Repository: PyFE/PyFENG (language: python)

Issue:
Revert "Asp risk-parity-project-fully-version-1.0"
Reverts PyFE/PyFENG#107


Discussion / hints:

Fix patch:
diff --git a/LiftedHeston.py b/LiftedHeston.py
new file mode 100644
index 0000000..891d269
--- /dev/null
+++ b/LiftedHeston.py
@@ -0,0 +1,178 @@
+import numpy as np
+import scipy.integrate as spint
+import scipy.special as scsp
+
+
+class LiftedHeston:
+
+    def __init__(
+        self,  vov=0.0, v0=0.0, rho=0.0, theta=0.0, lamda=0.0, H=0.0, n=20, rn=2.5
+    ):
+        """
+        Args:
+            vov: volatility of volatility
+            v0: initial variance
+            rho: correlation between price and volatility
+            theta: mean_reversion term
+            lamda: mean_reversion term
+            H: Hurst index, measuring regularity of the sample paths of V
+            n: number of factors
+            rn: rn ↓ 1 and n*log(rn) → ∞ as n → ∞
+            n and rn are fixed according to (26) in the paper
+        """
+        self.vov = vov
+        self.v0=v0
+        self.rho = rho
+        self.theta = theta
+        self.lamda = lamda
+        self.H = H
+        self.n = n
+        self.rn = rn
+
+    def weight(self):
+    	"""
+    	Generate cn from known parameters
+    	according to equation (19) in the paper
+    	"""
+    	cn_v = []
+    	for i in range(1,self.n+1):
+    		alpha = self.H + 1/2
+    		cn_i = (np.power(self.rn,1-alpha) -1) * np.power(self.rn,(alpha-1)*(1+self.n/2)) * np.power(self.rn,i*(1-alpha))/(scsp.gamma(alpha)*scsp.gamma(2-alpha))
+    		cn_v += [cn_i]
+    	return np.array(cn_v)
+
+    def mean_rev_speed(self):
+
+    	"""
+    	Generate xn from known parameters
+    	according to equation (19) in the paper
+    	"""
+
+    	xn_v = []
+    	for i in range(1,self.n+1):
+    		alpha = self.H + 1/2
+    		xn_i = ((1-alpha) * (np.power(self.rn,(2-alpha)) - 1) * np.power(self.rn,i-1-self.n/2))/((2-alpha)* (np.power(self.rn,1-alpha)-1))
+    		xn_v += [xn_i]
+    	return np.array(xn_v)
+
+    def gn(self, t):
+
+    	"""
+    	Generate gn(t) according to equation (10) in the paper
+    	"""
+
+    	mean_revs = self.mean_rev_speed()
+    	weight = self.weight()
+    	integral_array = []
+    	for i in range(0,self.n):
+    		integral = (1-np.exp(-mean_revs[i]*t))/mean_revs[i]
+    		integral_array += [integral]
+    	integral_array = np.array(integral_array)
+
+    	gn = self.v0 + self.lamda * self.theta * np.sum(weight * integral_array)
+    	return gn
+
+
+    def price(self, strike, spot, texp, cp=1):
+
+    	"""
+    	Calculate the price
+    	Firstly solve the phi function using discretization scheme in (A11) with a number of time steps N = 300
+    	Then get the characteristic function
+    	The call prices are computed via the cosine method (Fang and Oosterlee 2008) for the inversion of the characteristic function
+    	"""
+
+    	price=None
+    	return price
+
+class LiftedHestonMc(LiftedHeston):
+
+	def set_mc_params(self, n_path=1000, rn_seed=None):
+		"""
+		Set MC parameters
+
+        Args:
+            n_path: number of paths
+            rn_seed: random number seed
+		"""
+
+		self.n_path = int(n_path)
+		self.rn_seed = rn_seed
+		self.rng = np.random.default_rng(rn_seed)
+        
+
+	def vo_path(self, texp, N):
+		"""
+		Simulate one single path of Volatility process and U process of each factor
+		simulated according to modified explicit–implicit scheme (A12) and (A13) in hte paper
+		"""
+		dt = texp/N
+		tobs = dt*np.arange(0,N+1)
+		zz = self.rng.standard_normal(size=N)
+
+		u_n = np.zeros((self.n, N+1))
+		v_n = np.zeros(N+1)
+
+		for i in range(0,N):
+			v_n[i] = self.gn(tobs[i]) + np.sum(self.weight()*u_n[:,i])
+			u_n[:,i+1] = (1/(1+dt*self.mean_rev_speed()))* (u_n[:,i] - self.lamda * v_n[i] *dt + self.vov * np.sqrt( np.fmax(v_n[i],0) * dt)*zz[i])
+		#	u_n[:,i+1] = (1-dt*self.mean_rev_speed())* u_n[:,i] - self.lamda * v_n[i] *dt + self.vov * np.sqrt( np.fmax(v_n[i],0) * dt)*zz[i]
+		v_n[N] = self.gn(tobs[N]) + np.sum(self.weight()*u_n[:,N])
+		
+		return u_n, v_n
+
+	def vo_paths(self, texp, N):
+		"""
+		Montecarlo Simulation of paths of Volatility process and U process of each factor
+		simulated according to modified explicit–implicit scheme (A12) and (A13) in hte paper
+		"""
+		dt = texp/N
+		tobs = dt*np.arange(0,N+1)
+		zz = self.rng.standard_normal(size=(self.n_path,N))
+
+		u_n = np.zeros((self.n_path, self.n, N+1))
+		v_n = np.zeros((self.n_path, N+1))
+
+		for i in range(0,N):
+			v_n[:,i] = self.gn(tobs[i]) + np.sum(self.weight()*u_n[:,:,i],1)
+			u_n[:,:,i+1] = (1/(1+dt*self.mean_rev_speed()))*(u_n[:,:,i] - self.lamda * v_n[:,i][:,None] *dt + self.vov * np.sqrt( np.fmax(v_n[:,i][:,None],0) * dt)*zz[:,i][:,None])
+		#	u_n[:,:,i+1] = (1-dt*self.mean_rev_speed())* u_n[:,:,i] - self.lamda * v_n[:,i][:,None] *dt + self.vov * np.sqrt( np.fmax(v_n[:,i][:,None],0) * dt)*zz[:,i][:,None]
+
+			v_n[:, N] = self.gn(tobs[N]) + np.sum(self.weight()*u_n[:,:,N],1)
+		return (v_n, zz)    	
+
+	def mc_price(self, strike, spot, texp, N, cp=1):
+
+		"""
+		Calculate option prices
+		Parameters
+		----------
+		strike: strike prices
+		spot: spot prices
+		texp: time to expiration
+		N: time steps
+        ----------
+		"""
+
+		dt = texp/N
+		zz = self.vo_paths(texp, N)[1]
+		vo = self.vo_paths(texp, N)[0]
+		zz2 = np.random.default_rng(self.rn_seed-1).standard_normal(size=(self.n_path,N))
+		ww = self.rho * zz + np.sqrt(1 - np.power(self.rho,2))* zz2
+		st = np.ones(self.n_path)*spot
+
+		for i in range(0,N):
+			st = st + st * np.sqrt(np.fmax(vo[:,i],0)*dt) * ww[:,i]
+		#print(st)
+
+		n_strike=strike.size
+		price=np.zeros(n_strike)
+		for i in range(n_strike):
+			price[i] = np.mean(np.fmax(st-strike[i],0))
+		return price
+
+
+
+
+
+
diff --git a/Report-Lifted Heston_v3.ipynb b/Report-Lifted Heston_v3.ipynb
new file mode 100644
index 0000000..0a91dea
--- /dev/null
+++ b/Report-Lifted Heston_v3.ipynb
@@ -0,0 +1,1643 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img align=\"left\" src=\"https://statics.phbs.pku.edu.cn/uploadfile/2018/0119/20180119080526567.png\" style=\"margin-top:50px\">\n",
+    "<h1 align=\"right\" style=\"margin-top:60px\">Applied Stochastic Processes (FIN 514) </h1>\n",
+    "<h1 align=\"right\" style=\"margin-top:20px\">Module 3, 2021-2022  </h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1 align=\"center\" style=\"margin-top:40px\">Lifted Heston Model</h1>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<center>\n",
+    "<font color=black size=4 face=times> Team: Ji Yucheng & Wang Mengjie<br>\n",
+    "    Instructor: Jaehyuk Choi<br>\n",
+    "\n",
+    "<center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Main References:\n",
+    "\n",
+    "[1]Abi Jaber, E.,Lifting the Heston model. Quantitative Finance, 2019, 19(12), 1995-2013. \n",
+    "\n",
+    "[2]Abi Jaber, E. and El Euch, O., Multifactor approximation of rough volatility models. SIAM J. Financ. Math., 2019, 10(2), 309–349.\n",
+    "\n",
+    "[3]Cont, R. and Tankov, P., Financial Modelling with Jump Processes, Vol. 2, 2003 (CRC Press, Taylor & Francis Group: Boca Raton, London, New York, Washington).\n",
+    "\n",
+    "[4]Fang, F. and Oosterlee, C., A novel pricing method for European options based on Fourier-cosine series expansions. SIAM J. Sci. Comput., 2008, 31(2), 826–848.\n",
+    "\n",
+    "[5]Gatheral, J., The Volatility Surface: A Practitioner’s Guide, Vol. 7, 2011 (John Wiley & Sons: Hoboken, NJ).\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Brief Introduction\n",
+    "### 1.Model Set up\n",
+    "    \n",
+    "&emsp;&emsp;This paper defines the lifted Heston model as a conventional stochastic volatility model, with n (n ∈ N)\n",
+    "factors for the variance process:  \n",
+    "\n",
+    "$$dS_t^n = S_t^n \\sqrt[]{V_t^n} dB_t, S^n_0 > 0, $$\n",
+    "$$V_t^n = g^n_0(t) + \\sum^n_{i=1} c^n_i U^{n,i}_t ,$$\n",
+    "$$dU_t^{n,i} = ( −x^n_i U^{n,i}_ t − λV^n_t ) dt + ν\\sqrt[]{V_t^n} dW_t, $$\n",
+    "$$U_0^{n,i} = 0, i = 1, ... , n, $$\n",
+    "\n",
+    "\n",
+    "&emsp;&emsp;where λ, ν ∈ R+, \n",
+    "\n",
+    "&emsp;&emsp;$c^n_i , x^n_i ≥ 0$, for i =1, ... , n, \n",
+    "\n",
+    "&emsp;&emsp;$B = ρW + \\sqrt[]{1 − ρ^2}W^⊥$, with $(W, W^⊥) $ a two dimensional Brownian motion with ρ ∈ [−1, 1].\n",
+    "\n",
+    "&emsp;&emsp;$g^n_0$ is a continuous function on t\n",
+    "\n",
+    "##### &emsp;&emsp;[Conditions for $g^n_0$]\n",
+    "&emsp;&emsp;if $g^n_0$ is non-decreasing such that $g^n_0(0) ≥ 0$, or\n",
+    "\n",
+    "&emsp;&emsp;$g^n_0 : t → V_0 + \\sum_{i=1}^{n}c^n_i \\int_0^t e^{−x^n_i (t−s)}θ (s) ds$, with  $V_0$, θ ≥ 0,\n",
+    "\n",
+    "&emsp;&emsp;then the strong solution of $(S^n, V^n,(U^{n,i})_{1≤i≤n})$ exists, such that $V_t^n$ ≥ 0, for all t ≥ 0,(proof in Appendix1)\n",
+    "\n",
+    "&emsp;&emsp;Specifically, let\n",
+    "$g^n_0 : t → V_0 + \\lambdaθ \\sum_{i=1}^{n}c^n_i \\int_0^t e^{−x^n_i (t−s)}ds$, with  $V_0$, θ ≥ 0\n",
+    "\n",
+    "\n",
+    "\n",
+    "##### &emsp;&emsp;Setting n=1, $c_1^1$ = 1 and $x_1^1$ = 0, it's the standard Heston model\n",
+    "\n",
+    "### 2.Approximation of Rough Heston to reduce parameters\n",
+    "&emsp;&emsp;The weights and mean reversions:\n",
+    "\n",
+    "$$c^n_i , x^n_i ≥ 0, $$ for  i =1, ... , n \n",
+    "\n",
+    "&emsp;&emsp;They can be calculated by H and r_n, according to the approximation result (Abi Jaber and El Euch 2019, Theorem 3.5), that is, $c^n_i , x^n_i $ is chosen in such a way that sending the number of factors n → ∞ would yield the convergence of the lifted Heston model towards a rough Heston model with parameters (V0, θ, λ, ν, ρ, H).\n",
+    "\n",
+    "&emsp;&emsp;that is, $$\\Theta_0:= (V_0, θ, λ, ν, ρ, H)$$\n",
+    "$$ volitility smile: σ_n(K, T; r_n, \\Theta_0) → σ_∞(K, T; \\Theta_0),$$ for each pair of (K,T)\n",
+    "\n",
+    "$$c^n_i = \\cfrac{(r^{1−α}_n − 1)r_n^{(α−1)(1+n/2) }}{\\Gamma(α)\\Gamma(2 − α)} r^{(1−α)i}_ n $$\n",
+    "\n",
+    "$$x^n_i = \\cfrac{1 − α}{ 2 − α} \\cfrac{r^{2−α}_n − 1}{r^{1−α}_n−1} r^{i−1−n/2}_n ,  i = 1, ... , n, $$\n",
+    "&emsp;&emsp;where α := H + 1/2 for some H ∈ (0, 1/2)\n",
+    "\n",
+    "&emsp;&emsp;and the sequence $(r_n)_{n≥1}$ satisfies $r_n$ ↓ 1 and $n  ln r_n$ → ∞ as n → ∞\n",
+    "\n",
+    "\n",
+    "### 3.Tractability\n",
+    "&emsp;&emsp;Like the classical Heston model, the lifted Heston model remains tractable. Specifically, fix u ∈ C such that Re(u) ∈\n",
+    "[0, 1]. The Fourier–Laplace transform of the log-price is exponentially affine with respect to\n",
+    "the factors (Un,i)1≤i≤n:\n",
+    "\n",
+    "$$E\\left[exp(ulogS^n_t)|Ft\\right]= exp \\left(φ^n(t, T) + u log S_t^n + \\sum_{i=1}^n c^n_iψ^{n,i}(T − t)U_t^{n,i}\\right)$$\n",
+    "\n",
+    "&emsp;&emsp;for all t ≤ T, where $(ψ^{n,i})_{1≤i≤n}$ solves the following n-dimensional system of Riccati ordinary differential equations:\n",
+    "\n",
+    "$$(ψ^{n,i})' = −x^n_iψ^{n,i} + F \\left(u, \\sum_{j=1}^n c^n_j ψ^{n,i}\\right), ψ^{n,i}(0) = 0, i = 1, ... , n $$\n",
+    "\n",
+    "with\n",
+    "$$F(u, v) = \\cfrac{1}{2}(u^2 − u) + (ρνu − λ)v + \\cfrac{ν^2}{2} v^2 $$\n",
+    "and\n",
+    "$$φ^n(t, T) = \\int_{0}^{T−t} F \\left(u, \\sum_{i=1}^n c^n_j ψ^{n,i}(s)\\right)g^n_0(T-s)ds  , t ≤ T. $$\n",
+    "\n",
+    "\n",
+    "&emsp;&emsp;In particular, for t=0, since $U_0^{n,i}$ = 0 for i = 1, ... , n, the unconditional Fourier–Laplace transform:\n",
+    "\n",
+    "$$E\\left[exp(u log S_t^n)\\right] = exp\\left( u log S^n_0 + \\sum^T_0 F \\left(u, \\sum_{i=1}^n ψ^{n,i}(s)\\right)g^n_0(T-s)ds \\right) $$\n",
+    "\n",
+    "\n",
+    "##### &emsp;&emsp;Option prices can be calculated:\n",
+    "\n",
+    "&emsp;&emsp;(1)Solve the n-dimensional Riccati system of $(ψ^{n,i})'$ using explicit–implicit discretization scheme\n",
+    "\n",
+    "$$ψ^{n,i}_0=0,  ψ^{n,i}_{t_{k+1}}=\\cfrac{1}{1 + x^n_i\\Delta t} \\left( ψ^{n,i}_{t_k}+\\Delta t F\\left( u, \\sum_{j=1}^nψ^n_jψ^{n,j}_{t_k} \\right) \\right),$$ \n",
+    "\n",
+    "$$i = 1, ... , n$$\n",
+    "\n",
+    "&emsp;&emsp;(2)Get the characteristic function\n",
+    "\n",
+    "&emsp;&emsp;(3)Compute the call prices via the cosine method  for the inversion of the characteristic function(Fang and Oosterlee 2008) \n",
+    "\n",
+    "### 4.Simulation\n",
+    "&emsp;&emsp;Because the Lifted Heston Model is a Markovian and semimartingale model, one can adapt standard recursive Euler–Maruyama schemes,\n",
+    "\n",
+    "&emsp;&emsp;this paper  consider the modified explicit–implicit scheme\n",
+    "\n",
+    "$$\\hat{V^n_{t_k}}= g^n_0(t_k) + \\sum^n_{i=1} c^n_i \\hat{U^{n,i}_{t_k}} ,   \\hat{U_0^{n,i} }= 0 $$\n",
+    "\n",
+    "$$\\hat{U^{n,i}_{t_{k+1}}} = \\cfrac{1}{1 + x^n_i \\Delta t } \\left(\\hat{U^{n,i}_{t_k}} − λ \\hat{ V^n_{t_k}} \\Delta t + ν\\sqrt[]{(\\hat V^n_{t_k})^+} (W_{t_{k+1}} − W_{t_k}) \\right)\t, i = 1, ... , n, $$\n",
+    "\n",
+    "where $t_k = k\\Delta t, k = 1 ...N, \\Delta t = T/N $  and  \n",
+    "$W_{t_{k+1}} − W_{t_k}∼ N (0, \\Delta t)=Z_1$\n",
+    "\n",
+    "##### [the reason behind using explicit-implicit scheme]\n",
+    "\n",
+    "&emsp;&emsp;If using explicit scheme,\n",
+    "\n",
+    "$$\\hat{U^{n,i}_{t_{k+1}}}=（1-x^n_i \\Delta t) \\hat{U^{n,i}_{t_k}}-λ \\hat {V^n_{t_k}} \\Delta t + ν\\sqrt[]{(\\hat V^n_{t_k})^+} (W_{t_{k+1}} − W_{t_k}) $$\n",
+    "\n",
+    "&emsp;&emsp;$x^n_n$ grows very large as n increases. For instance,for n = 20, r20 = 2.5 and H = 0.1, $x^n_n$ = 6417.74. Consequently, if one needs to ensure the stability of the explicit scheme, one needs a very large number of time steps N. In contrast, the implicit Euler\n",
+    "scheme is stable for any number of time steps N.\n",
+    "\n",
+    "\n",
+    "\n",
+    "&emsp;&emsp;Given the paths of $\\hat{V^n_{t_k}}$, we can get the pahts of $S^n_{t_k}$, \n",
+    "\n",
+    "$$S^n_{t_{k+1}}=S^n_{t_k}+S^n_{t_k} \\sqrt[]{V^n_{t_k}}(B_{t_{k+1}}-B_{t_k})$$\n",
+    "\n",
+    "&emsp;&emsp;where $B_{t_{k+1}} − B_{t_k}∼ N (0, \\Delta t)=Z_2,  cor(Z_1，Z_2）=\\rho $ and finally get $S^n_T$ to calculate the price of options"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "&emsp;&emsp;This paper compares the estimated Hurst index of realized volatility of S&P Index, Heston Model and Lifted Heston Model,\n",
+    "founding that the Lifted Heston Model enjoys the similar roughness with realized value.\n",
+    "\n",
+    "<img src=\"./2.png\" width=\"70%\">"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.Strengths and Weakness\n",
+    "##### &emsp;&emsp;Strengths:\n",
+    "&emsp;&emsp;(1)Lifted Heston Model is an approximation to Rough Heston Model, keeping the characteristics of well reproduce the at-the-money skew of the implied volatility surface, therefore catching the roughness of volatility change.\n",
+    "\n",
+    "&emsp;&emsp;(2)By introducing n factors, Lifted Heston Model can get a semimartingale of $V_t$, therefore, $S_t$ is tractable and can be more easily simulated to price options.\n",
+    "\n",
+    "&emsp;&emsp;(3)Compared with other methods like adding jumps (Cont and Tankov 2003, Gatheral 2011) and stacking additional random factors (Bergomi 2005, Fouque et al. 2011), with more parameters slowing down the calibration process. Lifted Heston Model only introduces one more parameter H than Heston Model.\n",
+    "##### &emsp;&emsp;Weakness:\n",
+    "\n",
+    "&emsp;&emsp;(1)Compared with Heston Model, the prices and volatilities can only be simulated through conditional Monte Carlo, simulation process is slow\n",
+    "\n",
+    "<img src=\"./1.png\" width=\"70%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulation of Lifted Heston"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pyfeng as pf\n",
+    "import LiftedHeston as LF   #a module in pyfeng\n",
+    "h1=LF.LiftedHestonMc(vov=0.1, v0=0.05, rho=0.1, theta=0.05, lamda=0.3, H=0.1)\n",
+    "h1.set_mc_params(n_path=1000, rn_seed=1234)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "factor, variance = h1.vo_path(texp=1, N=1000)  #captures the change of a single path of simulated U and V"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.00000000e+00, -1.14908380e-03, -1.12144470e-03, ...,\n",
+       "         8.23606966e-03,  8.55297571e-03,  7.91712098e-03],\n",
+       "       [ 0.00000000e+00, -1.14908368e-03, -1.12144446e-03, ...,\n",
+       "         8.23524421e-03,  8.55214936e-03,  7.91629379e-03],\n",
+       "       [ 0.00000000e+00, -1.14908337e-03, -1.12144385e-03, ...,\n",
+       "         8.23318094e-03,  8.55008383e-03,  7.91422616e-03],\n",
+       "       ...,\n",
+       "       [ 0.00000000e+00, -8.14528398e-04, -5.57786731e-04, ...,\n",
+       "         4.30097229e-04,  5.29513895e-04, -7.53793362e-05],\n",
+       "       [ 0.00000000e+00, -5.66934251e-04, -2.66077125e-04, ...,\n",
+       "         1.29847010e-04,  2.20419033e-04, -2.04967119e-04],\n",
+       "       [ 0.00000000e+00, -3.22134366e-04, -8.25588728e-05, ...,\n",
+       "         4.72271521e-05,  1.02081333e-04, -1.49637968e-04]])"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Replication of Figure 7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1bf101914e0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import rcParams, cycler\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "df=pd.DataFrame(factor)\n",
+    "data=df.T\n",
+    "cmap = plt.cm.Blues\n",
+    "rcParams['axes.prop_cycle'] = cycler(color=cmap(np.linspace(0, 1, 20)))\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(15, 3))\n",
+    "lines = ax.plot(np.linspace(0,1,1001),df.T)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"./3.png\" width=\"85%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>991</th>\n",
+       "      <th>992</th>\n",
+       "      <th>993</th>\n",
+       "      <th>994</th>\n",
+       "      <th>995</th>\n",
+       "      <th>996</th>\n",
+       "      <th>997</th>\n",
+       "      <th>998</th>\n",
+       "      <th>999</th>\n",
+       "      <th>1000</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000664</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001510</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006947</td>\n",
+       "      <td>0.006498</td>\n",
+       "      <td>0.006520</td>\n",
+       "      <td>0.008262</td>\n",
+       "      <td>0.008510</td>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.008193</td>\n",
+       "      <td>0.008236</td>\n",
+       "      <td>0.008553</td>\n",
+       "      <td>0.007917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000664</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001510</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006946</td>\n",
+       "      <td>0.006498</td>\n",
+       "      <td>0.006519</td>\n",
+       "      <td>0.008261</td>\n",
+       "      <td>0.008509</td>\n",
+       "      <td>0.007529</td>\n",
+       "      <td>0.008192</td>\n",
+       "      <td>0.008235</td>\n",
+       "      <td>0.008552</td>\n",
+       "      <td>0.007916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000664</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001510</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006944</td>\n",
+       "      <td>0.006495</td>\n",
+       "      <td>0.006517</td>\n",
+       "      <td>0.008259</td>\n",
+       "      <td>0.008507</td>\n",
+       "      <td>0.007526</td>\n",
+       "      <td>0.008190</td>\n",
+       "      <td>0.008233</td>\n",
+       "      <td>0.008550</td>\n",
+       "      <td>0.007914</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000664</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001510</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006939</td>\n",
+       "      <td>0.006490</td>\n",
+       "      <td>0.006512</td>\n",
+       "      <td>0.008254</td>\n",
+       "      <td>0.008502</td>\n",
+       "      <td>0.007521</td>\n",
+       "      <td>0.008184</td>\n",
+       "      <td>0.008228</td>\n",
+       "      <td>0.008545</td>\n",
+       "      <td>0.007909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000664</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001510</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006926</td>\n",
+       "      <td>0.006478</td>\n",
+       "      <td>0.006499</td>\n",
+       "      <td>0.008241</td>\n",
+       "      <td>0.008489</td>\n",
+       "      <td>0.007509</td>\n",
+       "      <td>0.008172</td>\n",
+       "      <td>0.008215</td>\n",
+       "      <td>0.008532</td>\n",
+       "      <td>0.007896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000663</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001510</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006894</td>\n",
+       "      <td>0.006446</td>\n",
+       "      <td>0.006467</td>\n",
+       "      <td>0.008209</td>\n",
+       "      <td>0.008457</td>\n",
+       "      <td>0.007476</td>\n",
+       "      <td>0.008140</td>\n",
+       "      <td>0.008183</td>\n",
+       "      <td>0.008500</td>\n",
+       "      <td>0.007864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000663</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001510</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006815</td>\n",
+       "      <td>0.006366</td>\n",
+       "      <td>0.006388</td>\n",
+       "      <td>0.008130</td>\n",
+       "      <td>0.008377</td>\n",
+       "      <td>0.007397</td>\n",
+       "      <td>0.008060</td>\n",
+       "      <td>0.008103</td>\n",
+       "      <td>0.008420</td>\n",
+       "      <td>0.007784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000663</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001510</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006620</td>\n",
+       "      <td>0.006171</td>\n",
+       "      <td>0.006193</td>\n",
+       "      <td>0.007935</td>\n",
+       "      <td>0.008182</td>\n",
+       "      <td>0.007201</td>\n",
+       "      <td>0.007864</td>\n",
+       "      <td>0.007907</td>\n",
+       "      <td>0.008224</td>\n",
+       "      <td>0.007588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000663</td>\n",
+       "      <td>-0.000572</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001509</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006153</td>\n",
+       "      <td>0.005704</td>\n",
+       "      <td>0.005725</td>\n",
+       "      <td>0.007466</td>\n",
+       "      <td>0.007713</td>\n",
+       "      <td>0.006732</td>\n",
+       "      <td>0.007394</td>\n",
+       "      <td>0.007437</td>\n",
+       "      <td>0.007753</td>\n",
+       "      <td>0.007117</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001149</td>\n",
+       "      <td>-0.001121</td>\n",
+       "      <td>-0.000663</td>\n",
+       "      <td>-0.000571</td>\n",
+       "      <td>0.000016</td>\n",
+       "      <td>0.002173</td>\n",
+       "      <td>0.000821</td>\n",
+       "      <td>0.001509</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.005099</td>\n",
+       "      <td>0.004649</td>\n",
+       "      <td>0.004670</td>\n",
+       "      <td>0.006410</td>\n",
+       "      <td>0.006655</td>\n",
+       "      <td>0.005674</td>\n",
+       "      <td>0.006335</td>\n",
+       "      <td>0.006377</td>\n",
+       "      <td>0.006692</td>\n",
+       "      <td>0.006055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001148</td>\n",
+       "      <td>-0.001120</td>\n",
+       "      <td>-0.000662</td>\n",
+       "      <td>-0.000570</td>\n",
+       "      <td>0.000018</td>\n",
+       "      <td>0.002174</td>\n",
+       "      <td>0.000822</td>\n",
+       "      <td>0.001509</td>\n",
+       "      <td>0.000150</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.003056</td>\n",
+       "      <td>0.002606</td>\n",
+       "      <td>0.002626</td>\n",
+       "      <td>0.004365</td>\n",
+       "      <td>0.004609</td>\n",
+       "      <td>0.003627</td>\n",
+       "      <td>0.004287</td>\n",
+       "      <td>0.004327</td>\n",
+       "      <td>0.004641</td>\n",
+       "      <td>0.004003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001147</td>\n",
+       "      <td>-0.001118</td>\n",
+       "      <td>-0.000659</td>\n",
+       "      <td>-0.000566</td>\n",
+       "      <td>0.000021</td>\n",
+       "      <td>0.002175</td>\n",
+       "      <td>0.000822</td>\n",
+       "      <td>0.001508</td>\n",
+       "      <td>0.000148</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000344</td>\n",
+       "      <td>-0.000105</td>\n",
+       "      <td>-0.000083</td>\n",
+       "      <td>0.001656</td>\n",
+       "      <td>0.001900</td>\n",
+       "      <td>0.000919</td>\n",
+       "      <td>0.001579</td>\n",
+       "      <td>0.001620</td>\n",
+       "      <td>0.001934</td>\n",
+       "      <td>0.001296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001144</td>\n",
+       "      <td>-0.001112</td>\n",
+       "      <td>-0.000651</td>\n",
+       "      <td>-0.000558</td>\n",
+       "      <td>0.000030</td>\n",
+       "      <td>0.002178</td>\n",
+       "      <td>0.000823</td>\n",
+       "      <td>0.001505</td>\n",
+       "      <td>0.000145</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.000861</td>\n",
+       "      <td>-0.001304</td>\n",
+       "      <td>-0.001277</td>\n",
+       "      <td>0.000463</td>\n",
+       "      <td>0.000707</td>\n",
+       "      <td>-0.000272</td>\n",
+       "      <td>0.000390</td>\n",
+       "      <td>0.000432</td>\n",
+       "      <td>0.000745</td>\n",
+       "      <td>0.000109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001137</td>\n",
+       "      <td>-0.001098</td>\n",
+       "      <td>-0.000633</td>\n",
+       "      <td>-0.000536</td>\n",
+       "      <td>0.000051</td>\n",
+       "      <td>0.002185</td>\n",
+       "      <td>0.000825</td>\n",
+       "      <td>0.001497</td>\n",
+       "      <td>0.000137</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000550</td>\n",
+       "      <td>0.000100</td>\n",
+       "      <td>0.000121</td>\n",
+       "      <td>0.001844</td>\n",
+       "      <td>0.002069</td>\n",
+       "      <td>0.001078</td>\n",
+       "      <td>0.001723</td>\n",
+       "      <td>0.001748</td>\n",
+       "      <td>0.002043</td>\n",
+       "      <td>0.001393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001120</td>\n",
+       "      <td>-0.001064</td>\n",
+       "      <td>-0.000591</td>\n",
+       "      <td>-0.000487</td>\n",
+       "      <td>0.000099</td>\n",
+       "      <td>0.002198</td>\n",
+       "      <td>0.000825</td>\n",
+       "      <td>0.001474</td>\n",
+       "      <td>0.000112</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001966</td>\n",
+       "      <td>0.001478</td>\n",
+       "      <td>0.001461</td>\n",
+       "      <td>0.003121</td>\n",
+       "      <td>0.003283</td>\n",
+       "      <td>0.002243</td>\n",
+       "      <td>0.002832</td>\n",
+       "      <td>0.002802</td>\n",
+       "      <td>0.003039</td>\n",
+       "      <td>0.002342</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.001078</td>\n",
+       "      <td>-0.000986</td>\n",
+       "      <td>-0.000495</td>\n",
+       "      <td>-0.000379</td>\n",
+       "      <td>0.000196</td>\n",
+       "      <td>0.002208</td>\n",
+       "      <td>0.000804</td>\n",
+       "      <td>0.001400</td>\n",
+       "      <td>0.000038</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.002029</td>\n",
+       "      <td>0.001483</td>\n",
+       "      <td>0.001412</td>\n",
+       "      <td>0.002960</td>\n",
+       "      <td>0.003009</td>\n",
+       "      <td>0.001904</td>\n",
+       "      <td>0.002409</td>\n",
+       "      <td>0.002301</td>\n",
+       "      <td>0.002457</td>\n",
+       "      <td>0.001709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000987</td>\n",
+       "      <td>-0.000824</td>\n",
+       "      <td>-0.000314</td>\n",
+       "      <td>-0.000192</td>\n",
+       "      <td>0.000340</td>\n",
+       "      <td>0.002145</td>\n",
+       "      <td>0.000682</td>\n",
+       "      <td>0.001177</td>\n",
+       "      <td>-0.000157</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.001620</td>\n",
+       "      <td>0.001006</td>\n",
+       "      <td>0.000883</td>\n",
+       "      <td>0.002254</td>\n",
+       "      <td>0.002149</td>\n",
+       "      <td>0.001004</td>\n",
+       "      <td>0.001432</td>\n",
+       "      <td>0.001267</td>\n",
+       "      <td>0.001360</td>\n",
+       "      <td>0.000622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000815</td>\n",
+       "      <td>-0.000558</td>\n",
+       "      <td>-0.000071</td>\n",
+       "      <td>0.000014</td>\n",
+       "      <td>0.000427</td>\n",
+       "      <td>0.001832</td>\n",
+       "      <td>0.000340</td>\n",
+       "      <td>0.000729</td>\n",
+       "      <td>-0.000446</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000873</td>\n",
+       "      <td>0.000301</td>\n",
+       "      <td>0.000229</td>\n",
+       "      <td>0.001397</td>\n",
+       "      <td>0.001166</td>\n",
+       "      <td>0.000131</td>\n",
+       "      <td>0.000563</td>\n",
+       "      <td>0.000430</td>\n",
+       "      <td>0.000530</td>\n",
+       "      <td>-0.000075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000567</td>\n",
+       "      <td>-0.000266</td>\n",
+       "      <td>0.000095</td>\n",
+       "      <td>0.000092</td>\n",
+       "      <td>0.000335</td>\n",
+       "      <td>0.001230</td>\n",
+       "      <td>-0.000060</td>\n",
+       "      <td>0.000310</td>\n",
+       "      <td>-0.000518</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000274</td>\n",
+       "      <td>-0.000086</td>\n",
+       "      <td>-0.000032</td>\n",
+       "      <td>0.000844</td>\n",
+       "      <td>0.000538</td>\n",
+       "      <td>-0.000218</td>\n",
+       "      <td>0.000220</td>\n",
+       "      <td>0.000130</td>\n",
+       "      <td>0.000220</td>\n",
+       "      <td>-0.000205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.000322</td>\n",
+       "      <td>-0.000083</td>\n",
+       "      <td>0.000105</td>\n",
+       "      <td>0.000055</td>\n",
+       "      <td>0.000180</td>\n",
+       "      <td>0.000655</td>\n",
+       "      <td>-0.000195</td>\n",
+       "      <td>0.000138</td>\n",
+       "      <td>-0.000342</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000040</td>\n",
+       "      <td>-0.000115</td>\n",
+       "      <td>-0.000026</td>\n",
+       "      <td>0.000481</td>\n",
+       "      <td>0.000204</td>\n",
+       "      <td>-0.000218</td>\n",
+       "      <td>0.000125</td>\n",
+       "      <td>0.000047</td>\n",
+       "      <td>0.000102</td>\n",
+       "      <td>-0.000150</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>20 rows × 1001 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    0         1         2         3         4         5         6     \\\n",
+       "0    0.0 -0.001149 -0.001121 -0.000664 -0.000572  0.000016  0.002173   \n",
+       "1    0.0 -0.001149 -0.001121 -0.000664 -0.000572  0.000016  0.002173   \n",
+       "2    0.0 -0.001149 -0.001121 -0.000664 -0.000572  0.000016  0.002173   \n",
+       "3    0.0 -0.001149 -0.001121 -0.000664 -0.000572  0.000016  0.002173   \n",
+       "4    0.0 -0.001149 -0.001121 -0.000664 -0.000572  0.000016  0.002173   \n",
+       "5    0.0 -0.001149 -0.001121 -0.000663 -0.000572  0.000016  0.002173   \n",
+       "6    0.0 -0.001149 -0.001121 -0.000663 -0.000572  0.000016  0.002173   \n",
+       "7    0.0 -0.001149 -0.001121 -0.000663 -0.000572  0.000016  0.002173   \n",
+       "8    0.0 -0.001149 -0.001121 -0.000663 -0.000572  0.000016  0.002173   \n",
+       "9    0.0 -0.001149 -0.001121 -0.000663 -0.000571  0.000016  0.002173   \n",
+       "10   0.0 -0.001148 -0.001120 -0.000662 -0.000570  0.000018  0.002174   \n",
+       "11   0.0 -0.001147 -0.001118 -0.000659 -0.000566  0.000021  0.002175   \n",
+       "12   0.0 -0.001144 -0.001112 -0.000651 -0.000558  0.000030  0.002178   \n",
+       "13   0.0 -0.001137 -0.001098 -0.000633 -0.000536  0.000051  0.002185   \n",
+       "14   0.0 -0.001120 -0.001064 -0.000591 -0.000487  0.000099  0.002198   \n",
+       "15   0.0 -0.001078 -0.000986 -0.000495 -0.000379  0.000196  0.002208   \n",
+       "16   0.0 -0.000987 -0.000824 -0.000314 -0.000192  0.000340  0.002145   \n",
+       "17   0.0 -0.000815 -0.000558 -0.000071  0.000014  0.000427  0.001832   \n",
+       "18   0.0 -0.000567 -0.000266  0.000095  0.000092  0.000335  0.001230   \n",
+       "19   0.0 -0.000322 -0.000083  0.000105  0.000055  0.000180  0.000655   \n",
+       "\n",
+       "        7         8         9       ...         991       992       993   \\\n",
+       "0   0.000821  0.001510  0.000150    ...     0.006947  0.006498  0.006520   \n",
+       "1   0.000821  0.001510  0.000150    ...     0.006946  0.006498  0.006519   \n",
+       "2   0.000821  0.001510  0.000150    ...     0.006944  0.006495  0.006517   \n",
+       "3   0.000821  0.001510  0.000150    ...     0.006939  0.006490  0.006512   \n",
+       "4   0.000821  0.001510  0.000150    ...     0.006926  0.006478  0.006499   \n",
+       "5   0.000821  0.001510  0.000150    ...     0.006894  0.006446  0.006467   \n",
+       "6   0.000821  0.001510  0.000150    ...     0.006815  0.006366  0.006388   \n",
+       "7   0.000821  0.001510  0.000150    ...     0.006620  0.006171  0.006193   \n",
+       "8   0.000821  0.001509  0.000150    ...     0.006153  0.005704  0.005725   \n",
+       "9   0.000821  0.001509  0.000150    ...     0.005099  0.004649  0.004670   \n",
+       "10  0.000822  0.001509  0.000150    ...     0.003056  0.002606  0.002626   \n",
+       "11  0.000822  0.001508  0.000148    ...     0.000344 -0.000105 -0.000083   \n",
+       "12  0.000823  0.001505  0.000145    ...    -0.000861 -0.001304 -0.001277   \n",
+       "13  0.000825  0.001497  0.000137    ...     0.000550  0.000100  0.000121   \n",
+       "14  0.000825  0.001474  0.000112    ...     0.001966  0.001478  0.001461   \n",
+       "15  0.000804  0.001400  0.000038    ...     0.002029  0.001483  0.001412   \n",
+       "16  0.000682  0.001177 -0.000157    ...     0.001620  0.001006  0.000883   \n",
+       "17  0.000340  0.000729 -0.000446    ...     0.000873  0.000301  0.000229   \n",
+       "18 -0.000060  0.000310 -0.000518    ...     0.000274 -0.000086 -0.000032   \n",
+       "19 -0.000195  0.000138 -0.000342    ...     0.000040 -0.000115 -0.000026   \n",
+       "\n",
+       "        994       995       996       997       998       999       1000  \n",
+       "0   0.008262  0.008510  0.007529  0.008193  0.008236  0.008553  0.007917  \n",
+       "1   0.008261  0.008509  0.007529  0.008192  0.008235  0.008552  0.007916  \n",
+       "2   0.008259  0.008507  0.007526  0.008190  0.008233  0.008550  0.007914  \n",
+       "3   0.008254  0.008502  0.007521  0.008184  0.008228  0.008545  0.007909  \n",
+       "4   0.008241  0.008489  0.007509  0.008172  0.008215  0.008532  0.007896  \n",
+       "5   0.008209  0.008457  0.007476  0.008140  0.008183  0.008500  0.007864  \n",
+       "6   0.008130  0.008377  0.007397  0.008060  0.008103  0.008420  0.007784  \n",
+       "7   0.007935  0.008182  0.007201  0.007864  0.007907  0.008224  0.007588  \n",
+       "8   0.007466  0.007713  0.006732  0.007394  0.007437  0.007753  0.007117  \n",
+       "9   0.006410  0.006655  0.005674  0.006335  0.006377  0.006692  0.006055  \n",
+       "10  0.004365  0.004609  0.003627  0.004287  0.004327  0.004641  0.004003  \n",
+       "11  0.001656  0.001900  0.000919  0.001579  0.001620  0.001934  0.001296  \n",
+       "12  0.000463  0.000707 -0.000272  0.000390  0.000432  0.000745  0.000109  \n",
+       "13  0.001844  0.002069  0.001078  0.001723  0.001748  0.002043  0.001393  \n",
+       "14  0.003121  0.003283  0.002243  0.002832  0.002802  0.003039  0.002342  \n",
+       "15  0.002960  0.003009  0.001904  0.002409  0.002301  0.002457  0.001709  \n",
+       "16  0.002254  0.002149  0.001004  0.001432  0.001267  0.001360  0.000622  \n",
+       "17  0.001397  0.001166  0.000131  0.000563  0.000430  0.000530 -0.000075  \n",
+       "18  0.000844  0.000538 -0.000218  0.000220  0.000130  0.000220 -0.000205  \n",
+       "19  0.000481  0.000204 -0.000218  0.000125  0.000047  0.000102 -0.000150  \n",
+       "\n",
+       "[20 rows x 1001 columns]"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1bf092730b8>]"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1bf079b7898>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15, 3))\n",
+    "plt.plot(np.linspace(0,1,1001),variance,\"b\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "<img src=\"./4.png\" width=\"85%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Foundings:\n",
+    "\n",
+    "Different factors $(U^20_i)_{1≤i≤20}$ are highly correlated. \n",
+    "\n",
+    "Short-term factors : fast mean reversions, responsible of the ‘roughness’, \n",
+    "\n",
+    "Long-term factors: slower mean reversions,determining the level of the variance process"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### An example to use the module to calculate option price by simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.05      , 0.05121006, 0.05914061, ..., 0.05225546, 0.04408349,\n",
+       "        0.02695603],\n",
+       "       [0.05      , 0.04724264, 0.05275854, ..., 0.01732654, 0.01456418,\n",
+       "        0.01276219],\n",
+       "       [0.05      , 0.05668886, 0.04864526, ..., 0.04017572, 0.05075225,\n",
+       "        0.04905138],\n",
+       "       ...,\n",
+       "       [0.05      , 0.04062341, 0.03958003, ..., 0.04078279, 0.05564083,\n",
+       "        0.05612033],\n",
+       "       [0.05      , 0.04396372, 0.03284474, ..., 0.06108645, 0.06617895,\n",
+       "        0.06299508],\n",
+       "       [0.05      , 0.04195399, 0.05705365, ..., 0.02939353, 0.01993236,\n",
+       "        0.01616433]])"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h1.vo_paths(texp=1, N=1000)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.1606798 , 21.48967354])"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h1.mc_price(strike=np.array([85,78]), spot=98, texp=1, N=1000, cp=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparison Between Lifted Heston Model and Rough Heston Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lifted Heston model nests as extreme cases the classical Heston model (when $n=1$), and the rough Heston model (when $n$ goes to infinity)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to visualize the convergence to rough Heston model when $n$ goes to infinity, we generate our benchmark implied volatility surface, for $9$ maturities $T \\in \\{1w,1m,2m,3m,6m,9m,1yr,1.5yr,2yr\\} $, and with $26$ strikes $K$ per maturity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The parameters of rough Heston model is given by\n",
+    "$$V_0=0.02,\\; \\theta=0.02,\\; \\lambda=0.3,\\; \\nu=0.3,\\; \\rho=-0.7,\\; H=0.1$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The implied volatiliy surface can be computed by Fourier inversion techniques. For simplicity, we only consider the call option situation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We first define:\n",
+    "$$x=log(S_0/K),\\quad y=log(S_T/K)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then the price of the call European option will be\n",
+    "$$v(y,T)=K(e^y-1)^{+}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the method of inverse Fourier integral via cosine expansion, we have:\n",
