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+# Lab-P13: SQL Databases and Plotting
+
+In this lab, you'll learn to use SQL queries to extract data from a database. You will also write various plotting functions to visualize the extracted data.
+
+-----------------------------
+## Corrections/Clarifications
+
+
+**Find any issues?** Please report to us:
+
+- Ashwin Maran <amaran@wisc.edu>
+- Brandon Tran <bqtran2@wisc.edu>
+
+## Learning Objectives:
+
+## Learning Objectives
+
+In this lab, you will practice how to:
+
+* use SQL queries to extract data from a database,
+* use different SQL keywords to organize data,
+* write different plotting functions to visualize data and engage in data exploration.
+
+------------------------------
+
+## Note on Academic Misconduct
+
+You may do these lab exercises only with your project partner; you are not allowed to start
+working on Lab-P13 with one person, then do the project with a different partner. Now may be a
+good time to review [our course policies](https://cs220.cs.wisc.edu/s23/syllabus.html).
+
+**Important:** P12 and P13 are two parts of the same data analysis.
+You **cannot** switch project partners between these two projects.
+If you partnered up with someone for P12, you have to sustain that partnership until the end of P13.
+
+------------------------------
+
+## Segment 0: Setup
+
+Unlike previous labs, you will **not** be working on an Otter notebook in this lab. Most importantly, this means that `practice_test.py` will **not** be provided to you. There will be `assert` statements in your `practice.ipynb` notebook to guide you, but they will **not** be comprehensive. Instead, if you come across any syntactical or semantic errors, you will have to debug your code by yourself. Feel free to reach out to your TA or PM if you get stuck anywhere. and you will instead learn how to test your code by yourself.
+
+You **will** however be provided with a `p13_test.py` for the project.
+
+First, create a `lab-p13` directory and download the `practice.ipynb` file into the directory.
+
+## Segments 1-3: Web Requests, Caching, DataFrames and Scraping
+
+For the remaining segments, detailed instructions are provided in `practice.ipynb`. From the terminal, open a `jupyter notebook` session, open your `practice.ipynb` and follow the instructions in `practice.ipynb`.
+
+## Project 13
+
+You can now get started with [P13](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/p13). **You may copy/paste any code created here in project P13**. Remember to only work on P13 with your partner from this point on. Have fun!
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+# Images
+
+Images from lab-p13 are stored here.
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115889c5",
+   "metadata": {},
+   "source": [
+    "# Lab-P13: Analyzing World Data with SQL\n",
+    "\n",
+    "In this lab, you will practice how to:\n",
+    "\n",
+    "* write SQL queries,\n",
+    "* create your own plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daed65a3",
+   "metadata": {},
+   "source": [
+    "# Segment 1: Setup\n",
+    "\n",
+    "### Task 1.1: Import the required modules\n",
+    "\n",
+    "We will first import some important modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e59b7bdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n",
+    "\n",
+    "import sqlite3\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "import math\n",
+    "import numpy as np # this is *only* for the function get_regression_coeff - do NOT use this module elsewhere"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97a3f1e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this ensures that font.size setting remains uniform\n",
+    "%matplotlib inline \n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "matplotlib.rcParams[\"font.size\"] = 13 # don't use value > 13! Otherwise your y-axis tick labels will be different."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75adca21",
+   "metadata": {},
+   "source": [
+    "### Task 1.2: Use the `download` function to download `QSranking.json`\n",
+    "\n",
+    "Warning: For the lab and the project, do **not** download the dataset `QSranking.json` manually (you **must** write Python code to download this, as in P12). When we run the autograder, this file `QSranking.json` will not be in the directory. So, unless your `p13.ipynb` downloads this file, you will get a **zero score** on the project. Also, make sure your `download` function includes code to check if the file already exists. The Gradescope autograder will **deduct points** otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bb742ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# copy the definition of your 'download' function from P12 here - remember to import the necessary modules\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe96e53b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/raw/main/p13/QSranking.json'\n",
+    "# to the file 'QSranking.json'\n",
+    "\n",
+    "download(\"https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/raw/main/p13/QSranking.json\", \"QSranking.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0023581a",
+   "metadata": {},
+   "source": [
+    "### Task 1.3: Create a database called 'rankings.db' out of 'QSRankings.json'\n",
+    "\n",
+    "You can review the relevant lecture code [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Michael_lecture_notes/32_Database-1) and [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Gurmail_lecture_notes/32_Database-1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "270d8da5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a database called 'rankings.db' out of 'QSranking.json'\n",
+    "\n",
+    "# TODO: load the data from 'QSranking.json' into a variable called 'qs_ranking' using pandas' 'read_json' function\n",
+    "# TODO: connect to 'rankings.db' and save it to a variable called 'conn'\n",
+    "\n",
+    "# write the contents of 'qs_ranking' to the table 'rankings' in the database\n",
+    "# we have done this one for you\n",
+    "qs_ranking.to_sql(\"rankings\", conn, if_exists=\"replace\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84a77c79",
+   "metadata": {},
+   "source": [
+    "### Task 1.4: Read all the rows in rankings (the database table)\n",
+    "\n",
+    "You'll have to use pandas's `read_sql` function to make a query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a300adde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rankings', display its head\n",
+    "# remember to display ONLY the head and NOT the whole DataFrame\n",
+    "# replace the ... with your code\n",
+    "\n",
+    "rankings = pd.read_sql(\"SELECT ... FROM ...\", conn)\n",
+    "rankings.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e4d16ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(rankings) == 1201\n",
+    "assert rankings.iloc[0][\"country\"] == \"United States\"\n",
+    "assert rankings.iloc[-1][\"institution_name\"] == \"Wake Forest University\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b09ee5a",
+   "metadata": {},
+   "source": [
+    "# Segment 2: SQL Practice\n",
+    "\n",
+    "In practice, we often are more interested in writing more specific queries about our data. For example, we might be interested in finding institutions in the *United States*, or data collected in the `year` *2018*, or both. With **SQL**, **WHERE** and **AND** clauses can help filter the data accordingly.\n",
+    "\n",
+    "Before proceeding with this segment, it is **recommended** that you **review** the relevant lecture code:\n",
+    "1. [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Michael_lecture_notes/33_Database-2) and [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Gurmail_lecture_notes/33_Database-2),\n",
+    "2. [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Michael_lecture_notes/34_Database-3) and [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Gurmail_lecture_notes/34_Database-3)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cebe083",
+   "metadata": {},
+   "source": [
+    "### Task 2.1: Use WHERE to find institutions in the United States\n",
+    "\n",
+    "* Write a query to select the rows from the database with *United States* as the `country`.\n",
+    "* Keep only the `institution_name` column.\n",
+    "* Save these institution names to a **list**.\n",
+    "\n",
+    "**Hint:** You will need to use **quotes** (`'`) around the **strings** in your query and **backticks** (``` ` ```) around **column names** as in the example below. The **quotes** and **backticks*** are only **required** when the string or column name contains special characters or spaces. But even otherwise, it is a good idea to use them to be on the safe side."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64012949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "us_institutions_df = pd.read_sql(\"SELECT `institution_name` FROM rankings WHERE `country` = 'United States'\", conn)\n",
+    "us_institutions = list(us_institutions_df['institution_name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c035f899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"University Of Wisconsin-Madison\" in us_institutions\n",
+    "assert \"Tampere University\" in list(rankings[\"institution_name\"])\n",
+    "assert \"Tampere University\" not in us_institutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fe4da4e",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Add an AND clause to find institutions in the United States with at least 70 overall score\n",
+    "\n",
+    "* Copy your query from Task 2.1.\n",
+    "* Update it to only select rows with `overall_score` of **at least** *70*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12f341ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'good_us_institutions', but do NOT display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25e2d3cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"Massachusetts Institute Of Technology\" in good_us_institutions\n",
+    "assert \"University Of Wisconsin-Madison\" in good_us_institutions\n",
+    "assert \"Wake Forest University\" not in good_us_institutions\n",
+    "assert \"University of Connecticut\" not in good_us_institutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf715227",
+   "metadata": {},
+   "source": [
+    "### Task 2.3: Use an ORDER BY clause to display the top 5 institutions by academic reputation in 2019\n",
+    "\n",
+    "In addition to **WHERE** and **AND**, the **ORDER BY** keyword helps organize data even further. Much like the `sort_values()` function in `pandas`, the **ORDER BY** clause can be used to organize the result of the query in *increasing* (**ASC**) or *decreasing* (**DESC**) order based on a column's values.\n",
+    "\n",
+    "* Write a new query to select rows in rankings where the `year` is *2019*.\n",
+    "* Use **ORDER BY** and **LIMIT** to select the top 5 rows with the **highest** `academic_reputation`.\n",
+    "* Save these institution names to a **list**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "763304e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_5_institutions', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "404fa832",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(top_5_institutions) == 5\n",
+    "assert top_5_institutions[0] == \"Massachusetts Institute Of Technology\"\n",
+    "assert top_5_institutions[-1] == \"University Of Cambridge\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13e1803b",
+   "metadata": {},
+   "source": [
+    "### Task 2.4: Order by multiple columns\n",
+    "\n",
+    "If you print out the resulting dataframe from your query, you might notice that all 5 rows have the same academic reputation. This makes it hard to compare the universities, so we will add some **tiebreaking** rules. If two universities have the same `academic_reputation`, then we should order them by their `citations_per_faculty` (with the **highest** appearing first). You can do this by ordering by multiple columns.\n",
+    "\n",
+    "* Copy your query from Task 2.3.\n",
+    "* Update the **ORDER BY** clause to add this tiebreaking behavior.\n",
+    "* Save these institution names to a **list**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26f5a433",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_5_with_tiebreak', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5b2382b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert top_5_with_tiebreak[0] == \"University Of California, Berkeley\"\n",
+    "assert top_5_with_tiebreak[-1] == \"University Of California, Los Angeles\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b991dcf",
+   "metadata": {},
+   "source": [
+    "### Task 2.5: Use GROUP BY clause and SUM aggregate function to get the total number of international_students for each country in 2019\n",
+    "\n",
+    "The **GROUP BY** keyword groups rows that have the same value. It is often used with aggregate functions, such as **COUNT**, **SUM**, **AVG**, etc. to obtain a summary about groups in the data.\n",
+    "\n",
+    "For example, to answer the question \"What is the average rank of each country's institutions?\", we could **GROUP BY** the `country` and use the **AVG** aggregate function to get the average rank of each country.\n",
+    "\n",
+    "* Write a new query that uses **GROUP BY** and **SUM** to get the total number of international students in each country, using **WHERE** to filter by the `year`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `country` and the **sum** of the `international_students` for that country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f31786c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country', then display its head\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c84f12c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"country\"] == \"Japan\"].iloc[0][1], 280.9)\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"country\"] == \"Australia\"].iloc[0][1], 1895.5)\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"country\"] == \"United States\"].iloc[0][1], 3675.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06ecba29",
+   "metadata": {},
+   "source": [
+    "### Task 2.6: Use the AS keyword to rename the new column from Task 2.5 to total_international_students\n",
+    "\n",
+    "Although the dataframe does have a column for the sum of international students for each country, the name of the column looks strange:\n",
+    "\n",
+    "```sql\n",
+    "SUM(`international_students`)\n",
+    "```\n",
+    "\n",
+    "In SQL, the **AS** keyword allows us to create an simpler alias for the columns we create with our queries to make the resulting **DataFrame** easier to understand.\n",
+    "\n",
+    "* Paste your query from Task 2.5 and modify it so the **SUM** column has the name `total_international_students`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `country` and `total_international_students`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3947be0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country_renamed', then display its head\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e114959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"total_international_students\" in inter_students_by_country_renamed.columns\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"country\"] == \"Japan\"][\"total_international_students\"], 280.9)\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"country\"] == \"Australia\"][\"total_international_students\"], 1895.5)\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"country\"] == \"United States\"][\"total_international_students\"], 3675.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79fdda0c",
+   "metadata": {},
+   "source": [
+    "### Task 2.7: Use the HAVING keyword to only keep countries with more than 1000 international students\n",
+    "\n",
+    "In addition to **WHERE**, the **HAVING** keyword is useful for filtering **GROUP BY** queries. Whereas **WHERE** filters the number of rows, **HAVING** filters the number of groups.\n",
+    "\n",
+    "* Paste your query from Task 2.6 and modify it so that it only returns countries (`country`) and `total_international_students` with **more than** *1000* international students.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `country` and `total_international_students`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bc00cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country_more_than_1000', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1c5be56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(inter_students_by_country_more_than_1000) == 4\n",
+    "assert \"Australia\" in list(inter_students_by_country_more_than_1000[\"country\"])\n",
+    "assert \"Germany\" in list(inter_students_by_country_more_than_1000[\"country\"])\n",
+    "assert \"United Kingdom\" in list(inter_students_by_country_more_than_1000[\"country\"])\n",
+    "assert \"United States\" in list(inter_students_by_country_more_than_1000[\"country\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d83309db",
+   "metadata": {},
+   "source": [
+    "# Segment 3: Plotting\n",
+    "\n",
+    "SQL provides powerful tools to manipulate and organize data. Now we might be interested in plotting the data to engage in data exploration and visualize our results.\n",
+    "\n",
+    "Before starting this segment, it is recommended that you go through the relevant lecture code:\n",
+    "\n",
+    "1. [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Michael_lecture_notes/36_Plotting-1) and [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Gurmail_lecture_notes/36_Plotting-1),\n",
+    "2. [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Michael_lecture_notes/37_Plotting-2) and [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Gurmail_lecture_notes/37_Plotting-2),\n",
+    "3. [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Michael_lecture_notes/38_Plotting-3) and [here](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/s23/Gurmail_lecture_notes/38_Plotting-3)."
+   ]
+  },
+  {
+   "attachments": {
+    "bar_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "d27b7c2c",
+   "metadata": {},
+   "source": [
+    "### Task 3.1: Use a bar plot to plot the data from Task 2.7\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:bar_plot.png\" width=\"400\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e4dc5d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instead of specifically plotting just the DataFrame 'inter_students_by_country_more_than_1000',\n",
+    "# create a general function to create bar plots\n",
+    "\n",
+    "def bar_plot(df, x, y):\n",
+    "    \"\"\"bar_plot(df, x, y) takes in a DataFrame 'df' and displays \n",
+    "    a bar plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: set dataframe index to 'x'\n",
+    "    # TODO: use df.plot.bar to plot the data in black with no legend\n",
+    "    # TODO: set x as the x label \n",
+    "    # TODO: set y as the y label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e21ed94a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to plot the data from Task 2.7\n",
+    "# verify that this plot matches exactly with the image shown above\n",
+    "\n",
+    "bar_plot(inter_students_by_country_more_than_1000, 'country', 'total_international_students')"
+   ]
+  },
+  {
+   "attachments": {
+    "scatter_plot.png": {
+     "image/png": 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4ZWxYRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpVc2VyQ29tbWVudD5TY3JlZW5zaG90PC9leGlmOlVzZXJDb21tZW50PgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KHSl//AAAABxpRE9UAAAAAgAAAAAAAAEVAAAAKAAAARUAAAEVAACt3MmljwkAAEAASURBVHgB7J159FXVef53jXMAQQVFQUBGAQXBiUGIsY3GxBjj0moccLZGdFWtzcpq+/u3yySaAaPVRBMcgpqkxjRZdcgkEURpBQRBRCWCAyJSo7ba2jY/Pzt56eF4zr3n3Hvm++y1vt9z7xn28Ox993mf9333u//k9x8kpyQEhIAQEAJCQAgIASEgBISAEBACQkAIlILAn4iYl4K7ChUCQkAICAEhIASEgBAQAkJACAgBIeAREDHXQBACQkAICAEhIASEgBAQAkJACAgBIVAiAiLmJYKvooWAEBACQkAICAEhIASEgBAQAkJACIiYawwIASEgBISAEBACQkAICAEhIASEgBAoEQER8xLBV9FCQAgIASEgBISAEBACQkAICAEhIAREzDUGhIAQEAJCQAgIASEgBISAEBACQkAIlIiAiHmJ4KtoISAEhIAQEAJCQAgIASEgBISAEBACIuYaA0JACAgBISAEhIAQEAJCQAgIASEgBEpEQMS8RPBVtBAQAkJACAgBISAEhIAQEAJCQAgIARFzjQEhIASEgBAQAkJACAgBISAEhIAQEAIlIiBiXiL4KloICAEhIASEgBAQAkJACAgBISAEhICIucaAEBACQkAICAEhIASEgBAQAkJACAiBEhEQMS8RfBUtBISAEBACQkAICAEhIASEgBAQAkJAxFxjQAgIASEgBISAEBACQkAICAEhIASEQIkIiJiXCL6KFgJCQAgIASEgBISAEBACQkAICAEhIGKuMdA1Ar/73e/cxo0b3dtvv+123HFHt8MOO3SdpzIQAkJACAgBISAEhIAQEAJhBH7/+9+7999/3/Xt29cNHTrU7bHHHuFb9F0I1BIBEfNadlu1Kr1q1Sp39913u7Vr17o+ffq4nXfeuVoVVG2EgBAQAkJACAgBISAEGoEApPydd95xY8aMcaeffrqbOHFiI9qlRggBEXONga4ReOyxx9z111/vnn32WTd69Gi31157dZ2nMhACQkAICAEhIASEgBAQAmEE3njjDbdu3To3duxYd+WVV7pp06aFb9F3IVBLBETMa9lt1ar00qVL3Y033uhd2U8++WQ3bty4alVQtRECQkAICAEhIASEgBBoBALPPPOMu++++7wr+xe+8AV3+OGHN6JdaoQQEDHXGOgagX/91391t9xyi8/n4osvdlOnTu06T2UgBISAEBACQkAICAEhIATCCEjuDCOi701BQMS8KT1ZYjs0QZYIvooWAkJACAgBISAEhEAPISC5s4c6u8eaKmLeYx2eR3M1QeaBqvIUAkJACAgBISAEhIAQCCMguTOMiL43BQER86b0ZInt0ARZIvgqWggIASEgBISAEBACPYSA5M4e6uwea6qIeY91eB7N1QSZB6rKUwgIASEgBISAEBACQiCMgOTOMCL63hQERMyb0pMltkMTZIngq2ghIASEgBAQAkJACPQQApI7e6ize6ypIuY91uF5NFcTZB6oKk8hIASEgBAQAkJACAiBMAKSO8OI6HtTEBAxb0pPltgOTZAlgq+ihYAQEAJCQAgIASHQQwhI7uyhzu6xpoqY91iH59FcTZB5oKo8hYAQEAJCQAgIASEgBMIISO4MI6LvTUFAxLwpPVliOzRBlgi+ihYCQkAICAEhIASEQA8hILmzhzq7x5oqYp5Dh7///vvu3//9393bb7/t//7jP/7D/dd//Zf7n//5H/f73//e7bHHHm7YsGGuf//+kaX/93//t3v33XfdW2+95f7t3/7N50WepJ122sl99KMfdXvuuafr27ev22233dyOO+64XT7/+7//66wOb7zxhs/nP//zPx3nd9hhB7frrru6fv36+T/qsssuu2z3fNovmiDTIqb7hYAQEAJCQAgIASGQLwLInMie/CFbkpAZP/KRj/i/P/mTP2lZAXueZ5FjkS3t83vvvbftO7Jpnz59/B8y6s4779wy324vSu7sFkE9X1UERMxz6Jk333zTrV+/3q1bt84988wzbuPGjW7r1q0Ogs6EdvDBB7s5c+a4SZMmRZYOoX/55Zf9s0uXLvV5cY4EGT/wwAPd4Ycf7saNG+f2228/fy6YESSc+59//nn32GOP+Xxef/11xyQKCd93333dhAkT3Pjx4/1x0KBBwcdTf9YEmRoyPSAEhIAQEAJCQAgIgVwRQOZE9kP+fOedd3xZEOjdd9/dG2nChp1wZex5ZErk2N/97nfeWMTn1157zRuPyBvZdPTo0W7UqFFeRh0wYEA4q0y/S+7MFE5lViEERMxz6AxI8OrVq7f9vfjiiw7L9ZYtW/xENnXqVPf//t//c7Nnz96udCzaTIIQ+SeeeMI9/fTT7oUXXvCTIdfQbGLxxloOOZ84caIn6EOGDPEaUK6h3aQcFAKrVq1yy5cv9ySffM1izoS89957e2I/a9YsP5Gi4UTj2UnSBNkJanpGCAgBISAEhIAQaBICZmHu1EKdFRZYt/HcRPZ86aWX/JHvJOS9vfbayw0ePNh7bmKwQf4LWtKDXpc8v2nTJp+HEXO8OTnHMUjMx4wZ4/gbMWKEQzbFOzOPJLkzD1SVZxUQEDHPoReYCLGWQ7AhyUxkuKZDshctWuQnrChizkSKVpIJ5+677/aTHlZxSDgTHAlLOvlAvLF8n3HGGW7KlCnefYiJlZcCSoEf/vCHbu3atd5CzuQLicd1nvx5/vHHH/fa0j/7sz9zKAqYRONc69tBpAmyHUK6LgSEgBAQAkJACDQdAbMwd2qhzgofCDOemxhnfvOb37jf/va3Xj4kf4w8Q4cOddOmTfNWbkg6yxqDlnRkRXv+kUcecc8995y3vNuyTI5Y4u07pB6rOZZyvDCPOOIId+qpp3rPzKzaFMxHcmcQDX1uEgIi5jn0JhMaBJqJkUkLUo4L0VNPPeXuu+8+P2lFEXPWlEPmIe/33HOPXxP0uc99zk+ew4cP95Mpkyvu6f/4j//o1wedfvrpbvr06Z64s96cspYsWeJuueUWb51ncuQP13cmX5QETz75pLv33nu9JR53+sMOO8yXYeQ/LSSaINMipvuFgBAQAkJACAiBuiNgFnJIKrJf0MU7bKFGxkIOy3MNttUHWXHhwoXu0Ucf9X/IlnhVkvCeZBkkciHu5wMHDtxGzM2STlv+5V/+xcuTEHs8P3mO/FslDES0D7n0C1/4gj/m0V7Jna16QdfqjICIeQ69h8YUggwpZyKDcL/66queUH//+9/3ruhRxPyVV15xrClnMmTSwd387LPP9hZtJjYSWliu33nnnd4aD6nmD6s3LkOUw0R8++23eyJ/7rnnuhkzZnhlAEHfcE/Ckv7AAw+4Z5991rvOjx071p1yyinuoIMO6ggNTZAdwaaHhIAQEAJCQAgIgRojYBZy1lsjW7EEcdmyZV4WQ/7DOs3f8A+MK0cffbSbPHmy91DMaw221Qc5cf78+d7QQ91MSWBQIw+yLBJLuRFnAsKZJR359Ze//KVfErl582b/fDtSTt6Qf/LB6PP5z3/esVwSr8+s2yu503pSx6YhIGJeQI8SDA7tJe5AEGZcxqOIOcHaHnzwQbdy5UrHOvWRI0e6M8880x1yyCHb1RLL+1133eWDu+EyxHVc0smXFwMW9x//+Md+Irzqqqs8MUeLadrSDRs2+HuY2HB7P+CAA9xFF13kyf12BYW+MFGjRbU/vpNwq7///vu9G9Oll17qFQWhR/VVCAgBISAEhIAQ6DEEzIJb9prrrGFH/gmu4cbtG1kIYo6MBpmFoJIg6BDemTNn+j/IKkSd65B2UhxOJreRB6SbFFwLbs/7Cx/8I/gvnpFYuW+44QZv8cYgA/5RifxNPqSM/fff33tZUh+8L7G085m/NIkgcMcdd5z72Mc+5o466qhtyzHT5NHqXhHzVujoWp0REDEvoPeSEvM1a9Z4F3XINRM2FuyTTjrJYdEOJq5DhLmfiZTrJ598sifmuKmzfnzx4sV+Dfpll13mjjzyyG1aW/JBe7pixQpvwUcjus8++7grr7zSu7MHywl/Zu08VnbK58h3Eu1j3ToBP6644gpfXvhZfRcCQkAICAEhIAR6CwGz4Ja95jpr1MNruJGLkIX4gxjj2h4kzVilkbXwYGRXHjwdsVpbVPQ4nGzbMRQBSaKqoyxAxsNz8rbbbvNemJD1OGJOHe0P8k1wYNzZ+YyMR7+lJeVgTWwj4h8R5Pgzn/nMh+TYbvtDxLxbBPV8VREQMS+gZ5IScyzlCxYs8JZwJka2VfvEJz7hLefBapplnajrrGfCsk4QOFySWH/OhEVeaGgvvvjiD1nCmWx5iTBxs1adsr70pS95TW6wnPBnEfMwIvouBISAEBACQkAIhBEIW5SRH8ydGpKK3FHEmutwvbr9DkmF5IbXcPOd8xhLohLGFizTWI/nzp3r3dqR2SDFQct7GCeeIYEnJJnUCr+0xNxnGPhnJJ1TtLUTUs6zIuagoCQE0iMgYp4es9RPJCXmuD/dcccdPhImgTlwUT/22GP9eqRgobhM/eIXv/DuUqxLZ/3OWWed5S3muC+xvgmLNi7qF1xwgTv00EODj/ugdJB7AoPYmve//du/9WuBtrsx9IUXg7mxc+Q7Sa7sIaD0VQgIASEgBIRADyMQtihDXI3kQf6GF7TmOusuMMt2eA031uxWRBZizrZkWMqRy3Brx4KOHGXRz4PR08kLnEiGG5+NOMfhl9aVnTzDKarc8D3tvsuVvR1Cui4EohEQMY/GJdOzTSHmcaDIpSgOGZ0XAkJACAgBIdA7CEAioyzK4ajg7dZcVxWxMPHFS5E13HGWcmsHxBy3dCKhQ8xxaSfAL/GE4qKnowSwfC2oGqSZc3H4EXgYqzsk/6abbnJPPPGEX5tu+Vh98jpSP9qKYYkYSbiyK/hbXmgr3yYiIGJeQK8mJeZVd2WPg0rEPA4ZnRcCQkAICAEh0DsIxFmUzY3dkGi35truq9rRXMUhvrfeeqvfJQerd9wabqu/BVlj73Bc2TliQUfui4ueHrTAm6Xc8ovDj12A2HMcss/SSDwxqVvQ6m555HFkzTztYru0yy+/3HsGUFdbK59VmZI7s0JS+VQNARHzAnokKTEPBn9jciP4W1TQDAv+hgs5E26r4G+8ANjHPDipW/A3Im7iEp80+FscVJog45DReSEgBISAEBACvYNA2KKMnBEVFdzWXBOc1va7JvAYpK5V1PGykeyUmFtQNVzYL7zwQr+dGFhhcU8SPT3cbsPP1qyTb58+ffw2bZByFAcco/YfRx4khY9G3sPHcNmtvrNuftiwYT4a+3nnnee3h2t1f6fXJHd2ipyeqzoCIuYF9FBSYm5B3dCgbtmyZdt2aQSBCyauB7dL43rUdmnsUWnbpfGis203cCljSzUmNrZLwyUqyXZpwToEP2uCDKKhz0JACAgBISAEehMBI67tooJDLCHhxMAhRs7UqVPdbrvt5qOCQzAhssGo5VVBM6h4mDdvnifWQZfzuHoGg6GdcMIJPvAdch6yWJLo6eF8DT9bsw5BhxRj4MECD/4YYWztuz1vRhqOyIT2x3Xc3fkzS719tmeTHFlbfvzxx3tijtKFAH95JMmdeaCqPKuAgIh5Tr0Q1DgaMUd7SXA39hv/u7/7u+2CrTFJEsiNgCL8MekMHDjQnX322f6FhSsQiZce18iHSZ1JmT9eav369fPaUiZk9ksnTzSWrGUiL15yaK4JDPfAAw/4I9+xuJ9yyineQt8JHJogO0FNzwgBISAEhIAQaBYCaYg57s3sKnPMMcc4CB1EHXKOvGNR2zEwILtARCHApDws6shseCDy16occ9VfunSpJ9TIW6wTp91RyYgwctaJJ57o11yjjEBeS7OtWThv8AA/I+Z4RoITW+a2ssBbfYyQg/WAAQM8qUcZwnWivyO3shc77TJ5NlyH8HeepW3nn3++J+YYfWhnHklyZx6oKs8qICBinkMvmLaRIxpHopPiToRr0Z133umJ+d/8zd/47TIonsmMSZZI51iz2YP8nnvu8c9CmFmLNPyDCKak334Q2RTXpx/96Ef+mdNPP92v5UEryQuNwB+4jt1yyy1+0keLyoTN5M2Ljv01mbh/8IMfeGI/adIkf40yOtVsaoL0XaN/QkAICAEhIAR6GoGgRbkVQYQYIvdA3FhO17dvX7+dGOeQiZB5jj76aDdx4kS/9RakPck+3p2Cb4S73X7rRuDxcGQpIAYXZDZkt6hk7UQGwzNx1qxZvr1sg4Y8hlzYCqeoPDlHvigokPFYy41xhr3TTWHAkb5A0RBO4Msfz2PJRw6cMGGCGzNmjCf7GInYjhelAzJnEss5+VEnrOTUh6BvEH6UBXkkyZ15oKo8q4CAiHkOvcBkCMmGkKNx5G/Tpk0+yMfDDz/s1wHhumUu6ljQ2doMzSXEevny5e7uu+/2z7DOHI3y/vvv72v68ssvuxdeeMG7K+27775+//IpU6b4PJnoeWngnv7DH/7Q71XOy4yt13i54ebEi43nH3/8ca+dxgWeCX3EiBFeYdAJHJogO0FNzwgBISAEhIAQaBYCRnDD24kFLcrIKUYOIXMkjvYZIoihgHXTEEZkGGQZyyNoUcfgwPdOg4sRuI18iWT+0ksv+WOScrifeD9EPX/ooYc8kd26dauX4WgbiXbilo/iAc/FOXPmeEOIkVWINDjhyo5LO7Ki7VVuz5NHVKIMlBjky5JF5DjkO2S7JK7x9jzEfPLkyd4AxPa8gwYN8kYdiPmvfvUrt2LFCo8LSoS4ulA/rO14ZtJnRJ0n+jzthPznkSR35oGq8qwCAiLmOfQCk/O6des8wUbjCBFmAmZiw3WJiYrgGBBl0vjx492pp57qt5fg5cPkzGT/9NNP+/0tyY8XFYkXF65dbD8B2Wby4wVGnlxj4sTFncBwTKzsaY72E5d1rjEZM4GyTQekH4306NGj/YsNYt9J0gTZCWp6RggIASEgBIRAsxBAzsBK+9sPLK1Yk5GB+LPt0rgetMDa9zAKRvQwXPAZGYd7Scgxw/9oUYdUYljAOttJ6nS/dQg9BhgioOPFiKcipBh5yxQM1BejC2SVP6zl1BtCTQInLO+//OUvPVY8j/GF9vEsSg6T/cJtM2JN9POrr77aW6q5n3oktcCbPIhyg3xY+ogrOmUjd1I32nb//fd7mZL6Wh+E64PbOp6XWMoh+BiUzPshfG8W3yV3ZoGi8qgiAiLmOfQKExqTNZZvtKAbNmxoWQrEHJd1XjBoV7G4MzlDrnFHWr9+vX8BkAnuXryEcE8fN26c1yRzLpjMYm8vDLS6kH3Oo1XG0o4W2v7QkHaTNEF2g56eFQJCQAgIASHQLASiCC8kE3KHoQBSi1zCMYqAQhohuHbksxFe8onbx5v7kyQIZisFAnkkKccs58h7kFgUEDxnBNYIK/IdRhCMIhBWMMAyj+EGORFCjUEGYk/ieXCJI8NgQT6QYVzHIdYYdoLbr+GpGQ7+5jMP/CMPjDIW3R1jDUYjyqZ/UK58+9vf9pZ96kzbggm8+UOO/exnP+uJOd6geAnkmSR35omu8i4TARHzHNBHk8pkSPAMrNdB16So4lhjhdUbzTBaYSY+XNrZj5KXG5M3EyKJCRS3LazmEHLWlfNMMPE891MHXhq8+CDlnGcyx72IMvljAmYy7yZpguwGPT0rBISAEBACQqBZCMS5iCMPIY9gjf31r3/tj3yHgIaTkWwjf8HryEFRLuJheSj4TPAzxNNcyeP2Eef+duWY5RxZD1KNYQbZi/ZQF2Q05CwIOfXlMzIYchlGF4wvDz74oCfUyIzgg6wGMeePz1HkHEIdDP6GRZ4yCEQHmWbtuq0Rj3resCAfZEALIkc+1JNyX331VZ/H9773PR90OKqfkCmRS1E8fP7zn/demN0sjbR6tTtK7myHkK7XFQER87r2XIXqrQmyQp2hqggBISAEhIAQqAgCRoAhnBBWjhgekBsIhstyuyjC1676cZbepIYGykwSfC1pOdZOW7IIUeczbeUaJB2Sj9s467rBgvXluK9DoPGshOQaGedI4hhFrOMINYoCCD+4JlkjHpcPZSeJGs/zQQUBFneIPW3NM0nuzBNd5V0mAiLmZaLfkLI1QTakI9UMISAEhIAQEAIZImDEEnIJQYUQQ86JZH7jjTd6YoqHH9fTpDhCmZQQ4omYlHhGWZTD5Vg7IeQWYwjXdgLKkSDdWP7NtZ024zFALCDqAVE3DwEj5TzH5+B3zpFof5QLOnlY25KsEY/LhzKSKC7MYo5L/dy5c73FPAtPTMpvlSR3tkJH1+qMgIh5nXuvInXXBFmRjlA1hIAQEAJCQAhUGAGzLLeL2m5NaEVMkxBmyyd8NPKKtbpVFPOkCgDqiXLhtzFB7ygf93AizBMjiMSactak8xzXginKfd+IO/ehGGjlym8Bh8F5wYIFXgGCAgClSDCF8yG6O4SfOqEsQL676667/Bp43OTbPY9LfJ7R2K3ukjsNCR2bhoCIedN6tIT2aIIsAXQVKQSEgBAQAkKgZgi0I7Bch6QaIeezfQ82Nc7SW7YreyuFA/VnzTlrwUkWg8ja6k/+8R+WaNpoZJyjWd3Bo13wO4g1VnkUBQSXQwHB0aLjU0xUPsQ74jkIOBZzXOzZMg05Lxx1Pur54X+MOm/1DrYpy8+SO7NEU3lVCQER8yr1Rk3rogmyph2nagsBISAEhIAQKAGBuKjtVAUCimWd9dkERCM6eNaW2iSWe+oStijHWYSTrlk3gk3eUQoHzpPMRZwgvaxJJ9ivPcs1CDDrudttF9cOZ8uHqOpY4CH0wf3cwd/2eMc1nqDEWMSxqqepxx9ald1/yZ3ZYamcqoWAiHm1+qOWtdEEWctuU6WFgBAQAkJACJSCQFzUdioDGYboEuX86aefTmTpTWupbWe5px5pLMJJXePJ16zJ1CEuGTFnW9wTTzzRjRo1ypPmYBA5rNsEkwMvArBFpXY48zz5YJ3H1X316tU+ojuWdurH87jFoyBgb3Irk12EKNeeb1ePqLp1c05yZzfo6dkqIyBiXuXeqUndNEHWpKNUTSEgBISAEBACLRAwworlFKsyCTIIcQu6VrfIItUls1xjEWdNM6lPnz6emLNdF8HR2PoLoghRJmVpqU1qUW5nmU5DzH0j2vzbfffdvcs7+4ufddZZbuLEid7FnL4AH66nWcsdhzOu9Vi/sZKzzRou7/yFXd5ZG3/kkUc61qBPmjTJHXDAAX7rN8h5mnq0aXbiy5I7E0OlG2uGgIh5zTqsitXVBFnFXlGdhIAQEAJCQAikQyCOwKUlgklLjVMEoBjAUsu+4EHXavLN0lKb1KLcziKc1JU9KS64r0+ZMsXNmjXLHX/88d5ajWICpUQnipI4nDnPmnLkuFb7udP/AwcOdCgKzjzzTE/QURCwpj8PhU07nCR3tkNI1+uKgIh5XXuuQvXWBFmhzlBVhIAQEAJCQAikRCArgpqy2La3F6Uo6LYce75d8DeIMH8kO4ZBwNXdXNhnz57tDj30UL/OPHxfFt+TKhQg31HbsyUNtpdFXYN5SO4MoqHPTUJAxLxJvVlSWzRBlgS8ihUCQkAICAEhkAECUS7dRhwhisMTBhvLoCrbZUEdsJ7n7VrfbTn2PC73rVzCsXrzx/12DDYYrCHBhx9+uLvooou8xZygbFin80hJXfCpUzfb02Vdd8mdWSOq/KqCgIh5VXqixvXQBFnjzlPVhYAQEAJCoGcRSEoo223P1bMAhhoepeCAgJNwQ8eyznp6op3H7Qs+aNAgN2PGDHfuuee6uCjwoWI7/ipi3jF0elAI5IKAiHkusPZWpiLmvdXfaq0QEAJCQAg0A4GkLtis68ZyC2GcM2dO7oSxrui2WxIQjDZPkDXWz0PYSRD4ohUgcmWv60hTvZuKgIh5U3u2wHaJmBcItooSAkJACAgBIZARAnUlZhk1P7dsTOFRVrT5pA2zerZbG181xYzkzqQ9rPvqhoCIed16rIL11QRZwU5RlYSAEBACQkAItEGgrq7MbZpV+mVbIhBeG19UtPmkAFg9262NL9qS367+kjvbIaTrdUVAxLyuPVehemuCrFBnqCpCQAgIgQgETAAPEwWCOvFH0Cml3kNAxLycPjdLddiinte2dO1a2W5tfFnB/+LqLbkzDhmdrzsCIuZ178EK1F8TZAU6QVUQAkJACLRAoGpEoEVVdalABOTKXiDYgaKqpihrtzaefdyHDBni2u3nHmhirh8ld+YKrzIvEQER8xLBb0rRmiCb0pNqhxAQAk1DoG4Cd9Pwr3p7TGFTtzXGVcc1bf2qQtRtPFTFkh+Ho+TOOGR0vu4IiJjXvQcrUH9NkBXoBFVBCAgBIRCBQJSLKiSAVOb+1BFV1akSEDBCWLc1xiVAlWuRVSHENh6qvuRFcmeuw1GZl4iAiHmJ4DelaE2QTelJtUMICIGmIGACtghXU3o033ZEKXCC+29XbY1xvmgUl7s8WjrDWnJnZ7jpqeojIGJe/T6qfA01QVa+i1RBISAEegwBs8DJRbnHOr7D5oogdghcl49FKUTk0dIeVMmd7THSHfVEQMS8nv1WqVprgqxUd6gyQkAICAGnoF4aBJ0gYAqdqq8x7qRtVXoGV/H333/f4dGyaNEi9+ijj/rjxo0b3Q477OCrisdCt9uUmedM1V3T0/aN5M60iOn+uiAgYl6XnqpwPTVBVrhzVDUhIAR6EgFtg9WT3d51o5tK5LoGJuMM3nvvPfe73/3O4dGyYMEC9/jjj7vXXnvNoRAJpo9+9KNun332cTNmzHBz5sxxhx12mNt1113djjvuGLwt9nNTFS2SO2O7XBdqjoCIec07sArV1wRZhV5QHYSAEBAC/4dAWmJ+6KGHurPOOsvNnDnT7bfffq5fv37a3/z/4NSniiBQd8WBLRmAhD///PPusccec/fff7975plnHFZtc2M3uD/ykY+4nXbayR111FFu7ty57uijj3Z77LGH22WXXeyWyKOV88Ybb7iXXnrJcWROIEH2q7b9WWQjWpyU3NkCHF2qNQIi5rXuvmpUXhNkNfpBtRACQkAIGAJpXdknTZrkzjjjDDd9+nQ3ePBg179//1SWOStXRyGQJwJ1twDbmvJly5a5X/3qV27FihWeOGM9D5NycISYQ8KxlF9wwQVecYYFHXLdKlk5y5cvd7/5zW+8y7zl34TdGCR3tup9XaszAiLmde69itRdE2RFOkLVEAJCQAj8EQEjMK2CvyGoI6Qj/I8aNcode+yxbvz48W7PPfd0gwYNckOGDPGWNUjAzjvvLGyFQGkIZGUBZsxjmS56zbWVy5ryhQsXerLMunK+R1nKDei0xDxcDmXwl/XadatfWUfJnWUhr3LzRkDEPG+EeyB/TZA90MlqohAQArVCoJ2Abtc5Qs779u27zYWd9asjRozwbrOTJ0/2nwcMGFCr9quyzUIgKwuwKayKDm5n5QYVZZs2bXLvvPNOpKXcei+tK3tUObjNmxu75dvt2nXLp6yj5M6ykFe5eSMgYp43wj2QvybIHuhkNVEICIFSETAindbSFyY069evdwjvuM6++uqr7u2333ZEfyYSNCQAkp5FNOhSwVLhjUHAxr1Zmju1AGdlce8U2KRLS4L581vs06eP23fffRMHf0taTlrCH6xXFT5L7qxCL6gOeSAgYp4Hqj2WpybIHutwNVcICIHCETBLWFpLX5iQbN682W3dutWtXr3a/fznP/cBqMyVFiJgqe4WNWuHjvVGwMZ90NLciQU4rKCC6EP6SYz74cOH5+ohkjQYo/UWdYI8Uy8CMhL0bdasWf67KdDs3uAxaTnk0cna9WBZZX6W3Fkm+io7TwREzPNEt0fy1gTZIx2tZgqBBiFglri0FuhuIUhbbphYdxpd2QjOm2++6S3lixcv9ts0ERyK/ZSxkgdT3S1qwbaU8TltP6etY975p61PXvd3awEmNgK/8W4t7t22LylhphxIOZHXifFAUMZjjjnGsWsCy0vaLSlJWo6Iebc9queFQD4IiJjng2tP5Spi3lPdrcYKgUYgYEQ1rQW628anLTcrS58Rubfeesu98sorbtGiRe6OO+5wRIeG/EBegqnugnuwLWV8TtvPaeuYd/5p65PX/d0STazC7BnercW92/YlVTCYpXzcuHHupJNOctOmTXMjR470e5knCcKYtJy6K94kd3Y7IvV8VREQMa9qz9SoXpoga9RZqqoQ6HEEsrJAp4UxbbnsXZyHpa9bogM5UIpHIG0/p412n3f+8S0r5oopkMyThfGKlwj7fX/ve99zyBtpFEkEMiSeAluG3XDDDW7JkiXeQyRKEdXJfuFJUTFFSjsFwe677+5J+JFHHum3L2SbNKznu+66a6KikpZT96UqkjsTDQfdVEMERMxr2GlVq7ImyKr1iOojBIRAHAJZWaDj8o87n7ZcoqTnYenrFYtaXD/kfT5tP7dzTQ7XN+/8w+UV/d2IpXmyMF5ZZoGc8Z3vfMctXbo0FbEmP9akEzTutttu88+nIfZZKaJM4ZDEpX7GjBl+XTlH1pijMMDCnSSlKYe16/wlWbuepOwi75HcWSTaKqtIBETMi0S7oWVpgmxox6pZQqBBCJQlsHZa7uDBg/1WShCKLC19RnzaWe7qblEreuh22s8QL0gXLsytUt75tyq71TWrF+MKaz5HEpZqvAE4JmlfnCcA+ZHHiy++6H7961+7Z555xhEnAXIdTHHjlfuqQMytrlGKFYvvwM4IjAcCvXW7TWFR5Vi7ij5K7iwacZVXFAIi5kUh3eByNEE2uHPVNCHQEATKIqSdlnvQQQd5F1xcb7O09BmRSmK5q7NFrehh22k/46qMmzLks1XKO/9WZbe6ZvVi2z2i/WPpJuGSveeeezo8P5K0L4pIMlaNtGI1hnxTzrPPPuvLgsiSuGfo0KHe+hu2AEP4q+DK7iv6wb84BQTXad9ee+3lg75x5HvapQ5Fl2PlFX2U3Fk04iqvKAREzItCusHlaIJscOeqaUKgIQhgOStDQO+03ClTpnhXdlx3syTm1p1RRAiCAxniD6JD4Cksd6NHj3Z77713Isun5d9rx077Gesoa4gJUtYq5Z1/q7KjroUJ5qZNm/xa8Hfffdffvttuu3mSyR7cRBePI5pJFUWQ/DFjxniiz7pzysfLgL9WlmZTHFTNQ8TqZS774ABmjAOUNPaHt0ESj4OoPuJcuBzOsTc6ipMkChPur2KS3FnFXlGdskBAxDwLFHs8D02QPT4A1HwhUAMEygp61mm5hx9+uBecn3zyyUxd2a2rwsTKtmFDkIcUQBIgjBDyffbZZ1sAqnaWXcu/146d9jMWXvDFOtoq5Z1/q7KjrgUVO4888oh77rnnvCKJ8UOyMURE8dmzZ8e6ZhtxbEecIaz9+/d3RCv/2Mc+5oYNG+ZJJ+W0sjQnJf5xFvd2SwyisElyzuplQe7AgT8UMCg3KDcLAh0ux/qmW8KfpI153iO5M090lXeZCIiYl4l+Q8rWBNmQjlQzhECDESiL2HRa7lFHHeXJ8Jo1a9z8+fP99maslSW/YIKUQOwIFDVnzhyX1DXa8jBihDcB+W/ZssV7FkAOuNaO+Fg+vX7stJ/rRsyN6NlSCKKdL1y40K8BN48LxkLYko1nQFSQsbSeAIzviy66yE2dOtVbyiHsSQhsUJFAnam/ucm3srjnPa7jFGSU20rhkHe9qp6/5M6q95Dq1ykCIuadIqfntiGgCXIbFPogBIRARRFISwAgxnPnzvWBmJK4Gsc1u9NyIWwQjldffdUTH4LA8bdx40ZPSCiv1drapJY+I1oQ8nXr1rnly5f7raleeukl3yRIC3kNzygoVRxOdT/faT/XzZXdFDlYuNm+bNGiRV6h88477/glEMF+NKsv7uxxiqO0Cg0I+bnnnuuXWZh7PMqjdhbgqhLgKIUBv0mSfnfB0bT9Z8md2+Ohb81BQMS8OX1ZWks0QZYGvQoWAkIgIQJBQpGnBTpcnW7LJdjV+vXrPWHOw9JnxNwsoHkpAMK4xH23+piLL/clIV5x+RV1vtt+po2tUt75tyo7eC2ogJg3b962fcHN+hy8l88odgjcRryCKEVXWmKOxfyCCy7wgd6SLAEI18dwtLXdXE9icQ/nk/R73HgGFxLR5vE4KPt3l7Q9VblPcmdVekL1yBoBEfOsEe3B/DRB9mCnq8lCoGYImIBcNAHtttz333/fu6+zBhwrtq0FB/4sXF2NqLRb49uty3zS4WL1KYo4Ja1Xu/u67ed2Hg5559+ufXbdiDRKIvYVZ9wwRlGkRCUs2RBzCPWFF17oPVCChDpI9LPcFjCqLpwzHItS/MSNZ4u2jodKkYrCOFzqdl5yZ916TPVNioCIeVKkdF8sApogY6HRBSEgBCqGQJTrqFn78lxr2m25cQJ+t9GViyZGccOhqq7GcfWNO99tP8fla+fzzt/KiTtmTcxtXFdFMRTX7rTn241ntpEjmN3q1avdrbfeGqvgMMVGVktr0rajqvdL7qxqz6he3SIgYt4tgnreaYLUIBACQqAuCLQTmLPaRziMR7fl5mXpM6KFK20e27KFcYj7HkU4aTOpTmttu+3nOHzsfN75Wzlxx6AiJwtXdhvXRXuyxLUvq/PtxjPr7g855BC/L/sDDzzg1q5d6yOyhz0PIOYEuOvWhT+rdlUlH8mdVekJ1SNrBETMs0a0B/PTBNmDna4mC4GaI2Au4qzhfvPNN31rsGBhycJtG/fbPJJZCKviql02MW8qMeu0nw2Pdq7Wnebf7Zi2crMK/mb1iSKyRXiyWPlZHa3/2ikaBg0a5CZNmuTX4K9atcoHeUTpIWKerCckdybDSXfVDwER8/r1WeVqrAmycl2iCgkBIdAGgbKIuQnu7YhXm+pndjloAS1ijW+44kGi16S1tp32s+HRTnHTaf5h/NN+t3KNeHa7XZqVX7YngNWj26P1XzvX/N12283tueeenpizVSH9zZxkygirh1zZDYntj5I7t8dD35qDgIh5c/qytJZogiwNehUsBIRASgSaQgBSNjv29qREIq/gb2UrBmKBKfhC3cZl0ML9yCOPuOeee8699957jvFEIso85HPkyJFu9uzZbvLkyW7EiBFuwIABLZG18dhOMdEykxIvph3PYMR2jBByAjuCYTDl9bsLllHHz5I769hrqnMSBETMk6Cke1oioAmyJTy6KASEQIUQCBIK234MKyCpTmuZs4I0bAEtetumsl3ps8Kx23zqNi7DioRNmzZ5Yvnuu+96KCCcxGtgLfWQIUP8Z0imRSOPw8vGY1U8SuLqGXc+7XgePHiwmzBhgo8Wv2LFCrd582ZvRSd/yPrQoUP91nAzZ850s2bNcsOHD/d7treL4h9Xv6acl9zZlJ5UO8IIiJiHEdH31AhogkwNmR4QAkKgYARM4DcX3KIJaMHNTV1cFDE0t9o8o9WnJTJNC4JV93FpFm5iNWzdutW7ZDP42C0AV21iNuy6667egp56UNbwgbTjeezYse7444/3OD311FMOBUcRv7tOobXxWrbiRHJnpz2o56qOgIh51XuoBvXTBFmDTlIVhUCPI2AEot3az151HQ1bQLPeLz1u+KV1/W3atlF1H5dG1GgHY4gjCVd2rOMcWSfdKxbetOPZFE3jx4/3QShRcEDuScxFee0S4Qvo4J+N17KXGkju7KDz9EgtEBAxr0U3VbuSmiCr3T+qnRAQAs5vRUSQJdzXywhyVpc+KFrwtvJ6VWGSlsg1TTFRl99F0np2Op5Zg09CufHOO+/4z3369PGeB1XwOChLceeBiPgnuTMCFJ1qBAIi5o3oxnIboQmyXPxVuhAQAu0RSOtiapYs1nbus88+3nrVvpT632EW0KJcVa28Xl1ioHFZ/99MsAWdjudhw4b5bHBjh9yTzNugCh4HUUtdaCupjNgckjs99PrXQAREzBvYqUU3SRNk0YirPCEgBNIiIAKUFrFi748S/Ku81jYrdILj8tZbb3VLly71VlMUI8EEOcM13BRGRx99dK4KIyOYRSlogm1twudOx3PVcLf6VE1xJrmzCb8StSEKARHzKFR0LhUCmiBTwaWbhYAQKAEBuQyXAHqKIqvmKpui6l3dauNy4cKFbt68eW7JkiXeYmpKCcucAHyQ82nTprnLL7/cR+hmm61ddtnFbsn0aC7ZZa8lzrRRBWbW6XiuGu5Wn6otNZHcWeBgVlGFIiBiXijczSxME2Qz+1WtEgJNQqCqAmZdMTZLWtYWVeunXiGE77//vg/2BTG/7rrr3OLFix2Ygm8w4S4MMZ8+fbq7+uqrPTEnONhOO+0UvK3rz50Syq4LbmgGScdzVXE3xVHVYnNI7mzoD0bNciLmGgRdI6AJsmsIlYEQEAI5I2BEsmoumTk3O7fskxKOtBWwfsqa8KetR1H3B4n59ddf7xYtWtSSmM+YMcNdddVVuRHzKBdsUxKUsZa4qH7Iq5yk47mquAeXWtx2221+qQVknd9nMKE0wnvDllrkHZtDcmcQfX1uEgKNJua4gjF58OJDG4kgEXYPC3Ymmmc00KzjUkqOgCbI5FjpTiEgBMpFIEoAtvdCnvt1l9vq7EqvqmUvuxYWm1PQIokr+2OPPRYpqzA2CQZGVPY8XNmNQEpxVWz/Z4275ZeVYkvEvNjxoNKEQKOJuWmi2SJn69at3l2McyaEhbu/f//+7sADD3QDBgwIX9L3FgiImLcAR5eEgBCoFAIilt11R5RiAzJAkkU1PbZB4kPwN9byxlkk8wz+Zh4QVVtLnB7Rej2RNe6WX1ZLQYKKoyptMym5s17jXLVNjkAjiTmC19tvv+1ef/11t2HDBrd582b35ptvOiYqtIhxxJztKoh0attWJIext+/UBNnb/a/WC4E6IpC1AFtHDNLU2SxxsqimQa39vUFiXqarcFUJWHsE631HVrjnpXC0ebJqChvJnfUe96p9PAKNJOZvvPGGe/bZZ92yZcv8ei3IOQlXMP7Q6ocT5yZNmuTmzJnjj+Hr+h6PgCbIeGx0RQgIgWoiYEQzK5fParYyu1pVVUDProXl5JQVMeu29lVREHTbjro9nxXueXmy2DxZNYWc5M66jXTVNykCjSTm69evd7/4xS+8S9i6deu8Cztu6qwfZ40W5DwqjR8/3p1yyimOo1JyBDRBJsdKdwoBISAE6ohAVQhkHbFrVeeqKDyyIoit2qprH0agW9x333137wmaN3GOIv7mfYpMPXz4cO9xOnnyZDdixIjcl4RK7vzwWNKZZiDQSGKOpRyXsBdffNGNHj3arxtn0mDteBwppzv79evnhgwZ4o/N6N5iWqEJshicVYoQEAJCoCwEuiUQKMaVPoxAVSySUrx8uG+KONMt7kRDf++997whav78+d5L9LXXXvMGqWD9+f3ts88+jqj+eIYSPX3XXXf1xqrgfXGf83KVjyuv3XnJne0Q0vW6ItBIYk5UU/YDZV35qaee6qZNm+bXje+xxx517adK11sTZKW7R5UTAkJACHSNgIh51xC2zKBsi2RVLPctQWrgxW5xZykOAY6L2mfc6ptVcLlOu1RyZ6fI6bmqI9BIYs4+oF/+8pf9Nmlz5871GkLcfdgOTSl7BDRBZo+pchQCQkAIVAmBbi177HGsFI9A2RbJqlju4xFq5pVucYcgYyF/9NFHvafo0qVLY6P6Z7HPuNW37Ngckjub+XtQqz7Y3eSDH9kf9jlpEBpPPPGEYz9Qtka78MIL3RFHHOF222232hBzm/iwULDNGxHm3333Xb+3Kd2EOz7bpvTp08ftueee/sh3XJpIvODfeecd/1wwGj0B7uy5vfbayz/HRG3P+Yc7+KcJsgPQ9IgQEAJCoEYImKWsatGZawRhoqoazmVZJMu23CcCqYE3dYp73p4sJo+WTcTDXS65M4yIvjcFgUYS81WrVrl77rnHvfXWW95aPnHixFqtHefFDBEnmMfjjz/u1q5d61599VVPtJkkWRcEIR81apRXOnBk/TzKBxJk/vnnn3dr1qxxy5cvdy+99JLfKo7Ad9zH/bj3s/4ego43QTdJE2Q36OlZISAEhED1ETABnffSwoULvYUOK93GjRu3xW4hGNTQoUPdzJkz/d+sWbN8UCiUv1G7oVS/1cXX0HAuiwiVbbkvHvFqlNgp7nl7spStKIrrHcmdccjofN0RaCQxh8j+5Cc/8QIDxJOAbpDRvffe21uH4wLAQWyzIKrdDgrWCxG47umnn3ZLlizxe7EzOVoETAQc2jBo0CA3YcIEh+LhkEMO8XVnkiYqPe78q1ev9oQeBQUvexICEgFAiDxvz+27774+v04FJ02Q3fa4nhcCQkAI1AOBTi179Whd97Usm1h334I/5FBVQpZV+6qaT1rc7f6sPVk6VRQUhavkzqKQVjlFI9BIYv7MM8+4H//4x44f7uuvv+5w1z7wwAPdwIEDvXU4bq05BH769Ole4190RwTLe+GFF9zPf/5z99RTT/m1Q2z1dtRRR/l6YfXesmWLJ+3szw6Jx/J95pln+jZu3rzZR+e87777/LVDDz10m1KCyJ3PPfecJ/qbNm3ylozTTz/d79sOJp26tGuCDPaePgsBISAEmotA1QX2spE3olSWK3pW7W+KgiErPIrKJy3udn/WnixRCjjKImHEGV7w9mhh/CV3hhHR96Yg0FhiDjHF2ozlGZewwYMHb3P3jiPmY8eOdSeeeKLjWGaCkN91113e4o0VH+s29Ro3bpzf2gK3dtbRE32eSJz777+/u+qqqzxBx1sA9/eHHnrIu7afdtpp3t0dqzju8VjRweWBBx7w28JdcMEF3q096Aqftu2aINMipvuFgBAQAn9AwATrslyXO+2HphDQTtsffk4KizAi+l4kAlFE2rws8bBMSqRtPsqa6GeNheTOrBFVflVBoLHEHIv5ypUr/dpqJhrWZVuAtDiXbYjvSSedVDoxf/LJJ923v/1tr1RASTBlyhS/Vv6AAw7wLufm6s46vzvvvNMHcbvmmmu8yz5EHfKNRZz7TznlFHfwwQf79iP44dYOoWefd4KGfPrTn95uvXknA1MTZCeo6RkhIASEgPNBPfFmqpuF1QT4uikU8hpzUcQIjEhVsDDm1W7lWw0EslIMmcIta9f4rFGS3Jk1osqvKgg0kpjj4s0aa45pEkR2xowZntCmeS7re1EoYDGn/gTSYS04LvbDhg3zL3gj5rTx7rvv9sT8yiuv9B4B999/v8MV/qMf/ahff/6pT33KjRkzZlsVERTYTuPGG290L7/8sremH3744Y4/LO+tEhM/EeLtj+8klg5Qbt++fd2ll17qDjvssFbZ6JoQEAJCoPYIdEtMsxKk44Dstn5x+er89ggYzlW3MG5f6/TfrJ1SxKTHrsgnjFh3qujLO5hcVliImGeFpPKpGgKNJOZMSKzD5pgmEZ2cAHHdRilPU2bUvbjfY/kmqjpr5CHMJ5xwgndlxw0fV3bc1ZmYVqxY4a9fcsklfo34/PnzPaEfOXKkY3357NmzPaEPlsMz3IcgAfGfNGmSO+aYY9yIESOCt33o8xtvvOGeffZZHyWeI99JbMmGMgAFwBVXXOGOPPLIDz2rE0JACAiBJiHQrQCct4W12/o1qa/ybIvhXHULY7cYWDs7JXzdlq/nkyHQrQIl7+3XkrWi/V0i5u0x0h31RKCRxNy6ggmKPywTrK9GE4i2l0QQNVzbWcPNEVezOBd3y6+oI0oF1oqz1py15CQs2pBoiDnXuUagN9YOHXTQQe4zn/mMd1O/6aabvCV86tSp/hn2cA9bwon2znZyBILr16+ft6x/8pOf9EHiWrVRxLwVOromBIRALyDQraXbBOe8LKzd1q8X+jDLNtbFwthpmzWeOkWuXs/ZvMRyx1deecV7nd5xxx1u2bJl28nO1iqCBRNYGQ9JYhWxRSI7/uCtWUQSMS8CZZVRBgKNJuYEvnj//fe9RZe9vCG0aAMh4EwetpUaUc8hvHHbqBXdMe+88463ikO+CeK2bt06vw6RiZPJkHbxsiSKPPvEQsJZi47Vet68ef5ZXN8h5Vwj8F0wYYn/0Y9+5K3fKCUILsda86DLe/B++0yZ5sbOke8kubIbQjoKASHQdAS6tXSb5TEvC2u39Wt6/2XdvrpYGDttt8ZTp8jV6zmbl/CAxCtz8eLFbsGCBW758uVejrZActYqZFHkZnYMmjt3rjv66KPdHnvs4cm63ZPnUcQ8T3SVd5kINJKYYxWHkPNCgZCzlpoj39Fuk9D0EYkccotFmSPfmWg63TYsq47EfR2yu2rVKq+t3LhxoyM4EO1CeYBiAZK+3377eeKNKzrkmv3LWTv+2muv+TXpWRPzuPZpgoxDRueFgBBoCgJmUerW0p2XhTWr+jWlv4pqR1OJucZTUSMo23Ks35LGAgh7ROCJuXXrVh9EmG17n3/+eS97km/QqxTjFhZy4jLNmTPHW84Jsow3ahFJcmcRKKuMMhBoJDGHxKL1g9iyLRgkl3MkXNdJuLaTmEiIxn7cccf5IGuQc86VlZj8qDeu5rizUxcs3hBvJkEmPVyNEA4JDofSAaXCqaee6pUO3/3ud72Lex6u7HGYaIKMQ0bnhYAQaAoCZlHq1tKdF5HLqn5N6a+i2pGXoqWo+seVo/EUh0y1z1u/JY0FEPaIwMBDHubSjnck1nJIOfInR76ztBL3df7w3Bz+wb7mGLWC5D1PpCR35omu8i4TgUYSc1ujzQ/3kUce8RZkCDduNn369PF44y5OdHMI/KBBg7a5hEPSCQBXRmKyQ8vJdmZf//rXHZZy1pYTTA13IaLGM/FRZ7SYuBqxLRzKBtb4ELSO6OhoPEeNGuWDvzFhEs09mDoN/hbMI/hZE2QQDX0WAkKgiQhkRcDyIuZZ1a+JfZdnm4wIEZD15ptvdo8++qj3zjNjgJWNkh05BCJDsFbe65wrysJo9Uh61HhKilQ17gtbvokJxFxDCi7dZAkn3/EORd5s5QHE2DZLObvuYCRCjmbMEiwY9/XJkyf7z4ztIpPkziLRVllFItBIYk7E8J/97Gd+DTWTCpZmJg9cv+0lyIRDgAtIKnt+k0aPHu1OPPHEtmut8+oge8ETkf2rX/2qX899xhln+MkPbSQTH9pI3PRRLLBdGq7raEZPO+00x1p5gsXhCo8CYsKECY7t0miXJfCw7dJov22VlmS7NMsjfNQEGUZE34WAEGgaAlkR6rwIT1b1a1q/5d0e3qkQHIKqErsFY4DtqBIse+DAgT5QKzulnHLKKf79XKSFMViXJJ81npKgVJ17wpZvCDdjk4TcOPwDGTJIpCHaKI9aeQDxPM8yTjH2HHvssd57c8899/QGLbw1jegTr6jIJLmzSLRVVpEINJKYP/nkk+473/mOJ9xEjGTbMIj5vvvuu83NhgmHtdgQc+6HrELgL7roIjdlypQi+2BbWRBzhLaFCxe66667LpaYoxk1Yn7DDTd4rSjEHMUDAeBYT4+yAQs7AsDBBx/sNfPkj3vSkiVL3K233uqfI+jbtGnTPHlngu0kaYLsBDU9IwSEQJ0QyIqomAK2lUAMLmnXcGZVv6z7xIhr0jWvWZefd37WPhHzcqJz592/Vc/fxl8ry3eU6znWb+RIPDyQI5ELMfrwOw0mSDnWdWIZYSgisDDPYggq0+NDcmewl/S5SQg0kpgzweAKzjryc845x7uBo+Gz9eXWgVxHy8j97OvNJHPllVf6++2eIo82wVKfb3zjGx9yZWdND5MkdcaVHcv6vffe6wPZXXjhhV4bjxv/ypUr3cMPP+zbC2HHZQ6lA+1dvXq1by9r79GY4gIPMccaH8Ynads1QSZFSvcJASFQVwSysnTbPJ9WkMbi1crCmlX9su4fU0QkXfOadfl552ftkyt7OdG58+7fqudv4y+too+RJkpQAABAAElEQVRtdlnOibx52223eeMUc0gUMSdYMgaus846yy/FwAjEVrut5qO8cZPcmTfCyr8sBBpJzFl7/ZWvfMVPMFdccYXX8DGxMIkEExMQExH3f/Ob3/TXr7nmGn9/8L6iP9s+4wR/gywTNX7ixImeXBOVncmUwG/sQw7RRnt55plnemKO8AMxv++++/x9WP9Hjhzp183jtsQzPMt2GAh6p59+uteEdhONXhNk0SNE5QkBIVA0Ap0KwHhtRVmWolxPsWyRmOeZn4Oup+3WcGZdv27xTbvmtWhX2G7bZ89XVSFi9ev0WLXx1Gk7mv5cp+MP2RCZEG/RJMS8rP3K4/pPcmccMjpfdwQaSczRAH7ta1/zpPv888/3FvCo/RVtQuN+XLsh71jMsSCXmVgjDjm3P9zSeUkitGFtISG40SbW+LCWnOBwEHiUDS+++KJXNkDaWUeO+zrP2lohrOdEeYfsH3LIId7F37Zh66TdmiA7QU3PCAEhUCcEsrZ0Z01cs65ft30TpXiw91fUmtd2iodu65PX81VdQtBte6s2nrptT1Of73T8EVcIhSFLOZO4spe1X3lcv0nujENG5+uOQCOJ+bJlyzzRxq0bkg1xxQ2cF38w+BuCA+ux2Z6MQGoEZ8G1u6w15jaY0GJiFWe9OO5JWLkh66wHgmBjWcCNiDXkrPsZO3asbx9EncQelLi6E4Bm+fLl3iUeSzptBwOCwTHJjhkzxgfuIJp7N0kTZDfo6VkhIATqhEAU4czC0p2Vq3fW9UvbN71G6DolRkRnR0lOLIEqp7LHU1WwsXGN8QNDCQmZCk/MMl26Ox1/yIDIjMiJLOVEBibuEvkFU9pYF8Fn8/wsuTNPdJV3mQg0kpivW7fOR2XH4gyZJagZZJttw4LbpWFZhsSzrQTk1KKYQ1jLTEz8BOFgguSlSBuw7vMy4OWAdRvXc+rMxMpacT5zjmTB4dh/kq3VEPjIEysFpB4MwIRjlIt/2rZrgkyLmO4XAkKgrgjkZenOSuDPun5p+6nXXKDN846YL3W0PLbr37LHU7v6FXXdxnVWCrSs6t3p+EMxhAzIskYCDhMEjj+26UXGJKFwLHu/8jicJHfGIaPzdUegkcQc12+irUO6+cMCzZ6L7E9uAc4IhIZF/bcfbCmBOw9R2+2P6O1KyRHQBJkcK90pBIRAMxCoqqBu6JZVv06JAuvpUTSjLK5TMpzTBt+Kiz1Q1bZbO6tGTPPGq+qKCeuXTscfBpz169d770qUS8jE3XgA5d0flr/kTkNCx6Yh0EhiDhHH0ozlnEipBFHDFZy11ggNJF7+uIMPGjTIu3QTuZx9GnH1NvLetM7Oqz2aIPNCVvkKASFQVQSq6tpqeJVVv05da+vi2m342tFwhtDU0fJo7Wh3tHZm5dnRrryqXI9y5QcLUhViJVi/dDr+zDsTz1GWdnI0d3bc2PGuLHO/8rhxILkzDhmdrzsCjSTmTFT8bd682Ucof+aZZ/w67eD6GVs3AxkfN27ctqjnTLT8KSVHQBNkcqx0pxAQAkKgyQj0GjG3vowicHWwPFr9Oz0aMWwaYbd2dUp4i5Yjux1/Znmvi0eE5M5Of7F6ruoINJKYAzqTqrnU4arDOm0s6Uw+JIJ24MLOGhvWaJsLXdGTqa9Mzf9pgqx5B6r6QkAICIGMELD3blPXXMfBVHWX57h6d3u+boQuaXutXZ26iFug4aTldXtft+PPFBF1UbBI7ux2xOj5qiLQWGJeVcCbWC9NkE3sVbVJCAgBIZAegboRmvQtbP2Etb8ulsfWrYm/2i0RjM+5GlfqqmDqlfEnubMavxPVInsERMyzx7TnctQE2XNdrgYLASEgBCIRMMtbXVyAIxvRxUlrf10sj502Ncp1mraT8DwcPny4I6AfQXUJvlu3ferruiSjV8af5M5Of7l6ruoI1JqYs9f3hg0bPMbs6Y07OoktwtgKjSMpqXs6z7OlWv/+/f1z+pcMAU2QyXDSXUJACAiBXkEgirj1wprrovq3LAJm5TZd8SJiXtRI7qwcyZ2d4aanqo9ArYn5ypUr3YIFCzzKZ5xxhjv44IP9Z7ZKu/322/2WaZxISswPOeQQd84557hJkyb5fPQvGQKaIJPhpLuEgBAQAr2CQNNdncvux7Jclq3cuqy97rSf5MreKXLFPCe5sxicVUrxCNSamC9fvtx997vf9aidd9553mWKL/aD5ZiUlPPc1KlT3UUXXeSPfFdKhoDhzd0XX3yx8EsGm+4SAkJACDQeASNyTV9zXVRHlq3wqCthTds/Nm7rooAoe1ykxbfb+yV3dougnq8qArUm5q+88opj0iQddthhbr/99vOfX375Zb9/OXsypkns1ch+5vvvv3+ax3r+Xk2QPT8EBIAQEAJCIBIBc31u+prryMbncDJqiQAYk4pY211XF++0XWHjti4u+2WPi7T4dnu/5M5uEdTzVUWg1sTcXhCAu88++zj2Jie99dZbDnLOFmlsWbHDDjv483H/WPeGdpSt0yDl/fr1i7tV5yMQ0AQZAYpOCQEhIASEgBDICIGiiKKVE6dIwfPhtddec48++qi77bbb3NKlS/3WtNwfTB/5yEfcLrvs4o0mF1xwgZs5c+Z2clrw3ip/jiK8VYqVYP1VFwVCVn0tuTMrJJVP1RCoNTGHTONWReIFYPtG8uLYsmXLNrK98847t8TdXIB4fq+99nK77757y/t1cXsENEFuj4e+CQEhIASEgBDIEoGiXKutnLilBxBwAu/2yj71Jh++8cYbDi9MjhiFSBiDkBnxtuTI93byZpZjgrysv+ricp9V+yV3ZoWk8qkaArUm5nFgvv7662716tX+8vjx493AgQPjbvXnuX/NmjX+80EHHdT2/paZ9eBFTZA92OlqshAQAtshYJarOEtjmngn22WsL0LgAwTyXtudlICyew1GDILszp8/3y1atMhb0I2sWmdBUvFknDFjhpszZ463nO+6667bDCh2X12ORoDjFBZmGCq6PXmPi6Lbk7Q8yZ1JkdJ9dUOgkcTcXhh0Bi+EdlHWuf+OO+7wfXf22We3vb9unZx3fTVB5o2w8hcCQqDqCFRVcK86bqpfMgRs6V5eLuRRLtsom0jBtevIU2xPi9V84cKF3qWdOm3cuHHbskFcvYcOHerd13FhnzVrlt/XHPf2uiqoqqp4y3tcJBudxd8lubN4zFViMQg0kpjjYvX3f//3HsEvfelL7uijj45Fk8kWje+1117rXxhf/OIXvYY39gFd+BACmiA/BIlOCAEh0CMIJLU0luXq2iPd0Phm5kXAjHCmXaOM5XzDhg3eco7MxfNVWnvd+AHxxwbmNS6qjp/kzqr3kOrXKQI9Tcx5IfEigZh/5StfETHvcBRpguwQOD0mBIRA7RFIammcPHmyGzFihBswYEDt26wGFI9AXi7L5umRdo0ylnOexXJexbXXxfdQOSXmNS7KaU3yUiV3JsdKd9YLgUYQc6KvEyWUI2nZsmXu1ltv9Z+JBjplyhT/OfyPtYBYO1atWuV++MMfOjTAf/mXf+mOOuqo8K363gIBTZAtwNElIdDjCJhFrmlrr61daS2Nw4cPd3V26e3x4Vxa8zsl0Gwl22ptd7fEjrH83nvvuaqtvS6towouOK9xUXAzUhcnuTM1ZHqgJgg0gpg///zz7qGHHnIcSWyV9sQTT/jPRxxxhI+Y6b+E/kHKIfNofLdu3erGjRvnLrzwwlgiH3pcX/+IgCZIDQUhIATiEDDBsWmCu7UrraWxHVGKw1HnexuBvBRB3bpCs4sNSremKd7qMtryGhdVb7/kzqr3kOrXKQKNIOZPP/20u/feex1HAouwVdratWs9JmPHjnV77713JD4mWLHVGpHbcc06/vjj3ahRoyLv18loBDRBRuOis0KglxFo+trrbi2NvHeUhEBaBKKWTnSztjtIzPE0ZF9yfrsQ7WDCMs5WYCiW8EQkdg9R14m+rlQ+AlmPi/Jb1LoGkjtb46Or9UWgEcQcS/mDDz7onnvuOd8TWMx5uZAOP/xwt//++/vP9s+igtqLBuIOGcfFkEiiuLQrJUdAE2RyrHSnEOgVBKIERaw7pGCU57quvQ4Smttuu82/cyDrUYQGEm6EhijVIjS98ivIvp1ZK7xMwUSE9Xnz5rklS5b4teNG9q0FO+ywg1+CMW3aNHf55Zf7SOvISlIwGULlHrMeF+W2pn3pkjvbY6Q76olAI4j5pk2bfGRQjiQCkTz22GP+My+RIUOG+M/BfwiGO+20k9ttt928RR1STtRcNMIQdqXkCGiCTI6V7hQCTUegV1wrRcz/MJKtv+XKXOwv2zz+ul0i8v777zvGMsT8uuuuc4sXL/bKJVOiWauQmZCNpk+f7q6++mpPzLGWI0cpVQeBrMZFdVoUXRPJndG46Gz9EWgEMSfwyJtvvukDkNAlBIJbvny57x2sMVgnwomXjL1o0PjygoGUoxXmvFJyBDRBJsdKdwqBpiNggmHT116bpZGtom644QZvaYTkRFnMIS8EFZ07d653AW6SpdH6u1uC2PTfRdbty0ohEiTm119/vd+lhjEcR8xnzJjhrrrqKhHzrDs0o/yyGhcZVSe3bCR35gatMi4ZgUYQ8zCGkPTffrCnJmn4B5bw/v37+5cMrllRLxx/4wf/IOQ77rijJ+d2Tsf2CGiCbI+R7hACVUEgb8Gt6YTV8KOdBA9l/rvrrrs8MX/99dd9dOpgX6P0RTkMoZkzZ453aW8VJTv4bJU/95rrbJX7opu6BX+vuLLjbYiyJcqVHfkIBZNc2btBXM9mgYDkzixQVB5VRKCRxNw0wACOUMTLBEKOIIF1nZdOVMKqYZbzqOs6F42AJshoXHRWCFQRgbwtnE138Tb8aCc7emzYsMEvpWIefPzxx90rr7yyTbkLuSFuCevK+Zs1a5ZXFuMSXHfPrKbHEKjibzePOgV/rwR/w9MFsh7l+aHgb3n0gPLsBAHJnZ2gpmfqgEAjiTnCEMLT22+/7SO0IzzhZvfuu+9uI+ZhNy06a9CgQW7ChAk+QnsdOq8qddQEWZWeUD2EQDwCRVk4g4J+k4KixeGHh9Ybb7zhY5sQiPStt97y+0aj6GVpFF5bRLCua5C78IgyjwG80liX/Oijj/q/jRs39oRCIoxH3b9X/fdq4w1FgRlVMLag3GqCgqvu46es+kvuLAt5lZs3Ao0k5ghQkPJ169Z5wWHNmjVu8+bN3u2wlSv7oYce6rcB4aiUHAFNkMmx0p1CoCwEirJwBl1jm7T2Og4/88Tq16+fGzlypA82SiBRllDhgcVnApBybIJHlnkMND2GQFm/06LLrfrv1cabYhgUPTKqXZ7kzmr3j2rXOQKNJOZbt251WC4IAIc2H80+7u1Yzi1y+7777utd3LF2QNaJzs4awMsuu8xvsdY5pL33pCbI3utztbg+CJjFqSgLpwnSTSFuSfHbb7/93JFHHummTp3qJk2a5A444AC/9SZkvAlrym3EZ0XkDFdZQg3Zco5V/b1GeagQ04HUp08ft+eeezp+c2x327dvXx+8txwEVWoZCEjuLAN1lVkEAo0k5uxn/s///M9u1apVnowziY8bN85h8eA86YQTTvCT+7Jly7ybO+sAsZQfe+yx7sADDywC+8aUoQmyMV2phjQQgaIFbyNcRSkC8u6ypPjtvvvufhkUwbHOPPNMT9B597DrR5NcbrNyfTZcZQnNewS3zr+qv9coDxWUOCSWiCCzsR0uS0TGjh3rPVJat1RXm4SA5M4m9abaEkSgkcQcss3aRgTDESNGeFIO6WZ/85tvvtm3/y/+4i+8tpUfN9ursWYJQj579mw3bNiwIEb63AYBTZBtANJlIVAiAllZONM2IUqwtkjPadZeG3Eoy7JaFn5p8S7q/m6JOWvvycPW5XPkO6mJrv9F9Uu35WT1e+22HvZ7Dyv2CLLIvEFiLjAPFRRhEPRRo0bJct4t+DV6XnJnjTpLVU2FQCOJ+ZIlS9w3vvENv8781FNP9a7pAwcO9JFzr7vuOg/Q1Vdf7Q4++GBPyrGsE8SGNYJnnXWWO+SQQ1KB2Os3a4Ls9RGg9lcZgW6JFGSpkxTlitoJASvbsloWfp1gXsQz3SoqsJCvX7/eLzVjD3gIGGSMRKT6pgXLK6JPsigjq99rt3Wx33t4KQxu7LaTAeOF5Ye4s0+cONF94hOfcEcccYQs592CX6PnJXfWqLNU1VQINJKYL1q0yH35y1/2WtUrr7zSrx1HSw9hv/baa70QcM011/j1gLyMnnjiCXfLLbd4q/kVV1zhz6dCscdv1gTZ4wNAza80AmUTSxO007osV4UolI1f1QaX9WeYOJnSxeqLQie4fztr73kP47mmaO6GUvWO1r9pf69ZtSSs+GFfdWIEmbdNuBxc2qdPn+63ImQpIgEYm7R0JNxeff8DApI7NRKaikCjiTla1auuusoTcyZqiDkWc9ygLr/8cocLlJ2//vrrvTb2r//6r/39Te3wPNqlCTIPVJWnEMgGgbCgyzyIoGvrNa0U5kKIE/Pi3Llz/RZfe+yxh18jbfd0cjTX1LSu6FGutWVYVsvGrxPM83zG+jPsatxuuzQi0zPueF/Mnz/foUBnGVk7Qn/YYYc1Knhenn2TRd7Wv2l/r1mUTR6mCMObwvZVR0kXnq+sPBRAeETOnDnTnX/++d5DsknBFq2dOm6PgOTO7fHQt+Yg0EhijuD59a9/3QsBl1xyiRc0cXti7fm3vvUt7+L+uc99zrtA7bzzzu6pp57ya9IJ3oMlnejsSskR0ASZHCvdKQSKRsAsYGktnGURIiMGaYkfLtDdWMqs3DAh4XyRhDKuHrStm/ZlPe6iFCdm1YyKIcD7lZ1RIFxN2kYva1x7Pb+0xJyxRowg1pljcDn66KP9bggEXVRqLgKSO5vbt73eskYSc36w3/72t/2+5SeeeKKPjsv2aGyhdscdd7gXX3zRr2NjX1mIOVuo4c6ORp+JnS1vlJIjoAkyOVa6UwgUjYARvaKJbqftLEuRYOWGXXhR6hbpgh1XD4htlSyBaZcaoNzAQs4WpgRnXbp0qcMbIWwJRfkAqUIxdMEFF3hLKC7xncY66HQc6rlyEAh6qMybN8/hys5vwpQ+4VoxXpDjbLxAzDVewig177vkzub1qVr0BwQaSczXrl3rfvKTn7i33npr21YarEPasmWLe/jhh92aNWv8NQQLJnUmfIQGtlQ77bTT3IQJEzQ+UiCgCTIFWLq1NASMoIYtoswB/FlgodIqmHPBaS2cAwYMyLlG0dkHBfMiLKtJCSZjBGK5evVqb/VF0WFkIcpCnBa/pPVAoQxJhYxUISVVJJglVMS8Cr1W3TrYeMLz8aabbvKKHDwtmBeiEr9LfgvEMDjvvPO8Imfw4ME+QnvU/TrXDAQkdzajH9WKDyPQSGL++uuve/JNFM/+/fv79UdM1JBvrOZEYWfSZ00cLwHWJxGJfdKkSf4P67pScgQ0QSbHSneWh4AJfGGLaNUskXkhVBfiVzSBi1JYoMQhBaOEjx8/3lviUOwQwCzrbb6S1oN9m9kGNC3xz2tcJVV4Fa1wyau9yjdfBGw8rVy50t1zzz0+UOCzzz7rf29RJZsr++GHH+4uuugiHwRu0KBB8rCIAqtB5yR3Nqgz1ZTtEGgkMX/33XcdQs57773nG4srIkIME/jbb7/tyTnuUbi0Q9Yh4kzqY8aM8SRdLnPbjZG2XzRBtoVIN5SIQF0IaVEQVV1BURQxNwKQ1sXfgpjxnkH5S+rTp4/rVMHTaT26XVNf1Hizcmzc1SXWgdVbx2IRsN8DxPzee+91jzzyiGtHzLGaH3TQQe4zn/mMjylEZHZzZ6+KZ0mxKDa/NMmdze/jXm1hI4k5Fg2EcY5YPJi0WSPIZ4QDhCmsHVjOcEVk3R7EvW/fvn5tG4FElJIjoAkyOVa6s3gE6mqJzAspE3yr6tJflGW1U6Jo237Z+4R+4p3Be4Y/zqdJndajrOB8adoWvNfGXVpFSN0UEME263N6BOz3gFfjP/zDP/jo/W+++eY2Q0s4Rwwu/O7YQWL//fd3Bx98sPvYxz7mDj300Ep5loTrre/dISC5szv89HR1EWgkMWc9EtZw0rBhw/yE3aoLuH/Dhg3+lgMOOKDt/a3y6sVrmiB7sder32YRger3UVQNTTDP27JalAIgqo3Bc1WpR7BOWXy2319YAUTsF963K1asyGWtfhZ1Vx7lIZD092A1NOMLBJ2EzMfWafzNmjXLSbFjSDXrKLmzWf2p1vwfAo0k5rhA3XXXXb6VZ555pteg/l+TP/yJ+xcsWOAvnHHGGW3v/3AOvX1GE2Rv939VW18Uwatq++taLyN0eVtWi3KZb9cPValHu3qmvW6/v3BMB7zXuIZCPI+1+mnrqfurhUDS30Ow1pBz81RhWQlu7Gx7O2fOHB+tvUq7GQTrrc+dIyC5s3Ps9GS1EWgkMWef1L//+7/3yH/pS1/y+1q26gaixF577bV+Yv/iF7+ofcxbgRVxTRNkBCg6VToCSS0vttTlqKOOcnPnztU+uKX33B8qELUEIU0UdCP4YYutuZxDGKuwfVdSIkK967CNWNKYDrgeswSAOC9ZrNWvyLDtuhrtxq0R0K4LqmgGSX8PcdXHco7yh33NNZ/HoVT/85I769+HakE0Aj1PzHkJLlq0SMQ8enwkOqsJMhFMuqlgBJIKeHUhPAXDV3pxSQle3PZhcRZbC9IGYcdqiyK3iG3Z4gBtmgIpSqHCe5YEqRw+fLhXfrELCkvH+vXr5y3oXO9mrT7PNyG1G7dNj4GT9PcQ19fM59rXPA6d5pyX3NmcvlRLtkegEcQcywfB3IiSS2Jt4s033+w/X3LJJd6VyX8J/TNLyvLly933v/99H1n3qquu8prW0K362gIBTZAtwNGl0hAoipj3uoUr7w5OS1SSEnqz2LLWef78+V5BiwWdcRNM7NKRp2ustS/rNfVFj0srL+8lCMG+adLnpOM2ThHVFCzifg94VaDYYZzZX1SbzWLOtoKf//znvRKI7QXZOlepOQhI7mxOX6ol2yPQCGLOfuSLFy/269Vo3vr1690vf/lL39KPf/zjPjLn9s3+wzc0s2yfhjD2wgsvOLbYuPTSS2OJfFQeOuecJkiNgioikNTygoUF18dOXdlNkAyvpTXLbNMtXHn3vRE+U6RSXivLalqLLVbzhQsXOpY08cf7xAJJ4To/dOjQXINJWfuyJrRFj0srL2sFQ97jqyr5Jx23VdvHPmv84n4PBAzkd8lvkuUPtqwlXD7knb/x48e7z372s2727Nk+bhDKNaXmICC5szl9qZZsj0AjiPnatWvdP/3TPzmOpE2bNvmIr3zGXY59yqMSkztWdl4EBAeZOHGiO+mkk9zYsWOjbte5GAQ0QcYAU9PTJhglJUJVbWbeREEWrmr1vI3btAQXy3kVooRHETMjHxCS4X90AW9HzMoal0Upwqo16rqvTafjlvGAUhES2sQU9XvgnWQGFRRq3GOekmEMUKhNnz7dE3MMNBhemoxXuP1N/y65s+k93Lvtawwxv//++90zzzzjexIL+FNPPeU/H3LIId4NMdjF9iIzS9nee+/tRo0a5UaPHu3/cBVTSo6AJsjkWNXhTiO0dbcA5y3wRgmOlElijhmekEjVYUzUoY42btNabFHe8mzZUcKzItRljcuilo7UYSymqWOn47Zu+9inwYR7o34PuLNv3brVvfrqq94Qw2/9lVdeicw6uATl/PPPV3T2SJTqe1JyZ337TjVvjUAjiDnWDgK4cSQxabNunIR1YfDgwf5z8B+CM+6ru+22myfuY8aM8Zb1vn37+sAhwXv1uTUCmiBb41OXq1GCkK23RchBYTVkyBB/5DsBduqQoohKJ5ZIa2vehN/K0TEdAt1abFHUvvfee65shZQRtbT1yGpcWj5pPWZEzNONV7u723FLpP4mp/Dv4fXXX/ey3tKlS91Pf/pTT9AZs/wFkxleOl2iFMxLn6uHgOTO6vWJapQNAo0g5ggwW7Zs8QIVsLz88stuyZIlHiEm5f333/9DaEHM+WPyxo0dQm57Xdr6wg89pBORCGiCjISldiejCKwJO3W2AGetcDBBMa1ltukWrrIHfLfEkJgAkNG0hDTrdndKjLMal5ZPWsWACGZnI6HbcYuStMkp/Ht48803fTwh4grdfffdftli1JpzZLs6bC/Y5L7Ls22SO/NEV3mXiUAjiHkYwM2bN7tVq1b506wbHzRoUPgWfc8QAU2QGYJZQlYm+KRdm4urNsIPpL0OqVPCEW5bUwmIjYOyiWkY76Tfe53gdDsu+R2DITucvPTSS/7Id1ISjxn7fTVVYZXX76PXx23S37fdZ3ixzeGtt97qd+FB+cq8FUwi5kE0mvdZcmfz+lQt+gMCjSTmJqDQRAL7NN3Vq+zBrAmy7B7orvymC9SGTlaCtQmGRPC+7bbbHC6VzDl1FwxtHKS1lBq+ZR9t3i97X/KycOh2XEJu2NGEZWBgiKKO3wwpiceM/b6aquDL6/fR6+M27e8liNe8efO8d2Scxbyb3TbS1kv3F4uA5M5i8VZpxSHQSGKOq9OLL77oOJKSWvQg8cOGDdN+lynHnybIlIBV7PagoHPDDTdsE3SiiKYEHeetigSYrCsxNwJllnGEWsYA8yXxObCYQvJISSylVRnORpyaarFth3OnxHzGjBmOAKis3c1i27ioJTHdxHRo1+68r2e9FCZc314ft2E82n1HccgchfLopptuck888YQP3mhjjOeR+fr06ePjB82cOdPNmTNHwd/aAVuz65I7a9Zhqm5iBBpJzFesWOFuv/32bVumJSXmRHA/55xz/BZriRHUjdrHvOZjoFOBHoGHvWGbvsYx3L11V2QYETDLONHIEXTXrVvnHnvsMb+PdxpLaRifsr6bwqGpFtt2uHY6LqdNm+a9ylauXOnmz5/vA6mieDLljJXL75zfO0S+FdHJm8hafYo6Rikasvx99Pq4TduPRGV/7rnnvBKJNebswIOS0Yg58h5xgjCyzJo1yx199NH+OLxmS6/S4tJr94uY91qP9057G0nM7QfLMYqUM4EzkbP/5VtvveXd9dCuQjQuu+wyd/jhh/fOCMigpYY3WV188cVu6tSpGeSqLIpCQMQ8HdJGbOtmmY0iTGw/BDHHWgoxxy2f7YcsACZzJfsBMzfyh6BbdQE3ikiZ0J5mP/B0oyK7u42omUcDOe+4444+nkOrmA6djsuDDz7Ye0yglMnSY8bqYwog2sF7liB7FmiVc3mnTvG054pS9NR93BbVj9YfWMxtyQW/b/qLhMzH7wRDyxlnnOHnLLbD3XPPPfOuovIvEAHJnQWCraIKRaCRxJyo7I8//rgPYBOFJq6bCAsIoE8//bQn6exhfsQRR7jjjjvOjRw5MuoxnYtBQBNkDDA1Od2ppQ1LRC/GcChaYM9qGEUJ/pA/I+wQdKxRbBkWTEktpcFnyvxs7ek0iFmZdafsTgltp+Ny4MCBflcTthzNMmaC1SetgiFr/DvF054rSgFX93Gbdb+F84vqj02bNjmUi0bKeQblG4os5LlLL73UW8zZ6hOFkFJzEJDc2Zy+VEu2R6CRxJyJGlc8jlHJiPnGjRu9GxRrK/v37+/GjRvnjjnmGG8RinpO56IR0AQZjUtdzkYJPN24stal3d3WM4roVtEyawTJLE2sjedvw4YNXoilzlHBkwwfrE91jC1g47psi63h2O6YFTFLOy533nln/76sa8yEOFy7xbMshWXdxm0c/lmfL6s/sm6H8ssGAcmd2eCoXKqHQCOJub3YOEYlBFWuYSHCav7ss8/6ddJoVU8//XTHFmtKyRHQBJkcqyreGUfcUFzV2aU5b6y7Ffzzrp/lb/Nh2PKH4hK3T/rf/uyZ4LGu2w7ZuC7bYhvEstXnKEJNG0j00/AP1sjipTJ58mQ3YsQIN2DAgMjs0o5LyuBdiFtwlq7skZUr8GS3eJa1xKdu47aoLi2rP4pqn8pJh4DkznR46e76INBIYp4UfgSYt99+26+rvPPOOx2WA1yftMY8KYJ/uE8TZDq8qnp3lCBbRQtw1fAz4ltVy2xSS1McrnUl5nHtCZ8vmwhZ+WGPhrSKMcvHFBGMS/7of+KpQO6j1njb+A0rbjoN/hbGt+jvhkO3eIoIRvec4WvjjLuSxECIzi35WfVHcqx64U7Jnb3Qy73Zxp4m5rxYIOeLFy923/zmN33AkL/6q79y06dP783R0GGrNUF2CFzFHktraUORpeS8tZm5pGhBNSn2SQXauPzq6soe157weSOmZSlWrPxuibHlY+2AQO22224+4jrEyf7oT/4g6iQjWt0S2TCuZX03HLrFM6lCq+m/j3A/Gr42zrgepfAJP9ftd/VHtwg263nJnc3qT7Xm/xBoJDHnxcEkjqAclRBEsARyD1HZiUTM9moEORIxj0Ks9TlNkK3xqdvVsgSvuuFUl/qmJeYQNvtjrkToTrJNVlXxMOIZVpzwnUB3BLx76aWXStu/vVvCQV/Rx90Gu6uLx0xcf5rCAQVjFq75Ng92S/Cr+rtIW6+yFbfqj7Q91uz7JXc2u397uXWNJOYIKZs3b/aR16M61wS0LVu2+P0wn3nmGR+dnX0v5coehVjrc5ogW+NTt6vtBF+ztNWtXb1a36TEz/AhrgDB3uhnxsIBBxxQq+3SrB12NIE+bOEDl1dffdWtWrVq27ZLtJdE24cnXNNt5XR6TKo4gXjusssu7rDDDnMXXHCB7xMUJhCm9evXu+XLl3fVjrKJV1L84vrTtmGjXwle2W0wO5sHm+JJkBTfuPuiFDdF/l7UH3E905vnJXf2Zr/3QqsbSczZLg0rOMeoZBM80dhZx0cQJNz8JkyY4E4++WQfnT3qOZ2LRkATZDQuOisEqoAAhBRrKsG9brrpJvfEE0/4tccWP4A6QkQhNuz1269fP+89BAmEDBZFULPGqh3RhMDZlpmQOKzmZQQ77JSYz5gxw+29995+D/qFCxd6Iko70q5ND+PejvjyriwjtetPgrcOGTLE75EOpmyZmsX2b1GE1H47jJe6/j6S9qHJS1VRUPR6fyTtt6bfJ7mz6T3cu+1rJDHnB3vzzTf7SOtRXcvL1NaFIXwOHTrUTZ061R1yyCHuoIMOcuzrqpQcAU2QybHSnUKgaARw1X7uuecc5G3BggV+i0i8hoLWLuZE5sEjjzzSjR492hM+tpDEjd0ID0eW+9QltkCUAB9sMwQUpQUK2tdff/1DHlZF7d+e1KPB3llHHXWUmzt3rps2bZq3oK9cudLNnz/fsQ95FtscGhEzzzLGK2Sc8vkry2OmXX8aQeYdzlgFlyyizCdVCNTt95F0HjJFTVVc+nu9P5L2W9Pvk9zZ9B7u3fY1kpjj0oegsmLFisieRQhF0DChc8yYMV4gHTVqlN/PfNddd418ruiTvBCJpotFH6EEK4DtN4xwhLspwuMee+zhBREC/ZB4cfEMEecROhE+EbJ4BqHa2s3RrGLdtE0TZDfo6VkhkA8CRrDM0oXFHHL+4osv+hgbRlJNUYnH0CmnnOJdpQcNGuTnlSTBw/Kpfee5htuNFTnKkozVkz/ut2Ow1DARZqsy5lrmzCxTp8Tn4IMP9nFSHnvssUwIaLs2Ga5FE3Yr18ZxXH+iWJo5c6ZXWLCzCl4id9xxhw/umoXCwvopvCTCXOjL8iRo12/dXu9UcRT+vVg/ZjV+erU/uu3PpjwvubMpPal2hBFoJDGHjPISJwBMXDJyDlHt27evd+GEqEJ2EciqkCDWrIFkn/Vly5b5dYQQdIg3dWQfW6wECGhYUXDjI2Ehe/75592aNWv8ukNcNBEmEBx4BgUE1hYsY2j5ESy6SZogu0FPzwqBfBAwwdUsXRAaCApKO4RkSzYXQmYuueQSh4s07uzMheSBYN5quy3LpyrHcLvjLMlgYDjYMdgG5lhIeHhNN8rQLBNlQ1Z4Z6VxScezizgptC8Ll+12bTJciyamVq6N47j+NA8HdlU5++yz/buNJW0oLqIUM0bkIfOzZs3y71L6PM4jwPopK2LZDu+qXO90qQW4EgPBfi/Wj1mNn17tj6qMi7LrIbmz7B5Q+Xkh0EhiHgeWTeRcb/UCjnu+qPP2AoNQY/Vfu3at27BhgyfcCMlYd6g/1pv999/fTZw40c2ePdt/5jqBgBBeVq9e7Yk9kedpO4nneFmOHz/eP4f7/r777uvXVsYJJO3arQmyHUK6LgSKRyCppQtiDglHWYeLNEo+5gkIfJnRyjtFLGm72+UPBkUQc6tHlKs2cz2JPkIJixVy8uTJbsSIEd77KYsgZ1Z+3LFs1+Gk/Ul/MY7N1R+FNVZzFNR4i6D4aIUnSmulDyPQLTGnT8ij210DPlwznellBCR39nLvN7vtPUXMIby85EkIXFV1PUMg3rRpk7eS/+xnP/Pu6BBphDFINZZ9SDRCBq7trAXF+o0LPoIaloX77rvPewwceuih3kJOkCC2BmKtKSSf/BH0Tj/9dDdp0qSuPAU0QTZ7klDr6olAGoEazyEs5kT7RtHHs+vWrdtGaEyxx7zDvBEkiFUjNEnb3a5Xw0Qv7Jrb7vm019MSYPoki23B2tUzSmFQ5HhI2p9hRQrxErDW8t6ro4KpXb8Udb1TxYj9XrCQZ7FrQFHtVTn1QEByZz36SbVMj0AjiTlCA3+8EBAqEF4gu3znJU2CxOLCDcnF8oxwyXcEz04tx+nhj37CSDnRkxcvXuxY5/mpT33Ku1Tifkd9aR9twW0fawou6bTx6aefdkuWLHEPPfSQf+60005zRxxxhLeK446KFZ3rDzzwgHdXRRDHUkb7bY16dK3iz2qCjMdGV4RAWQikJTQEwDz33HP9khi8dFg+k4ULcNHtT9pu6hWe74Nzf9g1Gks1ylz7gwjyF3wmi7aax1Q7l1+7r52LN/M60fZx8T7vvPP8+4C2YclslXjHdOJij+ImS1yS9meYmJsrNUp43pXt8GyFRS9fSzrO7PfCUpg5c+b4gLqMMZQi7ZZo8Az9xZHxw3P0p5IQiENAcmccMjpfdwQaScyxJPMyYdsY1pixTy1rrlmPh4WZxMSPFXnkyJHeQoS1CNKL0AXRLTPhevejH/3Isb86Qh9rwo877ji/jRtClglUCE20h3uweBHU6ec//7mPugy5Z/9hgjnh0ocigvtxa2fNHWsSEXg+/elPb7fevJN2a4LsBDU9IwTyRSCtpYu11BdddJFX0OFxA+HLImhWvq38cO5J2828yVzPH5/tO0feIbYGGcUl7wcC4hWx1t4Icbu1zHYfLtqtiA/1xiuKdhx77LGOYKdJiHmnhIxxxPsmK4+0pP0JkePdaK7sZrHl3QiW7fD88EjSGRBIOs7s92Jr9ol5g3yCfNBq1wAMIngC4uFwxhlneAMExgfGkJIQiENAcmccMjpfdwQaSczNFRyCi8WZ4Gmsb0KoMtKN4AXJhZwjqCC0sM0K662xopeReAHyhzKB/YZfeOEFX7dx48b5PdapK8ISgiMCCPXE0s0RqwBE/p577vEuqAheRFnG0k77LFn+N954o9/nHWs6Qid/rFdvlXC1JCCd/fGdRLn333+/D6J36aWX+hdrq3x0TQgIgfwRSEusIFQnn3yynye/853v+HkIwRpCE0xxBIg5KEkyQT8vokSdUTricfTd737XvwNs/mfutAQhgLSyFIh3AUSS9wKJ98R+++3nrX4QDCN35EtifsVLiWtlb5MVdjVnuRL1RAlLIFBIDsSc5VC8C4Z/YJFMUu9uCXHS8eABbfEv7Tg2i23WCoIWVeyJS+FxhkIo+HthXAWXuPD7SrLUgt8kcwpyzkknneRlMQwmFjiO356SEAgjIGIeRkTfm4JAI4k5a6gJ9kLgNF4eTPqswYZ0I1CRIO9YgxBiEBCHDRvmhRdeLFiay0i85BBCcB/96le/6us+ZcoUv686rngoFhC4EBoh5AceeKAn1FjUEQ5xP8USjuWcFxvrywkKR9uCCVzQYIMNWm6EtmOOOcavYQ/eF/6McIuSg3JM2cE9uNObEuGKK67wmu/ws/ouBIRAsQgYAeZ33sqiapYu5ouxY8d619Pvfe973tIFOYsi5pAuiA9LYcxl2ObWdq00opWXa7ERc+aoX/ziF95DiDmP+d6s48y1tBsShwITEk6bgsSb9wbKR1xxEQJfeeUVrzilfZCJ4SEiwpxcRgqvTae/aTvLmp566ilPjnBlJ9I+fcT7Ikig4uoNFmDG+6iIqO9x2KUdx2axpX/ow6AyJq4MnW+PQHicIQ8Efy9hRRW/wyTjh5LpIxRIKIxMHmE+Iq5O3PhsX2Pd0WQERMyb3Lu93bZGEvOVK1e6BQsWeOGErdB4QbN+kqNZw7H6QmD5cXPEuoA1AVcqXL/LSAisCMK//vWv3bXXXust30RNx6pjpJ2XI58RMM3aj2DJywzX/ZtvvtkLkLQXKzgW8bAlHIENyzpKCYQ1nv/kJz/pBbZW7RYxb4WOrgmBaiKQ1NKF8hLixtKfNEQMcstchOspcxgJ6zOkKEiM0gr2aS1l4fyNoBJXA2KO9Q4hHwIQJKgEuxs8eLAn5ihsIYJY0Ddv3uy9BrLabivv0dFKIUE/kEwhAXltR2CrQswNt6Tj2KLWi9AZctkekyrWko4fqx3knHGKnMbYRHmUZBs7e17H3kJAxLy3+ruXWttIYo4L47x587wllzXUWJ2xlkNCbd0bLxfIOGuxn3zySffTn/7UuzRefvnlnsyWMQgQLBEMH3nkEW8xx9pBvXFB5CWFpQOBEqUCFhEEz2B0dUg9xByBkkA/kHIIOkJnMNkadvJA+CV/cAq6vAfvt8/Uz9zYOfKdJFd2Q0hHIVA9BMKENc7ShdIS6zjBIW+44QZ/hOxFWcyDa3lZBoS1mfuYv0jkhStrcK1xFLGCBJOysECH8w+6dHON+R9FJ4rIoEs31mTqCSngvWB/y5cvd3fccYd3hcfyZ9ZBX+EP/jEX425bFddpI+btXPiT1rsqruyGd9JxXPbSAqtvU4/mwcDvnd8KKUoRl3T8BHFiHmDuQO6pyu8qWD99rg4CIubV6QvVJFsEGknMWVf+la98xb80LrvsMk9Sg0HTDEIEGdwpuf9b3/qWX7d9zTXX+PvtniKPRnwh5tddd53D8g+pJijKqaee6hUMWHtQKGDVot4PPvigtwBdfPHFvv633367j0SfBzGPw0ITZBwyOi8EqoMAQnSr6NTUlOvtonwbscMjh3kJCxdknyUtRl65x1xbjfjiCp7Epb6dJTeMqBEFFJWt8k8aBC0poYDIBxUUKE+Zn7NaWx1uZ7vvWdfbxkvS8VAUkbJ65bUUoh3Oup4MAeun4PjBkGBeKXG5VO13FVdPnS8XAcmd5eKv0vNDoJHEHNfDr33ta9uIOVFaEZbMWm5w8uJAmOF+gqFx/corr/TBR+yeIo8oClhHDjHHlZ1126yzQuA7/vjj/fpPBEHuwy1z0aJF3kJOG3DBp41Y/nnx5eHKHoeFJsg4ZHReCFQHASOwcZYuasq1dgTX1qTjAs6aUJuLWBIUZQE3V3F2xmgVndkIf1qCF0UAoizcKGeTbBuW1AUXAsGc2+la+6xHRtb1tvGSdDykVah02n6rV9w4xuqqVD4C1k82foj7Q7wCvtN3NleEa1q131W4fvpeDQQkd1ajH1SL7BFoJDHHBZHgRRDUT3ziE37NONYS1psbOUeYwx0bt29cxh9++GF/nf03WaNWRuJlBenG6vPlL3/ZEcQON1HWWX384x/fLjgbLzWIOQSeyLuf+9znvGslz9Ju3N4h9TybVfC3OEw0QcYho/NCoH4IhF3CEaRZm0witgXzCeQZ6zCB0fDeabXfOZZ15lRiYKAAffzxx/08x3wXTJ1aypJaiqm7Wbi/8IUveIUn65DD2zJlTXCDbeSzEZasiWVe9W43HvCYQHmstd3hntZ3ELDxs2zZMverX/3Kx3tg3kChF0XOuyXmef2+yurNprUnKxwld2aFpPKpGgKNJOYIklid169f7yd+3MERDonwiVWGBHnlPtysXn31Vb/GketRUcyL6jQmYARgXNSx+FM/1sfjlh6Oms69QWLOfuVs+8N6edxKWafFWkq2SyOokyWeYzs2BGRcS8HF/sJB4uyZdkdNkO0Q0nUhUB8E2q3lZZ7BTZ356Qc/+IGfT6Is1GYBh7AxD+ENdOeddzoEdMh0FDHvxAKdhpBCzFFYnnPOOZ5MYv1n7XkwJSX6nSoSzMKftSt2XvVuNx5syYLWdgdHkT4bAjZ+mCPwmsFDke1ViU3DHIBMEkyd/q4sj7x+X5Z/0cemtScr/CR3ZoWk8qkaAo0k5hBytsnBco6VBpKKuyGWHj7zIkCYs6jsfMYlE80/24zh7hhMWIZ4FoG0iITgSlRktiUj2jER15O4sqOAIKAb7WItF9u+QdiJMm8Rk1mfTnCnW2+91WNA0Des8pB3BKtOkibITlDTM0Kg2gjECYQIzpBABOwkQeIIuPbnf/7nXvl57733eg+lMog5FnM8ppjjjz32WMcSJ+qGQrKI4G9GUFCcYjGMC8LXKcG1/gqu6W2lMOl0yUDWCoVq/wpUu6wQIH4FVnLGJ7vm4DnD+GQ8BZMp9NKOz7x/X8E6FvE57/YgB6MYydpzpwhsKENyZ1FIq5yiEWgkMTeLMJZnSDfCGC6LTPhBV3au4WbFxMQ1/iDu4W16EN6wsECQi0hEE37ggQf8xIM7O0qD0047za8bx7oDuWbLM9rHfSgOCP4GuUbgg9jjms+aSp4jeBzRg7FYsXUQxJzncO1nH2KIOfhwfydJE2QnqOkZIVBtBOIENwTsLVu2eI+dJNuqMX8yD5Ef2zSydIglO8y7wdSppSyppZi1x/wx76HEpF5/+qd/6ud1vmOtx5OKejIXZr1dmrn0ojBmvS0eB5RFol7Du3QJt/4i31ZB8CxGQNo14ZZ/XQX54FjT5+IRYNzwu2d84u3H8heOGE9QmpHSbOcXbkHev69weXl/z7s9psirq6JNcmfeI1D5l4VAI4m5/WDRzJrgA8DhoDB2zQS28HXrFAKpXXTRRZ4Y27k8jwiErNukHZBvEuQZSw9CJQqFdevW+TXouOFjzccixXUmcwS/++67z2uncYXnPJZ3BGpIP2Sf5xAETz/9dC+Y4uKJYNxJMrx5FgUBeCkJASHQTASYf7B0IVgnIea4jp911lme7P7sZz/z81MeltywpdhIts3z1hvM88x1zH+QU5b87Lfffp6Y0zYSSlruwWqFspM5jqU/wbX2PJ9kbbUR2rwIs7XLjlECfSf1tvx0FAJZIpD1+Cz695UlFlF55d2evC3xUW3K45zkzjxQVZ5VQKCRxPzll1/2blIIVEGhLEy87Vr4fLhjsFhjde50DXY4v3bfzQKEKzuWD464gHEezTICI5Z/AtqNGzfO70OOuzoukKaRhtBjHUeYxMKOYGYCKdZz9i4nWjJWI/YMJd92OMTVWxNkHDI6LwSah4DNT1h9k7iyM3cSbA1LLUpDPJpaBYvr1JIbJr58x0pnpDTYE8x1eEcNHDjQL1Fiz3Xm1OA7AbKOkpH5Hy8q8goSd+ZbrrVzPTfLVFhxYHlZvVAGZLEvelMEb8NFx2YhkPX4TPP7QmbCRf7cc8/1yxtZwmJelFVBOU17OpkvohQjwXkvqcKxbLwkd5bdAyo/LwQaScyxlGCR4ZhFQoBjAuRYVGKiZJ04gizWc9aO4z6KcIjLJcIgkdcJ3MaRdfHmik6UdoKsrFmzxj+PqxjuSryAcFnH5Z31lWPGjPH5IJR2kzRBdoOenhUC9UIgTnBkvjXlHvOXzZu2VhTPHTx1mM/MldtIM4rBbgVCEziZj4gxsmLFCl8eu29QHxM+DW3qagpJjvbHdeplLt94KzHPItSzHMhIPfNmEsE+rSKDuXnu3LneGs8yJVMKMPeDPYm5HAUtfySuha/TXhS11NnehfRJ0nr7jPWvYwTAP6pfrN/st9JxATV/0OaRbl2pk/6+wBvs+S3jAcmONUXLdUm6LGl7aIvtMhGcL5APo5KNx7ACM6ulBFFl5nlOcmee6CrvMhFoJDG3Cd+EmG4BRghKIoB1W074eVzP33zzTW8tR7hE08zkigCJsIaQBSHnyHcT0rgPQYxneJ4XHwICLyZ7DmLPc0zi9ly4/KTfNUEmRUr3CYH6IxAn4LFExsgG9xB8Euu3WcCxQDOnoTjMI/iZWeLIm3XsxNKAoKOk5F1gSoBgD1h9Odpnu24WbHbFOPvss/12YLwL7I95k7/wc/a8HdO6/of3RWeOBrc4AkM5UddR1CK4Uz97FwYJfbt6W/117AwBk0Pi+o2+6OVk80hYoZT0d2XYJf19cT9jHi/DE0880e/AwzIb4ktUKSVtDzgxN4TnC+atqGTjsSjPnag6ZHlOcmeWaCqvKiHQSGJuAJugBknlD00kLwEIKUIiggsCDVYFhDZelFgTEGaUkiOgCTI5VrpTCDQFAWJhrFy50lu/2f6IJTdG9hC6x44d60466SRv+WWpDRZnkgmIeREWlu5gBcIqf/vtt/tgmMzxzP1pEoJvGotUXN6dCtosAUDI5h0VpchglxBih5DwpkIJS1kknkvqau8f0L/MEDC5I6/o+5lVtKSMsiLkVv3g74vdZiCeJuvZPcEjRJzYO2yNe8IJJ/h5KomCLZhHnp+D7UkSwyMpMc/LEp8nFq3yltzZCh1dqzMCjSbmuDZiLbE/3NsRcnCpRGBkzTjnWL/NeQg7gdT+P3vn/rxVVf3x82P+IAIJg+HIJeQmKuoACkneysyxyxiijYZK0Q3GLM36C5pKsybFoUQjmEQbhhoioxDTGVBgJrwQF0GYGEBGiJnsh379+lp8F26O57LPec45zz7nWXvm+ZzzeZ5z2ee9915nvddae23CB634I2AC0h8rO9IQ6AoCZYl51Yp5HE9kObIfYr506VLxnKvxNX5s1v9FPVJp1yqrEGPMgNwxJUlD/8GOggGE+fF4/yisCX38+PHT4fr8PtYzOZ1cwP5UhoBOqagr+35lFe3Thao2zMXHF8s4Zo13nC8YtJhi89WvfjWaOXNmXyIi0+CPPw+RP0mGxaKGw7oIf9pz1P296Z11I2zX7xcCnSTmCDa8JqznzcsRYg4B58O8bRKfLVmyRJQaPCski8MjwVJkzO8joY8VfwRMQPpjZUd2D4G6iWZoiOnzunMVIY7IUtdjzhxtspZrKDtEsQnPlCr+WSGbSnDBln33f8W7KmLuUx/uGQ+dx+NNojyIRlKyPM0Xwrms0gEhZJoTxZ0jn4a/tmOvocRyQ/sjfQgs3XGR1G6auyCtXboKZV2RBDq+tm3bFj355JMyVjBoEZGTVIoS2qRr1PmdPk+W/OL+Ki80hwee86wpl0bM62w1u7YhUB0CnSTmeA5Y55vkPwg3wth5GSKY/vGPf0Tjxo2LHnjgAclIDlkneznZzxF08+bNk+VzqoO4+1cyYt79NrYnTEdAFam6QrPT79yfX/R544ojctYl5kQglcka3OtTKeFMI0j8DnFlq/v6v3vvqhT4vPpwT5dIa7I5yMXKlStlyUzeU7y/3EKYPctnUsgnglfNLXmKu7bjoPRbF5s69hXP+LiIt1teu9RRtxCuWVckgY4vptL8+c9/jl566SXR80g0mVSqMrglXbuK7/R50uQX93Dlha+Bpy5PfBXPXOYapneWQc3OaQMCnSTmeMj/+te/SvZfXgYoL4QFMgdv3bp1EgL4gx/8QJJm8B0v0meeeUasjd/85jfl+zY0Xih1NAEZSktYPZpEoC4PUJPPUOZebVHwkogACi0FzzJEiozlvAMIy4972KomUHn10dDzKVOmyPxw5u9nxElHMQAALZBJREFULUeX13ZphgWMJ5BFmwOdh2Cx39syLoo9Ve9H10U04zWDiO/YsUOIOXoeRJ1783FL6MRc6+orL6ZPny7OJiJoskrXDEemd2a1tv3WZgQ6ScwJX3/66aclIQ7zh1B0CE9HUDPnkHCfhx56KMIzgXJNqODjjz8uYZb333+/fN/mRm267iYgm0bc7hcCAkmKkyqBXZ7jC6nDg0uYbpXJiapu0zzDCUTq6NGjEl21efNmCcUvEgpetL559dFkbbyfwHjr1q2Z+ObdP42AUI+DBw/KNK+kuetqIPBV+PPqMSi/t2VcNN0eTRFCInaQS0Q//vrXvxaHC1ML1Binz51msEpbZkzPa3rrKy+QGxgRWXEnqzRlIMmqQ5W/md5ZJZp2rZAQ6CQxJ1nGz3/+c8nMee+998q8ceaPM1/vxz/+seAPMWduDsIKpeynP/2phGF+//vfl+9DaqTQ62ICMvQWsvpViUDbFRytf9m5xW0jIEoM4iHbEPM611VP63Np9dH1xamXj+Ej7fr6fZyY874j6RVTvSAvGFZsDrSi1fu2beOi9yf2u0JTkQQ6rro2lUCfKy6/VF4UXXYvyaCsxgsMk20xzJne6Tf+7Kj2IdBJYr5ly5bo4YcfluVxvvOd70SsQ8t8PL6PE3MEEsT8kUceEWL+4IMPGjEv2I9NQBYEzA5vNQKqKLVVAdT6l1X0mlK0q+okaYYIDBNkcK9rXfW0+qfVByLNB08ZK4Xgza4ylJ0IMbyChMivWLFC3ntJc9erDuFPw6Fr37dtXDSFf1MGCx1XVc/NbgqntPvoc5U1pMavW7UnPn79pv43vbMppO0+TSPQSWJOaPqjjz4qyXC+8Y1vSGg6a5aTtdMl5oS589LAw07oE6FA9913n3jYm26INt/PBGSbW6/3uletOPReo3qv0FYFvCqFTIl9Ww0T8d6hz1PWUBG/Xq//a33i+MaT62FsJsEehfdYXvI38qzQd3k/ZhH+toT69opz1eentRtt45ZBM3w0RcwV4654hPV56tpqfw1F7hV9TtM7iyJmx7cFgU4ScwbssmXLZMm0z372s9EVV1whWdnxFLjEHEWFJX7I1L5hwwZZLo11LS+//PK2tF8Q9TQBGUQz9K0SbX/BFwWuaUWzaP3Sjk9SWDGqUIrMiVdDTFc8U/o8VXmk0vD3/V7rE8f30KFDp5dDo65Dhw6Nxo8fL2134MAB8bJnzZFn3fMTJ06Ipzzk3AC+OIV2XFq7oWNktQuhwxhDdEWD0J6r1/o0bcisygDZ63OHfr7211DkXlG8TO8sipgd3xYEOknM9+zZE61du1ZI98iRI6MLLrggmjRpUoRig2ecF+CiRYuiMWPGRBzLixOFhSXVbrnlFjm2LQ0YQj1NQIbQCs3XYVAVoLYRc1XA4kSvV8KQRPTbOFex+ZHjd0fFl9woGI4xLJNBnkzy4KzEnGRxHMt4ZL4pnnQ+kPZrrrkmuuyyyyRrMxFhPnPXIYmEvLMu8sKFC2Udepa9w9NrJR8BbTeS0GpyvSLjQsdrWwlTHCE13MYjQOqOJND7ttUjHMfR/j8TAdM7z8TD/usOAp0k5seOHZM1zPGEo9SgsEydOlXC/VjjEuv19ddfL2GArHeOwjJjxgxRYKZNmxZB5q34I2AC0h+rLh2ZpICiVFKKeGDbhknTHqBe8VEFtWrFeFANM722h+/5ii/ecPKgMOWK6VhkkucdBvkmAdSoUaOiiRMnyprxkHQSnZLkbfTo0dHHP/5x+R5Szdj0mbtuoey+LZR8nLZb2eXodLx2hVCqoaFqw2Ay+h98q/ftioHjgyezPRAwvdP6QVcR6CQxV48WFuvnn38+OnLkiKwLi6fhrbfeEqVm8uTJ4gHAU/6xj30suummm6JLL71U1jhH2bHij4AJSD+suqIo6HM0rWj5oVz/Uao4V01066p53YYExaMrRKKudih6XR1nb7/9drRp0ybJpM4SarzPIM8UPLEQc7ziRHyRS+Wcc85JJOaQeZLdtaXfFsUrtOOLjoteCX2/n1/7axoRJtojvkxfkUiCfj+f3T8sBEzvDKs9rDbVIdBJYs4LEWWUsD3WLudlQNgmnga+o6DMQMhRZsaNGyfh64TrEcKnSk91MHf7SiYg/dq3qKLmd9Xmj9LnGFQFXxXQthgm1FBZ17rjikeaQt7VubN1jzx3nDEnHK/5u+++G2EAUUzBHi/5sGHDxNDMPhFgfOKh7IS+00Zt6bd141v39YuOi7ZHIGl/TTPQQcKRRWUjCepuL7t+uxAwvbNd7WW19Uegc8SclyHCHwUG7wAKCi88POWsWfvf//5X0CG0D2KO5/z888+X+XooNVaKI2ACMhuztntC4k9X1gP7iU98QqaPMA8WJY7CPoYwPko24vcL9f8kRTpED5BLzJcvX356eg8kzS20AfJS5xZfffXVp8Og3eNsvxkEfMeZ1kbD29nSD8mtwpjjM3fuXFmfmDY2z6UiFsZWCXxoBhOtV57Brej7TSM30gh80XW5w2hFq0XTCJje2TTidr+mEOgUMedFwoc5eS+88IJ4B1BK8I7zEkDRcQmBzscj/I/5eigtVoojYAIyG7MkAkc/pbRxLrZL9Ipkd77yyislzJZnZuknCss9MXWEsdg2hayoQgrp7UdRgvfyyy9Hv/zlL2WuMnJQjQhaJxRmZCDrXS9ZskTIHGHRRBFZaR4B33GmNWNc6Qf5wtgiCmzOnDnRggULxODCODPPpSIWxlY9zaFFIGm98gh00febRm7kEf4wWsdqESoCpneG2jJWr14R6CQx37FjR7RixQoh6XfddZfMv0PhRPG0Uj0CJiCTMUU57mLoqC9hYMxB6sjdcPvtt0sCRsgsuR64BoXIlY9+9KMStcKW//tFYJNbMf9bXwU2/0r1HMH61uANMX/kkUeiLVu2SL9U45DeFVJHm82ePTv63ve+J8Sc9sBoaaV5BHzHWVrNaEvaDoPY4sWLIyIgXENL6P027bm69r0azsjg3uT68vp+ihNk/ifa8OTJk9Hhw4cl9DxJXg8fPlwMqkwRRLYwVYZPr6s9dK197XnqQcD0znpwtav2H4FOEXPg5GVDghw8Q+x/+9vfjmbNmtXKUNn+dw+/GpiATMZJFd/QPCHJtfX/tqgiOWXKFFmGkGzRrJSAIqekEDI49v11fCEN06dPl3wPzJdtU0lTcCFGfHjGfhaXmP/sZz+Tucoo39oGWjcl5nhYv/vd7xoxV2D6tPUdZ2nVo+9lLXsWer9Ne66ufe9rgMlrz6K46Psp7hGn3zHtb+fOnaeXe1NZ4cprVrA577zzIpIT4gghBwI5fJTEa30w7iVFbrQtQkqfx7ZhIGB6ZxjtYLWoHoHOEXMgggg98cQTErp+xx13SAgfngKbQ159B+KKJiCTcfVVrFG4sjxbyVfv37eq0OUZHAhRHzFihCRYZNkmPOUYzXS5J56AsFoSMCbNhe03oe0fwtXe2e2HGCxfeeUVmdKTFMqOsoyH1ULZq22DMldLG2dMA2FsQJb0k3T9qolc0j3su94RaJqY503BQV4go1lKFrKd5gFniVmMqfy+dOlSke0YATH6uaVt7ze37rYfLgKmd4bbNlaz3hDoJDHfvXt3tHbt2uj48eOSbZ0EbxdddJGQhN7gsrOTEDABmYRKJJ4DPAh1ZcNOvmv936qnLS9ZEckViVYhhwNeFRQ4xiQeGreYR8VFo/p9V/En+RsGFZTvJAXakr9Vj3/ZK6aNs0OHDp0m5hChuIFF72eESJEIe+sazpoIZU+bE05/Y7ofBiGMqCQJ1FUAXARVXkPKb775Zjl21apVEVMI0+RKVuSGe23bNwR8ETC90xcpO65tCHSSmJOB/U9/+lO0f/9+edGQ/O2SSy6RsCs8QjrXPO6R0xcOSXOs+CNgAjIZK5cQFUmShveY0D/6Y8glScFTksAYI8yR+eWshMB43LNnT5REJEIjEEqIIK4oqRTkBvXkE5cbIbcRdWtbP+wa/r32j6RxRt+EBNG27733nswHZl6wW/R9Fk/+ZiHELkr930+LjKBt3dJre+q4yjKo0q84jg+ynA/7blF5jU41f/58kYfPPfdc9MYbbxgxd4Gy/VoRML2zVnjt4n1EoJPEnLlRq1evjrZt2xadOHFClGm8d25yKZTruILNuq+f/vSnI8JurfgjYAIyGaumPSHJtajv27yQSE0atmvXrmjNmjUyZzEt1DEkj4oqyvG5l23NHt+2ftg1/HsdgUnjjHB2PJq83/bt2ydL4NkUkV6R7s/5PoQZgtzrlB8dV1lTkFwSzr77v6IDMUdeQ8xvu+02OebZZ58VYp4m39s0VUuf07ZhI2B6Z9jtY7Urj0AniTlzo3hRMJ+VUGJeFlib8YSz5aVCiRNzwt3nzZsnYe/lIR28M01AJre5jyLEmb16QpLv3ty3+pxKZBlvjK1///vfEdErKIIsX0g4O8eqV11rqIqerp/dr4iBJAKkXivaqK3Z47V9shRy2qLf/bAt+CuRajqiQttRx9l//vMfGWMQc/IGMFVExxYRK2NbnlRR5cOgbJMiI6psT18DXR7eKq8vu+yy6M4775SpSuvXr49ee+01S/6WB579XhkCpndWBqVdKDAEOknMCWHfsGFDtHfvXln2g5cb4Xv60VD2eFtMmDAhuvHGG81jHgcm538TkMkAqQKfFTpI3+zVE5J89+a+1edUooKCCUlAUXvxxRejN998U7L8EtLOsXzcoqGRacs6ucfWuZ+kGGtdMTS0leho+4TeD9uCf5wg0ycx+tYdUaHtqOMMAxhkC885WbQxhHXBkFTnGA/52nUbpugbPjlP8jBSeU3+kG9961vy/kLWb9++3ZZLywPPfq8MAdM7K4PSLhQYAp0k5seOHYtef/31iG2Rwlx05sSypaiFmX13/Vf+t/IBAiYgP8AiaS+JcFTpCUm6Zz++U+LgEkDW5uV/nbvo1guyywdS04/ldLS+EC2U4n/961/Rq6++Kt7HLq7HG2o/1HZw+02I+NdNnNyxUWS/X4aCInW0Y/0RqKs9ixBz5DJFZbRb+3hkDVP/4surdfH95mJg+/1HwPTO/reB1aAeBDpJzEmCgxchngwnD0KWUxs6dOjpZdXISIq3j3LxxRdHI0eOzLvEQP5uAjK72UNV6LNrXfxXVSjjIdPqKY9fkcgVvC9j3w+57cdyaVpf6nfy5Enx8K9bt07WWu/ieryh9kNth3i/Ue+v9ps4IWDqAzK7qWRmSYYNjAoUCAz9+Oqrr5YlpMaNGxcNGzZMq17rVg0b6knnZmDC2OKjJKvWStjFK0OgrvZURwPG0qzs7/QXZLNu2dcow6QIL/L3oGshQw8fPmyRG5X1BLtQFgKmd2ahY7+1GYFOEvOqGoT5scydorAsyMSJE6u6dKeuYwLSrzmVgOgcUc5qIgTWr3a9H1VE8UPpO/vss09nbr/++uujK664ImqC0MQJKgY4lEpyU2zcuFHmwkNy1OujyGgIZ79C7qtS2Jvqh7719e03kAOSSF111VXR4sWLhQT7RDL51kPbOb7V80P36Mfrbf8bAi4COu7zDGBMyRgxYoQ4KdjHyKOykDGYZoDS63f1/eZiafv9R8D0zv63gdWgHgSMmGfgypyppUuXyhHMpZoxY0bG0YP7kwlIv7ZXBb+rni3fUEmUO5Q9QiAh5BBdMvyef/75koCMtbTrLHHP58GDByWUnSgbMlurh5/2cgvEvJ/Z46tSfJvqh7719e03ahhBDi9cuFCIuc+ygr71cNva3dfz8whNvz36bp1t3xCII6DjPs/AhBwmgolkuHjDkXkavUIfT0uCqdfv6vstjqf9318ETO/sL/529/oQMGKegS0hXz/60Y/kiB/+8IeiCGYcPrA/mYAc2KY/48GLEiyy+n7lK1+RcUUCvCFDhpxxvar/UcUxrpgeOnRIwjbxCkHC1DsUv3+/iHncw182yZc+f92Kc9H6ksTMJymVeszJA/LlL385mjt3rkRYMP0oqRStR5pByNejr4aDfkVUJGFg3xkCcQTihknkoco81yM+bdo0iWiCmLM8H6VLEV5xXOz/diFgeme72stq64+AEfMMrIyYZ4Dj/GQC0gFjgHd9CYwSLAgMkSjMy2U+LvOF6yxpnk+UTkLrIa76SapHv4hXkiJNPSnUe6zn3GZ9/rpDTYvWl3BZlv7ymfvK806ZMiX64he/GH3yk5+U3B94zZNK0XqkzQkvYnDqZ0RFEgb2nSEQR8DXYDV8+HCRycg9ZAfFchfE0bT/+4WA6Z39Qt7uWzcCRswzEDZingGO85MJSAeMAd5V4hdqyK+v4UCbEBKoH4gwBBJldfbs2dE999wTzZw5U0LvmfecVTgXL3VRT7WeF/fwsxQdxg0Knq685fZ8FXFCVAlVTfMcZz0jv5WtL6GzeM2RI0899VS0efPmxPWQ9f48L20AMb/uuutkSgTkgbbqpR4YONzr6P2MmCsStu0SAiqv6zbUdQkze5ZwEDC9M5y2sJpUi4AR8ww8jZhngOP8ZALSAWOAd8sSszRCVDWUvgRL7wv5xUPElmfDM0sYNcnHmBtPMkiIbB4xL6sA63m9Gjqq8hwrLmnbsvUl6R8Yslzdpk2bopdeeinasmVLhAEiqbhzue+9994onp29bD3i19F7+xp0+hVRofW0rSFQBAGV10UNhkXuYccaAnUhYHpnXcjadfuNgBHzjBZgLV13jjkJUax8GAETkGdiMugKTxIRTJrDOH369EaysGvrFCHmeF/VQw4xJ9SaucwQ86lTpwopx6CAtzfN09yrp9qXEFI/iG18agBh1SjdvXrcFb+8rW990wgsyfdYnhJi/oc//CHatWtX4tSCtPPx9PO8eADpg8jvZcuWRdu2bROPvPZBfY6064CbW6om+u61bd8QMAQMAUOgOAKmdxbHzM5oBwJGzDPayYh5BjjOTyYgHTDe31VFflBDBHslpGeiWd1/vsQRUg5pGzVqlGQm5n+WUmMuOqHsJKnDazthwgSZH59mYEgyUGC0oXDNsTlzw30NCUow48n0MCywvnCvHnffFihS36S52ISzkyGfSKXf/e53sq483yUR6qTz+Y7nJTkeCf1eeeWVaM2aNUL26ZOQdreAW9J1aFu3qKGtKQOHe2/bNwQMAUPAEPgwAqZ3fhgT+6YbCBgxz2hHI+YZ4Dg/mYA8BUaohNRpqkZ3ezVQKCGqKtRS65NHVCG0rOM7cuRIIef/+9//hJgfP35cCDsgZs3t5vcqPNW+RFdD7uPLz/EM1OPVV1+NHnvsMdlCdJMIqnrci6wPznO6xbe+aYSYa5GdHWK+fPlyMSj4EOpZs2aJoQRSfvjw4ejIkSPyweMOOSdEvgjBjxNzfcYkQ4saDWiDPEOLXse2zSBQtfxoptZ2F0MgbARCGVemd4bdT6x25REwYp6BnRHzDHCcn0xAngIjSXHnJUbx8ZCeukp3/vb6AlciXVXkgdYnz/PJ2r2QPbype/fulbnOtC0EnXbU4s51XrBgwem5zvxehae6iIefep199tmyvBHh9syBnzx5shDWnTt3SlK17du3R1wziZj7eI71udO2vvVVD398WTGu62Znh1SnEWrXkHDxxReLl3z37t1C6t9++215TkLjIfpZ69K712F1gHPOOUfaPekZzfCWhEq431UtP8J9UquZIdAcAqGMK9M7m2tzu1OzCBgxz8Dbkr9lgOP8NOgC0pfw5WXPdiAdyF3FEVILmTp58qQQK0gx3lgKZJg53Vlzu/PASzKguJ5PiPnll18evffee9G6desiCF+WpxkSz7JvZArH207BmEACs6VLl0Zbt27NPD9OUCHJFFWA4h5+JZpykPMHry2kF88t+TAIbZ80aZJ4kX/zm99I1vM6iXlafbXttKpVGTTA+6677pL+gNEBIo8xlTB2sKBNXWKP8UI/9DXWZCah35w5cyLXsELCv6yiz1mVwSjrXvZbcQTMgFIcMzvDEMhDILRxNeh6Z1572e/tRcCIeUbbGTHPAMf5adAFpCrqcQLlS0jyiIADdad3FUe8nHiqmde9Y8eO6J133pEkYDw8xGpsztzsPJDyFIyzzjpLlgwjFHr16tXR66+/nulphgDfeeedEdnFOZeCd51xsWrVKnmGMoRYDRWuhx+ZxP94vfndLUo6lXCSZZz1vqnLk08+GUFeswwMaQYC9x5Z+0n1hSj7Lu/GtXku93mzzic7/owZM8RbvnLlSjGE0HfS1qWHrOMhV9J+wQUXiAEDI8bcuXOlX2HYAMesos9JXemzFFvfOQuxZn9LMrzpWKlCfjT7NHY3QyAMBEIbV4Oud4bRK6wWdSDQSWLOSxhvCVtVVnXrgsjv7odjUNrYUiAFzHWkLFy4UDxQ8o/9OQOBQReQvYbwqof0DFAH6J84USYB2J49e4SYk6X73XfflXEJJIzrqiIP1BAQ93wiEwipZm4262pnhYCTCZy53ddee60kg9O2pE/s378/+vvf/y5bnrFsCLkqRIyzjRs3Rm+88YYYK9RzTn3doqHiEPOvfe1rYixYu3atzNmGuNZtMNL6vvbaaxJaDtFWeUxdaT9INUnzLrzwwujcc88VT7/K3bTzeUbk89j/N8xMmTJFvOX0kaw59Cr7CfXHQ052fdqJdiN8PS15H3WlzYyAu70r3H1tL1/DTlGDTLhPbjVrIwLaX0OXL1rP0MbVoOudbezzVmc/BDpJzBF0KMYogyipfHQ9YhcWfkc553j2UfpQtDmecvToUVHK2cczQ4irlQ8jMOgCEqID4cG7l0fkIAQQJgw9KIYQhbRkUx9GupvfuETs5Zdfjt56662I+cF8IMiMZbekhUIXjTxQhSOuGCET8LrSnlmED3mBrCBLO+0I8cMjS8EzDXGmX7DlmsgYt3CuzxxnNVyQ2AxSjsHghRdeiJhLnXZd+hke/LvvvlvC/olAwNCY5YGuiqhofcmOTp3ZMkaoK21EVAFzuSHk4Mb+Rz7yEfkNfNLO5zd3KgPncF2mCmSNO9oJnMeMGSMGAZa7Gz16tHyypkRQX6ZVxA03TFdw60u9rPQfAW0vi1zqf1tYDfIR0P4aunzReoY2rgZd78zvYXZEWxHoJDFHmScTLwoeyh8eEhQ6VZq1sVCeUewgACiPkHLCG1EUKUq42DcCBQrJZdAFpPYTI+bJ/SPtWyXGriU+K1Rbr+NLaPX6ceLN+XzUQ6vX1a2PIsK11ROrWwggHwokXIk4W47Xc/Q+RQ0MzHknLByMfvvb3wrRTgtNdw1A06ZNE1m2b9++MzzY1IP6qgc6zXOs9S26VRyRxxgoTpw4IYYWQuv5DZLuEu34evB6fpriisHG1yCG7CdJ3Je+9CUh58h57pdEsH0NA/H6FsXHjq8WAfoDfY3xkWVQ85UfebUrK1/yrmu/dxuBtsmXpseVb+sPut7pi5Md1z4EOknM8bitX79elNGZM2dGEydOlOWPUALdAqEiTBaFlXBVfr/55pslvJLjUAzVW4eiW9Qj596ry/uDLiBDfXGF3ueUeMUt8RqinVZ/FGuXeKZFHuj104hd2nhWhds1GLieZn53iTf/80kqEF81ALDV/zm/aEg+3luiCyAeJJXDcw4xVwOA3j9OPJg7zndEAbgebI7PIsZ6vbJbxRFCjowltJ0EbdSBolikGQb0/DTDCgquDxHjPhBz3gVf//rXJVJl2LBhkqgPXLR99DndCA41FGn7cmxaffV82/YHgaYNpGXlS3/QsbuGgkDb5EvT48q3nQZd7/TFyY5rHwKdJOYM2GXLlgkxv/XWWyWkkzV9NWOyNhMKO2sTQwyYg4mSumjRIjlej7FtPgKDLiBVQYsTTF5obqF/EXlRNAu0e4069vMIUJy4VFUHX4NG/H6QKaJbCNW+5557hGidd955EkrOsVV5JOIKFHPGaVPIIBnjkR9gpyRdyZvWF0II+SfEnTXRNdSd+vNbUYLXaz+jXk2GZmu/SjNwUJ8yBgrOo/SKR9wwU3d9T9Xa/taFQFMEoir5UhcOdt0wEWirfGlqXBVttUHXO4viZce3B4FOEnO8Mo8++qgobixjRKKhJI+3KnYc/8QTT4gSff/998vx7WnC/td00AVkW1+42nN0HBT1LOv5Zbe+L/z49dUDqsnNyKgN8cXwQYkTaoghbUQp4vGMK+Bch2gcssWTqZ1s8ZqfQq8vN/n/P2pA0ORwbJFDGAjLeKp77WdUi/qmeaCrNsBov6rLYNUrHvHnrbu+bt+w/eoR8DX0MS6JoCi7CkFV8qV6BOyKISPQVvnS1Lgq2naDrncWxcuObw8CnSTmmzdvjn7yk5+IMv7AAw+IhxJlPq6IqWLH8Q8//LD8/tBDD8nx7WnC/tfUBOSpNkhS2DTMuIyHtO6WjRNPTdLFfcsQx7z66nhTYggx554Yxn6Tsc52/Lpgibdz8uTJ0ec+9zlRsCG9kHPmDJO0kSRyhJ+7Iehcp4yHVnNRQMpJukZ9CctWYs5zJRUIAEQ8vpwaxJwlzcomEWtLP2tKoasKj7JTBcjsTl4S2tpK/xCom/io/MJAV6V86R9iducmEWhKHlb9THWPq7L1Nb2zLHJ2XugIdJKYb9myRYg2Svh9990XzZ49WyzkKPRugSCgdEPMf/GLX0h46YMPPmjE3AXJY98E5CmQmia6Hk2TeUgSoVGSWcSznHkT50d9watnHkWFMUr/yVpn27mE7CoxhwyxUgJJva655hrZEtJOtvIVK1bIuCY5GAYAtxSdUqDEfNu2bdHTTz8t62VjUNBQdvfa7r565mbNmhURuYMcgoxr9A6/84kbDN1rJO23pZ/5RkSoAaPsagVV4VE2uV5ajoOktrPv6kOgbuKs8quuCJD6kLErh4BAU/Kw6mete1yVra/pnWWRs/NCR6CTxBwF+vHHH5dkR/PmzZOkP3jT8FK5hWRIKO4c/9xzz4mXcMmSJRGKtBV/BExAnomVKnBKQPm1Fw/pmVfv/b+mX7RpxAmc8HyzgsLf31/vm7XLWSEBwq5FDQXuFmIOmVNDGxm2IUcsaUhmcTKXkxyNZbQg1Rjg3MK5RUJZfT0d7j0g27T5qFGjasspEHo/a1oRLYuH9s+iy9GVNSS4/cT2q0cgyeBYReSSrxwoKl+qR8CuGCICTcvDqjGoa1yVrafpnWWRs/NCR6CTxJy1fleuXCnZfydMmBBdcsklEu7KmrXqnULRP3LkiGQ2Zr4oYaqsbbtgwYLo0ksvDb3dgqqfCcgzm0OJr4Zs8ysEFIWNj/bBM89q7j8lME15fpJe6GCkyjIkGS822dgZhyRWg3TrMRyn+2wpLoYQYJLqQcpZVYHluFatWiXLiaFMJxFzvNa+xMpXodIWpG60M8ndMBgQ6sw8eP6vsv3BgmcLtZ81TWTK4qH9Ezm2ceNGWSueaQq6OoD2OW1f2rCIYUfPs20zCKihhaiWKlch8JUD9I8i8qUZVOwu/UagaXlY9fPWNa7K1tP0zrLI2XmhI9BJYn7gwAGZC7pr166I8ETWm8WbxvJEKFQUyAmeNZZJYzkflPspU6ZEn/rUpyLmq1rxR8AEpD9WIRxZtYKQRojUo41HPGtO5vDhw2VJQ7KWo/yiAEBuIeR4vBnDLGsIUYKEKqFXLJUoYYCbP3++nEsEDAa6pok59SbEHiMgBr5rr71WDAZ49YcMGSJyh3qHZKhRHKveNm0AKlp/7bfunGFdHo1+FifkXJ/2pZ+GuLpC0efv+vHa/4pGLmm/iBu8yEGArsDUt6eeekp0hyrkS9fbwZ7vFALaH5syiNeFuz5H0XFVdX1M76waUbteKAh0kpgTDnvw4EGZu4oHBM84yjKKMUoVyhVKPgo/xzJP9YYbboguv/zyaPz48RFr3FrxR8AEpD9WIRxZtecn7UXNkmYUEqVlzfnGuzR06FBJ5sZc8TFjxgiBhaCj+LJMGaHuzB3nOxRmt6iHCmJ+2223CaF69tlnhZg3GcqOXKEuJKX7/Oc/L6s7YOTDMAgRpy5Mn6GENLXBxbLKfSU4LvGtIhlfVXXUfhtXlNVTHr+Ptu/Y9yMfiIIgGqKOSIj4fe3/cgho/4sTbMYoH9ozqWi/iBMPzkEekfzxsccek2i7KuRLUh3su+4hoP0xVHnoi7g+R9Fx5Xt93+NM7/RFyo5rGwKdJOYacoPH/Pnnn4/YomzhaTvrrLOkjbB+49EjhHbq1KnRTTfdJFsUZiUUbWvMftXXBGS/kC9336qIuY6ztJBRjGAQbsbf8uXLIwhQliKry5+xPjljkxc/Yen0L5/QdM1+zhhfv369GASqSP6minqcwIGjW0jshieVHBV33HGHTKEhQgcynhZSS7I6MMI4wbFd9KRrqDgGGvVIa9QD7axEl6kI48aNK2wYLasoQrJYk546ZREtbWPqSvsQWYXhBVJO4kHyl1hpPwJ58oxxipENYvX73/9ePOZVyJf2I2dPUASBuuVhkbq0+VjTO9vcelb3LAQ6ScxR+lCmUbrwlhOyfujQIZm7CiGnsKwSL1nC2wk7ZX45L14ULxQwK/4ImID0xyqEI30JCR6irLm0SQoGJImCN4rEZ3ixMYr95S9/ifbu3ZsZWg4hv/vuu8XTrF5mvFassuCTzE2znzOmIYFMU6nCQ6vEz8fTMWfOHPGkskWeIHvefPPN04TUxYd6XnXVVdGFF14osoioni560vMID22NDGaLobSoYVQNJ3EPZ95ydL4GKh2zvBcYExDzL3zhC0bMFZiObPPkGZE8jGvGKYa2nTt3ViJfOgKfPYYnAnXLQ89qtP4w0ztb34T2ACkIdJKY67NC0PG6Ea6Ogsz8MDeU9NxzzxWF0Ai5IlZu2xYBqQSr3yFY5VCu7iwlMnke4LRlxSAnYJhHVPEkMs8aQoMSS0ItjAKc6xaul5Qsie8wpBWtJ+Hj3It7VumhTVLckzy/PDNzyjEMZs2tZwoNuS8g5iNGjBCFH2LeK1F1sQ1pX/tdUQKd9gy9KrhFiTnGJvqyevgJZYeoYWChH/BbFyMe0vDvyvf6XsiTZ7QzbT5t2jTRGxjfzDcnh0aSHCgbAdIVXO05shGoWh5m3617v7ZF7+we8vZEdSPQaWLOC5cP4bMogyhyCEMKChSeGbwqeAVRutLmnNXdCG2/flsEpL0IT/W0oopofC4txNqHMGtUCoSFfA6MwaxQ9iuvvDJavHixzN/FK8X49DEAqMKs9YTwUj+yu6eFkJfx0PoSQeqOfGG1h6y59eBD4juOV08xRgolfl1T7LXf0aauHOaZ+RSVv0mGEu5B4Vp5OPpGjpwaNaf+cl2MJ0SDEKFx++23yxQo+jVt3sWIB/f5u7iv7wVfAyDGNJZhpX8xjQfDv05rYRx31bDWxbbv5zNVLQ/7+Sz9uHdb9M5+YGP3bDcCrSbmqljRBCi3eNgoKOVYs/m9SOF8roPCbMUfgdAFpC+hKhtK649UWEcmERsfz4+Ou7y5uRByjF4YvxhXXBuyzLxxt6R55iE6lLL1VIW7Kg+t1jnvuhBP5E8ePno9xYktGOm67GpogACUIa56/a5tVaH19XCm4ajt6BKyY8eOSVSVEvwk7LS9SPJ3yy23RCzJSZvTX42YJSEW9ne+8owx6E7tYRoKOgNt70bi5U2hCBsNq50h0A4EQtc724Gi1TJEBFpNzFlC6Z///KfgetFFF51OwkNCFpZK4vcihdBb5sSSwMmKPwKhC8gkYqeKt49nzR+Jdh1Z1mDhGwKMIovXmwRnhH+COV5kxiXkhgIRjXu840S0bD2VwFXlodXWzbsuhgBkEPPbs5ZV0uvRB/XDtfG62nJcis6Ht0mEukwSLm1HJfgYUmgz/qfP8HtSoe9CwjE20bdJcsh3fAZZniRh1YbvisgziDhJKhcuXCjTGJgOhyGfPkmhXyD3zJDWhpa3OrYZgdD1zjZja3XvLwKtJuYsibZp0yZB8LrrrpOMvvzDskp/+9vfZFsEXuam2jrmRRA7dWyoAjKueKN0V5EMrDhCYZ+hRMfXs1xUkZ00aVL0mc98RggMBjO8kj6e+ThqResZP9/3f+03ZQm9Lz5p9Yl75liay40ISjtvUL4v6+FMw1ENdzt27IhefPFFMR4xBYKohyRyDvmmjSDi/M6W/yk+hqZBaae2PKfveKWNXWJOJAYGNKIkrFSPQK9yuPoa2RVDQiBUvTMkjKwu7USg1cR89+7d0Zo1awT5W2+9VbLl8g9edNYxJvmTb0HZwus+f/582fqeZ8dFspzVr371K4Fi0aJFEdm1QyhK5NxQ1TKetRCepc46FFWAihIj9TCxLCHzMcnSjjJMKRL6W7SeZTHTfuNrqIjfxxef+Hn6vxEARSJ5WzWR0ogMZANGXdap/uMf/xjt2bMn1XOuEQ5aQ/7Xkjc1Q4+zbRgI+I5XxqUbyp5m6Anjqdpfi17lcPsRsCfIQsCIeRY69lubEWg1MT9w4EC0ceNGwf+GG26Ixo8fL/v79++PNmzYELEtUpgreOONN8qcwSLnDfqxoQpIU7jq6ZmqMBU1eJDMjAIRCnFOphK0tHXZfZM6peHDM0PgMDDoJ6mF0og5c1fx4pf15Cfdq43fVU3MFQPNTUK/fuaZZ6KtW7fKlAQMNFpoN7cktaMROBeh8PfTxqsaD/UJzOCiSNS7rUoO11tLu3q/EQhV7+w3Lnb/9iPQamKuChrN4IaUoQBrEp8iTaTZdtla8UcgVAGp/SNvrm8aEbIQxeQ+ABmBHDIXN2s5sPjccdYBphDuizJMCWlOpoY0swa6LrOmRAxCPfb9JGx4yfKypafhc+jQodPEnCzeGs4vQDh/0ogd30Mey3rynVu0ercugxt9mnahX7MMFnKDLUttatg6baZ9Qts53o4mT9rVvbQdi8oz5AFt7UZLtOvJw6xtVXI4zKezWlWFQKh6Z1XPZ9cZXARaTcwHt9nCevJQBaQR83r7SZICpSQFIuNLZOutZf7V61LMk/CB/EEs6ZssIUeWesi2W+KeOdZFx5DBnOcql39z79mm/bo9nEntRr9WUs5UjHfeeUfaL8nAYsS8Tb3pg7qmtTtHtEmeffBE7dqrSw63CwWrrS8CoeqdvvW34wyBNASMmKchY997IxCqgKzLs+YNTMcP7ErIYV1ELwkfonmYZ3/ixIlo37590fbt26OjR4+K4k93gfzFIw1I/Ia3nYz2vXjyu9Id61bgk9oNQwr9BNLNFKl169bJHPQ0Ym5zkdvX29LanSfBWOY7laV9Tx5GjeuSw2E8ndWiagRC1Turfk673uAh0EliroobIZ8owShVkDQEv3r0kpqaZW9Gjx4dDRkyJOnnvn+Hp4YQfZ6J52HOKUr80KFDT2cJppIoGBAAjudYcMBTR8gdy1cRqo+SwZYssyibvZRQBaS96HtpVf9zFee6Qqx1PNOHuRelyhD4ug04cXzwfDOPHWJOojFCpdUby7MyplkjmaUbWSmCsUxItYZXa2g1OCQR+apCbOvGnfr3Uur2cMbbjf8h3Lt27YpWr159eg467xe3xCMeSH7Iklr0WZ8SOu4+z9DmY+LtzrPwrrT1yett1brlcL21t6s3jUCoemfTONj9uofA/wEAAP//UcVYTAAAQABJREFU7N2J231VWT/+0zx/U0vDBntUFEVxKqnMBAQlQcAhFUQFZHACzNTL+hPMS9MLwwEZFBHUjKkQleGD5VySgglimKZFarNl8+/3eS27P67PZu9z1j5nn+ecs5+1rus8+zzn7L2G9xrO/b7ve93rO/6/3WkysvTf//3fk29+85uTv/qrv5rceOONky984QuTr3/965NvfOMbk//6r/+a/O///m9rix/wgAdMnvKUp0we+MAHtn6/yg9102233Ta56qqrJp/61KcmX/3qVydbW1uTpz3taZOHPexhkx/6oR+afM/3fE+q4t///d9P/uIv/mLy2c9+dvJnf/Znky9/+cuTf/u3f5t893d/9+Sud73rZN9995380i/90uR+97vf5Md+7McmP/iDP7hQ0/70T/908uY3vznlcdppp01+7ud+bqH8hnoYZv/zP/8z+cu//MvJBz/4wckf//Efp5dx8Z3f+Z2pGGPhZ37mZyaPfvSj0+sxj3lMwvW7vuu7Jt/xHd8xVFVGnU/gDGtzTzLWYDgEjvL893//9zSGzWHph3/4h9O4/f7v//5UVvpwzj//+q//Ovnbv/3bNDbOO++8ySc+8YnJf/zHf6Sxk2epLd/3fd83+fmf//nJySefnMbLT/zET6S5l9/XfN/Exxok/3/8x3+c/M3f/M3k7/7u7ybqoJ1w+4Ef+IHJj/7oj07ucpe7pOvnP//5ySWXXDL52Mc+lurp3jyZ++rxy7/8y5MTTjgh1W8IXJaNe96Ged7/53/+Z8INfta4wFFeMLG2/fRP/3S6+v97v/d7exXT7DdrhXXD+vGhD30ovawpQ68n6457LxA38OZmv2vCkOvZBkKyLVVe9jq8LY2ohWwbAusqd24bALWg0SLwHbt/hEZHzAm8yPgtt9ySiDmhDVEPYbuLmD/0oQ+dPPvZz564rlNCSv7pn/4pteX3f//3Jx/96EcnX/rSlyb777//5CUvecnk4IMPnvy///f/EgnSRm0nOP75n/95Evz/+Z//eRLdjFwQ4j374Ac/ePKQhzxkss8++ySBc14iuu4L5D/8wz8kTCgp/uiP/igR9RgDBG0Kjl/5lV9JCo573/veSXmxTv2/U+uyLOLVFLwJhEjdRz7ykckFF1wwMZ5jrcixn5eY53nk74OAmdsUA5SH3lurfCcpEzG/8sor03rWplh0D6XcL/7iL05OP/30NJYRe0qEedKycJ+nLtOeiX7UVxQ2lI/WSnhoPzI+hIKiWYdlrSebgnsTj/p/RWAIBCoxHwLFnZPHusudO6cnakuHRmCUxDwsyzfddFOyShFa73nPe05+5Ed+ZCoBRdAOOuigyc/+7M8OjfNC+bGOf+Yzn0nW7z/5kz9JhJsVmGU/J+aEeQK+ey699NIk5D/84Q9PFvIf//EfT0IrIR+pv+OOOxIhPfbYY5MiAkYE2nnSui+QVeCdp1dX/0wbAQoFEyXS1pwKlSDEiBxCh9hR1BjHb3nLW5LFHAHmAZCnoQlwEEuE3JpFcUQ5QJEYSb3+5V/+ZY+CrYuYz2PJjzKa12Xh3ixn0f+jHwn0FBr6CxHnAcSjAib6bF6FY1f9lrWebAruXbjUzysCiyBgHTaPKc9f//rXJwPEdqzDi9S5Prs6BNZd7lwdMrXkTUdglMT8k5/85OScc85JliZuyve6170mLKF3u9vdkktauDE3O8/3973vfdN9ze9W8T+h3A8Tt/TrrrsutYdQSHC3KFEg5MScZRyBZ1F///vfn1xin/70p08OPPDAZBVniWNF9/3VV1+drOxccrm1c3HnQjtP2pQFMgT5IGTaOqRL9DzY1WfujEAQVsqnIbcgdBEq44Kr6he/+MXJrl27kmWa1w1BMU9Du4yXtBPZDGVE3B/eHlE35HMIYh75D4171HOoa1c/yn8IF/bSeg61nmwK7qW49L0v2r+srTB961PvXw0CMZ8YFt761rcmrz+GBoq3PA29Dud51/ebg8CmyJ2bg2it6bogMEpizur0mte8JlmanvGMZ6Q9z6zlhNcuUq5D7EFE1PruRVxWZyIGyLZ98u95z3vSe+6qtMrvete7ErHOiflXvvKVyTXXXDP59Kc/nSziFBJPfepTJwcccECyJBF85Acfe2n94D3xiU/ca7/5PG3ZlAWyCoDz9O72P7MsAa3LIhlEl9cIoY+F+nOf+9xErIZYL9wzdCyCknYas3nyf/OzoSz5JfVRl1ULxl39qG6LeFJ4vk8aaj3ZFNz7YNPn3mh/VZj2QW1898Z8WnfF4PiQ38wWbYrcuZno1lqvEoFREvMPf/jDk1e/+tXJtfGMM86YPOpRj0pkmwC7SYmLK4Ig2NvHP/7xZNE+/PDDE2F4wxvekKx8OTHnpv7Od74zucUSnh/0oAdNjjzyyMn973//Pc324ye41dlnnz1B5FnTH/nIR6bXT/3UT+25r+0NSxXSEi//S/byX3755WmrwAte8IIUfKrt+frZuBAIQWpoS1fTpZEiqcuFu2RvddRzlsDHY8ZcocSjtDK+ET2vZcQiaLaTJ0ub6+a0UaNulIniRCwa/K20PqWKgMB9qPER+c3qx6EVKF34R30Wbd/QuHfVd90+XxfPh3XDZafXp03xFsrTZazDOx3vTW1/Jeab2nO13rMQGCUxD+JJyD3xxBMTUURUCfGbkgh9t9566+SKK65Iruv2iLOAixzPtf2ss85KZCEn5tzUWcK55HLJt7+8bc88os9VjIBLiBXs7pBDDknu/tPwESCLokC9XP0vcfu9/fbbE6k588wzJ7/wC78wLZv63UgQWJalCynmwmiv4bnnnptiJhDiEaA8lbpwRz1nuUjyqBEJ3RwTUNFWEc9yc7d+LBrlO6+799FOkb2nRYNvPhf/NxUGAhgucqpAaX364j6UJbS0H7fLoh/1WbR9Q+Me42Pdr20EzO+eZGxvzRlDYt3bXes3HYGqsJmOT/32WwhUYl5HwlgRGCUxd0yY4GcI4yMe8YgU/Ozud797siwRKsM9tdmpIXASxFeZwoIiGNRll12WgrYhzvvtt1+KNsy1/XWve10SXnJizoWdJZ0l3JFlLOEs4k1LuH3oLOss7KK5s6w/4QlPSDhNa3cl5tPQWd/vhrLsRQuXLTjFqQqI+Tve8Y4UFK3LYl6ytzrmU2lQIcehnXrqqWkOWSuUsYxYBKWELHBvXtXNmuVkBUEckXJHIbL8l6TmuFCfeaLTs9RTHAq8hqzqK5jrx/w4OHVaRMHRtx+HilLfxHLo8V86DuL3qe9xfc36r/r/GHcUw0PGkFh1u5rlRzsX9aho5rtT/h9K8bVT8Npp7azEfKf1+M5p7yiJ+de+9rUUBI1l+a//+q+TGzsXVe6eApx1Wc5F873HPe6RhMdVDoGo/80335zc2FnxjjnmmHTuOKHQUWhtxJwlnCWdMMx9HylH0EWkzxPFhT3rrN720zs6zV7z3OU9vz/eKzvc2F39L1VX9kBoPa9DCzjLtnSxljtR4YYbbkiKKZ4ghFyvPCEqJa7sfYmPOXPibk8bQRFZyZHJZZxjXEo08zbn7xFz9TLPbSFhMVdf61hJao4L9eEySuDpE50eTpQXCIgo92JgIPgizduG4Jzv6LtFLKF9+3FZBHbo8V86DkrHe0nfr/KeGHezPFi2y/NhWVhEOxf1qFhW/dY936rYWPceWm39KjFfLf619OUhMEpiLsAZYZDFWTA0gtR97nOfRLoR8y6LOMsyK3PTwrw8+PfOmVDM2kShcO211yaXdMIYK9ihhx46+cmf/MlkhRI5ehXEfO/afvu/ukB+G4sh3g0lkAxt2Yt6LcvSFfnHKQSIuXgR5nJbygX35z73uWnLStu51YsSOkQX6exr+Yr2dD3nc+duNwkKcovAej5ebe1HzCkmEOM+55eHgk0MC4pLQe6UqT7WxtLo9NbKpz3tacnlGBFnIQ9iTrmImNtWpAx1lRYJordoPxovi6Toz6HHfxC45jjQ3jzl4/2EE07YM979Rui7rnFmLK1TGrsiYuh1d536rtalIrAuCFS5c116otZjaARGScwJifZBs9ZcddVVky984QvJosM6TIhpE1R8xlJ22mmnpevQQJfkR2BhiXbcm6jrjjd73OMel9zx7XdllSL8dhHzZbuyd7WhLpBdyMz3eQjqi1pahrbsRb0QiAsuuCB5bnz1q18d7DibPH97rnmGtB2XE6iKj8AzRByFxz72sSmuQtv8XpQIyBOB7tsf0Z6u54JQNYnel770pbRGIbHycG1LnremIciOPXz0ox89+Ymf+ImZHj+xPsa56RQfykA8JXkigdYiXjVd0ekf/OAHT376p386Wcj1FUKPHAYxYTn3LOzy1EUwuxSm8eyi/Wj9XCRFf85LoLvatyjhh/k843MRLBZ5dtUKlkXqXvLs0OtuSZn1norATkOgyp07rcd3TntHScwJiPaTEqDsp/ZDSShjXULA24i5Ln/Ywx42YYkQDG0ViSDMDZ2Qe9FFFyUB9+ijj54QgFn6Cc8sUvaYI+7a4Tg4FjNWfgL2u9/97nRUGiu74G/2nSL1eZo3+FueR/6+LpA5GvO/D0KDODmr3jWsZshMafCxRQX9rvlh7CHK9oWec845yRqKFAShi5YjjCUu5nF/XEuJV8zhBz7wgZMnP/nJiZg7EhApbUs5oQrCT6GAMEdbtcE+cnlEdHPrgGcRzD790bcf9THlIaJs3fJeHpRwrM08gJoYayecrWtdLtsxDsKSKk8E7i937+0VAf5jH/tYeilDXpI1xjibFp3eegKjH/3RH0242HIjgB2FAsu4PNpiAqQCdv+Zd3zk/dh1zrE2Wyu1gdLmpJNOSq7+5o8xuUgqHZ/ztq+N0MFSgutWIxiaNhk7i64Xi2Ayz7NjJeYx38yvMe+dn6fP6zMVgaERqHLn0IjW/NYFgVEScwIuix6XWISVIMmVXVAkVotwq2x2gr3c7nVdReJaav834VzwOgKz+tz1rndNwqwffgKv+0RBl0RfR9xZzQiErOasVEhGHJd2v/vdb09z5BFR6+XvuXjN68JfF8g98C70pk0w118SAtkUzI2LtlRCYDzX13JJaUQpxMX8yiuvTLEFon55PYzDaYQxvzd/Xyqwm79IFuL8zGc+Mymf7n3ve3fOW3VETsNFntCMlAqSGGuBe5x6wOrsRaGFeCKbFFnmJIE72jutP/r2Y5NgURqYw5SKtuII0jiPAiTGQVjs5Rn796+//vq01viMZ06uoLAdYFp0et8hvvCgCLSeyJfiRj4wilfev/F+3vEhTzgot434+B6RpVwxNigsbQGiZBiCmJeOz3nbt6hCBy4wkKaNz+iHVV2XreBYVbtivg3tUbGq9tRyKwLrjECVO9e5d2rdFkFglMScC7tzzBHYo446Kh3fxYJMmCQ0hTC+CHDLeJblw75MigUWKMQBAYn6Ejr9+BOmeQVIyBoCLgCUCOvINgJ1xx13JKLx1Kc+dcKaGBGTWd9YyxxDRdAU9I0Ai7zDZ55UF8h5UPv2M7MIhzv1fen5zEMLvlE/20NsDUHMbbcwztqSOTaPi3Uf4mNehAu3oGfTXLiD8FBm8UYx/j/+8Y+nuYLAsLBScpgnhx9+eLJAI+ks5W0EsKs/KNEkc7PPc+YwzJBOFm2eM+a4dcBxibDus2XAWIFlbklFmuWpz/Qj8mCtaEuh+IBvW3R6dTXGrLOvf/3rE54Uhupfkjw/j+Im8m4qPigutFd/+Y5CBTEX1BIphy+Xe+sbgm5s9kkx/q2dMDOGLrzwwqSkgkOz3Yu2LwheKFTUNT8VIMZKl4LC/X3WC/dvZ4r2DU1go5/0hzIkinh4eYXyaVltHXrdXVY9a74VgTEgUOXOMfRibUMbAqMk5gSn3/7t307uoKIVc2kk2BDI/Dgv+we6DeiSzwjl3FcjKFNYoKK+hF8CKNdRFkufc3V3bjjhk/BB8Gbh+8AHPpAIx9Of/vT0PeLCMibCNWJy9dVXT37kR34k7U1FzBETBGWeVBfIeVD79jNDC6p9CG4JQYr6IbOidZtfxiji0JaC2BlXfYKSlQq2ffMPIsey+773vS9FfDfPzAcERlBFii3Ha3HRRsoJ9LHlQ3vb9ro3PQ5shZEQ6i5Xa983n+OKTnEWhAvelAkIvrmKAFPU2aqSK+m6FDXalrvGI3DICnyR/iCw2t+W1IPiQ724guvHILVwsU7pf7jMc/565D/vcWahaAnFg/bZC8/DgMcQAm09o6iENaUs5Y3+4VnR5WnShoXPYvzD1foqIOHFF1+c+rnNZX/R9s0imPpSHwxNbLvaP/Tn0b6hFQvRT10KDWN3mWnodXeZda15VwQ2HYEqd256D9b6dyEwSmLOTdWxYX6oTznllERMkc5l/zB3gVz6OZKgzoS9NktMEBeC4Zve9KZEzPNjkpTDKsbdmCs8Adw57tzdnTNMmGNd4p5LwETmnX/MukQQJ1DOk+oCOQ9q334m+pW79DQLZKnAP7SAWFq/aJEo5ogcl3DzD+lti5Ye98c1BOuhCEcXAYi90MpFcmzhoNzivi6IHOsqpRgy3Kc/tFOiwOjzHNLIyktJkqdQKCD64UpvjZAQdPM3J5xczLWnSXiivZ5tI5J5md7HOAuPBP2Yn1NufCHFFAYX7N4yZP63rVeRbyhDXfVJcy+/oJvWH98bA5K1Wj28fN6WQlGJlDvFQn0oU3gXlCgwuvKNspoKgNhiQLlpi4GtEfDWpjyvLsVL/P7EuPRsn/ZGvUrnY/TjvAqQKG9Z177ju0uh0uwnY9MYlfSFtWgRj4nS9g+97paWW++rCOxEBKrcuRN7fWe0eZTEnEWZRYPlhLDNRZXwzUK8zonA5kWA9vI+T4h1uNdSPBAGX/ziF6fgVwR7wi0hhWCOvBMguV7CQX7uJ6yxnnPztDf9IQ95SDrfnSCbC5d5ubPe1wVyFkLTvx9aoBtacC+tX7Tynve8Z1IIiZZ+xBFHTPbbb7+pBCueC8LSJJalluLm+O0i+uGJolxlUiTc4x73SNZyx64JKmeesVZPswibS7nHgWCLEsVYn+cQ3zZX/L6Ewxxus6RGe7U1XqmiHX/kg0SylJ9xxhnpqp2IpLyML+uJeT/tvPPIXn5BvJXf3MuPNCHZLPjyl3LX7SC0kV9cg5hThJx//vlpzaOYbHoCzCLKkV/z2iSOvBCMqXBp530Q66o6Gn/+7/JkiPEZ43Jey27pfGyOz65x1mz3dv3fd3x3bUFo9pP1wziTYL7VUGB1EfxF2z30urtoferzFYExI1DlzjH37s5u2yiJ+S233DK5/PLLU8Tgu9/97klbXrLHPKL5EtTXMRFkCK4saO95z3tSFZ0jzEWT8En4lQinrDkCybkXsSEEEh4JJfaTs6LYf8masGh76wKZYJ/7z9CCdgj+Q1meS+tHCPZCxMV2QMyRVUS9T2oTtBEeCckrFbTnFZR5mSC4XN/7EOyhiXlgFv05i8iVtjfy7bpaS6ybvAic+sD9mxWSK7exoD7WEq72u3btSkEAfaf8PPGSEHAzXMqRe2Qx+o9ykEIC4e8T9T7KaLaXxbzNI0CZ1sZSyzFSp04IXlesABi4z3in8DXGKUfhAq/ckyGI4FBEtHQ+rjsxj34sHd9xf1xL+qlEURL5LXqNdsxad/3eml/G4/HHH5+OZ6WIivlhTNVUEagITEegyp3T8anfbi4CoyXmXLkFTbIXkqZdVHY/hn4Ug8A2u43lxn50Fo91TIQMP/6EYIKspK5cWAlhSIsUBJ5Fx70EeoKmH3xYEAIQ8lwYSA/O+acukHMC93+PNQkGSy2Coc/yVEowhhZYS+tn/KmjvcmChvFWQbyMsz5p1QSGCzdSaf3o45I+tCt7YBb9aTyY/xICCGuvEORLCVvk23W1plgHedMgmZSB9pMj4uoSShLrKBJvnWk779zefVhSBHKFt07F2mOtVXd793n2hKu+/CVt2pph6Yz2elYwS4TI2GmbN7lnwyzLcQnBUk911AZKX9HfKRooInhftLlOtymc+rQ3+qt0PqpbH4VE5L/dVxjos1nju1mvkn7yzLweE83yZv0f7Zim0DF3zAtKL1s4bCPjQUKpo54lW35m1aN+XxHYCQhUuXMn9PLObOOoiTmCQ5j0g8+iwXLBKt5FzMPS51pTOQJ1gSzHqu3OZQmYbUQgSFVueQ7hkGWzjfiZP9NcpLWJUIp0IeJxDngENUMi50mBS9NSHHMYMWqrb1+iisDkxI0li6DM46RPELe+wd+Q32c/+9nJ4wVG8VIfr2hHKXZBVO2Nn2bpz/NTRpQDV8TSKQ+IJoKJOMOhLfice+MoMmMNYYw2uCIc3OHjxAeYGiPRf5SLXRZp43SWSzilI/dyxPwd73jH1GBs+hcROvHEE+8UzK6Jd1/ia/4cd9xxSZnhd4YCIidYpYRtVnuj32JezLLMbhchjXpt97VvP5V6TCzajq51l7LVOmqdFfcllDfGi77arr3wi7avPl8RWAcEqty5Dr1Q67AMBEZLzC+77LIUfZlQTzAiKLEWTxN4H/CAB0yOOeaY5Iq7DLDHmmddIBfr2aEF96hNqeU5XHAJjm17fM0Z5Dy3BCFDtkgEqdMGxIILL4ska/nWbovntPkW9ey6Bi5NS5rP1XXWnuR5BXf1RyAFSOxDHPsel4a0sigjwNoCS+Xy6smJXRc+zc9L2xvPKS/vnwiCR6HCnR/B+P3f//0955Qj/nlCdpEK/Yycet5n2hCW4yCqPqcQRdij/6wbfRQfTUUPpcFNN92Uju+z3rO8y9srT9qo7PDkQNAppqKuTbxLFRzylYctDM961rPSuGcNRbxyXIcm0trXnI9tipNSop9jtUnv+/aT/j/55JNTP7XFdBiq7V3rLkVSnCRgq5lYBSGXmIvmUdsWiKHqVfOpCIwJgSp3jqk3a1tyBEZJzEUh5nrp2iex8MRxSX2e2+n31gVymBHQZWkJskHQRuZYZsM9OCcAXbUIYtC0PCMrvhPorGSPL8ESEULKxXBwHnZOzHmaUGwRLgVcRDiHTF0CrzLaLE7IV5ulv0kwuyyLXLSbx461eRw0j+Hq6kf1VCfkDTlkMaMsRLKiTm3tcE9Jin5mSWUx7zrmLfJSF+OHYkaduGJTSrjus88+iehOc+WP5xEKihhu7+qq31nSwyKI+OapVIGgbsZo09IZmCE31113XSLmgl1SFLUl5EcbBfUTdZ8CBVaUBG14U/og/bM8D9QPMZ9F+BZtrzLa0qxxtmyiFwqCpuIMLl6xNrTVfYjP1pWYR9tiPqqnNZY84tQAv5dOjhGY1RySSjxEIt96rQhUBCZpHr35zW9OUJx22mnpN7XiUhEYAwKjJOYIyLRzlrs6jgBpL2RTkOy6v37+LQQqMR9mJHQRzyARXIARDGOUxcf7EstqlwDNYhPCIrL9lzOiGSMsqyTmbURE26Q2ixNr7iKWRe0lVIeVq/QYpq5+VE9EEGlxD2WIuUNAn9aOCB7m+Wkp+rmUsAaxRlgF60OAkWsWbxZ80c6nucTD3Mv9iLwgaP6XxyGHHJIEJfuvWc/ztCihQlRD4VKigOAiTFFgvnjOmJC0X323GpZK6z8iZU5MU0zoxzbFQZNIL9peY6YtzRpny3aNDuLZVPjBr2RdamtTn8+WpfDoU4dp98Z8VE+eSOb6RRddlE57+NrXvpZiv+TP6+chtwLledf3FYGxIVDlzrH1aG1PIDBKYh6N88NIE03AJjwQZAgTLC6EtLC8hBWMkEbYCi125FOv0xGoC+R0fPp+GwIvcsByR8nkPbIUJL3N0mc8l6QQGBHxEldtXiReEiusF2siUm/OSPJsHoOF8AxhOetb36YrfexFLj0PvEmEoz/6EpDmc9pBueI8bBHfRRHv44IM57BOxlrmM5Zffe8Kb/mXuHhb53IXb1bv8HIosRgrW5KPsl1LxsGiRFVZJcTZ2PRCUCkIvL/99ttTQMxY49sslSz9fjOsa4u42n8LnUlS7pTgqe9KLPCRb1yb48zni2yJiHy7rqtWCES9ot3NvfZIcL4uURQY19YwxyE2t0REfsu6rrsCYVntrvlWBJaJQJU7l4luzXuVCIyamBNiCVgsbaxTYUUneDvDmyUH2XEPgZLA50ecsFpTOQJ1gSzHquTOIKLG62233ZaCWiFxxrCEVBA8txqWviah7CqrS6BFmPIUFhwRhI899tj01SWXXJLcMBGNpgCMDCzD4tO3vgTwE044YY8AjnxpW1/Ld2AR/RGk2OdBgqcpHprPaYcXBcGFF16Yzt2GYxfuzXYol8WXi70jESkKJGuWtYzFmqVSv5QGRUPoudWfdNJJyY3dnnCflRDfVPjuP8ZivLR51jhYlKjAsITohsIAJjBSN14iiGWeYpwH3vDwG2C+lSiumoqgIIVRxqLtbVrgI9+4NseZz0vGZzzf99rXc6V0Xepbj2h3U8GYKwzN/YidQPFkK4PAa9Pmbd96zLp/UUWU8VlTRaAisDcCVe7cG4/633gQGCUxJ0AThggQ9h1yFRXIKSyPLHtHHnlk2gNJwCWEEthYs+w/5AJbUzkCdYEsx6rkzi6B01ieZulD1EsEzr5EQSAwRFdiQXSMGGWWeZYnZZe49sYz0c5ZhLdvfZt7koPYBMHva/mO+i56nbcd2gNbhBtZvOOOO5KSgVJRsm6xCnMnZ+1FRJEBp1LMc4wYvMJVPLcYhyJGv8WrDRNjNMbBC1/4whRzADlTLyn6YZals4vgw7GUmKsHooygeRlr6p6nrnFrnLTFGIi2l8Z86GpvqUIGyV6HpN3waxLhodaledsYigJeKFddddXk5ptvTl4RxrA6i5/gKEPziPLF0anIbqmH0bz1iuf6EPM2RRmFW00VgYrA3ghUuXNvPOp/40FglMScQMU6xp3zfe9738SeS8kPNbdWgakIjPYdEiq4Nwpkxb39sMMOSz/c4+ni5bekLpDDYrxsQb6PoIikOT7rSU96Umqk6Nef+cxnkuKrjZi7f1YwrEAr2jmLKPetb1f5QSxmKQKifkNf520H7x7P8p644YYb0noW2xrUEXFDerlrH3TQQSnIGaJu/bNHmrcFRQpimqcuQooctBGwsETKR98184u8I1/Ryp/znOckYo7EilYuRT80CV7kH/d0bY1g8S6x6FMQqIukrl7Kbib3tI1bpB7uTU8LbYd5acyHrvaumtA2cZj1f8zXpkIFRnlqeiAs23U8XOtjflBIUR5SYBkD+oliyO/+4Ycfnk5CEAdhWZb8HAvvSxVyodCC16mnnpoCKnLBrxbzJqL1/4pADf5Wx8B4ERglMbfHEnnw40woZRX3A0eAcKSOY9F+67d+a/KQhzwkWaA+/elPp+i+BEfnCjv6p6ZyBCoxL8eq5M5SQS4IUJeFuKss86DE4hiEZWhiHoJ0k/CoLyG0GbQKqexT3y5i3oXHdn3eF3cu1SfuPnebFZzi0HoWQfpykskiTKjn7cNdV+R+FkL4zuMyHxbasETG3nzWY31HuckLiZdRG9FVF3lwGXYeuvFprdWO3FIZe+H7RvkvJYj6NdzKKRra6uqeGOdd4ybKowwwDsPzKpQj2to2bpsW2SaeFBOh3IAZj5d1Pi5r2euSvpgnGZO2d3zuc59Liqsg5voKrpK+71L0xBiZp+ySZ2L8NBUaPFAkdfOKeUM+Ofroo9O8MYfiaLfmeCope53u0UbzMBSjxn70T7w3l8xHr2X3yzphU+vSH4Eqd/bHrD6xGQiMkpjfcsst6TgngiSyzZXNj91XvvKVydve9ra0F/M3f/M3k+bcD7rowz5nNXnRi16UhNrN6L71qGVdIIfth74ErotQdNWqr4A9tCt7G0EhtEmEsSZBsT+4xEJKmAsX6tNPPz2RHF4w5vU6pL64h+WMxe/SSy+dEOxZAQn0gVe0C27h+v2oRz0qKRhF758nyFwIxE0FCiJNyUnpec0110w+//nPJyG7rS7y4IJrzzpFJ4JO0ZBbKucl5iHgNy3uTQt0KC9c433glV9njZsob9GYD008KU7MdamE2Od1XsX7Za9L87YJjhRXjiC7+uqr93Jlj7GsD2N+RCwB84unSSii5i1/1nMxfprjlYeI74xNZFVd1cWaRWZh4T/44IMnPE/yeTOrvHX9PhQU4SHlf+u1RPmq7frIer8d/bKuONV6lSFQ5c4ynOpdm4fAKIm5CfumN70pWYxCIHTuM2v5K1/5ytRLr3jFKyYEWD8IzsB93etel7S3L3vZy9Lnm9eVq6txXSCHxX7ZAnAISE0LTpCEaA2ywFrTFfyt6/4uwbdLQG0SKm7PAmpFUK2ho2RH+7b72oV77N1Wn5xAIA5PfvKTU4DKc845J5HsNpf0aAfrUygmzjjjjCTYIy2f/exn97K0u9+9W/9noUWcWRMpMdVRyi1XSEMedI4F/YorrkgWfFbJNkWBPJSB9Con70+WfemLX/zinuBqrObGQU6kjAMW5BgH8sktaW0KniA52sGir36Ur9rgu7aECFBiGLennHJKUszmxGDecdusb5Qd4yAIis83gZAse10KfEqv0S+2ql177bVpLE07zz7mB4+SVSjuYrzeeOONk127dqWtJjxPKB1j3hnf6imtysJfin/pfW0KKWtGkHH5aL+1yzxoekxtuqdAKU71vn4IVLmzH1717s1BYJTEnPv67/zO76S9XfZq+SFmvWEZz4k5QcyPuyOLXvWqVyWhEGH3eU3lCNQFshyrkjv7Wlb7urKHQNu04HQRZPMh5kR+XFrX/V1EKghJqUJAmYLODR0lu6QPlnFPG+7TCClL2X777ZcI6/nnn5+O7yLMIpltKSy/CD2CaVxQriDVgsZ1WWhZ6BBxeROYpZwoyleZ+o+QjVBzF0aCrJ3Gke+1L09Itpe88mj9D3vYw9JtCH5bcDlfyqv5XNPC2SbwI48IrzmEsF1//fXp6t4u3Fj1eYXYn3/EEUckzHMFwLzjtlnfwCbGQWDq81wREsqJuH9drstel/q2M/qFV4jz7I1F54M3FYaRrz5F8vTLySefnJQ+4SYe9yzzGuOVssjYNIcouCjOYv7kfT9r/C+zrkPmHQqJfEuMvtPmUJaFEs884B2wzls6hsSm5jU/AlXunB+7+uR6IzBKYk5gRLQt+r/+67+eSIUFH2HPiTnCThh1/2tf+9qkqX75y19eLeY9x2xdIHsCNuP2EDibBLZpWWXpW+R83qbAhGDlghKLXy4gqXZblGqfE6ya9zeDKy0q2CNci5SvnuuQSl24kUX9y033LW95S3Jl70PM9R3iwZUfOW9aaFmojDUWu2nEnQULwQ/LVYwb855LuxgdTr1gmUY6mwQ9FAahQLL/XaIoFZwOQWlrV/M57WnbmhDzJdrnav+3+r397W+fsFAae23EHBGyzemoo45KxJwyBFHP06Ljdl22UuRtmud94Nxcl5pEODxtQrHWpaCYpw75M3m/nHXWWen3XR1jDcvv9X7VxDzqAy9rgKP4eMLA0/hv1rt0/Ee+63YNBZTflfzYQS78kvbGnNTWsXkKrFt/jK0+Ve4cW4/W9gQCoyTmBD2u6TTULEdccWmfadZzYk5gIEzam8YiRYA688wz0/0BUL3ORqAukLMx6nPHNIEmLCqEmkXP5w0LDktqCTHTBkJl6f1B5KLtnmUtYtli4TIf2wgTIc1cDMsWCzyCiUguUn7UY9XXUmKOwHJnRcynRVeP9hBsc1f2ILL6gQDshbhIFJVcvQnJn/rUp/a4ugepNs62GoqZULTEuDFmkHLrLVdiVsA2YtTsT+RXQpjnGQeIX55ivkT7jCnknML17LPPTut7G/HXRnV75CMfuScKtnHmtyJPi47bZn3zvDfpfeDcJFp9PWeGanP0C6+TaccCRnnmh3FPIW+rR8yP7VackDkosqyDF1xwQVIg9VkHN2U8dSlywitHv+TrTfTTWDwFoj31uhwEqty5HFxrrqtHYJTEnMsUoi1QEaFeEBX7Fe0xR9ill7zkJelYIW6Zzj1lTWcZcrxPjcreb2DWBbIfXqV3h2USgXXsn+OvfMYaSKAZ6nzeEKDC4qh+hKOuIDx974/2hiA9LzEPgXTe8qMeq7p2ERvkOBQu7om9pQgjt2/uuZdcckkimEg9HEOgjbZ4PhdoT9wdzb3LUtlVj74EC7H3DGIkeCai3UaAl03MA4O4xvgoseyGx8k0vIYat1G/Tb/GuhSuyYh6WHtLPGeGan/0Sykxt37c/e53TzLBc5/73KSQyWMJDFWvWflEvRddB2eVs+rvc4+GaZ4xzXpaL0LBuIpYAM361P/XE4Eqd65nv9RaLY7AKIm5aMHvfe97J6KzExS5JnKjFFUXYSfE2nvuRxohp732A+1c88c97nH1HPOe46oukD0BK7w9LJO33377xN5ulknuvwIGEYBF677LXe6y8Pm8QdTC4qh6LEsEJK8gjVHtvvfHc6WC2izBbN7yox6runYRxuYWBQQbYWRZPvLII9N5y/ahIkLGADKMCMFB0j/GQxyXxhLo2LStRrC0aHdXPRCGPCEyLMhdLsnc45E0xIhlWt1KXHKHdmXP6+x9jI+hLLtDjdtmPTf1/1iX5vWcGardzX7xW942/qI8ynkBX80NQWEdRda2vsX9y7o26x3zxvqbp1nrYH7vOr4vVUA0667dbR5ToZht3l//35kIVLlzZ/b7Tmj1KIk5d1kull6sjH7wkG57Ke2JJMj+6q/+ahJ4uYn6IXDOLsv6/vvvnwj7Tuj8odpYF8ihkNw7nyAYXISvu+66tE/PtgvH/hmzEoIWFtauoGt757q6/4YihEO1IPAtVUgsWm6pQG590r/77rtvIhC2LCDvjkq77bbbkicQIh4KE0oUikX3C2DGyj7teKXSeqjDNMvVvP3ZFfytr2JgVn8MZdktbScPE8peSuDjjz8+BS2kZEEyYBn9Navem/J94FLqaTP0fIvym54RTUUXBebd7na3yYMf/ODJ4x//+BR1X1BFHnKrSF31Hnr8r6JteZmVmOdo1PdDI1DlzqERrfmtCwKjJOZhySHE2mfI2kSoZRlHaghIjmDiCrzPPvskl3aadEeqtQUXWpfOWtd61AVyOT2TC3D24rKac2UmCIeQT9jNXZhFMe9yYV5OLctzDcF8KEtmecntdwa+pcSiPZfyT/sIqvo3zgFnKVJX3hHId+yDjj38iAeSYS2zrjWDtTVrWFoPZHKa5Wre/mw7Lo1bb19X+ma7mv8PZdktbaffE/FMnCIQR9D5PdF/q3CZbuIx9P+BS6lia+j5FuU315N8awjFZfQLhYn95eaQuRXzZ2hcZuXXVe+hx/+seiz7+1IFYLMesxSCzfvr/zsTgSp37sx+3wmtHiUx98PnhYhz/2Q1D3Lux0IicBJkWchZyllxuLwH4dkJnT9UG+sCuRiSIag1BVyCLOsP0jJtj96mCTJDWTLnRX0owta3/FJCHPlai/Qt67iEdCN+SN8DH/jA5O7uO8SPRRDZKCGApfWYRcyjnvP257zPRbml16EIYVd9uU9TBjsHnnt0KEcoUvQNRUl85v9VEcJSvIa+b9nzra1frKWS+cGFHSH3G79KS3kT17Z6r2KvfrNeQ/0f867NoyHKCFnNWhey16YomqMN9boaBKrcuRrca6nLR2CUxBxsFnzC0j/+4z8mF3aBigikAmdJrEyEJMIUqwYhymc19UegLpD9McufCAGmabk1hm2/sAdxiOjVeZmrfL9sQX1W29oEYlhLhMOtjmjks/Kd9f08FqRcYLVecZVGzo899ti0Bx0Rp2RE9iIuQAi4XfUprUepwmfe/pz3ua52dX2ubxG1puJL+7xm4RX5dtXXb0zst7btxG+NftEnyxxPUa91vy57vrX1C4WmhORRWrGa//iP//hKLeXNfmqrNxlFGoNCJ+Zdm0eDNlJCmJPuMw9je86mbM3ShppWh0CVO1eHfS15uQiMlpi3wYYA5RZzgmxNiyNQF8j5MJwlmFEUEe6dJiAqt2OtjN+wBkWphJppLsdx37pduxQSXdHgF61/l6C4XS6k0V4B/N785jenrQkIXSgLZ7VPP0/b8z3r+Wi/MYS4mLcXXXRRUvyI/E4xlCfkYFrwt/xe76N9TQXTrP7ses74117k1j1SKB9gIQ1BuFNGPf5EfZEoirM4cg6eYkBEcEZZIh8stuI/rHsMiB4QFN0a461JzNrmmwCD8HGlGNPv0cdFhe2+Kfql7/grzX9Z921qvUvxaCpmvvCFL6S+MndzDwH9bX6LjyGA5axYGaXl1/vGiUCVO8fZr7VVuw1Eu388v2Uq2gFohKCgqX4ESi0lOwCahZpYF8j54GsKLATYmI7GJivPIx7xiGSBu/LKK9N2DG6zbcR8EcI2X+0Xfyrmo/a0Ea+h52cIwE3XyrBSRYv6EtJ4btY12mtbzRVXXDG54YYbUoBKwSpLkjVrEQVMtH9ZhDLa17c/u57zufFOcZFbQIPow4xX1HYTsajvshQcJWNhE+6J8TZrvulPCiCeIMcdd1yKkcGLjddBnxT90nf89SljGfduar1LsWhTQJvP5nYYR4wVv2E8HHb61o9SXHf6fVXu3OkjYLzt32hizoUQmWG1GCJxZxeYyLWmcgTqAlmOlTtDEJtlSYqgRSyHXGRZmkosm4JPTbM0Dk14+7V+uLsDx1JBHJGyVjjeaxV79kNAdYyjc+kFpnRyxLKJeZQb7tauyHmXC7axw62U5XIVlquu+ho5lCbWZ27JkiMwtSOUK9vpAjzveGIZRkCQEoREyj0BxjI/S/HRXkqnBzzgAZNjjjkm7Qe3Vx9Z15/GozleOs8ToPXP2iEQippQpAUZV9Eg6eZFKN6CtK9dQ2qF1gKBKneuRTfUSiwBgY0m5gK7vfWtb00uvkNgw3VKVGvBlWoqR6AukOVYuTMElBJLkj3FXGEJqiyHJa6yAk1NszSOReAJHEPQg+00wQ55Q4IF01vFnv3wkNCHf/AHf5DWrb//+79fuit7lGu9pJSgEKLUQIDXMWhZV331LxJnTiBxEiUHZZX2SL7f2iaFwrzjSXRwFmF1bfMEGMv8LMUn+g0m1i6/v4ccckiKocCtWVDDVXhGpAFV/wyGQFORyo2dAlCK92NUUA0GYM1oLwSq3LkXHPWfESGw0cTcxHzTm96U9krqE4KORGAndBLY/+Vf/iV9Lsgby2MIPe5BdATq8YPhx5916IwzzkjnnKaM6p8iBOoCWQTTnptKLUmsSKxFInAfddRRiXQi81/+8pcT8TZuw7Jpb6Zzep15LTmRICyj/i+xJDYFJ88NISgNne8si2qXK2QpUVjUZRxueYr25x4SQZBZAX3flqxnsab5ntKhz57vtnLbjiULz4xVH/NVWt+73vWu6WhLmDgSE5HPBfzt2tPddzwhnIL2OQnEGPb7Iw9p2vwMXDbNYlyKTwJg9x9j3dzb2q1Y4VVAgeHoR+N+nvUs8q3XikBFYHwIVLlzfH1aW/QtBDaamDuTXCAlVymEWISciyPX31tvvTUJbQ960IOSNt6PvMRS4bnPfOYzSVvrGBWCwBOe8IR0zmm6qf4pQqAukEUw7bmpVGANghgC/f3vf/+kSOK6a3wT1CmUEJV73OMeaf8x0o8AfuQjH0njP0ifuUHgneaa3NcCvadBM94Mne8si2pXO/sqRKwHp59+esKMNc/+7nlStL/pIUFpGP3Tli+yaQwE6ewbrbir3CCDUSbXUVZo7T3++OPTOdzWSe1Vfqyrcf+yrqX1pawy7iUY8g7JE5LbR4GRP9vnfd/xFAo2SuJPfvKTKUhc9P+0+Rm4lHqG9GnDMu8txSevAxyMPUeZUjQefPDBSTk5z3qW51vfVwQqAuNCoMqd4+rP2ppvI7DRxJyA+dWvfnWP1SGaxV3V2eUICiGeUI3cEGxzgQ5xF+navlPkhiXj0EMPndznPveJrOq1AIG6QBaAlN0yBDHnvktgN54FSUKguHsi7KyIn/jEJ4qjQyM26tTcg6zK0yx5WZP2ehsWPvVBnLhrm5PmYpDCRfI1rz/4wQ8ml/Q2C3CXxZQiQ52aBDnqFI0YmtiVEhSkxCuwQeCQ4zgKjcIhPCO00ZrV5tGgnfqU8lGf2st+7rnnpnb7PCIhR3uNHWR3KEVE5Nv3WorTrHy3qz1BmGeNp1B89N2SwpPBeDV/eMn09YCZhdOyv2/D54477kjjMhQSbXWgiDIe4fXIRz4yKa8o4JcV7T7Wq03zSGjDrn5WEdgpCFS5c6f09M5r50YTcz/8hDnXPDle6vd+7/dSUCDumfYjcvEVNCh3ZWd5ZDVnVSdcCSjEYvSQhzwkz66+n4FAXSBnANT4upSABMEISxtLknFKoSQPgiQi5z7j2v+xPYMwT6jPUxfhRJ4dYdPcg+xZ+W/NsLTnZXgfAjkybm7xSrnxxhuTOypSGAR03nxnEaGudsIJRvMSe/WeJ5UqYhAS/QgXBHzfffedIOeUL/re+sVNH9FDJpAXnzeDJel3ykZERtBApEawuc997nOJsMMgT3BZJNp7ntci70txmlXGdrUnCN2s8RRbBfoGcRRXggv3zTffvFdsAO2fZ17Owm3o75v42L5BkQYvY9D3bUn/GdvmsTFvzaCItLblqWuex298fu+097FebZpHwrQ21e8qAmNHoMqdY+/hndu+jSbmXd1GEBV1GXl5znOek/aMh/Upf8b39pi7/21ve1sSTrmuHnjggflt9f0MBOoCOQOgxtchCM4imF2Wtti6QWANAZcwK8X/jSLTvyHwhmU0okMT/uexQDeJanPvN7IvOBdiLvo475aop7p3Wbab+UZb+io0HL/0whe+cPKoRz0qkVfkk9BuzsfZ07HXW30k5BgxnubyH/UpvZYSzugfikF7kSkVefvwiIAJ4sBq2hWF3HiBEaUM5c0Xv/jFpHBByPUBa6WxF22N+is3J+aUApSUynW/BDf3eXX1T+Q377UUp1n5N9tjnEeE71nPzvN929aKwNh4Qsz7HHsY49b8oCzj/dLHM2RZ/TMPNp4JfCjnrr/++uSlxgOA8qiNnOs/xBx2cLSmeQWmUQ/fu896JjaMOdtny0lzvdo0j4TAoV4rAjsRgSp37sRe3xltHiUx57r5qle9Kv2Yn3nmmUkwJ3j6wc8ToZMg6/6zzjorCZ8ve9nL0v35ffX9dATqAjkdn+a3TUsSobtN8O6ytCEwkeTlFcJ4m6Ab9zYJC4GWIOtcbacbfOhDH0ou53n+ni21TIUAHpZ31nIk0osQzpIb9eyTb9S/lLhFOx/+8IdPnvWsZyWCy1qJuLIwE+bNfXXaDhfhUoVCEA3E7PnPf35ah9SXpRDRZjnVRwh39DM8t/5PkYBMsyzefvvtSRGibTDjEUEZAf8YL4GpaxChUNj80i/9UiLqyNB2Rg0vxSmve9v7Znv6Era2PKd9NovgGXu2I/DkuuSSSxIx1Vb45qk5bj33h3/4h4mc8z6Zd17mZazifeCjDTw47Be//PLLk9IOBjGWo27mAUWQq++CnLcRc/dxdz/11FMTMe+jgGmuVyz5UZd8Xjmt5d67o8PbOlJTRaAisB4IVLlzPfqh1mJ4BEZJzP3wv/rVr04WJhZzgiYXUAQjiIEfYIKOPbnuv/DCC5Pg/tKXvjTdPzzU482xLpDz9W2bYBjCJ6G01NJWWnpT8CfQIn7iLJx99tnpKDZ7kNsIQ1im2oKhmUueIdjmlnf/+zza1KxnXwLVh5gjQlyBHbvEJZxiDtGxBkTUdkoJgn3sx1a/NtfwZr37/i9/deeZc/755ydFIKv2LNfcOI8ewc5xRdKNDwm24Xlwt7vdLSlWEHfKFmtbW7IGxkvfaTNCYzw87WlPS0R/mmU+1lIYD5koSygPpnmSqK9XpPy9z7Qr2sNS7vhLkb1Z//X1MlPUv+kSrY6UQB/96EeLjunjMfH0pz89tfOd73xnUrLMMy+X2VZ5a5f57aXtEozNa6/4rU1f7P4TWyz078UXX5zWG2QdXnkyT40xyRrpe2W09bVy9O/JJ588ecxjHjO55z3vuSeOTJ5n/j7q3VyvuuaVcSTvrd0KsLZ25XnX9xWBisD2IFDlzu3BuZay/QiMkpjHhCXQCvrGckbodEaqH1bJD73vuQlysUNOfH/aaaclC9v2d8Xmlhh4a0HFr7wfw5KEBBmLTVfKUktbaYnGPoJtThx33HHpSpBmiX3729+e5sE0S14IwE3X4CAkTULF2koIbgrUUV/1yV2om/nGfXEttagirfK2fQXhtEdbu32GLBCwWVEjIKT7SohF1KPvNYg5l/Jrr702KQKtN1z72wh2EIE4j978muXRICo/ohykkJUcXm1JmfrdVd9EtHdRsJWJRE6zzC/LgjiLMPleP7k2X9qpb7XpZ3/2ZxOR0sfbSaii/k2iqs7mAq8YW6wQ9GlEGzF/xjOekdrzrne9KxHzeeZlW98P+VnM+xhz8p6m2IKLdiPExhc8XJuEWER2v9nwtKbYuuN9W9Lnfttf8IIXTA466KB0OgXl27QU9W6uV5vqkTCtrfW7isBYEahy51h7trZrlMScAPwHf/AHyWqE/DgKiDApAFxYeXwuOBK3W+6fBNUIsuVYqprKEagLZDlWbXeGoNgUcAmjJZa2tjx9RmiNl7y8R05ZkJ0+wD3T5wTlXbt2TT7/+c+n85UJ0HmaRaBLCXOep/ez8m3eHzhNE6ijndoapNc13ucW5iDAW4WWMHnDpkm88rzVUbKeuI+VMKJqw9naxKU5ToNg5aYYQCb0CzIZxJfrvf63D34aoVO+8iTld3kopBt2/5Evt3eBtSgtlGtfOQ8CCiKKmratFWGZ74tblFt6bXqSiFWgXbAQD4Fbvr7QTlcvKcY3YkvxhJRrG4xXmUrGrfoZAxRJ+v/II49MHhWLKMyW1eZZCsXwSOnyrGj2r3kRY9ZYjqPS9PeVV16Z5kzez9GuWNsoDPsQ89L1yvo0zVMo6lGvFYGKwPYjUOXO7ce8lrg9CIySmLM8OjLKWbFcQAmbCDnrHKFUQoL8QNPgI+wEYgF6kPJwo9ueLtj8UuoCuVgfEjrbCB+BvsTS1lU6IZdgSYANEuM9MsZNHhHwuTK4lLKythG7WQJqqYt5s56z8m3eHzgR5Ntcu30f7fRs/N/MJwgQMtrH1TkIVlOBkiv7YCmxHFpf8qjaFB+wQiyRdUQYiXzQgx6U1h0KAhbrIDTWJv2CJJ933nnJu0eexkqe+hLzsEiykFNGspgrE67vfve7UznKXZUFsUn8eBbAi0KDxwEcYZD3NTwCB8E7ETVrunbFmp9jtp3vZ41bddGWUHywACPnLMmLbDFZVhvbiLU2StYX4zhXMDX3Zjf7t+kpFNsOBCzk8k6JFf2dt0l/W0Ns+ejjyl66Xsm7j0dPXrf6viJQEVguAlXuXC6+NffVITBKYk54RTLss7zmmmtSRGI//j5HVCRCrx9dghvBmAWRkNoWvX113bMZJdcFcjn9hAAatyymb3jDG9Ie5TbirPSwHnnPBZ6VEAH3PgiiOYEAhEAbhN1nUhAdQrbvIs0isqWCbuTnKn/1YyHsS5DbiIG6E97Na+1E5uZVNOT19H4WkYg1xX36TIIZ3HnlIBjINQUh7CV11Xbu9GJgWH8oBT0X+ZXiGoRUvl3jw3eR7ne/+02OOOKICcv3AQcckKzn6irWBsu8KxxjXMRzMW5YKAXbYpHWBkqIZaRQhAgeSMGhXu94xzsSUVM/GOZJ/WAniJ1o/MghUojorUPqGrfqpu5BaClM7JUWKG3WFoZ55s+8WPRRMJjTxperdukXRDdP0b9NRZdyWMv19znnnJPc2bvGo3xjPOpveJhD01LpvKrEfBqK9buKwGoRqHLnavGvpS8PgVEScwKlH3KWKZYfQp0XAY8AKiHlXDkJQKB2ZMMAAEAASURBVCxIXki5H3pCUk3lCNQFshyrPneyErIOsg6zHDlyDBkJ61TkheQSIo1b73mAsLrd5z73SWPaHk2WRoJ+PJ8Tb/nEs977Lv43l8KS1+XCbE6VuFzLW4r6EtgJ0135fuvuO//tIsoEfHXRTq75rv5vEri+AncbodIHXoFj3ic+80I81Mm6Y7tMkHYtcn8EuXKc20knnZSOacyJeSmu+qqPKztr7IknnpiItb615gkUR3lw7rnnJiIE4yZu0a799ttvctRRR6U9vfYCW0OXkWCkDtZxCg77kbl284Tq6ldtUSdBP40t7bOur0PqGrfqpt8piXlMUKpRJmhzm2eI+0vmpfuGTEGkp20lUR4PBQTZ6QK2FCDOvEOaCpLoX30sb8k49j48hZyWEnvy2xRF+rvvcWml88o6Efm3Bb1MFa5/KgIVgZUgUOXOlcBeC90GBEZJzAM3P/xeNOQEY+QhhGPCA2HB/nNCUQid8Wy9liNQF8hyrEruJKhSLHEtjiBJBHTRtgmnxnQk4zZc041nCifBr1hhXQmWXIARe/EUuixPBGL5CCLm6rkg+0GgY+9z0zW1VGBXN3WkEPNiIWZ17co32th1jXLNaYK8q0jnxuMQe3ODOOiHLoKkDkEYEGSY6ROfxUs+zX7TJvfDmRKFBVqfwd7aFO7x+kt7ZllOS4K/wV/+iAzXX+XpD/vg7ePmmcEi3TVOoh8Qcdt+BNtieUfUo91xz5DXUgtnKChE4+eBoJ22CiC71vjAdMi6zZNXc9zKoy1oWptCKB9rs+blPHWb9kwpoTX+jYcHPOABk2OOOSaNM30S1uxZ/RD4zFIA6NN5PAaWnf80DOt3FYGKwDAIVLlzGBxrLuuHwKiJObhDuCZUEHL9KEuICKGYsLpMoTIVNvI/dYEctoObxwqxGHHNRj6bpNzYJfQedthhaUsGaxthFXkyvimjHNPlOECnD3RZGgnLcbyYq3mBIOaWPBa9NoITc2wagUUokEeu07aOENrvvTv4XL6nepbA3kQ5yg2Lm7Yh5x/+8IcH2ZtbIsCrQ/RJU7mXfxf35G3Qd/po//33TwSGgoLSQx96IWu+5wLfpRiAa3g0eIaHUNdxaQK+2a7DkvyUpzwl9YP8WctvuummyQ033DC57LLLkiInr3teZ++NC2OBp8Mpp5ySLP2xL7h57xD/9yGEoagy/pFy88IeZGOtqVAaom7z5AFbYzbGrTz8HhkP+W9RqYW9a17OU7dpz5QqSOShHyh9zG/bNRxbyJOhpB8Cn1nrSYz7vh43y85/Gob1u4pARWAYBKrcOQyONZf1Q2DUxJxgY59pWNIQc8KQH+a2hHRwaV/Wnsm2MsfwWV0gh+nFEMSRK27Y9lhefvnlk1tuuaV13IbFleD7zGc+c8IlGiFhjUaUEBp5zQoehgwg4gTnZz3rWYnIcLNGwNoseV2tnWXhUzdlIOasrBQIQxK6EiKt7rEHv8uFPNpXSgjj/r7X6D9bD1igkRZu1wgksmUtQmz0j37k+cCqjbC0WU4Rb55Bt99+e9r2wBVaGyT9K+AfsqosbsbKtR4aa9ddd10i5pQago5NS1FvFvftcPHt6tdQVDXX87DYhkW5L3Gb1vZVfBftD88QdegzL4eqcx9irsy8H/QBhVCf4+tmrSfRv/N63Cw7/6Fwr/lUBCoCd0agyp13xqR+Mg4ERkvMCWv26DqeiAXJHnM/xKyRBJ22xFJ4+OGHpyN22r6vn7UjUBfIdlz6fhqCIsv29ddfn4JcsZayejfJh7wRNlZmRAuhJvwiX8id7xD9kr3f7mU5RdYEzUJYkXJkrs2S19WuUCwIWKfezWjLCCcyzrLLpRopVzYBfogEI0QTcZ1mYWa5nxZ0LerSl4jEc6VX7faiKIANxaB+0KewcdQXd3EWdbhpWxuuSDwCr88Qcesecm3NYw2XkHaKEVZGeXlGWdZDLsOivts2QQGg3dNSjDsu+FzijTt5qv8yUle/6meYhJIiLxuuxph69Q0umOezDu+j/doav1195uVQbZhHUaUfKBEomfr2w6z1JMa9a5snz6x2Lzv/WeXX7ysCFYH5Eahy5/zY1SfXG4FREnP7TIPkCBSEmAsg5DMWJZZzQhuhgdVF8sPO5VGwGlFxaypHoC6Q5Vi13RmCdxBKVlFW7iAevm9LCBLyLLhSG0EKS9uy9mq21clnUe6qLHwx9+2VhmXbMWWIOcJrn/vW7iB0bS71QxBza4xXJO/1Z7zi8/zqHlZpMQLCysjSqI7WLutbrFttltPYCoGg58ScQoR7MdIvzUO0PBfE3HopaB1ijvRbU5eZol+tN4IZOkaL8oFXVBue6kn5YK/5dlj2l9n2RfKGDVK/KLGPeZ2vJ3fcccedttg067poP0S5y1pPlp1/E4/6f0WgIrA4AlXuXBzDmsN6IjBKYk5YM2lZHhFzAja3TQKro4sIKhGwiBun/0WwJsCJNkxYr6kcgbpAlmPVdmcIhkMLvCGQB+FH9r1YUxE/Kd+jjGD1cTVta4vPotxFiUBX/rM+b1rCtJ/njLkvsj0vAiSVZwGFHMs0Atx0iZ2XuEb9gmDD2vt4wQfBbrP05s+qG+u+fkGAEWFEUz7GjNRmOYW7/OHgJbHCe3keUZLmVTyEK3scU2XMqKf6LjNFv/Ia0I9iL8RpA/Bo4qmd0xRXy6zrOuUd68uixDbmdawnQykQZ2EV5S5rPVl2/rPaV7+vCFQE+iNQ5c7+mNUnNgOBURLzW2+9dXLFFVckiwpLOUuOYFPev//970898/jHPz4J5vZtElC5w7GiHXnkkRPn/NZUjkBdIMuxartzHgKInJW6iIalMSzIBOsgMUjW1v8dW9Ykpm113aTPkFNzGylH4OzZZ2Xlrh3kdJpigmV61jny+iFSEO/4n2s5wmrPP3d131MOOj5NHbr2SMfz7tc/thicccYZya2dy3tYvOO+ea+LEHMKAWvq0UcfnRSatgGFOzsFwCJpFlFiIadcQgzf9ra3JQWsvkbc8rTTiXkoMrq2lszrCh7rSZ8tN1VBko/M+r4iUBFYFIEqdy6KYH1+XREYJTFnJT/nnHOS8HvggQemSMT3ute9JrfddtvkTW96U+qL5z3veSkKtWOCEBVX7phPfepT0/3r2mHrWK+6QC7WK30JEsKGdGztJtSsqeHu7H+f+z5PyxLQ8zLW8X0Qc1Hpzz///BStHUlBuHOMWHrb9iJT5E07R14ezRfC6rMg/PbVCnZn7z/C6Ux5lntB1myxQSZDSdKGobwEzHvuc587Ofjggwc9l3sehZA6UhZoJ7d47RJpX91Ko263tTP/bJaF1/fIIWJ+9tlnd55zbS7sZFf2INC5Qs4YlIyrrd3rRZunSN4Xbe9jPaFcKglSudP7oQ3D+llFoCKwGAJV7lwMv/r0+iIwSmLOMvaa17wmCeAnnnhiCmbF0mQiv/KVr0y98YpXvCIJkvacs6JdddVVKXjSCSeckFxa17fL1q9mO3WB7LLsIS4SwoVESG0ux+mL3X/6EHMC9bzHEM0iPOo4psQijTwIBEdRZ6tAGxFuEgfKDt4ItsR4FgF0RaT1aU5uPMslHkGNPdbR7/aEIz48cewXNy64YXPBfve7352OKNMn04i5/hBJ/dnPfnaymLNMc8MfIsV4yLdQwMt4jBRtza/aod0xzik9Yea1yFaIIHyzLLzGv7Fq3Z51vnubwmVs4zz6Kq6xLoXL+bK2sEQsA+Pn4osvnnzsYx9L843LfJ66FF9j74ccg/q+IlARGBaBnSp3DotizW0dERglMRddGAEnhL/kJS9J0WC5VxKIg5i//OUvTy6YhEECxRve8IYk7L34xS9On69jZ61rnXbqAhnEprl3M1x5ja1pQbqiP0stl0g5QsSF+Jhjjpk4rqqPC3EI7OaFukuEY3l6yX9MCbHmbut87iuvvHJiiwsMgmRGW7U9d7UVawL5++xnP5uIH2KTu57Hc0FQnQ0uNoU4FcYCwqJPubLzwhHfwosLOms9xeEb3/jGdL58m6Ig8o/rsoh5jIcuAud7SoNQRsT/6pWPFUqMIQhwqYWXooMyQKyAadH35z3nOnDf1GusS9MULtq2KGE2dnmlGD9+c80T12XHsNjUfqn1rghUBIZDYKfKncMhWHNaVwRGS8xf9apXJYHyN37jN5LFHAFBwFnSCRNc2bm5E8q5ur72ta9NJIUlnftpTeUI7LQFcpZlj/us5L6wHhGCu/Z0hiBtfNpqQcBFUhC8PCF29isjjifu9gQJAjnUnuO8rO18HwRxlsKg9L4gDIj4e9/73mTxRtAR9bbUJOaOAYOpLTGvf/3r97hKyzdPiLm+tjffcXXOIeeqru/0vURJg7jqe0eWeTln/J3vfGdad3js5BbqPH/vEeBlubJHWUGIYbRr165Uv9izDXP1E92dkslYbVr44beIy3j0a5eCQD2V2STalCdf+tKXkuWcV4Pno276ZmtOV+3AZVOvpYq+Rfst8Inxk7vM134IdOq1IlARWAYCO03uXAaGNc/1RGCUxBzBed3rXpeE41NOOSUFT0KMuD6yjNs7+sQnPjEdl0QAv/nmm9MeVPewpFdi3m+w7rQFsk0QRS68wpLofSSfeW11EIUgJvYev+c970kWXtZapC1Pd7/73VP8A+7RT37yk9PeZYSIgL3JKRQTTc8DFmfzM1xeS+8LF1vrAFdnnjL6jLW6LTUJinPh5fGJT3wine/tiuw0ibnnEG+nOAgmKcCbucBd3b36Nci771jUuXtzeUfOb7rpprTXnIWxLRkznl9W8LcoMxRNsWeYcgERh4Hy/W9PvDgc7m3DIfc40MYIBBdlTLtGv/a18LKce5blfNr57pQi1vbwZJlWlzF8V7o1xvhdpN8Cqxg/s7Yg7LR+CHzqtSJQERgegZ0mdw6PYM1xXREYJTGPvYcE/Sc84QlpjydSw6LCUsXKwrXUvnOCyVe/+tVkHbNPlCXdMUA1lSOwUxbIINDGUZcLLaKQW4sIvwhWbvGj+PFiAczvveWWWyaXXXZZynsaMT/ooINSkEJBxSL/8t5anztLBHqu4CzOyKBo3Mgjkh1WZoTLPEYEEV54iHqORHIZhycsc0VJEwGkGWnQJ6eddlpSeCB7CP15552XCHoXMbd+7LPPPukZfclqyDKvvCgzCHYoZpyfrr7WHdHiKWScOd5UHGibdQvRdVyadSlXVDTbMe//0Q9txByeSC9ibtxPw0H9Tj755FTfPsR8UQuvOlIizFLszIvPpj233cQ88AkFS+2HQKReKwIVgWUhsFPkzmXhV/NdXwRGScxZo6655pokwLNSxYsrpj1wLDOsVQRjFqEI3uScYJb0eo55vwG7UxbIEDynWfaahAwpi4RoISwsoMcee2zy2OAyLLF8G5MXXHDBHgsvspEnpAwJRdQokOSzDKKWl7nM912eB8qEG8WFffSILywQxHBJzxUayDt3b8HW4PHXf/3XaTsATxjWaCR7WtIn9nFTeDj+y3phrbClYJorO0Ko35RJKUB5oE3qGqQ8ytUeCgZt4Sr/lKc8JfUld3njiXWfZdp9Xp6PoGqzou5HGfNeox9W5cq+KJHkWQF7L3NUGnPshFn9vKiig7JpnmTM1n6YB7n6TEWgItAXgZ0id/bFpd6/+QiMkphzAWYlI5AjQ/Z1iopMmGdJI4Bef/316b3PWMoJy1xYuUcS1GsqR2CnLJClAm8XcuHWLHibYGH77rtvEmTdj+Q5mkuQMpZzhD3IZ+QXRNDe8tNPPz1F/LbPdl5BOvLd7msI8CywXZ4H+bw0H5FdxPXTn/50UqjBQkIEnBWOWEeANZZfRDd3KZ/WRnvDH/vYx04O3n3kFxIsH+VNU8DILxQl3iO3LN7a5tWWov/1n3PJWc7vuOOOtFax7vPk0R4JsdzavfVBlPNlnS9f0g/qE+1xbY5JdQ2FE48Dp1r0tewvSsyVX9O3EShRILp70X77don1XUWgIlAR2F4Edorcub2o1tLWAYFREnMEyj5y5IaAi7iwqhAsCYGIu6OPCNPuYe1C3FmzEB0Cd03lCOyUBbKUQHQhh5gZj8YYK69x5zMJ4eGqzQ06xm6TBCGjxvK8LsNd9druz3PiwEOAFwvvFfjmyTx0NJgrgoj4UrYhzazKkjnte5hySYeR77mGc6kNUpnn23y/3377pSj3LOYs79YB5c1SHFD42U6gn3g7UAhMKy+IOSVgxL5QZ/Vl5bdHNzCIgHG8AJa1Nzfvh65jx7QnrPgxVl3jvbY3g7JRKOiH6KMm3s3/SxVe8lwkyFyz3LH+r89Kxu+i/TZW/Gq7KgIVgfVHYKfInevfE7WGQyMwSmJOWES4Q6gk0OWCou8IwAR9QqHvkCRCMsEvhM6hwR5rfmNfIEPQRZgRKETywgsvTJ4Xxg8huCQhKsaa8SVPV/9L8vCZFOWNlZhzE0diWcudLy64Wt7+BMI2/kGun/GMZyRXdkef2dcthYt3V7RpSgPWdnvar7vuuuSBM63a+tv6QrFy6qmnJus8bwDKFuR8u/fmlhLiGKe2/KivtTJcxX2HiPM0mNeyX6IggGu18E4bXXf+btb4XbTf7lxi/aQiUBGoCGwPAmOXO7cHxVrKOiIwSmJO4ESiCHwEYYIv0k2YlBAeRMB9CDqijjS5j9DpWlM5AmNfIIM4IGAs2gJhXXzxxSnQl7HTJNDTkAvrY9wTVsUg5fG5/5ufIfFjsBjC0HaSOF+c636zrYHDdlwPOOCAyfHHH5+I+f3ud79koVZuBEXrijbtHmTaXvbLL788nZPus64UBNe+ea7s3NRZ+kULtx55GWtSEF99HmOkK995P7f2UZDYSz8ryJ063ve+950ccsgh6WqN5IWELLPoL2LZ1/fa/pcztjZUC2+/np41fhftt361qXdXBCoCFYHhEBi73DkcUjWnTUNglMTcUT/OMEa8uQwTQAjATcIdFiOCt72evufW6v6ayhEY6wLZFGy5W3OR/vM///MUXPAv/uIvEqFALII8eR8kM66lSDZJu+ciX+833WIIDwTM3LzqqqsSMRf8DFFfVYLvrHPCQzHTtGj7X8wKxNZpD6KrT0uUgxG8T/T3oYP3Bb6lBL8PMbc2wsl57YJkagtizuXe1XaCUHxOw2Dad9XCOw2d+b/rGr9D9dv8NatPVgQqAhWB+RAYq9w5Hxr1qTEhMEpiLvDbpZdemlzVuVhyT0W2CSJ5Ilgj8Sx2hGvfP+lJT5oIzlVTOQJjXSCbRAEJI+SGS7s94azlyF2QEqTIZ16IUh9yHhbVIOOuPnOV36ZbDIMgfPzjH5+85S1vSVsCzD/zcBUp8I1gevaYI85IZp66CK/97pQzXPIvuuiitM88f675nhu4IHUs5cccc0xal4a0iAe+TQVCFwELxeQf/dEfFUWfp0h44QtfOHnUox6V1kpkfUjLflMRlu+5H8Iy3+yPnfJ/1/g19oYcfzsFz9rOikBFYPUIjFXuXD2ytQarRmCUxNye1d/93d9N+zaf+cxnpojrbQK3fZ3Il/u5JhPICZ6CM9VUjsDYFsgQZKe51iJBQbqRBvuSWQ8JuiyRrMBB3OO+WYgi4VzV7eWN4HBBHn23teBe3lnlL/v7UiI4ZD3gJ0UfxP+u+k1Ed9HE5zknnILGcWy7du1KruDc86OcZhuUx03+iCOOSC7z1hjR34dI8xLaIPKzos9vt6dG1KtUwTAEhjWPikBFoCJQEdgcBMYmd24O8rWmy0ZglMTcHuBXv/rVyW32zDPPTBYehAdpyhPrJqHW/WeddVb6/qUvfWm6P7+vvp+OwNgWyCAG0wgLAoZsGVP21yJa9uAKCOZYr/e///2Tz33uc2kMsnaXpCDmzePUWCXHYDEsdZ0uwarknugf95rrUngguDqJASmf95zwUOzZKx/nnYenRCrs//6oh/K4gAv6xjJP8UKRM0RqenZQKIWCQNlbHQod98BlmgJqFZ4aUS91MxelIS3zQ2Be86gIVAQqAhWB1SEwNrlzdUjWktcNgVESc1Gzf/u3fzsJp4g24bvNZS8EQG7siDwh9hWveEW6f906ap3rM7YFstSyG8SPi3kQc1smRG5HzG+77bYUWLCLmHs+XsYiosbdmcvwsccem865FlwOKRlyL++ssRTzoosYed53Xd9rU1vqQ8wjD3XpkzzHxZoiw3snL0SAPnkhyFy7HXXGrfzwww9PfSe6Oq+aPilX4Jx//vl7HfuW119deFQ8+tGPnssy31Wn6KdFiXUbsY8xC6+tDmLfVa/6+c5CIMZh3/VgZ6FUW1sRqAgMicDY5M4hsal5bTYCoyTmH/nIR/ZYzF/0ohdNREGO/ZB5d4VgzWLO4oUAvexlL6sW8xykgvdjWyD7EkikGfES0Z9nBjIo2jVX9iCFbTAiPcacK+H2Xve6VyJvFEleCD+CFPdRLrUpmNryXuSzmBddrsTynud4rz4Kj5zYwqYkeQY+lCP3uc990iPiAiCeeX5cyCk/7C2Hs3uRZ5HH+6QgJEGM7dWmFPzSl760V3kRG2Bey3xXnaKfpnl2eFbbKHy09YQTTkjHteXB2uZ1he+qV/18ZyEQ47BrvbDG1VQRqAhUBIZEYGxy55DY1Lw2G4FREnOC6hvf+MYUpOvII49MLqSEcZHZcwFd8CZux+6/8sor097euse8/4Ae2wLZh5hDK4izsZWTyCBuYX3MkXWvveQ/+ZM/ObnLXe6SSCHLJPL04Ac/OJFyFlxC7SxCHuUQkJEsV8mzyGbkEWM/r0f+fhZBU0+WZknQNsfHwUpC/hDiacdmhQA/i0iqMyWHRLlBCZDjmr7I/sT95jfMPIt4evbzn/98JzFnwXb8F5f2RRQgbRbnvA/06yLnfGdN3ettqaLD+Ck5Zi/6pxKsvWCu/3QgMGu9mLUedGRbP15TBOJ3pnpGrGkH7bBqjU3u3GHdV5s7BYFREnPHFjm+iLUMkRCVXSRk7qoEVIkl0/esXI6/chSW77kQI0Y1lSMwtgWylPAEQghvF+klzDRJpXuRpX333Xdy2GGHTR70oAelvekEWS+u1p4xVktc2INQIaKOc0OsJPnY8x5EdZblqo1gRt3VmVdAnFjgJIOvfe1re9rm+60ZBFRehLqwMNtC4iWAGuWGhCAj17CRbAegBPBc1CV9kf2BmWMOzfP9998/KQuuu+66dNoC5Zv+zBNcBH1jMT/uuOMS/otsGWgjKN/4xjdSkfpvWQSlVIFkrPEY+vmf//nJySefnLwyWNApU/IU/VMF7xyV+r4LgVnrxaz1oCvf+vl6IhC/M1Vxt579s9NqNTa5c6f1X21vNwKjJOZcSRHum266afLlL385ERN7gLkKx1FIrHDuY71DaFguEXIEngVtlYkrNKFHvbxHLPwoEpwRGIQNuUC4kBjvfd4khwgDgiAf1k0/qIRu97EyBmlwJbgT4OdJm7ZAziIgMDI+mpZdWMLO8/Fqw8s9zb7I/0eIEMNHPOIRk6OPPjoRSv2hnxFr0b5LiF2TEN5xxx0TR0wZM5KzppFCZfEYQdKVrZ+NJwlZD1L8xS9+MR39NY0wiywuIczGaDyLUIfLNku0ebS1m6grK2+7Z5sCPas2kqnd2m88I+YwFkBvGjGXt6B7hx56aCKe6qcdjmNz2gLCrT/zFHNoqCB7MZ70n37TltyTAOb5HGvikdetz/uhiXmfsuu964tAjMdlKVgi/1kKttL1YH2RrDWDQPN3ph5jWMfFOiCwaXLnOmBW67AZCIySmCOiXNT/7M/+bPKBD3xgcvvttydyEIRW1yCpfnAIGfaYPu5xj5s87GEPSwSGi/Eqk+BhFh1WSYoFxIQQTtBCpJBxLsOshKxgBKA2Yo3kOGfZue6wkJd2Rx7Ij/33yExYaudp96YtkLM0/8gkrJuCZ+wdRkLl4dqWEL+ckCJiPnPNCSzsWW0RN2XdfPPNE2d866fIWz5bHZbonOCKDK6vkfKcdBsXPEGcPX3AAQckpRPCnhN/SgHJGHnrW9+a9knbIx/kMtpo/uQu5khvnrRj2l7muLcp6Gk7As7T5dOf/nQi7uYgDLRRm8zTZoInfEQ7f+5znzt5+MMfnnD+1Kc+NbngggvSHEKW24i5OcD1PY6l0z/RR114N8uP/2M8wSss9EG+1VsfKAs++d7ueH7eq7Ypr/Qc8jivnVu9+qhXTeNDIMbjsiybkX9TcdlcL0rXg/H1wLhalP/OWGus17EeW+f6rpfjQqe2ZlUIbJrcuSqcarmbh8AoiTnBgdBK2L/22muTsO9caYIDgVxyD8scwfyhD31osrjd//73T8Jq3LOq7qRIuOaaaxJRQa6RKCTIjyHygCAR8HkAIOZceJHzUChE+7nqC0bFVV/7WSTjBxWhQaK4/vIUEKF6n3322UNO+rR9UxbIJiGcpfk3XmCIsBJIvJcH7wPKEwogeAamgVn0kf4I4keACfIXgox+4x6uHgIWfvSjH5187GMfS3nrHwk5jaBwYYkOj46wcKtbCEzuj/oo04sSBynTz5417gntEuEZ2VZXxPi8885LhNZ4C+VAurHgjzobm6UE0ByFoXl6/fXXp/Yj1baVwEr5eT2iPa7ayAod55CfeOKJaR4gqnDUDhbzNmLueXVVhnxcc7xLLX3GgvpTnBkP+tFcNf9iDfEeJuGdMm0PfgHEe90i7zbPjkqQ9oJpx/zTd30LhVxfgKpCqC9im3m/tbFNQd3celS6Xm4mCrXW64rApsid64pfrdf6IjBKYh4/KIRklj8uvgRnJDd38+Xay4UdIUVSCc8EdIL7KhNiTqGg7uoVAa0I+IgKi6pFCUFk6Ua4WPwjEjXB3LMsGpdeemmyqrEmspDbc0+Y5z7MAgybrd0WWXvrKSiUESSlFINNWSD7av6RVlgiXDBHGI0hBJbihIWa4NIksIgeHGe5SsNXvoi/49VYzGOMxhgMAppbonl2SLmFWz8a7+7Pk3zCpZ2V1BgPgdx3XsgtBQ3FDQ+TOOataWnO8217b9ywws7ayxzPmovay0vgoosuSte8/doSL88Erurs86bCghIEBlzx41xx86WtHdH2qIv/I5Va+oyLW2+9NfUDxYr5hCwbDzEmgvQj6jwXhgwCB4MqOEev1Wvf9c3vyjwpfl/Ms2kKsL7rwTx1qc8sD4Gq+FsetjXnxRHYFLlz8ZbWHHYaAqMk5tGJYZFj0UKABKtiUSPQIigRZZoFUSKQI+m+W2Vi3f7kJz+ZrJpbu0kzwqGuyB6rCAsjAmUPPXLApf2kk05KrvjqrZ3II8shwoeYPf3pT58ceOCBqX0IESu676+++upkLRUUims1Yc39fdK6L5CLEhgCCmGUIoTCg1UXbq6wDutzYBbErus8cvjqSxZvCpgPfvCDE0f2sUS0pSCk+uf0009P/eg+hBYBRQpzy3JbHj5DPoMohgIKgYwAib637UEbg2B25dX2uTyRfq7lxiMLf3gM5PeHZU85lBvqf/nll6etG4im/spTkGi4UiJQMMQ+9JzosvzPsiA38/Z/8zPt0D9dlv+wlFNu5Z4Ots+oK0y1w3t5wVxqKhK2ds/t6Ie8vX3ftxGyXDGgnByneQlZ33rV+7cHAeN3OxU0lZhvT7+uupTqGbHqHqjlT0Ng3eXOaXWv31UEpiEwWmJOWEFaWf8I/0gQi2e4mgaJQCTC7VQQqcMPP3xPROhpwC3zO+SCsE3YQkCCyBHwCdyIO1LI7dneYuTnJS95SSIS6hWu8PbrsqQiBE996lPTHmMu8PJlHUUqWDwIWk984hP32m/ep33rvkAuqvk3lmAUwclYRu3/Z7EO1/MgX/onXPscfeblf5+7x1iTHyLNowH+LE8IvjLaUhBFAQwpUASNkyhvBDmTT5dluJlfkFzXSOZAbIPgmm38qaNXn6R9CC2L+amnnpoCwCHSCHWegkjeeOONyYXdWKY4C6VZfq/3gRs3cHmLYo9sGte5a7iypxEU7dEP0S5X9/ssT/CeZvkPSznFSNPTQT7y9QqsI28Kv9zzQVuG2HMeig71gqNrjCXYLysqfLSrXleLwKLrW/z+lbaiErZSpDb7PmsI5Wn1jNjsfhxr7ddd7hwr7rVdy0dglMScRTiEf+QFKUeqfIYAITGsawRnRF0iwLL0OT6Ja/g6JwoG1nLE+n3ve1+yIr70pS9NAb7Um7XccXGUEtqFyDjP3R76SIiDPbhnn312CpTHmo74eYng3ZbCUoi8eflfQlJZPGH6ghe8IJGntudX9dm8giSLd4yR2ENMWGE5R35YSCl9jC0ECzFEIpHGaRbKULywlP/u7/5uIvfTLNRDEvN5+iAIJgKvj6W8/4OIaru62jt/1FFHJUUR921eKJ5rEmf74gl9ggkhyPJpS0HMjUtk1jzlzk+RxtqP8CpXPaWY+xEbQP7Id5BydY+YC22eBl3EXB+7X5/v2rUreTqYg12eDs22RD92WeKb9/f9PwjasoJ+9a1PvX97EJh3fbNGzRMEMMbZWIK/WXesP17aJlFWmK/5urI9vbk+pVRivj59UWtyZwQqMb8zJvWTcSAwSmJO6DZpWeQQcz8whHqECGn1Q8z9248uIdv/9mcTmBGKnMCuYzdTNCA12qg9Ano973nP20OItZsl1n3Ii/3lBx100J2OgWOpFIUbcWHVtcf8kEMOSXth29qNjHKjt6/W1f8SospKD7czzzxzgtCuU5pXwEAwtY2CAwFjKSe8IWeEYUKtffthtbXdoMRCicgjc4j52972tjRO2whiYBjElCv7GWecMbcre+TX9xqEGwnOj0vT/+ZOCLaIsTkFF7Eb7Fvnzm78mW8+Nwdzgb5rb3xex1AM8B5RB3nBQr7KQPxzy3OXBZnQrX5c0K+88sqkUGrD3T2UCE0CrZ2s+hRab3/729NWkLatDHnd8/fynWaJz++d5330QyUY86C3uc/Mu76Zmzw4rFl9UowzvxvWMMo1r00NChaKhqrQ2nsUbLfCZ+/S638VgekIVGI+HZ/67eYiMEpijjheccUVyd0bCWKtE4jLe66n0uMf//jkvmuvNcGGuydiyrIc5GPduhWJUFf7gO1xRoYJ+wjkMccck9qozojDG97whmTRZV1kBWcRb1rCw7KOqHBlZll/whOe0OnKv1OIuQj1z3jGM9IWAdjwTuCyzkKOpEqEU3hSQsDY2KEgQT4JujlRTA/s/hOEkbuxbQbI/nXXXZeUQ4TDpkt1PCc/0dsJ0nEsmO8i+BuhmMthW/C3yGOeaxBi88d2Ca7jFD3GYCgq4KDeyKBkPHpOCnwQXESaAsP9sJwVnC1l0PgTxF9d9JHxal6zyodyBFYs+1JT4PY/wm3OX3LJJakecNOePMkDYXHE3LOf/exUjrnHEk/Zpf6XXXZZIvZhhc+f73q/bGLeVW79fNwIGL+rcDnu8kyBtnVya4bn0Kp7JdZjv2t1C8ideyPWz1yROm29tG3rhBNOSAaCtt+/O5dQP6kIzI9AJebzY1efXG8ERknMWcnPOeecJKwgpIgrUsHy+cY3vjH1yPOf//xEMhyBRfPvSuC3F9v965hYbxEDe1tZ/AgWLNzaiKTYzyv5/qyzzkruusiF75FH7csTgv+e97wnWb+RGdZHe827PAaUF27suSvz2FzZtZ+CgnXTcXO8AyJaeI5fWHARz2c961lJIOFW7bmcoMYzIcj6QRHVHTnn3QFLhNWrLfFm0I+Pecxj0rF+EX3fWGCx4j3h6v8+RLGtrPyzsNQj44ceemhS2CC25grFEFwQ8rzMIPPyERvByQe2hlCEGaO8Coy7adGc8zo038tfvhQg8vJiQW/bOgBP9QsLsnpqE8uefvWaZumjTKDUUh4Fjaj5+k79p+2Jb9Y5/jcm2izxxktNFYF5EViVZXPTiW2sx/mWl1iDrTNba65YmHe8lD4X6yf5aAyeEaXtrvdtBgKVmG9GP9Va9kdglMScNe81r3lNOhrtxN3nGyM1IhGbyK985SsTSq94xSuSKyxXVC7dV111VQq0RuMbx1H1h3M5T4SlnCu1NiAISBEr6pOe9KQUDIzFH4GQlkXMu1q37gtkqeYf0YYpN2yKHJYo3gf2l7clwhuix2rOxdx2AeOMtSBPbQJOnDuONIYwmD8T75WB0IqqL39KI3WUcsFSEEAKEsob7tZeBPZpeUcZXdcgkubD8ccfn7Y7IOUUCqX1l3coFiiHDjjggITnBRdckMayOsJgngR7dSRA8ybworzwf5tiJMrIcYt2IO2SPPW/urLCU1i5H7Y8TKwV3O9n9VuUpf+8pGUFf4uyNu0a8yIUJ+pf9/b278XS9S08QYa2bEb5m+IKHuOuEs6ysTZrvbTetilGy3Kvd20qAjGPVrV+r7vcuan9Wuu9egRGScxZwhBwC4Zo5QQRArbjwYKYv/zlL097SGn9uaZy/SYUvvjFL06fr75rvl0DZIsQwRPA8VpclhEc7tNIUxAIZERaliv7t2u097t1XyDjB2SWIIaQIdmIte0QLKvcHJvHoeWtR7rsdeZifvDBBycSGhHO474QXJsugWEpj/uaV3kjiqy2guoh5kg5BYLUtFix6iKPFDeu3A613WuepGwWXgHXtE+5gvzpb3nPqn+UGa742vFrv/ZraZ+5aPLGKaWTeTpPCtLbl/A2cdPH4c6uruaRe1jFtZUywhyMV0Stn1Vn9ZMXHCVbHfooEGblv+nfx7zYFEK3rniXrm8UZMsYf1H+qgT0vv0S4665HscaEPktS5ER+W/KddZ6WU992JSeHLaeMY9WtX6vu9w5LNo1t52EwGiJ+ate9arkYvsbv/EbyWKOdCPgLOnIgGBpXLwJzizMr33taxMxZ0lH5Nch+UFEwrkoC+iGbAlW54fwV3/1VxMp556OmOQpD/4mOBniyJKIGOSpb/C3/Nn8/aYskE3NP8Llx4VAyWKKmCOPBLQIDhbf5e1tvrff2V5kxJnbN/ftPJW6mubPeI/QGZ8R9E0fcuFuuj7HDyS3eGOERZt7Jq8K5FJshS5yjjxKzavPQjBFzJ/85CcnD5R5CLV2mH+wdYwagn/ppZemIHDNPYt5Pb3P/1entgSjeVzEA7cQLJTF68SpB5QGPG/Com4clNQFjvLgYk9Bo60UPepnL/wyLUvqp55e2iatowW6r6APu01oV9vY3M7PmusbRWTuCbJVLZupO0rX43nXle3s8+0sq7leKpvsEWuctaam8SPQd/2OmC9DI7MpcufQ7a75jR+BURJzBPx1r3tdsnqdcsopyQqKZCCiLOOIir3U9lQTmlkYzz///EREWNLXhZjb12xfPKK9a/fxTCx1LOSIoH27iKT6N38QkbM4Ls0PJ1foZlA7Qjzy4bg0rtpIU7yaQeJmTYNNWSCbPygIWOwdD1KGVCPsv/d7v5fGBSVOCLddOMwi5og+Amo/c5+91QRDP2qIsfPLkbq2KMpByIwPLuzaZdsDhZMgiNyw24glEok0u8b7+F+bw8IWUdWR/AvmcEEPAZd7+EknnZTy5ZFgXOd7vLVDua7xPv7vwt7n8qesCJxYBdtwauYRuAWRJXh6UWpceOGFkw9/+MO9PAMCQ/PSWfOCSFKcGVMC6C3bsrQpgnMbgdQXEgy3GgQSdsZ2KFDcVwkBFPZOzfWt6Qmy7PG3d23W97/S9XjedWV9W75YzZrrpdzWUfG3WCvr07MQ6Lt+2963jLQpcucy2l7zHDcCoyTmYQkmyAnixeWbCzALAsKKtCCfFgwCPSLDTZxAnR87tqqup9GnPEAQudsJNqXOgrsdccQRiZx7T9nQlkRrF1xMNHH7Ye2XFtSO+zsiT4CXP9f+c889N1mIKSpYZpEJAlyftGkLpHERkXhZlb1HCCTCB9xFS+caDqtpxNxzs1zZc0EQ3vq0ZG91EHMkT3A5hNMYZYn1XdS52VdB0JVz8cUX74k+rt15YukwL1h3vSdkRVsR9CBIxoSxRoHVR7EQZakrqyfFDwWDcQgTSqewSOflwvyb3/xmch0Pi3oQt8gzvw4lQJda0vKy472+4M1gXaHwsyVCxHgKAsQ8J5LqO6QFeFMIWQj21uGSYFIwpGyBHW+QSjRjtE2/boqCZnorlvdtvh5PW8+GWleW15Kac0Vg+xDou36TV0pivszbgk2TO+dtZ31u5yEwSmIexJRmTwTreHELt/8cYUFaEXIEBNFBeFj0pkUl367hISAdMk5ZwKJI0FI3CgZCPyschULTUh718zyrOeL9gQ98ILnWCh5G0EUUkB5HRvledG0WKYQJMaesiCBykd+s66YtkKzkSLd6X3/99WmrgHEgIYj2TlNouLZZmgMPZMxzcJ0W/K1J+LhIl1ji5Y3Q6vdnPvOZaUuGvkek2zwlol7qLH8EKCKPuzbPGRabwI8njwrj35gitEqIeFjYECN5Gi/zHHMW7TC+Tj/99BTDgdBrPrLCNwkXvHhxUAQEcScUdJFzecGpee649vRJpQJ7M0/jQB0QcccWGg9bu62+zlzXT7wecssSLIe0AK+LBaOJS/P/IIzW37e+9a1pbIbiJb/X2LNO8Qo6eHfcBn1rzhi/MQZgDuNlbg3I67RJ70OANs5gLuXjD3Y7OTXXY+ua9RJeeRpqXcnzrO8rApuKQN/1e+ggk03cNk3ubNa//l8R6EJglMQ8iC23XkIeAdn+aqSLNZQLLULmvc+QEtY8lkkkiFC4ysSS+N73vjeRctZ/bXDcFEujtrBu5omlLs7Q9nkQDAuXvbxw0Db7nz2PFCCmrPAsUQTcY489NrWdEEwg6ZM2ZYEMgRVhnWaxMya83B/XNjyQLtgjt47fQwzbCHNY6JFMWym4mPuRk3eeCMwhNCsbodUX+u2www5LShnWV94Sbed2R/vkzYr69a9/PZ11j+Da3oEERxny1u+IDQIUyh5kWcotvP43ZqYRKmV7RYpyfCYvc6r5Qx35wicvV1nqypWcxd+eeUJznn+U4xpETv6C1LGytvVD/kzb+5g3pVsOoo1RPkJ+3HHHpfKNC3XI09CWbXjAZdZ4ji0Jy7Zg5G1te19KiEKRo97WZe00ZyhrfCeZO812RQwN31VC2tYD9TMIrBvBqL1SEdgEBErX7+1SaG2K3LkJfVvruF4IjJKYW0C4atOCsxSwnCGzBDzCN+IuoBpLk3tYjAl1++yzTyJaTYF6u7sMiXIOOfKoHcgy5QFLqbY0ibM9zo6zcpUIHjCgeEBuWMcJtfIitCIU8kCWWOARM89qP8HX933SpiyQpQKZcQID13i14cEN3PFl3LVEG2d5hmsTv7DQ689LLrlkD9FsEnPYx/Pxnc+MT/3Ps8N47goiFu1j6VcmhYzxjvjykkAM3SMPZDIs4si+Ma9s30u5hc3/0wggjDwXdY7xFRjaSoEUNolh5CvvvFz1NDcR5GmKDM9LCJojEQXfe+xjH5sUGYHjt+4o+1sqeERu0V8UHJQC2ufq/zYF19CWbZjNUpioaygOmooRfbydqVTxoe/gp97WPOPKmm0M56nZrjjm0vjJFT3W/nkUNXlZ9f14ELBeTVvPtNSYayp+zOt51pXxIFdbspMR6LN+k1P7xnzpi+2myJ1921XvrwiMkpjP6lZk3CJDEPeeqykLV1/X11nlzPu94FNcPVnLkZsgAE3CF/k3iXl8jpyJ4s4tXp5cQZE0AjmXdXuHWXnvf//7J5LWtMRHPrOum7JAlhIvOAfWJcSc1VnUcsQ8J2RNAZDFPFyzCX6+j6Q8BJyyxHtExBh1j/+DRLUJjEg7gqa/WZq54XMPDyKjTqzWiL1xzmtCOWHVjbyjLl3XNmIZ7WjDy2cE2a3dAm0fl+O+/UQ5An/EnFeJtkmBf5P4q1ObgF1CdOWJ5MEOafTab7/9prYv6jG0ZbsUJ201BhZ19e8aF6Wf9xHs1Ne6F9jlip8oL9oVAiAloyPtKKaUJeUKKIoo/y8rSnDUq143A4Gu9Uztjb2+69ZmtLrWsiIwHwJ91u9KzOfDuD5VEYDAjiTmyARh3Yvg50c4BMF1GBZxbjmLZ+qk7/g2UWyrH5KQu7LHPWE5IqjKEynXZoSJcIqsEVZd2yzxkc+s66YQ89IfFu3NiWZX+xE0xAzhOfHEE9M1yK5n2oge0syaZ9xFygks67v/g8DHGI36eKZpKeTqbksCl/UbbrghKWOQcuVLQWBY2llNWRYpZRD0NoKaHmr5YzzBEOnP94ZrSxAe70PxJYsYY22u9y1FpI9K+ynmbezBh5026hMp8G+6yndZUNUd3l0E2vfWDlsJKMMoYuwrV+a09kU9pm0FUN9mv85yyS/FSR9vh6CkDdNSqSJBv1IWqbf3MDf29E2eol0UI46PpNiy7cEc01eSebPVUzGUl1HfjxeBrvVMi83F8CiqCp3xjoHasnIEStdv6/J2KII3Re4sR7jeWRH4FgI7kpjXzh8WgU1ZIEuJTCk6Qagj6BeCjqQhvIgQQoEU2k7gWDpbFJDWJsGIfHguiLrv//e9733pHPK2++OHz57mF77whcnlkkeEfbhB6JWdkxMEh/IGeWW9dkVY5KW8PimIJjwpj7SHkiKCnPmfYkC+iHkXEe4qs7SfAoeI9q5drOWEAnk0FQjKKxG4Z1nS7McXiR8xRwqVOc1VelkCTR+c1oGYh8LGOHU8pXnByyM8O2I8CD5pm4gxG9sxphFz/aEvjHdeRhHUU37mQXVJDmTrtQ2BWM9KFXhtedTPKgJjRyDmydAK5nlx2xS5c9721ed2LgKVmO/cvh+s5ZuyQJYSpD7AIJ+s5Cym9urbZ4yssbIgiEgHfN7+9renoIPq0CTmypOPvdgs2ginM+YF52u73/eIFnLoGDVE5g//8A/TdoU2i3zkjyQjkco4cbeFf5ZFtgsHBEgb1I31n0DLlR6RUkaQVFbPsHz2UQCU9pPyYBzR3hFzfaE+4itQVoSiIldSbM2woM6ypNkGAsc4n1x7p7VvWQS6FCd12w4LRtd4ic+DmDui8Nprr02R1tuItK0ZBx54YJoT5oEtOJ5FsvMU7TL+9bvvKWOMxTz19UTIn63vx49ArGfWNORDmmfdGj9StYU7GYGYJ10eZbDZTkXopsidO3nM1LbPh0Al5vPhVp/KENiUBbJE4xsErnnNmnunt0g1koCcs2JzE2fxY0FGnuzz37VrV4qE32b5iwxZ2pF6QiFiKaq655tEXlnytp/6aU97WrIUvutd70pHALYRmMg/iCzLvuPdgsgi+X3SLOK6qAtoST+pb5NwOVIQAeVmPy3qfqkFNeqxqCVtWcQ86rcuFoyuMdQcLwQ75FxQSsScVZySQwwEfcrrhLLFWDZvbNFoO1YNIfdcWNb1U9v4DwK/6j32XfjUzysCFYGKwKYgMMujbJbie6h2borcOVR7az47B4FKzHdOXy+tpZuyQM7S+PqextfL+/jfdVZCzrlsIwqsdwgGQoA82ePPvdbV//JvSyyvnvVcWKERjSYxD4LNffcpT3lKysqxeM6ub7s/ygqCwlJ+yimnJGLO8quufVLbD3NgBIetGRbpWWXJS5v7auYpRrTfeCw5J3tWlPKox6KWtGVZtqN+fXHSP8aCvtqO1BwvjmqkrHBKBFd2Yz727NvOoX7c03ljUDrYAiJCP8u5sS+ZQ/b6iy/gM+RdnIUuRdY6uPJvB9a1jIpARaAisEwEmopWXkrWc4kssahivrTumyJ3lran3lcRCAQqMQ8k6nVuBDZtgWwSBcQmyDIhH3lGjAXMQ6j96ATxLAEpiLNrk+TH/235xHMsgQKYuRdxUZdI6oFQsaojMFzn/Y+4aEebxTCeXZSY9yWCrJ6slPa2x/7zPoRwVj9pP6u/YHasrBQjrK/c11//+tdPPvrRj7YqKgKHoS2ogU+TyPt8SIVB9Gdc++LEFX87UuAxTXEAK8ohBNt4OfTQQ9MpEQQ8xLy5JSGfp453ROgpvK6++urJrbfeWon5dnRsLaMiUBHY8QiExxb5xFotzRNTZl4gN03unLed9bmdh0Al5juvzwdv8aYtkLM0vqxuzn1nhUPynHk/jVA3AUWU44WcSP6XpuWDMCKwrIX2q3uWqy833ngWkZEQcz+C3N8lx6v5cfR9kJf0RfYniP+8ruzxQ1ziOs2a+YhHPGJy9NFHJ+Kc78dW95I0q5+amnnkF1aUFOedd17ap7+dFtTApymoULQM6WLfxK4vTsbYdqTAY9p4Mcbhoy+dRX/SSSel/eWIue8oxbqC+InCToHFJf7cc89N1nVjIOZItHFZipjIv14rAhWBisBOQ8D6bK31stZLftutt14h8ywLl02TO5eFQ813fAhUYj6+Pt32Fm33AjnUD0IQhyaRQuby48duu+22ZLVmjbXvu68F3Q+UHyopiLk2NJN7uNzG8U+I94033jhRPkWB8uMH0L2IduTj6tWVvzrIb5Hgb3ApsUhHe/fdd9/JYYcdliJmI+bIen6sGLKa/7CruzZJ8d4PvXYhXALpTdPM65dVEPNSYqzP1A+RjKB02ilp91bDA6CvZbtrPPeNip8qNMCf0vECl2nB6braFQoGQf6G2LowQJNrFhWBikBFoCKwDQhst9y5DU2qRVQEEgKVmNeBsDAC271AdgnqfQkIwpcTQ0Aggj4T2ZkbuUBiX/nKVxJRt4c7J1SeL0lhqXavvLvIcxAUe8BPPvnk5NKLyH3yk5+cXHHFFZNbbrklEXPl5tpo7+PlO68oQ5m+U4dFj0srJb5RpmBeImyzaiJR3M1z13MWTziHYkS/ImgSIq4vKBPC4qwd7pHaNPNDEcFUQI8/ba7kMTbUeev/CPf++++fFCPGQH4OvKKG2JunzLbxbFx5qct2ptLxom7T9oB3tSuUODxahgj2t53Y1LIqAhWBikBFYH4EtlvunL+m9cmKQD8EKjHvh1e9uwWB7VogSy2T3GIRnbCotVS56KNQANhr7hgy1mt7WeOIJ8QD2UEc4tWWMQKBSMa9SDMC5RmfBWHKLdonnHDC5IEPfGAKkOW853POOWePq25YWaMs+SM3iK5o8BQUOTFXtsByLNgHHXTQnj3ZfS2ypUQrr5ey1U9bHQdnT7zI9ZQP2sszgasyS3iQcc/D3rPa1HRZ7+rX6K9prtPyNjbCcwDOix4bN20PtX5oRoGPIHWzPADUdZNT6XiZRcxnYdCmGIk5YuxtLeiJMKv8+n1FoCJQEagIbC8C2yV3bm+ramkVgd3GtN0Cc5nZr6JVEehAYLsWyDYBPIYvcju0AC5vBBphZNllmfv4xz+ezl/+0Ic+lM4ZJ/gjAe4JMtCEKci3e4OkBjFHSnzmniCuyOtjHvOYtH+cxVxZF1xwQYo2zirs2TzJA1m9733vOzn44IMTDoGL+2IPr2BZuSt5F8HN887fl1qk45lot6v6hOLB/nn1ZB3/yEc+knBEqrUrMPSMdrmnaWnvUihEf/Ulylu7iZuylNknzasIiGPdlCcPiRJCHeapR586b+e9peMl+nneYHzbrbDbTgxrWRWBikBFoCJwZwS2S+68c8n1k4rAchGoxHy5+O6I3Je9QC6bcEX+iOE0otSmGPBMEAP7r7m/s4QGGZW3lxRk03vP+R8hc1wUl2/nkiOsD3/4w///9u47yr6qvP/4QRERqVJUpCoiCCq9l68awV6jYlAB0SgoGmNMYkxcK2vlH13WSDQiarB3xYKKYOiCIEVQqgKKIFJUBEGx/HjtuPkdrndm7szcfj97rfnOfO89Ze/3s885+/M8z96niFHCeTFzpu33whe+sETEq9h3LhH0pSy+Zt92mUuIinZ3a297X3+rE6EtgrzzzjsXLhwdpgpgRJTjohBrtlek4HNWVIeF/yu272YvGQ7zreZNiLdT6ucS+uUk8/wzLOE5TxXG+qu5+otIerv0K4Ohnq9OjXAOzqDFTnFp1y1/h0AIhEAIjB+BQY87x6/PpJKtAABAAElEQVTFqdGsEIgwnxVLD7Cdg75B1gH3oFKU6/EXGtBXAd5eJZooJcitim6RtrPPPrsIzSpUOyPpPlcIUcKTAJWybvVyEUNR7/pucdv0sthajThKET/88MPLa6ekrvtcIf6J/BqVrXUoXy7iH/UhnDsj0j/+8Y+LMK9CuUa9Ow9d60mI1dfB4Ya74vh+FHWs9SSuLBznveMHH3xwcTzYhj26LQbn/MRf205VDDp3r6nxzjFfGVaq9nx1GOfv5uovne8j70z15zjRV6r9e21jPZ8+6ppWap9fyvF6PW+2C4EQCIEQGC6BQY87h9uanC0E/j+BCPP/zyJ/LZHAoG+Qg4pMdhPavQi4TiFPPBOBhLnUbMJV6rvPRby9Z7ktOivmGkE2x/llL3tZSV8nQIlHpZ6HQ8Lrv6S0e686IVtFi+PWFHHCdTlzpmu9FhI4nRFp0WksfW71+LnaW4W5dtdzaONcQr7WR1vt6/Vr2rfNNtsUBjjMZy/naS8u53j9jKBGmFcLzf+7W6ZJtTkbEeL9yGCYvxb5NgRCIARCYFoIDHrcOS2c0o7JIxBhPnk2G7saD/oGOSgB1E0wEIwKMTiXYKiiskbmRMU5DwhTi5lJzfbjtViEurnpnZFz5yA2RbLNOfb+Zqna3mFuwTOlnueHP/xh861vfausPH3WWWeVYxM0dZvOuenqvZwIYXUIzJVB0BmR5iyQwm/V+hNOOKFRX2yq+CoVvesfda4RTHx9j4ttFyq2ryntou2irhwf89nLdo5d7eQc9fzL4VPrOiiHUT3+tPxeqgNsWtqfdoRACIRACPSXwKDHnf2tbY4WAr0TiDDvnVW2nIPAoG+Q/RbmVfB2pmQvN8W2ClrRc+ndRPnnPve55sILLywR5U4BShzO95qoKmh+9KMflWj5mWeeWRafE5UmVC3qZn60uen7779/mbdtobS55kzXds8lVIlkrHtNAa8RaRFywtz7pL/85S+X17vJFHCsdlFfq8bbjxPD970Kc8chtDe7y+mg7V435rzVQUHk9yslul3n+f6u9h7UFIv5zj2J31Veczl8OE1SQiAEQiAEQmAhAoMedy50/nwfAoMiEGE+KLIzdNxB3yD7HZmsAqHfgqoKX8JDNP60005r3ve+9xUxPVfE3GJoc61GXSP65q1/4xvfKAKfoLW4HCFqwbhddtml7C+N/aEPfei8r4mr7Z5LGBG6nYum9RKRdlxOBJkBnAccEtre6eio9SWsLfrme/t2RtbnunSwqmn+GNi3XXzXj9egtY8539/V3v128Mx3zkn+rvKayzGkX6SEQAiEQAiEwEIEBj3uXOj8+T4EBkUgwnxQZGfouIO+QVZB2S8h3W+h32nq5dZXJJ146RR8ovA1Quz7hzzkIeWd4F6t9rjHPa4sHNctRbtG3ueLhEuht5AaxtLluwnrhSLS1ZEgcn7qqaeW+lfRrd7233333YuIP/HEE4ujweJvnZH1Tp69/l/b53N09HqcxW63ULtF+TOHerFUx2/7OBbGzyapUQiEwGwSGPS4czapptXjQCDCfBysMOF1GPQNsg6IO4VqZ0R2IeFYMROCi3kNmcXZDj300DIHvK6YXo/V7fdy60t0W7Ss0xFRX0vmnM7hNVB1tfKXvOQljXpajb0zJbibcLS/IkpZBbOo/kknndRcdNFFhU+nYF4oIt2rA8DccFF1P2eccUaJnJfKLPOfhaYGLPPwc+7eS7uX8/74OU+cL4ZKoDrc5so46bzuhlq5nCwEQiAEZojAoMedM4QyTR0zAhHmY2aQSazOsG6Q3QRmOyLba2Ry0MK82nCp9e1XRL9XB0E7xVzaPIfHUlLva7sXEjCE+aWXXlrS2Y8//vjiCDBHneCpDoN6rMX8HpUwr3VcqN0RbpXUZP2O42Wy7JXahkAITD+BYY07p59kWjhuBCLMx80iE1ifYd0g+zVA7pfwXchUS61vvxwHVSh2Rt47I+HtRdmIZt+ru8h9u/QqfKtDwP7qoBCl9vdD9JvPfsUVV5SouXnp0uct6GafpYpzxx5FKntltFC7M4e6kpqs390cbLWPsmmvDsHJanVqGwIhEALjS2BY487xJZCaTSuBCPNptewQ2zXsG2QVnJ0ppQQmYWaw3E0QVmFU919IsC6Uut0r4nq+zvpKRe+Wet4vYd6rA8L87yqc/S0LYTnCvJPLXILVQnbE+QUXXFAEur99xjngRzp/tZljsm19lRxhT+C3S7/s1T7mMP+ei1N1aLRZDLNes3quao9+TaGZVY79bne1y1yOv2m7Tmatvf3uLznedBIY9rhzOimmVeNIIMJ8HK0yYXUa9g1yroGKz4k1K3abj62svvrqZS52WwDX/Yc14K7n63Ug2augXihCvBiB733qVZTXetZpArU7LnS+ul3n77kcE5wB7EWMewWcFeGluHsf+ve+973G+9HVyUBbXbwG7uEPf3g5/OWXX15Wvve94vte1xgoO4zhP3NxmsuBM4ZNmKoqVXsMy4E3VfAG2Jhql14dnQOsylAOPWvtHQrUnGTiCQx73DnxwNKAiSEQYT4xphrfio76BtktZbwtzB/wgAeUV4utt956JeJKhCrdUlSrGCX4RpWiWgdiCwkCgs17wb1u7cADD2x23HHH4ojwbnQi2sC110XuMBFxXmuttYrItVo6B0e7LDYi3c0uNY3esdZdd93GomjOSaSbZ16F+Xnnnddcd911pS7qwB7autVWW5UqXXLJJY06joO92oyW8nevnPDCrfbfpZwr+/ROoF8Ost7PmC3nIzBr18mstXc+2+e7EOgkMOpxZ2d98v8Q6BeBCPN+kZzh44z6BtlNYIv6KgRdXXV8u+22ax7xiEcUQei7cR341Aj7QhF9i7btuuuuRZA/5jGPaTbZZJMicok3GQIYSAn36rIjjzyyvGNchLqywUDBSJo4TjvvvHOZ4+0946LYvlOWEpHuZhdtU0TBq+Oj1t1UBCnqBDqHgv3bQn7ttdduOFeUG2+8sUTa299XoT9pArZXTvrv5ptvXjIHCoT8M1ACi8k44Qxb7NsbBlr5KTz4rF0ns9beKeyyadIACYx63DnApuXQM04gwnzGO0A/mj+qG+RcArbzfd9VwIose4/2FltscY/IeY1Qj1tqZLeBGYFMXJt/veaaa5Z3l9dXcRGu3SLR5nAfc8wxzemnnz7va9C23XbbZsWKFUWke5WZ1dnbEelNN9202XPPPRvbEfFSy+vcdBH6OrdzLrv0+no7jgPtm8se+ux836vTIEptl/rpM0q39vd67nq8hRwwk56i3yuPcdsuwnw8LDJr18mstXc8ellqMWkERjXunDROqe/kEYgwnzybjV2NR3WDrIK6M+W7833fIrHS2QnK/fbbr9lll13uETmvA6F+Ca5+GWiuiL452TfddFNZxfyHP/xhc8stt5QIuRTnbpFoUfNTTjmlOe2008rPXAJZ9F3Uz7x8aeTO0Y5IE/6i0VLoMRNl7zaHfy671GNVPpwI3gtP7B900EF3v4edyGeLuexh//m+rw6Cep5+/a7tmsthsFiHQD1eZ//tldNiz9cvDrNynKSyj4elZ+06mbX2jkcvSy0mjcCoxp2Txin1nTwCEeaTZ7Oxq/GobpCdA2dRXtHkGuXtBCXyuMceezT77LNP8/jHP75Em9uR3s7tx+X/daBGsBHZMgJEwXH3mrGFUs7N4a77SGsXoa2MpKrXlPKaKm3l83ZEGlNilyAl1jkGqnhsR+hrCjnRXlPo3/3ud9+dQl/PWbnWFHqZDEcccUSz9957l1R8acHjVOZykKhjt/b3Oge8s/96bVy3qQb6KCcITq961avGltM42awfdanXXRwn/aC59GPM2nUya+1des/InrNMYFTjzllmnrYPh0CE+XA4T/VZRnWDJA7NRSY2P/CBDzQG0ESUaGq3QkRZQGyvvfZqXvKSl5T51O3V2rvtMw6f1Yi+AZtsALw/9rGPFcFrATSCuV06I9HmcBMZxLJ3hXdGwjvnZhOB7Yi0lHpR9osuuqikw1999dV3v2u8HaGvwp4wrXZ5//vfX+zSzWFCmIv6mtf+spe9rAhOEXT1H6fSbUoBmyjd2i/Fv5dS+69Mhg9+8IPN2Wef3bBxZ/8lzDOHuRei/d2mXneZatBfros92qxdJ7PW3sX2h2wfAgiMatwZ+iEwaAIR5oMmPAPHH9UNsg5gehXmVQiaZ95LhLYOzAklwlZZzpzi5XaF5UZSCLx2JFx9uqWi13rW9i9WmFigrQpzDhP9A8POiDlRq0510SyZDA9+8IPvfld5rceofi+1/TIQtEv75iu1/0aYz0dp9N91c8zUvtwt46RXx8zoWzYZNZi162TW2jsZvTC1HDcCoxp3jhuH1Gf6CESYT59Nh96iUd0g20JVyrRUdgK6Dpo7QRBLosEitIceeuiCEdqaytqvOcWd9Vns/5c7YDM3vB0Jd/75HA21/YtN5d16663LvHeLzb33ve8tEXMi109nIV7Z47DDDmv23XffZoMNNhibiPlS28/R0EsmRrv/zrdqfu23SWXv7D3D+f+gpjIMp/aTf5ZZu05mrb2T30PTglEQGNW4cxRtzTlni0CE+WzZeyCtHdUNsgonKcBSgUUepXYTsN1KFTgLCfPFDsQ7U7+dez7B261uvXy2XGG+2BTxpQ4Qd9hhh5KSbc70fMKcKPdDyI6jMF9q+3udK1/772IdH70K/176VLbpnUC117g46nqv+WRvWbnPynUya+2d7N6Z2o+KwKjGnaNqb847OwQizGfH1gNr6ahukDXV2MrkJ554Yll5/Iwzzijzobs1ti42JpV9vkW0uqWu1mgvIbnZXanKxFedU925WJpzz5ci3q1uvXw2aKHYWYelOgJwEZnXL+ZLZWcPzpIdd9yxZDCMWyr7UttvDYNe5srX/rvYqQL6H276YsrwCFR7jcvUluG1fLRnqtxn5TqZtfaOtnfl7JNKYFTjzknllXpPDoEI88mx1djWdNQ3SIuZXXrppc13vvOd5vjjjy+LlN18883N7bfffrd4Mdghlru9nkt0W1nsgGih14vVRdW8qk1qMzElGqIsJaI+7EjKUoXp9ttvX9p47rnnNgst/ibbQAR4HBd/W2r7exXmBdJd/3RzBNXpGJnDXCnl96wTmLXrZNbaO+v9O+1fHIFRjzsXV9tsHQK9E4gw751VtpyDwKhvkFLPf/3rXzdXXHFFmWcuhbrzNWJE9yabbFJWZCecRGc7I4+LFb7ei75ixYoyb938diuXO4/SjqzbzqJmVta2qrqylIj6Yh0Hc7WzVKCHf5YaofeeeIWjZJJfl7bU9veayl5NsNipE72+jq0eP79DYBoIzNp1MmvtnYY+mjYMj8Cox53Da2nONGsEIsxnzeIDaO+43CBr5Pz8888vAp1QrpFH0WpCvJ2C3rl6cq9CrKbEey+6+eoEMxHa+T7xjTbaqDgCttlmm2bDDTcswlwUVjHfu0bU6/u/exVcw4qkLNZRseeeezYHHXRQSfHXRnY45phjyivWrNJe2+47pfO1buM2d3qp7V9qO+r5Mof5//pH/g2BbgRm7TqZtfZ2s3k+C4FOAuMy7uysV/4fAsslEGG+XILZf2zeJ1kj5zfeeGMRydLZ2xHqhYTwYlKXiWjCcq211iri36JzUufbxSro3pu+9tprN/6Wvt4tol7nqnc6CtrHav89rEjKUiP0m266aamu952fcsopZVE+C/NxlHBqKBwmHBui+suN7JcDDuCfpba/MxOj16rV82UOc6/Est0sEpi162TW2juLfTptXjyBCPPFM8sek0Egwnwy7DTWtRy3G+RSIwxtYW7RMqsAi6ITSu0i+m5uNJFZB03OWaPzdVvp7Lapv/3dT2G61HbW+vX6e6kR+qXu12u9hrXdtLRjWLxynhAIgRAIgRAYJIFxG3cOsq059mwRiDCfLXsPpLXjdoOsYnmxkcd2Kvt870UnrolzP/52PlHsTgEPdl052+/6dzXCclO5l9rOev5efy81Qr/U/Xqt17C2m5Z2DItXzhMCIRACIRACgyQwbuPOQbY1x54tAhHms2XvgbR2Wm6Qd955Z5kHLf367W9/e5kbTWwTwO1SRTZhXtPT7dtNmLf36/y7Rt532223eV/f1rnfqP6/1Aj9UvcbVTvnOu+0tGOu9uXzEAiBEAiBEJgEAtMy7pwE1qnjcAlEmA+X91SebVpukG1h/ra3va3xTvRuwpwR2+Lc/23Xmcru8/kKYW6ldouFHXrooWWudS/vv57vmIP8bqkR+qXuN8i2LOXY09KOpbQ9+4RACIRACITAuBCYlnHnuPBMPcaHQIT5+NhiYmsyLTfImsouYl5T2ecT3MS5VHa/O6Pq7f/7u/3/auhJE+a13vkdAiEQAiEQAiEQAqMiMC3jzlHxy3nHl0CE+fjaZmJqNi03yLr426mnntpY/O3ss89uFkpRr+KcyCbS65xz0fMqyP1d/9826qSlsrfrnr9DIARCIARCIARCYBQEpmXcOQp2Oed4E4gwH2/7TETtpuUGuVRhvvrqqzcPetCDyqvTvEbNvHMC3Zxkr1D75S9/2fz85z9vvJ+6XZa7+Fv7WPk7BEIgBEIgBEIgBGaBwLSMO2fBVmnj4ghEmC+OV7buQmBabpCdqexnnnlmEdcLzR3fbLPNmsc+9rHNlltu2ayxxhrlh+h2vGuvvbb5/ve/XxaSm7T3eHcxdT4KgRAIgRAIgRAIgZESmJZx50gh5uRjSSDCfCzNMlmVmpYbZF11+6yzzmqOOuqo5rTTTmu8w1rUe76yzTbbNM9//vObXXbZpVl//fWbtddeuxFFJ8yvu+665qKLLmqkx1911VV3LxAnok7Q77333s12223XbL755s0666wz32nyXQiEQAiEQAiEQAjMPIFpGXfOvCED4C8IRJj/BZJ8sFgC03KDNCfcYm8XX3xxc+yxxzYWgfve977XXH/99fMiedSjHtUceOCBZVX1TTfdtAhs6eyOdccddzQ333xzc8011zQ33XRTeR2bg4mor7vuus1GG21Ufvu/NPiUEAiBEAiBEAiBEAiBuQlMy7hz7hbmm1klEGE+q5bvY7un7Qb505/+tCz8dvLJJzfHHXdcc/nll3ddVR1Ci79tv/32zUte8pJmxYoVzcYbb9ysueaa96BbI/HmmN96663lOxH11VZbrVl11VXLnPR77JD/hEAIhEAIhEAIhEAIdCUwbePOro3MhzNJIMJ8Js3e30ZP2w2SeJaCTpi///3vb7Sv26rqdUX23XbbrXnVq17V7LvvviVaTmy3S43Ei6AT6YqIulXZ/ThOSgiEQAiEQAiEQAiEwMIEpm3cuXCLs8WsEIgwnxVLD7Cdw75BDlro1gj3Oeec03zoQx8qc81vuOGGkoZeRbQ6SD/fYIMNmj333LM55JBDmp122ikR8AH2sxw6BEIgBEIgBEIgBIY97gzxEBgWgQjzYZGe4vMM+wZZhfOgUsOr8LdYm3nmFm6zEJxV1dvCfJNNNimi3AJu++yzT1nMLRHwKe7oaVoIhEAIhEAIhMDICQx73DnyBqcCM0MgwnxmTD24hg7rBvm73/2uRK0tojaMxdSsyH7llVc2559//t2rqrdT0bOq+uD6VI4cAiEQAiEQAiEQAt0IDGvc2e3c+SwEBkkgwnyQdGfk2MO6QXYTyqLbikh2v4VyN0dAe/G2rKo+Ix08zQyBEAiBEAiBEBgbAsMad45Ng1ORmSEQYT4zph5cQwd9g+xMLZdWXlPLvQ9csTibFdH32muv8tPP1PJBp84PzjI5cgiEQAiEQAiEQAhMF4FBjzuni1ZaM0kEIswnyVpjWtdB3yCrMLYY2zHHHNOcfvrp5d3it9122z2IWIztgQ98YJn3fdBBB/VtMbbqGMiq6vfAnf+EQAiEQAiEQAiEwNAJDHrcOfQG5YQh8GcCEebpCssmMOgb5G9/+9vmV7/6VZnnfeSRRzZnnnlmc+eddzaEcrtYeO0+97lPU19fZlG2tdZaq7nvfe/b3ix/h0AIhEAIhEAIhEAITCiBQY87JxRLqj0FBCLMp8CIo27CoG+QIuPXX399SV//4Ac/2Jx99tkNsd5NmBPhXlt26KGHlpR2EXSR9JQQCIEQCIEQCIEQCIHJJzDocefkE0oLJpVAhPmkWm6M6j3oG2SE+RgZO1UJgRAIgRAIgRAIgRESGPS4c4RNy6lnnECE+Yx3gH40f9A3yKSy98NKOUYIhEAIhEAIhEAITD6BQY87J59QWjCpBCLMJ9VyY1TvQd8gR7342xihTlVCIARCIARCIARCYKYJDHrcOdNw0/iREogwHyn+6Tj5oG+QdVX0q666qjnllFPKXPNhvi5tOqyUVoRACIRACIRACITA5BMY9Lhz8gmlBZNKIMJ8Ui03RvUe1g3yF7/4RXPllVc2559/flmhnVD3/nLF+8w322yzxkrs2223XbP55ps366yzzhhRSlVCIARCIARCIARCIASWS2BY487l1jP7h8BiCUSYL5ZYtv8LAsO6Qf7ud79rLAR30003Nddcc035Xd9lbuX1ddddt9loo43Kb/9fZZVV/qKu+SAEQiAEQiAEQiAEQmByCQxr3Dm5hFLzSSUQYT6pluux3tLA77jjjka02bvAb7311obA9fnKK6/crLrqqs2aa655t5j1LvCVVlqpx6P/32bDvkHWOee/+c1vSnvUYvXVV29WW2210h7tSgmBEAiBEAiBEAiBEJg+AsMed04fwbRoXAlEmI+rZfpQL+Lbz7XXXtu4iX3/+99vrrjiiubGG28s7wAnZjfccMPmkY98ZLP77ruX9G/vAV+ssB32DVKbvMPcD5GuqDOnwlIcC31AnUOEQAiEQAiEQAiEQAgMgcCwx51DaFJOEQKFQIT5FHeEGim/9NJLm5NPPrm57LLLml//+teN148Rt0QsIb7ppps2u+22W7PNNtuUv9daa61FUckNclG4snEIhEAIhEAIhEAIhMASCWTcuURw2W3sCUSYj72Jll7B66+/vrngggua8847rzn77LPLAmki4xZJM//65z//eYmkS3Mnxgnz/fffv9liiy0WddLcIBeFKxuHQAiEQAiEQAiEQAgskUDGnUsEl93GnkCE+dibaOkVvPzyy5vjjjuuiHPp65tssknz7Gc/u9l2221LpNwCaieddFL5/uqrry6C/ZBDDml22GGHkhpupfNeSm6QvVDKNiEQAiEQAiEQAiEQAsslkHHncglm/3ElEGE+rpbpQ73OPffc5uijj268VkwU/DGPeUyz1157FQFOdFsMzuvHzjjjjOazn/1sYyXzV77ylWWbxaxqnhtkH4yVQ4RACIRACIRACIRACCxIIOPOBRFlgwklEGE+oYabr9rmj3u/N8H99re/vbnhhhuaJz3pSc0ee+xR0tU32GCDsru55uacm3/+zne+syyk9opXvKLZd999m/XXX78I9fZ5rOZu+/rj/8oll1zSHHvssc0aa6zRHHbYYc1OO+3U3i1/h0AIhEAIhEAIhEAIhEBfCESY9wVjDjKGBCLMx9Aoy62S1cqJ5lNOOaV5y1veUl4p9uIXv7jZe++977G4G/F+5513NqeeemrZ7pZbbmle8IIXlO0233zzZp111rlHVbw/3AJyFpPz2/+VX/7yl82PfvSjZsstt2xe/epXN7vuuus99st/QiAEQiAEQiAEQiAEQqAfBCLM+0ExxxhHAhHm42iVZdaJ2L799tvL/HHCnEgXyV6xYkUjWi5NvV1OP/305s1vfnN5jdrTn/70Zs8992y23nrrZr311mtvVoR4hPk9kOQ/IRACIRACIRACIRACQyQQYT5E2DnVUAlEmA8V93BOVlPOLez2tre9raS1H3HEESVFfd11123ud7/73aMi3/72t8t2Vmnfb7/9Ssq7BeJqynvduB43qeyVSH6HQAiEQAiEQAiEQAgMk0CE+TBp51zDJBBhPkzaQzpXFdD9FuZzVT83yLnI5PMQCIEQCIEQCIEQCIF+Esi4s580c6xxIhBhPk7W6FNdBpXKPlf1coOci0w+D4EQCIEQCIEQCIEQ6CeBjDv7STPHGicCEebjZI0+1WWuxd/22Wef8i7ztdZaq5zJ4m+///3v7178zevT5lv8ba7qnXPOOc173vOexuJxz3rWs5qtttpqrk3L51aNV0fl3ve+d7PSSiuVv/PPdBOI3afbvnO1Lnafi8x0fh57T6ddF2pV7L4Qoen8flR2v/jii8vbgFZfffXm8MMPb3beeefpBJxWzRyBCPMpNLkbZefr0p785Cff/bo0r0JTasq716W94x3vuPt1aQR8t0Xi5kJljrr9rdb+8Ic/vDGPfb5ClN9xxx1lk1VXXbWI8/m2z3fTQSB2nw47LrYVsftiiU329rH3ZNtvqbWP3ZdKbrL3G5Xdb7755vJ2oEc84hHNa1/72mb33XefbJCpfQj8mUCE+RR3hXPPPbc5+uijm6uuuqrZYostmu22267Za6+9yivT7nWvezUi5L7zvvPPfOYzZbX2V77ylWVVdl7IVVZZpSc6F110UfPJT36y3CSt+L7Qfl6vduWVV5Zjey3b2muv3dN5stFkE4jdJ9t+S6197L5UcpO5X+w9mXZbbq1j9+USnMz9R2V3gaXbbrutvKb3gAMOaCxYnBIC00AgwnwarDhHGy6//PLmuOOOay644ILyKrRNN920efazn11uYPe9732ba665phEtP//884tA32yzzZpDDjmk2WGHHZqVV165Id57KQT+T37yk8Zq7b3sJ7J+7LHHlkM/4xnPaHg8U6afQOw+/Tbu1sLYvRuV6f0s9p5e287Xsth9PjrT+92o7F6nYq6xxhrNxhtv3NQpmtNLOi2bFQIR5lNs6euvv76I8vPOO685++yzS8q4dB8CnTD3ejQLaEgJclPbZpttmv33379E1weJJYt2DJLu+B47dh9f2wyyZrH7IOmO37Fj7/GzyTBqFLsPg/L4nSN2Hz+bpEaTTSDCfLLtN2/tzeP+xS9+0VxyySUlMn7ZZZeVqLYUIPPQLbxGoBPqu+22WxHm/h605zE38nnNNrVfxu5Ta9p5Gxa7z4tn6r6MvafOpD01KHbvCdPUbRS7T51J06ARE4gwH7EBBnl64tvPtddeWyLj5oJfccUVzU033VRWRTePfMMNN2y23nrrsjCc+d6EunT0QZbcyAdJd3yPHbuPr20GWbPYfZB0x+/Ysff42WQYNYrdh0F5/M4Ru4+fTVKjySYQYT7Z9luw9oR5jZybC37rrbeW1dh9ToBbFX3NNdcsK6lbuG0Yry/LjXxBs03lBrH7VJp1wUbF7gsimqoNYu+pMmfPjYnde0Y1VRvG7lNlzjRmDAhEmI+BEWatChdeeGHziU98ojTbe9Mf9ahHzRqCmWxv7D6TZm9i99mye+w9W/aurY3dK4nZ+h27z5a909rBE4gwHzzjnKGDwFV3vaLtpJNOKp+uWLGisRp8yvQTiN2n38bdWhi7d6MyvZ/F3tNr2/laFrvPR2d6v4vdp9e2adloCESYj4b7TJ/VHHcL0SlbbrllSaOfaSAz0vjYfUYM3dHM2L0DyJT/N/aecgPP0bzYfQ4wU/5x7D7lBk7zhk4gwnzoyHNCq8J757niHZSrrLJKoMwAgdh9BozcpYmxexcoU/xR7D3Fxp2nabH7PHCm+KvYfYqNm6aNhECE+Uiw56QhEAIhEAIhEAIhEAIhEAIhEAIh8H8EIszTE0IgBEIgBEIgBEIgBEIgBEIgBEJghAQizEcIP6cOgRAIgRAIgRAIgRAIgRAIgRAIgQjz9IEQCIEQCIEQCIEQCIEQCIEQCIEQGCGBCPMRws+pQyAEQiAEQiAEQiAEQiAEQiAEQiDCPH0gBEIgBEIgBEIgBEIgBEIgBEIgBEZIIMJ8hPBn6dR/+tOfmjvuuKP5xS9+0fzqV79qbr311sZrNny+8sorN6uuumqz5pprlnea3//+92/ufe97NyuttNIsIZqYtt5+++3Fjl555+/f/va3ze9///tiy3vd617Nfe5zn2a11VYrr8JbZ511yt8+77Qn++sHjvPLX/6y+c1vftP84Q9/KNt5hd7qq69e+oPf973vfUufmBhIM1RR9vvZz35WbKgvsP3GG2/crL322sVmbK/E3pPdKVzjrnfXrPv4bbfd1tx5553NH//4x3LNuu7du9daa61y3d7vfveL3SfY5J7N7sfsfPPNN5f7NPvrB4rrut6nH/CAB5T7tf97diu53guGsfnHtcqW7td+PG/ZiI3Z2nW76aablvt2t0rX6/+WW265x/Vv23rt6wdegevaN65rF/eJWgfvPncczwuf60t1DGgcqC6e+SkhMIsEIsxn0epDbrObvp9rr722+e53v9t8//vfb6644ormxhtvLA8FwmvDDTdsHvnIRza77757s/nmm5ebcueNfcjVzunmIFDteMkllzTXXHNNsaMHvgc8mxHjG220UfOIRzyi2WmnnYpI6yasDfZ++MMfNhdffHFz/vnnl2MZLNRjbLHFFqU/PPzhDy8DfYIvZbwIuK4vv/zy5rjjjmsuuOCC5uc//3mz2WabNc997nOb7bbbrgg1gzYl9h4v2y22Ngbz1113XXPZZZc15513XnPllVeWAbrBPTHmumf7Rz3qUc1uu+1W7gGx+2Ipj8/2VYhdddVVzVlnndVceumlxf76geuekCLE3Kd32WWX8lsfqA6ZXO/jY0s14fx2zbpfe3b/5Cc/Kfdkz1y2dt0edNBBzWMe85iuFWf3n/70p2Xfs88+uxzLZwox/tCHPrTZeeedm6222qqM53zWLkS47T3zv/3tb5fj3HDDDSVgY3zwoAc9qNlmm23KONDvDTbYoL17/g6BmSEQYT4zph5dQ2uk3IP95JNPLgM7N2g3ag94gzo3Zt5aAzo3ZX/zmqaMH4Ef/ehHzQknnFAcLAZfImg84WzJ802IGbRtsskmRZhvvfXWRZzzhCsGAWxvkHD66ac3P/jBD8qAjwfdMRR94oEPfGB5SG+77bbNox/96PLgdvzOyHvZIf8MnYDrWvYLkfb5z3++OfPMM5sf//jHxWavfe1rmxUrVpQsGLaMvYdunr6d0PXK1pxwnC/u4+zs2mdXES82dr9+yEMe0rhe99133/J37N43Mwz9QK7tq6++utzn67WtL7C34j7sfkxAeWbX+/S6666b633o1lr4hESwZ239YVuRawGS66+/vtlxxx2bN73pTeXabR+NvdmdkP/Od75T+oMxgOvfd7UfcNIQ5/oBgc45z8muj3iuOw+HwEUXXVQc8UR+7U+24Xhfb731irDfZ599iqNHBk517LbrlL9DYJoJRJhPs3XHpG1u+gZ0BvA8rW7CIuOiK1LfRNlE0qVHGtx5yO+///7lxjwmTUg1WgQ8lE888cTyMCeeRUl4xz1ACXQDePbkoTdI86B+whOeUB7aDiO6rk+cc845zRe+8IUi7rbffvtibw9mIkBGhcG/FGn95IADDiiefOcgAlJGT8B1K/tFtgNbGvCJrnHEtIW5wVfsPXp7LbUGHG+uQ/fvr371q+W6lt0ks8n1L+PJ4Nwg3fVvCoMsF8652H2p1Ee/X3XAfu973yt2ZFeOc9NUCC5Cy/XvPk3Es/mBBx5Y7vPuDbm/j96G7RoQ4aLlBDbbsZmpCezMQe567ibMZcQIpHimf/KTnyz3AlFxIpz4VohsxyG8Rb5f8IIXNDvssEO5N3hmE+aeD5/97GeLY08g5sEPfnAZG+hXjm9/mRnuG8YLHAXq5PuUEJglAhHms2TtEbW1nerqgSCS+uxnP7vclN2gCbmTTjqpiHdeXELskEMOKTf26nEdUdVz2i4EpLOee+65ZY4aW3nAenh6AHuIS3X95je/2Vx44YXFIy+lnT2lNis89wZ0ojDHH398SX183vOeV9IhPdQNFjzEff/1r3+9RF0PPfTQ4sxpp0p2qVo+GgKBKsCkJH7rW98qThR2rw4Z2S5tYS4TIvYegmEGdIoqykXLzjjjjHK9PuUpT7l7mgpnqoE3hxpnHMcrhxxBH7sPyChDOCxB/rGPfazci6Wnc8Y87WlPKxFNz2XPAX1CWvKpp55aMiT+/u//vgh0WRVEVu7vQzBUj6cgfgloARD367peBDtzkMt86CbM3b+JeeL9U5/6VJmyZvxWgyucchyy+oHMKY5zjvQ99tijCHd9x7k8z4866qji5DH1wY/IunsFJ4Exxac//ekSiZdObxqcc1Tx32Mzs1kITDyBCPOJN+H4N8AN9+ijjy43b/PR3HT32muvIsAN4tyUpTUb9PGoSl965StfWbbxt6h6yvgQMAD3cDenXPqZBy9RzpZEmwGbDAkDM1MXCHdCTbRFaUdiDPo5ap7znOeUOW685Y5rMOBB/8EPfrBE2J/61KeWh3Sdbz4+NGavJtKT2UcE9XOf+1z5m21dxwZWpiy0hbnBoKkPBoCx9+T1F2tAsLNomEG4e7iMJlGzeu1rletWxNw27tmcrLH75Nm71thz+/3vf3+xI+eqCOiee+5Z7tf1uc3Gp5xySvPRj360REdf//rXFyFFqHOu5nqvNEf/W+YSgUyUe067h3tWe85+/OMfL+sFdBPm1pSR6SgDQtRcVtuLXvSiEtE2PlPMU/e9fiD4QlT7EfX2PHCe0047rfnwhz9c7g8HH3xw6UucAZ757hucORzxHPvqqs8ZF8jASgmBWSIQYT5L1h5yW0VRPAAI7re//e0lUvqkJz2peFLbi3sY6PPmEnHvfOc7y035Fa94RZnrtP766xehPuSq53TLICCNUbTcA/8b3/hG8cS/7nWvK3Z3WFE0nneZFB7s+oII3JZbbnn3WfUdg4H3vOc9xctfves87OaxpoyOgIGXwRPni4gZcUaomXP43ve+t6S5toW5aQmx9+jstdQzuwbrdciuHGquUYLcNWuAbgBNiHPMSWmX0VLfokDIx+5LpT/6/dzDRcylqktfNyVJFFRGDJtzxBHmIqlSnNndda8PHHvssaW/5P4+ejvOVQPZLSLdxl0Es6y3bsJcZpTnuP4g2+1hD3tYmbJg3Zd2qRkWtie4fS8l3XGJbv3ki1/8YukfMis4eapD33H0M9sQ/5w6HPYve9nLirhvnyd/h8C0E4gwn3YLj7B9Iii8szzqb3nLW0pq44tf/OJm7733vsfibjU1lpfddjy55ijZzhwjD/qUySFgsMaWHrBEuIHcy1/+8uJB1wqRVpFw23nIm19usSjbtQvhd8wxx5TBg4GhTIvHPvaxpU+0t8vfwyNAqBlkfelLXyqp68SZARSxZkD27ne/u2ROtIW5QVbsPTwb9etM7suEt0jXW9/61nIdippylsqaEX2zXoToqXt0XZVZRF16qn4Su/fLGsM/Tr2Py5ggyDhEn/zkJ5drnaASBZUV5T7vXu1793mpzO7bhFbu78O3W69n7FWYs78Uddcz24pgP+MZzygR7fa5fM8hY3v3DhHvZz3rWUWYy77QVwRpTFeTEbnrrrsWBw8nj1LXIuLQN0XK+hWeI9LZU0JglghEmM+StYfcVulJBm/mjxPcRPphhx3WrLhrtWYe1ZoGVavFW/rmN7+5pEI9/elPLx5VDwGD/5TxJ8DeBuoezFLSRNjaD3LiTREJF4GT4izVTRRcRLwzEl4j6yKu0uFE6WRcGPinDJ+AzBZRMou9iXwQZxwlBmDmGXO4vOtd7yqDrbYwF0mJvYdvr+WekShnc/dv92UZLqJg7t1VtNe0WOLcfVpE3XXKiWZe6vve977ymsxc58u1xvD3lxlDbLl+ZcYo7tWcpIS5730nQ4r9Pas9tznWc70P316LPWOvwlyk/BOf+ERxvHK4ea3afvvtV5wu7XPWyLpV12VPccoIsHg2ENscOI6l//zt3/7tX0TCLU6nv3EEcgQ41xve8IYypbF9nvwdAtNOIMJ82i08wvYZtElRN7B729veVgZzRxxxRImOuulKgW0XN2/bedC78Uubkz5nIJgy/gQ86EVZDOK+/OUvF0cM4UZ0G6xXO/peZFXEhY19b+BuLnq7EPjmtkqbNmfV4kPmmrdT3tvb5+/BEqiL9hl4iZBJURQ5Me/ftc6x1k2Y2zb2HqxtBnF0NrWAm1RXEXMiTLTLdSibiYOMc9U93jUqLdac4s3+/BYFop4wdz/PdT4ICw32mGzvHs3uFnHjmOGskTXD4co5o49YnMvrrdzDOek4ZHO9D9Y2/Th6r8Kc/T/ykY+UdYA23HDD4px7/OMf/xeZa9YJ8rYW25uXLoPmhS98YXlOyKDjuHWfkGFlMVeZcu1i3RriXoZlnfP+r//6r6VvtbfL3yEw7QQizKfdwiNsX4T5COEP8dQ1Ui51kVeccPMAlvL6zGc+sywa1HbERJgP0Th9OJUBOBsbNBl4cb4YmBNmBmgGawZ5HHAR5n0APiaHqPdvwpzDVLSL80wK6nOf+9xyXYuGiZC65qWpmovqMxExUVVzVw24I8zHxKiLqAZHXH3vNFElA0KWjClqIuRSkIl01z9RLkuC04ZAszaI1OTYfRHAh7xphPmQged0IdAjgQjzHkFls8UTSCr74plN4h71AW8eGeEm0iLdzUDNK9JEVES8iTklqeyTZeW6OCP7WnXd9BSL+phvbF0ArzycT5iLoCS1dbJsrrb1/k2YS2UXCRXlEi1/4hOfWKKjxLftTHGQMSFCrr9IYdUvvvKVr5T7QVLZJ8v+BDdni8X7pBdbOZtThvA299fr0jhkZElwyJqW5D7PYcP+H/rQh0qmROw+vnavz+2FFn9LKvv42jA1m04CEebTadexaNVci79Je5POJLKi1PmKdfE3g7ws/jYWJpy3EjXVVQRVRMWccFFV0XEDd6LcYM5qve1i27oolKirwb4+kcXf2pTG52+pylJaCS+rNLO7uaSmmZiO4vrljGFXwl0k7fnPf35ZtMe6ASJtn/nMZ0qac+w9PnZdqCb19Wd18U4CzEJMrtXHPe5x90hlJeTqGiHml3rPsTR3++obsftCtMfne9cz25ta5i0prl9zy2VKeC2iZzcnK2Hnfi9TwpoT7gVSlL1C0yJgpjDE7uNj186a9CrM24u/ccjUtQRMW2iXuvibLAv9Z77F3171qleVKWyeFZ2Lv3nfOQd/Fn9r083fs0QgwnyWrD3kthqseci3X5dmVVfpbeYcS3VW2imT73jHO+5+XZoBYLdF4obcjJxuDgIG4OYdEmTSmKU5EuMWiCLapDiKtHiYt0td1M2+RLu+4HVp5irXou/U16WZr2ZgWH86F4mr++T3YAhY5MngjOPsC1/4Qpk/yIliJW4DdLYSNbWdqKpi4R99gM1sI2quv8Teg7HRII7avn+7L4uOypJw/+58O4Jt28Lc+4etQSDLwqJOsfsgLDSYY5pH7l7uere2AMdcdZRbP8B1T0y55jld2F3qundZP+95zyt2N11JKnzsPhgb9eOovQpzzpf6ujT3ePf2Aw88sGTFteshss5xa3vjNllz3V6X9oAHPKCpr0szNjAtQuEA0pdMh/MmD4vE5XVpbcL5e1YIRJjPiqVH2E6Ds6OPProM7HjQibe99tqrREjdlEXIDfoIeJE1kRav0/CeSw92adAp40NAqqI0RnMJzznnnCLaRNM8jDle2Hc+hwrxdsIJJ5Q5qxaLEoExkPcgJ+QNDB2f5/wDH/hAWendom+idcS7iHzK8AgQVpwoVmO3Yq60VSnMdUBVM14Ib9kTigE8h4uF/ayoz7ki6h57D89u/TpTzXAREbPyuikqvaSyy5ax1oQ+Ebv3yxqDP05djV+2g7UF5hLmNWOKmDryyCPLfZow55B1j7/mmmti98Gba1Fn4EBT/K7CnJ0t7saR9m//9m/3WGyNA8a923PeD9EsoPKiF72orCtQ36zjbSy+cxzifaeddio/pjK4/7v3e3ZYc8IxDznkkDK+cyzPfE4e9wpvc/Hb/0XcjQtE6FNCYJYIRJjPkrVH1FaD+uOOO66s5OymLdom1VFEzTxED3DznAz8CXSDejdu0Zm2R3VE1c9pOwiIhIigcrh42BrItRf/MSBn185IeT1MXd2b8P7mN79ZUiAN6KRKSl8zh5nH3Pce1GussUZJkSTMRWs6V/Ovx83vwRAQPTOIc+0apImSGVz5UQyiDMzMSbUav8+lurPnZnddyyLmBmZWZ4+9B2OjQR7V6wpdhwbeHHDmErte66CbE00WDMeq7erib5xo7u2Efew+SAv199hEm1Rk91+LOXamsotkuqbrKtoi66awuOe/9KUvLULKvUIENXbvr22WczR2rT+cqezHacZ+H/3oR4swf+Mb31jWkHAe93F25pjRB1zf1hywL8Hseez+rhi3mfrgLSr2OeCAA0pmjXuF57Vnuv501FFHlUUBTYngtCXgOdoFZ4wnBGb0Hc4/3zmHY6SEwCwRiDCfJWuPqK1WZzUoN0CTnuzG7YZbF44yF82gT8TNoE6kbf/99y/z00ZU5Zx2HgIcLV/72teKKGdXXnOvtxPxFlEzx7Bd2JSt65oCRJw+weZSoz2UOWGkyNmfECQGiACCzsPfg97DWqRW/0kZHoEaESfAZUsYtLeLz9jQwM3iXwZ0hx12WBng1ewG17brP/Zuk5uMv92fOV1cr2ysuH+7XjnNXM/uCfV6da1bY8D3Bv8crrH7ZNi6Xcs65UimBHFlChFnOudpzXRjc/dqjlQOWSnOIpzS2gnz2L1NdLR/u08T2a5J17QfmSzVgSI70evNPMcVEXTZbJ7vhLXr+JOf/GTZh41d33VamSwqWRIc9l6paOqDZ7pjemZzCOgjn/3sZ8tigpw4Miv0J+MCzl77n3XWWcXBIwWe42/zzTcv9RgtuZw9BIZLIMJ8uLxn8myEloeBRUFExqUqeUBIhXPDJrTcqA3oeFIJ87aQm0loY9xoD0/vqZUCJ1rmwesh60HOjp3C2ZxzAza/lZoqKRXeQN8DWyTWsYjA6qk3ALQKsIe3fT3wDQh9nzI8Au0oC/v4f7u4vglz/UG/YJ/XvOY1zb777lsGXfqHa/2qP09Xib3b9Mb/7+p4IdDY2G/29rnr0fUuO8b0la222qpcswb3nDKcObH7+Nu4Ww1rZhOB7oeIc+9u3wPYn7AS1fTc9vwm1jjvRGNzf+9GdjSfcY62pyQRwvXezVHuGm6Puzx7rbLv2eu5TshbO0Bf8Ox2PH1B0Q/MHffucs9ra4voEzXj0TNDJLz9+j3PfPcH33lmcOhzzBP93vwg44ZTwPMjJQRmiUCE+SxZe0RtrQN7N2JRF9EXXnZzVz3AeVUJOzdkCwvxknoQzJUKPaJm5LR/JsBzfswxx5QsCA/UOjifSzB3CvMK0oPdQjG87I4pXU6khd2lrHswG+htueWWZZDfGYmvx8nv0RKoc03ZUCqjYkBnrYH2wCr2Hq2dlnN293DCjI3dvzlXDbTdv92riXDrhxiQ+22QXqecxO7LIT+6fatoI+DML/bMJtZFNwkya7+YPyyqKpvJnGBp7jUzKnYfne26nZk92NA1bF0A2Q7zFcJcyrr7uDEaR5zIOHEt85E4F2BRZM4Yt0lP55wznvNZu9SIvTpIe+fgI/Z9ri9xvHPu1B+OvpQQmEUCEeazaPURtNnArkbORVs83GvEnBCzAIiHvAGewbwozFxCbwTVzylbBOqiMeyosNN8tjJQa3vi66GqoPNwd0yi3EDfsTyoDQb0B7+7ReLrcfJ7tAQM0kXS2NCcYsUAXQaF65jjRom9C4aJ/cf9m41d9+2MJ/at1ytB7nr1/5o5E7tPpsndi+v6ETLePLOJKNe65zm7i2ZymLrHE2L+rhHO2H287F7t4RrmVPO8na8Yj4l6u48bo7nPS2mX2aY/mMKifyhsbtzm+tcPOOU6Ayv2t71+JCjjHqI/+VxfqmNA59WfPPNTQmAWCUSYz6LV0+YQCIEQCIEQCIEQCIEQCIEQCIGxIRBhPjamSEVCIARCIARCIARCIARCIARCIARmkUCE+SxaPW0OgRAIgRAIgRAIgRAIgRAIgRAYGwIR5mNjilQkBEIgBEIgBEIgBEIgBEIgBEJgFglEmM+i1dPmEAiBEAiBEAiBEAiBEAiBEAiBsSEQYT42pkhFQiAEQiAEQiAEQiAEQiAEQiAEZpFAhPksWj1tDoEQCIEQCIEQCIEQCIEQCIEQGBsCEeZjY4pUJARCIARCIARCIARCIARCIARCYBYJRJjPotXT5hAIgRAIgRAIgRAIgRAIgRAIgbEhEGE+NqZIRUIgBEIgBEIgBEIgBEIgBEIgBGaRQIT5LFo9bQ6BEAiBEAiBEAiBEAiBEAiBEBgbAhHmY2OKVCQEQiAEQiAEQiAEQiAEQiAEQmAWCUSYz6LV0+YQCIEQ6DOB3/zmN82NN97Y+L3SSis1q622WrPuuuuW330+VQ4XAn0n8Ktf/aq5+uqrmz/84Q/NAx/4wGattdZq7nvf+zYrr7xy38+VA4ZACIRACIRANwIR5t2o5LMQCIEQCIFFEfjxj3/cnHbaac1PfvKTIsw32WSTZs8992w23njjRR0nG4fAKAh873vfaz72sY81t912W7Pffvs1j370o5v111+/uf/97z+K6uScIRACIRACM0ggwnwGjZ4mh0AIhEC/CVxyySXNF7/4xebSSy9t7nWvezVbbbVV8/SnP715xCMe0e9T5XgTTOB3v/tdc+uttza33HJLI0q9yiqrNA95yEOaNddcs6+t+v3vf9/ccccdzS9/+cvm+uuvL5HvTTfdtFl77bW7nifCvCuWfBgCIRACITBEAhHmQ4SdU4VACITAtBKIMJ9Wy/a3XTfffHNzxRVXNBdffHFzwQUXNA94wAOa5zznOc3WW2/d1xMR/z/72c/KOb7xjW80q6++enPQQQc1j3nMY7qeJ6nsXbHkwxAIgRAIgSESiDAfIuycKgRCIASmlcAsC/M//elPd5vV/Pp+l3r8QRy733Vd6HjE8vnnn9+cddZZzSmnnNI86EEPal796lc3u+6660K7Lup7kfKrrrqqOfnkk5sPf/jDJVL+pje9qdl3330XdZxsHAIhEAIhEALDIhBhPizSOU8IhEAITDGBWRXmRHMVzsxLPPdTQDv2H//4x9JzTBHo57FH0R0jzEdBPecMgRAIgRCYBAIR5pNgpdQxBEJg4glUAXf77beXea+//vWvy0JT5twSXve+973LKtBSbq1mbtGp+9znPmWVc2LGfubjEmbmz9rPHFqrSBNstl9vvfWa+93vfoWV7aUNW8zK9o6/6qqrlrm8dVGrtoisEcbf/va3Jb3Y9tKBnePOO+8s53D+NdZYo9RPPdXPuZWFhLk2qof6qJf2O5f6q4djaYPjr7POOqUdPnf+X/ziF2U/9dC+DTbY4C8W5apziqUk//znPy/ttfCc1bUdx/ntr02O57fz+9z32qZN9fxY1f0cG59rrrmmtAFn7TZP2ir0bGHfjTbaqERmreRduRQ48/xT613nQqtPPb7Pqv3Mwd5ss82K/ezjvG2OPlNwtCK+dkgT97d2+FGWa+eaIs4ubKHdGOPVLvU86vXgBz+4bKcOdZHA73znO3ensv/1X/9188hHPrLsrg9YFV17rYrORlb7x9rfbOhawhdn2+sv2qs+PtenfvrTnzbnnHNOc+qppzZf//rXy/kPPvjgZrvttivnsa3rTN21AU8R9lpfc9Hbq7I7L1vot/qY7Stz9cDZPurhb3ZQOufUq7u2+e04OPq72k191Ev9qs3KgfJPCIRACITA1BOIMJ96E6eBIRAC40CgCtNrr722Oe+884qQ9Xom4srgnbAhyLbccsuS1vuwhz2sDPKtck5YEBoG7FUw33TTTc11111XBLt9N9980nzoHwAAGexJREFU82aPPfYoq6Ab6Nue+LnyyiuLoCAyCKRtttmm2Xvvvcv2xEAVkNKLjznmmOaGG25odt555yIMzAV2DmKE+HB+i7k5zxZbbFHq57jKQsJcGwmbH/3oR6VeFokjoDkQtIm4sjiXucY77rhjWRDM55wS3/3ud0s7CBltePzjH1/q37YrwaiuF154YfOtb32riKPnPe95ZXVtdXd+gtz5zz777DLPGUPCqLbtoQ99aDn/9ttvX86DTRXz+Hzuc58rLKw2r93f//73i1gnGvEgMM1hJqqqMGvXsdvfVeiab20utPM5PkeBc7K/OuLywhe+sCyqh+NVd4lI7bjsssvK4mY+I+SIPg4J27Ojv/tpZ31Cf7SgGltotzYT0+1S+5P2PelJTyr9Wv1+8IMfFI5squ/j2F6UjQ3Yd9ttty3XA1Eu5d2cdP1FXySItak6K3baaady/A033LBcR0TzueeeW1ZZP+OMM8q1YHvXFGeFwomy2267lf7hcw4D/b/Wl4DXJsJf4Whwveq3bGV72yqEvbcQ2Mf1oT0EtqKN7Tn17gNs4/dFF11U+rf21LZo9+67717qh1fEecGYf0IgBEJgJghEmM+EmdPIEAiBURMwiCccL7/88iI0DfKJKaJLhE8hHoidRz3qUSWCaGVzUVqCgTARjSMCROQM5glLotAxiGb7WeGa2PH5D3/4w7tFG2FqkE9IPfGJTyxiiiPAsZSTTjqp+fd///eyD2FIlDiGehM6NTJP/Oywww5FOKmfYzjufMKco0BdtZ2YJWw4KGr00/6EKEHLwUCYOLbIODF95plnFpFK1Dm/RbyIIMK9OhYIRaLc3GVtUa/DDz+8iFPbcDgQQoQVoUTkYVLZY4Ytkab9HCScBepGlDnmu971rsKTcFQ3x6jR/Ic//OHNc5/73EUL8xpZNheanfUJr+uS1UD8EXYixSLKBx54YLEvMa4deOhT7N+O3nLUbHZXdJ3wxFGf0rZ+2Jng/p//+Z8iUolodthnn32KGC0n+PM/eOlPnCmHHHLI3c4edefgWEiYcyDp0/oJR4D+ry9qK1HrR2EjfVrf54jQb/Hi/PL6s16FuVXZ2/VdsWJFEduEueO5XvUt/Rxz7ap9Rz8kxPVNonuXXXYpPPQp/Q6zb3/728VhpM+zCyHu2nZdOL5rxPb2f8pTnlIcaK5310VKCIRACITAbBCIMJ8NO6eVIRACIyZw1Z8XoiKmCDqDbpFZg3mCnAAjtkS6CWIC0WrVBvIWr5KWSyAT3hawIhyJBvsRuoQDkUgkEI2idqJ3IoREG5H+v//7v+V4hIOoNFFl8S2lCinHIo5F7izIJRJIHIhcEi9ECUHr2DUFmXhV926vS1NPokO7P//5z5ftRKiJRe+KJqC1kQglfLSRyFU3Ah0n3xFYn/70p0ubCW5R+3ZkWvtEnAl/HIhGEXPClPDRLvsTekSrH23DUHuq8FIX9fKjDtKT28JcxB2b+r12EP62E50mFLWvOgwW6nZVmOPfzlgQhSbS8MGP8GNT9SdsZUI4p/6DMTsTisSeviKyrK+oqwwJbe2HnXFcijDfa6+9imjWX73vfqFUdo4EDGVMiH67JvTr+rnjcPSwh/6BjYwCNsOfo2Yxqez6Vzdh7liuV44E/Ze9OKY4A9SHbXzP2cMZwP6uW9cX54p6E+ZS6r/2ta8Vx4K+y5HgOuMYYysOC9en8z35yU8u4t49wPFSQiAEQiAEZoNAhPls2DmtDIEQGBEBA3eCiaARwSMkDOhFBEUaDb7bwpdgET0lyF72speV1NxPfepTZXAvmkqEEZyigyKKRChBS1jYjwgl6kTlRP0IUKW+p5mIISA5Bf7qr/6qCFjft4W5OhHFz3jGM4qAIB5E/tSt/hD0L3/5y4t4J5AJk27C3LGkq6vjUUcdVYS91GP15yAgbAljEdGvfvWrRaCIhuKjnUQ64aJ9//3f/122fdGLXlSEuX0Ja9tXvrV9GHBgEEdEmqj7xz/+8SKMCB9OB+KK4BWFJei/8pWvFMFL/BG0IpdYtYU5Bwt2hOYTnvCEYg+RToKKg6L9g+tCpVOYsych7cc52I9A5VzhtJC+/qEPfag4ZPQfHDkJtFM/Y4cTTjihOEIcG7+/+Zu/KXZUt+XaWdbBUoS5vsg5oC8QqvrDfKuysyknCR5sqlTHB+cTRxFnz+mnn17sxoGjP2JGzLoOqjOsl1XZK5ca4a/1VQd9U8SbsGZr/dL1xSmicKbpXxw/+rL+VbM+2E17tfXLX/5yEfK+Z19OBA4V17Xril1F0GVs6J+uk3qOcqL8EwIhEAIhMNUEIsyn2rxpXAiEwKgJGJgTCYTlu9/97iIyn/3sZ5eBuyifqCfhZRtCynZHH310EWJStoligkBElwgVaXv6059ehCtBT7QSIAb+H/3oR8t+InYG/qLaBL4iok14+k3AiSQ/61nPKr99X4WJYxG0RB+BQBgSQgQVcU4IffCDH7RL85KXvKQIIY4Gc6G7CXNzeUUURUnrfgfftQiXY9tP+4gwkXgRRwLFOQiS17zmNUV4Yihqan/Hakf8CVLsCKP/+q//KgwJasKIE8O+nBIino6N9wEHHFCingS4bAXnF2n2PYFP8BGB2idq3Rbm/sYNH+Jd6jT7+amFAO61OB7m+IuYK+zOBoSs+ioEY43qf+lLXyoOmxe/+MVFwHEuEIycQASrdGvCVwo4Pn/3d39XhKR6cd6IDDvnUuwsSj0MYa4tfghdDglFX2Ev7fC5aLS+wmHDucKu+pXryr7aqL3LEeYcAyLZ+oU+oj/LFOEM4ZBSXBu+/+xnP1u2YTeOoRV3OSNMK6gR8+OOO644gl7xileU65MDwTVcHXef+MQnihNC37P/Yx/72Gazu66/lBAIgRAIgdkgEGE+G3ZOK0MgBEZEgGgkIAivd7zjHeVvETdRV0LbwLwWYoKIFNn19/Of//ySyk1ciKSJEIuy7b///iXaaz+ihRizzZFHHllEpoE/UUA0Ep+KSL1IqogncSvV+wUveEER+r5XP4JNdJnjwP6cAI6hEK9ErpTct7zlLaUd9ucAkJIr5bibMLe/tGv7ER7m1v7jP/5j2U+UuQpaApXw5GD4yEc+UrZ7wxveUAQwxwCHwhe+8IUSESaICGZz5Ql47SfQPvCBDxTRduihhxaBJqOAM8ExCXORT0LXYmSi5e1Sz28bKdDE3Wtf+9rC23f4mGNOEMpkwEequEjtckqnMCc+/+Ef/qEcv90/2EXfELllR4KvTiVon19/0FcIRRkInB///M//XDgStVgsx87E7jCFOdFLHLuG/I2/vuj6UKwr4HoR2eYwETHnkNK3+iHMOYz0O/1PX+XQkkniHO3CGXLssccWp4i62e6Zz3xm6W+Euf55/PHHl+vp9a9/femfHAzVicMZRNhzfLC7NQX0UxkPKSEQAiEQArNBIMJ8NuycVoZACIyIgDRVoopwfu9731uEJVEshbUtTGv1iBCCgggnkAk/0U+RcWKRMBdJI4aVKlwIx//8z/8sn4k0i4aKyNWoHnEs8kfcSb0ViZMSLvKn2J9gI+qkPhOeRAFxqxAbfkT03/rWt5btnva0pxWBQaQQx92Euf2JFvtJ5fV/wtM827YwaXMSARUBJuC1QxsIfxyIHCJG5NKiYsSxNHTfaZ/jE86i6o5x1V0szT13fmKVcLUPB0G7EHbqIDJt/rI5wv/0T/9UfreFOVF4xBFHFD7OVfm2j7WYv6sw1z9EzNXrX/7lX8rx2/2DzbRR5gFhro5sWCPq9Zyir5xB+pF9TAngYBDhJ/gIxOXYWdR4GMIcZ22pc+b1IX9rl+i47/Qf/7e+AAcQIaydHEocXmyP63Ii5jJBPvnJT5ZzuGZlSViczxSNdulc48D3HGsyVvRZGR36pykg7QyGegwOIQ4Ac805UAh715esj5QQCIEQCIHZIBBhPht2TitDIARGRKAKTsKXMDeAN2gn6kSCa8SsVo/4JUoIT2m5/iYuRAy7rYJNpLSFo+MRYoQ1kUecKkSKOhCn5uwSdVKhpcwqvluOYOuHMBfJV49uwtyK3JwLxKlUbpkA5hSLCBObhA3xzmFh9XLiTKmCiTAXXSXMRdm7RbqxV/y2v+MQRm2+vq98awq5z5ZaOoU5of2mN72pOCTafaMtzL0OjjDnqFGHbkUbiFdOE1Mb2Fk0frnCXD04EGQ3OL/jcp7oT0oV1Oz4H//xH8WhxIGiPy5mjjmHEycRu+r/Mj5Ey2Vt+NG+tjDXD5761KeWuqiTjAJ9fpjCXERcRgpnQacw129lKxDmr371q8sUhALsz/9wPHRzbHVG5tv75O8QCIEQCIHpIhBhPl32TGtCIATGjIDopSgsoSKVnVCXAi6VnXgQFe0sRIdIrGgbMfqZz3ymzMEehjAfVSp7nUNNvIhwciq0U9mJMVkDUrktIifCLbJPoIsgS1kXCSeo2xkFBCRxZo66hdFkEVi0Dcu5Cv4EL+HJsdEW5nM5PuY61kKft4X5fJFdduFUEXnVl/QdqdtS+ucq2sEBgQvBzxG03FR2Th2LGOqXsjpkXDzucY+7O4ODXTg/MDflwd+dwlw7CFXbzCVUOVlsJ9rMdupu3QB2c23UueY1lZ2QN7VBKrusEnyq7TkS2g6Pbrww5ZjSD9v1baeyq4NItjUeOgWzVPeays450ZnKHmHejXo+C4EQCIEQaBOIMG/TyN8hEAIh0GcCBKWotoitOeCEuRTXuqozAVgj5wb0hI3tFQJEmjZhQRANQ5iLMopu1lXBiVP1Uyfit72I21IXfyN8Ohd/8xowIswCbM5RF3+zOjUxTGTiY/43YU64+c5ntufgsMq8FHZi1aJnSqfAI8y9b5x4q3O46/HZCn8/2oy/z8ZBmIvCEsP4WERM9NvrwazczYlBiNZ2iJRrA6eQedF10TQ8qgBdqp2JXa9ra6dcW2xPZoHzc55IOSe662J/baGrTqLKHAzf/OY3i51MvXA9qKtjKOrnGOwtas4JwBFjigFng/4gi4TtZViwUVuYa3Oth7UHOHDe+MY3loh6e4pAOdld/1QuncJcZkJ7Ckh78TfMFU4BU0Tq4m8Wb+Ow4CCyjX4dYV5Q5Z8QCIEQCIF5CESYzwMnX4VACITAcgkQEISS6J9II8EhgmlxJ2KE2CYiiBKpuoSBKB2BQpwSY/Yz13UYwpzAkIZrDrjIoAi0+hHlXtVFYBBVIp0WmSOOCdiFXpdmnyqoveKr2+vSCM66ajw+HBjm9NaCZV1ki8OC+JTujKkU9vaib+qkSIEnztW7vs7KolrOj6d5w9gT4LYl9vyIlLOTyOw4CPOaeUGYi6yrkxRywpwjwhxrglNfIxT1IzZT/3bKexWgS7UzYW7OPrEprd2xZYBIH+fMIIY5oSz2R9ByhLSFOc5EvZR6qdsi2aYGcNSoq2Mo+v2JJ55YFu3Tt4hdayJ4h7j54+xO4DuPtQsUqezmmHO6EO91aoQpJI79ute9rjDzXXvRRftWLp3C3DXJMSBTQ7v1i+pY4yxQcNC/9S926va6tAjzgir/hEAIhEAIzEMgwnweOPkqBEIgBPpFgHgUASQsRT9F8CzMRXwb7BPiRIvUX9+Lgoq6SWG26jTBMwxhzoHgPASx6LN5wVUIEXMi28SKCKnIMwGt7gR1tzmytiOoLdjWjrTWNGjp+gS2jADihqi0YjqhJ3WZIGsXHIgcPxwF/k+MEqiHH354cXa0BR6mjqldzs/pQcQTlIQ3zpW9rADbi8Krl3RkdhoHYa5O6obj5z//+UZ/Ukf9R3sIYKIWa+JQW7Sb4wHLurp+FaBLtTMnh9Ryq9yLZmMtMsyZ42+srCxOuBOz6tgW5pwgbSGrTRxAnAv2J5rNF+ck0d/8SFfnaFlxVyaHvskBITKvz/he1Nw11H4/OB4cLJwEXj9oexF1/ZVNZapY58FvmRO265bK7lwEvrZanE37RO310ZqV4XvOA1w4Gszp1x99rw6JmLev4PwdAiEQAiEwF4EI87nI5PMQCIEQ6CMBQoMotBgZMSHyKDpHiBOmBBURQIAQC4SKubtS30XMhyXMLbJV08yJCvVWR+K3RvHNjxdJV0fCy+fzLV6lbSKcxDthSRQS+ESZ7+xPlGk7kUmQE8XEJNHULpgRZCKluPibyDa32Hus1audEu346s7B4dyitdoomky4cjL4nqiVdkxMEX/app2E1jgIc+2oHAlf7fCjT+kjxLh2aDtHinoT7YQoe1YHRxXmS7Wzc2GnD4sgE6TqVfsufmxIrH7lK18p9msLc9kXjqEPWDtBv2H7ajP7Pv7xj282u2sxOc4Ix7eomvraxrZEOOHNsaKPctJos+tF3xExJ+5dV6LqUsxlWNT+5jh4yPbQXzgV1KebMNf/sOUIcR7Rc8ydF29F3+HgwVu/cVz1V0+sIszbV3D+DoEQCIEQmItAhPlcZPJ5CIRACPSRQI14ijgSNUSqdN0qEIkb4sIAnyA1wJdOTlzUxc1EmWs0uUZACUvi2eBfRFhpz6EmZBTntQ3HAJEqFd370IkSpQo2okME07mkz6ufaCPxIcIoAq5eXqWmrsSHQiCLXNqH0BZpbwtC9XQcbSZwiMradscmJAls7RaRJLqIPSKqXYgk4lz2gfeKE/jOI62boCPsuhURZALb+UV7CT4ryWNHYDmXSCpxpY0W9+Ic0MZe+HY7Zy+fOTbbEIaErjocdNBBJcrdbX/9gbDl2NEOIr0KRd/hRfxWgawNHAzsrSzXzlg5D47szdnBBpwcHBvszn7s7Vy4mjpALOuz6uZaIHTZkHDWfhxcAwQtO6o3B5W+ygnjPPoLO7pOtIfTQXtdS/qQzAD9xzUiU0Dh0JKJ4TzOKRvF+fWvtjDH01oO6tGub3UM6Tv250hgK/3ctgqbabfzq7d+rD8r5qjrawS9/dRLRF0922Wh66e9bf4OgRAIgRCYTgIR5tNp17QqBEJgzAgQHX4IC8KSSG1HOn1HZBDSBI6UXtFoIoI4IUhFlGsarm2UKvgJByJEEQ0kDIiVKmyrMCUmHMv+hJLjKUSUiCEh4TVh5vISJY5BiBHbRLjt1cvvGum0v6g6oeu3bQkwQt5vpdZTm0XPaz0IvRoxty0hLBqqrT730y6inkSg1cXf9ra3FbHtfe/mFhM7BGm3UgWl8xNn9fztKCoRWdnj70cbbbMQ327n7OUzx662YWd1aAu7zmPgqC3tdtifjXyHF5uxFfvhqS/U/rJcO+unzuP87C1y7PzqVJ0C7GcbQlp9OHnUQZ20zzHs7/u6f7WDOot2qzf2jl3Po9/W82iPbRzfteQ3Ie8zfZN4V/QV/a1mp3AYOL/+1U5l12+vukt4q0dnfR0HX3V2Ln3B9rZVtEnf1UZ9RhvUXamOM+1UB/WqUyjKBn/+Z6Hrp71t/g6BEAiBEJhOAhHm02nXtCoEQiAEFkWgCjbioZ16TOyMUyFgCC2Lh1n1m8g6+OCDSxSfoCOKUuYmMCl2nrsF+SYEQiAEQiAEppNAhPl02jWtCoEQCIFFEZgEwUaEiypLCbY6OXEuev/Sl760rLJeI7KLaviMbTwJdp4xk6S5IRACIRACIVAIRJinI4RACIRACDTjKtiIcenLNZXcHHkLedV5zea6d5uzO2yTyjSoC/r1cm7pzxwJ0p9NKRhWpH9c7dwLs2wTAiEQAiEQAtNMIMJ8mq2btoVACIRAjwTGVbDVOdgWzLO6tldnmWts8S+vc6srxEtjH2VRP6vE+91LIcQJcq/Ee8ITnlBWgu9lv+VuM652Xm67sn8IhEAIhEAITDqBCPNJt2DqHwIhEAJ9IGDF9vlWpe7DKZZ0iLYwr+9Bt/CXSPmKu95rbcG39iJzSzpJH3aaFGE+rnbugwlyiBAIgRAIgRCYaAIR5hNtvlQ+BEIgBPpDQKr4fKtS9+csiz9KTWWXKm7VeatiW3Xcytui5FbBlhLus1GWSUllH1c7j9J2OXcIhEAIhEAIjAOBCPNxsELqEAIhEAIhEAIhEAIhEAIhEAIhMLMEIsxn1vRpeAiEQAiEQAiEQAiEQAiEQAiEwDgQiDAfByukDiEQAiEQAiEQAiEQAiEQAiEQAjNLIMJ8Zk2fhodACIRACIRACIRACIRACIRACIwDgQjzcbBC6hACIRACIRACIRACIRACIRACITCzBCLMZ9b0aXgIhEAIhEAIhEAIhEAIhEAIhMA4EIgwHwcrpA4hEAIhEAIhEAIhEAIhEAIhEAIzSyDCfGZNn4aHQAiEQAiEQAiEQAiEQAiEQAiMA4EI83GwQuoQAiEQAiEQAiEQAiEQAiEQAiEwswQizGfW9Gl4CIRACIRACIRACIRACIRACITAOBCIMB8HK6QOIRACIRACIRACIRACIRACIRACM0sgwnxmTZ+Gh0AIhEAIhEAIhEAIhEAIhEAIjAOBCPNxsELqEAIhEAIhEAIhEAIhEAIhEAIhMLMEIsxn1vRpeAiEQAiEQAiEQAiEQAiEQAiEwDgQiDAfByukDiEQAiEQAiEQAiEQAiEQAiEQAjNLIMJ8Zk2fhodACIRACIRACIRACIRACIRACIwDgQjzcbBC6hACIRACIRACIRACIRACIRACITCzBCLMZ9b0aXgIhEAIhEAIhEAIhEAIhEAIhMA4EIgwHwcrpA4hEAIhEAIhEAIhEAIhEAIhEAIzSyDCfGZNn4aHQAiEQAiEQAiEQAiEQAiEQAiMA4EI83GwQuoQAiEQAiEQAiEQAiEQAiEQAiEwswQizGfW9Gl4CIRACIRACIRACIRACIRACITAOBCIMB8HK6QOIRACIRACIRACIRACIRACIRACM0sgwnxmTZ+Gh0AIhEAIhEAIhEAIhEAIhEAIjAOB/werXs62zwORWQAAAABJRU5ErkJggg=="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0adf3bdd",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Use a scatter plot to plot the relationship between employer_reputation and academic_reputation in 2019\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:scatter_plot.png\" width=\"500\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8eb6036d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a general function to create scatter plots\n",
+    "\n",
+    "def scatter_plot(df, x, y):\n",
+    "    \"\"\"scatter_plot(df, x, y) takes in a DataFrame 'df' and displays \n",
+    "    a scatter plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use df.plot.scatter to plot the data in black with no legend\n",
+    "    # TODO: set x as the x label \n",
+    "    # TODO: set y as the y label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d77b0f09",
+   "metadata": {},
+   "source": [
+    "With the `scatter_plot` function defined, you are ready to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select rows from the database where the `year` is *2019*.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `employer_reputation` and `academic_reputation`.\n",
+    "* Call `scatter_plot`, passing in `employer_reputation` and `academic_reputation` as the `x` and `y` arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef617ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the scatter plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "horizontal_bar_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "d144417b",
+   "metadata": {},
+   "source": [
+    "### Task 3.3: Make a Horizontal Bar plot of average employer_reputation and average faculty_student_score across all years\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:horizontal_bar_plot.png\" width=\"600\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78e21b0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "def horizontal_bar_plot(df, x):\n",
+    "    \"\"\"horizontal_bar_plot(df, x) takes in a DataFrame 'df' and displays \n",
+    "    a horizontal bar plot with the column 'x' as the x-axis, and all\n",
+    "    other columns of 'df' on the y-axis\"\"\"\n",
+    "    df = df.set_index(x)\n",
+    "    ax = df.plot.barh()\n",
+    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.9))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cbdaa9f",
+   "metadata": {},
+   "source": [
+    "Use the `horizontal_bar_plot` function to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select `year`, **average** `employer_reputation`, and **average** `faculty_student_score` grouped by `year`.\n",
+    "* Save the resulting **DataFrame** with **three** columns: `year`, the **average** of the `employer_reputation` and the **average** of the `faculty_student_score`.\n",
+    "* Call `horizontal_bar_plot`, passing in `year` as the `x` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc779e0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the horizontal bar plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "pie_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "aaeeebe7",
+   "metadata": {},
+   "source": [
+    "### Task 3.4 Display a Pie Chart of the average overall score of the top 10 countries in descending order\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:pie_plot.png\" width=\"400\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aedb58d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "def pie_plot(df, x, y, title=None):\n",
+    "    \"\"\"pie_plot(df, x, y, title) takes in a DataFrame 'df' and displays \n",
+    "    a pie plot with the column 'x' as the x-axis, the (numeric) column\n",
+    "    'y' as the y-axis, and the 'title' as the title of the plot\"\"\"\n",
+    "    df = df.set_index(x)\n",
+    "    ax = df.plot.pie(y=y, legend=False)\n",
+    "    ax.set_ylabel(None)\n",
+    "    ax.set_title(title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "805c89c1",
+   "metadata": {},
+   "source": [
+    "Use the `pie_plot` function to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select the **top** *10* countries based on **average** `overall_score`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `country`, and the **average** of the `overall_score`.\n",
+    "* Call `pie_plot`, passing in `country` as the `x` argument, and the **average** of the `overall_score` as the `y` argument.\n",
+    "* Your plot must also have the **title** `Countries with top 10 overall scores` as in the image.\n",
+    "\n",
+    "**Hint:** If you are having trouble writing the SQL query, take a look at Task 2.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "777d3b49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the pie plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "regression_line_plot.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "de3777de",
+   "metadata": {},
+   "source": [
+    "### Task 3.5: Fit a regression line to the data from Task 3.2\n",
+    "\n",
+    "Your line of best fit should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:regression_line_plot.png\" width=\"500\"/></div>\n",
+    "    \n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68941bde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have defined this function for you\n",
+    "\n",
+    "def get_regression_coeff(df, x, y):\n",
+    "    \"\"\"get_regression_coeff(df, x, y) takes in a DataFrame 'df' and returns \n",
+    "    the slope (m) and the y-intercept (b) of the line of best fit in the\n",
+    "    plot with the column 'x' as the x-axis, and the column 'y' as the y-axis\"\"\"\n",
+    "    df[\"1\"] = 1\n",
+    "    res = np.linalg.lstsq(df[[x, \"1\"]], df[y], rcond=None)\n",
+    "    coefficients = res[0]\n",
+    "    m = coefficients[0]\n",
+    "    b = coefficients[1]\n",
+    "    return (m, b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb427287",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you must define this function to compute the best fit line\n",
+    "\n",
+    "def get_regression_line(df, x, y):\n",
+    "    \"\"\"get_regression_line(df, x, y) takes in a DataFrame 'df' and returns \n",
+    "    a DataFrame with an additional column \"fit\" of the line of best fit in the\n",
+    "    plot with the column 'x' as the x-axis, and the column 'y' as the y-axis\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use the 'get_regression_coeff' function to get the slope and\n",
+    "    #       intercept of the line of best fit\n",
+    "    # TODO: save them into variables m and b respectively\n",
+    "    \n",
+    "    # TODO: create a new column in the dataframe called 'fit', which is\n",
+    "    #       is calculated as df['fit'] = m * df[x] + b\n",
+    "    \n",
+    "    # TODO: return the DataFrame df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a70404d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you must define this function to plot the best fit line on the scatter plot\n",
+    "\n",
+    "def regression_line_plot(df, x, y):\n",
+    "    \"\"\"regression_line_plot(df, x, y) takes in a DataFrame 'df' and displays\n",
+    "    a scatter plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis, as well as the best fit line for the plot\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use 'get_regression_line' to get the data for the best fit line.\n",
+    "    \n",
+    "    # TODO: use df.plot.scatter (not scatter_plot) to plot the x and y columns\n",
+    "    #       of 'df' in black color.\n",
+    "    # TODO: save the return value of df.plot.scatter to a variable called 'ax'\n",
+    "    \n",
+    "    # TODO: use df.plot.line to plot the fitted line in red,\n",
+    "    #       using ax=ax as a keyword argument.\n",
+    "    #       this ensures that both the scatter plot and line end up on the same plot\n",
+    "    #       play careful attention to what the 'x' and 'y' arguments ought to be"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef4b46de",
+   "metadata": {},
+   "source": [
+    "Now, use the `regression_line_plot` function to create the required plot.\n",
+    "\n",
+    "* Call `regression_line_plot` on your data from Task 3.2 to show the correlation between `employer_reputation` and `academic_reputation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "065d0ef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the scatter plot with the best fit line using the DataFrame from Task 3.2 \n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdb5cdb7",
+   "metadata": {},
+   "source": [
+    "### Task 4: Closing the connection\n",
+    "\n",
+    "Now that you are done with your database, it is very important to close it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65557b40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# close your connection here\n",
+    "\n",
+    "# we have done this one for you\n",
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f20a99c",
+   "metadata": {},
+   "source": [
+    "### Congratulations, you are now ready to start P13!"
