diff --git a/topic/timeseries/README.md b/topic/timeseries/README.md
index cd243858..54e919d0 100644
--- a/topic/timeseries/README.md
+++ b/topic/timeseries/README.md
@@ -28,5 +28,17 @@ repository, e.g. about machine learning, to see predictions and AutoML in action
   window functions, interpolation of missing data, common table expressions, 
   moving averages, JOINs and the handling of JSON data.
 
+- `exploratory_data_analysis.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](exploratory_data_analysis.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/timeseries/exploratory_data_analysis.ipynb)
+
+  This notebook explores how to access timeseries data from CrateDB via SQL, 
+  and do the exploratory data analysis (EDA) with PyCaret.
+  
+  It also shows how you can generate various plots and charts for EDA, helping you understand data distributions, relationships between variables, and identify patterns.
+  
+- `time-series-decomposition.ipynb` [![Open on GitHub](https://img.shields.io/badge/Open%20on-GitHub-lightgray?logo=GitHub)](time-series-decomposition.ipynb) [![Open in Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/crate/cratedb-examples/blob/main/topic/timeseries/time-series-decomposition.ipynb)
+
+  This notebook illustrates how to extract data from CrateDB and how to use PyCaret for time-series decomposition.
+  
+  Furthermore, it shows how to preprocess data and plot time series decomposition by breaking it down into its basic components: trend, seasonality, and residual (or irregular) fluctuations.
 
 [CrateDB]: https://github.com/crate/crate
diff --git a/topic/timeseries/exploratory_data_analysis.ipynb b/topic/timeseries/exploratory_data_analysis.ipynb
new file mode 100644
index 00000000..247484c8
--- /dev/null
+++ b/topic/timeseries/exploratory_data_analysis.ipynb
@@ -0,0 +1,939 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b5838281",
+   "metadata": {},
+   "source": [
+    "# Exploratory data analysis\n",
+    "\n",
+    "Exploratory Data Analysis (EDA) is a critical step in the data analysis process that involves examining and summarizing the main characteristics of a dataset, often visually, before formal modeling or hypothesis testing begins. EDA is used to uncover patterns, spot anomalies, test assumptions, and check for inconsistencies within the dataset. This process can involve a variety of techniques, including statistical summaries, plotting data distributions, visualizing relationships between variables, and identifying potential outliers or errors in the data. \n",
+    "\n",
+    "In this notebook, you can learn how to perform EDA on weather data to uncover underlying patterns and understand relationships between different weather variables using the PyCaret library.\n",
+    "\n",
+    "## What is PyCaret?\n",
+    "\n",
+    "\n",
+    "PyCaret is an open-source, low-code machine learning (ML) library in Python that aims to reduce the cycle time from hypothesis to insights. It provides a simple and efficient end-to-end ML pipeline, enabling data scientists and analysts to perform complex ML tasks with just a few lines of code. PyCaret automates much of the ML workflow, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and model deployment, making it accessible for beginners while still being powerful enough for experts.PyCaret’s visualization tools can facilitate the process of time-series analysis, enabling users to generate plots and charts that summarize the data’s main characteristics.\n",
+    "\n",
+    "Integrating PyCaret with CrateDB presents a compelling opportunity for handling large-scale data analytics and ML projects. CrateDB is a distributed SQL database that excels in handling massive amounts of structured and unstructured data in real-time. This integration allows users to leverage CrateDB's efficient data storage and fast query capabilities to manage large datasets, while PyCaret's ML algorithms can be applied directly to this data for predictive analytics, anomaly detection, and other advanced analytics tasks.\n",
+    "\n",
+    "By following this notebook you will learn:\n",
+    "\n",
+    "- **Data Extraction**: how to efficiently extract data from CrateDB for analysis in PyCaret, including connecting to the database and querying data.\n",
+    "- **Data Preprocessing**: how to use PyCaret to preprocess data from CrateDB, including handling missing values and data aggregations.\n",
+    "- **Visualization**: you will discover how PyCaret can generate various plots and charts for EDA, helping you understand data distributions, relationships between variables, and identify patterns or anomalies.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ab53421",
+   "metadata": {},
+   "source": [
+    "## Getting started\n",
+    "\n",
+    "To get started with this notebook install necessary dependecies and import required modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69f4989b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install -r requirements.txt\n",
+    "\n",
+    "# Note: If you are running in an environment like Google Colab, please use the absolute path of the requirements:\n",
+    "#!pip install -r https://raw.githubusercontent.com/crate/cratedb-examples/main/topic/timeseries/requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5505a9fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import pandas as pd\n",
+    "import sqlalchemy as sa\n",
+    "import requests\n",
+    "import os\n",
+    "from pycaret.time_series import TSForecastingExperiment\n",
+    "import refinitiv.data as rd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33949dcd",
+   "metadata": {},
+   "source": [
+    "## Load data from CrateDB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "067cd043",
+   "metadata": {},
+   "source": [
+    "The following demonstrates how to connect to a CrateDB database instance using SQLAlchemy. It shows two scenarios: connecting to a local CrateDB instance and connecting to a CrateDB Cloud instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dc786d26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the connection string to running CrateDB instance.\n",
+    "CONNECTION_STRING = os.environ.get(\n",
+    "    \"CRATEDB_CONNECTION_STRING\",\n",
+    "    \"crate://crate@localhost/\",\n",
+    ")\n",
+    "\n",
+    "# Connect to CrateDB Cloud.\n",
+    "# CONNECTION_STRING = os.environ.get(\n",
+    "#     \"CRATEDB_CONNECTION_STRING\",\n",
+    "#     \"crate://username:password@hostname/?ssl=true&schema=notebook\",\n",
+    "# )\n",
+    "engine = sa.create_engine(CONNECTION_STRING, echo=os.environ.get('DEBUG'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdae15fa",
+   "metadata": {},
+   "source": [
+    "The next step fetches data from CrateDB and load it into a pandas data frame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "13d08f24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>location</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1672532724000</td>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>20.329405</td>\n",
+       "      <td>90.035633</td>\n",
+       "      <td>7.045718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1672533002000</td>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.485222</td>\n",
+       "      <td>90.233420</td>\n",
+       "      <td>4.534103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1672535400000</td>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>21.331066</td>\n",
+       "      <td>74.014164</td>\n",
+       "      <td>4.347653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1672536651000</td>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>21.034967</td>\n",
+       "      <td>35.431482</td>\n",
+       "      <td>13.160233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1672537835000</td>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.801286</td>\n",
+       "      <td>34.073989</td>\n",
+       "      <td>5.314130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       timestamp  location  temperature   humidity  wind_speed\n",
+       "0  1672532724000    Vienna    20.329405  90.035633    7.045718\n",
+       "1  1672533002000  Dornbirn    20.485222  90.233420    4.534103\n",
+       "2  1672535400000    Vienna    21.331066  74.014164    4.347653\n",
+       "3  1672536651000    Berlin    21.034967  35.431482   13.160233\n",
+       "4  1672537835000  Dornbirn    20.801286  34.073989    5.314130"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"SELECT * FROM weather_data\"\n",
+    "with engine.connect() as conn:\n",
+    "    result = conn.execute(sa.text(query))\n",
+    "    columns = result.keys() # Extract column names\n",
+    "    df = pd.DataFrame(result.fetchall(), columns=columns)\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e7b60c",
+   "metadata": {},
+   "source": [
+    "Next, we convert the `timestamp` column of the DataFrame df into datetime objects using pandas' `to_datetime` function, making it suitable for time-based operations. It then sets this `timestamp` column as the DataFrame's index, effectively transforming df into a time series DataFrame with a DatetimeIndex, which enables time-based indexing and analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3ec768a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 00:25:24</th>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>20.329405</td>\n",
+       "      <td>90.035633</td>\n",
+       "      <td>7.045718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 00:30:02</th>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.485222</td>\n",
+       "      <td>90.233420</td>\n",
+       "      <td>4.534103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:10:00</th>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>21.331066</td>\n",
+       "      <td>74.014164</td>\n",
+       "      <td>4.347653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:30:51</th>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>21.034967</td>\n",
+       "      <td>35.431482</td>\n",
+       "      <td>13.160233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:50:35</th>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.801286</td>\n",
+       "      <td>34.073989</td>\n",
+       "      <td>5.314130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     location  temperature   humidity  wind_speed\n",
+       "timestamp                                                        \n",
+       "2023-01-01 00:25:24    Vienna    20.329405  90.035633    7.045718\n",
+       "2023-01-01 00:30:02  Dornbirn    20.485222  90.233420    4.534103\n",
+       "2023-01-01 01:10:00    Vienna    21.331066  74.014164    4.347653\n",
+       "2023-01-01 01:30:51    Berlin    21.034967  35.431482   13.160233\n",
+       "2023-01-01 01:50:35  Dornbirn    20.801286  34.073989    5.314130"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n",
+    "df.set_index('timestamp', inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "988ba39e",
+   "metadata": {},
+   "source": [
+    "## Data interpolation\n",
+    "\n",
+    "The interpolation in this context fills missing values in the `temperature`, `humidity`, and `wind_speed` columns of the data frame by using time-weighted interpolation (method=`time`). This method estimates missing values based on the linear time difference between known points, leveraging the DatetimeIndex to account for the actual time intervals between observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "250d38fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 00:25:24</th>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>20.329405</td>\n",
+       "      <td>90.035633</td>\n",
+       "      <td>7.045718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 00:30:02</th>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.485222</td>\n",
+       "      <td>90.233420</td>\n",
+       "      <td>4.534103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:10:00</th>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>21.331066</td>\n",
+       "      <td>74.014164</td>\n",
+       "      <td>4.347653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:30:51</th>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>21.034967</td>\n",
+       "      <td>35.431482</td>\n",
+       "      <td>13.160233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:50:35</th>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.801286</td>\n",
+       "      <td>34.073989</td>\n",
+       "      <td>5.314130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     location  temperature   humidity  wind_speed\n",
+       "timestamp                                                        \n",
+       "2023-01-01 00:25:24    Vienna    20.329405  90.035633    7.045718\n",
+       "2023-01-01 00:30:02  Dornbirn    20.485222  90.233420    4.534103\n",
+       "2023-01-01 01:10:00    Vienna    21.331066  74.014164    4.347653\n",
+       "2023-01-01 01:30:51    Berlin    21.034967  35.431482   13.160233\n",
+       "2023-01-01 01:50:35  Dornbirn    20.801286  34.073989    5.314130"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['temperature'] = df['temperature'].interpolate(method='time')\n",
+    "df['humidity'] = df['humidity'].interpolate(method='time')\n",
+    "df['wind_speed'] = df['wind_speed'].interpolate(method='time')\n",
+    "df.fillna(method='bfill', inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee8b72dc",
+   "metadata": {},
+   "source": [
+    "To focus on analysis for a specific city we process the data from Berlin, convert timestamps to datetime format, calculate daily averages, and organize the data frame with a `timestamp` index for further analysis or visualization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "50d5da34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01</th>\n",
+       "      <td>19.273445</td>\n",
+       "      <td>60.066777</td>\n",
+       "      <td>9.090639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-02</th>\n",
+       "      <td>20.769394</td>\n",
+       "      <td>64.997035</td>\n",
+       "      <td>8.010616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-03</th>\n",
+       "      <td>20.849007</td>\n",
+       "      <td>65.351548</td>\n",
+       "      <td>7.674121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-04</th>\n",
+       "      <td>20.228274</td>\n",
+       "      <td>66.177554</td>\n",
+       "      <td>7.542323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-05</th>\n",
+       "      <td>20.795721</td>\n",
+       "      <td>64.429477</td>\n",
+       "      <td>7.872682</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            temperature   humidity  wind_speed\n",
+       "timestamp                                     \n",
+       "2023-01-01    19.273445  60.066777    9.090639\n",
+       "2023-01-02    20.769394  64.997035    8.010616\n",
+       "2023-01-03    20.849007  65.351548    7.674121\n",
+       "2023-01-04    20.228274  66.177554    7.542323\n",
+       "2023-01-05    20.795721  64.429477    7.872682"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_berlin = df[(df['location']=='Berlin')]\n",
+    "\n",
+    "# Ensure the index is in datetime format for resampling \n",
+    "df_berlin.index = pd.to_datetime(df_berlin.index)\n",
+    "\n",
+    "# Now aggregate to daily averages\n",
+    "df_berlin_daily_avg = df_berlin.resample('D').mean()\n",
+    "\n",
+    "df_berlin_daily_avg.reset_index(inplace=True)\n",
+    "\n",
+    "# Ensure 'timestamp' column is set as index if it's not already\n",
+    "df_berlin_daily_avg.set_index('timestamp', inplace=True)\n",
+    "\n",
+    "df_berlin_daily_avg.head(5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01475c6a",
+   "metadata": {},
+   "source": [
+    "## Set up the experiment\n",
+    "\n",
+    "Now, we will initialize the modelling environment by setting a few parameters. The `forecast_horizon` parameter is set to 10, indicating the number of days ahead for which forecasts will be made. Subsequently, `fold` is set to 3,  denoting the number of folds or partitions used in cross-validation techniques. We then instantiate a TSForecastingExperiment and initialise the newly created instance using the pre-defined parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "2df1c267",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_2c4e9_row25_col1 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_2c4e9\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_2c4e9_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
+       "      <th id=\"T_2c4e9_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_2c4e9_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
+       "      <td id=\"T_2c4e9_row0_col1\" class=\"data row0 col1\" >1425</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_2c4e9_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
+       "      <td id=\"T_2c4e9_row1_col1\" class=\"data row1 col1\" >temperature</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_2c4e9_row2_col0\" class=\"data row2 col0\" >Approach</td>\n",
+       "      <td id=\"T_2c4e9_row2_col1\" class=\"data row2 col1\" >Univariate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_2c4e9_row3_col0\" class=\"data row3 col0\" >Exogenous Variables</td>\n",
+       "      <td id=\"T_2c4e9_row3_col1\" class=\"data row3 col1\" >Present</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_2c4e9_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n",
+       "      <td id=\"T_2c4e9_row4_col1\" class=\"data row4 col1\" >(244, 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_2c4e9_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n",
+       "      <td id=\"T_2c4e9_row5_col1\" class=\"data row5 col1\" >(244, 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_2c4e9_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n",
+       "      <td id=\"T_2c4e9_row6_col1\" class=\"data row6 col1\" >(234, 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_2c4e9_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n",
+       "      <td id=\"T_2c4e9_row7_col1\" class=\"data row7 col1\" >(10, 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_2c4e9_row8_col0\" class=\"data row8 col0\" >Rows with missing values</td>\n",
+       "      <td id=\"T_2c4e9_row8_col1\" class=\"data row8 col1\" >0.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_2c4e9_row9_col0\" class=\"data row9 col0\" >Fold Generator</td>\n",
+       "      <td id=\"T_2c4e9_row9_col1\" class=\"data row9 col1\" >ExpandingWindowSplitter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_2c4e9_row10_col0\" class=\"data row10 col0\" >Fold Number</td>\n",
+       "      <td id=\"T_2c4e9_row10_col1\" class=\"data row10 col1\" >3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_2c4e9_row11_col0\" class=\"data row11 col0\" >Enforce Prediction Interval</td>\n",
+       "      <td id=\"T_2c4e9_row11_col1\" class=\"data row11 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_2c4e9_row12_col0\" class=\"data row12 col0\" >Splits used for hyperparameters</td>\n",
+       "      <td id=\"T_2c4e9_row12_col1\" class=\"data row12 col1\" >all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_2c4e9_row13_col0\" class=\"data row13 col0\" >User Defined Seasonal Period(s)</td>\n",
+       "      <td id=\"T_2c4e9_row13_col1\" class=\"data row13 col1\" >[1, 5, 20]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_2c4e9_row14_col0\" class=\"data row14 col0\" >Ignore Seasonality Test</td>\n",
+       "      <td id=\"T_2c4e9_row14_col1\" class=\"data row14 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_2c4e9_row15_col0\" class=\"data row15 col0\" >Seasonality Detection Algo</td>\n",
+       "      <td id=\"T_2c4e9_row15_col1\" class=\"data row15 col1\" >user_defined</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "      <td id=\"T_2c4e9_row16_col0\" class=\"data row16 col0\" >Max Period to Consider</td>\n",
+       "      <td id=\"T_2c4e9_row16_col1\" class=\"data row16 col1\" >60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "      <td id=\"T_2c4e9_row17_col0\" class=\"data row17 col0\" >Seasonal Period(s) Tested</td>\n",
+       "      <td id=\"T_2c4e9_row17_col1\" class=\"data row17 col1\" >[1, 5, 20]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+       "      <td id=\"T_2c4e9_row18_col0\" class=\"data row18 col0\" >Significant Seasonal Period(s)</td>\n",
+       "      <td id=\"T_2c4e9_row18_col1\" class=\"data row18 col1\" >[5, 20]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
+       "      <td id=\"T_2c4e9_row19_col0\" class=\"data row19 col0\" >Significant Seasonal Period(s) without Harmonics</td>\n",
+       "      <td id=\"T_2c4e9_row19_col1\" class=\"data row19 col1\" >[20]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
+       "      <td id=\"T_2c4e9_row20_col0\" class=\"data row20 col0\" >Remove Harmonics</td>\n",
+       "      <td id=\"T_2c4e9_row20_col1\" class=\"data row20 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
+       "      <td id=\"T_2c4e9_row21_col0\" class=\"data row21 col0\" >Harmonics Order Method</td>\n",
+       "      <td id=\"T_2c4e9_row21_col1\" class=\"data row21 col1\" >harmonic_max</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
+       "      <td id=\"T_2c4e9_row22_col0\" class=\"data row22 col0\" >Num Seasonalities to Use</td>\n",
+       "      <td id=\"T_2c4e9_row22_col1\" class=\"data row22 col1\" >1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
+       "      <td id=\"T_2c4e9_row23_col0\" class=\"data row23 col0\" >All Seasonalities to Use</td>\n",
+       "      <td id=\"T_2c4e9_row23_col1\" class=\"data row23 col1\" >[5]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
+       "      <td id=\"T_2c4e9_row24_col0\" class=\"data row24 col0\" >Primary Seasonality</td>\n",
+       "      <td id=\"T_2c4e9_row24_col1\" class=\"data row24 col1\" >5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
+       "      <td id=\"T_2c4e9_row25_col0\" class=\"data row25 col0\" >Seasonality Present</td>\n",
+       "      <td id=\"T_2c4e9_row25_col1\" class=\"data row25 col1\" >True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
+       "      <td id=\"T_2c4e9_row26_col0\" class=\"data row26 col0\" >Seasonality Type</td>\n",
+       "      <td id=\"T_2c4e9_row26_col1\" class=\"data row26 col1\" >mul</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
+       "      <td id=\"T_2c4e9_row27_col0\" class=\"data row27 col0\" >Target Strictly Positive</td>\n",
+       "      <td id=\"T_2c4e9_row27_col1\" class=\"data row27 col1\" >True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
+       "      <td id=\"T_2c4e9_row28_col0\" class=\"data row28 col0\" >Target White Noise</td>\n",
+       "      <td id=\"T_2c4e9_row28_col1\" class=\"data row28 col1\" >No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
+       "      <td id=\"T_2c4e9_row29_col0\" class=\"data row29 col0\" >Recommended d</td>\n",
+       "      <td id=\"T_2c4e9_row29_col1\" class=\"data row29 col1\" >2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
+       "      <td id=\"T_2c4e9_row30_col0\" class=\"data row30 col0\" >Recommended Seasonal D</td>\n",
+       "      <td id=\"T_2c4e9_row30_col1\" class=\"data row30 col1\" >0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
+       "      <td id=\"T_2c4e9_row31_col0\" class=\"data row31 col0\" >Preprocess</td>\n",
+       "      <td id=\"T_2c4e9_row31_col1\" class=\"data row31 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
+       "      <td id=\"T_2c4e9_row32_col0\" class=\"data row32 col0\" >CPU Jobs</td>\n",
+       "      <td id=\"T_2c4e9_row32_col1\" class=\"data row32 col1\" >-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
+       "      <td id=\"T_2c4e9_row33_col0\" class=\"data row33 col0\" >Use GPU</td>\n",
+       "      <td id=\"T_2c4e9_row33_col1\" class=\"data row33 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
+       "      <td id=\"T_2c4e9_row34_col0\" class=\"data row34 col0\" >Log Experiment</td>\n",
+       "      <td id=\"T_2c4e9_row34_col1\" class=\"data row34 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
+       "      <td id=\"T_2c4e9_row35_col0\" class=\"data row35 col0\" >Experiment Name</td>\n",
+       "      <td id=\"T_2c4e9_row35_col1\" class=\"data row35 col1\" >ts-default-name</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2c4e9_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
+       "      <td id=\"T_2c4e9_row36_col0\" class=\"data row36 col0\" >USI</td>\n",
+       "      <td id=\"T_2c4e9_row36_col1\" class=\"data row36 col1\" >39aa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x287f01270>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pycaret.time_series.forecasting.oop.TSForecastingExperiment at 0x286fcc8b0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forecast_horizon = 10\n",
+    "fold = 3\n",
+    "\n",
+    "eda = TSForecastingExperiment()\n",
+    "eda.setup(data=df_berlin_daily_avg, fh=forecast_horizon, target='temperature', seasonal_period= [1, 5, 20])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00ae4344",
+   "metadata": {},
+   "source": [
+    "After the instantiation, the model reports possible seasonality for 5 and 20 days options. After initial testing, it is reporting the **primary seasonality for 5 days**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99d5a717",
+   "metadata": {},
+   "source": [
+    "## Plot data \n",
+    "\n",
+    "To better understand the performance of the trained model, one can use the `plot_model` function.\n",