+    "$$price\\;of\\;call\\;option=v(x,T,V_0)\\approx Ke^{-r\\Delta t}\\cdot Re \\left\\{ \\dfrac 1 2 U_0+\\sum_{k=1}^{N-1} \\phi_{hes}(\\dfrac {k\\pi}{b-a};V_0)e^{ik\\pi\\frac {x-a}{b-a}}U_k\\right\\}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "where, $\\phi_{hes}(\\omega;V_0)$ is the characteristic function of the log-asset price $y$, when $x=0$, $[a,b]$ should cover $[x-10\\sigma,x+10\\sigma]$ (or other sufficiently large interval), with $\\sigma$ denoting the standard deviation of the density $f(y|x)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$U_k$ is given by:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$U_k=\\frac 2 {b-a}(\\chi_k(0,b)-\\psi_k(0,b))$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$\\chi_k(c,d)=\\frac 1 {1+(\\frac {k\\pi}{b-a})^2 }[cos(k\\pi\\frac {d-a}{b-a})e^d-cos(k\\pi \\frac{c-a}{b-a})e^c+\\frac {k\\pi}{b-a} sin(k\\pi\\frac{d-a}{b-a})e^d-\\frac {k\\pi}{b-a} sin(k\\pi \\frac {c-a}{b-a})e^c]$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$\\psi_k(c,d)=[sin(k\\pi\\frac {d-a}{b-a})-sin(k\\pi \\frac {c-a}{b-a})]\\frac {b-a}{k\\pi},\\quad for\\; k\\neq 0$$\n",
+    "$$\\psi_0(c,d)=d-c,\\quad for \\; k=0$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$\\phi_{hes} (\\omega;V_0)$ is derived from the fact that:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$\\mathbb{E}[ {exp}(u{ log} S_T)]= {exp}(u {log} S_0+\\int_0^T F(u,\\psi(s,u))g_0(T-s)ds)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$F(u,v)=\\frac 1 2 (u^2-u)+(\\rho \\nu u-\\lambda)v+\\frac {\\nu^2}{2}v^2$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$g_0(t)=V_0+\\lambda\\theta\\int_0^t \\frac {s^{H-1/2}}{\\Gamma(H+1/2)}ds$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$\\psi(t,u)=\\frac 1 {\\Gamma(H+1/2)}\\int_0^t (t-s)^{H-1/2}F(u,\\psi(s,u))ds$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$\\psi$ solves the fractional Riccati equation. We can use the Adams Predictor-Corrector scheme for the discretization of the fractional Riccati equation. The procedure is shown below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$\\psi(t,a)=\\dfrac 1 {\\Gamma(\\alpha)}\\int_0^t (t-s)^{\\alpha-1}F(a,\\psi(s,a))ds$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Over a regular discrete time-grid with mesh $\\Delta$, $0\\leq t_0\\leq...\\leq t_n\\leq t$,"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$\\psi(t_{k+1},a)\\approx \\hat \\psi (t_{k+1},a)=\\sum_{0\\leq j\\leq k} a_{j,k+1}F(a,\\hat \\psi(t_j,a))+a_{k+1,k+1}F(a,\\hat \\psi^P(t_{k+1},a))$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "with\n",
+    "$$a_{0,k+1}=\\dfrac {\\Delta^\\alpha}{\\Gamma(\\alpha+2)}[k^{\\alpha+1}-(k-\\alpha)(k+1)^\\alpha]$$\n",
+    "$$a_{j,k+1}=\\dfrac {\\Delta^\\alpha}{\\Gamma(\\alpha+2)}[(k-j+2)^{\\alpha+1}+(k-j)^{\\alpha+1}-2(k-j+1)^{\\alpha+1}]\\quad 1\\leq j \\leq k$$\n",
+    "$$a_{k+1,k+1}=\\dfrac {(\\Delta t)^\\alpha} {\\Gamma(\\alpha+2)}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$b_{j,k+1}=\\dfrac {\\Delta^\\alpha}{\\Gamma(\\alpha+1)}((k-j+1)^\\alpha-(k-j)^\\alpha) $$\n",
+    "$$\\hat \\psi^P(t_{k+1},a)=\\sum_{0\\leq j\\leq k} b_{j,k+1}F(a,\\hat \\psi(t_j,a))$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Following above steps, we can derive the price of the call option under rough Heston model. Since we know the call option price formula of BSM model, we can derive the implied volatility surface through Newton's method with good initial guess."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call option price formula of BSM model:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$C=S_0N(d_1)-e^{-rT}KN(d_2)$$\n",
+    "$$d_{1,2}=\\dfrac {log(S_0e^{rT}/K)}{\\sigma \\sqrt{T}}\\pm\\dfrac 1 2 \\sigma \\sqrt{T}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cmath\n",
+    "from scipy.special import gamma"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "V0=0.02\n",
+    "θ=0.02\n",
+    "λ=0.3\n",
+    "ν=0.3\n",
+    "ρ=-0.7\n",
+    "H=0.1\n",
+    "S0=100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculation of implied volatility surface of rough Hestion model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since the calculation speed of Python is relatively slow, we can only choose to run 26 maturies(and this will take many hours). Meanwhile, the time steps of Adams Predictor-Corrector scheme is set at 60 (200 in the paper). The N of cosine method is set at 160, same as in the paper. However, to reduce running time, we will stop the calculation once the variation of price in the last 5 steps is less than $0.005\\% S_0$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "104.2420576193561 0.013577110154244653\n",
+      "109.04631784921234 0.0021397512390339025\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-79-a0a987d36b05>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     92\u001b[0m         \u001b[0mcheck_end\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 94\u001b[1;33m             \u001b[0mchar_func\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcharacteristic_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcomplex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mH\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mρ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mλ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mν\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mV0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mθ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgamma_coeff\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mintervals\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m70\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     95\u001b[0m             \u001b[0mprice_add\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchar_func\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mU_k\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcomplex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpi\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     96\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-79-a0a987d36b05>\u001b[0m in \u001b[0;36mcharacteristic_func\u001b[1;34m(u, T, H, ρ, λ, ν, V0, θ, gamma_coeff, steps, intervals)\u001b[0m\n\u001b[0;32m     40\u001b[0m         \u001b[0mleft_s\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[0mright_s\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mleft_s\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mds\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 42\u001b[1;33m         \u001b[0mψ_right\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mψ\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mright_s\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mH\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mρ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mλ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mν\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     43\u001b[0m         \u001b[0mintegral_left\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mψ_right\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mρ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mλ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mν\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mg0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mleft_s\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mV0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mλ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mθ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mH\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgamma_coeff\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m         \u001b[0mintegral_right\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mψ_right\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mρ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mλ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mν\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mg0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mright_s\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mV0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mλ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mθ\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mH\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mgamma_coeff\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-79-a0a987d36b05>\u001b[0m in \u001b[0;36mψ\u001b[1;34m(t, u, H, ρ, λ, ν, steps)\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[0mψ_series\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mψ_series\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcomplex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0mψ_P\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[0mψ_P\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mψ_P\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcomplex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msteps\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[0ma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "def g0(t,V0,λ,θ,H,gamma_coeff):\n",
+    "    ans=V0+λ*θ/gamma_coeff/(H+0.5)*pow(t,H+0.5)\n",
+    "    return ans\n",
+    "\n",
+    "def F(u,v,ρ,λ,ν):\n",
+    "    ans=0.5*(u*u-u)+(u*ρ*ν-λ)*v+0.5*ν*ν*v*v\n",
+    "    return ans\n",
+    "\n",
+    "#Adams Predictor-Corrector scheme\n",
+    "def ψ(t,u,H,ρ,λ,ν,steps):\n",
+    "    α=H+1/2\n",
+    "    Δ=t/steps\n",
+    "    pow_temp1=pow(Δ,α)\n",
+    "    gamma_coeff2=gamma(α+2)\n",
+    "    gamma_coeff1=gamma_coeff2/(α+1) #gamma(α+1)\n",
+    "    ψ_series=np.zeros(steps+1)\n",
+    "    ψ_series=np.array(ψ_series,dtype=complex)\n",
+    "    ψ_P=np.zeros(steps+1)\n",
+    "    ψ_P=np.array(ψ_P,dtype=complex)\n",
+    "    for k in range(steps):\n",
+    "        a=np.zeros(k+2)\n",
+    "        b=np.zeros(k+1)\n",
+    "        for j in range(k+1):\n",
+    "            a[j]=pow_temp1/gamma_coeff2*(pow(k-j+2,α+1)+pow(k-j,α+1)-2*pow(k-j+1,α+1))\n",
+    "            b[j]=pow_temp1/gamma_coeff1*(pow(k-j+1,α)-pow(k-j,α))\n",
+    "        a[0]=pow_temp1/gamma_coeff2*(pow(k,α+1)-(k-α)*pow(k+1,α))\n",
+    "        a[k+1]=pow_temp1/gamma_coeff2\n",
+    "        for j in range(k+1):\n",
+    "            F_value=F(u,ψ_series[j],ρ,λ,ν)\n",
+    "            ψ_P[k+1]+=b[j]*F_value\n",
+    "            ψ_series[k+1]+=a[j]*F_value\n",
+    "        ψ_series[k+1]+=a[k+1]*F(u,ψ_P[k+1],ρ,λ,ν)\n",
+    "    return ψ_series[steps]\n",
+    "\n",
+    "def characteristic_func(u,T,H,ρ,λ,ν,V0,θ,gamma_coeff,steps,intervals):\n",
+    "    ds=T/intervals\n",
+    "    integral_value=0\n",
+    "    ψ_left=ψ(0,u,H,ρ,λ,ν,steps)\n",
+    "    for i in range(intervals):\n",
+    "        left_s=ds*i\n",
+    "        right_s=left_s+ds\n",
+    "        ψ_right=ψ(right_s,u,H,ρ,λ,ν,steps)\n",
+    "        integral_left=F(u,ψ_right,ρ,λ,ν)*g0(T-left_s,V0,λ,θ,H,gamma_coeff)\n",
+    "        integral_right=F(u,ψ_right,ρ,λ,ν)*g0(T-right_s,V0,λ,θ,H,gamma_coeff)\n",
+    "        integral_value+=(integral_left+integral_right)*ds/2\n",
+    "        ψ_left=ψ_right\n",
+    "    ans=np.exp(integral_value)\n",
+    "    return ans\n",
+    "\n",
+    "def χ_k(k,c,d,a,b):\n",
+    "    sincos_1=k*np.pi*(d-a)/(b-a)\n",
+    "    sincos_2=k*np.pi*(c-a)/(b-a)\n",
+    "    ans=np.cos(sincos_1)*np.exp(d)-np.cos(sincos_2)*np.exp(c)\n",
+    "    ans+=k*np.pi/(b-a)*(np.sin(sincos_1)*np.exp(d)-np.sin(sincos_2)*np.exp(c))\n",
+    "    ans/=(1+pow(k*np.pi/(b-a),2))\n",
+    "    return ans\n",
+    "\n",
+    "def ψ_k(k,c,d,a,b):\n",
+    "    sincos_1=k*np.pi*(d-a)/(b-a)\n",
+    "    sincos_2=k*np.pi*(c-a)/(b-a)\n",
+    "    if (k==0):\n",
+    "        ans=d-c\n",
+    "    else:\n",
+    "        ans=(np.sin(sincos_1)-np.sin(sincos_2))*(b-a)/k/np.pi\n",
+    "    return ans\n",
+    "\n",
+    "def U_k(k,a,b,call=1):\n",
+    "    if (call==1):\n",
+    "        ans=(χ_k(k,0,b,a,b)-ψ_k(k,0,b,a,b))*2/(b-a)\n",
+    "    else:\n",
+    "        ans=(-χ_k(k,a,0,a,b)+ψ_k(k,a,0,a,b))*2/(b-a)\n",
+    "    return ans\n",
+    "\n",
+    "maturities=[7/365,1/12,1/6,1/4,1/2,3/4,1,1.5,2]\n",
+    "log_moneyness_all=np.zeros(shape=(9,26))\n",
+    "impvol=np.zeros(shape=(9,26))\n",
+    "gamma_coeff=gamma(H+1/2)\n",
+    "for i_T in range(9):\n",
+    "    T=maturities[i_T]\n",
+    "    left_log=-1.2*np.sqrt(T)\n",
+    "    right_log=0.3*np.sqrt(T)\n",
+    "    strikes=np.linspace(left_log,right_log,26)\n",
+    "    for i_strike in range(26):\n",
+    "        log_moneyness=strikes[i_strike]\n",
+    "        log_moneyness_all[i_T,i_strike]=log_moneyness\n",
+    "        x=-log_moneyness\n",
+    "        K=np.exp(-x)*S0\n",
+    "        a=x-1.5\n",
+    "        b=x+1.5\n",
+    "        N=160\n",
+    "        price=0\n",
+    "        check_end=np.zeros(5)\n",
+    "        for i in range(N):\n",
+    "            char_func=characteristic_func(complex(0,i*np.pi/(b-a)),T,H,ρ,λ,ν,V0,θ,gamma_coeff,steps=60,intervals=70)\n",
+    "            price_add=char_func*U_k(i,a,b,1)*np.exp(complex(0,i*np.pi*(x-a)/(b-a)))\n",
+    "            if (i==0):\n",
+    "                price+=price_add.real/2\n",
+    "            else:\n",
+    "                price+=price_add.real\n",
+    "            flag=False\n",
+    "            if (i<5):\n",
+    "                check_end[i]=price*K\n",
+    "            else:\n",
+    "                for i_temp in range(4):\n",
+    "                    check_end[i_temp]=check_end[i_temp+1]\n",
+    "                check_end[4]=price*K\n",
+    "                check_min=check_end[0]\n",
+    "                check_max=check_min\n",
+    "                for i_temp in range(5):\n",
+    "                    check_min=min(check_min,check_end[i_temp])\n",
+    "                    check_max=max(check_max,check_end[i_temp])\n",
+    "                if (check_max-check_min)<0.005*S0/100:\n",
+    "                    flag=True\n",
+    "            if (flag==True):\n",
+    "                break\n",
+    "        price=check_max\n",
+    "      #  print(K,price)\n",
+    "        model=pf.Bsm(0.3)\n",
+    "        impvol[i_T,i_strike]=model._impvol_newton(price,K,S0,T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Store the result of rough Heston model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import xlwt\n",
+    "#wb=xlwt.Workbook()\n",
+    "#ws=wb.add_sheet('log_moneyness')\n",
+    "#for i in range(9):\n",
+    "#    for j in range(26):\n",
+    "#        ws.write(i,j,log_moneyness_all[i,j])\n",
+    "#ws=wb.add_sheet('impvol')\n",
+    "#for i in range(9):\n",
+    "#    for j in range(26):\n",
+    "#        ws.write(i,j,impvol[i,j])\n",
+    "#wb.save('rough heston impvol.xls')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xlrd\n",
+    "data=xlrd.open_workbook(\"rough heston impvol.xls\")\n",
+    "data_log_moneyness=data.sheet_by_name(\"log_moneyness\")\n",
+    "data_impvol=data.sheet_by_name(\"impvol\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the implied volatility surface under lifted Hestion model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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BQbB9O6SdrILX+C2cqdQGNsyB7wcABwB/4E/LBimEEMLsNrOZP/P6/bctAx0WgfdASJoA8a9CMX44I4QQopCVqQDd1kLtMNj0NOx539IRCSFEkSqVnUqrWc1GNgLg6wuJiaAcKnDfi6v5u2I4xM2DHV1B/w34YXQwCSGEKA0yyGAAA/DDj1/4JXcBGztoNw8aDYfv34GtI0FfM3+gQgghrJN9WeiyAur1gO0vwq7J8gBCCFFilbpOJY3mbd6mAx34hm8AqFfP6FiqeY8Dns8u4mD5YbB7MWwIhGuXMEYsfWfRuIUQQphHWcqynvVc4hL++LObPKZFKBsImgW+L8Hej2HDYLh21fzBCiGEsIgfgEv5FbAtAx0j4cH+kDheRrYKIUqsUteppFB8wXqaE0B/+jONaWg0NWpAXBw80tSW+5/5hO9sJ8C+VRDzEFyuAAQBWy0dvhBCCDNoRCMSSKA85QkkkO1sz11IKWg1HZq/Dvu+NNZaunrZ7LEKIYQwr7+5yo8M4VUSOJ9fQRs7Y8HuRsNkZKsQosQqdZ1K14ABVORv1tGR3rzCK4xiFNe4RqVKsHEjdOqkaPbCRNZmzITfN8Nyd7hYC2NXuJUWboEQQoiiptlPPaqRSCL3cA+hhBJDTO6CSkGLN8D/Hfg10tjx58pFs8crhBDCfKpzkm7EM5lgXmcNJ/MrrGwg6JMbI1s3DpGRrUKIEqXUdSrZAO8D/+DAdyzicV5gJjPpS18ucpGyZSE6Gp58EjqOf47P0xehj+yGJXZwvgHQA/jKom0QQghRdK6SwSmCOUYbamJPHHE8xEN0pzsLWJB3paYvQ5uP4PdVsKoLXL5g3qCFEEKYkRuViCeTBkylK9NZQFp+xf8b2froBPi/eTKyVQhRopS6TiUwlt5OBByxYRXvMYQZRBFFKKGc4Qx2dvD55zBmDAx5ty9T9q1Gn/odIs/AmRbAE8AHlm2EEKLY2bRpE76+vjRs2BBfX1+2br0xpXbPnj00bNiQunXrMnLkSLSsu2AxVyjLJ8yhAvv5Bz9cOMcWttCKVgxkIB/yYd4VG4+AkC8gZSMsbw+Xzpk3cCFEsSY5orhxozJbOUtr3mYg83iP3/IrrhS0nCgjW4UQd8xa80Sp7FQC8AJ2AnWAL3mJoXxDIon4408aaSgFb78N778P478I5bkdm9EZJyHyEPzTFngBGA9IUhdC3JoqVaqwevVqfv75Z+bPn0///v2vHxs+fDhz5swhOTmZ5ORkYmNjLRhp6eYAjKEDM9mELcc5Q0vK8SdrWUs3ujGKUbzO6+i8fv8bPgntF0JaAiwLgczTZo9fCFE8SY4ojpxwYS2n6Ml4XmI9r7KnoHsDGdkqhLhD1ponSm2nEkANIA5oBczhcfqxjj/4gxa04AAHABg1Cr7+Gj5b3Zzw6DiuXtWwZA8c6QhMBp7DWKlJCFFapKSk4OXlxZAhQ/D29iYkJISMjIwC6zVu3JgaNWoA4O3tTWZmJhcvXiQ9PZ2zZ8/SvHlzlFIMGDCAlStl/TZLsgNeoSXzieMCmgwCsOEHoohiEIN4kzd5nue5ltfvv1df6LQUju6BpW3gwj9mj18IYTmSI0obByoTyWme5nmm8jND2c6V/KtkG9kaJiNbhShlSlqesDPblayUM7AOeBL4krb0JI54wmhJS9awhha0oF8/cHGBnj0b0PZMIhuHBmO/dCt06QGes4BTGOss2VuwJUKUPqOAvYV8Th9ubXJrcnIyixcvZu7cuYSHhxMdHU16ejoLFy7MVTYgIICZM2dm+y46OprGjRvj4OBAWloaHh4e1495eHiQlpbv6gzCDBQwioZ8ThKtCKYWbbnCMr7gC1xw4T3e4xSnmMc87HP+/tfrBl1XQUx3iGoNPTdBBXdLNEOIUs1SeUJyRGljSyVmc5YqDGIKKznBKhbRBcebV2n4JNiXM9ZXWhYM3deDY2XzhSyEkHuJQmK2TiWlVCjwIWALfK61nprj+CBgOlxf5+5jrfXn5oitDLAA8ADeoTFtSSKFUIIIIpJIutCF9u1hyxbo0KE2vjMS+fbldpRdEQNhfaD+IuAMEAWUM0fIQggLq127Nj4+PgD4+vqSkpLCuHHjiIiIKLDuvn37eOWVV9i4cSNAnnOelVKFG7C4Y4PxZCmJnCOUh+jCOb5iBjNwxZXXeI3TnCaKKMpSNnvF2mHQbR2s7ARRraDnFnCuZZlGCCHMSnJEyfcbv1GHOthia/pG4cxkzuNGV0axlfYsYCUDcL75Ser3AbuysCYcotpAz41Qzs0s8QshLKsk5QmzdCoppWyBWUAwkAp8r5SK0Vrvz1F0idZ6hDliyskGmIrRsTSS+3iYRJzpSHe68wmf8DRP07w5xMdDSEg16k/YwU9vdqLi2iWQ2Rd8IoFQYDVQ0RJNEKLUseRy+Q4ODtff29rakpGRwfTp0wt8upCamkq3bt1YsGABderUAYynCampqdfLp6amXh/aKqxDL6qyge0k0JXW9OME/zCWsVSmMs/yLKGEEkMMFXP+/t8TCD1M0xuW+EOvrVDpPss0QohSyFJ5QnJEyfY3f9OMZrSlLV/zNY5ZRiSV53kycSWAJ/iRQD5mPSOoevOT1e0CXWNgVTdY0gp6bYYK8t+vEOYg9xKFw1xrKjUFDmqtD2mtLwGRQBczXfu2jACWAf+HG2fZSgChDGPY9UVZvb0hKQkcK1bE86UN/F2+I2xZDDt7gk4CWgPHLNoGIYRlREREsHfv3lyv/5LA6dOn6dChA2+//TYtW7a8Xs/d3R0nJyd27dqF1poFCxbQpYtV/kSWau1wpgzriKE7roziGK8xnGEsZCFJJNGGNhzneO6KNVtA+FZjzYwl/nDiF/MHL4SwOMkRJUd1qjOOcSxjGe1ox2myb8rgSD80q2jAAULw4y1S8l++27Md9IiFc4dhSQCc/bNI4xdCWKfimifM1alUEzic5XOq6buceiilflJKLVNK5TlHQCk1VCm1Wym1+/jxPP7xXgi6A1uAE5RnP6vozJO8yZs8zdNc4Qr33gsJCVC3flk8R0bzu8MASFoK2zqC/gXwByQZCCGy+/jjjzl48CCTJk3Cx8cHHx8fjh0zOqFnz57N4MGDqVu3LnXq1CEsLMzC0Yq8tMCR+4hiIYOpylv8zTD6Ek4MMRzgAP74czhbujOp5gvh2+HaVeOG4fhPZo9dCGHdJEcULy/xEgtZyE524o8/qaRmO25Pe+zZhAfHGUhLxvN/+S/f7RFgjFLKOAGR/nAquUjjF0IUP9aaJ1Re8+8K/SJK9QLaaa0Hmz73B5pqrZ/LUsYV+FdrfVEpNQwI11q3ye+8TZo00bt37y6yuA8AYcBxNJ0ZTyRT6ExnFrOYcpTj3Dno1g22br3G99NfwpcPwKsdtNsJts7AJqB+kcUnRGl04MABvLy8LB2GVcnrb6KU2qO1bmKhkKxGUeWJFDTrGcdw3iKd7rizkHi+pyMdqUhFNrOZ+7k/d8WTv8LSILhyAXpsgOqPFHpsQpR2kidykzxxc3ebJ7awhW50oyIViSUWb7yzHdf8zL+04zKZTGUtE2mecwW+7I7tNRbutrGDnpuhind+pYUQt0lyRN7uJk+Ya6RSKpB15JEHcCRrAa31Ca31RdPHuYCvmWK7KS9gJ3A/imVMZgCzWM1qggnmJCdxcoK1a6FHDxuajH6P2PNT4MAGiGkEly9ijFj6wbKNEEIIUeg8UfRgCu/yPu4sJ532+NOI7Wwnk0z88ON//C93RZcHoE88OFSCpW0hLdH8wQshhCg0QQQRRxxXuIIffsQTn+24oiFOJGGDK28QxOus50x+J6zqA73jAGWssXQ0j1wihBBWxFydSt8D9ZRStZVSZYA+QEzWAkqprHstd8YYKGRx7sAOjJWSFvAMfVnKbnbjhx9/8RcODhAZCcOGKcJeH8uCI5+iDyVA9D2Q6QgEAnGWbIIQQogiUBUYyiimsgA34jhKII2pSQIJOOJIa1qTQELuihVrGzcM5atDdDv4a5vZYxdCCFF4fPBhJzupRjWCCWY5y3OU8KQSCVzkAabQmakszn8FVlcvI0/Yl4elgXBkVxFGL4QQd8csnUpa6ysYa2BvwOgsitJa71NKvamU6mwqNlIptU8p9SMwEhhkjthuhTOwFngMWEQPwtjIEY7QnOb8zM/Y2sInn8CECTDwvaeZcSASnf4TRDnD+apAO2CdRdsghBDi1l26dGvlnIAX6M80YnDiAMfxpx4OJJKIO+6EEMJ61udR0QN67wBnT1jRHv6ILczwhRBCFCGtYexY+PHHG9954kkCCTSmMT3pySxm5ahVjcps5xwtmMLjfMrHpOR3kcp1jZGtZasY0+EO7yj8hgghRCEw10gltNbrtNb3a63raK2nmL6boLWOMb1/VWvtrbVupLUO1Fpb1fY4ZYCvgQhgFa3wJR4N+ONPHHEoBRMnwkcfwSvzwolIXI0+lQKRV+FMHYzN7iIt2AIhhBC34tIlCAuDV16Ba9cKLu8AvEJ7ZrEJO45xmpa4c5Z44vHCi850ZglLclcsX91YvNvFC1Z2huSVhd0UIYQQReDYMViwAPz9YePGG99XoQpb2EJHOjKCEbzGa+hs+75VxIVYTtOJCTzHct5gX377wjnfY4xYcqoFy8MgZUORtUkIIe6U2TqVSgIbYBrwAbCNhtQgiaqmJ9HRRAMwYgQsXAgfrmrHoDWbuXbhFESehBONMMY6fWa5BgghhCiQrS3Urw/TpsFjj0Fm5i3UAUbTkoXsIINrZOCPM7+zla20oAV96ctnef3+l6sCvbZAtYdhdU/4JY/OJyGEEFalWjXYtQtq14YOHeDLL28cK0c5lrOcIQzhLd7iCZ7gMpez1C6LC9GcZBAvMpEknmMX+TzBqFDDGNla+X7jAcTBmJuXFUIIC5BOpTvwPMaYo5+5F0jAm4fpRS9mMxuAvn1h9WpYmticTt/EceUKEPkHpDcHhgFTLRa7EEKI/Nnawscfw/TpsGQJBAfDyZMF11PACB5iI4kcxYVrBGHHLmKJpT3tGcYwpub1++9YGXpugpotYd1jsG9+obdJCCFE4fLwgPh4aNMGnnwSXn/dmBYHYIcdn/EZE5nIfObTmc78y79ZatvhwjxOM5ohzOIv+rGJfOZdl3OD8G3g5gOre8gDCCGEVZFOpTsUDmwEjuHKETbjTwee4RnGMx6NJjQUtmyBnckN8fs4gUuqEiz9Ef5sA7wKvAL5DXcVQpQ4KSkplC1bFh8fH3x8fBg2bNj1Y3v27KFhw4bUrVuXkSNHorX8PliSUjB6tLERw3ffQYsW8Mcft1Z3ELX5kUR+ox5l6MQlVrGCFTzGY7zKq7zCKzmmQwBlnKD7eqjVBmIHwY+fFnqbhBDWTXJE8ePsDGvWGJ1Kb74JgwbdWJNPoZjABOYyl41sJJBAjmVbnltRiemc4x3CWcxVuhDN+Ztf7L8HEO7N5QGEEKWUteYJ6VS6C62ABMCWcvyPFbTnKSYzmcEM5gpXaN4c4uLgrzP38dDbCZy3vw9WJMBvIRgT6YYBVy3aBiGEedWpU4e9e/eyd+9ePv30RsfB8OHDmTNnDsnJySQnJxMbKws3W4PevWHzZmP9jEcfhe+/v7V6PajGMXawi+Y48RhnmcPXfM1whjONaQxjGFdz/v7bl4Nuq+G+DrB5OOz5oPAbJISwapIjih97e/j8c6NTacECY02+M2duHB/MYFaxin3sowUt+J3fs9V34mUuMJdgNuJOMPPIZ2isgzP0WA/3BBkPIPbOLppGCSGsljXmCelUuksNgJ3APdixibl0YxzzmEc3unGBCzRoAElJcNXRnXqv7eBUGV9Ysxl+bg/MAR6H/Ia7CiGsTkpKCl5eXgwZMgRvb29CQkLIyMi44/Olp6dz9uxZmjdvjlKKAQMGsHJl6Vu0WSkVqpT6VSl1UCk1Jo/jg5RSx5VSe02vweaIy9/f+B0vXx5at4aYW1zOIpiK2BNLLJ1wZQT/8Caz+JjXeI05zOExHuNSzt9/O0fovBzq9YDtL8C3bxd6e4QQRUtyROmjFIwfD/PnGw+U/fzg8OEbxzvSka1s5TSnaUEL9rAnW/1yDOYKS3mEPTQlgA9Ju/l8Bvvy0DUG7ut1opAiAAAgAElEQVQEW56B3e8VWbuEEEWjpOUJO7NdqQSrBcQDXVGsYBI9qMFynqUtbVnDGjw9XUhIgLCwyniO3sSBaT2psXEdZLaHR5YAZ4FlQDmLtkOI4mYUo9jL3kI9pw8+fEDBI0SSk5NZvHgxc+fOJTw8nOjoaNLT01m4cGGusgEBAcycOROAP/74g8aNG+Ps7MzkyZPx9/cnLS0NDw+P6+U9PDxIS0srvEYVA0opW2AWEAykAt8rpWK01vtzFF2itR5h7vjq14edO6FTJ+jWzdjp85lnCq73KGX5mWiWMITeTCSdf5jMTCpTmdGM5hznWMYyymX9/bctAx0jYf1ASBgLVy5AizeNuxYhxG2xVJ6QHFE6DRgANWtC9+7G6Na1a8HHxzj2KI+SSCLtaEcrWrGc5YQQcr2uA925wnrq0IUKtOQtNvEq9fIeAWDnCJ2Xwbp+sOMlI088Os4sbRSiJJF7icIhnUqFpDKwAegPLGM4HXFjI4/jhx8b2ECtarXYtg26dClP7RdW8fO0AdwftwQyQ8FvPah2wBqgokXbIYS4NbVr18bH9C9FX19fUlJSGDduHBERETet4+7uzl9//YWrqyt79uyha9eu7Nu3L885z6r0dSA0BQ5qrQ8BKKUigS5Azk4li6lWDbZtMzZjePZZSEmBqVPBpoAxvw2xw4l5fE4VBjODI5zgJeZTkYoMZSjtaMca1lAx6++/jR2ELQC7srBrMlzOgFbTpWNJiGJCckTpFRQEiYnQvr0x0nXpUggNNY49wAMkkUR72tOBDnzJl/Sj3/W6drTBhm1UIYwhtOR1NjCBxtjndSHbMtBhkdHBlDgeLl8AvymSJ4QoJkpSnpBOpULkiLEr3AvAR/SkDVXYTRea05wNbMC7ojexsdCnTxm8XlrIrimVeeS7TyEzCIK2g00bIBZws2QzhCg2buUpQFFxcHC4/t7W1paMjAymT5+e79MFBweH6/V8fX2pU6cOv/32Gx4eHqSmpl4vn5qaSo0aNYq+EdalJpBlsgCpQLM8yvVQSgUAvwEvaK0P5yyglBoKDAW45557CjXI8uVhxQoYOdLYHe7PP43pDo6O+dfzRNGZ6XyIG8/zCkc4xWCiccaZfvQjkEA2sAG3rL//NrYQMse4YdjzLlzJgKCPQMnMdSFulaXyhOSI0q1BA9i1Czp0gI4d4dNPYbBpwnYNarCDHXSnO/3pTzrpjGY0CuMG0IYmlCeBq4QwmtZMIIYJtKJsXheysYPQr8CuHHz3tjFiqfX70rEkxC2Se4nCIZ1KhcwW+BDj7mgMrWlKHH8Sih9+rGENLR1bsmwZDBliS9Oxn7B+nCuhP02BTD9o/z3YBgCbAI98ryOEsD4RERH5Pl04fvw4Li4u2NracujQIZKTk7nvvvtwcXHBycmJXbt20axZMxYsWMBzzz1nxsitQl7/As752GU1sFhrfVEpNQyYD7TJVUnrORiL1tGkSZNC3/rC1hY+/hhq14aICDhyBFatAheX/OtVBZ7gZabjyosMJZ22hLMWZ2LoTnf88WcTm6hFrRuVlA20+cgYsbR7Bly7BG0/NTqchBDFiuSI0qVGDWN9pfBwGDLEGN06aZLR31ORiqxjHQMZyMu8zBGO8C7vYmOa7KZ4gIokcJJ2vE47JhLFGDrnPZ9B2UDb2Uae+OED4wFE29nyAEKIYqi45gn5tSkCCngF427nBxrhwk5cqEpb2hJDDHZ2MG8ejB6tCJs8ma8Pvwu/JcDKRnA5FfADDlq0DUKIwhcXF8dDDz1Eo0aN6NmzJ59++ikupp6I2bNnM3jwYOrWrUudOnUICwuzcLRmlwpZe1PwAI5kLaC1PqG1vmj6OBfwNVNsuSgFo0dDZCR89x20aAGHDhVczxl4jqeYwTJc+IGjBNCOhmxkI+mk44cfySTnvljANGO9jJ8/N3b8uXalKJolhLAgyRElj5OTsbnDkCEwZQr07w+XTPszOODAIhYxilF8wAc8xmNc5GKW2rVwIY7zPMQkujODBRy72YWUgtbvQdNX4ac5EPuE5AkhSiCrzRNa62L78vX11dZuvda6vNa6lj6mH9JNtY220XP13OvH33lHa9D63Se+0NfetdF60UNaZ1TWWlfXWv9koaiFsF779++3dAhWJ6+/CbBbW8Hv9K2+MEbOHgJqA2WAHwHvHGXcs7zvBuwq6LzmyBNxcVpXrqx11apaf/fdrdW5rLV+R2/VZ3UFfVx76qv6N71H79Fu2k1X1VX1Xr0374o7J2s9A61jeml95VKhtUGIkkTyRG4lIU8U1csceeLaNa2nTDH+zR8YqPWpU1mO6Wt6up6u0ehAHahP69M5ap/Vx3WQ1ho9Rb+n/yjoYjsnmfJEuOQJIfIgOSJvd5MnZKRSEQsFtgGZuJHKFpoSzBCGMJnJaDQvvwxz50LE/CcZtzMK/fcvEOUG5zXQCvjWsg0QQggz0FpfAUZg7HlwAIjSWu9TSr2plOpsKjZSKbVPKfUjMBIYZJlos/P3h6QkKFcOWreGNWsKrmMHRBDIl2xD8y/n8KMhinjiKUMZWtOaJJJyV3z0NWg1A35bCqt7wZWLucsIIUQJpZQKVUr9qpQ6qJQak8fxQUqp40qpvabXYEvEmZNSMHYsfP01JCSAnx/89ZfpGIrRjOZrviaeeAII4Ei2gbpOVGEtJ+jBWF4khtfYn2t2eBaPjoOA6fBbFKzuKXlCCFHkpFPJDB4BkoBKVOBHVhNIf8Yznud4jqtcZfBgiIqCGTE9eHr9Gq6dSoNIRzhTAQgCtlq2AUIIYQZa63Va6/u11nW01lNM303QWseY3r+qtfbWWjfSWgdqrX+xbMQ31K8PO3eClxd06WIsyloQBYykCTHEcwYHLtGaezhKAgm44UYwwWxiU+6KTV6CNh/D76tgVVdjZzghhCjhlFK2wCwgDHgQ6KuUejCPoku01j6m1+dmDbIA/frBhg2QmgqPPgr/+1+WY/RjLWs5xCFa0IJf+TVLTQdcWcIJhjCSt/iW4XzL1Ztf6JHRpjwRAys7GzvDCSFEEZFOJTOpi9Gx5IU9O/iKUCKYxSz60IeLXKRHD1i7FhYlBdMzcjNXz5+FyMtwojrQHoixbAOEEELkq3p12L4dwsJg+HB49VW4dq3gek9Rn50k8hc1sKEdLvxEPPHUox4d6EA00bkrNX4WgudCygZY2REuny/09gghhJVpChzUWh/SWl/C2HS5i4Vjum2BgZCYCHZ2EBAAsbE3joUQwna2k0EGLWjBLnZlqWmLK59xijE8wWek0ZfN5DMKqfGzEPIF/LkJVnSAS/8WWZuEEKWbdCqZUTVgO9AWG2KZRijvsoxlhBLKGc7Qti1s3QpxyY/Sdu4OLl8CIk/B3/cB3YHc2wsKIYSwHhUqwMqV8PTTMHWqsSjrxVuYedCbWhwinp9pQFm6Yc8GtrOdR3iEcML5ki9zV3poMITNh8PbIToULp4t9PYIIYQVqQkczvI51fRdTj2UUj8ppZYppWrlcdzivL1h1y6oWxc6doQvvrhxzBdfkkjCBRfa0IY1ZJ1TrajM25xlBt1ZiqYTK8ins6jhk9D+G0iNh2UhcPFMkbVJCFF6SaeSmTlh7IndD4jlRYL4hgQSaEUr/uZvmjY1th9NPtWQpu8nkKkrQtRh+KsB0B/4xKLxCyGEyJ+dHcyeDW+/DYsWQWgonD5dcL0OVOECW0mgFS4M5DJfsZGNtKUtT/IkH/BB7koP9ocOiyF9F0SHQOYtXEgIIYonlcd3ORcXWg14aq0fAjZjbMac+0RKDVVK7VZK7T5+/Hghh3lratQw/s3fti0MHgzjx4M2taYOdUgkEW+86UpX5jEvW11nXuI882jDFtxpywJO3PxCXo9BxyVwdDcsDYKMfMoKIcQdkE4lCyiDkeFeBrbwOE1Zy0EO0pKWHOQgDz5oDIs9b1cH70kJnLO5F5b/Agd9gWeBty0avxBCiPwpBWPGwDffGL/nLVveWJQ1PwE44cw61tAdN17gHFOJYRU96MELvMBEJqJz3kM9EA6dlsHRH2BpG7lhEEKUVKlA1pFHHpBtRWu01ie01v+ND50L+OZ1Iq31HK11E611Ezc3tyIJ9lY4OcHq1Uan0uTJMHAgXLpkHKtKVbaxjba05SmeYgpTsv3+l+cJLhPNw+zFlwBmkXbzC93fA7qsgH/+D6IC4fzRIm6ZEKI0kU4lC7EB3gE+AHYSQl22cpoztKQlP/AD995r7A5RsWYN6r62g39sGkHM/2Dfo8BYYAy5H84IIazZiRMnCAwMpEKFCowYMSLbsT179tCwYUPq1q3LyJEj0abHlSdPniQ4OJh69eoRHBzMqVOnLBG6uEOPP24sypqWZizKundvwXUexoF6RLGYp6jOZE7wApEsZBCDeIM3eJEXc3cs1e0CXVfByQMQ1VpuGIQohiRHFOh7oJ5SqrZSqgzQhxyLjiql3LN87Iyxm6hVs7eHOXOMTqWvvzbW5ftvdGsFKhBDDP3oxzjGMYIRXM2yQLcjXVGspzaH6UBLpvHbze8O7usA3dbA6YOwpBWcy6cTSghhlaw1T0inkoU9DywG9tMUFxKxx5FWtGILW6haFbZtg/o+rtR+eQuHVWuI3QU/tMToknoWuIVVYIUQVsHR0ZFJkyYxY8aMXMeGDx/OnDlzSE5OJjk5mVjTyp1Tp04lKCiI5ORkgoKCmDp1qrnDFncp66Ks/v5GJ1NBHsAWP+Yyl1eowaccZQBfMJuRjOQDPmAwg7PdWABQOwy6roHTh+SGQYhiSHJE/rTWV4ARwAaMzqIorfU+pdSbSqnOpmIjlVL7lFI/AiOBQZaJ9vYoBa+9BgsWGFPi/PzgsGn1qDKUYT7zeZmX+YRPCCecTDKv17UnEEe24cp5BuHHm/zv5vvC3dsWemyAf9NgSQCc/bPI2yaEKDzWmiekU8kK9AZigaM8wDWScMeT9rQniigqVjR2hQgMqUDdiLX8cqUbbEuEnS1Bz8bIlVcsGr8QpU1KSgpeXl4MGTIEb29vQkJCyMgoeFv38uXL4+fnh6OjY7bv09PTOXv2LM2bN0cpxYABA1i5ciUAq1atYuDAgQAMHDjw+veieMm6KGuHDjBvXsF1aqHozlQ+Yho1iSKdrnzAFCYwgXnMoy99ucSl7JXuDYIescYNQ1QrOHsLc+6EEIVKckTR0Vqv01rfr7Wuo7WeYvpugtY6xvT+Va21t9a6kdY6UGv9i2Ujvj39+xv/7j98OPvoVhtseId3eJ/3Wc5y2tGO09xYQ88GXyqQQBkcGUVr3iD+5vvCefhDr82QeRIiA+DUwSJvlxAiu5KWJ+wK/YzijrQBdgBh1OQYcdSnM33ow3GO82zZZ4mOhqeecqDB2CgSXx9Cs6Sv4GILaPU1qH8xxjs5WLQNQpjfKOAW5hPdFh/Ia0HkHJKTk1m8eDFz584lPDyc6Oho0tPTWbgw9y6NAQEBzJw586bnSktLw8PD4/pnDw8P0tKMUSZHjx7F3d0Yze/u7s6xY8dusz3CWvy3KGvPnvDUU8YaS6+/bjyhvhlX4AkieBcXRjGUdEKYyFoqUpGXeIlznCOaaMpR7kYlD3/ouQmWhxojlsK3QsXaRd4+IayTZfKE5Ahxp4KCjCUw2rc3RrdGR0NIiHFsFKOoTnUGMAB//IkllpqmDfAUD1CJRE4QwlhCmMQyxtCBCnldxL0Z9Npi7Ai3JAB6bQXX+mZroxDWQ+4lCoN0KlmRxsBOoB2V+ZWNPEIfRjCCoxxlov1EvvpK4epqR/M3viD2FWdC9syEi80geAXYdAJWAOUt2wghSonatWvj4+MDgK+vLykpKYwbN46IiIjbPtd/c56zUvn1NIhiy8kJ1qyBp5+GiRONjqXPPjPW1LiZCsAInuI9KjOSvvxNAC+wEWecGcpQ2tGONayhIhVvVKrxqOmGIdh4Eh2+FSrXK/L2CSEMkiPE3WjY0Bjd2r69Mbp1zhx44gnjWB/64IYb3ehGc5qzgQ144WWqWQtX4viH9rxBFyYxn+d5HJe8LlLtYei93dgRLqoV9NwMbg3N00AhRInKE9KpZGVqA4lAB8ryPdH4MYxJTOIoR/nE5hPee88WFxcb2k34gEXPVaYvE+FSEwjbDHbtgDVAJcs2QgizKfgpQFFxcLgxMtDW1paMjAymT59+R08XPDw8SE1Nvf45NTWVGjVqAFCtWjXS09Nxd3cnPT2dqlWrFmIrhCXY28MXX8C998Ibb8CRI7B0qdHhdDMOwIt050PWMpSunKAlT7AJZyJ5nMdpQxtiicWNLLsYVfOFXttgWVtjxFKvLeDqddNrCFEyWSZPSI4Qd6tmTYiPN0a3Pvkk/PnnjdGtQQSxgx2EEUZLWrKGNbSghammG1XYynG6MJF+vMVJBvEcNfK6SJUGEL4DlgUZmzz03GR0NglRasi9RGGQNZWskBuwFQjBjgTmEsBY5jCHXvTiospk/Hj46CPFYx+9wax978Nvu2HlQ3D5W4yJdMct2wAhSqmIiAj27t2b65VfEgBjKKqTkxO7du1Ca82CBQvo0qULAJ07d2b+/PkAzJ8///r3onhTyrg5+OIL2LwZAgIgPT3/OrbAC7TlK7aiOMNZ/OiGF6tYxX72E0AAaTm3lK7aCMK3g75m3DD8839F1CIhREEkR4jb5ewMa9fCwIHG6NannoLLl41jjWlMEklUoQptactqVmep6YQb6zhOF8YykiVM5Peb7QvnWh96x4F9BVjaBtK/LfJ2CSHyVlzzhHQqWakKwGqgP4o4puDHh6xgxfWF+UaMgG++gecXjOLNb+eh//oZltWDzP1AAOS8sRBCWAVPT09efPFFvvrqKzw8PNi/fz8As2fPZvDgwdStW5c6deoQFhYGwJgxY9i0aRP16tVj06ZNjBkzxpLhi0L25JPGdLjkZGNRVtP/HG5KASNoykriOY8tmQQQSCU2sIE00vDDj4PkWHS1ijf03gE2drCkNRz9X1E1RwhxlyRHiJzs7eHLL2HCBOM/O3aEs2eNY/dxH4kk0oAGdKUrn/N5lpqOuLGM4wziBd5gG6P46Wa7RleqA33ioGwVWNoWUuOLvF1CiDtjjXlC5TX/rrho0qSJ3r17t6XDKFLXgDHAdKApkfyPAXjhRSyxuOPOmjXQqxcMaR3NhyF9Ua73Qs90KOcGbAbqWDR+IQrbgQMH8PKSKTxZ5fU3UUrt0Vo3sVBIVqO45IkffjDWzcjMhFWrjJFLBYkkBV+C8eAIV1jOr7gSSij22LOJTTSgQfYKpw4aT6EvnYOeG6H6I0XTGCEsTPJEbpInbq645AkwRrc+/TQ0aADr1hkbQAD8y7/0ohexxDKJSbzGayj+W0/lGieIwJX3WEI/7mEezbnJQn7n0owp02f/gq4xxo6iQpQwkiPydjd5QkYqWTkbYBrwHvAdfajPWn7nd1rSkmSS6djR2Hr0q8QeDFi+mmsn0yDSFc6eAvyBfRaNXwghRMEefhh27oTq1SE4GJYsKbhOHzz5lQR+5X4c6UQdfieOOGywIYAAvuO77BUq1zWmODhWNp5EpyUVTWOEEEIUiaeeMqbD/f67Mbp1n+mf+RWoQAwx9Kc/4xnPCEZwlaumWja4MoNTTKE333CK7mzkJluXO9U0pkxXug9WdIA/1pujWUKIYk46lYqJF4BFwC8EU51tnOEcfvjxAz/QqhVs3w6xv7Sj84JNXD13BiLLwsnLQCtgj0VjF0IIUTBPT0hMhGbNoE8fePddKGgwcUeqcZptfE8zKtIXN+JJIIHKVCaIILazPXuFip5Gx1L5ahAdAqlxRdQaIYQQRaFdO4iLgytXoGVL2LbN+N4ee77iKyKI4BM+oQ99yCTTVEtRmbGcZTahrMWRUJZzJu8LlK9mbPLg+iCs7ALJK83SLiFE8VU6O5UuXLB0BHekL7AWOMojOJKAHY60pjXb2MbDDxs7RPz0T0taf7KdSxlXYYmGY2WAQEDmRgshhLVzcYGNG41pzaNHw/PPw9Wr+ddpTSXKsIHNhOHGMMoSSTxx3MM9hBHGOtZlr+DkYez243QPRIfCn1uKrkFCCCEKXePGxujWGjWMTqZFi4zvbbBhGtN4j/dYxjJCCeVMls4jZ4aRyWJakIQngSzgWN4XKFcFem01doJb3RN+uYXhs0KIUqv0dSppDa1bQ6dOUEzmT2cVDGwHrvAAF0jCjXsIJZRooqlfHxIS4Bg+PPJuPBmXykHUeUirBLQDNlo0diGEEAVzdITISHjxRfjoI6ODKeMmMxX+04RyeLCSFTxGdcaieZ8dbMcbb7rQhSiisleo4A69t0OlurCyI/wRW2TtEUIIUfjuvdcY3dqiBTz+OEydemN06wu8wEIWkkQSAQRwhCPX65WjN1dYzYP8QjP8+YS/8r6AY6X/Z+++42u8/gCOf04ikiAhCEHsPVpKWjVi772a0tq1S3VYbdGq6k/VatWm9t57hxCrpUVrBrVj05iRyPf3xwkJkrgkuTfjvF+v+3LdnOe553i1zzfP9znne6DZJshaFtZ+AIdnWmFUhmEkRskvqXTihF6AvHo1vP12okwulQJ2AunJRiDbyYcX7/EeE5lIjhx6xpJ9xoIUHexPUGhmWHwdzngA9QEzhdUwDCOhs7PTy99Gj4bly6FqVbh+PeZjiuBASWYxi+5kYwQP6csWNvAu79KCFvzGb88ekCoT+GyF9IVhRUM4tSrqExuGYRgJkpsbbNgALVrAl19Ct256WRzAB3zAGtZwmtOUpSzHOf70OCdqkYJNeHKF+pRjOMeIcrV1Shdoug6yV4b1beHQZGsMyzCMRCb5JZUKFoQzZ6BPH0iVKtEml/KhE0sFSc9xNlGcOnShC9/zPe6ZhK1bIXvRHBQYuIPrYQVg+UUIyAk0A+bYtvOGYRiGRXr2hEWL9O5w5crB6dMxt8+JHTX4hYkMxJNp3KETG1hJdarzER8xmtHPHuCcAd7bAu7FYWUTCFgaf4MxDMMw4pyjI8yeDX37woQJ0KQJ3Lunf1ad6mxjG/e5TznKsZe9T49LQTmc8MOVENrgzRD2E+Vqa4fU0GgV5K4FmzrBX2OtMi7DMBKP5JdUAnB3hx9/TPTJJQ/AD6hAKg6wjFK0ZgAD6ElPXNKGsX49eFXITP6vt3IxpCSsOg2H8wOtgEm27bxhJENnzpzB2dmZEiVKUKJECbp06fL0Z/v37+eNN94gX758fPLJJ0j4HPabN29SvXp18ufPT/Xq1bl165atum/YSNOmsGWLnqlUpszLQ1RmFM0ZxC+MwpOl3MSHFcyhKU35jM8YzGAk8jNpJze9xCHz27DKx9TOMAwbMTHCeF12dnr529ixene4KlXgani5pFKUYhe7SEtaqlCFdUTs6GZPcVzxx540fEJlvmcbj6L6AgdnaLAM8jYE3+6wb6RVxmUYxrMSapxInkmlJ5JAcskVWAc0w4H9TMOLLxjDGD7kQ+ydH7FsGdRp7EbB/psICK4E64/BX4WBzoAJCIZhbXnz5uXAgQMcOHCACRMmPP28a9euTJo0iYCAAAICAli/Xte4GTp0KFWrViUgIICqVasydOhQW3XdsKFy5XTtjFSpdFnAtWtjbp8W6MCnjGQaWfDlBnWZzzja0IaBDKQPfZ5NLDmmhWYbImpnHDUzWg3DFkyMMGKjWzdYuhT+/ls/hAgI0J/nIx872UlBCtKABswkoj6SIh/p8ecR2elLLX5gFVFuaZTCEeovggLvgd8XsPd/VhmTYRjPSohxwqKkklLqL6XUp0qpzHHeg4QgkSeXHIH5QDfs2MdwSjKM+cynPvUJdrjLrFnQ+qM0vDFwNQfvNgDfI7C3KPAF8B1EvYraMIxonDlzhsKFC9OxY0eKFi1KjRo1ePCySsoxCAwMJCgoiDJlyqCUonXr1ixfruufrVixgjZt2gDQpk2bp58nNEk+TiQAhQrp3X4KFIAGDWDq1JjbpwK605afWUwG/uIaVZnKELrTneEMpwtdeBx5scOT2hmeFWFtKzg8I17HYxhJlYkRUTNxwjoaNoStWyEoSCeW9uzRn3vgwTa2UZGKtKENwxgW6eFCNjKynTu8SX8aM5zZ3I7q5PYOUHcuFP4Q/L+CXd9GVAc3DMNiSS1OpLCw3ffAh8AQpdR2YBawTERef+QJ0ZPkUq9eMHw4/PqrTi6tXg2NG+uKqTly2LqXUbIHfkUviRtIb94gI1voSFWqssZuDWPHZiRdOie8vlvM1r5tKe8/F4KLgfc3oO4AwwBl0zEYxivb+ilcPRC358xUAiqPfmmzgIAA5s2bx+TJk/Hx8WHJkiUEBgYyZ86LMzwqVKjAL7/8AsC///7LW2+9haurK99//z3e3t5cvHgRT0/Pp+09PT25ePEiAFeuXCFLliwAZMmShatXo9n+1/aSR5ywMQ8P8PPTO8J16ADnzsG334KK5vKdEviUxoxhLR/RkJtUYBQbccWVH/iBO9xhBjNwwEEf4JAaGq+GFY1gfTt4/Aje7Git4RlG3LNRnDAxIkomTlhJ6dL6IUStWlC5MsybB40agSuurGENbWhDX/pymcsMZzh22AEZcGcLV2nEQFoxlFu0owcvZADtUkCtGWDnALsHweNgKP9D9IHIMBIycy8RJyxKKonIEmCJUio94AN0A8YppZYCs0XEN857ZktRJZeWLYONG2HQIF05NYWl+TjrUcAAdGKpC+3IRwYO8T7lKc9GtZEffshB2rQOVPhyFqt6ulKXCfCoKFQdDuouMJbkviLSMCyVO3duSpQoAUCpUqU4c+YM/fv3p3fv3tEekyVLFs6dO0eGDBnYv38/jRo14vDhw0/XPEemEtkvZ8kuTtiQiwusWgWdO8N338H58zBxIjg4RN3eDviEqkxkCz7UJghvvg1PLPWjH3e5y0IW4oSTPsAhFTRaCSub6qKsYSFQopvVxmcYSYGJES8yccK68uXTiaX69TxSiokAACAASURBVHXx7jFj4OOPwRFH5jKXzGRmFKO4whWmMY2UpARcyMQartCCfnzCz9yiEQPI+fyDZzt7qDkV7B3h96E6sVRxhEksGcYrSEpx4pUyIyJyUyk1E7gL9AGaAhWUUmFANxHZHN2xSqlawM/oSTVTRCTKxXxKqWbAIuBtEbHturMnyaWePeHTT/UWPL16waxZ+jf40qVt2r3odATcgeY0IBMbuUR9ylGOjWykb9/CpEtnR/2u45jdyZUPGKYTS7UmgN094Dde8T8Lw7AdC54CxBdHR8en7+3t7Xnw4AE//fRTjE8XHB0dnx5XqlQp8ubNy4kTJ/D09OTChQtP21+4cIGsWbMCkDlzZgIDA8mSJQuBgYFkypQpnkcWO7GJE4blHBz08rfs2XViKTBQh6g0aaJur4AulGY626lBDRyowCesw4WxfMzH1KUuK1hBGsJPkMIJGiyF1T6w5WOdWCrZ02rjM4w4Y6M4YWJE9EycsB53d/D1hQ8+gO7d4exZXdDbzs6O0YwmC1n4ki+5znUWsxgXXAAnMrOIq3SkJ98whZuUYySFn3/wrOyg2nidWNo/CkKDoeoY/blhJBbmXiJOWFpTyU4pVVMpNRu4hJ66OhTwEJF8wJfA7BiOt0dPg6kNFAFaKKWKRNHOBfgEIu13mRBkzQoLF+rtFHLlgoMH9SLlbt3gdpQrjm2uEbARCMIbJ7bzkFDKU5697KVzZ5g7V9Fm6lDGHhwCRw/DqsIQOgt4Hwi2becNI5Hq3bv308J5kV9Ppqteu3aNx491DZvTp08TEBBAnjx5yJIlCy4uLuzZswcRYebMmTRs2BCABg0aMGOGrm0zY8aMp58nNLGNE8arU0pPnp08GTZtgooV4fLlmI9pSzH82clV3BCq0oqCzGQmfvhRgxrcjlxF40lR1vxN9PTwP4bH74AMI4lLzjECTJywlVSpYMkSfdvy00/w4YcQHAwKRT/68Ru/sYUtVKEK17gWflQKMjGVa3xKB37mIO3YR+iLJ1dK35R79YaD42BTZwh7/GI7wzAskljjhKWp5EvACOAQUEREaovI3CdroMOnsx6N4fh3gJMiclpEHqHrSkc1msHo4j4PLR2AVdWpA4cPQ79+YG8P48fryqnz5iXIInUVgB1ACt4kmJ04kY6qVGUTm2jeHJYvV/Ra9BXf7xoDJ4/CsgIQshSdkopy3wfDMGJh+/btvPnmmxQvXpxmzZoxYcIE0qdPD8D48ePp0KED+fLlI2/evNSuXRuAfv36sWnTJvLnz8+mTZvo16+fLYcQk9jGCeM1degAK1fCsWP6ecfx4zG39yE3x/HnFHlwpA51ScNCFrKPfVSmcqSbCsA+JdSdDwXfh+29Ye8P8TsYw0jGkniMABMnbMbeXlfz+PFHmD8fataEJ7uKt6Mdy1jGP/xDOcrxL/+GH2WHOyO5wWCaM5PLNGN7VLdoSkGFH+HdAfD3FNjQDsKiSEAZhhFrCTZOiMhLX4CXJe1iOL4Zesnbk7+3An59rs1bwJLw99ui+06gE7AP2JcjRw6xmb//FilXTkSnk0Rq1BA5edJ2/YnBvyJSQEQcJVByS3FxEAdZIAtERMTPT8TFReTzmtMlbLidyNy8Ig8QkYoiEmSzPhtGdI4cOWLrLiQ4Uf2bAPskFtftV33FNk7E16tUqVKv8S+aOP3+u4i7u0j69CL+/i9vv1VuyB55V0LFTq7KNFkv68VZnKWQFJILcuHZxo9DRNa0FBmOyM5vRcLC4mcQhhEHTJx4kYkTJk48MXeuiIODSJEiImfPRny+U3aKm7iJh3jIATnwzDG3ZIyIIFulsqyN6f5g92AdJ1a9LxL6KH4GYBixZGJE1GITJyydqbQxqg+VUpaWDo+qStTTqT1KKTtgFHqP+xiJyCQR8RIRL3d3dwu/Ph4UKwbbt+t1B25uuoh3sWLw/fd6TmkCkgvwB97Ag7NsIzfv0pzmjGc8FSrobUdn7GtDhxULCQs8Bwtzwv0dQHXglk37bhhGohHbOGHE0ttv66KsGTJA1aqwdGnM7SuRHjs2s52quNOONznGetZzkYt44x3paTXhu/1Mh6JtYfe3sHNAgpyhaxhGgmbiRALQooW+bbl4Ed59V1f1AChLWXawgxSkoAIV8MPv6THp6M4dZlGe7WSkKou5EfXJ3+0PFYbB8QWw+n29g6hhGEmepUmlF/aUUUo5oItuW+ICkD3S3z3RU2CfcAGKAduUUmeAd4GVSikvC89vG3Z2et3BsWPQqhU8fAgDBkCJErBnj6179wx3wBeoQjpOsIFC1KMb3RjMYEqWEnbsgA2nm/LenFU8vnEVFmSBO38CVSDyUgjDMIyoxTZOGHEgb17YtQveeguaNdPLHWLyNqnJxCpW05QsfEpefNnCZm5zm/KU52jklShPdvt5oyPsHQI7+pnEkmEYr8LEiQSiUiXYuVMvi/P2hs3hpdGLUpRd7CIb2ahJTZYS8XTChZY8YinFOURhKjCTi1Gf/O3eUPlnOLlM7yIamrAethuGEfdiTCoppXYopbYDTkqp7ZFfwHFgl4Xf8weQXymVWymVEmgOrHzyQxH5T0QyikguEckF7AEaiK13f7NUpkwwcyZs2QIFCugkU+3acDVhPXhxAVYD7+PMUZZQhNYMZCA96UnBwmH4+8PB/2pSe/IGQm8Hwfz0cPsIUJFnc4CGYVtibmSfsvW/RRzGCSOOZMyow1H9+tCjhy4DGBYWffuiOFKE+SyiHdkYhAdz8WMrj3lMBSpwgAMRjZUdVJ8AxbvBH8PAr5dJLBkJkq2vjQmJrf8tTJxImIoW1c/Ac+XSty2zZunPs5OdHezgLd6iGc2YwISnx6SiAcI6cnOO8ngzgVNRn7zkJ3pnuNOrYUUjCHkQ/wMyjFdg6+tiQhPbf4+XzVSagt5jPhSYGuk1BegKNLHkS0QkFOgObEAX4FsoIoeVUt8ppRq8Zt8TnipV4NAhqFZN7wrXu7ete/QCR2Au0B0HjjCNInzOGMbQilZkyxWCvz9cTuGN9xhfHt0NgQWucOMsuuz3Wdt23jAAJycnbty4YYIBOgDcuHEDJycnW3YjTuKEEbee7PbTubMuzNqmDTyKYRVCHlJQjinM4DOy8zNpGc0OtuKMM5WpzG52RzRWdlD1V3jrE9g/Uu8MZ/5/NBIQEycimDhhxCRbNtixAypUgNat4X//05fzDGRgC1uoQx260pVBDELCK5c4UpmUbMWdIBpSnpH8Q5T/pxXvAjWmwpkNsLw+hJhNgIyEwcSIZ8VFnFCW/GMqpQqJyLHX/pZ44uXlJfv2JcDJTKdO6fR/cDD4+kLlyrbu0QsE+B4YiFCQHznOl9SmNotYRPDN1NSuDQ8v/MOeXtVwdgyGpqGQ2Q3YAuS3beeNZC0kJIQLFy7w8GHC3CTS2pycnPD09MTB4dlVBUqp/SJitSXEJk4kTCL6JuHrr/XzjiVLwNU1+vY3EBbxPV0YyDkaIwylGnUJJJCVrKQKVZ49uV8vnVgq3g2qjtEJJ8OwMRMnnmXiRMySe5wA/dChfXuYMwe6dIExYyBFCgghhI50ZAYz6EIXfuVX7MNXK4ZxhCCqE8YDJrKOPpSOeh3jkVmwvi1k84bGqyFlGmsOzTBeYGLEi2IbJ6JNKimlWonIrPD37aM7gYj89mpdjjsJOggMHgwDB0LBgroCnqOjrXsUpYlANyAXkzlDF0pTmtWsxuFOeho2hAv/BLC/X1VcUt6CxvaQzRnYDBS1bccNw4iRNW4WTJxIPKZPh44d9fOOtWsha9bo294BpvMLPejJOaqTknFUpzEBBLCEJdSlbkRjEV1b6Y9h8GZnqDbOJJYMI5EwcUIzcUILC9MPIIYO1cun582D1KlBEL7kS37kR5rQhDnMwQk9o0H4l5tUx5HLjGY5fahGyqhOfnQerGsFWd6FJmvBMYanG4ZhJBhxkVRaKyJ1wt9vjeZ4EZEq0fws3iXoIBAcDG++CSdO6B3hvv7a1j2K1lKgBZCJpVylBQUowAY24PYgKz4+cND/HAe+rkr6lBehoTPkVOgNPEratuOGYUTLSjcLJk4kIhs2QNOmene49euhcOHo2z4EJjCTHrQjkHdwZja1aM4BDjCHOfjgE9FYBHb2h70/QLGPoMYkk1gyjETAxAnNxIlnjRun6/F5ecHq1fBks+1RjOJzPqcSlVjOctKSNvyIQK5Sk7Qc5yfm8zmNSRXViU8shjUtIHMpaLIenNJZaUSGYbyuWCeVEoMEHwR8ffW+zk5OcPgw5Mlj6x5FayvQEHDCl3s0JBMZ2cQmcobko3Vr2LrmMge+rk5mxxOo+q6QNwRYB5SxbccNw4iStZc1JFQJPk5Y2f79ULeuXuqwciWULx9921BgLMvoQnNuUIg0LKYeH7GTnUxhCu1oF9FYBHZ9C3u+g6JtocYUvVucYRgJlokTmokTL1qxApo3B09PWLcO8uXTn89hDm1pSzGKsY51eOARfsRNrlCXjPzOj0zlY9o+TTk94+QKWPUeuL8JTTeCc3orjcgwjNdhaZyI9lGiUsrOklfcdjuJqVIFWraEhw/h448TdCHTyoAfYEcVHNjKbe5SnvIccTjI7NnQoLkHRb/bxrn7byIrb8MxJ6A6sM2m/TYMw3biOk4opWoppY4rpU4qpfrF0K6ZUkqUUsn+ZuhVlSoFu3frJ8/VqsHSpdG3TQH0oDGTWYMrp3hAHdYykWpUoz3t+ZVfIxorBeUGQdlBcHi6rp8R9jieR2MYRkJn7icSp4YN9bPxW7egTBnYu1d//iEfsopVnOAE5SjHqae7v6UnM5u5RlW+oh2T+ZlrUZ04X0NouByu/wOLqsD961YakWEY8Smmi3goEBLD68nPjZgMHw7p0um1BkuW2Lo3MXoL2AlkwItgdhBGSipSkd32/kycCO26ZuCN77ZwIqgMsuYq/O0K1AbW27bjhmHYSpzFCaWUPTAWfVEpArRQShWJop0L8AmwNw76nyzlzg07d0LJktCsGfz6a/Rt7YCPqcZcNpOS6wRTnSUMozGN6UEPfuTHZw8oMxDKD4Gjs3X9jLDQeB2LYRgJnrmfSKTKlIFdu8DFRe85tGqV/rwWtfDFl//4j7KU5S/+Cj8iNR6s4jJN6MWnLOBbLkS1L1yeOtBoJdw6Dosqw70rVhuTYRjxI6akUm4gTwyvJz83YpI5s956B6BnTwgKsm1/XiIv4A/kpxC38Cc1HtSgBuvUWn76CXp/7cpbQ9Zz4FYN2BgIf2YAGgDLbNtxwzBsIS7jxDvASRE5LSKPgPnoVbnPGwwMQ5f9MV5TxoyweTM0aKBrZ/Ttq4u0RkUBnXiX1fgRQiihVGMGffmAD+hHPwYw4OlW0wCU/gq8f4Rj82DNB/DY3C8aRjJm7icSsQIF9OzWokWhUSOYOFF/XprS+OOPI45UpCJbeVIuyxEPFnCZdnRnEBv5jJNEEVxy1YDGa+D2aVhYCe4GWmtIhmHEg2iTSiJy1pKXNTubaHXqBKVLw6VLeke4BC4LeilcGXJwiR24U4SGNGSumsOAAfC/n1Lx7tAV+F9pDFsvwh4PkGboe0DDMJKLOI4T2YDzkf5+Ifyzp5RSbwHZRWR1TCdSSnVSSu1TSu27di3KCfgGkCqVnkDbtSsMGwatW+taS9FpxZvswJ/bpMaO6oyjAx3owPd8zxd88Wxi6Z0+UHEEnFgEa5rD4xhObBhGkmXuJxK/zJlh61aoWRO6dIEBA3RFj0IUYhe7yEEOalGLxSwOPyIFHkzhCp/Snp/5gw78TRSzVnNUgabr4c4FWFhR/2kYRqKUIrofKKUmiUin8PezIKr5iyAireOpb0mHnR1MmKCLWYwZo39zL5mwd05LB2wA3sedVfiSm0a0pCW3uEXPnt1xdXWkSqeFrOzWnlrMgpDsUL4FqIdAW9t23jAMq4jjOKGiOjTSd9kBo7DgAiMik4BJoAuwWvDdyZa9PYwdq4uxfv01XL2qE00uLlG3f498rMWf3NQgN3UYxhJSk5pRjOIudxnPeOwJL9Dt9TnYpYCtPWGVD9RfCPZRbjZtGEYSZe4nkoY0aXTx7i5d9KbWFy7ApEng6eDJdrZTn/r44MM4xtGFLoAdmRnJVdLRgm9ZSRAPmcPbOD57Yk9vaLoBltaGBRXBxxdcc9pkjIZhvL5ok0rAv5Hen4zvjiR5JUro5W+jRukr8u7d+rf5BMwZWAp0wJUZrCUPLehBD25wg4HtBpImTQoatpzOnHapaMZEnViq3C48sdTFxr03DMMK4jJOXACyR/q7J3Ap0t9dgGLANqUUgAewUinVQETidtueO3f0nsqOjvrl5PTy987OkD27LlidyCgFX30FWbNChw5QsSKsXQseHlG3r4MnvvgRTE2K0IivmU0a0jCEIdznPtOZToonv16U/EQnlrZ8DCubQf1FkMIx6hMbhpEUmfuJJMLBAaZM0aFu0CAIDITFiyF9mvRsYhM++NCVrlzmMt/wDQpFJr7hBmlpwGds5g5+LKUiqZ89cbay0GwTLKkZnljaCmlz22aQhmG8HhF56QvweJXPrfUqVaqUJCpBQSLZsomAyLhxtu6NxR6LyBcigoRIbmkrCNJDeshjeSzr1ok4OYXJtLafiQxHZH12kceIyCgb99owkjdgn1jxehzbOIF+yHEaXV8jJXAQKBpD+22A18vO+1px4t9/9XX6VV9lyohcu/bq35eArF0rkjq1SK5cIsePx9x2t9yWnVJeQsVOLstU+UF+EARpLI3loTx8tvGB8TpGLK0rEvIg/gZgGIbFElucCG9bCziOTlD1i6FdM/SsqPiJE8nc5Mki9vYiJUuKBAbqzx7JI2kjbQRBukpXCZXQp+1vym8SKnayU8rJWrkV9Ukv7xP51U1kYnaRWyetMArDMF7G0jhh6RaeJ6L5/IiFxxug1xP8/LN+/+WXcPmybftjITvgJ2AoKfiXqeTkc8Ywhta0pmqtENavV/RYPIIxfwyAf87Duuzw+DPgBxv33DAMK4pVnBCRUKA7euXtUWChiBxWSn2nlGoQR320TJo00Ls3fPIJdO4MbdpA8+bQuDHUrg1VqkC5cnpJc7FikD8/uLrqGaje3nD+/Mu/I4GqXVvXzrh3D8qWhT17om/7LmlJxQb8qE5mPqINzvzMzyxjGY1oxAMeRDQu3gWqT4TTa2BlEwg1ddYNIxmKVZwwu4QmHB066OVwx47pWHHiBDjgwDSm0Yc+jGc8zWlOMMEAuNGO+yzgbX4nC5VZytUXT5q5FLznCyH39Yylm9H952IYRoJjSeYJuBPFZ67AdUuOj69XonyyEBYmUqeOfqr9wQe27s0rmyIiSsIke/gT6bpSV+7JPdm7V8TNTWRIw6H6afRyT5EQRGSAiITZuNeGkfxg/SfQyTtOXLok8sYb+tqePbvI0aPW+d54EhAgkjeviLOzyMqVMbc9Jg9ltTQVEeScDJIpMlmUKKkklSRIgp5tfGiKyHAlsrimyKP78dZ/wzBeLrHFCaAMsCHS378Evoyi3WigHvE5o/XxY5HOnUX27Xv1Y5OQvXtFMmYUyZBBZPfuiM9HyAhBkCpS5Zk4cFfWywNxlmNSQGbLuahPevWQyFh3kfFZRK4n7lhqGImdpXEixplKSqnzSqlzgLNS6lzkFxAILI+j3FbyoRT8+quuxTF3rt7TORH5CFiC4ipfkoWJrGUtNalJgXdus20b/Ly7L19u+gVOXoAV2SBkMNCHaOoyGoaRyJk4ES5LFvDz0zOYzp+H8uXhjz9s3avXli8f7NoVsY30lCnRty2II8WYzxLakp1vqMZR5jCbHeygBjW4xa2Ixm98BDWmwJmNsKIhhDyI/sSGYSQJcRgnEs4uoTNmwMSJ8M47embr/fuvfo4k4J139CTddOn0JN6VK/Xnn/M5M5mJH35UpjJXw2cmpaYmio14cpnylGcyAS+e1P0NXVdJwmBhJbh+2HoDMgzjtbxs+VtLoDXwCGgV6dUSKCkiHeK3e0lU7tx6P06Abt3gYeJaBtAYWA/cpRMZWMBe9lKZyni8eZUdO2DOkR50XzUVOXMJlmaFR8