+   ]
+  }
+ ],
+ "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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Sussex","228":"Universiti Putra Malaysia","229":"Universiti Kebangsaan Malaysia","230":"University Of Arizona","231":"University Of Wollongong","232":"Universidad Complutense De Madrid","233":"Loughborough University","234":"American University Of Beirut","235":"Al-Farabi Kazakh National University","236":"Universit\u00e9 Grenoble-Alpes","237":"University Of Leicester","238":"Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn","239":"Macquarie University","240":"Saint-Petersburg State University","241":"Universit\u00e9 Paris-Sud 11","242":"National University Of Ireland, Galway","243":"Tufts University","244":"Chulalongkorn University","245":"Simon Fraser University","246":"Queensland University Of Technology","247":"Rmit University","248":"University Of Massachusetts, Amherst","249":"Novosibirsk State University","250":"University Of Tsukuba","251":"University Of Miami","252":"Universiti Teknologi Malaysia","253":"Universidad Nacional De Colombia","254":"Universit\u00e4t Frankfurt Am Main","255":"Beijing Normal University","256":"Kyung Hee University","257":"Universidad De Los Andes Colombia","258":"Norwegian University Of Science And Technology","259":"Royal Holloway University Of London","260":"Universit\u00e4t Stuttgart","261":"Curtin University","262":"North Carolina State University","263":"Indian Institute Of Technology Madras","264":"National Taiwan University Of Science And Technology","265":"Universiti Sains Malaysia","266":"King Abdul Aziz University","267":"University Of Dundee","268":"Universit\u00e9 Paris 1 Panth\u00e9on-Sorbonne","269":"Ecole Des Ponts Paristech","270":"Universidad De Navarra","271":"Technische Universit\u00e4t Darmstadt","272":"University Of Surrey","273":"University Of East Anglia","274":"Universitat Polit\u00e8cnica De Catalunya","275":"University Of Turku","276":"University Of Indonesia","277":"University Of Strathclyde","278":"Dalhousie University","279":"University Of South Australia","280":"Universidad Carlos Iii De Madrid","281":"Wuhan University","282":"Rutgers - The State University Of New Jersey, New Brunswick","283":"University College Cork","284":"University Of Gothenburg","285":"Universit\u00e4t Innsbruck","286":"Link\u00f6ping University","287":"Universit\u00e4t Erlangen-N\u00fcrnberg","288":"National Taiwan Normal University","289":"University Of Ottawa","290":"Bauman Moscow State Technical University","291":"University Of Waikato","292":"Deakin University","293":"Indian Institute Of Technology Kanpur","294":"Universit\u00e9 Paris-Sorbonne","295":"Soas - School Of Oriental And African Studies, University Of London","296":"Universit\u00e0 Degli Studi Di Padova","297":"Universitat Pompeu Fabra","298":"Ewha Womans University","299":"Hong Kong Baptist University","300":"University Of California, Santa Cruz","301":"University Of Porto","302":"Universit\u00e9 De Strasbourg","303":"Indiana University Bloomington","304":"Universit\u00e9 Paris Diderot - Paris 7","305":"University Of Lisbon","306":"Politecnico Di Torino","307":"Birkbeck College, University Of London","308":"Indian Institute Of Technology Kharagpur","309":"Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster","310":"Universidade Federal Do Rio De Janeiro","311":"Heriot-Watt University","312":"University Of Tasmania","313":"Charles University","314":"University Of Tartu","315":"Massey University","316":"Tongji University","317":"University At Buffalo Suny","318":"Lincoln University","319":"Sun Yat-Sen University","320":"University Of Liege","321":"Hiroshima University","322":"Tomsk State University","323":"University Of California, Riverside","324":"Griffith University","325":"Harbin Institute Of Technology","326":"Universit\u00e0 Degli Studi Di Milano","327":"Yeshiva University","328":"National Yang Ming University","329":"Ecole Normale Sup\u00e9rieure De Cachan","330":"Bandung Institute Of Technology","331":"Universidad Austral","332":"Universit\u00e4t K\u00f6ln","333":"Belarus State University","334":"Mahidol University","335":"L.N. Gumilyov Eurasian National University","336":"Universidad De Belgrano","337":"Ume\u00e5 University","338":"Boston College","339":"Gwangju Institute Of Science And Technology","340":"Universit\u00e4t Jena","341":"University Of Hawaii At M?\ufffdNoa","342":"City University London","343":"Nankai University","344":"Xi'An Jiaotong University","345":"Brunel University","346":"Northeastern University","347":"University Of Victoria","348":"Qatar University","349":"University Of Brunei Darussalam","350":"Kobe University","351":"Ben Gurion University Of The Negev","352":"George Washington University","353":"University Of Essex","354":"Moscow Institute Of Physics And Technology State University","355":"Universit\u00e9 Paris Dauphine","356":"Tilburg University","357":"Universit\u00e4t Ulm","358":"University Of Jyv\u00e4skyl\u00e4","359":"La Trobe University","360":"Oxford Brookes University","361":"Stellenbosch University","362":"Universidade Nova De Lisboa","363":"Pontificia Universidad Cat\u00f3lica Argentina Santa Mar\u00eda De Los Buenos Aires","364":"Rensselaer Polytechnic Institute","365":"University Of The Witwatersrand","366":"James Cook University","367":"Tokyo Medical And Dental University","368":"University Of The Philippines","369":"University Of Tromso","370":"Virginia Polytechnic Institute","371":"University Of St Gallen","372":"Aston University","373":"Moscow State Institute Of International Relations \u2013 Mgimo University","374":"National Research Nuclear University \"Mephi\"","375":"Universidad Politecnica De Valencia","376":"University Of Kent","377":"Laval University","378":"Aalborg University","379":"Tampere University Of Technology","380":"Universit\u00e9 De Montpellier","381":"National Research University - Higher School Of Economics","382":"Stony Brook University","383":"University Of Southern Denmark","384":"Universit\u00e4t Konstanz","385":"Tomsk Polytechnic University","386":"University Of Kansas","387":"National Sun Yat-Sen University","388":"Universit\u00e4t Mannheim","389":"United Arab Emirates University","390":"Dublin City University","391":"National Central University","392":"University Of Utah","393":"University Of Colorado At Denver","394":"American University In Cairo","395":"Illinois Institute Of Technology","396":"Hufs \u2013 Hankuk  University Of Foreign Studies","397":"Goldsmiths, University Of London","398":"Johannes Gutenberg Universit\u00e4t Mainz","399":"Taipei Medical University","400":"Massachusetts Institute Of Technology","401":"Stanford University","402":"Harvard University","403":"California Institute Of Technology","404":"University Of Oxford","405":"University Of Cambridge","406":"Eth Zurich","407":"Imperial College London","408":"University Of Chicago","409":"Ucl","410":"National University Of Singapore","411":"Nanyang Technological University","412":"Princeton University","413":"Cornell University","414":"Yale University","415":"Columbia University","416":"Tsinghua University","417":"University Of Edinburgh","418":"University Of Pennsylvania","419":"University Of Michigan","420":"Johns Hopkins University","421":"\u00c9cole Polytechnique F\u00e9d\u00e9rale De Lausanne","422":"The University Of Tokyo","423":"Australian National University","424":"University Of Hong Kong","425":"Duke University","426":"University Of California, Berkeley","427":"University Of Toronto","428":"The University Of Manchester","429":"Peking University","430":"King'S College London","431":"University Of California, Los Angeles","432":"Mcgill University","433":"Northwestern University","434":"Kyoto University","435":"Seoul National University","436":"The Hong Kong University Of Science And Technology","437":"London School Of Economics And Political Science","438":"The University Of Melbourne","439":"Kaist - Korea Advanced Institute Of Science And Technology","440":"University Of California, San Diego","441":"The University Of Sydney","442":"New York University","443":"Fudan University","444":"The University Of New South Wales","445":"Carnegie Mellon University","446":"University Of British Columbia","447":"The University Of Queensland","448":"The Chinese University Of Hong Kong","449":"Psl University","450":"University Of Bristol","451":"Delft University Of Technology","452":"University Of Wisconsin-Madison","453":"The University Of Warwick","454":"City University Of Hong Kong","455":"Brown University","456":"University Of Amsterdam","457":"Tokyo Institute Of Technology","458":"Monash University","459":"Shanghai Jiao Tong University","460":"Technische Universit\u00e4t M\u00fcnchen","461":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","462":"University Of Texas At Austin","463":"Ruprecht-Karls-Universit\u00e4t Heidelberg","464":"Ecole Polytechnique","465":"University Of Washington","466":"Osaka University","467":"Zhejiang University","468":"Georgia Institute Of Technology","469":"University Of Glasgow","470":"University Of Illinois At Urbana-Champaign","471":"National Taiwan University","472":"Universidad De Buenos Aires","473":"Durham University","474":"Sorbonne University","475":"The University Of Sheffield","476":"Tohoku University","477":"University Of Zurich","478":"University Of Birmingham","479":"University Of Copenhagen","480":"Katholieke Universiteit Leuven","481":"The University Of Nottingham","482":"Pohang University Of Science And Technology","483":"University Of North Carolina, Chapel Hill","484":"The University Of Auckland","485":"Korea University","486":"Rice University","487":"Universiti Malaya","488":"Ohio State University","489":"Lomonosov Moscow State University","490":"The University Of Western Australia","491":"Lund University","492":"Boston University","493":"University Of Leeds","494":"Pennsylvania State University","495":"University Of Southampton","496":"University Of St Andrews","497":"University Of Science And Technology Of China","498":"Eindhoven University Of Technology","499":"Purdue University","500":"Sungkyunkwan University","501":"University Of California, Davis","502":"Washington University In St. Louis","503":"Kth, Royal Institute Of Technology","504":"Trinity College Dublin","505":"The Hong Kong Polytechnic University","506":"Yonsei University","507":"University Of Geneva","508":"University Of Alberta","509":"University Of Helsinki","510":"Nagoya University","511":"Technical University Of Denmark","512":"Universidad Nacional Aut\u00f3noma De M\u00e9xico","513":"The University Of Adelaide","514":"University Of Southern California","515":"Kit, Karlsruher Institut F\u00fcr Technologie","516":"Uppsala University","517":"Universidade De S\u00e3o Paulo","518":"Queen Mary University Of London","519":"University Of Groningen","520":"Humboldt-Universit\u00e4t Zu Berlin","521":"Leiden University","522":"Nanjing University","523":"Utrecht University","524":"Wageningen University","525":"Kyushu University","526":"University Of Maryland, College Park","527":"Chalmers University Of Technology","528":"Hokkaido University","529":"Freie Universit\u00e4t Berlin","530":"Lancaster University","531":"Pontificia Universidad Cat\u00f3lica De Chile","532":"University Of California, Santa Barbara","533":"University Of York","534":"University Of Oslo","535":"University Of Pittsburgh","536":"Centralesup\u00e9lec","537":"University Of Ghent","538":"University Of Bern","539":"Aalto University","540":"Aarhus University","541":"Michigan State University","542":"Newcastle University","543":"Rheinisch-Westf\u00e4lische Technische Hochschule Aachen","544":"Cardiff University","545":"Mcmaster University","546":"Technische Universit\u00e4t Berlin","547":"Emory University","548":"Universit\u00e9 De Montr\u00e9al","549":"University Of Lausanne","550":"Hanyang University","551":"University Of Liverpool","552":"\u00c9cole Normale Sup\u00e9rieure De Lyon","553":"Hebrew University Of Jerusalem","554":"University Of Exeter","555":"Politecnico Di Milano","556":"University Of Minnesota","557":"University Of Bath","558":"Universidad Aut\u00f3noma De Madrid","559":"University Of Basel","560":"University Of Technology Sydney","561":"Indian Institute Of Technology Bombay","562":"National Tsing Hua University","563":"University Of Waterloo","564":"Universit\u00e9 Catholique De Louvain","565":"Universitat De Barcelona","566":"Scuola Superiore Sant'Anna Pisa","567":"Eberhard Karls Universit\u00e4t T\u00fcbingen","568":"University Of California, Irvine","569":"Indian Institute Of Science  Bangalore","570":"University Of Bergen","571":"Indian Institute Of Technology Delhi","572":"University Of Aberdeen","573":"University Of Twente","574":"Scuola Normale Superiore Di Pisa","575":"Universit\u00e4t Wien","576":"University Of Otago","577":"Tecnol\u00f3gico De Monterrey","578":"Erasmus University Rotterdam","579":"Queen'S University Of Belfast","580":"Universit\u00e0 Di Bologna","581":"University Of Florida","582":"Dartmouth College","583":"Universiti Kebangsaan Malaysia","584":"University Of Rochester","585":"Case Western Reserve University","586":"Universit\u00e4t Freiburg","587":"Vrije Universiteit Brussel","588":"King Fahd University Of Petroleum & Minerals","589":"University Of Colorado At Boulder","590":"Technische Universit\u00e4t Dresden","591":"University Of Virginia","592":"Universitat Aut\u00f3noma De Barcelona","593":"University College Dublin","594":"University Of Reading","595":"Vanderbilt University","596":"Georg-August-Universit\u00e4t G\u00f6ttingen","597":"Keio University","598":"Technische Universit\u00e4t Wien","599":"Stockholm University","600":"University Of Cape Town","601":"Universiti Putra Malaysia","602":"Texas A&M University","603":"Radboud University Nijmegen","604":"Universidade Estadual De Campinas","605":"Universidad Complutense De Madrid","606":"Universiti Sains Malaysia","607":"National Chiao Tung University","608":"Universidad De Chile","609":"Waseda University","610":"Maastricht University","611":"Arizona State University","612":"University Of Notre Dame","613":"University Of Illinois, Chicago","614":"University Of Newcastle","615":"Western University","616":"Sapienza - Universit\u00e0 Di Roma","617":"Loughborough University","618":"University Of Wollongong","619":"Al-Farabi Kazakh National University","620":"Sciences Po Paris","621":"Victoria University Of Wellington","622":"Universit\u00e4t Hamburg","623":"University Of Antwerp","624":"University Of Leicester","625":"Georgetown University","626":"University Of Sussex","627":"Universiti Teknologi Malaysia","628":"University Of Calgary","629":"Tel Aviv University","630":"King Abdul Aziz University","631":"University Of Canterbury","632":"Vrije Universiteit Amsterdam","633":"National Cheng Kung University","634":"Saint-Petersburg State University","635":"Royal Holloway University Of London","636":"American University Of Beirut","637":"Tufts University","638":"Queen'S University","639":"Universit\u00e9 Libre De Bruxelles","640":"Universit\u00e9 Paris-Sud 11","641":"Universidad De Navarra","642":"University Of Miami","643":"Novosibirsk State University","644":"Queensland University Of Technology","645":"University Of Arizona","646":"Technion - Israel Institute Of Technology","647":"University Of Surrey","648":"Universit\u00e0 Degli Studi Di Padova","649":"Curtin University","650":"Macquarie University","651":"Rmit University","652":"Technische Universit\u00e4t Darmstadt","653":"Universidad Carlos Iii De Madrid","654":"Rheinische Friedrich-Wilhelms-Universit\u00e4t Bonn","655":"King Saud University","656":"National Taiwan University Of Science And Technology","657":"Wuhan University","658":"University Of Massachusetts, Amherst","659":"National University Of Ireland, Galway","660":"Universit\u00e4t Stuttgart","661":"University Of Tsukuba","662":"Ecole Des Ponts Paristech","663":"Indian Institute Of Technology Madras","664":"Kyung Hee University","665":"Simon Fraser University","666":"University Of South Australia","667":"University Of Strathclyde","668":"University Of East Anglia","669":"University Of Gothenburg","670":"Chulalongkorn University","671":"Universidad De Los Andes Colombia","672":"University Of Dundee","673":"University Of Waikato","674":"Universidad Nacional De Colombia","675":"Universitat Polit\u00e8cnica De Catalunya","676":"Hong Kong Baptist University","677":"Tomsk State University","678":"Dalhousie University","679":"North Carolina State University","680":"Universit\u00e4t Frankfurt Am Main","681":"Universit\u00e4t Innsbruck","682":"Indian Institute Of Technology Kanpur","683":"Rutgers - The State University Of New Jersey, New Brunswick","684":"Harbin Institute Of Technology","685":"University Of Turku","686":"University Of Tasmania","687":"Soas - School Of Oriental And African Studies, University Of London","688":"Universit\u00e9 Grenoble-Alpes","689":"University Of Ottawa","690":"Tongji University","691":"Beijing Normal University","692":"National Yang Ming University","693":"University Of Indonesia","694":"Indian Institute Of Technology Kharagpur","695":"Sun Yat-Sen University","696":"Universit\u00e9 Paris Diderot - Paris 7","697":"Universitat Pompeu Fabra","698":"Bauman Moscow State Technical University","699":"Universit\u00e4t Erlangen-N\u00fcrnberg","700":"Universit\u00e9 Paris 1 Panth\u00e9on-Sorbonne","701":"Heriot-Watt University","702":"Link\u00f6ping University","703":"University Of Hawaii At M?\ufffdNoa","704":"Ecole Normale Sup\u00e9rieure De Cachan","705":"Birkbeck College, University Of London","706":"Universit\u00e4t K\u00f6ln","707":"National Taiwan Normal University","708":"Deakin University","709":"Universidad Politecnica De Valencia","710":"Yeshiva University","711":"Moscow Institute Of Physics And Technology State University","712":"University At Buffalo Suny","713":"Xi'An Jiaotong University","714":"Gwangju Institute Of Science And Technology","715":"Khalifa University","716":"Charles University","717":"Lincoln University","718":"Ewha Womans University","719":"Tilburg University","720":"Hiroshima University","721":"University Of Tartu","722":"Indiana University Bloomington","723":"University Of Brunei Darussalam","724":"Universit\u00e0 Degli Studi Di Milano","725":"Northeastern University","726":"Universit\u00e4t Jena","727":"University Of Porto","728":"Griffith University","729":"National Research Nuclear University \"Mephi\"","730":"Westf\u00e4lische Wilhelms-Universit\u00e4t M\u00fcnster","731":"Brunel University","732":"Massey University","733":"Qatar University","734":"Universit\u00e4t Ulm","735":"University Of California, Santa Cruz","736":"University Of Jyv\u00e4skyl\u00e4","737":"Nankai University","738":"Ume\u00e5 University","739":"Universit\u00e4t Mannheim","740":"University College Cork","741":"Virginia Polytechnic Institute","742":"Aalborg University","743":"National Research University - Higher School Of Economics","744":"George Washington University","745":"Rensselaer Polytechnic Institute","746":"University Of Utah","747":"Universit\u00e9 De Strasbourg","748":"Stony Brook University","749":"United Arab Emirates University","750":"City University London","751":"Kobe University","752":"Tokyo Medical And Dental University","753":"Belarus State University","754":"Moscow State Institute Of International Relations \u2013 Mgimo University","755":"University Of Essex","756":"University Of Kent","757":"University Of Lisbon","758":"Bandung Institute Of Technology","759":"University Of Victoria","760":"Universidade Federal Do Rio De Janeiro","761":"Taipei Medical University","762":"Norwegian University Of Science And Technology","763":"Oxford Brookes University","764":"Technische Universit\u00e4t Graz","765":"Tampere University Of Technology","766":"Universidad Austral","767":"University Of Kansas","768":"James Cook University","769":"Pontificia Universidad Cat\u00f3lica Argentina Santa Mar\u00eda De Los Buenos Aires","770":"University Of Tromso","771":"Ruhr-Universit\u00e4t Bochum","772":"Tomsk Polytechnic University","773":"University Of Connecticut","774":"University Of St Gallen","775":"American University Of Sharjah","776":"Boston College","777":"University Of Oulu","778":"University Of Southern Denmark","779":"Mahidol University","780":"Aston University","781":"Indian Institute Of Technology Roorkee","782":"University Of The Witwatersrand","783":"Universidad De Belgrano","784":"University Of California, Riverside","785":"University Of The Philippines","786":"Hufs \u2013 Hankuk  University Of Foreign Studies","787":"Politecnico Di Torino","788":"Swinburne University Of Technology","789":"Wake Forest University","790":"Universitas Gadjah Mada","791":"University Of Liege","792":"Washington State University","793":"L.N. Gumilyov Eurasian National University","794":"University Of Warsaw","795":"Goldsmiths, University Of London","796":"Chung-Ang University","797":"La Trobe University","798":"Pakistan Institute Of Engineering And Applied Sciences","799":"Universit\u00e4t Konstanz","800":"University Of Colorado At Denver","801":"Massachusetts Institute Of Technology","802":"Stanford University","803":"Harvard University","804":"University Of Oxford","805":"California Institute Of Technology","806":"Eth Zurich","807":"University Of Cambridge","808":"Ucl","809":"Imperial College London","810":"University Of Chicago","811":"Nanyang Technological University","812":"National University Of Singapore","813":"Princeton University","814":"Cornell University","815":"University Of Pennsylvania","816":"Tsinghua University","817":"Yale University","818":"Columbia University","819":"Ecole Polytechnique F\u00e9d\u00e9rale De Lausanne","820":"University Of Edinburgh","821":"University Of Michigan","822":"Peking University","823":"The University Of Tokyo","824":"Johns Hopkins University","825":"Duke University","826":"University Of Hong Kong","827":"The University Of Manchester","828":"University Of California, Berkeley","829":"Australian National University","830":"University Of Toronto","831":"Northwestern University","832":"The Hong Kong University Of Science And Technology","833":"King'S College London","834":"Kyoto University","835":"Mcgill University","836":"University Of California, Los Angeles","837":"Seoul National University","838":"The University Of Melbourne","839":"New York University","840":"Fudan University","841":"Kaist - Korea Advanced Institute Of Science And Technology","842":"The University Of Sydney","843":"The University Of New South Wales","844":"London School Of Economics And Political Science","845":"University Of California, San Diego","846":"The Chinese University Of Hong Kong","847":"The University Of Queensland","848":"Carnegie Mellon University","849":"University Of Bristol","850":"Delft University Of Technology","851":"University Of British Columbia","852":"City University Of Hong Kong","853":"Universit\u00e9 Psl","854":"Zhejiang University","855":"Technische Universit\u00e4t M\u00fcnchen","856":"University Of Wisconsin-Madison","857":"Brown University","858":"Monash University","859":"Tokyo Institute Of Technology","860":"Ecole Polytechnique","861":"Shanghai Jiao Tong University","862":"The University Of Warwick","863":"Ludwig-Maximilians-Universit\u00e4t M\u00fcnchen","864":"University Of Amsterdam","865":"University Of Texas At Austin","866":"Ruprecht-Karls-Universit\u00e4t Heidelberg","867":"University Of Glasgow","868":"University Of Washington","869":"National Taiwan University","870":"Universiti Malaya","871":"Osaka University","872":"Georgia Institute Of Technology","873":"Universidad De Buenos Aires","874":"University Of Illinois At Urbana-Champaign","875":"University Of Zurich","876":"Sorbonne University","877":"Durham University","878":"The University Of Sheffield","879":"Katholieke Universiteit Leuven","880":"University Of Birmingham","881":"University Of Copenhagen","882":"Tohoku University","883":"Korea University","884":"The University Of Auckland","885":"Lomonosov Moscow State University","886":"Rice University","887":"The University Of Western Australia","888":"Pohang University Of Science And Technology","889":"University Of Science And Technology Of China","890":"University Of North Carolina, Chapel Hill","891":"The Hong Kong Polytechnic University","892":"Lund University","893":"Pennsylvania State University","894":"University Of Leeds","895":"Sungkyunkwan University","896":"The University Of Nottingham","897":"University Of Southampton","898":"Boston University","899":"Kth, Royal Institute Of Technology","900":"University Of St Andrews","901":"Ohio State University","902":"Eindhoven University Of Technology","903":"Universidad Nacional Aut\u00f3noma De M\u00e9xico","904":"University Of California, Davis","905":"Yonsei University","906":"The University Of Adelaide","907":"University Of Helsinki","908":"Trinity College Dublin","909":"Washington University In St. Louis","910":"University Of Geneva","911":"Purdue University","912":"Technical University Of Denmark","913":"University Of Alberta","914":"University Of Groningen","915":"Nagoya University","916":"Universidade De S\u00e3o Paulo","917":"Uppsala University","918":"Leiden University","919":"University Of Oslo","920":"Humboldt-Universit\u00e4t Zu Berlin","921":"Nanjing University","922":"Utrecht University","923":"University Of Bern","924":"Kit, Karlsruher Institut F\u00fcr Technologie","925":"Chalmers University Of Technology","926":"Wageningen University","927":"Queen Mary University Of London","928":"Pontificia Universidad Cat\u00f3lica De Chile","929":"Lancaster University","930":"University Of Southern California","931":"Freie Universit\u00e4t Berlin","932":"University Of Ghent","933":"Hokkaido University","934":"Kyushu University","935":"Aalto University","936":"University Of California, Santa Barbara","937":"University Of Maryland, College Park","938":"Universit\u00e9 De Montr\u00e9al","939":"Rheinisch-Westf\u00e4lische Technische Hochschule Aachen","940":"Centralesup\u00e9lec","941":"Mcmaster University","942":"University Of Pittsburgh","943":"University Of Technology Sydney","944":"Michigan State University","945":"Aarhus University","946":"Newcastle University","947":"Technische Universit\u00e4t Berlin","948":"University Of York","949":"Politecnico Di Milano","950":"Hanyang University","951":"University Of Basel","952":"Indian Institute Of Technology Bombay","953":"University Of Lausanne","954":"Cardiff University","955":"Universit\u00e4t Wien","956":"Emory University","957":"University Of Minnesota","958":"Tecnol\u00f3gico De Monterrey","959":"Universiti Putra Malaysia","960":"Ecole Normale Sup\u00e9rieure De Lyon","961":"Universiti Kebangsaan Malaysia","962":"Hebrew 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diff --git a/p13/README.md b/p13/README.md
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+++ b/p13/README.md
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+# Project 13 (P13): World University Rankings
+
+
+## Corrections and clarifications:
+
+* None yet.
+
+**Find any issues?** Report to us:
+
+- Iffat Nafisa <nafisa@wisc.edu>
+- Jodi Lawson <jlawson6@wisc.edu>
+
+
+## Instructions:
+
+This project will focus on **SQL**, and **Plotting**. To start, download [`p13.ipynb`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/p13/p13.ipynb), [`p13_test.py`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/p13/p13_test.py), and [`p13_expected.html`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/p13/p13_expected.html).
+
+**Important Warning:** You must **not** manually download any of the other files. In particular, you are **not** allowed to manually download the file `QSranking.json`. You **must** download this files using Python in your `p13.ipynb` notebook as a part of the project. Otherwise, your code may pass on **your computer**, but **fail** on the testing computer.
+
+You will work on `p13.ipynb` and hand it in. You should follow the provided directions for each question. Questions have **specific** directions on what **to do** and what **not to do**.
+
+------------------------------
+
+## IMPORTANT Submission instructions:
+- Review the [Grading Rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/p13/rubric.md), to ensure that you don't lose points during code review.
+- You must **save your notebook file** before you run the cell containing **export**.
+- Login to [Gradescope](https://www.gradescope.com/) and upload the zip file into the P13 assignment.
+- If you completed the project with a **partner**, make sure to **add their name** by clicking "Add Group Member"
+in Gradescope when uploading the P13 zip file.
+
+   <img src="images/add_group_member.png" width="400">
+
+   **Warning:** You will have to add your partner on Gradescope even if you have filled out this information in your `p13.ipynb` notebook.
+
+- It is **your responsibility** to make sure that your project clears auto-grader tests on the Gradescope test system. Otter test results should be available in a few minutes after your submission. You should be able to see both PASS / FAIL results for the 20 test cases and your total score, which is accessible via Gradescope Dashboard (as in the image below):
+
+    <img src="images/gradescope.png" width="400">
+- **Important:** After you submit, you **need to verify** that your code is visible on Gradescope. If you displayed the output of a large variable anywhere in your notebook, **we will not be able to view your submission**. Make sure you don't have any large outputs in any of your cells, and verify after submission that your code can be viewed.
+- If you feel you have been incorrectly graded on a particular question by the Gradescope autograder, please make a regrade request.
diff --git a/p13/images/README.md b/p13/images/README.md
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+# Images
+
+Images from p13 are stored here.
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diff --git a/p13/p13.ipynb b/p13/p13.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e33d91e",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "import otter\n",
+    "# nb_name should be the name of your notebook without the .ipynb extension\n",
+    "nb_name = \"p13\"\n",
+    "py_filename = nb_name + \".py\"\n",
+    "grader = otter.Notebook(nb_name + \".ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0611fe14",
+   "metadata": {
+    "deletable": false
+   },
+   "outputs": [],
+   "source": [
+    "import p13_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bcd01a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# enter none if you don't have a project partner\n",
+    "# you will have to add your partner as a group member on Gradescope even after you fill this\n",
+    "\n",
+    "# project: p13\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "372ed345",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " # Project 13: World University Rankings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b30c2df0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate how to:\n",
+    "\n",
+    "* query a database using SQL,\n",
+    "* process data using `pandas` **DataFrames**,\n",
+    "* create different types of plots.\n",
+    "\n",
+    "Please go through [Lab-P13](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/tree/main/lab-p13) before working on this project. The lab introduces some useful techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "479785c7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Note on Academic Misconduct:\n",
+    "\n",
+    "**IMPORTANT**: P12 and P13 are two parts of the same data analysis. You **cannot** switch project partners between these two projects. That is if you partnered up with someone for P12, you have to sustain that partnership until end of P13. Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/s23/syllabus.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e0e04f5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the file `p13_test.py`. If you are curious about how we test your code, you can explore this file, and specifically the value of the variable `expected_json`, to understand the expected answers to the questions.\n",
+    "\n",
+    "For answers involving DataFrames, `p13_test.py` compares your tables to those in `p13_expected.html`, so take a moment to open that file on a web browser (from Finder/Explorer).\n",
+    "\n",
+    "For answers involving plots, `p13_test.py` can **only** check that the **DataFrames** are correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. Your plots will be **manually graded**, and you will **lose points** if your plot is not visible, or if it is not properly labelled.\n",
+    "\n",
+    "**IMPORTANT Warning:** Do **not** download the dataset `QSranking.json` **manually**. Use the `download` function from P12 to download it. When we run the autograder, this file `QSranking.json` will **not** be in the directory. So, unless your `p13.ipynb` downloads this file, you will get a **zero score** on the project. Also, make sure your `download` function includes code to check if the file already exists. Otherwise, you will **lose** points for **hardcoding**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aad1951a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "For your final CS220 project, you're going to continue analyzing world university rankings. However, we will be using a different dataset this time. The data for this project has been extracted from [here](https://www.topuniversities.com/university-rankings/world-university-rankings/2023). Unlike the CWUR rankings we used in P12, the QS rankings dataset has various scores for the universities, and not just the rankings. This makes the QS rankings dataset more suitable for plotting (which you will be doing a lot of!).\n",
+    "\n",
+    "In this project, you'll have to dump your DataFrame to a SQLite database. You'll answer questions by doing queries on that database. Often, your answers will be in the form of a plot. Check these carefully, as the tests only verify that a plot has been created, not that it looks correct (the Gradescope autograder will manually deduct points for plotting mistakes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48aad11e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code. You **may not** manually download **any** files for this project, unless you are **explicitly** told to do so. For all other files, you **must** use the `download` function to download the files.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Functions:\n",
+    "- `bar_plot`\n",
+    "- `scatter_plot`\n",
+    "- `horizontal_bar_plot`\n",
+    "- `pie_plot`\n",
+    "- `get_regression_coeff`\n",
+    "- `get_regression_line`\n",
+    "- `regression_line_plot`\n",
+    "- `download`\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "Required Data Structures:\n",
+    "- `conn`\n",
+    "\n",
+    "You **must** write SQL queries to solve the questions in this project, unless you are **explicitly** told otherwise. You will **not get any credit** if you use `pandas` operations to extract data. We will give you **specific** instructions for any questions where `pandas` operations are allowed. In addition, you are also **required** to follow the requirements below:\n",
+    "\n",
+    "* You **must** close the connection to `conn` at the end of your notebook.\n",
+    "* Do **not** use **absolute** paths such as `C://ms//cs220//p13`. You may **only** use **relative paths**.\n",
+    "* Do **not** hardcode `//` or `\\` in any of your paths. You **must** use `os.path.join` to create paths.\n",
+    "* Do **not** leave irrelevant output or test code that we didn't ask for.\n",
+    "* **Avoid** calling **slow** functions multiple times within a loop.\n",
+    "* Do **not** define multiple functions with the same name or define multiple versions of one function with different names. Just keep the best version.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/blob/main/p13/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e04f805e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1363e20",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "995a9ea8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, you may copy/paste some of the functions and data structures you defined in Lab-P13 and P12, which will be useful for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4fab7ea",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this ensures that font.size setting remains uniform\n",
+    "%matplotlib inline \n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "matplotlib.rcParams[\"font.size\"] = 13 # don't use value > 13! Otherwise your y-axis tick labels will be different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4eac640",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'bar_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71c71935",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'scatter_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "153b23ad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'horizontal_bar_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f6d37df",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'pie_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88255766",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'get_regression_coeff' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8119a0ec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'get_regression_line' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13851f7d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'regression_line_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c12776a3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'download' from p12 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4fbd661",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-s23-projects/-/raw/main/p13/QSranking.json'\n",
+    "# to the file 'QSranking.json'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40f76941",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 1: `conn`\n",
+    "\n",
+    "You **must** now create a **database** called `rankings.db` out of `QSranking.json`, connect to it, and save it in a variable called `conn`. You **must** use this connection to the database `rankings.db` to answer the questions that follow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8de4b158",
+   "metadata": {
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create a database called 'rankings.db' out of 'QSranking.json'\n",
+    "\n",
+    "# TODO: load the data from 'QSranking.json' into a variable called 'qs_ranking' using pandas' 'read_json' function\n",
+    "# TODO: connect to 'rankings.db' and save it to a variable called 'conn'\n",
+    "# TODO: write the contents of the DataFrame 'qs_ranking' to the sqlite database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f28e183",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# run this cell and confirm that you have defined the variables correctly\n",
+    "\n",
+    "pd.read_sql(\"SELECT * FROM rankings LIMIT 5\", conn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d31f5dd9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** List **all** the statistics of the institution with the `institution_name` *University Of Wisconsin-Madison*. \n",
+    "\n",
+    "You **must** display **all** the columns. The rows **must** be in *ascending* order of `year`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**rank**|**year**|**institution_name**|**country**|**academic_reputation**|**employer_reputation**|**faculty_student_score**|**citations_per_faculty**|**international_faculty**|**international_students**|**overall_score**|\n",
+    "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+    "|**0**|55|2018|University Of Wisconsin-Madison|United States|94.0|62.1|84.0|54.2|53.2|30.9|75.8|\n",
+    "|**1**|53|2019|University Of Wisconsin-Madison|United States|88.5|51.2|87.4|52.6|58.8|30.6|73.2|\n",
+    "|**2**|56|2020|University Of Wisconsin-Madison|United States|87.8|49.7|85.5|50.0|57.2|30.9|71.8|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8eefb54f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'uw_rating', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a51b275",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "587fd6d2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** What are the **top** *10* institutions in *Japan* which had the **highest** score of `international_students` in the `year` *2020*?\n",
+    "\n",
+    "You **must** display the columns `institution_name` and `international_students`. The rows **must** be in *descending* order of `international_students`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**institution_name**|**international_students**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|Waseda University|35.8|\n",
+    "|**1**|Tokyo Institute Of Technology|31.3|\n",
+    "|**2**|University Of Tsukuba|30.4|\n",
+    "|**3**|The University of Tokyo|26.2|\n",
+    "|**4**|Kyushu University|21.5|\n",
+    "|**5**|Nagoya University|21.3|\n",
+    "|**6**|Tohoku University|17.6|\n",
+    "|**7**|Kyoto University|17.5|\n",
+    "|**8**|Hiroshima University|17.1|\n",
+    "|**9**|Tokyo Medical and Dental University|16.7|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b72f2999",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'japan_top_10_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f06aaae0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "341ac4b8",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** What are the **top** *10* institutions in the *United States* which had the **highest** *reputation* in the `year` *2019*?\n",
+    "\n",
+    "The `reputation` of an institution is defined as the sum of `academic_reputation` and `employer_reputation`. You **must** display the columns `institution_name` and `reputation`. The rows **must** be in *descending* order of `reputation`. In case the `reputation` is tied, the rows must be in *alphabetical* order of `institution_name`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**institution_name**|**reputation**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|Harvard University|200.0|\n",
+    "|**1**|Massachusetts Institute Of Technology|200.0|\n",
+    "|**2**|Stanford University|200.0|\n",
+    "|**3**|University Of California, Berkeley|199.8|\n",
+    "|**4**|Yale University|199.6|\n",
+    "|**5**|University Of California, Los Angeles|199.1|\n",
+    "|**6**|Columbia University|197.1|\n",
+    "|**7**|Princeton University|196.6|\n",
+    "|**8**|University Of Chicago|190.3|\n",
+    "|**9**|Cornell University|189.2|\n",
+    "\n",
+    "**Hint:** You can use mathematical expressions in your **SELECT** clause. For example, if you wish to add the `academic_reputation` and `employer_reputation` for each institution, you could use the following query:\n",
+    "\n",
+    "```sql\n",
+    "SELECT (`academic_reputation` + `employer_reputation`) FROM rankings\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "271b86d7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'us_top_10_rep', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96cacdd4",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21ba8c82",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** What are the **top** *10* countries which had the **most** *institutions* listed in the `year` *2020*?\n",
+    "\n",
+    "You **must** display the columns `country` and `num_of_institutions`. The `num_of_institutions` of a country is defined as the number of institutions from that country. The rows **must** be in *descending* order of `num_of_institutions`. In case the `num_of_institutions` is tied, the rows must be in *alphabetical* order of `country`.\n",
+    "\n",
+    "**Hint:** You **must** use the `COUNT` SQL function to answer this question.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**country**|**num_of_institutions**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|United States|74|\n",
+    "|**1**|United Kingdom|45|\n",
+    "|**2**|Germany|23|\n",
+    "|**3**|Australia|21|\n",
+    "|**4**|Canada|14|\n",
+    "|**5**|China|14|\n",
+    "|**6**|France|14|\n",
+    "|**7**|Japan|14|\n",
+    "|**8**|Netherlands|13|\n",
+    "|**9**|Russia|13|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1991dc45",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_countries', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e878347",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ef62b90",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** Create a **bar plot** using the data from Question 4 with the `country` on the **x-axis** and the `num_of_institutions` on the **y-axis**.\n",
+    "\n",
+    "In addition to the top ten countries, you **must** also aggregate the data for **all** the **other** countries, and represent that number in the column `Other`. You are **allowed** do this using any combination of  SQL queries and pandas operations.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `num_institutions` containing the **country**, and the **num_of_institutions** data.\n",
+    "\n",
+    "**Hint**: You can use the `append` function of a DataFrame to add a single row to the end of your **DataFrame** from Question 4. You'll also need the keyword argument `ignore_index=True`. For example:\n",
+    "\n",
+    "```python\n",
+    "my_new_dataframe = my_dataframe.append({\"country\": \"CS220\", \"num_of_institutions\": 22}, ignore_index=True)\n",
+    "```\n",
+    "will create a *new* **DataFrame** `my_new_dataframe` which contains all the rows from `my_dataframe`, along with the **additional row** which has been appended. You can **ignore** any warnings about `append` being deprecated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0b3223c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'num_institutions', then display it\n",
+    "# do NOT plot just yet\n",
+    "\n",
+    "# TODO: use a SQL query similar to Question 4 to get the number of institutions of all countries\n",
+    "#       (not just the top 10), ordered by the number of institutions, and store in a DataFrame\n",
+    "# TODO: Use pandas to find the sum of the institutions in all countries except the top 10\n",
+    "# TODO: create a new dictionary with the data about the new row that needs to be added\n",
+    "# TODO: properly append this new dictionary to 'num_institutions' and update 'num_institutions'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c95611c9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51a82c7e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `num_institutions` as **bar plot** with the **x-axis** labelled *country* and the **y-axis** labelled *num_of_institutions*.\n",
+    "\n",
+    "You **must** use the `bar_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q5.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "b7e7e295",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q5.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cd92732",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the bar plot using the DataFrame 'num_institutions' with the x-axis labelled \"country\" \n",
+    "# and the y-axis labelled \"num_of_institutions\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6617e42c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** Create a **bar plot** of the **top** *10* countries with the **highest** *total* `overall_score` listed in the `year` *2019*.\n",
+    "\n",
+    "The `total_score` of a `country` is defined as the **sum** of `overall_score` of **all** institutions in that `country`. You **must** display the columns `country` and `total_score`. The rows **must** be in *descending* order of `total_score`.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `top_10_total_score` containing the **country**, and the **total_score** data.\n",
+    "\n",
+    "Your **DataFrame** should looks like this:\n",
+    "\n",
+    "||**country**|**total_score**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|United States|4298.4|\n",
+    "|**1**|United Kingdom|2539.2|\n",
+    "|**2**|Germany|1098.2|\n",
+    "|**3**|Australia|1093.8|\n",
+    "|**4**|Japan|752.9|\n",
+    "|**5**|China|743.4|\n",
+    "|**6**|Canada|705.3|\n",
+    "|**7**|Netherlands|674.9|\n",
+    "|**8**|South Korea|612.8|\n",
+    "|**9**|France|595.2|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7cf3887",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_total_score', then display it\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64d40c82",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e7b11bc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `top_10_total_score` as **bar plot** with the **x-axis** labelled *country* and the **y-axis** labelled *total_score*.\n",
+    "\n",
+    "You **must** use the `bar_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q6.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "033d0733",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q6.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2192b4e4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the bar plot using the DataFrame 'top_10_total_score' with the x-axis labelled \"country\" \n",
+    "# and the y-axis labelled \"total_score\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88cbb812",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** What are the **top** *10* institutions in the *United States* which had the **highest** *international_score* in the `year` *2020*?\n",
+    "\n",
+    "The *international_score* of an institution is defined as the **sum** of `international_faculty` and `international_students` scores of that institution. You **must** display the columns `institution_name` and `international_score`. The rows **must** be in *descending* order of `international_score`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**institution_name**|**international_score**|\n",
+    "|---------|------|---------|\n",
+    "|**0**|Massachusetts Institute Of Technology|194.1|\n",
+    "|**1**|California Institute Of Technology|186.7|\n",
+    "|**2**|Carnegie Mellon University|183.5|\n",
+    "|**3**|Rice University|180.4|\n",
+    "|**4**|Northeastern University|179.1|\n",
+    "|**5**|Stanford University|167.5|\n",
+    "|**6**|Cornell University|166.1|\n",
+    "|**7**|Purdue University|158.2|\n",
+    "|**8**|University Of Rochester|157.9|\n",
+    "|**9**|University Of Chicago|151.2|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af3589cd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_inter_score', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41ee5bff",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4794b1a5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** Create a **scatter plot** representing the `citations_per_faculty` (on the **x-axis**) against the `overall_score` (on the **y-axis**) of each institution in the `year` *2018*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `citations_overall` containing the **citations_per_faculty**, and the **overall_score** data from the `year` *2018*, of each **institution**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92a32a11",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'citations_overall', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9a2b1ba",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68165402",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `citations_overall` as **scatter plot** with the **x-axis** labelled *citations_per_faculty* and the **y-axis** labelled *overall_score*.\n",
+    "\n",
+    "You **must** use the `scatter_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q8.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "667b4025",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q8.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e0b8a7d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot using the DataFrame 'citations_overall' with the x-axis labelled \"citations_per_faculty\" \n",
+    "# and the y-axis labelled \"overall_score\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ba5ed8c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9:** Create a **scatter plot** representing the `academic_reputation` (on the **x-axis**) against the `employer_reputation` (on the **y-axis**) of each institution from the *United States* in the `year` *2019*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `reputations_usa` containing the **academic_reputation**, and the **employer_reputation** data from the `year` *2019*, of each **institution** in the `country` *United States*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b04f767f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'reputations_usa', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05490b0c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f8fcce5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `reputations_usa` as **scatter plot** with the **x-axis** labelled *academic_reputation* and the **y-axis** labelled *employer_reputation*.\n",
+    "\n",
+    "You **must** use the `scatter_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q9.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0295c09c",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q9.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29894cd8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot using the DataFrame 'reputations_usa' with the x-axis labelled \"academic_reputation\" \n",
+    "# and the y-axis labelled \"employer_reputation\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e739c41",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** Create a **scatter plot** representing the `international_students` (on the **x-axis**) against the `faculty_student_score` (on the **y-axis**) for the **top ranked** institution of **each** `country` in the `year` *2020*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `top_ranked_inter_faculty` containing the **international_students**, and the **faculty_student_score** data from the `year` *2020*, of the **top** ranked **institution** (i.e., the institution with the **least** `rank`) of each **country**.\n",
+    "\n",
+    "**Hint:** You can use the `MIN` SQL function to return the least value of a selected column. However, there are a few things to keep in mind while using this function.\n",
+    "* The function must be in **uppercase** (i.e., you must use `MIN`, and **not** `min`).\n",
+    "* The column you are finding the minimum of must be inside backticks (``` ` ```). For example, if you want to find the minimum `rank`, you need to say ```MIN(`rank`)```.\n",
+    "\n",
+    "If you do not follow the syntax above, your code will likely fail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa9e1b6f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'top_ranked_inter_faculty', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4831be1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59b40839",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `top_ranked_inter_faculty` as **scatter plot** with the **x-axis** labelled *international_students* and the **y-axis** labelled *faculty_student_score*.\n",
+    "\n",
+    "You **must** use the `scatter_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q10.png": {
+     "image/png": 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hoaEICwvDwYMHTdodOnQIQgjce++9No3V3Wk0GmRnZ3OneyIicguSE6Rr164hMDCwyXp7zPTq2bMnAGDDhg0G5ZWVlfjss8/g5+eHO++8EwCQmpqKM2fOYMeOHQZtly1bhjZt2mDcuHE2j9cdabVaqFQqxMfHIzk5GXFxcVCpVNDpdI4OzW0w+SQisj/Js9iio6Pxww8/NFm/d+9efQJjKzNnzsSmTZuQnp6O48ePY/DgwdDpdFi3bh3OnTuHpUuXwtvbGwAwe/ZsbNu2DRMmTMAPP/ygX0l7z549mDNnDqKiomwaq7tKTU01GYyvVquRkpKCnJwcB0XlHrRaLVJTU5Gbm6svUyqVyMzM5JYnLkqj0aCgoAAxMTFOu0K7K8RIZBdCokWLFglvb2/x5ZdfikuXLgmZTCa++uorIYQQb775pvDw8BArV66UevgWKyoqEk899ZSIj48XPj4+wt/fXwwePFh88sknJm1LSkrE5MmTRXBwsPDy8hJ33nmnWL16tdnvWV5eLgCI8vJya/wILuvUqVMCQJMvjUbj6BBdmlKpFHK53OCcyuVyoVQqHR0ameny5ctCqVQafJZKpVJotVpHh6bnCjESWcqc67fkBKmmpkYMGzZMeHh4iPj4eOHh4SHuuusu0alTJ+Hh4SGUSqWor6+XeninxgTppqysrGYTpKysLEeH6LKYfLoXV0h2XSFGIkuZc/2WPAbJ09MT//73v/HWW2/B398f3t7eKCgoQGhoKJYsWYI9e/bAw8OldzKh24iOjm62PiYmxk6RuJ+CgoJm6/Pz8+0UCVlKo9EgNzcX9fX1BuX19fXIzc11irFlrhAjkb1JGoNUW1uLQ4cOoXPnzpg1axZmzZpl7bjIBcTFxUGpVEKtVhv8xyqXy5GUlMTxCxZg8uk+WpLsOvpvxRViJLI3SY945HI5Ro4cqV+pmlqvzMxMJCUlGZQlJSUhMzPTQRG5h4bkUy6XG5TL5XIolUperFyIKyS7rhAjkb1JTpBCQ0NNFoek1kehUCAnJwcajQZZWVnQaDTIycnhLCsrYPLpHlwh2XWFGInsTSYkZjmzZs3CkSNH8O233za6UKQ7q6ioQGBgIMrLy7ntCNlcXl4e8vPzOe3ahel0OqSkpDj1kg2uECORpcy5fktOkH755ReMHz8eHTt2xN///nfExMQ0up9ZRESElMM7NSZIRCSFKyS7rhAjkVR2SZA8PDwgk8kghGj2CZLxrAh3wASJiIjI9Zhz/Za8kvbcuXNbXdeaO+KquURERKYkP0FqzdzhCRK3sSAiotbGnOs3V3JspZrbQ42IiKi1k9zF1uCTTz7Bjh07cPr0aQA319N49NFHMXbsWIuDI9toWDXX2K2r5rK7jYiIWjPJCVJtbS1Gjx6NnJwcCCEQGBgIADh27Bi2bduGjRs3YteuXWjbtq3VgiXr4Kq5REREzZPcxfbmm28iOzsbU6dOxblz56DT6aDT6VBUVIRp06YhOzsbb775pjVjJSvhqrlERETNkzxIu0ePHujRowd27drVaP3DDz+MU6dO4eTJk5bE55TcYZC2SqVqcg+1nJwcB0ZGRERkG3YZpF1YWAilUtlkvVKpRGFhodTDk41xGwsiIqKmSR6D1K5dOxQXFzdZX1xcjHbt2kk9PNlYwx5qXDWXiIjIlOQnSEOGDMGqVavw008/mdT997//xb/+9S8MHTrUouDI9mJjYzFq1CgmR0RERLeQPAbpxIkT6N+/P2pqavDAAw+gZ8+e+vKsrCx4e3vj0KFD+nJ34g5jkIiIiFobu+zFBtyc0v/ss8/iu+++MygfNGgQ3nnnHfTp00fqoZ0aEyQiInJn7roNlV32YgOAvn374sCBA7h06RJOnz4NIQSio6PRsWNHSw5LRERmctcLGtkXt6H6H+7FJgGfIBGRs+AFjazJ3ZeAscs0/61bt2LixIlN1k+aNAnbtm2TengiImoB7qtI1tKwDdWtyRFguA1VayI5QVq1ahU8PJr+drlcjpUrV0o9PBER3QYvaGRNLdmGqjWRnCD9+uuvzQ7C7tOnD3755RephyciotvgBY2sidtQGZKcIF29ehVyubzJeplMhsrKSqmHJyKi2+AFjawpLi4OSqXS5Noul8uhVCpb3eB/yQlSZGSkyfT+Wx04cAARERFSD09ERLfBCxpZG7eh+h/JCdLo0aOxdetWrF+/3qRu3bp1+OyzzzBmzBiLgiMioubxgkbW1LANlUajQVZWFjQaDXJyclrljEjJ0/wrKyuRkJCAX3/9FT169ECvXr0AAD/99BNOnTqF+Ph4HDp0yC2nwXOaPxE5G+6rSHR7dltJu7y8HOnp6di6dSt0Oh2Am9lnSkoKXnvtNQQFBUk9tFNjgkREROR67JYgNRBC4NKlSxBCIDg4GDKZzNJDOjUmSERERK7HbluNNJDJZAgODrbGoYiIiIgcTvIg7f/85z9YtWqVQdnWrVsRGRmJwMBA/P3vfwd3MSEiIiJXJDlBysjIwN69e/VfFxYWYvLkyaitrUVsbCxWrVqFtWvXWiVIIiIiInuSnCAdP34cCQkJ+q8/+eQTyGQyHDt2DEePHsWf/vQnrFu3zipBEhEREdmT5ARJp9MhJCRE//WXX36JoUOHolOnTgCA5ORkLnNPRERELklygqRQKFBaWgoAqKmpwaFDhzBkyBCDNteuXbMsOrI6jUaD7OxsbmJJRETUDMkJUp8+fbBu3TocO3YMCxcuRE1NDZRKpb7+zJkz+qdJ5HharRYqlQrx8fFITk5GXFwcVCqVfv0qIiIie3L2G3bJCdKrr76K3377Dffeey9ef/11KJVK9O3bV1+/Z88e9O/f3ypBkuVSU1OhVqsNytRqNVJSUhwUERERtUaucsNu0UKRGo0Gubm5CAoKwl/+8he0bdsWAHD58mW89tprGD16tEm3mztwtYUiNRoN4uPjm63n1gRERGQPKpUKarUa9fX1+jK5XI6kpCTk5OTY9L3tvpJ2S4OaOXMmXnzxRfTo0cMeb2kzrpYgZWdnIzk5ucn6rKwsjBo1yo4RERFRa+ToG3Zzrt+Su9jMVV1djY0bN6KkpMReb0n/v+jo6GbrY2Ji7BQJERG1ZgUFBc3WO9Psd7slSAC4sraDxMXFQalUQi6XG5TL5XIolUp2rxERkV240g27XRMkcpzMzEwkJSUZlCUlJSEzM9NBERERUWvjSjfsVtmslpyfQqFATk4O8vLykJ+fj5iYGKf6RSQiotYhMzMTKSkpyM3N1Zc54w07E6RWJjY2lokRERE5jKvcsDNBIiIiIrtz9ht2jkEiIiIiMsIEiYiIiMiI3brYPD09MXToUCgUCnu9JRERmUmj0aCgoMBpx4UQ2YvkJ0hyuRwff/xxk/Vbt241mManUCiwb98+9OnTR+pbEhGRjbjK/lhE9iI5Qbrdoo9cFJKIyHVwQ2siQxaNQZLJZE3WnTp1CoGBgZYcnoiI7KBh4/FbNw8FgPr6euTm5iIvL89BkRE5jlljkDZu3IiNGzfqv37ttdewdu1ak3Y6nQ7Hjx/HQw89ZHmErQz7/4nI3lqyPxb/P6LWxqwEqaysDGfOnAFw8+nR77//jqqqKoM2MpkM/v7+mDJlCl577TXrRermtFotUlNTDVYWVSqVyMzM5MB2IrIpV9ofi8heZELiYCEPDw9s2bIFqamp1o7J6VVUVCAwMBDl5eUICAiwyjFVKhXUarXBI265XI6kpCTk5ORY5T2IiJrC/4OoNTDn+i15DNKZM2fwyCOPSP12ugX7/4nI0bihNZEhyesgdevWzZpxtGrs/yciR3OV/bGI7MWiWWwHDx7E+PHj0a9fP0RHRyMqKsrgdbt+bWspLy9Heno64uPj4e3tjfbt22PgwIHYuXOnQbsLFy4gLS0NnTp1gre3N3r16tXoIHN7Y/8/ETmL2NhYjBo1iskRtXqSnyBt2rQJU6ZMQdu2bREXF4eIiAhrxtViRUVFGD58OLRaLaZMmYKePXuiqqoKJ0+exLlz5/TtysrKMHjwYBQXF2PmzJmIjIzE7t27MW3aNJSUlGDevHkOiR8A4uLioFQqm+z/539URERE9iV5kHZ8fDzkcjnUajW6dOli7bhabNiwYTh16hQOHz6M8PDwJtulp6fjjTfewPbt2zFmzBh9+UMPPYScnBycOnUKkZGRLXpPWwzS1ul0SElJ4Sw2IiIiG7HLIO2zZ89i+vTpDk2O9u/fj2+++QYvvfQSwsPDUVdXh6tXrzba9qOPPkJkZKRBcgQAs2bNwvXr17F161Z7hNykhv5/jUaDrKwsaDQa5OTkMDkiIiJyAMkJUteuXVFbW2vNWMyWlZUFAIiKisKYMWPg4+MDf39/dO/eHatWrdK3Ky0tRVFRERISEkyOkZCQAJlMhsOHD9st7uaw/5+IiMjxJCdI06ZNw0cffWQyNd2eTp48CQCYOnUqiouLsW7dOmzatAmdO3fGM888g4ULFwIAiouLAQBhYWEmx/Dy8kLHjh1x/vz5Jt+npqYGFRUVBi9yHI1Gg+zsbC5/QERENiN5kHa/fv2wc+dO9OvXD08//TQiIyMhl8tN2g0ZMsSiAJtTWVkJAPDz88O3334LLy8vAMC4cePQs2dPLF68GDNmzNCv9t1Qb8zb29tkRfBbLV68GBkZGVaOnszF1caJiMheJCdII0eO1P976tSpJhvXCiEgk8ls+oTJx8cHwM1dqG9Nfjw9PTF+/HgsWLAA33//PYKDgwHcfBLUmOrqaoSGhjb5Punp6Zg1a5b+64qKimYHhJNtNLfbOFf6JSIia5KcIK1fv96acUjS0GXWuXNnk7qGMq1Wi969ewNAo91o165dw+XLl5GYmNjk+3h5eTX59Inso2G1cWO3rjbOcVtERGQtkhOkSZMmWTMOSQYMGID33nsPRUVFJnUNayB16tQJoaGhCAsLw8GDB03aHTp0CEII3HvvvTaPl6TjauNERGRPFq2k7WgPP/wwAgICsGnTJpSXl+vLKysrsXHjRigUCv3MtdTUVJw5cwY7duwwOMayZcvQpk0bjBs3zq6xk3m42jgREdmTRQlSUVER0tLSEBYWBk9PT3z11VcAgN9//x1paWk4cuSIVYJsSlBQEJYvX47S0lL069cPb731FpYuXYp+/frht99+w4oVK+Dr6wsAmD17NqKiojBhwgS88sor+OCDD/Dggw/iiy++QHp6OqKiomwaq620lhldDauNG08EkMvlUCqVfHpERETWJSQ6ffq0CAkJEQEBAeL+++8XHh4eYu/evfr6P/zhD2LatGlSD2+Wzz//XAwaNEj4+fkJX19fkZiYKLKyskzalZSUiMmTJ4vg4GDh5eUl7rzzTrF69Wqz36+8vFwAEOXl5dYIX5LLly8LpVIpAOhfSqVSaLVah8Vka1qtttX9zEREZD3mXL8lbzWSmpqKr7/+Gt9//z18fHwQEhICtVqNESNGALj5xOaLL77AiRMnrJLIORNbbDViLpVK1eTebe4+o4u7jRMRkRTmXL8lD9JWq9V45plnEB4ejsuXL5vUd+vWrdnFF0m61j6jKzY21q1/PiIicjzJY5AqKioanV7foLa2FnV1dVIPT81oyYwuIiIikk5yghQeHt5s99nBgwc5s8hGOKOLiIjItiQnSGPGjMGHH36In3/+WV/WsJr21q1bsW3bNowdO9byCMkEZ3QRERHZluRB2hUVFUhISEBhYSEGDRqEvXv3Yvjw4SgvL8exY8fQu3dv/Oc//4G3t7e1Y3Y4ZxikrdPpkJKSwn3JiIiIWsic67fkBKnhjV599VVkZmbqB2oHBQVh/PjxWLRokcOSB1tzhgSpAWd0ERERtYzdEqRb/f777xBCIDg42GTjWnfjTAkSERERtYxdpvkbCw4OttahiIiIiByqxQnSt99+K+kNhgwZIun7iIiIiBylxQnSsGHDJHWd3brSMxEREZEraHGCtH79epOylStX4uTJkxg/fjx69uwJADhx4gQ+/vhj3HHHHZgxY4b1IiUiIiKykxYnSJMmTTL4etWqVfjtt9/w66+/Ijw83KDulVdeQUJCAq5cuWKdKImIiIjsSPJCkStXrsQTTzxhkhwBN/dhmz59OlauXGlRcERERESOIDlBOnv2LIKCgpqsDwoKwtmzZ6UenoiIiMhhJCdIYWFh+OyzzxodhF1XV4etW7eia9euFgVHRERE5AiSE6QnnngC//nPfzBy5Eh88cUX+hWdP//8c4wcORKHDh3Ck08+ac1YiYiIiOxC8kKR//jHP1BSUoJ33nkH+/fvN6mfOXMmnn/+eYuCIyIiInIEi7ca0Wg0+Pzzz1FQUAAAiI6OxkMPPYS4uDirBOiMuNUIERGR63HIXmytCRMkIiIi12PO9VvyGCQiIiIidyV5DNKIESNu20Ymk2Hv3r1S34KIiIjIISQnSKdPnzbZm62urg6//fYbbty4gY4dO8LPz8/iAImIiIjsTXKCVFhY2Gh5TU0Nli1bhvXr1+Obb76RengiIiIih7H6GCQvLy+kp6ejf//+mDVrlrUPT0RERGRzNhukPXjwYOTm5trq8ERE5KI0Gg2ys7ORl5fn6FCImmSzBOnMmTOora211eGJiMjFaLVaqFQqxMfHIzk5GXFxcVCpVNDpdI4OjciE5DFI586da7Rcq9VCrVbjnXfewbBhw6QenoiI3ExqairUarVBmVqtRkpKCnJychwUFVHjJC8U6eHhYTKLrYEQAj169MDnn3+OmJgYiwJ0RlwokojIPBqNBvHx8c3Wx8bG2jEiao3MuX5LfoI0d+5ckwRJJpOhffv2iIuLQ1JSEjw8uA4lERFBvx1VU/Lz85kgkVORnCDNnz/fimEQEZE7i46ObrbeHXsbyLVJfsSTlpaG77//vsn6w4cPIy0tTerhiYjIjcTFxUGpVEIulxuUy+VyKJVKPj0ipyM5QdqwYUOzj0zPnDmDjRs3Sj08ERG5mczMTCQlJRmUJSUlITMz00ERETVNchfb7VRUVMDT09NWhyciIhejUCiQk5ODvLw85OfnIyYmhk+OyGmZlSD997//xY8//qj/ev/+/airqzNpp9Pp8O6776JHjx4WB0hERO4lNjaWiRE5PbMSpJ07dyIjIwPAzRlra9aswZo1axpt6+/vz8emRERE5JLMSpAmT56MYcOGQQiBESNG4JVXXjHpT5bJZPD390fPnj3h7e1t1WCJiIiI7MGsBKlbt27o1q0bAGDevHl49NFHcdddd9kkMCIiIiJHkbySdlPq6upQWVkJhUJhzcM6Fa6kTUREjqTRaFBQUMCB7mYy5/oteZr/559/jvT0dIOyZcuWwd/fHx07dsTDDz+MmpoaqYcnIiIiI9zw134kJ0hLly412LD2xIkTePHFF9GjRw+MHj0aX3zxBd555x2rBNmaaDQaZGdnIy8vz9GhEBGRk2luw1+yLskJ0smTJ9G3b1/9159++in8/Pxw4MABbNu2DX/961+xZcsWqwTZGvCugIiImqPRaJCbm4v6+nqD8vr6euTm5vLG2sokJ0jl5eXo0KGD/mu1Wo2RI0fC398fAJCYmIizZ89aHmErwbsCIiJqTks2/CXrkZwghYSE6BOgiooKHD16FIMHD9bX19TUmGS51DjeFRAR0e1ww1/7kpwgJSQk4L333sP27dvx3HPPoa6uDsnJyfr6goICdOnSxSpBujveFRAR0e1ww1/7kpwgzZ8/Hzdu3MBjjz2G9evXY8qUKfqtRYQQ2LlzJwYNGmS1QN0Z7wqIiKgluOGv/Vi0DpJWq8V//vMfBAUFITExUV+u0+mwadMmDBs2DH/84x+tEqgzscU6SCqVCmq12qCbTS6XIykpCTk5OVZ5DyIicg/c8Fcac67fVl8osik6nQ6PPvoo/vnPf6JPnz72eEubsUWCpNPpkJKSgtzcXH2ZUqlEZmamWy+6SUREZC/mXL/N2mrEErW1tfj66685bb0JCoUCOTk5Lb4r4CqqREREtmO3BIlaJjY2ttmER6vVIjU1tcknTUycqDXi7z0RWRsTJBfT1HpJjz76KDw9PdlFR63K7W4YiIikkjyLjeyvufWS9u3bx4UmqdXhAqtEZCtMkFzI7dZL4kKT1JpwgVUisiUmSC7kduslNYULTZI74gKrRGRLTJBcSFOrqHp4NP8xcqFJ16XRaJCdnc2nIY3gAqtEZEtMkFxMY6uo3nfffRgxYgSXn3cjWq0WKpUK8fHxSE5ORlxcHFQqFZfJuAW3XSAiW7JbguTj44NJkyZxfzYLNayXpNFokJWVBY1Gg5ycHGzbto3Lz7sRDj5uGW67QES2Inkl7cTERDz++ON47LHH4OPjY+24nJotVtK2Fi4/7/o0Gg3i4+Obredna4i/90TUEuZcvyU/QTp58iSmTJmCzp0746mnnsKxY8ekHoqsKDY2FqNGjeJFwoVx8LH5+HtPRNYmOUEqKSnB1q1bkZCQgPfffx/33nsv+vTpg3fffRfl5eXWjLHFqqqqEBUVBZlMhunTp5vUX7hwAWlpaejUqRO8vb3Rq1cvrF271gGREjWNg4+JiBxPcoLUtm1b/PnPf0Z2djbOnj2L+fPno6KiAjNmzECXLl0wceJEfPPNN9aM9bbmzp2L33//vdG6srIyDB48GJ988gn+9re/YeXKlYiIiMC0adOQkZFh1ziJmsPBx0RETkBY2d69e8XDDz8sPDw8hIeHh4iNjRX//Oc/RWVlpbXfysCxY8eEXC4XS5cuFQDEE088YVA/e/ZsAUBs377doPzBBx8Ubdu2FadPn27xe5WXlwsAory83CqxExnTarVCqVQKAPqXUqkUWq3W0aEREbksc67fVp3FdvToUXz22WfYt28fhBCIi4uDl5cXXnjhBcTGxuK7776z5tvp1dfX4/HHH4dSqcSjjz7aaJuPPvoIkZGRGDNmjEH5rFmzcP36dWzdutUmsRFJ0dRsRe4vRkRkHxYnSDqdDitXrkTv3r3Rv39/bNiwAQ888AC+/vpr/Prrrzh+/DjUajV8fX3x9NNPWyNmEytWrMAvv/yCVatWNVpfWlqKoqIiJCQkmNQlJCRAJpPh8OHDNomNyBIcfExE5BhtpH7j3r17sW7dOuzatQvXrl1Djx49sHTpUkyaNAnt27c3aDtixAjMnj3bJgnS2bNnMW/ePMyZMweRkZEoLCw0aVNcXAwACAsLM6nz8vJCx44dcf78+Sbfo6amBjU1NfqvKyoqLA+ciIiInJbkBOm+++6Dl5cXHn30UUybNg1Dhgxptn1MTAwGDRok9e2a9OSTT6Jbt2544YUXmmxTVVUF4GYy1Bhvb299m8YsXryYA7mJiIhaEckJ0rJlyzBp0qQWj4kYPnw4hg8fLvXtGvXxxx8jOzsb33zzDdq2bdtkO19fXwAweAp0q+rqaoSGhjb5/enp6Zg1a5b+64qKCoSHh0uMmoiIiJyd5DFIFRUV+q6rxpw4cQILFiyQevjbqq2txXPPPYcHHngAERERKCwsRGFhob6rrLKyEoWFhSgvL0fXrl0BoNFutGvXruHy5cuNdr818PLyQkBAgMGLiIiI3JfkBCkjIwP//e9/m6z/+eefbdotVVVVhYsXL2LPnj2IjIzUvxITEwHcfLoUGRmJ1atXIzQ0FGFhYTh48KDJcQ4dOgQhBO69916bxUpERESuRXIXm7jNFm5VVVVo00by4W/Lz88PO3fuNCm/ePEinnjiCSiVSkyfPh133nkngJubfy5ZsgQ7duwwmOq/bNkytGnTBuPGjbNZrERERORazMpgzp07ZzBL7OTJk/j2229N2ul0OqxZswZRUVEWB9iUtm3b4pFHHjEpb4ive/fuBvWzZ8/Gtm3bMGHCBPzwww+IjIzE7t27sWfPHsyZM8emsRIREVHLaDQaFBQUOHzzabMSpPXr1yMjIwMymQwymQyLFi3CokWLTNoJIeDh4YEPPvjAaoFaSqFQ4MCBA3j55Zexdu1aVFRUICYmBqtXr2503zYiIiKyH61Wi9TUVOTm5urLlEolMjMzHbJIrkzcrq/sFj/99BN+/PFHCCGQlpaGadOmmSy+KJPJ4O/vj3vuuQcRERFWD9gZVFRUIDAwEOXl5RywTUREZAUqlQpqtRr19fX6MrlcjqSkJOTk5FjlPcy5fpuVIN1qypQpmD59Ovr37y8pSFfGBImIiMh6NBoN4uPjm623RnebOddvyaOo169fL/VbiYiIiPQKCgqarc/Pz7f7eKQWJ0jnzp2T9Abu2s1GRERE1hEdHd1sfUxMjJ0i+Z8WJ0jdu3eHTCYz+w1u7UskIiIi12KPWWVxcXFQKpVNjkFyxGy2FidIc+fOlZQgERERkeux96yyzMxMpKSkGLxfUlISMjMzrf5eLSF5kHZrxkHaRETk7uwxq6wxeXl5yM/Pt8kTK7vMYmvNmCAREZE7s9esMnuzyyy2lg7a5iBtIiIi1+KMs8rsTXKC1NJB2xykTURE5FqccVaZvUlOkBobtF1XV4eCggLs3r0bf/jDHzBq1CiLAyQiIiL7csZZZfYmOUGaP39+k3WnT59GQkIC7rnnHqmHJyIiIgdytlll9mazQdpz585FVlYWjh49aovDOxQHaRMRUWthy1ll9maXQdq307VrV/zyyy+2OjwRERHZQWxsrMsnRlJ42OrAu3btsslCUkRERES2JvkJ0oIFCxot12q1+Oqrr/Dzzz/jxRdflBwYERERkaNIHoPk4dH0w6fQ0FDMmDEDL730EuRyueTgnBXHIJGrsMceSkRErsIuY5DOnDljUiaTydC+fXv4+/tLPSwRWYG991AiInI33GpEAj5BImfnqD2UiIicmTnXb8mDtM+cOYMvvviiyfovvvgChYWFUg9PRBJpNBrk5uaarGJfX1+P3Nxc5OXlOSgyIiLXITlBeuWVV7BkyZIm6//5z39izpw5Ug9PRBK1ZA8lIiJqnuQE6cCBA1AqlU3W33///di/f7/UwxORRNxDiYjIcpITpIsXLyI0NLTJ+pCQEFy4cEHq4YlIooY9lIxnkMrlciiVSs5mIyJqAckJUlBQULOP8vPz89GuXTuphyciC2RmZiIpKcmgrDXtoUREZCnJs9gee+wxfP311zhx4gRCQkIM6kpLS3HXXXdhyJAh2LFjh1UCdSacxUauwp32UCIispQ512/JCdKPP/6IAQMGQKFQYNasWejVq5e+fPny5SgrK8OBAwdwzz33SDm8U2OCRERE5HrsslBk7969sW3bNkyZMgUvvfQSZDIZAEAIgY4dO+Kzzz5zy+SIiIiI3J/kBAkAHnjgAZw7d06/tooQAvHx8bj//vvh4+NjrRiJiIiI7MqiBAkAfHx88Mgjj1ghFCIiIiLnIHkWGxEREZG7sugJUkFBAZYvX47vv/8eOp0ON27cMKiXyWS3XdWXLMPd2omIiKxP8hOk48ePo2/fvvjggw9QW1uL06dPw8/PD9euXUNhYSHkcjkiIiKsGSvdQqvVQqVSIT4+HsnJyYiLi4NKpYJOp3N0aER6Go0G2dnZ3P+NiFyO5ARp7ty58PT0xE8//YS9e/cCAN5++22UlJRgzZo1KCsrw7/+9S+rBUqGUlNToVarDcrUajVSUlIcFBHR/zCBJyJXZ9FebNOmTUN8fLzBFH8AePzxxzFq1CjMnj3bOlESgP/djX/55ZfcrZ2cGhN4InJ1khOkyspK/aaYnp6eAICrV6/q6wcNGoQDBw5YGB4BpnfjzW0SDHC3dnIsjUbDBJ6IXJ7kBKlTp074/fffAQDt2rWDn58fNBqNvl6n05n8B0nSNHY33hzu1k6OdLuJGUzgicgVWLSS9tGjR/VfDx06FG+//Tb69euHGzduYNWqVfjjH/9olSBbs4a78ZaQy+VISkribDZyqIYny01hAk9ErkDyE6Tx48fj4sWLqK6uBgAsWLAAZWVlGD58OEaOHImysjK8/vrrVgu0tTJnmQTu1k7OIC4uDkqlEnK53KBcLpdDqVQygScil9DiJ0hjxozBc889h8TERABAaGgoPvvsM/2WIn379sUvv/yCnTt3Qi6XY9SoUYiKirJN1K3I7e7Gv/zyS9TV1XEdJHIqmZmZSElJMXj6yQSeyPlxbb3/kYmGqWe34eHhgS1btiA1NRXAzbvBzZs3679uTczZDdgaVCoV1Gq1wZiuhu60nJwcm78/kVR5eXnIz8/nf7ZETk6r1SI1NdXgpkapVCIzMxMKhcKBkVmXOdfvFnexhYSEoLS0VP91C/MqspBGo0FaWhoSEhIMynk3Tq4gNjYWo0aNYnJE5OS4NIepFnexDRo0CIsWLcL58+cRFBQEANixY0ezM1JkMhnmzJljcZCtUWPZfGJiImbMmIE+ffrwgkNERFbR1GSgW5fmaI3XnBZ3sZ0/fx4TJ07EN998AyEEZDLZbZ8iyWQyt5zqb48uttt1q7GfmIiIrCE7OxvJyclN1mdlZWHUqFF2jMh2zLl+t/gJUlhYGL766ivU1taitLQU3bt3x4oVK/Dwww9bHDAZul02n5iYaLAIpzv2ExMRkX14eDQ/2qa1Ls1h9jpInp6eiIiIwKRJk9C/f39069bNFnG1areb2v/dd98ZfN3QT8wB20RE1FKNDeW4VWtfW6/FXWz0P7buYtNoNIiPj5f0fa31F5mIiMzT2FCOW0ntnXDmISA2mcVG9tPUQnu3ewzKLRyIiKglmtozscGXX36JnJwcs5Ij431D4+LioFKpoNPprBW2XTFBclKZmZlISkoyKBs4cGCz39Na+4mJiMg8txvKUVdXZ/Yx3W2pACZITkqhUOhnq2VlZUGj0WD//v3cwoGIiCxm7T0Tm3oidetSAa6GCZKTM15or7EnS1w0koiIzGHtPRNv90TKFYeAmD2LjRyr4ckSt3AgIiJLWHPPRGs/kXIGnMUmgb33YiMiIrIVa91wu8K+oZzFRkRERC1irT0T3W0ICLvYiIiIyGLuNgSECRIRERFZTWxsrEsnRg3YxUZERERkhAkSERERkREmSERERERGmCARERERGXHpBEmj0WDu3LkYMGAAgoOD0a5dO/Tu3RuLFi3C1atXTdpfuHABaWlp6NSpE7y9vdGrVy+sXbvWAZETERGRM3PpWWwffvghVq1ahQcffBCpqanw9PTEvn378Oqrr+LTTz/FoUOH4OPjAwAoKyvD4MGDUVxcjJkzZyIyMhK7d+/GtGnTUFJSgnnz5jn4pyEiIiJn4dIraR89ehQxMTEICgoyKH/11VexaNEirFq1Ck8//TQAID09HW+88Qa2b9+OMWPG6Ns+9NBDyMnJwalTpxAZGdmi9+VK2kRERK6n1aykfc8995gkRwAwduxYAMDx48f1ZR999BEiIyMNkiMAmDVrFq5fv46tW7faNFYiIiJyHS6dIDWluLgYABASEgIAKC0tRVFRERISEkzaJiQkQCaT4fDhw3aNkYiIiJyXS49Bakx9fT0WLFiANm3aYPz48QD+lzCFhYWZtPfy8kLHjh1x/vz5Jo9ZU1ODmpoa/dcVFRVWjpqIiIicids9QXr22Wdx6NAhzJ8/H/Hx8QCAqqoqADeTocZ4e3vr2zRm8eLFCAwM1L/Cw8OtHzgRERE5DbdKkF599VW8++67mDp1Kl5++WV9ua+vLwAYPAW6VXV1tb5NY9LT01FeXq5/FRUVWTdwIiIicipu08U2f/58LFq0CBMnTsSaNWsgk8n0dV27dgWARrvRrl27hsuXLyMxMbHJY3t5eTX59ImIiIjcj1s8QcrIyEBGRgb++te/Yv369fDwMPyxQkNDERYWhoMHD5p876FDhyCEwL333muvcImIiMjJuXyCtGDBAsyfPx/jx4/Hhg0bTJKjBqmpqThz5gx27NhhUL5s2TK0adMG48aNs0e4RERE5AJcuovtX//6F+bNm4eIiAjcd999yMzMNKjv1KkT7rvvPgDA7NmzsW3bNkyYMAE//PCDfiXtPXv2YM6cOYiKinLEj0BEREROyKUTpCNHjgAAzp07h8mTJ5vUDx06VJ8gKRQKHDhwAC+//DLWrl2LiooKxMTEYPXq1Zg+fbo9wyYiIiIn59JbjTgKtxohIiJyPa1mqxEiIiIiW2CCRERERGSECRIRERGREZcepE1EN2k0GhQUFCAmJgaxsbGODoeIyOXxCRKRC9NqtVCpVIiPj0dycjLi4uKgUqmg0+kcHRoRkUtjgkTkwlJTU6FWqw3K1Go1UlJSHBQREZF7YIJE5KI0Gg1yc3NRX19vUF5fX4/c3Fzk5eU5KDIiItfHBInIRRUUFDRbn5+fb6dIiIjcDxMkIhcVHR3dbH1MTIydIiEicj9MkIhcVFxcHJRKJeRyuUG5XC6HUqnkbDYiIgswQSJyYZmZmUhKSjIoS0pKMtm4mYiIzMN1kIhcmEKhQE5ODvLy8pCfn891kIiIrIQJEpEbiI2NZWJERGRF7GIjIiIiMsIEiYiIiMgIEyQiIiIiI0yQiIiIiIxwkDYRNUuj0aCgoIAz5IioVeETJCJqlFarhUqlQnx8PJKTkxEXFweVSgWdTufo0IiIbI4JEhE1KjU1FWq12qBMrVYjJSXFQREREdkPEyQiMqHRaJCbm4v6+nqD8vr6euTm5iIvL89BkRER2QcTJCIyUVBQ0Gx9fn6+nSIhInIMJkhEZCI6OrrZ+piYGDtFQkTkGEyQiMhEXFwclEol5HK5QblcLodSqeRsNiJye0yQiKhRmZmZSEpKMihLSkpCZmamgyIiIrIfroNERI1SKBTIyclBXl4e8vPzuQ4SEbUqTJCIqFmxsbFMjIio1WEXGxEREZERJkhERERERpggERERERlhgkRERERkhAkSERERkREmSERERERGmCARERERGWGCRERERGSECRIRERGRESZIREREREa41QgRkQ1oNBoUFBRwDzsiF8UnSEREVqTVaqFSqRAfH4/k5GTExcVBpVJBp9M5OjQiMgMTJCIiK0pNTYVarTYoU6vVSElJcVBERCQFEyQiIivRaDTIzc1FfX29QXl9fT1yc3ORl5fnoMiIyFxMkIiIrKSgoKDZ+vz8fDtFQkSWYoJERGQl0dHRzdbHxMTYKRIishQTJCIiK4mLi4NSqYRcLjcol8vlUCqVnM1G5EKYIBERWVFmZiaSkpIMypKSkpCZmemgiIhICq6DRERkRQqFAjk5OcjLy0N+fj7XQSJyUUyQiIhsIDY2lokRkQtjFxsRERGRESZIREREREaYIBEREREZYYJEREREZIQJEhEREZERJkhERERERpggERERERlhgkRERERkhAkSERERkRGupC2BEAIAUFFR4eBIiIiIqKUartsN1/HmMEGSoLKyEgAQHh7u4EiIiIjIXJWVlQgMDGy2jUy0JI0iAzdu3EBJSQnatWsHmUxm9vdXVFQgPDwcRUVFCAgIsEGE1IDn2r54vu2L59u+eL7tx1bnWgiByspKdOnSBR4ezY8y4hMkCTw8PBAWFmbxcQICAvhHZic81/bF821fPN/2xfNtP7Y417d7ctSAg7SJiIiIjDBBIiIiIjLCBMkBvLy8MG/ePHh5eTk6FLfHc21fPN/2xfNtXzzf9uMM55qDtImIiIiM8AkSERERkREmSERERERGmCARERERGWGCRERERGSECZIdZWZm4u6774aPjw86duyIlJQUnD171tFhuSyNRoO5c+diwIABCA4ORrt27dC7d28sWrQIV69eNWl/4cIFpKWloVOnTvD29kavXr2wdu1aB0TuPqqqqhAVFQWZTIbp06eb1POcW668vBzp6emIj4+Ht7c32rdvj4EDB2Lnzp0G7XiuLXPlyhUsXLgQd911F/z9/REcHIzBgwdjy5YtJm15rltu8eLFeOyxx/T/T3Tv3r3Z9uaeW5teVwXZxcqVKwUAMWjQILF69WqxcOFC0aFDB9GlSxdRXFzs6PBc0ksvvST8/PzEX/7yF/H222+L1atXi7FjxwoAolevXqKqqkrfVqfTiZiYGOHj4yPS09PF+++/L/70pz8JAGL+/PkO/Clc2/PPPy/8/f0FAPHEE08Y1PGcW+7cuXMiOjpaKBQKMWvWLPHBBx+Id955Rzz11FNixYoV+nY815apr68XAwcOFB4eHiItLU2sWbNGLFu2TPTp00cAEHPmzNG35bk2DwDRvn17kZSUJBQKhejWrVuTbc09t7a+rjJBsoNLly4Jf39/0bdvX3H9+nV9+ZEjR4RMJhN/+9vfHBid6zpy5IjQ6XQm5a+88ooAIFatWqUvmz17tgAgtm/fbtD2wQcfFG3bthWnT5+2dbhu59ixY0Iul4ulS5c2miDxnFtu6NChIjQ0VJw7d67ZdjzXlvnuu+8EADFz5kyD8qqqKhEaGio6deqkL+O5Nk9BQYH+33feeWezCZI559Ye11UmSHawbt06AUBs2LDBpG7o0KGiXbt2oqamxgGRuaeffvrJ5IIdHh4uIiMjTdru27dPABCLFy+2Z4gur66uTtx9990iOTlZnDlzptEEiefcMt9++60AIJYvXy6EEOL69eviypUrjbblubZMdna2ACCWLFliUnfHHXcYnFuea+lulyCZc27tcV3lGCQ7OHz4MABg4MCBJnUDBw5EZWUlTp48ae+w3FZxcTEAICQkBABQWlqKoqIiJCQkmLRNSEiATCbTf0bUMitWrMAvv/yCVatWNVrPc265rKwsAEBUVBTGjBkDHx8f+Pv7o3v37gbnnefacv369UNAQACWLFmCzz77DEVFRfj111/x3HPP4dSpU5g/fz4AnmtbMvfc2uO6ygTJDhou2GFhYSZ1DWXnz5+3a0zuqr6+HgsWLECbNm0wfvx4AM2ffy8vL3Ts2JHn3wxnz57FvHnzMGfOHERGRjbahufccg3/uU+dOhXFxcVYt24dNm3ahM6dO+OZZ57BwoULAfBcW0P79u2xa9cuBAYGYuzYsYiIiEDPnj2xYcMG7N69GxMnTgTAc21L5p5be1xX21j03dQiVVVVANDonjLe3t4Gbcgyzz77LA4dOoTXXnsN8fHxAJo//8DNz4Dnv+WefPJJdOvWDS+88EKTbXjOLVdZWQkA8PPzw7fffqs/l+PGjUPPnj2xePFizJgxg+faShQKBfr06YPRo0dj4MCBKCsrw+rVqzF27Fhs374do0aN4rm2IXPPrT2uq3yCZAe+vr4AgJqaGpO66upqgzYk3auvvop3330XU6dOxcsvv6wvb+78Azc/A57/lvn444+RnZ2N1atXo23btk224zm3nI+PDwAgNTXV4CLg6emJ8ePHo7q6Gt9//z3PtRUcP34cCQkJSEpKwltvvYXRo0djypQp2L9/P7p164a0tDTU1NTwXNuQuefWHtdVJkh20LVrVwCNP+5r7jEhtdz8+fOxaNEiTJw4EWvWrIFMJtPXNXf+r127hsuXL/P8t0BtbS2ee+45PPDAA4iIiEBhYSEKCwv157WyshKFhYUoLy/nObeChvPTuXNnk7qGMq1Wy3NtBcuXL8e1a9fw2GOPGZR7eXnhkUceQWlpKU6ePMlzbUPmnlt7XFeZINnBvffeCwD47rvvTOq+++47+Pv7o0ePHvYOy21kZGQgIyMDf/3rX7F+/Xp4eBj+WoeGhiIsLAwHDx40+d5Dhw5BCKH/jKhpVVVVuHjxIvbs2YPIyEj9KzExEcDNp0uRkZFYvXo1z7kVDBgwAABQVFRkUnfu3DkAQKdOnXiuraDhgnr9+nWTuoayuro6nmsbMvfc2uW6atEcOGqR33//Xfj6+ja5XkNaWpoDo3NtGRkZAoAYP368qKura7Ldiy++2OT6Gm3atDFYq4MaV1tbK3bu3GnyWrNmjQAglEql2Llzp9BoNEIInnNL6XQ6ERAQIEJDQ0VZWZm+vKKiQnTp0kUoFApx9epVIQTPtaVmzpwpAIg33njDoLyiokJEREQIPz8/UV1dLYTgubbE7ab5m3Nu7XFdZYJkJytWrNCv+Pnee++J1157TXTo0EGEhoaK8+fPOzo8l7Rq1SoBQERERIgNGzaIzZs3G7y+/PJLfVutViuioqKEr6+vePnll8XatWvFAw88YLJKLpmvqXWQeM4t17DWS1xcnFiyZIl46623RI8ePYRMJhMbN27Ut+O5tkxhYaFo3769kMlkYvz48WL16tXi9ddfF9HR0QKAWLp0qb4tz7V5Nm3aJBYuXCgWLlwoQkJCRFBQkP7rlStXGrQ199za+rrKBMmOtmzZIvr06SO8vb1F+/btxbhx47jqqgUmTZokADT5Gjp0qEH7kpISMXnyZBEcHCy8vLzEnXfeKVavXu2Y4N1IUwmSEDzn1vD555+LQYMGCT8/P+Hr6ysSExNFVlaWSTuea8sUFRWJp556SsTHxwsfHx/h7+8vBg8eLD755BOTtjzXLTd06NAm/49u7GmSuefWltdVmRBCWNZJR0REROReOEibiIiIyAgTJCIiIiIjTJCIiIiIjDBBIiIiIjLCBImIiIjICBMkIiIiIiNMkIiIiIiMMEEiIiIiMsIEiYiIiMgIEyQiJ/H1119DJpNhw4YNjg7F6chkMkyePNnRYZhwh89s2LBh6N69u6PDIHI6TJCI3MSGDRuwYsUKR4ch2fz587Fr1y5Hh+FUdu3ahfnz5zs6DKspLCzE/Pnz8eOPPzo6FKLbYoJE5CSGDBmC6upqTJgwQdL3u3qClJGR0WSCVF1djbVr19o3ICewa9cuZGRkODoMqyksLERGRgYTJHIJTJCInISHhwe8vb0hl8sdHYqJK1euOPT9vb290bZtW4fGQEStCxMkIidhPJ7l1q8/+OAD9OzZE15eXujWrRuWLFli8L0ymQzffPMNzp49C5lMpn8VFhbq2xw9ehSjR49Gx44d4eXlhfj4eCxatAh1dXUGx2oYk3L69Gn8+c9/Rvv27dGuXTsAwOTJkyGTyaDT6fD4448jJCQE3t7eGDRoEL7//nuD49y4cQOLFi3CkCFDEBoaCk9PT0RERODJJ5/E5cuXTX5uANi4caM+9lvHxTQ1Bmn9+vW455574Ovri3bt2mH48OH48ssvTdp1794dw4YNw4kTJ6BSqdCuXTsEBgbiz3/+M0pLSw3alpSU4Pnnn0fv3r2hUCjg7e2Nnj174s0330R9fX3jH54EmzdvRr9+/aBQKODr64uIiAiMGzcOv/32mz7mjRs36n/+htfXX39t8DMZa2pcVHFxMVJSUhAUFAR/f3+MGDECx44dazI+c39fzp8/j7Fjx0KhUMDPzw9KpRIajUbfbv78+Rg+fDgAYMqUKfqfp+FzvXbtGubPn48ePXrA19cXAQEB6NGjB5599llzTiuR1bRxdABE1LzVq1fj4sWLmDp1KgIDA7Flyxa89NJLCAsLQ2pqKoCbF9tFixbh0qVLWL58uf57g4ODAQBZWVkYPXo0YmJi8Pzzz6N9+/Y4ePAg5s6dix9//BGfffaZwXteuXIFQ4cOxeDBg7Fo0SJcvHjRoF6lUiEkJATz5s3DpUuXsGzZMiQnJ6OwsFCfTNXW1mLp0qV47LHHMHr0aPj6+uLw4cNYt24dDhw4gB9++AGenp644447sHnzZkyYMAGJiYmYNm0aAMDf37/Z8/Lyyy9j8eLFuPvuu7Fw4UJcu3YN69atg0qlwubNmzF+/HiD9sXFxRgxYgTGjBmD0aNH4//9v/+H999/HxUVFQZJ1X//+1/s2rULY8aMQWRkJGpra5GdnY3Zs2fj9OnTWLNmjTkfX6O2bNmCiRMnIjExERkZGfD19UVRURFyc3NRUlKCzp07Y8WKFVi2bBn279+PzZs367/3jjvuMPv9ysrKkJiYiLNnz2Lq1Kno3bs3jhw5gpEjR6JDhw4m7c39fbl69SqGDh2KhIQEvP766zhz5gzefvttPPzww/j5558hl8sxZswYXL9+Ha+//jqmTZuGxMREAEB0dDQA4Omnn8aHH36ICRMmYObMmbhx4wYKCgrw73//2+yfl8gqBBE5hX379gkAYv369QZfd+7cWeh0On27q1evio4dO4oBAwYYfP/QoUNFt27dTI5bXV0tQkJCRGJiorh+/bpB3bJlywQAsW/fPoPjABBz5841OdakSZMEAPHkk08alH/66acCgHjvvff0ZTdu3BBVVVUmx/jggw8EALF161aDcgBi0qRJJu0bqzt16pSQyWSif//+4tq1a/ryS5cuidDQUKFQKMSVK1f05d26dWv0PZ966ikBQPz666/6sqqqKnHjxg2TGP76178KDw8PUVJSoi8z/sxaavTo0SIgIMDk8zDWcL4b061bNzF06FCT8sZiSk9PFwDE6tWrDdq+9dZbAoDB743U35c333zToO2SJUsEAJGTk9NsbA0UCoVITk5u9GclcgR2sRE5uSlTpiAoKEj/ta+vLwYMGIC8vLwWff+///1vXLx4ERMnTkRZWRkuXbqkfyUnJwNAo91Ss2bNavKYzz33nMHXI0aMAACDmGQyGXx8fAAA9fX1+vduaGvcJWeO3bt3QwiBF198EV5eXvryDh064KmnnoJOp8O+ffsMvqdLly4YO3Zso3Hn5+fry3x8fPRdfrW1tdBqtbh06RKUSiVu3LiBo0ePSo67QVBQEK5evYo9e/ZACGHx8W5n9+7d6NChA6ZOnWpQ/swzz+if+DWQ8vvi4eFh0hXW2O9Ec4KCgvDzzz/j+PHjZv1sRLbCBInIyUVFRZmUdejQwWAcT3N+/fVXAMDjjz+O4OBgg1ePHj0AABcuXDD4nuDgYAQGBrY4poZuGuOYPv30U/Tv3x8+Pj5QKBQIDg7Wf69Op2tR/I05ffo0AODOO+80qfvDH/5g0KapmJuKu66uDq+99hri4uLg7e2NDh06IDg4WD+70JK4G7zyyiuIjIzE6NGjERwcjNGjR+u7+2yhoKAAsbGxaNPGcFSFl5eXyXmR8vvSpUsXeHt7G5Q19TvRlLfffhtlZWXo1asXoqKi8Le//Q07d+7EjRs3Wv6DElkRxyAROTlLZ7U1PKF44403cPfddzfapkuXLgZf+/r6Sorp1qch27dvx7hx49CvXz+8/fbbCA8Ph7e3N+rr66FSqSy68DX31KWpuubO463f89xzz2HVqlUYN24cXnnlFYSEhKBt27Y4duwYXnrpJatcsKOjo3HixAns27cParUa33zzDZ544gnMmzcPe/fuRc+ePW97jIanXMaMB1Hfrr0xKb8vLT23zXnwwQdRWFiI7OxsfP311/jqq6/w4Ycfon///ti3b5/+aSSRvTBBInITTV0A4+LiANxMepKSkuwWz5YtW+Dt7Y19+/YZJFwnT560+NgNA3tPnDiB+Ph4g7oTJ04YtDHXli1bMGTIEHzyyScG5bd2w1mDp6cnlEollEolgJuzz4YPH44333zTYPZaU9q3bw+tVmtSbvzkDLh5LjQaDerq6gyeItXU1OD06dNo3769vsyWvy+3S9IUCgVSU1P1kw8yMjIwf/58fPLJJ5gyZYpVYyG6HXaxEbkJf39/lJWVmdyxK5VKhISEYMmSJbh06ZLJ91VXV6OystLq8cjlcshkMoMnLkIIvPbaa4229/f3b3H31SOPPAKZTIalS5eitrZWX67VavHuu+9CoVA0OgW+pXEbn8OrV68azA60VGOfQ58+feDh4WGQ9DTM5GvsvMTFxeHkyZMoLi7Wl9XU1OBf//qXSdtHHnkEly9fxgcffGBQvnLlSpPP3pa/L039PA1j1Iz17dsXABpNBIlsjU+QiNxE//79sWfPHjz77LMYMGAA5HI5HnzwQfj5+WHTpk145JFH0KNHD6SlpSE2NhZlZWU4efIkduzYgZ07d0pOKJry5z//Gdu3b8eIESMwceJEXL9+Hbt27UJVVVWT8avVarz11lsIDw+Hn58fHnzwwUbbxsbGYvbs2Vi8eDEGDRqElJQU/TT/0tJSbNq0CX5+fpLjXrNmDcaNG4ekpCRcuHABH374YaPT4aW6//77ERgYiCFDhiA8PBzl5eXYtGkTbty4gYkTJ+rb9e/fH6tWrcLTTz+NUaNGoW3bthgxYgRCQkIwY8YMfPLJJ0hKSsL06dNRW1uLzZs3N9o9+o9//AOZmZl4+umn8eOPP+KPf/wjjhw5gp07dyI6OtqgW87X19dmvy89e/aEv78/3n33Xfj5+SEgIACRkZGIj49H586d8dBDD6F3797o1KkTzp49i/feew/+/v4YM2aMpPNMZBHHTJ4jImNNTfNvbEp0Y9O/KysrxcSJE0WHDh2ETCYTAMSZM2f09cePHxfjx48XXbp0EW3bthUhISEiISFBLFiwQFy+fFnfrqnlApp63wZoZJr++++/L+644w7h5eUlQkNDxeOPPy4uX77caNuTJ0+KESNGCH9/f5Op5421F0KIdevWib59+wpvb2/h5+cnhg4dajCtvIE5U+KvXr0qXnjhBRERESG8vLxETEyMWLx4sVCr1SZtpU7zX7t2rbjvvvtEaGio8PT0FJ06dRL333+/Sex1dXVi5syZIjQ0VHh4eJhMsd+wYYOIi4sTbdu2Fd27dxdvvvmm2Lt3b6MxnT9/XvzlL38RgYGBws/PTwwfPlz88MMPTX7elv6+nDlzRgAQ8+bNMyj//PPPRa9evYSnp6f+c62pqRGzZ88W/fr1E+3btxeenp4iIiJCTJgwQfzyyy9mnVsia5EJYYc5pkREREQuhGOQiIiIiIxwDBIRkZUY7+vWmMDAQE5ZJ3IB7GIjIrKSlqw1tH79+kY33iUi58InSEREVtKSjVUbW/2biJwPnyARERERGeEgbSIiIiIjTJCIiIiIjDBBIiIiIjLCBImIiIjICBMkIiIiIiNMkIiIiIiMMEEiIiIiMsIEiYiIiMjI/wfa3I8cFEB7BQAAAABJRU5ErkJggg=="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0ffca4e3",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q10.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f17934b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot using the DataFrame 'top_ranked_inter_faculty' with the x-axis labelled \"international_students\" \n",
