diff --git a/book/time_series/acf.md b/book/time_series/acf.md
index 9e40a42fd0472cf3d8f4c5fba183fbee41d00b9d..8f5e9709b3e4af4588123f5da3eee79335809c8a 100644
--- a/book/time_series/acf.md
+++ b/book/time_series/acf.md
@@ -1,31 +1,75 @@
 (ACF)=
 # Autocovariance function (ACF)
 
-Before we can look into the modelling of a stochastic process using an Autoregressive Moving Average (ARMA) model, we first need to introduce the autocovariance function (ACF) for a stationary time series, and describe the relationship between ACF and a power spectral density (PSD).
+Before we can look into the modelling of a stochastic process using an Autoregressive (AR) model, we first need to introduce the autocovariance function (ACF) for a stationary time series, and describe the relationship between ACF and a power spectral density (PSD).
 
-As in the Chapter on {ref}`OT`, the variance component is often determined based on the precision of an observation (at a given epoch), and the covarience components quantitatively indicate the statistical dependence (or independence) between observations. In this case, dependence is inherently introduced by the phyiscal processes that produce the signal (of which our time series is a sample), and in fact our time series methods seek to (mathematically) account for this.
-
-A preliminary motivation and explanation for why autocovariance is needed is provided nicely in this lecture, beginning at time 1:22:32 (and ending around 1:34:00).
-
-<p><iframe width="680" height="480" marginwidth="0" marginheight="0" src="https://collegerama.tudelft.nl/Mediasite/Play/1d74f018b9b54e918179570c75f6cd0c1d?playFrom=4952000&autostart=False"></iframe></p>
+As in the Chapter on #TODO (add reference to obs theory), the variance component is often determined based on the precision of an observation (at a given epoch), and the covariance components quantitatively indicate the statistical dependence (or independence) between observations. In this case, dependence is inherently introduced by the physical processes that produce the signal (of which our time series is a sample), and in fact our time series methods seek to (mathematically) account for this.
 
 ## Autocovariance and autocorrelation
 
-Let us assume an arbitrary (discrete) stationary time series, $S=[S_1,S_2,...,S_m]^T$, with mean $\mathbb{E}(S)=\mu$ and variance $Var(S_{i})=\sigma^2$.
+Let us assume an arbitrary (discrete) stationary time series, $S=[S_1,S_2,...,S_m]^T$, with mean $\mathbb{E}(S)=\mu$ and variance $Var(S_{i})=\sigma^2$. Remember that stationarity implies that the statistical properties of the time series do not depend on the time at which it is observed, i.e. expectation and variance are constant over time.
 
 The *formal* (or: theoretical) autocovariance is defined as
 
 $$
-Cov(S_t, S_{t-\tau}) = \mathbb{E}((S_t-\mu)(S_{t-\tau}-\mu))=\mathbb{E}(S_tS_{t-\tau})-\mu^2
+Cov(S_t, S_{t-\tau}) =\mathbb{E}(S_tS_{t-\tau})-\mu^2
 =c_{\tau}
 $$
 
 We have that $Cov(S_t, S_{t-\tau}) =Cov(S_t, S_{t+\tau})$.
 
 
-```{note}
-The reason to refer to *auto*covariance is that we are considering the covariance of $S$ with itself (with a certain time lag $\tau$). If the covariance of $S$ with the time series of another variable, $X$, would be considered, this is referred to as the *cross*-covariance. 
-```
+:::{card} Exercise covariance
+
+Show that the covariance can be written as: 
+
+$$Cov(S_t, S_{t-\tau}) = \mathbb{E}(S_tS_{t-\tau})-\mu^2
+=c_{\tau}$$ 
+
+
+````{admonition} Solution
+:class: tip, dropdown
+
+$$
+ Cov(S_t, S_{t-\tau})= \mathbb{E}[(S_t - \mathbb{E}(S_t))(S_{t-\tau} - \mathbb{E}(S_{t-\tau}))]\\
+ = \mathbb{E}((S_t-\mu)(S_{t-\tau}-\mu))\\
+ = \mathbb{E}(S_tS_{t-\tau} - \mu S_{t-\tau} - \mu S_t + \mu^2)\\
+ = \mathbb{E}(S_tS_{t-\tau}) - \mu \mathbb{E}(S_{t-\tau}) - \mu \mathbb{E}(S_t) + \mu^2\\
+= \mathbb{E}(S_tS_{t-\tau}) - 2\mu^2 + \mu^2\\
+= \mathbb{E}(S_tS_{t-\tau}) - \mu^2\\
+$$
+````
+:::
+
+:::{card} Exercise covariance
+
+Prove that $Cov(S_t, S_{t-\tau}) =Cov(S_t, S_{t+\tau})$: 
+
+
+
+````{admonition} Solution
+:class: tip, dropdown
+
+From the definition of covariance, we know that
+$$ Cov(a,b) = Cov(b,a)$$
+
+Hence, we have that
+
+$$ Cov(S_t, S_{t-\tau}) = Cov(S_{t-\tau}, S_t)$$
+
+Due to the stationarity of the time series, we have that
+
+$$ Cov(S_{t-\tau}, S_t) = Cov(S_t, S_{t+\tau})$$
+
+Therefore, we have that
+
+$$ Cov(S_t, S_{t-\tau}) = Cov(S_t, S_{t+\tau})$$
+
+
+````
+:::
+
+
 
 The *formal* autocorrelation is defined as
 
@@ -74,10 +118,6 @@ $$
 Here we use capitals for $\hat{C}_{\tau}$ and $\hat{R}_{\tau}$ since **estimators** are always a function of the random observables $S_t$.
 ```
 
-```{note}
-Software tools may have implemented one or both methods to choose from, so if possible good to check!
-```
-
 ### Covariance matrix based on autocovariance
 
 The structure of a covariance matrix for a stationary time series is purely symmetric and it looks like
@@ -91,9 +131,6 @@ c_2 & c_1 & \sigma^2 &  \ddots & c_2  \\
 
 There are $m$ (co)variance components - **one** variance component, $\sigma^2 = c_0$, and $m-1$ covariance components, $c_i$.
 
-```{note}
-The covariance matrix $\Sigma_{S}$ has constant values along the top-left to bottom-right diagonal and is called a _Toeplitz matrix._
-```
 
 (NACF)=
 ## Normalized ACF
@@ -226,9 +263,11 @@ $$
 ```
 :::
 
-## Power spectral density
+<!-- ## Power spectral density
 
-The power spectral density (PSD) explains how the power (variance) of the signal is distributed over different frequencies. For instance, the PSD of a pure sine wave is flat *except* at its constituent frequency, where it will show a peak. Purely random noise has a flat power spectrum, indicating that all frequencies have an identical contribution to the variance of the signal!
+The power spectral density (PSD) explains how the power (variance) of the signal is distributed over different frequencies. For instance, the PSD of a pure sine wave is flat *except* at its constituent frequency, where it will show a peak. Purely random noise has a flat power spectrum, indicating that all frequencies have an identical contribution to the variance of the signal! -->
+
+<!-- The power spectral density (PSD) explains how the power (variance) of the signal is distributed over different frequencies. For instance, the PSD of a pure sine wave is flat *except* at its constituent frequency, where it will show a peak. Purely random noise has a flat power spectrum, indicating that all frequencies have an identical contribution to the variance of the signal!
 
 ### PSD vs ACF
 
@@ -261,4 +300,4 @@ Time series data, autocovariance and its power spectral density plots of white n
 
 The PSD explains how the power (variance) of the signal is distributed over different frequencies. The PSD of a pure sine wave is flat except at its constituent frequency.
 Purey random noise (i.e., white noise) has a flat power, indicating that all frequencies have identical contribution in making the variance of the signal. This is however not the case for time-correlated noise because different frequencies have different power values in making the total signal variability.
-
+ -->
diff --git a/book/time_series/ar.ipynb b/book/time_series/ar.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..18a5416cdaf0843e799a78bc79ee45c5b9d60f64
--- /dev/null
+++ b/book/time_series/ar.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(AR)=\n",
+    "# AR process\n",
+    "The code on this page can be used interactively: click {fa}`rocket` --> {guilabel}`Live Code` in the top right corner, then wait until the message {guilabel}`Python interaction ready!` appears.\n",
+    "\n",
+    "The main goal is to introduce the AutoRegressive (AR) model to describe a **stationary stochastic process**. Hence the AR model can be applied on time series where e.g. trend and seasonality are not present / removed, and only noise remains, or after applying other methods [to obtain a stationary time series](stationarize).\n",
+    "\n",
+    "## Process definition\n",
+    "\n",
+    "In an AR model, we forecast the variable of interest using a linear combination of its past values. A zero mean AR process of orders $p$ can be written as follows:\n",
+    "\n",
+    "$$S_t = \\overbrace{\\phi_1S_{t-1}+...+\\phi_pS_{t-p}}^{\\text{AR process}} + e_t $$ \n",
+    "\n",
+    "or as\n",
+    "\n",
+    "$$S_t = \\sum_{i=1}^p \\phi_iS_{t-i}+e_t$$\n",
+    "\n",
+    "Each observation is made up of a **random error** $e_t$ at that epoch, a linear combination of **past observations**. The errors $e_t$  are uncorrelated purely random noise process, known also as white noise. We note the process should still be stationary, satisfying\n",
+    "\n",
+    "$$\\mathbb{E}(S_t)=0, \\hspace{20px} \\mathbb{D}(S_t)=\\sigma^2,\\quad \\forall t$$\n",
+    "\n",
+    "This indicates that parts of the total variability of the process come from the signal and noise of past epochs, and only a (small) portion belongs to the noise of that epoch (denoted as $e_t$). To have a better understanding of the process itself, we consider two special cases, $q=0$ and $p=0$.\n",
+    "\n",
+    "### First-order AR(1) process\n",
+    "\n",
+    "We will just focus on explaining $p=1$, i.e. the AR(1) process. A **zero-mean first order autoregressive** process can be written as follows\n",
+    "\n",
+    "$$ S_t = \\phi S_{t-1}+e_t, \\hspace{20px} -1\\leq\\phi<1, \\hspace{20px} t=2,...,m $$\n",
+    "\n",
+    "where $e_t$ is an i.i.d. noise process, e.g. distributed as $e_t\\sim N(0,\\sigma_{e}^2)$. See later the definition of $\\sigma_{e}^2$.\n",
+    "\n",
+    ":::{card} Exercise\n",
+    "\n",
+    "In a zero-mean first order autoregressive process, abbreviated as AR(1), we have $m=3$ observations, $\\phi=0.8$, and the generated white noise errors are $e = [e_1,\\, e_2,\\, e_3]^T=[1,\\, 2,\\, -1]^T$. What is the generated AR(1) process $S = [S_1,\\, S_2,\\, S_3]^T$?\n",
+    "\n",
+    "a. $S = \\begin{bmatrix}1 & 2.8 & 1.24\\end{bmatrix}^T$  \n",
+    "b. $S = \\begin{bmatrix} 0 & 2 & 0.6 \\end{bmatrix}^T$  \n",
+    "c. $S = \\begin{bmatrix} 1 & 2 & -1 \\end{bmatrix}^T$  \n",
+    "\n",
+    "```{admonition} Solution\n",
+    ":class: tip, dropdown\n",
+    "\n",
+    "The correct answer is **a**. The AR(1) process can be initialized as $S_1=e_1=1$. The next values can be obtained through:\n",
+    "\n",
+    "$$ S_t = \\phi S_{t-1} + e_t $$\n",
+    "\n",
+    "Giving $S_2=0.8 S_1 + e_2 = 0.8\\cdot 1 + 2 = 2.8$ and $S_3=0.8 S_2 + e_3 = 0.8\\cdot 2.8 - 1= 1.24$, so we have:\n",
+    "\n",
+    "$$S = \\begin{bmatrix}1 & 2.8 & 1.24\\end{bmatrix}^T $$\n",
+    "\n",
+    "```\n",
+    ":::\n",
+    "\n",
+    "### Formulation\n",
+    "\n",
+    "Initializing $S_1=e_1$, with $\\mathbb{E}(S_1)=\\mathbb{E}(e_1)=0$ and $\\mathbb{D}(S_1)=\\mathbb{D}(e_1)=\\sigma^2$. Following this, multiple applications of the above \"autoregressive\" formula ($S_t = \\phi S_{t-1} + e_t$) gives:\n",
+    "\n",
+    "$$ \\begin{align*}\n",
+    "S_1&=e_1\\\\ \n",
+    "S_2&=\\phi S_1+e_2\\\\ \n",
+    "S_3 &= \\phi S_2+e_3 = \\phi^2S_1+\\phi e_2+e_3\\\\ \n",
+    "&\\vdots\\\\ \n",
+    "S_m &= \\phi S_{m-1} + e_m = \\phi^{m-1}S_1+\\phi^{m-2}e_2+...+\\phi e_{m-1}+e_m\n",
+    "\\end{align*} $$\n",
+    "\n",
+    "of which we still have (in order to impose the *stationarity*):\n",
+    "\n",
+    "$$\\mathbb{E}(S_t)=0 \\hspace{5px}\\text{and}\\hspace{5px} \\mathbb{D}(S_t)=\\sigma^2, \\hspace{10px} t=1,...,m$$\n",
+    "\n",
+    "All the error components, $e_t$, are uncorrelated such that $Cov(e_t,e_{t+\\tau})=0$ if $\\tau \\neq 0$, and with variance $\\sigma_{e}^2$ which still needs to be determined.\n",
+    "\n",
+    "### Autocovariance\n",
+    "\n",
+    "The mean of the process is zero and, therefore:\n",
+    "\n",
+    "$$\\mathbb{E}(S_t) = \\mathbb{E}(\\phi S_{t-1}+e_t) = \\phi\\mathbb{E}(S_{t-1})+\\mathbb{E}(e_t) = 0$$\n",
+    "\n",
+    "The variance of the process should remain constant as:\n",
+    "\n",
+    "$$\\mathbb{D}(S_t) = \\mathbb{D}(\\phi S_{t-1} +e_t) \\Leftrightarrow \\sigma^2=\\phi^2\\sigma^2+\\sigma_{e}^2, \\hspace{10px} t\\geq 2$$\n",
+    "\n",
+    "resulting in\n",
+    "\n",
+    "$$\\sigma_{e}^2 = \\sigma^2 (1-\\phi^2)$$\n",
+    "\n",
+    "indicating that $\\sigma_{e}^2$ is smaller than $\\sigma^2$.\n",
+    "\n",
+    "The autocovariance (covariance between $S_t$ and $S_{t+\\tau}$) is\n",
+    "\n",
+    "$$ \\begin{align*}\n",
+    "c_{\\tau}&=\\mathbb{E}(S_t S_{t+\\tau})-\\mu^2 =\\mathbb{E}(S_t S_{t+\\tau})\\\\\n",
+    "&= \\mathbb{E}(S_t(\\phi^\\tau S_t + \\phi^{\\tau-1} e_{t+1}+...)) = \\phi^\\tau\\mathbb{E}(S_t^2)=\\sigma^2\\phi^\\tau\n",
+    "\\end{align*} $$\n",
+    "\n",
+    "In the derivation above we used that:\n",
+    "\n",
+    "$$ \\begin{align*}\n",
+    "S_{t+\\tau}=\\phi^\\tau S_t + \\phi^{\\tau-1} e_{t+1}+...+e_{t+\\tau}\n",
+    "\\end{align*} $$\n",
+    "\n",
+    "and the fact that $S_t$ and $e_{t+\\tau}$ are uncorrelated for $\\tau \\neq 0$.\n",
+    "\n",
+    "```{admonition} Derivation (optional)\n",
+    ":class: tip, dropdown\n",
+    "\n",
+    "$$ \\begin{align*}\n",
+    "S_{t+\\tau}&= \\phi^{t+\\tau-1}S_1 + \\phi^{t+\\tau-2}e_2+...+ \\phi^{\\tau} e_{t}+ \\phi^{\\tau-1} e_{t+1}+...+e_{t+\\tau}\\\\\n",
+    "&= \\phi^{\\tau} \\left(\\phi^{t-1}S_1 + \\phi^{t-2}e_2+...+  e_{t}\\right)+ \\phi^{\\tau-1} e_{t+1}+...+e_{t+\\tau}\\\\\n",
+    "&=\\phi^\\tau S_t + \\phi^{\\tau-1} e_{t+1}+...+e_{t+\\tau}\n",
+    "\\end{align*} $$\n",
+    "\n",
+    "```\n",
+    "\n",
+    "### Model structure of AR(1)\n",
+    "\n",
+    "$$\\mathbb{E}(S) = \\mathbb{E}\\begin{bmatrix}S_1\\\\ S_2\\\\ \\vdots\\\\ S_m\\end{bmatrix} = \\begin{bmatrix}0\\\\ 0\\\\ \\vdots\\\\ 0\\end{bmatrix}, \\hspace{15px} \\mathbb{D}(S)=\\Sigma_{S}=\\sigma^2 \\begin{bmatrix}1&\\phi&...&\\phi^{m-1}\\\\ \\phi&1&...&\\phi^{m-2}\\\\ \\vdots&\\vdots&\\ddots&\\vdots\\\\ \\phi^{m-1}&\\phi^{m-2}&...&1\\end{bmatrix}$$\n",
+    "\n",
+    "* Autocovariance function $\\implies$ $c_{\\tau}=\\sigma^2\\phi^\\tau$\n",
+    "* Normalized autocovariance function (ACF) $\\implies$ $\\rho_\\tau=c_{\\tau}/c_0=\\phi^\\tau$\n",
+    "* Larger value of $\\phi$ indicates a long-memory random process\n",
+    "* If $\\phi=0$, this is called *purely random process* (white noise)\n",
+    "* ACF is even, $c_{\\tau}=c_{-\\tau}=c_{|\\tau|}$ and so is $\\rho_{\\tau}=\\rho_{-\\tau}=\\rho_{|\\tau|}$\n",
+    "\n",
+    "Later in this section we will see how the coefficient $\\phi$ can be estimated.\n",
+    "\n",
+    "## Simulated example\n",
+    "\n",
+    "If you have run the python code on this page, an interactive plot will be displayed below. You can change the value of $\\phi$ and the number of observations $m$ to see how the AR(1) process changes. At the start, the process is initialized with $\\phi = 0.8$. Try moving the slider and see the response of the ACF; pay special attention when $\\phi=0$ and when $\\phi$ becomes negative. \n",
+    "\n",
+    "Lastly, focus on the case where $\\phi=1$ and $\\phi=-1$. What do you observe? You will notice that the function will \"explode\". This makes intuitive sense, since the effect of the previous epoch is not dampened, but rather amplified. This also means that the process is not stationary anymore. So, the AR(1) process is stationary if $|\\phi|<1$.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3cffa76164d46349e929a2a83006563",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.8, description='Phi:', max=1.0, min=-1.0), Output()), _dom_classes=(…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from statsmodels.graphics.tsaplots import plot_acf\n",
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import interact\n",
+    "\n",
+    "\n",
+    "def acfplot(phi=0.8):\n",
+    "    # Parameters for the AR(1) process\n",
+    "\n",
+    "    sigma = 1          # Standard deviation of the noise\n",
+    "    n = 500            # Length of the time series\n",
+    "\n",
+    "    # Initialize the process\n",
+    "    np.random.seed(42)  # For reproducibility\n",
+    "    X = np.zeros(n)\n",
+    "    X[0] = np.random.normal(0, sigma)  # Initial value\n",
+    "\n",
+    "    # Generate the AR(1) process and noise series\n",
+    "    noise = np.random.normal(0, sigma, n)  # Pre-generate noise for the second plot\n",
+    "    for t in range(1, n):\n",
+    "        X[t] = phi * X[t-1] + noise[t]  # Use pre-generated noise\n",
+    "\n",
+    "    # Create the 2x1 subplot\n",
+    "    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 6), sharex=False)\n",
+    "\n",
+    "    # Plot the AR(1) process in the first subplot\n",
+    "    ax1.plot(X, label=f\"AR(1) Process with φ={round(phi, 2)}\")\n",
+    "    ax1.set_ylabel(\"Value\")\n",
+    "    ax1.set_title(\"Simulated AR(1) Process\")\n",
+    "    ax1.set_ylim(-4, 4)\n",
+    "    ax1.legend()\n",
+    "    ax1.grid(True)\n",
+    "\n",
+    "    # Plot the white noise in the second subplot\n",
+    "    lags = 20\n",
+    "    plot_acf(X, ax=ax2, lags=lags, title=\"ACF of White Noise\")\n",
+    "    ax2.set_xlabel(\"Time\")\n",
+    "    ax2.set_ylabel(\"ACF Value\")\n",
+    "    ax2.set_title(\"ACF of AR(1) Process\")\n",
+    "    ax2.set_xlim(-0.5, lags)\n",
+    "    ax2.grid(True)\n",
+    "\n",
+    "    # Display the plot\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "interact(acfplot, phi=widgets.FloatSlider(value=0.8, min=-1, max=1.0, step=0.1, description='Phi:'));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Estimation of coefficients of AR process\n",
+    "\n",
+    "If the values of $p$ and of the AR($p$) process are known, the question is: **how can we estimate the coefficients $\\phi_1,...,\\phi_p$**\n",
+    "\n",
+    "Here, we only elaborate on AR(1) using best linear unbiased estimation (BLUE) to estimate $\\phi_1$. The method can be generalized to estimate the parameters of an AR($p$) process.\n",
+    "\n",
+    "**Example: Parameter estimation of AR(1)**\n",
+    "\n",
+    "The AR(1) process is of the form\n",
+    "\n",
+    "$$S_t=\\phi_1 S_{t-1}+e_t$$\n",
+    "\n",
+    "In order to estimate the $\\phi_i$ we can set up the following linear model of observation equations (starting from $t=2$):\n",
+    "\n",
+    "$$\\begin{bmatrix}S_2 \\\\ S_3 \\\\ \\vdots \\\\ S_m \\end{bmatrix} = \\begin{bmatrix}S_1 \\\\S_2 \\\\ \\vdots\\\\ S_{m-1} \\end{bmatrix}\\begin{bmatrix}\\phi_1 \\end{bmatrix} + \\begin{bmatrix}e_{2} \\\\ e_{3}\\\\ \\vdots \\\\ e_{m} \\end{bmatrix}$$\n",
+    "\n",
+    "The BLUE estimator of $\\phi$ is given by:\n",
+    "\n",
+    "$$\\hat{\\phi}=(\\mathrm{A}^T\\mathrm{A})^{-1}\\mathrm{A}^TS$$\n",
+    "\n",
+    "Where $\\mathrm{A}=\\begin{bmatrix}S_1 & S_2 & \\cdots & S_{m-1}\\end{bmatrix}^T$ and $S=\\begin{bmatrix}S_2 & S_3 & \\cdots & S_m\\end{bmatrix}^T$.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/ar.md b/book/time_series/ar.md
new file mode 100644
index 0000000000000000000000000000000000000000..4789a2a5a394993466a1bf0b78c0d6053c43f6b4
--- /dev/null
+++ b/book/time_series/ar.md
@@ -0,0 +1,161 @@
+(AR)=
+# AR process
+
+The main goal is to introduce the AutoRegressive (AR) model to describe a **stationary stochastic process**. Hence the AR model can be applied on time series where e.g. trend and seasonality are not present / removed, and only noise remains, or after applying other methods [to obtain a stationary time series](stationarize).
+
+## Process definition
+
+In an AR model, we forecast the variable of interest using a linear combination of its past values. A zero mean AR process of orders $p$ can be written as follows:
+
+$$S_t = \overbrace{\beta_1S_{t-1}+...+\beta_pS_{t-p}}^{\text{AR process}} + e_t $$ 
+
+or as
+
+$$S_t = \sum_{i=1}^p \beta_iS_{t-i}+e_t$$
+
+Each observation is made up of a **random error** $e_t$ at that epoch, a linear combination of **past observations**. The errors $e_t$  are uncorrelated purely random noise process, known also as white noise. We note the process should still be stationary, satisfying
+
+$$\mathbb{E}(S_t)=0, \hspace{20px} \mathbb{D}(S_t)=\sigma^2,\quad \forall t$$
+
+This indicates that parts of the total variability of the process come from the signal and noise of past epochs, and only a (small) portion belongs to the noise of that epoch (denoted as $e_t$). To have a better understanding of the process itself, we consider two special cases, $q=0$ and $p=0$.
+
+### First-order AR(1) process
+
+We will just focus on explaining $p=1$, i.e. the AR(1) process. A **zero-mean first order autoregressive** process can be written as follows
+
+$$S_t = \beta S_{t-1}+e_t, \hspace{20px} -1\leq\beta<1, \hspace{20px} t=2,...,m$$
+
+where $e_t$ is an i.i.d. noise process, e.g. distributed as $e_t\sim N(0,\sigma_{e}^2)$. See later the definition of $\sigma_{e}^2$.
+
+:::{card} Exercise
+
+In a zero-mean first order autoregressive process, abbreviated as AR(1), we have $m=3$ observations, $\beta=0.8$, and the generated white noise errors are $e = [e_1,\, e_2,\, e_3]^T=[1,\, 2,\, -1]^T$. What is the generated AR(1) process $S = [S_1,\, S_2,\, S_3]^T$?
+
+a. $S = \begin{bmatrix}1 & 2.8 & 1.24\end{bmatrix}^T$  
+b. $S = \begin{bmatrix} 0 & 2 & 0.6 \end{bmatrix}^T$  
+c. $S = \begin{bmatrix} 1 & 2 & -1 \end{bmatrix}^T$  
+
+```{admonition} Solution
+:class: tip, dropdown
+
+The correct answer is **a**. The AR(1) process can be initialized as $S_1=e_1=1$. The next values can be obtained through:
+
+$$
+S_t = \beta S_{t-1} + e_t
+$$
+
+Giving $S_2=0.8 S_1 + e_2 = 0.8\cdot 1 + 2 = 2.8$ and $S_3=0.8 S_2 + e_3 = 0.8\cdot 2.8 - 1= 1.24$, so we have:
+
+$$
+S = 
+\begin{bmatrix}1 & 2.8 & 1.24\end{bmatrix}^T 
+$$
+
+```
+:::
+
+**Formulation**
+
+Initializing $S_1=e_1$, with $\mathbb{E}(S_1)=\mathbb{E}(e_1)=0$ and $\mathbb{D}(S_1)=\mathbb{D}(e_1)=\sigma^2$. Following this, multiple applications of the above "autoregressive" formula ($S_t = \beta S_{t-1} + e_t$) gives:
+
+$$
+\begin{align*}
+S_1&=e_1\\ 
+S_2&=\beta S_1+e_2\\ 
+S_3 &= \beta S_2+e_3 = \beta^2S_1+\beta e_2+e_3\\ 
+&\vdots\\ 
+S_m &= \beta S_{m-1} + e_m = \beta^{m-1}S_1+\beta^{m-2}e_2+...+\beta e_{m-1}+e_m
+\end{align*}
+$$
+
+of which we still have (in order to impose the *stationarity*):
+
+$$\mathbb{E}(S_t)=0 \hspace{5px}\text{and}\hspace{5px} \mathbb{D}(S_t)=\sigma^2, \hspace{10px} t=1,...,m$$
+
+All the error components, $e_t$, are uncorrelated such that $Cov(e_t,e_{t+\tau})=0$ if $\tau \neq 0$, and with variance $\sigma_{e}^2$ which still needs to be determined.
+
+**Autocovariance**
+
+The mean of the process is zero and, therefore:
+
+$$\mathbb{E}(S_t) = \mathbb{E}(\beta S_{t-1}+e_t) = \beta\mathbb{E}(S_{t-1})+\mathbb{E}(e_t) = 0$$
+
+The variance of the process should remain constant as:
+
+$$\mathbb{D}(S_t) = \mathbb{D}(\beta S_{t-1} +e_t) \Leftrightarrow \sigma^2=\beta^2\sigma^2+\sigma_{e}^2, \hspace{10px} t\geq 2$$
+
+resulting in
+
+$$\sigma_{e}^2 = \sigma^2 (1-\beta^2)$$
+
+indicating that $\sigma_{e}^2$ is smaller than $\sigma^2$.
+
+The autocovariance (covariance between $S_t$ and $S_{t+\tau}$) is
+
+$$
+\begin{align*}
+c_{\tau}&=\mathbb{E}(S_t S_{t+\tau})-\mu^2 =\mathbb{E}(S_t S_{t+\tau})\\
+&= \mathbb{E}(S_t(\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...)) = \beta^\tau\mathbb{E}(S_t^2)=\sigma^2\beta^\tau
+\end{align*}$$
+
+In the derivation above we used that:
+
+$$
+\begin{align*}
+S_{t+\tau}=\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}
+\end{align*}
+$$
+
+and the fact that $S_t$ and $e_{t+\tau}$ are uncorrelated for $\tau \neq 0$.
+
+```{admonition} Derivation (optional)
+:class: tip, dropdown
+
+$$
+\begin{align*}
+S_{t+\tau}&= \beta^{t+\tau-1}S_1 + \beta^{t+\tau-2}e_2+...+ \beta^{\tau} e_{t}+ \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}\\
+&= \beta^{\tau} \left(\beta^{t-1}S_1 + \beta^{t-2}e_2+...+  e_{t}\right)+ \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}\\
+&=\beta^\tau S_t + \beta^{\tau-1} e_{t+1}+...+e_{t+\tau}
+\end{align*}
+$$
+
+```
+
+**Model structure of AR(1)**
+
+$$\mathbb{E}(S) = \mathbb{E}\begin{bmatrix}S_1\\ S_2\\ \vdots\\ S_m\end{bmatrix} = \begin{bmatrix}0\\ 0\\ \vdots\\ 0\end{bmatrix}, \hspace{15px} \mathbb{D}(S)=\Sigma_{S}=\sigma^2 \begin{bmatrix}1&\beta&...&\beta^{m-1}\\ \beta&1&...&\beta^{m-2}\\ \vdots&\vdots&\ddots&\vdots\\ \beta^{m-1}&\beta^{m-2}&...&1\end{bmatrix}$$
+
+* Autocovariance function $\implies$ $c_{\tau}=\sigma^2\beta^\tau$
+* Normalized autocovariance function (ACF) $\implies$ $\rho_\tau=c_{\tau}/c_0=\beta^\tau$
+* Larger value of $\beta$ indicates a long-memory random process
+* If $\beta=0$, this is called *purely random process* (white noise)
+* ACF is even, $c_{\tau}=c_{-\tau}=c_{|\tau|}$ and so is $\rho_{\tau}=\rho_{-\tau}=\rho_{|\tau|}$
+
+Later in this section we will see how the coefficient $\beta$ can be estimated.
+
+**Simulated example**
+
+## Estimation of coefficients of AR process
+
+If the values of $p$ of the AR($p$) process is known, the question is: **how can we estimate the coefficients $\beta_1,...,\beta_p$?**
+
+Here, we only elaborate on AR(2) using best linear unbiased estimation (BLUE) to estimate $\beta_1$ and $\beta_2$. The method can be generalized to estimate the parameters of an AR($p$) process.
+
+**Example: Parameter estimation of AR(2)**
+
+The AR(2) process is of the form
+
+$$S_t=\beta_1 S_{t-1}+\beta_2 S_{t-2}+e_t$$
+
+In order to estimate the $\beta_i$ we can set up the following linear model of observation equations (starting from $t=3$):
+
+$$\begin{bmatrix}S_3 \\ S_4 \\ \vdots \\ S_m \end{bmatrix} = \begin{bmatrix}S_2 & S_1 \\S_3 & S_2\\ \vdots & \vdots\\ S_{m-1}&S_{m-2} \end{bmatrix}\begin{bmatrix}\beta_1 \\ \beta_2\end{bmatrix} + \begin{bmatrix}e_{3} \\ e_{4}\\ \vdots \\ e_{m} \end{bmatrix}$$
+
+The BLUE estimator of $\beta=[\beta_1,\beta_2]^T$ is
+
+$$\hat{\beta}=(\mathrm{A}^T\mathrm{A})^{-1}\mathrm{A}^TS$$
+
+
+where $\mathrm{A}=\begin{bmatrix}S_2 & S_1 \\S_3 & S_2\\ \vdots & \vdots\\ S_{m-1}&S_{m-2} \end{bmatrix}$ and $S=\begin{bmatrix}S_3 \\ S_4 \\ \vdots \\ S_m \end{bmatrix}$.
+
+Notice that S and A are vectors of length $(m-2)$ and $(m-2\times 2)$, respectively.
\ No newline at end of file
diff --git a/book/time_series/ar_exercise.ipynb b/book/time_series/ar_exercise.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..827e95b614c00720c9a836d8ca9eaa006e02e731
--- /dev/null
+++ b/book/time_series/ar_exercise.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fit AR(p) model\n",
+    "In this exercise, we will fit an AR(p) model to a time series.The timeseries is already stationary and we will use two different methods to choose the number of lag (p) to include. First we will start by loading and displaying the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#load data\n",
+    "data = np.loadtxt('generated_time_series.csv', delimiter=',', skiprows=1)\n",
+    "\n",
+    "#plot data\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "plt.plot(data)\n",
+    "plt.xlabel('Time')\n",
+    "plt.ylabel('Value')\n",
+    "plt.title('Generated stationary Time Series')\n",
+    "plt.grid(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next we can use `plot_acf` to plot the autocorrelation function of the time series. From the resulting plot we can see that that there is autocorrelation present in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Plot the ACF of the time series\n",
+    "fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "plot_acf(data, lags=40, ax=ax)\n",
+    "ax.grid(True);\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A good indication for the number of lags to include in the AR model is the number of lags that are significant according to the PACF (partial autocorrelation function). We can use the `plot_pacf` function to plot the PACF of the time series. From the resulting plot we can see that the first lag and the second lag are significant. This suggests that we should use an AR(2) model.\n",
+    "\n",
+    "```{note}\n",
+    "The PACF is similar to the ACF, but it is the correlation between the time series and a lagged version of itself that is not explained by correlations at all lower-order lags. In other words, the PACF is the ACF with the linear dependence of the earlier lags removed.\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the PACF of the time series\n",
+    "fig, ax = plt.subplots(figsize=(12, 4))\n",
+    "plot_pacf(data, lags=40, ax=ax)\n",
+    "ax.grid(True);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make sure that we have selected the correct number of lags, we can test for significance using the generalized likelihood ratio test (GLRT). For a detailed explanation of the GLRT, see the chapter on observation theory. We will apply the GLRT to the AR(1) vs AR(2) and AR(2) vs AR(3) models. \n",
+    "\n",
+    "The AR(1), AR(2) and AR(3) models are defined as follows:\n",
+    "\n",
+    "AR(1): $S_t = \\phi_1 S_{t-1} + \\epsilon_t$\n",
+    "\n",
+    "AR(2): $S_t = \\phi_1 S_{t-1} + \\phi_2 S_{t-2} + \\epsilon_t$\n",
+    "\n",
+    "AR(3): $S_t = \\phi_1 S_{t-1} + \\phi_2 S_{t-2} + \\phi_3 S_{t-3} + \\epsilon_t$\n",
+    "\n",
+    "Since we need $S_{t-3}$ we will have $y = S[3:]$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AR(1) vs AR(2) test statistic: 65.46386398456401 Critical value: 3.841458820694124\n",
+      "Reject AR(1) in favor of AR(2)\n",
+      "AR(2) vs AR(3) test statistic: 3.5318291727430835 Critical value: 3.841458820694124\n",
+      "Fail to reject AR(2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.stats import chi2\n",
+    "\n",
+    "y = data[3:]\n",
+    "s1 = data[2:-1]\n",
+    "s2 = data[1:-2]\n",
+    "s3 = data[:-3]\n",
+    "n = len(y)\n",
+    "\n",
+    "# AR(1) model\n",
+    "X1 = np.column_stack((s1)).T\n",
+    "phi_ar1 = np.linalg.inv(X1.T @ X1) @ (X1.T @ y)\n",
+    "e1 = y - X1 @ phi_ar1\n",
+    "rss1 = e1.T @ e1\n",
+    "\n",
+    "# AR(2) model\n",
+    "X2 = np.column_stack((s1, s2))\n",
+    "phi_ar2 = np.linalg.inv(X2.T @ X2) @ X2.T @ y\n",
+    "e2 = y - X2 @ phi_ar2\n",
+    "rss2 = e2.T @ e2\n",
+    "\n",
+    "# AR(3) model\n",
+    "X3 = np.column_stack((s1, s2, s3))\n",
+    "phi_ar3 = np.linalg.inv(X3.T @ X3) @ X3.T @ y\n",
+    "e3 = y - X3 @ phi_ar3\n",
+    "rss3 = e3.T @ e3\n",
+    "\n",
+    "# test ar(1) vs ar(2) using log likelihood ratio test\n",
+    "dof = 1\n",
+    "crit = chi2.ppf(0.95, dof)\n",
+    "test_stat = n * np.log(rss1 / rss2)\n",
+    "test_stat = -2 \n",
+    "print('AR(1) vs AR(2) test statistic:', test_stat, 'Critical value:', crit)\n",
+    "\n",
+    "if test_stat > crit:\n",
+    "    print('Reject AR(1) in favor of AR(2)')\n",
+    "else:\n",
+    "    print('Fail to reject AR(1)')\n",
+    "\n",
+    "# test ar(2) vs ar(3) using log likelihood ratio test\n",
+    "dof = 1\n",
+    "crit = chi2.ppf(0.95, dof)\n",
+    "test_stat = n * np.log(rss2 / rss3)\n",
+    "print('AR(2) vs AR(3) test statistic:', test_stat, 'Critical value:', crit)\n",
+    "\n",
+    "if test_stat > crit:\n",
+    "    print('Reject AR(2) in favor of AR(3)')\n",
+    "else:\n",
+    "    print('Fail to reject AR(2)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the GLRT results we can see that an AR(2) model is the best fit for the data. Finally we will fit the AR(2) model to the data and plot the residuals. The residuals should be white noise, which is confirmed by the ACF plots of the residuals. Plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AR(2) Coefficients:\n",
+      "Phi_1 =  0.4955394214306651 +/- 0.0601858844661936\n",
+      "Phi_2 =  0.25270004768701176 +/- 0.06018454960839631\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the residuals\n",
+    "\n",
+    "fig, ax = plt.subplots(2,1,figsize=(12, 6))\n",
+    "ax[0].plot(e2)\n",
+    "ax[0].set_title('AR(2) Residuals')\n",
+    "ax[0].grid(True)\n",
+    "\n",
+    "plot_acf(e2, lags=40, ax=ax[1])\n",
+    "ax[1].grid(True)\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "\n",
+    "sigma_e2 = np.std(e2)\n",
+    "var_phi2 = sigma_e2**2 * np.linalg.inv(X2.T @ X2)\n",
+    "sigma_phi2 = np.sqrt(np.diag(var_phi2))\n",
+    "\n",
+    "# print the AR(2) coefficients with confidence intervals\n",
+    "print('AR(2) Coefficients:')\n",
+    "print('Phi_1 = ', phi_ar2[0], '+/-', 1.96 * sigma_phi2[0])\n",
+    "print('Phi_2 = ', phi_ar2[1], '+/-', 1.96 * sigma_phi2[1])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/components.md b/book/time_series/components.md
index 3f14a52fe826ef8881c216668d685fbb71e6cf96..530981306ba40d6c818be6436a5710167ad6e70b 100644
--- a/book/time_series/components.md
+++ b/book/time_series/components.md
@@ -1,7 +1,9 @@
 (components)=
 # Components of time series
 
-A time series is a sequence of data points indexed in time in order to study a phenomenon. It is thus the data collected at different points in time. The data are usually collected at fixed time intervals rather than just recording them intermittently or randomly. The fixed interval, in the time domain, is defined as 'sampling interval' and, in the frequency domain, is defined as 'sampling rate' or 'sampling frequency', expressed for example in Hz.
+A time series is a discrete time sequence of data points indexed in time which can be used to study a phenomenon. It is a record of the data collected at different points in time, it consists of discrete time samples of typically a continuous-time phenomenon in reality.  The data are usually collected at fixed time intervals rather than just recording them intermittently or randomly. The fixed interval $\Delta t$, in the time domain, is defined as 'sampling interval' and, in the frequency domain, is defined as 'sampling rate' or 'sampling frequency', expressed for example in Hz.
+
+$$ \Delta t = \frac{1}{f_s} $$
 
 A time series is denoted as 
 
@@ -23,7 +25,7 @@ Example of time series with equally spaced time interval $\Delta t$
 
 A time series can be decomposed as follows:
 
-$$Y(t) = tr(t) + s(t) + o(t) + b(t) + N(t)$$
+$$Y(t) = tr(t) + s(t) + o(t) + b(t) + \epsilon(t)$$
 
 where we distinguish the following components:
 
@@ -31,11 +33,11 @@ where we distinguish the following components:
 2. $s(t)$ = seasonality, shows the regular seasonal variations
 3. $o(t)$ = offset, is a discontinuity (or jump) in the data
 4. $b(t)$ = irregularities and outliers (also referred to as biases), due to unexpected reasons. Irregularities will not be considered in this book.
-5. $N(t)$ = noise, can be white or colored noise.
+5. $\epsilon(t)$ = noise process, can be white or colored noise.
 
 ## Trend
 
-The trend is the general pattern of the time series and shows its long-term changes. It is observed when there is an increasing or decreasing slope in the time series.
+The trend is the general pattern of the time series and shows its long-term changes. The trend can be linear, however higher order polynomials are also possible.
 
 ```{figure} ./figs/trend.png
 :name: trend
@@ -47,25 +49,8 @@ Monthly time series of global mean sea level measurements using Satellite Altime
 
 {numref}`trend` shows a positive trend (red line) of around $3.5$ mm/year, which in this case indicates sea level rise. This however needs to be further investigated and tested statistically (see {ref}`hypothesis_testing` and also {ref}`modelling_tsa`).
 
-Trend analysis expresses the changes of the variable of interest with respect to time $t$.
-We address here two types of trend analysis.:
-
-1. Linear trend analysis. The time-dependent variable $Y(t)$ changes at a (constant) linear rate over time: $Y_t = y_0 + r t + \epsilon_t$
-2. Log-linear trend analysis. The time-dependent variable changes at a (constant) exponential rate over time: $ln(Y_t) = y_0 + r t + \epsilon_t$
-
-:::{card} Example - linear trend
-
-Assume $\hat y_0 = 5$ and $\hat r = 2$.
-
-Therefore, $Y_t = \hat y_0 + \hat r  t = 5 + 2  t$. At $t=10$, $Y_{10} = 5 + 2 \times 10 = 25$.
-:::
-
-:::{card} Example - log-linear trend
+Trend analysis expresses the changes of the variable of interest with respect to time $t$. Different types of trend are possible and for now we will mainly focus on linear trend, i.e. the time-dependent variable $Y(t)$ changes at a (constant) linear rate over time: $Y_t = y_0 + r t + \epsilon_t$. Other trends are however also possible, for example, quadratic, which includes $c t^2$, or log linear $\log(Y_t) = y_0 + r t + \epsilon_t$.
 
-Assume again $\hat y_0 = 5$ and $\hat r = 2$.
-
-$ln(Y_t) = \hat y_0 + \hat r  t = 5 + 2  t$. At $t=10$, $ln(Y_{10}) = 5 + 2 \times 10 \Rightarrow Y_{10} = 72004899337.4$
-:::
 
 ## Seasonality
 
@@ -73,73 +58,85 @@ Seasonal variations explain regular fluctuations in a certain period of time (e.
 
 From {numref}`trend` it is also possible to see the seasonal variations: in fact sea levels are higher in summer and lower in winter. The annual warming/cooling cycle is the main contributor to these seasonal variations.
 
-Regular seasonal variations in a time series might be handled by using a sinusoidal model with one or more sinusoids whose frequency may be known or unknown depending on the context. A harmonic model for seasonal variation can be of the following two equivalent forms (using that $\sin(u+v)= \sin u \cos v + \cos u \sin v$):
+Regular seasonal variations in a time series might be handled by using a sinusoidal model with one or more sinusoids with frequency that may be known or unknown depending on the context. In fig {numref}`trend`, cyclical behavior with a period of 1 year can be observed. A harmonic model for seasonal variation can be of the following two equivalent forms (using that $\cos(u+v)= \cos u \cos v - \sin u \sin v$):
 
 $$ 
 \begin{align*}
-Y(t) &= \sum_{k=1} ^p A_k  \sin(k \omega_0  t + \theta_k)  + \epsilon_t\\
+Y(t) &= \sum_{k=1} ^p A_k  \cos(k \omega_0  t + \theta_k)  + \epsilon_t\\
 &= \sum_{k=1} ^p \left(a_k  \cos(k \omega_0  t) + b_k  \sin(k \omega_0 t) \right)+ \epsilon_t
 \end{align*}
 $$
 
-with the coefficients $a_k =A_k\sin\theta_k$ and $b_k=A_k\cos\theta_k$, and where $\omega_0$ is the base (fundamental) frequency of the seasonal variation and is fixed or is determined by Spectral Analysis methods such as {ref}`dft` or FFT. To be more specific, we can use the {ref}`psd` and {ref}`LS-HE` to determine the unknown frequencies. 
+With the coefficients $a_k = A_k\cos\theta_k$ and $b_k=-A_k\sin\theta_k$, and where $\omega_0$ is the base (fundamental) frequency of the seasonal variation and is fixed or is determined by Spectral Analysis. To be more specific, we can use the {ref}`psd` to determine the unknown frequencies. 
 
-The coefficients $a_k $ and $b_k$ can be determined using the least-squares method. From this the original sinusoids can be obtained using:
+Once $\omega_ 0$ is set, the coefficients $a_k $ and $b_k$ can be determined using the least-squares method, since the equation is linear in $a_k$ and $b_k$. From this the original sinusoids can be obtained using:
 
-$$ A_k = \sqrt{a_k^2 + b_k^2}, \hspace{1cm} \theta_k = \arctan(\frac{a_k}{b_k}), \hspace{1cm} k = 1, \ldots{}, p $$
+$$ A_k = \sqrt{a_k^2 + b_k^2}, \hspace{1cm} \theta_k = \arctan(-\frac{b_k}{a_k}), \hspace{1cm} k = 1, \ldots{}, p $$
+
+```{note}
+This transformation is necessary to make the seasonal component phase-independent. Using regular estimation methods, we cannot linearly estimate the phase of the sinusoidal function. However by transforming the sinusoidal function into a linear combination of sine and cosine functions, we can estimate the phase of the seasonal component.
+```
 
 :::{card} Worked example - seasonality signal
 
 Show that the time series 
 
-$$Y(t)=A sin(\omega_0 t + \theta)$$ 
+$$Y(t)=A \cos(\omega_0 t + \theta)$$ 
 
 with given $\omega_0$, can be rewritten as
 
-$$Y(t)=a cos(\omega_0 t) + b sin(\omega_0 t)$$
+$$Y(t)=a \cos(\omega_0 t) + b \sin(\omega_0 t)$$
 
 and derive the formulation of $A$ and $\theta$.
 
-Hint: you might need to know sine properties $sin(u+v)=sin(u)cos(v)+cos(u)sin(v)$
+Hint: you might need to know trigonometric identity $\cos(u+v)=\cos(u)\cos(v)-\sin(u)\sin(v)$
 
 ````{admonition} Solution
 :class: tip, dropdown
 
-[This video](https://youtu.be/8kqQiI4ni68) includes the solution to this exercise. 
-````
+Using the trigonometric identity to rewrite:
 
-:::
+$ Y(t)=A \cos(\omega_0 t + \theta) = A (\cos(\omega_0 t)\cos(\theta)-\sin(\omega_0 t)\sin(\theta)) $
 
-(season)=
-:::{card} Example - seasonal variations
+Retrieving the functions for a and b
 
-```{figure} ./figs/sine_wave_1.jpg
-:name: trendab
-:width: 600px
-:align: center
+$ a = A \cos(\theta) \hspace{1cm} b = -A \sin(\theta)$
 
-Seasonal variations components: blue line is the time series $Y(t)$; red and green lines represent the contributions $a  \cos(0.5\pi t)$ and  $b   \sin(0.5\pi t)$, respectively.
-```
+Squaring both functions in order to get rid of the sin and cos
+
+$ a^2 = A^2 \cos^2(\theta) \hspace{1cm} b^2 = A^2 \sin^2(\theta) $
+
+Adding both functions together
+
+$ a^2 + b^2 = A^2 (\cos^2(\theta) + \sin^2(\theta)) $
+
+Using this property to simplify:
 
-The seasonal variation is given as $y = A \sin(\omega_0 t + \theta)$.
+$ \cos^2(\theta) + \sin^2(\theta) = 1 $
 
-Assume amplitude $A=2$, base frequency $\omega_0=0.5\pi$ and initial phase $\theta = -0.8 \pi$ (rad), see top panel of {numref}`trendab`.
+$ a^2 + b^2 = A^2 $
 
-$y(t) = 2 \sin(0.5 \pi t - 0.8\pi)$
+Take square root to find A
 
-The time-delay of the phase is $0.5 t - 0.8 = 0 \Rightarrow t = 1.6 \equiv \theta_t$.
+$ \sqrt{a^2 + b^2} = A $ 
 
-Alternatively we can write 
+For $\theta$ we rewrite the second function
 
-$y(t) = a  \cos(0.5\pi t) + b   \sin(0.5\pi t)$
+$ a = A \cos(\theta) \hspace{1cm} -b = A \sin(\theta)$
 
-where $a = A  \sin(\theta)=-1.1756$ and $b=A  \cos(\theta)=-1.6180$.
+$ \frac{-b}{a} = \frac{\sin(\theta)}{\cos(\theta)} = \tan(\theta) $
+
+$ \theta = \arctan(\frac{-b}{a}) $
+
+
+[This video](https://youtu.be/8kqQiI4ni68) includes the solution to this exercise. 
+````
 
 :::
 
 ## Offset (jump)
 
-Offsets are sudden changes in time series. There are different underlying reasons why we encounter offsets in time series. 
+Offsets are sudden changes or shifts in time series. There are different underlying reasons why we encounter offsets in time series. 
 
 ```{figure} ./figs/offset.png
 :name: offset
@@ -149,9 +146,9 @@ Offsets are sudden changes in time series. There are different underlying reason
 Example of time series with two offsets. 
 ```
 
-As a deterministic sudden change, offsets can be handled by a step function such as a Heaviside step function whose epoch (time instant) can be known or unknown (to be detected) depending on the time series.
+As a deterministic sudden change, offsets can be handled by a step function such as a heaviside step function with an epoch (time instant) that can be known or unknown (to be detected) depending on the time series.
 
-In this case the time series is written as 
+In this case the time series is written as: 
 
 $$ Y(t) = \sum_{k=1}^q o_k u_k(t)+\epsilon_t$$
 
@@ -164,21 +161,23 @@ $$u_k(t) = \left\{
 \end{array} 
 \right.  $$
 
+Once the time instant ($t_k$) of the offset is known, the amplitude can be estimated using least-squares.
+
 ## Noise 
 
 Noise simply refers to random fluctuations in the time series about its typical pattern. In general we can talk about white and colored noise in time series analysis. The following characteristics are associated with noise:
 
-- Noise is not necessarily synonymous to error, but part of noise is the random error.
+- Noise is not synonymous with error, although random variation, including measurement errors, contributes to noise. Essentially, noise represents the unpredictable fluctuations in data, while errors encompass any inaccuracies that may arise from a range of factors, including both random variations and systematic issues.
 - It is required to filter out unwanted random variations, and detect meaningful information (i.e., a signal) from noise processes.
 - Transforming data from the time domain to the frequency domain allows to filter out the frequencies that pollute the data.
-- White noise can be decomposed into its constituent components (frequencies) like white light.  In principle, white noise contains all wavelengths/colors, each contributing equally to the fluctuations observed in the data.
+- White noise can be decomposed into its constituent components (frequencies).  In principle, white noise contains all wavelengths/colors (like white light), each contributing equally to the fluctuations observed in the data.
 - Colored noise can seriously affect the analysis of time series, and their parameters of interest. Short-term colored noise has also predictive property (used for forecasting).
 
-A purely random process (or white noise process) yields a sequence of uncorrelated zero-mean random variables. This zero-mean random process is of the form
+A purely stationary random process (or white noise process) yields a sequence of uncorrelated zero-mean random variables. This zero-mean random process is of the form
 
 $$ Y(t)=Y_t=\epsilon_t $$
 
-where $\epsilon_t$ is the independent identically distributed (i.i.d.) error at epoch $t$. Therefore, the observation/noise at time $t$ is not dependent on the previous observations.
+where $\epsilon_t$ is the independent identically distributed (i.i.d.) error at epoch $t$. Therefore, the observation/noise at time $t$ is not dependent on any of the previous observations $Y_t$.
 
 ### Stochastic model
 
@@ -194,7 +193,7 @@ $$
 \mathbb{D}(Y) =  \Sigma_{Y} = \sigma^2 \left[\begin{array}{ccc} 1 & 0 & \ldots{} & 0 \\ 0 & 1 & \ldots{} & 0 \\ \vdots & \vdots & \ddots & \vdots \\ 0 & 0 & \ldots{} & 1 \end{array}\right]
 $$
 
-The noise can be represented with a Gaussian distribution with mean $\mu=0$ and variance $\sigma^2$, that is $\epsilon(t) \sim \textbf{N}(0, \sigma^2)$.
+The noise can be represented with, for example, a Gaussian distribution with mean $\mu=0$ and variance $\sigma^2$, that is $\epsilon(t) \sim \textbf{N}(0, \sigma^2)$.
 
 :::{card} Example - time series consisting of a trend, annual signal (seasonality), an offset and pure random noise (white noise)
 