OBHkCYbTtuGEZ8MHHiCTc32LgR6taFGzegcuVE9+AgskyZ9FK4GjWgY0ddmFWieT6QkxSUYyqz+YScjKQMW1nCQv7kT6pQhWtEuml7oz3U/A3OboblDfRSB8MwkrK4ihOW7hL6xctOJCKTRMRLRLzc3d0t/PpI3nsPPvtMvx8+XC+F3rTp1c+TBDx5CFGsmF4lPmGC/rwVrVjBCo5whPKU5wxnAHCkPCnZihv3qY83Yzj04qPnjEXBZxsoO1hYGa4dsuKIDMN4VTEmlUTET0S2ARnD3z95bReR49bpYhLVqxcULgwBATBsmK1788oqoecUK97DmVUc4wTeeONU4Bw7dsD6C+1pv2gOYReuwOIs8HAs0Al4bMtuG4YRx0yceE6qVLBsGbRsqQsT1amjt8dJpNKk0U+e27aFb7+FTp0gNDTqth7YUYfRTGUAuZhCcRayiiUc5zgVqcilyJv5FWsLtabDuS2wvD6E3Iv/wRiGYRNxGCdeZZfQM8C76F1CvWI3giikSQMjR+rCc8WLw7//6gx827b6oUIy8+QhRK1a0LUr9O+vH0LUpS6b2cw1rlGWsvzN3wA4UJLU7CAlDrSkIiPZ/eKj5wyFdGLJPiUsrAJXD1h7WIZhWEhJdI8dn2+oVAnAG8hIpCcFIjIwfrr2cl5eXrJvX9zuJG1Vfn5QqZLejvrvv3Wx10QmAKgBXGEXdtTBDRc2sYm0gYWoXh2KpFzOvJbvY5/RDZpdgVQfADPgyXbThmHEC6XUfhGJ+1+kY/5OEyeeCAuDL76A0aP1sufx43XR70RKRE+wHTIE6tWDBQt0/iwqd4A5DKcLvTlLHc7zKbVpQmYys4Ut5CRnROMjs2F9G/CsAI1Xg0PqqE9qGEacS2xxQimVAl3suypwEfgD+EBEolwfpZTaBvQSkRiDQKzjREgIjBihM+/BweDurq/9LVro638yEhqqk0pTpug9LiZPBgcH+Id/qElN7nOf1aymHOUAEM5ynWo4E8hYlvMF1V68Q7h9Ss9WCrkLzTbpgt6GYViFpXHCot3flFKdgJ1AFaAv8AZ6amm+2HQy2atYUV9xg4OhVSu4c8fWPXpl+QF/IA9lCcaPu4TgjTeXsuxn2zY4pRrRYOpKHl//Dxa4w925QHP0DGjDMJIKEyeeY2enn2IPGaIzMl26RLxPhJSC77+HsWNhzRpdO+P69ajbugBt6cVYJpKddeRgCFtZwQ1uUIEKnORkROMiLaH2LLiwHZbWgUd3rTIewzCsL7ZxQhLSLqGROThAv376AXGlSnDtGnz4oV4KffaszbplCylSwKRJOr82YwbUrw9370IxirGLXWQiE9Woxmp0yStFTjKyg3vk4VPqMoLl4fvFRZIuL7zvByldYVFVCPzd2sMyDOMlLEoqoSst1xKRxsCD8D+bASHx1rPk4qefwNMT9u7VezknwsRSNmA7UIri/McOhNRUpjJHMm7H1xf+c6tJtfHrCbn5ABZkhKAl6P98XggbhmEkXiZOPE8p+OorXWBCKb0e4LPP9CymRKpbN1iyBA4e1DXJz5yJup0T0IlOjGcOWdhJZvrhxzLuc58KVOAoRyMaF/4A6syBi/7hiaXEFwcNw7BIrOOEiKwVkQIikldEhoR/NlBEVkbRttLLZinFqfz5wddXT9NJlw7WrdO7Hfz8MzxOPuUflIJvvtGzlDZv1nm2K1cgJznxx59iFKMRjZjBDN0eDzKzjVu8xRc042dm80KlvbS5dWLJKT0srg6Xdlt7WIZhxMDSpFImEdkR/j5MKWUnIuuA+vHUr+TD3R22bdOJpZ07E21iKT2wGahGfm7gjzOe1KQm/mnXsGEDOOSuiPcvm3h4OxTmu8GtVUBDwOz8YxhJhIkT0encGRYuhJQp9c1FmzZ6uUQi1bixrkd79SqUKaMTTFFxALrSgsksw51DZKQ721mEIFSkIgeJdGCh5lB3HlzaBUtqm8SSYSRNST9OKAUffQRHj+pi3vfuwaefQtmyeiZTMtKhAyxfDkeO6OEHBIA77vjiS2Uq05a2DGd4eOv0eLCJq1SgF60ZxwT+e/6ErjnBxw9SucPiGnDB38ojMgwjOpYmlS4opXKFvz8BNFRKeWPWMMWNvHmTRGIpDbAKeA9PrrKddOFPIlaknsvKleDx1ru8O3Ir94PsYUFauLEBqAeYAq2GkQSYOBGTZs30urHUqWH2bGjUKFFvQV2+PPj766UOFSroh/NRsQO6Uo+ZrMWVM7jRke3MwwknKlGJ34m0jKGgD9SbD4F7YEktCA6yylgMw7Ca5BMnPDz0w4QVKyBbNvj9dyhZUmda9uxJtEuhX1W9erqAd1CQTiz9/ju44MJqVuODD73pTR/6IAjgQlbWcJm69KIrMxjGC6usXbPrxFKarLC0ll46bRiGzVmaVBoGFA5//x0wG/AFBsVHp5KlJJJYcgTmAZ3JyGW24E55WtKS35zGsWgRFK1UAq/hfty544wscIGrW4Ha6NKuhmEkYiZOvEy1avq36wwZYO1a8PLSGzYkUkWL6m2kPT31jj8LFkTdTgEdqcJiNuPIddLRmm1MJz3pqUY1/In0tLlAM6i/EC7/bhJLhpH0JL840aCBnqrTrZteAjd1qp7iWayYrrt39aqtexjvSpfWtzYuLlC5sg5/jjgyl7l0oxs/8RPtaU8ooYAzWVnKJZrzCX1ZQn8CeS4B55JN7wrnkl3PbD2/zQajMgwjMouSSiIyPXx6KuF/ugFuIjI+PjuX7CSRxJI9MB74ClcCWYsH9fiYjxnmMIQZM4Vy9YtQcth2bt9JiyxMBZd3oveQu23bjhuG8dpMnLDQ22/rKT758+vlEZUqQevWuuBEIpQ9ux5O6dLQvLle3RcVBbTlayOJyQAAIABJREFUXdaxFeEh6fiArUwgK1mpSU02szmicf4mUG8BXPnDJJYMIwlJtnHC1VXvcnD0KPTuDZky6UTTF1/oWUxNm+pMSxKuu1SggH4IUaiQzrP99hvYY8+v/Mq3fMt0ptOEJjzgAeBAVmZziQ50Zggb6ckZnqtFmCYL+GyFtLl0Lb5z0UyXNQzDKqJNKiml7KJ7AaHA/fD3RlxKIoklBQwBRuBMIEvIQkv6059+9r2ZOEmo80F+Sg7bzvW7mZBFTnDxD6AacNO2HTcMw2ImTrymQoXg0CEYNAgcHWHWLChYEMaNS5Q3FW5usHGjrrX06afQt2/0tcg/oAT+bOc+KUjH+2xkFHnJSz3qsYY1EQ1NYskwkgQTJyIpWBCGDYMLF2DZMr02LCwMli7VO8XlzKk3dDh1ytY9jRceHvoWp2pVXXZq8GBAFN/wDWMZy2pWU5Oa/Md/gD1ZmUQgn9OGMezlI44R+uwJU3vAe76QNg8sqwdnt9hgVIZhQMwzlULRuzFE93rycyOuPUksZc8eu8TSxYt6D2gvL2jSRG/DcO5cnHc3Jp8D03DgCjPIRHdGMIKOqgPDR4fSonNu3hq6nctBnsgSBzh3CL3L7DWr9tEwjNdm4sTrcnKCgQPh8GG9duy//+Djj/WUnz/+sHXvXpmzMyxaBF276numtm2jr0XehEIcZAfXcCMDPqzhe4pRjMY0ZglLIhrmbwL1FoYnlmqaxJJhJE4mTjzPwUHX1Vu1Cs6fhx9+gHz59O/tQ4bo95Ur6/p7D5LWhjYuLnrYrVrpENi1K4SGQje6MZ/57GEPFanIZS4DiiwM5wqDeJ/pBNCcg8/vHJ06M/j4Qrp8sLwenNlok3EZRnKnJJpCcUqpnJacQETOxmmPXoGXl5fs22e9nUKt7tQpHVTOn9d7N69bp6/GMQkN1VNoJ0/Wf0b1uLhIEX0TU7s2eHvrJ+XxbAXgg+DKN1xnMM1oxmxmM3yII2N+vMzvvauR3SUA1VAgdwH0XnIe8d4vw0iqlFL7RcQrnr/DxIm4IKKfWvfsqZ9gKwVduuibCzc3W/fulYjo+6P+/aFmTVi8GNKkibrtFi6Rherk4TTXmElzRrOXvcxgBh/yYUTDgGWw2gcye0HTDeDoap3BGEYSZ+KEliDihAhs365rLi1eHJFMypABOnbU2ZccOWzbxzgkAl9/Df/7n14ON28epEoFG9lIE5rggQcb2Uge8gBwlVFk4nM2UxMXllKaVM+e8P51WFwVbh6Hhsshdy0bjMowkh6L44SIWPxCz2zKAti9ynHx9SpVqpQkeSdPimTPLgIi5cqJBAVF3e70aZGvvxbJmlW3BREHB5H33hNZuVJkwgSRRo1E0qSJ+DmIpEolUreuyJgxIgEB8ToUXxFxERE3GSEIUlNqyj25J6NGiWRIdU1OflNCwkY6iASkFJGCInIhXvtjGEkZsE9scF02cSIW7twR6d1bJEUKfX12dxeZMUMkLMzWPXtlU6aI2NuLeHmJXLkSfTt/uSZ/SkkJFgc5JzOlslQWJUomy+RnG55YKjIyhcicd0Ue3o7fzhtGMmHiRAKNE7dv69/b33pLnv6+bmcn0rixiK9voowJ0RkzRkQpkTJlRK5f15/tkT2SXtKLh3jIQTn4tO11mSKPRYm/eMtWiSIO3L8uMqOEyKiUIqfWWGkEhpG0WRonLL34uwIz0Vt+hgHBwAwgrSXHx9crwQWB+BJdYik4WGThQpHq1eWZRFGBAiI//RT1b/LBwSLbton07StSvPizx4FIvnwi3buL/P57vAxln4hkFBFXmSJKlJSX8nJbbsvEiSJuqW7Kkf7vSNhIe5FjTiKSV0TOxks/DCOps/bNgokTcejvv0W8vSOuyxUqiPzzj6179cpWrRJxdhbJm1eHsejsk9uyW8pJqNjJWZkotaSWIMgYGfNsQ5NYMow4ZeJEAo8TYWEiO3eKtGgR8bABRIoV00mnu3dt3cM4sWSJiKOjSMGCIv/+qz87LIfFUzwlraSVHbLjadtbMl8eSQrZJ6VknVx78WT3b4jMLKkTSydXWWcAhpGEWRonLC2M9wuQGigGOANvAKnCPzfiW968ehvqyDWWevfWxbx9fGDTJr2ErWVLvT31sWPQq5feXeJ5KVNCxYowdCgcOKDXb0+bps+TLh2cPAm//grvvAPly+spuKGhL57nNZUCdgCufIQT89nDXipTmcadrvHLRDfKjNjEoatlkDWP4PAloCLwb5x9v2EY8cbEibhSrJi+ls+YAe7ueklEiRK6kOsPP+if3b9v616+VL164OsLt25B2bLw559RtytFWtKwgR1UJQedGU9NGtKQHvRgBCMiGuZvDPUWwZV94cW7/7POQAzDiCsmTrwKpfTFc+5cXRP1m290tet//tFLpLNlg88/T/SFvZs00bcyV67o4R48CEUowk524oEH1an+dCOHdLzPQ5ZTlMNkpyLLufTsyZzTw3ubIeMbsLIJnFxpgxEZRjJkSeYJuAykeu6zNMAVS46Pr1eCfbIQXyLPWHryeuMNkV9+Ebl5M/bnDwkR2bVL5PPPRdKmjfiOnDlFhg/X03HjyBkRyS8iKWWtpBQnKSSF5Lycl0WLRFyd78qeXlUlbLgSOZhKRDxF5EScfbdhJAdY/wm0iRPx4eZNka5d9fqAyNf+FClE3n5bpGdPPWP1QsJdLnz0qEiOHHr19aZN0bcLkIeyThqJCHJaBouP+AiCDJbBzzY8sSx8xlJpM2PJMGLBxIlEGCeCg0XmztXrxZ7EA6V0KYt16xL10rh//hHx9BRxddWr/ERErspV8RIvsRd7mSkzn7a9J1vlnqSRAMkr8+XfF0/24JbI7LdFRjromGEYxmuxNE5YOlPpIeD+3GcZ4fkS/Ea8erIrXLVqei/OPXt0Or9Hj7gp5poiBZQpAyNG6GKxY8boHSjOntUznzw94ZNP9GymWMoJ+ANFqM1jNnCWi5SnPCWanWTuotTUHL+KHedrw6b78Oct9Iyl47H+XsMw4o2JE/HBzQ3GjdPX4Tlz9A5xJUroTRj++AN+/lnPNPX0hFy54IMPYOxY+OsvfbuRABQqBLt3Q+7cUKcOzJ8fdbt8OFKEhazgQ3IzgP+Rm1a0ZAAD6E9/hPDx5G8UPmNpf/iucGbGkmEkEiZOxFbKlNCiBezaBfv2QZs2+rM1a/RKhnfegS1bbN3L11K0qB5W9ux6P6GFC8Edd3zxpSIVaU1rRjMagFRUwo7NZOYGZfFmxvP3CE7poNkmyFwSVr8HAUttMCLDSEYsyTwB/YETQBegdvifx4H+lhwfX69E9WQhsXr8WBfGqFLl2SciDRrESbHA2yJSXkSQfZJaMkhmySyH5JBs3iyS1iVYNnzcWGQ4Ir+nEZHMInI4DgZlGEkf1n8CbeKENQUF6Wk/334rUqOGiIuLyPM18ooWFZk2TT/ZTgBu3dLloUBk9Ojo2wXKY1konUQEOSU9pIN8JAjSS3pJmESKOQHLzYwlw4gFEyeSSJy4elXkhx9EPDwirv/Vq4vs32/rnr2Wmzd1WUGlImLFQ3koTaWpIMhX8tXTWBAsB+S2uMtlySSTIhX1furhfyJzyoiMsBc5vsiKozCMpMHSOGFpEFBAe/Q+70fC//wIUJYcH1+vRB8EEpuDB0Xat9fV9J4EreLF9U1LSMhrn/aeiNQWEeSwuEpWcRM32SN7xN9fJH26R7KiU3OdWNqdRkTcReRQ3IzHMJIwG9wsmDhhS6Gh+ho9bpxIy5bP3lxkzSry449xuoT5dT14oDcwApF+/aJ/LnFdwmSOfCoiyEn5SLpLV0GQ7tJdHsvjiIYmsWQYr83EiSQWJ+7eFRkyRK8fe3L9f//9eN/dOT48eCDSpIkeQp8++hl3qIRKJ+kkCNJJOkmohIqISIgckxuSTW6Im/wqe+WFsBIcJDK3nE4sHZ1v9bEYRmIW10kle0vaWfuVZIJAYnPlisigQSKZM0cErSZNYpVYChaR90UEOS3pJI+kltTiK76yb59Ixgyhsqh9K51Y8ncRCUsvIgfiajSGkSTZ4GbBxImEJDhYZPp0vUvQk+u0i4vIF1+InD9v066Fhop07qy71LatyKNHUbf7T8JkmugJDCflA+ktnwqCdJSOUSSWHMITS/9ZZQyGkRSYOJFE48T16/pa/+QhcIoUIt26iQQG2rpnryQ0VHcbRFq10rEiTMLkK/lKEKSZNJOH8lBERB7LabkqeSRI0sho2RY5QmjBQSLzvEVG2IkcmWP1sRhGYmVpnLC0ptJlpdQ4pVQ5C9sbSVmmTDBwoK7xMW2a3jVu6VLo1Om1a3ikBOYAXcjNbfxJSS5qU5uLpVayxdee7qunMefQR7DnDuwIAakMRLOVkGEYtmDiREKSMqWutXHoEKxdC5Urw507umZe7tz6Z3//bZOu2dvD+PEwaBBMnw6NGsG9ey+2c0XxHoOZyv/Iy1y6cIaB9GUyk2lLW0IJ35k0X0OotzC8xlItCA6y6ngMw7CYiRPWkCEDDB8OJ05Au3a6Bt+4cbo264ABEJQ4rpH29npD6u+/h1mzoH59uHdXMYQhjGQki1lMPepxl7vYkZuMbOc+2elELUaxnseRT5bSBZquA88KsK4VHJllq2EZRpJkaVKpBnAXmKeUOqOU+p9S6o147JeRGDg6Qtu2ujigs7NOMPXq9dqJJXtgHPAlWbiFH6l5kyY04Z8357J1mz19Nk3itz+7wh93YFsoSBXgjzgckGEYsWDiREKklC7e6uurC3v7+OgbjJkz4c03I372mtft2HRr4ECYMAHWr4eqVeHGjRfbpQY+pB+T+IU8LKctf/E/BjCLWbSkJSGE6Ib5G4Unlv6ApbXh0R2rjscwDIuYOGFNOXLAb7/phwsNG8L9+zpDkycPjB4NwQm/PrpS8PXXMHUqbN4MlSrBlSvwGZ8xgxlsZStVqcoNbqDIRib8uE1hetCA0Sx5EiE0h9TQeA1krwTr2sA/020yJsNIiixKKonIXyLSR0RyAG0AN2CLUupQvPbOSBzKloVly8DBAUaOhP/977VPpYAfgGFk4CZbSIs3LWmJX+EJ+G2349sdYxm/tyf8eQe2CEhVYHdcjcQwjNdk4kQi4OUFCxZAQAB07w6pUkVkdLy8YONGq3epc2dYsgQOHIDy5eHcuRfbOAHt6MFEppKTTTRnG6MZzAIW4IMPwU82jsrfGOrOh8C9sMQklgwjoTFxwkaKFoXly2HnTvD21hn8zz6DAgXgu+/g6FFb9/Cl2reHFSvgyBEoVw5OnYLWtGYpSznIQSpQgYtcROFOFny5jhef4sOvzORh5BM5pIJGqyBnNdjQHv6eaqshGUaSYulMpciOA0eB80CuOO2NkXjVrAmzZ0c8Uhg/Plan6w1MxoWbrCUddehKV5bl+4nt2xXD949i1M4+cDAINioIqw74x8kwDMOIEyZOJGR58sCYMTqD89134O4Of/6pr+NNmuilzVbUqJHOZwUGQpkyUa/KcwA60J7JzCUbu2nEKiYylOUspzGNecAD3bBAU6g7DwL3wNK68OiuVcdiGIbFTJywtrJlwc8PVq+GYsV0DPjmGyhSRCeevv0WDh+2+sxVS9WtC1u3wu3beij790MDGrCBDZznPOUoxwlOAOnIykYCqcRntGEi43kmEjikgoYrIFcN2NgBDk2y0YgMI+mwKKmklEqnlPpIKbUFOAVUAn4EMln6RUqpWkqp40qpk0qpflH8vItS6m+l1AGllL9Sqoil5zYSCB8fvZYB4OOPYd68WJ2uA7AQZ+6wlHT40Ic+/JZrIH7bYcLhoQzdNgD+CYINdhBWE/CL9RAMw3g9cREnDCvLkEHX1zh7Fn78EVKn1rNOCxeGwYPh4cOXnyOOVKgAO3bo997eEe8jswc60pzpLMGDA9RmLtP4ifWspz71uUd4YaaC70HduXBpFywziSXDSChMnEgAlNLZmQMHdL29du3AzU1PARo0SCebihTR65P//jvBJZhKl4Zdu/RE24oV9QOJilRkG9u4z33KU56/+AtIgydrOE89etKNGfzE7cgncnCGhsshdx3Y1BkOxO5huGEke5ZU8wbuE7HtZ1pLjnnueHt08MiDrsl8ECjyXBvXSO8bAOtfdt4kt1tDUjF0aMRuE2vWxPp060TESUIlrbQXBPlMPpNLgWFStKjIN7UG613hVrmKhDqJyJZYf59hJAVYf1efWMWJ+HqZOPEKLlwQad5cnu4WlzevyOrVVu3CmTMiBQvqTYuWLYu6TZiITJWNck+c5aIUlPkySuzETrzFW4IkKKLh0fl6p5/5FUUe3bVG9w0jUTFxwsQJEdHbqq1fL/LRRyLp00fEABApUEDk669F/vpLJCzM1j196tIlkeLF9a3G7Nn6s2NyTHJIDnEVV/ETv/CWj+Sc+IgIMlEGyDV5bgwhD0WW1tf3En+OseYQDCNRsDROWLr8La+IVBORqSLy32vkrt4BTorIaRF5BMwHGkZuICKRtyJIDSSs1Lhhub59oU8fCA2Fpk2jfuT8CmoBG7EnjMm40JNRjGKgRye2bHvMyiv9+XLdMDgeBGtSwuM6gPXrghiGEes4Ydhatmx6hunWrXopxKlTUK+e3nLn1CmrdCFnTl32o0QJHT6mTHmxjQLaU50lbCANl6jILyxmNLvYRQ1qcPvJ8+hC70Pt2XBxByyrByH3rTIGwzCiZeJEQuTgoJc/T5kCly/r6T+dOkHGjHoHuSFD4K23oGBB+PlnuGv72Z9ZsuiVfN7e0LKlLulakIL4409WslKTmqxiFeBAduZynvZ0YjBr+ZzAyLeYKRyhwWLI1wh8e8D+0TYbk2EkZpYW6g6M5fdkQ6+ZfuJC+GfPUEp9rJQ6BQwDPonldxq2NHQodOyol0/Uqwd//RWr03kD27AjJaNITX+mMIXPMrZi/ZYQfO/05vNVoyEgCFY6Qmh9YPX/2bvvuKyqP4DjnwsCCrj3TnNrjlTce+HeA1dpbkvNNDO3lpmaOVJz5c6RA7e4Zzlw5F7hwkkuFBQEvr8/Dv1Ec5ACD+j33eu+4rnPfc49x5c+X+4Z3xMlzVBKRU4UxAkVW5Qvb76zf/wREiUy+Tfy5jXLIQKjv2MmeXLYvBmqVjVhZPjw56/AaEUZ1rIZR+5Shu9ZyVgOcOD/OwEBkNsDqs8F3x3gWVs7lpSyIY0TcYCDA1SpAlOmmER3mzZBp06QKpXZ5KFHD7Or3IABcPOmTauaODGsWweNG8MXX0Dv3pA+LCM72Uk+8lGf+sxlLmBPRqbhSzdaM5YddOAioU8Ksnc0u4dmbwjbPgfvH2zWJqXiqtdJ1P06rOec+9eviCIyUUTeB/oA/Z9bkGV1sCzL27Isbz8/vyiupooylmWSdTduDP7+ZgTkzJk3KvJDYCcWSRhGAr5nAQton7QRqzY+Yn9od7osmww+/rAiPjyuByyOkqYopdQ7x8HBPDycPg2tWpmtp4cNM7k2PD2jPc+GiwusXGlGoPv1M1UJC/v3dc0oyja2EcpjSjCUtYzlBCcoT3lucMNclLs5uM+GS1vBsw48fhitdVdKqbdCvHhmd9DJk+HqVfPdX6oU3LkD33xjOpc6dTKdTTbi5GQm2H76KYweDR99BIkfp2ALWyhHOVrTmnGMA+zIwFh86UdTpnOYlpzl8ZOC7B3MJg85GsP2XrDve5u1Sam4KKY6lXyBjBFeZwCuvuT6hUC9570hIlNFpIiIFEmZMmUUVlFFOXt7syNc1arg52dGPi5ffvXnXiI3Zp+3dHyJI5NYyUqaJ6rFkvUPOB2/E58snoFcuA/LXCCoGTArChqilFLvqDRpYM4cs4y5QAGT1Lt+fahe3WRLjcbOJQcHmD0bevaE8eOhRQsIDv73dQ3Izz528gAn3OiPFz/ggw/lKMcVrpiL8rQE91lwaQusqKsdS0op9V/Y20PdurBrlznq1jWDDVOmmGVxjRrBvn02q9r48WaV3rx5ZsW29SAha1hDferTgx4MZCACZOAbrjCCuizEh4acIMKGFPYOZpOHnM1g51ew51ubtEepuCimOpX2A9kty8piWZYj0AxYGfECy7KyR3hZE7Bdt7eKOo6OsGyZ2Sf60qUnHUxv4D1gJ5CdzsRjDlvZSgOXasxbfZdrydviMe9XwnwDYUlCeNgGmBQFDVFKqXdY6dLg7Q0//QRJkoCXlxmxLljQ7Pp5/3603NbOzow+f/89LFxoVlM/71a1ycFxduJHUgrTh02M4CpXKUtZLnLRXJS3NbjPhIubwjuWdCmcUkr9Z6VKmVlLJ07AJ5+YEYClS83WbOXLm13lonk267MsC77+GmbMMCv2KlaE+37xWcxi2tKWYQzjUz4llFDS04drTKQaq/CjFoeJkCPKLh7UmAu5W8Lu/rCrX6zbAU+p2MiSF/xDsSyrbWQKEJFfInUjy6oBjMXsBPeLiHxrWdZQTEbxlZZljQMqA4+BO8CnInL8ZWUWKVJEvL29I3N7ZWt37pi9P48ehcKFzTd+kiRvVOQtoAbgzTIsmpGffKwM8qJrk5SEnVvF8raNiZfCERrdB5eRQO+oaIlScYJlWQdEpEg03yNK40R00DgRDW7eNPmWZsx4MkiQMKFZJte5s9mSOhrMmgXt2pl8sWvXwvMmK2/mKumoTBbOc4TvqMYQEpGILWzhfd43Fx2bBV5tIWM5qLcSHBNGS32Viu00ThgaJ97Q1atmqtDkySblBZg40Ls3NGtmBphj0KpV0KQJZMxoxj/eyyJ8yZeMZjRNacoc5uCIIzeYQwra4E0xYC3FiPBcEhYKmzrD0WlQqBtU+BGsmJqLoVTsEdk48bJOpa0RXwKlgOuYhNsZgdTAbhGp8ObVfT0aBOKYa9fMNg1//WW+6WfPhgpv9tfnPlAH2IYXDtQnG++xJngjvZun586fm1nXqS6OSSxo/AASDQQG8/wUX0q9XWLoYUHjxLssKMjMRJ08+eldPsuUMZ1LDRqYhBdR6J+HhUyZwh8W3vv3NTvwIxHVyMMxjvEdVfkOJ5zYzGZykctcdHIBrGsFadygwVqI/2aDHErFRRonDI0TUcTfH6ZOhbFj4Ur40uO0aU3Co44dzS4MMeT3383MVicnWL/erN4eyUj60IeqVGUpS3HFFT+WkhgPTpIXfzZQhgijFSImv9KBMZC3DVSdBnb2MdYGpWKDyMaJF3a5ikiFfw7gKNBbRDKKSEkRyYiZ9nE06qqs3npp05otfYoWNbmVKlY02zU8evTqz75AQmAtUItqPGY9PvhSybEMwxf6kKpIJcpN2MijO/aw0AXuDAV68Zwc8Uqp16Bx4h3n5AQeHrBjBxw5Al26mBlLO3dC8+Zm8KBvX7hwIcpuWbu2meh68yaULGkmvz6rLCl5xBYOUYT89GEzXxBKKOUox9F//jrm9oDai+GGN/xWCR7eirI6KqWe0DjxDkmUCHr1Ah8fmDnTzFa6ds3stpAxoxlsOH06RqpSsiTs3m1W5pUtC9u2wZd8yQxmsIlNVKYyt7hFShrygJXk5BQpKcumf/LwgVlTV240lBgEx2fCmuYQ+pzEfkqpF89Ueuoiy7oDpBCR0Ajn7IG/RSRpNNbvpXRkIY56/NjsET1sGISGmq2q580zuTlet0jgI2AB+4mPO0lxYl3IRn5om5cjmw+zu0dVnF0DsBoFQsqOmDxLOo1Vvb1iYgT6mftpnFAm4dGvv8KkSaajCcwv5u7uZuZStWrm4eINHTtmigoMNLOXSpf+9zUHeEAAdSjNNk4wGHem8pCHbGQjH/KhuchnLaxsAEmzQ6NN4JL6jeumVFyhccLQOBFNRMxg8pgxsG7dk/M1a8Lnn5vBZSt6Vw/4+prwc/YszJ9v8ol74kkzmpGVrHjhRUYyco8d2FOLm6TgFJuoQdanC9o/Gnb0hqy1oPZvEC9+tNZbqdjijWcqPeM6ZpVRRLWBm/+1Ykrh4ACDBpm5qTlywPHj4OYGI0aYTqbXKRKYC3SkKI/YwT2gYryydJm5n8LuBSk8agf+95Mii+LDtSnAx0BIlDVJKfXmccKyLHfLsk5blnXOsqyvnvN+J8uyjlqWddiyrF2WZeV5wzqrqJYwoVnmcPiwGSZu2dJ8569bB+3bm3VrefOaWaobN772TNV8+UwISZ3abCy6cuW/rymMK0lZwyZqkI9BbOQjEpKQilRkD3vMRVlrQP01cNcHFpWF+75v0Hil1Cvo88S7xLKgcmWTBO/ECejQAeLHhzVrzPmCBU2yvKCgaKtChgxm8qybm1k6PXky1KMe61mPL76UohSnOEViygKbScFdClCGFZx8uqCivaDyZPBZA8trQvCD595PqXeWiLzyAKoA94DfgUXAH+Gvq0bm89F1FC5cWFQcFxAg0rWriBnPEClVSuSvv167uDAR6SUiyF/iKlnEVVxlc+hW6dhR5L1kPuI3IquEjXMUuYSINBCRoKhph1KxDGYThBj7Pn7TOIHZxOEvICvgCPwJ5HnmmkQRfq4DrH9VuRonYoGbN0UmTRKpW1fE1fXJ9z2IJEggUr26yLhxIqdPi4SF/aei/fxE3NxE7OxEfvnl+declCBZJY1EBDklPSWbvC+u4ipbZeuTi3x3iYxPKDIti8hdn9duqlJxSVyLE9F1aJyIQX5+IsOGiaRO/SQOpE4tMnSoiRXRJDBQpE4dc7tBg0yoOSgHJZWkkuSSXPbJPhERCZAjcktSy01JIYvl4L8LOj5H5Ac7kfklRB7eibb6KhVbRDZORGqmkohsBN4HJgMHw/+fVUQ2vE5HllL/5+xstqhet87kXNq922TTmzHjtbbwtICRwBCy8oCd2JGJmnbVqTV5DbVbZCH/8J1cD8iGLHWA88uAesDDKG6UUu+eKIgTbsA5EfERkWBgIVD3mXv4R3jpgiZIixtSpjS5NDw94dYt2LoVvvrKjFI/fGi+/7t3h5w5IWvvpYZAAAAgAElEQVRWc+3q1RAW9sqiU6QwqysqV4a2bWHUqH9fkwtHcrOA3/iInIxhHVXJTCbccWc1q81F6UtB480QdBcWloXbZ6L4D0Eppc8TihQpoH9/uHjRzFIqUABu3ICBA83S6MaNzdTT4KjNXZQgASxdauLEkCEmBWD+0ELsYhcJSUgFKrCJTTjzAS7sBBJQlfLMY+vTv2jkaWWWv93whsUVINAvSuupVJwVmZ6nfw7MLg3F/8tnovPQkYW3zN9/izRu/GTkonZtkevXX7u4H0QE8ZPEUkTiSTyZH/ar9OwpktzZTy5+86GEjbEXOY2IlBeRe1HVCqViBWJ4BPqf43XjBNAImB7hdSvgp+dc1xUzo+kykP0FZXUAvAHvTJkyRe0frIpaV6+KzJol4uEhkjy5PDWLyc1NZP/+SBUTFCTSrJn5WK9ez5/wdFFCZa50ERHER9pIUSks8SSe/Cq/PrnoxmGRiSlFJqUW8TsaNW1UKpaKa3Eiug59nrChsDCRzZtFatUSsawn3//Jk5uVDHv2/OcZrK+6Xd++5hYNG4o8fChyRa5IPsknDuIgv8lvIiISLJfksuSRR+Ios2ShhD5bkM86kbHxRX7JLeLvG2X1Uyq2iWyciNRMJcuyMlmWtRs4BWwKP9fIsqzpb9yrpdQ/kieHRYtM0u7EiU321Q8+gOXLTXLv/6gnMIUU3GMzrpSipdWC7KOn0L5bCj74ZgvnHhRHVltwbAdQHrgRxQ1S6t0RBXHiedk6/zUTSUQmisj7QB+g//MKEpGpIlJERIqkTJnyeZeo2CJtWvjoI5Pc+8YN2LvXDCOnSwf79plEGB07mhlOL+HoaJKwdu0Ko0dDmzYQ8kzavEzYUYmfmMGXZGEmS8hCWUrQghZMZaq5KFUBaLodLDtYVB5uHIqediv1DoqK5wnNvfeWsSyTsHvVKrh0yeRXzZvXfOdPnAjFi0OuXGZzn/Pno+R2w4fD2LFm5lL16uByLx072IEbbjShCVOYggMZSccufCnGRzRjAT/y1JNIFndo6AUPfGFRGbj35nVTKk6LTM8TsA74GpPY+074ucTAxch8ProOHVl4i126JFKx4pMRi3jxRHLkMCMZPXuKTJ5sRjYuX37lCMZ8EbGTQEkiNQVBRoR9LwMGiDg7PpAjg6qIjEZkn4NIWFYRef18TkrFJsR8row3ihNACcArwuu+QN+XXG8H3HtVuRon4ih/f5Hevc13P4gkS2a+90NCXvqxsDCRwYPl/5NdAwP/fY2fiIyXESKCXJJq0kDcBUFGysgnF90+KzIlk8iExCJX/ojSpikVW8TBOKG5994FYWEiBw+KfP7507mXQKR0aZEpU0Ru337j28yfb0JMwYIi166JBEiA1JAagiDDZJiESZiEyUM5KQ1FBFkmPSXg2TlL1/aJ/JRU5Of0In+ffOM6KRXbRDZORDYI3ALswn++HeH83ch8ProODQJvudBQkR9/FMmc+emA8uzh7CxSoIBZOtevn8jcuaazKYLlIuIgQZJEmgqC9JW+MmRomDjaP5LdfZuajqWtTiJhqUTkkC1aq1SUssHDwhvFCSAe4ANkifCwkPeZa7JH+Ll2ZNqocSKOO3FCpFKlJ9/3hQuL/PHqTp6JE81KijJlRO48J5fqPREZJVMkVCzxlVLSRuoLgvSTfhIm4QMV9y6ITH9fZJyryKVtUdsupWKBOBgn/uvggwew7lXlapyIxR4/Flm3TqR5c7Oxwz+xwNHR/N6/Z88bFe/lJeLiIpI1q8i5cyLBEiwtpaUgSDfpJqESKiIhclS6igjiJR5yWx49XcjNP0UmpRKZmELk+oE3qo9SsU1UdyqdAHJIhCAA5AGORObz0XVoEHiHBASIHDkismSJyPDhIm3amJ3iUqaUF3Y25ckj0qOHyNq1Ig8eyAYRiS8hklg6CIJ0kS4yfESoWFaorP6iu+lYWu0s8thVJOKuQErFQTZ4WHjjOAHUAM6Ej0T3Cz83FKgT/vM44DhwGNj6bKfT8w6NE2+BsDCRxYtFMmR48v3etu0rdwpauFDEwUEkf36TuulZgSIyQhZJkDjINSkgn0tzQZBP5dPwBwkRuX/F5MwYG1/kr9VR3zalbCiuxQk09967zd/f5OCrVOnp/EsVK4ps3PjauZf27jUpnFKlMhOkQiVUekgPQZDG0lgeykMRCZMj4TNc/5CKcvXZXKy3z5jZreMTiVze/sZNVSq2iOpOpbbhv+i3AfzDe/6PAi0i8/noOvRhQYmImQK7d6/IvHkiAweaJXLPblvt6ChSoYKc/+47KX3AWxKH9hIEaSkt5fsxwQJhMvvT703H0mIXkUcOIrLE1i1T6rXZ4GFB44SKXg8emAyrDg7mez1JEpEJE8xI9gts2PD0KPSzgkVkpKyXB+IsNyWbDJN2giCtpbU8lvByA26KzC0s8oO9yIl50dM2pWwgrsUJoPFzOpUmvOT65sDsV5WrcSIOunxZpE8fkYQJn/yuX6SIyLJlZqXDf3TqlEimTKa4zZtFwiRMRstoQZCyUlZui1lud1TmSLDEk+NSQHzkytOF+F8W+SWXDkKot0pk44Rlrn01y7LqhffqZw7v+f9ZRDwj9eFoUqRIEfH29rZlFVRsFRwMe/bAhg3m8PY2ISfc3ylSsK1yOtZUPQJVq5B7uSd9PnPmx/Zz6J77E6zkTtAwAFx+Bjrarh1KvSbLsg6ISJEYvqfGCRX9zpyBbt3Ay8u8LlDAJHQtVeq5l+/bBzVqQLx4sH49FCz49PthwFj+oA01AGcW0JCuTKA+9VnAApxwgiB/WFEPLm+FCuPhw8+itYlKxYS4FicsyyoBDBaRauGv+wKIyHcvuP6f3E2JX1auxok47O5dmDTJZN728zPncueGPn2geXNwcIh0UVeugLu7CTHz50OjRrCABXzER2QnO+tZT0Yychov0tOQ26TgAevJQ64nhQT6wbLq4PcnuM+B3B5R3GClYlZk40SkO5ViIw0CKtJu3YItW0wHk5cXXL78/7fCLPi9ZhIuv/8zLcY1ZmDrDQwq3AjLOQwaPoSkQ4ABPH9zKqViJ1s8LMRGGifeUiLg6Qk9epgdgwCqVoWvvoLy5c0WPxGcPGne9veHlSuhXLlnigN+5ij1qIorwazkY1oyhipUYTnLccEFQh7BGg845wklBpnD0rig4q64Ficsy4qHmelUCbgC7Aeai8jxCNdkF5Gz4T/XBga9qo0aJ94CgYEwY4bZ/vOfmJApE/TuDW3bgrNzpIq5cwdq14bffzdjFZ07wxa2UJ/6uOLKOtaRn/yc5wCu1MCeEC6yikKUfFJIkD941gHfHVBpIhTsHA0NVipmvHGnkmVZrURkbvjPbV9UgIj88tq1fEMaBNRrEYEzZ/DbsIE/N2ygzIb1OAWbvaf/TpOZYdc/x7nBBwyv1AwLf2gQBGm6AOMxG48oFfvFxMOCxgllc4GBZgvqMWMgIMCcc3MznUt164Kd3f8vvXzZdCydPw+LFpm3nzULH8pQhXTcYBOdqccYilOcNawhCUkgLAQ2tIfjs6Dgp1BxHFh2/y5IqTggLsYJy7JqAGMxv5D9IiLfWpY1FLNEY6VlWeOAysBj4A7wacROp+fROPEWefwYfv3VxIVTp8y5lCnNAESXLpAkySuLePgQmjUzAxBffw3ffANHrSNUpzoPeIAnnlSgAlfx4THVSIUvR1mIGxGCyuOHsLop+KyC0t+CW18dhFBxUlR0Kq0VkRrhP299wedFRCq+fjXfjAYB9aYuAU39/Kg0fRAdJ08h4+UwAB7ggvd7tSnbeid2yW5CnceQpTEwF3CyZZWVipQYeljQOKFih9u3zbDyuHFmZipArlzw5ZfQogU4OgLw999Qs6ZZET19OrRp8++ilnCNnFQlJ2fYRTfcGUce8rCOdaQlLUgYbO8NB8ZArubgPgvsI7/EQqnYQuOEoXHiLRQWZmazfved+cIHcHWF1q1N51LevC/9eEgIdO0KU6eaj0yfDtccLlGd6pzlLHOYQzOacQs/rlGL3Hizl4mUpNOTQkIfg1dbODkPivSCsiO1Y0nFObr8TalI+huz5dThkDO0WF2BVj9do+LmJ/8uJKcrVvEA6CFQsCKwHEhko9oqFTlxbVlDdNE48Y4JCIBffnl6CUSGDNCzJ7RvD66uPHgADRrAxo0wcqRZHfGsddwhCTVxYy/76UFlppCCFHjhRU5ymhmv+0bArq8hSw2o/Rs4RG55hVKxhcYJQ+PEW0wENm82nUtbtjw5X7686VyqV++FeZdE4NtvYcAAqFIFli6FkIR3qEtddrKT0YymJz0JIJBjNKU4a9jFV5TiWyzCZ7BKGGzpBocnQr5PoMoUsNNVDyruiGyceOGcbcuy7CJzRG21lYp5KYAtQPl4OZhV7yjtNpUhz3FY17g493HFOv0AZguUBzptheslges2rbNSsYHGCRXruLjAZ5/BuXMwZ44Zjfb1NZ1KmTLBoEG4PvqbVaugSRMzkalPn6f2cQCgOkl5zEa2UZnijGEPn/CQQEpSkj/4w4w2F+trHhDOr4Ol1eDRXdu0WalYTOOEsinLgsqVTcfSkSPQqZOJE9u2mSCQOTMMHgxXrz73o/37m3GKLVtMLr5H15KygQ00ohG96MXnfI4zCfgQT7bQkdKMYB8teExQeCF2UHECFB8Ax2aYJXEhQTH6R6BUTHjZl3gIZj3yi45/3lcqznMFVgFNScZ5NiB5PKixeA9NDzTkU7uxXHbOAfeAKQLvHYef84GcsXGtlbI5jRMqdnJwgFatzEPEypVQooTJwDp0KGTOjFOvz/i19yE6dxJGjjSTmEJCni6iLC4kYRWeNCEf4/mDKqQgCZWoxEpWmovyd4Bai+DaXlhcDgJ0wEGpZ2icULHDBx/A5MmmA2nCBLNL3LVrMGSI6Vxq0sR0Nj0zytCmDaxebXaFK1ECLpyKzyIW0Z3ujGMczWhGGCFUYDIbGEExFnKKqtzntinAsqDUUCg/Bs4uBc/aEPwg5tuvVDR6WU6lzJEpQEQuRmmN/gOdrqqiWijQDZhEGB8wgKMMJ/8Vd47mXsTnWfYwKkFn7Pb6mIsbOMD0tZC0si2rrNRzxVCuDI0TKm4QgV27TPLWtWufnM6dm81pWtJua3MK1XuPBQsgfvynP3qKMLbzFR0ZxRUq0oI77ORPfuZn2tPeXHRhA6yoD65podFGSJwlBhun1OvROGFonHhHiZhOpIkTTf6l0FBzPk8eszSuVStI9CTdxYEDJiff48dmrKJkKWEMY+hFL8pSFk88SUpStrCAUnzMVbLiwlpSESEeHJsJG9pBGjdosBbiJ43ZNiv1H0VLTiXLsizMaqG/JRYkY9IgoKKDAEOBwUB+pnOcTmS4k49L+ddQOkt6NlX5BMdvf4EgIAOwaDSU/MKWVVbqX2yVK0PjhIr1/vzTZF1duNBk7g63gzJ452hBO6/GJHov2VMfuQ7MYCp96MIdcvEFKZnLNgaF/2dhwdU9sLwG2MeHRhsgRb4YbphS/43GCUPjhOLKFZOVe+pUuB4+4zRhQmjbFrp1g6xZAbN7qLu7Sdn3669Qvz4sYAEf8RHv8z6rWEU2srGHHeSkLiE4cp81ZCXCP7Ozy2FNM0ia08QKlzQ2aLBSkfPGOZWeKSyJZVlzgUfADeChZVlzLctK9oqPKhXnWMAgYCJwlHbkYg23k/qQ9HQxdt8/QoX1MwjwnA2Z7MEXKNsLvm34ZIRDqXeQxgkVZxQoYJY+XL1q1jR4eECCBJRlJz3PdCJB1jQE1ahvsrI+egRAGqA7HRjCWhy5zE+cZiC1GMIQOtCBEEIgXXFousPcY1FZuLbPdm1UKhbSOKFirfTpzTK4ixdh0SIoWxbu3zc7imbLZnqPtm8ny3vC7t1QqBA0bAg//QQeeLCBDdzkJm64sZnNFKcsV/mdhziTmnIcY9WTe2WvD/XXwD0fWFga7l2wWbOViiqRTYw3E0gAFMSknymE2Vf9l2iql1I21wVYCJyhGmnYiZMzOO0rzZ7EXpQf0Jo7XofAPYlZM9d/GVTJAVd8bVxrpWxG44SKWxwczFqGX3+FGzdg9mz8ClXBTkJxWucJjRpBmjTQrh3s3ImrCIOoyjh2cxtH+rKF2TRiOtOpT30CCTSzkzx2g1NS+K0SXN5u61YqFZtonFCxm6Ojya20fTscOgQffWRihaen2TGucGFSrJ3DpjVB1Klj9oX46isoG1ae/ewnPempRjUmMIE85MLiD86Tm9zUw5tJT+6TubJZKv3wlulYunXSZk1WKipEavmbZVl3gbQi8jDCOWfgqogkicb6vZROV1UxYSNQH0iGLy7U5EzYcey6TyDXts5sXnObVFMqwIQjcB9IHh9mLoDa9Wxca/Wui+llDRon1Nti34preDZbSLPQeeR/fPDJG6VKwcCBSJUqTLBuUJw6FMGbHTSgIkspRnFWsYoUpID7V2BJFfA/D3WWQZbqtmuQUi+gccLQOKFe6vp1+PlnmDQJ/PzMudSpCevUha8udGLU7FS0aGF2iQtyvE9LWrKSlbSjHROZSACPOUkzSrKavfTGjRFY/8zr8DsCS6qChEJDL0j9oe3aqdRzROnyN+A08N4z5zKFn1fqrVYF2AoEkoFb7KKUXXVCJnThVJfPKO2eCN+O3jCtOeQEbj2COvWhe9f/L5tQ6h2hcUK9FdzqpsVj3+e4pzhA8UQn8G3dF5Ilg927oVo1rJIl6bb+MD6ylRXUpzxLOUo1jnCQUpTiAhcgYXqzFC5ZHvCsC6d/s3WzlIoNNE6ouCdNGhg82CRSmjkT8ueHGzewGzKI7xdm4nDhthyZfwR3d3h8OyHLWU4/+jGd6VSiEo8J4EOWs4HOFGMUB/EglPBnhJT5odlOiOcMiyuA706bNlWp1xXZTqXNwAbLsoZbltXZsqzhwAZgk2VZbf85oq+aStlWUWAXEJ+EHMKTRvQipPNP+EyoSclaAfgUmQdzf4BagD0wfhKUcIPT+nuSemdonFBvjQ8+MH1It1LlJueS4WycdgGGD4fkyWHPHqhenWYlKpF1bRvGSS/y4sVpCvGIm5SgBIc4BM4poMkWs8vPmmZwbJatm6WUrWmcUHFX/Pjw8cdw+DBs2QJ16mAFB1PgwEyOUID+2yrT7YOtnDlp8Q3fsIAFeONNUYpymuNUZiJrGElhFnOKKgRyy5SbNDs02wUuaWFpVTi/zqbNVOp1RHb529ZIlCUiUvHNqxR5Ol1VxTRfoCZwHGjBDOaHdULOZSNl21Vsm56NXI5rYVIDmBMEfoBzApjwE7RpA5Zl28qrd4oNljVonFBvnRs3zE4/x46Z1Q+fNH1glkCMGvX/neMCixZl0cBitKo5iXtWNmpyn+PcZwlLqEY1eBwAK+rDxY1QcQIU+tTGrVLK0DhhaJxQr+3cObPxwy+/wIMHAOy1L4HD4P582K863tYB6lGPO9xhLnNpQAO8WER5WnOd93BlLcl535QV6AdLq8HfR6HGfMjZxIYNU8qIbJyIVKdSbKVBQNnCfaAJsB7wYDtrQxrg7w+J2i5lx9Dy5E93DH6tBHNuwoHwD1WqZB5EcuSwWb3Vu8VWW0XHNhon1Jvy9zd5W728TELWb78Fu4cBMHmy6Vy6eROAi4Wzk2KgL1LblQ5WEhbjwxSm8AmfQEiQma10zhNKD4difW3cKqU0TvxD44R6Y3fvwsSJhP7wI/Z3zAykmxkKkfLHflxvUJz6dg3Zy16GMIT+9GcXv5OXuljYEcgKMlDSlBN0D5bXgiu7ocpUyN/Oho1SKupzKv1TaCLLstJFPF6/ikrFTQmBVUAnYAHlKBVvH++5pOLeb1UoMX0G+3zyQbuj8EVB8AASA5s3mzXYQ4ZAUJBN669UdNI4od42iRLBqlXQoQOMGAEeHvDQzgV69YLz5+GHHyB1ajIfOItL3Yc4FL7H7BUXGSZ5aEc7BjAAiecItRZD7haw62vY2Rfi8KCeUm9C44R66yRJAv36YX/pAkHfjua2UxpS+R7CatyINHmrsGNeRz4OackgBtGUphSmEJfYw12SkIKKnGWxKccpsUnY/V412NgevH+wbbuUiqRIdSpZllXFsiwf4C5mBdA/x+VorJtSsVY8YBIwCljL+6Rw2kPxkEoEjm9H6b1fsO1Acmi8B1o1gJ5AZUxn0uDBpnNpa2RmgCsVd2icUG8zBwez/G3UKFi82Ew+9fMDnJ2hZ0/w8YGxY5G0aXE6FIxDvWD6lj7K7t+zMZxvaE1rgu0Fqs+B/B1h3wjY8hlImK2bplSM0Tih3nqurjh9/QVJbp9nRdWJXCQT1qmTOLZqyy85f8drWiNWBy2lNKVJjhNh/MExipCdppzge0DAwRnqrYAcjWF7L9g9QAchVKwX2ZlK04HhQCLAIcLhGE31UirWs4BewG/AURJzM8FqGgZ04/FnY6gUUAfPHUHgvgSq9Qd3YACQMxmcOQMVK0Lr1k+2JlUq7tM4od5qlmUmJy1ZAocOQfHiEfZicHaG7t2x/voLGTeOgJQp4XcoWeocd+u7cvLUPNxx567lD5UnQ5FecHgirG8DYSE2bZdSMUjjhHon2DnHp65XF36fdZaO8WZwIV42LB8fqnZYwu1syak84RSlHxbGl2NkZBNeNCMPX3GSjsBjsHeEmgsg3yew5xvY0k0HIVSsFtlOpfjATBF5ICKhEY/orJxScUEjYCtwn3hscRlHx4DJhFX2okHqkkzbfAGKDYNa8yG5PXS/DX1zgJMTzJ0LOXPC9OkQpoFCxXkaJ9Q7oWFD2LbN5GQtUQK2b4/wZoIEWN264XLuHH8O6E+QsyMJPR+wPx907LSNutfcuGhdgrIjoeRQODEHVjc1OZeUevtpnFDvFI+PHGmzsy0lk52ibYJfuZ85Hwl8/2ZUt0ccynyH7UMq4vn3D5RkHov4mtxM4yy1EPzBzh6qToPCPeHwT7C2JYQ8snWTlHquyHYq/Qh8aVm6fZVSz1Mc2AOkAma6dKL74/XYZbxChwJuDNm8C3I2hya7AFdIdwa8MkGV8nDnDrRvD2XLwvHjNm2DUm9I44R6ZxQrBnv2QJo0UKWKGSN4SqJEFBg6jH3nLrC2Y20Amk4RvLKdZenAvBx+sAtKDIDyP8LZZeBZG4L8Y74hSsUsjRPqnVO8OOz1tudQTg+SXvqTlW2WI0WKkNwvlEGDhdYZ+7O3c25qnmnCbKbzHlvwpTTBXDZTZMuNhtLfwakFsLgiBNywdZOU+pfIdiotBdoD9yzL8ol4RGPdlIpTsgK/AyWAcQkq08FpD/EfJWVwmYq03PILpC0OLY5Bwoxw4CyMPA4LfoTUqWH3bihYEPr2hcBAG7dEqdeicUK9U7JkMV/dZcqY1cyDB/877UWZtGnJ+PNKPI5t4l69RMQPhJ7DAkj/flmOT+wC+btCtV/g0hZYWBr8NbWMeqtpnFDvpIwZYdcuqFPPjroz69E+/z4eb9iK1KxBgkdQ+eezOOcqSKP6i9myexSJ5AL3KcYDDpiOpWJfQe0l4HcY5ruB3xFbN0mpp1gSicRflmX9CRzGpI95GPE9EdkcPVV7Nd0CVMVGwUA7YC7QOOQOW4815e+CG3Hb3YNdJUfhEBwIa6rB+T1QOD7kXwL915gssCKQIQN89hm0awfJktm4NSquiumtojVOqHdVcDB07AizZkHLlmZFs5PT09dcARoSwHe7a1Gh9zb4w5y/ly0Vib+bCIUTwerG4OAC9VdB6sIx3Ar1LtI4YWicUDElLAwGDYJvvjEDEr/9BqlvneDymM9JNXcDTsHmutvF8hPa6yqu9QMItF9EcsyMV24cfDKzteav8H5t2zVGvRMiGyci26nkDyQRiV0ZwjQIqNhKgKHAYKBsWAg3d/TmVPmxpDtehcM5F5HSLiFsawuH5kJWO6j5CxzMCZ06wZ9/mkKcnc3wd7dukDu37Rqj4iQbPCxonFDvLBEYPhz69zermZcv//eYgD/QBKGIfMM3ngMJ/soOxzPmn4sULYr1aQsI/AGCbkGthfqwoKKdxglD44SKaQsXQtu2kDw5eHpC4cLgc/13NkysS+NJf5P8trkuKKsjDp8/5mabkaRx6WVOPrgKnnVMB1O5USbnkq4oVdEksnEissvfVgAV36xKSr07LGAQMAfYYxeP4HI/UnbTDK5m30aWm24cDDoLFedAxRFwPgwWfgx5l8PBA7BmDVStapbB/fwz5MkD1avD+vWa0FvFZhon1DvLsqBfP/j1V5NrqXhxOHny6WsSAauwuG4NoG59T0KPJSBwsgO3UoO1fz981APGJYCTqWBZXTg4wSZtUSoaaZxQCmjWzCyftrOD0qVh/nzImqYkLYedp/ulenT9CW5kdcHJJxi7z4Q0GXvjP9AN7v4Nrumg6Q7I0RC294IN7SE02NZNUu+4yHYqOQErLcvysixrTsQjOiunVFzXCtgOBFpwsHJb6hzYSoC9P26hxVnovxYK9YH6K+BePJg/EvwaQo0q4OVlEnd37AgJEpgOperVIW9emDwZAgJs3TSlnqVxQr3zPDxgyxa4d88k81616un3HYBpQFHqUsRhL3c6ZSLxX3bMHwfXMzrCqTMw5QKMig/DusHaLhCmG2Opt4bGCaXCFSoE3t7g5maWTvfqBfFDXJnrsoxMXUeQ4Uwgny/NhH/xgnAHEg3bT1iWtDC8PwSFQa1FULw/HJsBS6rCw1u2bpJ6h0W2U+k48D0mD/FfzxxKqZcoDngDuYGVJUpR695+Qs++j4drLb6+MwrJUhs8DoJdYli4As4UBK6aGUo//wyXL8N335lcS6dOQZcu5ucvv4RLl2zbOKWe0DihFFCqlHlQyJED6tY1uTMiZhqwgP5AH/JSiH3sd6lIi26w5FwovWYm5VGuLOD3EDyBJpOhdT7wu2Kj1igVpTROKBVBypSwaRN07Qo//AA1asCd2xZ96MMa+/XMbuUYgnIAACAASURBVHCfzH9cYOuOUVwqnxO7uyHQ71ska2YYOw4+/BpqzINre+DXYnDrlK2bpN5RkcqpFCU3six3YBxgD0wXkRHPvN8Tk984BPAD2orIxZeVqWugVVzyEOgAzANK+wWy5/c2hNRdTK27LfktyTTiB9yDFaXg2l+Qxwkq/ALxmz8p4PFjk6hj7Fj4IzzLq729eWpp2xaqVYN48WzQMhUbxXSujNhK44SylYcPoUMHmDcPGjY0ibxdXZ++ZjPQmBBG8gXtGM9WnGgZZoenZ3eKfrfJ9E4BJLCDTp2hzwCzY6hSUUDjhKFxQsUGM2Y8GTdesQLy5QMffKhPfY5ylKEyjDxbXKjVrzeOe0PMh9KnN8n83PPAusYQGgS1FsN7VW3bGPXWeONE3ZZllRWRHeE/v3D9s4hsiURl7IEzQBXAF9gPeIjIiQjXVAD2ikigZVmdgfIi0vRl5WoQUHGNAD8AfYBsD4Urs4YT0Lk/uf3d2JRoOelCU8Cez2HfJEgAVKkB7y/BvIhg3z4YNw4WL4aQ8MCSJg20agVt2mhibxUjDwtRGSeii8YJZUsiMGaMmViaN695UMiS5elrjgN1AHemMp6uXCQe1XhERxnJF5sLYg3sBX+Ebx/t5AhtP4Hevf9dkFL/kcYJQ+OEii3++MMMQvj7w5w50KABBBBAe9qzgAXUpS71ZBhl19QlS//zWOF7+5AlC3zZFRxnwd2TUGEcFOpqy6aot0RUdCodE5F84T+ff8HnRUSyRqIyJYDBIlIt/HXf8A9/94LrCwE/iUipl5WrQUDFVeuBZoB9KFjjPbnVviVJ7ROxPoEnbrjBjb2wvhb8/TfkSQwV1kD85/xzuHrVRJ2ZM+HMmSfnixWDjz82mQCTJImhVqnYJIYeFqIsTkQXjRMqNtiwAZo2NUlZFy+GSpWefv9voDEgbGMNDQnjAXUI5n0+YRKTcFy3APp2hD+DzAfixTMzVPv3h4wZY7o56i2hccLQOKFik6tXTWfS3r0wYAAMHgyWnTCe8fSiF1nIwmDm4RI2irpLl/B4YCIcTvmbD+fIDrUSQpqDUKC96VxySPDS+yn1Mm+8+9s/ASD85ywvOCIbANIDlyO89g0/9yKfAOue94ZlWR0sy/K2LMvbz88vkrdXKnZxB/YBKe3hXo96pF/8B3euO1EqpCyzmIWkdoOWV6B4czh5D2aXBp9OmLlOEaRLB199ZXIt/f47tG8PCROaSNS5M6RNC82bmyeaUE32qqJWFMcJpd5aVavC/v1mQmm1amaiacQxvRTABiAX5fmA/dwhG5uwIx4zqEpVblWvBTvOwrfZobBlkndPnQrZssGnn5qnEKViIY0TSv036dLBtm1m4cGwYVCvHtz3t+hOd7awhfvcpz0V8LVryE+Nh+FwzJ+7c7KaPEtnzsKYgzAlFSycBvOLwq0Tr7ynUm8qsom635T1nHPPnSJlWVZLoAgw6nnvi8hUESkiIkVSpkwZhVVUKmblAPYCVS240vYD0p/bT8iuErShDfWlAdftb0Op+dBiI8RPCMunwLqM8OjsvwuzLChRwjxkXL9uknhUqgSPHsGCBeYp5r33zKj2hQsx21CllFJkywZ79kDt2tCjh3lgePToyfsOwGTgC7JSgD/YjTs/A03ZSSncOJUoAL7whq+rQy+BshlNrr2JE+H996FnT7hxw0atU0opFVXixzc5liZMgLVrzQKEM2egDGU4yEEKUYhP8eA0t/nefjHxWt3g7qlgQqb0NUmZ/roJM4Fhp2FoITj6y9MjGUpFsZjqVPIFIs7PzgD8a1jNsqzKQD+gjogExVDdlLKZxMBKTI6lK1VSkDrtJhg8klWP15EnLC8LWICkrgQt/aB4NTh5BWbnAp9hLy7U2RlatDDbSVy4AEOGmLXWvr7w7beQMyd8/TUEBMRMI5VSSgFmIunSpTBoEMyeDeXKPT3JyAK6AktIRANW8hO96EwYU7lMNYqx0Wkv1F8FDb+HetdgYBqoUd70Tv34I2TNCn36mKXTSiml4izLMhNRN24EPz8oWhRWroS0pGUrW+lGN37iR9YwgZ9ZxV0HJ0I7jOHu2WEmHqRIAT4hMD4Ymn4Ck2pCkL+tm6XeUjHVqbQfyG5ZVhbLshwx6WRWRrwgPI/SFEyH0s0YqpdSNmcPjADmA/dy2JPki95Ih8M8OpKD5jSnAQ24YX8XSq2HFgshvgMsHwjr8sKj6y8vPHNmGDgQzp0zc2mbNYPgYPjuO5PMe8kSHblQSqkYZGdncmQsWwbHj0Phwk829PxHJeAP7JnIKD5hJiWx2M4jeuDOZGsKuH0JzXZBeieotBPmdDVToAIDYeRIM5DQvz/cuWOLJiqllIoiFSrAgQOQPbvZ8HnAALALdWAc45jHPLzxZgwtWMZE9lKcJPHbcL3HDfjrjHkGcHGBk8Bn66BCeti/ytZNUm+hGOlUEpEQ4FPAC/PXerGIHLcsa6hlWXXCLxsFuAK/WZZ12LKslS8oTqm3UnNgtwWpEgIzcxG2fRdOg0eyJnQdecgTPmupCbS8AcULw8kTMDsTnBkLEvbywu3szJD4ggUm91KhQnD5MjRubJJ9nDoVE01USikVrn59sxzO2dl8PU+Z8nQff3ZgD3CVjynHFpKRiL3Y4UkXOtKRgLT5oPVhyNEIbk6EVoGwfR1Urw4PHpiZqVmywNChcO+erZqplFLqDWXODLt2mf0ZvvkGataE27ehBS3Ywx6cceYr6rKO+sylI2kYwfVEDZEhH8Fff8Fnn5kNHv54ACXqQPNycFPncKioE1MzlRCRtSKSQ0TeF5Fvw88NFJGV4T9XFpHUIlIw/Kjz8hKVevt8CBwEPrEgqLs9do1787jWYVyuZKc5zWlIQ27YP4JS3tBiDMQPg1Wfw6xkcGw0hAa/+iYlSpiMsZMmQdKkZplc/vxmycSDB9HdRKWUUuHy5TNfx5UqQadOZq+FiHmWEgOrgRKU4gO8uUEe1mGRjKkUJj+7nI5CzQVQdTpc/R2OtYaJn5nBg8qVTWfSoEFm2fPChTozVSml4qj48WH6dDMAsWULFCkChw5BfvLjjTfuuDOCHqwkgOFMwhlvgviAgNQLYfyPcOo0NGsEYcCCHZA5HfTvA/fv27pp6i0QY51KSqnIcQGmAUuBBHnAfnkuLg/aTb65I1kra8lLXhayEEndA1pdhBruYO8PXr1hejI4MBiCX9E5ZG9vdoc7cwbatYOQELNkIlcuWLRIHzyUTViW5W5Z1mnLss5ZlvXVc97vaVnWCcuyjliWtdmyrMy2qKdSUSlZMli9Gvr1M4lZy5Y1E0n/YQ+MBgaTmSLsxouGfAcsx5felKG39SWPPmgBLQ+Aa1pYVgOClsD6NbB9O5QsaRJ4e3hAjRpw/kW7uiullIrNLAs6dICdO002i5IlYc4cSEISVrCCYQxjKfNZyM+MZRVbKI8LPbhDGcgaDAt+g4OHoFQeeBQK346ELJlMRvDgSAxMK/UC2qmkVCzVADhiQXknYLo9x1x6k77VITI+yoYHHjSiETfs4kHudaZzqUENSBoI24bAtBSw+wsI9Hv5TVKkgGnTzBqMIkXgyhWTd6lSJZPsQ6kYYlmWPTARqA7kATwsy8rzzGWHgCIikh9YAoyM2VoqFT3s7c2ShuXLzWrkwoVNGryI2gCrceVjFuPBUjKQgt1AVkZTngLsT/4Amu+Fgl3hwBhYUAo+SGeePqZNgyRJYP16yJsXRo0yO8cppZSKc4oVg4MHoXhx+Ogjk9A7JNiO/vRnLWvx5TLfUpVlFKQP0wjjNI8pSDDfQcG8sOs4LJ8GWePDrbvQrZsZWF6wAMJekVJDqefQTiWlYrH0wAbLJByLVw/Oj8zNWY/dtDvzPWtYQ17yMoMZBFmpIMsaaPIXNK8FGYJgzxiYlg42t4d7F15+Izc307E0ZYoZNt+6FQoWhC++AH/dKULFCDfgnIj4iEgwsBCoG/ECEdkqIoHhL/dgdhJV6q1Rrx7s2wfJk5vVa2PGPD1xtBRwEAt/GpCOUyzhczpixyrOMY5iDIz3LcGVxkCdZXDvL5hbCE4vMDNST52C5s3h4UP48kuzldC+fTZrq1KvQ2e0KmWkSmV2hvviC5g40ST0vnoV3HHnOMdpQhNmMJwFDKM7I/GkNo58TQDFgMNQrx2cuAH9y0EqzCzW5s3NM8HmzbZunopjtFNJqVjODugF7LODrCkgYKk901d8SZdZh8gu2WlHOzKRiUEM4joJIO0qqHsaPq4JuULgyHSYkRXWNgW/Iy++kb29mVN75oxJ7hEaap5oPvhAHzxUTEgPRFj0g2/4uRf5BFgXrTVSygZy5YK9e6FOHfOw0Lw5BAQ8eT89Js/SeBLSnjGUxpsQCjIPoQzf0JiCHM2eDVodhlQFYW1LWO0BrgLz55vZSlmywJ9/mmHuzz7TwQMVJ+iMVqWeFi8ejB5tMlf8+Sd8+KGZnJqWtMxlLjvYQRISM592fM89PmEc97lKKEUJZQA4OcHQrbBxFngkMIn8Dhwwoxru7qZQpSJBO5WUiiMKAUcc4ZNQoDf8mD83yXr/zspHGyhKUYYylExkohWt8MYfkq+GakehXXX4UODcYphTAJZWgQsbXpw3KXlymDzZZI8tXBguXYLSpWH8eM21pKKT9Zxzz/0LZ1lWS6AIZhLf897vYFmWt2VZ3n5+r1gCqlQslCgRLF0Kw4ebh4USJcwGPv+wMMvhjgEJKUQG9jGOiZTDmUWcZBkFGZloNiFNNkLJoXBuGczMBYcnQ5XKcOyY2ZzBzg5++gny5DFr75SK3XRGq1LP0aSJGYxIlAgqVnzyK3sZynCQg4xnPOfYx1x60YCmzKMx9nxDEB+CtQ/yfwRT/4Kfq5su2wT24OVldotu3RouXrR1E1Usp51KSsUhzsB0B1gu4JwL1g61+Hh8FaotWs2JkDN0ohOeeFKUopSiFIs5QUjClVD+IHSoatZO+G2GpdVgdnYzi+nxw+ffrHBhs4NQt24m90b37tC4sW5NraKLL5AxwusMwNVnL7IsqzLQD6gjIkHPK0hEpv6PvfsOj6raGjj8W2kkkACh1xB6lSIdxIIKihUbCldFrhVR/NSLei0oVtRrQVEvelUUUbHSBaRZQDoioXcSIJSQkJ7MzP7+2CcSehKSmcxkvT7nyZQze/bZhrNy1tnFGNPJGNOpevXqJVJZpUqaCDzxBMycCfHxdtq7mcf1zasP/AS8TzBPMZRWbGEH/RmJh2t4huFBHdjY/Sa47S+o2QnmDoUve0DqRnjlFTspR9eudj69666z4+/yzxKuVOlSbD1a9eaDCjStW9v7wVdcYf9k79/f3hcOIYQHeICNbGQQg1jMGB7mF/rzGAc4goceGB6ByEowYDq8/hk8UwEuCIbgIPj8c2jWDB59FJKSfH2YqpTSpJJSfuhagS3loXMGJI2AB6+DTvOb4pk7ho258bzJm+xjHwMYQEMa8gqzOBQ+EbotgTsHwmWhELQV5twFH9aA3x+D9H0nflFYGLz9NnzzDURF2VvnHTvaNUxVyXGnQML78Mfl4DlF0i/wLAOaikhDEQkDbgam5N9BRDoA/8UmlPb7oI5KeV3fvrB8OTRoYC8Wnn/+2HlUBbgb+AtoQG1a8j1PMJNa1GAsG1hBK8ZUGU/mDVOg3xdwZAd80QnmPwTNY+H3321vpagomDzZ9lp66y27KqhSpUux9WjVmw8qEFWqBN9/b4fEzZkDLVvaxZ1zcqAmNfmET1jEIhpQgx8ZzXk04r/ciPAGLpqBfAatBsGwDTC8H/zLDT2q2QL+8x9o3NgWmFlm/jZVBaRJJaX8VG1gaTX4y0D/nZDTAcZeDPVTKzF17UNMyN3Ej0ymKU15gieoT33u5iOWhjyIp/VeuHUM3NQQ6qTBH6/Ch3Xhpyth/+oTv+yGG+wd7fbt7RiM7t1h3DgdDlcs3GD+gqSXYFVP+LES5r3K8NVQ+P0ncg/96OsKeoUxxgUMA2YB64FJxpg4ERklIlc7u70GRALfiMhqEZlyiuKUCiiNGtmOowMHwjPPQL9+J45GiAXmYCecGcNlNGMHf/AQN2IYwkv8T2ryRcuDuO5YC23vgZVj4NOWsPUHGDoU1q+H66+HtDT4v/+zNxB+/937B6vUqRVbj1alAlVQkJ2Pb9066NPHjnTu0AEWLrTvd6c7y1jGe7zHEf5iGN9xIQNYRU1gMC46QuR6uHYy/OMzuMUFj4RBl2aQnGwLbNYMJk7U6wD1NzF+/MvQqVMns3z5cl9XQ6lSIdfAS8thTAok9QLKQd2DMLwydApZw0TeYQITyCKL6lTnci6nH5dzOVWpePi/sHIyxLkgF6jfDDo+B41uAsmXe87KgocesqvEAQwaBB98AJGRvjhkP2SABGApZMyDnfMwOzcju1yQavfIrATrG8CsBvBl/VC+jFhFa1oX+ptEZIUxplOxVt8PaZxQgcQYO+XdiBH28Qsv2Hm2Q0KO3W8rMAT4BbiTTTzF/dTnZ9KAL6hGA97m8r2NkTn3woHVEHsZXDwWKjeCadNsoTt22MIGD4bRo+1SQyqg+FucEJEQYBNwMTaYLgMGGmPi8u3TATtB92XGmM0FKVfjhApk+U/p//iH7cVUs6Z97yAH+Tf/5iM+IoxK9OdSRrOEGHbh5kqCeRXSKsOce2DbVDjYCqYb+Gu9LeDSS21QatzYZ8enSlZB44QmlZQKMMbAd/NhxErYfj7QBYLc0A+4MTgJNzP5mRn8xE8kkUQQQfSgB9fSmwFZqdT962tk1R6b5KgcBR3vgdbPQWj5o1/yxRdwzz12SaIWLeDbb+1gbnUSOcB8cE/C7P4R2ZkEOwFnCoescFgTA1Nj4LcG9ahT+Xy6Of+1ox1hhBXpW/3tYqGkaJxQgWjXLtu5aPp026Howw/tnej8PMC7wOPYdPZI1jKAu2nIYpKBb4jhHM8HdFu9CX57CowLuj4FnR6FXA+8/LJNJuXkQOXKdtbwu++2K4WqgOCPcUJE+gFvAcHAx8aYF0VkFLDcGDNFRH4GzgH2Oh/ZZYy5+hTFARonVODLyLCn9FdfhYgIePFFu9Bz3ul8FasYyUimMpVIormPLjzFYiJJB+4myIyE9XNg/oOQkwGHr4GxP9s5lsLDbRfaRx6x02aogKJJJaXKOGNg7lx4bDysbANBg8FTE4INNBNojZvKLCWZGaxnBnGsBKAOdRjq7shdm3dQfWUcstcD5UPg3Cug3TsQ7vQ837DBDouLi7MR6v334fbbfXfApUomMBvc3+DZ9QNBGzMwW0CyITcY/qwDMxuEsrdBO6rUuISuQT3oSldqcLQnQAawC2gChJziW07HHy8WSoLGCRWojLHT3T34IBw8CA8/DM8+C+XLH7vfduB54DMgDBjFcq7lTprwJ4eA6bSke+prNF3wKWz6Fqq0gN7vQoOLYfNme4t71ixbWKdO8N570LmzF49UlRSNE5bGCVVWbNwIw4bBzz/bGxLvv3/s6Xw5y3mWZ5nOdGoSzZO04D6WAuUJ5gkkbQDMecj2WopoB/PrwLfOXPitW9uRDD17+uTYVMnQpJJSCrAXHgsWwLMvwC+hwHlQvgvQBjJq8ffMauFmLzXlJ2AGicwmiyNUNkGMi6/PNUsTCduRZa9I2rWHc1+HyIttT6X774fx420hQ4bAO++ceFVTJqQBMzCeSXh2TyV4Yw6ezRCUBelhMLNJOXY160F0zHV0Cr2AOrQinmB2wjHbDo7pyMQ2oGERaqMXC5bGCRXoDh+2U1x8+CE0bGhHJPfpc+J+m7HJpS+ACOB5fqUfd9KcTewH5tOFS7bdRdV5L0HKdmh8NVzwOlRuYmd+feghuwydiO2x9NJLUKWKV49VFS+NE5bGCVWWGAOTJtmp8/btsz2WXnwRoqOP7rOUpTzLc8xkBi2ozCvU5hrWk019ypkXYWMwLHjULvLj7gfj1sHW7fbDd99tVxfNX6DyW5pUUkqd4PffYd4827koLg427AZXU6AN0BYiOoGnFWRXzQUWA9MJNt/ilm3cuj+IV5aWo/amTDvNUusadphE9DD45Cs7FiMrC845x94+b97cp8fqHYeBqeR6viQofg7Bm9x4NkFQJmSEwq9NqrCrWV9SYodxOKQbawn6O2mUclxJ4UCDk2xXApWLUDO9WLA0Tqiy4pdf7N/yGzfaeTPeeANOtqjVBmAU8BV21vsXmMkl3EsrdrEXWOG6iN4rulF+yTvgzoYOD0C3p8EVYpeee+MNuzJc1ap2eNwdd9iZYZXf0ThhaZxQZdGRIzByJIwZY0/nTzxhp9DLnwtawhKe4Vlm8xMXUpHXiaIjCWTRkfDsZ2DxQlj5NgRVgvU94JNZkJtr5+B76y24+WZ7I0L5LU0qKaXOKDcXtmyxK0TkJZri4mDjIXA1A9oC3Q1y6UpM9UnAJDok7+CdZdA9DoI8YJqFIp0HwP6b4IZHYdMmO3H3uHFwyy0+PsKSkA3MIM28R0TCPII3enBvguAMyAyF3xrFsKh5f+bFPsyq0Ji8+bcJBloAjTg2aRTr/KzOyddKLiq9WLA0TqiyJCvLzpvx8ssQFWXzP7fddvK/6eOA54BvgErA83xNbx6kNfvZh7A2vSs9fqtH+bXfQURV6DEK2t4FGzbZHqoLFtiCune3Q+Lat/facSpHVjLsWQSN+hXp4xonLI0Tqiz780/bEXXBAjubxaBB9hSf/5S+mMU8xbPMZzYDqchLBBFDMhn0pvzBITB3HMT/Au5z4PtgWOKsJN23r40PjRr55NgUkLIDDq6FxlcW6eOaVFJKFVlurp1KIy7OJp22boO4TNhUx5B0wXLoOYmaoRP5z8o93LgawnKAWNharQ3hI6Oou3CxLWfIPYSOfctO4ufXDLCUVDOW0MRJhG/Ixr0RgtMgK0SY3ag5E5oPYlrDB8kMrUgk0A5oD3RwfrbG9kbyFr1YsDROqLJo3Trba+n336F3b7vaT/v2J08urQGeBX4AojE8x0dcwDO0ZR/JwNrEZnRcUJ6I+NVQtTVc+CY0uAS+/NJOzLpvn+2pNHw4PPeczWapkuPOhR2zYN1nsHUyxuNC7t0L5Qu/Op/GCUvjhFKwerXN/3zxhZ3Yu0cPm1y6/nooV87us4hFPMFIlvAz91CBJ4EapJNsrqLy+m6wcAykJcK+C+GT1XA42V4DjBxp40VoqC8PsezIOACbvoH1X9gbD+UqwX37IbjwE6lrUkkpVSJyc+3KQ4v3GL6MWML6Gp8zZM9nDF+VRlQGEA3u9QJjIDjHEFehLaOaf0d4myY0bswxW7Vqpb1X7E7S+AD3oXFU2pCEZwMEJUNuEMxs2JIvm9/H0sZ30DwskvYcTSI19MChA7B3r9327bM/k5Ls9VZ0tJ2KJDr6xC0vcJ8tvViwNE6ossrjsR1GH3vMDnNo3hxuuslurVufeO5diU0uTcUmwO9lMtfzCD3YSo6B9Vtq0nohhKUkQqOr7HxLIbXg6afh3XftF9ata8dS9O9f2k/u/sUYSFwB6z7Cs2EiQZmpeCKEoBYGTytw10wgVOoUuliNE5bGCaWOSk6GTz+1CabNm+1Itrvusos+13fW6vmd33mWl1nMdIYTxr8QKpLDgezrqLEoEln1OeRWhl+bwfQ/7IfOOQf+9z9d6KGk5KTB1smwfqK98WDcmGr1yGlRkYwWIURX+rNIxWpSSSnlNR48zHAtZOu6F+i14Vfax+cSFA9MAA6AKzKYsU89wL+PvEDGrxVgBZBhEyx5CaZGjY5NONWvDyFFWfbsrB3hCONJSXmT+hu2w0Z7DB6BhfVjmdr4LnZXuo+aidHU2gTZW48mjfK2/fvB7T6x5IgIyMw8/beXL39skunzz6FBg8IfhV4sWBonVFl36BB8952dmHX+fJv7adnSJpcGDLCP81sJ/A/4EjtrXHcWMYL7uZzVhLhg+8pIGi5xEeRyIXnzLa3bbmd7XbbMFnLFFTbRFBvr1WMNOEe2k7Z+JCHrfiQ8KRUTDNIYMlrBnFhhVnAjZtGXubxCLIXvIaZxwtI4odSJPB67StzYsTBtmn3tmmts76Xeve19gz/5k+d5hfl8zSMEMRyhHIbE/VdTZ+5uZM9yONgSvkyFnfG2V+tDD8GoUVChgm8PMBC4c2DHbNgwEbZMBlcGrqho0ltEUqFlPCHVDbsJYTIVGMoBgih8TzFNKimlfMKDh5UZs9i25U2arV5AuzdyESc57r4WFr7enfdjH2bVoSuI2BSBWQ2pf8DenyE38Wg5ISE2mZI/4dSokU1EhYRAcPCxP0/2ODjYJnHS049uaWnHPk9Ng7TIQ1TrOImOTT/iov2rCNtoYK+tx4oatfg0dzDfLBhO4txasOfY4w0Kgpo1oVYtqF3bbnmPj38tIsLOb5ucbFdsytuSko59nv+1r76yny0svViwNE4odVRiol3IbdIkWLjQdoBp0+ZoD6b86ytkA9OA8cAMoAYbeJp7+Qe/EJVu2LsojFprciG8CtL139DmLvj4czvb65Ej9oT3zDPw8MMQVvgu92WRwbA75yeyN71AzfUridqVhQCmLmxvJXzXLIbJ4Zeyk4FcQnf6Ek5voPAD3yyNE5bGCaVOb+dOu7LoRx/BwYPQogUMHAhXXmmHVm+VLbzMq8ziU0bg4l4EY4I4sK43dReuQI4kwfJW8MN6m61q2NB2pb3kEl8fmv8xHkj4HTZMxGz8Bsk6RG54BGnNw4lucRjqwnoRfsAw2YQSn3IBzTb2ZXbXhwil8HfrNamklPI5g2FN5gLM88No+9o6glxAPeAeyO0WxO/NujGx7m0sCe7GOloR7Q4lJgWi90LoJshZBYd+gZ1rbILlrNUBWoO08dDxot+4ovN/ud41izY7DyHbwSTaybK3VI5mimsg27Y+QrinIZUrH9t7KP/zatVs8qrYpaXZiVG6dCnSx/ViwdI4odTJ7d17tAfTb7/ZUsGEUgAAIABJREFUBFO7dja51KePHamQNxx3PzARm2DawV6Gch8PMZXq+z0cWRhExV0eTPkaSOcRUONaePxpO+cS2LF2778PvXr56EhLv0T3SvbtHEqD9cupvMUNLjtyZE3LSnzSqjfTKt9PZy7gUkK4BLvgA9gbFLt2Qdu2RftejROWxgmlCiYryy7w/N//wqJFNm7UrWuTS1ddBc17JzA24g2m8z6PkclgICsrnCN/tKPWn38iO3JgSmXYlmQLvOMOO+lflSq+PCz/kLId4j4jZ914wlK24woJIaNxCBVbZkEsLA22cyPOSWtC4u9XkfJRX1Kn94LM8tSqBQkJRVuoVZNKSqlSxSxbSu6N1xK2cy+uCsAACGkFhAJ1wFVP2F2vNstqdWd+yCUsphtxtMZFKHWBZrlQOwnCsm2S3ngADxi3velh8m9uu7mApGjYWwXSoxLpFfoB16VNos+OjVTc7sbsAsmyQ9sSatciK/YqGjd7kKCqbXzZVNZjj8Frr8Grr8Kjjxb643qxYGmcUOrMEhLg229tgmnRIvtaaKhNLHXsCJ062Z/nnAMbwmxy6VtSuYKH+BcTaBifQ+piIWqXwZSvjnQaAQebw4MP29UeAIYMgdGjbSZekWkOsXnvfdRYP5WaG7OQTHCFw9JmsXzQaij76/wfF5sQ2iRCuY2wfSts2wZbt9pt2zbbo1XE9sgtynx8GicsjRNKFV5iIsyYYYfGzZple/9HRNjORxfdcIjd/d9hYdSbjOAIA4C09HAOL21NvVVrkbk5MCcIct22u/8778ANN+hcfMfLTYdN35EZ9ykRu+djgMyYEMq3duFqAgvCYEpueeYsu5iN/+uPmXUpJNSjZUu7MGu3bvZny5ZFvwGuSSWlVOlz+LC9KzF5MgBbBrdg34B0GifupfZBFwAmBKQ2UA9y6gnba9diQ2gblnM+c+jLAaoQhMfZchFyCSLnJFsuESTT1/0d1+35jeY70pDtwAFblcwK5UiO7U6VhkMoF3MFRJSiuyRr10KHDnZipiVLijSpoV4sWBonlCqchARYvBhWrIDly+3PvJ6iYWE2sdSpE7TvBDkXwm8Nc4gMfown+ICmCVk2ubTT4ImoRlDb/4NZafDqfyAnB6pWtcnywYPL5MWDwc2mpJGErX+fmA1JBCeDJwQ2NKrK583uY03u05SbH0b6HNi5CbZvt82WJ29YeP4h4Y0b2ymsNKlUdBonlDo72dmwYIFNME2daofLAbTvlUr1J8eR0fsV7g89yE1AbloIictaUn/OBoK+zoVtTiHXXGMncKpb10dHUUoYAwm/kbn2E4I3fUNYbhoZlUMIb+0iqBXMi4IJO5vw4xc3cvjH/lTcei7dOgf/nUTq2tWOpCgumlRSSpVOxsBbb8GIEXaCofbtYdw4ctrUY2/8JLLifyI6fjnV9x9EAE8weGpBSD3s0LlQINfZXKd4nLdlg0kAyQFPEKTUaUhY7EAqNLwBqrcrnRc1xsAFF8Cvv8J999nlN4pALxYsjRNKnR1jbHIjf5JpxQo79ApsoqlDH6j8QC6NL3yWYWFv0zIhnbTFELkTPBFVCapxB7y3HOYvsB/q0wcmTIDq1X12XN60O/0rUjf+mybrthOWCAbYExPBdw2v5vNN77HtwyokLbD7RkVBkyYnJo5KYgELjROWxgmlio8x9t5oXoLpjz/AhGUR/cgn1H/8eR6I2sttRpB0IeGPpsSM24JMdSNZQMVIePV1u9xcUcZq+bMju8iOG0/2uvFUTN5KTmgQNPcQ1hq214FPE2IZP+4OcmbcxWXtatOjx9FeSCXZVJpUUkqVbn/8AbfcAjt22OTOsGHwwgtQsaJ9PysZEn4jN/5ncuJ/JiJxPUHGc9oijQju0BDcoaG4Q8PwhIZhavUgquFtENMbylUq+eM6W599Brffbi+2Nm4s8u0GvViwNE4oVfyMscOvVqywC74tWGAfGwPlL3UzcOxohjd5hTZ7U0lfBBV2gis8mpCkvjBmDhw8ZO9GT5oEPXr4+nBKRHruFjZsuYOmcYupuMsNBlJqBDMjpgsj533A5k/bwi47X+1550HPnvZnSV8g5KdxwtI4oVTJOXAApk+3c/jNmp9D7k0TaDBqFA/V28ndRghPNyRMb0C9F3Yha528RM+u8OkEm2EPZFnJuLb8yOH1E6i6ax5BGNJjhAqtDelN4NuUWoyfeDOu30ZwVZfaXH65nabQm/fENamklCr90tPhuefgjTfsUK+6de246muvPfGMmZMK+5aBJxdCKkBoBQgtb3+GOD+Dw0pn76OCOnzYLr904ACMHw+33VbkovRiwdI4oZR3JCXZ5NLcuXbblOih/3/H8PR1o2i//zAZi6H8DsjOrEjY1xWRtfG2281rr8Hw4f597s5jXPy1/1kq//Uu9TakINmQXREW1m/M84tfZtF7N3JurE0g5W116viuuhonLI0TSnlHSortvTTpexczK31F9BPPMbzZFoaZICqlejjwejWqvXkQSQPCQ+C5h+DR0YHVayknDbZOJXXjV4TvmEmoO5f0SkJEa0NQK/hFKvPNnKvIXf0sl3dqRO/etgerr2hSSSnlP/78E+6+G5Yutc+vvtoml2JifFsvbxs61K6SdP759ursLC6y9GLB0jihlG/Ex8O8eTB3rsHVcRwP3fU0nZMPkPEHhG8BmQ6y0Nn5uv7w8SdQyQ96k55EQsavHFx7P63WryX0oMEEw8aGUby59y42TXuVi7oEc955djHPyEhf1/YojROWxgmlvC81FabOcDMm/ns29H2OoW3i+D8TRPVdHjKGRFB+XiYApkMk8trj0PN+CK/s41oXUW4m7JhJ7oavYdtUQl2ZpEUGEdLcQ3hz2F0ljOl/XoJryzNc3KErLVqUnvssmlRSSvkXtxs++ACeeMJGmgoV4Pnn4YEHincSidJq2TI7u15wMKxebfu3ngW9WLA0Tijle8bAxo2GZQkTaNLl33Q38XjWguszCBsPZEF2jWhyPv2MqMuv9HV1C+RAZhJr1j9C+11fU3V7JnjgcE3h++hOLF71P65pdw69e9tQVlppnLA0TijlW6npHl5YNY0JdZ7l+kareNQTTMwEN2YYSCoQBZ5bwgi67h/Q7l6oVfgFbLzOnQM7f8Zs/ArX5h8JzU0lLSKY7GZuqraAzDowL7497B3BBa1vIjKyiMuzlTBNKiml/FNCgh0K8d139vm558K4cXY960DldtuE0ooVdgLz0aPPuki9WLA0TihV+mS7N7DoyCgaRk4m9tcMzJ3Y1TlDYPM1TVnU5gVqd72WnueHlZqkjNsNK9a7WXPwc3q4RtJiyy6C0sEVAX80rsLP5lFujB1Bq+bBpeYO85lonLA0TihVOhgMU7JmM/LIaOrUmM/wHdBn8NFere7zggju58FdvyPBbe+FlrfY6S9Ki5xU2DUftk3FtfF7QnKSSCsXwqGmbuo1NwTHwKqM2qQk/pPu9R+mXFgxLtNWQjSppJTyb1On2sm7d+2yY6kfeMD2XPLlwOKSMnasPdb69WHdumIZH6EXC5bGCaVKM8NhZrAp/Xk6DF1C2GfOy50h6cYKfLzzHlZsvYdaLZrRpg2cc47txOmNRNOhQ/DHEpi9NZX6tcbQJ/JjWiVuJ+SQwQjsig1iTv0L6dniA1pGNS35CpUAjROWxgmlSp/tbOej3PHMyPqIl99PoO9TILngjhWO3F6R6KgUXGEVCW51G9LuXqh2dj38i8R4YP9q2DELz/ZZsGcRQSaX9JBQdjeB2i1yqRQL+0w421KupFP0SMKC2ni/nmdBk0pKKf+XlgbPPgtvvgkeD9SrZ3vxDBhgh4kFgn37oEULO3vh999D//7FUqxeLFgaJ5TyF9nsH387Ve+bRHCmgQZgbgGpBlvC6zB7/xUs+PMSFv5yAZHRNTnnHJtkyks2NWtW9JHSLhfExcHixfDLatgTuZaBHd+gX+gU6iYcQlxggiGxLiyLqUNQqyfpE3UXoYQWbxN4mcYJS+OEUqWXBw8LWcjPy/7DPbfPIGa9wYSAPAKJPatSZUsKoR4XmZExhNU7j+B6vaBuL6jaEqQEJvhOT4SdszHbZ+HeNoeQnP0A7IiuyJHGWdRsmEPNOpAdLMRld6RZuceIlGsB/5zKQ5NKSqnAsWqVncg779978+bw1FNw883+P9/SrbfChAnQrx9Mm1ZsM/PpxYKlcUIpP/PXX5gbrkU2bcMdBcH/BpqBJx6Ccu0uWyvWYYHrcmbF9WXBwgs4sLYGYWHQsqXdwsPtfQi32255j0/2My0d4jyZXHL5NO5s+T49UhYRdTgbAHcl2BQbxOqGLShX/yEuCRtARSr6rm2KmcYJS+OEUv7hSGYiux8bSOt35gGQeR5EjIP0rDAOxVenUnwqlTKOAJAeUoXsqj2