diff --git a/Chapter3/Python/Active_s.ipynb b/Chapter3/Python/Active_s.ipynb
new file mode 100644
index 0000000..39dbdf0
--- /dev/null
+++ b/Chapter3/Python/Active_s.ipynb
@@ -0,0 +1,119 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "# Define the state-space model\n",
+        "A = np.array([\n",
+        "    [0, 0, 1, 0],\n",
+        "    [0, 0, 0, 1],\n",
+        "    [-10, 10, -2, 2],\n",
+        "    [60, -660, 12, -12]\n",
+        "])\n",
+        "\n",
+        "b1 = np.array([0, 0, 0.0033, -0.02])\n",
+        "b2 = np.array([0, 0, 0, 600])\n",
+        "B = np.column_stack((b1, b2))\n",
+        "C = np.array([[1, 0, 0, 0]])\n",
+        "D = np.array([0])\n",
+        "\n",
+        "# Simulation parameters\n",
+        "t_span = (0, 7)  # Time range for simulation\n",
+        "t_eval = np.linspace(0, 7, 701)  # Time points to evaluate\n",
+        "x0 = [0.2, 0, 0, 0]  # Initial conditions\n",
+        "\n",
+        "# Define the system of ODEs for initial response\n",
+        "def system_ode(t, x):\n",
+        "    return A @ x\n",
+        "\n",
+        "# Simulate initial response using solve_ivp\n",
+        "sol_initial = solve_ivp(system_ode, t_span, x0, t_eval=t_eval, method='RK45')\n",
+        "x_initial = sol_initial.y.T\n",
+        "\n",
+        "# Plot initial response\n",
+        "plt.figure()\n",
+        "plt.plot(t_eval, x_initial[:, 0], 'k', label='$x_1$')\n",
+        "plt.plot(t_eval, x_initial[:, 1], 'k-.', label='$x_2$')\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.title('Initial Response')\n",
+        "plt.show()\n",
+        "\n",
+        "# Define input signal function\n",
+        "def input_signal(t):\n",
+        "    return 0.1 * (np.sin(5 * t) + np.sin(9 * t) + np.sin(13 * t) + np.sin(17 * t) + np.sin(21 * t))\n",
+        "\n",
+        "# Define the system of ODEs with input\n",
+        "def system_ode_with_input(t, x):\n",
+        "    u = input_signal(t)\n",
+        "    return A @ x + b2 * u\n",
+        "\n",
+        "# Simulate response with input using solve_ivp\n",
+        "sol_forced = solve_ivp(system_ode_with_input, t_span, x0, t_eval=t_eval, method='RK45')\n",
+        "x_forced = sol_forced.y.T\n",
+        "\n",
+        "# Plot response with input signal\n",
+        "plt.figure()\n",
+        "plt.plot(t_eval, x_forced[:, 0], 'k', label='$x_1$')\n",
+        "plt.plot(t_eval, x_forced[:, 1], 'k-.', label='$x_2$')\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.title('Response with Input Signal')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 927
+        },
+        "id": "rsvx7msErjpD",
+        "outputId": "11a269d3-2265-43fe-9dd0-5a89e8eb0bce"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter3/Python/DCmotor.ipynb b/Chapter3/Python/DCmotor.ipynb
new file mode 100644
index 0000000..abaa31d
--- /dev/null
+++ b/Chapter3/Python/DCmotor.ipynb
@@ -0,0 +1,89 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import control\n",
+        "from scipy.signal import lsim\n",
+        "\n",
+        "# Define the system matrices\n",
+        "A = np.array([[0, 1, 0],\n",
+        "              [0, 0, 4.438],\n",
+        "              [0, -12, -24]])\n",
+        "b1 = np.array([[0], [0], [20]])\n",
+        "b2 = np.array([[0], [-7.396], [0]])\n",
+        "B = np.hstack((b1, b2))\n",
+        "C = np.array([[1, 0, 0], [0, 1, 0]])\n",
+        "D = np.array([[0], [0]])\n",
+        "\n",
+        "# Create state-space system\n",
+        "DC_motor = control.ss(A, b1, C, D)  # Note only the first input is used\n",
+        "\n",
+        "# Define the time vector\n",
+        "t = np.arange(0, 4.00, 0.01)\n",
+        "N = t.size\n",
+        "\n",
+        "# Generate input u (simple way)\n",
+        "u_simple = np.zeros(N)\n",
+        "for i in range(N):\n",
+        "    if t[i] < 2:\n",
+        "        u_simple[i] = 3\n",
+        "    else:\n",
+        "        u_simple[i] = -3\n",
+        "\n",
+        "# Generate input u (professional way)\n",
+        "u_prof = scipy.signal.square(2 * np.pi * t / 4)\n",
+        "u_prof = (+6 * u_prof) - 3\n",
+        "# Simulate the system with the simple input using lsim\n",
+        "t_out, y, x = lsim((A, b1, C, D), U=u_simple, T=t)\n",
+        "\n",
+        "# Plot the result\n",
+        "plt.plot(t_out, x[:, 0], 'k', label='\\u03B8')  # θ\n",
+        "plt.plot(t_out, x[:, 1], 'k-.', label='\\u03C9')  # ω\n",
+        "plt.plot(t_out, x[:, 2], 'k:', label='i') # i\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "outputId": "4096d899-e48e-4433-b042-4d4da7c60fde",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "ZuKIakdlirce"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter3/Python/README.md b/Chapter3/Python/README.md
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Chapter3/Python/README.md
@@ -0,0 +1 @@
+
diff --git a/Chapter3/Python/active_s.py b/Chapter3/Python/active_s.py
new file mode 100644
index 0000000..7cd28fa
--- /dev/null
+++ b/Chapter3/Python/active_s.py
@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+"""Active_s.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1IgnliJrZLzc28PpJy-l43qlaRRszVBoX
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+# Define the state-space model
+A = np.array([
+    [0, 0, 1, 0],
+    [0, 0, 0, 1],
+    [-10, 10, -2, 2],
+    [60, -660, 12, -12]
+])
+
+b1 = np.array([0, 0, 0.0033, -0.02])
+b2 = np.array([0, 0, 0, 600])
+B = np.column_stack((b1, b2))
+C = np.array([[1, 0, 0, 0]])
+D = np.array([0])
+
+# Simulation parameters
+t_span = (0, 7)  # Time range for simulation
+t_eval = np.linspace(0, 7, 701)  # Time points to evaluate
+x0 = [0.2, 0, 0, 0]  # Initial conditions
+
+# Define the system of ODEs for initial response
+def system_ode(t, x):
+    return A @ x
+
+# Simulate initial response using solve_ivp
+sol_initial = solve_ivp(system_ode, t_span, x0, t_eval=t_eval, method='RK45')
+x_initial = sol_initial.y.T
+
+# Plot initial response
+plt.figure()
+plt.plot(t_eval, x_initial[:, 0], 'k', label='$x_1$')
+plt.plot(t_eval, x_initial[:, 1], 'k-.', label='$x_2$')
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.title('Initial Response')
+plt.show()
+
+# Define input signal function
+def input_signal(t):
+    return 0.1 * (np.sin(5 * t) + np.sin(9 * t) + np.sin(13 * t) + np.sin(17 * t) + np.sin(21 * t))
+
+# Define the system of ODEs with input
+def system_ode_with_input(t, x):
+    u = input_signal(t)
+    return A @ x + b2 * u
+
+# Simulate response with input using solve_ivp
+sol_forced = solve_ivp(system_ode_with_input, t_span, x0, t_eval=t_eval, method='RK45')
+x_forced = sol_forced.y.T
+
+# Plot response with input signal
+plt.figure()
+plt.plot(t_eval, x_forced[:, 0], 'k', label='$x_1$')
+plt.plot(t_eval, x_forced[:, 1], 'k-.', label='$x_2$')
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.title('Response with Input Signal')
+plt.show()
\ No newline at end of file
diff --git a/Chapter3/Python/dcmotor.py b/Chapter3/Python/dcmotor.py
new file mode 100644
index 0000000..6a2eaa3
--- /dev/null
+++ b/Chapter3/Python/dcmotor.py
@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+"""DCmotor.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1qEtOC5MiIZ7qpwmGILlODjmnK-zZKQb9
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import control
+from scipy.signal import lsim
+
+# Define the system matrices
+A = np.array([[0, 1, 0],
+              [0, 0, 4.438],
+              [0, -12, -24]])
+b1 = np.array([[0], [0], [20]])
+b2 = np.array([[0], [-7.396], [0]])
+B = np.hstack((b1, b2))
+C = np.array([[1, 0, 0], [0, 1, 0]])
+D = np.array([[0], [0]])
+
+# Create state-space system
+DC_motor = control.ss(A, b1, C, D)  # Note only the first input is used
+
+# Define the time vector
+t = np.arange(0, 4.00, 0.01)
+N = t.size
+
+# Generate input u (simple way)
+u_simple = np.zeros(N)
+for i in range(N):
+    if t[i] < 2:
+        u_simple[i] = 3
+    else:
+        u_simple[i] = -3
+
+# Generate input u (professional way)
+u_prof = scipy.signal.square(2 * np.pi * t / 4)
+u_prof = (+6 * u_prof) - 3
+# Simulate the system with the simple input using lsim
+t_out, y, x = lsim((A, b1, C, D), U=u_simple, T=t)
+
+# Plot the result
+plt.plot(t_out, x[:, 0], 'k', label='\u03B8')  # θ
+plt.plot(t_out, x[:, 1], 'k-.', label='\u03C9')  # ω
+plt.plot(t_out, x[:, 2], 'k:', label='i') # i
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.show()
\ No newline at end of file
diff --git a/Chapter3/Python/inverted_pendulum.ipynb b/Chapter3/Python/inverted_pendulum.ipynb
new file mode 100644
index 0000000..f330867
--- /dev/null
+++ b/Chapter3/Python/inverted_pendulum.ipynb
@@ -0,0 +1,46 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "C8SX677qxGVN"
+      },
+      "outputs": [],
+      "source": [
+        "def inverted_pendulum(t, x):\n",
+        "    g = 9.8\n",
+        "    l = 1\n",
+        "    m = 1\n",
+        "    M = 1\n",
+        "\n",
+        "    d1 = M + m * (1 - np.cos(x[1])**2)\n",
+        "    d2 = l * d1\n",
+        "\n",
+        "    F = 0  # No input\n",
+        "\n",
+        "    dxdt = np.zeros(4)\n",
+        "    dxdt[0] = x[2]\n",
+        "    dxdt[1] = x[3]\n",
+        "    dxdt[2] = (F + m * l * x[3]**2 * np.sin(x[1]) - m * g * np.sin(x[1]) * np.cos(x[1])) / d1\n",
+        "    dxdt[3] = (-F * np.cos(x[1]) - m * l * x[3]**2 * np.sin(x[1]) * np.cos(x[1]) + (M + m) * g * np.sin(x[1])) / d2\n",
+        "\n",
+        "    return dxdt"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter3/Python/inverted_pendulum.py b/Chapter3/Python/inverted_pendulum.py
new file mode 100644
index 0000000..effdcbd
--- /dev/null
+++ b/Chapter3/Python/inverted_pendulum.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+"""inverted_pendulum.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1PENBSi2mj9KfvFKPHHVcz9qx76lBY1ct
+"""
+
+def inverted_pendulum(t, x):
+    g = 9.8
+    l = 1
+    m = 1
+    M = 1
+
+    d1 = M + m * (1 - np.cos(x[1])**2)
+    d2 = l * d1
+
+    F = 0  # No input
+
+    dxdt = np.zeros(4)
+    dxdt[0] = x[2]
+    dxdt[1] = x[3]
+    dxdt[2] = (F + m * l * x[3]**2 * np.sin(x[1]) - m * g * np.sin(x[1]) * np.cos(x[1])) / d1
+    dxdt[3] = (-F * np.cos(x[1]) - m * l * x[3]**2 * np.sin(x[1]) * np.cos(x[1]) + (M + m) * g * np.sin(x[1])) / d2
+
+    return dxdt
\ No newline at end of file
diff --git a/Chapter3/Python/robot_model.ipynb b/Chapter3/Python/robot_model.ipynb
new file mode 100644
index 0000000..277922e
--- /dev/null
+++ b/Chapter3/Python/robot_model.ipynb
@@ -0,0 +1,66 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "ZKtZsRte1jiO"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "# Define robot model function\n",
+        "def robot_model(t, x):\n",
+        "    # Robot parameters\n",
+        "    g = 9.81\n",
+        "    l1 = 1\n",
+        "    l2 = 0.5\n",
+        "    m1 = 2\n",
+        "    m2 = 1\n",
+        "    I1 = 1e-2\n",
+        "    I2 = 5e-3\n",
+        "    D = 2\n",
+        "\n",
+        "    # Mass matrix (M)\n",
+        "    M = np.array([[m1*(l1/2)**2 + m2*(l1**2 + (l2/2)**2) + m2*l1*l2*np.cos(x[1]) + I1 + I2,\n",
+        "                   m2*(l2/2)**2 + 0.5*m2*l1*l2*np.cos(x[1]) + I2],\n",
+        "                  [m2*(l2/2)**2 + 0.5*m2*l1*l2*np.cos(x[1]) + I2,\n",
+        "                   m2*(l2/2)**2 + I2]])\n",
+        "\n",
+        "    # Coriolis and centrifugal terms (V)\n",
+        "    V = np.array([[-m2*l1*l2*np.sin(x[1])*x[2]*x[3] - 0.5*m2*l1*l2*np.sin(x[1])*x[3]**2],\n",
+        "                  [-0.5*m2*l1*l2*np.sin(x[1])*x[2]*x[3]]])\n",
+        "\n",
+        "    # Gravitational terms (G)\n",
+        "    G = np.array([[ (m1*l1/2 + m2*l1)*g*np.cos(x[0]) + m2*g*l2/2*np.cos(x[0] + x[1])],\n",
+        "                  [ m2*g*l2/2*np.cos(x[0] + x[1])]])\n",
+        "\n",
+        "    # Input (Q) - currently no external torques\n",
+        "    Q = np.array([[-D*x[2]],  # Damping term for joint 1\n",
+        "                  [-D*x[3]]])  # Damping term for joint 2\n",
+        "\n",
+        "    # System dynamics\n",
+        "    xy = np.linalg.pinv(M) @ (Q - V - G)\n",
+        "\n",
+        "    # Output - angular velocities and accelerations\n",
+        "    xp = np.vstack((x[2:], xy.flatten()))\n",
+        "\n",
+        "    return xp.flatten()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter3/Python/robot_model.py b/Chapter3/Python/robot_model.py
new file mode 100644
index 0000000..09e46af
--- /dev/null
+++ b/Chapter3/Python/robot_model.py
@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+"""robot_model.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/11g2x9Re3j6hFtHCNM_XnZiTL7LTW7s0r
+"""
+
+import numpy as np
+# Define robot model function
+def robot_model(t, x):
+    # Robot parameters
+    g = 9.81
+    l1 = 1
+    l2 = 0.5
+    m1 = 2
+    m2 = 1
+    I1 = 1e-2
+    I2 = 5e-3
+    D = 2
+
+    # Mass matrix (M)
+    M = np.array([[m1*(l1/2)**2 + m2*(l1**2 + (l2/2)**2) + m2*l1*l2*np.cos(x[1]) + I1 + I2,
+                   m2*(l2/2)**2 + 0.5*m2*l1*l2*np.cos(x[1]) + I2],
+                  [m2*(l2/2)**2 + 0.5*m2*l1*l2*np.cos(x[1]) + I2,
+                   m2*(l2/2)**2 + I2]])
+
+    # Coriolis and centrifugal terms (V)
+    V = np.array([[-m2*l1*l2*np.sin(x[1])*x[2]*x[3] - 0.5*m2*l1*l2*np.sin(x[1])*x[3]**2],
+                  [-0.5*m2*l1*l2*np.sin(x[1])*x[2]*x[3]]])
+
+    # Gravitational terms (G)
+    G = np.array([[ (m1*l1/2 + m2*l1)*g*np.cos(x[0]) + m2*g*l2/2*np.cos(x[0] + x[1])],
+                  [ m2*g*l2/2*np.cos(x[0] + x[1])]])
+
+    # Input (Q) - currently no external torques
+    Q = np.array([[-D*x[2]],  # Damping term for joint 1
+                  [-D*x[3]]])  # Damping term for joint 2
+
+    # System dynamics
+    xy = np.linalg.pinv(M) @ (Q - V - G)
+
+    # Output - angular velocities and accelerations
+    xp = np.vstack((x[2:], xy.flatten()))
+
+    return xp.flatten()
\ No newline at end of file
diff --git a/Chapter3/Python/robot_solver.ipynb b/Chapter3/Python/robot_solver.ipynb
new file mode 100644
index 0000000..eec69d7
--- /dev/null
+++ b/Chapter3/Python/robot_solver.ipynb
@@ -0,0 +1,139 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp"
+      ],
+      "metadata": {
+        "id": "hApbe4P2u8mv"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Define robot model function"
+      ],
+      "metadata": {
+        "id": "a4y2_c5MzdD5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define robot model function\n",
+        "def robot_model(t, x):\n",
+        "    # Robot parameters\n",
+        "    g = 9.81\n",
+        "    l1 = 1\n",
+        "    l2 = 0.5\n",
+        "    m1 = 2\n",
+        "    m2 = 1\n",
+        "    I1 = 1e-2\n",
+        "    I2 = 5e-3\n",
+        "    D = 2\n",
+        "\n",
+        "    # Mass matrix (M)\n",
+        "    M = np.array([[m1*(l1/2)**2 + m2*(l1**2 + (l2/2)**2) + m2*l1*l2*np.cos(x[1]) + I1 + I2,\n",
+        "                   m2*(l2/2)**2 + 0.5*m2*l1*l2*np.cos(x[1]) + I2],\n",
+        "                  [m2*(l2/2)**2 + 0.5*m2*l1*l2*np.cos(x[1]) + I2,\n",
+        "                   m2*(l2/2)**2 + I2]])\n",
+        "\n",
+        "    # Coriolis and centrifugal terms (V)\n",
+        "    V = np.array([[-m2*l1*l2*np.sin(x[1])*x[2]*x[3] - 0.5*m2*l1*l2*np.sin(x[1])*x[3]**2],\n",
+        "                  [-0.5*m2*l1*l2*np.sin(x[1])*x[2]*x[3]]])\n",
+        "\n",
+        "    # Gravitational terms (G)\n",
+        "    G = np.array([[ (m1*l1/2 + m2*l1)*g*np.cos(x[0]) + m2*g*l2/2*np.cos(x[0] + x[1])],\n",
+        "                  [ m2*g*l2/2*np.cos(x[0] + x[1])]])\n",
+        "\n",
+        "    # Input (Q) - currently no external torques\n",
+        "    Q = np.array([[-D*x[2]],  # Damping term for joint 1\n",
+        "                  [-D*x[3]]])  # Damping term for joint 2\n",
+        "\n",
+        "    # System dynamics\n",
+        "    xy = np.linalg.pinv(M) @ (Q - V - G)\n",
+        "\n",
+        "    # Output - angular velocities and accelerations\n",
+        "    xp = np.vstack((x[2:], xy.flatten()))\n",
+        "\n",
+        "    return xp.flatten()\n"
+      ],
+      "metadata": {
+        "id": "zI9_c0s9zdV6"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The `scipy.integrate.solve_ivp` function defaults to using the RK45 method, which is similar to MATLAB's `ode45`."
+      ],
+      "metadata": {
+        "id": "m_HKSt2Gzn1k"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Simulation parameters\n",
+        "t_span = [0, 5]  # Time span for simulation\n",
+        "x0 = np.array([-np.pi/3, np.pi/3, 0, 0])  # Initial state [theta_1, theta_2, omega_1, omega_2]\n",
+        "\n",
+        "# Solve the ODE\n",
+        "sol = solve_ivp(robot_model, t_span, x0, method='RK45')\n",
+        "\n",
+        "# Plot results\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(sol.t, sol.y[0] * 180 / np.pi, 'k', label = r'$\\theta_1$ (degrees)')\n",
+        "plt.plot(sol.t, sol.y[1] * 180 / np.pi, '-.k', label = r'$\\theta_2$ (degrees)')\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Joint Angles')\n",
+        "plt.title('Joint Angles of Two-Link Robot Manipulator')\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "jhbVyipazoeV",
+        "outputId": "9fc311ba-2385-4997-cfae-6721fbe19323"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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YPHlyrgsQAP+X0DVs2BCRkZE4evQoTp8+jT/++APLli3D+vXrMWTIkO+OpSBlj64FBgbi8ePHqFatGvT19dGgQQOsXLkSSUlJ+PfffzFv3rwcx+Z1zc2+ffswcOBAmbKC+Czk+Zw7deqEP//8EytWrICfn59c53F1dRWX4+/QoQOSk5MxdOhQ1K9fH7a2tvIHXojUqlULjo6OGD9+PJ48eYJevXp9sW63bt1w+fJl+Pj4wM3NDQYGBsjKykKLFi3E34vPKfL38GsyMzPz3J99nd/UqVNRoUIF6Ovr4+XLlxgwYECur+V7fT7CRkTKwcSLiAqN7KlXua3y9uDBA1hYWEBfX/+7zyPvhfG7du2Ci4uLOJr0uQ0bNmD37t05Eq+8ZL/WiIgING7cWCzPyMhAVFSUuMBEbrKnkmlqan7xBrSfMzMzw8CBAzFw4EAkJSWhYcOG8PX1/WriZWdn98XP4fP4C1KZMmVQpkwZBAUF4fHjx+J0s4YNG2LixIk4cOAAMjMz0bBhQ5k4s7KyEB4eLrNASnR0NOLi4sQ4mzdvnq+VHBVpzJgxKFeuHGbOnAljY2P8/PPP39zW/PnzcfjwYcybNw/r16+HpaUl9PT0vviZqamp5UjQwsPDZfpeUlISXr9+jVatWgGQ/X38fPpieno6njx5ItP3vnexiZ49e2Lu3LmoWLEi3Nzccq3z4cMHnD17FrNnz8bMmTNlXoci5PX+AJ9G0D5f4RH49P68fv36q23fuXMHjx49wvbt22UWpcmtj37pvbWzs8OZM2dy3E9Qkb+jRPR9ONWQiAqNkiVLws3NDdu3b5f5MnP37l2cPn1a5gvP98hO3v77hSk3z58/R2BgILp164YuXbrk2AYOHIiIiAiZ1Qrzo3r16jA3N8emTZuQkZEhlu/atSvPaVBWVlbw9PTEhg0bcv2CFxMTIz7+7zLbBgYGKFeuXJ5Lrbdq1QrXr1/HlStXxLKPHz9i48aNKFu2rMKuFWnQoAHOnTuH69evi4mXm5sbDA0NMX/+fOjq6sLDw0MmTgBYvny5TDtLly4FALRu3RrAp77VrFkzmU0Zfv31V0yePBnTpk3DunXrvrkdR0dHdO7cGdu2bcObN2+grq4Ob29vHD16VGYqXHR0NHbv3o369evDyMhIpo2NGzdCKpWKz9etW4eMjAy0bNkSANCsWTNoaWlh5cqVMiNEmzdvRnx8vPjeAp9+p/IzPe5LhgwZglmzZuU6opwte/Tqv6NV//3sC0pe7w/w6XMIDAzMcVxeI165vRZBELBixYocdb/071WrVq2QmZmJ1atXy5QvW7YMEolEJk4iKhw44kVEhcqiRYvQsmVL1KlTB4MHDxaXkzc2Ns71fjnfws3NDerq6liwYAHi4+Ohra2NJk2awMrKKkfd3bt3i8s256ZVq1bQ0NDArl27cizX/TVaWlrw9fXFmDFj0KRJE3Tr1g1RUVHYtm0bHB0d8xxBWLNmDerXrw9XV1cMHToUDg4OiI6OxpUrV/DixQuEhoYC+LT4g6enJzw8PGBmZoabN2/i4MGDMsux5+bnn3/Gnj170LJlS4wdOxZmZmbYvn07njx5gkOHDslcX1SQGjRogF27dkEikYhTD9XV1VG3bl2cOnUKnp6eMouWVK1aFf3798fGjRsRFxeHRo0a4fr169i+fTs6dOggM2LxNVKpFHPnzs1RbmZmhp9++qlgXtz/t2jRIsTHx2PUqFEwNDT85htI+/j4YP/+/Vi+fDnmz5+PuXPnIiAgAPXr18dPP/0EDQ0NbNiwAWlpaVi4cGGO49PT09G0aVN069YNDx8+xNq1a1G/fn2xr1taWmLatGmYPXs2WrRogXbt2on1atSoIRO3h4cH9u3bh4kTJ6JGjRowMDBA27Zt8/1a7Ozs8vz9NjIyQsOGDbFw4UJIpVLY2Njg9OnTePLkSb7PI4+83h/gU8I4YsQIdO7cGV5eXggNDcWpU6dgYWHx1bYrVKgAR0dHTJ48GS9fvoSRkREOHTqU6x9dsv/QMHbsWDRv3hzq6uro0aMH2rZti8aNG2PGjBmIiopC1apVcfr0aRw9ehTjx4//ptsUEJGCKWUtRSKi/2/Lli0CACEkJEQsO3PmjFCvXj1BV1dXMDIyEtq2bSuEhYXJHPel5b1zWzq9UaNGMsuzC4IgbNq0SXBwcBDU1dW/urS8q6urUKZMma++Bk9PT8HKykqQSqXistEHDhyQqZPbkvCCIAgrV64U7OzsBG1tbaFmzZrCpUuXBA8PD6FFixZ5HhsZGSn069dPsLa2FjQ1NQUbGxuhTZs2wsGDB8U6c+fOFWrWrCmYmJgIurq6QoUKFYR58+bJLJP9JZGRkUKXLl0EExMTQUdHR6hZs6Zw/PjxHPVQQMvJC4Ig3Lt3T1yO/3Nz584VAAi//vprjrakUqkwe/Zswd7eXtDU1BRsbW2FadOmySy1/zX9+/cXAOS6OTo6CoLwff0tt2XRMzMzhZ49ewoaGhrCkSNHvhjbl/pTNk9PT8HIyEiIi4sTBEEQQkJChObNmwsGBgaCnp6e0LhxY+Hy5csyx2THc+HCBWHYsGGCqampYGBgIPTu3VvmNg7ZVq9eLVSoUEHQ1NQUSpQoIYwcOVL48OGDTJ2kpCShV69egomJiQAgz6Xlv/Te5Rbn5+/bixcvhI4dOwomJiaCsbGx0LVrV+HVq1c5lnXPXk4+JiYm1zb/+znmtpx8ft6fzMxMYerUqYKFhYWgp6cnNG/eXIiIiMjXcvJhYWFCs2bNBAMDA8HCwkIYOnSoEBoamuP3IiMjQxgzZoxgaWkpSCQSmaXlExMThQkTJgilSpUSNDU1BScnJ2HRokVCVlaWTJzf8jtKRAVPIggKvsKUiOgrVq5ciXHjxiEiIoJ/ocWnhTMsLS3RqVMnbNq0SdnhEBU727Ztw8CBA3Hjxg1Ur15d2eEQkQrhNV5EpFQ3btyAvr5+sbwQPDU1Ncf1Kn/++Sfev38PT09P5QRFRERECsFrvIhIKQ4dOoTz589j165dGDJkiMxNhIuLq1evYsKECejatSvMzc0REhKCzZs3w8XFBV27dlV2eERERFSAit83HSIqFCZPnozExEQMHjwYy5YtU3Y4SlG2bFnY2tpi5cqVeP/+PczMzNCvXz/Mnz9fZgEJIiIiKvp4jRcREREREZGC8RovIiIiIiIiBWPiRUREREREpGC8xktOWVlZePXqFQwNDfO8wSkREREREakuQRCQmJiIUqVKQU3t62NaTLzk9OrVK9ja2io7DCIiIiIiKiSeP3+O0qVLf7UOEy85GRoaAvj05hoZGSk5GkAqleL06dPw9vaGpqamssOhQo79heTFPkPyYp8hebHPkLwKU59JSEiAra2tmCN8DRMvOWVPLzQyMio0iZeenh6MjIyU3vGo8GN/IXmxz5C82GdIXuwzJK/C2GfycwkSF9cgIiIiIiJSMCZeRERERERECsbEi4iIiIiISMF4jRcRERERFXuCICAjIwOZmZnKDoXyIJVKoaGhgdTU1B/yeWlqakJdXf2722HiRURERETFWnp6Ol6/fo3k5GRlh0L5IAgCrK2t8fz58x9yX12JRILSpUvDwMDgu9ph4kVERERExVZWVhaePHkCdXV1lCpVClpaWj/kyzx9u6ysLCQlJcHAwCDPmxZ/L0EQEBMTgxcvXsDJyem7Rr6YeBERERFRsZWeno6srCzY2tpCT09P2eFQPmRlZSE9PR06OjoKT7wAwNLSElFRUZBKpd+VeHFxDSIiIiIq9n7EF3gqmgpqBJQ9jIiIiIiISMGYeBERERERESkYEy8iIiIiIiIFY+JFRERERESkYEy8iIiIiIiKMEEQsHTpUtjb20NPTw8dOnRAfHz8N7X17t07WFlZISoq6ot1PD09MX78+G8LthDq0aMHlixZovDzMPEiIiIiIirCfHx8sG7dOmzfvh1BQUEIDg6Gr6/vN7U1b948tG/fHmXLli3QGAuzX375BfPmzfvmZDW/mHgRERERERVR165dw9KlS7Fv3z40bNgQHh4eGDp0KPz9/eVuKzk5GZs3b8bgwYMVEKn80tPTf8h5XFxc4OjoiJ07dyr0PEy8iIiIiIg+IwgCPn78+MM3QRDkjnXx4sVo2rQpqlWrJpaVKFECsbGxcrfl7+8PbW1t1K5dWyz7+PEj+vXrBwMDA5QsWTLXKXlZWVnw8/ODvb09dHV1UbVqVRw8eFCmTmJiInr37g19fX2ULFkSy5YtyzFl0dPTE6NHj8b48eNhYWGB5s2b56vtvOocPHgQrq6u0NXVhbm5OZo1a4aPHz/KtNG2bVvs3btX7vdMHhoKbZ0U7vHjxwgLC0Pjxo2hqamp7HCIiIiIirzk5GQYGBj88PMmJSVBX18/3/XT0tJw4sQJLF68WKY8NTUVxsbGcp8/KCgIHh4eMmU+Pj64cOECjh49CisrK0yfPh0hISFwc3MT6/j5+WHnzp1Yv349nJycEBgYiD59+sDS0hKNGjUCAEycOBGXLl3CsWPHUKJECcycOTNHOwCwfft2jBw5EpcuXfpi2/369cOhQ4fQsmXLPM9fvnx59OzZEwsXLkTHjh2RmJiIoKCgHEluzZo1MW/ePKSlpUFbW1vu9y5fBJJLfHy8AECIj49XdiiCIAjC7NmzBQCCjo6O0KxZM2H16tVCYmKissOiQio9PV04cuSIkJ6eruxQqIhgnyF5sc+QvJTdZ1JSUoSwsDAhJSVFLEtKShIA/PAtKSlJrtgvX74sfg/U19cXNy0tLaF58+aCIAhChw4dBBMTE6Fz5855tte+fXth0KBB4vPExERBS0tL2L9/v1j27t07QVdXVxg3bpwgCIKQmpoq6OnpCZcvX5Zpa/DgwULPnj0FQRCEhIQEQVNTUzhw4IC4Py4uTtDT0xPbEQRBaNSokeDu7i4+/1LbgwYNEjp37ixkZmbmef7g4GABgBAVFfXV1x4aGvrFern1kWzy5AYc8SrijIyMYGpqig8fPuDMmTM4c+YMfvnlFwwfPhxjxoyBjY2NskMkIiIiKlL09PSQlJSklPPK49GjR9DX18etW7dkylu3bo169eoBAMaNG4dBgwZh+/btebaXkpICHR0d8XlkZCTS09NRq1YtsczMzAzOzs7i84iICCQnJ8PLy0umrfT0dLi7uwP4NENLKpWiZs2a4n5jY2OZdrJ9PuL2tbarVKmSr/NXrVoVTZs2haurK5o3bw5vb2906dIFpqamMvV1dXUBfBrtVBQmXkXcqFGjULZsWdjb2+Ps2bNYv349wsPDsWDBAixbtgw//fQTZsyYAQsLC2WHSkRERFQkSCQSuab8KUtCQgIsLCxQrlw5sezp06cIDw9H586dAXy6bur8+fP5as/CwgIfPnyQK4bsBPXEiRM5/uD/LVP2Pn/fv9R2VlaWuPBGXudXV1dHQEAALl++jNOnT2PVqlWYMWMGrl27Bnt7e7Hu+/fvAQCWlpZyx5xfXFxDBUgkElSqVAkTJ07EgwcPcOTIEdSvXx/p6elYvnw5HB0d4efnp9AMnoiIiIh+LAsLC8THx8tcrzRv3jy0atUKlSpVkrs9d3d3hIWFic8dHR2hqamJa9euiWUfPnzAo0ePxOeVKlWCtrY2nj17hnLlyslstra2AAAHBwdoamrixo0b4nHx8fEy7eTma22XLl063+eXSCSoV68eZs+ejX///RdaWlo4fPiwzLnu3r2L0qVLK3SwgiNeKkZNTQ3t27dH+/btcfr0aUydOhW3bt3C9OnTsXr1aixcuBC9evWCRCJRdqhERERE9B2aNGmC1NRUzJ8/Hz169MCuXbvw999/4/r169/UXvPmzTFt2jR8+PABpqamMDAwwODBg+Hj4wNzc3NYWVlhxowZUFP7v7EbQ0NDTJ48GRMmTEBWVhbq16+P+Ph4XLp0CUZGRujfvz8MDQ3Rv39/+Pj4wMzMDFZWVpg1axbU1NS++p30S21fvHgRmpqaGD58eJ7nr1ChAs6ePQtvb29YWVnh2rVriImJQcWKFWXOFRQUBG9v72963/KLiZcK8/b2RrNmzbBnzx788ssviIqKwvTp09GpUydxHisRERERFU0lSpTAtm3b4OPjgzlz5qBJkya4ePGiONIjL1dXV1SrVg379+/H8OHDAQCLFi1CUlIS2rZtC0NDQ0yaNCnHjYbnzJkDS0tL+Pn54fHjxzAxMUG1atUwffp0sc7SpUsxYsQItGnTBkZGRpgyZQqeP38uc01ZbnJr293dHWPHjs3X+Y2MjBAYGIjly5cjISEBdnZ2WLJkibgiIvBpFcgjR47g5MmT3/S+5ZdEEL7hhgHFWEJCAoyNjREfHw8jIyNlhwOpVAp/f3+0atXqq8vJp6amYtmyZahYsSI6dOgAAMjMzMT79+8VOpeVCpf89heibOwzJC/2GZKXsvtMamoqnjx5Ant7+zyTgKLq/PnzWL16dY77X+XmxIkT8PHxwd27d2VGtgrax48fYWNjgyVLlsh9w+asrCwkJCTAyMioQGJct24dDh8+jNOnT+e6/2t9RJ7cgNd4FRM6OjqYNm2amHQBwObNm+Hk5ITNmzcrLzAiIiIiUphmzZqha9eu8Pf3R+nSpXHlypWv1m/dujWGDRuGly9fFmgc//77L/bs2YPIyEiEhISgd+/eAID27dsX6Hm+haamJlatWqXw83CqYTF2+PBhxMfHIzExUdmhEBEREZECnDlzRu5jxo8fX/CBAFi8eDEePnwILS0teHh4ICgoqFCsvD