@@ -208,5 +207,6 @@ where
 - $a$ and $b$ are the coefficients of the signal, (e.g. annual signal)
 - $\omega_0$ is the frequency (e.g. 1 cycle/year)
 - $o$ is the offset starting at time $t_k$
-- $\epsilon(t)$ is the i.i.d. random noise, i.e. $\epsilon(t) \sim \textbf{N}(0, \sigma^2)$.
+- $u_k(t)$ is the Heaviside step function
+- $\epsilon(t)$ is the i.i.d. random Gaussian noise, i.e. $\epsilon(t) \sim \textbf{N}(0, \sigma^2)$.
 :::
\ No newline at end of file
diff --git a/book/time_series/exercise1.ipynb b/book/time_series/exercise1.ipynb
index b0b19b93633837fd3b776cc1849d100aaa93488c..11b9d9ef71c87d4acfa43c34c04c58bb4c1c8010 100644
--- a/book/time_series/exercise1.ipynb
+++ b/book/time_series/exercise1.ipynb
@@ -24,16 +24,17 @@
    "source": [
     "**Introduction:** \n",
     "\n",
-    "The four components of time series are the trend, seasonality, offset, and noise (white/colored). We use simulated data to show these components here. The observation equation of time series should have the following mathematical representation:\n",
+    "The four components of time series, we will consider here, are the trend, seasonality, offset, and noise (white/colored). We use simulated data to show these components here. The observation equation of time series should have the following mathematical representation:\n",
     "\n",
-    "$$Y(t) = y_0 + r t + a \\cos(\\omega_ot) + b\\sin(\\omega_ot) + o {u_k(t)} + \\epsilon(t)= y_0 + r t + A \\sin(\\omega_o t+\\phi_0) + o {u_k(t)} + \\epsilon(t)$$\n",
+    "$$Y(t) = y_0 + r t + a \\cos(\\omega_0t) + b\\sin(\\omega_0t) + o {u_k(t)} + \\epsilon(t)= y_0 + r t + A \\sin(\\omega_0 t+\\phi_0) + o {u_k(t)} + \\epsilon(t)$$\n",
     "\n",
     "where\n",
     "- $y_0 $: intercept (e.g. in mm)\n",
     "- $r$: is the rate (e.g. in mm/day)\n",
     "- $a$ and $b$ are the coefficients of the periodic signal \n",
-    "- $\\omega$ is the frequency of signal (e.g. cycle/ day)\n",
+    "- $\\omega_0$ is the frequency of signal (e.g. cycle/ day)\n",
     "- $o$ is the size of the offset at time instant $t_k$\n",
+    "- $u_k(t)$ is the unit step function which is 1 if $t_k \\leq t$ and 0 otherwise\n",
     "- $\\epsilon(t)$ is the random noise with a given variance which follows a Normal distribution: $ \\epsilon(t) \\sim \\textbf{N}(0, \\sigma^2)$\n",
     "\n",
     "Here, we are assuming only a single seasonality and offset component. However, in many practical scenarios, there could be multiple components related to these.\n",
@@ -54,22 +55,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, '$Y$(t) = 1 + 0.02 t $')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -92,7 +83,7 @@
     "plt.plot(time, y1, color='red')\n",
     "plt.ylabel('Y(t)')\n",
     "plt.xlabel('Time (day)')\n",
-    "plt.title('$Y$(t) = 1 + 0.02 t $')"
+    "plt.title('$Y(t) = 1 + 0.02 t $');"
    ]
   },
   {
@@ -119,7 +110,7 @@
     },
     {
      "data": {
-      "image/png": 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8HMPW8Hrq1Cn++OOPzNt79uxh8+bNFC9enOjoaO688042btzIl19+SVpaGn/++ScAxYsXJygoyK6yRURERMQmtobX9evX06pVq8zbAwcOBKBbt27ExMSwcOFCABo0aJDleUuXLqVly5beKlNEREREfISt4bVly5bkNOTWR4bjioiIiIiPsHV5WBERERGRvFB4FRERERHHUHgVEREREcfw6amyRERERMT7Xn7Zj/Bw31zcSeFVRERERDKtXAlPPeVPUFArbrnFomxZuyvKSsMGRERERASAtDTo189st2x5kFKl7K0nOwqvIiIiIgLA7NmwaRMUKWLRtesvdpeTLYVXERERESExEUaMMNtDh6ZTpEiyvQWdh8KriIiIiPDii3DoEFSsCI8+mm53Oeel8CoiIiJSwP39N0ycaLaffRYKFbK3npwovIqIiIgUcGPGQHw8NGwI995rdzU5U3gVERERKcB+/x1mzDDbU6aAn4+nQx8vT0REREQ8afhwSE2Fdu3g+uvtrubCFF5FRERECqgtW+Cjj8DlggkT7K4mdxReRURERAqomBjz7113Qb16tpaSawqvIiIiIgXQhg3w2WdmjOuoUXZXk3sKryIiIiIFUEZg7dIFatWyt5a8UHgVERERKWB++gm++gr8/WHkSLuryRuFVxEREZECJiOw3n8/XHaZvbXklcKriIiISAGyahUsWgQBATBihN3V5J3Cq4iIiEgBktHr2qMHVKliby0XQ+FVREREpIBYutR8BQaaxQmcSOFVREREpACwrLO9rg8/DBUq2FvPxVJ4FRERESkAvvvOjHcNDoZnnrG7moun8CoiIiKSz1nW2Yuz+vSBsmXtredSKLyKiIiI5HPffGPmdg0JgSFD7K7m0ii8ioiIiORj/x7r2rcvREXZW8+lUngVERERyccWLoQNGyAsDJ5+2u5qLp3Cq4iIiEg+lZ4Oo0aZ7SeegJIl7a3HHRReRURERPKpTz+FLVsgIgKefNLuatxD4VVEREQkH0pLO9vrOmAAlChhbz3uovAqIiIikg/Nmwc7dkDRoia85hcKryIiIiL5TEoKxMSY7UGDTIDNLxReRURERPKZ2bPhjz8gMtJcqJWfKLyKiIiI5CNJSTBmjNkeOhTCw+2tx90UXkVERETykTffhP37oUwZeOQRu6txP4VXERERkXzi9Gl49lmzPXy4WQ42vwmwuwARERGR8zl6FL75Bn74AQ4ehD//hEKFzFjO6tWhRQto1ix/XZB0KV57DY4cgYoV4cEH7a7GMxReRURExKdYFixbBhMmwHffmdvn8/zzEBgId94JffqYIOtyea1UnxIfb84ZwMiREBxsbz2eovAqIiIiPuOXX6B3b1i58uy+hg2hbVuoVg2iouDMGfj7b9i40YTc33+HuXPNV6tWMGUKNGpk21uwzXPPwbFjcNll8MADdlfjOQqvIiIiYru0NJg6FUaMMFfLBwebP3sPGgSVKuX83A0bYMYMeO89WLoUGjeGhx4yvbKFC3ulfNsdOWLCK8D48RCQjxOeLtgSERERWyUkmD/7P/20Ca433WR6U1955cLBFUwv6xtvwG+/wX33mX1vvgn168Py5R4t3WeMGgWJidCkCdxxh93VeJbCq4iIiNjmyBFz0dVnn0FQEMycCV9/DeXL5/21KlY0va/LlpnQu3cvXH+9GUaQ07hZp9uxA956y2w/91z+H/Nra3hdsWIFt956K9HR0bhcLj777LMs91uWRUxMDNHR0YSEhNCyZUt+/vlne4oVERERt/rrLzNGdcMGM3vAkiXw8MOXHr5atICtW824z/R006N7zz1mGqn8aMgQ8z5vuw2uvdbuajzP1vCakJBA/fr1mT59erb3T548malTpzJ9+nTWrVtHVFQUbdq0IT4+3suVioiIiDsdOwatW5s/9VeoAGvWuDd4RUTAO++YoQcBAfDRR+air5Mn3XcMX7B8OXzxBfj7n51pIL+zNbzefPPNjBs3jttvv/2c+yzL4sUXX2TYsGHcfvvtXH755bz77rskJiYyZ84cG6oVERERd0hMhHbtYPt2swrU999D1aruP47LBY8+aqbbKlIEVq0yU2kdPOj+Y9khNRX69TPbvXpBjRr21uMtPjvmdc+ePfz555+0bds2c19wcDAtWrTgxx9/tLEyERERuViWBT16wLp1UKKECa7Vqnn2mC1amKm3oqPh55/hmmvMlFxON2MGbNkCxYrB6NF2V+M9PjuRwp9//glA6dKls+wvXbo0+/btO+/zkpKSSEpKyrwdFxcHQEpKCikpKR6oNKuMY3jjWOIZakPnUxs6m9rP+XJqw2ef9eOjj/wJCLD46KM0qlWz8EZT16xp/sTevn0AO3e6aNXK4n//S6V2bc8f2xOOHoXhwwMAF2PGpFG0aLpbz6O3vw/zchyfDa8ZXP8ZtW1Z1jn7/m3ChAmMzua/H4sWLSI0NNTt9Z3P4sWLvXYs8Qy1ofOpDZ1N7ed8/23DTZtKMnr0NQD07r2Z+Pj9fP21d2saPjyIUaOasmdPUVq0SGPcuB8pX95519JMm9aA2NiKVKnyD9HRyz12Hr31fZiYmJjrx7osyzcmj3C5XCxYsIBOnToBsHv3bqpWrcrGjRu54oorMh/XsWNHihYtyrvvvpvt62TX81q+fHmOHTtGYS/MVJySksLixYtp06YNgYGBHj+euJ/a0PnUhs6m9nO+7Nrwzz+hceMAjh510adPGi+/nG5bfcePw003BbBli4tSpSwWLXJWD+yaNS6aNzf9jytWpNKkifujnLe/D+Pi4oiMjCQ2NvaCec1ne14rV65MVFQUixcvzgyvycnJLF++nEmTJp33ecHBwQRns5hvYGCgV38Ievt44n5qQ+dTGzqb2s/5MtowPR169jR/6q5XD154wZ/AQH/b6oqKMmNt27SBTZtctG0byNKlOCLA/vsirR49oFkzz0Y5b30f5uUYtl6wderUKTZv3szmzZsBc5HW5s2b2b9/Py6Xi/79+zN+/HgWLFjA9u3b6d69O6GhoXTp0sXOskVERCQPXnrJXPEfGgrz5kGhQnZXZC4W++47uOIKE6pbtTKT/fu6KVNg0yYoWhQmTrS7GnvY2vO6fv16WrVqlXl74MCBAHTr1o133nmHp59+mtOnT/Poo49y8uRJrr76ahYtWkRERIRdJYuIiEge/P47PPOM2Z46FWrVsreefyte3ATY1q1NILz+eli61Ldq/LcdOyAmxmy/9BKUKmVrObaxNby2bNmSnIbculwuYmJiiMloKREREXGM9HR48EE4c8YExF697K7oXBkB9oYbYPNm0wO7bJmZncCXpKWZoRfJyWaO3Pvvt7si+/jsPK8iIiLibDNm+LFyJYSFwRtvXPqyr56SEWDr1Tu7ZO1vv9ldVVbjxsFPP0HhwvD66757Lr1B4VVERETc7uTJYEaONDFj8mSoVMneei4kY8GEunXNzAitWsHOnXZXZaxYAWPGmO1XX4Vy5eytx24KryIiIuJ2775bh7g4F1deCX362F1N7kRGmgB7+eVw5IgJsL//bm9Nx49Dly5mCEa3btC1q731+AKFVxEREXGrH35wsWxZeVwui1deAT8HpY2SJU2ArV0bDh82AfaPP+ypJTUV7rkHDh2C6tVh+nR76vA1Dvo4iYiIiK9LTYUnnjBzuPbsaXHllTYXdBFKlYIlS8ysA4cOQYsWsG2b9+sYMMCMxQ0Lg/nzITzc+zX4IoVXERERcZtXX4Vt21xERCQzdmya3eVctNKlTYCtU8f0wDZrZsaeesurr57taX3/fXMxmRgKryIiIuIWx47ByJFmu2vXX4iMtLeeSxUVBStXwnXXQWwstG0LCxZ4/rjvvw+PPWa2n30WOnXy/DGdROFVRERE3GLsWBPy6te3aNNmr93luEWxYrBoEXTsCElJcMcdMGEC5DBN/SWZP99cmGVZ8OijMHSoZ47jZAqvIiIicsn++MP8qRtg4sQ0/P3trcedQkLg44/NrAmWZVYMu/NOiItz73GmTzcXaGUs7jBtWsGez/V8FF5FRETkkg0bZi7WuvFGuOEGD3VL2iggAF57zSwQEBgIn35qxqEuXXrpr52SAv36weOPm+D60EPmOE6apcGbdFpERETkkvz0E3z0keklnDzZ7mo8q1cvc+FW5cqwbx9cf73Z9+efF/d6W7bAVVfByy+b2xMnwsyZ5Kuea3dTeBUREZGLZlkwaJDZ7tatYFwV36SJCZ29epnbb7wB1arBiBFmaq3c2LsX+vaFK6+EzZvNCl+ffgqDB2uowIUovIqIiMhF+/JLc0V+oULmgq2CIiLC/Gl/xQoTQBMSYNw4qFgROnQw41U3bzYzMKSlwcmTsH07zJgBt94Kl11mxginpJjZBH7+GW67ze535QwBdhcgIiIizpSebnobwYzZLFfO3nrs0KwZrFljek2nTTNh9osvzNeFtG5txgq3aKHe1rxQeBUREZGL8umn5s/nERFnhw4URH5+ZvaBO++EHTvgs89g+XJYt870uGYoXtwsO9uuHdxyC9Sta1vJjqbwKiIiInmWlgajRpntAQPMmE0x4bR2bTOdFphhASdPmoAfEmJvbfmFwquIiIjk2UcfmV7GokVNeJXsBQZCqVJ2V5G/6IItERERyZPUVIiJMdtPPmkCrIi3KLyKiIhInsydCzt3mjGcTzxhdzVS0Ci8ioiISK6lpMDo0WZ70CAoXNjeeqTgUXgVERGRXHvvPdi1C0qWhMces7saKYgUXkVERCRXkpNhzBizPXgwhIfbW48UTAqvIiIikiuzZsG+fRAVBY88Ync1UlApvIqIiMgFnTljlj8FGDoUQkPtrUcKLoVXERERuaA334SDB6FsWejVy+5qpCBTeBUREZEcnT4N48eb7WHDoFAhe+uRgk3hVURERHI0YwYcOQIVKkDPnnZXIwWdwquIiIicV0ICTJxotkeMgOBge+sRUXgVERGR83rlFTh6FKpUgW7d7K5GROFVREREziM+HiZPNtsjR0JgoL31iIDCq4iIiJzHyy/D8eNQvTp07Wp3NSKGwquIiIic4+RJmDLFbI8cCQEB9tYjkkHhVURERM4xdSrExkKdOnDPPXZXI3KWwquIiIhk8fff8OKLZnvMGPD3t7UckSwUXkVERCSLyZPh1Clo2BBuu83uakSyUngVERGRTEeOwPTpZnvcOHC57K1H5L8UXkVERCTT+PFw5gxccw3cdJPd1YicS9cOiohIvrZ3L3z3HezZA4cPg2VBiRJQvjxcdx00aKAr6TPs2wevv2621esqvkrfriIiku8kJMAbb8DMmfDLLzk/tnBhuPde6NPHBNmCbNw4SEmB66+HVq3srkYkewqvIiKSb6SlwbRp8OyzcOyY2efvb/4EXq8elC0Lfn7mvl9/hZUrzXRQr79uvtq0MRcrFcQQ++uvMGuW2R471t5aRHKi8CoiIvnCzp3Qowf8+KO5XaUKDB4MnTtDsWLZPyctDVasMMH1009h8WJzhX2PHvD881C0qNfKt92QIeZ8dOhgwr6Ir9IFWyIi4ngLF5rQ+eOPEBEBM2bAb79Br17nD65gemVbtYJ588zj77nHjIl9+22oW9eE2YJg5Ur4/HNzPiZNsrsakZwpvIqIiGNZlplMv1MnM861ZUvYvh169877RViVK8PcubBqFVStCgcPQtu2ZmnU9HQPFO8jLAsGDTLbDz0ENWvaW4/Ihfh0eE1NTWX48OFUrlyZkJAQqlSpwpgxY0jPzz9FREQk10aPhgEDTADr3RsWLYIKFS7tNa+9FrZsMRdwgRn/2aGDGRubH338Mfz0E4SFQUyM3dWIXJhPh9dJkyYxY8YMpk+fzi+//MLkyZOZMmUK06ZNs7s0ERGx2aRJJrwCTJwIr70GgYHuee2wMPN6774LhQrBV19B8+Zmqq38JDkZhg4124MGQVSUvfWI5IZPh9fVq1fTsWNH2rdvT6VKlbjzzjtp27Yt69evt7s0ERGx0WuvmQuMACZMMBdmeWJO0gceMMMIoqJg61ZzIdNvv7n/OHZ5+WXYtcu8vyeftLsakdzx6fB63XXX8f3337Nz504AtmzZwqpVq2jXrp3NlYmIiF0WL4bHHzfbI0acDbGe0qiRuRDsssvMJP7XXmv+zO50hw+f7bkePx7Cw+2tRyS3fHqqrMGDBxMbG0vNmjXx9/cnLS2NZ599lnvvvfe8z0lKSiIpKSnzdlxcHAApKSmkpKR4vOaMY3jjWOIZakPnUxs6W07tt3Mn3HVXAGlpLu67L53hw9PwRjOXKwfLlkHHjv6sX+9H69YWX36ZxjXXWJ4/uIcMHOjPqVN+XH11Ol26uPc86nvQ+bzdhnk5jsuyLJ/9zps3bx6DBg1iypQp1KlTh82bN9O/f3+mTp1Kt27dsn1OTEwMozP+K/kvc+bMITQ01NMli4iIh5w5489TT7Xg4MEIatQ4wdixPxAU5N0LeE+f9mfChKvZurUkhQqlMmrUamrVOuHVGtxh27YSjBhxHS6XxXPPLadq1Xx6NZo4RmJiIl26dCE2NpbChQvn+FifDq/ly5dnyJAh9O3bN3PfuHHjeP/99/n111+zfU52Pa/ly5fn2LFjFzwZ7pCSksLixYtp06YNge66ckC8Sm3ofGpDZztf+/Xu7c+sWX5ER1usWZNq28VFiYlw++3+LFniR3i4xRdfpHHttT77q/QcKSlw5ZUB7NjhonfvNKZNc/9/APQ96HzebsO4uDgiIyNzFV59ethAYmIifn5Zh+X6+/vnOFVWcHAwwcHB5+wPDAz06jeQt48n7qc2dD61obP9u/3mzTNLl7pc8P77LsqXt69dixSBL76Ajh3hu+9c3HprAN98A9ddZ1tJeTJtGuzYASVKwPjx/gQG+nvsWPoedD5vtWFejuHTF2zdeuutPPvss3z11Vfs3buXBQsWMHXqVG677Ta7SxMRES/Zv9/M4QowbJhZEctuoaFmRarWreHUKbjpJjMrga/btctc5AZmerHixe2tR+Ri+HR4nTZtGnfeeSePPvootWrV4qmnnqJ3796MHTvW7tJERMQLLAsefhji4qBpUxg1yu6KzgoNNcvStm5tVve6+WYzK4GvSk+HBx80wx5atYKePe2uSOTi+PSwgYiICF588UVefPFFu0sREREbzJplVs0qVMhs53XJV08LCTE9sB06wPffmx7Y//3PBG1fM2MGLF9uQvebb4KfT3dfiZyfProiIuKTDh2CgQPN9tixUKOGvfWcT0YPbKtWEB8PN94Ia9bYXVVWv/0GTz9ttidNgipV7K1H5FIovIqIiE8aNMif2Fi4+moYMMDuanIWGmou4mrZ8myAXbvW7qqMpCS45x4ztOH66+HRR+2uSOTSKLyKiIjP2bKlJB9/7IefH7z+Ovh77oJ4twkLgy+/hObNzRjdtm1h3Tq7qzI9rps3Q2QkvPeehguI8+kjLCIiPiU5Gd54oy4AfftC/fo2F5QHYWHw1Vdm2qzYWGjTBtavt6+eDz+El1822+++C9HR9tUi4i4KryIi4lOmTfPj4MEISpWyGDPG7mryLjwcvv4arr32bID94Qfv17F+PXTvbrYHDYJ27bxfg4gnKLyKiIjPOHgQxo0zv5rGj0+jaFF767lYERHwzTcmwP7zj5lOa+FC7x3/0CGziMKZMya0TpjgvWOLeJrCq4iI+Iynn4aEBBc1apzgvvucs+RqdiIizDRft9xiQuRtt5kpqjzt6FETlg8fhtq1Ye5cZ4wZFskthVcREfEJ69aZoOVyWfTqtTVfXFgUGgoLFpgFAdLTzYILzzwDaWmeOd6JE2aYwq+/Qrly5gKyCywTL+I4+eBHg4iIOJ1lmXGZAF27WlStGmtvQW4UEGB6XIcNM7cnTID27U3QdKd9+6BFC9i6FaKizKIJlSu79xgivkDhVUREbPfVV2b1p+BgiInxULekjVwuGDcOPvjArMr1v/+ZWRS++849r79unZkPd/t2KFPGvG716u55bRFfo/AqIiK2Sk09u/pT//5QoYKt5XhUly6wejVUq2YuTmvTBnr3hr//vrjXS02FZ581F4b99RfUqwc//QR16ri3bhFfovAqIiK2mjULfvkFSpSAIUPsrsbz6tc3iwY89pi5PXMmVK0K48fnfiiBZZnpuK6+GoYPh5QUc0HYqlVQvrzHShfxCQqvIiJim4QEGDnSbI8YgWOnxsqrsDCYNs0MlWjUyCwpO2wYlC0LDzwAH39sZg34N8syIf/5581z2reHjRuhWDGzctYnn5gZDkTyuwC7CxARkYLrlVfgzz/NhUV9+thdjfc1bw5r15pZFqZMgS1bTBB97z1zf9Gipkc6Lc1MfZWcfPa5YWHwyCPw1FNQurQt5YvYQuFVRERsER8Pkyeb7VGjzMVaBZGfH3TtasbDrl1rLupatgy2bTMLHPzzz9nHBgVBy5am17VrVxNsRQoahVcREbHFyy/D8ePmqviuXe2uxn4ulxnDevXV5nZsLBw5Ys6Ry2WGFJQpYwKsSEGm8CoiIl4XGwvPPWe2R40yc6FKVkWKmC8RyUoXbImIiNe9+KL5c3jt2nD33XZXIyJOovAqIiJedeIETJ1qtmNiwN/f1nJExGEUXkVExKumToW4OKhbF+64w+5qRMRpFF5FRMRrjh2Dl14y26NHmyvtRUTyQj82RETEa6ZMgVOn4IoroFMnu6sRESdSeBUREa/46y+YPt1sjxljpn8SEckrhVcREfGKSZMgMRGuuspMsi8icjEUXkVExOMOH4bXXjPb6nUVkUuh8CoiIh43YQKcOQPXXgtt29pdjYg4WZ7XNNm7dy8rV65k7969JCYmUrJkSa644gqaNm1KoUKFPFGjiIg42IEDMHOm2Vavq4hcqlyH1zlz5vDyyy+zdu1aSpUqRdmyZQkJCeHEiRPs2rWLQoUK0bVrVwYPHkzFihU9WbOIiDjIs89CcjK0aAGtWtldjYg4Xa7Ca8OGDfHz86N79+589NFHVKhQIcv9SUlJrF69mnnz5tG4cWNeffVVOnfu7JGCRUTEOfbuhbfeMtvqdRURd8hVeB07diztc7g0NDg4mJYtW9KyZUvGjRvHnj173FagiIg419ixkJoKrVtD8+Z2VyMi+UGuwmtOwfW/IiMjiYyMvOiCREQkf/jjD3j3XbM9Zoy9tYhI/pHn2Qb8/f05evToOfuPHz+Ov7+/W4oSERHnGz0a0tLg5puhaVO7qxGR/CLP4dWyrGz3JyUlERQUdMkFiYiI8+3YAR98YLbHjrW3FhHJX3I928DLL78MgMvl4s033yQ8PDzzvrS0NFasWEHNmjXdX6GIiDjOqFFgWXD77dCokd3ViEh+kuvw+sILLwCm53XGjBlZhggEBQVRqVIlZsyY4f4KRUTEUTZtgo8/NjMLjB5tdzUikt/kOrxmzCDQqlUrPv30U4oVK+axokRExLlGjDD/3nsvXH65vbWISP6T5xW2li5d6ok6REQkH1i9Gr76Cvz9ISbG7mpEJD/KVXidOHEijz/+OGFhYRd87E8//cSxY8fyNL2WiIivsixz8dEvv8ChQ5CQAMWLQ+nScNVVULas3RX6loxe1+7d4bLLbC1FRPKpXIXXHTt2ULFiRTp37kyHDh1o3LgxJUuWBCA1NZUdO3awatUq3n//fY4cOcLs2bM9WrSIiKf99htMmwZffAH795//cVWrmouSevWCatW8V58vWroUvv8eAgPPhlgREXfLVXidPXs2W7du5ZVXXqFr167Exsbi7+9PcHAwiYmJAFxxxRX06tWLbt26ERwc7NGiRUQ85Y8/YNgwmD/f9LoCFCoEDRpAuXIQEQEnT8K+fbBlC+zaBVOmmK/27WHCBKhb19a3YAvLgmeeMdu9ekHFivbWIyL5V67HvBYvXpzXX3+dGTNmsHXrVvbu3cvp06eJjIykQYMGWlVLRBwtPR2mT4chQ+D0abOvQwfo3RtatYKQkHOfExsLS5bAm2/CN9+YsZ7ffAM9esDkyWZ4QUHxySewZg2EhZnwLyLiKblepODyyy/nvffew+VyUb9+fTp27Mg999xD69atFVxFxNH++cesAtWvnwmu118PW7fC559Du3bZB1eAIkXgtttMaP3tN+jc2YTgt94yV9l/841X34ZtkpNh6FCz/dRTUKaMvfWISP6W6/A6fvx4+vbtyx133MHx48c9WZOIiNfs2QPXXguLFkFoKLz2Gnz3Xd7/9H/ZZfDRR7BqFdSoAUeOmOA7YACkpHimdl8xc6YZblG6NDz5pN3ViEh+l+vw+uijj7JlyxZOnjxJnTp1WLhwoSfrEhHxuF9+gaZNzWwCZcvCDz9Anz5mcv2Lde21ZpL+/v3N7RdfhDZt4OhRd1Tse2Jjzy5EEBNjxgSLiHhSrsMrQOXKlVmyZAnDhw/njjvuoF69ejRs2DDLl7sdOnSI++67jxIlShAaGkqDBg3YsGGD248jIgXLH3/ADTfAX39BvXrw00/moix3CAmBF16ATz+F8HBYvhyaNIHff3fP6/uSyZPh2DHT2/zgg3ZXIyIFQZ4XKdi3bx+ffPIJxYsXp2PHjgQE5Pklcu3kyZNce+21tGrVim+++YZSpUqxa9cuihYt6rFjikj+d/CgCa5Hjpixqd9/D54Yun/bbbB2Ldx6q5mV4Npr4euvoXFj9x/LDvv2mZAOMGmSmSJLRMTT8pQ833jjDZ588klat27N9u3bM+d69ZRJkyZRvnx5Zs2albmvUqVKHj2miORvCQlmFoH9+6F6dTO+1ZPXnNaqZYYjtGsHGzdCy5amR7ZtW88d01sGDDAXuLVoYc6piIg35Dq83nTTTaxdu5bp06fzwAMPeLKmTAsXLuTGG2+kc+fOLF++nLJly/Loo4/y8MMPn/c5SUlJJCUlZd6Oi4sDICUlhRQvXDWRcQxvHEs8Q23ofOdrw/R0eOABfzZt8iMy0uLLL1MpXtzzF1QVLw6LF8Ndd/nz/fd+tG9vMW9eGh06WJ49sActWuRiwYIA/P0tXnghldRU9722vgedT23ofN5uw7wcx2VZVq5+erZp04ZZs2ZRrly5iy4srwoVKgTAwIED6dy5M2vXrqV///68/vrr5w3QMTExjM64euBf5syZQ2hoqEfrFRHfNn9+dT74oBYBAemMHv0Ddeqc8OrxU1JcvPRSQ1atKoe/fzpPP72Oq6/+06s1uENKih/9+rXi8OFwOnT4g549f7a7JBFxuMTERLp06UJsbCyFCxfO8bG5Dq92CAoKonHjxvz444+Z+5544gnWrVvH6tWrs31Odj2v5cuX59ixYxc8Ge6QkpLC4sWLadOmDYEaAOZIakPny64Nf/jBxQ03+JOe7mLGjFR69rTnR19qKvTo4c+HH/oREGAxd24aHTv67I/hbE2a5MeIEf5ERVls356Ku3+06nvQ+dSGzuftNoyLiyMyMjJX4dVzV1u5QZkyZahdu3aWfbVq1eKTTz4573OCg4OzXZ42MDDQq99A3j6euJ/a0Pky2vDECXjgATNs4P77oXdv+370BQbC+++Dnx/Mnevi3nsDmD8fOnWyraQ82b/fLIELMGWKixIlPPc9ou9B51MbOp+32jAvx8jTVFnedu211/Lbb79l2bdz504qatFsEckly4JeveDAAahWDV55xe6KICAAZs+Ge+81PbGdO8OCBXZXdWGWBY88AomJ0KwZdO1qd0UiUhD5dHgdMGAAa9asYfz48fzxxx/MmTOHmTNn0rdvX7tLExGH+PBD+OQTExjnzfOdSfT/G2DvusssR+vLZs82U30FB8Prr1/aYg4iIhfLp8PrlVdeyYIFC5g7dy6XX345Y8eO5cUXX6Sr/rsvIrlw9Cg89pjZHj4cGjWyt57/yq4H9osv7K4qe4cPn101LCbGTAEmImIHnx7zCnDLLbdwyy232F2GiDhQv37+HD9uVtAaOtTuarKXEWDT000v8R13mHlgfenHnmXBww/DP/+YBRaeesruikSkIPPpnlcRkYu1fn1pPvnED39/mDULgoLsruj8AgLMRVydO5s5Z++4w/x53le8+OLZ4QKzZpl6RUTsovAqIvnOmTPwxht1AbMKVMOGNheUCwEB8MEHJrgmJ5ulZb/91u6qYMMGGDzYbL/wgllOV0TETgqvIpLvPP+8H3/9FUZ0tMXIkXZXk3uBgTB3rgmuyclm+qz//c++ek6cgLvvNr3Bt90GffrYV4uISAaFVxHJV/buNZPoA0yalOYzswvkVmCgmRWhUydISjL/fvWV9+tISTHDGHbtgooV4c03NbuAiPgGhVcRyVcGDIAzZ1zUrfs3d93lrJWrMgQFmYu3OnY0QyA6doR33vHe8S3LzCywZAmEh5sZEIoX997xRURyovAqIvnG//4Hn30GAQEWDz+8zdE9hUFBMH8+dOsGaWnQowdMnGiCpaeNGwevvmp6Wj/4AOrW9fwxRURyS+FVRPKFtDR48kmz3bdvOhUqxNtbkBsEBpqr+zMumBo61Mxbm5LiuWM+9xyZ44Sffx46dPDcsURELobCq4jkC++8Az//DMWKwTPPpNtdjtu4XKbH9YUXzO1XX4UbboA//3TvcSwLnn0WBg0yt8eNM0MwRER8jcKriDheQsLZ3sLhw02AzW/69zfLxxYuDCtXwhVXuG8u2ORkMyxh+HBze8QIGDbMPa8tIuJuCq8i4ngvvGCWL61UCfr2tbsaz+nQAdauhdq1Tc9r+/Zm5avjxy/+NXfsgOuug3ffBX9/07M7Zoz7ahYRcTeFVxFxtKNHYdIksz1+vFkFKj+rUQPWrzd/0ne5zBRWVavClCmmBzq3YmNNSL3iCli3DooWhS+/hEce8VjpIiJuofAqIo42ZgycOgWNG5sJ9QuCkBCYOhWWLYP69U0QffppiI6Gxx+HVavMHLH/lZ5ugurQoWbu1lGjzJCBdu1g+3a46SavvxURkTzTCtUi4lh798Lrr5vtyZPBr4D9d7x5c7N86/vvw9ixZkGB6dPNV6FCZinXyEjTG33kiLn/30MMatUy41vvuUcLEIiIcyi8iohjjR0LqanQpg20amV3Nfbw9zdzwd5/P3z/Pbz9tvn377/N8IL/iogw56trV7N6V0EL/CLifAqvIuJIf/xhLjICXWAEJoS2aWO+LAt+/dWco+PH4fRpKFMGypWDevXMAggiIk6l8CoijjRmjFmYoF07aNLE7mp8i8tlhgTUqmV3JSIi7qc/GImI4/z6q1m2FGD0aHtrERER71J4FRHHGTPGXDnfsaOZZUBERAoOhVcRcZTt22HePLOtXlcRkYJH4VVEHGX0aHNB0p13mjlORUSkYFF4FRHH2LwZPv7YXJAUE2N3NSIiYgeFVxFxjIzAes89UKeOraWIiIhNFF5FxBHWr4fPPzfzmY4aZXc1IiJiF4VXEXGEjMB6331Qo4a9tYiIiH0UXkXE561ZA19/bZZCHTHC7mpERMROCq8i4vNGjjT/dusG1arZW4uIiNhL4VVEfNrKlbB4MQQEqNdVREQUXkXEx2X0uj74IFSqZGspIiLiAxReRcRnLV0Ky5ZBUBAMG2Z3NSIi4gsUXkXEJ1nW2WECvXpB+fL21iMiIr5B4VVEfNKiRfDDD1CoEAwdanc1IiLiKxReRcTnWBYMH262+/aF6Gh76xEREd+h8CoiPufzz82KWmFhMHiw3dWIiIgvUXgVEZ+Snn52rGv//lCypK3liIiIj1F4FRGf8tFHsH07FCkCTz5pdzUiIuJrFF5FxGekpsKoUWb7qaegWDF76xEREd8TYHcBIvldWhrs2weHDsGxY1C4MERGQvXqEBJid3W+5f33YedOKFEC+vWzuxoREfFFCq8iHpCeDl98AR9/DN9+a0LrfwUGwlVXQbt20KMHlCnj/Tp9SXIyjB5ttocMgYgIe+sRERHfpGEDIm6Ung7vvQd16kCnTqYn8dgxCA6GqlWhSRNzX4kSkJJi5jEdNsxMwN+5M+zYYfc7sM8bb8DevRAVBY8+anc1IiLiqxReRdxk9264/np44AH49VcoWhQGDjTLm8bHwx9/wOrV5mKkv/82t2fOhGuvNUMLPv4Y6taF3r2z76nNz+Lizva6Dh8OoaH21iMiIr5L4VXEDebMgXr1YPlyE7zGjzfjXJ9/Hlq0MEME/s3lMj2xDz8Mq1bBli1w222m53bmTNM7u3ChPe/FDlOmmEBfvbpZClZEROR8FF5FLoFlQUwMdO0KCQkmqG7bZpYzLVw4969Trx58+imsWAG1a8PRo9CxowlyZ854rHyfcPiwCfkAEyeeG/RFRET+TeFV5CKlp8ODD579c/fTT8OSJVClysW/ZrNmsGEDDBpkemffeMME4gMH3FOzLxo5Ek6fhmuuMeOERUREcuKo8DphwgRcLhf9+/e3uxQp4CwLHnkEZs0Cf3/zp/5Jk8DPDd9RhQrB5MlmloJixWDtWrjySti48dJf29ds327OIZihAy6XvfWIiIjvc0x4XbduHTNnzqRevXp2lyIFnGXBgAEmsPr5mRkFHn7Y/cdp29b0wtarB3/9ZXpgv//e/cexi2WZHub0dLjjDtPzKiIiciGOCK+nTp2ia9euvPHGGxTTkjtis2nT4KWXzPbbb8M993juWJUrm3GwrVrBqVNw883w4YeeO543LVxoepcDA2HCBLurERERp3DEIgV9+/alffv2tG7dmnHjxuX42KSkJJKSkjJvx8XFAZCSkkJKSopH68w4zr//FefJqQ0XL3YxYIA/4GLSpDS6dEnH000dGmqCXvfu/nzyiR/33mtx4kQaDz1kefbAHnT6NPTvHwC4GDAgjUqV3Hse9X3obGo/51MbOp+32zAvx/H58Dpv3jw2btzIunXrcvX4CRMmMDrjCpp/WbRoEaFenDxy8eLFXjuWeMZ/2/Dw4TCeeqoF6ekubrhhH9Wrb+brr71XT5cucPp0Xb7+ugqPPhrAtm2bufHGfd4rwI3mzq3B3r01iYxMpGHDJXz9dZpHjqPvQ2dT+zmf2tD5vNWGiYmJuX6sy7Isn+2+OXDgAI0bN2bRokXUr18fgJYtW9KgQQNefPHFbJ+TXc9r+fLlOXbsGIXzMnfRRUpJSWHx4sW0adOGQM3540jZtWFSEjRvHsCmTS6aNk1n0aI0goO9X5tlwdNP+/HSS/4AvPJKGg8/nO79Qi7Brl3QoEEASUku5s5N5Y473P8jSN+Hzqb2cz61ofN5uw3j4uKIjIwkNjb2gnnNp3teN2zYwNGjR2nUqFHmvrS0NFasWMH06dNJSkrC398/y3OCg4MJziZVBAYGevUbyNvHE/f7dxsOHgybNpllXT/6yI/wcPuGi7/wgpnhYOpU6NvXH39/f3r3tq2cPHvqKfOfgdat4e67Azw6w4C+D51N7ed8akPn81Yb5uUYPh1eb7jhBrZt25ZlX48ePahZsyaDBw8+J7iKeMLXX5vACGZap3Ll7K3H5YLnnjPbU6dCnz6mR7ZPH3vryo358+Grr8xFWtOmaWosERHJO58OrxEREVx++eVZ9oWFhVGiRIlz9ot4wsmT8NBDZvuJJ+DWW+2tJ8N/A+wjj5hpu3x5adW//4a+fc324MFQs6a99YiIiDM5YqosEbsMHAhHjkD16mbpUl+SEWAHDjS3e/eGN9+0t6acPPGECbB16sDw4XZXIyIiTuXTPa/ZWbZsmd0lSAHx7bcu3nnHhMRZsyAkxO6KzpURYNPT4cUXzWIJfn7Qs6fdlWX1yScwb56pbdYsbLnYTURE8gf1vIpk48wZf/r2NWOq+/f37dWfXC4zdOCJJ8zthx46u+SqL9i37+zQi8GDzVK3IiIiF0vhVSQbH39cnQMHXFSsCBdYF8MnuFym5/Wxx8zFWw8+CO++a3dVkJoKXbvCP//AVVdBNlMwi4iI5InCq8h//P47fPZZVcDMMuDFtS0uicsFL78Mjz5qAmyPHvDee/bWNGIE/PADFC4Mc+eaWQZEREQuhcKryL9YFgwc6E9qqj833phOp052V5Q3LhdMn25mH7As6NbNvh7YOXPOXuQ2cyZUqWJPHSIikr8ovIr8y+efw//+50dAQBpTp6Y5ch7SjADbu7cJsN27n51Wy1vWrj170djgwXD33d49voiI5F8KryL/7/Rpc3EWQKdOu7jsMlvLuSR+fvDqq2en0Ro0CJ580sxK4Gk7d0KHDmYVrVtvhWef9fwxRUSk4FB4Ffl/L75orowvX97izjt32l3OJfPzg+efhylTzO2pU+G++0xI95Tdu+H66+Gvv6B+fXj/fbOUrYiIiLsovIpgJs+fMMFsjx2bRqFCafYW5EZPPQWzZ0NAgLloqlkzE9Ld7bffTHA9dAhq14bFi82FWiIiIu6k8CoCjB0L8fHQsCHcc49ldzlud//98O23UKIEbNgAjRub8b3usmwZNG1qQvFll8F330HJku57fRERkQwKr1Lg/f47vPaa2Z4yxfy5PT+64QYTXBs2hGPHoFMnMxvBiRMX/5ppaeactW0LJ09CkyawahWUKeO2skVERLLIp7+mRXLvmWfMZPo332z+7J2fVawIP/4ITz9tZiWYPRuqVTPjYZOS8vZaW7bAddeZ10pJgbvugiVLoFQpz9QuIiICCq9SwK1ZAx9/bHpbJ0+2uxrvCA6GSZNg5Uq4/HLTY/rkkybYPvOMmS3AOs/IieRkMyTg1luhQQNz/goXhjffhHnzICTEq29FREQKoAC7CxCx09Ch5t/u3U2QK0iuvRY2b4Z33oFRo8yFVhMmmK+yZc0Y1jJloEgRM8xg3z4zJCA+3jzf5TK9rVOmQPnydr4TEREpSBRepcBautRcaBQUBDExdldjD39/ePBBeOAB+OILeP11c14OHTI90tkpVcqMl33ySahe3avlioiIKLxKwWRZMGKE2e7VSz2HgYFw++3mKzHRDAfYvBmOH4d//jGzFJQtC1dcYWYqyK8XtYmIiO9TeJUCafFi+OEHKFTo7NABMUJDzYVr+f3iNRERcSb1n0iB8+9e10cegehoe+sRERGR3FN4lQLn669h7VrTwzh4sN3ViIiISF4ovEqBYlkwcqTZfuwxKF3a3npEREQkbxRepUD5/HPYuBHCw2HQILurERERkbxSeJUCIz3dzGcK0K8fREbaW4+IiIjkncKrFBiffAJbt5oVoQYOtLsaERERuRgKr1IgpKWdXYhg4EAoXtzWckREROQiKbxKgfDhh7BjBxQrBv37212NiIiIXCyFV8n3UlNh9Giz/dRTUKSIvfWIiIjIxVN4lXxvzhzYudMscfr443ZXIyIiIpdC4VXytZSUs72ugwdDRIS99YiIiMilUXiVfG32bNi9G0qVgkcftbsaERERuVQKr5JvJSfD2LFme8gQCAuztx4RERG5dAqvkm+9/Tbs2wdlykCfPnZXIyIiIu6g8Cr50unTZ3tdn3kGQkLsrUdERETcQ+FV8qXXX4fDh6F8eXj4YburEREREXdReJV859QpmDDBbI8cCcHB9tYjIiIi7qPwKvnO9Olw9ChUrQrdutldjYiIiLiTwqvkK7GxMHmy2Y6JgcBAW8sRERERN1N4lXzlhRfg5EmoVQvuvdfuakRERMTdAuwuQHLn2DFITIS0NIiM1EpR2Tl+HKZONdtjxoC/v731iIiIiPspvPqouDj48ENYuBDWr4c//8x6f7ly0LAh3HYbdOgAxYvbU6cvmTwZ4uOhQQO4/Xa7qxERERFPUHj1MX//bcZqvvOO6Wn9t+Bg8PMzc5gePGi+Fi404zrvv9+sInXZZXZUbb8jR2DaNLM9dqw5TyIiIpL/6Fe8j0hPh5deMuHz1VdNcK1ZEyZOhB9/NNM/nTlj9p84AatWmT+N16sHKSlmNamaNaFXL3N/QTNqlAn1TZpA+/Z2VyMiIiKeovDqA44dM4Grf39ztfwVV8D338OOHTB4MDRtCmFhZx9frBhcey2MGAFbtphw2769CcBvvGFC7Ny5tr0dr9uxA956y2w/9xy4XPbWIyIiIp6j8GqzrVvN2NVvv4VCheC112DdOrj++tyHsKZN4csvYcUKqF3bDD3o0gV69ICEBM/W7wuGDDHB/bbbTKgXERGR/Evh1UZr1kCLFnDggBku8NNP0KfPxV8l36wZbNpk/oTu52fGzV55Jfz+u1vL9inLl8MXX5hzlrGqloiIiORfPh1eJ0yYwJVXXklERASlSpWiU6dO/Pbbb3aX5RbLl0Pr1vDPP3DNNbB2rRm/eqmCgswFX0uWQHQ0/PILXH01LFt26a/ta9LT4amnzHavXlCjhr31iIiIiOf5dHhdvnw5ffv2Zc2aNSxevJjU1FTatm1LgsP/Fr5lC9x6q/mTfuvWsGgRFC3q3mO0aAEbNpjgevIktGkDH3zg3mPY7cMPzTRi4eGmt1lERETyP5+eKuvbb7/NcnvWrFmUKlWKDRs20Lx5c5uqujT79sHNN5v5SFu0MH/yLlTIM8eKioKlS6FnT5g3z0ynFRsLjz7qmeN506lTMGiQ2R48GEqXtrceERER8Q6fDq//FRsbC0DxHGbkT0pKIikpKfN2XFwcACkpKaSkpHi2wP8/zr///bdTp6BduwCOHHFRu7bF/Pmp+Pubqa48JSDAjH0tUcKPV17xp29fOHEijcGD0z13UC+IifHj0CF/qlSx6Ncv1a3nMKc2FGdQGzqb2s/51IbO5+02zMtxXJZlWR6sxW0sy6Jjx46cPHmSlStXnvdxMTExjB49+pz9c+bMITQ01JMl5siyYOrURqxcWY5ixc4wZcpyIiPPePX4c+bUZP58MzD0jjt2ct99vzhyWqkDB8Lp378VaWl+DBu2hiuv/MvukkREROQSJCYm0qVLF2JjYylcuHCOj3VMeO3bty9fffUVq1atoly5cud9XHY9r+XLl+fYsWMXPBnukJKSwuLFi2nTpg2BgYGZ+6dP92PgQH8CAiy++y6Na66x57RPnerHkCFmOoPBg9MYMybdUQHWsuDmm/1ZssSPdu3S+eyzNLcf43xtKM6hNnQ2tZ/zqQ2dz9ttGBcXR2RkZK7CqyOGDTz++OMsXLiQFStW5BhcAYKDgwkODj5nf2BgoFe/gf59vPXr4emnzf7nnnPRooV9p33wYAgJgX79YNIkfwoV8icmxrZy8mz+fDOTQnAwTJvmR2Cg56459PZnRtxPbehsaj/nUxs6n7faMC/H8OnZBizL4rHHHuPTTz9lyZIlVK5c2e6S8iwx0VwolZoKd94JTzxhd0WmhqlTzfbo0TB2rL315FZsLAwYYLYHD4YqVeytR0RERLzPp3te+/bty5w5c/j888+JiIjgzz//BKBIkSKEhITYXF3uDBkCv/4KZcrA66/7ztKlAwaYQP300zBypLmwa+hQu6vK2ZNPwqFDUK2aOa8iIiJS8Ph0z+trr71GbGwsLVu2pEyZMplfH374od2l5cp338G0aWZ71izIYZIEWwwaBOPHm+1nnoEpU+ytJyeLFsFbb5nw//bbZuiDiIiIFDw+3fPqkGvJsnX6tFnqFcy8qjfeaG895zN0qOmBHTnS9ML6+ZkeTl8SGwsPP2y2H3vMLIMrIiIiBZNP97w62cSJfuzaZZZonTDB7mpyNmKECa9gllt9/nl76/k3y4LevWH/fqhc2ffPpYiIiHiWwqsHHDgQznPPmVP78svghRm6LllMTNYA+9xztpaT6e23zTKw/v4wZw6EhdldkYiIiNhJ4dXNLAtmzKhPSoqL9u3h9tvtrih3XC4z88CoUeb2oEH2B9iff4bHHzfbzz4LTZrYW4+IiIjYT+HVzd57z8XPP0cSEmIxfbrvzC6QWzExWQPs5Mn21HH8OHToYMYOt2ljahERERFReHWj2FgyV68aPjydSpXsredi/TvADh5svrx57VxKipkTd/duM851zhxzIZmIiIiIIoEbFS4ML7+cRoMGR+nfP93uci5JTMzZi6MmT4bu3U2o9LT0dOjVC5Ytg/Bw+OILiIz0/HFFRETEGXx6qiyncbngzjstQkNXExjYzu5yLtmQIRAVBQ89BLNnw9Gj8NFHEBHhmeNZlpkK6513zAVa8+ZBnTqeOZaIiIg4k3peJUfdu8Pnn5tFAb791lw0tXOn+4+Tng79+sFrr5n/BMyeDe3bu/84IiIi4mwKr3JB7dvD0qVmztodO+DKK2H+fPe9fmKiGeOasRrZW29Bly7ue30RERHJPxReJVeuvho2bIDrroO4OLjrLhMwT5y4tNfduROaN4cFCyAoCN5/H3r0cE/NIiIikv8ovEquRUXBkiUwfLgZkzp3LlSvbhZiSE7O22ulpMALL0D9+iYUlygB330HXbt6pnYRERHJHxReJU8CA2HsWPjxR3Mx1fHjZqxq9eowcSL89VfOz09IgNdfhxo1YOBAOHMGWrc2AbZZM++8BxEREXEuzTYgF+Wqq2DzZjM+ddQo2LcPhg6FYcOgYUMzFKBiRdOjmpAAhw7BmjVmCqyMXtqSJWHcOHj4Yect5iAiIiL2UHiVixYQAL17w/33mym0XnsN1q6F9evN1/lUqWJ6ax96CEJDvVeviIiIOJ/Cq1yy0FAzpVb37nDgACxfbnpZ//rLDCsIC4OyZc3Qgptvhpo11dMqIiIiF0fhVdyqfHm47z7zJSIiIuJuumBLRERERBxD4VVEREREHEPhVUREREQcQ+FVRERERBxD4VVEREREHEPhVUREREQcQ+FVRERERBxD4VVEREREHEPhVUREREQcQ+FVRERERBxD4VVEREREHCPA7gI8zbIsAOLi4rxyvJSUFBITE4mLiyMwMNArxxT3Uhs6n9rQ2dR+zqc2dD5vt2FGTsvIbTnJ9+E1Pj4egPLly9tciYiIiIjkJD4+niJFiuT4GJeVm4jrYOnp6Rw+fJiIiAhcLpfHjxcXF0f58uU5cOAAhQsX9vjxxP3Uhs6nNnQ2tZ/zqQ2dz9ttaFkW8fHxREdH4+eX86jWfN/z6ufnR7ly5bx+3MKFC+sb1uHUhs6nNnQ2tZ/zqQ2dz5tteKEe1wy6YEtEREREHEPhVUREREQcQ+HVzYKDgxk1ahTBwcF2lyIXSW3ofGpDZ1P7OZ/a0Pl8uQ3z/QVbIiIiIpJ/qOdVRERERBxD4VVEREREHEPhVUREREQcQ+FVRERERBxD4dXNXn31VSpXrkyhQoVo1KgRK1eutLskAVasWMGtt95KdHQ0LpeLzz77LMv9lmURExNDdHQ0ISEhtGzZkp9//jnLY5KSknj88ceJjIwkLCyMDh06cPDgQS++i4JrwoQJXHnllURERFCqVCk6derEb7/9luUxakPf9tprr1GvXr3MCc+bNm3KN998k3m/2s9ZJkyYgMvlon///pn71Ia+LyYmBpfLleUrKioq836ntKHCqxt9+OGH9O/fn2HDhrFp0yaaNWvGzTffzP79++0urcBLSEigfv36TJ8+Pdv7J0+ezNSpU5k+fTrr1q0jKiqKNm3aEB8fn/mY/v37s2DBAubNm8eqVas4deoUt9xyC2lpad56GwXW8uXL6du3L2vWrGHx4sWkpqbStm1bEhISMh+jNvRt5cqVY+LEiaxfv57169dz/fXX07Fjx8xfjGo/51i3bh0zZ86kXr16WfarDZ2hTp06HDlyJPNr27Ztmfc5pg0tcZurrrrK6tOnT5Z9NWvWtIYMGWJTRZIdwFqwYEHm7fT0dCsqKsqaOHFi5r4zZ85YRYoUsWbMmGFZlmX9888/VmBgoDVv3rzMxxw6dMjy8/Ozvv32W6/VLsbRo0ctwFq+fLllWWpDpypWrJj15ptvqv0cJD4+3rrsssusxYsXWy1atLD69etnWZa+B51i1KhRVv369bO9z0ltqJ5XN0lOTmbDhg20bds2y/62bdvy448/2lSV5MaePXv4888/s7RdcHAwLVq0yGy7DRs2kJKSkuUx0dHRXH755WpfG8TGxgJQvHhxQG3oNGlpacybN4+EhASaNm2q9nOQvn370r59e1q3bp1lv9rQOX7//Xeio6OpXLky99xzD7t37wac1YYBXjtSPnfs2DHS0tIoXbp0lv2lS5fmzz//tKkqyY2M9smu7fbt25f5mKCgIIoVK3bOY9S+3mVZFgMHDuS6667j8ssvB9SGTrFt2zaaNm3KmTNnCA8PZ8GCBdSuXTvzl57az7fNmzePjRs3sm7dunPu0/egM1x99dXMnj2b6tWr89dffzFu3DiuueYafv75Z0e1ocKrm7lcriy3Lcs6Z5/4potpO7Wv9z322GNs3bqVVatWnXOf2tC31ahRg82bN/PPP//wySef0K1bN5YvX555v9rPdx04cIB+/fqxaNEiChUqdN7HqQ19280335y5XbduXZo2bUrVqlV59913adKkCeCMNtSwATeJjIzE39//nP95HD169Jz/xYhvybjSMqe2i4qKIjk5mZMnT573MeJ5jz/+OAsXLmTp0qWUK1cuc7/a0BmCgoKoVq0ajRs3ZsKECdSvX5+XXnpJ7ecAGzZs4OjRozRq1IiAgAACAgJYvnw5L7/8MgEBAZltoDZ0lrCwMOrWrcvvv//uqO9DhVc3CQoKolGjRixevDjL/sWLF3PNNdfYVJXkRuXKlYmKisrSdsnJySxfvjyz7Ro1akRgYGCWxxw5coTt27erfb3Asiwee+wxPv30U5YsWULlypWz3K82dCbLskhKSlL7OcANN9zAtm3b2Lx5c+ZX48aN6dq1K5s3b6ZKlSpqQwdKSkril19+oUyZMs76PvTapWEFwLx586zAwEDrrbfesnbs2GH179/fCgsLs/bu3Wt3aQVefHy8tWnTJmvTpk0WYE2dOtXatGmTtW/fPsuyLGvixIlWkSJFrE8//dTatm2bde+991plypSx4uLiMl+jT58+Vrly5azvvvvO2rhxo3X99ddb9evXt1JTU+16WwXGI488YhUpUsRatmyZdeTIkcyvxMTEzMeoDX3b0KFDrRUrVlh79uyxtm7daj3zzDOWn5+ftWjRIsuy1H5O9O/ZBixLbegETz75pLVs2TJr9+7d1po1a6xbbrnFioiIyMwpTmlDhVc3e+WVV6yKFStaQUFBVsOGDTOn8hF7LV261ALO+erWrZtlWWaKkFGjRllRUVFWcHCw1bx5c2vbtm1ZXuP06dPWY489ZhUvXtwKCQmxbrnlFmv//v02vJuCJ7u2A6xZs2ZlPkZt6Nt69uyZ+bOxZMmS1g033JAZXC1L7edE/w2vakPfd/fdd1tlypSxAgMDrejoaOv222+3fv7558z7ndKGLsuyLO/184qIiIiIXDyNeRURERERx1B4FRERERHHUHgVEREREcdQeBURERERx1B4FRERERHHUHgVEREREcdQeBURERERx1B4FRG5RDExMTRo0MC2448YMYJevXrl+JiWLVvSv39/tx0zKSmJChUqsGHDBre9pohIbii8iojkwOVy5fjVvXt3nnrqKb7//ntb6vvrr7946aWXeOaZZ7x63ODgYJ566ikGDx7s1eOKiATYXYCIiC87cuRI5vaHH37IyJEj+e233zL3hYSEEB4eTnh4uB3l8dZbb9G0aVMqVark9WN37dqVQYMG8csvv1CrVi2vH19ECib1vIqI5CAqKirzq0iRIrhcrnP2/XfYQPfu3enUqRPjx4+ndOnSFC1alNGjR5OamsqgQYMoXrw45cqV4+23385yrEOHDnH33XdTrFgxSpQoQceOHdm7d2+O9c2bN48OHTpk2ZeQkMADDzxAeHg4ZcqU4fnnnz/nee+//z6NGzcmIiKCqKgounTpwtGjRwGwLItq1arx3HPPZXnO9u3b8fPzY9euXQCUKFGCa665hrlz5+b2dIqIXDKFVxERD1iyZAmHDx9mxYoVTJ06lZiYGG655RaKFSvGTz/9RJ8+fejTpw8HDhwAIDExkVatWhEeHs6KFStYtWoV4eHh3HTTTSQnJ2d7jJMnT7J9+3YaN26cZf+gQYNYunQpCxYsYNGiRSxbtuycsanJycmMHTuWLVu28Nlnn7Fnzx66d+8OmKESPXv2ZNasWVme8/bbb9OsWTOqVq2aue+qq65i5cqVl3q6RERyzxIRkVyZNWuWVaRIkXP2jxo1yqpfv37m7W7dulkVK1a00tLSMvfVqFHDatasWebt1NRUKywszJo7d65lWZb11ltvWTVq1LDS09MzH5OUlGSFhIRY//vf/7KtZ9OmTRZg7d+/P3NffHy8FRQUZM2bNy9z3/Hjx62QkBCrX79+531va9eutQArPj7esizLOnz4sOXv72/99NNPlmVZVnJyslWyZEnrnXfeyfK8l156yapUqdJ5X1dExN3U8yoi4gF16tTBz+/sj9jSpUtTt27dzNv+/v6UKFEi80/1GzZs4I8//iAiIiJzDG3x4sU5c+ZM5p/p/+v06dMAFCpUKHPfrl27SE5OpmnTppn7ihcvTo0aNbI8d9OmTXTs2JGKFSsSERFBy5YtAdi/fz8AZcqUoX379plDG7788kvOnDlD586ds7xOSEgIiYmJeTo3IiKXQhdsiYh4QGBgYJbbLpcr233p6ekApKen06hRIz744INzXqtkyZLZHiMyMhIwwwcyHmNZ1gVrS0hIoG3btrRt25b333+fkiVLsn//fm688cYsQxQeeugh7r//fl544QVmzZrF3XffTWhoaJbXOnHixHnrExHxBIVXEREf0LBhQz788ENKlSpF4cKFc/WcqlWrUrhwYXbs2EH16tUBqFatGoGBgaxZs4YKFSoAJtzu3LmTFi1aAPDrr79y7NgxJk6cSPny5QFYv379Oa/frl07wsLCeO211/jmm29YsWLFOY/Zvn07V1xxxUW9ZxGRi6FhAyIiPqBr165ERkbSsWNHVq5cyZ49e1i+fDn9+vXj4MGD2T7Hz8+P1q1bs2rVqsx94eHhPPjggwwaNIjvv/+e7du307179yxDGCpUqEBQUBDTpk1j9+7dLFy4kLFjx57z+v7+/nTv3p2hQ4dSrVq1LEMRMqxcuZK2bdu64QyIiOSOwquIiA8IDQ1lxYoVVKhQgdtvv51atWrRs2dPTp8+nWNPbK9evZg3b17m8AOAKVOm0Lx5czp06EDr1q257rrraNSoUeb9JUuW5J133mH+/PnUrl2biRMnnjMtVoYHH3yQ5ORkevbsec59q1evJjY2ljvvvPMS3rmISN64rNwMkBIREZ9kWRZNmjShf//+3HvvvW5//R9++IGWLVty8OBBSpcuneW+zp07c8UVV3h9dS8RKdjU8yoi4mAul4uZM2eSmprq1tdNSkrijz/+YMSIEdx1113nBNekpCTq16/PgAED3HpcEZELUc+riIic45133uHBBx+kQYMGLFy4kLJly9pdkogIoPAqIiIiIg6iYQMiIiIi4hgKryIiIiLiGAqvIiIiIuIYCq8iIiIi4hgKryIiIiLiGAqvIiIiIuIYCq8iIiIi4hgKryIiIiLiGAqvIiIiIuIY/wfX+wGqgdkBzAAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -139,7 +130,7 @@
     "plt.plot(time, y2, color='blue')\n",
     "plt.ylabel('Y(t)')\n",
     "plt.xlabel('Time (day)')\n",
-    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€)$')"
+    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€)$');"
    ]
   },
   {
@@ -166,7 +157,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -186,7 +177,7 @@
     "plt.plot(time, y3, color='g')\n",
     "plt.ylabel('Y(t)')\n",
     "plt.xlabel('Time')\n",
-    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t)$')"
+    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t)$');"
    ]
   },
   {
@@ -213,7 +204,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x400 with 1 Axes>"
       ]
@@ -233,7 +224,7 @@
     "plt.plot(time, y4, color='red')\n",
     "plt.ylabel('Y(t)')\n",
     "plt.xlabel('Time')\n",
-    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t) + N(0,0.5^2)$')"
+    "plt.title('$Y(t) = 1 + 0.02 t + sin(0.02Ï€t + 0.2Ï€) + 5 u_{300}(t) + N(0,0.5^2)$');"
    ]
   }
  ],
@@ -253,7 +244,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
diff --git a/book/time_series/exercise3.ipynb b/book/time_series/exercise3.ipynb
index 9fa20c445fd0e599b669329e16c2ae59d8b78baf..86820769aa4d05a6689a234e3c86c780ba0ca329 100644
--- a/book/time_series/exercise3.ipynb
+++ b/book/time_series/exercise3.ipynb
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,18 +133,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "y0: True value is: 1 ,  Estimated value is: 1.0108670424240747\n",
-      "r: True value is: 0.02 ,  Estimated value is: 0.020019382911012365\n",
-      "Am: True value is: 1 ,  Estimated value is: 1.0971057283981764\n",
-      "phi0: True value is: 0.6283185307179586 ,  Estimated value is: 0.6899387295464887\n",
-      "Ok: True value is: 5 ,  Estimated value is: 4.929702224035138\n"
+      "y0: True value is: 1 ,  Estimated value is: 1.0108670424241462\n",
+      "r: True value is: 0.02 ,  Estimated value is: 0.020019382911012216\n",
+      "Am: True value is: 1 ,  Estimated value is: 1.0971057283981793\n",
+      "phi0: True value is: 0.6283185307179586 ,  Estimated value is: 0.6899387295464864\n",
+      "Ok: True value is: 5 ,  Estimated value is: 4.929702224035133\n"
      ]
     }
    ],
@@ -174,22 +174,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1fd586d5a50>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -213,7 +203,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "mude2",
+   "display_name": "TAMude",
    "language": "python",
    "name": "python3"
   },
@@ -227,7 +217,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
diff --git a/book/time_series/figs/signal_noise.png b/book/time_series/figs/signal_noise.png
index 1fdd2a299baa8f5a305c25e5bae2cfb07dfed00d..271e640cd8353b7b086c79d5dc7a4fd9ddad9c0b 100644
Binary files a/book/time_series/figs/signal_noise.png and b/book/time_series/figs/signal_noise.png differ
diff --git a/book/time_series/figs/tsa_cover.png b/book/time_series/figs/tsa_cover.png
new file mode 100644
index 0000000000000000000000000000000000000000..934a85cd2c199821c3cfe6aacaaa23c90294cc24
Binary files /dev/null and b/book/time_series/figs/tsa_cover.png differ
diff --git a/book/time_series/fit_BLUE.ipynb b/book/time_series/fit_BLUE.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3f3da0ea28374dc22fa52c96b723e7ec6b02a0de
--- /dev/null
+++ b/book/time_series/fit_BLUE.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Time series modelling\n",
+    "We have a time series with a clear trend, seasonality and jump. The researcher knows that there is jump present at $t=270$ due to a change in the data collection method. First we will load the data and plot it. After that, we will use the PSD to identify the frequency of the seasonality and the trend. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import necessary libraries\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Import the data\n",
+    "data = np.loadtxt(\"y_values.csv\", delimiter=\",\")\n",
+    "t = np.arange(0, len(data), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the data\n",
+    "plt.figure(figsize=(12, 3))\n",
+    "plt.plot(t, data, label=\"y_values\")\n",
+    "plt.xlabel(\"Time\")\n",
+    "plt.ylabel(\"y_values\")\n",
+    "plt.title(\"y_values over time\")\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now inspect the time series and identify the jump at $t=270$. We can also see that there is a clear trend and seasonality. We will now use the PSD to identify the frequency of the seasonality and the trend.\n",
+    "\n",
+    "remember that to compute a PSD we need to make our time series stationary. In the cell below I have shown how not to do it. without stationarity, the PSD is not useful. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Ng7q6Olx//fWorq5GVlYWRo4ciRUrVuDcc8+NyM+LBnoqZycIgiCIqCJb+DQ1NeGee+5BWVkZzjzzTCxcuBCzZs3CL7/8AgAoKCjAypUrMXLkyIgs9K233lL0epme7ZjCUl0U8SEIgiCI6CDb4/PAAw9gw4YNuPLKK7Fz505ccMEFcDgc2LBhAzZt2oShQ4fikUceieRauxysgaGFIj4EQRAEERVkR3y+/fZbfPDBB5g6dSrmzZuHoqIirF69GhMmTAAAPPvss5gzZ07EFtoVoYgPQRAEQUQX2RGf2tpaDBo0CADQq1cvGAwGFBUViV8vLi5GfX19+FfYhdGJ09njPy1HEARBEF0B2cLH6XRCrVaLj9VqtcfsrkQZDBpP6KicvVPK61vRbnXEehkEQRBEF0FRVdebb74pdka22+1YsmQJ8vLyAAAtLS3hX10Xxx3xIeHjjzX763Dj4s248owiPDM3MqZ5giAIIrmQLXyKi4uxaNEi8XFhYSHee+89n9cQ8qEGhh2z5OejAIB9NSSqCYIgiPAgW/gcPXo0gstITlgDQytFfHyoamrHuoOCZ6zNYo/xagiCIIiuQlAjK4jwoKMGhgH5aHMlWCumVhI+BEEQRJiQFfF55ZVXZB/w7rvvDnoxyYaOytn94nDy+HhLpfi41UzChyAIgggPsoTPiy++6PG4vr4eJpNJnIbe1NSE1NRUFBQUkPBRAEV8/PPTwXqcaDZDp1HBanei1WoHz/NUOUgQBEGEjKxU15EjR8R/Tz31FEaNGoW9e/eisbERjY2N2Lt3L8aMGYMnnngi0uvtUrgbGFIfHyn/2yxEe+aO6QUA4HnARCXtBEEQRBhQ7PH561//in/+858YPHiw+NzgwYPx4osv4tFHHw3r4ro6YsTH4UyI2WLRoKHVglV7agEAN5SWQOUK8pDPhyAIgggHioVPdXU17Hbfi5DD4UBtbW1YFpUssIgPQFEfxmdlx2F38hhVlI0hhZlI1wvZ2Bby+RAEQRBhQLHwmT59Om677TaUlZWJz23duhV//OMfMWPGjLAurquj8xA+5PMBgGXbTwAArjhDGIeSYdACoJJ2giAIIjwoFj5vv/02CgsLMW7cOOj1euj1eowfPx7du3fHm2++GYk1dllYqgsggzOjvsUCABjRKwsAkKYXxqRQqosgCIIIB4pGVgBAfn4+li9fjoMHD2Lv3r0AgCFDhogDTAn5qFUcVBzg5CniwzDbBBOzQSsIHkp1EQRBEOFEsfBhDBw4EAMHDgznWpISnUYFs80JC0V8AABm1z4YtEI0LN2V6qKID0EQBBEOZKW6nnnmGbS3t8s64KZNm/DNN9+EtKhkQktNDEWcTl5M+bGIT4Yr4kMeH4IgCCIcyBI+e/bsQXFxMf70pz/h22+/RX19vfg1u92OHTt24F//+hdKS0txxRVXICMjI2IL7mroJSXtyY406sWED3l8CIIgiHAiK9X17rvv4rfffsOrr76Kq6++GkajEWq1Gnq9HiaTCQAwevRo/OEPf8C8efNgMBgiuuiuhBjxsVM5O/P3AIDBJQjT9UKqizw+BEEQRDiQ7fE5/fTTsWjRIrzxxhvYsWMHjh07hvb2duTl5WHUqFHIy8uL5Dq7LDqK+IiY7YLw0ag4aNTM4yO8RVsttpitiyAIgug6KDY3q1QqjBo1CqNGjYrAcpIPFvGhcnbAbPP09wBSjw+NrCAIgiBCR3EfHyK8kLnZjbuU3f22TKNydoIgCCKMkPCJMTSh3Q0TPnqNO+JDqS6CIAginJDwiTE6tTCFkyI+0lSX+21JqS6CIAginJDwiTFkbnbDzM1Sjw9LdVE5O0EQBBEOFAufxYsXiyXsROiQudmNxeYrfGhkBUEQBBFOFAufhx56CIWFhbj55pvxyy+/RGJNSQWb0E4RnwCpLvL4EARBEGFEsfCpqqrCO++8g4aGBkybNg1DhgzBs88+i5qamkisr8uj1bAGhiR8xKoujW/Ex2xzwk7ikCAIgggRxcJHo9Hgd7/7HZYtW4bKykrccssteP/991FcXIw5c+Zg2bJlcDrpAiUXvVjOTp2bvSezA26PD0AGZ4IgCCJ0QjI3d+/eHZMnT8bEiROhUqmwc+dO3HDDDejfvz/Wrl0bpiV2bbSU6hJhk9n1klSXTqMSDeAtlO4iCIIgQiQo4VNbW4sXXngBp512GqZNmwaj0Yivv/4aR44cQVVVFS6//HLccMMN4V5rl0SrEcrZydzsP+IDuEvaqbKLIAiCCBXFwueiiy5CUVERlixZgltuuQVVVVX48MMPMWPGDABAWloa7r//flRWVoZ9sV0RnVq4yFPER2Ju1ngKH9bEsI2ED0EQBBEiimd1FRQU4Mcff8TEiRMDviY/Px9HjhwJaWHJAov4kLnZ/8gKAEjTUUk7QRAEER4UR3ymTp2KMWPG+DxvtVrx7rvvAgA4jkOfPn1CX10SoCePj4jFTwNDQDq2goQPQRAEERqKhc+NN96I5uZmn+dbWlpw4403hmVRyQQNKXXjr48PIB1bQcKHIAiCCA3FwofneXAc5/P88ePHkZWVFZZFJRPuIaVUzh7I3EwT2gmCIIhwIdvjM3r0aHAcB47jMH36dGg07m91OBw4cuQILrjggogssitD5exu/DUwBCjVRRAEQYQP2cLnkksuAQBs374d559/PtLT08Wv6XQ6lJSUYO7cuWFfYFeHOje7aXcJH32AVFcrRXwIgiCIEJEtfB577DEAQElJCa644goYDIaILSqZIHOzG7fHxyviwzw+VhI+BEEQRGgoLmenxoThRSxnJ+FDHh+CIAgi4sgSPrm5uThw4ADy8vKQk5Pj19zMaGxsDNvikgHWwNBCqS5xDwwaz1QXeXwIgiCIcCFL+Lz44ovIyMgQ/78j4UMoQ6umiA+j05EVFPEhCIIgQkSW8JGmt+bNmxeptXTIggUL8Nlnn2Hfvn1ISUlBaWkpnn32WQwePNjntTzPY9asWfjuu+/w+eefi8bseMRdzk7CJ5DwoYgPQRAEES4U9/EpKyvDzp07xcfLli3DJZdcgocffhhWqzWsi5Py448/4o477sDGjRuxatUq2Gw2nHfeeWhra/N57UsvvZQwUSkdNTAUCdTAMI2GlBIEQRBhQrG5+bbbbsNDDz2EESNG4PDhw7jiiitw6aWX4uOPP4bJZMJLL70UgWUC3333ncfjJUuWoKCgAFu3bsWUKVPE57dv346FCxdiy5Yt6NGjR6fHtVgssFgs4mOj0QgAsNlssNlsYVo9xGN5H1MF4WJvtTvD+vMSDZ7nYXaNrFDDcy8MrgBQq9ne6R4F2mci/NBeRwfa5+hA+xwdIrnPco+pWPgcOHAAo0aNAgB8/PHHmDp1Kj744AP8/PPPuPLKKyMmfLxhYzNyc3PF50wmE66++mq89tprKCwslHWcBQsW4PHHH/d5fuXKlUhNTQ3PYiWsWrXK4/GxVgDQoLmlDcuXLw/7z0sU7E6A54W3409rVyNV8s5ssgCABi1mK775ZjnkBPO895mIHLTX0YH2OTrQPkeHSOyzyWSS9TrFwofneTidQpTi+++/x4UXXggAKCoqQkNDg9LDBYXT6cS9996LSZMmYfjw4eLzf/7zn1FaWoqLL75Y9rHmz5+P++67T3xsNBpRVFSE8847D5mZmWFbs81mw6pVq3DuuedCq9WKz++tbsE/dm6ARqfHrFnTwvbzEg1juw3YtAYAcNHM86GX+HxazDY8VrYGDp7DjPM8v+ZNoH0mwg/tdXSgfY4OtM/RIZL7zDI2naFY+IwbNw5PPvkkZsyYgR9//BGvv/46AODIkSPo3r270sMFxR133IFdu3Zh/fr14nNffvklVq9ejW3btik6ll6vh16v93leq9VG5M3vfdxUgw4AYHXwSf1hc7QLaS6OA9JS9B4erSy1+21qdnJIl7FPkfr7Eb7QXkcH2ufoQPscHSKxz3KPp9jc/NJLL6GsrAx33nknHnnkEQwYMAAA8Mknn6C0tFTp4RRz55134uuvv8aaNWvQu3dv8fnVq1ejvLwc2dnZ0Gg04iyxuXPnYtq0aRFfV7CQuVmAGZv1GpWPMV2t4pCmE6I8VNJOEARBhILiiM/IkSM9qroYzz//PNTqwCmIUOF5HnfddRc+//xzrF27Fn379vX4+kMPPYQ//OEPHs+NGDECL774Ii666KKIrStUqJxdgBmbvUvZGekGDdqsDqrsIgiCIEJCsfBhWK1W1NXViX4fRnFxcciL8scdd9yBDz74AMuWLUNGRgZqamoAAFlZWUhJSUFhYaFfQ3NxcbGPSIonWANDu5OH08lDpUqMMvxwE2gyO0MoabeQ8CEIgiBCIqiqrptvvhm//PKLx/M8z4PjODgcjrAtTgrzEnmnrRYvXhyzporhQCcZz2B1OGFQRS5qFs8E6uHDoO7NBEEQRDhQLHxuvPFGaDQafP311+jRo0fUGgXyPB+V74k2WrX7Qm9zOAOmero6gbo2M6h7M0EQBBEOFAuf7du3Y+vWrRgyZEgk1pN06DyET/wLtUjBhE+gUvU0HQkfgiAIInQUV3UNGzYsav16kgGVioPG5etJZoOzOcBkdgZFfAiCIIhwoFj4PPvss/h//+//Ye3atTh58iSMRqPHP0I5Wipp7zTVRR4fgiAIIhwoTnXNmDEDADB9+nSP5yNtbu7K6DQqtNscsCRxxMfiEj4p5PEhCIIgIohi4bNmzZpIrCOpoYhP51VdNKGdIAiCCAeKhc/UqVMjsY6kRk9NDCnVRRAEQUQFxR4fAPjpp59w7bXXorS0FFVVVQCA9957z2N2FiEf1sQwqSM+Mjo3AxTxIQiCIEJDsfD59NNPcf755yMlJQVlZWWwWCwAgObmZjz99NNhX2AyQGMrJLO6AqS60vXC8DkSPgRBEEQoKBY+Tz75JP79739j0aJFHpNQJ02ahLKysrAuLllgHh9LMkd8Oh1Z4RpSSsKHIAiCCAHFwmf//v2YMmWKz/NZWVloamoKx5qSDmbcNVmStyLObW4O5PFxRXzI40MQBEGEgGLhU1hYiEOHDvk8v379evTr1y8si0o2ROOuxRbjlcQOt8eHGhgSBEEQkUOx8Lnllltwzz33YNOmTeA4DidOnMD777+PBx54AH/84x8jscYuD4v4tCRxNMPSSVUXS3W1We1wOpN3tAdBEAQRGorL2R966CE4nU5Mnz4dJpMJU6ZMgV6vxwMPPIC77rorEmvs8lA0Q850diHVxfOAyeZAul7xW5cgCIIglAsfjuPwyCOP4MEHH8ShQ4fQ2tqKYcOGIT09PRLrSwqoR03n5maDVgW1ioPDyaPVbCfhQxAEQQRFUFcPnudhNBrRvXt3DBs2LNxrSjrYRbzNmsTCp5M+PhzHIV2vQXO7LakjYwRBEERoKPL41NTU4Prrr0dOTg66d++OgoIC5OTk4KabbkJtbW2k1tjlYamuZPb4dNbHB3ALRBI+BEEQRLDIjvgYjUaUlpaitbUVN954I4YMGQKe57Fnzx58+OGHWL9+PcrKyijlFQR0Qe98ZAUg2ackFogEQRBEaMgWPi+//DLUajV2796N/Px8j689+uijmDRpEl555RU8/PDDYV9kVyfDQBd00dwcwOMDUBNDgiAIInRkp7q++eYbPPzwwz6iBwAKCgowf/58fPXVV2FdXLJA4xik5eyB35Jio8ck9kIR4cNsc6DZlLy9swgiWZEtfA4cOIDS0tKAXy8tLcX+/fvDsqhkgzw+nZubASBN5zKBJ7FAJEKnurkdz363D2cu+AETFnyPWqM51ksiCCKKKPL4ZGdnB/x6dnY2jEZjONaUdCS7x8fh5GFzCE0JOxQ+4j4l72gPpTSZrHjk81246PQeuGB4j1gvJ6YYzTb87Ytd+HpHNeySJpj7alrQPdMQw5URBBFNZEd8eJ6HShX45RzHgeepo24wZEgaGCbjHjJjM9BZqksQRZTqks9/Nx7DNzur8dL3B2O9lJjz3a4afLH9BOxOHmf2y0X//DQAQH2LJcYrI4jwYrU7seeEMSmvJ3KQHfHheR6DBg0Cx3EBv04EB4v4OJw8zDYnUnSBox5dEQ/h06G5ObkjY0rheR5fbD8BADjS0Aank4dK5f/zmwycbLUCAC4e1RMvXzka93/0G8rr21DXQqkuomvx54+245sd1Vg87wycPaQg1suJO2QLn8WLF0dyHUlNqk4NjhPGMbRYbMknfOxCRZdOrerwwiw2eiThI4s91UYcqmsFAFjsTlQ1taMoNzXGq4odze2CkTk3TQcAyM/QA6CID9G12FZxCt/sqAYA/Hq0kYSPH2QLnxtuuCGS60hqOI5Duk6DFosdrWY7CjJivaLowiI+HTUvBASBCABtVvL4yGGZK9rDKK9vJeEDICtFqKIk4UN0RV5Y6S4yKnfd+BCeKJ7OTkSGZB5UKqd5IeBOdVHEp3OcTh5fuoRPpuu9VV7fFpGfteHwSdz23pa4r44ymj2FT4FL+NSR8Akap5NHi5laAsQLPx9qwM+HToqPy+tJ+PiDhE+ckMyVXZ1NZmdQqks+vx5tRI3RjAyDBlecUQQgMidBiwO4/+OdWLG7Fu/8cjTsxw8nxgARnwYSPkFzxwdlOOOp71Hd3B7rpSQ9PM/j+RVCtOfcYd0BAMdOmmBzOGO5rLiEhE+ckJ7E3ZstnUxmZ1A5u3yWba8CAMwcXohhPTMBAIcjIHx+qFKh3mUaXnewPuzHDycs1ZVpoFRXuPj1SCPMNid2V1Erk1jz/d46bK9sgkGrwlOXDEeqTg27k0dFoynWS4s7SPjECUkd8ZHRvBAA0nRUzi4Hi92B5TtrAACXjOqF/vnC/Dylqa4TTe0dvh+rm81YXe02o++qMqKhtWMRUV7fisoYnYhFj0+qZ6qrxWJHO/nGFGO1O3GyTRC9lC6MLU4nj4Uub8+Nk/qiINOAfq52DeTz8YWET5yQkdQeH3mpLvL4yOPH/fVobrehIEOPCf26oW+eu18Nu/h3xt5qI6Y9vxbXvLkJTqf/VhX/WHUQNieHcX2yMayHEFVaf7Ah4DHrWyyY88/1uPyNDQGPGUm8U13peo34nqOoj3LqJSKX9i+27D5hxL6aFqTp1LhtSj8ACPqGJxmQVdV13333yT7gP/7xj6AXk8ywiE8yjq2Qa25O5qiYEpb9Jpia55zeE2oVhwyDFt0z9ag1WnC4vhWji3M6PcaSn4/C6nDit8om/LCvTvQMMHYcb8IXvwklsw/PHIyVexuwp9qIdQfqccnoXn6PuXpfLdqsDrRZHWhos6AgI3rdknmeh9H12WLCh+M45GfoUdnYjvpWM4q7JW/FWzBIzezUCym2sL9F/4J0ZKcK7RrcwociPt7IEj7btm2TdbBAzQ2JzknmQaUs4qPvxOPDytnNNiccTh7qJG7GFwink8e6A4LXZvZI94iK/vnpqDVaUF7f1qnwaTbZsOy3KvHxq2sOYcbQAvHzzfM8nvxmLwBgXJ4TI3plwWwH/v1jOdYdbADP837PBav21In/X3WqParCp9Vih8MVZWIeHwAoyDCgsrEddUaKWCilzkP40P7FkpNtwv53c/WoAkj4dIQs4bNmzZpIryPpSWZzs1nGZHbAneoCgDar3eMCRggcqGtBi9mOVJ0aI3plic/3z0/HL+UnZZ0EP95aCbPNiX55aahqasdvlU34+dBJTB6YBwB455ej+PVII/QaFS4sFt6vY0tykKJVo6HVgr3VLaKhmtFudWD9Ibf5+fipdlmRp3DBUnw6tcrjfZaf7jI4d+JNInypNVKqK15ocBUYdHO9nwGgf4Hb4xPoZiRZIY9PnJCRxGkcueZmvUYFjSvKQz4f/2w5egoAMLo4Gxq1++PN5lJ1VtnldPJ4f1MFAOAPZ/XDVeOLAQCvrhFmfZVVnMJTy4Voz/3nDkSO6zyr16gxsX83AP6ru34+1CBG9gCgqim65c9iRVeK1uMCQJVdwSNNb/nbv693nMDdH24j43gUOCkKH3fEp6RbGjgOMJrtojAiBGR3bpayZcsWfPTRR6ioqIDV6rmhn332WVgWlmykJaDHZ1dVMyx2J8b2Ce3OXa65meM4pOrUMJrtaPMqaT/ZasH+2haMK8oM8N3JwZajjQCAcX1yPZ7vJ9PouP5QA440tCFDr8HFo3qiud2G9zcdw8bDjVi1pxZ/W7YLNgeP2SN6YN7EYnz77W7xe6cMzMPqfXVYd6Aet0/t73Hc7/fWAgDUKg4OJ4+qU9EVPsZ25u/xPOWJTQwp1aUY74iPd1Th5e8P4mBdKy4e1RPTh3b3dwgiTLBUV74k4mPQqlGUk4qKRhPK61tFkU8EEfFZunQpSktLsXfvXnz++eew2WzYvXs3Vq9ejaysrM4PQPjF3bk5MbqgOpw8rl60EVct2ogmU2h3E3L7+ACBmxj+5dMduHrRJpRVNIW0lkRnyzEh4jOuxFOM9i8QhM+xk20dNjR7b+MxAMDcsb2RptegZ3YKLh3dGwBw23tbUN1sRr+8NDwzd4RP6HzKoHxhDUdPebQccDp5fL9X8PdccFohgNhFfJixmSFGfCjVpRipudnqcHpUDPI8j+MucSu3kpAIHn8RH8Ad6SWfjyeKhc/TTz+NF198EV999RV0Oh1efvll7Nu3D5dffjmKi4sjscakINFSXSdbLTCa7bDandhzIrTmZXKruoDAJe1HTwq9YQ4lcelmTbMZx0+1Q8XBxz/TI9OAFK0aNgcfsI9OVVM7fnBFZq49s4/4/B+n9YeKA5w8kKJV4/VrxyLDj7+qb14aemWnwOpwYuNhd9v83443oaHVgnS9BpeOESq+oh/xcae6pFCqK3i8o2RSg/Mpkw3trs81CZ/Iw/pndUvzjOqIBue65D0v+kOx8CkvL8fs2bMBADqdDm1tbeA4Dn/+85/xn//8J+wLTBYSzdwsDXPvqQ5V+MhLdQFAKhM+Xr6BJpNwck3mC9iWY0Kaa2iPTDEyxlCpOHdDswDi8INNx+DkgdL+3TDAFSECgJK8NFxxRhFUHLDg0hEYXOh/ii7HcWLUZ90Bdz8fluaaOjgfJa6eQsdPmcDz0evlEyjiwyrLqBxbObWuPdOqhcif9LN3QhLRY2nGRMVij3+PEmsk6R3xYZ9jacSn1WLHit01YpVjMqJY+OTk5KClpQUA0KtXL+zatQsA0NTUBJOJWmMHS3qCjWOQhrlDFj4yzc0AkK53TWiXRHx4nhfv6JM5ZcGMzeMCeK7Y3Z8/g7PDyePTrUIJuzTaw3jykhHY/MiMgD16GFMHCZVfX++oRlmFsJ7vXWXs5w7tjl7ZKQAE4RrNSID3gFKGOK+r1RqTpoqJitnmEG82BnUXhLBUPB6XRPSMCTzEdO3+Oox4bCX+/uXuuH1/OJ08Gl3CJy/dK+LjR/jc9UEZbntvKz7aUhm9RcYZioXPlClTsGrVKgDAZZddhnvuuQe33HILrrrqKkyfPj3sC0wW3MInMU4StZKT3N7qlpCOxao+9HJSXTrflGC7zQGry7dS35K81Qss4jOuJNfv1/t1kO//9Yh7qOn0oQU+X1erOI9S2UCcNTAfvXNS0NBqwdzXf8H9H/2G/bUtUKs4TBucD4NWjTzXXenxKKa7AkV8uqXrwHGC8DsVolctmWDRHZ1GhYGui6s04lPlEfFJjHOaP8oqmmB1OLHkl6P467JdcSl+mtptYvQmJ9Xb4yP8baqa2tFudWDT4ZNYs1+outx8pDEq66tqascDH/+GXVXNUfl5clAsfF599VVceeWVAIBHHnkE9913H2prazF37ly89dZbYV9gssBGVphtzoSYpitNdR2qa4HVHvyaza7vNWg6fzv6MzdLIwehRHycTh4PfboDb68/EvQxOsNotuGdX46GbAj3ptViF71W3sZmRkct7L90NSycNbxHp40kOyJNr8FXd07G3DG9wfPAp2XHAQBnlOSIHWV75QgdkqNpcA4kfLRqFXJd66ImfPJhEd/umXoUZLrShZJzQlUXifgw/yEAvL+pIi7Fz0nXOS8rRQud1zk0N02HnFQteB443NCK51zT2wFhzEU0ePTznfhk63Es+eVoVH6eHBQJH7vdjq+//hpqtXBiVKlUeOihh/Dll19i4cKFyMmJXkOyroZHc74EMDhLu7baHHxIVQNKzM2pLNUl8fhIhU9DCBevPdVGLN1ciWe+3ReSkOuIpb9W4LEvd+Pe/20P63G3VzTByQO9slPQIyvF72uY8DnkamjGkA41vXh0z5DXkpOmw8LLT8e7N41H7xxhLb+TpMh6u9Jd0TQ4e09ml0IGZ+WwG5/uGQZ3SwCPiI/b9pDIHh8WjR7eKxMcJ4ifx77c3cl3RZeGABVdDPa5/8+6w9h67BR0rv5eB+taIt5jaUO5O8IUT9c1RcJHo9Hg9ttvh9lMRsBwo5V0lE2EXj5Sjw8gDLUMFkuIVV3MawAAda1WBOuZZdVOVocTB2o7Tt+V17fir1/s8hCAcmB3xWv312ND+clOXi2fzax/T4BoDyBUXXGcIAKYJwBwDzXtnqnHhL7dwramKYPyserPU/HN3ZNx+bgi8fleLjEUzVRXoKougIRPMDA/T/dMg9/9O9Hk/lzIifjEq9GWVabNHtETCy87HRwntHw4FEcTz1kPn7w0/6loJnyWbRdm+N00uS/y0nVw8sC+mshFfXiexzPf7RMfm+KokaXiVNf48eOxffv2CCylc9atW4eLLroIPXv2BMdx+OKLLwK+9vbbbwfHcXjppZeitr5QCWVel9nmwDVvbsTTrq66kYbd8ZW4BjuGUtKupKorXddxqstqd6I9yM+X9EK843jH+eg3fzqC9zYew7Pf7e/wdd60SfrbPPPdvqArm/62bBemL1yL1fuEiqmtYv8e//4eAEjRqdHTFQ3aICk3Z0NNLxrZM+zzz1J0apzWM8uj5w8zOEujApEmUKoLcDd9o1SXfNjnvyBTLwofqbnZw+PTifDZc8KICU9/jwc//i0CKw0NJnxStCpcOqY3xrs+X+xGIx5gPXzyMgJEfFyjKwAg06DBH6f2x7CeQs+9SKa7vttVg98qm8TH7bYEFj5/+tOfcN999+HVV1/Fhg0bsGPHDo9/kaStrQ2nn346XnvttQ5f9/nnn2Pjxo3o2TP0sH00yTAE38unrOIUfj50Eot+OuwTjYkE7CQ3bbBghN3rdeew/mADvnRdUDtDSVWXv3L2ZpPnidUYpH3m+Cn3hXjH8aYOX8siPct3VqNFgYdB2nH6t8omrNhdo2yRAGwOJ5b+Wony+jbctGQL7l26TaygClTRxZg5XGgg+PBnO3G0oQ2tFju+3yOIp4tHdVyxFS7cwieaqS7PyexS8jO7ZsSH54WeTZFoG1BndEd8CrwiPiar3SOi2FGqq8lkxW3/3YKGViu+21UT1RYHcjC7zjMprgHJLKLKbjTigZMBevgwWMQHAG6f1h9ZqVoMd83S230iMoZjm8OJ511+otN7CyLLHEfCR/HICmZsvvvuu8XnOI4T25U7HJH75WbOnImZM2d2+JqqqircddddWLFihdhvqCMsFgssFvcJz2gULuA2mw02W/hMeexYHR0zzfXhamozK/7Z5a7UDM8DX20/jhsm+pYkB2JnVTMMWrVYndEZNodTzCufNSAXS345ij0njLBareA4Dk0mG256ZzOsdicGdEvBwO4dH5edXDTgO/292cSBlnar+NrGNk+h12LjgvrbVTS6Tb+/VTZ1eIz6VuFnttsc+KLsOK48o7esn9FiFvatd04Kjp9qx3Pf7cPUAbkec7U6Y39NC6wOJzQqDk6exxeuEHaGQYO+uYYO133v9P7YeqwR2yqbccu7m3HN+CJY7E70y0vF4IIURfsm5z3tj+4ZgvioOtUe1s9YIHieR3O7sO+pWt/1dksV1lNrjM56lBLsPn9aVoWHPt+N+RcMwk2TSsK6pppmQbTmpWqQYxDOW0azHS0msxg55TjhfGQ022CxWKHyiiY6nDzu+qAMlY3C61ssdtQ2mzwmjEcTf/vMOpBrVcLzo3oLgmHL0ca4ea+wm9CcFI3fNQ3pnga9RoXcNB2uPaM3bDYbhnQXokC7qpoj8nt8uLkShxvakJumxR+n9MXtH2xHm8XucV2NxM+Ve0zFwufIkchVvISK0+nEddddhwcffBCnnXaarO9ZsGABHn/8cZ/nV65cidTU1HAvUWwF4A9zqwqACus3boHpkLI7n9VHhe8FgPd/2of8U54GvAYzoFcDGV43vGY78MgWNfRq4ImxDsi5/p6yAIAGao5H475foYIap0w2LF32LbJ0wLpqDla7cDL895c/YXqvjn+XFpMaAIdfN6xHRSdbfqCBA6BGZXU9li9fDgAoq3D/7gDQbO14nwOxr1JYBwDsrzXii6+WQxcgCFVV737tm6t3I7NeXrSzslpY66ScVnzdosLhBhMef3cFJnaX//feXC/sQZ80Jy7u48CH