p3LQXQfV7Qc1z7SrRBWUM5KZBxgFI2Q4755CzeRZhyasBSA6pQEJsKBWaHCE21gMVINmEsM3TmvrBA6jOPUCV4m4Cr9OkklIqsLjd8Omn8MILsGOHfa1pU5tcGjjQP5NLCxbARRfZK6C4OGjUqNiK1osFS+OEUn7oyBG46y6YNAmA/Q+1ZecrmTRN2kbl3W6IPzbJtKlKbRZX7MPy5CtYuKInue4QoioeISryCJHljxAVcYSocilEhh2hYuhhaoTuo3rQfqpwgNquXTTZt51gl4EQyKwHKxuGsi62O/WiH6G39KUc5XzYGCVH44SlcUIpP/PTT7juuI2QfQfIiBJWjDU0GgR1BUiBg/FRJCVUJzIhgzqH9wGQRQQJId2QqudRsWkncGXhSj2AyTiIyThIUPYBgrMPEuI6SJjrAOHmIMHk/P2VLhPMzirReFpkEtM4nXI1IAdYZaqTFXQhHbibilyIv/ZIOhVNKimlAlNuru3Z8+KLsHWrfa1JE3jySRg0CEL9ZDhCTg60bw/r18OoUfD008VavF4sWBonlPJTxsC778Ijj9jz/nnnwaSvyaidwG7G4/H8TP3ELUQ6SSZ3PATnFrDsICAMKGe3pHrwW2x5dtS7gg6hw+hBT4IJkCHWp6FxwtI4oZQfOnjQ3nz48UcA/ry1Le9+EEb58qvphYuLgKoA6bA7vgqH9tQmMiGLhvu3E2w8xxSVHFyJ1NAo0stFkBMheCq4kQo5hFbIJDwym2pR6VSs64EwWGtCWUsrqsqNXMB9hAVAb6TT0aSSUiqwuVzwxRe259KWLfa1Ro3g3/+G224r/cml0aPh8cdtb6s1a2xvpWKkFwuWxgml/NzixXDDDbBnD9SqZXsv9erlvOkml+XsZQJuz2xqJ24hfK/n76RRThgcKRfKkbAwUsIiSCkXSWpYJdKDK5MtVcilKobadOF62tIWwU+WbSsmGicsjRNK+Slj4JNP7GI+GRnQti3Z33/JssZJLGAeCUynCqs4j1zOByoA7mzYfaAaYWE5RJdPI6K8EzPycQGJwD5gvwljv1RiPz3pxL1cSJ8yFSs0qaSUKhtcLvjyS5tc2rTJvhYba5NLt98OYYUYP+0tO3dCq1Y2AM6eDZdeWuxfoRcLlsYJpQJAYqKdQ2/BArtIw+uvw/DhJ5mDLgeIw1461AKioAz98V9YGicsjRNK+bm1a+G662DzZqhUCT7/HK66CoAccljBCn7hZ/YzjSqs4hxySQaSCCGbaAy1CKUu5WlEZVpQgxbEEEs96hFBhG+Pzcc0qaSUKlvcbvj6a3j+ediwwb4WE2PnXBo8uHT1XLr2Wpg82a5i99VXJfIVerFgaZxQKkC4XPZmwWuv2ecDBsBHH0FkpG/r5cc0TlgaJ5QKACkp9mby5Mn2+VNP2RWkj1stOpdctrGNalSjClXKVK+joihonCiBdfaUUsoHgoPthN1r19qeS61awa5ddtW4Vq1g4kS71I+vTZ1qA15UFLzxhq9ro5RS/iEkBF59Fb791iaSvv4aunaFjRt9XTOllFK+VqkSfP89vPwyBAXZEQz9+sGhQ8fsFkoozWlOVapqQqkYaVJJKRVYgoPtMIm//rLJpWbN7JxLgwZBu3Z2Qj9f9dDMyIAHH7SPR42COnV8Uw+llPJX118Py5ZBy5awbh107mwvJJRSSpVtQUF2vtLZs6FaNfvz3HNBeyKWOK8llUTkMhHZKCJbROTxk7x/voisFBGXiNzgrXoppQJUUJBNLsXFwf/+Z4fCrV0L/fvbu9uzZ3s/ufTSS7BjB7RtC8OGefe7lVIqULRoAUuWwI03QmqqTTQ99pgdIqeUUqpsu/hiWLkSunSxoxZ69rTDpVWJ8UpSSUSCgbHA5UAr4BYRaXXcbruAwcBEb9RJKVVGhITAkCF2Eu933oGaNe1d7r594cIL4bffvFOPDRvs0A2A99+39VJKKVU0UVF2CNx//mN7qL76KvTpA/v3+7pmSimlfK1+ffjlF7jvPsjJgbvugn/+EzIzfV2zgOStnkpdgC3GmG3GmBzgK+Ca/DsYY3YYY9YApWDSE6VUwClXzvYO2rYNRo+GKlVssOnVCy6/HFasKLnvTkyEq6+G3Fyb4OrRo+S+SymlygoRePhhmDfP3jCYP98OdZg/39c1U0op5WvlysF778H48RAeDh9/DOedZ0cNqGLlraRSXWB3vufxzmuFJiJ3i8hyEVl+4MCBYqmcUqoMKV8eRoywyaWRI+2Erz/9BJ062SEUu3YV7/clJ8Nll9llTtu318m5lVKquJ1/vh3q0KMHJCRA7952FSDttaSUUuq222DxYmjUyMaKc8+1PV19NcdqAPJWUulkU6sX6f+iMWacMaaTMaZT9erVz7JaSqkyq1Ilu9To9u3w6KP2Dsb330O3brBmTfF8R0YGXHUVrF4NTZva5FWlSsVTtlJKqaPq1LE9lEaNsnenP/vMzr304YelY+VPpZRSvtO+vZ2w+8or4fBhO+/qFVdor6Vi4q2kUjxQP9/zesAeL323UkqdWrVq8NprtufShRfC3r32rvevv55dubm5dhLZ336DunVhzhw7PEMppVTJCAuDp5+2izJceqm9cLj7bjvM+a+/fF27M8vMtDchUlN9XROllAo80dEwebK92VC5MsycCa1bw+uv60IPZ8lbSaVlQFMRaSgiYcDNwBQvfbdSSp1Z7do2uNxwA6Sk2AuSH38sWlkejx16MWMGVK1qE0oNGhRvfZVSSp1ckyYwaxZ8+aVN5i9aZIc7PPYYpKf7unYnMga++872rOrQwfZobdkSbr0V3nrL3pxIS/N1LZVSyv8FBcGdd8L69ba3UkYG/Otf0LmzXchHFYlXkkrGGBcwDJgFrAcmGWPiRGSUiFwNICKdRSQeuBH4r4jEeaNuSin1t/Bw+Ooru1JEdradY+nDDwtXhjHwwAP2YiZvvqaWLUumvkoppU5OxF4wbNgAQ4eC221XiGvdGqZN83Xtjlq/3q5ad8MNdk6/2rXt6qAbNsCECfB//2d7WlWsCK1a2blB3n4bfv+9dCbIlFLKH9SqZf9WnzEDYmNtL9Fu3WD4cO0tWgRi/HiCqk6dOpnly5f7uhpKqUBjDDz/vJ3IG+zjJ5+0Fyln8vTT8MILdk6PmTPhootKtq6nICIrjDGdfPLlpYjGCaUUAEuXwj332AsHgP79YcwYqFfPN/VJTbXzP731lh12UaUKvPSSvYPuctnheitW2DlAVqywz48fnhEUZHs3vfWW7V1bSP4YJ0TkMuBtIBj4yBjzynHvnw+8BbQFbjbGfHumMjVOKFXGpafDc8/ZxXTcbhsX3n0XrrnmzJ/1towMOw9UTo7dcnNP/zPv8a232iF/hVTQOKFJJaWUOpX//tfe4fZ44P777d3h4OBT7//GG/DII3af777zaTDSiwVL44RS6m8ul71QePppO5wsMtImdoYOtTcCvMEYmDjRDrfYu9ferLj7bnjxRTtc+lSysmxiKS/JtHw5xMXZY1q82N5hLyR/ixMiEgxsAi7Fzte6DLjFGLMu3z6xQEXgUWCKxgmlVIGtXm3Px3nD4Pr3h3fesXOj+srhw7Zn6q+/wi+/2PN/bm7hy9mwAZo3L/THChonQgpfI6WUKiPuuQeqV4eBA2HsWLs89eefn/zi45NPbEIJ4H//K513N0ox52JhLPkuFkRkSv6LBWAXMBh7saCUUoUTEgIPPWSHmg0fblf8fPhhm9D5xz9gyBBo27bkvn/NGhg27OhCEF272tjSseOZPxsebuf86Nz56GtZWbbMkqxz6dIF2GKM2QYgIl8B1wB/xwljzA7nPV3yTylVOO3b2yT92LF2hMIPP8DPP9sYMWQIVKhQ8nXYu/doAunXX+3NhPydgETsvIHly0NoqF2gIizs6OOTvRYWZodQlyBNKiml1Olcd52d8PXqq+Gbb+DQIRtk8p+cf/jBDlkAOwzh9tt9U1f/phcLSinvqFfP9iadNg2eegr+/NP2RH37bZvgGTIEbrnFrhRUHJKT4Zln7IWKx2NvVrz6qp0fKegspjcND4cuXYqnjv6hLrA73/N4oGtRChKRu4G7AWJiYs6+ZkqpwBAcDA8+aHspPfCAXS3uwQdt79KLL4arrrJbcfReMgY2bz62J9LWrcfuExZmbyacf76dX69HD7uYQymjSSWllDqTCy6wJ/rLLoN58+DCC+18STVrwty5djJYj8deNAwf7uva+iu9WFBKedeVV8IVV8CqVfDxx/DFF3ZowYoVtgfTddfZBFPv3kVL/qSk2OTV44/DgQP2YmX4cHj22SLNbaE42cSGRZrHwxgzDhgHdvjb2VRKKRWA6te3q0D/8AOMHm3n5Zsxw2733WdXFL36aru1b1+weVeTk205f/xhtyVLICnp2H0iI23iqFcvm0jq3BkiIkrmGIuRJpWUUqog2rWzy1L36WMvQHr2tJOq/vOfdhK8YcPshYIqKr1YUEp5n4i9ODj3XHj9dXsR8fHHdsjDl1/aLSYG7rgDBg+2qwTlSU62E6bmbTt3Hvs8Ofnovuefb+fmKDtD1UpCPFA/3/N6wB4f1UUpVRb072+3xESYPh2mTIHZs2HlSrs9+6zt/ZrXg+mii2wvUrcb1q07mkBavNiu9nm8WrWge3ebROrVyyaoQvwvReN/NVZKKV9p2NB2Ub3iCjtJ6oAB9vVBg+ywiYLcpVCnohcLSinfCg+3PU9vvtkmiMaPt/Pl7dhhVwZ67jk7IXZGhn0/JeX05ZUvD82awYgRtkyNEWdrGdBURBoCCcDNwEDfVkkpVSbUrGl7rg4ZApmZduTClCkwdSrEx8P779utQgU45xxYu9YuCJFfWJi9gdGtm00kdetme0QFQGzQ1d+UUqqwUlPh+uthzhw7fOL77+1keKWIH67qE4Jd1edi7MXCMmCgMSbuJPt+CkzTVX2UUiXO44EFC2zvpe++s5Nj56lQwfZcytsaNDj2ebVqpfpiwd/iBICI9MOuAhoMfGyMeVFERgHLjTFTRKQz8AMQDWQB+4wxrU9XpsYJpVSReTy2x9LUqTbJtHr10fcaNDiaPOrWzfZC8tZKo8WkoHFCk0pKKVUUubl23o1OnUplN1W9WLA0Tiilik1ysh3CUL26TRpVrVqqk0Zn4o9xoiRonFBKFZtdu2DDBttbqXZtX9fmrBU0TpS+KyGllPIHoaH2roMqNsaYGcCM4157Jt/jZdhhcUop5X2VK8Pll/u6FkoppUqrmBi7lTFnsY6pUkoppZRSSimllCqrNKmklFJKKaWUUkoppQpNk0pKKaWUUkoppZRSqtA0qaSUUkoppZRSSimlCk2TSkoppZRSSimllFKq0DSppJRSSimllFJKKaUKTZNKSimllFJKKaWUUqrQNKmklFJKKaWUUkoppQpNjDG+rkORicgBYKev63ES1YCDvq5EKabtc3raPqen7XN6ee3TwBhT3deV8bVSHCcKQn/XT6RtciJtkxNpm5zoZG2icYJCxYlA+L3y92Pw9/qDHkNp4O/1B+8dQ4HihF8nlUorEVlujOnk63qUVto+p6ftc3raPqen7RM49P/libRNTqRtciJtkxNpm5y9QGhDfz8Gf68/6DGUBv5efyh9x6DD35RSSimllFJKKaVUoWlSSSmllFJKKaWUUkoVmiaVSsY4X1eglNP2OT1tn9PT9jk9bZ/Aof8vT6RtciJtkxNpm5xI2+TsBUIb+vsx+Hv9QY+hNPD3+kMpOwadU0kppZRSSimllFJKFZr2VFJKKaWUUkoppZRShaZJJaWUUkoppZRSSilVaJpUKiIRqSIic0Rks/Mz+hT7/SQiySIy7bjXPxWR7SKy2tnae6fm3lEM7dNQRJY4n/9aRMK8U3PvKET73O7ss1lEbs/3+gIR2Zjv96eG92pfMkTkMueYtojI4yd5v5zzu7DF+d2IzffeE87rG0Wkrzfr7S1FbR8RiRWRzHy/Kx94u+6qYApyXhCRBiKywvl/GSci9/qirt5SwDZpLyKLnfZYIyIDfFFXbznb+BooziZmBKoCtMn5IrJSRFwicoMv6uhPCvFvzZ0vxk7xdj1Pp6DH4OxbUUQSRORdb9bxdAIhLgZCHPPXuBMIccJfzuuaVCq6x4G5xpimwFzn+cm8Btx6ivf+ZYxp72yrS6KSPnS27TMaeNP5/GHgnyVSS985Y/uISBVgJNAV6AKMPO4kPijf789+b1S6pIhIMDAWuBxoBdwiIq2O2+2fwGFjTBPgTezvCM5+NwOtgcuA95zyAsbZtI9ja77flVL1x5Y6RkHOm3uBHsaY9thzw+MiUseLdfS2grRJBnCbMSbvHPCWiFT2Yh29rTj+/vBrxXBODDgFbJNdwGBgondr57cK+m8tM1+Mvdp71SuQgh4DwPPAQq/UquACIS4GQhzzu7gTCHHCn87rmlQqumuA8c7j8cC1J9vJGDMXSPVWpUqRIrePiAjQG/j2TJ/3YwVpn77AHGNMkjHmMDAHG2gCURdgizFmmzEmB/gK20b55W+zb4GLnd+Va4DvaDjwAAAgAElEQVSvjDHZxpjtwBanvEByNu2j/McZzwvGmBxjTLbztByBH8cL0iabjDGbncd7gP1Ada/V0Pv07w89J57MGdvEGLPDGLMG8Piign6oQP/WSrkCHYOIdARqArO9VK+CCoS4GAhxzB/jTiDECb85r5e2f3T+pKYxZi+A87Mow49edLo4viki5Yq3ej53Nu1TFUg2xric5/FA3WKun68VpH3qArvzPT++HT5xuvo+XcpOgEVxpmM9Zh/ndyMF+7tSkM/6u7NpH4CGIrJKRBaKSK+SrqwqsgKdN0Wkvoiswf7/Hu38ARqoChVLRKQLEAZs9ULdfKU4/v7wd2d7TgxEZSEWeltB/62Fi8hyEflDREpb4umMxyAiQcB/gH95uW4FEQhxMRDimD/GnUCIE35zXg/xdQVKMxH5Gah1kreeLIbinwD2YU8a44DHgFHFUK7XlGD7nCxBYs6yTK8rhvY5XTsMMsYkiEgU8B22q+lnha9lqVGQ/+en2icgfl/O4GzaZy8QY4w55NyJ/FFEWhtjjhR3JdWZFcd50xizG2jrdO//UUS+NcYkFlcdva24YomI1AY+B243xvh1T4wS/vsjEJzNOTFQlbXjLRbF9G8txhizR0QaAfNE5C9jjNcSAsVwDEOBGcaY3b64RxkIcTEQ4lgAxp1AiBOlvX5/06TSaRhjLjnVeyKSKCK1jTF7nRNAoea0ycv2Atki8gnw6FlU1SdKsH0OApVFJMTJGtcDStMdhwIphvaJBy7M97wesMApO8H5mSoiE7HdI/05qRQP1M/3/GT/z/P2iReREKASkFTAz/q7IrePMcYA2QDGmBUishVoBiwv8VqrExTnedO5iIkDenF0uLDfKY42EZGKwHTgKWPMHyVUVa8pyb8/AsTZxIxAVRZiYbErjn9reb1ijDHbRGQB0AEv9jIphmPoDvQSkaFAJBAmImnGmNPNv1RsAiEuBkIcC8C4Ewhxwm/O6zr8reimAHmrcd0OTC7Mh51/kHnzB10LrC3W2vlekdvHuQieD+TNYF/o9vUDBWmfWUAfEYl2JujuA8wSkRARqQYgIqHAlfj/788yoKnYVf/CsBNvH7+CSv42uwGY5/yuTAFuFruCQ0OgKbDUS/X2liK3j4hUdyb6w7mL2hTY5qV6q8I543lBROqJSITzOBroCWz0Wg29ryBtEgb8AHxmjPnGi3XzlbP6+yNAnE3MCFQFaRNVOAU5/0TnTWHh/G3WE1jntRqe2RmPwRgzyBgTY4yJxd7k/sxbCaUCCIS4GAhxzB/jTiDECf85rxtjdCvChh1vORfY7Pys4rzeCfgo336/AgeATGy2sa/z+jzgL2wyYAIQ6etjKmXt0wibGNgCfAOU8/Ux+ah9hjhtsAW4w3mtArACWAPEAW8Dwb4+pmJok37AJuzdvSed10YBVzuPw53fhS3O70ajfJ990vncRuByXx9LaWof4Hrn9+RPYCVwla+PRbdT/j8+43kBuNT5t/+n8/NuX9e7FLTJP4BcYHW+rb2v6+7LNnGenzS+Bsp2NjEjULcCtEln53chHTgExPm6zqV5K+D5pwf27/k/nZ//9HW9C3sMx+0/GHjX1/Uu5P+DUh0XAyGO+WvcCYQ44S/ndXEqo5RSSimllFJKKaVUgenwN6WUUkoppZRSSilVaJpUUkoppZRSSimllFKFpkklpZRSSimllFJKKVVomlRSSimllFJKKaWUUoWmSSWllFJKKaWUUkopVWiaVFKllojsEJFLfF0PpZRSpZPGCaWUUqeiMUIp79CkklJKKaWUUkoppZQqNE0qKaWUUkoppZRSSqlC06SS8gsiUk5E3hKRPc72loiUy/f+CBHZ67x3p4gYEWlyirIWiMgLIrJIRNJEZKqIVBWRL0TkiIgsE5HYfPv3cF5LcX72OK6s50XkdxFJFZHZIlIt3/vdnO9JFpE/ReRC5/UbRWTFcfV6RER+dB5/KiJjRWS6U+4SEWmcb98WIjJHRJJEZKOI3JTvvX4iss75XIKIPOq8Xk1Epjl1SRKRX0VEzwFKqYCgcULjhFJKnYrGCI0RqgQZY3TTrVRuwA7gEufxKOAPoAZQHVgEPO+8dxmwD2gNlAc+BwzQ5BTlLgC2AI2BSsA6YBNwCRACfAZ84uxbBTgM3Oq8d4vzvGq+srYCzYAI5/krznt1gUNAP2wC91LneXWgHJAEtMxXr1XA9c7jT533uzjf+wXwlfNeBWA3cIfz3rnAQaC18/5eoJfzOBo413n8MvABEOpsvQDx9f9n3XTTTbeibhonNE7opptuup1q0xihMUI372yaWVT+YhAwyhiz3xhzAHgOe3IGuAl74o4zxmQ4753JJ8aYrcaYFGAmsNUY87MxxgV8A3Rw9rsC2GyM+dwY4zLGfAlsAK46rqxNxphMYBLQ3nn9H8AMY8wMY4zHGDMHWA70M8ZkA187+yAirYFYYFq+cr83xix16vRFvnKvBHYYYz5x6rQS+A64wXk/F2glIhWNMYed9/Nerw00MMbkGmN+NcaYArSVUkr5A40TGieUUupUNEZojFAlRJNKyl/UAXbme77TeS3vvd353sv/+FQS8z3OPMnzyFN8b9531833fF++xxn5PtsAuNHpIposIsnAediTMcB4YKCICDaoTXICREHK7XpcuYOAWs7712PvaOwUkYUi0t15/TXsXZXZIrJNRB5HKaUCh8YJjRNKKXUqGiM0RqgSEuLrCihVQHuwJ8A453mM8xrYLpr18u1bvwS+N78Y4KcCfHY38Lkx5q6TvWmM+UNEcrBdRwc6W0HsBhYaYy49RbnLgGtEJBQYhr3jUd8Ykwo8Ajzi3M2YLyLLjDFzC/i9SilVmmmcOLZcjRNKKXWUxohjy9UYoYqN9lRS/uJL4CkRqe5MXvcMMMF5bxJwh4i0FJHyznvFZQbQTEQGikiIiAwAWnFs19JTmQBcJSJ9RSRYRMJF5EIRyR+0PgPeBVzGmN8KWKdpTp1uFZFQZ+vsHH+YiAwSkUrGmFzgCOAGEJErRaSJczcj73V3Ab9TKaVKO40TR2mcUEqpY2mMOEpjhCpWmlRS/uIF7BjiNcBfwErnNYwxM4ExwHxsl8zFzmeyTyymcIwxh7Djjh/BTow3ArjSGHOwAJ/dDVwD/Bs4gL0r8C+O/Xf3OdDG+VnQOqUCfYCbsXc/9gGjsRP2ge3+ukNEjgD34oy1BpoCPwNp2DZ6zxizoKDfq5RSpZzGiaPlapxQSqljaYw4Wq7GCFWsROfWUoFGRFoCa4FyzsR0pZaIRAD7sasqbPZ1fZRSqizQOKGUUupUNEYoVTjaU0kFBBHp73TXjMZm2qeW9iDguA9YVpAgICKfisgLXqiTUkoFHI0TSimlTqUsxAjQOKFKhk7UrfySiKTle1oeMBxNki4Hri5gOTdjlw2the3iOhN4wBhzpPhqe8rv3gEIcO1J3hsM3GmMOa+k66GUUoGouOKEU1Yj7NCIC7Cx4mNjzIhiqurpvncHGieUUqpEHBcnKuR77AaGF6KccsArwAAgAjt/03BnTqISc7oY4bw/GI0Tygu0p5LyS8aYyLwN2AX0NcaIs3U2xuwtYFG/Az2NMZWARthEq1ey98aYWGNMA2PMKm98n1JKlSXFFSdEJAyYA8zD3oCox9HJXUuUxgmllCo5x8WJncClTowIMcaMLURRjwOdsHMbNQPOBZ4q/hofS2OEKi00qaTKNGPM7uMmynMDTU61v4gYERkqIptFJFVEnheRxiKyWESOiMgk5wIkb/+7RGSLiCSJyBQRqXNcWfc6ZR0WkbFitQQ+ALqLSJqIJOerQrSITHe+e4mINHbKEhF5U0T2i0iKiKwRkTbF1lBKKVV2DQb2GGPeMMakG2OyjDFrTrWzxgmllCpzrgLGGGOSjDEHsD1bh5xqZ40TKtBoUkkFJLHLdiafZovJt+95IpICpALXA2+dofjLgI5AN+wKDuOAQUB97B2KW5xyewMvAzcBtbF3QL46rqwrgc5AO2e/vsaY9diVFhY7d08q59v/FuxwvWjs6hQvOq/3Ac7H3h2pjO1+e6gATaWUUmVSIeJEN+wqODNF5KCILBCRc85QvMYJpZTyc4WIE+Js5HteT0QqnaZ4jRMqYGhSSQUkY8xEY0zl02y78u37mzP8rR7wGrDjDMWPNsYcMcbEYVeGmG2M2WaMScHOydTB2W8Qdt6NlcaYbOAJ7N2C2HxlvWKMSXbqMx9of4bv/t4Ys9SZOPCLfPvnAlFAC+yqjusLMQRQKaXKnELEiXrYZZfHAHWA6cDk/HeRT0LjhFJK+blCxImZwHARqS4itYAHndfLn6Z4jRMqYGhSSSmHMSYB+IkTs//HS8z3OPMkzyOdx3WwdxPyyk/DZvvr5tt/X77HGfk+eyon3d8YMw94FxgLJIrIOBGpeIaylFJKnVkm8JsxZqYxJgd4HagKtDzNZzROKKVU2fEisApYDSwCfsQmaPaf5jMaJ1TA0KSSCkgiMsgZP3yqLeYUHw0BGhdTNfYADfLVqQL2QiShAJ81hf0yY8wYY0xHoDW22+q/CluGUkqVFYWIE2sowjm5gDROKKVUKVXQOGGMyTTGDDPG1DXGNMImfVYYY9zFUA2NE6rU06SSCkjGmC/yr+hwkm0X/B0sYpyJ6Rpg7zTMLaZqTPx/9u47rOryfeD4++EwVdwjlEpEKzQTw9ziQFTEiYr6rTRnbk3DUea2NG2ZZqmVmubEvXCg4sh+SdkwKyypQBy5U5w8vz8eUoRjOeCcA9yv6/K6OOc8n4f7Q98vN5/7WUAXpZS/MkeNvg58qbWOv4trj2PWYv/b8oqblFLPKKWqKaVcgIvAZcym40IIIay42zyBOemtulKqoVLKAgwC/gIOZUIYkieEEMJB3cPzRCmlVMnU54nqwGvA6EwKQ/KEcHhSVBK5XXnMNNW/gT3Az0CPzOhYa70Nk1QigSTMDKgOd3l5NHAQOKaU+uu/GgP5gdnAGcwU2VOYJRpCCCEegNb6Z+A5zCk6Z4CWQIvUpXAP2rfkCSGEyP58Mc8TF4F5wHCt9ebM6FjyhMgOlNZZNaNbCCGEEEIIIYQQQuRUMlNJCCGEEEIIIYQQQtwzKSoJIYQQQgghhBBCiHsmRSUhhBBCCCGEEEIIcc+kqCSEEEIIIYQQQggh7pmzvQN4EEWLFtWlS5e2dxhCCOFwYmNj/9JaF7N3HPYmeUIIIayTPGFInhBCCOvuNk9k66JS6dKl2b9/v73DEEIIh6OU+t3eMTgCyRNCCGGd5AlD8oQQQlh3t3lClr8JIYQQQgghhBBCiHsmRSUhhBBCCCGEEEIIcc+kqCSEEEIIIYQQQggh7lm23lNJCJH7XLt2jYSEBC5fvmzvUByCu7s73t7euLi42DsUIYRwCJInbid5QgghbpEckdGD5gkpKgkhspWEhAQ8PT0pXbo0Sil7h2NXWmtOnTpFQkICPj4+9g5HCCEcguSJWyRPCCHE7SRH3C4z8oQsfxNCZCuXL1+mSJEikgQApRRFihSRkRYhhEhD8sQtkieEEOJ2kiNulxl5wmZFJaVUE6XUz0qpw0qp4XdoE66U+lEpdVAp9bmtYhNCZC+SBG6Rn4UQQmQkvxtvkZ+FEELcTn4v3u5Bfx42Wf6mlLIAM4BgIAH4Sim1Rmv9Y5o25YARQC2t9RmlVHFbxCaEEEIIIYQQQggh7p2tZipVBQ5rrX/TWl8FFgMt07XpAczQWp8B0FqfyLpwJgCTsq57IYSw4tSpU9SvX598+fLRr1+/2z6LjY2lYsWKlC1blgEDBqC1tlOU9uMwM1q1hq194cfPsqR7IYSwRnJENnLjKqxtD/Fb7B2JECIXcdQ8YauiUingzzSvE1LfS+sx4DGl1B6l1D6lVBNrHSmleiql9iul9p88efI+QkkBDmEmRY0CJCkLIWzD3d2d8ePHM3Xq1Ayf9e7dm1mzZhEXF0dcXBybNm2yQ4T2k2ZGawhQHuiolCqfrk3aGa0VgEFZEsyNK3DmJ9jYGb7/JEu+hRBCpCc5Ihu5+rfJE6uaQ3yUvaMRQuQSjponbFVUsrZIL301xxkoB9QDOgJzlFIFM1yk9SytdRWtdZVixYrdRyhOwHygKzAeiLASihBC3Fl8fDx+fn706NGDChUq0KhRI5KTk//zurx581K7dm3c3d1vez8pKYnz589To0YNlFJ06tSJVatWZVX4jspxZrQ6u0OrtfBoMGzuBt/NypJvI4TImSRH5AIehaFdNBT2g1Ut4chGe0ckhMhGclqesMmeSpiZSQ+nee0NHLXSZp/W+hpwRCn1M6bI9FXmh2MBZgN5gLeAZOB95DA8IbKXQYPgwIHM7dPfH95997/bxcXFsWjRImbPnk14eDiRkZEkJSWxcOHCDG0DAwOZNm3aHftKTEzE29v75mtvb28SExPvK/5szNqM1mrp2jwGoJTag/lFPkZrnWEYRinVE+gJ8Mgjj9xfNC55oNVqWNMGtrwIN65B5b7315cQwm7slSckR+QCHkWg3TZYHgyrW0HzSPBtZu+ohBD3QJ4lMoetikpfAeWUUj5AItAB+F+6NqswM5TmKqWKYh4efsu6kJyAaYAHMAW4BMzBPKcIIcS/8/Hxwd/fH4CAgADi4+MZOXIkERER99yXtTXPufBUinud0eoN7FJKPam1PnvbRVrPAmYBVKlS5f6nojq7Q4sVsC4covtByjUIyJoVd0KInEVyRC7hURjabYXljWBNGDRfDmVb2DsqIUQ2kJPyhE2KSlrr60qpfkAUpmrzidb6oFJqHLBfa70m9bNGSqkfgRtAhNb6VNZGpoDJQF5gDGbG0meAS9Z+WyFEpribUYCs4ubmdvNri8VCcnIyU6ZMua/RBW9vbxISEm6+TkhIoGTJkpkbsONzqBmt8fFQvDjkyeMGzZfB+o6w4yVTWHrm3pO9EMI+7JUnJEfkIu6FoO0WiGwEa9tCs6VQrpW9oxJC3AV5lsgctpqphNZ6A7Ah3Xuj0nytgcGp/2xIAaMxM5aGAZeBJYDbv10khBAZRERE3NfogpeXF56enuzbt49q1aoxf/58+vfvnwUROjSHmdGanAzVRkXhc+0xts72IV8+VwhdDBueg5ihprBU7ZXM/rZCiBxOckQO5l4wtbDUGNa1g2ZLoFyYvaMSQmQz2TVP2Kyo5PiGYvZY6o/ZG3ZF6mshhMg8pUuX5vz581y9epVVq1axefNmypcvz8yZM3nhhRdITk4mJCSEkJAQe4dqUw41o9UjmasfduHL007U6b6NHR89ToECLhC6EJycYferkHIdaoz6776EEOIeSI7IxtwKQJvNENkE1oZDs8XwWFt7RyWEyGEcMU8oa+vvsosqVaro/fv33/uFp34CiysULGPlw48xBwzVBdYAng8UoxAicx06dAg/Pz97h+FQrP1MlFKxWusqdgrJYdxvnujDd8y7GsylM1B+wFZiPqhIkSJAyg1zItzBeVB9JNQcB7K3iRAORfJERjklTyilmgDvYQYf5mitJ1lpE47Z10ID32qt0896vc19P0/cydULEBkCSfug6UJ4on3m9S2EeGCSI6x7kDyR+4470ymwvj0srg1/HbTSoBuwANgFNAbOWmkjhBAip0oGvuQprrruJF8hZ378oB7V+uzn+HHAyQKNP4GK3WHfBNg1ArLx4IwQQmQXSikLMAMIAcoDHZVS5dO1KQeMAGpprSsAtj9dwdUT2myEkjVhw//g0CKbhyCEELaU+4pKysmMGgAsCYSkL600+h+wFNgPBAF/2Sw8IYQQ9uUB7OBbgijBRdddFPDMz6+zgnhm0B4SEjB5JPgjqNQbvpoMO4dIYUkIIbJeVeCw1vo3rfVVYDFmz4q0egAztNZnALTWJ7IiEI3ZkdXa8DRgCkthG6BUHdj4HPy4ICvCEEIIh5D7ikoARZ+EDrvBrSAsC4Lft1lpFIbZE/ZHoD5wzKYhCiGEsJdkPAlhPfUIIy/nXGMo5PEQf85pxDPDojlyBFNYCpoBlftD7DuwfaAUloQQImuVAv5M8zoh9b20HgMeU0rtUUrtS10ul4FSqqdSar9Sav/JkyfvOZATwGzMZhlf36mRaz4IWw/edWFjJzg4/56/jxBCZAe5s6gEZj+lDruhgA+sbApxq6w0agqsB45g0kaClTZCCCFyFg9gHhYOs5RAuqA547qTIi5lOPZxU54ZvYFffsHspVT/PQgYDN+8D9v6mCXWQgghsoK1DezSV/OdgXJAPcxpoXOUUgUzXKT1LK11Fa11lWLFit1zICUwG2Xkwww9771TQ5e80HodPNIANr0AP8y95+8lhBCOLvcWlQDyeUH4TiheGda2vcMIQgPMgUPHgEBMgUkIIUTOFgxsxoljfEwdBnCBU647KGqpwKk5rag6OZIffsAUlupOharD4dsPYXNPKSwJIUTWSAAeTvPaGzhqpc1qrfU1rfUR4GdMkSnTrZwKn8WbAlMwYG3dAwAueaDVWni0IUR1he8/zopwhBDCbnJ3UQnAozC03QoP14NNneHr9600qoVJFWcxhaVfbBmhEEIIu6gF7EBxiXepw0iO8pfLNoqpKpz7qD01Zizg668xhaXar0P11+CHj2FTF3NKnBBCiMz0FVBOKeWjlHIFOmCOak5rFWbyEEqpopjlcL9ldiDHjsGkSRBeA2b9BGWAUGDdnS5w8YCWq6F0Y9jcHb6bldkhCSGE3UhRCcya59broGwr2D4AvhhnZW+MKsAO4AqmsPSDraMUQmRzW7ZsISAggIoVKxIQEEB0dPTNz2JjY6lYsSJly5ZlwIABaNmfx0FUBmJQODOOukzhF066bKY4gfw9oxO1581i3z5MYanWOKg5Fn6cb/bPSLlu7+CFENmI5Ih/p7W+DvTDLCE4BCzVWh9USo1TSrVIbRYFnFJK/QhsByK01qcyO5aHHoKYGHBygja14O2voSLQGnPUj1UuHtByJfg0hS0vwoGZmR2WECKHc9Q8IUWlfzi7Q/NlUKEz7B0NO16ysoThKSAGsGCWan9j6yiFENlY0aJFWbt2Ld9//z3z5s3j+eefv/lZ7969mTVrFnFxccTFxbFp0yY7Ripu5wfsQlGIIQTxEfs54byeEilNSH7vRequeI+dO1Ob1hhlZi399DmsfxZuXLNn4EKIbERyxH/TWm/QWj+mtfbVWk9MfW+U1npN6tdaaz1Ya11ea11Ra704q2J5vPwNdu+GggUhrC6M2QXVMRs5zb3TRc7u0GIFlGlu9uH7ZnpWhSeEyIEcNU/kyqLSamLZbu0QUCdnaPwJPD0Qvn4PorpZGWl+AlNYyouZXbsvy+MVQjiW+Ph4/Pz86NGjBxUqVKBRo0YkJyf/53WVK1emZMmSAFSoUIHLly9z5coVkpKSOH/+PDVq1EApRadOnVi1ytrhAcJ+fDCFpUfoSQiL2c5x55WUuNGaq28OomH060RFpTatNsLss/TLUljfAW5ctWfgQggbkxyR853nPDWpyXafT9i1Cx55BNo2gkGbIAjoAsy408XObtBiuVkhEd0fYt+1XeBCCIeQ0/KEs82+k4O4Tgod6MZVfmcB6+hIrdsbKCeo9w64FYIvxsCVcxC6yCSAm3wxZz40wGzNtx6zJE4IYUuDgAOZ3Kc/cDd/3sXFxbFo0SJmz55NeHg4kZGRJCUlsXDhwgxtAwMDmTZt2m3vRUZGUrlyZdzc3EhMTMTb2/vmZ97e3iQmJj7gnYjMVxLYCTShPS3xZCGhlqWUuNGZ42Nfpekbl1hxZTwtWyioMsQMVGwfBGvbQbOl6fKIEMIW7JUnJEfkbBYsFKYw3ejGlJKn2bnzZZo0gQ7N4dPPIU87s07vb2CY1Q5cTV5Y3yF1dcQNkzeEEDYlzxKZI9cVlZxxYjEraUNj/kdDjrKUITS/vZFSUHM0uBc0DwQrm5k10K750jR6BDNjqSHQBFiNKTAJIXIDHx8f/P39AQgICCA+Pp6RI0cSERHxn9cePHiQYcOGsXnzZgCra56VsnZysrC/opiDG5rRlI7sZDZ1LfMpkZKH4yMm0vq9i3y+5G06tFdm1quTC2zrC2vCoEWkWfoghMjxJEfkbHnJy2pW04lORBDB6aKn2RY9kRbNFZ3aw4xzkKc7DMcUlsYBGf6LWVwgdDFseBZ2vmwOeKg61Ob3IoSwj5yUJ3JdUQnA9wcflhTcw/+8m/IyrUlkFm/TNWPDpweCW0GzDG55Q2i9wZwWd1NJzObdjYBmwHJIX6ASQmQZe04Yd3O7NevEYrGQnJzMlClT/nN0ISEhgdatWzN//nx8fX0BM5qQkJBws31CQsLNqa3CERXA7AUbRiDdiOU8zzh9RLEUD04OfJeOHyWTPO8DunR2Av8+prC05UVY1RJarjKbtQohbMJeeUJyRM7niisLWUhBCvIGb3A6/2nWbZxB+7YW+vSAKRcg70swAbgIvMWdCkufg7LArmFmxlK1ETa/FyFyK3mWyBy5rqikNTz3HBw9WozFW7fT6ak2vEM3EjjGEkag0v+6r9AZXAvA+vawtC602Qz5vNI0KA5EY2YrhQGfA+1sdTtCCAcSERHxr6MLZ8+eJTQ0lDfeeINatW4tvfXy8sLT05N9+/ZRrVo15s+fT//+/W0RsrhveTAzVJ/laV7iBy5QyeldiqXk5eSLk+g6/xKXPvqEvi86w1M9TGEpqiusagat1oBLXnvfgBDCxiRH5DwWLMxkJoUpzBu8wdk8Z1m6aj5dn3MlYjC8dhYGjIF3lJmxNBNz3M9tnJyh6WfgZIHdr5j9XGu8ZvN7EULYX3bNE7luo26lYMkSyJMHnq+ZjznRaynOsyzjVQIZSArpT3wDyrWC1uvh3BFYUgfOxadrUBjYijnzoQOwIKtvQwiRDU2fPp3Dhw8zfvx4/P398ff358SJEwDMnDmT7t27U7ZsWXx9fQkJCbFztOK/uQGLgc74MSXEJ4AAACAASURBVIpfGMoFp4kUSRkPnT6jX+GOTHkvdZPuJ1+AkPnw5w5Y0RSuXrBf2EIIhyQ5Iru5BIBC8TqvM4UpLGEJ7VxbMmfRRbp2hfHjgJfgFQ2zgU6A1TNBnZyhyTwo3wn2joK9Y8xIuBBCpOGoeUJZW3+XXVSpUkXv37//vq5NSoKQEDh4ED74NIUpz0UQx9s8TjjfMB8PrGyoenQfrGwKzh7QdgsUKZ+uwUWgJWbm0kdAj/uKTQhxZ4cOHcLPz8/eYTgUaz8TpVSs1rqKnUJyGA+SJ+5eCjAQmE4SPfHjA5z0NM6owbAulNE/LGfM8NS9lH5aDBueA69qELYR3PJncWxC5D6SJzKSPHFn95cnTgE1gc7ACP5Z2PYxH9OTnlSnOmtS1jFhSCHefRe6dIGyc+BVJ/OksASsPWmYfZU2d4eDc6H6SKg5zoyICyEyjeQI6x4kT+S6mUr/8PKCmBioWxd6Pu9Elzffooaews8spQyhnOJ8xotKVofwnaBTYHEgHEufgPIC64AQoCcwLUMXQgghchonzO/7V/BiFr/yPC6qHwX1TGi2nrFVmhEx5qIZdH6iAzRbDMf+DyIbweWzdo5dCCHEvcsPVAVexQwqmJUO3ejGUpayn/00cKrH0LePMXo0fPopfNMe3rluFk634J95Tuk4WaDxx1CxO+ybYJbDZeMJAEKI3CHXFpUA8ueHDRugY0d4ZRgEDHyZ1inzOMYOylCP3zie8aJiFaHDbnD1hGUN4M+d6Rq4Aysx+ysNBCZn+X0IIYSwNwVMBCZRhEXE0Yb86gU89TxosJ2pDRvT95Vz5tngsbbQfDkc/xqWB0PyaTvHLoQQ4t64APOAwcD7wP+AKwC0oQ3rWMev/EodVZvOY47w1luwfDlENYeZV8ymGU3A2hA2KCcI/gieehH+bxLEDJPCkhDCoeXqohKAqyssWABDhsD098HSvhN9r6/lPD9Tnlp8xa8ZLyroawpL+bxhRRP4dV36XjETWztiDhMdA0gyEEKInG8YMIP8rOUQoXipMPKoxagaXzIzrCFdhpwmJQUo2xJarIC/voNlQZB8yt6BCyGEuCdOmDPd3sT83d8MMPvlBRPMVrZymtPUohaNBx9k9myIioLPg+GTi/AFEIRZSJeBcoKGH0ClPrB/CuwcIoUlIYTDyvVFJQAnJ5g6lZujCD80DGHspWiucJYa1GQ9X2e8yLMUtI+BIk/CmtZwaFG6Bs7AZ0AXYCymuCTJQAghcr4+wHzysJNvCeZx1RA3p5U4Vf6eeS/Uo/2A41y/Dvg2g5ar4fQhWFofLp2wd+BCCCHuWQQwF9gO1AfM7/LqVCeGGAACCaRi9y9ZtAi++AKmBcK8c/A9UA84Zq1b5QRB06HyAIh9B7YPksKSEMIhSVEpjcGD4fPPYe9eWFatGu+f3kMK7jSnLrPZlvGCPEWh3TYoVRs2PAvffpiugQWYA/TGjGLcWnMthBAiJ3seWIY7X/Ml9XhGPYPFeR3O5X9leb+6tOqXwNWrgE8TaL0Ozh42haWLVh8thBBCOLTOmN2SfgRqAUcAeJIn2c1uClKQIIIo0n4rq1fDjz/ChBow/6RpWQf4w1q3SkH9dyHgJfhmGmzrZ/Z2FUIIByJFpXQ6doSNG+H33+FN/8eZE78XC6XpSQhjWZrxArf80HoDlGkGW3vDl5PSNXACZnBrzXUvpLAkhBC5QWtgLS7EEU0gDXkM7RyFi+9R1g8NJKTPES5fBh5tCGEb4Fw8LKkHfx+1b9hCCCHuQyiwjVsnw30LQBnKsJvdlKEMoYSS3DSSTZsgIQGGVYVPE+AkprAUZ61bpaDuW1AlAr79ALb2kcKSEMKhSFHJiqAgczLctWvw8tOlmPN1DB5UZwwd6Mn0jBe4eECLSHjif7B7BMQMTzc9VQFTMSdEzAZeAK7b4E6EEI4kPj4eDw8P/P398ff3p1evXjc/i42NpWLFipQtW5YBAwagZYp7DtEI2Iwzx1hHHcIowQ2Xbbg+fJbo0YEE9f6FS5eAh+tBm03wdyIsrQcXEuwbthDC5iRH5AQ1gN2YbTACAXOgjxde7GQnAQQQTjiH635MdDScPw8Dq8Lsw+Y0uEDgB2vdKgWBk6HqcPjuI9jcUwpLQuRCjponpKh0B/7+ZhlcsWLQq1Yhpm2MohAtmE1/mvEaOv3+SBYXaPqZ2VDvq8mwtRek3EjTQAETUv99hjkl4pqtbkcI4SB8fX05cOAABw4c4MMPby2Z7d27N7NmzSIuLo64uDg2bdpkxyhF5qoNbMeJSyymDl1w56rLDtwfusLeNwIJ7PMDFy4A3nWg7Wa4eByW1IXzv9s7cCGEjUmOyAnKA3uBUkBjYAUAhSjEFrYQTDDd6c72KlOIMVsu0asaTP/ePC3UBfZb61YpqP06VB8JP3wMUd3SPWsIIXIDR8wTUlT6Fz4+sGcPVKoELzbzYOTHy/GmO+uZwDP05Fr62Ub/bKhX7RX4bhZseA5upC8cvYo5KWIZ0JZ/jh8VQmQf8fHx+Pn50aNHDypUqECjRo1ITk6+7/6SkpI4f/48NWrUQClFp06dWLVqVSZGLOzvaSAGJyzMpi4vcZnLLjF4FLYQ+1ZdavaL5cwZoGQNaLsFLp82haVzR+wduBDiHkmOEPAwZsbS05i/982DX17ysoY1tKc9QxnKggoj2LVbU6AAdK8JU/aBJ9Ag9eoMlIJa46HGGDg4F6K6SGFJiGwop+UJZ5t9p2yqaFHYtg3at4ch3Z0Z8ecs1o1+iFg1gcc5yXcsIh8ety5QCmpPBNcCsGsYXLsAzZaZJXI3DQY8MCcEtQBWAnlseVtC5AiDGMQBDmRqn/748y7v/me7uLg4Fi1axOzZswkPDycyMpKkpCQWLlyYoW1gYCDTpk0D4MiRI1SuXJn8+fMzYcIE6tSpQ2JiIt7e3jfbe3t7k5iYmHk3JRyEH7AbRUPeIoj8rGWsawx5PYP4YVoDqg3cyJ4pNSnmVdUcArG8ISwOhPDtUKisvYMXIluyV56QHCGgMLAVCMcc2nMcGIUrrixkIQUpyCQmcabMGXbsmkFIIwvd68PMdfBGkFk8vRoIttZ1zdHgZIE9r5miUsg8cJLHOiHulTxLZA757XMX8uaFVavgxRfhjbGKrgnjKTarBNFOAyhNI35gDQ9R6PaLqg4F94KwpResaAKt1ppNvW/qDbgB3TEb+60F8tnqloQQD8jHxwd/f38AAgICiI+PZ+TIkURERNzxGi8vL/744w+KFClCbGwsrVq14uDBg1bXPCulsix2YU8+wC4UwYwmhPxEMsR1F/lUEHHTG/HMgDV8MbEBXl5PQ7vtprC0tC60i4bCj9s7eCHEXZIcIYw8mMHjHsAYTGHpfSxYmMlMilCE13mdM6XOsGXnZ7Ro4kqPJvD+UpjZGpoBS4GW1rquPhKUBXa/AinXoekCsx2HECJbyEl5QopKd8nZGebMgVKlYPx4aHa8Hx1XFmeR83OUJZD9bOIJSt1+0VM9wTU/bHweljWAsE2Qp2iaBl0xM5aex4xHbAQK2OqWhMj27mYUIKu4ubnd/NpisZCcnMyUKVP+dXTBzc3t5nUBAQH4+vryyy+/4O3tTULCrY2ZExISKFmyZNbfhLCTksBOFE14iZYUYCHdXWLwpCG/zwilSv8V7BsdwsMPVzKzlJYFmc27222DIuXtHbwQ2Yq98oTkCHGLC/ApUAJ4EzgBLEDhzkQmUpjCvMzLnCt6jrXRkXRonpc+beCtT2FxZ2gDzMfsxppBtRFmhlLMUNA3IHSRFJaEuAfyLJE5pKh0D5SCceOgZEno2xeq1glnwLYiTMvTmqeoyTaiqMMTt1/0RAdw9YS1bWFJoNkrwzNt8akjZsZSB6AhEIWZLiuEyG4iIiL+dXTh5MmTFC5cGIvFwm+//UZcXBxlypShcOHCeHp6sm/fPqpVq8b8+fPp37+/DSMXtlcU2IaiGV3pSH5mE+6yg/w05ugHLQkYsJh9Q8MoU+ZJCN9hBiaW1IPwaCj6pJ1jF0LcD8kRuZkCJgMPYbbBOAWsAgowhCEUohA96EGb/MEs27ieHm0LMfgFGH8WPAbCc8BFzHynDJ6JMIWlHYNhXTg0WwIWVxvdlxAiM2XXPCEbdd+HXr1g+XL45huIejqIMad2cI3L1KM2kfxfxgvKhJpZSn8nwOLacPbXdA3CMCdDfAfUx4xgCCFympiYGJ566ikqVapE27Zt+fDDDylc2BSRZ86cSffu3Slbtiy+vr6EhITYOVqR9QoAUSga0o5uRLGA8y7byO9UhZPTw6ny9uf89BNQxM8UliwuprB04lv7hi2EyBKSI3KDl4AFmG246wJJAHSlK8tYRiyxhOSpy8xVSYSHw2uDoNpYaKKhJ/DOnboNeAnqT4PDq2BNW7guBwEJkRM5ap5Q1tbfZRdVqlTR+/dbPXTTJnbvhubNwd0dXt57mKE+jUnhGO8TST+aZLzg2H6IbGIeDNpusTLavAWzaro0sA3wyupbECLbOXToEH5+fvYOw6FY+5kopWK11lXsFJLDsHeeuDtXMAsbVrCXcdRiEAWut+Cc0048h8xmT9duVKwInDlsZixdu2hySImn7Ry3EI5J8kRGkifuzD55IgqzsK04sBkwhzFsYxstaUkJSrDpxhbe7FWGOXOg90A4/g6sUDAOGImZ+5TBgQ9gW1/waQotIsHZ3Ub3I0T2ITnCugfJEzJT6QHUrm0KS87OMM6/LG/H7sGFx+hPc0awIOMFD1WBDjGgnMxSuKT0s5qCMfsq/QEEAn9m+T0IIYSwNzdgCdCJmoziAOM457yeAroxF97pTvWF7xMbizkBrv1Os6R6WRAc+8rOcQshhLg/jYFo4AJQE4gFIIggoonmLGcJtNSi/6zvGTIEZr4HHl3guRQYBQwDrE4L8O8DwR/BkQ2wuhVcu/8jyoUQ4m5JUekBVagAX3wBjzwCETUeYtKGneQlkEk8TyfeznhBkfLQYTe4FTIPBX9sT9egLmbE4gSmsHQky+9BCCGEvTljNnLtSyWm8guDuWiJpMCNVlyaNIBaayazdy9QwMcUltwLw7KGcPQLO8cthBDi/lQF9mBOiKsHbE19tyq72IUFC3VVIGFTvmD8eFg4Dy60hRdvwBSgD5BirdunekKjjyF+M6xqAdcu2eZ2hBC5ls2KSkqpJkqpn5VSh5VSw618/oJS6qRS6kDqv+62iu1BeXtDTAxUrw5DQvMz7KMNFKEtnzGEYIah048lFPAxhaX8j8KKEDi8Jl2PNTHL384BdYBfbHIfQggh7MkJeB8YQTk+4jd6cNWykPw3OnBl7HDq7RzN9h3a5I72OyFPcVjeCBJ22ztwIYQQ9+UxYC/gAzQFFgNQnvLsZjfFKEawasgzI6OYNg1Wr4TDTeClq/Ah8AJw3Vq3FbtCk0/hj22wsplZNi2EEFnEJkUlpZQFmAGEAOWBjkopa+ciL9Fa+6f+m2OL2DJLoUKweTO0aQOjernx7KuLeVT3Zitv4k8XrnLt9gvyeZmHgmKVYE0YHEp/dGAVYDtwFTNj6aBN7kMIIewlJw8+3D0FvA68wcN8TjwdUZY5eKZ05dqIcQQfiGBTlAZPb5ND8pWCFU3gz532DlwIIcR9KQnEADUwp0JPA6A0pdnFLh7jMZrTnOL9lzB3LmyPhr114dVk+Axoj9mZL4MKnaHpZ5CwE1Y0hat/2+Z2hBC5jq1mKlUFDmutf9NaX8WU4Vva6HvbjLs7LFkCffvCtNct1Ooyg8opY/mOeZSlNedJN/3Uowi02wregbDheTgwM12PlYAdmP9M9YADNrgLIYSwvdww+HBvhgMzKM4ajtCSPE7v4JnSjxuD3iI0vi8rV6dAvpLQfsetWa+/b7N30EIIIe5LQczm3a2BgcCrgKYEJdjBDqpTnY505HLnj1i2DL75GtZWh/HnzfnRrSD9U4bh9yw0/RwS95jDgq5esNkdCSFyD1sVlUpx+67TCanvpddGKfWdUmq5Uuphax0ppXoqpfYrpfafPHkyK2J9IBYLvP8+TJwIn89TFGs6iuDrM/mTDfgQzFFO336Bqye0Xg9lmsG2PvDlpHQ9lgd2Ah5AfUA2ZhVC5Ei5YvDh3vQB5lGI7cTRhMJOY8mnh5Ly4kzanOnGoqU3IO9DEL4dCpaFVc0gPsreQQshhLgv7sAyoCdmxmp34DoFKEAUUYQSSi968VPY66xdpzl8GOZXgcmnTDkqBDhvrdsn2kOzxXDsS1jeGK6cs9kdCSFyB1sVlaydepn+0IK1QGmt9VOYnermWetIaz1La11Fa12lWLFimRxm5lAKXnkFPvkEtm2FMzV60TF5GafZTznqcJCE2y9w8TDHfj7REXaPgJjhoNP+eMphpsUWAoIwm/oJIbKbU6dOUb9+ffLly0e/fv1u+yw2NpaKFStStmxZBgwYgE79HXD69GmCg4MpV64cwcHBnDlzxh6h20KuGXy4N52AZXiyn0M04GE1mDwpY9EvzOV/+lnmzLtm9lZqFw2FnjCbsv62wd5BCyHug+QIARbMbkmjgU+AMOASHniwghU8x3O8yqtEBb/M5i2akydhemV466jZmakhpB++Nh5rC82WwvGvzF58l8/a7I6EEJnHUfOErYpKCUDaP/69gaNpG2itT2mt/1kSPBsIsFFsWaZLF1i9Gg4ehP+r2Ia+pzdxiT+pTE1iOHR7Y4sLhHwGT70IX02GbX1Bpz3ToTSmsPQQ5hjS9KfGCSEcnbu7O+PHj2fq1KkZPuvduzezZs0iLi6OuLg4Nm3aBMCkSZMICgoiLi6OoKAgJk1KP5sxx8hVgw/3JgxYiwe/8A11Ke/UFQ/9JrRfQo8C7Zj20RXIUxTabYMiT5pjpDMcACGEcHSSI4ShgDHAB8A6oBFwGhdcmMc8+tOft3mbOTW7snXHda5cgTf8Yepv8B1mw4zj1rot1xqaR8KJb2B5Q0i2Wn4SQjgwR80TtioqfQWUU0r5KKVcgQ7AbX/xKqW80rxsAemrLtlTaChER8PZs7DMrz7D/9jJda5Sn9osY9/tjZ0s0HAmPDMUvp0JGzvBjbQbfHtjCkuPYk6IkGUOQthDfHw8fn5+9OjRgwoVKtCoUSOSk5P/87q8efNSu3Zt3N3db3s/KSmJ8+fPU6NGDZRSdOrUiVWrVgGwevVqOnfuDEDnzp1vvp8D5crBh7vXGIjCjST2UpuqKgw3PR1arWZg6RZMfv8SeBQ2+/QV94e1bSBuhb2DFiJXkhwhMkdvYCnmMaoOkIATTrzHe4xlLHOZy4RK7di6+zLu7jD6aZj8A/ya2voPa12WbQEtV8Jf36cWlk7Z7naEEDfltDzhnOk9WqG1vq6U6oepgliAT7TWB5VS44D9Wus1wAClVAvMyZinMadk5gjVq8OePdC4MUyvUJlRu/Yw3r8x4QTxLssZSMitxkpBnUngVgB2v2o21Gu2BJz/+R/OQ5jNu4MxtbdIoJmN70gIRzGIzN/A3h949z9bxcXFsWjRImbPnk14eDiRkZEkJSWxcGH6kxwhMDCQadOm3bGvxMREvL29b7729vYmMTERgOPHj+PlZWruXl5enDhx4h7vJ9u4OfgAJGIGH/6XtoFSyktrnZT6MscMPty9OkA0LjRmG3VopbYQpT/mWqPuDHcP4eKUdYyLKARtt0BkCKwNh9BF8Hg7ewcuhB3ZJ09IjsgaSqkmwHuY54k5WutJ6T5/AZiCySMA07P3oQ5tgaKYLQZrAlEo/BjFKApTmP7051y5pmzas5rWDT15pRpMiIax1UzG2AaUTd9lmVBotcbMal3WANpuhTw5YVavEPdDniUyg02KSgBa6w3AhnTvjUrz9QhghK3isbXHH4e9eyEkBCY+48vIdXuY1DiEQbTgKJ8wmedvNVYKqr0CrgUguh+sDIWWq8E1X2qDYkA0Zjpsa2AJZnmEEMJWfHx88Pf3ByAgIID4+HhGjhxJRETEPfeldfpVXqCUtdVgOVduH3y4ewFADBYasppAnlNRLNULuVHneca7BfP3+I28NbIQqm2UOUJ6fUdIuQ5+He0duBC5iuSIzJfmlNBgzOzWr5RSa7TWP6ZrukRr3S9DB9lWPcxKhSZAbcySuBr0ox+FKERnOtP54Qas2LWBZxsVY1gdmLgB3mxoCktbgCfTd1m6MbRaC6uam8JSu21mfz4hhM3kpDxhs6KSgJIlISYGWrWCcU1KMHTODmZ0a82bdCKREyxgyO0XVO4LbvlhUxczRbX1BrO8AYDCmPGHECAcWIAZ2BciN/nvUYCs4ubmdvNri8VCcnIyU6ZMua/RBW9vbxISbm3gn5CQQMmSJQEoUaIESUlJeHl5kZSURPHiOfePvtw++HD3ygO7cSKIhTQgv1rHbJajn2nPO24NuDRqMzPHFUOFbTQPDBufA30dyj//nz0LkfPYJ09IjsgSN08JBVBK/XNKaPqiUg5UCbMVdyPMoT1LgWY8y7MUpCBtaUub4nVYtmMLLzZ9mOGNYdwy+CAM6mJGa6qk7/LRhuYE6pXNYUk9CI82J4oKkavIs0RmsNWeSiJVgQKwaRO0awdvds/Pc6M2UFS3ZSEv05Ch6PT70pZ/HpovM5vqLa0HF4+l7Q2TJmoBz3KHPWuFEDYSERHBgQMHMvz7tyQAZiqqp6cn+/btQ2vN/PnzadmyJQAtWrRg3jzz/+158+bdfF/kdmWAXShKMZMmDFHuaKc1OFX8iY861KPz8CRSnPOZB4aH68PGzvD9J/YOWohcTXLEA8u0U0KzJx/MCdDlgVbAXABCCWUzm0kiidCCtZix9WeCgmBkG+j6KeQHGgC7rHX5SAMI2wAX/jCFpb+PWmslhLCR7JonpKhkB25usHgx9O8PH413o17XxZTWvdnGFPzpyjWu335BudbQah2c/RUW14Hzv6f50BPYiEkXXYBZNrsPIcS9K126NIMHD2bu3Ll4e3vz449mgHXmzJl0796dsmXL4uvrS0iI2Wtt+PDhbNmyhXLlyrFlyxaGDx9uz/CFQ/EGdqJ4nMm0YLz6mxTnjVge/53PugcS/vIf3HDKY5Y4PBoMm7vBd5IjhHBkkiP+VaadEqqU6qmU2q+U2n/y5MlMDjMrFcecAP3P3/2TAU0d6rCTnVzhCo3y1GbsuljCwmBCV2jzDpTS/xz3YMXDdSFsE/ydaAawLyRYayWEcBCOmCeUtfV32UWVKlX0/v377R3GfdMaJk+GESOgQWPNqXXj+NZ5DI/QjB9Ygid5br8gca/ZX8klnznhp/DjaT68DLTBrBx5H8hBS8mFSOPQoUP4+fnZOwyHYu1nopSK1VpnmO2e22T3PHF3zgKhaPbxPp8wkMdwvhbC9cSChL6zjZVTfXFRl2FNGziyAYJmgH8fewctRJaRPJFRTsgTSqkawBitdePU1yMAtNZv3KG9BTittS7wb/1mzzxxFbOt4CJgIPA24EQccQQTzGlOs/LGGhZ0r8fcufDia/DlWDioYDF32In16BcQ2QQ8ikL4dsj/iK1uRgibkhxh3YPkCZmpZEdKwfDhMHcu7NyqcKo2mvqXP+AP1lOaRiRx5vYLStWE8B2QctXMWDqRdqd6d2AFZml5f+AtG92FEEII+yoIbEbRgAG8wFxiue4SjUupv1k/NJAmg37iyg13aLECfFvAtr7w9b9PoxZCCAd085RQpZQrZjPRNWkbKKW80rzMwaeEumL2Ux2IOQzvOeAq5SjHHvbwMA8TamlCi49XM2AAfDQeKvSHp/WtnVgzKFnDnB56+RQsqQvn4m12N0KI7E2KSg6gc2dYswZ+/gl+r9CbsHNLOM1XlCOQQzdPRE1VvBK0jwGLm5mimrg3zYduwDKgHfAyMNFWtyCEEMKu8mJWfbSkM/1ZQRTXXHbgUvwG0aMDqT/oWy5ddTN79JULg+0DYb8MPgghsg+t9XXMVPwoTLFo6T+nhKaeDArmlNCDSqlvgQHk6FNCnYB3gEmYGUuhwAVKUYoYYvDHn3ZObaj87jxGjYKFM8CrE9RJgU7Ah9a69KpqToK7es4Uls7+arvbEUJkW1JUchBNm0J0NJw7B7sfa0eXoxu5SDyVqMVufr69ceHHoeNu8CgGy4Ph961pPnQBPseMWIwEXiPjcnMhhBA5jztmYOF/tOYVtrKAay47cS3kxhcT61Nn8Ff8newKoYvhsXaw82X4cpK9gxZCiLumtd6gtX5Ma+2rtZ6Y+t4orfWa1K9HaK0raK0raa3ra61/sm/EWU0Bw4BPMXst1QdOUIQibGUr9alPF/UCBce+w1tvwaoF4NwSmtyA3sAUa12WCIB20XDtotm8+0ycze5GCJE9SVHJgVSrBnv2gIcHLHu8AX0P7eAal6hLbVby1e2N8z8KHXZBQV+zz9Lh1Wk+dMacCNENmIBJNlJYEkKInM8F+Ax4kSAm8wXvc81lB26eBfn6zSBqROzm3N8uEPo5PNERdo+AfRPsHbQQQogH8gKwGvgRcyr0b+QjH+tYR1vaMpjBnB48ktlzNNEb4FwQhF2FocAorDwlFPeH8Gi4cdmsjDj9c/oWQghxkxSVHMzjj8PeveDjA7MqBTBw1x4gH22oz0y23N4470Nmj6Vi/mYD1h/TrpC2YE6C64MZhxiEFJaEECI3cAJmAi9TnRl8yziuu0TjnseLH6Y25pnh2zh1xhlCPoPyz8Oe12DvGHN6hBBCiGwqFNgGnAZqAt/ghhuLWUx3ujORicR268Pni2/w1V74rSZ0TIbxwGCsPCUUe8ps2J1y3RSWTv1o07sRQmQfUlRyQCVLQkwM1KwJ7wWWo9fivTjjSx9CGcWS2xt7FDYnwXkHwsZO8G3aFdJOwHTgJWAapsCUYqvbEEIIYTcKeBMYT0Xm8zNDSHHegrtbGeLeCSXgtQ2c+MsCjT+FCl3gi7GwZ6QUloQQIlurAezGbORdF4jGgoVZzGIYw/iQ49vyzQAAIABJREFUD1nZ7lki117l5x8h9mnoegHeBXoCN9J3V/RJM4ANsLQ+/PWDze5ECJF9SFHJQRUsCJs2QZs28EFHL56dspM8ujrj6Uh3pt/e2NUTWq+HMqGwtTf83+Q0HyrMSXDDMVvy9cBKyhBC2EB8fDweHh74+/vj7+9Pr169bn4WGxtLxYoVKVu2LAMGDECnPtyfPn2a4OBgypUrR3BwMGfOnLlT90KkozB7672DLyv4lRdxct6Au3MFfn+3FZUnRJKYZIHGc6BiD/jyddg1XApLQtiJ5AiROfyAvcAjQAiwFIViEpN4kzdZwhJmNG7Bqi0XOXYUtjwJfU7BHMyOrNfSd1fEzxSWnJxNYenkdza9GyHELY6aJ6So5MDc3WHJEujTB+YOLUiTAVEU0i34mP6EMgqddqKqi4c5LvrxDuahYPeraR4MFPA6MBr4BLPu+rqN70YIAeDr68uBAwc4cOAAH354a2Zh7969mTVrFnFxccTFxbFp0yYAJk2aRFBQEHFxcQQFBTFpkmysLO7VIGAO3kRxhGdxd16Jh1MVjr7dnspTF/L7H04Q/CFU6g1fvWk28JbCkhB2ITlCZA5vYBfwDNABUgekI4hgDnPYwhbG1ApmVcxpki9BZAUYmASLgTbA5fTdFX4cwneCxd0Ulo5/Y8N7EUKk5Yh54q6KSkqpb5RSg5RSJTI9AvGvLBaYPh0mTIAV0z2oHLacUind2MB4qtGH62lnHVlcoOmCWyPO0QNA/7PcTQFjgInAAuBZrIxFCCHuQnx8PH5+fvTo0YMKFSrQqFEjkpOT77u/pKQkzp8/T40aNVBK0alTJ1atWgXA6tWr6dy5MwCdO3e++b6jkTzh6LoBiyjOF/xGG/I7L8JD1eHkW89TecYcDv/qBEEzoHJ/iH0btg+SwpIQ90lyhHWSJ2ytELAFaA70x8xc1XSjG8tYRiyxDKhUl8i9STg7w7zyMOQ3WAs0A/7O0F1ZaL8TXPLBsgZwbL9N70aInCSn5Qnnu2w3AVOFmKiUisEcLbNSa33/dy7umlLw6qvw0EPQs6czlWrOxnNXcb5yeYMn+IvvWYAHbqaxkwWCPwK3ArB/Kly7AI3mmCmrALyCWWcdgSkqLU59LUQ2tH0QnDiQuX0W94f67/5ns7i4OBYtWsTs2bMJDw8nMjKSpKQkFi5cmKFtYGAg06ZNA+DIkSNUrlyZ/PnzM2HCBOrUqUNiYiLe3t4323t7e5OYmAjA8ePH8fLyAsDLy4sTJ05kxl1mBckTDq89kJdCtOUwoTxlWcOxG30582YPAkYms+/Z/vjVf8/ki9h3zOasQe+DkknNIhuzU56QHGGV5Amb8wAigd6YgeVjwIeEEcZ61tOKVrxQrhYL922he31fZlaEof8HUytAY2A9UDBtdwXLmMLS0vqwvCG02QxeVW1+V0JkGnmWyBR3VVTSWkcCkUqpwkA4ZsfnD5RSK4AFWuvoTI9MZNCtGxQvDu3bK0pWeJ3q3xZjn8dgSnOag6ykKPlNQ6Ug8E1TWNrzGly9AE0/B+fUwhMvYwpJA4EwYDngbo9bEiLb8vHxwd/fH4CAgADi4+MZOXIkERERd7zGy8uLP/74gyJFihAbG0urVq04ePDgzTXPaSmlsiz2rCB5IrtoBmwkH805SGMCLOs5kjKM8xMG8My4ZPa0HEqlum+Bcob9U0Bfh4YzpbAkxD2SHJGR5Al7ccacCP0Qpq53ElhEQxoSTTRNaUoH79os/CKKQQ2e4t2nYUgMvFsNGgBRQLG03RUoDe13wNIGsDwY2myCkjVsfE9CZH85KU/c7UwlALTWp5VS8zEzIodilt0GKqVSgD5a661ZEKNIo3lz2LoVmjWDi2VeovHBYkQV7kIZ6vMtG/GhuGmoFFQfaTbx3j4IVrc0ey655EntaQDgBvQCWgIrgTzWvqUQjusuRgGyipub282vLRYLycnJTJky5V9HF9zc3G5eFxAQgK+vL7/88gve3t4kJCTcbJ+QkEDJkiUBKFGiBElJSXh5eZGUlETx4sWz+M4ejOSJ7KA+sBUPQjhAELWcNvF9ijsXRw2j+uRLxFwbzTOBk82Mpf97w8xYajRbCksie7JTnpAccWeSJ+xBAeMxhaX+QCNgDVWpyi52EUwwYUUDWbR7PeMa1+LtWjBoE8xoaM6Q2wKUSttd/kfTzFhqBGEbwbu2ze9KiAcmzxKZ4m73VHJSSjVWSi0AjmKmrk4CHtJalwVGYDbqETZQsybs3g3OzvCFz3O0+XM1FziEH7WI5cjtjZ8eCI0/gd+3QGQTuHIuzYcvYjbu3oIZvb5os3sQIieKiIi4uXFe2n//TFc9efIkN26YfdB+++034uLiKFOmDF5eXnh6erJv3z601syfP5+WLVsC0KJFC+bNmwfAvHnzbr7vaCRPZDfVgR24cpW9NKC601DcUrpwedhYau8ezp4vgNoTofpr8MMnENUVUuTkUCEeRG7OESB5wjH0BZYAXwF1gAT88GMPeyhOcdrkDyYieiP16sFbwdAzEhJSWx5J35Wntyks5SsJK/6fvfuOjqraAjj8O5kEEgidIL33JggC0kvovYbeiyAgoCioqBRFEQXkSRGk9xJqICTUFARBQXpHASnSpZNy3h8nmICUQJK5k2R/a2W9MHPnzr4xb3buKXvXgbPb7HolQiRE8TVPRHfa8TymL/0+oLDWuq7WesGjPdARy1kPx3p04pkKF4bt2yFrVliTtx7t9m3iAVcpSwX82P/4wUW7QP2FcOFnWFoD7l2N8mQXYA6wDdN29JbdrkGIxCYgIIDixYvz+uuv06JFC6ZMmULatGkBmDx5Mt27dydv3rzkyZOHunXrAjBkyBD8/f3Jly8f/v7+DBkyxMpLeB7JE/HO60AALiRhE9Wp6dSDJOF9eDhgDFX39WfzNg0VRkD54XBwNvh2MquWhBBxIoHnCJA84SBaAr7AWaA8cJgc5CCIIApSEC+3RnRYt5DGjeH7FtD2J7ihoSJP+Y/jnhlabYUU2cG7LpyRHYxCxCVHzRPqafvv/nOQUqW11g5X4r906dJ6926HC8uurl2DRo3MAFN7v4PM86wN3GE2a+jAE8tQT/nAmhaQKg+08Af3TFGeXAK0xbQeXc8TZfmEcBiHDx+mUKFCVofhUJ72M1FK/aq1Lm2vGCRPxGd/AjUI5xJtWY239iFEfYttRndWZ5lCvdo22PEFBH8CBbxMl1Gnl9o9L4RdSZ74L8kTz5Z488QezIRyCLAWeIub3KQRjQgkkAlh/2NXlz7MnQsdv4EN70GYAj+g5JOnunPJTFzfPAmNV0POmna+FiGiT3LE08UkT0R3pZLf0x5USjl0i4nEIG1a8PMzNZbm1ixC6znBOOnX6EhNvmHN4wfnrm/2PP/zJyyuBDf/iPJkK0zB7l8BT+CavS5BCJEwSJ6It3IAgTiRg4XUo7OqhrMeRljX6TS82okVa0Kh3MdQ6Ws4uhh82kBYiNVBCyHiH8kTDqUksB1IC9QA1pKKVPjiS0Ma0t/2DnlnjeSdvpo5g6HyMHDTpirfz0+eKvlr0GoLpM4HKxvCaV87X4sQwkrRHVRyefIBpZQLYIvdcMSrSJYMvL1Nd7iFnXLQcFQQSXUxPqAp7zH78YOzVYWWG80WuEWV4NrRKE82wRTsPoDp93DFXpcghIj/JE/Ea5mArSiKMJWmvKuKYdNfEt52Ps0ftmbhsodQ5gOo8i0cWwZrvSDsodVBCyHiF8kTDic3EAwUxtwHzMQNN5aznI505DOnT7F9P5CPh4Wz9Aso3hfSa6gJ/GejWzIPaLkZ0hYyDYJO+dj5WoQQVnnuoJJSKlApFQC4KqUCon4BRzHD28IBODvDtGnwySew8tP0VOq1mRS6Ot/RmTaMffzgTGVNYb3wh2Zg6e/fozxZH1iF+c9bDZDJI+F4orNtN7Gw+mcheSIhSQ9sQlGWb2jNMJUJJz0e3Xw5bd2aMWPBfSg9CKpNgBMrYHULCH1gddBCPJXVn42OxOqfheQJR5cB2IKZUO4KfIUzNmYykwEM4Hs1gTMjOvPVtyH4TILs7SFHONTDbJp7TLL00HITpC8Gq5rCiVV2vhYhosfqz0VHE9Ofx4uKIkzH9KB8E/gp6vsCl3jKILWwjlIwciRkzAj9+rnz5vk1nFrdiUVOg7nEJTYxBoUyB3sUB69AWOYJS6qabXGZy0WcqTbgAzQEqgKbMLPYQljP1dWVq1evki5dOpRSVodjKa01V69exdXV1cowJE8kKKkAXxTN+IwupFITGaSnouu9TbeNDbk7cyV9u/QHZYPNfWFNc2i4DJwt/R0U4jGSJyJJnhDRkwIzRNQZ04TvAk6M4zu+Ix3pGMYwGg66wf9SL6ZfdzfK3oTCK6Gps2nX5xX1VG5pocVGWF7b1HJtsATyNbX/JQnxDJIjHhcbeSK6hboLaq2PvPK7xJHEW1jvxZYuhfbtIXfRcO7u6McZl0kUpzO7mYZL1LHEf/40hfXuXIQmqyF79ShnCcDMQ2TB5Pssdr0GIZ4mJCSEc+fOcf/+fatDcQiurq5kzZoVF5fHdxVYUIBV8kSC8gBoA6xgBl/SjSwQ3gWCKjDm8FoG90oJv0+FjW9DzjrQeIUMLAmHIXnicZInnk/yRFThwPvAOKA1MAtIymQm8w7vUIlKdPZeTU+vVBR5C9w2ws4kMA3o9uSpHtyE5XXg4i7ThbpAS7teiRDPIjniv2KaJ545qKSU6qC1nhvxfddnnUBrPePlQo49kgSeb8sWaNwYUqbVpDo8gkNun5ODRhxgEe64RR54+wIsrwXXj5sZ5zwNopwlGNMZIgNmYCm7Xa9BCPFq7HGzIHkioQsFugDzWMYQWlISwtvBrjf4fKcvn/VPA/umg39PyOEJjVeBi9uLTiqEcBCSJwzJE0/SwFjgA0zzHm8gBYtYRAc6UIxivL/Rl24NM5C9IGT6Gba5wnjg3SdP9eAf8K4HF3aYzqEFW9v3UoQQMRIbg0rrtNb1Ir7f8ozXa6119Wc8F+ckCbzY3r1Qpw48DIH8J35gZ5p+pKcSB1jNa6SKPPDeVTObcHkv1J0HBaMuZN2J2RKXBjOwlMuu1yCEeHl2ulmQPJHghQPvAFPwpS/1qAFhXuh9hflwsx+jB3mgDs6EDd3MStcmq8ElmdVBCyGiQfKEIXniWeZgaiy9DqwDXsMXX5rRjKxkZeROP3rUzEmajFDwN/Bzh5HAx8BjG4oe3oYV9eGvIKg7Bwq1s/+lCCFeSYwHleIDSQLRc+oU1K4Nf/0Fbx1bxOasHXGnML/hSz4yRh744B9Y0cB86NeaBsWiLmT9FdPrwR0zsJTXrtcghHg59t7W4KgkT8QGjZmxHksQnalKK8LDmqOP5KLf6o1MGJIJdXgu+HaGrJWh6VpwSW5xzEKIF5E8YUieeJ51QEtMbdUNQB6CCaYBDUhOcsYd9KN3lcLYkkKZA7A2jckWX/HEwFLIHVjREM5uhTozoUgnu1+JEOLlRTdPPLP7m1LKKTpfsRu2iAu5c0NwMBQuDNtytqbe/rXc5jjFqMgvnIo8MGlKaO4LOWuDX3f4bUKUs5TCDCbdBapgmnUIIRIzyROJhQLGACOoyCx2MQNn22pUwT+Z2KIyPUeeRRfqAHXnwrkAWF7XzEwLIRI9yRPxXT1Mw57rQAVgDxWowDa2EUYYbxepxP9++QVnICgvNLtoskVfzDrXf7kkNxMO2WuAbxfYb9luRyFEHHjeh3goEPKcr0fPi3ggQwZTY6lqVVhXvBYNNm/mAdcpTwXWsy/yQJdk0Hgl5G0KWwbAji/g39VsJYCtmP/0VYFD9r0IIYSjkTyRaChgGDCOkixjH9+R1LYWp1x/M71DZTp8eorwAm2h3gI4v91sp354y+qghRDWkzwR75XD1FhNiplY3kxxihNEEKlJTffc1flq90bSpQHfvOD1B0zCVOQLjXoal2Rmi3TOWuDXDfb9aPcrEULEjecNKuUCcj/n69HzIp5IkQJ8fMDLC9bWKEvdeYGEa2fqU5nZBEYe6JwUGi6Bwh0g+BMIHBplYKkoZmAJzMDSfnteghDCsUieSHQGANMogC+H+ZxkzmtwyvYP83tVpuUnRwnL5wUNFsHFnbCstun+I4RIzCRPJAgFge1ADqAOsIQ85CGIIHKRi+6Z6jNk13Jy5wLvAtDusKnI5IXpJfovFzczeZ2rHvj3gr2T7X8pQohY98xBJa31n9H5smewIuaSJoUFC6BfP1jfoTDVvw7GpjPSmVqMYU3kgU7OUGcWvP427PoaNvcD/WghayFgG5AEqAbstfdlCCEcgOSJxKo7ivnkJIhjDCaV80psGR/i3b8KjT46QGjuFtBgCVzaBctrw/0bVgcshLCI5ImEJAsQgFm51BqYSCYyEUAApShFrzSt6L5jOiVLwKJi0H6X6RvXBFM841/OrtDIG3I3hE194LeJ9r8UIUSscn7WE0qpH7XWPSO+n4up1PkfWuuOcRSbiCNOTjBhAmTMCB8PzU65c4HsmViPD1VTLjCdcXQ2ByonqDEJXNxh91gIuQ21ppsBJ/JjBpaqR3z5AYm+1qMQiYrkicSsDZCMTLTiGH0p4uzN1fRerBtclTpD/Vn3RVOSNFwOa1rAsprQwg9c01gdtBDCziRPJDRpMAW72wL9gYukYRT++NOCFgxI3oMRAddwr/cB88pAu02woDrUBdYAKR+dxjkpNFoGa71gS3/QoVBqoDWXJISIsedtfzsd5fsTwMlnfIl4SCn46COYNg1+mexBgWabcQ+vyni60Iaxjx9YeQyUHw4HZ4NPGwh7GPFkHszAUirAE/jF7tchhLCU5IlErTGwlvSc4BjdyOS8GOfUydn0cTWqD93J/ayNzGz0lX2w1BPuXbM6YCGE/UmeSHDcgKVAT+BLoDvJScoqVuGFF58m/ZASGz6kYSPN/BrgtRK2a3On8FgWsCUxq1rzt4Ctg2DX2Ke8lxAiPnjmSiWt9ego/5yqtb745DFKqYxPPibil+7dwcMDWrdOQZbyPiQJbs8i22AucwV/RqNQZmDprU/NiqVt70HIXWi4zOyLJidmYKkaUBPwBd6y8IqEEPYieUKYz30/UlGPw3TgDee5nHbvSvDnnlT62IdtIxuQrPFKWNUUltaAlhvBLZ3VQQsh7ETyRELlDEwBMgIjgMskYRHzmU8a0vCd8xi6rrhGm05TWNjURrOZ4NMJqijwj3gVADYXqL8QlDMEDIbwUCg7xKJrEkK8qui28Dz2jMej3f5LKVVHKXVUKXVCKfXMTwulVAullFZKyV4qO2ncGPz84OrRpCQpsIgsD3qxia95gx6ERO3bUHoQ1JwKp9fDivpROvtkxwwsZQBqAUF2vwYhhOUkTyRaFYDNuHOLfbSmsPM0nJNlZffIOrw1bCO30teFJqvg+hFYWh3uXrE6YCGENWKcJ4QjUcBwTK+3tUBNbNxkEpP4hE+Y4TSdB3O86NnvAd5dwHM8nNJQGTgT9TROzlBvLhRsC0FDYccoC65FCBET0R1UUv95QKmUQPhTjv3vi5WyAT9gttQWBtoopQo/5bgUmA26O6MZl4gllSpBQAA43bNxK8tk8t7+hL38RH5acYf7kQcW72k++M8FmDoZ969HPJEVM7CUBdMVYpvdr0EIYSnJE4laKWAbrmh20YrStok4J83Lvi8aUGa4DzfT1IYma+D6sYiBpctWByyEsL8Y5QnhqHoDS4DdQCUU5xjJSMYxDm+1nFMTGjDo09v4DILyn8PfGiph9kL+y8kZ6s6J6Dw9DLYPt+A6hBCv6rmDSkqps0qpM4CbUupM1C/gArAymu9TBjihtT6ltX4ILMIUY3jSSGAMRB3FEPZSrBhs3w4Z0ynOeoyk+OVx/MEKclGPy/wTeWChdmb72997YEk1uPt3xBOZga2YlUt1gU32vgQhhJ1JnhCRigCBJCEZgTSnsu1rnF2KcuTLprwxyptrKTyhqQ/cOGFyx51LVgcshLCDWMwTwmG1wBTwPgeUBw4xgAHMYhZb1BaChtfgk3FX2TgCig2AuxEDSweinsLJBrVnQpEu8PPnEPwp6KfWdRdCOJgXrVRqD3QEHgIdony1B97QWneP5vtkAc5G+fe5iMf+pZQqCWTTWq993omUUj2VUruVUrsvX37Fmc7z5yFcJkWeJkcOCAqC4sXgYKYBlDk5h8sEkIfqnCbKzztfk8hZ50WV4da5iCcyYgaW8gANMF3hhBAJmMPlCWGlvEAQzryGHy2oaxuGs600p0a3osSYBVx2qw7N1sHN0xEDS/8pryKESHhiK08Ih1YVCABCgYrAdjrRCW+8+Z3f8R5QmdFz/iJ4IuTqAiocqmDWN/3LyQa1p0Ox7rBjJAR9LANLQsQDzx1U0lpv01pvBdJHfP/oK0BrffQl3uc/y12J0lJUKeUEjAPee9GJtNY/aq1La61Le3h4vEQIEUJDoWZNePNN2CQraZ7GwwM2b4bq1eGXvB2osHslt/RBClOR36Pugs5ZC5pvgDvnYXFlc5MAmNpKm4H8QCNgvd2vQQhhH46YJ2Jl8kHEQDYgABu5WYkXLW0DcHaqyNkv21Ni/EwuOFeF5uvh1hkzsHT7gtUBCyHiUCzmCeHwXge2A+kx/d7W0IhG+OLLWc4ypUMFxq46zm/zIH0zcA+D6kBg1FMoJ1PDtXgv+GU0BHwoA0tCOLho1VTSWt9VSpVQSvVTSg1XSo149BXN9zmH+SvzkazA+Sj/TgEUBbYqpf4AygGr46QI66lTcOMG/PYbeHpCnTrw+++x/jbxnbs7rF0LrVtD8JsNqLDOj/v6EqWpwBYORx6YtRK03AQPbsCiSnDt0d8GHpiBpcJAE0wBPyFEQuVIeSLGkw8A338Ph6R27KvLCGzDieLMpx1dnbrijCfnv+hKiSlTOEdlaLberHJdUhVu/WV1wEKIOBYLeUIaOsQLuYBgTMpuCsykKlXZwhbucIevG1bk2017OeYLSWtChhCozRN7G5QTeE6GEu/A7m9g2/sysCSEA4vWoJJSqifm06E68CFQDDNbnDea77MLyKeUyqWUSgK0BlY/elJrfVNrnV5rnVNrnRPYATTSWu9++uliIH9+OH4cvvgCUqaEDRugZEno1AnOnHnx6xORJElg/nzo1w+CG1Si3PRthOkQPKnEUn6JPDDjm9BqK4SHmBVLl/dFPJEOU1epONAM2TIvRMKVoPLE4cPw7rtQpAg0bAiBgfLH7CtJC2xEUZ4pdOZdp+bYwuvz9/DevD5zPH+EVoLmvnDnQsTA0rkXnVAIEY/FNE9IQ4f45NHkcg2gKzCaUrxBIIEkJSmfVqnC19sDubALQitAzgfQEFgR9RRKQfWJULI//PodbBkguVgIBxXd7m8fAHW01k2BexH/2wIIic6LtdahQF9MBbfDwBKt9cGI2YlGrxB3zCRLBh99BCdPmhsHZ2eYM8cMOA0eDNevv/gciYSTE0yYAKNGwY6er1NyeDDolLSiOpPYGHmgR3HwCgAnF3NzcHFXxBNpAH/gDaAlsNzelyCEsI+EkydSpIDevcHV1SzZrFwZypeHFSsgLMyuocR/KYH1KGoxlrf5xKkGNt2Ma8MGUmLRV5y4V8Fso757yeSOf86+6IRCiPgrRnkCaegQz7gDa4B2wEfAAAqSnyCCyEQmhrxRi+G7fbh1Eq6XhEJ3zZ3C/KinUAqqjYdSA2HP97CpL2ipiyuEw9Fav/AL+CfK91cBp4jvr0Xn9XH1VapUKR0rTp7Uuk0brc34t9Zp0mj9zTda37sXO+dPIH78UWsnJ60Ldz+vXcKLabSL/kwvffygG6e0npZL6+9TaH02IMoTN7XW5bXWNq31IvsFLUQiBezWdvw8TpB54tIlrYcNMznhUX7In1/rqVMlP7y0+1rrJlpr9Ld6lHYKb6PRaPexn+lDh8O1Pr9D6+9Tmvxx8w+rgxUiUYhveQIzADU9yr87AP974piSwPKI77cCpZ9xrp6YGtG7s2fPHts/WvGYMK31QG1+9K211vf13/pvXUqX0jZt06PPzNMZM2qdNofWpW5qrbTWU588RXi41lsHaz0Wrf16aR0eZt9LECKRim6eiO5KpXNKqZwR3x8DGiulKmG6OMR/uXPDggWwe7epTn39ulmxVKCAWcEkM9MA9OgBy5bBybmZyNpwG67hbzKcVrzN1MiDUuUCr0BwzwLLa8Mf/hFPpAR8gbeAtsACu8cvhIhTCS9PZMgAI0aYrdETJpj2mMeOQa9ekDMnfPmlrGyNtqTAEqAtg/iEKSoHTroTt98bTmn/oey/WgZaboT712BxVbj5h7XhCiHiQkzzhOM0/hEvwQn4FrN4bBHQAA9c2cxmKlOZodna03PfRFIqOJoXyl6FXpj/kP9SCip/DWWGwr6p4N9LViwJ4UCiO6g0BigU8f0IYB5mo+zwuAjKMqVKwcaNsH49FC9ubiQ6dYI33jCjKXfvWh2h5Zo2NWWorgamIXVZf1KE1WEqb9OML9GP8nqKLOC1DdLkg5UN4MSjsigpMJ3gKmMml+ZZcg1CiDiRcPOEuzv07w8nTpgJiJIl4dIl+PhjyJYNBg6UmnzR4gLMAXrQg6+Yq1KhdC/u9vuaMsED+O1CaWix0TR+WFwFbpyyOmAhROyKaZ5wnMY/YDpKi2hSwGBgNrAFqEpK7rGOdTSmMSM8+tPs9+FkTa/ZkwsqXIBBmF+SKKOGUPELKPcJ7J8OG7pBuEz8C+EIlFnV9JIvMkVUk2itb8d+SNFXunRpvXt37NdoBczqpHnzYNgwOBtR48HNzXSLa9YMGjSA1Knj5r3jgb17zY/igVsItqOduZpkAZUYxDbGoh5NJN27Bt514dKvUG8eFGwd8eo7mHJ8W4GZQCcrLkGIBE0p9avW2rKuNwk6T2gNmzbBmDHgH7Ea02aDFi1MLabKlc0fv+IZNOZ2YTyr6UaN8CUCAAAgAElEQVRTnYxwNZEkM3uxrcgkyuXYC8tqgkty0wQidW6L4xUiYYpveUIp5YxZ4VQD+AvT4KGt1vrgM47fCryvX9DQ4ZXyxPXrULgweHmZjjZ58rzc6xO1dZjqSZkAP0LJTg96MItZdL/bj18rj2ffAScqHIaAXPA+ZjTysay6fTj8/DkU7gC1Z4KTzf6XIUQiEN088cyVSkopp2d9AaHA3YjvEyabzaxSOnYMxo+HsmXh3j1TqLVDB/DwgNq1YepUuHjR6mjtrkQJ2L4d0tlcuPPaXDLf6Usg31GcroQQMXPjlhZa+EOWCuDTFg7MjHh1cmAtpvlHF2CGJdcghIiZRJsnlAJPT/Dzg99+g7ZtzeOLF0PVqlC0KEycCDdvWhqm41LAd8AwGvETfuoKznowD7tMpdLxrgSceB1aboKQO6Z4942TFscrhHhVsZkntCM1dPDxMX//T5gA+fJBo0awebN0J4uWepju0NeB8jizn5/4iYEMZHqyieTb3olyb4UQkBeqHYSxQB/gsc1u5T+D8iPg0Fzw7QThsmpMCCs9c6WSUiqcKCsOn3YIoLXWlg0Nx+lKpac5dw5WrgRvbwgIiKy1pBRUqGD2hjVtCrly2S8mi126ZFYs7TukyXF+OKfTDScHTTjIQpLjag4KuQurmsKfflD9f1DynYhX38M07fAHpgHdLbkGIRIie8xAS56I4swZmDYNpk+PnGhIlswMOPXubbZRi6f4GhjCdhpTjWI8ZBS2pa3xzTAHz0KHYGkNcHaDVlsgTbS6jgshoknyhPHKeWLPHvj+e7Mt+mFEWaiiRU1n6XbtzA4H8RyHgdrADWAlmmqMZjQf8zF1wxoQ2mwJ/qvdqLYTtpQxhTNmAM5RT7FzNAR9BAVaQ7254OT8lPcRQryq6OaJ5w0q5YjOG2mt/3zJ2GKN3QeVorpyBdasMQNMfn6RyQTMMp4uXaBnT9OSOoG7eROaNIGtW6HAH99zNMe7eFCNg6zEg5TmoNAHsNYLTq6CSl9DmQ8iXn0PaIqZdJqKacYhhIgpO90sSJ54UkgIrFoFkyebWetH3nzTDC55eZnBJhHFD0Bf9lCLClTkHp/itKopq5Mton6JI2ZgyZY0YmApn9XBCpFgSJ4wYpwn/v7b7FyYNClyUiFdOnMf0KcPZM0aO4EmSOeAOsBxYC7QislM5h3eoWJ4JdJ1Wc3KOamo5g9bPKEZptVP0qin+GUMBH4I+VuZchs2F/tfhhAJVIwHlZ5xUifgNeCS1taX3Ld0UCmqW7dMcW9vb7Mc9nbE1vBs2eDzz6FjR3BO2CPn9++bCfkVK6DInnkcLNGZFJTgd9aTi4iuGmEhsL4jHF0E5T6F8p9H1B25DzTH7LGeBPS26jKESDCsqpUheSKKo0dhyhSYNQtu3DCPpU4NnTubDnIFC1oTl0OahaYbR6lAGepziyGodfXxdlpGk1LHYWl1sCWBllsgbX6rgxUiQZA8YcRannj4EJYsMVviHp3P2dnU23v3XShXLubvkSBdBxoBwcBE4B0WsYgOdKCYLkbBgb4snJCBysshoJkZgloOPDY9s/tb2PY+5GsO9RfKwJIQsSTGNZWeOFlKpdQczN3/X8A9pdRspVSqGMaZMKRIAa1awaJFcPkyLF1qlr+ePQvdukGxYrB8eYLeZ+3qavJo9+5wsGR7ivqv5JY+SGEqsY+IQuc2FzODULQr7BgBAR9E/ExcAW+gAWbX9A+WXYcQ4tVInniKAgVg3Dg4fx5mzoQyZczg0vjxUKgQ1KhhJiOkgxDQGcVCCvAze1lOGsai6/nQzKkxi3fkMauUwkJMjaVrR60OVgjxChJ8nkiSBNq3h19+geBgc2+gtbk/eOstU5911qzIyWcRIQ3gh2ni0xcYRmu8WM1qjqgj/DquEt1G/ElAcyg/AzZoU5XpVtRTlH4Pqn4Hx5ebnRFhD5/yPkKIuBLdAqrfY6orFwXcgGKYAeLv4yiu+MvV1cxI7N0Lc+ea+kpHjpjHypQxnYIS6OCSszP8+CN89BEcqNWAwvM3cF9foBQV2MYRc5CTDWpNgxLvwO6xsKkv6HDMQtZlmJmKvsivlhDxjuSJZ3FzM6uTdu6EX3+FHj3MFrjNm6F5c8iZE0aOTJRNHx7XCsUKcrGP/cwmA+PQNf1pnaI+cwNymoElHQZLqsHVI1YHK4R4eYkjTygF5cub5g2nT8OQIZA2rRls6tIFMmY0OWHbNgi3fKGWg3DDrD/qBowCelGXmvjjzyV1iQ2fVOSd/x1mezd4czwEa/AErkU9RamBUG0CnFgBa1qa0htCCLuI1vY3pdRFILfW+m6Ux9yBk1rr1+IwvudymO1vz/PwoSncGvWGoVo1GD3azFgkUOPHw8CBkP+bPRx/rw5KhbMUX5pRyhygNQR8CLu/gSJdzECTkw14CLQGVmC6Aw207BqEiM/sva1B8sRLunkTZs82NTiORqy8cXExg0zvvGOaPyj1/HMkWJvQNOIyWXmDd/hLD4Ttb/HjuXX08DwHS6qbn03LzZCukNXBChFvSZ4w7JIn7t41K5ZmzoSgoMjHc+Uy3aY7dTITDImeBoYBXwBNgAX8zjFqU5tQQum4wJdx7UpT4lM49DkUVGaN02O/PHt+gM19IXd9aLgcnJP+922EENESq9vfMMtUPZ54LD0gQ8AvkiSJKdJ34oQZSEqdGrZsMfuqmzSBgwetjjBODBhgFmqdGlqSXO8FgU5OC6oxja3mAKWg8tfw1mdwcCasa2+2NpAEWIypsTQI00hUCBEPSJ54GalSQf/+cPiwWcHapInpKLpoEVSqZBo+/Pgj3LljdaQWqIHCDw8usp9x5FIT4K2d9MxVk//5ZopYsaQjViwdsjpYIUT0Jd48kSwZdO0KgYFw/Dh88ompvXr6tKm/misXVK8Oc+Yk0s/9RxRmpdL3wCqgNq+TgyCCSEEKprWtxmCfLewbBfkGwgkNVTDlvv9V8h3wnAynfGB1Uwi9b8F1CJG4RHdQaTrgr5R6WylVVyn1NqZd149xF1oCkzy5Wf566hQMHWqSy6pVpt5Sx45w7JjVEca69u3NJV6Yko+MbYOx6Wz0pA5fssocoJQp1l3pK1O8e22riKWqLsBCoCUwGNNyWgjh4CRPvAqlwNPTdDk4fdrsH/bwgH37TDHvLFnMKH0CzBHPVwHFJtLwD3v5igJO46H0HvoVrsG3azJAq63mZ7ekGlxJmJMzQiRAkicA8uY1OxhOnzaTCm3bmvIZW7aYFUsZM5qarIGBCbZkxov1w9wL7AAqk5dkBBFEDnLwfb26vBe4kmOTIEs3OB8OlYBTUV/++ttQ80c4vR5WNYGQe1ZchBCJRnS3vymgC9AWyAycx/w/fYZ+mfZxscxhtzVEx8WLMGqUmYkOCTGPVatmam00bWqSSwLx889Qvz7YSl7lln89Hjj9yrv8xHg6RR7020TY0h9y1oFGy8ElGRAKtMesXBoFfGxJ/ELERxZsa5A8EVsePIBly8zWuO3bIx+vUQPefhsaNzZb5RKF/UBN7hJOOT5nf/ggOJifL3Zu5KNm182gUngotNoM6YtaHawQ8YrkCcMh8sTNm6bjzcyZ5g/nR/LnN/cGnTqZCYdEZyPQFEgHbOAaHtSjHrvYxbv7fmJy2c5krAc3lkAymzn6sU3R+2eAX3fI4QmNV0bcXwghoiu6eSK6g0o2rXVYrEQWixwiCcTUqVPw5ZewYAHcixhFT5vWrF7q0QMKF7Y2vlhy8CDUqgW30t4mbE8T7jpvoj3jmcu7kQftmw7+PSFbFWiyBpK4YwaWOgPzgc+BTzFLY4UQz2PBzYLkibiwZ48ZXJo/PzJHPJrF7tEDcuSwNj67OAbU4D53qMRIdocPhmM5GLZ1EyNa3Yal1Uynn5abwKO41cEKEW9InjAcLk8cPWq6xM2ZY7qHgplIaNIEevY02+ScorvZJCH4FagLhAPruE1hmtKUjWykz8lvmVtyEO7lIGQdKGdTY6lE1JcfnA2+XSB7NWiyGlySW3ERQsRLsV1T6aJSapJSqkIM4xJPyp3bFPK+cMHcOJQoAdeumUrXRYpAxYqmmOvduy8+lwMrUsRMuGd66E5oTh9SPmjKPAZQj8/RRAxsFu8O9ebCuUBYVgvu3wCcgdlAJ8yg0jAgsS4FFsKhSZ6ICyVLwrRp5sbi++/NRMPFi/DFF6YGR4MGsHatqceUYOUHAnElNcEMpaL6GpXvHCM9q/DBfDezFc7malYtXdpjdbBCiGeTPBEdBQqYOqx//mnqSDRoYD7jly6FmjUhXz7z/IULVkdqJ6WAYCAlUB13glnLWlrQgkl53qPlkU948JtGVQHbQ6iG2TT3ryKdoO4cOLsVlteFh7csuAYhErboDirVAm4DC5VSfyilRiulisVhXIlPqlTQuzf89hvs2mVmItzdITjYtB3NnNl0BNq71+pIX1mOHKbhRbGMSbnlsYS0dzqznuGU513CiGipWqgdNFgMl3bD0hpw7ypgA2YA3THdIIYgA0tCOBzJE3EpdWro1w8OHDB1Ntq1MzPXPj7QsKEZYBo5MnJWO8HJCQSShCxsUR9S02kUKtfffNOoMu/Odka32gYu7rC0OlzcZXWwQoinkzzxMpydoVEjWLPGDDANH26Ke586ZervZcsGzZrB+vUJfGIBIB+wHcgLNCApy1jEIrrTnRmZv6DmsT7Y/gzj3puQ4j54wqPWQEbh9lBvAZzfHmXiWggRW6K1/e2xFyhVBWgDNAMuaq0tW2vucMtVY9vt27B4sam79MsvkY+XLm0Gndq0MQNP8cytW2YF7+aAcF67+D6X0o2jEB3Yw08kJaJOyCkfWN0c0uSDFhsh+WuYZa99gcnAQOBbZCucEE9n720NT7y35Al7uHLFbJGYOtV0GAWw2UzNpV69TAHwBLdF4m+gJmEcobkezerwUegL7vRatJnJPVxQS6uZyYjmGyBzOauDFcKhSZ4w4lWeCAsDPz9zb7BmTeRgUvbsZlt0587m+wTrJtAEM2T0HZoBDGUoX/M19W97cbDUHC6HJ8FjL1xMDt6YjXP/Or4C1nqZrdLN/cAtrQXXIET8Edvb36I6ChwGzmKmDkVccXc3CWLnTvj9d+jb16xo2r3bDCplzmxWN8Wz1UspUpjJ9WaNnLjk8S1ZzoziMHPJRwtuE9H2M3d9aOoDN07B4spw6xzm1/UHTEeIccC7yIolIRyS5Al7SJ8e3n/f1N/w94cWLUxHNG9vqF3bdBj68ssEtkUiA7AFG6/jrT6kjdOHqEz3mNq+Mp2m3DYrlpJlgOW14FyQ1cEKIZ5N8sSrsNmgbl3TMfTsWfMZnysXnDkDn30GOXOaLXILFsT70hlPlwpYDzQHBqEYwleMZgxj8HFfTM7fG5M15R0uFIAsN6ExsDzqy/M1hcYr4MoBU4/v7mUrLkKIBCdag0pKqdRKqW5KqU3ASaAqps97hjiMTURVvDhMnGhuDmbPhgoVzJKfKVNMzY2yZeGnn+DOHasjjRZXV9Pkons3xV85Pibb/v9xltXkoi5/8485KEcNM9t85wIsqgQ3TmJWJk0ABgETgT7waOucEMIykics5ORkViUtXWpuLEaNMjPVp0/Dxx+bLRJNmsC6dQlki0RaYCNOvMU89RHdnAagPGBujyp4TblEeMttkDwzeNcxNTSEEA5B8kQsy5QJhg41K1X9/cHLC5IkgY0bzRbpTJnMJPTPP4N1zfXigCumM3RvYAzQmcEMYDrTCXD1I+WOWhTIe50/ckOuK9AKmBv15bnrm4Ld14/DkqpwOyFNvAhhjeh2f7uL2ci6EFimtb4Z14FFR7xarhoXDhwwy1/nzDGtSAFSpoT27U0Sef11a+OLBq3NtvCvvoLsW+dzpkonUlCSfawnJ+nNQRd3w/La4OxqtsKlK4RZoTQU87dId2Aqr7bwToiEyYKuPpInHElYmLmx+PFHWL0aQkPN49myQdeu5iveb5G4i9kG4c9A/RkTwmej716l/uS1rO6dH6flNeDmaXPzkMPT6mCFcDiSJ4wElSeuX4dFi8zW6KilMwoUMFvjOnSALFmsii6WaWAUpjN0XWAp3mygDW3IG1YA9+Yb2LUpEwWPwOEspnjG21FffnYrrGgA7pmh5WZIkdX+lyCEg4tunojuoFImrbXDDeMmqCQQE3fvmhnqqVPNbMQjZcuauhpeXpAsmXXxRcO335pdHNmWruVs85a4qpz8gj/FiPiAv3IAlnqCDjN7oF8riUkmn2ISSifgJ0xRbyGEBTcLkicc1cWLZoXrtGlw8qR5TCmoUwd69DCdhVxcrI3xld0HvIDVjNQf8Hn4KsJD/qTGpOVs6PEmNu8acOM4NFoBuepYHawQDkXyhJFg88TBg+azf+5ckwfArGytWRO6dDH191xdrY0xVvyIWbX0JuDDRvbQhCZkCH+NbN38CViYm8KH4FBu+Ar4MOpL/woG77rglt4MLKXKaUH8QjiuWK2p5IgJQESRLBl06gTbt8O+fZG1l3buNLPRmTPDwIFmK4SDeu89mDkTzrduQJbJvtzXf1GKCgRy3ByQvii0DgRnN7MH+vzPmK1wI4HhwGzMwFKoVZcgRKImecKBZcwIH34Ix47Bpk3QurUZRFq/3nQOyp7dLBl14BzxbK7AMqA9w9QY/meris2lIJv6NabKjK2ENtsCaQvBqsZwcq3VwQqRqEmesLMiRWDMGFN7ae1aU3fPZoMNG0weyJQJ+vQxXafj9fa4npjKSXuBiniSn81s5qbTDY7NqIBnn/0cKgBF95v+0UOJUpE1SwWzC+L+dVhcBa6fsOgahIjfZL9QQlOsmKm9dP48zJgB5cqZrXHjx5uirc2bQ1CQQyaPzp1h+XK4MqgKr322lRB9l6pUZDURhcjT5DMDS24esKwmnNkS8cpPgS+A+UB7IMSK8IUQwrE5OUH16rBwIfz1F3z3HRQsaGawR4+GPHmgXr3HOwrFCy6YiYX+9GYqC2wFSeJUhuC+rSk3dxUPG2+C9MVhdTM4vtLqYIUQwr6cnaF+fbOr4cIFc59QqhTcuAGTJ0OZMqZ263ffwd9/Wx3tK2oC+AEXgLcoQzICCcSmbOz+tjL1Pt/OgRJQLNisVnqsImumMmaVUsgdWFIFrh6x6BqEiL9kUCmhSpbMLG39+Wf47Tfo2NHMTnh7Q6VKJoEsWAAhjjUA07ixmUC5N+EN0vUOROukNKEqswg0B6TMAV4BkDInrKgHp9ZFvPIjTLG+xZgOtQ+tCF8IIeKH9OnNCtZDhyAw0NTZSJLErF5q1Mh0Exo1KnLLhMNzAsYDI2jFItbaUpNMVefXPt14Y8lsHjTaCK+VgrUt4dgyq4MVQghrpEtndjTs3m12NwwcaPLBgQNm20CWLNC0qanF52D3CC9WGQjErEOqRGGuEkwwHsqDLR950vgHX/ZXhGI+MAXoQJRp6NdKgtdWCA81A0tXDlhzCULEUzKolBiULGn2VP/5p+kElC6dSSbt2pkbh6++gmvXrI7yX1WqwNatYFtRkBTNgnDSGelCLb7BxxzgnglabYW0hWFVkyg3CIMxNxXLgUZA/OiEJ4QQllEKKlY0DR/OnYNvvjErls6ehWHDTGHvVq1gyxaHXOH6OAUMA37Ak/VscbpPqvBGHOw+kKKrxnO3vi9kLAtrW8PhhVYHK4QQ1ipWzKxO+usvM+ncsKH5nF+50szyZstmCp4ePGh1pC+hGKYWfAagFjnYSyCBFFAF8OnVkGbL57O/ARSZBwuAZsC9Ry9NXxRabQNlg8VV4dIeay5BiHjomYW6lVJdo3MCrfWMWI3oJSTYwnpx7e5dmDfPbIk7fNg89qgu04ABkD+/tfFFOH4catWCS5kvExpQlxDbXt5jFmNpbw54cBO868OFn6H2DCjSKeKVP2H2V5cD1gJpLIlfCCvZowCr5IkEKjzcdI6bMsXMVj/aClegALz9tskVaRz9c3Uhmo4cpijVwgvwt9Nisi0byCHP4bhvaAjnAsBzMrzey+pAhbCM5AlD8kQUFy+awt4zZ0beI4DZ4dCli6nFlDq1dfFF2xWgPrAbmMpNWtKUpmxhC40DxrKqynsUHA9H+0NlBauBlI9eev0ELK0OD/+BZush81tWXYQQlotx9zel1Jao/wQqABeBs0A24DUgWGtdLebhvhpJAjEUHg5+fjBunPnfR+rXNzcOtWtb3hHo/HkzsHQ05BbOBxpz32ULHZjAHPqbA0LuwMomcGYj1PgBSvSJeOUyoC1QCNgAZLQkfiGsYqebBckTCd25czB9uukcd/68eczNzdxY9OxpuowqZW2Mz+SLphlnyEpVXZ4/1Gwyru3G4QrjSL21DZzygYpfQJmhDnwNQsQdyROG5Imn0No0/Jk5ExYtgn/+MY+7upqC3127mq0FTo686eUO0ALwBUbygPfpSCeWsITaBwbhV+Ib8n7sxOnPoYSC9UD6Ry/950/TdfrOBWi8EnJ4WnQNQlgrxoNKT5xsInBSaz0+ymPvAnm01v1jFGkMSBKIRQcPmpVLc+fCgwfmMQ8PaNPG1NooVcqyP7qvXTPjXDtO3cf9z7bcdl1BHYaxjuEoFITeh7VecHI1VPoaynwQ8Up/oCmQKeL7nJbEL4QVLGgVLXkiIQsJMd2DJk8Gf//Ix4sWhe7dTZ5Im9a6+J5pO5r6XCEZVXVDDqmppNvYisOlZuDxcy84PB9KDYIq34By5JsjIWKf5AlD8sQL3L0LK1aYBkCbN0c+niuXWb3UqZPpIuqQQoBuwFzgHcIZxwDeYyITqXSmLTsKzyRLlyRc/B5yK1PqO8ujl965aBoDXT8GDZZA3sZWXYQQlontQaXrQHqtdViUx2zAFa21ZWvgJQnEgcuX4aefzODSoUORjxcqZG4a2rWzJHHcvm06X/sHh5L6/NvcSPUTpenNz0zEGRuEhcD6jnB0EZQbBuWHRwyC7QDqAW6YgaXCdo9dCCtYcLMgeSKxOH7crFyaNcvkDICkSc2HdPfuULWqg81e7yec2tzmATVpxy9MJGVwXfYXWEz2fR/Dnolm+3St6eDkbHWwQtiN5AlD8sRLOH3afPbPnGlq74H5e7tmTejWzdRiSprU0hD/KxwYAnwDNESzgK+YyEd8xBtXa3K48HLSeKbg5lzwcIKNQJ5HL713DbzrwqVfoc4sKNzeomsQwhrRzRPR/avvIqbycVQNgfjad1I8i4cHDBliukDs3g3vvmseO3wYPvoIcuY0LalnzYJbt+wWlru76XLdop4zN9JNw+Pih+xmMoVpx30egs0F6s2Dot1gx0jY3A/CwzB1lbZhEkolYJfdYhYikZE8kVjkywdjxpitccuWQZ068PAhLFwINWqYunyjR5vW1Q6hGE4E4U4atjIDTwbwz1sbKHixOvsKfwTlR8DB2bC6OYTce/HphBCvSvJEfJcrFwwfbgaX/PzMVmgXF/O9lxdkzgz9+8PevVZHGoUTpkP0D4APiioMpTMzmcnv6TaT/WRVbu+6RPKGcCMUKgL/9n5zSwstN0LWyrC+A+ydZNVFCOHQortSqSamSM1BzB7o7JglHy211n7Pe21ckpkFOwkJMcli7lzTEeLR9jg3N2jSBDp2NDMUNluchxIWBr17w7TpkPHIN1zM/wGZqM0hlpOa5GYPeOAQ2DUG8reCunPAOSlwCvAELmPK8Vm2dV8Iu7BgBlryRGL2559m5nrGjMjZa5vN7F3u0cMMPDlbvQroIuHUIZxDtGEgy0Im4nwhExuc1lP9ykbY1NfcODRZBUlTWRyrEHFP8oQheSKGrl2DBQvM5/+eKB3TSpY02+PatjWdpx2CD+AFpAV8WMdZWtKStA8ycq/KBsJseXHZAqFJTI2lso9eFnof1rSCU2ug4mgoO8SqCxDCrmJ1+1vECdMDdYHMwAXAR2t9NUZRxpAkAQvcvGlmpufMgYCAyMfz5oWBA6FzZ9NJLg5pbRZNffUVZNkxg7/K9iA1ZdiPD1mJqOmxaywEDIbsNaDxCkiSAjgP1AaOA4uAJnEapxBWsvfNQsR7Sp5I7MLCzCTE9Ommc1xoqHk8c2ZT2LtnT8iUycIAb6BpiCaYvrzH5HuzcLqnmffPGtrc+wN8O0L6YtDcF5JlsDBOIeKe5AlD8kQs2rPHTDDMmwfXr5vHkiQxk9BduthtEvr59mA6w90BlrGTlNSnPoTacGmyjttnSpFyJ9xwMyOgdR+9LCwEfDvBkYVQZghU/FKaPIgEL9YHlSJOmg3IorXeEZPgYoskAYv98QfMn29qMJ0+bR5Lmxb69IG+feG11+L07b/5Bj74ALKuXsm5Bq1JpvKyiw0UflRi7+Ac2NAVMpSAZusibhCuYWos7QJmAJ3iNEYhrGLFzULE+0qeEMbFi2YCYvp0U4cJzGqlFi1Mjihf3qI/yO+haY1iNSPpw2e3fdG284w9v5D3nFxhdTNIkRWa+0GqnBbEJ4R9SJ4wJE/Egfv3YdUqM8Dk52dmhAGyZjU7HLp0MRPSljmLGVg6DEzhKBWpTW2uhF8lbQ9v/t5Uk6y/wx+pYBrQ5dHLwsNgUx/Y9yO83gdqTJQmDyJBi9WaSkqp7EqpYOAIpn4ZSqkWSqnpLxFQHaXUUaXUCaXUf9YMKqXeVkrtV0rtVUoFKaWkorKjy5kTPv7Y3CwsXWpaS1+7BqNGmWLe3bs/Xuw7lg0eHNHlukkTsv60nrv6DCWoyHYibl6KdDTbGK4egkUV4eZpzHLXjUB1oDMwIc7iEyIxiY08IRKYjBnNyP/Ro7BxIzRtCuHhpj11xYrwxhtmUuLuXTsH5oZiOdCdYUxiqnt5nB8W4/1czRgUfhJabIS7l03euBp3OUyIxEbyRCLi6mpqLPn6mu3Ro0ZBnjymFt+XX5rafFWqmBqtt29bEGA2IAioAXSnALPZThC5nXJxYXo9MrWfzx85odAF6AqMAjSAkw08p+/DuQIAACAASURBVEDpwfD7JFjfCcJDLYhfCMcS3aHVqZhNqCkwvRnBtNKqGZ0XR3R2+AGzgrAw0OYpg0YLtNbFtNYlMNXUvotmbMJqNpuZef75ZwgKMjcOISHmZqFIEVNTY/PmyFmKWNS9OyxZAn+/U42MX2whRN+mMhXx5hdzQO765gbh3hVYWAEu7wfcgbVAc2AA8BkRqUII8epilCdAJh8SLKVMAW9vb7OqdehQSJ/eFHLt3t3MXA8eDKdO2TEoZ+BH4BN6MI9lKTOQ/GZdxuXpS6uHqwj32go6HBZVggs77RiXEAlajPOEiIeyZYuchN62LbJURkCAWbGUKZPpHBccHCf3Cs+WElgD9ARGk5n3CcCPCqoCf4xqT87RYzmQQ1PqMAwD+gBhYHJa5a+hwig4PA9WtzA1l4RIzLTWL/wCrgJOEd9fi/L4jWi+/i1gQ5R/DwWGPuf4NsD6F523VKlSWjioY8e07t1bazc3rU2K0LpkSa3nzdP64cNYfzt/f62TJ9fao88R7RSeS6Pd9DfaO/KAywe0npJF64mptT4bGPFgiNa6qza/Tn201qGxHpcQVgF262h8PsfWVyzkCRtwEsgNJAF+Bwo/cUzKKN83AnxfdF7JEw7q3j2t58zRukyZyByhlNb162u9fr3WYWF2DOYHHa6VDg5/S6e/3kWj0RX/bKPvXz+k9fQ8Wk9IrvXpDXaMRwj7iG95Iq6+JE9Y4J9/tP7pJ60rVIjMAaB1oUJaf/ed1leu2DGYcK3119r8OlTQ9/RZ3UK30Gh07vW9NbYQXW6zebax1vpO1Jf++r3WY9F6SQ2tH9yyY8xC2Ed080R0VypdAh7b+BoxQ3wmmq/Pgtm8+si5iMceo5R6Ryl1ErNSqX80zy0cUb58MGkSnDkDI0aAh4cp3te+PeTODSNHwl9/xdrbeXrCpk0QtqgAqRvtwCX8dQbTnF58h0ZD+iLQJhiSvQbLa8KJ1ZiZ6unAh8AkoC3wINZiEiKRiWmeKAOc0Fqf0lo/xFTTbxz1AK31P1H+mRxZYhh/ubpChw6wcyf88gt06mSKufr4QN26UKAAfPstXLlih2D6oFhCOfUr21LtJOfNIQRlX0iJe3242doHUueFFQ3g6FI7xCJEghbTPCESihQpoGtXs8PhyBEYMsRsmT58GAYNMs0d2raFrVvtsHpJAR8AS4DduFKNxYxiMIM5VWcy2X5vyI7G/1BuPqzWppf0v5Xl3+gHdWbB2S2wrCbcuxbHsQrhmKI7qDQWWKuU6gI4K6XaAIuBr6P5+qdV4vzPJ4TW+getdR7MXf4nTz2RUj2VUruVUrsvX74czbcXlkmfHoYNM4NL06ZBwYJmP/Wnn5q6Sw0bPt4hKAbKloXAQEi2NwMur28meUgzfuQ9atKXUEIhZQ5oHWQ6+6xuBgdmYn41v8L8ii/BFO27FeNYhEiEYponYm3yQfJEPPPmm6auxtmzMHq0yQ0nTsD770OWLGYyIigojm8sWuDEBvKrcwSlnEfxO19xJH0wBe8351yreZCpLKz1gt+nxGEMQiR4Mc0Tsk06ISpQwHz2nzkDK1aYiYWQ/7N339FRF9ECx7+z6aTQe2ihhd6bARWRDqEISJeioIIIClJUmnSV3nvT0HuTKr0jPfQWek9IIWV33h8TnzyfaIAkG8L9nJMj2fz2tzPnCDe/mbn3RkNAAFSubH7+449w924CD6QxsBV4jAU/RuDPVKZys+AmMpyvyL7e1yg1HI5o8AOu/Pm2Qh9B3cVw9wgsegdCbyXwOIVIguJynMmcfKI+sA44BWwA6r/Ae180/c0CBP/XfeW46mvIatX6t9+0btRIa0fHv467Zsmi9XffaX358it/RFCQ1oULa+2QzqrTPumh0egCurYO0bHHUiOfaL24qjmuun+Y1jZb7DvnaK0dtNaltNZ3X3kcQtgTiZzWoF89TjQGpj/zfStg3L9c3xyY81/3lTjxGoqO1nrlSq1r1TIpcX/GiUKFtB47VutHjxLww4/qKJ1JP7Sl1m9HjNE88dKewVn0sch9Wi+rY+LG3h+eiRtCvL5ewzghadJviitXtO7bV+usWf+KAU5OWjdubGpeJGiK9AWtdT6ttbPW+he9UW/UXtpLe4Vm0pQ+qIt8rnVKq9aZtNZ//J8xbzbp0tN8tH50MQHHJ0TiiWuciHMPRK31Cq11La11Ia11Da31ijivXJn+7XmVUrmUUs5AU2DVsxcopfI+821t+LOFl0hWLBaoVs10i7txA0aMMKlyN2+azhA+PlC9OixZAlFRL/UR3t7mxFLFQhYepB1B1tuTCWQDualEEDfA2QMarIH8TWFnL9j+tSnISmtgJXAaqMgzexBCiDh4xThxHdOO5U/ewM1/uX4B5uFEJDeOjuDvb1LhLl2CPn0gY0Y4dQq6dDFpEe3ambQ5Hd+nl4rhxF7cVXp+c+lJPZcfeBKpKGOryuY6naFgK9j9PfzeLTZuCCFexCvGCUmTflPkyAEDBsCVK7B6tclssFrN80PVqubZYehQuJUQp4JyA3sxZyJaUJU97GE3qd1dcN77NqfvrCB9Q1BWeBvY8r9jrgKNt0LkY9M99N6JBBibEEmT0s/5hUwp1UprPS/2z+2edwOt9cw4fZBStYDRmF2GmVrrwUqpgZjVr1VKqTGYNNVo4BHQWWt96t/uWbp0aX3o0KG4fLxIyrQ23SCmTYOlSyEytq5RhgymQ8THH5vg8YKePoXWrWHxEvA5uoFLRRvjRkq2s5YyFDMPBNu6wh/jwLcZVJ8Fji7AbqAOkAL4DSgcb1MVIrEopQ5rrUsn8GfEW5xQSjkC5zD9fW9gNiOaPxsHlFJ5tdbnY/9cF+j3X3OUOJFMREWZVOnJk00BvT+VKAEdO5raG56e8fiB94iiDg76EJ/ahjH9+lwsWc8yS8+k9a7DcGQ0FGgJ1WeCg1M8fq4Qiec1jBONgBpa64//vDdQTmvd+W/XdQK+wpxmeu/PuPG3azpg2n6RPXv2UlevXo37pIR9XL8Os2bB9OkmVQ5MB+q6daFDB7Np7eAQjx8YhflfZA7QgjsMwZ/GHNQHcfr2R9Jt/QqP7YrLLjALaPHn2+6fgqXVIDocGq6DLBXicUxCJK64xol/W1Rap7WuFfvnbc95v9Zav/fyw3w18rCQDD18CPPmmQWmU8+sKdaoAV27moCh/qlE1z+z2Uy9vzFjIM/qY1yoUxsHglnIIj6gplnQOjAcdvWG7O+B/3Jw8QJOAtWBcGANJntaiNdHIj0sxGuckM0HESfnz8PUqebh4kFsudQ/i7526WJOvMaLUKJojDMb+E5/y+DzuyDfdgZFDqfP0WjUru/ApzbUWQROKeLpM4VIPK9bnFBKNQaq/21RqazW+ovnXN889vqP/u2+EideM1YrbNxonhVWrTLfg6nH166d+cqW7d/vEWcaGAp8C1QknF/4iK9ZwhJc53TEbfB4fA45ctjLFAbrQWwh4eArpnB36E2otxxyVoun8QiRuF55Uel1IEEgGdMa9u0zAWPBAoiIMK8XKABffmm6BqWI2y/xWpsmQj16gM+YG1z6og6oEwxnAt/Q0Vx0ai5sbA9pC5ldBY8smPS3apisnMWYrEwhXg+J8bDwOpA4kYw9fWpOt06ebAp5g0mxrlcPunWDihVfaBPin0UTzcc4MZcptKfT2RCs+RfzccQXTDlXAMvmTpDlLZNS7ZrqlackRGJ63eKEUqoC0F9rXT32+94AWuuhz7neAjzSWqf8t/tKnHiN3bplGj1Mn27SpcHEgZo14ZNPoHZtk1L9yhZhymRkxcZqvmUewxiG6/bqWD5aRNl9XvyeCT4HxmD6SxN2B5ZWhwenodYvkL9xPIxDiMQV1zjx3JpKSilLXL7id9hCxFIKKlSAmTP/6giUNatpNfrpp2YHondvcxQ2Drfq3h1++QWCumfF+8sdWHR1evIp7emBDRsUam0eCh5fgIC34MEZICewCyiISdmfm6BTFuJ1I3FC2JWrK7RoYYro/fGHyXd2cDDdg95+23SV++WXl67PZzjhxGxs9KYjM1ieP5wUF7ow3W0cdfJsIarOPLh9QDr+CPEc8RwnpEar+L8yZzbPA+fPw6ZN0KSJiQNr10L9+ub00nffweXLr/hBTYDfgVAs+DGUqkxnOjFvb8G6yY+dla5SNxAmAg2AMAD3jNDkd8hU1nQPPT7tFccgRNL1b/+Ix2BSDJ739efPhUhYadNCr14mIAQEQNmyJk1u2DDImROaNYP9+//zNs2bw/r1EDzbk7T1VuJi+5yZ/MT7NCGccMhZHT7cDjERsMAPbuwBMgDbgHeBj4CfE3KmQrxuJE6IpKF4cZgzB65eNQ8Q6dLB4cPQsiXkygVDhvyVKvfCFBaGoJlIbdazLfdeMgQNZL3bMsp7TyakQQA8vmgKsz6+GK/TEiIZiLc4obWOATpjCl4GAou01qeUUgOVUv6xl3VWSp1SSh3F1FX619Q3kUxYLPD++7BwoWkE9NNPkC+fOck0eDDkzm0aAa1c+Ve63AsrD+wHsgDVaY9ig9qAS54gLPvKsbr1QRpshnXaPDXcAXOCtdFGyFUDNnUwJTeESIb+raZSjrjcQGttt8p2clz1DbZvH4webbrE/Rkcypc3dZcaNgSn5xdOPXrUnIoNzaJR+0bxxKk7uSnFDlaShSzw+JI5rhp6HWovhDz+QCSmw/li4BtgGLFZ00IkSYlUK0PihEiaIiJg/nwTJ06fNq+5uZnTTF27gq/vS954FTE05ZrOTI2HXTjv+Q05YnKzN3gImZe1B4sTfPAbZCgWb1MRIqFInDAkTiRTWpuTrFOnmueFPxsBZc9ush4+/hjSp3+JGwcDjYFNQE9O05JatroExdzG9tFMar3TjN87QgYF6wFfAGsUbGgDZwKgdA94e3g8pGcLkfASpKaSUkoB6YD7OgkUY5IgIAgKggkTTMB49Mi85u0NX3xhOkGk+ucaF1eumNrfl8Ig3alV3PJqjhep2MZqSlICwu/B8tpw5zBUmQjFOgJW4AtgEvBJ7H/js8uEEPHHXrUyJE6IJEVrU9B11Cj47be/Xq9Z09Tnq1rV7HC/kP1EU4cQrakbPpC9ug+pHVKwJ3w8vku6QHQo1F8N3pXidSpCxDeJE4bEiTfAw4em9tLEiXAx9kSps7NJl+vUCcqVe8FFnmjMM8EU4APu8TMf6FbsVDthSG8qPhjE2Z8sxChYAbwNpuv0li/g2EQo3B6qTgGLPEeIpO2Vayr97WaplFLzgKeY03wRSql5Sqk0rzhOIV5NtmwmDS4oCCZNMrvP169Dz57mZ127/mMedc6csHs3lMkOtzL5k/fybkKwUI6KLGYFpEgPTbZBzhqw+VPY3Re0BZiA6QAxDWiGaTcqhJA4IZIkpUzKw4YNpqNohw6mFtP69WZnoVAh85ARGvoCNy2HE3vxVKnYkqI7/i4DeRTmQAnPNuxuNgJSZDTtpC+tTbBpCfE6kjgh7CZNGtMO+tw5WLfOFPCOjjYnWitUgNKlTR3XPxsD/ScnzObySGAZ6WnMZjWfT/gE+gxl17v1Sd8shHQxUBVYAKAsUGU8lP8OTs6ANU0g5mkCTViIxBXX7blZgBtQHPAASgAuwMwEGpcQL8bd3RxlPXXKBIsqVcxDwpgxkCcPNG5sUuaekTYtbN4MH/rD+dzFKPjbAWIoTBMa0o/haKcUUH8lFG4H+36AjZ+AzQoMAn7CpML5E1uOT4g3ncQJkbQVLAhTpphNiCFDTPOHM2fMLrW3N3z99V/dg/5THpzZg6MqwjLHbnyW7lOePsjBO+5tWPxhV9NJdEU9OD0/QackxGtG4oSwrz87w61ZY04s9ehhFpyOHIH27U0s6NEjjrFAAd2A5cApnPFjCh0YxzgstdcR2PctQutdomiY2YYeDmilwO8HeHcUnF8GS2vA08cJOWMhEkWc0t+UUo+BzFrriGdeSwHc1FrbrYeuHFcV/+roURg50hT3jokxr731ltmpqF/fdIcAbDYYMAAGDoS8QyO42LMtNrWQhrQhgCk4ayfY088sLPnUhjoLwckdmAF0ACoAawBpJy2SjsROa5A4IV470dGmU9zYseboKpiTTXXrmtS4ypXjkA4RhpVmOLCaYXxJn+tH0d7b+SmiP1+t3o4K2gaVx0DJLgk+HSFelMQJQ+LEGy4iwhT4njAB/vz/QCmz+NS5sznt+p9p0n9gOkXfA2ayhQw0jGnMkxBFio+XUG5yZbZmgE+BcYAjQGAAbPgI0vjCBxvAI0vCzVGIlxSv6W/AWUx/9Wdlj31diKSpeHGYO9cUUOrVy9RX2rMHGjUyHSHGjYPQUCwWs6j0yy9wrb8bGT8MwNXWj2XMpgxVua8egN9AeH8yXF4Pi96D8PtAe2AhcACoTGyfByHeVBInxOvFycnU09i1yzxItG5tXlu1ypx2LVoUpk2D8PB/uYk7DixD8ym9GMM870w432hMd7f+dPX3ReepB9u+jE2htnvpGCHsTeKESHrc3KBNGzh40HSTbt3a1Ftatw5q1TLPDKNG/VW79R+VAA4BZYDmVGEzRxz3kdsjI2GLqvL7oIn4n4bJQH0gFKBAM2iwFoIvQ8Bb8FD+GojXV1xPKg3BtL6aBwQB2YCWsd//b/9crXWiHl+VnQXxQkJDYdYs0w3oz2OtqVJBx46msHfWrOzdaw4xRWQG530BPHBtS3qysp01FKAAXFgJa5uCZw5o9Bt45cB0tm0AeAObMb8fCWFfdtiBljghXn937pgUuUmT4PZt81rq1KYW05dfQubMz3mjxnQF7cNWXZkGdwsSknECDSIbsuh3TxxPzoFin0OVcaauhhBJgMQJQ+KE+H/u34cZM0wsuBrbmDBFCmjZ0qRMFy36nDdGAV0wBbxrEcJkGkd9xkbntTDpU+r5jGV1NSeKK1gLZALTFGhpTVPIu+FayFwuESYoRNzEa/c3pdS2OHym1lq/F5fBxRcJAuKlWK2wciX8/LM5uQTg6AhNm8JXX3EldQnq1oXTNyH76X1cyVgfF56yksVUpypc3wUr6oKjmzmumr4osBuoDXhiWoy+bLtqIeKHHR4WJE6I5CMqyrSgHjMGDhwwr7m4mN3sHj0gd+7nvHEeNtpxWvtSK7geQakGUyHybbbuK4broXGQvynUnAMOzok1EyGeS+KEIXFCPJfVCmvXwvjxsGnTX69XqmRS4xo0MCdc/5/JmO5wubGyjF4xc/nJcTj8/g7vXVjC/vbpSBu7sFQY4NEFWFodwm6D/xLIVTMxZifEf4rXRaWkSoKAeGX79pm6S0uXmuJKAJUrE/7pVzSeVYt1myz47r3GmTJ1UZziZ8bSjc/h/klTXC86FOqthGzvAEeB6oANc3qppN2mJYS9WkUnNRInxCvbtw9+/NHUX9La1NZo0sR0GS1e/B/esJkYPuCedqd2RCf+cB5APmt+DhzzJ+XOIZCzOvgvja3NJ4T9SJwwJE6IODlzxnQLnT0bnjwxr2XJYjIeOnSATJn+9oYdQCPM6aUA5tke0tbaHuv1zBRfsIrbPYsQboElmA5xhN02J5YenIRqM6BQ68SbmxDPEd81lf68qZdSKsuzXy8/RCGSgPLlYdEi0wGia1fw8IBt20jxYV3WXC7IwnemcLVsOvLP3IXWNfmKTrTlC2LS+UKzPeCe2ewsnF+GaWayE0iBqbG0065TE8IeJE6IZKd8ebPxcPo0tG1rFpUWLIASJUy9jR07/lYv6X0c2U165chOt6HU0v04Z7tK/uLzuFltIFzdBIurQsRDu01JCHuSOCFeS76+prHDjRumqHeBAnDzJvTrB9mzQ/PmfxX6BuBt4CCQC6hNK8sN9jrtIGWGKI5+UQGnQSvIEg01gWkA7pngw+3g/bYp4H3wR6nFJ14bcVpUUkpVVUpdAh4D15/5CkrAsQmReHLmNEX4rl+Hn36CbNlQZ8/SZOunPHDPTquPR1Cq3WQc9dfMZjx+1OCBlzs03QUZSsDqxnBsMpAP2AVkBqoB6+w5KyESjcQJkez5+sLMmaYmX9eupr7G+vXwzjtQsSKsXv3XiVcK48h+XFR+Vjn25TO3ztyJjMbXdxSBdQfC3cOw6B0IvWnXKQmRmCROiGTB0xM+/xxOnYKtW6FhQ5MmFxAAZcqYmLBqVWw8yIEpkdEE6EkZRhPovoN8MYUI6tuA65MGUjbERgegJ2Bz8YIG6yBfE9jxDWz/2tRaEiKJi+tJpenAEMALcHrmS4oCiOQlZUr4+mtzcikgAEqXxi3sAd/qQeye7cPsLA8p/8cgDrCTQpTllNttaLzF5D5v/gz2DADtjTmlVBDTXjTAvnMSInFInBBvhmzZzCbEtWtmhzpNGlOfz98fihWD+fMhJgbIjCPbQdViIkMZ4VmX0LB0FPMZxM6G30LwFQjwM7U0hHgzSJwQyYdSULmyOcl6+TJ07w5eXub0ar165iTT5MkQDuZZYAiwgMw04Viq+dR51IrQLv04sK0JVa+GMQKz9BTh6AJ1AqBEFzg8Cta1BGuUPWcqxH+K66KSKzBLax2qtbY++5WQgxPCbpycTOHuAwdg506oXx9nFU2L27PYW/I71tcpT5YjwZSkPCucNoH/cijUBvb2N4tLtjTAVuAtoDnwM6Y7kBDJlsQJ8WZJmxb69zedgUaOhKxZ4eRJaNUK8uY16RERDjiwAk0nejCNRany4xBShHe8B7CkSSdTl29BRbh71N6zESIxSJwQyVP27Kb2XlCQ2XTIkQPOnYPPPjM/+74v3G4LrALO40pFVqX+hL4hP2Gts5xNj9+i6v4rLMMU0LijLFB5NFQcCmcCYHkdiHpi3zkK8S/iuqg0CvhGKaUScjBCJDlKmbSG5ctR587xtH0nIi2u1Fi7gyOlHrCkrgODD9anr8MwdPUZULY3HJ8Ca5pAjAuwAVOkrzumC4T83iSSLYkT4s3k4QHdupm0uBkzIF8+uHLFdAbKkQOGDEc9/gEYyQd6LVtTKdI9qULjTMMZ9eEHaAdns7B0bqm9ZyJEQpM4IZI3Ly+THn3hAixcCGXLwoMHMGiQiQftlsHJ+UBKFO8xwMuJFdFrccxzlU0+ZSi7ejvHNZQHTisF5XpB9VlwbauJE8GX7T1DIf5RXBeVlgKfAMFKqUvPfiXg2IRIWvLkwXX6eLh0mfUFvyYcN+quCeZgWShfqy899r1PWKVvzc7C+WWmgPfTSGAhZlFpAtAACLPrNIRIIBInxJvN2RnatTMFvZcsgVKl4N49+PZbyJ4DvrmFuj2Nshxnr9dF8oV+wFdpp/BVUz9s6QrD6kaw61uwyeaDSLYkTog3g6Oj6RK6b5/JeGjQAKKjYdYsKFIPamSHTWVAf0k917mcdNtGGls69td4n8yLJxFhM7kOmwEKt4GGayHkGswvDVe32HduQvwDpeNQVV4pdQzTL30xEPHsz7TWdvs/W1qACnvRGsZ9f5fwwSP5wmk87tFmoWhXVQ/y9J1FpnQxsL41pPGFDzaARxbMolIXoCSwBshovwmIZC+xW0VLnBDib7Q2RVyHDoUtsX8FnJ2hTS2iu+/gSR5N/Sh/drrMoWZkVZZvz4LLiTmmRl+tX8E1lX3HL5I9iROGxAmRKC5cgNGjzcJSeLh5rXhm6H4bmhTgidNc/K7240SOtaRc25GMVcdyydmZyUB7MPX3VtaHh4Hwzk9QsqvJqBAiAcU1TsR1USkESKV10io/L0FA2NvSpdC15X26Zx1F27uj8XpigsTjyiVI1aEF3O0Prqmh7hLIXBaTS90Us6C0HvC129hF8maHhwWJE0I8z8GDMHw4LFtmFpssFqyN3bF+E0Gbom0IULPJrX3Ydao5mbYOBq8cUG8FpCtk75GLZEzihCFxQiSqhw9hyhQYMwbu3DGvZbdANyesH0+nxd1TLPQZhtPBShTNv4TDXhnoBQwGLFFPYP1HcGE5FGgJVaeCk5s9ZyOSubjGibimv60E3nu1IQmR/HzwASzbkY5hYYMpnC6IsT078TilhVTb/oBm3SEgHwRGmQ4/RyeCrgtsx7SCeAvYYd8JCBF/JE4I8TxlypiUuMBAkyLn4IDDwic4l4rh1zrTmb67KpejH5C38Cj2NhliCnj/Ws6kUguRfEicECJNGujd29TemzYN8ueHazboFolDtlYsmLGdCYfHEVPkIIcfl6H8jaMMw3SGC3X2BP8l4PcDBM43dZZCrtl5QkLE/aTSIqAOpk/6nWd/prVunTBD+2+ysyCSiqAgqFsXjp8Dv11XeXdtVbqNOk+aR7EX5EsFFR9Dg2ZQfRo43QVqApeBOZjTS0LEHzvsQEucECKurl83HYKmTIEwkz79oEoqWvVOxYb3rjI+rA+frdqCurUPyn0Lbw0Ai4OdBy2SG4kThsQJYVc2G6xZAyOGwe695jVnxc0Pa1C94xFOlgih5KXZHC3UhAIKVgB5AC6uhnUtwcEF6i6GbO/YcRIiuYrvk0qngOHAHuDi376EeONlywa7dkGd92FXqRxsKh5Izivd6DMYHqd1hHOPYSbwaQB084V7EZi/TuWBZpi/Xv+9wCtEEiZxQoi48vaGn3+Ga9egfz9I6UbaLY9Z9/4VdtdMzYyzg2nXOBcxRdrA/sGwwh+ePrb3qIV4VRInhPg7iwX8/WHXHti9G+qXhGhNlnnrOVHxDmv93XAN/pDsB7/hpo6iDKaABrnrQvP94JoGlrwPf4w36dVC2EGcTiolVbKzIJIaqxW++QZGjoQSg+BknwBcwtrx1RRnvv3REec7D82FmSzQvTN0HQoO7YEFQEdgPOBovwmIZCOxd6CTKokT4rXw6BGM7IkePR0Van4vW14fFvQryGTVitRb+0qdJRHvJE4YEidEknN2BYxsDXOeQKR5aZcfDGufhzPNV3HJpQCDgN6Aigw2J5YurYFCbeH9ieDoas/Ri2TklQt1K6Xe1lrviP3zjQUz9QAAIABJREFUc/OftdZbX3qUr0iCgEiqpk6FTp0gez0IDTjGXacGuEdcZ8PMD6g4bAdcv2ku9E4FA0ZAq/Pg9CNQC1gIeNhx9CI5SIyHBYkTQsSz+1eIGl4T5wlnIAJsClY2dqXQ573Id3oSRIdBzTmQt6G9RyqSAYkThsQJkTQ9gjuNYdwWmOgMj6IA2F9aMb37Z0xvMo6GysJswFPbYE9/2PcDZCoL/svAM6s9By+SifhYVDqptS4c++fLz3m/1lr7vPwwX40EAZGUbdliCnk7pAOf/Q85lLY58BvtotoyeXZJnPp/C7dCzMXZvaH329A2AFyKAUsBu/3VEslAIj0sSJwQIt5pom//jGVILyxTrKgosFog6MPS5CwTDbZjULY3+A0Ei5xsFS9P4oQhcUIkXTZgEDzpBxPTEfNjNI4PggE4ViQl/QeM5ny91iy3WMgLcH45rG8NTu6mzpJ3JXsOXiQDr7yo9DqQICCSurNnoX59OHcBamy1sq5iX1BDKEJZ1sUsxHvkTzByItyJ/XuYJS30DIeOLuDyK6aYtxAvTtIaDIkT4nWlOcbDKw1IPewyegY4xIDVQWGpmhdV6hwU8oPav4JXdnsPVbymJE4YEidE0rcZ+BjCrmKd6EfwkGOkeRwKwImiufjpu+F8+MEH1LJY4P4pWFkfgi+ZRg8V+soGhHhp8V2oWwjxEvLnhwMHoL4/rHvbgco/DCalbRknOE0hx3Js/6YxHD8On2WFzMDNB/BlBPiGwy+1wNYPs0shhBDiTaIoRtqcx7g+tjkO5+BGG3c0GrXhHHqEI0w+ABOKwoWV9h6qEEKIBPU+cBLcu+DQYw9pbqbmULcPuZ7BkSLHLzOnSRNyFCnM8oAAdGpfaHkYCrQ06XALKsHjS/aegEjmZFFJiATm6QlLlsDQofB7f8hUpQFFnh4ghNRUpgo/ZdiCHh0IU5tCGyCbB1yJgpZA6YGwqTzw0K5zEEIIYQ+eZHeez+1s00gzw8qD0x5saqbQMTGwIxoGPIFP68OqjhATae/BCiGESDAewBhgJ7i5U3rkQlyPNeHbtpW4mh0KnQ6kQfPm3CxYkIiFK6DqDKgdAA8DYV5xOD3f3hMQyZgsKgmRCJSCXr1gwwa4dxyuZi1AvYsH0NShB11p4PwZ4bWnw5cToFs0tEgBGVPDH0C1g1AtK/wRYO9pCCGESHSKTI4fo9V+InJm4b1fFZMPebKqHhBpgy1A06nQ3AeuH7P3YIUQQiQoP8wDwreky7SQH6afY+aw3rT/KSUXfSDruXO4ffQRUb6+cCgGWhyB9MVgfStY2wIig+09AZEMyaKSEImoWjU4dAh8ssOqvF40W7AMV/0DK/mVwsqPS8VrwEfHoFZR6PoIWheGlO6w6SmUbA4tKsDl59W5FEIIkVylUEXJ4XyYAyEt+LzkE9IvS8sHe+GPyqkgAlh8EwqWgO/aQKScWhJCiOTLFRgEHMJiycKAZkPp3qAi74yuTus5cC6vG84XL0KrVmi/OqA6Qfn+cHYhzC0ON/bYefwiuZFFJSESWa5csHs3tGwJAc0svNXhO3JZ13KZqxSiNKvTXISmO6HyUChxDvq4Qtvq4Kzg133gmwe6dYH79+09FSGEEIlI4UEFr7mcCQuggLYyp7wL4zeHU/83L4JL+cATDYPnQPZ0MGMKWK32HrIQQogEUxw4AAyjgM9mLtfcj4dzGwoe0rSb4cqlXOlQgYHQtBl8vgzSDDFvW1gJ9vQHW4wdxy6SE1lUEsIOUqSAOXNg3DjYMRssxWpS8/EhnuKNP7Wpa/mI2+XamEJ7mbNDod9gcl1o5gvRNhg9DnL7mEJN4eH2no4QQohE5OvelBSW41wK8WOGJYqP3oc8By8xc2ktdK50cDcUPv4UCuaDpUvhNe70K4QQ4t84Aj2BYzg5FmZi09nsuVaKrb7F8T1zn88m5ee6d2bTGKhtT5iaBsLehT0DYOE7EHzFvsMXyYIsKglhJ0pB586wbRuEPYAd3rn57MB+XOnLGpaQE1/GpNuOtfkeqNAPHq2Fd0Jg9cdQ3QFCnkCfPpA3L8yeLTvSQgjxBnFW2SjqtYlTj36mFk85oVKwuOE6ip3x5sGoDpDWAc5dgkaNoFw52CPpDkIIkXzlB7YDEyhT6Bjnyxyn69pGTGt5g7xnH9NlTEMeZMwIh45A360QkA/2HIW5xSDwV3sPXrzmEm1RSSlVQyl1Vil1QSnV6x9+/pVS6rRS6rhSaotSKkdijU0Ie6pYEQ4fhmLFYFI5N1r3HUCNmBNEUpqudKawQyUOvVUHWuwHl9RwZjp81RDWe0NJ4OZNaNsWSpeGrVvtPR0hXprECSFelIVCqb/CZjtIWGhu1gMdnQLJ/cV8Fp0fjv6kAHgBBw+Cnx80bw5BQfYetBBCiARhAT5HqVM4Ob3HiAZLOH0vJwWOlGBcl2XkuJiL3j/2ICJtWjhyDsaHw2QFE1rAmmYQLqU1xMtJlEUlpZQDMAGoCRQEmimlCv7tsj+A0lrrosASYERijE2IpCBLFnNiqXNnmPoDnM2Xj4GHN5GOAM5wnTKU5ZOMswhuuQXKfAMnlkCQAyytDPMAb2c4ehSqVIG6dSEw0N5TEuKFSJwQ4uW5ORYlt8cBTt79ik4qkn0OmuGpu9N4UjGervoWqlrACQgIgPz5oX9/SZ0WQohkKzuwClhM3pz3OfTWPkZvqoGOPsPwr8aS4XIXZgwaiDVVKjgdbH77+mYhDM0P55fbeezidZRYJ5XKAhe01pe01lHAAqDesxdorbdprf/8DWcf4J1IYxMiSXB2NjWWtm8HFxfoW1pRuW1T2oadAToxnUnkdCzOr28XRzfdCRYnWLoNMleDE24w2AE8XGDNGihSBD7/HO7etfe0hIgriRNCvBJXCmf4meDwzaSOSMM+bSGfZSE5/H7lWMB8+LEYFAMiImDAALO49MsvUm9JvFbkRKsQcaWARigViMXyCV9W3cClGA+q7y1JqGc/OvRZifflNRz+/nu0hwcEahj8EFo2hDn15NSSeCGJtaiUFXj2vPX12Neepz2w/p9+oJTqoJQ6pJQ6dO/evXgcohBJw9tvm0NHAwbAyl9huXdKvl0+jvz6AI/xpgXNqZS1H+dbL4GSX8LRTbDABRoUg/OR0DGjeUiYNAny5IFhw8xDhBBJm8QJIeJByhRVyOh2ggt3GzNEaZY63qRhmuZ0+PxdoqYNgs5OkM0Brl83bUj9/Ex6nBBJnJxoFeJlpAImAzvIkNaD9X57WbTPj3SPrnA7ZWVKD7TQ+GIgIZ07g4OjaSb3ySpomAOOzLPz2MXrIrEWldQ/vPaPW2NKqZZAaeDHf/q51nqq1rq01rp0+vTp43GIQiQdLi7Qt69p1FC8OAxuCOkql+Lr2/twYgK7OUQBp7J8V9mLpy13gkd2WHcEdhaEIeFwwglqFYQnT6B3b/D1NTvSNpu9pybE80icECLepKZAxgDuPJxP0WgXTuJAFqcxeBebzs7+U2FIaWgCpHKBvXuhbFn46CNTo0+IpEtOtArx0iqh1FGgP43KHeSCi5W2+0oBA1iWvhbpx7Vg1OnTRDduDNHAb+FQqTV0KAmPJTaIf5dYi0rXgWzPfO8N/L//O5VS7wPfAv5a68hEGpsQSVb+/Kb29qxZEHgCxmZ3oMPPn/OO9QxWGjOYH/DJ2IJ5zTthqzIO7tyAuZHwyBtWnIZNRaGoL1y7Znaky5eHXbvsPS0h/onECSHilSJjmhZ4Op7m+p0m9Af2O1/n57RtqVi3IOE9foBeQFVXcHKEuXMhXz4YPFhOt4qkSk60CvFKXIB+KHUUT/fCzKywjx2ni5Dn3h2iqMBXeX8g26LRrN+7F12xIoQD0/6AvNlg9NeSLi2eK7EWlQ4CeZVSuZRSzkBTTPWw/6WUKgFMwTwoSCEYIWIpBW3awJkz0LQpTOgO130z0efkfFKylVuko7WlLQWKT2Fj27HofI1h30WYkx5yn4cj12BmK8ic2aQ4VKoErVvDnTv2npoQz5I4IUQCUCoreTP9SnT0NpxC8rMC+C7lLEoVG8vU5iOhTQnoHgPlM0NYGHz3HRQsCKtX23voQvydnGgVIl4UALYDU6hY4BqnUgczcPdbeEQt4K4tH7XKb6Xcjo2cXbkS8uaC+zboNhLyp4PfVtp78CIJSpRFJa11DNAZ+A0IBBZprU8ppQYqpfxjL/sR8AAWK6WOKqVWPed2QryR0qc3G8mbNgEahhSBGp9V5suwAziziHNEUt39I8rVusKJxqPBkgaWRcBaD2g0D877Qt8vTW7dvHlmR3rcOIiJsffUhJA4IUQCc3J6F2+vPwgJG41fjDvHHO/zMFsnfKpl4pb/AGj6CLp4QL5scOUK+PtDnTpw8aK9hy7En+REqxDxxgJ0QKlAnJwa8L3fHq5GeNHxeB4c+ZbDuhC+/lZanT7Lo0kTIa0HnH8INerDe6Wl07T4P5R+jY+xlS5dWh86dMjewxAi0UVEmAyF4cPB1RVafwMRX0czN8UsrPQHblE7pibTD+ch075poDS8paGEC1zpCV12w7rYE+HFi8PEiVChgj2nJOKZUuqw1rq0vcdhbxInhPgnd7gV3JPMKedwQ8PX2h2XU32ZdXIllqA9cLYALA6CJ6FmI6JXL+jZE9zc7D1wEY9etzihlHIEzgFVgBuYE67NtdannrmmBKZAdw2t9fm43FfihBBgKnR3B3Zy6bY3X0UrVmYLQukquKjR9A3LRfdBPXEaPQme2sBBQYc2MGQkpEpl57GLhBLXOJFY6W9CiHjk5gaDBplC3nXrwuT+EJDWiZb9O1A/7AKKYax13EvmcuPp0qYG4dnegu1RMN8GLt/BmoewYiTkyGFazb31FrRvD1JXQAgh3gAZyZxyNrAXIgqywBJG2yI9KV1XsaV4PygWBN2joEYxiIw07UgLFZKUOGFXcqJViIRUFpMSt5JcGT1YkS2I3Vd8KBt6gKe6OH3ce5Fr6EBWXrqGblAObBomzYJcWWHKRLBa7T0BYUdyUkmIZOD8eRg2zKTHKQX+veDRN4/Y6jEcGIODjmHiheq033YUhyc3ILczVIoGt89gSAr4cSxERUHq1DBkCHzyCTg42Hta4hW8bjvQCUXihBD/xUq0dRrhfI27JZwJyon5Z79i+dFLeF9fDHezwEpnOHvFXF6nDowZAz4+dh21eHUSJwyJE0L8XQwwE5utLxbLHZZcyk2vHJe4RGq0w0D86MiMXZvJ37EVnL5v3lI4D0yZYzaqRbIhJ5WEeIPkzQszZsCFC9ChA6wZAb+nTE2NvsMoFXYBq2pHx7wbSNf2Mb9VrIwOcoY5wM6J0HMOnBgKVavCo0fw2WemS9zBg/ae1pst/D6cWSCdNoQQCcwBJ4dPSelwjfvRLfhCR/Nb/uGMabKZ5mV7EJbLA9pdgY+LgZcnrFljCnn37y9d4uwp5Crs7wlLi0mcEELEM0egAxbLBaA/DXPe5gwOjL7lRlo6s9dWAt+KiqYn7/BoTB9I7QgnL4CfH3zYAG7+v1JnIpmTRSUhkpEcOWD8eLh8Gbp1g50j4bBHVir1m0K+sNM8dqpNjXLbyPqxZnvJ0uhAJ5h5H25/DSsjYPFo8PaGQ4egXDn49FN48MDe03ozaA0PTsOB4RBQESZnhLXN4N4xe49MCPFGSEsm5/lY1B+ERpfhR/WIEZV+5Ot2T/kxVxusBc9CjxioWfL/psStWWPvgb85IoPhxFhYmB+m5YRdI9DRxyFCCuYKIRKCB9APi+U8jg7t+SLrba48TcE3j2/gRg0W6fdJ/4U/fa7cJaptFbMWtWgF5MkJg38wsULYh7bBjd2wrRusbpTgHyfpb0IkY/fvmyyFceMgOBhK9oOQr05ywWswsJA8wa78sjsnZQLPoFyBsgrydoGhCkbGdobLkAEmTIBGCf8P0hvHGgXXd8DF1XBpDQRfAiAygwtOPpFYcsO9jOtJr2q88K0lrcGQOCHEy7GxjZvWT/B2uMgZYOzDgnywJB1VnuyA+zlhhQXOmH+zqF/fNHzInNmeQ06erNFwZRWc/hl9cT/KaoPUcL+ghTkFbMxI6cJ2gkhP+he+tcQJQ+KEEHEVCPQCVvEg3IMRWjPJPYwnuiEeDGbSiYc0//RDLHuvm8uzZ4JxU0wBWKXsOfA3g80KN3bCuaVwfjGE3UE7KCJzOuNa9wk4OL3wLeMaJ2RRSYg3QHCwWRcaOdIcPMpYH1KMCORyniGgfqXkXSfm7sxAoStB4An4pQX6QaelsH27uUmjRuYmGTLYcyqvv4gHcHmdWUi68htEhWBzcCAkuwspcofj7ANXPGGxzsAc7c8v/EAxS6YX/hh5WDAkTgjxKjRRLOGB7QsyW+5wEFh5pgSfbbhH1qjrcLEMBASaLnGpU8PYsdCihTw8vCqt4fZeOP0j+ux6VEQkuEF4flhREMZkcCT0cm30oaY8uFaHW909sLxE7oHECUPihBAvajcwCNhAcKQro5SVsU4xPFLtyWTtx+oFCyj1zXeom7Enlaq8DROnQb589hx08mSNhqDf4fxSuLAMwu9hc1SQS2PJCxd9YINLSjpwEydSvPDtZVFJCPH/RETAypUwZw5s3Ai2nJB2/HkeVh+MtsynyjULc3a4k/XOY0gH+JWBjbWh508QGgpp05pjT02bykPDi4gON/WRTs2Gm7tB24hwT8FtH0VanzC8csADR1ikcvILjdkT2Rl9MDvshY21oGqhF/9IeVgwJE4IER+shDKJp7oP6dQTtkQrru4sQqs/AlFP3HDcmBn2njWX1q0LkydDliz2HfLrKOQqnBqODlyAevQIHCA6N2wvCCO9HdhzvCYhgU3RaetCJS/wAmWDC0/AJ+WLf5zECUPihBAv6xAwGFhBaIwT45WVUcqJu5YuFH/ShQ0DviDjxBUQATg5QJ8e0GcAODvbedyvOWsUXNsC55bAheXw9BE2Jwv42LDkg6u5YK2TOxepy00+x9HqxzyHl6t6JItKQoh/desW/PqrWWA6cQMs31+Ez4ainWfT9CxM2u1IyseRkAXIUsFsSGzea95crx5MmiSpDv/l4Vk4NtksJkU+5laalFzNF0n23E/JkhHClWIVeQkIacH6jd2I3uWJxwmokhbefxuqVAFf35dbv5OHBUPihBDx6SkPGYKDHkFKFcn6B054bPSm0o3LRB/xwnF1FCr0KaRKZXKvW7WSDYj/YouBS4vQJ4bA5VMoDTZvOF4QRvsoFp2vRkRIc/DxB+9UAGQMg2oW8HeD94A0L/nREicMiRNCvKqTwBC0XkikTTHFYuVH7ckNS29an6nC1C9a4LL5grk0VzqYOQ/effHSDm80reHOITg+Dc4tgshgbM4O6NxWHPLBjRyw2tGdA9G12Bnehcseb2F1tEAUuB2EJ37wMn29ZVFJCBEnWsOxYzB3LsxfAveaXUH1Hoaj5wy+OmHju4MOeIREm99ar+WCkfcgJDbVYfRoeWj4O2s0XFwJxybBta3EWCwE5nXGrfhT8mQFK7Al0pd5B9qxYuqnxOzz5O3cZgGpShUoXhwcXuZf/b+RhwVD4oQQCSGEm3QnlZ6Js7Ky8bozabZnpPyZIKxLXXAIjE15qF0bpkyBrFntO9ykKOQqnOiH9eRCHEKfgjvcLgITCsKkkMoEO7UiJks9IA1eMfC+A1RTUBXwiachSJwwJE4IEV/OAcOw6XnEaCuzLJrhtnRc5gd+XuhI16+7YbkVCgpoUBomLYUM2e096KQtMhgCf4HjU+HeMWyOFmz5NI75NXeyw3KVglUhtdjk2IUYTz/AYtb4NkGmE1AuEsoUhD59EnaTWhaVhBD/KzrapMXNmQfLPIKwDhiOY6bpfHI2kh8OOpP2fhREAWs8Yc8T86ZatcxDg7e3Xcdud0+uw/Fp6BPTUGG3CPZ0JrhoFNmLAO6w90lOAgI6sGRoO3J7Z6RKFXjvPdNkz8Ul/ocjDwuGxAkhEo7mNtf4nAx6JW7Kxq4gC+7b01Ji7T30SoWK0JAyJYwaBW3ayAaELQYu/crTowNwuXoJBUTngs1FoH+qkhz26ojVpSFOOh1+sQtIVYGSvNwO83+ROGFInBAivl0FRmDV09FE8auCn3Qazke0Z/HXQdSethBl1ZBKQe/m0HUSOHvae9BJh9Zwcw+cmIY+uxAV85SYDArHoprHvrDEIQW/htVge8ovsVER7lhIfRgK34KqCt7NC0WLmvD7qmRRSQjxSh4/hkWLYNSpO5zpMBYKjKfJ1RB+2u9CtuuRcAzT/SfUBl5epgp4u3Zv1kODtsHVzXBsEvriatBWHuS0kLq4DYdccCw6LQFbmrPr524U881FrVrwzjvg4ZHwQ5OHBUPihBCJ4T436IOnnoMXURy9Bm6rPMk/6YlpFgRQvRpMmw7Zstl1pHYRcpnHR7qSInAdzuEx4A7XisDYnDmZlq4zIS4tSKkzUV9BI6Ay4J4Iw5I4YUicECKh3ETzE1Y9EUcVyXYN41BcPFaJRR9dJe/xq+ayYs4wpAdU7QNOL15MOtmIeACn56FPTEY9OIvNWWHx1ViLwsb0Tsy2vsMqp2+IjKxCtssWKoTCB15QNye4JlCZKllUEkLEm1On4Md1IfxSaTIxZUfx3p3bjN/viu+Rp6jlmGOWANWqwbRpkD2ZH2UNvQWn5hBzciqOjy8T6aZwKKxxLAYXPNwIOFGD07O/p2TWEtSuDQUKJP5amzwsGBInhEhModxjOOjRpCeUi1fAfYQzmeZEQQTYUrhhGT0aPv4k+W9AWKO5c2os+uRwMt66hwIic8G6/Knon/tjjrt2wVNno56CJkA1IAEOrf4riROGxAkhEtojYCZRtjE4W4IIslmYrG3YJqShb+8nuIVHgytQ3xO+HgDFPwNHV3sPOnFoDde3o49PQZ9fgsUagy0TWIrC6XyKWU7F+cXSjfshTalwz4mOGaG+By/Rx+3lyKKSECLeRUXB/M1P+TblXG6XG0HJ4ItM2e9MqV+jzOJSOODpCuMnQ6vWyeuhwRoFl9aiT85AX16PRduwZgWHYnA9j4XFd8txaWMvKrnXpVo1RapU9h2uPCwYEieEsIcoQpjCEwaSVd/nzgHw/EyR4g/zO2dYiUKkWLEKlT2+qgMlHWF3T3Bpbwfy3TiAS4QN7QGXCjgxqlBNpqYdhrMugH/sQlINzHOUvUicMCROCJFYrMBaYmxjcLRs5alWrLquydhJ8c7q2DWJnEDrDFCnOxRuD24v24ogCdMa7hyGswuJPjsbpyf3sbmApQA8KgLz0vswR3Xg3L3OVA115/MsUNklYdKg/4ssKgkhEtSFmzG0urWU/YWG4RN9lNmbnPDrH406EXtBVW+YswYyF7PrOF/Zg9NwYibRp2bi9PQRVndwKAQhhWCxsw+BJz6noeVLypV2jJcC2/FFHhYMiRNC2JONCBZzi1742K4QOgZSfA+WMLC5K843bkCa70eR3uf1Pt1qi47gwPZ+eN+YjPf9J6DgqQ+sKuhL7zwjuaVqUkfBh0BNEm+H+b9InDAkTghhD4FoPZ5o2yycLRGcXaLI0EWT+jZoB1BVgaquULgVlPgC0hex94BfjdZw/wScXYj17HwcHl/DZgFLTojJB+vypWOWU1MO3OxLbWt6OmWFohZT09yeZFFJCJEorFrT6cpGZqUfiqfjdpYPt1BxmA31FHRKoGdWVJuhkKnl63NyKTIEzi4k6o8pON8/bP7Rzw3WwrAhmwe/32yEf8wgKuVOuh2N5GHBkDghRFKgiWYbF/kK36Bj2JqDZZf5ia087H+rAbpCd8rVr4CD42sSJ4BjxzYRFtiV0vdP4xwJpITzhT0YXrgdi92HUUO58QFQm8SpkfSiJE4YEieEsKdgYDZPIsfiGX6JsJ4K92lmfeJRXldSNrJhSRsF2d41i0u5/cHiaNcRv5AHgXB2IfpsAOrhObQCnR0s+eFIXldmOddg160fqO1YmA4ZIaltsciikhAi0c16up/vnk7A5f4C1jePJv/B2B9UBFtrNyxlmkCBEeCSwa7j/Ee2GLixi8gjM3C4tAhHWxS2tGApDCcLKBbFlCdryPd0yFYDZfd9g/8mDwuGxAkhkpoTnLMNIvuopbj2sUIU2DKApRn8kbIYFxx6ULpFY3LlTqCqo6/o1p177NzxNX6hi8j6MBIc4GkexfKipRjhPZmillJ8gOna5mbvwf4HiROGxAkhkgIb8BsPI8aRZs96dFtQQRDlCls/TkXZ/A6kiXwAntmh+OdQ5GNwS2vvQf+zxxfh7ELzde84GojxBidfuJFXMfd/2Lvv+Krq+4/jr0922MgSCHuIDMsIIlNZCg5cOHDbqhWrVKuordZWrbWOtv4cteKqKI4qiogIyHIBClhFATGojACyZUPW9/fH90QCJJBxc0fyfj4e55Hce84993NvkvvJ9/MdJ7UbUzbcTn93LtcdDVHYKvqZikoiEjHr2MzNu56j1T/+xl33bCExB/IaQdwF4JpBTqvjSOz2Zzj6rMiNXnIOtn6LW/E+u5dOJmnDbBLz9pCXBHHtYHNHeLFOI9bvvJbbav+OWhaN/cxFU2PBU54QiVa72f3l/SRc/CBJi7NwCbBvGKScAGvtKN774bcc1WskQ8+tR0okFx7Kzeb7ZdP4+uunabV3Nsds3UZCDlAHvj2uNo8d8ztyq97KWSRxEhCdpbDCKU94yhMi0eZ79m59Gn77GCkv7gJgXX8Yd0cSfVfVp8eWTL+Qd7uL/eil+hFcaiM3CzZ8Aevmwtp5sG4ebF8BwN6GRko7x6628EbVJry95Wra7h7NjWkpHB39/dOAikoiEgXyyGP6widpdfHttFq2k5xE2HFJArWOy8FyIatmDXKaDyC1zflYo2GQWM6Fm90bYdUM9mVMghVTSc7aBPhpetYM9jWDic2S+DD3DM5P/TN9rWP5xlOO1FjwlCdEotyePXD7VfDoy/6UTQqpAAAgAElEQVR2d9j9S6iyC/bGxfPf9eeSueVKjurUl579qtKxI+W7fl1uFrk/fso3S18kLnMyrbatJSkn+F+5DuxuEs+kY/rxTaPHOSmuPb2JzOKpoaA84SlPiEQrBxPuJ++ae4nbuBeqw6b/g/GnQ+25dTnzu+0k52VB4z7QYig06gVHdy/f9sSOTFg71xeP1s3zC27n7gNgT9VEshvlUSMtl7zWMKN6dd7YcRZVtt7D6CbNaRRXfmGVFxWVRCR67N7Nztt+Q7XH/wPAgt6w4w9J9N6aQ9KPeQA4g93V67KrVjdSWp1DjXbnQJW6ZXvenL2w5mNyf5hIdsYkUrb/4O9PBppCVjOY3wym1KjJDzkDOCHpCq5hKEkklu15o4AaC57yhEiMmDoVrrgcflxPXo044h7II7cVuCWQkANZFsdc14YPlwxl6+7hNDjueHr3TSQ9nbKNZMrNgnWfsTPzXbZ89xZHb8wgKdfnJeqCS4Nv0+rwUYPTcDWvo4t1pytxxGDb4BDKE57yhEiU27ABrr0a3poIgBsGNgY+qwmrvkyk8+fVaL17q99n8Vi9X0Cjnr7I1Kgn1GhespkRzsHerbBjFWxfCVsz4MdPfTFp5xoA8uLj2Vk/ifhG+6jaKA8awpbq8LGrxdzdXdmx6Q/c1nggTWJo+afCqKgkItFn6lTclVdi69axs3o81z+WS8YFcMm6KgxYE0/LzF0k/pjnrzgK7EquyaaUzuytM4yG6WdTI625Twp5ObB7A3k71rF38xqytv5A3rbl2O5VxO9ZS8K+DSTu20pC1k4MB3FAI8hqCv9rDu80gI84Djifq+POYgTtiY+BdZJKQo0FT3lCJIZs3AhXXw1vvw3A3hEtyfq/NdTYtQ9WQvYqSNjgr4azKyGe2bltmbHoFDZsuYwmHTrTt6/RsydUrQq5uX7LyYHcrCzY9gNu23L27fiK7J1fE7czg6o7V1Jz50YS8oLOjXpgabCtSSLTG3RjVcIvaVvlHPpShxoRfFvKi/KEpzwhEgOcgxdfhBtugO3bya1blU3/qkKD8zaSBXy7BzasA7cylQarUmi9ZRcpeVn+sVWPhoY99xea6v0C9m6G7UHRaMeqQ7/P3nnA02fXrM7uox3JjXaR0shBPVgZDx9Tm092dmXl2rPpnXcRN7StTfWK0OsQUFFJRKLT5s3w61/D+PEAZFyQzk1PpzGr+kfsYTNdcuDi9bUZnGkcs2YbSWtzwY8qZU98KgYk5+4pvARUBX+JnWr+654qxtJGjolNYFpSdb5yp9HThjGKUziVoypET3NR1FjwlCdEYoxz8MwzcOONsHs3NG0K4+4mu88uVjGZanvm0mD1VlgFeSsh7if/sK0pCczIacmMb06iRsp22tZdzrE11tDStlA/ax9xBf7dzUsAakFcLf81txEsbNSIT3KHUY0rOalGOq2Jq2BdDYdSnvCUJ0RiyOrV8MtfwvTpAORcPJRlj7ZiV435NI77hsZx2/xxebBjE6xfY+StTKHuOuOo3buLPG1OagpZ1RPJqm7kVc8hsWY2VapnE18TqAFUga+BjziKj3d358tlwzn2x3P5XZva9Gxd7q86YlRUEpHo5RyMHQvXXw87d8Kxx5I3/nW+PDaL95nBBKazkI/IZjcd8mDE5oYMycyj3bqt5MXFsb1aEluqJrK+mrGmWg4rqu3i+yrZrIuHDcB6YBMNyKYVqe5EBttpXM8JDCA+Zte+KCk1FjzlCZEYlZEBF18M8+f7RZQefBBuuimYwrCWn5jDCqZRdfs0WqxaRcJKB6uA/DZDKhAUjajpv26qlcKqGnVZldyYTTkt2L6nLdm7O9GxZn8G1ahDcoReaqQoT3jKEyIxJi8PnnwSRo/26/I1buzbFQMGsJ2tfMNXZGTPIs99SPPEpXSy9dQiD3YB62DXRiOlivMFo+r4olGiTx+ZwGriyaQqq6lJJvVYubcpn311JkcvOp1Lq9flyhOhQYNIvgHho6KSiES/Zcvg3HNh8WKoVg2efRbOPx+AfexjHvOYzgwmMZ1FfEZe/rw4IJ4UatOSOrSkPi1pSEvSaElTWtKC5tShKtWA9lABVkgqOTUWPOUJkRiWnQ1/+AM8/LC/fe658NxzUOPAyWh57GY5n7HazabK9qnsSG3AvqSOJNCGZJpSnabUIY1GJBPJC8lFG+UJT3lCJEZ9+y1cfjnMm+c7HO64A/70J0jYv5BRNvAljrm5y/g26y2qJ35As4Tv2EhN1mU3Yv2+ZmzZ1ZqdO9qTsK0NDXYcTf29KdTNhnrZ0CAP6tSA3r391OrKRkUlEYkNu3b5NTReecXfvvFG3yOdeGApaDvbmcc8qlKVlrTkaI7GKvzkhNJTY8FTnhCpAN56C664ArZvhzZt4M03oWPsXp0zWihPeMoTIjEsJwfuvddvzkHfvvDyy5CWVujhDtiEH8CaFM44Y1Rx80RFXlJERGJB1aowbhw89pjvWXjkEejfH9auPeCwGtTgZE6mN71pSEMVlEREKouzz4YFC6BTJz8trkcPeOmlSEclIiKRlpAAd98NM2ZAw4bw0Ufwi1/AO+8UergB9VBBKdRUVBKRyDPz6yt9+KGfF/3JJ9C1K3zwQaQjExGRaNCmjZ/icNllfgHvSy+F666DffsiHZmIiERa//7wxRcwZAhs2QLDhvl1+LKyIh1ZpaCikohEj5494fPPfWJYvx4GDvRracTwNF0REQmRKlXgP/+Bp56CpCS/UGvfvrByZaQjExGRSKtfH9591y+jkT/7oVcv+O67SEdW4amoJCLRpX59mDYNbr8dcnP9lR2GD/draYiISOVmBtdc40e0Nmvmrw7XtStMmRLpyEREJNLi4nzb4aOPoHlzWLgQunSBV1+NdGQVmopKIhJ9EhLg/vthwgR/lZ8334Tu3f1V4kRERNLT/cjWoUP9VIdTT4U//9l3RoiISOV2wgnwv//5q4bu2AEjRvgOid27Ix1ZhaSikohErzPP9D0Mxx3nLxt6/PH+ctKVZTpcXl6kIxARiV5HHQWTJvmr/oBfrPW002DjxsjGJSIikVerFrz+OvzrX5CcDE8/7dsS6qQOORWVRCS6tW4Nc+f6RVl374Zf/QpOPhm+/z7SkZWfrCwYMwbattU8cBGRw4mLgzvvhKlToU4d/7V9ez/VoSJ3QOTl+asbnXkm7N0b6WhERKKTGYwcCZ9+Cscc4wtK3bv79ZY0sjVkVFQSkehXpQq88AK8+KJvNEyfDh07wt//Djk5kY4udLKz4ZlnfDHp17/2BaVnnol0VCIi0W/wYD/VoX9/2LTJT3U46yxYsybSkYVWdjaMHQudOvmrG02c6G+LiEjRfvELWLAALr8c9uzxV4bLnyJX0YWhg0VFJRGJDWZwySWwdClcdJFPCLfc4q8Yt2hRpKMrm+xsP63vmGPg6qv9lYyOPdb3tN93X6SjExGJDU2awIwZfqRnjRq+4NKhgy/Ox/qopV274NFH/ejdyy+HJUsgLQ3++U+fE0VE5PCqVfNXEJ040eeLBQv8qKXRo/1nbEWydatvR1x2GbRoUe4jWlVUEpHYUq8ejBvn19HITwjdusEdd8TeFICcHJ/c2rXz0/p++MEXll5+Gb76Ci64wE/tEBGR4jHzxfnFi+H002HbNn978ODYnDa9eTPcc4+/0t1vfwurVvmc8fzzfjTrjTf6hlIlYWZDzGyZmS03s9sL2d/PzD43sxwzGx6JGEUkyp1xhs8Rv/2tn0r88MN+BkQsX0XUOd92eOAB6NfPt5dGjPCzPFau9FdMLUdha60oCYhISJ12mk8I11/v50T/9a/QubO/hGi0y8nx0/natYMrr/QNnbZt4aWX/GsaMQLi4yMdZdgpT4hIyKSl+d7ol1+GunX9CKZOnWJnHY3Vq/30jKZN4U9/8sWl44+Ht97yeeKKKyApKdJRhpWZxQNPAEOB9sAIM2t/0GGrgCuAl8MbnYjElOrVfT749FM/NW7FCn810YsugvXrIx1d8eza5dfWGznSdzwcdxzcfrtvC5n56eAPP+xHtg4YUK6hhKWopCQgIuWienV47DH4+GM/XWzZMl+dv+462L490tEdKjfX9xi0b+8bBN9956cyjB3rGwkXX1wpi0mgPCEi5cDMF+mXLIELL/QXe7jpJujb10+ljkZLl/rOhpYtfYNn92445RSYNQvmzfPrRFXeEazHA8udc98757KAV4EzCx7gnFvhnFsE6PKpInJk3bvD/Pnw4IOQmgqvvOLbFM8+G33TpvPyfD577DFfAKtTx6+t9+9/+46IBg18/nj9db+24MyZcPPN/vWYlWtoCeV69v1+TgIAZpafBJbkH+CcWxHsUxIQkZLp1csvtPfXv8L998OTT/oe6ief9ENcw23XLv/hXnBbtcr3HGRk+GNatYI//tEXkhLC9VEc1ZQnRKR81KvnGwojRvge3blz/cjWu+6CW2+FxMTwx7RrF3zzjS8iLVnit6VL4dtv/f64OF8Iu/VW6NIl/PFFp8bA6gK3M4EeEYpFRCqKxES/rtLw4T5HTJ0KV13lO4KfesovTREJP/3kR1LNnes7FT791N+Xz8yPYD31VD+Do2vXiHU6hKslE7IkYGbXANcANG3atOyRiUjFkJwMd98N553nE8Gnn/rqfYcOMHCg3048EWrWLPtz5eb6UVFffOHnKR9cPNq6tejHtmjhi0mXXBKZhkz0Up4QkfI1bJgfzTp6tF+8+847fY/uVVf5zonjjgt9kX/btgMLR/nFoxUrCj8+Odn3NN9yi+98kIIK62ov1VAC5QkROUSLFvDee74T4sYb4YMP9k8pu/BCX1wqr6JNXp7vaJg7d/+2dOmho6XS0qB3bz9SaehQqF+/fOIpoXAVlUKWBJxzY4AxAOnp6VE2Jk1EIq5jR78Y3eOP++LN4sV+e/RRP7UsPR0GDfJFpp49ISXl8Odzzi+gPX++3xYsgIULYefOoh+TlOQ/9Js08ethNGnitxYt/JxmFZMKozwhIuWvVi14+mnfQLj6avjyS7jhBr+valXo0cP/w96rl7/cdK1axTtvdrYfZbRokV8sddEiv61eXfjxCQl+Lb327f3UhPbt/da27ZHzUuWVCTQpcDsNWFuaEylPiEihzPy6SkOG+A6I557zF0u45x6/7Ea3bn7KXP7WrFnJppY5B+vW+ZkL337rvy5adOgoJPDtia5dfXslf0tLC+3rDZFwFZVClgRERI4oPt5f0eHaa/2H9IwZfvv00/3bfff5f9z79Nk/kqlrV784X8EC0oIFfoHUgzVp4hNLq1aHFo/q1avMa16UlvKEiITPwIG++PPyy35q8pw5fp27mTP9Br6h0KHD/iJT795+raP16/cXjfILSEuWQFbWoc+TkuIvynBw8ahVK3UwlNx8oI2ZtQDWABcCF0U2JBGpkI46yq+rdOmlfg2jzz6DzEyYPdtv+erW9cWl9PT9haajj/Zth/zCUX7xKP/rrl2FP2fjxgcWkLp29aNXY4C5MCxAZWYJwLfAQHwSmA9c5JxbXMix/wEmOefeONJ509PT3YIFC0IcrYhUWDt2+MZDfpHpyy8P3J+cDPv2Hfq4evUO7JVIT/eL4UUxM1vonEuPdBzFpTwhIhH3449+ysGcOX7E68KFhxaKUlNhz57CH9+ihZ8qkb916uQvxhClF2CItTwBYGanAo8A8cBzzrn7zOweYIFzbqKZdQfeAmoDe4EfnXMdDndO5QkRKZYff9zf8Zy/FdbxXLVq0YUj8AWrtm2hTRv/tV07P0q2SZOiHxMhxc0TYSkqgZKAiEShjRv9FXWmT/dFpu+/92supacf2OPQpEm5XzUh1NRY8JQnRKTU9u71haX8ItOcOT5v1Ky5v2iUX0Dq2NFPjYghsZgnyoPyhIiUinN+fbyCRab8JTKqVj2wcJT/fZs2/qptMSLqikrlQUlAREJq61bfWKgAU9fUWPCUJ0QkZJyDLVt8L3OMdTQURnnCU54QkZDJzfXtiTp1KlWe0HWsRUTy1a4d6QhERCRamcVUD7OIiIRZfLxfZ6mSif3ueBERERERERERCTsVlUREREREREREpMRUVBIRERERERERkRJTUUlEREREREREREpMRSURERERERERESkxFZVERERERERERKTEVFQSEREREREREZESU1FJRERERERERERKzJxzkY6h1MxsI7CylA+vC2wKYTihpvhKL5pjA8VXVtEcXzTF1sw5Vy/SQURaGfNEaUXT70F50OuLbRX99UHFf42hen3KE4QlT8Tq76PiDi/FHT6xGDNEJu5i5YmYLiqVhZktcM6lRzqOoii+0ovm2EDxlVU0xxfNsUn4VPTfA72+2FbRXx9U/NdY0V9fRROrPy/FHV6KO3xiMWaI7rg1/U1EREREREREREpMRSURERERERERESmxylxUGhPpAI5A8ZVeNMcGiq+sojm+aI5Nwqei/x7o9cW2iv76oOK/xor++iqaWP15Ke7wUtzhE4sxQxTHXWnXVBIRERERERERkdKrzCOVRERERERERESklFRUEhERERERERGREqs0RSUzO8rM3jezjOBr7SKOa2pm08xsqZktMbPmURZfrpl9EWwTwxFbSeILjq1hZmvM7PFoic3MmpnZwuB9W2xm14YjthLE19nM5gaxLTKzC6IpvuC4KWb2k5lNCkNMQ8xsmZktN7PbC9mfbGavBfs/DdffaQni62dmn5tZjpkND2dsEn4l+Bt6MPgbX2pmj5qZhTvW0oj2/FlW0ZzfQiHac1BpRXueCIVivMbfBX9ri8xshpk1i0ScciAzOy/4W8ozsyIv/32kn2+4xWoui9UcFau5J5ZySqzmiVj87K80RSXgdmCGc64NMCO4XZixwEPOuWOB44ENURbfHudc52AbFqbYoPjxAdwLfBCWqLzixLYO6OWc6wz0AG43s0ZRFN9u4DLnXAdgCPCImdWKovgAHgIuLe9gzCweeAIYCrQHRphZ+4MO+xWw1TnXGvgn8EB5x1XC+FYBVwAvhysuiagj/g2ZWS+gN3Ac0BHoDpwYziDLINrzZ1lFc34LhWjPQSUW7XkiFIr5Gv8HpDvnjgPeAB4Mb5RShK+Bc4APizqgmD/fcIvVXBarOSpWc09M5JRYzROx+tlfmYpKZwIvBN+/AJx18AHBDyzBOfc+gHNup3Nud7TEF2HFis/MugENgGlhiguKEZtzLss5ty+4mUx4f/eLE9+3zrmM4Pu1+ERXL1riC+KaAewIQzzHA8udc98757KAV4MYCyoY8xvAwDD2lB0xPufcCufcIiAvTDFJZBXnb8gBKUAS/jMoEVgflujKLtrzZ1lFc34LhWjPQaUR7XkiFIqTa2YV+DubB6SFOUYphHNuqXNu2REOK87vcLjFai6L1RwVq7knVnJKrOaJmPzsr0xFpQbOuXUAwdf6hRzTFvjJzN40s/+Z2UNBtTBa4gNIMbMFZjbPzMJZeDpifGYWB/wdGB3GuIoVG4CZNTGzRcBq4IHgQy5q4stnZsfjk/V3YYgNShhfGDTG/4zyZQb3FXqMcy4H2AbUCUt0xYtPKpcj/g055+YCs/CjJtcBU51zS8MaZelFe/4sq2jOb6EQ7TmoNKI9T4RCSXPNr4D3yjUiCaVo/F8iVnNZrOaoWM09sZJTYjVPxORnf0KkAwglM5sOHF3IrjuKeYoEoC/QBT995TX8FJZnoyQ+gKbOubVm1hKYaWZfOedC8kcagviuAyY751aHusgbivfOObcaOC6Y9jbBzN5wzoWkdyVEP1vMrCHwInC5cy5ko1xCFV+YFPbL40pxTHmJ5HNLhJT1b8jMWgPHsr836X0z6+ecK3J6RDhFe/4sq2jOb6EQ7TmoHER7ngiFYsdvZpcA6UR+GlKlcbi/Oefc28U5RSH3lfvvZ6zmsljNUbGaeypITonVPBGTn/0VqqjknBtU1D4zW29mDZ1z64Jf8MLm0WYC/3POfR88ZgJwAiH6wAlBfPlDCHHOfW9ms/EfjiEpKoUgvp5AXzO7DqgGJJnZTudcmRcfDMV7V+Bca81sMT65vFHW2EIVn5nVAN4F7nTOzQtFXKGML4wygSYFbqcBB48qyz8m08wSgJrAlvCEV6z4pIIJwd/Q2cA859zO4DHv4fNLVBSVoj1/llU057dQiPYcVA6iPU+EQrFyjZkNwjf0TiwwzV/K2eH+5oopIv9LxGoui9UcFau5p4LklFjNEzH52V+Zpr9NBC4Pvr8cKKwXYT5Q28zy53wOAJaEITYoRnxmVtvMkoPv6+IXyoua+JxzFzvnmjrnmgO3AGPD9A93cd67NDNLDb6vjX/vjjTfPZzxJQFv4d+z18MUV77i/G2E03ygjZm1CN6XC/ExFlQw5uHATOdcuHoWihOfVC7F+RtaBZxoZglmlojvVYr0lIHiivb8WVbRnN9CIdpzUGlEe54IhSO+RjPrAjwFDHPORbpDSEomGv+XiNVcFqs5KlZzT6zklFjNE7H52e+cqxQbfn7kDCAj+HpUcH868EyB4wYDi4CvgP8ASdESH9AriOvL4Ouvou39K3D8FcDj0RJbgZ/rl8HXa6LpvQMuAbKBLwpsnaMlvuD2R8BGYA++in5KOcZ0KvAtfhTeHcF99+A/PMEvEvk6sBz4DGgZrp9nMePrHrxHu4DNwOJwxqctvFsx/8bj8f8ALMX/I/uPSMcdytcX3I5I/gzX6ytwfNjyW7heXyRzUBleV1TniTC9xun4RZLzf2YTIx2zNgd+NE8msC/4+UwN7m+En8pU5M83wnHHZC6L1RwVq7knlnJKrOaJWPzstyAwERERERERERGRYqtM099ERERERERERCREVFQSEREREREREZESU1FJRERERERERERKTEUlEREREREREREpMRWVRERERERERESkxFRUkqhlZivMbFCk4xARkeikPCEiIkVRjhAJDxWVRERERERERESkxFRUEhERERERERGRElNRSWKCmSWb2SNmtjbYHjGz5AL7bzWzdcG+q8zMmVnrIs4128z+YmZzzGynmb1jZnXMbJyZbTez+WbWvMDxvYL7tgVfex10rnvN7BMz22Fm08ysboH9JwTP85OZfWlmJwX3n2dmCw+K62YzmxB8/x8ze8LM3g3O+6mZtSpwbDsze9/MtpjZMjM7v8C+U81sSfC4NWZ2S3B/XTObFMSyxcw+MjN9BohIhaA8oTwhIlIU5QjlCClHzjlt2qJyA1YAg4Lv7wHmAfWBesAc4N5g3xDgR6ADUAV4EXBA6yLOOxtYDrQCagJLgG+BQUACMBZ4Pjj2KGArcGmwb0Rwu06Bc30HtAVSg9t/C/Y1BjYDp+ILuIOD2/WAZGALcGyBuP4HnBt8/59g//HB844DXg32VQVWA1cG+7oCm4AOwf51QN/g+9pA1+D7+4F/A4nB1hewSP+ctWnTpq20m/KE8oQ2bdq0FbUpRyhHaAvPpsqixIqLgXuccxuccxuBu/EfzgDn4z+4Fzvndgf7juR559x3zrltwHvAd8656c65HOB1oEtw3GlAhnPuRedcjnPuFeAb4IyDzvWtc24P8F+gc3D/JcBk59xk51yec+59YAFwqnNuH/BacAxm1gFoDkwqcN43nXOfBTGNK3De04EVzrnng5g+B8YDw4P92UB7M6vhnNsa7M+/vyHQzDmX7Zz7yDnnivFeiYjEAuUJ5QkRkaIoRyhHSDlRUUliRSNgZYHbK4P78vetLrCv4PdFWV/g+z2F3K5WxPPmP3fjArd/LPD97gKPbQacFwwR/cnMfgL64D+MAV4ALjIzwye1/wYJojjn7XHQeS8Gjg72n4vv0VhpZh+YWc/g/ofwvSrTzOx7M7sdEZGKQ3lCeUJEpCjKEcoRUk4SIh2ASDGtxX8ALg5uNw3uAz9EM63AsU3K4XkLagpMKcZjVwMvOueuLmync26emWXhh45eFGzFsRr4wDk3uIjzzgfONLNE4Hp8j0cT59wO4Gbg5qA3Y5aZzXfOzSjm84qIRDPliQPPqzwhIrKfcsSB51WOkJDRSCWJFa8Ad5pZvWDxuruAl4J9/wWuNLNjzaxKsC9UJgNtzewiM0swswuA9hw4tLQoLwFnmNkpZhZvZilmdpKZFUxaY4HHgRzn3MfFjGlSENOlZpYYbN2D159kZhebWU3nXDawHcgFMLPTzax10JuRf39uMZ9TRCTaKU/spzwhInIg5Yj9lCMkpFRUkljxF/wc4kXAV8DnwX04594DHgVm4Ydkzg0es+/Q05SMc24zft7xzfiF8W4FTnfObSrGY1cDZwJ/ADbiewVGc+Df3YtAx+BrcWPaAZwMXIjv/fgReAC/YB/44a8rzGw7cC3BXGugDTAd2Il/j/7lnJtd3OcVEYlyyhP7z6s8ISJyIOWI/edVjpCQMq2tJRWNmR0LfA0kBwvTRS0zSwU24K+qkBHpeEREKgPlCRERKYpyhEjJaKSSVAhmdnYwXLM2vtL+TrQngcBIYL6SgIhI+VKeEBGRoihHiJSeFuqWiuLXwH/w83o/AK4zs50F9lfBD2HNn/f7a+fcuOKc2Mw6An8HugF1nHMWioDNbAVgwFmhOJ+IiOxXSA5w7O9MWwAMK+Z5SpQDzMzhr7KTPxT8VefcVSUIveC5VqA8ISISDgXbEjWAfUEeKUsb4nJgFH7a2HbgZeAPoSpWKUdItND0N6kUgg/dq5xz00vx2GPwl+/cBEwIVVGpLMwsIUZ6T0REIi6cOSAoKrVxzi0vTayhFiyoas65vEjHIiISa8qYP0bip9F9CtQDJgKvO+f+FtIgSxaT2hAScpr+JnIEzrllzrln2X8J0iKZ2RNm9veD7nvHzG4Mvm9kZuPNbKOZ/WBmowocd7yZzTWzn8xsnZk9bmZJBfY7M/uNmWUAGuIqIhIGJckBJWFm55nZwoPuu9nMJgTfJ5vZw2a2yszWm9m/g7UzMKbCiSgAACAASURBVLPaZjYpyCVbg+/TCpxntpndZ2af4EdNtTSzK8zsezPbEeSfi0P5ekRE5EDOuSedcx8557Kcc2uAcUDvwo4NZxvCvH+a2QYz22Zmi4JRuSKloqKSVFrBpT1/OszWtBSnfQEYYWZxwXPUBQYCrwT3vQN8CTQO7r/RzE4JHpsL3ATUBXoG+6876PxnAT3wlyIVEZFSKqcckO9DM/vRzN40s+ZFHDMRaBEsCJvvEvZfwecBoC3QGWiNzxv5l7mOA54HmgFNgT34S0oXdClwDVAdf9WgR4GhzrnqQC/gi9K+OBGRyqwM+aMfRXdQhLMNcXIQS1ugFnAB/sp0IqWiopJUWs65l51ztQ6zrSrFOT8DtuE/zMFfqnO2c2490B2o55y7J+ix+B54OjgG59xC59w851yOc24F8BRw4kFPcb9zbotzbk/pXrWIiED55IDAiUBzoB3+Us2TzOyQNSydc/uA1wgu1WxmHYLHTQqmrF0N3BR85u8A/sr+fLHZOTfeObc72Hcfh+aL/zjnFgfTHHKAPKCjmaU659Y550I68kpEpLIoTf4wsyuBdODhIs4ZzjZENr7DoR1+evRS59y6sr0rUpmpqCQSei8QNBI4sNe5GdCoYE8G8AegAYCZtQ2mMPxoZtvxDYi6B517dfmHLyIipeWc+zD4p/8n4LdAC+DYIg5/AbgoKCJdCvw3KDbVwy8Ou7BAvpgS3I+ZVTGzp8xsZZAvPgRqmVl8gXP/nC+cc7vwPdHXAuvM7F0zaxfK1y0iIoUzs7OAv+FHi246zKFhaUM452biR7c+Aaw3szFmVqNsr1IqMxWVpNIys4vNbOdhttJOfXgJONPMfoFvSEwI7l8N/HBQT0Z159ypwf4ngW/wC7zWwCeLgxeE1cr6IiIhUI454GCOQz/L/Q7n5gFZQF/gIvY3IDbhp7R1KJAvajrnqgX7bwaOAXoE+aJf/ss66HkLPtdU59xgoCE+1zxd5lcmIlIJlSR/mNkQ/OftGc65r45w6rC1IZxzjzrnugEd8NPgRpf4jRAJqKgklZZzbpxzrtphtlXgr5xjZilAUnA7xcySD3PeTGA+vnEwvsBUtc+A7WZ2m5mlmlm8mXU0s+7B/ur4y43uDHqQR5bPKxcRkfLIAWbWwcw6B5/v1YC/A2uApYcJZSy+xzjHOfdxEFsevhHyTzOrH5y7cYH1M6rji04/mdlRwJ8O91rNrIGZDTOzqvhLY+9k/+WxRUSkBEqQPwbgF+c+N5jedqTzhqUNYWbdzayHmSUCu4C9KCdIGaioJHJkzfD/vOevP7EHWHaEx7wAdGJ/rzPOuVzgDPyiqz/ge6KfAWoGh9yC76negW9MvBaa8EVEpAwOmwPM7D0z+0NwswH+s3s78D1+jaTTnXPZhzn/i0BHCuSLwG3AcmBeMJ1hOn50EsAjQCo+j8zDT407nDj86Ka1wBb8WhsHL+IqIiKh9Uf8//mTC4xieu8IjwlHG6JGcNxWYCV+ke5C13oSKQ5zTrNpRELNzPrhh7A2D3qcRUREDmFmqcAGoKtzLiPS8YiISOSoDSGxSCOVREIsGEr6W+AZJQMRETmCkcB8FZRERCo3tSEkVqmoJBJCZnYs8BN+IdRHIhyOiIhEMTNbgW9A3BzhUERigpkNMbNlZrbczG4vZP8/zeyLYPs2uEqWSNRTG0Jimaa/iYhIVAiukPJ/QDy+l+5vB+3/J9A/uFkFqO+cqxXeKEVEJBLMLB74FhgM5C9oPMI5t6SI428Aujjnfhm+KEVEKp+ESAcgIiISNBaeoEBjwcwmFmwsOOduKnD8DUCXsAcqIiKRcjyw3Dn3PYCZvQqcCRRaVAJGcIQrI4qISNnFdFGpbt26rnnz5pEOQ0Qk6ixcuHCTc65epOMogXJpLChPiIgULgbzRGNgdYHbmUCPwg40s2ZAC2BmEfuvAa4BqFq1ard27dqFNlIRkQqguHkipotKzZs3Z8GCBZEOQ0Qk6pjZykjHUELl0lho2rSp8oSISCFiME9YIfcVtY7HhcAbwaXYD32Qc2OAMQDp6elOeUJE5FDFzRNaqFtERKJBSBsLzrl051x6vXqx1AkvIiKHkQk0KXA7DVhbxLEXAq+Ue0QiIqKikoiIRAU1FkRE5HDmA23MrIWZJeFzwcSDDzKzY4DawNwwxyciUimpqCQiItFAjQURESmScy4HuB6YCiwF/uucW2xm95jZsAKHjgBedbrEtYhIWMT0mkoiUvlkZ2eTmZnJ3r17Ix1KVEhJSSEtLY3ExMRIh1ImzrkcM8tvLMQDz+U3FoAFzrn8ApMaCyJyWMoTB6ooeQLAOTcZmHzQfXcddPvP4YxJRGKLcsShyponVFQSkZiSmZlJ9erVad68OWaFLcNTeTjn2Lx5M5mZmbRo0SLS4ZSZGgsiEgrKE/tVtDwhIlJWyhEHCkWe0PQ3EYkpe/fupU6dOkoCgJlRp04d9bSIiBSgPLGf8oSIyIGUIw4UijyhopKIxBwlgf30XoiIHEqfjfvpvRAROZA+Fw9U1vejkhaVsoNNRESkENl7QMs2iYhIkfZEOgARkahQCYtKefh1Xi8GciIci4hUJps3b6Z///5Uq1aN66+//oB9CxcupFOnTrRu3ZpRo0ahdagjKHsPjD8ZZv9OhSURCRvliFiyCegGPBLpQESkEonWPFEJi0pxQE/gdeAiVFgSkXBJSUnh3nvv5eGHHz5k38iRIxkzZgwZGRlkZGQwZcqUCEQoACSkQINu8Pkj8OFtKiyJSFgoR8SSWrCrJbibgKcjHYyIVBLRmicq59Xf3O8ABzYaMGAclfWtEIllN94IX3wR2nN27gyPHKHjccWKFQwdOpQ+ffowZ84cGjduzNtvv01qauphH1e1alX69OnD8uXLD7h/3bp1bN++nZ49ewJw2WWXMWHCBIYOHVqm1yKlZAYn/RNys2HBQxCfCL3/4u8XkZgSiTyhHFEJ7NoEL34O7ZrDideAVcV3VotILFFbIjQqXyXFOfj4DxCXCL0eBLsVX1h6icr4dohI6WRkZPDKK6/w9NNPc/755zN+/HjWrVvHuHHjDjm2X79+PProo0Wea82aNaSlpf18Oy0tjTVr1pRL3FJMZjDwMcjLhk//CnFJ0OtPkY5KRGKEckQFV6UBtD0PFj4Kic2g92VAKnB2pCMTkRhRkfJEJayiONizCb56Btwd0PsBsNvwhaUXqZRviUiMOlIvQHlq0aIFnTt3BqBbt26sWLGCO++8k9GjR5f4XIXNedZVKaKAxcHgf0NeDsz9sx+x1OMPkY5KREogUnlCOaKCM4P+j0DOHpj3NCQ0gR4XABOBIZGOTkSKSW2J0Kh8FRSLg8FPAQaf3gfuduiTX1gCFZZEpDiSk5N//j4+Pp49e/bw0EMPlap3IS0tjczMzJ9vZ2Zm0qhRo9AGLKVjcXDy0+By4OM7/CjX7iVP9iJSuShHVHzbbDs1Bz3pC0sfvwQJjaDb2cAU4MRIhyciUa4i5YnKWT3J7322OPjsb+Buhb5/A7sdP2JpLJX1rRGR0hs9enSpehcaNmxI9erVmTdvHj169GDs2LHccMMN5RChlEpcPJzyvF9j6cNbIS4But0U6ahEJMYoR1QcG9jACZzAlXFX8schz/vC0uzxkHA0/OJ0YAZwfKTDFJEYE6t5olJWTvYAcRZH8qB/+cLS/AfB3QL97gf7Pb6w9AKV9O0RkXLUvHlztm/fTlZWFhMmTGDatGm0b9+eJ598kiuuuII9e/YwdOhQLcAabeLi4dQX/Yil2b/zI5a6XH/kx4mIlIByRGyovq8ueXP7cddJd5EYl8jtp70ME8+B6ZMhoS50OAWYDfwiwpGKSEUTjXmi0lVN8oBz8WWj8RZHysAnwOJhwcP+sqAn/hUsf80MFZZE5FDNmzfn66+//vn2LbfcUuzHrlixotD709PTDzinRKG4BDj1Zb/G0swbfGHpF7+OdFQiEmWUIyq+vbvjiB/3LHGbsvn98N+TGJ/IzWe8AW+dAVNnQkItOGYw8CHQLtLhikiUqWh5Ii4izxpBcfjrMrwHnAHsNoMBj0KXUbDwnzB7Pbi/Ai8DlwO5EYxWRESiSnwinP4atDwdpl8LXz0b6YhERCTM9tWGtWPiSW7xAnFvn8ct3MJjCU/DWROgUS+YvB2+ywIGAT9EOlwRkXIVtqKSmQ0xs2VmttzMbi/imPPNbImZLTazl8srlhE74aksmAmcBuzMv4JD1xvh8/+DWevA3YcKSyIicoj4JDjjDWg+BKZdDYtfiHREIiISRkcD/zHY1zWB5EbjiJt0NqMYxb8TX4Sz34X6XeCd3bBiGzAQCN+lvUVEwi0sRSUziweeAIYC7YERZtb+oGPaAL8HejvnOgA3lkcseXlw1lnw6lB4Zi98BJwCbDODk/4B3X4H/3sMZq4F9xdgHHAFKiyJiMjPEpJh2JvQdCBMuRKWlls/iIiIRKELgJcM9qUnklzvVeKnns5IRvJs8utwzhQ4qj28nQ2ZP+JHLG2IcMQiIuUjXCOVjgeWO+e+d85lAa8CZx50zNXAE865rQDOuXL55I2LgyuugNmz4dlB8Pxu+AwYDGw1gxMfhvTR8MUTMGMNuHuAl1BhSUREDpCYCme9DU1OhPcuhW9ei3REIiISRiOAsQb7jk8iscYbxM8YwtXuasamToLh06BGS3gTWPcDcDKwNbIBi4iUg3AVlRoDqwvczgzuK6gt0NbMPjGzeWY2pLATmdk1ZrbAzBZs3LixVMFccgm89hp8+ik8eiL8Zwd8iR+cuskM+j0A3W+DL5+E6SosiYhIERKrwFnvQKPeMPli+PaNSEckIiJhdDHBVLgTkklMeZO4jwZwpbuSV6rMgPOmQ9VGMD4R1i8GhgDbIxyxiEhohauoZIXc5w66nQC0AU7CF/6fMbNahzzIuTHOuXTnXHq9evVKHdDw4fDWW7BoETzYB174CZYAA4ANZtD3fjj+97DoKXh/lQpLIiJSuKRqcM670PAEeHcEZLwV6YhERCSMLgWeM9jXK5UEm4jN7cul7lJer/YJnDcDko6CN1Jh00L8iq67IhyxiEjohKuolAk0KXA7DVhbyDFvO+eynXM/AMvwRaZyc/rpMGkSZGTA3T3hhU2wHF/VWmcGfe6DE+6Er56BaStUWBKRMnn//ffp1q0bnTp1olu3bsycOfPnfQsXLqRTp060bt2aUaNG4dzBdXeJaknV4ZzJ0KA7TDoflr8d6YhEJMYoR8S2K4BnDPb1rUJCziSYfwIXuYuYUON/vrAUXw1erwZbPsGvArInsgGLSMyJ1jwRrqLSfKCNmbUwsyTgQmDiQcdMAPoDmFld/HS478s7sMGDYcoUyMyEO06A53+EVcCJQKYZ9LoHTrgLvn4Opn4HeSosiUjp1K1bl3feeYevvvqKF154gUsvvfTnfSNHjmTMmDFkZGSQkZHBlClTIhiplEpyDTj3PajfFd45D76bFOmIRCSGKEfEvl8CY4B9/aqRsGsy/K8b5+Wdz7u1l8F5M4FkeL0m/DQDGA5kRTReEYkt0ZonEsLxJM65HDO7HpgKxAPPOecWm9k9wALn3MRg38lmtgRfrRntnNscjvj69YPp02HIELi1Bzz3CVydBv2AmWY07303xMXDnD+BuxROuRfi/oifwfdC8JJEJNxuBL4I8Tk7A48c4ZgVK1YwdOhQ+vTpw5w5c2jcuDFvv/02qamph31cly5dfv6+Q4cO7N27l3379rFlyxa2b99Oz549AbjsssuYMGECQ4cOLeOrkbBLrgnnToU3BsM758Kwt6DlqZGOSqTSikSeUI6o3K4G8oBr+9cgaeYU8r4axNmdzuGdOhM55bwZ8NpJ8N+j4MLJUGME8BphapKJyEHUlgiNcI1Uwjk32TnX1jnXyjl3X3DfXUFBCef9zjnX3jnXyTn3arhiA+jRA2bOhF274Mbj4ekf/PUZTgS+A+h5F/T+Cyx5Ed5bGoxYGgdcjkYsiVQ+GRkZ/OY3v2Hx4sXUqlWL8ePH89BDD9G5c+dDtlGjRh3y+PHjx9OlSxeSk5NZs2YNaWlpP+9LS0tjzZo14Xw5EkoptfxVf+p0hInnwIqpkY5IRMJMOaJy+zXwBJA1oBZxG6fhlrTnzLyzmFF3PQx/H7Ly4L91YMebwGWoLSFS+VSkPKGyeAFdusAHH8DAgXB9D/j3x/CbtsGIJeCYE+4Ai4ePfw/uAjj1Hoi7K3i0RiyJhNuRegHKU4sWLejcuTMA3bp1Y8WKFdx5552MHj36iI9dvHgxt912G9OmTQModM6zWWHXN5Bw+ZYcGpFAtdKeIKW2bzi8PhAmnOmvENd8cChDFJFiiFSeUI6Q6/Ajlm4YdBRJU98nN6E/p7c9g8kN3qX/uVPhjUHwel244BWomgI8Qxj7+0UEtSVCRZ9cB+nQAT78EJKTYeQJ8NjXkIMfsbQYoMft0O9BWPYavLsIcjViSaQySk5O/vn7+Ph4cnJyitW7kJmZydlnn83YsWNp1aoV4HsTMjMzDzimUaNG4XsxcoA9ZLGW03mNP7P1kAuVlkDqUf5y0kcdA28Pg5UzQhekiEQ15QgBuB7faM06pS62YgY5y1twWt7pfNBwD5zzHuzcA6/Xg93PAzdw6MWxRaSiqkh5QiOVCtG2rS8sDRwIv+4F/5oFt3bzV4V7H+jcfTTEJcDs30FeLpx+D8TfhU8EY9GIJZHKafTo0YftXfjpp5847bTTuP/+++ndu/fP9zds2JDq1aszb948evTowdixY7nhhhvCEbIUIpU4mtOIk7ibcfzEIP5Bg9L2waTWgeHT4fUBMOEMOPtdaNo/tAGLSExQjqicfosfsfS7IfVJfG8m2dafU1udxpTG79H37Enw5lB4ox6c9y9ITQUeAjQSTaQyitU8oZFKRWjRwheWGjaEX/eDv82BVGAA/lJ2dLsJ+j8Cy9+CdxYGI5ZeRvOiRaQojz/+OMuXL+fee+/9uedhw4YNADz55JNcddVVtG7dmlatWmkB1ohKYO2cZ1i290Yu5v/4mF+xipzSn65KPX856Zot4K3TYfUHoQtVRCoM5YiK6ybgYSB7aANs+UyyfkhjaN6pzGmSBGe+DVu2wfj6sO/vwF1HOJuIVFbRmiessPl3sSI9Pd0tWLCgXJ9j/XoYNAgyMuBfk+EvA2Az8B7QC+B/T8DM66Hl6XDG8ZBwF3ARGrEkUj6WLl3KscceG+kwokph74mZLXTOpUcopKhRmjyxZw+0bAlVqzre/vweOtT4M5M5lzaMow3JRz5BUXath//2hx2r4JwpkNan9OcSkSIpTxxKeaJo4WhP5HsYGA0kvrcW1+4kUpr9yPtx0zjhu03+wg4NasPwDZB0H/CHsMQkUtkoRxSuLHmiUo5UWstaNrO5WMc2aACzZ0PHjnDtELhtMjQATgY+AOjyGxj0JHw/CSbOg5y72T9iqQw92yIiEhGpqfDWW7Bli3HysX/iy03/5FTGk8kwvmJX6U9ctQGcPxOqpfnpDmvmhC5oEZFKwMyGmNkyM1tuZrcXccz5ZrbEzBab2cvhjvFwbsFPbsse2gi+mcXeVfU5Oe8UPmtVH05/DX7cDG/Vh+w7iOwSwiIixVfpikp55HEmZ9KXvqxmdbEeU6cOzJgB3bvDdWfAqPHQFBgKTAf4xbUw+Cn4YTK8PReyC06FU2FJRKQ4oqmx8MkJf+fhhbNwDgYccyPzVz9LP6azi1P4jJ9Kf+KqRweFpYbw5hBYOzd0QYuIVGBmFg88gf8XvD0wwszaH3RMG+D3QG/nXAfgxrAHegT5haWcoY1h6Sz2rK7LoLyTWdCmCZw6DtZsggn1IPsm4N8RjlZE5MgqXVEpjjge4iHWsIZe9GIpS4v1uJo1YepUOOkkGHUe/HIstAFOByYDHHcNnPwsrJgKb38UFJZeAS5FhSURkcOLpsbCHvbwHM8xssUQ/vj5W9SqBf2P/SWfLH2NrnxGMv35gA2lf4JqjeC8Wb7ANP4UWDsvdMGLiFRcxwPLnXPfO+eygFeBMw865mrgCefcVgDnXBk+rMvPLcCDQM7QJrBkFrvX1GZg3mA+b9cGTnkeVm2CifUgZyTwXISjFRE5vEpXVAI4iZP4gA/IJps+9GEexfuHvlo1mDQJhg6F0ZfDBU9BR+AsYAJAp1/CkOdh5XSYMBuy/4LPd5egwpKIyGFFTWMhlVQ+5EO60pXrjx7OdQufpWVLGNx5ODM/m0hblnE0/ZhazNGuhare2BeWqjTwhaV1n4buBYiIVEyN4YAP3szgvoLaAm3N7BMzm2dmQwo7kZldY2YLzGzBxo0byyncwxtNfmGpKfb1LHatq8GA3EF80eE4GDwGVmyESfUg91fASxGJUUSkOCplUQmgM52ZwxxqU5uBDOQ93ivW4/LX2jjnHLjjWhj6d+gGDAdeA+hwOQwdC6tnw1vTIesvwZ6LUWFJRKRIUdVYqEMdpjOdwQzmllpXcc68B+jazXFazyG8O3UajVlHO/owgYxSnR/whaXzZ/mrw71xsgpLIiKHZ4Xcd/AVhxLwkwlOAkYAz5hZrUMe5NwY51y6cy69Xr16IQ+0uPYXlprDolnsXF+NAbmDWHTc8TDgcfhuI7xbD3IvI2hpiIhEnUpbVAJoSUs+4ROO4RiGMYyXitkLkJQEr70GF18Mf7kF+twDvZy/5tuLAO0vgaEvQeaH8ObUoLD0X/wR2eX2ekREYljUNRaqUpWJTGQEI7i7yu0c/8FoBg7O47whfXjtlVnUZDcn0JfXWFTq56B6mh+xlFo3KCx9VvpziYhUbJlAkwK304C1hRzztnMu2zn3A7AMnzei1mjgASB3aEv4chbbN6bQP3cgX3c5Efo/AhkbYXIdyLsIeCvC0YqIHKpSF5UAGtCA2cymL325lEv5B/8o1uMSEuCFF+Cqq+DhP8Fxt0N/B5cDzwAcOwJOewXWzoHxk2HffcDrqLAkUnmtWLGC1NRUOnfuTOfOnbn22mt/3rdw4UI6depE69atGTVqFM4dXE+p8KKysZBEEi/xEtdzPY8m/p2j3/0l516YzVUXdeWZxz4iwSVwMifyAvMOqYAVW40mcP5sX1gafzL8OD+Er0BEYoVyxBHNB9qYWQszSwIuBCYedMwEoD+AmdXFj3D9PqxRlsKt5BeWWsEXs9i2KYmTcgewuOsg6PcQfLsJ3qsNeecDkyIcrYhESrTmiUpfVAKoQQ0mM5nhDOdmbuY2bsMVo3kQHw9jxsCoUfDEg9B8FJzigkU/AI45P7g86GcwfmJQWHoD38GuwpJIZdSqVSu++OILvvjiC/797/1XdRk5ciRjxowhIyODjIwMpkyZEsEoIyJqGwtxxPEoj3I3d/Ni/AvsffkcrrxuD6NHtePhP37MPleXcxnE08woY2FpFqQcBW8Mhh8XhPIliEiMUI4omnMuB7gemAosBf7rnFtsZveY2bDgsKnAZjNbAswCRjvnNkcm4pL5ubA0pA18PpOftsRzUu4AlnQ/Ffr8Fb7ZDFNrQN45wLQIRysikRKNeSIhbM8U5VJI4VVe5QZu4EEeZCMbGcMYEo7wFpnBI49A1apw//1w0S4Y9gxcHwd7gZvbngtxb8A758HrufD/7N13XJbV+8Dxz+ERcO+FYoriwImrEgVUREBcIGpuK7NMM9NMK7OhlmmW+c00K3OWC7cMUWaQ5UgrXJR7b83UZJzfHzf5s2Q8IvAwrvf39byEh3Of5zq9/HJ5X/cZAdOg6JsY90srAOtcGJ0QBdMYxrCXvdnapzPOzGZ2hm2OHTuGj48P7dq1Iy4ujurVq7NhwwaKFSuWpc88e/YsN27coE2bNgAMHjyY9evX4+Pjk6X+8iOtdZJS6p+bBROw8J+bBWCX1npj6s86p94sJJOLNwsKxWQmU4lKjFQjaftZZ8ZU3sSH79TiyqUYJs/rzBDVhfmsYDh+mLLyIaUfMwpLK9sbhaXe26BKy2weiRCFiyXyhOSInKO1DiL14OX73pt839caGJv6yndew1j3PdGnPqbgCK626kD78h2JeiICp5REiHsbrMpD5+6ggkl9ziKEyCK5l8geMlPpPiZMzGUu7/AO3/AN/vhzi1uZXqcUvP8+TJ0K334DRfpDQLJxXOhUAMce0H0tXPoFVq+B29OAtUBf4G6OjkkIkTMSEhIYOXIk8fHxlC1blsDAQGbOnHlvOur9r9GjR9+77ujRozRv3hx3d3diYmIAOH36NPb29vfa2Nvbc/r06Vwfk6VprYO01vW01nW01tNS35ucWlBCG8ZqrRtqrZtorVfkdowjGMEKVvCj+pHwt91587OzfPlFVcb0ieRcSguGE8AXLM76b/bSNaFvJNiWNQpL5/dkY/RCiNwiOUJk1QRgOpDs0wB2R3DlCrgnd+BAm97w5CT47QpsKw7aF/jewtEKIbKqIOUJman0HwrF27xNZSozkpF0pjOb2EQ5ymV67ZtvQvHiMHYs+NyC/uvgLZMxY2lKna6o7utgoz+sXmXMWCr+JkZhaSVgk8MjE6LgyewpQE5ycHDA2dkZgJYtW3Ls2DEmTZrE+PHj073Gzs6OEydOUKFCBXbv3k3Pnj2Jj49Pc82zUmntWy3ygj70oRzl8MOP70a2Y1rFrbzVvw7XOofxRag/L5qG8gXXGMTLFM/KB5SuacxYWtUe1nSCgG1QpUX2DkKIQsJSeUJyhHgUEzBOr5jg3QDT1kiuNG+Pe/kORLmE45ScCDs/BKsy0NEH1DbgCQtHLET+JPcS2UNmKqVjBCNYyUp2shM33DiNeZW+V16BefMgeBOc84ahiTAN42QHXbsL9NwAVw/B6hVwaxrGFiF9kBlLQuQvtra29742mUwkJSVl+nTB1taWChUqAEbyqFOnDocPH8be3p5Tp07d6+/UqVNUq1YtdwckHoonnkQQwXWuM6dvW6aH7CUmpiR9Ht/E4bv+PM8Yf35XZQAAIABJREFUVvAO17K6y1KZWsbm3daljMLS+Z+zM3whRA6THCEe1WvADCC5cwPYG8GVq+Ce0pGDrkOg5VjYex0iTaA7A7stHK0Q4mEVpDwhRaUM9KY3wQRznOO44MJBDpp13QsvGCfDRYbD4Q4w/G+YBbwEpNTygp6b4NrvsGo5/DUN2AD0RgpLQuRv48ePv7dx3v2vOXPmAHDx4kWSk5MBOHLkCAkJCdSuXRs7OztKlSrFjh070FqzZMkSevToYcmhCDO0pjXf8z022DDV050PYqM5dMgW30Yr2X/raZ7hXYIYwwVSsvYBZWoZS+GsSxqFpQvZu+ZfCJG7JEeIhzUe+AhI9nSCfeFcuaqNwpL7MGg+GvZch2hAewL7LBusEOKR5dc8IUWlTHSkI5FEcoc7tKMdP/GTWdcNHgwrV8JPP8KutjDqtnEi3HAguWYn8A+C68dg1VK4+T7GIUcBwN85NhYhhGVFR0fTtGlTmjVrRkBAAPPnz6d8+fIAzJs3j2HDhuHo6EidOnUK5Qas+VEDGhBLLNWpzhutOvP2ng1cvVoEjzpfs/faWPozh1ie5gRJWfuAMg6pM5ZKwGoPKSwJUYBJjhBpGQd8DCR7NIRfIrh8NQX3lI4c6jACmo2AXTcgNgm0BxBv4WiFEDkpr+YJldb6u/yiVatWeteu3Dl2+Xd+xwsvznGOQALxxtus67ZsgV69wLEueP0AH5eEAcAioMipGFjrAyWrQ++hUOoNwBcIBGwz6FWIwuvAgQM4OTlZOow8Ja3/Jkqp3VrrVhYKKc/IrTxxmcv44stOdvLumQXMf/xZbt7UhB6YxhN2bxFKDxxYQT2KZu0Drh0x9lhK/At6b4fKztkavxAFieSJB0meSF9u3k88ik+BMYApYj806UCFciairbZTP+xj+PUraFMSXEoAkUADi8YqRF4mOSJtj5InZKaSmRxxJJZY6lGPbnRjOcvNus7X1ygsHT0Cm1vCa9dgOdAPuGvvCr1C4a+zsOpruPE+sAXwx9jeWwghRH5QgQpsZzueePJWtWEM+HU6latAhzqTiPr9f3ixgXP4so8/s/YBZWvLjCUhhCjEXgb+ByR3aAjx4Vy6loRbigeHPcdBo6Hww03YcRPoCCRYNFYhROEiRaWHUJWqRBGFK64MZCCf8IlZ13l4wNatcO4crHSGNy7BGozFbneqt4VeW+HWRVj1JVyfBgQhhSUhhMhfSlCCjWykP/2ZUe51Ov48lgYNU+jkNIote5biQhSJdOIHLmftA/5bWJLNu4UQolAZBXwGJLs3QsVHcOlaEq4pHTnc+TVoOAhi/4IfbwAdgD8sG6wQotCQotJDKk1pgggigADGMpYJTECbcbpP27YQHg5//gmLmsHkc7AJ6AHcqvYk9N4Gd67Cyi/g2vtAMNATKSwJIUT+YYMNS1nKy7zMF8VnUzduMC7t79K15UDWbFtHE/ZRGnfCOZO1D7hXWCopp8IJIUQhNBL4HEh2a4Q6kDpjSXciwesNaNAfvv8LfrqKUVg6atlghRCFghSVsqAoRVnBCkYwghnM4GmeJpHETK9r2RKioiA5GeY1hXdOQhjGLko3q7Y29slIvAkrP4er7wNbMcpOt3N2QEIIIbKNFVZ8wie8z/ussllO0ZAedO37F/08u7HwuxBq6eM40JbNWX2KXLY29ImQwpIQQhRSI4D5QHK7xqiD4Vy8loir9iDBZxLUfwpibsHOSxhL4U5YNlghRIEnRaUsMmFiLnN5l3dZzGL88OMWtzK9rnFjiI4GW1uY3RTe+R1iAC/gepUW0Dscku/Aqs/gylSMspMUloQQIj9RKF7ndb7kS7aZtnLhOw/6jbrMi/3bM3tOBOX0n7SkHav5JWsfIIUlIYQo1J4HFgDJbRvDoXAuXr+La0onDnd5C+r1gejbsPscxoylU5YNVghRoElR6REoFJOZzHzmE0wwHnhw2Yy9MurVg5gYKF8eZjaHt/fDTqATcKVyM+NGISUJVv0PLk8FtgHdwYyilRBCiLxjGMMIJJB9ai9757gyfMpJJo1pxbtvxGCtTXTCnSXEmbGIOg3/KizJHktCCFHYPAd8Beg2jeFwBBevJ+Ka0olDXd6CegEQeQf2nMYoLGVx2bUQQmRCikrZ4HmeZzWr2cMeXHHlJCczvaZWLaOwVKMGfNAKJu2GXzF+5V+o2NjYMwNg5Wy4NBXYDnRDCktC5F+XL1+mQ4cOlCxZklGjRv3rZ7t376ZJkyY4OjoyevRotDbKDFeuXMHT05O6devi6enJ1atXLRG6eAQ96UkooZxWpwma5MLYLw8we7oTrzwTy21diQA68TUhWS8s9Y0E61JSWBIin5McIbLiWeArBfqJxqjfI7h0LRk33ZlDXd6Guv4Q8Tf8fAJjKdw5C0crhHgUeTVPSFEpm/jjb9w0cBoXXNjP/kyvqVbN2GOpXj2Y5gKvxxoHgLYHzlRwMgpLJmtY9QlcmAJEAl2Bv3JwJEKInFK0aFGmTJnCRx999MDPRowYwYIFC0hISCAhIYGQkBAApk+fjoeHBwkJCXh4eDB9+vTcDltkA3fciSKKRBJZNKwdE9fv4NslNRnW9XsupdRnMN35gpUkZaXzMg5SWBKiAJAcIbLqGeAbBSmPN0IdMwpLrtqTg75vQ50eEH4X9h4FPIALFo5WCJFVeTVPSFEpG7WnPdFEk0QS7WhHHHGZXlOpEkREQPPmMMUdXt0GJwE34ET5+tAnCkxFYfXHcP5dIApja28pLAkBYzDKsNn5GpPppx47dgwnJyeee+45GjVqROfOnbl9O/N9z0qUKEG7du0oWrTov94/e/YsN27coE2bNiilGDx4MOvXrwdgw4YNDBkyBIAhQ4bce1/kP844E0cc5SjHnB4evB4dzPZtlentEsnJpCcZTj++4gv+zkrnUlgSIh25nyckRwhLGAIsUaBbNkSdiODyVY2r7syBbu9C7W6w/S7sS8DYcOOShaMVIq+Qe4nsIEWlbNaMZsQRRwUq0IlObGZzpteUKwdhYeDqClM7w6gNxq96N+CPco7QN8rYM2P1LDj3DsbW3l2Amzk6FiFE+hISEhg5ciTx8fGULVuWwMBAZs6cibOz8wOv0aNHZ9jX6dOnsbe3v/e9vb09p0+fBuD8+fPY2dkBYGdnx4UL8oQxP6tNbWKJpR71+LBtd8b9vIwD+8vQpVEoh/725QVeYBkfcDMri+EeKCztyfb4hRDmkRwhLGEgsFwBzRuiTkdw5Sq4aS8OdHsPavvCtkT45QCpO7laNlghCrmClCeKZHuPAgcciCWWLnShJz35ki95mqczvKZUKQgKgl69YHpPGLccFvU3Ckvby9amwVPRsKoDrP4Ier0D1d4FfIAgoFTOD0qIPGm2xT7ZwcEBZ2dnAFq2bMmxY8eYNGkS48ePf+i+/lnzfD+l1CPHKPKmKlQhiih60pMPGg5izP7zLG8xDg+HtYQcfppnS77Bci7jw0zK85B/D/4pLK3qYJwKFxAGVVrmyDiEyB8skyckRwhLeQoooqBfMyf4NZIrdMC1nBfR3UJouDEFwoJBxUMTT4zDgMpZOGIhLEnuJbJDrs1UUkp5K6UOKaV+V0pNTOPnQ5VSF5VSe1Nfw3IrtpxQmcpEEEFHOvIMzzCd6ehMnjwXKwbr1oG/P8waAAO+hGTAHfi1dE1jKVzxShA4E05PBn7AKCzdyPkBCSH+xdbW9t7XJpOJpKSkLD9dsLe359Sp/z/u99SpU1SrVg2AKlWqcPbsWcCY2lq5cuUcGI3IbaUpTTDBBBDAbPtX6XZwPNa2JlyrL+GnS6MYwCwiGcaZrOyyVMbB2JPPpjSs7gTndmV7/EKIjEmOEJYUAKxSoJo0QJ2P5OoVE654Ed99CtTygq3J8Ns+oDNwzcLRClE4FaQ8kStFJaWUCZiLUQFpCPRTSjVMo+lKrbVz6uur3IgtJ5WiFJvZTD/68Tqv8wqvkEJKhtfY2sLKlTBwIMwZDj0+BmttrM7cU7qGUVgqYQeBM+DkW8AOwBspLAlheePHj2fv3r0PvObMmZPhdXZ2dpQqVYodO3agtWbJkiX06NEDgO7du7N48WIAFi9efO99kf/ZYssKVvAiL7Kw/Ec8Hv809g7JuFafQ+TxyfizkF/owxHuPHznZWoZS6dtyxozls7tzPb4hRAPR3KEyE1+QKAC1bA+6nIE164UwQ0f4ntMg5qdIDQF4n8GvIDrFo5WCAH5N0/k1kylx4HftdZHtNZ3gRVAoch6NtiwjGWMYQyf8ikDGchd7mZ4TZEisHgxPP88LBgHnaZAaW0cBPpDqerGE+hSj8HaD+H4JGAnkhCEyB9q1arF2LFjWbRoEfb29uzfb5wUOW/ePIYNG4ajoyN16tTBx8cHgIkTJxIWFkbdunUJCwtj4sQHJnqKfMyEic/4jPd4jzXFl1D9p540b3cLj9rvsvmX2XizjtP48ht/PnznpWsahaWi5Y0ZS2d/zP4BCCGyleQIkZ26A+utwKp+fdTVSK5dtsYVb37rMQ0e84CQFIjfjTygFiL/yIt5QqW1/i7bP0SpAMBbaz0s9ftBwBNa61H3tRkKfABcBA4Dr2itT6bR13BgOMBjjz3W8vjx4zkef3bQaGYwg4lMpBOdWMtaSmWyF5LW8Oqr8PHH0Gcc7JkJ5xRsBtxvXYDVHnDtd+jxGtR6H2gBhAJlc2FEQljGgQMHcHJysnQYeUpa/02UUru11q0sFFKe0apVK71rV/5Y/rWABYxgBC2TW1O6/xa2r6rAsvAl9G3/DPtUC5IJ4nEqPnzHN07CqvZw+xL0CoVqT2Z77ELkJZInHiR5In35KU9kVSjQPQWSjySgS3agTMW/idKbabL+TTgRDj4KGj6R2lL2ahUFm+SItD1KnsitmUpp7RL132rWJqCW1ropxq5xi9PqSGu9QGvdSmvdqlKlStkcZs5RKCYwgW/4hggi6EAHLpDxzutKwUcfweTJsGoWNHoRamhjDWFY8crQOwLK1Yf10+HIROBnwBO4mgsjEkIIkZ2GM5w1rOEX015OrWhHz9EnGNhxMJ+vXEtj/QulcCOCU5l39F+laxgzXItXgsDOcOaHbI9dCCFE3uUFbLGCIrXrov6K5PrForgpX37pORUe6wjBGvb/iHGXkYWZsUKIQi23ikqngBr3fW8PnLm/gdb6stb679RvvwQK5HE1QxnKBjawn/20pS1HOJJhe6Xg3XdhxgzYMB8eGwx1U6ArsKl4RegdDhWbwIYP4ffxwD6MwpIcEyqEEPmNH35sZSvn1Fl2znZh4AfxvNyvO9Nmh1JDn6I2bdnMoYfv+F5hqQqs6Qyn47I9diGEEHlXJyDICmwcHFF/R3LjYnHclC97e74Hj3VILSzFAV2AmxaOVgiRn+RWUWknUFcp5aCUssE47XLj/Q2UUnb3fdsdOJBLseU6X3zZznaucIW2tGUvezO9Zvx4mDsXQpdB+d7QJBn8gdXFykPANqjcHDbNgMOvAr9ipA4pLAkhRH7jhhvRRJOsktk8oR3PLYplylh3xr8WRWl9m8dxZTW7H77jUvZGYamkHQR6wenYbI9dCCFE3tURCLEC28fqoO5GcuNCSdyUL3vuFZaA/bGAL/CXZYMVQuQbuVJU0lonAaMwFuoeAFZpreOVUu8ppbqnNhutlIpXSu0DRgNDcyM2S2lDG77ne6yxxh13IojI9JoXX4RFiyB6PVh3gdZJRnVuWdGyELAVqrSGzTPg4CvAfsADuJyzAxFCCJHtmtKUOOKopCqxdEgnXgzexIKPmzN88PdoXRwvOrCYyAfWkWfqn8MeSlYzCkunYnIgeiGEEHmVG7DVCorVqI1VShQ3z5XFXfmyq+fbRmEpBNgfg7Eu4pZlgxVC5Au5NVMJrXWQ1rqe1rqO1npa6nuTtdYbU79+XWvdSGvdTGvdQWt9MLdisxQnnIgjDnvs8cabNazJ9JohQ2DlStgVDrfbQ9tEGAx8aVsGAkKhmgsEzYT9ozHqdx7ApZwdiBBCiGzngAOxxNKYxnzh7ccLPy1k46p6PNU5luspNeiLN1+znpSH7bhkNegTASXtYa0PnIrOifCFEELkUW2BMCsoXq0WVlaR/HW2Au1VN37q+RbYtzcKSweigG5IYUkIkZlcKyqJtNljTwwxtKY1fejDXOZmek1AAKxfDwd2weU20PGOcRzeHJtS0CsY7N0h+CP4bRRwCGOy68UcHokQQojsVolKRBCBBx583vJZBh14n10/VqNL8xhOJznzNL34im9IfNiO/ykslaoBgT5wMionwhdCCJFHPQmEW0HJKjWxso7k1pnKdFDd2eE3ySgsBQMHwjF2Jblt0ViFEHmbFJXygPKUJ4wwutGNUYziLd5CZ7KowdcXgoLg+EE41gK8b8HLwIfWJcBvM9TsBKEfwy8jgASMwlLGp80JIXLWsWPHKFasGM7Ozjg7O/PCCy/c+9nu3btp0qQJjo6OjB49Gq2N3wFXrlzB09OTunXr4unpydWrcrpjYVOSkmxiEwMYwNe138T3j9Gcv1CGjo7bOHSnE8N5hqV89PD/5C9pZxSWSteEtV3gRObLsIUQOUdyhMhtrYBIE5SuVANVLJLbp+zoqHoQ5/d66kNqBQe2Az2QwpIQlpdX84QUlfKIYhQjkECGMYypTGU4w0kiKcNrOnaEsDC4dAbiG0O3GzAReNu6OLrnRqjlBWGfwN7hwB9AB+B8LoxGCJGeOnXqsHfvXvbu3cv8+fPvvT9ixAgWLFhAQkICCQkJhISEADB9+nQ8PDxISEjAw8OD6dOnWyp0YUE22LCEJYxlLCsrfUarw/2wKmpNW7tN7LnRh2cYz1omcu1hd1kqUdUoLJWpBet84fj2HIlfCGEeyREZU0p5K6UOKaV+V0pNTOPnQ5VSF5VSe1NfwywRZ37iDESboHz56qhSkdw5aY+H8iPGb8J9haUwwA+4Y+FohRB5MU8UMaeRUupnYDHwndZaqhI5pAhFWMACqlKVqUzlAhf4ju8oTvF0r2nTBiIioHNn+MkJ/H6D98rB7SJF+bDHetSm3rB9DqSMhBbfYBSWwoGquTUsIXJOxBi4kPnpiQ+lsjN0mJ1hk2PHjuHj40O7du2Ii4ujevXqbNiwgWLFimXpI8+ePcuNGzdo06YNAIMHD2b9+vX4+PiwYcMGIiMjARgyZAjt27fnww8/zNLn5KTsyBNKKW/gU8AEfKW1nv6fnw8FZgKnU9/6TGv9VZaDzmessGIWs6hGNV4t9Spt912iRMd1uFT+lm1HyzPA7kPWchkX5lMVk/kdl6hiFJZWe8D6rtBjI9TyzLmBCJGbLJAnJEek7VHzhFLKBMwFPIFTwE6l1Eat9f7/NF2ptR71yAEXIo2BGBO0L2vHRRXB3yc86FyzFyF+q3FfBwRHAaHg5AesA4paNF4hso3cS2QLc2cqTcU4LOCIUipYKdVfKZW1EYsMKRRTmMJnfMYmNuGJJ1e4kuE1zZtDVBRYaYisC70vGHddLxWxJaX7GnD0g4i5sHMwcAJoD5zJ+cEIUYAlJCQwcuRI4uPjKVu2LIGBgcycOfPedNT7X6NHj7533dGjR2nevDnu7u7ExBgnb50+fRp7e/t7bezt7Tl92qibnD9/Hjs7OwDs7Oy4cCHPLmN9pDxx382CD9AQ6KeUaphG05Vaa+fUV6EpKN1vHONYylJ+tI1BRbvj7HMBt+qfE3xoEv58xV76cpS/H67T4pWhdziUqwfru8Gx0JwJXohCQnJEmh71fuJx4Het9RGt9V1gBca6LJENGmAUlqqWqYqqEMGdo7Xxsgog3G8sVHdLnbEUgsxYEiJ7FKQ8YdZMJa11IBColCoP9AFeBD5XSq0Flmmtw7M9skJuJCOpQhUGMABXXAkhhBrUSLd9w4YQEwMeHhBSF/rth7nV4Y7Jhi+6rsQUNACi50PKcHjiW4zCUgRQPZdGJEQOyOQpQE5ycHDA2dkZgJYtW3Ls2DEmTZrE+PHj073Gzs6OEydOUKFCBXbv3k3Pnj2Jj4+/t+b5fkqpHIs9J2RDnrh3swCglPrnZuG/T6AFMJCBVKQiAaYAKga60HFUKF0aTOHbuAr0a/MKMVzjNutoSCnzOy1eCQK2wxpPWN8DeqwDB5+cG4QQucFCeUJyxIOyIU9UB07e9/0p4Ik02vVSSrkBh4FXtNYn02gj0lCX1BlLJStz2iqcv490wqd2H4L8V+KxTqXOWAoBJ39gLTJjSeR7ci+RLR5qTyWt9RVgCTAfY8pLL2CBUuqwUqpTDsRXqAUQQAghnOIULriwP5N7qzp1jMKSXVVYXw8GHoGvgcEma5J8v4UG/eH7BRDXB/RZjMLSqVwYiRAFj62t7b2vTSYTSUlJmT5dsLW1pUKFCoCRPOrUqcPhw4ext7fn1Kn////iqVOnqFatGgBVqlTh7NmzgDG1tXLlyrk1xCx5hDyR1s1CWlXvXkqpX5RSa5RSaVbalVLDlVK7lFK7Ll4suCdfeuNNBBH8ZXWTfXPb0u29nfR3GcP/1i2hjY7kDh35iUsP12nxitB7O1RoCBt6wh+bcyZ4IQo4yRHpe4Q8kdYd0n/vpDYBtbTWTYFtGMvtHuyokOSJrKiNUVh6rHgllN127v7RAB+rPmz1G516KpyC/cGAPzJjSYisK0h5wqyiklLKSinlpZRahrFuagAwHaiqtXYEXgeWZXt0gg50IIookkiiHe2IIy7D9jVqQHQ0ONaBVU4w+AB8C/SxKsJdnyXQaAj8sBBie4E+h1FYkgc4QmSH8ePH39s47/7XnDlzALh48SLJyckAHDlyhISEBGrXro2dnR2lSpVix44daK1ZsmQJPXoYM/q7d+/O4sXGv4kXL1587/28JhvyRLbdLGitF2itW2mtW1WqVOlhh5KvtKY1scRSUpUkfFIH+i4MZbT/IKbOX0dD/RtlaEcEJx6u02LljcJSxSaw0R9+35gzwQtRyBTmHAHZkidOwb+m7dvzn/0ctNaXtdb/rP/9EmiZVkeFKU9kRU2MwlLtYhVR9ttJ/KMRvlZPEeT3olFYCvmnsNQLKSwJkX3ya54wd6bSGWAW8AvQUGvto7X+Vmt9G+5NZz2Q7dEJAJxxJo44KlIRDzzYxKYM21epApGR0KwZfNsUhuwxttTzszJx22shNBkGPy6GmJ6gLwDuwPFcGIkQhVt0dDRNmzalWbNmBAQEMH/+fMqXLw/AvHnzGDZsGI6OjtSpUwcfH2PZ0cSJEwkLC6Nu3bqEhYUxceIDh93kFY+aJ7LtZqGwqUc94ojDUTkS+HRXBgQv490XuzF2Uhh2+hyOtGXLw6boouUgYBtUbg6bAiBhfc4EL4S4p4DnCHj0PLETqKuUclBK2QBPAf+qeiul7O77tnsm/YkM2GMUluralkc9to2khGZ0t+rPRr/hUKODMWMpPgijsPSQ+/gJIbIkr+YJldb6uwcaKdVKa70r2z/9EbVq1Urv2pXnwsoxF7lIF7rwMz/zBV/wLM9m2P7GDejWzVgSNygGlrY1zn7bqFMosX0U7JsHLZ6C9kGgymPssVQrF0YiRNYdOHAAJycnS4eRp6T130QptVtr3Sq3YnjUPKGUKoKx/4UHxuluO4H+Wuv4+9rYaa3Ppn7tB0zQWj+ZUb+FKU9c5zp++BFBBP32zGT1E6/SfcAvfPGNF0rdJZwgeqe5/UgG/r4Ogd5wfhf4roB6vXImeCGykeSJBxWEPJHaRxdgNsYpoQu11tOUUu8Bu7TWG5VSH2AUk5KAK8AIrfXBjPosTHkiK84DHZPhQNJ19B8+mBrsZFXyN/ivXwQnwsFbQ6MuGHss2WbcmRB5gOSItD1KnjB3ptLWtN5USuXpIyYKmkpUIoIIPPBgGMOYxjT0A6tD/l/p0hAcDJ07w5J2MGArRAJeyoobHnOhxcuwZwWEe4G+ijFj6WgujUYIUcA8Up7QWicBo4BQjCfLq7TW8Uqp95RS3VObjVZKxSul9gGjgaGPHnbBUYYyBBNMb3rzXYvxdD88juA1jfHvHMudlHL44MEStmaQNdJgWwZ6hULVx2FzXzi0OqfCF0IUfI98P6G1DtJa19Na19FaT0t9b7LWemPq169rrRtprZtprTtkVlASmasCRJmgSZEy4BhK8oEn6W0ayqqeg+AxD2Mp3G9BGHssyYwlIQojc4tK1v99QylljfGUQOSikpRkE5sYwAAmMYmXeIlkktNtX7w4bNgAfn6wzAv6rIUfAQ+luNL+E2j1KuxdDds8QN/AKCz9kVvDEUIUHI+cJ+Rm4dHZYst3fMcoRrHW4WNcjw0k/jd7vFp8z4UkR56iKwtZScpDdVoaeoVAtTawpR8cXJlT4QshCja5n8inKgKRJmhZpBTUDyZlvytPmZ7h255PQc1OEPpPYUmWwglRGBXJ6IdKqRiMjVKLKqWi//Nje8hk12iRI2ywYQlLqEpVZjGL85xnKUspms6xnra2sGoVPP00LOsFAYtg42DooBRhbjOobGUNP30AyT2gcwxYtcdYCueYa2MS4mForfPlcco5wZwlzDlJ8kTeY8LEHOZQneq8XvF12vx+kROtA3FziCQsoTtPF+3HN1xmEC9iY26nNqXAPxjW+UJQf9DJ4NQ/J4chxCORPPH/JE+I7FAOCLcCb0ryg9MWdHwPBjZ+jsQenzNkg4LQMNBboEkvIBBZCifyMskR//aoeSLDohLwFcaJPK0xTqe/97kYS2zDH+nTRZZZYcVHfIQddrzKq1ziEutZTxnKpNm+SBFYvBhKlIAvhkL3PyFsJLgrxfZ206hmsoEf3oUUX/DeAVbuGIWlerk5LCEyVbRoUS5fvkyFChUKfTLQWnP58mWKFk27oJxLJE/kQQrFRCZSlaoMKzEMp73tuesZxJNVQgk/8RTPlhnJEi7Si8mUSPPgvTTYlAT/IFjXFYIHGYWlhoNydiBCZIHkif8neULOrX2wAAAgAElEQVRkp9LAVivollKcyEYb0b/5M7TJCBJ7zGHYRgVbQ4Et0CQAWIMUlkReJDni37IjT5i7UXeDvLjMQDbWMyxnOUMZSiMaEUwwdtil21ZrGD8eZs0C7/fh+4lQRcF2oOaOaRA7Cep7g88uMFlj5PkGuTUUITKVmJjIqVOnuHNHjrAFIzHa29tjbf3vVQUW2IBV8kQeFUwwAQRQKbkK5fuH8utaB7afeA43u0WsZhQd+ZQKZq+GBxJvwfruxgatXguh8dAci12IrJA88W+SJzImeeLh3QZ6psBWqzvwSwA03cLnSR8zYsNWOBYCnkDTrkhhSeRFkiMe9Kh5It2ZSkqpQVrrpanfuiilXNJqp7Ve+DABi+w3gAFUohL++OOCC6GEUi+dGUZKwcyZxibeb78BHf+EPdPAVUH4k2/iaLKB6NcgxRN894KpPcaMJdkhX+QN1tbWODg4WDoMgeSJ/MIHHyKIwNfky18rXHhyXBDu1RaycX9Fejt9RBCXaMpi7M1dDGddHHpugg09IfQZSEmCpsNydhBCPATJE3mH5ImCqRiwwQp6pxRlc9O18GsfXmwylrvdP+TlTQrCgoHN0FSWwom8R3JE9sto+Vs/4J8kkN78dg1IEsgDOtOZSCLpQhfa0pYtbOFxHk+zrVIwebKxFO7VV6Hdn3BwDrgp2NZ6PA2trCHyFdjUAbruhyLtMWYsNcrNIQkh8j7JE/nE4zxOLLF4KS/2zGqPp30g3RvO5JvoSgx1nUAkV7lFIPUoYV6H1sWg5wbY4AdhzxlL4Zo9n7ODEELkR5InCqiiQKAV9NM2rG2yGn7pz5imE0jsNpVXNykICzL2WGomhSUhCjqzlr/lVTJd9UEJJOCFF+c5zxrW4INPhu2/+AJGjIDWQ+H4V5BsBWGA889zIXwUOLhB98NQJBljkVyTXBiFEOJR5fayhrxK8sS/neMcPvjwm/4Nr9Vfs6XvYGasXMjY3s/xs2qNiS00p4L5HSb9DZt6wZEt0PEzaD4y54IXQmQryRMGyROPJgkYrOE7lQS/DoImK3g/8R1e37zTyA0egHMXjMKSRff2EkI8JHPzRLqbKCilrMx5ZW/Y4lHVpS5xxFGPenSnO0tYkmH755+HJUtg9xKwewpsU6AD8FPzkeC5AI7GwPo6kGgCOgL7cmMYQoh8QPJE/lOVqkQRhbtyZ0ufIfhGf8hrfZ9m4uxAGuu9FMOV7zlpfodFbKFbINTpYTyI2PNpzgUvhMh3JE8UfEWApQqG6iLQZCn8NpA3rN/h3a7N0LW7Gc+kfw4C/AHZw0aIgiijX+JJQGIGr39+LvKYf24a3HBjCEOYyUw06c9IGzgQVq+G/RugTFcokwydgJimzxmbsB6Pg3U14a4NRmFpb24NRQiRt0meyIdKU5ogguhHP7a4TqTzgTF8MqE7L7waSnV9msdoSwgPsZduEVvotgrq+kPEGNj1cc4FL4TIbyRPFAIm4GsFw3URaLwIfnuad6zf561uTmjHnsYuGnuCAT+ksCREwZPRnkqye1U+9s9NwxCG8BqvcYYzzGIWVunUEf38YONG489qHaHKNvCyho2Nh9LJZA3Bg2FtC/A/CzYdgW1Ai1wdkxAiz5E8kU/ZYMMyllGVqnzS4BNcT5xldcOlnD8VxdIV3rRS7VhLEP7p7M33AJMN+K6AoAEQNc7YvPvx13J2EEKI/EDyRCFhBcxXUFSbmNP4K4i3YVqjGdzu8jIfBfujItZCSgi06gmsR5bCCVFwpFtU0lofz81ARPazxZZv+ZaqVGU2sznPeRaxCJt0Tvjx8oKQEOjaFcq5QM3voastrHEaQFcra9jSH9Y0hV6XwNYDY/elQr8UX4hCS/JE/maFFR/zMdWpzqtVX6XFsYv85LweH7dY1kd2prOpI8tZS386o8zp0GQNvt+CMkHMBEhJhCffzOlhCCHyMMkThYsCZisohhUfNpoH+234uOGn3PUewRwVgIpaAzoUWvfAKCwVs3DEQojskG5RSSm1QGs9PPXrpZD2+imt9eAcik1kAyus+IRPsMOOiUzkIhdZy1pKUSrN9m5usH07eHtDYkuouxP8isG39fvQ28oaNveF1U4QABTtBGwFc59kCyEKFMkTBcM4xlGVqgwtPZTaB1w56xaMa8NYwn7zprd1VxaxhCE8leF6+XusikCXpcafsZOMGUsub+f0EIQQeZTkicJHAR8AxVC80/BTOGDLZ04fccf7WRaovqjolZCyFZ7oAWxACktC5H8Z/Rvx6H1f/w78kc5L5HEKxQQmsIhFRBBBe9pznvPptm/dGiIjIeUSnG0MjW/CU8DSun7QPRAuHYRVpeBWWcAT2JFLIxFC5DGSJwqIAQwgiCDO2B6FWBesna7yeLUoDt1qwxD6s5jPuGtuZ1ZFwHsRNBoCP7wDsZMhH580K4R4JAU3T8jvtXQp4G1gOgqcZsDBN/nK+muGdrYhpUE/+B7YEQb0AG5bNFYhxKNT2oxfiEqpqlrrc+a+n1vkCNCHF0wwAQRQlaqEEoojjum2TUgADw+4kQyO+2FPGZgPDD8aAht6QjkH6H0bil8BQgCX3BqGECITuX1UtOSJgmEPe+hCF/5OuUvNUZs5uLAFEaf70abCepYxmZ68Q0nzFsOBToGtw+G3r+HxidDufVBmXiuEyHGSJwxZyhO3b4OvL4waBf7+ORNYAfE/YDTA4SlQbzJ97vblu+1FsNq/3Lh1aNMJY8ZScUuGKYRIg7l5wtwjPA+n8/5+80MSeYEPPoQTznWu44ILu0g/idatCzExUKk4HKgDj1+G54FPHbzBbzNcOw4rbeBmRcAL47GDEKKQkjxRALSgBXHEUcmqIofmetD8nVBcq6wm9OizDOQ9QhjJJZLN60xZQecF0PR5+Gk6RL8mT/aFKNwKTp5YuhQiIqBXL3jnHUhJsXREedZLwAJA1X0Lfp/OKpuVBHS8TXKjwRAHxG0D3RW4ZdlAhRBZZm5R6YFHi0qp0oD8Bs2HnuAJYomlOMVpT3u2sjXdtjVrQnQ0OFSFvQ7gchbGAB/U7AT+QfDnGVil4M/KgDcQnVvDEELkLZInCoja1CaWWJqqpvw0wR+XxV/jXftLvt01kQDmsZN+nOBv8zpTVtBpHjiPhF0fQeQrUlgSovAqOHniuedgxgxj9uW77xrFpT//tHRUedZzwBIFqvYEOPox62zX0q3jFZIbD4EfgLiI1MLSXxaOVAiRFRkWlZRSJ5VSJ4BiSqkT97+Asxjb9ot8qD71iSMORxzxxZflLE+3rZ0dREVB43rwowO0Ow5vAG/VaI/2D4GbF2BVCtyoCvgAkbk0CiGEpUmeKJgqUYlwwvFW3sQMeAG3yLcZ0Pp9Pt08Cx9WcxxfDmHmDZRS0PF/0GIM7PkUwl8ylsYJIQqFApknlILx4yEoCMqUgfXroU0b+CN/bg+VGwYCK63AVPMVODaXYJvNeLc/R1KTp43tWb+PAO2LFJaEyH/SPf0t1UCMpwpBwKD73tfAea31oZwKTOS8alQjiih60pOBDOQ85xnL2DTbVqhgnArXtSvEOoJbPEytB7fs2/FRQBgq0AtWloE+1aBMF2Az0DFXxyOEsAjJEwVUCUqwgQ08z/MsdJ+Cy/4zvNJsPufnVOK955/mN9WB3QTRksqZd6YUtP8YrKxh10xISTRmMClzJ0wLIfKxgpsnvL3hp5+gRw+IjzdOu1m5Ejw9LR1ZntQbKGoF/jVeJOmkDduqD8fDvQPbrJ7F+qevISUK3HxABQElLR2uEMJMGRaVtNZRAEqpilprWehaAJWhDCGEMIhBjGMcZzjDDGZglcYktjJlICQE/PwgrD6474WPm8Gtak8yt/d2rNZ4wsoU6G0P5XyBTUCnXB+TECL3SJ4o2IpQhK/4impUY6rTVFoeP8esRis5f7ICn00N4JRqRzShuOGQeWdKgduHYLKGH9+HlCTwXABWppwfiBDCYgp8nqhXD378EQYOhE2bjELTRx/BmDFyOEEaugFBJuhWbRh/n7MhusrTuLVLJEo9h82uL0HHgLs3qGCglKXDFUKYIbOZSgBorW8ppZwBV6Ai962J1lpPzqHYRC6xxZbv+I4qVGEWszjHORayEBtsHmhbooSRL/v2hQ3O4B4H89vA7aqt+Lp3OKY1nrAqGXo/BuW7YZzm0DnXxySEyF2SJwouhWIKU6hOdUbajaT+MQ9WO2/m/KntLFvkSwnVliBC6EJTMzpT0HaqMWPph3eNGUte30hhSYhCoEDnidKljSVwb78NU6fC2LGwdy988QUULWrp6PIcTyDUBD6VB3P7og07Kg7EpW0isVYvYLt7PqTEQQcvUCFAaUuHK4TIhFnzzpVSw4FYjPVME4AmwDjI4Dz6B/vwVkodUkr9rpSamEG7AKWUVkrl2hGnAkyYmMMcpjGN5SynG924yc0029rawurV0L8/RLmA2zZYDAyo0pzEPhGQnAyrrsHlWkB3ICQXRyKEsITsyBMib3uBFwgkkKOl91H+QFt+OlgVb88YTClWuODGGmLM60gpcHkH2k6B/UsheJAxa0kIUaAV+DxhZQVTpsCqVVC8OCxZAm5ucPq0pSPLk9yB7UWgZMWn4Moqdpt207rNTm63HAk/a9i+A7QXcN3SoQohMmHuZgavAd5aaz/gduqfAUCiORcrpUzAXIxdnBsC/ZRSDdNoVwoYDfxoZlwiGykUb/AGC1nIdrbTgQ5c4EKaba2tjVz53HMQ7Qlt18FKIKBSE+72jQSsYOUluOgA9AC25N5AhBCW8Eh5QuQPPenJNrZxw/YixLpwrtRd2jaJ41qiHb50ZikbMftstycnget0OPgdbOkPyfJXRYgCrnDkid69IS7OOEJ5505o1Qp++MHSUeVJbYCoIlCmnD/cWMuvVr/S4okYbj0+GvZpCPsRdGeksCRE3mZuUamy1vqfR5ApSikrrXUwxrJYczwO/K61PqK1vguswKg0/NcUYAZwx8x+RQ54mqdZz3riiactbTnCkTTbmUzGrN6xYyHWH55cChuBbhUacrtPFJhsYdUFOF8H8MPYY0kIUUA9ap4Q+URb2hJLLMWK2HAp0A1rj0O0fiyGI7eb0R8/FrMQs+cdPT4B3GfB4dWwuS8k383J0IUQllV48kSzZrBrF7RvD+fOGX8uXGjpqPKkFsD31lChdFe4tYmDKoGmrcL488kx8KuG0J2Q4glcs3SoQoh0mFtUOqWUqpX69WGgh1LKFTD3X3/VgZP395f63j1KqeZADa31ZjP7FDmoK13ZznaucIU2tGEPe9Jsp5SxF+Hbb8OOwdD6c9imwad8PW72jQLrkrD6DJxzBHph7LEkhCiAHjVPiHzECSd+4AfqWNXm90+7UG1sCE9U2sauK54M5Vm+Yzp3zJ2z1GosdPgUfl8Hm3pD0t85G7wQwlIKV56oWBG2boWXXoK7d+HZZ2HECLgjz87/qzEQZw1VS3SGxCD+4ASNW27huss4iNcQugtSOgFXLR2qECIN5haVZgBOqV+/BywDwoF3zbw+raMP7v1rUyllBXyCsa46446UGq6U2qWU2nXx4kUzP/4/hg6F996Dq/KLKSNtaMP3fE9RiuKOO9vYlmY7peCdd2DmTNg5EprNhO81eJatw42+UWBbDlafhjN1MWY5r83NYQghcsej5gmRz1SjGtFE46pc+WX8IBp8/TmuVTew9Xg/BvE6mxjLdVLM66zFaPD4HP7YCBv9IUluuoQogApfnrC2hjlz4OuvwcYG5s+HJ56AgwctHVmeUw/4wRpqFG0PKVs5kXyehs3XcrXtONivIfhnSPEArlg4UiHEf5lVVNJaL0qdnkrqn+WAclrreWZ+zimgxn3f2wNn7vu+FEaROlIpdQx4EtiY1mbdWusFWutWWutWlSpVMvPj73P4MCxebEytqVUL3nwTslqcKgSccCKOOGpRiy50YQUr0m376qswbx7snQgNJ8MeDe3L1OJK32goXgnWnIBT9YE+wJpcG4MQIudlQ54Q+VAZyhBMME/xFLv7TqBp5Hi86yzmuz0v05vZfM9gzpk7CcF5BHgugKPBsL4HJN7O2eCFELmqUOeJZ54x9lVydIRffjH2WVq82NJR5Tm1MApLtW1cwLSdM4nXaOC8ikuu4+FgCmzZB8kdgcsWjlQIcb90i0pKKav0XkAScCv1a3PsBOoqpRyUUjbAUxjb7wCgtb6uta6ota6lta4F7AC6a613ZXlk6alXD6KioFMnuHED3n/fKC6NGwdnz2b7xxUE1alODDG0oQ396MdsZqfb9oUXjA28938AtcfCAQ1upWtwvk8UlKwOgUfhpBPGX4GVuTYGIUT2y+Y8kbfcLZirMXKCLbYsZzljGctul//R9EB/+rt8wOyg9/FlOYfpzhH+Mq+zps+B10I4Hgbru0KimdcJIfKkAp0nHlaLFrBnj3F88l9/GSsnhgyBm2mftlxYVcdYCudkagVFI7jw923qN1vGeffX4HAKbPkVkjsAlywdqhAiVUa/xJMwTmNI7/XPzzOltU4CRgGhwAFgldY6Xin1nlKqe9bDzyI3NwgLM54Y+PrCrVvw8cfg4ACjRsGJE7keUl5XlrKEEoo//rzCK0xgAinpLGsYOBBWr4Y/5kL15+CoBtdS1TndNxLK1IK1f8CxhkB/4NtcHIUQIptlW57IUxITjafIY8bIMmkzWWHFrNT/7au7hgYnvBg/9AVeX/AlbXUYV/HgF3NvABoPBZ8lcDIS1naBu3/mZOhCiJxVMPNEVpUqBcuWGcvhihUznsS2agX79lk6sjylCsbm3c40g+JRXLmlqd/kG053eA0SUmBTPCS1h3ROqRZC5C6lddobaSqlaprTgdb6eLZG9BBatWqld+3KhslMP/8MU6fC2tS9fqytjScHEydCnTqP3n8BkkwyL/ES85jHIAbxNV9jjXWabUNDwc8PKveAK8uhvBVE3rpIrTWd4Moh6FEPHOKBRcCg3ByGEAWeUmq31vqBJcTZ/BkFM0+Eh4OnJ6SkGButTp0Kw4YZR16KTK1gBUMYgt1NRy61DqFP7918/u5THFcOXCGUNjxmXkcHV0LQALB7AvyDwbZ0zgYuRCEjecKQbfcTD2v/fujbF377DWxtjQfcI0YYm5UKAK4D3omww+owXPegZIlbxB94jse2fwi1raBbPSgSiVGGEkJkN3PzRLozlbTWx//7wjjB7S5w8r738r/mzSEw0Pil3r8/JCfDV19B/foweDAcOGDpCPMMEybmMpcpTGEpS+lGN26S9rRdLy/4P/buO6zK8g3g+PdhCYhbxAXiwpVb3LhRXIg4MUfubVla2fiVZZmpZZp7j9xbERcOHGhirtwLBfceKLKe3x8PpiniSTjnMJ7PdZ0reM8577nfynP7PuO+N26Eu/6QyQcexEINe0fOtNkGOUrCmtNw/j2gC2pgSdO01CS584QQwksIcVoIcU4I8Xkir2sthJAJ1d1LFvXqwcGDULs23L6t9vVWrKi2Tmtv1Z72bGQj9xzCyXikGmuCCuPbdTN55DWcqc4Wjht2ouLtoNkSuP4nLPeESN1OWtNSmzSbJ5JDyZKwfz/07AnPnkH//tCmDdzX33XPZQG2WkOdODfIHsTjR5kpUXIK5z0/hQtxsOYMRNcCdAkTTTMng/YwCyEyCyHmAZHAFeCpEGKuECKLUaMztVKl4I8/1CBS165qpmD+fHW8ZUsICFADTumcQPAVXzGd6WxhC/Woxy0SLnZeqxYEBsLT3WDTEKJiwMMuB8fbBIJjWVh7As6WAboBM0x6HZqmJZ+k5gkhhCUwEWgMlAT8hBAlE3hdJmAQsD/Zgk9IuXKwfTssXQouLmprQp060LYtXEob8ynGVJe67GIXVjaS6EAPjkZL6nsGkSEulop4sIa9hp3IrRU0XwE3D8Gy+vBUd/3RtNQqzeWJ5GBvD9OmweLFamvcihVqsnt/yg/dVDICAdbgFVMQcgbx5IEjpYpP4nSjzyBUwurzEO2B6gulaZo5GFoYbzzqz/R7gB1QGrCPP572uLnBrFlw9qyaoba2htWroUkTVXfp22913SWgBz1YxSqOcYwa1OAiFxN8nbu7muAXxyHWA0QU1LLNxqHWW8DJHdb9DafLAD2BqSa9Bk3Tkk1S80Rl4JyU8oKUMgpYDLRI4HXfo9pSG7/nvBBq1vjUKfjuO1X/YtkyKF5cdRB98sToIaRmZShDMMHkt8zLrQUNuVPpFFXL7OVhdE48acBi1pPwBvxXFPGGFqvhznFYVg+e6I6tmpZKpb08kVzatVPlOCpWhNBQqFkTRo9W27A1bIE1VuAb4wyOQTy770zpYuM51ngohElYGQpRHoC+P9M0czB0UMkL6CSlPCOlfCalPAN0jT+edrm6wuTJ6sv9xx+hUCEIC4Phw9VzjRurOkzR6ae+4Ku88WYrW7nNbapTncMcTvB1pUtDUBA4XIEn7pAhEupkyMK+1psgbzXVyeFkOaAPMMmk16BpWrJIap7Ih9oS8Vx4/LF/CCHKA85SyvXJEbDB7Ozg66/h9Gnw84PISDXIVKyYml1+Q21CDVxwYTe7cRfuhI5sj/WANVQusJvQJyVpjQ8LmINB638LNQGfdXDvNCytCxE3jB26pmnJL8XkCSFELyFEiBAi5NatFDJQXbgw7N0LgwdDTAx8+ik0bKjuQzRsgCVW0CE2D+TaQfRdN8q7jeNgk4/VurcVYfDMAwg1b6Calg4ZOqgUCTi+ciwn8Cx5w0mh8uSBYcPUyqXAQGjfXq1e2rgRWrWC/Pnhs8/U8+lQDWqwm91YYUUtarGNbQm+zs0Ndu0CpydwvwxkfgINbDKxo9VGyF8bNhyB4+WA/qTVRXCaloYlNU8kVJn0n9Ga+JbTvwKfvPVExrpZcHaGhQvVF1n58hAergaZatVSbaK1BGUnO1vYQkvRktN9BpNzxs9UyRNIyJ26dKIri/mZZ4asWXL1hJYb4MFFWFoHHusaGpqWyqSYPCGlnCalrCSlrOTo+GpIZmRjowp2r1unGkUEBsJ778HEiXrVEmAFzLeEXrG5wGkbsXfKUrnoOPY0/RCuAyuuQGRN4LyZI9W09MXQQaUZwBYhRB8hRGMhRB9gEzDNeKGlQBYWqoDrokVw9Sr8+qsqsnfzJvz8sxo1qVNH1WWKTD0rbpNDSUoSTDAuuNCYxixlaYKvK1BArVgqZAO3SkLOR9DYOiObWq6HAg1g4xE4Wh74EPX3Ak3TUomk5olwwPml3/MDV1/6PRNqy8QOIUQoUBVYm1ARVqPfLNSsCQcOwPTp4OgIu3erltBdu+oZ5Teww46lLKU//TnZZCz5d/ahtusKAi62430+Yy1DeIQBN0wudaHVRngUDktrq39qmpZapJg8keI1a6a6w7VtCxERMGCAugc5rwdLLIApljA4Ljs4bSXuVjU8io4jsFl/uCFg+XV46gGcMXeompZ+SCnf+kDNDHQDtgIn4v/ZHRCGvN9Yj4oVK0qzi4uTcu9eKbt2ldLeXkq1EULKHDmk/PJLKa9cMXeEJnVX3pU1ZU0ppJDj5fg3vu7WLSkrVJDSKreUBe9KaS2lXB39VMoVjaUcg5SHykn1n3m0qULXtDQFCJEm/D5Oap5ATUBeAAqiVrkfAUol8vodQKW3ndfoeeL+fSk/+URKKyv13W9tLWX//lJevWrcz02l4mSc/En+JJHIIpfqSpHlrpx/YICUEukvO8obMsqwE4XvkXJ8ZimnF5LyQahRY9a0tErniRR0P5GY5culzJVL5Rh7eynHjZMyNtbcUZldnJTym1gpkY8l1zwlErnuXH8pf7WRcq6llBFOUsqT5g5T01I1Q/OEoSuVLKSUs6SUDaSUJeP/OTP+g9I3IaBaNVXY+9o1mDJFbYu4cwd++EEtzXn/fTWrnQ5kIxub2UwLWjCIQXzBF8gEtjXkzAnbtkGVwhBaGFxuQisrW5Z4r4JCzSHwMPxVHhgK/GTy69A07T9LUp6QUsYAA1Cz1ieBpVLK40KI74QQ3sYMPEmyZIExY1Qx744dVR2MiRNVDb6hQ+H2bXNHmKIIBJ/xGfOZT6jLLvKH1qZr688YvXYETVjASbwJJeLtJ8pXHVpvgci7sKQ23L9g/OA1TUuq9JknkqpVKzh+HDp0UA0iPvoIatdOt2U3nhPAtxYwWmaE3GvhRnOaF57IshYfwD0rWHYHIjyA42aOVNPSPmHI97gQ4hawDPhDSrnH6FEZqFKlSjIkJMTcYbxOSlVob9w4Vcj7+R7oatVUIvD1BSsr88ZoZLHE0p/+TGUqH/AB05iGNdavvS4iAnx8YOs+KHoKzuWDWbFRfODvB2dXQu3yUOkQ8E38I6Ht9JqmvUoIcVBKabIl/zpPxDt+HP73P/XdD6pF9ODB8PHHagBK+8cWtuCLL3aR2XjsEUDfrnv5uW8fDgt3bPGnFDnefpIbf8FyT7CyhzaBkN3N+IFrWhqh84SSYu8nErJmjepMff062NrCiBHq3sLS0tyRmdVUCX2Ihpvvg9My5oR2pfOaxYjMUdAmCzhsB8qYO0xNS3UMzROGrlRqCDwGFgkhQoUQI4UQpZMUYVomBNSooVpPX7igZquzZoXgYNUytGBBGDUK7t41d6RGY4klk5nMt3zLHObggw8RCcw+Z8yoahF614OzRaHIRehqacPkpouhWDvYeQj2VwSGA5+CYQ2oNU0zPZ0nAEqVghUrICQEvLzg0SPVKa5gQfjpJzWSrgHgiSdBBGFpG41VcE1mbypO52HLKSUPY0VN9hvSGtqpArTdDrHPYEktuP238QPXNO1d6TyRVC1aqMmLTp1U/dYhQ1Sdv1OnzB2ZWfUWsEBYIxwXwq3OfOA6m8k+LZGPMsDSh/CoNryhQ7WmaUln0KCSlPKQlPJTKaUL0AXIBgQKIY4aNbq0oEABVcQ7PBwmTVItqMPD4fPPVde4Pn3g5DgaHKoAACAASURBVElzR2kUAsE3fMMUprCRjdSnPrd5fSuIrS0sXw7tW8DZ4uB2EvpZWjO2yQIo8T7sPgh7KoEcg1r1rLtfaFpKo/PEKypWhIAA1SmuVi24d091ES1cGMaPh2fpo3nq25SnPMEEk9fKiYhVnmyLiKXZ+5vJLa+Rj+oEGrJtwbEMtNsJwgKW1IEbh4wet6Zp/53OE8kke3aYN0/NyubNC/v2QblyasI6Jsbc0ZnN+8AKCysss8+G273pX2AhY32aIiMywJLH8LA2kD7KkWiaqRm6Uullp1H7mMMA12SNJi3LmBH69lWdHDZsgEaN4OlTmDpVdZCrV08liDQ4i92b3qxgBYc5TE1qEkroa6+xtoYFC6BHZzhTBooegiEWVnzvNRf5XjfYFwJB7iAnoWo6xpr6MjRNM5zOE8/VrAk7dsDmzeDuDjduwIcfQtGi6vtfDy7hiit72ENFiwrcGN+WMxUOU6tOEDaxsVSgJuswYJdMjhLQLgisM8KyunB1n/ED1zQtKXSeSKpmzdSqpa5dVS75/HOVZ4KDzR2Z2bQE/C0tsM46Ge4MZqjLMr5rUR8ZaQdLnsD9usBuc4epaWmOQYNKQoisQojuQohA4DxQBxgF5DJibGmThQU0bgwbN6oBpj59wM4Otm+HLl0gd27o1g2Cgl7UYkoDfPBhK1u5wQ2qU52jvD4pZWkJ06bBh/3hbCUouhf+Z2HJFw2nI8sNgJADEOgOcg7QAYg29WVomvYGOk8kQgjw9IT9+2H1aihdGsLC1Pd/oUKq/l4anFD4L3KQg0ACaSFaEDbkQx4OWECVMru5H+VIAxqwlHVv3/ycrQi0DwK7nKrOUniQKULXNM1AOk8YQdasqllQQIDaHXH4MFSvDt27w61b5o7OLBoBgVYC28xj4d5XfOuyls98aiCjMsGSZ3DHE9hm7jA1LU0xdKXSVcAPWAjklVK2lFIulVJGGi+0dKBECZg8Ga5eVTPW1avD48cwe7bq6lCkCAwfDhcvmjvSZFGTmuxmN5ZY4oEHO9jx2muEgF9/ha++gLM1ochW+ElYMKDeeGSloXDkAGx2h7ilQCtA/y+oaSmEzhNvI4Sqh3H4MCxeDGXKqO//wYPB1RV+/BEePDB3lGZjhx3LWU5f+hLaZjTM+JrKRbdx7vF7+NKShcx6++bnzAXUiqVM+WGFF4RuMUXomqYZRucJY/HyUpPVX3yhlv/PmqVKbkydCrHpb3W/BxBkLXDI+D08+JHR+TcyoGUFZFw2WBINt7yAAHOHqWlphqGDSoVfavuZfv/GayxZs0KvXrBnD5w+rRJC/vxqMOnbb9VMdt26MGeOGnRKxUpRir3sJT/5aUQjlrHstdcIAd9/Dz+NhHOeUGQVTBKCzrVGEVftG/j7AARUgth1QHMwpP20pmnGpvOEoSwsVNOGw4dh7VqoXBlu34Yvv1QzzV9/rX5PhyyxZCIT+ZEfCa22CPvAzlQrtprgm/V5n+4sYSTP3rZmySGvqrGUrSisbgbn15kmeE3T3kbnCWOyt4cffoC//1arY+/dUytiq1ZVzSPSGXcg2Aay2Q6Dx+OYlDeQLj7FiLN0hKVxcL05sMrcYWpammBooe5rxg5Ei+fmphJCaChs2QLvv6+2x+3YofZM584NH3ygir/K1NkJzRlndrGLylSmHe2YwIQEX/fZZ/D773DOFwrPggVC4Fv9W6I9foJTIbC+IsQEAl7AQ5Neg6Zp/6bzxDsQApo3V0VWt2yBOnXUSqURI9TKpSFD4Fr6+9cqEAxjGPOYx9Uiu8h8uDH1K09hzZkO+PEF/nzEo7etWbLPBW22Q84ysNYXTr8+gaFpmmnpPGEibm6waRMsXQr58qkBpcqVVW3XNNx5OiHvAfszQC6rD+HJDOY77cG3pTNxNnlgGXClNbDYzFFqWur3LoW6NVOwtIQGDVT16mvXYPp0VfA1IgLmzlXdhEqXVqMuqXC7RHays5nNtKAFgxjEF3yBTGD2uX9/tUDrYk9wHQtrgMaVP+NZ3fFw7iCsLQvRwUB9IH0lSk3T0ggh1Pf99u2we7equxcRAWPHQsGC0K+fmmhIZzrRiQ1s4JHjRTKdqEWbdp8zbfdgfBnPHt7nJm8pcm6XHdpshdxVwL89nFhgmsA1TdPMTQho00Z1mB4yRN1XTJmitsTNnp2m6ra+TVHggC24iO4Qs4g12Q/h2TIbsRldYDlw2Q+YY94gNS2V04NKqUGWLNCjh1qddOaM2h7n5KQ6PgwcqNqJ9ugBBw+aO9L/5Hn9jN70ZiQj6UpXohMovt2lCyxbBleGgfNXsENCnQoDifCcBhePwKqSEHUEVe/xhqkvQ9M0LfnUqKE6hIaEgK+v6ugzebLqFtexoyr2nY544skudmFjH4PNnx589IM3wxePwovFnKUZoTxK/AQZskCrjeBcBwI6w9HpJolb0zQtRciUCUaPVtuta9dWW6u7dQMPDzhyxNzRmYwLEGIHJWPagVjNtixnqNHCmugshWGVBVzsCkw2d5ialmrpQaXUpmhRtT0uLEwta61XD548gZkzoVIltbx11ix1LBWwxJLJTGY4w5nLXFrQgogEaiT5+sK6dXD7F3AaCH9JqFamJ/cbz4PwE7CiKDw7B9QGwk1+HZqmacmqYkVYsUJNHnTsqGaV//hD1caoUkX9HBVl7ihNohzl2Mc+nK3zEOPfiF+3udJn9ByqyO08oC7HuZn4CWwcwGc9FPSCLb3gr/GmCVzTNC2lKFVKrYZdsEBNTO/dCxUqQM+e6p4iHXAE9tlD1cimYB3AfocrVGwRzbPsxWG1gLP9gF/NHaampUpCvqEujxCimyEnkFLOStaI/oNKlSrJkHRYeO41p0+r7g5z5qiifKBWN3XpAr17Q8mSZg3PUNOZTh/6UJGK+OOPI46vvWb3bmjaFGybwqMFkM8C9p5ZgaN/e3AsDK2uqHbSBAKFTH4NmpZSCCEOSikrGfkzdJ4wlUuXYNIktRX6+fe8k5Mqwtq7N+TJY974TOAud2lBC3azm5w//kKNa24sHN+GayIft9lElbd958c8A38/OLcKPH6Cyp+ZJnBNS6F0nlDSTJ4w1IMH8L//wcSJqjNchgxqm/WwYeD4+t+905pngE8EbMy4DyIbU/iZPcf8c2J3/Rg0llBiBPClucPUtBTB0DyR2KDS9pd/BWoA14EwwBlwAvZIKesmPdx3k+6SwNs8fapWL02Zogq/Plerltoe5+sLGTOaLz4DrGUt7WiHM85sZCOFErhJ+OsvaNQI4qpA9BrIYgnBF/zJv7YVZHOGNrfB3g7YCqSOATVNS24mulnQecLUnjyBhQth/Hg4dkwds7ZWtTMGDVKrmNKwSCLpSEdWsAKnhYNxXdaaDSuaE2VhzVH8aUjFxE8QGw0bu8CpRVDlC6gxQtUe0bR0SOcJJc3lCUOdPg3ffANLlqjfHRzg44/VI0sW88ZmZLFAlwj4I+NReOpJvphYTm0ogEP4X9AQKP0l8D3qf1lNS78MzRNv3P4mpaz7/AEcA4ZKKZ2llNWllM7A0PjjWkphZ6dWJwUHw6FDavY6Y0YICoLOnVXnuG7d1O8ptECfN94EEshtblOd6hzi0GuvqVBBXYLtIaAORERDhUJNOddyPdy/AouzwsNooBbwl4mvQNPSD50nzMDeXk0SHDmiuoL6+qqZ5oULX2yNW7BA1WJKg2yxZQlLGMQgbnT4lcuDf6NGnUCiY2ypTm2WsyGBlg8vsbSGxvOhdE/Y/yNsGwgyZeZDTUsLdJ5IwYoVg8WL1T1Dkybw+DF89x0UKgRjxqjJ6jTKEpifET56UgbsgrhiaYdrs/PcL1AZNgOHfgCGQOIZRdO0eG9cqfSvFwlxD8gppYx96ZglcFtKmc2I8SUq3c4s/BcPH8KiRapjXHDwi+MFC6qBps6dVfJIYU5ykkY04j73WclKGtDgtddcvKgaJl3PDA7B8MwWdoXvovSqZpDBAVpLyB4B+AM1TX4NmmZOppiBfuXzdJ4wl0uXVDHv6dNftIt2coJevdQjf37zxmcEEslYxjKUoTidroVoM4UtwR0pnvEIS5hEO3phlegJJAR9CiFjoERH8JoNFom+Q9PSHJ0nlHSRJwyxe7dqBrRrl/o9b161Ta5bN7UiNo368Sl8aXcJntQnk7jG+U1lcTwfrOam3bsDU1HDUJqW/iR5pdIrrgPerxxrDm+rjqmZXebMasXS3r1w6pRKFvnzqxGZ4cOhcGHVDWLWLHj0li46JlSCEgQTTAEK0IQmzEmg1WfBgirvuUbBgzLg8Biq5Pdgb9vtEBMNi6PgRlbUOtbNpr4ETUtvdJ4wlwIF4KefVLHV6dOhTBm4cQO+/14917IlbNmSYleovguBYAhDWMhC7roFE7O1NVXd57PrWiPepzcr+YLHJHK9QkCtn9X2t5MLYG1riIk03QVoWvqk80RKVrMm7NwJAQFQvjxcvarq9hUvrppDxMa+/Ryp0Bd2MO1ZAciwi0cUxKXxQa4U84AgYPdMkO2B9NEYQ9PelaGDSoOAOUKIvUKIJUKIYGAuMNB4oWnJrlgx1TkuNFTdYHTsqLbMBQVB9+5qe1ynThAYmCJuPvKRj13soja16UpXPuVTYvl3QsubV+W/0lngejHIdQ/qOFXAv/0usLKHpfcgPC/q7yyrzHIdmpZO6Dxhbs+3xh0+rL4Y27UDCwtYvRoaNlQ3Br/88mI1Uxrghx+bxCaic13BMqQeDVt9xaLDPWnLSHbRiWsksg1QCKj6JdSbAOfXwKpmEPXYdMFrWvqj80RKJwR4eUFICCxbpu4dLlxQ9wzlyqljaXBwqWcGWBmXB2Gxk8i4Mrg22sv50nVhP7BtOUhvIHV01tY0czBo+xuAECIn0BjIC1wD/KWUd4wY21vp5arJ4NEjWL5cdY4LCnpx3NVVtRnt2tXsXYWiieZDPmQyk/HGmwUsIBOZ/vWahw+heXMIOgpFT8DZPDDjYRjdl3vCo0vQvAAUOgfMBjqZ5To0zZRMva0h/jN1nkhprl+HmTNVh9DnbaNtbcHPD/r2BXd388aXTE5wgqY05WrMDWSHPxjud5JhLb8kmDpkYxXFyZr4CY7Pg03dILc7tPQHu+ymCVzTzEjnCSXd54nExMTA/Pnw7bdw+bI6Vry42vng5wdWaWvb8I4Y8Hz2kBir5mAdxF9BDSh/cCuUABpVB8sNQNouYq5pL0vu7W9IKW8DO4CdUsp55k4AWjLJlEkNHO3cCefPq6Th6qpWM335JTg7q0KwGzeabfWSNdZMYhITmMB61lOTmlzi0r9ekzmzWq3buBqcLQTvnYEemZ35rv0uZPaSsOY8nCwGdAYmmeU6NC2t03kiBcqdW32XX7gAa9ao1pmRkTB7NlSurAaVZs9WXeVSsZKUZB/7KGdVmpglrRhx0I5eI+dRUe5BUoO9XE78BKU6Q/NlcPMvWFoHIq6bJG5NS290nkhlrKzUfcKZM6p2X4ECqpxG587g5qYmLNJQY4g6VnDALjN2kQEQ04wKtbeytXo9OAmsC4aY2ujdmpr2OoMGlYQQLkKIPcApVJ92hBCthRAzjBmcZmKFCqnWoufPq0EkX1+1DHbVKmjcWD3/ww9qj7UZDGAAAQRwiUtUpjLBBP/reXt7tcujvQ/8XQLKBsM39o70abuduLw1YMNJOFwa6A+MMss1aFpapfNECmdlBd7e6rv97FkYMgSyZ1dbHLp1U7X2Bg1SW+dSKSec2M52fIUvT0Z8zOJ8+2nebQN55RVcqYp/At1E/6VoS7VK6f55WOwBDy8l/npN0/4TnSdSsQwZVH2ls2fV7gY3N1WftU8fVZ/1t99S/eTEc+Us4Fhme7I9WgmxXfCsto1FdWrDeQkrj0FUTSDM3GFqWopi6EqlqagWWpmA6PhjWwBPYwSlmZmFhZrNXrFCLXX98UdVFfvSJfjqK3BxUYVfAwJMvq+6IQ0JJphMZKIudfmDP/71vI2NqiXYvy8cqQ7lV8K0DJnx8Q0gplAzCDwG+0qD/Bz4Et0qVNOSjc4TqUWRIjB6NISHq5uDKlXg3j2YMEEVZ61QASZOVMdSGXvsWcpShjCER50nsq/bOGp4boFYK2pRi4VsTPxbv0ADaLMVnt6GRTXhzilTha5p6YHOE6mdtTV06QInTsDixaoxxJUr8NFHaqfDTz+pmhSpXGEBp3JY43JnFsR9QoeKO/nNsyoyHFh2Hp5WA86aO0xNSzEMHVSqDPwkpYwj/i5cSvkAvak07cuTB4YNg3PnYNMmaNVKrV5avRqaNFGzEyNGqJsTEylBCfazn6pUpSMd+YqviHupy4+Fhbo3+uYbONQKyv0O/lZ2eHivILJER9hzDHa+B/JHVM1I8xcl17Q0QOeJ1MbOTt0c7NsHhw7BwIGQLZv6ecAA9f3v55fqOsdZYMFoRjOZyUTU3MjlKb2oVH0VV54WoS3NmMeMf+5mE5S3GrTdAXFRsKQW3HjLCidN0wyl80RaYWmpmkEcPgxr16rt1LduqXuGAgXUX8LvpO6djbmA404WlLs5BhjFR2X28WXjsshbVrDkOjyuBhwxc5SaljIYOqh0Ayjy8gEhREl4W5ECLc2wsFDdg5YvV8VeR45U2+EuXYKvv1YJpEkT9bwJ9lbnIAeb2UwPevADP9CGNkQQ8c/zQqjyUBMmwJFBUOJLOGxhTdnGc3lQfiAc/Bs2lYK434FuQIzRY9a0NE7nidSsXDkYP15tb168WH3fR0W9+LlgQfWlGhpq7kgN1oc+rBfrofB5HgS0wL3eRP682YAu9GQZX/EwsTVLucpCu11gZQvL6sKVPaYLXNPSLp0n0hohVKecfftg82aoXRvu34fvvlP3BoMGqbIaqZQD8GduaHjtU2AmI4sdoWezQsiHGWDxfbjvAew1c5SaZn6GDiqNAdYLIboCVkIIP2AJujBN+pQ7N3z+udpXvWULtGmj6nUEBKif8+WDwYPh2DGjhmGDDdOYxi/8wmpWU4tahPPvFVMDBqjtcKdHg3NXuCktKFb3N65V+xaOH4d1xSFmLtAaeGrUeDUtjdN5Ii2wtVWzz5s2qQGk4cPVlobLl9XPBQtCgwawcCE8TfnfmV54sVvsJns2QdSORtTu3o+Vx7vTgR/YRUcuEfnmN2d3g/a7wd4JlnvChQ2mC1zT0iadJ9IqIcDTE3bsgF27VBmNiAg1u1u0KPj4qKZABnYdT0msgY15oOv1bmCxgpkFL+LjnYu4Zxlh8RO4XR+1i1PT0i8hDfzDLYTwAXoBBVDVyaZIKVcb/EFCeAG/AZbADCnlT6883wdVQTkWeAz0klKeSOycugVoCnLnjhq9mTkTjh59cbxSJVUE1s8Psr6lpXMS+OOPH3444MAa1uDOv9tkb9yodu7l9IDo9fDYCkL++g237R+Bixu0OAM2tYE16FXYWlpgplbRScoTxqDzRDKIi1M3CjNnqlp7z1ejZskC7dur73h3d3VTkUJd5SrNaMbhuCMwcAJjGj3gY+8v2EdNbFhFBXK++c1PbsIKL7h9DLzmQokOpgtc04xI5wlF5wkjOXoUxo1T9wdRUepY+fKq/lL79qoQairzv9vwffadEOdN9Rv27FwXjVXsffAVkGcJ4GvuEDUtWRmaJwweVEpiMJbAGVQhvnDgAOD38qCRECKzlPJh/M/eQD8ppVdi59VJIAWSUtXjmDlTJZEHD9RxW1s1qtOtG9Spo7bTJbO/+ZvmNOc615nFLPzw+9fzwcHQtClYu0LmvXDJFoJOzKfqxq6QywV8L4N9aWAj4JTs8WmaKZnjZiEl0nkimd27p7bEzZqlOsc9V7KkajvdsaNazZoCPeYxfvixnvVY/fYx3R9XYtwXXQkXzpxnA40o+uY3P3sIq70hfCfUHQ8VBpoucE0zEp0nFJ0njOzGDZg8GSZNUnWXQOWJ/v1V97iciQzqp0DT7kPvjIdAeFHqbhQH19qS4clN8JHgMhXoae4QNS3ZJHlQSQjRSUo5P/7nbm86gZRylgHBVAO+lVI2iv99WPx7R77h9X5AZyll48TOq5NACvf0qSroPXMmBAa+OF6wIPTooQaYkvnm4xa3aE1rgghiGMMYwQgsXtrlefy4Kg/y2BoKHoEjWWDN+XV4r28LmXJA69uQ2RnYDBRM1tg0zZRMcbOQnHnCWHSeMKJjx1T3uPnzX9woWFqq+npdu6pR/BQ2Ex1LLIMZzAQmYLO5GR5/DGLp7A5Iizg2sRo/PHjjequYSPD3g3OroerXUH14il6dpWlvo/OEovOEiURGqq3T48a9KJFhawudOqnVSyVLmje+/8D/CXhzjjibhrg8us7xNY443A+DJhLcvkd1mNb5QUv9kmNQaYOUskn8z9vf8H4ppaxnQDCtAS8pZY/43zsBVaSUA155XX/gY8AGqCelfK1XoxCiF2rZLC4uLhUvXbr0to/XUoLQUHXzMXu2qs0Bqg5Ty5ZqlqJu3WT7y3kUUQxkINOYRnOas4AFZCbzv0Jp2BDCbkGFk7A3N/wevpt+q5shrG2g9TPIkRE1sPRessSkaaZmopuFZMsTxqJvFkwgOhr8/dV3vL8/xMQ3PsiZU61c6tpVtZ1OQSYxiUFyEFZnS+A88He2rOtNHpuLzGMWH/A+1m96Y1wMbO4Fx2dD2b5QbwJYWJoydE1LNjpPKDpPmJiUarL5119hw0u16ho2VKuXmjZVExQp3IEo8Ii4xrNMXuR4doJTawqS89pZaACU7Q+Mx/DyxZqWMqW07W9tgEavDCpVllImuH5cCNEh/vVdEjuvTgKpUFyc6g4xdapqQfq8TbWbmxpc6tIFsmdP8sdIJJOZzCAGUYxirGUthSn8z/M3b4KXFxw9DvWOwpZiMPTWUUat8ELEPoGW1pA3BvAHqic5Hk0zNb2tQdF5wsRu3FBbn2fPhr//fnG8QgU1uOTnBzlymC++l2xlK61lG548sCKj3zwCF/5MhWw7mMNwWvA12d40yywlBH0GIaOhWDtoPA8sU9aKLE0zhM4Tis4TZnT6NPz2m5qUeN78wdkZevVSuxpS6Hbq5y7EQfnb93mYszl2Mbs5tq4khUNPQDWgWhsQ84EM5g5T096ZoXnijcOnQggLQx4GxhMOOL/0e37gaiKvXwz4GHhuLTWxsFCjOatWwaVLqkV1vnxw5gx8/DHkzQudO8PevUnqECEQ9KMfm9nMda7jjjuBvNiClyuXqjvrUR22FIcmO2C0Yxn8/PYQa5sTlkVAqB1quiEgiRetaWlTMucJLS1wclLf5UePwoED0K+fatLw118wcCDkyaPq661bp1Y4mVEDGvCn2I9rluw8XNeCqkPex/9UFz7gG4LowgWeJfxGIaD2z1DrZzi9BFY1h6jHpg1e01KJ5M4TQggvIcRpIcQ5IcTnCTzfRwhxTAhxWAixWwiRevZUpUfFiqlaS+HhMGYMFCkCYWHw9ddqcKltW9i+PcV2jStkAeccs5Lv2maeWvtS3OcE+4qXgmAgcBnENQEemTtMTTO6xL7EY4DoRB7PnzfEAaCoEKKgEMIGaA+sffkFQoiXK2Q2BV7b+qalMfnzwzffqP1oq1erwaaoKFWfo0YNKFtWJZqHD9/5I+pRjwMcIC95aUQjfud3JCoxZc4MAQGqe/aGulB/JqzMXJC67fcQla0YrLoNp5wAb2BhslyypqUxyZkn9M1CWiKE6v45cSJcu6aKezduDLGxsHIleHurHPDJJy9qa5iBG27sF/uoZ1mb6Jk9abbWkfHrhtOC+VyjEX9y981vdh8KDWfC5a2wvAE8vWO6wDUt9Ui2PBHf+Gci0BgoCfglkAcWSilLSynLAT8DvyTHRWhGlj27ygenT6sdDS1bqoGkZcugXj1Vb+m33+D+fXNH+hpHAWfy2VEufCkxlv2p1uQ4K8qVgCPA+m0QUwu4ae4wNc2oEqupVMCQE0gpDSpqJIRoAowDLIFZUsofhBDfASFSyrVCiN9Qy0KigXvAACnl8cTOqZerpkEXLsD06aq49/PCrxkzQocOantchQrvdNpHPKIjHVnLWnrSk9/5HRvUdoW4OPjiCxg1Cqp8Dqd+hNxRDwhZ3QKH8CCoVxjKn0PtjdYdf7TUwUS1MpItT+guoenE1atq4mDOHDh16sXxChXggw/U9jgzdAKKJprBDGYiE7Hwb0bfkz6M/aQfl4QrZ9hAs5e2T7/m7Grwbw9ZCkHrzZApv8ni1rSkSIV5Qjf+SU/Cw2HGDHVfcDV+g4udncoTffuqiYsUJAbwCZf45/8Z+Jyxe4vycfBZcLaAFgUgQyC6CZCW2hicJ6SUBj9QZewdiR+MMvejYsWKUkujIiOlXLxYyjp1pFRzFerh7i7ljBlSPn78n08ZK2Pll/JLiUTWlDXlDXnjX89PmSKlhYWUJdpImSdGypzRT+WNVS2kHIOUe4pJGYeU8n9SyrjkuUZNMyLUgL3Jv5ffNU+gKhBseun3YcCwRF7vBwS87bw6T6RQcXFS7tsnZZ8+UmbJ8uI73tpaSl9fKdeulTIqyuRhTZQTpWWcpbQ8/p707LdU3o3NIW/KnHKW3J34N/+lbVKOzyTlVBcp75w2VbialiSpME+0Bma89Hsn4PcEXtcfOA+EAUXfcK5eQAgQ4uLiksz/ZrVkFRUl5YoVUjZo8O97gkqVpJw5853uCYwlTko56KqUyHmSWCs56C9nGfeLpZRzLaV85CilPGzuEDXtPzE0Txi0h1kIkVUIMR+IBG4AT4UQ84UQSa+orGkJyZBB7Uvbvh1OnFCtRrNmVTU6evRQtZcGDvx3Idi3sMCCEYxgEYsIIQR33DnM4X+e791blfm4vAFENcgWY4uL93LOvdcNgk9DYHGI+w71d5XY5L9mTUvFkiFP5EPdADwXHn/s1c/pL4Q4j9rWMOgNsfQSQoQIIUJuPV/xqKUsQkCVKjB5Mly/DkuWJLw97nl9JhPp1L03rAAAIABJREFURz82io1kLBbOtu/6U63bBCKjstGBekxmPk/f9EaXutB2B8Q8hcU14MZBk8WsaalFMuSJhKrnv7blQko5UUpZGPgM+CqhE0kpp0kpK0kpKzk6Ohr48ZpZWFuDry9s2aK2xw0eDNmyQUgIdO+uarMOGgTHE93gYhIC+C0PjLvbCeI2ML7MPVo3zk7cfWtYfBfu1QR2mjtMTUt2hhbGmw3YAeUAB6A8qpT9LCPFpWkvlCih2o5evaq2TFSrpuos/f47lC4NHh6q21BkpEGna097drObWGKpQQ2Ws/yf55o0gaAgiAuDG25Q8r4VRRvOINj9UzhyCjYUh9jJQBt48+2FpqVHSc0T+mYhvbK1VcVYN2xQBVpHjYLixVWbzl9/VfX1KlRQ9TRMMEjYgAYcsNyPa9asnJnehbKDB3Hibg360ZmlfM414hJ+o1MFaL8brDPCkjpwUTd50LRXJDVP6MY/6Z2bG/zyC1y5AnPnQtWq8OABTJgA770HtWr9p3sCY/kwO6yK9MQiKoiVRS2p5WNFdJQ9LHoKNzyBVWaNT9OS2xtrKv3rRULcB/JIKZ++dMweuCqlzGrE+BKl90CnY0eOwNSpqjbH4/iuOzlyqJocvXqppPMW17lOS1qyj318xVcMZzgW8eOsly+rAabTF8H9GAQXguUHxtAqaCgUKAbNT0OGasA6IGW0x9a0l5m6VXRS88Q71MqwAO5JKbMkdl6dJ1IpKdXK1DlzYNGiF8VZraygWTP1Xd+kiZrBNpJ73KN1XFu2WWzFauxgltR7gm/5qWzCGycWUI5MCb/x8VVY1QxuHYX6E6Fsb6PFqGlJkQrzhBWq9l594Aqq9l4H+VINViFEUSnl2fifmwPfvO0adZ5I5d50T9C1q7onKFo08fcb0YFo8HgSyjMHL4rfvcDBVZmxj7wHLSQUmILahalpKZehecLQlUqnAddXjrnEH9c003veGe7qVZVIypeHO3dg7FjVntTLC/z9VRXuN8hNbnawg250YwQjaEELHvAAABcX2L0balWF4MLgsRtauw9hnNccZNh5WOwCjw4C1YGLprlmTUvZkpondJdQ7QUhoHJl9T1/7RosXaoGkeLiVLdQHx+15eGjj+Dw4bef7x1kIxsbLTbQT/Yn5pNfaXXzIt/MG0UDuR5LahDAG+oKO+SFdkHg2gi29oGgz0C+ORdpWjqSpDwhpYwBBgCbgJPAUinlcSHEd/HNGwAGCCGOCyEOAx8DXZIlci3levWeoFw5dU8wZoyaZPb0hBUrINrgZrTJxt0aTmd2JfudPZzKUYkifne4ndkJVgKnegNfwJtWv2paKmLoSqUfUcXw5qNqXjgDHeN/P//8dVJKk26H0zML2j+ez2pPmaJaVz+NnwQrXBj691ezFVkTngSTSCYxiY/4iMIUZg1rKEYxAKKioGdPmDcPqs6FPztB30tbGL+uNRY2NtDyGeSyB/yBiqa5Vk0zgBlmoJOcJ3SXUO2trl1TWxtmz1b19p4rW1Z9z3fsqGaok9l0ptMvrj9xFwvQfPanzPt+KJHCBn9W8QE1Ety7SVwMbBsIR6aAWxvwmgvWdskem6a9q9SYJ4xB54k05uV7gkWLXmyFy51brXLt0UPdH5jQPcD96lPO5+1AlsjVhKzMT5Fr4VADqNIOxBzA1qQxaZohDM0Thg4qbTfgM6WUsp4hwSUXnQS0BN29CzNnwsSJcCl+JjljRujcGQYMgJIlE3zbTnbShjY84xkLWUhTmgIqNw0frh5lv4Yzw6H27WOsXdUU68g70NwBCkYAy4BEu9ZqmsmY4WZB5wnNdKSEgwdfbI+7e1cdt7FRq5i6d4f69cHSMtk+cg978Ilpxd1nERT5YhSbfx5H7gyXmMU0PqALCQ4XSQkhYyFoKOStDi3WgH3OZItJ05JC5wlF54k07N49tS1uyhQ4efLF8Xr11Kxxy5aqOZAJRAINrsSyJ98gbGImEeBfgHrnLkEJoGF1sFqLLqmhpTTJOqiUUukkoCUqNla1c5swAbZte3G8fn3VOa5Zs9duOC5zGR98OMxhRjCCYQxDxM9Bz5mj8o9La3iwAJyeXmX/qmY43DoKDfJCmavANKCbyS5R097E1DcLKZXOE+nAs2ewdq2aTNi8WQ3kgNrH/MEHagWTq2uyfNQVruAd25K/LA+Q7adPCeh6kCpOgcxnKPUZSV7eMIh1ehkEdIJMzuC7AbKZr8aHpj2n84Si80Q6IKWqazFjhtpO/Xz1Uo4catK5R483Tjonpzig4zXJojyjQA5j3E5nPjwYBnkFtHAF+y2AaVdRaVpijDKoJITIjOrW8A8pZWJdF4xKJwHNYMePq25x8+bBkyfqmKur2hrXvbtqTRrvCU/oSU8WspDWtGY2s3GI/99+61Zo1QpsioP9Tnhs8Zi/17cjz8UN4O4KHqEgvgX+R8LNrDTNNMx1s6DzhGZWYWFqBmDWLAgNVceEUJMJ3burVUy2SdtiEEkkvWUf5om5WK32ZnYeRzpWmckWmpGThZR/UwHvK3thTQt1c+OzBvLVSFIcmpZUOk8oOk+kM/fvq23U06erIt/PVa+uZo/btgV7e6OG8L/b8H225SA60/WYAzN23MciYyy0zAw5NgDVjPr5mmaoZC3ULYTwFEJcAO6j2nk+f4QlKUpNM5VSpWDyZAgPV8W8CxVSNxxDh6pir337/rMs1h57FrCAMYxhJSupTnUucAGABg0gOBgcbsLNIuD4yAEXnzUcKdsHDoTCeleI+RbVzSHGPNeqaWag84SWIjg7w9dfw/nzahagQwe1JW7rVvDzg7x51UrVJBT3tsWWOWI24xhHnLc/XTIHM2TOt9SVAVhTHX9CE35jvurQYR/YZYdl9eHUkneOQdNSI50ntBQha1Y1qXzokKq91KsXODjA3r1qZWuePNCvn3reSL7LCYuftMYiKojZ71nh0dKaqGgHWPQQQmsDK4z22ZpmDIZ2f5sB/AhkBqxfetgYKS5NM45s2eDjj+HMGbU1ztNTFfWeMkUte23cGDZtQkj4hE8IIIBwwnHHna1sBdTL9u2DsvnhtDOUPGtFufqTWF3rZzgTCsvyw5MZQAvgsTmvVtNMSecJLeWwsFCrk/74QxX3/v131SX03r0XP1eqpL77Hzz4z6cXCD7kQzZbbCJT0ev82nIcTUZ+j3NcOJVxZza7Eu7nk7Uw+AVDbnfwbw9/jnqxXU/T0j6dJ7SUQwiVB6ZOVXlixgyoUgUePlQT0RUqgLu7Oh4Rkewf3y4T/GlZCbsHf7I3nxtF/R5xx8ERVkbDkdbAWEDnBy11MHRQyRaYLaV8LKWMfflhzOA0zWgsLVVNpc2b1da43r3Bzg42bgQvLzVyNGUKDSNqcIAD5CUvjWjEL/yCROLkBNu3Q5tmcLQElN4qaOk+lNHNliJv3ILFueBeAFAXuGHuq9U0U9B5QkuZsmVTs9J//aUeAwaomeqDB9Uq1Tx5oEsX2LXrPw/w1Kc+h6xCKJHRhS2ffYX7rz15/DQ7HanHLH7ncUI3BHY5oPUWKNYedn0OW/uqTnGalvbpPKGlTA4Oaov0vn1w9CgMGqTyREiI2hL3fPXSy9vlkkFFazifLT95r+/iclZvCvrd4JiLC2wFtg+BuH7onQ9aamDooNKvwKdCCF0kRkt74geQCAuDkSPVdrhTp9TNhrMzhT+fzr6wZfjgwyd8Qmc684Qn2NnB4sUw7HM41hCKTYLP3NrQq8024p7GwkIHuHIMqAq8+1YLTUsldJ7QUr7y5VXzhqtX1SqmunXVatV586BWLSheHEaPhhuGTwYUpCD7rfbSWrTi7CejKb+uDEGXPOnBQLbThUs8ef1NVrbQ9A+oPAyOToVVzeDZf18xpWmpjM4TWspXujT89htcuaJq9FWrBo8eqdVL5cqp32fPflGjNYnyCDiXz4HKoSt5lOFTyvleZmlpF/gLWDMFopqhdz5oKZ1BhbqFEEWBTUBO4PbLz0kpCxkntLfThfU0o4iOhpUrYdw4NWMBYGmJbN2KOR9lpXvV6bzHeyxnOW64AaombO/ekKcf3PwVajw4S8DKJtg8ugyNM0KxSNSq7w5muywtfTFDq2idJ7TU6fx59SU+e7baAgFgZQXe3mrmulGj1zqFJkQiGclIvpJfIY6UYcT1OnzWaDwnRBkiWEkV3vDH4OgMCOwLWYuAzzrIViQZL07T3kznCUXnCe2tjh2DadNg/vwXW6azZIFOndQNwHvvJfkj4oBul2BugVkQ15thB3Pyw66biJxx4FMKMm8G8ib5czTtv0jW7m9CiCOopRbLgKcvPyelDHzXIJNKJwHN6PbtU7MVy5ZBrFqdfb9KcT4aGs5qH5hlORdffAHYtk11hhPVQK4Bx+jbhKxpQeare6GyM9QIA4uPgVGAlfmuSUsXzHCzoPOElrrFxEBAgKqf4e//z3c++fOr4q09e6pC4G+xgQ34xXTk0ZNYmi4awPxek4gTgmAW0hSvhN8UtgPWtlI/N18OLnWT55o0LRE6Tyg6T2gGi4iApUtVHab9+18cr14d+vSBNm2S3GF0zDUYmnMHWLTC+0IMKzdGYWn1DHwcIc9GoHySzq9p/0VyDyo9BLJKKROsO2kuOgloJhMeDhMnqlmKu3cBCCuSgR8+eUbWLoP43m4M1lhz6hQ0aQJX7CD7foiwfcbhbQModGwGuLpAk8tgVxdYAjia9ZK0tM0MNws6T2hpx7VrMHeuGmA6f14ds7BQtfj69oWGDdXvbxBKKL7RbTlkfYBC8z9gXZuDFLf9m/V8R2O+wDqh6gP3z8Oq5nD/LNSfCGV6GefaNC2ezhOKzhPaOzl8WN0XLFigtscB5MihJiH69IHChd/51BvuQwtxlhiHZpS6c5H9ax3I+PgeNLSBkguBVslzDZr2FobmCUNrKq0B6iUtJE1LxfLnV/WWwsLU4FKhQjife8aUvvBxgfHM/b4I1+78TfHiauKiYha47grZbmSgcMPprPWchgy7Dn84ws09QEXgoJkvStOSlc4TWtqRJw98/rnqFLp9O7Rvr7bArV2ruoQWKQKjRsGtWwm+3RVX9lnvZkDcIC50mkOloxlYebkF3nzNn/hwmwTqJ2UtDB2CoYAnbOkN2wbpAt5aWqPzhJZ2lCsHkyapGn3Tp6tucXfuwJgxKkd4ecGaNWoV7H/UJCscsy1Kpuv7OJ7TgwId7hGaOx8EREFQa4gbju4Mp6Ukhq5UWgo0A3bxSisrKWVn44T2dnpmQTObmBhVd+nnn1UHIeCJPdzt3pL8H/9CZG5XPvgAlqyGIoFwrgZ8enUfI9e1xiLyNjR0gBKPgalAF3NeiZZGmWEGWucJLW27cUPVXZo6FUJD1TEbG7XvuW9fqFlTtah+xXKW0zm6G08fWvHJibaMrDmTMFGQaFZSjATqcMTFQtCncPAXKNAQmi0B26zGvTYtXdJ5QtF5QksWUsKBA2qgackSiIxUx52doVcv6NEDcuf+T6e8FwcVz0Vz0W0QVrFTWBGYD+9jV6Ag0NQXMiwA7JL9UjTtueReqXQcVQhmL3D+lYempT9WVtC2rUoe27bx2Ksm9k8g/4RVxBUpTIaufiwc8hdfDYVzNcF5LIzNU5U6HQ/y1KkybLgD23NB7AfAQCDazBekaUmm84SWtjk5qdVL587Bhg3QvLmaYFi0SHWOK10afv/9RRHXeK1pzRHrg7jZuzDWYyr1NrbHPu4R+anCfpa8/jkWllBnLDScAWHbYWFVuHfWRBepaUal84SWdgkBlSurjnFXrsDYsWrFUlgYfP21Glxq1w527FADUAbIZgFn3KxpeGIyMZYzaNHgNp/Wzoa8ZAELV8K9KsBVY16VphnEoJVKKZWeWdBSkoijwRwc40e1RZewfr7StUED9tb8lMZjGyDrC1gCGSyjObBzCK6Hxqttdc3Dwd4DWAr8txkMTXsTU89Ap1Q6T2hGdfmy2vYwYwZcv66O2dtDhw4wYACULfvPS5/ylF6Rg1hgO4M8B6qyunQMlW1D2MvHVGUUFgk1cAgPgjW+QFx8AW+9c0hLPjpPKDpPaEYTFweBgTB5stoKFxdfTqxECbXCtUsXyJzZoFONvgif5joAtq2oF3adAH9LbGQkNMsOrpuAdP9HWTOCJBfqFkLUklIGxf/8xr/FSCm3vXOUSaSTgJbSSCSzw77jwbjh9JwGDo/Vn6/I0pX4/OGXjBfe5Aq24JYTrDm5gKZbeiJs7cE7AvLkAFYAVc16DVraYIqbBZ0nNC1edLS6YZg8WbUCfa5mTTW45OsL1tYAzImbR6+YvnAvI7/L6vTKvYbjeJCPP8hKAt3lHlxUBbzvnoJ6E6BcXxNdlJbW6Tyh6DyhmUR4uJqEmD5dNYMAcHBQA0v9+6uBprcIuQ917t0iomB7XB/8n737Do+q+Bo4/p3NpieEACEBEmpooQZClSKIBRWxIIgiWLH3hqCovHbsvWClCFJEEBA7TXovoUlLIAEChPS2O+8fs/wIGCRlW+B8nuc+2XJ399yb3T07596Z+YNlP4RR8+hxVE9faDce1CAXb4Q43zijqLRJa93ScXn3GR6vtdYNyx9mxUgSEN5qKUu549h1XPvJYUa+E0jgITMrRHLVFjyV8zQLfh7E/l5WRhxcy0uzrsWSvR8uCoNWGcD7wJ3Av8fmEKK03NRYkDwhxOm2bTNjanz9NWRkmNtq1YK77jLjatSqxWY2c0nGAA4Eb2dY4jV80OJnipQfKYyjOdf++znzM2DujbBrDrS9D3q9A5YSzmwSogwkTxiSJ4RbnTgI8cEHsGDBydsvusgchOjXz0wMcQbZdui+poi1CSMJLhjL3DlV6bErHVoAfUaB9f+QNoRwlgoXlSoDSQLCmx3mMDdyI0tyfuPNL5pz5+vpWJPNkYmdNOL9a5/i04lD6ayzmDdnMIF7f4XW0dArGaw3Ax8DwR7dBlF5SbcGQ/KE8JjMTDPV9AcfwJYt5jarFQYMgPvvJ6trawYdv5u5VScRu74LU1pk0866gTUMpw1v40PQqc9nt8GiEbDqDYjpBVdMgmDpMi3KT/KEIXlCeMzGjWZW6fHjISfH3Fa3Ltx7L9x+O9SoccaHPr4W3mw2FeV/C68ugSdX5EAtoH8/CJ4Mp+cQIcrB2QN1CyHKKIII5jOf14Pe54kH9hH5TyYLxw1Fx8YSyz+8O2M4O8MbkfDRBJpfOp29HUfAhmSYUgeOjgc6Als8vRlCCCHKIzTUjJmxaZPpEnfttWY8jcmToVs3Qtr14Kfpvfgg7X32NN1A55xdvJ12Ke34jGQSOMT6U5/P4gM9x8Jl30DKMvi2Lez93TPbJoQQouJatYJPPjEDe7/9thnYe98+MylEdDTceuv/Zpk+3RvxMD/tenyTVvJUt2iuv1JhO+yDnjAbDiYAye7dFnFek6KSEC5kwcL93M9GNtLWryM9b/+WSxNjODzpXYqatyQ6bz9vPP4wKxs04rNFVfil17fo9FwY7w9r9oFOAMZ7ejOEEEKUl1LQqxdMnw67d8PIkebo87p1qDvu5L4mozn87E10396SR2vM59K97fHTR6hCJ7bwPnDaGeUthsJNKyGgGky7GJY8Z85iEkIIUTlVrQoPP2y6T8+dC5dfDvn5pht1QgJ06WJmGi08dbboS2IgOSKO+ktXMK3pVbQfbOO48kdPToQtzYG5Htkccf6RopIQbtCABvzGb3zKpyyzrqLB4JF8umk4RTNmkFy7AxFHDvPSqJF07P0A8/cNpTC8B/yZBdP8IWMoZoylXE9vhhBCiIqoWxdeeslMMf3tt2b66WPHqPrGZ/zWeSUbr2nH8b2baJ2Vy58FTYnjQbZwFQUcPvV5arSAISuhxTBYNgam9YGsFM9skxBCCOewWKBvX5gzB3bsgEcegbAwWLbMzCrasCGMHQvp6f97SEQQ7OoSxpA/ZrA+/EWa3JTP+pqBMC8L5l0BBQ8CBZ7bJnFekKKSEG6iUAxnOJvYRDe6cb/lQS665h3ykiey4sVfWGztSdXjx7ns9XfIfWA5h//pB7sK4Rt/2DwOdCdgu6c3QwghREUFBMDNN8Py5WYZPBgFtJy5hmU985nXSzFu9gYeOdKchvzKcdpwgN9OfQ7fYLjsK7jsa0hZAePbwp5fPbE1QgghnC02Ft56y3SN++QTaNbMzCD35JOma9xDD8GuXYA5IXZ8bwvjE0dx5PjPJAz055VOvuhEYML7cLAt8I9HN0ec26SoJISb1aUu85jHV3zFetbTWrVh8aiNRG3/nduaLObXsD5Uycgg4uPZ5L7ig31xFMwEZm2FnHhgiqc3QQghhLN07AiTJpmucU8+CWFhJKzOYPoAeLD9Vt57HY4e10TpS0jkKf51xLnFMHPWUmAETL8UljwL9iKPbIoQQggnCw42s4du3mzOYLroIsjOhvfeg8aN4brrYMkS0JohrWGH76WEL1vHyG7tufB6yCjwR09KhNUtQE/29NaIc5QUlYTwAIXiFm5hC1voQx8e4zGGNujOwxurs/ihX+kVtICFTXoSeDwDy/d7sY0NgnF2+DQfdtwA3A/ke3ozhBBCOEtMDLz2mjkS/f770KgRDfZqnnwqn6i6qWx6JITme15nNxeQw85TH1s9Dm5aAS1vhWUvwtQ+kHXAM9shhBDC+SwWM9bSb7/BunUwbBj4+MCMGdCtmxl36fvvaRhexMEu9bhp/kIWRjxFg2H5/FkvGP7Khx8GQ84QIMfTWyPOMVJUEsKDalObH/mRiUxkG9vo6NcW/xdeZuzSzjwc9Ce92/7O0g5d8TmeAz/Z0M8peAz44UPI7wzs8vQmCCGEcKaQELj/fjNg68yZ6B7dCcuA1u9mYmsEtQeuxbK8Jbt5Byg2QLdvEFz6BfT9FlJXmtnh9sz32GYIIYRwkTZtzCDee/fCqFFQrZrpSj1oEDRqhOWdt5jQJZffk18lN2keF10TyIO9fLHvs6C/nQh7mwObPL0V4hwiRSUhPEyhuJEb2cIW+tGPUYzi5tZteHXln1w8sDc9dy3myqfms6xTJ1RmEcwGhgHD1sG2VsB0D2+BEEIIp/Pxgf79UQsWwqpVpFzTD7tF4T/VRkDnfBpc+AgHf25Jnt5y6uPiboYhqyA4EqZfBotHSXc4IYQ4F9WqBS++CPv2wUcfme5w+/bBY49BTAy9x40gPbANFy5az/ttu9H2JjupvkHoaftgUVuwfcy/ZhgVohykqCSEl4gkkqlMZS5zKaSQS60XseHpwfy6NIUjCy6hS8RS+k+Zw+r27SETM7RSpxx4aABk9QcOengLhBBCuET79tSaMQvbjj1MHtiL41WABRDZdyt+8S05PPkGKMo7uX715nDjcmh1Byx/GSZ3hyOJHgtfCCGECwUHwz33wNatMGsW9OwJGRnw2mv4xdbnzwnPM2v+R2xRLxA7NJfvmofCChtMuReOXwmkn+0VhPhPbisqKaUuU0ptU0rtVEqNKOH+R5VSW5RSG5RSvyul6rkrNiG8SV/6solNPM/z/MAP9GvcjAFL3mbsRTZ+eexyuoxcSf+ZM9nZuhUcB94DGs6Cd+qB/VvkiIMQQpybAurX5YYpf7BiwSpGP1yPg1FgWa+JGDyFoqZhFH7yDOTmmpV9g+CSz+GK7+DYdjM73PJX5KwlIYQ4V1ks0K8f/PUXLFsG11wDhYXw+ef0uyKOnDs30Pf7j7np8lAGXe5DwREr+tu5sLUJsMTT0YtKzC1FJaWUD/Ah0BeIAwYrpeJOW20tkKC1bg1MA153R2xCeKMAAniO59jMZrrRjcctj/Ltw+34auliur6nmPVdf+IWrOPGKVPJaFQXDgOP5EOTYTArHkjy9CYIIYRwkYvbtmf0m7t4cvIbDH/fj5RGCuuuAnzveQlb/Qh45f8g3XHkudkNcMsWaHQVLB4JEzvBofWe3QAhhBCu1amTGcQ7MRFuvx2sVvxmTmfakLvZ3akh6TvjaXJzEVvDqsCcwzCnG2TfhukOIUTZuOtMpY7ATq31Lq11ATAZ6F98Ba31n1rrE0PRLwOi3RSbEF6rEY2YwxxmMIN00hkc3Z16f97Ca5cdwreDhe+rDKB64k5e+PgjiqqHwj9A//XQpR6sGQnYPb0JQgghXMBqsfBNz8e444ZE2v/Qk9umwJF4Kz6HsmHkaHTdOvDUU5CSYsZX6jfVLFnJMDEBljwHtgJPb4YQQghXatoUxo2DPXvgiScgNJT6KxYz/6pVzOpWm5d8MnmlXRXs2y3w1Vewvi7oWZ6OWlQy7ioq1eHUUyeSHbedye3AvJLuUEoNV0qtUkqtOnz4sBNDFMI7KRTXcA2JJDKCEXynJvHKLU15etlH9H7PRtF1vrx0+T2E7U3hh9FPooOssExDwitwXQ3Y85enN0GIUpFu0kKUXccaDdnf6g/CWn1KxIIARs3zJa+3FZWZA6+/DvXrw913m1mCmgwwZy01vQGWjYEJ7SF1lac3QYhSkzwhRDnVrm1ywr598MorEBlJ680HmDBEM+j2HF6w2dkQHA6/pcN3/eFwX+CQp6MWlYS7ikqqhNtKHPhFKTUESADGlnS/1vozrXWC1johIiLCiSEK4d2CCeYVXmEDG2hHO56tfh9H53TitceXEX0Z5LwUzPVPv0b07hQSb+2P9gFmHIMmveCBrpAmA3kL7yXdpIUoP4Xi7ebD2eq7mSmNehP5exEzllSDa0EXFsCnn0JsLNx5J6RmwOXj4erZkHcUJnWChSOgMNfTmyHEf5I8IYQTVK0KI0aYM5ccuaHh7iJeeALCRx9j/FFfcg/6osf/DAvqQeFnyHit4mzcVVRKBmKKXY8GDpy+klKqDzAKuEprne+m2ISoVJrRjN/4je/4jgPqAE/16EKbjdfxeGwigfGQ8nsN4r6cSa9Nm8m4pDG6CPhgKdSvBS8+CNnZnt4EIUoi3aSFqKAmAXXZ0XgeIw58zcBWdi60IOtgAAAgAElEQVT63pfkzWFwE2h7kekC0aSJGV+D5jBsM7S8DVa+BuPjYb8M1Cq8muQJIZwlIACGDzczxk2ZAi1bEpMMN79cyLGxhazfFghL8+Cru2BnPLDb0xELL+auotJKoLFSqoFSyg+4ATils6ZSKh74FFNQknPthPgPCsUN3MA2tvECL/Cbzy+8dVtL+q25ncHrk6AHLM6PI2z+dp7/bTL21kGQreHZ96FRTRg/Duwy3pLwKtJNWggnUCierj2MvSGbObq/L02aH2fkl1Fkb/HHPlSh7Tb48kszzsbdD0ODJ+G6X8CWB5O7w58PQ6EcfBBeSfKEEM7m4wMDB8L69TBtGvbWramdAm0/yyXtbTj+hx9MXQ8/NoaMZwGZQVT8m1uKSlrrIuB+YD6QCHyvtd6slBqjlLrKsdpYIASYqpRap5SSEcKEOItQQhnNaHaxiwd5kOmBE5j+amOGTnucTs8dgbvgpfhB+K3NZNHnw9F1FRzMgaF3QtfGsGK5pzdBiBOkm7QQTlRH1WZN3Zl8lPk9n2QGE9s0n8+/qIl9GxTc4odGwzffQLNmMHo8dJ4Bbe+DNe/Cl00hcSJoOfggvIrkCSFcxWKB667DsnYt/PADOW1aU+MQhE0pIOsNhf1LG3z6IqxqAPbVno5WeBl3namE1nqu1rqJ1rqR1volx22jtTbDy2ut+2itI7XWbR3LVf/9jEKIEyKI4G3eZjvbGcxgJtR8m00zGjL4oRepfWUWtg8t9Lj1U5qt38/hx9ugQ4Hlu6BTZ7iln5kdSAjPkm7SQjiZQnFr6PUcrJ7I41kf81iOhbaxmr/GhaK22cm+rQraomD8eGjbAb48Cu0nQnAUzB0C33WFA8s8vRlCnCB5QghXs1jg6qsJWrsOZs0ipXUzQo5qLLOg8EUFzyfDuARIuh445ulohZdwW1FJCOF69ajHV3zFBjbQS/Xiu7hnKVgcS/9mHxJ4YQE71tWi5th13LTqTwquqgo+wDc/QeMYeO1ZyJffXl4h/R9Y+n8wvh0UZHo6GneRbtJCuIgvvjwecjeHquzkmszXuSrPTt9GsHccqO02jt5RC+1jgUmToPcQmNcE4l6HjH3wXReYcxNkJJ39hYRwLckTQriLUtCvH7XWbaHwpx/Z0qYevhka5oB+DLhnGkyMgP2PAoWejlZ4mBSVhDgHtaAFM5nJ3/xNU9WUHy++n4g/m9Fz50TUNXYmcyH+M4/y8exX0G2skG2DES9C81owazpomeXB7XIOwZr3YVIX+CIW/h4NfpmQ/a+DsOck6SYthOsFEcSY0Cc4GLybppnP0K6giHvqKWyfHUNvLyJ1eFO01QqTvoOrRsL6fhD7EOyYDl81hb9fgMKcs7+QEC4geUIID1AK3yuuIm7tbnb+OImVCTVQWZji0v02ePht+L4qpL6DzBJ3/lK6EjceExIS9KpVqzwdhhBeTaP5mZ95mqdZz3qa5Lekxmej+Xv7dajnLASE5bDgvcEkjJ2FOuh40MVt4J1JEHf6TL3CqQoyYedMM3bJ3t9A2yAiApppaJYGVcDMjty2zE+tlFqttU5wdsiVjeQJIc7sEId4OvNlZgR+xAiLnYcV2Pb5kv5sE2pN3ISy2yEoCO67HdonQ/IPEBINPV6FZoNBybHJykzyhCF5Qogy0Jpp096i0UvPEb/eTOqgI0BdClxdA7p/DDUHeDZG4TSlzRPya0CIc5xC0Ze+rGENk5iE8i/k7wcGEju2Ne2+m0rumwF0vO9Hmq7fSdqdjSAQ+HU9tG4BDw6FY9Jf2qlsBfDPbPjpBvg4EuYNhaOLoIMVhgFDj0PHdlDlPfLZQnkKSkIIURo1qckXoe+wzrqDNdnDaKbtzIopova3G8hcH0jKlc0gJwfGvg/3LYTj94FvhIy35E3y0j0dgRDifKIUA65/jLZrjvPNx4+zPdYPdRiYAPqRNHjmevixAaQt9HSkImMvrP8Eljzr8peSM5WEOM/YsPE93zOGMWxlKw1zWxD45Wg2Vx0AN1kYtOIHxj99M75/ZpuzWKv7wzuvwk0Pmf7Vouy0htQVsPkr2DYZ8o5DgBWaFkFzoHZNUFeiuYLt1OVHFjKVn1nHIvawhzpElvkl5Qi0IXlCiNLbylYey3qO5ODvGaMt9LfYSV8SSsFTNai5ZLdZKSYG7rwYqs2BvIPQ/Cbo9hJUqefZ4M8XBZmQvBD2ToO9P8Pxw3BfFlgDyvxUkicMyRNClJ+tMI8v37qby98bT50DZsZQHQv0BXVpW+jyLVRr5dkgzxe2QjiwBHbNhd1z4chmAIrCo7DekgQWa5mfsrR5QopKQpynbNiYylTGMIZEEqmfEwcTR7On6QAs3eC9SSO494W3UDsdU0p3qw5ffgeNL/Zs4JVJ7hFIHA8bP4S0nWBVEKtNIaleW/C5iiwuZB7HmMB8FuifOa72OR4cB/bLmJn9GP1Da5f5paWxYEieEKLstrGN0dmvsTvgW16w2OmL5ujcKugRIVTf5BjnrVlTuLkVBM4CNLS8FTqNlOKSs9kKIXUl7JsPe3+AlM1gt4MVqAP5dcPxb7sG/OqX+aklTxiSJ4SouOyco0wYM5TrP5tLtWOO+kIr0JeB6tUTOn4M1Zt7NshzUVYK7J5nikh7f4WCDLTFh9zoEGiQTVCDIo5XgyqqEIUUlUokSUCIirNhYxrTGMMYtrCFellx5M96ltSE6wmul8OsZwfT66M5qGzAF3iwAbw4FQLaezp076TtsO8P2PgO7PwZbDaIAlpZoGlvtP8ANtCADwrWMsc2j5SAJaCKgFCgDyRdBvMvI+j3usRnwjfvQqNGZQ9DGguG5Akhyi+JJP4v5022+X3M8z4F9NJwZFIVfJ71peqeI2alDu1gcF1Qc833XwtHcSmsvkdjr7S0hqNbzTh7++ZC0l9QkGfuiwRdz0JS3ZpMrmPhI+sBkrCQRhrhhJf5pSRPGJInhHCeQ4f28uPzN3LjN38TnANagUoALgHax0P71yCmj/R+KC+7DVKWmyLS7rlwaC0AtpAAChvYCGhQCPUg1Q/+xp8DtCWbQTzOA/hIUalkkgSEcB47dqYxjRd4gS1sITqrOTm/j+Zo++uJsSQz77araTF/nVm5NvBWCxj4FagOHo3ba2Qkweb3YdMXkHEUAjBnJLVqR26Nobx2MIxx9oWkRPyM3TfF8aA2cOgy/Ff0pdn6LvTSfnRrDvHx0KBBxfKtNBYMyRNCVNxhDvNa7nts8nmbZ/2yuaAA0j4LJWCMIuRwhlnpmivh2nBIm+IoLt3iKC418GjslYK9yHRp2z4V/pkBWYcA0FVB1YXcegEsianKZ4FHmEshBfgSS1dCuZgCLmYl7bHiU+aXlTxhSJ4Qwvm271zH0jFDGDx5M36FoP1A9Qa6A9HRkPA8NL0ZfPw8HGklYCswB6x3zICdP0BuGlopimpb8W1QCA3hWA1YoBQ7acg+rmQHQ1lPW1KwEA6kUb7BtKWoJIQolxPFpTGMYTObicxpQMHyezjW6FYu2baKSbffTPWkNLNyT+DN1tDufVA9PBq3R9gKYNcU2DgW9mw0Y1DVBVrVI63RIEanV2WidTEZVX8HlQ9URWVeQo09l9F136UM9K9NrxZQq5bzQ5PGgiF5QgjnySCDd/M/Zq1+lZEB6SRkwZE3Qgh9PR+/3EK0vz/q/jugZyH8842Z0TJumCkuVW3o6fC9i70I9v0JO6bCjmmQe8x0kW6goT7sqxfGlLBCppDDGiCKFoRyMUe5mDR6ACEE2aDRIVhQi3KcpyR54gTJE0K4zt8rfif1xdu5dvZeAIpqKKxXaWgGhIZC/MPQ+mEIrObZQL1NYTbs/tkUknbNgoIstK8F3dCOJRay68PCAFhNOEn0ZgeD2cbFHDBTR1PTDnGHofoGiNwCHzwE5TlWLUUlIUSF2LEzgxm8z/ssZCG+Nn8CEweR538Hoyb8wshXXsFaaIMgYAjwRHNo9JZjTtFz+JRWbYf9f8DWN2H7n5CbDyGgW1ZlX9ylPKMimOq/jPxAx3eTvSEBqf3pdugqXq5zAR0ifN0SpjQWDMkTQjhfHnl8WvAlq4pe4LGgQ7RNhoIRFvwmmjH4CqOi8B39BDTeDZs+NwWUFsOg06jzu7hkK4SkP2Db97BzOuQdR/sqVEONrYliZYMgvvTN5icgk0hC6cMxLiGPPkBtwrKg2mYo+AtSp4FtLWCD9HQICyt7OJInDMkTQrjeljkzKXj6XtpuNGfrZ7ewEDzQDsGA1Qotb4J2z0B4rGcD9aS8Y2aG6B3TzUQMRQXoAAsq1g6xkFgPfrJa2EwCOxjEbq4khcaAIiwfoneCdREcngoH/jj5tI0bw7Zt5esBIUUlIYTTbGITH/Mx3/ItWWQRmNqOmO3X8slz8+n11yKzUiPgDqB/DDR+Cqy3YDLFOUBrSFkK296F7XMgKxusoBv6sysunlE16zA1cAV2axKgIK8TVXdfRb8j/Xm5eXOiq7u/yCaNBUPyhBCuU0QRk4um8kvGG/SttoYBS8H3EWC5uf9Iu3hCXn4Wf9+/YMOnprgUNxQSHoMaLTwZuvvYCsz4SNunwj/TIS8T7XeykLSwvh/f+OYzGyiiG1n6KuzqUqAVYUkKtQTS5wCLgL0QEQHt2pklPt78bdjQtY2Fc53kCSHcxG7nyBdfY3vmMWoeSgcg5UoLkdfaUemg7EDsRdD+OajT7fwYdyn7oOnStmMqJC0Auw0dolCNNbbGsLAOzLVY2cCFbNe3sEddCYQRmA2hayB7DmTPBraY3dWkCbRta/LDiSUiovzhSVFJCOF0mWQygQl8yIdsZjM+uWEM/rIzbz2/goi0Y2iro7/0FUBrP2h9A1QbBTTxcOTloDUcXA3bPoVtUyHzOPiArm/hUJM4RteJ4osqK7CpDNCBcPRiam29iluyr+CJjlGEV/Vs+NJYMCRPCOEeu9jFewc+ILzKOO7/MZPqTwH7zX2bru9HyHOjqH/oO1NcKsozDYbWd0GTAWAN8GjsTleYDXvmm6PNu36E/GxTSIrVFDZW/FrfwiSrjTnanywupUj1B/uV+CfWJH8OsBD4G2JCThaOTiy1azuvnSV5wpA8IYSb5eSQ/+ob6LEvE5CXT74fbLtX0fwyje9OIA8Ij4Zmt0PzIefe2Us5h01+2DYRkpeA1uhwUI0hKxZmR8E85ccG3ZfdthvJsF4O9hD81kDBdGAu+G6DVi1OLR61bg0hIc4NVYpKQgiX0WgWsYiP+IjpejpVjhbx2iO1uWO8mWo6t0EIAQOyUJFADNCmFcSOBp9roByDibqN1nB4A2z7BraNh+NpZlS7epDTuDHv1mzJa9USOe67FXQQKmkAsesG8BAXMbRXEKGhnt6Ak6SxYEieEMK9bNiYdHA+q9PG8MD3y2k0FsgFm7/i10cHUvjQK/TdNx3rxs/g2A4IqGa6xrUaDtWbeTr88ss9Crtmw47vzbTORYXoAIVqpClooviprmaKFX7W1chQ/YCrsSRfjH1eMPwKVVZD9+ZwwQXQvn3Fjy6XhuQJQ/KEEB6yfz/2p0dhGf8NAIcjYOUL0KOzhZAddnSSY0CNqHbQfBg0HQTBkR4Nudxy0swZSdvGQ9Lik4WkprC/KUytDj8TyMbCqznocz02n0tRWUHon4HZELEKLmwBPXpAt24QFwd+bhjjXIpKQgi3SCWVcYzjw6JPabYoma9usVB/n518PyurB3ejXce1BOQeN2MvtagCre+Eqk8BLv61XFo5h8zppknzYd88OHbAZLC6UBhbi8lhPXi+2nF2VfkNVBFkdqbRptt4OmsQN3arQmCgpzegZNJYMCRPCOE5e7LS+OLPF7hp/Oc0m5oPQGGUYuarPUm8+UNuTk6lwfpPzA9texFE9zRnLzW+Fqz+Ho6+FDL3w86ZsHMiJC0HbUeHgIqFzMY+zKhj40cfmK/rkaOuhaz+8OsF8KuVyI1wYTR07wbdu0PLlmApz9Q8FSB5wpA8IYSHrVqFfuQR1OLFAGxs6cOsD2xcHO9Lx22F6ERQhwFlgXoXQ/ObIPZq8POio7klyT1qcsS2r2HfEjMuazjQFHY1UUysoZmlQ9mSfy05gdcDfWC3P8yGGsugjx/0vsAUkpo08UxvQCkqCSHcqogi5jCH749/Ra/HZ3PHODNg69LOtfnqiTt4OGMezY+uQmkN9RS07g6NXgafrrh1YO/sg5D8FyT9ZAZMPWrOrsIXqAO2hiH8EXA5L4QEsaTOfLCkgC2C2klDeTT7Nh5uFoePF59sdYI0FgzJE0J4XpFNM/3Tz+j88RPU25RpbuwNiR9X5ddG19Ew70Yu3rQc/42fw/HdEFgDWtwCrYdDeGOPxn4KreHYdtNtYecESE00t4cDjSEpNpAJkbnMssAKGmLXN8C6gTCjNfV2KPpUhR5dTRGpfn3PDxciecKQPCGEF9Aapk1DP/EEaq+ZKe6bAdGM+3A/19fU3HLYQpWtdvRWKyqjCKyB0Kg/NL8R6l8KPm44bac0sg6Y7s/bv4K9S8BuhzCgKSQ2gfE14UdbOIl6ANp3ANh6wVJfqi2FnhlwVSxc2BPq1fN8jgApKgkhPOg4x1nx8xja3vEhEfvzyQ2AEa/WYvrNQ3nm1xRuSZtBQF6WGce7XgBENIKIjhDRF4L6UL7Jkc8gOxWS/oTkGZC0CI4eNLf7AtGgo/1ID4tnTnZXJvhY+b3ZUor8F4O2EJZxObfm386rEVfgr9wza5uzSGPBkDwhhBex29n18hfUfP0RQjKzsfsBI8EyAnb6VWVdYX/aHWhLgw0LUTtngbZB3d7QsB9Ed4eINmCxui/enDQ4uBJSl0PqH5CyDnIdRbFIsMcqNjQK48vqmcy22NhDPSgaBEsHEvlLOy4vUlzZwRSRXN2VrTwkTxiSJ4TwInl58Prr8PLLkJ9PXmg4z953ER+N3MDA0O3cZYfOB8C21YJlmy8qL990o47uAbW7miWyvXvG6tMa0ndC8iLYPx/2L4B0RzujCtAU1jeBbyJhpi2C3daBwHWQ3J3w5VY6HoMB4dC3M9Sp4/pwy0OKSkIIzzt2jNyHhhM4fhoAf/WEW7+CffW6cu3qZoxet4Km+f/gl5978jEhQEQQRNSHiASIuAzC+4GlhJHntIbcI5C1BzITIWsHZO6GrCTITIWMQ5B+zKzrB9QBYsLIi+zIjwWd+aLAyh81dmGLWARql1mtsAlXFN7GG0E305DaLtw5riWNBUPyhBBeKC0NnnwSvvoKgNRGfuz9vID4XuarOsUeyp5jfWi7I4zAzYsg/R/zOL9QqNXFFJjqdIeojuDrpD7IhTlwaC2kroCURZC6FI6nnry/OhAFBZEB/B5djS+rH2a+pZBM6kD+QPyXD6Ljpo4Mqaa4orv3NhCKkzxhSJ4Qwgvt2AH33gu//QZAUZduvHLHvbzebQENYydwlyWbIUVQZS8UbAvFN8WCSj9uHmvxhch2J4tMtbtCiBN+09ttkLYJ9i+E5Lno/X+jsjPMfQFANBTUgdXRMD4SZurapFgGQuEAQtd1IT7VwoAQGNwaalSveDjuIEUlIYT3+PFHGD4cDh2iINiPUWOr88bdKaB8gARqpLSm67oqDDqSQg8SiSr8B5/0DDO1KIAVqBEENaLBZofMY5CVDZn5YDvtO0xhzoAKdSxRURTFdGWef0feydAsqrKRwqoLQSWb1W3ViMrpQV//ntzq14MLiEe5szuei0hjwZA8IYQXW7AA7r4btm4F4K9LE5j63jF6NfmHvpiv8nR7IIeONCPyUA2qpOSjkpPgyG7zeB8/iEwwBabo7lD7AggoNvWm1lCUY8a1yD8GeUch77S/OSlwcCmk7TDjXYAjd4A9SpFSsyp/Vgvkl6AMllizMIcfoiDnemptHsQ1h7pwb1MLcY28o6tCWUieMCRPCOGltIbJk+GRR+DgQbBa4fHH2X7b49x1ZD5rYz/n+up/MURBB60IytVwAPIP+GE5EID1YA6qqMg8V5V6p57JZLGCrRDsxZbi120F/7us89IoPPATPvvX4pNfYJ4vFIiGzDqwPBrmVoPFRLJWxVNEO/wO96fVrg5c5acY3hiinDwrm7tIUUkI4V3S0swRh6lTAci8pAsvjEtgWu31JKmV2C0nzlaKhMLOBPyTwHV7Ari9aBcdrasJytiJOnIMfC0Q4g+hIRASDqER2ENrkxtSl4PB0Wz2qcpWm2aLrZBN9nQ2+68gN3ghqEPm6QujqH6sJxfae/BoRA86+8Rhwc2jo7qBNBYMyRNCeLn8fBg7Fl580VyuVo2dz4zgvp55BDf5gstD9tIaiMOcyApALuTvD8a6Pxyf/YVw8LAZtwIF1ZoB2lE4OmoGAD8TBToQVAQQBVlRfqysGcj8kBz+thSyGsjBD2gJBfGEHI6naXp77vbtwLBYH3wreeqQPGFInhDCy6Wnw6hR8PHHptBUvz58+CFcfjnfb9/H8/nTSApfQ2yNFXTy30UnZaMT0KwILIeBA5B7wB/fA2DNyi9fDNWAaDhcBxbV8ePnsAYsogNb7d3xSW1FrZQ4WtvC6FkN+tWFZn5uHTHWZaSoJITwTlOmmOLS0aMQFgYffEDhkEFsYCM/sYyf8pexWS0j12+H4wEW0K2xHO1MzYMd8LFosoNSyQtKpTAgFZt/KlhTQKUC2f9+vfy6BKf0pP2xHjwe0ZMro2PPiTORzkYaC4bkCSEqiR074J574PffzfUePeCTT1he05cPk1eywLoGn5rLaFJtHXE+WcQBcRrisFC1yA4pwH7QqT5gtaMCNPhjuiQ4FlsAZPj7kRbgQ4q/Itm3kP2WQtYoWArsJRSIh6K2WA/EE3Mgnp4FzRkY48cl9cHnHEsdkicMyRNCVBLLl5uzW9etM9evuw7eeQeiowEzHNPvawv54vB2loZsILfuChJi/qaT/1Y6kUEnDZGZwGHH8/kAFsfiA9kWyLRAhkWR4eNLuiWQ45YgDvpWZZF/BxYf6Uvqlt7UTatJWx/oHQHXNIPaThwK1ttIUUkI4b1SU+Guu2DWLHN9yBBzxKFKlf+tkkYav+sVjM9cxjKWcjR4Odon8+RzFFXFpyAKa24tAnOjCM6JIjw3ioi8KGIKa9GwKIo4Sy0uaVODsDA3b58XkMaCIXlCiEpEa5g0yXR1OHwYfH3hqadg5EgINGMnpR7UjF+XxJSitWypuZbc+muoVX01cZYDxAGxQCZwEDjk+GsuWzhKdTQ1gQighvlrrwnJLQnfEU/8sQb0rW5hUBzERHpkD7iV5AlD8oQQlUhREbz/Pjz7LGRnQ0iIOdP1vvtM97jTVl23Dmavhp9yjrEpegO12v1FizpryCkM43huFOkZdUg/VpeMw/WwH6lFUGYNQgp8CdVmrO1wH4j0hV5R0Cfee2ZlcxcpKgkhvJvW8OWX8OCDkJMDjRrBd99Bhw4lrm7Dxj/8gz/+RBJJAG6Y1aESk8aCIXlCiEro6FEYMQI+/9xcb9rUDOrdpcu/Vk1NhT8WwqTkNJbUXEd67BbID8KSUYOArAhCciKoklODaoVVqW61UMMPIgIgKghqh0BMVWjfBoKD3byNXkDyhCF5QohKKCkJHnoIfvjBXO/Y0bQrWrQ440PsdkhMNIWm4GCoVg3Cw83fatX+d+xCFCNFJSFE5bB1KwwebL7hrVZ46SV4/HGwVPLBKjxMGguG5AkhKrHFi80kD4mJ5tDwo4/C//3ff/7yT08Hf39pHJSG5AlD8oQQldjs2eYspaQkc3brs8+agxK+vp6O7JxQ2jwhrTYhhGc1awbLlsHDD5vzVJ96Ci69FFJSPB2ZEEIIT+rWDdasMQ0EpeDNN6FtW1iy5IwPqVpVCkpCCHHe6NcPNm0yYy0VFsLo0abXw9q1no7svCJFJSGE5/n7w9tvw08/QY0a8Ntv0Lo1zJ3r6ciEEEJ4UkAAvPKKOfgQFwfbt0P37mbcpZwcT0cnhBDC06pUMTPD/fEHNGwI69ebwtIzz5hZRYXLSVFJCOE9rrgCNmyAPn0gLc1cf/hhSQhCCHG+69DBnLU0cqTpHv3OO9CmDSxa5OnIhBBCeINevUw74qGHzABKL70E8fFm1jjhUlJUEkJ4l1q1YP58eO01M8bSu+9C585m7CUhhBDnL39/00hYtgxatoSdO6FnT9OAyM72dHRCCCE8LTjYHHRYtAiaNDFj8nXtasZrlbNbXUaKSkII72OxwJNPmnEzGjUyg3i3b29mdajEkwsIIYRwgoQEWLXKdG2wWOC990yX6QULPB2ZEEIIb3DBBab98NRT5vqbb5qzWxcu9Gxc5ygpKgkhvFfHjqa7w003maMLt98O114LycmejkwIIYQn+fubmeBWrDAFpV274MIL4YEHICvL09EJIYTwtMBAePXVf5/dev/9kJHh6ejOKVJUEkJ4typVYMIE+PZbCA2FmTOheXNzZNpm83R0QgghPKldO1i50sz4Y7XCBx+YLg/ffmvG1BBCCHF+69ABVq+G554zeeLDD6FxY9MDQvKEU0hRSQhROdx8M2zZAtdcY45CP/SQGWtpzRpPR+Y6Bw7IOCFCCHE2fn7wwgumuNShA6SkwLBh0KWLDNAqhBDC5Innnzddp7t2hUOHTA+Ijh3NcBuiQtxWVFJKXaaU2qaU2qmUGlHC/T2UUmuUUkVKqQHuiksIUYlER8OMGeZspehokxg6dIBHH6383R2ys00/77FjYcAAiImBOnXgl188HZnbSJ4QQlRI27amm8PXX0NUlOka17kzDB1qivTnktRU+PlneOUVOHrU09G4jeQJIUSFtGkDixfDxInmd/bq1dCtG9x447k7vEZBgctfwi1FJaWUD/Ah0BeIAwYrpeJOW20fcAswyR0xCSEqsf79zVlLjzxirr/9NsTFwaxZno2rtOx22LzZnHZ7112mIRQWZvp5P/kkTJ9uEluVKpCW5ulo3ULyhBDCKSwWc5bS9u3w9NPm6Ngtkl4AABYrSURBVPT48aZL3MsvQ16epyMsG7vdjAMydSqMGgWXX25mSa1VC/r2hZEjzQGW84DkCSGEUyhlikjbtsGzz0JAAHz3HTRtasbqy831dITld+gQ/PorvPGG6eXRujWEhLj84IPVpc9+Ukdgp9Z6F4BSajLQH9hyYgWt9R7HfdKxUQhxdqGh8NZbMGQIDB9ujjT0728G8n7vPXP0wVukp8Pff5vTa5ctM100MjNPXcfHxxSXOnU6uTRrZhpI5wfJE0II5wkNNUWkO+4wU0n/8IMpynz+ufmxfe21pmHhTQoLzQGHtWvNrEUn/p6eL8AcdGjbFuLjTYHp/CB5QgjhPMHBMGYM3HabOag7daoZn++LL072HPC2PHFCUZEpiq1ff+qSmvrvdZUy63bp4rJw3FVUqgMkFbueDHQqzxMppYYDwwHq1q1b8ciEEJVbu3ZmzIwPPzQNhhkzTIX+pZfg3ntNscbdkpLMqbWLF8OiRbBpE2h96joxMacWkNq3h6Ag98fqPSRPCCGcr2FDkxf++MOMxbdpk2koXHghvPuuOYrrKUeOwNKlZvn7b9NdLyfn3+vVqmWKR8WXBg28t7HjOpInhBDOV78+fP89LFhg8sT69TBwoOlB8M47poDvSScOOKxcaZY1a0wuy8//97ohIaaLX/GlZUtTQHMhdxWVSsp6uoTbzkpr/RnwGUBCQkK5nkMIcY7x8YEHHzSDeD/4oBlz6cEHzew/994LCQlmxjirC77y7HZITDTFoxOFpL17T13Hz8+M/dStmxnfo1On8+nIcmlJnhBCuE7v3ubMn88/N90d/vrLFGfuvBNuuME0GqpWdd3rn8gVJwpIf/9tjhyfrlEjc5DhRPGobVuIjHRdXJWL5AkhhOv07Gl6PowbZw5UL1hgvo9vuw2uvtpcjopybQx2O+zYcbKAtHKlyV0ldd2uX//fBaQGDTzSy8FdRaVkIKbY9WjgHBsxUQjhcTExpovDzJnwwANmnInbbjP3BQWZH+gJCSeXJk1K/8Wbl2eKRbt2mWX3bti61TQQTu+nHBYGF1xgikjdu5vXCghw7raeeyRPCCFcy2qFe+6BQYPMbHEffgiffmoWMGc1tWtnlvh487dmzbK9RmGhGRQ8KcksO3aYPLFsmekKXVxAgJl5qGtX0y2hSxeIiHDOtp6bJE8IIVzLx8eMdzpwoOka98EHpsg0bpy5v3ZtU1xKSDB/y1tostnMuKkpKfDPPycLSKtWQUbGv9ePjTUHqDt0MK/Zpo1pb3gJdxWVVgKNlVINgP3ADcCNbnptIcT55uqr4aKLTAJYutR8Qe/ebcY0Kj5taGioaTScKDK1bg3Hjp0sHJ0oHu3aBfv3n/n16tQxxaPu3U0hqUULz3S7q9wkTwgh3KNaNdP17a67TINh1SrYsOHk9/60aSfXrV371EJTs2bmQMKJolFy8qmXU1P/3d35hDp1zAGHrl3N0qaNOZNVlJbkCSGEe4SHm4mAhg83YyytWmW6nR04YJbZs0+uW7v2qUWmJk1M9+aUlJNLauqp1w8dMoWlktSpc7KA1KGDee7wcPdsdzkpfabE5+wXUupy4B3AB/hSa/2SUmoMsEprPUsp1QH4AQgH8oBUrXWL/3rOhIQEveo8mfFCCFFBR46YU1pXrTq5JCWd/XEn+PhAvXrmtNKGDU8unTpB3bpeN7aFUmq11jrB03GUheQJIYTHFBaa7mlr15qGw9q1ZsnKKtvzKGW6N8fEQHS0yRsnzkaKiTn7491I8oQheUIIUSonZuM80Z5Yvdrki5ImUyiNGjXMWU4xMaYYdaKI5EVDZJQ2T7itqOQKkgSEEBVy8OCpRabNm03Xg4YNTy0eNWhgvvBdMSaTi1TGxoIrSJ4QQpTbiQZE8ULTjh0mT0RHm7xwonh04nKtWuDr6+nIS0XyhCF5QghRbsULTSeKTXv2mDxRq9bJJSrq1OuRkZXiTNXS5onK00ISQghni4yEK64wixBCCFGcxWK6MTRpYsZhEkIIIYornicGD/Z0NB7j/qHBhRBCCCGEEEIIIUSlJ0UlIYQQQgghhBBCCFFmUlQSQgghhBBCCCGEEGUmRSUhhBBCCCGEEEIIUWZSVBJCCCGEEEIIIYQQZSZFJSGEEEIIIYQQQghRZlJUEkIIIYQQQgghhBBlJkUlIYQQQgghhBBCCFFmUlQSQgghhBBCCCGEEGWmtNaejqHclFKHgb3lfHgNIM2J4TibxFd+3hwbSHwV5c3xeVNs9bTWEZ4OwtPKkCe86X93Jt4eo7fHB94fo7fHBxKjM3hLfJInqHB7wp285X3jjWTflEz2S8lkv5zZ6fumVHmiUheVKkIptUprneDpOM5E4is/b44NJL6K8ub4vDk28d8qw//O22P09vjA+2P09vhAYnQGb49PeCd535yZ7JuSyX4pmeyXMyvvvpHub0IIIYQQQgghhBCizKSoJIQQQgghhBBCCCHK7HwuKn3m6QDOQuIrP2+ODSS+ivLm+Lw5NvHfKsP/zttj9Pb4wPtj9Pb4QGJ0Bm+PT3gned+cmeybksl+KZnslzMr1745b8dUEkIIIYQQQgghhBDldz6fqSSEEEIIIYQQQgghykmKSkIIIYQQQgghhBCizM6bopJS6nql1GallF0pVeI0eUqpGKXUn0qpRMe6D3lTfI71LlNKbVNK7VRKjXBjfNWUUr8qpXY4/oafYb3XHduRqJR6TymlvCi2ukqpXxyxbVFK1Xd1bGWJz7FuFaXUfqXUB+6IrbTxKaXaKqWWOv63G5RSg1wc03++z5VS/kqpKY77l7vrf1mG+B51vMc2KKV+V0rVc2d8omRl+J7do5TaqJRap5RaVez2Un+WXRXff+UppdTzju+PdY7lcmfGV9oYHeuV+BlRSjVwfGZ3OD7Dfi6IsTTfab2K7ad1Sqk8pdTVjvu+VkrtLnZfW3fH51jPViyGWcVu95Z9eMa84Kp9WJHcoJR62nH7NqXUpc6Ip5wxnjE/nOl/Ls5PFc1Z57KK5qJzVUXzy7mmIjnjXFaK/XKLUupwsffIHWd9Uq31ebEAzYGmwF9AwhnWqQW0c1wOBbYDcV4Unw/wD9AQ8APWuzG+14ERjssjgNdKWKcrsMQRpw+wFLjQG2Jz3PcXcLHjcggQ5C37rti67wKTgA/cEVsZ/rdNgMaOy7WBFKCqi+I56/scuBf4xHH5BmCKG/dXaeLrdeL9Bdzjzvhk+c//3Vm/Zx3r7QFqlHB7qT/Lrorvv/IU8DzwuKf34X99RoDvgRsclz8B7nFBjGX6PwHVgKPFPrNfAwNcuA9Lm7OyznC7V+zD/8oLrtiHFckNQJxjfX+ggeN5fFyw3yqUH870P5fl/FxK833rWG8PJeSsc3mpaC46V5eK5pdzaalIzjiXl1Lul1soY1v0vDlTSWudqLXedpZ1UrTWaxyXM4FEoI63xAd0BHZqrXdprQuAyUB/10cHjtf5xnH5G+DqEtbRQADmDeoP+AIHvSE2pVQcYNVa/wqgtc7SWue4IbZSxQeglGoPRAK/uCmuE84an9Z6u9Z6h+PyAeAQEOGieErzPi8e8zTgIqVcf1ZcaePTWv9Z7P21DIh2U2ziP5Tye/a/lOqzXF7enqccr1nuXOX4jPbGfGbBBfvQoaz/pwHAPG/LCSXxpn3o5rwAFcsN/YHJWut8rfVuYKfj+dweo+QHUVpOyFnnrErQbvIUl/5OqWS8vT3hKS75XJw3RaWycpz+Fg8s92wkp6gDJBW7noz7GhORWusUMI0aoObpK2itlwJ/Yo5WpgDztdaJ3hAb5ohqulJqhlJqrVJqrFLKxw2xlSo+pZQFeBN4wk0xFVea/fc/SqmOmMLhPy6KpzTv8/+to7UuAo4D1V0Uz+nK+jm8HZjn0oiEs2ngF6XUaqXU8GK3l+mz4mpnyFP3O7rVfHmm097d4EyfkepAuuMzW/x2Zyvr/+kG4LvTbnvJsR/fVkr5eyi+AKXUKqXUMuXomoeX7sMz5AVn78OK5AZ3/X6qaH4o6X8uxNmcKWed7zzZbvKUiuSXc423tyc8pbSfi+scOXyaUirmbE9qdVZ03kAp9RsQVcJdo7TWP5bheUKA6cDDWusML4qvpMqprlhUxZ78P+Ir5eNjMaejnjjq9qtSqofWeqGnY8O817tjGmD7gCmYU/u+qGhsTorvXmCu1jrJFQVyJ8R34nlqAeOBYVpruzNiK+llSrjt9Pe5Sz8LZ1Hq11ZKDQESgJ4ujUj8j5PywAVa6wNKqZqY77Gtzvgec2J8Z8pTHwP/h3k//h+mUH2bB2I802fEaZ9bJ3+ntQLmF7v5aSAVUyT5DHgKGOOB+Oo63ocNgT+UUhuBkn6TeMM+PD0vVHgflvRSJdxW2tzgrpxR0fzwr/+51tpVB3CEF/D2nOVJ3t5u8hRX5Zdz8LvG29sTnlKabZ4NfKe1zldK3Y05m6v3fz3pOVVU0lr3qehzKKV8MT/UJ2qtZ1Q8qpOcEF8yULxSGA0cqOBz/s9/xaeUOqiUqqW1TnH8gDxUwmrXAMu01lmOx8wDOgMVTmxOiC0ZWKu13uV4zExHbE4pKjkhvi5Ad6XUvZjxnvyUUllaa6cMKuiE+FBKVQHmAM9orZc5I64zKM37/MQ6yUopKxCGGRPFHUr1OVRK9cEk+J5a63w3xXbec0YecHTlQWt9SCn1A+ZU4YVAqT4rro7vTHlKa32w2DqfAz+V5/ldmKvSgKpKKavjiGC5c5gzvtMcBgI/aK0Liz13iuNivlLqK+BxT8RX7H24Syn1F+agyHS8aB+eKS84Yx+WoCK5waW/n8oY4xnzwxn+5+daQ08U4+KcVal5e7vJU1yYX8617xpvb094yln3i9b6SLGrnwOvne1JpftbMY4+lF8AiVrrtzwdTwlWAo2VmfnFD3PKvrtG7J8FDHNcHgaUdIRgH9BTKWV1NHp6Ysb78IbYVgLhSqkT4z30Bra4ITYoRXxa65u01nW11vUxP76/dVZByRnxOd5vPzjimurieErzPi8e8wDgD621u44snDU+pVQ88Clwlda6zIUH4TlKqWClVOiJy8AlwCbH3aX5rnF1fGfMU44fkCdcw8m43a3Ez4jjM/on5jMLrtuHZfk/Dea0rm8n9qNjX1+N8/djab5zw090GVNK1QAuALZ40z78r7zgon1YkdwwC7hBmZl+GgCNgRVOiKnMMZ4pP5zpf+6CGMU55Cw563znyXaTp5Q7v7gtQvfx9vaEp5QmTxX/PXkVpWnPay8YhdwdC+YHdjKQjxk8er7j9tqYbkcA3TCnf20A1jmWy70lPsf1/2/v3mLmqso4jD9/aSkgkXAyIgINVEKpF0g8Ekm4aD1UDhoEtYVEosZDvKsSY4yJQILGxBCjxgsTkLZBSzQGajFQpA2K2CpaS1GoNSUNUDmU0hIItvh6sVfT+UoHZkq/Y59f8mb23mvvtVfXzOz369qHmU/3az+b6C7/HKv+Ox64G9jYXo9ry98F/LRNH0b3h9I/6A5O358obWvz89p7u57ul2kOn0jt61n/M4ztr78N8t5eAezq+V78DThnFNv0is853a0TF7fpI4Bb6R62ugY4faz6a8D2rWzf4z19ddtYts/o+74NkgdOp/sljHXAht7jbL/vyhi3r2+eorsFaX0ruw04aTz6sM3vN1e1/l3Tvru3AjNGoY2D5oSZwGPAG/bZ/netHx8ElgBHj3X76H5NdX37HK4HPjvR+pBXyQuj1Yf7+1wxYG6guzJoE/Aw8JGD3WdDtHG/+eHV3nPj0IxBjre8Ss6ayjFI37T5cfl/0zj2y+vKL1MtXk/OmMoxQL9c344n6+hOZJ31WnWmbShJkiRJkiQNzNvfJEmSJEmSNDQHlSRJkiRJkjQ0B5UkSZIkSZI0NAeVJEmSJEmSNDQHlSRJkiRJkjQ0B5U0YSXZnGTueLdDkjQxmSckSf2YI6Sx4aCSJEmSJEmShuagkiRJkiRJkobmoJImhSQzktyQ5PEWNySZ0VN+dZInWtnnklSSWX3qWpXkuiT3JXk+ye1Jjk+yNMmOJGuTzOxZ/7y27Ln2et4+dV2b5A9Jdia5M8kJPeXva/vZnmRdkgva8suS/GWfdi1K8us2fVOSHyX5Tav3T0nO6Fn3rCR3JdmW5OEkl/eUzU/yUNvusSRfbctPSLK8tWVbknuTeAyQNCWYJ8wTktSPOcIcoVFUVYYxIQPYDMxt09cA9wNvBk4E7gOubWUfBrYCc4CjgMVAAbP61LsK+BdwBnAM8BDwCDAXmAbcDNzY1j0OeBa4spV9us0f31PXJuBM4Mg2/51WdjLwDDCfbgB3Xps/EZgBbANm97Trr8ClbfqmVv6ett+lwM9b2RuBLcBVrexc4GlgTit/Aji/TR8LnNumrwd+AkxvcT6Q8X6fDcMwDjTME+YJwzCMfmGOMEcYYxOOLGqyWAhcU1VPVtVTwLfpDs4Al9MduDdU1Qut7LXcWFWbquo54A5gU1WtrKrdwK3AO9t6HwU2VtXiqtpdVbcA/wQu2qeuR6rqRWAZcE5bfgWwoqpWVNX/quou4M/A/Kp6CfhFW4ckc4CZwPKeen9VVWtam5b21HshsLmqbmxtegD4JfCJVr4LODvJm6rq2Va+Z/lJwGlVtauq7q2qGqCvJGkyME+YJySpH3OEOUKjxEElTRZvBR7tmX+0LdtTtqWnrHe6n//0TL+4n/mj++x3z75P7pnf2jP9Qs+2pwGXtUtEtyfZDnyA7mAM8DNgQZLQJbVlLUEMUu9796l3IfCWVn4p3RmNR5OsTvL+tvx7dGdV7kzy7yRfR5KmDvOEeUKS+jFHmCM0SqaNdwOkAT1OdwDc0OZPbcugu0TzbT3rnjIK++11KvDbAbbdAiyuqs/vr7Cq7k/yX7pLRxe0GMQWYHVVzetT71rgkiTTga/QnfE4pap2AouARe1sxj1J1lbV3QPuV5ImMvPEyHrNE5K0lzliZL3mCB00XqmkyeIW4JtJTmwPr/sWsKSVLQOuSjI7yVGt7GBZAZyZZEGSaUk+CZzNyEtL+1kCXJTkQ0kOS3JEkguS9Catm4EfArur6vcDtml5a9OVSaa3eHf79x+eZGGSY6pqF7ADeBkgyYVJZrWzGXuWvzzgPiVpojNP7GWekKSRzBF7mSN0UDmopMniOrp7iP8OrAceaMuoqjuAHwD30F2S+ce2zUuvrGY4VfUM3X3Hi+gejHc1cGFVPT3AtluAS4BvAE/RnRX4GiO/d4uBd7TXQdu0E/gg8Cm6sx9bge/SPbAPustfNyfZAXyRdq818HZgJfA8XR/9uKpWDbpfSZrgzBN76zVPSNJI5oi99ZojdFDFZ2tpqkkyG3gQmNEeTDdhJTkSeJLuVxU2jnd7JOlQYJ6QJPVjjpCG45VKmhKSfLxdrnks3Uj77RM9CTRfAtaaBCRpdJknJEn9mCOkA+eDujVVfAG4ie6+3tXAl8e1NQNIshkI8LFxbookHQrME5KkfswR0gHy9jdJkiRJkiQNzdvfJEmSJEmSNDQHlSRJkiRJkjQ0B5UkSZIkSZI0NAeVJEmSJEmSNDQHlSRJkiRJkjS0/wMzpmJNh+h2mAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1bf5e6a7b38>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T=1 week:  [0.00358235 0.00276278 0.00299642 0.00420163 0.00385517]\n",
+      "T=1 month:  [0.00309864 0.00467375 0.0053564  0.00473218 0.00695323]\n",
+      "T=2 months:  [0.00405539 0.00635089 0.00496945 0.00623992 0.00868845]\n",
+      "T=3 months:  [0.00511969 0.00558735 0.00561834 0.0090895  0.00732372]\n",
+      "T=6 months:  [0.00799324 0.00743358 0.00786481 0.00704351 0.00927781]\n",
+      "T=9 months:  [0.00802688 0.00870764 0.00731743 0.0061944  0.01133724]\n",
+      "T=1 year:  [0.00874137 0.00945326 0.005941   0.01162977 0.01082401]\n",
+      "T=1.5 years:  [0.00871327 0.00948823 0.00815824 0.00953475 0.01558524]\n",
+      "T=2 years:  [0.01055309 0.01022309 0.01102695 0.01279537 0.01229152]\n"
+     ]
+    }
+   ],
+   "source": [
+    "n=[10,20,50,100,500]\n",
+    "n=np.asarray(n)\n",
+    "r=1+10*pow(n,-0.9)\n",
+    "maturities=[7/365,1/12,1/6,1/4,1/2,3/4,1,1.5,2]\n",
+    "titles=['1 week','1 month','2 months','3 months','6 months','9 months','1 year','1.5 years','2 years']\n",
+    "colors=['b','#00FFFF','#00FF00','#FFFF00','#FF8C00']\n",
+    "fig,ax=plt.subplots(3,3,figsize=(20,15))\n",
+    "MSE=np.zeros(shape=(9,5))\n",
+    "for ix in range(3):\n",
+    "    for iy in range(3):\n",
+    "        i_row=ix*3+iy\n",
+    "        log_moneyness=np.asarray(data_log_moneyness.row_values(i_row))\n",
+    "        impvol_rough=np.asarray(data_impvol.row_values(i_row))\n",
+    "        strikes=S0*np.exp(log_moneyness)\n",
+    "        plt.subplot(3,3,i_row+1)\n",
+    "        for i_n in range(5):\n",
+    "            LHM=LF.LiftedHestonMc(vov=ν, v0=V0, rho=ρ, theta=θ, lamda=λ, H=H,n=n[i_n],rn=r[i_n])\n",
+    "            LHM.set_mc_params(n_path=1000, rn_seed=1234)\n",
+    "            prices_lifted=LHM.mc_price(strike=strikes, spot=S0, texp=maturities[i_row], N=300, cp=1)\n",
+    "            BSM_model=pf.Bsm(0.3)\n",
+    "            impvol_lifted=BSM_model._impvol_newton(prices_lifted,strikes,S0,maturities[i_row]) \n",
+    "            plt.plot(log_moneyness,impvol_lifted,color=colors[i_n])\n",
+    "            MSE[i_row,i_n]=np.mean(np.power(impvol_lifted-impvol_rough,2))\n",
+    "        plt.plot(log_moneyness,impvol_rough,color='r',linewidth=2)\n",
+    "        plt.legend(['n=10','n=20','n=50','n=100','n=500'])\n",
+    "        plt.xlabel('log moneyness',fontsize=12)\n",
+    "        plt.ylabel('implied volatility',fontsize=12)\n",
+    "        plt.title('T='+titles[i_row],fontsize=12)\n",
+    "plt.show()\n",
+    "for i in range(9):\n",
+    "    print(\"T=\"+titles[i]+\": \",MSE[i,:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result in the paper (Figure 2) is shown below:"
+   ]
+  },
+  {
+   "attachments": {
+    "figure2.JPG": {
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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![figure2.JPG](attachment:figure2.JPG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can find that, although in our simulation, the results of lifted Heston model and rough Heston model differ a lot, the trend of implied volatility is similar. Meanwhile, the result of rough Heston model is closer to the result in the paper. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We believe the error in rough Heston model is due to choosing fewer steps in Adams Predictor-Corrector scheme(only 60) and cosine methods(less than 160), as well as fewer intervals to calculate the integral(only 70). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The error in lifted Hestion model, is probably due to fewer MC simulations(only 1000) and fewer steps when simulating the price and volatility(only 300)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyfeng/heston_mc.py b/pyfeng/heston_mc.py
index 0c3c0f5..283d54c 100644
--- a/pyfeng/heston_mc.py
+++ b/pyfeng/heston_mc.py
@@ -40,7 +40,7 @@ def phi_exp(self, texp):
         phi = 4*self.mr / self.vov**2 / (1/exp - exp)
         return phi, exp
 