+    "The sample data is generated, which can be seen in a few of the plots."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d5b6390d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly\n",
+    "\n",
+    "# Plotly plots will be rendered as PNG images, uncomment the following line to crate an interactive chart\n",
+    "plotly.io.renderers.default = 'png'\n",
+    "\n",
+    "eda.plot_model() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "633c26a8",
+   "metadata": {},
+   "source": [
+    "The Autocorrelation Function (ACF) is a statistical tool used to quantify the relationship between observations in a time series and their lagged values. In this context \"lag\" refers to the difference in time between observations.For instance, a lag of 1 means comparing each observation with the observation that occurred one day prior. ACF essentially measures the correlation between a series and its lagged versions at different time lags. When examining the ACF plot, each point represents the correlation between the original series and a lagged version of itself. The horizontal axis of the ACF plot displays the time lags, while the vertical axis represents the correlation coefficients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3540858e",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eda.plot_model(plot=\"acf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "597ce7b2",
+   "metadata": {},
+   "source": [
+    "In the context of the Autocorrelation Function (ACF), the findings indicate that there is a stronger correlation between recent weather temperatures and today's temperature compared to temperatures measured further in the past."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d83e75ce",
+   "metadata": {},
+   "source": [
+    "PyCaret offers an easy way to plot all these graphs in a single multi-plot by using the diagnostics plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5524f3fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CHSEHyPJuLNhswRgh6Zyx+EoS582Cjs2ggQMHqlABhAywkAAJkAAJkEBOBCjQOT9IgARIgARcBHxZ0M+dO6eszEgQBksfhLd2H/cU6NqS7ClU4VoOF3OjizsyicPFGNnfYXGFVRsZu2EVh1XdTAy6pwUdln3EVnsmf8MRYE888YQrQzuSwSEpnU7aBQiwbiPOGaIMbvAo6BcEn84ermF5SxKnLfTYvMDz64JkdnCBhxUX94VYNhZ9D8Rf+3N1tyrQcZ9mzZopLwbPjYn3339fbbQgRhpHnRkLssufOHFC9ccpgQ6RjYR+8F7QOQQw7hDV4If5YfSswLisXbtWHb2Xk0DHcyLEAjHeOns6ng2Z1yGujQIdiduQr8AYL685QFAvX75cZdeHaz0KEtuBFTwxPLO4exPoesMKfzfw9NDtoC0kEER4B5LcsZAACZAACZAACFCgcx6QAAmQAAlcINAhJOEWDEEEcYlj0PD/KJ7i01OgI2s43HlhEUY8NgQexAwSwaEYBTrEC1zmURcCBhnXja7muRHoyCJ+zz33qCO10A9kWoc4RuI4FO36rkUc6jRu3FiJdYh7iDijtROWVZxf3bRpU+U2fejQIZV9G9nFPbO4Q8xpKynaqFChgmzdulXFyOPfYApBCsEM93+ciw7BDWs94vlhOcaxZjmV3Ah0HIOHcdLZ0SG+EfMNkQuLMvjjWeDyjvFDYjOIUn10nl0CHceq4bnBEhsc8MhADDzc2Y1nyevkgcjKjv5hbmzZskVZsiHccaxdTgIdzJE5HdfhmD5wxtjoo82MAh1zG/eGcEaYATajkKUemwDIswCPDMxZbPhg8wgu67qYEeioi+Pm0Gf0B/dArgQkzMN8w9+G3gziVxEJkAAJkAAJUKBzDpAACZAACbgIaOu3EQmECwQFrL5a7Bg/X7JkieBYMWSohvhEgeiBuNMJ2PA7WDJhIUZcuPF8bFgkIfoh3uDeDaEKN3jE7RrPiPaVZAzWT1hBjZngYflFsjFjkjpsOsCqWq9ePdVH3A9u1YiVNxYIQli09ZFvEIJwt0b7epMC98NzQNTC4o4+6AILMP4NjwFd4O4P0VmpUiVlqfVMngcxiKO/zGR1z41Ax7hC/Ov7QijqxHzgjyzoOnGd7jM2EWDJhru/tmobjydDPe2tgLhxJJTTRceVP//889KlSxf1a33MGkQ3chigoB/YuIBgNm5MQLxjrsBTQzNHfXgBwC3/xhtvFGR1xxFwvpLEwVqtj/PDtZjHGFtY5RGHDi8LXZBJHvV1wjwdU4+xR+Z6PL8u8PZAVnj8Dp4V2FTy1xdsGmFzAeETxg0oPD/yMhjPmOfXEQmQAAmQQHgToEAP7/Hn05MACZCAYwRgiUTMLwqEJxLKmS2wdsOqPG3aNKlRo4bZyy6oB4EFqycEvzEzu7EixCCO4kLBUWO++gnLMuqhLTMJvZDJHt4HqB8fH+/WN7jSQ6hBfEI4+os7N16cG4Gur0eSNPQL98Q55MaCPuH54HaNz62Ml5kBMm6wwHMCohYbPzl5DEDY4ng0bLjACq7PtDdzP9TB80J041kg7v3dC3VxD332OtpAH+A1AW5oI1AuaAdjj6P2kDgRYR0sJEACJEACJKAJUKBzLpAACZAACVxSArCSw+VZCyi4+8LSDbdiJBCLioq6pP0LtpsHItAv5bP48oC4lH3ivUmABEiABEgg2AhQoAfbiLA/JEACJBBmBOBK7Vngjo7M5v4SpoUZKvW4FOjhOOp8ZhIgARIggXAhQIEeLiPN5yQBEiCBICUAwYlEWXB7hvsw3OERhx6oK3GQPm7A3YJL/ubNmxUnuM+HSkFcOtzakZSPhQRIgARIgARIwDsBCnTODBIgARIgARIgARIgARIgARIgARIIAgIU6EEwCOwCCZAACZAACZAACZAACZAACZAACVCgcw6QAAmQAAmQAAmQAAmQAAmQAAmQQBAQoEAPgkFgF0iABEiABEiABEiABEiABEiABEiAAp1zgARIgARIgARIgARIgARIgARIgASCgAAFehAMArtAAiRAAiRAAiRAAiRAAiRAAiRAAhTonAMkQAIkQAIkQAIkQAIkQAIkQAIkEAQEKNCDYBDYBRIgARIgARIgARIgARIgARIgARKgQOccIAESIAESIAESIAESIAESIAESIIEgIECBHgSDwC6QAAmQAAmQAAmQAAmQAAmQAAmQAAU65wAJkAAJkAAJkAAJkAAJkAAJkAAJBAEBCvQgGAR2gQRIgARIgARIgARIgARIgARIgAQo0DkHSIAESIAESIAESIAESIAESIAESCAICFCgB8EgsAskQAIkQAIkQAIkQAIkQAIkQAIkQIHOOUACJEACJEACJEACJEACJEACJEACQUCAAj0IBoFdIAESIAESIAESIAESIAESIAESIAEKdM4BEiABEiABEiABEiABEiABEiABEggCAhToQTAI7AIJkAAJkAAJkAAJkAAJkAAJkAAJUKBzDpAACZAACZAACZAACZAACZAACZBAEBCgQA+CQWAXSIAESIAESIAESIAESIAESIAESIACnXOABEiABEiABEiABEiABEiABEiABIKAAAV6EAwCu0ACJEACJEACJEACJEACJEACJEACFOicAyRAAiRAAiRAAiRAAiRAAiRAAiQQBAQo0INgENgFEiABEiABEiABEiABEiABEiABEqBA5xwgARIgARIgARIgARIgARIgARIggSAgQIEeBIPALpAACZAACZAACZAACZAACZAACZAABTrnAAmQAAmQAAmQAAmQAAmQAAmQAAkEAQEK9CAYBHaBBEKJQEpKiixatEiio6Pl8ssvD6Wuq75u375dtm3bJpmZmdKwYUMpV65cyD2D7vDOnTtl9+7d0rp1a8mbN6/s27dPtm7dKqVLl5batWuH7HOx4yRAAiQQqgSwtvz1119qbalRo0aoPgb7TQIkcAkJUKBfQvi8dRaB1atXy65du6Rz584SFRUV1lj27t0ry5cvlzZt2kiJEiWCksXEiRPl/fffl6efflq6desWUmOo+67BPvzww9KrV6+g5GymUx9++KHg5+eff1bz5eTJk9K/f3/Zv3+//PDDD1K4cGEzzbAOCZAACQQlgRkzZsi3337r6ltsbKxUq1ZNvS80aNDAtj6//PLLsnDhQpkyZYoULVo0oHbPnTsnbdu2lR49esjjjz8eUFu8mARIIDwJUKCH57gH1VOPGjVKvvrqK/nnn3+UFTCcy++//y4jRoxQArh58+ZBh+LAgQPSpUuXC148QmEMjx49Ktddd500a9ZMhg4dKsWKFZOIiIig3QgxM/ieAh3XHDx4UL283nLLLfLkk0+aaYZ1SIAESCAoCXz00UfywQcfSMuWLSV//vyC73Fs6qM8//zzaj2yo7z55puycuVKGTt2bMAbmxTodowI2yCB8CZAgR7e4x8UTx8K4u5igQp2gQ5rANzbPa2zoTCGq1atkvvuu0/Q1w4dOlysIXXkPufPn1ebC94EOm44YcIEGT9+vHz22WdSp04dR/rARkmABEjAaQJaoE+bNs3lLr5p0yaX55MOt3K6H2ba19/Ldgl03Z6Ze7MOCZDAf4sABfp/azxD7mlgOccCnJSUJE2bNlVxzSijR48WuLIdP35cWZOxCCO+tnr16nLXXXcpCyEECsozzzwjJUuWVG5vX375pWzYsEEuu+wyZSUtU6aM2n2Hdf7w4cNKmMGqWKRIEXXtkSNH1C78VVddpSyPc+fOlT179qjd+scee0wqV67sYmqmLxBE6Osbb7whcM2DKMzIyFDPM2nSJIEAR/soxYsXl2uuuUZ69+6tLAPYvYebHT7Hc8LCizJw4ECpX7++DBs2TOrVq6dEpi7ghue/8cYbpVOnTi4epUqVUlbu6dOny+bNm9X1uC49PV258P3yyy8qDhsxcugDPgPvnApit2+//Xbp16+f6pMuOY0hLB2ffvqp6jvCGObPny/Hjh1TbDF+EMuIocbY5suXT40beMDKrYse31q1ailPC7SJfj/wwANyww03uOqhXTDGPcCwYsWK0rhxY7n77rvlzJkz8sILLwhitjXbuLg4NU54CYIL5XfffSd48cN1mCdGJnqedO3aVcqWLav4od+33nqrYos5BI6HDh2SX3/9Vc2rFi1aqDAA/A5zEKEL8BBBG+Cn5zoeAGPx7rvvqjooENWDBw92c+FMTU2Vjz/+WLmz678FtIE+axd3DeP06dPSvn179RxgzEICJEACoUjAm0DHc+D7/KefflJrQtWqVdWj/fHHH2p9wxqBNR7r+EMPPSRYD1EWL17scz1CzPiCBQuUBV2H2iHfCjY60S6+c+vWrau+8/Edbizr16+X9957T7WPdQwhaljrPV3c8f2OTVX8V/dvyJAh6v3FWH788Uf1LoPvdtS74oor1Dpy7733qvckFKyL3tZ5rM94x9myZYtah5KTk1U+EvT75ptvdt1Gs7jzzjvlt99+U+9IKHiPADO8C6EPer3FmnX99deH4hRin0kgJAlQoIfksP13Og0xA/ECQXXTTTe5RAsEHQQJ3HQhQiHEIJYhjCCysDjpxQYLCsQ3Srt27ZR72vfff68WShSIoiuvvFItsMuWLVPCTotc/A73RcFCiGRb69atU/3B9bgf/ovYXjN9gcDGvbHgok/4L8QSFn8IrrS0NCXo0Cbu8/fff6tF75VXXlGLMdzsdAw6NhdQbrvtNiUqIVo7duwoI0eOdE0A7c5sfCbwwD2xMKNAzELcQqgh5hoCFm3hWZcsWaJeKsAXfc+pzJo1S5577jn5+uuvpUqVKq6qOY0hFnlcA7YYR70x8vbbbysWjz76qHr5gOiFiMYLF+p98cUX6pn1C4MeXySlw0sJBCme788//5QCBQookd2nTx+1OYO4eDzzjh07BH3GXAHzt956S22e4KWtfPnyakMC8+ydd96RyZMnq9/jB2IZ1+FFCC9n2EjQ80Q/B8YP94X4x9zScwi/hyg+deqUzJs3z/XceJZGjRrJ0qVL1dzCnNcvWngBwksVru3evbt65pkzZyoOxpdPvMjhBRJjh40MMMFLIDh4CnS0Ae6IqcTLFwsJkAAJhCIBXwL9pZdeUp5c33zzjXo3gDDHdzw2WLFhjQ1hfC9iDcZGNb7v9RrmbT3Cuo2NWm2Rx8b6Pffco9YUCHLcA+s4Nt2N663x+xvu9tj4xWY7fm8U6Fh3sf6ifzAwYI3G5jW+93FvvTbq58VYQVTHxMSo73l832PtxzuAXhe9rfP/93//p8LjsEGLdScyMlKJbaxrxvcmzQJtoU+4ZuPGjep59ftLq1atlJEDazzWGTyDv438UJxj7DMJBCMBCvRgHJUw65Mv92iI1c8//1w++eQTlyURFmCISb2o6YUqT548ygIJ4YUCdzhYrSGqH3nkESXSIeJ69uypFjwsjChaeGHRgxUUiysysKItiLannnpKiSazfdECHbvS2O02JulC341WU9xfiy6IZOza5+TibkWgYzFH3yHW9YIKMQtBjN1xiFldUG/27NlKHCP7t68CywI2LLCwexZfY6hfArx5JOAFCOIXLxC6YNOib9++SjjDU8I4vhDSeJFAwW4/PCTwMnL11VerpGh4idKbHbo9WNUh/CtUqOC6ZsyYMUpUG8cfmwSYL7ov+iUJFna86Oh5AuEPXhgLbWXRn2Fz6MUXX1TCHWX48OFKpBvjJNGfa6+9Vj0jNmxQ7rjjDtV/vEzCkwIFmwt4udNx5PrlDs8I4a2LLxd3fI6XKljwMaeYLC7MvlT5uCTwHyHgTaBjvRw0aJASkrA243sV7wUQlOPGjXM9OYT5a6+9Jq+++qrKP5LTevS///3PTaDr70+sM9iIRcGahftio19viiLJKDbX4TGHdQbF08Ud7x4wKEDkop5eI7TBAO8LeE/RG+7YAMd7j66HjV1YsD0Furd1Hvf3fNeAJwDWHWwK410GRbPAZgM2IrCewTKUpsMAACAASURBVCiiNwCwtuiTQPS7A94B4B3AQgIk4DwBCnTnGfMOfgj4EnewHMOtCwukscCCi91gvZsLEYoFDaJaF72AQzAb3aC1W5zeJdfiCmIJokmXtWvXqkVL74Cb7YsW6FhQtQu+bhPCHxZQWK1xXyS7gRAzWoLtEuiePNAHCFBsXOAFo2DBgq5nRV9xX3+J6XAdXlBgVfYs/gQ6sqfDguxZ8EIyZ84c5YaPlw0wgYcENhCwkYDibXzhugcxDnd4bHLghUTHlQ8YMEBZwmHxMG6IaFFvFOiat/F3uCfmHawwun1f8wR1fX0GTogFxwuk9oZAfbjCIxwDn2vBDtdJbYXXjPDCCGZgp638ePnES6guOQl0PYeN1np+GZEACZBAKBHQAh3fkdhcx2Ym1gps0mPNRyZ3uKAjbAprhXGdSUxMVN/BONkCP1qUeluPPAW6XtOwPhUqVMiFTAt3WOtxL4THoW960x8VPQU6+oH3EIh5WNGNBd/78fHxygNAC2GsYffff7/bGu1NoHtb53ERNqVhNceGNxK7Yp3BJgIEN+6D4osFvLlwjXEjPiEhQXmmPfjgg8rwwEICJOA8AQp05xnzDn4IeBN32HHWWcyN7tTGprDIQmh6E3A6IZinQMdOOnaw/Ql0uChj4cWuM64x2xdfAh072MjOjk0F7Po3adJEWauxg45FVLtqOynQtXu7L55IAJdT5nhwxg68N1f43Ah0LZgxphDUlSpVUh4M8FzwJ9B1jLXxhQfuh+gbXMNR8AIHywcs8fh/bwIdOQPgbm/00jC+YOmQgtwIdMSLIy7RU6DjhQxjD+GsPQbQPx0naZzjYAIrvrbGIyQCjMwIdP1ShU0puFSykAAJkECoEdACHWE9EMr4noQnFdYjvdGs3duxtmovJONz4vsPa4oVga6927ChbvTy0pv/WM8ROoU1yDMvi6dA19/znt5r6COu3bp1q3Kfnzp1qmCzGBsPWBN18WVB9ybQ8b0PgwPWLAhyhLfhCE60DW7+BDos6gjDMgp0b6F0oTaP2F8SCDUCFOihNmL/wf5qcecZ34QFGInUjDvT3h7fikD33CX3Jby065kWimb74kugw3KOBR+ubPivtuzCZQzP5ynQPRdoPDfcquFGDUu4Lr5i0L0t3Lpv+l5WpxIWbrjBGT0VdBu+xjCnFyJYpxHvhuRsOjQBsf6whOdGoGthjeQ4aBfCGFYDuPXDjdybQEesN1zQsTkBbwldtPUZSfFgmXFKoGvXfKPLu7dx0Z4fOt7SjECnBd3qDGd9EiCBYCPgKwbd2E+9zvhzwbYi0HWMuzF7PO6J9Rr3QXhVzZo1lWUZ7weor4unQN+7d69ycfdM2gmXcriMQzjDHR8hZIgTx5qDtSc3Ah1eVkiWanSHRzvYSEChQA+2Gc7+kIB3AhTonBmXnIB238Xiacxmih1qWJSx2GCn2liwS6zjvZwQ6LCqwrqqY5zN9sWXQNeLOuK/EO+sixZeWjTrWGPEzcF6byzYaYd1GAu5LjpBjWeSOG8CHeIOizZi3bBRYCxwGUe8W04JYMAE7n3eYtB9jaGvFyK4+yPLOZLYICGcLti5R8y/VYEOYY+XIlgKdNFiH4nl4I7oTaDr43owJjo2D9fDOwMu/zqpjlMCHSED8NRAjgQkKDKGHsCLBPfF5gUs/NgYQfy5MYOw3nDyliROe2MwBv2Sf8WxAyRAArkkYEagI+QNm7CeG9i4JbzX4BGHtcGKQEcG89dff90tHwraw1qLDXyspwhdatu2rUrwhu9gvfGuQ6R0iJwOwUI4m/FYOCSTgyu7TtKqT0qBtwA2vYsWLarCt8AAa5JnDLq3dV5b/uHirtcTrI3oi3alx3P4YkELei4nKi8jAZsJUKDbDJTNWSegFwrsLiORC+KfdMw3xBrcfyHY4KoFizFczrCw6ezUdgh07GAjIReONoM7GkQj4sqwOQD3Ni0c/fXFl0DXbnFYULFQQnwhtk0fq6UFut5pRz1kmseCjsUa/0bCPIhI9BPHcKGfsD7rlwadmd4bD9TBIo0keXgWZIdFLDPaRzvIFu5pnfUcST1O3ur5GkM8I0Slt5g/bDhggwF9wvPg5QTu7ShWBTqOucE12HjA0WqIVcS9keVXu3h7E+i4Fyzs4I/rkdkeeQHwcoTNIoRDQDw7JdBxf7zYPfvssyrzPPqA+6IPENaIo4dlRscwoj7cNeG6iD7jRVG3YdycwO/w3HhmZnG3/p3EK0iABIKDgBmBjp7iJBSsh3ANxyYmEsfCmwobn3D5RmiRFYF+9uxZ1Q42xbFBr7O4w1XcaDGHSzp+h+9vvLegPvLkYG01ZnHXgh+bwbCOI0wLG9tYW4wno2BTGJZ0FHjNYV3UYVtmBLrOfYL74J0KeV2wjmmXd1rQg2Nesxck4I8ABbo/QvzccQI4bgsuYzhqRB8NpgUrLJzYxYaQ0wUiGSJVJ4/DYolzUI3ZW3UMOhZt49mdvlzc9bEi+h5Y2JCt25j92kxfckoSB/GJxVEvtniRgMs43N+NbucQwFjw9XnpOjEYRBqStCCJGgp27SFIYVlFAhkIXhRvPPRzwVKO9pCx3VgQa42zU43JcDwHXlspsOOPJDbG4msMEVfnS6DjObDhgOdHwbhCrEPMG12+vT2PjkFHjDnmAZIGQcjCOqELNliQ4R2u9EjYpwW6pxcD2oIgN3oGIPkPMrJr13vtiu6ZTBD38vWZtnp7ZsfHiyIsL8Zke9hMQOiCPk4O7eKFD5z1+fbwrsCc1H8j8CrBhhIyxeOFDuEguiBJEKxJyECPGHYWEiABEghFAkjyhu9K49Gb3p4DG9BYN/G9q78jUQ/f5cjhgfXAikDHtTiqDesXQqZ0wfc32tPHuOK7FmFSWMN1wRqJjQWjQIe3FDbZ8a6jC9ZweMsZc7/AYg6PO6yL2MivV6+eSiqKdwtjklBf6zyeHZsGevMe98L7EvKXGEMGfbFAMliszcb1UHsEGN8zQnEusc8kEEoEKNBDabT+433FwgSBArcuT1druKnpzK343Ji0JRAsRssoxC6ynUKU5+TqHUhf8IywjuIZjILK2zPAWwAvAUa3Z1wPQQjBCQGXWw6aNe6LzQnP4998MYUlAS8OcLP31v+cxtBXm1j88ZIDMQzLdyAFMX1oD+76+lxZs+3hxQYeDOBxqY4lg1s+5iDmh3Hc9TOAL14a0b+c5g8S0OEFEWEa8E5gIQGnCJgJj3Hq3myXBDwJQNTCagwLOLyK7Di3G9/JmOcIqzMm6TTeG/fE5jsSe8J676vgOxyb72gHyUI9T3vxdh0292Ft9+flZrwWG894VyhbtqzruDbOFhIggdAhQIEeOmPFnjpAICfXZQduF/JNYpMEO/c6u72Zl4uQf+gQewDt6aDP1g2x7rO7QUhAJ57CcVaIY0VBHhB4r2hPH1gW4YVjdrMvCB+TXSKBS04Alm8IfSSgQ0b6jRs3Ku8qeALAm4Br7iUfInaABC4KAQr0i4KZNwlWAhTo1kdGx9Ih5h0JZViChwC8B3S2XsQdwpOAhQQCIYDTEBDugWIU6Aj3wPxCrgN4+yCUBB42OiQjkHvyWhIIVwI64arx+ZGHBn9nOuQqXNnwuUkgnAhQoIfTaPNZLyCA2GkkYYG7sD+Xc+LLIgAXPVhpET9fo0YNYgkiAsePH1diCS7wcJ9kIYFACCCJ5bBhw9RpBsh/oAW6PiHBmPwReRywQWQ8BjKQe/NaEghHAvr0DrjUoyBfCcKuaDkPx9nAZw5nAhTo4Tz6fHYSIAESIAES8EIAruvwxoDwRiwvMk9rgY48GkiAZUxOiPOiZ86c6TpnmVBJgARIgARIgARyR4ACPXfceBUJkAAJkAAJ/CcJnDhxQrms33XXXUqI6/OZtUDHqRo4NcJ41jISRyJG1pj92ZjZWoPCyQIsJEACJEACJEACvglQoAc4O5Dd0+6is4QiCylL7gjg7GokK0J2cJbACSCbfGZmpiCDPUvgBJBoCywRYsFiDwEn5yiy6odT+f3331U8ORINwrUWSasgvG+99Va5+eab1WkLZizoOI7Ks8AFHmsb1zdzMwqnUSB0Ba7PLDkTQPZ0ZG0/deoUUZkgwHXIBKR/q+DEHJysgr9FFnMEwm3dNEfFfC0KdPOsvNakQA8QoEOXU6DbC9ZJ8WNvT0OjNb4Y2T9OTs7RcHvRgAu70fqNI6SQHBJW8+uuu07l6+jQoYMYY9BHjhwpiYmJpmLQKdDNz38KdPOsKNDNs0JNrkPmeVGgm2ela4bbummdUM5XUKAHSJQCPUCADl1OgW4vWCfFj709DY3W+GJk/zg5OUfD/UXD08Udozdo0CD1gp+bLO4U6ObnPwW6eVYU6OZZUaBbY0WBbo0Xaof7ummdmPsVFOgBEqRADxCgQ5dToNsL1knxY29PQ6M1CnT7x8nJORruLxreBPquXbvUOeg4qhKlc+fO8vTTTwtEkr9Cge6PUPbnFOjmWVGgm2dFgW6NFQW6NV4U6NZ5eV5BgR4gQwr0AAE6dDkFur1gnRQ/9vY0NFqjQLd/nJyco+Eu0HMarcOHD0v+/PnVj9lCgW6WlAgFunlWFOjmWVGgW2NFgW6NFwW6dV4U6IEzc2uBAt1moDY1R4FuE8h/m3FS/Njb09BojQLd/nFyco5SoNs7XhTo5nlSoJtnRYFunhUFujVWFOjWeFGgW+dFgR44Mwp0mxk60RwFur1UnRQ/9vY0NFqjQLd/nJycoxTo9o4XBbp5nhTo5llRoJtnRYFujRUFujVeFOjWeVGgB86MAt1mhk40R4FuL1UnxY+9PQ2N1ijQ7R8nJ+coBbq940WBbp4nBbp5VhTo5llRoFtjRYFujRcFunVeYSvQ09PTBUfF4CzREiVKSFRU1AX0jhw5IgUKFFDnaJotdHE3S+ri1gslgT5qzk75cvlBiYuJlBduqC4daxW7uLBM3M1J8WPi9v+5KhTo9g+pk3OUAt3e8YJAn748QV7+dbucSz8vdzcvIw+3r2TvTf4jrVGgmx9ICnTzrCjQrbGiQLfGiwLdOq+wFOjffPON4IxWXUqWLClvvPGG1K1bV/0qISFBZaPds2eP+vdNN90kTz75pERHR/slTIHuF9ElqRAKAh2bRS//ukO+W3PYjdEnd9WXRuXiHeO2IuGkrN5/SjrXKylF80XLxIV7JSk5XUrGx0jflmUlIiJCthxOljlbjqo+3NywpFQrXUQyMzMlJSXFsX6FU8MU6PaPNgW6/UydanHtniPS5b3Fbs2/2Km6dKlfwqlbhmy7FOjmh44C3TwrCnRrrCjQrfGiQLfOKywF+qxZs6RQoULSpEkTgSUd4hv/HT9+vOIxePBgZTnPzXmuFOiBT0InWggFgQ7r0YzV7uJcs+jftrzc16qcREdFytEzaZI/b5TERkfKPzuSZNGuExIdFSFd65eUKsXiLOEb88dumbJ0v+uasoXyyv4T51z/7taopPRqVlZu/Xi18jZBKRgbLXMebiv5YyIp0C3R9l2ZAt0mkIZmKNDtZ+pUi2/P3iRvzt3u1nz/NuXlgcsrOHXLkG2XAt380FGgm2dFgW6NFQW6NV4U6NZ5haVA93zop556SlkDYVU/efKkdOjQQSZOnCiNGjVSVUeNGiWHDh2S0aNH+yVMge4X0SWpEAoCvenri1wi2BukphUKysFTqbLveJbVulPd4jJrwxFX1QJ5o+T3wc0lJirCFOP0zPPS4o1FOdaF9RztnUvPdKv3YLsqMrhdZQp0U6T9V6JA98/Iag0KdKvELl19WtDNs6dAN8+KAt08Kwp0a6wo0K3xokC3ziusBfrMmTNl3rx5sm3bNiXOa9asKTt27JAePXrIL7/8IsWLF1d8pk2bJqg7ZcoUv4Qp0P0iuiQVQkGgdxq/Qg6ezLZe5wbUm91rS7vqRVyXpqRnyufLDkhKaoYknU2XUvExEhcTJYdPnZOVCSdl/cEzubmNuuat2+rLlVWcc73PdcdC8EIKdPsHjQLdfqZOtWiMQU9Jy5Ti+fPI7EHNnLpdSLdLgW5++CjQzbOiQLfGigLdGi8KdOu8wlqgjxs3TlatWiWJiYny7LPPSrNmzWT16tXSr18/mTt3rhQsWFDxmT59ukyYMEHgGq8LrvUscI3Hi4bdRce+ww2fJXcEkAQQX6hpaWm5a+AiXLVg+zG5b8oqyXIkz125pXFZ+X71AXXxIx2rydfL98vOo8m5a8zPVVfVKiHv3dHAkbbDrdGYmBgVZgNPHhZ7CODlHGEZTnxvxsVZCyWx54n+u63oLO6nUtKl3dil6kHnDWku8bH+8778d6l4fzIKdPMjToFunhVqcqPYPC8KdPOsdE0mV7XOzHhFxHkdaBpYOyF1NdzZv/jiC/ntt99MW9AnTZp0wTP27dvXEYGORQYlmMVlsA84NjnwhZqamupYV0+fS5e4PFESFWnOxdxbR1qMnCenzmXI/Ecvlz83H5HnZm6WzMxAJLtjjyudG5SW17tnJVZkCYwAPDwgJDMyMgJriFe7CFCgh85kMB6zdv+09bI84aR81LOeIKyHxZ0ABbr5GUGBbp4VBbo1VhTo1nihNgW6dWZhL9DnzJkjTzzxhCxatEiSk5MviEGH+zus7IxBD2xyXcqrnXRxR1K1B7/aIHuSsmLDR3atKdfWzj4abdPB0/LXjuPSunJhaVC2wAUYVu49KYt3nxSEjr8/P0F9vuLx1uq/CUkpcsvHqyU949JbVvPHRMmZ1CwBidj0T+5uJA1LXxxL4rerD8nhU6kqs/wtjUpdyqnkyL1pubAfK13c7WfqVItGgT51+QF5Y84uubNZGXm0Q2Wnbhmy7VKgmx86CnTzrCjQrbGiQLfGiwLdOi/PK8LCgv7hhx9K69atpUaNGoKYcSSJw1nnOov7oEGDlKsPs7gHPqGCpYXcCPSfNyTKpkPJEpsnUvq2LCdxeSLdHuf0uQz5auVB+WvbMVmz/7TbZ3MGN5fCcdHy66Yj8tSPW12fjbimivRoXNr1b2Rgh7g3FliNYD3SZWtistz+yWrHUSJ2HTHp8ASIjY6S42ezwwGiIyPk07vry4mUDBn45QaJzxsli5+40nKSOMTDz91yTAntu5uXlbql8/t9LpwL/8Xyg656dzQtLY9fXcXvdaFUgQLd/tGiQLefqVMtGgU6rOewouM7Zt7QFk7dMmTbpUA3P3QU6OZZUaBbY0WBbo0XBbp1XmEp0CG8f/rpJ9ezI1v7Sy+9JOXKlVO/27VrlzoHfd++ferfnTt3lqefflq0q3lOmJkkLvBJ6EQLVgX692sPy4s/Zx/7U7d0AZnSOzve+lhymvT4eLXgv74KjiPLPH9eIOR1gfv70kdbuf49eu4ulcTNWHD2L84A1gUe7s1eXxgwlvi80XLqXFYeg2YVC0riqVTZ/a/Vv1bJ/PJZ7wYCIY6Smp4pMzcckaTkNMnIPC/dGpVSiZtQOryzVI6fTZebGpWW+1uVFRzNZqZMX31IXvl1h6tqgbzRMvehZq57+mqj6aiFbnH56OHyfz0MUjOy3P/NZq43089LUYcC3X7qFOj2M3WqRaNAxz3avb1EhfpM69NAapW60OvIqX6EQrsU6OZHiQLdPCvU5DpknhcFunlWuiZd3K0zM14RFhZ0JUBSU5Xbev78+aVw4cJeqR0+fFh9jh+zhQLdLKmLW88o0OdsOSav/LpdklMz5d5WZeWqGsVk6rL9kngmTdpXLyK3NS4tg7/eKAt2Hnfr5D/DWrqs6B/8s1c++CfLHd1KiYgQWf5Ylvv6rmNn5fHvt8i2RPckbjfWKy4v31jDrdken6y+oJ6v+1YsEutyt9d17m1VTvDcu49lJTHsWKuYvNalhny35pDkiYoUbApEonN+yseL9sm4v/a4akGcX1G1iERGisTmiVLu5zuOJMusjUckT2SE3NSgpDT5N4501O875YsV2ZZwNNKpXnFpWCZeejTJ9irw7MLlby1WY6VLvphImf9wS3l4+ib5a1uS+vWV1YvIW91r++t+0H7OFyP7h4YC3X6mTrXoKdDfmLtLpi47IAPalhech46QGpYsAhTo5mcCBbp5VqjJdcg8Lwp086x0TQp068yMV4SNQA8Mk++rKdCdIpv7dpH3cGNiqizdc0LWJCTJH1uPuTUGF3Yc7aMLrNeo+9O6RLd6i4e3VGIWZfz8BPlwwV7LnerVvIw8clVWXGX3CauUSPdWIJ6vr5N1zB/KoVOp8s68PbIi4YR0rF1c9iSddYlTfH5Z+XhpWamwin2HaJ64cJ+cSkmTMoVipWuDkjJt+QH5yKO/P/RvLOULx1p6hge+WC9L9pz0eQ2s7EfOZHsV4LX6ze61ZPX+07J41wnZcNA9FEA3dHODkvLcDdW8tvv75qNqI0MXhAjULVNAXpi1za3+CzdUk8vKFxRkgobHQyi90/PFyNI0NFWZAt0UpqCo5CnQn/hhi/y26ajqGzYcP76rvhTNl+XBE+6FAt38DKBAN88KNbkOmedFgW6ela5JgW6dmfEKCvTA+KmYdruLPtLHiSPc7O5rsLWXmpEpfaesk02HzJ/3XbtUfhU/vnj3CTn/bwL1O5qUlsc7Zsc9awuP1edF7DVcy2+oW1xmbTji8/J+rcrJoCsr+vz8bFqmvP3nLll34LTUL1NAhravfEGMvPHid/7aI58sygrZ0OXdHnWldeVClh7h1dk75NtVhyxdA6GsOeZ04dD2laRswbzy1/Yk5dLeqW5xaVOlsOw/kSKdP1hp6Z5VisXJpF71BW79oVD4YmT/KFGg28/UqRaNAv3gyXPSafwKt1v5+z50ql/B2C4FuvlRoUA3zwo1uQ6Z50WBbp6VrkmBbp2Z8QoK9MD4UaAHyM/uyyct3idj52W7ZOe2/ZioSBlweXm5p2U5eX3OLmWR1gWx5nBLn2ZIZJbb++jrHryiotzXOisngh3lj61JMnzGJldTOA7uj4eaSUy0e+I7f/fyzFjvr36gn/dvW0EmLdon2GixWvq3LS8PtK1g9bJLUp8vRvZjp0C3n6lTLRoF+pp9p6Tv5+vcbtW7RVl5uH0lp24fUu1SoJsfLgp086xQk+uQeV4U6OZZ6ZoU6NaZGa+gQA+MHwV6gPzsvjy3seK++lG5aJzsPZEi6f8mJ9P1JvdqIH2mrPV6WbH8eeSowe1bVzJaluGWDvGLgpjt93vUlTw4d83GMnvTUfl98xEpGJtHbr2slMBTILfliR+2yW+b3EMA0BZi2xNPp8rqfady27Rt1yF29YHLKdBtAxpiDVGgh86AGQU6klLCgo7vEV3e7F5bcMoEC2PQrcwBCnQrtCjQrdCiQLdCK6suBbp1ZsYrKNAD40eBHiA/uy9POJ4iN3+0ShCH7lTJGx0pCx9pqY5cg3UdmdvvalZWOtcrLsULxMhXKw76jVef0ruh1CqZT2VMt2rVduq5cmoX4ud0SqocO5Wszif/euUhQeb7tlULy6mUDFmzP2eBfnnVIlKndP4L4uLtfJZWlQurbPW9mpUJeqa0XNg58lltUaDbz9SpFj1j0LFZ+fysbYIj13geujt1WtDNz0IKdPOsUJPrkHleFOjmWemaFOjWmVGgB8bM7WrGoNsI06amftt8VJ74fouyHMNtHO7oyNK+0UJcek5dGdO9tsr+7qsgKd3wGZtzfJoBbSuojMWhUjzFT/9p62VZgu/kccbnqlY8n4zpVlNOp2bKXZPXWHpkiO5Fu9yz6xsb8JbBHvf78p6GprLUW+qMjZX5YmQjzH+bokC3n6lTLXoKdNxHez9RoFOg53beUaBbI8d1yDwvCnTzrCjQrbPydgUt6AFypEAPEKADl8MKc/+09dKichEZ3yPrKK6EpBR58sctsuGg+eRx3roGwY94cX/l+7WJ8tGCBOXqfi79wnjqER2r5HjUmL/2L/bnRvGDo9u6TVjltws4A37RIy0F/9UFlnYclYYj3iYszDkrft+W5WRIu4qyJTFZhn6zUWW29yw4bk0fvWb8DCEDLS0mxPP7QDZW4IuRjTAp0O2H6XCL3gT6j+sOy/OztqsjIHGyBksWAVrQzc8ECnTzrFCT65B5XhTo5lnpmrSgW2dmvIICPTB+dHEPkJ8Tl3sT6LhPekamPPTtJnX8F0qJAjFucY+6L8iSDtdzT4s7jvL6qGe9HLOnez7P8bNp8sj0zbLKEKPdrEJB+eCOuiF11q9RoIOLWUv4ouGtJMZLbP0/O4/LQ19vzHH4fxrQWMoWyj4W7kxqhlz9zlJJ9cgH4K0RjFPTf89jd2KOBdomX4wCJXjh9bSg28/UqRa9CfTNh05Lz8lrpVap/DKtT0Onbh1y7VKgmx8yCnTzrCjQrbGiQLfGC7Up0K0zo0APjJnb1bSg2wjTpqa0JaZjnRIyqsuFlpj0zEyJiohU52anZWRKr0/XydbEbMv6Y1dXlqTk9AssvD8PbCql4mNy3UsclYZ49fwxUblu41JdaBQ/iO+/Y/Ja2XrY3RsBdnJj5D8S7E2/7zKvXe7z2VpZe8D7Gen6gr8fbuHGCmPVcvRivwjqlMovU3o3COoNEAp0v8NouQIFumVkl+wCbwJdH68YnzdK5g1tccn6Fmw3pkA3PyIU6OZZUaBbY0WBbo0XBbp1Xp5X0IIeIEMK9AABOnD51OUH5I05u6RP64oy9Ar/R5cdO5Mqny8/JCdT0qVmiTi5rXFpOXE2XfpNWyc7jpxVPezZtIxAuIdr8RQ/Z9My1DnrExZmn7X+8o01ZOfRZFmy+4QgDhzhAOUKZ1vAjezunLzmgrPqcc32I8mq2qArKki/1hfG6Pf4ZLVsS8yqg9KwbAFZs99d6C99tJWbW30wjhkFuv2jQoFuP1OnWvQm0HGvJqMWqluueLy1U7cOuXYp0M0PGQW6eVaoyXXIPC8KdPOsdE1a0K0zM15BgR4YP7q4B8jPict1sqHB7avKvS1KBXSLY8npyqUdFjMtnAAAIABJREFUP+FcfImfpOQ0+Wt7krSoVEjKFMxrGpFnIr32NYrKmG611CZJbHSkzyzsiEN/Z94eWZFwQjrWLi4Pt68oyamZsunwGRk+fZOcOpch84Y0l/jYaNN9uRQV+WJkP3UKdPuZOtWiL4He7u0l6m/YM7zFqX6EQrsU6OZHiQLdPCsKdGusKNCt8UJtCnTrzCjQA2PmdjUt6DbCtKkpOwW6TV0K+WacED8r956UJbtPSskCeaRbo8A2UgC48wcr1NnyofByT4Fu/5+EE3NU95IvGvaOly+B3nPyGtl86IxM69NAapUqYO9NQ7Q1CnTzA0eBbp4VBbo1VhTo1nhRoFvn5XkFLegBMqRADxCgA5dToNsP1UnxY1dvQ+nlngLdrlHPbsfJOUqBbu94+RLoOAv9x3WJ8mKnatKlfkl7bxqirVGgmx84CnTzrCjQrbGiQLfGiwLdOi8K9MCZubVAgW4zUBua0y95I7vVlWtrFLKhRTbhpPixiy6O1kMG/2DP4M4XI7tG3L0dJ+douAr09PR0OXLkiDrqK29e7yEs+LxAgQISG+s934S30fYl0PXm6oC2FWRA2wtzUDgzc4K7VQp08+NDgW6eFdcha6wo0K3xokC3zosCPXBmFOg2M7S7uUdmbJI/tybJu3c0lNYV89vdfFi256T4sQtoKFnfaEG3a9Sz23FyjoajQJ80aZKMGzfOBbhjx47y5JNPSqFCWZueCQkJMnToUNmzZ4/690033aQ+j472n//Bn0C/s1kZebRD+CblNP51UKCb/66gQDfPigLdGisKdGu8KNCt86JAD5wZBbrNDO1uTltSP7unqdQrkftj0ezuVyi356T4sYsLBbpdJEOzHSfnaDgK9BkzZkj58uWlQYMGsnfvXhk4cKD06dNHevXqpSbI4MGDleX8hRdekIMHD0rv3r1lxIgR0qlTJ78TyJdA10dkdqlfQl7sdOERmX4b/g9WoEA3P6gU6OZZUaBbY0WBbo0XBbp1XhTogTOjQLeZod3NUaDbTVTESfFjV29DyT2WFnS7Rj27HSfnaDgKdM8Revnll2Xfvn0yfvx4OXnypHTo0EEmTpwojRo1UlVHjRolhw4dktGjR/sdXF8CffOh09Jz8lqpVSq/TOvT0G874VCBAt38KFOgm2dFgW6NFQW6NV4U6NZ5UaAHzowC3QvDRbtOyObDZwSWj6L58thM2VpzOpv33GFtpXCeTGsXs7ZXAk6KH7uQU6DbRTI023Fyjoa7QEcseteuXeX666+XIUOGyI4dO6RHjx7yyy+/SPHixdWEmTZtmsycOVOmTJnidwL5EuinUtKl3dilEp83SuYNbeG3nXCoQIFufpQp0M2zokC3xooC3RovCnTrvCjQA2dGge7B8OmftsrPG46o30ZGRMjEO+tKo3IFXbXSMs5Lxvnz6nxrb+X8eZG0zPMSExVhy+jos3SXjmgnUZmptrQZ7o04KX7sYmunQM88L/LJor1yIiVdyhWKldublPbZzdT0TDkvInl9zG9vF9KCbteoZ7fj5BwNd4H+yiuvyOzZs+Xbb7+VEiVKyOrVq6Vfv34yd+5cKVgw67t++vTpMmHCBJk1a5ZrULxZ04cPHy4Q/PjxVmq/MEf9etMLV9s/SUKwRSTnO3fuXAj2/OJ3GSIqKipK0tLSLv7NQ/CO2NDIyMiQzEwaMvwNX0REhMTExPBv0R8ow+dWEodaaDZsqobNMWtmstHmZtTDPYt7SnqmtBmz2A3dTQ1KyvM3VFO/e/fvPTJx4T71/w3KFpCP7qgnp85lyLDpm2TdgdPK2o5/p2VkSvnCsdK5fgmBYL+2djGpUiwuN0MiTUYtVNdtfrGjnDlzJldt8CJ3Ak6KH7tY2xm/qhMN6r71blFWHm5f6YKuPvPTVpn17+ZUi0qF5P0edSXCxD4TBbpdo57djpNzNJwF+ocffij4mTx5stSrV08BN2tBnzp16gUDfeeddyoB5UtEXTN2oew7niK/DWkl5Qrnbg2wf3ZduhYxr+FxcB4LI0uOBCDOITpTUlJIygQBCCj8HUKks+RMAAIdvPC3yGKOAL67WHJPICwEur9stGPGjBHPFwnE1SG+zl8Jd4F++lyGXPn2EjdMENcju9aUHUfPyq0TV7l99sDlFWTn0bPy68Ysi7uvgi9DxCBWLhYrKWmZyjL/1cqDqjqsmWv2nZJPl+6XM+cy5NbGpaVr/RKupijQ/c1a6587KX6s98b7FVqgt69RVF6+sbrE5YlU8yY3Rc8hfW1MdKQseqSlW1Pzth6TYTM2u/3usasrS8+mZfzekgLdLyLLFZyco+Eo0GFVGzt2rLKMQ6DXrl3bNSbeYtBHjhwpiYmJAcWg4wY9J6+RzYfOyLQ+DaRWqQKW58F/7QK6uJsfUbq4m2eFmlyHzPOii7t5VrpmOK6b1inloIPOh8G2rL9stHDDQ5baYcOGuUjBraxUqVJ+WYe7QH9+1naBMDIWuKqP6V5bjp5JE2TWNpbi+fPIseR0yTRhDahcNE52Hbtwt7JkfIwcPuXuuj66Wy25qkZRdSsKdL/T1nIFJ8WP5c74uABnoCNBoC7Q5m91ry1XVCui5tGPaw8LHPnaVi0iTcsXzNHS3WrMYoHrui6F8+WR3wc1lcW7TsiSPSeU8N8LK9+mo269MXt+M1+M7Br17HacnKPh+KLx0ksvyQ8//KBEeuXK2UeelSxZUh2lNmjQIPWCb2cWd4xmKJ3GYP8svrBFCnTzlCnQzbOiQLfGigLdGi/UDsd10zqlMBfono9vzEaLzyDQT5w4IXghsVrCWaB/s+qQvDZ7h1dkeaIi5ZpaxWTWhkSrSHNVv2mFgtK2amG5qmYx6fbRSilXOFbmDrucLu65onnhRU6KH5u6KDoDtLG96KhIea1zdXnx5+1yJvVCNz54atzcsIT0b1NBPl60T3YdTZZmFQtLpaKxMuKHLZa79lHPeoK56K9QoPsjZP1zJ+doOL5o4FxzZG33LLCoV6xYUXbt2qXOQdd1OnfuLE8//bRyMfZXfCWJw3V25pLw149Q+JwC3fwoUaCbZ0WBbo0VBbo1XhTo1nl5XhEWLu7Gh/bMRqsF+pw5c6RFixZSuHBhadeunTRu3NgU3XAV6At3HZfBX29U8eJOlPwxUV4Flb97Fc8fI0fOpErt0gXk+4GtKND9ATP5uZPix2QX/FbbfyJFOn+w0m89bxVioiIlNSPbYt6pXnGZtT7nMAzPdkrFxwhCPrA5NLxDZWlULl52HElWwn9PUopcUa2wDLqiorqMAj1Xw5TjRU7O0XAU6GZH6PDhw5I/f371Y7bkJNCnLj8gb8zZJXc2KyOPdsi23Jtt+79WjwLd/IhSoJtnxXXIGisKdGu8KNCt8wp7ge6ZjRZAkHV2z549KkPjhg0b5M8//xTE03Xs2NHFa926dRfQrl+/vjoP1u4C93qUYM7c2vGdxRe4mdvJAXHEmw6dkYMnc5e9tkXlIvJ5v+ZM6GHToCA5CmJSU1ODNys+jmhqOyYrQWCwlkFXVpIBl1dU58qDpa9M1sHa/2Dul5NzVGcqD+bnD6W+5STQ/9x6TB6ZsVkd2flip+qh9FiO9JUC3TxWCnTzrCjQrbGiQLfGiwLdOq+wFujestF6Q/jcc8/J8ePHVeydLn379r2gKpLPOfGCjS8ClGA++qLGs79eYD2/q0UF+Wr5XsGxaoGWgrHR8kC7qjLqV+tuxvre43s1kY61s5PHBdqncL4+FOYkxqf6M78G/TDFx0bLkzfUltublWdmZhtHy8k5iphrFvsI5CTQdahKrVL5VaLQcC8U6OZnAAW6eVYU6NZYUaBb40WBbp1XWAr0nLLRekM4btw4WbVqlTrT1V8JVxf3kb/tdGVVB6Pr6hSX/3WpIf2nrZdlCRd6FeSNipBzGeelQN4olUBu+Z6T8sE/Cf7wBvQ54uAXD3fPvB1Qg2F8sZPuw3Zi9cy+bmfbaAuJ56oWyyfbjyQH3PR7d9SXVhXjA26HDWQRcHKO0sXd3lmWk0CHJ0y7sUslPm+UzBvawt4bh2BrFOjmB40C3TwrCnRrrCjQrfGiQLfOKywFur9stBDknTp1UolvtmzZIg888ID06dNH+vXr55dwuAp0gHlk+ib5c1uSSs429pY6Mn9Hkgz9dpNXZjVK5JNP724geaOzvAO+W3NYXvplu1++Ziqg3Xunrpd0Qwyxvm75Y61NnUtt5j7hXMdJ8WMn17ZvLpGzaRnSpEJBWZlwUgL35biwdw9eUUEw1RCzXqdUfpmwcJ9sOXzG8mMMvKKS3N+6rOXreIF3Ak7OUQp0e2ddTgIdd9Ibbd/f31gqFIm19+Yh1hoFuvkBo0A3z4oC3RorCnRrvCjQrfMKS4HuLxtt7969Vey5LshGO2LECEFMo78SzgLd8zgcnX3XF7OfBzaRUvFZ8fUoD361URbtOu4PcY6f1yyZX77o21CW7TkpL/2yTfYez45ZRxz6+B7ZZ/cGdKMwv9hJ8WMX2uEzNskfW5NUc8je3rNJafls6X5LzReKi5aTZ9NzFPa9mpeVR66q5Go3ISlFXv11uyzdc1IkQkwnThx1cx3pWLOwpf6xsm8CTs5RCnR7Z15OAv3h6Zvlr23HXDf88I560qyi/5MRvPXwnx1JsmjXCYmOipCu9UtKlWJx9j7IRWiNAt08ZAp086xQk8lKzfOiQDfPStfkummdmfGKsMvi7gvX6dOnJSkpSUqUKGFKmOt2KNAT5cVO1aRL/ZIqqdudk9f4nJF/DGkuhWLdYzmT0zKUoPnwn73y9cqDShidM5w/7dnY4CsrSrXicbJm/2mJjoyQ3i3KCjK+6zJp8T5JOJ4iJeLj5MF2VSUzLSWwvxBerQg4KX7sQHwsOU06jlvm1tRdzcpI0tl0mbXe+1F/5QrFyus315R525Lk/L+SvHujUrLn2FmZvvqwJKdlqqMCn5251a3dh9tXUvPOW1mRcFLe/TtBEpLOqntnZGbZ8MsXjpXmFQvJjDWH1L8Rf/7MdVUlLS3NjsdnGw7PUb5o2DvFfAn0s2mZ0vbNxW43u7FeCXn5RuvJ4pbsPiEPfJm98Y7wql8fbCZxebK8uEKlUKCbHykKdPOsUJMC3TwvCnTzrHRNrpvWmRmvoEAPjJ+Es0C/f9p6WZ5wUoxnP8/dclRG/7Fb0tIzBS9b+uzpq2oUldHdavmlDUHzwYK9sungGcFRbvF5o+XhqyrJmXMZ6nzpmiXz+W0DFZAJH4mdzpyx7nps6gZhVinYBTqy/Xcav8JtVG5vUlquqFZEHQfoWZCf4N3b6piyzM3fniTPztouJ86mSdcGJeX566sKzk/3V2BZn7k+USIjI6Rn09JqLvecvEY2HzojPzzYSqoUiaFA9wfRwudOzlG+aFgYCBNVfQl0rBdXvLXErYVO9UrIK34E+vvzE5Q3VuWicXJv6/JSqUisjJ+fIB8u2OvW1pPXVJHbGpc20cPgqUKBbn4sKNDNs6JAt8aKAt0aL9TmummdGQV6YMzcrqZAdxfoRjgQ6D+tOyxVi+dT4vpiFgp0e2k7KX7s6undn62V9QdOu5p759Y6Kj/ChoNn5O/tSVIkLlq6NSwpx1MypGi+aImK9C+y7eqbbkdvak25t5k0KpOPAt1GwE7OUb5o2DhQIur4S/x4K56JRsfdVkfaVPEdCvLRgn3y/vw9rqbgrfJD/8biLeTKuJls7xM51xoFunm2FOjmWVGgW2NFgW6NFwW6dV6eV9CCHiBDCnTfAj1AtAFdToEeEL4LLnZS/NjVUyRt+2zpAXXMH2JWm13kTSEzz/HIjE3y59Yk+b/u9aRTnWIU6Gagmazj5BylQDc5CCar5STQz58/L0/8sFV+33xU7m9TXgZeXiHHVgd8sT4r/4Oh3Ne6vKzce1J5eOlSNF8e+X1wM5M9DJ5qFOjmx4IC3TwrCnRrrCjQrfGiQLfOiwI9cGZuLYSzQO/8wQrZf+Kc/DSgsZQt5D+hns3oc2yOAt1e2k6KH3t7Gtyt6cSKFOj2j5OTc5QC3d7x8pfFXVu/B7StIAPals/x5v/7bafKX+KrwAKvw1zmDWku8R55UOx9Mvtbo0A3z5QC3TwrCnRrrCjQrfGiQLfOiwI9cGYU6P8SoEC3eTIFcXNOip8gfmzbu6aFx0NXVZP+bcrRgm4jYSfnKAW6jQPlx8Udd/px3WF5ftZ26VK/hLzYKecEcYdPp0rPSaslKTndayd/7N9ERv+xU3mumBH89j5p4K1RoJtnSIFunhUFujVWFOjWeFGgW+dFgR44Mwr0fwnos2pXPN7aZqqBN0cLeuAMjS04KX7s7Wlwt0aB7tz4ODlHKdDtHTd/FvQ/tx6TR2ZslvY1isiYbv6PytRrUdcGJeSHte6nNnx3f2NJPJ0qyP8QnzdK/hzSQkzkeLT3gQNojQLdPDwKdPOsKNCtsaJAt8aLAt06Lwr0wJlRoFOg2zyLgr85J8VP8D+9fT2kQLePpWdLTs5RCnR7x82fQEfsOAQ1kosisVtORdetVSq/PNqhsrpOlwZlC8jkXg3UP5uOWvjvYYoiPRqXlhHXVLH3oRxqjQLdPFgKdPOsKNCtsaJAt8aLAt06Lwr0wJlRoFOg2zyLgr85J8VP8D+9fT3UrrvdG5dVrrs8B90+tk7OUQp0+8YJLfkT6PtPpEjnD1ZK2UJ55acBTXK8+dTlB+SNObtc7vDbEpNlzpZjgnPP72hSWp3W8MO6RHlh1ja3dsZ0ry3tqxcx/WAp6Zmy8+hZqVY8n8REXbwTICjQTQ+RUKCbZ0WBbo0VBbo1XhTo1nlRoAfOjAKdAt3mWRT8zTkpfoL/6e3rIQW6fSw9W3JyjlKg2ztu/gT6qZR0aTd2qXJJnze0RY431ycjYMMLMeveircj16zEo8/fkSRDvtmkmoZ7/Hu31ZWWlQvJ6XMZMmz6JpUtvlBctIzsUlP93s5CgW6eJgW6eVYU6NZYUaBb40WBbp0XBXrgzCjQRcSKhcNm5KaaYwy6KUymKzkpfkx34j9QUbvjtqxSVCbcWZ8WdBvH1Mk5SoFu40CZsKDjbmZznJhJVros4aTgfHVj+aJvI6lZMp+pB7vy7SVKjOtSumBemfVAE3l9zi6ZtvyA6/cx0ZGyYFgLibQxyJ0C3dQQqUoU6OZZUaBbY0WBbo0XBbp1XhTogTOjQKdAt3kWBX9zToqf4H96+3qoBXqdMvHy1b2NKdDtQytOzlEKdBsHykaBrjeKzVjaZ286Iq/8ukMJ7Sc6VpHbm5Q2/VB6s0BfEBERIcsfayX9v1gvyzzOYP/74RaSPybKdNv+KlKg+yOU/TkFunlWFOjWWFGgW+NFgW6dFwV64Mwo0EWUS5/ZJD42IzfVHC3opjCZruSk+DHdif9ARS0oyhWOlV8GtaBAt3FMnZyjFOg2DpRJgW7GMv7jukR5ftY209neURfXvNipmnSpX9LUQ2WeF+k2YaUkJKW46ndrVFKeva6avD1vj0xevM+tncvKx0up+Lxyd/OyUrd0flP3yKkSBbp5hBTo5llRoFtjRYFujRcFunVeFOiBM6NAp0C3eRYFf3NOip/gf3r7eqhjawvGRss/w9tQoNuHlhZ0G1k63ZS/GHTcv+fkNbL50BmZ1qeB1CpVwGuX3pi7S6YuO2D6fHMdi24m/nzxrhMqvhzJ4VDKFMor+fJEyfYjySqzPDLMQ7Tf9NFK9XmeqAhJyzjv6meBvNHyx0PNVJK6QAoFunl6FOjmWVGgW2NFgW6NFwW6dV4U6IEzo0CnQLd5FgV/cxTo9o2Rdpdd+/SVFOj2YaVAt5Gl002ZEejw0IKnlhbDnn3KPH9eeny8WnYcPeuzjuc1OuP7nc3KqCPZciptxix2iXPUu6pGUWlasaDKGK+v1xZ8JKeDW/sXKw66Nflj/8YCb5lACgW6eXoU6OZZUaBbY0WBbo0XBbp1XhTogTOjQBcRnY0aLybInhtshS7u9o4IBbp9PCnQ7WNpbMnJOUoXd3vHzIxAz8kdffW+U/LgVxvkbFqWdXt0t1pKQPsrf249Jo/M2OzXJT4tI1Najl7s1hy8XiC4dXZ5HP/W/8sNysqPNXDPsbMycZG7u/s/w1pIXJ7A4tEp0P2NavbnFOjmWVGgW2NFgW6NFwW6dV4U6IEzo0CnQLd5FgV/c06Kn+B/ent72O7tJXLqXIYsGN5GYqOyXWLtvUv4tebkHKVAt3c+mRHo2n390asry51Ny7h1oPdna2XdgdOu3+WJipTFw1v67eTmQ6el5+S1UqtUfpnWp2GO9W+duEpZ53W57bJS8uS1VVXuFVj2H+1QSd6Yu1t9vOLx1pKcmiEDvtwg6//tV58WZWVo+0p+++SvAgT6jv2Jsv7AKWlYNl6wUcDinQAFurWZER8fLykpKfTkMoGNAt0EJI8qXDetMzNeEXH+/Hm+IQbA8NixYwFc7f3SuLg49QFeYoK1WInluxTPQAu6vdSdFD/29jT4W9PJr34d3EJK5AvMuhb8T3vxeujkHOWLhr3jaEag57TGeLqfo3fLH2utzijPqVg5X/2Prcdk+IzNAvF/c4MS8njHKiqeXLvJ6/t4epFNWbpfxvyx2+UGHyi5BQkpMnhaVpw7ythbasvl1YoE2qzb9ceS0+TzpfuVR0Kj8gXlutrFbG3/YjVGgW6NNAW6eV4U6OZZ6ZpcN60zM15BgR4YP6FAryAD2pYPkKL9l1Og28vUSfFjb0+DvzWd/Orrfo2lWrHA4lNzelpY+XAEVLOKBVVCK8+CvdmjZ9IkPjZa8kZHBj84Pz10co7yRcPe6RGoQH/zj93y2dL9rk5hfiNW3Uwxe766rwR0a/eflj5T1rpuNfDyinJ/m3Kuf+sTTswc/Wamv23eXCIpadlnsBfNl0d+H9zMzKWm6yBL/e5j2VnqX+1cQ26oW9z09cFSkQLd2khcLIF+4myafLnykMAciA2tsoXyWutoENSmQLc+CFw3rTOjQA+MmdvVTgl0JMD5YdVe2XjwjMRERUrn+iVUjFv9sgWkcFwey0+AHfJvVh0SGBjuaFJavZQHUmhBD4Re6F3rpPgJPRqB9bjf1HWycu8peevWunJl1UKuxpQr0/nzgjOWAy1frTwoI3/b6WpmxDVVpEfj7HOfkX0a7rgHT55TdYZ3qCx3NXN3Iw60Dxf7eifnKF807B1NMwLdX56Tmz9aKXuSUuTGesXl6WurSWwec5tMZo5vw9Pqep5J6rSLuyZSMj5GfhnY1A2QDmP5aUBjKVsosE04zzPYIyMiZNljrWwbEGMmet3orZeVkqeurWrbPS5WQ3YLdAhKG76OL9bjW77PxRDo59Iz5eaPVsmhU1lrTUxUhEy/D38XoSXSKdAtTy/hummdmfEKWtAD4+eIBX3J3mQZOG212m30VsZ0qyXtTSTE0ddCnHefsEpOpqSrX+GFYsZ9jSXO5AuNtz5QoAc4cULscifFT4ihCKi7nyzaJ+/8tcfVxju31pY2VQrLsBlb5K9tWeEy97cuLwOvqJDjffA3Das3Mkd7K63GLJbUf4+HUi9F0ZGy6JHsGF2Id4h4Y1k0vKXaDAzV4uQc5YuGvbPCjED3l9BNi+CcjmHz1utHZmySP7cmSU7r6P4TKdL5gyy3csSXG4unYPZWRye48xY/b5Xky7/vkRkrspPPYSMNG2p2Fe32b2zPrvh5u/poth27BDo8j574YYskJadJteL5ZEy3mlKhSFbo4X+pOCnQl+w+Ifgb3pqYrHI2GEv/NuXlgctzXuOCjTMFuvUR4bppnZnxirAR6Onp6XLkyBFBwhW4P3sr+LxAgQISG2t+x9tuCzrcTluNWSLIIuurxOeNlnlDm5se+Q/+SRAIamN56MqKck+rbLc8M41B4G85fEbqlSmgrHM4YsaOFxAz97Zahy7uVonlXN9J8WNvT4O7Nc+X+wpFYuWOJmXk9TnZ1m48waRe9VVCKF3SM89LVESEpGZkyj2fr5NNh86oj9pXLyo1S+aTQnF55I4mpVzW9+ZvLJKMzOwdPsTOLn002+qG2FrE2BrLn0Oah3QCKifnKF807P27MiPQtau4L/d1s67qnj3PKfmcrquPT2tfo4iM6VbbrYknvt8iv20+6vpd7VL5ZapHwjkdp+7teqsk8c5S5clZrst8HTtntV1j/ZG/7ZCvVh5y/eq1LjXk+jrh6+J+9bhlSpzrcnnVIjL2Vvd5EAjvYLnWKYGO9enOyWt8PibCIge0pUAPlnngVD+4bgZGNiwE+qRJk2TcuHEuUh07dpQnn3xSChXKci9NSEiQoUOHyp49WZatm266SX0eHe3fDdxugY4X8RZvLMpxVOEA27dlWUEsXPUS+dQXXaG4rL4ipvS12dtl/o7j0qFmMXn62qoybfkBeX9+glubw9pXkmaVCqnYtsvKFZQlu4/Lq7N3yuHTqcoF/mGP7LN4IcGLCQpccKsVj5NticnyYqdq0qV+ycBmoQNXU6DbC9VJ8WNvT4O3NXjENH19oVsHcQRT5WKxKpTFWAZfWVHubVVOeb0M/HKDbDx0Rv3d1SmVXzYczM5ebbzmyupF5a3utdR3wJcrDsiEhdlWt0FXVJR+rbM35L5ddVD9vetSuWiscjsM5eLkHOWLhu+ZkZuNbTMCXVux4QqLI82MxUo2ds+em/H+0lZ2HJ+GmFljQbb2//22U4n0VpUKyYhrq0rp+Bi3OlaS0fn7mztwNlJufOcfVzUzZ7j7ahMhAT+tSxSE0F1Vs6jUK11AVdWbFoibxwkTVmL6/fX/Yn5uhwXd2/c0PIvgYfRfK04JdP035osXNrSwsRVKhRZ066PFddM6M+MVYSHQZ8yYIeXLl5cGDRrI3r17ZeDAgdKnTx/p1auXYjF48GBlOX/hhRfk4MGD0rt3bxkxYoR06tTJL127BTpueNena2Wjj5dwbx2qX6aAfHp3A/URrGs4I1aXK6vsJbQlAAAgAElEQVQXkZc7VZfbPlkth0+lun6PL0dthatYJFYSjp8TY0L/q2sWlcvKF1Rxq3miIsTTZVY35MRuvl/oJipQoJuAZKGKk+LHQjdCvircJn/blG19i46MEGzKeZZZA5vK3qQUdWbz6XNZoSlmSrnCsbLveFayJ4Sw6HOib72stHRvVNLtpejh6Zvkr21Jqu742+tKi0rZ8fBm7hVsdZyco3zRuHC0A9nYNiPQcxK52v09N0LSX2w7PE/ggYISSAy5TgZ5d/OyKpFqPh/hKP7+jubsOCOPfbNGapXML5sPn5HcJp8DzxvGL5fk1CzvPGz4fdm3odrk132F2z/c8yHSsRGA94DG5S9MMOmvz5fqczsEOvp+28erZfuRZNdjIAfQS52q2/5YM9cnyqFTqeq7OTe5hQLtkFMCHUah1+fscute1wYlZN/xc8rdHZ4lI7vUVKFXoVIo0K2PFNdN68yMV4SFQPdE9PLLL8u+fftk/PjxcvLkSenQoYNMnDhRGjVqpKqOGjVKDh06JKNHj/ZL1wmBfiYjSq4YPd/vvY0V9BEz3uLjYDmDOIfbntWC+KsRHavI/V+s93ppsCaXokC3OtI513dS/Njb0+BvbeLCvfLu3wkqqVVK2oWhLDg7GTGgV72zVE6cNS/O/T057jd3cHNXMi1jsqtgDVXx90zGz52co3zRuHAkAtnYNiPQcUdfbuzaQpcba3JOrvM/rU+U52Zucz3srw82lRIF3K3jZubklsPJ0nPSatFbb5WLxsk3/S6TyFzkfxy34KB8PH+n8pSbumy/Es+52Rj/ZPE+eWdedv4LPAfa7FK/uFu8/f1fbJDle064HnN0t1pylYWcN2b4BFpn7f5TsmDnCalUJK9cXzfbw8Euga5zCOh+2hGq4PnM901bLyv+jc3OGx0hU3o3VPHuF7M4JdDhoXHf1PWy6l9jETZ/sQmMTaJr3l0mqRlZfxlNKhSU93vUVUagYC8U6NZHiOumdWbGK8JOoCMWvWvXrnL99dfLkCFDZMeOHdKjRw/55ZdfpHjxrJiradOmycyZM2XKlCl+6Toh0I+dE+n49kJBtlZ80ZkpWGhLxcfIS79s91odn2Gn1onyZvda0q56USeaznWbFOi5Ruf1QifFj709DY3W2o9dqtzX4bIO93Vjwd9yp7rF5aaPss8+tuuprq1dTOqXjZe7mpaR9mOXqJd9lMvKxUvV4vnUdwisRYhRh4cNEkr+r0uNkLCiOTlH+aLhPgMD3dgOVKCbiSP39Tfjy3UeeV9ajl7sdlnvFmUvCPcy87f43t973EJMcM27t9WR1lUKm7ncrc6D32yRRTuOKlGOvBFTlx3I1RnrOq7e2DhCX/A3DkEKEfp/XWtewKBV5cLyXo86lvvt1AV/bDkmw7/b7Gq+a/0S8sK/1m27BLrO4G/0KMBcQLiDHSJ659GzcsvEVW6IkBMIuYEuZnFKoOMZdJjIc9dXk5sbZoVBHjyVKp3eX+72iAMvryD3twm+o3o9x4EC3frM5LppnZnxirAT6K+88orMnj1bvv32WylRooSsXr1a+vXrJ3PnzpWCBbNcuaZPny4TJkyQWbOyE7N4c3fH55mZvpO55XZoFu88Jj0/Wiy1SsXL5kPZ7uq5bc/p6x5oV1Uev66W07ex1L4+qsrotm+pAVZ2I0Ce9k6IOycsUS/draoWlUU7spK1FYzNIydT0uTauqXkn+1H5YwF13arvetQu4TM3eTdowa2DOO2YMn4vLLoyQ5Wb3HR6zs5R/FyxpJNINCNbbMC3deRaNr7IzeWZDyFN8s8vFXgtWIsneuVkJdutO7a7E2gT7iznjTJhbu47uu8Ic2VRXLot5tUF1tULChPX1dNkGgypzJ/e5LM2nhEhcysO+Cev2L2oGbyzrzdroSvCGnzzIETSIK0MX/sks+XHVDGhseurux21GNu/55wIs2uY2fdLl/+eGt1hKwdAl1vZOjcB8g38LXhxItAzohPSc9UPHYfPSvw1jCW2OhI9b3brVEpeaxDJVuO2/TH2EmB7u2owQU7j8vgrze6datvy3IypN3F3Zjwx8Xb5+Es0BGGhyNZ4U2E02PMFgp0s6S81wsrgf7hhx8KfiZPniz16tVTRMy+aMDl3bOUKlVKjh8/HtgIeLl6wqIDMu7PHXJX87JSrVicvPLrDmVJb16xkIrfMWtVt71jPhrs37aCPHhFcH3BxsTEqCR/ycnZcWQXi8d/8T5xcXFqM+rcuayzTFlyT2D3sbNy92dr5eTZ7CzBaA3nDr82e4c6d9fTcSYyMkIyvcSq574X1q5c+UTboD8P2Mk5WriwdcunNcKhVdvsxvbtt99+wYN9+eWX6rvEzOZp53H/yMYDp+SnwW2kTpnsWOjGL/+uPFDmPXqllC9i3S1YX7/ymaulYFweVx/bvfGX7E3KXjOeubG23NPG+pFm+46flS7jFsiJf//GL6tQWL59wPrZ5egL+lQwNlpWPttRBk1dKb+sz34XqV06XmY+1Nbn5Fm2O0lu/9DdK+CZTrXlu1X7Zd3+kzKkQ3WZtGCXG8vHvl0r0w3Hun3ct5m0q2E9ozvuMfxr90ze3z7QWi6rEFiui2vfmi/bE903Gra9er0S6Nikw09uDCf4zp28cJd8smC37E06K6NuaSC3NCkn9V/8Tc6mZnkaocTFRMm656+x/AcLkXPD2/NlxxF3jylvDQ3pUE2GXl3D8j2sXgDRib9DM3+LVtrW87Z8kTiZ92g716VnkZT4pd/d8q68dXsj6dKwjJXmbauLMTmXliH58/pPCI2bgldu5pZtHb4EDS3ZeUz6fIKwhCxj5Ng7LpMbG5Q21ZOoKO/HwJq6mJUkLAQ6/qDGjh2rLOMQ6LVrZx+X4c1Vb+TIkZKYmHjJYtDf+muvfLooQcWHIbmMLnDBazf2/9k7Dygpiq+LP3LOOecMkgUjiAkRRQyIimBOIIJ/s58JE2bEjKigCEYUBURQFBUVBQHJGck55/idW0vN9vROqJ7unu3duXWOR92prq7+VXVX3apX7/0d8dyqk76MkEs6BBO8v7s551q3bCF57+pGUeMxO6mXl3lp4u4lTRE/zYe9rWnwSxs8ZZUMm5buYV3X2L5zHetJTPMiwgKcYrlJOD87+qZmbopIyrV+9lHuBIQ3oenC9tKl6ee5dQm1a9dWC30HDqQ5M4yV+n05X2DSPOiyBnJW3VKhrE2fTfPRMvvB0+MVEfH3bu/PlEUb98pnNzSTeuXSPJkjTVmyVfp+sUBKF84rvU6uJD3bOAtFar3ZnoNH5d6vF8rvy7fLbadXldsTWMT+afFW6fflAulQr5S8cmkDafbsb2HWLfEYvPXbKnn71/Bz573PrCotqhSTGz+eE6oudou/uyM9dCt21Yf+vlodXRt8RcOEGEe6NxiAhZs0edFW6T86fRe2S5OyMqBzXVUkFuUx9u/dG18E2+twy6i5Mm1l+oaL7nPNBk4NE7BYAJj1QPRFkWjP9vnM9fKU7QginBw2qVgkw3jghrsTtoUKFVLvIo5+Okk/L90m89ftkby5c0ivNpUkT67wXdVv5mySR8YuloublJUnT7SNLn/mml3y2pSVMmPVLqleqqCMuSU8QoOTerjJa22POmUKyZCrGknJQtH9TUCcw5k0NENQ0w+LtsqYfzeq+fjlzctLq6qxF8P+XLFD7huzUGkA9MXBVzSSwvnCRXX3D2aFOa2GfvjnfrP+ryNlBZVX0OuVEgJ9wIAB8s033yiRXr16+mp42bJl1Qe9d+/eAlOfIHhxf+CbJTJx4RbVb+CBfdCl6YsJ8WJLWjubOkvaqIy892e4EICzl8cuqCXfzd+i4pnDWqXnR3MjepI26bzf39FKyhRO34EwuSYZeSjQvaXsp/jxtqbBL02fnzWtqXVBTV9zao3icvjYcVm6ea+0r1NKvp2zKeI7jEW+0oXzyNPfLze9XVg+vNvvdG8kEOlBT372UQr08NZ3u7BtauKunXVZw3m6CbGmn0KXizPG7S0O0LR5M84aI8Sa26S9zSfqZMweEu62T+bJX6vSBULFYvmVp/loacT09fLy5HBv2i91rS/ta5eQ0wZNC1vs/6hnk1DYNS8YI1oFolZY07vdG0nLqu69wlud4cL0v0j+tB3QRE3cDx45Jqe8HG5pAJEM/zqDfv5PPvxrXegxEvVLMGbOJnniu3AfQbecVlluPqWyOlZgPVbUo3UFufssc8uNbfsOy1f/bpJKxfI5il+fiIn7hAVb5KFvl4R4INrP+1enWaTa369ozkd1hAbkt7af2/fN9Pp9h4/J6a+Et/f1bSvLnWdGj80edBP3qct3yJ1fhB8fmNS7lZQqFHl+DkvcU1/5Sw4dST+me1XLCuooijWdPugvQWhJa3rv6sZSuXi+uA40OW6a9sjI+VJCoCOuOby22xN21KtWrSorV65UcdB1ns6dO8vDDz+sPvbxkpdO4hBy4xGLB1nc+8kLa8uFjdK8lO4+eETavRp+Rs5avyYVC0ubasWkVKG80vWksvL6L6sEA7ROiKsMpzAwobWmLXsPy1ezN2aIlY48f9zdRp6csEzGz09bNLCn969uLM0qF4mHKem/U6B7i9xP8eNtTYNf2i9Lt0m/0elOjlDjWJYsWNm/ZVR4FAUcKbnJEtdcPzWsbCYs2KqcPiH1a19NMKGEdTyc0fX8aE4Gc8bShdIcw0Xa1U8kjFVmtYCffZQTjYyt6mZh21SgR3IG5ybEmn6KaE7m3Difi9Tv3cZD12ft9ULC6u0H5N4xi2XxprQd4tcubyCn1Yx+/AKT8BtHzguFXu1Qt6S8eEk9+Xnpdrl7dNpZdp3gqAsOu5Dc1htlRIqFjcXGEgVyCyJV6HmN0++FXjzQ11kXWRIV6BApCCVrTefWL6Wc5iFZHez9c98pTqus8qMtrhk+R4XK09/8T69vKmUL55Vfl22X+8YsFiwUID18fk25rGk5o/tg46bXiLmCbz+S9phucnEiAh0+EFBfa/r97jaCM/Q6ad8Ro3o1CbNQCetvo+apI5uJ+pEweb5oef7bfkC62pywXtasvDx8Xo2oxdoFOrhPWbpdiubPJVe2KK/8LGRmevu31TLk9zVhVcCCAxYerAn+G0bN2KCcRmMuYk2tqhaVId3DF1tgTfPBtPQFKmt+aIo+MRwbctx01yNSQqCbItq0aZPA5Af/mCYvBXqkAc1u5j5u3hZ5dNySDGZuqO9jHWtJlxPeMnfsPyIdbA5v4r1Ml78/S5ZvSXe+UiBPLpna/2SFAgPYpEXb5JFx6SunOBc3qXfLDOZNpuz8zEeB7i1dP8WPtzXNGqX9s/6g/LFss+Q8flyKFsgtlzYtpzyn/2kxscSTwMv7x71Okie/X64W0ZAaVyisJjWRnLXgLGGPD+eEPMMj9vIn150klYunOZKKZIXTqWEZeapzbZm6fLvc+UX4pJ0CPa0/caKR8b1ys7BtKtDtO8ioRaS/OX3ro5Wh44F7KRoiOcsyra++dtxtLaRC0Xyhy+7+apFgocIkPKJedLAK8Gkrd8rtn80Pq4b1d/wQLcRdvLr/37glMn5e+oI+RNr6XYcEdbYmeIaHh3inaeSM9fKiJca2dY6UqEBHHf5v7JKwjYh3ujdUfn900n0jluiM9yy6LbCR8lyXusq3gDX9vGS78n7uJNb9qz//J8MtO/woD1YVsK6IlxIR6P83bqmMtzm4+/N/bSTvCTN36+54rMUMzSKRUInxniva73/9t0temLxChR1GPa2pZdVi0rJyEWlYvrCyXrUnq0D/67+dctun6e8PnKdt3nNIjckDOtUWLO4kO8HKA9Ye1gSxDdGtE462dnxrhmzdG+7/Rv+O4wp32Rz26agXCNOKbxAiEFjTn3e3iRrPnuOmu15Age6On3gp0LHDdc3wcKcqmJxjkm5Ph48el5tGzZU569KcpeDj8m739LNiOF+HAcWaIpmvWH/HaiY8bGIVF2uBgy6rL2fUCv9Q4cOMUC/FC+ZRHlnrlHHuoMclcqPLKdCNMBlnokA3RmWUMdrE6Ot/N8n6nQdk9Y6DKvwRFtX0JA7vJXZJCsdwaBPJrNQ+8bbGei5RMI8K/1T/xDcGHoZfspjEak/GRg+VyZn87KOcaERv3EQWtk0FuhZj1km8F7vc8Mvw2PhlKmyW1ZQ9UVEaq+trc/poYhrO7mBCWt4iwFGeVejMvP/UMMsXzcXEdD6ax/vrP54b2lkvmDenjOzVVKpaPMKb7ILan9v+/cDvP/RpJZ/P3CjvTF0dlt2++WD6+dDhu+qVK6T8CFjb0I1A1zvzlYrnl3eubCj49lmTF/3OZAHoqmH/ql123P/suqWUh3NYHkRLL//0n4z4O3yHEz5DTI4lJSLQh/6xVhClQKduzcvJA+fWDP2/qYUL5pvom2jHUb1OMm3+hPPhnDU2raxHCcAIx0Gn/bczrNxnOteRjg3DHSNaBTqsSnGkIFpCny9ZML4FbsIPE+FCiO/WL/4Z9ot9oSaSLoD/AG19geNsrW1HUPS3Bu8ZvlM/Lg7fdY91RIHjprsWpkB3x89TgY6qwOTkvWlpZuk3tqkgZ8aJL75q+wHJlSOHVCoePphEWimzmstHe2x4tVy746BUKJo36qqYS2RJuZwC3VvMfoofb2uaNUpLZGJk8mTwLfHw2HQrF1xjF+j42/7Dx9SKP86R2U3z8A1YtW2/XP7+bDVJHHtr5jjxMXleax4/+ygnGk5bI3Z+U4EeSUi7DbGGms3bsEeu/XCO6t8fXnuSlCyYW5nb+iEYIi0yaDoPj10q381PC7dVs1QBeeeqRpInZw4VRuzRcUsF43vF4gXk0+tOkkJ5002InZigW8O06bPauB+sbcbO2yJb9h6Sy5uWC53j1nXTQth+Tj9Wy0ZygDn48voqKoUOD6evN5mPRLqXNUY5duWt3yg3Al2bsUdb9IjVjiZvh+nO8tXD/1WWTjoh7Noj56cLYP139JuFG/ep0HlwvKZTw/KFZERPM8GbyDik+YP7up0Hw6w4YGn56PilMnHh1gxOjiMx0hYiXp9DRzi3X5duk1y5csr59UspR3xj/t0kT9ic9N3YtpLccmplaWM73gCRCrFqTVaB/vTE5fLlrIyRnXR+9HmEJ0xmsoYHhOUb/h//ho+NK5qVU/P5aJa1JQvlUVYpkSzm9PcWC5m5cojAgkKnemULyajrovc1jpvuegAFujt+ngt0VAfhgtQken+4KYnTquqzJlv3HlJC/+LGaWfZUyFRoHvbyn6KH29rmjVKS2RiZPJkOOd45Qf/yrItaaGisPOCc46Y/DtJppNJJ2X6ndfPPsqJhretZyrQI+3GuZ3UY/G6+7D0d6RAnpzy+Q3NBJ6pMUn1ykGcJqZ3Zu2LXSu3HZBLh840AosjMP9nE2maQyxz/Gj3NrlpIjvGn83cIAMnrQgr/ue+rZUVkNXZGgTT8B6NTaoRlkeb22qWdosHNwI93tEJt47zTHaWsZDR8oU/wp45b64c8uf/wkP0RXI4B7GEnXf4GICvAZNkOg7BYhOm9NNX71L+D8AfEYZghaJFHRzV4XgV4mUjNa9cVEX4iZXSxV8tQcQRL9KcdbvVmXydsAA94fYWyjL0oiHh79udZ1aVa0+uqJz0WVO72iXklUvry/Z9h2XAhGXyy9LtckqNEvLcFU2lkBxUCyI3jZwX8agpytF93ovnMS0j3aFmbeWX4pzXp4cuxRE3CGmEfbx06Cw5dDTNjqBW6YLquByENxZedh88KtYjHJHmAVh8efr7ZSqv/eiNva4cN01bL3I+CnR3/AIt0F0+Wpa+nALd2+bzU/x4W9OsUZrpxCiRp8EuBpxDYlIFsWE31TQt0w9zX9N7J5LPzz7KiUYiLRL9GlOBHkkUue2X8OUAnw7WBG/a63ceVLtOJue6ndKItKgAs9rbLedYY5VZu3RB+eyGpmFZtICOZSruxot8PMEarb44JocdTJj4Pn5BLTndckzOrZWCfZfbfi7cjUA3scxw0/dM2gtMzxj0l+y1eM2OtJurGVvbAIs4o2dvNNq51teZjkP28/nNKhWRVy+rr8L+6vPyb/22Wt61OSkb3qOJwHlxtDRyxgZ58ccV6pw0+orJuflY78mBI8dk6O9r5H1b9CI4R76gYSnp/E66QMfxTXjph4C3HxOAFcVLl9SXvl8uEHhH16lZleLy/lUN1P9qPxB4//YcPKIcr2GBHOnJC+sIRL49ZJnT74Zp/rU7DoQWH2DW/tOS7WFH1VAOjq8g8gu+c3DQCG/tVh8I1ugy59YrpaI9wZwdwt++s253Xhmtnhw3TVswcj4KdHf8KNBd8vPrcgp0b8n6KX68rWnWKM10YpSZT+NmMpoZ9fazj3Ki4W2Lmgp0+47p57M2yrMTl0uV4vllzC3Rw4vFqm00gQ6PzDij6cYJWLT7RprQ4txnx7f+Ubt08VKkHXSTHdlERTbqo48XmJxzt9Y/3s67GwsI+3l++/+7Eegm9UrkXL5mY3L+HHmxcNPvy4Uhj+4QSoj1bk0wIx87N+1ohE6wsHjq++Vi0l4wTf9y9kY5liOXNK9UWM6sGTteNnb1T2jP0P3gAM66qIEY8jifbk2DL6sftkBj7+dwtAaHa0g4ZT/06kZq591pwqL0zZ/MC/lksl8Piw2IaDg4w6L14xfUzhDN6J/Vu2TW2t0yfNo6FTEpWtKO7yK15zdzNsvj36WbgMPMHebufiXshN/6SXqUBu3BHwsUiOIULT10Xg25vFn5sJ+xsw6rW50uaFBacuTIIePnb1Z+OsBNp3jvuM7HcdNdy1Ogu+NHge6Sn1+XU6B7S9ZP8eNtTbNGaRTo3reTn32UEw1v28tUoOOueqHo5lMqybsWAYD/v/2Mqo4rZjdxRwFDujeUWz5J88qcaBitWBW5f8ximbRoqzoHevdZ1ZSDVSSEq8LZ7EJ5c6kz4No8GL8VL5BHduw/LO3qlpGnOtUMO4OO363mp3d3qC5Xt8wY6sl0pytS3RM16Y63E+3GrFkLZG3Wbz8XnqhANz3TH8/hX7Q+4PTIEPwDvD0VO9JrI54L/nHRVhVuTyeEujqtRjG5aniaX4VYfkPQ/zu9/Y/yQaITzLmx4xstITwnzrrrBOeiP/ZpJVZP7KfVKC69P0+Pww2nh9/c0lxyR3FwZ2Wiy72qZXm59+zooc7s9Zu8eJv8u263qhsEtkmCU9RTakSPHnD269OjLprVLFNYvri+ibpNpAXsSJ7Un+9SV86p549X90ih1X7o01pwLOKSd2cKjh1ESjefUlluPyM95nukoxXW6+yLRKb+GDhumvTI6Hko0N3xo0B3yc+vyynQvSXrp/jxtqZZo7SsIND1ZNg0ZE9mk/ezj3Ki4W3rJiLQYYqqTUhRG/z/9HvDz+aa1hLnUT/9Z4MSyDC71g6v/AgrOBnh0GwhxrTA1CbfuO+jHWsJvKBDOJ1ao5ggLjNSiRIlZMeOHWFe3PF3OAmDkzmd2lYvJm92S4/kgr+7CfFmKlrtzOOFI4s3uYdlwb5Dx6RYgfQQZHPW75H+Xy4MCQ4sqLSqWkyFmoOpsfYEnqhAt7YD2iZailf3aNeZWDvYrwV/fS7Y/g3WCwUwRb7vnBpSqlCax/BoDgGtZf+7drdc93H6GW381vPkitKvfbWoz23dkYXPBkT4QQg665EF9OGR09eriEItqxRRi1CxPJnDq/pZtlDA3VuUV89jkiJFLLFeh11fnJfHO2VN8aIHaIaR6jCm96lSpdDx0OKYPRweFlWGTA2PRV6tZAFpVKGwdGpYWk6NsjDw/YItavcaEQTe/m2NwHKobtlC8kKXulLFElnBXqdXfvpPPrJ58B96VSNpUaWoelcQPeHgkaMyzBbHHAs6iBBjTee9MV22RAm/hogyE25vGcpuunjHcdOkJ0fPQ4Hujh8Fukt+fl1Oge4tWT/Fj7c1zRqlUaB7305+9lFONLxtLycCXYtM7D7DlFUnxBz+4+42rioGEXTemzNC5sRwmvRxryahmM6uCj9xcaRJuxYJJoIvmkDv9sFsWbo5zRmkTtPubiN5cqd5e09UYFvLS+SYS7xr9OQ+UqxvawhIfR4ZYgXmz9bwWFow2J8xUYFuehQgkTP0Ayctl89mpnn8xrlnnN02TXqH+qSKRZTjt2tbV1Bmx9EWT03M6GGlgR10a4I3895nRrdG0fd7pGMtuaRJGVUHnXAO3RpT3MmC7g0j58qsNbtDZVnDKUJc/7Z8h3SoW0qql8wY0/3ZSSvk85kbIqLEDvKUu05WC3D3WSwNkBkLEViQiJbsXvSxCPLnyh3KKdqsR86RYwf3hhYm7At6dgd19nvAuz687FsT6vfDoq0Rq3Ny1aLyts2bvDWj9X3B3/OdeG58K60Jzi/xrUFqVrmovHNlA0F4NWuC47sHv12iYsRHSlbLItNvC8dN0zc9cj4KdHf8KNBd8vPrcgp0b8n6KX68rWnWKC0rCPR4O2FBI+1nH+VEw9vWdiLQtTg4qWJh+XddupntYxfUli5N3EUm2bb3kJzzxoywh0PYpdtOTzf/dPvksXbQTYRhNIF+86j5MmN1ePzmv+5pGzIpNt0VjvV8Ts9cm07cI533jhTHOVbdtGCwWgks3XpQZq7dIzmPH5XLmpYLOcjUu4kwHUfYMsS+tiYdUs5+1tZ+f6em6pF2eZ+7uK6cW9/M5Bmh9iDCdDqrTkkpXiC3isEdydrD9Gzw/75aJD8tSYtnnT93Thl7W4uou926H0Uynf/7v51yq8XRYZnCeeX7O9J3WuO9O7CIgSXL2p0H1e47EsR/qypF5O9V6SbrL1xST862eaa3RgXQ9zmtZgkpXiCXdGteIeScru8XC+W35dtVFix0wLN8rLjyG3YfkoETl8uf/+0UiPMHz62hjqGAwzs9WkjrivmU2IXotS4o6DroEG//rt8rCzakf6/w+xXNy8uVzX3SqJwAACAASURBVMtJzdIFVXYsOLa1hXizM4Ogb1yhiPQ+s4oUyZduVaLz6YU67Jr3bVdN8J2MlA4dPSbHjqe1d7xkXzSAiT5M9a3JxGcDx814pGP/ToHujh8Fukt+fl1Oge4tWT/Fj7c1zRqlZQWBHu8sadBI+9lHOdHwtrWdCHSEbppvmegivNMZtUpm2IlKpIY4t3rTqHlhl/ZoXUHuPqt6IsVFvea9P9YKvFxDkFjNeE1ijUcT6BAwcBClE3YNr2tbSQrmySXXtKqgHHa9M3V1RBFh+nAm9bOWZboooMu9B2fnW1VQRew7fExOf2WaUdWsns2v/WiOzFu/R3kCn24RdRCL429rIfsPHws7jwsT7TG3tJDSJ8zCcUPTxUgIqnPfmKGciJ1Zq4Q8cG4NwVnraEkvwFh/j2dibc0by9z6yhbl5X6bObgWjvFCBepvu75XJMeIq7cfkMe/WxaKsY574Z7WZPd+jt8g0MHeaWo/+G/ZdSCyczYcd/jpztZhRcIk/MaRc2X7vrRrrmpZQXkmj5SwQIO2i9VWseqrFz4e6dxAujYsLiYLa+PnbQ6LGW4tv06Zgsrx2qbdB6Xf6EVGqCJ588eFJscajG5gyYRjJm/9tkaWb9mnFhNuP71yhh13k28Dx02n5MPzU6C740eB7pKfX5dToHtL1k/x421Ns0ZpFOjet5OffZQTDW/by1SgW82DUQOEB3rywtqeVQZekBHWChNSnXAW/JKTvInJbK1opDBbJibJ0QQ6yt6857CMnbdJCdM/VqSHg8JOYaF8udTf4u0KmwgT09BzJib7uN8j45bKuBM7w4jZPOjS+mk7p7YY4Na61S5TUDm/su4mfjlrgzw9MTzuuvWaYdc0VrG77R6tscgDoaxTPLN8ne+J75YJYpDrVLdsQfnkuvDwd9b7w2z4xpHhC0DYwTX1VH7Wa9NV7Opoye7Q0CTuvdUK4NLmFWX0zHURF3GuGf6vLNi4N3TrNtWLyVs2HweIjT78r3Vh1fuhT6uYZ88jPQvevzYvRV+cgTn2tP9lPM6iFxrwTcC3wa+k+/VlLSrJw+dUDXmvf7lrPWlfJ3rMeYjvX5amWSqYpqaVigj8BFiPdOhr7e2dyJEL03rEy2e31vhz5U6Zuny7EvIdG5RSZ+g5bsajGPt3CnR3/CjQXfLz63IKdG/J+il+vK1p1igtKwh07YjoiU615KLG3gsWr1vKzz7KiYa3rWUq0O07iHZnRV7UCrvob/y6WlZv3y/XtK4ovWKcT3Vzv0i7mybCMJZA1/V5btJy+fTEOWd7HdtWLy5vdkuL3ew0mewUWss0yb940z7pPmx2WFUgzmF+jlS2cF5pcMKs9+//dihTZ5y/xpEGe1xpq6l2pGfDDjpiOb80eWXYz4iLfeMpaU6ynIicti9NE5gKW9OMe0/JELLL+jtCpv2ybLvasf9fh+pyfoPSxs3w27Lt0vfLhVHzR7p3vD6lY8nDRL7/uXWlx/vTQ7HMrTeKtHtvF4gLN+6VXiPmhha4dJgv4we0ZIy1g96yalF5N8JZbDdOEJ3UUS98NKxYVEb0aGRscYF7wILji5kb5JWf/4t6S8SKP7VGCTmlRjFlho90xfuzZdmWdB8Tkb59+n2LZGrv5PkSyWv9nmFB07oQBf8gE+9oKdUqBn/ekMizJ+saCnSXpLdtc7Y6ZnK7AgUKqGyYxDAlRoACPTFu0a7yU/x4W9OsURoFuvft5GcfpUD3tr2CJNC9fbLopdk9H5ueZzYR6Ho3K9rdP7uhqdQ+ce7VyfM6jYVuYvY6asYGeeHH6LveN51SWe6whICKVd+Bk1bIZ1EchV3QsLQ83bmOHD56XOD4yyp2fu13sgptt3H3QXlm4grlTAwm6/BOHivd8dl8wU6hTpFMr+3XuxWRew4elYWb9srcdXtk8JR0kYdz7DjPbk/xzPXTF15ry9Wn1JQWT/2oHKDpyAK6PLtAxK4/dv/tCebjOBNfqVg+6ehg8cFezvRVO+XBb5bI1n2HpXGFwoL2g6f3UTPWq/ohQbwi5FeNUgU8cYLo5F2wLnzEWwSxlwuRfv6b0wVtGSlFEtjWyAU4M/9WtwYqcoE1pYcsDI9T7uS5Es1r/Z61q11ShkxdHVZU//bV5K6OjRMtntfBH8NxvWxJHAkRoEBPCJvvF1Gge4vYT/HjbU2zRmlZQaCb7IYFibaffZQC3duWNhXodhP3Zy+q42gH0ttauyvNLshNz2ubCHR4j77+43lhYeistR3SvZE6o+00mYZT0uWa+K2AoDv39ekRTXhRDpxo3X9uTaOq4pw0nJTp+PH3nltLVm0/KJ9OXyNWs3z7AsbLiPtdq4RcPGSmrN15IHSvSM7IrBVZuXW/9Bu9UFZtP6A8/b9+BURTdK6xHKwZPaAtE7x9w3M/wpd1s50H11m1AI/mS0GLS3hbr1e5jDzx7TwZ8ddaJX6vaF4uZCoOS4frPp4jBw4fk/rlCqnFgFghvxJ5HpNr7OflcSb6ixuaRvWkblJmInm0w0RYlD02flncePP2e6zYul++mbtJjhw9ro6eLN+avgE36NJ6cmbtjKby8ZwuWttSRz1I5NkSucb6PcOC2pu/hgt0LHZd0rpWIkXzmhMEKNBddgUKdJcAfbqcAt1bsH6KH29rmjVKo0D3vp387KMU6N62l6lAx13hCRuT2ZOrFTU+u+ttbb0rzbqbCgGEGN7t65SQl7tG37k1Eeio4ZY9h2TMnM1y9Phxefu39MlyhaL55Jtbmsf0XB3tCeMJBPt1Jp6dcc1f/+2UwVNWyda9h2TH/iOhMHf47YFzakQVn5HqiT2mrXsPS5H8uaVwgXzyycyN8vT4RWJ1lKZ3lcH65yXblQd0OBTrPuzfsCJjORrTGRH2rfM7M40Eml7kjOe0zasetnnPIWUtAB5IOM/8wTVpu5g4H/zcDytlzY4DUr4oHOi1FIxDFwyeKks3p581f/yCWnJxkzTTZKc7xV49h7Wcls//kWExB6b2yTbvhsO8b+Zskva1i8vPS3dE9KJv+vxwhvfx3+vlyPHj0qJyUYEfhmgp2jtl4m/AtD6J5tN1w0JVn88XhIo5pUZxeeOKBjyDnijYE9dRoLsESIHuEqBPl1OgewvWT/HjbU2zRmkU6N63k599lALd2/ZyItC9vXPmlmY1AV+0aZ/ysh7Pq7epQLc+mXYah1DHV7aoIKUsHsudEnAi0pzk1fX4d91ueW3KKlmxdZ90b1lBYOKeaEIc9K0Hjkv7l34NO1dtrdfpg/6SfYeOSs4cOTJYHFzftpLcGSMeuK4XyosUx91eb73rajcfT/T54l339tTVMmTqmrBsr1xaX2qXLiAXDZkZ9neEzerQqJK0euansL9f3LiMPN6ptuiFCJPnjFcvN7/3/myB/LEy3QFi5eL51YKTthQwdWDopg64dsjva+Xt31aFion33rq9n74+2pEFU4/9XtUjUjn6e4YQfOt2Hgxl0f2d46Y7+hTo7vjRSZxLfn5dToHuLVk/xY+3Nc0apVGgu2snhKvCuT6cI9XJzz7KiYa79rJfnaoC3erJHaG6EPs5nsBIRKB71Vo4M3vh2/+osGIwqb63Q/WoO/FOzeG9qqO1HAj0/Pnzh85VT+nbWhZv3qe8btcrV0heuqSu2v2OlCoVyy9Dr24o5YpED5umrzNZiNACF9fYnav58ewoE6H83v09XKBjR3zL3sMZPNnfcmpluevc+tLoiR/CqnNNq4ryvw7Vkm5CHo3J+l0H5f4xi2Xu+rSY4gMvrivn1S8VctSWrMWPKUu3S//R6Q774r23XrVxpIUIHO1ArPJFm/a6itLgpo6wXLnmwzkCR4FIGItxFALHOrRjWY6bbgjzDLo7eiIU6K4J+lMABbq3XP0UP97WNGuUlhUEunYQlSzzTNOWg0MnnAOF0+dyRfLKa5c3EIRg8rOPcqJh2jpm+VJVoFt3vbDjhMlsPIGRmQLdfv4Xps8QfJHSz0u2GZnsm/WQxHJpgX7zh38rU3YIhXU7D4XiweOc7os2B3VYeLj0pLJSp0yhmN7YrTXSO+NYAIBpvT1hhx4O3T6buTHuEYbEnjTyVQs37pNrPvw35BEf3rThEA/WGtd+GG7Oj6MEN7arI4N/WCSvnXA+V6xAHvngmkZSvWSBpJuQx+NgD+tlskgSr0wnv+89dEzOGJQeCi7ee+uk7Fh57b5gYB1zyyfpofvOqFVCXo3j3NCruljL+fCvdTLI5pm+R+uKMuLvdSGrII6b7shzB90dPwp0l/z8upwC3Vuyfoofb2uaNUqjQA9vp8XYCfhumYq7e2btEvLUhXVCIZVU8KXjx1WcZCTEy7XGrT69ZgkZfHl9CvSs0fVVLVNVoFt3mdftOKC8U0cTebo5M1Og20NtwSx8+r1tI/a0IDiV1AL9xQkLQqJ80ca9J3b1akuzykWly5B/wurv9Mw7Lo7lKR3nv6/8YJZs23dE3adBuUIyoudJxuLf7WuMb+nkJdtl1PR1qn+N6tVELSJYLQe0B3g9Dk1bvlVuOmFlMKrXSaoKdkHstl5ur9cLQPAfcE+HanLV8DlGfgDc3ldfnzNnTjlz0DTB+XGkeO+tV/e1R1J4ZNxSGTdvc1jxU/u3kQJ5cnp1S6NyXv7pPyXGremsuiXlp8XbRHulp0A3Qhk1U0oJdJhkHD16VHLnzrjimShGnkFPlJy/11Gge8uXAt1bnllBoFsnRNgt8DNdPOQfWbMj/QwbYgU/07m29P9qsfyyNC2U5c2nVJZbT68irV74I6wqOtSRn32UEw1vWz9VBbrV87EmGs/8OTMF+lmv/a1CXekEE9aRJwScvUcEQdBpgf7z/LUhs3a9EAKv5dhBHz9vswyYsFzFM7+kSVl5NIpFQKwer8/eRtpFhYO+ITYz8wEX1pbOjcp4+xLFKU23BxzjFcmXWxD/HNZQj19QO7RYYB2H7DvSJh75k/lA1ncH5uUv/rgyqdYJR46JnPP6dNl1IM0B3+gbm0n1Umkhkf1Mdmdw/zduqerD1qTDBvpZD3vZkXbQ0c8fHbc01C4cN921SEoJ9PHjx8vrr78u+Lc1vfzyyzJy5MiwvzVt2lTee++9uHQp0OMiypQMFOjeYvdT/Hhb06xRWlYQ6JHCQO3Yf1je+m2NzN+wR5pWLCJ3ta8meXKl7WwnmrBDDk+91oQSYbr3y7LtYX8f1qOxIPaxPveGH2FWd/dZ1biDnmgDZMJ1qSrQgVp7PsZ/41y03rGM1gyZKdCnrdwpd41eKIegTvCeVimqTPKPHjsuL/+0Uv5etUvqlS0kt55WWVnAmJjs+9ndtEBft3m7tBv8d+hWcGI19tYWnt06PZZ4LbmocZrHc50iOWp74Nwa0q15ec/ub1JQpMUgvUihr7eOQ9psHzvu9coVDvVT+zUm9/Yrj1400E7JkuWoDc/zzKQV8sXMDaFHq1oiv3x9c3O/HjWsXOviye8rdoR5TG9VpagM8XkBPdJDYsPziQnLlWd7HI14slMtqVm6QFiEAwp0d90jJQT66tWrpU+fPrJ27VopW7ZsBoH+0ksvyZo1a6R///4hmhB45cqVi0uXAj0uokzJQIHuLXYKdG95ZgWBHsnp0w0fz5VZa3eHYHRqWFqe6lzHEZxjx0Vy2jT9BW/9Ixt3p++gRysQZ2D3HjoqPy7aqrIUL5BbfuzTSpm/+9lHOdFw1MRxM6eyQNe7r4CkBW8sYJkp0FEvOGSE2fbl781SJtPNKhWRA0eOhS2S1SxVQPLkzikwJ9cCL24n8CGDFui7d+8OmaHjNvFC2TmtSixz/pXbDsgV789SixhI8DqOuN15cyfXBHnC/C3y0NglYY+GcFin1kgP6WUdh+zOyJJ9xtukDbQPB5335a71pH2djPHDTcpymqf94L9D5u362un3npJhLHNarkl+a3hGWIGc+epfAgeOOJ6B2PX6+JdJWX7nsfYbjpvuaKeEQD9y5Ihs3bpVpkyZIsOGDYso0Hfu3CkDBgxwTJMC3TGypFxAge4tZj/Fj7c1zRqlZQWBrr0QwxHbd7e3VGDtZ1JxLHzGvadEhL7/8FE5ekxCZ8lxvvx/Xy2SDbsOSomCueXFS+oLHN5MX7VDTfytu+LRWjF3rpxyBIVakjYR9rOPcqLh7XuVygJdmx6DqD6rGYtuZgt0XbeHvl0iExZsidsR3urWUNpULxY3nx8ZrAIdO/pj5mxSt7nltMpy22lVPLtlvPP2+vfWVYvKi13rKRPzZCddB+t97TvO1nFIi1/0ybPqlAwdEYhn4ZHM59KLxvqeydzdh9f0H04sDOP+hfPlll/uap2Ux7eGZ6xbtmDYLnVSKuDgJta47dUqhluXOCiGWSXFvLhPnDhRBg0aFFGg//jjj3LyySdL8eLFpV27dtK8uZnpCgV6MN8jCnRv28VP8eNtTbNGaUEX6EeOHZfrRsyR+RvSQqg0q1xE3ruqkZzzxgzZvi/tDB4SdoUua1pWWlUtpiZ1Ot3z9SKZvDjt7HgLmOB1byjXDJ+jwsLolCtnjtAuk0mrYdM9bU8qPLWrXUKwWXVJ80pyQcMycuDAAZPiHOWhQHeEK27mVBbo1l1AExPdoAj0li/8oSInxEt4r/+4u43ktpvJxLvQg9+1QP9h7hq5/dP5oRLh3G5i75ZSsmAeD+4iEi/CRRDO4/+2fLv0/SI9LBge/MNrm0jjCoVDDKzjkD7ShGMXV7esoOKMe2154BY+xqMeFm/0D59fUy5rGt/S1e19cf2mPYel/1eLZMH63QK/Jy90qSetqhb1oui4ZVj7ExZ70DZBi66iH8LqQPGUBlXjPhszRCeQEjvo+vGjCXScSV+1apXkzZtX5s+fLz///LMMHDhQzjnnnBC5559/PgPF++67z5fJoHZih51/psQI5MqVS+B18/DhdDGRWEm8CgTQJ7WTRRJxTwATSTisPHYsfDfYfcnelPDBH6vkue/DzSMHXFRfxUB+eMyCiDd5vftJsmXPIVm8cY+M/Ds8Fu8DHevKwAmLjSoXTYibXPx2j+bSvrb3Jo+IrczkHYFUFugDJiyTr/9N29ltU62YvHVlw5hggyLQLx06U2C+bZIm3N5SyhbJa5LV0zxaoL/w3fwMjtrgL6PXyRU9uV+8kHJBcbA2evZGFQord86cct851aVjg9Jhz28V6PrMepF8ueTqVhWVF3yTBSRPgBoWctfoRfLrCaeh+pJkmZljPlm0aFHZvn1H0jzy62e0WjegneDw74lOtZVID1rSu/0IcdjrzPpBq16Wqg8FeoTmevTRR2XHjh0yePDg0K+ffvpphpxXXnml7Nu3z/MGxyCDRHGZOFoISnxQDx06lHghvDJEAItXEOjsk950Cgg+sIRID2J6Y8pKeWPKirCq1SlbSKqXLCiTFoZ7kNWZYO4ebYftptOqypJNe2XKkrSz47FS0fy5M5z1i3eN/r1P+1pyx5ner9rDgoTJOwKpKtCn/bczbGcXRB86r6Zc3iz6LmBQBPqsNbul3+iF6t3Mnzunir0My5p3f18jQ/9YG+ocQdhBH/zDQnltyqqwDvtmt4bS1iPT+0j+Oaw3s5r5RoqT7t2b5K4kuyWXdhSHXXT4EwiaCOzy7kxZvT18kejPu9sk5Xy/FujQBslO1gWhxZv2ybqdByWZ5v1Onlfv9mNx58GL0kL2MSVGgAI9Ajd4ep81a5YMHTo0LlWauMdFlCkZaOLuLXaauHvLM+gm7iu27pfL359lZNLqLRmRnDlzyLETDpYK5s0l+w6ZL2L061BTerby3uSRJu7etnKqCvQhU9cIvHxbU7zz0UER6KgzFmnhpLFQ3tyhXUQ4Q0Pop+8XbJHShfLIkxfWyfQz6Nt37lJnqGefcGh5QcPS8rRDZ5axerz2zxHJO7zVe3q8EHrevlXOS7OPQ1YHhigtUhg553fx7grE3Ub8bZ0aVSgsH13bxLsbxCgpMwW67m+wboC/Fq+jEngJUPs+gC+DZy73LnKCl3XMKmWlhEDHoAJz8UmTJqkwa2PGjFFeD7UpOf7WqVMnqVq1qixevFhuu+026dWrl9x4441x25ECPS6iTMlAge4tdgp0b3kGXaDjaees2y1Pfb9clmz23krICc1YO/PWclpUKSYf9GwmRw97bzVDge6kxeLnTVWB/t+2/dJ16KwwQK9dXl9Oq1kiKrQgCfT4LZu5OaxO4lATeLrOnSuH2vH3OkXzcm49yx0kB2uRnt8+DtkdywVxgQFm+0s375NCeXPJ9W0rCRZxk5EyU6Dj+awOWoN6/hz11P4Z4L9g2I2nJqNpsu09UkKgL1u2TGCObk0Q5Npre8+ePdXZc506d+4sDzzwgJicO6RAD+a7QYHubbtQoHvLMysIdGxit3ohPD65txTSSnu8Uy2Zs26P2jXH7vmXszaG3eamUyrJxt2H1Lk7nRCDdpXN1PGN7ifJWXVL+eIXJFUFOha2t2zZIhCJ+KZGSvi9cOHCRuOlvj5VBTqeH867YH69//Axue20ytKpUexzpBTo5l8du0A3v9J5zmgCHd+pIDpYMxHo2pQaebFbO+Wuk52DyaZXZLZA18cPgPees6srR35BTNbjH9/3bx/EKmaZOqWEQDdpjT179sj27dulTJkyjiYaFOgmdJOfhwLdW+YU6N7yzAoC/dCRY9L25WkRH7x2mYJybr1SKh7yB3+mnz91Qql04bzy8Hk1BV7YdTp89LhgIrJ5T/ouOM6OtqlWVAb/skomLtgqJ1UqIn3OqCKHjh6XiQu3CiaV8A7/7CUNpMtJ5SnQnTRCjLwISQrrMp3gNPXBBx+UYsXSQmitXr1a7rrrLuVgFalLly7qd22ZFqsaqSzQnTYPBbo5sWQKdC2Y7GeB44VgM38a/3PaxyFE6Dj79enqxvXLFZKRvXiGWLdCZgp0jMWXvjdLnT1HeuXS+mHjpv89xfwOVmeDc57oaH4hc2YgQIHuslNQoLsE6NPlFOjegqVA95ZnVhDoeOJrP5oj89bvCT38JSeVlTNrl5R2tUqEzqDiTC3O1lpT33ZVZeryHQJzz0jphraVpE8UZ27rdx1Uu+g429q6WjE5tUbxmPC1U5oHO9aRa0+uTIHuUVf96quvpHLlytKkSRNZs2aN3H777eroV48ePdQd+vTpo3bOH3/8cdmwYYPAEg2WZ7BOi5co0OMRSv+dAt2cVTIFuvbUPqpXE6lXLj10WboX62B62bbStI9Dt382X6at3BnKck+H6oKzxEzwjZLmxT0znMQ9PXF5mGUZLMi+vtksFHRmtJ22Llk58MLMuH22uScFusumpEB3CdCnyynQvQVLge4tz6wi0OGgDTvkcAyFXfNLI8ScRWi1y9+fHfK8Xq1kfvns+qbS5qXIu+9lCueVYT0aS4WikU2mnZLWO1Z92teUO86sRoHuFKBh/ieffFLWrl0rb7/9tuzatUs6dOgg7733njRt2lSVgFCkGzdulJdeeiluiRTocRGFMlCgm7NKpkDXQvzlrvWkfZ300I46DnTQHKxFomgdh44cOyYnvxj+zca3+vs7Wpo3QDbOmZkCvf3gvzNENklWeLlEmlRbl1CgJ0Iv/RoKdHf8hALdJUCfLqdA9xYsBbq3PLOKQDd96h37D8vnMzdK7pw51I5Lvtw55YxBfylhr1Ot0gXl3rOrS4sqRVU+rxIFulcko5eDs+gXX3yxdOzYUfr27SvLly+Xbt26yYQJE6R06bTYyqNGjZJx48bJiBEjQgVt3ZoxrF6pUml+AvAPU3wCOFKABRE4u2WKTQDHK+A7CEcW/U4v/LhCPv57nQy4sLZc3CQ9ckSzgVPVrX/t10aCHGINdYQFDN5DvN/oXs2fS6u7Tk3gJb1X2gJcqicIdPDCu5jsdM9XC+WHRenf0sL5cstv/dskuxrG9+s/eoH8tHibUKAbI4uYkQLdHT8KdJf8/LqcAt1bshTo3vLMbgI9Ep3fV+yQvl8slGPHjyuPu/BW3axyUW9BisjIGevlxR9XStdmFeTpi+v7Ivyyk5O49evXy/fffx+1Hbp3757BD8tTTz0lEydOlC+//FL5aZk9e7aKcjJ58mRl9ok0evRoFZp0/PjxobLPO++8DPdBORCbFJxmrwKEwbFjx8wyp3guROdBSkbfevXHpfLqj0vkrrPryF1n11b33bX/sDR78gcpmj+3zHr03MC3hp3XB1NXypPjFqh6w2HniBtPlrY10q0DAv9APlcws97FdTv2y80fzZAF63dL8YJ55M2rW0jbmsFtF/Qh9CUKdHcdkgLdHT8KdJf8/LqcAt1bshTo3vJMBYEOYjhHjritxfKnx032lmR6WBcKdDOycOr2xRdfRM2MMKN433UaMmSI4J/hw4dLo0aN1J9Nd9Cj3YQm7mZthVw0cTdnlUwT90jO4HSItZZViqoY4kFPkcah1TsOyj+rd0qHOiUDbwGQTL6ZaeKunxNWDifWoJL56I7vpd8NCnTH6MIuoEB3x48C3SU/vy6nQPeWLAW6tzxTRaB7Sy1yaXpSfHL1EjKsZzPuoHsEHbu2gwcPVjvjEOj169cPlRzpDPrAgQNl8+bNPIPuEX9dDAW6OdBkCnQd79kak1pb8+CYDxysBT1xHDJvoSAIdPPaZm5O/W5QoLtrBwp0d/wo0F3y8+tyCnRvyVKge8uTEyPveGqBXr98YRl9S2sKdI/QDhgwQL755hsl0qtXTxcbZcuWVaHUevfuLejH9OLuEfAoxVCgm/NNpkDX3532dUrIy13TFq+yUog11JfjkHnfokA3Z9Vv9EL5Zel2mribI4uYkwLdJUA6iXMJ0KfLKdC9BUuB7i1PToy847lu5wHp/M5MqVQ8v0zqewoFukdoEdccXtvtCTvqVatWlZUrV6o46DpP586d5eGHHxaIpHiJJu7xCKX/ToFuziqZAn3Rxj1y1fA5Uq9cIRl1Il54NM/u5k+Q3Jwch8x5U6CbLX12PQAAIABJREFUsRo3b7M8Mm6pyswddDNm0XJRoLvjxx10l/z8upwC3VuyFOje8uTEyDueuw8ckXaD/5Yi+XLJtPvPpED3Dq1RSZs2bZJChQqpf0wTBbopKZ5BNyclanEIXtx3797t5LKE8urvTsVi+WTsrS1k3c6Dcssn89S/R/Y6SeqXM38fEqqABxdxHDKHSIFuxkpbkVCgm/GKlYsC3SVD7qC7BOjT5RTo3oKlQPeWJydG3vJs8fwfqsD5j55Fge4tWl9Ko0A3x8oddHNWyRToqJX+7ozo2USu/WiOClWG1KRiYRneo4l5xTMpJ8chc/AU6GasFmzcK9cM/1dl5g66GbNouSjQ3fHjDrpLfn5dToHuLVkKdG95cmLkLU8KdG95+l0aBbo5YQp0c1bJFujtXv1LRam4vm1l+eDPNWEV/fT6plKnTHo0BPOnSF5OjkPmrCnQzVn9snSbvPvHWhl3Vzvzi5gzAwEKdJedgjvoLgH6dDkFurdgKdC95cmJkbc89UR52n1nSB454m3hIpKd4qB7DieBAinQzaFRoJuzSrZA7/zOP8qk/fwGpeX7BVso0M2bKsvlpEB33mQcN50zs15Bge6OH3fQXfLz63IKdG/JUqB7y5MC3VueeqI8qW9bKZU/h7eFU6B7zpMC3RwpBbo5q2QL9JtHzRN4c+/XvqoM+nlVqKI0cTdvs6ySkwLdeUtRoDtnRoHujlnY1dxB9xCmh0VRoHsIU0Qo0L3lSYHuLU89UR7eq7k0KZff28Ip0D3nSYFujpQC3ZxVsgX6Y+OXyrdzNwtCrf28ZLvAYVyPVhWlW4tykjOH9wuF5iTMcnIcMuOEXBTo5qx0Tgp058wo0N0xo0D3kJ9fRVGge0uWAt1bnpwYectTC/TXujWW06oX8bZwCnTPeVKgmyOlQDdnlWyB/uLklTJy+vpQBZ/oVFsualzGvMKZnJPjkHkDUKCbs6JAd84q0hU0cXfJkTvoLgH6dDkFurdgKdC95cmJkbc87/9miUxauEX6nlVDrmtd3tvCKdA950mBbo6UAt2cVbIF+lu/rZZ3f09zDqfDrZnXNvNzchwybwMKdHNWFOjOWVGge8MsrBQKdB+gelAkBboHEC1FUKB7y5MTI+94jpi+Xl6evDJU4N0dqkuPVhW8uwEFuqcsURgFujlSCnRzVskU6NP+2ym3fzo/VLk21YrJW1c2NK9sAHJyHDJvBAp0c1YU6M5ZUaB7w4wC3QeOXhdJge4tUQp0b3lyYuQdz5Nf/FOOHDsRgFhEcufMIX/d09a7G1Cge8qSAt0ZTgp0c17JFOiPjFsq4+ZtDqvcb/1PloJ5cplXOJNzchwybwAKdHNWFOjOWVGge8OMAt0Hjl4XSYHuLVEKdG95cmLkHU8KdO9YJqsk7qCbk6ZAN2dFgW7OCjk5DpnzokA3Z0WB7pxVygv048ePy9GjRyV37twR6W3ZskUKFy4s+fObewGmibs3HdHrUijQvSVKge4tT06MvONJE3fvWCarJAp0c9IU6OaskinQ7SbubasXlze7NTCvbABychwybwQKdHNWFOjOWaW8QB8/fry8/vrrgn9b0+rVq+Wuu+6SVavS4lh26dJFHnzwwahC3notBbo3HdHrUijQvSVKge4tT06MvOX5+4odsnDzQTmpUhFpVamgt4XTxN1znhTo5kgp0M1ZJVOgo1bLtuyXHxZtlXJF8solJ5U1r2hAcnIcMm8ICnRzVhTozlmlrECHAO/Tp4+sXbtWypYtm0Gg4zfsnD/++OOyYcMG6dmzpzzwwAPSqVOnuJQp0OMiypQMFOjeYqdA95YnJ0be8kRpfvZRxnP1tr0o0M15UqCbs0q2QDevWTBzchwybxcKdHNWFOjOWaWsQD9y5Ihs3bpVpkyZIsOGDQsT6Lt27ZIOHTrIe++9J02bNlWMnn/+edm4caO89NJLcSlToMdFlCkZKNC9xe6n+PG2plmjNE6MvG8nP/soBbq37UWBbs6TAt2cFQW6OSvk5DhkzosC3ZwVBbpzVikr0PWDT5w4UQYNGhQm0JcvXy7dunWTCRMmSOnSpVXWUaNGybhx42TEiBFxKVOgx0WUKRko0L3F7qf48bamWaM0Toy8byc/+ygFurftRYFuzpMC3ZwVBbo5Kwp0Z6wo0J3xQm6Om86ZWa/IcRye01IkRRLos2fPlhtvvFEmT54sRYsWVSRGjx4tQ4cODRPyM2bMyECpZcuWsnv3bs/p5c2bV5V56NAhz8tOlQIxUOfKlUsOHDiQKo/s63NiwQOfCvZJbzAXKFBAsYTTSiZvCPjZR7GgwuQdAQp0c5YU6OasKNDNWVGgO2NFge6MFwW6c172K1JeoJvuoN9yyy0ZaA8ZMkQOHz7svhVsJUBYInHynjhafExz5MhBhokjDLsSfRIC/dixYx6VmNrFIJIEWJKnd/3Azz6KiT+TdwQo0M1ZUqCbs6JAN2dFge6MFQW6M14U6M55UaDbTNwjnUEfOHCgbN68mWfQ3fevTCuBJu7eovfTfNjbmmaN0mji7n07+dlHaarnbXtRoJvzpEA3Z0WBbs6KAt0ZKwp0Z7wo0J3zSkmBjp0/OIqbNGmSCrM2ZswYtbuq46H37t1bOctIxIs7BL7XSe/W+LE773Vdg1oe2hYfVJpke9NCOHaB3V68R0zuCeTPn19Z39BKxj1LXQL6KL71fnw39fEn72qb2iVBoPvRTtmRKhaewCuFTiMm3IywosHYf/DgwYTLSKULOQ6ZtzY0A46m7du3z/yiFM/JcdNdB0gJE/dly5bJlVdeGUYKIdQGDBig/rZy5UoVBx1h2JA6d+4sDz/8sGSWWSM8yGMwLl++vLvWTeGr4bUfH9IqVaqkMAXvHh3vBgRQmTJlvCs0hUvCN6lcuXIqvCOTNwTWr1+vFuXAlSm4BBDKFInjm1kbzZ07V+rXrx/aUDC7KjVz7dy5U+C4t0aNGqkJwOFTr1ixQkqVKhXyv+Tw8pTKjgXFxYsXS6NGjVLquRN9WH7nEyWXfl1KCHRTTJs2bZJChQqpfzIzIeQbVoDvuOOOzKxGlr73119/LXPmzJFHHnkkSz9HUCqPkIMVKlSQq6++OihVytL1wLvdq1cvadOmTZZ+jiBVHtZRWPC47rrrglQt1sVGAL5bsAB96623ko0BgfPPP18+/vjjUJQZg0tSNsvPP/8s3377rdHxxJSFZHnwfv36yWWXXSZnnHEGccQhAH2AMfu7774jKwMC/M4bQIqThQLdPUPPS6BAd4+UAt09Q2sJFOje8qRA95YnSqNA956pHyVy4uaMKgW6OS8KdHNWyEmBbs6LAt2cFXLyO++MV6TcFOjuGXpeAgW6e6QU6O4ZUqB7y9BaGgW692wp0L1n6keJnLg5o0qBbs6LAt2cFQW6M1YU6M548TvvjBcFunteSSmBAt09Zgp09wwp0L1lSIHuH0+UTIHuL1+vSufEzRlJCnRzXhTo5qwo0J2xokB3xovfeWe8KNDd80pKCWPHjlUebrt27ZqU+2XHm/z222+ydOlSnkf1qHE/+eQTdQbynHPO8ajE1C4GRwYuuOACadiwYWqD8PDpv/zyS4HHa3BlCi6Bb775Rp1B79KlS3ArGaCa/e9//5NHH31UihUrFqBaBbMqs2bNEoz9ffr0CWYFA1arwYMHS7t27aRp06YBq1nwqrNjxw556qmn5MUXXwxe5QJYI37n3TcKTdzdM2QJJEACJEACJEACJEACJEACJEACJOCaAAW6a4QsgARIgARIgARIgARIgARIgARIgATcE6BAd8+QJZAACZAACZAACZAACZAACZAACZCAawIU6K4RsgASIAESIAESIAESIAESIAESIAEScE+AAt09Q89LOHDggGzfvl3KlSsnOXPmzFD+sWPHZPPmzVKqVCnJnTt32O9HjhyRLVu2SIkSJSRfvnwZrj148KD6Hc6UkCc7p3gsYnHEb2iDPHnySNGiRVOao/Xh2TejvzEzZ85UfaVWrVpRM+3atUvwDpYpUyZinkOHDgmc0eD3HDlyhOWJx37Pnj3qWrzXhQoVCtSrDadg+O4UKVJE8ufP71vd/HznUfbWrVuVgzO0T65cuXx7juxcMPopWBYvXjw7P6ajZ4s1FkUrCAwxP4g0R3B08yyYOdZ30v448cbyLPj4jquMb2/hwoWNvr2pNEeMBNJJ33LcENnwAn7P/WtUCnQHbJcsWSJXXXWVugIC95dffnFwtVlWeGydMmWKyoyJ9kUXXSR9+/YNXQwPpQ899JDs27dP/Q3/femll6r/HjZsmAo1pBM8bj/44IMh768DBgwQeFbUqVmzZsojZbImSuPHj1feaK+//nrp3bt3qB7wEI56wPPqddddZwYqTq54LGJxnDZtmtx7770hxi1btpS77ror5HE7szni0Z988kkZM2aMwAvrqaeeGqKh+8/QoUMF7etlys59087p888/l+eee079+bzzzpNnnnkmLsr+/ftL48aN5cYbb8yQFxOkW265RVatWqV+q1GjhnoPOnXqpP4fog9t9s4774Te/ZdfflmaNGmi/j8We3wLbrjhBhW1QKdu3bqpazJbRGKRC8/0448/qgUvpKpVq8r9998vbdq0icvUSQY/3/kvvvhCBg4cGKpO2bJl1TeLXvjNWwj99JFHHgmNb3hXwBDRIVI5xRqLonHBYt21116r3vtUiloQ7ztp5xVvLM/u/W716tVq7qLHHUROwJzQvrGjOQRhbpNZbeK0b1nriXe4X79+MmjQIDn99NMz6xGSel+n33OEQLznnnsy1HHq1KkRNxOT+jABvRkFuoOGwUosOiU62vPPP++LQH/77bdVKKvKlSvL33//LZj0Y+KJyQwGZYiFW2+9Va688kr59ddflZCEUKtUqZJ89dVX6jpM6tesWSO333679OrVS3r06KGeEvHVIebq1Kkj69evV4M7Fhzw72QkLdBxr8mTJ6vdRuwCXHzxxYIYkxDtEC1epFgs4nEEd1go4EOLvM8++6wSUPj4BoEj6qAFOkQ4hB3SypUr5fLLL1f/7YdAz859097n0C/R9q+++qrs3r07TJxF65+xBDr607fffisXXnih2t0eOXKkjBgxQiZOnKh2NWbPnq2EPdqtUaNG8tZbb8mECRMEIRexQxaLPVawUVbnzp2lQoUK8ueff6pJmR99wOm7+cADD8iyZcsEEz8sSqxbt06+++47ZR2k+6rTMqPl9/Odx7cLYa5atGihvlmY5OLfaBcmMwIYx9BG6JcFChRQfbR69epKtKdqijcWReKCb9JHH32kfsI4kEoCPd530s4r3lie3fsdNj2wc/7444/Lhg0bpGfPnoJvsl4Ytj9/Zs8RM7M9nPYtXVds3GHshjZIJYHu9Hv+008/yWOPPabmKtZUpUqVDNaCmdkPgnRvCvQEWgO7QU888URIoGOCgUkxdogwCYWA1vGiEe8Yq5UrVqyQf/75R84880y57bbblJA2SfiQYiILEa1X6X7//XfJmzevuhy759gt6969e4biMHivXbs24iQScdZRNupy2WWXmVTFdR5MciFMIEggfrFb/v3338vHH3+sTMnxNwh0mOliNXL58uXqng0aNFArb1hYwGQf7LE7OGrUKCWkMajES1YWTjnqhQW0sX3lOTM46okZFg1gEYHnRxxTLCSAIywStDhDn8PfN27cqBCh/913331qcQTWF7AO0O2P8u6++24l9M4+++x4SFX/yS59M9rDvvDCC8qsGbun8+fPF+xq6wURXAPrFvTZ5s2bq8W0aDvo9vLxXmI3Q7cTLCEWLVokb7zxhsqKfo2JNwaz+vXrZ6ielb39R7w3+CZ8+umnMc3t4zawBxnQ3zB5wUJhpIQ+h/jl+C5gIQRMrrjiCiXg8d7BSgkLGpMmTVIWRei7p512mlHN/HrncXO8O1iwte6qG1UqhTNhoRjjoraS+uGHH5RYgIiyH+dIFUxOxyJwwfgIM2R8d+68886UEuhOv5P2fhRrLM9ufQ7HqTp06BCaH+D5sLGEuQDmpfFSZs1t4tXLr98T6VuwjMOiB95DzLNgaZcqO+hOv+cQ6OCDsZzJjAAFuhmnsFx2gQ5BVLt2bTWBxK42zMyRBzsuEJoQSXfccYfKg49A69at1QsdL8EsCQJcr8qNHj1aTdjxb50gVKtVqxZmBo/f9M50x44dw37D+ZoPP/xQiX2YFmJlFSusyUhaoGNRABNciHNMMrCggefSAh0DC4QndoexEIH6YncYeebOnasmeDAxxWQeYj/a5F8/k52FE44oA2b5ED3Wlb/M5Ig6QXygf2Fii7o9/PDDgrbGDlXXrl1Dwg+iEosa9erVk/3798tTTz2lRDr6H54Hwgg7uzCFxhnqm2++WTBxjnfsIbv1zWj93yrQp0+frha08G+dzj33XNU/zjjjDEcCHf0bu8paeOJ9AHMIUJ1atWoVcUXezl7nh+hH38ZACEsb1DWzEyYt2DG/6aab1O4zzudj91QnWAlg0Eb/xXcMCxbo11j0RP/Etw/XYuEDQh7vv8kA78c7jzqPGzdOmWjjOAHEed26dTMbcZa5P747eFf04vXChQuVdZe2psoyD+JhRZ2ORdZbY/zDO55KO+hOvpORminSWO5hcwaqKL1Qi2+sPkaCTQ18w+y7mNaKZ/bcJrMgOu1bsH7BsTVYpeI9xPctlQS60+855iWw+MUGEPxjYVMDY0G04xaZ1Q+CdF8K9ARawy7Qjx49KjBzwQ4Ydr5g9ghRifOJEOgQmnrX4Ouvv1Y7nPgnVtq7d6/aeYJ4xhlOCCiYlEA8WT+u+KhghwkTXGuCEIP5LCa1VodU+KhAlC9evFj9HbvR5cuXT4CC80u0QAcbmOjDLB+WBZik4EOnBTpKRj0xGYcwh8iEoIEw0gIdk2RTR1h2Fk446hV37Gxaz8xmJkerQAdHmE2DHdoTlgbYYbSaN2OVd9asWapvou/CUdcrr7yidmLwgdTn2CGKsAOPSUyq9c1oz+uHQIe4g0XM1VdfHRLRMEWE2LP6m8AAiPcbCy86Rfou6N8geN5//3210IIFA5hhoz0zM2GxDZNCWMlovxnY3cf7jgUJfONg4qYtgPANxe4ORBu+kX/88UcGqwJ8L3COPVby453H/bD4qt8lvC9YRGGKTwCWEliYtpqAagGBYxzJGoPi1zS5OZyMRfaapaJAN/1ORmrFaGN5cls8eXfTJtvWBTB8OzE3AItoKbPnNskjFH4nJ30L1lMYm5EgynEMLZUEeiLf83nz5in9ggV4HLGFNsFcwLopkVltH9T7UqAn0DJWgY4JM87SQfC2a9dOmWZi0NXnxu0CHR30tddeU+fGY30gIbRgivTuu++GdjNNV9uHDBki+Gf48OHqPGukhBcMHyTUN54gSwBRxEu0QMcCgx4sMcnFRAMTdS3QseOLFUkISey6YUUX+a0C3dQsMhILU44waQcjCJ1oxwAygyPg6h10CDp9Hh2OrHAG2SrQsUiDgQSLRNhFhzjE6iVEORIWa3CGGc94/vnnhxaWUq1vRnterwU6zmBjRxhHC8BeO3FDG8ECByvMOtl30DFxivRdsNcdohir1HDEhsWbICRMaLDDj3cYi0NYWILPCVggwOGm3VEYTDGx824V6HgO+6p9pGfz+53HPXGsBAsIJrv5QeAfhDqg7XAGUR+f4Q66qMVpU6s4exumokA3+U5G6usmY3kQ3hEv65DoDrquQ2bNbbxk4KQsJ31LH0G75JJLQhtFWITGwjjGXG0l5OT+WS2v2+85NBDmrpGOjmY1Fn7VlwI9AbIwzX766afV+UjtmdC6SomJdaICHecwMQmHOTJElNXUWJ9Xw6RV74xhkIajN+xAYRKMazDoY5Ia6eyq9XEhPrBDrc+9JoDC0SVWgY7zTVjJxU4iBKNVoOOcL3bS3nzzTSVg5syZo0zhnQj0WCziccRD6fORmFDCk36slGyOqItVoMNTK/oEViNxNtEq0HGeF+bOMF1HgvUCOGqBrlfZcZYclgr4PVrKzn0z2jPDRHvnzp3KnFkfAUjUxF0vPGEhD2dvraZdaA8s8ukoDPYz6LHYR6o7jsbA+aJXUREcveiWzFhUsIdVg2k/xDosg2DijAmNjo5hvQ+Ei1WgY3EDz4TrsMBhT8l857FIiwUQTi7Me4bTM4vmJWfdnCZjUbSnS0WBHu87GYmVk7E86/akjDWPdAYd4xjGFpMz6CgxM+Y2mdUGTvoWrMHsVrCYr+K4CSzeTP2kZNazenFft99z+NLCBhO+gX6GXvXiWTOrDAp0Q/I6fjHEpA65hI/dX3/9pc6Xw4wT56Ih3rH7k4hAhyiHwwmYzCPEkzbhhvkMTADxO1bo4MgLYszuxV2HyMCHBt5xdUK9MFH+4IMPlNiEg7oFCxaoHSyc344UFsoQi6NsVoFuv9Aq0DEBx7PBHBJnSbHYYDdxj7eDHosFFgdicYTJJXY3cb4fYkonLJZABGQ2R9THKtCtLO0CHVzh+wBnzuHZH30Xz6EFOq7F4g521mN5BM7ufdPKEN5usasLUYx3G+8kLCgwKGPVGDvAiJQA6wS86/j/eGfQdYhGDN6wDtGxi3EftIfVgyzOW2PRDN8S9EW0aazvAq7FgtZZZ52lzMdw5hD9H9Y3OOeVWQmWLxDf+M6cfPLJyiIGi20YlOF3AotGMMnHtxMM4QwSpm/wo6B9JOD5YXEEBvgOQLDDZ0KkAd2vdx5thHufcsopylHltm3blFUK6kAv7ua9y6nXX/OSs27OeGM6rJvwvcD7j4VWJIyJ2N3EoiqscfBNyeyjLMlqgVjfSXxTsaiHTRPtyDPWWI73OrsnfHvx3Y3kxd3et/D/QZjbZFabxOtbM2bMUOMU5lCRjlilkok72ije9xybELDohIUx0meffabGT4zz2PTAsVxsUnAMjd7jKdANvwbaRArZ27Ztq0RyzZo1lWBDR8QOOhIEHc5HJyLQEWosUvgLmL5qU0qUDeGoE3ZxsEuKhBV17EzZE3bUS5UqpSbFMCvUCWawMG3GokMykqlAh0CCFYGuK5xwYLUNO5c4x4JFhXgCPRYLfFxjccTCC8zF7QlHAWCemdkcUS+IaQg7u7NBu0BHv8UxAkQYwIQEH0gM2DpkHMrCUQiY7KKPResL2b1vWtsa4hgTFSRtKq4dm2nzafwGUa4XknA8A+8ljpRECluojxrY+xTedwhLTLixMKUnlmgrCFN454/HHn4Z4EFexxnHPfB9whn3zEwQErBAQL/S589Rn2uuuUYdHYGogIiH1QCcFeqE3XGw0E7i9N/hswKWS1jAiJT8euexa48JLib7OqFd0G6oE5MZARwHw6QMOyZI8NGCnTyrjxSzkrJXrlhjESayGHOs4zzmG9gVtiaMV9ZF+exFKP1pYn0nkQvjGuY7sG5EijWW473O7gkWkhBIel6I8QzvIL699r6F9zMIc5vMapN4fQt9CuMqFpQxj7KnVBPo8b7n2PjBN0lHOsF8BnNNnTCOYzznGBq9x1OgO/gaYIURKZLXc4Rigjl2PO/XDm4XNSt22HE+HRMbpyvneKmwA4Qzn1Zvyl7Uy+sysJsGnn7VM1U4Qihh0QNWGJE8ZmIXBscyvPD6nR2YYqDG5AX9LtKCBd4hMMVutdcJli4Q2vANoXfZTe6h64xvVLR2NinHjzyoGxwSQozju6PP3VvvBZ74LiH8n94d1ybumHTjubBQ6Ta56Z+oP8xDYdmUjO+822cN6vWwRtN9Iah1THa93PTLZNc1CPdL9DsZhLpnRh2wyIvvlolj3aw0R/SDJfuWM6pOvudgC6fF0FAcQ+NzpkCPz4g5SCDbEsC5c5hPprIn5WzbuFn8wexn0LP447D6JEACJEACJEACJGBEgALdCBMzkUD2JIBjBNhdb9++ffZ8QD5VliUAvwjY+cERFyYSIAESIAESIAESSBUCFOip0tJ8ThIgARIgARIgARIgARIgARIggUAToEAPdPOwciRAAiRAAiRAAiRAAiRAAiRAAqlCgAI9VVqaz0kCJEACJEACJEACJEACJEACJBBoAhTogW4eVo4ESIAESIAESIAESIAESIAESCBVCFCgp0pL8zlJgARIgARIgARIgARIgARIgAQCTYACPdDNw8qRAAmQAAmQAAmQAAmQAAmQAAmkCgEK9FRpaT4nCZAACZAACZAACZAACZAACZBAoAlQoAe6eVg5EiABEiABEiABEiABEiABEiCBVCFAgZ4qLc3nJAESIAESIAESIAESIAESIAESCDQBCvRANw8rRwIkQAIkQAIkQAIkQAIkQAIkkCoEKNBTpaX5nCRAAiRAAiRAAiRAAiRAAiRAAoEmQIEe6OZh5UiABEiABEiABEiABEiABEiABFKFAAV6qrQ0n5MESIAESIAESIAESIAESIAESCDQBCjQA908rBwJkAAJkAAJkAAJkAAJkAAJkECqEKBAT5WW5nOSAAmQAAmQAAmQAAmQAAmQAAkEmgAFeqCbh5UjARIgARIgARIgARIgARIgARJIFQIU6KnS0nxOEiABEiABEiABEiABEiABEiCBQBOgQA9087ByJEACJEACJEACJEACJEACJEACqUKAAj1VWprPSQIkQAIkQAIkQAIkQAIkQAIkEGgCFOiBbh5WjgRIgARIgARIgARIgARIgARIIFUIUKCnSkvzOUmABEiABEiABEiABEiABEiABAJNgAI90M3DypEACZAACZAACZAACZAACZAACaQKAQr0VGlpPicJkAAJkAAJkAAJkAAJkAAJkECgCVCgB7p5WDkSIAESIAESIAESIAESIAESIIFUIUCBniotzeckARIgARIgARIgARIgARIgARIINAEK9EA3DytHAiRAAiRAAiRAAiRAAiRAAiSQKgQo0FOlpfmcJEACJEACJEACJEACJEACJEACgSZAgR7o5mHlSIAESIC2MV6KAAAgAElEQVQESIAESIAESIAESIAEUoUABXqqtDSfkwRIgARIgARIgARIgARIgARIINAEKNAD3TysHAmQAAmQAAmQAAmQAAmQAAmQQKoQoEBPlZbmc5IACZAACZAACZAACZAACZAACQSaAAV6oJuHlSMBEiABEiABEiABEiABEiABEkgVAhToqdLSfE4SIAESIAESIAESIAESIAESIIFAE6BAD3TzsHIkQAIkQAIkQAIkQAIkQAIkQAKpQoACPVVams9JAiRAAiRAAiRAAiRAAiRAAiQQaAIU6IFuHlaOBEiABEiABEiABEiABEiABEggVQhQoKdKS/M5SYAESIAESIAESIAESIAESIAEAk2AAj3QzcPKkQAJkAAJkAAJkAAJkAAJkAAJpAoBCvRUaWk+JwmQAAmQAAmQAAmQAAmQAAmQQKAJUKAHunlYORIgARIgARIgARIgARIgARIggVQhQIGeKi3N5yQBEiABEiABEiABEiABEiABEgg0AQr0QDcPK0cCJEACJEACJEACJEACJEACJJAqBCjQU6Wl+ZwkQAIkQAIkQAIkQAIkQAIkQAKBJkCBHujmYeVIgARIgARIgARIgARIgARIgARShQAFeqq0NJ+TBEiABEiABEiABEiABEiABEgg0AQo0APdPKwcCZAACZAACZAACZAACZAACZBAqhCgQE+VluZzkgAJkAAJkAAJkAAJkAAJkAAJBJoABXqgm4eVIwESIAESIAESIAESIAESIAESSBUCFOip0tJ8ThIgARIgARIgARIgARIgARIggUAToEAPdPOwciRAAiRAAiRAAiRAAiRAAiRAAqlCgAI9VVqaz0kCJEACJEACJEACJEACJEACJBBoAhTogW4eVo4ESIAESIAESIAESIAESIAESCBVCFCgp0pL8zlJgARIgARIgARIgARIgARIgAQCTYACPdDNw8qRAAmQAAmQAAmQAAmQAAmQAAmkCgEK9FRpaT4nCZAACZAACZAACZAACZAACZBAoAlQoAe6eVg5EiABEiABEiABEiABEiABEiCBVCFAgZ4qLc3nJAESIAESIAESIAESIAESIAESCDQBCvRANw8rRwIkQAIkQAIkQAIkQAIkQAIkkCoEKNBTpaX5nCRAAiRAAiRAAiRAAiRAAiRAAoEmQIEe6OZh5UiABEiABEiABEiABEiABEiABFKFAAV6qrQ0n5MESIAESIAESIAESIAESIAESCDQBCjQA908rBwJkAAJkAAJkAAJkICXBA4fPix58uTxpMijR4/K8ePHJXfu3J6UZy9k2rRpki9fPmnWrJkv5bNQEiCB4BGgQA9em7BGKU7gq6++ki+//FKeffZZqVKlShiNdevWyX333SeXXHKJXH755eq3J598Uv744w8ZMWKElCxZ0ojeDz/8IPnz55fTTz/dKD8zkQAJkAAJkEBWJvDbb7/JJ598Iv/++6/s27dPqlatKs2bN5fbbrtNypQp4+jRjhw5Ip9++qlMnjxZZs+era5t2LChnHHGGdKzZ08lqOMlPdbrfBiTa9WqJZ07d5YmTZqELu/SpYuUL19e3nnnnXhFhv3Ocd4RLmYmgUARoEAPVHOwMiQg8u6776qBeNSoUVKnTp0wJCtXrlTC/MYbb5Tbb79d/fbKK6/IzJkzZfDgwVK8eHEjhJdeeqmUKFFC3nvvPaP8zEQCJEACJEACWZXAyy+/LCNHjpSCBQvKueeeK2XLlpV58+bJ77//rv722muvSdOmTY0e78CBA9K7d28lzCHysdCN3XOUtXTpUqlRo4Yaw+MtmOuxvk2bNlKoUCHZunVrSOw/9thjctFFF6n6JCrQOc4bNSczkUAgCVCgB7JZWKlUJuBUoCfCysuBG6Z9OXLkSKQavIYESIAESIAEfCUwZ84cuf7665WYfvPNN9VutE7jxo0TiOHatWvLxx9/LLly5Ypblw8//FAtiJ9zzjny+OOPK2s0JOyqv/jii/LFF1/I1VdfLXfffXfMsiKN9QsXLpQePXqo6/78808l/CnQ4zYJM5BAtiNAgZ7tmpQPlNUJOBXomFRg5R4TBj25mDJlitqBnz9/vjK1q1mzpto1wO77gAED5JtvvlGYsHKPBFP6Bx54QP33jBkzZMiQIerf2GVHnr59+6odB2v69ttvlYkfJhTIB9O+jRs3yg033CAtW7ZUWf/v//5PypUrJ926dZPRo0fLokWLpHHjxsoC4MEHH5TFixfLli1blLlh/fr1Vf1gvq8Tzt5hMoTJzqRJk2Tq1Knqp06dOsmdd96pzAtRB+xkVKpUSVkVdOzYMat3AdafBEiABEjAIwIYvzBGDh06NOI57kcffVTGjx+vxHX79u1j3hW75+edd57Kg2sKFy4clh9n2yGoN23aJBMmTJDSpUtHLS/aWA/RP3bsWPnss8/U2B1JoK9fv15effVVNU4fPHhQmddjV1+bxscb5z1Cy2JIgAR8IkCB7hNYFksCiRLQgzYGXwzO1rRq1So1CFtN3HFWHWfW9Wo7ROxdd92ldgTOPvtswYQCQhfX/vLLL0p84x+Y9V1wwQWqeIhoCGuc0evXr5/aacA5uD179iiBjLxjxoxRQhxJ1xH/DVGdN29ewXk3TEoGDhyodha0kEYZEOBIENH16tWT5557Tlq3bq0mQ6hnzpw5ldiGeSBEvRbpmABh8oSEOuGaBQsWqIUHLBjgfm3btpUKFSrI999/r+6DZ9A7Gom2Aa8jARIgARLIHgTOPPNMNcZ9/vnnER9I77Bjlx3ja6y0ZMkSueqqq2LukOsd9tdff12NT9FSNIGuxTV24qtXr55BoGPcw7iL8e66665T4x0WwPH3t956S42Tscb57NGqfAoSyN4EKNCzd/vy6bIgAav4jVb9WAIdTuMgpvXgjjJghg7HOPqMXSQTd+SBMMagD+c1emdg+vTpyomONtnbsGGDEu84Z/fBBx+E8v39999qB9su0DFpeOihh9Sut1U4wxzQ6vVW70xg9x3n6pG0QMf9MXmChcChQ4dCCwCYhGDnHennn3+We+65R1kSnHrqqVmw5VllEiABEiABLwngXPf555+vxgyMTZHStm3b1K54hw4d5Pnnn495eywkw1Hr/fffL1dccUXEvD/99JPce++9Kh+sx5wIdCymY5EAC9CwUsOYZ99B14vyw4YNUxZpSDt27JCLL744bCHCy6NsXrYJyyIBEohPgAI9PiPmIIGkEtACHWIXO8PWBLGLVflYAh1eamGqB5NzTCAaNGgQ2vnWZUUauDdv3qx21HH+Dbvo1oQJQpEiRZSneC2Eb731Vrn55ptD2aIJdAj5N954IwPDvXv3ql3zuXPnCsz1MEmCuTwEN+6DpAU6nNlZHfjg+XENftdp9erV0rVrV7njjjuUNQATCZAACZBAahNYs2aNWnjGojJMxyMlLEpjl71Vq1by9ttvCwT2Cy+8EJYVR8T69++vTM9RjtWJm71MbcWGMRxjVbSkx3qYp8MKDVFaMMbDYg1jpjZXtwt0jN/Ij7HemrRwx7hatGhRoUBP7b7Pp8/aBCjQs3b7sfbZkIDTM+h2E/ddu3YJPNZiIqET4qf26dMndP4u0sANoQxzOZzt7tWrVxhZTDJg2gcTeXjCRfmYQOgz7MjsRKBDTKM+a9euVYIcZu8Ic4OyYcoeT6BjRx0m+1aBrnf28dtNN92UDXsGH4kESIAESMAJAVhqwcwcYyDOoEdKK1asUIvZWsTPmjVLWaBZE66HWTl+w/hiX6C25oUF2tNPPx1TxCO/HutRdrFixdTuN8Y/WJtBYOtkFeiwdIMJO54Ji/XWhMUFPCP8siBcGwW6k57CvCQQLAIU6MFqD9aGBByHWbMLdI0QghVntf/66y812cCqPM6JY+U90sCtdxrsZn4wKYfJOCYOOOcGxzc4Jw4TviuvvDLUYk4EOiYWMM+zmsOjIO29lgKdLwIJkAAJkIAXBCC+IcK/++67iPHOMd4MGjQoptm6roc2mcfCMs6aw3+KPcECDb5QIoVKteaNtRhvzWffQcduPhzDYaff6nVeO8PTzuko0L3oPSyDBDKHAAV65nDnXUkgKgG3O+jLly9XjmWsEwecy8ZkYvjw4dKoUSMlhHfv3q3OquuEnQaIc5j7aYdz+A0x1mHKDvN3nG9ftmyZEuZY9cd5PcR6xbWoN0zR7WfQI5m464mENsXDfTDhwHk9bUqPv0UzcecOOl8gEiABEiABEwLaLB0RPmCebvV9Amuua665Ro171vEoVrnay3qkM+b6jDqOZGE8jJUSFejwtYKjZhjTYR6PBB8uiMcOR64TJ05UoU8jjfMmvJiHBEgg8wlQoGd+G7AGJBBGwK1Ax+o9Qpfh3F21atXUWe3XXntNCV+YkMNRm3YkB9P1unXrKrGO/DCNw9k7nF+HCIcHdlwLU3R4wIXYRsIOOlbpkXBuD6J9+/bt6v9NBLo2xcN9sCiAXQmYBWqTd+6g86UgARIgARLwgsCxY8eUbxI4PIWgveiii6RUqVLq2BbGWySTEGu6LvDXAqepGPPgfA7CGKIf5X/99dfKWg3hTxG+1A+BrmOlw6oNR8UwpuM8OkLJPfLII8qpHFK0cd4LpiyDBEjAXwIU6P7yZekk4JgAzpBBwGLARQgya1q5cqU6B4czcNhFRrKbuENIw7u5FsyYLCCc2bXXXit16tRR18AZDXbVYfKOBE+wMDk/evSomljgN52wIv/MM8+oc286Ycccq/eYEOBMHHblceYNEwJraBmcpUOoOPtZOexW4Bw7JjM6XXbZZfLrr7+quLEoGynaDjq83MJk0XoGHTHYL7zwQuVJPpZjHscNwgtIgARIgASyNAGMWR999JGKPKLDfuKB4C0d4xaihzhJkXy94HqUA5FcuXLluMXFGuutF0Nww2Es5gU6wREddvL1OI+/Y2cdC+vYPY81zsetGDOQAAlkOgEK9ExvAlaABPwhgIF7586dahXfek7NejdMVDDRgCi2mv1hMgMnbAUKFJDy5cuHBvxYNYX5PHbbreHd4j0ZduixWFCxYsVQuLZ41/B3EiABEiABEkiUAHbAsaCLXfVY8dFNyseiNsawRYsWyQMPPBAzPrpJeU7ywDIAFnLwE4Nx3jqGm4zzTu7FvCRAAsklQIGeXN68GwlkCwLY+cYCAMzjCxUqJAsWLJCXXnpJhULDroBewc8WD8uHIAESIAESyHYE9FErWJDBKzos1J566qmoQjceAFhuzZ49W9q1a6es3yDeYYLORAIkQAJOCVCgOyXG/CRAAmqnHDvm1gSncTC5MzHtI0ISIAESIAESyEwCO3bsUCE9cVRKxx+Hk7doO9Hx6ooz7TiaNmnSJGVGD6GOhWsmEiABEnBKgALdKTHmJwESUOfO4dANzuiQcD4OZ/m4c87OQQIkQAIkkJUIwFQcUURwpMuLhCNi2D3Ply+fF8WxDBIggRQkQIGego3ORyYBEiABEiABEiABEiABEiABEggeAQr04LUJa0QCJEACJEACJEACJEACJEACJJCCBCjQXTY6zjAxeUsAMT1hcgbPpEzeEUC4NTCF+R2TdwTgJO/AgQPKpJHJOwJFihQRePnHcYrMTsWLF8/sKmSr+6fquImzzXnz5g0L85WtGpYPE5GANnWHGT1T6hBI9bkBx013fZ0C3R0/2bZtm8sSeLmdAD5qEJEczLztG4ULF1ZMDx8+7G3BKV5a0aJF1YSbCx/edgQM7ggBiMW6zE4lS5bM7Cpkq/un6riZJ08ewQL07t27s1V78mFiE9Bn2/fv309UKUQAi8xYvE/VORfHTXednQLdHT8KdJf8Il1Oge4DVBEVZ5wC3Xu2FOjeM0WJFOj+cA1CqRToFOhB6IfJqgMFerJIB+s+FOhc2HbTIynQ3dAToUB3yY8C3QeAUYqkQPeHNQW6P1wp0P3hGoRSKdAp0IPQD5NVBwr0ZJEO1n0o0CnQ3fRICnQ39CjQXdKLfDl30H3Byh10f7AKBbo/YCnQw7nCPBYm0onGaPanlRIrlQKdAj2xnpM1r6JAz5rt5rbWFOgU6G76EAW6G3oU6C7pUaD7ApA76MnESoHuE+1UFujjx4+XkSNHyuuvv65M/V999VX56KOPFOlBgwbJ6aef7hP15BRLgU6BnpyeFoy7UKAHox2SXQsKdAp0N32OAt0NPQp0l/Qo0H0BSIGeTKwU6D7RTmWB3rdvXylWrJg8+eSTsnTpUunevbt06dJFdu7cKZs2bZIPP/zQJ+rJKZYCnQI9OT0tGHehQA9GOyS7FkEW6Hv37pWffvpJNmzYIL169VLWWV4nOolzR5QC3R0/nkF3yS/S5TRx9wEqncT5A1Uk2wn0Fs//4RsrpwXPeuC0lPTiDjHes2dPueyyy2TEiBFq1/zXX39VYecuuOACmThxomTlyQ8FOgW6029BVs5PgZ6VWy/xugdRoM+bN0+mTZumxDlEOtKAAQOkcePGiT9olCuz8hjlOYwECqRATwCa9ZJUnWi4xBbzcgp0f+jSSZw/XLPbGXQK9Iz9JNkTDYjzDh06yHXXXSd9+vRRoXqGDh2qws7h79hBb9iwoT8dOgmlpuq4yTBrSehcAbwFBXoAGyUJVQqKQIcQHzt2rPpHi3I8fqNGjeTiiy+W1q1b+0Ij2eOmLw+RiYVSoLuEn6oTDZfYKND9BBilbAp0f6BToPvDFaWm6g76W2+9Je+995507NhRJkyYIA8//LB07dpV7aL379+fO+j+dTlfS6ZA9xVvYAunQA9s0/hascwU6NqEHbvl2DXXCRtgnTt3Vv/gv/1MFOju6FKgu+NHE3eX/CJdzh10H6DSxN0fqDRx941rKgt0TK6effZZZYp4xhlnyEMPPaS8t/fo0UNy5szJM+i+9jr/CqdA949tkEumQA9y6/hXt8wQ6BDjkydPVibsVlF+1llnSZs2bdSuebISBbo70hTo7vhRoLvkR4HuA8AoRXIH3R/W3EH3h2sqC3T/iAaj5FS1PKNAD0b/S3YtKNCTTTwY90uWQI90rhwEIMYhyiHO/d4tj0ScAt1dP6RAd8ePAt0lPwp0HwBSoCcPKnfQfWWdqibugLpjxw6ZMmWKrF+/Xtq1aycNGjRQ5u6lSpXy7cygr41pKZwCnU7iktXXgnAfCvQgtELy6+CnQI9mwl62bFllvp5ZotxKmQLdXZ+jQHfHjwLdJT8KdB8AUqAnDyoFuq+sU1WgI/RNt27dZN++fYovvOx26tRJBg8eLN9++6189913yuQ9qyYKdAr0rNp3E6k3BXoi1LL+NX4IdOyWf/LJJxnOlWeGCXu8FqJAj0co9u8U6O74UaC75EeB7gNACvTkQaVA95V1qgr0IUOGyC+//CLPP/+8PPPMM0qc458FCxbItddeK19//bVUrlzZV/Z+Fk6BToHuZ/8KWtkU6EFrkeTUxyuBHsuEvXv37kk9V+6EHAW6E1oZ81Kgu+NHge6SHwW6DwAp0JMHlQLdV9apKtAhxq+//nq54oorVJg1LdB37twpZ599tgwfPjywkzKTDkGBToFu0k+ySx4K9OzSks6ew41AjxUaLTPPlTshQIHuhBYFujtaEa5O1YmG5yAtBdKLuz906STOH650EucPV5SaqgId8c+bNWsm/fr1CxPoM2bMkFtvvVWdRS9durR/4H0uOVXHTTqJ87ljBbR4CvSANozP1XIq0GOFRguiCXs8fBTo8QjF/p076O74cQfdJT/uoPsAkDvoyYPKHXRfWaeqQB82bJi8//778uijj8oXX3whF154odSoUUP9PxaE8HtWThTo3EHPyv3Xad0p0J0Syx75TQV6JBN2bFRlRVFubTkKdHf9mALdHT8KdJf8KNB9AEiBnjyoFOi+sk5VgX7kyBElxidOnBjGt1KlSvLKK69IzZo1feXud+EU6BTofvexIJVPgR6k1kheXWIJ9E2bNsnYsWNVvHLsnOuU2aHRvKRDge6O5v+zdyZwNpX/H//OYmYMxgxjp0RT1hClnUQhUpJUQlHy41+khUiSfvwqpdImilJU0mYrsleWFFmzZwljmTHGmDHb//V56lx35t6Ze+6ce+49957P83rNS819zjnPeT/fuee8n5WCbowfBd0gPwq6CQAp6P6DSkE3lbVdBV2DunXrVtmxY4ekp6dLrVq11J62MTExpjL3x8kp6BR0f8SZVa5BQbdKTfi3HIUFvbgh7NgaDT+B2K/cLCoUdGNkKejG+FHQDfKjoJsAkILuP6gUdFNZ213QTYUbwJNT0CnoAQw/v1+agu535Ja4oCboGzZsCJqt0XwJjoJujCYF3Rg/CrpBfhR0EwBS0P0HlYJuKms7CTpe4l599VW1tdq8efNk8+bNRbJ98cUXJTY21lT2Zp6cgk5BNzO+rHZuCrrVasT88mBe+e+//y7ff/+9yxD2Nm3aqPnloZ4o6MZqmIJujB8F3SA/CroJACno/oNKQTeVtd0EfeLEiTJ+/HiZP3++4AWvqPTCCy9Q0E2NPHNOzlXczeFq9bNS0K1eQ74pX1FD2ENpXrk3pCjo3tByzWs7Qc/OzpZjx46pLWqioqIKEDl+/LhgKypv5vjZtSfAWNgVfzS3WTOHLrdZM4crt1kzhyvOaidBN4+i9c5s1+cmBd16seiPElHQ/UE5cNdAY+qsWbMKNKriPfaWW26RFi1aSL169QJXuABemYJuDL5tBH3fvn0yduxYwTBCpGHDhkm3bt3Ufx84cEAee+wx2b9/v/r/Ll26yPDhwyUyMtIjXbu+aHgEYyADBd0AvGIOpaCbw5WCbg5XOws6hrDXrVtXevToUQDuzp071bPq008/lfj4ePPAm3xmuz43KegmB5ZFT09Bt2jFGCiWu63RcDr0luN7G//q3WbNQDEsfSgF3Vj12ELQsZ1Bx44d5eabb5bu3bur1qzMzEzHC86gQYNUz/no0aPlyJEj0qtXLyXwOMZTsuuLhicuRj6noBuhV/SxFHRzuFLQzeFqZ0EfOnSo1K9fX/r161cALkZ5tW/fXmbMmBHUvTJ2fW5S0M37rrDymSnoVq4d/WXDEHZsjYYfPVujUdAr6IfLnC4EbCHo2DcWc/oWLFjg0iuelpYmWLBh6tSp0qRJEwUIi/QcPXpUJkyY4DFk7Pqi4RGMgQwUdAPwijmUgm4OVwq6OVztKOjbt28X7IGOZ1bt2rXljjvucMDF77Ev+ueffy6rVq3yaiqWeTVUsjPb9blJQS9ZvAT7URT04K3B4rZGw0Jv2PoSveXuEgWdgm4k8m0h6HfddZfgC7Jq1aqqh/zSSy9VPRNVqlSRPXv2qF71hQsXqnnpSDNnzlSr6KKXwlOy64uGJy5GPqegG6FX9LEUdHO4UtDN4WpHQW/Xrp2kpKQUCTQhIUF69+4tPXv2NA+6H85s1+cmBd0PwWXBS1DQLVgpHoqEIexLliyRpUuXOnLi3dSTlDufloJOQTcS+bYQdCzSgJ/bbrtNLQw3bdo0ycjIkM8++0y2bt0qffv2VX+IeNFGmjNnjkyZMkX1umtp8ODBLpyx4u65c+eM8Oexbghg7n9eXp76YfIdAXL1HUvnM+GlOzc3N2Ti9ZJRi8wBVYKz7nzhZsnPzy/Bkb49pPCCor49+/mzocEYPeUYxYU56HfeeafjQ8TZhRdeKOHh4WZd3m/npaBzmzW/BZsFLkRBt0Al6CgCpsNi+DqkXM8Qdk+npKBT0D3FSHGf20bQX3nlFWndurVigQXjsEAcesojIiJ09aCvXLnSheP1118v6enpRvjzWDcEoqOjlexgxX0m3xHA7gRgCplk8h0BvHxlZWWFjKA3Grvcd3AMnmnLs60tIegYfeLPdPbsWSXi+C4MxURBp6CHYlwXdU8UdOvWdnFD2O+++27VY46e85IkCjoFvSRxox1jC0HHcEBsd3D//fer+9aGtU+fPl1q1arlMgcd+9BiKzbOQTcSWiU/lkPcS86uuCM5xN0crhzibg5XnNVO26zt3btXjdq67777ZOPGjXLo0KEiwaJnPZjlnYJOQTfvW8N6Z6agW69OitoazZsh7J7uioJOQfcUI8V9bgtB/+ijj+Tjjz8WCDkk5c0335Rly5bJd999pxbaGThwoNoOgau4Gwkl3x1LQfcdS+czUdDN4UpBN4er3QQdC79hKhWmWL399tuyePHiIsE6T8kyj755Z6agU9DNiy7rnZmCbo06KW5rNCwWDTn3ZaKgU9CNxJMtBB3zxCHfWAEXqXLlymqOX6NGjdT/Y8g79pbVeiw6deokI0aMEMz585Ts+qLhiYuRzynoRugVfSwF3T2by1/6xRzgXp71t6eu9vIIc7JbhYfdBB1TT/CsQqNxWFiYOZVrkbPa9bnJReIsEoB+LgYF3c/AnS5X1BB2rLyOFdiNDGH3dFcUdAq6pxgp7nNbCLoG4PTp02rhB6ze7u4FCAtEQA69mW9i1xcNI0Hn6VgKuidCJfucgk5B1xM5FHRXShUq+PdF49dff5Xy5ctLUlJSgcJgH/S1a9fKzTff7LJlqJ66tUoeuz43KehWiUD/loOC7l/eeM/H9yRGGqHXXEversJutNQUdP8+N43Wl9WOt5WgmwHfri8aZrB0/hLFSsZYeIvJdwQo6BR0PdFEQQ+8oA8dOlTq16+vtgN1TocPH5bOnTvL7Nmz1T7pwZrs+tykoAdrxBorNwXdGD+9Rxc3hL1Hjx5F7leu9/ze5qOgU9C9jRnn/BR0I/RExK4vGgaxFXs4e9DNoUtBp6DriSwKunUFffv27WoP9K+++kotcGp2QkPpiRMn1Er6lSpVUrueFE7o1cd3C4bm6012fW5S0PVGSGjlo6CbV5/oLcfWaPjxxdZoviyplQU9+fQ5+fz3I5JxLleqxkXL/VdUF1/PrPL3yDNf1p0VzkVBN1gLdn3RMIiNgm4mwCLOTUGnoOsJOwp64AR91KhRkpqaKhs2bJCEhO4vjjgAACAASURBVAS177mWMD8dQ9/r1asnM2bM0FOVhvKglx47mmgJa7dgu9IGDRqoXx04cECt3bJ//371/126dJHhw4frGnpv1+cmBd1QSAbtwRR031ZdcVujYU45tkfzZqqqL0u358RZWbT9hMSWCpd+rZIkL+ecfL3hbzmQkikxpSLkvhbVpFREmKRn5crH6/6W7Nx8qVE+Wu5sWsVRDEjz9LV/S1pmjpSPiZQHrqoh0ZHhLsVctTtFft6XKvl5+XLFhfHS5hJ9PdZnzuVKl8m/ycmMHMc5r6+bIK/fWc+XKISCbgwnBd0YP/agG+Tn7nD2oJsAVUT1cmHaAPeXL8jXKkLKReJc495O26zh7l944QU5deqU2mYNvS916tRxQEEPdfPmzeW6665TvdlmJ2z5hnnwl19+uaAnHfKNf99991116UGDBqnvFO5+or8mKOj6WYVSTgq6b2oTQ9gxrxzzy7Xecl/MK8//t3iLtx+X3cfPSqmIcImNCpdTZ3OUGP9+8LT8tCdFLqtRTpIql5GvNh5Vv7/n8qqybFeK7D6eIWWjI6RL48ryya+HHTdbM6G0NK5eThZsSXb8rl6VsjL13oZy94cb5WBqpuP3t9RPlHGdkyQ/X6THtD9k57Ezjs8aVisrH9/fuADE5btOypA5fxb43X87J0n7+okeYX+w+pBMWvFPw6pz+uXxqyQ60ncLlFLQPVZFsRko6Mb4UdAN8qOgmwCwiFNS0N2DoaAX5GIVHiiV3QRdqwlsAYoea6wybJX0zDPPSF5enupVT0tLE2xLNHXqVGnSpIkqInZGOXr0qEyYMMFjkdmDzm3WPAZJCGWgoJe8MoubV47vx2tvaC17T+UpQT6dlSvlS0fK6cwcwRBu/H96Vo7k5OUr8c3LF8nOzVPDuk9l5siPO05KQulIOXr6XMkLWIIjq5SLcnvN1UNbyo9/npQRc3e6nHVm78vk0iplHL9/8NPNsuFgwe+RmvEx8u3DzTyW6L2fDsp7Px1wybf80SukXEykx+P1ZqCg6yXlPh8F3Rg/CrpBfhR0EwBS0L2CahUhZQ+6a7XZVdA1EhDis2fPuoDx5/DNefPmyfLly2XXrl1Kzi+55BLZs2ePdO/eXRYuXCiJif/02MycOVOQV8/wewo6Bd2rL+kgz0xB964CixrCHhsbK83adJbo2pdLXkS0fLspWU5mZHt3cgvnXvboFfLNpmR5belfLqV89+4GcuWF5R2/7zplg+w7WfDZgEaKFY9d6fEO/0w+I/dM+6NAvloJMfLNQ57l3uPJnTJQ0L2h5ZqXgm6MHwXdID8KugkAKeheQaWgF8RlFR4olV0F/dixY6p3+scff5SUlBSXeMYwz7i4OK/ivKSZJ02apObEo0zPPvustGjRQg3B79u3rxpuqpVjzpw5MmXKFMHQeC0hf+GEYfx23aEjPDxcLbTHaUYljcbgPA51jq19MUWEyZUAereRvln6i/y0YI788cd5eYSIp1dqJBkVLpbMuAuCDl9EeJjkouveKY3seKmMnV9weHrVuBhZPvQ6OXU2W6763wrJ06CISGxUhKx5upVEOc1Df3/VPnll0a4C5+133YXyZLuCW3MWBezH7cdk+Ndb1DD+JjXLy/g7GkidxPM99L4AHR0d7YvT2PYcFHSDVW/XngCD2Io9nHPQzaHLIe7uuVpFSNmD7lo/dhX0l19+WT777DPp37+/VKtWzWXhtZtuukkwn9mfCQ0Gs2bNkkWLFunuQXeWda2sHTt2LLDasj/vIdDXgqih3jIzz889DXSZeH3zCaDOIehY6NGuCT3dC7ckS0oGGISpYefr9qXItq1bJPbkTil7bLOE557fWjczrpakVW0uGRX0CacVuGLOeVZOrhz7d8h8q0sS5cFrLpAhX2ySk2eyBbO7n7v1Ernr8uoyd9NReXbun3IuJ09qVygtL3VtIA2rlVO3sXZfqoz8brscSs2U2hVLy9jO9aRZrfO959q9Tvhxt0xffVDJ/N3Nq8vI9kkqzqyS/DnSyyr37MtyUNAN0qSgGwTo5nAKuu+Z4owUdAq6nsiySoMFympXQW/Xrp107dpVBgwYoKfK/JIHvflPP/20rF69WjIyMlzmoGP4O3rZOQe96OrgInF+CVXLXcROQ9xz8/PlaFqW/H4oXQ6cPCsbDp2WtX+dKlAn4TmZUvbYFiXlURnnF1CDlGckJEl6pYaSF6l/20YzKhwLxWG+OhaDu+OyyrLzWIZcXClWMM/7j0On1e9vrl9R/jqZKUdPZ0npf1doLxcdIQdTs9Rc7lqVE1RjHEbMZOXkSVREuMtWZugot5BT+xQlh7gbw0lBN8aPQ9wN8nN3OAXdBKgU9CKhWkVI2YPuWkV2FfRHH31UatasKU899ZQ5XwY6zjp58mS5+uqrJSkpST3nsEgcVpLXVnEfOHCgWmmeq7jrgPlvFgq6flahlDOYBR1bfa3YnSIbDqbJ0p0pakh0/r/DrwsO3PZcY3GH10tsyk6JSTu/QNn5IexJAkH3V6qTGCtnsnLUyuwXVigtWEXu6oviVS82RpKHh4UpqXa3vZneMlp5H3S992AkHwXdCD0RCroxfhR0g/wo6CYALOKU7EF3D4aCXpCLVXigVHYVdPRSQ84xr1tbhM1/3xT/XAniPXfuXMdlsVr7mDFjpEaNGup3+/btU/ugHzp0SP1/p06dZMSIEbqG3tt15BkF3d9RbI3rWVHQMbR69V+nZGdyhhpujoSh0odOZcn2I+lyOC1LzmbnGQYIGXc3hD0j4WJJq9bCkJRjfndSpVjBHt4xkeGSnZf/z7+5+VKxzD9TgJrUKKeGfWPl9JzcPJ+uUu4JDgVd377snjja9XMKusGat+uLhkFsxR7OHnRz6FLQKeh6IouC7krJ3z0B6K3+4Ycfiqwufy0ShzmzGLaO7+T4+Hi35UlOTlafezPf0K7PTQq6nm+g0MsTSEE/knZOvv7jqBJvrN6NudBmr3yOIexxR9YLeswLzysvyRD2SmWjpGmNclIlLkpiS0VIXEyk3Na4stpazcqJgk5BNxKfFHQj9ETYg26Qn7vDKegmQOUQ9yKhWkVIOcTdtYrs2oO+bNkyOXjwYJExe9ddd0kwr5BLQec2a+Y85ax5Vn8J+t+nMuW1pftlyc4Tat9vfyZtXrm7Iexp1Zqrldhzol0XOqtcLlqurxuvpFvyReomlparLopXc7ojw0RKOa1c7s/78cW1KOgUdCNxREE3Qo+CbpCe+8Mp6KZg5SJxRWCloBcEYxUeKJVdBd2cbwDrnJWCTkG3TjSaXxIzBB294BsOnpa9J87KmXO56t81f6VKpg+GpXtDRBvCjh5zLRWeVx4dGSbdmlaVstGRapG1+lXLSJmoCKlc9p/V7UM1UdAp6EZim4JuhB4F3SA9CropAIs4KYe4uwdjFSFlD7pr/dhd0NPT091uyVWxYsWgfrGloFPQ/fnsC/S1fCnoR0+fkzELd8sve1MDcltYiRwrr0cd/sNla7T6DRpK3YbNJLv6ZSKRMVI1Lkra1080tNBaQG7SRxeloFPQjYQSBd0IPQq6QXoUdFMAUtC9wkpBZw+6p4Dx9xx0zOt+4oknZOvWrW6L5q856J64lPRzCjoFvaSxE4zH+UrQdyRnyIOfbpaMc7mmY0DPdkLpSGlXr6Iafp51NkNy92+Q1H2bZcuWLY7rY8QjFojEjzfrUJh+Axa4AAWdgm4kDCnoRuhR0A3So6CbApCC7hVWCjoF3VPA+FvQx40bJ5DwBx54QF599VV57rnn1CJtb7zxhlSpUkUmTpwokZGRnopt2c8p6BR0ywanCQXzlaD/3+xt8tMeYz3nGFCOPbpbJ1VQQ8wjI8LVcPPaFWLkksplJb50hGAPcC1BxmfNmuUi5TfeeKO0bNlSGjZsaAKx0DglBZ2CbiSSKehG6FHQDdKjoJsCkILuFVYKOgXdU8D4W9B79OghHTp0kHvuuUeuueYa+eKLL+Siiy6SVatWyeDBg2X58uVB3VtFQaege/qbC6XPfSXoLSeskexc/VufNapWVi6oUFqqlouSRtXLSeuLE3RhhZSvWbNGli5dKmfOnHEcAxnHdxOlXBdGoaBT0PVFivtcFHQj9CjoBulR0E0BSEH3CisFnYLuKWD8LehdunSR3r17S9euXeWGG26QsWPHqn+x5zg++/DDD6Vx48aeim3ZzynoFHTLBqcJBfOVoA/96k9ZuvNksSWMigyXG5MqyJ1NqkiLC+J03w1EHEKOn7179xaQcvSUo8ecQ9h141QZKegUdO8ipmBuCroRehR0g/Qo6KYApKB7hZWCTkH3FDD+FvQ+ffpIkyZNZMiQITJs2DBJTU2V8ePHy/z589WQd/xbuXJlT8W27OcUdAq6ZYPThIL5StD3p2TKI59tlSNpWaqUWBH91oaJanX0Dg0SpUJsKa9Kr0k5essLzyvnEHavULrNTEGnoBuJIgq6EXoUdIP0KOimAKSge4WVgk5B9xQw/hb0d955R3bs2CGvvfaabNq0Sc1F11Lbtm2VrAdzoqBT0IM5fr0tu68EHdfNzcuXFbtTJCJM5Pq6FaQku5QVNYT9yiuvlM6dO3MIu7cVXER+CjoF3UgomSLoR44cke+++0569uwp2heTkUJa+Vi7vmiYWSfcB90cutxmzT1XCjoF3dNfnL8FvXB5du3aJevWrZOkpCRp3rx5UG+xhnuz63OzVKlSEhMTI6dPU9A9/c2F0ue+FPSSckFv+dy5c9VP4XnlHMJeUqrFH0dBp6AbiSxTBH3Dhg3Sr18/WbhwoSQmJqp5c8OHD1et/tWrVzdSXssda9cXDTMrgoJuDl0KOgVdT2RZpcECZbX7Puh66isY89j1uUlBD8ZoNV7mQAl6cUPYuTWa8Xr1dAYKOgXdU4wU97lfBH3Pnj3SvXt3x0q0RgpstWPt+qJhZj1Q0M2hS0GnoOuJLAq6KyV/96C/99578scffxRZXS+99FJQL9hk1+cmBV3PN1Do5fGVoC/YelxeWbJPUjKy5bo6CfJch7pSsYzrvHNujWaNGKKgU9CNRCIF3Qg9Gw/VM4it2MMp6ObQpaBT0PVEFgU98IL+8ccfF1i0SSvR4sWL1XZrH330UVBPH6Ogc4i7nu+iUMlTUkE/cy5X3lyxX2b/flTy8vNdcNSvUkY+6X2Z+j1WXtdWYecQdmtEDgWdgm4kEinoTvSOHz8ukBjMEdOb7PqioZdPSfJR0EtCzfMxFHQKuucoEaGgB17Qi6qnyZMny4oVK5Sgh4eH66lOS+ax63OTPeiWDEfTC6VH0DOz82TLkXRZfyBN4OIQ8k9/PSyQ9KJSeE6mjGx4Wtb/uq5Agx4a8bAKO7dGM71qi70ABZ2CbiQCTRX0p59+Wu0DiEXjJk2aJP/3f/8nVapUKVDem266SfDQ8ldCOaZNm6ZaGlE2pAMHDshjjz0m+/fvV/+PfWYxZz4yMtJjsez6ouERjIEMFHQD8Io5lIJOQdcTWRR06wo6Fovr0aOHzJ49W2rXrq2nOi2Zx67PTQq6JcPR9EJB0NMyc+TztX9J+rk8x/Xwu4XbjklKRo5XZYg9uVPijqyXmLQDjuPw3sSt0bzCaHpmCjoF3UiQmSroegq2ZMkSiYuL05PVcB6sLP/888+r8zgL+qBBg1TP+ejRo1VjQq9evdTesx07dvR4Tbu+aHgEYyADBd0APAq61/CsIqS/PXW112U34wCr8MC9cZG4gjWMldwHDBhAQTcj8P1wTgq6HyBb4BJZOXmyIzlDft6bKtuPpqtt0dyMUPeqpJBxiHnZY5slPPeffdCRGjZsqBrt8C+TtQhQ0CnoRiLSFEE/d+6c7m1UKleu7JeheuvXr5chQ4bIyJEj5ZlnnnEIelpamrRp00amTp0qTZo0USyxAM/Ro0dlwoQJHtlS0D0i8joDBd1rZLoOYA+6e0xWEVIKumv92FXQZ82aJdu3by8A5NSpU7Jy5Upp1KiRGgUWzMmuz00KejBH7fmyn8vNl283HZUTZ/7p+c7Ly5PMnDxZvjtV9p8867ObxBD2sse2SNyRXyUyK81x3sy4WtLm+qtlQI/OQb1YpM9AWfREFHQKupHQNEXQjRTIjGMxdB17skO8K1WqJHfffbdD0LUV5rUt4XD9mTNnyrx582TGjBkei2PXFw2PYAxkoKAbgFfMoRR0CrqeyLJKgwXKaldBx1Ss33//vUB14WXvuuuuk1atWqnnWDAnuz43KejBF7W7jp2R5btSBb3iaVk58uOfJ+TEmWzTbkST8tiUnQWGsIdHxUir1jdKbM0G0vLyJtKoelnTysAT+4YABZ2CbiSSTBf0VatWyW+//SYbN25Ui69dfvnlcu2110q9evWMlFv3seh1wJD1++67T231tnv37gKCjnL17dtXnIfaz5kzR6ZMmSLz5893XOeHH35wuebNN98s6enpusvCjPoIIE5yc3MlO9u8h6C+koRWLnDNyclRP0znCTQau9wSODaPbGWJcliFB2BsHXWj6p0KdELjFpPvCFDQuYq776LJnDNt+jtdHp+zXU5k+Oc9xN0Q9ryIaMlJuFA6d+4s9918lZSNjjDnZnlWUwhQ0CnoRgLLNEHPz89XC8NNnz5dla9BgwaCoe9Y5AbpxRdflFtuucVI2XUdi21pMJ/83nvvlbCwMElJSVHi3a1bN7n99tslKipKibunHnQsGlc4jRs3Tt0Tk28JRERECOLHCi/mvr2zwJ4Nix6i4QNsmc4TuGTUIkvg2DGmnSXKYRUegGEVJnhO+DNlZmaqxUsPHjyoFjPF/FJtJWitHFjJHVPE2rdv78+i+eRaFHQKuk8CyaSTHD19Trp/sEFOZxW9grovLo3eciz2Fnd4vcu88pYtW8rV17WSivH/LGbMFHwEKOgUdCNRa5qgYx/X119/Xfr06SP9+/d3rNSOFw8sxgZxfuuttwRfQmYmDGFftmyZ4xInTpyQzz77TPWao4EgMTHRZQ76+PHj5dixY5yDbmbFFHNuDnE3BzyHuLvnapUh3ZyD7lo/dhvijtEtGMGFxu2MjIwCQLBGysCBA9UoNCQ8R1NTU2XixInmfGGYeFYKOgXdxPAyfOqXFu+VWb8dMXwe5xNclBgr7S6tIFF55+TUrvWyc9NvcmTfDkcWvPd06tRJ/eC/mYKfAAWdgm4kik0RdPTSoVUfQ9nxElE44fMHHnhAKlSo4PeXi8JD3FE2vPTgD4mruBsJJd8dS0H3HUvnM1HQKeh6IssqDRYoq90EHYuVvvPOO9KiRQs1yqtq1apq1AvWUUGPORYv/fDDD6Vu3bpq5WZMFXP3jNVTz4HMQ0GnoAcy/jxd+6GZW9R+5CVJpSLC5coL46RB1bISHham/vuapCryxx9/yJdffilr164tIOXcGq0klIPjGAo6Bd1IpJoi6MePH1eC/v7770uzZs3clg9fVOhhX7FihZHye32sO0Hft2+f2gf90KFD6nxowRwxYoSu/dnt+qLhNXgvDqCgewHLi6wUdAq6nnChoLtSQmOy2Qmjtjp06KCmYz3++OMul0PvOp5TO3bskCpVqqhV3vEMRUN4sCW7Pje5SFxwROorP+6TT9cfLrKw5WNKSfsGFaV6+WhpVjNOasbHSGxUuJQKD5ewsPOHbdmyRdasWaMWJT5z5ozjA0xZwehRyDl7y4MjJkpSSgq6+c/NktRLsBxjiqBjnjla97ESOl4k3KXVq1cL9h//5ZdfdImwP4AmJyerL0tvvjDt+qJhZn1Q0M2hS0GnoOuJLAp6YAQdC6oOHjxYli9fXuQzCC/8vXv3losvvlhNH2vbtq1gbYlgS3Z9blLQgyNSU8/mSL+Zm2XP8X+2TEssU0qebneR3FC3gpSKcDJwN7cDEYeQQ8zx96qlOnXqSOvWrSnlwRECPiklBZ2CbiSQTBH0DRs2SL9+/dTc76JWv920aZMa5u68erqRGwnUsXZ90TCTNwXdHLoUdAq6nsiioAdG0PWMKtMzOk1PHQc6j12fmxT0QEee/utjMdWf96ZKZk6+tKobL5ER4cUeDBmfNWtWASnHuwx6ya+//nq57LLL5OxZ3+2Rrv9OmDNQBCjoFHQjsWeqoGOoOB5I7hJeNFauXElBN1J7IXosBd2ciqWgU9D1RBYFPTCCvm7dOhkwYIAsWLCgyH3O0SuHNVOcdx3RU6dWy0NB5xx0q8VkScpT3BB2jCLFUHYkbQcGCnpJKAfvMRR0CrqR6DVF0Ldt26a2NtOTPvnkkyJ72fUcH+g8dn3RMJM7Bd0cuhR0CrqeyKKgB0bQ09LS1PonWKUdO4nExMQUKAi2CMXuI9iG8osvvtBTlZbNY9fnJnvQLRuSugumDWHHMPa9e/c6jituXjkFXTfekMpIQaegGwloUwTdSIGC7Vi7vmiYWU8UdHPoUtAp6Hoii4IeGEHHVdEzPnLkSElISJDbb79datasKdnZ2fLXX3/JzJkzVcFmzJihVm8P5mTX5yYFPTijtqh55doQdiz4pvWWu7tDCnpw1rvRUlPQKehGYshUQT9y5Ihs3rxZLWKD1dzLly9vpKyWPNauLxpmVgYF3Ry6FHQKup7IoqAHTtBxZazh8uabb8rGjRsLFATzWLGKe+3atfVUo6Xz2PW5SUG3dFi6FK6oIewY6eJJyp1PRkEPrnr3VWkp6BR0I7FkmqBjfvmQIUMcZYuNjZW3335bGjVqZKS8ljvWri8aZlYEBd0cuhR0CrqeyKKgB1bQtauj5xyN3Gjgrly5shraHirJrs9NCrr1Ixi95XPnzlU/vtoajYJu/Xo3o4QUdAq6kbgyRdCxXyv2c61Ro4YMHTpU8P//+9//5Ny5c2ruHF80jFRZ6B9LQTenjinoFHQ9kUVBt4ag66mrYM1DQecicVaK3eKGsKO3HD/ebL9b+N4o6Faqbf+VhYJOQTcSbaYI+v79+6Vr164yffp0x7wcDNfDAjffffedVKtWzUiZLXWsXV80zKwECro5dCnoFHQ9kUVBp6DriRMjeez63GQPupGo8f2xxW2N5s0Qdk8lo6B7IhSan1PQKehGItsUQdf2OF+8eLHEx8er8mGF2jZt2sj777+v5qOHSrLri4aZ9Rdqgm4l4dk04ga16BTTeQJWqZ/fnrraEtViFR6AsWHYtZKXlxdwLhUq8EXDl5Vg1+cmBd2XUVSycxW3NRreUbFvua8TBd3XRIPjfBR0PjeNRKopgo5Fbvr16yezZs1ybKEGKbjjjjtk7Nix0rRpU0eZMbcuPDzcyD0E9Fi7vmiYCZ2Cbh5dCrorW6sIKQXdtW4o6OZ9FwTyzHZ9blLQAxN1RQ1hx/snhq9DygsPYT+bnSdHT2dJjfLRUirC2DsqBT0w9R7oq1LQKehGYtBUQddTsCVLlkhcXJyerJbMY9cXDTMrg4JuHl0KOgXdU3RZpcGCPeieaip4P7frc5OC7t+Y1TuEPTcvX/5MPiOr952S7Nx8WbLjpOw8dkYVNjwsTF7reqlcXzehxIWnoJcYXVAfSEGnoBsJYFMEPSUlRdauXaurXBhShIdWsCa7vmiYWV8UdPPoUtAp6J6ii4LuSigQQ9xTU1Nl+fLlcvjwYWnVqpXUr19f7ZNesWJFueKKKzxVo08+xwKvx48fV/uyR0dHuz0nPsf6FjExMbqvadfnJgVdd4iUOGNxQ9h79Ojhsl/5qt0p8vjXOyQnt+ipNGFhYfLLkCslKrJkPekU9BJXZ1AfSEGnoBsJYFME3UiBgu1Yu75omFlPFHTz6FLQKeieoouCHnhBx/Zq3bt3l4yMDFWYMWPGSMeOHeWNN95QC60uWLBAbb9mZpo2bZpMmjTJcYm2bdvK8OHDpXz58up3Bw4cUPuyY1FYpC5duqjP9ZTLrs9NCro5EasNYV+6dKns3bvXcZGGDRuq/cq1IewnM7JlzsZkycnLk/x8kU1/n1a95nrSJ70vk/pVyujJ6pKHgl4ibEF/EAWdgm4kiCnoRuiJiF1fNIrDZpUXfM7pda0lCjoF3dNXnlX+flFOu85Bnzx5sqxYsUJeeukl+e9//6vkHD/btm2T+++/X77++mupWbOmp6o09PlXX32lrtG4cWM5ePCgDBgwQHr37i09e/ZU5x00aJDqOR89erTar71Xr14ybNgwVU5Pya7PTQq6p8jQ/3lR88qlVIykVWwordp1kPiKiXI07Zys2H1S0rNy9Z/cTc5lj14hcTElaxSjoBtCH7QHU9Ap6EaCl4JuhB4F3S09q7zgU9Ap6Hr+vBmvBSlZhYedBR2S+8ADD8hdd92lRFgT9FOnTslNN91UYAtTPTHuizwvvPCCHDp0SN59913HrixTp06VJk2aqNOjMeHo0aMyYcIEj5ejoHMfdI9BUkQGd0PY8yKiJb1SIzlbIUnOxtUq6amLPO6e5tXkyZtql/i8FPQSowvqAynoFHQjAUxBN0KPgk5B1xE/VhIeHcX1SxarNJ7gZq1SP1ZhYhUedhb0Pn36qN1OBg8eXEDQ169fL/3791dz0RMTE/3yt4qLYC76bbfdJu3bt5dHH31U9uzZo4bgO5dj5syZMm/ePJkxY4bHclHQKejOQZL/7/8cTDkr328/IWv/OiWbD6dLVs4/Q9HDczIl7sh6iTu8XsJzsxyHZsbVkoyEJEmv1FDyIvWvgeAxQP/N0OriBOnWtKpcW+ef7YJLmijoJSUX3MdR0CnoRiKYgm6EHgWdgq4jfqwkPDqK65csVpFRCrprdVspXu06xB3zvz/44AMZNWqUzJ49W2699Va56KKL1P9j1xN87s+E7VF/+OEH+fLLL6VSpUqyceNG6du3rzjvwjJnzhyZMmWKzJ8/31E0HFc4jRw5UrKyzkuWP+8j0NfClrIRERGCbWftmDKzc+XEmWxZ91eK/HUiQ5LTsuSrjYcFq6gXTpDysse2SGzKTolJO+D4GL3ladWaS1rV5j6X8pa145JqXQAAIABJREFUE6RDoypyRe0EqZNYRsLDfFNLqHMsNIeGLib7EMCUltzcXMnLK3oBwlCmUdTCoqF8z768Nwq6QZp27QkoDptVXvCtIoFW4WEw1H16uFXqhoJOQdcT2P5exR0v8pBxSLFzqlGjhrz22mtSp04dPcX2SR7Mh8fP9OnTHStg6+1Bx1z5wun2228XzB+2Y4Ko4aU9MzMzpG8/JSNbPv31b/l242E5ln5O3Su2L8tDd7iHBBmPP/iTi5RjCHtGhSRBr7kvExz8mroVpH2DStK1aTVfntpxLtQ5BP3cuX9YMNmDAHa2QGMcJN2OCQs+M5WcAAW95OzUkRR0V4BWEVKrSKBVeBgMdZ8ebpW6wU1ZpX6swsQqPFA3du1B1/7Ytm7dKjt27JD09HSpVauWWpHam+3MjPzRotcHq8ajZxyCXq9ePcfp0tLSBFukOs9BHz9+vBw7doxz0IuBHuqLxOXn58un64/Iq0v3qaHpehOkPPbkTil7bLPbIezoMTeasMDblReWl9oVYqRWQmm5MamCxJQKl0hfdZMXU0AOcTdae8F5PIe4c4i7kciloBuhR0F3S88qL/gUHoPBbeLhVqkbCrp1G9go6Cb+Aeo4NbZ2+/bbb5Wk1659foGsypUrq63UBg4cKHgB5SruOmD+myWUBf3nvany5Nc75Gy2vt7Cooaw50THSVrVFiWeV16tfLR0qJ8ojauXlSrloiSpUhmJ8IOEFxcFFHT9fyOhlJOCTkE3Es8UdCP0KOgUdB3xY5UGCx1F9VsWCroraqswsVK82qkHffny5WqVdD3pzjvvFLPn92Ffc3flQY/6BRdcIPv27VP7oGt5OnXqJCNGjFBDuD0lu448C1VBxxZmrV5fK3o6zfUOYcfQ86jIMKlSLlpqJcRIYpkoiYkMl5ioCImOCFNDxpHg3vGlI6XFBegdLy3//tpTCPr1cwq6X3Fb5mIUdAq6kWCkoBuhR0GnoOuIHysJj47i+iWLVWQUN2uV+rEKE6vwsFsPOvYQX7x4sa6/P+fF2XQdYGKm5ORkwVxDb+YbUtBDaxX3bzcly+gFu4uMsuKGsKfWvFbqN2gol1UvJ+HhIlERYdKsZpxccUF5S8p2Sf6UKOgloRb8x1DQKehGopiCboQeBZ2CriN+rCQ8OorrlyxWkVEKumt1Wyle7dSD7pc/PItchIIeWoK+7egZuW/6HwWiSxvCHnfkV4nMSnN81rBhQ2nQtIXEJzWXGxtUV73ioZ4o6KFew+7vj4JOQTcS+RR0I/Qo6BR0HfFjJeHRUVy/ZKGgu2K2ChMrxavdBX3Xrl1qCDkW38Kwcn+u3m7mFwEFPbQEHbHyyKwt8uueZLdbo2F0xY033qh+sF2g3RIF3W41/s/9UtAp6EYin4JuhB4FnYKuI36sJDw6iuuXLFaRUfagswddT8D7e5s1rNo+aNAg2bx5c4HitWjRQp5//nmpUqWKnmJbNg8FPbQEfcuWLbJ69WpZuGiJ5J47q+JOk3LsPIBeczsnCro9a5+CTkE3EvkUdCP0KOgUdB3xQ0G3bm8xBZ2CruNPWPwt6OPGjZMvv/xSSXqzZs3Uqunr1q2TTz75RPWkY3uzYE4U9OAXdKw9MHfuXFm6dGmBfe0h45By9JZ7sy5BMMezp7JT0D0RCs3PKegUdCORbRtBz8nJkePHj0tCQkKRq9/i87Jly3q1z6xdXzSKCzqrCKlVemmtwsPIF4Wvj7VK3VDQKeh6Ytvfgt6uXTtp3ry5YG9x5/TZZ5/Jyy+/rMSoatWqeopuyTx2fW4G+yruZ86cUUK+Zs0aQa+5liDiWMUfP5Ry1z85Crolv4ZMLxQFnYJuJMhsIejTpk2TSZMmOTi1bdtWhg8fLuXLl1e/O3DggNouZv/+/er/sb0MPkevhadk1xcNCrqnyDj/OQXdlRUF3bpMrBSvdp2Djj3G69atK48//niBQMF8dDyfIOr4PFiTXZ+bwSrokPFZs2a5SDl6yTmE3fNfIQXdM6NQzEFBp6AbiWtbCPpXX30lNWvWlMaNG8vBgwdlwIAB0rt3b+nZs6dih2GE6DkfPXq0HDlyRHr16iXY8qZjx44e2dr1RYOC7jE0HBmsJDz6S21uTgo6BV1PhNlV0LGNGp5H6CmPi4tzoPrpp59U4/GiRYtM3wddT/2UNI9dn5vBJOiQcvSUcwh7SaP8/HEUdOMMg/EMFHQKupG4tYWgFwb0wgsvqJVx3333XUlLS5M2bdqoOX1NmjRRWV966SU5evSoTJgwwSNbu75oUNA9hgYFvRhEFHQKup6/ILsKut490TEffc6cOXpQWiqPXZ+bVhf0ooawc165sT8fCroxfsF6NAWdgm4kdm0n6JiLftttt0n79u3l0UcflT179kj37t1l4cKFkpiYqFjOnDlT5s2bJzNmzPDI1q4vGhR0j6FBQaeg6w8SEbFKo4WVRnzYVdCXLVumRnt5Spjve8cdd3jKZrnP7frctKqgY6SGu3nlHMLumz8dCrpvOAbbWSjoFHQjMWs7QR87dqz88MMPaoXcSpUqycaNG6Vv376CIYXaUEL0SEyZMkXmz5/vYIt5VoUTHmhMrgRqD5tnCSz7xt9qiXJYhYclYPxbCKvUDYpjlfqxChOr8EDdWIWJlf52QqEsFPTAr+Je3BD2Hj162H5rNF/+nVHQfUkzeM5FQaegG4lWWwn65MmTBT/Tp093PHz09qCj571wwiJydn3RKC7orNIDxx5JI18N5h5rlbrBXTJeC9a1VXigVHbtQddqJC8vTzIyMlz+GLFmSjAnuz43A92DjiHs6C3HD/5bS74Ywp6akS2HT5+TuomlJSoiPJjD0+dlp6D7HGlQnJCCTkE3Eqi2EHS85Lzxxhtqrh4EvV69eg5m7uagY2ubY8eOcQ56CSPLKi/4VpFAq/AoYXWacphV6oaC7lq9VopXuwr633//rZ5VGNnlTtCdR3yZ8gdq8kkp6P7rQS9uazQMYcfWaJUrVzZU488v2C3fbEpW5wgPC5M3utWTay6KN3TOUDqYgh5Ktan/XijoFHT90eKa0xaCPmbMGPn222+VpNeuXdtBAQ8l9IJjSxv8IXEVdyOhdP5Yq7zgW0UCrcLDN7Xrm7NYpW4o6BR0PRHt733QsUjczz//LA8++KBUqVJFwsML9khiYVP0xgZroqCbL+juhrBjzQJfziv/fttx+XDN37Ij+XxvPGIyKjJcfh5ypZJ1JhEKuj2jgIJOQTcS+bYQdOwbi1XbCyf0qGMV3H379ql90LU8aFEeMWKErhcgu75oFBd0VhFSq0igVXgY+aLw9bFWqRsKOgVdT2z7W9DbtWsn3bp1k/79++spXtDlsetz0+wh7mYOYdeCbPvRMwIx/+Nwuvx+IK3I2Fvyf1dIfOnIoItNMwpMQTeDqvXPSUGnoBuJUlsIul5AycnJghZm/OhNdn3RoKDrjRDrzHHWX2Lzc1LQXRlbhYmVGpTsOsR9yJAhkpCQIKNGjTL/jzEAV7Drc9MMQS9uCDs6G/DjzTtN4XBIy8yRhVuPy8FTWbJw6zE5fiZbV8SsfeIqiQxnDzpgUdB1hUzIZaKgU9CNBDUF3Qg9ES4S54afVV7wKTwGg9vEw61SN+xBZw+6njD3dw/6hg0bpF+/fjJ16lS120jhVLVqVZdh73ruwyp5KOjGh7hjCPusWbME/2rJmyHsEO+UjGxZtSdV4mIiBf+fnZsnGedy5URGjpzOzJGVu09KVk6+12EztE1tua9FNa+PC9UDKOihWrPF3xcFnYJuJPIp6EboUdDd0qOgF8RiFR4GQ92nh1PQXXFahYmV4tWuPej79++Xnj17ul0gDpHDReJ8+nXkt5MZ7UEvamu0Bg0bSssrW6r55aViSsvhtCxZtP2EVCxTSk6cyZZSEWGSmyeyP+WszN18TLxXbn2ISkWEy8Sul8rVXCCuADAKur74CbVcFHQKupGYpqAboUdBp6DriB8rCY+O4voli1VkFDdrlfqxChOr8EDd2FXQMcR98+bNagFT9KBHREQU+Lts0aKFWuA0WJOde9Ajo6Jl2spdciozRzo2SJRqcdEy67cjcjorR+pWjJXYqHD5ftsJycrJk8tqlJO/j5+SI9vWSkTyDtn151ZHlWfG1ZLMiklStk5zOfjvGm0YUG6WfBcXa7huzyuqS79raki56OCNS7P+nijoZpG19nkp6BR0IxFKQTdCj4JOQdcRP1YSHh3F9UsWq8goBd21uq0Ur3YV9I4dO8odd9whDz30kF/+Hv19kWAWdIhzVESYhBVaofxcbp7sPn5WqpWLkvjYf1bYP5CSKTPXH5a/T2XJxZVipc/VF0ifj/+QPcfPr3qeVLmM7Cy0CjqOLXtss/qJSTvgqJ68iGhJr9RIMiokCQQ90AkcrrooXh65tpbUq6J/7Z5Al9vf16eg+5u4Na5HQaegG4lECroRehR0CrqO+LGS8Ogorl+yUNBdMVuFiZXi1a6Cjm3WMBz6hRde8Mvfo78vEoyC/vPeVHny6x1yNjtXDRkff9slcmPSPy+g+OzR2dslL/+f/us7mlSRx2+8UDq997ucOnt+UTVIff6/edwxh4zHntypxDw8N8uRBTKeWvNaS0h5kxrl1BD2bk2rSIV/GyL8HT/Bdj0KerDVmG/KS0GnoBuJJAq6EXoUdAq6jvixkvDoKK5fslhFRnGzVqkfqzCxCg+/BKLOi+wbf6vOnL7JtnLlSsEw94kTJ0piYqLLSZOSklyGvfvmyv45i9UEfc7Go/L570clIgxDtatJhwYFF+ZLz8qVVq+vdRk+vvA/zSWxTCm55rW1ci4nrwC8htXKypbD6R6BhudkStyR9RJ3eL2LlGckJEl6pYaSFxnj8Ty+zoBh65ER4dLlsspStVyUtKgVJ42qlxMuzO49aQq698xC4QgKOgXdSBxT0I3Qo6BT0HXED4XHFZJVZJSC7lo3jFdXJv4W9KFDh8ry5cuL/HbhInE6vnh1Zvn89yMyftHeArlf7HRxAUn/dlOyjF6w2+WM/3fDBaq3vM2b61w+K24+OKS87LEtEpuy0+0Q9rRqzSUnurzOO/AuW634GGlZO14tIIeRAFGR4VI+JlLQCJFUKVbKRUfIpRyu7h1UD7kp6D7FGTQno6BT0I0EKwXdCD0KOgVdR/xQeCjoOsJErNJowXgNvKBjFfe0tLQiw6ZevXpcJE7PH5WOPLe8vV6OpZ8rkBNDuN+6q77jd9uOnpH7pv/hcraJXevJNXXi5cpXVrt89kDL6vLhmr8L/D4+45CEJ/9ZYAh7dOlYOVa+vppXHlmptnRrWlXQaIC57pB/zE//49BpubZOgpSJjpCf9qSo/87MzpUdyRnSOilBykRFiISFSVREuOTk5kn18tFSMz5GqsdHy5msXImNilDbpUWGi/o9k38JUND9y9sqV6OgU9CNxCIF3Qg9CjoFXUf8UHgo6DrChIKuB1KA8vi7Bz1At+m3y1ppiPtNk35V+4E7pysvLC/v3t2gwO8embVF1u4/32jStEacfHBfQ5Xnk18Py4Ql+xz5MU/7g3sbyrr9aTJ+/jZJ3bbKZQh7Q2yN1vKfrdFiSsdKZk7eP6LNFHIEKOghV6W6boiCTkHXFShFZKKgG6FHQaeg64gfCjoFXUeYUND1QApQnkAI+okTJ+TPP/90uxd669at2YPuo1j4cPUheXPF/gJne7Z9XbnjssouV5i94YgcT8+W8qUjpcfl1dBp7Uhbj5yRlbtTpGZ8tLSuHStLly6VNWvWCPYu11KZMmWka9eucvPNNwv+m8keBCjo9qjnwndJQaegG4l8CroRehR0CrqO+KGgU9B1hAkFXQ+kAOXxt6BjD/Q+ffoUebecg+7bQICkz/j1sESGh0nfq2tI92ZVS3QByPisWbNcpBy95Ogtb9q0qcTExMjp06dLdH4eFJwEKOjBWW9GS01Bp6AbiSEKuhF6FHQKuo74oaBT0HWECQVdD6QA5fG3oD/xxBNy5MgRefrpp+WBBx5Q0le5cmUZM2aM2qZrwoQJASLhm8taaYi70TuClKOnHD3mZ86c398cQ9jbtGmjhrBrCVvnUdCNEg++4ynowVdnvigxBZ2CbiSOKOhG6FHQKeg64oeCTkHXESYUdD2QApTH34KOYdA9evRQw6GvuuoqmTFjhmBhuI0bN0rfvn1lwYIFUqlSwa3AAoSmRJcNdkGHiLsbwu48r9zdEHYKeonCJegPoqAHfRWW6AYo6BT0EgXOvwdR0I3Qo6BT0HXEDwWdgq4jTCjoeiAFKI+/Bb1Lly5y3333Sffu3QX//dBDD0mnTp0Eq7tD2qdOnSpNmjQJEA3jlw1WQYeUY3pB4Xnl2hB2CHpxiYJuPHaC8QwU9GCsNeNlpqBT0I1EEQXdCD0KOgVdR/xQ0CnoOsKEgq4HUoDy+FvQH3nkEalevbqMGjVKxo0bJ6tXr5YhQ4bIsmXLZO7cuerfsmXLBoiG8csGk6AXN4Qdoxw8SbkzLQq68dgJxjNQ0IOx1oyXmYJOQTcSRRR0I/Qo6BR0HfFDQaeg6wgTCroeSAHK429B//777+Wvv/6Shx9+WI4dOyb33nuvpKSkqLt/7LHH5P777w8QCd9c1uqCjiHsaAjBT+F55drWaCVZhZ2C7pv4CbazUNCDrcZ8U14KOgXdSCRR0I3Qo6BT0HXEDwWdgq4jTCjoeiAFKI+/Bb3wbebk5MiuXbukZs2aQd1zrt2XFQW9qHnlEHG9Q9g9hScF3ROh0Pycgh6a9erprijoFHRPMVLc5xR0I/Qo6BR0HfFDQaeg6wgTCroeSAHK4y9Bz83NVXcYERHhuNO8vDzZtGmTpKamSrNmzSQuLi5AFHx3WTMEff2BNPl1f5pUKltK7WEe5rxJeTFFdzeE3ZdS7nxpCrrvYiiYzkRBD6ba8l1ZKegUdCPRREE3Qo+CTkHXET8UdAq6jjChoOuBFKA8/hB0bJ926623CiTu66+/VoKJnvOePXuq3nOkhIQEeeutt+SSSy4JEAnfXNbXgv7B6kMyacV+R+EaVy8n0+5rJEU5enJyshq+7m5rNCND2D3RoaB7IhSan1PQQ7NePd0VBZ2C7ilGivucgm6EHgWdgq4jfijoFHQdYUJB1wMpQHn8IeiQcCw6Nn78eGnbtq26U0jk6NGj1Vx0SPkbb7yhetCnTZsWIBK+uawvBT07N09aTljjUrBh7S6S7s2qOn5f3BB2rJCPn5LMK/eGCAXdG1qhk5eCHjp16c2dUNAp6N7ES+G8FHQj9CjoFHQd8UNBp6DrCBNmsTABfwj68uXLZejQobJ48WKJj49XNLBy+59//infffedGvaOxeNGjBih/q1YsaJfiKFnH0PvIyMj3V7v+PHjal58TEyM7vL4UtBTz+ZImzfXuVy7/7W1pP+1NdWWaLNmzSrx1mi6b0pHRgq6DkghmIWCHoKVquOWKOgUdB1hUmQWCroRehR0CrqO+KGgU9B1hAmzWJiAPwQdw9rHjh0rv/76q4PEDTfcIK1bt5YxY8ao3x08eFBuv/12mT59ulfbexlBO3/+fJk0aZLgX+d04MABtaI89mZHwn7tw4cPL1LknY/1paDn54tc+9oayczJc1wiJu2ANI86LKk7f3VZhb1NmzZq0bdAJAp6IKgH/poU9MDXQSBKQEGnoBuJOwq6EXoUdAq6jvihoFPQdYQJs1iYgD8EXetBx7D2qlWrCgT4jjvukMGDB6t56Ejbtm1TW6yhR/jiiy82lRiuP2jQIDl06JBUrlzZRdDxGXrOMQT/yJEj0qtXLxk2bJh07NjRY7l8Kei42Jq/Tsljs7dJzMFfJTZlp0DQtYR9ys2cV+7xZp0yUNC9oRU6eSnooVOX3twJBZ2C7k28FM5LQTdCj4JOQdcRPxR0CrqOMGEWCxPwh6Bjv/MOHToowYXsYp75woULZd68eVKlShVFZ/bs2WqOuvMweLOwYYG6EydOCBoOUBbnHvS0tDRBT/TUqVOlSZMmqggvvfSSHD16VCZMmOCxSL4WdFxw3bp1Mm7cOHVts1Zh93hjHjJQ0I0SDM7jKejBWW9GS01Bp6AbiSEKuhF6FHQKuo74oaBT0HWECbNYmIA/BB23j6Hrb775poPEPffco+alI2VmZkrnzp2VrM+YMcNvtH744QeZOHFiAUHfs2ePdO/eXTUgJCYmqrLMnDlTNSboKZsZgo4yoBHhwgsv9Nvwf28rgYLuLbHQyE9BD4169PYuKOgUdG9jxjk/Bd2JRqAXuzFSkVY61ipC+ttTV1sCi1V4WALGv4WwSt2gOKwfK0WGNcviL0HH3f/+++9qKHuLFi0KbKe2c+dOJcD4/XXXXec3UO4EfePGjdK3b19ZsmSJY1/2OXPmyJQpUwqIPHrVC6ennnpKNTbYMYWHh6vF/rKzs+14+7a9Z22BRYxKYbIPATTIYYHNvLzz62PY5+7Fq4VD7cRF771S0EXUXD8rLHajt9Ksns8qwmMVCbQKDyvFjVXqhoJupaiwbln8KehWo2CkB/2zzz5zuZ27775bMjIyrHabfikP5Bwv7XZtoPALZAteBHWOxIYZC1aOiUXCzhaoc0i6HVNsbKwdb9tn90xBF1EL4VhlsRuf1WwAT2QVIbWKBFqFRwBDwuXSVqkbCrqVosK6ZaGgFxzi7m4OOubGYx59oOagWzd6zpeMQ9yDoZZ8X0YOcfc902A4I4e4c4i7kTi1vaBbcbEbIxVqhWOtIqRWkUCr8LBCbGhlsErdUNCtFBXWLYsdBR37n2NI7qJFi9Q2a998842EhYU5tlEbOHCg4AXUCqu4WzdyCpaMgh4sNeXbclLQfcszWM5GQaegG4lV2wu60cVuag+bZ4Q/jzWRgFUkkILuWslWqRsKuol/gCF0ajsK+u7duwXD0Z0TVpjX9mTft2+fmhqGbdiQOnXqJCNGjFBDuD0lsxaJ83TdQH9OQQ90DQTm+hT0wHAP9FUp6BR0IzFoe0HXu9jN2LFjXTiPHDlSKOhGws/cY/98vq25F9B59kufW6wzp32yWaVuQJz1Y5+4K+md2lHQ9bJKTk5W25rhR29KTU3VmzWk8mGxsKioKNvOwQ+pyvTiZqKjo1XurKwsL45i1mAngDnYqHO7zkGPj48P9ioMaPltL+h6e9C//vprl4pq27atYBggWsmYfEfg1KlTahilNy98vrt66J4pJSVF0JKPhUuYfEcAuz/ExcWpF28m3xGA+FWoUMExpNp3Z+aZSCAwBPCynp6eLhUrVgxMAXjVgBBAnfNdMSDoA3rREydOqPWttAaagBaGFw86ArYXdCOL3UyePFl96fbv3z/oKt7KBR43bpwkJSVJt27drFzMoCvbE088oYahtm7dOujKbuUC9+nTR+1V3bhxYysXM+jKhj2/33vvPalevXrQlZ0FJgF3BH755Rf55JNP1Jx+JvsQ4Luifera+U7/85//SO/evaVly5b2BMC7NkTA9oIOeiVd7IZfuoZir8iDKejmcKWgm8OVgm4OVwq6OVx51sARoKAHjn0gr8x3xUDSD9y1KeiBYx8KV6agi0hJF7vhl645fwIUdHO4UtDN4UpBN4crBd0crjxr4AhQ0APHPpBX5rtiIOkH7toU9MCxD4UrU9CdatHbxW74pWvOnwAF3RyuFHRzuFLQzeFKQTeHK88aOAIU9MCxD+SV+a4YSPqBuzYFPXDsQ+HKFHQDtThz5kw1B/3ee+81cBYeWpjA22+/LRdddJF06NCBcHxI4MUXX5SbbrpJrrrqKh+eladCw0e/fv2kXr16hOFDAmCKmK1SpYoPz8pTkUDgCPz2229qP/nnn38+cIXglf1OgO+KfkduiQs+++yzcuedd0rTpk0tUR4WIrgIUNCDq75YWhIgARIgARIgARIgARIgARIggRAlQEEP0YrlbZEACZAACZAACZAACZAACZAACQQXAQp6cNUXS0sCJEACJEACJEACJEACJEACJBCiBCjoIVqxvC0SIAESIAESIAESIAESIAESIIHgIkBB96K+sCBcbm6uREZGuj3q+PHjUrZsWYmJifHirMzqiSsJeU8gJydHEI8JCQkSHR3tcoK8vDw5duyYVKxYsch49v6qoX9EZmampKSkqIXLwsPDQ/+G/XSHWVlZKl5jY2NVzBZOjFc/VQQv41cCnr6n/VoYXsw0Aunp6YK6jo+PN+0aPLG1CPBdwVr1EYyloaB7UWvz58+XSZMmCf51TgcOHJDHHntM9u/fr37dpUsXGT58OMVHJ9uiuL766qvy6aefFjhLkyZNZOrUqTrPbM9s06ZNU3GqpbZt26p4LF++vPrVqlWr5JlnnpGMjAz1//jvrl272hOWF3c9dOhQWb58uToCEoltwB599FHHGe666y7Zu3dvgTM+/PDDgh+mogmMGTNGvv32W0cGrHj7yiuvOF5mGa+MnlAk4Ol7OhTv2W73hGcsVvLWnhuNGjVS322JiYl2Q2Gr+/X0rmArGLzZEhOgoOtABwEfNGiQHDp0SCpXruwi6PgMPeejR4+WI0eOSK9evWTYsGHSsWNHHWe3bxZPXCdMmCAHDx6UIUOGOCChN5jbLhUfM1999ZXUrFlTGjdurPgNGDBAevfuLT179hS06t58883Sv39/ufvuu2XlypXy5JNPqq1/atSoYd9g1HHn7777rqCxA2zXrVun4hIv2XjpQoKgt2/fXtq1a+c4W1xcHHtNPLBFg9s111wjSUlJcvjwYXnwwQflnnvuUf8yXnUEJrMEJYHivqeD8oZYaBcCeD6gnqdMmSKlS5dWHTm1a9dW0s4UugQ8vSuE7p3zznxJgIKugyaGJp04cUK1guIL17kHPS0tTdq0aaN6ddG7i/TSSy/J0aNHBYLJVDSB4rjiKPA7deqUoIeNqeQEXnjhBdW4hIcGeiMHDx4sP//8s0RFRamTove8e/fu0qNHj5JfxIbIC/YDAAAgAElEQVRHogGuW7duSiQ1QUcjCEbQMJWMQHZ2tmrYfOSRR9T+sYzXknHkUcFHwPl7OvhKzxK7I4DnARp1+/Tpoz5evHix6rxBA29YWBih2YRA4XcFm9w2b9MgAQq6FwB/+OEHmThxYgFB37Nnj5KbhQsXOoYtzZw5U+bNmyczZszw4uz2zeqOqyboP/74o1x55ZWqF7JVq1bSrFkz+4IqwZ2jEeS2225TPbsYjj1nzhwVl/hXSxiOdeGFFxYYrl2CS9nqEExnQcMGvg+uu+46h6CXKVNG6tSpI1WrVpUOHTpIrVq1bMWlpDd77tw5+eijj5SQY/gnRiNhVBLjtaREeVwwESj8PR1MZWdZiyZwww03yKhRo5SkI23fvl2NZFuyZIlgdBVT6BNw964Q+nfNO/QFAQq6FxTdieTGjRulb9++Bb5w8VKJIU2F56p7cSlbZS1K0MEPX27o6d26dassW7ZMxo8f73jY2QpSCW927NixAr5ffvmlVKpUSY0AQSu+c+MR5qBDLEeMGFHCq9jrsDNnzqi/eQjke++9JxEREQqA9t9Y9BCjbRC7n3zyCSVdR3hgKDukfMeOHSpOn3/+edXIwXjVAY9ZLEUA0zS+//77IsuEkUqFF5It/D1tqRtiYUpEAM+BK664okAjrtahM3fuXPX9xhTaBIp6Vwjtu+bd+YoABd0LkuxB9wKWF1mLEvTCp0BLdGpqqrzxxhtenN2+WSdPniz4mT59ujRs2FCBYI+ksXiASD7xxBNqCsv7779f5PxyDNXGUHfMpb7//vuNXdRGR+OlFmt6YJ0J/L0zXm1U+SFyq2iYmz17dpF3g+kb2K1AS+6+p0MEhe1vAz3ozz33nNx0002KBXvQ7RMSet8V7EOEd+otAQq6F8TciaS7Oejo5cUWVpyDrg+uXkHHyuQbNmxQoxOYiiaALanQiAG5wctfvXr1HJm1Ob2//PKLlCpVSv1eE0nOQS8+qk6fPq3k/OzZs4qvpy1zsFjk9ddfLw899BDD1QsCL7/8suzbt0/eeustxxx0xqsXAJk1KAgU9z0dFDfAQnokwDnoHhGFZAZv3xVCEgJvyjABCroOhOjVwRyxRYsWqe2rsOI1FvjQ9kMfOHCglCtXjqu462DpnMUTV7DG4hoXXHCBGvqKngesRo7hxUxFE9C2rYJEYsVYLWEHAvTsQhoff/xxtXYCV3HXF0mQcgh3bm6u/O9//1NTApCwFzqGKmJHAgxrxwru2Fse3xVYqRe97Fw3oWjG2B/4ww8/VFvWYXX8bdu2Cb5Ptb9zcGe86otR5gouAsV9T2vvFsF1RyxtYQJcxd1+MeHpXcF+RHjHJSVAQddBbvfu3WpLKucEcdRWF0dvD7bPwErZSJ06dVLzebUeSh2XsGUWT1whRJh7riVwxQqohefv2RJeMTeNHnEtFp2zoUcdjR0QSSwMp6Wnn35abRHGVDSB5ORkt9smYj90yDgEHVvXIZ+W0Ahy7733EmsxBDBHD9ww9NP573z48OGCLRWRGK8MoVAk4Ol7OhTv2W73hO83vAti5BpSgwYN1MhKrLPBFJoEPL0rhOZd867MIEBB9yFV/GGiZ03rXfPhqW17KvSwpaSkqAcaxdx3YYCeYMyjBlc2JPmGK0aEIFYzMjJUrzp7wfRzxYvsyZMn1Qru2C+4cGK86mfJnCRAAtYigKmQ2KkC329MJEACJKCHAAVdDyXmIQESIAESIAESIAESIAESIAESIAGTCVDQTQbM05MACZAACZAACZAACZAACZAACZCAHgIUdD2UmIcESIAESIAESIAESIAESIAESIAETCZAQTcZME9PAiRAAiRAAiRAAiRAAiRAAiRAAnoIUND1UGIeEiABEiABEiABEiABEiABEiABEjCZAAXdZMA8PQmQAAmQAAmQAAmQAAmQAAmQAAnoIUBB10OJeUiABEiABEiABEiABEiABEiABEjAZAIUdJMB8/QkQAIkQAIkQAIkQAIkQAIkQAIkoIcABV0PJeYhARIgARIgARIgARIgARIgARIgAZMJUNBNBszTkwAJkAAJkAAJkAAJkAAJkAAJkIAeAhR0PZSYhwRIgARIgARIgARIgARIgARIgARMJkBBNxkwT08CJEACJEACJEACJEACJEACJEACeghQ0PVQYh4SIAESIAESIAESIAESIAESIAESMJkABd1kwDw9CZAACZAACZAACZAACZAACZAACeghQEHXQ4l5SIAESIAESIAESIAESIAESIAESMBkAhR0kwHz9PYjcPDgQdm1a5e68fDwcClXrpzUqVNHypcvH1Iw0tPTZeHChbJgwQLJysqS8ePHS82aNR33ePToUdm2bZs0bdpU4uPjXe59+/btcuLECbn22ms9ctm8ebO88sor6icxMdFjfmYgARIgARLwPYGcnBxZtWpVsSfGd/TFF18sjzzyiPq56qqrfF8Qk864ceNGOXTokHTs2NHwFTIzM9X9P/TQQx6fc+fOnZMNGzaod4f8/HzFr0mTJhITE2O4HL46gR42eP/5/fff5frrr3f73PdVWXgeEgh1AhT0UK9h3p/fCXz22Wfy8ssvu1x36NChcs899/i9PGZd8P3335ePP/5YunfvLmXKlJFbb71VKleu7LjcgQMH5I477pCBAwfKAw884FKMrl27yoUXXiivvfaaxyKuXr1aBg0aJN98843UqFHDY35mIAESIAES8D2BtLQ0adOmTbEnxuejRo2S1q1by4svvii33HKL7wti8IxoVEbj8OjRo6VTp06Os6G8X331lfz6668GryBy9uxZJapjxowpVvj37NkjzzzzjJJz7RmanJwsF110kfz3v/+VpKQkw2Xx5gR62fTr108uuOACVddaQqP9yJEj5aOPPpIGDRp4c1nmJQEScCJAQWc4kICPCWiCvnLlStX6ffz4cdW7vHz5cvn6668L9DL7+NJ+Pd1dd90l1113nTz22GNFXrdv376SkpIic+bMKZAHPev333+/4tK2bVuP5aage0TEDCRAAiTgFwLoRdcSvttfeuklWbRokRotpiX0HltZ0FE+PL+ee+456dy5s6PckOrs7GyJi4szzFKPoIMlGrlPnz6tGqsbNWqkrotnJBr1S5UqJZ9//rlER0cbLo/eE+hlg+d7rVq1VCOHlsDuzJkzUrZsWYmMjNR7SeYjARIoRICCzpAgAR8TcBb00qVLq7OjNR5D3SZOnKheCpBmzJihxBUCj9S4cWOVB/8izZ49Wx3XsmVLwTn//vtvQa/zfffdJ5UqVXKUevHixTJlyhTV+o7WbLS64/Nhw4apPLm5uTJr1izVOLB37141dA4t357EeP78+aoVHOfFMb1795YOHTqoc6KFHC3l6M3GcEYMY/+///s/F5K45tixY9W91qtXz/E5OOB3GCqJRgxPLAoLOvKj18G55f67776TJUuWFOiR//nnn2Xq1KmCoXkoK3pK0JuPFwcMKcRneLFEHVSpUkVuuOEGt/fh4xDh6UiABEggJAjgOYWG1qVLlxYQdEyBgqD3799fPXfwXX/JJZeoUWQ33XST4973798vr7/+uqxbt05JKHq10eibkJCg8uC7Gc+LNWvWqKlUrVq1ksGDB0vFihULPCcffvhhJbK41oABA9Qzqbjv/yFDhgga0bVnGE721ltvqSlbuBbuSXt+fvnllzJ37lzZt2+fesbivvAMRb4333xTcA8ZGRnqOYmGZ4wmQ9Ij6DgvBHfChAnq3pwTyv/oo4+q3nU8+9euXSvvvfeeumZsbKzKiqliGLGHnv+qVat6LBPkG6Pa8Cz/7bff3NaLHjYffPCBvP3226ocWg8/jkNCQ8P//vc/x3S04uoB+efNmyczZ85UHFHvzZo1UyPmOJ0tJL4ieBMlJEBBLyE4HkYCRRFwJ+gYMocH6Pfff+94sXj33XeVPOOhrkk0XgAgxmh9xkN4+vTpasgbHs6QykmTJkmfPn3UwwsJQvrUU0+poWTIgxcCDDuvVq2akk8knOeLL74Q9HijdR5C+sMPP8iHH37oaAwofC/aMDXMHWzXrp06BpKsDVfES8yzzz6rJL958+bqelrDg/O5Tp06pV7GevXqpV40kNBjgJcDvOSMGDFC/c4Ti8KCjpcnzEuHqGsJjRRoUFixYoX61U8//aRe9DCX8MYbb1T5wRPlQHlwTRyDlz28pO3YsUM++eQTx/GMcBIgARIggeIJeBJ0HN2+fXs1nxpCDFnTZP7YsWPqWQCZxvMLo62mTZsm9evXlzfeeEM9K9C7DEmH+CLh+QZxg4zjmag9J/EZzoPn5Z133ikQ0eK+/7VnslY2HI8pWZi6hcZePIeRUA48VzBUHc8yNFjjuYPGczSOo2EBjepoaMYoOTwb8VxBWfQIOkYf4F5wnrCwMBfYaDTGFAE8K/Hchqzjua/18EOy0TiBeqhdu7bHMmkNJ8XVix42aJx4/vnnVV1oIxDwDvDXX38VmI7m6TmM86DBAHV29dVXy+HDh9X7CkY2IGaYSMCuBCjodq153rdpBDRBh0Tigbt7926ZPHmy6r11HgqmFQAvIampqao1Gw9fvKBApPHi8e2336qXBW2hmFdffVW1eGtDxiHdkHvnIeS4BuZ/Q9BPnjwpN998s+oVRg+4JsiYI9ilSxc1hM5dwssSWsadBbhHjx6q11m7VosWLeTpp59W4l9cQk/++vXr1YsLXqi0BzKYXH755QUOLYpFSQQd5cXLAxo1tISyoD7wAgBRx4I2eLHBYn5IeKmz0qI8pgUpT0wCJEACPiDgSdDRgAzJRoKAo8FXm9qEnnE8T9AgrPUIa+eDjOKZiO9s55FnkHz01KKHFsKsNUDjeec8V9vT939Rw7jfeecdh6CjYQACD3kcPny4gxYaFpxHsWFRN8zNx/1169ZNNQygQUGPoKOxHdfBKDd3CY3JUVFRSvr1CLp2jqLKpAl6cfWihw2u426Ie+Fntad6QOMHGkHwfqAxxTtNXl6eGt7PRAJ2JUBBt2vN875NI+BukTj04mKhGOeEHlsM7YOwOif07EJ+8eKBFnosjKYlCDNeVtB6D1m+5pprlHg7Dy93FnStdR09xM7zAzEsDj0C7hZo0xaIce6px/W1ngr0gOCFQa+go1cBDQEYDnfllVcqDniIY2ifJsaeWHgr6JgHh9Z4DJfD0HUtaUMRwQ8vhliAB2wwtBCNBWASERFhWmzwxCRAAiQQSgQ8CXrhReLw3MCopZ49e6rh72i8dZ7+hLnYWEUdzzrIOIZ0Ow+f1xapw7FYHd3dc1LP978eCdWmprkbfo46RMM6nsfo0cYQdy1pZdMj6E888YTs3LmzwHPeOT7QAH7ppZeqqWJ6BN1TmTRBL65e9LDRI+gYzeDpOYx7x7QHNNBgRN5ll12mOhW0BptQ+lvhvZCANwQo6N7QYl4S0EHAeYg7WoCffPJJ9aKBoXOYW4WkvWSgpxzz5TBvHL9Da3Nxgo5Wdmw1hhcH7SGqvQxoRXMWdG0OG8rgvAUa8mLrs4YNG7rcERZ4gbD+5z//kQcffNDxOVrwUTZt3rheQUdDAh686DlBOTAMDvP3MN9eLwt3go4taZx7HZyHuGv3gCGLGErvnDCqAQ0bSDgH5sljbh9WzcVUAQyDdzfUUEfVMwsJkAAJ2IqAEUFH7zAaaSHahRNEDd/FGFGGYdLaImnac097hrgTdD3f/3okVBuejUYCTOUqnNCIjcYENEDjWYoRW7fddpsaMYB70iPoGOGFe0RDNnZDcU5aGdF7j158TdB//PFHx7athYe4eyqTPwUd7xh4l/D0HMbUPsQRnsfoPICcY9g/5tQzkYBdCVDQ7VrzvG/TCBSeg44HIhYmw77g6BXAIjOaOGNYnjbPStuWTK+g4wYgvdhjHcdAKjGsDS3yaEXHuTGE+/bbb1fD8/CAd07IW5SIaufFi4mW8EKE+WWYj46kV9CRF4vYgAvmlWHemjZfDp/pYVFY0HG/WLhHKwvOo237ps1Bxz3gpUpb7Ee7D+2+MYxO6y3H73A+5C28oJ1pgcITkwAJkECQEzAi6GhMRuMoRjM5Ty3SvqMxxQsjrpwFWevV1lZfdyfo2rOxuO9/TKfCGisYQo9h6VpyHuKuPZMLjybDswPyjcZfDFHH51rCc8cbQcee4ZB5NFjjGeuctOHfaIjGWjXa9DDMEcfq6UgaD9QDGgg8lUmPoOthg2vj3rFejvMztvCz2pvnMM6JOf7oqAi1bWmD/M+cxQ8AAQp6AKDzkqFNwN0icVj45O6771ZDrtErgBcQPLgwLx0vB+i9xaqoaD32RtC1Ie94eKNnGg9rLIYG6dcWiYOwL1u2TK28joVrMC8dPQOQc22xucI1ghZ9tOxjjhkWWMPx6KF2fhnxRtA3bdrk2AsdvdR48dCSNi+xOBaFH/pYlR1lw0Mc59NWt0XLuyboaIHHAjza6vMY9ojjMAIA94YXIjDDSxqG7GPxIbz4OM+FC+1I5d2RAAmQgDECRgQdzzsMdcf3ML7PIXt//vmnaiTFdzQaULEiOhq1MVIMzyw8HzFVCSt/Y9pWUYLu6fsfd411SNDbjpFdGFqPEW6FF4lDHvTsYk455ryjtxfrp0CaUXaMAEAeSDumo6GX25sh7igHGgkwnU27Bn6HZy6ew5B/LKKGe9d61LVnJWQWK8/jGao1ensqkx5B18sGz0zwwjB/PEMxnQxrvOA9ASwwfcxTPaCe0diBxQKxMj/eTSD8mH6HKWdMJGBXAhR0u9Y879s0Apqga0PBtQtpkgp5Ro8AHvB40Gtz1/DQxbxsrbcADy70EDvPQdfODRFHwkIqn376qXopQMJ8L7zg4PdYeAUJQ+fxEEcPsZbQUICXEsz1cpcwLB3HOw8hxzwxzHXHgxgJgl6496EoqGiQQE8+hgO6W1gOL2TFsdB6DtCjUr16dbW6L+aP4/+R0ECBleQxn1ETdLwwgRde6JznB2pz9rXVebUyo/ECL0iFt7oxLVB4YhIgARIIcgKaoEMoIdhaKk4EscgbtgtFgpChIRXPBi1hChJ+h151PDfxzEAjNhLmNeMzbb9wd89J5PP0/Y88eK5gyhi2ZkPCswONx3iuaKu4nzhxQo0Ag0BrCb3UOA4Nx+PGjXOUHZKJBl6tN1wTaowCwDo0RSU8zyDjuLbzs8rd8xXPZDS+Q8rRII1nOKZpaYLuqUx660UPG2z9invT3kfwnoGEBgXtWe2pHrCzDVjifpAw3Q+NMs6jEoL8T4TFJ4ESEaCglwgbDyIB3xDAgmzoXcdcK1+sHo6HIRaVwQsOes6dE14CsFosroO5YXoSXjCOHDnis/IVd82SsECvB1rf8dJWVELjAF6y8G+FChUKLAKnMcHcP+dF9PSwYR4SIAESIAHfEEBDMuQRw7S1RmDtzPjuxhQxJPTSerNGSHHf/9r5sSo7vv+Lewbj+YTnJ3p5Cw/Hx9QvPFu0rc9KSkS7T8jxCy+8UGBof+FnOZ7LaJR2t6gpzuOrMulhgyl1GElQ3P0XVw/4DIKO9xfn1fFLypHHkUAoEKCgh0It8h5sSwBD7zA0Hvuwli5dWg3fRms2eo7r1q1rWy68cRIgARIgARIIRgIQVewtjlEDLVu2VP/OnDlTbVPKRAIkYA8CFHR71DPvMkQJYD4c5n9hTh5a0iHlmIOmDf8L0dvmbZEACZAACZBAyBLAWjGYNoBGeExJwzx3bgEastXNGyMBFwIUdAYFCZAACZAACZAACZAACZAACZAACViAAAXdApXAIpAACZAACZAACZAACZAACZAACZAABZ0xQAIkQAIkQAIkQAIkQAIkQAIkQAIWIEBBt0AlsAgkQAIkQAIkQAIkQAIkQAIkQAIkQEFnDJAACZAACZAACZAACZAACZAACZCABQhQ0C1QCSwCCZAACZAACZAACZAACZAACZAACVDQGQMkQAIkQAIkQAIkQAIkQAIkQAIkYAECFHQLVAKLQAIkQAIkQAIkQAIkQAIkQAIkQAIUdMYACZAACZAACZAACZAACZAACZAACViAAAXdApXAIpAACZAACZAACZAACZAACZAACZAABZ0xQAIkQAIkQAIkQAIkQAIkQAIkQAIWIEBBt0AlsAgkQAIkQAIkQAIkQAIkQAIkQAIkQEFnDJAACZAACZAACZAACZAACZAACZCABQhQ0C1QCSwCCZAACZAACZAACZAACZAACZAACVDQGQMkQAIkQAIkQAIkQAIkQAIkQAIkYAECFHQLVAKLQAIkQAIkQAIkQAIkQAIkQAIkQAIUdMYACZAACZAACZAACZAACZAACZAACViAAAXdApXAIpAACZAACZAACZAACZAACZAACZAABZ0xQAIkQAIkQAIkQAIkQAIkQAIkQAIWIEBBt0AlsAgkQAIkQAIkQAIkQAIkQAIkQAIkQEFnDJAACZAACZAACZAACZAACZAACZCABQhQ0C1QCSwCCZAACZAACZAACZAACZAACZAACVDQGQMkQAIkQAIkQAIkQAIkQAIkQAIkYAECFHQLVAKLQAIkQAIkQAIkQAIkQAIkQAIkQAIUdMYACZAACZAACZAACZAACZAACZAACViAAAXdApXAIpAACZAACZAACZAACZAACZAACZAABZ0xQAIkQAIkQAIkQAIkQAIkQAIkQAIWIEBBt0AlsAgkQAIkQAIkQAIkQAIkQAIkQAIkQEFnDJAACZAACZAACZAACZAACZAACZCABQhQ0C1QCSwCCZAACZAACZAACZAACZAACZAACVDQGQMkQAIkQAIkQAIkQAIkQAIkQAIkYAECFHQLVAKLQAIkQAIkQAIkQAIkQAIkQAIkQAIUdMYACZAACZAACZAACZAACZAACZAACViAAAXdApXAIpAACZAACZAACZAACZAACZAACZAABZ0xQAIkUCICubm5EhERUaJjeRAJkAAJkAAJkIBIXl6ehIeHEwUJkAAJOAhQ0BkMJEACMnr0aNm1a5c0adJEnnzyySKJ7NixQz766CPZunWr7N+/X2JjYyUpKUk6dOgg7dq1k/Lly8tXX30lX375ZZHnePfdd6Vs2bKkTgIkQAIkQAKWJVD4WRYTEyN169aVTp06SePGjV3KnZGRIY888ogS7h49eqh87lJ2drbMnDlTfvrpJ9m2bZvguBo1asiVV16pjrnsssskLCxMXnzxRfW5u1S7dm0ZO3asZdmxYCRAAsYIUNCN8ePRJBD0BA4fPiydO3d23MfixYslPj7e5b4++eQTee2119TvIeQ1a9aUAwcOyJo1ayQlJUVuv/12GTlypLz//vvy3nvvSaNGjSQuLs7lPOPGjZMyZcoEPTfeAAmQAAmQQOgS0J5lLVu2VM+sEydOyMaNG9UNP/fccwWem/jdwoUL1TMQqV69ejJjxgwXOMnJyTJw4EDZu3evytOiRQvJz8+X7du3y/r161X+7777TqpVqyb9+vWTDRs2yDXXXONyHjx/n3rqqdCFzzsjAZsToKDbPAB4+yQwbdo0mTRpktx5552q53v48OHqv50TXibuuusuqVy5srzxxhty8cUXOz7OzMyUt99+W/UCOAs6egjQu85EAiRAAiRAAsFGQBN052cZRLpnz57qVlavXi2RkZGO2xo0aJD88ccfcsMNNyhZ/+yzz1SPu3MaNWqUzJ8/X3r16iX/+c9/ChwP+cfnGGWmCTpGra1YsSLY0LG8JEACBglQ0A0C5OEkEOwEunbtKpDszz//XFq3bq16viHtzgkt9UuWLJGJEyfKdddd5/aW09PT1dB1dy81wc6I5ScBEiABErAXgaKeZZgSNnfuXPXMrFOnjoKCnvGOHTtKly5d1AgzDHXv27evDBgwwAENsn3vvfeqnnNMFXM37xzPYkg/ftCDTkG3V8zxbklAI0BBZyyQgI0JYC45WvIffvhh9YMecLT8z5kzRy644AIHGfQIJCQkyDfffOORlvZS8+GHH7r0oEdFRXExHI8EmYEESIAESCDQBIoS9DFjxsi3334rs2fPFswFR9KmgL3zzjvSrFkzJelIeJ5qi6niufrf//5XzS2/5ZZbPN6eNsR91apVLnkxH56JBEggdAlQ0EO3bnlnJOCRwKuvviqffvqpfPHFF3LRRRfJypUrZciQIdK/f3956KGH1PGYX44F4Fq1aiUTJkzweE7tpcZdxqlTp6qF6JhIgARIgARIwMoE3Ak61lzBHHJM98JccU2+MQUsNTVVFixYoHq/X3/9dfn444/VcHXMM0fSnreYm45edE9JE3R3+datW6cWkmMiARIITQIU9NCsV94VCXgkkJOTI23atFE95dpiNufOnZO2bduqoerz5s1TLwAHDx5UC8Bh+B56Djwl7aUGQ/nwEuOc0GtQqVIlT6fg5yRAAiRAAiQQUALas6xBgwaC0V9///23GsqO3Uveeustx0ruO3fulHvuuUeNRnv00UdVmbH6+v3336+GvD/77LPqd9rQeOee9+JuUBP0wYMHF8iG5/J9990XUDa8OAmQgLkEKOjm8uXZScCyBLDFy2OPPaYkGtu6aOnnn39WC75pvd0Q+auuuqrIVWkL3yDnoFu2ylkwEiABEiABnQS0Z1nTpk3VFqJVqlRRDdporHbeoeTNN9+U6dOnC0S+evXqjrNjRxQkDFHHkHTkQd7i1nJxLhrnoOusKGYjgRAkQEEPwUrlLZGAHgLafPPCQ86zsrLUli/dunWTYcOGqVNhT1fsk459YWvVqlXs6SnoeugzDwmQAAmQgJUJ6HmWoQEb883x3Cy8a8nx48fl0KFDMn78eDUyDauxP/74444tST3dOwXdEyF+TgKhS4CCHrp1yzsjgSIJnD59Wm688UbB/q4Yquec8vLypFOnToJV2RctWiTR0dFqXh2G6WEuHeahF97HHPPyIPAYdqfnpYZVQwIkQAIkQAJWJqDnWabNSccWa3369ClwO5BzDHHHztoIHwcAACAASURBVCfoNccUMjR279+/320vOp65kydPVkPlExMTuYq7lYODZSMBkwlQ0E0GzNOTgBUJYHGb559/Xv3ceuutLkV87733lGi//PLLSuTz8/PliSeekOXLl6sh8VgQp0aNGmpO3oYNG9QQPsxT5z7oVqxtlokESIAESMBbAnoEXZtX/vXXX0vNmjVdLoGt1rC/OVZzh3Tjv/E7JPS8o9Ebc8rRwI2GcCzKiucz90H3traYnwRCiwAFPbTqk3dDAroIYBVatPwvW7ZMLQhXOO3Zs0f+n73zgJKiStvwOzkPOUgUyRkEQVwUxYwoqATFgKuu4gqrrnnNGfnN4uq66BowoIIJEBEBMYFKlqzEAQYYmBx7uvs/X/V0T/d0uNVVXRPfew4H6P7ureqnbvetp26aMGGCNixPhudJkp512VpGnvDLQjnuJAvmSJwsCtelSxfMmjVLW7n2ww8/1P7PRAIkQAIkQAJ1jYCqLZM9y6V3XKaJyZotgZJ7azWZLibTxiRJz/rzzz+vtb/eSVZ2lwfd8kdWgpfdVGSxORkaz0QCJNCwCFDQG9b15qclgYgQkEXkMjMzkZSUpPWou7eaiUjhLIQESIAESIAE6jkBeegtD7uLi4u13vW0tLR6/on58UiABPQSoKDrJcU4EiABEiABEiABEiABEiABEiABErCQAAXdQrgsmgRIgARIgARIgARIgARIgARIgAT0EqCg6yXFOBIgARIgARIgARIgARIgARIgARKwkAAF3UK4LJoESIAESIAESIAESIAESIAESIAE9BKgoOslxTgSIAESIAESIAESIAESIAESIAESsJAABd0k3Ly8vJAlJCYmwmazwW63mzwSswciIKuIl5aWaluAMUWegGyhJivMyj7oTJEnkJKSgsLCwsgXzBI1AlJ/ZccBsyk9Pd1sEczvRUDVbgaDxe9L6Goku2nExcVBtv9iCkwgPj5eu18oLy8noiAE2O6HrhrR0dFISEjQ7o2YghNgu2mudlDQvfjJj7aISDhbRh07dizkFZBtM6SxFElnijyBRo0aoaCggA9AIo9WK7FJkybIycmhoFvEt2nTplD9hlh06HpfbFRUFBo3bozs7GzTn1WuE1NgAla0m8FY8/sSuhaKnEunQH5+PqtrEALykEfkXB7sMwUmIL+b8hCNHR+B+YgjpKamIjc3l1UoBAG2m+aqBwW9gp+I+RNPPKH97/7779dNVXVzTUHXjdJQIAXdEDbdmSjoulEZCqRwGMKmKxMFXRcmU0FWtZsUdGOXhYKu5kZBVzOioIdmREFX1yGJoKDr4xQsioIOYMmSJXj66ae1npaxY8dGVNBTUlNRVlrKHnRz9TRobgq6RWAriqWgW8uXgm4dXwq6dWylZCvbzWBn3rhJU+Rkhx61Zu2nrt2lU9DV14eCrmZEQaegq2uJOoKCrmYUKoKCDmjzSGQ4z8yZM7V5JZHoQd+XXYK7vtiObYdc80v/enJbTDutg7mrxdx+BCjo1lYKCrq1fCno1vGloFvHVkq2ot0Mdsardufi7i+2I6+kHElxMZh+URec2pnTDqryoqCr6zwFXc2Igk5BV9cSdQQFXc2Igq6T0fTp07W5SYEEvayszK8UWWwk2PzGSW9vwJbMAp88T17YFef3auHzWrnDgWOF5WicHIv4mGidZ8owNwFZhELmoHOulDV1QhpqmYPOZA0BeQASiTnS1pxd3S81UvVXrhNTYAKRbDcDHaHI5sCpz6+Co8pClUumnoRmKXHKy5JfUo6P1mai3OHE2H6t0CotXpmnrgbExsZqc9ClTWQKTEAWQJP7vED3dGTmIiAdH9JpxcVhA9cIWSRO5qAbXeyyodQztpvmrjR70L34hbrRGDZsmB/pn3/+OeAPmNxHnPCvhX7xN53eGXed293z+te/Z+Km99bAvT72/Rf0xHXDO/nlKy13IKeoDC3SEhAdFWXuitez3NJLJokNiTUXlnyt4eouVfiy7lrDOJJ1112WNWdat0uNVLsZjMLcNRm44+MNfm/ffV53TBnROSS8HYfyccmrP6Og1LVit7QW710/FMM6N6vb0IOcfSTrfL0EJHWA9wzKS0tGoRGRj7IKuX5v6Sv6QAX7PXfy7tCDJtSNRjDKwRaJG/R/Iu++uf42rB1uOrW99mKxzY6/PP+LX7EfXtMf3Vome15/ZuluvP/bQe3/0sP+4rgeGNqxkV8+m90BuxNIjG1YvfAc4m7q+6/MzCHuSkSmAjjE3RS+kJk5xN06tt4lR7LdDHTGGw/kY/Ls3/3eevbi7jija+hh7k9+swufrM30ydujZQrev6Zf9cCp5qNwiLsaOIe4qxlxiHtoRlwkTl2HJIJD3PVxChbFHnQvMpG80Xj31wN4ftkeT+npibH4+Nr+aJHqGl63em8u/vbhZr/rMmV4e9xwSjvt9RV/ZOPWeVt9YkTAf7xtKNwPpuQhwG3ztmLFn66thLq1TMELl/RA63T/YXzHimyIi45CWmKsuVpTi3JT0K29GBR0a/lS0K3jS0G3jm11Croc628fbMLqfXmew/Ztk4q3r+yr/IA3fLgJv+2tzCcZpBd99V3+I+KUhdWBAAq6+iJR0NWMKOgUdHUtUUdQ0NWMQkVQ0AFtD22ZwywrucvcpPvuu0/bC13mmahSqG3W5vy4Fa99tBCtWrTAK7dd5jNf7mihDWe/8ptf8c9f0gMjurjmO772wz68/lOGX8zSaSehcZJLst9cuR8zV+z1iZEe9lcn9vK8lpFTgmkfb8Ge7BLttYHt0vHqxJ5+c94P5pXiq81H0L1lCv5yQt2Yc0lBV9VQc+9T0M3xU+WmoKsIGX+fgm6cnZ6cVrWbgY4tD6IfeHcJfly9EUMG9MGTk89CTLR6uteLy/fg7V8O+BQpve7S+14fEwVdfVUp6GpGFHQKurqWqCMo6GpGFHQFo7lz5+Kpp57yiXrggQcwZswYJd1Qgr5p0yZIOX379sUjjzziV9aji3bisw2HPK8Pbp+O1y/v7fm/LGwz/ZtdfvlW3T4UcRULyv3ryx1YtCXLL2aNVw/BzR9vwc+7fBf6uvbktpjqtar84q1Hcc8X2z3lnNg+HbO8zsX9Rm5JOXYcKYIME0xNiFHysTqAgm4tYQq6tXwp6NbxpaBbx1ZKtqrdDHbWc+bMgfyZOHGi9kdPkvVbbvxwEzYccC2adkLzJLwyvle9XSiOgq6uFRR0NSMKOgVdXUvUERR0NSMKujlGIXObEXQpeOa85Zi3/DecP3ww7pxwus+xpNfgmvd+h8zBcyeRapFrd3p26W68VzFH3f1a0+RYyAq37nTijJ/9PsNpnZvghUt7aK/L6rgnPbPSb878v87phHEDWnvyzvo5A//+fp/n//ee3QnjB1a+735D5sPb7E4kx1sv8BR0Cys3AAq6tXwp6NbxpaBbx9ZsyaHazWBlGxF0d1ljLrsKzqgYfPHBW2ZPvVbnp6CrLw8FXc2Igh6aEeegq+uQRFDQ9XEKFsUh7ub4waygq246ZA2/O179DGu3/om/jTkdl5852OeM92aXQLZ0Kyqze15//pLuGNGlcvGci/+71jO83R102YmtcddZrhXjC0vtOPVF/wXrbvxLO9z4F9eidn9mFWP8m+v8aC2dNhiNkyq3unnkqz/x+cbDWly7xol4Zmx3n0Xv3AW494fv3irF1BWgoJvCp8xMQVciMhVAQTeFL2RmCrp1bM2WXN2Cfskll2inPG/ePLOnXqvzU9DVl4eCrmZEQaegq2uJOoKCrmYUKoKCbo6f5YIup/fyyy9j2bJlmDZtGs444wy/M5bF3yY/NguFhYV4atoVGNr1OJ+YX/bkYsqcygXpGiXF4Z0r+6B9k0QtTnrqT35updbr7Z0ePr8zLurbUnvpjZX78UqVue7y+ouX9sCpnV3z1T/fcBiPLPrTpwwZUvjJtQM8rx0pKNOGHO4+5poPf3zTRPznst6exfPcgfJg4lhROZLiokP2xFPQTVZgRXYKurV8KejW8aWgW8fWbMkUdLMEA+enoKu5UtDVjCjooRmxB11dhySCgq6PU7AoCro5frVC0OUjTJkyBYcPH8Zrr72Gli1dUu2dNu3JxLRnZyMtJRnvP3IjUqoMP//+z2zcMrdyxfi/nNAYL4/r6Sli/f58/PU9/61uFt00CC3TXCvGv/TdXry1ar/fsVffOcyz6vwDC/7Agk1HfGIu6N0Cj13QxfPahgP52sr02UWuvWtlNICMCgiUEpJTYSsphsNROYLA5CVldi8CFHRrqwMF3Tq+FHTr2JotmYJulmDg/BR0NVcKupoRBT00Iwq6ug5JBAVdH6dgURR0c/zqjKCLvIvEi7yLxAdKi5d9j2c+/AZnntgdd994hV/IvV/uwNdeC9JNPLE17q4YJi/Br/6wD/+tsup8bHQUfrnjZE9Zp734CwpKfWVaFptbccsQT8xFr6+FrDzvneQ4cjx3WrUnF3d+tk0rS1bzfWJ0F5zTo3nAzyUjBNzb0pm83A0uOwXd2ktOQbeOLwXdOrZmS6agmyVIQTdKkIKuJkdBp6Cra4k6goKuZhQqgoJujl+9EnQZRi/D6WUYvQynD5RenbcUHy9fg0kjT8T1Y0f6hOQU23DZWxtwOL/M8/pD53fGmIph8vLi7Z9uw7Idx3zyeW97IzI96P/8F7W7Zmhb/GNEBy1fWbkDf3nhF9gdvkPyP//bQM+wfYlbuOkIHv16pxbfsUkinryoG3oGmPNe7nDC4XAiPla9rZ7J6lLnslPQrb1kFHTr+FLQrWNrtmQKulmCFHSjBCnoanIUdAq6upaoIyjoakYUdHOMQua2epE4ObhqDrrEqIa46+lB1yPoqkXtCsvs+Nv/vYfMQ0dw25WjceEQ36HpskXb9e9vQn6pa/h6WkIsZk3qja4tkj2chz27EqVV5sPfcEo7TBnuWrBuzb48XP/BJr/rctPw9vjbKe201zceLMDkdzf6xLRKS8BXN53o89o9X+zA4q2ubepE3l8a19Nnv3p3cH6pHTIaQObEN6REQbf2alPQreNLQbeOrdmSKehmCVLQjRKkoKvJUdAp6Opaoo6goKsZUdDNMaKgexFQCbqEyt7vsgf8Y489ht69K/d1dxdTbHNg9K3Ttf/Of+EeP+ldsu0Y7vp8m+eoxzdNwuzJ/ZBcIccH80pxwWtr/K6LDHM/v1cL7fX//JiB//xYuSWcO3j5P05CemKs9t93fjmAF5bv8Sln+AlN8NI41/ZzkjLzSvGPuVvxx5Ei7f9DOjTCzAk9NVmvmuThRGJstDbkvr4kCrq1V5KCbh1fCrp1bM2WTEE3SzBwfs5BV3OloKsZUdBDM+IcdHUdkggKuj5OwaI4xN0cvwY3xD0Sgi7IVdveLF2zDY+/9SXat26BWXdfjbgYX+md9skW/Lgzx3P1+rZJxdtX9vX8/61VB/DSd77yLW+uvH0o4mNcveD3zf8DX232XbBOXl9z1zBPObJwniyg552kl156691pbUYe7vxsO2Q1fUnXD2uLv5/qGo7vnbIKbdiSWYDex6WiaXLl1nQmq6Cl2SnoluLVGjAjsmLtWdWP0inotfc6GqnzetqeYJ9Y1d7UXlLhnRkFXc2Lgq5mREEPzYiCrq5DEkFB18cpWBQF3Rw/CnoAfqoedMmiumGSHngpR3rgpSc+UHr8g2VY+vManDyoPx6/+kxEe60EV2Z3Ytwb63wWmzu3Z3M8dWFXT1HPLN2N93876FN0k+Q4fDu1cq/5E2f4z4evuqr8WTN/88i5u7Cqe9G/vGIv/reycoX7287oiKtOauP3sWQ+vJy7e7SAyeppOjsF3TTCkAVQ0K3jS0G3jq3ZkinoZgkGzk9BV3OloKsZUdAp6Opaoo6goKsZhYqgoJvjR0GvQUF396hcdtllmDBhgt+ZyArvt/z7c+zcsxejTx+G28cO9YnZmVWESW9v0ITYnZ4Z2x0juzX1/D/QivKXndgad1WsXi/D2k994Re/Y9/wl3aY8hdXL/u2w4W4/K0NfjFLpg726Umf/s1OfLT2kBZ3XHoCnrm4e8BF7crsDm3v+oRqWNSOgm7yB0KRvS4KunxbZH1Gh9MJJ6K0uhjoNc/7FfESJ69JXne84JEy3OW51310/d83zv2au1zJ64qR/E7tPNzvuf8e0L4R0ux5pi8ibzRMI/QpgIIeWZ7u0ijoaq4UdDUjCjoFXV1L1BFsN9WMKOjmGIXMzUXi/PFUVw+6StDlzFQL7B3KL8P1T76JwsJCPPr3SRje4zifD/Tzrhzc/PEWz2vJ8dF496p+6NQsSXtNVpI/6ZmVfhBkxXlZeV7Smyv3Y+aKvX4xL17aA6d2bqK9LivO37/gD5+Ydo0T8MUNlYvayWr00z7Zil/35mpx/dumaXvVyzZ1VZPEylx4s/PhKegW/nhUDAFTyYrdAdgrZFiEVJ4nya4D2t8Vgiz/1mKcUdrfksfhEWmXwLpfk1iRWVce979FnOvP2gnuqzawAwXd2hpsrHRVnQ9UKoe4q1lT0NWMKOhqRhR0Crq6lqgjKOhqRhR0c4wo6F4E9Nwk1SVBl4+mWgF/3c6D+OcL7yE1JQXvPHg9Gif5zh//9/d7MevnyuHrnZsn473JfT1z3X8/WICrq6woL8ddfPNgNE9xlfXKir14w2sIvBv5b3cOg3vNuf/7dhc+WJ3pUx8v6tMCD4/q4nltX3YJbv9sm2dRu4v7t8ID555g+BtAQfdH55ZbTYArRFiTX+kdRpT20Ebr7a34t/TwymuaDGsPdVzviWSnpKUjOzdXE2spz12OJt/OKC2eyTgBCrpxdlbmpKBbQ5eCruZKQVczoqBT0NW1RB1BQVczoqCbY0RBb+CCrmeLurcWr8Y785ejW+fj8dLUiz1y7kb3yFd/4vONhz0k/3pyW0w7rXIhuUCrzssc2tV3nuzJIyvXywr23ik+Jgorb6+MkQcB8kDAO91/7gm4pH8rz0u7jxVj+uKd+GVvHk5onox7zu6Ewe3TA9bz+iLoNrsIchRsFaJc7oxCud2J8goR1l53QPu/q8faNXTaLdaVPdCR7WVOTU1FQYHv9bLw56rBFU1Br52XnIJuzXWhoKu5UtDVjCjoFHR1LVFHUNDVjCjo5hhR0CnoWi97y5Yt8dprrwWsD3r2kJ/16RK8/+0aXHXmAPz14nN8yikoLcektzf6LGp391mdMPHE1p642+ZtxXd/+K4o3+e4VLxzVeXq9YEWtZPF6GRROklOpxMXvb4O+3NLPOXKg4DFfx/ks/+7SP7rP2Zgw4ECnNmtKW45vYNnezoLv04hi3bJdRRsdkAW03MJt+vfrr9dYi3/1v7W4lzSXVsTBd3aK0NBt5av0dIp6EbJhc5HQVdzpaCrGVHQKejqWqKOoKCrGVHQzTGioFPQIyLoqukBJeUOXPf0ezh46DBuuWI0xgzt7lP3Nh4owDXvbdTmD7vTfy7rhZM6NPL8/+RnV/oseidvXHdyW9xc0Vu/M6sY495c51en/35qe1w/rJ32umwXd/6ra2CTruSK1LNVCt6b3M8n34vf7cE7qw5ow7BP69wET13UzW9P+6oH8shzhVBLL7brNVfvtkuy3T3dUZAF8dyvWfg1rrGiKejWoqegW8vXaOkUdKPkKOhmyVHQ1QQp6BR0dS1RR1DQ1Ywo6OYYUdAp6NUi6IJZNX//QG4JrnxgpnZF3njoZs9ide5LJMPoZTi9O6UnxuLDa/qhdXqC9tKB3FKM/s8avzp92+kdcdUQ17Zv//0pA6/+sM8vZum0wWiUGKeJ9LwNhzHjm50+Mad0boZ/ntVN69EWqRa5/vDXDCzblomEmGiMGtABp3RpaeG3se4VTUG39ppR0K3la7R0CrpRchR0s+Qo6GqCFHQKurqWqCMo6GpGFHRzjCjoFPRaI+hyKVR7yH++chteeH8+WrVsiX//8zKfrdwk/+TZGyG98e7UNDkOs64ciNjYGMhc7TmrM/DRbxl+9f6pcSciKSFee332T3/gt11ZfjEvXFE5H37u6t34fqvvonZXndIFgzo19+QrLrPjs9W7sXpPFto2TsHoge3RtVXliAALv7q1omgKurWXgYJuLV+jpVPQjZKjoJslR0FXE6SgU9DVtUQdQUFXM6Kgm2NEQaeg1xlBL3MA6zf8jqcefRBde/bG3+95DKXlTsjrZXag1O5EsQ3435ffYmfGIRzfvg3Gn3kKmqa6etgl5ZfY8PCna2GXJcorUvumKbj9/Mq57nN/3Y3vt/vKd0JcDJ6ecJInzz8/+AUOrzLkjYEdm2Py8MpV519cvAm7juT7fMfuHd0frRq5trGTVFRWjmWbD6Kg1Ia0xHic1aeN3yJ8Fn7FLS2agm4pXlDQreVrtHQKulFyofNxDrqaKwVdzYiCHppRTEwMpO3OzXVtecsUmAAF3VzNiHLKqlFMhglwH3R/dKph2pJD1Qu8adMmbbh379698dhjjwW8PpHYB10KVm2zpmcVdz2LxKnmoMu5BGMnPdtljihNtK+7/BKNx4w352r/FwEvdbgWTJNvc8aOTfjkpQfRrmtvjPvHowHZrVw4Byu/+ggnnz8BJ4+a6Bez52gB5ixbjcxCJ2T++RUjByI5PtYTtzsrHy98vckn38ShJ2CY1xD2u+b8AtmP3Tud1KkFrjils/aSzHG/88Nf/I59bt92OL+faz68/DxNn78Bh/KKPXHtmqbgDq+HBfJGbnEZftp+GOVOB9o0Tsag4yt76Q1/uashIwXdWsgUdGv5Gi2dgm6UXOh8FHQ1Vwq6mhEFPTQjCrq6DkkEBV0fp2BRFHRz/EBB9wdIQZ8WsFYFEnRZh02TbBFwO/DM4w/ij62bcN2dj6Jtl94eAZctv9zphWmXav+89eW5AY8TCUGXghfPnonNq5bhnCunotfQM/yOlZFdhHdmv4eSkmJcOOZinNStvU/Mkk0HMH/dXp/Xbji9B3q1bay9Vm534I4Agn5e33Y4r0LQf92Zhfd+/sPv2CLoIuqS8otteGr+eq2n3Z3O6tMWo/v7ns+6vUexZvdRbb/y/u2b4qQTWpj89pvPTkE3zzBUCRR0a/kaLZ2CbpRc6HwUdDVXCrqaEQU9NCMKuroOSQQFXR+nYFH1WtCzsrK0YSiJiYnmKIXITUH3h0NBn6atbK4NKa/o3RYBnz93DhZ//hFOv3ACTrngMu092Q7MO0nPtwi29HxLD3igVFsEXc7tzYduQt6xw7j2kVeR3tR/AbhlG3dh8YqfkRAbg/EXnIve7Vxy7k6vLNmCHYe8holFAQ+OGYCmKa7v7PKtB/HZ6j1+GG4+q6dnrvrCDRlYvNF/zrz3fHiR87e+3+FTTtUef3nz280HsD0zF2mJcRjauSW6tgq8P3ykflAo6JEiGbgcCnr4fGu63Qx2xnpGIAXLqxqxFT6l2pmDgq6+LhR0NSMKemhGFHR1HZIICro+TsGi6qWg79u3D7fccgv27nX13o0ZMwb33nsvYmMrh+h6A3nuuefw/vvv+zDq378/3njjDSVdCro/ovou6DLU3D2v2/33TyuW4ZM3Z6LfsDNw7pVTteHmVZNqWLnE1zdBF3kXiRd5F4mvmmSbtXeXrMKm/XlonRaH8WechI7NUz1hxbZy3P/JGp/58ElxsXh8/CDERLlGFSxYtw/fbNrvV/bzk4ZC9niX9Pyi3yHD9r1To+R4PHLxiZ6XPvplF37accgn5pZzeqNTizTPa6U2OxZtzMC+Y4VolZ6I03u2QYs04w8AKejKn1hTARR0/fhqS7sZ7Iwp6OprSUFXM6KgqxlR0EMzoqCr65BEUND1cQoWVS8FferUqVrP+cMPP4zMzExcffXVuOeeezBq1KiAHJ599llkZGTgtttu87yfkJCAVq1aKelS0P0R1QdBFwkvtjlRbAf2Zx7GE3dMQeNmLfG3R18NKN8yFFyGhMtQcBkSHihR0P0FXTip2P1xOA8frdyJw/klaNMoEROHdUHHZpUSv2HfMby5YrsP8rZNk3Hn+ZX7tss89szcIp+Y5IQ4PDlukOe1W99b6XfZzuzdBhcO6OB5/dlFv2Ofl+inJMThwbEDkRAb7YmRofbfbc1EabkdjZPjMaJ7a8+DgqoHoKArf2JNBVDQ9eOrLe0mBV3/NasaSUFXs6OgqxlR0Cno6lqijqCgqxmFiqh3gp6Xl4eRI0dqvd/SCy5pxowZOHToEETEAyV5XVZjfPTRwAtqhQJIQa+7gl5qB0rKgTumTcHRrMO44+nXkNioJYpsDji85nyreoH1SKbEUNCNCboedsu2HMAXa/ZqUwvaNUnG5OHd0CK9smf7h22Z+OS33T6V9fx+7XFu37YhBX1krza4aKBL0I8WlOKxz9f6VXh5X+IkyYiAJ+evw7GCUk9czzaNceMZPfzy/b4/GyX2aPQ9LhWyAj5T5AlQ0PUxrU3tJgVd3zULFEVBV7OjoKsZUdAp6Opaoo6goKsZNShB37lzJyZMmIBFixaheXPXSs4ffPABFixYgNmzZwcV9G+//RZDhgyB/DCNGDECAwcO1EWWgl67Bf3Fl17Gd8uX4aobpqL/sDNQYgeKy11/HBXzv1XzqCnogReJkytfl9gt3XwQ83/dBmdUNM4ecAJGVSxE567Bb63YgXX7jvpUaO9F7Q7nleDJL9f5VfgL+rfH2X1coi/bwX2+1n/O/PQJJyGxQsJl9foXv/4dssiepNiYKEwZ2QtdWlYOpdf148MgJQEKuhKRFlCb2k0Kur5rRkE3xomCruZGQaegq2uJOoKCrmbUoAR9/fr1uO6667B06VKkp7sWeJo3bx5mzZqFhQsXBmQhr8t89fj4eGzevBnLly/H9OnTcdZZZ3nib731Vr+8L7zwAsrKyoLy3bBhA+644w6tJ////u//Asa9++67kD9XXXWV9idQeuaZZ7B48WKtrHPOOSdgjOSVUQJSVqCh+fKexMh7Ch675QAAIABJREFUEhMoyTHkWHIMOVagpOd8Ja98dimrX7/KYcbe5bk/hxwzUHKzk/xSTqhzmXTFVRh72ZUosjm1P8Xl8scl4V+88Rw2/rQEF/z1NvQ9pfJ6epf36j3XIvfoIdw0/U00auY/rUHekxh5T2ICJTnGgv89rx1DjhUo/fDFe/jhy/cx/MJJGH7RFQFj3n/mHuzdthGT7piODt0r9x33Dp7+twu0/97z3wUBy5D8Uo7kl3KMnot8Hvlco6/9J/oMOzNgOfWJXbnDibe+WY3f9uUiOcaJK0b0x6AqK70/+PFKiKi7U3RUFJ6edIq2qJyk+Wt2Y/5a3556eX3GpFOQnhSvxXy5ehcWrPOV+BbpSXhs/FAfxgvW7sHSTRmIi43G6IHHY3j34wJeA74YmICsP9CjRTyOSwqwIESY0KRtqM+pNrWbwTjraXuC5VW1N/Xl2kZHR0P+lJdX7mhRXz5bpD6HrEXkcDi0P0yBCchIDKlD3IU5eNsi9chms7EKhSBQ39tNqy9+vRvibqQnoCrkBx98EDk5OXjppZc8b33//fd+1+LUU09FQYHvwlPeQRs3btQWpxPJfPLJJwNeS1mcTv5MmjRJ+xMoPf/885Aefpkjf+aZgUXp2muvhezX/eabb6JlS//VtOU9iZH3JCZQkmPIseQY3vPxvWP1nK98ZvnsTz31FPr2DSyZo0eP1oqdP39+wHNxs5P8Uk6Jl3QXlTu1oelfzX0f3335IU4Zfbm2Knqg9NU7L2LTz0tx/tW3oPewkQFjXr//b8g7ehg3PP5fpDfzZyfvSYy8JzGBkhxDjiXHkGMFSj/N/wA/LfhQO1c550BpzvP3Yd/23zHxtifQvlufgDHP3DRGe/2OVz8P+L7kl3Ikv5Rj9FzIzp/d0fwSvLtsPf44Voa2TRIxYVh3dG5ZudK7zHN//LM1PsjTk+PwpJd8f/zLTny35YDfZZk5ebjnNdmebtF63y3qrh7eFUM6+z9AKrHZNYl3L5pndaNRl8rv1zYVTaOLTZ+yrBVQn1NtajeDcdbT9gTLq2pv6su1lcWr5Ka4uNh8na8vTKp+DllfSOScchX8Cssog6KiIgp6EETyEEx2hxJGTMEJ1Pd20+prX+8EPdBcOukNP3LkSNA56FUhz5w5E+vWrdN63VWJQ9z9CZlZJK6oHCiyAes3/o5Xpj+IDt1645JpgdcGcM/pHjZqIoaePyHgpVLt5S2Z6tIwbTnfurTNWkOcHrAxIxuzf9yurfTfulESrjilC9pX7Nku1++r9fvw9e++q843T0vE/RcN8NThu+b8irJyu0+d7tOuCa4f0d3z2u6sfPxvxQ7kFrtG8YzsdRwuGtgx4PdAekLcK9qrftPq0/sc4q7vatamdjPYGXMVd/W15Bx0NSMOcVcz4hD30Iy4iru6DkkEh7jr4xQsqt4JunzQm2++GWlpaQFXcZce7ylTpmgru7uHvYmQywrvHTp0wPbt27X3J0+erA2VVyUKujlBf+HteSgsBwrLnBA5dyfZC1y2HJO9wGVP8ECJgj43IJdw2J18/gScPGoiH25UEKgOdnrmoN/70W+QLea8U7/2TXHtad08Lz3y2VpkF1YuRidvXDO8CwZ0dK29IWlHZh5m//wHcovKINvKXTmsC7q2tnZvd9VvZnW+T0HXT7u2tJvBzpiCrr6WFHQ1Iwq6mhEFPTQjCrq6DkkEBV0fp2BR9VLQd+/ere2Dvn+/q5dKhrfdd999kMZLVmuXIdx33303xo8fr70vsi5zz91J4mVbNhnCokoUdH9CVXvQnU6goBzILXUi3xal/T3j75dqGW992bxksgfd9xpUh2S6j8jRB8Yfbrz07HRNnG+68UY0b+k7dF0WtPuiymJz15zaFQM6NNPQy2rxd3y4yu/Ld27fdji/YvG74rJy3PfJajjkC1iRZM78E+MGISk+1vPapv05+PjXXdq59GvXBBOGdoJsH1cfEgVd/1WsLe0mBV3/NasaSUFXs6OgqxlR0EMzoqCr65BEUND1cQoWVS8F3f1hZd61/BjLH1WSnvXs7Gy0aNFCl5i7y6Og+5O9/4EHsHnTJvz97kfR4oTeyLc54XBG+QRGcpg2BZ2CLgRkxIU8nJARFzLyIlCKZL2zevSBbA33+YpfEe0ox+Xnn+aRc/lcMmT9tvf9Bd17RfkfdhzCJ7/s8sMwbkgnDO/qeiAgw+Rf+HqTT0zH5qm47Vzf9Q++3piBJZsOaDsfnNa9JcaceLzqJ7VWvE9BD/8y1HS7SUEP/5q5c1DQ1ewo6GpGFHQKurqWqCMo6GpGoSLqtaCbQ6MvNwXdtVp6Xpmrh1z+fue56hUlCjoFvT4KunymUA8Uvt18AF+urVxIrmV6Iu4c1Q9xMdFahdi8PwevL9/q90PmvXXcwg0ZWLwxwy/m2cuHICbaVc7yrQfx2WrfVefP6dMOo/q38+STBwZfrtuHX3ceQeOUeMge873aNNb3I2phFAXdQrgmig7VblLQjYOloKvZUdDVjCjoFHR1LVFHUNDVjCjo5hiFzN3QBP3DD+fgo4/m4LyxE3HKqAnIK3OivErveHX3ZFLQKegNUdDlM6/csA2LvlqAJs2a4+ZrrkRsrEuq3em5Rb9j79HKnSY6NEvFP8+r7B3/amMGvt4QQNAnDfWsCj99/npk5vquCp0cH4snxw/2HOfdn/7A6l1ZPse++aye6Nqqkee1UpsdizZmYN+xQrRKT8TpPdugRZp6GpGZn28Kuhl61uWloFvDloKu5kpBVzOioFPQ1bVEHUFBVzOioJtj1KAFvczu2zv+zRdzsHLhRwg1vJeCPjVgnXEvaleb2Fk9TLshruIuFz8SuwdIOWaH5Jc7HHjniyXY/cd2HN+lG66+6CzEVvSMS/nHCkvw+BcbfPYE7tehKa49tXIxuhcXb8KuI/k+dbpJSgIeGjvQ89qt7630q/Nn9mqDCwd28Lz+7KLfsc/rYYHMc39w7EAkeD1UyDhWhBXbDmoPBDq1SNPm0yfGxRj+BaegG0ZnaUYKujV4KehqrhR0NSMKOgVdXUvUERR0NSMKujlGDUbQly5dhpkzX8bQU8/ARddM04asi6B7p9oomexB971GXCSufsxBj4SgSxmq72zGsUJ88PlXyC4swaD+fXDJ8AGI8loyYv3eY/jf99t9KtmkUzpjSKcWugX9aEEpHvt8rd9v6UUDO2Bkrzba64WlNjz86VrIKvfuVLXHX16XlelX78lCcWk5uh7XyDOfPtAPNQXdwsbPRNEUdBPwQmSloKu5UtDVjCjoFHR1LVFHUNDVjCjo5hjVa0G3OZzIKQWyS4AV3y3DwndmotfQM3DOlXWnF5iCTkEXAtU9csPq0QfVJeh62Mne7h/O/RRi7uPGXISBHV2rybuTaoj74bwSPPnlOr/fUu9F7b75fT8WrN/nFyND6WVIvaQ/D+fj5W98F7U7u3dbXDCgvU8+Gda/6UAOjktPwMU909G+ibmh9LzRiGwjSkGPLE93aRR0NVcKupoRBZ2Crq4l6gi2m2pGFHRzjOqdoP/fS68huxQ4WuzQtj1zp82rlmlDcynovpecw7SnanUiUOI2a8a3WVOxq02CrjoXWSRuzooN+HVXFlLjgIlnDPJbJO7xz9ciq6By33bZ8u3RSwchNcEl37JS/FcB5sPLtnDubd++WLMXS7cc8KmKUVFReH7SUM9rWw7k4D/LKhfHk9EAb13RF33bpBpuCXijYRhdwIwU9MjypKDr50lBV7OioFPQ1bVEHcF2U82Igm6OUZ0XdOkl/zPjCP516xQ0atYSf3341YCfiYL+KtKbtvRjQ0GnoHtXCtWQcYmtLXPQ5Vz0nG91jD44WlCC97/7HX9m29C2cSIuHdoZJ7RI96DNKyrDQ5+uhROV+7a3aZKMu0b188R8tnovlm/1FXTt4cEVJ3tipKdeeuy905i+LfHQ+Z0NtwS80TCMjoIeWXQhS2MPuho2BV3NiIJOQVfXEnUE2001Iwq6OUZ1VtBL7cDhIuBQkROZhw9DeuxEQK99hILufVFVPZkUdAo6Bd38gnV6HhbsOpyPD1esx6HiKPRsmYhJI/ojLdHVwy5p/d6j+N/3O3x+k2W4/eThXT2vyf7w0qPvnYZ0bITXJvYy3BLwRsMwujor6IfyyzBjyS4s33EM3Vum4J8jj8fgDpUPlCJLJDKlUdDVHCnoakYUdAq6upaoI9huqhlR0M0xqlOC/u9XXwNSW+BQEZBbVnnq9VkyOQfdt4pykTguEhfuA4Xq6EHXI+gSoxp9sOrPw/j45+0oRwxO7NAYVwzvgZjoyqk6s3/6A79V2fLt+mFt8fdTK1eUD7dJ4I1GuMRCx9eFIe7j3lyHnVmV2wtKDVt40yC0SouPLIwIlkZBV8OkoKsZUdAp6Opaoo5gu6lmREE3x6hOCPoNN05B1pHDuP7RV5HapGEN06agU9CFQF2STDlf1cgNiTG7zZpeKa5P7Ipt5Xh92TbP1nCndWmCZ8d295H4cJsE3miES6xuC3pmXilGvbbG70NMGd4eN5zSLrIwIlgaBV0Nk4KuZkRBp6Cra4k6gu2mmhEF3RyjWivoxeUyfN01hP3V+2+C9JLL8PWGNo+agk5Bp6D3xrh/PBrwt6q2zEHX+7BA1YOu9+HG8/8Yr/H4dO4nplsA3miYRuhTQG3vQc8qtOGcV37z+9BTT+uAa09uG1kYESyNgq6GSUFXM6KgU9DVtUQdwXZTzYiCbo5RrRL04SNO1+aVy598W+WpqXrjOMSd86i9K7IeaYuEKNXneteQtlmTulNdvfmRqHfe5ztv3jzTLQBvNEwjrFOCLid7zeyN2HCgwHPeKfExWDjlRJ81ESJLxXxpFHQ1Qwq6mhEFnYKuriXqCLabakYUdHOMao2gX/rXqWh/Ire7cl8Qt2SyB923inIOOuegh/swpj4NcXd/dvcDBQq6hQ2gwaJrew+6fKzScgfu/3Alfty6D11bpeOhy0fghObJBj9x9WSjoKs5U9DVjCjoFHR1LVFHUNDVjCjo5hjVqKDLSuxPP/8y1v20DOdcyV7gQOJBQaegC4H6LJm3vjw34O9QpB7G1Gd2FHQLG0CDRdcFQZePtmzZMrz88ss444wzMG3aNIOftvqyUdDVrCnoakYUdAq6upaoIyjoakYUdHOMakTQZZMgGV23K8+BRe++AtmjnILueynYg26ttEViqDGHuBt/qCa1vbqGlVPQQzcSvNGIbCNKQY8sT3dpFHQ1Vwq6mhEFnYKuriXqCLabakYUdHOMql3QZW759mwnCivmmFOUQi9+xR509qCzB52LxFX9oeYQdwsbPpNFU9BNAgySnYKu5kpBVzOioFPQ1bVEHUFBVzOioJtjVG2Cfun4idiT78T+ynVptGNT0Cno3pWwunpVWe+Mr4oeCXZyzavrWrMHPXQjwRuNyDaiFPTI8nSXRkFXc6WgqxlR0Cno6lqijmC7qWZEQTfHqFoE/cJLJqLnmRNQ5vA/XCRu9uvzUGP2oPvWmUjNS2a9o6DX1QdD3g83OAfdwgbQYNEUdIPgFNko6GquFHQ1Iwq6NYK+7XAhlu/IRlxMFCae2BqyM0R9ThR0c1c3yul0ynRnJoMEQt1obNq0CQ888AD69u2LRx55JOAR3vtgDuZ+PAdWb9lEQTc+F7g+s2O9q70PN7wlk4vE+V4n1baSFHSDDVo1ZaspQX/o9cDrdgT72LI46+dvzcSAU87AmGumVhMd44eJjY1FfHw8ioqKjBdSz3MmJSXBbrejrKysnn9S4x8vLS0NhYWFcDgC9BgZL7be5IyJiYHUo4KCKsNdQ3zCtXuz8dKSbZ6I9KRYPHxRPzRJibecizwG6NM8yvLjVD0ABd0ccgq6OX4wI+hlduDVtz/Ed/M/oqBXuQ7h9AKzB732SiYfbhh/MERBjww79qCbbOQsyF5dgn6oyIndecDTf79U+xTBHnQF+4iyOKuMJOo19AxtkdbankQcRNCLi4tr+6lq51dudyI6BohG9clDQkKCJp42W8UiP3WCVPWepIwykIc87L8LzD06OhqJiYlhPQj71ye/oai03KfAs3u3xQUD2lt+cWOinPhLm2jLj0NBjyxiCroXz6ysLKSmpmpfPL3JqKCX2IENR5xY/uUcrPyKgl6VNwW9fuzlTUGPjGSyB933F4I96HpbKOvjIt1uBjvjOXPmQP5MnDhR+xMqHS0BdudVLrSqWssh0oKeX2zDDzsOAVFR6NoyDV1apYd1IURcY6Ilu35x/XVnFuau3oWSMjv6dWiKSSd3RmJc7RxCe6ywFG9+tw0Z2a6e/sGdmuPKU7qExchoMAU9NLntmbnYl1OK1PgoDD2hhVHM9TqfEUG/9b2VfkxO694alww+3nJWFHTLEVtyAAo6gH379uGWW27B3r17NchjxozBvffeCxkupkpGBF1Waf89ywmbA3BvFcahxr6kKegUdO8aoed7Up8XOqOgU9BVbVF1v29Vu2lG0HNLndiZ60S+zVdsq1PQs/JLMWPhepSVVw4Pvnp4V5zYsZnyEu3OysfbP/yB7MJSLfbCgR1wZq82ynyb9+fg9eVbfeK6t26Em87sqcxbEwEzl2zGH4fyfA596eDjcWr31pafTnUL+rKtB7F00wHtc43s3QZn9DjO8s9o9AAL1u3FNxXnKmV0apGGW87pbbS4epvPiKA///Um7MnK92Fy0cCOGNnL+vpAQa+bVZGCDmDq1Klaz/nDDz+MzMxMXH311bjnnnswatQo5VUNV9CPlQCbjznhqJj5r0c8uFgXV3H3roiqm81wHm7wwZDxB0NWs5Mzq65rXZ8fbnCIu7IZMxRgVbtpRNBlS9JduU4cc3mtX1J9j4Id08gQ9/nr9mKJl+RI2dKTPX3CSUrOD81bg9xi37nRN5zRHb3aNAmZd/66fViyab9fzPOTTpZO/FqXarI3sToFfcXWg5i3eo8P/0sGdcRptVDSS2123P3Rr3515fKTT8DQzi1rXR2qyRMyIuiH84rx+vJtyMov0U69V9vG+NuIHtXy/aSg12RtMX7sBi/oeXl5GDlyJN544w30799fIzljxgwcOnQIzz77rJJsOIKeWeTa39w7UdADL9oTjmRyDnrtlUwOcecQ95r4vfN+uEFBVzZjYQdY2W6GI+il9ijsynPgsGJNtOoU9Hm/7caKbZn+DwmuODkk5xKbHfcEEKRz+7XD+X3bhcy7cH0GFv+eEfYxw77wEcogn1M+r3c6q3cbjB7QIUJHCF6MUUEvKivH/uwitG+aonvqwGOfrcPRQpeQuVOzlEQ8MHaA8nPanU4s3bQfhWUOJMZG46zebREbY93TlsO5xXhy/nq/8zqvbzuc1y90/VN+GAsD7A6HtpZBQjVO5zAi6IJA7odmPXor0ps0w/UPvWwhFd+iKejVhjqiB2rwgr5z505MmDABixYtQvPmzTW4H3zwARYsWIDZs2crYesV9L/e/jD2+o5u0cqmoFPQvStZfevJpKBT0L3rd3X93skx3VJGQVc2Y2EHWNluBjsZ7znoF4+biH0FThwoAPRsQ1Odgv79tkzM/W23z8eQOehTz+oVkrMI2e3vr/KLEWkVeQ2VMnOLMX3BBsBrU57h3Vph3Emdwr621ZFhzZ6jeOeHHZ5DxcdG465R/dE8LcHywxsRdHn4IQ9B3Gn8SZ3wl26tlOf6xBfrcKSix9Qd3CItEfddpBb0F77eBJny4E7tmqbg9vP6hLUugfIEvQKk/t354S9wuId3Vrz39zN7olvrRuEUVW2xH6zciVV/HtaO1yw1Adee1h1tmyRbfnwzgi7rp6Q3bYlrH3nV8vN0H4CCXm2oI3qgBi/o69evx3XXXYelS5ciPd21kIvc0M2aNQsLFy70wP7ss8/8wI8dOxYvvfRS0Asii+esWLECjVsch46nulaRrZoyt/yCQ1t+QaueQ9C655CAMXtXL0H2nq3oMOgsNOnYI2DMlkVvo6woHz3Pm4z45DS/GHlPYuQ9iQmU5BhyLDmGHMvo+f654lMUZO1H59MuRmrztgHLWT9vpvZ6/0sCr4wr+aUcyS/lhDqX1r2GolWPwMMHyS40O9Y735oVTr2zmp2cWSS/J6HOt7q/s9XJ7pJLLjHdaP7jH/8wXUZ9KsDKdjMYpy1btkD+HN+zP5r0HA5HGCt/q75HwY6pp00MlHd1dhwyil1r2DSKdWBIszIkx6gfJWzKi8MfBZVr36TGOnBG81JE61iAOccWjU1/7kVxmQ3t2rRCj+b6RcXpdGD/+u+R3qoD0o8LT+rzD+9D7oE/0abvcETHqNftcfM6lG/D1i2/IyYuASf1640EHXwkr8MZhR0FMbAVFyMtNQkdk31XxlZ9zw5tXonYhGQ069xPFaq9n1cejWWH/R8cnN+6GPGK67K3MAZrc3230RrYqAwdUnxHD1Q9kaOl0fjhqP8xBzUpQ7uk0HndZR3cuxNltjJ07Bz4njHQhz9UGo1Vx+LhdLp66rul2tAzXT/f7L3bUJRzGG37naqLret6AlvyYpFVGo3UOCdOSLGjSbx6e7dAbNNiHRjZMsg8lypnZLTOlzmAnQWxsNvLkRYfo7yW3ofVcx8eDJzcm2Tv247jeg5FbKL+77aUt3/bGrSKt6F7J+tXjPc+f7abur8GAQMbvKDr7Ql47LHH/ADKHudXXXVV0CtQXl6OgwcPIj69OWI7DgoYV35sH8r2b0Zcq66Ia3lCwJiyA1tRfnQPEjoNRkxq4IVmSv5cCUdRHpJ6jURUgEbSaS9H8ealiEpKQ1KXYQGP4yg8hpKdvyK2WQfEtwm8uIztyE7YMndo70tcoFS6bwPsOQeR1P1URMUH/iEp3vytljWp15mBz6W0CCXbv0dMo9ZI6OCaelA1lR/dh7IDmxHfuhtiWwS+qXCzSzzhJESnNA1YTsmfq+AoytHOJSi7LUsRnZiOxC6BhynaC46hdFdoduVHdqMscxvijuuBuOYdA1/rfRtQnnMQid1ORXRCYHZFws7pRHLvwA9RHGVFKNmmYHcsA2X7N4Wudwe3ojxLVe9WwV6Yg+TewdkVbV6KmKQ0JAapd/bCYyjd+avGRNgESrYju2DL3I7443ogNgg7d71L7DYc0QkpAcuRc4HDjuQ+ZweudxXsYhu1Rnyweudh1wVxLTsHvo5udscPQkyaa2RO1VTy5y+wF2YHZSfxRb8vRlRsPJJ6nB6wDGdpIYq3/4DYxm0Q375vEHa7YdPqXXfENQ+8YqwedsVblmnskoLUO2dZMYq3rQj9nc3ej7KM3xHXsgviWgVmZzu4Dbas3UjoNAgxqYHZSX0RdsF+71zsvoH0dLRtY34RnnfffTfo73xDfMPKdjMYz/z8fOTk5CBZHvq27gVE6bDWisKKtiwH7GVI7nNOWJfLXpCF0l2rte+NfH/CSSW7pI7mIbnniIDtSrCycqOSUXjsMGJSGqNFvB3RusYIuEorq2g/QrW9gY7rbvuljZS2Mpwk9wySP1QbG6g8uScp2vQtYlKaILFz4M6JqvnkEcfOpG4oja7caSetPBftS33neYc6/4LNywGnA6m9R+r6mEfiWuJIvP/idR1KdiPV7rvQXaACc2KbIMvuOt/mMSVoXJ6tPG5uTGPsT/S/v2pVdhDNbEdC5ncgGrsTO6MkJkmLi3eUoGPJbsQ59e37Xl5wDIWZfyIxvRkSgtyTBjsB+f2XdiCp+2mIincdX5X2JHZGYYx3e+1E16KtiHOG3gbvUNxxOBrvv8p8r8INqkNq79sLspF/aDfiEhKQ0i706BZ3gfJt/DO5O8qj4jzHaFx+DG1K/aeXRKrOu8spzdgIe/YBJLTri5gm6oUjJZ8tKg57EjuhrOL7kl6Whb5ZyxDt1PeQRxfIEEFsN80RbPCCHmgu3fTp03HkyBHTc9Dl0sh+m3KT+GeuE/sLAl+swtxspDQKvgiM0+FAcUEektMbB73atrJSOOzlSEgKLCaSsbS4UHvCHRcffBhZUX4uElNSES2bkwZJBbnHkNoosOxqP3zlNpSVFCMpNfjWMrbSEm2PzfjE4D/ixYV5iItPQmxc5Y9h1VNylJVoTxOFc8AfRB3sym1lkD+JyalBP3NZSRGiomOU7JJS0xAV4sZRxU6uYWlxkWl2JYX5iI1PDMlOT72Lkh/ymOD89bKTm+n4hODbFxbm5SA5Lb1a2EldSUgK/gRa2MUlJCEmxC4Owk6+j8G2QZK6XZyfG/I7a7fZEB8XA2n0gyX5zkq9U7GTemfmOyv1rqSoEMlpwYcyyndWxU5+p+KFXYjvrB52RXk5IX8ThZ2trASJKf6jhTw3NMVFGHh8EzSP1XdjGqopbdo0+O+duSa4bua2ut0MRsXdnpbaXduoHVLMPXeXU1ZaoklZfJg9T5JfVV+DnaueOhosr7TDqrYkSIMHEV95qBdO0rPmS6Dy9h0twAfzl+BYYRn69+qKCSMGICaMVem039r4xJC/F97HDbTomrz/9MQhSIgN/cAmr8SG/yzdos0jl9S5ZZq2yn2sYnjCriP5eHHxJr+P//DFJ6Jxsj7Ocj8kKdT9jvcBbHYH7v34V21utTtJn/bj4wYjJSH0KIXP1+zBsi0Hfc63T7smuH6E/gdMTlspouLCn26gZ/tL7xMrtztwx4e/+LHVM+994YYMLN7oK8bRUVF4btJQZdXfeSQP/122HcU21+iAHsc1xpSR6pEGVac6uA/07OVDEKNnmAuAcOu8HCMzpwgffb0CB47locfx7XDFOScjTvZhVKQ3V2zHhn3HfKImD2mDW04P3EGkKi/c99luhkvMN77BC7rguPnmm5GWlhbxVdylbCm3pKQENpsNGQWyBYy5C8bcvgSSk5M1vsEEnbzMEZDdDQoKgjxZMlc0cwPa7hHka11VGNihEdJ09HKpzoA3Gv6ErGw3g10PuQ7e677I6u3y8DtH36hW1WWu8+8JKV6pAAAgAElEQVTHxMQgPj4excUuIdSTZE/y/32/HfuOFmqTBkb1b4+z+wSemuZdXk5RGR79bC0cXvPeux/XGDfpEB095xUoZtGGDCyqImUS9+jFJyJdIcv/+34H1u896lOsbGEnW9mp0ts/7MDaPZV5ZTs42RbOyiTb0L3/85+Q65OeFIcrhnVB9+PUc8EDbWEn1/V5xSKF3p8lJSUFRUVFWieK3pRfbMPM/76pdQSNG38Z+pyg7uWVBxEy771qOrdvO5yvWJiusNSGGQs3Ireo8gGs3hXnpd4KV++kZ3X9rzZm4OsN/r3lT084ybJF6orLyvHIZ2t9FlU8vnkqbj23j/LS3PbeSr/xN4M7pOP1y6pn6zy2m8pLFDKAgg5g9+7d2j7o+/e7tikZPXo07rvvPsSF6AVyUw21SJzEeAu6/P9goRN/5Pqs5WLuCjbw3BR0aysABZJ8rSVgbekUdOv4WtluBjvrqoLujssucWJnHiDC3pCTEUF/ftHv2HPU9yHs5OFdMVCxb3swWX7msqGWrTaecawQz3y10ecSJ8XH4slxg5XbVd0qC/BVEc6urdJxs2LxPvfBdmTm4s/DeZokd2oRfHRgTde/j3/ZiR93uBZOc6cOzVLxz/PUQueOD1fQs/JLMWPhepSVV45kvHp4V5yoqENyPBmdIKMUvNO9o/ujVSP1EHnZCWDFtoOQgQZdWqZDrqcqSRW47f2VfmF6HtbsOJSHV5Zs9skri9M9MGag6rCG3/9m034sWLfPL/+T4wcjOT70aIpnFm5