5WpUt3MoSbFixXffdvr9vysAymvUOFjXhse/3guAw2BDC779tvPv9YfSvRYCABqcMtnw+VfLoVdYRGayAxtqOYzN45EtY/yQ1QHYHMKpbtO6NTB4nfWOu95T+49VY/ny48oWE0WU7vMX5cJ77fst+1DYvCesaymvFt7/R/b+Bk0VDw2nhp3n8PFXK1DXLuxnnp5HvZkDzwOff/0tvMak4ZsKFX6qUkGr4qHhgHYHh6Vff4++/vtjRg3pPlfXCb/nnh3boT6+DSbXe/foSRP+t2y5z/k0Fuxx/Z2rjx7A8uX+U+8PDAcMaivWfL8CgHA9ADTYc6IZX329XNZ5Xy48D/yjTNi3s/PNOLBjCwANThlbxXM2ENw5ujPk9hJULHyOHTuG0tJSaDSe32q32/HLL7+gTx/5kYZw8+yzz0Kj0XhEozpj/vz5uO+++8THRqMRRUVFOO+885CZGb6BlzabDatWrcK5554Lrdb/p+XLU9twyFiPAUNHYJbMCIL4ve9vA6oF9/yRFg6jJ52DHllCmenBulb8v9c3ojg3BcvvmuTxfeX1bbBv/hl2O9B/7GQM69H577y9sgko+xXdM1NwyUVT8MaRn3Govg09h52BqYPy8ca/NgAQIlA1qjzMmnVGh8f7f5u/B+DEudOnoSinY+WTeqAeSw5ugyEjE7NmTQQAbPpqD1DlvmC12NDhPvuD53k8XLYagMM1noFDyahSjCrK9vv6+Vt/ACBEqo61cug/5qyAHY2lvFmxETAaMb10HPoPNuHpb/djTX0q/nb9WdDKPPvs+G4/cOgYSocV448XDsXNdifW7K/H2D7ZPg3MAjFkbBOueWszWPT5vt9PQUm3tI6/yQs57+lAPL1zNYxmO4aPn9JpRNCbN9YdwZebDyKloDeenDWs09fXGs3Ar+ugVnH43UUzfWaMdTvSiHcPboFTl4ZZsyYrWks0CHaf339rM4BTSMstwKxZY8K6pke3rQZgx0UzpqB/fhpe2LcOx5vMGD6uFPtqW4B9ezG8Tz5+Lm+E1e7EmVPOFlOcAPDDvjqs3LAdALDg0pH4tKwKGw43oseg0zGrkyaZkcLfPr9++BegtRWTzhyPyQME4//bx37Gwbo2dBs0DjP89LyKNu9W/Qo0NmHKhDG44DR5w2B5nsdLe9egxWzHwHFnYYiMc5dcqpvNaNq4DhoVh8evPw/HGk34x64NgEaHWbPODum80RksY9MZioXP2WefjerqahQUeP7Bm5ubcfbZZ0c01dURW7duxcsvv4yysjKfE1tH6PV66PW+FwutVhv2P0pnx81MEUK87Xan4p99zBUuTtOp0WZ1YMWeetwypR8A4J9rDsNid+JQfRtUao2HgbXN5i7b3lPThtOLO6/qOWkS/sYFmQZotVoM65mFQ/Vt2F9nQo8cE/ZUt4hTuLdWNKHd4b+MGBA+gBZX6Xh6ir7T3zsrVRBzJqt7j1oswvdr1RxsDh5GK6f479dksor+mwl9u2HD4ZPYW9uGM/rl+7zWZLWLFQqTB+Rh/aEGfLq9Go9dFNhYzGDepKxUPW4Y0h0vfn8ItS0W1Lba0TdPnvDYVytUlAzvne36PYELRykTyuP75ePJS4bjL5/uxPi+uRhYmK3o+6UE81nplZMKY7URta02DOut7HvLG4S7uvL6Nlk/12QXUgGZBg10Ot80SmG2sO/1rdaIfObDhdJ9rnCdE5rN9rD+XiarXaw87ZWbBq1Wi4JMA443mdHYbketq4loUW4aMqta0NBqgckGjzUs2VABALhhYh/8flwxyiqbseFwI443WWL+N5DuM+sxlpGiE58bV5KLg3Vt2H7ciJkjYyPSpDS6PI7ds1IV7d2wHpnYdKQR+2rbMKKo83OXXA6fFPxPffPSkJ5qQEa7sIftVs/rWiSusXKPpzjAxbw83pw8eRJpacruGMPJTz/9hLq6OhQXF0Oj0UCj0eDYsWO4//77UVJSErN1KUHs3qywnN3h5FHhGtJ5rcvb8/XOagCCSffbXYKBlufhY8SVloJ3ZuhluEtZBcE41BUl2lttxMdbhMjL+ad1R//8NDicPNYfbPB/IEAUPYDccnbhNa1+qrqYcDAGkTpmvoT8DD3O6CucBH6r9G/8Y1UUOo0KN0/uCwD4fFuVrJk+Jpe4StNroNeoJSMT5BlreZ4XO2UPlRGd64grzijG8rvPwn+uGxvScYKB3f0fD8LgfKShzeO/ndFRRRfgLmdvMdvjyoAZCmabAzUuA7K3+T9UWEuGVJ1aPGdJS9pZf6ae2SnIdOW3vCu7WFXYrBE9ALirQ+X+TZWy+WgjrnhjA7YorMZq99NqY2wf4fywJU4MzuKA0gB9fAIxvFdkKrsOum7M2CgTZgxvtznixrwuO+Jz6aWXAhCMzPPmzfOIkjgcDuzYsQOlpaXhX6FMrrvuOsyYMcPjufPPPx/XXXcdbrzxxhitShnioFKF87pONLXD6nAKF+JJfbFo3WH8VtmEykYTXlh5wOO1TSab2EEX8CwF3x7gQu8NO/F1d3VsHeaqENhxvFk8wV02rgg/H2xAef0RrNlXJ57gvJFeaAwyOgazkRUmqfBxdUEeWJCBA7WtQQofQTj2zknBSNcJYWdVk9/XinNx0nSYMigfPbIMqG42Y9WeWlw4suNKQlbOnuo6GeSl61DRaJLdeLHWaEFjmxVqFSeeWEKB/e2iDWtsGEwTw6MnhYvjyTYrmk02ZKV2fJfHLvz+evgAQiRIr1HBYneivsWCotzwe/uiTUWj2+sQ7lEcrGq0IEMv3gS7h71axGq9XtkpYqTXe2zFSa+LNUuzsr9tuPnPusPYdKQRt723FV/fPTlgk09v2r2qugB35eTO482w2B0hdToPFbPNIUbfAvXxCcTwXpGp7NrvKrTxFj6AcKMbu91yIzvik5WVhaysLPA8j4yMDPFxVlYWCgsLceutt+K///1vJNeK1tZWbN++XewjdOTIEWzfvh0VFRXo1q0bhg8f7vFPq9WisLAQgwcPjui6wkWw87rYXVKf3FQUZBpwZj8hXfX4V7ux7kA9NCoOGa5jN7UHjvgcqJXXybNWUsoKAEN7CG/wikYTmkxCI7wpA/Nx9hAhHbr2QH3ANu+sh4+Kc0957og0STk7OyYTb2wSsdGqvBcNi/j0zknFSFf55aG6Vr/dRlnDsNx0HdQqDr8fK6SZ/re546F/PM+Lx2N/a+bJkRvx2VMtnKT656fJipDFK8GWtDeZrB7v2cMNnTeSCzSglMFxnKQXTdcoaT8qiZw0t9vCOmaB7REbVQG4Iz51RnfEp1dOiig2jZIottXuFB+zEmwWrT3W0HH5vd3VXFRJ5MDucGKjq1noyTYr/vR+mezO7Oz8lCL5rPXploq8dB2sDmfM50+xmzCtmhOja3I5zdXLZ88JY1jfHwdF4SOcj6V7Fy9NDGXv1OLFiwEAJSUlePDBByNS8dQZW7Zswdlnny0+ZqbkG264AUuWLIn6esJNsH18mPBhJ48LR/bEL+Un8f1eYSr2FWcUYXtlE3afMPrc/UmFkMPJY091sxjKDUQtO/G5TnYFGQbkpevEEve5Y3pDreIwriQHqTo16lss2FNtFEOrUqTjKuR4s9Iloz1MNgfS9Rrxd2Am2VBSXb2yU1CQaUBhpgE1RjP2VBtxhldTQLFFvOukPXdMb/xz9SH8fKgBVrvTZ14Ow2J3gp1fWD+iPJYiaJV3Vx6uNFescXdvVtbE0DsVcqShDaOLO+5d1NxB12ZGfoYex0+1d5lePsdOuvfVyQtpvM4iY3LxvvEB3OeCE83tYoqtd3YKMl3nNGnEh52D1CpOFKNFuangOKGk/WSbNaBJ/+HPd+KjLcfx1g3jMH2oPCPvzqpmtFjsyNBrwHHAtoomPL18L/4+p+PKX7vDKQ4/ll68OY7DmOIcrNxTiy1HT3V6vowkJyXnIiXeVgDol5cGg1aFNqsDR0+2oV++siIDfzidPA64Ul0DXREftYqDTqMSmsvaHMjQKbYWhx3FHp/rr78eVVVVPs8fPHgQR48eDceaAjJt2jTwPO/zL5DoOXr0KO69996IrimcsIu60pEV3sLnguGFooFZr1HhrnMGItt10vPO9zd7CaFAvhYpdX5OfNILMYuA6DVqTBqQBwBYu7/O77GUNC8UXqcC82abLHY4nbx4Uh1YIHzQTHbOwzskB3fER7ggj3BFffx1cD7pNRunODfVVQkGNLUHFjBSQZuqZakuveuYMiM+rny8nOq7eKZXkPO6pBd0ADjsZ+CqN515fABImvB1jXE83imjjt6XnfH9nlq8uvog7C4RwCI+3TPc4qTA5ffbVdUMJw/o1CrkpeslER/JPD3Xez0nVSf29jFo3V3Fjwbw+ZTXt+LjrYKHcFtFk+z1/3xI8BhOHpiHf1w+CgCw5JejeHfDUdQZzQGjR2bJOSTFq69FvDQybGgLzt8DABq1CkMKhfPIrjD5fKqa2tFuc0CnVom+LcAtHCM9G0wuioXPvHnz8Msvv/g8v2nTJsybNy8ca0pa3Kmu4IRPiUv45KbpcNZAQXBcd2YfFGYZkO2qGPOeCs6iJUwYyTE4+7vjYxfiM0pyPO4cznZ1dmaD6rwRx1XImMwOCHdbzOfTarGj1WoXoyh9uqWK6TK5QoIh9fgAEH0+/vaDHZuJFpWKE31Tp9oCh5uYsTlVpxZP+PmuE5b8VJdL+MTImxMu2D7XtVhkmcIZ7L3OhL0cM2xHA0oZXW1el7dAPBWCwfnRL3bhhZUHxFlP/j7/+ekGj5/TI9sAlYoT91zqJRQ9cl4X65I84UJ59KT/KOBraw6Jc/iUpEjXu4RP6YA8zBjWHXec3R8A8LdluzH+6R8w7G8rcOE/f8JOr7SV9CKt9zo/sSjP1mOnYmrYdd+EKfP3MESfT1Uz9lYbcdt7WzDq/1Zio2SkjRL21wgR6X75aR5NWZnwiZfiAcXCZ9u2bZg0aZLP82eeeWbMZnh1FURzs8KID7u7k/Zhefp3I/D4nNPwwPmCv4kJG2+PDzshTXZFZjqbUWWxO8STG6vqAoDrS0swa0QhHrvIM3w8bbBQDr6t4hROtfnedSqZzM5wDyp1iBEsg1YFg1Ytdn2VmzoCBO9NlcTjA7gjPjv97Ac7cedKOsyy/e3ISOo2NrtDvW6PT+frbbPYxb91oqe6ctN04my26ib5URb2+491pbcOyxA+xg7GVTDYhbteoWCOV465upCz7EewBmebwymmrv619hAcTt5tbs70jfgwWESP7bl0bAW7WOd6dWjuwwzOfv6mFSdNovAC5EcK260OlB1rAuA+x9137mDMKy1B75wUqDih2mhXlRFfbK/2+F6zOKfLNw0/vFcmdBoVTrZZAwq1aCDehAXZ7Xq4y+fzwaYKzHz5J6zYXYsmkw0fdeJXbLPYccPbv+LGxb96DJg9UCcIH++eZixiFi8eH8XCh+M4tLT4Tq5ubm6OWQ+frkKGjCGlTSarh2q22p1imkbaB6ZndgpuKC0RBYUofLzu/NhjFiE63NDmcXfmDavo0mlUHheSXtkp+Nc1Y318PD2zUzCkMANOHlh30Dfqw34XvSLh4y5p905jeM8NkoOx3Y4W156LEZ/e2QCE/fAuxW1oY3l198kmV4z4dCB8XD8jTdKqOE9BOfu+mhbwvPA7ym1UGK9wHBeUwZldFJlx/mhDW6fGTFmprky3OTfRsdqdojAY4Iq+BlvSLv0clde34btdNT5VnYDwWZBqA/a39VfOfrLNf5Sir0v4HPFT2cVEV6HrZ8p9z2w+2girw4meWQYx9aJWcfj7nNOw/i/nYN8TM/HHaUIEyPu8Kw4o9dO+Xa9Ri1FhpSXy4cS9l8EJH2ZwZue/0cXZAIDNxwL/Tk4nj3v/tx0/HqjHmv312FbhTvcdqPGs6GKIqa5EjfhMmTIFCxYs8BA5DocDCxYswOTJ8df1NJEQL+gBIj7Vze04c8EPuPW9reJzladMcDh5pGjVHhEYbwKlutw9cNJRlCucrPxFORjSHj5yzXRskOlPfvr5KJnMzmARH5PVLgo39vuJKQsFd+6VrjRXXrpeFIq5aTpRBHlXbninugC4U10dXGBY88I0fxEfGUKtq6S5GL1c0TW5Bmee58XU1lkD86BRcWiX9KsJhFGG8CnMUnZBjWeOnzLByQsXG3YBCjbi4z3w+NU1h/ymujRqlceNADOv+ytndw/V9E51uSq7vIRPVVM7Pi0TvD1/u0jo1F1jNIueo474uVw450wakOf3fKXTqEQx5R2NEEvZA9yUjXX5fH49Ejvh4+7hE9yN0Gk9M3HV+GJcPKonvr3nLLx703ioOKCysR01zf4/V8+u2IdVrsHGALBqr/v/D3j18GGIvXwSNeLz7LPPYvXq1Rg8eDBuvPFG3HjjjRg8eDDWrVuH559/PhJrTBrEVJfV7vcudneVEWabE+sO1It3vkcl/p6OhEhWgFQXE0LZqVoxyvFbBz4f1nise4Yh4Gu8Gevqe+Gv9JP5O+T08GFIPT7ed/NMSCiJ+HgbmxkjernLPaV4m5sBIDet81SXyV/Ex3WMNquj05PCXpfwSfQ0F8Md8ZGX6moy2cQy6P756SjOldf0Tk7Ehx2rIkLTzKMJ8/f06ZYaMNIrF/Z575+fhjSdGnurjaKAL8jwvNjmS84J7oiPbzm7uxLJS/i4IjJHvUra/722HDYHj9L+3XDBaYXQqoWu8LUyPuPM2MyKLPyRKqZh/Ed8At2UTR0opPG/31sLmwwRFgkC7aVcVCoOCy4dgZevHI2hPTKRYdCKN1ab/USyPtpSiTd+PAwAmD1S6M3GRJDDyeNQPRM+nhVi7oiPMhtHpFAsfIYNG4YdO3bg8ssvR11dHVpaWnD99ddj3759GD58eCTWmDSwVBfPC6Xa3kijGN+4OjO7K7o6bi+QneJ7AnQ6efGikJ2ixem9Axt6Gf7u9jqD9fkpr2/16Z/BxkT4CycHwsPjwy5qqSzVpdzjw+7yvYVPv3x2B+qOSPA879fjkyMj1cVC6VKPT7peIxonO0t3dZWKLkaBwq7VLAXSI8uAFJ1aTO125vNhaZaO+pz0zkkBxwl3/XL8VvEM80H16ZYqvi+9I71yYRHeQd0zcO2Z7jmMGXqN+Dlk5EuEkCh8/JSzs/RMrld6hpW0t1rs4t+grsUs9se665yBUKk4MTp3opPo3Kk2q9iVuHRA4FE87giyV8Sng1QXAIzvm4u8dB1OmWzYUB6cGThUWE+xcKa+x7HO1F7CZ+uxU3jk850AgLvPGYBnLh0BrZrD4fo2lNe34tjJNljtThi0Kp+Zi+6IT2wEojdBzWTt2bMnnn76aXzzzTf45JNP8Le//Q25ubHrZdBVMGhVYrWKv3SXNIrxzQ5P4dPZgMkc10Va6t9psbgrojJT3BGfjgzO7A7Q28zYEb1c/TxsDh4H6zz9YQdcza6UDMhkEROT1S6W6YYW8RGETS8v4cOiAMckXXBbLHaxt0c3SadUtr+NHUV8XCdWaS8ijuPca+5AADicPPbVdK2ID4t2ya3AO+r1XmfC9EgnJe1yIj56jRo9XGJe2vU4EWFCvaRbWsCiBrmwdEf3TANuPquv2KPK3+dfGgESU11+ytnZxbqbV6dhaUk7S3f979dKWB1OjC7Oxpn9hGuM3FYIGw6fBM8Dg7tniJ2l/cEiPm1eHfPNnaS6NGoVLhheCMB9Po42/qLPoTLeNbLn16Oepfr/WnMINgePmcMLce+MQcgwaMVmud/vqRXP5QMLMsSqVUbCe3wAYS7Wtddei9LSUrGnz3vvvYf169eHdXHJBsdxHXZvll7M91Qbcbi+Vby762zApTvi474ws7swVhE1olcWVJwwXbcuQD8Tfz18OoPjOMk8L0/hw9Jqpxf5NjcMRJo+cKqrIAiPz3Gvii5Gca6wp5WSCyE70aTp1B53gjkyUgruiI/niVQ0OHcg1o6ebIPZJtxNyR1mGu90E3sYyYtGuNO6wt+pb54QTu+oe7PN4RQFZ0fCBwCKu7F0V2TGJkSLY2LEJ02W96wjpDc6BRkGXHVGEQD4HfnAIj4c5/468/i0Wtzp+0Dl7ID7b3ukoQ0OJ48PfxWGmV4/sY+Yyu+VLbymMz+Wu4y948HL6Z1EfDqqOJ09QhhR893uGtndoMMFz/PiZye8ER/BmrCvxigK1oZWC9YeEIpT7j9vsChszh0mNJH8fm+tpHGhbyNEdx+fBE11ffrppzj//PORkpKCsrIyWCzCB6O5uRlPP/102BeYbLiFj59Ul+vCyKw8y3dW46hrUnVnF0OWCpK2r/c2BqfpNeLYhx0BGhnWeg0olYt0kCnD5nCK6RsWbZJDupjqsoviLVuM+LhSXS3yQ/uBPD7sQnjcZSAHpDOGPH9/llJo7CDVxTwEPikCsZdP4O9l+zSkMFOMCiY6Ssd1HJFEMgD3e74jj480wpnRQR8fQOLzOZnYBmd3xCdV/Fx4NyqVi1jM4IqY3DtjEK4aX4Q7zxng81p3J3e9GBli6UWed1cOBSpnF9bsTi+v3leHE81m5KRqMXO4e9Zfr2xhLcc7ifj8whoXduDvAdyp57YAHp9AER9AiI7kZ+jR3G4TjdTRwmh2R5/97WWwFGQa0KdbKngeKHM1aPzqtxNwOHmM7J0lXiMAYIare/bWY6fEdN9gPzMEpYNK4wHFwufJJ5/Ev//9byxatMhjBPykSZNQVlYW1sUlIxkd9PJhUQzWFPCzbVXiXU9JZ8LHdQJk7esBdzfXbEkr+xG9sgEEntgbjLkZcFciSYXP/poWWOxOZBo0Hl0+OyNNPFE5RPHm9vi4L6ZyTaos1VXkJXwKMw3QqjnYHDyqm9tdx/UfWmapro68FG3iZHaviI8MAfBbZRMAoQqjq9BNTHXJuyizSAZ7r7NUV2WjKeDdNhM+GXpNp4KR9ZE5lsARH7vDKVYp9slLQ45oug824uMZ4c1J02HBpSPFFIcUloLqI0lb6zVq0RxsbLfBbHOIkU9/lUiimD3Zhv9uPAYAuHxckUfUhaXROvL41LWYcfSkCSrOnboJhDt1HqCqqwP/oVrFYVaM0l3sJixDrwn73D42pocZnD/fJmR2Lh3dy+N1PbNTcFrPTDh5IbUI+FZ0AV3A47N//35MmTLF5/msrCw0NTWFY01JjZxU1zUTikVTGSC88Ttz9es1ajHFwgSPKBokKYCerrsplof3xt28TKHwcUV89lQbRUHCvEQje2crmjPDTlRtflJd7GRqc/CyKlma222iEGQhdIZaxYkmPeb7aPTTwweQF/Fp82NuBuQJnzJXr4wxncylSiTYNOkWi73Tjq7SUnZ2cSzI0CNNp4aTD5yeYhHBjuZ0MdhU9soYeHw+KzuOEY+twPinvsesl3/CdW9twkdbOm4i54/qZjNsDh46tVCmnRWgjYVc2I1OYVbnEd5pgwvwwHmD8NfZwzyel3Zv9hiqafA1mzPRtPlIo9j36+oJxR6vkZPqYn/DHlkpnUb62OfRZHVAWkxrlhHxAYDZI4V014rdNYq6kIdKqD18OuIMV6n+5qOncKiuBTuON0Oj4nDR6T19XsvSXYxBhX6ET6JXdRUWFuLQoUM+z69fvx79+vULy6KSGVbS7j2vi+d5Mew8oCAdZ7lKKQGgb37HpewMd4WHcDHwHlch/H/gC7jJahfXpTTVNaAgHWoVhyaTTey7wqIYbBq6XNIkqS5v8abXqJCqEc5ecnw+x8UePjq/d3beF0N3DxLvVJe7bDdQfxF/5mb2s4HAwsdiFzrLAsCYPl1H+GSmaNwjRjoQjIDwfmwx28Fx7pQUx3Ho64r6sJsAp5PH+oMNYlRBjrGZ0YeZ2WPQiffzbVVosdhR5xro+9PBBjy2bLfi47C1F+WmQK3iZL0vA2G2uasm5dzo6DQq3HnOQLHrOUNqcJamufyds1h1al2LBTwPTBmU7xFBAtwRn6pT7QGjupWN/tPX/pBGYG2SLZLj8QEET0z3TD1azHas99OrLFIESruHAxbx2V7ZJFbVTR2U7/dnzZAMi03Xa9Azy/e9kprofXxuueUW3HPPPdi0aRM4jsOJEyfw/vvv44EHHsAf//jHSKwxqQg0r6vVYheb/eWl6zF7hDvnLbciip38meDx19iN9aPxFy1hHVtTdWqfi3dnGLRq9HddpFi6ixmblfh7gADl7JLfIVPrud6OYJUhvXL8p9r6dPO8GAa6y5L+/EAVNO6RFYHMzf4v/ntOGGF1OJGbplOUEox3OI4TBWRnlV3MxN8j0+BxIWIGZxYNeuzL3bj2rU346xe7ACgTPkxQ1bVYon6CZsLtud+PxOvXjAEgXHiVGma9x9dIf2+jwlE4LLqbolUjQ+HnXYq7pN0uRpJz0/xfrFlJO+Nar2gPILQzAIT9CRTVFdPXuZ1/XoSRFML/S62VLC3TWasNlYrDLNf5OFC66+XvD2Lq82swfeFaXPDSOvzuXz97NAEMhoYQe/h0RN+8NHRL08Fqd+KdX4SU46Vjevt97Wk9M0WxM7B7ul9Ba0j0qq6HHnoIV199NaZPn47W1lZMmTIFf/jDH3DbbbfhrrvuisQak4pAHh+W5krTqZGm12DGsO7QuYbAdebvYbibmVk9/suiPNL/99eIT5rvV5KaYgyTVHa1Wx04WCdUASip6AKEPQAEISGamyW/Q6aORXw6b4wnGpuz/d8ZShvbAYE7pWrU7hEegdIK7pEVylJdZa5J1KOLlKUEEwG5Pp8jLhO/93u9n8TgvPTXCrzn8oV89dsJ1LWYZXVtZmSnasXPX6XMbtLhwGS1i2mbc4d290gdeDfV6wxpRRcgvC/Z76S0e7Po51PQpd0f/iI+/iq6ACElz0rae2QZcI5rNIkUg1YtfmYCpbsCFSz4Qzr42CwVPjJTXQBwoauZ38o9tT5p21qjGa+sPohjJ00or2/DvpoWbKtowr/W+mZOlBDqgNKO4DhOnEBvdTiRYdBg+lDfvwV7LXvPBmq14U51JajHh+M4PPLII2hsbMSuXbuwceNG1NfX44knnojE+pKOQBEfJnxYyDkrRSv2kBgrM/3h3cXVn8fHOx0mhXVK9e7YKhf2odhzwojdJ5rhcPLIz9CLLePlwoRDc7tNrBQJNuLT2QnSN9UV+C6LpRUaA0xod5ub/QufQKk55u9hc3S6Et1kVnZ5G5sZzOC87kA9/rpMiPKk6dSwO3l8tLlSjHJ01LyQwXGcpLIresKHRXty03TISdNBo1aJTS07mtvnDzYws0TS0NT7hkcuwfr5vJGOrQjkkZPCyqGvPKPYY8K3FJbuClTZVSkWLMiLkLIorNR7K9fjAwCji3LQKzsFrRY7lm2v8vja/zZXwuHkMaooG0tvPRMLLh0hrDFEL5m7eWH4Iz6AO90FALNH9Ogw5XffuYNx9zkDcJefaj9AmupKUI8PQ6fTISMjAz169EB6um/dPhEc7KLY4i18XBeGfIm6f2buCHx55yRMHZQPObiNjoE9Pu6Lt+9JMpgePlKkJe2/uYzNp/fOUnw3ycRhtWSWjNQoyYRPZ00MG9usWLmnBoC7dN0bMdXlbW72c7JhlV2B7qzFcnav0Dn7m7aY/Zt8t7siPl3J2MzIk1HKD0g6lHuldZnR+YTL1DtzeCGeuEToIP/Bpgrx7yUn4gP4/r39sWD5Xkx6ZnXY5nqVu9r8s1QwELibcGcwgVgsSfF0dDPTEcF0afeHe1CpHQ2dpLoA4KGZQ3D/uYNw29TAnlFW0h6OiA/g3m/PVJfL4yOjq7xKxeHGSSUAgDfWHRZbhjicPJa6ehHNKy3Bmf26iaMeGlqtYhQ4GEIdV9EZUuETKM3FyErV4r7zBvvt7wS49zBhU112ux1//etfkZWVhZKSEpSUlCArKwuPPvoobLbgSiYJN2LEJ0CqS9oWPlWnUeSPEZvsuaq6mr36+ADui3e7zeFzEa4LU8TnyMk2bHSVPir19wCSuzOX/yFDr/G4M2SprroOhI/V7sTt/92K46faUZybigtH+FYrAO47xiaTDc3ttoBdZ4HOx1aw3kzeVV2ZKRoxbelt8q01mlHV1A4VB4wsyg74+yQqeenKPD7eER9p/6ohhRl44bLTMWtED+Sm6XCi2Sx6LuQKn84qu348UI831h1GVVM7vt0ZnvLlclfEp3+++wZSHFis4MLI87yYkpX6/tjvrrSknX1+ugf5eWd4RHxkdBoeUpiJu6YP7DDCwErn/ZW0O5y8+HxvGR4fwH1OsTjcN2Fmu/yIDwBcOb4YmQYNDte3YaXLv7N2v7sXEYvQZxq04t8klJQqO4/nREj4nNYzE1MG5WPG0O5iU8NgcTcwTFDhc9ddd+E///kPnnvuOWzbtg3btm3Dc889h7feegt33313JNaYVIgenwCprvwQTkIsstMsRnx8+/hk6DXQuPqdeN8hss7CeUGuIT9Dj/wMPXgeWLOvDoDyii7AtyrKu1Q5o5OID8/z+OsXu/DrkUZk6DV464ZxYh8gb9L0GjEqcexkW4ddZ3M66ZLLIj7e6+c4TrwQeHdvZg3EBnXPUGwoTwTY3WpHVV08z+OY2KjT80KWYdBiYr9u6JllwH+uG4c0V0+Ty8cJHYZZBaHsiI+rW7e/sRWtFjse/myn+HjrsVM+rwkGd8RHInxYibWfRqaBMLa7CyB6ZLujNMHO62IRn0I/VTpK8PD4yEh1yaGjsRW1RiH6p1FxstPobL8t0qquTkZWeJOu1+C6icI8s3//WA6e5/H+JiHac5lXL6JiUWAHHzW0uP7WegUDnpWgUavw7k3j8eYN43xGUCgl3oSP4jPpBx98gKVLl2LmzJnicyNHjkRRURGuuuoqvP7662FdYLKR7hpUKifioxQW2WGpGH8VLxzHITtVi4ZWKxrbrB4nvYYwnLSG9shEfUs97K5QcDARH2+PTLaXaMlyLY+duL15a/0R/G9LJVQc8MrVozHQT8MtKcW5qWhotWLH8Waxz4e/uywWUfOX6nI6eTFtkar3PVHlpetR3Wz28bpsc5X8d6UydilyPD7VzWa0WOzQqDi/VTof3DIBdicPrSTqd82EYryxrhys2llOHx9AMp/tpG9foBdW7EdVUztSdWqYrA5sOXYKPM+HbDgvd5n8+xf4prqURHzY+y5Vp/a4GAY7oZ3N6QrV48POL8Z2ifAJ0ZDLqjD9pbpYmqtndorsLucswibVmaLHRyc/PjCvtC8W/XQE2yub8Pm2KqzZL9zgXTXeszqtODcVO6uaQ5oLZ3FFvPUBpsfHE6mJnurS6/UoKSnxeb5v377Q6SITcksmWD7ce9ilP4+PUrK8Bhb6MzcD7gop7zvEUCM+gOdk8aLclKBarXvfgXmvP1cvXO2On2oXc+2M46dMeHr5XgDAI7OHiV2wO4JdDLe5vDZZKVqPiyxD9Pj464Ek+cCn6XzvNwL18mERn67o7wHkeXzYuI4BBel+7245jvP5exTlpnr8beUKH+bxqfR672w52oh3NhwFALxy5Who1RzqWyw+d+yfbj2OS//1s+wxHA6nuzGjNOLjHpzpK3yOnzKJokQKEz45qZ6fKfHz3K4s4hP+VJdd0nsmtGsFa7TqL9XF0pRFufL8PQCQ6s/jI7OPj5T8DD1+P1bww/zl0x3geWDSgG4+I4XC0SyTNUtkRvh4JuHL2e+880488cQT4owuALBYLHjqqadw5513hnVxyYh7Vk2bR8Ox8ER83Kkus80h3jF4R0xyA6RsxCqCDoyJnTG0hzu6Eky0BxCMhFKDsI9w0wtdl60Op0+l1K4qI5y8EHm6yWVG7Ixi199kW6UgQgKdtHM6aAVgcl3AVBzEFv5S3CXt7u+12p3YUSWYwLtiRRcgz+PD+j4pnUp/7Znuu2y5qa4eWQZoVBysdqc4l85id4gXscvG9saMYd0xvJeQomUt/QEhqvfcin0oq2jC2v31sn7eiaZ2WOxO6NQqjyG57sGZnsLHbHNg5ss/Yc6r631Evb9iBcD9uVfi8eF5PgLmZlvYDLm9Xd2bT7ZZfdIn7hYVSsbg+EZ8lJSzS7nlrH7gOKF7PABcM6GPz2uYKAtN+LBUV/wLH9YLyWxz+rxvY4HiHdu2bRu+/vpr9O7dGzNmzMCMGTPQu3dvfPXVV/jtt99w6aWXiv8I5fTKTkGKVg2bg/cIg4bH48Pu/GxitEet4ny8I9l+UjZOp2QScEbwJy1pxGdUkMIH8Ex3eZ/o1RzQw9VZ2vvEwh73l9ntGnBHfFjZcSDhl9vBXCSWskjTafz+XBZFk/qS9lYbYbU7kZ2qFfvVdDWYiGxsswY8Ie6tEYTPMIXCZ+qgAgwpzECaTu1TDRYIjVollkqzkvb/ba5EeX0b8tL1eNQ1joGZPbdIfD6/HW8Se9/I7b9zyOXv6ZuX5pGWYQZ472HFJ10drOtaLB4DWAFpXy7PzwOb19WsQPi0WuxiarZAYZd2b1jEp8ZoFsVEqKmuzBSNeN7yTnex5oVyK7oA935bnO6/gdwGht70zUvDTJeROT9D7zPSAfDtDxYMVntkPT7hRNq01RzFsR6BUOzxyc7Oxty5cz2eKyoqCtuCkh2VikP/gjTsqjLiYF0r+uWnw+HkxdB5KMInR9LPg4ma7BStz4XYX3WS0WwTfTmhTALum5cGvUYFi90ZlLGZka7XiKF4f2mM3jkpON5kRuUpE8ZJyjLZAMo+CjogF3v5SgL9/tkdVHWxi4i3P4nhr4mh2L+nCzYuZLDqOLuTR3O7za93iqW6lEZ81CoOH90+ERabU1HlS3FuKo6dNOFYowlj++Rg0U+HAQB3Tx8gpovHleRi0U9HsPWYO+KzYre7E69cb44/fw8ApIuDMz2P02J2i5dGk9Xj9zrV5tvME/D19smBCbgMg8anClEpmSmeHiOdRuXT0kEpHMehZ7YBB2pbUdXU7jExvFJB12YG8/hY/Xl8ghgAev95g1HZ2I55pSV+0+JS4ROsTyyRIj4GiTiLhyaGit/RixcvjsQ6CAkD8tOxq8qIQ3WtOP80190wD3BcaKKDnYCcvDsc7C8F4O5H4z7JsgtypkET0h2GRq3CXy8choO1LR6CRClSg7C0HJ/ROycVOHLKx4PBRk94i5mO8BZJgVJduR308REHlPoxNgP+PT5lXbh/D0OnUSHToIHRLIwz8BYobRa72FNHmiaVS6ZBCyjM1BRL/Bff7qpBZWM7clK1uGys+waPNQ09UNuKZpMNmSkarNxdI35dbjWWv1J2wO058RldIyl6aGyzor+khVeT6PHx9uwpNzeznl1Km4v6w3sYaV6AOV1K6ZWdggO1rT4+H6U9fADfPj48zwed6gKEv+dXd00O+PWe2SlQcYJ4qW+xBGUgt9iYxyf+Iz4qFSfe8MZDZVfI9bE//vgj2traMHHiROTkdN0TdDRhdy/sbpClP3JTdX7vHuRi0KqRolWj3eYQq1b8lXHn+On02iC2mg+9Pfq1Z/rmvJUiNQj7E2/spBco1VWcKz91lJ+uFz+0QOAwfbbEPO5w8h6pizarO9XlD2+Pj9PJY6vLPzK6CwsfQPjdjWY7GlqtGODlNd9X0wKeF8YmRKI1vz+KJcNKV7vaLtxQWuKR8shL16NvXhqONLShrOIUeuek4HCDuxJMdsTHTyk7IPH4eAmoFi/hI4XdqHibm4MpZ68Jk78HgM909NwwdRqWDitl2B1OsbGpooiPVzm7zcHD4Ypwy2lgqBStWoUeWSmoampH5SlTcMLHdT7SJUDEBxDSXRa7My4MzrJ37Nlnn8Vf//pX8THP87jgggtw9tln48ILL8TQoUOxe7fyacKELwMKhDtbNsuqPgxpLga7OLOGcNn+Ij5+TLosEhEO4RMOpL6kDoWPpEGY3eEU7waVpLpUXmXUgVrEs33jefcAWIZ7XEWgiI9nquvn8gacaDYjXa/BmD7ZsteaiLgNzr4X5mCNzaHA3hs/7K3F7hNGGLQqXD+xxOd1Y0WfTyNWSKI9gHyPj5jq8o74uC62rd6pLomg8k6pNvmZWyc8Fj4fbVb5Q09ZqitUfw8gXJilURN/zT+DoSfr5SOJ+FQ3m+Fw8tCpVYoqYFO9zM3Si7MhQhGVUHw+DicvWg8SIdUFuCNn/rrTRxvZO/a///0Pw4cPFx9/8sknWLduHX766Sc0NDRg3LhxePzxxyOyyGRDjPjUt8Lp5MNibGYwkcBSPt4nSeE519gKSWj8pIyOq9EktQNzMwD0dpW7SlNd1c1m2F0nRaV3sn0kwifQiVurVolTrL3bEbRZOov4uMcK2BxOvLtBGLY5d0yvkD0W8U63AKX8ALAnBsKHidw2V0j+inFFflPMzOC8+egp0d8zpFC4aWmTkeo61WYV+9r0y/eMQIojKyyBPT7eTR+bJL49KZkGrTh9XG5Je7gquhjSm5NwnUN6+RE+7MamV06KoqZ77lSX8D3s4qxWcdCqI+Ovc8+FU97EUCpgE6GPD+COnCkdwxIJZO/YkSNHMHLkSPHx8uXL8fvf/x6TJk1Cbm4uHn30UWzYsCEii0w2+nRLhVbNwWR1oNpoDqvwYVEJFvHxFy1hJ/mmuI74BC5nB9wRn+rmdthcbQHYnVXvXPmNzRjSiE9HPqvsNP8DIdvE5oX+RUxOqk5c047jzfhhr3AhZZ1guzLuCe2+wodFfJRWdIWC1P+lVnH4w1n+Z0ax6dXbKk5hZ1UzOA64ZHQvAO7UZkccbhCiPT2yDD6mdxbR9BZQUo+Pd8RH7OOT5vl5UKk48TMi1+dT5yrlD7WHD0M6JDZcs6V6+0l1VQZR0QX4ifhIujZHqrCAlbQHE/GxSCqjdCHYH6JJqi4BIz52ux16vftDsGHDBpSWloqPe/bsiYaGhvCuLknRqlViP5+DtS1hFT4sOlLVgbnZX3VSQ5xFfDrz+OSn66HTqODkgeom4STOolx9FOT+GdLUWEfTkMUeSF4T2tmde3qAVJdKxYmC6pUfDsLJA6X9u4lpz64Mi6A1eF3IHU4e+2taAEQ34pNh0Ip/i9kjegT0ivTPT0dOqlbs1zKuT4743pIzfLK8zr+xGZA0MPQSUFLvkHdUMVBVF6B8UClLdYUr4pNpkEZ8wiOm+uenQ63iUNXUjkN1wvvEbWxW9hkXIz6uQEowzQuVIjYxDGJeF/P3qFVcwAn28YY4tiIOqrpk71j//v2xbt06AEBFRQUOHDiAKVOmiF8/fvw4unXrFv4VJiks3XWorjUsXZsZTPiwlin+0kTM3Gw028UmivEW8ZFGTvwZtFUqzsfnUyEam5ULH+n3dHTiZhcd74sS82p0lLZie/vjAaH53fVJEO0B3D2MvOeUHTvZBpPVAYNW5dP5NtJMGZiHdL0Gfzq7f8DXcBwn+nwA4PzTCsX3pb9UF8/zHlEtf1PZGe6Ij3eqK7C5mfX18TY3A9JBpcpSXaGOq2BIW06EUpkqJTtVh7MHC2VtH289DgA4HkTXZsB9I2X18vgoGVehlOIQujdbE6iUnWFIRI/PHXfcgTvvvBM333wzZs6ciYkTJ2LYsGHi11evXo3Ro0dHZJHJyECp8HGFncPj8fFvfPR8jfs5Zpg8KQqf+Ij4sMiJigPSA4gJNlmdnVgqXD18imU2s5PCIj5qFefXEM7wlyYE3NU5HfUvke5tYaYBM4b6Nj7riuQFGFS6t1q4ix9cmKk4NRkqL14xCr8+Mh1DCjuONI3t427JcP5pheL70l+q6/kV+zH2ye/x9y93w2p3uoVPgb+Ij/8Ghi0BUl1Wu1OMBnmXs0ufk9PEkOd51LkiPqEOKGVIS9rDeQ65zDWM9rOyKo/iBeURH89Ul1nhgNJgYMKnxmj2SF3JIZHGVTDYXiaUx+eWW27BK6+8gsbGRkyZMgWffvqpx9dPnDiBm266KewLTFb6ewif8Ke6xMd+euBo1CqJJ0A4uYaznD0csNB0Voo2oIlRbAvviviEkurqn5+Oq8YX4Z7pAzs0TYrGcK9Ul1jO3sGEdWlE7+oJxQkTwg6VbgHGVrj9PdFP93EcJ8tUPnVQPlQccEZJDopyU8Xv8Rfx2ekaP7Lkl6O4atFG7HY1ZvSX6go0siKQuZmZljnOt3wckKSvZUR8TplssLoiveGIMgPeEZ/wnUPOGVKAvHQd6lss+PFAfVBdmwHPzs1OZ2g9fOSSm6ZDqk4Nnvc/Zb4jzBGezB4J4snjo6hc5Kabbgoobv71r3+FZUGEwEBJSbvTNWK6ICzmZq9J5n7uDtnrmtttYm+Qk3GW6mKh6Y5mMLkjPu3geV4cQaCklJ3BcRwWXDqy09flBuiZ4m5g2EGqy/X31ao5XDk+ebqh54nmZs89i0VFl1KG9czEt/dMET+bgVJUgJA6ZmyVjLrw6/HRu++OpaM8WgOUs0sHDvuLjkl7THUGS3N1S9OFrUeMh8cnTKkuQPBDXjKqF95cfwQfbKpAtWvtRUFGfAAhzRUNjw/HcSjOTcW+mhZUNJrQz8/7IBCJ1sMHcI/+SCiPDxFd+uWngeOEvD0Lb+enhx529kl1BRAOolfFNQSQVSXFi7mZCbCCjMB7IjUPNplsYg8UJY3NlJKd5t43KeLIig5SXSy9edHInh3+Xl0NFvFpsdg97gZjUdEVDIMLM8SO06niyd0hNsBjtLqiNc/NHSn+ThkGDbr76ZUj7VNlkuyJNNXVZnWI+8VEkD9/D+CO7MppYhhufw/gVdUV5nMIS3f9sK8OPC8MAVaaThOqt4T/N1kd7qquCDQvlBLslPZE9vh0ic7NRGQwaNUoykkVDbk6tcrj5BEs3qmuQBETafdmZmzWa1Q+A01jxZn9cvHo7KE4s19gQ7004sPGHnTP1Ef0Li43QPWM2Meng/373eheyEvXY2L/5CoSyDRooFVzsDl4nGyzold2CppMVrED75A4Fz5SpH9fk9XukXZiomVYz0x89qdSLFp3GAO7Z/gtl9ZrVFBxQhGCNHrk3RH6lMmKHlkpASezM3LENgudR3yYvyccEWYGO8+kaNVh70s1uDADp/fOwm/HhVRi75xUxSXoQmpTjTaLAyaJoIxkqgsIvomh6PFJkB4+gOdNAWI8ejBxdi0JGSgxPeZn6MPST0K28JHM65JWdMXLsEyNWoU/nNUPw3sFHnTKPD4NrRYccJVF91EwqiIYcsTmj96pLhbxCXzS16hVOHtIQUSFWTzCcZxY0s5SqizNVZybGjdiWw56jUpMNXmbOJnwyTBoYNCqcdf0gbjANcXbG47j3E0MrdKIj6dwYZFF95wu/5EOJVVdDW3h8xQyWKorUhFjFvUBlPt7GOyz2Wa1R8XjAwBF4mgdZR4fMdWVQD7AhOzcTEQf6cThvDCdhKRm5gy9JqCBVjqh3W1sjo80l1yyUrRiJ+VfyoUeU5FMcwFuwejbwJBFfJJL1MglL8Oze/PuKubvSaw+RixyAHhGZ+wO94wif+Zjf6T5MUoz8cRSpkz4MC9eoNS1kj4+TeLML3nrlAM7l7HO1uHmotN7imkfpf4ehtg7yeJAu1UQFpGY0yWluFuwEZ/EMzcbErGqCwBsNhs0Gg127doVqfUQEqRlruGqrpBGfAIZmwH3Se+UySrehUdrSGS44DgOvV1CZ/2hkwCCMzYrwT3nzAaed3s85KS6khmxiWGrFTzP47NtVQCAM0pyO/q2uMTfgFGpCJIbwUrzKo232p3iBY+1ZHALH9e4ikAeHwUT2tkxc8JoQh7YPQNrHpiGV66KTMuTrBQtLhzZE0DwZvhUcaRC9CI+0l4+0vNFZ4gen4RKdQnv+3iI+Cg6C2u1WhQXF8PhiP3CkwFpqiscwwIBQXUbtCqYbc6AfgBAWv4qTXUlVsQHEELJe6uN4u8QaeHD9tTh5GE028UUgziyIsJ3kIlKN0ll18bDjdhbbUSKVo3LxiZedZu/iA+L1Og1KtmVOGKFmOu9Iz1eca7wvmam5uZOojRixEfGrK7OjNLBEukmlE9cchrOHdYd04cWBPX9qZJZUtHy+LB+Qy0WO5rbbeJ51+ZwYsvRU1izvw5mmwOPzh7m8b5JyD4+rmaQ8VDVpfj285FHHsHDDz+M9957D7m5iXc3lkhEIuIDCOmuGpu5w1JwaSM+97iKxIr4AL6prUinugxaNVJ1apisDpxqsyIrRQubwyneoSWSXyWa5Et6+bz98xEAwO/H9u4wKhmv+OvB4/b3yP992B2yyWKHCm7hk6JVi/4bn4hPgCgN20ezzYmfDzVgTHFOwIqlU534heKVVJ0moGdKDmkSoRmtqi6DVo2CDD3qWiyY+vxaFGYakJumw64TzR4VfFMH5WO6pKGpxcbK2RPnRso9siL2gRPFZ+FXX30Vhw4dQs+ePdGnTx+kpXmq+LKysrAtLtnJNGhRmGlAjdEcVqNhdqoWNUaz3+aF0tcAwok13sZVKKHIy+gYTPNCpeSk6mCytuOUyYoSpHmkPLr6pPVgYRGfrRWnsL2yCQAwb1JJ7BYUAu6uy1LhI0RkpB2MO0N6Ic6ApzmaNQFkJvpTnUR8MvQapOnUaLM6cM2bm6BRcTitZyYem3MaxhTneLyWHStcoyUShTRJxCcafXwYs0b0wJJfjqK53SaOHQGE/Xc4eTS323yq+SwJWM6e4vpcJGQ5+yWXXBKBZRCBGNsnB9/srMbgMJoCWaSnY4+P2wx5MkHNzYBn6/p0vSYqJ/OcNC2qmtrFO2fm0dCquYRqOBZNmMdnW0UTAODswfl+G/slAv6rsVxDahUJH2a2tSMDbiGVbtAgV3JjArjN9IFuZjiOwz+vHo1Pt1Zh67FTqDGa8dvxZrzzy1Ef4SN6fBIw2hYK0ghbtDw+APD3OafhzzMGocZoRo3RjIYWC0ry0jCqKBu3vbcF3++t8xELidjHJ56quhQLn8ceeywS64gIzzzzDObPn4977rkHL730UqyXExTPzB2BP07rj9N6hq+XCRM1Hc2ccnsCbOKQ1ISM+EgiPEW5yvt7BEOO14R2k4xxFcmOd5nzTZP7xmgloSMVLAwmWjKCjPgAQKskXZab7pnqYqbljnx75wzpjnOGCOmSDzZV4OHPd4rjcBh2hxNGV3QqnObmRECs6pJ6fCI4pFRKVqoWWalanxtcQ4D0kNvjQ6muYAjqr9rU1IQ333wT8+fPR2NjIwAhxVVVVRXWxYXC5s2b8cYbb2DkyM7HDMQzGQYthvfKCusFu69rGnRHZkOpSZeNeoiXrs1KkPb0iEaaC5BWdgkXpVYZPXySHamoHliQjskD8mK4mtBI8zOhnaW6MvTyoyhpOk8BJYonvUZslHmqTageFEvQZYoVaY8rKc3tNrDioo5ujLoiaRLh0x6FIaVycE809zQEJ/bIigQUPjt27MCgQYPw7LPP4oUXXkBTUxMA4LPPPsP8+fPDvb6gaG1txTXXXINFixYhJyen829IMu6ZPhAf3TYRvxvdK+BrmEkXgDiwMBEjPml6jTgbKNIVXQyWTmORMhOb00UVXQGRvrdumtw3bhplBoP7Airx+FiCSXV5pszEVJdeI3ZiPtlmhcnqED+jctNTeaKZ3LPKi4n1TEPgHl9dFX/l7LFuJhooSpKYVV3xM6tL8S3offfdh3nz5uG5555DRoY7LDdr1ixcffXVYV1csNxxxx2YPXs2ZsyYgSeffLLD11osFlgs7rseo1FonGaz2WCzdd7zQi7sWOE8ZrCoAYzunQHe6YDNGVh9Z6doxZOuigPStVxcrL8j/O1zrxwDTrZZ0TNLH5X198sT7qZ3VjbBZrOh2SS8v1J16rjfPyWE8z2dbVChf34aeB64cHhBQu9Tiuti1NJuFX+PZldKKk2nkv27GTSC+Gsx24B0t48nTa9Cpl74GadMVtQ1u8baaFTQwCnr+NkG4fsbTVa0my2iyKk3Ch2Ec1J1Cf03CAa2361mG9qZL0/Fx3Qf2L1Sm9nqsQ6z67ysVcXHNUUOGgiCx2p3wslHZt1yj6lY+LAUkje9evVCTU2N0sOFnaVLl6KsrAybN2+W9foFCxbg8ccf93l+5cqVSE0Nf4Rg1apVYT9mpFDZ1WBDVVI1PFZ8921sF6QA6T6fbuBgylSBO7ETy5fvjPjPbm0DAA22Hj2Jr79ZjrIGDoAa7S2nsHz58oj//GgTrvf0nf2F2VSrV60Iy/FixbETwt/7wJEKLF9+FACw67AKgAo1lUexfPlhWcc5Uisc59jxamAIsGvfIQAqNJw4jk3rKgBo4HDy+OS7tQDUMHAOfPutvM+okwc4qMHzHD756jtkujJkOxqFn8lZ27rke7UjjtQJv3vFiVo0mjgAHMp+3YiGPbFb0/EK4X1z4NARLF9eLj5/xPV8+cF9WN66N2brU4Kg1QTJYXVG5lpoMsnrgK1Y+Oj1ejEqIuXAgQPIz89XeriwUllZiXvuuQerVq2CwSBvsvD8+fNx3333iY+NRiOKiopw3nnnITMzfIZim82GVatW4dxzz4VWmxi584/qtuJ4udDxuGdOBmbNKo3xijrH3z7PivIaHE4er+1fjTaLA/3HnIWW403Aob0o7tEds2ZFpnNtLEjE93Q0MG4+jmXH9iAnz/33/uHjnUBtNUYPH4JZMsv07b9V43+HdyI1KxdAA/J69gZOnMCIIQNw8fQBeGzbD2izOpDffziwZy8KFX5Gn9y5FifbrDh9wlniaJDWLceB/XtQ0jMfs2aNUfibJzbO36rwQflupGbmgLOaAKsN06dN8WgkG20q1x3BiqqDKOjVG7NmDRef/+bD7UBDHU4fMRyzxidGk0+e5/Hgr4LYsTqAiy4I/3nDnzbxh2LhM2fOHPzf//0fPvroIwBCmWRFRQX+8pe/YO7cuUoPF1a2bt2Kuro6jBnj/sA6HA6sW7cOr776KiwWC9Rqz5ytXq+HXu/rXdFqtRE5mUfquJEgV+K7yM/UJ8y6gdjusxbA6KIcrD/UgB0nWmC2C27RjBRdQu2hXBLpPR0NMl3GY5PNKe4Lq8zKTpX/OcpKFT5/7S4jq8nKi89rtVrkpuvQ1tiOY64Blzlpyt5feel6nGyzosnsEL/PaBF+Vrd0Q9L9TTNd+22yOkUzcUZKbM97aa6GlxY777EOm0N4L6TqE+uzl6JVo93mgM0ZmfOG3OMpdkYtXLgQra2tKCgoQHt7O6ZOnYoBAwYgIyMDTz31lOKFhpPp06dj586d2L59u/hv3LhxuOaaa7B9+3Yf0UN0jNQoyfqsEPIYU5wNACirOCVW95C5OTlwT/n27eOjpHOzd3VYi8VVGeYySLPKrsP1bQCUd1r2HgwLuM3NuWmJczENF6kSU7rYxyfGn9lAVV3MzJ5I5mbAvZ+WGPubFUd8srKysGrVKqxfvx47duxAa2srxowZgxkzZkRifYrIyMjA8OHDPZ5LS0tDt27dfJ4nOkc68DARK7piyeg+QjVh2bFTYlUZjatIDlJdfXxMllCrujynWbO2COwYrHqwvL4VAMRKL7n4q+xifYECDTvtyjDBekoyyDXW5eyBmv6xkRWJ1McHcP8+sa5oV3wmNpvNMBgMmDx5MiZPnhyJNRFxQq404pOAPXxiyZgiQfgcPWlCpSsVQeMqkgNxuKifkRVBNTBkfXzM7nJ2wN2zp6pJeH9ldTCCxh9M+EgjPk1ixCf5Pu9MsEpnZMW6nD1wA8PEjvhYnbFtV6H4TJydnY3x48dj6tSpOPvsszFx4kSkpKR0/o0xYu3atbFeQsIibYYWziGpyUBWqhYDCtJxqK4Vv5Q3AHDfwRNdm9QOUl2KZnVJjsPz7qgRS5exVBdrOKh0xAQTPvUS4dMYocnsiUCaV1pLp1FBrYrtBdqgFYSNT8QnAfv4AO6IjzXGqS7Fu/b999/jggsuwKZNmzBnzhzk5ORg8uTJeOSRRxKqVJvoHGm4myI+ymE+H6OZRlYkE9KRFTzPg+d5SfNBJR4f4TgOJw877478iB4fr8+kYo9POvP4uFNdTZ0MO+3KeHvwYp3mkq7BO+IjzurSJpjw0SWo8Jk8eTIefvhhrFy5Ek1NTVizZg0GDBiA5557DhdccEEk1kjECOnJjzw+yvEe/kjm5uSACVy7k4fV4US7zQGH01XZpyDiI02NWhye09kBd8SH0dGcLn/kZbhSXZJ5XY1JnOoyaNTgwIuP40L4uM4ZZmugVFfs16iEhPX4AELPnrVr14r/LBYLLrzwQkybNi3MyyNiSQ5FfEJibB9P4UOzupKDVMkFs83igM1VgaPilIlftYoTy39bbYKQAnw9PgylQ0XzvTw+DieP5vbkHFAKACoVB53KXXEU64ouQFLVZU/8WV2AW/gkXFVXr1690N7ejmnTpmHatGn4y1/+gpEjRyb0bB3CP3npeug1Kqg4jiI+QdA/Px2ZBg2lupIMjVoFg1YFs82JNotdvEil6zWKz5NpekH4NFmF7+M4t4Du5iVOlA4VZTczJ9uscLpET7IOKGXo1e6LcqyNzYAk1eUd8bElpseHCf9Yj+tSvGv5+fkwmUyoqalBTU0Namtr0d7eHom1ETEmRafGkhvHY8mNZ8TFSSDRUKk4jJKku8jcnDy4jcl2SUWXcjHBxHKzy4aTrtNA5TLcekdllJags95cLNLDjM3JOKCUIf2IpsSBf8Yd8XGA591pOHcfn8Q6pxjEwdexXYfiv+z27dtRU1ODhx56CBaLBQ8//DDy8vJQWlqKRx55JBJrJGLIxP7dMKFft1gvI2EZKxE+VM6ePEibD3p7c5TA3jOi8JEcwyfio9Djo9OokOWK7DS0WiTNC5MvzcXwED5xkeoSLtE8705vOZy82Lk5UVNdCVfODggl7XPmzMGkSZNQWlqKZcuW4cMPP8SmTZti3r2ZIOKJMX2yxf+nBobJg9gF2GIXK7qCET7prisxS3VJj5Fp0ELFCQNHM/QaaIOI0uSl69DcbkN9q0XsE5SMzQsZeskWxoO5WRppN9scMGjVYkUXkLiprlhXdSn+JH722WeiqXnPnj3Izc3F5MmTsXDhQkydOjUSaySIhGVUUTZ0rgtSZgoJn2SBiVxTuFNdEvGsUnHISdXhZJsV2UGOmMhL16O8vg0NrVa0WwXhk8wRH52aByCIzHhI72vVKmhUHOxOXhxbwXr4AIknfNiexjrVpfhMfPvtt2PKlCm49dZbMXXqVIwYMSIS6yKILkGGQYu3550Bm8NJqa4kItUlUFolqa5gIn5pYqqLRXw8BU5umkv4KOzazJCWtLNUitKUWVfC4OHxib3wAYR1tFjc88NYxEet4hLOiyWWsydaxKeuri4S6yCILsvkgXmxXgIRZdLFOVv2kDw+zBDvz+MDuA3OwYoVaUk76zXk3R8omdBJU11x4PEBBENwi8UuVnaJpewJJnqA+GlgGNQtqMPhwBdffIG9e/cCAIYNG4aLL76Ypp8TBEHAbUputdiDmszufZwW19xM75EXzOAc7IgJd/dmi3v0RRKnuvRxGPERx1bYmfBxlbLHQdWZUhLW43Po0CHMmjULVVVVGDx4MABgwYIFKCoqwjfffIP+/fuHfZEEQRCJhOjxsTiCGlDqfRze5TvxTpfliMInuIhPN8mEdtZiKBnndDGkwicePD6AZEK7V8Qn0fw9gNTjE9uqLsU7d/fdd6N///6orKxEWVkZysrKUFFRgb59++Luu++OxBoJgiASCnZn2xpiVVeqV+8n76jR5AF5MGhVmNg/uHSqdEL7KdecrtwgjdJdAb1aMrIiTlJd3vO6EnVcBRA/Q0oVfxJ//PFHbNy4Ebm5ueJz3bp1wzPPPINJkyaFdXEEQRCJSJpHVVco5eyaDh/PGtED5w3rHrTJVTqolEUQkjriE2fl7ACgZxEfVtVlS8xxFUACp7r0ej1aWlp8nm9tbYVOl7wfGIIgCEaa2MfHnepSMpmd4V0J6G1uBhBSZQ+L+NS3WsSLEnl8BOJF+PhGfBJzXAXgTnXFekip4p278MILceutt2LTpk3geR48z2Pjxo24/fbbMWfOnEiskSAIIqEQOzdb7WgJQwNDhre5OVTyXeXsVrsTTa5UV1JHfKQenzhNdVkT2OMTL1VdinfulVdeQf/+/TFx4kQYDAYYDAZMmjQJAwYMwMsvvxyJNRIEQSQU7pEVoaW6fCI+QUSNOsKgVfukz5K5j088prpYVZelC3h8EjbVlZ2djWXLluHQoUNiOfvQoUMxYMCAsC+OIAgiEZHO6mKjIDKCEC1pXqIkGPHUGXnpOg8DdjCjL7oKOqm5OU6ED4uS+PTxScSIj2tPHTwHm8MJbYw0tuxPkdPpxPPPP48vv/wSVqsV06dPx2OPPYaUlJRIro8gCCLhYB6f5nabmKIIpYEhw5/HJ1S6petx9KQJQHKPqwC8h5TGh7AwdEGPDyDMHks1xGYdsnfuqaeewsMPP4z09HT06tULL7/8Mu64445Iro0gCCIhYZGauhaz+FwwoiVNF52IDyOZ/T2A58iKeOnjY/Cq6hI9PnGyPiXoNSqoXC182mM4t0K28Hn33Xfxr3/9CytWrMAXX3yBr776Cu+//z6czhgn6wiCIOIMJlhsDiF1YtCqgkoheftvgkmXdQar7AKCb4TYVdDFoccncB+fxIv4cBzn8/vEAtk7V1FRgVmzZomPZ8yYAY7jcOLEiYgsjCAIIlHprPFgMMdRqzjR6BpOPIQPpbpE4q2BoZkJnwTu4wMAJXmp6J4iVITHCtk7Z7fbYTB4JuS0Wi1sNlvYF0UQBJHI+ERqgkxR6TVqaNWuyex6DTgu/K3+2YR2ILkHlAJCxKdbmg5pOnXQE+/DjTirqwt4fADgiz9OxMOjHCjplhazNcj+NPI8j3nz5kGvd39IzGYzbr/9dqSluX+Bzz77LLwrJAiCSDCYl8E18BwZ+uC9Oak6NZrb7T49fcJFvtTjk+QRH44D/nfreIBTxU3Ex9vcbE3gcvZ4Qfan8YYbbvB57tprrw3rYgiCILoCHMchTa8JaTI7I02nEYRPCMfoCE+PT3ILHwDok5sKbazqrP3ABJi5C3h84gXZwmfx4sWRXAdBEESXIk2nCal5IYM1fYtUxEcqfJJ5QGm8YtCwiI9rVpcr1ZWoHp94gHaOIAgiAkh78Hh7fpQdR/jeSJSyA0A3SaormyI+cYcY8bFSxCdc0M4RBEFEAGnX5dBSXSziExnhk67XiJVD3ZLc4xOPiH187F4enzgpt09EIvNJIgiCSHKkzQdDidZEOuLDcRzuP28QyutbMaAgPSI/gwgeVtXlPbJCn8SjRUKFhA9BEEQEkKa6wuPxidzp+g9n9YvYsYnQ8G1g6Cpnj0BPp2SBdo4gCCICeKa6ghct/fOFdiH98mLX94SIHSzVxRoXsv+Sxyd4KOJDEAQRAVJ14fH43HpWX+jq9+HS0T3DsSwiwWARH6vDCbvDCauD+viECklGgiCICJAepqoutYpDj1REpGszEf9IGyma7c6EH1kRD9DOEQRBRIDUMJmbieRGmtIy2xwJP7IiHqCdIwiCiADpYSpnJ5IbjuM8KrssNLIiZEj4EARBRIDUMFV1EYR0Qru7jw9dvoOFdo4gCCICpIepqosgxCaGNqcY8dFRH5+goZ0jCIKIAMzjo1Zx4h07QQSDtJcP9fEJHdo5giCICMAaGKbrNVSRRYQEi/i0We2wOXgA5PEJBRI+BEEQEaBHVgoAoFd2SoxXQiQ6zNxsbLeJz1FVV/BQ4pkgCCIC9M1Lw7s3jUdRbmqsl0IkOKyXT7NE+FAfn+Ah4UMQBBEhpgzKj/USiC4A8/g0mQTho+IAjYrSp8FCkpEgCIIg4hi91jPio9eoyTcWAl1O+HzzzTeYMGECUlJSkJOTg0suuSTWSyIIgiCIoEnxFj5U0RUSXSrV9emnn+KWW27B008/jXPOOQd2ux27du2K9bIIgiAIImi8hQ/18AmNLiN87HY77rnnHjz//PO4+eabxeeHDRsWw1URBEEQRGiwqq5mE0V8wkGXET5lZWWoqqqCSqXC6NGjUVNTg1GjRuH555/H8OHDA36fxWKBxWIRHxuNRgCAzWaDzWYL9G2KYccK5zEJX2ifowftdXSgfY4O8bzPOrXg52kyWV2PVXG5TjlEcp/lHpPjeZ4P+0+PAUuXLsVVV12F4uJi/OMf/0BJSQkWLlyIlStX4sCBA8jNzfX7fX//+9/x+OOP+zz/wQcfIDWVylAJgiCI2LKqisPXFWpk6Xg0Wzn0SuXx/053xHpZcYfJZMLVV1+N5uZmZGZmBnxd3Aufhx56CM8++2yHr9m7dy/KyspwzTXX4I033sCtt94KQIjm9O7dG08++SRuu+02v9/rL+JTVFSEhoaGDjdOKTabDatWrcK5554LrZYmNUcK2ufoQXsdHWifo0M87/OSDcfw1PL9MGhVMNucGFWUhY9vnRDrZQVFJPfZaDQiLy+vU+ET96mu+++/H/PmzevwNf369UN1dTUAT0+PXq9Hv379UFFREfB79Xo99Hq9z/NarTYib/5IHZfwhPY5etBeRwfa5+gQj/ucbtABEIaUAsIIi3hbo1Iisc9yjxf3wic/Px/5+Z03ARs7diz0ej3279+PyZMnAxCU5dGjR9GnT59IL5MgCIIgIoL3kFua0xUacS985JKZmYnbb78djz32GIqKitCnTx88//zzAIDLLrssxqsjCIIgiOAweFVx0biK0OgywgcAnn/+eWg0Glx33XVob2/HhAkTsHr1auTk5MR6aQRBEAQRFAafiA8Jn1DoUsJHq9XihRdewAsvvBDrpRAEQRBEWPAVPpTqCgWSjQRBEAQRx/h4fKiBYUjQ7hEEQRBEHJOi8xQ+NLIiNGj3CIIgCCKOMWgo4hNOaPcIgiAIIo4x6Dwv1eTxCQ0SPgRBEAQRx/j28aFLdyjQ7hEEQRBEHEPl7OGFdo8gCIIg4hitWgWNihMfk/AJDdo9giAIgohzpOku8viEBgkfgiAIgohz9FLhQ1VdIUG7RxAEQRBxToqksov6+IQG7R5BEARBxDkpFPEJG7R7BEEQBBHnGMjjEzZI+BAEQRBEnOMpfOjSHQq0ewRBEAQR50hTXToSPiFBu0cQBEEQcY5B4uuhVFdokPAhCIIgiDgnhVJdYYN2jyAIgiDinBQdpbrCBe0eQRAEQcQ50vQWRXxCg3aPIAiCIOIcacRHryWPTyiQ8CEIgiCIOIc8PuGDdo8gCIIg4hxW1cVx8JjUTiiHhA9BEARBxDks4qPXqMBxJHxCgYQPQRAEQcQ5BlH4kL8nVEj4EARBEEScY5BEfIjQoB0kCIIgiDiHpbqoh0/o0A4SBEEQRJyTqheETwqVsoeMJtYLIAiCIAiiY8YU52D2iB44e0hBrJeS8JDwIQiCIIg4x6BV47VrxsR6GV0CSnURBEEQBJE0kPAhCIIgCCJpIOFDEARBEETSQMKHIAiCIIikgYQPQRAEQRBJAwkfgiAIgiCSBhI+BEEQBEEkDSR8CIIgCIJIGkj4EARBEASRNJDwIQiCIAgiaSDhQxAEQRBE0kDChyAIgiCIpIGED0EQBEEQSQMJH4IgCIIgkgYSPgRBEARBJA0JI3yeeuoplJaWIjU1FdnZ2QFft2TJEowcORIGgwEFBQW44447ordIgiAIgiDiGk2sFyAXq9WKyy67DBMnTsRbb73l9zX/+Mc/sHDhQjz//POYMGEC2tracPTo0egulCAIgiCIuCVhhM/jjz8OQIjo+OPUqVN49NFH8dVXX2H69Oni8yNHjozG8giCIAiCSAASRvh0xqpVq+B0OlFVVYWhQ4eipaUFpaWlWLhwIYqKigJ+n8VigcViER83NzcDABobG2Gz2cK2PpvNBpPJhJMnT0Kr1YbtuIQntM/Rg/Y6OtA+Rwfa5+gQyX1uaWkBAPA83+HruozwOXz4MJxOJ55++mm8/PLLyMrKwqOPPopzzz0XO3bsgE6n8/t9CxYsEKNJUvr27RvpJRMEQRAEEWZaWlqQlZUV8OsxFT4PPfQQnn322Q5fs3fvXgwZMqTTYzmdTthsNrzyyis477zzAAAffvghCgsLsWbNGpx//vl+v2/+/Pm47777PI7T2NiIbt26geM4Bb9NxxiNRhQVFaGyshKZmZlhOy7hCe1z9KC9jg60z9GB9jk6RHKfeZ5HS0sLevbs2eHrYip87r//fsybN6/D1/Tr10/WsXr06AEAGDZsmPhcfn4+8vLyUFFREfD79Ho99Hq9x3MdVY2FSmZmJn2oogDtc/SgvY4OtM/RgfY5OkRqnzuK9DBiKnzy8/ORn58flmNNmjQJALB//3707t0bgODTaWhoQJ8+fcLyMwiCIAiCSGwSxuNTUVGBxsZGVFRUwOFwYPv27QCAAQMGID09HYMGDcLFF1+Me+65B//5z3+QmZmJ+fPnY8iQITj77LNju3iCIAiCIOKChBE+f/vb3/DOO++Ij0ePHg0AWLNmDaZNmwYAePfdd/HnP/8Zs2fPhkqlwtSpU/Hdd9/FhUNfr9fjscce80mrEeGF9jl60F5HB9rn6ED7HB3iYZ85vrO6L4IgCIIgiC5CwoysIAiCIAiCCBUSPgRBEARBJA0kfAiCIAiCSBpI+BAEQRAEkTSQ8Akjr732GkpKSmAwGDBhwgT8+uuvHb7+448/xpAhQ2AwGDBixAgsX748SitNbJTs8+7duzF37lyUlJSA4zi89NJL0VtogqNknxctWoSzzjoLOTk5yMnJwYwZMzp9/xNulOz1Z599hnHjxiE7OxtpaWkYNWoU3nvvvSiuNnFReo5mLF26FBzH4ZJLLonsArsISvZ5yZIl4DjO45/BYIjsAnkiLCxdupTX6XT822+/ze/evZu/5ZZb+OzsbL62ttbv63/++WderVbzzz33HL9nzx7+0Ucf5bVaLb9z584orzyxULrPv/76K//AAw/wH374IV9YWMi/+OKL0V1wgqJ0n6+++mr+tdde47dt28bv3buXnzdvHp+VlcUfP348yitPPJTu9Zo1a/jPPvuM37NnD3/o0CH+pZde4tVqNf/dd99FeeWJhdJ9Zhw5coTv1asXf9ZZZ/EXX3xxdBabwCjd58WLF/OZmZl8dXW1+K+mpiaiayThEybGjx/P33HHHeJjh8PB9+zZk1+wYIHf119++eX87NmzPZ6bMGECf9ttt0V0nYmO0n2W0qdPHxI+Mglln3me5+12O5+RkcG/8847kVpilyHUveZ5nh89ejT/6KOPRmJ5XYZg9tlut/OlpaX8m2++yd9www0kfGSgdJ8XL17MZ2VlRWl1ApTqCgNWqxVbt27FjBkzxOdUKhVmzJiBDRs2+P2eDRs2eLweAM4///yAryeC22dCOeHYZ5PJBJvNhtzc3Egts0sQ6l7zPI8ffvgB+/fvx5QpUyK51IQm2H3+v//7PxQUFODmm2+OxjITnmD3ubW1FX369EFRUREuvvhi7N69O6LrJOETBhoaGuBwONC9e3eP57t3746amhq/31NTU6Po9URw+0woJxz7/Je//AU9e/b0EfeEJ8HudXNzM9LT06HT6TB79mz885//xLnnnhvp5SYswezz+vXr8dZbb2HRokXRWGKXIJh9Hjx4MN5++20sW7YM//3vf+F0OlFaWorjx49HbJ0JM7KCIIjE4JlnnsHSpUuxdu3ayJsUk5SMjAxs374dra2t+OGHH3DfffehX79+4vgeIjRaWlpw3XXXYdGiRcjLy4v1cro0EydOxMSJE8XHpaWlGDp0KN544w088cQTEfmZJHzCQF5eHtRqNWpraz2er62tRWFhod/vKSwsVPR6Irh9JpQTyj6/8MILeOaZZ/D9999j5MiRkVxmlyDYvVapVBgwYAAAYNSoUdi7dy8WLFhAwicASve5vLwcR48exUUXXSQ+53Q6AQAajQb79+9H//79I7voBCQc52itVovRo0fj0KFDkVgiAEp1hQWdToexY8fihx9+EJ9zOp344YcfPJSslIkTJ3q8HgBWrVoV8PVEcPtMKCfYfX7uuefwxBNP4LvvvsO4ceOisdSEJ1zvaafTCYvFEokldgmU7vOQIUOwc+dObN++Xfw3Z84cnH322di+fTuKioqiufyEIRzvZ4fDgZ07d6JHjx6RWiaVs4eLpUuX8nq9nl+yZAm/Z88e/tZbb+Wzs7PFsrzrrruOf+ihh8TX//zzz7xGo+FfeOEFfu/evfxjjz1G5ewyULrPFouF37ZtG79t2za+R48e/AMPPMBv27aNP3jwYKx+hYRA6T4/88wzvE6n4z/55BOPstSWlpZY/QoJg9K9fvrpp/mVK1fy5eXl/J49e/gXXniB12g0/KJFi2L1KyQESvfZG6rqkofSfX788cf5FStW8OXl5fzWrVv5K6+8kjcYDPzu3bsjtkYSPmHkn//8J19cXMzrdDp+/Pjx/MaNG8WvTZ06lb/hhhs8Xv/RRx/xgwYN4nU6HX/aaafx33zzTZRXnJgo2ecjR47wAHz+TZ06NfoLTzCU7HOfPn387vNjjz0W/YUnIEr2+pFHHuEHDBjAGwwGPicnh584cSK/dOnSGKw68VB6jpZCwkc+Svb53nvvFV/bvXt3ftasWXxZWVlE18fxPM9HLp5EEARBEAQRP5DHhyAIgiCIpIGED0EQBEEQSQMJH4IgCIIgkgYSPgRBEARBJA0kfAiCIAiCSBpI+BAEQRAEkTSQ8CEIgiAIImkg4UMQBEEQRNJAwocgCIIgiKSBhA9BEBFn3rx54DjO518kJzATBEH4QxPrBRAEkRxccMEFWLx4scdz+fn5Ho+tVit0Ol00l0UQRJJBER+CIKKCXq9HYWGhx7/p06fjzjvvxL333ou8vDycf/75AIBdu3Zh5syZSE9PR/fu3XHdddehoaFBPFZbWxuuv/56pKeno0ePHli4cCGmTZuGe++9V3wNx3H44osvPNaQnZ2NJUuWiI8rKytx+eWXIzs7G7m5ubj44otx9OhR8evz5s3DJZdcghdeeAE9evRAt27dcMcdd8Bms4mvsVgs+Mtf/oKioiLo9XoMGDAAb731Fniex4ABA/DCCy94rGH79u0U7SKIGELChyCImPLOO+9Ap9Ph559/xr///W80NTXhnHPOwejRo7FlyxZ89913qK2txeWXXy5+z4MPPogff/wRy5Ytw8qVK7F27VqUlZUp+rk2mw3nn38+MjIy8NNPP+Hnn39Geno6LrjgAlitVvF1a9asQXl5OdasWYN33nkHS5Ys8RBP119/PT788EO88sor2Lt3L9544w2kp6eD4zjcdNNNPlGuxYsXY8qUKRgwYEBwG0YQRGhEdPY7QRAEz/M33HADr1ar+bS0NPHf73//e37q1Kn86NGjPV77xBNP8Oedd57Hc5WVlTwAfv/+/XxLSwuv0+n4jz76SPz6yZMn+ZSUFP6ee+4RnwPAf/755x7HycrK4hcvXszzPM+/9957/ODBg3mn0yl+3WKx8CkpKfyKFSvEdffp04e32+3iay677DL+iiuu4Hme5/fv388D4FetWuX3966qquLVajW/adMmnud53mq18nl5efySJUtk7BpBEJGAPD4EQUSFs88+G6+//rr4OC0tDVdddRXGjh3r8brffvsNa9asQXp6us8xysvL0d7eDqvVigkTJojP5+bmYvDgwYrW89tvv+HQoUPIyMjweN5sNqO8vFx8fNppp0GtVouPe/TogZ07dwIQ0lZqtRpTp071+zN69uyJ2bNn4+2338b48ePx1VdfwWKx4LLLLlO0VoIgwgcJH4IgokJaWprf9E5aWprH49bWVlx00UV49tlnfV7bo0cP2d4YjuPA87zHc1JvTmtrK8aOHYv333/f53ulpmutVutzXKfTCQBISUnpdB1/+MMfcN111+HFF1/E4sWLccUVVyA1NVXW70AQRPgh4UMQRFwxZswYfPrppygpKYFG43uK6t+/P7RaLTZt2oTi4mIAwKlTp3DgwAGPyEt+fj6qq6vFxwcPHoTJZPL4Of/73/9QUFCAzMzMoNY6YsQIOJ1O/Pjjj5gxY4bf18yaNQtpaWl4/fXX8d1332HdunVB/SyCIMIDmZsJgogr7rjjDjQ2NuKqq67C5s2bUV5ejhUrVuDGG2+Ew+FAeno6br75Zjz44INYvXo1du3ahXnz5kGl8jydnXPOOXj11Vexbds2bNmyBbfffrtH9Oaaa65BXl4eLr74Yvz00084cuQI1q5di7vvvhvHjx+XtdaSkhLccMMNuOmmm/DFF1+Ix/joo4/E16jVasybNw/z58/HwIEDMXHixPBsFEEQQUHChyCIuKJnz574+eef4XA4cN5552HEiBG49957kZ2dLYqb559/HmeddRYuuugizJgxA5MnT/bxCi1cuBBFRUU466yzcPXVV+OBBx7wSDGlpqZi3bp1KC4uxqWXXoqhQ4fi5ptvhtlsVhQBev311/H73/8ef/rTnzBkyBDccsstaGtr83jNzTffDKvVihtvvDGEnSEIIhxwvHcSnCAIIgGZNm0aRo0ahZdeeinWS/Hhp59+wvTp01FZWYnu3bvHejkEkdSQx4cgCCJCWCwW1NfX4+9//zsuu+wyEj0EEQdQqosgCCJCfPjhh+jTpw+amprw3HPPxXo5BEGAUl0EQRAEQSQRFPEhCIIgCCJpIOFDEARBEETSQMKHIAiCIIikgYQPQRAEQRBJAwkfgiAIgiCSBhI+BEEQBEEkDSR8CIIgCIJIGkj4EARBEASRNPx/qKiHmcLJjfQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.psd(data, Fs=1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we already now there is a linear trend and a jump we can remove them from the time series. We can do this by fitting a linear model to the time series and subtracting it. The resulting residuals will be stationary and we can compute the PSD.\n",
+    "\n",
+    "Matrix A is defined as follows:\n",
+    "\n",
+    "$$\n",
+    "A = \\begin{bmatrix}\n",
+    "1 & t_1 & 0 \\\\\n",
+    "\\vdots & \\vdots & \\vdots \\\\\n",
+    "1 & t_{269} & 0 \\\\\n",
+    "1 & t_{270} & 1 \\\\\n",
+    "1 & t_{271} & 1 \\\\\n",
+    "\\vdots & \\vdots & \\vdots \\\\\n",
+    "1 & t_{500} & 1 \\\\\n",
+    "\\end{bmatrix}\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QQgghhBBCiBNSvaVtKJVf3bGG4L9XJ0yllOgeHx8fYmNjSUhIYPTo0Tz44IN89913LF68mPfeew+Al156iWHDhhEQEEBiYiK33347ZrMZgJUrV3LDDTdQX1/vrbp67LHHAPjwww8ZO3YsQUFBxMbGcuWVV1JRUXGMHqkQQgghhBBCiN8bU7Odi99Yw7+WZR7rqXRLXZOtzdf5NRJKiS5SFAWLzXFMPnpi08QZM2YwYsQIvvnmG0BtPj5v3jx2797N+++/z4oVK7jvvvsAmDx5Mq+88grBwcGUlpZSWlrKvffeC6i7Ajz55JNs376dBQsWkJeXx/XXX9/t+QkhhBBCCCGEEACrM6vYlF/Ly8v28/Pe3iuCcLoU7vpsK5e9tRarw9nj4x9YKVVwkldKyZqtbmiyOxn86E/H5NwZT8zB39j9py89PZ0dO3YAcNddd3lvT0lJ4amnnuKPf/wjr7/+OkajkZCQEDQaDbGxsW3G8DSbB0hLS2PevHnenl6BgYHdnqMQQgghhBBCiJNbXqvw5v5vdrDkrumE+Pf8rnRv/pLNgm0lAGwrqGNCWkSPju/pKZUY7kdhTRP51Sd3TymplDrJKYqCRqMBYNmyZcycOZOEhASCgoK45pprqK6uxmI5cnK7efNmzjnnHJKSkggKCmL69OkAFBQU9Pr8hRBCCCGEEEL8/rUOb8pNVp5elNHj59hSUMtLS/d7v95dYurxc9S7Q6lhCSEAmJod1FlsR7rL75pUSnWDn0FHxhNzjtm5e8KePXtITU0lLy+PuXPnctttt/H0008THh7Ob7/9xo033ojNZsPf3/+Q929sbGTOnDnMmTOHjz/+mKioKAoKCpgzZw4228n7xhJCCCGEEEKIk129xU6wn95bCNEdee5Q6tKxffhiUxGLd5bx3IXD0Wq7PzZAs93JXZ9tw+lSCDDqaLQ5eyWUqnMv34sN9iM6yIeKBiv51RZC/Y09fq4TgVRKdYNGo8HfqD8mHz3xpl6xYgU7d+7koosuYvPmzbhcLl588UUmTpzIgAEDKCkpaXO80WjE6Wy7pnbv3r1UV1fz3HPPccopp5Ceni5NzoUQQgghhBDiJLdwewkjnljCZxsLe2Q8zy51l4xNxM+go8HqIKfK3CNjA7z1Sw4FNRZig3154ryhAGSU9kallFq8EepvIDlCLf7YkFuDy9X9vtEnIgmlThJWq5WysjKKi4vZsmULzzzzDOeddx5z587l2muvpV+/ftjtdl599VVycnL48MMPefPNN9uMkZKSgtlsZvny5VRVVWGxWEhKSsJoNHrvt3DhQp588slj9CiFEEIIIYQQQhwPvtykhlFLdpd1e6xmu5PS+mYA+kYFepe+bS2o6/bYAIU1Fl5fmQXAQ2cPYkJaOACZ5Q093uzcs3wv1N9Av2i1B/PTi/Yw7R8/k9ELlVnHOwmlThI//vgjcXFxpKSkcMYZZ/Dzzz8zb948vvvuO3Q6HSNGjOCll17i+eefZ+jQoXz88cc8++yzbcaYPHkyf/zjH7nsssuIiorihRdeICoqivfee48vv/ySwYMH89xzz/HPf/7zGD1KIYQQQgghhDhxOZwubv1wE48t3N3jYzdaHZSbmnt83EOx2Bysz6kBeqbaqLBGrZIK8tUT5m9gRKIaSm0vquv22ADPLNqD1eFiYlo4c4fHkRDqR4ifAYdLIbO856qxoGX5XoifgT/N6M/FY/oQ5KOnqLaJx77v+ef9eCc9pU4C7733Hu+99167x919993cfffdbW675ppr2nz9xhtv8MYbb7S57YorruCKK65oc5uinJylh0IIIYQQQgjRVRmlJn7aXQ7A+aMSGJkY2mNj3/T+JrYU1PLDn6fSLzqox8Y9lHU51dicLkBtSl5lthIZ6NPl8Tw776VEBKDRaBiZGAbksq2wrttztdgc/Oiu5vr7OUO8rXKGxAezJruajBITQ92VWT2hdSgVH+rHPy8ZwV9mD2DaCz+zIbeGjXk1jEsJ77HzHe+kUkoIIYQQQgghhDgOZFe2VOX899ecHhu3qNbC2pxqrA4XX24q6rFxD+eXfZVtvu7usjTPznueHkyeSqm9pQ0027u3vC6z3IyiQGSgkUFxwd7bB7s/311S363xD9SyfK+lsXlciB8Xje4DwGs/Z/Xo+Y53EkoJIYQQQgghhBDHgeyKRu/ni3eVUVRr6ZFxl+9p2Yzqu20lvdJUu7S+iZeW7CO/upFf9quhVKi/Aej+Ej7PznspEQEAJIT6ERnog8OldDs02lfeAMCAmLbVY0MSPKFUz/Z58oRSIX6GNrf/cXpftBr4ZV85u4p7Ngg7nkkoJYQQQgghhBBCHAc8lVJaDThdCu+tzuuRcZftKfd+XmZqZl1utffrgmoL320r7nYLlldXZDFvRRZnz/uNvGoLBp2GK8cnAT1RKaWGc55KKXUJn1otta2wewFO5uFCqXh1/D2lJqrN1m6dA3sz1BVgryvGbHUAEKaY4Ie/wOdXwztzSPl4Chl+N3Kdbglv/JLdvfOdQKSnlBBCCCGEEEIIcRzIqVQrgq6ZmMz7a/P5fGMh984ZiK9B1+UxG5rtrMtRQ6iJaeGsy6nhu60lTO4bSVl9Mxe+sYYqs5UAo55Zg2O6fJ4NuWpjc0/oMjY5nHGp4bAyu+cqpSIDvLeNTAxl2Z4Kvt1aRHFtE6cOjGLagKhOj73P3cj8wFAqLTIAH72WRpuTMU8tY0BMIF/cOqll2Z3DBo0VoNVDUKx6m6UGVj4H5nJorFT/NFeCVQ3OHKNuBGYCEORngI3/bXNOX2BmH0g7a1CnH8eJSkIpIYQQQgghhBDiGHO6FHKr1PDlD1NTWbanguK6JpbtKWfu8Pguj7tqfxV2p0JaVAB3zhzAupx1LNpZyhUTknj8+91UuauAlu+t6HIoVdNoI6tCDXcuGdOHb7YWc/GYPgxx92XKqTTTZHPiZ+x8uGZzuCiubQJaKqUAd7Nz2FVsYlexia+3FLHlkdPRaTUHjVFhasag0xIWoAZKq/ZXogDTB0Sxv0ytlBoYGwhOhxomaXXoA6P565yBLFi7iwtMnxBVU4f5LTuhhgY1bGquUwcfdzOc3WoH+g1vHfqB6IzYrOq1DvbVo/MPh2l/hYBoCPR8xHBKUCwY/bDb7Z2+ViciCaWEEEIIIYQQQohjrKjWgs3pwqjX0ifMn3NHxvPGymwWbC3pVijlWbp3+qAYJqSGkxDqR3FdE+e/thpQlwq6FHdQoyje3ec6Y3N+LQD9ogP5xyUjeOqCofjodSiKQmSgkSqzjb1lJkYlhXV67MyKBlwK+Bt1RLXawW9S3whuPiWVarONn3aXUd9kJ6PExLA+bXfKszRbufSl7wjwMfD93y7AYnfylw9WcgvfMjLdyD+aMoky1jPgMws01QAKjLsJzn6Rm05J46YxofCCe1f6A1cKavXgtOJ0KdQ02ogMCEVzyl/cQVMUBMZAQDSugCgaNQFkVTTCljWE+BtAq4UZD3f6evzeSCglhBBCCCGEEOKEZnO4qGm0ERvie6yn0mWeflJpkQHotBouGJXAGyuz+WV/BbWNNm+VT2c0253eUGrmoBi0Wg3zbxjHK8v2s2JvBQ6nwr8uH8Xdn2+juK6JnKpG+kYFdmhsRVGobLASHezLpjx16d64FDV08tGrFVEajYZBccH8mllFRmnnQqmGZjt/X7ib77eXAJAaGdASmLlc6JrreOjUGAiI4Kb3N7FhTw7OJY9AmA3MFepHYwV+jZWsxMVHjTMpM51JTaMNq8PFzb4/QBac4ineanL/qdGqPaA8/MJwTr6TV9ebyGny58Jpozl19BA1cPINBa2Wv325na82FxEb7MuYlLnEh/gS5/BjsH8wDZUO/vHRDrIrG7l+cgoAoX6dfy5/rySUEkIIIYQQQogTSGZ5A++vzePGqWmktuqxc7xTFIW/fbOL/EItZ/Tw7m8PL9jJ11uK+e+1YzktPbpHxz5aPDvveUKhATFBDIoLZk+piR92lnL1xOROj7k0o5yGZgcJoX6MTQ7zjvv6VWOw2ByYrQ6ig3z5ZEM+q7Oq+WVfZYdDqReX7OffP2fx9AVD2egOpcYmhx903OB4NZTaVdxOXylFgeZ6UFwofmHc99UOVu/K5i799wwIbWasnw3eekjt0dRYAS4HjP0DzH2ZSX0j2LAnh5EF70NB22E1gEvR4KexkVPZSI3FhokA3nKcTZUSQpUSQmxCMn+7eJpa4eQfDtpWyww1GnSzn8DPJ5uFi/eyd08g0+ekt6ko8+w2WGZq5ocdpYd9iB+szQNadiUUEkoJIYQQQgghxAnlP7/m8MWmIn7aXc5nt0zscIhwrFWarXyztQTQklttIT3+4GqRH3aUUlLXxE2npHZqGdmG3BqcLoXHv9/NlH6RGPUn3kbzOVVqpVTfqJag8YJR8ewpNfHdtuIuhVJfbylyj5OA9oBeS/5GPf5GNRKY1j+K1VnVrMqs5A9TUw85VpPNSUZpPaMSwyiua+LtVTkAPP3DHuxOFwDjUg4IpRSFsbF6PsXMFvcSP5rrYc2rLdVMrZuCO20w5gbeDbuTxbvKCNNpuUO/EJqB4kNMyqpes8l9I3gSf95TzubqmWPZUWvELyyeQf378eEuC48tr8CJjieqzNQ22gENzzqu8g5zc0oqxAw+4rW8YkISr67IYn+5mdVZ1UztHwlAfZOdyga1V9Q7140lu9JMhclKfo2F3cX1mJodnD8qno/XF2B3qmFssJ+EUh4SSp3ETj31VEaOHMkrr7xyrKcihBBCCCHE78aryzPZXlTH61eN6ZVwpLReXVpU2WDlirfX8e0dU0gI9evx8/S0gmqL9/OdxfWkx4ce9P0/f7YVp0thcr8IhsSH0BFOl0Jxnbr2Kq/awgdr87jplDQAXC4Fq8PVpQbbR0tNow1/o66lUiq6JWQ8Z0Q8zyzay8a8WqrNViJa9VRqT4WpmVXuCp4LRycc8djpA6N4dvFe1uVU02x3HnK3v+cW7+H9tfmcMyIeo6sZX2cDNgKw2JwEYuE+/yUkrlmmVjF5wiZzBac7mrhfP4OHKm6ivslOiEYDq/5x2LmY6mt4du0eAO46awyYboOASG8jcAKiWv7Uq8HmwJggwgN8eKzxKvZUJPL5pkKCffVsmzqM/et340TdfTCnspE6iw0AjUYtzgLof8DOe4cS7GvgwtEJfLA2n0825HtDKU+D97gQX2YOimHmoLbN4j19ugpqmrzPR6iEUl4SSp0Err/+et5///2Dbl+/fj2DBrVsNZmSksJdd93FXXfddRRnJ4QQQgghxO+Hw+ni3z9nYXW42FJQy8S0iB4/R7lJDaUCffRUNFj5dH0B984Z2OPn6Wn5rUKpHUX1XDIO1uVUU9No48yhsby6IhOne1nfruL6DodSZaZmbwUKwL+WZVJQY8HmcLFibwUVDVbOGBLLnbP6M8i9G1xn7Sk1ccfHW/jL7IGcPTyuS2McSoWpmen/WEmAj44mmxOAtMiWUCouxI+BMUHsK29gXU5Np869YFsxLgVGJ4WS1k413cCYIBKDNNQ2qBVNk/tFgrUB1r3hDZguy8rmemMVUXvrCdQ084n+NHwu+Df3f7MDnHCz6wvYfOjx43yaURpha0Etpw6IgvG3gn9ES9Dk2X0uIJoPfi3EsWs/p/SP5NrJKaB5rt3HqtVqmJQWwQ87S/l8UyEApmYHRbVNFNS0vO5yqhqpd4dS5wyPZ6G7X9XADoRSAFdOSOKDtfks2V1ORUMz0UG+ZLtDqX7Rh77Gnoq/K8cntYRSsnzPS0Kpk8QZZ5zB/Pnz29wWFRWFTnf8/o+BEEIIIYQQJ5q8agtWh7qUKavC3CuhVIV7qdBZw2L5YlMRedWNPX6O3pDfKhzYUWyiodnO9fM30Gx3cdHoPizY1rI+a3dJO/2HWil0j5sY7keIn4FdxSY+WJvf5pgfd5exJKOML26dxNgDl5h1wMLtJeRUNfLY97uZOSj6kJVEXfFrZhVNdidNdqf3trSotn3CJvWNYF95A2uyq9qEUhklJhwuF8P7hB5y7AVbSzDg4LIRrZqLW82w/k1vE3DPEjqNuYJf7fV8qj+N/eVj1VAK4OenvXcdDNCq8G9wqIORY/pQ0WDl+R/3kN/3SpL7JLaqaIr2hk3ff5sJW4vZnF/LqQOj4awXDntNNuSpy/xmpkd3agnnpL5qKNXavvIGCmtbhVKVZuqb7ADcdEoqm/NrsTtdDOhgKJUeG8zopFC2FNTx5aYi7jitH1mVnmWXRw7+Zg6KJjrIh4oGKyFSKeUlodRJwsfHh9jY2Da3tV6+d+qpp5Kfn8/dd9/N3XffDahlhkIIIYQQQoiO21fW4P3cs6ynJzXbndRZ1F+qxyaH88WmIgprm9q51/GhoFV4tqesgaUZ5TTb1QDP0/so0EeP2eroVCjlqYRJiQjgn5eM4MddZVQ0NONwKkzuF0lUoA9/X7iLjXm1fL+9pEuhVL577pUNVr7cVMg1k1IOeZzF5uBfyzI5fXBMh87jaRA+ICaQnMpG0uOCCPBp+2v6lH6RvLcmj7XZ6hI0nA4aGs1c8uY6HC6F1fdMIDLj/TY9mpTGSj6uKSLM14y5+ErgjZYBVzx52PlEaEys8VS0GQPVRuJ+4TT5RvKXH0qoVEJ54JJp/Fys4fpThwJw26l9uWFKCr6GuYcdd0xKGN9sLWaTO3A6HKdL8faeGpfauedp+oAoDDoNgT56BsUFsya7mn1lJopavT88n2s0asP3//1pKgp0annnlROS2VJQx2cbC7htel/v+7zvYSqlPAw6LQ+clc6ry7MOWuJ3MpNQqifYjvA/ExodGHw7eKwWDH7tH2vs+R02vvnmG0aMGMEtt9zCzTff3OPjCyGEEEIIcTLYV9YSpmRWNBzhyK7xNFT20WsZkqAuRStsVYHU2+osNnwNui5VCrVeRmVzqMscAUYlhbKjqB6XovDoOYO576sd7Ck14XQp6LTtV8oUeSul/IkJ9uW6ySkHHXPtpBQ25tWytbCu0/MGyKtqmfubv+Rw+fgkrA4Xv+6vZHN+LeePSmBoQgjv/JrLW6ty2JRfy9e3TW533A25aih13+n9GRHvR0Cgu2LH1ggb3wFzOdNN5Xxk3EOkqR7n8xZ0TTXUJF1Ao+1iQF0COXfZY23G1QBh7kvnb69u+YZPIIy5HvzC2vZnCozmq/027v0+n9M84aFGA3NfBiC7uJ5Frt+IDDQyevQ4Ro9u+zjaez14duXbVliH3enCoDt0r7U9pSbMVgdBPnrSYzu31DIx3J/v7phKWICBb7cWsya7ml8zq7A5XOi0GvwNOhqsDgASQv26/DqeOzyOJ77fTWFNE2tzqsl2V0r168CGAxeM6sMFo/p0+py/ZxJK9YRn4g//vf6z4aovW77+Rz+wH+YfjeSpcMMPLV+/Mgws1Qcf91h9p6f4v//9j8DAljfJmWee2eb74eHh6HQ6goKCDqqoEkIIIYQQQnTMnlaVUpnlPV8p5eknFRPsS1K4P6A2ym60OthZXM/DC3bx+LlDmOJZftWDimotzH55FVP6RfKfa8d26D5bC2ox6rUMiQ/xhlKBBgWzXUNOpRp+PH7uEPRaLY02B6MSQ3lkwS4sNid51Y0d2lnQUymWGOZ/2GNGJ6tL2DJKTIdt5A1qJdpPu8uYMyTWe4yiKN5KKV+DluK6JiY9u4KaRivuFlj8lFHGj3dO46P16rLB3KpGtYu2w9pSpGCzYNvwDkvX7yDN30Jfv0b+bcoh0qeeqK9NaEZcCee/5p6JBpY+AoARmOrJcNxFP/VVJd45/5JrYe7oa9WgKUBdOpfd5McfFxSjCYxiyRXnt32Q5/zrkI893lQFFJBXffDvq55+YMkRXSuQ6B8dSLCvHlOzg72lDQzr07ZfmNXhxKjTekO6MSlhHQokDzQ4Xg2yPD2iNrmrruJCfIkI9GG7O5RMjex6oYevQcfZw+P5dEMBX20u8obCh+spJY5MQqmTxGmnncYbb7SUbAYEBHDFFVccwxkJIYQQQghx9FWbrZz/+mp0Gg2XjUviivGJhPobe2z81sv3Khqs6m5jPdg/ptykVkpFB/kQ5Gsg1N9AncVOYa2FrzcXkVVh5n87SnsllPo1swqLzckv+ys7VMVU22jj8rfXYdBpWXHvdKrMaoPpUeEKv5ar940P8WVYQkib3kGD4oLZVljH7hJTh0IpT9jlCekOJT7E19vPZ2dxPeMOs7Ru3vJMXl+ZzRXjE3n2wuEAVJqtNNqcaDVw54z+vPzTLqrceePACD0zLYsJMFWz4V9v8mxTOZHGeqIc9ShPmtAMvwzOf109WKPBuOxhzgZwv0wGe8ImBbXHk4fRH0ZdDb6hak+mbCdf7LUyIn0At8+dxOUvb/UeujqrCuX+eW2uYeauUjIVDSNDQ0HbsR0gPUFNYY0Fh9OFvlU1k6dvWfIRrvGRaLUaRieHsXJfJetzq9uEUmuyq7j2nQ1cMjaR2kb1NXK456ejPD2iPI3zk8L9iQ329YZSad0IpQDOGR7HpxsKWLi9BJcCIX4GIgN77u+Rk4mEUj3hwZLDf09zQAL/16wjHHvAXxZ37ez6nA4QEBBAv379emw8IYQQQgghTkQLtpVQWKOWmzz/416+2VLEojtPOexyos5otDq8AUmQj54Gq4OsCjND4oPZUVRPXlUjAT76bu3e1rpSCtRftuss9RRUW8goVZcOVjY0d+tx1Fvs/H3hLk4fHNtmrjuK6gB16V1BjaXdapOMUhNWhwurw8WXm9SeUWH+BgaGNvNruXrMnKGxBzWzHhLvCaXqOXfEEValuLVudH44Go2GUUmh/LS7nK0FtW1DD0UBlwNFq2fRzlJ8sOG79V0a/cIIsFdjqCxigTGPWJ2JmN9MXDDobIqmv0hShD/RvsDTl6q/WVuA1r/+uQBzecvXBj8yYs5hbbGDKiWEGk0oZa4QJgxN5/ZzJoP/AUHiea95Pw2NruTXjA3sK/QhMduBxa4GbVWNNkrqm8mpaltVVlqvvgbiQnzpqJggX3z0WqwOFyV1zSRFtARQBe5Kqda3ddbEtAhW7qvkn0v2ERFo5IJRfVAUhWcW7cHhUvh0Q4E36BzfyX5SB0oI9cPfqMPi3tEwMcy/zeujO5VSABPSIogM9KHKrIbE/aIDO9WUXbSQUKondKbHU28d2wOMRiNOp7P9A4UQQgghhDhBLXTv8HbOiHh+y6wks8LMV5uLuGJ8EquzqgjzN3qXAHXW/nK1/CUqyIf02CB+zawis7yB53/c612WBBAfOplRSWGHG+aIyt2BU3SwD6D+sr2jqJ7cqkbvckFP36muenVFJgu2lbAqs4pZg6Px0atJy/bCljYimeUN7f5iv6e0pb/WpxsK1PmG+5EU2NJ4+owhB7cOGRKvVtFkdKDZebPd6d2N8KBKKZcTtO6UyN7MVbrlpOv2kb7JBiWKtyk4jZUw+Dyyp/yDvGoLRjT8XTcf1qt3DQPCtKjVTE6I1dYT2yrUcg2/jIV7G8ls9KdGEwoBUewy+XHneVOYNXZomyn9O/huFuWXtbntoiGjIOjILVTGJocTHmCkosHK375WixfOHh7H7hITa7KrWZ1VdZhQ6vBB3YG0Wg3JEf7sLzeTV93YJoDKr1ErpVK6uHwP4JqJyazNruaX/ZXc/fl29pQ2MCoxlF3FJrQacClqZZNRr2X4Acv7Okur1dA/JshbGZUY7kdqZMv1SelmKKXTajhrWKx3l8e+UUf3d/ffk+7/d8BRVFxczNVXX01ERAR+fn4MGzaMTZs2eb+vKAqPPvoocXFx+Pn5MWvWLDIzM4/hjE8sKSkprFq1iuLiYqqqqo71dIQQQgghhOhReVWNbC+qR6uBR+cO5k8z+gPqkq23fsnmqv+u59p3N3R5F+q97qV76bFB3v4yX20uYkNuDQadhlB/dRlf6yV+nVXpXr7nqZTq467++HlfBTanupNdRTdCqXJTMx+uU3/Rrmm0sTRDrfRpsjnZV96qX1YHdhbcU9pyvGfXs6Qwf0KMcM2ERC4YlXDIHeqGuEPB3SWmg58Ll6vlc3szptX/4f903/Ksz/uE/O8mePcMmDcKnukDC25vOVajYdr+Z7jb8DXTTN/D3v9B0Qaoy1d7/porWJqhLp8LDAjgG+dUPmM21lPuZ0naA9xsu4c3+r0Fd+6Ayz9uMyXthW8Tdem/eJsL0I29Fmvf2exU0tjXFEy9XcO1727gI/c13eu+JmH+LUs6x3dgqZqfUceHN44nvlXl0xlD45jaX62u+jWz7e9vJXXq9Y4P7XilFLT0jMqrbrvpVn4PVEoF+Oh59/px/HmGuoLn7VU53Pn5