKumEWwU3ruy/QtCgIKV51/65xsnlJ0mKTSLjYYbOMpVFlwXpXMI5/rn2D+527lxLvsbZhLOkpqdibab+WPyy7pfK58zLX5f/YlbzWbnGAwsHsu/yPgBqedYiNilWSZ6JkQltyrWhTbk2XE+5zsJjC5l3dB5fhH9Bk1JN6B3Ym5Y+LTHSGD1/MiFewqmbp4hPiQey7pnO3DpDysMUAG6l3lKaVcGtAhXcKvB5nc+5lnyNtWfXsvbsWj7a/hE+zj45ZXJVilZRllnetTzlXcvzSe1PiEuOY925daw7t45Pd3xKCccSTAidQPMyzZVk2VvY09G/Ix39O5KekU54dDjrzq6j55qeJKcl80PTH+hcobOSrB5retAroBelnXV/wB64eoBfDv3Czh479Zq/oPeVmbEZU5tOZVzoOC4mXgSgpFNJrEzVlroDdFrVib6V+tK1YlfiU+JpuKAh5V3Ks+j4IuJT4hkZou43Q0JqAiPDRxIeHc6NezfI1OoWTSeOSFSWBZB4PzHPstiyhcqSeF9d1pQmU+i8qjOrz6zms9qfUcqpFAArTq2ghkfuf7/RV/b3oxBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCvMmkIE8IIYR4zZQqVYrly5fz6aef6owvW7aM0qXVtdk1GN+AbcO30Xxm86yyKwMK6BFAoXKFiJwWyelVpwFwKedCzz09cQ9yf22zWsxqwdYPt7Ky00oy07P+8N/IxIjA3oE0/K6h0qyC5vuWL+8eeJf9k/dzdvVZAAqVK8S7ke9SJLCI0qxqA6uxddhWUuJTcPV3xdjUWGe5WwU3vTOOzjtK2C9h+IT56MxrW8yWjQM2Ki3IC+geoGyu5+ke3r3Ask4sPcHby9+mdDPDtmq2mtMq53Hrua0NmvW4xpMb4+DtQNLlJEK/DcXMxgyA5Lhkqg6oWiDbcHHbRZzLOCuds84XdTCzNWPfxH3s+GQHALZFbQn5MkRZCVqRSkW4dfoWhXwK6YzfOn0Lt4r6n7+Pe3jvIZsGbuLo/KNoM7VAVhlfhW4VaPZDM0ytTJVlhXwZQvm3y2PhYKFszqc59fspKnSpAMC5dee4E32HD858wNEFR/nzsz/ptbeX0ryU+BTc/LP2jZmNGWlJaQCUaVGG8C/ClWadWHqCP7r9QanGpbi49SIlG5Uk4VwCKddTKNfm9SyTHb9nPKN2jqJvpb581+g7LiReoOsfXakwqwIL2ywk2CNYSY7RaKPnloRq0PBo5CMleR52Hvx6+FfGh47XGf/18K942GcVbCbcT8DR0lFJ3uPcbNyo5VmLcwnnOJdwjuM3jtN9dXccLR2Z02oOdb3rKs9USaPRkJyWjIWJRVZJLRpSHqZwN+0uQM5/X0cFVSa35cIW7C3sAcjUZrLj0g5O2JwA4M6DO8pyABrMb4BWq8153mJxCyBrP2aXDBtCUdui9K/Sn/5V+nPv4T02XdjE2rNrabKwCUODh/Jp7U+fP8lLKmJbhL6V+9K3cl/uPbzH1otbMTcxV54DYGpsSqOSjWhUshE/NPuBw3GHeZSp5voEcDjuMDXDauYar+5enQ82fqAsBwpmXyU9SCJDm4GTpRP+bv4544n3EzExMsHO3E7vjGwnbpygWrFqACw/uRw/Vz/29trL1otb6b++v9KCvK5/dOVC4gV6B/bGzcYNjaFatf9PxcIVmR45nWlNp+mMT4+cTkW3ispyKrhV4Ph7x3ONf9foO4w1xnm8Iv9KTC3B333+xtlK93fBnQd3qDS7EpcGX1KaJ4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQr4oU5AkhhBCvmdGjR9OhQwd27dpFzZpZBQB79+5lx44dLF++XFnO6m6rSU9NZ1bFWRibGWNiqXvbMCJxhLIsAPcgd9wXqS2oe9VZplamNP+xOQ2/a8jti7cBcCzpiJm1mcGzC0LRykVpu7CtwXOWv5V1XK/ptSZn7PGCkpEZ+hc23E+8T6GyhXKNFypbiPuJ9/WeHyDtbtoLrWdup66UxDvEW9lcz2NsZoxTKacCywOYV38eHVZ1yFVQlnY3jaWtl9L9T3UFgcamxtT4sEau8eAhaoquAM6uPZvn+IOkB8QdjOPQL4cI+yVMWR5knUvBQ4IJHhJMWnLWMWpuq7YYp9qgamwevJnEC4m4V8+69l7Zf4W/Z/xNg/ENuH7ses66+hZebhm6hZiIGDqu64hnTU8AYvfEsmnQJrYM20KLmS30mv9xlftUVjbX86QmpGJT2AaA8xvP4/u2L85lnAnsFciBqQeU59m525Ecl4y9pz2OJR25uPUiRSoV4erfVzE2V1vysvub3TSe3Jhq71djnO04mkxtgkNxB9b3W49NERulWQVl6oGprO6wmqalmwLg5+pH5LuRfLrjU+rOq0va5y/2ffA8f3T446nL9l3Zx7QD08jUZirJAvi+0fe8/fvbbLqwiapFs4pB/7n2D2dunWFF+xUA/H31bzqU76As83rKdRYcW8CcI3O4dPsSrcu2Zn2n9YSWCOXew3uMiRhD99XdiflfTL7m/+XQL9iYZR1njzIfMffIXApZZd0PJKclK3sfWq2WMtPL6DwPnB2o81xl6dqbWCbXfbXud3q/9f10nqvKiRocpWQefVmbWdPOtx3tfNuRkZlB4v1EJfMeijuEqZFpTuHamjNrmHNkDr4uvnxZ90valGujJAdg84XN2JjZUMuzFgAzImfw86Gf8XXxZUazGQQWCXzODC8nu4jySdlFc4ZiqH31zsp3aFmmJQOqDtAZX35yOWvPrmVj541KcgDSM9JzihG3X9pOWJms+82yhcoSlxKnLAdgd+xu9vTcQ8XC6srpnuXb0G9pvrg52y9tJ9g9675935V9XL57mY2d1H2G2Q5eO8jpW1lF/L4uvlQqUkl5RvSd6DyP6bRHaVxNvqo8TwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghXhUpyBNCCCFeM2+99RYHDhxg8uTJrF69GoBy5coRGRlJYKC6koHGUxorm+tFJF5M5MicI9y+dJsmU5pg7WrN+U3nsfe0x7W8q97zn1x+krKty2JsllXsc/fKXWyL2qIxyirTSE9NJ3J6JDU/qql31pNS4lJIjkvGq44XppamyktQXlatT2th6WSp9zzaTC2JFxK5d+Me2kytzjKvOl56z59tcNRgZXM9TeGKhYmcHknTaU11xiOnR+JWUb/CrmzjHcY/c7+rLPx7XMzuGA7OPsjtS7d5+/e3sStmx9EFR3Es7ohnLU9lOcHDgtk/dT/NpjcrsOM7emc0GQ9zl0M8evCI2N2xSrOOzj/6zOUVu+lf8rG09dI8x81tzXH2cSbslzD83vHTO+dpVBfjZVvZcSUA2z7alucylYWXp1eepv2K9njX9c4ZK92sNCaWJqxov0LvgrxlbZfRem5rzO3MWdZ22TPX7bBKXUmYjZsNN0/dxKaIDRc2X6D5zOZA1neXkbGRspxsZduUJWpHFO5B7lQbWI0/uvzB4V8PkxSbRPUh1ZVm3b54mzLNs4rDjM2MeXjvIRqNhupDqjO//nzqja6nNK8gHH/veE7JWjZTY1O+a/QdLcqoK2lsVbZVrrGzt87y8Y6PWXd2HZ0rdGZM3THK8sJ8wjjz/hlmH5zNuYRzADQt1ZTV76zG28EbgPeqvqcsr+WSlmy5sIUyzmXoU6kP3Sp2w8ny3yJWazNrhtUYxnd/fZev+T3tPfn50M85zwvbFGbBsQW51lEhvHu4knle1JtWJpc5Sl3R4/N4Oai7h30Z9x7eIyImgtikWB5mPMwZ16BhYNBAXKxdlOT0W9+Pj2t+jL+bP5duX+Kdle/Qpmwbfj/1O6npqUxpMkVJDsDwbcOZEDoBgOPXjzNs6zCGBg8lPDqcoVuHMqfVHGVZAHW86jBuzziWvLUEY6Os33wZmRmM2zMup6RPhYLaVweuHGBSo0m5xut61+WzPz9TkpGtvGt5Zv0zi+alm7Pt0ja+qvcVANeSr+Fs6aw0q2yhstx/pKaA/EWEeIdw9oOz/Pj3j5xJOANA23JtGVB1AEVtiyrLuXHvBh1WdCAiOgIHCwcgq5C0XvF6LH1rqZLjYu3ZtTmPHy9ChaxjfUfUjpzvYyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEeBNIQZ4QQgjxGqpcuTILFy40aEZA9wCDzv+46IhoFjVdhGdNT2J2xVD/6/pYu1pz/eh1Dv96mPYr2uudsbLjSobFDcPa1RqAGb4z6H+kP44lHAFIS05jxyc7lBbkpSaksqL9CqLCo9BoNAw8PxDHEo6s7b0WC0cLGk/Uv4Tw7NqzL7yuT5gPALU/qa137pX9V1jZaSVJMUlotbrleKpL3hy8HJTN9TSh34ayuPliLm2/hHuwOwBX9l3h7uW7dNrYSUlG9/Duz19JsVMrT/FH1z/w7+xP3KE4MtKyyuTSktLY/c1uOm/srCzr8p7LRIVHcWHTBVzLu2JkqlvapbIs7Pqx6zmPb566SUp8Ss7zzIxMLmy+gG0xW2V5AJsHb9Z5npGeQXpqOsZmxphamSopyBuVOUrvOV5WyvUUtn24jUs7LnHvxj3QPZ2VnMsFUXKZLT01HWs361zj1q7WpKem6z2/hb0FaB57XEACegbwe/vfsS1ii0ajoURoCQCuHrhKobKFnvPqlxc6PjTnsV8HP+w97bmy7wpOpZ3waemjNMvC0YK05DQAbIvZcuPEDdz83Xhw54GSffYqFLIqxJ0Hd1hxagUXEy8yvOZwnCydOBR3iFJOpQySeS35GqPCRzHv6Dwal2rMkf5H8HNVX6hZ3LE440PHK583L65WrkT0iCDYI/ip67hYueS7pC36f9H53LKXF+IdUmBZ/z+UyRnSsevHXmi9Cm4VlGUejjtMs8XNSE1P5d7DezhZOnEr9RZWpla4WrsyMGigsqxzCecIKBwAwO8nf6eOVx0Wv7WYvbF7eWflO0oL8qJuR+Hr4gvAytMraVGmBd80+IZDcYdotqiZspxsE0InUGdOHXym+1DbK+v3zu6Y3dxNu8uf3f9UklGQ+yotI41HmY9yjadnpHM/XW3B3ITQCbRZ1obv/vqO7hW7U7Fw1n3t2rNrqVasmtKsH5v9yMc7PmZknZH4ufphamyqs9zO3E5pHkAxu2KMbTBW+byPG7hpICkPUzg54CTlXMoBcOrmKbqv7s6gzYNY8tYSvTNaL22d8/jJIlRTY1O8HbyZ2Gii3jlCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPFfIQV5QgghxGvG2NiYuLg4XF1ddcYTEhJwdXUlIyNDeeajB4/IeKg7r7mdubL5d3y8g/pf1yd4aDDjbMfljBevX5zI6ZFKMp4scXuyBMoQtgzZgpGpEUNihzCj3Iyc8fIdyrN16FZQ8LfrS1sv1Xmu0Wh03qtGo8l5rLK0bn3/9RStUpROGzphW8Q2pzDKUBIvJrJ/yn5unb4FgIuvC0GDg3Aq6aRkfu8QbwaeG0jkjEgSziQAUK5tOaoOqIptUTVFa94h3krmeRm7v95Ni1ktqNitIieXnswZ96jpwa6vdynNsnCwoFybckrnfJpZAbPQaDRoNBrm1Z+Xa7mppSlNf2iqNHPE7RG5xhLOJ7DhvQ3UGF5DadaLmuk/k04bO2HvYZ/vOdb0WENSbBJ1vqhjsHO5IEous3kEe7Bz1E7azG+DiUXWz930++lEjI7IKb/UR6s5rfJ8bGh1v6yLq58rSZeTKP92eUzMs96bxlhDzY/VFbs+jUewBx7BHgaZ26uOF5e2XcLN3w3ft33ZPHgzUX9GcWnbJYo3KG6QTEM7dv0YofNDsbewJ/pONH0q98HJ0olVp1cRmxTL/DbzlWUlPUjim93f8EPkDwQUDmBHtx05xVCGcOfBHSKvRnLj3g0ytbpFbN0qdlOaFeIdQqUilXKNP8x4yNITS+lWsRsajea1KGm7m3b3hdYzRDGUIRVUmdzas2tfaL0wnzC9cgACZgXkup9+kkajIWOkut9cQ7YMoWWZlsxqMQv78fbsf3c/pkamdPmjC4OD1JbMarXanHN3e9R2WpRuAYCHvQe3Um8pzTIzNiM1PTUr69L2nGuEk6XTC58TL8PXxZdj7x1jeuR0jl4/iqWJJd0qduODah/gZKnmN0NB7qtqxarx08Gf+KHZDzrjs/6ZReWilZVm1fWuy63ht7ibdhdHS8ec8b6V+2JlaqU0y8HCgbtpd6k/v77OuFarVX5uAcw5PAcbMxveLv+2zvjvJ38nNT2V7gFqSsw3X9jM9q7bc8rxIOuYnNFsBo0WNFKSkV2AWnxqcf7u8zeFrNSXJAshhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxH+JFOQJIYQQr5mnlTWkpaVhZmamLOfhvYdsH7Gdk8tPcj/hfq7lKsvWrh+/TtvFbXONW7tak3orVVlOQbu49SJdtnTBzl237MS5tDN3Yu4oyRiVOSrn8aXtl9g+Yjv1v6mfU2R0ed9lwj8Pp/439Z82Rb4knk+k/Yr2OJVSUzbxLBe2XGBp2FIKBxTGo+b/va+9l/mx/I90XNeRkg1LKsmxLWpLg7ENlMz1PJkZmZz54ww3T98Esgr/yrYqi5GJkdKcW2dv4VUnd3GQhb0FD+48UJpVkGVhg6MGgxamlphKn8g+WLn8W9xhbGaMtas1RsZqP8u8OJd2JnR8KKu6rOKDMx8YPO9Jd6LvkJme+fwVnyF2Tyw9d/ekcEBhRVuVN0OXXGZrMrUJCxsvZJL7JApXzHpP8UfjMbEwocuWLkqzCppvO99cYwHdAwyWl3wtmdg9sdy7cQ9tpu69R9CgIGU5zaY349GDRwDU+awOxqbGXP7rMuXeKkedz+soyylIQ7YMoUdAD75t+C224/4tWW1WuhmdVnZSlvPt3m+ZsHcChW0Ks+StJbQqa9jr8Lqz6+i8qjMpD1OwM7fTKeHVoFFekNdzTU+alGqCq4luKXRyWjI91/TUO2/agWkvtN6goEF65QA4jHfQ+byepLIY6k0sk2u9tPVz11H1+UUNjtJ7jpd1JP4Is1vMxkhjhLHGmLRHaZRwKcG3od/SfXV32pbL/Tspv6oUrcLXu78mtHgoEdERzGw+E4Co21G4WbspywGo5VmLoVuGUtOjJpFXI1nWbhkA5xLO4W6nf2ltXoraFuWbBt8YZG4o2H31db2vCV0QytHrR2lQPOs3yo6oHfx97W+2dtmqLCebsZExjzIfsSd2DwA+zj54O3grz+m8qjOmRqYsbrsYNxs3NAZuWh+3ZxyzW8zONe5q7Urf9X2VFeRlajMxNTbNNW5qZJqrUFZfo+uOxtYsd4n74wWyQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjxJpCCPCGEEOI1MW1aVomHRqPhl19+wcbGJmdZRkYGu3btomzZssrytn20jejwaJrPbM4fXf+g2YxmJF9N5uDsgzQYr7ZEzMLBgpS4FByLO+qMxx2Ow66Y3VNe9d+Xfi8dU6vcfyR/P/E+Jubqb8M2/28zLWa1wLOWZ85YqcalMLUyZX3f9bx/+n1lWcWCipF4IbFACvJ2fLyD6kOqEzo+VGd8+8fb2T5iu14FeUmxSS+0nr2nfb4znnTj5A2Whi0lJT4FZx9nAPZO2Iu1izUd13XE1c/1OTO8OJvCNiReSMTB20FnPHZPLI4lHPN+UT4d/u0w3vW8c53HhuDg5QDoFkS+KkYmRiRfS37Vm5Fvdh52zyw2UqGgSi4BXP1cGXh+IMcWHePWmawyPr+Ofvh39sfUMvf1+GXNDpzNi/a49DvUT++8x13acYmoHVF5Fta1+k1tMdqRuUdY3289xmbGWDpb6hZ7adQV5GU+yuTc+nOUbJx1DGiMNNT6uJaSuV+lf679w08tfso1Xsy2GPEp8cpyPt7+MZamlpRyKsW8o/OYd3Renuut6rBKSd6wrcPoFdiLbxp8g5Wp1fNfoCetVptncdKVu1ewN9f/e3ny/snPXUeDRklBXnj3cL3neFFvYplc5ii15VLP4uWQu1jY0EyNTTHSZBX7ulq7EpsUSzmXcthb2HP57mWlWVOaTKHzqs6sPrOaz2p/RimnUgCsOLWCGh41lGZNbzadARsGsOL0CmY2n0kxu2IAbDq/iSalmijNAth8YTM2ZjbU8sz6HpkROYOfD/2Mr4svM5rNwNFS/3vUgtxXNT1rsq/3Pr776zuWn1qOpYklFdwq8GvYr5R2Lq00697DewzcNJD5R+fnlLkZGxnTrUI3fmj2g9Jr/okbJzjc7zA+hXyUzfkssUmxFHcsnmvcy8GL2KRYZTn1i9dn8ObBLHlrCUVtiwJw9e5VhmwZQoMSav/9xNAFskIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII8V8hBXlCCCHEa2Ly5KwSD61Wy6xZszA2Ns5ZZmZmhre3N7NmzVKWd27dOdrMb4N3XW/W9FyDV20vnEo5Ye9lz/FFx6nQuYKyLL93/Ng+Yjtv//42aECbqSV2byzbPtxGhW7qci5suYCFvQWQlXFpxyVsTmQVDT6480BZTjbP2p4cnX+U+l/Vzxr4v/e299u9eNfzVp53++JtLBwsco1b2FtwJ/qO0qxqA6uxddhWUuJTcPV3xdjUWGe5WwU3ZVk3T9+k3fJ2ucYDewWyf8p+veae4j1Ft/jp/2i12n/HNTDy0Ui9ch637t11uJR3oc8/fbB0tATg/u37rOmxhnV919H7r97Ksir1qcTmwZsJ+y0MNJB8LZnL+y6z9cOt1PmijrIcgD3j9rCuzzpsi9niHeKNV4gX3nW9DVqieGTeEawKWVGmeRkgq9jz4E8HcfF14a0lb+UU6alwdu1ZnedarZaUuBQip0fiWdPzKa/672sypQk7Pt5Bi9ktchUpqmLIksu8mFqZUrlPZaVzZvNpXTBlLk/aOXonu8bsomiVotgUscnzuqVS+Bfh1BlZh9qf1EZjZLgsIxMj1vdXW+D6X2BubM7dtLu5xs8lnMPF2kVZTreK3Qx+LDzuavJVBgUNMng5XuDsQDRo0Gg0NJjfABOjf//pKkObQdTtKCXlWgVV7gYQ4h1SYFlvepmcoZ1POM/InSOZ3WI2dua6Rd1JD5J4b8N7fF3/a0o4llCWGVg4kL+v/U1p59KEeIUwcudIbqXeYsGxBfi5+inLAajgVoHj7x3PNf5do+8w1hjn8Yr887T3ZH2n9bnGJzd5fjllfgzfNpwJoRMAOH79OEO3DmVY8DDCo8MZunUoc1rN0TujIPcVQEDhABa1XaR83icN3TKUiJgI1nVcR03PmgDsid3DoE2DGLZlGDNbzFSWVaVoFS7fvVxgBXmu1q4cu34MbwdvnfGj8UdxtnRWljO96XTClobhPcUbD/v/K4ROuoyfqx8L2y5UlgOGL5AVQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQoj/CinIE0IIIV4TUVFZJR716tVj1apVODo6GjTvfuJ9HEtkZZjbmXM/8T4AnrU82fDeBqVZDb5pwIb3NzDZYzKZGZnM8J2BNkOLfyd/6nyursRrdffVOs/X99MtbFBdMtPw24bMbzCfuH/iyHiYwfaPtnPj5A3uJ96n195eSrMAilYtypahW2izoA02blnFfynXU9g2fBvFqhVTmrX8reUArOm1JmdMo9HkFMuNzFBXKGftYk38kXicS+sWGMQficfa1Vqvufsd7pf3Ai2cWHqCA9MOYGZjplfGk+KPxOuU4wFYOlpSf2x9fq76s9KsWh/XQpupZX6D+aSnpjOnzhxMzE0I/jCYoIFBSrMGnh/I3at3id4ZTcyuGP76/i/W91uPTREbvOt603ZhW6V5AHu+2UPzmc0BuLzvMpHTI2kypQnn1p9jy5AtdFjVQVnW0tZLdZ5rNBqsXKwoXr84jSY2UpZT0FZ0WEF6ajrTSk7D1MoUI1MjneUjEkfonWHIksuCVndU3Zd+zfElx/EJ88HMOv/XkoOzDtJqbisqdq2Y7zleRnpqOn7v+Bm0HC9bsWrFiD8Sr7TQ8lUL8wljzK4xLG+X9V2pQUNsUiwjto/grXJvKcuZ23qusrleROOSjfnn2j9Ki8Hy0tqnNQBH4o/QuGRjbMxscpaZGZvh7eDNW77qPseCcC35GpP2TWJkyMg8S9e+3vU1H9b4EDcbdQXDBaGgyuQOXjvIh9s+ZM07a/LMab2sNVMaT6FiYf2vkd/99R0edh65cgDsLezxsPPgu73fKS0M+6bBNySnJQMwtsFYuv3Rjfc2vEdp59L8FvabspxnsTDJXbKt0oNHD3iY8VBnLK/PWB9Rt6PwdfEFYOXplbQs05JvGnzDobhDNFvUTEnGf2FfGcLK0ytZ0X4Fdb3r5ow1K90MSxNL2q9or/R4H1htIIM3D2Z4jeH4u/pjamyqs7yCm7qCfICOfh0ZtGkQtma21PHK+reFiJgIBm8ezDt+7yjL8bD34FDfQ2y/tJ0zt84AUM6lHKElQp/zyhdXUAWyQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjxXyEFeUIIIcRrJjw8vEByHEs4cjvqNvae9hQqW4iTy09SrFoxzq07h4WD2gIFYzNjwn4OI2RkCDeO3+BhykMKBxbOVYimj1GZo5TN9aJc/Vz54NwHRE6PxMzWjIcpDynXthxV36+KbRFb5XmtfmvFsjbLmOI5BTuPrMKJu5fv4lTaiQ5/qCsKAxgcNVjpfM9SqU8l1vddz+1Lt/Go4QHA5b2X2TthL9WHVtdr7sIVC+cau7T9Ets/3k7CuQRqflST4GHBemU8ybmMM/eu34PyuuP3btzDqZST0iyNRkOdz+pQc3hNEi8k8jDlIS6+LspL/7LZFbOjQucKlGtTjpjdMZxYcoLji45zYukJgxTkJV1OyvnMzqw+g287Xyr3rYxHTQ/m1Z2nNOtVXEMKQuPJjcHAHWiGLLl8Hazvtx73IHfMSuT/vMt4mJFz/SsIgb0DOfX7KWp9XMvgWVUHVGXr0K3cvXyXIpWL5CoSdKvwehWGAUxsNJF2v7fD9XtX7qffJ2RuCHEpcQS7BzO2/thXvXn51rx0c4ZvG86pm6fyLDUK8wlTkjOqbtb11tvBmw5+HQxW3PVn1J98sPED9r+7P8/StRq/1WBm85k5hUr6mLRvEnfT7j61dC35YTKT9k1iQsMJeme9iWVyE/dNpL53/afmNCzRkO/++o6FbRfqlQNZxVkL2zx9nvbl29NpVSe9MmLuxODl4JXzvErRKjmPXa1d2dxls17zP0tGZgaT909m+cnlxCbF5iqtSxyRqCzr3sN7jNg+guUnl5NwPyH3tozMUJYFWQWaqempAGy/tJ1uFbsB4GTpxN20u/ma81Xuq4KUmp6Km3Xu71tXa9ecz1SVDiuyfpf2WvNvYfzjReuqj4uv6n9FdFK0TqFcpjaTbhW78U2Db5RmaTQaGpZsSMOSDZXOm+1NLJAVQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQohnkYI8IYQQ4jWTkZHB3Llz2bFjBzdu3CAzM1Nn+Z9//qkkJ6BnANePXsc7xJtaH9diScslRE6PJDM9k0aTGinJeJK9hz32HvYGmftVsbC3oM5n+hervAinUk70P9afS9svcev0LQAKlStEidASaDRqG7AsHS0xtzPPc1niBXXFGgB1vqiDma0Z+ybuY8cnOwCwLWpLyJchBA0KUpYTdyiO7SO2E7M7hkrvVqLzxs7KyrvS7qblPG4wrgGbBm2i7pd1ca/uDsCV/VeIGBNB6IRQJXlPMjYzxsXXxSBzZ7u49SLRO6OJ3hlN/OF4CpUrhFeIF2+veBuvOl7PnyAfzGzMSE1Ixd7TnktbL+UUJppYmJB+P90gmQBarRZA+Xn1KgT0CHjqMlWfoSFLLl8LWv2nCHw3kOOLjxPyRYj+k72ABuMasKTFEi5svoCrvyvGpsY6yxtPaqwsa8U7KwDYNGhTztjjRTkjM0Yqyyoo9hb2bOu6jT2xezh2/RgpD1OoXKQyDUo0eNWbppc+6/oAMCZiTK5lhig16h7QXel8T5qyfwp9KvV5aulav8r9mLx/spKCvM0XNjOrxaynLu9WsRt91vVRUpD3ppXJARy4eoCPa3381OUty7Tkl0O/6J0DEJsUi6u161OXF7IqxOWky3plLDy2kLiUOKY3m67XPPkxOmI0vxz6hWHBw/g8/HM+q/0Z0XeiWX1mNSND1F5vP9r2EeHR4cxsPpOuf3RlRrMZXE2+yuyDsxnfYLzSLIBanrUYumUoNT1qEnk1kmXtlgFwLuEc7nbu+ZrzVe6rghTsEcyonaOY32Z+Tinp/fT7jI4YTbC72rLwqMFRSud7HjNjM5a1W8ZX9b7iaPxRLE0t8Xf11yk+zK9pB6bRt3JfLEwsmHZg2jPXHRQ0SO+8ly2QXXJ8CWE+YVibvfmF1EIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIN5MU5AkhhBCvmcGDBzN37lyaN2+On5+fwQqagof8+4fwJUJL8P6Z94k7GIdTKSfcKrgZJFPkz6Jmi3hryVtY2Fug0WiIOxhHlf5VsHDI+oP51IRU5tSew/un3leWubj5Yrpu64qJhe7t5K2zt5jfYD5DrwxVlqXRaAgeEkzwkGDSkrOK5sxt8y7ny4/Ei4n8+emfnFp5ivLty/P+qfdxLOGobH6A8Q7jdc5VrVbL7+1/zxnLLlxb0nKJ0jKoh/cesmf8HqJ2RHHvxj20mbpNXYMvDVaWtbDJQqxdrAkeFkznjZ1zjj9DKtmwJOveXUfhwMIknEugdLPSANw8eRMHbwfleUfnH+Wv7/4i4XwCAM5lnKkxvAYVu1ZUnvWkB3ce5PpMW8xugbWbfoUXmwZtoum0prnGH957yJIWS+gern9BVUGVXL7JHj14xKGfDhG1PQrXCoYtrAPYM24PF7ZcoJBPIW5wQ/deQ/Ftx+AoddehV23f5X0k3E+gRZkWQFZZ08XEi3y791tS01NpXbY1PzT9AXMTdd9hBSlzVObzV9KT0wQnzg08RyGrQjhOcETzjAMucYR+hbxHrx9lQujTC+kalWzE9399r1dGtqg7UXjaez51ubudO9F3opVkvWllcgBX717F1sz2qcttzGyIS4nTOwfA3tyei7cvPrU860LihTzLB1/G0OCh9FnXh7eWv8XK9isJnB34zGP9UL9DeuU9btHxRfzc8meal2nOlxFf0tGvIyWdSlLBrQL7r+xXUuKVbd25dcxvM5+63nXpuaYntb1qU8qpFF72Xiw6vojOFTorywKY3mw6AzYMYMXpFcxsPpNidsUA2HR+E01KNcnXnK9yXxWkqU2m0nhhY9wnuVOxcNZ97dH4o1iYWLClyxalWSqK6fKjjHMZyjiXUTrn5P2T6ezfGQsTCybvn/zU9TRolJ5bL1og2299P4LcgyhhVkJZthBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCFCQpyBNCCCFeM0uXLmX58uU0a9asQHMdvBxw8HIo0EzxYi5uuUhGWkbO893f7KZ8+/I5ZVqZjzJJOJugNNPMxoxlbZfRcW1HjEyMALh5+ibz68/Ht72v0qzHqSzGA9gwYAOHfz2Mdz1v+v7Tl8IBhZXOn01FyVh+rHt3HdER0VToWgHbIrbKi60e13hSY2J2xbD3270cmHoArxAvvOt6413XG+cyzgbJbDajGX9+/id3L9+l/cr2WDlbAXDt4DX8Ovopzdo3aR/hX4RT9YOq1K9ZH4DYPbFs6L+B1FupOqWi+tozYQ8O3g74dch6D7+3/53TK09jU9iGThs7Ubhi1nHq38lf76zzG85j4WhBvdH1csYe3nvIoiaL9J47m6FLLh+XdDkJjUaDnXtWedHVyKscX3wcF18XKvetbJDMgnDj2I2c69PNEzd1FxrgvN43cR+tfmtFQI8A9ZM/4U26txizawx1vermFOQdv36cPuv60L1id8q5lOO7v76jqG1Rvqz75avd0P+wyY0n5xShTW48+ZlFVPq6nnIdU2PTpy43MTLhZurNpy5/GZYmlkTfiX5qSV70nWgsTSyVZL1pZXIALtYunE04S3HH4nkuP3PrDIWsCumdA1DHqw4/RP5A/eL181w+LXIatb1q65VhaWrJwrYLmXZgGgCtfFoZ9Fh/XHxKPP5uWfcPNmY2JKUlAdCiTAu+CP9CaVbi/URKOGYVc9mZ25F4P6vUspZnLd7b8J7SLABPe0/Wd1qfa3xyk6cXlz3Pq9xXj7ty9wqQVaZpCH6ufpwfeJ5FxxZx5tYZADr6daSzf2csTdVcm7LNPzr/mcu7Veymd8bQLUP5qt5XWJtZM3TLs4vbJzWelO+cqMFReT7+r9Ciff5KQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjxHyYFeUIIIcRrxszMjFKlShVI1tW/rxIdHs29G/fQZur+cXXjSY0LZBvE82m1T/zhewH8HXz7Ve1ZELqAVZ1X8dbSt7h58ibzG8zHv7O/kmNjdqXZdNvRDUtHS2YHzn5mAVS/Q/3ynfPPrH8wsTDh3o17rOm1xiAZAN4h3nq9Pr/ObzpPpw2d8KyZdyGPStX/V53q/6sOwPXj14mJiOHC5gts/GAj1q7WDL3y7HKK/LBwsKDZ9NxloY+XvakS+UMkzWc2p2K3