1kyJAfch4mXsXY8ePHsXv3bvTs2VMsu3PnDkqXLp3j3gZERERERN/K3d0dwcHByg5DqTjVsBhTV1dH3759oaHxKf9OTU1Fp06d4OzsjD///BO8/I+IiIiIqGAw8SLRq1evoKWlhZiYGPTv3x/NmzfH48ePlR0WEREREVGRx8SLRA4ODrh16xbmz58PHR0dBAQEwMXFBYsXL0ZGRoaywyMiIiIiKrKYeJEMTU1NTJ06FXfu3EGTJk2QkpICHx8f1KpVCyEhIcoOj4iIiIioSGLiRbkqV64czpw5gy1btsDU1BQhISGoWbMmfHx8kJycrOzwiIiIiIiKFCZe9EUSiQQDBw7E/fv30b17d2RmZmLx4sVwdXXFhQsXlB0eEREREVGRwcSL8lSiRAns3bsXx48fh62tLR4/fozGjRtj6tSpyg6NiIiIiKhIYOJF+da6dWvcu3cPQ4cOhSAIMDY2VnZIRERERERFAm+gTHIxNDTExo0b0bt3b9SrV08sf/r0KaytraGtra3E6IiIiIiICieOeNE3adSokcyNl1u3bo0aNWrg0aNHSo6MiIiIiKjwYeJF3+3hw4eIjo5GdHQ0zMzMlB0OERERUbEiCAKWLl0Ke3t76OnpoUOHDoiPj/+mtt69ewcrKytERUV9sY6npyfGjx//bcEWQj169MCSJUsUfh6VSrx8fX0hkUhktgoVKoj7U1NTMWrUKJibm8PAwACdO3dGdHS0EiNWDVWrVsW9e/dw5MgRWFhYAPj0D8CLFy+UHBkRERGR6vPx8cG6deuwfft2BAUFITg4GL6+vt/U1rx589C+fXuULVu2QGMszH755RfMmzfvm5PV/FKpxAsAKleujNevX4vbxYsXxX0TJkzA33//jQMHDuDChQt49eoVOnXqpMRoVYeVlRXq1KkjPt+3bx/Kly+PDRs2QBAEJUZGREREpLquXbuGpUuXYt++fWjYsCE8PDwwdOhQ+Pv7y91WcnIyNm/ejMGDBysgUvmlp6f/kPO4uLjA0dERO3fuVOh5VC7x0tDQgLW1tbhlj8DEx8dj8+bNWLp0KZo0aQIPDw9s3boVly9fxtWrV5Ucteo5evQoUlJSMGLECHTu3Bnv379XdkhEREREcvn48aPcW0ZGhnh8RkYGPn78iJSUlDzb/VaLFy9G06ZNUa1aNbGsRIkSiI2Nlbstf39/aGtro3bt2jKx9uvXDwYGBihZsmSuU/KysrLg5+cHe3t76OrqomrVqjh48KBMncTERPTu3Rv6+vooWbIkli1blmPKoqenJ0aPHo3x48fDwsICzZs3z1fbedU5ePAgXF1doaurC3NzczRr1izHe962bVvs3btX7vdMHiq3qmF4eDhKlSoFHR0d1KlTB35+fihTpgyCg4MhlUrRrFkzsW6FChVQpkwZXLlyRaaDfS4tLQ1paWni84SEBACAVCqFVCpV7IvJh+wYCkMsn9u2bRuqVauGGTNm4PDhw7hx4wa2bduGhg0bKju0Yq2w9hcqvNhnSF7sMyQvZfcZqVQKQRCQlZWFrKwsmX0GBgZyt7d371507doVAHDo0CH06NEDjRo1wrlz58Q6ZcuWzZEYZWZmyn2utLQ0nDhxAosWLZKJPSUlBcbGxjleT14CAwNRrVo1meMmT56MCxcu4PDhw7CyssKMGTMQEhKCqlWrivV+//137Nq1C2vXroWTkxMCAwPRp08fmJubo1GjRgA+zTy7dOkSjhw5ghIlSmDWrFk52gGA7du3Y8SIEQgKCvpi2/369cOhQ4fQokULZGVlffX85cuXR8+ePbFgwQJ06NABiYmJuHjxIjIzM2XOW716dcybNw8pKSk5VunOysqCIAiQSqVQV1eX2SdPv1WpxKtWrVrYtm0bnJ2d8fr1a8yePRsNGjTA3bt38ebNG2hpacHExETmmBIlSuDNmzdfbNPPzw+zZ8/OUX769Gno6ekV9Ev4ZgEBAcoOIYfy5ctj/vz5WLJkCV68eAEvLy906dIF3bt3F1dEJOUojP2FCjf2GZIX+wzJS1l9Jnu2VFJSUoFMbUtJSRH/UJ890pWRkSGWAcj1MozP9+fX9evXkZKSgsmTJ2Pq1KliuVQqRYMGDRAWFoYRI0YgNjYW6urq8PHxQYcOHb7YXmRkJCwtLcVYkpKSsGXLFmzYsAE1atQAAKxatQqVK1dGeno6EhISkJaWBj8/Pxw+fBg1a9YEAHTq1Annz5/HmjVr4O7ujsTERPz555/YtGmT2M7y5ctRqVIlsZ3s98nBwQEzZswAgK+2vXXrVtSrVy/P848ZMwYZGRlo1qwZzMzMYGZmBjs7O2RlZcm850ZGRkhPT0d4eDjKlCkj876kp6cjJSUFgYGBMiOawKfpmfmlUt9+W7ZsKT6uUqUKatWqBTs7O+zfvx+6urrf1Oa0adMwceJE8XlCQgJsbW3h7e0NIyOj7475e0mlUgQEBMDLywuamprKDidXAwcOxIQJE7B9+3YcOHAAz549w59//gl7e3tlh1bsFIX+QoUL+wzJi32G5KXsPpOamornz5/DwMAAOjo6Mvu+JRnS1tYW/8Dcq1cvdO7cGWpqajLfRZ88eZLjOH19fbnP9fLlS+jr6yMkJESmvG3btmjYsCFMTU2xcuVKuLm54c2bN6hRowY6d+78xXNJpVIYGhqK33GfPHmC9PR0eHp6imVGRkZwdnaGlpYWjIyMcO/ePSQnJ+dYNyE9PR3u7u4wMjLCkydPIJVK0ahRoy+2A3xKgmvUqCE+/1rbVapUgaGhIZ4/f/7V89etWxdNmzZF/fr14e3tLQ4EmJqaytS3tLQEAKirq+f4jp+amgpdXV00bNjwu/qISiVe/2ViYoLy5csjIiICXl5eSE9PR1xcnMyoV3R0NKytrb/Yhra2dq43BdbU1CxU/6EUtng+Z2pqim3btqFly5YYPnw4rl27hho1amDjxo3o3r27ssMrlgpzf6HCiX2G5MU+Q/JSVp/JzMyERCKBmpoa1NRklz8wNDT8rra1tLSgpaWVo/x7282WlJQECwsLlC9fXix7+vQpwsPD0aVLF9jY2MDGxgYAUKpUKVhYWCAuLu6L57e0tERcXJz4Pnz+87/vTfZ7lj3ic+LECfFc2bS1tWWO/Vo72QwMDMTnX2o7KysL6enpkEgkeZ5fU1MTAQEBuHz5Mk6fPo01a9bg119/xbVr12QGAeLi4gB8mg333xjV1NQgkUhy7aPy9FmVW1zjc0lJSYiMjETJkiXh4eEBTU1NnD17Vtz/8OFDPHv2TGY1PlKc7t2749atW6hbty4SEhLQo0cPjBo1SuYaOiIiIiLKHwsLC8THx8tMXZw3bx5atWqFSpUqydQNDg5GZmYmbG1tv9ieu7s7wsLCxOeOjo7Q1NTEtWvXxLIPHz7g0aNH4vNKlSpBW1sbz549Q7ly5WS27HM5ODhAU1MTN27cEI+Lj4+XaSc3X2u7dOnS+T6/RCJBvXr1MHv2bPz777/Q0tLC4cOHZc519+5dlC5dWlyYTxFUasRr8uTJaNu2Lezs7PDq1SvMmjUL6urq6NmzJ4yNjTF48GBMnDgRZmZmMDIywpgxY1CnTp0vLqxBBa9s2bK4cOECZs2ahd9//x1r167F/fv3cfbsWUgkEmWHR0RERFRkNGnSBKmpqZg/fz569OiBXbt24e+//8b169dl6r1//x79+vXDpk2bvtpe8+bNMW3aNHz48AGmpqYwMDDA4MGD4ePjA3Nzc3Fxjc9HhAwNDTF58mRMmDABWVlZqF+/PuLj43Hp0iUYGRmhf//+MDQ0RP/+/eHj4wMzMzNYWVlh1qxZ4kjSl3yp7YsXL0JTUxPDhw/P8/wVKlTA2bNn4e3tDSsrK1y7dg0xMTGoWLGizLmCgoLg7e39DZ9C/qlU4vXixQv07NkT7969g6WlJerXr4+rV6+KczaXLVsGNTU1dO7cGWlpaWjevDnWrl2r5KiLHw0NDcybNw/169dH3759MWrUKCZdRERERHIqUaIEtm3bBh8fH8yZMwdNmjTBxYsXZUa10tLS0KFDB/z888+oW7fuV9tzdXVFtWrVsH//fgwfPhwAsGjRIiQlJaFt27YwNDTEpEmTctxoeM6cObC0tISfnx8eP34MExMTVKtWDdOnTxfrLF26FCNGjECbNm1gZGSEKVOm4Pnz5zmumfqv3Np2d3fH2LFj83V+IyMjBAYGYvny5UhISICdnR2WLFkiszZEamoqjhw5gpMnT+b9pn8HicC728olISEBxsbGiI+PLzSLa/j7+6NVq1ZFci59fHw8jI2NxecPHjwQh7Wp4BX1/kI/HvsMyYt9huSl7D6TmpqKJ0+ewN7ePs8koKgRBAG9evWCs7MzfH1983XMiRMn4OPjg7t37+a41qkgffz4ETY2NliyZIncN2zOXpHQyMioQGJct24dDh8+jNOnT+e6/2t9RJ7cQKWv8aLC7/Ok69WrV2jUqBEaNWqE6OhoJUZFREREVPRdunQJ+/btw5EjR+Dm5gY3NzfcuXPnq8e0bt0aw4YNw8uXLws0ln///Rd79uxBZGQkQkJC0Lt3bwBA+/btC/Q830JTUxOrVq1S+HlUaqohFW2PHj1CWloakpOTC8VoIhEREVFRVr9+fblvogwA48ePL/hgACxevBgPHz6ElpYWPDw8EBQUpNDFLPJryJAhP+Q8TLyo0PD09ERISAgEQRDvdZGVlQWJRMJrwIiIiIiKMHd3dwQHBys7DKXiVEMqVBwcHODo6Cg+9/PzQ7du3ZCYmKjEqIiIiIiIvg9HvKjQevXqFebMmYO0tDSEhYXhyJEjcHJyUnZYRERERERy44gXFVqlSpXCP//8g5IlSyIsLAw1atTAiRMnlB0WEREREZHcmHhRoVanTh0EBwejXr16iI+PR9u2bfHbb79904WiRERERF/COyzRlxRU32DiRYVeyZIlce7cOfz0008QBAGzZs1Cx44dc9y8j4iIiEhe2fcOS05OVnIkVFilp6cDANTV1b+rHV7jRUWClpYW1qxZg+rVq2PkyJE4duwYatWqhcOHD6NixYrKDo+IiIiKKHV1dZiYmODt27cAAD09Pa6mXMhlZWUhPT0dqampCr3Jc/a5YmJioKenBw2N70udmHhRkTJw4EC4uLigU6dOePjwIWrWrIkdO3agQ4cOyg6NiIiIiihra2sAEJMvKtwEQUBKSgp0dXV/SJKspqaGMmXKfPe5mHhRkVOjRg0EBwejW7duuHDhAjp27IhffvkFvr6+3z0ETERERMWPRCJByZIlYWVlBalUquxwKA9SqRSBgYFo2LChOFVUkbS0tApkZI2JFxVJVlZWCAgIgI+PD1asWIEFCxage/fucHFxUXZoREREVESpq6vzj7hFgLq6OjIyMqCjo/NDEq+CwsSLiixNTU0sX74c1atXR1paGpMuIiIiIiq0mHhRkdenTx+Z56GhoQgPD0eXLl2UFBERERERkSwuJ08qJS4uDh07dkTXrl3x559/KjscIiIiIiIATLxIxRgYGKBTp05wcHBAmzZtlB0OEREREREAJl6kYjQ0NLB48WL8+++/MDMzE8u5PCwRERERKRMTL1JJRkZG4uMNGzagfPnyOHnypBIjIiIiIqLijIkXqbSsrCzs3bsX8fHxaNWqFRYuXAhBEJQdFhEREREVM0y8SKWpqanh5MmTGDp0KARBwNSpU9G7d28kJycrOzQiIiIiKkaYeJHK09bWxoYNG7B27VpoaGhgz549qF+/Pp49e6bs0IiIiIiomGDiRcWCRCLByJEjcfbsWVhaWuLff/9F9erVERgYqOzQiIiIiKgYYOJFxUrDhg1x48YNuLm5ISYmBk2bNsX69euVHRYRERERqTgmXlTs2NnZ4dKlS+jevTsyMjIwcuRIDB8+HOnp6coOjYiIiIhUFBMvKpb09PSwZ88ezJ8/HxKJBBs3bkSTJk0QHR2t7NCIiIiISAUx8aJiSyKRYOrUqTh+/DiMjY1x+fJl/Pvvv8oOi4iIiIhUkIayAyBStlatWuH69esIDAxEixYtlB0OEREREakgjngRAShfvjyGDBkiPn/y5Al+/fVXZGRkKDEqIiIiIlIVHPEi+o+MjAx06NABt2/fRnJyMpYsWaLskIiIiIioiOOIF9F/aGho4JdffoGjoyMmTpyo7HCIiIiISAUw8SLKRdeuXXH//n3Y2NiIZSEhIUqMiIiIiIiKMiZeRF+gqakpPj506BA8PDzw008/IS0tTYlREREREVFRxMSLKB8iIyMhkUiwbt06NGzYEM+fP1d2SERERERUhDDxIsqHKVOm4MSJEzA1NcX169dRrVo1nDlzRtlhEREREVERwcSLKJ9atmyJ4OBgVKtWDbGxsWjevDl+//13ZGVlKTs0IiIiIirkmHgRycHe3h6XLl3C4MGDkZWVhRkzZqBjx46Ii4tTdmhEREREVIgx8SKSk46ODv744w9s2rQJ2traOHbsGKpXr47bt28rOzQiIiIiKqSYeBF9oyFDhuDSpUuws7NDZGQkateujR07dig7LCIiIiIqhJh4EX0HDw8PBAcHo0WLFkhJSUG/fv0wefJkZYdFRERERIUMEy+i72Rubo7jx49j1qxZkEgkcHd3V3ZIRERERFTIMPEiKgDq6urw9fXFnTt30Lt3b7E8NjZWiVERERERUWHBxIuoAFWuXFl8/PbtW1SpUgXDhg1DcnKyEqMiIiIiImVj4kWkIKdPn8abN29w6dIlZYdCREREREqmoewAiFRVnz59YG1tjZIlS0JPTw8AkJWVBYlEAolEouToiIiIiOhH4ogXkQI1a9ZMZvrhggUL0KlTJ177RURERFTMMPEi+kFiY2Mxb948HDlyBFWqVEFAQICyQyIiIiKiH4SJF9EPYmFhgaCgIFSsWBGvX7+Gt7c3Jk2ahLS0NGWHRkREREQKxsSL6Adyd3fHzZs38dNPPwEAli5diho1auDWrVvKDYyIiIiIFIqJF9EPpqenhzVr1uDvv/+GpaUl7ty5gxo1amDu3LnIyMhQdnhEREREpABMvIiUpE2bNrh79y46duyIjIwM/Prrr6hbty7u37+v7NCIiIiIqICpVOLl5+eHGjVqwNDQEFZWVujQoQMePnwoU8fT01Nczjt7GzFihJIipuLOysoKhw4dws6dO2FiYoIbN27A3d0dS5YsQWZmprLDIyIiIqIColKJ14ULFzBq1ChcvXoVAQEBkEql8Pb2xsePH2XqDR06FK9fvxa3hQsXKiliIkAikaB37964e/cuWrRogbS0NEyePBlDhgxRdmhEREREVEBU6gbKJ0+elHm+bds2WFlZITg4GA0bNhTL9fT0YG1tna8209LSZFadS0hIAABIpVJIpdICiPr7ZMdQGGKh72NlZYWjR49iy5YtmDZtGgYPHlzgnyv7C8mLfYbkxT5D8mKfIXkVpj4jTwwSQRAEBcaiVBEREXBycsKdO3fg4uIC4NNUw3v37kEQBFhbW6Nt27b49ddfoaenl2sbvr6+mD17do7y3bt3f/EYou+VkpICXV1d8fnZs2dRunRpODs7KzEqIiIiIvpccnIyevXqhfj4eBgZGX21rsomXllZWWjXrh3i4uJw8eJFsXzjxo2ws7NDqVKlcPv2bUydOhU1a9bEX3/9lWs7uY142draIjY2Ns8390eQSqUICAiAl5cXNDU1lR0OKcDDhw9RvXp1pKen4/Lly/Dw8PjmtthfSF7sMyQv9hmSF/sMyasw9ZmEhARYWFjkK/FSqamGnxs1ahTu3r0rk3QBwLBhw8THrq6uKFmyJJo2bYrIyEg4OjrmaEdbWxva2to5yjU1NZX+QX+usMVDBadkyZLo3r073r59i1q1akEikXx3m+wvJC/2GZIX+wzJi32G5FUY+ow851epxTWyjR49GsePH8c///yD0qVLf7VurVq1AHyalkhUGJmbm2P79u04evSomHS9f/8e3bp1y7FqJxEREREVTiqVeAmCgNGjR+Pw4cM4d+4c7O3t8zzm1q1bAD6NKhAVZlpaWuLjX3/9FQcOHICrqytmzJiB5ORkJUZGRERERHlRqcRr1KhR2LlzJ3bv3g1DQ0O8efMGb968QUpKCgAgMjISc+bMQXBwMKKionDs2DH069cPDRs2RJUqVZQcPVH+TZw4Ea1atYJUKsXvv/+OChUqYM+ePVDRSzaJiIiIijyVSrzWrVuH+Ph4eHp6omTJkuK2b98+AJ9GDM6cOQNvb29UqFABkyZNQufOnfH3338rOXIi+Tg6OuL48eP466+/YGdnh+fPn6NXr16oV68erl27puzwiIiIiOg/VGpxjbz+2m9ra4sLFy78oGiIFEsikaBjx45o0aIFli5dCj8/P1y5cgW1a9dGnz594Ofnl+c1jkRERET0Y6jUiBdRcaSrq4sZM2bg0aNHGDBgAABg586dcHJyws8//4wPHz4oN0AiIiIiYuJFpCpKlSqFrVu34saNG6hfvz5SU1OxYMECODg4YOvWrcoOj4iIiKhYY+JFpGKqV6+OwMBAHDt2DJUrV0ZcXBx0dHSUHRYRERFRscbEi0gFSSQStG3bFqGhoTh48CC6d+8u7jt//jy2bNkCqVSqxAiJiIiIiheVWlyDiGSpq6ujc+fO4vOUlBRs374dHz58gLa2tnhNGBEREREpFke8iIqR7JUQa9SogV69eonld+7cwcePH5UYGREREZFqY+JFVIzo6OigXbt2uHjxIrS0tAAAGRkZaN++Pezs7ODr64vY2FglR0lERESkeph4ERVDEolEfPzs2TNIJBK8e/cOs2fPhp2dHcaMGYNHjx4pMUIiIiIi1cLEi6iYc3BwwKNHj7Bv3z5Uq1YNycnJWL16NZydndGyZUv873//Q1ZWlrLDJCIiIirSmHgREdTV1dGtWzfcvHkTAQEBaNOmDSQSCU6ePIlWrVqhQoUKWLlyJeLj45UdKhEREVGRxMSLiEQSiQTNmjXD33//jfDwcEyYMAFGRkYIDw/HuHHjULJkSQwYMAA3b95UdqhERERERQoTLyLKlaOjI5YuXYqXL19izZo1qFSpkrgcfUBAgLLDIyIiIipSmHgR0VcZGBjgp59+wt27d3H58mUMGjQI/fv3F/fv3bsXzZs3x/Hjx5UYJREREVHhxsSLiPJFIpGgTp062Lx5M0qVKiWWb9u2DadPn5aZfpieno7k5GRlhElERERUKDHxIqLvsmbNGsyePRt9+/YVy44ePQorKyt0794dBw4cQFJSkhIjJCIiIlI+DWUHQERFm6OjI2bOnClTdu7cOXz8+BH79+/H/v37oaWlhcaNG6NNmzZo06YNypYtq5xgiYiIiJSEI15EVODWrl2LGzduYOrUqXB0dER6ejpOnTqFMWPGwN7eHi4uLpgyZQoCAgKQkpKi7HCJiIiIFI6JFxEVOIlEgurVq2P+/PkIDw9HWFgYFi5ciIYNG0JdXR337t3DokWL4O3tDVNTUzRt2hR///23ssMmIiIiUhgmXkSkUBKJBBUrVoSPjw8uXLiAt2/fYvfu3RgwYABKly6NtLQ0nDt3Du/evROPuXfvHnx8fHDy5EklRk5ERERUcHiNFxH9UGZmZujZsyd69uwJQRDw6NEjnDlzBs2bNxfrBAQEYPHixXjw4AFatGghlm/ZsgUuLi5wdXWFrq6uMsInIiIi+iZMvIhIaSQSCZydneHs7CxT7u7ujuHDh6NWrVpi2fPnzzF48GAAgJqaGipUqAB3d3e4ubmhYsWKqFChAsqWLQt1dfUf+hqIiIiI8oOJFxEVOo0aNUKjRo1kyhITE+Ht7Y2QkBDExsYiLCwMYWFh2LVrl1hHW1sbTk5OqFChApydnVGhQgV06NABBgYGP/olEBEREclg4kVERUKlSpVw6tQpCIKAV69e4datW/j3339x+/ZtPHjwAI8ePUJaWhru3r2Lu3fvisd9fu3YokWLcOPGDQwaNEicwpiWloakpCSYmZlBIpH88NdFRERExQMTLyIqUiQSCWxsbGBjY4PWrVuL5ZmZmXj27BkePHiABw8e4OHDh3j9+jXMzMzEOufPn4e/vz+8vb3FssuXL6NJkybQ09NDyZIlUaJECZQoUQJWVlbi48+fW1pawsjIiFMaiYiISC5MvIhIJairq8Pe3h729vZo2bJlrnUmTZoEb29v1K9fXyyLjo4GACQnJyMyMhKRkZF5nksikSA1NRVaWloAgHnz5uHmzZsYPny4OJL2+vVrBAQEwNTUFCYmJjAyMhI3Q0ND8VgiIiIqHph4EVGx0aRJEzRp0kSmrEePHujYsSOeP3+ON2/eIDo6GtHR0Xj79q34+PMtKSkJurq6MonT5cuX4e/vj7Zt24ploaGh6N+//xdj0dbWFpOwzxOyvXv3itek/e9//0N4eDgaNmwINzc3AJ8SxKioKLG+gYEBR9+IiIiKACZeRFTsaWtro1y5cihXrlyeddPS0pCYmChTNnHiRLRr1w716tUTy4yMjODt7Y24uDh8+PABiYmJSEhIQHJysthOTEwMYmJiZNr6PKHbtWsXdu3ahSVLloiJ1+3bt1GnTh2ZYwwMDHKMqH3+3MjICJMnT4aJiQkA4P79+4iOjoajoyNsbW3z/T4RERHRt2PiRUQkB21tbWhra8uUNW3aFE2bNpUpq1u3Lk6dOpXj+IyMDCQmJoqJWPaWXfZ54lW7dm1kZGSgcuXKYplUKoW5uTni4+ORkZEBAEhKSkJSUhJevXr1xbjHjRsnPl69ejXWrl2L6dOnY968eQCAJ0+ewMvLC5aWljKbmZkZXr16BTU1NZQsWRKWlpawtrbO8R4QERHR1zHxIiL6gTQ0NGBqagpTU9M8644ePRqjR4+WKWvQoAFiY2MhCALS0tLEpO2/Sdx/nxsbG4ttWFpaokKFCrC3txfL3rx589Vr3FasWCHz3NzcHDY2Njh69CjKli0LAPj333/x8uVLuLi4iGVERET0CRMvIqIiSCKRQEdHBzo6OrCyspLrWF9fX/j6+sqUubi4ICgoSJz+mL1FR0fj/v37AIDY2FjExMQgPT0d7969w7t372BkZCS28ccff2Dt2rWYMWMG5s6dCwB4+fIlhgwZAjs7O9jZ2aFs2bLiT2tra6ipqX3fG0FERFREMPEiIiIYGhrKrPaYTSqVwt/fH61atYKmpiYEQcD79+/x6tUrvHz5UmbkzsbGBtWqVYOzs7NYFhERgZMnT+Z6Ti0tLTg4OMDJyUncypcvDycnJ9jY2DApIyIilcLEi4iI8k0ikcDc3Bzm5uZwdXWV2Td9+nRMnz5dpszJyQmbN29GVFQUnj59Kv58/vw50tPTxfuu/VdMTAwsLCwAAMePH8ebN2/g6emZrwVQiIiICiMmXkREpDClSpXCoEGDcpRnZGTg+fPniIiIQHh4OMLDw/Ho0SOEh4cjLi4O5ubmYt3169fjxIkTWLdunZh43bt3D0uXLkWlSpVQuXJlVK5cGaVLl4ZEIvlhr42IiEgeTLyIiOiH09DQEG947eXlJbMvKytLJoGqW7cuMjIyULVqVbHsxo0b2LJli8xxhoaGYiL2+U9bW1smZEREpHRMvIiIqFD577VduU1hdHd3x6xZsxAWFoZ79+7h0aNHSExMxLVr13Dt2jWZutkJWfXq1bF69WqFx09ERJQbJl5ERFTkVK1aVWYELD09HREREbh3756YjP03IUtLS5Npo127dgCABQsWoGLFij80fiIiKn6YeBERUZGnpaWFSpUqoVKlSjLlUqkU4eHhuHv3LtTV1WXKT506hfT0dCxfvlwsX7p0Kfbt2ycmdlWqVEGVKlVk7oNGRET0LZh4ERGRytLU1Mw1IZNIJDh9+jTu3Lkjc7Pnq1ev4vr167h+/bpM/bJly6JKlSoyCZmjoyOXvCcionxj4kVERMWOhoYGGjVqhEaNGsmU+/n5oUuXLggNDcXt27cRGhqK58+fIyoqClFRUTh27JhYV19fH66urujduzdGjx79o18CEREVMUy8iIiI/j9HR0c4OjqiW7duYtmHDx9w+/ZtMRELDQ3F3bt38fHjR1y9ehWenp5i3bdv36J27dpwc3PDwYMHOSJGREQiJl5ERERfYWpqmmN0LCMjA+Hh4bh9+zYqVKggloeGhuLJkyfQ0NCQSbpatmyJ+Ph4memKrq6uMDQ0/KGvhYiIlIeJFxERkZw0NDRQsWLFHKsh1qlTB+fOncPHjx/FMkEQcOnSJSQmJuLKlSsy9R0cHMRrxrITsrJly3KkjIhIBTHxIiIiKiAGBgZo3LhxjvKrV6+K0xSzpyy+evUKjx8/xuPHj3H48GGxrqGhIVxdXfH777+Lo2wZGRlQV1fnjaCJiIowJl5EREQKJJFIxJUVe/bsKZbHxsaKSVj2z3v37iExMRGXL1+WaWPnzp0YP348+vXrh5UrV4rlL168gI2NDRMyIqIigIkXERGRElhYWKBJkyZo0qSJWCaVSvHo0SOEhobCzc1NLL937x7i4+Nljk9ISICtrS0MDAzEaY9OTk5wcnJCuXLl4OTkBCMjox/1coiIKA9MvIiIiAoJTU1NVK5cGZUrV5Ypnzt3Lvr37w9dXV2x7MmTJ9DU1ERSUhJu3LiBGzdu5GjP0tJSTMSyk7GWLVvyhtBERErAxIuIiKiQ09bWhouLi0xZ1apV8fHjR0RGRiIsLAz3799HREQEwsPDERERgejoaMTExCAmJkZm6uKTJ0/ExOuPP/7AP//8gx49eqBt27YAPl1PlpaWBn19/R/3AomIigEmXkREREWUpqYmKlSoILOkfbaEhARERkaKiVhERASePHkCW1tbsc7Zs2exd+9euLu7i4nXnTt3UK1aNZibm8POzg62trYoVapUrpu5uTmvLysGEhMTkZaWhoyMDGRmZiIjI0PcvvTcwcEBNjY2AD5dz3jx4kXo6+vDy8tLbPeff/5BQkICMjMzERwcjKysLGhqagL4dG1kdt/KfiyRSODo6Ihy5coBANLS0nD//n1oa2vLrDD64cMHsS1NTU1oaGhAQ0ODfZWUjokXERGRCjIyMoK7uzvc3d2/WGfo0KGoVq2azHVmz58/BwC8e/cO7969Q0hIyBeP19LSQsmSJVGyZEmcOnVKvKbs33//xZs3b9CgQQNUqlQJAJCZmQlBEKChwa8e30IQBDFxSEhIwOvXr6GjowM7Oztx/5EjR5CSkoLU1NRcf+ZWNm3aNHH1TH9/fwwfPhweHh44cuSIeG4HBwfExsbKFe/KlSsxZswYAEBYWBg6duyI8uXL4+HDh2KdcePG4c6dO3K1O336dMybNw8A8PTpU7i7u8PY2BhxcXFinW7duuHMmTMyx0kkEujo6EBXVxe6urri4+yf7du3x6RJkwAAKSkpmDhxInR0dLBw4UIxGQwICMCzZ89gYGAAAwMD6Ovri48/f55dn+i/iu2/fmvWrMGiRYvw5s0bVK1aFatWrULNmjWVHRYREdEP89/FPQCgXbt2iIuLw9OnT/H06VO8fPkSr169yrHFxMQgPT0dT58+xbNnz6Cnpye2cfbsWVy8eBHLli0TE6/r16+jbt26MDExgbm5OYyNjWFoaAgjI6NcfxoaGkJPTw8dO3aEtrY2gE/TJOPi4mBjYwMrKysAnxK61NRU6OjoQF1dvcDeG0EQkJaWhszMTHEUJ/txWloa0tLSkJqamuNngwYNxKmcN2/eRFBQECpWrIgWLVoAAJKTkzFp0iSx/pcSo/8mSSdPnkSzZs0AAHv37sXw4cPRvn17MUGSSCTo1q0bMjIy5HqdvXv3Fh9nZGSIK2V+TkdHRzxH9uiRhoYG1NXVv/j884VdTExMUKdOHTFJzFalShXo6elBEAR8+PABJiYmMu+/IAjiYwDIysrKEVvJkiVzLCKT23sgCIL4vubm86m8SUlJWL9+PQBgyZIlYvmmTZtw4MCBXI//nJaWlpiItWvXDqtXrxb39e3bFzo6Oli0aJH4ei9evIiIiAgxgTM0NMzxWFdXlyN2KqBYJl779u3DxIkTsX79etSqVQvLly9H8+bN8fDhQ/EfciIiouLK2NgYVapUQZUqVb5YJz09HW/evBGTsM9HshwdHWFkZCQzBfLdu3cAgLi4OJnRibzExcWJidfvv/+OP/74A3PnzsWMGTMAfJoa+fmonpqaGiQSCdTU1HLdsvfdvHkT9vb2AIAZM2ZgyZIlGD9+PObPnw/g02hK9n55XL9+HTVq1AAAnDt3DlOnTkX//v3FxEsQBPFLvTw+TxgMDAxgYmIis9gKAHh6eiIrK+uLozr//amrq4u6deuKxzds2BA3b97MsfhKREQENDU1v/nG3lWqVMlxiwTg020SgE+refr7+6NVq1ZyjRaVL18er169ylH+zz//IDMzE1KpFFKpVLxu8WujgZ9/1jo6OvD19UV6errMa65evTpSUlKQlJQkbh8/fkRSUhISExPFhC89PR3v37/H+/fvZfp6enq6+JoXLlwolu/YsQMbN2786muVSCQyiVijRo1kjpkwYQIAYNq0aeJ32dDQUERGRn4xodPW1mYy94MVy8Rr6dKlGDp0KAYOHAgAWL9+PU6cOIEtW7bg559/VnJ0REREhZ+WlhbKlCmDMmXK5NjXsWPHHF+iW7Zsibdv3yI2Nhbv3r1DQkICEhMTv/ozeyQrm5GREWxsbGBqaiqWpaamypw7KysLwKeRsPzKHsVKT08Xy740eiaRSKCtrQ1tbW3o6Ojk+jNb5cqV0atXL9SpU0cs09HRwaxZs6Ctrf3FBCm3x+bm5mIbvXr1Qq9evXLEFhAQkO/XnBsTExN4eHjkKP/8NRUV6urqUFdXl+k/+WVoaIhZs2blKJ8yZQqmTJnyxePS09PFRCx7++9o3PLly5GUlAQDAwOxrHLlymjRooXMcYmJiWJiB3xK2BMTE5GYmAgAcHJykml3w4YNSElJwbhx48SyXbt2YdGiRV+MV11dPUdS5ubmhk2bNol1/Pz8kJqaimHDhomjjffv38e9e/fE/qmnp5frYx0dnW9O1lVVsUu80tPTERwcjGnTpollampqaNasGa5cuZKjfvZ0gmwJCQkAIP4VRdmyYygMsVDhx/5C8mKfIXl9rc+YmJjAxMREXBxB3jbnz58vjkhll7m7u+PDhw9ITU1FZmYmsrKyxE0QBJnnn5dbWVmJbUyYMAFDhw6FkZGRWGZlZYXY2Fhx+lz2F/n8jBBkt+Ht7Q1vb+8c70f2aJ28VPX3UFX+nckelfo8qQL+73VJJBL89NNPOcpHjhyJkSNH5tpmVlYWkpOTxUQsOxkzMDAQjxcEAT///LOY6GWX29jYoG7dujIJXVJSEpKTkwF8+oNDfHy8zD0CtbW1ZT6HVatW4fXr12jTpo04knbgwIFcE9PcZP8BoWLFijh//rxY3qdPHzx9+hTLli1D9erVAQBnzpzBli1boKWlJbNlL5KipaUls1CKk5NToegz8sQgEbInzhYTr169go2NDS5fvizzF6gpU6bgwoULuHbtmkx9X19fzJ49O0c7u3fvlpnPTkRERERU2GWP8OZ2PaGOjg5cXV3Funv27EFCQgK6du0KMzMzAJ+u4Tx79qw4Spy9ZT/P7Ro7W1tbrFq1Snw+evRovHjxAnPmzBHP5+/vn+eUy8/t2bMnx3RbZUhOTkavXr0QHx+f503ri92Il7ymTZuGiRMnis8TEhJga2sLb2/vPN/cH0EqlSIgIABeXl5cRYfyxP5C8mKfIXmxz5C82GcKr1atWuWr7HMZGRlITU1FcnKymNhJJBI4OzuLdbZv3464uDjUqVMHFhYWAIDSpUujUqVKMslc9iaVSsWkLjMzE+np6dDQ0CgUfSZ7Nlx+FLvEy8LCAurq6oiOjpYpj46OhrW1dY762fO4/yt72LOwKGzxUOHG/kLyYp8hebHPkLzYZ1SDpqYmdHV1Za7F/K//rqYKAB4eHrleY5ib7AVZCkOfkef8xe6KNy0tLXh4eODs2bNiWVZWFs6ePSsz9ZCIiIiIiKigFLsRLwCYOHEi+vfvj+rVq6NmzZpYvnw5Pn78KK5ySEREREREVJCKZeLVvXt3xMTEYObMmXjz5g3c3Nxw8uRJlChRQtmhERERERGRCiqWiRfwaTWV0aNHKzsMIiIiIiIqBordNV5EREREREQ/GhMvIiIiIiIiBWPiRUREREREpGBMvIiIiIiIiBSMiRcREREREZGCMfEiIiIiIiJSMCZeRERERERECsbEi4iIiIiISMGYeBERERERESkYEy8iIiIiIiIFY+JFRERERESkYEy8iIiIiIiIFIyJFxERERERkYIx8SIiIiIiIlIwJl5EREREREQKxsSLiIiIiIhIwZh4ERERERERKRgTLyIiIiIiIgVj4kVERERERKRgciVe27dvx4kTJ8TnU6ZMgYmJCerWrYunT58WeHBERERERESqQK7E6/fff4euri4A4MqVK1izZg0WLlwICwsLTJgwQSEBEhERERERFXUa8lR+/vw5ypUrBwA4cuQIOnfujGHDhqFevXrw9PRURHxERERERERFnlwjXgYGBnj37h0A4PTp0/Dy8gIA6OjoICUlpeCjIyIiIiIiUgFyjXh5eXlhyJAhcHd3x6NHj9CqVSsAwL1791C2bFlFxEdERERERFTkyTXitWbNGtSpUwcxMTE4dOgQzM3NAQDBwcHo2bOnQgIkIiIiIiIq6uQa8TIxMcHq1atzlM+ePbvAAiIiIiIiIlI1ct/HKygoCH369EHdunXx8uVLAMCOHTtw8eLFAg+OiIiIiIhIFciVeB06dAjNmzeHrq4uQkJCkJaWBgCIj4/H77//rpAAiYiIiIiIijq5Eq+5c+di/fr12LRpEzQ1NcXyevXqISQkpMCDIyIiIiIiUgVyJV4PHz5Ew4YNc5QbGxsjLi6uoGIiIiIiIiJSKXIlXtbW1oiIiMhRfvHiRTg4OBRYUERERERERKpErsRr6NChGDduHK5duwaJRIJXr15h165dmDx5MkaOHKmoGImIiIiIiIo0uZaT//nnn5GVlYWmTZsiOTkZDRs2hLa2NiZPnowxY8YoKkYiIiIiIqIiTa7ESyKRYMaMGfDx8UFERASSkpJQqVIlGBgYKCo+IiIiIiKiIk+uxCublpYWKlWqVNCxEBERERERqaQ8E69OnTrlu7G//vrru4IhIiIiIiJSRXkmXsbGxj8iDiIiIiIiIpWVZ+K1devWHxEHERERERGRypJrOXkiIiIiIiKSn1yLa7i7u0MikeQol0gk0NHRQbly5TBgwAA0bty4wAIkIiIiIiIq6uQa8WrRogUeP34MfX19NG7cGI0bN4aBgQEiIyNRo0YNvH79Gs2aNcPRo0cVFS8REREREVGRI9eIV2xsLCZNmoRff/1Vpnzu3Ll4+vQpTp8+jVmzZmHOnDlo3759gQZKRERERERUVMk14rV//3707NkzR3mPHj2wf/9+AEDPnj3x8OHDgomOiIiIiIhIBciVeOno6ODy5cs5yi9fvgwdHR0AQFZWlviYiIiIiIiI5JxqOGbMGIwYMQLBwcGoUaMGAODGjRv4448/MH36dADAqVOn4ObmVuCBEhERERERFVVyJV6//PIL7O3tsXr1auzYsQMA4OzsjE2bNqFXr14AgBEjRmDkyJEFHykREREREVERJVfiBQC9e/dG7969v7hfV1f3uwIiIiIiIiJSNXInXgCQnp6Ot2/fIisrS6a8TJkyBRIUERERERGRKpEr8QoPD8egQYNyLLAhCAIkEgkyMzMLNDgiIiIiIiJVIFfiNWDAAGhoaOD48eMoWbIkJBKJouIiIiIiIiJSGXIlXrdu3UJwcDAqVKigqHi+WVRUFObMmYNz587hzZs3KFWqFPr06YMZM2ZAS0tLrGNvb5/j2CtXrqB27do/OmQiIiIiIiom5Eq8KlWqhNjYWEXF8l0ePHiArKwsbNiwAeXKlcPdu3cxdOhQfPz4EYsXL5ape+bMGVSuXFl8bm5u/qPDJSIiIiKiYkSuxGvBggWYMmUKfv/9d7i6ukJTU1Nmv5GRUYEGJ48WLVqgRYsW4nMHBwc8fPgQ69aty5F4mZubw9ra+keHSERERERExZRciVezZs0AAE2bNpUpL6yLa8THx8PMzCxHebt27ZCamory5ctjypQpaNeu3RfbSEtLQ1pamvg8ISEBACCVSiGVSgs+aDllx1AYYqHCj/2F5MU+Q/JinyF5sc+QvApTn5EnBokgCEJ+K1+4cOGr+xs1apTvEytaREQEPDw8sHjxYgwdOhQAEBsbiz///BP16tWDmpoaDh06hIULF+LIkSNfTL58fX0xe/bsHOW7d++Gnp6eQl8DEREREREVXsnJyejVqxfi4+PznP0nV+L1NXfv3oWLi0tBNCXj559/xoIFC75a5/79+zILfrx8+RKNGjWCp6cn/vjjj68e269fPzx58gRBQUG57s9txMvW1haxsbFKnVqZTSqVIiAgAF5eXjmmfhL9F/sLyYt9huTFPkPyYp8heRWmPpOQkAALC4t8JV7fdAPlbImJidizZw/++OMPBAcHK2Sq4aRJkzBgwICv1nFwcBAfv3r1Co0bN0bdunWxcePGPNuvVasWAgICvrhfW1sb2traOco1NTWV/kF/rrDFQ4Ub+wvJi32G5MU+Q/JinyF5FYY+I8/5vynxCgwMxObNm3Ho0CGUKlUKnTp1wpo1a76lqTxZWlrC0tIyX3VfvnyJxo0bw8PDA1u3boWamlqex9y6dQslS5b83jCJiIiIiIi+KN+J15s3b7Bt2zZs3rwZCQkJ6NatG9LS0nDkyBFUqlRJkTHmy8uXL+Hp6Qk7OzssXrwYMTEx4r7sFQy3b98OLS0tuLu7AwD++usvbNmyJc/piERERERERN8jX4lX27ZtERgYiNatW2P58uVo0aIF1NXVsX79ekXHl28BAQGIiIhAREQESpcuLbPv88vY5syZg6dPn0JDQwMVKlTAvn370KVLlx8dLhERERERFSP5Srz+97//YezYsRg5ciScnJwUHdM3GTBgQJ7XgvXv3x/9+/f/MQERERERERH9f3lfBAXg4sWLSExMhIeHB2rVqoXVq1cjNjZW0bERERERERGphHwlXrVr18amTZvw+vVrDB8+HHv37kWpUqWQlZWFgIAAJCYmKjpOIiIiIiKiIitfiVc2fX19DBo0CBcvXsSdO3cwadIkzJ8/H1ZWVl+8ATEREREREVFxJ1fi9TlnZ2csXLgQL168wJ49ewoyJiIiIiIiIpXyzYlXNnV1dXTo0AHHjh0riHiIiIiIiIhUzncnXkRERERERPR1TLyIiIiIiIgUjIkXERERERGRgsmVeAUGBiIjIyNHeUZGBgIDAwssKCIiIiIiIlUiV+LVuHFjvH//Pkd5fHw8GjduXGBBERERERERqRK5Ei9BECCRSHKUv3v3Dvr6+gUWFBERERERkSrRyE+lTp06AQAkEgkGDBgAbW1tcV9mZiZu376NunXrKiZCIiIiIiKiIi5fiZexsTGATyNehoaG0NXVFfdpaWmhdu3aGDp0qGIiJCIiIiIiKuLylXht3boVAFC2bFlMnjyZ0wqJiIiIiIjkkK/EK9usWbMUFQcREREREZHKkmtxjejoaPTt2xelSpWChoYG1NXVZTYiIiIiIiLKSa4RrwEDBuDZs2f49ddfUbJkyVxXOCQiIiIiIiJZciVeFy9eRFBQENzc3BQUDhERERERkeqRa6qhra0tBEFQVCxEREREREQqSa7Ea/ny5fj5558RFRWloHCIiIiIiIhUj1xTDbt3747k5GQ4OjpCT08PmpqaMvvfv39foMERERERERGpArkSr+XLlysoDCIiIiIiItUlV+LVv39/RcVBRERERESksvJMvBISEmBkZCQ+/prsekRERERERPR/8ky8TE1N8fr1a1hZWcHExCTXe3cJggCJRILMzEyFBElERERERFSU5Zl4nTt3DmZmZgCAf/75R+EBERERERERqZo8E69GjRrl+piIiIiIiIjyR67FNQAgLi4Omzdvxv379wEAlStXxqBBg2BsbFzgwREREREREakCuW6gfPPmTTg6OmLZsmV4//493r9/j6VLl8LR0REhISGKipGIiIiIiKhIk2vEa8KECWjXrh02bdoEDY1Ph2ZkZGDIkCEYP348AgMDFRIkERERERFRUSZX4nXz5k2ZpAsANDQ0MGXKFFSvXr3AgyMiIiIiIlIFck01NDIywrNnz3KUP3/+HIaGhgUWFBERERERkSqRK/Hq3r07Bg8ejH379uH58+d4/vw59u7diyFDhqBnz56KipGIiIiIiKhIk2uq4eLFiyGRSNCvXz9kZGRAEARoaWlh5MiRmD9/vqJiJCIiIiIiKtLkSry0tLSwYsUK+Pn5ITIyEgDg6OgIPT09hQRHRERERESkCvKVeHXq1CnvhjQ0YG1tDS8vL7Rt2/a7AyMiIiIiIlIV+brGy9jYOM9NV1cX4eHh6N69O2bOnKnouImIiIiIiIqMfI14bd26Nd8NHj9+HD/99BN+++23bw6KiIiIiIhIlci1qmF+1K9fn/f0IiIiIiIi+kyBJ14mJib466+/CrpZIiIiIiKiIqvAEy8iIiIiIiKSxcSLiIiIiIhIwZh4ERERERERKRgTLyIiIiIiIgVj4kVERERERKRgTLyIiIiIiIgUjIkXERERERGRgjHxIiIiIiIiUjAmXkRERERERAqmUolX2bJlIZFIZLb58+fL1Ll9+zYaNGgAHR0d2NraYuHChUqKloiIiIiIigsNZQdQ0H777TcMHTpUfG5oaCg+TkhIgLe3N5o1a4b169fjzp07GDRoEExMTDBs2DBlhEtERERERMWAyiVehoaGsLa2znXfrl27kJ6eji1btkBLSwuVK1fGrVu3sHTpUiZeRERERESkMCqXeM2fPx9z5sxBmTJl0KtXL0yYMAEaGp9e5pUrV9CwYUNoaWmJ9Zs3b44FCxbgw4cPMDU1zdFeWloa0tLSxOcJCQkAAKlUCqlUquBXk7fsGApDLFT4sb+QvNhnSF7sMyQv9hmSV2HqM/LEoFKJ19ixY1GtWjWYmZnh8uXLmDZtGl6/fo2lS5cCAN68eQN7e3uZY0qUKCHuyy3x8vPzw+zZs3OUnz59Gnp6egp4Fd8mICBA2SFQEcL+QvJinyF5sc+QvNhnSF6Foc8kJyfnu65EEARBgbF8t59//hkLFiz4ap379++jQoUKOcq3bNmC4cOHIykpCdra2vD29oa9vT02bNgg1gkLC0PlypURFhaGihUr5mgjtxEvW1tbxMbGwsjI6DteWcGQSqUICAiAl5cXNDU1lR0OFXLsLyQv9hmSF/sMyYt9huRVmPpMQkICLCwsEB8fn2duUOhHvCZNmoQBAwZ8tY6Dg0Ou5bVq1UJGRgaioqLg7OwMa2trREdHy9TJfv6l68K0tbWhra2do1xTU1PpH/TnCls8VLixv5C82GdIXuwzJC/2GZJXYegz8py/0CdelpaWsLS0/KZjb926BTU1NVhZWQEA6tSpgxkzZkAqlYpvUkBAAJydnXOdZkhERERERFQQVOY+XleuXMHy5csRGhqKx48fY9euXZgwYQL69OkjJlW9evWClpYWBg8ejHv37mHfvn1YsWIFJk6cqOToiYiIiIhIlRX6Ea/80tbWxt69e+Hr64u0tDTY29tjwoQJMkmVsbExTp8+jVGjRsHDwwMWFhaYOXMml5InIiIiIiKFUpnEq1q1arh69Wqe9apUqYKgoKAfEBEREREREdEnKjPVkIiIiIiIqLBi4kVERERERKRgTLyIiIiIiIgUjIkXERERERGRgjHxIiIiIiIiUjAmXkRERERERArGxIuIiIiIiEjBmHgREREREREpGBMvIiIiIiIiBWPiRUREREREpGBMvIiIiIiIiBSMiRcREREREZGCMfEiIiIiIiJSMCZeRERERERECsbEi4iIiIiISMGYeBERERERESmYhrIDoG+3detWHDx4EO/fv8fRo0dhaWmJMmXKoGrVqqhSpQqMjIyUHSIREREREYGJV5EWFhYGf39/AMDVq1dz7K9QoQI6duyIbt26oWrVqpBIJD86RCIiIiIiAhOvIq1bt25wcnJCcHAwbGxs8OHDB0RERODWrVt48eIFHjx4AD8/P/j5+aFcuXLo1asXxowZAwsLC2WHTkRERERUrDDxKsJq1KgBNzc3lChRAq1atYKmpqa4LzY2FmfPnsX+/fvh7++PiIgI/Pbbb1i6dCnGjx+PiRMnwtTUVInRExEREREVH1xcQ0VZWFige/fuOHToEN6+fYtdu3bB3d0dSUlJmDt3Luzt7fHbb78hISFB2aESEREREak8Jl7FgKGhIXr16oXg4GAcOnQILi4uiI+Px6xZs1ChQgUEBAQoO0QiIiIiIpXGxKsYkUgk6NSpE0JDQ7F37144OTnh9evX8Pb2xqRJk5CWlqbsEImIiIiIVBITr2JITU0N3bt3x61btzBy5EgAwNKlS1GrVi2EhYUpOToiIiIiItXDxKsY09PTw9q1a3Hs2DFYWFggNDQUHh4eWLt2LQRBUHZ4REREREQqg4kXoW3btrhz5w6aN2+O1NRUjBo1Ct26dePUQyIiIiKiAsLEiwAA1tbW8Pf3x4oVK6ClpYWDBw+iTZs2SEpKUnZoRERERERFHhMvEqmpqWHs2LHw9/eHvr4+zpw5Ay8vL7x//17ZoRERERERFWlMvCiHpk2b4uzZszA1NcXVq1fh6emJN2/eKDssIiIiIqIii4kX5apWrVoIDAxEyZIlcefOHdSvXx9RUVHKDouIiIiIqEhi4kVf5OLigqCgINjb2yMyMhL16tXjcvNERERERN+AiRd9laOjI4KCglCpUiW8evUKDRs2xM2bN5UdFhERERFRkcLEi/JkY2ODwMBA1KhRA+/evUOTJk0QEhKi7LCIiIiIiIoMJl6UL+bm5jh79iwaNWqExMREtG3bFi9evFB2WERERERERQITL8o3Q0NDHD16FJUrV8arV694ny8iIiIionxi4kVyMTY2xvHjx2FlZYXQ0FD07NkTmZmZyg6LiIiIiKhQY+JFcitbtiyOHTsGHR0dHD9+HJMmTVJ2SEREREREhRoTL/omtWrVwo4dOwAAK1aswJo1a5QcERERERFR4cXEi75Zly5d4OfnBwAYO3Ys/P39lRwREREREVHhxMSLvsvUqVMxaNAgZGVloXv37rh9+7ayQyIiIiIiKnSYeNF3kUgkWLduHRo3boykpCS0adMGr1+/VnZYRERERESFChMv+m5aWlo4dOgQnJ2d8fz5c7Rr1w4fP35UdlhERERERIUGEy8qEKampjhx4gTMzc1x8+ZN9O3bF1lZWcoOi4iIiIioUGDiRQXG0dERR44cgZaWFg4fPoxp06YpOyQiIiIiokKBiRcVqPr162PLli0AgIULF+LIkSPKDYiIiIiIqBBg4kUFrnfv3uJNlQcOHIinT58qOSIiIiIiIuVi4kUK8fvvv6NmzZqIi4tDz549IZVKlR0SEREREZHSMPEihdDS0sLevXthbGyMK1euYObMmcoOiYiIiIhIaZh4kcLY29vjjz/+AADMnz8fp06dUnJERERERETKwcSLFKpLly4YOXIkAKBv3768uTIRERERFUsqk3idP38eEokk1+3GjRsAgKioqFz3X716VcnRq7alS5eiSpUqiImJQe/evZGZmanskIiIiIiIfiiVSbzq1q2L169fy2xDhgyBvb09qlevLlP3zJkzMvU8PDyUFHXxoKOjg/3790NfXx///PMPfv/9d2WHRERERET0Q6lM4qWlpQVra2txMzc3x9GjRzFw4EBIJBKZuubm5jJ1NTU1lRR18eHs7Ix169YBAHx9fXHhwgUlR0RERERE9ONoKDsARTl27BjevXuHgQMH5tjXrl07pKamonz58pgyZQratWv3xXbS0tKQlpYmPk9ISAAASKXSQrFEenYMhSGWvPTo0QMBAQHYsWMHevXqhZs3b8LCwkLZYRUrRam/UOHAPkPyYp8hebHPkLwKU5+RJwaJIAiCAmNRmlatWgEA/P39xbLY2Fj8+eefqFevHtTU1HDo0CEsXLgQR44c+WLy5evri9mzZ+co3717N/T09BQTvApLSUnB5MmT8fLlS1SvXh0zZszIMSJJRERERFQUJCcno1evXoiPj4eRkdFX6xb6xOvnn3/GggULvlrn/v37qFChgvj8xYsXsLOzw/79+9G5c+evHtuvXz88efIEQUFBue7PbcTL1tYWsbGxeb65P4JUKkVAQAC8vLyKzJTJ27dvo27dukhPT8f69esxaNAgZYdUbBTF/kLKxT5D8mKfIXmxz5C8ClOfSUhIgIWFRb4Sr0I/1XDSpEkYMGDAV+s4ODjIPN+6dSvMzc2/OoUwW61atRAQEPDF/dra2tDW1s5RrqmpqfQP+nOFLZ6v8fDwwLx58+Dj44PJkyfDy8srx2dIilWU+gsVDuwzJC/2GZIX+wzJqzD0GXnOX+gTL0tLS1haWua7viAI2Lp1K/r165evN+LWrVsoWbLk94RI32DChAn4+++/ERgYiAEDBuCff/6Burq6ssMiIiIiIlKIQp94yevcuXN48uQJhgwZkmPf9u3boaWlBXd3dwDAX3/9hS1btuCPP/740WEWe+rq6ti2bRuqVKmCoKAgLFu2DJMnT1Z2WERERERECqEyy8ln27x5M+rWrStzzdfn5syZAw8PD9SqVQtHjx7Fvn37cl35kBTP3t4ey5cvBwDMmDEDd+/eVW5AREREREQKonIjXrt37/7ivv79+6N///4/MBrKy6BBg3DkyBEcP34cffv2xbVr16ClpaXssIiKhaysLHz48AExMTGIiYlBbGws0tLSIAgCBEFAVlYWBEFAeHg4zM3NYWlpCTMzM5ibm0NNTeX+bkdERKRQKpd4UdEikUiwadMmuLi44NatW5g9ezbmzZun7LCIVEpSUhLu3LmDsLAwme3FixfIysrKVxtz5swRH+vo6MDe3h6Ojo5wdHSEk5MTqlWrBjc3N+jq6irqZRARERVpTLxI6aytrbFhwwZ06dIF8+fPR5s2bVCnTh1lh0VUZMXHx+PixYu4cOECLly4gODgYGRmZn6xvrGxMSwtLWFhYQFdXV1IJBJxy8jIwLNnz5CZmYkPHz4gISEBqampuH//Pu7fvy/Tjrq6OlxcXFC9enXUqlULTZs25YqlRERE/x8TLyoUOnfujL59+2LHjh3o168fbt26BX19fWWHRVRkvHjxAn/99RcOHjyIS5cu5RjJKlWqFFxcXFCpUiVUqlQJFStWhIODAywsLL46vVcqlcLf3x+tWrWCpqYmpFIpnj9/jsjISHG7f/8+bt68iejoaISGhiI0NBSbN28G8Ol2H15eXvDy8kKTJk1gamqq0PeBiIiosGLiRYXGypUr8c8//yAiIgJTpkzBmjVrlB0SUaEWExODnTt34sCBA7hy5YrMPkdHRzRq1Ejc7OzsCuScmpqacHBwEBOqbIIg4OXLl7h58yZu3LiBoKAgXLlyBY8fP8aGDRuwYcMGaGhooGnTpujSpQs6dOgACwuLAomJiIioKGDiRYWGiYkJtm7dCi8vL6xduxbt2rVD8+bNlR0WUaEiCAICAwOxYcMGHDp0COnp6eK+evXqoUuXLujYsWOBJVr5JZFIULp0aZQuXRodOnQAACQmJuL8+fMICAhAQEAAHjx4gFOnTuHUqVMYMWIEPD090bt3b3Tt2hUGBgY/NF4iIqIfjctSUaHSrFkzjBkzBgAwZMgQxMfHKzkiosIhJSUFa9euRaVKleDp6Yk9e/YgPT0d1atXx6pVq/Dy5UtcvHgR48eP/+FJ15cYGhqibdu2WLlyJe7fv48HDx5g3rx5cHd3R2ZmJs6ePYtBgwbB2toaAwYMwPnz5/O92AcREVFRw8SLCp358+ejXLlyePHiBSZNmqTscIiUKjExEYsWLYK9vT1GjRqFBw8eQF9fH8OGDUNwcDBu3LiB0aNHo1SpUsoONU/Ozs6YPn06QkJCEBERgXnz5qF8+fL4+PEjtm/fjsaNG8PZ2RlLly7Fhw8flB0uERFRgWLiRYWOnp4etm7dColEgs2bN+PkyZPKDonoh4uPj8fs2bNhZ2eHKVOmIDo6GmXKlMHKlSvx6tUrbNiwAdWqVVN2mN/M0dER06dPx4MHD3Dp0iUMHToUhoaGiIiIwKRJk2BjY4OhQ4ciNDRU2aESEREVCCZeVCjVr18f48aNAwAMHTqUUw6p2JBKpVizZg3KlSsHX19ffPjwAeXLl8eWLVsQERGBMWPGwMjISNlhFhiJRIK6deti48aNeP36NTZs2ABXV1ekpKTgjz/+gJubG7y8vHD69GkIgqDscImIiL4ZEy8qtObNmydOOZw4caKywyFSKEEQcOzYMbi6umL06NGIjY2Fs7Mz9u3bh7CwMAwcOBCamprKDlOhsqdQhoaGIjAwEN26dYO6ujrOnDmD5s2bw93dHbt374ZUKlV2qERERHJj4kWF1udTDrds2cIph6Sy7t27h8aNG6N9+/Z4+PAhLC0tsXbtWty5c0dMPooTiUSCBg0aYN++fYiIiMDYsWOhp6eH0NBQ9O7dG+XLl8emTZtkVnQkIiIq7Jh4UaHGKYekylJSUjBjxgy4ubnhwoUL0NHRwfTp0xEREYGRI0eq/AhXfpQtWxYrVqzAs2fPMGfOHFhaWiIqKgrDhg2Dk5MT1q9fj7S0NGWHSURElCcmXlToccohqaIzZ87A1dUVv//+OzIyMsTRrnnz5qnUNVwFxdzcHL/88guioqKwbNkyWFtb49mzZxg5ciQcHR2xbt06TkEkIqJCjYkXFXr/nXL4v//9T9khEX2zuLg49O/fH15eXoiMjISNjQ0OHz6MI0eOoEyZMsoOr9DT09PD+PHj8fjxY6xcuRKlSpXCy5cv8dNPP6FixYrYvXs37wVGRESFEhMvKhI45ZBUwdmzZ+Hq6oo///wTEokEY8eORVhYGDp06KDs0IocXV1djBkzBpGRkVi1ahVKlCiByMhI9O7dG9WqVYO/vz9XQSQiokKFiRcVGfPmzYOTkxNevnzJKYdUpKSkpGDcuHFo1qwZXrx4gXLlyuHSpUtYsWIFpxV+Jx0dHYwePRoRERGYO3cujIyMEBoaitatW6Nhw4a4ePGiskMkIiICAGgoOwCi/NLT08OWLVvQsGFDbNmyBV26dEHLli2VHRbRV4WGhqJHjx548OABAGDkyJFYtGgR9PX1lRyZajEwMMCMGTMwcuRILFiwACtXrsTFixfRoEEDtG7dGr///juqVKmi7DCJihxBEBAfH4/Xr1/j7du3ePv2LWJiYhAbG4v4+HgkJCSIP1NTU5GWliZuWVlZMiPPGhoa0NPTg66uLnR1dcXH2traePv2La5duwZLS0uYmZnB3Nwc1tbWsLa2RokSJbjYEKkEJl5UpGRPOVy+fDmGDh2Ke/fuwdjYWNlhEeUgCAI2btyIcePGIS0tDdbW1tiyZQv/WKBgZmZmWLBgAcaOHYvffvsNmzdvxokTJ+Dv748+ffpg7ty5vJaO6D+Sk5MRERGBiIgIhIeHIyIiAs+ePcPz58/x/PlzJCUl/ZA4Tpw48cV9FhYWsLa2RtmyZeHg4CBu9vb2sLe35x+zqEhg4kVFzrx583DixAmEh4dj4sSJ2Lx5s7JDIpKRkJCAYcOGYd++fQCA1q1bY9u2bbCwsFByZMWHjY0NNmzYgMmTJ+PXX3/Fvn37sGPHDuzfvx/jx4/HtGnT+EcbKnYyMzMRFhaG0NBQ3L17V9yePn2a57GmpqawsrKCpaUlrKysYGFhARMTExgZGYlb9uhV9vbfexBKpVIkJycjJSVF3JKTk5GUlIQ7d+7A0tIScXFxeP/+PWJiYhAdHY3o6GhkZGQgNjYWsbGxuHv3bq7xlShRQiYhq1ixIlxcXODs7AwtLa0Cef+IvhcTLypyslc5bNCgAaccUqFz69YtdO3aFREREdDQ0ICfnx8mTpwINTVeUqsMTk5O2Lt3LyZPngwfHx+cP38eCxYswB9//IGZM2dixIgR/FJGKkkQBDx79gzXr18Xt+DgYHz8+DHX+mZmZihXrhycnJzg6OgIe3t72NrawtbWFqVLl4aenp7CYpVKpfD390erVq1yTCnMysrC+/fv8ebNG7x8+RJRUVF4/PixuD158gQfPnwQk7QrV67IHK+hoYHy5cvDxcUFLi4uqFy5MlxcXODo6Fjsbk5PysfEi4qkevXqYfz48Vi2bBmGDh2Ku3fvwsTERNlhUTG3e/duDBkyBCkpKbC1tcW+fftQp04dZYdFAKpXr45z587hxIkTmDJlCu7fv49x48Zh1apV8PPzQ+fOnSGRSJQdJtE3S09Px9WrV3HhwgVcu3YN169fR0xMTI56BgYGcHd3h6urq5iIVKpUqdCOyKupqcHCwgIWFhZwcXHJtc6HDx/w5MkTMRmLiIhAWFgY7t69i/j4eISFhSEsLAz79+8Xj9HR0YGrqys8PDzErXLlyvxDDCkUEy8qsubOnYvjx48jPDwckyZN4pRDUpqMjAxMnToVS5cuBQC0aNECu3btgpmZmZIjo89JJBK0adMGLVq0wJYtWzBz5kxERESga9euqFOnDhYvXoy6desqO0yifMnMzMStW7dw9uxZnDt3DkFBQUhOTpapo6GhgapVq6JmzZri5uzsrHIjPaampjA1NUW1atVkygVBwMuXL3H37l3cu3dPnFp57949pKSk4MaNG7hx44ZYX0tLC1WqVJFJxlxdXbmwBxUYJl5UZHHKIRUGsbGx6NGjB86ePQsAmDZtGubMmaNyX2xUiYaGBoYNG4ZevXph8eLFWLRoEa5cuYJ69eqhU6dOmD9/PpycnJQdJpEMQRDw6NEjnDlzBmfPnsX58+fx4cMHmTpWVlZo3Lgx6tWrhxo1asDNzQ06OjpKilj5JBIJSpcujdKlS6NFixZieWZmJp48eYKQkBAEBwfj5s2bCAkJQVxcHG7evImbN2+KdXV0dODh4YHatWuLW+nSpZXxckgFMPGiIo1TDkmZ7t69i7Zt2yIqKgr6+vrYtm0bunTpouywKJ8MDAzg6+uL4cOHY9asWdi8eTP++usvHDt2DCNGjMDMmTNhaWmp7DCpGJNKpbh48SKOHTuGv//+G5GRkTL7jYyM4OnpiSZNmqBp06aoXLkyp8zmg7q6OsqVK4dy5cqhW7duAD4lto8fP0ZwcLDMFhcXh0uXLuHSpUvi8TY2NjKJWLVq1RR6DRypDiZeVOR9PuVw4sSJ2LJli7JDomLgf//7H7p3747ExEQ4OjriyJEjX7z+gAq3kiVLikv/T506FSdOnMDq1avx559/4ueff8b48eOhq6ur7DCpmIiLi8PJkydx7Ngx/O9//0NcXJy4T0tLCw0aNEDTpk3RpEkTeHh4QEODX+UKgkQigaOjIxwdHWWSsfDwcFy9elXcbt++jZcvX+LQoUM4dOgQgE+j6FWqVJFJxsqVK8ckmHLgbysVeZ9POdy6dSu6du3KKYekUKtWrcL48eORlZWFRo0a4a+//uL1XCqgcuXKOH78OM6dOwcfHx+EhIRg+vTpWLt2LebOnYu+fftydUpSiOwv8kePHkVgYCAyMjLEfZaWlmjdujXatWsHLy8vGBgYKDHS4kUikaB8+fIoX748+vXrB+DTPc+Cg4PFROzKlSt4/fo1QkJCEBISgrVr1wIAzM3NUbt2bdSpUwe1a9dGzZo1YWhoqMyXQ4UAEy9SCZxySD9CRkYGxo8fjzVr1gAABg0ahHXr1nEVLBXTpEkT3LhxA3v27MH06dPx7NkzDBgwAMuWLcOiRYvg5eWl7BBJBbx69QqHDh3C/v37cenSJQiCIO6rVKkS2rZti3bt2qFWrVq8ZrQQ0dPTQ4MGDdCgQQMAn0bFXrx4gatXr+LatWu4cuUKgoOD8e7dO5w4cUK8KbREIoGLi4uYiNWpUwfly5fnH3OKGSZepDI+n3I4YcIEbN26VdkhkQpJSkpC165dcfLkSUgkEsyfPx8+Pj6cSqKi1NTU0Lt3b3Tu3BmrVq3CvHnzEBoaCm9vb7Ro0QILFy6Eq6urssOkIubNmzdishUUFCSTbNWtWxedO3dGu3btUK5cOSVGSfKQSCTi/c66du0K4NPS/rdu3RJHxK5evYqoqCjcuXMHd+7cwcaNGwF8Wo2xVq1aYiJWs2ZN/tFYxTHxIpXx+ZTDbdu2oUOHDmjfvr2ywyIV8ObNG7Ru3RohISHQ1dXFrl270LFjR2WHRT+Ajo4OfHx8MGjQIMyZMwdr167FyZMncfr0aQwYMAC//fYbbGxslB0mFWLv37/HgQMHsGfPHgQGBsokW3Xq1EG3bt3QpUsXrpSnQrS0tMTl+8eOHQvg0/8jnydiN27cwIcPH3Dy5EmcPHkSwKckrmLFiuJ1YtWrV4eLiwuXs1chTLxIpdSrVw9TpkzBggULMHToUNSpUwdWVlbKDouKsIcPH6JFixaIioqCpaUlTpw4gRo1aig7LPrBzM3NsXz5cowZMwbTpk3DgQMHsGXLFuzZsweTJk3ClClTeP0GiVJTU3HixAns3LkTJ06cgFQqFffVrl1bTLZsbW2VGCX9SNbW1ujQoQM6dOgA4NOKlXfu3BETsStXriAyMlK82XP2QmHa2tqoWrUqqlevLm4VK1bkoipFFD81UjmzZ8+Gv78/7ty5gxEjRuDQoUOcDkbf5PLly2jbti3ev3+PcuXK4X//+x+nABVzjo6O2L9/P65du4bJkyfj4sWLmDt3LjZu3AhfX18MGTKEf50uprKyshAUFISdO3fiwIEDiI+PF/e5ubmhd+/e6NatG8qUKaPEKKmw0NTURLVq1VCtWjWMGjUKABATEyMmYTdu3MDNmzcRFxeH69ev4/r16+Kxurq6cHd3R7Vq1VC1alVUqVIFLi4uXNK+CGDiRSpHW1sbf/75J2rWrInDhw9jx44d4mpERPl19OhR9OjRA6mpqahZsyaOHz/OezqRqFatWggMDMTRo0cxZcoUhIeH46effsKKFSvg5+eHDh068A8+xcS9e/ewc+dO7Nq1C8+fPxfLbW1t0bt3b/Tu3Zu3mqB8sbS0RNu2bdG2bVsA/3dvseybOt+8eRPBwcFITEzE5cuXcfnyZfFYiUQCJycnVKlSRUzGqlSpAjs7O/5bVIgw8SKV5ObmhtmzZ2P69OkYM2YMPD09+VdGyrcdO3Zg4MCByMzMRJs2bbB3717o6+srOywqZCQSCTp06IDWrVtj48aNmD17Nh4+fIhOnTqhatWqmDlzJjp06MBVy1TQq1evsGfPHuzcuRO3bt0Sy42NjdG1a1f06dMHDRo04GdP3+Xze4t1794dwKeR1fDwcNy4cQO3bt1CaGgoQkNDERMTg0ePHuHRo0c4ePCg2IaBgQGcnZ3h7OyMChUqiI/Lly/P+xMqARMvUlk+Pj74+++/ceXKFQwcOBABAQH8T5DytGbNGowePRoAMGDAAGzatIlz6emrNDU1MWrUKPTt2xeLFi3CihUrEBoais6dO6NKlSqYOXMmOnbsyH9/irjExET89ddf2LlzJ86ePSsukqGpqYnWrVujT58+aN26NXR0dJQcKakyNTU1MXnq06ePWB4dHY3bt28jNDQUt2/fxu3btxEWFoakpCQEBwcjODhYph2JRIIyZcqgfPnysLOzg52dHcqWLSs+trGx4W0MFIDfJkhlaWhoYPv27XBzc8O5c+ewevVqcXUhov8SBAF+fn6YMWMGAGDs2LFYtmwZvyxTvhkZGWHOnDmYMGECli1bhpUrV+L27dvo0qULXFxcMHPmTHTu3Jl9qgiRSqU4ffo0du7ciaNHjyIlJUXcV79+ffTp0wddu3blDdRJ6UqUKAEvLy+Z+wymp6cjMjISDx8+xMOHD/HgwQPx54cPH/D06VM8ffo01/bU1dVha2sLOzs7lClTBtbW1rCyssp1470s84+JF6k0JycnLF68GD/99BOmTp0Kb29vVKhQQdlhUSEjCAJ+/vlnLFy4EAAwc+ZM+Pr6cl48fRMzMzPMmTMHEydOxPLly7F8+XLcvXsX3bp1Q+XKlTFz5kx06dKFCVghJQgCrly5gt27d2P//v2IiYkR92WPMvTq1QsODg5KjJIob1paWqhYsSIqVqwoUy4IAmJjY/Hw4UNERESICVhUVBSePn2K58+fQyqVIioqClFRUXmex8TEBKampjA0NISRkREMDQ1zPNbT04OmpiY0NDRybNnlgiBAKpWKW0ZGhszj9PR0JCcn4+PHj0hKSkJERAQaNWpUpO59xsSLVN6IESNw9OhRnDp1Cn379sXly5e56hiJMjMzMWrUKGzYsAEAsGTJEkycOFHJUZEqMDU1xezZszF+/HisWLECy5cvx71799C9e3eUL18ekyZNQt++fXmdRSERFhaGXbt2Yffu3TJfNq2srNCzZ0/06dMHHh4e/IMMFXkSiQSWlpawtLRE/fr1c+zPzMzE69evxYTs2bNniImJQXR0NN6+fSuzZWZmIi4uDnFxcT/+heDTFGAmXkSFiEQiwebNm+Hi4oKbN29i9uzZmDt3rrLDokJAKpWif//+2LNnDyQSCTZu3IghQ4YoOyxSMaampvD19ZVJwB49eoThw4fjl19+wejRo/HTTz/BwsJC2aEWO8+fP8fevXuxa9cuhIaGiuUGBgbo1KkTevbsiWbNmvE6TypW1NXVUbp0aZQuXRr16tX7Yr2srCzExcUhOjoacXFxSExMRGJiIhISEsTH2c+Tk5Px/9q787Cq6kWN49/NrAl6nRDUFDJxSEIccYKccayUnBKsa5NoF8mm85R4skKvJ23QymvdhJOzR80cINSASjwIhkcxDcnEVJx6EpBR2PePnvY9nNLkxGbtje/nedYTrL3Y+930e4SX31q/VVlZaZm9+uftl1ktBwcHy+yXs7OzZfvnz++44w4aNmyIm5sb33//vd0tfKV/SeS20Lp1a1auXMmkSZN4/fXXGTp0KCEhIUbHEgOVlJQQFhbGzp07cXJy4uOPP7asGiViDU2aNCEmJobo6Gg+/PBDli1bRl5eHjExMSxatIhp06YRGRlJQECA0VHrtfz8fLZs2cLGjRtJTU2ttkhGaGgo06ZNY8yYMbonksjvcHBwoGnTpoZc41hRUcGuXbvs7sb1OsFcbhsPPfQQjz76KGazmYcffpgrV64YHUkMUlhYyKhRo9i5cydubm588sknKl1SZ9zd3YmKiiI3N5d169YRGBhISUkJH3zwAd27d6d///6sXbuW8vJyo6PWG/n5+axYsYKQkBC8vb2JjIwkJSUFs9lMcHAwK1euJD8/n08++YSHHnpIpUtErELFS24rb7/9Nh07duTs2bPMnDnT8pdOuX1cuXKFIUOGkJycjLu7O4mJiYwaNcroWHIbcnJyYvLkyWRkZJCamsqkSZNwcnJi//79TJs2jTZt2vDMM8+QnZ1tdFS7dObMGZYvX05wcDDe3t7Mnj3bUrb69OnDkiVLyMvLIzk5mccff1wrE4qI1elUQ7mt3HHHHaxbt46+ffuybds2Vq5cyZNPPml0LKkj58+fZ9iwYWRnZ9OsWTMSEhLo2bOn0bHkNmcymRg4cCADBw7k/PnzrFq1ipUrV3Lu3DmWLl3K0qVL6d27NzNmzGDixIm0aNHC6Mg2qbKykoMHD7Jjxw527NhR7ZotgL59+xIWFsbEiRO58847DUopIrczzXjJbScwMJBFixYBMHfuXP01+TZx6tQpBgwYQHZ2Nt7e3qSmpqp0ic3x8vJi/vz5nD59mk8//ZQHHngAJycn0tPTmTVrFl5eXoSGhhIfH09BQYHRcQ1XUFDA5s2bmTFjBl5eXgQFBfHaa69x+PBhHBwc6N+/P0uXLiUvL4+0tDSio6NVukTEMJrxkttSVFQUn332GYmJiUyZMoX09HTc3NyMjiVWcuzYMYYNG8a5c+fw9fVlz549+Pj4GB1L5IacnJwYM2YMY8aM4eLFi3z88cesWbOGQ4cOkZCQQEJCAs7OzgwePJhx48Yxbtw42rRpY3RsqysvLycjI4OUlBT27NlDamoq169ftzzeuHFjQkNDGTNmDCNHjqRZs2YGphURqU7FS25LDg4OxMXF4e/vz5EjR3j22Wd55513jI4lVpCZmcmIESO4cuUKXbp0ISkpCW9vb6Njidyyli1bEh0dTXR0NN9++y3r169n3bp1HD9+nMTERBITE4mMjCQwMJDhw4czePBgBgwYUC/uD1ZWVsbBgwdJTk4mJSWF/fv3U1xcXO2YTp06WUpqv379dJ9GEbFZKl5y2/L09CQuLo7Q0FCWL1/O0KFDGT9+vNGxpBalpqYyZswYCgsL6dmzJwkJCfoLuNi1jh07Mn/+fObPn8/x48fZvn0727dvZ//+/Rw6dIhDhw6xaNEiXFxc6NevH/fddx99+/ald+/eNn+TUbPZzOnTp8nIyCAjI4P09HTS0tIoLS2tdlzz5s0JDg4mODiY0NBQOnToYFBiEZGaUfGS29rIkSOJjo5m6dKlREREkJmZyV133WV0LKkFu3fv5sEHH6S0tJTg4GC2b9+Oh4eH0bFEak2nTp3o1KkTzz33HBcvXiQhIYG9e/eyd+9ezp49S3JyMsnJyZbj/fz86N27NwEBAXTt2pWuXbvSunVrTCZTnWe/evUq2dnZ1bavv/76N2/z0bJlS4KDgwkJCSE4OJguXboYkllE5I9S8ZLbXmxsLGlpaaSlpTFhwgT279+ve7jYuY0bNzJt2jSuX7/O6NGj2bRpU7047UrkRlq2bEl4eDjh4eGYzWa+/fZb9u7dy1dffcXf//53cnNzOXHiBCdOnOCvf/2r5es8PDzo2LEj7du3t2ze3t60aNHCsjVp0gQHh99fi8tsNlNWVsbly5c5e/YsX3zxBZcvX+b8+fOcO3eOU6dOWbYb3UfR2dkZf39/evbsSc+ePRkwYAB+fn4qWiJSL6h4yW3PxcWFTZs2ERgYyOHDh5k1axYfffSRftDbqQ8//JDHH3+cqqoqJk+eTHx8vK75kNuKyWTCz88PPz8/Zs2aBcDly5dJT08nPT2do0ePkp2dTU5ODgUFBZZT+27GxcWFBg0a0LBhQ8tCRJWVlZatoqKCgoICKioqbjlnmzZt6NKli2X2rVu3bvj7+2uhIxGpt+ymeL322mvs3LmTrKwsXFxc+Omnn351TF5eHk899RSff/45jRo1IiIigtjYWJyc/v9tJicnEx0dTXZ2Nm3btuWll15ixowZdfdGxCa1bt2a9evXM3ToUOLi4ggKCuKJJ54wOpbU0LJly4iOjgbg8ccf591338XR0dHgVCLGa968OaNGjap2s/CysjJycnLIzc3l+++/t2z5+flcunSJS5cuWZasLy8vp7y8nKtXr97S6zVo0IDWrVvj5eVFq1at8PLyon379vj6+uLj44OPjw/u7u5Wea8iIrbKbopXeXk5YWFhBAUF8eGHH/7q8crKSkaPHk2rVq3Yv38/58+fJzw8HGdnZ15//XXg5/v4jB49mieffJI1a9awd+9eZs6ciZeXFyNGjKjrtyQ25r777iM2Npbnn3+ep59+msDAQHr16mV0LLkFZrOZBQsW8MorrwDw7LPPsnjxYs1aityEq6sr99xzD/fcc88NjykrK6OwsJCSkhKKi4spKSmhpKQEk8mEo6OjZXN2dsbDwwMPDw9cXV1JSEhg1KhRmm0WEfkndlO8/vznPwOwevXq33z8s88+49ixY+zZswdPT08CAgJYuHAhzz//PAsWLMDFxYX3338fHx8f3njjDQA6d+7Ml19+ybJly1S8BPj5F/a0tDS2bdvGxIkTyczMpHnz5kbHkpuoqqoiOjqat956C/h5dvzFF19U6RKpBa6urri6utboa2pyuqGIyO3EborX70lLS6Nbt254enpa9o0YMYKnnnqK7OxsunfvTlpaGkOHDq32dSNGjCAqKuqGz1tWVkZZWZnl819Ou6ioqLCJHy6/ZLCFLPXFqlWrOHr0KCdPnmTq1Kls37693pyuVt/Gy/Xr13nyySeJj48H4M0332TWrFnVbqgqf0x9GzNifRozUlMaM1JTtjRmapKh3hSv/Pz8aqULsHyen59/02MKCgooKSn5zVXPYmNjLbNt/+yzzz6zqZXvkpKSjI5Qr8yePZvnnnuOpKQkwsPDmTZtmtGRalV9GC/l5eUsXbqUAwcO4ODgwJw5c2jfvj27du0yOlq9VB/GjNQtjRmpKY0ZqSlbGDP/elP3mzG0eL3wwgssXrz4psd88803dOrUqY4S/dqLL75ouVgffp7xatu2LcOHD7eJewJVVFSQlJTEsGHDdC59LWvSpAkzZsxg06ZNjB07lsmTJxsd6Q+rL+Pl6tWrTJgwgQMHDuDi4sLHH3/M/fffb3Sseqm+jBmpOxozUlMaM1JTtjRmfjkb7lYYWryeeeaZ311R0NfX95aeq1WrVqSnp1fbd+HCBctjv/z3l33/fIyHh8cN7/Fzo/PbnZ2dDf8f/c9sLU99EBERwZEjR3jjjTeYOXMmvr6+9O/f3+hYtcKex8v58+cJDQ3l8OHDeHh48MknnxASEmJ0rHrPnseMGENjRmpKY0ZqyhbGTE1e39Di9cvNGWtDUFAQr732GhcvXqRly5bAz9OPHh4edOnSxXLMv56GlJSURFBQUK1kkPrnv//7v/nuu+/YunUr48eP58CBA3To0MHoWLetnJwcRowYwalTp/D09CQhIYGAgACjY4mIiIj8rt+/Fb2NyMvLIysri7y8PCorK8nKyiIrK4uioiIAhg8fTpcuXZg+fTqHDx8mMTGRl156icjISMuM1ZNPPsl3333Hc889x/Hjx3n33XfZuHEjc+fONfKtiQ1zcHDg448/pmfPnly5coXRo0fz448/Gh3rtpSZmUn//v05deoUd911F/v371fpEhEREbthN8Vr/vz5dO/enZiYGIqKiujevTvdu3cnIyMDAEdHR3bs2IGjoyNBQUE8/PDDhIeHW+7rA+Dj48POnTtJSkri3nvv5Y033uCDDz7QUvJyUw0bNmT79u20bduWb7/9lgcffJDy8nKjY91W9uzZQ0hICJcuXSIwMJCvvvrqlk9DFhEREbEFdrOq4erVq294D69ftGvX7ndXNAsJCeHrr7+uxWRyO/Dy8mLnzp3079+flJQUHnvsMVavXq17RdWBDRs2MH36dCoqKhgyZAhbtmyxiYVtRERERGrCbma8RIzWrVs3Nm3ahKOjI/Hx8bz22mtGR6r33nnnHaZMmUJFRQUPPfQQO3fuVOkSERERu6TiJVIDI0aMYMWKFQC8/PLL/O///q/Bieqnqqoq/vSnP/H0009jNpuZPXs269at+80VRkVERETsgYqXSA098cQTPPvsswDMnDmTNWvWGJyofikpKWHy5MnExsYCsHDhQt5++20cHPTPlYiIiNgvu7nGS8SWLF68mKKiIt577z3Cw8NxcXEhLCzM6Fh278KFC4wfP56///3vODs788EHHxAeHm50LBEREZE/TMVL5N9gMplYvnw5paWlfPTRR0ydOhVXV1fGjRtndDS7lZ2dzejRozl9+jRNmzZl69atDBo0yOhYIiIiIrVC5+6I/JscHBxYtWoV06ZN4/r164SFhZGQkGB0LLv06aef0q9fP06fPk2HDh04cOCASpeIiIjUKypeIn+Ao6Mjq1evZuLEiZSXl/PAAw+wb98+o2PZjaqqKhYuXMi4ceMoKChg0KBBHDhwgLvvvtvoaCIiIiK1SsVL5A9ycnJi7dq1jBs3jtLSUsaOHavydQsKCwuZMGEC8+fPByAyMpI9e/bQrFkzg5OJiIiI1D4VL5Fa4OzszMaNGxk5ciTFxcWMHDmSdevWGR3LZuXk5NC3b1+2bduGi4sLH374IcuXL8fZ2dnoaCIiIiJWoeIlUktcXV3ZunUrYWFhVFRUMHXqVP7yl79gNpuNjmZTNmzYQI8ePTh27Bje3t6kpqby6KOPGh1LRERExKpUvERqkZubG+vXrycqKgqAZ599lqioKCorK40NZgOKi4t57LHHmDx5MoWFhQwYMIDMzEz69OljdDQRERERq1PxEqllDg4OLFu2jDfeeAOAt99+m0mTJlFaWmpwMuMcPXqUXr168cEHH2AymXjppZf4/PPPadWqldHRREREROqEipeIlURHR7Nu3TpcXFz429/+xrBhw7hw4YLRseqU2Wzmf/7nf+jVqxfHjh2jVatWJCUlsXDhQpycdBtBERERuX2oeIlY0eTJk0lMTKRx48Z8+eWXBAQE3DYrHubl5REaGsoTTzxBaWkpI0aM4PDhwwwZMsToaCIiIiJ1TsVLxMpCQkJIS0uja9eu5OfnM3ToUGJiYurtdV9VVVW89957dO3alcTERFxdXVmyZAm7du2iZcuWRscTERERMYSKl0gd6Ny5M+np6cycOROz2cwrr7zCkCFDOHv2rNHRatXJkycZPHgws2bNoqioiH79+nH48GHmzZuHg4P+uREREZHbl34TEqkjDRs2ZNWqVaxZs4ZGjRqRkpJCQEAAn376qdHR/rBr164RExNDt27dSElJoWHDhrz11lukpqbi5+dndDwRERERw6l4idSxqVOnkpmZSUBAAJcvX2bcuHGMHz+e7777zuhoNVZVVUV8fDwdO3bklVdeobS0lCFDhnDkyBGefvppHB0djY4oIiIiYhNUvEQM0LFjR9LS0pg3bx5OTk5s376dLl26MH/+fIqLi42O97vMZjPJycn06dOHiIgIzp07h4+PD5s3byYpKQlfX1+jI4qIiIjYFBUvEYO4ubmxZMkSy0p/ZWVlLFy4kM6dO7NlyxbMZrPREX/FbDaza9cuBg4cyH333UdGRgbu7u4sXryYY8eOMWHCBEwmk9ExRURERGyOipeIwbp06UJSUhKbN2+mbdu25OXlMWHCBHr06EF8fDzl5eVGR+T69ets3ryZHj16MHr0aL766itcXV2ZNWsWOTk5PPfcc7i5uRkdU0RERMRmqXiJ2ACTycSECRM4fvw4L730Eg0aNODrr78mIiKCdu3a8eqrr3Lp0qU6z3X48GGio6Np3bo1YWFhfP3119xxxx0888wznDp1ihUrVuDp6VnnuURERETsjYqXiA1p2LAhCxcu5MyZM7z++ut4e3uTn5/Pyy+/zJ133smMGTPYsmULBQUFVstw7tw5li1bRkBAAAEBASxbtoyLFy/SokULXn75ZU6fPs1f/vIXvLy8rJZBREREpL5xMjqAiPxas2bNePHFF5k3bx6bNm1i2bJlZGRkEBcXR1xcHE5OTgwYMIDQ0FBCQ0Pp2rXrv32frLy8PFJTU0lJSSElJYWcnBzLYy4uLowdO5aIiAhGjhyJs7Nzbb1FERERkduKipeIDXN2dmbq1KlMmTKFtLQ0Nm7cyO7du/n2229JTk4mOTmZ559/HldXV3x9fenQoQMdOnTg7rvvpmnTpphMpmrb5cuX2bt3Lxs3buTMmTN89913v7qJs8lkok+fPoSHhzNp0iSaNm1q0LsXERERqT9UvETsgMlkol+/fvTr148333yT3Nxcdu/eza5du/j8888pLS3lm2++4Ztvvqnxczs6OtKjRw+Cg4MJDg6mf//+NGnSpPbfhIiIiMhtTMVLxA7dddddzJ49m9mzZ3P9+nXOnDnDyZMnLVtOTg4FBQWYzWbLBtCoUSMABg4ciK+vL+3ateOee+7B3d3dyLcjIiIiUu+peInYOScnJ3x8fPDx8WHYsGE3PbaiooJdu3YxatQoXa8lIiIiUoe0qqGIiIiIiIiVqXiJiIiIiIhYmYqXiIiIiIiIlal4iYiIiIiIWJmKl4iIiIiIiJWpeImIiIiIiFiZipeIiIiIiIiVqXiJiIiIiIhYmYqXiIiIiIiIlal4iYiIiIiIWJmKl4iIiIiIiJWpeImIiIiIiFiZipeIiIiIiIiVqXiJiIiIiIhYmYqXiIiIiIiIlal4iYiIiIiIWJmKl4iIiIiIiJWpeImIiIiIiFiZk9EB7I3ZbAagoKDA4CQ/q6iooLi4mIKCApydnY2OIzZO40VqSmNGakpjRmpKY0ZqypbGzC+d4JeOcDMqXjVUWFgIQNu2bQ1OIiIiIiIitqCwsJDGjRvf9BiT+VbqmVhUVVVx7tw53N3dMZlMRsehoKCAtm3bcubMGTw8PIyOIzZO40VqSmNGakpjRmpKY0ZqypbGjNlsprCwEG9vbxwcbn4Vl2a8asjBwYE2bdoYHeNXPDw8DB94Yj80XqSmNGakpjRmpKY0ZqSmbGXM/N5M1y+0uIaIiIiIiIiVqXiJiIiIiIhYmYqXnXN1dSUmJgZXV1ejo4gd0HiRmtKYkZrSmJGa0piRmrLXMaPFNURERERERKxMM14iIiIiIiJWpuIlIiIiIiJiZSpeIiIiIiIiVqbiJSIiIiIiYmUqXnZsxYoVtG/fHjc3N/r06UN6errRkcSGpaamMnbsWLy9vTGZTGzbts3oSGLDYmNj6dWrF+7u7rRs2ZL777+fEydOGB1LbNh7772Hv7+/5YamQUFB7N692+hYYicWLVqEyWQiKirK6ChiwxYsWIDJZKq2derUyehYt0zFy05t2LCB6OhoYmJiOHToEPfeey8jRozg4sWLRkcTG3Xt2jXuvfdeVqxYYXQUsQMpKSlERkZy4MABkpKSqKioYPjw4Vy7ds3oaGKj2rRpw6JFi8jMzCQjI4PBgwczfvx4srOzjY4mNu7gwYOsXLkSf39/o6OIHejatSvnz5+3bF9++aXRkW6ZlpO3U3369KFXr14sX74cgKqqKtq2bcucOXN44YUXDE4nts5kMrF161buv/9+o6OInbh06RItW7YkJSWFQYMGGR1H7ETTpk1ZsmQJ//mf/2l0FLFRRUVFBAYG8u677/Lqq68SEBDAm2++aXQssVELFixg27ZtZGVlGR3l36IZLztUXl5OZmYmQ4cOtexzcHBg6NChpKWlGZhMROqrq1evAj//Ii3yeyorK1m/fj3Xrl0jKCjI6DhiwyIjIxk9enS132lEbiYnJwdvb298fX2ZNm0aeXl5Rke6ZU5GB5Cau3z5MpWVlXh6elbb7+npyfHjxw1KJSL1VVVVFVFRUfTv35977rnH6Dhiw44cOUJQUBClpaU0atSIrVu30qVLF6NjiY1av349hw4d4uDBg0ZHETvRp08fVq9ejZ+fH+fPn+fPf/4zAwcO5OjRo7i7uxsd73epeImIyE1FRkZy9OhRuzqPXozh5+dHVlYWV69eZfPmzURERJCSkqLyJb9y5swZ/uu//oukpCTc3NyMjiN2IjQ01PKxv78/ffr0oV27dmzcuNEuTmlW8bJDzZs3x9HRkQsXLlTbf+HCBVq1amVQKhGpj2bPns2OHTtITU2lTZs2RscRG+fi4kKHDh0A6NGjBwcPHuStt95i5cqVBicTW5OZmcnFixcJDAy07KusrCQ1NZXly5dTVlaGo6OjgQnFHjRp0oSOHTty8uRJo6PcEl3jZYdcXFzo0aMHe/futeyrqqpi7969OpdeRGqF2Wxm9uzZbN26lX379uHj42N0JLFDVVVVlJWVGR1DbNCQIUM4cuQIWVlZlq1nz55MmzaNrKwslS65JUVFReTm5uLl5WV0lFuiGS87FR0dTUREBD179qR37968+eabXLt2jUceecToaGKjioqKqv1F6NSpU2RlZdG0aVPuvPNOA5OJLYqMjGTt2rV88sknuLu7k5+fD0Djxo1p0KCBwenEFr344ouEhoZy5513UlhYyNq1a0lOTiYxMdHoaGKD3N3df3XN6B133EGzZs10Lanc0Lx58xg7dizt2rXj3LlzxMTE4OjoyJQpU4yOdktUvOzUpEmTuHTpEvPnzyc/P5+AgAASEhJ+teCGyC8yMjK47777LJ9HR0cDEBERwerVqw1KJbbqvffeAyAkJKTa/o8++ogZM2bUfSCxeRcvXiQ8PJzz58/TuHFj/P39SUxMZNiwYUZHE5F64ocffmDKlClcuXKFFi1aMGDAAA4cOECLFi2MjnZLdB8vERERERERK9M1XiIiIiIiIlam4iUiIiIiImJlKl4iIiIiIiJWpuIlIiIiIiJiZSpeIiIiIiIiVqbiJSIiIiIiYmUqXiIiIiIiIlam4iUiIiIiImJlKl4iIlKvzJgxg/vvv9+w158+fTqvv/56nbzWCy+8wJw5c+rktURE5I8xmc1ms9EhREREboXJZLrp4zExMcydOxez2UyTJk3qJtQ/OXz4MIMHD+b06dM0atTI6q93+fJlfH19ycrKwtfX1+qvJyIi/z4VLxERsRv5+fmWjzds2MD8+fM5ceKEZV+jRo3qpPDcyMyZM3FycuL999+vs9cMCwujffv2LFmypM5eU0REak6nGoqIiN1o1aqVZWvcuDEmk6navkaNGv3qVMOQkBDmzJlDVFQU//Ef/4GnpyerVq3i2rVrPPLII7i7u9OhQwd2795d7bWOHj1KaGgojRo1wtPTk+nTp3P58uUbZqusrGTz5s2MHTu22v53332Xu+++Gzc3Nzw9PZk4caLlsaqqKmJjY/Hx8aFBgwbce++9bN68udrXZ2dnM2bMGDw8PHB3d2fgwIHk5uZaHh87dizr16//d76dIiJSh1S8RESk3ouLi6N58+akp6czZ84cnnrqKcLCwujXrx+HDh1i+PDhTJ8+neLiYgB++uknBg8eTPfu3cnIyCAhIYELFy7w0EMP3fA1/vGPf3D16lV69uxp2ZeRkcHTTz/NK6+8wokTJ0hISGDQoEGWx2NjY4mPj+f9998nOzubuXPn8vDDD5OSkgLA2bNnGTRoEK6uruzbt4/MzEweffRRrl+/bnmO3r1788MPP/D999/X8ndNRERqk041FBERu7R69WqioqL46aefqu2fMWMGP/30E9u2bQN+nvGqrKzkiy++AH6emWrcuDEPPvgg8fHxwM+nMHp5eZGWlkbfvn159dVX+eKLL0hMTLQ87w8//EDbtm05ceIEHTt2/FWebdu2MXHiRCoqKizXom3ZsoVHHnmEH374AXd392rHl5WV0bRpU/bs2UNQUJBl/8yZMykuLmbt2rX86U9/Yv369Zw4cQJnZ+ff/D4UFBTQuHFjkpOTCQ4Ortk3UURE6oyT0QFERESszd/f3/Kxo6MjzZo1o1u3bpZ9np6eAFy8eBH4eZGMzz///DevF8vNzf3N4lVSUoKrq2u1BUCGDRtGu3bt8PX1ZeTIkYwcOZIHHniAhg0bcvLkSYqLixk2bFi15ykvL6d79+4AZGVlMXDgwBuWLoAGDRoAWGbrRETENql4iYhIvfevxcVkMlXb90tZqqqqAqCoqIixY8eyePHiXz2Xl5fXb75G8+bNKS4upry8HBcXFwDc3d05dOgQycnJfPbZZ8yfP58FCxZw8OBBioqKANi5cyetW7eu9lyurq7A/5eqm/nxxx8BaNGixe8eKyIixlHxEhER+ReBgYH87W9/o3379jg53dqPyoCAAACOHTtm+RjAycmJoUOHMnToUGJiYmjSpAn79u1j2LBhuLq6kpeXd8NTBP39/YmLi6OiouKGs15Hjx7F2dmZrl271ug9iohI3dLiGiIiIv8iMjKSH3/8kSlTpnDw4EFyc3NJTEzkkUceobKy8je/pkWLFgQGBvLll19a9u3YsYO3336brKwsTp8+TXx8PFVVVfj5+eHu7s68efOYO3cucXFx5ObmcujQId555x3i4uIAmD17NgUFBUyePJmMjAxycnL461//Wm0J/S+++IKBAwfe0uyYiIgYR8VLRETkX3h7e/PVV19RWVnJ8OHD6datG1FRUTRp0gQHhxv/6Jw5cyZr1qyxfN6kSRO2bNnC4MGD6dy5M++//z7r1q2zzE4tXLiQl19+mdjYWDp37szIkSPZuXMnPj4+ADRr1ox9+/ZRVFREcHAwPXr0YNWqVdVmv9avX89jjz1mpe+EiIjUFq1qKCIiUktKSkrw8/Njw4YN1VYqtJbdu3fzzDPP8I9//OOWT4kUERFjaMZLRESkljRo0ID4+Pib3mi5Nl27do2PPvpIpUtExA5oxktERERERMTKNOMlIiIiIiJiZSpeIiIiIiIiVqbiJSIiIiIiYmUqXiIiIiIiIlam4iUiIiIiImJlKl4iIiIiIiJWpuIlIiIiIiJiZSpeIiIiIiIiVqbiJSIiIiIiYmX/B4tbL5QgFcM0AAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter3/Python/robot_solver.py b/Chapter3/Python/robot_solver.py
new file mode 100644
index 0000000..acccc3b
--- /dev/null
+++ b/Chapter3/Python/robot_solver.py
@@ -0,0 +1,65 @@
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+"""## Define robot model function"""
+
+# Define robot model function
+def robot_model(t, x):
+    # Robot parameters
+    g = 9.81
+    l1 = 1
+    l2 = 0.5
+    m1 = 2
+    m2 = 1
+    I1 = 1e-2
+    I2 = 5e-3
+    D = 2
+
+    # Mass matrix (M)
+    M = np.array([[m1*(l1/2)**2 + m2*(l1**2 + (l2/2)**2) + m2*l1*l2*np.cos(x[1]) + I1 + I2,
+                   m2*(l2/2)**2 + 0.5*m2*l1*l2*np.cos(x[1]) + I2],
+                  [m2*(l2/2)**2 + 0.5*m2*l1*l2*np.cos(x[1]) + I2,
+                   m2*(l2/2)**2 + I2]])
+
+    # Coriolis and centrifugal terms (V)
+    V = np.array([[-m2*l1*l2*np.sin(x[1])*x[2]*x[3] - 0.5*m2*l1*l2*np.sin(x[1])*x[3]**2],
+                  [-0.5*m2*l1*l2*np.sin(x[1])*x[2]*x[3]]])
+
+    # Gravitational terms (G)
+    G = np.array([[ (m1*l1/2 + m2*l1)*g*np.cos(x[0]) + m2*g*l2/2*np.cos(x[0] + x[1])],
+                  [ m2*g*l2/2*np.cos(x[0] + x[1])]])
+
+    # Input (Q) - currently no external torques
+    Q = np.array([[-D*x[2]],  # Damping term for joint 1
+                  [-D*x[3]]])  # Damping term for joint 2
+
+    # System dynamics
+    xy = np.linalg.pinv(M) @ (Q - V - G)
+
+    # Output - angular velocities and accelerations
+    xp = np.vstack((x[2:], xy.flatten()))
+
+    return xp.flatten()
+
+"""The `scipy.integrate.solve_ivp` function defaults to using the RK45 method, which is similar to MATLAB's `ode45`."""
+
+# Simulation parameters
+t_span = [0, 5]  # Time span for simulation
+x0 = np.array([-np.pi/3, np.pi/3, 0, 0])  # Initial state [theta_1, theta_2, omega_1, omega_2]
+
+# Solve the ODE
+sol = solve_ivp(robot_model, t_span, x0, method='RK45')
+
+# Plot results
+plt.figure(figsize=(10, 6))
+plt.plot(sol.t, sol.y[0] * 180 / np.pi, 'k', label = r'$\theta_1$ (degrees)')
+plt.plot(sol.t, sol.y[1] * 180 / np.pi, '-.k', label = r'$\theta_2$ (degrees)')
+plt.xlabel('Time (sec)')
+plt.ylabel('Joint Angles')
+plt.title('Joint Angles of Two-Link Robot Manipulator')
+plt.legend()
+plt.grid(True)
+plt.show()
diff --git a/Chapter3/Python/tank_model.ipynb b/Chapter3/Python/tank_model.ipynb
new file mode 100644
index 0000000..ace6609
--- /dev/null
+++ b/Chapter3/Python/tank_model.ipynb
@@ -0,0 +1,37 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "X0fxu1Ha4orR"
+      },
+      "outputs": [],
+      "source": [
+        "# Define tank model function\n",
+        "def tank_model(t, x):\n",
+        "    A = 1.0\n",
+        "    C = 2.0\n",
+        "    F_in = 0\n",
+        "    u = 0.1\n",
+        "\n",
+        "    xp = 1/A * (F_in - C * u * np.sqrt(x))\n",
+        "    return xp"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter3/Python/tank_model.py b/Chapter3/Python/tank_model.py
new file mode 100644
index 0000000..0c3686f
--- /dev/null
+++ b/Chapter3/Python/tank_model.py
@@ -0,0 +1,18 @@
+# -*- coding: utf-8 -*-
+"""tank_model.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/16PYpakoWAxUODinO25FW74gghJLFSYsD
+"""
+
+# Define tank model function
+def tank_model(t, x):
+    A = 1.0
+    C = 2.0
+    F_in = 0
+    u = 0.1
+
+    xp = 1/A * (F_in - C * u * np.sqrt(x))
+    return xp
\ No newline at end of file
diff --git a/Chapter3/Python/tank_solver.ipynb b/Chapter3/Python/tank_solver.ipynb
new file mode 100644
index 0000000..17a0d69
--- /dev/null
+++ b/Chapter3/Python/tank_solver.ipynb
@@ -0,0 +1,97 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "Duagnpzk2kbM"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "FAleFqli3cOa"
+      },
+      "outputs": [],
+      "source": [
+        "# Define tank model function\n",
+        "def tank_model(t, x):\n",
+        "    A = 1.0\n",
+        "    C = 2.0\n",
+        "    F_in = 0\n",
+        "    u = 0.1\n",
+        "\n",
+        "    xp = 1/A * (F_in - C * u * np.sqrt(x))\n",
+        "    return xp"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 507
+        },
+        "id": "0LjQ7GhL3fLs",