NkDdwW+uuypvdFKoNwjtjoExLSFUYrg/aa2Co7TI7vd/OqdVFZ/0k+q6E6ZSqra2lilTpnDaaaexePFioqKiyMzMJCys5X8YXnjhBebNm8f7779PamoqjzzyCHPmzCEjIwNf3869GU9GTzzxBLfeeit9+/bFarV2+R9jIYQQQgghelqj1cG8FZlcMqYP/aKDujTG99vVthtT+kUSFeTDlROSeHtVDqX1zTy7eC8AVWYrtRY74QGd7w+zr1Uo5anK8DRaPndEAgE+Oj5Ym09+J3bLc7kUtK16N3krpYLUSilPdVDrSqzKButB9+uo13/OwurerczpUvh8YyFzh8eTUVrv7c8DaqVUe/aWHVzplBjuBzZ4dO4gDAZ3MKMo6k5vAA4rg8u+4w79aiKtdTR/+iF+1mp3RVMFDDgTLnxLPVajIXrl37jXk+9kHHCy1j2a9D44Bl/ElzvrqFBCMIbEsq3WyJSRg7lm5jg0QTEse2c7AHfN6s+/1/yVnMpGGn0Gs0Vfy1JXKROSB0FY8iEf65R+kWx+5HQCjHpec+8qmFfVyOKdpazaX8mOojrOH5VArjvseer8YdzxyRYGxgQR28EldkPiQ1jwf1P421c78NHrGJUYikGn4QX2sSariiW7y5g1KAatVkOZu1Kqo2N7eKrf8qosFNZY+GV/JeePSvBWXnWnUgrUCqN7Zg8kOSKA+77egc3hIshXzy2n9MWo1zIyMZRTB0Z36xwenr5S0BJKRQb64KPXkhDW8QqywxmTFEZ8iC8l9c2d3ilQtDhhQqnnn3+exMRE5s+f770tNTXV+7miKLzyyis8/PDDnHfeeQB88MEHxMTEsGDBAi6//PKjPufjxXvvvXfI21euXNnm64kTJ7J9+/ben5AQQgghhPjdWLK7jIXbS3jmwmEE+/ZcQ+8DfbQun7d+ySGz3My714/r9P0VRWGhO5TyVDj4GnT8aWY/Hvp2V5tji2otXQqlPCHMwNjgg5aSXT85hfW56s7aBR0Mpf6zKodXlu3nwbMHcdUENQwpP6BSyrPjXKu8CIdLodZiIyLQ54jjWx1O3l+TR0GNhVqLnXqL3TvHZy8Yxn1f7+DXzCoKayxscy/d8/Tp2d/OzoIOh9O7nLBPkI4JjT8TpanjvHI9gfVZ6D7+j7pszlyh7ljuCZrQYPjhz/zV85vq/gMGbt2jSe9Dbuwc1hdZCY5M4KxJIyEwCgJj3LvQtQ039Je+y0dlv7K7xATuTc5/2gKFAc3cOFXHlgI1QDx9cAxajYaHF+zisw0FGPXqAqP2dp7zvP6T3dVE+TUWPE9LncXOt1uKUBSIDDRy9vA44kMnEx3cudAoOsiX+TeM9349JD6EftGBZFWYueXDzUxMC+fDGyd0afle67nnVTdyxydb2FFUz9oc9WIF+ejbVHh1x0Vj+hAeaOTFJfu4+ZQ0Qtzj3nRKWo+MDweEUmH++Oh1LL9nOhotXdrZ70BarYbXrx7D5vxaTunf8xsLnCxOmFBq4cKFzJkzh0suuYRffvmFhIQEbr/9dm6++WYAcnNzKSsrY9asWd77hISEMGHCBNauXXvYUMpqtWK1tpS3mkzqPyR2ux273e693W63oygKLpcLV+uS0eOUp8rJM+fjjcvlQlEU7HY7Ol33SzOF6CrP+7z1+10I0Za8T4TomJPxvWKxObjvqx3UNdkZnRjCNROTOj1GdmUjb/6Sw5XjExmVFHrY47YXqoHB1oJabDZbp5sKZ5abyawwY9BpmDkgwvs8XTAilp1FdUQH+bAqs4pthfXkVTYwKKZzFSGKonh7KPWL9CM+tCUQGpUYQnqMP8U1akiTX9XYodfJV5sLabQ5eejbXRRWN3L3zH7eRufhfjrsdjtxwYcOCUpqGwn2OXK3li83FfHMor0H3T6lbwQXjIxlwbYi1mTX8Mn6PO/yuzlDYvh2awkFlXXYNn+MzlIBjZVozO4/G9U/zXGnYHNeTIBRx/kjE7h345vq4Hnuk9S2nM/VUIrTez006Aadx8ZiK5trjAzql8YpIwdDYDRKQLQaOLW6dh/GP8q7efn8IS2Z00cPPPhBHnCdp/ePZHeJiekDIhmTFMpLy7L4z6+5/Pe3XBQFhsYHE+mv56whUTz5Py2ZFWZvEVdCiLFDz1tCiPrc51c1UmFqaTr/n19zABgQE4jdbmdoXKB7it37O+OjG8bw3toC5q/JZ11ODeuzK72vk6gAfafG7+Oe++qsKqwO9ffIH3aUAmqVm8Ph6NZcW5uaFsbUP04EeufvzQHR/uqyWT8DIT4a7HY7nkytp843JDaAIbEBPXpdPE70f1M6Ou8TJpTKycnhjTfe4J577uHBBx9k48aN/PnPf8ZoNHLddddRVqY2i4uJiWlzv5iYGO/3DuXZZ5/l8ccfP+j2JUuW4O/f8r8ber2e2NhYzGYzNputhx5V72to6Pp69d5ks9loampi1apVvfIGFqKzli5deqynIMRxT94nQnTMyfReWVmqoa5J/Q/G79dlEFGzq517HOzrXC2ryrR8v6OEy/u6GB916BYSG7N0gIZai50PvllMVCdX36ws1QA6+gY6+e3nts/RJD3QBKubtICWZWu3ohR0rpVFZRPUN+nRaxSyt/xGvhaCDTpMdg3DfGtYtGgRJRYAPdnl9SxatOiI4zU5YH+5+pgB3lyVS1VhFg3N7qbj61axXw8OF2jQobiP02kUnIqGRSt+IydUfQw2J2yo1DAyQiGwVYb19X718Q4KdZEeqhCkczDGupFkVx15/5nHfeZ66g0mItfWE62p51TDUPY13YpBo6XJoWD4/g40HPo6VRfuAyDK6EBXk8Mi53iaMDIwOhh8QrAaQrDqg2k2hNJsCMXe+nr4XsTPfhoWOHQMK3dhyncBJvdH293MN+5VH4OpNIdFi7LbfZ76K/DoKAj3KUPTWMblaRq+y9fS5FSvX19Drfe5GRqqZXOVVl1diELGhlVkdqArc6MdQE/5Ab29Cty7PhotVe0+/501CEgP1rK9RsvbizbgcGnRoLDptxXoOpHf1lgB9N5AqjWjrf3X7fHmzsHgo7OzePHiYz2VLjtR/02xWDpWEXrChFIul4uxY8fyzDPPADBq1Ch27drFm2++yXXXXdflcR944AHuuece79cmk4nExERmz55NcHDLutDm5mYKCwsJDAw8IfpTKYpCQ0MDQUFBx+XWlM3Nzfj5+TFt2rQT4nqK3y+73c7SpUs5/fTTW/oaCCHakPeJEB1zPLxXXC6F+7/dhVGv5ZGz0vEx9F5Fut3p4rmXfwPUioximx9nnjmt0z97fvfRViirxKlo+DhLx/dFevqE+fH3uYMY7a6camh2ULl2hfc+oX1HctbI+MOMeGjff7wVqGTu+IGcNS31kMfsXZrJ5lW5BMWmcNZZgzr3OLaXwradDO0TyrlzJwAQ0K+SPaUN3HxKKjqthiabk+e3L6fJqWHKaacT4nf418lvWdUoGzfTJ8yPc4fH8fovOaypCQCa8TNoufCcM73X+p97V1Fa34xeq2FMchjrc2tJGTScs0YlgNPOb/97n4CCDILMNuam6dReS+YKJjYW8pthCDEXvsOE1HBwOdA/+4e2QVOrl1AstZxywUx+nb+ZPWUNlMfPIjo8BCUgCgI8lUzRKAFRfL/JAuvqmDgokT+enc7V70Zi1Gv5z1XDWb5sWbvvk4jcGha8u4kaxZ+zzpp2yGNsDhdP7FgF2Jg7fTxT+kZ0+PnyOAt4QlEormum0mxleEKId2lXSHY117+3GVCXwZ0399DzOJTndq3A1Kz+53tEgJHqxpbChjMmDuOs0Qmdnmt7ykLy2P7jfvY2+gFWYoJ9Oefs6Z0aw+VSeHbHcmzuUOrBMwfyzGI1YJwwuC9nze7f09MWh3E8/JvSHZ5VaO05YUKpuLg4Bg8e3Oa2QYMG8fXXXwMQG6tuoVleXk5cXJz3mPLyckaOHHnYcX18fPDxOXittcFgaPPEO51ONBoNWq0Wrfb437TQs2TPM+fjjVarRaPRHHSdhThW5LUoRPvkfSJExxzL98q2wjq+3aYutSmoaeby8Ym8tyaPiAAf3rx6NHpdz/1cuHBHEaX1zUQG+lDfZKOiwUppg73dvjsHKnP3SJraL5K1OdWYmh1klDbw8YYiJvSNAiCrqG31/86SBi4e1/Fr7HQpbMhT14tNHRB92Ocnyd2cvLi+udPP4a4SdY6jksK89501JJ5ZQ1qOMRgMRAf5UNFgpcRkIzL44J3MrA4nPnod24vVX+jGJodxzeQU3liVQ4mnT1CQAWPWYrUfk7mCJ7U7UQwVJBobiK6qZ4VhIBWWl9V56LRM3/Ugpxlc0AC0aiEbA8RrahiVGonBoAMMkH426H28PZkc/lG8ttHEknwFn7A+fBMSwIDYIPaUNfB1/+e447R+OF0K93yxjQExQdwxRt1ZbfvSDYDa88jP14evb58CtCzpae99MixR3ZGuuK6ZRrtCqP/BPb6W7CmlutFGdJAPU/pHY+jG6zs12kjqAf21TxkQ421knRIZ0KnXREpkADuK1D5cZwyNZW12NTlVapPzoX3CeuXviHFpkcB+7+6L8aF+XTpPUrg/WRVmTukfyc3T+vJLZhWrs6oZkdQ78xZHdqL+/NXROZ8wodSUKVPYt29fm9v2799PcrLa8C81NZXY2FiWL1/uDaFMJhPr16/ntttuO9rTFUIIIYQQJ7CCagsLtxeTGO7PyMTQTgctHZVX1ciD3+5kRno0N05N7XaF+9rs6pbPc6q9DYoBvtlazKVjE7s1vsfeMhNP/aBudXbTKakszShnc34tG3JrOn2tSuvVJU0PnjWIpAh/Fu0o5b6vd7TZuW13ifrLvVGvxeZwsa2wrlPn2FNqwtTsINBHz9D4w++S5Wka7umf1Gx3otVovI2uj8Qzp5GJoUc8Lincn4oGK/nVFob3aXWs08G3X7zDtj37uWV0IMOysnjTUM7oMhvR79UzP7Q/19feAEBUkC98eQko6n9EzwK1osmpfiQQwS532IdWx3a/CRSbXVQpIcQlJDNnwnBWFmt4cU0d8YmpTGpdUXf5x23mqwf+OMKJ/5p8Rier8/U0kM6qUHtkbS+q47ttJRh1Wv44vS86rYY9pWpINyiua7uShfgZSAz3o7CmiYwSE5P7HdxI+pMN+QBcNi6xW4HU4ei0Gi4bl8TLy/YzNCGkU/dNjmgJpcamhGHQacmpakSrgX7RgT0+V4Ch8SHe9wh0vsm5xyn9I8mvbuT/TuuHRqPhrWvGsjGvhun9o3pyukIAJ1AodffddzN58mSeeeYZLr30UjZs2MDbb7/N22+/DagVQXfddRdPPfUU/fv3JzU1lUceeYT4+HjOP//8HpvH8dg0/EQk11EIIYQQx7OHv9vFqv2V3q/nXz+O09K7v025xebg3d9yOXVgNANjg7jzs61sL6pnTXY1piY7d58+oFvBlCeEunhMH37eW4HN6WJkYii/Zlbxr2WZnDcyHh9995b0ZZY3cNV/1lNrsTOiTwjXTkqmzmJnc34tG/NquKQTwVeTzUmtRa2ciQ/1JdBHzykD1PAhu7LRWzW0u0QNqOYOj+ObLcVklJpotjvx7eDyRE9YNy4l7IjVYn3c28QX1Vqw2Byc8cqv+Bt1LPrzKWiPsFuX1eEkwz3HNqGUywmZS7wVTZjLediyH5uxjAE/NkHuNDj/NfVYjZZz993PBVoXbIMEUIOmOvXbg8Mjvc3Bo0L8Ifh00BshIJqc5gC+2GNl7pSR5DYF8vSvdYwxt/Qz+pvxAfbb1QDJWKxl7YAZfLMvg51KCTP6t78cy0ev4+ZpLbuieUKV/eVq8JTl3mXP5nRRXNtEsJ+eMnej7QGxQXTVkLgQNZQqPTiUyq1qZHVWNRqNGkr1ljtO60t6XBCTO7k0MLnV7otjk8MJD/DhvTV5pMcGd/h121lGvZYRfULY6K4KjAvpWpuUR84ezF2zBniXlwb66DltYPf//hPiUE6YUGrcuHF8++23PPDAAzzxxBOkpqbyyiuvcNVVV3mPue+++2hsbOSWW26hrq6OqVOn8uOPP/ZIzyKj0YhWq6WkpISoqCiMRuNx2avJw+VyYbPZaG5uPq6W7ymKgs1mo7KyEq1Wi9HY+a12hRBCCCF6U53FxpqsKgDv0p0NeTU9Ekp9v72Efy7Zz7zlWUzpF8H2onp89FqsDhfzVmThZ9Rz26l9uzS23eliU14NADdOTeXJ84Z6dw2b/o+fKa5r4pP1Bdww5dD9lDrqr1/toLrRxtCEYD64cQL+Rj3jU8N48xe8vwx3lKdKys+g8/4CHBvsS6i/gTqLncxyM0MTQryh1JwhsazaX0mV2cbuEhNjksM6dJ517rBuUjvBQnyoHxoNNNtd/LirjIIatVFvVaOV6CD37xQuJ2Qtd/dlKgdzJZaKIj7QZRFjMJH062ktQRMa+OwqUJzec4wE0AJNQG1uy8m1WtZrR2K2a6hSQqgklHptKA9deiq64Bj8fKIxvroPm8NFTLAvzP3Ce9c04H7353k7SihjK5XuSilFUSh2V37FBPtQbrLy3OK93msyMa3zfZjS3UFTZrkZu9PlDacAcqrM+LlDl4RQP4J9u77saEh8MD/uLvM+/619uqEAgFMHRNEn7OBlkD1Fr9MyZ0hsp++XHKHOKTrIhz5hfvQJ8+Nfl48kPbZrlWMdNTo5rCWUCu1apZRWqzlivzMhelKvh1JbtmzBYDAwbNgwAL777jvmz5/P4MGDeeyxxzoVSsydO5e5c+ce9vsajYYnnniCJ554otvzPpBWqyU1NZXS0lJKSkp6fPyepigKTU1N+Pn5HZfhmb+/P0lJScdVYCaEEEIIAbA0oxyHSyE9NoiLx/ThqR/2UFjTsV2E2pNXrY5jc7r4eZ9aifXCxcOpMFl5etEeXlm2n/NHxXdp2c2OojosNidh/gYGxgS1qez588z+PPTtLv750z5K65u5bnIKCV34hbWwxsK2wjq0Gnj3unHeX1zHJIej0ajVKxUNzS0BTjtKPT2SQn29P7NqNBrSY4NYl1PD3rIG+scEkukOPYbEBzMyMZRleyrYWlDboVDK4XSxIVcN6yalHbwEDFCDppyfMZor+Yv/anys1UQt+Q8fGyqI1NSj/X4KXPm2+2ANfHYFuFp2kA4DJnp+rK3JaRlXq4W+p4FGB4HREBjD9jojb202Ex2XyGPnz/YeqigK11n/it3Z0mR8UloEuqETAQgC5gyp5/vtJaRFHX6JpOfaVzSo17bOYqfRpoZij8wdzP99spUvNxcBYNRpGeVuJt8ZSeH+BPnqaWh2kFluJtO9jA8gp7IRg3u544CY7i1TG5KgBjie5ZseVoeTr9yP4aoJyd06R2+ZOSiGEX1COH9Ugve1fd7Inm9ufqCxyeG8hfoa7GqllBBHU6+HUrfeeiv3338/w4YNIycnh8svv5wLLriAL7/8EovFwiuvvNLbU+gxRqORpKQkHA4HTqez/TscQ3a7nVWrVjFt2rTjrimaTqdDr9cfl2GZEEIIIcSPu8oAtTmxpwKj0F1p0l2eipXBccHsK2/gotEJnDcyAUVRWJpRzoa8GuYtz+TZC4d3emzPErUJqREHLTW7dGwi32wpZnN+LW+vyuGzDQX8dPe0TodfP+1Wr8341HCig1t+4Q3xU4OwvWUNbM6r5cxhcYcboo2SOvV6xB8wj/TYYDWUKjWRHhuEw6UQ4mcgIdSPUUlhLNtT4e3Xc1guF+T+QnFBLpc5NtHH18SQDd/BigporISE0XDuq+6DNfDJZeBy8H+g/pbUjHfnuarqrJZxtVpInQYaLQTGQGA032baWVEI00cP4eLTJrSdx9Vft/nSkV/Loo1riDf78lhYivf2+ia7N5Ay6rTYnC5vDyePpy8Yyoz0KM4edvidB6OC1E2cPM2ui93XODLQh7nD49FqNPz1y+002pyMTAzt0lIyjUbDkHj1OdpVUu8NDUENJj26s3QP1CbpoC7lbL1c88ddZdQ02ogL8eXUgcdnn6PwACPf/d/Uo37e0a1CRgmlxImg10Op/fv3exuPf/nll0ybNo1PPvmE1atXc/nll59QoRRwwuwYp9PpcDgc+Pr6HvdzFUIIIYQ4XjQ02/k1U126d+bQOBzuPphFPVQp5Qlh7jitH6elR3mXOWk0Gu47YyAXv7mWLzYVcdMpafSN6lyVydojLFEz6LR8ceskVu6r4Pkf97K/3Mwzi/by6hWjOnWOxe7A7syhB4dOwxJC2FvWwN6yhg6HUmWeSqkDfnke7G6OvafMREqhWhU0JD4YjaIw1rWTc7WbGFFsg6UL1IDJXK72bIofBefOUwfRaODjS0h22XnY8+PwjlYnMba6vlotpEwFNGysNrCl2kClEkqlEkIVIZyZNo6rW0/wmm+9nyqKwr+2riTPZeGiYeMg4sjLPD3LukpNzeRXN2KxORkUF0ylO0QK9Tdw09RU3lqVwzkj2oZPwb4GLhjV54jjR7tDKYvNidnqoKhWfe16+mWdNSyO/tGBvLEym8vHJx1xrCMZGh/Cupwa1uVUe3cFBHX5nsMdrg2I7l4oFR3kQ2SgD1VmKzuL6xmXou7I9/F6deneZeMSe3RHyd+DiEAfZqRHs7+8gYHdDAWFOBp6PZRSFMXb1HrZsmXe5XeJiYlUVVX19umFEEIIIYTosBXu5uBpkQEMiAmkwaou0aputNFodRDg070fnz1VKwlhfvgb2441NiWcWYOiWbangleWZXYqMGq2O9mcr/aROVzfJJ1Ww8xBMcQE+3LOv3/j++0lXDUhqcM9hcpNzd5zHKrHjmdHtswKtWrG1GxnZ1E9k/tGHLZCvqS+GQ0uxmr2wK5sb8A0q7yQdwyZxBXVk1cxALiWKf0iQaNh3OpbmGC0gRlYfcCAhlYVVxoNJE9md0k9mRZ/+qakMmzgAPcyumgIOaA59rXfAfDrkn3MW5HV5ltJ1sMHTetza8irtuCj1zIqqf3lhBEBRvyNOiw2J9P/sRKAhf83BXOz+lqLCvTh/2b05/9mtN+A/FACfPQEGHU02pxUNli9OwkmhLVcm/4xQbx02cguje/h2Y1uye7yNrfnuKuagG6HIhqNholp4fxvRym/7q9kXEo4WRUNbMitQdvLDc5PZO9cNxaXor7nhTje9XooNXbsWJ566ilmzZrFL7/8whtvvAFAbm4uMTExvX16IYQQQgghOmxJhvoL9hlDY9FoNAT7GgjxM1DfZKew1tKtJsV2p4ty945k8aGHXlZz16wBLNtTwY+7SqmzDCHUv2P9Vz9eX0Cz3UVCqB/929lufmhCCFeOT+Lj9QU8tnB3uzvLeXiW7o1OCiX2EMuC+sd4dmQzg6Lw4eefs3NfJoFjgxgRanU3Bnc3B48dDue8Qml9EwoaLs74E+yyeccKB2a6V5U1NevwM+i4cnwSaDTYE8azJa+GKoI5e9JItEFqryYCoiGkbRWR/eoFXPL4Eix2Jz+cORXcy8GOpHXTbE/DdU+10aG8vUrt33PRmD4dag6t0WjoHxPE9sI6723bC+sIdt/Xs/yuO6KDfdX+XqZmbxDaJ6xrTa8PZ6i735PZHdwOjgsmo9Tk7ROm0bTs0tcd0wZE8b8dpfySWcU9swfyyfpCAGakx3Sp99rJQKPRoJM8Spwgej2U8uyQt2DBAh566CH69esHwFdffcXkyZN7+/RCCCGEEOIY2ltmIirQh4jA7v+i3REvLtnHjqJ63r52DD76zvXKURTF25ep9U57SeH+7Cyup7CmqVuhVFl9My5F3bY9MuDQ12NoQgiD4oLZU2rifztKuXpi+02cTc12/r0iE4A/zejXob6d984eyFebi9hb1kBedSNp7SwVdLoUvt5UyChNJn+MCYJN+9wBU4V36dy48EHAHPKqGrE5XNyUexc+RnvbJXMeGvW5Ka1rBjQ0RI4i1FcHgVHegOnFNbVkNPhSrERxycQ+hAWoAZ3+hv9xzcOLcbgUxk6ZccRgYmdxPRabk1B/A4M6+Nz1CW8Z74JRCcxfneetNjpQZnkDK/ZWoNHAzaekdWh8gOcuHMaa7Gq2FNTyw45SCmubiHaoq0t6IpSKCvRxN51vqZTq08Wd2A4nNTIQP4OOJndV1PjUcErqm6iz2AFIDvfvUr+qA00foPaM2lFUR2GNhS83qaHUVRO6vvRQCHH86PVQavjw4ezcufOg2//xj3+g03X/LykhhBBCCHF82pxfwyVvrmViWgSf3Dyxx8dXFIUvNxdR02jj1mlpWGxO3liZjcOlsK2gjgmd3Oo+s8JMTaMNX4OWEX1Cvbcnhvu5Q6nu9ZUq9jb19j1iZdKFoxJ4utTEt1uLOxRKvf1LDrUWO32jArh4zJH7DXmEBRhJCvcns6IBU/YGqLG19GVq9ARNlRA9COa+xH9/zWF7cT17fZ7Cd6cdDv7xHn8UgnzOpsHqYE1ONX6uvuhwYTGEM23UEHdTcHfo5F46V1KvXpPKi74mNKbtUq+c4i0s31GKRgNvTU313q7TaogJ9qW4romSuuYjhlItzd/DO1QNBpAcEeA9z8Vj+jB/dR7FtU24XMpBY3iqpOYMjiU18vA74h1oUFwwg+KC0a5GDaVqLHhGjuqBADcquKXZefEhlu/1BJ1Ww+D4YO+Szv4xgaRFBrCloA5oWc7ZXTHBvqTHqk307/xsKw1WB32jArxhlRDixNbroRRAXV0dX331FdnZ2fz1r38lPDycjIwMYmJiSEjo/W0xhRBCCCHE0ffu6jxcCuxsb5e0LlAUhecW7+UtdygwPjUcU5Mdh0ttsFzaqvFyR61zNwofmxyOUd/SPDnRuwNf50OpZruTT9YXMHd4nLfJeXvhwHkj43l28R4259eSV9VIyhHCjiqzlXd+ywXgvjPS1abPigKl21s1AHcHTOZy9baogXD2i8SH+pFZ0cDQJVeA6zDXy2kjo8TEP5fsAzQ0hA/D11/b0pcpwP1nYAya0ET6LbCwtaCOLzYVssj2qDqGDTZOn3VQBZDZ6qDB3Ucp7hBVPKMSQ/lhRylnDo31BkUecSFqKFXW6nlutjt5ZVkmM9KjGZ+qNsT2PKeTOhFQJoT68eR5Qwh27yio02qwOV1Umq3EtNpxsLDGwoJtxQDcMr3jVVKttX5teZre98jyPfcYZfVNrRqd+x/pLl0ytFUoNSAmiNTIwB4PpUBdwre3rME79i3T0jocMgohjm+9Hkrt2LGDmTNnEhoaSl5eHjfffDPh4eF88803FBQU8MEHH/T2FIQQQgghxFFWYWrmJ/dObQ1WB6ZmO8G+PbMjsKIoPPLdLj5aV+C9bfmecpyulmM8VUmd4QkwJqaFt7m9T7g7OKjp/Jgfrs3n6UV72FJQy0D3L+kJ7Syjig72ZUq/SH7NrOLbrcXcffoANWgq39WyZM7dn6kqK4v/UEhdaCqzB5+lDqDRwPyzwN546BPY1NvVcExDecAAEoJ0rQImd4+mwGgyrWHc9P5G7E6F0wfHEHnNCnX8wxgQvYOtBXUszWjb/HpLQe1BzdFL3c9RkK+ewEM0kL9mUjJh/kZmDzm4D62np1Vpfctz8sHaPN78JZvPNhaw/J7pBPka2JTnaf4eedg5H8o1k1K8n8eF+FJU20RhjYUdRfU02Z2cMzyOfy3PxO5UmNovktEdaHB+KImtXlth7v5hPRFKDXH3zvp6SzEmd/DX3uuuS+dJaOnR1T86kLSolvBwQA/u/DZ9QJS3Ki0qyIfzR0lhgxC/F70eSt1zzz3ccMMNvPDCCwQFtfzFdNZZZ3HllVf29umFEEIIIcQx8NnGQm/VEkBxbRPBcT0TSn27tZiP1hWg1cDpg2P4aXc5y/dU4NOqf01JJ0MpRVFYl1MDcNBudEne4KDzlVIb89Qxf8uqwt+ozi/eEw4oClTsadUAvMJbzfTP5nwW6YP4ZOcdaiil0cA7cw4KmtIBdFDrq23bSypmCDia3OFSDAREeYMmQtVePJ6Q4qXEf/PipSO4+/Nt7Mk18dVtkwn00fP99hL+8vV2726Ez144rN1+VZ5m53an+tyH+RuotdjZnH9wKFXirnKKP8zyOx+9josOsxwxzhtKNbsvpcKnG9ReQ3UWO88u3ku/6ECa7E7CA4ztNn8/kj5hfhTVNrExr5Z//LQXlwK/7q/kmy1FANw7Z2C3xgaob7KTU6k+tz0RSp07Ip6Xl+73hrOh/oZu7xx5KGOSw9Bq1CWPof5G0lpV9Q3swUqpsSlh3v5V109O6XS/OCHE8avXQ6mNGzfy1ltvHXR7QkICZWVlvX16IYQQQghxlDmcLj5Zr1YxaTXgUtSQaFBc15uEexRUW3j0u90A3D1rAFdPTGZpxlL2ljXQejVPZ0Op1v2khrfqJwWQ6A4OCmstKIrSfiPxqv3QXA3mCgbl/8ZIfTVRtjoSdjeQro8hKPSf6nEaDbxzOtjMBw0RA4zWpvF4hZnKBqsaVEQPAnuTt5ppX6M/X+614fCN5OHz5rQd4Kal7T5mTyhVUtdEs93Jd9uKcSnwW2YVpw+O4dHvdmFzujh9cAwvXjqiQ5VuBy7ZumZiMvNWZHmXeLXmqZSKO8xOhEfi6SPlWb63Nrua3KpGfPRarA4XX20u8h5749TUbi31Upe91fDfX3Pw5KxfusefNSiGkYmhXR47wEdPRICR6kabN0DqiVDKqNfy55n9+NvXavOvnt55z6NvVCCf3DzRO+e+7vBPr9V0qsdWe3z0Ou6dM5ANudVcO6n9PmtCiBNHr4dSPj4+mEymg27fv38/UVHSnE4IIYQQ4vdmc34tZaZmwgOMjOgTws/7Kru0nO5ATpfCXZ9vxWx1MD4lnNtP64dOq2Fscjgb8mpoVZhFSZ0aVvyWWcWm/Br+PKP/EYOJw/WTAnWZW5q2lEh7HeYtDQQ5atpUNRGeBmc86z1eP3+2N2i6G1p+4lYgUJuKufUyqsgBYLe06s8U4w2dPl5WB1Xq3M4ZEQ83L/fezepwctdra9jjNHHfKQPRp/br9PX0VGwV1zWRU9novX7rcqpJCPWj1mIn0EfPG1eNVntVdUDrUCo8wMiFo/swb0UWO4vqabY72+zG5nlNHKlR+eF4KqU8jdI/3qCGoJeM7YPT1VI1de/sAdx+at9Oj9+aJ9CpbrQBMDM9mhX7KgD4y+wB3RrbM75nbOiZRucAF47uw2s/Z1NQY+mVpXserSsL+0cHcsu0NBJC/Q56H3XXjVNTubFVw3shxO9Dr4dS5557Lk888QRffPEFABqNhoKCAv72t79x0UUX9fbphRBCCCHEUVZmUgOh9Fi18XFPhVLv/pbLloI6gnz0vHTZCHTukGnmoGg2uJfJjUsJY2NerbdS6v5vdlBU28SktIiDd+OryfEum9Nu3MDd+iJmOoBPX4GwVDjjGUCt0vjO+AhBWOD7Q0wsdnibL5WIfmhsjdRowlhTrqVSCVE/CKVIieK51lUrt/x82McbULgbqvJaQim3JbvLePKHDAprmvA1aLlyfFLHLuAB4kNb+jLtK2/5T+S12dXeht4T0yI6HEgBxAT7EOSrp6HZwajEUJIj/IkMNFJltrElv5bJ/dTeTnUWmzc4GhTX+WVensboZfXNVDZYvf3LrhyfTEKYHzqthnEp4Zw3svu9hxJbNQj3M+j41xWjyKow43C6eqT6r0+4P9vdmwHotBpvb6nuMui0PHz2IP706VZmph/cl6s3aDQaHjxr0FE5lxDi96HXQ6kXX3yRiy++mOjoaJqampg+fTplZWVMmjSJp59+urdPL4QQQgghjrJqs1r1ER5g9AYfnm3pv99eQlK4PyM6ueQpt6rRvQMcPDx3UJudxGYOiuHZxXsBuCZdgyM/k0h7PTW/5HKhaSNR+joSlrwDqYNgTqufP986FaxqGHA1qD8Zl7k/DgiayvUJVNoaqEINmALC4zh19DAIjPL2aPJw/mEZWoOB//y4lzeKshmaEMyu4pbQx9Okuz2T0iKYvzqPte4qLoD86kZu/3gLDpdCdJAPT18wjNAuhhixwb5oNWr/p7XZLefYV97A99tLAJjar+O71oEaSgyMCWJTfi2jk8PQaDSMTw1n0c4yrn9vI1dPSObaScm8uiKLKrOVftGBXDYusdNz91RKlZuaWbi9BIdLYURiKIPj1ZDoqfOHdXrMw2m99O2MobEE+ui7tWTvQK1Dr8hAY4/uKjd7SCwZT5zhDXCFEOJ40+uhVEhICEuXLuW3335jx44dmM1mRo8ezaxZs3r71EIIIYQQ4hiobrQCEBno06Zv0c6iev706VYSQv1Yff+MDo/XXJ3Pm5+sYIqriMlxTi61ZMAi9/K5kET6zn6K8SnhZFWambv2Ms71UYMmfoZ7PG2QygAq2g4clgxWE7lNAexr9Mc/LJZpo4a4m4G37Vvz30Hv8tnGQgKMOhrtTozVWjZNmHXEPkvbC+sAtXrn2cV7aGh2EB3k0+EmzRNSI9BoIKeykXJTMzHBvuwuMeFwKQyMCeLbOybjb+z6j/N6nZbYYF9K6ptZua+yzfcyStUQbWr/zu1aB3DbqX35cF0+l4xVm5Q/eNYgqsw2NuTW8O7qXN5dnQuoLbWev2h4l5pWRwb6oNdqcLgUPlmfD8DcYXGdHqcjPLsvAlw0+tCN17sjMbwl9OqJflIHkkBKCHE86/VQymPq1KlMnTr1aJ1OCCGEEEIcIzXu/jgRAUYSwlr6Fq3PrfZ+bqooJNjZqjeTZ/e5xgoIToDZTwJgc7hwvjaF510NYARqgdYr3mLUHeE+vGk8Lhdo302ivKKcUkcQTv9o9pn9qCSEPn1SuGjG5LYT/eOv5FSamfXSL7gU+OHyqRAfwqH8eWZ/UiMDOH9UAlf/dz2ZFWZ+2lXGJWMPXeXjcinscC/JGp0cysS0CJZmlHuvR0eE+BsYHBfM7hIT63KqOW9kAnnV6g5tg+ODuxVIeSSE+VFS30xFgxok9osOJKtC7YcVE+xD36jO71o3c1AMMwe1LBfrE+bP57dMZFVmFe/8lstvmZW4FLhhcipjksO6NG+dVkNMsC/FdU1ku3etO31w7yxRiwv2ZdagGBRFYVLfzlWOdUTrSqme6iclhBAnil4PpZ544okjfv/RRx/t7SkIIYQQQoijqMpsY5p2O1MqNtLf0sTLhp2UW8LZkHuX9xj/d6aCte7QA8QMBdRQ6t4vt3ObI4xQjYGgiHgCIxLUJXOBMeqHu6LJW23zx9946P1NLNtTjsGpwe5Uu3dP1IZz0YBJbU5T0dDM/V/vxKXArEHRDDlMIAVqU/Bbp6sNs88dEc+LS/ezcHvJYUOpnCozZqsDf6OO/tFBnDYwmqUZ5aTHdq5/0qS0iDahVEG1BYCkVtU73aE2O2/ZGe+aicn8faG6u+GUfpHt7zTYQRqNhukDopg+IIqKhmb2lDYwtV/nq7Baiwvx9fYqGxATSEoP7vbWmlar4b/Xje2VsQESWz2XvVEpJYQQx7NeD6W+/fbbNl/b7XZyc3PR6/X07dtXQikhhBBCiN+Z0Nqd/MP4PKgtoLhAB3tciby8v2WJmNkYRajBtyVgCoh27zoXoy6rA4pqLSzcXsIP2ud49/rxTB/QsZ2bE9x9rDyBlDpW20brq/ZXcvfn26hutOGj13L36R3fRe3ckWootTqrisoG6yGDhC0FdQAMTQhBp9Vw+bhEwvwNBzdbb8fEtAj++1suG3LVRu6eSqmUyJ4JpVrvyhbsq+ecEfHeUKq7odHhRAf5Eh3Usb5aR9K6N1dvVUkdDfGhvmg0oCgSSgkhTj69Hkpt3br1oNtMJhPXX389F1xwQW+fXgghhBDihLeloJbNebXcdEpqj1Wu9KbQRrVnkDUgHp8RF/Hmpga2mUOxulzeY/494H0ePmfIEcfZnK9W8AyJD+lwIAWe6p+2yuqbcboUdFoNH6/P59HvduN0KaTHBjHvilEMiOl4BVNyRAAjE0PZVljHA9/s4JkLhx0Usny7pRhQQyVQq23O7ELPI09D+JyqRhqtDvLdlVLJET1TFdT6WvWPCSI8wMhZw2LZXljPaQOje+QcvSWuTSgVewxn0j0+eh2xwb6U1jfL8j0hxEmn4/u79qDg4GAef/xxHnnkkWNxeiGEEEKIE4bd6eKWDzbz9KI9bXZhO575W9WKKFufyTD7KVbHXMmPrvFtjsmqamx3nK3uaqPRSZ3rO9Q6aAkw6jDo1IbY5aZm/vtrDg99uwunS+HC0QksuGNKpwIpj9tO7YtOq2HZngpmvfgLK/aWe7+XWW5mbU41Wg1c3oWd5VqLCvIhOsgHRVEbp5fWNwOQ0kOhVOseV/2j1f5Rr181htX3zyAsoGu7+h0tcSHq3KODfBiecPillyeCfu5rnxTRMxVwQghxojgmoRRAfX099fX1x+r0QgghhBAnhOV7yqkyq02oD1yCdjxqtjsJdarhmSEsAYD4kJbgw/PLt6eZ9pF4KqVGd7IZdutQKj0u2BteFNU28Z9fcwC447S+vHjJCHwNnd/5DWDOkFi+u2MKQxOCMTU7uPmDzXzlro76aEMBALMHxx6yaquzhsQHA7B4VxkAQb56wvwPv+tfZyQcUCl1Ipk5KJqEUD9uP7Uv2hN8h7knzxvKCxcNZ/qA47s6TQghelqvL9+bN29em68VRaG0tJQPP/yQM888s7dPL4QQQghxQvtkQ6H383J3lczxrKbRxvvO2WxgCK+PvBBoW41z6dg+PLNoL8V1TVhsDu8Ocs12J1qNBqNe/T9Ti81BRqkJoNM7tLUOWgbFBWHUaSmosbA+p5pykxW9VsP/nda/20shhyaE8O3tU7j/6518vaWIB77dzfBwLVnmUgCunZTcrfE9hsSH8PO+Sm8olRIR0GPLONss34vu/E57x1JyRACr759xrKfRI1IiA3qtUbsQQhzPej2Uevnll9t8rdVqiYqK4rrrruOBBx7o7dMLIYQQQpywCmss/JrZ0hy8vOHECKXylDia/FPQxA4D2gYfMwfF8OYvOdQ02sipbGRoQggNzXZmv7yKmGBfvr19MhqNhh1F9ThdCrHBvsSHdK4pdlSQD3qtumRvUFwwVrvay+rbbWol05CEEPyMXauQOpBBp+WflwwnKsiHN3/JZkeNFnDSLzqQSX0719T8cDyVUp6KuZ5c4hXooyclwp/S+mbveYQQQoijpddDqdzc3N4+hRBCCCHE79IXmwpRFNBqwKVAucl6rKfULk9wEhHQ0rC5j7tSKtTfQFpkAP2iAtnQWENWhZmhCSFsyqultL6Z0vpmKhusRAf7sqXAs3QvtNNVQTqthtTIADIrzIzoE0plgzqnnEq1j9W4TlZetUej0XD/memcMyya575cTYkriIfOGtRj1UxD4tv2S0rp4b5DX/xxEuZmBxHSZFsIIcRR1uuhlBBCCCHEyeJfyzIJ9tNzw5TUHhlvgbuy59wR8SzYVkKF6fivlKpusHKjbhFh2iRwTAC9kbHJYVw5IYlxKWFoNBr6RgeyIa/G21fK0zsKIKPUpIZSnn5SnWxy7vHvK0eTU6mGXnvLGtp8b2xKz4ZSHgNigrg4zcVZZ03BYOiZnk8AieF+BPnqaWh2AD23855HdJAv0SdWOykhhBC/E70SSl144YUdPvabb77pjSkIIYQQQhxV+dWNvLxsP1oNXDE+qcsNtD0KaywU1jSh12q4bFwSC7aV9HqllKIo5FdbSAz3R9fFxtGNdeU8YvgIqgHNPQDodVqeuWCY95gDm523DqX2lDYwfUAUWzw773WxqmlgbBADY9WkJeGAZuNjksO7NOaxotFoGBwXzPrcGqDndt4TQgghjrVeCaVCQk7sLVmFEEIIITprd4nalNulqIFSd3cyW5ut7mA3IjGUvlFqCFFptuJ0KV0OjNqzYFsxd3++nfvPTOeP0/t2aQx7XQkAZn0YgbpDVwt5Gmrvr2jA4XSxrbDO+709pSayK83UNNrw0Wt7pM9Rn1aN1lMi/IkKOvGWqQ2JD2kVSvXs8j0hhBDiWOmVUGr+/Pm9MWwbzz33HA888AB33nknr7zyCgDNzc385S9/4bPPPsNqtTJnzhxef/11YmJien0+QgghhDi57S6p936eX939UGpNdhUAk/tGEBHog1YDTpdCtVntudQddqeLrAoz6bFBbfoeLd9T4f6zvMuhlGJSd55r8o3mcHu5DU0IQa/VkFPZyNdbimiyO73f21Nq4rdM9bGPSwnHR9/9huSxIb7evlxjU06sKikPTzjnZ9CdkKGaEEIIcSjaYz2Brti4cSNvvfUWw4cPb3P73Xffzffff8+XX37JL7/8QklJSaeWEgohhBBCdFWGu1IKIK+6sVtjKYrCGnel1KS0CHRajTeI6IklfPNX53Lmv37lzV9y2ty+q7je/acJh9PVpbF1ZjWUsvsf/j8FwwOMnDUsDoCnftgDQLp7qV1OVSPL96rh2JR+kV2aw4EMOi2x7iBvbA83OT9aJvWNwEevZXxqeI81UBdCCCGOtaMSSn311VdceumlTJw4kdGjR7f56Cyz2cxVV13Ff/7zH8LCWn6oqK+v55133uGll15ixowZjBkzhvnz57NmzRrWrVvXkw9HCCGEEOIgGaUtoVRBjaVbY2VXNlLRYMWo13p7KsW4Q5XyHmh2vsG9DOw/v+bQ7K5SMjXbyatW591kd5JVae7S2MZmNVByBcYe8bjrJqcAeJt3nzk0jjB/A06Xwq/uSqmpPRRKAVw9KZkRiaHMGXLkeR2v4kP9WPfATP573dhjPRUhhBCix/T67nvz5s3joYce4vrrr+e7777jhhtuIDs7m40bN3LHHXd0erw77riDs88+m1mzZvHUU095b9+8eTN2u51Zs2Z5b0tPTycpKYm1a9cyceLEQ45ntVqxWlv+x9FkUn+gtNvt2O32Ts/veOGZ+4n8GIQ4GuS9IkT75H3SvmqztU0FU16VuVvX67dMNdgZnRiCDhd2u4uoQCMAxbWN3X4ust0NxmsabXyxMZ8rxiWyPb+mzTFb8mroG+F3qLsfUYC1EgBNYMwR5zksLoBhCcHsLFZ/9hrZJ4iBMYGsy1Wbnof6Gegf5ddjr7ubpyRz85RkoPdey739Xgk0asDlxO5ytn+wEMcp+TdFiI450d8rHZ13r4dSr7/+Om+//TZXXHEF7733Hvfddx9paWk8+uij1NTUtD9AK5999hlbtmxh48aNB32vrKwMo9FIaGhom9tjYmIoKys77JjPPvssjz/++EG3L1myBH//E7+J5NKlS4/1FIQ4Ich7RYj2yfvk8PbWaYCW3kd7CqtYtGhRl8dbsE8LaAl3tIzTXKvetmbLbkKrdnZ5bKcL8mt0gLoE7N9LMgiq2MnK0raP4X9rdxJQvr1TYysKBNsrQQu5FWY2tXMNhvtp2IkODQplGevxaVYfI0CKn5WfflzcqfMfL+S9IkT75H0iRMecqO8Vi6VjVeO9HkoVFBQwefJkAPz8/GhoaADgmmuuYeLEifz73//u0DiFhYXceeedLF26FF/f7jX3bO2BBx7gnnvu8X5tMplITExk9uzZBAd3f7eXY8Vut7N06VJOP/10DIZD73wjhJD3ihAdcbTeJ/vLG/jfzjJum5aGn7H7za2PpqJfc2FPJiMTQ9hWWE+dXcvsOaej13WtU8JzGauAZq6aPYHx7sbcuStzWF2eRXBsIpVhgXy5uZgrxidy6ZgEDJ04T3ZlI671q/EzaNHrtFQ0O/BLG4ujsQwoY3BcEBmlDdTrQjjrrEmdmnej1cEl68v4VHMaL55zOX7RaUc8fqbDReOC3SRH+HPhaX1RthTzy7e7Abhw6hDOGpfYqfMfa/JvihDtk/eJEB1zor9XPKvQ2tProVRsbCw1NTUkJyeTlJTEunXrGDFiBLm5uSiK0uFxNm/eTEVFRZs+VE6nk1WrVvHvf/+bn376CZvNRl1dXZtqqfLycmJjD987wMfHBx+fg3cwMRgMJ+QTf6Dfy+MQorfJe0WI9vXm+8Ric3DLR9sormsiPiyAayYm98i4iqIclabQ+8rVxuazBsWQUdqAzeGiyuIkMbzzu6TZHC7K3H2jBsSGeq95fKhawV1S38xPu8sxNTt47Ps9fLS+kE9unkB0UMf+066gVh07LSqQU/pH8eYv2fzr5xwsNrW309UTU3jw253sKzPjRIuvoeMBYUODnUylDwW6JILiB7R77Q0GeOWKlp/thvZp6Rc6fWDMCfv3svybIkT75H0iRMecqO+Vjs651xudz5gxg4ULFwJwww03cPfdd3P66adz2WWXccEFF3R4nJkzZ7Jz5062bdvm/Rg7dixXXXWV93ODwcDy5cu999m3bx8FBQVMmtS5/+UTQgghxNH16oosiuuaANhb2rH/WWvPb5lVDH70J8Y9vYwr3l7HupzqHhn3UDxNzockhJAYpvZhyq/uWrPz0vomFAV89Foi3X2kAKKD1YBrXU4NpmYHwb56gn31ZFWYWbW/qsPj51SpAVpaVCC3TksjyFfPnlKTd75nDI0lIsCIw6Wwp5PPRZVZ7asVGejTpTAwPTaIGenRXDAqgaTwE7+NghBCCCGOrNcrpd5++21cLnVL4TvuuIOIiAjWrFnDueeey6233trhcYKCghg6dGib2wICAoiIiPDefuONN3LPPfcQHh5OcHAwf/rTn5g0adJhm5wLIYQQvyc7i+pJCPMjPMDY/sHHkczyBv6zKqfl64qu7frWmtXh5KEFO2myO2myO6lssFL0lYXl95yKUd9z/ye3cHsJP+0qI9u9U92QuGCSIwLIrmwkv6aRqXR+97iiWjWc6xPm1ybY8ey+53SplebnjUzAbHXw7dZiqs3Wgwc6jBz3XPtGBRAWYOSO0/rx3OK9ACSEqq+f4X1C+HlfJTuK6hmVFHak4dhdUk9SuD9BvgZqqsq4UfcDRkMSMKPDc/LQ67S8e/24Tt9PCCGEECemXq+U0mq16PUt2dfll1/OvHnz+NOf/oTR2LM/NL/88svMnTuXiy66iGnTphEbG8s333zTo+cQQghxcqi32NlX1nCsp9FhWwtqOeffv/HnT7f22jlWZ1fz0+7Dbx7SUdsK6/jXskzsTvU/rV5ZlonDpdA3KgCArB4IpeavziO/2kJUkA9f/nESkYE+FNY08cWmwm6P7ZFTaebPn27lh52lKAoMiQ8mKsjHW+FT0MVKqaJa9X6JB1QKeUIpj7OGxXkrqaraCaWsDid7Sk0oikJOZUulFMD1k1OID1HHHpqg9tMckRgKwJaC2iMMambHuiX8698vs3j+07DyORLXPMIjho+5pemdDjxSIYQQQpzser1Sql+/flx99dVceeWVDBgwoEfHXrlyZZuvfX19ee2113jttdd69DxCCCF+/37YUcqO4jr+NicdrVbDbR9vZk12NcvumU6/6MBjPb12/ZqpLt9am1ON2eog0Kdn/4k32+HeD7fgdCmse2Am0cFd33Tk0e92saOonvhQXy4e04c12ercnzxvKFe9s56aRhtVZiuRgZ3vxwRQYWrm1eWZANx/RjrjUsL5v9P68tj3Gby6IpOLx/TpVJ+kw1nrXg6YHhvE388ZwqikUDQaDckRapiUV93YpXFbV0q1FuZvwKDTYHcqRAYaGZ8azvaiOgCqzbYjjvnvFVm8uiKLv58zuGX5XqQaAvoadDx5/lDu/2Ynlw4Ph6LNnKHPoky3gZT9ZpQfgtCYy6GxEkZfByOvUAet2MPwHy/hbSNQrn54ftIzB6Vx5PoqIYQQQoijEErdcccdfPLJJzz55JOMHj2aq6++mssuu+yIzceFEEKIo+3vC3dTZbYyZ0gso5PC2FlcD0BWRcMJEUptdVe0OF0Km/NrmT4gqkfH31SlhiGgVjJ1NZTyVOwArMmuZlRSKLUWO74GLWNTwkkM86egxkJmubnLodTXW4pptDkZkRjKBaMSALhiQhJvr8qhpL6Zj9cXcOPU1C6N3dqG3BoA5gyJZVLfCO/tKRFq2NPVnlItoVTbSimNRkN0kC/FdU3MGRKLTqvxXqPKI1VK2SyUZKzlVG0Rect+5XJbNVH6etJ/+wIsVTD6GmaOuJyND8VA4Ub47yzSgecMgAvY2GqspFZ9OoNiqNDFUGwPolIJYcrIwXy9z87+Rj/Omn4TJ9a+eUIIIYQ4Fno9lLr77ru5++672b9/Px9//DGvvfYa9957L6eddhpXX3011157bW9PQQghhDiihma7d/lTQbWFvlGBNDSrO5FVtlOBcjxQFIWthXXer9flVPd4KLWxsmXFf251I5P7db5XEkBmudkbbq3JrmJcSjgAIxNDMeq1DIgJVEOpioY2QU/nzqEuu5w9OAatVu3J5KPXcftp/Xh4wS6+3FTY4VBqY14NiWH+xIa0DeEURWF9jhpKTUgNb/O9JHelVEGNpcO7/zU022m2u4gK8vEu3zuwUgpgZFIoZaZmLh4RCaU76N+QyUXaDQypaoYfvwNzOTRWwMirYMTlADjLdvNi3Z1gBBTAsxnOHvefia16OAXFQHAfCIxmS42BfWZ/hvTvx/D0ARAYBTEt/T1dwYnMdLxKg3vXvtf6j+bxzVtxuhT+L7Vfu49ZCCGEEKLXQymPAQMG8Pjjj/P444+zbt06brvtNm644QYJpYQQQhxzBTUtFS2FNRaK3ZUqQKcaSB8redUW6ix279fre3iXuX1lDRQ1tgQruZVdW5YGeCvQAMpNVm+Pp/HucKpfdBDL9lSQWd71vlJZrRp5t3b2sDge/W4Xe8saKK5rIiH04NCntf3lDVzy5lqSI/xZds90DLqWYK6otokyUzN6reagRuBqg3Kw2JxUmW1EBbVf8XXNOxsorKhhybUJxFWv43xtJaOKdkOZCcyVatg04gpeuOgi7pszkOSmvfDWDEYALxqBJmBdqwHjR3s/LbAF4aeEUamEUKWEUKmE4hsWy7lTRkFAFMQOa7lfaBLcsxuAdSuzeOHHfczWxPD2hLEHzTmnqpEGq8P79cLtxThdCsG+emKCu1blJoQQQoiTy1ELpQA2bNjAJ598wueff47JZOKSSy45mqcXQgghDql1Q+rCWou3UgXabyB9PPAs3UsI9aO4rokdRfVYbA78jT3zz/y320oAvP2Mcqu6HkrtahVKgdr0HGCsO5Tq714qub+8a03mFUUh290o/cBll2EBRsYkh7Exr5YVe8q5ZlIKZfXNhAcYD7kjn6fRfX61he+2lXDxmD7e7613L90b3icEP2Pb/lQ+eh3xIepzUVhZQ1SzSQ2VzBXuD3d/JnM5DLuUitTz2FZYx3BNLhEfXs08UKuaNhwwobjhBIy4jAAfPdRFQ0A0dr9I1pbrqCKUC6aOQhMUA4HRbYKm7Q1B3GV9jSHxwZSbrFSZrVzbL5lzJw7lSKb0jQT2sS6nGqdLQadtW/G1w93PymPF3goA0mODO1QdJoQQQgjR66GUZ9nep59+Sm5uLjNmzOD555/nwgsvJDDw+O/RIYQQ4vevbaVUE8V1rSuljv/le1sL6gA4c2gsi3eVUVzXxOb8Wk7p3/0lfBklJr7ZqoZSl49L5MN1Bd0LpUrUflIDYgLZ766G0mpgdHKY+/YgoGM78NU32flsQwGXjUsk1F/dha7cZKXR5kSn1ZAUHnDQfWakx7Axr5ZleyqIDvblto82c/GYPrxw8YiDji2rb/Z+/vrKLC4YlaAGMw4buXu3MkGzjyuCfWHdTnXJnLlcrWoadjFJ4UkU1zVRm7sdfr3s8A8iejBb9dMBqFRCqdcEU+YMploTyqThg9AEukOmgGiIG95yv9BE+GsmisPFtQ8vBuC0qacTFnDwzsa7S9QgcHRSGKOTQ3n8+wzOHBrX7vUdmhBCkK8eU7ODXcX13h35PLa7A8URfULYXlTvXZY5MDao3bGFEEIIIeAohFLp6emMGzeOO+64g8svv5yYmJjePqUQQgjRKfk1B1ZKtYRSJ0KllKfaaFRSGDUWG99sKWZdTnW3Q6nFO0u554vtNNmdxPopXD8piQ/XFVBQY8HudLVZztYRdqfL2+T85lPS+OtXOwAYHB/s3S2wb7QaJFU32qg2W4k4QrPz5xbv5dMNBZTWN/PYuUOAljArOdz/kNVPMwdF8/yPe1mbXU1GqQmXAkszylEcVjT1RWolU6Na0TRwz26e0ecRpTHxffVEFu8awNzh8VCxm79mXg0+QJb7o7WoAaREprM2p5rspgBm+oaqwVJgjLpcLjBG7c8UGANxI9i6tQ6AUiIY0fQmoFZ5LbtoervX1KjXEuwOjqobrYcJpdRrPiQ+mAtG9eGCUX0OOuZQdFoNE9MiWJpRzr1fbicx3J87TuvLmGS1qm17kRp2XT4+iR3FO1HUTIoBEkoJIYQQooN6PZTat28f/fv3b/e4Tz/9lHPPPZeAgIP/V1MIIYToTYWtQqnS+mbyq1sqgXqqUqq20cbC7SVcMrZPjy2rA2iytexmNyoplEabg2+2FHubcHfVqv2V3PHJFlwKTO0Xwdlh5fQJ9cPPoKPJ7qSotonUyM79m51VYcbmcBHko+fckfE8+t1umuxOb7NzAH+jnsRwPwprmsisMB82lLI5XCzeVQrA2uyWHlrZnn5S0YHgtIOpuM2yuf7mCl4O3IaftZpFjRNYyGRqLXaK9m8h8Ysz2pxjGnh/UspVYnl56X5mDYqhpCmAKMWPKiWExKQU9MEx7sApWg2f4keStE+9NhnmQLg//4jXZVvh2oNuO1ST88OJDPLB1OygssFGv+i231MUpVUoFdLhMT1mpkezNKOczAozmRVm7E4XH944AZvDRYb7dTcpLYLUyABy3L3G0iWUEkIIIUQH9Xoo1ZFACuDWW29lwoQJpKWl9fKMhBBCiLbyW/WUcroUNuXXer+u7KFKqWcW7eHLzUXkVJp5/Lwj9/LpiGqzlT9/tpVdxSYcLoWYYB/iQnwZ7W66vbvEdMg+QB2RXWn2BlIXjErgmfMGseSnH9FqNSRH+LO3rIHcKnOnQylPP6nB8cH46HXMSI/mh52lzEhvm6T0jw7yhlIT01p24Gu2O9mQXcHkaBs79uxnTPM6onT1RFbV0/zdt/g2V+FvGQUMpG9UIFRkwFvT2oytAS4A0EGBEs26gNOoaLCyrdpAoiHAXdGkfvyY5yLD5MfUUYNYtSeI7MpGHv8+g60Ftey1vsPM9GjeuX4ch5JcpQZmravwDsXhdLHDXXHk6QkGkBjm38GrCpEBPuRUNlLdePBrtbiuifomO3qthgGxnW+bcMnYRGJCfNlX1sBzi/eyq7geRVHYV9aAzeEixM9AcoQ/Q+NDvKGUZwmmEEIIIUR7jmqj8yNRPDXfQgghxFFkd7q8QUCQj54Gq6PNTnYNzQ6sDidv/5LDV1uK+OLWScQE+3bqHDaHi592lwHw1eYi7p0zkCBfQ7fm/ePuMlZntVQIXTY2EY1GQ2pkgLeaKbfKTL/ozgUEWRVm/vDeRhqaHYxJDuO5i4ahVVze76dFBbC3rIGcykZmpHduzp6KnaEJasXOsxcN49bpaQyPDwJTibcn00WabaTrsui3+VsodEL62TDsYp5dtIeN635hkc+DjAXeab1Sbav6R0CAHjWUCoCAEND7tvRkci+bq1BCeHNTA4nDJnNRSB/eWJnNr2V6znmopM18n3xuBcXOJk4dP5mbhtn5bf5GPt1QAEBEgJFnLxzG4SSFq6FS68AT1NebS1Hw0avN0feXm7HYnAT66LlyQhL/+Gkf0NlKKfVCHKqqz3PN+8cEec/ZGTqthtMGRjMpLYJ//rSPWoudkvpmthaqwe3wPiFoNBqGJgSzcHsJ8SG+hPh177UthBBCiJPHcRNKCSGEOHHVNNr4YWcpcwbHEN3JwOZYUhSF0rpmnC4Fo17LyKRQfs2s8n5fqwGXov6y/+XmIgpqLKzLqea8kQmdOs/anGpMzQ4AGm1Ovt1azLWTUjp8/8IaC/Ghfm2qnjxhwzUTk3ngrHTvkkCdVsOguCC2FNSxu8TUqVBqWUY5d32+DbPVQZ8wP968egw+eh12e0so5amOyqtup9m5ywmNVa0agFeQtn8jD+tLmVOmhZ0XEjzsYob3CYWynfDmVO9dzwbONgCV7o+gWMwDzuerzUX4KyHYFHW3uUolhGafCHKbAkhITOGUkUP4eql6nftFB0JQKDxUBgfsBBcNPDxXQavVsCyjHIDN7mbx3um7FMpNaqPz2GC1Au2GKSnMX50HwD8vHXHE13pyhBpK1TTaaGi2E+RrYGlGOY8s2EWzw8lj5wzhvJHx3nBnRGIIU/tFtgqlOl4pFRGgLnE8VP+z1v2kusPXoGNATBAZpSZ2FtWzLkcNRCekqksvZ6TH8OKS/cwZGtut8wghhBDi5CKhlBBCiG7ZUlDLHR9vobS+mXd+zeGr2yYTeYTm1EdiarZz0/ubmD4gijtO69fDM23rpvc3sa/cxL2zBwJqZYunugUg1N+Ar15HmamZ0vomimrVipfWO7J11I/u3kfhAUZqGm18sDafayYmo9G0v7Tu18xKrnlnA1eMT2pTmZPhDhvGp4Yf1KNqSHyIN5TqaIBW32Tn/z7dQrPdxfjUcF6/avTBz6PiYmCQjQGaQvyL8mHHHnfg5N51bsBsGHqRemzFHnhzSpu7XwvqTx5FQEISDLtY/UZgDGh07oqmKEz6cH7Kc2E2RHDD7PGQMIaF20potDmxasMYYP0A0BAf4st9Z6Rz/+fbGGoLZuSIiSz/dgkAaVGBB4VRrWndAZ9n17/sykZqG23eRuHVjTYcLgWtBqKC1OvwtzPScboU+scEcdrA6EMP7BbkayAiwEh1o438agufbSzgo3UF3u/f9fk2vttWTJPdCcCoxDCGJoQQ4megvslOSmQnlu8FekKpgyulPE3wh3YzlAIYmhBMRqmJXcX1rHP3LPMsr+wXHcjOx+ag78JyUSGEEEKcvCSUEkKIk0iV2crOonpOHRjVoUCkPauzqrh+/gbvVvB51Raun7+BT2+e2KXlaSv3VbIht4bN+bWcNzKe2GBf3vwlG6vDxZD4EBqa7ewuMTExLYIzulGRYbY6WLZHrZB54Ue1MiUp3J/EVqFUQqi6fKrM1MyW/Dpc7lXmpR0Mpb7dWsTzi/dx16z+LNmtnuvp84fyly+3k1VhZm12NZP7RbY7jqdh+ecbC7j5lFTSogJxuhT2lqmh1OBDhA2eqpjdJfXtT1RRoKmW/H1ZjHLupF9gE48Ni0S37ie1OXi/mTDwXPXYij2cu2Q65/oAVcA3B4zlF9YSSgVGAxoIiITAGCzGCBbnOanThnHD7PFo+7TqxRQQBY9UgVbdLU9ndfDXv/8EDjhv+OmEBxj55LtfAfjrnHRW7qtkbU41545M8IYiGSUmtheqjzcqyKfDS8jCA4ykRalNurcU1DJzkLpLsCd8jAz08e4y6GvQ8UQn+oElRfhT3WhjSUY5H60rQKuBm6elEWDU8+qKTH7eV+k9dlRSKDqthtevGk1OpblTTckjAtUg7cBKKZvDxcZcd3jUN+Kg+3XWsIQQvthUxHfbi6lptOFr0KqVbm6H2u1QCCGEEOJIJJQSQoiThMulcM07G9hTauLDG8dzSv+obo/56opM7E6FmenR3DVrANfP38CuYhPzlmfy0NmDOz2eZxc5p0vhnd9yiQn25Z9L9h903OcbC5mRPrvLvwTvK2vwfu7pJ5UU7t+muXSfMD+a3cvWNuS17GTnWdLVnu+2lVBmaub+b3YCEOJnYNbgGM4bGc+nGwpZklHeoVDKs0zOpcBrP2fz4qUjyK1qpNnuws+gIyXi4GbjQ+KCCcJCQ3ElSp4DTWNlmx3o6DsDhl6oHlyRAW9MZjjwqRFwAEtaDWYMbAmlAtXqoBolkEollH5pfdEFuXs1BUTBoYImnfqjxk9bi/hL5nbGJodx45TJbSes0bSpagrw0XubfmeWN+Bv1LOr2IRRp+WSsYlcNTGZJbvLOHNoHH5GHckR/uRXW3h5mfpa6RfVuYbeY5PDyKlsZFN+q1DK/TzHhXR9OWpyuD9bC+qY/1suALMHx/LAmYMAOGtYLO+vyWfB1mL8jDrGuZfBTekXyZQOvC5a81RKVR8QSm0rrKPJ7iQiwMjAHmg+7ukFVlijvmfGJodLECWEEEKIbun1UOq6667jxhtvZNq0aUc8Ljk5GYNBGmMKIURvWbi9xBv6bC+s63YolVvVyLqcGjQaeOL8oSSE+vHgWYP4y5fb2XpAf56O8ixJA/hsQyEu9yYYM9KjqWhoxt+oJ6PEhNnqYGdxHWOSw7t0Hk+VUWvJEf4khrc0l+4T5k+tRV0OtalVKNXRSqkDG1yfPjgGg07LqKQwPt1Q2CYY6+g4S7ZlUzxGR1l+HmdoNzIqxIZu5Wa1b1PqdG/QNFBfxE7fm0AB3jvEoAa/llAqUA1hmnRBFNuDMITEkJyU6m0K3iZo8o9EebiSOc+vorLBykdTJjC1/2ECFI3GG0gB7Cpu2+S8PQNiAtVQqsLsfb7OGBpLuHt53YWj+3iPHZ8STn61hc3uXRM72z9pTHIYX2wq4qN1+aTHBnHeyATK6tXgpbNN7VtLcgeGDVaHe84tSyn7RQfx5PlDeWTuYDQavNVYXRHprZRqu3xvdZbaH21S34geqYwcFBeMTqvB6S4b9PSTEkIIIYToql4Pperr65k1axbJycnccMMNXHfddSQkHNzfYteuXb09FSGEOO5syK3B36jr8C/qXWVzuHhpaUvFUVaFudtjfr6xEIDpA6K8S90GxalhwP7yBhRF6fQvwp7QLMhXT4O7Mfi0AVG8c91Y71h//HAzP+4uY11OTddDqVI1EPJU44BaKdW6uXRCqJ+3P05tq934OlIp5XC6KKxRw6S/nZHOyn0V3HxKGgDpsWrFyj7PNbI3uZuBV6pVTI0V3qomJXUaedXqtT0tvJr5lj/Bh5AATDUCZmCV+6RagzdoMgbHAdCg+KENiiEgPK7tDnSJrYOmCHi4ghvnb2VNdjX/mD6c5LGJbR+Q3f34NRo0egOnDojiy81FLNtTfvhQ6gC7itWldR0NjPrHBPHzvkr2lTWweKe6c+FFY/oc8tg/TE2lsNZCTLAvI/qEcvHYQx93OOeMiOezjYVsLajjzs+2kV3ZiNOlVsl1t1LKI8zfwKmH6EPVE5VGh6uUWputNiPvbOXV4fgadPSPDmSvO1DtiSWBQgghhDi59XootWDBAiorK/nwww95//33+fvf/86sWbO48cYbOe+886Q6SghxQrE7Xei1mh6pOiiua+LK/6zD16Bj3YMzCfTpnb+SFUXhP7/mUFDTUnGTVdm9UMrudPHV5iIALh/XEmCkRQWg1YCp2UFlg7VTO/FVm61UNFjRaOCBMwfx4Lc78TfqeOaCoW2u9/jUcH7cXcaG3BruOK1r8/dU3tw5qz//WpZJRUMzg+KCCfM3EGDU0Whz0ifMz1sR0lpFgxWnS2mzE96BSqrriXZVEqdv4NYYDbcFVcL+32BLBQP7TEajURue1+TvJOK9Uw47jtWp0NA8C40Gbp87Gb4Ai+JDnS6MUkcQUXFJJCUmq0FTm4qmcB4YtIRPt1Zx59T+3H36gMNfDI0G9D5kuoPK/h1Y5jVzUAxfbi5i+d5y/n7O4HbfDy6X4t0FrqMBbL9odQned9uKMTU7CPU3MPkwIciguGA+u2VSh8Y9FH+jni9vncQryzL5989ZvPtbLqcOVCsJY7oRSrVuVn7uiPheW+rm6SnVaHPSZHPiZ9RhsTm8O/sd7rp1xZD4EPaWNbj7SfVumC6EEEKI37+j0lMqKiqKe+65h3vuuYctW7Ywf/58rrnmGgIDA7n66qu5/fbb6d+//9GYihDiGHp9ZRYVJiuPzh3s3fnqRLKzqJ6L31zDDVNSuf/M9G6Pt3JfBQ6XgtnqYGlGGReM6lx1R0dkVZi5/+sdbHIva7p+cgrvrckju6IRl0vp8vOwfE8FVWYrkYE+3h48oFZSpEQEkFPVyP5yc6dCqT3u6qXkcH8uH5eI3ekiPTaoTfUSwIQ0tTpqU14NDqcLfSeXPSmK4q2UGtEnlK9vm0ytxUa8u9prUt8IfsuqYnifUMzuZVcAehxEYCJSqce04wfClDr3rnMVkDSJ3JjT+WFHCTcPdpL05gTWeB76523P76O4SIk4h9yqRvab/ZgEoPd1VzFFe3egIzCGIl+1L1dcsC/jBvXl5oQFLM1uBNTn7duzJ5OUFHbwg9RoGNAnGrZW8VtWFVdPTPbuIOdhd7q4+f/ZO+/wJq6sjb+SLNmWe++ATe8dAqRAaAHSC+mbkJ6QQshmU770zSa72d10Uja99wYJIThASCD0Xg0G9967ZZX5/ri6mpGsrpFsmfN7Hj8jq4yuZua2d95z7oc7kREbjvvnD0VNC3PZcDHIGWcMToRGpURJPQuvG+JCyCqqb0erzoDQECUGu7F/AJZ9Npsdc/PM4Y/+IkSlxPK5Q/DlzhJUt+iw7kg1AB+dUpJ8X9JwQ7mJDA1BaIgSOoMJta06ZMVrsaOwAXqjgIzYcKtVJX1lXL9YfLO7FJMHxCM0RCXbfgmCIAiCODUJaKLziooK5ObmIjc3FyqVCgsXLsSBAwcwYsQIPPfcc7j33nsDWRyCIALIH8drLKucnT8uHRPsTaR7OZ9uL4LOYMJPB8plEaV+PyauvPXD3nKHopQgCHh3cyHadQbcNdszAf+Bb/ZjV1EDwtRK3HrmQNwxayA+2VaEDr0RFc2dlrA7T/nlEAunumh8ejehYFBypFmUanE7tAsADlew8K4R6dFQKhW4bvoAu+8blhqN6LAQNHcacLii2Wr1L3cobehAi84AtUqBnIQwqDvrkaqoBvKrgNYa/C+nCvrESoSWdSExkiXkHqCowG+h94k7+d5mp8YuPLApBdsL6hGvzMRVALoEFVpD4hGfkmkWnJLMoXOnYWh9FApq23CoXonovxzEE78U4+YzB2LeSOsVBQ/sKQWwj4kbCgVumzcWua9vAQAoFexYOGJcFjsuu4oacNqz63DN1H54/LyRFiHyUHkzfjOv/sYdLxmx4W459iJCQzB9UAJ+y6vBr0eqUNXciYZ2Pc4bk2bXNcVD94alRbstItqKYwtHp7n1OV9QKhWYNzIFH28tRofeCMC3nFKJkaG46+xB6DKa/OoqUigUSIwMRVljB/JrWrG7uAHvbS4EAMwYJE8+Kc7iSZlo7tBjgQ+rXxIEQRAEQXD8Lkrp9XqsXLkS7733HtauXYsxY8Zg2bJluOqqqxAdzQbT3333HW644QYSpQiij9JlMOHxlYcs/+8oqA86UcpgNOGXQ1UA2MpTDW1diDMnXHYXo0nA8i/3IitOi3vmDMaf+XWW1/44Xou6Vh0SIkO7fe6NjSfxrzVHAQAXT8x0KiR16o1o1RmQGBkKg9GEA6VMDPj29hkYYc7l0z8hAvnVrcivbvVKlBIEwZJA2V6OnCEpUVh7uMoSDuYu3Ck13InQAgAqpQKTB8Rj3dFqbDtZ312UMpmAjgazi6kKaKsRHU1ZU3BUmAIAOCOhBepnkgHBZPVxJYBQADB1ImHiTABAvcDKZIQStUI0wuLSEZOYYXE2NSROwI7NLBn65lIDSseuwWvb6nDT5Bw8cm73VQiHFh3DmkOVOFrViu2FeuwoasTuT3bj1SvHY4FEfCmsZSGXPAxsYv94zBiUgM35dchJikS4xrFTZVxWLP5z2Vh8sq0Ie4ob8cEWJqo+c9FoKJUKFNSK5+fldfkAgMEp7q9aN3t4Cn7Lq8Gr6/PR3sUEHLVSYVV+DhelRnmQgDxSsgJfTLhatrxIrpg/MhUfby22/J8W451wy7lv3lBfi+QWiZEalDV2YMl7O6yenz9SXvEoNESFpbMGybpPgiAIgiBOXfwuSqWlpcFkMuHKK6/E9u3bMW7cuG7vmTVrFmJjY/1dFILwK0V1bSiub/d5RbO+yDubCnCyps3y/47Cetx61sAeLJH7lDV2IC06DNsL6lHfJq5sdaCsCWcOYee6vq0LN36wA/NHpuI2J7/rSEUzfthbDoAlN27RGRCnVSM9NhyHypux+mAlrj2tP6pbOnH/V/tR3aLD0JRIfG/+DABUNjl2NxlNAq58aysOlzfj1+VnQWcwostoglajsiTXBoBBSZEWUeqsIdbXa1O7HjuL6jFraLLD0L4TNa2obtFBE6LExP7dxUUubByvcm91OenxAcRk6VYIAtDZaE4AXoWrIg6iv+owUnf8AEPCeQgZeR57X/1J4JVJgGC0/yUTl+ColrncUlMzgGYTAAUQkchcTOawOUQmAf2mIcksEjZDixuTP0NoVBJWH6rGE5NH4PoZ2Zbd/rC5AIJwGACwu6QRnWnRABTonxhhWwIAYrLznYX1lkTrRpOAuz7bg1cVCpxjdqEU1bF6Iw0De+CcYbju3e24aHz3RUOkKBQKXDoxE5dOzMTKfeVY9vkefL6jBLFaDR5cMMyqTvIyuBtaBwCzhyXjUcAiSAHAP1YfwaxhyQhTM7Hsf7+fwDubCiyrwnma0H9QMluBb66fQ/eknJaTYHHiAUCqD06pQDIsNRr7zCL0oORIzB+ZgnPHpNuvTwRBEARBEL0Ev4tSL7zwAi677DKEhTke1MXGxqKgoMDfRSEIv3LXZ3uwv7QJ39w+zesVwXoSQRDw8dYijMqIwXgZXUztXQas2MBcGH+Z1h8fbinCjsIGn/IZBYr1R6tww/s7MXNoElKirNuw/aWNFlHq54MV2FPciJM1bbjljByHv4tP/AFYVsI7Y3ASxmTG4FB5M97fXACj0YQ3fz+Jiia2whsXapQKwCQANS2OV377fEcx9hQ3AmDOq5hwtpDE4JQoqzINSo4EDtlfge/fa4/i463FePbi0bhySj+737PZ7PCa1D/OIj5IGZzMBBeXK/AJAqBrAVqr0dVcicE1v2KqqhFTC7cDyinAsEXsfQ2FwKuTAaMoCs4GMFsNoAX48sta5E8dhIcXDge0iaIgFR4vCkyRKWhUxqIiYjz2lbCJ+4CMVOC8PPYZlf3uMM7IXVQKJCRnQKth76tstl7lbLV5dTgAqGjqtAg1AxLs5/IZahalCuuYE2pYahSGpUbh+73luPPT3Vhx9QTMH5lqeV26nzGZsdj96FyPQrLOH5uOTr0Rf/t6Pz74sxD3zx+Kk7Vt3d7nTpJzTnpsOC6ZkInDFc14aMEw/O3r/Sht6MC7mwtwx8xBEAQBb2w8aRFzY8LVljrjLtdPH4CmDj1uOyvHo8/5glqlxOzhKfhuTxliwtVO3Wi9iUfPG4FLJmZiUHIk4j10cRIEQRAEQfQUfhelrr32Wn9/BUH0OAajCYfNK0vlHq4OSlHq1yPVePSHQxieFo2f73G8Gpin5B6uQqvOgH7xWjyyaAS+2lmKpg49jle3WibmvZXPt5cAgCXvDsASPP9xvBb7zY4EANhZyJKIN3XokVfV4tCZUCERpThnDknC6YMS8c+fj+JETRueWMXcNgOTInDHzEHYV9qIOK0GhyuakXu4CtUtum774N/937XHLP/vLWmwODyG2QgNPFfPCTsr8B0sY9fx+qPVTkQpFrrnKJwqJwYYoKhCvK4JzbtbEWNqQHVFMVYcjcDIWVdh8eQsoKEIWDEFMDCRTQPgFb4Y63YAhutEUUqbIApSYTFAZAqEiCQcbArDgaZQ/NE1GD//fhLXTR+AjJgo4N7DLKxOJa7uujm/Fje8vwM6gwkAC8MclhYDRDkXSdQqJeK0ajS065GdGAmu7VU2ieeyurkTO4pY6B4PN2vq0AMABiTYd0r1T4hAmFqJTj0TvS4an4GbzsiBAJZfbOknu/HWdZPsOqUAeJUj6JIJmXhi5SG0dxlRUNtqcUolRGhQZxaOPHFKAcB/F4+1PH5gwVDc+8U+rFifjysn94POYEJ9WxdUSgXW33cWUmPCPE6MPWtYMmYN6x4i6m8WjErFd3vKMMCB0603EhkaginZwdf3EARBEARxahPQROcE0Vcpa+yAwbx0/MZjNbIkwQ40vx7m+ZLaZd0vD1e7YBxbDn1C/1hszq/D9oK6Xi1KtXTq8Zs5ETl3KcWEq3HbWQPxx/FaHCgTRakdhfWWx9tO1jkUpcrN7qeYcLVFtDhzcCKSo8Pw6c2nYd2RKhwoa8KAxAg8uGAYosPUuGQiS37+yPcHAADVzfZFqVfWHUd9W5dlBa69JY3INk+oh9gc54FJZlHKjlOqtIGd/+0F9dZuNn0H0FoNY0sVwk6uxVWqelzcvAM4OgUYtpC9p7EEeO00hHW14jeeGmsV2yQDGG6Yic92nM5EKW28RZCCJgq1iMbJzkgoI1MwaeRQoP90sVCaSGDZQRZap2ZCmwLAaAAjTQK++98WoLABvxysxA2nZwMx1mFtf56oxY0fMEEqQqNCW5cRUaEhGO1mKFlSVKhZlIqAzsAcUJXNnfhhbxne/7MQgsBMXxP6xWJMZize/7MQAKBWKSwr+tmiUiowODkKB8qaoFCw5P8qpQL/vWwsBAHmcLu9luukvwPHlSeolAqMSIvGzqIG7C9tQqHZKXXrWTl4ZjXLWeaJU8qWC8ZmYMWGE8ivbsXWk3UIVbNwu4FJEd1Etd7O3BEp+M9lYzHSgxxYBEEQBEEQhOeQKEUQMiANgzlS0Yyq5k6fVmwKNCaTgPV5bPnzVp0BrTqD1QpcrToDQpQKu6Fazqhv67KsMHfBOCYUTB4Qz0SpwgZcO22ALOX/cEshXvr1OMb3i8W8kam4eHwGQlRK/Li/HE+sPIRXrpyAaQMTPNrnr0eq0GUwYWBSBO46ezD+9vV+XDElC2OzYqFQsBCt6pZOmExsNTfO9sJ6q1xDUsrNTqlbzszB/tJG9IvXItl8nUzJjnfqckg2hw9WOwjf+2EfE/+ePH8kHvz2AI5Xt6KxnQkaw2xFqWQmELS0taGxogCxpnqgtQa6pgpc3vEnjij7YX3HBByuaMaoyBbgtWmAjjmoVABeBgA1gD0AcK0oSoXHAV1M6OpShKLSGA1NbBoaFLHYVafGdtNwnKhuZSF9mkjgnn1ARDIKmwXMfn4jjCYB314xHbANH1UogNgsu79bqVRgwag07ChswC+HzKKUhC6DCUs/2Y1OvQmzhibhjWsnorShA+FqlduJ6pfNGYJ1R6oxc2gS9pU0AgCK69rxxMpDaDAfYwBYNCYdSVGhFlEqK14LlZMQ1aGpTJQ6LTvBkkw7RKXEfy4bi+PVrZbQzeSoUEvYoK+MyojBzqIGrDtSjQ69ESFKBa49bQB2FjYg3c2V9xyhVCowLScB+dWt2FXUgFgtc6qNCMKcRjwfF0EQBEEQBOFfSJQiCBkotMnN8vuxGlw2yf4kujdysLwJNZKwsOrmTkSa3TR1rTqc/d+NGJQcia9vm+ZR2NBP+8thMAkYnRFjCRmbPIAJLzsK6p3nG3KTVp0B//4lDy2dBvx6pBq/HqkGBGDx5Cz8sLccta1deH3jCY9FqZ/2VwBgQsOF4zNwzqhUiyg30Jwo/GBZkyV3ULhahQ69Edud/C4uSmUnRni8elVyFLMe1dgJ39MZjJbn5w9LwGcxHTA0VyKprQlnqpowtvAwYBoDDJkPANB2VGF/2C2IRivwprifUAD3q4EvDDOx3jQBW0/WYdTUFIsgBVUoGlWxKOyMBCKTMW7YEKD/DMkOIoG7dgORyXh5Qxle/e0EUG1T2E4Dalu7kBQVCsQNAAC8vG4vjCYBs4YmebUq47yRKXjqx8PYUViP2lYdEiUrGB4oa0JDux5xWjVev2YiQkNUFqeYuywcnYaF5hXlUmOYOMhdb+kxYbhu+gDoDCZcPbWfJQwOcBy6x7lqaj8cqWjG8nlDrJ7XhCjx/OKxuODVzegymlzuxxO48+fXI8wZ2S9ei3CNCv/7yyRZ9j+xfxw+2lqEXcUNSDMfqxHkNiIIgiAIgiAcQKIUQcgAF6U0IUp0GUzY6CdRSg4Rh1PTosN1727HWUOTuq1qVdWsQ4554r6toB5NHXrsKmrA7uJGu6ut2XKwrAkbjlbjq12lAFjoHmd8v1iEKBWobO5EYV27JcTMWz7fXoyWTgOyEyOQnRiB9UercbC8CYuRhZPmnEmbjteguqXT4jZyRqXZAfX7MZY36dwxTIyQusTGZMYgv7oV+0ubLG6kC8dn4Nvdpaht7cKJmlYMSu4eBsWTl/PJutuYjMjQtGC4oggZ9fnA3hNAaxWQPBwYMh+VTZ1IRgPWhD6IuOdb8APAFCbOJgDjrrGIUgiPY4IUAKMiBKootupcLWKwrkSBbSYWfrrlRB1uOiMHuHMnEJGE17bW4LlfWN6q1xZMwDizUGNFAlt98JzRafhqdymqzOGGN8zIxq9HqlBc344TNa1MlAK7Vr7fWwYAWD53qGfHxUxmnBajM2JwoKwJH28tQkl9BxIj2Qpz2wtYaOWU7HiPnX72sHVA/mX6AKuVJNNjwpAaHYbK5k6XIXcT+sXhp7vt528bnhaNv50zFE//dAQTB8i38MDoTBayyHJrATlJ8obV8fbhYFkTqszX+4g0z1bcIwiCIAiCIE4dgkaUevbZZ/Htt9/i6NGjCA8Px/Tp0/Gvf/0LQ4eKk5jOzk7cd999+Pzzz6HT6TB//ny89tprSElJ6cGSE6cCBeYVss4fm46vd5Xij+O1MJoEp6E7ntDSqce/f8nDd3vKcMfMQbjtrByfxanVBypwuKIZhyuaoQlhopRCwXLjSEPE9pU2Wh5/vavEpSjVqTfi8je3oM3sINKolDh/rChKaTUsGe+fJ+qw5mAlbp850NGuXKI3mvDe5kIAwM1n5ECtUmD90WrkV7fCYDSh2JwfyyQAP+6r6BbaZcsvhypx60e7LP8PSYnEEDs5dsZkxODb3WX4+UAlDCY2uZ8xKAGFtW3YcrIO2wrqu4lSBqMJVc3suGbEhgMmE9DRwMSltmqg1fyXOAQYMo99qLkCePNMoL0WZwgm/BwKoAXA9+adjrsaGDKfJdZGBOIVLez3KlSoNUWhRoiBITwRY4cNBfpPEwuj0eL98V/ihS2NmD1+CJ6/fDwAYNXmAjxZcBhZ8eFAfQe2F9TDYDQhJHEwPtlWZBGkHjhnmMU55IhRGTHY9vAc6I0mdOiNiA5T42Rtq0WUOi0nATUtOtzy4U6YBGDh6FSLYOIN80em4EBZE1789bjluQvGZWB7AVspcEq2Z045R4SpVZbE52FqJa6YbC0+KxQKzBiUiG92l2JUum9izE1n5ODsYcnIjPM9nxRnUFKkJe8YAJ9FYVsy48KRFBWKmhadxU1GTimCIAiCIAjCEUrXb+kdbNy4EUuXLsXWrVuRm5sLvV6PefPmoa1NDJu69957sWrVKnz11VfYuHEjysvLcfHFF/dgqYlTBe6Uunh8BqLDQtDUocdBSSJsXzhU3oR5L/yOD7cUoaXTgH+tOYqnfzoCkzmxurdwBwnA8u4oFMCMgWw1NS6eAMABySpzq/ZVoMMsNjniRE0r2rqMiNCo8LdzhuLr26dZ8iZxFpndR6sPVPj0G1YfqEBZI3PFXDwhw2pVuZKGDuiN4jHibhxnrDLnZYoMDUFGbDjunj3Y7vvmj0pFTLgaeVUtOGFewWxS/3hMzWYOpMIje4CCP4ADXwNbXweO56KqRQeTAKSpmpD01jjg6STg3znA69OADy8Avr0ZWPt/wKHvxC8Kj2WClWCCAAVqhGgcMfWDkDMLGHMF0O80AEB5Yyd00OChlDeB+09gz3XHMEX3GhZ1PYvvRr4CXPQ6MP4aq9/Qf+h4NCESeyXnt6SehRfOG5GK6LAQtOgMOFDWhD3FDXhi5SEAwN1nD/JISFSrlIgOY7mFxATrbegymHD7x7tQ3tSJnMQIPHvxGLf3aY9zRqVKvpMJtj/sLbOsjDhVxlXJuFvqwnEZiNV2z0v16LnD8frVE3DR+Ixur3lKTlKkRTSWgxCVEsMkOZ5yPAxldIVCocBESQhmWkwY4t3M3UUQBEEQBEGcegSNU2rNmjVW/7///vtITk7Grl27cOaZZ6KpqQnvvPMOPv30U5x99tkAgPfeew/Dhw/H1q1bcdppp/VEsYlTgC6DybJi2aDkSIzJjMWm/Focq2rB2KxYn/f/5MrDqGjqRL94LeaOSME7mwrwzqYCDEiMwLWn9fdqn4IgYJtZlDp/bDpW7ivHadkJGJkejU35tZaQK5NJsKwyp9Wo0Koz4JdDlbjQyWT7eBULCxuZHoM7ZtrPm3TOyFQ8+v1BHChrQlFdm1crc52sacWTqw4DAK6bNgBhahUGmkWpqmYd9psdXhmx4ahs7sT+0iacqGl1mE/IZBLw5wnmqnn3+sli0nFBAHQtzMXUVg20ViGttQY/LkjFJblaVLfoMDpWh9R3JuLu1mosC+sCCsH+OGOvRMX4iQCA8OgEKFokYlx4PBCZzP4ikoGsKeJr6nDgtk1ARDKMYXGY8lguBAHYfuFsK6GP56oyJY0AIhIxUsMSWBtMgsMVDseZr82TNW1oatcjRqtGifk6HpCgxWk5CVh7uArXvbsdmhAV9EYB54xMxb1zh9jdnztYRKmaVvy4vxw7ixoQFRaCt66bhJhwtdf7BYBByVF4fvFYdBlMCNeocM/ne/HhliJ06NlKe45WRPSGSydm4vMdJQ7FuVitBgtcOMl6klHp0ZaE7XI7pQAWwrfmUCWA4ExyThAEQRAEQQSOoBGlbGlqYhPl+Hg2cdy1axf0ej3mzJljec+wYcPQr18/bNmyhUQpwm8U17fDJAARGhWSokKRnRiBTfm1KLBJfu4Ne4obsL2wHmqVAl/eOg2pMWHQhCjx+m8nsKOg3mtRqrCuHbWtOmhClHju0jG47ayByIgNx9e7WQ4o7pQqrGtDS6cBoSFK3DAjG69uyMdXu0qcilJ5VSyEbHCKYwdGQmQopg1MwOb8Ovx0oMKheOWI6uZO/OXd7ahv68KYzBjceAYLy4sOUyM5KhTVLTrkHmaJnMdlxaK9y4ANeTX4dncp7p8/DEcrm/HS6r1YPj0O2aHNSGvcgerfCjGyox27NeMxvl8s0FYLvDULaK0BDB3dypA15gp8fdsL+Mfqwzh3RAKwqtRiPW0StIhISEdIVCoQmQRkTkKZWThKjI0G/vI7EJEEaBOBEBcuktTRAFhjnRARitpWHapbdHZFqfRYtoJbmFqFM4ckYdPxWocOobgIDQYkaFFY1469pY04a0gSSszhjpnxWjywYBiK69txtLIFgAE5iRH492VjfAobHWjOX3SiphW/mEWLJTOyPU487oiLJ7DV0jq6jNBqVJYk9JMGxMkWSguwsLqbzsiRbX+BZlSGGFYod04pAJggCfGl0D2CIAiCIAjCGUEpSplMJixbtgwzZszAqFGjAACVlZXQaDSIjY21em9KSgoqKysd7kun00GnE1ezam5mq0zp9Xro9XpHH+v18LIH82+QE73RhHu+2I+0mDA8snCobMnCASC/igmk/eK1MBgM6BfPxIIT1S0+H///bTwBgCXbTtCqoNfrMTyFTSJL6tvc3v+h8mYMSNAiwrzc+5Z8tiTamIxoqGDC4CQmZiRq2euVTR3Q6/XYU8TcVCPSonDx+FS8uiEff56oQ1l9q2U1OFvyzMvYD0zUOi3fOSNSmCi1vxw3z3BfXNtWUI+/fn0Alc069I/X4n9Xj4NaIbDvMnRialwrSlqLgLw9uFJVj3GqMQgbOxsb8mrw685DuO/I5ejfVIXXhQ6gmO1zCgAUABeqTodqwNmAyQi9KhzqxmLL9wqaSCAiCUIkSwoupI1HWrQar14xFgBgSMyFEJGEc987jrw6Pd6ePR5nDUmyfL70jwIAQFp0KPSJI8w7BeDBNZIUqUFtqw4VjW0YmizmGSozO5ySo9SWY/78paPQ3GlAWkyow/MwJiMGhXXt2FVYh2kDYiyiVFqUBv1iQ/H97adh5b4KbMqvw52zchCm8q1N6R/Hrpmyxg7UtbJ29+whCbK3UyEKYPawJKzaz9r+if1iqS2UMDKViYCx4WrEhirdOjae9ClDk7VQqxTQGwUMSY6gY0+cUtD4iyBcQ/WEINwj2OuKu+UOSlFq6dKlOHjwIDZt2uTzvp599lk8+eST3Z5fu3YttFr5ksv2FLm5ubLub1u1Ar9XKnHjUCPi7WsSvZITzUDuEXa5q+oLMC7Bt3xMUjaUKwCoEKpvwurVq1HbwP7fX1iF1atXe73f2k5gzSEVAAWGmIqxejUTSEpaASAEJyob3dr/0UYFXj+iwtQkE64axJIbf5evBKBEvKHeah8nmtm+CysbsHr1aqwsZO+L0jfgwJbf0D9ShaJWBZ7/cj3OTLN/DPcVsjLXFxzC6vqDDsul1ANKqHCovAXvfr0aqQ6qm8JkQKihCWGGJhTXNuNgZQsGCokwhY3CX/o3Y/f673HGsacRZmiC2tiOVwBx5Tk1sLd4Ok6ExkEbosLJlhAo9YUIN7/cCQ2Mmhjo1DE40hGDA/ocxHWJ5y1uyOPQhURBFxIDo8rmgq8G0O34VyBKwY7Zt7/tRFu+eIy2nmTPt9WWYfXqEofHxSmdbB/rNu9Eu2Tfx8rYMS/J24/VlfusPrLHye7Uzexazd19HCnNeWjrYnXk4LaNyDPbvkIBzI4AjmwvwRHvSm1BEACtSoV2owIdehPiNAIK92xC0V4fd2yHVB37bQBgqDiC1at9LX3f4vIcBeJDjfj55589+py7fcq0JCWONynQemIXVhd5U0KCCG7kHn8RRF+E6glBuEew1pX29na33hd0otSdd96JH3/8Eb///jsyMzMtz6empqKrqwuNjY1Wbqmqqiqkpqba2RPjoYcewvLlyy3/Nzc3IysrC/PmzUN0dPCGHej1euTm5mLu3LlQq33L1SLl3Te3obStCcbUkVg4zbvQMXsIgoCdRY0YnhaFyFD5L8uPthYDh44CAFZXanH34hmyfc/WlYeBolJMGzUQC+cMxsj6drx5dBPqu1Q455x5UHoZNvTvtccgoBBnDk7ATZdOtDzf2K7Hfw5sQLNegbPnzne5zP2R3OPAkQKc6AjDggVnQaFQ4N/P/wGgA1fMnoQzBida3ltc346XD21Cq0mFBQvm4aN3dgBoxLnTx2Dh+HRUxRbhmZ/zUCQkYOHCKd2+q73LgGVb1wMArjl/NhIcJTg2GYC2Gpyo34QThYWIagIWTZoKYcCZ7PWOBoR8uAhorYais9H6s2pgR9RsDL71Hub8MuqhPrDU8rJRoUaFKQa1QgxqhBgMGzELI+cvxD7FUXy4tRiXdD2BOiEKNUIs4mJisfae6Vj/Sy7+b5cGOqMJq8+d7jT00BXVW4qwc3UedBGpWLhwvOX5lZ/sAapqcPqEkVg4JcvJHhzzh+4QjuwuQ2r2UCycycLHBEHAQ7vWAzDionlnYoAH+bkyS5vwzZvbUNkViqETJwA7tyElKhQXnDvPq/K5wwdl27CnhLkLzx3fD4sWDffL98wxmLD+tS0QBAE3XzJd1mThfYGFHr7f0z7F0/0TRF/BX+MvguhLUD0hCPcI9rrCo9BcETSilCAIuOuuu/Ddd9/ht99+Q3a29dLuEydOhFqtxrp163DJJZcAAPLy8lBcXIxp06bZ2yUAIDQ0FKGh3S0/arU6KE+8LXL/jmJzeE9xQ6es+33x12N48dfjGJCgxdvXTcKgZPvJmb3lqDn5NsCSYL+2sQD/t2iELPsubmD5fAYmR0OtVmNAYhTUKgV0BhNqO4zIiA13sQf7HChjuZnOHZthdawTo0MQFcpWRqtq1WNQcpijXQAA8mvYOatt7UJVqwEqpQKlDR1QKoApA5OgVovNQEY8E2M69Sa06YFD5awMEwbEQ61W47xxGXjm5zzsLGpEbbsBaTGS32YyoqysFMNQhBxtG1ILm4HodCDnLPZ6RwPw3kKgtQporwcg4DEA0ACoBNp2XISIwbPZe5XxQO1xsPg2AMoQICIZR1rCUGGMxogRpyE20vzdajWwZA2gTQAik7G1VI+r39luKdb+OfOgVqtxxZT++HBrMXaZxETdHc06CAoVClrY+UqKCsXwjFifwjvH92M5nA6WN1udt4omFq7WLyHC67qTaj7edW16yz4a27ssuZOyEqKgdiFSShmdFQ9NiBIN7XpsLWCr1GXFa/3a9g1KjrKIUueMTvfbd6nVwM/3nAmlgq04R8hDX+kbCcLfUF0hCNdQPSEI9wjWuuJumYNGlFq6dCk+/fRT/PDDD4iKirLkiYqJiUF4eDhiYmJw4403Yvny5YiPj0d0dDTuuusuTJs2jZKcy0RThx4N7Swu9GSN70m8OUV1bXjtN5Y7qbCuHReu+BPvLZmMyQPkW8L9sDnP0eWTsvDFzhK89UcBxveLw0IfV8gymQTkVTLhhq9iFaJSol+8Fidq2lBQ0+aVKCUIgiVh+DCb1dMUCgUy4sJxtLIFJQ0dVgKezmBEaIi1KHG8usXyeHdxA7oMLIRvZHpMN7dYmFqFmHA1mjr0+P14DTr1emRoOpBtKgFOViOttRrXpdXjg4os/LS/AjdNigfeP9csNNVihGDCz6EATAC+BzDqUlGU0kQB1UdgEZoUSiAiCUW6CBR2RkLXkgqLP0cVAlz/o1loSgHCYtFpFLDgUbYK596z5lofsP6i8DwopdPyODEyFNFhrDEckR6N0RkxOFDWhOFp0SisbUOH3ojypg7kNzMRasbABJ/zjY1Ij4ZSwcTPquZOpJgTklc0MfHSSsjzkCRzHq/qFvE3ljeyxwkRGpeuOVs0IUqMSo/G7uJG/O/3kwCYKOVP+AqJ0WEh4gqHfoLcUQRBEARBEATRuwmaEfvrr7+OpqYmzJw5E2lpaZa/L774wvKeF154Aeeeey4uueQSnHnmmUhNTcW3337bg6XuWxTXiTGhJ2tanbzTM55adRhdBhOmZsdjSnY8WnUGvPjrMdn2rzeacKySlffOswfh+ukDAADLvtiLXeZE3t5yqLwZta1d0GpUGJUhhntmJ7KJd0FtK4wmAW06g0f7rWnVob6tCwoFMNiOa4wLB6X14jlZc7ASox9fi/c3F1ie6+gyWtxtALC7qAG/HKxENFpxaf92oOAP4OA3QKGYny07yoBVmodxxsozcCz0OmxW3gzVG9OADy8Avr0Z12k2AAC+31sGozoSqD4EtFUDggkCFKgVolEVlgPkzARSJG40VQhw3Urg9j+Bv+YDj9YCfz2G8it+xXX6B3Fn8Vl49PuDOFjGXDQYcDqQPBzQxgNKpWWFOa2GCWeOSI4KtYhttiuLLZ87BKnRYXhwwTD0Mx/DkvoOlJk11rFZsQ736y5aTYjlnO0vZb+lo8toEXTTvXTOAbAkl69pERdnsF15z1Punj0YYWolmjvZNepvUWrW0GSoVQpcNbU/1ORgIgiCIAiCIIhTmqBxSgmC68TUYWFhWLFiBVasWBGAEp16FNaJ7qjypk50dBkRrvHMmWHLH8drsO5oNdQqBf5x0WiYBAHzXvgdu4qYo0cTosTRymb0i9dCq/Hucs2vbkWX0YSosBBkxoXj0XNHoLShA78eqcKdn+7B5gfO9jrv04Y8tord6YMSrRxKOUkRwBHgZG0b7v96H37cX4Hv75jh9vLo3H01ICHC7jHOijMLKubQwc4uA/69cgfSTVWoPlgBxO4DIpJwQj0KggBEoh0fa55B2t4WxJkaoAkzALvB/gBg5EVMBAIQHR2LEU1FUJkEgB+W8HggMhmITEZy+liElilxsKwZT/x4FE9d8x0UWvb6jV8VYP2xejy9YBSuOc1OzrHsM7s9dVpOPOYMT8GvR6rw0dYifLytCC9dMR7nj023eh93BKXHhjt1MykUCgxMjsS+kkbkJFqLUrOGJWPrwyxE8KMtRcirakFxfTvK2tj+RqTJk0dudGYM8qpacKC0EXNHpFjqToRGhegw75vd5GjulJKIUk1clHIexumImUOT8cPS07H0093Ir27F2MwYr8vnDkNTo3D07wvgZZUjCIIgCIIgCKIPETSiFOEd645Uo1lnwuLJ3iVWliJ13ABAQW2b2yKLIzbn1wEALhqfgUHJkRAEAfERGtS3dWF/aSMa2vW4+cOduGRCJv67eKxX33GonIXujUiLhkKhgEoBvHzlOIx7MhcVTZ0obehAvwTv3CHrjzJRatawZKvneSjfxrwanKxlgsRXu0rwePpIt/bLRanRSSFAfQHQWs3cSNpEoP80ZMWHIwIduGLfDcCxNqiaK7HOpGNLpVUA+ArAiAtxbNA/AAAJcbEY1V6IEMFkEZqE0GgoIlOY2JQ0zPLdSdERuF7/ABqESNQKMfjhgQuREiee50gAz6dU4M7PduOjrUVIjRmKpbMGsXJXHwbAhAd3USgUePPaifjzRC0++LMQvx6pxn1f7kVihAbTB4lJ2LkjyJ1wyNEZ0dhX0oiRTq7P/uZzvr+sGQ1d7KAM9/F65ozNjMHXu0qxz+yU+n5PGQBgcna8T+GByVFMeKpu0UEQBCgUCpT56JQC2Pn66e7TUVzXjkHJ3id5dxcVKVIEQRAEQRAEQYBEqT5NlxG4/8v96DKYMCojxmcBqbDWOo+UHKIUn1APSWEihkKhwNTsePx8sBLbCuqxu4glX157qBJ642ivwn0Oc1FKUlatJgSDkiNxuKIZRyqbvRKl6lp12FfaCICFJEnhotRJyTH7+UAlHl00AkqjjglMreY/bTzQz5z3TNcKfHwJLqgqwRWhdYgs6ARelux4xIVA/2nIjNOiHaHorzsK6EzgwWytQhgalXHIzOwPJA7BMXOC9zOGpmL5gYdR1K5BrRCDC84Yh78tGmf3d6VEh+Ib0xgALJxNKkhxFo1JQ03LCDyx6jBe/PUYrp8+AAajYDmfgz0UNlRKBc4YnIQZAxNx1+d78NP+Ctzy0S78cu+ZFhHKE/HlvrlDMSU7AfNGpDh8Dxel1h+tAQBkxYVb8k/5yoT+cQCAzfm1OFLRjC93lgAArp7q24qVPKdUl8GEurYuJEaGooI7yHzIVQUAoSEqDE6Rd4EBgiAIgiAIgiAIZ5Ao1YcpbFVYklqvP1rls4BUZM4pFaFRoa3LKEteqdIGtk+p++W0nAT8fLASvxyqtAhKLToD9pU0YpIXyc8PlTO3ysh067Ck4WnRTJSqaMb8kake73fjsRoIAnNgpUYogaYyi9g0xCSKXOHoxAea55DQ2QTTP1uh7Gqx3tGIC0RRSq0FynYiyWQQQ+dCwi2hc0gYCADIig+HACWWKf6GqSMG4s3drUBEMkpaFYgOC8H+G+cDAI6/vwMAE/3+bDkb+w6xBQLmjnYsjvDE3AAwd3iyw/ddPyMbb/1RgLLGDuwpbkSnnq0Al5MUgVitxvUBtINSqcDzi8eitKED+0oa8fn2Ytw3bygAqVPKdZhaXISmW/ifLTx3UmMHy/U0PE0+QWZkegxmD0vGuqPVuPad7Who1yMtJgyzhib5tN8wtQoDErQorGvH/V/tw1t/meRzTimCIAiCIAiCIIiegrLM9mFONIshMuvMYWa+wPPinD6YhVSdrPV9Bb4yc06kjDhxQj01hwlP+0ubYDCJucT+OF7r0b4FQUBTh96y8p5tviAuQhytaOn2WQCA0QC0VAIV+4DjvwIlO8TXutoxMvcarNXcj29argKeTgJeGAH8bybw6WLE7XsTEeZcUIPTkzBBmY+BygqEcEFKpQFisoD0CUBctrhfpRLGyz7C1cbHMVP3XxTcnAf8XwWwbD9w06/A7McAiDmlVnWMwX+PxKJESMFtc0cDAJo7DdAbmRh5zLzy3uDkKEzoHwsASI0Ow9jMWIfHLcWctwgA5jhxGgGwrJ62vaAOW0+yUMyp2QlOP+OK0BAVbjkjBwDw1c5SGMy/RcydJI/40t8mobdc+aQ4/7doONQqBWpbWf6nKyb3Q4gMib1fuHwcwtRKbMirwdwXfseuYuYm9DanFEEQBEEQBEEQRE9BTqk+zIlm8fHekkbUtuqQGBnq+ANOaO8yWJIrnz0sGb8cqvJZlNIZjJZ9ZsaJAsGQ5CjEadWW1cqGpkQhr6oFfxyvwb1zh7i17+ZOPS5csRkna1gZNSoly5VjMgLt9UBrFU5DPsYqinC00vzd+g7gsyvEsLr2OgCSBPvDzwMu/5iVXaFBTsd+qJVGgC+spwwBIpKByCQo4gZgZEYMthfU46azBmF/1Ut47rcKKCJT8Omy86AIjwUc5BYqTjoLm/UCQkOU6JeWYvd9EaEhltxbDe16JEZqcMmETDz6/UGYBKChrQuRYSEoqefhkZEYlByJdUeqcfnkLKeJ3QclR0KhAHISIzDURTjXlOx4fLenDFsL6tHexQ7EaTmeu9lsmTMiGfERGlQ2d+L34zU4e1iKRcCUS5TKiAuHQgHwNRTkdEoBQE5SJJbMyMb/fj8JlVKBy2XI6wYA4/vF4aUrxuO2j3ehwFwHZw9LxqgM/yYoJwiCIAiCIAiCkBsSpfooXQYTCluY8JAQoUFdWxd+y6vBpRMzvdofT3Ieq1VjXBbLl3OyptWSbNkb+Gpq4WoV4rRiLh+lUoEp2fH45VAVAOCZi0fhkte3YG9JI5o69IgJt8n7YzIBnY1AaxX7C43Cnw0ZOFnThlB04d2wFzBI2w7Ni/cAbTWAwJw3owDcHjIZt9cPQpvOgAhNGFD0J2DsEvetULLk4pEpQEw/y9N/HK/DV113IUQbg1duOgfKqBQgPA5Qik6Y/9S142hlM+aOSIHOcCUObMpFW7MRWytMmDbQ8THLq2Rq4uCUSKcJoTPjwlHfxsp6ycRMhKlViNOyc13X1oXKZnZ8EyI0SDCLkV/cOs3h/jiDkqPw2c2nIcPFKncAMNXslNpb3AiDiR3X03J8c0oBzC118fgMvL2pAJ9vL8HMIckob2K/x51E5+5+R3pMuCVX1XAPkrO7y11nD0JRXRvGZsUiNUY+J9P8kal469pJyK9pxaLRaZZQRIIgCIIgCIIgiGCCRKk+yqHyZugFBeK0alw9tR9eXp+P9UervBalCmuZKNU/IQL9E7RQKICWToMl2bI3SEP3bMWPGQMTsOXQSczOAiaaDmFBfDl+rk/HlhO1OGdYPPDFNWYRqoblcTIZxA8POxd7Yx4FAFw0KQczjhwF2nWSvStYcvHIFDTWJkHQA3lVLZjQLw64+H9AaDQToSKTAW0CoFR1K/tPByrwi2kKlowdAGXqCLu/r1+C1pJAPUytwnlj0/H5jhI8/N0B/HjX6YgItV/9jppX3hua4jycLCtOi/3m1d2umMwEs3izAFnf1oVKs4gzOMXz1dTcFZayEyOQGBlqCVHLToywyknlC5dPzsLbmwqw7mg1jla2oMtggkIBWcWdfvFalDV2ICJEQGq0d9exM6LC1Hjz2kmy7xdgoZVz4Dy8kiAIgiAIgiAIojdDolQfZXshyzMzqX8czh6egpfX5+P3Y7XoMpigCXE/r81Lvx7Hqv3lGGAWVwYkaBGmViEjNhylDR04WdPmviglCEBXqyU8rq2QCSqZceGAQQd8eR0TmtpqcG1rFf4S1gXUAPgAuCfmDPyM27HxWC3OGZkKFPwOGDqt9x8Wy8SkqDTsMefZGd8/Dhj2hlloSmKvaxMBFbv0V7+7HThWgyMVzUyUGnmRy5/RqTci9zBzcZ07xnkybSkPLRiOjcdqUFDbhqd/OoxnLx5j9315ZlFqmAvnDhe8TsuJt6z2Fx/BEozXtXWhxJxEfkBChNtl9BS+WuJPByoAiM4pORicEoWJ/eOwq6gBr6w/DgBIiQrzagVGR/SL12LLyTpkRHjv+CMIgiAIgiAIgiC8g0SpPsqOIibKTB4QhzEZMUiKCkVNiw6b82sxa5jjFdV0BiPq27qQFhOODXnVeOHXYwCA/Gq20h5PDj0wKRKlDR3YcqIOUzLCUF9dgj2Hj2Ha0Axo+09gOzN0AV8vMYtQVWxr6LB818C4MwHcxsKxVBrg5AaL0GSRB0JjgMhkhMdkAlVgScsVCuCCFUBoFBCRxBxNEUlACBPHjCYBB574BQDLv4OUix3+3uGpUfj9WI3jZOcSns89hrzKZozLikOrzoD0mDCMz4p1+TlOjFaN/y4ei6vf3obPtpdgfL84LJ7UPc8QF6WGuhClrp7aD+WNHbh95kDLcwmRTJSqb9WJTjQ/r8o2RSJKyRG6J+XyyVnYVdSAnw+yVQPlTuY9oX8svthZgiExgus3EwRBEARBEARBELJColQfxGgSsKuoEQAwZUAclEoFFo5KxQdbirBqf7mVKNXUrkdNq44lAQew9JM9+PVIFS4fl4Qj+fkYq6hBWkIM1tSypexzEjTAF9fi+bZSNGvKkLypCdjUiXgAswHs3DUVI+/7GeEaFaBSA/nrrIQoAIAmEohMRo2JJWbOiAtnQtP5r1heY0JTMqBmIkRjSSNweDOqzCFpGH2pw99/rKoF7V1GRIaGYGCS89C14eYV145UNDt9X0eXEa+uPw6TAEuuq0Vj0pwmDLfH9IGJWDpzEF7dkI8Hv9mP6LAQnDMqzfJ6p95oWeXQlVMqM06Ll64Yb/Ucd0rVt3VZciVJVzb0B1Mk7qipMiQ5l7JodBqeWnUYrToWnilXknPO4klZGJ8ZjUPbNsq6X4IgCIIgCIIgCMI1JEr1QRrauzA8LQpHyuotwsZ5Y9Px6ZYT2HfoMHTFRoRqQoHUUbjhgx3YX1yLvSO/QoS+Hv9XdBL/DW1EzFEW+oVQwJg6H7nnvIQ/T9Rh3ugsYM06JOjbkCCJouoU1KgRYnGiLRwvfbQTb183CaEhKuC8FwFNhHlVumQcaAzFA6tO4NFFI/BC7jEA9eLKe2MWO/xNaeY8QjWtOhiMJoQ4CeHaU9zIdpcZ4zRROAAMM6+4drSyBSaTAKVSgSdXHUJdaxceWDDM4jI6WtkMkwCr1drOH5vhdN+OuG/eENS06PDFzhIs/XQPRqafwJjMGNw/fxiK69phEoA4rRpJUZ7nOIqPYJ+pk4pSfnZKDUuNwg0zsqHVqJAWI+93RYSG4Lyx6fhsezEA+X+LQqFAdmIEjlDkHkEQBEEQBEEQRMAhUaoPkhgZik+XjEfl6xdC89kbQFsNJrZW43hYPXvDuwAGz4fpyi9woLQJekGF0IJfAWM7sgFL7FwXQoCIZGgi4nHOqDTR0bPoP4A6HH9WqfBwbjVqhRi0Ihy3zxyED/4sRPvxWjz94xH8/cJRwNgrrMr26W8HcLiiGf9Zm2dJxO2O0JAQGQqVUgGjSUBta5fTZNd7S8z5pPrFutzvwKRIhKmVaNUZcKKmFdrQELy3uRAAsP5oNZ48fyQumZiJQ+XMSXXm4CTcfEYOOvRGjM6Mcbl/eygUCjxz8WjoDEZ8v7cc+0ubsL+0Cemx4Ugy5+camhrlVY6jBLNTqrZVhwrz6ob+dkopFAo8dp79ZO9ycMXkLIsoJbdTiiAIgiAIgiAIgug5SJTqqyhDkNa8F8pG6xxNekGFtpA4xIbHorpFhy6jCQDwW85fMTgzCQ/8UgWDNglf33cBVJoYqOw5ksZdBQA4bbiA0H1/oLWqBQtHp+KBc4ZhSnY8lry3Ax9tLcIZgxMxb2Sq1UePV7F8SbuKGsA1l0w3RBOVUoGUqFCUN3WioqnDhSjVyIqZFedyv2qVEmMzY7GtoB67ihoQpmYr7SkUQKvOgPu/3odpAxNwqJwlZR+ZHo3TBye63K87v+fFK8bjnjlD8PHWIryzqQB/HKvFqAwWTjgs1fnKe47g4XvHqlrRZTRBqYBsq+H1FGMyYzAyPRqHypsxJMV5SCNBEARBEARBEAQRPJAo1Yc5kHEVRk+YjJCYdCAiGYdawnDu24cQagrB7nPnoqRczKP0U8hszIpNxlbTHkxOjAPC46BysX+lUoFXrxqPVfvKceMZOQCAWUOTcfMZ2XjrjwL87Zv9GJMZaxGQBEHAcXPCdPY/oFEpLe4gV6TGhKG8qRNVzeKqe43tXVjy/g6MSIvGQwuHo01nsHzHODeTkE8aEIdtBfXYWdSAUPPKhEumZ2NXUT32lTZh47Eai1NqZLp37ihHZCdG4Oqp/fDOpgLsKmqAwcREQldJzh3BnVIFtSwvVWq0vKvV9QQKhQJvXzcJB8uacZrMOasIgiAIgiAIgiCInoNEqT5MceJMjBq1EFCrAQAjkgWkRBegsrkTh8qbUVzXbnnvydo25JiFjP4JEW5/x+CUKCyfN9TqufvnD8OWk3U4WNaMp348hNeunggAqGnRoalDb/Xe9Ngwt5OFc3GrokkUpTYeq8Ge4kbsKW7ExmM1aOrQQxCAnKQIt3MyTeofD+AEdhU1QGMWcKbmxCNWq8a+0iasO1KFo+YV8biTSU6yEyOQERuOssYO7ChkoYfeilLx5tX3OP4O3QsUaTHhsuerIgiCIAiCIAiCIHqW4LZQEB6hUCgsYsexqhaUNIiiVEFNKwrMq74NSND69D2aECX+c9lYKBXA6gOV+DO/1vydzMGUERuOqFCmh3oimqRGs/dWSkQpLhYBQGlDB1o6DRiZHo1Xrhzf7fOO4LmnCmrbkGcOL5zUPw5nDWErDq47Wo0ugwlRoSHIivPt2NhDoVDgzCHWIYHehqnx8D2Ov5OcEwRBEARBEARBEIS3kCh1ijEkJRIAcLyqFSX1HZbnmzsN2Gtetc4Tp5QjhqVG49rT+gMAnlh1CAajCceqRLfRwtEsaXq/ePe/K82OU+qYWZT667whWDprIP5z2VisvPN0j8LsYrUaDE6OtPw/MCkCCZGhGJ0Rg/gIjWW1veHp0W67ujzl9EFJlseZceGIDPXOxBin7ZtOKYIgCIIgCIIgCKLvQeF7pxiDk5kDJ7+61ZLknHPSEr4njxvo3rlDsHJfOY5VteLLnaWWXE9DUqJw/fQBiNGqcc3U/m7vj4fvVTZ3d0pNHhCPqTkJXpd10oA4S/kmD2B5i5RKBc4YnIgf9pYDYEnO/cWMQQlQKFierWFehu4BLHF7rFaNxnYWJpkRK7+ziyAIgiAIgiAIgiDkgJxSpxiDzU6pY1UtKK1n4XvRYdbaZH8P3EvOiNVqsHTWIADAp9uLLCvvDUqOREJkKB5eOBz9PBDALKKU2SnV0qlHWSNze3m7Wh1nYn8xgTYXpQBYQvgAYJTMSc6lxGo1GJPB9u9tPimONISPnFIEQRAEQRAEQRBEb4VEqVOMQeYwteoWHSrMjqMzBovCS5xWjRitWrbvu2RCJjQqJQ6WNWNvSSMA7/MlpUaLopQgCJZwwNToMJ/LPLF/nOWxVJSSHpuRfkhyLmXprEEYlRGNSyZk+rSfBKkoRTmlCIIgCIIgCIIgiF4Khe+dYkSFqZEWE4aKpk4IAhCuVmHSgDj8dKACgDz5pKTERWgwZ0QyVh+ohMEkQKVUICfJu+9IMYtSXUYT6tu6kFdpDgf00VkEsOTuN5+RDYVCgax4UchJigrF/y0cjrq2Lgz1Ukxzl3kjUzFvZKrP+4knUYogCIIgCIIgCIIIAkiUOgUZnBJlSRaeGReOnCQxybevK+/Z47KJWVh9oBIAy1cVGqLyaj+aECUSI0NR26pDZXMn8iqbAfiWg4mjUCjwf4tG2H3t5jNzfN5/IImPCAXAHFPhGu+ONUEQBEEQBEEQBEH4GwrfOwWRrjSXFa9FTqLoXOons1MKAM4YnIiUaCaUDEn2TUBKjWH7qWzqtCQ597eDKdjg4Xvp5JIiCIIgCIIgCIIgejEkSp2CWIlSceFIjw2HJoRdCv5wSoWolLj2NLbK3mk58S7e7ZzUaCa0VDR1Is+cU8rXxOB9DZ4QXq5VFAmCIAiCIAiCIAjCH1D43inIYImzKCteC5VSgVHp0dhd3IhRGf5ZYe6OmYMwc2iyz6F2aWbBZX9pIxrb9VAqxOTtBOP8cemoadHh/HHpPV0UgiAIgiAIgiAIgnAIiVKnIFIRJzOOuWneuHYiShs6vF4ZzxVKpUIWwYu7gL7fUw4AGJAYgTA15U2SEh2mxr1zh/R0MQiCIAiCIAiCIAjCKRS+dwoSE662hOkNSWECVXJUGCb0i+vJYrlFqmQFvjC1EvfNHdrDJSIIgiAIgiAIgiAIwhv6pCi1YsUKDBgwAGFhYZg6dSq2b9/e00Xqdbx93SS8t2Sy1cp7wcDpgxMxOiMGiydl4re/zsKiMWk9XSSCIAiCIAiCIAiCILygz4XvffHFF1i+fDneeOMNTJ06FS+++CLmz5+PvLw8JCcn93Txeg2DkqMwyMeV8HqClOgwrLrr9J4uBkEQBEEQBEEQBEEQPtLnnFLPP/88br75ZixZsgQjRozAG2+8Aa1Wi3fffbeni0YQBEEQBEEQBEEQBEGY6VNOqa6uLuzatQsPPfSQ5TmlUok5c+Zgy5Ytdj+j0+mg0+ks/zc3NwMA9Ho99Hq9fwvsR3jZg/k3EEQgoLpCEK6hekIQ7kF1hSBcQ/WEINwj2OuKu+XuU6JUbW0tjEYjUlJSrJ5PSUnB0aNH7X7m2WefxZNPPtnt+bVr10Kr1fqlnIEkNze3p4tAEEEB1RWCcA3VE4JwD6orBOEaqicE4R7BWlfa29vdel+fEqW84aGHHsLy5cst/zc3NyMrKwvz5s1DdHR0D5bMN/R6PXJzczF37lyo1eqeLg5B9FqorhCEa6ieEIR7UF0hCNdQPSEI9wj2usKj0FzRp0SpxMREqFQqVFVVWT1fVVWF1NRUu58JDQ1FaGio5X9BEAAAHR0dQXniOXq9Hu3t7ejo6IDBYOjp4hBEr4XqCkG4huoJQbgH1RWCcA3VE4Jwj2CvKx0dHQBEjcURfUqU0mg0mDhxItatW4cLL7wQAGAymbBu3Trceeedbu2jpaUFAJCVleWvYhIEQRAEQRAEQRAEQfR5WlpaEBMT4/D1PiVKAcDy5ctx3XXXYdKkSZgyZQpefPFFtLW1YcmSJW59Pj09HSUlJYiKioJCofBzaf0HD0MsKSkJ6jBEgvA3VFcIwjVUTwjCPaiuEIRrqJ4QhHsEe10RBAEtLS1IT093+r4+J0pdfvnlqKmpwWOPPYbKykqMGzcOa9as6Zb83BFKpRKZmZl+LmXgiI6ODsoLmCACDdUVgnAN1ROCcA+qKwThGqonBOEewVxXnDmkOH1OlAKAO++80+1wPYIgCIIgCIIgCIIgCCLwKHu6AARBEARBEARBEARBEMSpB4lSfZTQ0FA8/vjjVisLEgTRHaorBOEaqicE4R5UVwjCNVRPCMI9TpW6ohBcrc9HEARBEARBEARBEARBEDJDTimCIAiCIAiCIAiCIAgi4JAoRRAEQRAEQRAEQRAEQQQcEqUIgiAIgiAIgiAIgiCIgEOiFEEQBEEQBEEQBEEQBBFwSJTqo6xYsQIDBgxAWFgYpk6diu3bt/d0kQgiYPz+++8477zzkJ6eDoVCge+//97qdUEQ8NhjjyEtLQ3h4eGYM2cOjh8/bvWe+vp6XH311YiOjkZsbCxuvPFGtLa2BvBXEIR/efbZZzF58mRERUUhOTkZF154IfLy8qze09nZiaVLlyIhIQGRkZG45JJLUFVVZfWe4uJiLFq0CFqtFsnJybj//vthMBgC+VMIwm+8/vrrGDNmDKKjoxEdHY1p06bh559/trxOdYQg7PPPf/4TCoUCy5YtszxH9YU41XniiSegUCis/oYNG2Z5/VStIyRK9UG++OILLF++HI8//jh2796NsWPHYv78+aiuru7pohFEQGhra8PYsWOxYsUKu68/99xzePnll/HGG29g27ZtiIiIwPz589HZ2Wl5z9VXX41Dhw4hNzcXP/74I37//XfccsstgfoJBOF3Nm7ciKVLl2Lr1q3Izc2FXq/HvHnz0NbWZnnPvffei1WrVuGrr77Cxo0bUV5ejosvvtjyutFoxKJFi9DV1YU///wTH3zwAd5//3089thjPfGTCEJ2MjMz8c9//hO7du3Czp07cfbZZ+OCCy7AoUOHAFAdIQh77NixA2+++SbGjBlj9TzVF4IARo4ciYqKCsvfpk2bLK+dsnVEIPocU6ZMEZYuXWr532g0Cunp6cKzzz7bg6UiiJ4BgPDdd99Z/jeZTEJqaqrw73//2/JcY2OjEBoaKnz22WeCIAjC4cOHBQDCjh07LO/5+eefBYVCIZSVlQWs7AQRSKqrqwUAwsaNGwVBYPVCrVYLX331leU9R44cEQAIW7ZsEQRBEFavXi0olUqhsrLS8p7XX39diI6OFnQ6XWB/AEEEiLi4OOHtt9+mOkIQdmhpaREGDx4s5ObmCmeddZZwzz33CIJAfQpBCIIgPP7448LYsWPtvnYq1xFySvUxurq6sGvXLsyZM8fynFKpxJw5c7Bly5YeLBlB9A4KCgpQWVlpVUdiYmIwdepUSx3ZsmULYmNjMWnSJMt75syZA6VSiW3btgW8zAQRCJqamgAA8fHxAIBdu3ZBr9db1ZVhw4ahX79+VnVl9OjRSElJsbxn/vz5aG5utjhJCKKvYDQa8fnnn6OtrQ3Tpk2jOkIQdli6dCkWLVpkVS8A6lMIgnP8+HGkp6cjJycHV199NYqLiwGc2nUkpKcLQMhLbW0tjEaj1YUKACkpKTh69GgPlYogeg+VlZUAYLeO8NcqKyuRnJxs9XpISAji4+Mt7yGIvoTJZMKyZcswY8YMjBo1CgCrBxqNBrGxsVbvta0r9uoSf40g+gIHDhzAtGnT0NnZicjISHz33XcYMWIE9u7dS3WEICR8/vnn2L17N3bs2NHtNepTCAKYOnUq3n//fQwdOhQVFRV48sknccYZZ+DgwYOndB0hUYogCIIgTnGWLl2KgwcPWuU1IAiCMXToUOzduxdNTU34+uuvcd1112Hjxo09XSyC6FWUlJTgnnvuQW5uLsLCwnq6OATRK1mwYIHl8ZgxYzB16lT0798fX375JcLDw3uwZD0Lhe/1MRITE6FSqbpl6a+qqkJqamoPlYogeg+8HjirI6mpqd0WBjAYDKivr6d6RPQ57rzzTvz444/YsGEDMjMzLc+npqaiq6sLjY2NVu+3rSv26hJ/jSD6AhqNBoMGDcLEiRPx7LPPYuzYsXjppZeojhCEhF27dqG6uhoTJkxASEgIQkJCsHHjRrz88ssICQlBSkoK1ReCsCE2NhZDhgxBfn7+Kd2nkCjVx9BoNJg4cSLWrVtnec5kMmHdunWYNm1aD5aMIHoH2dnZSE1Ntaojzc3N2LZtm6WOTJs2DY2Njdi1a5flPevXr4fJZMLUqVMDXmaC8AeCIODOO+/Ed999h/Xr1yM7O9vq9YkTJ0KtVlvVlby8PBQXF1vVlQMHDliJuLm5uYiOjsaIESMC80MIIsCYTCbodDqqIwQhYfbs2Thw4AD27t1r+Zs0aRKuvvpqy2OqLwRhTWtrK06cOIG0tLRTu0/p6UzrhPx8/vnnQmhoqPD+++8Lhw8fFm655RYhNjbWKks/QfRlWlpahD179gh79uwRAAjPP/+8sGfPHqGoqEgQBEH45z//KcTGxgo//PCDsH//fuGCCy4QsrOzhY6ODss+zjnnHGH8+PHCtm3bhE2bNgmDBw8Wrrzyyp76SQQhO7fffrsQExMj/Pbbb0JFRYXlr7293fKe2267TejXr5+wfv16YefOncK0adOEadOmWV43GAzCqFGjhHnz5gl79+4V1qxZIyQlJQkPPfRQT/wkgpCdBx98UNi4caNQUFAg7N+/X3jwwQcFhUIhrF27VhAEqiME4Qzp6nuCQPWFIO677z7ht99+EwoKCoTNmzcLc+bMERITE4Xq6mpBEE7dOkKiVB/llVdeEfr16ydoNBphypQpwtatW3u6SAQRMDZs2CAA6PZ33XXXCYIgCCaTSXj00UeFlJQUITQ0VJg9e7aQl5dntY+6ujrhyiuvFCIjI4Xo6GhhyZIlQktLSw/8GoLwD/bqCADhvffes7yno6NDuOOOO4S4uDhBq9UKF110kVBRUWG1n8LCQmHBggVCeHi4kJiYKNx3332CXq8P8K8hCP9www03CP379xc0Go2QlJQkzJ492yJICQLVEYJwhq0oRfWFONW5/PLLhbS0NEGj0QgZGRnC5ZdfLuTn51teP1XriEIQBKFnPFoEQRAEQRAEQRAEQRDEqQrllCIIgiAIgiAIgiAIgiACDolSBEEQBEEQBEEQBEEQRMAhUYogCIIgCIIgCIIgCIIIOCRKEQRBEARBEARBEARBEAGHRCmCIAiCIAiCIAiCIAgi4JAoRRAEQRAEQRAEQRAEQQQcEqUIgiAIgiAIgiAIgiCIgEOiFEEQBEEQBEEQBEEQBBFwSJQiCIIgCILoZVx//fW48MILe7oYBEEQBEEQfiWkpwtAEARBEARxKqFQKJy+/vjjj+Oll16CIAgBKhFBEARBEETPQKIUQRAEQRBEAKmoqLA8/uKLL/DYY48hLy/P8lxkZCQiIyN7omgEQRAEQRABhcL3CIIgCIIgAkhqaqrlLyYmBgqFwuq5yMjIbuF7M2fOxF133YVly5YhLi4OKSkpeOutt9DW1oYlS5YgKioKgwYNws8//2z1XQcPHsSCBQsQGRmJlJQUXHvttaitrQ3wLyYIgiAIgrAPiVIEQRAEQRBBwAcffIDExERs374dd911F26//XZcdtllmD59Onbv3o158+bh2muvRXt7OwCgsbERZ599NsaPH4+dO3dizZo1qKqqwuLFi3v4lxAEQRAEQTBIlCIIgiAIgggCxo4di0ceeQSDBw/GQw89hLCwMCQmJuLmm2/G4MGD8dhjj6Gurg779+8HALz66qsYP348nnnmGQwbNgzjx4/Hu+++iw0bNuDYsWM9/GsIgiAIgiAopxRBEARBEERQMGbMGMtjlUqFhIQEjB492vJcSkoKAKC6uhoAsG/fPmzYsMFufqoTJ05gyJAhfi4xQRAEQRCEc0iUIgiCIAiCCALUarXV/wqFwuo5vqqfyWQCALS2tuK8887Dv/71r277SktL82NJCYIgCIIg3INEKYIgCIIgiD7IhAkT8M0332DAgAEICaEhH0EQBEEQvQ/KKUUQBEEQBNEHWbp0Kerr63HllVdix44dOHHiBH755RcsWbIERqOxp4tHEARBEARBohRBEARBEERfJD09HZs3b4bRaMS8efMwevRoLFu2DLGxsVAqaQhIEARBEETPoxAEQejpQhAEQRAEQRAEQRAEQRCnFnSbjCAIgiAIgiAIgiAIggg4JEoRBEEQBEEQBEEQBEEQAYdEKYIgCIIgCIIgCIIgCCLgkChFEARBEARBEARBEARBBBwSpQiCIAiCIAiCIAiCIIiAQ6IUQRAEQRAEQRAEQRAEEXBIlCIIgiAIgiAIgiAIgiACDolSBEEQBEEQBEEQBEEQRMAhUYogCIIgCIIgCIIgCIIIOCRKEQRBEARBEARBEARBEAGHRCmCIAiCIAiCIAiCIAgi4JAoRRAEQRAEQRAEQRAEQQQcEqUIgiAIgiAIgiAIgiCIgEOiFEEQBEEQBEEQBEEQBBFwSJQiCIIgCIIgCIIgCIIgAg6JUgRBEARBEARBEARBEETAIVGKIAiCIAiiF/HEE09AoVC49V6FQoEnnnjCr+WZOXMmZs6c6dfvIAiCIAji1IREKYIgCIIgCAe8//77UCgUlr+QkBBkZGTg+uuvR1lZWU8XjyAIgiAIIqgJ6ekCEARBEARB9HaeeuopZGdno7OzE1u3bsX777+PTZs24eDBgwgLC5P1ux555BE8+OCDsu6TIAiCIAiiN0KiFEEQBEEQhAsWLFiASZMmAQBuuukmJCYm4l//+hdWrlyJxYsXy/pdISEhCAmhIRpBEARBEH0fCt8jCIIgCILwkDPOOAMAcOLECctzR48exaWXXor4+HiEhYVh0qRJWLlypdXn9Ho9nnzySQwePBhhYWFISEjA6aefjtzcXMt77OWU0ul0uPfee5GUlISoqCicf/75KC0t7Vau66+/HgMGDOj2vL19vvfeezj77LORnJyM0NBQjBgxAq+//rpbv/+VV17ByJEjodVqERcXh0mTJuHTTz9167MEQRAEQRAcug1HEARBEAThIYWFhQCAuLg4AMChQ4cwY8YMZGRk4MEHH0RERAS+/PJLXHjhhfjmm29w0UUXAWDi0LPPPoubbroJU6ZMQXNzM3bu3Indu3dj7ty5Dr/vpptuwscff4yrrroK06dPx/r167Fo0SKffsPrr7+OkSNH4vzzz0dISAhWrVqFO+64AyaTCUuXLnX4ubfeegt33303Lr30Utxzzz3o7OzE/v37sW3bNlx11VU+lYkgCIIgiFMLEqUIgiAIgiBc0NTUhNraWnR2dmLbtm148sknERoainPPPRcAcM8996Bfv37YsWMHQkNDAQB33HEHTj/9dDzwwAMWUeqnn37CwoUL8b///c/t7963bx8+/vhj3HHHHVixYgUAYOnSpbj66quxf/9+r3/Txo0bER4ebvn/zjvvxDnnnIPnn3/eqSj1008/YeTIkfjqq6+8/m6CIAiCIAiAwvcIgiAIgiBcMmfOHCQlJSErKwuXXnopIiIisHLlSmRmZqK+vh7r16/H4sWL0dLSgtraWtTW1qKurg7z58/H8ePHLSv1xcbG4tChQzh+/Ljb37169WoAwN133231/LJly3z6TVJBiotuZ511Fk6ePImmpiaHn4uNjUVpaSl27Njh0/cTBEEQBEGQKEUQBEEQBOGCFStWIDc3F19//TUWLlyI2tpaiyMqPz8fgiDg0UcfRVJSktXf448/DgCorq4GwFbxa2xsxJAhQzB69Gjcf//9Lt1ORUVFUCqVGDhwoNXzQ4cO9ek3bd68GXPmzEFERARiY2ORlJSEhx9+GACcilIPPPAAIiMjMWXKFAwePBhLly7F5s2bfSoLQRAEQRCnJhS+RxAEQRAE4YIpU6ZYVt+78MILcfrpp+Oqq65CXl4eTCYTAOCvf/0r5s+fb/fzgwYNAgCceeaZOHHiBH744QesXbsWb7/9Nl544QW88cYbuOmmm3wup20yc47RaLT6/8SJE5g9ezaGDRuG559/HllZWdBoNFi9ejVeeOEFy2+yx/Dhw5GXl4cff/wRa9aswTfffIPXXnsNjz32GJ588kmffwNBEARBEKcOJEoRBEEQBEF4gEqlwrPPPotZs2bh1VdfxQ033AAAUKvVmDNnjsvPx8fHY8mSJViyZAlaW1tx5pln4oknnnAoSvXv3x8mkwknTpywckfl5eV1e29cXBwaGxu7PV9UVGT1/6pVq6DT6bBy5Ur069fP8vyGDRtclh8AIiIicPnll+Pyyy9HV1cXLr74YvzjH//AQw89hLCwMLf2QRAEQRAEQeF7BEEQBEEQHjJz5kxMmTIFL774IqKjozFz5ky8+eabqKio6Pbempoay+O6ujqr1yIjIzFo0CDodDqH37VgwQIAwMsvv2z1/IsvvtjtvQMHDkRTU5NVSGBFRQW+++47q/epVCoAgCAIlueamprw3nvvOSyHo9+g0WgwYsQICIIAvV7v8vMEQRAEQRAcckoRBEEQBEF4wf3334/LLrsM77//PlasWIHTTz8do0ePxs0334ycnBxUVVVhy5YtKC0txb59+wAAI0aMwMyZMzFx4kTEx8dj586d+Prrr3HnnXc6/J5x48bhyiuvxGuvvYampiZMnz4d69atQ35+frf3XnHFFZbV/u6++260t7fj9ddfx5AhQ7B7927L++bNmweNRoPzzjsPt956K1pbW/HWW28hOTnZrrAmZd68eUhNTcWMGTOQkpKCI0eO4NVXX8WiRYsQFRXl5dEkCIIgCOJUhEQpgiAIgiAIL7j44osxcOBA/Oc//8HNN9+MnTt34sknn8T777+Puro6JCcnY/z48Xjssccsn7n77ruxcuVKrF27FjqdDv3798fTTz+N+++/3+l3vfvuu0hKSsInn3yC77//HmeffTZ++uknZGVlWb0vISEB3333HZYvX46//e1vyM7OxrPPPovjx49biVJDhw7F119/jUceeQR//etfkZqaittvvx1JSUmWcERH3Hrrrfjkk0/w/PPPo7W1FZmZmbj77rvxyCOPeHEUCYIgCII4lVEIUt82QRAEQRAEQRAEQRAEQQQAyilFEARBEARBEARBEARBBBwSpQiCIAiCIAiCIAiCIIiAQ6IUQRAEQRAEQRAEQRAEEXBIlCIIgiAIgiAIgiAIgiACDolSBEEQBEEQBEEQBEEQRMAhUYogCIIgCIIgCIIgCIIIOCRKEQRBEARBEARBEARBEAEnpKcL0NswmUwoLy9HVFQUFApFTxeHIAiCIAiCIAiCIAgiqBAEAS0tLUhPT4dS6dgPRaKUDeXl5cjKyurpYhAEQRAEQRAEQRAEQQQ1JSUlyMzMdPg6iVI2REVFAWAHLjo6uodL4z16vR5r167FvHnzoFare7o4BNFrobpCEK6hekIQ7kF1hSBcQ/WEINwj2OtKc3MzsrKyLBqLI0iUsoGH7EVHRwe9KKXVahEdHR2UFzBBBAqqKwThGqonBOEeVFcIwjVUTwjCPfpKXXGVFokSnRMEQRAEQRAEQRAEQRABh0QpgiAIgiAIgiAIgiAIIuCQKEUQBEEQBEEQBEEQBEEEHBKlCIIgCIIgejtvvAGccw7Q2trTJSEIgiAIgpANEqUIgiAIgiB6O889B/zyC7B5c0+XhCAIgiAIQjZIlCIIgiAIgujNGAxASQl73NnZs2UhCIIgCIKQERKlCIIgCIIgejPl5UyYAkiUIgiCIIi+wI4dwF/+wvr4UxwSpQiCIAiCIHozhYXiYxKlCIIgCCL4efVV4KOPgC++6OmS9DgkShGEXHz8MXD99YBe39MlIQiCIPoSJEoRBEEQRN+io4Ntm5t7thy9ABKlCEIu/v534IMPgE2berokBEEQRF+CRCmCIAiC6Ft0dbFte3vPlqMXEFSi1O+//47zzjsP6enpUCgU+P77761eFwQBjz32GNLS0hAeHo45c+bg+PHjPVNY4tSDq9xVVT1bDoIgCKJvIRWldLoeKwZBEARBEDLBo2va2nq2HL2AoBKl2traMHbsWKxYscLu68899xxefvllvPHGG9i2bRsiIiIwf/58dNJdRSIQtLaybU1Nz5aDIAiC6FuQU4ogCIIg+hbklLIQ0tMF8IQFCxZgwYIFdl8TBAEvvvgiHnnkEVxwwQUAgA8//BApKSn4/vvvccUVVwSyqMSphiCIKjeJUgRBEISckChFEARBEH0LckpZCCqnlDMKCgpQWVmJOXPmWJ6LiYnB1KlTsWXLlh4sGXFK0NHBhCkAqK7u2bIQBCEPnZ1AaWlPl4I41TEYgJIS8X8SpQiCIAgi+OGiFDmlgssp5YzKykoAQEpKitXzKSkpltfsodPpoJPkZ2g25wXS6/XQB/EqarzswfwbgorGRqjND01VVTDScQ8aqK4QFo4ehaKgAILZkau65BIo1qyB4cgRIDu7hwvXs1A96UGKi6E2GCz/GtvbYaLz0GuhukIQrqF6QhCASqeDEoCptdXh3DHY64q75e4zopS3PPvss3jyySe7Pb927VpotdoeKJG85Obm9nQRTgm0VVWYa35cf+wYNq9e3aPlITyH6gox8557EFNUhNzXX0d7Whrm7NqFCJMJOz77DDXjxvV08XoFVE9co+rogKBSwaTRyLK/hEOHcLrk/9L8fOylPqbXQ3WFIFxD9YQ4lZlZV4cYAI3l5fjDRb8erHWl3U0XWJ8RpVJTUwEAVVVVSEtLszxfVVWFcU4mEw899BCWL19u+b+5uRlZWVmYN28eoqOj/VZef6PX65Gbm4u5c+dCrVa7/gDhGwcPWh4mGI1YuHBhDxaG8ASqKwQn5IYbAACzhg2DMGMGQpQswn3K2LEQTvE6TfXETXQ6hAwZAsTFwbB3ryy7VNTVWf2flZiI9FP8euzNUF0hOIqtW4H8fAjXXNPTRel1UD0hCCDkwQcBAHFqtcO5Y7DXFR6F5oo+I0plZ2cjNTUV69ats4hQzc3N2LZtG26//XaHnwsNDUVoaGi359VqdVCeeFv6yu/o9UhCQBU1NXTMgxCqKwRPNBnS1QWo1ZYY/xCTif1PUD1xRWkpUFEBVFSwkG45jhXPa6ZUAiYTlF1dUNI56PVQXSFw/fXAyZPAGWcAgwbJv39BYO1NWhqgUMi//wBA9YQ4pTGH5is6OlzWg2CtK+6WOagSnbe2tmLv3r3Ya777WFBQgL1796K4uBgKhQLLli3D008/jZUrV+LAgQP4y1/+gvT0dFx44YU9Wm7iFEC6akJ9vaWRIQgiSDAaRXGZW435ViI6E4RTmprEx3KtpsNX3hswgG3peiSI4IDntK2q8s/+n38eyMgAPvvMP/snCMK/dHWxLa2+F1yi1M6dOzF+/HiMHz8eALB8+XKMHz8ejz32GADgb3/7G+666y7ccsstmDx5MlpbW7FmzRqEhYX1ZLGJUwHbxsQm3IIw8+mnwLPP9nQpCKI70pj3jg4mLPPkjCQCEO4iFaXkWk2Hi1LDhrEtrb5HEL0fg0FsA1pb/fMdBw6wLYlSBBGc0Op7FoIqfG/mzJkQBMHh6wqFAk899RSeeuqpAJaKINB9wFFTA9isBHnK09UF3Hgjm1BdeaV4158gegPSAUF7OxOmOCRKEe7iD6dUSQnbDhkCrF5NohRBBAMtLfYfywlvCzZsYGMsmRZXIAgiQHBRqq2NheMGaRiuHASVU4ogei22k4/q6p4pR2/m8GFxAEXHh+htSOtwe7u1SMXt1QThCn84pWpq2LZfP7YlUYogej/S5L7+ckrxtqCtDdi82T/fQRCE/+DjS5PplB9rkihFEHJgzylFWLNnj/i4vr7nykEQ9nAmSpFTinAXuZ1Ser24z6wstiVRiiB6P1JRyt9OKQBYu9Y/30EQvY36emD0aODxx3u6JL7DnVLAKZ9XikQpgpAD24aERKnuSEUpyrlF9DZsw/eCVZSqrqaFFnoSuZ1StbVsq1SyFbYAEqUIIhgIpFMKAH75xT/fQfQcu3ez1RsJa7ZvBw4eBFasYCFvwYzUHXWK55UiUYrwDKNRzG9BiNgOOCg8rTvklCJ6M7ZOqWDMKXXsGJCeDvzlLz1dklMXuZ1SXJRKSAC0WvaYRCmC6P0Ewikl7Zv27KGxZ1+irg6YOhU4++yeLknvg/etdXXBPScVBDav5pBTiiA84O67WV4Lil23hjckPEFdsDqlysr8MwE3mYC9e8X/SZQieht9IXxv3z42wNm5s6dLcuoinYjKcdeT9yWJiQBfSZhEKYLo/QTSKcXHnrm5/vkeIvCUlzPXc1ER0NDQ06XpXUjHa9Ib3sGGNHQPIKdUTxeACDIOHmTbw4d7thy9DT7gyMhg22AUpU6eZILjpZfKv+/8fOtBGYlSRG/DWfhesCSf5PWK6lfP4S+nVFISEBrKHgeLSNoXaG5mK5tJ72YThDsEMqfUiBFse+SIf76HCDzSMXNBQc+Vozci7Vt37+65cviK7diSnFIE4QG8wvjrrk+wwo9LdjbbBqMotXcvczQdPSr/vm3vZNCkmehtSAcDHR3BGb7H76Y2NLC6HIzo9cGdQ0NuUYqcUj3LAw+w8JnFi4OnHSB6B4F0SiUns+0p7rToU0iFTBKlrCGnVJ+ERCnCM3hDcIqrud3gAw4uSgVjXH9lJdv6Y8LDO42ICLYlUYrobfSF8D0uSplMQGNjjxbFa+67Dxg4MHjDUPyV6DwpSRSljEZKZh8oDh1i22+/Bc47z1qsJoIboxG44w7gzTf9s/9AOqXi49n2FJ/U9imkQmYw36jxB+SU6pOQKEV4Bjml7MOPy4ABbBuMTil/ilK80zjrLLYlUYrobfSF1fek9SpYV7jkK0gFaxhKIJxSALmlAkV5OdsqFEwo/fzzni0PIR/btgGvvw7cfjtbzUtuAumUIlGq70Hhe46RHpuysuA0AgDklLKBRCnCM8gpZR/b8L36+uC7k11Vxbb+mOzwJOezZ7NtsE6Yib5LX3JKAcFZx5qb2QqCgP+cBf7Gn04pnlMKIFEqEAiCKErxGyr85g0R/BQXs60gMGFK7rxh5JQifIFEKcfYzkGDNYSPnFJWkChFeAY5pezDj0f//uyOqiAE36SQD7blnoC3tIh3+6dPZ1tyShG9DVtRShqmEyyJzoNdlNq3T3zcF0QpOROdJyYCKhWgVrP/SZTyP01NYjswcCDb0qS/71BaKj7evRt47TV59+9vp5QgBLUopbrpJkx44QX2O4juSPvAYA/fa24G3npLvnFJXxGlyCllBYlShPsYjaJgcYqrud3gxyM6WhwcBFsIHxel9Hp57xjygV9MDBPtgOBOxBys5Sac09fC94JR+JXmhgjGGx9Go/VEQo4BJu9HkpLYlpKdBw7ukoqNBRIS2ONTfNLglLffBqZOBSoqerok7lFWxrb9+rHtv/4l7/797ZSS3iwJNlGqtRXKDz9E1saNwXkDJRBI+8DCQnnHnidOiKHygeC114BbbgH++1959sfnXHxOEax5pWxFqVN8bk2iFOE+0soSjBMGf8KPR0SEOHngd7iDBWlYgpyT8JISts3KAuLi2GOTyXrAFiwcOcIGf08/3dMl8Z6CAuDjj+UPVairA/7v/4LXZk7he+7R3u6/JOq7domPg9EpZVtmuZ1SgBjCR6KU/+GiVHo6oNWyx/6a9AsC8NVX1u4dX/e3fDnw1FPy7M8dXnmF5Wb69dfAfacv8GN9yy1sW1Ymb9vmb6eUtA0INlFK2jZK3aWEiPSa6eqSV+xdvBg45xxxIQd/w8eFvE31FX798OiLgwfl2a89/v534Kqr/HND2taFHyz110+QKEW4D4lSjuHHJjKSuaWA4JpUCYKYUwqQd8LDRanMTHaXnw/ug9HJsWEDG0CtWSPfPo8dk28i4g633gpcey2wbp28+73/fuCZZ4CHH5Z3v4FC2r51dFiH75EoxRAEhEydCgwe7N8FEYDgaj85tkK7r6KUIDh2SgXLNRnMBFKU+vZbNlG8/np59ldcDLzwAvD442LuJH9iMgHHj7PHwdK38353+HAgNZU95r9BDqTtQUeH/HlGpW1wbCzbBsuklkQp19jOs+QM4eO5G/Py5NunM/j8Qq5zza8fHlbtrzanpQV44gngs8/8c6zIKWUFiVKE+0grSzBWnN27mQVWbrq6xIYlIoL9AcF1jJqarCc5ck44+cAvK4tt+R29YBm4SuGTFLk61uZmYPx4YNq0wORVEARg5072WK47VgBz2X3yCXu8fn1w5ogI9vA9g8F6EuQHUUrd0gJFXh5z78gdotPebr3iXjDe+LBtF3ydILa0iH0LDx8LRPheZSXwwAMsxONUJpCi1Pr1bLtxozz9izR9wOrVvu/PFWVlopAfLH07H5tkZgJDhrDHck485RapbeFtQFiYOO4MFlFKUk6Fv5y3wY7tjRm5XOitrWL/ykNY/Q1fHU+uCAlelzIy2NZf19DWraJDyh/iKTmlrCBRinAffzqlTCZm+fZXhaypYbkOZs2Sf8IsPS5SUUruYyQIQH6+fyb8tisK+SN8LzOTbYNZlOIduFwdYFERu+ZLS/3XqUqprBTdNHJen6+9Jnau1dXA4cPy7TtQSOtxZ2d363xvx/b68YMoFS4NSZZ7grVvn7U9PhidUraDVl+PERcWtFpRFAmEKPXuu8BzzzGnzamMP0UpkwlYtUqst1u2sK3BIApUviCtqz/95Pv+XMGdF0Bw9O0GgyisZ2YCQ4eyx9Lf4Su2E3C52zQ+TgsN9b9oKjfStlHusY80AXwww8cgGg3byiVKScf7gRKluFPKX6KUTmftbpeLTZvEx/4QpcgpZQWJUoT7+NMp9dFHwNy5wD33yLtfTnExG4QUFlqHqckB7zjUatZ5REay/+U+Rn//OwubWbFC3v0C3UUpf4TvcacUv+MfjMkt5XZK8btH0n37E2n+ALlEqY4OcdWimBi23bBBnn0HEtv6Kr0+g8EpJQ3dA/wyMdT6U5TioXu8/ewLopSvE0R+vHnoHhAYUYq3S8G2WIfc+FOUWrUKOP984IYbWFssXXlSjgTE0rq6bh1rp996i421/IE07M3folRREbBokW8h6FVVLK+iSgWkpIhOKblEKZOpexsm941KqVMqmEUpR+Op5mbvHLn338/CGYPx5pgUfr0MH862coXv9YQoxfsUucP3UlMBpVnK8MeNXako5Y88uOSUsoJEKcJ9/OmU4pPYL77wz2Bb2hAeOCDvvqX5pAD/hO+VlADPPssef/ihfPvl2Ap1tuegpcX7fAh9MXyvpUWeROFSUSoQgwN/iFKff84EnOxs4L772HPBKErZDgaCTZSyrU/+cEpJRQq5+wAuSs2Y4Z/9BwLez/D8LnI5pXiScyAwohQf3J/qYTX+FKWKitj2xx+BtWutXYJr1vjuiJbW1Y4OJn7dcgvLWeWPO/6BdEp99x0LSXz9de/3wccl6elMmLJ1SuXn+xai3NYmnkNef+UW2h2JUsEQPu9O+N6ZZ7KcQbY3XFyxaRPrs3NzvS9fb4D3gWPGsG2wOqXa28XfIrdTKjJS7G/l7q/0eha+xyGnlN8hUaqPo/jsM/ks+LZOKTk7vj172LalhQ3Q5MafopR05T1AFKfknFQ98og4ANmxQ758LrxBdOaUamhggtK8ed59R18M3wPk6VylYmAgnFLSFUrkuj55jqrLL2duRwD47Tf/rFTiT2wHA1KnQTCIUnzgrlCwrb9FKV8HT88/z9wJfHLI8xdNnsy2ck3g8vLY5OaNN/w/WeP9TFoa2warU4r/DjkH+atXy5uoNxD4U5Ti+9Hr2aqlAHDuucxtXVTku2PHdvXfzz9nW5PJPwl7AylK8bbOl+/hfTkfl0idUiUlwLhxwJw53u+fjw9CQsT6GwinlNHYfaLbG3HllGpqYu7Bjg7PxRhet/qKU2r0aLaVq/2Uzh8CIUpJb77KLUpFRPhPlNq717q994dTyrauklOK6LMIAlS33caWBebCgC9IOxGDQb48Kzqddefx5Zfy7FdKIJxSXJSS2ym1d69ouU9PZ1s5ckScPMlC6e66y7kolZfHjt+mTZ4LDU1N4uQy2J1SHR3Wd+zk6AB7MnxPrkk/XzwgJweYOJGJsvX1wP793u+ztTXwd3ttBwPSSV0w5JTi1yavZ709p9Tnn7OQn99+Y//z9iA7m23luj4/+gj44w/g9tuBm2/2r8BoK0r1dqfUqlWsrL/+av08b9vkujO8Zw8Lt7r2Wnn2FwgEwb+ilDT/ydGjbDt7NhNQAd9XeOV1dcqU7q/1VlGqoYGFGLo6xnKIptIk5wBrd1QqVmdXrGDbo0e974f4BDY6GoiKYo8D4ZQCgmNi60qUki564akY0FdEKX69jBrFtuXl8giOtk4pf4+1pDdfOzp8/w3S+ac/RSlp6B4QmETn5JQi+ioh7e1Q8IGPHGq4bWWRq/IcPMgaGR4XvHKl/HeBpY2J1C0iB/xuhm34nlx3xd5+m3Ual18O3HYbe27VKt/3u20b6/Q++KD79SGduPGGXq+3FlHcgQ/84uLE4xKsopStaCRHBxXI8D1B8E/4Hg9DGTCA5VXjkypvQ/hKSliOj2uukaV4bmPbnkkHC8HglOL1adAgtm1rk73cVqKUr9cPb1e4eMa3/fuzrV4vjxi4d6/4+J13gMce832fjrAVpXQ638J87TmlQkPZVo4+8qef2ATl22+tn5c7fI8L1IEQ3uWirk6cPKWm+s8pJWX6dGD+fPbY19Ajfu0sXszq1MCBwCWXsOfkFqX0emsXh7eC+KuvshDDhx5y/j5ezzwN65JiK0ppNOzGCiCGBZpM3k+g7YlS/nRKqdXMlQWI11ZvvpniKnxPKkp5OtbiffmhQ8ERyugIfr3k5LDzKwjdbyB7g3QfbW3+cQBJsZ03+Pp90rGaNHzPl/bAHlyUCg9nW386pfh3BIOg7EdIlOrDaKR3ZTwVE+xhO2mTq4PloXszZ7IBgj9C+KSd2qFD8uQD4tg6peROdM7vop5zDkuMCrABq68rTfCBY0sL8PPP1q9JJzzSAQMfyLmLbege0HdEqWBzSpWVWXeqctRfQRCdUgMGsC3PCSQVAzxh/37WMQcyH4TRKF7zcXHdXw8GUYoPyLKzRYFfZreUVs7wPd4m19Wx68hWlALkcRbw65CLnBs3+r5PR/DfxB2tgG+DTGdOKTmuSd4G2IbHyC1K8f0H04Cbt8eJif5Z3cy2/w4LYyFj06ax/311dHNRKiuLuZgOHhTbZrlFqYIC1oby0OHGRu/GWDys6Msvnbuy5bg++ViGr94FiCF80n7S2/MtFaVsUzpUVbEVFrdt827fHKkoBVhfo99+y8QwHrbZ23C1+p4vohQ/Zw0N8sx9egJBEK+X6GjxRoccNy9t03/4e+xpm7PW1xu6/LioVExM9odTShBEUWr2bLb1p1NKrjyUQQ6JUn0YK1FKjhXn/C1KTZgAXHope7xypTz75kgbk44OeXNb+DvROV/VZvBglvAwK4v9Bl+XjZZOWG3zT8glStkmOQdEUSrYVt/zh1MqkDmlbB2CctTfujrxOu/Xj2356ore3lXix7WmJnArsEkniFIBgGM0yitk+wMuSiUk+Ef41esRJr0T6Uv7Jghiu1JbyyYRXGRJSREnWb6e/9pacRDPXaZ5ef67e86v+ZQUcYLuy3Hyd04p3gbY9oe8Dup08nwP378/luz2F3zixgVGf4lSvL2ZPJlNsHjC7eJi375Leu1oNOy64fuWW5TioXt8lTBB8K5/5MekshL480/H7+P7bmryPnehrVMKEEUpKXKIUtLwvdtuY8672bOB007zLcTMkSjV1sbE964u58exJ7EXvnfypOjmliN8D7B2hwcTnZ3itR0ZKYqncohStm4rf7v0beefcjmlIiJYP8tvJMopSpWVMUFTpQJmzWLP+TPRORelgunGjR8gUaoP43enlFyiCxelxo9nf4A8ObCk2DYmcuaV8mei885O8VgMHswa4IUL2f++3vG3JwolJ4vfy5E29J6eF/5+e6JUMDilBIHlW2lu7t5xy+2U8vfAgA/OoqPZVo7rk4fupaWJYUW+5s+Q1tVAJUaWtmVcVLOlt7uleH2KixN/g5zCb3k5FNIJoC/XT2eneIewrk4sp1rN2k+5crDs28e2OTnspgfA6q2tCC8X/NqNiZFHxODl9FdOKX4OCwvFCZDJZF0H5WjnuFMqmEQpfpOAOxT4+TQY5Mnrwq+L5ctZuNqLL7L/ExPFPtKXZOf2XHZcdDl+XN6FKHg5R44U6643/bv0+vj6a8fv49enIHg/wbUnSnHRzlGZPMGRU+qrr9hjLlr7sqKaM6cUP0b+XBDBFyR9rqKxkZ3LmTNZDrTycu+dUnq99UrRwZpXStr3abWiOC6nKMXHCf4ee/orfI/PufzhlOLz0hEjxHmRP8P3yCkFgESpPk1QOKWMRjHfxPjx/nPR8E6NDwTkFKX86ZQ6eZJ11tHR4t1yfsfE1wbS3jHmIVi2q+9x5Ajf4x1hMIhSP/zAVpS76y7nTqnOTrbk9nffOd+fIACPPw58+in7X9pZV1b6143DRSme+FYOF5Jt6B4g1gNv9y+9rgMtSmm1Yvlt6epibZ4vd14bGljibblzi/B9A34TpRS2dd+X9k1ad2prxbYgIYG10XIJ+1yUGjeO5Wzgbj5fVzZzhFSUkqMfCJQo1dUltm+trdaChRwDfV6PDQbrCWNvRprkHJA/kTQXOxISgGeeEUVTABg2jG29dTSZTGLdl147PO9fZydzYskFr09Dhvh200kqAH3zjWPhTNp+eJNHRhC6r74HWDuleAi0nE6pkhLxuPAbsL5cS85EKf79vfVmivR3NzWx81hSwp7/+mtrsc4TUcq2vQ1WUYq3zVotc+vI5ZQyGsVx56RJ8uzTFXKH7wVSlBo/nvXnQGDC97q6gqeP9AMkSvVhgsIplZ/P9qPVsgGBvwQL3piMGcO2ciY7t3VKyZnonIfuDRokCmpyhRHYm7DySZu9ROeA504pZ+F79fW9Pwklz0fz44/dBTnpcdmwAXjvPeCJJ5zv78QJ4KmnWDLXlhbrc2g0ine37dHW5n0ix8ZGcTUnnrNEjuvTniglp1PqxAk2MXn0UeCzz7zbnzvw86DVWk8+peh0wPXXs5Vw+IDFU/75T+AvfxET6coJvzbi4/0j7ttOYn1p/6V1R+qU4uWWyynF6+/YsWzLnRCBEKXkaKelOUU4/hClAHESaDuw93Ug3tlpLegHyi311lusnfVW6LcVpTQa34UKKXwfPMGtFH6d8nySntLYKAo6UudnSIi4EIKcdYBP/AcPlk+UKi0Fduyw/z5fnXy1tWzyp1CITjiArR6bmsoW6+BjFm/PNS+j1CnFb8CmpIjuC3+JUtIQ3N6IbU4paZ6jl16yFiQ9uQFrezyDXZTifSEXpXxN81BbK+Z/GzeOPScVpUwm+VexC5RTSs5E53zsMG6c2P8GwikFnNIhfCRK9WE00grUWxOd88ndmDHsboA/wk4AsYM+/XS29YdTyh+JzqX5pDhyi1KpqWybmCh2gHInOrcnShmNgcsZ5C28s66vB9atY4+5cCcdGHMxyVVYEP9MW5s44A4Pd53E0mRiA+bBg72b1N13Hxv0DR4MXHYZe87fopS3+7d1Sv3+O/D008BVVzFRp64OeP99YNcu7/ZvD2kdtp0k8hWNdDpxIuetg4t/3h8DZT+H7ylsBWlfrh9p3ZGKUrzccofv8cE3d0LInVOHI7dTSiqWcvwlSvFr2nZC4usEhbcRnECJUo88woQpb/t62/A9hULevFL8ONgTpXx1SvF+KDqaiWlS5M4rVVcn5i2aMcM3QZxf03wSeM013Zdll+ajA7ybiPJxTHKy9fGJjmbie26uWH/lTHTOr8WcHOv8T97CBSceOh+s4XstLdY3PB3luHMH2/MVrDmlbFf1lsspxUP3kpLERUWk+7z+elYvvBXE7cGdUnI5jmznXP7IKRVop1RUlGg8IFHKdxrlVlYJn/F7+J4cosuJE2zLE2RKkyTLkbeBwxsTvlz9sWPyNWC2nYec4Xv5+WzrD1GKD1x5cvmMDPsTHm9Eqc8/BxYtEkU1qUU+PFz8HrkmzUVFwGuvyd+YSztrfrxGjGBbW7cH3zpzf0knZL/9xrYpKa7vgu3cySYRdXWe1+U1a4B332Ud3nvviWGgra2+O9V4Tinpimly55SSrlD00ENMRF2yRFze3Bc++IC5sKSDHKkAEBoqThp1OrGuezvQ5+eXHzc58XP4Hp80CFzElsspVVvrWJTyRfjS6UTxz1aUCmT4ni9tkj03jb9FKduBt6/9pG3OnEAMuI1Gsb329jrlNxpSUsTneNsgh7DG92HPmemrcGQvQb5c+7blhx/Y8R47lrmw5HBKPfII6xPz89mY7aefrN8jDW/x5vrkN2l5WyZFrWZCFa9zcuSUsr3Zl5MjT/vQk+F7JhNzfXu7Urbt77YngnDB0BOHCq/v0dFszFNba+1A7+3ufA4fP/lLlEpN7Z6nqquLhc3q9b7f9NPp2A1Fk0msb3we09tzSjU0iDdTxo0TRSl/OqU0GnmE6iDHK1HqX//6F7744gvL/4sXL0ZCQgIyMjKwj9+ZJHoctb/C97iaK4fTgndMvNOOjRX3L2cIHx9oDx7M/qTLffqKbQPpj/A9qSjFB0tyOaVuuw14+GHgv/8V77g5E6VcJUjV6ViI0urVbLA6bBhbpl4KHyzLtVzvAw8AS5ey75Vz0GFvAMBFKVu3B8B+u7PzIh3g8kT1ycmuk1iuXi0+9nSQ+cwzbHv33exONh/kCILvkytXOaW8ORe24Xvbt7PHEyeyLZ+QFBf7noPr3nuZC4t/h234Xni4WCd0OrGue3vcAiFKxcf7JQyaO6UEfgNBrpxSnZ2i2M3L7WteMoAJUgYD61O4U9OfopQ06bI0fM/b4yRNqG0rlALsety7F3jhBe9v4LgTvie3KOUvp9QLL7D6zJ00vJ/y9vvs5fOS0ynlTvietytF2iu7vX3LAU9Izm9uySFKTZjAUiwsWMB+vzRXo61o6o1TiosU9kQ7jq/n2p5TijNwoDwT0J5MdL5jB8uPec893n3e9ndLE5tzeJ41b5xSiYniuOSnn9j1eMUV7Jxv3uxxcQOOM6eUL2NcHiaZmtpd6Nq+XTx+zlJJuMPLLwNnncXGV3x8HCyiFA/dy85m++bOzfZ2ec0SgOiUUqvlEaqDHK9EqTfeeANZ5kFebm4ucnNz8fPPP2PBggW4//77ZS0g4T1WTqm6Ot+Tp/GGgA905BSleGeqUokNjJx3+aV3sM86iz3+/Xd59m3befCtTuf7pNlf4XtdXeKELzUV+Mc/2BLF9u7CSwd9er3rzkrqctuxg+VRUKut38NDIqR5BHyB2+K/+QZ4/nnx+T//ZKsVejsAtycS8Um58hx1QQAAsbdJREFUtAOUDsCdDcalEyTuAJKKUo6cUlJRytNBJj/GPGxPqxWFX18m/YLgPHzPaPRuQCwdsBQWAlu3ssfPP8/O586d4vf7MggxmcRrm99MsXVKabWiCMATnQPe/S6TSTwXcghqUqRiqJ/D9wQeWuRL+2973rhIJGdOKWnoHr/e+YQ8P1/+RQWkCcKjo313zErbCkfhe8uWsdXbXn7Z8/3r9dYCt7/C92xDcfwhSpWUsBDlF19kjgtpGHVvFaWche8NHMjChtvavHNG2Ft5jyNnCGtjI1udFpBXlAoPZ+PABQvY/9J2QA4nnzPRjiOnKMXbM440fC9Yc0pxx7a3Nz4cJSTnrlYAOO00tvVGlNJqgbPPZo+XLGECwxdfsD6Ri9e9GducUnyM2Nbmm6gjdUpxUaqqis0P168X3+erKMXDJl9+mR1rpVK8Od3bE53z0D1+LUpzOsqdcoScUlZ4JUpVVlZaRKkff/wRixcvxrx58/C3v/0NOxwlJiQCjpUoJQi+L4PNG3tuZ5ej4tgbmNlOqBoafOtA9Hrxe2JixBA+7lTxFUdOKelr3tDRIcbZ2xOlfBnc84GEQmGdYI8PbhwlOgdch/DxDjMigq3uYStIAfKKUiaTGAYKMNcU71TeeQf4+Wfg4489329np3gNcmEiLk4suz2nlO1jW6TnjB/j5GT74Xu5uSzBelWVdcJXTweZvB3gHatSKY+br6FB3DfPswVY3xX2Zv/S42owsGOiVLK7ptOmMceUL8uO2ysbT0DrTJTq6BDbQG9EqZoaUQgxGOQTZAFRXFMq2Xn2Y/ieJd+NXE4pQJwgyxm+x8vLEzsD7DrVaFgdknP1sa+/Zi5EQBxc+jrp5J9TKMRrELAWpbjg8/zznrcLtucvmJ1Sn30mjhEqK30XpYxGsU752yllL3xPrWbCBeBdbhd3nFIlJb47NletYuOrESPEdkEuUQqwn2BYDqeUO6KUnOF79pxS/gzfa2gQ2wN/OaX4cfd2ku4oIfnVV7NxukoFnHEGe86bROdaLXNPLlvG9tXczMbRWi0bT/34o3flDhT20oLwMDJfQvi4KJWWxsaeKhUbQ1dVyStKSVNaAKyu8dxP/kx0LofYyJ1SfIVMtVpsD+TOK8VFKXJKAfBSlIqLi0OJecC3Zs0azJkzBwAgCAKM/lzSnPAIjW1n4WteKd4Q8FVD/OGUAqwnVNu2scbMFweetBGJjhadUrt2yaN6266+FxrKGnrpa97AhZaYGOsVdOQYGPOOIi5OLCvQ3SkldaNwMcbVCnz8mNreHZTC9+XrSiK8PDoda9Rnz2YTCt658rJ4mqBdWrawMMDcxiEjQxwYOHJKuStKcVJSuofvFRezu8TnnQfcfLP1+z0dZPIBgPR88IGOL9cnn9CkpFiLylLRy5v6ZW/AMnKk9cBejtXlpN/DRRFn4XvSyY83A31bEUrOED5etthYdvx9mRjaY8cOKBobYQwJgcDDV+XKKQWIjlA5w/ds7zQDrK2Te/WxsjLmQjxwgF33Tz3FhCRfnVLSvpE7vQCxjW5vF6+p8nLPhXfbul9ezton20G3r4NwfzulBAH46CPx/6oq30UpR6vXBcopBfiW7NyZ6JKYCMyaxR77Gtnw7bdsy11SgP9FKdu2w1+iVKCcUv4I3+PCA+A/pxQ/D7Y5vtzF/LuNfCERfr1kZzP33c8/s34f8KwNkgoWUVFMmNq7F3jjDWD3buCuu9jrjz3mOhVFT2IrSgHyrMAndUqpVOJYfPt2YMsW8X1yiVKclBT5VrFzlOjcaJTHLCFNcs7xV14pafgeOaW8E6UuvvhiXHXVVZg7dy7q6uqwwGyx3bNnDwZJ70oSPYpG6lgBfM/fwyuKnE4pV6LU5s2s4+AhPN7AOzStllX8fv1YuJHRaN0Iews/DrzzkGNCAlgnOZdOSuQUpaQDbqC7KNXRISr5o0axrSuBx9aZYw8uwsjhFuET2pwcMTSBd+h8682dJf6ZjAwmdgHs2uF3ZRw5pdwN3+PYC9974w3RVbNqlfX7PRlkSl2C0vMhhyhlL3TPdv/eiAr8uEpXbJwyxfo9cuRMkg4u+LG255TiyVal59ibia7tQNJ2VTJvEQRRwOaDM7lXi1mxAgBQPmMGBGmifG+xLRdvb+Rcfc+eKAWIThG5RCneHqakMIH8gQfY/3I5pWydNLyNLi21ngw+95xnIYn8+MTGiseoqEicbPLr3henlCCIohT/DrlFqf37Wf4hTlWV9YTKm++Trl4ndfrKlc9RWi57TinAt9xPzhKdAyzMUakEvvrK2h3hKfy48zApwLe22RunVDCE70mFhbAwJgT40yklHVf52ykFeNcXmMfGOt5ncdLTmcN+7lxrIcBdAcleuzlqFHDrrew83H8/a4v27mXCV2/FNtE5IE+yc2lOKQCYP59tr71WFEgA30Up2/qfnOy/1ffCw8V22ldnryCI7tTRo8XneVvkL6eURkNOKXgpSr3wwgu48847MWLECOTm5iLSXGkqKipwxx13yFpAwks6OxHCJ6/8jptcTikuSsnhlHIWvldfLw74fWkgpfmkOHKG8PHOQxq2J0d4lL18UoB/RSnbROe8gVepxFxKrpxS9pw5tsgZvic9TrZii1yi1G23AQ8+yJI2Sp1S3CrsTfgeRxq+V1rKjv3bb7P/uRAI2E9Cbw+dThSgpZN6uZ1SzkQpb0UFQRDrK09yCnQXpeRwStkbXEREWLdF0vA96SDLm4G+rSglh1OqsJCtbHneeex/Xp/lvKtXW8tW0wRQsGiRPIK7o4GjnOF79gb1gLw5dQDxGkxPF0VBQD6nlK2Thk9AefhhXBz7O3bMM4FBKtrxXB8nT4rnhofk+jLIb2gQr0Hef8gtStk6xKqrfXdKORIt5HJK6fWioOjIKcVFKbnD9wBgzBjg9tvZ43vu8c4xIghimyZdXdfbttlkEm+49IbwPblEqago6743J4fdZPSnU0o6rvLVKSUIwCefWKdIAKzbBXfb6Q0bxBvM5t/dYTsG5TfoAPH8C4L7x8lZWCzA+pjzz2ePpWJ2b8PeTRU5RCmpUwoA/vMf1tbz48vHc8HklJKmIfFVlGpvF9tmaftATqmA4JUopVar8de//hUvvfQSxkvsbffeey9uuukm2QpH+IC5QRBUKlHU8MUpJQhiY8/D9wLhlPKXKMVD+HwVpQRBLBs/LoA4EfLlGPE7zAMHWj8vx91aPihz5ZSShgVx54q7TqmeFKV4GeQSpcLDgWefZXZe3vnp9eJx8ibROSc5Gejfn4kf9fVMMK2pYcd761bgqquAxYvF1edcDTLPP59NEkpLxQ40LMz6jr8cThR+HUgdTbb791RU6OwUBwRS67Qjp5Rc4XscZ+F70u/qLaLUH3+w/apU7ObDffex5/ngr6XF94Te77wD6HQwTZiABmkd0+m8XzzD1o3D4RNaf4XvAWL4nm1YmbdIc2ZI8bdTigsJ2dnMWQBYh+24QhoewvMXSUUpPjnxZZDP80mlpornVs67wILA8kkBYvsoR/iev0UpaZkciVK83/emnXBHdHnqKfbdBw+KfagnNDeLx4H354D34XvSNpUfE3v9FB/P8QmpN9ens0TwtmXw5voxmcRyxcVZC+P8vPoz0bmcolRuLnDNNcAtt1g/LxUD3WmnW1qAc85hzhzJIgud/HrhcLEEYOeAh/e561BxJUoB4nn3RtAEWOqPIUOY09BfOAvf81aUEgRx3MbFv9hYJuwrzXLA4sVs68ucy2QS6z9PfZGZKZ9TyjZlCiCfKMXHhUql9TVETqmAEOLuG1euXOn2Ts/nKjTRc/CBcny82Mj74pTq6BBdIf52SknvtPEGtKGBTYBC3L5kReyJUqefzrY7drDfJQ2P84SWFvE38OMCyOsmcDQw5kuG20skLkWvZ06HWbPEO5quwvf4QIaXITZW/Kwc4Xty5pSSilL8XNg6pZqa2LmQdmKukIpSUiIjWYfFB54qlXVd8CanVHQ08NprwE03iUnNb72VlfeTT9j/c+eyrTNBpKuL3Y3U61niUH6cbc+FHE4pe2Isx1vRi9dVhUK0ToeFibklOHKH73Hshe/xO1lyOaX69WMuFznC9/jxuugi6wGytK1rabFezMATTCYWSgrAdMcd1qHJAKtT0u9yF17u7Gxrx5Kc4XuOnFJy9IdSHAn8cq2+50iU4mRkiH2zJxNQe6LUiRPiuZFDlOIh6Dk5vieOtsfx46ydDg0FrruOTRb5SlKc3ihKST9vez450v7W0zGKO6JLfDxrV3fuZCtlcWeWu/D2LDbW+hrl47eGBtZ+KN28921PqJM6K/gxkF6fhw755pRyFN4I+HaueXkBJkpJx2i8rvkzfE8qKPgavrd7N9vaOkul7YI77XR1NetLu7qsxpBWolRCgvWiDgoF61/q6tx3qNi6aOzBHa3etm1r1rC25+OPxVWN5cYfolR9vXi9SW8mnnEG8L//AWvXMgflc8+xeuXO/MIe0nDLd95hY4g77xTHUHI5paTHRprs3BekN9Wlba6/nFLSROd83MMTrZ+CuD3Dv/DCC916n0KhoGTnvQAFr/wJCeKk0RenlHRgzTvyQDilpKFidXXWwo+78EZEOnnidwm6uthgyNldFWfwiU1EhHUnKMekX5qTQIq0rO3trieFq1YBf/kLcPnlljAch3f3bZ1S9kQpOcL3+PGvrvZebORIRSnufrAVpQDWkfPQHXdwJErxgVJDAxsg204W3BGlEhLE9/H6ecMNbF833sgGZrauUz5YczbxPHJE7OQaG8Vr0p+ilL2BvbdOF+k1f9ZZ7K7y/PndB0ZyJzrn2BOl+DmTK6fU9OlMlJLDKWVPcAfYtaLRsPatudl7UWrfPiaeRUZCuOwyJniGhoqirLeiFG9XBg50Lkr5cn06ckrxPsTXHIscRwK/r1Z8R3f8pZM2gLWlfJLtrSglDRXj56Z/f7b1RZTibfOQIWK7JKcotXkz206eLIYbVldbr8DUG0Up6c04R2IT73fa21k996QOu+OUAqxFqYsvdn//gNieSV1SgDjhN5k8a3v4mCMkRBwP8H7LaBTHaVIn36FDnl+fJpPj8Y8UX841nxiHhVkLRu3toijlaftQX8+SUc+fL14zjkQp6fXvq1OKr4pXXm4tUniaU0ra35pvyAgKhXVOKWnoHic6mp0vOZ1S/Du9FTD4OeOiuz+wd1PFdkEcT+Fj9+Tk7g7NG29kf0Yju774iu229dsdeP3Salm7/Mwz7H9+s0Du8D3Ad6GR42je5S+nFL/pqdGwqIh332VC3pVXirlsTyHcDt8zmUxu/ZEg1Uvg4XsJCeIg3Jc7w7wRCA8XK6e/V9+rrra2IXtrJ7U3cYuIEDt2XxpIfkxtxTI5nFKOGkeNRrz76M6AW7qiG8fdROf2RKmyMufLrrrjlEpKYr9BEHybHBoMohDlLKcUL7cnOBKlAOu8UrbCiDvhezxfkkJhfQ6WLGETrU2bul9Tti42e+zbJz5ubHR8DflblPLVKRUdzY5Lfr4lybYV/nJK2YbvOUp07otTato0ti0q8n35YkeilPQ5XwZRa9ey7axZ4iBWofA9PJmXSRqaHBkpHms5w/dsnVJSUUqO5aMdtaW+OiFche9x0tNFoUqaqNYV0kkPz/dkT5Ty5frhyeQHD/aPU4qLUjNmWI9zgiV8z9nEOTxcnGh50nfp9eI5cyVK8ZyF3uTW4e2ZrZAQFib+Lk/aZ3uueXvjNFsnn6fCQlOTGNJsW2el+HK98jJJBRfeJ/I2z9P24eGH2Yq8770nPudIlJKi1/u2yhwXpaQ5xADPnVLS/pbfkNFq0SVtn+0JIJ72Y+6IUr66ani/d+KE/1bwc5ZTytsIAz4H4AK+PVQqsV54O+fi9d42NJOfy64u3xx89kQpucL3HKUfCYRTavZsMdffkiXyC2BBgFc5pYjej0IavienU0rqCPJ3ovPDh63zoUgHmp5gb+KmUIiNji+NjG3SQI4cx8iRoCBNkunOgIY3stIO2FNRKi5O/I1dXc47c3dySqlU4iTCl7xSxcWsUQ8NZXZkqcPCNjmmnKKUdAU+W1HKHafUtGnMPbBgQXeX2LRpYn4UKe4kOpeKUtIkw7bnQk5Ryt7Ex1unizORRYo/E507yinlTfje7t3A9dezdoIPJCdPZoJsZ6fvbh1/i1K5uWzLQ0c5vrZvUqcUR9oW+TN8j/eHXV3yDPpciVJyO6Xshe9xMc9bpxRfDKWoSOzT+KS/vd0zsUuK1CnlD1Fq0ya2Pf10a1FKrtX3/B2+5yifFMfdkHkpvOxKpbUoYg8eFn3okPv75zgSpQDv8krZGwsqFN2TI/M6y0VTnc6zCS4/PlFR3V2HUuRwSkmP/+mns/95fkRPnVJc4OVud0Cs7/x3OBJivHVLmUzMfc2RuuQ9zSllT5SKiIBeKiw4ckrZft4Z/Hy5E77nqyil0/mWdNwZzsL3Kiu9yxXpjigFiH2kt6KUoz5R+lt8mXf5U5TqKacUdyD++99sXFRSwkIqTzG8jplpa2vDxo0bUVxcjC6bAcvdd9/tc8EIH5E2CnI6pSIi5EnizXHmlLLtMOR0SgGskWlu9m3i48gpJccxchYGFx7OOi25RSlb4UOa6DwsjA026+vZgNT2Log75ZaSlsYEKV9EKT7pGTiQDcKlDovOTuu7WJ4MHqR3BV05pfhgmlue3XFKxcezwZ4neULccUpJY9EbGx271uQIj/KHU8peqK095HRK8bAKwH74ni+i1EsvAR9+yK5L3lYMGMAG36WlbHDuTkiyycSS3o8ZYz2wkzrLbPF1pZv2dpZIHQDmzbN+zRfBxWgUrwupKCVtT/wZvhcWJrb9VVXehzZy/CVguFp9jyN1SnkrSiUmim07f146cWlqcp5/xxHS0GqeK08uUaqmRgz9nD7duu+STp57oyhlT4CxR0YGcOCAZ30XP+ZZWa7zOXGnVF4emxzZLjzgDFeiVGmpZzcN+DGxvb6jo9n1ZytKZWWJfW5Dg/thRu6GNsotSn35JTvGtq4md/fP+58NG9j+4+Lcc0oB7H2urjV7lJRYl0/qtvd09T3pZJ7nU9RqXYtSnt5c4X2SO+F73goY0t+bn29/sRdfsSdK8WuW5zN15vSzh7uiFG/r5XZKqVTs97S2svpsLx+pO/SkU8qfic4B9puWLAEeecS7mwVBjldOqT179mDQoEG48sorceedd+Lpp5/GsmXL8PDDD+PFF1+UuYiEV5gbBcHWKeVtuIK/nFL2Bt6OGlp/iFJA8IXvAZ4NaPh+GhrE8+9NonPAvVXz3AnfA8QBiC/JzqWTHsDaAWR7fXoysK+tFe9g2Bvs2nNK8YGJO06p8HA2YfBElHLllBIEx04pR+F73gqybW3ib5Ezp5QzkUWKnDmlxo4Vn9NqrdsiqSjlTfgerz8ffSQm/eWrLQLuJzv/4QcWonT//fZ/gz+cUr//zupAv37dc7H5IrpL21tHTim+f+lqjJ7iyCkFyOMe5vQGp5SvopRCIYbwceLjxXrozUC/vl48NoMGybNqrJQ//2Tb4cNZWaVjEz7QB3qnKOVOiBHgnVOKh+LxhSJc7T86mtUxT1fgC4RTCuh+g4O3Z7Gx3k1E3UkCD8gvSimV1nVX2j64My7nx9JgAFavZo/dFaW8dUrx0D0OF3v1euvxlQ/hewZpmZ2JUp46pQKRUwpgIXxy0d7OXPI33GC//1KrxePhTeRIoEQpR30iII+44yynlJyJzqXIMV+0h61TChBXCJbz2goSvBKl7r33Xpx33nloaGhAeHg4tm7diqKiIkycOBH/+c9/5C4j4QUKaaMgR7iCPaeUXu+9rR9gd8z55+2t3mKLnOF7QGBEKW+FO0FwLu54E74njeN2tGKUs5xSgHtCkidOKUAep5TcohR/b3Ky/bvHUqcUHyzyMtTXOx5kunuH3B6unFLl5dbCiTSnlCfhe7t3A3fc4XxAwq+f0FD7k/5AOaXkEKXGjxefcxa+Jz1W7k50ebvJvys1ld0t5KFR7iY753nTuNuE48/wPR66N29ed/HUl/aNl0ertRZ87YXvAd4Jp3yVJ9t9cTx1D69fb+0SkOIq0bm3Aoa7q+95m1PK9k48D+EDRNeptJ3zFN42Z2RY1yu5nFI8nxRfSRew7zrsjaKUJ04pwLO+i4tS3AXlDIUCGDHC+nPu4kyU8mZs5eiY2O5LOibxZiLqzsp70nJ4c/1I0x44gl9LRqO1iOoIqcD3/fds64lTyhsciVK27YGXopRgG75n7wagp2FTnuSUamz07ka9VJSSM9n5vn1s/PXee2LfZNt/+TL24X2YK2eXP0UpOeZdPRG+FyinFCCKUv5MpN9L8UqU2rt3L+677z4olUqoVCrodDpkZWXhueeew8MPPyx3GQlv4E6phATWufKGzdsQPqkl1nZJcG+RdvbSDiQiwr4Q0BudUo5ySkmdBDqd58u/63RiYyWXKAWIyzRLV2eU4kqU8sQpFQhRijuDuJPDmSjljiOrpoYlGvznP9n/9kL3APtOKd6RGI2OrylfRClXTinbZWTdcUrZExX++1/g9deBzz5zXBZp6J49t5evOaVcOaX4ddvS4t6A3h5SpxT/DfbC9+y1Re4O8m3bRz6B49e+u+0x38/x49aDaGeilK/tG09ybptPCvDNKcXblJgY9qdSsf+lbZFGIx53b4Qv6WfsiaaeiFKHD7MEpPaW/haEwDul1GrxetVorJdR98Ypxeuq1CkVG8u+w5eBvjTJOSB/TimeT2rGDPE5uUQpR6uzBVqUki4u4i6eiFLS93kaKsL7bXuilDc3JdwVpaRtnjfXZyDD95yFBkvH0a6+o6vLuh35+WfWBwXKKcXHt45EKU9X3+PiiD/C9zzJKWU0ete/+EuUkgpNPPWEbf/Fr9ve7JRyFL4H+C7uGI3i9dzXEp1zuIO8slKeiKQgwitRSq1WQ2mOVU9OTkax+UKPiYlBiavl4onAYDtQ5o29twKAVJnWaMQKJJcoJb37a7siGQ916Y2ilDtOqeuvB7KzgT173N+vtEz2JlWe3HWW7quhgR0P3uF5kugccM8p5W74nq+iVGOjeLf87LPZlnckXV3dQwfcGdh/8AHwxhvAF1+w/x2JUvZW38vIEAfUju5i+dMpxQU6adm8EaV4fXE2YXeWTwrwffU9V04pPmkGvM8rxb8rOZnlalKp2B1EaVskDd+T4qsoxc+Hu8dH6riStoP+ckrl5bHJrUJhf1liXwQXqdAtbettB7DuXkPt7cDKldYTO35dh4ZaD/Y4noTv8XZj377uKy21t4vXgiMBQ25RSqEQr8n0dPa/r4nOAWunFB/gS8V3T7F1scopSul0wK5d7LHcopTBIIoKPR2+x/sfd8P3BMFzUYonO/fEKSXNuehMlPJkQuWOKCUI1m5aX5xSgQ7fs0WtFgV5V20E359Cwa6JtjZgzRox2bW/nVLz57MtFzVsj7c7/Zi0DeEh2Z44pdwdp7uTUyo8XGwzvQn3CoQoxZFLlNLrxbG2u6KUt+Ht/nRKSY+9PVHqyBHg3ntZ7jVv6OlE5wD7LfzYcZf8KYJXotT48eOxwxxGcNZZZ+Gxxx7DJ598gmXLlmGUux0h4VcUjkQpb/P32Nol5Vi9i3f2YWHdE3JKG7Nx49g2mML3pE4CLpx4I0pFRdlPVuqLU4pfGxER3UNB+GTHaLQenHvilHI3fM/Xa/KXX1gZhw/vvswyILrY+LVUUeF6+V5eloED2XV388323yedrEldZ64ScPvTKcVFqTPPZFtvE53za8qZPdxVXg5vc0o5y6MmRaUSz4G3opT0u3Jz2aQsNZXVN35+pOF7UnwVpTwV7RwNgv0lSr3yCtued579gaUc4Xu8fHz/jlbqcXWMXnoJuOACtuXYSxIrxROnFK+z9lZa4nVEre7+XfwYdXR4t2y4M+GCt9v8evI1pxRg7ZTi50YOpxR3scopSu3dywbziYnWecmkiXO5aO3p90nbE1tRwReh4vBhtvLmjz/6L3yvooL12SoVMHSoe5/xxinV0CBea/aEBH85pdraRCHG304pX65Xd0QpT1ZR5tdkXJx4k2DbNvF1fzilBEFceY+LUnKF73G0Wpg0Ghhvvhm4/HL7Yom3TilnopTUBSqHKOUoBFAQWH3nY1FX2Btz2f4Ob8P3ystZP6TRuE4w7o1T6tAh4MYbWZ/qKBIDkE+UUiis5y/czVdZCbz4IsvL5Q095ZSydeSfoiF8XolSzzzzDNLMHdE//vEPxMXF4fbbb0dNTQ3+dwouYdgrMTdYAr/7zAcOngoA338PXHON+Dk+0JYjkbezzkPamPGcL73NKSUI4qTGNnyPH5/aWrEj98QR5ErY8SRprCNRyl6HIW3kdTrvckoFKnxv1Sq2Pfdc8Tm1Wpyg8YEAX5nPYHB954dfY7feykTE88+3/z57TimpKNUTTqn9+9n2rLPYVhq+5yinlL3BpCeiVE85pQDfk51LRamkJGunCG+THDmlPM0pxd2efNDt7d1fQHSgSFexkzN8r6kJeP999tjRSrpyhO/xNoUPkG2vJXevIX43X5oU1FmSc+l3unMnWCpA2g4QpXXfNoxV2q95M7F1tPoeILYFXLSQI6dU//7ifmydUr7klHLklPr4Y2D7ds/3C4ifmzLF+rhLbw7xx54eey5axMUBITYLVPsiSq1cCezcydy4niY6r611TwjnbqfBg7vfcHIEd0rl57t/rHj/Lw0dleIvUYr3DyoVO3bB7JQC3B9HS0Upng9I2t7xc+BoXOGOKCUILGfhb7+xsc+OHex4K5WiE72ujh0Pb5xS9voh8+83rVgBfP65/VQA/hClAN9W4JOer7Y2x/3I2rXsxs4dd7i3X96f8DY5MrL7TWlvnVLSfFKuVuX0RpT6+9+Bd98FXn5Z/B3+CN+TGiSk18vYsSz1xI03sv/LyrzLF+aOU8rbBcPsYc8pBYg3W04xUSrE9Vu6M2nSJMvj5ORkrFmzRrYCETJgNIoNra/he3/7Gxtc8oZETqeUswl6IEQpPnDyVpRqaRF/g6PwvQMHxOe8EaUcOUZ8cUrZy+HCkQ4wOzv9u/qeNK8OX5nMXQwGllcBYJ2+lMhINgjjolRMDDs/FRWso7IVEKXwwYWrJKh8UnvihNiZxMe7Fkr86ZTiA1fueGhsdJyjyVn9lVOU8janlDuiVEICO/6+OqXsfZdWy36/XOF7b7/Nwi1uvZX974tTik/2pZ+1V9e8Hfy99x77vpEjxcmILb7clLA9xw8/zBK/S8VlwP1riIc2Sa9X23xJtkidUu3twIMPspxRZ5zR/b22otSsWeL/zgR+6aSovd15jhN7BNopxd01+/eL7b23ic4FQbxObZ1S7e3A0aPAtdey1/LyPNs3YC1KSZH2w1lZrA/wVpSyJ1r4IlTwdqquzv1+IC6OnevOTiYE5eQ4f7+noXsA6w/5d1RViYswOMNZ6B7gnSjF65mz1fdsQ38DsfpeVxcbb9x+O6sP7izm5K4o5alTKj5eFKK5KCUNA+Qr/PFjGRHB2mh3+qtff2WLWtgyaBAb70RFsXNQUuJ7TikzgivhCPD85oo7OaUA31Zr4/1eSAi7NvLzxbantla8ScHd6+6G3/L+5IYbgHXr2Ep8tnjrlHI3nxTgnSjF5zr79vk3fI9fa7bnV6EAli9n5/+dd5gDqaXF9TzEFldOKYOB1SdvxvD2cOWUOsVW4PPKKUX0coxGmP7zH+QtXixOkr0JlaqtFQeWfCJh65SSI3zPXsfEy61Uiksb19Z6rlAbDGIHIrdTirukpEtRc/hAX9p5u2vhBVwLO/YGMzodS05t21nZ5pTi5bYnKKhUosjS0eHcKWXvXHR1iRMjV06plBTWkRgMnt/12bKFDdTi4oBp06xf48eeH+/ISPfDIHgn7MrefPrp7FgdPSrWEX+H77lySvF983Ok14vn2hNRitcXZ+fEX04pd8P3AN+cUiaT8zo2ZgwbJAwZYj/RuU7nui0SBPFYjhrFJjO8zJ62PdLzxO+c8TZZo7HvivBGlDKZxNC9u++2f+ca8K39t21T5s1jzizb9tnd8D1ep6V1zpVTik8gqqvZHfpXXgEef9z+e6Wihu1dS0ermALWYaDeiHeOVt8Dujul3M0pVVDA7mg3NNgPceRuQV+dUlVV7BwolaKQIs2DyCdp3i68wkWpqVOtn5eKUnzyxfvIt99mwqOrY+QvUYofw9pa9/sBhcKzZOfeiFLSMDJPnVJyilKeOKV4W+HP1fek9e7ECXb9/Pe/7rkRPXVK+SJK2bb90nLzcYw7YjW/dqKi2HnVatm1ceWVbMsdWiUl4u/j9c3TnFL2yuoIT/oxaZ/rrlPK2bWzYQMTB/iiHwDrI/n54jcA8/NZe3rbbey6uv9+9jw/R+62F9JFcw4eZK5KW3x1SnkiStXXi+GyztDpxHDt/fvdC9+TwyllD61WbEO8SfniSJSSOrPkzCtlL9E5cMo6pbwSpbKzs5GTk+Pwj+hhNBqY7roLR6+6SrzQvRGltm7t/pytU0qO8D1nTqm0NNHZotd7LiBJ3+9IlPJmyXHAcT4pwH6D6W+n1DvvAFddBTz5pPicIFhPHBsanK+aA4iDnJoaMRcK78C5u0ka2idFeixdiVJqtdj5uXsnicND9xYu7B5iIYco5WrAGhcnuir4Xcj4eP+G77lySvF9JyWJx4SHjjrLKWUrrrjjlHI1sJcKCp4IyZ46pQDvnFLS322vjn33HWsr09PtO6UA1wP9jg7xO2zbAzmcUs6cXoB3ontlJUusqVQCV1/t+H2+tP/unmN3jpEgeOeU4pO1qipR4HB0V9id8D1HrgtfRAx/OKWeew547DHgf/+zL0qddhrb8gExb/c9rWP8Gu3Xr3toUUeHOBH0tH0A2Gf5BGjyZOvXbJ1S/PsAtqLq119b5+Kxh7uilDflBsQQKOn+nOFJsnNvRCnA8/xJrkQpb3IKeiNK+TOnlFTsKSrqXk5neOqUcjfRuVSU4sfCHVHKHacUPy7XXcfGSW1tbMz9xBPseakoxY83f87H8D2neNKP6fWiiOKqbrmTU+qbb5iwxBe+Aazb8rFj2fb555mz+M032f+//sq2PEm1u/2kM4cRJxCiFP8O6eqyzjh2TExeL70+7IXv+eJQA8Q+2Nm1w8vvzQ1LR3MvpVJ8ztsk6vbgIjfllALgpSi1bNky3HPPPZa/O+64A9OmTUNTUxNuueUWuctIyIG7olR7u9h4bdnS/XVbUcoXxdjZnWDeKGdmskEK/1537aSCwO4I84mVVttdiZbLKWUvHMzeHXp/iFLSARIfbEvj29varAfPDQ2uB5R8kMNFHakTIyxM7FTsXUt8cBIe3l0ssgcXsT1ZYaKjA/jwQ/b4ggu6v84novZEKWcDe0FwX5QCuocbJSSInbC9SZzRKHZAcjulDAZxUBAeLg64+F0YRzmlBKH7IFsqSjmaeLnrlDIYPAspCpRTin9PSIh9l5FaLbZBjkQpV5MT6UDUto3zNaeUILgWd7xxSkmPv7NBn1yr7znDnfC9xkbxPEjrnLuJzltagD/+YI8dDZKdOaVcTSJ8OU7OhIszz2S/bfp09r+7OaV4m3j0qP1jtHQpyynz17+y/70d3PM+SLp6qVT44OfKZPJ8ZTDzIjsYOLD7cZc6XPmEuavLOqWBq3PhjiglCJ4nj7YnSrnTD7jrlDKZxPxqPE+Uu8gtSvlr9T3bNs9T0VevF68DV6KUQiGWxRNRShC6r1rsCF/C9ziORKnQUPHYuXOt2rvueVggIIoZUqcUr2Nehu/J7pSSHkc5ckrx9lIaQiVtPyZMYNv9+9m8ibc/eXmsPvLPySlKBSJ8LyREHF85mnP99BO7MZuf73jlTnuiFH/OG1Fq0yYWRgs4Ti0AeC/cAc5z4l56Kdtecw1bpVsOHDmluChVUuLdQgVBilc5pe655x67z69YsQI7d+70qUCEn5AmOhcEx2EZ117LXCi//y6KUnPmiMo/H2Tzzqiw0PsyORt08zACficiMZE17LW1YmV1xv797I4whydbleKrKMU7LE+cUs6OvRRvnFI8plvagNnewZKGbTgaUPIJDhfdYmKsy5yWJjqubAe/7q68xxk0iDnyPLkb8O67rGz9+wMXXtj9dXtOKd4ROhOlWlvFY+euKMUnb3z1LWcDBunkyxenlL0OSjpQDg9nAy5ph2x7HYWHs3MqCOwasc1/w7+nrc3+xN6VKCX9TGur+0l3vXFK+SJK/X97Zx4mRXW2/bt6mX0DBmZAVgVB9k0IEnFBRSUa4xJjjAt51U8liVs0alzimyiJiRpj3GJcY/KqqLgCQgRBRJFVUJHNYZNlgGH2rWe6vj/OPF2nqqu6q7qreqaH53ddXD309FRVV531PvfzHGPZNsNKlIo3maaBaFaWfnAPJOeUqqkR998LUcruJgVu7r5nhR23hVyfSURVlPjhewUFQmxvbtYm8lZuIPk5b9umb8PjTSLsOiGIO+8UybDffz92//joo8Cf/6ytrtp1StG937xZO758jzIytI0SAGeD+61bxYQnI8O8/zJzSgGiDDlpD63ySQHmTilAPEO6pngCQCxRSr7O+nr77RqgfeemJu0cbjqlduwQ5Swjw94YScYrUYrq4Y4dwNy5wJVXWj/rREQp+qxdYZPquKLEF4wA8XwaGrQJPRC//NTUaG4dLxKd9+ih5TICovsnKlOFhebu6poakTfw/PM1wROI7/qk+rRzp/ZcnTil6Nl1766NH+w4pehZNzaK9tosnJ6Qcz3F+hxgz7FDC8nyoqkcPvbznwsBPjtbzFmmTRNtUGOj6Cuo3DgN30vEKXX11eIev/qq+ZjGiSgFiOdUUWEtSj3/vBCJHn/cvB0rKDBfmE7UfbtzJzB9uqiPZ50lXL9WxBsb/uUvolzR7trr14t+fdSo2HMvEqKefVYkrz/jjPh5/mKhqtaiVPfuom+urRVh9/Ius50YV3NKnXXWWXjjjTfcPGRCPP744+jfvz+ysrIwceJEfJ7oDi+dCRKl6uv1HcjXXwNvv639f8MGUUkeeEAb/D3wgDZwpU6EkpeSjT4RYoUy/eAHQqx4+GHxf6eJ90hQ6dtX5Fn64IPoz7jllLIrSjU22p8gxhOljLvvtbRokyt5tdz43SortY7WbCtnQBtoUydmzK8Uy3VnN8k5QQ2t3WR+oZDWGd12W3RDDmhllVbr8/L01nNA3Id//EPfMdLnc3LsDZYGD9bEzq5dxUAglnvHKBw5hZ6L2QBcPnZWVrQLxcyKbCYshELaYBew7tTjJYul3ZEA+8JLa6t2LV6H7zlxZCUrSpmVJZq0NTZqgxI7xyK2bIkv7sjtm91QI7uispu771lhR7iT3SPNzVp7GC98T1Gi2+2GhviCb12dPg9SrJxSgP2cMcTf/w4sXCgGyfHcNPKky6koJW/AYSXcAfZFqU8/FW3htdeK/5v1A2ZOKfmzdrHKJwWIukD3RRalDh/W6lk84SWWKBUMan2O05BM2ZFBApOdfsBu6Dkt7AwcaM+lLONUlKJrsStK3XWXmMTNnm19TDq3UeiTN6Shfof6Wfqs3eumZ9u1a/RCgRl0X5yIUiRyZGTEf76xnFLl5WKBOBzWO6V8Pv3YzcopJYtScrvwr38BN9ygT/MAxA9rNMspRe/V1WmpHsxoatLGpdLk2laic7kNjzdWdxIWayd8jxY2d+/W7qHcrxcWArNmiQXwH/9Y/J/mRh98oImTdsN9nTilKiq0e15TI3KezZ5tHQ1D/ZbVuN9IvLaf+tj339ecUnLZsfoOiTqlXn9dPP8xY0RYZSzRMda1Hzwocn7NnCnuXygkHF8nnijakVgLc4EA8MwzQrxSVW3H60SRx9rG76MoR2QIn6ui1Ouvv46uZna9FPLqq6/i5ptvxr333os1a9Zg1KhRmDZtGsrtbP3cmaEGFNA3WpdeKtwm33wj/k8N7rvvip8LCsQOELffLhozWkWlhpdyRyRCrA5EUcSgkwb1TkUpavAGDAB+8hNz4SiV4Xs0ALIbwhdvcmgczGzZonWasihl5pSyG75XViZejd8v1g58dp0WhNNG99//FgPEkhJgxgzzz9C9pw7bTJR69FGxE9qjj0b+TLGbAFWGQvioA44llNDAWd4txwl2nFJZWearwGbCiFmyc6PIYCZKNTfrVz2tMHO6xBJg4u0mZySZ8D2rXQnNMBssAO6IUoC9STkdi+qeHVGK3g+F7DsJnDqlUpFTysyNRW2c0T1CZSGeUwow7xPMBsrGeye3VXZzStm5T7RbEB03Vni7EbuJzune03loty4rqD2LJ0rRpIQWRmI5pVpa9CHmTtx2qhrbKaUoYhW9d2+RV4kEJHmTkXgCBl1bPPeb01395LJF/ZCT8L14Tikql4msqBsXuWLR0gJ89ZX42cqRZRSl6NpjfYd4Tqmamujv6NQpZXfnPYKetZPwPTmfVDwXbizR+oYbgHPPFZN+WZQC9CF8sUQps4UsugcbN+r/Lp7ATvdcTmQtu25itXHyGFt2lthZ/AsEtM/FW9R1IkrFC99TVa3dUFWtDMRLtE1RHu+/rz+WVbkhsaq+XntOdkSpcFi7dnlOZ9VW292VkIi38ETvb9mi5Vj68Y+jr9NIok6pjz4Srz/9afx2M5ZTispQKCTuSVWVKJ81NcJkQfMGq7GhoogFaUCbIyWKPFczW2A/AnfgS0iUGjNmDMaOHRv5N2bMGPTs2RN33nkn7rzzTrev0REPP/wwrr76asyYMQNDhw7FU089hZycHDz33HPtel0dAjOHCwkL9J6xAZo4UQxcf/tb8RnqhMghsmWLqMRvvinUYxqs2MFJXgUaRDgVpWLZp+UVuESI5ZSSO8W8PO1+2RWlnO6+Jyv2scL3KirsJzqn0Ezjykosp5TT8D2nTqn//Ee83nijdbkxTkTz8vQDe1XVJlGy68FJPini0ktFZ0ITpFg5pZJJcg7ETnRurEuyCyU/X9RhI2aTfuPg2KxTp0GP32+vflEZXLVKvHf//eafp8FCZqa1O0mmvZxS9Izt5pQyE0aCQa2eORGlRo8Wr7IoZfUd8vKc7xaTivA9u04pq/C9554T53/77Wj3CJXXeE4pwHyHTbOyZEeUcsMpJU+ODh50NsGym1PKWA7kMmIG9bv19bHLO107Hd+sfsnfQ+47nJShdetEm52drdUFI3PmiMlCXp7WHsrtfLxnQc/XKiwjkeT18gQS0Lty40Hji3hjH9kp5RQnQtuGDdqC5dCh5p+hekcOGqpXsdwRdsL3aBGUvmOiTqlkRCm7Tim74YGA+cSf6sjatfpE54A9UaqgwHwhi+6VcUId795MmCCezb59mtOyZ09tbBGrH6P2IC9PP162U/4B+zu2JSJKWZXJmhr9szYmLbcSd2hHPmMybLNyU1Eh5lTXX6/1JYFA7H4rI0O7H/TM4olSsihmd/wZb+HJmFIAEIIRYWVQoferquzt7AeIzy1dKn4++eT4n4/llJKfQ22tvv8hc4bPF7sMJZIL1wx5kdbM+XUE7sCXUE6p8wx5XHw+H7p3746TTz4ZQ0glbgeam5uxevVq3HHHHbprO+200/CpWdJuAE1NTWiSGuzqtsYzFAohZCesooNC1y5/B39pKXwbN6Jl506obe8H6uqgAGipqoIaCkX+T7ROmICw2X3o3RsBvx9KfT1CO3bA/8gj8K1fj9bHH0dYcp/EwldbCz+A1qws83PIn+3WTXy2vDzuZwHAd/Ag/ADChYVotfp8djaCANDQgFB9vblSHQP/3r3wAWjp1i1yP2UCWVlQGhuhHnMM1C5d4PvmG7Ts2mX62ahjV1bCB6A1N9f0+yoZGQgACNfVoTUUgm/dOpD3JtzUFPnOSkUFAgDUjAwozc1Qv/0WCpWNbt1MnSv+zEz4AKhlZVAAtPboobsGX48e4t5+913UvVUqK8V15eVZ33eZfv3EM9i1C6Hq6rgdpv/wYXHPhwyxvI++nJzIvQCAlqwsqD16iPM0NiK0dy8CW7dCARCurY3UkdZ9+8S1Fxfbu3YAGDlSdBhdu4p7mZODIAC1qgotxmNUV4vfZWdH/84Ofr/4+6am6L+vqdEd21dQELkHakGB6fkCubmi7ldWaveyqgpyLWjZty/6Pu/ZI87VrRtaWlstBxaBvDzd8X2vvgp/UxPCixej9bbbov/g0CFxXIvrNaIUFIiyfeiQ4/upHD4snnV+ftxnrfj9kY5S9fuBwkIohw6hpbY2+t60tgqhpG9fKFVV4hw5OabnCBQUQGlsROjQIWuBuO2YwbY+qnXECPjnzUN482aoAwaINjE/37JNDOTnQ6muFueItfrahq+yUtTt3Nyoa9b1KZmZ4lnV1Tm+94HKSigAQrm5MZ1zSk6OuH/V1bpr8b//PnwtLQjPng20tVVES3k51FAI/qoq0X5mZ1veG3/37lGrci0HDkQ9U39dne5zrZs2RY4ZOHRIlPHCQtP2yJ+VJa6jujp+v1VeHql7rd99B3+bwBQKBuOHeMZqGwhVRaCqSte/q3l5sZ9fdjYCgQCUlhaE9u3T56CRoP6W2j0qR605Odr39vkQUBQoqgp19+7IdbQcPmyrTwQA32uvifJ5xhloDQRi35dQCIHsbCjV1Wj57rtIHW6trbV+Fk1NCLT1e6GjjzY9fiAnRzzz6mrL644af1VWImgSvtMSDMb/7gUF4tkePBjzWfm3bBFlbcAAW2Mk3d+21aOY96YN38cfi2fwve+h1ar9z8qKlOXQ4cMIHD4s+tuDBy3bW399vejbjfeExmn19VC/+UY8mwEDgFAISjAo+oDGRlvtkG//fnHt3brZ6uP92dliLCSX1xjPHQCUAwdEu1VUFPccvsxMUU9M7ru/oQE+AOFNm6BUVIgyV1Ag+tKePbXxXmamvn1sa3PC+flQMzLE8evrI8encTf27EGopkaIWqoaactChYXm9crng3/KFPg++CAifofy8kQfU1WFUEWF9YIe9e2FhQh36RK59pbMTDH+jnOfAgUFUPbuRUtFRex739bnqjk5ccuDkp8vPltRYf7ZXbt0Y6HWLVsQnjpVG+Na9OvKwIGirTG4VkOVlVHuYGXlSgR274b66qtomTFDG1fJYV0mBLp1E+3a/v1QBwyAsnNnpH1r2bs3+h41NyPY5gAK+f22UgZQ2bfqu4xzRbWkBC3jxok2t6EB4S5dzMt/Xp64r6qK0MGD1