ijljPmE+uJR3IeLLCKUFeQdnHaTtorYAXNx2kUvbLtF5U2dOLj/JtuHb6Lq1q7KsLlu7MKf2HCwdLan+v+qkJaexsPFCjEyM6Lyps7KcbIYqxsu2qtMqKvWtRMWuFUmJT2FBwwW4lHfh+KLjpMSnEDIyxKD5hlLQJZvG5sZ41PQokKyj848+c/nj59x/3ZH4I3xV76uc50tPLKVasWr8HPYzAB52HozaOeq1KsibdmAafSv3xcLEIqcg6mkGBQ3SO697wL/Heo+AHnrP9yzF7Ipx4sYJSjnlfV997PoxitgUUZIV5B7EgqMLqONVJ8/l84/Op1qxakqy3rQyOYDQEqGM3T2WJqWa5Fqm1WoZu3ssoSVC9c4B+KTWJwT/Gky75e34qOZH+Dj7AFmf27d/fcuWC1v4q/dfSrKyz5mCvCa427kTlxyHp70nJR1LsvXiVioVqcTfV//G3Fjtd3QJxxJE3Y7C096TsoXKsvzkcqoVq8a6c+twsHBQmpXtYuJF5hyZw8XbF5naZCqu1q5sOr8JT3tPyruWz/e8r2JfZWoz+XrX10zcN5GUhykA2JrZMix4GJ/V+QwjjZHSPCtTK/pU7qN0zrwM3jxY53l6Rjqp6amYGZthZWqlpCDvcPxh0jPTATgUdwiNJu8fkyrLDvfE7qGWZy1l8wkhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhJCCPCGEEOK1M2zYMKZOncr06dOf+ofeKuz+Zjd/fv4nhXwKYe1mrZtluFjxmjC1NKXThk7MrTuXFe1XELMrhgrdKtDou0ZK5vdp5YOJuUnOY0MdcyGjXk1Z1oM7Dzj06yFunb4FgEt5FwJ7BWJhb6E0x9LREksnS6VzPotWqyX+cDzRO6OJDo8mdk8s2kwt1i7WBst8cOcBVyOv5i7y1EDFruqKtZLjkvGokbswzKOGB8lxycpyAFLiU7DzsAPg3Ppz+Lb3pWSjkjh4O/BL0C9Ks5xKOtFlcxfm1ZuHxkjDiSUnMDY3ptOGTphZm+V73ucVWz5O3wLKx904cYNi1YoBcHL5SVz9XOm1txcXt15kff/1r21BXkELGhxE5A+RNJ3W1OBZmwdv1nmekZ5Bemo6xmbGmFqZvlYFebfv38bN2i3neURMBE1L/fsZVi1Wlct3L7+KTcu3yfsn09m/MxYmFkzeP/mp62nQKCnIu5t294XXtTO30yurWalmfBH+BU1KNcHCRPf79376fUbtHEWLMi30ysj2YfCHNFzQEHsLe4bXGI6bTdZxcj3lOt/u/Za5R+aytetWJVlvYpnc57U/p/JPlQn6JYhhwcN0cibum8i5hHPMbT1X7xyAwCKBrGi/gl5revHHmT90ljlbOrP87eVUKlJJSdar0KZsG3ZE7SDIPYiB1QbS5Y8u/Hr4V2KTYhlSfYjSrJ4BPTl6/Sgh3iF8XOtjWi5pyfTI6aRnpjOp0SSlWQAR0RE0XdSUmp412RWzi7H1x+Jq7crR60f59fCvrGi/QnmmIX224zN+Pfwr4xuMp6ZnTSCrhO3LnV/y4NEDxjYYq9f8a8+ufeF1w3zC9Mp63O0Rt3ONnU84z3sb3mN4jeFKMsK7h+c83tljp5I5n6f+vPoUsytGR7+OdPbvrFchoxBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiCxSkCeEEEK8Zvbs2UN4eDibNm2ifPnymJqa6ixftWqVkpwDUw/Q6rdWBPQIUDLfs0zxnkJgr0ACegRg72lv0KzV3VcT2DsQrzpeBs0pSBqNJncRlQEK5dLupulGGGlot6wdCxouoNxb5Qj5IiRnHXM7c72y6o6q++/jL+s+dT19PZ7zImL3xlK0StGc8r78uPbPNRY2XoiJpUlOkdf+SfvZPXY3Xbd2pUilIvme+0n1vqrHzpE7aT2vNaZWps9/gR6WtFxC7N5Y0u6mUbhiYbzqelGpTyW86nhh4aC2+C/b2XVnWdV5FQ9THmJuZ56ryFNlQZ5TKSdOLj9J7U9r64yfXHYS59LOynIALBwtuHv5LvYe9lzcfJF6X9cDsoqNMjMylWYBuFVwo+P6jixouAD3IHc6ru+IqaV+x4tPax9FW/dyMtIzcs7PS9svUSasDACFyhYiJS7llWyTvZc9RqZGL/26ZW2X0Xpua8ztzFnWdtkz1+2wqkN+Ny9P1yKvEfVnFOfWn8O1vGuu7VeZN+L2iFxjCecT2PDeBmoMr6EspyC42bgRdScKD3sPHmY85FDcIUbXHZ2zPDktGVMjw16LVYsaHJXnY0NxGO/w3AJorVaLRqMhY2SGXlmf1/mcVT+toswPZfig2gc6pWsz/p5BhjaDz2p/pldGtnrF6zGj2QwGbx7M5P2TsTO3Q4OGpLQkTI1M+aHpD9QvXl9J1ptYJlfSqSTbu22nx+oevLPinZxjRKvV4uviy7au2yjlVErvnGwtyrQg5n8xbL6wmQuJF9CipYxzGRqVbISVqRWJ9xNxsnTSK8NxgiOaF7xhTxyRqFfW48aHjs953MGvA572nuy7so/STqVp6dNSWQ7AkOB/C/dCS4Ry5v0zHIw7SCmnUlRwq6A0C+DjHR/zdf2vGRo8FNtxtjnj9YvXZ3rk9HzP+6r21byj8/gl7BedcroKbhUoZluMARsH6F2Q13ppa53nGo0GrVabawzQ+3r7PKWdSzM+dDxdVnXhzAdnlM2bnpGO5VhLjvQ/gp+rn7J583Jt2DWWnljKkhNLGL9nPBXcKtDZvzMd/Tvibudu0GwhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhHhTSUGeEEII8ZpxcHCgTZs2Bs/RGGnwqOlh8ByA6v+rzpG5R4gYE0HxesUJ7B1I2TZl9Sohe5oHSQ+YHzofBy8HAnoGULF7ReyK2Smbf3bg7Bcup+t3qJ+STK1Wy5oeazA2Nwbg0YNHbOi/AVPrrBKejDQ1hQbjHcbnWVqj1Wr5Z9Y/HJx9MKe0ZmTGSCWZAFNLTKXP332wcrbSGX9w5wGzK81m8KXByrKeZ1HTRfQ/0h/HEo75nmPLkC34hPnQ8ueWGJlklU5lPspk7btr2fy/zfTc1VPV5rJv4j4SLybyvdv3OHg75Cq5UnUMAjiXdaZyv8p41vbEwt4whXhP2jpsK4G9AmnwTQODFwDWHV2XFR1WELMrJufaeHnvZaJ2RNFueTulWeXalmNlp5U4l3YmNSGV0k1LAxB/OB6nUvoV8sDTr1Mm5iYkX0vmt5q/5Yzl9xh52fJJVVzLu/LPrH8o3bw0l7Zdot5XWeWCydeSsXS2VJp19e+raDO1uAfplp5cOXAFI2MjilYpCsCAEwPyNb+FvUXOfiqocyon28GCcm3LFWjm45xLOxM6PpRVXVbxwZkPXtl2vKxmpZrx8faPmRA6gdVnVmNlakVtr39LNY9dP0ZJp5KvcAv1c+LGiacWDK0+s5rWZVvrnRHePVzvOV6Um40bf/X6i/c2vMcnOz7JKYbSaDQ0LtmYGc1m4GbjpiyvX5V+tCjTguUnl+uUrrXzbYe7nTvpGemYGuv/XfZfK5NT4cGjB1QpWoUTA05wJP4I5xPO5+QEFA4A4HzCeUo7l9Y7q+7cusxtPRdvB2/alMv9m+uP038wYOMA4obF6ZUzpfGUnMcJ9xP4etfXNC7VmGD3YAD2XdnHlgtb+KLOF3rlzDsyj+ru1fEplHdxbbBHMMEewXplZJt2YBqVilSilmetPJd7OXjh5WC4kvLj14+zuO3iXOOu1q7cSr2V73kLal89KfF+ImULlc01XrZQWRLv61/Elznq39Ll7Ze2M2L7CL6p/03O8bDv8j4+D/+cb+p/o3fWizAxMuFa8jWlc5oam+Jp70lGpmEL/gAKWRXig2of8EG1D4i6HcXi44uZd3Qen+z4hDpedfiz+58G3wYhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhHjTSEGeEEII8ZqZM2dOgeRUH1Kdv2f8TZMpTQyf9b/qVP9fdeIOxXFk7hE2DdzEhgEb8O/kT2CvQIpUKqIs653V73Dv5j2OLTjG0XlH2TlqJyVCSxDYOxCfVj4YmxrrNb9P67zLJwwpoHuAzvMKXSrkWqdit4p653QP7673HPlxJ/oO2gxtrvFHaY+4e+VuwW5M7s14adf+uaZTjgdgZGJEzY9q8lOVn/QPeExBHo/VB1fHzj3vsskr+6/gXt09z2X6SL6aTNCgIIOX4wH4vuXLuwfeZf/k/ZxdfRaAQuUK8W7kuxQJVHeNAmg8uTEO3g4kXU4i9NtQzGzMAEiOS6bqgKp6z/8qrlMP7jzg1IpTJF5MpObwmlg6WRJ3KA5rN2ulJaWhE0JZ1mYZf333FxW7V6RwxcIAnF17lmLViinLAdj4/kZqflQTgnTHk68ms3fCXt498K5e87ea0yrPxwWhoPPyYmRiRPK15Fe9GS/lq/pf0XZZW0LmhmBjZsO81vMwMzbLWf7bkd9oVKLRK9xC/TRe2Jg9PfdQ3LG4zvjKUyvptrob9z69p3dGiHeI3nO8DC8HLzZ23sjt+7dzyt1KO5XG0TL/Rbh56fpHV2Y0m0Exu2IMCR6Sa/k/1/6hx+oenBhwQkne88rkVLM0tcyzTA4gNT1V76K8gFkBzGs9jyD3IAIKB+R6H5P2TeKL8C+UHIO25rZUmFmB7xp+R78q/5bEJt5PZMCGAaw5u4aRdfQvgu4e8O999VvL32JMvTF8UO3fQtBBQYOYHjmd7Ze253nMvKgitkVotLARy9oto7p7ddaeXfvM9cN8wvKdVd29Ou1/b8/kxpNpU64N0w5Me+b6g4IG5TsrLw4WDsSlxOW6Rh2OO0wxu/zfAxTUvnpSxcIVmR45nWlNdT/H6ZHTqeim/2+7x/1v8/+Y1WKWTrlh41KNsTK1ou/6vpx+/7SyrCePQa1WS1xKHNMjp1PTs6aynGyf1f6MT//8lAVtFuBkqX/R9Iso7licj2t9TMXCFfki/AsiYiIKJPdJXvZemBoZ/neSEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIYikar1Sqo+RBCCCFEQXr06BE7d+7k4sWLdOrUCVtbW65du4adnR02NjZKMrSZWhY3X0zCuQRcfF0wMjXSWd5hVQclOXnJSM/g7x//ZvuI7WSmZ+Lq70rQoCACegag0WiUZsUdiuPwnMMc/uUwZjZm+Hfxp+qAqjiXdlaaI17e2bVZJWRLWy+l9bzWWNhb5CzLzMgkakcUl7Zd4oOzHzxtCuXG2Y6j/9H+OJbIf3HO927f02ZBG0o2KqkzfmHLBVZ3W82H1z/UdzNfiRm+M+i1pxeWTpY647F7Y1ncfDEf3/lYeeaytsvwe8eP8u3LK587W9rdtBdaz9zO3GDb8Lq7fuw680PnY2FvwZ3oO3xw9gMcSzjy5+d/khSbRJv5eZcq5VdmRiZpd9OwdPz3WLwTfQdTK1OsXa2V5Xxj8w3vHXsv1/XgdtRtZlWYxSfJnyjLepNlX+uzabVaUuJSiJweib2HPZ03dX5FW5Z/SQ+SsDGzwdhIt3Q38X4iNmY2OqV5r5NR4aNYeHwhe3vtpbBNVvnkshPL6LW2F3NbzeXt8m/rnXHs+jH8XP0w0hhx7PqxZ65bwS13IXB+3Lx3ExdrlzyXHb9+HH83f70zKv9Umesp1/k17Fcal2qcM56ekc7I8JFM3DeRXoG9mNVilt5Z2R48eoCFiUWey+KS4yhiq67ctcH8BsxvPT9XCdmBKwfo+kdXzg08p9f8AzcOZPbB2QwLHsaYemMwNc4qfDqfcJ4ea3pwLuEc05pMo6N/R71ysv12+DeGbhlKdffq/BL2C39f/Zv3NryHu507c1vPxc/VT0lONptvbDjS/wilnErpjF9IvEDArABSPk3Ra/6j8Ufp+kdXjr13DKPRRk9dT6PRkDEyQ6+sK3ev8M6Kd9jTaw/FpxZ/6noaNFwafEmvrCd9uPVDDlw9wO9v/06ZH8pwqN8hrqdcp9vqbnSr0I1RdUfpnWHoffW4iOgImi9ujqe9J8HuwQDsu7KPy3cvs7HTRmp71VaWZTnWkr/7/J3r2D52/RhBvwRx/7P7yrKePAY1Gg0uVi7UL16fiY0mKr02AQTODuRC4gXSM9LxcvDC2lT3XvBQv0NK8/bG7mXR8UWsOLWCB48e0KpsKzr7d6ZJKcP/jweEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOJNY/KqN0AIIYQQLycmJoYmTZoQGxtLWloaDRs2xNbWlgkTJpCWlsasWWqKNTYN2kRUeBTF6xXH0tlSeTFdXjLSMzjzxxmOzDnCxW0Xca/uTmDvQO5eucuOT3dwafsl3lr8lrK85LhkLm67yKVtl9AYayjdrDQ3jt/gR98fCf02lOAhwcqy3kQP7jzgauRV7t24hzZTt3O5YreKes+/tPVSIKs0YXX31TrLjE2NcfB2oNHERnrnFLTyHcqztvdaGn7fEI8aHgBc3nuZbcO34ddRTeHK00rdTK1NMTJ+ejGKPtyru7Og0QK6h3fH3DarLC5mVwxLWi4h5MsQg2SWbl6abcO3cfPUTVz9XTE21S2j8gnz0TtjvMP4F7r+jcwYqXdWtqPzjz5zuYrz60kZDzPyPJftPe31nnvL0C0E9Aig4bcNGWc7Lme8dLPSrOy0Uu/5c9FC3ME4Ei8m4t/JH3Nbc4zNjDG1MlUaY2JuQsr1lFwFeSlxKRiZGOY8KyizA2dDHoe9RqPBxMIEp1JOVOxRkeL1nl5+9KKyr/WPZ1i5WFG8fvHX8hoPYG+R93njZOlUwFui1uh6o0m8n0jo/FB29dzF5gubeXftuyxos4C3fNXcnwXMCiD+w3hcrV0JmJVVjJzX/9dBRZFXNv+Z/vwa9ivNyzTXGf/+r+/5IvwLJcVQB949wJiIMbRc0pKeAT2Z2HgiZ26dofvq7qQ8TGF9p/U0Kqn2eK80uxKL31pMQOEAnfGVp1bSf0N/bg6/qSzLwsSCCrMq8GOzH+ng14FMbSZjIsbwze5vGFB1gN7z/9DsB1qXbU3vtb1Zf349c1rNYXfMbj778zMalmzIyvYrc0obVegV2IvQEqF0+6MbZX4oQ6Y2k89qf8antT/NVXypgrOVM2vOrGFYjWE642vOrMHZSv/S7oqFK7Kr5y4AMkdl6j3fs7jbuRPePRyAqMFRBs160jcNvuH9De/jMdmDjMwMfGf4kqHNoJN/Jz6v87mSDEPvq8eFeIdwbuA5ZkTO4EzCGQDalmvLgKoDKGpbVGlW1aJVGbplKAvaLMDNxg2A6ynXGb5tONWKVVOaZehj8EmtfFqhyeumRrFPtn/C0pNLuZZ8jYYlGjK1yVRalW2FlamV8qyMzAwm75/M8pPLiU2K5WHGQ53liSMSlWcKIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcSrIAV5QgghxGtm8ODBVKlShaNHj+Ls/O8f4bdp04Y+ffooyzk67yjtV7anTPMyyuZ8mrhDcRyec5gTS06gMdJQsVtFGk9uTKGyhXLWKdemHD9X/VnvrIz0DM6uPZtVwrf1Im4V3Kj+v+pZJUp2WaVep/84zdpea/UuyMvMyGT/5P2cXH6SpNgkMh7qFrmMSByh1/yv0tl1Z1nVeRUPUx5ibmeuWyCmUVPgNSpzFABTi0+lz999sCqkvlzgVWj0fSPQwOpuq8l8lIlWq8XYzJgq71UhdHyokoynlbppjDU4Fnck+MNgKveprCQrW9gvYSxvt5wlLZfQZUsXLv91maVhS6n3dT2qD66uNCvbuj7rAIgYE5FrmUajUVJa1z28+79PtLCo2SLCfgnDtpit3nM/zebBm3WeZ6RnkJ6anlPyprIgL+FcAmt7r+XyX5d1xrVarbLP8Nrf12gxu0WucdtitqTEp+g9/+PuxNxhUZNFJMUm8SjtESUblsTc1pw9E/aQkZZBi1m5tyO/SjYqyY5PdvDOmnewsLcAsopDd3y6gxINSyjLeRVKNinJPzP/wc3fjaLVskp4rv19jevHrhPQI4Cbp26yIHQB7Ve1p2yrsnplZV/rxevhh2Y/0HlVZ6r/Up2ryVdZ8tYSWpVtpWz+qMFRuFi55DwuCEODh/LW8rfoGdCTSY0nkXg/kW6ru3H8+nEWt12sJMPEyIQx9cYQ5hNGj9U9KPNDGW6l3qJrha5MbjIZO3M7JTmPq+tdl+q/VGd03dGMqDWCew/v8f7G91l+cjlj649VmrWh0wZmRM6g19perDm7hug70cQkxSgt/mtQogHH3ztOlz+6EPRLEFamVsxuMZuuFbsqmf9JZ26d4eLti7hYuxCfEo+RxshgheGj647m3bXvsjNmJ0HFggA4cPUAmy9s5ueW+v/+AXCwcFAyz4swNVZbSPuizIzN+DnsZ74I+YITN06Q8jCFwMKBlHYurSyjIPbV44raFmVsA7Xna15+a/UbbZa1wXOKJx52/1fgffcypZ1Ks/qd1QbNzsjM4PiN43jZe+Fo6fj8F7ykL+t+qXzOvOyK3cXwGsNpX749hawKPf8FehgdMZpfDv3CsOBhfB7+OZ/V/ozoO9GsPrOakSHqSruFEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOJVk4I8IYQQ4jWze/du/vrrL8zMzHTGvb29uXr1qrIcSydLnEo6KZvvWX6u+jMlGpag+czmlG1dFmNT41zrOBR3wO8dP72zJhaZiDZTi19HP/pE9qFwQOFc6xSvVxwLBwu9syJGR3Dol0MEDwsm/PNwan9WmzvRdziz+gwhI0P0nv9V2jpsK4G9AmnwTQNMrQxbgjE4arBB538pCnpRjM2MaTq1KaHjQkm8mAiAU0knpZ+jTqnbYx7ceUDcwTi2Dd+GkYkRgT0DlWVqjDS0W9qOxc0XM7/+fK4fu06DcQ2o9kE1ZRlPKohiLe8Qb53nRsZGuFd3x7GE+gKPbCNu5y7PTDifwIb3NlBjeA2lWWt6rsHIxIiO6ztiW8RWyTH+JGNzY9LupuUaTziXgLWLtdKszYM3U7RKUfof7c+3zt/mjJdrUy6nUFGVht83ZG6duUzxmkKRwCIAxB+Jx9rNmjYL2ijNKmipt1IJHhZMyBe631W7vt7FnZg7dN3alfBR4ez6apfeBXkRYyKo8WGNXNfA9Pvp/PXdX6/99+Xrbu3ZtbnG2pZty+6Y3XT064hGo8lZJ8wnTO88LwevPB8b0kc1P6JhiYZ0/aMrFWZVIPF+IkHFgjj23jEK2+S+T9SHhYkFpsamJKUlYWZsRr3i9QxSjgfwY/MfaV66Oe+ue5f159cTlxyHjZkNkX0i8XPV/576Se9Xe58rd68wYe8ETIxM2NljJzU81H5nLTmxhPCocIKKBXEo7hC7YnbRplwbbMxslGXce3iPIVuGMO/oPD6t9Smf1fmMrRe30nddX1afXc381vMp51JOWR5Aj4AelCtUjmmR01h1ehUA5VzKsafnHoLcg/Sef9qBaS+87qCgQXplDd0y9IXXndR4kl5ZT+Np74mnvadB5jb0vgKITYp9ofVUvsdSTqU41v8Y2y5t48ytMwCUK1SO0BKhyosh/7f5f/i7+tO7Um8yMjOoM7cO+y7vw8rUivWd1lPXu67SvBJTS/B3n79xtnLWGb/z4A6VZlfi0uBLSnL29tqrZJ4Xsej4In5u+TPNyzTny4gv6ejXkZJOJangVoH9V/brfR4LIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcR/hRTkCSGEEK+ZzMxMMjIyco1fuXIFW1tbZTkhX4awc9ROWs1pZdACtMyMTMJ+C8MnzAdLR8unrmdmbUarOa30zms8uTHl3y6PicXTb4MsHCyUlLIdX3Sclj+3pEzzMkR8GYFfRz+cSjrhVsGNK/uvEDRITYnCq5B8NZmgQUEGL8cD2DRoE06lnHJ9XpHTI0m8kEiTKU0Mvg05tPl/6bK2y567jpGJETaFbSjRsAQ+LX3ynfVkqdvjyrYqi4O3A5E/ROpdkHf92PVcYyFfhrCy40r8u/jjVccrZx23Cm56Zf3/zrm0M6HjQ1nVZRUfnPlA2bzxR+Lpe7AvhcoWUjbnk3zCfNg1ZhftlrfLGtBAUmwS20dsp9xbakuGYnfH0uuvXhib6RatOng7cPfqXaVZdsXs6H+sP8cXHSf+aDymlqYE9AzAr6NfnkWvr5NTy0/R92DfXON+7/jxU+WfCPs5DP+O/uyftF/vrIjREVTpXyV3QV5qOhGjI6Qg7xVrvbT1U5f9dvg3fjv8GwAajYaMkbnvT/V19tZZfoj8gdO3TgNZhU0Dqw3Ep1D+vyPzUsqpFH6ufqw8vRKADuU7KC3H02q1jN8zntERo+no35Hw7uEsPr6Y9za8x6rTq5jdYjYu1i7K8rI1Ld2UtmXbMvOfmZgYmbCu4zqDlOPdvn+bd9e9y45LO5jdYjYRMRE0WtCIbxt+y4CqA/Se/+rdq/Ra24vIq5FMazqNHgE9OBp/lO6ru1P+x/L8FvYbDUo0UPBOwG+mH7ZmtuzrvY9KRSoB0Kx0M04MOMEHGz+g0k+V+DLkS0bUyl1mq48g9yAWuS9SOme2yfsn6zy/ee8mqempOFg4AFlFYVamVrhau+pdrHU4/rDO80Nxh3iU+Qgf56xz9lzCOYyNjKlcpLJeOU+69/AeE/ZOYNXpVUTfiUaj0VDcoTjtfNvxYY0PsTK1UpZlyH0F4D3FO89SOq1WmzOuQcOjkY+U5mo0GhqVbESjko2UzvukFadW0KVCFwDWnVtH9J1oznxwhgVHF/DZn58pL5qLvhNNhjb391PaozSu3L2iNGvB0QXMOjiLqNtR7Ou9Dy8HL6bsn0Jxh+K0Kqv/v2Vki0+Jx9/NHwAbMxuS0pIAaFGmBV+Ef6EsRwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghXjUpyBNCCCFeM40aNWLKlCn89NNPQNYfsqekpDBq1CiaNWumLCdyWiSJFxP53u17HLwdMDI10lne71A/JTlGxkas77cerzpezyzIUyEjPYM1PddQJLAIrn6uBs0CSIlPwc0/qxTMzMaMtKQ0AMq0KEP4F+EGzzekko1Lcu2faziWcDR41umVp3ln7Tu5xj1qeLBn/J4CLcj7JPmTfL/Wwt7iuetoM7Uknk/k0C+HqPFhDeqNqZfvvGfxCvFi8/826z3PrIBZaDQatNp/mwOznx+cfZBDPx3KKfMYmTFS77xsB6YdeKH1XucSyrwYmRiRfC1Z6Zwuvi6k3kpVOueTGk1sxO/tfud71+9Jv5/O3JC5pMSn4BHsQf2x9ZVmaTO1aDNyN1nevXIXc1tzpVmQVd5aua/akp+nubTjElE7orh34x7aTN332Oo3daUrACYWJlz+6zJOpZx0xi//dTmnYFabqX1m2eyL0mq1kLsHiOtHr2PpZNj7AvF8maMyX1n2ylMreWflO1QpWoVg92AA9l/Zj99MP5a+tZS3fN9SkrM3di9d/uiCk6UTx/ofY+/lvQzcNJCNFzYyq/ksHC31v9ep/mt1Lidd5ve3f6elT0sABlQdQJNSTeixuge+P/oyvel0Ovh10Dsr28XEi3Ra1Yn4lHi2dNlCREwEYUvDGBw0mLH1x2JqrK7k2G+mH8UdinO432GKOxanT+U+LDuxjAEbB7Dh/AY2dNqg9/xBxYI4/t5x3O3cAahYuCJ/9/mb0RGjabqoKb0DezOzxUy930uH8h0YU28MZsZmOuMOFg4sbLuQt06/xXsb3lNekGdIUYOjch4vPr6YH//+kV/Dfs0pmjx76yx91vWhX2X9f9+Fd//3N86kfZOwNbNlXut5OefR7fu36bmmJ7U9a+udle1hxkNC5oZw4sYJmpZuSssyLdGi5fSt04zdPZZNFzaxq8cupce8IR3udzjPcS1alp5YyrQD07Axs1GeGxEdwff7vuf0zaxCUl8XX4bXGE5tL3X7CuBW6q2cAtKN5zfytu/blHEuQ6/AXkw9MFVZztqza3Meb7mwBXsL+5znGZkZ7IjaQXHH4sryZv49k5E7R/K/oP8xdvfYnFI+BwsHphyYorQgz93OnbjkODztPSnpWJKtF7dSqUgl/r76N+bG6u93hRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDiVZGCPCGEEOI1M3HiRBo3boyvry8PHjygU6dOnD9/nkKFCrFkyRJlOT6tfZTN9Tyufq7cvnQbx+KGLVszNjXG3tOezIyCKXuxc7cjOS4Ze097HEs6cnHrRYpUKsLVv69ibG5cINtgKKWbl2bb8G3cPHUTV39XjE11349PmLrjJzUhNc9yOXM7c2XFXinXU9j24TYu7bjEvRv34Il+LRXlbq3mvHgpwrn159gwYIPBCvLSktJeqLDveQZHDVawNS9v/+T9z19JY8CCvDzKvFQ6u/asznOtVktKXAqR0yPxrOmpNCt0QijbPtpGg28a5Hkum9vpX7JhYW9B121did0Ty/Vj13mY8pAilYpQIrSE3nM/qWSjkuyfsp+WP2UVUKGBhykP2TlqJ6WblVael3A+gejw6DxL60JGhijL2Tl6J7vG7KJolaLYFLFBozHsQVhtYDXW91/PtYPXKFa1GABX/77K4V8OU+vTWgBc2HKBwgGF850xwXECaLJKNaeXma5zXmkztDxMeUjl/gVTPij+mz7a/hGf1PqEMfXG6IyPCh/FR9s/UlaQV39+fYZUH8JX9b7C1NiUci7lqOddjy5/dMF/pj9Xhl7RO6O4Q3E2dd6Ek6Vu6WQJxxJE9Ihgyv4p9F7bW2lBXsDsAJqXbs6WLltwsHCgYcmGNCvdjG5/dGPbpW1PLeHKj/6V+/NZnc8w0vxbqN3BrwM1PWvSc01Pvecf12Ac/av0zzVuamzK1/W/ppVPK3qs6aF3DsD40PHPXN6mXBvqeNVRkvUqfBH+BSveXpFTjgfgU8iHyY0n0+73dnSu0FlZ1sR9E9naZatOyaSjpSNf1/+aRgsaMazGMCU5M/+eyZW7Vzja/6jO+wI4c+sMdefWZdY/sxgYNFBJnqFVLFwx19j2S9v5ePvHnEs4x0c1P2JYsJrPLtvCYwvpuaYnbcu1ZVDQIAD2xO6hwfwGzG09l07+nZRludm4cermKYrYFGHzhc3MbJ5VbJmanoqxkbrfyK2Xtgay7jO6r+6us8zU2BRvB28mNpqoLO+HyB/4ueXPtC7bmvF7/72OVClahQ+3fqgsB6BN2TbsiNpBkHsQA6sNpMsfXfj18K/EJsUypPoQpVlCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPEqabRarfb5qwkhhBDiv+TRo0csXbqUY8eOkZKSQqVKlejcuTOWlpavetPy5cLmC+z4ZAf1vqpHkcpFMLM201muoqAp26FfD3Fm1RnaLGiDpZNhP6/tH2/H3M6c2p/W5sSyE/zR5Q8cvB1Iik2i+pDqhI4PNWi+IY02Gv3UZRqNRkmhXLYf/X6kSv8qVPugms74gR8O8M/Mf3j/1Pt6Zyxquoik2CSqflAV2yK2uQrQyrYqq3fGy3hw5wFreq2hwyp1RTnZMtIzWN1tNRnpGbRf0V75/G+aZW2X6Tw/t+4cxesXx9TaVGdc5b568vzSaDRYuVhRvH5xGk1slHWMKs56snBNq9UqO5eTLidh72Gv9zwv4u6VuyxsvBCtVkvi+USKVilKwvkErApZ0XNXT6xdrZVlHfz5IBve24BVIStsCj9RWqeBfof6KcuaWGQiod+GUrFr7tIcQzm26Bh/T/+bW2dvAVDIpxDVBlbDv5M/AOn309FoNJhY5K93/8i8I6CFNb3W0GRKE8zt//2uNzYzxsHbAY9gD73fh1ArIjqC7/d9z+mbpwHwdfFleI3h1PaqrTzLaqwVx947RimnUjrj5xPOU3FWRVI/U1OSGxEdQYh37kLLTG0mY3eN5YuQL5TkPM/5hPOUdlZX5Lng6AK6Vuyaazw5LZn/bf4fv7b6VVnWf8HDjIeYGZs9f8UXpNVqORh3kOg70WjQUNyxOIGFAw1eUGpoVmOtiOgRQdViVXXGI69GUnduXWXnFYDtOFvWdVxHXe+6OuPhUeGELQ0j+ZNkJTkhc0No79ue96vl/ZvghwM/sOL0CiJ6RCjJK0iH4g4xYvsIdsfs5t1K7zIyZCSu1q7Kc8rNKEffSn0ZEqxbrjZp3yR+PvQzp98/rSzry51fMmX/FIrYFiE1PZVzH5zD3MSc3w7/xs+HfmZf733KsgCKTy3O333+ppBVIaXzPslyrCVn3j+Dl4MXtuNsOdr/KCUcS3A+4TwVZlXg/mf3DZa97/I+9l3ZR2mn0rT0aWmwHCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEKGhSkCeEEEKIV+7xMqjHSydUFjRlmx04m8QLiWSkZ+Dg5ZCr5EplodGTruy/wuW/LuNU2gmflj4Gy3nTHP7tMBs/2EiN4TUoXr84AFE7otg3cR+NpzSmcp/KemeMsx1Hz909KRxQWO+5/gueLHXLlpaUxo2TN9BoNPTc3ROnUk5KcxPOJxAdHs29G/fQZur+zAgZmbt8qKDM9J9Jp42d8lXStqbnmhdar9WcVi89939BdET0M5d7h3jrnTHGeAyetTzx7+KPbztfLB0NW06a+SiTE0tPcP3YdR6mPKRIpSL4d/bH1NL0+S9+CVO8plBlQBVqjaildN68fOv8Le9GvotTSbXn7H9BdEQ0njU9MTIxetWbIp5j4bGF9FzTk7bl2lLToyYAey/v5Y/TfzC39Vw6+XdSmtdsUTPe9n2bnoE9dcbnHJ7D0pNL2dJli9K8gnIr9VZO6Zq3gzfOVs6vepP0dj7hPGvOrtEpk2tdtjUlHEsozUl6kMS2S9t0ckJLhGJnbqc0JzwqnN5rexOTFEP2P5tqNBqKOxTnt1a/UcerjtK8gtRySUuu3r3KL2G/UKlIJQAOXjtI3/V9KWZbjLUd1yrL6vZHN3bH7mZio4lUK5ZVdH3gygGGb8sq1ZzXep6SHJfvXNjZfSflXcvnufzEjRPUm1ePm8NvKskrCBcTL/Lpn5+y8tRK2pdvz9f1v1Z+Pj3O/GtzTg44mauQ9ELiBfx+9OPB5w+U5q04tYLLSZd5u/zbuNu5AzDvyDwcLBxoVfb1vKf2neHLuAbjaFW2lU5B3g8HfmDOkTkc6nfoVW+iEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEK8dkxe9QYIIYQQ4vnWrl1L06ZNMTU1Ze3aZ5cWhIWF5TtngtMEBp4biFUhKyY4TgDN09cdkTgi3zlP6h7eXdlcz+PT+tUV07lXd8e9uvsry39dBfYK5FHaI3aP3c2ur3YB4ODtQPOZ/4+9+wyL6lrbOP4fYOi9o4KoIKJii71ixYpGo6ixRxM1RmPUaE5ijT2JLeaoKbZorLH3GlHs3dhjAyNgQUFApMy8H3ghjmBj9gyS8/w+zay1Z91LmNl7T66LOy0o3628Ihn23vb8m3qjLR0scx2397YnsF0gQe8HvfCYvDrx0wk299uMtas1tp62OmWXqPK3IO/RzUdo0jR5em1+F989W8xjCEoU4L1Kn+N9OPfbOcLHhbP1k634NfWjXJdylGxVEjML5b+SmpiZUK5LOcXXfd6Th08o0z73Mh6lVexdkXO/naPeyPz7HBlKWlIa13dfxy/kuVKe7X+h1Wjxb+afTzsTz5uwfwJTG01lcI3B2WMDqw1k2qFpfB3+tSIFeRsu/3OfGxoQyvBdwzkRfYLqRaoDcPj2YVZdWMXY4LEvWuKNaLQaFp5eyJqLazJL1/6/BO290u/RtVxXRc+95++ep9/mfkREReiM1ytajzkt5hDgquw96p4be3L9dyld7jZp/yRG7h2JFi3uNu5otVruJd9jxK4RTGw4kaE1hyqSs+TsEgZsGUDC0wSdcQdLB+a2mEtY2TBFcv6K+4uWy1pSrXA1podMp5RrKbRouXDvArOOzKL50uac7XfWoGVlhjQ/dD7d13Wn8o+VUZtmFsema9IJKRHCz6E/K5o1t+Vchu4YSuffO5OmSQPAzMSMDyp+wDeNv1Es51HKo5cWTbpYuRCfEq9YnqH139yfX079Qn3f+hz/8DgVPCsYPNPb3pvd13fnKMjbdX0X3g7eiue9V/q9HGPdKxjuvwkkpSax79Y+IuMjSc1I1ZkbWG2gIhmf1fiMj7d8TEp6ClqtlqN/H2XZuWVMOjBJkc/WhssbaObXDLWpWudamZvQgLz/NyEhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhHibqLT/piYQIYQQ4l/KxMSEmJgY3N3dMTExeeFxKpWKjIyMPOecXnSash3LYmZhxulFp196bIXuFfKc879i/6T92HrYUrFXRZ3xU/NPkXQvidrDa+fTzvJu+2fbcx23cLDApaQLgW0DDVJ4lSXpXhJqKzXmtuaKrnttxzUOfXeIlvNa4ujrqOjaBUFkRCSFKhfS63c3o+gMKvev/Fa+ryfZTaLvmb44FXfK7628tjOLz3Dwm4M8uPoAAJeSLtQcVpPyXZUphcxyK/zWS+eL1i2qWJZWq+XmHzc599s5Lv5+Ea1GS2DbQFrP16+E8PKGy/g188NUbcrlDZdfemxAqHIFVOs/WE/hKoWp3LeyYmu+yNZBWzm7+Cwe5TxwL+eOqdpUZz5kWojeGflVkjun3BwaTW6Ef3PdIry/tv3FruG76Humr2JZQj8W4y043/98jgKlv+L+oux/y5LyVYreGSZjX3yf+yyVSkXGqLzf80LmOanVslZsubqF8p7lM4vQtFou3r/IudhzhAaEsq7jOr0yssQkxlD2v2Vxs3Gj7zt9dUrXfjr5Ew+SH/Bn/z9xt3FXJK/vpr78eOJHnKycKOlSEq1Wy9W4qzxKeUT/yv35vvn3iuTsvbGXRr82YmTdkQyqNggnq8zrbNyTOGYcnsHE/RPZ032P3qV8J6NPUu3narwf9D6Dqw/W+fnNODyD5X8u51ifY5T31P8aOWDLAC7ev8jubrtzzGm1Whr92ojSrqUV+xm+zLW4a/TZ2Ic93fcovvbVB1e5eP8iAKVcS1HSpaTiGVmSUpO49vAaACWcSmBjbqPo+qbjTIkZEoObjVuu87GJsRSaVkjvc8bLKPm7MhlrgqWZJaVcS730uJMfndQ7q9f6XsxsOpMlZ5fw6fZP6VWhFzW9awIQERXBwtMLmdl0Jh9V/kjvrGftvr6b3Td2czfpLhqtbon1/NbzFc06FX2K5r81JzktmaTUJJytnLmffB9rtTXuNu5cH3RdsaylZ5cyZt8YrsVlvt8L2RVibPBYPqj0gd5rm4w1IWZoDO427i+9VipxfRRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiLeF4dpLhBBCCKEYjUaT62OlZZXeadI1qFQqSoSUwNbD1mB5z0tLTiM+Mp6MVN0/6PYo52G0PSjpxLwTtPutXY5xtzJuhHcMfyuLxF4l5lRMruMpj1KI+yuOvSP30n1Pdxx8HBTN1aRruPnHTeKuxRHUOQiAx3ceY2FvoUhZ3uqw1aQlpzGrxCzU1mpM1LqlA0qWQb2NljZbSt/T+hXIPXn4hDLtyyi4q/9dh6YdYu/IvVQZUIUGtRoAEHkgks19N5N8P5kag2solrUweGGOMZXqn2a0URmjFMtSqVQUq1+MYvWLUaVfFTZ8sIEzi87oXZC3vM1yhsYMxcbdhuVtlr80X8l/j7OfM3tH7uX24du4B+Usras2sJpiWXfP3sWzgicA9/68pzv5kiK7NxEyPQRzO/Psx0qt+ypxV+NwK52z1Mi1lCtxf8UZZxPitXjbe7P7+u4cBXm7ru/C28FbkQzNaMPd5z5v4emFhN8KZ3e33dQvVl9nbs+NPbRZ3obFZxbTrXw3vbOmH5pOUceiRPSKwNLMMnu8qV9T+lXuR+0FtZl+aDqTGk3SO2vtxbUsOL2A+a3n07189+xzukarYeHphfTb3I/GJRoTGhCqd9bcE3PpXbE3Y4LH6Iw7Wzkzrv44YhJjmHN8jt4Fed8f/Z42pdqwsM1CnfFKXpVY/O5iktOSmXlkpiLFWn/c/INJDXP/PahUKj6t9ilf7P5C75zXkZiayL5b+wyytr+LP/4u/q8+UAE25jaU8yhnsPW1Wi0NFzfEzCT3/8ydrkk3WHYWJX9Xo+uNVmSd17HozCImN5pMvyr98LT15LtD37HywkoAAl0DWfHeClqX0u8+7Xlj/xjLuPBxVC5UGS9bL537TkMYvH0wrUq2Ym7LuThMduBw78OoTdR0WduFQdUGKZKRrknnt3O/EVIihPfLvU9yWjKJqYmKlZ6C7vXRmNdKIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYTIT1KQJ4QQQogcTMxM2NR3Ex9f/NgoeUn3kljfcz1/bf0r13klC400GRoOTz/M+ZXncy3jU7IILTEmEVuvnAWDNm42PI5+rFiOMXXf2/2Fc08TnrLm/TXsGrEr12LAvHp06xFLmy4lPjKe9KfplGhcAgs7Cw5MOUDG0wxazm2pd0bIjBAFdlqAafVfonT70lzbcY3KfSvrv9j/uKPfH6XFnBaU71Y+eywgNAC3Mm7sG7NP0YK84Q91z3maNA3Rp6LZO3IvDSY0UCwHIOF2Aud+O8e5385x98+7eNfwpvkPzfVed7RmdK6PDe3kjycxtzXn1r5b3Np3S3dSpWxB3svOvUrJKskFqNCjwguPU5qFgwUPrz/E0ddRZzzurzjUNmqj7UO82pAaQxi4bSCnY05T07smABFRESw8vZCZTWfm8+7e3LI/l/GfOv/JUY4H0KBYA0bUHsHSc0sVKcjbeX0nI2qP0CnHy2KltmJYzWFMjZiqSEHegtML+Kz6Z/So0ENn3ERlQq+Kvbh8/zK/nPpFkYK8o38f5dd3f33hfNdyXem2Tv+fX0RkBP9t8d8Xzvet3Jf+m/vrnQMQGR9JkEfQC+fLupflVvytF86/iVlHZr10/u+EvxXJ+bd7nUK5doH6fTcx5u9qdPCb3ctEREZQuVBlLMws3jhLq/3nS8C7ge/ybuC7b7zGm5p7Yi4LWy+ka/muBs8COB1zmnkt52GiMsFUZcrT9KcUdyvO1EZT6b6uO20D2+qdYWZiRt9Nfbn48UUArNXWWKut9V5XCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCH+10lBnhBCCFEAHTt2jL1793L37l00Go3O3LRp0xTJKFy1MDGnYnAs6qjIei+z/dPtpDxKofeR3iwMXkjY2jCSYpMIHx9Ok++aKJq1b+w+Tv58khpDarD3q73U+bIOj24+4tK6S9QbVU/RLAdvB6IionAq5qQzHhkRiV0hO0Wz3gYW9hbUHVmXVe1XKbrutkHbKFS5EH3P9GWqy9Ts8cB3A9nYZ6MiGc8WQ4m8cfZzZu/Ivdw+fBv3IHdM1aY680qWhf3bPY5+jHdN7xzj3jW9FS/XtHTIWdZUonEJTM1N2fHZDj488aHeGcfnHefP3/4kMiIS11KuBL0fRMf1HY1yfTGkQTcG5fcWDGac6TiGRA/Bxt1GZzz5QTLfun+raHFtQOsAtn26jbC1YTiXcAYyy/F2DNlBQGiAYjlCf/2q9MPT1pPvDn3HygsrAQh0DWTFeytoXaq1QTKTUpPYd2sfkfGRpGak6swNrDZQr7XPxp5lauOpL5xv5tfslaVYr+v6w+tU8qr0wvnKhSpz/eF1RbJORp/kq7pfvXC+bWBb2q1Upsg4NjEWX0ffF84XcypGTGKM3jl3Ht+hpEvJF86XdCnJ34+VKShLTE18abGVtdqa5LRkRbI+3fYpXnZemJua5zr//Hte5M4YhXJv8++q2dJmnO57muJOxfP0+sdPH+da3vksewv7PK2dm9SM1OySVWNQm6oxUZkA4G7jTmR8JIFugThYOhCVEKVYTtXCVTkVc4qijkUVW/NFBm4diJ+zX47r4Oyjs/kr7i9mNJ1h8D0IIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQxSEGeEEIIUcBMnDiRr776ioCAADw8PFCpVNlzzz7WV5X+VdgxZAcJtxPwescLcxvdMgCPch6KZd3Yc4OO6ztSqHIhVCYqHIs6UqJxCSzsLTgw6QAlW7y4EONNnVt6jlY/taJki5LsG7OPsp3K4lzCGY9yHtw+fFvREq9KfSqx7dNtZKRlUKxBMQBu7L7Bzs93UmNIDcVy3ibWrtY8iXui6JqR+yPpdbAXpua6hWuOvo4k/J2g19pPE56+1nEW9q9foPG/6uSPJzG3NefWvlvc2ndLd1IlBXlvwtnPmfMrz1PnP3V0xs+vOI+Lv4tR9mDrYcv9y/cVWWv/+P2U7VSWprOa4lneU5E1n3Vk1pHXPrYgvQ9XtF1Bm4VtsLC3YEXbFS89NmxNmKLZWq021/GMpxk5zsX6ajy1MUubLuWHUj9gXySzgCfhdgI+dXxo8q2yJblCf+8Gvsu7ge8aJetU9Cma/9ac5LRkklKTcLZy5n7yfazV1rjbuOtdkBf3JA4Pmxffz3rYevAw5aFeGVkepz5+acGUnbkdiamJimTdT75PEfsiL5wvYl+EB08eKJKVkp7ywsIwALWJWpHisOS05JcWeFmYWpCSnqJ3TpYL9y68sNjvfrIy10aAoo5FmdJoCh3KdMh1/nTMad758R3F8kSmvBTKvc2/Ky25X7NfV8nZL/6urdVqUalUZIzK0CvjWb0r9ua3c78xst5IxdZ8mYqeFTl25xj+Lv7UK1qPUX+M4n7yfX49+ytl3csqltO/Sn+G7BjC7YTbvOP1DjbmuiXD5TzKKZb1+8Xf2dBxQ47xmt41mXxgshTkCSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCH+NaQgTwghhChgZs6cyfz58+nRo4dBc1Z3XA3A1oFbs8dUKlX2H8mPyhilWFZqUio27pl/QG7lZEXSvSRcSrrgHuRO9MloxXIAEmMS8QjKLEMxtzXnaXxmQVrJliXZO3Kvolk1h9Uk+UEyW/pvISM1s1TAzNKMWsNrUeeLOq94dcF0+/BtnEo4KbqmVqNFm5Gz+CHhdgIWdvoV1012nPzSYklDvN//rQbdGJTfWwAg5VEKlo66JTot57XExsPmBa94+wSPDWZ12Gpuhd/Cu5Y3AFERUdzYfYP3Vr6naFbs2Vid51qtlsToRA5MPoBnBWXK7D6N/FTRAtfnHZ5++PUOVKiocftn21/ruJBpIXrlWDpYguqZx0aQVTaoUqk4+XNm6WUWTYaGyPBIXEu5Kppp6WBJr4O9uL7zOjFnYlBbqfEo50HRukUVzRHKSkxNRKPV6Iy9rAAuLwZvH0yrkq2Y23IuDpMdONz7MGoTNV3WdmFQNf2vORnaDMxMXvyfxUxVpqRr0vXOyfL46eMXlrwlPE3Qu+QqS2pGKmoT9QvnzUzMFCmty/LzyZ+xNbfNde7x08eK5Wz/azsOlg65zj1KeaRYDkDDxQ1zLQp99ruQEt7xeocTd068sHRNheqFhaUi7/LyWfs3/65Wt1+Ns5Wz0fJS0lP48eSP7Lqxi3Lu5VCb6p6vpoVMUzRvYsOJ2eeiCQ0n0G1tN/pt7oe/iz/zQ+crltNxdUcABm79p7z12XOGkiWDD5If5Ho+tLewV7TEUwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh8psU5AkhhBAFjImJCbVq1TJ4jjHLrlwDXLl/+T6Ovo54lPfgxLwTOPo6cnzucey87BTNsi9iz+Poxzj4OOBUwolrO67hVcmLv4/9jamFqaJZKpWKxlMaU29kPe5dvIfaSo2zvzNmFgX3Fuz5Mq0sKfEpRJ+IZv/E/dQbXU/RzBJNSnB4xmFa/dgqc0AFqYmp/DH6D/yb++u1dve93RXY4b+A4brLDOrAlAM4+jpSNqwsAKs6rOLi7xex9bSl85bOeJbPLHgL6hyUn9t8Y6Xblab3kd4cnn6Yy+suA+Aa6Ervo73xquilaNbcCnOzyzueVaR6EVrPb61Ihkql4tb+W5yYd4KH1x7SfnV77Avbc+bXMzgVc8Knto9e6xu7nDHmVMyrD1LgM9V6QetcHxtSVtmgVqvl+NzjmJiaZM+Zmpvi6OtIi7ktFM9VqVSUaFKCEk1KKL62UM6NhzcYsHUAf9z8g5T0lOxxQ5T/AJyOOc28lvMwUZlgqjLlafpTirsVZ2qjqXRf1522gW31Wl+r1dJjfQ8sTHMv232a8VSv9Z/PKjm75EvnlSwSHbl3JNZq61znktOSFcvxcfDhp5M/vfIYJXRf9/J7NqV+fjcG3VBkndcxrv64l/4+SruVVnQ/Wq2Wm49u4u3gnV2UuPbiWp5mPKW5f3NcrZUpQH2a/hQTlUl28dm1uGvMPzWfyIRIijoU5YOKH1DMqZgiWcZi7N+VMdXyqYW7jbvR8s7ePUsFzwoA/HnvT505lQG+lFQuVDn7sbuNO9u6bFM8A4x77vBz9mPbX9sYUHWAzvjWq1sp7lTcaPsQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghDK7jtLEIIIcT/qMGDB/PDDz8wY8YMg+Y4FnU06PrPqjaoGonRiQDUG12PpU2Xcm7pOUzNTWm9UNlSoFLvluLG7hsUqVaEqp9UZW2XtZz65RTxkfFUH1xd0aws5rbmFK5S2CBrG9uLyrQArF2tqfFZDar0r6JoZpPvmrAkZAk/lP6B9JR01nRew4OrD7B2tabdsnZ6re1bz1eZTRZ0OX+db2x9r/UvnVeqbO1ZJ+aeoO3SzIKkazuvcX3ndd7f+j7nV55n57CddN3RVfFMQ3qa8E8Zk4u/Cy3+m7OI7GnCUyzscy9zyovny+VUJips3Gwws1Tuq+KF3y+wtutagt4PIvpUNBlPM0u0nsY/Zf/E/by/5X3Fsozh31ysmfV+WFR/ER3WdMDKycrgmfvG7XvpfL1RypauirzrsrYLWq2W+aHz8bD1MEiR0bPUpmpMVJklje427kTGRxLoFoiDpQNRCVF6r9+9wqs/y93Kd9M7B2Bv972KrPM66haty+UHl195jBJufnpTkXVeRTNaY5QcgKKORd/o+P6b+zOu/rg8lcuVdiv90nm1qVpnPxGREVQuVBkLsze/D7h8/zIhS0KISoiiuFNxdnTZQftV7bl0/xJatFirrTnY6yD+LvqVTwOELAlhQNUBvFf6PSIiI2i4uCEBrgEEugay5eoWph+ezq6uu6jhXUPvLGN509+VeDFjng+NyZi//89qfMaALQO4l3SPBsUaALD7xm6+O/QdM0JmGG0fQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghhaCptbu0mQgghhHhraTQaWrRowZUrVyhdujRqtVpnfs2aNYpl3b98n6PfH+X+xfsAuAa6UvWTqrgGvHkBxJtIS07j/qX7OPg4YO1qbdCsqENR3D50G2d/ZwJaBei93oq2K2izsA0W9hasaLvipceGrQnTO8/YHt16lOu4hb2FQYuUNOka/lzxJ7FnYklNTMWrkhdB7wehtlK/+sWvm5Gh4dLaS9y7eA8At9JulGpdChMzE8Uy/s1WvKv7fs9Iy+Dun3dJeZRCsQbFDPJ+n2A1gQFXBuDg7cDWQVtJT0mn1bxWPLjygJ+r/czwh8MVzzSksSZjUaleXTg1KmOUEXaja07QHDpv6YyDt8Mbv3ZexXlUH1yd8t3KM8luEn3P9MWpuBPRp6JZ2mwpQ2OG6rW37Z9tp/7X9TG3MWf7Z9tfemzItBC9sv5XaTI03D13F4eiDoqf6+dVnKfzPCMtg0c3HmFiZoJTCSc+OvmRonki72wn2nLiwxMEuOp/v/Q6mvzahB4VetA5qDN9NvTh7N2zDKw6kF/P/srDlIcc6X3EKPvIcjvhNoXsCmWX9hnS5AOT6Vu5L46WjgbPMqagOUFs6bwFbwdvg+a0+K0FP7f6GS87L4PmANhPsud039MUdyr+Vme1Wd4GLVrG1x/P/FPz2X5tOyVdSrKq/So0Wg3tV7XHwdKBX9/9Ve99Okx24Hif4/i7+BO8MJhKXpWYFjIte37knpHsvbmXA70O6J2VF3aT7DjT94xRfmfGoM/7otjMYhzvcxwXa5fXOl6fksbc3E64DUAR+yKKrJel0rxK7O62GycrJyrOq/jSQteTH51ULPfy/ct8f/R7Lt6/CECgayCfVP3EINfNOcfmMGH/BO48vgOAr6MvY4LHKFbsKoQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQbwOz/N6AEEIIId7MwIED2bt3L/Xr18fFxeW1ypTy4sLvF/i94+8UqlyIIjUy/2D99uHbzCk7h3bL21G6XWmD5AKordV4VTJ8oQWAdw1vvGsoV9Jh6WBJ1t/fWzpYKrbu28KxqOMbHf9bi99o9XMr7Lzs8px5K/wW3jW9Kfd+OXj/n3FNuoZb4bcoWrdontfOcvf8XZaHLicxJhGXgMyCiIgpEdi42dBpYyfcy7rrnZEfEmMT2Tl0J9d3XyfpbhI8V42tZNFa2NqcBXhajZZN/TbhXMJZsZxnWTpZkhCVgIO3A9e2XaP++PqZuVotmgyNQTINqfve7v880cLS5ksJ/TkUu8J5//wo5dHNR2jS8vYzvX/5fq6fU0sHS1Iepei7NWJOxWTvLeZUzIsPVPhyubLdSgpVLUTt4bV1xiOmRnDn2B3ar2qvbKARbft0G+5B7lT6oBKaDA0L6y4k6lAUams1nTd1xjfYV7Gsj07lLMB7mvCUdT3WUerdUorlCP1VKVyFqIQooxXkTWw4kcdPHwMwoeEEuq3tRr/N/fB38Wd+6Hyj7OFZpX8obbQitIn7J9KhTAejFOQZs+Dt5qObpGnSDJ4TfiucJ+lPDJ4DoH3+5uotzToYdZAdXXcQ5BHE+AbjmXlkJj+2+hG1aWbZ9IjaI+j0eydF9pmhySBDmwHApfuXmNl0ps58jwo9mHFkhiJZefGysrSCSJ/3xY1BN97o+GZLm+l9vtBoNYwPH893h74jMTURADtzO4bUGMKXdb9UpIS0dUDr7BK/NgFt9F7vdfx+4Xc6/t6RyoUqU6NIDQAO3z5M2TllWd5uOe1Kt1M0r1+VfvSr0o97SfewUltha26r6PpCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCPE2kII8IYQQooBZtGgRv//+Oy1atDBozq7Pd1H7i9rUH1dfZ3zv6L3s+nyXogV5Wq2WC6svcHPvTZLuJqHV6P6Rf9ianMVbb+LyhsuvfWxAqH6lL60XtM718f+qW+G3SH+Srtcai+ovYkj0EGzcbXTGU+JTWFR/kSIlbxt7b8StjBt9jvfByskKgCcPn7C+x3o2friRDw5+oHdGfljfYz3xkfHUHVk3s6TQyH0gKhMVNT6rwaLgRdT6vJbi6we2DeT3zr/j4u9C8oNk/Jv5A5klac5+hinlMyTfer46z01MTShSvQhOxZ3yZ0MKsfW0Je6vOBx9HXXGIw9EKvJve7ZYUKdk0MBuhd+i3ph6Ocb9mvlx6LtDRtuHIVxYdYFyXcoBcGXjFR7dfMSASwM48+sZ9ny5h14RvQyab2FvQfDYYJa1Wkb5ruUNmiVe38+tfqbv5r78nfA3Zd3LZhdrZSnnUU6xLK1Wi7uNO2XdywLgbuPOti7bFFs/T3sqIEVob3OWyD+JqYk4W2XeG9mY22BjboOX7T+F5N723sQmxiqSVa1INTZe3kgp11KUcC7BmdgzlPf851x+OuZ09l7yQ0F6z8enxBOTmFn+62nriYOlQ45jHn/x2Gj7UeJn9+XuL/nl1C9MbjiZWj6Z3w8ORB5gzB9jSElPYULDCXpnjA4enetjQ/p81+d8UfsLxtUfp7uXvaP5fNfnihfkZXGzcTPIukIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII8TaQgjwhhBCigHF2dqZEiRIGz3kc/Zjy3XKW0pTrUo6D3xxUNGvbp9s4Me8ExeoXw8bDBpVK2Rav5W2W6zxXqVRotdocY4AiZWtCWVqtNtditycPnqC2UeecyIOY0zE65XgAVk5WNJjQgJ+q/KRIRn6IPBBJz/098azgmW97eHjtIZp0jUHWDpkegqOvI/FR8TSa2ghzW3Mg8/xVpX8Vg2SKN1epTyW2DdpG6PxQUMHjO4+JOhTFjqE7qDuybn5vL89SE1MxNTfNMW6qNuVpwlOD5aanpGNmadiv8skPkrH1tAXg6parlG5fGpeSLlTsVZEjM48YNDvL0/inPI033M9RvLl7yfe4FneNnut7Zo9l3VOpVCoyRmUolqVFi98sP873P4+/i79i6wrxv6qQXSEi4yPxcfABYGqjqbjbuGfP30u+h5OVMoW84+uPp9nSZiSlJdGpbCeG7BjC1QdXCXQL5PL9y8w6Oosvan+hSNbz3rZCubz6+eTPTDs0jcsPdIveA1wCGFJjCB9UKpjl3QCLzizi59CfCQ0IzR4r51GOwnaF6b+lvyIFefkh+nE03cp3yzHepVwXvjn4jaJZsYmxDN05lN3Xd3M36W6O4kIlr8dCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCJGfpCBPCCGEKGDGjBnD6NGjWbBgAdbW1gbL8Q325db+Wzj7OeuMRx6IpGidoopmnf31LGFrwvBvbpgClNGa0dmPr++6zq7hu2gwsQHeNbwBiDoUxd6v9tJgYgNFcxNjE9k5dCfXd18n6W4Sz/3dupTxvcKKtiuAzPKd9T3WY2rxTxGVNkNL7NlYvGt6K5LlUtKFpNgkKKM7nnQ3KcdnoCCx97bPUQZpKNs/267zXKvVkhidyNXNVynfPWfZphJM1abUHFozx3iNwTUMkifypvaI2mg1WhY3XExachoL6i7AzMKMGkNrUO2Taopmpaekc+T7I9zce5Oku0loNbrv/49OfqRYlnuQO+dXnKfeqHo6438u/xO30m6K5QBoNVrCJ4RzYu4JEmMT+eTKJzgVd2LPyD04+jpS6YNKiubZethy78I9bL1s+WvbX7SY0wKAtOQ0TExNFM06Mku3cC/r3HH217P4NfNTNEvop9f6XlT0qsiydsvwsPVAlVt7rUJMVCb4u/jz4MkD/JGCPCH01ah4Iy7dv0Rtn9oA9KvST2d+x7UdVPJS5lpSw7sGW9/fymc7PuPI7cxz/IT9maVnhewKMabeGAZVH6RIVpb8KJRLSU/B0sxS8XW/ifiGMfvGMLDqQEL8QvCw8QAgNimWHdd2MGjbIB6mPGRozaGKZxtD3JM4SrmWyjFeyrUUcU/iFM8zGWvy0gJ+pcrkgn2D2X9rP37OuvcuByIPUKdoHUUysvRY34PI+EhG1h2Jl52XQa/HQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgiRn6QgTwghhChgZs2axbVr1/Dw8MDX1xe1Wq0zf/LkSUVyAkID2DV8F9EnoilSvQgAtw/f5sKqCwSPDebyhss6x+rD0sESp+JOeq3xurZ9uo2Wc1viU9sne8wvxA+1tZpNH27i44sfK5a1vsd64iPjqTuyLnZedsjfrb8ZS4fMwgmtVou5nTlqq3/e6ybmJlSqXol3+ryT5/WfJjzNftxwUkO2DtxK8Jhgnff7vnH7aDSlUZ4z8lvTGU3ZPWI3Lee1xNHX0aBZMadidJ6rTFRYu1nT5LsmVOxV0SCZZxafeel8+W6GKeYzqn/BeUOlUlH3y7rUGlaLuL/iSE1Mxa20G+a25opnbfhgA9d2XCPwvUAKVy1s0J9f3ZF1Wdl2JQ+vPcS3gS8AN3bf4M9lf9J+VXtFs8LHh3Nm0RkaTW3Exj4bs8fdy7pzZMYRxQvyKvSswKoOq7DzskOlUlG8UXEA/j7yN66lXBXNOjz9sM7zrHNH+e7lqf1FbUWzhH5uxd9iQ6cNOcp/DGVyw8kM2zmMOS3mUNa9rFEyhXib6VOCNbfl3JfOh5UJo3v57nle/3k1vGtw6IND3Eu6x/WH19FoNXjZeeHr6KtYRhZjFspptBomhE9g7om5xCbGcuWTKxR3Ks7IPSPxdfRVpIhv9rHZLGi9gA5lOuiMB7oFEuwbTHmP8gzbOazAFuSV9yzP7KOzmdVsls747KOzKe+h/L372rC1Os/TNGmcij7FojOLGBs8VrGc0IBQhu8azonoE1QvUh2Aw7cPs+rCKsYGj2XD5Q06x+rjQOQB9vfcTwXPCnqtI4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQbzspyBNCCCEKmDZt2hglZ3P/zQAc++8xjv33WK5zkFl8NCpjlF5Z9cbUY9/YfYTOD9UpQTOEh9ceYulomWPc0sGSRzcfKZoVeSCSnvt74lnBU9F1/1e0XtAaAAdfB2oOrYm5jbJlWpMdJ6NS/VM0otVqWdVhVfaYVqsFYFmrZXq/x/PL6rDVpCWnMavELNTWakzUJjrzw+OGK5bVfe/rlapERkRSqHIhzCz0/yqybdA2necZaRmkJadham6K2lpd4AryVrRdofM8PSWdzX03o7bRPS+GrQkz5rYUY2puiltpN4NmXNl0hc5bOuNTy+fVB+spoFUAYevCODDxABdWX8DMygyPch503dUV33q+imadWXyGlj+2pHjD4mzu+8812LO8J/cv3Vc0CyB4TDDuZd2Jj4qnTPsy2Z9XlamKWiNqKZo16MYgRdcThtOgWAPOxJwxWkFet3XdSE5Lpvzc8pibmmNlZqUzHzc8zij7yKJPOdnb7N/67zKWLkFdsLewN0qWFq2i691OuE0hu0KYqEwo5lRM0bWzuNm44Wbjlp1lCMYslBsfPp5FZxYxtdFU+mzskz1e1r0sM47MUKQg727SXYLcg144H+QRxP1k5a/9r0OJ88XURlNp8VsLdl3fRY0iNQA4dPsQUQlRbOm8Re/1n9e6VOscY++Vfo8y7mVYcX6FIr8zgP6b+wPw32P/5b/H/pvrHGT+95OMURl6ZXnbe2d/VxVCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiH8zKcgTQgghCpjRo0cbJ0djnByAMh3K8OeyP/nW/VscfR1zlHh9dPIjxbIKVSnE9s+28+6v72LrYQtAYmwiO4ftpHDVworlANh728sfrisgeHQwAEn3knhw+QEALgEu2LjZ6LXu6xa6FWQhM0Lyews5LG22lL6n++JU3EnvtYY/zFnw9+DqAzb320zNYTX1Xt/YLB10yzvLdSmXL/tIeZSSo0i05byW2Hi8/mfu+bK/l1Gy8M+usB0WdhaKrfcqJVuUpGSLkgbPefz3Y5z9nHOMazVaMtL0K1l5kdLvlc4xVqF7BZ3nc4Lm0HlLZxy8HfKUkZGWwQSrCfQ93Rf3su55