+        "outputId": "91361af6-42f3-40e8-cf08-fa0bdfc09607"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-2-546ba543a0cf>:8: RuntimeWarning: invalid value encountered in sqrt\n",
+            "  xp = 1/A * (F_in - C * u * np.sqrt(x))\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "t_span = [0, 100]  # Simulation time from 0 to 10 seconds\n",
+        "x0 = [100]        # Initial tank level (example: starting at 0.5 units)\n",
+        "\n",
+        "# Solve the ODE\n",
+        "sol = solve_ivp(tank_model, t_span, x0)\n",
+        "\n",
+        "# Plotting the tank level over time\n",
+        "plt.plot(sol.t, sol.y[0])\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Tank Level (units)')\n",
+        "plt.title('Tank Level Simulation')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Chapter3/Python/tank_solver.py b/Chapter3/Python/tank_solver.py
new file mode 100644
index 0000000..7acc86b
--- /dev/null
+++ b/Chapter3/Python/tank_solver.py
@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+"""tank_solver.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1viFJz4TvlG_1ahi0DEEtIoTbjjnrc6zy
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+# Define tank model function
+def tank_model(t, x):
+    A = 1.0
+    C = 2.0
+    F_in = 0
+    u = 0.1
+
+    xp = 1/A * (F_in - C * u * np.sqrt(x))
+    return xp
+
+t_span = [0, 100]  # Simulation time from 0 to 10 seconds
+x0 = [100]        # Initial tank level (example: starting at 0.5 units)
+
+# Solve the ODE
+sol = solve_ivp(tank_model, t_span, x0)
+
+# Plotting the tank level over time
+plt.plot(sol.t, sol.y[0])
+plt.xlabel('Time (sec)')
+plt.ylabel('Tank Level (units)')
+plt.title('Tank Level Simulation')
+plt.grid(True)
+plt.show()
\ No newline at end of file
diff --git a/Chapter4/3_3.ipynb b/Chapter4/3_3.ipynb
new file mode 100644
index 0000000..ef5aa6c
--- /dev/null
+++ b/Chapter4/3_3.ipynb
@@ -0,0 +1,91 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Y2WafZqIcspc",
+        "outputId": "4ac6bd7d-e5f7-44da-c69a-9bdb24ed6d8c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix([[3*exp(-2*t) - 2*exp(-3*t), 6*exp(-2*t) - 6*exp(-3*t)], [-exp(-2*t) + exp(-3*t), -2*exp(-2*t) + 3*exp(-3*t)]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import sympy as sp\n",
+        "\n",
+        "A = sp.Matrix([[0, 6],\n",
+        "               [-1, -5]])\n",
+        "\n",
+        "t = sp.symbols('t')\n",
+        "exp_At = (A*t).exp()  # Compute the symbolic matrix exponential\n",
+        "\n",
+        "print(exp_At)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import sympy as sp\n",
+        "\n",
+        "A = sp.Matrix([[0, 6],\n",
+        "               [-1, -5]])\n",
+        "\n",
+        "t = sp.Symbol('t')\n",
+        "exp_At = (A*t).exp()  # Compute the symbolic matrix exponential\n",
+        "\n",
+        "\n",
+        "\n",
+        "sp.pprint(exp_At, use_unicode=True)\n",
+        "print('-------------------------------------')\n",
+        "print(exp_At[0],'   ',exp_At[1],'\\n',exp_At[2],'   ',exp_At[3])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rOAPh1tVdoCB",
+        "outputId": "a11aad62-8100-430d-946c-4f67eba90555"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "⎡   -2⋅t      -3⋅t      -2⋅t      -3⋅t ⎤\n",
+            "⎢3⋅ℯ     - 2⋅ℯ       6⋅ℯ     - 6⋅ℯ     ⎥\n",
+            "⎢                                      ⎥\n",
+            "⎢    -2⋅t    -3⋅t        -2⋅t      -3⋅t⎥\n",
+            "⎣ - ℯ     + ℯ       - 2⋅ℯ     + 3⋅ℯ    ⎦\n",
+            "-------------------------------------\n",
+            "3*exp(-2*t) - 2*exp(-3*t)     6*exp(-2*t) - 6*exp(-3*t) \n",
+            " -exp(-2*t) + exp(-3*t)     -2*exp(-2*t) + 3*exp(-3*t)\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/4-1.ipynb b/Chapter4/4-1.ipynb
new file mode 100644
index 0000000..826f4c2
--- /dev/null
+++ b/Chapter4/4-1.ipynb
@@ -0,0 +1,70 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## What is expm?\n",
+        "The expm function, short for \"matrix exponential,\" is a mathematical function used to compute the exponential of a square matrix.\n",
+        "The matrix exponential is defined using a power series expansion, similar to the exponential function for scalars:\n",
+        "\n",
+        "expm(A) = I + A + (A^2)/2! + (A^3)/3! + ...\n",
+        "\n",
+        "where A is a square matrix, I is the identity matrix of the same size as A, and A^n represents the matrix A raised to the power of n.\n",
+        "\n",
+        "The expm function calculates the matrix exponential by evaluating this power series expansion up to a certain level of accuracy. It is a useful tool for solving systems of linear differential equations, computing matrix logarithms, and solving matrix equations.\n",
+        "\n",
+        "In Python, the expm function is available in the scipy.linalg module, which is a part of the SciPy library. It can be used to compute the matrix exponential of a square matrix."
+      ],
+      "metadata": {
+        "id": "45zH4QggvKee"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n81KDO8lu3f6",
+        "outputId": "7855c39c-ac69-4227-8d43-4a4142df1430"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.99094408,  0.08610666],\n",
+              "       [-0.17221333,  0.73262409]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.linalg import expm\n",
+        "A = np.array([[0, 1], [-2, -3]])\n",
+        "t = 0.1\n",
+        "\n",
+        "phi = expm(A * t)\n",
+        "phi"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/ex3_2.ipynb b/Chapter4/ex3_2.ipynb
new file mode 100644
index 0000000..89e7552
--- /dev/null
+++ b/Chapter4/ex3_2.ipynb
@@ -0,0 +1,78 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sympy import symbols, exp, integrate, Matrix\n",
+        "\n",
+        "# Define matrices A and B\n",
+        "A = Matrix([[1, 0], [1, 1]])\n",
+        "B = Matrix([[1], [1]])\n",
+        "\n",
+        "# Assign values to variables u and x0\n",
+        "u = 1\n",
+        "x0 = Matrix([[1], [1]])\n",
+        "\n",
+        "# Symbolically define time variable t\n",
+        "t = symbols('t')\n",
+        "\n",
+        "# Calculate the matrix exponential phi = expm(A*t)\n",
+        "phi = exp(A * t)\n",
+        "\n",
+        "# Calculate the state equation x1 = expm(-A*t) * B * u\n",
+        "x1 = (exp(-A * t) * B) * u\n",
+        "\n",
+        "# Perform symbolic integration x_zs = int(x1)\n",
+        "x_zs = integrate(x1, t)\n",
+        "\n",
+        "# Calculate the initial state x_zi = phi * x0\n",
+        "x_zi = phi * x0\n",
+        "\n",
+        "# Compute the total state x = x_zi + x_zs\n",
+        "x = x_zi + x_zs\n",
+        "\n",
+        "print(\"phi =\", phi)\n",
+        "print(\"x1 =\", x1)\n",
+        "print(\"x_zs =\", x_zs)\n",
+        "print(\"x_zi =\", x_zi)\n",
+        "print(\"x =\", x)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pra0ICqInU5R",
+        "outputId": "c916955f-891c-4abb-d538-e36f01d912bd"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "phi = Matrix([[exp(t), 0], [t*exp(t), exp(t)]])\n",
+            "x1 = Matrix([[exp(-t)], [-t*exp(-t) + exp(-t)]])\n",
+            "x_zs = Matrix([[-exp(-t)], [t*exp(-t)]])\n",
+            "x_zi = Matrix([[exp(t)], [t*exp(t) + exp(t)]])\n",
+            "x = Matrix([[exp(t) - exp(-t)], [t*exp(t) + t*exp(-t) + exp(t)]])\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/ex3_8.ipynb b/Chapter4/ex3_8.ipynb
new file mode 100644
index 0000000..568c47b
--- /dev/null
+++ b/Chapter4/ex3_8.ipynb
@@ -0,0 +1,73 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#   Example 3-8 of Modern Book\n",
+        "\n",
+        "import numpy as np\n",
+        "from scipy import signal\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "A = np.array([[0, 1],\n",
+        "              [-2, -3]])\n",
+        "\n",
+        "B = np.array([[1],\n",
+        "              [1]])\n",
+        "\n",
+        "C = np.array([1, 0])\n",
+        "\n",
+        "D = 0\n",
+        "\n",
+        "sys = signal.StateSpace(A, B, C, D)\n",
+        "eigs = np.linalg.eigvals(A)\n",
+        "poles = sys.poles\n",
+        "zeros = sys.zeros\n",
+        "\n",
+        "print(\"Eigenvalues (eigs):\")\n",
+        "print(eigs)\n",
+        "print(\"Poles (poles):\")\n",
+        "print(poles)\n",
+        "print(\"Zeros (zeros):\")\n",
+        "print(zeros)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dBqNzr0kYjYX",
+        "outputId": "1525e2cf-9670-4e79-fd6c-1fcd4b230c02"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Eigenvalues (eigs):\n",
+            "[-1. -2.]\n",
+            "Poles (poles):\n",
+            "[-2. -1.]\n",
+            "Zeros (zeros):\n",
+            "[-4.]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/python/DCmotor_jordan/DCmotor_jordan (1).ipynb b/Chapter4/python/DCmotor_jordan/DCmotor_jordan (1).ipynb
new file mode 100644
index 0000000..a028443
--- /dev/null
+++ b/Chapter4/python/DCmotor_jordan/DCmotor_jordan (1).ipynb	
@@ -0,0 +1,92 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy.signal import StateSpace\n",
+        "from scipy.linalg import eig\n",
+        "\n",
+        "# Define the system matrices\n",
+        "A = np.array([[0, 1, 0],\n",
+        "              [0, 0, 4.438],\n",
+        "              [0, -12, -24]])\n",
+        "b1 = np.array([[0],\n",
+        "               [0],\n",
+        "               [20]])\n",
+        "b2 = np.array([[0],\n",
+        "               [-7.396],\n",
+        "               [0]])\n",
+        "B = np.hstack((b1, b2))\n",
+        "C = np.array([[1, 0, 0],\n",
+        "              [0, 1, 0]])\n",
+        "D = np.array([[0], [0]])\n",
+        "\n",
+        "# Create a state-space model\n",
+        "DC_motor = StateSpace(A, b1, C, D)  # Note only first input is used\n",
+        "\n",
+        "# Compute the eigenvalues and eigenvectors of the matrix A\n",
+        "eigenvalues, eigenvectors = np.linalg.eig(A)\n",
+        "\n",
+        "# Compute the eigenvalues and eigenvectors of the transpose of matrix A\n",
+        "eigenvalues_transpose, eigenvectors_transpose = np.linalg.eig(A.T)\n",
+        "\n",
+        "# Create diagonal matrices of eigenvalues\n",
+        "e_matrix = np.diag(eigenvalues)\n",
+        "ee_matrix = np.diag(eigenvalues_transpose)\n",
+        "\n",
+        "# Print the results\n",
+        "print(\"v:\\n\", eigenvectors)\n",
+        "print(\"ee:\\n\", e_matrix)\n",
+        "print(\"v:\\n\", eigenvectors_transpose)\n",
+        "print(\"ee:\\n\", ee_matrix)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oiGXq_KpoTmp",
+        "outputId": "598d18ba-25fd-474a-ff2a-475ac61791e3"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "v:\n",
+            " [[ 1.         -0.33290784  0.00938002]\n",
+            " [ 0.          0.82362581 -0.20191394]\n",
+            " [ 0.         -0.45914364  0.97935835]]\n",
+            "ee:\n",
+            " [[  0.           0.           0.        ]\n",
+            " [  0.          -2.47403548   0.        ]\n",
+            " [  0.           0.         -21.52596452]]\n",
+            "v:\n",
+            " [[ 0.          0.          0.90907852]\n",
+            " [-0.48691774 -0.97940144  0.40967937]\n",
+            " [-0.87344783 -0.20192283  0.07575654]]\n",
+            "ee:\n",
+            " [[-21.52596452   0.           0.        ]\n",
+            " [  0.          -2.47403548   0.        ]\n",
+            " [  0.           0.           0.        ]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/python/DCmotor_jordan/dcmotor_jordan (1).py b/Chapter4/python/DCmotor_jordan/dcmotor_jordan (1).py
new file mode 100644
index 0000000..fc1ce05
--- /dev/null
+++ b/Chapter4/python/DCmotor_jordan/dcmotor_jordan (1).py	
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+"""DCmotor_jordan.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1S14TkOUwps0aH2bIltpl-r70GklmZFyS
+"""
+
+import numpy as np
+from scipy.signal import StateSpace
+from scipy.linalg import eig
+
+# Define the system matrices
+A = np.array([[0, 1, 0],
+              [0, 0, 4.438],
+              [0, -12, -24]])
+b1 = np.array([[0],
+               [0],
+               [20]])
+b2 = np.array([[0],
+               [-7.396],
+               [0]])
+B = np.hstack((b1, b2))
+C = np.array([[1, 0, 0],
+              [0, 1, 0]])
+D = np.array([[0], [0]])
+
+# Create a state-space model
+DC_motor = StateSpace(A, b1, C, D)  # Note only first input is used
+
+# Compute the eigenvalues and eigenvectors of the matrix A
+eigenvalues, eigenvectors = np.linalg.eig(A)
+
+# Compute the eigenvalues and eigenvectors of the transpose of matrix A
+eigenvalues_transpose, eigenvectors_transpose = np.linalg.eig(A.T)
+
+# Create diagonal matrices of eigenvalues
+e_matrix = np.diag(eigenvalues)
+ee_matrix = np.diag(eigenvalues_transpose)
+
+# Print the results
+print("v:\n", eigenvectors)
+print("ee:\n", e_matrix)
+print("v:\n", eigenvectors_transpose)
+print("ee:\n", ee_matrix)
\ No newline at end of file
diff --git a/Chapter4/python/README.md b/Chapter4/python/README.md
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Chapter4/python/README.md
@@ -0,0 +1 @@
+
diff --git a/Chapter4/python/ex3_1/ex3_1.ipynb b/Chapter4/python/ex3_1/ex3_1.ipynb
new file mode 100644
index 0000000..35576df
--- /dev/null
+++ b/Chapter4/python/ex3_1/ex3_1.ipynb
@@ -0,0 +1,65 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import control as ctrl\n",
+        "from scipy.linalg import null_space\n",
+        "# Define matrices A and C\n",
+        "A = np.array([[-1.5, 0.5], [0.5, -1.5]])\n",
+        "C = np.array([[1, -1]])\n",
+        "\n",
+        "# Compute the observability matrix using control library\n",
+        "O = ctrl.obsv(A, C)\n",
+        "\n",
+        "# Compute the rank of the observability matrix\n",
+        "rank_O = np.linalg.matrix_rank(O)\n",
+        "\n",
+        "# Compute the null space of the observability matrix\n",
+        "null_O = null_space(O)\n",
+        "\n",
+        "print(\"Observability matrix O:\\n\", O)\n",
+        "print(\"Rank of O:\", rank_O)\n",
+        "print(\"Null space of O:\\n\", null_O)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uUA74VEX9Vpb",
+        "outputId": "e0768c47-cfd0-475a-b45d-3c9c01a162e6"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Observability matrix O:\n",
+            " [[ 1. -1.]\n",
+            " [-2.  2.]]\n",
+            "Rank of O: 1\n",
+            "Null space of O:\n",
+            " [[0.70710678]\n",
+            " [0.70710678]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/python/ex3_1/ex3_1.py b/Chapter4/python/ex3_1/ex3_1.py
new file mode 100644
index 0000000..8266443
--- /dev/null
+++ b/Chapter4/python/ex3_1/ex3_1.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+"""ex3_1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1B1kMIbvyikf7cNz9rhl4Je4Lz9mQo8TK
+"""
+
+import numpy as np
+import control as ctrl
+from scipy.linalg import null_space
+# Define matrices A and C
+A = np.array([[-1.5, 0.5], [0.5, -1.5]])
+C = np.array([[1, -1]])
+
+# Compute the observability matrix using control library
+O = ctrl.obsv(A, C)
+
+# Compute the rank of the observability matrix
+rank_O = np.linalg.matrix_rank(O)
+
+# Compute the null space of the observability matrix
+null_O = null_space(O)
+
+print("Observability matrix O:\n", O)
+print("Rank of O:", rank_O)
+print("Null space of O:\n", null_O)
\ No newline at end of file
diff --git a/Chapter4/python/ex3_2/ex3_2.ipynb b/Chapter4/python/ex3_2/ex3_2.ipynb
new file mode 100644
index 0000000..89e7552
--- /dev/null
+++ b/Chapter4/python/ex3_2/ex3_2.ipynb
@@ -0,0 +1,78 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sympy import symbols, exp, integrate, Matrix\n",
+        "\n",
+        "# Define matrices A and B\n",
+        "A = Matrix([[1, 0], [1, 1]])\n",
+        "B = Matrix([[1], [1]])\n",
+        "\n",
+        "# Assign values to variables u and x0\n",
+        "u = 1\n",
+        "x0 = Matrix([[1], [1]])\n",
+        "\n",
+        "# Symbolically define time variable t\n",
+        "t = symbols('t')\n",
+        "\n",
+        "# Calculate the matrix exponential phi = expm(A*t)\n",
+        "phi = exp(A * t)\n",
+        "\n",
+        "# Calculate the state equation x1 = expm(-A*t) * B * u\n",
+        "x1 = (exp(-A * t) * B) * u\n",
+        "\n",
+        "# Perform symbolic integration x_zs = int(x1)\n",
+        "x_zs = integrate(x1, t)\n",
+        "\n",
+        "# Calculate the initial state x_zi = phi * x0\n",
+        "x_zi = phi * x0\n",
+        "\n",
+        "# Compute the total state x = x_zi + x_zs\n",
+        "x = x_zi + x_zs\n",
+        "\n",
+        "print(\"phi =\", phi)\n",
+        "print(\"x1 =\", x1)\n",
+        "print(\"x_zs =\", x_zs)\n",
+        "print(\"x_zi =\", x_zi)\n",
+        "print(\"x =\", x)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pra0ICqInU5R",
+        "outputId": "c916955f-891c-4abb-d538-e36f01d912bd"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "phi = Matrix([[exp(t), 0], [t*exp(t), exp(t)]])\n",
+            "x1 = Matrix([[exp(-t)], [-t*exp(-t) + exp(-t)]])\n",
+            "x_zs = Matrix([[-exp(-t)], [t*exp(-t)]])\n",
+            "x_zi = Matrix([[exp(t)], [t*exp(t) + exp(t)]])\n",
+            "x = Matrix([[exp(t) - exp(-t)], [t*exp(t) + t*exp(-t) + exp(t)]])\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/python/ex3_3/ex3_3.ipynb b/Chapter4/python/ex3_3/ex3_3.ipynb
new file mode 100644
index 0000000..9b04a2a
--- /dev/null
+++ b/Chapter4/python/ex3_3/ex3_3.ipynb
@@ -0,0 +1,64 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy\n",
+        "from sympy import symbols,exp, integrate, Matrix\n",
+        "from scipy.linalg import expm\n",
+        "\n",
+        "A = Matrix([[0,6],[-1,-5]])\n",
+        "# Symbolically define time variable t\n",
+        "t = symbols('t')\n",
+        "\n",
+        "# Calculate the matrix exponential phi = expm(A*t)\n",
+        "phi = exp(A * t)\n",
+        "# Print the symbolic expression\n",
+        "phi = np.array(phi)\n",
+        "print(phi)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "306ZILH_q6IX",
+        "outputId": "e3f0a244-9f1c-4f0a-8308-cf1faa34cb08"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[3*exp(-2*t) - 2*exp(-3*t) 6*exp(-2*t) - 6*exp(-3*t)]\n",
+            " [-exp(-2*t) + exp(-3*t) -2*exp(-2*t) + 3*exp(-3*t)]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "d_bXuuvZtj8l"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/python/ex3_3/ex3_3.py b/Chapter4/python/ex3_3/ex3_3.py
new file mode 100644
index 0000000..6bc5fa0
--- /dev/null
+++ b/Chapter4/python/ex3_3/ex3_3.py
@@ -0,0 +1,16 @@
+
+
+import numpy
+from sympy import symbols,exp, integrate, Matrix
+from scipy.linalg import expm
+
+A = Matrix([[0,6],[-1,-5]])
+# Symbolically define time variable t
+t = symbols('t')
+
+# Calculate the matrix exponential phi = expm(A*t)
+phi = exp(A * t)
+# Print the symbolic expression
+phi = np.array(phi)
+print(phi)
+
diff --git a/Chapter4/python/ex3_8/ex3_8.ipynb b/Chapter4/python/ex3_8/ex3_8.ipynb
new file mode 100644
index 0000000..568c47b
--- /dev/null
+++ b/Chapter4/python/ex3_8/ex3_8.ipynb
@@ -0,0 +1,73 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "#   Example 3-8 of Modern Book\n",
+        "\n",
+        "import numpy as np\n",
+        "from scipy import signal\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "A = np.array([[0, 1],\n",
+        "              [-2, -3]])\n",
+        "\n",
+        "B = np.array([[1],\n",
+        "              [1]])\n",
+        "\n",
+        "C = np.array([1, 0])\n",
+        "\n",
+        "D = 0\n",
+        "\n",
+        "sys = signal.StateSpace(A, B, C, D)\n",
+        "eigs = np.linalg.eigvals(A)\n",
+        "poles = sys.poles\n",
+        "zeros = sys.zeros\n",
+        "\n",
+        "print(\"Eigenvalues (eigs):\")\n",
+        "print(eigs)\n",
+        "print(\"Poles (poles):\")\n",
+        "print(poles)\n",
+        "print(\"Zeros (zeros):\")\n",
+        "print(zeros)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dBqNzr0kYjYX",
+        "outputId": "1525e2cf-9670-4e79-fd6c-1fcd4b230c02"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Eigenvalues (eigs):\n",
+            "[-1. -2.]\n",
+            "Poles (poles):\n",
+            "[-2. -1.]\n",
+            "Zeros (zeros):\n",
+            "[-4.]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/python/jordan_forms/jordan_forms.ipynb b/Chapter4/python/jordan_forms/jordan_forms.ipynb
new file mode 100644
index 0000000..7365a16
--- /dev/null
+++ b/Chapter4/python/jordan_forms/jordan_forms.ipynb
@@ -0,0 +1,143 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from sympy import Matrix\n",
+        "\n",
+        "# Inverted Pendulum example\n",
+        "A = np.array([[0, 1, 0, 0],\n",
+        "              [0, 0, -9.8, 0],\n",
+        "              [0, 0, 0, 1],\n",
+        "              [0, 0, 19.6, 0]])\n",
+        "\n",
+        "B = np.array([[0],\n",
+        "              [1],\n",
+        "              [0],\n",
+        "              [1]])\n",
+        "\n",
+        "C = np.array([[1, 0, 0, 0],\n",
+        "              [0, 0, 1, 0]])\n",
+        "m = Matrix(A)\n",
+        "T, J = m.jordan_form()\n",
+        "\n",
+        "# Convert SymPy Matrix P to NumPy array\n",
+        "T_np = np.array(T).astype(float)\n",
+        "J_np = np.array(J).astype(float)\n",
+        "# Transform B and C matrices\n",
+        "Bn = np.linalg.inv(T_np) @ B\n",
+        "Cn = C @ T_np\n",
+        "\n",
+        "\n",
+        "print(\"Transformation matrix T:\")\n",
+        "print(P_np)\n",
+        "print(\"\\nJordan form J:\")\n",
+        "print(J_np)\n",
+        "print(\"\\nTransformed B matrix (Bn):\")\n",
+        "print(Bn)\n",
+        "\n",
+        "print(\"\\nTransformed C matrix (Cn):\")\n",
+        "print(Cn)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3Bl14H_nEvm1",
+        "outputId": "40cd3790-3a3e-4ebc-b47d-9aeab66eb5c9"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Transformation matrix T:\n",
+            "[[ 1.          0.          0.11293849 -0.11293849]\n",
+            " [ 0.          1.         -0.5        -0.5       ]\n",
+            " [ 0.          0.         -0.22587698  0.22587698]\n",
+            " [ 0.          0.          1.          1.        ]]\n",
+            "\n",
+            "Jordan form J:\n",
+            "[[ 0.          1.          0.          0.        ]\n",
+            " [ 0.          0.          0.          0.        ]\n",
+            " [ 0.          0.         -4.42718872  0.        ]\n",
+            " [ 0.          0.          0.          4.42718872]]\n",
+            "\n",
+            "Transformed B matrix (Bn):\n",
+            "[[0. ]\n",
+            " [1.5]\n",
+            " [0.5]\n",
+            " [0.5]]\n",
+            "\n",
+            "Transformed C matrix (Cn):\n",
+            "[[ 1.          0.          0.11293849 -0.11293849]\n",
+            " [ 0.          0.         -0.22587698  0.22587698]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Example 3-13\n",
+        "A = np.array([[0, 1, 0, 3],\n",
+        "              [0, -1, 1, 10],\n",
+        "              [0, 0, 0, 1],\n",
+        "              [0, 0, -1, -2]])\n",
+        "m = Matrix(A)\n",
+        "T, J = m.jordan_form()\n",
+        "\n",
+        "# Convert SymPy Matrix T to NumPy array\n",
+        "T_np = np.array(T).astype(float)\n",
+        "J_np = np.array(J).astype(float)\n",
+        "print(\"Transformation matrix T:\")\n",
+        "print(T_np)\n",
+        "print(\"\\nJordan form J:\")\n",
+        "print(J_np)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sSa67ZIqFKpC",
+        "outputId": "3cd75594-1cb3-4934-8205-93b1f4edc07a"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Transformation matrix T:\n",
+            "[[ 9. 11. 11.  1.]\n",
+            " [-9.  1.  0.  0.]\n",
+            " [ 0.  1.  1.  0.]\n",
+            " [ 0. -1.  0.  0.]]\n",
+            "\n",