uKVzNq1CFZVibHhsGFxr99XVCTq+YED0XOU6urIvQodPgw0NWn97ldfiX4sLy/mM1D69hVlYNs2+3MFM+rrtXYSiP5eV1wB5ZxzoA4aZDqnTyfsXndCotS9996byJ95zsGDB9Ha2ooSg3OlpKQE35ACamDWrFm4zxhbDWDBggXIsavid2AWLlwY+XlsOIw+ADZ99BG2dukCqCrObVON1y1bhj2trTi3rSLW9uqFvD17sDw3FxVz55oee2pJCfL27MHKl17ChJUr4QNQ/847WDRtmq1rG/rllxgEoGzfPnxlcQ5i0KFDGArgu7VrsTbOZwFg6OrVGATg28pKy2MroRDObft54ZtvImTX3dPGadu3IxfA8m+/xWGTc5yZkYHMxkbsyc1FuO3ef/PRR9gWzyEA4ISyMnQHsHbbNnxncuySr7/G9wBU7d2LpXPnYsKiRSA/U/XBg1jS9je9ly3DOAB1Xbsib98+KG2r/k0FBZhPyeuN566pQXcA6q5dUABsrKjANukaeu3bh+MBHP76aywzXNvRK1ZgBIA9NTVYbeM5QVVxdk4OgvX1+Pill1Aj5wEx4ZTychQAWLF+PQ5arPAfu3cvjpP+v3rTJuz78ENMKypCVmUlPnnlFUzevBlBAPu2bcPKtjqy7dNPMRTA7qYmW2XMjKwDBzANgHr4MOa+/77OhdBl0yZMAVCvqvhvAsfP/e47nAagpbYWcw1/33XjRpwIoC4cxodz52JoRQUotX+tomCRyfkmNzejGMCapUuxt63eF2zfjlOkz3z98ccoMzhxir/4ApMB1GRlYXGM7zE5FNIdf/K8eSgGULl7Nz42+buuX38tvkMggA9t3J+sigpMA6Ds3o21d92FvbQTmQ2onH5XW4s1cc5VuHUrTm77uSUzEw0tLaIMLlmCg4ZV1mHPPYeB77yD5ffdh2BNDY4HcKixEctNzjHV50MegE8XLsRhCucxIVBfj+ltP68Jh3E8gOo1a3C4thYDAGwtL8c3Ft/h9IwM5ABYPm8eKm2stg1ctQrDAOyurrasAwsXLkTunj2iLFZVRZXFmKgqzmkTpRatXo1G2YFgoOeWLZgAoGLnTnwinWPK+vXoAqDuo49QX1ICubdf+9//Yk9jIyZ++y1KAawvK8NOi+sbWlMTqSONXbog6/BhrFqwAPsN4Rzjy8pwFIC6khLk7t+PvcuWRdq2s/ftQxDAki+/RK3JivvYykr0AbBxzRpdG2pGl82bMaXt5+0ffwwKwpq/dCnCcZL25u/ciVMBNNfUYL7FeXxNTTjHMDCsA+LWtWn5+cg6fBjL3noL1RbuoZHr12MAgNaKCsydOxfjvvkGvQF8/d13+FY6/vRgEIHmZoR27gR9I7n9icepL7+MfABrjj7atF80cpqqIhfA5qVLQZ6erevXW9aX/J07cWo4jFB2NuauXSucWQZObmlBIYDPFy9G3Vdfob601NJtRuOvnP37cbrJ7z/fsAEHzFysEhlVVTgLYsI97913hTBuwilffCHapUOHcMBh/zL28GHb5XTsG2+gD4DNXbtik9VnVRXn+HzwhcNY9M47mNom1Oz75hustPib7+/Zg24A1mzciL3SZ+RxmtLmuFqwbRta9u5F/vbtotxXVVmWe5ljly/HcQB2NjTgCxufP6GuDt0BKFL5XPPJJ9gbI39S348/xhgA5aEQVsQ5x+A9ezAEwM6NG7He8NmTDxxAIYDKVauQt38/MgAs2bABtZWVGFhdjWFtnyuvqtKdZ/ShQ+gHYHdNDZpVFQMBfPv11/i67TOjNm9G/7bPLn3pJdT27o1AXR2mt33H+StXImyRK+eYXr0wXPr/orVrMSUQQDaATz74AFUWjvduGzbg+xBjkW379mF02/ufbdgADByom6eYcaKqoiuA1YsWYV+MMMHSzz/HRACHm5tNxxgyhd9+i5MBNO3fjw9MPtvtq6/wfen/Zf/9L77q2xdHffIJxgM41NBg2q8XHDyoG0MRH8+fHzW+7fnZZ5gAUa7Xvv46JgCoyciIOa4CgCmBALoAWDV/PvZXVGDg4sWR8vD10qUoM8xl5DHE/CVL4vYnADDy4EEMALBl3TrTen7GoUOQl5APlpRg+QcfYErv3uiyZQu219Rgg8X3mJ6VhUBjIz6aMwf1NnJcHfP22xgOYP+xx2LFggVxP3/Url3iGW3eHPWMijdswOS2n5d98AH8oVCk39370UfoDaAxGMSCGM+g+4EDOAFA3YYNpmNru2QdOoRpAMJ+P+bOm2f9QcnxFa+udFTq7TqLVZtUVVXZ/tdefPfddyoAdfny5br3b731VnXChAmmf9PY2Ki79l27dqkA1IMHD6rNzc1p+6+urk5966231Lq6ush7LbfcoqqA2vKrX4n3ampUVRg71dBTT6nN+/dH/t9cVqaGFi2KeY7Ws84Sx7vmmsjfqYDavG2brWtsuf568fe33x73s6GnnlJVQG096yxbx26dMUMc+777Yn4unJUlrnnzZmf3uKlJDWdmxvzbcL9+4hpuvVVtuekm8fONN9q7/vHjxXOZM8f8fsyfr6qAGh42TJyrf//I/Q8fd5x2jx97TNy3k0/WPaPwiBHW5z77bN1nQy++qD/34sXiGEcfHf1M77lHfM+rrrJ9L8OjR4vzvPlm/M8efbT47JIl1uXqwQf11//BB+J7jRsn/v/MM5HftU6bFqkrzb/8pbj2m29OuN41Hzyo1YOqKv19W7BA3LchQxI79tat4u8zM6PLw7x54nfDh4t78Ic/aN/x+ONjPufQP/6hHefjj3X3ruXuu6PP9a9/ieOedFLsMnzmmZH73VxXp4azs3XXGHXc114Txx0/3vY9aWl7ZuHsbDX06af2/+7uuyNtV9zPr1mj1ZtevdTWsWPF93r77ejvPHmyOO6f/qSG/vEP8X3OPjt2uX/33djn37FDnFtR1OYvvhA/5+WprT/5SeRclvVl6FBxjnnz7N2X228Xx7zuuqjf6fqU7dvFdfh8anNTk/0yXFmp1Y+KipifDb37rjjHqFH671RcrD2Pvn3Fa06OuO6//108h5NOEt/75Zetv+uf/yz+tqREbT39dPH5Z5+1rCet06eLz48ZI35XW6t9lz17zM9x9dWW9Sjq+77zjlZnzztPe+Z27u9XX4nP5+dbf2bnTl3d1n2XGP/Cw4bFLUOtP/6xdi/q6rS25emn9cfq2jXqGkL//Ke9srNunbjmjAy12eb4LDxkiLj/M2dqbVqM9j306qtx26DWCRMifahZ/xhVV5qb1eYVK6K+d7x+LPKvvl67t7t3m3+msVEby2zcaL8+0nf6+c9tjZeam7WxRmju3NifKywU1yO1n61Tplh/ntrDd96J/l3bd1MBNdyjh/a7r7+OX+6lf9RftNx6q7370lbndc/s+edjn+NPfxLf9ZJL4l/PrFnis5deGv2djz1WfLcuXdSwooh7uXOnKKcvvKDd0/PP1x+TxtW//KXacttt4mca8zc3R/oN3b3euDHSjsa85tWrdfei+fBhNTx4sDjWwoXW9eqNN8S1TpighmbPjvx9/erVUfMU0+dw2mniHM89F/NzkbHJySfHf76bN4vvnJVlfqyXX9Z919ZzzxXvP/lkpD8wPW5lZeR56crN8uXR5/jnP3XtkgqorZMnxy+X06bp2s6WX/1KO85dd0X/za5dzvqT5ma15cYbY7aX4S5dIvePyltzc3NkPtjy+99bHjvcu7flPYlVD1v++Edbnw+9/36kjY763Zw52jP56KPIXIrGGipsjNGpvmRlqc2NjbauKWYZzM6O+1mzOX06/Tt48KAKIK5GZNspVVRUBMXOVvaAsPS2A8XFxfD7/dgv5w8AsH//fpSaJaMGkJmZiUyT/CXBYBBBh+FcHRHd92hT6f379sEfDOpiaQMNDVo+imAQwf79gf79Yx988GBg3jz4DTuqBJcuFVv/xqPN3urPzxfXE4s2i6evthY+O8+lLfbcX1wc+9gFBUBjI4KNjc7C9w4ejFx/sF+/mLvA+YcMicSt+8vL439XIBKjHeja1fzYbe4VpaEBwYYGLf8TxMpi0LBDkK9PH7Gaq6riM0cdZV2+DSF0gd699dfQVi6U3bsRbG3V5zRoO5+/qMje9wREvq116xDYvj3+M2iLiw/k51t/1mCRDhQVic/27QusXo3Axx9Hfuerr4/cB3/bKqy/pMT+tRvp0kXEo4fDCFLeDYJCKnNyEmtb2sqT0tSEYCCgX503HlvKC+ErLDSvM205FQLV1dq9NFjO/YcPR9+LtrLs6949dl1s++6B+nqRVLXt2Sl1debfv83J4xs82F4dB4BHHgG2bYMydy4Cl11mf+OFtrbPVjmVckcoeXlQ2hy0gVAougy25WvzV1dH6oUvL8/8+9D9aWiIXe7b2mUlNxfBwYMBnw9KbS2UtlVpf9eu1t+hzZUZsBueTPW3sNDymMFgEMG2sqOEwyIsIFaybJPjw+9HsKgodj4jOkdtrVZeamt1q4ZK265YyogRwIoV8FdW6vq2SN03Y5hYW1ZOOQVKW7sYqKmJ/nzb/feNHQu8/z6Ub75BUFG0PBqKgmCPHuabF7StWPubmuKXMykni68tV5aSk4OgjVVtXdtgdR4Th4GSnx+/LWoLyQlUVlrfSykvY7ChQbv/Xbro/8YkPDtu+SfeeUdc8+mnI2gjFBVAJC+IX8rHFPNZUBs0ZIh1G9TWHihteXUCq1cDl19u+tHI+Msib1bMfkw7iKgLhw8jWF2tzydEfPedyH0WCCCYyO57bd/J39wcu5zu3SvGGoqCwOTJsa89P1+Eg0t5NH2Vldb3ta3vMb0n+fmR3G7KoEFamW2rX0pDg70+1Wkfb5IPMNDcHPt7t9UFX7du8fuxtj7A19gY/Vlq9yUHZrBHD20s04YvJ0f/t1OnAv/6F/ynnSbyUcHwXKUceYFdu8Tx2sbLSnFx7Ps4ZozY+GbfPjFPKCy014/ROLSwED7JGRMoLBRhcvHmW9SP1dXZ6it9ubnx731bTkGlsTF6HAto/UxBAVBdDd/27eKYbffPl59vfo7CQqBfP21MHggALS3m5UZqk/0rV4rjFhfHv3Zqkw8fFseUNnLwV1REl+02F5ySlWWvPwG0vqux0byutF27ct11wCOPwP+jH4nP3X030L8//FdfbV3HunQBdu8272+NtLYCy5aJa5k61V69bZvvK4cORZcrKe9ioLFRN+ZV2naTVwoLY5fHY44R47DGRgTjpV+IBc3HHOgN6apN2L1m24nOFy9ejEWLFmHRokV47rnn0KNHD9x2222YM2cO5syZg9tuuw0lJSXtmlA8IyMD48aNw4cffhh5LxwO48MPP8SkSZPa7bo6DMYE1bKdrq4ufgI/I7QDH3WaFO4o3f+YOElK6HSXFbvJJhPdgY8S7Hbvbr016fjxosH9/vdj71hnRrxEzHKyVUraTcjJbuk4hYV6sSaWZdbYORs/26+fSFbZ3KzthGQ8n5NQSCfJzu3sSGWW6BzQduCjXTwAfR1IZPc9Iz6fdVLOZBOdy8/FmNDYeGw5RNSqDJntPmMn0bndhPByInU5p59VYmPaucsqea4Zfj/w4ovi561b7SdNTjTReW6u+Y5GgBhgULtw+HD89tRu2yMfJzNTm5B88YV4jbWLHf3Oq0Tn8vXZgcpaYWH8nanMEvFL4ruOkSPFKyVUtpPo/MwzgSVLgCeftLdBwciRoi1paBAiKz3rbt2sd9N0skthoruzAfpE520D3SjMykCs3QmJWDsZEfK1025GQPT9N/s+duvsG2+I1wsusPd5+Xzy7nuxwggozUOs3KjGvsdqC3YZq2TKdlNExHsGFJrbr59zQQqwv/seteMjRsRvO+nZyyG6iSQ6B/TnkhO5yzs62gkBTXT3PbPrtMKtROfGnTSp/QdiJzo//3zRzp57rvY7s0TngJbsPN4uooSiAKe3BaLS7oJWG1LIyDuuyuewO9ewm+jcyRwmP1/rg8x24KM2g+aO334r2tZYG5gQcvtBO7WZPWO571+9WrzaEdyNibzjJTpPZOwZq+9qadHGoHfdJcrXKW1Bi717A3fcEbssxepvjXzxhXjuBQXWm1sYke+PsT80znvl/ofuU7zxjywMJ7MDH4XT2xUKjwBsi1InnXRS5N9LL72Ehx9+GLNmzcK5556Lc889F7NmzcJf/vIXPP/8815eb1xuvvlmPPPMM3jxxRexceNGXHfddairq8MMq+3jjyRIXDATpWprExeliGuvFa///a/1wFjGSUNpNRG0IlWilNmqJfHcc2KXnWOPjSj3jkUpq8ZRFqVoJxQSHc1238vP19+LWMq+cZBjdBkqirYDhnGHEbuTWhkaZNrZYcJOmYknSrW5KwDo6oBCOyKZ7crlBBKEjAMdt3bfA6LrQSxRyupZ0GfkiYIdUaptRQ8DBsS6Wv3ue3ZEKdqimnavsUu3blp9MLhkLUlUlMrLs26LKiq0umdHlDLuTmiF8Ti0kycNCmOJUnYH80S8XT8Jv9/Z7oGEPDmJh9n9MROlcnK03XCovNLfxJo4KAowZYqoB7F2YqLnnJMDjBsnfl61Cli+XPxM75kRa9JpRD43TYjsihZyGbVKKEr3Xm7f7IhSsXYyIuS2ThaljOXI7PvYEaVqajQR9pxz4n+eMNt9L5aosGmTeHUiShl3gDTDSoyx2xfYFaUS2XkPsL/7HpV5O/n7qP7K/W2sSahdUYraP+Nn7YwNne6+Z3Ytdnffs5E7NOZOjsbvIyeEjiVKAdqOeGY79srnogm1k/sii1KA+eKBEbk9oBxsfr+99gfQ+ot443QnC90+n/aMliwBZsww35n8e98T10suXTvzJBrDHHWUVnfjiVJ07XZEKWN7EE+Uoudv19EMxBal5Pdyc52LKvF2PpShHd3HjbMvuNP9aW6Ovv5Yu+8RdsaFNP5NZgc+KTqJEdgWpWQ+/fRTjB8/Pur98ePH43OjcyLFXHzxxfjLX/6Ce+65B6NHj8a6deswf/78qOTnRyQkROzdq1f8gcScUvLgAABuuknbMpZcD7Fw0oE43frXrihFHapTUWr3bvEaS5SSOz0nTqnWVu1Z2HFK0YCU3AKyiyZZUSoz03xwRasiVqKUnUadsCtKtbZqk/5kRCkZuQ644ZQCrN0pbopSxlVU47HlZ52IU4qcH8ZJUGWl5oQ891zERF5BlUWphoboHZvCYU2UcuKUAsSAkYRT2RERC9lBGA+7opQ8MKyosC9KOXFKAdETTztOKbvtmxNRmSYw8baql6GyZmfCRuWnvl4rLzSJkt2b8sCfyqsdp5RMrJVbes7Z2cL9CghRqi2kACeeaH1cemZ2knyanduuKCVPCoxtA0Ht0bHHatfllihl5ZQytj1mbZ8dUZOea3a2fUFBPp8dp5Sqak4pcjeYQWWX6qEdpxSVe+OE06koZfUMyGVMrmOn0HXEG1/RtuxORCnZKVVXF+3yJRJxSsljFSeilN0+3qz+2RWl7DilYk38jfVYFqVyc7W2PZbQYOaUkq+fJtRORKkLLgB+/GPg9tvF/+0srsjtQWGhcKc+/bS+b42FXcevkzkFoD2jn/8ceOEF4MEHtd9Rm9G/vzbO37bN3jypLTQcAwfGFh7N+mWnTilV1bdBZv2x204pes/nS8zl48QpRW2/kzl8To5W9o1tptW8V8bOuIEWwpIRpdgpFUVColSfPn3wzDPPRL3/z3/+E33i7J6VCn7xi19gx44daGpqwooVKzBx4sT2vqSOAQ3kGxpE455s+F7v3lrFP/po8X8aoFuF8O3ZI6ydO3Zo57fTUHbU8L1YopQM3fuqqvgDP1m5jydKtbRoeXRoIG0mShUU6O9FrPA9eaBgtbMQiVKffaZ/JsmE7+3YEXurV/k8scqM8dxUns3aJiqDqmo/LC0eXjmlFEXrvIwDVmNdshO+F8spRaKlsUN/913xjIYNiz1xA7TnsHBhtMXZOBDYtUucOxhMbGJlV5R67TXgsce0wW0i4XtWEzjZMeEkfK+mRhzr//7PfOBq5ZQyHscMr8L3AG2QaNedJl+HE6cUoN0Dckqdd54mnPburQ3kKypEXaY21O5KfKyVW3rOWVmaKLVyJUC56b7//ei/IRJ1Shn/Ph6xBGuC7n1RkVbHnIiPVoKIqkY7pawWJxIN36PJi53tw83OJ5d/q/533z7Rf/l8sR1HN98sFuD+/W/x/z17hKgeC3q2xuO6Hb6XqFPKrE0zfqd160S59/tF3qJ4mDmlAGt3RCJOKXlSbGfB0qlTymtRKpZgYazHxuPR2DOWKGXmlDIL36P7YkcQyckBXn1VyxtrJ3zPKFL/v/8H/M//xD8XYdfxm6goRX83f772OxpLlJbqBQhqr2LNky66CLjuOuD3v48t7iQqSsntQWWl/vmmInxPHpPYzDWtw4lTito8u3kEAXFNspD/v/8rREfAnlPKiShlFr739tuifsQbG9F8h51SERISpR555BE89thjGDFiBK666ipcddVVGDlyJB577DE88sgjbl8j4xbZ2dpEdO/e5MP3fD5tgPC974lXGqBbOeaefhr44x9FgmI7+YEIJ+F7LS1aYx9vEJusKNW7t73PFxZq3yGeW4quJSPDeiVJ7lzIYUIigTyYkUWiRJxSFhsEYNAgIWw1NeldMIk4pXr1EueUBTYz5MGUXadUIKANWs2eVVuZ9zc2QqGy5ZYo5bZTCrCuB4k4pczEM2oDSMAzToLefFO8nn9+/GulRLhtySMxbJgmJBgHAuSsPPbYxHKi2BGlDhwAfvpT4Fe/AlasEO/ZKafyKlYsp5RTUUpeYX78cXFtv/1t9OfiiVJehO95JUo5cUplZmplga6LRKnjjtOcoUcdpbXzhw6JukATarviuJ3wPaNTau9eUb4nTLA+rhOnVDKilN+v1S0rJ4osCJJ44YZTSt4kBRD1jPogt0QpJxP9eOezehYUujdgQOyJ/pAhwMMPa/lNQqHY+bYA7fqNddduX0DPIFWi1MUXi7ZYnpD+9a/i9aKL7CX2dSJKtbZqEzSze2/llJI/H29sWF+vPftkwvfczCll1T60tES7iY1jWRKlYrmN6HdWTqnKSvHPbk4pM+yE7zlZjDDDruPX6RzG2A9t2qT1MTRO79lTL0rZOUdBAfDEE2KR3gtRSm6TjU5NszxKXopSiZCIU8qJKAVo92jhQuDee4EbbhD/T0X43v33i1yn55wTe0GKw/eiSEiUOvvss7F582acc845qKioQEVFBc455xxs3rwZZ599ttvXyLiJ3Lgn65QCtIHZSSeJ1+OPF6+rVpl/nga227d7F74nT7LjTX5S5ZRSFPshfHby3WRmaisUNCClHF8tLdqkLNnwPStHlaJobik5cXgiOaV8Pi0c4PbbrfOR0bPPyLBOLAzoJ1p5edp96tVLy7UgHzMcRiYNmrKzE+9oCapjbjulAPNBptmx5cGf1bMwm4hTnaQkjrW12rlqa7WVRDvJhidPFmLTpZeK+3755dqzsRKlnOaTIuyIUm+9pQ30qYzZGXwEAlq5sRu+5zTROeVNaNthTEcyopSX4XuUm4hysdlBTnQeD7MkurQqOWCA1mYcfbTeKSWXLbuijp1E51lZwmFUWKiVn+OPj12fU+WUAqzbBkKeGP7wh+LYsUIPiXguHWM7R6HtQLTolWj4XrJOKRmrMYSd0D2ZjAxtASNeCJ+ZUyozM7o/siLWM1BV98P33n9fHJMWvPbtE05OALjxRnvHpDbEmHMrlhtRvhazY/XoYS10xhsb0r0LBu2PT1LllDK2D2Z12Fj2Kcw91hjUrK8y3qeyMucOMhmz8L0nn9S7jpzkcDTDa6fUoEEARdR88IEYQ5NzvrRUEyDKypzPk7wO36O2h4SzUCj6uHIfZpdY152sKOW1U0r+/FtvidfqajH+i5XonEg2fI/6xJUrgUsuiR4rtrQIAZTD96JISJQCRAjfAw88gDfffBNvvvkm7r///g4RusfEQV7VSNYpBQB/+pOwRZIVl1aSN20y70Co4/ruO+/C96ihy8+P77pIlSgFaAKPPGg3w04Hrihap0EClJx4nho7K1EqVny2HacUYJ7sPJHwPQB49FExWHz3XW3wa8RueTGKUkQgoD0DQ9LwTLru7t0TsyPLtKdTispERob2sxOnlBy+RxMmGhTMny/Oe/TRmkslHgMHAi+/LDrhW2+1FqUSzSdF2BGlZs8Wr/36ae/ZHSRTeYm1+57RKRUvhEwezNNua99+G51bzdguDxigPRu/P/YAvKOG79lxSsnXYXRK9e8P3HmnWJGcOVMvKlFdzs21P+m345TKyhJtg5xPM1boHl0DkHhOKSdthV1RqqhICMTV1cC0afGPG88pZbxnVJZzcqL7X/n7WLkmzUiFKGUnybkR6v/jJTuneyTvjufk2cYSpQ4cEM9SUbSJklPk3ffCYa3NofM9+aRY1Z80SZu8x4PaPWMYYCzhV74WGWqnjYI8YH/BUhZe7PbxTkWppibtntnZMMVq4m82zjWW/fvuA957Tyz6WBHLKUVlym1RqqwMuP56kTicSFaU8iLROSDyYpaUiPJNGyjMny/qlKqK/qO4WHOOf/ed83mS0XEkO+CoTZbLo5PwvYoKrb095hjtXMa2Wnb72iUVTiknopTTtp/Kshy1I89zzf5POHFK7dkTXV/lPu3dd4FRo7R5kqqKRd0hQ4C5c8V77JSKYFuUWr9+PcJtncv69etj/mM6MPLKc7KJzgExyb/iCm2AWVwsJgyAtsWpDHVcu3cnFr7X2hp/618nK1VyXhcnJCJKUfLDdetif86usCPft6IifdgZDULkwQDdj+7dYyvzdpxSgOaOW7FCcw4kEr4HAMOHA/fcI37+5S9jD1zjdaxy+TUKAjS4kB05dXXIoMFBsqF7QMdwSgHa804k0Xlenj4kChCdKyBC95wKd4qid76k2il16BCwaJH4ef58IaL/8If2w13ovufl2csp1dqqiTV2nFI0sATESq2MsV3OyNCEtcLC2M+io4bv2Q3jkBdRqqq0tr1/fyGc3nmnmPxRWW1t1VaOnQjjVBcoJxWhqtEDelmUiuc0SsQpJQtpTpxSVvnmCOO9j+U2lYknSlk5pczaHfn7UN+ZalHKSlQgd7ddpxSgOY7jOaXoHnXpok0o3RKlZs0Sr0OGOHNCyMhtmvw86Hy0UEQhMHawqn+xnFIZGeZCMgk8Zv2D3QVLcr44EV7k8krXEEv8+vZbIcLl5dlLzCyL1rJ4R3XY79fqibHsFxYC06fbG8uZiVI0HpVFKaduFCC6T6f7LJdVt8L33HZKXXaZiFyYOlUT6D/8UAs5LSkRz4BSP+zapbXldvMVyuLO7t1ijHnTTeI9Gp/LC8p2nkFJiSgPra0iTyYgxuo0fjW21R0tfE/ub+ORrFNKpqbG2ikltzt2xg7du4vvr6rRuwLTOOqJJ8TYdPNm4NRTxeL7Cy9ojnjKS8lOqQi2RanRo0fjYFtBHz16NMaMGYPRo0dH/RszZoxnF8u4gLyq4YZTygw574YRqqz792sNsp2GUh5suRnTn4hTqqFBa0ydiFK0dbiZWCdjV9iRO97+/fVqO8Uqy5NMcifEywdhTHRuBQk8zc3iPC0t2rNx6pQCgN/8RnTOFRXAvHnRv7crYmZkaI28lSg1cKBWpurr9U6pZPEq0TlgP6cUoK3kUCieEbrOhgZt0EptQE6OPo+JqmorPbQldCKYiVKq6r1T6u23xQBu9GgxefvnP4Wt266TRi5PdsL3AE1osrP7nuyejCdKAZpjIN4gn9pAOezAiqYmrd3oKE4peRGFBn7FxeZhYVT2abcvu5MGQJvwtbTo71NzsyZS0XOXRal4u5DFGtgbkd00RCLhe3ZySjmB2oGGBnNBx8opZdZ/ye0TtUsdIXxvxQqxm2IgYM89Rjh1SsmilJNna7X73iefiIkOIPJcJQpdS0OD/nnQhJC+n8mu25ZYtSGJLDhdeaVIVmyWc8+pU8pJH29WXmM5pSgn5qBB9hZu5DIg9yfUH2dmaoJFIi4mY6LzUEhz6pAo9e237uSUonJDY6lQSGuLUhW+l8gchp7T2LHi+9fUaLkzaVxB48bduxMP36urE+3M4cOaQ4buy9ix2ufttHF+v9jsA9DGC716We+I67YoRXUglU6pRHNKyZjNe2ksSmYKwF45VRRtjC3vMCo7Tc8/X4xtKZLoxhtFAnyCXPHslIpgW5QqKytD97bGvKysDN9++y3Kysqi/n2bzPaIjPdYhe8l6pQyg/JKrVwZ/TtqhOUVaCdOKSD+ipjXohRNPuXE8XaQxTqrvEnytTgVpYzJblVVL0rRIITygFlh1yklTwQPHdIPGBIZfASDmoVazlNFOOlYqZwbJ6YUmjFihK7T9cQp5UX4nhOn1MsvC1fQqFHmxyoo0AZkJKDJK43yRKisTEw2g0GRKypR6HnIA519+8T5fT79iqET4olSFLp34YWJHd9p+B6gPf94Til5J1JAiH+ysGC22w85vOIJDMcfL1Z5y8s1J6JMOCy29777bv1k1OucUomE78mhe2ZQeaUBohNhPCdHGxjKA2WzHT9PPll893PPjT+BsBu+19SkfUYOUfIqp5QT8vI0UdbMLWUU30mUMrv/ZpN8J04prxKd/+EP4vWyy/SiYDzsOqXMRKlknVKhkNjKXlWFaHPmmfaPZ0R2ShlFKTlBuJP+0fj8ScSOt5mAGd26iTbKrO7bdUolEqIm1z8nopTdfkw+vnxc+i6ZmeJ7/+xnwtnrFKNTSj4HjdMXLXJXlJLLD/XzbobvxRo7UxvnZEGC8Pk0QZqEXhr/kvhcXa2NMZyG79XXa+0YhQfSfaEF6/x8+64Z41hGFqXcdEo1NkYn3U8Hp5RdUYq+i1xn7Y4dqE2QN3Oor9fKKJkBnnlGhNsCoi5SXy1vasUAcCBK9evXD0rbBKZfv34x/zEdGHnl2WunlJkoZbYqamfgLW/966YoJbsV7CKH7jkJYxo+XEx8Dh+OtnvK2O3A5Q6G6p08MWlq0kId8/NFqMlXX4n4+VjYzSkF6HO5UOdRUJC48k95qsxEKSc5yKicGwcnt9widsW48cZIuVMaGhCksu900mNGR3FK9esXe9Xf59MGe1RnzESpQ4c0l9SECcm1D/S38kSUknwffXTi4SdUTvfvj85h0tIiLPmAvQTtZsjhe2bPIBTSxBnj4CmeU4rufdeuQuyorQWWL9c+F8spZUe4fuop8fNf/6rtOkhs3SoEuz/+URvQZ2XZ2wExGadUIuF78UQpaosScUopivlAmeqVomhtWnGxCPmYMyf+ceVV8liTKSoDxrxAHUGUkrfXNgsfM4oMVLftOqXaI6eU/CzWrhW5eXw+sdGGE2iyGkuUUlX9mIQmS06eLf2NHF66fr0ICykoSM4lBViLUgcPaq6LzExnQq/xs1SuY4XvJdI3JpJTyi7yM6IxVqzz0E6zZrmvzPD7tXorL9RQHc7KEhvK/Otf7jilqH9XFOHiyMwUCf5pnOhG+J5cfug9t5xSqhq7vSBxINH8xvfeK+o03X8aV+Tna+0mtYGJ5JSivz18WIsuALTchFZ9mxlTp+rb8l69rMP35LyIdpG/n1GIdSunVH29dX8FiOs25kCzi9PwPTlsOxlRisq/z6e1Z4oiFgWffFIsHpDoSbBTKkJCic5ffPFFvP/++5H/33bbbSgqKsIJJ5yAHbKNjel4pMIpRar/jh3RNlIzUcruQMTu4EOe5MXDiVPqL38B/t//03aAchK6B4gBACWIttqdUL4Wp04pQBPumpv134kGDkOHxr/fdp1SgH6ykkyyTOLEE0VjvnVrdEJ4JwNXK1GqsFAk+S0o0CUZDVJdSDTngfEcQPs7pexgzCsl27LJmjx3riZK0Y6LiWIWvrdkiXh1EhpihASSUCh60lNRoSX+T3TL9Hg5pfbtEwPmYDB6QhJPlCL69gXOOEP8vGCB9r5ZDouzzxaD0B/9KP61T58ukuGGw9E5Yag9bmnR2jW7AzK654cOxc/zRzh1SsmLKPLOe2Yk45QCzEMKjEnOCZ/PXugnPXtVjT34pnMWFekTJLuZUyqZvC6x8krRMzW6aJyIUkYh2YibolRrq9YeAEKQBYCLL3bu1CSnVKzwvfp6rX4UFSXnlGpp0fp1ej3qqOQXU+RE50anFI3hnG4CYqx/tDNgMvkizfDSKWW28Gc3fM8uVMc//xz4xz9E+ZTD95LB6JSSUyBQTipCdgE7wTiGNjqlGhs152+i46ucHC0KwCqEr7ZWE30SNUcMGgR89plYQAb07YFR6EokfE8u+9SfKYpwrb33npYfyg4ZGXr3nNvhe/JnjSF8cpqHRJBzYcYK4aPn6fcnHnYOaOcy5lKWw/fkZ21XPDUTpeQNbozt5bXXitQkp56qf59FqQgJiVIPPPAAstsK7Keffoq///3vePDBB1FcXIybKIEb0zGxyinV3KwNLpMVpQoLtQpuFF+MopTsgIqH3cGHE6u/E1HqnnvEoOGxx8T/KfmhE+zklXJLlKJ7nZtrP6ktoB+YxNtBRnZKuSFKFRZq8fVGt5STxPhWopSMtIIVcFOUag+nlBMXmQxdq5lT6rrrRKf6zjtaYkYvRKmFC8VrMrmqMjK0iZsxhI8GNl262HMAmTFhgri3I0eaPwOalPbqZd8pZazfffpoopScV8psseDYY8U57W7P/uc/i9cVK/TOJvk50O5jdgdk3bqJ9ltVowfBViTqlJJ3KLRaBU/GKQXEdkolWmfltipWXikzJ43T83qVUwqILUqZ5cIC4otS8nOMF97oZKHJ6nzyBIGe6/79Wv6Y3/zG2bEBe04puvZAQNRhupdOnm1WllaWqD1zaxFRvhaz8D1ZlHKClSjltlPKauMJI6kM33MiStHzu/hisei5ZIk+fC8ZrJxS9L1+8hPts4mO24z5noxOKbPFUacoSvwd+KjdLypKbhzXu7dwKs+ZIzbdkd+XSSZ8DwC2bROv+fmiH50+3dnOn4A+hM/t8D15h2+3nVI+X/TY0wx55z2nm+vI4zCad1VXW5sx3HZKxTqGcQzD4XsREhKldu3ahYFtK85vvfUWLrzwQlxzzTWYNWsWPqZs8kzHxGr3PUALP3FjkEPx6rL4Yma9zcmx39hYTciNeJFTqqlJa9jpOzl1SgGxk8ATyYhSspPGyU5aMnSfu3WL31jKTqlEY7+NkPBB7hzCDaeUjOyUorpwpDul5BWwwYO1QU9NjSgLkyY5vmQdRlHq8GEtzDcZUQqwzivlRrl89lkxOevXL74oZWx37DqlZFFqzRqtPXZj8tmzJ0CbkPz3v9r7ch/wzTfm12WF368Ngu3mlUo0p1RtrTbxt2p36fnSxDBRUcrKKZUIgYDWhsqD4fJyvZBhJUq5Fb6nqt47peyIUsbd98htFi+Ezw2nlDyxofbypZeE+2jiROvce7Egp1R5ud59JSM/W0VJLHwPiE527oUo1dqqL/+HDml126koZax/XjmlzNrjpiaxiCiHKye7+x5NQK3Er/p6zd2diFOKwjLLy/Xhe8kgO6VUNfo+T5+uPadERSlqT+rrRR0wOqXk3aTtbixiRrxk5yRKOQmBsyI/XyQSl59/sqKUHL4HaKJUoiGNgBgzDRokHOBui1KAdbJzN9oeO3mlkhm7DR0q7stFF2l9U6xE5337in5g2LD4i/EEHdfKKWVFVpZ+d052SkVIqIXIy8vDobbCsmDBApzeNpnIyspCQ7zVCqZ9sQrfA9wVpShM7csvtfeM294CzlcLAe9237OTQFEmGVFq9Wrr88mdeCzM8h2YOaWcilJDhogG9aST4n/WbacUYJ1Xyo2cUjJeO6VqavQJIt0UpezklLJDLKcUANx5p/bZSZOSu3YgWpRavFi0CYMHJ54HgvBSlFIUrbzIjs3du4FXXtHyHZmF0li1p8GgftLRp48YqNBGBOQgc2vySaKfHBpo5pRy0l44ySvV0qKdLxGnlJzLzwwSCFRVtOs//rG9cxCxwveSKfdy+AYgRJzRo8WgmfpcWXSRy6lbolRDgxZC5pVTyjgZjJfovGtX/SJZLNwQpQoL9Sv/qip24QSAq65ydlyiuFjUY1W13mTBOB754Q9FW3rFFc7OZczr5aYoJZczWWB2yymVkaFN6lPhlHr3XeD3v9c7SRPZfa+0VPwbMUKrA1ZOKRIZ5BBNOxivp6HBfacUIMaExv49J0cLAUtWlALE2NXolEp0HGp1HqsFZOqDvcprLI9PFMV+WZWFHVmAoZ3XkhlzZmWJnHgbNoh2iMqSG+F7gLeilJ0d+JIZu+XkiBxvr70We9d5WURavhz44gv7jnoSqnft0ua2dsu7vCs2O6UiJCRKnX766bjqqqtw1VVXYfPmzTj77LMBAF999RX6u6FSM95hVTkBd0Up2umNkhgD5p2Jk0G33fA9J6IUdQjyTnX0fxljKBaQmCg1bJhogCorxVa8ZtB12E10XlCgiQtmOaWcrsT06CEG2LRjWSzczikFiKSPfr+4P/IKhJOOle5HrA5fTnTuRU4pQF/m3Qzfs3JKOV19N4YaGgeto0cDP/iB+JlcPMlgFKXcCN0jrEQpKpfJOvgIWRyfORO45BJtq3KjUyozM3borDxwoUkbJaenED63Jp/0/BYu1No3WZRy6pQCnIlScl2wW8+ovFRVieTigCY+Gbn+euCuu4BXXxVlIFaSfzNihe8l41gwDuz/9CfxXaqqxKYLgPdOKarfPl9iITRUd5Yv15xoxmM7Dd+TRalYTqmGBu05JLP7XkGBXsD4+GMxacnL04cxOcHn0/IuWoXwGd2BAweK+3juuc7O5aUolZmpuci8EKW6do09CU2mnpk5paiMrl+vTRYTGZ9kZYljrVypH3+a5UCTd95zEmr02GMi8TGVBzn5c7JOKVmUknf4lNuVG28U98RpeSSCQe3eVFXFdkolg5UDnYi3EUayyE6pnBz7ri9ZCHfbKQXoc4FZLR4k6vhNZ6cUoNVDed4rfxd5p7y8PPFMnaQ66dVL/E0opI2BqPzH62dlUYqdUhESEqUef/xxTJo0CQcOHMAbb7yBbm0FZvXq1bjkkktcvUDGZax236P3AHdFqU2bNFt7MknOAW/C97KztY6bdraZOlXkkJHPQx2hrKAnIkplZGhhAp9/bv4Zp+F7/ftrja8b4XuAKAN2Ol0vnFIFBVqiyS++0N53Irz86ldim+xY7ZFX4XsZGVq5lsXMRPM+yXgVvmd0SsltwIsvAk8/Ddx8s7Njm5EqUertt0VCSSC57a7NkNuh9evFz/Q8jjpK7+aINzCR6zitxMrJzsNh9yafkyeLa9+7F/j6a/GeLAZQziYn7QXZ3O2IUlQXcnLsD8LoWsrKhNNHUax3BO3eXbgjfvzjxOqY104pCu/561+13z3zjPXubPLf2iFWonM5dM9pbg5A2zntv/8Vk275OySSU8rvF3XDTJRqbdUvCtHx/X7nE7hYTqlnnxU//+Qniee6AbRxgFWycyfjkVhQufBClFIUrV0z5pyj75WMKNWli34Salz0c9spRUmk6+vF4paqJj4+ycsT/a5cF83GoE533iPGjBHjFbo/sijlplNKTkEh3+fx44XweP31iZ9HFoysnFLJii+pDN8zQxalnNQ5K6eUW6KUTDqF73ntlJKRdzq2CrNOJGl7IKC1/1T+qC9jp1RCJCRKFRUV4e9//zvefvttnHnmmZH377vvPvyWVoyZjkms8D3CjUFO376iMw+FtBUk6pxkYcdJQ+BF+J68Ffjhw2LitGiRyPlECVABrSMcMkQMYseMSSwHBSAmh0B0ziQiEVGKcCN8zwle5JQCtO9EE2XAWcc6dizw/POxhUOvwvfk48gDKC8TnScbvmfllALE4OGaa5JftQX0k9CyMjEw8/u1kM1kILFi/nyRD+L888V9cbNcAtp9qKnRnHw0YB05Ut/uxGtL5bpJotTkyeL+798vbPluTT6zsoApU8TPFMJn5lDxyilFdcFuPin5Wii0sEcP71YVU+GU+t3vRN2dOFHUhS1bRGJjN51SZonOk8knBYgdS597TizWAMCHH2q/o7ajtFQ/CY4lSlF+J2P4XlOT6GNll5u8cYlTQS2WU4pyQ15wgbNjGiHnnpVTyi1RykunFKDdG2N+OHJQOhWlMjO1sV7Xrtr3b26OHsO5nVOKXDOAaEOrq7Xw1UT7AfnazMbOiSQ5l5EFU7fC9xRFGxM2Npr3724gj3dkR6wXTqn2Ct9LVpQyOqVo7OCmKEV11Ci+dERRKhVOKYLKntU4JTvbmUNKxpjsPJHwPXZKRUg469zHH3+Mn/3sZzjhhBPwXdtKyr/+9S8sW7bMtYtjPMDKxijjxiDH5xM5MwAthI8qa//+mgvHy/A9u/knZMVe7jSefFL7WR7U/9//iUTEiYoLp50mXuWBvYxdUYo6X8pBA+hFKTPXi9t44ZQCtAm6LEq54TSSkXK9BKnTdkuUMtuBryMnOqdBDA1k3B60ErIoRU7B8ePdGZiRKEV57BobhSvIbVGK7vGhQ8LJlJUlnEcrV4rJtBNRSv7eJKBmZmrJ/j/4wN3JpxzCB7gnStlJdE51wUkdo/JCbWIi7lS7mA2Sk010Dmh16fBhLVzvoYeASy8VP//jH/qcSTk5WhlLZPe9eE6pRPD7gRkzgFtu0R8P0Ppb465XZnV64EAhVIwYIf4vL5IBQqjeulU4sihEKtF8UkBspxS1C1bOO7vQAoqcVFuGxCq7yXOtaG9Ryun1K4r2fLt0EXWZRCqjO8IrpxQgRCkam+TmJt7/+v3mmxYQbopSboXvyceQw/fcGkMRsmDUXk4pr8P35JxSTuqcPJaSFwyofXNTlOrSRRNX5Hy+HVGUsnJKNTeL3YK3bPFOlPL59NeejFPWKErZSXQu/x3AopREQqLUG2+8gWnTpiE7Oxtr1qxBU1sDWlVVhQceeMDVC2Rcxix8z5jUza1BjjGvFHVOXbpoA8Fkw/dUVYQWUZiXvPuH3ZVJeTIiT0iWLdMa9kRW+a2YMkXc82+/1Q+eCLui1FVXCbfV7bdr78kTE68GIDJe5JQC9AkECTdEHRnKKXXgABQaILjtlKKJuNnON4lgVgdaWjQxKRmnlDzQToUoRZO1AQPcObbZ5HLvXu9yShFHHy0GPePH652XgH2nVI8e+lXxqVPF69KlWrlxo12mzQvWrBGvZgsTiTql/vMf4L77rDdwSMYpRVjlk3IDKocrV2r33I06S89t0yZRV7OzgRNOEO5DAHjjDa2fobJDO5U5+b5eilKE0QHa2qr1V1266I9vVo6OOkr0ee++K/5vDN+j/ldVtbLpdJFJxihKyQJGMmKXzMUXi9fZs80TwVPuSHqmieLl7nuAtShF53HqlAK0MkDOOCt3RDJhssY+MRzWQmkAIUolsvOeGdQvmrn1O6JTSj5GY2PiuSfjYRW+lyqnVEODVm69EqXy87UxeaKilBluilJ+v+b8vOsu7f1E65fs8pJxY8Gb+jY5RQcA/OtfwG23Ab/8pdZOuC1K5eToRaNkRCnjDnyc6DwpEhKl/vCHP+Cpp57CM888g6Ck8E2ePBlraLDLdEzMwveMgw2vRClZbCErbLLhe8uXA9deK5I0trbqnSl2Jz9WTilACF5AYqv8VuTnm4dBAGIwbleUCgZF2JPc0chOKa8GIDJeO6XMEp279X2onLclUFYDAfcELyp7NHmTJ4rJXL/ZxFOuD8k4pag9kFeE3UaehFLiakoUnCxmotSePd7llCKMk015kmtXlDLuPHj88eL1k0/sH8sO1NZTuUzWKUXuiS++ECFev/udCH02w5jw2Q7Ga/HSKTV5shgoVlUBc+aI99xwStFzI8dJnz5igj52rFigaG4WOygBWn2cM0f0DU4mWHZzSiWDUZSSXQt2nFKA6PupDTSG78liBfWDbjmlCgq08x48qLkWkhWljj9eCNJNTSLE0QiJUpSXK1G8dkrRvbFyPSYjSlG5tnJH0Hdxwym1Z48+dGnDBk2El0OwEkEWjmQqK7UNNo49Nvlju5VTCjB3SqVKlHLTKRUr0TmJkPn57iwcW0Hlx0md8/lil2s3RSkAuP9+seg9d662g3WiYejGnWMJN9qec84Rrx9/rM/Ht26deP3kE60tSlaUontMddQoSiXzPazC9+IJXXKYKTulIiQkSm3atAlTKDeFRGFhISrNdiljOg40SKir0yYkRlu2W4McSlZtdErl52uNu5NBiFn4Hk1sd+4UuaBosFNQYD9GWF69owEfNWIvvSQ6c7cG9QQ5IYyiVFOTlvsgkZUlOa+I284iM6izOHxYu/duOFLMwve8ckpRGUo0CbAZxvC9ZIQjGTOnlHxsp4MOM6dUTo5798EIddR1de6LUn37inubna3lbZNFKa+cUkZRKpHwPaMoNXq0eAZyf+pGuaf2q7FRtDVuhe/t2ycWBQD9jqsyibShxoGdl04pn0+EqAEiHx3gbqJzyoslr5A+8YTeqUyT9oEDgVNPdXYeL3NKEUaxncpnbq4YWMsTLDuTLWP4nrwoZBSlEsnJZOWU2r1bvGZkuDPeoSTRTz2l1QNALDJRQuNkRSkvE50D2r2h8YexD0hElKL6S+XayilFTsFE7pFxsZLCuKj8bdkC/POf4ufzz3d+fBm6R0ZRauNG8XrUUYnXMfnYbobvmTmlvArfM3NKuZXbNFb4nhy659XYBdD6aafOGlkENG4g5LYoNXAg8P/+n/j5ttuSc+nHC99LRtzs21eM01QVeO017X15ExZarHHLKUX9TE6O9+F78cp7cbFWv9kpFSEhUaq0tBRbt26Nen/ZsmU4OtmOl/EWufJR5ZEHGz6fO6szgOaU2rIlejc4WvFOxCklT8hlK+8LL2grYk4aMXn1jgZL06aJBry6Wqj4XolSixbpw13k75NIQymvlnu1KiZD9y4c1mLk3RSlvvtOG+S7PaCiTolWT9x6tvKxqNzQtft8ya2KxHJKZWba36aYMHNKeVlevHRKFRSIlcHPPtOciHv2uB++Zyx/xj7PiShF9ceYnDUvDxg8WPu/ky2oYyEPlKqq3BOlZKxEqY7ulALEjp2AWCzYvt3dROckSskC5LBhwK9/rf0/mWTYqQzfq67W7xpIz9SOU0rGKnyPziG/l4ijKSNDm6TKic5JlKKwsmS5+GLx7MrKxEYLxOHD2vdINqyIygbVI69EKUKua8FgYmXH6JQy7vYKiP6dks5Tu+0E42IlpUQYP160+eGwmNz6fLF347WDlVOKJtKURzXZY7sZvtfeTim7zn+75zAL3/N65z0iEaeU8fPG8YKb407i7rtFnV25UtybjphTChCbRgHAK69o71FdArQFFrdEKSI3173wvUQTnSuK9rfslIqQ0Cj36quvxg033IAVK1ZAURTs2bMH//73v3HLLbfguuuuc/saGTfJyop2EMlOqdxc91YajjpKdEQtLWK7XLmyUm6T8ePtH88sfE/uoN58U0vE+tOf2j+umVOquFi7L+Xl7uaUAoDvfU90EOXl2rbygD7+PpFJqFn4npdOqYwMfYNeWOhOA9url/j+oZAWB+5RonOF8k24uWJl5ZTKzk6ufsVySiUy0JSdD/IqkleYiVLJJhqWmTBB7IBHjprvvnMvLwERCOjrptEplZWlPad4g7arrhK5hWbOjP7d2LHaz25NPP1+bbBkJUo5qQfyggZ9Z3lQKZOIMJJqUap/f7FgoKoiKbmbic6pnspOKUBMIIYNEwJJMnmHYolSboWf09+Hw6LsGHeWi5dTyojX4XuKovUXcqJzEqXcahNycoArrhA//+c/2ulJIOnZM/l21egU8VqUkif4xcWJ9VunnKLlUAO0Sf2iRdpnNm0Szz8nBzjuOOfnsHJKDRigJdQHxAYzyS6AWOWUojaPFmKTPbab4XuyU8prUaq8XHPaAal3Snm18x5BgpLTtkiuo8bwTredUoBYLKIFo4MH3RWlVNW9TZQuukiMpT7/XDhKKyq0RWIZt0UpN3NKUX9+6JA+AsnOMVmUiiIhUer222/HT3/6U0ydOhW1tbWYMmUKrrrqKlx33XW46qqr3L5Gxk3kHVEIoyjl5rnkvFJy53TBBWKg7ETENAvfk0WpxkbhjBg4EPjtb+0f1yynVNeu2oTrwAH3nVLyDlsXXaTlnUh2VUkO4UhFonNA30G7OfEnYYFC+NzOKWU4jpoKp1SyzyKWUyqRY9NkMhzWx9t7BXXUra3aQNItp5QMHXPjRs1p51bZVBS9SGEmJFCdiNeeHnOMyFtnlhxXFqWSGTQZkcsmDaDk63QyecjMFAN1n0/ksgCsRalEnFLZ2XoB0MvwPYLEhffeczfROWEM1czJESvaO3cmN3FLRU6p7Gwt3LCqKvqZ0vF9PnvtiDGswm1Riq4ZsHZKucWFF4rXuXO1nEZu5ZMCol1qqRSlEgndA4A77xRlZNw48f9rrxWv//mPlhicdmEdNy560x07WDml+vfXi1KXXeb82Fbnqq8XIUf//rf4v9tOKa923/M6fI/qFSE7pdxKdB4vfM9Lrr5aLCLQ4rdd5LawpETfB3ohSgF6V2KiiytmolRDgxbdkWzbU1Kiham/8ooWBmvEC1FKvvZkvkdhofYMd+1yJsKef77ogyjdBJOYKKUoCn7729+ioqICX375JT777DMcOHAAhYWFGODWTkqMd8gTHL/fWbiJU0iU2rgxurI6HSDHCt+TG4Cnn3bW6Zrtvtetm16UcjPROfHII0Ip37IFmDRJdOjJilLyxCQVic4BfYfhVjJpIDqvlFfhe4SXTim3tgWP5ZRKdOciErroGt1uA2TkskiiqReiFIkXNFnIzXVngE/QsRTFfCBMbUoy95Imcskex4g8uKfBprx663TyMH8+8Omnmpizfbu5AyuRNlRR9P2V104pQJtY7tnjrlOKMDqlAFF3k33GsXJKuZXsX1H05ceY74l+V1Bgz1njdfgeoBelvHJKAcL9XFwMVFZCWb4cgOSUckOUov6ptVW0nekgSgH6fCljxwI/+IFYBCERe+VK8ZpI6B4Qneic7vmAAcI1C4h79KMfJXZ8GSo/Bw4Al14K/Oxnor3ryOF7qXBKUdk0ilKyUyoV4XteO6W6dwf+93+d7xgs11F5bgF4L0odOKC515JxSu3aJdzDsijoRjmiEL633tLqEW30Aog+ItmcS146pQB9CJ8Tp9R11wknm5OIoU6OI1GqqakJd9xxB8aPH4/Jkydj7ty5GDp0KL766isMHjwYjz76KG666SavrpVxC7mCurkLgRnUeO/cmfyKiVn4HnV4//M/Yie63/3OeYJYM6dUt27m4XtuilLHHismc4MHi3O8+aZ7olR7OaXcFKWoofdKlDJ2qF46peSBcjK47ZQCNAGNdkDxUsT0+/XHz8hILo+OFSRK0eDezcknoN3r3r3NJw70nZIZ7Iwerf3slShFAyg5f5XT9nnQIDGhlNtM2mkOEOLOf/4DUB5KpyHQdD2Zme46W6ygcNL9+91pQ+M5pdwiVvgehSi70T7L5ce446osStnBTvgehQgm+uwvv1wM+seN054jHdvN8uT3A9OnAwCU994Tr26KUrm5WuoFWVD2SpSS+6pkRCkjd98tXl9+WYTskFNKnog6wbhQI7tmzj1XPPd773XnPsmbFtBE/9VXtXwyiYQfGo/tVaLzVOSUkjemAdx1SsUK33NrfOUVctnr2lXfDnslSlHbRouNQOKiVH09cOONIufiSy+J98xSwSTCD34gFjBWrQL++1/x3ve/r41J3Bi7GcdhbotSNN7cu9d5uKqXifnTEEei1D333IMnn3wS/fv3R1lZGS666CJcc801eOSRR/DQQw+hrKwMv/nNb7y6VsYtjKKUWzZGMyiHwO7dya+YxArf69cPWLxYDD6cYpZTyuiU8kKUAkRjdtpp4ufy8uTvkdnue+nulKIBn9sim6Gsuxq+Z3RKuTVoctspBWjlnwYvXpcX+b6XlnrTKRvDvNwWpeg5WOUAcsMpVVSkHd9rUSoZp5QMOWPlEL5LLhGugg0bxP+dipA0YOzVKzUDOBLWWlu1lX83nVLtKUq5IS7I5cfowHIqSjkJ30tUvL7/fuHGyc2NfhZui5znngsA8L33nghxcVOUUhTtvlZXuy9KGe+N7DpJ1uErM2ECcOaZon5ddx3wxRfa+4kgO6VaWjRhZMAAcd2rVgG33pr8dcvnkkX3v/1NvJaWJleeUpHo3OvwPWMCeC+cUo2Nejdofb2WeqCjbrQl161UO6VosRFw3o/RddfVaf33J5+IV7fanZIS4TIFgNdfF69Dh4roEcCdsZtxMTQ31915L7n99+zRyrubKReOIByJUrNnz8ZLL72E119/HQsWLEBraytaWlrwxRdf4Cc/+Qn8bqimjPfIlcVrp5QcgpVswsNY4XvJNOxy7LVZTikvEp3L0ID+4MHkV5XMdt9Lx5xSgPfhe8ZBeLIreTKydRrw1imV7HNOpVMK0Lc3XoTuAeJZyudxUywF4otSF1wgnH5OXZtGKK9UuohSFL4ii1LbtonXUaPEfaFNLuxC15OKfFKASDpK5YVcF27svgeIttKr8NhYohQ5mtwWpYxOKTq+3X4gFeF7MsY20m2x+owzgIwMKNu2IW/3bnedUoDeLeKlUyorS7+zpptOKQB4+GFRXhcuFPm3unVLPB+QPC7cvVuIXZmZ7m6gQVDfaHSCAsklOZePLTul0iV8z7igR8/ETaeU/PdyCB+10YWF3riu3cDYB6RSlKLymZXlfFGHrru6WhvD0g7nbvZjbWJ+ZAfv444DTjxR/OxWvx8rQsgtp9SePVpf5uZ84gjCkSi1e/dujGvLczF8+HBkZmbipptugsL2s/SivZ1SbobvuSFK0WC3uloIUIA+FMVLpxSgd2S5Gb6X7k4pq/A9jxKdu/psKXH1zp1i8uC2U8rN8D16Zl99JV47gygF6AczXjmlrCabV1wh8lzIIXiJQO4BN6+f2pb9+7Uk8GSVt5ug2goSpagsAVrbOXu2WAl1Wk6pv0hFPimCJrTk0nQrfM8sn5RbyG2/TGOjNlB2M3yvsjJalJo2DbjjDmDWLHvHihe+19KilR8vRCm3nVJ5eREhus+SJVr5cUuUont/4IBWd70QpfLz9W2O26LUcceJ3DzEhAmJuyBlpxT1s/36JbZ7cTyobZTdJ0Qy+aTkY6dzonOC+vXaWvecUsGgdo/kED43NxPwilg5pbwSL6hto12OEylLdN1btmjhqlT2vRCliOOOE+7qBx6w35fEI9a81y1RqqxMu08sSiWEo1a7tbUVGVLCsUAggDy2qKUfcmXJzfXWKUWiVF2dNkBLtLKahe+50eHJ7idaSZI7jh07tN10Oroo1d6773kVvhcKJZ6s0Qovw/e6d9ee68aN7jul3AzfmzZNvNLgxctE50DnEKWozA8Z4u5xjVxzjciTd+ed7h2Tyrmca2L0aODii4GbbkouRM7olGpt1QSRROtXqp1SgCZKUbvvVvieV6F7gLVTityawaA7/Vcsp1RmpphIUOhFPKifq6wUZYXCnQHRF1I+KcAdl7JRcHW7XQAiu7wNnDMHSmurKDtuuXbofsl1Nx1FKUDsYDZxovg5md2nqG62tOhFKS8w9rHyVu7JilKyuEZ9ero6pai8NzW5O0k324Gvo+eTAvT3W3ZK5ed7I54C0eF7iYwPqW0xc9+6WYaOO05znVMYbGamWOAYPtydc3jplKJx7ObN2ntej6M7KY72X1VVFVdeeSUy2xq5xsZGXHvttcg13Pw333zTvStk3McYvuelUyo7WwxYDx7UBpwdLXwvGBTXRAKXoogBMHUctBLj83kTJ2wWvse772kTuP379YMQr8L33LZRDxsGfPSRSORKk7dktyz2ItE5iRE0eEylU8qLEAtCFjHcDt/705+AU04BzjnH3eMaKShILE9eLGhgLw9WAwGxJXOyUAhLWZl+xV8+r1PIsZrKlXBjuUwHp1Q8Uaq42J2cXCQOmYlSTiH32+HD0Tt3VVdr196li14ASBSvnVIA8JOfIPz44/C17cCHAQPcm3hSHaIFhGDQnfsCRItSGRnauMgLUcrvB959VzgoL7888ePI100ChVcCtrFvvOACrd10yykFaOMdt3NKpUqUMltscmPs3K2bKPuUZgNIP6eULEp5FboHRIfvJSNKOf2dUxRFuKUeeSS5zQJiId9rt80Y1N5QWczJcScJ/BGII1HqCtryuY2f/exnrl4MkyJSufseINxSNHg1nt8JscL3kl2F6dJFE6WKikSDQpMhCocoLPQm0a5X4XvpnlOqe3cxKGtq0nbuAtyxtAPi2D6fFsfutguORKn33xf/LypKfrU/VqLzRAea3buLxLNtO0Zx+J4NxowR/9IRoyjlptDevbu414cOCcs/lffMzMQnWL/9LTBwoAiJTBVGUSqdnVJu5pMCzJ1SidYvahMrK4G1a/W/k0Upt649FU4pnw+tf/sbMGECfOGwdd65RDC6HN0crxlFKUDkf1u+3D23gpHu3YHrr0/uGHLdpEmhV4sdxvJz5ZXAvHnCUZnsPZLvPzkE3dx9r7HRu/A9at+p7SkuFmNoCjHNy3NHmJXzvBLp4JQyhu9R+fQyBxaNy2ms2JFFKQC44Qax6cHNN7t7XCIV4XtyeWcSwpEo9fzzz3t1HUwqSaVTChAD8XXrtP+7tfueqrrjlAJEA07hhTRQNQ6EvQjdk89z6JDmJks2fK8zOKUURZSdrVs1W2x2tnvCoKKIe5NseJEV5BpZtEi8ujFokp+vqorv4MZA87LLWJQ6UvBSlALEgPvQISFYUF1Npm717w+kelffdHRKWeWUkp1SbkDPUnb2JnPs/v3F+IAS6BJeiFKpcEoBwMiR2HbuuRj01lvA+PHuHdcYvufmeE1u92kC9/bboq/xemErGXw+UfabmzWBwqt+xdg3HnccsGyZuEfJliW/XxN2KLeaG04pena1td45pQDRLpBYVFAgyqZbi8aEvIBLpINTiu53ZqZ4HiedJELzzzjDu3MaBa+OLkr16wd8+KG7x5TxMnzPOF7gfFIJ41EwK9OhSWWic0DLK2V2ficYXSJNTVrOj2RFKbkBpwlsbq6+IfdKlKLzqarIXwUkv/teba3mAErXnFKA5izYtEm8uv1dpMGZ6raVmuz8VF7dEKWoDoTDWridG6LUOedoZY5Fqc4NtWNebV0s72ZKYShehil4QWdySrkt7FD5kcPak3GAUkhzKkQpY9vmlSgF4OvLL0fL4sXuCqrG8D2vnVI+X8cWpAiqn1QmvepX5Hvh84k+ZvhwoG0DqKSh8kljBjdEKapf27Z5u1Apj4+Nu9+61f4bRSlVTS+nVLduYqEmIwN4+mkR/ukVRlEqmUTnxPHHW/+uo+NlhFBmpn6MyaJUwrAodSSS6vA9eSCelSXylySCMXyPJlVA8hMrqxA0CuEDvBOlgkFtUE/bpycbvicnjPVaZCgp0Tpatwf5XotScnn3yilFuOmUArTJpxuiVHY2MHOm+FkeeHhBe4hSboul6YyxnLvd5puJUl61nV5RUqL/fzKiVHvnlPIqfI/6qq5dk8ufYRSlqN567ZTKyvK2b/T5oE6e7F64OZB6USpdoGvfv1+8psIpddRRiY9l7RwfcKfs0E7A69dr73khNBpFKblseuWUOnhQLMAqinfJ7d2A7oWHIngUxnMl8syDQX0Znz5d+zmdRancXHfD9wD9eJPD9xKGRakjEWP4ntxYee2USqZzMobvkTU4Ly/5pHLyqoLcmMuDYS8nVjRpplCyZMP3SJTy+dxLhGpFly7As88CL73k/rloEieH77mJPAh0+/kWF+tFTbdFKaoHbuWJeOABISTY3TkrUaj9URT9/XEbeWLCTikNYzlPhVMq3UQpN8P3CguFm2LoUC2xtxfYSXTuBvQsKe9NssclUYqEFvq/106pVE4Q3YLGBSS+sCglMNbPVOSU8sL1aBSl3HBKHXuseJVD3lIhSqXCKSUntndT/HWb4cPFHMXrBT8Zo3s10WdObYzfrw83TGdRyu3wPUAvSqVb+9mBcFnmZ9ICo2Ls84nXujrvnVLJVFY5fE/OJ+VGA2DllJIHw25sSW1F9+76ZN7JOqVoMpiT401ydiMzZnhzXCo7W7aIV7dXttvKe5jqgNsMG6blWXBDlPL7xcpVS4s2+XQroT3tOuk1NADo3t39lWaZo44Sg3pV9Vb8Sje8FqWoLa2o0NrmdBelkpnw+HzCBaQo3u7II++8KuNV+B6RrOBr3JG0f3+RXLulRcvz6IVTKh2Farr3lFCXRSmBsX6mInzPC9ejF06p4mJtMwFAtBNe9Lvt4ZSicM2OHLoHiAWJfftSK4T7/WIeQfOkZESpqirhRJPd/+kuSrmdtkZuc9gplTDslDoSMVZOQKuUHdkpRR20qoqklm4lOQfMc0oBqQnfA6IH3W6JUumQDyIWJEols4NILNrKf4tX4p3cibs1cKKBDU3YvNpRxyuow/YydA8Q9+Ptt4E5c7wPYU0n2CkVn65d9RO3ZOuWMQzCC8hVEQqJPpLwKnyPcMspRfTtq7XFFCLohSiVzk4pgkUpgXzteXneTQrlfsQLUcrYzrjhlFIUzS1ldg63iOWUcqs80Xjc6JTqyEnOieJid3YgdILcxiUqcFIbM3CgaH/IEZTuolRxMTBihNiIwo3vwk4pV2BR6kjEGL4nv9eRRSm5M21s1HJKuSFK2XFKpSJ8j0g2fI+SnKf7ZNw48PNIlAp5dZ8o2TkQPflKlBNOEK9Ll4rXdBOlKPfDccd5f65p04Czz/b+POmEsW1hUSoan0+fV6ojh4YQ8gRW3oHPa6dUsqKUMRdMcbE2TiBRyi2nY7qH73mZD85s9710Qa6fXi52eC1KeRG+B2h5pczO4RZyv2J0SnkdvtfRnVLthbzYnmz43jHHiNfBg8VrurmBjKKU3w+sXQusWOHOgjSLUq7AotSRiJlT6pprgMmTgYkT3T9fVpY2cE2mcyIXECBEqVQ4pVIlShknDMnuvkeki1BhhTFvg0eJzlu8GqiNGCFee/Vy79pPOkm8LlkiXtNNlDr9dGDBAuCxx9r7So5MAgH3k3zKdAZRCtCH8KWbKCWH8HmVU4pI9rhFRfqw4a5dtT6dHLJuCWqZmdoEJJ3D9wh2Sgnka/cqn5TxPOkSvgek3ilVUOCNU4ragcOHhSOUwvfSwSnVHrgpSg0cKF5vuUUs9slJz9MBY9oaQAhTbrnXOHzPFViUOhIxE6V+8xtg2TLvLJkkLiTTOSmKfgc+N0UpedVU/lleofUy3448sM/ISHyFzChKpbtTqqBA/3zd/j7klPKq3J9wAvDb3wJ//7t7xyRRatkykXfFy22evcDnE8IU74jXfsgTCLfLvpxTikQpt1bKUwlNbjMyUh92kQhy20+iVGureA6AuyFw8oYWbtRj2UUqi1KEW9euKNrkLB2dUhy+Zw47pWIji1JejRNi5ZRyq/3v2lUTlQ8e1JyU7JQyR27jEhWlxo4V93zKFPH/6dOB+fO9SfTvJWbzXjdhp5QrpMFIi3Eds/A9r6EQvmQrq7wDX2d1SiXzfYyDmHRxz8RCHvylW/iezwf84Q/Aj37k3jFHjhRlsaYGWLcu/ZxSTPsjt2VeOqWojU5np1Q6uKQA0dZQ3ioK36uo0PJLueUMUhT980yFKOWmgE3tJDul9KSzKCVfu5eilNxWei1KBQLuieGpCN9LRU4pv1+rt7t3a3k15e/HaLjhlHr0UeG2HT/enWtqL1IpSrFTKmFYlDoSyc3VVhtSlayOOvBkJyfyDnyp3n0vHUSpzha+B+hXZNItfM8L/H7gxBPFz0uWsCjFOCdVolRnCN9Lp3pFixLklKLQvS5d3E20Lj9PN8SdWKJUQYF7jhFAm5CwU0qPHNqYbqJUqpxSBQXAnXcC99yjn/C7hTwOcVMMl0WbVCU698IpBWhj5c8/F7lT8/P1+f8YDbmMJlqeFCU9BXwjVAYVxd3+hJDDhtOt/exAeLwdDNMhURQxEampSZ1T6pprgF27gMsvT+44cviem4nOu3XTVqXkVdlU7b4nn9NNUSqdxBYrvBSl2gYzjek2QTnpJOC994Ann9RWC70ML2U6F6kQpaqqhDBlPF+6kG5OKUAMtuvqokUpt8LfiFQ6pdy+9nR2SgWD4vppIcJNUUpRxHihri79wm1TlVMKAO6/37tjy+M1NyfOBQXivuzb571TKhAQ1+6FUwoQ7cHGjcDy5eL/gwZ5s3NyZ8CN8L3OQr9+Ip1Gv37elJeMDNEXHjzITqkkYFHqSKWkRIg6qcrrMnKk2J49WbwK38vPBx5/XAhT8kAv3ZxSnTF8Txal3B5QXX01WrOzsTU/H/3dPbK3UF4pyqlwySXarigME49UiFKqqgmm6SxKpVMbanRKHTwoXtNJlOrSxVtR6thjgc2b9TujphOFhd6IUoBwAG3enH7hUKlySnmNV04pQJT7VIhS+fli0u+1U0oWpRhz3Ajf6ywEAsAnn3h7joEDRZ+bzm1QO8Oi1JHKCy8A33yTfhNZs/A9tzq8a6+Nfi83Vyjr5eXAUUe5cx4z2ClljZc5pQoLEb7mGjTPnevucb1mzBjhjKqsBK66CnjqKV4tZOzjpSiVkSHazbq69M4pNXasGMgOH97eV2Ifav8pp1S6OKUoUXFurujj5T5Qdiu7wauvAnv2aLtJpRsFBUJcANwXpW67zd3jpYpU5ZTyGq+cUoAQb5Yu9U6cGDZMCF+TJ4v/e+WUovZg+3bxyqKUNSxKpZZ//hNYtQr43vfa+0rSFhaljlQmT9Y6j3TCq933YvHJJ95b2vPyxCCkqYlzShnxMnwvXQkEhPOwrEyExLIgxTjBy933ADEYrqszP1+6cPTRQrzwIneMV1jllHLbEU3P0+9359kOHy7EdVok89IplZOTvoIU4H3dTUc6i1NKHt+4LUpNmAA8+6xYZPWC3Fyx0G2Wr9bt8D0ZFqWsSZPwPVVV0dLSgtbW1va+lOQ45hjxj/pfFwmFQggEAmhsbOyQ98nv9yMQCEBJci7CohSTXsjhe5RTyuukcl46pAhFEZ3t7t3JfR/jQKYzOKVYlDJnyhRtm16GcYI86fci/0GXLqItMztfOuG2IOI1qQ7f69rVnR3CfD7gmWe0/3spSqU7LEpFQ+OCQCA9E9gTXobv/fznQvSdONHd48rIE1K5X/EifI9gUcoaNxKde0xzczP27t2L+vr69r6UDo2qqigtLcWuXbuSFn68IicnBz179kSG0RzhABalmPTCy/C99qa4WEzkkvk+fr8YGNA24J1BxOndW/u5M4hsDNPeeBm+B+gnhn4/19tUIYtS27YBFJbsdggclR+vclKyKGWNfG9YlBLQOKe01B2RtL3wMnwvENByUaYCdkq1Px08fC8cDqOsrAx+vx+9evVCRkZGhxVc2ptwOIza2lrk5eXB18HaOFVV0dzcjAMHDqCsrAyDBg1K+BpZlGLSi/YI30sV1Nkm830URYTw0Up5Z5gMZmWJSVV5eYfsWBkm7fBalJIHw4WFHF6aKmgi+9prwDvviJxzJSXAhRe6ex4WpdoPdkpFQ+PCdA7dA7wVpVKN3K+42cfI7UFRUXruopkqOnj4XnNzM8LhMPr06YOczjBX8ZBwOIzm5mZkZWV1OFEKALKzsxEMBrFjx47IdSZCx/tmDBMLr3bf6wgcfbR4lZ1BiSAPZjpgR5QQdG+Kitr1MhimU5BqUYpJDWSbf+klIUhNmgSsWZN8n2KE2mOvXAosSlnDTqloaPfGYcPa9TKSxsvwvVTTo4dYjCgpEW5Zt5Dbg0GDeMEjFvn5mnOwA88FOqLIwjjHjefITikmvaCOur5eyynVWUSp3/8eOPlk4Ec/Su44cjxvZ1l9eOQRYP58YOrU9r4Shkl/SCjy+byZ/LAo1T6QU6S0FLjpJuDGG6M3v3CDc88FPvgAOP54948NsCgVC3ZKRTNtGvDxx8DIke19JcnRmZxSJSXAnDnuhw4bRSnGGp9P3KNt29LfRcgcEbA8yaQXNIGiBK5A5xGluncHfvKT5Acj8iSkA6+OOOJ73wN+9ztvJlgMc6RBE9vcXG9WmlmUah8eewx47z2xK+dtt3nXXvr9wBlneLczIYtS1rAoFY2iAN//fvqPBTuTKAUAP/yhcGu6iRwyzKJUfBYuBFat8i7UmnGd7du3Q1EUrFu3zvIzH330ERRFQWVlpavnVhQFb731lqvHdELaiFL3338/TjjhBOTk5KDIIoRn586dmD59OnJyctCjRw/ceuutaGlpSe2FMt5CIkt5uXgNBDpH5+0m8v3oLE4phmHcY+hQYPhw4IILvDm+nMsi3SeK6UTXrsD06ekf+sOilDV0b3w+Hvt0NjpT+J5XyDssHnts+15LOtCnDzBqVHtfRafjyiuvhKIoUBQFwWAQAwYMwG233YbGxsakj92nTx/s3bsXw4cPd+FK04u0Cd9rbm7GRRddhEmTJuHZZ5+N+n1rayumT5+O0tJSLF++HHv37sXll1+OYDCIBx54oB2umPEE6qhJlCoo4JhyI53RKcUwjHtkZQHr13vXdrJTikmG3r2FO7akhPswI167HJn2Qy7rLDhac9xxwPLlwLhx7X0lzBHMmWeeieeffx6hUAirV6/GFVdcAUVR8Kc//Smp4/r9fpSWlrp0lelF2jil7rvvPtx0000YMWKE6e8XLFiAr7/+Gi+//DJGjx6Ns846C7///e/x+OOPo7m5OcVXy3iGmSjF6GGnFMMw8fByQsuiFJMMfj/w6adAO4YRdFhozMOhe50PdkrZY84cYPVqYPDg9r4S5ggmMzMTpaWl6NOnD8477zycdtppWLhwIQCxW96sWbMwYMAAZGdnY9SoUXj99dcjf3v48GFceuml6N69O7KzszFo0CA8//zzAMzD9+bOnYvx48cjNzcXp5xyCrZv3667lt/97ncYPXq07r2//vWv6E+bQABYuXIlTj/9dBQXF6OwsBAnnXQS1qxZY/n9mpub8Ytf/AI9e/ZEVlYW+vXrh1mzZiV2s2ySNk6peHz66acYMWIESkpKIu9NmzYN1113Hb766iuMGTOmHa+OcQ1aSdqzR7yyKBUNO6UYhmlPWJRiGG/o10+8ur2jItP+dLacUl7RvTuH9XZWVFVsZNUe5OQkvFj35ZdfYvny5ejX1j7PmjULL7/8Mp566ikMGjQIS5cuxc9+9jN0794dJ510Eu6++258/fXXmDdvHoqLi7F161Y0NDSYHnvXrl248MILcdVVV2HmzJlYs2YNbrnlFsfXWFNTgyuuuAKPPfYYVFXFQw89hLPPPhtbtmxBfn5+1Of/9re/4Z133sFrr72Gvn37YteuXdi1a5fj8zqh04hS+/bt0wlSACL/37dvn+XfNTU1oampKfL/6upqAEAoFEIoFPLgSlMDXXs6fwczfMEg/ABQUQEACA8fjtZO9h2TxR8MRiyQoWAQ4PsTk85aVxjGTRzVk/x8BNt+bM3LQ5jrFnME4WmfcswxUN5/H+rRR3Pf3tkIBLR2MxDo9O0mj72ObEKhEFRVRTgcRjgcFm/W1cHXTmaDcHW1bQeqqqp47733kJeXh5aWFjQ1NcHn8+Fvf/sbGhoa8MADD2DBggWY1Jbov3///vj444/x1FNP4cQTT8SOHTswevRojB07FgDQt29fcQ3SvaCfn3jiCRxzzDH4wx/+gPz8fAwePBjr16/Hgw8+GPmMqqqRv5GvUX7v5JNP1n2Hp556Cl27dsXixYvxgx/8QLsPbcfcsWMHBg0ahBNOOAGKoqBPnz5R59Ddv7brCIVC8Pv9ut/ZrePtKkrdfvvtcWMvN27ciCFDhnh2DbNmzcJ9990X9f6CBQuQ0wlCn8hK2Fnou3kzyPMWysnBolNPRePcue16TR2NE2prQWtIH61YgfqdO9v1etKFzlZXGMYL7NSTjOpqnNX284Zdu7CD22jmCMTTPmXTJvGP6VT8ICMD/uZmbN65E5uPkHaTx15HJoFAAKWlpaitrdXS7NTVoaidrqe6uhpobbX12VAohBNPPBEPPfQQ6urq8OSTTyIQCOD000/HunXrUF9fj2nTpun+prm5GSNHjkR1dTUuv/xyXHHFFVi1ahVOOeUUTJ8+HRMnTgQA1NbWAgDq6upQXV2NDRs2RKK9ampqAACj2pLX19TUwOfzoampCa2trRFjDQA0NjYiHA5H3isvL8f999+PZcuW4cCBAwiHw6ivr8fmzZt1f9fQ0IDq6mpceOGF+NGPfoTBgwdj6tSpmDZtGk499VTLe9Lc3IyGhgYsXbo0apO5epvut3YVpW655RZceeWVMT9z9NFH2zpWaWkpPv/8c917+/fvj/zOijvuuAM333xz5P/V1dXo06cPzjjjDBSkcWhYKBTCwoULcfrppyMYDMb/gzRBaauQAKD85S849fLL2/FqOib+J54ANmwAAJx81llAr17tfEUdm85aVxjGTRzVk5YWoK1tHn7CCRh29tkpuEKG6Rhwn8Ikii8vD6iowLEjRmBgJ283uZ4c2TQ2NmLXrl3Iy8tDFuVQy88XjqV2oMBB+F4wGERBQUEkj9OkSZMwZswYzJ49O7Jr3rvvvoujjjpK93eZmZkoKCjABRdcgClTpmDu3Ln473//i/POOw/XX389/vznPyMvLw8AkJubi4KCAgQCgUj9yM/Ph6IoyG5LzZKfn4+CggJkZ2fD5/PpdAu/36977+KLL0ZFRQUeffRR9OvXD5mZmZg8eTL8fr/u77Kzs1FQUIATTzwR3377LebNm4cPP/wQP//5zzF16lTMnj3b9J40NjYiOzsbU6ZM0Z5nG9U2n2m7ilLdu3dHd5figidNmoT7778f5eXl6NGjBwChvhcUFGDo0KGWf5eZmYlMk9jtYDDYKRrJzvI9IowZIxqNs85C4NprefcZM6TyHCwsBDrT8/eQTldXGMYDbNWTYBDIzwdqahDo2pXbIOaIhPsUxjE5OUBFBfw5OfAfIWWH68mRSWtrKxRFgc/ng88n7btmkt+oo6EoSuTaAcDn8+HOO+/EzTffjM2bNyMzMxO7d+/GKaecYnmMkpISzJgxAzNmzMDTTz+NW2+9FQ899JDumD6fD0OHDsU777wTOa/P54uYcOgzPXr0wL59+yLXBQBffPFF5DMAsHz5cjzxxBORUL1du3bh4MGDuu8hHxMAioqKcMkll+CSSy7BRRddhDPPPBOVlZXo2rVr1Pfx+XxQFMW0Ptut32mTU2rnzp2oqKjAzp070draGslKP3DgQOTl5eGMM87A0KFDcdlll+HBBx/Evn37cNddd2HmzJmmohOTpgwbBuzbB3TrxoKUFXJ550TnDMO0Bz16ADU1QHFxe18JwzBMekBpQ3j3PYZJKy666CLceuutePrpp/HrX/8aN910E8LhML7//e+jqqoKn3zyCQoKCnDFFVfgnnvuwbhx4zBs2DA0NTXhvffew3HHHWd63GuvvRYPPfQQ7r77blx//fVYu3YtXnjhBd1nTj75ZBw4cAAPPvggLrzwQsyfPx/z5s3TOaAGDRqEf/3rXxg/fjyqq6tx6623RhxXZjz88MPo2bMnxowZA5/Ph9mzZ6O0tBRFRUVu3C5TfPE/0jG45557MGbMGNx7772ora3FmDFjMGbMGKxatQqAsKm999578Pv9mDRpEn72s5/h8ssvx//+7/+285UzrtOjh9gymjGHdt/z+fQ78TEMw6SKRx4B7rwTGD++va+EYRgmPSBRihfTGSatCAQC+MUvfoEHH3wQd9xxB+6++27MmjULxx13HM4880y8//77GDBgAAAgIyMDd9xxB0aOHIkpU6bA7/fjlVdeMT1u3759MXv2bMydOxdjxozBU089hQceeED3meOOOw5PPPEEHn/8cYwaNQqff/45fv3rX+s+8+yzz+Lw4cMYO3YsLrvsMvzqV7+KRJaZkZ+fjwcffBDjx4/H8ccfj+3bt2Pu3Ll6V5vLKCqlZ2cAiLjHwsJCVFVVpX1Oqblz5+Lss89mW+yRxowZwAsviF0k2hLmMdZwXWGY+HA9YRh7cF1hEuaEE4BPPwX+8x/gkkva+2o8hevJkU1jYyPKysowYMCAqBxEjB5KWF5QUOCpKJQMsZ6nXW2lY34zhmESh1bYOsHukQzDMAzDMEcE554L9OwJfO977X0lDMMwKYVFKYbpbFDIHueTYhiGYRiGSQ9uvx347jugLcyHYRjmSIFFKYbpbJAoxU4phmEYhmGY9IE38WEY5giERSmG6WxQ+B47pRiGYRiGYRiGYZgODItSDNPZYKcUwzAMwzAMwzAMkwawKMUwnQ3OKcUwDMMwDMMwTAdGVdX2vgTGBdx4jixKMUxng3ffYxiGYRiGYRimAxIMBgEA9fX17XwljBvQc6TnmggBty6GYZgOQq9e4rV37/a9DoZhGIZhGIZhGAm/34+ioiKUl5cDAHJycqBwkn9TwuEwmpub0djYCJ+vY/mJVFVFfX09ysvLUVRUBL/fn/CxWJRimM7GBRcA77wDfP/77X0lDMMwDMMwDMMwOkpLSwEgIkwx5qiqioaGBmRnZ3dY4a6oqCjyPBOFRSmG6WwEg8A557T3VTAMwzAMwzAMw0ShKAp69uyJHj16IBQKtffldFhCoRCWLl2KKVOmJBUe5xXBYDAphxTBohTDMAzDMAzDMAzDMCnF7/e7Imp0Vvx+P1paWpCVldUhRSm36FiBiQzDMAzDMAzDMAzDMMwRAYtSDMMwDMMwDMMwDMMwTMphUYphGIZhGIZhGIZhGIZJOZxTyoCqqgCA6urqdr6S5AiFQqivr0d1dXWnjj9lmGThusIw8eF6wjD24LrCMPHhesIw9kj3ukKaCmksVrAoZaCmpgYA0KdPn3a+EoZhGIZhGIZhGIZhmPSlpqYGhYWFlr9X1Hiy1RFGOBzGnj17kJ+fD0VR2vtyEqa6uhp9+vTBrl27UFBQ0N6XwzAdFq4rDBMfricMYw+uKwwTH64nDGOPdK8rqqqipqYGvXr1gs9nnTmKnVIGfD4fevfu3d6X4RoFBQVpWYAZJtVwXWGY+HA9YRh7cF1hmPhwPWEYe6RzXYnlkCI40TnDMAzDMAzDMAzDMAyTcliUYhiGYRiGYRiGYRiGYVIOi1KdlMzMTNx7773IzMxs70thmA4N1xWGiQ/XE4axB9cVhokP1xOGsceRUlc40TnDMAzDMAzDMAzDMAyTctgpxTAMwzAMwzAMwzAMw6QcFqUYhmEYhmEYhmEYhmGYlMOiFMMwDMMwDMMwDMMwDJNyWJTqpDz++OPo378/srKyMHHiRHz++eftfUkMkzKWLl2Kc845B7169YKiKHjrrbd0v1dVFffccw969uyJ7OxsnHbaadiyZYvuMxUVFbj00ktRUFCAoqIi/M///A9qa2tT+C0YxltmzZqF448/Hvn5+ejRowfOO+88bNq0SfeZxsZGzJw5E926dUNeXh4uuOAC7N+/X/eZnTt3Yvr06cjJyUGPHj1w6623oqWlJZVfhWE848knn8TIkSNRUFCAgoICTJo0CfPmzYv8nusIw5jzxz/+EYqi4MYbb4y8x/WFOdL53e9+B0VRdP+GDBkS+f2RWkdYlOqEvPrqq7j55ptx7733Ys2aNRg1ahSmTZuG8vLy9r40hkkJdXV1GDVqFB5//HHT3z/44IP429/+hqeeegorVqxAbm4upk2bhsbGxshnLr30Unz11VdYuHAh3nvvPSxduhTXXHNNqr4Cw3jOkiVLMHPmTHz22WdYuHAhQqEQzjjjDNTV1UU+c9NNN+Hdd9/F7NmzsWTJEuzZswfnn39+5Petra2YPn06mpubsXz5crz44ot44YUXcM8997THV2IY1+nduzf++Mc/YvXq1Vi1ahVOPfVU/PCHP8RXX30FgOsIw5ixcuVKPP300xg5cqTufa4vDAMMGzYMe/fujfxbtmxZ5HdHbB1RmU7HhAkT1JkzZ0b+39raqvbq1UudNWtWO14Vw7QPANQ5c+ZE/h8Oh9XS0lL1z3/+c+S9yspKNTMzU/2///s/VVVV9euvv1YBqCtXrox8Zt68eaqiKOp3332XsmtnmFRSXl6uAlCXLFmiqqqoF8FgUJ09e3bkMxs3blQBqJ9++qmqqqo6d+5c1efzqfv27Yt85sknn1QLCgrUpqam1H4BhkkRXbp0Uf/5z39yHWEYE2pqatRBgwapCxcuVE866ST1hhtuUFWV+xSGUVVVvffee9VRo0aZ/u5IriPslOpkNDc3Y/Xq1TjttNMi7/l8Ppx22mn49NNP2/HKGKZjUFZWhn379unqSGFhISZOnBipI59++imKioowfvz4yGdOO+00+Hw+rFixIuXXzDCpoKqqCgDQtWtXAMDq1asRCoV0dWXIkCHo27evrq6MGDECJSUlkc9MmzYN1dXVEScJw3QWWltb8corr6Curg6TJk3iOsIwJsycORPTp0/X1QuA+xSGIbZs2YJevXrh6KOPxqWXXoqdO3cCOLLrSKC9L4Bxl4MHD6K1tVVXUAGgpKQE33zzTTtdFcN0HPbt2wcApnWEfrdv3z706NFD9/tAIICuXbtGPsMwnYlwOIwbb7wRkydPxvDhwwGIepCRkYGioiLdZ411xawu0e8YpjOwYcMGTJo0CY2NjcjLy8OcOXMwdOhQrFu3jusIw0i88sorWLNmDVauXBn1O+5TGAaYOHEiXnjhBQwePBh79+7FfffdhxNPPBFffvnlEV1HWJRiGIZhmCOcmTNn4ssvv9TlNWAYRjB48GCsW7cOVVVVeP3113HFFVdgyZIl7X1ZDNOh2LVrF2644QYsXLgQWVlZ7X05DNMhOeussyI/jxw5EhMnTkS/fv3w2muvITs7ux2vrH3h8L1ORnFxMfx+f1SW/v3796O0tLSdrophOg5UD2LVkdLS0qiNAVpaWlBRUcH1iOl0/OIXv8B7772HxYsXo3fv3pH3S0tL0dzcjMrKSt3njXXFrC7R7ximM5CRkYGBAwdi3LhxmDVrFkaNGoVHH32U6wjDSKxevRrl5eUYO3YsAoEAAoEAlixZgr/97W8IBAIoKSnh+sIwBoqKinDsscdi69atR3SfwqJUJyMjIwPjxo3Dhx9+GHkvHA7jww8/xKRJk9rxyhimYzBgwACUlpbq6kh1dTVWrFgRqSOTJk1CZWUlVq9eHfnMokWLEA6HMXHixJRfM8N4gaqq+MUvfoE5c+Zg0aJFGDBggO7348aNQzAY1NWVTZs2YefOnbq6smHDBp2Iu3DhQhQUFGDo0KGp+SIMk2LC4TCampq4jjCMxNSpU7FhwwasW7cu8m/8+PG49NJLIz9zfWEYPbW1tdi2bRt69ux5ZPcp7Z1pnXGfV155Rc3MzFRfeOEF9euvv1avueYataioSJeln2E6MzU1NeratWvVtWvXqgDUhx9+WF27dq26Y8cOVVVV9Y9//KNaVFSkvv322+r69evVH/7wh+qAAQPUhoaGyDHOPPNMdcyYMeqKFSvUZcuWqYMGDVIvueSS9vpKDOM61113nVpYWKh+9NFH6t69eyP/6uvrI5+59tpr1b59+6qLFi1SV61apU6aNEmdNGlS5PctLS3q8OHD1TPOOENdt26dOn/+fLV79+7qHXfc0R5fiWFc5/bbb1eXLFmilpWVqevXr1dvv/12VVEUdcGCBaqqch1hmFjIu++pKtcXhrnlllvUjz76SC0rK1M/+eQT9bTTTlOLi4vV8vJyVVWP3DrColQn5bHHHlP79u2rZmRkqBMmTFA/++yz9r4khkkZixcvVgFE/bviiitUVVXVcDis3n333WpJSYmamZmpTp06Vd20aZPuGIcOHVIvueQSNS8vTy0oKFBnzJih1tTUtMO3YRhvMKsjANTnn38+8pmGhgb1+uuvV7t06aLm5OSoP/rRj9S9e/fqjrN9+3b1rLPOUrOzs9Xi4mL1lltuUUOhUIq/DcN4w89//nO1X79+akZGhtq9e3d16tSpEUFKVbmOMEwsjKIU1xfmSOfiiy9We/bsqWZkZKhHHXWUevHFF6tbt26N/P5IrSOKqqpq+3i0GIZhGIZhGIZhGIZhmCMVzinFMAzDMAzDMAzDMAzDpBwWpRiGYRiGYRiGYRiGYZiUw6IUwzAMwzAMwzAMwzAMk3JYlGIYhmEYhmEYhmEYhmFSDotSDMMwDMMwDMMwDMMwTMphUYphGIZhGIZhGIZhGIZJOSxKMQzDMAzDMAzDMAzDMCmHRSmGYRiGYRiGYRiGYRgm5bAoxTAMwzAM08G48sorcd5557X3ZTAMwzAMw3hKoL0vgGEYhmEY5khCUZSYv7/33nvx6KOPQlXVFF0RwzAMwzBM+8CiFMMwDMMwTArZu3dv5OdXX30V99xzDzZt2hR5Ly8vD3l5ee1xaQzDMAzDMCmFw/cYhmEYhmFSSGlpaeRfYWEhFEXRvZeXlxcVvnfyySfjl7/8JW688UZ06dIFJSUleOaZZ1BXV4cZM2YgPz8fAwcOxLx583Tn+vLLL3HWWWchLy8PJSUluOyyy3Dw4MEUf2OGYRiGYRhzWJRiGIZhGIZJA1588UUUFxfj888/xy9/+Utcd911uOiii3DCCSdgzZo1OOOMM3DZZZehvr4eAFBZWYlTTz0VY8aMwapVqzB//nzs378fP/7xj9v5mzAMwzAMwwhYlGIYhmEYhkkDRo0ahbvuuguDBg3CHXfcgaysLBQXF+Pqq6/GoEGDcM899+DQoUNYv349AODvf/87xowZgwceeABDhgzBmDFj8Nxzz2Hx4sXYvHlzO38bhmEYhmEYzinFMAzDMAyTFowcOTLys9/vR7du3TBixIjIeyUlJQCA8vJyAMAXX3yBxYsXm+an2rZtG4499liPr5hhGIZhGCY2LEoxDMMwDMOkAcFgUPd/RVF079GufuFwGABQW1uLc845B3/605+ijtWzZ08Pr5RhGIZhGMYeLEoxDMMwDMN0QsaOHYs33ngD/fv3RyDAQz6GYRiGYToenFOKYRiGYRimEzJz5kxUVFTgkksuwcqVK7Ft2zZ88MEHmDFjBlpbW9v78hiGYRiGYViUYhiGYRiG6Yz06tULn3zyCVpbW3HGGWdgxIgRuPHGG1FUVASfj4eADMMwDMO0P4qqqmp7XwTDMAzDMAzDMAzDMAxzZMHLZAzDMAzDMAzDMAzDMEzKYVGKYRiGYRiGYRiGYRiGSTksSjEMwzAMwzAMwzAMwzAph0UphmEYhmEYhmEYhmEYJuWwKMUwDMMwDMMwDMMwDMOkHBalGIZhGIZhGIZhGIZhmJTDohTDMAzDMAzDMAzDMAyTcliUYhiGYRiGYRiGYRiGYVIOi1IMwzAMwzAMwzAMwzBMymFRimEYhmEYhmEYhmEYhkk5LEoxDMMwDMMwDMMwDMMwKYdFKYZhGIZhGIZhGIZhGCbl/H/fjI0DoFgx1wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency corresponding to the highest peak: 0.084\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "offset = np.zeros_like(data)\n",
+    "offset[270:] = 1\n",
+    "\n",
+    "A = np.column_stack((np.ones_like(t), t, offset))\n",
+    "xhat = np.linalg.solve(A.T @ A, A.T @ data)\n",
+    "yhat = A @ xhat\n",
+    "ehat = data - yhat\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 1, figsize=(12, 5))\n",
+    "\n",
+    "# Plot data and fit\n",
+    "axs[0].plot(t, data, label='Data')\n",
+    "axs[0].plot(t, yhat, label='Fit', linestyle='--')\n",
+    "axs[0].set_xlabel('Time')\n",
+    "axs[0].set_ylabel('y_values')\n",
+    "axs[0].set_title('Data and Fit')\n",
+    "axs[0].legend()\n",
+    "axs[0].grid()\n",
+    "\n",
+    "# Plot residuals\n",
+    "axs[1].plot(t, ehat, label='Residuals', color='red')\n",
+    "axs[1].set_xlabel('Time')\n",
+    "axs[1].set_ylabel('Residuals')\n",
+    "axs[1].set_title('Residuals')\n",
+    "axs[1].legend()\n",
+    "axs[1].grid()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Create a sample signal\n",
+    "N = len(data)  # Number of points\n",
+    "T = 500/N  # Sampling interval (1000 Hz sampling rate)\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "pxx, freqs, line = plt.psd(ehat, return_line=True, Fs=1, NFFT=1024, pad_to=2048)\n",
+    "\n",
+    "plt.xlabel(\"Frequency\")\n",
+    "plt.ylabel(\"Power Spectral Density\")\n",
+    "plt.title(\"Power Spectral Density of the residuals\")\n",
+    "plt.grid(True)\n",
+    "\n",
+    "\n",
+    "# Find the frequency corresponding to the highest peak\n",
+    "highest_peak_freq = freqs[np.argmax(pxx)]\n",
+    "print(\"Frequency corresponding to the highest peak:\", round(highest_peak_freq,3))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using peak detection we can identify the frequency of the seasonality and the trend. \n",
+    "\n",
+    "Finally, we can use the identified frequencies to model the time series.\n",
+    "\n",
+    "For this, we need to expand our matrix A to include the seasonality and the trend. The new matrix A is defined as follows:\n",
+    "\n",
+    "$$\n",
+    "A = \\begin{bmatrix}\n",
+    "1 & t_1 & 0 & \\cos(2\\pi t_1 f) & \\sin(2\\pi t_1 f) \\\\\n",
+    "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots \\\\\n",
+    "1 & t_{269} & 0  & \\cos(2\\pi t_{269} f) & \\sin(2\\pi t_{269} f) \\\\\n",
+    "1 & t_{270} & 1  & \\cos(2\\pi t_{270 f}) & \\sin(2\\pi t_{270} f) \\\\\n",
+    "1 & t_{271} & 1  & \\cos(2\\pi t_{271 f}) & \\sin(2\\pi t_{271} f) \\\\\n",
+    "\\vdots & \\vdots & \\vdots & \\vdots & \\vdots \\\\\n",
+    "1 & t_{500} & 1  & \\cos(2\\pi t_{500 f}) & \\sin(2\\pi t_{500} f) \\\\\n",
+    "\\end{bmatrix}\n",
+    "$$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f = highest_peak_freq\n",
+    "\n",
+    "A2 = np.column_stack((np.ones_like(t), t, offset, np.cos(2*np.pi*f*t), np.sin(2*np.pi*f*t)))\n",
+    "\n",
+    "xhat2 = np.linalg.solve(A2.T @ A2, A2.T @ data)\n",
+    "yhat2 = A2 @ xhat2\n",
+    "ehat2 = data - yhat2 # removing the functional components from the data\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 1, figsize=(12, 5))\n",
+    "\n",
+    "# Plot data and fit\n",
+    "axs[0].plot(t, data, label='Data')\n",
+    "axs[0].plot(t, yhat2, label='Fit', linestyle='--')\n",
+    "axs[0].set_xlabel('Time')\n",
+    "axs[0].set_ylabel('y_values')\n",
+    "axs[0].set_title('Data and Fit')\n",
+    "axs[0].legend()\n",
+    "axs[0].grid()\n",
+    "\n",
+    "# Plot residuals\n",
+    "axs[1].plot(t, ehat2, label='Residuals', color='red')\n",
+    "axs[1].set_xlabel('Time')\n",
+    "axs[1].set_ylabel('Residuals')\n",
+    "axs[1].set_title('Residuals')\n",
+    "axs[1].legend()\n",
+    "axs[1].grid()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
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diff --git a/book/time_series/forecasting.md b/book/time_series/forecasting.md
index f515eea28dbc0495c68cbc20d9bf3d9c8ad98913..595c8c6f5f87b4a5ba6d6d9f6c7d33cd4ba5e643 100644
--- a/book/time_series/forecasting.md
+++ b/book/time_series/forecasting.md
@@ -8,13 +8,11 @@ The material on this page is provided to give you extra insight into time series
 