WkMYT6uSrRi8fTDn7p4jyD0ItalueWdoQKiieTNCZii6nr6ULid7mTo+dXIUAhqKMf9d81rOw8PGw+A5/6n9H5ytcp4z39SjlEf8cvIXLt6/CEAZtzL0qtgLB8t/znlzWs7ROwcgPiWemMQYADxtPXUysjz+4rEiWVlK/1Ca031PU9ypuKLrGjvLmIVyi88s5seWP9KweEP6bu6bPV7eszyX7l9SJKNKoSpMjpjML6G/YGai+5/uMzQZTImYQpXCVRTJelNKnC/q+dbj8oDL/PfYf7n0IPNn1jawLf2r9DdYiWJuqhepzocbP1RsPc1ojWJrvcqMpjMYsXsE81rOw9fR12i5QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghhbFKQJ4QQQoh8t677OqJPRBPUJSiztE5luKzW81uz4t0VzPCZgb23PQAJUQk4+zvTcV1HRbOazmjK7hG7aTmvJY6+joquXVDU/k9trJz1K3hJS05jy4AtnFl8Bq0ms5TBxNSEct3K0fz75qit1a9YIXe+9Xz12ldB8HyZ1VvBwD08Lv4uNJrciDVd1jDg0gDDhims9YLWRs88MOUAjr6OlA0rC8CqDqu4+PtFbD1t6bylM57lPQEI6vzi4pncPFv2p9VqubT2EhYOFhSqnFl8En0impRHKQS2DVToX5KpyXdN2DV8Fy3mtsCxqKOia7/InRN3uH8xs3jHrYwbXhW9FM9wK+1G5P7IHP+mC6svGCTvdT26+QhNWt4LYUzVpjj4OKDJMF6pjMi7vpsyy6DG7RuXY06lUpExStmyxu4V8qfINuFpAntu7CHAJYBAt3/OURf6X9C7vOlk9EnUJmqCPDLPqesvrWfB6QWUdivNmOAxmJuaA7Dl/S165byJre9vpbCd/iXRSalJ7Lu1j8j4SFIzUnXmBlYbCEDnoM56ZSw6vQhXa1dalGwBwOc7P+fHEz9S2q00y9oto6hjUQC+qPOFXjkAx+8cJ2RJCFZmVlQtXBWAaYenMWH/BHZ03UElr0p6ZwD8fPJnph2axuUHl3XGA1wCGFJjCB9U+kCRnNwYsxzRkFnGLJT7+/Hf+Dn75RjXaDWkZaQpkjG7+WxCloTg+a0ndYvWzS6VjE2KJfxWOOam5uzoukORrGcZs6SxsH1hJjScoMhaefEk7QmzjsyisL2yBfmvI2hOEFs6b8HbwTvPa4StDiM5LZkSs0pgrbZGbaL7fThueJy+2xRCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiLeCFOQJIYQQBUxGRgbTp09n5cqVREZGkpqqW0ARF1fw/hj66uardNneBZ/aPgbPcvZzpu/ZvlzfeZ37lzILjVwDXSneqDgqlbLNfKvDVpOWnMasErNQW6sxUZvozA+PG65onqFd3nD51Qf9v4DQAADqfFFH79xtg7dxa98tOm3shE+tzPdI5IFItg7cyvYh22k5p6XeGQApj1I4+ctJnaKrir0q6hR9FRRPE56+1nEW9hYG3kn+MDEz4fEdZQo8/u1OzD1B26VtAbi28xrXd17n/a3vc37leXYO20nXHV3ztO6zZX87h++kdIfStJzbEhPTzPOgJkPD5v6bFX8PFqpciPSUdGYVN/x5N+luEqs7rubmHzexdMw8T6Q8SqFY/WK0W94OGzcbxbLqjqrLuu7rSPg7Aa1Gy8U1F7l/+T5nF5+l06ZOiuXkhzpf1mHPf/bw7q/v6l2oKgxLM9r4RYbX4q6x4PQCrj28xsymM3G3cWfr1a34OPhQxr2MIhkdVnWgbtG6DKg6gCdpT6j8Y2VuPrqJFi3L2y2nXel2AHqVGWX5aNNHjKg1giCPIK4/vE7H3zvybql3WXVhFclpycxoOkPvDMgsq5sSMYU1F9dw89FNVCoVxRyL8V7p9xhacyjWauvsY2v71NY771T0KZr/1pzktGSSUpNwtnLmfvJ9rNXWuNu4Zxfk6WvigYnMaTEHgENRh/jh2A9MD5nOpiubGLx9MGvC1iiSAzB4+2BCA0L5qdVP2aVr6Zp0em/ozafbPiW8Z7jeGd9EfMOYfWMYWHUgIX4hOkVoO67tYNC2QTxMecjQmkP1zvo3M2ahXGm30uyP3J9dxphl9YXVVPSqqEhGOY9yXBlwhSVnl3D478Ncf3QdyCytG99gPJ2DOmNvYa9IFhi/pHHBqQXYmtvSvkx7nfFV5zPPg0qXozpNcUL1TAO/Fi2Pnz7GWm3NkrZLFM16HTcf3SRNo1+Z4oyQGcpsRgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh3nJSkCeEEEIUMGPHjuXnn39myJAhfPXVV3z55ZfcvHmTdevWMWrUqPzeXp7Ye9sbtahLpVJRokkJSjQpYdCckBkhBl3f2Ja3Wa7zXKVSodVqdZ5nGZWh3Hvx4u8X6bC6A77Bvtlj/s39MbMyY3WH1YoU5N05foclIUswszKjcNXCAByedpj9E/bTdUdXvCp56Z1hTJMdJ7+08FGr1aJSqRT9PeWH50sbtVotidGJHJ19NLtMUbxcYkwi9t6ZJStXNl2hdIfSlGhSAkdfR36u9rMiGafnn6bngZ7Z5XgAJqYm1PisBvNrzqfJN00UyQH4vdPvJPydQIOJDbD1sAVle091bP1kK6mPU+l/vj9ugW4A3Ltwj3Xd17Ft4DbaLWunWFap1qXotLET4ePCUduo2TtqL16VvOi0sRMlGhv2WmZox2YfI+6vOL4r9B2ORR1R26h15j86+VE+7UxkORR1iAdPHtCy5D/X28VnFjP6j9EkpSbRplQbvm/2PRZmyt7L7bu5j2ZLm1HLpxbht8KZ0GAC7jbunIk9wy+nfmF1h9WK5ITfCufLOl8CsPbSWrRoeTTiEYtOL2L8/vHZBXlKuPLgChU8KwCZZVB1i9blt3a/EREZQcffOypSkJeakUq9hfX48+6fNPNvRquSrdCi5eL9i0zYP4Gtf20lvEc4alP1qxd7TYO3D6ZVyVbMbTkXh8kOHO59GLWJmi5ruzCo2iDFcqLio/Bz9gNg3aV1tAtsx4fvfEgt71oELwpWLAfg+J3jOuV4AGYmZnxe63Mq/1hZkYzZx2azoPUCOpTpoDMe6BZIsG8w5T3KM2znMIMV5P2n9n9wtnI2yNrGzDJmodyouqPovq47fyf8jUarYc3FNVy+f5nFZxezqdMmRTIA7Czs6FelH/2q9FNszdzkR0njpAOTmNdyXo5xdxt3Ptz0oeIFedNDpusU5JmoTHCzcaNa4Wo4WTkpmmUsSv+MhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDibSUFeUIIIUQBs3TpUn766SdatGjBmDFj6NSpEyVKlKBcuXIcPnyYgQMH5vcW31iT75qw8/OdtJzbEkdfR4PnXd99nRu7b5B0NwmtRqsz13p+a8VyKnSvoNhab4PRmtHZj6/vus6u4btoMLEB3jW8AYg6FMXer/bSYGIDRXPTktOw8bDJMW7jbkNacpoiGdsHbycgNIBWP7XCxCyzxEuTrmFD7w1s+3QbPcN7KpJjLN33/m+UJuRW2mjtZk2xBsVo8p1ypWv/ZpZOliREJeDg7cC1bdeoP74+kFk2qMnQKJKhSddw/9J9XANcdcbvX7qf4xysr6iDUXxw6AM8y3squm5u/tr2F113dc0uxwNwK+1G8x+a82uTXxXPK1qnKF13dlV83fwW0DrAoEWGQn/jwscRXDQ4uyDvXOw5PtjwAT3K9yDQLZBvDn5DIbtCjAkeo2juiN0jGN9gPJ/V+Ay7SXbZ4w2KNWD20dmK5cQ/jc8u7tr21zbaBbbDWm1Ni5ItGLZzmGI58P/nVm3muXXXjV209M/8mXo7eHM/+b4iGXOOzeF2wm3O9D1DgGuAztyl+5cIXhjM3ONz+aTaJ4rkAZyOOc28lvMwUZlgqjLlafpTirsVZ2qjqXRf1522gW0VybE1t+VB8gN8HHzYcX0Hn1X/DABLM0uepD1RJCOLvYU9kfGRlHItpTMeFR+FnYXdC171Zu4m3SXIPeiF80EeQYq9L3LzRZ0vDLZ2blk3Ht7A1txWp3RQKcYqlGtdqjUbO21kXPg4bNQ2jNo7ikpeldjYaSONSzRWNCtDk4GpiWn28yO3j/A04yk1itRQrOAyP0oaI+MjKeZULMd4UceiRMZHKpaTpUeFHoqv+Ta4FneNBacXcO3hNWY2nYm7jTtbr27Fx8GHMu5l8nt7QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgihCCnIE0IIIQqYmJgYgoIyixRsbW2Jj48HoGXLlowcOTI/t5Zna7usJS05jVklZqG2VmOiNtGZHx43XLGsP8b+Qfi4cApVLoStly0qlWFbebQaLXF/xeVaxle0blGDZhvStk+30XJuS3xq+2SP+YX4obZWs+nDTXx88WPFsrxrePPH6D94d/G7mFlm3r6mPUlj39h9FKlRRJGMO8fv6JTjAZiYmVDr81r8WPlHRTKMybeeb35v4cUU/Mg9W9oo8iawbSC/d/4dF38Xkh8k49/MH4CYUzE4+zkrklGhZwU2fLCBh9ceUrhqYQBuH7lNxOQIKvSsoEhGFtdSrqQ/SVd0zRfRarSYqk1zjJuoTRQv/tvQewPlupTDN9hX0XXfBsFjgvN7C+IVTsec5uv6X2c/X/7ncqoVrsZPoT8B4G3vzeg/RitekHcu9hy/tf0tx7i7jbuipWHe9t4cun0IZytntv21jeXvZZavPnzyEEszS8VyACoXqsz4/eNpVKwR+27uY06LOQDceHgDDxsPRTLWXFrDyLojc5TjAZRyLcWXdb5k9cXVihbkqU3VmKgy76HcbdyJjI8k0C0QB0sHohKiFMtpXKIxvTf2pqJnRa48uEJz/+YAnL93Hl9HX8VyAMLKhPHBhg/4tvG31PSuCUBEVATDdg6jU9lOimRUKVSFyRGT+SX0lxylcRmaDKZETKFK4SqKZAFEP45m943dOFs506h4I8xNzbPnklKT+O7Qd4yqN0qxvOcFzA7gTN8zBLoFGmR9YxTKAdQpWoedXXcqtt7zoh9H035Vew7fPkwtn1qsC1tH17Vd2XJ1CwD+Lv780f0PvOy89M7Kj5JGdxt3zsaezfGZPRNzBhcrF0WzAM7Gnn3tY8t5lFM83xD23dxHs6XNqOVTi/Bb4UxoMAF3G3fOxJ7hl1O/sLrD6vzeohBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCKEIK8oQQQogCpkiRIkRHR+Pj40OJEiXYsWMHlSpV4tixY1hYWCiaFXctjtMLTvPw2kOazmyKjbsNV7dexcHHAfcy7orlhMwIUWytVzkx9wStF7amfNfyBs+6ffg2v3f+nfhb8Wi1umVJKpWKURmGK6AwtIfXHmLpmLM0xtLBkkc3HymaFTIjhKVNlzKtyDQ8y3sCEHMmBjNLM7ps76JIhoW9BfGR8biWctUZj4+Kx8JO2c+VsWkyNFxae4l7F+8B4FbajVKtS+mUARqVsr1h/yz7/58xQ5de/tuETA/B0deR+Kh4Gk1thLltZlnO4+jHVOmvTClPk2+bYOtpy6HvDvE4+jEAdl521BxWkxpDaiiSkaXR5EbsGLKDBhMa4B7knqPAzsJeuc9zsQbF2DZoG+2WtcOukB0ACX8nsH3wdoo3LK5YDkDyvWSWNF2CjZsNZTqWodz75fCs4KloRn6ZWXwmfY71wdrFWmc85VEK8yrNY9D1Qfm0M5Hl4ZOHOuVt+27to5lfs+znVQpXUbQELYujpSPRidEUcyqmM34q+hSF7QsrlvNp9U95f8372JrbUtShKMG+wQCE3wonyOPFxVF5MaPpDN5f8z7rLq3jyzpf4ufsB8DqC6uzS9j0deHehex/Q27qF6vPuPBximRlqehZkWN3juHv4k+9ovUY9cco7iff59ezv1LWvaxiOT80/4Gv9nxFVEIUv3f4HRfrzDKtE3dOKFZal+XbJt+iQkW3dd1I12QWr6pN1PSr3I/JjSYrkjG7+WxCloTg+a0ndYvWzf6cxSbFEn4rHHNTc3Z03aFI1rG/j9FkSRM0Wg1pGWkUti/MurB1lHEvA0BiaiJj941VpCCv7Yq2uY5naDMYuG0gduaZ18w1YWv0zgLjFsoZw/Bdw9GiZW3YWpaeW0rLZS0xVZkSNTiKDG0GnX/vzIT9E5jdfLbeWcYuaQToVLYTA7dmvg/qFq0LZF5XBm0bRMeyHRXNAqgwt8Irvx9otVpUKhUZozIUzzeEEbtHML7BeD6r8Rl2k+yyxxsUa8Dso/q/L4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ4m2h0j7f1iKEEEKIt9qIESOwt7fnP//5DytWrKBLly74+voSGRnJ4MGDmTxZmcKGm/tusrTZUnxq+XAr/BYfX/wYp+JOHJh8gDvH79BhdQdFcoxtqstUeh/tjXMJZ4Nnza0wF5eSLgSPDcbOyw6e+7t8S4ecBXMFxYK6CzCzNOPdX9/F1sMWgMTYRNZ1W0d6Sjo99vVQNC8tOY2zS89y/9J9ANwC3Qh6Pwi1lVqR9bcO3MqltZdo/G1jvGt6AxAVEcXOYTsJbBdI0xlNFckxtrvn77I8dDmJMYm4BGSWyDy48gAbNxs6beyEe1nlii7zy5nFZzj4zUEeXH0AgEtJF2oOq2mUEkzx5p4mPAVyL6qLjIikUOVCmFnkvcd9rMlYIGdRYlbxiZLFpPFR8SwPXc7d83dx8HbIHnMv606nDZ2wL2KvWBbAk4dPuLDqAud+O0fk/khcS7kS9H4QQZ2DcPR1VDQr4XbCC/d/+/BtilQvAsC5384R0DoAcxvzPGeNNRnL0Jih2Ljb6IwnxiYy3Xs6I1NH5nltoYyiM4ry67u/UrdoXVIzUnGc7MjGThtpWLwhAOdiz1FvYT3ihscpmjt0x1CO/H2EVe1XUfL7kpz86CSxibF0W9eNbuW6MTp4tGJZx+8cJyo+isYlGmNrnnlfs/nKZhwtHanlU0uxnBdJSU/BVGWK2lT/+xr112qiBkfhaZt7iWb042iKzihK6shUvbOyHL9znMdPH1O/WH3uJt2l29puHIw6iL+LP/ND51Pes+Bek5PTkrkWdw2AEs4lsFZbv+IVb+bx08csObuEw38fJiYxBgBPW09qFKlB56DO2Fsocy1p/GtjvO29+Tn0Z5JSkxi+azgrz69kZ9edVPSqSGxiLIWmFVKkIMxkrAl1i9bNUW65+MxiQgNCcbR0BGBB6wV6ZwF0W9uNaw+vMaLWCJaeW0pUQhSmKlOWtVuWXShXwbNCngvlnKY4oXr+i9wLKHEeLPRdIdaEraF6kerEPYnDdaorO7vuzD7n7rmxhz4b+3Bt4DW9s87GniVkSQhpGWkvLWlUsugyNSOVrmu7sur8quxSPo1WQ7fy3Zjbci7mpnm/p8jNukvrGLpjKMNqDqOGd2Yx86GoQ3x36DumNp5KRc+K2ccWdSyqaHZu7CbZcabvGYo75b1M2XaiLef6naOYUzGd9W4+ukmp2aVI+SpFwR0LIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIUT+kYI8IYQQooA7fPgwBw8exN/fn1atWim27i81fqF0+9LU+KwGk+wm0fdMX5yKO/H30b9Z0XYFn93+TK/1nyY8zS5IyipMepHcipTyaufwnZjbmlNvZD3F1nyRiTYT6XumL85+hi/jM7a4v+JY8e4KHlx5gL13ZnFHQlQCzv7OhK0Nw8XfRZGcjLQMfij1A502dcIt0E2RNXPNSc1gx7AdnJh7Ak26Bq1Wi6m5KZX7VabR5EZ6FXblp19q/IK1mzVtFrXByskKyCzZWt9jPUn3kvjg4AeKZSXGJrJz6E6u775O0t0keO5bhpLFZFkOTTvE3pF7qTKgCj61fACIPBDJsR+OUX98fWoMrqF45r/NmcVnXjpfvpvxSo0m2U+i7+nMa01e3dx386XzvvV887x2brRaLdd3Xdcp7yzeKO+FJ68r4XYC55ad4/T80zy4+oBR6cp+vn4o/QO9DvTCytlKZzwyIpLfWvzGiEcj9M64vOEyAMvbLKfNojY6pbGaDA03dt/g+s7rDLg8QO8soZ9+m/pxJvYMUxpNYd2ldSw6s4g7Q+5klxgtPbuUGUdmcKzPMUVzUzNS+Xjzxyw8s5AMTQZmJmaZZVdBnVnYeiGmJqaK5kHmZxpylmwWJKbjTIkZEoObTe73TUoWoeWHRymP+OXkL1y8fxGAMm5l6FWxFw6WDormxKfEk6HNwNlK9z4+7kkcZiZmihXXGYvzFGcO9z5MSZeS2WOTD0xmasRUtnfZjo+Dj2Lvi+V/LmfYzmGMCx5Hz4o9s8fVX6s50/cMpd1K653xLEMXyi06vSj78YMnDxgfPp4QvxBqFPn/srXbh9j+13ZG1h3J4BqD9f73WE2w4sqAK3g7ZJZ220605XTf0/g5+wEQGR9JqdmlSP4yWe8sMF5J4/OuPrjK6ZjTWKmtCHIPMlg5XdWfqjImeAzN/ZvrjG+5uoWRe0dy4sMTBsl9VnJacna55m/nfqN1QGtszG1e8aoXKzKtCCvbr6Smd02dgry1F9cydOdQRcoThRBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDibVAwmz6EEEKI/2Hh4eHUrFkTM7PMy3j16tWpXr066enphIeHU7duXUVyYs/F0va3tjnGbdxtSL6v/x/jT3GawpDoIdi42zDZcXKuRSRarRaVSqVosVZ6SjonfzzJjV03cC/njqlat1wlZFqIYlmFqxUm7q+4f2VBnrOfM33P9s0shrqYWQzlGuhK8UbFFS2VMVWbkp6Srth6L8wxN6XZzGY0mtSIuGtxADiXcEZtrTZ4tiHFnI6hz/E+2eV4AFZOVjSY0ICfqvykaNb6HuuJj4yn7si62HnZgRG6hY5+f5QWc1rolLgFhAbgVsaNfWP2SUHea9g2aJvO84y0DNKS0zA1N0VtrTZqQd7zpYp5oXQB3quoVCpKNC5BicYljJaZkZbBneN3+PvI3zy6+QhbD1vFM4pUL8KvTX6l+97uWNhlltTeCr/FslbLqDdGmYLZ5W2WA5k/w3Xd1+nMmapNcfR1pMl3TRTJEvr5usHXtF3RlnoL62FrbsuiNouyy/EA5p+eT5Piyv+uzE3N+Sn0J0bWG8mfd/8kMTWRip4V8XfxVzxr8ZnFfHPwG64+uApASZeSDKs5jK7lu+q9tvMUZ658cgVXa1ecpjiheskFMm54nN55Wq2WhosbYmaS+3/yS9cY/r7KUI7fOU7IkhCszKyoWrgqANMOT2PC/gns6LqDSl6VFMvq+HtHWpVsRf8q/XXGV55fyYbLG9jy/hbFsjI0GTqFj0duH+FpxlNqFKmB2lS5e9GU9BSd5yNqj8DMxIwmS5owP3S+Yjkdy3akepHqdFnThU1XN/Fzq59xssp7+e2rPEx5SGG7wgA4WzljrbbWKVvzc/Yj+nF0ntfvXqF79uN2K9sxrv44BlT9p7x1YLWBzD46m13XdylSkOdu4050YnR2Qd6AqgN0ihofPnmoV7na8+ws7OhXpR/9qvRTbM3X4e/i/9Lzuf0ke073PU1xJ/2Kh8/dPUcxx2I5xos5FuPCvQt6rf2shosbsrjNYgrbF9YZP/r3Ubqs6cKVT64A0Dmos95ZHct2ZPiu4axqvwoVKjRaDRGREQzdOZRu5brpvb4QQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQrwtpCBPCCGEKGDq169PdHQ07u7uOuPx8fHUr1+fjIwMRXIsHS1JjE7EqZhumUH0qWjsC9vrvX63Pd2wcs4s7eq+t/srjlbO3bN38azgCcC9P+/pTipQ6BV7Njb7cdVPqrJjyA4SYxJxD8pZxudRzkP/QCNb2nwp7Za1w9LBEpVKRfSJaCr3rYyloyUAyQ+SWVBnAR9f+FixzCofVyFiSgShP4diYmai2LoAK9queOUxJmYm2HraUrxxcQJaBSiab2guJV1Iik2CMrrjSXeTFC9ujDwQSc/9PbM/X8bwOPox3jW9c4x71/TmcfRjo+2jIBv+cHiOsQdXH7C532ZqDquZDztSRlpyGvGR8WSk6l4TlTjvRh2K4smDJ5RsWTJ77MziM/wx+g9Sk1Ip1aYUzb5vhpmFsl+3b+y9wbnfznHx94toNVoC2wbSaVMnijXIWfqir9CfQ1n53kqWtVpGl+1diDoYxfLQ5dQfX5/qg6orkjFaMxqAmcVm0udYH6xdrRVZVyjP1dqV8J7hxKfEY2tuq1PkBbCq/SpszZUvaszi4+CDj4OPwdafdmgaI/eOZECVAdRqUAuAA5EH6Lu5L/eT7+tdeDU9ZDp25nYAzAiZoe92X2l0vdGvPKZdYDtFM2MTYxm6cyi7r+/mbtJdtM81nmaMUub7yeDtgwkNCOWnVj9lFwCma9LpvaE3n277lPCe4YrkQGZJ3bQm03KMB/sG8+WeLxXJiH4cTftV7Tl8+zC1fGqxLmwdXdd2ZcvVzPI9fxd//uj+B152XnpnlXUvy8Gog5TzKKczPrTmUDRaDZ1+76R3xrN8HX0J7xnO2D/GUn5ueX5q9dNLyyH1YcxCue1/bWdKoyk5xpv6NWXErhGKZFTwrMChqEPZJZCTG03WmT8QeSDH71FfxippfBPPn0fyKtA1kEkHJvFz6M/Z5a6pGalMOjCJQNdARTIALM0sKTe3HP9t/l/Cyoah0WoYt28cE/dPzFG0qa+JDSfy8eaP8Z7uTYYmg9I/lCZDm0HnoM58VfcrRbOEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEECI/SUGeEEIIUcBotVpUqpzlAg8ePMDGRpk//Aco27Esu4bvov2q9qACrUZLZEQkO4fupFw3/f8g37eeLwCadA239t2iYq+K2BfRv3jvVQxdxje3wlxUKhVa7T9/0L++1/rsx1lzKpWKURmjDLoXQ7i2/RoZT/8pOdk/cT9lOpTJLsjTpGt4cPmBopl3jt3h+u7rXNtxDY8gD9Q2uiUNYWvC8ry2pYPlK4/RarTEXY3j5M8nqTm0JvXH1c9znjE8TXia/bjhpIZsHbiV4DHBFKleBIDbh2+zb9w+Gk1ppGiuvbe9zvveGJz9nDm/8jx1/lNHZ/z8ivO4+LsYdS//Ji7+LjSa3Ig1XdYw4NKA/N7OG0m6l8T6nuv5a+tfuc4rcd4NHxdO0eCi2QV5sedi2fDBBsr3KI9boBsHvzmIXSE7gscE652VZVrhaTyJe4JfUz9a/diKkq1KKl7A9yyViYr3lr/Hby1+Y3GDxcSejaXhpIZUHVBVsYysosFBNwZljxmjaFDknYOlQ67jzxZRKSlDk8HC0wvZfSOzdE2j1ejM7+m+R5Gc749+z5wWc+hWvlv2WGhAKGXcyjBm3xi9C/K6V+ie62NDGR386oI8pfVY34PI+EhG1h2Jl52XwYrQjt85rlOOB2BmYsbntT6n8o+VFc16mvGUdE16jvG0jDSepD1RJGP4ruFo0bI2bC1Lzy2l5bKWmKpMiRoclVl29XtnJuyfwOzms/XO6la+G/tu7aNv5b455j6v9TlarZa5J+bqnfMsE5UJY+uPpXGJxnRb240MrTJFic8zZqGci7UL6y+tZ0jNITrj6y+tx8VamXvP9R3Xv3S+SuEq1POtp0iWMUsa88vclnNptawVRaYVyX4fnI09i0qlYmOnjYrlbO68mR+O/kCvDb1Yf3k9Nx/d5Fb8LTZ13kSTEk0Uy9FqtcQkxjCr2SxG1RvFubvnSExNpKJnRfxd/BXLEUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIt4H8pbkQQghRQLRt2xbILFjr0aMHFhYW2XMZGRmcPXuWmjVrKpbXcGJDNn+8mene09FkaPih9A9oM7QEdQ6i7ld1FcsxMTPh4DcHKd+tvGJrvq6E2wkAihbzPVv082+UowDNCH1olo6WlG5X2iBrt17Q+rWPvbLpCpv7b37rC/ImO07WKdHUarWs6rAqeyzrd7is1TJFSxqbzmjK7hG7aTmvJY6+joqt+zLBY4NZHbaaW+G38K7lDUBURBQ3dt/gvZXvGWUP/1YmZiY8vvM4v7fxxrZ/up2URyn0PtKbhcELCVsbRlJsEuHjw2nynTLlJDGnY6j/9T/ngT+X/0nhaoUJ/SkUyCyL/GP0H4oW5NUbU48y7f8pIzWE2LOxueb+3ul3groEUbRu0exjPMp56J23b+w+fOv7GrVoUBQsg7YNYuHphbQo2YKy7mUNVroW/Tiamt4576Fretck+nG04nkZmgzWXlrLxXsXASjtVprWpVrrlL7p49czv9KiZIsXFhcmpSbx3aHvGFVPuXuAA5EH2N9zPxU8Kyi2Zm7sLeyJjI+klGspnfGo+CjsLOwUzapauCo/nviR75t/rzM+9/hc3in0jiIZu67vYk3YGqoXqU4tn1q4TnVlZ9edFLYvDMC4+uPos7GPIlm9K/Wmd6XeL5wfXns4w2sPz34eERlB5UKVsTCzeOFrXldtn9qc7XeWa3HX8HP2yzGvb5YxC+XGBo+l94be/HHrD6oVrgbAkb+PsO2vbfzU6idFMl4lqwhQCcYsacwvVQtX5frA6yw9t5RL9y8BEFYmjM5BnbExV+5/MADwcdWPuZ1wmykRUzAzMeOPHn/ken3RhxYtfrP8ON//PP4u/ng7eCu6vhBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCvE2kIE8IIYQoIBwcHIDMcis7OzusrKyy58zNzalevTp9+ihToABgam5K6E+h1BtVj7vn7pKamIpnRU9c/F0Uy8hSrEExbu67SQXfCoqv/TytRkv4+HAOfXeI1MRUAMztzKkxpAZ1v6yLykS/8hXHoo4K7FI8601K7AzJp7YPhSoXyu9tvFL3vd3zJXd12GrSktOYVWIWams1JmoTnfnhccNf8Mq8K92uNL2P9Obw9MNcXncZANdAV3of7Y1XRS/F8/6NLm+4rPNcq9WSGJ3I0dlH8anlY9zNKNB9dWPPDTqu70ihyoVQmahwLOpIicYlsLC34MCkA5RsUVLvjCcPn2Dj8U+hyq19t/Br9k/hT+EqhUmIStA751nv9FGmjOll5laYi0ql0ilCzXp+Yt4JTv54Eq1Wi0qlUqRcM/ZMLA3GN8h+boyiQVGwLP9zOSvbr6S5f3OD5vg5+7Hy/Er+U+c/OuMrzq/A38Vf0azzd88TujyUmMQYAlwCAJgSMQU3Gzc2dtpIWfeyemd0X9ed4k7FWddxXa7rJaYmMnbfWEUL8rztvXOWKBtAWJkwPtjwAd82/ja7dCoiKoJhO4fRqWwnRbPG1x9Po18bcSb2DA2LNQRg943dHLtzjB1ddiiS8TDlIYXtMsvwnK2csVZbU9SxaPa8n7OfQUoaX0ezpc043fc0xZ2KK7Kerbkt5T1zL0RXOut5ShbK9ajQg0DXQGYdncWai2sACHQL5EDPA1QrUk2xHIBJ+yfhYetBr4q9dMbnn5rPvaR7OoWGeWXMksb8ZGNuw4fvfGjQjIdPHtJ7Y292X9/NvJbz2HdrH01+bcLUxlPpX6W/YjkmKhP8Xfx58OQB/ih7jRJCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiLeNFOQJIYQQBcSCBQsA8PX1ZejQodjY2LziFfrZN24fNYfWxMHbAQdvh+zxtCdpHPzmIPVG1VMsy6+ZH7tH7Obuubt4veOFuY25znxAaIBiWbu/3M2pX07RcHLD7PKnyAOR/DHmD9JT0mk4oaFiWfsn7cfWw5aKvSrqjJ+af4qke0nUHl5bsSxjUalUOUusFCi1eh1Jd5O4f/k+AK4Brti4G/Yz8DxLR0vC1oQZNTMvfOv55ktuyIwQo2U9TXia/djF34UW/22R6zEW9hZG21NBtbzNcp3nKpUKazdrijUoRpPvmhh3Mwp0K6UmpWafG6ycrEi6l4RLSRfcg9yJPqlMyZCthy2PbjzCwduBjNQMok9GEzw2OHv+6eOnOQoi82JF2xW0WdgGC3sLVrRd8dJjlTg3DboxSO813kR+FA2KgsXc1Bw/Z79XH6inscFjCVsdRvitcGp51wIyS9d239jNyvdWKprVe2NvyriV4Xif4zhZOQGZpUo91vfgw40fcvCDg4rkFHcqTs1farKwzULaBrZVZM2XmdF0BiN2j2Bey3n4OvoaLOfbJt+iQkW3dd1I16QDoDZR069yPyY3mqxoVi2fWhz64BDfHPyGlRdWYmVmRTmPcvwS+otixYnuNu5EJ0bj7eANwICqA3C2cs6ef/jkITbmxr3fzaJV4qJs5CxjFMoBVCtSjaVFliqy1svMOzGP39r9lmO8jFsZOoZ3VOTf8zaXNKr0+JK54fIGmvk1Q22qZsPlDS89NjQgNM85zyo7pyzFHItx6qNTFHMqRp93+rDizxX039KfzVc3s7nzZkVyACY3nMywncOY02KOIsWqQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjxtpKCPCGEEKKAGT16tFFy9o3dR+W+lVFbq3XG05LT2Dd2n6IFeZv7Z/6x+KFph3LMqVQqRmWMUizrzKIzhP4cqlO651HOA7vCdmzpv0XRgrwT807Q7rd2OcbdyrgR3jG8QBbkabVa1vdYj6mFKQDpKels7rsZtU3m+yTjaYbimU8TnrLl4y38ufxPNBkaAExMTSgTVobmPzTH0sFS8cx/k5RHKZz85ST3L2aWC7qVcaNir4qK/9wqdK+g6HovM9lxcmZZ4ysoee74txqtMc415XV88fgLvddwDXDl/uX7OPo64lHegxPzTuDo68jxucex87JTYJfg19yPXSN20WhKIy6tu4TaWk3ROv+UycSejcW5hPNLVng9lg6W2QWkxjjPORZ1NHjGs4xVNCgKriE1hjDz8ExmN5/9Wuf8vGpXuh1Heh9h+uHprLu8DoBA10CO9j5KRa+KL3/xGzodc1qnHA/AycqJCQ0mUOWnKopkqFQqlrRdwk8nfqLDqg58WedLxtYfq8jaLxK2OozktGRKzCqBtdoatYnu94e44XGK5JibmjOz2UwmNZrEtbhrAJRwzsw0hAqeFVja1nBFaBU8K3Ao6hBVC1cFyFHydyDyAOU8yhks/9/GGIVyxhSTGIOXrVeOcTcbN8VK6/6tJY1tlrchZmgM7jbutFne5oXHqVQqMkYp8/217zt9+bLul5io/rl3CSsbRi2fWvRc31ORjCzd1nUjOS2Z8nPLY25qjpWZlc68UudcIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYTIb1KQJ4QQQhRAq1evZuXKlURGRpKamqozd/LkSUUytFptdjHPs2LPxGLlbJVzQg/GLGh6EvcE11KuOcZdS7nyJO6JolmJMYnYetnmGLdxs+Fx9GNFs4zl+RK0cl1ylnaU71Ze0cyNfTYSfSqaTps64V0js7wh6lAU2wZtY9NHm3hv+XuK5v2b3Dl+hyUhSzCzMqNw1cIAHJ52mP0T9tN1R1e8KuUs3XhTTxOevtZxFvYWemdl6b63+z9PtLC0+VJCfw7FrrAyBWj/q7TazCISJYqo5lWcl+s1JDcfnfxI77ws1QZVIzE6EYB6o+uxtOlSzi09h6m5Ka0XtlYko8HXDVjRdgUL6y3E3NacNovaYGpumj1/ev5pijcprndO6wWtc31sDKcXncba1ZqSLUoCsPPznZz48QRupd1ot6ydImV6xioaFAXXgagD7L2xl61/baWMe5kcpWtrwtYolvVOoXdY0naJYuu9SEmXksQmxVKGMjrjd5Pu4ufsp0hG1rn8y7pfUt6zPF3WdOHs3bMseXeJwYquZoTMMMi6L2KttibII8hoeSnpKaRm6H7nsrew13vd9R3Xv3S+SuEq1PNVrpT8384YhXLG5O3gTURUBMWciumMR0RGUMiukCIZb0NJY4Ymg3N3z1HUoahOeejW97dS2K5wntbUjNbk+tgQxu0bx9CaQxlZb2Su80Xsi7Cz605FM419zhVCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiPwiBXlCCCFEATNr1iy+/PJLevTowfr16+nZsyfXrl3j2LFjfPzxx3qvP8VpCqgyC5Jml5ytU3CkzdCSmpjKO33f0Tsny6Obj7i28xqaNA1F6xXFvYy7YmvnxrO8J0dnH6XZrGY640dnH8WjvIeiWQ7eDkRFROFUzElnPDIiErtCBbPIy9glTQBXNl2hy/Yu+NT2yR7zC/Gj1U+tWNp0qdH3U5BsH7ydgNAAWv3UChMzEwA06Ro29N7Atk+30TO8p94Zkx0nv7RQTavVolKpGJUxSu+sLL71fHWem5iaUKR6EZyKO+X+AvFSZxaf4eA3B3lw9QEALiVdqDmsJuW75r3sMqBNQPbj9JR0jv/3OG6l3ShSowgAtw/f5t75e1TuX1m/zZNZ0phVwPhsaWehdwrx6a1PuX/pPg4+Dli7WuudBWDtak3P8J6kxKdgbmuOiamJznz7Ve0xtzVXJCu/HJh4gBZzWgCZhaRHZx+l6YymXNl0he2DtxO2JkzvDGMVDYqCy9HSkXcD3zVK1rW4ayw4vYDrD68zo+kM3G3c2Xp1Kz4OPpRxL/PqBV4i4WlC9uNJDScxcOtAxgSPoXqR6gAcvn2YcfvGMaXRFL1yctOyZEsO9z5Mm+VtqPZzNTZ02oCNWvmSvO4Vur/6IAUkpSYx+cBkdt/Yzd2ku2i0usVX1wddVywrOS2Zz3d+zsrzK3nw5EGO+YxRGYplvUhWaZl4PcYolDOmPpX68Om2T0nLSKNBsQYA7L6xm893fs6QGkMUyciPksZPt31KkHsQH1T6gAxNBvUW1uNg1EGs1dZs6ryJYN9gAGr71FY091mPUh7haOmoyFpj942lb+W+WKuVuc98HcY65wohhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghRH6TgjwhhBCigPnvf//Ljz/+SKdOnVi4cCGff/45xYsXZ9SoUcTFxem9fsiMENDC+l7rCR4bjIWDRfacqbkpjr6OeNfw1jsH4MbeGyxruYy0J2kAmJiZ0Hp+a52CI6U1mtqI31r8xvVd1/8pajp0m4SoBDpv6axoVqU+ldj26TYy0jIo1iCzqOHG7hvs/HwnNYbUUDTr38zKxUrnfZjF0sESSyfLfNhRwXHn+B2dcjzI/JzV+rwWP1b+UZGM7nuloKEgOzQ2s2OaAAEAAElEQVTtEHtH7qXKgCo0qJVZvhJ5IJLNfTeTfD+ZGoPzdq4KHh2c/XhD7w1UHViVBl830Dlm7+i9JEQloK8pTlMYEj0EG3cbFjVYRNiaMCwdM88Nams1XpW89M7IjaVD7ucfK2crRdafV3GeTknty3x08iNFMrPER8Xj7OcMwKV1lyj9Xmne+fAdvGt5syh4kSIZ/wtFg0I/C1ovMErOvpv7aLa0GbV8ahF+K5zxDcbjbuPOmdgz/HLqF1Z3WK3X+o6THXWKZLVaLR1Wdcge02q1ALRa1kqR0rXnS2tLuZbiaJ+jdPq9E1V+qsKMkBl6Z0Bm8Z+9hX3245fJOk5fvTf2Zt/NfXQt1xUvOy9Ur3uSzINhO4ax9+Ze5rSYQ9e1Xfmh+Q/8/fhv5p2Yx+SGkxXNmrR/Eh62HvSq2EtnfP6p+dxLusfw2sMVzXsdhvzZGirLGIVyxjSs5jAeJD+g/5b+pGakAmBpZsnwWsP5os4XRtmDIUoaV19YTZdyXQDYeGUjNx7d4NKAS/x65le+3PMlEb0iFM2bcmAKvo6+hJXNLPdtv6o9v1/4HS87L7Z03kJ5z7wXQsM/53Bjyyp2vfbwGjObzlS02FUIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIId4WUpAnhBBCFDCRkZHUrFkTACsrKx4/fgxA165dqV69OrNnz9Zr/QrdKwDgWMwRn1o+OsVaSts7ci/FGxenxZwWmFmaseerPez8fKdBC/J86/ky4PIAjv33GA8uPQAgsG0gVfpXwa6QnaJZNYfVJPlBMlv6byEjNbPwxMzSjFrDa1HnizqKZv2b1f2qLjs+28G7v76LractAIkxiewctpO6I+vm8+7ebhb2FsRHxuNaylVnPD4qHgu7nKWDeeFbz1eRdUT+OPr9UVrMaUH5bv+UgwSEBuBWxo19Y/bluSDvWRdWXaDP8T45xst1KcdPlX+i9fzWeq1vbmtO8oNkbNxtuPnHTTLS9C+YehsEtAnIt+ysn6mDjwPXd1yn+mfVgcxrWFaprVIMXTQoxKuM2D2C8Q3G81mNz7Cb9M+9YINiDZh9VL/7aoC93ffqvcabyK2syd7Cnk2dNvHlni/psb6HIjlOU5yIHhKNu417jhLAZ/eiUqkUKf4D2Hp1K5s7b6aWTy1F1nuZjVc2svjdxQT7BtNzfU/qFK2Dn7MfRR2KsvTcUt4v975iWfNOzOO3dr/lGC/jVoaO4R3zpSBPi/FKv5TKyo9CuYSnCey5sYcAlwAC3QIVXVulUjGl8RRG1hvJxXsXsVJb4e/sj4WZMvfwzzJmSeP95Pt42noCsOXqFtqXbk9Jl5L0qtiLmUdmKpaTZe6JuSxtuxSAndd2suv6LrZ12cbK8ysZtnMYO7ru0DvDmIWSkLPYdUKDCYoWuwohhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxNtCCvKEEEKIAsbT05O4uDiKFi2Kj48Phw8fpnz58ty4cSPXQoy88q3nS9y1OE4vOM3Daw9pOrMpNu42XN16FQcfB9zLuOudcffPu3xw8APsvDLLSJp804QT806Q/CAZaxdrvdd/EfvC9jSc0NBg62dRqVQ0ntKYeiPrce/iPdRWapz9nTGzkFuwN3F8znHi/opjus90HHwcAIiPjMfMwoyke0mcmHci+9iPTn6UX9t8K5UJK8OGDzbQ+NvGeNf0BiAqIoqdw3ZStlNZxfM0GRourb3EvYv3AHAr7Uap1qUMWrSZzbi9FP8aj6MfZ783nuVd05vH0Y8VyTCzMiMqIgoXfxed8aiIKMws9T8fFm9UnEX1F+EW6AbAindXYGpumuux3fd01zvPWIJHB7/xa84tO0dAaADmNuZ6ZZdoXIKNvTfiWdGTB1ce4N/cH4B75+/h6Ouo19pCvEzFeRVfu2jo5EcnFck8F3uO39rmLCdzt3HnfvJ9vdev51tP7zXeRPcK3bEyy1kwqVKpmNhwIhU8KzD3+Fy9c/Z024OzlTNgvBJAJyun7ExDi3sSR3Gn4kBmwWDckzgAavvUpt/mfopmxSTG4GXrlWPczcaN6MfRimYBxKfEE5MYA4CnrScOlg45jnn8hTL3AMbMMkahXIdVHahbtC4Dqg7gSdoTKv9YmZuPbqJFy/J2y2lXup1iWVliEmOIexJHXfe6WJhZZBdPKsmYJY0eth5cuHcBL1svtv21jTkt5gCQnJaMqUnu92/6iEmMwds+815305VNdCjdgSYlmuDr6Eu1n6spklFydslXXrvihscpkgWGL3YVQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQoi3hbSzCCGEEAVMgwYN2LBhAxUrVqRnz54MHjyY1atXc/z4cdq2batYzs19N1nabCk+tXy4FX6LBhMaYONuQ+yZWE79cooOqzvonfE04SnWrv8U4amt1ait1DyNf2qwgrxTC05hbmtOmfZldMbPrzpPWnIaFbpXUDwzMSaRJ3FPcK/rjpmFmUFKDf7NAtoE5PcWCqwm3zYBFazrtg5NugatVoupuSmV+1Wm0eRGimbdPX+X5aHLSYxJxCUgswgtYkoENm42dNrYCfey+pdqZlnRdoXO8/SUdDb33YzaRq0zHrYmTLHMfytnP2fOrzxPnf/U0Rk/v+J8jkK7vKr+aXU299tM9MloClctDMDtI7c5Pf80dUfW1Xv9d5e8y5lFZ4i7FsfNfTdxK+OG2lr96hf+C236aBNFqhXBvLh+BXnNf2jOnq/2kBCVQIffO2Rfk++cuGOQck0hsrQJaGP0TEdLR6IToynmVExn/FT0KQrbFzZIZnJaMpHxkaRmpOqMl/Mop/faC1ovAOBB8gNcrDPP41HxUfx08ieepD2hVUAr9nTfo3fOs8V/xioB/Lr+14z6YxSL2izCWm24Mm2A4k7FufHwBj4OPpRyLcXK8yupWrgqG69sxNHSUdEsbwdvIqIicrwHIyIjKGRXSLGcn0/+zLRD07j84LLOeIBLAENqDOGDSh8UyKznGbJQLvxWOF/W+RKAtZfWokXLoxGPWHR6EeP3j1e0IO9B8gM6rO7A3ht7UalUXP3kKsWdivPBhg9wsnTiu5DvFMsyZkljzwo96bCqA152XqhUKhoVz/xOcuTvI5RyLaVoFoCTpRNRCVF4O3iz7do2xtcfD4BWqyVDk6FIxtjgsThY5Cx/NBRDF7sKIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIcTbQgryhBBCiALmxx9/RKPRAPDxxx/j4uLCwYMHCQ0N5aOPPlIsZ/eI3TQY34Aan9Vgkt2k7PFiDYpxdPZRxXL+2v4Xlg6W2c+1Gi3Xd1/H9k/b7LGAUOUK0g5MOkDLeS1zjNu427Dpw02KFuQlP0hmdYfV3Nh7A5VKxSdXP8GpuBMbPtiApZMlId+FKJb1bxY8Ovi1jju37BypSamY2+hXDPVvYmpuSrOZzWg0qRFx1+IAcC7hbJDysI29N+JWxo0+x/tg5WQFwJOHT1jfYz0bP9zIBweVK0J59pwBUK6L/oVC/6uCxwazOmw1t8Jv4V3LG4CoiChu7L7BeyvfUySj9ojaOBV34sjMI5xdchYAt0A3Wi9oTZkOZV7x6ldTW6mp3LcyANHHo2k8pTGWjpaveNW/lFaZZSwdLWk+u3mO8fpj6ysTIMQLjA4ebfTMjmU7MnzXcFa1X4UKFRqthojICIbuHEq3ct0UzbqXdI+e63uy9a+tuc5njNK/rOlc7DlaLWtFVEIU/s7+LH9vOU2XNCUpLQkTlQnTD09ndYfVtCnVRu+s5xmy+A/gu0PfcS3uGh7feuDr6IvaRPd+5uRHJxXJgcwSrzOxZ6jnW48RtUfQalkrZh+dTZomjWlNpimWA9CnUh8+3fYpaRlpNCjWAIDdN3bz+c7PGVJjiCIZ30R8w5h9YxhYdSAhfiF42HgAEJsUy45rOxi0bRAPUx4ytObQApX1LGMUysU/jcfZyhmAbX9to11gO6zV1rQo2YJhO4fpvf6zBm8fjNpETeTgSAJ/CMweDysTxmc7PuM7lCvIM1ZJI8CY4DGUdS9LVHwU7cu0x8LMAgBTlSkjao1QNAugbWBbOv/eGX8Xfx4kP6CZfzMATsWcws/ZT5GMjmU74m6jXBn4q+RHsasQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQuQHKcgTQgghCpD09HQmTpxIr169KFKkCAAdO3akY8eOimfFnoul7W9tc4zbuNuQfD9ZsZx13dflGNv00absxyqVilEZoxTLi4+Mx6mYU45xx6KOxEfGK5YDsH3wdkzUJgyOHMwPgT9kj5cJK8OOz3agYKeBIPN9U6RaEcyLS0HeirYrXnmMiZkJtp62FG9cnIBW+pdQxpyO0SnHA7BysqLBhAb8VOUnvdd/VusFrRVd739Z6Xal6X2kN4enH+byussAuAa60vtob7wqeimWU6ZDGUXK8F6l+97uBs/4t4o9G4t7WXdUJipiz8a+9FiPch5G2pUQhjex4UQ+3vwx3tO9ydBkUPqH0mRoM+gc1Jmv6n6laNan2z/lUcojjvQ+QvDCYNaGrSU2KZbx4eP5rokyN4af7/qcII8glrZdyq9nf6Xlby1p4d+Cn0Izr8WfbPmEyQcmK1qQZ4ziP4A2AW0UWed1DK4xOPtxo+KNuPjxRU5Gn8TP2U+xwr8sw2oO40HyA/pv6Z9dLmhpZsnwWsP5os4XimTMPjabBa0X0KFMB53xQLdAgn2DKe9RnmE7hylSWmfMrGcZo1DO296bQ7cP4WzlzLa/trH8veUAPHzyEEszZct5d1zbwfYu2yliX0Rn3N/Fn1uPbimaZYySxme9VzpnCXP3Coa5h5seMp1ijsWIjI9kaqOp2JpnFvFHP46mf5X+eq+vUqn0XuNNGbPYVQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh8pMU5AkhhBAFiJmZGVOnTqVbN8P/0bOloyWJ0Yk5yuSiT0VjX9hekYzRmtGKrPMmbNxtiD0bi6Ovo854zJkYrFyscn9RHl3bcY0u27tgX0T35+Xi78KjW48UzRKANr838PawdHh1QYdWoyXuahwnfz5JzaE1qT+uvl6ZLiVdSIpNguc60JLuJuHs56zX2kJ5TxOeZj928XehxX9b5HqMhb2FYpl3Ttzh/sX7ALiVcVO0gE/ob26FuQyNGYqNuw1zK8xFpVKh1eY8sSpdXCvEi5iMNXlp8ZBSpWvmpub8FPoTo+qN4tzdcySmJlLRsyL+Lv6KrP+sPTf2sL7jeioXqoyJyoSijkVpXKIx9hb2TDowiRYlc56L39Sxv4+xp/seynmUo7xneX488SP9q/THRGUCwCfVPqH6z9X1znmWMYr/AEYHG/+7QxZfR198HX0NsrZKpWJK4ymMrDeSi/cuYqW2wt/ZHwsz5a7Bd5PuEuQe9ML5II8g7iffL3BZzzJGodyn1T/l/TXvY2tuS1GHogT7BgMQfiucII8X/5vzIiktCWu1dY7xuCdxir43wDgljc/ad3Mf3x76lov3LgJQ2q00w2oOo07ROormJDxN4MjtI5RyLUW38t1ws3HLnnu2BFMfud0rGZoxi12FEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEECI/SUGeEEIIUcA0bNiQffv24evra9Ccsh3Lsmv4Ltqvag+qzDKtyIhIdg7dSblu5Qya/SK/tfiNVj+3ws7LLs9rlO1Ulq0Dt2JuZ07RukUBuLXvFtsGbaNsx7JKbRWAtKQ01NbqHONP4p5gZiG3YcJwWi9o/drHXtl0hc39N+epIO/ZkrWGkxqydeBWgscEU6R6ZjHJ7cO32TduH42mNHrjtYVhTXac/NLiqSxKFKEl3U1idcfV3PzjJpaOmeWNKY9SKFa/GO2Wt8PGzUbvDKG/QTcGYe1mnf34RdKS0oy1JfE/bm3YWp3naZo0TkWfYtGZRYwNHqt4nreDN94O3oqv+6yk1CTcbdwBcLJy4l7SPUq6lCTIPYiT0ScVyYh7EoenrScAtua22Jjb4GT1T+G1k6UTj1MfK5KVxRjFf1kepTxi9YXVXIu7xrBaw3C2cuZk9Ek8bDwobF9YsRyA3dd3M/3wdC7ezyzxCnQN5NPqn9KouGHua2ISY4h7Ekdd97pYmFmg1Wpf61r9OqoUqsLkiMn8EvoLZia630MyNBlMiZhClcJVClzWs4xRKNe/Sn+qFq5KVHwUjUs0zi6eLO5UnPH1xyuSkaWOTx0Wn1nM1w2+BkCFCo1Ww9SIqdT31a/Y+nnGKGnMsuTsEnqu70nbwLYMrDYQgIioCBoubsjCNgvpHNRZkZzTMadpvrQ5sUmxaLVa7CzsWPneSkL8QhRZP4tmtEbR9V5HVrHryHoj+fPunwYtdhVCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiPwkzSxCCCFEAdOsWTNGjBjBuXPneOedd7Cx0S0WCg0NVSSn4cSGbP54M9O9p6PJ0PBD6R/QZmgJ6hxE3a/qKpLxpm6F3yL9SbpeazT4ugHxN+NZ3HAxJmaZhQZajZby3crTcGJDJbaZzaeOD2cWn6HB1w0yB/6/aDBiagS+9X0VzRIir3xq+1CocqE8vfb5kjWtVsuqDquyx7RaLQDLWi1TpGhNKKf73u7/PNHC0uZLCf05FLvCeS8gfZGtn2wl9XEq/c/3xy3QDYB7F+6xrvs6tg3cRrtl7RTPFG/Osahjro+zpD9N59gPx4iYGsHQmKHG25j4n9W6VM6y1/dKv0cZ9zKsOL+CDyp9oEhOu5XtqFqoKsNrD9cZnxoxlWN3jrGq/SpFcgACXAO4fP8yvo6+lPcoz7wT8/B19GXu8bl42XkplqNC9dLnSjNG8R/A2dizNFrcCAdLB24+ukmfd/rgbOXMmotriIyPZPG7ixXL+u+x/zJo2yDeK/0eg6plloYevn2Y5kubMz1kOh9X/VixrAfJD+iwugN7b+xFpVJx9ZOrFHcqzgcbPsDJ0onvQr7TO2N289mELAnB81tP6hati4eNBwCxSbGE3wrH3NScHV136J1j7KxnGatQrnKhylQuVBmtVptdYqhkCWSWqY2n0nBxQ45HHyc1I5XPd33O+bvniXsSR0SvCMXzwLAljVkm7J/A1EZTGVxjcPbYwGoDmXZoGl+Hf61YQd7wXcMp5lSM3zv8jqWZJV+Hf82ArQO4+slVRdZ/G/g4+ODj4JPf2xBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCIORgjwhhBCigOnfvz8A06ZNyzGnUqnIyMhQJMfU3JTQn0KpN7Ied/+8S2piKp4VPXHxd1Fk/fxiam7Keyveo/7X9Yk5E4PaSo17kHuuZUD6ajy1MYsbLib6eDQZqRns+nwXd8/f5UncE3pF9FI8T4i8sHS0JGxNWJ5eq1OyJgoU33q+Os9NTE0oUr0ITsWdFM/6a9tfdN3VNbscD8CttBvNf2jOr01+VTzvf5lDUQdM1CZ5fn3603T+GPMH13dex9TclFqf16JUm1KcWnCKPV/uwcTUhOqDqyu4YyHeXPUi1flw44eKrRd+K5wx9cbkGG/m14zvDulfTPasQdUGEZ0YDcDoeqNpurQpS88txdzUnIWtFyqW02N9DyxMLQBISU+h7+a+2KgzS7WfZjxVLCeLsYr/Ptv+GT0q9GBq46nYTfqn0LW5f3M6/65MqVaWifsnMj1kOgOqDsgeG1htILW8azHxwERFC/IGbx+M2kRN5OBIAn8IzB4PKxPGZzs+4zv0fx+W8yjHlQFXWHJ2CYf/Psz1R9cB8LT1ZHyD8XQO6oy9hb3eOcbOepaxCuUWn1nMNwe/4eqDzKK1ki4lGVZzGF3Ld1UsA6Cse1muDLjC7KOzsTO3IzE1kbaBbfm4yseKfq7AOCWNWa4/vE6rgFY5xkMDQvnP7v8olnPizgl2dN1BJa9KAMxvPR/nKc4kPE0wyPvPmDI0GSw8vZDdN3ZzN+kuGq1GZ35P9z35tDMhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhFCWFOQJIYQQBYxGo3n1QQpy8HHAwcfBqJnG4OjriFarxbmEMyZmeS8Tehn3su4MuDKAo7OPYm5nTmpiKoFtA6nycRXsvOxevYAQb7nnS9aEyI1Wo8VUbZpj3ERtglajVTwv5VEKfx/9m6S7STnWL9+tvOJ5xvD3sb/RarQUqVZEZ/z2kduYmJpQqHIhAPr/2V+vnL2j9nJi3gmKNypO1MEoVrVfRYWeFbh9+DYh00Io3b40JqaGuWYK8TqepD1h1pFZFLYvrNiaiamJmJua5xhXm6pJeJqgWA5Al3Jdsh+/U+gdbn16i0v3L+Hj4IOrtasiGd0r6JbXPpuZpVv5bopkZTFW8d+xO8eY13JejvHCdoWJSYxRLAfgUcojmvo1zTHepEQThu8armjWjms72N5lO0Xsdc/x/i7+3Hp0S7EcOws7+lXpR78q/RRb823IymKMQrlph6Yxcu9IBlQZQK0GtQA4EHmAvpv7cj/5PoNrDFYkJy0jjaZLmzK3xVy+rPulImu+jDFKGrN423uz+/pu/Jz9dMZ3Xd+Ft4O3YjlxT+J0PlOOlo7YmNvwIPlBgS/IG7RtEAtPL6RFyRaUdS+LClV+b0kIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIQxCCvKEEEKIAmbx4sWEhYVhYWGhM56amsry5cvp1i3vhRfbP9v+2seGTAvJc05+SktOY8snWziz6AwAn1z5BKfiTmz5ZAv2he2pPaK2YlnxkfHYe9tT98u6uc79G4sH85NDUQdM1FLclJ9SHqVw8peT3L94HwC3Mm5U7FURSwfLfN6ZyE/FGhRj26BttFvWDrtCmeWgCX8nsH3wdoo3LK5o1uWNl1nz/hpSE1OxsLdApXqmMERVcAvytny8hVqf14JquuOP/35MxJQIeh/prUjOhVUXeHfxuwSEBnD3z7vMKTcHTbqGvmf66v4shTACpylOOqU/WrQ8fvoYa7U1S9ouUSwnyD2IFedXMKreKJ3x5X8up7RbacVycmOttqaSVyVF11zQeoGi670OYxT/AViYWuRaWnjlwRXcbNwUywEIDQhl7cW1DKs1TGd8/eX1tCzZUtGspLQkrNXWOcbjnsRhYWaRyyvyLkOTganJP6W1R24f4WnGU2oUqYHaVF1gs4xVKPf90e+Z02KOTslkaEAoZdzKMGbfGMUK8tSmas7GnlVkrddhrJJGgCE1hjBw20BOx5ympndNACKiIlh4eiEzm85UNOvCvQs65ZlarZaL9y/yOPVx9lg5j3KKZhrD8j+Xs7L9Spr7N8/vrQghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhEFJQZ4QQghRwPTs2ZOmTZvi7u6uM/748WN69uypV0FezKmYVx8EUIA7cnZ9sYvYM7H0+KMHS5r+U65SvFFx9o3Zp2hB3sxiMxkSPQQbdxud8eQHycwsNpNRGaNe8EqRF/3/7J/fW/ifduf4HZaELMHMyozCVQsDcHjaYfZP2E/XHV3xquSVzzsUr2Sgc3uz2c1YHrqcGb4zcPDOLAaNj4rHvaw7bZe0VTRrx5AdVOxVkYYTG6K2VrZ8Jz/du3Av18+QZ0VP7l24p1hOwu0EvN7JzHEv646ZhRnVB1eXcjyRL6aHTNcpyDNRmeBm40a1wtVwsnJSLGdk3ZG0XdmWaw+v0cC3AQC7b+xm2Z/LWNV+lWI5kFkYtvD0Qnbf2M3dpLtotBqd+T3d9yial18MUfwHmUVk48LHsfK9lQCoUBEZH8nwXcNpF9hO7/VnHZmV/bi0W2km7J/AH7f+oEaRGgAcvn2YiKgIhtQYonfWs+r41GHxmcV83eBrIPPfpdFqmBoxlfq+9RXJiH4cTftV7Tl8+zC1fGqxLmwdXdd2ZcvVLUBmEdof3f/Ay07/+zVjZmUxVqFc9OPo7FK3Z9X0rkn042hFs7oEdeGXU78wudFkRdfNjTFLGvtV6YenrSffHfqOlRcyP8uBroGseG8FrUu1VjSr4eKGaLVanbGWv7VEpVKh1WpRqVRkjMpQNNMYzE3N8XP2y+9tCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEwUlBnhBCCFHAZP0h9/Nu376Ng4ODXmt339tdr9cXBJfXXea9Fe9RpHoRnZ+jexl34q7FKZql1WpzLZxKTUzFzFJuw15mitOU1y7rGh433LCbEa9l++DtBIQG0OqnVpiYmQCgSdewofcGtn26jZ7hPfN5h+JZK9qu0HmenpLO5r6bUdvolsqFrQnTO8vB24EPT37I9V3XuX/pPgBugW4Ub1Rc77Wf9/jvx1QbWO1fVY4HYGZhRmJsIk7FdUvBEqMTsz9vStBmaDE1N81+bmJmgrmtuWLrC/EmelToYZScVgGtWBe2jokHJrL6wmqszKwo51GOXV13Uc+3nqJZg7YNYuHphbQo2YKy7mV1CgALsnYr21G1UFWG19a9J5saMZVjd44pVjT4XZPveG/Ve7h/686TtCfUW1iPmMQYanjXYEKDCXqvP/3wdJ3nTlZOXLh3gQv3LmSPOVo6Mv/UfL6q+5XeeVmmNp5Kw8UNOR59nNSMVD7f9Tnn754n7kkcEb0iFMkYvms4WrSsDVvL0nNLabmsJaYqU6IGR5GhzaDz752ZsH8Cs5vPLlBZzzJGoZyfsx8rz6/kP3X+ozO+4vwK/F38Fc1K16Qz//h8dl3fxTte72Bjrlu6Pi1kmmJZxihpfNa7ge/ybuC7iq/7rBuDbhh0/fw0pMYQZh6eyezms6XEWAghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgjxrybNLEIIIUQBUbFiRVQqFSqVioYNG2Jm9s9lPCMjgxs3btC0aVODZMdHxQOZJUf5qfZ/amPlbKXXGkn3krBxt8kxnpqUqtgfl2//bDsAKpWKvSP36hQ1aTI0/H3kbzwreCqS9W8VMiMkv7cg3tCd43d0yvEgs1yr1ue1+LHyj/m4M5EbSwdLneflupQzaJ5KpaJE4xKUaFzCoDklQkpw5/idHEVyBV2JJiXY/cVuOq7vmP27S3mUwu7/7KZ4Y+WKBrVaLet7rMfUIrMkz5DFiUK8iv/3/rwf9D7vB72vePHU81qUbEGLki0MmgGw/M/lrGy/kub+zQ2eZUzht8IZU29MjvFmfs347tB3iuU4WDqws+tODkQe4GzsWRJTE6nkVYlGxRspsn5+FWqVdS/LlQFXmH10NnbmdiSmJtI2sC0fV/kYLzsvRTJ2Xd/FmrA1VC9SnVo+tXCd6srOrjspbF8YgHH1x9FnY58Cl/UsYxTKjQ0eS9jqMMJvhVPLuxYAEVER7L6xm5XvrdR7/Wf9ee9PKnlVAuBK3BWdOaXLNY1R0vi81IxU7ibdRaPV6Iz7OPgosn5Rx6JvdHz/zf0ZV38crtauiuQrre2KtjrP99zYw9a/tlLGvQxqE937tDVha4y5NSGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEMBgpyBNCCCEKiDZt2gBw+vRpQkJCsLW1zZ4zNzfH19eXdu3aKZanSdfwx9g/ODrrKKmJqZk5tuZU/aQq9UbXw1Rtqtf6lzdcfu1jA0IDAKjzRR29MgEKVS7Elc1XqPZJtcyB/+8WOPnzSYrUKKL3+gAxp2KAzKKhu+fuYmr+z8/K1NwUj/Ie1BxaU5Gsf6sK3Svk9xbEG7KwtyA+Mh7XUrqlEvFR8VjYWeTTrsSLtF7Q2qh5N/fd5NC3h7h38R4AbqXdqDmsJkXrvFl5SW6evZ74t/Bn57Cd3LtwD/cg9xzXqqzrSUHT+NvGLKy7kBlFZ+BVMbMsKeZ0DDYeNrz767uK5Tx/7jV0caIQL9O/cn9++/M3xu0bxzuF3qFLUBfCyobhaVtwS4bNTc3xc/bL720oLjE1EXNT8xzjalM1CU8TFM+r7VOb2j61FV83P6RlpNF0aVPmtpjLl3W/NFjOw5SHFLbLLKhztnLGWm2tUyDm5+xH9OPoApcFcP3hdXwdfY1SKNeudDuO9D7C9