ERnahT95xA1vjX2eHt86E0ZOnoBsl58pHQffid/jwYcgPk/zRm8IVdCn3qOyFftQZxswyvWfT8OIo6NZecwo6+VpLwNrSKejW8pXSrWg3g511MEF3x2cWOrEnH5Ah8A0xhSvo0kN6W4CV48/s3QYXKrY8+2pDBr4O0Js9Y+IQyKrsVqXfdmVpPcvSc984OQFXD++ME3QI84wFG3Agx3dOxCldWmLC0MBr/1h1/laXK6vGywrw0sMsSYZ9Tz27py5G7nMLV9Dnr9uLJZsO+Hy0xLgYTJ+gXs+guMyOz1bvxuo9WWjbOAWjB7ZH11bqkQJmON415xefhwlS1nl92+K8fupF1GTV+Dmrdmn1r3WjJFz1l66Wrhwv3zH5rlVNj48bhNSE4NMPJX5HZh7+/e1mj2ukxMfg7Sv74IQwFpA0w5mCboYeBd0cPcBnuF2gwqr2oLtjZEjepqMO2CtWyzR9Ig20AAq6tReegk6+1hKwtnQKurV8jZauerBtVNAln93hxP5CYG+eM6yV3o1+ltqUL1xBl3MPNAxWzxDjg7nFeLrKvtnh9tSaYSf7X+ud9yvH2Z6Zi39/u8VzyLiYKG2YcNsm+jtkzJxvdeYtLXfgx+2Z2ndhWNdWSFXMW696buEK+rzfdmPFtky/j/hCGMPqq5PPxoxsvPHdNs8hj2ucjNvO7aP7wZKsK5WQkIjiYp2LYJj4cDKV5JFP1/h06MmDgXtGB148ueqhJP8vOw/jhEbRGD+gFdIS9e+0YOK0tawUdHME2YNujp9hQZfDynC8jUedKGugT/tNoteyU9AjQTF4GRR08rWWgLWlU9Ct5Wu0dCsF3X1O0q7uyXMis7jhTCkzIujf/L4fC9ZXDqGV3m+5+W+aol4kbNvBXHy2eg8O5hXhxA7NtHnZKYmhe/WM1plI5DuSV4J1GTkAnDj5hOZIq8XnGonPa7SMcAX9+22ZmPvbbp/DdWmVjqk6pw8YPU8z+WSkwZrdWVp9lZEUMtJAbzK6D7re8qvGSU+4LP63P7sQfds3xSWDj9e9QKGUxX3QjZKv2XwUdJP8VTcawXrQ3YeVoXjrjzhRQkk3dCUo6Iaw6c5EQdeNylAg+RrCpjsTBV03qmoNVLWbwU5GNcQ9UL7icmBnrlObWlbfkxFBFyayAJrMVZUVzYd2bqFLzusqy4SEBG1RWVm4lykwgXAFXUqZ/dMfkOkHkto1Sca1I7rX23pU3YJutp5S0M0SrJn8FHST3FU3GipBl8PLuhp78oNvw2byFOt1dgq6tZeXAkm+1hKwtnQKurV8jZauajcjKehGz7Eu5pOFbRMTEyF7xjMFl8/y8nKUlnLp/2B1pHHjxpCtFMPdHcfudMJud+oeKl5X66g8CJN7o9xcbssU6hpyiLu5Gk5BN8fP1BD3qofOtwHbs50NfiXacC4JBT0cWuHHUtDDZxZODvINh1b4sRT08JlVRw4KujWUKehqrtI7TEEPzcmooKvp148ICrq+60hB18cpWBQF3Ry/iAq6nIrsOnmgANiV54CDC8gprw4FXYnIVAAF0hQ+ZWbyVSIyFUBBN4XPsswUdGvQUtDVXCnoakYU9NCMKOjqOiQRFHR9nCjo5jgFza260dAzxD1Q4TI3XXrTszkKK+SVo6BbVLEriqVAkq+1BKwtnYJuLV+jpavazWDlGpmDbvQc62I+Crr6qlHQ1Ywo6BR0dS1RR1DQ1YxCRbAH3Ry/iPegVz2drGLgjxwnyhwmT7SeZqegW3thKejkay0Ba0unoFvL12jpFHSj5ELno6CruVLQ1Ywo6BR0dS1RR1DQ1Ywo6OYYhcytutEw2oPufVBZRG5fgROZhU7YHPq3grDwY9eaoino1l4KCjr5WkvA2tIp6NbyNVq6qt0MVi570CnoRuucOx8FXU2Qgk5BV9cSdQQFXc2Igm6OUY0LuvcJyJB3EfWsYiecoKxT0C2s3IC2UmlBQYG1B2nApZOvtRefgm4tX6OlU9CNkqOgmyVHQVcTpKBT0NW1RB1BQVczoqCbY1SrBN19MuUOJ44URyGz0IF8W8MVdQq6hZWbgm4tXPK1nC8F3XLEhg5AQTeETZmJQ9yViEBBVzOioFPQ1bVEHUFBVzOioJtjVCsF3fukSsqBzCLgUJETsrhcQ0oUdGuvNnt4yddaAtaWTkG3lq/R0inoRsmFzkdBV3OloKsZUdAp6Opaoo6goKsZUdDNMar1gu59grmlThwqAo4UA3bZs62eJwq6tReYgk6+1hKwtnQKurV8jZZOQTdKjoJulhwFXU2Qgk5BV9cSdQQFXc2Igm6OUZ0SdPfJOpzQ5qmLrNfnrdoo6BZWbg7BthYu+VrOl4JuOWJDB6CgG8KmzMQedCUiDnFXIwIFnYKuo5ooQyjoSkQhA7jNmjl+lm+zZvL0tOyyRduhQpesF5VHosTaUwYF3dprwR508rWWgLWlU9Ct5Wu0dAq6UXKh81HQ1VzZg65mREGnoKtriTqCgq5mFCqCgm6OX50QdO+PKIKeXQIcLXEitxSo66PgKegmK7AiOwWdfK0lYG3pFHRr+RotnYJulBwF3Sw5CrqaIAWdgq6uJeoICrqaEQXdHKOQuVU3GpHYB92q05c56iLpx0qcOFYClNTBBeYo6FbVDle5FHTytZaAtaVT0K3la7R0VbsZrFzug05BN1rn3Pko6GqCFHQKurqWqCMo6GpGFHRzjOqtoFf9YMXlwNFiJ/JsQH4Z6sSK8BR0Cys3Bd1auORrOV8KuuWIDR2Agm4ImzITh7grEXEOuhoR56ArGMXExGidF7m5uTpoNtwQCrq5a88h7ub41bkh7uF83DI7NFnPK3UirwwosAGy+FxtShR0a68Ge9DJ11oC1pZOQbeWr9HSKehGyYXOR0FXc2UPupoRe9BDM6Kgq+uQRFDQ9XEKFkVBN8evXgt6VTROp0i6W9ajkFfmhPS612SioFtLn4JOvtYSsLZ0Crq1fI2WTkE3So6CbpYcBV1NkIJOQVfXEnUEBV3NKFQEBd0cvwYl6IFQlTuAfK9edhkaX16NvewUdJMVWJGdgk6+1hKwtnQKurV8jZZOQTdKjoJulhwFXU2Qgk5BV9cSdQQFXc2owQp6VlaWNk8kMTHRHKUQuVU3GrV5kTiroBTZnMi3uXrYRdhlaLxViYJuFVlXuRR08rWWgLWlU9DD51sb2s1gZ81F4ijo4ddo3xwUdDVBCjoFXV1L1BEUdDWjBifo+/btwy233IK9e/dqn33MmDG49957ERsbG5DFc889h/fff9/nvf79++ONN95Q0qWgKxHB7hBhdy08p4l7qVPbmz0SiYIeCYrBy6Cgk6+1BKwtnYKun29tajcp6Pqvm3ck56CruVHQ1Ywo6BR0dS1RR1DQ1YwanKBPnTpV6/l7+OGHkZmZiauvvhr33HMPRo0aFZDFs88+i4yMDNx2222e9xMSEtCqVSslXQq6ElHAgFK7S9hzy5yasEsvuxNRYRdGQQ8bWVgZKOhh4Qo7mHzDRhZWBgq6fly1qd2koOu/bhT08FhR0NW8KOgUdHUtUUdQ0NWMGpSg5+XlYeTIkVrvt/SCS5oxYwYOHToEEfFASV6X7RIeffTRsGlS0MNGFjRDqV2GxTs0WS+Q3vYyJ8qdoaWdgh45/oFKokCSr7UErC2dgq6Pb21rNyno+q5b1Sj2oKu5UdDVjCjoFHR1LVFHUNDVjBqUoO/cuRMTJkzAokWL0Lx5c+2zf/DBB1iwYAFmz54dVNC//fZbDBkyRNv/ccSIERg4cKBPbE5Ojl9eic3Ozg55BURwSktLYbNZOBHbXB2o1bllq7cSO1Bc7lox3vXH9W9ZoE4EXfja7fZa/Tnq6slJ/S0sLIRTlvBnijgBPgCJOFJPgVFRURjQPh3pjnzTB2nSpInpMmpzAbWt3QzGSq6Dqs2tzZytPjcRdBn9V1BQYPWh6mz5FHT1pWvUqBHy8/PhcERoLqL6kHUqQrZZk3okDzaZghOo7+2m1de+Tq3ivnbtWqxfvz4gE5HlsWPHau9fd911WLp0KdLT07XYefPmYdasWVi4cGHAvPK6zFePj4/H5s2bsXz5ckyfPh1nnXWWJ/7MM8/0yytSrxIXuUlUxVh9ketr+W55LyyzQxamkz/e8l5fPzc/FwmQgD4C3ZrFon1atL7gEFHyO15XU11sN4OxZnuqroVkpGbEiNAEWIfUNYSM9DFSRzEiaHvnrEP2+OOPP2LVqlUBP4sMpbjmmmtgpCegaoEPPvggpMf8pZdeUtYcDnFXIrI0QJ70Sm9B1R70MrsTJfYolEiPu9YDDxRrAl+928BZ+uGroXD28FoLmXyt5csh7kBdbDeD1Qqu4h76+8Ih7urfE/agqxlxiHtoRtKDLm23TI1lCk6AQ9zN1Y461YOu56MGmksnveFHjhwJOge9arkzZ87EunXrtF53VaKgqwhZ+34wQQ91VJvdiWLKu64LQ4HUhclwEPkaRqcrIwVdFyZtqGbVtVtqst2koOu7blWjKOhqbhR0NSMKOgVdXUvUERR0NaNQEfVO0OXD3nzzzZD9xwOt4i69rVOmTNFWdj/nnHM0NiLkssJ7hw4dsH37du39yZMna0PlVYmCriJk7ftGBF0t73D1vLt7322u3nhbA5yORYG0tv6Sr7V8Kej6+damdpOCrv+6eUdS0NXcKOhqRhR0Crq6lqgjKOhqRg1O0Hfv3q3tg75//37ts48ePRr33XcfpPGSISkyn/zuu+/G+PHjtfdF1mXuuTtJvGzLlpiYqKRLQVcisjQg0oIe6mTtDqBU/sjCdeWyl7sMoXdq/3f92wGHYtV5S2FYUDgF0gKoXkWSr7V8Kej6+damdpOCrv+6UdDDY0VBV/OioFPQ1bVEHUFBVzNqcILu/sCHDx/WVlqUP6okPeuyOmyLFi10ibm7PAq6iqy171enoOv5JDJ83uaMckl7xZ9Su8g8UCoy73D1xNeVRdEpkHquuvEY8jXOTk9OCroeSr4xtaHdpKCHf90kB3vQ1dwo6GpGFHQKurqWqCMo6GpGDVbQzaHRl5uCro+TVVG1TdD1fk63sGsyX9Eb7/rbJfYi9TZHza8cTYHUe0WNxZGvMW56c1HQ9ZKq3jhVu0lBN3Y9KOhqbhR0NSMKOgVdXUvUERR0NSMKujlGIXOrbjRkLnxJSQn3QbfoGtRVQdeDQ3rZ3SJf5hF5EXjpoXd6RN5u4bB6CqSeK2U8hnyNs9OTk4Kuh1L1x6jaTQq6sWtCQVdzo6CrGVHQKejqWqKOoKCrGVHQzTGioFvIz2zR9VnQ9bKxOyqG1Vf0xLv2h3f1wMu8ePlbhN7I/HgKpN6rYCyOfI1x05uLgq6XVPXGUdCt4U1BV3OloKsZUdAp6Opaoo6goKsZUdDNMaKgW8jPbNEUdP0Ey2X4fMVceNdQ+iiUOZyQxe/siEK53YlyJ7T/u/9OSknV9plnsoYABd0aru5SKejW8jVaOgXdKLnQ+Sjoaq4UdDUjCjoFXV1L1BEUdDUjCro5RhR0C/mZLZqCbpZg6PxNmjRB1rEcaHJvd0CG04u8y2J4dvlbZN7uEnqJ0f64/+2sO4vhWUsxeOkUdGvJU9Ct5Wu0dAq6UXIUdLPkKOhqghR0Crq6lqgjKOhqRhR0c4wo6BbyM1s0Bd0sQbWg5+TkwGlw2fly6aF3ulaul3+Xi+BLj70m/E5fofd6XyTf4CGtBRLh0inoEQZapTgKurV8jZZOQTdKjoJulhwFXU2Qgk5BV9cSdQQFXc2Igm6OEQXdQn5mi6agmyVoraCbOTvpoReZt4m4a4Ivcu8v/NKbXyn/rocAdUXuKehmaog6LwVdzagmIijo1lDnEHc1Vwq6mhEFnYKuriXqCAq6mhEF3RwjCrqF/MwWTUE3S7D2CrqZTxZK7l3z652axNudTjgRBUdFj70D0P6t/R9RWpyU5XC65uo7ENmt7yjoZq6yOi8FXc2oJiIo6NZQp6CruVLQ1Ywo6BR0dS1RR1DQ1Ywo6OYYUdAt5Ge2aAq6WYL1U9CtpOISe5F1wKFJu2vIvki8CLxIvSb5Ff/2Fn2nO1YeCjicSElLR3ZuLpzOKJ9yXA8FoiDxTMYJUNCNs7MyJwXdGroUdDVXCrqaEQWdgq6uJeoICrqaEQXdHCMKuoX8zBZNQTdLkIJuLcHQpUsDppIVbZV9r55+Td7dPftwDeeX17QYZ5T2t6u33z0aQEYIVL4msZV53P82tg1fTbLTc2wKuh5K1R+jqvPBzkjP96X6P03tOSIFXX0tKOhqRhR0Crq6lqgjKOhqRhR0c4wo6BbyM1s0Bd0sQQq6tQTNC3p1n5/M93cN8a8c7i+C75oK4Prb/Z6IvjZywCteztc9bcD9nrxWOXXA9W/v19zlul/zHKuifPf/ZVRBOImCHg6t6ouloFvDmoKu5kpBVzOioFPQ1bVEHUFBVzOioJtjREG3kJ/ZoinoZglS0K0lWPcEvSZ5hHNs1zQA14ME14MC74cK8moUWjdrjKL8nHCKDRjLGw3TCH0KoKBHlqe7NAq6misFXc2Igk5BV9cSdQTbTTUjCro5RhR0C/mZLZqCbpYgBd1aghT0muIbFRUFudHMzs42fQq80TCNkIIeWYQBS6OgqyFT0NWMKOgUdHUtUUew3VQzoqCbY0RBt5Cf2aIp6GYJUtCtJUhBrym+FPSaIq8+LnvQ1YyMRFDQ1dQo6GpGFHQKurqWqCMo6GpGFHRzjCjoFvIzWzQF3SxBCrq1BCnoNcWXgl5T5NXHpaCrGRmJoKCrqVHQ1Ywo6BR0dS1RR1DQ1Ywo6OYYUdAt5Ge2aAq6WYIUdGsJUtBrii8FvabIq49LQVczMhJBQVdTo6CrGVHQKejqWqKOoKCrGVHQzTGioFvIz2zRFHSzBCno1hKkoNcUXwp6TZFXH5eCrmZkJIKCrqZGQVczoqBT0NW1RB1BQVczoqCbY0RBt5Cf2aIp6GYJUtCtJUhBrym+FPSaIq8+LgVdzchIBAVdTY2CrmZEQaegq2uJOoKCrmZEQTfHiIJuIT+zRVPQzRKkoFtLkIJeU3wp6DVFXn1cCrqakZEICrqaGgVdzYiCTkFX1xJ1BAVdzYiCbo4RBd1CfmaLpqCbJUhBt5YgBb2m+FLQa4q8+rgUdDUjIxEUdDU1CrqaEQWdgq6uJeoICrqaEQVdJyOHwwGn04mYmBidOQDVjUZaWhpKSkpgs9l0l8lA/QQo6PpZGYls0qQJcnJytO8FU+QJSAOm+g2J/FEbRokU9Oq5zla0m8HOnN+X0NeUgq6u8xR0NSMKOgVdXUvUERR0NSMKug5GIiBPPPGEFnn//ffryOEKUd1cU9B1ozQUSEE3hE13Jgq6blSGAikchrDpykRB14XJVJBV7SYF3dhloaCruVHQ1Ywo6BR0dS1RR1DQ1Ywo6ApGS5YswdNPP43s7GyMHTuWgm6uTlVrbgq6tbgp6NbypaBbx5eCbh1bKdnKdpOCbuzaUdDV3CjoakYUdAq6upaoIyjoakYUdAWj4uJi5OXlYebMmUhISKCgm6tT1Zqbgm4tbgq6tXwp6NbxpaBbx1ZKtrLdpKAbu3YUdDU3CrqaEQWdgq6uJeoICrqaEQVdJ6Pp06ejvLycgq6TV20Io6BbexUo6NbypaBbx5eCbh1b75KtaDcp6MauHQVdzY2CrmZEQaegq2uJOoKCrmbUYAV97dq1WL9+fcDPLz9AMpxd743G9ddf71fOrFmzNKEPlWTBOfciOuYuFXMHIiB87XY74VhEIDY2VlnHLTp0gyiWfK29zJHiK+U0lFQb2s1grCN1PevrtZSHUtHR0WwTQ1xg4SNrJ3Dh0+CQeF+l/oUgIzWjhtRuqmmEHxHlrMe/Uj/++CNWrVoVkIo82bnmmmt0C/q6dev8yhkwYIA2ND5USk5ORllZGSUn/LqpK0dqaqo21JKSrgtX2EGyyGFBQQFvZsImpy9Denq68jdEX0mMqkpAZEV+H/Lz803DkevUUFJtaDeDseb3JXQtlBvi+Ph4FBUVNZTqGvbnTEpK0u4X5L6MKTABafcLCwu1ziUmfwIi51KP5N6IKTiBhtRuWlEP6rWghwvMiqF6XMU93KsQXjyHuIfHK9xoDnEPl1h48RziHh6vcKI5xD0cWsZjrWg3g50Nvy+hrxOHuKvrMYe4qxlxiHtoRiLo8vA3NzdXDbMBR3CIu7mLT0EHtKep8qRQVnKXIev33Xefthe6DIVSJW6zpiJk7fsUdGv5UtCt5UvhsI4vBd06tlKyle0mBd3YtaOgq7lR0NWMKOgUdHUtUUdQ0NWMQkVQ0AHMnTsXTz31lA+nBx54AGPGjFHSpaArEVkaQEG3FC8o6NbypaBbx5eCbh1bKdnKdpOCbuzaUdDV3CjoakYUdAq6upaoIyjoakYUdHOMQuamoFsIV0fRFHQdkEyEUNBNwNORlYKuA5LBEAq6QXDVkE3VblLQjV0ECrqaGwVdzYiCTkFX1xJ1BAVdzYiCbo4RBd1CfmaLpqCbJRg6PwXdWr4UdOv4UtCtY2u2ZAq6WYKB81PQ1Vwp6GpGFHQKurqWqCMo6GpGFHRzjJibBEiABEiABEiABEiABEiABEiABCwnwDnoFiOeNm0aLr/8cpxyyikWH6lhFj9p0iQ89NBD6N69e8MEYPGnPvfcc/Hee++hefPmFh+pYRY/ePBg/Pbbbw3zw1v8qY8ePar99i5evNjiI7H46iIg7eiyZcuQkJBQXYesU8f5+eeftd/rmTNn1qnzrs6TlfWGunbtinHjxlXnYevUsUaNGoX//e9/aNWqVZ067+o62S1btuCJJ57A7Nmzq+uQde44r7/+urY974033ljnzr22nDAF3eIrQUG3FjAF3Vq+FHRr+VLQreNLQbeObU2VTEEPTZ6Crq6ZFHQ1Iwp6aEYUdHUdoqCrGakiKOgqQibfp6CbBKjITkG3li8F3Vq+FHTr+FLQrWNbUyVT0CnoZuseBV1NkIJOQVfXktARFHSzBAEKunmGIUugoFsLmIJuLV8KurV8KejW8aWgW8e2pkqmoFPQzdY9CrqaIAWdgq6uJRR0s4xU+SnoKkIm36egmwTIHnRrASpKp6Bbi5+Cbh1fCrp1bGuqZAo6Bd1s3aOgqwlS0Cno6lpCQTfLSJWfgq4ixPdJgARIgARIgARIgARIgARIgARIoBoIUNCrATIPQQIkQAIkQAIkQAIkQAIkQAIkQAIqAhR0FSG+TwIkQAIkQAIkQAIkQAIkQAIkQALVQICCXg2QeQgSIAESIAESIAESIAESIAESIAESUBGgoKsImXw/KysLqampSExMNFkSswsBh8MBp9OJmJgYPyDy3pEjR9CsWTPExsYSWJgESkpKkJ2djVatWiE6Otovd1lZGXJyctCiRQtERUWFWTrDS0tLIb8HycnJaNKkCflWc5Vg/a1m4BE+HH/fIwy0gRQn9wt2uz3oPQHv0ULfVzWQahLyY+bl5UHab7n3CZQKCgpQXl6Oxo0bN0hc8tnleyT3NQkJCQ2SgRUfmoJuBVUA+/btwy233IK9e/dqRxgzZgzuvfdeiqMJ3tLQPvHEE1oJ999/v09JP/zwA/71r3+hqKhIe13+fckll5g4WsPKevvtt+O7777TPrT8yF544YX4xz/+of1fuM+aNQv/+c9/PO8/99xz6Nu3b8OCZOLTPvroo/jiiy88JQwYMADPPPOM1qCTrwmwVbIeOHAAl112GcaPHw/ZQYP1N3Jsa7Ik/r6Hpi8PVs8++2y/oFdffRUnnXRSTV66Gj/2woULMXPmTMjf3on3aC4awe6rWKegSecNN9zguY/v1KkT/vrXv0JWuZck95sPPPCA596pT58+WrvevHnzGq/31XUCb731lvb9cqezzjpLc51GjRppL8m94vvvv+9zOv3798cbb7xRXadYZ49DQbfo0k2dOlXrOX/44YeRmZmJq6++Gvfcc4/ni23RYettsUuWLMHTTz+t9fCOHTvWR9Cl5/ecc87BjTfeiIkTJ+L777/HnXfeic8//xxt27att0wi+cFee+01yA9ru3bt8Ouvv+K2226D/PBKg7N+/Xpcd911mqT37t0bctO3aNEizJ8/P2BPeyTPq76UJY2RbBHVtWtXHDx4ENdeey0uv/xy7W/yjcxVll4MuXnatWsXJpYd6jIAAAxOSURBVE+e7BF08o0M35oqhb/vavLHjh3T2sCXXnpJ+w13p5YtWzbY0Xsi4HIftn//fgiHqoLOezQg1H0V6xS0EZlffvklLrjgAqSkpGiiOXv2bCxevFj7Xsk90qeffqrdGyUlJWmdcscff7wm7Q0lyeeX3xzpsMnIyMBNN92ktb9XXnmlhuDZZ5/VXpd7SneSXnYZqckUmgAF3YIaIsNhRo4cqT0hkidFkmbMmIFDhw5plZUpfALFxcUQrvKkTr7c3j3o0rty66234qeffkJ8fLxWuPSeT5gwQetNYwqfgDwhHjdunCaQctO3bds2vPLKK1pB0midf/75WkPVo0eP8Atv4DlsNpv2oG7KlCm49NJLyTcC9UGG2P3zn//UGv38/HztwZy7B531NwKAa7AI/r6r4btl6pNPPtEEgQnakOOjR49qvZsiUt6Czns0Vw0JdV/FOuX/LZKHPTIaVoRcRsGJhErHxjXXXKMFywMP6YiTTo6GOg3wscce0x6KSaePJHGe3NxcyChCpvAIUNDD46UreufOnZocSi+je6jLBx98gAULFmhSw2ScwPTp07WG11vQ582bp3GVv91Jhmx37NjRM0zb+BEbXk6ZliEPOF544QUMHz5cmy4gQ7HvuusuD4zBgwd73m94hIx9YpkD/c4770CEQ34XZHSNjLIhX2M8vXPJsMI//vgDL7/8stZ74S3o5Gueb02WwN93NX23TI0YMUIbWtqlSxeMHj0a6enp6sz1PEJ6O6Ut8xZ03qP5XvRA91WsU/5fDJmmJqL5zTffaFMBTzvtNDz44IOapEvaunWrJu1Lly5tkN89uTe/6KKLcN5553nuvUXQv/32WwwZMkS7j5TfqIEDB9bzX53IfDwKemQ4+pTiHlLp/SWVmwx56lZ1mJUFh6/XRQZqSOTpuDy59H74ITflMiTpvvvuq9c8Iv3hCgsLteHsIo4y51wW45OhgN26dfN52CENkzCWH2ImfQRkqK5I+fbt27XFZh555BG0bt2afPXhCxr18ccfa999efghciI9GN6CzvprEnANZ+fvu/oCyPSOf//739rvivxbREIWS5XvhHtUmbqU+hkRSNB5j6YWdNYpX0byAFhGFE6aNEkb/SZz92V9B3dHhkS7H/zI9D9p2xtaevzxx7Xh/3PnzvUsqCfOI50+8ju0efNmLF++HHIf736o0dAYhfN5Kejh0NIZy6ezOkEZCGMPugFoOrOIQN5xxx3aVIz//ve/nhVJRcTlabHM63cn9qDrhBogTBp2kUYZji1P38nXOEvJKUMO27dvj86dO2sFyQ1AWloazjzzTG1OOvma41vTudmDHv4V2L17tzZFyb2OSPgl1J8c7EFXX8tA91VVczXkOiWLj15//fUYNGiQ9pDdvYuQdFQ89NBDWlvz/+3dy00kMRQF0NqwIQgCIAciIgAiQCzIgA0iDCJgy44FIgGiGF2PLPW0+vMQ/cSIOpZmMdIbozldVPuWy3bammfQHx4elvx5enoaexXtaxnz5DSgLD3TDgsI6A1XyK71TbkBZu2uNejfA9/1RTLXKL68vCxnZ2fjB2TQnk24rEGveWfdbsJ51qTlxrl5XEj+nlnfuVOnNeg100NV9/f3SwY8WdfP93ueWXebe+5smb3IA6Xsk5CQwvd7vj/9r93fv/4J5E2ovEqadaB5mLrmtiugG6P9e0VUAvpar6mPj48xY57fp7ydtXmErzXof4/oy3dsHqQmoB/blyjjyNfX1/FGsSag/8g1cH19PWZx7OJ+Gv6cY5obQXZyzzqXvLqep5g5rzuh8urqamwSlbX/dnH/mnn8cspAjOObpQFpsc1rWpu7YGdX94TK5+dnu7gXmfOq4OPj4zi6Lrudvr29Lbk/ZKfTLCfgW4Qslm2/4s63CPeflrm/H/9g8hAjb0BlnWcCRF53z2vueVi11nXoeVMpY4WsF04oyKku2bhrBixjtGV85+8bV7mmluX9/X1M9GQpX0J6xkRp5+fnYxLDLu7LWJOfe01C+uYGlTk5Ib9r+d3LprgXFxdjoieOc+xz/M627goz6E2ff2bHcuRCdjNMy4YtCZVzhrfpx/7abrOm5e7u7p//XzaDykx5WnZqzcZws93c3IyzkLXjAp+fnzuP/8ssZAY3GehkPfp84pkvp2zGNU8oOP4T1l2RmYccAZjX32bL/SBnheZEAr6nvT62Azrf0/r+RG/u74fVs99NJgNyLnNa7t23t7cjsK+1ZeYzx65utgSFuZu0Mdoy1grvG1e5ppaxnjpLpLbbvI7y3Z5xfR5mpF1eXo63ZLMXxFpaxuAz52z+nzOjnlCeyZ+sPd8c++Q7OsfUaYcFBPTmKyThJzOSc1ay+cetuvs8Dc766dwcPQg5/aWQGZqcQ5+10/NJ8ul/yu/tMV/m2Rk3O7jnzNTtxrf3s+fb69vdu/v7YeHMFuf+kpbvwLUe8/TV69AYbb+Ya6p2NWXJRE5pmac21f7VeqryFmHGjrkvCeb1z11Ar1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAgJ6G62OCRAgQIAAAQIECBAgQIBAXUBAr1upJECAAAECBAgQIECAAAECbQICehutjgkQIECAAAECBAgQIECAQF1AQK9bqSRAgAABAgQIECBAgAABAm0CAnobrY4JECBAgAABAgQIECBAgEBdQECvW6kkQIAAAQIECBAgQIAAAQJtAn8AwX2Yr2drHBwAAAAASUVORK5CYII="
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eda.plot_model(plot=\"diagnostics\", fig_kwargs={\"height\": 800, \"width\": 1000})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d9a7716",
+   "metadata": {},
+   "source": [
+    "For our example, diagnostic plot also generates other diagrams such as histogram and Q-Q plots. Histogram for weather data analysis can provide valuable insights into the distribution and frequency of weather-related variables over a specific period (e.g., temperature). Q-Q plot can be used to evaluate whether variables such as temperature follow a normal distribution or another theoretical distribution, helping to validate assumptions for statistical modeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5121f73f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/topic/timeseries/requirements.txt b/topic/timeseries/requirements.txt
new file mode 100644
index 00000000..8ab68b1d
--- /dev/null
+++ b/topic/timeseries/requirements.txt
@@ -0,0 +1,3 @@
+pycaret>=3.0
+refinitiv-data
+crate[sqlalchemy]==0.34.0
diff --git a/topic/timeseries/time-series-decomposition.ipynb b/topic/timeseries/time-series-decomposition.ipynb
new file mode 100644
index 00000000..c6e88764
--- /dev/null
+++ b/topic/timeseries/time-series-decomposition.ipynb
@@ -0,0 +1,1352 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b5838281",
+   "metadata": {},
+   "source": [
+    "# Time series decomposition\n",
+    "\n",
+    "Time series decomposition is a statistical method used to dissect a time series into multiple components, typically including trend, seasonal, and random (or irregular) components. This process helps in understanding the underlying patterns of the time series data, such as identifying any long-term direction (trend), recurring patterns at fixed intervals (seasonality), and randomness (irregular fluctuations) in the data. Decomposition is crucial for analyzing how these components change over time, improving forecasts, and developing strategies for addressing each element effectively.\n",
+    "\n",
+    "\n",
+    "## Components\n",
+    "\n",
+    "**Seasonality**: The seasonal component captures regular patterns that repeat over fixed periods, such as daily, monthly, or quarterly fluctuations. Seasonality is influenced by various factors like weather, holidays, and business cycles.\n",
+    "\n",
+    "**Trend**: The trend component represents the long-term progression of the series, showing how the data moves up or down over a lengthy period. It's essentially the underlying direction in which the data is heading, excluding shorter-term fluctuations.\n",
+    "\n",
+    "**Residual (Irregular Component)**: After the trend and seasonal components are removed from the original time series, the remainder is the residual component. It consists of irregular or random fluctuations that cannot be attributed to the trend or seasonal effects. This component can sometimes reveal unusual events or outliers not explained by the trend or seasonality.\n",
+    "\n",
+    "\n",
+    "## What is PyCaret?\n",
+    "\n",
+    "\n",
+    "PyCaret is an open-source, low-code machine learning (ML) library in Python that aims to reduce the cycle time from hypothesis to insights. PyCaret automates much of the ML workflow, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and model deployment, making it accessible for beginners while still being powerful enough for experts.\n",
+    "\n",
+    "Integrating PyCaret with CrateDB presents a compelling opportunity for handling large-scale data analytics and ML projects. CrateDB is a distributed SQL database that excels in handling massive amounts of structured and unstructured data in real-time. This integration allows users to leverage CrateDB's efficient data storage and fast query capabilities to manage large datasets, while PyCaret's ML algorithms can be applied directly to this data for predictive analytics, anomaly detection, and other advanced analytics tasks.\n",
+    "\n",
+    "By following this notebook you will learn how to extract data from CrateDB for analysis in PyCaret, how to further preprocess it and how to use  PyCaret to plot time series decomposition by breaking it down into its basic components: **trend, seasonality, and residual (or irregular) fluctuations**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ab53421",
+   "metadata": {},
+   "source": [
+    "## Getting started\n",
+    "\n",
+    "To get started with this notebook install necessary dependecies and import required modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69f4989b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install -r requirements.txt\n",
+    "\n",
+    "# Note: If you are running in an environment like Google Colab, please use the absolute path of the requirements:\n",
+    "#!pip install -r https://raw.githubusercontent.com/crate/cratedb-examples/main/topic/timeseries/requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5505a9fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import pandas as pd\n",
+    "import sqlalchemy as sa\n",
+    "import requests\n",
+    "import os\n",
+    "from pycaret.time_series import TSForecastingExperiment\n",
+    "import refinitiv.data as rd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33949dcd",
+   "metadata": {},
+   "source": [
+    "## Load data from CrateDB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "067cd043",
+   "metadata": {},
+   "source": [
+    "The following demonstrates how to connect to a CrateDB database instance using SQLAlchemy. It shows two scenarios: connecting to a local CrateDB instance and connecting to a CrateDB Cloud instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "dc786d26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the connection string to running CrateDB instance.\n",
+    "CONNECTION_STRING = os.environ.get(\n",
+    "    \"CRATEDB_CONNECTION_STRING\",\n",
+    "    \"crate://crate@localhost/\",\n",
+    ")\n",
+    "\n",
+    "# Connect to CrateDB Cloud.\n",
+    "# CONNECTION_STRING = os.environ.get(\n",
+    "#     \"CRATEDB_CONNECTION_STRING\",\n",
+    "#     \"crate://username:password@hostname/?ssl=true&schema=notebook\",\n",
+    "# )\n",
+    "engine = sa.create_engine(CONNECTION_STRING, echo=os.environ.get('DEBUG'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cdae15fa",
+   "metadata": {},
+   "source": [
+    "The next step fetches data from CrateDB and load it into a pandas data frame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "13d08f24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>location</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1672532724000</td>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>20.329405</td>\n",
+       "      <td>90.035633</td>\n",
+       "      <td>7.045718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1672533002000</td>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.485222</td>\n",
+       "      <td>90.233420</td>\n",
+       "      <td>4.534103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1672535400000</td>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>21.331066</td>\n",
+       "      <td>74.014164</td>\n",
+       "      <td>4.347653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1672536651000</td>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>21.034967</td>\n",
+       "      <td>35.431482</td>\n",
+       "      <td>13.160233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1672537835000</td>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.801286</td>\n",
+       "      <td>34.073989</td>\n",
+       "      <td>5.314130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       timestamp  location  temperature   humidity  wind_speed\n",
+       "0  1672532724000    Vienna    20.329405  90.035633    7.045718\n",
+       "1  1672533002000  Dornbirn    20.485222  90.233420    4.534103\n",
+       "2  1672535400000    Vienna    21.331066  74.014164    4.347653\n",
+       "3  1672536651000    Berlin    21.034967  35.431482   13.160233\n",
+       "4  1672537835000  Dornbirn    20.801286  34.073989    5.314130"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"SELECT * FROM weather_data\"\n",
+    "with engine.connect() as conn:\n",
+    "    result = conn.execute(sa.text(query))\n",
+    "    columns = result.keys() # Extract column names\n",
+    "    df = pd.DataFrame(result.fetchall(), columns=columns)\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64e7b60c",
+   "metadata": {},
+   "source": [
+    "Next, we convert the `timestamp` column of the DataFrame df into datetime objects using pandas' `to_datetime` function, making it suitable for time-based operations. It then sets this `timestamp` column as the DataFrame's index, effectively transforming df into a time series DataFrame with a DatetimeIndex, which enables time-based indexing and analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3ec768a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 00:25:24</th>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>20.329405</td>\n",
+       "      <td>90.035633</td>\n",
+       "      <td>7.045718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 00:30:02</th>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.485222</td>\n",
+       "      <td>90.233420</td>\n",
+       "      <td>4.534103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:10:00</th>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>21.331066</td>\n",
+       "      <td>74.014164</td>\n",
+       "      <td>4.347653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:30:51</th>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>21.034967</td>\n",
+       "      <td>35.431482</td>\n",
+       "      <td>13.160233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:50:35</th>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.801286</td>\n",
+       "      <td>34.073989</td>\n",
+       "      <td>5.314130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     location  temperature   humidity  wind_speed\n",
+       "timestamp                                                        \n",
+       "2023-01-01 00:25:24    Vienna    20.329405  90.035633    7.045718\n",
+       "2023-01-01 00:30:02  Dornbirn    20.485222  90.233420    4.534103\n",
+       "2023-01-01 01:10:00    Vienna    21.331066  74.014164    4.347653\n",
+       "2023-01-01 01:30:51    Berlin    21.034967  35.431482   13.160233\n",
+       "2023-01-01 01:50:35  Dornbirn    20.801286  34.073989    5.314130"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n",
+    "df.set_index('timestamp', inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "988ba39e",
+   "metadata": {},
+   "source": [
+    "## Data interpolation\n",
+    "\n",
+    "The interpolation in this context fills missing values in the `temperature`, `humidity`, and `wind_speed` columns of the data frame by using time-weighted interpolation (method=`time`). This method estimates missing values based on the linear time difference between known points, leveraging the DatetimeIndex to account for the actual time intervals between observations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "250d38fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>location</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 00:25:24</th>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>20.329405</td>\n",
+       "      <td>90.035633</td>\n",
+       "      <td>7.045718</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 00:30:02</th>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.485222</td>\n",
+       "      <td>90.233420</td>\n",
+       "      <td>4.534103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:10:00</th>\n",
+       "      <td>Vienna</td>\n",
+       "      <td>21.331066</td>\n",
+       "      <td>74.014164</td>\n",
+       "      <td>4.347653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:30:51</th>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>21.034967</td>\n",
+       "      <td>35.431482</td>\n",
+       "      <td>13.160233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01 01:50:35</th>\n",
+       "      <td>Dornbirn</td>\n",
+       "      <td>20.801286</td>\n",
+       "      <td>34.073989</td>\n",
+       "      <td>5.314130</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     location  temperature   humidity  wind_speed\n",
+       "timestamp                                                        \n",
+       "2023-01-01 00:25:24    Vienna    20.329405  90.035633    7.045718\n",
+       "2023-01-01 00:30:02  Dornbirn    20.485222  90.233420    4.534103\n",
+       "2023-01-01 01:10:00    Vienna    21.331066  74.014164    4.347653\n",
+       "2023-01-01 01:30:51    Berlin    21.034967  35.431482   13.160233\n",