+    "# and the y-axis labelled \"faculty_student_score\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dab472c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Correlations:\n",
+    "\n",
+    "You can use the `.corr()` method on a **DataFrame** that has **two** columns to get the *correlation* between those two columns.\n",
+    "\n",
+    "For example, if we have a **DataFrame** `df` with the two columns `citations_per_faculty` and `overall_score`, `df.corr()` would return\n",
+    "\n",
+    "||**citations_per_faculty**|**overall_score**|\n",
+    "|---------|------|---------|\n",
+    "|citations_per_faculty|1.000000|0.574472|\n",
+    "|overall_score|0.574472|1.000000|\n",
+    "\n",
+    "You can use `.loc` here to **extract** the *correlation* between the two columns (`0.574472` in this case)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09ade4a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 11:** Find the **correlation** between `international_students` and `overall_score` for institutions from the `country` *United Kingdom* that were ranked in the **top** *100* in the `year` *2020*.\n",
+    "\n",
+    "Your output **must** be a **float** representing the absolute correlations. The **only** `pandas` operations you are **allowed** to use are: `.corr`, `.loc` and `.iloc`. You **must** use SQL to gather all other data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "706db815",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'uk_inter_score_corr', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea738710",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "314d22d6",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Let us now define a new score called `citations_per_international` as follows:\n",
+    "\n",
+    "$$\\texttt{citations}\\_\\texttt{per}\\_\\texttt{international} = \\frac{\\texttt{citations}\\_\\texttt{per}\\_\\texttt{faculty} \\times \\texttt{international}\\_\\texttt{faculty}}{100}.$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cef190f0",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 12:** Find the **correlation** between `citations_per_international` and `overall_score` for **all** institutions in the `year` *2019*.\n",
+    "\n",
+    "Your output **must** be a **float** representing the absolute correlations. The **only** `pandas` operations you are **allowed** to use are: `.corr`, `.loc` and `.iloc`. You **must** use SQL to gather all other data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "777d001c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'cit_per_inter_score_corr', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee14b0ac",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc72c981",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 13:** What are the **top** *15* countries with the **highest** *total* of `citations_per_international` in the `year` *2019*.\n",
+    "\n",
+    "\n",
+    "The *total* `citations_per_international` of a `country` is defined as the **sum** of `citations_per_international` scores of **all** institutions in that `country`. You **must** display the columns `country` and `sum_inter_citations`. The rows **must** be in *descending* order of `sum_inter_citations`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**country**|**sum_inter_citations**|\n",
+    "|----|-----------|-----------------------|\n",
+    "|**0**|United States|2623.8207|\n",
+    "|**1**|United Kingdom|2347.1602|\n",
+    "|**2**|Australia|1255.5530|\n",
+    "|**3**|Netherlands|748.4268|\n",
+    "|**4**|Canada|724.5029|\n",
+    "|**5**|Switzerland|561.8790|\n",
+    "|**6**|China|482.2577|\n",
+    "|**7**|Germany|455.5466|\n",
+    "|**8**|Hong Kong|375.3032|\n",
+    "|**9**|New Zealand|327.3357|\n",
+    "|**10**|Sweden|305.3745|\n",
+    "|**11**|Belgium|255.0750|\n",
+    "|**12**|France|198.0860|\n",
+    "|**13**|Denmark|186.4904|\n",
+    "|**14**|Singapore|160.3000|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14aaad72",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b44e985d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59a993ce",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 14:** Among the institutions ranked within the **top** *300*, find the **average** `citations_per_international` for **each** `country` in the `year` *2019*.\n",
+    "\n",
+    "You **must** display the columns `country` and `avg_inter_citations` representing the **average** of `citations_per_international` for **each** country. The rows **must** be in *descending* order of `avg_inter_citations`.\n",
+    "\n",
+    "**Hint:** To find the **average**, you can use `SUM()` and `COUNT()` or you can simply use `AVG()`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** whose **first ten rows** look like this:\n",
+    "\n",
+    "||**country**|**avg_inter_citations**|\n",
+    "|----|-----------|----------------------|\n",
+    "|**0**|Singapore|80.150000|\n",
+    "|**1**|Switzerland|75.497000|\n",
+    "|**2**|Hong Kong|62.550533|\n",
+    "|**3**|Australia|61.362388|\n",
+    "|**4**|Netherlands|56.166733|\n",
+    "|**5**|New Zealand|53.226220|\n",
+    "|**6**|United Kingdom|52.889084|\n",
+    "|**7**|Canada|50.779723|\n",
+    "|**8**|Denmark|46.196200|\n",
+    "|**9**|Norway|46.083300|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dac3e940",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "946bb83c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfded4bf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 15** Find the **institution** with the **highest** value of `citations_per_international` for **each** `country` in the `year` *2020*.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** with the columns `country`, `institution_name`, and a new column `max_inter_citations` representing the **maximum** value of `citations_per_international` for that country. The rows **must** be in *descending* order of `max_inter_citations`. You **must** **omit** rows where `max_inter_citations` is **missing** by using the clause:\n",
+    "\n",
+    "```sql\n",
+    "HAVING `max_inter_citations` IS NOT NULL\n",
+    "```\n",
+    "\n",
+    "**Hint:** You can use the `MAX()` function to return the largest value within a group.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** whose **first ten rows** look like this:\n",
+    "\n",
+    "||**country**|**institution_name**|**max_inter_citations**|\n",
+    "|----|-----------|--------------------|----------------------|\n",
+    "|**0**|United States|Massachusetts Institute Of Technology|99.8000|\n",
+    "|**1**|Switzerland|Ecole Polytechnique Fédérale De Lausanne|98.9000|\n",
+    "|**2**|Netherlands|Eindhoven University Of Technology|95.4493|\n",
+    "|**3**|United Kingdom|London School Of Economics And Political Science|91.1000|\n",
+    "|**4**|Hong Kong|The Hong Kong University Of Science And Technology|89.5000|\n",
+    "|**5**|Singapore|Nanyang Technological University|88.8000|\n",
+    "|**6**|Australia|The University Of Western Australia|88.3000|\n",
+    "|**7**|Belgium|Katholieke Universiteit Leuven|76.7700|\n",
+    "|**8**|New Zealand|University Of Waikato|73.6434|\n",
+    "|**9**|Canada|Western University|72.3240|\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fba4a1c2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'max_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c4db997",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da9cb13f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 16**: Among the institutions ranked within the **top** *50*, create a **horizontal bar plot** representing the **average** of both the`citations_per_faculty` and `international_faculty` scores for **all** institutions in **each** `country` in the `year` *2018*.\n",
+    "\n",
+    "You **must** first create a **DataFrame** `country_citations_inter` with **three** columns: `country`, `avg_citations` and `avg_inter_faculty` representing the name, the average value of `citations_per_faculty` and the average value of `international_faculty` for each country respectively.\n",
+    "\n",
+    "You **must** ensure that the countries in the **DataFrame** are **ordered** in **increasing** order of the **difference** between `avg_citations` and `avg_inter_faculty`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9e566a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'country_citations_inter', then display it\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60d1c6f7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e859552",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `country_citations_inter` as **horizontal bar plot** with the **x-axis** labelled *country*.\n",
+    "\n",
+    "Then, you **must** use the `horizontal_bar_plot` function to plot this data. Verify that the countries are **ordered** in **decreasing** order of the **difference** between `avg_citations` and `avg_inter_faculty`. Verify that the **legend** appears on your plot.\n",
+    "\n",
+    "**Hint:** If you want the countries in the plot to be ordered in **decreasing** order of the difference, you will need to make sure that in the DataFrame, they are ordered in the **increasing** order.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q16.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "fb3e7670",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q16.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "259af611",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the horizontal bar plot using the DataFrame 'country_citations_inter' with the x-axis labelled \"country\" \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a5d4543",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 17:** Create a **scatter plot** representing the `overall_score` (on the **x-axis**) against the `rank` (on the **y-axis**) for **all** institutions in the `year` *2020*. Additionally, **plot** a **regression line** within the same plot.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** containing the **overall_score**, and the **rank** data from the `year` *2020*. You **must** use the `get_regression_line` function to compute the best fit line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d51299b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'overall_rank', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a422be6a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c062dae",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `overall_rank` as **scatter plot** with a **regression line** with the **x-axis** labelled *overall_score* and the **y-axis** labelled *rank*.\n",
+    "\n",
+    "You **must** use the `regression_line_plot` function to plot this data.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q17.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "aee08178",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q17.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c914693",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot and the regression line using the DataFrame 'overall_rank' with the x-axis labelled \"overall_score\" \n",
+    "# and the y-axis labelled \"rank\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "effa2591",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Does our linear regression model fit the points well? It looks like the relationship between the `overall_score` and `rank` is **not quite linear**. In fact, a cursory look at the data suggests that the relationship is in fact, inverse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f1de243",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Food for thought is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26e4e3c1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 18:** Create a **scatter plot** representing the **inverse** of the `overall_score` (on the **x-axis**) against the `rank` (on the **y-axis**) for **all** institutions in the `year` *2020*. Additionally, **plot** a **regression line**  within the same plot.\n",
+    "\n",
+    "The `inverse_overall_score` for each institution is simply defined as `1/overall_score` for that institution. You **must** first compute a **DataFrame** containing the **inverse_overall_score**, and the **rank** data from the `year` *2020*. You **must** use the `get_regression_line` function to compute the best fit line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c535d83",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'inverse_overall_rank', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22a6a736",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e64a0040",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `inverse_overall_rank` as **scatter plot** with a **regression line** with the **x-axis** labelled *inverse_overall_score* and the **y-axis** labelled *rank*.\n",
+    "\n",
+    "You **must** use the `regression_line_plot` function to plot this data.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q18.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "baeb0d40",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q18.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd8efd5b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot and the regression line using the DataFrame 'inverse_overall_rank'\n",
+    "# with the x-axis labelled \"inverse_overall_score\" and the y-axis labelled \"rank\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f9f2089",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "This seems to be much better! Let us now use this **regression line** to **estimate** the `rank` of an institution given its `overall_score`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0849a83f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 19:** Use the regression line to **estimate** the `rank` of an institution with an `overall_score` of *72*.\n",
+    "\n",
+    "Your output **must** be an **int**. If your **estimate** is a **float**, *round it up* using `math.ceil`.\n",
+    "\n",
+    "\n",
+    "**Hints:**\n",
+    "1. Call the `get_regression_coeff` function to get the coefficients `m` and `b`.\n",
+    "2. Recall that the equation of a line is `y = m * x + b`. What are `x` and `y` here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f3fa177",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rank_score_72', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1559986",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "547f4135",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Can you find out the `overall_score` of the university with this `rank` in the `year` *2020*? Does it match your prediction?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "60915e12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Food for thought is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ab4005",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 20:** Using the data from Question 5, create a **pie plot** representing the number of institutions from each country.\n",
+    "\n",
+    "You **have** already computed a **DataFrame** `num_institutions` (in Question 5) containing the **country**, and the **num_of_institutions** data. Run the following cell just to confirm that the variable has not changed its values since you defined it in Question 5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a86a546",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d95601d7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `num_institutions` as **pie plot** with the **title** *Number of institutions*.\n",
+    "\n",
+    "Now, you **must** use the `pie_plot` function to create the **pie plot**. The **colors** do **not** matter, but the plot **must** be titled `Number of institutions`, and **must** be labelled as in the sample output below.\n",
+    "\n",
+    "**Important Warning:** `p13_test.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q20.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "76ce5db5",
+   "metadata": {},
+   "source": [
+    "<div><img src=\"attachment:q20.png\" width=\"400\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fdcbe48",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the pie plot using the DataFrame 'num_institutions' titled \"Number of institutions\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bce0354",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** It seems that we'll run out of colors! How can we make it so that **no two neighbors share a color**? You'll probably have to look online."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7bd4d538",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Food for thought is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "936abcda",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Closing the database connection:\n",
+    "\n",
+    "Now, before you **submit** your notebook, you **must** **close** your connection `conn`. Not doing this might make **Gradescope fail**. Additionally, **delete** the example images provided with plot questions to save space, if your notebook file is too large for submission. You can **delete** any cell by selecting the cell, hitting the `Esc` key once, and then hitting the `d` key **twice**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9515f232",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# close your connection here\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27a5f70c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "Make sure you have run all cells in your notebook in order before running the following cells, so that all images/graphs appear in the output. The following cells will generate a zip file for you to submit.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. Check **Gradescope otter** results as soon as the auto-grader execution gets completed. Don't worry about the score showing up as -/100.0. You only need to check that the test cases passed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9419c771",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b54d6127",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "!jupytext --to py p13.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11da7246",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "p13_test.check_file_size(\"p13.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=True, files=[py_filename])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a44ca87a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "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": "3.9.13"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "q1": {
+     "name": "q1",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q1\", uw_rating.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q10\", top_ranked_inter_faculty[[\"international_students\", \"faculty_student_score\"]].reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q11\", uk_inter_score_corr)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q12\", cit_per_inter_score_corr)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q13\", top_cit_per_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q14\", avg_cit_per_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q15\", max_cit_per_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q16\", country_citations_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q17\", overall_rank[[\"overall_score\", \"rank\", \"fit\"]].reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q18\", inverse_overall_rank[[\"inverse_overall_score\", \"rank\", \"fit\"]].reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q19\", rank_score_72)\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q2\", japan_top_10_inter.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q20\", num_institutions.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q3\", us_top_10_rep.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q4\", top_10_countries.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q5\", num_institutions.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q6\", top_10_total_score.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q7\", top_10_inter_score.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q8\", citations_overall.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 5,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> p13_test.check(\"q9\", reputations_usa.reset_index(drop=True).to_html())\nTrue",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p13/p13_expected.html b/p13/p13_expected.html
new file mode 100644
index 0000000000000000000000000000000000000000..6d4e54944aa7cf2837224d624e451c3c3faf1de0
--- /dev/null
+++ b/p13/p13_expected.html
@@ -0,0 +1,8726 @@
+<html>
+  <body>
+    <h1>Question 1</h1>
+    <table border="1" class="dataframe" data-question="1">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>rank</th>
+          <th>year</th>
+          <th>institution_name</th>
+          <th>country</th>
+          <th>academic_reputation</th>
+          <th>employer_reputation</th>
+          <th>faculty_student_score</th>
+          <th>citations_per_faculty</th>
+          <th>international_faculty</th>
+          <th>international_students</th>
+          <th>overall_score</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>55</td>
+          <td>2018</td>
+          <td>University Of Wisconsin-Madison</td>
+          <td>United States</td>
+          <td>94.0</td>
+          <td>62.1</td>
+          <td>84.0</td>
+          <td>54.2</td>
+          <td>53.2</td>
+          <td>30.9</td>
+          <td>75.8</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>53</td>
+          <td>2019</td>
+          <td>University Of Wisconsin-Madison</td>
+          <td>United States</td>
+          <td>88.5</td>
+          <td>51.2</td>
+          <td>87.4</td>
+          <td>52.6</td>
+          <td>58.8</td>
+          <td>30.6</td>
+          <td>73.2</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>56</td>
+          <td>2020</td>
+          <td>University Of Wisconsin-Madison</td>
+          <td>United States</td>
+          <td>87.8</td>
+          <td>49.7</td>
+          <td>85.5</td>
+          <td>50.0</td>
+          <td>57.2</td>
+          <td>30.9</td>
+          <td>71.8</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 2</h1>
+    <table border="1" class="dataframe" data-question="2">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>institution_name</th>
+          <th>international_students</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>Waseda University</td>
+          <td>35.8</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>Tokyo Institute Of Technology</td>
+          <td>31.3</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>University Of Tsukuba</td>
+          <td>30.4</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>The University Of Tokyo</td>
+          <td>26.2</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Kyushu University</td>
+          <td>21.5</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>Nagoya University</td>
+          <td>21.3</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>Tohoku University</td>
+          <td>17.6</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Kyoto University</td>
+          <td>17.5</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>Hiroshima University</td>
+          <td>17.1</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>Tokyo Medical And Dental University</td>
+          <td>16.7</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 3</h1>
+    <table border="1" class="dataframe" data-question="3">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>institution_name</th>
+          <th>reputation</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>Harvard University</td>
+          <td>200.0</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>Massachusetts Institute Of Technology</td>
+          <td>200.0</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Stanford University</td>
+          <td>200.0</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>University Of California, Berkeley</td>
+          <td>199.8</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Yale University</td>
+          <td>199.6</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>University Of California, Los Angeles</td>
+          <td>199.1</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>Columbia University</td>
+          <td>197.1</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Princeton University</td>
+          <td>196.6</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>University Of Chicago</td>
+          <td>190.3</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>Cornell University</td>
+          <td>189.2</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 4</h1>
+    <table border="1" class="dataframe" data-question="4">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>country</th>
+          <th>num_of_institutions</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>United States</td>
+          <td>74</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>United Kingdom</td>
+          <td>45</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Germany</td>
+          <td>23</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>Australia</td>
+          <td>21</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Canada</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>China</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>France</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Japan</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>Netherlands</td>
+          <td>13</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>Russia</td>
+          <td>13</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 5</h1>
+    <table border="1" class="dataframe" data-question="5">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>country</th>
+          <th>num_of_institutions</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>United States</td>
+          <td>74</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>United Kingdom</td>
+          <td>45</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Germany</td>
+          <td>23</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>Australia</td>
+          <td>21</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Canada</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>China</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>France</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Japan</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>Netherlands</td>
+          <td>13</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>Russia</td>
+          <td>13</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>Other</td>
+          <td>155</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 6</h1>
+    <table border="1" class="dataframe" data-question="6">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>country</th>
+          <th>total_score</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>United States</td>
+          <td>4298.4</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>United Kingdom</td>
+          <td>2539.2</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Germany</td>
+          <td>1098.2</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>Australia</td>
+          <td>1093.8</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Japan</td>
+          <td>752.9</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>China</td>
+          <td>743.4</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>Canada</td>
+          <td>705.3</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Netherlands</td>
+          <td>674.9</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>South Korea</td>
+          <td>612.8</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>France</td>
+          <td>595.2</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 7</h1>
+    <table border="1" class="dataframe" data-question="7">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>institution_name</th>
+          <th>international_score</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>Massachusetts Institute Of Technology</td>
+          <td>194.1</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>California Institute Of Technology</td>
+          <td>186.7</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Carnegie Mellon University</td>
+          <td>183.5</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>Rice University</td>
+          <td>180.4</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Northeastern University</td>
+          <td>179.1</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>Stanford University</td>
+          <td>167.5</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>Cornell University</td>
+          <td>166.1</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Purdue University</td>
+          <td>158.2</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>University Of Rochester</td>
+          <td>157.9</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>University Of Chicago</td>
+          <td>151.2</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 8</h1>
+    <table border="1" class="dataframe" data-question="8">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>citations_per_faculty</th>
+          <th>overall_score</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>99.9</td>
+          <td>100.0</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>99.4</td>
+          <td>98.7</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>99.9</td>
+          <td>98.4</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>100.0</td>
+          <td>97.7</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>78.3</td>
+          <td>95.6</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>76.3</td>
+          <td>95.3</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>74.7</td>
+          <td>94.6</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>68.7</td>
+          <td>93.7</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>85.9</td>
+          <td>93.5</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>98.7</td>
+          <td>93.3</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>83.3</td>
+          <td>92.2</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>99.2</td>
+          <td>91.2</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>100.0</td>
+          <td>91.0</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>96.2</td>
+          <td>90.7</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>66.2</td>
+          <td>90.5</td>
+        </tr>
+        <tr>
+          <th>15</th>
+          <td>63.2</td>
+          <td>90.4</td>
+        </tr>
+        <tr>
+          <th>16</th>
+          <td>83.9</td>
+          <td>89.8</td>
+        </tr>
+        <tr>
+          <th>17</th>
+          <td>62.3</td>
+          <td>88.9</td>
+        </tr>
+        <tr>
+          <th>18</th>
+          <td>67.4</td>
+          <td>88.7</td>
+        </tr>
+        <tr>
+          <th>19</th>
+          <td>85.6</td>
+          <td>87.1</td>
+        </tr>
+        <tr>
+          <th>20</th>
+          <td>91.4</td>
+          <td>87.0</td>
+        </tr>
+        <tr>
+          <th>21</th>
+          <td>66.7</td>
+          <td>87.0</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>64.8</td>
+          <td>86.9</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>55.5</td>
+          <td>86.9</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>75.3</td>
+          <td>85.6</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>46.3</td>
+          <td>85.5</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>99.3</td>
+          <td>84.9</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>75.4</td>
+          <td>84.8</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>73.3</td>
+          <td>84.8</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>87.7</td>
+          <td>84.3</td>
+        </tr>
+        <tr>
+          <th>30</th>
+          <td>50.0</td>
+          <td>84.0</td>
+        </tr>
+        <tr>
+          <th>31</th>
+          <td>61.4</td>
+          <td>83.9</td>
+        </tr>
+        <tr>
+          <th>32</th>
+          <td>97.5</td>
+          <td>83.6</td>
+        </tr>
+        <tr>
+          <th>33</th>
+          <td>47.5</td>
+          <td>83.0</td>
+        </tr>
+        <tr>
+          <th>34</th>
+          <td>71.7</td>
+          <td>81.8</td>
+        </tr>
+        <tr>
+          <th>35</th>
+          <td>56.4</td>
+          <td>81.5</td>
+        </tr>
+        <tr>
+          <th>36</th>
+          <td>70.6</td>
+          <td>81.5</td>
+        </tr>
+        <tr>
+          <th>37</th>
+          <td>61.7</td>
+          <td>80.8</td>
+        </tr>
+        <tr>
+          <th>38</th>
+          <td>90.3</td>
+          <td>80.8</td>
+        </tr>
+        <tr>
+          <th>39</th>
+          <td>63.7</td>
+          <td>80.6</td>
+        </tr>
+        <tr>
+          <th>40</th>
+          <td>99.5</td>
+          <td>80.4</td>
+        </tr>
+        <tr>
+          <th>41</th>
+          <td>77.2</td>
+          <td>80.4</td>
+        </tr>
+        <tr>
+          <th>42</th>
+          <td>100.0</td>
+          <td>79.9</td>
+        </tr>
+        <tr>
+          <th>43</th>
+          <td>56.4</td>
+          <td>79.5</td>
+        </tr>
+        <tr>
+          <th>44</th>
+          <td>79.8</td>
+          <td>78.9</td>
+        </tr>
+        <tr>
+          <th>45</th>
+          <td>50.7</td>
+          <td>78.8</td>
+        </tr>
+        <tr>
+          <th>46</th>
+          <td>95.6</td>
+          <td>78.6</td>
+        </tr>
+        <tr>
+          <th>47</th>
+          <td>83.4</td>
+          <td>78.6</td>
+        </tr>
+        <tr>
+          <th>48</th>
+          <td>91.5</td>
+          <td>78.4</td>
+        </tr>
+        <tr>
+          <th>49</th>
+          <td>70.3</td>
+          <td>78.0</td>
+        </tr>
+        <tr>
+          <th>50</th>
+          <td>63.7</td>
+          <td>77.9</td>
+        </tr>
+        <tr>
+          <th>51</th>
+          <td>23.4</td>
+          <td>77.3</td>
+        </tr>
+        <tr>
+          <th>52</th>
+          <td>92.5</td>
+          <td>76.5</td>
+        </tr>
+        <tr>
+          <th>53</th>
+          <td>91.6</td>
+          <td>76.1</td>
+        </tr>
+        <tr>
+          <th>54</th>
+          <td>54.2</td>
+          <td>75.8</td>
+        </tr>
+        <tr>
+          <th>55</th>
+          <td>62.4</td>
+          <td>74.8</td>
+        </tr>
+        <tr>
+          <th>56</th>
+          <td>54.1</td>
+          <td>74.4</td>
+        </tr>
+        <tr>
+          <th>57</th>
+          <td>83.5</td>
+          <td>74.3</td>
+        </tr>
+        <tr>
+          <th>58</th>
+          <td>50.8</td>
+          <td>74.0</td>
+        </tr>
+        <tr>
+          <th>59</th>
+          <td>68.5</td>
+          <td>73.1</td>
+        </tr>
+        <tr>
+          <th>60</th>
+          <td>98.8</td>
+          <td>72.9</td>
+        </tr>
+        <tr>
+          <th>61</th>
+          <td>81.5</td>
+          <td>72.5</td>
+        </tr>
+        <tr>
+          <th>62</th>
+          <td>63.1</td>
+          <td>72.1</td>
+        </tr>
+        <tr>
+          <th>63</th>
+          <td>26.6</td>
+          <td>72.0</td>
+        </tr>
+        <tr>
+          <th>64</th>
+          <td>55.5</td>
+          <td>71.6</td>
+        </tr>
+        <tr>
+          <th>65</th>
+          <td>55.1</td>
+          <td>70.8</td>
+        </tr>
+        <tr>
+          <th>66</th>
+          <td>95.7</td>
+          <td>70.6</td>
+        </tr>
+        <tr>
+          <th>67</th>
+          <td>34.8</td>
+          <td>70.4</td>
+        </tr>
+        <tr>
+          <th>68</th>
+          <td>93.5</td>
+          <td>70.3</td>
+        </tr>
+        <tr>
+          <th>69</th>
+          <td>99.1</td>
+          <td>70.1</td>
+        </tr>
+        <tr>
+          <th>70</th>
+          <td>88.9</td>
+          <td>69.4</td>
+        </tr>
+        <tr>
+          <th>71</th>
+          <td>99.8</td>
+          <td>69.4</td>
+        </tr>
+        <tr>
+          <th>72</th>
+          <td>26.9</td>
+          <td>69.2</td>
+        </tr>
+        <tr>
+          <th>73</th>
+          <td>31.9</td>
+          <td>69.2</td>
+        </tr>
+        <tr>
+          <th>74</th>
+          <td>2.6</td>
+          <td>69.1</td>
+        </tr>
+        <tr>
+          <th>75</th>
+          <td>74.1</td>
+          <td>69.0</td>
+        </tr>
+        <tr>
+          <th>76</th>
+          <td>48.4</td>
+          <td>69.0</td>
+        </tr>
+        <tr>
+          <th>77</th>
+          <td>76.3</td>
+          <td>68.5</td>
+        </tr>
+        <tr>
+          <th>78</th>
+          <td>51.8</td>
+          <td>68.5</td>
+        </tr>
+        <tr>
+          <th>79</th>
+          <td>61.5</td>
+          <td>67.8</td>
+        </tr>
+        <tr>
+          <th>80</th>
+          <td>59.8</td>
+          <td>67.2</td>
+        </tr>
+        <tr>
+          <th>81</th>
+          <td>48.1</td>
+          <td>67.0</td>
+        </tr>
+        <tr>
+          <th>82</th>
+          <td>50.1</td>
+          <td>67.0</td>
+        </tr>
+        <tr>
+          <th>83</th>
+          <td>46.3</td>
+          <td>66.8</td>
+        </tr>
+        <tr>
+          <th>84</th>
+          <td>47.2</td>
+          <td>66.8</td>
+        </tr>
+        <tr>
+          <th>85</th>
+          <td>40.3</td>
+          <td>66.1</td>
+        </tr>
+        <tr>
+          <th>86</th>
+          <td>80.2</td>
+          <td>65.9</td>
+        </tr>
+        <tr>
+          <th>87</th>
+          <td>68.3</td>
+          <td>65.7</td>
+        </tr>
+        <tr>
+          <th>88</th>
+          <td>96.7</td>
+          <td>65.6</td>
+        </tr>
+        <tr>
+          <th>89</th>
+          <td>32.8</td>
+          <td>65.5</td>
+        </tr>
+        <tr>
+          <th>90</th>
+          <td>49.6</td>
+          <td>65.5</td>
+        </tr>
+        <tr>
+          <th>91</th>
+          <td>69.6</td>
+          <td>65.3</td>
+        </tr>
+        <tr>
+          <th>92</th>
+          <td>80.2</td>
+          <td>65.2</td>
+        </tr>
+        <tr>
+          <th>93</th>
+          <td>82.3</td>
+          <td>65.2</td>
+        </tr>
+        <tr>
+          <th>94</th>
+          <td>6.3</td>
+          <td>65.0</td>
+        </tr>
+        <tr>
+          <th>95</th>
+          <td>54.5</td>
+          <td>65.0</td>
+        </tr>
+        <tr>
+          <th>96</th>
+          <td>96.7</td>
+          <td>64.9</td>
+        </tr>
+        <tr>
+          <th>97</th>
+          <td>70.0</td>
+          <td>64.7</td>
+        </tr>
+        <tr>
+          <th>98</th>
+          <td>82.1</td>
+          <td>64.7</td>
+        </tr>
+        <tr>
+          <th>99</th>
+          <td>78.3</td>
+          <td>64.6</td>
+        </tr>
+        <tr>
+          <th>100</th>
+          <td>42.3</td>
+          <td>64.5</td>
+        </tr>
+        <tr>
+          <th>101</th>
+          <td>41.5</td>
+          <td>63.9</td>
+        </tr>
+        <tr>
+          <th>102</th>
+          <td>56.0</td>
+          <td>63.9</td>
+        </tr>
+        <tr>
+          <th>103</th>
+          <td>83.3</td>
+          <td>63.6</td>
+        </tr>
+        <tr>
+          <th>104</th>
+          <td>58.0</td>
+          <td>63.2</td>
+        </tr>
+        <tr>
+          <th>105</th>
+          <td>31.8</td>
+          <td>62.3</td>
+        </tr>
+        <tr>
+          <th>106</th>
+          <td>69.8</td>
+          <td>61.9</td>
+        </tr>
+        <tr>
+          <th>107</th>
+          <td>37.9</td>
+          <td>61.6</td>
+        </tr>
+        <tr>
+          <th>108</th>
+          <td>70.9</td>
+          <td>61.5</td>
+        </tr>
+        <tr>
+          <th>109</th>
+          <td>55.2</td>
+          <td>61.5</td>
+        </tr>
+        <tr>
+          <th>110</th>
+          <td>75.5</td>
+          <td>61.5</td>
+        </tr>
+        <tr>
+          <th>111</th>
+          <td>56.5</td>
+          <td>61.4</td>
+        </tr>
+        <tr>
+          <th>112</th>
+          <td>53.6</td>
+          <td>61.3</td>
+        </tr>
+        <tr>
+          <th>113</th>
+          <td>85.3</td>
+          <td>60.8</td>
+        </tr>
+        <tr>
+          <th>114</th>
+          <td>24.3</td>
+          <td>60.8</td>
+        </tr>
+        <tr>
+          <th>115</th>
+          <td>37.0</td>
+          <td>60.7</td>
+        </tr>
+        <tr>
+          <th>116</th>
+          <td>69.3</td>
+          <td>60.7</td>
+        </tr>
+        <tr>
+          <th>117</th>
+          <td>61.6</td>
+          <td>60.4</td>
+        </tr>
+        <tr>
+          <th>118</th>
+          <td>74.6</td>
+          <td>59.3</td>
+        </tr>
+        <tr>
+          <th>119</th>
+          <td>33.6</td>
+          <td>59.2</td>
+        </tr>
+        <tr>
+          <th>120</th>
+          <td>25.7</td>
+          <td>59.1</td>
+        </tr>
+        <tr>
+          <th>121</th>
+          <td>35.1</td>
+          <td>59.0</td>
+        </tr>
+        <tr>
+          <th>122</th>
+          <td>3.8</td>
+          <td>59.0</td>
+        </tr>
+        <tr>
+          <th>123</th>
+          <td>56.7</td>
+          <td>58.7</td>
+        </tr>
+        <tr>
+          <th>124</th>
+          <td>48.3</td>
+          <td>58.4</td>
+        </tr>
+        <tr>
+          <th>125</th>
+          <td>55.3</td>
+          <td>58.4</td>
+        </tr>
+        <tr>
+          <th>126</th>
+          <td>43.3</td>
+          <td>58.3</td>
+        </tr>
+        <tr>
+          <th>127</th>
+          <td>31.9</td>
+          <td>58.2</td>
+        </tr>
+        <tr>
+          <th>128</th>
+          <td>75.8</td>
+          <td>58.1</td>
+        </tr>
+        <tr>
+          <th>129</th>
+          <td>49.9</td>
+          <td>57.7</td>
+        </tr>
+        <tr>
+          <th>130</th>
+          <td>81.0</td>
+          <td>57.5</td>
+        </tr>
+        <tr>
+          <th>131</th>
+          <td>70.4</td>
+          <td>57.2</td>
+        </tr>
+        <tr>
+          <th>132</th>
+          <td>70.3</td>
+          <td>57.1</td>
+        </tr>
+        <tr>
+          <th>133</th>
+          <td>97.5</td>
+          <td>56.9</td>
+        </tr>
+        <tr>
+          <th>134</th>
+          <td>63.2</td>
+          <td>56.3</td>
+        </tr>
+        <tr>
+          <th>135</th>
+          <td>53.0</td>
+          <td>56.3</td>
+        </tr>
+        <tr>
+          <th>136</th>
+          <td>63.8</td>
+          <td>56.0</td>
+        </tr>
+        <tr>
+          <th>137</th>
+          <td>39.5</td>
+          <td>56.0</td>
+        </tr>
+        <tr>
+          <th>138</th>
+          <td>11.4</td>
+          <td>56.0</td>
+        </tr>
+        <tr>
+          <th>139</th>
+          <td>35.7</td>
+          <td>55.8</td>
+        </tr>
+        <tr>
+          <th>140</th>
+          <td>42.1</td>
+          <td>55.6</td>
+        </tr>
+        <tr>
+          <th>141</th>
+          <td>48.9</td>
+          <td>55.5</td>
+        </tr>
+        <tr>
+          <th>142</th>
+          <td>51.3</td>
+          <td>55.5</td>
+        </tr>
+        <tr>
+          <th>143</th>
+          <td>27.2</td>
+          <td>55.2</td>
+        </tr>
+        <tr>
+          <th>144</th>
+          <td>50.4</td>
+          <td>54.7</td>
+        </tr>
+        <tr>
+          <th>145</th>
+          <td>30.9</td>
+          <td>54.5</td>
+        </tr>
+        <tr>
+          <th>146</th>
+          <td>70.5</td>
+          <td>54.4</td>
+        </tr>
+        <tr>
+          <th>147</th>
+          <td>74.3</td>
+          <td>54.4</td>
+        </tr>
+        <tr>
+          <th>148</th>
+          <td>59.5</td>
+          <td>54.3</td>
+        </tr>
+        <tr>
+          <th>149</th>
+          <td>94.4</td>
+          <td>54.3</td>
+        </tr>
+        <tr>
+          <th>150</th>
+          <td>57.2</td>
+          <td>54.2</td>
+        </tr>
+        <tr>
+          <th>151</th>
+          <td>80.9</td>
+          <td>53.7</td>
+        </tr>
+        <tr>
+          <th>152</th>
+          <td>54.6</td>
+          <td>53.6</td>
+        </tr>
+        <tr>
+          <th>153</th>
+          <td>19.4</td>
+          <td>53.3</td>
+        </tr>
+        <tr>
+          <th>154</th>
+          <td>29.7</td>
+          <td>53.2</td>
+        </tr>
+        <tr>
+          <th>155</th>
+          <td>33.4</td>
+          <td>52.9</td>
+        </tr>
+        <tr>
+          <th>156</th>
+          <td>30.1</td>
+          <td>52.8</td>
+        </tr>
+        <tr>
+          <th>157</th>
+          <td>59.0</td>
+          <td>52.7</td>
+        </tr>
+        <tr>
+          <th>158</th>
+          <td>58.6</td>
+          <td>52.7</td>
+        </tr>
+        <tr>
+          <th>159</th>
+          <td>56.9</td>
+          <td>52.6</td>
+        </tr>
+        <tr>
+          <th>160</th>
+          <td>90.2</td>
+          <td>52.4</td>
+        </tr>
+        <tr>
+          <th>161</th>
+          <td>42.9</td>
+          <td>52.4</td>
+        </tr>
+        <tr>
+          <th>162</th>
+          <td>63.1</td>
+          <td>52.2</td>
+        </tr>
+        <tr>
+          <th>163</th>
+          <td>20.4</td>
+          <td>52.1</td>
+        </tr>
+        <tr>
+          <th>164</th>
+          <td>59.7</td>
+          <td>52.1</td>
+        </tr>
+        <tr>
+          <th>165</th>
+          <td>89.2</td>
+          <td>52.1</td>
+        </tr>
+        <tr>
+          <th>166</th>
+          <td>88.4</td>
+          <td>51.9</td>
+        </tr>
+        <tr>
+          <th>167</th>
+          <td>48.4</td>
+          <td>51.5</td>
+        </tr>
+        <tr>
+          <th>168</th>
+          <td>70.5</td>
+          <td>51.3</td>
+        </tr>
+        <tr>
+          <th>169</th>
+          <td>53.1</td>
+          <td>50.9</td>
+        </tr>
+        <tr>
+          <th>170</th>
+          <td>53.8</td>
+          <td>50.8</td>
+        </tr>
+        <tr>
+          <th>171</th>
+          <td>91.2</td>
+          <td>50.7</td>
+        </tr>
+        <tr>
+          <th>172</th>
+          <td>27.6</td>
+          <td>50.3</td>
+        </tr>
+        <tr>
+          <th>173</th>
+          <td>38.7</td>
+          <td>50.3</td>
+        </tr>
+        <tr>
+          <th>174</th>
+          <td>53.3</td>
+          <td>50.3</td>
+        </tr>
+        <tr>
+          <th>175</th>
+          <td>45.8</td>
+          <td>50.1</td>
+        </tr>
+        <tr>
+          <th>176</th>
+          <td>31.8</td>
+          <td>50.0</td>
+        </tr>
+        <tr>
+          <th>177</th>
+          <td>39.2</td>
+          <td>49.8</td>
+        </tr>
+        <tr>
+          <th>178</th>
+          <td>50.8</td>
+          <td>49.7</td>
+        </tr>
+        <tr>
+          <th>179</th>
+          <td>92.2</td>
+          <td>49.7</td>
+        </tr>
+        <tr>
+          <th>180</th>
+          <td>23.9</td>
+          <td>49.6</td>
+        </tr>
+        <tr>
+          <th>181</th>
+          <td>83.7</td>
+          <td>49.5</td>
+        </tr>
+        <tr>
+          <th>182</th>
+          <td>30.0</td>
+          <td>49.5</td>
+        </tr>
+        <tr>
+          <th>183</th>
+          <td>52.0</td>
+          <td>49.5</td>
+        </tr>
+        <tr>
+          <th>184</th>
+          <td>23.8</td>
+          <td>49.5</td>
+        </tr>
+        <tr>
+          <th>185</th>
+          <td>42.6</td>
+          <td>49.3</td>
+        </tr>
+        <tr>
+          <th>186</th>
+          <td>24.7</td>
+          <td>49.2</td>
+        </tr>
+        <tr>
+          <th>187</th>
+          <td>36.9</td>
+          <td>49.1</td>
+        </tr>
+        <tr>
+          <th>188</th>
+          <td>51.6</td>
+          <td>49.1</td>
+        </tr>
+        <tr>
+          <th>189</th>
+          <td>100.0</td>
+          <td>49.0</td>
+        </tr>
+        <tr>
+          <th>190</th>
+          <td>35.9</td>
+          <td>48.9</td>
+        </tr>
+        <tr>
+          <th>191</th>
+          <td>9.4</td>
+          <td>48.5</td>
+        </tr>
+        <tr>
+          <th>192</th>
+          <td>98.3</td>
+          <td>48.5</td>
+        </tr>
+        <tr>
+          <th>193</th>
+          <td>96.0</td>
+          <td>48.5</td>
+        </tr>
+        <tr>
+          <th>194</th>
+          <td>49.2</td>
+          <td>48.1</td>
+        </tr>
+        <tr>
+          <th>195</th>
+          <td>79.0</td>
+          <td>48.1</td>
+        </tr>
+        <tr>
+          <th>196</th>
+          <td>57.0</td>
+          <td>48.1</td>
+        </tr>
+        <tr>
+          <th>197</th>
+          <td>43.7</td>
+          <td>48.1</td>
+        </tr>
+        <tr>
+          <th>198</th>
+          <td>3.7</td>
+          <td>48.0</td>
+        </tr>
+        <tr>
+          <th>199</th>
+          <td>73.3</td>
+          <td>47.9</td>
+        </tr>
+        <tr>
+          <th>200</th>
+          <td>12.8</td>
+          <td>47.8</td>
+        </tr>
+        <tr>
+          <th>201</th>
+          <td>50.9</td>
+          <td>47.4</td>
+        </tr>
+        <tr>
+          <th>202</th>
+          <td>7.6</td>
+          <td>47.2</td>
+        </tr>
+        <tr>
+          <th>203</th>
+          <td>73.6</td>
+          <td>47.1</td>
+        </tr>
+        <tr>
+          <th>204</th>
+          <td>96.0</td>
+          <td>47.0</td>
+        </tr>
+        <tr>
+          <th>205</th>
+          <td>44.4</td>
+          <td>47.0</td>
+        </tr>
+        <tr>
+          <th>206</th>
+          <td>76.8</td>
+          <td>46.9</td>
+        </tr>
+        <tr>
+          <th>207</th>
+          <td>46.9</td>
+          <td>46.9</td>
+        </tr>
+        <tr>
+          <th>208</th>
+          <td>73.0</td>
+          <td>46.7</td>
+        </tr>
+        <tr>
+          <th>209</th>
+          <td>22.6</td>
+          <td>46.6</td>
+        </tr>
+        <tr>
+          <th>210</th>
+          <td>72.1</td>
+          <td>46.6</td>
+        </tr>
+        <tr>
+          <th>211</th>
+          <td>33.4</td>
+          <td>46.5</td>
+        </tr>
+        <tr>
+          <th>212</th>
+          <td>54.9</td>
+          <td>46.4</td>
+        </tr>
+        <tr>
+          <th>213</th>
+          <td>56.8</td>
+          <td>46.3</td>
+        </tr>
+        <tr>
+          <th>214</th>
+          <td>40.1</td>
+          <td>46.1</td>
+        </tr>
+        <tr>
+          <th>215</th>
+          <td>72.0</td>
+          <td>45.9</td>
+        </tr>
+        <tr>
+          <th>216</th>
+          <td>43.5</td>
+          <td>45.8</td>
+        </tr>
+        <tr>
+          <th>217</th>
+          <td>55.8</td>
+          <td>45.6</td>
+        </tr>
+        <tr>
+          <th>218</th>
+          <td>45.8</td>
+          <td>45.5</td>
+        </tr>
+        <tr>
+          <th>219</th>
+          <td>3.8</td>
+          <td>45.2</td>
+        </tr>
+        <tr>
+          <th>220</th>
+          <td>13.2</td>
+          <td>44.5</td>
+        </tr>
+        <tr>
+          <th>221</th>
+          <td>49.3</td>
+          <td>44.3</td>
+        </tr>
+        <tr>
+          <th>222</th>
+          <td>21.0</td>
+          <td>44.2</td>
+        </tr>
+        <tr>
+          <th>223</th>
+          <td>61.9</td>
+          <td>44.0</td>
+        </tr>
+        <tr>
+          <th>224</th>
+          <td>79.4</td>
+          <td>44.0</td>
+        </tr>
+        <tr>
+          <th>225</th>
+          <td>49.1</td>
+          <td>44.0</td>
+        </tr>
+        <tr>
+          <th>226</th>
+          <td>27.5</td>
+          <td>43.8</td>
+        </tr>
+        <tr>
+          <th>227</th>
+          <td>54.7</td>
+          <td>43.7</td>
+        </tr>
+        <tr>
+          <th>228</th>
+          <td>17.2</td>
+          <td>43.6</td>
+        </tr>
+        <tr>
+          <th>229</th>
+          <td>11.9</td>
+          <td>43.4</td>
+        </tr>
+        <tr>
+          <th>230</th>
+          <td>66.4</td>
+          <td>43.4</td>
+        </tr>
+        <tr>
+          <th>231</th>
+          <td>59.4</td>
+          <td>43.3</td>
+        </tr>
+        <tr>
+          <th>232</th>
+          <td>14.1</td>
+          <td>43.2</td>
+        </tr>
+        <tr>
+          <th>233</th>
+          <td>41.4</td>
+          <td>43.1</td>
+        </tr>
+        <tr>
+          <th>234</th>
+          <td>12.5</td>
+          <td>43.0</td>
+        </tr>
+        <tr>
+          <th>235</th>
+          <td>1.1</td>
+          <td>42.9</td>
+        </tr>
+        <tr>
+          <th>236</th>
+          <td>38.2</td>
+          <td>42.9</td>
+        </tr>
+        <tr>
+          <th>237</th>
+          <td>50.9</td>
+          <td>42.8</td>
+        </tr>
+        <tr>
+          <th>238</th>
+          <td>23.7</td>
+          <td>42.6</td>
+        </tr>
+        <tr>
+          <th>239</th>
+          <td>47.2</td>
+          <td>42.5</td>
+        </tr>
+        <tr>
+          <th>240</th>
+          <td>4.0</td>
+          <td>42.5</td>
+        </tr>
+        <tr>
+          <th>241</th>
+          <td>24.1</td>
+          <td>42.3</td>
+        </tr>
+        <tr>
+          <th>242</th>
+          <td>17.9</td>
+          <td>42.0</td>
+        </tr>
+        <tr>
+          <th>243</th>
+          <td>62.8</td>
+          <td>42.0</td>
+        </tr>
+        <tr>
+          <th>244</th>
+          <td>8.9</td>
+          <td>41.8</td>
+        </tr>
+        <tr>
+          <th>245</th>
+          <td>78.0</td>
+          <td>41.8</td>
+        </tr>
+        <tr>
+          <th>246</th>
+          <td>36.8</td>
+          <td>41.6</td>
+        </tr>
+        <tr>
+          <th>247</th>
+          <td>23.0</td>
+          <td>41.6</td>
+        </tr>
+        <tr>
+          <th>248</th>
+          <td>63.1</td>
+          <td>41.5</td>
+        </tr>
+        <tr>
+          <th>249</th>
+          <td>11.0</td>
+          <td>41.3</td>
+        </tr>
+        <tr>
+          <th>250</th>
+          <td>23.8</td>
+          <td>41.3</td>
+        </tr>
+        <tr>
+          <th>251</th>
+          <td>46.5</td>
+          <td>41.2</td>
+        </tr>
+        <tr>
+          <th>252</th>
+          <td>12.5</td>
+          <td>41.1</td>
+        </tr>
+        <tr>
+          <th>253</th>
+          <td>4.5</td>
+          <td>40.9</td>
+        </tr>
+        <tr>
+          <th>254</th>
+          <td>20.6</td>
+          <td>40.9</td>
+        </tr>
+        <tr>
+          <th>255</th>
+          <td>44.5</td>
+          <td>40.6</td>
+        </tr>
+        <tr>
+          <th>256</th>
+          <td>21.1</td>
+          <td>40.6</td>
+        </tr>
+        <tr>
+          <th>257</th>
+          <td>6.5</td>
+          <td>40.6</td>
+        </tr>
+        <tr>
+          <th>258</th>
+          <td>43.9</td>
+          <td>40.5</td>
+        </tr>
+        <tr>
+          <th>259</th>
+          <td>63.4</td>
+          <td>40.5</td>
+        </tr>
+        <tr>
+          <th>260</th>
+          <td>48.8</td>
+          <td>40.5</td>
+        </tr>
+        <tr>
+          <th>261</th>
+          <td>35.0</td>
+          <td>40.4</td>
+        </tr>
+        <tr>
+          <th>262</th>
+          <td>46.7</td>
+          <td>40.3</td>
+        </tr>
+        <tr>
+          <th>263</th>
+          <td>67.6</td>
+          <td>40.2</td>
+        </tr>
+        <tr>
+          <th>264</th>
+          <td>37.1</td>
+          <td>40.2</td>
+        </tr>
+        <tr>
+          <th>265</th>
+          <td>22.1</td>
+          <td>40.2</td>
+        </tr>
+        <tr>
+          <th>266</th>
+          <td>15.8</td>
+          <td>40.1</td>
+        </tr>
+        <tr>
+          <th>267</th>
+          <td>25.7</td>
+          <td>40.1</td>
+        </tr>
+        <tr>
+          <th>268</th>
+          <td>3.9</td>
+          <td>40.0</td>
+        </tr>
+        <tr>
+          <th>269</th>
+          <td>9.6</td>
+          <td>39.8</td>
+        </tr>
+        <tr>
+          <th>270</th>
+          <td>18.8</td>
+          <td>39.8</td>
+        </tr>
+        <tr>
+          <th>271</th>
+          <td>69.6</td>
+          <td>39.6</td>
+        </tr>
+        <tr>
+          <th>272</th>
+          <td>47.8</td>
+          <td>39.6</td>
+        </tr>
+        <tr>
+          <th>273</th>
+          <td>49.8</td>
+          <td>39.5</td>
+        </tr>
+        <tr>
+          <th>274</th>
+          <td>22.3</td>
+          <td>39.4</td>
+        </tr>
+        <tr>
+          <th>275</th>
+          <td>25.3</td>
+          <td>39.3</td>
+        </tr>
+        <tr>
+          <th>276</th>
+          <td>1.6</td>
+          <td>39.2</td>
+        </tr>
+        <tr>
+          <th>277</th>
+          <td>32.6</td>
+          <td>39.2</td>
+        </tr>
+        <tr>
+          <th>278</th>
+          <td>68.5</td>
+          <td>39.1</td>
+        </tr>
+        <tr>
+          <th>279</th>
+          <td>41.9</td>
+          <td>39.1</td>
+        </tr>
+        <tr>
+          <th>280</th>
+          <td>8.4</td>
+          <td>38.9</td>
+        </tr>
+        <tr>
+          <th>281</th>
+          <td>37.5</td>
+          <td>38.8</td>
+        </tr>
+        <tr>
+          <th>282</th>
+          <td>32.0</td>
+          <td>38.6</td>
+        </tr>
+        <tr>
+          <th>283</th>
+          <td>36.7</td>
+          <td>38.6</td>
+        </tr>
+        <tr>
+          <th>284</th>
+          <td>32.9</td>
+          <td>38.6</td>
+        </tr>
+        <tr>
+          <th>285</th>
+          <td>18.1</td>
+          <td>38.5</td>
+        </tr>
+        <tr>
+          <th>286</th>
+          <td>30.4</td>
+          <td>38.4</td>
+        </tr>
+        <tr>
+          <th>287</th>
+          <td>97.3</td>
+          <td>38.4</td>
+        </tr>
+        <tr>
+          <th>288</th>
+          <td>11.9</td>
+          <td>38.3</td>
+        </tr>
+        <tr>
+          <th>289</th>
+          <td>66.0</td>
+          <td>38.3</td>
+        </tr>
+        <tr>
+          <th>290</th>
+          <td>1.1</td>
+          <td>38.2</td>
+        </tr>
+        <tr>
+          <th>291</th>
+          <td>64.4</td>
+          <td>38.1</td>
+        </tr>
+        <tr>
+          <th>292</th>
+          <td>38.9</td>
+          <td>37.9</td>
+        </tr>
+        <tr>
+          <th>293</th>
+          <td>73.2</td>
+          <td>37.9</td>
+        </tr>
+        <tr>
+          <th>294</th>
+          <td>7.6</td>
+          <td>37.9</td>
+        </tr>
+        <tr>
+          <th>295</th>
+          <td>13.8</td>
+          <td>37.8</td>
+        </tr>
+        <tr>
+          <th>296</th>
+          <td>39.6</td>
+          <td>37.8</td>
+        </tr>
+        <tr>
+          <th>297</th>
+          <td>47.7</td>
+          <td>37.8</td>
+        </tr>
+        <tr>
+          <th>298</th>
+          <td>16.4</td>
+          <td>37.7</td>
+        </tr>
+        <tr>
+          <th>299</th>
+          <td>52.6</td>
+          <td>37.7</td>
+        </tr>
+        <tr>
+          <th>300</th>
+          <td>94.4</td>
+          <td>37.6</td>
+        </tr>
+        <tr>
+          <th>301</th>
+          <td>52.8</td>
+          <td>37.6</td>
+        </tr>
+        <tr>
+          <th>302</th>
+          <td>27.9</td>
+          <td>37.5</td>
+        </tr>
+        <tr>
+          <th>303</th>
+          <td>19.6</td>
+          <td>37.4</td>
+        </tr>
+        <tr>
+          <th>304</th>
+          <td>38.1</td>
+          <td>37.3</td>
+        </tr>
+        <tr>
+          <th>305</th>
+          <td>37.5</td>
+          <td>37.3</td>
+        </tr>
+        <tr>
+          <th>306</th>
+          <td>63.8</td>
+          <td>37.0</td>
+        </tr>
+        <tr>
+          <th>307</th>
+          <td>61.7</td>
+          <td>36.9</td>
+        </tr>
+        <tr>
+          <th>308</th>
+          <td>81.8</td>
+          <td>36.9</td>
+        </tr>
+        <tr>
+          <th>309</th>
+          <td>22.2</td>
+          <td>36.9</td>
+        </tr>
+        <tr>
+          <th>310</th>
+          <td>10.2</td>
+          <td>36.7</td>
+        </tr>
+        <tr>
+          <th>311</th>
+          <td>37.5</td>
+          <td>36.6</td>
+        </tr>
+        <tr>
+          <th>312</th>
+          <td>46.2</td>
+          <td>36.5</td>
+        </tr>
+        <tr>
+          <th>313</th>
+          <td>11.1</td>
+          <td>36.3</td>
+        </tr>
+        <tr>
+          <th>314</th>
+          <td>17.9</td>
+          <td>36.3</td>
+        </tr>
+        <tr>
+          <th>315</th>
+          <td>19.7</td>
+          <td>36.2</td>
+        </tr>
+        <tr>
+          <th>316</th>
+          <td>43.8</td>
+          <td>36.2</td>
+        </tr>
+        <tr>
+          <th>317</th>
+          <td>73.3</td>
+          <td>36.1</td>
+        </tr>
+        <tr>
+          <th>318</th>
+          <td>33.3</td>
+          <td>35.9</td>
+        </tr>
+        <tr>
+          <th>319</th>
+          <td>31.0</td>
+          <td>35.9</td>
+        </tr>
+        <tr>
+          <th>320</th>
+          <td>78.6</td>
+          <td>35.9</td>
+        </tr>
+        <tr>
+          <th>321</th>
+          <td>17.5</td>
+          <td>35.8</td>
+        </tr>
+        <tr>
+          <th>322</th>
+          <td>2.7</td>
+          <td>35.7</td>
+        </tr>
+        <tr>
+          <th>323</th>
+          <td>77.9</td>
+          <td>35.7</td>
+        </tr>
+        <tr>
+          <th>324</th>
+          <td>40.2</td>
+          <td>35.6</td>
+        </tr>
+        <tr>
+          <th>325</th>
+          <td>60.0</td>
+          <td>35.6</td>
+        </tr>
+        <tr>
+          <th>326</th>
+          <td>41.1</td>
+          <td>35.6</td>
+        </tr>
+        <tr>
+          <th>327</th>
+          <td>65.9</td>
+          <td>35.6</td>
+        </tr>
+        <tr>
+          <th>328</th>
+          <td>37.4</td>
+          <td>35.5</td>
+        </tr>
+        <tr>
+          <th>329</th>
+          <td>31.6</td>
+          <td>35.4</td>
+        </tr>
+        <tr>
+          <th>330</th>
+          <td>2.9</td>
+          <td>35.3</td>
+        </tr>
+        <tr>
+          <th>331</th>
+          <td>1.8</td>
+          <td>35.3</td>
+        </tr>
+        <tr>
+          <th>332</th>
+          <td>12.0</td>
+          <td>35.3</td>
+        </tr>
+        <tr>
+          <th>333</th>
+          <td>1.6</td>
+          <td>35.1</td>
+        </tr>
+        <tr>
+          <th>334</th>
+          <td>7.0</td>
+          <td>35.1</td>
+        </tr>
+        <tr>
+          <th>335</th>
+          <td>1.3</td>
+          <td>35.0</td>
+        </tr>
+        <tr>
+          <th>336</th>
+          <td>1.1</td>
+          <td>34.9</td>
+        </tr>
+        <tr>
+          <th>337</th>
+          <td>23.4</td>
+          <td>34.8</td>
+        </tr>
+        <tr>
+          <th>338</th>
+          <td>43.4</td>
+          <td>34.7</td>
+        </tr>
+        <tr>
+          <th>339</th>
+          <td>100.0</td>
+          <td>34.7</td>
+        </tr>
+        <tr>
+          <th>340</th>
+          <td>20.1</td>
+          <td>34.6</td>
+        </tr>
+        <tr>
+          <th>341</th>
+          <td>46.2</td>
+          <td>34.6</td>
+        </tr>
+        <tr>
+          <th>342</th>
+          <td>37.5</td>
+          <td>34.2</td>
+        </tr>
+        <tr>
+          <th>343</th>
+          <td>58.6</td>
+          <td>34.0</td>
+        </tr>
+        <tr>
+          <th>344</th>
+          <td>38.5</td>
+          <td>34.0</td>
+        </tr>
+        <tr>
+          <th>345</th>
+          <td>49.8</td>
+          <td>33.9</td>
+        </tr>
+        <tr>
+          <th>346</th>
+          <td>40.1</td>
+          <td>33.9</td>
+        </tr>
+        <tr>
+          <th>347</th>
+          <td>74.4</td>
+          <td>33.9</td>
+        </tr>
+        <tr>
+          <th>348</th>
+          <td>7.9</td>
+          <td>33.8</td>
+        </tr>
+        <tr>
+          <th>349</th>
+          <td>3.8</td>
+          <td>33.8</td>
+        </tr>
+        <tr>
+          <th>350</th>
+          <td>15.2</td>
+          <td>33.6</td>
+        </tr>
+        <tr>
+          <th>351</th>
+          <td>49.2</td>
+          <td>33.5</td>
+        </tr>
+        <tr>
+          <th>352</th>
+          <td>33.8</td>
+          <td>33.5</td>
+        </tr>
+        <tr>
+          <th>353</th>
+          <td>29.9</td>
+          <td>33.5</td>
+        </tr>
+        <tr>
+          <th>354</th>
+          <td>6.9</td>
+          <td>33.4</td>
+        </tr>
+        <tr>
+          <th>355</th>
+          <td>9.2</td>
+          <td>33.4</td>
+        </tr>
+        <tr>
+          <th>356</th>
+          <td>74.5</td>
+          <td>33.3</td>
+        </tr>
+        <tr>
+          <th>357</th>
+          <td>51.2</td>
+          <td>33.3</td>
+        </tr>
+        <tr>
+          <th>358</th>
+          <td>20.2</td>
+          <td>33.3</td>
+        </tr>
+        <tr>
+          <th>359</th>
+          <td>28.5</td>
+          <td>33.2</td>
+        </tr>
+        <tr>
+          <th>360</th>
+          <td>13.5</td>
+          <td>33.1</td>
+        </tr>
+        <tr>
+          <th>361</th>
+          <td>52.9</td>
+          <td>33.1</td>
+        </tr>
+        <tr>
+          <th>362</th>
+          <td>20.5</td>
+          <td>33.1</td>
+        </tr>
+        <tr>
+          <th>363</th>
+          <td>1.2</td>
+          <td>33.0</td>
+        </tr>
+        <tr>
+          <th>364</th>
+          <td>78.2</td>
+          <td>33.0</td>
+        </tr>
+        <tr>
+          <th>365</th>
+          <td>33.7</td>
+          <td>33.0</td>
+        </tr>
+        <tr>
+          <th>366</th>
+          <td>56.8</td>
+          <td>32.9</td>
+        </tr>
+        <tr>
+          <th>367</th>
+          <td>23.9</td>
+          <td>32.9</td>
+        </tr>
+        <tr>
+          <th>368</th>
+          <td>2.1</td>
+          <td>32.9</td>
+        </tr>
+        <tr>
+          <th>369</th>
+          <td>10.0</td>
+          <td>32.9</td>
+        </tr>
+        <tr>
+          <th>370</th>
+          <td>44.1</td>
+          <td>32.9</td>
+        </tr>
+        <tr>
+          <th>371</th>
+          <td>34.9</td>
+          <td>32.8</td>
+        </tr>
+        <tr>
+          <th>372</th>
+          <td>40.2</td>
+          <td>32.5</td>
+        </tr>
+        <tr>
+          <th>373</th>
+          <td>1.0</td>
+          <td>32.5</td>
+        </tr>
+        <tr>
+          <th>374</th>
+          <td>3.9</td>
+          <td>32.5</td>
+        </tr>
+        <tr>
+          <th>375</th>
+          <td>15.9</td>
+          <td>32.5</td>
+        </tr>
+        <tr>
+          <th>376</th>
+          <td>24.5</td>
+          <td>32.5</td>
+        </tr>
+        <tr>
+          <th>377</th>
+          <td>30.8</td>
+          <td>32.4</td>
+        </tr>
+        <tr>
+          <th>378</th>
+          <td>26.9</td>
+          <td>32.2</td>
+        </tr>
+        <tr>
+          <th>379</th>
+          <td>24.7</td>
+          <td>32.1</td>
+        </tr>
+        <tr>
+          <th>380</th>
+          <td>42.3</td>
+          <td>32.0</td>
+        </tr>
+        <tr>
+          <th>381</th>
+          <td>1.5</td>
+          <td>31.9</td>
+        </tr>
+        <tr>
+          <th>382</th>
+          <td>43.3</td>
+          <td>31.9</td>
+        </tr>
+        <tr>
+          <th>383</th>
+          <td>26.0</td>
+          <td>31.6</td>
+        </tr>
+        <tr>
+          <th>384</th>
+          <td>21.8</td>
+          <td>31.5</td>
+        </tr>
+        <tr>
+          <th>385</th>
+          <td>2.4</td>
+          <td>31.3</td>
+        </tr>
+        <tr>
+          <th>386</th>
+          <td>21.5</td>
+          <td>31.3</td>
+        </tr>
+        <tr>
+          <th>387</th>
+          <td>52.2</td>
+          <td>31.2</td>
+        </tr>
+        <tr>
+          <th>388</th>
+          <td>32.0</td>
+          <td>31.2</td>
+        </tr>
+        <tr>
+          <th>389</th>
+          <td>11.7</td>
+          <td>31.1</td>
+        </tr>
+        <tr>
+          <th>390</th>
+          <td>17.3</td>
+          <td>31.0</td>
+        </tr>
+        <tr>
+          <th>391</th>
+          <td>40.3</td>
+          <td>31.0</td>
+        </tr>
+        <tr>
+          <th>392</th>
+          <td>38.2</td>
+          <td>31.0</td>
+        </tr>
+        <tr>
+          <th>393</th>
+          <td>18.8</td>
+          <td>30.8</td>
+        </tr>
+        <tr>
+          <th>394</th>
+          <td>5.1</td>
+          <td>30.7</td>
+        </tr>
+        <tr>
+          <th>395</th>
+          <td>49.7</td>
+          <td>30.7</td>
+        </tr>
+        <tr>
+          <th>396</th>
+          <td>2.1</td>
+          <td>30.6</td>
+        </tr>
+        <tr>
+          <th>397</th>
+          <td>19.7</td>
+          <td>30.5</td>
+        </tr>
+        <tr>
+          <th>398</th>
+          <td>20.1</td>
+          <td>30.5</td>
+        </tr>
+        <tr>
+          <th>399</th>
+          <td>18.7</td>
+          <td>30.5</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 9</h1>
+    <table border="1" class="dataframe" data-question="9">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>academic_reputation</th>
+          <th>employer_reputation</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>100.0</td>
+          <td>100.0</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>100.0</td>
+          <td>100.0</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>100.0</td>
+          <td>100.0</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>98.7</td>
+          <td>81.2</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>99.6</td>
+          <td>90.7</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>99.9</td>
+          <td>96.7</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>98.7</td>
+          <td>90.5</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>99.9</td>
+          <td>99.7</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>99.7</td>
+          <td>97.4</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>95.4</td>
+          <td>92.6</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>98.9</td>
+          <td>90.2</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>89.7</td>
+          <td>56.9</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>88.2</td>
+          <td>74.7</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>100.0</td>
+          <td>99.8</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>100.0</td>
+          <td>99.1</td>
+        </tr>
+        <tr>
+          <th>15</th>
+          <td>85.0</td>
+          <td>73.1</td>
+        </tr>
+        <tr>
+          <th>16</th>
+          <td>91.7</td>
+          <td>56.1</td>
+        </tr>
+        <tr>
+          <th>17</th>
+          <td>95.6</td>
+          <td>91.3</td>
+        </tr>
+        <tr>
+          <th>18</th>
+          <td>78.5</td>
+          <td>79.0</td>
+        </tr>
+        <tr>
+          <th>19</th>
+          <td>88.5</td>
+          <td>51.2</td>
+        </tr>
+        <tr>
+          <th>20</th>
+          <td>62.5</td>
+          <td>52.7</td>
+        </tr>
+        <tr>
+          <th>21</th>
+          <td>93.8</td>
+          <td>78.1</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>85.0</td>
+          <td>46.5</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>73.5</td>
+          <td>74.6</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>89.1</td>
+          <td>53.6</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>68.1</td>