MPTWXd5HQCBroEc7X2Uil4VFcm4/vA6xRyLsbf7XkXWex2GLmm89ehW9nvg6oOr9NrQi4NRB3WO0Wq1qFQqMkZl6J2XF0vOLmFozaFvbUGeg6Xu/6zg3UDl7v+EEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOJtJQV5QgghRAExevRoAHx9fQkLC8PS0vKlxy9btozQ0FBsbGzylLflky1cWnOJRlMb4V3DG4CoQ1HsG7OP5AfJtJzTMk/rZlneZrnOc5VKhVar1XmeZVTGKL2yntVwYkOWNlvKvQv30KRrODLzCPcu3CPqYBQ99vVQJKP73u4ArO+5nqYzm2JhL+VgSklPSScjVbc0QX6+b4cyYWXY8MEGGn/bGO+a/3/OiIhi57CdlO1UNp93J/LT2SVnWd9zPYFtA6k2MLOcNPJAJIsbLqbNwjYEdQ7Sa/3nrycA+8btyzGmUqkUvZ4Yk31he/qe7cu5peeIOROD2kpNhZ4VKNuprN6Ftc8ydnGiEC8zuMZgBtcYzJUHV1h6dik/HPuBoTuHUt+3Pl3KdaFb+W6K5GRoMph+eDorz68kMj6S1IxUnfm44XGK5AAMqTGEmYdnMrv5bJ173YIuyD2IFedXMKqe7jl2+Z/LKe1WWrGcWUdm5TquQoWlmSV+zn7ULVoXUxNlzou3E26z4fKGXN8X00KmKZKhNlVzNvasImu9jLuNO9GJ0Xg7ZN6jDag6AGcr5+z5h08eYmOet++N+ZkF4P+9P9FDorML5cJWhzGr6Sw8bD0Uy3jWO4XeYUnbJQZZG/7597jbuAOG//cYo6Sx98beOFs5s+K9FfRY3wMzEzM2ddqEl52XYuWF+tKiffVB+WhB6wX5vQUhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhDA6lfb/2LvvsKrr///j98OSJUsBN0Nw494798SVW3M1NM0ytfGtTFtqSyst01yVOTL3Hrn3XrhFnJgKiot9fn/483w6ITg4cAQft+vy6pzX+3Vez8c5wgG8Lh79u4lGREREsg03Nzf2799PYGDgUz1+lPso2s1qR3DTYLP1k8tO8lfnv3jv5nuWiAnAmTVnWPPuGup9Uc+sjG/dh+uo90U9CjcsbLFZAFGno9g8ajNXDlwh/nY8ecvnpca7NfANyZjSAUmf+DvxrHl3DUfmHOHe9XsprmfVwqvsJik+iVVDV7Fnwh6SE5MxGo3YOthSsV9FGoxqgF0OdXM/r8YXH0/5V8tTbVA1s/Vt325j76S99D/a30rJRCSr2X5hO/2W9uPglYMkDUt69AMew7B1w/hl7y8MrjaYD9d9yAe1PuDsjbMsOLaAYXWGMbDKQIvMAWgzuw3rwtfh5eRFSZ+S2NvYm12f13GexWZlpsXHF9N2Tlu6hHShnn89ANaGr2Xm4Zn82f5PWhdrbZE5Ad8FcPXOVe4m3MXTyRO4X7bmbO+Mq4Mr/9z5h0DPQNb1WGcqZ3taa8+sJXRWKIGegRy7doxSPqU4e+MsRqOR8nnL83ePvy3xlAAYtGIQOexyMKrBKIud+V+tZrWinn893qz65kOvj985nnnH5rH2pbVZahaAzQgbIodEmgrl3Ea6sb/vfgI9n+7n4EdJNiZzKuoU/9z5h2Rjstm12n61033+f59PzpE5OdD3QIY9HwDvr7zZ2nsrwbmCH735KRy6coju87uzv+9+XL5wYc+reyiWu1iGzHpamfE6i4iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjIk1FLhYiISDaV3g5c2xy2ePh7pFj3CPDA1sE2XWf/14q3VtBiQgsK1SxkWgtqHIS9sz1LXl1i8fIkr8JehE4KteiZknFWv7Oas+vO0vyn5szvPp9m45tx6+It9vy8h/qj6ls7nvx/tg62NP2uKQ1GNiDqdBRw/3PN3tn+EY+U7C76TDRFWxZNsV40tChr/88y5TgPHPj1ACU7lkxRyJgUn8ThWYcp81IZi87LTNdPXufsurPc+ecOxmTzr/F1htWxUiqRzLHz4k7+OPQHs4/MJiYuhvYl2lvs7BmHZjCp5SSaF2nO8A3D6VyqM4W9ClPatzTbL2y3aEGeh6MHbYq3sdh5z4qWRVuyoOMCvtj8BXPD5uJk50Rp39Ks6b6GOv6We3/6ot4XTNw7kV9a/kJhr/sF2qeiTvHaktd4tfyr1ChUg05zOzFo5SDmdpibrlnvr32fIdWGMOKFEeQcmZO/OvyFj4sPXed1pUnhJpZ4OiaJyYlM2T2FNWfWUCFvBVwcXMyuf9v423TPWNhpYZrXK+WvZLG/q8yc9TBGMu7/BbP9wna6/NWFiJsRKX7eNhgMFivuzGzdQroxed/kDCtpfPHPFxnXdBwAJbxLcO3utQyZk52V+7kcBgyPtXfva3szOI2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiISOZQQZ6IiIg8VOUBldn46UZaTW1lKhpKjEtk0+ebqDSgkkVnRZ+OxtHDMcW6o7sjN87esMiM5MRkkpOSzUqTbl+5ze4Ju0m4k0DR0KJmBX3y7Dix+ARtfm2Df11/FvZaiF8tP7yCvHD3c+fQjEOU7lra2hGfa7Pbzn7kHhs7G1zzuBLYMPChRWmSvbkVdOPM2jN4BXmZrZ9Zcwb3gu4WnbWw10KCmgRh52P+o27crTgW9lqYZQvy9kzaw9J+S3HO7YxrHlcMhn8VpBhUkCfZ04nrJ5hxcAYzD88k/EY49QLqMbrBaNoWb4urg6vF5kTejiTENwQAVwdXbsbdBKBFkRZ8tO4ji80BmNpqqkXPe5Y0L9Kc5kWaZ+iMD9d9yF8d/jKV4wEEeQXxdcOvaTenHWfePMOXDb+k3Zx26Z519NpRZrabCYCdjR33Eu7h6uDKJ3U/odWsVvSr1C/dMx44fPUw5fOWB+BE1Amza49biJVelfNXzpQ5GTHLYDCkeJ0y6nXru6QvFfNVZGmXpeTNmTdD5mTm83kgo0saC7oV5Nvt39KwcENGNxjNO6vf4Yv6XxDiE4K9rXmZtlsOt3TNyq5aF21tuh2bGMuPu3+khHcJqhWoBtwvbzxy9QivV3zdSglFRERERERERERERERERERERERERERERERERCzvqQvyjEYjCQkJJCcnWzKPiEi2ZmNjg729vXmpicgzKnJfJGfWnmFMgTH4lvEF4MqBKyTFJxFQP8CsFKvjvI7pmpWvUj5Wvr2SNr+1wdX3fuHJ7Su3WT10Nfkr50/X2Q8sfmUxNg42tPy5JXC/LGlSpUkkxiaSM29Oto/ZTqeFnQhuFmyReWI596Lu4RnoCUAOtxzci7oHQKGahVjab6k1own3iywfxZhsJOpkFHt/2Uv1IdV54ZMXMiGZPCuqDa7GioEriNwfScHqBQE4v+U8+6ftp8l3TSw6y2g08rAOmZgLMeRwz2HRWZlp02ebqPd5PWq+W9PaUUQyTbFxxaiUvxL9K/WnU6lO+Lr6ZsicAm4FuHzrMoXcC1HYszCrTq+ifN7y7Lq4ixy2Wfd9wxr2XNrD0WtHASjpXZJyectZ9PzLty6TmJyYYj0xOZHI25EA5MuZj1txt9I9y8XehfikeADyuubldPRpSvqUBODa3WvpPh/gTPQZAjwCWNdjnUXOexwjN43E19WX3uV6m61P2TeFq3eu8m7Nd7PcLKPRSM+FPU2fr7GJsfRd2hcXe/OSt3kd56V71smok8ztMJcgr6B0n5WazHw+D2R0SeOal9Zw9sZZABr82gCA+r/WN9tjNBoxGAwkDUtK97zs6OO6H5tuv7zoZQZWHsin9T4137PuY87HnM/saCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiGeaJC/Lu3r3LzZs3uXXrFklJ+oU1EZEnZWtrS86cOXF3d8fZ2dnacURS5ejhSIl2JczW3Au6Z8isVlNaMbvNbMYWGotbQTcAYs7H4BXsRacFnSwy49yWczQb18x0/8CvBzAmGXnj5Bs4ujuy+t3VbP1qqwrynkGegZ5Eh0fjXsid3MVyc2TOEfJXzs+JxSdw9Hh0OZtkrFZTWz323hNLTrD09aUqyHvOVOpXCdc8rmz7Zhthc8IAyF08Ny/OfpFirYpZZMbP5X4GAxgMBn6t/ys2djama8YkI9Hh0QQ1ybgym4x2L/oeJduXtHYMkUx1fMBxgnM9+vuymYdmElo0FBcHl0fufZg2xdqwNnwtVQpU4Y3Kb9Btfjcm75vMuZvnGFR10FOdmZort68wZPUQ1p5Zyz93/sGI0ex6Vi2G+ufOP3Sa24n1Z9fj4egBwI3YG7wQ8AKz2s3C28XbInNeCHiB15a8xi8tfzGV7+27vI9+S/tRL6AeAIeuHCLAM+CpZ3yy4RMGVxtM1QJV2XxuM8W9i9MsuBmDVw3m0JVDzDs2j6oFqlrk+QT/EMzlwZfxcfEBoOPcjnzf5PsMK4ME+HnPz/zR7o8U6yW9S9JpYyeLFuRl1qweZXuY3e9WuptFzn2YKvmrcCrqVIYW5GXm88nMkkZ/D3+ANGcd+udQhudITbeQbrjlcLPa/CfxZ9if7H5ld4r1bqW7UXFSRaa0mmKFVCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIilvdEBXm3bt3iwoUL2Nvb4+HhgYuLCzY2NhgMhozKJyKSbRiNRpKTk7lz5w4xMTHcuHGDAgUKkDNnTmtHE3moJym9Si+vIC/6HuzLmdVnuHbsGnC/PCmwQaDFvs+4dfEWXsFepvvha8Mp3q44ju73C9bK9ijL/qn7LTLr3w78doA9E/YQHR5Nn2198PDzYPvY7XgEeFisGCq7K9urLFcOXMG/jj8136vJzJYz2TluJ8kJyTT6tpG148kTKFSzEPkq5rN2DLGC4m2KU7xN8Qw7v2jrogBE7o+kcOPCOLg6mK7ZOtji4e+RovQ1KynRvgSnV52mYt+K1o4ikmkepxwP4LUlr1GlQBUCHQKfas6oBqNMtzuW6kgh90Jsu7CNYK9gWhZt+VRnpqbnwp6cu3mOj2p/RN6ceTGQPf497Y3lb3Ar/hZHXj9Cce/77/VhV8PosaAHA1cMZGa7mRaZMzl0Mt3nd6fCxArY29oDkJicSP2A+kwOnQyAq4Mr3zT65qlnjNgwgr4V+/Jt42+5HX/7/lrdEdyOv83sI7MJzhXMt42+Tf+T4f6/EfzbspPLGFl/pEXOTk3k7UjyuuZNse7t4s3lW5ez5KypraZa7KxHeaPyGwxeNZjI25GE+ISYPg4fKO1bOt0zMvP5WKOksY5/HbP7t+JuMfPwTH7Z+wt7Lu9hQOUBFp03fP1whtUZho3Bxmz9ZuxN+i7ta3p/+qnFTxadm5Gc7JzYcn5Liq+TW85vwdFOBfIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIikn08dkHe3bt3uXDhAm5ubuTLl0+leCIiT8nFxQVvb28uXbrEhQsX8PPzw9nZ2dqxJBvy8/PD3t7+0RufEQaDgcKNClO4UeEMOd/O0Y7Ee4mm+xe2X6DhVw3NrsffjrfozF0/7WL9sPVUeasKmz7fhDHpfgmGo4cjO8buUEHeY6o2qJrpdmCDQPof68/lPZfxCvLCt3TGlTeI5Tl6ONJxXkdrxxArSYpP4s4/dzAmmxcCuRdyT/fZdT+uC4CHvwelOpbCzvGJuuCfeV5BXqz7aB0Xtl/AJ8QHW3tbs+tVBlaxUjIR6zNifPSmJ1CtYDWqFaz26I1PYfO5zWzqtYmyecpmyPnWsuLUCtZ0X2MqxwMo4V2C8c3G0+g3y5UZ53HNw+ruqzl27Rgnrp8AoGiuohTNXdS054WAF9I140FpXaDn/woXXRxcmNBiQrrOfVYUdC/IlvNbCPAMMFvfcm4L+XJatsQ4M2dllnZz2gHQe2Fv05rBYMBoNGIwGEgalmStaE/FGiWND2yM2MjkfZP5K+wv8uXMR9vibRnfbLzF50zeN5lVp1fxe9vfTZ/X68+u56X5L5HHNY/F52WGt6q+Rb+l/dh7eS+V81cGYMeFHUzeN5lhdYZZOZ2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiOU8dmvAzZs3sbe3VzmeiIgFGAwG8uXLx71797h586YK8uSpxMfH888//5CcnGy2XqhQIQAOHz6crvPvXr/LumHrOLvu7EMLjd6Nejdd5wPMaDaDdjPb4ejuCMDmUZup2Lcijh6OpgxTa02lf1j/dM/KUzYPB347QIORDYjYFMGdK3cIqPe/soao01HkzJcz3XP+becPO2k5qSXFWhdjy6gtpvV8FfOxasgqi856nnj4eeDh52HtGCLymK6fvM6i3os4v/W82fqDMplhSZYr8ijbo6zFznqW7J24FwdXByI2RBCxIcL8okEFeSJPatHxRTQNaoq9rT2Lji9Kc29o0VCLzS3oVjBFIVV2kGxMxt42ZTG3vY09ycbkhzwifYrlLkbRXPdL8TLi3ygNZM6/exoMhhSzMnr2K+Vf4a0Vb5GQlEC9gHoArA1fyzur32FwtcFZdlZmCX8z3NoRsrTI25FM2z+NyfsmExMXQ4cSHYhLimNBpwWU8C6RITMP9j3Ia0teo+yEsnzT6BtOXD/Bdzu+Y2j1oYx4YUSGzMxo79V8j0DPQL7b8R2/H/wduF9KOr31dLOiUhERERERERERERERERERERERERERERERERGRrO6xCvKMRiO3bt3Cw8ND5XgiIhZiMBhwc3Pjxo0b5MmTR++v8thOnjxJ79692bp1q9n6g6KhpKQki8yZ330+UaeiKNenHK6+rmREV8PpladJivtf3k1fbKJkh5KmgrzkxGSuH79ukVm1h9VmRtMZhM0J49blW5TpWYacef9XiHds/jEK1ShkkVkP3Ai/QZ5yeVKs2+awJeFOgkVnZTc7vt9BhVcrYOdox47vd6S5V8VQIs+2hT0XYmNnQ+clne+/71r468lor9G8ceINnHM7M9pzdJrnW6Lc1RreDH/T2hFEspXWs1oTOSQSHxcfWs9qneo+g8FA0jDLfG8NMLbJWN5b+x4/t/gZfw9/i51rbfUC6vHmijeZ2W4m+XLmA+BizEUGrRxE/cD6Fp3164Ff+WrrV5y8fhKAIrmKMLT6ULqX6W6xGUXGFXlkUV3Uu1HpnmM0Gum5sCc5bHMAEJsYS9+lfXGxdzHbN6/jvHTPemBo9aFcv3ud15e9TnxSPACOdo68W+Nd3q/1vsXmZPaszOLn4WftCBaVmSWNLWe2ZGPERpoHN2ds47E0CWqCrY0tE/ZMyJB5D3g6eTKn/Rz+b+3/8dqS17CzsWN51+UWf2/KbB1KdqBDyQ4AxMTFMPPQTL7a+hV7Lu+x6NctERERERERERERERERERERERERERERERERERFreqyCvISEBJKSknBxcXn0ZhEReWzOzs5cv36dhIQEHBwcrB1HsoiePXtiZ2fHkiVLyJs3b4aVK57bdI5em3uRp0zKgjdLMRqN/1nIsFH41/Hn1T2vcnrVaVzzuFKyfUmz63nK5iF/5fwWnekR4EHk/kg8/DzM1k+tOEXu4rktOiu72T5mOyFdQ7BztGP7mO2pbzSoIE/kWRe5P5JX97xK7mIZ877XeExjHHI6mG5nUK+LiGQjyR8nP/R2Rus4tyN3E+5S+PvCONs7Y29jb3bdEqVr1jCu6ThCZ4XiP9afgu4FATh/8zylfErxe9vfLTbn223f8tG6jxhQaQA16tUAYPO5zfRd2pdrd68xqNogi8wZUXcE7jncLXJWWnqU7WF2v1vpbhk+02AwMLrhaD6q8xFHrx7Fyd6JYK9gctjlyNKzMtNvB35jwp4JhEeHs63PNvw8/Bi7fSwBHgG0KtbK2vGeSGaWNC4/uZyBVQbSr2I/gnMFp/u8J/HDjh/4bsd3dA7pzJ5Lexi4YiB/tP2DMnnKZGoOS9sYsZHJ+ybzV9hf5MuZj7bF2zK+2XhrxxIRERERERERERERERERERERERERERERERERsZjHKshLTr7/y8I2NjYZGkZE5Hlja2sL/O99VuRx7N+/nz179lCsWLEMnZO7WG4S7yVm6IzM5l3cG+/i3g+9VuHVCmb3/2j+By1/aUnOvDmfel61t6uxrP8yEmMTMRqNXNx5kUMzD7F55GZCfwl96nOfB2+Gv/nQ2yKS9XiX8ObutbsZdn7ZHmX/d7tn2VT3ZUUr3175WPsaf9s4g5OIiCWMbTzW2hEyREH3gux9dS9rzqzh2LVjABT3Lk6DwAYWnfPDzh/4qflPvFTmJdNaaNFQSnqXZPiG4RYryOtUqhM+Lj4WOSstU1tNzfAZqYm8HUnUvShq+9Qmh10OjEZjhhWvZ+asjPbTrp8Ytn4Yb1V5i883fU6SMQkAD0cPxu4Ym+UK8jKzpHFz781M3juZChMrUNy7ON1Ld6dTqU4ZNu+BJr83Yfel3UxvPZ0XS7zIvYR7vL3ybapOrsqIuiN4p8Y7GZ7BkiJvRzJt/zQm75tMTFwMHUp0IC4pjgWdFlDCu4S144mIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIWNRjFeQ9kFV/eVFE5Fml91V5GiVKlODatWsZPqfZj81Y+95aag+rjU8pH2ztbc2u53DLke4ZBoMB/vtp8Ix8WkRsjEh3QWD5l8tj52THug/XkXA3gb+6/EXOfDlp8l0TSnUqZaGkIiLPnriYONPtBqMbsPqd1dT/oj4+IRnz9eSB+S/Nx/8Ff/xq++FV2Mti51pL5L7IR296Rr5uiliLn7sf9jb26Tpj7Zm1jNk+hqPXjgJQPHdx3qr6lsUL3v5bRpXV/R3+NwOWDWD7y9txy+FGw8INaVi4IQA3Y29S8seSTGg+gVp+tSwy7/Kty1QvWD3FevWC1bl867JFZmT3n9Gv371Oh7kdWBe+DoPBwMk3ThLoGUifRX3wdPTkm8bfZMlZmeWHnT8wqeUkWhdrzagto0zrFfNVZMiqIVZM9nQys6SxaoGqVC1QlbFNxjL7yGym7JvC2yvfJtmYzOrTqynoVpCcOZ6+nD41ScYkDvY7SL6c+QBwsnfipxY/0aJIC15e/HKWKshrObMlGyM20jy4OWMbj6VJUBNsbWyZsGeCtaOJiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiGSIJyrIExEREesbPXo077zzDl988QUhISHY25sXgri5uVlkjqOHI3Excfxa71ezdaPRiMFgYFjSsHTPMBqNLOy5ENsc98uSEmMTWdp3KfYu959TUlxSumdYS3JiMof+OEThxoUp3bU0CXcTiL8dj4uPi7WjZTlz2s0hX+V81Hy3ptn6li+3cGnXJdr/2d5KyUQkNaM8RpmVDBmNRn6tn3FfTx6wdbBl88jNLOqzCLf8bvjV8cO/rj9+dfzIFZzLYnMyS4912atMSyQjHH79cLoe/+OuH3lzxZu8WOJF3qzyJgDbL2yn2YxmjGk8hv6V+1siZgqxibHEJ8WbrbnlsMz38Zll7PaxvFL+lYfmdnd057UKr/Ht9m8tVpAX5BXEnCNz+L9a/2e2PvvIbIJzBVtkhtFotMg5z6pBKwdhb2PPuUHnKD6+uGm9Y8mOvL3qbb7BcqV1mTkrs4TfCKdcnnIp1nPY5uBOwh0rJMp6XBxc6F2uN73L9eb4teNM3jeZUVtG8d7a92gY2JBFnRdZdN7q7qsfut68SHMO9Ttk0VkZbfnJ5QysMpB+FftZ7D1PRERERERERERERERERERERERERERERERERORZpoI8ERGRLKZBgwYA1K9f32z9QdFQUpJlSuXmdZ2Hjb0Nbf9oi6uvKxge/ZgnVbZHWbP7pbuVTrGnzEtlLD84E9jY2bCk7xL6H71fqmLvbI+9s/0jHiUPE7ExgjrD66RYD2oaxLZvtlkhkYg8irWK3UJ/CQUg5mIMERsjiNgQwbZvtrHktSW45nXl7QtvWyWXiDwez9GeGB7zm86od6MsMvOLTV8wpvEYBlQeYFobWGUgNQrW4IvNX1i0IO9O/B3eXfMuc47M4fq96ymuJw3LWuXQB64cYHSD0aleb1S4EV9v/dpi80bUHUHHuR3ZGLGRGgVrALDl/BbWhq9lzotzLDIj+eNki5zzrFp1ehUru62kgFsBs/XgXMFE3IjIsrMyS4BHAPsj9+Pn4We2vuLUCornLp7KoyQ1RXMX5cuGXzKy/kgWn1jMlH1TLD7DaDSy5/Iezt44iwEDAZ4BlMtTDoPBQG7n3Bafl5E2997M5L2TqTCxAsW9i9O9dHc6lepk7VgiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhlGBXkiIiJZzLp16zJlzj+H/+G1fa+Ru2jG/dJ4q6mtMuzsZ0H+yvmJ3BeJh5+HtaNkafG347F1sE2xbmtvS1xMnBUSicij+Nfxt+p8J08nnHM54+jpiKOHIzZ2Nrh4u1g1U3rMaTeHfJXzUfPdmmbrW77cwqVdl2j/Z3srJROxrLGNx2b6zBuxN2gS1CTFeqPCjXh3zbsWnfXO6ndYd3YdPzX/ie7zuzO+2Xgu3rrIz3t+ZlT9URadlRmu3L6CvW3qBdB2NnZcvXvVYvPalWjHjpd3MGb7GBYcXwBA8dzF2fnyTsrlLWexOdnZnYQ7ONs7p1iPuhdFDrscWXZWZnm72tv0X9af2MRYjEYjOy/uZOahmYzcPJJfQn+xdrwsy9bGltbFWtO6WGuLnrsufB19FvUh4mYERqMRAIPBQIBHAFNaTaG2X22LzstoVQtUpWqBqoxtMpbZR2YzZd8U3l75NsnGZFafXk1Bt4LkzJHT2jFFRERERERERERERERERERERERERERERERERCxGBXmSYXr27Mn06dPx8/Pj7NmzGTLD39+fiIgIevTowbRp0zJkhojIs6ZOnTqZMidfxXzEnI/J0IK87K7S65VYNXgVMRdiyFshLw4uDmbXfUv7WilZ1uIT4sOR2UeoM8z8Y//wrMN4l/C2UioRScuVg1cee68l3wvX/t9azq4/S+S+SHIXz41fHT9qvFcDv9p+OHk6WWxOZovYGEGd4Sm//gc1DWLbN9uskEgkY/Qo2yPTZ4YWDWX+0fkMrTHUbH3h8YW0KNLCorMWn1jMr21+pa5/XXot7EUtv1oEeQXh5+7HjEMz6Fq6q0XnZbT8bvk5/M9hgryCHnr94JWD5HXNa9GZFfJV4Pe2v1v0zOdJrUK1+PXAr3xa71MADBhINibz5ZYvecH/hSw7K7O8XP5lnOyc+HDdh9xNuEuXv7qQL2c+vmvyHZ1KdbJ2PPmXU1GnaDGzBVXyV2FM4zEUy10MI0bCrobx/Y7vaTajGQf7HSTQM9DaUZ+Yi4MLvcv1pne53hy/dpzJ+yYzasso3lv7Hg0DG7Ko8yJrRxQRERERERERERERERERERERERERERERERERsQgV5D3D7ty5w2+//caiRYs4cOAA169fx2g04ubmhr+/PyEhIVSrVo0mTZpQsGBBa8fNks6ePUtAQMBj7//4448ZPnx4xgUSEXlMN27cYPLkyRw9ehSAkiVL0rt3b9zd3S02o/IblVnx5gqqD62OT4gPtva2ZtdV7vZoczvNBWD5wOWmNYPBgNFoxGAwMCxpmLWiZSm1P6rNnLZziD4djX89fwDC14ZzeOZh2v/Z3rrhROShJpSdYPZ+lxZLvhduHrUZF28X6nxch+Jti5OrSC6LnW1N8bfjsXWwTbFua29LXEycFRKJZK7YxFjik+LN1txyuFnk7BLeJfh80+esj1hPtQLVANh+YTtbzm9hcLXBfL/je9PegVUGpmtW1L0oUxmTWw43ou5FAVCzUE36Le2XrrOtoVlQMz5a9xFNgprgaOdodu1ewj0+Xv+xxUsGk43JnIo6xT93/iHZmGx2rbZfbYvOyo6+bPgl9X+tz+7Lu4lPiuedNe9w5J8jRN2LYkvvLVl2VmbqWrorXUt35W7CXW7H38bHxcfakeQhxm4fS9UCVVn70lqz9WK5i9GmWBsa/NaAMdvG8EOzH6yU0DKK5i7Klw2/ZGT9kSw+sZgp+6ZYO5KIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiMWoIO8ZtW3bNjp16sS5c+dSXLt27RrXrl1j9+7dTJ06FV9fXyIjI62QUqxl+PDhjBgxAgCj0WjlNCKS2Xbv3k3jxo1xcnKicuXKAHz77bd8/vnnrFq1ivLly1tkztyO98vdFvZeaFp7nsrdav5fTZy8nNJ1xpvhb1oozfOtaMuidFzQkc1fbCZsbhh2Tnb4lval+5ru+Nfxt3Y8EXmIf7//Re6LZNWQVVQfWp2C1e4Xe5/fdp5t32yj4ZcNLTr3tX2vEbEhgrPrz7Ltm23YOtjiV8cP/7r++Nf1z7KFeT4hPhyZfYQ6w+qYrR+edRjvEt5WSiWSse7E3+HdNe8y58gcrt+7nuJ60rAki8yZvG8ynk6ehF0NI+xqmGndw9GDyfsmm+4bMKS7IC/QM5Dw6HAKuReiWO5izDkyh8r5K7P4xGI8HD3SdbY1fFj7Q+ZNnEeRH4owoPIAiuYqCsCxa8cYv2s8ScYkPqj1gcXmbb+wnS5/dSHiZkSKfwsxGAwW+5jIzkr5lOLEgBOM2zmOnA45uR1/m7bF29K/Un/y5sybZWdZg7O9M872ztaOIalYf3Y9I+uPfOg1g8HAW1Xe4v2172dyqoxja2NL62KtaV2stbWjiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhYjMH4GO1asbGxhIeHExAQgKOjY2bkeq6dOHGCihUrcuvWLQBCQ0N58cUXKVKkCA4ODly7do0DBw6wevVq1q1bh5eX13NbkOfv709ERAQ9evRg2rRpT/z4s2fPEhAQAECrVq347LPP0tzv4+ODj4/P00S1KBXkZR96f5WnUatWLYKCgpg0aRJ2dve7bhMTE3n55Zc5c+YMGzdutMicGxE30rzu4edhkTmZ4fii44+9t2ho0QxMIiLy/JlUeRJ1h9cluFmw2frJZSdZ99E6Xt3zaobNjjwQyfYx2zk04xDGZGOWLXc9vvg4c9rOIaRLCP71/AEIXxvO4ZmHaf9ne4q1LmbdgCIZoP/S/qw7u45PX/iU7vO7M77ZeC7eusjPe35mVP1RdC3d1aLzrt29BkBu59wWPfffxmwbg62NLQOrDGTNmTW0nNkSo9FIfFI8YxqP4c2qWa9cOeJGBP2W9mPl6ZWmf58wGAw0LtyY8c3GE+AZYLFZZSeUpUiuIoyoO4K8OfNiwGB23d3R3WKzsqOEpASazGjChOYTCM4V/OgHZJFZmaHe9HqPte/vHn9ncBJ5XG4j3TjY7yD+Hv4PvR4eHU7pCaW59f6tzA0mIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKPzc7aASSlDz74wFSON3XqVHr27JliT8OGDRkyZAhXr15lzpw5mZwwe/Lw8KBUqVLWjiEi8ki7d+82K8cDsLOz45133qFixYoWm5OVCvAeZVbrWWb3DQaDWcGowfC/cg1LlydFnY5i+9jtXDt6v3TFu4Q3Vd6sgldhL4vOeR5c2nPpf69jSW/ylstr5UQi8jj+OfQPHgEeKdY9Ajy4GnbVorOMRiOR+yI5u/4sZ9ef5dzmc8TFxOFb2he/On4WnZWZirYsSscFHdn8xWbC5oZh52SHb2lfuq/pjn8df2vHE8kQi08s5tc2v1LXvy69Fvaill8tgryC8HP3Y8ahGRYpyLsRe4MP1n7A7COziY6NBsDT0ZNOpTrxWb3P8HD0SPeMfxtUbZDpdoPABhzrf4w9l/cQ7BVMiG+IRWdlFj8PP5Z1XUb0vWhORZ3CiJFgr2A8nTwtPutk1EnmdphLkFeQxc9+Htjb2nPwysFsNyszrD+7Hj8PP5oHN8fext7aceQx3I6/jbO9c6rXne2duZtwNxMTiYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIjIk1JB3jMmKSmJpUuXAlCxYsWHluP9m7e3N/3798+EZCIi8qxwc3Pj3LlzFCtWzGz9/Pnz5MyZ06Kzsku528fJH5tun1lzhjXvrqHeF/UoWK0gAOe3nWfdh+uo90U9i849tfIUs0JnkadsHgrW+P+ztpznx5I/0nlxZwo3LGzRednVnX/uMLfTXM6uP4ujhyMAsTdiCXghgHaz2uHi7WLlhCKSltzFc7N55GZCfwnF1sEWgKT4JDaP3Ezu4rktOutLry+Jvx2Pb5n7hXjlXymPXy0/03tHVlakeRGKNC9i7RgimSbqXhSBnoEAuOVwI+peFAA1C9Wk39J+Fjm/2uRqXIy5SNeQrhT3Lg5A2NUwpu2fxtrwtWztvdUiRW9/h//NgGUD2P7ydtxyuJnW/Tz88HD0oPqU6kxoPoFafrXSPctaPJ08qZS/UobOqJK/CqeiTqkgLx26hXRj8r7JjGowKlvNymijG4xm6v6p/Bn2J11DutK7XG9K+eh/svGsC7saRuTtyIdeu3b3WianERERERERERERERERERERERERERERERERERGRJ5WlC/KSEpKwtbdN9X5WdPXqVe7duwdAUJBlfuH37NmzfPfdd6xatYpz586RlJRE/vz5qVevHgMGDCAkJOSRZ9y6dYuJEyeydOlSwsLCiIqKwtHRkcKFC1OzZk06depEjRo1zB7Ts2dPpk+fjp+fH2fPnk1x5p