+            "Jordan form J:\n",
+            "[[-1.  1.  0.  0.]\n",
+            " [ 0. -1.  1.  0.]\n",
+            " [ 0.  0. -1.  0.]\n",
+            " [ 0.  0.  0.  0.]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter4/python/jordan_forms/jordan_forms.py b/Chapter4/python/jordan_forms/jordan_forms.py
new file mode 100644
index 0000000..1300adb
--- /dev/null
+++ b/Chapter4/python/jordan_forms/jordan_forms.py
@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+"""jordan_forms.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1h3p8bWuCzDPy1j91FbktzffhvE6UrfiC
+"""
+
+import numpy as np
+from sympy import Matrix
+
+# Inverted Pendulum example
+A = np.array([[0, 1, 0, 0],
+              [0, 0, -9.8, 0],
+              [0, 0, 0, 1],
+              [0, 0, 19.6, 0]])
+
+B = np.array([[0],
+              [1],
+              [0],
+              [1]])
+
+C = np.array([[1, 0, 0, 0],
+              [0, 0, 1, 0]])
+m = Matrix(A)
+T, J = m.jordan_form()
+
+# Convert SymPy Matrix P to NumPy array
+T_np = np.array(T).astype(float)
+J_np = np.array(J).astype(float)
+# Transform B and C matrices
+Bn = np.linalg.inv(T_np) @ B
+Cn = C @ T_np
+
+
+print("Transformation matrix T:")
+print(P_np)
+print("\nJordan form J:")
+print(J_np)
+print("\nTransformed B matrix (Bn):")
+print(Bn)
+
+print("\nTransformed C matrix (Cn):")
+print(Cn)
+
+#Example 3-13
+A = np.array([[0, 1, 0, 3],
+              [0, -1, 1, 10],
+              [0, 0, 0, 1],
+              [0, 0, -1, -2]])
+m = Matrix(A)
+T, J = m.jordan_form()
+
+# Convert SymPy Matrix T to NumPy array
+T_np = np.array(T).astype(float)
+J_np = np.array(J).astype(float)
+print("Transformation matrix T:")
+print(T_np)
+print("\nJordan form J:")
+print(J_np)
\ No newline at end of file
diff --git a/Chapter5_6/Python/README.md b/Chapter5_6/Python/README.md
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Chapter5_6/Python/README.md
@@ -0,0 +1 @@
+
diff --git a/Chapter5_6/Python/ex4_3/ex4_3.ipynb b/Chapter5_6/Python/ex4_3/ex4_3.ipynb
new file mode 100644
index 0000000..f6939f9
--- /dev/null
+++ b/Chapter5_6/Python/ex4_3/ex4_3.ipynb
@@ -0,0 +1,76 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ardo8rHTIMpg",
+        "outputId": "17273098-0d4e-49fa-d5df-186ad9c66d74"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Observability matrix (O):\n",
+            "[[ 1. -1.]\n",
+            " [-2.  2.]]\n",
+            "\n",
+            "Rank of the observability matrix:\n",
+            "1\n",
+            "\n",
+            "Null space of the observability matrix:\n",
+            "[[0.70710678]\n",
+            " [0.70710678]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import control\n",
+        "from scipy.linalg import null_space\n",
+        "\n",
+        "# Define matrices A and C\n",
+        "A = np.array([[-3/2, 1/2],\n",
+        "              [1/2, -3/2]])\n",
+        "\n",
+        "C = np.array([[1, -1]])\n",
+        "\n",
+        "# Compute the observability matrix O using control library\n",
+        "O = control.obsv(A, C)\n",
+        "\n",
+        "# Calculate the rank of the observability matrix\n",
+        "rank_O = np.linalg.matrix_rank(O)\n",
+        "\n",
+        "# Calculate the null space of the observability matrix using scipy\n",
+        "null_O = null_space(O)\n",
+        "\n",
+        "print(\"Observability matrix (O):\")\n",
+        "print(O)\n",
+        "\n",
+        "print(\"\\nRank of the observability matrix:\")\n",
+        "print(rank_O)\n",
+        "\n",
+        "print(\"\\nNull space of the observability matrix:\")\n",
+        "print(null_O)\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex4_3/ex4_3.py b/Chapter5_6/Python/ex4_3/ex4_3.py
new file mode 100644
index 0000000..94fff2c
--- /dev/null
+++ b/Chapter5_6/Python/ex4_3/ex4_3.py
@@ -0,0 +1,29 @@
+
+
+import numpy as np
+import control
+from scipy.linalg import null_space
+
+# Define matrices A and C
+A = np.array([[-3/2, 1/2],
+              [1/2, -3/2]])
+
+C = np.array([[1, -1]])
+
+# Compute the observability matrix O using control library
+O = control.obsv(A, C)
+
+# Calculate the rank of the observability matrix
+rank_O = np.linalg.matrix_rank(O)
+
+# Calculate the null space of the observability matrix using scipy
+null_O = null_space(O)
+
+print("Observability matrix (O):")
+print(O)
+
+print("\nRank of the observability matrix:")
+print(rank_O)
+
+print("\nNull space of the observability matrix:")
+print(null_O)
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex4_9/ex4_9.ipynb b/Chapter5_6/Python/ex4_9/ex4_9.ipynb
new file mode 100644
index 0000000..138dd29
--- /dev/null
+++ b/Chapter5_6/Python/ex4_9/ex4_9.ipynb
@@ -0,0 +1,77 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import control\n",
+        "from scipy.linalg import null_space\n",
+        "\n",
+        "# Define matrices A and B\n",
+        "A = np.array([[-3/2, 1/2],\n",
+        "              [1/2, -3/2]])\n",
+        "\n",
+        "B = np.array([[1/2],\n",
+        "              [1/2]])\n",
+        "\n",
+        "# Compute the controllability matrix C using control library\n",
+        "C = control.ctrb(A, B)\n",
+        "\n",
+        "# Calculate the rank of the controllability matrix\n",
+        "rank_C = np.linalg.matrix_rank(C)\n",
+        "\n",
+        "# Calculate the null space of the controllability matrix using scipy\n",
+        "null_C = null_space(C)\n",
+        "\n",
+        "print(\"Controllability matrix (C):\")\n",
+        "print(C)\n",
+        "\n",
+        "print(\"\\nRank of the controllability matrix:\")\n",
+        "print(rank_C)\n",
+        "\n",
+        "print(\"\\nNull space of the controllability matrix:\")\n",
+        "print(null_C)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ISFR-GvIM6-u",
+        "outputId": "1bf6e633-231a-463f-91e6-3cfd2db7cdd8"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Controllability matrix (C):\n",
+            "[[ 0.5 -0.5]\n",
+            " [ 0.5 -0.5]]\n",
+            "\n",
+            "Rank of the controllability matrix:\n",
+            "1\n",
+            "\n",
+            "Null space of the controllability matrix:\n",
+            "[[0.70710678]\n",
+            " [0.70710678]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex4_9/ex4_9.py b/Chapter5_6/Python/ex4_9/ex4_9.py
new file mode 100644
index 0000000..471f27a
--- /dev/null
+++ b/Chapter5_6/Python/ex4_9/ex4_9.py
@@ -0,0 +1,30 @@
+
+
+import numpy as np
+import control
+from scipy.linalg import null_space
+
+# Define matrices A and B
+A = np.array([[-3/2, 1/2],
+              [1/2, -3/2]])
+
+B = np.array([[1/2],
+              [1/2]])
+
+# Compute the controllability matrix C using control library
+C = control.ctrb(A, B)
+
+# Calculate the rank of the controllability matrix
+rank_C = np.linalg.matrix_rank(C)
+
+# Calculate the null space of the controllability matrix using scipy
+null_C = null_space(C)
+
+print("Controllability matrix (C):")
+print(C)
+
+print("\nRank of the controllability matrix:")
+print(rank_C)
+
+print("\nNull space of the controllability matrix:")
+print(null_C)
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex5_1/ex5_1.ipynb b/Chapter5_6/Python/ex5_1/ex5_1.ipynb
new file mode 100644
index 0000000..5165f0b
--- /dev/null
+++ b/Chapter5_6/Python/ex5_1/ex5_1.ipynb
@@ -0,0 +1,195 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import control\n",
+        "import numpy as np\n",
+        "\n",
+        "# Definition of System 1\n",
+        "A = np.array([[0, 1, 0], [0, 0, 1], [-5, -11, -6]])\n",
+        "B = np.array([[0], [0], [1]])\n",
+        "C = np.array([[1, 0, 1]])\n",
+        "D = np.array([[0]])\n",
+        "sys1 = control.ss(A, B, C, D)\n",
+        "\n",
+        "# Conversion to Transfer Function (System 1)\n",
+        "tf_sys1 = control.tf(sys1)\n",
+        "print(\"tf1 = \")\n",
+        "print(tf_sys1)\n",
+        "# Definition of System 2\n",
+        "a = np.array([[0, 0, -5], [1, 0, -11], [0, 1, -6]])\n",
+        "b = np.array([[1], [0], [1]])\n",
+        "c = np.array([[0, 0, 1]])\n",
+        "d = np.array([[0]])\n",
+        "sys2 = control.ss(a, b, c, d)\n",
+        "\n",
+        "# Conversion to Transfer Function (System 2)\n",
+        "tf_sys2 = control.tf(sys2)\n",
+        "print(\"\\ntf2 = \")\n",
+        "print(tf_sys2)\n",
+        "# Definition of System 3\n",
+        "A = np.array([[0, 1, 0], [0, 0, 1], [-5, -11, -6]])\n",
+        "B = np.array([[1], [-6], [26]])\n",
+        "C = np.array([[1, 0, 0]])\n",
+        "D = np.array([[0]])\n",
+        "sys3 = control.ss(A, B, C, D)\n",
+        "\n",
+        "# Conversion to Transfer Function (System 3)\n",
+        "tf_sys3 = control.tf(sys3)\n",
+        "print(\"\\ntf3 = \")\n",
+        "print(tf_sys3)\n",
+        "# Observability Check (System 3)\n",
+        "observability_matrix = control.obsv(sys3.A, sys3.C)\n",
+        "print(\"\\nobservability_matrix = \")\n",
+        "print(observability_matrix)\n",
+        "# Definition of System 4\n",
+        "A = np.array([[0, 0, -5], [1, 0, -11], [0, 1, -6]])\n",
+        "B = np.array([[1], [0], [0]])\n",
+        "C = np.array([[1, -6, 26]])\n",
+        "D = np.array([[0]])\n",
+        "sys4 = control.ss(A, B, C, D)\n",
+        "\n",
+        "# Conversion to Transfer Function (System 4)\n",
+        "tf_sys4 = control.tf(sys4)\n",
+        "print(\"\\ntf4 = \")\n",
+        "print(tf_sys4)\n",
+        "# Controllability Check (System 4)\n",
+        "controllability_matrix = control.ctrb(sys4.A, sys4.B)\n",
+        "print(\"\\ncontrollability_matrix = \")\n",
+        "print(controllability_matrix)\n",
+        "# Conversion from Transfer Function to State-Space (MySys)\n",
+        "num = np.array([1, 0, 1])\n",
+        "den = np.array([1, 6, 11, 5])\n",
+        "sys = control.TransferFunction(num, den)\n",
+        "mysys = control.ss(sys)\n",
+        "A, B, C, D = mysys.A, mysys.B, mysys.C, mysys.D\n",
+        "print(\"\\nA =\")\n",
+        "print(A)\n",
+        "print(\"\\nB =\")\n",
+        "print(B)\n",
+        "print(\"\\nC =\")\n",
+        "print(C)\n",
+        "print(\"\\nD =\")\n",
+        "print(D)\n",
+        "print(mysys)"
+      ],
+      "metadata": {
+        "id": "S8KWFV3XefVg",
+        "outputId": "64183108-fd1b-42ae-8e36-55add8ba0df0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tf1 = \n",
+            "<TransferFunction>: sys[156]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "s^2 - 8.882e-15 s + 1\n",
+            "----------------------\n",
+            "s^3 + 6 s^2 + 11 s + 5\n",
+            "\n",
+            "\n",
+            "tf2 = \n",
+            "<TransferFunction>: sys[158]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "s^2 - 8.882e-15 s + 1\n",
+            "----------------------\n",
+            "s^3 + 6 s^2 + 11 s + 5\n",
+            "\n",
+            "\n",
+            "tf3 = \n",
+            "<TransferFunction>: sys[160]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "s^2 - 3.375e-14 s + 1\n",
+            "----------------------\n",
+            "s^3 + 6 s^2 + 11 s + 5\n",
+            "\n",
+            "\n",
+            "observability_matrix = \n",
+            "[[1. 0. 0.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 0. 1.]]\n",
+            "\n",
+            "tf4 = \n",
+            "<TransferFunction>: sys[162]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "s^2 + 5.329e-15 s + 1\n",
+            "----------------------\n",
+            "s^3 + 6 s^2 + 11 s + 5\n",
+            "\n",
+            "\n",
+            "controllability_matrix = \n",
+            "[[1. 0. 0.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 0. 1.]]\n",
+            "\n",
+            "A =\n",
+            "[[ -6. -11.  -5.]\n",
+            " [  1.   0.   0.]\n",
+            " [  0.   1.   0.]]\n",
+            "\n",
+            "B =\n",
+            "[[1.]\n",
+            " [0.]\n",
+            " [0.]]\n",
+            "\n",
+            "C =\n",
+            "[[1. 0. 1.]]\n",
+            "\n",
+            "D =\n",
+            "[[0.]]\n",
+            "<StateSpace>: sys[163]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "States (3): ['x[0]', 'x[1]', 'x[2]']\n",
+            "\n",
+            "A = [[ -6. -11.  -5.]\n",
+            "     [  1.   0.   0.]\n",
+            "     [  0.   1.   0.]]\n",
+            "\n",
+            "B = [[1.]\n",
+            "     [0.]\n",
+            "     [0.]]\n",
+            "\n",
+            "C = [[1. 0. 1.]]\n",
+            "\n",
+            "D = [[0.]]\n",
+            "\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex5_1/ex5_1.py b/Chapter5_6/Python/ex5_1/ex5_1.py
new file mode 100644
index 0000000..32c62cf
--- /dev/null
+++ b/Chapter5_6/Python/ex5_1/ex5_1.py
@@ -0,0 +1,72 @@
+
+
+import control
+import numpy as np
+
+# Definition of System 1
+A = np.array([[0, 1, 0], [0, 0, 1], [-5, -11, -6]])
+B = np.array([[0], [0], [1]])
+C = np.array([[1, 0, 1]])
+D = np.array([[0]])
+sys1 = control.ss(A, B, C, D)
+
+# Conversion to Transfer Function (System 1)
+tf_sys1 = control.tf(sys1)
+print("tf1 = ")
+print(tf_sys1)
+# Definition of System 2
+a = np.array([[0, 0, -5], [1, 0, -11], [0, 1, -6]])
+b = np.array([[1], [0], [1]])
+c = np.array([[0, 0, 1]])
+d = np.array([[0]])
+sys2 = control.ss(a, b, c, d)
+
+# Conversion to Transfer Function (System 2)
+tf_sys2 = control.tf(sys2)
+print("\ntf2 = ")
+print(tf_sys2)
+# Definition of System 3
+A = np.array([[0, 1, 0], [0, 0, 1], [-5, -11, -6]])
+B = np.array([[1], [-6], [26]])
+C = np.array([[1, 0, 0]])
+D = np.array([[0]])
+sys3 = control.ss(A, B, C, D)
+
+# Conversion to Transfer Function (System 3)
+tf_sys3 = control.tf(sys3)
+print("\ntf3 = ")
+print(tf_sys3)
+# Observability Check (System 3)
+observability_matrix = control.obsv(sys3.A, sys3.C)
+print("\nobservability_matrix = ")
+print(observability_matrix)
+# Definition of System 4
+A = np.array([[0, 0, -5], [1, 0, -11], [0, 1, -6]])
+B = np.array([[1], [0], [0]])
+C = np.array([[1, -6, 26]])
+D = np.array([[0]])
+sys4 = control.ss(A, B, C, D)
+
+# Conversion to Transfer Function (System 4)
+tf_sys4 = control.tf(sys4)
+print("\ntf4 = ")
+print(tf_sys4)
+# Controllability Check (System 4)
+controllability_matrix = control.ctrb(sys4.A, sys4.B)
+print("\ncontrollability_matrix = ")
+print(controllability_matrix)
+# Conversion from Transfer Function to State-Space (MySys)
+num = np.array([1, 0, 1])
+den = np.array([1, 6, 11, 5])
+sys = control.TransferFunction(num, den)
+mysys = control.ss(sys)
+A, B, C, D = mysys.A, mysys.B, mysys.C, mysys.D
+print("\nA =")
+print(A)
+print("\nB =")
+print(B)
+print("\nC =")
+print(C)
+print("\nD =")
+print(D)
+print(mysys)
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex5_2/ex5_2.ipynb b/Chapter5_6/Python/ex5_2/ex5_2.ipynb
new file mode 100644
index 0000000..97db764
--- /dev/null
+++ b/Chapter5_6/Python/ex5_2/ex5_2.ipynb
@@ -0,0 +1,94 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import control as ctrl\n",
+        "import numpy as np\n",
+        "\n",
+        "# System 1 definition\n",
+        "A1 = np.array([[-1, 1, 0],\n",
+        "               [0, -1, 0],\n",
+        "               [0, 0, -2]])\n",
+        "B1 = np.array([[0],\n",
+        "               [1],\n",
+        "               [1]])\n",
+        "C1 = np.array([4, -8, 9])\n",
+        "D1 = np.array([[0]])\n",
+        "\n",
+        "sys1 = ctrl.ss(A1, B1, C1, D1)\n",
+        "tf_sys1 = ctrl.ss2tf(sys1)\n",
+        "\n",
+        "print(\"System 1 Transfer Function:\")\n",
+        "print(tf_sys1)\n",
+        "\n",
+        "# System 2 definition\n",
+        "A2 = np.array([[-1, 0, 0],\n",
+        "               [1, -1, 0],\n",
+        "               [0, 0, -2]])\n",
+        "B2 = np.array([[4],\n",
+        "               [-8],\n",
+        "               [9]])\n",
+        "C2 = np.array([0, 1, 1])\n",
+        "D2 = np.array([[0]])\n",
+        "\n",
+        "sys2 = ctrl.ss(A2, B2, C2, D2)\n",
+        "tf_sys2 = ctrl.ss2tf(sys2)\n",
+        "\n",
+        "print(\"\\nSystem 2 Transfer Function:\")\n",
+        "print(tf_sys2)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M4abquQsPP8h",
+        "outputId": "7133b616-45f8-46d2-e935-439e4e79e52e"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "System 1 Transfer Function:\n",
+            "<TransferFunction>: sys[3]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "    s^2 - 2 s + 1\n",
+            "---------------------\n",
+            "s^3 + 4 s^2 + 5 s + 2\n",
+            "\n",
+            "\n",
+            "System 2 Transfer Function:\n",
+            "<TransferFunction>: sys[5]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "    s^2 - 2 s + 1\n",
+            "---------------------\n",
+            "s^3 + 4 s^2 + 5 s + 2\n",
+            "\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex5_2/ex5_2.py b/Chapter5_6/Python/ex5_2/ex5_2.py
new file mode 100644
index 0000000..1470377
--- /dev/null
+++ b/Chapter5_6/Python/ex5_2/ex5_2.py
@@ -0,0 +1,36 @@
+
+
+import control as ctrl
+import numpy as np
+
+# System 1 definition
+A1 = np.array([[-1, 1, 0],
+               [0, -1, 0],
+               [0, 0, -2]])
+B1 = np.array([[0],
+               [1],
+               [1]])
+C1 = np.array([4, -8, 9])
+D1 = np.array([[0]])
+
+sys1 = ctrl.ss(A1, B1, C1, D1)
+tf_sys1 = ctrl.ss2tf(sys1)
+
+print("System 1 Transfer Function:")
+print(tf_sys1)
+
+# System 2 definition
+A2 = np.array([[-1, 0, 0],
+               [1, -1, 0],
+               [0, 0, -2]])
+B2 = np.array([[4],
+               [-8],
+               [9]])
+C2 = np.array([0, 1, 1])
+D2 = np.array([[0]])
+
+sys2 = ctrl.ss(A2, B2, C2, D2)
+tf_sys2 = ctrl.ss2tf(sys2)
+
+print("\nSystem 2 Transfer Function:")
+print(tf_sys2)
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex5_4/ex5_4.ipynb b/Chapter5_6/Python/ex5_4/ex5_4.ipynb
new file mode 100644
index 0000000..091dd8b
--- /dev/null
+++ b/Chapter5_6/Python/ex5_4/ex5_4.ipynb
@@ -0,0 +1,76 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy import signal\n",
+        "\n",
+        "# Define state-space matrices directly\n",
+        "A = [[-1, 0], [1, -1]]\n",
+        "B = [[2], [1]]\n",
+        "C = [[1, 0]]\n",
+        "D = [[0]]\n",
+        "\n",
+        "# Create the state-space system\n",
+        "sys = signal.StateSpace(A, B, C, D)\n",
+        "\n",
+        "# Define a function to simplify state-space representation (mimicking minreal)\n",
+        "def my_minreal(sys):\n",
+        "    # Extract matrices from the system\n",
+        "    A, B, C, D = sys.A, sys.B, sys.C, sys.D\n",
+        "\n",
+        "    # Reduce to minimal realization (example here is a basic form, not full reduction)\n",
+        "    A_min, B_min, C_min, D_min = A, B, C, D  # Placeholder for minimal realization\n",
+        "\n",
+        "    # Create a new StateSpace system with minimal realization\n",
+        "    return signal.StateSpace(A_min, B_min, C_min, D_min)\n",
+        "\n",
+        "# Simplify the state-space system (mimicking minreal)\n",
+        "sys_min = my_minreal(sys)\n",
+        "\n",
+        "# Display the simplified state-space system\n",
+        "print(sys_min)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "S_WwT6ti4qgP",
+        "outputId": "8437d557-982e-4d47-d888-9b8243e388f9"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "StateSpaceContinuous(\n",
+            "array([[-1,  0],\n",
+            "       [ 1, -1]]),\n",
+            "array([[2],\n",
+            "       [1]]),\n",
+            "array([[1, 0]]),\n",
+            "array([[0]]),\n",
+            "dt: None\n",
+            ")\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex5_4/ex5_4.py b/Chapter5_6/Python/ex5_4/ex5_4.py
new file mode 100644
index 0000000..2b09ca6
--- /dev/null
+++ b/Chapter5_6/Python/ex5_4/ex5_4.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+"""ex5_4.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1BbSmTDXQ5XRyLRD889Btajuz2dvcAJTl
+"""
+
+import numpy as np
+from scipy import signal
+
+# Define state-space matrices directly
+A = [[-1, 0], [1, -1]]
+B = [[2], [1]]
+C = [[1, 0]]
+D = [[0]]
+
+# Create the state-space system
+sys = signal.StateSpace(A, B, C, D)
+
+# Define a function to simplify state-space representation (mimicking minreal)
+def my_minreal(sys):
+    # Extract matrices from the system
+    A, B, C, D = sys.A, sys.B, sys.C, sys.D
+
+    # Reduce to minimal realization (example here is a basic form, not full reduction)
+    A_min, B_min, C_min, D_min = A, B, C, D  # Placeholder for minimal realization
+
+    # Create a new StateSpace system with minimal realization
+    return signal.StateSpace(A_min, B_min, C_min, D_min)
+
+# Simplify the state-space system (mimicking minreal)
+sys_min = my_minreal(sys)
+
+# Display the simplified state-space system
+print(sys_min)
\ No newline at end of file
diff --git a/Chapter5_6/Python/ex5_6/ex5_6.ipynb b/Chapter5_6/Python/ex5_6/ex5_6.ipynb
new file mode 100644
index 0000000..c8d6caf
--- /dev/null
+++ b/Chapter5_6/Python/ex5_6/ex5_6.ipynb
@@ -0,0 +1,61 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy import linalg\n",
+        "# Define matrix A\n",
+        "A = np.array([[-1, -2],\n",
+        "              [ 1, -4]])\n",
+        "\n",
+        "# Define the identity matrix Q\n",
+        "Q = np.eye(2)\n",
+        "\n",
+        "\n",
+        "\n",
+        "P = 60*linalg.solve_continuous_lyapunov(A.T, Q)\n",
+        "# Compute the determinant of matrix P\n",
+        "det_P = np.linalg.det(P)\n",
+        "\n",
+        "print(\"Matrix P:\")\n",
+        "print(P)\n",
+        "print(\"Determinant of P:\", det_P)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yYVA9rjDscm4",
+        "outputId": "21c2b008-b06f-4c7d-fd46-fa1e9aba2432"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix P:\n",
+            "[[-23.   7.]\n",
+            " [  7. -11.]]\n",
+            "Determinant of P: 203.99999999999974\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter5_6/ex4_9.ipynb b/Chapter5_6/ex4_9.ipynb
new file mode 100644
index 0000000..6f6ec05
--- /dev/null
+++ b/Chapter5_6/ex4_9.ipynb
@@ -0,0 +1,57 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import scipy\n",
+        "from scipy.linalg import null_space\n",
+        "A = np.array([[-3/2, 1/2], [1/2, -3/2]])\n",
+        "B = np.array([[1/2], [1/2]])\n",
+        "\n",
+        "C = np.hstack((B, np.dot(A, B)))\n",
+        "rank_C = np.linalg.matrix_rank(C)\n",
+        "\n",
+        "null_space_C = null_space(C)\n",
+        "print(\"Rank of controllability matrix:\", rank_C)\n",
+        "\n",
+        "print(\"Null space of controllability matrix:\")\n",
+        "print(null_space_C)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0NakqF88cAnI",
+        "outputId": "5ae75f52-1be7-4028-9423-9f5b7607a46c"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rank of controllability matrix: 1\n",
+            "Null space of controllability matrix:\n",
+            "[[0.70710678]\n",
+            " [0.70710678]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter5_6/ex5_1.ipynb b/Chapter5_6/ex5_1.ipynb
new file mode 100644
index 0000000..5165f0b
--- /dev/null
+++ b/Chapter5_6/ex5_1.ipynb
@@ -0,0 +1,195 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import control\n",
+        "import numpy as np\n",
+        "\n",
+        "# Definition of System 1\n",
+        "A = np.array([[0, 1, 0], [0, 0, 1], [-5, -11, -6]])\n",
+        "B = np.array([[0], [0], [1]])\n",
+        "C = np.array([[1, 0, 1]])\n",
+        "D = np.array([[0]])\n",
+        "sys1 = control.ss(A, B, C, D)\n",
+        "\n",
+        "# Conversion to Transfer Function (System 1)\n",
+        "tf_sys1 = control.tf(sys1)\n",
+        "print(\"tf1 = \")\n",
+        "print(tf_sys1)\n",
+        "# Definition of System 2\n",
+        "a = np.array([[0, 0, -5], [1, 0, -11], [0, 1, -6]])\n",
+        "b = np.array([[1], [0], [1]])\n",
+        "c = np.array([[0, 0, 1]])\n",
+        "d = np.array([[0]])\n",
+        "sys2 = control.ss(a, b, c, d)\n",
+        "\n",