-    def var_mean_var(self, var_0, dt):
+    def var_mv(self, var_0, dt):
         """
         Mean and variance of the variance V(t+dt) given V(0) = var_0
 
@@ -49,7 +49,7 @@ def var_mean_var(self, var_0, dt):
             dt: time step
 
         Returns:
-            (mean, variance)
+            mean, variance
         """
 
         expo = np.exp(-self.mr * dt)
@@ -112,9 +112,9 @@ def var_step_ncx2_eta(self, var_0, dt):
         chi_df = self.chi_dim()
         phi, exp = self.phi_exp(dt)
         chi_nonc = var_0 * exp * phi
-        nn = self.rng_spawn[0].poisson(chi_nonc / 2, size=self.n_path)
-        var_t = (exp / phi) * 2 * self.rng_spawn[0].standard_gamma(shape=chi_df / 2 + nn, size=self.n_path)
-        return var_t, nn
+        pois = self.rng_spawn[0].poisson(chi_nonc / 2, size=self.n_path)
+        var_t = (exp / phi) * 2 * self.rng_spawn[0].standard_gamma(shape=chi_df / 2 + pois, size=self.n_path)
+        return var_t, pois
 
     @abc.abstractmethod
     def cond_states(self, var_0, dt):
@@ -127,17 +127,17 @@ def cond_states(self, var_0, dt):
             dt: time step
 
         Returns:
-            (var_final, var_mean)
+            (var_final, var_avg)
         """
         return NotImplementedError
 
     def cond_spot_sigma(self, var_0, texp):
-        var_final, var_mean = self.cond_states(var_0, texp)
+        var_final, var_avg = self.cond_states(var_0, texp)
 
-        spot_cond = ((var_final - var_0) - self.mr * texp * (self.theta - var_mean)) / self.vov \
-             - 0.5 * self.rho * var_mean * texp
+        spot_cond = ((var_final - var_0) - self.mr * texp * (self.theta - var_avg)) / self.vov \
+             - 0.5 * self.rho * var_avg * texp
         np.exp(self.rho * spot_cond, out=spot_cond)
-        sigma_cond = np.sqrt((1.0 - self.rho**2) / var_0 * var_mean)  # normalize by initial variance
+        sigma_cond = np.sqrt((1.0 - self.rho**2) / var_0 * var_avg)  # normalize by initial variance
 
         # return normalized forward and volatility
         return spot_cond, sigma_cond
@@ -167,7 +167,7 @@ class HestonMcAndersen2008(HestonMcABC):
         array([44.31943535, 13.09371251,  0.29580431])
     """
     psi_c = 1.5  # parameter used by the Andersen QE scheme
-    scheme = 2
+    scheme = 4
 
     def set_mc_params(self, n_path=10000, dt=0.05, rn_seed=None, antithetic=True, scheme=4):
         """
@@ -178,7 +178,7 @@ def set_mc_params(self, n_path=10000, dt=0.05, rn_seed=None, antithetic=True, sc
             dt: time step for Euler/Milstein steps
             rn_seed: random number seed
             antithetic: antithetic
-            scheme: 0 for Euler, 1 for Milstein, 2 for NCX2, 3 for NCX2 with Poisson, 4 for 2 for Andersen (2008)'s QE scheme
+            scheme: 0 for Euler, 1 for Milstein, 2 for NCX2, 3 for Poisson-mixture Gamma, 4 for Andersen (2008)'s QE scheme
 
         References:
             - Andersen L (2008) Simple and efficient simulation of the Heston stochastic volatility model. Journal of Computational Finance 11:1–42. https://doi.org/10.21314/JCF.2008.189
@@ -187,7 +187,7 @@ def set_mc_params(self, n_path=10000, dt=0.05, rn_seed=None, antithetic=True, sc
         self.scheme = scheme
 
     def var_step_qe(self, var_0, dt):
-        m, s2 = self.var_mean_var(var_0, dt)
+        m, s2 = self.var_mv(var_0, dt)
         psi = s2 / m**2
 
         zz = self.rv_normal(spawn=0)
@@ -258,9 +258,9 @@ def cond_states_old(self, var_0, texp):
         n_dt = len(tobs)
         var_paths = self.vol_paths(tobs)
         var_final = var_paths[-1, :]
-        var_mean = spint.simps(var_paths, dx=1, axis=0) / n_dt
+        var_avg = spint.simps(var_paths, dx=1, axis=0) / n_dt
 
-        return var_final, var_mean
+        return var_final, var_avg
 
     def cond_states(self, var_0, texp):
 
@@ -274,45 +274,46 @@ def cond_states(self, var_0, texp):
         weight /= weight.sum()
 
         var_t = np.full(self.n_path, var_0)
-        var_mean = weight[0] * var_t
+        var_avg = weight[0] * var_t
 
         if self.scheme < 2:
             milstein = (self.scheme == 1)
             for i in range(n_dt):
                 # Euler (or Milstein) scheme
                 var_t = self.var_step_euler(var_t, dt[i], milstein=milstein)
-                var_mean += weight[i + 1] * var_t
+                var_avg += weight[i + 1] * var_t
 
         elif self.scheme == 2:
             for i in range(n_dt):
                 var_t = self.var_step_ncx2(var_t, dt[i])
-                var_mean += weight[i + 1] * var_t
+                var_avg += weight[i + 1] * var_t
 
         elif self.scheme == 3:
             for i in range(n_dt):
                 var_t, _ = self.var_step_ncx2_eta(var_t, dt[i])
-                var_mean += weight[i + 1] * var_t
+                var_avg += weight[i + 1] * var_t
 
         elif self.scheme == 4:
             for i in range(n_dt):
                 var_t = self.var_step_qe(var_t, dt[i])
-                var_mean += weight[i + 1] * var_t
+                var_avg += weight[i + 1] * var_t
 
-        return var_t, var_mean  # * texp
+        return var_t, var_avg  # * texp
 
 
 class HestonMcGlassermanKim2011(HestonMcABC):
     """
-    Exact simulation using the gamma series based on Glasserman and Kim (2011)
+    Exact simulation using the gamma series based on Glasserman & Kim (2011)
 
     References:
         - Glasserman P, Kim K-K (2011) Gamma expansion of the Heston stochastic volatility model. Finance Stoch 15:267–296. https://doi.org/10.1007/s00780-009-0115-y
     """
 
     antithetic = False
-    KK = 1  # K for series truncation.
+    scheme = 3  # Poisson mixture gamma
+    kk = 1  # K for series truncation.
 
-    def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, KK=1):
+    def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, kk=1):
         """
         Set MC parameters
 
@@ -321,168 +322,241 @@ def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, KK=1):
             dt: time step
             rn_seed: random number seed
             scheme: simulation scheme for variance
-            KK: truncation index
+            kk: truncation index
 
         """
         super().set_mc_params(n_path, dt, rn_seed, antithetic=False)
         self.scheme = scheme
-        self.KK = KK
+        self.kk = kk
 
-    def gamma_lambda(self, dt, KK=None):
+    def laplace(self, aa, var_0, var_t, dt):
         """
-        gamma_n and lambda_n in Glasserman and Kim (2011)
+        MGF of the average variance given the initial and final variance
+
+        Args:
+            aa: dummy variable in the transformation
+            var_0: initial variance
+            var_t: final variance
+            dt: time step
+
+        Returns:
+            Conditional MGF at dummy variable aa
+        """
+
+        vov2dt = self.vov**2 * dt
+        mrt = self.mr * dt
+        iv_index = 0.5 * self.chi_dim() - 1
+
+        gamma = np.sqrt(mrt**2 + 2 * vov2dt * aa)
+
+        var_mean = np.sqrt(var_0 * var_t)
+        phi_mr, _ = self.phi_exp(dt)
+        cosh_mr = np.cosh(mrt / 2)
+
+        phi_gamma = 2 * gamma / vov2dt / np.sinh(gamma / 2)
+        cosh_gamma = np.cosh(gamma / 2)
+
+        part1 = phi_gamma / phi_mr
+        part2 = np.exp((var_0 + var_t) * (cosh_mr * phi_mr - cosh_gamma * phi_gamma) / 2)
+        part3 = spsp.iv(iv_index, var_mean * phi_gamma) / spsp.iv(iv_index, var_mean * phi_mr)
+
+        ch_f = part1 * part2 * part3
+        return ch_f
+
+    def cond_avgvar_mv_numeric(self, var_0, var_t, dt):
+        """
+        Mean and variance of the average variance conditional on initial var, final var.
+        It is computed from the numerical derivatives of the conditional Laplace transform.
+
+        Args:
+            var_0: initial variance
+            var_t: final variance
+            dt: time step
+
+        Returns:
+            mean, variance
+
+        See Also:
+            cond_avgvar_mv
+        """
+        # conditional Cumulant Generating Fuction
+        def cumgenfunc_cond(aa):
+            return np.log(self.laplace(-aa, var_0, var_t, dt))
+
+        m1 = derivative(cumgenfunc_cond, 0, n=1, dx=1e-5)
+        var = derivative(cumgenfunc_cond, 0, n=2, dx=1e-5)
+        return m1, var
+
+    def gamma_lambda(self, dt, kk=None):
+        """
+        gamma_n and lambda_n below Eq. (2.8) in Glasserman & Kim (2011).
+        gamma_n is the original value * dt to make x1, x2, and x3 the average variance.
 
         Args:
             dt: time step
-            KK: number of terms
+            kk: number of terms
 
         Returns:
-            gamma, lambda
+            gamma_n, lambda_n
         """
 
-        if KK is None:
-            KK = self.KK
+        if kk is None:
+            kk = self.kk
 
         mrt2 = (self.mr * dt)**2
-        vovn = self.vov**2 * dt
+        vov2dt = self.vov**2 * dt
 
-        n_2pi_2 = (np.arange(1, KK + 1) * 2 * np.pi)**2
-        gamma_n = (mrt2 + n_2pi_2) / (2 * vovn * dt)
-        lambda_n = 4 * n_2pi_2 / vovn / (mrt2 + n_2pi_2)
+        n_2pi_2 = (np.arange(1, kk + 1) * 2 * np.pi)**2
+        gamma_n = (mrt2 + n_2pi_2) / (2 * vov2dt)  # / dt
+        lambda_n = 4 * n_2pi_2 / vov2dt / (mrt2 + n_2pi_2)
 
         return gamma_n, lambda_n
 
-    def x1star_mean_var_asymp(self, dt, KK=0):
+    def x1star_avgvar_mv_asymp(self, dt, kk=0):
         """
-        Mean and variance of the truncated terms of X1 in Lemma 3.1 in Glasserman & Kim (2011)
-        (v_0 + v_t) dt and (v_0 + v_t) vov^2 dt^3 need to be multiplied afterwards.
+        Asymptotic mean and variance of the truncated terms of X1/dt.
+        (v_0 + v_t) need to be multiplied to mean and variance afterwards.
+        This is NOT used for pricing, but for verification purpose.
 
         Args:
-            dt:
-            KK:
+            dt: time step
+            kk: number of gamma expansion terms
 
-        Returns:
+        References:
+            - Lemma 3.1 in Glasserman & Kim (2011)
 
+        Returns:
+            mean, variance
         """
 
+        vov2dt = self.vov**2 * dt
         # remainder (truncated) terms
-        rem_mean = 2 / (np.pi**2 * KK)
-        rem_var = 2 / (3 * np.pi**4 * KK**3)
+        trunc_mean = 2 / (np.pi**2 * kk)
+        trunc_var = 2 * vov2dt / (3 * np.pi**4 * kk**3)
 
-        return rem_mean, rem_var
+        return trunc_mean, trunc_var
 
-    def x1star_mean_var(self, dt, KK=0):
+    def x1star_avgvar_mv(self, dt, kk=0):
         """
-        Mean and variance of the truncated terms of X1 in p 281-282 Glasserman & Kim (2011)
-        (v_0 + v_t) dt and (v_0 + v_t) vov^2 dt^3 need to be multiplied afterwards.
+        Mean and variance of the truncated terms of (X1^*/dt).
+        (v_0 + v_t) need to be multiplied to mean and variance afterwards.
 
         Args:
-            dt:
-            KK:
+            dt: time step
+            kk: number of gamma expansion terms
 
-        Returns:
+        References:
+            - p 281-282 in Glasserman & Kim (2011)
+            - Proposition 3.1 in Tse & Wan (2013)
 
+        Returns:
+            mean, variance
         """
 
         mrt_h = self.mr * dt / 2
+        vov2dt = self.vov**2 * dt
         csch = 1 / np.sinh(mrt_h)
         coth = np.cosh(mrt_h) * csch
 
-        x1_mean = (coth/mrt_h - csch**2) / 2
-        x1_var = (coth / mrt_h**3 + csch**2 / mrt_h**2 - 2 * coth*csch**2 / mrt_h) / 8
+        mean = (coth/mrt_h - csch**2) / 2
+        var = vov2dt * (coth / mrt_h**3 + csch**2 / mrt_h**2 - 2 * coth*csch**2 / mrt_h) / 8
 
-        if KK > 0:
-            n_2pi_2 = (np.arange(1, KK + 1) * 2 * np.pi)**2
-            term = 8 * n_2pi_2 / (4*mrt_h**2 + n_2pi_2)**2
-            x1_mean -= np.sum(term)
-            x1_var -= np.sum(4 * term / (4*mrt_h**2 + n_2pi_2))
+        if kk > 0:
+            gamma_n, lambda_n = self.gamma_lambda(dt, kk)
+            mean -= np.sum(lambda_n/gamma_n)
+            var -= 2*np.sum(lambda_n/gamma_n**2)
 
-        return x1_mean, x1_var
+        return mean, var
 
-    def x2star_mean_var_asymp(self, dt, KK=0):
+    def x2star_avgvar_mv_asymp(self, dt, kk=0):
         """
-        Mean and variance of the truncated terms of X2 (with delta=1) in Lemma 3.1 in Glasserman & Kim (2011)
-        (vov * dt)^2 and (vov * dt)^4 need to be multiplied afterwards.
+        Asymptotic mean and variance of the truncated terms of X2/dt (with shape=1 or delta=2).
+        This is NOT used for pricing, but for verification purpose.
 
         Args:
-            dt:
-            KK:
+            dt: time step
+            kk: number of gamma expansion terms
 
-        Returns:
+        References:
+            - Lemma 3.1 in Glasserman & Kim (2011)
 
+        Returns:
+            mean, variance
         """
 
-        rem_mean = 1 / (4 * np.pi**2 * KK)
-        rem_var = 1 / (24 * np.pi**4 * KK**3)
+        vov2dt = self.vov**2 * dt
+        trunc_mean = vov2dt / (2 * np.pi**2 * kk)
+        trunc_var = vov2dt**2 / (12 * np.pi**4 * kk**3)
 
-        return rem_mean, rem_var
+        return trunc_mean, trunc_var
 
-    def x2star_mean_var(self, dt, KK=0):
+    def x2star_avgvar_mv(self, dt, kk=0):
         """
-        Mean and variance of the truncated terms of X2 (with delta=1) in p 284 in Glasserman & Kim (2011)
-        (vov * dt)^2 and (vov * dt)^4 need to be multiplied afterwards
+        Mean and variance of the truncated terms of X2/dt (with shape=1 or delta=2)
+
+            X2 = sum_{n=1}^kk standard_gamma(1) / gamma_n
 
         Args:
-            dt:
-            KK:
+            dt: time step
+            kk: number of gamma expansion terms
 
-        Returns:
+        References:
+            - p 284 in Glasserman & Kim (2011)
+            - Proposition 3.1 in Tse & Wan (2013)
 
+        Returns:
+            mean, variance
         """
 
         mrt_h = self.mr * dt / 2
+        vov2dt = self.vov**2 * dt
+
         csch = 1 / np.sinh(mrt_h)
         coth = np.cosh(mrt_h) * csch
 
-        mean = (mrt_h * coth - 1) / (8 * mrt_h**2)
-        var = (mrt_h * coth + mrt_h**2 * csch**2 - 2) / (32 * mrt_h**4)
+        mean = vov2dt * (mrt_h * coth - 1) / (4 * mrt_h**2)
+        var = vov2dt**2 * (mrt_h * coth + mrt_h**2 * csch**2 - 2) / (16 * mrt_h**4)
 
-        if KK > 0:
-            term = 1 / (4*mrt_h**2 + (np.arange(1, KK + 1) * 2 * np.pi)**2)
-            mean -= np.sum(term)
-            var -= 2 * np.sum(term**2)
+        if kk > 0:
+            gamma_n, _ = self.gamma_lambda(dt, kk)
+            mean -= np.sum(1/gamma_n)
+            var -= np.sum(1/gamma_n**2)
 
         return mean, var
 
-    def draw_x1(self, var_sum, dt):
+    def draw_x1(self, var_0, var_t, dt):
         """
-        Simulation of x1 using truncated Gamma expansion in Glasserman and Kim (2011)
+        Samples of x1/dt using truncated Gamma expansion in Glasserman & Kim (2011)
 
-        Parameters
-        ----------
-        var_t : an 1-d array with shape (n_paths,)
-            final variance
-        dt: float
-            time-to-expiry
+        Args:
+            var_0: initial variance
+            var_t: final variance
+            dt: time step
 
-        Returns
-        -------
-         an 1-d array with shape (n_paths,), random variables x1
+        Returns:
+            x1/dt
         """
         # For fixed k, theta, vov, texp, generate some parameters firstly
 
-        vovn = self.vov**2 * dt
-        gamma_n, lambda_n = self.gamma_lambda(dt, self.KK)
+        gamma_n, lambda_n = self.gamma_lambda(dt, self.kk)
 
         # the following para will change with VO and VT
-        Nn = self.rng_spawn[3].poisson(lam=var_sum * lambda_n[:, None])  # (KK, n_path)
+        pois = self.rng_spawn[3].poisson(lam=(var_0 + var_t) * lambda_n[:, None])  # (kk, n_path)
 
-        rv_exp_sum = self.rng_spawn[1].standard_gamma(shape=Nn)
+        rv_exp_sum = self.rng_spawn[1].standard_gamma(shape=pois)
         x1 = np.sum(rv_exp_sum / gamma_n[:, None], axis=0)
 
-        rem_mean_x1, rem_var_x1 = self.x1star_mean_var(dt, self.KK)
-        rem_mean_x1 *= dt
-        rem_var_x1 *= vovn * dt**2
-
-        rem_scale = rem_var_x1 / rem_mean_x1
-        rem_shape = rem_mean_x1 / rem_scale * var_sum
+        trunc_mean_x1, trunc_var_x1 = self.x1star_avgvar_mv(dt, self.kk)
+        trunc_scale = trunc_var_x1 / trunc_mean_x1
+        trunc_shape = trunc_mean_x1 / trunc_scale * (var_0 + var_t)
 
-        x1 += rem_scale * self.rng_spawn[1].standard_gamma(rem_shape)
+        x1 += trunc_scale * self.rng_spawn[1].standard_gamma(trunc_shape)
         return x1
 
-    def draw_X2_AW(self, mu_X2_0, sigma_square_X2_0, ncx_df, texp, num_rv):
+    def draw_x2_aw(self, mu_X2_0, sigma_square_X2_0, ncx_df, texp, num_rv):
         """
-        Simulation of X2 or Z from its CDF based on Abate-Whitt algorithm from formula (4.1) in Glasserman and Kim (2011)
+        Simulation of X2 or Z from its CDF based on Abate-Whitt algorithm from formula (4.1) in Glasserman & Kim (2011)
+        Currently NOT used for pricing.
 
         Parameters
         ----------
@@ -536,8 +610,19 @@ def draw_X2_AW(self, mu_X2_0, sigma_square_X2_0, ncx_df, texp, num_rv):
 
         return X2
 
-    def eta_mean_var(self, var_0, var_t, texp):
-        phi, exp = self.phi_exp(texp)
+    def eta_mv(self, var_0, var_t, dt):
+        """
+        The mean and variance of eta RV.
+
+        Args:
+            var_0: initial variance
+            var_t: final variance
+            dt: time step
+
+        Returns:
+            eta (n_path, 1)
+        """
+        phi, exp = self.phi_exp(dt)
         zz = np.sqrt(var_0 * var_t) * phi
 
         iv_index = 0.5 * self.chi_dim() - 1
@@ -550,17 +635,19 @@ def eta_mean_var(self, var_0, var_t, texp):
 
         return mean, var
 
-    def draw_eta(self, var_0, var_t, texp):
+    def draw_eta(self, var_0, var_t, dt):
         """
-        generate Bessel random variables from inverse of CDF, formula(2.4) in George and Dimitris (2010)
+        generate Bessel RV from p 285 of Glasserman & Kim (2011)
 
         Args:
-            zz:  Bessel RV parameter (n, )
+            var_0: initial variance
+            var_t: final variance
+            dt: time step
 
         Returns:
             eta (integer >= 0) values (n, )
         """
-        phi, exp = self.phi_exp(texp)
+        phi, exp = self.phi_exp(dt)
         zz = np.sqrt(var_0 * var_t) * phi
 
         iv_index = 0.5 * self.chi_dim() - 1
@@ -573,48 +660,45 @@ def draw_eta(self, var_0, var_t, texp):
 
         return eta
 
-    def draw_x2(self, ncx_df, dt, size):
+    def draw_x2(self, shape, dt, size):
         """
-        Simulation of x2 (or Z) using truncated Gamma expansion in Glasserman and Kim (2011)
-        Z is the special case with ncx_df = 4
+        Simulation of x2/dt (or Z/dt) using truncated Gamma expansion in Glasserman & Kim (2011)
+        X2 is the case with shape = delta / 2 and Z is the case with shape = 2
         
         Args:
-            ncx_df: ncx2 degree of freedom
+            shape: shape parameter of gamma distribution
             dt: time-to-expiry
             size: number of RVs to generate
 
         Returns:
-            Random samples of x2 (or Z) with shape (n_path,)
+            x2/dt (or Z/dt) with shape (n_path,)
         """
 
-        vovn = self.vov**2 * dt
         gamma_n, _ = self.gamma_lambda(dt)
 
-        gamma_rv = self.rng_spawn[1].standard_gamma(ncx_df / 2, size=(self.KK, size))
+        gamma_rv = self.rng_spawn[1].standard_gamma(shape, size=(self.kk, size))
         x2 = np.sum(gamma_rv / gamma_n[:, None], axis=0)
 
         # remainder (truncated) terms
-        rem_mean, rem_var = self.x2star_mean_var(dt, self.KK)
-        rem_mean *= ncx_df * vovn * dt
-        rem_var *= ncx_df * (vovn * dt)**2
-        rem_scale = rem_var / rem_mean
-        rem_shape = rem_mean / rem_scale
-
-        x2 += rem_scale * self.rng_spawn[1].standard_gamma(rem_shape, size=size)
+        trunc_mean, trunc_var = self.x2star_avgvar_mv(dt, self.kk)
+        trunc_scale = trunc_var / trunc_mean
+        trunc_shape = trunc_mean / trunc_scale * shape
+        x2 += trunc_scale * self.rng_spawn[1].standard_gamma(trunc_shape, size=size)
         return x2
 
-    def cond_intvar_mean_var(self, var_0, var_t, dt, eta=None, KK=0):
+    def cond_avgvar_mv(self, var_0, var_t, dt, eta=None, kk=0):
         """
-        Mean and variance of the integrated variance conditional on initial var, final var, and eta
+        Mean and variance of the average variance conditional on initial var, final var, and eta
 
         Args:
             var_0: initial variance
             var_t: final variance
             eta: Poisson RV
             dt: time step
+            kk: number of gamma expansion terms
 
         Returns:
-            (integarted variance / dt)
+            mean, variance
         """
 
         # x = np.arange(1, 10) * 0.02
@@ -626,23 +710,18 @@ def cond_intvar_mean_var(self, var_0, var_t, dt, eta=None, KK=0):
         # y2 - y1
 
         if eta is None:
-            eta_mean, eta_var = self.eta_mean_var(var_0, var_t, dt)
+            eta_mean, eta_var = self.eta_mv(var_0, var_t, dt)
         else:
             eta_mean, eta_var = eta, 0.0
 
-        vovn = self.vov**2 * dt
+        x1_mean, x1_var = self.x1star_avgvar_mv(dt, kk=kk)
+        x1_mean *= (var_0 + var_t)
+        x1_var *= (var_0 + var_t)
 
-        x1_mean, x1_var = self.x1star_mean_var(dt, KK=KK)
-        x1_mean *= (var_0 + var_t) * dt
-        x1_var *= (var_0 + var_t) * vovn * dt**2
-
-        z_mean, z_var = self.x2star_mean_var(dt, KK=KK)
-        z_mean *= 4 * vovn * dt
-        z_var *= 4 * vovn**2 * dt**2
-
-        x23_mean = (eta_mean + self.chi_dim()/4) * z_mean
-        x23_var = (eta_mean + self.chi_dim()/4) * z_var
-        x23_var += eta_var * z_mean**2
+        x2_mean, x2_var = self.x2star_avgvar_mv(dt, kk=kk)
+        x23_mean = (2*eta_mean + self.chi_dim()/2) * x2_mean
+        x23_var = (2*eta_mean + self.chi_dim()/2) * x2_var
+        x23_var += eta_var * (2*x2_mean)**2
 
         return x1_mean + x23_mean, x1_var + x23_var
 
@@ -651,14 +730,14 @@ def cond_states(self, var_0, texp):
         var_t, _ = self.var_step_ncx2_eta(var_0=var_0, dt=texp)
 
         # sample int_var(integrated variance): Gamma expansion / transform inversion
-        # int_var = X1+X2+X3 from formula(2.7) in Glasserman and Kim (2011)
+        # int_var = X1+X2+X3 from formula(2.7) in Glasserman & Kim (2011)
 
         # Simulation X1: truncated Gamma expansion
-        x123 = self.draw_x1(var_0 + var_t, texp)
-        x123 += self.draw_x2(self.chi_dim(), texp, size=self.n_path)
+        var_avg = self.draw_x1(var_0, var_t, texp)
+        var_avg += self.draw_x2(self.chi_dim()/2, texp, size=self.n_path)
         eta = self.draw_eta(var_0, var_t, texp)
 
-        zz = self.draw_x2(4, texp, size=eta.sum())
+        zz = self.draw_x2(2.0, texp, size=eta.sum())
 
         total = 0
         for i in np.arange(eta.max()):
@@ -666,13 +745,12 @@ def cond_states(self, var_0, texp):
             count = eta_above_i.sum()
             if count == 0:
                 continue
-            x123[eta_above_i] += zz[total:total+count]
+            var_avg[eta_above_i] += zz[total:total+count]
             total += count
 
         assert eta.sum() == total
-        var_mean = x123 / texp
 
-        return var_t, var_mean
+        return var_t, var_avg
 
 
 class HestonMcTseWan2013(HestonMcGlassermanKim2011):
@@ -693,9 +771,9 @@ class HestonMcTseWan2013(HestonMcGlassermanKim2011):
         >>> # true price: 44.330, 13.085, 0.296
         array([12.08981758,  0.33379748, 42.28798189])  # not close so far
     """
-    dist = 0
+    dist = 'ig'
 
-    def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, dist=0):
+    def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, dist=None):
         """
         Set MC parameters
 
@@ -704,92 +782,54 @@ def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, dist=0):
             dt: time step
             rn_seed: random number seed
             scheme: simulation scheme for jumping from 0 to texp
-            dist: distribution to use for approximation. 0 for inverse Gaussian (default), 1 for lognormal.
+            dist: distribution to use for approximation.
+                'ig' for inverse Gaussian (default), 'ga' for Gamma, 'ln' for LN
         """
         super().set_mc_params(n_path, dt, rn_seed, scheme=scheme)
-        self.dist = dist
-
-    def mgf(self, aa, var_0, var_t, dt):
-        """
-            MGF of the integrated variance given the initial and final variance
+        if dist is not None:
+            self.dist = dist
 
-        Args:
-            aa: dummy variable in the transformation
-            var_0: initial variance
-            var_t: final variance
-            dt: time step
-
-        Returns:
-            Conditional MGF at dummy variable aa
-        """
-
-        vov2 = self.vov**2
-        mrt_h = self.mr * dt / 2
-        iv_index = 0.5 * self.chi_dim() - 1
-
-        gamma = np.sqrt(self.mr**2 - 2 * vov2 * aa)
-        #decay = np.exp(-self.mr * texp)
-        #decay_gamma = np.exp(-gamma * texp)
-
-        var_mean = np.sqrt(var_0 * var_t)
-        phi_mr, _ = self.phi_exp(dt)
-        cosh_mr = np.cosh(mrt_h)
-
-        phi_gamma = 2 * gamma / vov2 / np.sinh(gamma * dt / 2)
-        cosh_gamma = np.cosh(gamma * dt / 2)
-
-        #part1 = gamma * np.exp(-0.5 * (gamma * texp - self.mr * texp)) * (1 - decay) / (self.mr * (1 - decay_gamma))
-        part1 = phi_gamma / phi_mr
-
-        #part2 = np.exp((var_0 + var_final) / vov2
-        #    * (self.mr * (1 + decay) / (1 - decay) - gamma * (1 + decay_gamma) / (1 - decay_gamma)))
-        part2 = np.exp((var_0 + var_t) * (cosh_mr * phi_mr - cosh_gamma * phi_gamma) / 2)
+    def cond_states(self, var_0, texp):
 
-        part3 = spsp.iv(iv_index, var_mean * phi_gamma) / spsp.iv(iv_index, var_mean * phi_mr)
+        tobs = self.tobs(texp)
+        n_dt = len(tobs)
+        dt = np.diff(tobs, prepend=0)
 
-        ch_f = part1 * part2 * part3
-        return ch_f
+        var_0 = np.full(self.n_path, var_0)
+        var_avg = np.zeros_like(var_0)
 
-    def cond_intvar_mean_var_numeric(self, var_0, var_t, dt):
-        # conditional MGF function
-        def mgf_cond(aa):
-            return self.mgf(aa, var_0, var_t, dt)
+        for i in range(n_dt):
 
-        # Get the first 2 moments
-        m1 = derivative(mgf_cond, 0, n=1, dx=1e-5)
-        m2 = derivative(mgf_cond, 0, n=2, dx=1e-5)
-        return m1, m2 - m1**2
+            var_t, eta = self.var_step_ncx2_eta(var_0, dt[i])
+            # m1, var = self.cond_avgvar_mv_numeric(var_0, var_t, dt[i])
+            m1, var = self.cond_avgvar_mv(var_0, var_t, dt[i], eta=None)
+            var_0 = var_t
 
-    def cond_states(self, var_0, texp):
+            if self.dist.lower() == 'ig':
+                # mu and lambda defined in https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution
+                # RNG.wald takes the same parameters
+                lam = m1**3 / var
+                var_avg += self.rng_spawn[1].wald(mean=m1, scale=lam)
+            elif self.dist.lower() == 'ga':
+                scale = var / m1
+                shape = m1 / scale
+                var_avg += scale * self.rng_spawn[1].standard_gamma(shape=shape)
+            elif self.dist.lower() == 'ln':
+                scale = np.sqrt(np.log(1 + var/m1**2))
+                var_avg += m1 * np.exp(scale * (self.rv_normal(spawn=1) - scale/2))
+            else:
+                raise ValueError(f"Incorrect distribution: {self.dist}.")
 
-        var_t, eta = self.var_step_ncx2_eta(self.sigma, texp)
-        #m1, var = self.cond_intvar_mean_var_numeric(var_0, var_t, texp)
-        m1, var = self.cond_intvar_mean_var(var_0, var_t, texp, eta=None)
-
-        if self.dist == 0:
-            # mu and lambda defined in https://en.wikipedia.org/wiki/Inverse_Gaussian_distribution
-            # RNG.wald takes the same parameters
-            lam = m1**3 / var
-            var_mean = self.rng_spawn[1].wald(mean=m1, scale=lam) / texp
-        elif self.dist == 1:
-            scale = var / m1
-            shape = m1 / scale
-            var_mean = scale * self.rng_spawn[1].standard_gamma(shape=shape) / texp
-        elif self.dist == 2:
-            scale = np.sqrt(np.log(1 + var/m1**2))
-            var_mean = self.rv_normal(spawn=1)
-            var_mean = m1 * np.exp(scale * (var_mean - scale/2)) / texp
-        else:
-            raise ValueError(f"Incorrect distribution: {self.dist}.")
+        var_avg /= n_dt
 
-        return var_t, var_mean
+        return var_t, var_avg
 
 
 class HestonMcChoiKwok2023(HestonMcGlassermanKim2011):
 
-    dist = 0
+    dist = 'ig'
 
-    def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, KK=0, dist=0):
+    def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, kk=0, dist=None):
         """
         Set MC parameters
 
@@ -798,53 +838,48 @@ def set_mc_params(self, n_path=10000, dt=None, rn_seed=None, scheme=3, KK=0, dis
             dt: time step
             rn_seed: random number seed
             scheme: simulation scheme for jumping from 0 to texp
-            dist: distribution to use for approximation. 0 for inverse Gaussian (default), 1 for Gamma, 2 for LN
+            dist: distribution to use for approximation.
+                'ig' for inverse Gaussian (default), 'ga' for Gamma, 'ln' for LN
         """
-        super().set_mc_params(n_path, dt, rn_seed, scheme=scheme, KK=KK)
-        self.dist = dist
+        super().set_mc_params(n_path, dt, rn_seed, scheme=scheme, kk=kk)
+        if dist is not None:
+            self.dist = dist
 
-    def draw_x123(self, var_sum, dt, eta_sum):
+    def draw_x123(self, var_sum, dt, shape_sum):
         """
-        Simulation of X1 + X2 + X3 using truncated Gamma expansion in Glasserman and Kim (2011)
+        Samples of (X1 + X2 + X3)/dt using truncated Gamma expansion improved in Choi & Kwok (2023)
 
         Args:
-            var_sum: initial + final variance. (n_paths,)
-            eta_sum: Bessel RV
+            var_sum: sum of v_t at the observation times. (n_paths,)
+            shape_sum: sum of gamma shape parameters
             dt: time step
 
         Returns:
-            X1 + X2 + X3  (n_paths,)
+            (X1 + X2 + X3)/dt  (n_paths,)
         """
-        vovn = self.vov**2 * dt
         gamma_n, lambda_n = self.gamma_lambda(dt)
 
-        Nn = self.rng_spawn[3].poisson(lam=var_sum * lambda_n[:, None])
-        x123 = np.sum(self.rng_spawn[1].standard_gamma(shape=Nn + eta_sum * 2) / gamma_n[:, None], axis=0)
-
-        # The approximated mean and variance of the truncated terms
-        #rem_mean_x1 = 2 * dt / (np.pi**2 * self.KK) * var_sum
-        #rem_var_x1 = 2 * self.vov**2 * dt**3 / (3 * np.pi**4 * self.KK**3) * var_sum
-        #rem_mean_x23 = (self.vov * dt)**2 / (4 * np.pi**2 * self.KK) * ncx_df
-        #rem_var_x23 = (self.vov * dt)**4 / (24 * np.pi**4 * self.KK**3) * ncx_df
-
-        m1, var = self.x1star_mean_var(dt, self.KK)
-        m1 *= var_sum * dt
-        var *= var_sum * vovn * dt**2
-
-        m1_x23, var_x23 = self.x2star_mean_var(dt, self.KK)
-        m1 += m1_x23 * 4 * eta_sum * vovn * dt
-        var += var_x23 * 4 * eta_sum * vovn**2 * dt**2
-
-        if self.dist == 0:
-            lam = m1**3 / var
-            x123 += self.rng_spawn[1].wald(mean=m1, scale=lam)
-        elif self.dist == 1:
-            rem_scale = var / m1
-            rem_shape = m1 / rem_scale
-            x123 += rem_scale * self.rng_spawn[1].standard_gamma(rem_shape)
-        elif self.dist == 2:
-            scale = np.sqrt(np.log(1 + var / m1**2))
-            x123 += m1 * np.exp(scale * (self.rv_normal(spawn=1) - scale / 2))
+        pois = self.rng_spawn[3].poisson(lam=var_sum * lambda_n[:, None])
+        x123 = np.sum(self.rng_spawn[1].standard_gamma(shape=pois + shape_sum) / gamma_n[:, None], axis=0)
+
+        trunc_mean, trunc_var = self.x1star_avgvar_mv(dt, self.kk)
+        trunc_mean *= var_sum
+        trunc_var *= var_sum
+
+        mean_x23, var_x23 = self.x2star_avgvar_mv(dt, self.kk)
+        trunc_mean += mean_x23 * shape_sum
+        trunc_var += var_x23 * shape_sum
+
+        if self.dist.lower() == 'ig':
+            lam = trunc_mean**3 / trunc_var
+            x123 += self.rng_spawn[1].wald(mean=trunc_mean, scale=lam)
+        elif self.dist.lower() == 'ga':
+            trunc_scale = trunc_var / trunc_mean
+            trunc_shape = trunc_mean / trunc_scale
+            x123 += trunc_scale * self.rng_spawn[1].standard_gamma(trunc_shape)
+        elif self.dist.lower() == 'ln':
+            scale = np.sqrt(np.log(1 + trunc_var / trunc_mean**2))
+            x123 += trunc_mean * np.exp(scale * (self.rv_normal(spawn=1) - scale / 2))
         else:
             raise ValueError(f"Incorrect distribution: {self.dist}.")
 
@@ -862,15 +897,16 @@ def cond_states(self, var_0, texp):
 
         var_t = np.full(self.n_path, var_0)
         var_sum = weight[0] * var_t
-        eta_sum = np.zeros_like(var_t)
+        shape_sum = np.zeros_like(var_t)
 
         for i in range(n_dt):
             var_t, eta = self.var_step_ncx2_eta(var_t, dt[i])
             var_sum += weight[i+1] * var_t
-            eta_sum += eta
+            shape_sum += 2*eta
 
-        eta_sum += self.chi_dim() / 4 * n_dt
-        var_mean = self.draw_x123(var_sum, dt[0], eta_sum) / texp
+        shape_sum += 0.5 * self.chi_dim() * n_dt
+        # self.draw_x123 returns the average by dt. Need to convert to the average by texp
+        var_avg = self.draw_x123(var_sum, dt[0], shape_sum) / n_dt
 
-        return var_t, var_mean
+        return var_t, var_avg
 


Test patch:
diff --git a/tests/test_heston.py b/tests/test_heston.py
new file mode 100644
index 0000000..70326f9
--- /dev/null
+++ b/tests/test_heston.py
@@ -0,0 +1,96 @@
+import unittest
+import numpy as np
+import sys
+import os
+
+sys.path.insert(0, os.getcwd())
+import pyfeng as pf
+import pyfeng.ex as pfex
+
+
+class TestHestonMc(unittest.TestCase):
+    """
+    Tests for Heston MC models
+    """
+    def test_conv_mv(self):
+        """
+        Check the infinite sum for the mean and variance
+        Returns:
+
+        """
+        for no in range(1, 5):
+            m = pfex.HestonMcGlassermanKim2011(sigma=0.01, mr=1, vov=1)
+            kk = 10000  # number of exact terms
+            for dt in [1, 2, 4, 8, 16]:
+                mean, var = m.x1star_avgvar_mv(dt, 0)
+                mean_1, var_1 = m.x1star_avgvar_mv(dt, kk)
+                mean_2, var_2 = m.x1star_avgvar_mv_asymp(dt, kk)
+
+                np.testing.assert_(mean_1 > 0)
+                np.testing.assert_(var_1 > 0)
+                np.testing.assert_allclose(mean_1/mean, mean_2/mean, atol=1e-7)
+                np.testing.assert_allclose(var_1/var, var_2/var, atol=1e-7)
+
+                mean, var = m.x2star_avgvar_mv(dt, 0)
+                mean_1, var_1 = m.x2star_avgvar_mv(dt, kk)
+                mean_2, var_2 = m.x2star_avgvar_mv_asymp(dt, kk)
+
+                np.testing.assert_(mean_1 > 0)
+                np.testing.assert_(var_1 > 0)
+                np.testing.assert_allclose(mean_1/mean, mean_2/mean, atol=1e-7)
+                np.testing.assert_allclose(var_1/var, var_2/var, atol=1e-7)
+
+    def test_avgvar_mv(self):
+        for no in [1, 2, 3]:
+            m, p, rv = pfex.HestonMcGlassermanKim2011.init_benchmark(no)
+            ratio = np.random.uniform(0.25, 4, 10)
+            m1, v1 = m.cond_avgvar_mv_numeric(m.sigma, m.sigma * ratio, rv['args_pricing']['texp'])
+            m2, v2 = m.cond_avgvar_mv(m.sigma, m.sigma * ratio, rv['args_pricing']['texp'])
+            np.testing.assert_allclose(m1, m2, rtol=5e-3)  # default: rtol=1e-7
+            np.testing.assert_allclose(v1, v2, rtol=5e-3)
+
+    def test_price_mc(self):
+        """
+        Compare the implied vol of the benchmark cases
+        """
+        for no in [1, 2, 3]:
+            m, p, rv = pfex.HestonMcAndersen2008.init_benchmark(no)
+            m.set_mc_params(n_path=1e5, dt=1/8, rn_seed=123456)
+            m.correct_fwd = False
+
+            vol0 = pf.Bsm(None, intr=m.intr, divr=m.divr).impvol(rv['val'], **rv['args_pricing'])
+
+            vol1 = m.vol_smile(**rv['args_pricing'])
+            np.testing.assert_allclose(vol0, vol1, atol=5e-3)
+            np.testing.assert_allclose(m.result['spot error'], 0, atol=2e-3)
+
+            m, *_ = pfex.HestonMcGlassermanKim2011.init_benchmark(no)
+            m.set_mc_params(n_path=1e5, rn_seed=123456, kk=10)
+            m.correct_fwd = False
+            vol1 = m.vol_smile(**rv['args_pricing'])
+            np.testing.assert_allclose(vol0, vol1, atol=5e-3)
+            np.testing.assert_allclose(m.result['spot error'], 0, atol=2e-3)
+
+            m, *_ = pfex.HestonMcTseWan2013.init_benchmark(no)
+            m.set_mc_params(n_path=1e5, rn_seed=123456, dt=1)
+            m.correct_fwd = False
+            vol1 = m.vol_smile(**rv['args_pricing'])
+            np.testing.assert_allclose(vol0, vol1, atol=5e-3)
+            np.testing.assert_allclose(m.result['spot error'], 0, atol=2e-3)
+
+            m, *_ = pfex.HestonMcChoiKwok2023.init_benchmark(no)
+            m.correct_fwd = False
+            m.set_mc_params(n_path=1e5, rn_seed=123456, kk=10, dt=None)
+            vol1 = m.vol_smile(**rv['args_pricing'])
+            np.testing.assert_allclose(vol0, vol1, atol=5e-3)
+            np.testing.assert_allclose(m.result['spot error'], 0, atol=2e-3)
+
+            m.set_mc_params(n_path=1e5, rn_seed=123456, kk=1, dt=1/4)
+            vol1 = m.vol_smile(**rv['args_pricing'])
+            np.testing.assert_allclose(vol0, vol1, atol=5e-3)
+            np.testing.assert_allclose(m.result['spot error'], 0, atol=2e-3)
+
+
+if __name__ == "__main__":
+    print(f"Pyfeng loaded from {pf.__path__}")
+    unittest.main()