07d1iyZAmrV69m9+7dhIeHc/fuXTw8PChRogQtW7akb9++uLq6WuR1sKT4+HhWrlzJypUr2bFjB6dOneL27du4ubkRFBRE06ZNGTBgALlzP7rwIy4ujunTp7Nw4UL279/PtWvXsLOzw9/fn2rVqtG+fXsaNWqEwWBg2rRp9OrVy+zxBoMhxZnh4eH4+/ubrV29epXvvvuOpUuXEh4eTmxsLHny5KFWrVq89tpr1KxZM9WM/v7+RERE0KNHD6ZNm8aePXv44Ycf2LBhA5cuXSI+Ph6j0Wjaf+PGDcaPH8+SJUs4duwYt2/fxsPDA29vb4oWLUqjRo1o27Ytvr6+j3x9RCSljh070qdPH77++muqV68OwJYtWxg6dCidO3e22JzsWu624q0VtJjQgkI1C5nWghoHYe9sz5JXl9D/qOWKZ9e+t5aqg6rSYFQDs/U1761hzbtrsuxrmNmWv7Gc+FvxvH7kdbyLewNwNewqC3osYMXAFbSb2c7KCUUkLS0mtGBmy5l8W+BbfEvf//7vysErGAwGOi+23NctgDa/t8Gvlh853HJY9NxnxaU9l/5XWlvSm7zl8lo5kUjGCfQMJDw6nELuhSiWuxhzjsyhcv7KLD6xGA9Hj3Sf/8mGT3CwdeD0wNP4upr/bPrJC5/Q6LdGfLLhE8Y0GZPuWWO3j+WV8q+YleM94O7ozmsVXuPb7d9m6YK8zPBG5TcYvGowkbcjCfEJwd7W3ux6ad/SVkqWdSQmJzJl9xTWnFlDhbwVcHEwL5r+tvG3WXJWRhtaYyhDawxl2/ltTNk3hRpTalA0V1F6l+tNl5AuD/3cFuur/2t9s3+vfMBgMGA0Gh/6b6oiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIvLsyJIFecmJyQAcm3eMsLlh3Iu+h5OnEyVeLEHxdsUBsLGzsWbEp+bg4GC6ffTo0XSf9+uvv/Lqq68SFxdntn7q1ClOnTrF5MmT+fTTT3n//fdTPWPNmjV07tyZa9euma0nJCSwf/9+9u/fz7hx4x76C4dpad68ORs2bEixfu3aNTZu3MjGjRv58ccfWbZsGcWKFXuiszPaq6++yvTp01OsR0VFsXPnTnbu3Mm4ceNYuHBhiuLAf9u/fz9t27YlPDzcbD0+Pp6wsDDCwsKYPHnyQwvvnsSqVato3749MTExZusRERFERETw+++/079/f77//ntsbNL+3JkwYQJvvPEGiYmJD71+9OhRGjRowKVLl8zWr127xrVr1zh69CgLFiwgKSmJAQMGPPVzEnmeff311xgMBl566SXT56K9vT39+vVj1KhRFpuTXcvdok9H4+jhmGLd0d2RG2dvWHTW1aNXeXHOiynWy/Uux/ax2y06Kzs7teIU3dd0N5XjAXiX8KbZ+Gb81ug3KyYTkceRv3J+Bp4ZyKEZh7h27P7PFCU7liSkSwgOLg6PePSTKdK8iEXPe1bc+ecOczvN5ez6s6avYbE3Ygl4IYB2s9rh4u3yiBNEsp5eZXtx4MoB6vjX4b2a79FyZkvG7RxHQnIC3zZKf7HWgmML+LnFzynK8QDyuObhy4Zf0ndJX4sU5B24coDRDUaner1R4UZ8vfXrdM/J7trNuV+K3Hthb9Pav4uukoYlWStalnH46mHK5y0PwImoE2bXDFi2LCwzZ2WWagWrUa1gNb5r+h1/HvmT8bvGM2TVEC4NvqSSvGdM+Jvhj94kIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLPtCxXkGdMNnJ61WkW9l7InSt3zK6FzQ3DxdeFVlNaEdQkCINN1vtlSy8vL/z8/IiIiODAgQOMHj2aoUOHPrK07GGWLl1Kz549MRqNuLq6MnjwYBo0aICdnR1bt25l5MiRXLt2jf/7v//Dw8ODfv36pThj3bp1NG3alMTERGxtbenevTutWrWiUKFCxMbGEhYWxvLly1m8ePET50tMTCQkJITQ0FAqVqxIvnz5MBqNREREMH/+fObMmUN4eDitW7dm//79ODqmLDOylsTERAIDA2nTpg2VK1emUKFC2NnZERERwZo1a5gyZQrXr1+nTZs2HD58GB8fnxRnHD16lFq1anH79m0A2rRpQ6dOnQgMDCQpKYkTJ06watUq5s+fb3pM69atqVixIj/++CM//fQTAIcOHUpxdv78+U239+/fT8uWLYmPj8fe3p4BAwYQGhqKi4sL+/btY9SoUYSHhzN+/HhcXFwYPTr1X9rftWsXv//+OwULFmTIkCFUrFiRxMRENm3aZNrTvXt3Ll26hL29Pa+88gpNmzYlT548JCcnc+HCBbZv3272nETkyTk4OPDdd98xcuRITp8+DUDhwoVxdna26JzsWu6Wr1I+Vr69kja/tcHV1xWA21dus3roavJXzv+IRz8ZF28XIvdHkis4l9l65P5IXHxUZvS4jMlGbO1tU6zb2NtgTH6ygl4RsQ4HFwcqvFrB2jGyrOVvLCf+VjyvH3ndVBZ6NewqC3osYMXAFbSb2c7KCUUsb1C1QabbDQIbcKz/MfZc3kOQVxClfUun+/zLty9T0qdkqtdL+ZQi8nZkuucAXLl9BXtb+1Sv29nYcfXuVYvMys5UePX0zkSfIcAjgHU91mWrWday9/JeNkRs4Oi1o5TyKYW9Teqf32Idfh5+1o4gIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLpZDAajY9sFYmNjSU8PJyAgACrlpQlJyZzetVpZobOxJiUemyDrYHOizpTuFFhbOyevFjO2r755huGDBliuu/v709oaCjVq1encuXKBAQEPPKMhIQE/P39uXTpEq6urmzatImyZcua7YmIiKBatWpcvnwZZ2dnIiIiyJ07t+l6bGwshQsX5tKlSzg7O7N06VLq1q370Hnnz5+nYMGCZms9e/Zk+vTp+Pn5cfbs2RSPOXnyJMHBwak+hzVr1tC4cWOSk5P55Zdf6NOnT4o9/v7+RERE0KNHD6ZNm5bqWak5e/as6fVs1aoVn332Wap7PT09TcVzp0+fJjAwEIPh4SWMhw4donr16ty+fZsPP/yQTz/9NMWeChUqsHfvXmxsbJgxYwadOnV66FnXr1/H2dkZJycn09rw4cMZMWIEAI/6FK5cuTK7du3C1taWZcuW0ahRI7Pr0dHR1KxZk7CwMGxsbDh48CAlS5qXBDx4nQFCQkLYuHEjHh4eKWadOXOGwoULA/DDDz8wYMCAh2YyGo3cuHEDT0/PNLM/D56V91eRhxlTcAyNvm1Eyfbm7wlH5hxh1ZBVDDo3KJVHPtuiTkUxu81srp+4jltBNwBizsfgFexFpwWd8ArystisDZ9sYPuY7dR4rwYFq9//Onl+y3m2jN5C1berUuejOhablZ3NajWL2BuxtJvZjpz5cgIQczGGeV3n4eTpRMf5Ha2cUET+6/ii44+9t2ho0QxMkj2Mch9F9zXdyV/JvMj14s6L/NboN9678Z6VkolkXfm/zc/sF2dTs1DNh17fFLGJjnM7cmnwpXTPKvx9Yb5p9A2ti7V+6PV5R+cxZNUQzrx5Jt2zRB7G9hNbLg++jI/L/f+BQ8e5Hfm+yff4uvpm6VmZ6dKtS0zbP41p+6cRExdDt9Ld6F2uNyW8S1g7mqRhxakVuDq4mt7rx+8cz6S9kyjhXYLxzcbj6aR/mxQREREREREREREREREREREREREREREREREReVbZWTvAk1rYe2Ga5XgAxiQji/osYtD5rFncM2jQIMLCwpgyZQpwv8Tt+++/5/vvvwfA19eXunXr0rVrV1q0aPHQkrb58+dz6dL9X+L+8MMPU5TjAfj5+fHVV1/RrVs37t69y9SpUxk6dKjp+q+//mo644svvki1HA9IUY73ONIqxwNo0KABoaGhLFiwgAULFjy0IM+SFi5cyMKFC1O9/u8SvgclcKkJCQnh5ZdfZuzYsSxYsCBFQd6qVavYu3cvAAMHDky1HA8gV65cj/kMUtq5cye7du0C4JVXXklRjgf3i/8mTpxIzZo1SU5O5scff2T8+PGpnjl+/PiHluMBREZGmm7Xrl071TMMBoPK8USeUNu2bZk2bRpubm60bds2zb3z5s2zyMzyr5RnyatLiD4T/dByt6zKK8iLvgf7cmb1Ga4duwZA7uK5CWyQevHp06r9UW0ccjqw7ZttrH1/LQA58+WkzvA6VBlYxaKzsrOm45oyK3QWY/3H4l7QHYCb527iE+JD29/T/nwQEeuY1XrWY+0zGAwMSxqWwWmyPmOyEVt72xTrNvY2GJMf2XkvkiUNXD6QIK8gBlYZaLY+buc4TkWdYmyTsek6v3Hhxnzw9wes7r4aB1sHs2txiXF8tO4jmgQ1SdeMB5oFNTOd52hnXox+L+EeH6//mBZFWlhkVnaz6PgimgY1xd7WnkXHF6W5N7RoaCalynr++z9XWHZyGSPrj8zyszJLsxnNWHd2HY0KN+Krhl/RvEhz7Gyy3D+rP5eGrh7K6AajATh05RCDVw3m7Wpvs+7sOt5e9TZTW021ckIREREREREREREREREREREREREREREREREREUlNlvlNvqSEJI7OO8qdK3cea//tyNscnXeUYm2KPbRI4FlmY2PD5MmT6dSpE99++y1r1qwhMTHRdP3KlSvMnj2b2bNnU7FiRWbNmpWisG3NmjXA/cKJ3r17pzqrffv29O/fn5s3b7JmzRqzgrwlS5YA4OLiwiuvvGLJp/hQV69e5caNG8TFxZnWvL29AThw4ECGz0+P6OhooqKiiI2NNf0i8IMSubCwMBISErC3tzftf/DaArz11lsZluvBxwGQZsFgjRo1KF68OEePHjV7zH8VLFiQWrVqpXo9b968ptvTpk3j22+/fcLEIpIad3d3U3mbu7t7pszMzuVuBoOBwo0KU7hR2oWnlphTbVA1qg2qRtyt+1/fcuTMkaEzsyP3gu68uvdVzqz5X6mhdwlvAusHWjmZiKTm4+SPrR0hWwmoF8CKN1fQbmY7cubLCUDMxRhWDlqp90LJtv46+heLOqUsRKtesDqjNo9Kd0HeJy98QsWJFQn+IZj+lfpTLHcxjEYjR68d5cddPxKXFMdvbX5L14wHPqz9IfMmzqPID0UYUHkARXMVBeDYtWOM3zWeJGMSH9T6wCKzspvWs1oTOSQSHxcfWs9qneo+g8FA0rCkzAsmz5UVp1aQN2dezt08x4gNIxixYcRD9+19bW8mJ5NHCY8Op4R3CeD+15UWRVrwRf0v2Ht5L81mNLNyOhERERERERERERERERERERERERERERERERERSUuWKciztbfl6NyjT/SYsLlhlOxQMoMSZbyGDRvSsGFDYmJi2LJlC7t27WL37t1s3LiRmzdvArB7925q1arFnj17zMrJDh8+DEBAQICpZO5hHBwcKFeuHOvXrzc95oF9+/YBUKFCBZydnS399ADYsmUL33//PWvWrCEqKirVfdeuXcuQ+f/Wo0cPpk2b9tj7Dx06xJgxY1i+fDmRkZGp7ktOTiY6OhofHx/T2oPXtlChQvj5+T115kd58Hfq4OBA2bJl09xbpUoVjh49ysmTJ4mPj8fBwSHFntKlS6d5RkBAALVq1WLTpk2MGTOGlStX0q5dO+rWrUvVqlUz7ONI5HkwderUh97OSNmt3G1Gsxm0m9kOR3dHADaP2kzFvhVx9Lh//+71u0ytNZX+Yf0zZH5Wfu2s5fy289y7fo8iLYrcLzVsWJhbl26x/uP1JNxNoFjrYjT9oSl2ObLMt7QikgkO/HaAPRP2EB0eTZ9tffDw82D72O14BHhQrFUxa8d7Kk3HNWVW6CzG+o/FveD9otyb52/iU8qHtr+3tXI6kYxx/e513B1TFkO75XDj2t30/xtBAbcCbOuzjdeXvc77a983ld0bDAYaBjZkXLNxFHQvmO45AL6uvmztvZV+S/ulmNW4cGPGNxuPr6uvRWZlN8kfJz/0tjwZg8GAAYP52n/uZ8VZmeXjOir+zaocbB24m3AXgDVn1vBSmZcA8HLyIiYuxprRRERERERERERERERERERERERERERERERERETkEbJUm8i96HtPtD82OjaDkmQuNzc3mjZtStOmTQGIi4vjjz/+YPDgwURHR3P58mU++ugjfvnlF9NjHpTN/buULTV58uQxe8wDD0rp/l28Z0nDhw9nxIgRj7X33r0n+7vPaJMnT6Zv374kJiY+1v7/5s/o1/aBB3+nXl5e2Nml/en+4OPAaDQSHR2Nr2/KX8739PR85MyZM2fSvn17tm3bRlhYGGFhYXz66afY29tTtWpVunTpQs+ePXF0dHyKZyQiAOHh4SQmJhIcHGy2fvLkSezt7fH390/X+Qn3Ejiz+gz+L/ibSt0e/DcuJo6z689SuHHhLFdKdnrlaZLikkz3N32xiZIdSpoK8pITk7l+/LpFZ969fpd1w9Zxdt1Z7vxzB2Oy0ez6u1HvWnRedrPxk4341fWjSIsiAFw5dIXFryymTI8yeBf3ZutXW8mZLyd1h9e1blARSWHH9zuo8GoF7Bzt2PH9jjT3VhlYxWJzd/20i/XD1lPlrSps+nwTxqT777uOHo7sGLsjyxbkuRd059W9r3JmzRmuHbv/s4R3cW8CGwRaOZlIxgnyCmLFqRUMqDzAbH35yeUEelrmYz/AM4DlXZcTfS+ak1EnTXO9nLwscv6/+Xn4sazrMqLvRXMq6hRGjAR7BePp9Oifs0XSy2g00nNhT3LY3v+5LjYxlr5L++Ji72K2b17HeVlqVmb5uO6TFeRtObeFivkqksNOJenWVrNQTd5e+TY1CtZg58WdzH5xNgAnrp+ggFsBK6cTEREREREREREREREREREREREREREREREREZG0ZKlmGydPpyfa7+iZPQu4cuTIQa9evciXLx9NmjQBYN68eUycOBEbGxuzvQaDwRoRH2nt2rWmcrzAwECGDBlCzZo1KVSoEC4uLqYyt2HDhvHpp59aM2oKx44dM5Xj+fj4MHToUOrVq4e/vz85c+bE3t4egClTptCnTx/g/i8HW5OlPg5sbW0fuSd//vxs3bqVtWvXMm/ePDZs2EBYWBgJCQls2rSJTZs28fXXX7Ns2TKKFClikVwiz5uePXvSu3fvFAV5O3bs4JdffmH9+vXpOn/PxD2cWHSCoqFFU1zL4ZaDHd/v4Ob5m1TuXzldczJbivfiTHhrnt99PlGnoijXpxyuvq7wbH5ZfmZF7o/khU9fMN0/POsw+SvnJ3RSKABuBd1Y//F6FeSJPIO2j9lOSNcQ7Bzt2D5me+obDZYtyNv5w05aTmpJsdbF2DJqi2k9X8V8rBqyymJzrMFgMFC4YWEKNyxs7SgimeLtam8zYNkArt65Sr2AegCsDV/LN9u+YWzjsRad5enkSeX8mfO9raeTJ5XyV8qUWdnNwOUDCfIKYmCVgWbr43aO41TUKcY2GWudYFlAj7I9zO53K90tW8x6VjWd0ZT9ffdbrMxTnt64ZuN4fenrzD06l5+a/0R+t/zA/bLVJkFNrJxORERERERERERERERERERERERERERERERERETSkmUK8pISkijxYgnC5oY99mNKvFiCpIQkbO0fXeqVFTVu3JiCBQty/vx5oqOjuX79Ot7e3gB4eXkBcOXKlUeeExkZafaYB3Lnzs2FCxe4fPmyhZPDpEmTAPD09GT79u2m3P8VFRVl8dnpNW3aNBITE7G1tWXDhg0UK1bsofvSyp47d26ADHlt/+3B3+n169dJTEw0FQ8+zIOPA4PBgKenZ7pn169fn/r165vmr1mzhokTJ/L3339z+vRpOnbsyL59+9I9R+R5tG/fPmrUqJFivWrVqgwYMCDd5x+acYjaH9VO9XrVt6qy4ZMNWa4gzxrObTpHr829yFMmj7WjZEn3ou/h4utiuh+xIYKgpkGm+/kr5SfmfIw1oonII7wZ/uZDb2e0G+E3yFMu5XuubQ5bEu4kZFoOSzm/7Tz3rt+jSIv/FUsf+PUA6z9eT/ydeIq1LkbTH5pilyPL/Ggv8th6l+tNXGIcn2/6nE833i/O9/fw56fmP/FSmZesnE6s4a+jf7Go06IU69ULVmfU5lEqyEvD1FZTs+WsZ5UxM9rY5bEUci/Eki5LUqyPaTLGCmlERERERERERERERERERERERERERERERERERORJ2Fg7wOOytbeleLviZiUpaXHN40rxtsWzbTneA/ny5TPdNhgMptulSpUCIDw8nKtXr6b6+ISEBFNJ2YPHPFC+fHkAdu/ezd27dy2WGeDIkSMAvPDCC6mW4z2Y/ax5kL1MmTKpluNB2tkfvLbnzp0jIiLiiTP8++86LQ/+TuPj49m/f3+ae3fu3AlAcHAwDg4OT5wpLbly5aJjx46sXbuW0NBQAPbv38/JkyctOkfkeWEwGLh161aK9Zs3b5KUlJTu86NORqVZ6OZb2peok89egemjGAwG+O/b5+O9nT613MVyk3gvMWOHZGOuvq7cCL8BQFJ8Epf3XqZA1QKm63G34rCxzzLfzopIJvAI8CByf2SK9VMrTpG7eG4rJEqfjZ9s5J8j/5juXzl0hUV9FhHQIICa79XkxOITbB652YoJRTJWv0r9uPD2Ba4MuULM+zGcefOMyvGeY9fvXsfd0T3FulsON67dvWaFRCKSFSQlJ/FX2F98tvEzPtv4GfOPzicpOf3/diIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhkryzWKtJrSCoNt2m02BlsDoZNDMymR9dy9e5ewsDAA3NzcyJUrl+lagwYNADAajUydOjXVM+bOncvNmzfNHvNAy5YtTXMmTpxo0eyJiffLgu7cuZPqnn379rFjxw6LzrWEx8l++fJlFi1alOr1B68twJgxY544g6Ojo+l2XFxcqvv+/Xc6ZcqUVPdt27bN9LH0348DS6tfv77p9rVr+gV2kadRu3ZtRo4caVaGl5SUxMiRI6lZs2a6z09OTObO1dTf4+5cvUNyYnK652Q2o9HIwp4Lmd12NrPbziYxNpGlfZea7i/qnfr79tNq9mMz/v7gb85uOMvd63eJi4kz+yNpC2oWxJr31hCxKYI176/B3tkev1p+putXDl7Bq7CXFROKyONITkpm7+S9/NXlL35t8CvT6003+2NJ1d6uxrL+yzg8+zBGo5GLOy+y8fONrH1/LTXeqWHRWZkhcn8kgfUDTfcPzzpM/ir5CZ0USrW3q9Hk+yYcmXPEiglFMoe3izeuDq7WjiFWFuQVxIpTK1KsLz+5nEDPwIc8QkSed6eiTlF8fHFeWvAS847OY97ReXSb342SP5bkdNRpa8cTEREREREREREREREREREREREREREREREREZE02Fk7wJOwsbMhqEkQnRd1ZlGfRdyOvJ1ij2seV0InhxLUJAiDTdpFes+i27dvU79+fT766COaNWuGjc3DOwyTk5N54403uHXrFgChoaEYDP97vq1btyZfvnxcunSJzz//nKZNmxISEmJ2xvnz5xkyZAgAzs7O9OrVy+x6t27dGD58OBcvXuSDDz6gXLly1KlT56F5Lly4QIECBR77eQYHB3Ps2DE2b97MqVOnCAoKMrt+9epVunfv/tjnZabg4GBWrFjByZMn2bp1K9WrVze7fvfuXbp06cK9e/dSPaNBgwZUqFCBPXv28MMPP1C1alU6der00L3Xr1/H2dkZJycn01revHlNt0+fPk2JEiUe+tjKlStTsWJFdu/ezaRJk2jXrp1ZQR3AzZs3ee211wCwsbGhX79+ab8Aadi/fz8AZcuWfeh1o9HImjVrADAYDPj7+z/1LJHn2ejRo6lduzZFixalVq1aAGzatImYmBj+/vvvdJ/vXdKbM2vOkK9CvodeP73qNN4lvdM9J7OV7VHW7H7pbqVT7CnzUhmLznT0cCQuJo5f6/1qtm40GjEYDAxLGmbRedlNvU/rMbvtbKbVmYaDqwOtp7fG1sHWdH3/lP0ENlIZisizbsWbK9g/bT9FmhfBp5QPZOCPaeVfLo+dkx3rPlxHwt0E/uryFznz5aTJd00o1alUxg3OIPei7+Hi62K6H7EhgqCm//vZKX+l/MScj7FGNJEMUf7n8qx9aS2eTp6U+7kchjTeMPa+tjcTk8mz4O1qbzNg2QCu3rlKvYB6AKwNX8s3275hbOOx1g0nIs+kgcsHUtirMNtf3o6X0/1y9et3r9NtfjcGrhjI0i5LrZxQRERERERERERERERERERERERERERERERERERSk6UK8gAMNgYKNyrMoPODODrvKGFzw4iNjsXR05ESL5ageNvipn1Z1c6dO2nZsiX58+endevWVKtWDT8/P3LmzMmNGzfYt28fU6ZM4dChQwC4u7vz6aefmp3h4ODAxIkTadmyJTExMdSoUYOhQ4dSv359bG1t2bp1K6NGjeKff/4B4OuvvyZ37txmZzg6OvLbb7/RqFEj7t69S4MGDejevTutW7emQIECxMXFcezYMZYtW8aiRYuIi4t77Of40ksvsXjxYu7cuUOdOnV47733qFChAgBbt27l22+/JTIykmrVqrFt27b0vJwW1717d3744QeSk5Np3rw5Q4cOpWbNmjg6OrJnzx7GjBnDyZMnqVGjBlu2bEn1nN9++43KlStz+/ZtOnfuzJ9//kmnTp0IDAwkKSmJU6dOsWrVKubOncvhw4fNyuT+Xco3aNAgPvjgA/LmzWsqSfT398fO7v6n96RJk6hSpQrx8fE0a9aMN954g5YtW+Li4sK+ffsYNWoUZ86cAWDIkCGUKvX0xSH79++nV69eVKpUiZYtW1K+fHny5MlDQkIC4eHhTJ06ldWrVwP3Sx3/XfQnIo+vRIkSHDx4kHHjxnHgwAGcnJx46aWXGDBgAF5eXuk+v1zvcqx8eyU+JX0o0qKI2bXji4+z6fNNNP62cbrnZLZWU1tl+sx5XedhY29D2z/a4urrmqGlUNmRc25nem3sRezNWBxcHbCxNS8Obv9nexxcHayUTkQe1+FZh2k/pz3BzYIzZV7prqUp3bU0CXcTiL8dj4uPy6Mf9Ixy9XXlRvgN3Au6kxSfxOW9l6k7oq7petytOGzsH16qLpIVtSraihx2OQBoXbS1dcPIM6d3ud7EJcbx+abP+XTj/X8H8/fw56fmP/FSmZesnE7kf9Iq95TMtSFiA9v7/K8cDyCXcy5G1R9FjSk1rJhMREREREREREREREREREREREREREREREREREQexWA0Go2P2hQbG0t4eDgBAQE4OjpmRq7HkpSQhK29bar3s6LY2FgCAgKIjIx8rP3BwcHMnDnTVC73X9OnT+e1115LtbzO1taWTz/9lPfffz/VGStXrqRz585ER0enmeW/H0o9e/Zk+vTp+Pn5cfbs2RT7e/fuzdSpU1PN9c033xAdHc2IESMeej7cL4KLiIigR48eTJs2Lc18D3P27FkCAgIAnuiMTz75hI8//jjV64MHD6ZUqVL06tULgPDwcLOCuwf27NlDmzZtOH/+fJrzHvb4jh07MmfOnMfav2rVKtq3b09MTEyqM/r378/333+PjU3Kgo3HfZ2nTZtmes5pqV69OosWLSJXrlyP3JvdPavvryLzus3j0B+HyF0sN7mL3i9QvXbsGtdPXKdkh5K0m9nOygmzhs+dP+e1fa+ZXkMRkefRN/m+oef6nuQqkvHf+238bCMhXUPwDPDM8FmZYUm/JVw5cIUGoxtwbMExDkw/wOBLg7F1uP9z78EZB9kxdgev7HrFyklFRDLX1TtXcbJ3wtXB1dpRRFLIOTInB/oeINAz0NpRnnteo71Y0mUJ1QtWN1vfcm4LLWe2JOrdKCslExERERERERERERERERERERERERERERERERGRR7GzdoD0+G8ZXlYvxwNwdHTk4sWLbN++nTVr1rB9+3aOHz/OlStXiI2NxcXFhXz58lGmTBlatWpFu3btcHBwSPW8Hj16UKdOHcaOHcuqVas4d+4cycnJ5MuXj3r16vHGG28QEhKSZqbGjRtz5swZfvrpJ5YsWcLx48e5ceMGLi4uBAUFUbt2bbp06fLEz3XKlCnUq1ePiRMnsn//fuLj48mTJw+1a9dmwIABVK5cmeHDhz/xuZlh2LBhVKxYke+++45du3Zx584dfHx8qFy5Mn379qVhw4aPVbZXoUIFjh8/zi+//MKCBQs4fPgwUVFRODo6EhAQQLVq1ejYseNDy/V+//13KlasyNy5czl+/Di3bt0iOTn5oXMaNWrEqVOnGDt2LMuWLePMmTPExcXh6+tLrVq16Nu3LzVr1kznqwKdO3fG19eX1atXs2vXLi5evMiVK1dITEzEx8eH8uXL07FjRzp16vTQIj4ReTJ3797l3LlzxMfHm62XLl063We3/b0tRUOLcuiPQ1w/cR2j0UiuormoO6IuJTuUTPf5z4t8FfMRcz5GBXki8lyrNrga27/bTrNxzTAYDBk6K+zPMNZ/vJ78VfJTultpSnYoiXNu5wydmZHqfVqP2W1nM63ONBxcHWg9vbWpHA9g/5T9BDZS+Y6IPH+8XbytHUGeUzdjbxJ5+/7/2CSPax7cHd1T7Ln1/q3MjiWpaFGkBa8ufpXJoZOpnL8yADsu7qDv0r6EFg21cjoREREREREREREREREREREREREREREREREREUmLwWg0Gh+1KTY2lvDwcAICAnB0dMyMXCIizwW9v8rTuHr1Kr169WL58uUPvZ6UlJTJiSQ1R/48wobhG6g+tDo+IT4pynx9S/taKZmISMaa3Xa22f3wv8Nx8nLCp6QPNvbmRckd53W06Ox/jvzDoRmHODzrMDEXYijcsDAhXUMo1roY9s72Fp2VWWJvxuLg6oCNrflrdy/qHg6uDmaleSLZhedoTwykLNU0GAw42jkS5BVEzzI96VWulxXSiTUEfBfw0I+JB868eSYT08jz5pe9v/Dttm85fv242XrRXEUZXG0wfcr3sVIyScuN2Bv0WNCDxccXY297//vAxOREQouGMq3VtIcWHIqIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIyLPBztoBRERE5Mm89dZb3Lhxgx07dlC3bl3mz5/PlStX+Oyzz/jmm2+sHU/+ZW7HuQAs7L3QtGYwGDAajRgMBoYlDbNWNBGRDOXobl78W7xN8Uyb7VPSh/pf1Kf+F/U5t+Uch/44xIq3VrCk7xLej3k/03JY0n9fzwecvJwyOYlI5hlWexifb/qcpsFNqZyvMgA7L+1kxakV9K/Un/DocPot7UdiciKvVHjFymklM7xV5S2z+wnJCeyL3MeKUysYWn2odULJc+GrLV8xfMNwBlYeSOOgxvi63C86v3LnCqtOr+LNFW8SHRvNkOpDrJxU/s1oNBITF8OsdrO4eOsiR68eBaC4d3GCvIKsnE5ERERERERERERERERERERERERERERERERERB5FBXkiIiJZzN9//83ChQupWLEiNjY2+Pn50bBhQ9zc3Bg5ciTNmze3dkT5/94Mf9PaEURErKLV1FbWjgCAg4sD9k722DrYEn8r3tpxROQJbD6/mc/qfUbfin3N1n/e/TOrzqzirw5/Udq3NN/v/F4Fec+JN6s+/Hvr8TvHs/vy7kxOI8+TcbvGMbXVVDqU7GC2Xty7OHX961LGtwxDVw9VQd4zxoiRoO+DOPL6EYJzBasUT0REREREREREREREREREREREREREREREREQki7GxdgARERF5Mnfu3MHHxwcAT09Prl69CkBISAh79+61ZjT5Dw8/jzT/iIg8D6LDo7l+8nqK9esnr3Pj7I0Mmbfx8438WPJHJlacSOS+SOqOqMvgyMEWnyUiGWflqZU0CGyQYr1+YH1WnloJQLPgZpyJPpPZ0eQZ0zS4KX+F/WXtGJKN/XPnH0J8QlK9HuIbwrW71zIxkTwOG4MNwbmCuX4v5fehIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLy7FNBnoiISBZTtGhRjh8/DkCZMmX4+eefuXjxIhMmTCBv3rxWTiciImJuYc+FnN96PsX6xR0XWdBzgUVn/VL1F34I+oGjc49StldZ3op4i5fWvkT5PuVxdHe06CwRyVheTl4sPr44xfri44vxcvIC4E7CHXI65MzsaPKMmRs21/QxIZIRKuWrxKgto0hMTkxxLSk5idFbRlMpfyUrJJNHGVV/FENXD+XwP4etHUVERERERERERERERERERERERERERERERERERJ6QnbUDiIiIyJN58803uXz5MgAff/wxTZo0YcaMGTg4ODBt2jSLzVnYeyFNvmtCjpw5zNbj78Sz/I3ltJrSymKzREQk+7q87zKhNUJTrBeoWoBlA5ZZdFZA/QBaTWmFdwlvi54rIpnvo9of0W9pP9adXUfl/JUB2HVpF8tOLmNC8wkArD69mjr+dawZUzJRuZ/LYcBgum/ESOTtSK7eucqPzX+0YjLJ7sY1G0fj3xuT5+s81Parja+LLwBX7lxhY8RGHGwdWNV9lZVTysO8tOAl7ibcpcyEMjjYOuBk52R2PerdKCslExERERERERERERERERERERERERERERERERGRR1FBnoiISBbTrVs30+0KFSoQERHBsWPHKFSoELlz57bYnAPTD9BgVIMUBXmJ9xI58OsBFeSJiMhjMRgMxN2KS7EeezMWY5LRorPqf17foueJiPW8UuEVSniXYNyuccw7Og+AormLsqHnBqoXrA7A4OqDrRlRMlnroq3N7tsYbPB28aauf12K5S5mnVDyXCjtW5oTA07w+8Hf2X5xO2dunAEgj2sePqv3GV1CuuCWw83KKeVhxjQeY1asKSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiWYfBaDQ+spEgNjaW8PBwAgICcHR0zIxcIiLPBb2/yrMoLiYOo9HIaM/RvHHyDVy8XUzXkpOSObH4BGveW8PgSyokERGRR5vZciZ2Tna0m9kOG1sb4P7Xk7kd55JwJ4Guy7um6/yVb6/khU9fwMHFgZVvr0xzb+NvG6drloiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhkPDtrBxAREZEn07t37zSvT5kyJV3nj/IYhcFgwGAwMK7IuJQbDFB3RN10zXheLOixgHJ9yuFX28/aUURErKbB6AZMrT2VcUXH4Vfr/vthxKYI4mLi6PF3j3SfH7kvkuSEZNNtEck+kpKTWHBsAUevHQWgpHdJQouGYmtja+Vkklli4mIee69bDrcMTCJy/z3p3+8/Oy7sIC4pjmoFqmFva2/FZJIa209suTz4Mj4uPmbr1+9ex+drH5KGJVkpmYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIPIoK8kRERLKY6Ohos/sJCQkcPnyYGzduUK9evXSf32NdDzDC9HrT6fBXB5y8nEzXbB1s8fDzIGe+nOme8zyIvRnLrw1+xcPPg7K9ylKmRxnc8qu4Q0SeL94lvOl3sB87x+3kyoEr2DnZUealMlQeUNnsa8zT6rGux0Nvi0jWdirqFM1mNOPirYsUzVUUgJGbR1LQrSBLuyylsFdhKyeUzOAxygODwZDmHqPRiMFgUNGVZJjLty7T/s/2bL+wnRqFarCg4wK6z+/OspPLAAjOFcz6HuvJmzOvlZPKfxmNxoeuxyXF4WDrkMlpRERERERERERERERERERERERERERERERERETkSRiMqf2W2L/ExsYSHh5OQEAAjo6OmZFLROS5oPdXsZTk5GT69etH4cKFeeeddyxy5o2IG7gXdMdgk3YhhaTtztU7HPztIAemH+Bq2FUCGwRSrk85irYqiq29rbXjiYg8M5a+vpQXPnkB59zOFj/72rFrzAydyRsn3rD42SKSMZrNaIYRIzPazsDLyQuA63ev021+N2wMNiztstTKCSUzbDi74bH2HfrnEAMqD8jgNPK8emn+S5yOPs17Nd5jxqEZnI85j63BlpntZpJkTKLLX10om6cs45qNs3ZU+f++3/E9AINWDuLTFz7F1cHVdC0pOYmN5zZy9sZZ9r22z1oRRURERERERERERERERERERERERERERERERETkEVSQJyJiRXp/FUs6fvw4devW5fLlyxY7M/ZGLHsn7+Xa0WsAeJf0plzvcji66+P1aVzee5l9U/ex75d9OLg6ENIthEqvVyJXcC5rRxMRsbqRbiPpu78vnoGeFj878kAkE8tPZFjSMIufLSIZw+ULF7b32U6Ib4jZ+oHIA9SYUoPb/3fbSsnkWXEr7hYzD8/kl72/sOfyHpKGJVk7kmRT+b7Jx7yO86haoCpR96LI/WVuVndfTf3A+gD8Hf43ryx+hdMDT1s5qTwQ8F0AABE3IijgVgBbm/+V0zvYOuDv4c8ndT+hSoEq1oooIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKPYGftACIiImIZp0+fJjEx0WLnXdp9id8b/46dkx35K+cHYPu329n0+Sa6r+pO3vJ5LTbreXDr8i1Orz7NmdVnMNgaCG4WzD+H/uHHEj/S4MsGVBtUzdoRRUSs65HV7SLyPMlhm4Nb8bdSrN+Ov42DrYMVEsmzYmPERibvm8xfYX+RL2c+2hZvy/hm460dS7Kx6Nho8ue8/zOxl5MXzvbO+Hn4ma4HeQVx+Zbliuol/cLfDAfghekvMK/DPBKTEzEYDOR2zm3lZCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi8rhUkCciIpLFvP3222b3jUYjly9fZunSpfTo0cNic1YOWknR0KK0nNQSGzsbAJITk1n08iJWvLWCXht7WWxWdpWUkMTxRcfZP3U/p1edxre0L1XfqkpIlxByuOUA4Oj8oyzqvUgFeSIiIiL/0qJIC15d/CqTQydTOX9lAHZc3EHfpX0JLRpq5XSS2SJvRzJt/zQm75tMTFwMHUp0IC4pjgWdFlDCu4S140k25+Piw+XblynoXhCAAZUH4OXkZboefS8aFwcXa8WTVNyIvUHx3MUJ/iGY6NhoADwdPelUqhOf1fsMD0cP6wYUEREREREREREREREREREREREREREREREREZE0qSBPREQki9m3b5/ZfRsbG7y9vfnmm2/o3bu3xeZc2n3JrBwPwMbOhhrv1GBixYkWm5OdfZP3G4zJRkp1LsUrO18hT9k8KfYEvBCAo4ejFdKJiIiIPLu+b/o9PRb0oNrkatjb2gOQmJxIaNFQvmvynZXTSWZqObMlGyM20jy4OWMbj6VJUBNsbWyZsGeCtaPJc6JsnrJsO7/NVNY5qsEos+ubz22mtG9pa0STVETdi6La5GpcjLlI15CuFPcuDkDY1TCm7Z/G2vC1bO29FU8nTysnFRERERERERERERERERERERERERERERERERGR1KggT0REJItZt25dpszJ4ZaDm+dukrtYbrP1m+dvkiNnjkzJkNU1HtOYku1LYueY+rdcjh6OvBn+ZiamEhHJfkZ7jgZD6teTE5MzL4yIWISHowcLOy3k5PWTHLt2DIDi3sUJ8gqycjLJbMtPLmdglYH0q9iP4FzB1o4jz6GFnRameb1S/krU8a+TSWnkcXyy4RMcbB04PfA0vq6+5tde+IRGvzXikw2fMKbJGCslFBERERERERERERERERERERERERERERERERGRR1FBnoiISBZTr1495s2bh4eHh9l6TEwMrVu35u+//7bInJIdS7KozyIaft2QgtULAnB+y3lWD11Nqc6lLDIjO0tKSGJhr4XkLZcXn1I+1o4jIpKtNR7b2NoRRCSDBOcKVinac25z781M3juZChMrUNy7ON1Ld6dTqU7WjiViUjl/ZWtHkP9YcGwBP7f4OUU5HkAe1zx82fBL+i7pq4I8EREREREREREREREREREREREREREREREREZFnmAryREREspj169cTHx+fYj02NpZNmzZZbE6jrxuBARa8tIDkxGQAbOxtqNivIg1GNbDYnOzK1t4W90LuJCclWzuKiEiWENIthBxuOZ7qsWV7lH2i/YdmHqJoaFEcXByeap6IZIy3V7792Hu/bfxtBiaRZ0nVAlWpWqAqY5uMZfaR2UzZN4W3V75NsjGZ1adXU9CtIDlz5LR2THkOjNw0El9XX3qX6222PmXfFK7eucq7Nd+1UjL5r8u3L1PSp2Sq10v5lCLydmQmJhIRERERERERERERERERERERERERERERERERkSelgjwREZEs4uDBg6bbYWFhREb+75e5k5KSWLFiBfnz57fYPFsHW5p+15QGIxsQdToKAK/CXtg721tsRnZX64Na/P1/f9PmtzY4eTlZO46IiFWM9R9Lud7lKNuzLO6F3FPd1+KnFpmWaclrSyhQpQAOgSrIE3mW7Ivc91j7DBgyOIk8i1wcXOhdrje9y/Xm+LXjTN43mVFbRvHe2vdoGNiQRZ0XWTuiZHM/7/mZP9r9kWK9pHdJOm3spIK8Z0hu59ycvXGWAm4FHno9PDocLyevTE4lIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiJPQgV5IiIiWUTZsmUxGAwYDAbq1auX4rqTkxM//PCDxefaO9vjG+Jr8XOfB7vG7SLqVBTf5PsGDz8P7F3MywVf2/ualZKJiGSeqm9VZf+0/Wz4ZAMBLwRQrk85irUphl0OK/44arTeaBFJ3boe66wdQbKIormL8mXDLxlZfySLTyxmyr4p1o4kz4HI25Hkdc2bYt3bxZvLty5bIZGkpnHhxnzw9wes7r4aB1vzQuS4xDg+WvcRTYKaWCmdiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIg8DhXkySMNHz6cESNGAGA0pmyS8Pf3JyIigh49ejBt2jSLz69bty4bNmygTp06rF+//qnPMRgMAHz88ccMHz7cMuEygKWer4hkP+Hh4RiNRgIDA9m5cyfe3t6maw4ODvj4+GBra5vuOQt7L3z0JgO0mtwq3bOyu6Kti1o7goiI1VV9qypV36rK5b2X2T9tP8vfWM7S15cS0iWEcr3Lkbd8yqIZEZF/uxBzAYACbgWsnESeNbY2trQu1prWxVpbO4o8Bwq6F2TL+S0EeAaYrW85t4V8OfNZKZU8zCcvfELFiRUJ/iGY/pX6Uyx3MYxGI0evHeXHXT8SlxTHb21+s3ZMERERERERERERERERERERERERERERERERERFJgwrynkHr16/nhRdeAJ79Mjd5Nj0oA3wcGVVsKCKW5+fnB0BycnKGzomNjk31WnJSMmfWnCEpLkkFeY+h7sd1rR1BROSZkbd8XvKWz0ujbxqx68ddrHl3Dbt/2o1PiA9VBlahbK+yT/R9rIhkb8nGZD7b+BnfbPuG2/G3AcjpkJPB1QbzQe0PsDHYWDmhiDxvXin/Cm+teIuEpATqBdQDYG34Wt5Z/Q6Dqw22cjr5twJuBdjWZxuvL3ud99e+b/qfvhgMBhoGNmRcs3EUdC9o5ZQiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIpIWFeSJyDNh2rRp9OrVC4Dw8HD8/f2tG0jkGbNo0SKaNm2Kvb09ixYtSnNvaGhoumZ1nN/xoevHFh7j7//7G7scdtQZViddM0RE5PmTlJDEsfnH2D91P6dXn6ZA1QKU61OOmAsxrP2/tZxZc4Z2f7SzdkwReUZ8sPYDJu+bzKj6o6hRqAYAm89tZvj64cQmxvJ5/c+tnFBEnjdDqw/l+t3rvL7sdeKT4gFwtHPk3Rrv8n6t962cTv4rwDOA5V2XE30vmpNRJwEI8grCy8nLyslERERERERERERERERERERERERERERERERERORxqCBP0u3s2bMZev769esz9PzsrGLFikydOjXNPZ6enpmURkTSo3Xr1kRGRuLj40Pr1q1T3WcwGEhKSrLo7HNbzrH2vbVc3nuZSgMqUfO9mjh5Oll0RnaVnJTM9jHbOTLnCDfP3SQp3vzv5t2od62UTEQk81zee5l9U/dxeOZhDDYGyrxUhsZjGpO7WG7TnuJtijOp0iQrphSRZ830A9P5JfQXQov+r/y5tG9p8ufMz+vLXldBnohkOoPBwOiGo/mozkccvXoUJ3sngr2CyWGXw9rRJA2eTp5Uzl/Z2jFEREREREREREREREREREREREREREREREREROQJqSBPJBtzcXGhVKlS1o4hIhaQnJz80NsZ6WrYVda8u4ZTK05R+qXStJvZDrcCbpkyO7vYMGIDe3/ZS7XB1Vj34TpqfVCLG2dvcGzBMeoMq2PteCIimWJSpUkENgyk+U/NKda6GLb2tin2eAR4UKpT5n3f6u7njo29TabNE5EnF3UvimK5i6VYL5a7GFH3oqyQSETkvsjbkUTdi6K2T21y2OXAaDRiMBisHUtERERERERERERERERERERERERERERERERERCRbydKNAAlJCWneFxERyU62bdvGkiVLzNZ+/fVXAgIC8PHx4dVXXyUuLi7dc26ev8nCXguZUGYCNnY29D3Yl1aTW6kc7ykcmnGIlpNaUn1wdWzsbCjVuRShv4RSZ1gdLmy/YO14IiKZYuCZgXRb0Y2S7Us+tBwPwMHFgVZTW6V71sVdF7mwI+X764UdF7i0+5Lp/uuHX8e9oHu654lIximTpwzjdo5LsT5u5zjK+JaxQiIRed5dv3ud+r/Wp8gPRWj2RzMu374MQJ9FfRi8crCV04mIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiGQvWbIgLzE5kcTkROYdm0f7P9vT4NcGtP+zPfOOzTNdy87Wr1+PwWDAYDCwfv16AObMmUP9+vXx9vbGycmJokWL8s477xAVFfXI8y5cuED//v0JDAzE0dGRfPnyERoaypo1ax4rj7+/PwaDgZ49e5qt16tXD4PBQMGCBTEajWmeERsbi7u7OwaDgQ4dOphdq1u3LgaDgbp166Z5xh9//EHdunXx9PTE1dWVUqVK8fHHH3Pjxo1HPoeePXtiMBjw9/dPc9+0adNMr/3Zs2dTXI+Pj2fx4sUMGDCASpUq4enpib29Pbly5aJKlSoMHz6ca9euPTKPNSQnJ/P3338zZMgQatSoQe7cubG3t8fDw4OyZcsyZMgQzp0799hnzZw5k3bt2lGoUCGcnJxwcnKiSJEidO3alblz55KQcL/Q8sHHc69evUyPDwgIML3O//1Y/7fbt28zatQoqlWrhpeXFzly5KBAgQK8+OKLKUrE/uu/H1cnT55kwIABBAcH4+zsnOLvODY2lu+//566devi7e2Nvb09Xl5eFC1alKZNm/Ltt98+9GNCxJI++eQTjhw5Yrp/6NAh+vTpQ4MGDXjvvfdYvHgxI0eOTPeccUXHcWTOEaq+XZWyvcoSdTKK44uOp/gjj3Y78ja+Ib4AOLg6EHfzfoFhkRZFOLn0pDWjiYhkGg8/j0ybtaz/MmLOx6RYv3XxFsv6L8u0HCKSfl82+JIp+6ZQYnwJ+izsQ5+FfSgxvgTTDkzjq4ZfWTueiDyHBq0chL2NPecGncPZ3tm03rFkR1acXmHFZCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItmPnbUDPKlkYzKrTq+i98LeXLlzxeza3LC5+Lr4MqXVFJoENcHGkCX7/55IcnIy3bt35/fffzdbP3HiBF999RXz589n06ZN5MmT56GP37RpEy1atCAm5n8lEpcvX2bx4sUsXryY4cOHP3W2rl27sm7dOi5cuMDGjRupU6dOqnuXLFliytC1a9cnmpOYmEiXLl34888/zdaPHDnCkSNH+P333x+77C+9Xn31VaZPn55iPSoqip07d7Jz507GjRvHwoULqVGjRqZkelyffPIJI0aMSLF+8+ZNDhw4wIEDB/jpp5/4/fffadOmTarnnD17ljZt2rB///4U106ePMnJkyf5448/WLdu3SNLD9Oyb98+WrRowaVLl8zWL168yF9//cVff/1F27ZtmTFjBo6OjmmetXDhQrp27cqdO3ceev3y5cs0aNCAsLAws/Xo6Giio6M5ceIEK1as4NKlS3z99ddP/ZxEHmX//v18+umnpvuzZs2iSpUqTJo0CYCCBQvy8ccfp+u9GyAx9n7R7NavtrL1q60P3WMwGBiWNCxdc54HbgXcuHX5Fu6F3PEs7MnpVafJWz4vF3ddxDaHrbXjiYhkmNGeo8HweHvfjXrXYnOvhl0lb/m8KdbzlMvD1bCrFpsjIhmvjn8dTrxxgvE7x3Ps+jEA2hZvy+uVXidfznxWTiciz6NVp1exsttKCrgVMFsPzhVMxI0IK6USERERERERERERERERERERERERERERERERERHJnrJUQV5iciKrTq8idGYoScakh+65cucKoTNDWdR5EY0KN8LOJks9xSf20UcfsXXrVlq3bs1LL72En58fV65cYfz48SxdupRTp04xaNAgZs6cmeKx586dM5Xj2djY8Oqrr/Liiy/i7u7OwYMHGTVqFMOHD6dixYpPla1du3b079+fuLg4ZsyYkWZB3h9//AGAp6cnTZs2faI5Q4YMMZXjFS1alHfeeYfSpUtz8+ZN/vzzTyZNmkTHjh2f6jk8qcTERAIDA2nTpg2VK1emUKFC2NnZERERwZo1a5gyZQrXr1+nTZs2HD58GB8fn0zJ9TgSExPJmzcvbdq0oVq1agQGBuLo6Mj58+fZunUrP/74I7dv36ZLly7s3buX4sWLpzjjypUr1KhRw1RaV69ePXr06EGxYsUwGAyEh4fz999/m5UZVqpUiUOHDrFw4UI+/PBDAFauXEm+fOaFBwEBAabbFy9epH79+kRHR2MwGOjZsyedOnUiV65chIWF8c0333DgwAHmzZtHz549mTVrVqrP+9y5c3Tr1g1nZ2c++ugjatWqha2tLbt27cLV1RWAN954w1SO161bN9q2bUu+fPmwtbXl8uXL7N69m4ULFz7lKy/y+KKjo/H19TXd37Bhg9l7ZqVKlTh//ny653yc/HG6z5D7irUpRvjacApUKUDlNyozv9t89k3ex81zN6k6qKq144mIZJjGYxtbZa5dDjtuX7mNZ6Cn2frty7exscv+Beoi2U2+nPn4vP7n1o4hIgLAnYQ7ONs7p1iPuhdFDrscVkgkIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKSfWW59rjeC3unWo73QJIxiT6L+nB+UPpLgp51W7du5bPPPuODDz4wW2/SpAlNmjRh1apVzJ07l++//x5vb2+zPYMHDyYmJgaA33//nc6dO5uuVaxYkfbt21OrVi127979VNk8PDxo3rw58+bNY+7cuYwbNw4HB4cU+27evMmyZcsAaN++/UP3pObQoUP88MMPAJQvX54NGzaYSs0A6tevT/Xq1enRo8dTPYcnNWLECAIDAzEYDGbrFStWpF27drz++utUr16dq1ev8sMPP/Dpp59maJ47d+5w+PDhVK+7uLiYiudefvllPv74Y+zt7c32lC9fnlatWvHGG29QtWpVLl68yBdffMFvv/2W4rx+/fqZyvFGjx7NO++8Y3a9UqVKdOjQga+//pqEhARThlKlSpl9nBUpUgR/f/9Uc7/11ltER0cDMGnSJPr06WO6VqFCBTp06EDTpk1Zt24ds2fPpkePHqkWL4aHh5MvXz62bdtGoUKFTOtVqlQBIDY2lkWLFgH3P2e+/vrrFGe0bNmSESNGEBUVlWpmEUvw9fUlPDycggULEh8fz969exkxYoTp+q1bt1J8DmeGP5r/QctfWpIzb85Mn/2sazCqgel2qY6lcC/kzoVtF/AK9qJoy6JWTCYikrHK9igLQHJiMof+OEThxoVx9XVN+0EWULhRYda+v5ZOCzvh6O4IQOyNWNb+31oCGwZm+HwRSZ+DVw5SyqcUNgYbDl45mObe0r6lMymViMh9tQrV4tcDv/Jpvfv/nmfAQLIxmS+3fMkL/i9YOZ2IiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiEj2YmPtAI8rISmBv8L+4sqdK4+1P/J2JPOOziMhKSGDk1lXhQoV+L//+78U6waDgbfffhuAxMREtm3bZnY9MjKS+fPnA9CiRQuzcrwHcubMycSJE9OVr2vXrgBER0ezfPnyh+6ZO3cucXFxZvsf14QJE0hOTgZg4sSJZuV4D7z00kuplqNZWuHChVOU4/1bSEgIL7/8MgALFizI8Dy7d+8mJCQk1T+9evUy7fX390+zWKtAgQIMHToUgEWLFmE0Gs2uHz9+3PScWrdunaIc799cXV3x9PR8qud06dIl08dukyZNzMrxHsiRIwdTpkzBzu5+B+i4cePSPHPUqFFm5Xj/FhUVZSrzq127dprneHl5PTK/SHo0a9aM9957j02bNvH+++/j7OxMrVq1TNcPHjxI4cKFMz1XxMYIEu8lZvrcrKhgtYJUe7uayvFE5LlhY2fDkr5LSIzNnK8TDb9uSMz5GMb6jWX6C9OZ/sJ0vgv4jtuRt2n0TaNMySAiT6/shLJcu3vNdLvcz+UoO6Fsij/lfi5n5aQi8jz6suGXTNw7kaYzmhKfFM87a96h1I+l2BixkdENRls7noiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiISLZiZ+0Aj8ve1p65R+c+0WPmhs2lQ8kOGZTo2dClS5dUC9kqVKhgun3mzBmza+vWrSMpKQnArCTtvypXrkzJkiU5cuTIU+Vr3rw57u7u3Lx5kz/++INWrVql2PPHH38AUKhQIbOip8exZs0a4H7x3L+f73/17t071YK+jBQdHU1UVBSxsbGmQjkPDw8AwsLCSEhISLOUzppiYmK4fv06d+/eNWV3dnY2XQsPDycwMNC0f+nSpaZ9gwYNyrBc69evN33sPqwc7wF/f38aNmzI8uXLTY+xtbVNsc/BwYH27dunek6uXLlwcHAgPj6e3377jWbNmpmK90Qy26effkrbtm2pU6cOrq6uTJ8+HQcHB9P1KVOm0KiRyn+s7fii44+9t2ioivJEJPvLXzk/kfsi8fDzyPBZbvnd6HuwL4dmHCLyQCT2TvaU7VWWUp1LYWuf8ntBEXm2hL8Zjrezt+m2iMizpJRPKU4MOMG4nePI6ZCT2/G3aVu8Lf0r9SdvzrzWjiciIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIpKtZKmWp+h70U+2P/bJ9mdFxYoVS/Wal5eX6fatW7fMrh06dMh0u1KlSmnOqFy58lMX5OXIkYMXX3yRyZMns3jxYm7dukXOnDlN1y9dusT69esB6Ny5c6plfw8TFxfHyZMngcd7Dpnl0KFDjBkzhuXLlxMZGZnqvuTkZKKjo/Hx8cmwLHXq1DG9vo8jIiKCr7/+msWLFxMREZHm3mvXrpkV5O3btw8Ae3t7qlat+lR5H8fhw4dNt6tUqZLm3ipVqrB8+XLu3r3LmTNnCA4OTrEnODgYR0fHVM/IkSMHHTt25LfffmPu3Lns2rWLDh06ULduXapXr24qPBTJDLlz52bjxo3cvHkTV1fXFKWPf/75J66urlZKJw/Maj3L7L7BYDAViP57DWBY0rBMyyUiYi2VXq/EqsGriLkQQ94KeXFwcTC77lva16LzHFwcqPBq6uXZIvLs8vPwM912dXAll3MuAM7fPM+kvZO4l3CP0KKh1PJ7snJ9EZH0SkhKoMmMJkxoPoEPan9g7TgiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLZXpYqyPN08nyy/Y5Ptj8rcnZ2TvWajY2N6XZSUpLZtaioKNPtRxW0+fqmr7Cia9euTJ48mXv37jFv3jx69OhhujZr1iySk5NN+55EdHS0qXAoo5/D45o8eTJ9+/YlMTHxsfbfu3cvgxM9vuXLl/Piiy9y9+7dx9r/3+zXrl0D7hczOjg4POwhFvEkH7t58uR56OP+zdPz0e8T48aN48aNG6biwK+++oqvvvoKGxsbypcvT4cOHXj11Vdxd3d/zGchkj6pfaz9uxhVrOfj5I9Nt8+sOcOad9dQ74t6FKxWEIDz286z7sN11PuinrUiiohkqrmd5gKwfOBy09qD8lCDwWDxstDrJ69zdt1Z7vxzB2OyeUFpnWF1LDpLRCzv0JVDtJzZkvMx5wn2CmbWi7No8nsT7iTcwcZgw5jtY5jbYS6ti7W2dlQReY7Y29pz8MpBa8cQERERERERERERERERERERERERERERERERERF5bmSZgryEpAReLPEic8PmPvZjXizxIglJCdjb2mdgsqzPYDBk6Pl16tQhf/78XLx4kT/++MOsIO+PP/4AICQkhJCQkKeekdHP4XEcO3bMVI7n4+PD0KFDqVevHv7+/uTMmRN7+/sfh1OmTKFPnz4ApoI/a7t27RpdunTh7t27uLq6MmTIEBo3bkzhwoVxd3c3Fd79/fff1K9fH3g2slvi793W1vaRe9zc3Fi0aBE7d+5kzpw5rF+/nv3795OUlMTu3bvZvXs3X3/9NQsWLKBatWrpziQi2ceKt1bQYkILCtUsZFoLahyEvbM9S15dQv+j/a2YTkQkc7wZ/mamzdozaQ9L+y3FObczrnlczb9fNKggTyQreGfNO4T4hjCj7Qx+O/gbLf5oQfPg5kwKnQTAG8veYNTmUSrIE5FM1y2kG5P3TWZUg1HWjiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIpLtZZmCPHtbe9oVb4eviy9X7lx55P48rnloW7wtdjZZ5ilmKk9PT9PtK1euULBgwVT3Xrny6Nc7LTY2NnTu3Jmvv/6atWvXcuXKFXx9fTlx4gR79uwBoGvXrk98roeHx2NnfNR1GxsbAJKTk9Pcd+fOnVSvTZs2jcTERGxtbdmwYQPFihV76L6oqKg0Z1jD3LlzuXHjBgDz58+nQYMGD92XVvbcuXOb9sTHx5tK9SzNy8vLdPtRH7uRkZEPfdzTqly5MpUrVwbg1q1brF+/nmnTpjFv3jz++ecf2rVrx+nTp3Fyckr3LBHJHqJPR+Po4Zhi3dHdkRtnb2R+IBERK/Dw88i0WZs+20S9z+tR892amTZTRCxr18Vd/N3jb0r7lqZMnjJM3DOR1yu9jo3h/s/tb1R5g6q/VLVyShF5HiUmJzJl9xTWnFlDhbwVcHFwMbv+beNvrZRMREREREREREREREREREREREREREREREREREQk+7GxdoAnNaXVFGwNtmnusTXYMjl0ciYlyppCQkJMt3ft2pXm3kddfxwPCvCSkpKYPXs2ADNmzADAYDDQuXPnJz7T0dGR4ODgx8r4qOs5c+YEMJXEpebEiROpXjty5AgAZcqUSbUcD2D37t1pzrCGB9m9vLxSLceDtLOXL18egISEBLZt2/bEGQwGw2PtK1WqlOn2jh070ty7c+dOAJydnQkMDHziTGnJmTMnLVu25K+//mLgwIEAXL58mc2bN1t0jkhWUPP/auLkpWLIh8lXKR8r317J7Su3TWu3r9xm9dDV5K+c34rJREQy39Wwq5xacYrji46b/bGke9H3KNm+pEXPFJHMFXUvijyueQBwdXDFxcEFT6f/lfx7OnpyK/6WteKJyHPs8NXDlM9bnpw5cnIi6gT7IveZ/uyP3G/teCIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiItmKnbUDPAk7GzuaBDVhUedF9FnUh8jbkSn25HHNw+TQyTQJaoKNIcv1/2WaF154AVtbW5KSkpg+fTpt27Z96L5du3Zx+PDhdM8rW7YsJUqUICwsjBkzZjBw4EBmzpwJQK1atShUqNBTndugQQNOnjzJoUOH2LdvH+XKlXvovilTpqR5TkBAAAC3bt3i+PHjFC1aNMWe+Ph4/vrrr1TPSExMBODOnTup7rl8+TKLFi1KM4s1PMgeGxtLcnIyNjYpP3fu3r3Lb7/9luoZzZs3Z/DgwRiNRsaOHUudOnWeKIOjo6PpdlxcXKr76tata/rYnTJlCi+++OJD9507d47Vq1ebPSaj1K9fn++//x6Aa9euZdgckczwJEVFRUPvv1fWer9WRsXJ8lpNacXsNrMZW2gsbgXdAIg5H4NXsBedFnSycjoRkcwRfSaa2W1mc+XQFQwGA0ajEfhfQfKwpGEWm1WifQlOrzpNxb4VLXamiGQ+A4Y074uIZKYz0WcI8AhgXY911o4iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLy3MhSBXkANgYbGhVuxPlB55l3dB5zw+YSHRuNp6MnL5Z4kbbF25r2Sery5s1Lq1atmDdvHosWLWLOnDl06NDBbM/t27d57bXXLDaza9eufPDBB+zcuZOZM2dy8uRJ0/rTeu2115gwYQJGo5FXX32V9evX4+LiYrZnxowZLFu2LM1z/l3m9s033zBx4sQUe95++20uXryY6hnBwcGsWLGCkydPsnXrVqpXr252/e7du3Tp0oV79+49zlPLVMHBwcD9jHPmzKFTJ/PSpqSkJF5++WUuXbqU6hlFihShTZs2zJs3jwULFvDVV18xdOjQh+69c+cO8fHxeHp6mtby5s1run369OmHlhQC5MuXjzZt2jB37lyWL1/O9OnT6dGjh9me+Ph4evfuTUJCAgADBgxI49mn7cyZM5w/fz7Nwr9Vq1aZbj8oWxTJqma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      "text/plain": [
       "<Figure size 5000x5000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 3, figsize=(50, 50))\n",
    "plt.subplots_adjust(hspace=0.75, wspace=2)\n",
    "\n",
    "# Go through possible values for 'Situacion_tratamiento' and 'Group'\n",
    "for sit_tto in range(1,2):\n",
    "    for group in range(1,2):\n",
    "        plt.subplot(3, 3, 3*(sit_tto-1) + group)  # Calculate the subplot position dynamically\n",
    "        plot_heatmap(sit_tto, group)\n",
    "        \n",
    "# Adjust layout to prevent overlapping titles\n",
    "plt.tight_layout()\n",
    "\n",
    "# Save the figure in SVG format in the \"./EDA_plots\" folder\n",
    "plt.savefig('./EDA_plots/heatmaps_one_hot.svg', dpi=550, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}