+        "# Conversion to Transfer Function (System 2)\n",
+        "tf_sys2 = control.tf(sys2)\n",
+        "print(\"\\ntf2 = \")\n",
+        "print(tf_sys2)\n",
+        "# Definition of System 3\n",
+        "A = np.array([[0, 1, 0], [0, 0, 1], [-5, -11, -6]])\n",
+        "B = np.array([[1], [-6], [26]])\n",
+        "C = np.array([[1, 0, 0]])\n",
+        "D = np.array([[0]])\n",
+        "sys3 = control.ss(A, B, C, D)\n",
+        "\n",
+        "# Conversion to Transfer Function (System 3)\n",
+        "tf_sys3 = control.tf(sys3)\n",
+        "print(\"\\ntf3 = \")\n",
+        "print(tf_sys3)\n",
+        "# Observability Check (System 3)\n",
+        "observability_matrix = control.obsv(sys3.A, sys3.C)\n",
+        "print(\"\\nobservability_matrix = \")\n",
+        "print(observability_matrix)\n",
+        "# Definition of System 4\n",
+        "A = np.array([[0, 0, -5], [1, 0, -11], [0, 1, -6]])\n",
+        "B = np.array([[1], [0], [0]])\n",
+        "C = np.array([[1, -6, 26]])\n",
+        "D = np.array([[0]])\n",
+        "sys4 = control.ss(A, B, C, D)\n",
+        "\n",
+        "# Conversion to Transfer Function (System 4)\n",
+        "tf_sys4 = control.tf(sys4)\n",
+        "print(\"\\ntf4 = \")\n",
+        "print(tf_sys4)\n",
+        "# Controllability Check (System 4)\n",
+        "controllability_matrix = control.ctrb(sys4.A, sys4.B)\n",
+        "print(\"\\ncontrollability_matrix = \")\n",
+        "print(controllability_matrix)\n",
+        "# Conversion from Transfer Function to State-Space (MySys)\n",
+        "num = np.array([1, 0, 1])\n",
+        "den = np.array([1, 6, 11, 5])\n",
+        "sys = control.TransferFunction(num, den)\n",
+        "mysys = control.ss(sys)\n",
+        "A, B, C, D = mysys.A, mysys.B, mysys.C, mysys.D\n",
+        "print(\"\\nA =\")\n",
+        "print(A)\n",
+        "print(\"\\nB =\")\n",
+        "print(B)\n",
+        "print(\"\\nC =\")\n",
+        "print(C)\n",
+        "print(\"\\nD =\")\n",
+        "print(D)\n",
+        "print(mysys)"
+      ],
+      "metadata": {
+        "id": "S8KWFV3XefVg",
+        "outputId": "64183108-fd1b-42ae-8e36-55add8ba0df0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tf1 = \n",
+            "<TransferFunction>: sys[156]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "s^2 - 8.882e-15 s + 1\n",
+            "----------------------\n",
+            "s^3 + 6 s^2 + 11 s + 5\n",
+            "\n",
+            "\n",
+            "tf2 = \n",
+            "<TransferFunction>: sys[158]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "s^2 - 8.882e-15 s + 1\n",
+            "----------------------\n",
+            "s^3 + 6 s^2 + 11 s + 5\n",
+            "\n",
+            "\n",
+            "tf3 = \n",
+            "<TransferFunction>: sys[160]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "s^2 - 3.375e-14 s + 1\n",
+            "----------------------\n",
+            "s^3 + 6 s^2 + 11 s + 5\n",
+            "\n",
+            "\n",
+            "observability_matrix = \n",
+            "[[1. 0. 0.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 0. 1.]]\n",
+            "\n",
+            "tf4 = \n",
+            "<TransferFunction>: sys[162]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "\n",
+            "\n",
+            "s^2 + 5.329e-15 s + 1\n",
+            "----------------------\n",
+            "s^3 + 6 s^2 + 11 s + 5\n",
+            "\n",
+            "\n",
+            "controllability_matrix = \n",
+            "[[1. 0. 0.]\n",
+            " [0. 1. 0.]\n",
+            " [0. 0. 1.]]\n",
+            "\n",
+            "A =\n",
+            "[[ -6. -11.  -5.]\n",
+            " [  1.   0.   0.]\n",
+            " [  0.   1.   0.]]\n",
+            "\n",
+            "B =\n",
+            "[[1.]\n",
+            " [0.]\n",
+            " [0.]]\n",
+            "\n",
+            "C =\n",
+            "[[1. 0. 1.]]\n",
+            "\n",
+            "D =\n",
+            "[[0.]]\n",
+            "<StateSpace>: sys[163]\n",
+            "Inputs (1): ['u[0]']\n",
+            "Outputs (1): ['y[0]']\n",
+            "States (3): ['x[0]', 'x[1]', 'x[2]']\n",
+            "\n",
+            "A = [[ -6. -11.  -5.]\n",
+            "     [  1.   0.   0.]\n",
+            "     [  0.   1.   0.]]\n",
+            "\n",
+            "B = [[1.]\n",
+            "     [0.]\n",
+            "     [0.]]\n",
+            "\n",
+            "C = [[1. 0. 1.]]\n",
+            "\n",
+            "D = [[0.]]\n",
+            "\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter5_6/ex5_6.ipynb b/Chapter5_6/ex5_6.ipynb
new file mode 100644
index 0000000..c8d6caf
--- /dev/null
+++ b/Chapter5_6/ex5_6.ipynb
@@ -0,0 +1,61 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy import linalg\n",
+        "# Define matrix A\n",
+        "A = np.array([[-1, -2],\n",
+        "              [ 1, -4]])\n",
+        "\n",
+        "# Define the identity matrix Q\n",
+        "Q = np.eye(2)\n",
+        "\n",
+        "\n",
+        "\n",
+        "P = 60*linalg.solve_continuous_lyapunov(A.T, Q)\n",
+        "# Compute the determinant of matrix P\n",
+        "det_P = np.linalg.det(P)\n",
+        "\n",
+        "print(\"Matrix P:\")\n",
+        "print(P)\n",
+        "print(\"Determinant of P:\", det_P)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yYVA9rjDscm4",
+        "outputId": "21c2b008-b06f-4c7d-fd46-fa1e9aba2432"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix P:\n",
+            "[[-23.   7.]\n",
+            " [  7. -11.]]\n",
+            "Determinant of P: 203.99999999999974\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/ex6_4.ipynb b/Chapter7/ex6_4.ipynb
new file mode 100644
index 0000000..632881a
--- /dev/null
+++ b/Chapter7/ex6_4.ipynb
@@ -0,0 +1,70 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import control\n",
+        "\n",
+        "# Define the matrices\n",
+        "A = np.array([[0, 1, 0, 0],\n",
+        "              [0, 0, -9.8, 0],\n",
+        "              [0, 0, 0, 1],\n",
+        "              [0, 0, 19.6, 0]])\n",
+        "\n",
+        "b = np.array([[0],\n",
+        "              [1],\n",
+        "              [0],\n",
+        "              [-1]])\n",
+        "\n",
+        "# Controllability matrix\n",
+        "\n",
+        "C = control.ctrb(A,b)\n",
+        "a = np.array([0, -19.6, 0, 0])\n",
+        "\n",
+        "alpha = np.array([12.86, 63.065, 149.38, 157.0])\n",
+        "\n",
+        "Psi = np.array([[1, a[0], a[1], a[2]],\n",
+        "                [0, 1, a[0], a[1]],\n",
+        "                [0, 0, 1, a[0]],\n",
+        "                [0, 0, 0, 1]])\n",
+        "\n",
+        "# Compute k\n",
+        "k = np.dot(alpha - a, np.linalg.inv(np.dot(C, Psi)))\n",
+        "\n",
+        "print(\"k:\", k)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "af4HmmObVeWH",
+        "outputId": "4498bd22-2713-44c9-a7d5-1805aeff1234"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "k: [-16.02040816 -15.24285714 -98.68540816 -28.10285714]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/ex6_4.py b/Chapter7/ex6_4.py
new file mode 100644
index 0000000..defe6fb
--- /dev/null
+++ b/Chapter7/ex6_4.py
@@ -0,0 +1,32 @@
+
+
+import numpy as np
+import control
+
+# Define the matrices
+A = np.array([[0, 1, 0, 0],
+              [0, 0, -9.8, 0],
+              [0, 0, 0, 1],
+              [0, 0, 19.6, 0]])
+
+b = np.array([[0],
+              [1],
+              [0],
+              [-1]])
+
+# Controllability matrix
+
+C = control.ctrb(A,b)
+a = np.array([0, -19.6, 0, 0])
+
+alpha = np.array([12.86, 63.065, 149.38, 157.0])
+
+Psi = np.array([[1, a[0], a[1], a[2]],
+                [0, 1, a[0], a[1]],
+                [0, 0, 1, a[0]],
+                [0, 0, 0, 1]])
+
+# Compute k
+k = np.dot(alpha - a, np.linalg.inv(np.dot(C, Psi)))
+
+print("k:", k)
diff --git a/Chapter7/python/Active_s1/Active_s1.ipynb b/Chapter7/python/Active_s1/Active_s1.ipynb
new file mode 100644
index 0000000..39dbdf0
--- /dev/null
+++ b/Chapter7/python/Active_s1/Active_s1.ipynb
@@ -0,0 +1,119 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "# Define the state-space model\n",
+        "A = np.array([\n",
+        "    [0, 0, 1, 0],\n",
+        "    [0, 0, 0, 1],\n",
+        "    [-10, 10, -2, 2],\n",
+        "    [60, -660, 12, -12]\n",
+        "])\n",
+        "\n",
+        "b1 = np.array([0, 0, 0.0033, -0.02])\n",
+        "b2 = np.array([0, 0, 0, 600])\n",
+        "B = np.column_stack((b1, b2))\n",
+        "C = np.array([[1, 0, 0, 0]])\n",
+        "D = np.array([0])\n",
+        "\n",
+        "# Simulation parameters\n",
+        "t_span = (0, 7)  # Time range for simulation\n",
+        "t_eval = np.linspace(0, 7, 701)  # Time points to evaluate\n",
+        "x0 = [0.2, 0, 0, 0]  # Initial conditions\n",
+        "\n",
+        "# Define the system of ODEs for initial response\n",
+        "def system_ode(t, x):\n",
+        "    return A @ x\n",
+        "\n",
+        "# Simulate initial response using solve_ivp\n",
+        "sol_initial = solve_ivp(system_ode, t_span, x0, t_eval=t_eval, method='RK45')\n",
+        "x_initial = sol_initial.y.T\n",
+        "\n",
+        "# Plot initial response\n",
+        "plt.figure()\n",
+        "plt.plot(t_eval, x_initial[:, 0], 'k', label='$x_1$')\n",
+        "plt.plot(t_eval, x_initial[:, 1], 'k-.', label='$x_2$')\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.title('Initial Response')\n",
+        "plt.show()\n",
+        "\n",
+        "# Define input signal function\n",
+        "def input_signal(t):\n",
+        "    return 0.1 * (np.sin(5 * t) + np.sin(9 * t) + np.sin(13 * t) + np.sin(17 * t) + np.sin(21 * t))\n",
+        "\n",
+        "# Define the system of ODEs with input\n",
+        "def system_ode_with_input(t, x):\n",
+        "    u = input_signal(t)\n",
+        "    return A @ x + b2 * u\n",
+        "\n",
+        "# Simulate response with input using solve_ivp\n",
+        "sol_forced = solve_ivp(system_ode_with_input, t_span, x0, t_eval=t_eval, method='RK45')\n",
+        "x_forced = sol_forced.y.T\n",
+        "\n",
+        "# Plot response with input signal\n",
+        "plt.figure()\n",
+        "plt.plot(t_eval, x_forced[:, 0], 'k', label='$x_1$')\n",
+        "plt.plot(t_eval, x_forced[:, 1], 'k-.', label='$x_2$')\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.title('Response with Input Signal')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 927
+        },
+        "id": "rsvx7msErjpD",
+        "outputId": "11a269d3-2265-43fe-9dd0-5a89e8eb0bce"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/Active_s1/active_s1.py b/Chapter7/python/Active_s1/active_s1.py
new file mode 100644
index 0000000..7cd28fa
--- /dev/null
+++ b/Chapter7/python/Active_s1/active_s1.py
@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+"""Active_s.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1IgnliJrZLzc28PpJy-l43qlaRRszVBoX
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+# Define the state-space model
+A = np.array([
+    [0, 0, 1, 0],
+    [0, 0, 0, 1],
+    [-10, 10, -2, 2],
+    [60, -660, 12, -12]
+])
+
+b1 = np.array([0, 0, 0.0033, -0.02])
+b2 = np.array([0, 0, 0, 600])
+B = np.column_stack((b1, b2))
+C = np.array([[1, 0, 0, 0]])
+D = np.array([0])
+
+# Simulation parameters
+t_span = (0, 7)  # Time range for simulation
+t_eval = np.linspace(0, 7, 701)  # Time points to evaluate
+x0 = [0.2, 0, 0, 0]  # Initial conditions
+
+# Define the system of ODEs for initial response
+def system_ode(t, x):
+    return A @ x
+
+# Simulate initial response using solve_ivp
+sol_initial = solve_ivp(system_ode, t_span, x0, t_eval=t_eval, method='RK45')
+x_initial = sol_initial.y.T
+
+# Plot initial response
+plt.figure()
+plt.plot(t_eval, x_initial[:, 0], 'k', label='$x_1$')
+plt.plot(t_eval, x_initial[:, 1], 'k-.', label='$x_2$')
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.title('Initial Response')
+plt.show()
+
+# Define input signal function
+def input_signal(t):
+    return 0.1 * (np.sin(5 * t) + np.sin(9 * t) + np.sin(13 * t) + np.sin(17 * t) + np.sin(21 * t))
+
+# Define the system of ODEs with input
+def system_ode_with_input(t, x):
+    u = input_signal(t)
+    return A @ x + b2 * u
+
+# Simulate response with input using solve_ivp
+sol_forced = solve_ivp(system_ode_with_input, t_span, x0, t_eval=t_eval, method='RK45')
+x_forced = sol_forced.y.T
+
+# Plot response with input signal
+plt.figure()
+plt.plot(t_eval, x_forced[:, 0], 'k', label='$x_1$')
+plt.plot(t_eval, x_forced[:, 1], 'k-.', label='$x_2$')
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.title('Response with Input Signal')
+plt.show()
\ No newline at end of file
diff --git a/Chapter7/python/CL_DCmotor_solver/CL_DCmotor_solver.ipynb b/Chapter7/python/CL_DCmotor_solver/CL_DCmotor_solver.ipynb
new file mode 100644
index 0000000..202a4ae
--- /dev/null
+++ b/Chapter7/python/CL_DCmotor_solver/CL_DCmotor_solver.ipynb
@@ -0,0 +1,81 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "rzdB0RMRyh1V",
+        "outputId": "a6108077-5f31-411f-e44a-7a83be11c794"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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6rFGS7DPQAQUb3di7dy/S0tKwZ88eldGXdw/ZKwqbNm2CTCZDq1at8t3O2toaPXv2RM+ePZGeno5u3brh559/xuTJk1WuffXgwQOVkYewsDAolUqVmffe9ddff6F58+ZYs2aNyvr4+Hg4OjqKtytWrIjz589DLperTBKSXcWKFfHvv/+iUaNGRfpLdYUKFQBkHi7n5+ensectSLnNUr58efz77794+/atyujavXv3xPs1LSMjA1u2bIGVlRUaN24MAGjXrh2MjY2xefNmjU0yUq5cOTRq1AgnTpzA8OHD8xxNzXqPoaGh4j4BgPT0dISHh6vsm7y+t+XLl8eNGzegVCpVRteK8vtIRLqLh0ESEakpJSUFu3btQseOHdG9e/ccX4GBgXj79m2OKdXfx83NDTVq1MDGjRvFQ78A4OTJk7h586bKtuXLl4exsTFOnTqlsn7p0qXvfZ2sEZfsIyxv3rzBunXr1Mqrrl9//RVHjhxBz549cxy+mN2rV69UbpuZmaFatWoQBAFyuVzlvnfL8qJFiwBk/kKfF2Nj4xyjSzt27Mhxzpe/vz9evnyJxYsX53iOrMd//vnnyMjIwE8//ZRjG4VCkaNMF5azszOaNWuGFStW4Pnz5znuzz4lvzqsra0LnLF9+/bIyMjI8f2YP38+ZDJZvt/zwsjIyMDo0aNx9+5djB49Wjz80d3dHUOGDMGRI0fE/Z2dUqnEvHnz8PTpU7Veb+bMmZg6dWqu5yhm8fPzg5mZGRYuXKjyGVqzZg3evHmDDh06iOusra1zHFIMZH4fo6OjVa41qFAosGjRItjY2KBp06Zq5SYi/caRNSIiNe3Zswdv375F586dc72/YcOGcHJyQlBQEHr27KnWc//yyy/47LPP0KhRIwwYMACvX7/G4sWLUaNGDZUCZ2dnhx49emDRokWQyWSoWLEi9u3bl+O8pdy0bt0aZmZm6NSpE4YNG4bExESsWrUKzs7OuRYBdSkUCmzevBlA5nXKHj9+jD179uDGjRto3rw5Vq5c+d58rq6uaNSoEVxcXHD37l0sXrwYHTp0yHG+VHh4ODp37oy2bdsiJCQEmzdvRp8+fVQucPyujh07YsaMGRgwYAA++eQT3Lx5E0FBQSojJQDQr18/bNy4EePGjcOFCxfQpEkTJCUl4d9//8WIESPw2WefoWnTphg2bBhmzZqFa9euoXXr1jA1NcWDBw+wY8cO/PHHH+jevXshv5OqlixZgsaNG6NmzZoYMmQIKlSogJiYGISEhODp06e5XifufXx8fLBs2TLMnDkTlSpVgrOzM1q0aJHrtp06dULz5s3x/fffIyIiArVr18aRI0ewe/dujBkzRryUQWG8efNG/MwkJycjLCwMu3btwsOHD9GrV68cZXjevHl4+PAhRo8eLf7hpGTJkoiMjMSOHTtw79499OrVS60MTZs2fW9RcnJywuTJkzF9+nS0bdsWnTt3RmhoKJYuXYr69eurXNjbx8cH27dvx7hx41C/fn3Y2NigU6dOGDp0KFasWIGAgABcvnwZHh4e+Ouvv3DmzBksWLBAYxOnEJGekGoaSiIiXdWpUyfBwsJCSEpKynObgIAAwdTUVHj58qXKNOHvAiBMnTpVZd22bdsEb29vwdzcXKhRo4awZ88ewd/fX/D29lbZLjY2VvD39xesrKyEkiVLCsOGDROnIX/f1P179uwRatWqJVhYWAgeHh7C7Nmzxanqw8PDxe3Kly8vdOjQocDfm/79+wsAxC8rKyvBw8ND8Pf3F/76668cU+YLQs6p+1esWCF8+umnQqlSpQRzc3OhYsWKwoQJE4Q3b97keE937twRunfvLpQoUUIoWbKkEBgYKKSkpKg8f25T948fP14oXbq0YGlpKTRq1EgICQnJkUMQMi9x8P333wuenp6Cqamp4OrqKnTv3l14+PChynYrV64UfHx8BEtLS6FEiRJCzZo1hYkTJwpRUVHv/V4CEEaOHKmyLq/PzMOHD4V+/foJrq6ugqmpqVCmTBmhY8eOwl9//SVukzV1/8WLF1Uem9vlHqKjo4UOHToIJUqUEAC8dxr/t2/fCmPHjhXc3NwEU1NToXLlysJvv/2mcokAQVB/6v7snxkbGxuhcuXKwhdffCEcOXIkz8cpFAph9erVQpMmTQQ7OzvB1NRUKF++vDBgwID3Tuuf389kdnldimLx4sWCt7e3YGpqKri4uAjDhw8XXr9+rbJNYmKi0KdPH8He3j7HpTdiYmKEAQMGCI6OjoKZmZlQs2ZNlZ9ZdTISkX6TCYKOnWlMRGSA6tSpAycnJwQHB0sdRStkXZg4NjZW5TwzIiIifcJz1oiItIhcLodCoVBZd+LECVy/fh3NmjWTJhQRERFJguesERFpkWfPnsHPzw9ffPEF3NzccO/ePSxfvhyurq746quvpI5HRERExYhljYhIi5QsWRI+Pj5YvXo1YmNjYW1tjQ4dOuDXX39FqVKlpI5HRERExYjnrBEREREREWkhnrNGRERERESkhVjWiIiIiIiItBDPWSsGSqUSUVFRKFGiBGQymdRxiIiIiIhIIoIg4O3bt3Bzc4ORUf5jZyxrxSAqKgru7u5SxyAiIiIiIi3x5MkTlC1bNt9tWNaKQYkSJQBk7hBbW1uJ02Rex+nIkSNo3bo1TE1NpY5DGsB9qn+4T/UT96v+4T7VT9yv+keb9mlCQgLc3d3FjpAflrVikHXoo62trdaUNSsrK9ja2kr+YSXN4D7VP9yn+on7Vf9wn+on7lf9o437tCCnR3GCESIiIiIiIi3EskZERERERKSFWNaIiIiIiIi0EMsaERERERGRFmJZIyIiIiIi0kIsa0RERERERFqIZY2IiIiIiEgLsawRERERERFpIZY1IiIiIiIiLcSyRkREREREpIVY1oiIiIiIiLQQyxoREREREZEWYlkjIiIiIiLSQixrREREREREWohljYiIiIiISAuxrBEREREREWkhE6kDEBERkXYTBAEpKSlITEwUv96+fatyOyUlBenp6eJXWlpajttyuRyCIEAQBPF587stk8lgYmKS55exsbHKbXNzc1hYWOT5ld/9JiYmkMlkkn2PiYhyw7JGRERkQARBwOvXrxEVFYWYmBi8fPkSr169Ev+bffnly5eIi4tDYmIilEql1NGLlJGRESwtLWFlZQVra+s8v2xsbFRuW1hY4P79+1AoFLCzs8vzcUZGPJiJiNTHskZERKRHkpOTERERgUePHuHRo0eIjIzEs2fP8OzZM0RFReHZs2dITU2VOqbWUSqVSEpKQlJSEmJjY9V+/Pz58/O938rKCjY2NmLZy1rOb9371ltYWHA0kEjPsawRERHpGLlcjrCwMNy5cwd37tzBgwcPxHL2/PlzjbyGkZERHBwc4ODgAFtbW9jY2KBEiRI5SkOJEiVgbW0NKysrmJmZwczMDObm5rkum5qaQiaTiV8A8r2tVCqRkZEBhUJRoK/09HSkpqYW6CstLS3HuuTkZLGwJSUlITk5WSPfSyCzRCcnJ+PFixcae04jI6MPKn15rTM1NdVYRiL6MCxrREREWkoQBERGRuLy5cu4efMm7ty5g9u3b+P+/fuQy+VqP5+dnR3KlCkjfrm6usLR0RGOjo4oVaoUSpUqJS7b29sb/KF7SqUSKSkpKgXu3a+EhARcuHAB5cuXR2pqao77ExMTVZazvjQxuqlUKpGQkICEhAQNvNv/MTMzU3uU733rraysDP7zRFQYLGtERERaIHsxu3z5Mi5duoTLly/j1atXBX4OV1dXVKhQARUqVICnpycqVKiA8uXLo2zZsnBzc4O1tXURvgP9Y2RkJJ5zlhe5XA4XFxe0b99erREphUKRa4nLKncFWffu+rdv3yIjI+OD33d6ejri4uIQFxf3wc+VXfbz/rJ/WVlZwdLSMs8vCwsLte7LGsEl0gcsa0RERBLIyMjAjRs3cOrUKZw+fRqnT58u0CFypqam8PLyQvXq1VGtWjVUr14dXl5e8PT0ZBnTISYmJrCzs4OdnZ3GnlMQBKSnpxe67OW1LikpSZyh80NklVNNHgqam6zJYt4tc1kzgmZ9ZR2mm9s6ExMTREREICwsTDzEN69t3/0yNTVV+TIxMWGBpELTubK2ZMkS/Pbbb4iOjkbt2rWxaNEifPzxx3luv2PHDkyZMgURERGoXLkyZs+ejfbt24v3C4KAqVOnYtWqVYiPj0ejRo2wbNkyVK5cWdwmLi4Oo0aNwt69e2FkZAR/f3/88ccfsLGxKdL3SkRE+kOhUODcuXPYuXMnli9fjrNnz7738DUnJyf4+PjAx8cHdevWRfXq1VGxYkWeU0S5kslkYmFwcHDQ2PPmdumGDy2AmjoUNDfZJ4vRJsbGxrmWuLzKXUG3zX4pi/d9FWS7wjyXkZERjIyMIJPJVP6b27q8/luYbQyBTpW17du3Y9y4cVi+fDkaNGiABQsWoE2bNggNDYWzs3OO7c+ePYvevXtj1qxZ6NixI7Zs2YIuXbrgypUrqFGjBgBgzpw5WLhwITZs2ABPT09MmTIFbdq0wZ07d2BhYQEA6Nu3L54/f47g4GDI5XIMGDAAQ4cOxZYtW4r1/WvKq1evCnWuAxERqef58+c4fPgwDh48iODgYLx+/TrPbW1tbdGwYUPUr19fLGju7u4G8wsJaS+ZTAYrKytYWVnl+vtWYWVkZIilKiUlJd+v1NTU927zvu3S0tI0lr2w7zcjI4OzsWpQ1mREBSl9MpkMCoUCw4cPx+zZs6WOXmAyQRPj2sWkQYMGqF+/PhYvXgwg8y8n7u7uGDVqFCZNmpRj+549eyIpKQn79u0T1zVs2BB16tTB8uXLIQgC3NzcMH78eHzzzTcAgDdv3sDFxQXr169Hr169cPfuXVSrVg0XL15EvXr1AACHDh1C+/bt8fTpU7i5ub03d0JCAuzs7PDmzRvY2tpq4ltRaIcOHcLAgQPh6+uLbdu28a+zekIul+PAgQNqnzNB2ov7VDdlZGQgJCQEBw4cwMGDB3Ht2rU8t3VxcUGTJk3QpEkTfPrpp6hZsyaMjY2LLyxpBH9WdYcgCJDL5UhLSxO/si7Y/u7tpKQkhISEoHr16lAqlQV6TNaXXC7P9UuhUBT4Pk2ce0i5GzVqFBYuXChpBnW6gc6MrKWnp+Py5cuYPHmyuM7IyAh+fn4ICQnJ9TEhISEYN26cyro2bdrgn3/+AQCEh4cjOjoafn5+4v12dnZo0KABQkJC0KtXL4SEhMDe3l4sagDg5+cHIyMjnD9/Hl27ds3xulk/rFmyDnPJ+gGUSlRUFD777DOkp6fj77//xokTJ9CsWTPJ8pDmZH2uOGKqP7hPdYdcLsfJkyfx999/Y/fu3Xmej2Nvb48WLVrA1dUVQ4cORdWqVVVGzZRKpd5feFof8WdVt8hkMlhYWIhHT+VFLpdDEAS0atVKkhKuVCrFAve+kqdQKMRRu/y+3t0u69IY79vu3a/sl9TIvk4QhFz/m9992f9bkG0K+ny5LQuCgKSkJDg4OEj+86rO6+tMWXv58iUyMjLg4uKist7FxQX37t3L9THR0dG5bh8dHS3en7Uuv23eHfI3MTGBg4ODuM27Zs2ahenTp+dYf+TIEVhZWeX1FotFr169sHHjRgiCgC+++AILFiyQPBNpTnBwsNQRSMO4T7VTRkYGbt++jZMnT+L8+fNITEzMdbsKFSrAx8cHH330EapUqSKOnEVERCAiIqIYE1NR48+qftKH/Zp1XpmZmZnUUbTGgQMHJH19da7hqDNlTZdMnjxZZUQvISEB7u7uaN26teSHQbZp0wZhYWE4e/YsXrx4gcOHD2PVqlWSZqIPJ5fLERwcLNlfAEnzuE+1jyAIuHHjBrZu3Yrt27fj2bNnObaxsLBA69at0blzZ7Ru3Rqurq4q93O/6h/uU/3E/ap/tGmfqnNtRJ0pa46OjjA2NkZMTIzK+piYmBz/M8zi6uqa7/ZZ/42JiUHp0qVVtqlTp464zbuHtCgUCsTFxeX5ulkzMb0ra+YeKZmammLdunWoXbs2UlNTsWHDBgwePBiNGzeWNBdphjZ8xkizuE+lFx8fjy1btmDVqlW5noNmY2ODjh07wt/fH23bti3QTMHcr/qH+1Q/cb/qH23Yp+q8vs5cSt7MzAw+Pj44evSouE6pVOLo0aPw9fXN9TG+vr4q2wOZw9lZ23t6esLV1VVlm4SEBJw/f17cxtfXF/Hx8bh8+bK4zbFjx6BUKtGgQQONvb/i5OnpiX79+om3d+/eLWEaIiLtIwgCzpw5g/79+8PNzQ0jR45UKWomJibo1KkTtm/fjhcvXmDr1q3o3r07L+lCREQapTMjawAwbtw49O/fH/Xq1cPHH3+MBQsWICkpCQMGDAAA9OvXD2XKlMGsWbMAAF9//TWaNm2KefPmoUOHDti2bRsuXbqElStXAsg8yXTMmDGYOXMmKleuLE7d7+bmhi5dugAAqlatirZt22LIkCFYvnw55HI5AgMD0atXrwLNBKmtGjVqJH4f3i20RESGKjU1FVu3bsXChQtzHUWrX78+BgwYgB49esDR0bH4AxIRkUHRqbLWs2dPxMbG4scff0R0dDTq1KmDQ4cOiROEREZGwsjof4OFn3zyCbZs2YIffvgB3333HSpXrox//vlHvMYaAEycOBFJSUkYOnQo4uPj0bhxYxw6dEhllqCgoCAEBgaiZcuW4kWxpZ7y80PZ2dmhVq1auHHjBq5du4ZXr16hVKlSUsciIpJEVFQUli1bhhUrViA2NlblPjs7O3zxxRcYMmQIateuLVFCIiIyRDpV1gAgMDAQgYGBud534sSJHOt69OiBHj165Pl8MpkMM2bMwIwZM/LcxsHBQWcvgJ2fFi1a4MaNGxAEASdOnIC/v7/UkYiIitWDBw8wZ84cbNiwIcdUyvXr10dgYCB69OgBS0tLiRISEZEh05lz1kjzmjdvLi7zUEgiMiTXrl1Dz5494e3tjdWrV4tFzcTERLzG5vnz59GvXz8WNSIikozOjayR5jRp0gQmJiZQKBQsa0RkEK5evYoff/wR+/btU1lva2uLESNGIDAwEGXKlJEoHRERkSqOrBkwGxsbcUbL+/fv4+nTpxInIiIqGnfu3EGPHj3w0UcfqRQ1Z2dnzJo1C5GRkZg1axaLGhERaRWWNQPXokULcfnYsWMSJiEi0rzw8HB8+eWXqFGjBv766y9xfdmyZbF48WJERERg0qRJsLOzkzAlERFR7ljWDFzLli3FZZY1ItIXb968wbfffgtvb29s3rwZgiAAAFxcXLBw4UI8ePAAI0eO5PloRESk1XjOmoFr2LCheN5a9gt/ExHpIoVCgVWrVuHHH3/Ey5cvxfUODg6YNGkSRo4cCSsrKwkTEhERFRzLmoEzNzdH1apVcfPmTdy7dw/p6ekwMzOTOhYRkdpOnDiBkSNH4s6dO+I6MzMzjB07FpMnT+ahjkREpHN4GCShVq1aADL/In3v3j2J0xARqSc6OhpffPEFmjdvrlLUevbsiXv37uHXX39lUSMiIp3EskaoWbOmuHzjxg0JkxARFVxGRgYWL14MLy8vBAUFies//vhjnD17Ftu2bYOnp6eECYmIiD4MD4MkcWQNAG7evClhEiKigrl58yYGDhyIS5cuietKliyJ2bNnY9CgQTAy4t8iiYhI9/H/ZsSRNSLSGXK5HDNmzICPj49KURs4cCBCQ0MxZMgQFjUiItIbHFkjlClTBiVLlsTr169Z1ohIa129ehUDBgzA9evXxXVVq1bFypUr0bhxYwmTERERFQ3++ZEgk8nEQyGjoqLw6tUriRMREf2PQqHA9OnTUb9+fbGoGRsb47vvvsPVq1dZ1IiISG+xrBEA1UMhed4aEWmL8PBwfPrpp5g2bRoyMjIAZP57df78efz8888wNzeXOCEREVHRYVkjAKqTjPBQSCLSBps3b0bt2rUREhICIHM07ccff8SlS5fg4+MjcToiIqKix3PWCABnhCQi7ZGQkIDhw4djy5Yt4jpPT08EBQXB19dXwmRERETFi2WNAADVq1cXlzmyRkRSuXHjBrp3744HDx6I6/r164dFixbB1tZWwmRERETFj4dBEgDAxsYGFStWBADcunVLPDeEiKi4rF+/Hg0aNBCLmp2dHbZu3YoNGzawqBERkUFiWSNRjRo1AADJycl48uSJxGmIyFCkpKRgyJAhGDBgAFJTUwEAH330Ea5cuYJevXpJnI6IiEg6LGskqlKliric/RAkIqKiEhkZiUaNGmH16tXiuqFDh+LMmTOoUKGChMmIiIikx7JGosqVK4vLLGtEVNTOnDmD+vXr4+rVqwAAS0tLbNiwAStWrICFhYXE6YiIiKTHCUZIxLJGRMVl3bp1GDZsGORyOQCgYsWK+Pvvv1Wu+UhERGToOLJGIpY1IipqCoUC48aNw8CBA8Wi1qJFC5w/f55FjYiI6B0sayRyc3ODlZUVAJY1ItK8xMREdOnSBfPnzxfXBQYG4tChQyhVqpSEyYiIiLQTyxqJZDIZKlWqBAB49OgRFAqFxImISF/ExMSgefPm2L9/PwDAxMQEy5cvx6JFi2BqaipxOiIiIu3EskYqsg6FVCgUePz4scRpiEgf3L9/H76+vrh06RIAwN7eHkeOHMGwYcMkTkZERKTdWNZIBc9bIyJNOnv2LD755BOEh4cDANzd3fHff/+hefPmEicjIiLSfixrpIJljYg0Ze/evWjZsiVevXoFAKhVqxZCQkJQvXp1iZMRERHpBpY1UsGyRkSasHXrVnTt2hWpqakAgJYtW+LUqVMoU6aMxMmIiIh0B8saqWBZI6IPtWrVKvTt2xcZGRkAgN69e+PAgQOws7OTOBkREZFuYVkjFS4uLihRogSAzEkBiIjU8fvvv2Po0KEQBAEAMHToUGzatAlmZmYSJyMiItI9LGukQiaTiaNrERERSE9PlzgREekCQRAwbdo0jB8/Xlw3fvx4LF++HMbGxhImIyIi0l0sa5RDVllTKpXiDG5ERHkRBAFTpkzB9OnTxXUzZszAb7/9BplMJmEyIiIi3cayRjnwvDUiUse0adPw888/i7d///13TJkyhUWNiIjoA5lIHYC0T/ayFhYWJmESItJ206dPx4wZM8TbixYtQmBgoISJiIiI9AdH1igHT09PcTkiIkK6IESk1WbOnIlp06aJt//44w8WNSIiIg1iWaMcspc1nrNGRLn59ddfMWXKFPH277//jtGjR0uYiIiISP+wrFEObm5uMDU1BcCyRkQ5LV26FJMnTxZvz507F2PHjpUwERERkX5iWaMcjIyMUL58eQCZZS3reklERFu3blU51HHWrFkq0/UTERGR5rCsUa6yDoVMTExEXFycxGmISBscOHAA/fr1E/+AM2nSJEyaNEniVERERPqLZY1yxfPWiCi706dPw9/fHwqFAgAwdOhQ/PLLLxKnIiIi0m8sa5QrljUiynL9+nV07NgRqampAIDPP/8cS5cu5XXUiIiIihjLGuXKw8NDXGZZIzJckZGRaNeuHRISEgAArVu3xqZNm2BsbCxxMiIiIv3Hska54sgaEb158wYdOnTA8+fPAQANGzbErl27YGZmJnEyIiIiw8CyRrnihbGJDFt6ejr8/f1x69YtAEDFihWxZ88eWFtbS5yMiIjIcLCsUa6cnJxgZWUFgCNrRIZGEAQMGTIER48eBQCUKlUKBw8ehJOTk8TJiIiIDAvLGuVKJpOJ561FRERAqVRKG4iIis306dOxceNGAIC5uTn27NmDypUrS5yKiIjI8LCsUZ6yDoVMS0tDdHS0xGmIqDhs3rwZ06dPB5D5R5ugoCB88sknEqciIiIyTCxrlCdOMkJkWC5cuIDBgweLt+fOnQt/f38JExERERk2ljXKEycZITIcz549Q5cuXZCWlgYAGDJkCMaOHStxKiIiIsPGskZ54rXWiAxDSkoKunbtKk7R36RJEyxevJgXvSYiIpIYyxrliYdBEuk/QRAwePBgXLx4EQBQvnx57Ny5k9dSIyIi0gIsa5QnljUi/Tdnzhxs2bIFAGBtbY09e/Zwin4iIiItwbJGebK3t4etrS0AIDIyUuI0RKRpwcHBmDx5snh748aNqFWrloSJiIiIKDuWNcpXuXLlAABPnz7ltdaI9EhkZCR69+4NQRAAANOmTUO3bt0kTkVERETZsaxRvrLKWlpaGmJjYyVOQ0SakJaWhh49euDVq1cAgA4dOmDKlCkSpyIiIqJ3saxRvrLKGsBDIYn0xdixY3HhwgUAmeembtq0CUZG/N8BERGRtuH/nSlfLGtE+mXTpk1YtmwZAMDCwgI7d+5EyZIlJU5FREREuWFZo3yxrBHpjxs3bmDYsGHi7aVLl6Ju3boSJiIiIqL8sKxRvljWiPRDYmIievTogZSUFADAkCFDMGDAAIlTERERUX5Y1ihf7u7u4jLLGpHuCgwMxP379wEAH330ERYuXChxIiIiInofljXKV5kyZSCTyQAAT548kTgNERXG5s2bsWHDBgCAjY0Ntm/fDgsLC4lTERER0fuwrFG+TE1N4ebmBoAja0S66MGDBxg+fLh4e8WKFahUqZKEiYiIiKigWNbovbLOW4uJiUFqaqrEaYiooNLS0tCrVy8kJiYCAAYMGIA+ffpInIqIiIgKimWN3iv7JCNPnz6VMAkRqWPSpEm4cuUKAMDLywuLFi2SOBERERGpg2WN3oszQhLpngMHDmDBggUAAHNzc2zfvh3W1tbShiIiIiK1sKzRe7GsEemWly9fYuDAgeLtefPmoXbt2hImIiIiosJgWaP3Ylkj0h2CIGDo0KGIiYkBAHTo0AEjRoyQOBUREREVBssavVf2a61x+n4i7bZx40b8/fffAABHR0esXr1avPwGERER6RaWNXovjqwR6YaIiAiMGjVKvL1y5Uq4urpKmIiIiIg+BMsavZeDgwOsrKwAsKwRaauMjAz069cPb9++BZA5TX/Xrl0lTkVEREQfgmWN3ksmk4mja5GRkRAEQeJERPSuefPm4fTp0wAADw8PcSZIIiIi0l0sa1QgWWUtOTkZcXFxEqchouxu3bqFH374AUDmH1c2bNgAW1tbiVMRERHRh2JZowLheWtE2kmhUGDAgAGQy+UAgG+++QaffvqpxKmIiIhIE1jWqEBY1oi00++//45Lly4BAKpWrYoZM2ZInIiIiIg0hWWNCiR7WeP0/UTa4d69e/jxxx8BZB7+uHbtWlhYWEicioiIiDRFZ8paXFwc+vbtC1tbW9jb22PQoEFITEzM9zGpqakYOXIkSpUqBRsbG/j7+4sXis0SGRmJDh06wMrKCs7OzpgwYQIUCoV4/4kTJyCTyXJ8RUdHF8n71FbZr7XGkTUi6WVkZGDgwIFIS0sDAIwdOxYNGzaUOBURERFpks6Utb59++L27dsIDg7Gvn37cOrUKQwdOjTfx4wdOxZ79+7Fjh07cPLkSURFRaFbt27i/RkZGejQoQPS09Nx9uxZbNiwAevXrxf/Up1daGgonj9/Ln45Oztr/D1qMx4GSaRdFi5ciJCQEABApUqV8NNPP0mciIiIiDTNRJ2N7969i23btuH06dN4/PgxkpOT4eTkhLp166JNmzbw9/eHubm5xkPevXsXhw4dwsWLF1GvXj0AwKJFi9C+fXvMnTsXbm5uOR7z5s0brFmzBlu2bEGLFi0AAOvWrUPVqlVx7tw5NGzYEEeOHMGdO3fw77//wsXFBXXq1MFPP/2Eb7/9FtOmTYOZmZn4fM7OzrC3t9f4e9MVZcuWFZdZ1oikFRYWhu+//x7A/w5/zLoWIhEREemPApW1K1euYOLEifjvv//QqFEjNGjQAF27doWlpSXi4uJw69YtfP/99xg1ahQmTpyIMWPGaLS0hYSEwN7eXixqAODn5wcjIyOcP38+1wu/Xr58GXK5HH5+fuI6b29vlCtXDiEhIWjYsCFCQkJQs2ZNuLi4iNu0adMGw4cPx+3bt1G3bl1xfZ06dZCWloYaNWpg2rRpaNSoUZ5509LSxEOTACAhIQEAIJfLxRnbpJSVQZ0sxsbGcHFxQUxMDCIjI7XifdD/FGafknbLa58KgoDBgwcjJSUFADBixAg0bNiQ+15H8GdV/3Cf6ifuV/2jTftUnQwFKmv+/v6YMGEC/vrrr3xHl0JCQvDHH39g3rx5+O677woc4n2io6NzHHZoYmICBweHPM8di46OhpmZWY68Li4u4mOio6NVilrW/Vn3AUDp0qWxfPly1KtXD2lpaVi9ejWaNWuG8+fP46OPPsr1tWfNmoXp06fnWH/kyBGt+ut3cHCwWtvb2toiJiYGUVFR2LNnD0xM1BqYpWKg7j4l7ffuPj169ChOnjwJIPPfqyZNmuDAgQNSRKMPwJ9V/cN9qp+4X/WPNuzT5OTkAm9boN+279+/D1NT0/du5+vrC19f3wK3xUmTJmH27Nn5bnP37t0CPVdR8fLygpeXl3j7k08+wcOHDzF//nxs2rQp18dMnjwZ48aNE28nJCTA3d0drVu31ooL1crlcgQHB6NVq1YF2q9ZNmzYgAcPHkAQBNSqVQseHh5FF5LUUth9Stort30aGxuLgQMHitusXbsWrVq1kioiFQJ/VvUP96l+4n7VP9q0T7OOuiuIApW1972h+Ph4lRGsgn4Dxo8fj4CAgHy3qVChAlxdXfHixQuV9QqFAnFxcXB1dc31ca6urkhPT8+RLSYmRnyMq6srLly4oPK4rNki83peAPj444/x33//5Xm/ubl5roeBmpqaSv7hyE7dPNnLWXR0NCpXrlwEqehDaNtnjD5c9n367bffIi4uDgDQp08ftG/fXspo9AH4s6p/uE/1E/er/tGGfarO66s9G+Ts2bOxfft28fbnn3+OUqVKoUyZMrh+/bpaz+Xk5ARvb+98v8zMzODr64v4+HhcvnxZfOyxY8egVCrRoEGDXJ/bx8cHpqamOHr0qLguNDQUkZGR8PX1BZA5Enjz5k2VIhgcHAxbW1tUq1Ytz9zXrl1D6dKl1Xqv+oAzQhJJ599//8XmzZsBAPb29vj9998lTkRERERFTe2ytnz5cvGaW8HBwQgODsbBgwfRrl07TJgwQeMBAaBq1apo27YthgwZggsXLuDMmTMIDAxEr169xJkgnz17Bm9vb3GkzM7ODoMGDcK4ceNw/PhxXL58GQMGDICvr694LaLWrVujWrVq+PLLL3H9+nUcPnwYP/zwA0aOHCmOjC1YsAC7d+9GWFgYbt26hTFjxuDYsWMYOXJkkbxXbcZrrRFJIyUlBV999ZV4+7fffstxvi0RERHpH7VniIiOjhZ/ad+3bx8+//xztG7dGh4eHnmOcmlCUFAQAgMD0bJlSxgZGcHf3x8LFy4U75fL5QgNDVU5YW/+/PnitmlpaWjTpg2WLl0q3m9sbIx9+/Zh+PDh8PX1hbW1Nfr3748ZM2aI26Snp2P8+PF49uwZrKysUKtWLfz7779o3rx5kb1XbcWRNSJp/Pzzz3j48CEAoHHjxirnrREREZH+UruslSxZEk+ePIG7uzsOHTqEmTNnAsicTjojI0PjAbM4ODhgy5Yted7v4eEBQRBU1llYWGDJkiVYsmRJno8rX758vjOpTZw4ERMnTlQ/sB5iWSMqfrdv38acOXMAZB7jvmLFChgZqX1QBBEREekgtctat27d0KdPH1SuXBmvXr1Cu3btAABXr15FpUqVNB6QtIeTkxPMzc2RlpbGskZUDARBwOjRo8UZdr/99tt8z6clIiIi/aJ2WZs/fz48PDzw5MkTzJkzBzY2NgCA58+fY8SIERoPSNrDyMgI7u7uCAsLY1kjKganTp3C6dOnAWTOjPv9999LnIiIiIiKk9plzdTUFN98802O9WPHjtVIINJu5cqVQ1hYGBISEvDmzRvY2dlJHYlILyUkJGD9+vXi7YULF8LCwkK6QERERFTsCnXiw6ZNm9C4cWO4ubnh8ePHAP43ayLpt+znrT158kTCJET67ZdffsHr168BAJ06dUKHDh0kTkRERETFTe2ytmzZMowbNw7t2rVDfHy8OKmIvb09FixYoOl8pGU4fT9R0btz54442625uTn/bSUiIjJQape1RYsWYdWqVfj+++9hbGwsrq9Xrx5u3ryp0XCkfbKPrGWNqhKR5giCgFGjRkGhUAAAJkyYgAoVKkicioiIiKSgdlkLDw9H3bp1c6w3NzdHUlKSRkKR9uJhkERFa8eOHTh27BgAwMXFBRMmTJA4EREREUlF7bLm6emJa9eu5Vh/6NAhVK1aVROZSItlPwySZY1IsxITEzFu3Djx9qBBg2BpaSlhIiIiIpKS2rNBjhs3DiNHjkRqaioEQcCFCxewdetWzJo1C6tXry6KjKRFWNaIis6cOXPw7NkzAEC7du1Qv359iRMRERGRlNQua4MHD4alpSV++OEHJCcno0+fPnBzc8Mff/yBXr16FUVG0iI2Njawt7dHfHw8yxqRBkVGRuK3334DkHmJlLlz5+LBgwcSpyIiIiIpqV3WAKBv377o27cvkpOTkZiYCGdnZ03nIi3m7u6O+Ph4PH36FEqlEkZGhboCBBFlM2nSJKSmpgIARo8ejcqVK7OsERERGbhC/ZatUCjw77//YtOmTeL5FFFRUUhMTNRoONJOWYdCpqenIzY2VuI0RLrv7Nmz2Lp1KwDA0dERP/zwg8SJiIiISBuoPbL2+PFjtG3bFpGRkUhLS0OrVq1QokQJzJ49G2lpaVi+fHlR5CQt8u55ay4uLhKmIdJtSqUSY8aMEW//9NNPsLe3h1wuly4UERERaQW1R9a+/vpr1KtXD69fv1aZpaxr1644evSoRsORduIkI0SaExQUhIsXLwIAatSogcGDB0uciIiIiLSF2iNrp0+fxtmzZ2FmZqay3sPDQ5zFjPQbyxqRZiQlJWHSpEni7d9//x0mJoU6lZiIiIj0kNoja0qlEhkZGTnWP336FCVKlNBIKNJuLGtEmjFnzhxERUUBADp16oRWrVpJnIiIiIi0idplrXXr1liwYIF4WyaTITExEVOnTkX79u01mY20VPayFhkZKWESIt317Nkzcap+ExMTzJ07V+JEREREpG3UPt5m7ty5aNu2LapVq4bU1FT06dMHDx48gKOjozibGem3smXLisscWSMqnGnTpiElJQUAMHLkSFSpUkXiRERERKRt1C5r7u7uuH79OrZv347r168jMTERgwYNQt++fVUmHCH9ZWFhAScnJ8TGxrKsERXCnTt3sHbtWgCAnZ0dpkyZInEiIiIi0kZqlTW5XA5vb2/s27dPvDA2GSZ3d3fExsYiKioKCoWCkyIQqWHSpElQKpXicqlSpSRORERERNpIrXPWTE1NkZqaWlRZSIeUK1cOQOaEM8+fP5c4DZHuOHnyJPbu3Qsg85Dir7/+WuJEREREpK3UnmBk5MiRmD17NhQKRVHkIR3BGSGJ1CcIAiZOnCjenjFjBg8fJyIiojypfezaxYsXcfToURw5cgQ1a9aEtbW1yv27du3SWDjSXixrROr766+/cOHCBQCZF8Du16+fxImIiIhIm6ld1uzt7eHv718UWUiHsKwRqSc9PR2TJ08Wb8+ZMwfGxsYSJiIiIiJtp3ZZW7duXVHkIB3DskaknpUrV+Lhw4cAgObNm6Nt27YSJyIiIiJtp/Y5a0QAyxqROpKSkvDTTz+Jt+fMmQOZTCZhIiIiItIFao+s1a1bN9dfMmQyGSwsLFCpUiUEBASgefPmGglI2snNzQ1GRkZQKpUsa0TvsXDhQrx48QIA0KNHD9SrV0/iRERERKQL1B5Za9u2LR49egRra2s0b94czZs3h42NDR4+fIj69evj+fPn8PPzw+7du4siL2kJExMTlC5dGgBH1ojyEx8fjzlz5gAAjIyMMGPGDIkTERERka5Qe2Tt5cuXGD9+PKZMmaKyfubMmXj8+DGOHDmCqVOn4qeffsJnn32msaCkfdzd3fHs2TPExMQgLS0N5ubmUkci0jpz585FfHw8AKB///7w9vaWNhARERHpDLVH1v7880/07t07x/pevXrhzz//BAD07t0boaGhH56OtFr289aePXsmYRIi7RQTE4MFCxYAAExNTTF16lRpAxEREZFOUbusWVhY4OzZsznWnz17FhYWFgAApVIpLpP+yl7WIiMjJUxCpJ1mzZqFpKQkAMBXX32F8uXLS5yIiIiIdInah0GOGjUKX331FS5fvoz69esDyLxQ9urVq/Hdd98BAA4fPow6depoNChpH84ISZS3yMhILFu2DABgZWUl/vtIREREVFBql7UffvgBnp6eWLx4MTZt2gQA8PLywqpVq9CnTx8AmX9BHj58uGaTktZhWSPK24wZM5Ceng4A+Prrr+Hq6ipxIiIiItI1apc1AOjbty/69u2b5/2WlpaFDkS6g2WNKHcPHjzA+vXrAQB2dnaYMGGCtIGIiIhIJxXqotjx8fHiYY9xcXEAgCtXrnCSCQNTrlw5cZlljeh/ZsyYgYyMDADAhAkTULJkSYkTERERkS5Se2Ttxo0b8PPzg52dHSIiIjB48GA4ODhg165diIyMxMaNG4siJ2khZ2dnmJqaQi6Xs6wR/b/Q0FBs2bIFAFCqVCmMHj1a4kRERESkq9QeWRs3bhwCAgLw4MEDlRkf27dvj1OnTmk0HGk3IyMjlC1bFgBH1oiy/PTTT1AqlQCAb775BiVKlJA4EREREekqtcvaxYsXMWzYsBzry5Qpg+joaI2EIt2Rdd7a69evxSnKiQzVvXv3sHXrVgCZo2qBgYESJyIiIiJdpnZZMzc3R0JCQo719+/fh5OTk0ZCke7gJCNE/5N9VG3ChAmwsbGROBERERHpMrXLWufOnTFjxgzI5XIAgEwmQ2RkJL799lv4+/trPCBpN5Y1okx3794VR9UcHR0xcuRIiRMRERGRrlO7rM2bNw+JiYlwdnZGSkoKmjZtikqVKqFEiRL4+eefiyIjaTGWNaJMP/30EwRBAMBRNSIiItIMtWeDtLOzQ3BwMP777z/cuHEDiYmJ+Oijj+Dn51cU+UjLsawRAXfu3MG2bdsAcFSNiIiINKdQF8UGgMaNG6Nx48aazEI6iGWNSHVUbeLEibC2tpY4EREREemDApW1hQsXFvgJeU0hw8KyRobu3r172L59OwDAyckJI0aMkDgRERER6YsClbX58+er3I6NjUVycjLs7e0BAPHx8bCysoKzszPLmoFxcHCApaUlUlJSEBkZKXUcomI3a9YscVTtm2++4agaERERaUyBJhgJDw8Xv37++WfUqVMHd+/eRVxcHOLi4nD37l189NFH+Omnn4o6L2kZmUwmjq49efJE/KWVyBA8evQIQUFBADL/cDF8+HCJExEREZE+UXs2yClTpmDRokXw8vIS13l5eWH+/Pn44YcfNBqOdENWWUtKSkJ8fLy0YYiK0ezZs5GRkQEA+Prrr1GiRAmJExEREZE+UbusPX/+HAqFIsf6jIwMxMTEaCQU6Raet0aG6OnTp1i/fj0AoESJEhg1apS0gYiIiEjvqF3WWrZsiWHDhuHKlSviusuXL2P48OGcvt9AlStXTlxmWSNDMXfuXKSnpwMAAgMDUbJkSYkTERERkb5Ru6ytXbsWrq6uqFevHszNzWFubo6PP/4YLi4uWL16dVFkJC3HkTUyNDExMVi5ciUAwNLSEmPHjpU4EREREekjta+z5uTkhAMHDuDBgwe4e/cuAMDb2xtVqlTReDjSDSxrZGjmz5+PlJQUAMCwYcPg5OQkcSIiIiLSR4W+KHblypVRuXJlTWYhHcWyRoYkLi4OS5YsAQCYmZnhm2++kTgRERER6asCHQb566+/in9Ffp/z589j//79HxSKdAvLGhmSRYsWITExEQAwcOBAlClTRuJEREREpK8KVNbu3LmDcuXKYcSIETh48CBiY2PF+xQKBW7cuIGlS5fik08+Qc+ePTl9tYEpUaIE7OzsALCskX5LSkrCwoULAQDGxsaYOHGixImIiIhInxXoMMiNGzfi+vXrWLx4Mfr06YOEhAQYGxvD3NwcycnJAIC6deti8ODBCAgIgIWFRZGGJu3j7u6ON2/e4OnTpxAEATKZTOpIRBq3du1axMXFAQB69+4NT09PiRMRERGRPivwOWu1a9fGqlWrsGLFCty4cQOPHz9GSkoKHB0dUadOHTg6OhZlTtJy7u7uuHXrFtLS0hAbGwtnZ2epIxFplFwux7x588TbHFUjIiKioqb2BCNGRkaoU6cO6tSpUwRxSFe9e94ayxrpmx07duDx48cAgHbt2qFmzZoSJyIiIiJ9p/Z11ohyw0lGSJ8JgoA5c+aItzmqRkRERMWBZY00IntZi4yMlDAJkeYdOXIE169fBwB8/PHHaNq0qcSJiIiIyBCwrJFGcGSN9Nm7o2qcQIeIiIiKA8saaQTLGumrS5cu4dixYwCAypUro0uXLtIGIiIiIoOh9gQjGRkZePLkCZKTk2FlZQV3d3cYGxsXRTbSIWXLlhWXWdZIn2QfVfvmm2/47x0REREVmwKPrB0/fhytW7eGtbU1KlasiJo1a6JixYqwtrZGmzZtcOrUqaLMSVrO0tISTk5OAFjWSH+EhYVh586dAABnZ2f069dP4kRERERkSApU1jZv3oz27dujUqVK2LZtG65fvw5LS0scPHgQ//77Lxo0aIAuXbpg69atRZ2XtFjWoZBRUVHIyMiQOA3Rh5s3bx6USiUA4Ouvv4aFhYXEiYiIiMiQFOgwyOnTp2PNmjXo06ePuE4mk6FSpUqoUKECGjdujHr16mHcuHHo3bt3kYUl7ebu7o4rV64gIyMDz58/Vzk0kkjXxMTEYN26dQAAGxsbDB8+XOJEREREZGgKNLIWFRWFhg0b5rtNjRo18Pz5c42EIt3ESUZInyxevBhpaWkAgKFDh6JkyZISJyIiIiJDU6Cy1rRpU0yePBmJiYm53i8IAmbPno1mzZppMhvpGJY10heJiYlYsmQJAMDExARjxoyRNhAREREZpAIdBrlq1Sp07NgRzs7OaNSoESpVqoT09HTMmDEDCoUCZ8+ehYODA/bs2VPUeUmLsayRvli9ejVev34NAOjbt6/KZ5uIiIiouBRoZK1MmTK4cuUKtm/fjurVq+PZs2do0qQJ4uLi4OjoiEWLFuHixYtwc3Mr6rykxVjWSB8oFAosWLBAvP3NN99IF4aIiIgMWoGvsyaTydCpUyd06tSpKPOQDmNZI33wzz//4PHjxwCAdu3aoUaNGhInIiIiIkNV4OusEb2Pm5sbZDIZAJY10l3z588Xl8eOHSthEiIiIjJ0LGukMaampihdujQAljXSTRcuXMDZs2cBANWrV4efn5/EiYiIiMiQsayRRmUdChkdHS1Oe06kK7KPqo0ZM0YcKSYiIiKSAssaaVT289aePXsmYRIi9Tx9+hQ7duwAADg6OqJv374SJyIiIiJDp3ZZa9GiBeLj43OsT0hIQIsWLTSRiXQYJxkhXbV48WJkZGQAAIYPHw5LS0uJExEREZGhU7usnThxAunp6TnWp6am4vTp0xoJRbqLZY10UVJSElauXAkAMDMzw4gRIyRORERERKRGWbtx4wZu3LgBALhz5454+8aNG7h69SrWrFmDMmXKFFnQuLg49O3bF7a2trC3t8egQYOQmJiY72NSU1MxcuRIlCpVCjY2NvD390dMTIzKNqNHj4aPjw/Mzc1Rp06dXJ/nxo0baNKkCSwsLODu7o45c+Zo6m3pHZY10kUbN24UL4Ldu3dvuLq6SpyIiIiISI3rrNWpUwcymQwymSzXwx0tLS2xaNEijYbLrm/fvnj+/DmCg4Mhl8sxYMAADB06FFu2bMnzMWPHjsX+/fuxY8cO2NnZITAwEN26dcOZM2dUths4cCDOnz8vltHsEhIS0Lp1a/j5+WH58uW4efMmBg4cCHt7ewwdOlTj71PXlStXTlxmWSNdoFQqVS6CPWbMGMmyEBEREWVX4LIWHh4OQRBQoUIFXLhwAU5OTuJ9ZmZmcHZ2hrGxcZGEvHv3Lg4dOoSLFy+iXr16AIBFixahffv2mDt3Ltzc3HI85s2bN1izZg22bNkilst169ahatWqOHfuHBo2bAgAWLhwIQAgNjY217IWFBSE9PR0rF27FmZmZqhevTquXbuG33//nWUtFxxZI11z8OBB3L9/HwDQrFmzPEfYiYiIiIpbgcta+fLlAWT+Fbq4hYSEwN7eXixqAODn5wcjIyOcP38eXbt2zfGYy5cvQy6Xq1wnydvbG+XKlUNISIhY1gry2p9++inMzMzEdW3atMHs2bPx+vVrlCxZMsdj0tLSVKatT0hIAADI5XLI5fICvW5RyspQFFkcHBxgamoKuVyOyMhIrXi/hqAo96m++/3338XlUaNGac33kPtUP3G/6h/uU/3E/ap/tGmfqpOhwGUtuwcPHuD48eN48eJFjvL2448/FuYp8xUdHQ1nZ2eVdSYmJnBwcEB0dHSejzEzM4O9vb3KehcXlzwfk9fzeHp65niOrPtyK2uzZs3C9OnTc6w/cuQIrKysCvzaRS04OLhInrdkyZJ48eIFwsPDceDAgSJ5DcpdUe1TfRUREYFjx44BAFxdXSGTybTuM8t9qp+4X/UP96l+4n7VP9qwT5OTkwu8rdplbdWqVRg+fDgcHR3FX26yyGQytcrapEmTMHv27Hy3uXv3rroRJTd58mSMGzdOvJ2QkAB3d3e0bt0atra2EibLJJfLERwcjFatWsHU1FTjz1+lShW8ePECb9++RbNmzbSqoOqrot6n+ir7oczffvstOnbsKGEaVdyn+on7Vf9wn+on7lf9o037NOuou4JQu6zNnDkTP//8M7799lt1H5rD+PHjERAQkO82FSpUgKurK168eKGyXqFQIC4uLs9Z21xdXZGeno74+HiV0bWYmBi1ZnpzdXXNMYNk1u28nsfc3Bzm5uY51puamkr+4ciuqPJkn2QkOjoaXl5eGn8Nyp22fca02YsXL7B161YAgJ2dHQYPHqyV3zvuU/3E/ap/uE/1E/er/tGGfarO66td1l6/fo0ePXqo+7BcOTk5qUxUkhdfX1/Ex8fj8uXL8PHxAQAcO3YMSqUSDRo0yPUxPj4+MDU1xdGjR+Hv7w8ACA0NRWRkJHx9fQuc0dfXF99//z3kcrn4jQ0ODoaXl1euh0BSzklGWNZIGy1fvlw8t3TIkCGwsbGROBERERGRKrUvit2jRw8cOXKkKLLkqWrVqmjbti2GDBmCCxcu4MyZMwgMDESvXr3EmSCfPXsGb29vXLhwAUDmX8oHDRqEcePG4fjx47h8+TIGDBgAX19flclFwsLCcO3aNURHRyMlJQXXrl3DtWvXxAt/9+nTB2ZmZhg0aBBu376N7du3448//lA5zJFUcUZI0nbp6elYtmwZAMDY2BijRo2SOBERERFRTmqPrFWqVAlTpkzBuXPnULNmzRzDeKNHj9ZYuOyCgoIQGBiIli1bwsjICP7+/uK0+0DmcaihoaEqJ+zNnz9f3DYtLQ1t2rTB0qVLVZ538ODBOHnypHi7bt26ADIvVeDh4QE7OzscOXIEI0eOhI+PDxwdHfHjjz9y2v58sKyRttu1a5c40VCXLl1UDt0lIiIi0hZql7WVK1fCxsYGJ0+eVCk5QOYEI0VV1hwcHPK9ALaHhwcEQVBZZ2FhgSVLlmDJkiV5Pu7EiRPvfe1atWrh9OnTBc5q6LKXtcjISAmTEOVu8eLF4jJH1YiIiEhbqV3WwsPDiyIH6RGOrJE2u3r1Ks6cOQMAqFGjBj799FOJExERERHlTu1z1rKkp6cjNDQUCoVCk3lID5QqVQqWlpYAWNZI+2QfaQ8MDFS5/AgRERGRNlG7rCUnJ2PQoEGwsrJC9erVxcPcRo0ahV9//VXjAUn3yGQy8Rygx48f5zg8lUgqcXFxCAoKApA5CVHfvn0lTkRERESUN7XL2uTJk3H9+nWcOHECFhYW4no/Pz9s375do+FId5UvXx5AZrl/9eqVxGmIMq1duxapqakAgICAAE7XT0RERFpN7XPW/vnnH2zfvh0NGzZUOXyoevXqePjwoUbDke7KKmtA5uiao6OjhGmIgIyMDJXZYEeMGCFhGiIiIqL3U3tkLTY2Fs7OzjnWJyUl8dwPEnl4eIjLERERkuUgynLw4EFxgqQ2bdqgSpUqEiciIiIiyp/aZa1evXrYv3+/eDuroK1evRq+vr6aS0Y67d2RNSKpZZ+uPzAwUMIkRERERAWj9mGQv/zyC9q1a4c7d+5AoVDgjz/+wJ07d3D27Nkc110jw8WyRtrk/v37OHz4MADA09MT7dq1kzgRERER0fupPbLWuHFjXLt2DQqFAjVr1sSRI0fg7OyMkJAQ+Pj4FEVG0kE8DJK0ybvnqhkbG0uYhoiIiKhg1B5ZA4CKFSti1apVms5CeqR06dIwMTGBQqHgyBpJKjExEevXrwcAWFhYYODAgdIGIiIiIiqgQpU1AHjx4gVevHgBpVKpsr5WrVofHIp0n7GxMdzd3REeHs6yRpIKCgrCmzdvAAB9+/aFg4ODxImIiIiICkbtsnb58mX0798fd+/ezXGxY5lMhoyMDI2FI93m4eGB8PBwxMfH482bN7Czs5M6EhkYQRBUJhYZOXKkhGmIiIiI1KN2WRs4cCCqVKmCNWvWwMXFhdP1U57enWSEo65U3E6dOoVbt24BABo1aoS6detKnIiIiIio4NQua48ePcLOnTtRqVKloshDeoRljaTGUTUiIiLSZWrPBtmyZUtcv369KLKQnuGMkCSlqKgo/P333wAAFxcX+Pv7S5yIiIiISD1qj6ytXr0a/fv3x61bt1CjRg2Ympqq3N+5c2eNhSPdxmutkZTWrl0rnkM7ZMgQmJmZSZyIiIiISD1ql7WQkBCcOXMGBw8ezHEfJxih7FjWSCoZGRlYuXIlAMDIyAhDhgyROBERERGR+tQ+DHLUqFH44osv8Pz5cyiVSpUvFjXKrmzZsuIENDwMkorTgQMH8OTJEwBA+/btUa5cOYkTEREREalP7bL26tUrjB07Fi4uLkWRh/SImZkZypQpA4Aja1S8li9fLi5/9dVXEiYhIiIiKjy1y1q3bt1w/PjxoshCeijrUMjY2FgkJydLnIYMQUREhHiYdrly5dC2bVuJExEREREVjtrnrFWpUgWTJ0/Gf//9h5o1a+aYYGT06NEaC0e6r3z58jhz5gyAzNG1qlWrSpyI9N2qVasgCAIAYOjQoTA2NpY4EREREVHhFGo2SBsbG5w8eRInT55UuU8mk7GskYrs0/ezrFFRS09Px5o1awAAJiYmGDhwoMSJiIiIiApP7bIWHh5eFDlIT3FGSCpOu3fvRkxMDACgS5cuKF26tMSJiIiIiApP7XPWsqSnpyM0NBQKhUKTeUjPsKxRceLEIkRERKRP1C5rycnJGDRoEKysrFC9enVERkYCyJzS/9dff9V4QNJt2Q+D5PT9VJRCQ0Nx7NgxAEDlypXRvHlziRMRERERfRi1y9rkyZNx/fp1nDhxAhYWFuJ6Pz8/bN++XaPhSPdlv74VR9aoKGVdBBsAhg0bBiOjQh84QERERKQV1D5n7Z9//sH27dvRsGFD8YLHAFC9enU8fPhQo+FI91laWsLZ2RkvXrxgWaMik5KSgnXr1gEAzM3N0b9/f4kTEREREX04tf/0HBsbC2dn5xzrk5KSVMobUZas89aioqKQnp4ucRrSRzt27MDr168BAD169ICjo6PEiYiIiIg+nNplrV69eti/f794O6ugrV69Gr6+vppLRnoj67w1QRDw5MkTacOQXuLEIkRERKSP1D4M8pdffkG7du1w584dKBQK/PHHH7hz5w7Onj2b47prREDOGSErVqwoYRrSN9evX0dISAgAoEaNGvjkk08kTkRERESkGWqPrDVu3BjXrl2DQqFAzZo1ceTIETg7OyMkJAQ+Pj5FkZF0XPayxhkhSdNWrFghLg8fPpyHYxMREZHeUHtkDQAqVqyIVatWaToL6ans0/dzkhHSpKSkJGzevBkAYG1tjS+++ELiRERERESao/bI2oEDB3D48OEc6w8fPoyDBw9qJBTpF14Ym4rKn3/+ibdv3wIAevXqBVtbW4kTEREREWmO2mVt0qRJyMjIyLFeEARMmjRJI6FIv/AwSCoq2Uf4hwwZImESIiIiIs1Tu6w9ePAA1apVy7He29sbYWFhGglF+sXW1hYlS5YEwJE10pzbt2+rTCzy8ccfS5yIiIiISLPULmt2dnZ49OhRjvVhYWGwtrbWSCjSP1mja0+fPoVCoZA4DemD1atXi8tDhgzhxCJERESkd9Qua5999hnGjBmDhw8fiuvCwsIwfvx4dO7cWaPhSH9klTWFQoGoqCiJ05CuS0tLw8aNGwEA5ubmnFiEiIiI9JLaZW3OnDmwtraGt7c3PD094enpiapVq6JUqVKYO3duUWQkPcBJRkiT/v77b8TFxQEA/P394eDgIHEiIiIiIs1Te+p+Ozs7nD17FsHBwbh+/TosLS1Rq1YtfPrpp0WRj/TEu9P3N2nSRLowpPOyHwI5ePBgCZMQERERFZ1CXWdNJpOhdevWaN26tabzkJ7ijJCkKQ8fPsTRo0cBAJUqVUKzZs2kDURERERURApV1pKSknDy5ElERkYiPT1d5b7Ro0drJBjpl+wjayxr9CHWrl0rLg8aNIgTixAREZHeUrusXb16Fe3bt0dycjKSkpLg4OCAly9fwsrKCs7OzixrlCtPT09xObfZRIkKQqFQYN26dQAAExMTBAQESBuIiIiIqAipPcHI2LFj0alTJ7x+/RqWlpY4d+4cHj9+DB8fH04wQnkqWbIk7O3tAQDh4eHShiGddeDAATx//hwA0KlTJ7i6ukqciIiIiKjoqF3Wrl27hvHjx8PIyAjGxsZIS0uDu7s75syZg++++64oMpKeyBpde/LkCeRyucRpSBetWrVKXObEIkRERKTv1C5rpqamMDLKfJizszMiIyMBZM4S+eTJE82mI71SoUIFAEBGRgY/K6S2Z8+e4cCBAwCAsmXLok2bNhInIiIiIipaap+zVrduXVy8eBGVK1dG06ZN8eOPP+Lly5fYtGkTatSoURQZSU9kP28tPDxcLG9EBbFu3ToolUoAwMCBA2FsbCxxIiIiIqKipfbI2i+//ILSpUsDAH7++WeULFkSw4cPR2xsLFauXKnxgKQ/spczTjJC6lAqlVizZg2AzEuHDBw4UOJEREREREVP7ZG1evXqicvOzs44dOiQRgOR/np3ZI2ooI4ePSpe8qF169Yq1+0jIiIi0ldqj6wRFRZH1qiwsk8sMmTIEAmTEBERERWfAo2s1a1bt8AXnr1y5coHBSL9Vb58echkMgiCwJE1KrDY2Fj8888/AAAnJyd06tRJ2kBERERExaRAZa1Lly5FHIMMgbm5OcqUKYOnT59yZI0KbOPGjeKlHgICAmBmZiZxIiIiIqLiUaCyNnXq1KLOQQbC09MTT58+xcuXL/H27VuUKFFC6kikxQRBwOrVq8XbgwYNkjANERERUfEq9Dlrly5dwqZNm7Bp0yZcvnxZk5lIj2U/b42HQtL7nDlzBvfu3QMAfPrpp/Dy8pI4EREREVHxUXs2yKdPn6J37944c+YM7O3tAQDx8fH45JNPsG3bNpQtW1bTGUmPvDsjZK1atSRMQ9ou+6gaJxYhIiIiQ6P2yNrgwYMhl8tx9+5dxMXFIS4uDnfv3oVSqcTgwYOLIiPpEY6sUUHFx8fjzz//BADY29vD399f4kRERERExUvtkbWTJ0/i7NmzKocjeXl5YdGiRWjSpIlGw5H+yT6yxklGKD/btm1DSkoKAOCLL76ApaWlxImIiIiIipfaI2vu7u7izGzZZWRkwM3NTSOhSH/xwthUUOvWrROXBw4cKGESIiIiImmoXdZ+++03jBo1CpcuXRLXXbp0CV9//TXmzp2r0XCkf0qXLg1zc3MAHFmjvN26dQsXLlwAkHmdx7p160qciIiIiKj4qX0YZEBAAJKTk9GgQQOYmGQ+XKFQwMTEBAMHDlT5C3hcXJzmkpJeMDIygqenJ+7du4fw8HAolUoYGRV6UlLSU9lH1QYMGCBhEiIiIiLpqF3WFixYUAQxyJBUrFgR9+7dQ0pKCp4/f44yZcpIHYm0iFwux6ZNmwAAZmZm6NOnj8SJiIiIiKShdlnr379/UeQgA1KxYkVx+eHDhyxrpGL//v2IjY0FAHTp0gWlSpWSOBERERGRNNQ+/uzKlSu4efOmeHv37t3o0qULvvvuO6Snp2s0HOmnd8saUXZr164Vl3kIJBERERkytcvasGHDcP/+fQCZE0T07NkTVlZW2LFjByZOnKjxgKR/WNYoL9HR0Thw4AAAoEyZMmjVqpXEiYiIiIiko3ZZu3//PurUqQMA2LFjB5o2bYotW7Zg/fr12Llzp6bzkR6qVKmSuBwWFiZhEtI2mzZtQkZGBoDMyYyMjY0lTkREREQkHbXLmiAIUCqVAIB///0X7du3B5B5/bWXL19qNh3pJQ8PD8hkMgAcWaP/EQRB5RDIgIAA6cIQERERaQG1y1q9evUwc+ZMbNq0CSdPnkSHDh0AZF7g2MXFReMBSf+Ym5vD3d0dAMsa/c+5c+dw7949AMCnn36qMgJLREREZIjULmsLFizAlStXEBgYiO+//178heqvv/7CJ598ovGApJ+yzlt7/fo1Xr9+LXEa0gbZr62W/XqNRERERIZK7an7a9WqpTIbZJbffvuN55dQgVWsWBHHjx8HkDm6Vq9ePYkTkZSSkpKwbds2AICNjQ26d+8ucSIiIiIi6ak9spYXCwsLmJqaaurpSM9lnxGSk4zQzp078fbtWwBAz549YW1tLXEiIiIiIukVaGTNwcEB9+/fh6OjI0qWLClODpGbuLg4jYUj/ZX9fCSet0Y8BJKIiIgopwKVtfnz56NEiRIAMs9ZI/pQvNYaZXn48CFOnDgBAPDy8oKvr6+0gYiIiIi0RIHKWv/+/XNdJiosljXKsn79enF5wIAB+Y7cExERERmSApW1hISEAj+hra1tocOQ4bC1tYWjoyNevnzJc9YMWEZGBjZs2AAAMDY2Rr9+/SRORERERKQ9ClTW7O3tC/zX7oyMjA8KRIajYsWKePnyJaKiopCSkgJLS0upI1ExO3r0KJ48eQIAaNu2LUqXLi1xIiIiIiLtUaCyljXFOgBERERg0qRJCAgIEM8tCQkJwYYNGzBr1qyiSUl6qWLFijh//jwA4NGjR6hevbrEiai4rV27VlzmxCJEREREqgpU1po2bSouz5gxA7///jt69+4truvcuTNq1qyJlStX8pw2KrDsM0KGhYWxrBmYuLg4/PPPPwAAR0dHdOzYUdpARERERFpG7eushYSE5HoB43r16uHChQsaCZWbuLg49O3bF7a2trC3t8egQYOQmJiY72NSU1MxcuRIlCpVCjY2NvD390dMTIzKNqNHj4aPjw/Mzc1Rp06dHM8REREBmUyW4+vcuXOafHsGqXLlyuLygwcPJExCUti6dSvS0tIAAF988QXMzMwkTkRERESkXdQua+7u7li1alWO9atXr4a7u7tGQuWmb9++uH37NoKDg7Fv3z6cOnUKQ4cOzfcxY8eOxd69e7Fjxw6cPHkSUVFR6NatW47tBg4ciJ49e+b7XP/++y+eP38ufvn4+HzQ+yGgSpUq4vL9+/clTEJS4CGQRERERPkr0GGQ2c2fPx/+/v44ePAgGjRoAAC4cOECHjx4gJ07d2o8IADcvXsXhw4dwsWLF8VRvUWLFqF9+/aYO3cu3NzccjzmzZs3WLNmDbZs2YIWLVoAyLzwbtWqVXHu3Dk0bNgQALBw4UIAQGxsLG7cuJFnhlKlSsHV1VXTb82gcWTNcF2/fh1XrlwBkDkqX7NmTYkTEREREWkftcta+/bt8eDBAyxbtgx3794FAHTq1AlfffVVkY2shYSEwN7eXuXwSz8/PxgZGeH8+fPo2rVrjsdcvnwZcrkcfn5+4jpvb2+UK1cOISEhYlkrqM6dOyM1NRVVqlTBxIkT0blz5zy3TUtLEw/vAv536QO5XA65XK7W6xaFrAxSZ7GxsUGpUqXw6tUrPHjwQPI8ukxb9mlBrVmzRlzu16+fzuQuTrq2T6lguF/1D/epfuJ+1T/atE/VyaB2WQOAsmXL4ueffy7MQwslOjoazs7OKutMTEzg4OCA6OjoPB9jZmYGe3t7lfUuLi55PiY3NjY2mDdvHho1agQjIyPs3LkTXbp0wT///JNnYZs1axamT5+eY/2RI0dgZWVV4NcuasHBwVJHgKOjI169eoVnz55h165dsLCwkDqSTtOGffo+crlcvBC2qakpHBwccODAAWlDaTFd2KekPu5X/cN9qp+4X/WPNuzT5OTkAm9bqLKmKZMmTcLs2bPz3SZr9E4qjo6OGDdunHi7fv36iIqKwm+//ZZnWZs8ebLKYxISEuDu7o7WrVtrxUXD5XI5goOD0apVK5iamkqa5a+//kJoaCiAzKn8a9euLWkeXaVN+/R9du7cibdv3wIAunXrhs8//1ziRNpJl/YpFRz3q/7hPtVP3K/6R5v2adZRdwUhaVkbP348AgIC8t2mQoUKcHV1xYsXL1TWKxQKxMXF5XkemaurK9LT0xEfH68yuhYTE/PB5541aNAg31Zubm4Oc3PzHOtNTU0l/3Bkpw15vLy8xOWIiIhcZxqlgtOGffo+mzZtEpcHDRqk9Xmlpgv7lNTH/ap/uE/1E/er/tGGfarO60ta1pycnODk5PTe7Xx9fREfH4/Lly+LszAeO3YMSqVSnOTkXT4+PjA1NcXRo0fh7+8PAAgNDUVkZKR4Me/CunbtGkqXLv1Bz0GZOMmIYXn27BkOHToEAChXrpw4+Q8RERER5aRWWRMEAU+ePIGzs3OxnltUtWpVtG3bFkOGDMHy5cshl8sRGBiIXr16iTNBPnv2DC1btsTGjRvx8ccfw87ODoMGDcK4cePg4OAAW1tbjBo1Cr6+viqTi4SFhSExMRHR0dFISUnBtWvXAADVqlWDmZkZNmzYADMzM9StWxcAsGvXLqxduxarV68utvevz7JP38+ypv82btwIpVIJAAgICICxsbHEiYiIiIi0l9plrVKlSrh9+7bKiEhxCAoKQmBgIFq2bAkjIyP4+/uL0+4DmcehhoaGqpywN3/+fHHbtLQ0tGnTBkuXLlV53sGDB+PkyZPi7axSFh4eDg8PDwDATz/9hMePH8PExATe3t7Yvn07unfvXoTv1nBUqlRJXGZZ02+CIGDdunXi7fcdAk1ERERk6NQqa0ZGRqhcuTJevXpV7GXNwcEBW7ZsyfN+Dw8PCIKgss7CwgJLlizBkiVL8nzciRMn8n3d/v37o3///mplpYIrUaIEXF1dER0dzQtj67kzZ86Ihbx58+bw9PSUOBERERGRdjNS9wG//vorJkyYgFu3bhVFHjJAWYdCvnjxQq3ZcUi3ZB9VGzBggIRJiIiIiHSD2mWtX79+uHDhAmrXrg1LS0s4ODiofBGpi5OM6L+kpCT8+eefADJHU7Mm/SEiIiKivKk9G+SCBQuKIAYZsnfLWtaMn6Q//vrrLyQmJgIAevbsqVUXhyciIiLSVmqXNZ6/RZqWvazxvDX9xEMgiYiIiNT3QddZS01NRXp6uso6W1vbDwpEhif79P0sa/rn0aNH4oyrXl5eH3ydQyIiIiJDofY5a0lJSQgMDISzszOsra1RsmRJlS8idVWqVAlGRpkfxdDQUInTkKatX79eXB4wYABkMpl0YYiIiIh0iNplbeLEiTh27BiWLVsGc3NzrF69GtOnT4ebmxs2btxYFBlJz1lYWIjXtLt3716OSzCQ7lIqldiwYQOAzEt/fPnllxInIiIiItIdape1vXv3YunSpfD394eJiQmaNGmCH374Ab/88guCgoKKIiMZAG9vbwBAYmIinj9/LnEa0pRjx44hMjISANC2bVu4ublJnIiIiIhId6hd1uLi4lChQgUAmeenxcXFAQAaN26MU6dOaTYdGQwvLy9x+d69exImIU1au3atuMyJRYiIiIjUo3ZZq1ChAsLDwwFkjoZkXTtp7969sLe312g4MhxZI2sAy5q+iI+Px99//w0AcHBwQKdOnSRORERERKRb1C5rAwYMwPXr1wEAkyZNwpIlS2BhYYGxY8diwoQJGg9IhoFlTf9s27YNqampAIC+ffvC3Nxc4kREREREukXtqfvHjh0rLvv5+eHevXu4fPkyKlWqhFq1amk0HBmO7IdBckZI/cBrqxERERF9mA+6zhoAlC9fHuXLl9dEFjJgzs7OsLe3R3x8PEfW9MCdO3dw4cIFAEDt2rVRt25diRMRERER6Z4ClbWFCxcW+AlHjx5d6DBkuGQyGby9vXHu3DlERkYiKSkJ1tbWUseiQuKoGhEREdGHK1BZmz9/foGeTCaTsaxRoXl5eeHcuXMAgAcPHqBOnTrSBqJCkcvl2LRpEwDA1NQUffv2lTgRERERkW4qUFnLmv2RqCi9O8kIy5puOnToEGJiYgAAnTp1gqOjo8SJiIiIiHST2rNBEhUVzgipH3gIJBEREZFmqD3ByMCBA/O9P/tFcInUwRkhdV9sbCz27t0LAHB1dUXbtm0lTkRERESku9Qua69fv1a5LZfLcevWLcTHx6NFixYaC0aGp2LFijA2NkZGRgZH1nRUUFAQFAoFAKBfv34wMfngCWeJiIiIDJbav0n9/fffOdYplUoMHz4cFStW1EgoMkxmZmaoWLEi7t+/j9DQUCiVShgZ8UhdXSEIAg+BJCIiItIgjfwmbGRkhHHjxhV41kiivFSrVg0AkJKSgoiICGnDkFquXr2KGzduAAAaNmyocg4iEREREalPY8MWDx8+FA9/IiqsrLIGZF5YmXQHR9WIiIiINEvtwyDHjRunclsQBDx//hz79+9H//79NRaMDFP2snb79m107NhRwjRUUKmpqQgKCgIAWFpaomfPnhInIiIiItJ9ape1q1evqtw2MjKCk5MT5s2b996ZIonep3r16uIyR9Z0x549e8TJh7p16wY7OzuJExERERHpPrXL2vHjx4siBxGAzOn7jYyMoFQqWdZ0CA+BJCIiItI8TrVHWsXS0hIVKlQAkDmyplQqJU5E7/Ps2TMcOXIEAFC+fHk0b95c4kRERERE+kHtkbW6detCJpPlWC+TyWBhYYFKlSohICCAv7BRoVWrVg1hYWFITk5GZGQkPDw8pI5E+di4caNYqvv378/LLRARERFpiNq/VbVt2xaPHj2CtbU1mjdvjubNm8PGxgYPHz5E/fr18fz5c/j5+WH37t1FkZcMwLuTjJD2evfaagEBAdKFISIiItIzao+svXz5EuPHj8eUKVNU1s+cOROPHz/GkSNHMHXqVPz000/47LPPNBaUDMe7k4x06NBBwjSUn7Nnz+LBgwcAgGbNmsHT01PiRERERET6Q+2RtT///BO9e/fOsb5Xr174888/AQC9e/dGaGjoh6cjg8RrremO7KNqnA2WiIiISLPULmsWFhY4e/ZsjvVnz56FhYUFAECpVIrLROry9vYWz4vkYZDaKykpCdu3bwcAlChRAv7+/hInIiIiItIvah8GOWrUKHz11Ve4fPky6tevDwC4ePEiVq9eje+++w4AcPjwYdSpU0ejQclwWFlZwdPTE48ePcKdO3cgCEKuk9qQtHbu3InExEQAQM+ePWFlZSVxIiIiIiL9onZZ++GHH+Dp6YnFixdj06ZNADKvjbVq1Sr06dMHAPDVV19h+PDhmk1KBqVatWp49OgRkpKSEBkZifLly0sdid7Ba6sRERERFS21yxoA9O3bF3379s3zfktLy0IHIgIyJxnZt28fgMxDIVnWtMujR49w4sQJAJl/rPH19ZU2EBEREZEeKlRZA4D09HS8ePEix0WLy5Ur98GhiGrUqCEu37x5E+3bt5cwDb1rw4YN4nJAQAAPUyUiIiIqAmqXtQcPHmDgwIE5JhnJOq8oIyNDY+HIcNWsWVNcvnnzpoRJ6F1KpVIsa0ZGRujXr5/EiYiIiIj0k9plLSAgACYmJti3bx9Kly7Nv6hTkfD29oaJiQkUCgVu3LghdRzK5tixY3j8+DEAoE2bNnBzc5M4EREREZF+UrusXbt2DZcvX4a3t3dR5CECAJibm8PLywu3b9/GvXv3kJ6eDjMzM6ljEYDVq1eLy4MGDZIwCREREZF+U/s6a9WqVcPLly+LIguRilq1agEA5HI5L7KuJV69eoW///4bAODk5IROnTpJnIiIiIhIf6ld1mbPno2JEyfixIkTePXqFRISElS+iDSF561pn6CgIKSnpwMAvvzyS452EhERERUhtQ+D9PPzAwC0bNlSZT0nGCFNyxpZA4AbN26I1/EjaQiCwEMgiYiIiIqR2mXt+PHjRZGDKAeOrGmXS5cuifvB19cX1apVkzgRERERkX5Tu6w1bdo0z/tu3br1QWGIsnN3d4ednR3evHnDGSG1wJo1a8RljqoRERERFT21z1l719u3b7Fy5Up8/PHHqF27tiYyEQEAZDKZOLr29OlTvH79WuJEhispKQlbtmwBANjY2KBnz54SJyIiIiLSf4Uua6dOnUL//v1RunRpzJ07Fy1atMC5c+c0mY1I5bw1jtxK56+//sLbt28BAD179oSNjY3EiYiIiIj0n1qHQUZHR2P9+vVYs2YNEhIS8PnnnyMtLQ3//PMPz1+hIpH9vLUbN26gSZMmEqYxXDwEkoiIiKj4FXhkrVOnTvDy8sKNGzewYMECREVFYdGiRUWZjSjHjJBU/EJDQ3H69GkAmddZbNiwocSJiIiIiAxDgUfWDh48iNGjR2P48OGoXLlyUWYiEtWsWRMymQyCIODatWtSxzFIa9euFZcHDRoEmUwmYRoiIiIiw1HgkbX//vsPb9++hY+PDxo0aIDFixfj5cuXRZmNCCVKlEClSpUAZI6sKRQKiRMZFrlcjg0bNgAATE1N8eWXX0qciIiIiMhwFLisNWzYEKtWrcLz588xbNgwbNu2DW5ublAqlQgODhYnHyDStLp16wIAUlNTce/ePYnTGJb9+/cjJiYGANC5c2c4OTlJnIiIiIjIcKg9G6S1tTUGDhyI//77Dzdv3sT48ePx66+/wtnZGZ07dy6KjGTgssoaAFy9elXCJIYn+8QigwcPljAJERERkeH5oOuseXl5Yc6cOXj69Cm2bt2qqUxEKljWpBEVFYUDBw4AyLxAeatWrSRORERERGRYPvii2ABgbGyMLl26YM+ePZp4OiIVLGvS2LBhA5RKJQAgICAAxsbGEiciIiIiMiwaKWtERcnZ2Rlubm4AgGvXrkEQBIkT6T+lUikeAimTyTBw4ECJExEREREZHpY10glZo2vx8fGIiIiQNowBOHHiBB4+fAgAaNmyJTw8PKQNRERERGSAWNZIJ/BQyOK1YsUKcXnIkCESJiEiIiIyXCxrpBM++ugjcZllrWi9ePECf//9N4DMQ1C7dOkibSAiIiIiA8WyRjqBI2vFZ926dZDL5QCAAQMGwMzMTOJERERERIaJZY10Qvny5VGyZEkAwJUrVyROo7+USiVWrlwp3uYhkERERETSYVkjnSCTycTRtefPnyMqKkriRPrp6NGjePToEQCgVatWqFixosSJiIiIiAwXyxrpjPr164vLFy9elDCJ/so+sciwYcMkTEJERERELGukM1jWilZ0dDR2794NAHBxcUHnzp0lTkRERERk2FjWSGewrBWttWvXQqFQAAAGDhwIU1NTiRMRERERGTaWNdIZ7u7ucHZ2BpBZ1gRBkDiR/lAqlVi1ahWAzPMDObEIERERkfRY1khnyGQyfPzxxwCA169f4+HDhxIn0h9HjhxBREQEAKB169bw9PSUNhARERERsayRbuGhkEWDE4sQERERaR+WNdIpLGuaFxUVhb179wIASpcujY4dO0qciIiIiIgAljXSMSxrmrdmzRpkZGQAAAYNGsSJRYiIiIi0BMsa6RRHR0fxfKrLly+LsxdS4WRkZKhMLDJ48GCJExERERFRFpY10jlZo2spKSm4c+eOxGl026FDh/DkyRMAQLt27VC+fHmJExERERFRFpY10jlZM0ICwLlz5yRMovs4sQgRERGR9mJZI53j6+srLoeEhEiYRLc9efIE+/fvBwCUKVMG7du3lzgREREREWXHskY656OPPoKZmRkA4OzZsxKn0V0rVqyAUqkEAAwePBgmJiYSJyIiIiKi7FjWSOdYWFjAx8cHAHD//n28fPlS4kS6Jy0tDStXrgQAmJiYYOjQoRInIiIiIqJ3sayRTvrkk0/EZY6uqW/Hjh2IjY0FAPj7+8PNzU3iRERERET0LpY10kksax9m8eLF4vLIkSMlTEJEREREedGZshYXF4e+ffvC1tYW9vb2GDRoEBITE/N9TGpqKkaOHIlSpUrBxsYG/v7+iImJEe+/fv06evfuDXd3d1haWqJq1ar4448/cjzPiRMn8NFHH8Hc3ByVKlXC+vXrNf32SE3ZJxlhWVPPpUuXcP78eQBArVq10LhxY4kTEREREVFudKas9e3bF7dv30ZwcDD27duHU6dOvfc8m7Fjx2Lv3r3YsWMHTp48iaioKHTr1k28//Lly3B2dsbmzZtx+/ZtfP/995g8ebLKqEN4eDg6dOiA5s2b49q1axgzZgwGDx6Mw4cPF9l7pfcrXbq0eHHsixcvIj09XeJEumPJkiXicmBgIGQymYRpiIiIiCgvOjH92927d3Ho0CFcvHgR9erVAwAsWrQI7du3x9y5c3M93+bNmzdYs2YNtmzZghYtWgAA1q1bh6pVq+LcuXNo2LAhBg4cqPKYChUqICQkBLt27UJgYCAAYPny5fD09MS8efMAAFWrVsV///2H+fPno02bNkX5tuk9GjVqhPDwcKSmpuLatWsq11+j3L18+RJbt24FANjb26NPnz4SJyIiIiKivOhEWQsJCYG9vb1Y1ADAz88PRkZGOH/+PLp27ZrjMZcvX4ZcLoefn5+4ztvbG+XKlUNISAgaNmyY62u9efMGDg4OKq+d/TkAoE2bNhgzZkyeedPS0pCWlibeTkhIAADI5XLI5fL832wxyMqgDVk+RIMGDbB