 Time series analysis is about analyzing time series of data points of a variable, with the goal to extract meaningful characteristics and statistics of the data, e.g., to study the trend and seasonality. Very often we do so in order to be able to predict future values based on the previously observed ones, which is referred to as **forecasting**.
 
-In this part of the book, you first learned about the different types of time series ([Chapter 4.2](timetypes)) as well as the components that can be distinguished ([Chapter 4.1](components)). Some of the components may relate to the *signal-of-interest*. However, another important component that we have to deal with is the noise.
+In this part of the book, you first learned about the components that can be distinguished ([Chapter 4.1](components)) in time series. Some of the components may relate to the *signal-of-interest*. However, another important component that we have to deal with is the noise.
 
 Modelling and estimating the signal-of-interest ([Chapter 4.3](modelling_tsa)) using the concepts of observation theory. The remainder of this chapter focused on the noise modelling. In order to do so, we need to work with *stationary*, i.e., time series of which the statistical properties do not depend on the time of observation ([Chapter 4.4](stationary)). An example of a stationary time series are the residuals after best linear unbiased estimation. Using these residuals as the input for noise modelling makes sense, since in fact the residuals are estimates of the noise. 
 
-Note: other methods to "extract" a stationary time series from the original time series, such as differencing or taking a moving average, were discussed as well.
-
-A problem with the noise process of a time series is that often there is time-correlation: in contrast to a white noise signal, the observations with different time lags depend on each other - this is referred to as colored noise. The dependency can be modelled by the autocovariance function ([Chapter 4.5](ACF)). With that, the noise process can be modelled using the Autoregressive Moving Average model ([Chapter 4.6](ARMA)).
+A problem with the noise process of a time series is that often there is auto-correlation: in contrast to a white noise signal, the observations with different time lags depend on each other - this is referred to as colored noise. The dependency can be modelled by the autocovariance function ([Chapter 4.5](ACF)). With that, the noise process can be modelled using the Autoregressive model ([Chapter 4.6](AR)).
 