+          <td>43.1</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>38.7</td>
+          <td>18.5</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>64.8</td>
+          <td>55.6</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>62.4</td>
+          <td>66.0</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>77.7</td>
+          <td>70.4</td>
+        </tr>
+        <tr>
+          <th>30</th>
+          <td>65.7</td>
+          <td>60.7</td>
+        </tr>
+        <tr>
+          <th>31</th>
+          <td>71.4</td>
+          <td>40.4</td>
+        </tr>
+        <tr>
+          <th>32</th>
+          <td>43.4</td>
+          <td>18.7</td>
+        </tr>
+        <tr>
+          <th>33</th>
+          <td>57.0</td>
+          <td>51.4</td>
+        </tr>
+        <tr>
+          <th>34</th>
+          <td>54.8</td>
+          <td>29.1</td>
+        </tr>
+        <tr>
+          <th>35</th>
+          <td>64.6</td>
+          <td>16.7</td>
+        </tr>
+        <tr>
+          <th>36</th>
+          <td>44.5</td>
+          <td>20.5</td>
+        </tr>
+        <tr>
+          <th>37</th>
+          <td>61.4</td>
+          <td>54.8</td>
+        </tr>
+        <tr>
+          <th>38</th>
+          <td>27.2</td>
+          <td>12.3</td>
+        </tr>
+        <tr>
+          <th>39</th>
+          <td>66.9</td>
+          <td>25.6</td>
+        </tr>
+        <tr>
+          <th>40</th>
+          <td>46.1</td>
+          <td>21.6</td>
+        </tr>
+        <tr>
+          <th>41</th>
+          <td>48.2</td>
+          <td>35.7</td>
+        </tr>
+        <tr>
+          <th>42</th>
+          <td>21.9</td>
+          <td>34.5</td>
+        </tr>
+        <tr>
+          <th>43</th>
+          <td>24.4</td>
+          <td>9.3</td>
+        </tr>
+        <tr>
+          <th>44</th>
+          <td>20.6</td>
+          <td>11.3</td>
+        </tr>
+        <tr>
+          <th>45</th>
+          <td>37.5</td>
+          <td>30.0</td>
+        </tr>
+        <tr>
+          <th>46</th>
+          <td>38.6</td>
+          <td>40.1</td>
+        </tr>
+        <tr>
+          <th>47</th>
+          <td>29.1</td>
+          <td>20.0</td>
+        </tr>
+        <tr>
+          <th>48</th>
+          <td>59.1</td>
+          <td>48.0</td>
+        </tr>
+        <tr>
+          <th>49</th>
+          <td>41.6</td>
+          <td>27.0</td>
+        </tr>
+        <tr>
+          <th>50</th>
+          <td>30.3</td>
+          <td>30.8</td>
+        </tr>
+        <tr>
+          <th>51</th>
+          <td>46.4</td>
+          <td>28.9</td>
+        </tr>
+        <tr>
+          <th>52</th>
+          <td>35.1</td>
+          <td>38.7</td>
+        </tr>
+        <tr>
+          <th>53</th>
+          <td>19.8</td>
+          <td>14.2</td>
+        </tr>
+        <tr>
+          <th>54</th>
+          <td>21.1</td>
+          <td>16.9</td>
+        </tr>
+        <tr>
+          <th>55</th>
+          <td>40.1</td>
+          <td>22.5</td>
+        </tr>
+        <tr>
+          <th>56</th>
+          <td>44.8</td>
+          <td>16.1</td>
+        </tr>
+        <tr>
+          <th>57</th>
+          <td>32.0</td>
+          <td>12.7</td>
+        </tr>
+        <tr>
+          <th>58</th>
+          <td>39.4</td>
+          <td>25.9</td>
+        </tr>
+        <tr>
+          <th>59</th>
+          <td>19.8</td>
+          <td>5.4</td>
+        </tr>
+        <tr>
+          <th>60</th>
+          <td>3.3</td>
+          <td>2.9</td>
+        </tr>
+        <tr>
+          <th>61</th>
+          <td>20.1</td>
+          <td>11.0</td>
+        </tr>
+        <tr>
+          <th>62</th>
+          <td>42.7</td>
+          <td>31.2</td>
+        </tr>
+        <tr>
+          <th>63</th>
+          <td>18.8</td>
+          <td>22.2</td>
+        </tr>
+        <tr>
+          <th>64</th>
+          <td>20.1</td>
+          <td>15.1</td>
+        </tr>
+        <tr>
+          <th>65</th>
+          <td>31.4</td>
+          <td>32.3</td>
+        </tr>
+        <tr>
+          <th>66</th>
+          <td>28.2</td>
+          <td>24.9</td>
+        </tr>
+        <tr>
+          <th>67</th>
+          <td>11.9</td>
+          <td>12.4</td>
+        </tr>
+        <tr>
+          <th>68</th>
+          <td>16.4</td>
+          <td>6.9</td>
+        </tr>
+        <tr>
+          <th>69</th>
+          <td>22.1</td>
+          <td>7.2</td>
+        </tr>
+        <tr>
+          <th>70</th>
+          <td>16.6</td>
+          <td>12.2</td>
+        </tr>
+        <tr>
+          <th>71</th>
+          <td>16.7</td>
+          <td>7.4</td>
+        </tr>
+        <tr>
+          <th>72</th>
+          <td>23.4</td>
+          <td>29.4</td>
+        </tr>
+        <tr>
+          <th>73</th>
+          <td>18.2</td>
+          <td>22.8</td>
+        </tr>
+        <tr>
+          <th>74</th>
+          <td>6.5</td>
+          <td>6.8</td>
+        </tr>
+        <tr>
+          <th>75</th>
+          <td>28.5</td>
+          <td>19.5</td>
+        </tr>
+        <tr>
+          <th>76</th>
+          <td>8.3</td>
+          <td>6.8</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 10</h1>
+    <table border="1" class="dataframe" data-question="10">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>international_students</th>
+          <th>faculty_student_score</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>64.7</td>
+          <td>77.4</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>99.8</td>
+          <td>37.5</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>92.5</td>
+          <td>26.2</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>20.6</td>
+          <td>94.2</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>37.5</td>
+          <td>11.8</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>3.7</td>
+          <td>25.2</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>66.2</td>
+          <td>94.9</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>93.0</td>
+          <td>71.5</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>4.2</td>
+          <td>28.6</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>30.1</td>
+          <td>92.4</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>3.1</td>
+          <td>27.6</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>46.6</td>
+          <td>43.2</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>29.4</td>
+          <td>100.0</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>3.8</td>
+          <td>43.7</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>23.3</td>
+          <td>82.7</td>
+        </tr>
+        <tr>
+          <th>15</th>
+          <td>6.8</td>
+          <td>66.8</td>
+        </tr>
+        <tr>
+          <th>16</th>
+          <td>72.1</td>
+          <td>100.0</td>
+        </tr>
+        <tr>
+          <th>17</th>
+          <td>76.8</td>
+          <td>89.1</td>
+        </tr>
+        <tr>
+          <th>18</th>
+          <td>99.1</td>
+          <td>87.2</td>
+        </tr>
+        <tr>
+          <th>19</th>
+          <td>1.6</td>
+          <td>45.8</td>
+        </tr>
+        <tr>
+          <th>20</th>
+          <td>5.0</td>
+          <td>43.4</td>
+        </tr>
+        <tr>
+          <th>21</th>
+          <td>93.5</td>
+          <td>35.8</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>11.6</td>
+          <td>77.9</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>54.5</td>
+          <td>6.3</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>26.2</td>
+          <td>93.3</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>26.1</td>
+          <td>98.6</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>64.6</td>
+          <td>63.1</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>96.2</td>
+          <td>24.3</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>57.2</td>
+          <td>90.7</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>4.3</td>
+          <td>57.6</td>
+        </tr>
+        <tr>
+          <th>30</th>
+          <td>87.8</td>
+          <td>46.4</td>
+        </tr>
+        <tr>
+          <th>31</th>
+          <td>91.8</td>
+          <td>25.0</td>
+        </tr>
+        <tr>
+          <th>32</th>
+          <td>30.5</td>
+          <td>77.7</td>
+        </tr>
+        <tr>
+          <th>33</th>
+          <td>7.6</td>
+          <td>74.5</td>
+        </tr>
+        <tr>
+          <th>34</th>
+          <td>NaN</td>
+          <td>96.4</td>
+        </tr>
+        <tr>
+          <th>35</th>
+          <td>1.5</td>
+          <td>62.0</td>
+        </tr>
+        <tr>
+          <th>36</th>
+          <td>17.6</td>
+          <td>59.3</td>
+        </tr>
+        <tr>
+          <th>37</th>
+          <td>32.5</td>
+          <td>15.6</td>
+        </tr>
+        <tr>
+          <th>38</th>
+          <td>98.3</td>
+          <td>66.4</td>
+        </tr>
+        <tr>
+          <th>39</th>
+          <td>56.1</td>
+          <td>99.7</td>
+        </tr>
+        <tr>
+          <th>40</th>
+          <td>60.6</td>
+          <td>71.3</td>
+        </tr>
+        <tr>
+          <th>41</th>
+          <td>74.2</td>
+          <td>93.9</td>
+        </tr>
+        <tr>
+          <th>42</th>
+          <td>40.9</td>
+          <td>28.3</td>
+        </tr>
+        <tr>
+          <th>43</th>
+          <td>11.9</td>
+          <td>88.0</td>
+        </tr>
+        <tr>
+          <th>44</th>
+          <td>33.8</td>
+          <td>32.5</td>
+        </tr>
+        <tr>
+          <th>45</th>
+          <td>70.1</td>
+          <td>50.1</td>
+        </tr>
+        <tr>
+          <th>46</th>
+          <td>98.0</td>
+          <td>85.0</td>
+        </tr>
+        <tr>
+          <th>47</th>
+          <td>30.4</td>
+          <td>41.9</td>
+        </tr>
+        <tr>
+          <th>48</th>
+          <td>2.5</td>
+          <td>25.6</td>
+        </tr>
+        <tr>
+          <th>49</th>
+          <td>55.6</td>
+          <td>77.3</td>
+        </tr>
+        <tr>
+          <th>50</th>
+          <td>98.5</td>
+          <td>100.0</td>
+        </tr>
+        <tr>
+          <th>51</th>
+          <td>94.1</td>
+          <td>100.0</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 13</h1>
+    <table border="1" class="dataframe" data-question="13">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>country</th>
+          <th>sum_inter_citations</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>United States</td>
+          <td>2623.8207</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>United Kingdom</td>
+          <td>2347.1602</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Australia</td>
+          <td>1255.5530</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>Netherlands</td>
+          <td>748.4268</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Canada</td>
+          <td>724.5029</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>Switzerland</td>
+          <td>561.8790</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>China</td>
+          <td>482.2577</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Germany</td>
+          <td>455.5466</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>Hong Kong</td>
+          <td>375.3032</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>New Zealand</td>
+          <td>327.3357</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>Sweden</td>
+          <td>305.3745</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>Belgium</td>
+          <td>255.0750</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>France</td>
+          <td>198.0860</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>Denmark</td>
+          <td>186.4904</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>Singapore</td>
+          <td>160.3000</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 14</h1>
+    <table border="1" class="dataframe" data-question="14">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>country</th>
+          <th>avg_inter_citations</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>Singapore</td>
+          <td>80.150000</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>Switzerland</td>
+          <td>75.497000</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Hong Kong</td>
+          <td>62.550533</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>Australia</td>
+          <td>61.362388</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Netherlands</td>
+          <td>56.166733</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>New Zealand</td>
+          <td>53.226220</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>United Kingdom</td>
+          <td>52.889084</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Canada</td>
+          <td>50.779723</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>Denmark</td>
+          <td>46.196200</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>Norway</td>
+          <td>46.083300</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>Sweden</td>
+          <td>45.522983</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>Ireland</td>
+          <td>40.810833</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>Belgium</td>
+          <td>39.861500</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>United States</td>
+          <td>38.357020</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>China</td>
+          <td>37.164742</td>
+        </tr>
+        <tr>
+          <th>15</th>
+          <td>Israel</td>
+          <td>36.224833</td>
+        </tr>
+        <tr>
+          <th>16</th>
+          <td>Finland</td>
+          <td>32.759067</td>
+        </tr>
+        <tr>
+          <th>17</th>
+          <td>South Africa</td>
+          <td>31.579800</td>
+        </tr>
+        <tr>
+          <th>18</th>
+          <td>Austria</td>
+          <td>31.025500</td>
+        </tr>
+        <tr>
+          <th>19</th>
+          <td>Saudi Arabia</td>
+          <td>24.431567</td>
+        </tr>
+        <tr>
+          <th>20</th>
+          <td>Germany</td>
+          <td>19.839767</td>
+        </tr>
+        <tr>
+          <th>21</th>
+          <td>France</td>
+          <td>16.605891</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>South Korea</td>
+          <td>14.925775</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>Taiwan</td>
+          <td>13.880733</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>Lebanon</td>
+          <td>13.756600</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>Malaysia</td>
+          <td>9.417440</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>Italy</td>
+          <td>8.174883</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>Japan</td>
+          <td>7.546055</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>Spain</td>
+          <td>7.496975</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>Brazil</td>
+          <td>3.194100</td>
+        </tr>
+        <tr>
+          <th>30</th>
+          <td>India</td>
+          <td>2.952050</td>
+        </tr>
+        <tr>
+          <th>31</th>
+          <td>Mexico</td>
+          <td>2.350200</td>
+        </tr>
+        <tr>
+          <th>32</th>
+          <td>Chile</td>
+          <td>1.985200</td>
+        </tr>
+        <tr>
+          <th>33</th>
+          <td>Indonesia</td>
+          <td>1.690200</td>
+        </tr>
+        <tr>
+          <th>34</th>
+          <td>Thailand</td>
+          <td>1.365900</td>
+        </tr>
+        <tr>
+          <th>35</th>
+          <td>Argentina</td>
+          <td>1.335000</td>
+        </tr>
+        <tr>
+          <th>36</th>
+          <td>Colombia</td>
+          <td>1.329300</td>
+        </tr>
+        <tr>
+          <th>37</th>
+          <td>Russia</td>
+          <td>0.626680</td>
+        </tr>
+        <tr>
+          <th>38</th>
+          <td>Kazakhstan</td>
+          <td>0.412800</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 15</h1>
+    <table border="1" class="dataframe" data-question="15">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>country</th>
+          <th>institution_name</th>
+          <th>max_inter_citations</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>United States</td>
+          <td>Massachusetts Institute Of Technology</td>
+          <td>99.8000</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>Switzerland</td>
+          <td>Ecole Polytechnique Fédérale De Lausanne</td>
+          <td>98.9000</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Netherlands</td>
+          <td>Eindhoven University Of Technology</td>
+          <td>95.4493</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>United Kingdom</td>
+          <td>London School Of Economics And Political Science</td>
+          <td>91.1000</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Hong Kong</td>
+          <td>The Hong Kong University Of Science And Technology</td>
+          <td>89.5000</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>Singapore</td>
+          <td>Nanyang Technological University</td>
+          <td>88.8000</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>Australia</td>
+          <td>The University Of Western Australia</td>
+          <td>88.3000</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Belgium</td>
+          <td>Katholieke Universiteit Leuven</td>
+          <td>76.7700</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>New Zealand</td>
+          <td>University Of Waikato</td>
+          <td>73.6434</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>Canada</td>
+          <td>Western University</td>
+          <td>72.3240</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>Sweden</td>
+          <td>Chalmers University Of Technology</td>
+          <td>70.7608</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>Denmark</td>
+          <td>Technical University Of Denmark</td>
+          <td>65.5705</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>France</td>
+          <td>Ecole Polytechnique</td>
+          <td>63.1176</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>Finland</td>
+          <td>Aalto University</td>
+          <td>62.9799</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>China</td>
+          <td>Zhejiang University</td>
+          <td>60.9606</td>
+        </tr>
+        <tr>
+          <th>15</th>
+          <td>Austria</td>
+          <td>Technische Universität Wien</td>
+          <td>59.0250</td>
+        </tr>
+        <tr>
+          <th>16</th>
+          <td>Ireland</td>
+          <td>Trinity College Dublin</td>
+          <td>56.5160</td>
+        </tr>
+        <tr>
+          <th>17</th>
+          <td>Norway</td>
+          <td>University Of Bergen</td>
+          <td>53.8688</td>
+        </tr>
+        <tr>
+          <th>18</th>
+          <td>Israel</td>
+          <td>Technion - Israel Institute Of Technology</td>
+          <td>44.4800</td>
+        </tr>
+        <tr>
+          <th>19</th>
+          <td>Macau</td>
+          <td>University Of Macau</td>
+          <td>43.9000</td>
+        </tr>
+        <tr>
+          <th>20</th>
+          <td>Germany</td>
+          <td>Kit, Karlsruher Institut Für Technologie</td>
+          <td>43.5600</td>
+        </tr>
+        <tr>
+          <th>21</th>
+          <td>United Arab Emirates</td>
+          <td>Khalifa University</td>
+          <td>42.2000</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>Saudi Arabia</td>
+          <td>King Fahd University Of Petroleum &amp; Minerals</td>
+          <td>40.1000</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>Spain</td>
+          <td>Universitat Pompeu Fabra</td>
+          <td>36.8628</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>South Korea</td>
+          <td>Pohang University Of Science And Technology</td>
+          <td>35.0529</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>South Africa</td>
+          <td>University Of Cape Town</td>
+          <td>31.6572</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>Malaysia</td>
+          <td>Universiti Malaya</td>
+          <td>26.0620</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>Italy</td>
+          <td>Politecnico Di Milano</td>
+          <td>25.3774</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>Japan</td>
+          <td>Tokyo Institute Of Technology</td>
+          <td>18.8191</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>Taiwan</td>
+          <td>National Tsing Hua University</td>
+          <td>16.1868</td>
+        </tr>
+        <tr>
+          <th>30</th>
+          <td>Qatar</td>
+          <td>Qatar University</td>
+          <td>14.3000</td>
+        </tr>
+        <tr>
+          <th>31</th>
+          <td>Lebanon</td>
+          <td>American University Of Beirut</td>
+          <td>13.7104</td>
+        </tr>
+        <tr>
+          <th>32</th>
+          <td>Oman</td>
+          <td>Sultan Qaboos University</td>
+          <td>11.2000</td>
+        </tr>
+        <tr>
+          <th>33</th>
+          <td>Brunei</td>
+          <td>Universiti Brunei Darussalam</td>
+          <td>6.2000</td>
+        </tr>
+        <tr>
+          <th>34</th>
+          <td>Egypt</td>
+          <td>The American University In Cairo</td>
+          <td>5.3523</td>
+        </tr>
+        <tr>
+          <th>35</th>
+          <td>Estonia</td>
+          <td>University Of Tartu</td>
+          <td>4.8416</td>
+        </tr>
+        <tr>
+          <th>36</th>
+          <td>Mexico</td>
+          <td>Tecnológico De Monterrey</td>
+          <td>4.5172</td>
+        </tr>
+        <tr>
+          <th>37</th>
+          <td>Czech Republic</td>
+          <td>University Of Chemistry And Technology, Prague</td>
+          <td>4.4590</td>
+        </tr>
+        <tr>
+          <th>38</th>
+          <td>India</td>
+          <td>Indian Institute Of Technology Kharagpur</td>
+          <td>4.1552</td>
+        </tr>
+        <tr>
+          <th>39</th>
+          <td>Russia</td>
+          <td>Moscow Institute Of Physics And Technology State University</td>
+          <td>3.7278</td>
+        </tr>
+        <tr>
+          <th>40</th>
+          <td>Brazil</td>
+          <td>Universidade Estadual De Campinas</td>
+          <td>3.2373</td>
+        </tr>
+        <tr>
+          <th>41</th>
+          <td>Portugal</td>
+          <td>University Of Lisbon</td>
+          <td>3.0560</td>
+        </tr>
+        <tr>
+          <th>42</th>
+          <td>Chile</td>
+          <td>Pontificia Universidad Católica De Chile</td>
+          <td>2.6384</td>
+        </tr>
+        <tr>
+          <th>43</th>
+          <td>Colombia</td>
+          <td>Universidad De Los Andes Colombia</td>
+          <td>2.6001</td>
+        </tr>
+        <tr>
+          <th>44</th>
+          <td>Indonesia</td>
+          <td>University Of Indonesia</td>
+          <td>1.7955</td>
+        </tr>
+        <tr>
+          <th>45</th>
+          <td>Argentina</td>
+          <td>Universidad De Buenos Aires</td>
+          <td>1.2168</td>
+        </tr>
+        <tr>
+          <th>46</th>
+          <td>Thailand</td>
+          <td>Chulalongkorn University</td>
+          <td>0.9775</td>
+        </tr>
+        <tr>
+          <th>47</th>
+          <td>Poland</td>
+          <td>University Of Warsaw</td>
+          <td>0.6256</td>
+        </tr>
+        <tr>
+          <th>48</th>
+          <td>Kazakhstan</td>
+          <td>Al-Farabi Kazakh National University</td>
+          <td>0.4896</td>
+        </tr>
+        <tr>
+          <th>49</th>
+          <td>Belarus</td>
+          <td>Belarus State University</td>
+          <td>0.0935</td>
+        </tr>
+        <tr>
+          <th>50</th>
+          <td>Philippines</td>
+          <td>University Of The Philippines</td>
+          <td>0.0378</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 16</h1>
+    <table border="1" class="dataframe" data-question="16">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>country</th>
+          <th>avg_citations</th>
+          <th>avg_inter_faculty</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>Canada</td>
+          <td>55.700000</td>
+          <td>92.950000</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>Hong Kong</td>
+          <td>69.050000</td>
+          <td>99.950000</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>United Kingdom</td>
+          <td>65.988889</td>
+          <td>95.788889</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>Singapore</td>
+          <td>74.750000</td>
+          <td>100.000000</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Australia</td>
+          <td>79.260000</td>
+          <td>98.940000</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>Switzerland</td>
+          <td>98.950000</td>
+          <td>100.000000</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>France</td>
+          <td>100.000000</td>
+          <td>94.400000</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>China</td>
+          <td>66.900000</td>
+          <td>58.133333</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>United States</td>
+          <td>87.461111</td>
+          <td>73.894444</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>Japan</td>
+          <td>64.850000</td>
+          <td>11.400000</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>South Korea</td>
+          <td>85.050000</td>
+          <td>24.100000</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 17</h1>
+    <table border="1" class="dataframe" data-question="17">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>overall_score</th>
+          <th>rank</th>
+          <th>fit</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>100.0</td>
+          <td>1</td>
+          <td>78.221321</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>98.4</td>
+          <td>2</td>
+          <td>78.073809</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>97.4</td>
+          <td>3</td>
+          <td>77.926297</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>97.2</td>
+          <td>4</td>
+          <td>77.778785</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>96.9</td>
+          <td>5</td>
+          <td>77.631273</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>95.9</td>
+          <td>6</td>
+          <td>77.483761</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>95.0</td>
+          <td>7</td>
+          <td>77.336249</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>94.8</td>
+          <td>8</td>
+          <td>77.188737</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>94.1</td>
+          <td>9</td>
+          <td>77.041225</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>92.0</td>
+          <td>10</td>
+          <td>76.893713</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>91.8</td>
+          <td>11</td>
+          <td>76.746200</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>91.8</td>
+          <td>11</td>
+          <td>76.746200</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>90.9</td>
+          <td>13</td>
+          <td>76.451176</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>89.3</td>
+          <td>14</td>
+          <td>76.303664</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>88.9</td>
+          <td>15</td>
+          <td>76.156152</td>
+        </tr>
+        <tr>
+          <th>15</th>
+          <td>88.6</td>
+          <td>16</td>
+          <td>76.008640</td>
+        </tr>
+        <tr>
+          <th>16</th>
+          <td>87.7</td>
+          <td>17</td>
+          <td>75.861128</td>
+        </tr>
+        <tr>
+          <th>17</th>
+          <td>87.4</td>
+          <td>18</td>
+          <td>75.713616</td>
+        </tr>
+        <tr>
+          <th>18</th>
+          <td>87.4</td>
+          <td>18</td>
+          <td>75.713616</td>
+        </tr>
+        <tr>
+          <th>19</th>
+          <td>86.2</td>
+          <td>20</td>
+          <td>75.418592</td>
+        </tr>
+        <tr>
+          <th>20</th>
+          <td>86.0</td>
+          <td>21</td>
+          <td>75.271080</td>
+        </tr>
+        <tr>
+          <th>21</th>
+          <td>84.3</td>
+          <td>22</td>
+          <td>75.123568</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>84.3</td>
+          <td>22</td>
+          <td>75.123568</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>83.9</td>
+          <td>24</td>
+          <td>74.828543</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>83.8</td>
+          <td>25</td>
+          <td>74.681031</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>83.8</td>
+          <td>25</td>
+          <td>74.681031</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>82.7</td>
+          <td>27</td>
+          <td>74.386007</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>82.6</td>
+          <td>28</td>
+          <td>74.238495</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>82.1</td>
+          <td>29</td>
+          <td>74.090983</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>82.1</td>
+          <td>29</td>
+          <td>74.090983</td>
+        </tr>
+        <tr>
+          <th>30</th>
+          <td>81.5</td>
+          <td>31</td>
+          <td>73.795959</td>
+        </tr>
+        <tr>
+          <th>31</th>
+          <td>80.6</td>
+          <td>32</td>
+          <td>73.648447</td>
+        </tr>
+        <tr>
+          <th>32</th>
+          <td>80.5</td>
+          <td>33</td>
+          <td>73.500935</td>
+        </tr>
+        <tr>
+          <th>33</th>
+          <td>80.5</td>
+          <td>33</td>
+          <td>73.500935</td>
+        </tr>
+        <tr>
+          <th>34</th>
+          <td>80.4</td>
+          <td>35</td>
+          <td>73.205911</td>
+        </tr>
+        <tr>
+          <th>35</th>
+          <td>80.4</td>
+          <td>35</td>
+          <td>73.205911</td>
+        </tr>
+        <tr>
+          <th>36</th>
+          <td>79.6</td>
+          <td>37</td>
+          <td>72.910886</td>
+        </tr>
+        <tr>
+          <th>37</th>
+          <td>79.5</td>
+          <td>38</td>
+          <td>72.763374</td>
+        </tr>
+        <tr>
+          <th>38</th>
+          <td>78.8</td>
+          <td>39</td>
+          <td>72.615862</td>
+        </tr>
+        <tr>
+          <th>39</th>
+          <td>78.6</td>
+          <td>40</td>
+          <td>72.468350</td>
+        </tr>
+        <tr>
+          <th>40</th>
+          <td>77.9</td>
+          <td>41</td>
+          <td>72.320838</td>
+        </tr>
+        <tr>
+          <th>41</th>
+          <td>77.8</td>
+          <td>42</td>
+          <td>72.173326</td>
+        </tr>
+        <tr>
+          <th>42</th>
+          <td>77.1</td>
+          <td>43</td>
+          <td>72.025814</td>
+        </tr>
+        <tr>
+          <th>43</th>
+          <td>77.0</td>
+          <td>44</td>
+          <td>71.878302</td>
+        </tr>
+        <tr>
+          <th>44</th>
+          <td>76.6</td>
+          <td>45</td>
+          <td>71.730790</td>
+        </tr>
+        <tr>
+          <th>45</th>
+          <td>75.9</td>
+          <td>46</td>
+          <td>71.583278</td>
+        </tr>
+        <tr>
+          <th>46</th>
+          <td>75.7</td>
+          <td>47</td>
+          <td>71.435766</td>
+        </tr>
+        <tr>
+          <th>47</th>
+          <td>74.8</td>
+          <td>48</td>
+          <td>71.288253</td>
+        </tr>
+        <tr>
+          <th>48</th>
+          <td>74.5</td>
+          <td>49</td>
+          <td>71.140741</td>
+        </tr>
+        <tr>
+          <th>49</th>
+          <td>74.2</td>
+          <td>50</td>
+          <td>70.993229</td>
+        </tr>
+        <tr>
+          <th>50</th>
+          <td>74.1</td>
+          <td>51</td>
+          <td>70.845717</td>
+        </tr>
+        <tr>
+          <th>51</th>
+          <td>73.6</td>
+          <td>52</td>
+          <td>70.698205</td>
+        </tr>
+        <tr>
+          <th>52</th>
+          <td>73.4</td>
+          <td>53</td>
+          <td>70.550693</td>
+        </tr>
+        <tr>
+          <th>53</th>
+          <td>72.4</td>
+          <td>54</td>
+          <td>70.403181</td>
+        </tr>
+        <tr>
+          <th>54</th>
+          <td>72.3</td>
+          <td>55</td>
+          <td>70.255669</td>
+        </tr>
+        <tr>
+          <th>55</th>
+          <td>71.8</td>
+          <td>56</td>
+          <td>70.108157</td>
+        </tr>
+        <tr>
+          <th>56</th>
+          <td>71.5</td>
+          <td>57</td>
+          <td>69.960645</td>
+        </tr>
+        <tr>
+          <th>57</th>
+          <td>70.9</td>
+          <td>58</td>
+          <td>69.813133</td>
+        </tr>
+        <tr>
+          <th>58</th>
+          <td>70.9</td>
+          <td>58</td>
+          <td>69.813133</td>
+        </tr>
+        <tr>
+          <th>59</th>
+          <td>70.5</td>
+          <td>60</td>
+          <td>69.518109</td>
+        </tr>
+        <tr>
+          <th>60</th>
+          <td>70.5</td>
+          <td>60</td>
+          <td>69.518109</td>
+        </tr>
+        <tr>
+          <th>61</th>
+          <td>70.4</td>
+          <td>62</td>
+          <td>69.223084</td>
+        </tr>
+        <tr>
+          <th>62</th>
+          <td>69.6</td>
+          <td>63</td>
+          <td>69.075572</td>
+        </tr>
+        <tr>
+          <th>63</th>
+          <td>69.0</td>
+          <td>64</td>
+          <td>68.928060</td>
+        </tr>
+        <tr>
+          <th>64</th>
+          <td>68.6</td>
+          <td>65</td>
+          <td>68.780548</td>
+        </tr>
+        <tr>
+          <th>65</th>
+          <td>68.4</td>
+          <td>66</td>
+          <td>68.633036</td>
+        </tr>
+        <tr>
+          <th>66</th>
+          <td>68.2</td>
+          <td>67</td>
+          <td>68.485524</td>
+        </tr>
+        <tr>
+          <th>67</th>
+          <td>67.9</td>
+          <td>68</td>
+          <td>68.338012</td>
+        </tr>
+        <tr>
+          <th>68</th>
+          <td>67.3</td>
+          <td>69</td>
+          <td>68.190500</td>
+        </tr>
+        <tr>
+          <th>69</th>
+          <td>67.1</td>
+          <td>70</td>
+          <td>68.042988</td>
+        </tr>
+        <tr>
+          <th>70</th>
+          <td>66.5</td>
+          <td>71</td>
+          <td>67.895476</td>
+        </tr>
+        <tr>
+          <th>71</th>
+          <td>66.2</td>
+          <td>72</td>
+          <td>67.747964</td>
+        </tr>
+        <tr>
+          <th>72</th>
+          <td>66.0</td>
+          <td>74</td>
+          <td>67.452939</td>
+        </tr>
+        <tr>
+          <th>73</th>
+          <td>65.9</td>
+          <td>75</td>
+          <td>67.305427</td>
+        </tr>
+        <tr>
+          <th>74</th>
+          <td>65.8</td>
+          <td>76</td>
+          <td>67.157915</td>
+        </tr>
+        <tr>
+          <th>75</th>
+          <td>64.9</td>
+          <td>77</td>
+          <td>67.010403</td>
+        </tr>
+        <tr>
+          <th>76</th>
+          <td>64.2</td>
+          <td>78</td>
+          <td>66.862891</td>
+        </tr>
+        <tr>
+          <th>77</th>
+          <td>64.2</td>
+          <td>78</td>
+          <td>66.862891</td>
+        </tr>
+        <tr>
+          <th>78</th>
+          <td>64.1</td>
+          <td>80</td>
+          <td>66.567867</td>
+        </tr>
+        <tr>
+          <th>79</th>
+          <td>64.0</td>
+          <td>81</td>
+          <td>66.420355</td>
+        </tr>
+        <tr>
+          <th>80</th>
+          <td>63.8</td>
+          <td>81</td>
+          <td>66.420355</td>
+        </tr>
+        <tr>
+          <th>81</th>
+          <td>63.7</td>
+          <td>82</td>
+          <td>66.272843</td>
+        </tr>
+        <tr>
+          <th>82</th>
+          <td>63.4</td>
+          <td>83</td>
+          <td>66.125331</td>
+        </tr>
+        <tr>
+          <th>83</th>
+          <td>63.4</td>
+          <td>83</td>
+          <td>66.125331</td>
+        </tr>
+        <tr>
+          <th>84</th>
+          <td>63.2</td>
+          <td>84</td>
+          <td>65.977819</td>
+        </tr>
+        <tr>
+          <th>85</th>
+          <td>63.1</td>
+          <td>85</td>
+          <td>65.830307</td>
+        </tr>
+        <tr>
+          <th>86</th>
+          <td>62.9</td>
+          <td>86</td>
+          <td>65.682794</td>
+        </tr>
+        <tr>
+          <th>87</th>
+          <td>62.6</td>
+          <td>87</td>
+          <td>65.535282</td>
+        </tr>
+        <tr>
+          <th>88</th>
+          <td>62.3</td>
+          <td>89</td>
+          <td>65.240258</td>
+        </tr>
+        <tr>
+          <th>89</th>
+          <td>62.2</td>
+          <td>90</td>
+          <td>65.092746</td>
+        </tr>
+        <tr>
+          <th>90</th>
+          <td>61.9</td>
+          <td>91</td>
+          <td>64.945234</td>
+        </tr>
+        <tr>
+          <th>91</th>
+          <td>61.8</td>
+          <td>92</td>
+          <td>64.797722</td>
+        </tr>
+        <tr>
+          <th>92</th>
+          <td>61.7</td>
+          <td>93</td>
+          <td>64.650210</td>
+        </tr>
+        <tr>
+          <th>93</th>
+          <td>61.7</td>
+          <td>93</td>
+          <td>64.650210</td>
+        </tr>
+        <tr>
+          <th>94</th>
+          <td>61.0</td>
+          <td>95</td>
+          <td>64.355186</td>
+        </tr>
+        <tr>
+          <th>95</th>
+          <td>60.8</td>
+          <td>96</td>
+          <td>64.207674</td>
+        </tr>
+        <tr>
+          <th>96</th>
+          <td>60.6</td>
+          <td>97</td>
+          <td>64.060162</td>
+        </tr>
+        <tr>
+          <th>97</th>
+          <td>60.5</td>
+          <td>98</td>
+          <td>63.912649</td>
+        </tr>
+        <tr>
+          <th>98</th>
+          <td>60.5</td>
+          <td>98</td>
+          <td>63.912649</td>
+        </tr>
+        <tr>
+          <th>99</th>
+          <td>59.9</td>
+          <td>100</td>
+          <td>63.617625</td>
+        </tr>
+        <tr>
+          <th>100</th>
+          <td>59.5</td>
+          <td>101</td>
+          <td>63.470113</td>
+        </tr>
+        <tr>
+          <th>101</th>
+          <td>58.9</td>
+          <td>102</td>
+          <td>63.322601</td>
+        </tr>
+        <tr>
+          <th>102</th>
+          <td>58.8</td>
+          <td>103</td>
+          <td>63.175089</td>
+        </tr>
+        <tr>
+          <th>103</th>
+          <td>58.7</td>
+          <td>104</td>
+          <td>63.027577</td>
+        </tr>
+        <tr>
+          <th>104</th>
+          <td>58.7</td>
+          <td>104</td>
+          <td>63.027577</td>
+        </tr>
+        <tr>
+          <th>105</th>
+          <td>58.6</td>
+          <td>106</td>
+          <td>62.732553</td>
+        </tr>
+        <tr>
+          <th>106</th>
+          <td>58.4</td>
+          <td>107</td>
+          <td>62.585041</td>
+        </tr>
+        <tr>
+          <th>107</th>
+          <td>58.3</td>
+          <td>108</td>
+          <td>62.437529</td>
+        </tr>
+        <tr>
+          <th>108</th>
+          <td>58.3</td>
+          <td>108</td>
+          <td>62.437529</td>
+        </tr>
+        <tr>
+          <th>109</th>
+          <td>58.1</td>
+          <td>110</td>
+          <td>62.142505</td>
+        </tr>
+        <tr>
+          <th>110</th>
+          <td>57.8</td>
+          <td>111</td>
+          <td>61.994992</td>
+        </tr>
+        <tr>
+          <th>111</th>
+          <td>57.4</td>
+          <td>112</td>
+          <td>61.847480</td>
+        </tr>
+        <tr>
+          <th>112</th>
+          <td>57.3</td>
+          <td>113</td>
+          <td>61.699968</td>
+        </tr>
+        <tr>
+          <th>113</th>
+          <td>56.1</td>
+          <td>114</td>
+          <td>61.552456</td>
+        </tr>
+        <tr>
+          <th>114</th>
+          <td>55.8</td>
+          <td>115</td>
+          <td>61.404944</td>
+        </tr>
+        <tr>
+          <th>115</th>
+          <td>55.5</td>
+          <td>116</td>
+          <td>61.257432</td>
+        </tr>
+        <tr>
+          <th>116</th>
+          <td>55.5</td>
+          <td>116</td>
+          <td>61.257432</td>
+        </tr>
+        <tr>
+          <th>117</th>
+          <td>55.4</td>
+          <td>118</td>
+          <td>60.962408</td>
+        </tr>
+        <tr>
+          <th>118</th>
+          <td>54.7</td>
+          <td>119</td>
+          <td>60.814896</td>
+        </tr>
+        <tr>
+          <th>119</th>
+          <td>54.5</td>
+          <td>120</td>
+          <td>60.667384</td>
+        </tr>
+        <tr>
+          <th>120</th>
+          <td>54.5</td>
+          <td>120</td>
+          <td>60.667384</td>
+        </tr>
+        <tr>
+          <th>121</th>
+          <td>54.5</td>
+          <td>120</td>
+          <td>60.667384</td>
+        </tr>
+        <tr>
+          <th>122</th>
+          <td>54.3</td>
+          <td>123</td>
+          <td>60.224847</td>
+        </tr>
+        <tr>
+          <th>123</th>
+          <td>54.2</td>
+          <td>124</td>
+          <td>60.077335</td>
+        </tr>
+        <tr>
+          <th>124</th>
+          <td>54.0</td>
+          <td>125</td>
+          <td>59.929823</td>
+        </tr>
+        <tr>
+          <th>125</th>
+          <td>54.0</td>
+          <td>125</td>
+          <td>59.929823</td>
+        </tr>
+        <tr>
+          <th>126</th>
+          <td>53.9</td>
+          <td>126</td>
+          <td>59.782311</td>
+        </tr>
+        <tr>
+          <th>127</th>
+          <td>53.4</td>
+          <td>127</td>
+          <td>59.634799</td>
+        </tr>
+        <tr>
+          <th>128</th>
+          <td>53.2</td>
+          <td>128</td>
+          <td>59.487287</td>
+        </tr>
+        <tr>
+          <th>129</th>
+          <td>53.1</td>
+          <td>129</td>
+          <td>59.339775</td>
+        </tr>
+        <tr>
+          <th>130</th>
+          <td>53.0</td>
+          <td>130</td>
+          <td>59.192263</td>
+        </tr>
+        <tr>
+          <th>131</th>
+          <td>53.0</td>
+          <td>130</td>
+          <td>59.192263</td>
+        </tr>
+        <tr>
+          <th>132</th>
+          <td>52.9</td>
+          <td>132</td>
+          <td>58.897239</td>
+        </tr>
+        <tr>
+          <th>133</th>
+          <td>52.9</td>
+          <td>132</td>
+          <td>58.897239</td>
+        </tr>
+        <tr>
+          <th>134</th>
+          <td>52.4</td>
+          <td>134</td>
+          <td>58.602215</td>
+        </tr>
+        <tr>
+          <th>135</th>
+          <td>52.3</td>
+          <td>135</td>
+          <td>58.454703</td>
+        </tr>
+        <tr>
+          <th>136</th>
+          <td>52.1</td>
+          <td>136</td>
+          <td>58.307190</td>
+        </tr>
+        <tr>
+          <th>137</th>
+          <td>52.0</td>
+          <td>137</td>
+          <td>58.159678</td>
+        </tr>
+        <tr>
+          <th>138</th>
+          <td>51.9</td>
+          <td>138</td>
+          <td>58.012166</td>
+        </tr>
+        <tr>
+          <th>139</th>
+          <td>51.4</td>
+          <td>139</td>
+          <td>57.864654</td>
+        </tr>
+        <tr>
+          <th>140</th>
+          <td>51.2</td>
+          <td>140</td>
+          <td>57.717142</td>
+        </tr>
+        <tr>
+          <th>141</th>
+          <td>51.2</td>
+          <td>140</td>
+          <td>57.717142</td>
+        </tr>
+        <tr>
+          <th>142</th>
+          <td>51.2</td>
+          <td>140</td>
+          <td>57.717142</td>
+        </tr>
+        <tr>
+          <th>143</th>
+          <td>51.1</td>
+          <td>144</td>
+          <td>57.127094</td>
+        </tr>
+        <tr>
+          <th>144</th>
+          <td>51.0</td>
+          <td>145</td>
+          <td>56.979582</td>
+        </tr>
+        <tr>
+          <th>145</th>
+          <td>50.9</td>
+          <td>146</td>
+          <td>56.832070</td>
+        </tr>
+        <tr>
+          <th>146</th>
+          <td>50.6</td>
+          <td>147</td>
+          <td>56.684558</td>
+        </tr>
+        <tr>
+          <th>147</th>
+          <td>50.5</td>
+          <td>148</td>
+          <td>56.537045</td>
+        </tr>
+        <tr>
+          <th>148</th>
+          <td>50.4</td>
+          <td>149</td>
+          <td>56.389533</td>
+        </tr>
+        <tr>
+          <th>149</th>
+          <td>50.3</td>
+          <td>150</td>
+          <td>56.242021</td>
+        </tr>
+        <tr>
+          <th>150</th>
+          <td>50.0</td>
+          <td>151</td>
+          <td>56.094509</td>
+        </tr>
+        <tr>
+          <th>151</th>
+          <td>49.4</td>
+          <td>152</td>
+          <td>55.946997</td>
+        </tr>
+        <tr>
+          <th>152</th>
+          <td>49.2</td>
+          <td>153</td>
+          <td>55.799485</td>
+        </tr>
+        <tr>
+          <th>153</th>
+          <td>49.0</td>
+          <td>154</td>
+          <td>55.651973</td>
+        </tr>
+        <tr>
+          <th>154</th>
+          <td>49.0</td>
+          <td>154</td>
+          <td>55.651973</td>
+        </tr>
+        <tr>
+          <th>155</th>
+          <td>48.6</td>
+          <td>156</td>
+          <td>55.356949</td>
+        </tr>
+        <tr>
+          <th>156</th>
+          <td>48.6</td>
+          <td>156</td>
+          <td>55.356949</td>
+        </tr>
+        <tr>
+          <th>157</th>
+          <td>48.5</td>
+          <td>158</td>
+          <td>55.061925</td>
+        </tr>
+        <tr>
+          <th>158</th>
+          <td>48.4</td>
+          <td>159</td>
+          <td>54.914413</td>
+        </tr>
+        <tr>
+          <th>159</th>
+          <td>48.3</td>
+          <td>160</td>
+          <td>54.766901</td>
+        </tr>
+        <tr>
+          <th>160</th>
+          <td>48.3</td>
+          <td>160</td>
+          <td>54.766901</td>
+        </tr>
+        <tr>
+          <th>161</th>
+          <td>48.2</td>
+          <td>162</td>
+          <td>54.471876</td>
+        </tr>
+        <tr>
+          <th>162</th>
+          <td>48.1</td>
+          <td>163</td>
+          <td>54.324364</td>
+        </tr>
+        <tr>
+          <th>163</th>
+          <td>48.1</td>
+          <td>163</td>
+          <td>54.324364</td>
+        </tr>
+        <tr>
+          <th>164</th>
+          <td>47.9</td>
+          <td>165</td>
+          <td>54.029340</td>
+        </tr>
+        <tr>
+          <th>165</th>
+          <td>47.9</td>
+          <td>165</td>
+          <td>54.029340</td>
+        </tr>
+        <tr>
+          <th>166</th>
+          <td>47.8</td>
+          <td>167</td>
+          <td>53.734316</td>
+        </tr>
+        <tr>
+          <th>167</th>
+          <td>47.7</td>
+          <td>167</td>
+          <td>53.734316</td>
+        </tr>
+        <tr>
+          <th>168</th>
+          <td>47.7</td>
+          <td>167</td>
+          <td>53.734316</td>
+        </tr>
+        <tr>
+          <th>169</th>
+          <td>47.3</td>
+          <td>169</td>
+          <td>53.439292</td>
+        </tr>
+        <tr>
+          <th>170</th>
+          <td>47.3</td>
+          <td>169</td>
+          <td>53.439292</td>
+        </tr>
+        <tr>
+          <th>171</th>
+          <td>47.3</td>
+          <td>170</td>
+          <td>53.291780</td>
+        </tr>
+        <tr>
+          <th>172</th>
+          <td>47.0</td>
+          <td>172</td>
+          <td>52.996756</td>
+        </tr>
+        <tr>
+          <th>173</th>
+          <td>46.8</td>
+          <td>173</td>
+          <td>52.849243</td>
+        </tr>
+        <tr>
+          <th>174</th>
+          <td>46.8</td>
+          <td>173</td>
+          <td>52.849243</td>
+        </tr>
+        <tr>
+          <th>175</th>
+          <td>46.8</td>
+          <td>173</td>
+          <td>52.849243</td>
+        </tr>
+        <tr>
+          <th>176</th>
+          <td>46.7</td>
+          <td>176</td>
+          <td>52.406707</td>
+        </tr>
+        <tr>
+          <th>177</th>
+          <td>46.6</td>
+          <td>177</td>
+          <td>52.259195</td>
+        </tr>
+        <tr>
+          <th>178</th>
+          <td>46.6</td>
+          <td>177</td>
+          <td>52.259195</td>
+        </tr>
+        <tr>
+          <th>179</th>
+          <td>46.4</td>
+          <td>179</td>
+          <td>51.964171</td>
+        </tr>
+        <tr>
+          <th>180</th>
+          <td>46.3</td>
+          <td>181</td>
+          <td>51.669147</td>
+        </tr>
+        <tr>
+          <th>181</th>
+          <td>46.2</td>
+          <td>182</td>
+          <td>51.521635</td>
+        </tr>
+        <tr>
+          <th>182</th>
+          <td>46.1</td>
+          <td>183</td>
+          <td>51.374123</td>
+        </tr>
+        <tr>
+          <th>183</th>
+          <td>45.9</td>
+          <td>184</td>
+          <td>51.226611</td>
+        </tr>
+        <tr>
+          <th>184</th>
+          <td>45.6</td>
+          <td>185</td>
+          <td>51.079099</td>
+        </tr>
+        <tr>
+          <th>185</th>
+          <td>45.2</td>
+          <td>186</td>
+          <td>50.931586</td>
+        </tr>
+        <tr>
+          <th>186</th>
+          <td>45.2</td>
+          <td>186</td>
+          <td>50.931586</td>
+        </tr>
+        <tr>
+          <th>187</th>
+          <td>45.1</td>
+          <td>188</td>
+          <td>50.636562</td>
+        </tr>
+        <tr>
+          <th>188</th>
+          <td>45.0</td>
+          <td>189</td>
+          <td>50.489050</td>
+        </tr>
+        <tr>
+          <th>189</th>
+          <td>45.0</td>
+          <td>189</td>
+          <td>50.489050</td>
+        </tr>
+        <tr>
+          <th>190</th>
+          <td>44.8</td>
+          <td>191</td>
+          <td>50.194026</td>
+        </tr>
+        <tr>
+          <th>191</th>
+          <td>44.7</td>
+          <td>192</td>
+          <td>50.046514</td>
+        </tr>
+        <tr>
+          <th>192</th>
+          <td>44.7</td>
+          <td>192</td>
+          <td>50.046514</td>
+        </tr>
+        <tr>
+          <th>193</th>
+          <td>44.6</td>
+          <td>194</td>
+          <td>49.751490</td>
+        </tr>
+        <tr>
+          <th>194</th>
+          <td>44.5</td>
+          <td>195</td>
+          <td>49.603978</td>
+        </tr>
+        <tr>
+          <th>195</th>
+          <td>44.2</td>
+          <td>197</td>
+          <td>49.308954</td>
+        </tr>
+        <tr>
+          <th>196</th>
+          <td>44.1</td>
+          <td>198</td>
+          <td>49.161441</td>
+        </tr>
+        <tr>
+          <th>197</th>
+          <td>44.1</td>
+          <td>198</td>
+          <td>49.161441</td>
+        </tr>
+        <tr>
+          <th>198</th>
+          <td>44.0</td>
+          <td>200</td>
+          <td>48.866417</td>
+        </tr>
+        <tr>
+          <th>199</th>
+          <td>44.0</td>
+          <td>200</td>
+          <td>48.866417</td>
+        </tr>
+        <tr>
+          <th>200</th>
+          <td>44.0</td>
+          <td>200</td>
+          <td>48.866417</td>
+        </tr>
+        <tr>
+          <th>201</th>
+          <td>43.7</td>
+          <td>202</td>
+          <td>48.571393</td>
+        </tr>
+        <tr>
+          <th>202</th>
+          <td>43.5</td>
+          <td>203</td>
+          <td>48.423881</td>
+        </tr>
+        <tr>
+          <th>203</th>
+          <td>43.4</td>
+          <td>204</td>
+          <td>48.276369</td>
+        </tr>
+        <tr>
+          <th>204</th>
+          <td>43.3</td>
+          <td>205</td>
+          <td>48.128857</td>
+        </tr>
+        <tr>
+          <th>205</th>
+          <td>43.2</td>
+          <td>206</td>
+          <td>47.981345</td>
+        </tr>
+        <tr>
+          <th>206</th>
+          <td>43.1</td>
+          <td>207</td>
+          <td>47.833833</td>
+        </tr>
+        <tr>
+          <th>207</th>
+          <td>43.1</td>
+          <td>207</td>
+          <td>47.833833</td>
+        </tr>
+        <tr>
+          <th>208</th>
+          <td>43.1</td>
+          <td>207</td>
+          <td>47.833833</td>
+        </tr>
+        <tr>
+          <th>209</th>
+          <td>42.9</td>
+          <td>210</td>
+          <td>47.391297</td>
+        </tr>
+        <tr>
+          <th>210</th>
+          <td>42.8</td>
+          <td>211</td>
+          <td>47.243784</td>
+        </tr>
+        <tr>
+          <th>211</th>
+          <td>42.7</td>
+          <td>212</td>
+          <td>47.096272</td>
+        </tr>
+        <tr>
+          <th>212</th>
+          <td>42.7</td>
+          <td>212</td>
+          <td>47.096272</td>
+        </tr>
+        <tr>
+          <th>213</th>
+          <td>42.1</td>
+          <td>214</td>
+          <td>46.801248</td>
+        </tr>
+        <tr>
+          <th>214</th>
+          <td>42.0</td>
+          <td>215</td>
+          <td>46.653736</td>
+        </tr>
+        <tr>
+          <th>215</th>
+          <td>42.0</td>
+          <td>215</td>
+          <td>46.653736</td>
+        </tr>
+        <tr>
+          <th>216</th>
+          <td>41.4</td>
+          <td>217</td>
+          <td>46.358712</td>
+        </tr>
+        <tr>
+          <th>217</th>
+          <td>41.4</td>
+          <td>217</td>
+          <td>46.358712</td>
+        </tr>
+        <tr>
+          <th>218</th>
+          <td>41.3</td>
+          <td>219</td>
+          <td>46.063688</td>
+        </tr>
+        <tr>
+          <th>219</th>
+          <td>41.3</td>
+          <td>219</td>
+          <td>46.063688</td>
+        </tr>
+        <tr>
+          <th>220</th>
+          <td>41.3</td>
+          <td>219</td>
+          <td>46.063688</td>
+        </tr>
+        <tr>
+          <th>221</th>
+          <td>41.2</td>
+          <td>222</td>
+          <td>45.621152</td>
+        </tr>
+        <tr>
+          <th>222</th>
+          <td>40.9</td>
+          <td>223</td>
+          <td>45.473639</td>
+        </tr>
+        <tr>
+          <th>223</th>
+          <td>40.7</td>
+          <td>224</td>
+          <td>45.326127</td>
+        </tr>
+        <tr>
+          <th>224</th>
+          <td>40.7</td>
+          <td>224</td>
+          <td>45.326127</td>
+        </tr>
+        <tr>
+          <th>225</th>
+          <td>40.4</td>
+          <td>225</td>
+          <td>45.178615</td>
+        </tr>
+        <tr>
+          <th>226</th>
+          <td>40.1</td>
+          <td>226</td>
+          <td>45.031103</td>
+        </tr>
+        <tr>
+          <th>227</th>
+          <td>40.0</td>
+          <td>227</td>
+          <td>44.883591</td>
+        </tr>
+        <tr>
+          <th>228</th>
+          <td>40.0</td>
+          <td>227</td>
+          <td>44.883591</td>
+        </tr>
+        <tr>
+          <th>229</th>
+          <td>40.0</td>
+          <td>227</td>
+          <td>44.883591</td>
+        </tr>
+        <tr>
+          <th>230</th>
+          <td>39.9</td>
+          <td>230</td>
+          <td>44.441055</td>
+        </tr>
+        <tr>
+          <th>231</th>
+          <td>39.8</td>
+          <td>231</td>
+          <td>44.293543</td>
+        </tr>
+        <tr>
+          <th>232</th>
+          <td>39.8</td>
+          <td>231</td>
+          <td>44.293543</td>
+        </tr>
+        <tr>
+          <th>233</th>
+          <td>39.7</td>
+          <td>233</td>
+          <td>43.998519</td>
+        </tr>
+        <tr>
+          <th>234</th>
+          <td>39.6</td>
+          <td>234</td>
+          <td>43.851007</td>
+        </tr>
+        <tr>
+          <th>235</th>
+          <td>39.6</td>
+          <td>234</td>
+          <td>43.851007</td>
+        </tr>
+        <tr>
+          <th>236</th>
+          <td>39.6</td>
+          <td>234</td>
+          <td>43.851007</td>
+        </tr>
+        <tr>
+          <th>237</th>
+          <td>39.5</td>
+          <td>237</td>
+          <td>43.408470</td>
+        </tr>
+        <tr>
+          <th>238</th>
+          <td>39.2</td>
+          <td>238</td>
+          <td>43.260958</td>
+        </tr>
+        <tr>
+          <th>239</th>
+          <td>39.0</td>
+          <td>239</td>
+          <td>43.113446</td>
+        </tr>
+        <tr>
+          <th>240</th>
+          <td>39.0</td>
+          <td>239</td>
+          <td>43.113446</td>
+        </tr>
+        <tr>
+          <th>241</th>
+          <td>39.0</td>
+          <td>239</td>
+          <td>43.113446</td>
+        </tr>
+        <tr>
+          <th>242</th>
+          <td>38.9</td>
+          <td>242</td>
+          <td>42.670910</td>
+        </tr>
+        <tr>
+          <th>243</th>
+          <td>38.8</td>
+          <td>243</td>
+          <td>42.523398</td>
+        </tr>
+        <tr>
+          <th>244</th>
+          <td>38.7</td>
+          <td>244</td>
+          <td>42.375886</td>
+        </tr>
+        <tr>
+          <th>245</th>
+          <td>38.5</td>
+          <td>245</td>
+          <td>42.228374</td>
+        </tr>
+        <tr>
+          <th>246</th>
+          <td>38.4</td>
+          <td>246</td>
+          <td>42.080862</td>
+        </tr>
+        <tr>
+          <th>247</th>
+          <td>38.3</td>
+          <td>247</td>
+          <td>41.933350</td>
+        </tr>
+        <tr>
+          <th>248</th>
+          <td>38.3</td>
+          <td>247</td>
+          <td>41.933350</td>
+        </tr>
+        <tr>
+          <th>249</th>
+          <td>37.8</td>
+          <td>250</td>
+          <td>41.490813</td>
+        </tr>
+        <tr>
+          <th>250</th>
+          <td>37.6</td>
+          <td>251</td>
+          <td>41.343301</td>
+        </tr>
+        <tr>
+          <th>251</th>
+          <td>37.6</td>
+          <td>251</td>
+          <td>41.343301</td>
+        </tr>
+        <tr>
+          <th>252</th>
+          <td>37.5</td>
+          <td>253</td>
+          <td>41.048277</td>
+        </tr>
+        <tr>
+          <th>253</th>
+          <td>37.5</td>
+          <td>253</td>
+          <td>41.048277</td>
+        </tr>
+        <tr>
+          <th>254</th>
+          <td>37.5</td>
+          <td>253</td>
+          <td>41.048277</td>
+        </tr>
+        <tr>
+          <th>255</th>
+          <td>37.3</td>
+          <td>256</td>
+          <td>40.605741</td>
+        </tr>
+        <tr>
+          <th>256</th>
+          <td>37.2</td>
+          <td>257</td>
+          <td>40.458229</td>
+        </tr>
+        <tr>
+          <th>257</th>
+          <td>37.2</td>
+          <td>257</td>
+          <td>40.458229</td>
+        </tr>
+        <tr>
+          <th>258</th>
+          <td>37.1</td>
+          <td>259</td>
+          <td>40.163205</td>
+        </tr>
+        <tr>
+          <th>259</th>
+          <td>37.0</td>
+          <td>260</td>
+          <td>40.015693</td>
+        </tr>
+        <tr>
+          <th>260</th>
+          <td>36.9</td>
+          <td>261</td>
+          <td>39.868180</td>
+        </tr>
+        <tr>
+          <th>261</th>
+          <td>36.8</td>
+          <td>262</td>
+          <td>39.720668</td>
+        </tr>
+        <tr>
+          <th>262</th>
+          <td>36.8</td>
+          <td>262</td>
+          <td>39.720668</td>
+        </tr>
+        <tr>
+          <th>263</th>
+          <td>36.8</td>
+          <td>262</td>
+          <td>39.720668</td>
+        </tr>
+        <tr>
+          <th>264</th>
+          <td>36.7</td>
+          <td>265</td>
+          <td>39.278132</td>
+        </tr>
+        <tr>
+          <th>265</th>
+          <td>36.6</td>
+          <td>266</td>
+          <td>39.130620</td>
+        </tr>
+        <tr>
+          <th>266</th>
+          <td>36.6</td>
+          <td>266</td>
+          <td>39.130620</td>
+        </tr>
+        <tr>
+          <th>267</th>
+          <td>36.5</td>
+          <td>268</td>
+          <td>38.835596</td>
+        </tr>
+        <tr>
+          <th>268</th>
+          <td>36.5</td>
+          <td>268</td>
+          <td>38.835596</td>
+        </tr>
+        <tr>
+          <th>269</th>
+          <td>36.3</td>
+          <td>270</td>
+          <td>38.540572</td>
+        </tr>
+        <tr>
+          <th>270</th>
+          <td>36.1</td>
+          <td>271</td>
+          <td>38.393060</td>
+        </tr>
+        <tr>
+          <th>271</th>
+          <td>36.1</td>
+          <td>271</td>
+          <td>38.393060</td>
+        </tr>
+        <tr>
+          <th>272</th>
+          <td>36.1</td>
+          <td>271</td>
+          <td>38.393060</td>
+        </tr>
+        <tr>
+          <th>273</th>
+          <td>35.9</td>
+          <td>274</td>
+          <td>37.950523</td>
+        </tr>
+        <tr>
+          <th>274</th>
+          <td>35.9</td>
+          <td>274</td>
+          <td>37.950523</td>
+        </tr>
+        <tr>
+          <th>275</th>
+          <td>35.8</td>
+          <td>276</td>
+          <td>37.655499</td>
+        </tr>
+        <tr>
+          <th>276</th>
+          <td>35.7</td>
+          <td>277</td>
+          <td>37.507987</td>
+        </tr>
+        <tr>
+          <th>277</th>
+          <td>35.7</td>
+          <td>277</td>
+          <td>37.507987</td>
+        </tr>
+        <tr>
+          <th>278</th>
+          <td>35.6</td>
+          <td>279</td>
+          <td>37.212963</td>
+        </tr>
+        <tr>
+          <th>279</th>
+          <td>35.4</td>
+          <td>280</td>
+          <td>37.065451</td>
+        </tr>
+        <tr>
+          <th>280</th>
+          <td>35.2</td>
+          <td>281</td>
+          <td>36.917939</td>
+        </tr>
+        <tr>
+          <th>281</th>
+          <td>35.2</td>
+          <td>281</td>
+          <td>36.917939</td>
+        </tr>
+        <tr>
+          <th>282</th>
+          <td>35.2</td>
+          <td>281</td>
+          <td>36.917939</td>
+        </tr>
+        <tr>
+          <th>283</th>
+          <td>35.1</td>
+          <td>284</td>
+          <td>36.475403</td>
+        </tr>
+        <tr>
+          <th>284</th>
+          <td>35.0</td>
+          <td>285</td>
+          <td>36.327891</td>
+        </tr>
+        <tr>
+          <th>285</th>
+          <td>35.0</td>
+          <td>285</td>
+          <td>36.327891</td>
+        </tr>
+        <tr>
+          <th>286</th>
+          <td>34.9</td>
+          <td>287</td>
+          <td>36.032866</td>
+        </tr>
+        <tr>
+          <th>287</th>
+          <td>34.9</td>
+          <td>287</td>
+          <td>36.032866</td>
+        </tr>
+        <tr>
+          <th>288</th>
+          <td>34.9</td>
+          <td>287</td>
+          <td>36.032866</td>
+        </tr>
+        <tr>
+          <th>289</th>
+          <td>34.9</td>
+          <td>287</td>
+          <td>36.032866</td>
+        </tr>
+        <tr>
+          <th>290</th>
+          <td>34.8</td>
+          <td>291</td>
+          <td>35.442818</td>
+        </tr>
+        <tr>
+          <th>291</th>
+          <td>34.8</td>
+          <td>291</td>
+          <td>35.442818</td>
+        </tr>
+        <tr>
+          <th>292</th>
+          <td>34.8</td>
+          <td>291</td>
+          <td>35.442818</td>
+        </tr>
+        <tr>
+          <th>293</th>
+          <td>34.8</td>
+          <td>291</td>
+          <td>35.442818</td>
+        </tr>
+        <tr>
+          <th>294</th>
+          <td>34.8</td>
+          <td>291</td>
+          <td>35.442818</td>
+        </tr>
+        <tr>
+          <th>295</th>
+          <td>34.7</td>
+          <td>296</td>
+          <td>34.705258</td>
+        </tr>
+        <tr>
+          <th>296</th>
+          <td>34.7</td>
+          <td>296</td>
+          <td>34.705258</td>
+        </tr>
+        <tr>
+          <th>297</th>
+          <td>34.6</td>
+          <td>298</td>
+          <td>34.410234</td>
+        </tr>
+        <tr>
+          <th>298</th>
+          <td>34.6</td>
+          <td>298</td>
+          <td>34.410234</td>
+        </tr>
+        <tr>
+          <th>299</th>
+          <td>34.5</td>
+          <td>300</td>
+          <td>34.115209</td>
+        </tr>
+        <tr>
+          <th>300</th>
+          <td>34.4</td>
+          <td>301</td>
+          <td>33.967697</td>
+        </tr>
+        <tr>
+          <th>301</th>
+          <td>34.3</td>
+          <td>302</td>
+          <td>33.820185</td>
+        </tr>
+        <tr>
+          <th>302</th>
+          <td>34.3</td>
+          <td>302</td>
+          <td>33.820185</td>
+        </tr>
+        <tr>
+          <th>303</th>
+          <td>34.3</td>
+          <td>302</td>
+          <td>33.820185</td>
+        </tr>
+        <tr>
+          <th>304</th>
+          <td>34.2</td>
+          <td>305</td>
+          <td>33.377649</td>
+        </tr>
+        <tr>
+          <th>305</th>
+          <td>34.2</td>
+          <td>305</td>
+          <td>33.377649</td>
+        </tr>
+        <tr>
+          <th>306</th>
+          <td>34.1</td>
+          <td>307</td>
+          <td>33.082625</td>
+        </tr>
+        <tr>
+          <th>307</th>
+          <td>34.0</td>
+          <td>308</td>
+          <td>32.935113</td>
+        </tr>
+        <tr>
+          <th>308</th>
+          <td>33.9</td>
+          <td>309</td>
+          <td>32.787601</td>
+        </tr>
+        <tr>
+          <th>309</th>
+          <td>33.7</td>
+          <td>310</td>
+          <td>32.640089</td>
+        </tr>
+        <tr>
+          <th>310</th>
+          <td>33.6</td>
+          <td>311</td>
+          <td>32.492576</td>
+        </tr>
+        <tr>
+          <th>311</th>
+          <td>33.5</td>
+          <td>312</td>
+          <td>32.345064</td>
+        </tr>
+        <tr>
+          <th>312</th>
+          <td>33.5</td>
+          <td>312</td>
+          <td>32.345064</td>
+        </tr>
+        <tr>
+          <th>313</th>
+          <td>33.4</td>
+          <td>314</td>
+          <td>32.050040</td>
+        </tr>
+        <tr>
+          <th>314</th>
+          <td>33.4</td>
+          <td>314</td>
+          <td>32.050040</td>
+        </tr>
+        <tr>
+          <th>315</th>
+          <td>33.4</td>
+          <td>314</td>
+          <td>32.050040</td>
+        </tr>
+        <tr>
+          <th>316</th>
+          <td>33.4</td>
+          <td>314</td>
+          <td>32.050040</td>
+        </tr>
+        <tr>
+          <th>317</th>
+          <td>33.4</td>
+          <td>314</td>
+          <td>32.050040</td>
+        </tr>
+        <tr>
+          <th>318</th>
+          <td>33.3</td>
+          <td>319</td>
+          <td>31.312480</td>
+        </tr>
+        <tr>
+          <th>319</th>
+          <td>33.2</td>
+          <td>320</td>
+          <td>31.164968</td>
+        </tr>
+        <tr>
+          <th>320</th>
+          <td>33.2</td>
+          <td>320</td>
+          <td>31.164968</td>
+        </tr>
+        <tr>
+          <th>321</th>
+          <td>33.1</td>
+          <td>322</td>
+          <td>30.869944</td>
+        </tr>
+        <tr>
+          <th>322</th>
+          <td>33.1</td>
+          <td>322</td>
+          <td>30.869944</td>
+        </tr>
+        <tr>
+          <th>323</th>
+          <td>33.0</td>
+          <td>324</td>
+          <td>30.574919</td>
+        </tr>
+        <tr>
+          <th>324</th>
+          <td>33.0</td>
+          <td>324</td>
+          <td>30.574919</td>
+        </tr>
+        <tr>
+          <th>325</th>
+          <td>32.9</td>
+          <td>326</td>
+          <td>30.279895</td>
+        </tr>
+        <tr>
+          <th>326</th>
+          <td>32.7</td>
+          <td>327</td>
+          <td>30.132383</td>
+        </tr>
+        <tr>
+          <th>327</th>
+          <td>32.6</td>
+          <td>328</td>
+          <td>29.984871</td>
+        </tr>
+        <tr>
+          <th>328</th>
+          <td>32.4</td>
+          <td>329</td>
+          <td>29.837359</td>
+        </tr>
+        <tr>
+          <th>329</th>
+          <td>32.4</td>
+          <td>329</td>
+          <td>29.837359</td>
+        </tr>
+        <tr>
+          <th>330</th>
+          <td>32.3</td>
+          <td>331</td>
+          <td>29.542335</td>
+        </tr>
+        <tr>
+          <th>331</th>
+          <td>32.3</td>
+          <td>331</td>
+          <td>29.542335</td>
+        </tr>
+        <tr>
+          <th>332</th>
+          <td>32.3</td>
+          <td>331</td>
+          <td>29.542335</td>
+        </tr>
+        <tr>
+          <th>333</th>
+          <td>32.2</td>
+          <td>334</td>
+          <td>29.099799</td>
+        </tr>
+        <tr>
+          <th>334</th>
+          <td>32.1</td>
+          <td>335</td>
+          <td>28.952287</td>
+        </tr>
+        <tr>
+          <th>335</th>
+          <td>32.0</td>
+          <td>336</td>
+          <td>28.804774</td>
+        </tr>
+        <tr>
+          <th>336</th>
+          <td>32.0</td>
+          <td>336</td>
+          <td>28.804774</td>
+        </tr>
+        <tr>
+          <th>337</th>
+          <td>31.9</td>
+          <td>338</td>
+          <td>28.509750</td>
+        </tr>
+        <tr>
+          <th>338</th>
+          <td>31.9</td>
+          <td>338</td>
+          <td>28.509750</td>
+        </tr>
+        <tr>
+          <th>339</th>
+          <td>31.8</td>
+          <td>340</td>
+          <td>28.214726</td>
+        </tr>
+        <tr>
+          <th>340</th>
+          <td>31.8</td>
+          <td>340</td>
+          <td>28.214726</td>
+        </tr>
+        <tr>
+          <th>341</th>
+          <td>31.8</td>
+          <td>340</td>
+          <td>28.214726</td>
+        </tr>
+        <tr>
+          <th>342</th>
+          <td>31.8</td>
+          <td>340</td>
+          <td>28.214726</td>
+        </tr>
+        <tr>
+          <th>343</th>
+          <td>31.7</td>
+          <td>344</td>
+          <td>27.624678</td>
+        </tr>
+        <tr>
+          <th>344</th>
+          <td>31.7</td>
+          <td>344</td>
+          <td>27.624678</td>
+        </tr>
+        <tr>
+          <th>345</th>
+          <td>31.6</td>
+          <td>346</td>
+          <td>27.329654</td>
+        </tr>
+        <tr>
+          <th>346</th>
+          <td>31.3</td>
+          <td>347</td>
+          <td>27.182142</td>
+        </tr>
+        <tr>
+          <th>347</th>
+          <td>31.2</td>
+          <td>348</td>
+          <td>27.034630</td>
+        </tr>
+        <tr>
+          <th>348</th>
+          <td>31.1</td>
+          <td>349</td>
+          <td>26.887117</td>
+        </tr>
+        <tr>
+          <th>349</th>
+          <td>31.1</td>
+          <td>349</td>
+          <td>26.887117</td>
+        </tr>
+        <tr>
+          <th>350</th>
+          <td>31.0</td>
+          <td>351</td>
+          <td>26.592093</td>
+        </tr>
+        <tr>
+          <th>351</th>
+          <td>31.0</td>
+          <td>351</td>
+          <td>26.592093</td>
+        </tr>
+        <tr>
+          <th>352</th>
+          <td>30.9</td>
+          <td>353</td>
+          <td>26.297069</td>
+        </tr>
+        <tr>
+          <th>353</th>
+          <td>30.9</td>
+          <td>353</td>
+          <td>26.297069</td>
+        </tr>
+        <tr>
+          <th>354</th>
+          <td>30.8</td>
+          <td>355</td>
+          <td>26.002045</td>
+        </tr>
+        <tr>
+          <th>355</th>
+          <td>30.7</td>
+          <td>356</td>
+          <td>25.854533</td>
+        </tr>
+        <tr>
+          <th>356</th>
+          <td>30.7</td>
+          <td>356</td>
+          <td>25.854533</td>
+        </tr>
+        <tr>
+          <th>357</th>
+          <td>30.6</td>
+          <td>358</td>
+          <td>25.559509</td>
+        </tr>
+        <tr>
+          <th>358</th>
+          <td>30.5</td>
+          <td>359</td>
+          <td>25.411997</td>
+        </tr>
+        <tr>
+          <th>359</th>
+          <td>30.5</td>
+          <td>359</td>
+          <td>25.411997</td>
+        </tr>
+        <tr>
+          <th>360</th>
+          <td>30.5</td>
+          <td>359</td>
+          <td>25.411997</td>
+        </tr>
+        <tr>
+          <th>361</th>
+          <td>30.5</td>
+          <td>359</td>
+          <td>25.411997</td>
+        </tr>
+        <tr>
+          <th>362</th>
+          <td>30.5</td>
+          <td>359</td>
+          <td>25.411997</td>
+        </tr>
+        <tr>
+          <th>363</th>
+          <td>30.4</td>
+          <td>364</td>
+          <td>24.674436</td>
+        </tr>
+        <tr>
+          <th>364</th>
+          <td>30.4</td>
+          <td>364</td>
+          <td>24.674436</td>
+        </tr>
+        <tr>
+          <th>365</th>
+          <td>30.3</td>
+          <td>366</td>
+          <td>24.379412</td>
+        </tr>
+        <tr>
+          <th>366</th>
+          <td>30.2</td>
+          <td>367</td>
+          <td>24.231900</td>
+        </tr>
+        <tr>
+          <th>367</th>
+          <td>29.8</td>
+          <td>368</td>
+          <td>24.084388</td>
+        </tr>
+        <tr>
+          <th>368</th>
+          <td>29.8</td>
+          <td>368</td>
+          <td>24.084388</td>
+        </tr>
+        <tr>
+          <th>369</th>
+          <td>29.7</td>
+          <td>370</td>
+          <td>23.789364</td>
+        </tr>
+        <tr>
+          <th>370</th>
+          <td>29.6</td>
+          <td>371</td>
+          <td>23.641852</td>
+        </tr>
+        <tr>
+          <th>371</th>
+          <td>29.6</td>
+          <td>371</td>
+          <td>23.641852</td>
+        </tr>
+        <tr>
+          <th>372</th>
+          <td>29.4</td>
+          <td>372</td>
+          <td>23.494340</td>
+        </tr>
+        <tr>
+          <th>373</th>
+          <td>29.4</td>
+          <td>372</td>
+          <td>23.494340</td>
+        </tr>
+        <tr>
+          <th>374</th>
+          <td>29.3</td>
+          <td>374</td>
+          <td>23.199315</td>
+        </tr>
+        <tr>
+          <th>375</th>
+          <td>29.2</td>
+          <td>375</td>
+          <td>23.051803</td>
+        </tr>
+        <tr>
+          <th>376</th>
+          <td>29.1</td>
+          <td>376</td>
+          <td>22.904291</td>
+        </tr>
+        <tr>
+          <th>377</th>
+          <td>29.0</td>
+          <td>377</td>
+          <td>22.756779</td>
+        </tr>
+        <tr>
+          <th>378</th>
+          <td>29.0</td>
+          <td>377</td>
+          <td>22.756779</td>
+        </tr>
+        <tr>
+          <th>379</th>
+          <td>28.9</td>
+          <td>379</td>
+          <td>22.461755</td>
+        </tr>
+        <tr>
+          <th>380</th>
+          <td>28.9</td>
+          <td>379</td>
+          <td>22.461755</td>
+        </tr>
+        <tr>
+          <th>381</th>
+          <td>28.9</td>
+          <td>379</td>
+          <td>22.461755</td>
+        </tr>