+       "2023-01-01 01:50:35  Dornbirn    20.801286  34.073989    5.314130"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['temperature'] = df['temperature'].interpolate(method='time')\n",
+    "df['humidity'] = df['humidity'].interpolate(method='time')\n",
+    "df['wind_speed'] = df['wind_speed'].interpolate(method='time')\n",
+    "df.fillna(method='bfill', inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee8b72dc",
+   "metadata": {},
+   "source": [
+    "To focus on analysis for a specific city we process the data from Berlin, convert timestamps to datetime format, calculate daily averages, and organize the data frame with a `timestamp` index for further analysis or visualization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "50d5da34",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>wind_speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>timestamp</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2023-01-01</th>\n",
+       "      <td>19.273445</td>\n",
+       "      <td>60.066777</td>\n",
+       "      <td>9.090639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-02</th>\n",
+       "      <td>20.769394</td>\n",
+       "      <td>64.997035</td>\n",
+       "      <td>8.010616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-03</th>\n",
+       "      <td>20.849007</td>\n",
+       "      <td>65.351548</td>\n",
+       "      <td>7.674121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-04</th>\n",
+       "      <td>20.228274</td>\n",
+       "      <td>66.177554</td>\n",
+       "      <td>7.542323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2023-01-05</th>\n",
+       "      <td>20.795721</td>\n",
+       "      <td>64.429477</td>\n",
+       "      <td>7.872682</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            temperature   humidity  wind_speed\n",
+       "timestamp                                     \n",
+       "2023-01-01    19.273445  60.066777    9.090639\n",
+       "2023-01-02    20.769394  64.997035    8.010616\n",
+       "2023-01-03    20.849007  65.351548    7.674121\n",
+       "2023-01-04    20.228274  66.177554    7.542323\n",
+       "2023-01-05    20.795721  64.429477    7.872682"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_berlin = df[(df['location']=='Berlin')]\n",
+    "\n",
+    "# Ensure the index is in datetime format for resampling \n",
+    "df_berlin.index = pd.to_datetime(df_berlin.index)\n",
+    "\n",
+    "# Now aggregate to daily averages\n",
+    "df_berlin_daily_avg = df_berlin.resample('D').mean()\n",
+    "\n",
+    "df_berlin_daily_avg.reset_index(inplace=True)\n",
+    "\n",
+    "# Ensure 'timestamp' column is set as index if it's not already\n",
+    "df_berlin_daily_avg.set_index('timestamp', inplace=True)\n",
+    "\n",
+    "df_berlin_daily_avg.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01475c6a",
+   "metadata": {},
+   "source": [
+    "## Set up the experiment\n",
+    "\n",
+    "Now, we will initialize the modelling environment by setting a few parameters. The `forecast_horizon` parameter is set to 10, indicating the number of days ahead for which forecasts will be made. Subsequently, `fold` is set to 3,  denoting the number of folds or partitions used in cross-validation techniques. We then instantiate a TSForecastingExperiment and initialise the newly created instance using the pre-defined parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2df1c267",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_81c5d_row25_col1 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_81c5d\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_81c5d_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
+       "      <th id=\"T_81c5d_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
+       "      <td id=\"T_81c5d_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
+       "      <td id=\"T_81c5d_row0_col1\" class=\"data row0 col1\" >7223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
+       "      <td id=\"T_81c5d_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
+       "      <td id=\"T_81c5d_row1_col1\" class=\"data row1 col1\" >temperature</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
+       "      <td id=\"T_81c5d_row2_col0\" class=\"data row2 col0\" >Approach</td>\n",
+       "      <td id=\"T_81c5d_row2_col1\" class=\"data row2 col1\" >Univariate</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
+       "      <td id=\"T_81c5d_row3_col0\" class=\"data row3 col0\" >Exogenous Variables</td>\n",
+       "      <td id=\"T_81c5d_row3_col1\" class=\"data row3 col1\" >Present</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
+       "      <td id=\"T_81c5d_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n",
+       "      <td id=\"T_81c5d_row4_col1\" class=\"data row4 col1\" >(244, 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
+       "      <td id=\"T_81c5d_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n",
+       "      <td id=\"T_81c5d_row5_col1\" class=\"data row5 col1\" >(244, 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
+       "      <td id=\"T_81c5d_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n",
+       "      <td id=\"T_81c5d_row6_col1\" class=\"data row6 col1\" >(234, 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
+       "      <td id=\"T_81c5d_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n",
+       "      <td id=\"T_81c5d_row7_col1\" class=\"data row7 col1\" >(10, 3)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
+       "      <td id=\"T_81c5d_row8_col0\" class=\"data row8 col0\" >Rows with missing values</td>\n",
+       "      <td id=\"T_81c5d_row8_col1\" class=\"data row8 col1\" >0.0%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
+       "      <td id=\"T_81c5d_row9_col0\" class=\"data row9 col0\" >Fold Generator</td>\n",
+       "      <td id=\"T_81c5d_row9_col1\" class=\"data row9 col1\" >ExpandingWindowSplitter</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
+       "      <td id=\"T_81c5d_row10_col0\" class=\"data row10 col0\" >Fold Number</td>\n",
+       "      <td id=\"T_81c5d_row10_col1\" class=\"data row10 col1\" >3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
+       "      <td id=\"T_81c5d_row11_col0\" class=\"data row11 col0\" >Enforce Prediction Interval</td>\n",
+       "      <td id=\"T_81c5d_row11_col1\" class=\"data row11 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
+       "      <td id=\"T_81c5d_row12_col0\" class=\"data row12 col0\" >Splits used for hyperparameters</td>\n",
+       "      <td id=\"T_81c5d_row12_col1\" class=\"data row12 col1\" >all</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
+       "      <td id=\"T_81c5d_row13_col0\" class=\"data row13 col0\" >User Defined Seasonal Period(s)</td>\n",
+       "      <td id=\"T_81c5d_row13_col1\" class=\"data row13 col1\" >[1, 5, 20]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
+       "      <td id=\"T_81c5d_row14_col0\" class=\"data row14 col0\" >Ignore Seasonality Test</td>\n",
+       "      <td id=\"T_81c5d_row14_col1\" class=\"data row14 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
+       "      <td id=\"T_81c5d_row15_col0\" class=\"data row15 col0\" >Seasonality Detection Algo</td>\n",
+       "      <td id=\"T_81c5d_row15_col1\" class=\"data row15 col1\" >user_defined</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
+       "      <td id=\"T_81c5d_row16_col0\" class=\"data row16 col0\" >Max Period to Consider</td>\n",
+       "      <td id=\"T_81c5d_row16_col1\" class=\"data row16 col1\" >60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
+       "      <td id=\"T_81c5d_row17_col0\" class=\"data row17 col0\" >Seasonal Period(s) Tested</td>\n",
+       "      <td id=\"T_81c5d_row17_col1\" class=\"data row17 col1\" >[1, 5, 20]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
+       "      <td id=\"T_81c5d_row18_col0\" class=\"data row18 col0\" >Significant Seasonal Period(s)</td>\n",
+       "      <td id=\"T_81c5d_row18_col1\" class=\"data row18 col1\" >[5, 20]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
+       "      <td id=\"T_81c5d_row19_col0\" class=\"data row19 col0\" >Significant Seasonal Period(s) without Harmonics</td>\n",
+       "      <td id=\"T_81c5d_row19_col1\" class=\"data row19 col1\" >[20]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
+       "      <td id=\"T_81c5d_row20_col0\" class=\"data row20 col0\" >Remove Harmonics</td>\n",
+       "      <td id=\"T_81c5d_row20_col1\" class=\"data row20 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
+       "      <td id=\"T_81c5d_row21_col0\" class=\"data row21 col0\" >Harmonics Order Method</td>\n",
+       "      <td id=\"T_81c5d_row21_col1\" class=\"data row21 col1\" >harmonic_max</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
+       "      <td id=\"T_81c5d_row22_col0\" class=\"data row22 col0\" >Num Seasonalities to Use</td>\n",
+       "      <td id=\"T_81c5d_row22_col1\" class=\"data row22 col1\" >1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
+       "      <td id=\"T_81c5d_row23_col0\" class=\"data row23 col0\" >All Seasonalities to Use</td>\n",
+       "      <td id=\"T_81c5d_row23_col1\" class=\"data row23 col1\" >[5]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
+       "      <td id=\"T_81c5d_row24_col0\" class=\"data row24 col0\" >Primary Seasonality</td>\n",
+       "      <td id=\"T_81c5d_row24_col1\" class=\"data row24 col1\" >5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
+       "      <td id=\"T_81c5d_row25_col0\" class=\"data row25 col0\" >Seasonality Present</td>\n",
+       "      <td id=\"T_81c5d_row25_col1\" class=\"data row25 col1\" >True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
+       "      <td id=\"T_81c5d_row26_col0\" class=\"data row26 col0\" >Seasonality Type</td>\n",
+       "      <td id=\"T_81c5d_row26_col1\" class=\"data row26 col1\" >mul</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
+       "      <td id=\"T_81c5d_row27_col0\" class=\"data row27 col0\" >Target Strictly Positive</td>\n",
+       "      <td id=\"T_81c5d_row27_col1\" class=\"data row27 col1\" >True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
+       "      <td id=\"T_81c5d_row28_col0\" class=\"data row28 col0\" >Target White Noise</td>\n",
+       "      <td id=\"T_81c5d_row28_col1\" class=\"data row28 col1\" >No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
+       "      <td id=\"T_81c5d_row29_col0\" class=\"data row29 col0\" >Recommended d</td>\n",
+       "      <td id=\"T_81c5d_row29_col1\" class=\"data row29 col1\" >2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
+       "      <td id=\"T_81c5d_row30_col0\" class=\"data row30 col0\" >Recommended Seasonal D</td>\n",
+       "      <td id=\"T_81c5d_row30_col1\" class=\"data row30 col1\" >0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
+       "      <td id=\"T_81c5d_row31_col0\" class=\"data row31 col0\" >Preprocess</td>\n",
+       "      <td id=\"T_81c5d_row31_col1\" class=\"data row31 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
+       "      <td id=\"T_81c5d_row32_col0\" class=\"data row32 col0\" >CPU Jobs</td>\n",
+       "      <td id=\"T_81c5d_row32_col1\" class=\"data row32 col1\" >-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
+       "      <td id=\"T_81c5d_row33_col0\" class=\"data row33 col0\" >Use GPU</td>\n",
+       "      <td id=\"T_81c5d_row33_col1\" class=\"data row33 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
+       "      <td id=\"T_81c5d_row34_col0\" class=\"data row34 col0\" >Log Experiment</td>\n",
+       "      <td id=\"T_81c5d_row34_col1\" class=\"data row34 col1\" >False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
+       "      <td id=\"T_81c5d_row35_col0\" class=\"data row35 col0\" >Experiment Name</td>\n",
+       "      <td id=\"T_81c5d_row35_col1\" class=\"data row35 col1\" >ts-default-name</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_81c5d_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
+       "      <td id=\"T_81c5d_row36_col0\" class=\"data row36 col0\" >USI</td>\n",
+       "      <td id=\"T_81c5d_row36_col1\" class=\"data row36 col1\" >5685</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x284a69de0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pycaret.time_series.forecasting.oop.TSForecastingExperiment at 0x282fd30d0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "forecast_horizon = 10\n",
+    "fold = 3\n",
+    "\n",
+    "eda = TSForecastingExperiment()\n",
+    "eda.setup(data=df_berlin_daily_avg, fh=forecast_horizon, target='temperature', seasonal_period= [1, 5, 20])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00ae4344",
+   "metadata": {},
+   "source": [
+    "After the instantiation, the model reports possible seasonality for 5 and 20 days options. After initial testing, it is reporting the **primary seasonality for 5 days**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "46087227",
+   "metadata": {},
+   "source": [
+    "## Plotting timeseries decomposition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62867669",
+   "metadata": {},
+   "source": [
+    "The default setup with regards to seasonality is the period detected during PyCaret initialisation which would be 5 in our case. We can use the 'decomp' plot parameter to ask for corresponding graphs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "08e97e18",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly\n",
+    "\n",
+    "# Plotly plots will be rendered as PNG images, uncomment the following line to crate an interactive chart\n",
+    "plotly.io.renderers.default = 'png'\n",
+    "eda.plot_model(plot=\"decomp\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01d638ae",
+   "metadata": {},
+   "source": [
+    "We can also pass specific parameters to change the seasonality period to explore other possible underlying components within the time series. For instancem we can check the classical decomposition for other sesonal period."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7c0d1dcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eda.plot_model(plot=\"decomp\", data_kwargs={'seasonal_period': 20})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "988a2553",
+   "metadata": {},
+   "source": [
+    "PyCaret allows for plotting STL (seasonal and trend decomposition using Loess) decomposition used also to estimate both the trend and seasonal components. Compared to classical decomposition, this method is more flexible, allowing for the seasonal component to change over time and the trend to be more smoothly adjusted to the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "6c3e5831",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "eda.plot_model(plot=\"decomp_stl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "117e29e4",
+   "metadata": {},
+   "source": [
+    "Observations:\n",
+    "\n",
+    "- There is a **downward trend** and **seasonality** present in the series based on decompositions results.\n",
+    "- The residuals in classical decompositions are also interesting, showing periods of **high variability**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a0c6bd1",
+   "metadata": {},
+   "source": [
+    "# Summary statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99c82aa5",
+   "metadata": {},
+   "source": [
+    "In PyCaret you can use `check_stats` function to get summary statistics and run statistical tests on the original data or model residuals. You can perform the following tests:\n",
+    "- _summary_ - Summary Statistics\n",
+    "- _white_noise_ - Ljung-Box Test for white noise\n",
+    "- _adf_ - ADF test for difference stationarity\n",
+    "- _kpss_ - KPSS test for trend stationarity\n",
+    "- _stationarity_ - ADF and KPSS test\n",
+    "- _normality_ - Shapiro Test for Normality\n",
+    "- _all_ - All of the above tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "0e6f2004",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Test</th>\n",
+       "      <th>Test Name</th>\n",
+       "      <th>Data</th>\n",
+       "      <th>Property</th>\n",
+       "      <th>Setting</th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Length</td>\n",
+       "      <td></td>\n",
+       "      <td>244.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td># Missing Values</td>\n",
+       "      <td></td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Mean</td>\n",
+       "      <td></td>\n",
+       "      <td>23.949556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Median</td>\n",
+       "      <td></td>\n",
+       "      <td>25.05107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Standard Deviation</td>\n",
+       "      <td></td>\n",
+       "      <td>5.077964</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Variance</td>\n",
+       "      <td></td>\n",
+       "      <td>25.785721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Kurtosis</td>\n",
+       "      <td></td>\n",
+       "      <td>-0.947567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Skewness</td>\n",
+       "      <td></td>\n",
+       "      <td>-0.505172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Summary</td>\n",
+       "      <td>Statistics</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td># Distinct Values</td>\n",
+       "      <td></td>\n",
+       "      <td>244.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>White Noise</td>\n",
+       "      <td>Ljung-Box</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Test Statictic</td>\n",
+       "      <td>{'alpha': 0.05, 'K': 24}</td>\n",
+       "      <td>3877.999384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>White Noise</td>\n",
+       "      <td>Ljung-Box</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Test Statictic</td>\n",
+       "      <td>{'alpha': 0.05, 'K': 48}</td>\n",
+       "      <td>4913.892275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>White Noise</td>\n",
+       "      <td>Ljung-Box</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>p-value</td>\n",
+       "      <td>{'alpha': 0.05, 'K': 24}</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>White Noise</td>\n",
+       "      <td>Ljung-Box</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>p-value</td>\n",
+       "      <td>{'alpha': 0.05, 'K': 48}</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>White Noise</td>\n",
+       "      <td>Ljung-Box</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>White Noise</td>\n",
+       "      <td>{'alpha': 0.05, 'K': 24}</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>White Noise</td>\n",
+       "      <td>Ljung-Box</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>White Noise</td>\n",
+       "      <td>{'alpha': 0.05, 'K': 48}</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>ADF</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>ADF</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>p-value</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.962989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>ADF</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Test Statistic</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.056851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>ADF</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Critical Value 1%</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>-3.457664</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>ADF</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Critical Value 5%</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>-2.873559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>ADF</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Critical Value 10%</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>-2.573175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>KPSS</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Trend Stationarity</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>KPSS</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>p-value</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>KPSS</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Test Statistic</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.558277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>KPSS</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Critical Value 10%</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>KPSS</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Critical Value 5%</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>KPSS</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Critical Value 2.5%</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Stationarity</td>\n",
+       "      <td>KPSS</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Critical Value 1%</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Normality</td>\n",
+       "      <td>Shapiro</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>Normality</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Normality</td>\n",
+       "      <td>Shapiro</td>\n",
+       "      <td>Transformed</td>\n",
+       "      <td>p-value</td>\n",
+       "      <td>{'alpha': 0.05}</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Test   Test Name         Data             Property  \\\n",
+       "0        Summary  Statistics  Transformed               Length   \n",
+       "1        Summary  Statistics  Transformed     # Missing Values   \n",
+       "2        Summary  Statistics  Transformed                 Mean   \n",
+       "3        Summary  Statistics  Transformed               Median   \n",
+       "4        Summary  Statistics  Transformed   Standard Deviation   \n",
+       "5        Summary  Statistics  Transformed             Variance   \n",
+       "6        Summary  Statistics  Transformed             Kurtosis   \n",
+       "7        Summary  Statistics  Transformed             Skewness   \n",
+       "8        Summary  Statistics  Transformed    # Distinct Values   \n",
+       "9    White Noise   Ljung-Box  Transformed       Test Statictic   \n",
+       "10   White Noise   Ljung-Box  Transformed       Test Statictic   \n",
+       "11   White Noise   Ljung-Box  Transformed              p-value   \n",
+       "12   White Noise   Ljung-Box  Transformed              p-value   \n",
+       "13   White Noise   Ljung-Box  Transformed          White Noise   \n",
+       "14   White Noise   Ljung-Box  Transformed          White Noise   \n",
+       "15  Stationarity         ADF  Transformed         Stationarity   \n",
+       "16  Stationarity         ADF  Transformed              p-value   \n",
+       "17  Stationarity         ADF  Transformed       Test Statistic   \n",
+       "18  Stationarity         ADF  Transformed    Critical Value 1%   \n",
+       "19  Stationarity         ADF  Transformed    Critical Value 5%   \n",
+       "20  Stationarity         ADF  Transformed   Critical Value 10%   \n",
+       "21  Stationarity        KPSS  Transformed   Trend Stationarity   \n",
+       "22  Stationarity        KPSS  Transformed              p-value   \n",
+       "23  Stationarity        KPSS  Transformed       Test Statistic   \n",
+       "24  Stationarity        KPSS  Transformed   Critical Value 10%   \n",
+       "25  Stationarity        KPSS  Transformed    Critical Value 5%   \n",
+       "26  Stationarity        KPSS  Transformed  Critical Value 2.5%   \n",
+       "27  Stationarity        KPSS  Transformed    Critical Value 1%   \n",
+       "28     Normality     Shapiro  Transformed            Normality   \n",
+       "29     Normality     Shapiro  Transformed              p-value   \n",
+       "\n",
+       "                     Setting        Value  \n",
+       "0                                   244.0  \n",
+       "1                                     0.0  \n",
+       "2                               23.949556  \n",
+       "3                                25.05107  \n",
+       "4                                5.077964  \n",
+       "5                               25.785721  \n",
+       "6                               -0.947567  \n",
+       "7                               -0.505172  \n",
+       "8                                   244.0  \n",
+       "9   {'alpha': 0.05, 'K': 24}  3877.999384  \n",
+       "10  {'alpha': 0.05, 'K': 48}  4913.892275  \n",
+       "11  {'alpha': 0.05, 'K': 24}          0.0  \n",
+       "12  {'alpha': 0.05, 'K': 48}          0.0  \n",
+       "13  {'alpha': 0.05, 'K': 24}        False  \n",
+       "14  {'alpha': 0.05, 'K': 48}        False  \n",
+       "15           {'alpha': 0.05}        False  \n",
+       "16           {'alpha': 0.05}     0.962989  \n",
+       "17           {'alpha': 0.05}     0.056851  \n",
+       "18           {'alpha': 0.05}    -3.457664  \n",
+       "19           {'alpha': 0.05}    -2.873559  \n",
+       "20           {'alpha': 0.05}    -2.573175  \n",
+       "21           {'alpha': 0.05}        False  \n",
+       "22           {'alpha': 0.05}         0.01  \n",
+       "23           {'alpha': 0.05}     0.558277  \n",
+       "24           {'alpha': 0.05}        0.119  \n",
+       "25           {'alpha': 0.05}        0.146  \n",
+       "26           {'alpha': 0.05}        0.176  \n",
+       "27           {'alpha': 0.05}        0.216  \n",
+       "28           {'alpha': 0.05}        False  \n",
+       "29           {'alpha': 0.05}          0.0  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check statistical tests on data\n",
+    "result=eda.check_stats()\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fda3641e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}