 Now that we are able to model both signal-of-interest and the noise process, we can start forecasting.
 
@@ -22,16 +20,12 @@ In summary, given a time series $Y=\mathrm{Ax}+\epsilon$, the workflow is as fol
 
 1. Estimate the signal-of-interest $\hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y$.
 
-2. Model the noise using the Autoregressive Moving Average (ARMA) model, using the stationary time series $S:=\hat{\epsilon}=Y-\mathrm{A}\hat{X}$.
+2. Model the noise using the Autoregressive (AR) model, using the stationary time series $S:=\hat{\epsilon}=Y-\mathrm{A}\hat{X}$.
 
 3. Predict the signal-of-interest: $\hat{Y}_{signal}=\mathrm{A}_p\hat{X}$, where $\mathrm{A}_p$ is the design matrix describing the functional relationship between the future values $Y_p$ and $\mathrm{x}$.
 
-4. Predict the noise $\hat{\epsilon}_p$ based on the ARMA model.
+4. Predict the noise $\hat{\epsilon}_p$ based on the AR model.
 
 5. Predict future values of the time series: $\hat{Y}_p=\mathrm{A}_p\hat{X}+\hat{\epsilon}_p$.
 
-```{note}
-Interested readers can find a brief introduction on how to predict signals that are partially deterministic and stochastic using Best Linear Unbiased Prediction (BLUP) in the section on [Supplementary Material](BLUP).
-```
-
 
diff --git a/book/time_series/generated_time_series.csv b/book/time_series/generated_time_series.csv
new file mode 100644
index 0000000000000000000000000000000000000000..76309a92cac0a5b402b66281e4c5e57d13a5da9c
--- /dev/null
+++ b/book/time_series/generated_time_series.csv
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diff --git a/book/time_series/intro.md b/book/time_series/intro.md
index dcb1dd0b017debdaaf57eccc54940c2a32275e7a..8b8c6dc679d8db9641773a7dbe39a1594260fefe 100644
--- a/book/time_series/intro.md
+++ b/book/time_series/intro.md
@@ -2,5 +2,12 @@
 
 In this chapter, we will first introduce the components to describe a time series: trend, signal, offsets, irregularities and noise. It will then be shown how to estimate the signal-of-interest (everything except noise).
 
-Next, we will consider stationary time series, meaning that the statistical properties do not depend on the time when the time series was observed. The stationary time series describe an underlying stochastic process, which can then be modelled, for instance using an Autoregressive Moving Average (ARMA) model. The final goal is to use the time series for estimating the components such as trend and seasonality, as well as to predict future values, for which we do need to take into account the stochastic process.
+Next, we will consider stationary time series, meaning that the statistical properties do not depend on the time when the time series was observed. The stationary time series describe an underlying stochastic process, which can then be modelled, for instance using an Autoregressive (AR) model. The final goal is to use the time series for estimating the components such as trend and seasonality, as well as to predict future values, for which we do need to take into account the stochastic process.
 
+```{figure} ./figs/tsa_cover.png
+:name: cover
+:width: 600px
+:align: center
+
+Recorded and expected global warming from 1960 to 2100 ([Huseien, Shah (2021)](https://www.mdpi.com/2071-1050/13/17/9720))
+```
diff --git a/book/time_series/intro_graph.ipynb b/book/time_series/intro_graph.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..159f2a26b319d0f0bbea114ae6100b76844960f3
--- /dev/null
+++ b/book/time_series/intro_graph.ipynb
@@ -0,0 +1,387 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Time Series introduction\n",
+    "\n",
+    "The code on this page can be used interactively: click {fa}`rocket` --> {guilabel}`Live Code` in the top right corner, then wait until the message {guilabel}`Python interaction ready!` appears.\n",
+    "\n",
+    "This page creates an interactive plot of a simulated time series. Throughout this section we will discuss these components.\n",
+    "\n",
+    "(components)=\n",
+    "# Components of time series\n",
+    "\n",
+    "A time series is a discrete time sequence of data points indexed in time which can be used to study a phenomenon. It is a record of the data collected at different points in time, it consists of discrete time samples of typically a continuous-time phenomenon in reality.  The data are usually collected at fixed time intervals rather than just recording them intermittently or randomly. The fixed interval $\\Delta t$, in the time domain, is defined as 'sampling interval' and, in the frequency domain, is defined as 'sampling rate' or 'sampling frequency', expressed for example in Hz.\n",
+    "\n",
+    "$$ \\Delta t = \\frac{1}{f_s} $$\n",
+    "\n",
+    "A time series is denoted as \n",
+    "\n",
+    "$$Y(t) = [Y(t_1), Y(t_2), \\ldots{}, Y(t_m)]^T$$\n",
+    "\n",
+    "The $Y(t_i)$ are random variables, since the data is affected by noise.\n",
+    "\n",
+    "The time instants, also defined as epochs, are $t_i = i \\Delta t$, indicating that the samples are equally spaced in time intervals of $\\Delta t$. Assuming a unit time interval (i.e., $\\Delta t=1$), then $t_i = i$ and we can write the time series as \n",
+    "\n",
+    "$$Y(t) = [Y(1), Y(2), \\ldots{}, Y(m)]^T = [Y_1, Y_2, \\ldots{}, Y_m]^T$$\n",
+    "\n",
+    "```{figure} ./figs/time_series.png\n",
+    ":name: time_series\n",
+    ":width: 700px\n",
+    ":align: center\n",
+    "\n",
+    "Example of time series with equally spaced time interval $\\Delta t$\n",
+    "```\n",
+    "\n",
+    "A time series can be decomposed as follows:\n",
+    "\n",
+    "$$Y(t) = tr(t) + s(t) + o(t) + b(t) + \\epsilon(t)$$\n",
+    "\n",
+    "where we distinguish the following components:\n",
+    "\n",
+    "1. $tr(t)$ = trend, provides the general behavior and variation of the process\n",
+    "2. $s(t)$ = seasonality, shows the regular seasonal variations\n",
+    "3. $o(t)$ = offset, is a discontinuity (or jump) in the data\n",
+    "4. $b(t)$ = irregularities and outliers (also referred to as biases), due to unexpected reasons. Irregularities will not be considered in this book.\n",
+    "5. $\\epsilon(t)$ = noise process, can be white or colored noise.\n",
+    "\n",
+    "## Trend\n",
+    "\n",
+    "The trend is the general pattern of the time series and shows its long-term changes. The trend can be linear, however higher order polynomials are also possible.\n",
+    "\n",
+    "```{figure} ./figs/trend.png\n",
+    ":name: trend\n",
+    ":width: 600px\n",
+    ":align: center\n",
+    "\n",
+    "Monthly time series of global mean sea level measurements using Satellite Altimetry technique. Source image: https://www.cmar.csiro.au/sealevel/sl_hist_last_decades.html\n",
+    "```\n",
+    "\n",
+    "{numref}`trend` shows a positive trend (red line) of around $3.5$ mm/year, which in this case indicates sea level rise. This however needs to be further investigated and tested statistically (see {ref}`hypothesis_testing` and also {ref}`modelling_tsa`).\n",
+    "\n",
+    "Trend analysis expresses the changes of the variable of interest with respect to time $t$. Different types of trend are possible and for now we will mainly focus on linear trend, i.e. the time-dependent variable $Y(t)$ changes at a (constant) linear rate over time: $Y_t = y_0 + r t + \\epsilon_t$. Other trends are however also possible, for example, quadratic, which includes $c t^2$, or log linear $\\log(Y_t) = y_0 + r t + \\epsilon_t$.\n",
+    "\n",
+    "\n",
+    "## Seasonality\n",
+    "\n",
+    "Seasonal variations explain regular fluctuations in a certain period of time (e.g. a year), usually caused by climate and weather conditions (e.g. temperature, rainfall), cycles of seasons, customs, traditional habits, weekends, or holidays. For example, the weekly signal is usually evident in the volume of people engaged in shopping (likely more people prefer going shopping in the weekends)\n",
+    "\n",
+    "From {numref}`trend` it is also possible to see the seasonal variations: in fact sea levels are higher in summer and lower in winter. The annual warming/cooling cycle is the main contributor to these seasonal variations.\n",
+    "\n",
+    "Regular seasonal variations in a time series might be handled by using a sinusoidal model with one or more sinusoids with frequency that may be known or unknown depending on the context. In fig {numref}`trend`, cyclical behavior with a period of 1 year can be observed. A harmonic model for seasonal variation can be of the following two equivalent forms (using that $\\cos(u+v)= \\cos u \\cos v - \\sin u \\sin v$):\n",
+    "\n",
+    "$$ \n",
+    "\\begin{align*}\n",
+    "Y(t) &= \\sum_{k=1} ^p A_k  \\cos(k \\omega_0  t + \\theta_k)  + \\epsilon_t\\\\\n",
+    "&= \\sum_{k=1} ^p \\left(a_k  \\cos(k \\omega_0  t) + b_k  \\sin(k \\omega_0 t) \\right)+ \\epsilon_t\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "With the coefficients $a_k = A_k\\cos\\theta_k$ and $b_k=-A_k\\sin\\theta_k$, and where $\\omega_0$ is the base (fundamental) frequency of the seasonal variation and is fixed or is determined by Spectral Analysis. To be more specific, we can use the {ref}`psd` to determine the unknown frequencies. \n",
+    "\n",
+    "Once $\\omega_ 0$ is set, the coefficients $a_k $ and $b_k$ can be determined using the least-squares method, since the equation is linear in $a_k$ and $b_k$. From this the original sinusoids can be obtained using:\n",
+    "\n",
+    "$$ A_k = \\sqrt{a_k^2 + b_k^2}, \\hspace{1cm} \\theta_k = \\arctan(-\\frac{b_k}{a_k}), \\hspace{1cm} k = 1, \\ldots{}, p $$\n",
+    "\n",
+    "```note\n",
+    "This transformation is necessary to make the seasonal component phase-independent. Using regular estimation methods, we cannot linearly estimate the phase of the sinusoidal function. However by transforming the sinusoidal function into a linear combination of sine and cosine functions, we can estimate the phase of the seasonal component.\n",
+    "```\n",
+    "\n",
+    ":::{card} Worked example - seasonality signal\n",
+    "\n",
+    "Show that the time series \n",
+    "\n",
+    "$$Y(t)=A \\cos(\\omega_0 t + \\theta)$$ \n",
+    "\n",
+    "with given $\\omega_0$, can be rewritten as\n",
+    "\n",
+    "$$Y(t)=a \\cos(\\omega_0 t) + b \\sin(\\omega_0 t)$$\n",
+    "\n",
+    "and derive the formulation of $A$ and $\\theta$.\n",
+    "\n",
+    "Hint: you might need to know trigonometric identity $\\cos(u+v)=\\cos(u)\\cos(v)-\\sin(u)\\sin(v)$\n",
+    "\n",
+    "````{admonition} Solution\n",
+    ":class: tip, dropdown\n",
+    "\n",
+    "Using the trigonometric identity to rewrite:\n",
+    "\n",
+    "$ Y(t)=A \\cos(\\omega_0 t + \\theta) = A (\\cos(\\omega_0 t)\\cos(\\theta)-\\sin(\\omega_0 t)\\sin(\\theta)) $\n",
+    "\n",
+    "Retrieving the functions for a and b\n",
+    "\n",
+    "$ a = A \\cos(\\theta) \\hspace{1cm} b = -A \\sin(\\theta)$\n",
+    "\n",
+    "Squaring both functions in order to get rid of the sin and cos\n",
+    "\n",
+    "$ a^2 = A^2 \\cos^2(\\theta) \\hspace{1cm} b^2 = A^2 \\sin^2(\\theta) $\n",
+    "\n",
+    "Adding both functions together\n",
+    "\n",
+    "$ a^2 + b^2 = A^2 (\\cos^2(\\theta) + \\sin^2(\\theta)) $\n",
+    "\n",
+    "Using this property to simplify:\n",
+    "\n",
+    "$ \\cos^2(\\theta) + \\sin^2(\\theta) = 1 $\n",
+    "\n",
+    "$ a^2 + b^2 = A^2 $\n",
+    "\n",
+    "Take square root to find A\n",
+    "\n",
+    "$ \\sqrt{a^2 + b^2} = A $ \n",
+    "\n",
+    "For $\\theta$ we rewrite the second function\n",
+    "\n",
+    "$ a = A \\cos(\\theta) \\hspace{1cm} -b = A \\sin(\\theta)$\n",
+    "\n",
+    "$ \\frac{-b}{a} = \\frac{\\sin(\\theta)}{\\cos(\\theta)} = \\tan(\\theta) $\n",
+    "\n",
+    "$ \\theta = \\arctan(\\frac{-b}{a}) $\n",
+    "\n",
+    "\n",
+    "[This video](https://youtu.be/8kqQiI4ni68) includes the solution to this exercise. \n",
+    "````\n",
+    "\n",
+    ":::\n",
+    "\n",
+    "## Offset (jump)\n",
+    "\n",
+    "Offsets are sudden changes or shifts in time series. There are different underlying reasons why we encounter offsets in time series. \n",
+    "\n",
+    "```{figure} ./figs/offset.png\n",
+    ":name: offset\n",
+    ":width: 700px\n",
+    ":align: center\n",
+    "\n",
+    "Example of time series with two offsets. \n",
+    "```\n",
+    "\n",
+    "As a deterministic sudden change, offsets can be handled by a step function such as a heaviside step function with an epoch (time instant) that can be known or unknown (to be detected) depending on the time series.\n",
+    "\n",
+    "In this case the time series is written as: \n",
+    "\n",
+    "$$ Y(t) = \\sum_{k=1}^q o_k u_k(t)+\\epsilon_t$$\n",
+    "\n",
+    "where $q$ is the series of offsets (in {numref}`offset` there are two offsets, hence $q=2$) and each of them is expressed as a Heaviside step function \n",
+    "\n",
+    "$$u_k(t) = \\left\\{\n",
+    "\\begin{array}{ll}\n",
+    "      0 & \\text{if} \\hspace{0.3cm} t<t_k \\\\\n",
+    "      1 & \\text{if} \\hspace{0.3cm} t\\geq t_k \\\\\n",
+    "\\end{array} \n",
+    "\\right.  $$\n",
+    "\n",
+    "Once the time instant ($t_k$) of the offset is known, the amplitude can be estimated using least-squares.\n",
+    "\n",
+    "## Noise \n",
+    "\n",
+    "Noise simply refers to random fluctuations in the time series about its typical pattern. In general we can talk about white and colored noise in time series analysis. The following characteristics are associated with noise:\n",
+    "\n",
+    "- Noise is not synonymous with error, although random variation, including measurement errors, contributes to noise. Essentially, noise represents the unpredictable fluctuations in data, while errors encompass any inaccuracies that may arise from a range of factors, including both random variations and systematic issues.\n",
+    "- It is required to filter out unwanted random variations, and detect meaningful information (i.e., a signal) from noise processes.\n",
+    "- Transforming data from the time domain to the frequency domain allows to filter out the frequencies that pollute the data.\n",
+    "- White noise can be decomposed into its constituent components (frequencies).  In principle, white noise contains all wavelengths/colors (like white light), each contributing equally to the fluctuations observed in the data.\n",
+    "- Colored noise can seriously affect the analysis of time series, and their parameters of interest. Short-term colored noise has also predictive property (used for forecasting).\n",
+    "\n",
+    "A purely stationary random process (or white noise process) yields a sequence of uncorrelated zero-mean random variables. This zero-mean random process is of the form\n",
+    "\n",
+    "$$ Y(t)=Y_t=\\epsilon_t $$\n",
+    "\n",
+    "where $\\epsilon_t$ is the independent identically distributed (i.i.d.) error at epoch $t$. Therefore, the observation/noise at time $t$ is not dependent on any of the previous observations $Y_t$.\n",
+    "\n",
+    "### Stochastic model\n",
+    "\n",
+    "A stationary zero-mean random process has an expectation of zero (functional model), and a scaled identity matrix as its covariance matrix (stochastic model). The functional and stochastic models of white noise are of the form \n",
+    "\n",
+    "$$\n",
+    "\\mathbb{E}(Y) =  \\mathbb{E} \\left[\\begin{array}{c} y_1 \\\\ y_2 \\\\ \\vdots \\\\ y_m \\end{array}\\right] = \\left[\\begin{array}{c} 0 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{array}\\right]\n",
+    "$$\n",
+    "\n",
+    "and \n",
+    "\n",
+    "$$\n",
+    "\\mathbb{D}(Y) =  \\Sigma_{Y} = \\sigma^2 \\left[\\begin{array}{ccc} 1 & 0 & \\ldots{} & 0 \\\\ 0 & 1 & \\ldots{} & 0 \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ 0 & 0 & \\ldots{} & 1 \\end{array}\\right]\n",
+    "$$\n",
+    "\n",
+    "The noise can be represented with, for example, a Gaussian distribution with mean $\\mu=0$ and variance $\\sigma^2$, that is $\\epsilon(t) \\sim \\textbf{N}(0, \\sigma^2)$.\n",
+    "\n",
+    ":::{card} Example - time series consisting of a trend, annual signal (seasonality), an offset and pure random noise (white noise)\n",
+    "\n",
+    "It can be written as \n",
+    "\n",
+    "$$Y(t) = y_0 + rt + a \\text{cos}(\\omega_0 t) + b \\text{sin}(\\omega_0 t) + o u_k(t) + \\epsilon(t)$$\n",
+    "\n",
+    "where \n",
+    "- $y_0$ is the intercept (e.g. in mm)\n",
+    "- $r$ is the rate (e.g. in mm/year)\n",
+    "- $a$ and $b$ are the coefficients of the signal, (e.g. annual signal)\n",
+    "- $\\omega_0$ is the frequency (e.g. 1 cycle/year)\n",
+    "- $o$ is the offset starting at time $t_k$\n",
+    "- $\\epsilon(t)$ is the i.i.d. random Gaussian noise, i.e. $\\epsilon(t) \\sim \\textbf{N}(0, \\sigma^2)$.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import ipywidgets as widgets\n",
+    "from ipywidgets import interact, Layout\n",
+    "import micropip\n",
+    "await micropip.install(\"ipympl\")\n",
+    "\n",
+    "%matplotlib widget\n",
+    "\n",
+    "x = np.linspace(0, 10, 500)\n",
+    "\n",
+    "def gen_seasons(a=0, b=0, frequency=0):\n",
+    "    y = a * np.cos(frequency*x) + b * np.sin(frequency*x)\n",
+    "    return y\n",
+    "\n",
+    "def gen_offset(offset_loc=0, offset_size=0):\n",
+    "    offset = np.zeros_like(x)\n",
+    "    for ind, i in enumerate(x):\n",
+    "        if i >= offset_loc:\n",
+    "            offset[ind] = offset_size\n",
+    "    return offset\n",
+    "\n",
+    "def gen_trend(trend_slope=0):\n",
+    "    y = trend_slope * x\n",
+    "    return y\n",
+    "\n",
+    "def gen_noise(std=0):\n",
+    "    np.random.seed(int(std*100))\n",
+    "    y = np.random.normal(0, std, size=x.shape)\n",
+    "    return y\n",
+    "\n",
+    "def generate_data(trend_slope=0, std=0, a=0, b=0, frequency=0, offset_loc=5, offset_size=0):\n",
+    "    y = gen_trend(trend_slope) + gen_noise(std) + gen_offset(offset_loc, offset_size) + gen_seasons(a, b, frequency)\n",
+    "    return y\n",
+    "\n",
+    "# Function to plot the data\n",
+    "def plot_data(trend_slope=0, a=0, b=0, frequency=1, offset_location=5, offset_size=0, standard_dev=0):\n",
+    "    y = generate_data(trend_slope, standard_dev, a, b, frequency, offset_location, offset_size)\n",
+    "    y_trend = gen_trend(trend_slope)\n",
+    "    y_seas = gen_seasons(a, b, frequency)\n",
+    "    y_offs = gen_offset(offset_location, offset_size)\n",
+    "    y_noise = gen_noise(standard_dev)\n",
+    "\n",
+    "    fig, axs = plt.subplots(5, 1, figsize=(8, 8))\n",
+    "    # plt.figure(figsize=(10, 6))\n",
+    "    axs[0].plot(x, y, label=\"Generated Data\")\n",
+    "\n",
+    "    axs[0].set_title(\"Data with Optional Trend and Noise\")\n",
+    "    axs[0].grid(True)\n",
+    "    axs[0].tick_params(axis='x', labelbottom=False)  # Remove x-tick labels\n",
+    "\n",
+    "    axs[1].plot(x, y_trend, label=\"Trend\")\n",
+    "    axs[1].grid(True)\n",
+    "    axs[1].legend()\n",
+    "    axs[1].tick_params(axis='x', labelbottom=False)  # Remove x-tick labels\n",
+    "    axs[1].set_ylim([-20, 20])\n",
+    "\n",
+    "    # plt.ylabel('Different time series components', loc='bottom')\n",
+    "    axs[2].plot(x, y_seas, label=\"Seasonality\")\n",
+    "    axs[2].grid(True)\n",
+    "    axs[2].legend()\n",
+    "    axs[2].tick_params(axis='x', labelbottom=False)  # Remove x-tick labels\n",
+    "    axs[2].set_ylim([-5.5, 5.5])\n",
+    "\n",
+    "    axs[3].plot(x, y_offs, label=\"Offset\")\n",
+    "    axs[3].grid(True)\n",
+    "    axs[3].legend()\n",
+    "    axs[3].tick_params(axis='x', labelbottom=False)  # Remove x-tick labels\n",
+    "    axs[3].set_ylim([-5.5, 5.5])\n",
+    "\n",
+    "    axs[4].plot(x, y_noise, label=\"Noise\")\n",
+    "    axs[4].grid(True)\n",
+    "    axs[4].legend()\n",
+    "    axs[4].set_ylim([-3, 3])\n",
+    "\n",
+    "    # plt.tight_layout()\n",
+    "    plt.xlabel('Time')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b5c7b334f4fa45b98636bd8dd1785a4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=1.0, description='Trend Slope', layout=Layout(width='40%'), max=2.0, m…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "    \n",
+    "# Creating interactive widgets\n",
+    "style = {'description_width': 'initial'}\n",
+    "interact(plot_data,\n",
+    "         trend_slope=widgets.FloatSlider(value=1, min=-2.0, max=2.0, step=0.05, description=\"Trend Slope\", style=style, layout=Layout(width='40%')),\n",
+    "         a=widgets.FloatSlider(value=0, min=0, max=5.0, step=0.05,description=\"a\", style=style, layout=Layout(width='40%')),\n",
+    "         b=widgets.FloatSlider(value=2, min=0, max=5.0, step=0.05,description=\"b\", style=style, layout=Layout(width='40%')),\n",
+    "         frequency=widgets.FloatSlider(value=4, min=0, max=10.0, step=0.05,description=\"frequency\", style=style, layout=Layout(width='40%')),\n",
+    "         offset_location=widgets.FloatSlider(value=5, min=0, max=10, step=0.1, description=\"offset location\", style=style, layout=Layout(width='40%')),\n",
+    "         offset_size=widgets.FloatSlider(value=5, min=-10, max=10, step=0.1, description=\"offset size\", style=style, layout=Layout(width='40%')),\n",
+    "         standard_dev=widgets.FloatSlider(value=0.5, min=0, max=2, step=0.005,description=\"Standard deviation\", style=style, layout=Layout(width='40%')));\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/modelling.md b/book/time_series/modelling.md
index bcb4bca4ef774e7dd3f7043c080e878814125a33..195e972a40bfe1bc45f6855a73c05c5ddc518418 100644
--- a/book/time_series/modelling.md
+++ b/book/time_series/modelling.md
@@ -4,22 +4,21 @@
 The goal is now to:
 
 * estimate parameters of interest (i.e., components of time series) using **Best Linear Unbiased Estimation (BLUE)**;
-* evaluate the confidence intervals of parameters of interest;
-* identify an appropriate model using **hypothesis testing**.
-
-$$\mathcal{H}_0: Y=\mathrm{Ax}+\epsilon \hspace{5px}\text{vs.}\hspace{5px} \mathcal{H}_a: Y=\mathrm{Ax+C}\nabla+\epsilon$$
+* evaluate the confidence intervals of the estimators for the parameters of interest;
 
 ## Components of time series
 
 As already discussed, we will distinguish the following components in a time series:
 
 * **Trend:** General behavior and variation of the process. This often is a linear trend with an unknown intercept $y_0$ and a rate $r$.
-* **Seasonality:** Regular seasonal variations, which can be expressed as sine functions with (un)known frequency $\omega$, and unknown amplitude $A$ and phase $\theta$, or with unknowns $a(=A\sin\theta)$ and $b(=A\cos\theta)$, see [example](season).
+* **Seasonality:** Regular seasonal variations, which can be expressed as sine functions with (un)known frequency $\omega$, and unknown amplitude $A$ and phase $\theta$, or with unknowns $a(=A\sin\theta)$ and $b(=A\cos\theta)$.
 * **Offset:** A jump of size $o$ in a time series starting at epoch $t_k$.
-* **Noise:** White or colored noise (e.g., ARMA process).
+* **Noise:** White or colored noise (e.g., AR process).
 
 ## Best Linear Unbiased Estimation (BLUE)
 
+#TODO: add reference to the chapter on estimation
+
 If the components of time series are known, we may use the linear model of observation equations to estimate those components.
 
 Consider the linear model of observation equations as
@@ -80,7 +79,7 @@ $$\Sigma_{Y}=\begin{bmatrix}\sigma_1^2&\sigma_{12}&\dots&\sigma_{1m}\\ \sigma_{2
 
 :::{card} Exercise
 
-A time series exhibits a linear regression model $Y(t)=y_0 + rt + \epsilon(t)$. The measurements have also been taken at a measurement frequency of 10 Hz, producing epochs of $t=0.1,0.2, \dots,100$ seconds, so $m=1000$. Later an offset was also detected at epoch 260 using statistical hypothesis testing. For the linear model $Y=\mathrm{Ax}+\epsilon$, establish an approprate design matrix that can capture all the above effects.
+A time series exhibits a linear regression model $Y(t)=y_0 + rt + \epsilon(t)$. The measurements have also been taken at a measurement frequency of 10 Hz, producing epochs of $t=0.1,0.2, \dots,100$ seconds, so $m=1000$. Later an offset was also detected at epoch 260 using statistical hypothesis testing. For the linear model $Y=\mathrm{Ax}+\epsilon$, establish an appropriate design matrix that can capture all the above effects.
 
 ```{admonition} Solution
 :class: tip, dropdown
@@ -144,6 +143,8 @@ The design matrix $\mathrm{A}$ is usually assumed to be known. So far, we have a
 
 ***How to determine $\omega$ if it is unknown a priori?***
 
+<!-- Remember from last week, that we can use the **Discrete Fourier Transform (DFT)** and **Power Spectral Density (PSD)** to detect the frequency of a periodic pattern. The DFT decomposes a time series into its frequency components, and the PSD shows the power of each frequency component. Using these transformations, we can identify the frequency (or frequencies) with the highest power in a time series. By doing so, we can estimate the frequency $\omega$ of the periodic pattern, which can then be used to construct the design matrix $\mathrm{A}$.
+
 ### Discrete Fourier Transform (DFT)
 
 The first method we will study is the **Discrete Fourier Transform**. The DFT or fast FT (FFT) of a real time series, $Y_t$, is a complex array as
@@ -154,7 +155,7 @@ having a real and an imaginary part. The power at each frequency component can b
 
 $$S_{Y}(\omega)=P_{\omega}=\frac{1}{m\Delta t}|Y_s(\omega)|^2$$
 
-where $|Y(\omega)|$ is the magnitude at the frequency $\omega$. If a significant seasonality is present at frequency $\omega$, there should be a clear peak at this frequency, so that $S_{Y}(\omega)$ is more peaked than the neighboring powers.
+where $|Y(\omega)|$ is the magnitude at the frequency $\omega$. If a significant seasonality is present at frequency $\omega$, there should be a clear peak at this frequency, so that $S_{Y}(\omega)$ is more peaked than the neighboring powers. -->
 
 #### Example power spectral density
 
@@ -167,90 +168,6 @@ where $|Y(\omega)|$ is the magnitude at the frequency $\omega$. If a significant
 
 Left: time series (grey) and estimated linear trend and sine wave with period of 100. Right: estimated PSD.
 ```
+This means we can estimate the frequency $\omega$ of the periodic pattern using the techniques discussed in the chapter on signal processing. Once we have the frequency, we can construct the design matrix $\mathrm{A}$. 
 
-(LS-HE)=
-### Least-Squares Harmonic Estimation (LS-HE)
-
-The second method we will study is BLUE in combination with hypothesis testing, here called **Least Squares Harmonic Estimation** (LS-HE). We make use of the hypothesis testing to test the validity of the linear model and, hence, to improve it.
-
-We put forward two hypotheses:
-
-$$\mathcal{H}_0: Y=\mathrm{Ax}+\epsilon \hspace{5px}\text{vs.}\hspace{5px} \mathcal{H}_a: Y=\mathrm{Ax}+\mathrm{C}\nabla+\epsilon$$
-
-The null hypothesis could be a model without a seasonal component, while the alternative hypothesis would include a seasonal component with a certain choice for $\omega$.
-
-:::{card} **Example**
-
-$$
-\begin{align*}
-\mathcal{H}_0: &Y_t=y_0+rt+\epsilon_t \\
-\mathcal{H}_a: &Y_t=y_0+rt+a\cos{\omega t}+b\sin{\omega t}+\epsilon_t
-\end{align*}
-$$
-
-$$\begin{align*}
-\mathcal{H}_0: &\begin{bmatrix}Y_1\\ Y_2\\ \vdots\\ Y_m\end{bmatrix} = \begin{bmatrix}1&t_1\\ 1&t_2\\ \vdots&\vdots\\ 1&t_m\end{bmatrix}\begin{bmatrix}y_0\\ r\end{bmatrix} + \begin{bmatrix}\epsilon_1\\ \epsilon_2\\ \vdots\\ \epsilon_m\end{bmatrix} \\
-\mathcal{H}_a: &\begin{bmatrix}Y_1\\ Y_2\\ \vdots\\ Y_m\end{bmatrix} = \begin{bmatrix}1&t_1\\ 1&t_2\\ \vdots&\vdots\\ 1&t_m\end{bmatrix}\begin{bmatrix}y_0\\ r\end{bmatrix}+\begin{bmatrix}\cos{\omega t_1}&\sin{\omega t_1}\\ \cos{\omega t_2}&\sin{\omega t_2}\\ \vdots&\vdots\\ \cos{\omega t_m}&\sin{\omega t_m}\end{bmatrix}\begin{bmatrix}a\\ b\end{bmatrix}+\begin{bmatrix}\epsilon_1\\ \epsilon_2\\ \vdots\\ \epsilon_m\end{bmatrix}
-\end{align*}$$
-
-![hypotheses](./figs/hypotheses.png "hypotheses")
-:::
-
-The [Generalized Likelihood Ratio Test](GLRT) statistic is given by
-
-$$\begin{align*}
-T_q &= \hat{\epsilon}^T\Sigma_Y^{-1}\hat{\epsilon}-\hat{\epsilon}_a^T\Sigma_Y^{-1}\hat{\epsilon}_a \\ &=\hat{\epsilon}^T\Sigma_{Y}^{-1}\mathrm{C}(\mathrm{C}^T\Sigma_{Y}^{-1}\Sigma_{\hat{\epsilon}}\Sigma_{Y}^{-1}\mathrm{C})^{-1}\mathrm{C}^T\Sigma_{Y}^{-1}\hat{\epsilon}
-\end{align*}$$
-
-where $\hat{\epsilon}$ and $\hat{\epsilon}_a$ refer to the BLUE residuals obtained with the null and alternative hypothesis, respectively. 
-
-The derivation of the second equality is beyond the scope of this book, but the advantage of this expression is that it only requires to apply BLUE with the model of the null hypothesis; the alternative model is accounted for with matrix $\mathrm{C}$.
-
-This test statistic, having a central $\chi^2$-square distribution under $\mathcal{H}_0$, can be tested for a given confidence level: 
-
-$$T_q\sim\chi^2(q,0)$$
-
-In our example above, we have that $q=2$, the number of extra parameters in $\nabla=[a,b]^T$.
-
-**Special case:** for a zero-mean time series and white noise time series with $\Sigma_{Y}=\sigma^2 I$ we have
-
-$$Y=\hat{\epsilon} \quad \Rightarrow \mathbb{E}(Y)=0 \quad \Rightarrow \mathrm{A}=0$$ 
-
-In this case the test statistic simplifies to:
-
-$$T_q = \frac{1}{\sigma^2}Y^T \mathrm{C}(\mathrm{C}^T\mathrm{C})^{-1}\mathrm{C}^TY$$
-
-:::{card} **Proof**
-
-```{admonition} MUDE exam information
-:class: tip, dropdown
-This proof is optional and will not be assessed on the exam.
-```
-
-If we assume $\Sigma_{Y}=\sigma^2I$ and $Y=\hat{\epsilon}$ such that $\mathrm{A}=0$, we have
-
-$$
-\begin{align*}
-\Sigma_{\hat{\epsilon}}&=\Sigma_{Y}-\Sigma_{\hat{Y}}\\
-& = \sigma^2I - \mathrm{A}(\mathrm{A}^T(\sigma^{-2}I)\mathrm{A})^{-1}\mathrm{A}^T \\
-&=\sigma^2(I - \mathrm{A}(\mathrm{A}^T\mathrm{A})^{-1}\mathrm{A}^T )\\
-& = \sigma^2I
-\end{align*}
-$$
-
-and
-
-$$
-\begin{align*}
-T_q&=\hat{\epsilon}^T\Sigma_{Y}^{-1}\mathrm{C}(\mathrm{C}^T\Sigma_{Y}^{-1}\Sigma_{\hat{\epsilon}}\Sigma_{Y}^{-1}\mathrm{C})^{-1}\mathrm{C}^T\Sigma_{Y}^{-1}\hat{\epsilon}\\
-&= \frac{1}{\sigma^2}Y^T \mathrm{C}(\mathrm{C}^T\mathrm{C})^{-1}\mathrm{C}^TY
-\end{align*}
-$$
-:::
-
-This, in fact, can be shown to be identical to a scaled version (by a factor 2) of the PSD.
-
-```{admonition} Optional: proof of equality of PSD and LS-HE
-:class: tip, dropdown
-[On the equality of the PSD and the LS-HE T-test statistics](./proof.pdf)
-```
\ No newline at end of file
+It is also possible to infer the frequency of the periodic pattern by reasoning. For example, if we know our model depends on temperature, we can assume that the frequency of the seasonal pattern is related to the temperature cycle (e.g., 24 hours). However, this is a more qualitative approach and should be used with caution. Best practice is to use the DFT or PSD to estimate the frequency.
diff --git a/book/time_series/noise.ipynb b/book/time_series/noise.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4a177a79baa3dcb020bc7d258c1a93d64d3c8417
--- /dev/null
+++ b/book/time_series/noise.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(noiseandstoch)=\n",
+    "# Noise and stochastic model\n",
+    "The code on this page can be used interactively: click {fa}`rocket` --> {guilabel}`Live Code` in the top right corner, then wait until the message {guilabel}`Python interaction ready!` appears.\n",
+    "\n",
+    "In the previous section you have learned about the different components that can be present in a time series. Removing all these component, i.e. the functional model, we will be left with the residual term $\\epsilon(t)$. In this section we will take a closer look at the difference between signal and noise, and introduce other types of noise than the traditional white noise.\n",
+    "\n",
+    "## Additional concepts\n",
+    "\n",
+    "In [Signal Processing](SP) the data is just considered to be the signal of interest, whereas here we assume the data is \"contaminated\" with noise, i.e.\n",
+    "\n",
+    "$$Y = \\text{signal} + \\text{noise} $$\n",
+    "\n",
+    "Time series analysis means understanding patterns and, hence, extracting the **signal of interest** from the noisy data.\n",
+    "\n",
+    "### Signal and noise\n",
+    "\n",
+    "How can we describe both signal and noise?\n",
+    "\n",
+    "* **Signal** - the meaningful information that we want to detect: deterministic characteristics by means of mathematical expressions to capture for example trend, seasonality and offsets.\n",
+    "\n",
+    "* **Noise** - random and undesired fluctuation that interferes with the signal: stochastic process are needed to describe this. Parts of the time-correlated noise  needs to be accounted for in prediction, see later {ref}`AR`. \n",
+    "\n",
+    "The example in {numref}`signal_noise` shows that the *signal* can be described by $\\cos(2\\pi t f) + \\sin(2\\pi t f)$. The stochastic model (assuming independent normally distributed observations) would be a scaled identity matrix with variance equal to 1 (middle panel) and 9 (bottom panel), respectively. The signal of interest has been entirely hidden in the background noise in the bottom panel. Techniques from signal processing can be used to detect the frequency.\n",
+    "\n",
+    "```{figure} ./figs/signal_noise.png\n",
+    ":name: signal_noise\n",
+    ":width: 600px\n",
+    ":align: center\n",
+    "\n",
+    "Example of a time series (top graph) affected by noise with different strengths (middle and bottom figures). Note the different scales on the vertical axes.\n",
+    "```\n",
+    "\n",
+    "#### Signal to noise ratio\n",
+    "In signal processing the signal to noise ratio is commonly used to report on the amount of noise present in the model. If we analyze the model $Y = signal + noise$, then Y is a random variable with $E[Y] = E[signal] = \\mu$, and its variance $D(Y) = D(noise) = \\sigma^2$. Using this the signal to noise ratio is often defined as:\n",
+    "\n",
+    "$$ SNR = \\frac{\\mu}{\\sigma}$$\n",
+    "<!-- or alternatively as:\n",
+    "\n",
+    "$$ SNR = \\frac{\\mu^2}{\\sigma^2}$$ -->\n",
+    "\n",
+    "The signal to noise ratio is a measure of how much the signal stands out from the noise. The higher the signal to noise ratio, the more the signal stands out from the noise. Better equipment or more data can increase the signal to noise ratio.\n",
+    "\n",
+    "## Different types of noise\n",
+    "In the ideal case, when the signal is removed, you are left with white noise. A white noise stochastic model has the following properties:\n",
+    "\n",
+    "$$\n",
+    "\\mathbb{E}(Y) =  \\mathbb{E} \\left[\\begin{array}{c} y_1 \\\\ y_2 \\\\ \\vdots \\\\ y_m \\end{array}\\right] = \\left[\\begin{array}{c} 0 \\\\ 0 \\\\ \\vdots \\\\ 0 \\end{array}\\right]\n",
+    "$$\n",
+    "\n",
+    "and \n",
+    "\n",
+    "$$\n",
+    "\\mathbb{D}(Y) =  \\Sigma_{Y} = \\sigma^2 \\left[\\begin{array}{ccc} 1 & 0 & \\ldots{} & 0 \\\\ 0 & 1 & \\ldots{} & 0 \\\\ \\vdots & \\vdots & \\ddots & \\vdots \\\\ 0 & 0 & \\ldots{} & 1 \\end{array}\\right]\n",
+    "$$\n",
+    "Most notable, all observations are uncorrelated (off-diagonal elements of the covariance matrix are equal to 0). When we compute the PSD, the resulting density will be flat over the entire range of frequencies. In other words, a white noise process has equal energy over all frequencies, just like white light. We will show this in the interactive plot at the bottom of this page.\n",
+    "\n",
+    "### Colored noise\n",
+    "In time series it is not guarantied that the individual observations are uncorrelated. At the bottom of this page you will find an interactive plot. You can select four different types of noise: white, pink, red and blue. The noise processes are plotted in combination with the PSD. The PSD #TODO(add ref to psd)is a measure of the power of the signal at different frequencies. The white noise process has a flat PSD, while the other noise processes have a different shape. The pink noise process has a PSD that decreases with frequency, the red noise process has a PSD that decreases quadratically  with frequency, and the blue noise process has a PSD that increases with frequency. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "tags": [
+     "auto-execute-page",
+     "thebe-remove-input-init"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "## create a white noise signal and plot it\n",
+    "import numpy as np  \n",
+    "import matplotlib.pyplot as plt\n",
+    "import ipywidgets as widgets\n",
+    "\n",
+    "# create a white noise signal\n",
+    "np.random.seed(0)\n",
+    "N = 1000\n",
+    "x = np.random.randn(N)\n",
+    "\n",
+    "# Function to generate pink noise\n",
+    "def pink_noise(N):\n",
+    "    uneven = N % 2\n",
+    "    X = np.random.randn(N//2+1+uneven) + 1j * np.random.randn(N//2+1+uneven)\n",
+    "    S = np.sqrt(np.arange(len(X)) + 1.)  # +1 to avoid divide by zero\n",
+    "    y = (np.fft.irfft(X/S)).real\n",
+    "    if uneven:\n",
+    "        y = y[:-1]\n",
+    "    return y\n",
+    "\n",
+    "# Function to generate red (brown) noise\n",
+    "def red_noise(N):\n",
+    "    return np.cumsum(np.random.randn(N))\n",
+    "\n",
+    "# Function to generate blue noise\n",
+    "def blue_noise(N):\n",
+    "    uneven = N % 2\n",
+    "    X = np.random.randn(N//2+1+uneven) + 1j * np.random.randn(N//2+1+uneven)\n",
+    "    S = np.sqrt(np.arange(len(X)))  # no +1 here\n",
+    "    y = (np.fft.irfft(X*S)).real\n",
+    "    if uneven:\n",
+    "        y = y[:-1]\n",
+    "    return y\n",
+    "# BEGIN: white_noise function\n",
+    "def white_noise(N):\n",
+    "    return np.random.randn(N)\n",
+    "\n",
+    "\n",
+    "# Generate different noise signals\n",
+    "pink = pink_noise(N)\n",
+    "red = red_noise(N)\n",
+    "blue = blue_noise(N)\n",
+    "x = white_noise(N)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "tags": [
+     "thebe-remove-input-init",
+     "auto-execute-page"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd052dd2ce6a4a3087fe78cee870785d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='Noise Type:', index=3, options=('Pink Noise', 'Red Noise', 'Blue N…"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "noise_options = ['Pink Noise', 'Red Noise', 'Blue Noise', 'White Noise']\n",
+    "\n",
+    "# Create a dropdown menu for noise types\n",
+    "dropdown = widgets.Dropdown(\n",
+    "    options=noise_options,\n",
+    "    value='White Noise',\n",
+    "    description='Noise Type:',\n",
+    ")\n",
+    "\n",
+    "# Function to update the plot based on selected noise type\n",
+    "def update_plot_dropdown(noise_type):\n",
+    "\n",
+    "    plt.figure(figsize=(12, 4))\n",
+    "    plt.subplot(2, 1, 1)\n",
+    "    \n",
+    "    if noise_type == 'Pink Noise':\n",
+    "        plt.plot(pink, label='Pink Noise')\n",
+    "    elif noise_type == 'Red Noise':\n",
+    "        plt.plot(red, label='Red Noise')\n",
+    "    elif noise_type == 'Blue Noise':\n",
+    "        plt.plot(blue, label='Blue Noise')\n",
+    "    elif noise_type == 'White Noise':\n",
+    "        plt.plot(x, label='White Noise')\n",
+    "    \n",
+    "    plt.title(f'{noise_type} Signal')\n",
+    "    plt.xlabel('Time Index')\n",
+    "    plt.ylabel('Amplitude')\n",
+    "    plt.legend()\n",
+    "    plt.grid()\n",
+    "    \n",
+    "    plt.subplot(2, 1, 2)\n",
+    "    if noise_type == 'Pink Noise':\n",
+    "        plt.psd(pink, NFFT=2048, Fs=1, color='r', label='Pink Noise')\n",
+    "    elif noise_type == 'Red Noise':\n",
+    "        plt.psd(red, NFFT=2048, Fs=1, color='r', label='Red Noise')\n",
+    "    elif noise_type == 'Blue Noise':\n",
+    "        plt.psd(blue, NFFT=2048, Fs=1, color='r', label='Blue Noise')\n",
+    "    elif noise_type == 'White Noise':\n",
+    "        plt.psd(x, NFFT=2048, Fs=1, color='r', label='White Noise')\n",
+    "\n",
+    "    # plt.yscale('log')\n",
+    "    plt.show()\n",
+    "\n",
+    "widgets.interactive(update_plot_dropdown, noise_type=dropdown)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{note}\n",
+    "If you are interested, you can read more about the different types of noise in the [Wikipedia article](https://en.wikipedia.org/wiki/Colors_of_noise). In here you can also listen to the different types of noise, which might give you a better understanding of the differences.\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "TAMude",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/book/time_series/stationarity.md b/book/time_series/stationarity.md
index ab8975d45ac18500f1f9d40453b4b733865b3b29..20c4f44b7fed27ae972b515ece868e389288f75b 100644
--- a/book/time_series/stationarity.md
+++ b/book/time_series/stationarity.md
@@ -1,3 +1,4 @@
+(stationary)=
 # Time Series Stationarity
 
 
@@ -23,6 +24,7 @@ $$
 Var(S_t)=\mathbb{E}((S_t-\mu)^2)=c_0=\sigma^2
 $$
 
+Notice that we have introduced the new notation $S_t$ to denote a stationary time series. The time series $Y_t$ is then a non-stationary time series.
 ## Why stationary time series?
 
 Stationarity is important if we want to use a time series for forecasting (predicting future behaviour), which is not possible if the statistical properties change over time.
@@ -32,202 +34,11 @@ In practice, we may in fact be interested in for instance the trend and seasonal
 (stationarize)=
 ## How to "stationarize" a time series?
 
-In general, there are five ways to make a non-stationary time series to a stationary one. They are known as transformation methods. An important requirement is that such transformation is regular, or admissible, meaning that a back-transformation is possible. 
-
-Common methods are:
-
-* Difference transformation of data
-* Moving average of data
-* Least-squares fit (detrending)
-
-### Single and double differencing
-
-Single differencing of $Y=[Y_1,...,Y_m]^T$ makes a time series $\Delta Y_t=Y_t - Y_{t-1},\; t\geq 2$ with starting value of $\Delta Y_1 = Y_1$. This is a **regular transformation** of data, and hence allowed, as shown in the equation below.
-
-(SD)=
-$$
-\begin{bmatrix}
-    \Delta Y_1 \\ \Delta Y_2 \\ \Delta Y_3 \\ \vdots \\ \Delta Y_m
-\end{bmatrix} = 
-\begin{bmatrix}
-    1 & 0 &   & \dots & 0\\
-    -1 & 1 & 0 &   &  \\
-    0 & -1 & 1 & \ddots & \\
-    \vdots & \ddots &\ddots & \ddots & 0 \\
-    0 & \dots & 0 & -1 & 1
-\end{bmatrix}
-\begin{bmatrix}
-    Y_1\\ Y_2\\ Y_3\\ \vdots\\ Y_m
-\end{bmatrix}\Longleftrightarrow
-\begin{bmatrix}
-    Y_1\\ Y_2\\ Y_3\\ \vdots \\Y_m
-\end{bmatrix} = 
-\begin{bmatrix}
-    1 & 0 & 0 & \dots & 0\\
-    1 & 1 & 0 & \dots & 0\\
-    1 & 1 & 1 &  & \vdots\\
-    \vdots & \vdots & \vdots & \ddots &0\\
-    1 & 1 & 1 & 1 & 1
-\end{bmatrix}
-\begin{bmatrix}
-    \Delta Y_1 \\ \Delta Y_2 \\ \Delta Y_3 \\ \vdots \\ \Delta Y_m
-\end{bmatrix}
-$$
-
-#### Worked example
-
-Let us consider the following time series
-
-$$
-Y_t = y_0+rt+\epsilon_t
-$$
-
-This time series is non-stationary, due to the presence of a linear trend (the expectation is a function of $t$).
-
-Now we will apply single differencing to the time series:
-
-$$
-\begin{align*}
-\Delta Y_t = Y_t-Y_{t-1} &= y_0+rt+\epsilon_t-(y_0+r(t-1)+\epsilon_{t-1}) \\
-&= r+\Delta \epsilon_t
-\end{align*}
-$$
-
-It follows that $\mathbb{E}(\Delta Y_t)=r$, and therefore not a function of $t$ anymore.
-
-Now consider
-
-$$
-Y_t = y_0+rt+at^2+\epsilon_t
-$$
-
-We again apply single differencing:
-
-$$
-\begin{align*}
-\Delta Y_t = Y_t-Y_{t-1} &= y_0+rt+at^2+\epsilon_t-(y_0+r(t-1)+a(t-1)^2+\epsilon_{t-1}) \\
-&= r-a+2at+\Delta \epsilon_t
-\end{align*}
-$$
-
-In this case we find that $\mathbb{E}(\Delta Y_t)=r-a+2at$, which is still a function of $t$ and therefore not stationary. The solution would be to continue the process, which is referred to as *double differencing*:
-
-$$
-\Delta^2 Y_t = \Delta Y_t - \Delta Y_{t-1}
-$$
-
-:::{card} Exercise
-
-Show for yourself that applying double differencing to the time series $Y_t = y_0+rt+at^2+\epsilon_t$ results in a stationary time series $\Delta^2 Y_t$.
-
-````{admonition} Solution
-:class: tip, dropdown
-
-$$
-\begin{align*}
-\Delta^2 Y_t = \Delta Y_t-\Delta Y_{t-1} &= r-a+2at+\Delta \epsilon_t-(r-a+2a(t-1)+\Delta \epsilon_{t-1}) \\&= 2a+\Delta^2\epsilon_t
-\end{align*}
-$$
-
-with $\mathbb{E}(\Delta^2 Y_t)=2a$.
-
-```{figure} ./figs/doubledifference.png 
----
-height: 300px
-name: doubledifference
----
-Original time series (second-order polynomial) on the left; double differenced time series on the right.
-```
-````
-:::
-
-### Moving average
-
-The moving average of $Y = [Y_1, ..., Y_m]^T$ will create a time series $\bar{Y}_t = {\bar{Y}_1,...,\bar{Y}_m}$, with 
-
-$$
-\bar{Y}_t = \frac{1}{k}\sum_{i=1}^{k}Y_{t-i}
-$$
-
-where the length of the interval over which the average is taken is equal to $k$ (hence, the moving average only uses past values up till $k-1$ epochs ago).
-
-The difference between two time series provides a (nearly) stationary time series $\Delta Y_t = Y_t - \bar{Y}_t$.
-
-```{figure} ./figs/moving_avg.png
----
-height: 300px
-name: moving_avg
----
-Original time series and moving average on the right; stationarized times series on the left.
-```
-:::{card} Exercise
-
-Consider a random process time series as:
-
-$$
-Y_t
-= U \cos (\theta t) + V \sin (\theta t)
-$$
-
-where $U$ and $V$ are two uncorrelated random variables with zero means, and unit variances and $\theta$ is a deterministic value in the interval $\theta \in [-\pi, \pi]$. Show that this noise process is stationary.
-
-```{admonition} Solution
-:class: tip, dropdown
-
-Because $\mathbb{E}(U)=\mathbb{E}(V)=0$, it simply follows that 
-
-$$
-\mathbb{E}(Y_t)=0
-$$
-
-For a given $\tau$, the covariance between $Y_t$ and $Y_{t+\tau}$ is obtained as:
-
-$$
-\begin{align*}
-c_\tau = Cov(Y_t, Y_{t+\tau})
-&= \mathbb{E}(Y_tY_{t+\tau}) - \mathbb{E}(Y_t)\mathbb{E}(Y_{t+\tau})
-= \mathbb{E}(Y_t Y_{t+\tau})\\
-&= \mathbb{E}
-\biggl(
-    \bigl[ U \cos(\theta t) + V \sin(\theta t) \bigr]
-    \bigl[ U \cos(\theta t + \theta \tau)
-         + V \sin(\theta t + \theta \tau) \bigr]
-\biggr)
-\end{align*}
-$$
-
-The multiplication consists of four terms in which the terms $U^2$, $V^2$, $UV$ and $VU$ appear. Because the two random variables $U$ and $V$ are uncorrelated with zero means and unit variances, it follows that:
-
-$$
-\mathbb{E}(U^2) = \mathbb{E}(V^2) = 1
-\quad \mathrm{and} \quad
-\mathbb{E}(UV) = \mathbb{E}(VU) = 0
-$$
-
-This, with the previous equations, gives:
-
-$$
-Cov(Y_t, Y_{t+\tau})
-= \cos(\theta t) \cos(\theta t + \theta \tau)
-+ \sin(\theta t) \sin(\theta t + \theta \tau)
-$$
-
-Using the identity $\cos(a-b)=\cos(a)\cos(b)+\sin(a)\sin(b)$, it follows:
-
-$$
-Cov(Y_t, Y_{t+\tau})
-= \cos(\theta t + \theta \tau - \theta t)
-= \cos(\theta \tau)
-$$
-
-Which is a function of $\tau$, but not a function of time $t$. This shows that the random process is stationary.
-
-```
-:::
+There are several ways to make a time series stationary. In this course we will focus on detrending the data using least-squares fit.
 
 ### Least-squares fit
 
-If we can express the time series $Y=[Y_1, ..., Y_m]^T$ with a linear model of observation equations as $Y = \mathrm{Ax} + \epsilon$, we can apply [best linear unbiased estimation](BLUE) (equivalent to weighted least-squares) to estimate the parameters $\mathrm{x}$ that describe e.g. the trend and seasonality:
+If we can express the time series $Y=[Y_1, ..., Y_m]^T$ with a linear model of observation equations as $Y = \mathrm{Ax} + \epsilon$, we can apply [best linear unbiased estimation](BLUE) to estimate the parameters $\mathrm{x}$ that describe e.g. the trend and seasonality:
 
 $$
 \hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y 
@@ -271,16 +82,23 @@ $$
 
 The time series of residuals (left panel) is indeed a stationary time series.
 
+
+### Other ways to make a time series stationary
+When model specification is not straightforward, other methods can be used to make a time series stationary. Two common methods are single differencing and moving average. Single differencing of $Y=[Y_1,...,Y_m]^T$ makes a time series $\Delta Y_t=Y_t - Y_{t-1}$. Another way to create an (almost) stationary time series is by taking the moving average of the time series. Where we apply a moving average of $k$ observations to the time series $Y$ to create a new time series $\bar{Y}_t = \frac{1}{k}\sum_{i=1}^{k}Y_{t-i}$, and then take the difference between the original time series and the moving average to obtain a (nearly) stationary time series $\Delta Y_t = Y_t - \bar{Y}_t$.
+
+Both these methods do not require a model specification. So in cases where the model is not known, these methods can be used to make the time series stationary.
+
+
 ## ... and then what?
 
-We have seen different ways of obtaining a stationary time series from the original time series. The reason is that in order to make predictions (forecasting future values) we need to account for both the **signal-of-interest** and the **noise**. [Estimating the signal-of-interest](modelling_tsa) was covered in the previous section. In the next sections we will show how the noise can be modelled as a stochastic process. Given a time series $Y=\mathrm{Ax}+\epsilon$, the workflow is as follows:
+We have seen different ways of obtaining a stationary time series from the original time series. The reason is that in order to make predictions (forecasting future values, beyond the time of the last observation in the time series) we need to account for both the **signal-of-interest** and the **noise**. [Estimating the signal-of-interest](modelling_tsa) was covered in the previous section. In the next sections we will show how the noise can be modelled as a stochastic process. Given a time series $Y=\mathrm{Ax}+\epsilon$, the workflow is as follows:
 
 1. Estimate the signal-of-interest $\hat{X}=(\mathrm{A}^T\Sigma_{Y}^{-1}\mathrm{A})^{-1}\mathrm{A}^T\Sigma_{Y}^{-1}Y$ (Section [Modelling and estimation](modelling_tsa)).
 
-2. Model the noise using the Autoregressive Moving Average (ARMA) model, using the stationary time series $S:=\hat{\epsilon}=Y-\mathrm{A}\hat{X}$ (Section [ARMA](ARMA)).
+2. Model the noise using the Autoregressive (AR) model, using the stationary time series $S:=\hat{\epsilon}=Y-\mathrm{A}\hat{X}$ (Section [AR](AR)).
 
 3. Predict the signal-of-interest: $\hat{Y}_{signal}=\mathrm{A}_p\hat{X}$, where $\mathrm{A}_p$ is the design matrix describing the functional relationship between the future values $Y_p$ and $\mathrm{x}$ (Section [Forecasting](forecast)).
 
-4. Predict the noise $\hat{\epsilon}_p$ based on the ARMA model.
+4. Predict the noise $\hat{\epsilon}_p$ based on the AR model.
 
 5. Predict future values of the time series: $\hat{Y}_p=\mathrm{A}_p\hat{X}+\hat{\epsilon}_p$ (Section [Forecasting](forecast)).
diff --git a/book/time_series/y_values.csv b/book/time_series/y_values.csv
new file mode 100644
index 0000000000000000000000000000000000000000..23ec73383f20059fa387e5d827ec2d591fcf743f
--- /dev/null
+++ b/book/time_series/y_values.csv
@@ -0,0 +1,500 @@
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