+        <tr>
+          <th>382</th>
+          <td>28.9</td>
+          <td>379</td>
+          <td>22.461755</td>
+        </tr>
+        <tr>
+          <th>383</th>
+          <td>28.8</td>
+          <td>383</td>
+          <td>21.871707</td>
+        </tr>
+        <tr>
+          <th>384</th>
+          <td>28.8</td>
+          <td>383</td>
+          <td>21.871707</td>
+        </tr>
+        <tr>
+          <th>385</th>
+          <td>28.8</td>
+          <td>383</td>
+          <td>21.871707</td>
+        </tr>
+        <tr>
+          <th>386</th>
+          <td>28.8</td>
+          <td>383</td>
+          <td>21.871707</td>
+        </tr>
+        <tr>
+          <th>387</th>
+          <td>28.7</td>
+          <td>387</td>
+          <td>21.281658</td>
+        </tr>
+        <tr>
+          <th>388</th>
+          <td>28.7</td>
+          <td>387</td>
+          <td>21.281658</td>
+        </tr>
+        <tr>
+          <th>389</th>
+          <td>28.6</td>
+          <td>389</td>
+          <td>20.986634</td>
+        </tr>
+        <tr>
+          <th>390</th>
+          <td>28.6</td>
+          <td>389</td>
+          <td>20.986634</td>
+        </tr>
+        <tr>
+          <th>391</th>
+          <td>28.6</td>
+          <td>389</td>
+          <td>20.986634</td>
+        </tr>
+        <tr>
+          <th>392</th>
+          <td>28.5</td>
+          <td>392</td>
+          <td>20.544098</td>
+        </tr>
+        <tr>
+          <th>393</th>
+          <td>28.5</td>
+          <td>392</td>
+          <td>20.544098</td>
+        </tr>
+        <tr>
+          <th>394</th>
+          <td>28.5</td>
+          <td>392</td>
+          <td>20.544098</td>
+        </tr>
+        <tr>
+          <th>395</th>
+          <td>28.4</td>
+          <td>395</td>
+          <td>20.101562</td>
+        </tr>
+        <tr>
+          <th>396</th>
+          <td>28.4</td>
+          <td>395</td>
+          <td>20.101562</td>
+        </tr>
+        <tr>
+          <th>397</th>
+          <td>28.4</td>
+          <td>395</td>
+          <td>20.101562</td>
+        </tr>
+        <tr>
+          <th>398</th>
+          <td>28.3</td>
+          <td>398</td>
+          <td>19.659026</td>
+        </tr>
+        <tr>
+          <th>399</th>
+          <td>28.3</td>
+          <td>398</td>
+          <td>19.659026</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 18</h1>
+    <table border="1" class="dataframe" data-question="18">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>inverse_overall_score</th>
+          <th>rank</th>
+          <th>fit</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>0.010000</td>
+          <td>1</td>
+          <td>0.010503</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>0.010163</td>
+          <td>2</td>
+          <td>0.010566</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>0.010267</td>
+          <td>3</td>
+          <td>0.010629</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>0.010288</td>
+          <td>4</td>
+          <td>0.010691</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>0.010320</td>
+          <td>5</td>
+          <td>0.010754</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>0.010428</td>
+          <td>6</td>
+          <td>0.010817</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>0.010526</td>
+          <td>7</td>
+          <td>0.010879</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>0.010549</td>
+          <td>8</td>
+          <td>0.010942</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>0.010627</td>
+          <td>9</td>
+          <td>0.011005</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>0.010870</td>
+          <td>10</td>
+          <td>0.011068</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>0.010893</td>
+          <td>11</td>
+          <td>0.011130</td>
+        </tr>
+        <tr>
+          <th>11</th>
+          <td>0.010893</td>
+          <td>11</td>
+          <td>0.011130</td>
+        </tr>
+        <tr>
+          <th>12</th>
+          <td>0.011001</td>
+          <td>13</td>
+          <td>0.011256</td>
+        </tr>
+        <tr>
+          <th>13</th>
+          <td>0.011198</td>
+          <td>14</td>
+          <td>0.011318</td>
+        </tr>
+        <tr>
+          <th>14</th>
+          <td>0.011249</td>
+          <td>15</td>
+          <td>0.011381</td>
+        </tr>
+        <tr>
+          <th>15</th>
+          <td>0.011287</td>
+          <td>16</td>
+          <td>0.011444</td>
+        </tr>
+        <tr>
+          <th>16</th>
+          <td>0.011403</td>
+          <td>17</td>
+          <td>0.011507</td>
+        </tr>
+        <tr>
+          <th>17</th>
+          <td>0.011442</td>
+          <td>18</td>
+          <td>0.011569</td>
+        </tr>
+        <tr>
+          <th>18</th>
+          <td>0.011442</td>
+          <td>18</td>
+          <td>0.011569</td>
+        </tr>
+        <tr>
+          <th>19</th>
+          <td>0.011601</td>
+          <td>20</td>
+          <td>0.011695</td>
+        </tr>
+        <tr>
+          <th>20</th>
+          <td>0.011628</td>
+          <td>21</td>
+          <td>0.011758</td>
+        </tr>
+        <tr>
+          <th>21</th>
+          <td>0.011862</td>
+          <td>22</td>
+          <td>0.011820</td>
+        </tr>
+        <tr>
+          <th>22</th>
+          <td>0.011862</td>
+          <td>22</td>
+          <td>0.011820</td>
+        </tr>
+        <tr>
+          <th>23</th>
+          <td>0.011919</td>
+          <td>24</td>
+          <td>0.011946</td>
+        </tr>
+        <tr>
+          <th>24</th>
+          <td>0.011933</td>
+          <td>25</td>
+          <td>0.012008</td>
+        </tr>
+        <tr>
+          <th>25</th>
+          <td>0.011933</td>
+          <td>25</td>
+          <td>0.012008</td>
+        </tr>
+        <tr>
+          <th>26</th>
+          <td>0.012092</td>
+          <td>27</td>
+          <td>0.012134</td>
+        </tr>
+        <tr>
+          <th>27</th>
+          <td>0.012107</td>
+          <td>28</td>
+          <td>0.012197</td>
+        </tr>
+        <tr>
+          <th>28</th>
+          <td>0.012180</td>
+          <td>29</td>
+          <td>0.012259</td>
+        </tr>
+        <tr>
+          <th>29</th>
+          <td>0.012180</td>
+          <td>29</td>
+          <td>0.012259</td>
+        </tr>
+        <tr>
+          <th>30</th>
+          <td>0.012270</td>
+          <td>31</td>
+          <td>0.012385</td>
+        </tr>
+        <tr>
+          <th>31</th>
+          <td>0.012407</td>
+          <td>32</td>
+          <td>0.012447</td>
+        </tr>
+        <tr>
+          <th>32</th>
+          <td>0.012422</td>
+          <td>33</td>
+          <td>0.012510</td>
+        </tr>
+        <tr>
+          <th>33</th>
+          <td>0.012422</td>
+          <td>33</td>
+          <td>0.012510</td>
+        </tr>
+        <tr>
+          <th>34</th>
+          <td>0.012438</td>
+          <td>35</td>
+          <td>0.012636</td>
+        </tr>
+        <tr>
+          <th>35</th>
+          <td>0.012438</td>
+          <td>35</td>
+          <td>0.012636</td>
+        </tr>
+        <tr>
+          <th>36</th>
+          <td>0.012563</td>
+          <td>37</td>
+          <td>0.012761</td>
+        </tr>
+        <tr>
+          <th>37</th>
+          <td>0.012579</td>
+          <td>38</td>
+          <td>0.012824</td>
+        </tr>
+        <tr>
+          <th>38</th>
+          <td>0.012690</td>
+          <td>39</td>
+          <td>0.012886</td>
+        </tr>
+        <tr>
+          <th>39</th>
+          <td>0.012723</td>
+          <td>40</td>
+          <td>0.012949</td>
+        </tr>
+        <tr>
+          <th>40</th>
+          <td>0.012837</td>
+          <td>41</td>
+          <td>0.013012</td>
+        </tr>
+        <tr>
+          <th>41</th>
+          <td>0.012853</td>
+          <td>42</td>
+          <td>0.013075</td>
+        </tr>
+        <tr>
+          <th>42</th>
+          <td>0.012970</td>
+          <td>43</td>
+          <td>0.013137</td>
+        </tr>
+        <tr>
+          <th>43</th>
+          <td>0.012987</td>
+          <td>44</td>
+          <td>0.013200</td>
+        </tr>
+        <tr>
+          <th>44</th>
+          <td>0.013055</td>
+          <td>45</td>
+          <td>0.013263</td>
+        </tr>
+        <tr>
+          <th>45</th>
+          <td>0.013175</td>
+          <td>46</td>
+          <td>0.013325</td>
+        </tr>
+        <tr>
+          <th>46</th>
+          <td>0.013210</td>
+          <td>47</td>
+          <td>0.013388</td>
+        </tr>
+        <tr>
+          <th>47</th>
+          <td>0.013369</td>
+          <td>48</td>
+          <td>0.013451</td>
+        </tr>
+        <tr>
+          <th>48</th>
+          <td>0.013423</td>
+          <td>49</td>
+          <td>0.013514</td>
+        </tr>
+        <tr>
+          <th>49</th>
+          <td>0.013477</td>
+          <td>50</td>
+          <td>0.013576</td>
+        </tr>
+        <tr>
+          <th>50</th>
+          <td>0.013495</td>
+          <td>51</td>
+          <td>0.013639</td>
+        </tr>
+        <tr>
+          <th>51</th>
+          <td>0.013587</td>
+          <td>52</td>
+          <td>0.013702</td>
+        </tr>
+        <tr>
+          <th>52</th>
+          <td>0.013624</td>
+          <td>53</td>
+          <td>0.013764</td>
+        </tr>
+        <tr>
+          <th>53</th>
+          <td>0.013812</td>
+          <td>54</td>
+          <td>0.013827</td>
+        </tr>
+        <tr>
+          <th>54</th>
+          <td>0.013831</td>
+          <td>55</td>
+          <td>0.013890</td>
+        </tr>
+        <tr>
+          <th>55</th>
+          <td>0.013928</td>
+          <td>56</td>
+          <td>0.013953</td>
+        </tr>
+        <tr>
+          <th>56</th>
+          <td>0.013986</td>
+          <td>57</td>
+          <td>0.014015</td>
+        </tr>
+        <tr>
+          <th>57</th>
+          <td>0.014104</td>
+          <td>58</td>
+          <td>0.014078</td>
+        </tr>
+        <tr>
+          <th>58</th>
+          <td>0.014104</td>
+          <td>58</td>
+          <td>0.014078</td>
+        </tr>
+        <tr>
+          <th>59</th>
+          <td>0.014184</td>
+          <td>60</td>
+          <td>0.014203</td>
+        </tr>
+        <tr>
+          <th>60</th>
+          <td>0.014184</td>
+          <td>60</td>
+          <td>0.014203</td>
+        </tr>
+        <tr>
+          <th>61</th>
+          <td>0.014205</td>
+          <td>62</td>
+          <td>0.014329</td>
+        </tr>
+        <tr>
+          <th>62</th>
+          <td>0.014368</td>
+          <td>63</td>
+          <td>0.014392</td>
+        </tr>
+        <tr>
+          <th>63</th>
+          <td>0.014493</td>
+          <td>64</td>
+          <td>0.014454</td>
+        </tr>
+        <tr>
+          <th>64</th>
+          <td>0.014577</td>
+          <td>65</td>
+          <td>0.014517</td>
+        </tr>
+        <tr>
+          <th>65</th>
+          <td>0.014620</td>
+          <td>66</td>
+          <td>0.014580</td>
+        </tr>
+        <tr>
+          <th>66</th>
+          <td>0.014663</td>
+          <td>67</td>
+          <td>0.014643</td>
+        </tr>
+        <tr>
+          <th>67</th>
+          <td>0.014728</td>
+          <td>68</td>
+          <td>0.014705</td>
+        </tr>
+        <tr>
+          <th>68</th>
+          <td>0.014859</td>
+          <td>69</td>
+          <td>0.014768</td>
+        </tr>
+        <tr>
+          <th>69</th>
+          <td>0.014903</td>
+          <td>70</td>
+          <td>0.014831</td>
+        </tr>
+        <tr>
+          <th>70</th>
+          <td>0.015038</td>
+          <td>71</td>
+          <td>0.014893</td>
+        </tr>
+        <tr>
+          <th>71</th>
+          <td>0.015106</td>
+          <td>72</td>
+          <td>0.014956</td>
+        </tr>
+        <tr>
+          <th>72</th>
+          <td>0.015152</td>
+          <td>74</td>
+          <td>0.015082</td>
+        </tr>
+        <tr>
+          <th>73</th>
+          <td>0.015175</td>
+          <td>75</td>
+          <td>0.015144</td>
+        </tr>
+        <tr>
+          <th>74</th>
+          <td>0.015198</td>
+          <td>76</td>
+          <td>0.015207</td>
+        </tr>
+        <tr>
+          <th>75</th>
+          <td>0.015408</td>
+          <td>77</td>
+          <td>0.015270</td>
+        </tr>
+        <tr>
+          <th>76</th>
+          <td>0.015576</td>
+          <td>78</td>
+          <td>0.015332</td>
+        </tr>
+        <tr>
+          <th>77</th>
+          <td>0.015576</td>
+          <td>78</td>
+          <td>0.015332</td>
+        </tr>
+        <tr>
+          <th>78</th>
+          <td>0.015601</td>
+          <td>80</td>
+          <td>0.015458</td>
+        </tr>
+        <tr>
+          <th>79</th>
+          <td>0.015625</td>
+          <td>81</td>
+          <td>0.015521</td>
+        </tr>
+        <tr>
+          <th>80</th>
+          <td>0.015674</td>
+          <td>81</td>
+          <td>0.015521</td>
+        </tr>
+        <tr>
+          <th>81</th>
+          <td>0.015699</td>
+          <td>82</td>
+          <td>0.015583</td>
+        </tr>
+        <tr>
+          <th>82</th>
+          <td>0.015773</td>
+          <td>83</td>
+          <td>0.015646</td>
+        </tr>
+        <tr>
+          <th>83</th>
+          <td>0.015773</td>
+          <td>83</td>
+          <td>0.015646</td>
+        </tr>
+        <tr>
+          <th>84</th>
+          <td>0.015823</td>
+          <td>84</td>
+          <td>0.015709</td>
+        </tr>
+        <tr>
+          <th>85</th>
+          <td>0.015848</td>
+          <td>85</td>
+          <td>0.015771</td>
+        </tr>
+        <tr>
+          <th>86</th>
+          <td>0.015898</td>
+          <td>86</td>
+          <td>0.015834</td>
+        </tr>
+        <tr>
+          <th>87</th>
+          <td>0.015974</td>
+          <td>87</td>
+          <td>0.015897</td>
+        </tr>
+        <tr>
+          <th>88</th>
+          <td>0.016051</td>
+          <td>89</td>
+          <td>0.016022</td>
+        </tr>
+        <tr>
+          <th>89</th>
+          <td>0.016077</td>
+          <td>90</td>
+          <td>0.016085</td>
+        </tr>
+        <tr>
+          <th>90</th>
+          <td>0.016155</td>
+          <td>91</td>
+          <td>0.016148</td>
+        </tr>
+        <tr>
+          <th>91</th>
+          <td>0.016181</td>
+          <td>92</td>
+          <td>0.016210</td>
+        </tr>
+        <tr>
+          <th>92</th>
+          <td>0.016207</td>
+          <td>93</td>
+          <td>0.016273</td>
+        </tr>
+        <tr>
+          <th>93</th>
+          <td>0.016207</td>
+          <td>93</td>
+          <td>0.016273</td>
+        </tr>
+        <tr>
+          <th>94</th>
+          <td>0.016393</td>
+          <td>95</td>
+          <td>0.016399</td>
+        </tr>
+        <tr>
+          <th>95</th>
+          <td>0.016447</td>
+          <td>96</td>
+          <td>0.016461</td>
+        </tr>
+        <tr>
+          <th>96</th>
+          <td>0.016502</td>
+          <td>97</td>
+          <td>0.016524</td>
+        </tr>
+        <tr>
+          <th>97</th>
+          <td>0.016529</td>
+          <td>98</td>
+          <td>0.016587</td>
+        </tr>
+        <tr>
+          <th>98</th>
+          <td>0.016529</td>
+          <td>98</td>
+          <td>0.016587</td>
+        </tr>
+        <tr>
+          <th>99</th>
+          <td>0.016694</td>
+          <td>100</td>
+          <td>0.016712</td>
+        </tr>
+        <tr>
+          <th>100</th>
+          <td>0.016807</td>
+          <td>101</td>
+          <td>0.016775</td>
+        </tr>
+        <tr>
+          <th>101</th>
+          <td>0.016978</td>
+          <td>102</td>
+          <td>0.016838</td>
+        </tr>
+        <tr>
+          <th>102</th>
+          <td>0.017007</td>
+          <td>103</td>
+          <td>0.016900</td>
+        </tr>
+        <tr>
+          <th>103</th>
+          <td>0.017036</td>
+          <td>104</td>
+          <td>0.016963</td>
+        </tr>
+        <tr>
+          <th>104</th>
+          <td>0.017036</td>
+          <td>104</td>
+          <td>0.016963</td>
+        </tr>
+        <tr>
+          <th>105</th>
+          <td>0.017065</td>
+          <td>106</td>
+          <td>0.017088</td>
+        </tr>
+        <tr>
+          <th>106</th>
+          <td>0.017123</td>
+          <td>107</td>
+          <td>0.017151</td>
+        </tr>
+        <tr>
+          <th>107</th>
+          <td>0.017153</td>
+          <td>108</td>
+          <td>0.017214</td>
+        </tr>
+        <tr>
+          <th>108</th>
+          <td>0.017153</td>
+          <td>108</td>
+          <td>0.017214</td>
+        </tr>
+        <tr>
+          <th>109</th>
+          <td>0.017212</td>
+          <td>110</td>
+          <td>0.017339</td>
+        </tr>
+        <tr>
+          <th>110</th>
+          <td>0.017301</td>
+          <td>111</td>
+          <td>0.017402</td>
+        </tr>
+        <tr>
+          <th>111</th>
+          <td>0.017422</td>
+          <td>112</td>
+          <td>0.017465</td>
+        </tr>
+        <tr>
+          <th>112</th>
+          <td>0.017452</td>
+          <td>113</td>
+          <td>0.017528</td>
+        </tr>
+        <tr>
+          <th>113</th>
+          <td>0.017825</td>
+          <td>114</td>
+          <td>0.017590</td>
+        </tr>
+        <tr>
+          <th>114</th>
+          <td>0.017921</td>
+          <td>115</td>
+          <td>0.017653</td>
+        </tr>
+        <tr>
+          <th>115</th>
+          <td>0.018018</td>
+          <td>116</td>
+          <td>0.017716</td>
+        </tr>
+        <tr>
+          <th>116</th>
+          <td>0.018018</td>
+          <td>116</td>
+          <td>0.017716</td>
+        </tr>
+        <tr>
+          <th>117</th>
+          <td>0.018051</td>
+          <td>118</td>
+          <td>0.017841</td>
+        </tr>
+        <tr>
+          <th>118</th>
+          <td>0.018282</td>
+          <td>119</td>
+          <td>0.017904</td>
+        </tr>
+        <tr>
+          <th>119</th>
+          <td>0.018349</td>
+          <td>120</td>
+          <td>0.017967</td>
+        </tr>
+        <tr>
+          <th>120</th>
+          <td>0.018349</td>
+          <td>120</td>
+          <td>0.017967</td>
+        </tr>
+        <tr>
+          <th>121</th>
+          <td>0.018349</td>
+          <td>120</td>
+          <td>0.017967</td>
+        </tr>
+        <tr>
+          <th>122</th>
+          <td>0.018416</td>
+          <td>123</td>
+          <td>0.018155</td>
+        </tr>
+        <tr>
+          <th>123</th>
+          <td>0.018450</td>
+          <td>124</td>
+          <td>0.018217</td>
+        </tr>
+        <tr>
+          <th>124</th>
+          <td>0.018519</td>
+          <td>125</td>
+          <td>0.018280</td>
+        </tr>
+        <tr>
+          <th>125</th>
+          <td>0.018519</td>
+          <td>125</td>
+          <td>0.018280</td>
+        </tr>
+        <tr>
+          <th>126</th>
+          <td>0.018553</td>
+          <td>126</td>
+          <td>0.018343</td>
+        </tr>
+        <tr>
+          <th>127</th>
+          <td>0.018727</td>
+          <td>127</td>
+          <td>0.018406</td>
+        </tr>
+        <tr>
+          <th>128</th>
+          <td>0.018797</td>
+          <td>128</td>
+          <td>0.018468</td>
+        </tr>
+        <tr>
+          <th>129</th>
+          <td>0.018832</td>
+          <td>129</td>
+          <td>0.018531</td>
+        </tr>
+        <tr>
+          <th>130</th>
+          <td>0.018868</td>
+          <td>130</td>
+          <td>0.018594</td>
+        </tr>
+        <tr>
+          <th>131</th>
+          <td>0.018868</td>
+          <td>130</td>
+          <td>0.018594</td>
+        </tr>
+        <tr>
+          <th>132</th>
+          <td>0.018904</td>
+          <td>132</td>
+          <td>0.018719</td>
+        </tr>
+        <tr>
+          <th>133</th>
+          <td>0.018904</td>
+          <td>132</td>
+          <td>0.018719</td>
+        </tr>
+        <tr>
+          <th>134</th>
+          <td>0.019084</td>
+          <td>134</td>
+          <td>0.018845</td>
+        </tr>
+        <tr>
+          <th>135</th>
+          <td>0.019120</td>
+          <td>135</td>
+          <td>0.018907</td>
+        </tr>
+        <tr>
+          <th>136</th>
+          <td>0.019194</td>
+          <td>136</td>
+          <td>0.018970</td>
+        </tr>
+        <tr>
+          <th>137</th>
+          <td>0.019231</td>
+          <td>137</td>
+          <td>0.019033</td>
+        </tr>
+        <tr>
+          <th>138</th>
+          <td>0.019268</td>
+          <td>138</td>
+          <td>0.019095</td>
+        </tr>
+        <tr>
+          <th>139</th>
+          <td>0.019455</td>
+          <td>139</td>
+          <td>0.019158</td>
+        </tr>
+        <tr>
+          <th>140</th>
+          <td>0.019531</td>
+          <td>140</td>
+          <td>0.019221</td>
+        </tr>
+        <tr>
+          <th>141</th>
+          <td>0.019531</td>
+          <td>140</td>
+          <td>0.019221</td>
+        </tr>
+        <tr>
+          <th>142</th>
+          <td>0.019531</td>
+          <td>140</td>
+          <td>0.019221</td>
+        </tr>
+        <tr>
+          <th>143</th>
+          <td>0.019569</td>
+          <td>144</td>
+          <td>0.019472</td>
+        </tr>
+        <tr>
+          <th>144</th>
+          <td>0.019608</td>
+          <td>145</td>
+          <td>0.019534</td>
+        </tr>
+        <tr>
+          <th>145</th>
+          <td>0.019646</td>
+          <td>146</td>
+          <td>0.019597</td>
+        </tr>
+        <tr>
+          <th>146</th>
+          <td>0.019763</td>
+          <td>147</td>
+          <td>0.019660</td>
+        </tr>
+        <tr>
+          <th>147</th>
+          <td>0.019802</td>
+          <td>148</td>
+          <td>0.019723</td>
+        </tr>
+        <tr>
+          <th>148</th>
+          <td>0.019841</td>
+          <td>149</td>
+          <td>0.019785</td>
+        </tr>
+        <tr>
+          <th>149</th>
+          <td>0.019881</td>
+          <td>150</td>
+          <td>0.019848</td>
+        </tr>
+        <tr>
+          <th>150</th>
+          <td>0.020000</td>
+          <td>151</td>
+          <td>0.019911</td>
+        </tr>
+        <tr>
+          <th>151</th>
+          <td>0.020243</td>
+          <td>152</td>
+          <td>0.019973</td>
+        </tr>
+        <tr>
+          <th>152</th>
+          <td>0.020325</td>
+          <td>153</td>
+          <td>0.020036</td>
+        </tr>
+        <tr>
+          <th>153</th>
+          <td>0.020408</td>
+          <td>154</td>
+          <td>0.020099</td>
+        </tr>
+        <tr>
+          <th>154</th>
+          <td>0.020408</td>
+          <td>154</td>
+          <td>0.020099</td>
+        </tr>
+        <tr>
+          <th>155</th>
+          <td>0.020576</td>
+          <td>156</td>
+          <td>0.020224</td>
+        </tr>
+        <tr>
+          <th>156</th>
+          <td>0.020576</td>
+          <td>156</td>
+          <td>0.020224</td>
+        </tr>
+        <tr>
+          <th>157</th>
+          <td>0.020619</td>
+          <td>158</td>
+          <td>0.020350</td>
+        </tr>
+        <tr>
+          <th>158</th>
+          <td>0.020661</td>
+          <td>159</td>
+          <td>0.020413</td>
+        </tr>
+        <tr>
+          <th>159</th>
+          <td>0.020704</td>
+          <td>160</td>
+          <td>0.020475</td>
+        </tr>
+        <tr>
+          <th>160</th>
+          <td>0.020704</td>
+          <td>160</td>
+          <td>0.020475</td>
+        </tr>
+        <tr>
+          <th>161</th>
+          <td>0.020747</td>
+          <td>162</td>
+          <td>0.020601</td>
+        </tr>
+        <tr>
+          <th>162</th>
+          <td>0.020790</td>
+          <td>163</td>
+          <td>0.020663</td>
+        </tr>
+        <tr>
+          <th>163</th>
+          <td>0.020790</td>
+          <td>163</td>
+          <td>0.020663</td>
+        </tr>
+        <tr>
+          <th>164</th>
+          <td>0.020877</td>
+          <td>165</td>
+          <td>0.020789</td>
+        </tr>
+        <tr>
+          <th>165</th>
+          <td>0.020877</td>
+          <td>165</td>
+          <td>0.020789</td>
+        </tr>
+        <tr>
+          <th>166</th>
+          <td>0.020921</td>
+          <td>167</td>
+          <td>0.020914</td>
+        </tr>
+        <tr>
+          <th>167</th>
+          <td>0.020964</td>
+          <td>167</td>
+          <td>0.020914</td>
+        </tr>
+        <tr>
+          <th>168</th>
+          <td>0.020964</td>
+          <td>167</td>
+          <td>0.020914</td>
+        </tr>
+        <tr>
+          <th>169</th>
+          <td>0.021142</td>
+          <td>169</td>
+          <td>0.021040</td>
+        </tr>
+        <tr>
+          <th>170</th>
+          <td>0.021142</td>
+          <td>169</td>
+          <td>0.021040</td>
+        </tr>
+        <tr>
+          <th>171</th>
+          <td>0.021142</td>
+          <td>170</td>
+          <td>0.021102</td>
+        </tr>
+        <tr>
+          <th>172</th>
+          <td>0.021277</td>
+          <td>172</td>
+          <td>0.021228</td>
+        </tr>
+        <tr>
+          <th>173</th>
+          <td>0.021368</td>
+          <td>173</td>
+          <td>0.021291</td>
+        </tr>
+        <tr>
+          <th>174</th>
+          <td>0.021368</td>
+          <td>173</td>
+          <td>0.021291</td>
+        </tr>
+        <tr>
+          <th>175</th>
+          <td>0.021368</td>
+          <td>173</td>
+          <td>0.021291</td>
+        </tr>
+        <tr>
+          <th>176</th>
+          <td>0.021413</td>
+          <td>176</td>
+          <td>0.021479</td>
+        </tr>
+        <tr>
+          <th>177</th>
+          <td>0.021459</td>
+          <td>177</td>
+          <td>0.021541</td>
+        </tr>
+        <tr>
+          <th>178</th>
+          <td>0.021459</td>
+          <td>177</td>
+          <td>0.021541</td>
+        </tr>
+        <tr>
+          <th>179</th>
+          <td>0.021552</td>
+          <td>179</td>
+          <td>0.021667</td>
+        </tr>
+        <tr>
+          <th>180</th>
+          <td>0.021598</td>
+          <td>181</td>
+          <td>0.021792</td>
+        </tr>
+        <tr>
+          <th>181</th>
+          <td>0.021645</td>
+          <td>182</td>
+          <td>0.021855</td>
+        </tr>
+        <tr>
+          <th>182</th>
+          <td>0.021692</td>
+          <td>183</td>
+          <td>0.021918</td>
+        </tr>
+        <tr>
+          <th>183</th>
+          <td>0.021786</td>
+          <td>184</td>
+          <td>0.021980</td>
+        </tr>
+        <tr>
+          <th>184</th>
+          <td>0.021930</td>
+          <td>185</td>
+          <td>0.022043</td>
+        </tr>
+        <tr>
+          <th>185</th>
+          <td>0.022124</td>
+          <td>186</td>
+          <td>0.022106</td>
+        </tr>
+        <tr>
+          <th>186</th>
+          <td>0.022124</td>
+          <td>186</td>
+          <td>0.022106</td>
+        </tr>
+        <tr>
+          <th>187</th>
+          <td>0.022173</td>
+          <td>188</td>
+          <td>0.022231</td>
+        </tr>
+        <tr>
+          <th>188</th>
+          <td>0.022222</td>
+          <td>189</td>
+          <td>0.022294</td>
+        </tr>
+        <tr>
+          <th>189</th>
+          <td>0.022222</td>
+          <td>189</td>
+          <td>0.022294</td>
+        </tr>
+        <tr>
+          <th>190</th>
+          <td>0.022321</td>
+          <td>191</td>
+          <td>0.022419</td>
+        </tr>
+        <tr>
+          <th>191</th>
+          <td>0.022371</td>
+          <td>192</td>
+          <td>0.022482</td>
+        </tr>
+        <tr>
+          <th>192</th>
+          <td>0.022371</td>
+          <td>192</td>
+          <td>0.022482</td>
+        </tr>
+        <tr>
+          <th>193</th>
+          <td>0.022422</td>
+          <td>194</td>
+          <td>0.022608</td>
+        </tr>
+        <tr>
+          <th>194</th>
+          <td>0.022472</td>
+          <td>195</td>
+          <td>0.022670</td>
+        </tr>
+        <tr>
+          <th>195</th>
+          <td>0.022624</td>
+          <td>197</td>
+          <td>0.022796</td>
+        </tr>
+        <tr>
+          <th>196</th>
+          <td>0.022676</td>
+          <td>198</td>
+          <td>0.022858</td>
+        </tr>
+        <tr>
+          <th>197</th>
+          <td>0.022676</td>
+          <td>198</td>
+          <td>0.022858</td>
+        </tr>
+        <tr>
+          <th>198</th>
+          <td>0.022727</td>
+          <td>200</td>
+          <td>0.022984</td>
+        </tr>
+        <tr>
+          <th>199</th>
+          <td>0.022727</td>
+          <td>200</td>
+          <td>0.022984</td>
+        </tr>
+        <tr>
+          <th>200</th>
+          <td>0.022727</td>
+          <td>200</td>
+          <td>0.022984</td>
+        </tr>
+        <tr>
+          <th>201</th>
+          <td>0.022883</td>
+          <td>202</td>
+          <td>0.023109</td>
+        </tr>
+        <tr>
+          <th>202</th>
+          <td>0.022989</td>
+          <td>203</td>
+          <td>0.023172</td>
+        </tr>
+        <tr>
+          <th>203</th>
+          <td>0.023041</td>
+          <td>204</td>
+          <td>0.023235</td>
+        </tr>
+        <tr>
+          <th>204</th>
+          <td>0.023095</td>
+          <td>205</td>
+          <td>0.023298</td>
+        </tr>
+        <tr>
+          <th>205</th>
+          <td>0.023148</td>
+          <td>206</td>
+          <td>0.023360</td>
+        </tr>
+        <tr>
+          <th>206</th>
+          <td>0.023202</td>
+          <td>207</td>
+          <td>0.023423</td>
+        </tr>
+        <tr>
+          <th>207</th>
+          <td>0.023202</td>
+          <td>207</td>
+          <td>0.023423</td>
+        </tr>
+        <tr>
+          <th>208</th>
+          <td>0.023202</td>
+          <td>207</td>
+          <td>0.023423</td>
+        </tr>
+        <tr>
+          <th>209</th>
+          <td>0.023310</td>
+          <td>210</td>
+          <td>0.023611</td>
+        </tr>
+        <tr>
+          <th>210</th>
+          <td>0.023364</td>
+          <td>211</td>
+          <td>0.023674</td>
+        </tr>
+        <tr>
+          <th>211</th>
+          <td>0.023419</td>
+          <td>212</td>
+          <td>0.023737</td>
+        </tr>
+        <tr>
+          <th>212</th>
+          <td>0.023419</td>
+          <td>212</td>
+          <td>0.023737</td>
+        </tr>
+        <tr>
+          <th>213</th>
+          <td>0.023753</td>
+          <td>214</td>
+          <td>0.023862</td>
+        </tr>
+        <tr>
+          <th>214</th>
+          <td>0.023810</td>
+          <td>215</td>
+          <td>0.023925</td>
+        </tr>
+        <tr>
+          <th>215</th>
+          <td>0.023810</td>
+          <td>215</td>
+          <td>0.023925</td>
+        </tr>
+        <tr>
+          <th>216</th>
+          <td>0.024155</td>
+          <td>217</td>
+          <td>0.024050</td>
+        </tr>
+        <tr>
+          <th>217</th>
+          <td>0.024155</td>
+          <td>217</td>
+          <td>0.024050</td>
+        </tr>
+        <tr>
+          <th>218</th>
+          <td>0.024213</td>
+          <td>219</td>
+          <td>0.024176</td>
+        </tr>
+        <tr>
+          <th>219</th>
+          <td>0.024213</td>
+          <td>219</td>
+          <td>0.024176</td>
+        </tr>
+        <tr>
+          <th>220</th>
+          <td>0.024213</td>
+          <td>219</td>
+          <td>0.024176</td>
+        </tr>
+        <tr>
+          <th>221</th>
+          <td>0.024272</td>
+          <td>222</td>
+          <td>0.024364</td>
+        </tr>
+        <tr>
+          <th>222</th>
+          <td>0.024450</td>
+          <td>223</td>
+          <td>0.024426</td>
+        </tr>
+        <tr>
+          <th>223</th>
+          <td>0.024570</td>
+          <td>224</td>
+          <td>0.024489</td>
+        </tr>
+        <tr>
+          <th>224</th>
+          <td>0.024570</td>
+          <td>224</td>
+          <td>0.024489</td>
+        </tr>
+        <tr>
+          <th>225</th>
+          <td>0.024752</td>
+          <td>225</td>
+          <td>0.024552</td>
+        </tr>
+        <tr>
+          <th>226</th>
+          <td>0.024938</td>
+          <td>226</td>
+          <td>0.024615</td>
+        </tr>
+        <tr>
+          <th>227</th>
+          <td>0.025000</td>
+          <td>227</td>
+          <td>0.024677</td>
+        </tr>
+        <tr>
+          <th>228</th>
+          <td>0.025000</td>
+          <td>227</td>
+          <td>0.024677</td>
+        </tr>
+        <tr>
+          <th>229</th>
+          <td>0.025000</td>
+          <td>227</td>
+          <td>0.024677</td>
+        </tr>
+        <tr>
+          <th>230</th>
+          <td>0.025063</td>
+          <td>230</td>
+          <td>0.024865</td>
+        </tr>
+        <tr>
+          <th>231</th>
+          <td>0.025126</td>
+          <td>231</td>
+          <td>0.024928</td>
+        </tr>
+        <tr>
+          <th>232</th>
+          <td>0.025126</td>
+          <td>231</td>
+          <td>0.024928</td>
+        </tr>
+        <tr>
+          <th>233</th>
+          <td>0.025189</td>
+          <td>233</td>
+          <td>0.025054</td>
+        </tr>
+        <tr>
+          <th>234</th>
+          <td>0.025253</td>
+          <td>234</td>
+          <td>0.025116</td>
+        </tr>
+        <tr>
+          <th>235</th>
+          <td>0.025253</td>
+          <td>234</td>
+          <td>0.025116</td>
+        </tr>
+        <tr>
+          <th>236</th>
+          <td>0.025253</td>
+          <td>234</td>
+          <td>0.025116</td>
+        </tr>
+        <tr>
+          <th>237</th>
+          <td>0.025316</td>
+          <td>237</td>
+          <td>0.025304</td>
+        </tr>
+        <tr>
+          <th>238</th>
+          <td>0.025510</td>
+          <td>238</td>
+          <td>0.025367</td>
+        </tr>
+        <tr>
+          <th>239</th>
+          <td>0.025641</td>
+          <td>239</td>
+          <td>0.025430</td>
+        </tr>
+        <tr>
+          <th>240</th>
+          <td>0.025641</td>
+          <td>239</td>
+          <td>0.025430</td>
+        </tr>
+        <tr>
+          <th>241</th>
+          <td>0.025641</td>
+          <td>239</td>
+          <td>0.025430</td>
+        </tr>
+        <tr>
+          <th>242</th>
+          <td>0.025707</td>
+          <td>242</td>
+          <td>0.025618</td>
+        </tr>
+        <tr>
+          <th>243</th>
+          <td>0.025773</td>
+          <td>243</td>
+          <td>0.025681</td>
+        </tr>
+        <tr>
+          <th>244</th>
+          <td>0.025840</td>
+          <td>244</td>
+          <td>0.025743</td>
+        </tr>
+        <tr>
+          <th>245</th>
+          <td>0.025974</td>
+          <td>245</td>
+          <td>0.025806</td>
+        </tr>
+        <tr>
+          <th>246</th>
+          <td>0.026042</td>
+          <td>246</td>
+          <td>0.025869</td>
+        </tr>
+        <tr>
+          <th>247</th>
+          <td>0.026110</td>
+          <td>247</td>
+          <td>0.025932</td>
+        </tr>
+        <tr>
+          <th>248</th>
+          <td>0.026110</td>
+          <td>247</td>
+          <td>0.025932</td>
+        </tr>
+        <tr>
+          <th>249</th>
+          <td>0.026455</td>
+          <td>250</td>
+          <td>0.026120</td>
+        </tr>
+        <tr>
+          <th>250</th>
+          <td>0.026596</td>
+          <td>251</td>
+          <td>0.026183</td>
+        </tr>
+        <tr>
+          <th>251</th>
+          <td>0.026596</td>
+          <td>251</td>
+          <td>0.026183</td>
+        </tr>
+        <tr>
+          <th>252</th>
+          <td>0.026667</td>
+          <td>253</td>
+          <td>0.026308</td>
+        </tr>
+        <tr>
+          <th>253</th>
+          <td>0.026667</td>
+          <td>253</td>
+          <td>0.026308</td>
+        </tr>
+        <tr>
+          <th>254</th>
+          <td>0.026667</td>
+          <td>253</td>
+          <td>0.026308</td>
+        </tr>
+        <tr>
+          <th>255</th>
+          <td>0.026810</td>
+          <td>256</td>
+          <td>0.026496</td>
+        </tr>
+        <tr>
+          <th>256</th>
+          <td>0.026882</td>
+          <td>257</td>
+          <td>0.026559</td>
+        </tr>
+        <tr>
+          <th>257</th>
+          <td>0.026882</td>
+          <td>257</td>
+          <td>0.026559</td>
+        </tr>
+        <tr>
+          <th>258</th>
+          <td>0.026954</td>
+          <td>259</td>
+          <td>0.026684</td>
+        </tr>
+        <tr>
+          <th>259</th>
+          <td>0.027027</td>
+          <td>260</td>
+          <td>0.026747</td>
+        </tr>
+        <tr>
+          <th>260</th>
+          <td>0.027100</td>
+          <td>261</td>
+          <td>0.026810</td>
+        </tr>
+        <tr>
+          <th>261</th>
+          <td>0.027174</td>
+          <td>262</td>
+          <td>0.026872</td>
+        </tr>
+        <tr>
+          <th>262</th>
+          <td>0.027174</td>
+          <td>262</td>
+          <td>0.026872</td>
+        </tr>
+        <tr>
+          <th>263</th>
+          <td>0.027174</td>
+          <td>262</td>
+          <td>0.026872</td>
+        </tr>
+        <tr>
+          <th>264</th>
+          <td>0.027248</td>
+          <td>265</td>
+          <td>0.027061</td>
+        </tr>
+        <tr>
+          <th>265</th>
+          <td>0.027322</td>
+          <td>266</td>
+          <td>0.027123</td>
+        </tr>
+        <tr>
+          <th>266</th>
+          <td>0.027322</td>
+          <td>266</td>
+          <td>0.027123</td>
+        </tr>
+        <tr>
+          <th>267</th>
+          <td>0.027397</td>
+          <td>268</td>
+          <td>0.027249</td>
+        </tr>
+        <tr>
+          <th>268</th>
+          <td>0.027397</td>
+          <td>268</td>
+          <td>0.027249</td>
+        </tr>
+        <tr>
+          <th>269</th>
+          <td>0.027548</td>
+          <td>270</td>
+          <td>0.027374</td>
+        </tr>
+        <tr>
+          <th>270</th>
+          <td>0.027701</td>
+          <td>271</td>
+          <td>0.027437</td>
+        </tr>
+        <tr>
+          <th>271</th>
+          <td>0.027701</td>
+          <td>271</td>
+          <td>0.027437</td>
+        </tr>
+        <tr>
+          <th>272</th>
+          <td>0.027701</td>
+          <td>271</td>
+          <td>0.027437</td>
+        </tr>
+        <tr>
+          <th>273</th>
+          <td>0.027855</td>
+          <td>274</td>
+          <td>0.027625</td>
+        </tr>
+        <tr>
+          <th>274</th>
+          <td>0.027855</td>
+          <td>274</td>
+          <td>0.027625</td>
+        </tr>
+        <tr>
+          <th>275</th>
+          <td>0.027933</td>
+          <td>276</td>
+          <td>0.027750</td>
+        </tr>
+        <tr>
+          <th>276</th>
+          <td>0.028011</td>
+          <td>277</td>
+          <td>0.027813</td>
+        </tr>
+        <tr>
+          <th>277</th>
+          <td>0.028011</td>
+          <td>277</td>
+          <td>0.027813</td>
+        </tr>
+        <tr>
+          <th>278</th>
+          <td>0.028090</td>
+          <td>279</td>
+          <td>0.027939</td>
+        </tr>
+        <tr>
+          <th>279</th>
+          <td>0.028249</td>
+          <td>280</td>
+          <td>0.028001</td>
+        </tr>
+        <tr>
+          <th>280</th>
+          <td>0.028409</td>
+          <td>281</td>
+          <td>0.028064</td>
+        </tr>
+        <tr>
+          <th>281</th>
+          <td>0.028409</td>
+          <td>281</td>
+          <td>0.028064</td>
+        </tr>
+        <tr>
+          <th>282</th>
+          <td>0.028409</td>
+          <td>281</td>
+          <td>0.028064</td>
+        </tr>
+        <tr>
+          <th>283</th>
+          <td>0.028490</td>
+          <td>284</td>
+          <td>0.028252</td>
+        </tr>
+        <tr>
+          <th>284</th>
+          <td>0.028571</td>
+          <td>285</td>
+          <td>0.028315</td>
+        </tr>
+        <tr>
+          <th>285</th>
+          <td>0.028571</td>
+          <td>285</td>
+          <td>0.028315</td>
+        </tr>
+        <tr>
+          <th>286</th>
+          <td>0.028653</td>
+          <td>287</td>
+          <td>0.028440</td>
+        </tr>
+        <tr>
+          <th>287</th>
+          <td>0.028653</td>
+          <td>287</td>
+          <td>0.028440</td>
+        </tr>
+        <tr>
+          <th>288</th>
+          <td>0.028653</td>
+          <td>287</td>
+          <td>0.028440</td>
+        </tr>
+        <tr>
+          <th>289</th>
+          <td>0.028653</td>
+          <td>287</td>
+          <td>0.028440</td>
+        </tr>
+        <tr>
+          <th>290</th>
+          <td>0.028736</td>
+          <td>291</td>
+          <td>0.028691</td>
+        </tr>
+        <tr>
+          <th>291</th>
+          <td>0.028736</td>
+          <td>291</td>
+          <td>0.028691</td>
+        </tr>
+        <tr>
+          <th>292</th>
+          <td>0.028736</td>
+          <td>291</td>
+          <td>0.028691</td>
+        </tr>
+        <tr>
+          <th>293</th>
+          <td>0.028736</td>
+          <td>291</td>
+          <td>0.028691</td>
+        </tr>
+        <tr>
+          <th>294</th>
+          <td>0.028736</td>
+          <td>291</td>
+          <td>0.028691</td>
+        </tr>
+        <tr>
+          <th>295</th>
+          <td>0.028818</td>
+          <td>296</td>
+          <td>0.029005</td>
+        </tr>
+        <tr>
+          <th>296</th>
+          <td>0.028818</td>
+          <td>296</td>
+          <td>0.029005</td>
+        </tr>
+        <tr>
+          <th>297</th>
+          <td>0.028902</td>
+          <td>298</td>
+          <td>0.029130</td>
+        </tr>
+        <tr>
+          <th>298</th>
+          <td>0.028902</td>
+          <td>298</td>
+          <td>0.029130</td>
+        </tr>
+        <tr>
+          <th>299</th>
+          <td>0.028986</td>
+          <td>300</td>
+          <td>0.029256</td>
+        </tr>
+        <tr>
+          <th>300</th>
+          <td>0.029070</td>
+          <td>301</td>
+          <td>0.029318</td>
+        </tr>
+        <tr>
+          <th>301</th>
+          <td>0.029155</td>
+          <td>302</td>
+          <td>0.029381</td>
+        </tr>
+        <tr>
+          <th>302</th>
+          <td>0.029155</td>
+          <td>302</td>
+          <td>0.029381</td>
+        </tr>
+        <tr>
+          <th>303</th>
+          <td>0.029155</td>
+          <td>302</td>
+          <td>0.029381</td>
+        </tr>
+        <tr>
+          <th>304</th>
+          <td>0.029240</td>
+          <td>305</td>
+          <td>0.029569</td>
+        </tr>
+        <tr>
+          <th>305</th>
+          <td>0.029240</td>
+          <td>305</td>
+          <td>0.029569</td>
+        </tr>
+        <tr>
+          <th>306</th>
+          <td>0.029326</td>
+          <td>307</td>
+          <td>0.029695</td>
+        </tr>
+        <tr>
+          <th>307</th>
+          <td>0.029412</td>
+          <td>308</td>
+          <td>0.029757</td>
+        </tr>
+        <tr>
+          <th>308</th>
+          <td>0.029499</td>
+          <td>309</td>
+          <td>0.029820</td>
+        </tr>
+        <tr>
+          <th>309</th>
+          <td>0.029674</td>
+          <td>310</td>
+          <td>0.029883</td>
+        </tr>
+        <tr>
+          <th>310</th>
+          <td>0.029762</td>
+          <td>311</td>
+          <td>0.029946</td>
+        </tr>
+        <tr>
+          <th>311</th>
+          <td>0.029851</td>
+          <td>312</td>
+          <td>0.030008</td>
+        </tr>
+        <tr>
+          <th>312</th>
+          <td>0.029851</td>
+          <td>312</td>
+          <td>0.030008</td>
+        </tr>
+        <tr>
+          <th>313</th>
+          <td>0.029940</td>
+          <td>314</td>
+          <td>0.030134</td>
+        </tr>
+        <tr>
+          <th>314</th>
+          <td>0.029940</td>
+          <td>314</td>
+          <td>0.030134</td>
+        </tr>
+        <tr>
+          <th>315</th>
+          <td>0.029940</td>
+          <td>314</td>
+          <td>0.030134</td>
+        </tr>
+        <tr>
+          <th>316</th>
+          <td>0.029940</td>
+          <td>314</td>
+          <td>0.030134</td>
+        </tr>
+        <tr>
+          <th>317</th>
+          <td>0.029940</td>
+          <td>314</td>
+          <td>0.030134</td>
+        </tr>
+        <tr>
+          <th>318</th>
+          <td>0.030030</td>
+          <td>319</td>
+          <td>0.030447</td>
+        </tr>
+        <tr>
+          <th>319</th>
+          <td>0.030120</td>
+          <td>320</td>
+          <td>0.030510</td>
+        </tr>
+        <tr>
+          <th>320</th>
+          <td>0.030120</td>
+          <td>320</td>
+          <td>0.030510</td>
+        </tr>
+        <tr>
+          <th>321</th>
+          <td>0.030211</td>
+          <td>322</td>
+          <td>0.030635</td>
+        </tr>
+        <tr>
+          <th>322</th>
+          <td>0.030211</td>
+          <td>322</td>
+          <td>0.030635</td>
+        </tr>
+        <tr>
+          <th>323</th>
+          <td>0.030303</td>
+          <td>324</td>
+          <td>0.030761</td>
+        </tr>
+        <tr>
+          <th>324</th>
+          <td>0.030303</td>
+          <td>324</td>
+          <td>0.030761</td>
+        </tr>
+        <tr>
+          <th>325</th>
+          <td>0.030395</td>
+          <td>326</td>
+          <td>0.030886</td>
+        </tr>
+        <tr>
+          <th>326</th>
+          <td>0.030581</td>
+          <td>327</td>
+          <td>0.030949</td>
+        </tr>
+        <tr>
+          <th>327</th>
+          <td>0.030675</td>
+          <td>328</td>
+          <td>0.031012</td>
+        </tr>
+        <tr>
+          <th>328</th>
+          <td>0.030864</td>
+          <td>329</td>
+          <td>0.031074</td>
+        </tr>
+        <tr>
+          <th>329</th>
+          <td>0.030864</td>
+          <td>329</td>
+          <td>0.031074</td>
+        </tr>
+        <tr>
+          <th>330</th>
+          <td>0.030960</td>
+          <td>331</td>
+          <td>0.031200</td>
+        </tr>
+        <tr>
+          <th>331</th>
+          <td>0.030960</td>
+          <td>331</td>
+          <td>0.031200</td>
+        </tr>
+        <tr>
+          <th>332</th>
+          <td>0.030960</td>
+          <td>331</td>
+          <td>0.031200</td>
+        </tr>
+        <tr>
+          <th>333</th>
+          <td>0.031056</td>
+          <td>334</td>
+          <td>0.031388</td>
+        </tr>
+        <tr>
+          <th>334</th>
+          <td>0.031153</td>
+          <td>335</td>
+          <td>0.031451</td>
+        </tr>
+        <tr>
+          <th>335</th>
+          <td>0.031250</td>
+          <td>336</td>
+          <td>0.031514</td>
+        </tr>
+        <tr>
+          <th>336</th>
+          <td>0.031250</td>
+          <td>336</td>
+          <td>0.031514</td>
+        </tr>
+        <tr>
+          <th>337</th>
+          <td>0.031348</td>
+          <td>338</td>
+          <td>0.031639</td>
+        </tr>
+        <tr>
+          <th>338</th>
+          <td>0.031348</td>
+          <td>338</td>
+          <td>0.031639</td>
+        </tr>
+        <tr>
+          <th>339</th>
+          <td>0.031447</td>
+          <td>340</td>
+          <td>0.031764</td>
+        </tr>
+        <tr>
+          <th>340</th>
+          <td>0.031447</td>
+          <td>340</td>
+          <td>0.031764</td>
+        </tr>
+        <tr>
+          <th>341</th>
+          <td>0.031447</td>
+          <td>340</td>
+          <td>0.031764</td>
+        </tr>
+        <tr>
+          <th>342</th>
+          <td>0.031447</td>
+          <td>340</td>
+          <td>0.031764</td>
+        </tr>
+        <tr>
+          <th>343</th>
+          <td>0.031546</td>
+          <td>344</td>
+          <td>0.032015</td>
+        </tr>
+        <tr>
+          <th>344</th>
+          <td>0.031546</td>
+          <td>344</td>
+          <td>0.032015</td>
+        </tr>
+        <tr>
+          <th>345</th>
+          <td>0.031646</td>
+          <td>346</td>
+          <td>0.032141</td>
+        </tr>
+        <tr>
+          <th>346</th>
+          <td>0.031949</td>
+          <td>347</td>
+          <td>0.032203</td>
+        </tr>
+        <tr>
+          <th>347</th>
+          <td>0.032051</td>
+          <td>348</td>
+          <td>0.032266</td>
+        </tr>
+        <tr>
+          <th>348</th>
+          <td>0.032154</td>
+          <td>349</td>
+          <td>0.032329</td>
+        </tr>
+        <tr>
+          <th>349</th>
+          <td>0.032154</td>
+          <td>349</td>
+          <td>0.032329</td>
+        </tr>
+        <tr>
+          <th>350</th>
+          <td>0.032258</td>
+          <td>351</td>
+          <td>0.032454</td>
+        </tr>
+        <tr>
+          <th>351</th>
+          <td>0.032258</td>
+          <td>351</td>
+          <td>0.032454</td>
+        </tr>
+        <tr>
+          <th>352</th>
+          <td>0.032362</td>
+          <td>353</td>
+          <td>0.032580</td>
+        </tr>
+        <tr>
+          <th>353</th>
+          <td>0.032362</td>
+          <td>353</td>
+          <td>0.032580</td>
+        </tr>
+        <tr>
+          <th>354</th>
+          <td>0.032468</td>
+          <td>355</td>
+          <td>0.032705</td>
+        </tr>
+        <tr>
+          <th>355</th>
+          <td>0.032573</td>
+          <td>356</td>
+          <td>0.032768</td>
+        </tr>
+        <tr>
+          <th>356</th>
+          <td>0.032573</td>
+          <td>356</td>
+          <td>0.032768</td>
+        </tr>
+        <tr>
+          <th>357</th>
+          <td>0.032680</td>
+          <td>358</td>
+          <td>0.032893</td>
+        </tr>
+        <tr>
+          <th>358</th>
+          <td>0.032787</td>
+          <td>359</td>
+          <td>0.032956</td>
+        </tr>
+        <tr>
+          <th>359</th>
+          <td>0.032787</td>
+          <td>359</td>
+          <td>0.032956</td>
+        </tr>
+        <tr>
+          <th>360</th>
+          <td>0.032787</td>
+          <td>359</td>
+          <td>0.032956</td>
+        </tr>
+        <tr>
+          <th>361</th>
+          <td>0.032787</td>
+          <td>359</td>
+          <td>0.032956</td>
+        </tr>
+        <tr>
+          <th>362</th>
+          <td>0.032787</td>
+          <td>359</td>
+          <td>0.032956</td>
+        </tr>
+        <tr>
+          <th>363</th>
+          <td>0.032895</td>
+          <td>364</td>
+          <td>0.033270</td>
+        </tr>
+        <tr>
+          <th>364</th>
+          <td>0.032895</td>
+          <td>364</td>
+          <td>0.033270</td>
+        </tr>
+        <tr>
+          <th>365</th>
+          <td>0.033003</td>
+          <td>366</td>
+          <td>0.033395</td>
+        </tr>
+        <tr>
+          <th>366</th>
+          <td>0.033113</td>
+          <td>367</td>
+          <td>0.033458</td>
+        </tr>
+        <tr>
+          <th>367</th>
+          <td>0.033557</td>
+          <td>368</td>
+          <td>0.033520</td>
+        </tr>
+        <tr>
+          <th>368</th>
+          <td>0.033557</td>
+          <td>368</td>
+          <td>0.033520</td>
+        </tr>
+        <tr>
+          <th>369</th>
+          <td>0.033670</td>
+          <td>370</td>
+          <td>0.033646</td>
+        </tr>
+        <tr>
+          <th>370</th>
+          <td>0.033784</td>
+          <td>371</td>
+          <td>0.033709</td>
+        </tr>
+        <tr>
+          <th>371</th>
+          <td>0.033784</td>
+          <td>371</td>
+          <td>0.033709</td>
+        </tr>
+        <tr>
+          <th>372</th>
+          <td>0.034014</td>
+          <td>372</td>
+          <td>0.033771</td>
+        </tr>
+        <tr>
+          <th>373</th>
+          <td>0.034014</td>
+          <td>372</td>
+          <td>0.033771</td>
+        </tr>
+        <tr>
+          <th>374</th>
+          <td>0.034130</td>
+          <td>374</td>
+          <td>0.033897</td>
+        </tr>
+        <tr>
+          <th>375</th>
+          <td>0.034247</td>
+          <td>375</td>
+          <td>0.033959</td>
+        </tr>
+        <tr>
+          <th>376</th>
+          <td>0.034364</td>
+          <td>376</td>
+          <td>0.034022</td>
+        </tr>
+        <tr>
+          <th>377</th>
+          <td>0.034483</td>
+          <td>377</td>
+          <td>0.034085</td>
+        </tr>
+        <tr>
+          <th>378</th>
+          <td>0.034483</td>
+          <td>377</td>
+          <td>0.034085</td>
+        </tr>
+        <tr>
+          <th>379</th>
+          <td>0.034602</td>
+          <td>379</td>
+          <td>0.034210</td>
+        </tr>
+        <tr>
+          <th>380</th>
+          <td>0.034602</td>
+          <td>379</td>
+          <td>0.034210</td>
+        </tr>
+        <tr>
+          <th>381</th>
+          <td>0.034602</td>
+          <td>379</td>
+          <td>0.034210</td>
+        </tr>
+        <tr>
+          <th>382</th>
+          <td>0.034602</td>
+          <td>379</td>
+          <td>0.034210</td>
+        </tr>
+        <tr>
+          <th>383</th>
+          <td>0.034722</td>
+          <td>383</td>
+          <td>0.034461</td>
+        </tr>
+        <tr>
+          <th>384</th>
+          <td>0.034722</td>
+          <td>383</td>
+          <td>0.034461</td>
+        </tr>
+        <tr>
+          <th>385</th>
+          <td>0.034722</td>
+          <td>383</td>
+          <td>0.034461</td>
+        </tr>
+        <tr>
+          <th>386</th>
+          <td>0.034722</td>
+          <td>383</td>
+          <td>0.034461</td>
+        </tr>
+        <tr>
+          <th>387</th>
+          <td>0.034843</td>
+          <td>387</td>
+          <td>0.034712</td>
+        </tr>
+        <tr>
+          <th>388</th>
+          <td>0.034843</td>
+          <td>387</td>
+          <td>0.034712</td>
+        </tr>
+        <tr>
+          <th>389</th>
+          <td>0.034965</td>
+          <td>389</td>
+          <td>0.034838</td>
+        </tr>
+        <tr>
+          <th>390</th>
+          <td>0.034965</td>
+          <td>389</td>
+          <td>0.034838</td>
+        </tr>
+        <tr>
+          <th>391</th>
+          <td>0.034965</td>
+          <td>389</td>
+          <td>0.034838</td>
+        </tr>
+        <tr>
+          <th>392</th>
+          <td>0.035088</td>
+          <td>392</td>
+          <td>0.035026</td>
+        </tr>
+        <tr>
+          <th>393</th>
+          <td>0.035088</td>
+          <td>392</td>
+          <td>0.035026</td>
+        </tr>
+        <tr>
+          <th>394</th>
+          <td>0.035088</td>
+          <td>392</td>
+          <td>0.035026</td>
+        </tr>
+        <tr>
+          <th>395</th>
+          <td>0.035211</td>
+          <td>395</td>
+          <td>0.035214</td>
+        </tr>
+        <tr>
+          <th>396</th>
+          <td>0.035211</td>
+          <td>395</td>
+          <td>0.035214</td>
+        </tr>
+        <tr>
+          <th>397</th>
+          <td>0.035211</td>
+          <td>395</td>
+          <td>0.035214</td>
+        </tr>
+        <tr>
+          <th>398</th>
+          <td>0.035336</td>
+          <td>398</td>
+          <td>0.035402</td>
+        </tr>
+        <tr>
+          <th>399</th>
+          <td>0.035336</td>
+          <td>398</td>
+          <td>0.035402</td>
+        </tr>
+      </tbody>
+    </table>
+
+    <h1>Question 20</h1>
+    <table border="1" class="dataframe" data-question="20">
+      <thead>
+        <tr style="text-align: right;">
+          <th></th>
+          <th>country</th>
+          <th>num_of_institutions</th>
+        </tr>
+      </thead>
+      <tbody>
+        <tr>
+          <th>0</th>
+          <td>United States</td>
+          <td>74</td>
+        </tr>
+        <tr>
+          <th>1</th>
+          <td>United Kingdom</td>
+          <td>45</td>
+        </tr>
+        <tr>
+          <th>2</th>
+          <td>Germany</td>
+          <td>23</td>
+        </tr>
+        <tr>
+          <th>3</th>
+          <td>Australia</td>
+          <td>21</td>
+        </tr>
+        <tr>
+          <th>4</th>
+          <td>Canada</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>5</th>
+          <td>China</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>6</th>
+          <td>France</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>7</th>
+          <td>Japan</td>
+          <td>14</td>
+        </tr>
+        <tr>
+          <th>8</th>
+          <td>Netherlands</td>
+          <td>13</td>
+        </tr>
+        <tr>
+          <th>9</th>
+          <td>Russia</td>
+          <td>13</td>
+        </tr>
+        <tr>
+          <th>10</th>
+          <td>Other</td>
+          <td>155</td>
+        </tr>
+      </tbody>
+    </table>
+  </body>
+<html>
diff --git a/p13/p13_test.py b/p13/p13_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd4e42a5161b9edf2206f4d7330ee6a8722e5d18
--- /dev/null
+++ b/p13/p13_test.py
@@ -0,0 +1,352 @@
+#!/usr/bin/python
+import os, json, math
+from collections import namedtuple
+import pandas as pd
+import numpy as np
+from bs4 import BeautifulSoup
+
+HTML_TEST_FILE = 'p13_expected.html'
+
+MAX_FILE_SIZE = 500 # units - KB
+REL_TOL = 6e-04  # relative tolerance for floats
+ABS_TOL = 15e-03  # absolute tolerance for floats
+
+PASS = "PASS"
+
+TEXT_FORMAT = "text"  # question type when expected answer is a str, int, float, or bool
+TEXT_FORMAT_NAMEDTUPLE = "text namedtuple"  # question type when expected answer is a namedtuple
+TEXT_FORMAT_UNORDERED_LIST = "text list_unordered"  # question type when the expected answer is a list where the order does *not* matter
+TEXT_FORMAT_ORDERED_LIST = "text list_ordered"  # question type when the expected answer is a list where the order does matter
+TEXT_FORMAT_SPECIAL_ORDERED_LIST = "text list_special_ordered"  # question type when the expected answer is a list where order does matter, but with possible ties. Elements are ordered according to values in special_ordered_json (with ties allowed)
+TEXT_FORMAT_DICT = "text dict"  # question type when the expected answer is a dictionary
+HTML_FORMAT = "html" # question type when the expected answer is a DataFrame
+FILE_JSON_FORMAT = "file json" # question type when the expected answer is a JSON file
+
+def return_expected_json():
+    expected_json =    {"1": (HTML_FORMAT, None),
+                        "2": (HTML_FORMAT, None),
+                        "3": (HTML_FORMAT, None),
+                        "4": (HTML_FORMAT, None),
+                        "5": (HTML_FORMAT, None),
+                        "6": (HTML_FORMAT, None),
+                        "7": (HTML_FORMAT, None),
+                        "8": (HTML_FORMAT, None),
+                        "9": (HTML_FORMAT, None),
+                        "10": (HTML_FORMAT, None),
+                        "11": (TEXT_FORMAT, 0.5213253604130499),
+                        "12": (TEXT_FORMAT, 0.557397228343763),
+                        "13": (HTML_FORMAT, None),
+                        "14": (HTML_FORMAT, None),
+                        "15": (HTML_FORMAT, None),
+                        "16": (HTML_FORMAT, None),
+                        "17": (HTML_FORMAT, None),
+                        "18": (HTML_FORMAT, None),
+                        "19": (TEXT_FORMAT, 56),
+                        "20": (HTML_FORMAT, None)}
+
+    return expected_json
+
+def check_cell(qnum, actual):
+    expected_json = return_expected_json()
+    format, expected = expected_json[qnum[1:]]
+    try:
+        if format == TEXT_FORMAT:
+            return simple_compare(expected, actual)
+        elif format == TEXT_FORMAT_UNORDERED_LIST:
+            return list_compare_unordered(expected, actual)
+        elif format == TEXT_FORMAT_ORDERED_LIST:
+            return list_compare_ordered(expected, actual)
+        elif format == TEXT_FORMAT_DICT:
+            return dict_compare(expected, actual)
+        elif format == TEXT_FORMAT_NAMEDTUPLE:
+            return namedtuple_compare(expected ,actual)
+        elif format == HTML_FORMAT:
+            return check_cell_html(qnum[1:], actual)
+        elif format == FILE_JSON_FORMAT:
+            return check_json(expected, actual)
+        else:
+            if expected != actual:
+                return "expected %s but found %s " % (repr(expected), repr(actual))
+    except:
+        if expected != actual:
+            return "expected %s" % (repr(expected))
+    return PASS
+
+
+
+def simple_compare(expected, actual, complete_msg=True):
+    actual = getattr(actual, "tolist", lambda: actual)()
+    msg = PASS
+    if type(expected) == type:
+        if expected != actual:
+            if type(actual) == type:
+                msg = "expected %s but found %s" % (expected.__name__, actual.__name__)
+            else:
+                msg = "expected %s but found %s" % (expected.__name__, repr(actual))
+    elif type(expected) != type(actual) and not (type(expected) in [float, int] and type(actual) in [float, int]):
+        msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+    elif type(expected) == float:
+        if not math.isclose(actual, expected, rel_tol=REL_TOL, abs_tol=ABS_TOL):
+            msg = "expected %s" % (repr(expected))
+            if complete_msg:
+                msg = msg + " but found %s" % (repr(actual))
+    else:
+        if expected != actual:
+            msg = "expected %s" % (repr(expected))
+            if complete_msg:
+                msg = msg + " but found %s" % (repr(actual))
+    return msg
+
+
+def list_compare_ordered(expected, actual, obj="list"):
+    msg = PASS
+    if type(expected) != type(actual):
+        msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+        return msg
+    for i in range(len(expected)):
+        if i >= len(actual):
+            msg = "expected missing %s in %s" % (repr(expected[i]), obj)
+            break
+        if type(expected[i]) in [int, float, bool, str]:
+            val = simple_compare(expected[i], actual[i])
+        elif type(expected[i]) in [list]:
+            val = list_compare_ordered(expected[i], actual[i], "sub" + obj)
+        elif type(expected[i]) in [dict]:
+            val = dict_compare(expected[i], actual[i])
+        elif type(expected[i]).__name__ in namedtuples:
+            val = namedtuple_compare(expected[i], actual[i])
+        if val != PASS:
+            msg = "at index %d of the %s, " % (i, obj) + val
+            break
+    if len(actual) > len(expected) and msg == PASS:
+        msg = "found unexpected %s in %s" % (repr(actual[len(expected)]), obj)
+    if len(expected) != len(actual):
+        msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+
+    if len(expected) > 0 and type(expected[0]) in [int, float, bool, str]:
+        if msg != PASS and list_compare_unordered(expected, actual, obj) == PASS:
+            try:
+                msg = msg + " (%s may not be ordered as required)" % (obj)
+            except:
+                pass
+    return msg
+
+
+def list_compare_helper(larger, smaller):
+    msg = PASS
+    j = 0
+    for i in range(len(larger)):
+        if i == len(smaller):
+            msg = "expected %s" % (repr(larger[i]))
+            break
+        found = False
+        while not found:
+            if j == len(smaller):
+                val = simple_compare(larger[i], smaller[j - 1], False)
+                break
+            val = simple_compare(larger[i], smaller[j], False)
+            j += 1
+            if val == PASS:
+                found = True
+                break
+        if not found:
+            msg = val
+            break
+    return msg
+
+
+def list_compare_unordered(expected, actual, obj="list"):
+    msg = PASS
+    if type(expected) != type(actual):
+        msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+        return msg
+    try:
+        sort_expected = sorted(expected)
+        sort_actual = sorted(actual)
+    except:
+        msg = "unexpected datatype found in %s; expected entries of type %s" % (obj, obj, type(expected[0]).__name__)
+        return msg
+
+    if len(actual) == 0 and len(expected) > 0:
+        msg = "in the %s, missing" % (obj) + expected[0]
+    elif len(actual) > 0 and len(expected) > 0:
+        val = simple_compare(sort_expected[0], sort_actual[0])
+        if val.startswith("expected to find type"):
+            msg = "in the %s, " % (obj) + simple_compare(sort_expected[0], sort_actual[0])
+        else:
+            if len(expected) > len(actual):
+                msg = "in the %s, missing " % (obj) + list_compare_helper(sort_expected, sort_actual)
+            elif len(expected) < len(actual):
+                msg = "in the %s, found un" % (obj) + list_compare_helper(sort_actual, sort_expected)
+            if len(expected) != len(actual):
+                msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+                return msg
+            else:
+                val = list_compare_helper(sort_expected, sort_actual)
+                if val != PASS:
+                    msg = "in the %s, missing " % (obj) + val + ", but found un" + list_compare_helper(sort_actual,
+                                                                                               sort_expected)
+    return msg
+
+def list_compare_special_init(expected, special_order):
+    real_expected = []
+    for i in range(len(expected)):
+        if real_expected == [] or special_order[i-1] != special_order[i]:
+            real_expected.append([])
+        real_expected[-1].append(expected[i])
+    return real_expected
+
+
+def list_compare_special(expected, actual, special_order):
+    expected = list_compare_special_init(expected, special_order)
+    msg = PASS
+    expected_list = []
+    for expected_item in expected:
+        expected_list.extend(expected_item)
+    val = list_compare_unordered(expected_list, actual)
+    if val != PASS:
+        msg = val
+    else:
+        i = 0
+        for expected_item in expected:
+            j = len(expected_item)
+            actual_item = actual[i: i + j]
+            val = list_compare_unordered(expected_item, actual_item)
+            if val != PASS:
+                if j == 1:
+                    msg = "at index %d " % (i) + val
+                else:
+                    msg = "between indices %d and %d " % (i, i + j - 1) + val
+                msg = msg + " (list may not be ordered as required)"
+                break
+            i += j
+
+    return msg
+
+
+def dict_compare(expected, actual, obj="dict"):
+    msg = PASS
+    if type(expected) != type(actual):
+        msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+        return msg
+    try:
+        expected_keys = sorted(list(expected.keys()))
+        actual_keys = sorted(list(actual.keys()))
+    except:
+        msg = "unexpected datatype found in keys of dict; expect a dict with keys of type %s" % (
+            type(expected_keys[0]).__name__)
+        return msg
+    val = list_compare_unordered(expected_keys, actual_keys, "dict")
+    if val != PASS:
+        msg = "bad keys in %s: " % (obj) + val
+    if msg == PASS:
+        for key in expected:
+            if expected[key] == None or type(expected[key]) in [int, float, bool, str]:
+                val = simple_compare(expected[key], actual[key])
+            elif type(expected[key]) in [list]:
+                val = list_compare_ordered(expected[key], actual[key], "value")
+            elif type(expected[key]) in [dict]:
+                val = dict_compare(expected[key], actual[key], "sub" + obj)
+            elif type(expected[key]).__name__ in namedtuples:
+                val = namedtuple_compare(expected[key], actual[key])
+            if val != PASS:
+                msg = "incorrect val for key %s in %s: " % (repr(key), obj) + val
+    return msg
+
+def parse_df_html_table(html, question=None):
+    soup = BeautifulSoup(html, 'html.parser')
+
+    if question == None:
+        tables = soup.find_all('table')
+        assert(len(tables) == 1)
+        table = tables[0]
+    else:
+        table = soup.find('table', {"data-question": str(question)})
+
+    rows = []
+    for tr in table.find_all('tr'):
+        rows.append([])
+        for cell in tr.find_all(['td', 'th']):
+            rows[-1].append(cell.get_text())
+
+    cells = {}
+    for r in range(1, len(rows)):
+        for c in range(1, len(rows[0])):
+            rname = rows[r][0]
+            cname = rows[0][c]
+            cells[(rname,cname)] = rows[r][c]
+    return cells
+
+def check_cell_html(qnum, actual):
+    try:
+        actual_cells = parse_df_html_table(actual)
+    except Exception as e:
+        return "expected to find type DataFrame but found type %s instead" % type(actual).__name__
+    try:
+        with open(HTML_TEST_FILE, encoding='utf-8') as f:
+            expected_cells = parse_df_html_table(f.read(), qnum)
+    except Exception as e:
+        return "ERROR! Could not find table in %s. Please make sure you have downloaded %s correctly." % (HTML_TEST_FILE, HTML_TEST_FILE)
+
+    for location, expected in expected_cells.items():
+        location_name = "column {} at index {}".format(location[1], location[0])
+        actual = actual_cells.get(location, None)
+        if actual == None:
+            return "in location %s, expected to find %s" % (location_name, repr(expected))
+        try:
+            actual_ans = float(actual)
+            expected_ans = float(expected)
+            if math.isnan(actual_ans) and math.isnan(expected_ans):
+                continue
+        except Exception as e:
+            actual_ans, expected_ans = actual, expected
+        msg = simple_compare(expected_ans, actual_ans)
+        if msg != PASS:
+            return "in location %s, " % location_name + msg
+    expected_cols = list(set(["column %s" %loc[1] for loc in expected_cells]))
+    actual_cols = list(set(["column %s" %loc[1] for loc in actual_cells]))
+    msg = list_compare_unordered(expected_cols, actual_cols, "DataFrame")
+    if msg != PASS:
+        return msg
+    expected_rows = list(set(["row at index %s" %loc[0] for loc in expected_cells]))
+    actual_rows = list(set(["row at index %s" %loc[0] for loc in actual_cells]))
+    msg = list_compare_unordered(expected_rows, actual_rows, "DataFrame")
+    if msg != PASS:
+        return msg
+    return PASS
+
+
+def check_json(expected, actual):
+    msg = PASS
+    if expected not in os.listdir("."):
+        return "file %s not found" % expected
+    elif actual not in os.listdir("."):
+        return "file %s not found" % actual
+    try:
+        e = open(expected, encoding='utf-8')
+        expected_data = json.load(e)
+        e.close()
+    except json.JSONDecodeError:
+        return "file %s is broken and cannot be parsed; please redownload the file" % expected
+    try:
+        a = open(actual, encoding='utf-8')
+        actual_data = json.load(a)
+        a.close()
+    except json.JSONDecodeError:
+        return "file %s is broken and cannot be parsed" % actual
+    if type(expected_data) == list:
+        msg = list_compare_ordered(expected_data, actual_data, 'file ' + actual)
+    elif type(expected_data) == dict:
+        msg = dict_compare(expected_data, actual_data)
+    return msg
+
+def check(qnum, actual):
+    msg = check_cell(qnum, actual)
+    if msg == PASS:
+        return True
+    print("<b style='color: red;'>ERROR:</b> " + msg)
+
+
+def check_file_size(path):
+    size = os.path.getsize(path)
+    assert size < MAX_FILE_SIZE * 10**3, "Your file is too big to be processed by Gradescope; please delete unnecessary output cells so your file size is < %s KB" % MAX_FILE_SIZE
diff --git a/p13/rubric.md b/p13/rubric.md
new file mode 100644
index 0000000000000000000000000000000000000000..8a073a0e04683da00f5fc5cae1ad038220afd139
--- /dev/null
+++ b/p13/rubric.md
@@ -0,0 +1,137 @@
+# Project 13 (P13) grading rubric
+
+## Code reviews
+
+- A TA / grader will be reviewing your code after the deadline.
+- They will make deductions based on the Rubric provided below.
+- To ensure that you don’t lose any points in code review, you must review the rubric and make sure that you have followed the instructions provided in the project correctly.
+
+## Rubric
+
+### General guidelines:
+
+- `import` statements are not mentioned in the required cell at the top of the notebook or used additional import statements beyond those that are stated in the directions (-3)
+- Required outputs not visible/did not save the notebook file prior to running the cell containing "export" (-3)
+
+### Question specific guidelines:
+
+- `bar_plot` (2)
+	- Function does not create correct bar plot (-2)
+
+- `scatter_plot` (2)
+	- Function does not create correct scatter plot (-2)
+
+- `horizontal_bar_plot` (1)
+	- Function does not create correct horizontal bar plot (-1)
+
+- `pie_plot` (1)
+	- Function does not create correct pie plot (-1)
+
+- `get_regression_line` (1)
+	- Function logic is incorrect (-1)
+
+- `regression_line_plot` (1)
+	- Function logic is incorrect (-1)
+
+- `regression_line_plot` (2)
+	- Required function is not used  (-1)
+	- Function does not create correct scatter plot or the correct line plot using `df["fit"]`(-1)
+
+- `conn` (3)
+	- Data structure is defined more than once (-1)
+	- Did not close the connection to `conn` at the end (-2)
+
+- Q1 (3)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-1)
+
+- Q2 (4)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+
+- Q3 (4)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+
+- Q4 (4)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+
+- Q5 (6)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)
+
+- Q6 (6)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)
+
+- Q7 (4)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+
+- Q8 (6)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)
+
+- Q9 (6)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)
+
+- Q10 (6)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)
+
+- Q11 (3)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-1)
+
+- Q12 (3)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-1)
+
+- Q13 (4)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+
+- Q14 (4)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+
+- Q15 (4)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+
+- Q16 (6)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)
+
+- Q17 (6)
+	- Did not use SQL to answer (-2)
+	- Incorrect logic is used to answer (-2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)
+
+- Q18 (4)
+	- Incorrect logic is used to answer (-2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)
+
+- Q19 (2)
+	- Incorrect logic is used to answer (-1)
+	- Required function is not used (-1)
+
+- Q20 (2)
+	- Required function is not used (-1)
+	- Plot is not properly labeled (-1)