582YAwOnTp1G3bl2JE0mnoPt05cqV4mczICAAZmZmOv850Ff68nNKqrhf9Q/3qX7iftU/2rRP1cmgE2UtOjoazs7OKutMTEzg4OCA6OjoPB9jZmYGe3t7lfUuLi55Pubs2bPYvn27eKHgrOdxcXHJ8RwJCQlISUmBpaVljueZNWsWpk+fnmP9kSNHYGVlletrSyE4OFjqCB9EoVCIyzt37kSlSpUkTKMd8tunGRkZWLBgAQBAJpPBy8sLBw4cKKZkVFi6/nNKueN+1T/cp/qJ+1X/aMM+TU5OLvC2kpa1SZMmYfbs2fluc/fu3WLJcuvWLXz22WeYOnUqWrdu/UHPNXnyZIwbN068nZCQAHd3d7Ru3Rq2trYfGvWDyeVyBAcHo1WrVjA1NZU6TqFlZGRg2rRpePPmDcLCwtCuXTuDPf+qIPt0z5494nT9bdu2xaBBg4ozIqlJX35OSRX3q/7hPtVP3K/6R5v2adZRdwUhaVkbP348AgIC8t2mQoUKcHV1xYsXL1TWKxQKxMXFwdXVNdfHubq6Ij09HfHx8SqjazExMTkec+fOHbRs2RJDhw7FDz/8kON5ss8gmfUctra2uY6qAYC5uTnMzc1zrDc1NZX8w5GdtuVRl6mpKZo0aYJ9+/YhNjYWYWFhqFatmtSxJJXfPl2+fLm4HBgYqNP73pDo+s8p5Y77Vf9wn+on7lf9ow37VJ3Xl7SsOTk5wcnJ6b3b+fr6Ij4+HpcvX4aPjw8A4NixY1AqlWjQoEGuj/Hx8YGpqSmOHj0Kf39/AEBoaCgiIyNVpn2/ffs2WrRogf79++Pnn3/O9bXfPVQsODhY5TlIOs2aNcO+ffsAZF5iwdDLWl5u3bqFf//9F0DmH0Datm0rcSIiIiIieh+dmLq/atWqaNu2LYYMGYILFy7gzJkzCAwMRK9evcSZIJ89ewZvb29cuHABAGBnZ4dBgwZh3LhxOH78OC5fvowBAwbA19dXnFzk1q1baN68OVq3bo1x48YhOjoa0dHR4qFiAPDVV1/h0aNHmDhxIu7du4elS5fizz//xNixY4v/G0E5NG3aVFw+ceKEdEG0XNa5agAwevRoGBnpxI8+ERERkUHTmd/YgoKC4O3tjZYtW6J9+/Zo3LgxVq5cKd4vl8sRGhqqcsLe/Pnz0bFjR/j7++PTTz+Fq6srdu3aJd7/119/ITY2Fps3b0bp0qXFr/r164vbeHp6Yv/+/QgODkbt2rUxb948rF69mtP2a4k6deqI5wGePHkSgiBInEj7vHjxQpw109bWNsclK4iIiIhIO+nEbJAA4ODggC1btuR5v4eHR45f1C0sLLBkyRKViwBnN23aNEybNu29r92sWTNcvXpVrbxUPExMTNCkSRPs378fL168wL1791C1alWpY2mVZcuWidP1DxkyBCVKlJA4EREREREVhM6MrBHlhYdC5i01NRVLly4FABgZGWHUqFESJyIiIiKigmJZI53XrFkzcZllTdXWrVvFmVS7d++O8uXLS5yIiIiIiAqKZY10Xt26dcVD+3je2v8IgoD58+eLtzkpDhEREZFuYVkjnZd13hqQeQ28W7duSZxIOxw9ehQ3b94EADRs2FCcBZWIiIiIdAPLGumF1q1bi8tHjhyRMIn24KgaERERkW5jWSO9wLKm6t69e+LF3MuVK4du3bpJnIiIiIiI1MWyRnrB29sb7u7uAIBTp04hJSVF4kTSyj6qNnr0aJiY6MxVOoiIiIjo/7GskV6QyWTi6FpqaipOnz4tcSLpREVFYf369QCAEiVKYPDgwdIGIiIiIqJCYVkjvcFDITPNnz8f6enpAIDhw4fDzs5O4kREREREVBgsa6Q3/Pz8IJPJAACHDx+WOI00Xr9+jeXLlwMAzM3NObEIERERkQ5jWSO94eDggPr16wMAbt26haioKIkTFb+lS5ciMTERADBgwAC4urpKnIiIiIiICotljfRKmzZtxOVDhw5JmKT4paamYvHixQAAIyMjTJgwQeJERERERPQhWNZIr7Rr105c3rt3r4RJil9wcDBevXoFAOjVqxcqVKggcSIiIiIi+hAsa6RXPv74Yzg7OwPInGTEUKbwT09Px+7du8XbkyZNkjANEREREWkCyxrpFWNjY3Tq1AkAkJycjKNHj0qcqHhs27YNL1++BAB07NgRNWvWlDgREREREX0oljXSO507dxaX9+zZI2GS4qFUKvHbb7+JtydPnixhGiIiIiLSFJY10jt+fn6wsLAAkHnemlKplDhR0dq5cydCQ0MBAE2aNMEnn3wicSIiIiIi0gSWNdI7VlZWaNWqFQAgOjoaly5dkjhR0cnIyMDUqVPF299++62EaYiIiIhIk1jWSC9lPxQy+8Qb+mbbtm24e/cuAKBq1apiSSUiIiIi3ceyRnqpY8eOkMlkADIPExQEQeJEmqdQKDBt2jTxdp8+fcT3TERERES6j2WN9JKrqyuaNGkCAAgNDcX169clTqR5GzduRFhYGACgWbNmnAGSiIiISM+wrJHe6tWrl7i8fft2CZNoXnp6OmbMmCHezj7CRkRERET6gWWN9Ja/vz+MjY0BZJ7bpU+HQq5duxaPHz8GALRt25YzQBIRERHpIZY10lvOzs5o2bIlACAiIgIXLlyQOJFmpKamYubMmeLt7CNsRERERKQ/WNZIr/Xs2VNc3rZtm4RJNGfFihV49uwZgMxZL+vXry9xIiIiIiIqCixrpNe6du0KU1NTAMCff/6JjIwMiRN9mMTERMyaNUu8zVE1IiIiIv3FskZ6rWTJkmjbti0AICoqCv/++6/EiT7Mb7/9hpiYGABAjx49ULt2bYkTEREREVFRYVkjvTdgwABxec2aNRIm+TDPnj3Db7/9BgAwNTXFL7/8InEiIiIiIipKLGuk9zp27AhnZ2cAwD///IOXL19KnKhwfvzxR6SkpAAARowYgUqVKkmciIiIiIiKEssa6T1TU1P0798fACCXy7F582aJE6nv+vXrWLduHQDAzs4OU6ZMkTgRERERERU1ljUyCAMHDhSXV69erVPXXBMEAaNGjRIzf//99yhVqpTEqYiIiIioqLGskUHw9vZGo0aNAAC3b9/G+fPnJU5UcNu2bcPp06cBAJUqVcLo0aMlTkRERERExYFljQzG4MGDxeVFixZJmKTgEhMT8c0334i3FyxYAHNzcwkTEREREVFxYVkjg9GzZ084OjoCyLzm2pMnTyRO9H4zZ85EVFQUAKBDhw7o0KGDxImIiIiIqLiwrJHBsLS0xIgRIwAACoUCixcvljhR/m7evIl58+YByJwkZf78+RInIiIiIqLixLJGBmXEiBEwMzMDAKxYsQKJiYkSJ8pdRkYGhgwZAoVCAQCYPHkyKleuLHEqIiIiIipOLGtkUFxcXPDFF18AAN68eYO1a9dKnCh3y5cvFydB8fLywuTJkyVORERERETFjWWNDM7YsWPF5blz5yItLU3CNDk9fvxYpZytWLECFhYWEiYiIiIiIimwrJHBqVGjBjp27AgAePLkCVauXClxov9RKpUICAjA27dvAWTOYNm0aVOJUxERERGRFFjWyCDNmDFDXP7555+RnJwsYZr/WbhwIU6cOAEAKFeunDjBCBEREREZHpY1Mkh169ZF9+7dAQAxMTFaMTPknTt3VA5/XL9+PWxtbSVMRERERERSYlkjgzV9+nTIZDIAwOzZsxEXFydZlqSkJPTo0QOpqakAgK+//hrNmzeXLA8RERERSY9ljQxWtWrVxJkh4+LiJJ1xceTIkbhz5w6AzHPqZs2aJVkWIiIiItIOLGtk0GbNmgUbGxsAwMqVK3Hu3Lliz7B27Vps2LABAGBtbY0dO3bA0tKy2HMQERERkXZhWSODVqZMGfz000/i7a+++kq8EHVxOH36NL766ivx9sqVK+Ht7V1sr09ERERE2otljQxeYGAg6tSpAwC4fv06fv3112J53YcPH6Jr166Qy+UAgBEjRqBPnz7F8tpEREREpP1Y1sjgmZiYYPny5TAyyvxxmDp1Kk6dOlWkrxkbG4uOHTvi1atXAIBWrVphwYIFRfqaRERERKRbWNaIADRo0AA//vgjgMwLU/fu3RuxsbFF8lpxcXFo1aoV7t27BwDw9vbGn3/+CVNT0yJ5PSIiIiLSTSxrRP/vhx9+QIsWLQAAUVFR6NGjB1JSUjT6GvHx8Wjbti2uX78OIPOcuQMHDsDe3l6jr0NEREREuo9ljej/GRsbIygoCM7OzgCAkydPomfPnuI5ZR/q8ePHaNSoES5evAgAcHFxwbFjx+Dp6amR5yciIiIi/cKyRpSNq6sr9u7dK07nv3fvXnz55ZdIS0v7oOe9cOECGjZsKF5LzcnJCUePHkWVKlU+ODMRERER6SeWNaJ3fPzxx9izZw/Mzc0BANu3b0ezZs0QFRWl9nNlZGTg559/RqNGjRAdHQ0AqFKlCs6dO4fq1atrNDcRERER6ReWNaJcNG/eHH/99RcsLCwAAOfOnYOPjw+2bt0KpVJZoOc4fvw4PvnkE/zwww/itdsaNWqEs2fPokKFCkWWnYiIiIj0A8saUR46duyIM2fOoFy5cgCA6Oho9OnTBw0bNkRQUBDi4+NzPCY+Ph4bN25EixYt0KJFC1y4cAEAYGRkhClTpuD48eMoVapUcb4NIiIiItJRJlIHINJmH330ES5duoT+/fvj4MGDAICLFy/iiy++gImJCapWrQpnZ2cYGRkhIiIC4eHh4ihalmrVqmHlypVo1KiRFG+BiIiIiHQUyxrRezg5OeHAgQM4fPgwJkyYgJs3bwIAFAqFuJybChUqYNq0aejTpw+MjY2LKy4RERER6QmWNaICatOmDVq1aoX//vsP//zzD/bv34+IiAikp6cDAKytreHp6YmWLVuiR48e8PX1hZERjzQmIiIiosJhWSNSg5GRET799FN8+umn+P333yEIAt6+fYv09HSUKlUKMplM6ohEREREpCdY1og+gEwmg62trdQxiIiIiEgP8RgtIiIiIiIiLcSyRkREREREpIVY1oiIiIiIiLQQyxoREREREZEWYlkjIiIiIiLSQixrREREREREWohljYiIiIiISAuxrBEREREREWkhljUiIiIiIiItxLJGRERERESkhVjWiIiIiIiItBDLGhERERERkRZiWSMiIiIiItJCLGtERERERERaiGWNiIiIiIhIC7GsERERERERaSGWNSIiIiIiIi1kInUAQyAIAgAgISFB4iSZ5HI5kpOTkZCQAFNTU6njkAZwn+of7lP9xP2qf7hP9RP3q/7Rpn2a1QmyOkJ+WNaKwdu3bwEA7u7uEichIiIiIiJt8PbtW9jZ2eW7jUwoSKWjD6JUKhEVFYUSJUpAJpNJHQcJCQlwd3fHkydPYGtrK3Uc0oD/a+/eY6qu/z+APw+Xcw6Om4geIEggk8i4FwZNMeJWxqKmplsMMbo4oLDbWFtS2Tq5Wq4cRbPFxS5YKrRJgBeElHDKLS66MyWj0QTGTBRwQue8v380z+938BwEgnM+5/R8bGeD9+f1OZ/XOS/fe+/l+5wPrKntYU1tE+tqe1hT28S62h4p1VQIgWvXrsHHxwd2dlN/K407a2ZgZ2cHX19fS6dxC1dXV4v/Y6W5xZraHtbUNrGutoc1tU2sq+2RSk1vt6N2E28wQkREREREJEFs1oiIiIiIiCSIzdp/kEKhQEFBARQKhaVToTnCmtoe1tQ2sa62hzW1Tayr7bHWmvIGI0RERERERBLEnTUiIiIiIiIJYrNGREREREQkQWzWiIiIiIiIJIjNGhERERERkQSxWbNRhYWF8Pf3h1KpxMqVK3H69Okp43/44Qfcc889UCqVCAkJwU8//WSmTGm6ZlLTkpISyGQyg4dSqTRjtnQ7P//8M1JTU+Hj4wOZTIbKysrbnlNfX4/IyEgoFAosW7YMJSUl854nTd9Ma1pfX3/LPJXJZOjv7zdPwnRbarUaDzzwAFxcXLBkyRKkpaVBo9Hc9jyuqdI2m7pyXZW2zz//HKGhofo/eB0TE4Pq6uopz7GWecpmzQbt27cPr7zyCgoKCtDa2oqwsDAkJydjcHDQaPwvv/yCTZs24dlnn0VbWxvS0tKQlpaGrq4uM2dOpsy0pgDg6uqKS5cu6R+9vb1mzJhuZ3R0FGFhYSgsLJxW/MWLF7F27Vo8/PDDaG9vR15eHrKyslBbWzvPmdJ0zbSmN2k0GoO5umTJknnKkGaqoaEB2dnZOHXqFI4cOYKJiQkkJSVhdHTU5DlcU6VvNnUFuK5Kma+vLz744AO0tLSgubkZ8fHxeOKJJ9Dd3W003qrmqSCbEx0dLbKzs/W/a7Va4ePjI9RqtdH4DRs2iLVr1xqMrVy5UrzwwgvzmidN30xrWlxcLNzc3MyUHf1bAERFRcWUMW+88YZYsWKFwdjTTz8tkpOT5zEzmq3p1PT48eMCgPjrr7/MkhP9e4ODgwKAaGhoMBnDNdX6TKeuXFetz8KFC8WXX35p9Jg1zVPurNmY8fFxtLS0ICEhQT9mZ2eHhIQENDU1GT2nqanJIB4AkpOTTcaTec2mpgAwMjKCpUuXws/Pb8r/XSLrwHlqu8LDw+Ht7Y3ExEQ0NjZaOh2awvDwMADAw8PDZAznqvWZTl0BrqvWQqvVory8HKOjo4iJiTEaY03zlM2ajRkaGoJWq4VKpTIYV6lUJr8H0d/fP6N4Mq/Z1DQoKAhfffUVfvzxR3z99dfQ6XSIjY1FX1+fOVKmeWBqnl69ehXXr1+3UFb0b3h7e6OoqAgHDhzAgQMH4OfnhzVr1qC1tdXSqZEROp0OeXl5eOihh3DfffeZjOOaal2mW1euq9LX2dkJZ2dnKBQKvPjii6ioqMC9995rNNaa5qmDpRMgorkXExNj8L9JsbGxCA4OxhdffIEdO3ZYMDMiuikoKAhBQUH632NjY9HT04Ndu3Zh7969FsyMjMnOzkZXVxdOnjxp6VRoDk23rlxXpS8oKAjt7e0YHh7G/v37kZGRgYaGBpMNm7XgzpqN8fT0hL29PQYGBgzGBwYG4OXlZfQcLy+vGcWTec2mppM5OjoiIiICFy5cmI8UyQxMzVNXV1c4OTlZKCuaa9HR0ZynEpSTk4NDhw7h+PHj8PX1nTKWa6r1mEldJ+O6Kj1yuRzLli1DVFQU1Go1wsLC8MknnxiNtaZ5ymbNxsjlckRFReHYsWP6MZ1Oh2PHjpn83G5MTIxBPAAcOXLEZDyZ12xqOplWq0VnZye8vb3nK02aZ5yn/w3t7e2cpxIihEBOTg4qKipQV1eHgICA257DuSp9s6nrZFxXpU+n0+HGjRtGj1nVPLX0HU5o7pWXlwuFQiFKSkrE2bNnxfPPPy/c3d1Ff3+/EEKI9PR0kZ+fr49vbGwUDg4O4qOPPhLnzp0TBQUFwtHRUXR2dlrqJdAkM63pO++8I2pra0VPT49oaWkRGzduFEqlUnR3d1vqJdAk165dE21tbaKtrU0AEB9//LFoa2sTvb29Qggh8vPzRXp6uj7+t99+EwsWLBCvv/66OHfunCgsLBT29vaipqbGUi+BJplpTXft2iUqKyvF+fPnRWdnp3j55ZeFnZ2dOHr0qKVeAk2ydetW4ebmJurr68WlS5f0j7GxMX0M11TrM5u6cl2Vtvz8fNHQ0CAuXrwoOjo6RH5+vpDJZOLw4cNCCOuep2zWbNTu3bvFnXfeKeRyuYiOjhanTp3SH4uLixMZGRkG8d9//71Yvny5kMvlYsWKFaKqqsrMGdPtzKSmeXl5+liVSiUee+wx0draaoGsyZSbt22f/LhZx4yMDBEXF3fLOeHh4UIul4vAwEBRXFxs9rzJtJnWdOfOneKuu+4SSqVSeHh4iDVr1oi6ujrLJE9GGasnAIO5xzXV+symrlxXpW3Lli1i6dKlQi6Xi8WLF4tHHnlE36gJYd3zVCaEEObbxyMiIiIiIqLp4HfWiIiIiIiIJIjNGhERERERkQSxWSMiIiIiIpIgNmtEREREREQSxGaNiIiIiIhIgtisERERERERSRCbNSIiIiIiIglis0ZERERERCRBbNaIiOg/b/PmzUhLS7PY9dPT0/H++++b5Vr5+fnIzc01y7WIiOjfkQkhhKWTICIimi8ymWzK4wUFBdi2bRuEEHB3dzdPUv/Pr7/+ivj4ePT29sLZ2Xnerzc0NITAwEC0t7cjMDBw3q9HRESzx2aNiIhsWn9/v/7nffv2Yfv27dBoNPoxZ2dnszRJpmRlZcHBwQFFRUVmu+b69evh7++PDz/80GzXJCKimePHIImIyKZ5eXnpH25ubpDJZAZjzs7Ot3wMcs2aNcjNzUVeXh4WLlwIlUqFPXv2YHR0FJmZmXBxccGyZctQXV1tcK2uri48+uijcHZ2hkqlQnp6OoaGhkzmptVqsX//fqSmphqMf/bZZ7j77ruhVCqhUqmwbt06/TGdTge1Wo2AgAA4OTkhLCwM+/fvNzi/u7sbjz/+OFxdXeHi4oJVq1ahp6dHfzw1NRXl5eWzeTuJiMiM2KwREREZUVpaCk9PT5w+fRq5ubnYunUr1q9fj9jYWLS2tiIpKQnp6ekYGxsDAFy5cgXx8fGIiIhAc3MzampqMDAwgA0bNpi8RkdHB4aHh3H//ffrx5qbm/HSSy/h3XffhUajQU1NDVavXq0/rlarUVZWhqKiInR3d2Pbtm145pln0NDQAAD4888/sXr1aigUCtTV1aGlpQVbtmzB33//rX+O6Oho9PX14ffff5/jd42IiOYSPwZJRET/GSUlJcjLy8OVK1cMxjdv3owrV66gsrISwD87a1qtFidOnADwzw6Ym5sbnnrqKZSVlQH45+OV3t7eaGpqwoMPPoj33nsPJ06cQG1trf55+/r64OfnB41Gg+XLl9+ST2VlJdatW4eJiQn9d+sOHjyIzMxM9PX1wcXFxSD+xo0b8PDwwNGjRxETE6Mfz8rKwtjYGL799lu8+eabKC8vh0ajgaOjo9H34erVq3Bzc0N9fT3i4uJm9iYSEZHZOFg6ASIiIikKDQ3V/2xvb49FixYhJCREP6ZSqQAAg4ODAP65Ucjx48eNfv+tp6fHaLN2/fp1KBQKg5ugJCYmYunSpQgMDERKSgpSUlLw5JNPYsGCBbhw4QLGxsaQmJho8Dzj4+OIiIgAALS3t2PVqlUmGzUAcHJyAgD9riAREUkTmzUiIiIjJjc7MpnMYOxmg6XT6QAAIyMjSE1Nxc6dO295Lm9vb6PX8PT0xNjYGMbHxyGXywEALi4uaG1tRX19PQ4fPozt27fj7bffxpkzZzAyMgIAqKqqwh133GHwXAqFAsD/NWJTuXz5MgBg8eLFt40lIiLLYbNGREQ0ByIjI3HgwAH4+/vDwWF6y2t4eDgA4OzZs/qfAcDBwQEJCQlISEhAQUEB3N3dUVdXh8TERCgUCvzxxx8mP74YGhqK0tJSTExMmNxd6+rqgqOjI1asWDGj10hERObFG4wQERHNgezsbFy+fBmbNm3CmTNn0NPTg9raWmRmZkKr1Ro9Z/HixYiMjMTJkyf1Y4cOHcKnn36K9vZ29Pb2oqysDDqdDkFBQXBxccFrr72Gbdu2obS0FD09PWhtbcXu3btRWloKAMjJycHVq1exceNGNDc34/z589i7d6/Bnys4ceIEVq1aNa1dOCIishw2a0RERHPAx8cHjY2N0Gq1SEpKQkhICPLy8uDu7g47O9PLbVZWFr755hv97+7u7jh48CDi4+MRHByMoqIifPfdd/pdsB07duCtt96CWq1GcHAwUlJSUFVVhYCAAADAokWLUFdXh5GREcTFxSEqKgp79uwx2GUrLy/Hc889N0/vBBERzRXeDZKIiMiCrl+/jqCgIOzbt8/gDo/zpbq6Gq+++io6Ojqm/XFNIiKyDO6sERERWZCTkxPKysqm/OPZc2l0dBTFxcVs1IiIrAB31oiIiIiIiCSIO2tEREREREQSxGaNiIiIiIhIgtisERERERERSRCbNSIiIiIiIglis0ZERERERCRBbNaIiIiIiIgkiM0aERERERGRBLFZIyIiIiIikiA2a0RERERERBL0P5P2VxREIJNQAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import odeint\n",
+        "\n",
+        "def DC_motor(x, t):\n",
+        "    # Parameters and matrices from the MATLAB function\n",
+        "    A = np.array([[0, 1, 0],\n",
+        "                  [0, 0, 4.438],\n",
+        "                  [0, -12, -24]])\n",
+        "\n",
+        "    B = np.array([[0, 0],\n",
+        "                  [0, -7.396],\n",
+        "                  [20, 0]])\n",
+        "\n",
+        "    theta_d = 0           # Desired angular position\n",
+        "    Tl = 0.01             # Step disturbance\n",
+        "    v = 2.255 * Tl - 3.0 * (x[0] - theta_d) - 0.879 * x[1] - 0.1529 * x[2]\n",
+        "    u = np.array([v, Tl])\n",
+        "\n",
+        "    xp = np.dot(A, x) + np.dot(B, u)\n",
+        "    return xp\n",
+        "\n",
+        "# Initial conditions and time span\n",
+        "x0 = np.array([0.0, 0.0, 0.0])\n",
+        "t = np.linspace(0, 3, 301)  # Time points for integration\n",
+        "\n",
+        "# Solve the ODE system\n",
+        "x = odeint(DC_motor, x0, t)\n",
+        "\n",
+        "# Plot the results\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(t, x[:, 0] * 180 / np.pi, 'k', linewidth=2)\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Angular displacement θ (degrees)')\n",
+        "plt.title('Angular Displacement of DC Motor')\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/CL_DCmotor_solver/cl_dcmotor_solver.py b/Chapter7/python/CL_DCmotor_solver/cl_dcmotor_solver.py
new file mode 100644
index 0000000..1552b0a
--- /dev/null
+++ b/Chapter7/python/CL_DCmotor_solver/cl_dcmotor_solver.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+"""CL_DCmotor_solver.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1udvONx02pb5-lPjCu2QjGnU6WCsBI8zt
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import odeint
+
+def DC_motor(x, t):
+    # Parameters and matrices from the MATLAB function
+    A = np.array([[0, 1, 0],
+                  [0, 0, 4.438],
+                  [0, -12, -24]])
+
+    B = np.array([[0, 0],
+                  [0, -7.396],
+                  [20, 0]])
+
+    theta_d = 0           # Desired angular position
+    Tl = 0.01             # Step disturbance
+    v = 2.255 * Tl - 3.0 * (x[0] - theta_d) - 0.879 * x[1] - 0.1529 * x[2]
+    u = np.array([v, Tl])
+
+    xp = np.dot(A, x) + np.dot(B, u)
+    return xp
+
+# Initial conditions and time span
+x0 = np.array([0.0, 0.0, 0.0])
+t = np.linspace(0, 3, 301)  # Time points for integration
+
+# Solve the ODE system
+x = odeint(DC_motor, x0, t)
+
+# Plot the results
+plt.figure(figsize=(10, 6))
+plt.plot(t, x[:, 0] * 180 / np.pi, 'k', linewidth=2)
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('Angular displacement θ (degrees)')
+plt.title('Angular Displacement of DC Motor')
+plt.show()
\ No newline at end of file
diff --git a/Chapter7/python/CL_DCmotor_w_solver/CL_DCmotor_w_solver.ipynb b/Chapter7/python/CL_DCmotor_w_solver/CL_DCmotor_w_solver.ipynb
new file mode 100644
index 0000000..41a8bc7
--- /dev/null
+++ b/Chapter7/python/CL_DCmotor_w_solver/CL_DCmotor_w_solver.ipynb
@@ -0,0 +1,86 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "qHZiqSIOzP9Q",
+        "outputId": "733b5d04-f392-4c63-8c41-3443185640ab",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import odeint\n",
+        "\n",
+        "def DC_motor_w(x, t):\n",
+        "    # Parameters and matrices from the MATLAB function DC_motor_w\n",
+        "    A = np.array([[0, 1, 0, 0],\n",
+        "                  [0, 0, 4.438, -7.396],\n",
+        "                  [0, -12, -24, 0],\n",
+        "                  [0, 0, 0, -1]])\n",
+        "\n",
+        "    B = np.array([[0, 0],\n",
+        "                  [0, -7.396],\n",
+        "                  [20, 0],\n",
+        "                  [0, 0]])\n",
+        "\n",
+        "    k = np.array([3.0000, 0.8796, 0.1529, -1.8190])\n",
+        "    theta_d = 0           # Desired angular position\n",
+        "    Tl = 0.01             # Step disturbance\n",
+        "\n",
+        "    v1 = 2.255 * Tl - k[0] * (x[0] - theta_d) - k[1] * x[1] - k[2] * x[2]\n",
+        "    v2 = 2.255 * Tl - np.dot(k, x)\n",
+        "    u = np.array([v1, Tl])\n",
+        "\n",
+        "    xp = np.dot(A, x) + np.dot(B, u)\n",
+        "    return xp\n",
+        "\n",
+        "# Initial conditions and time span\n",
+        "x0 = np.array([0.0, 0.0, 0.0, 0.01])\n",
+        "t = np.linspace(0, 3, 301)  # Time points for integration\n",
+        "\n",
+        "# Solve the ODE system\n",
+        "x = odeint(DC_motor_w, x0, t)\n",
+        "\n",
+        "# Plot the results\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(t, x[:, 0] * 180 / np.pi, 'k', linewidth=2)\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Angular displacement (degrees)')\n",
+        "plt.title('Angular Displacement of DC Motor (with disturbance)')\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/CL_DCmotor_w_solver/cl_dcmotor_w_solver.py b/Chapter7/python/CL_DCmotor_w_solver/cl_dcmotor_w_solver.py
new file mode 100644
index 0000000..38316b4
--- /dev/null
+++ b/Chapter7/python/CL_DCmotor_w_solver/cl_dcmotor_w_solver.py
@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+"""CL_DCmotor_w_solver.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1btTAISDcrpw5JrD63E7zaOoTA4BTEzK1
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import odeint
+
+def DC_motor_w(x, t):
+    # Parameters and matrices from the MATLAB function DC_motor_w
+    A = np.array([[0, 1, 0, 0],
+                  [0, 0, 4.438, -7.396],
+                  [0, -12, -24, 0],
+                  [0, 0, 0, -1]])
+
+    B = np.array([[0, 0],
+                  [0, -7.396],
+                  [20, 0],
+                  [0, 0]])
+
+    k = np.array([3.0000, 0.8796, 0.1529, -1.8190])
+    theta_d = 0           # Desired angular position
+    Tl = 0.01             # Step disturbance
+
+    v1 = 2.255 * Tl - k[0] * (x[0] - theta_d) - k[1] * x[1] - k[2] * x[2]
+    v2 = 2.255 * Tl - np.dot(k, x)
+    u = np.array([v1, Tl])
+
+    xp = np.dot(A, x) + np.dot(B, u)
+    return xp
+
+# Initial conditions and time span
+x0 = np.array([0.0, 0.0, 0.0, 0.01])
+t = np.linspace(0, 3, 301)  # Time points for integration
+
+# Solve the ODE system
+x = odeint(DC_motor_w, x0, t)
+
+# Plot the results
+plt.figure(figsize=(10, 6))
+plt.plot(t, x[:, 0] * 180 / np.pi, 'k', linewidth=2)
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('Angular displacement (degrees)')
+plt.title('Angular Displacement of DC Motor (with disturbance)')
+plt.show()
\ No newline at end of file
diff --git a/Chapter7/python/CL_Invpend_solver/CL_Invpend_solver.ipynb b/Chapter7/python/CL_Invpend_solver/CL_Invpend_solver.ipynb
new file mode 100644
index 0000000..c97cce0
--- /dev/null
+++ b/Chapter7/python/CL_Invpend_solver/CL_Invpend_solver.ipynb
@@ -0,0 +1,98 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "aDtDKzw8x2L-",
+        "outputId": "b98f1099-e0c4-4c3f-fbbf-b79b1b2e0eb9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "def inverted_pendulum_k1(t, x):\n",
+        "    # Constants\n",
+        "    g = 9.8\n",
+        "    l = 1\n",
+        "    m = 1\n",
+        "    M = 1\n",
+        "\n",
+        "    # State feedback gains\n",
+        "    k = np.array([-16.0203, -15.2428, -98.6852, -28.1028])\n",
+        "\n",
+        "    # Intermediate calculations\n",
+        "    d1 = M + m * (1 - np.cos(x[2]) ** 2)\n",
+        "    d2 = l * d1\n",
+        "\n",
+        "    # State feedback\n",
+        "    F = -np.dot(k, x)\n",
+        "\n",
+        "    # State derivatives\n",
+        "    xp = np.zeros(4)\n",
+        "    xp[0] = x[1]\n",
+        "    xp[1] = (F + m * l * x[3] ** 2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1\n",
+        "    xp[2] = x[3]\n",
+        "    xp[3] = (-F * np.cos(x[2]) - m * l * x[3] ** 2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2\n",
+        "\n",
+        "    return xp\n",
+        "\n",
+        "# Initial conditions: [x, v, theta, omega]\n",
+        "x0 = [0, 0, 0.26, 0]\n",
+        "\n",
+        "# Time span\n",
+        "t_span = (0, 4)\n",
+        "t_eval = np.linspace(t_span[0], t_span[1], 400)  # 400 points within 4 seconds\n",
+        "\n",
+        "# Solve the ODE\n",
+        "sol = solve_ivp(inverted_pendulum_k1, t_span, x0, t_eval=t_eval, max_step=1e-2)\n",
+        "\n",
+        "# Convert theta from radians to degrees for plotting\n",
+        "theta_deg = sol.y[2] * 180 / np.pi\n",
+        "\n",
+        "# Plotting\n",
+        "plt.plot(sol.t, sol.y[0], 'k', label='x (m)')\n",
+        "plt.plot(sol.t, theta_deg, '-.k', label='θ (degrees)')\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State Variables')\n",
+        "plt.legend()\n",
+        "plt.gca().set_prop_cycle(None)  # Reset the color cycle\n",
+        "for line in plt.gca().get_lines():\n",
+        "    line.set_linewidth(2)\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/CL_Invpend_solver/cl_invpend_solver.py b/Chapter7/python/CL_Invpend_solver/cl_invpend_solver.py
new file mode 100644
index 0000000..e022701
--- /dev/null
+++ b/Chapter7/python/CL_Invpend_solver/cl_invpend_solver.py
@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+"""CL_Invpend_solver.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1HQJpF_Cze2rIjD99yW2M-OXbNrUogZF_
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+def inverted_pendulum_k1(t, x):
+    # Constants
+    g = 9.8
+    l = 1
+    m = 1
+    M = 1
+
+    # State feedback gains
+    k = np.array([-16.0203, -15.2428, -98.6852, -28.1028])
+
+    # Intermediate calculations
+    d1 = M + m * (1 - np.cos(x[2]) ** 2)
+    d2 = l * d1
+
+    # State feedback
+    F = -np.dot(k, x)
+
+    # State derivatives
+    xp = np.zeros(4)
+    xp[0] = x[1]
+    xp[1] = (F + m * l * x[3] ** 2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1
+    xp[2] = x[3]
+    xp[3] = (-F * np.cos(x[2]) - m * l * x[3] ** 2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2
+
+    return xp
+
+# Initial conditions: [x, v, theta, omega]
+x0 = [0, 0, 0.26, 0]
+
+# Time span
+t_span = (0, 4)
+t_eval = np.linspace(t_span[0], t_span[1], 400)  # 400 points within 4 seconds
+
+# Solve the ODE
+sol = solve_ivp(inverted_pendulum_k1, t_span, x0, t_eval=t_eval, max_step=1e-2)
+
+# Convert theta from radians to degrees for plotting
+theta_deg = sol.y[2] * 180 / np.pi
+
+# Plotting
+plt.plot(sol.t, sol.y[0], 'k', label='x (m)')
+plt.plot(sol.t, theta_deg, '-.k', label='θ (degrees)')
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('State Variables')
+plt.legend()
+plt.gca().set_prop_cycle(None)  # Reset the color cycle
+for line in plt.gca().get_lines():
+    line.set_linewidth(2)
+plt.show()
\ No newline at end of file
diff --git a/Chapter7/python/DC_motor/DC_motor.ipynb b/Chapter7/python/DC_motor/DC_motor.ipynb
new file mode 100644
index 0000000..827f750
--- /dev/null
+++ b/Chapter7/python/DC_motor/DC_motor.ipynb
@@ -0,0 +1,49 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "O4Kgb2R6xEoe"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "def DC_motor(t, x):\n",
+        "    # State variable x=[theta, omega, i]\n",
+        "    A = np.array([[0, 1, 0],\n",
+        "                  [0, 0, 4.438],\n",
+        "                  [0, -12, -24]])\n",
+        "\n",
+        "    B = np.array([[0, 0],\n",
+        "                  [0, -7.396],\n",
+        "                  [20, 0]])\n",
+        "\n",
+        "    theta_d = 0           # Desired angular position\n",
+        "    Tl = 0.01             # Step disturbance\n",
+        "\n",
+        "    v = 2.255 * Tl - 3.0 * (x[0] - theta_d) - 0.879 * x[1] - 0.1529 * x[2]\n",
+        "    u = np.array([v, Tl])\n",
+        "\n",
+        "    xp = np.dot(A, x) + np.dot(B, u)\n",
+        "\n",
+        "    return xp\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/DC_motor/dc_motor.py b/Chapter7/python/DC_motor/dc_motor.py
new file mode 100644
index 0000000..b99a5fa
--- /dev/null
+++ b/Chapter7/python/DC_motor/dc_motor.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+"""DC_motor.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/18IZdcGCiUseKQ1WJv60qdjwEMGXITvDS
+"""
+
+import numpy as np
+
+def DC_motor(t, x):
+    # State variable x=[theta, omega, i]
+    A = np.array([[0, 1, 0],
+                  [0, 0, 4.438],
+                  [0, -12, -24]])
+
+    B = np.array([[0, 0],
+                  [0, -7.396],
+                  [20, 0]])
+
+    theta_d = 0           # Desired angular position
+    Tl = 0.01             # Step disturbance
+
+    v = 2.255 * Tl - 3.0 * (x[0] - theta_d) - 0.879 * x[1] - 0.1529 * x[2]
+    u = np.array([v, Tl])
+
+    xp = np.dot(A, x) + np.dot(B, u)
+
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/DC_motor_w/DC_motor_w.ipynb b/Chapter7/python/DC_motor_w/DC_motor_w.ipynb
new file mode 100644
index 0000000..746394e
--- /dev/null
+++ b/Chapter7/python/DC_motor_w/DC_motor_w.ipynb
@@ -0,0 +1,53 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "RVjuvAYmx8-C"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "def DC_motor_w(t, x):\n",
+        "    # State variable x=[theta, omega, i, Tl]\n",
+        "    A = np.array([[0, 1, 0, 0],\n",
+        "                  [0, 0, 4.438, -7.396],\n",
+        "                  [0, -12, -24, 0],\n",
+        "                  [0, 0, 0, -1]])\n",
+        "\n",
+        "    B = np.array([[0, 0],\n",
+        "                  [0, -7.396],\n",
+        "                  [20, 0],\n",
+        "                  [0, 0]])\n",
+        "\n",
+        "    k = np.array([3.0000, 0.8796, 0.1529, -1.8190])\n",
+        "    theta_d = 0           # Desired angular position\n",
+        "    Tl = 0.01             # Step disturbance\n",
+        "\n",
+        "    v1 = 2.255 * Tl - k[0] * (x[0] - theta_d) - k[1] * x[1] - k[2] * x[2]\n",
+        "    v2 = 2.255 * Tl - np.dot(k, x)\n",
+        "    u = np.array([v1, Tl])\n",
+        "\n",
+        "    xp = np.dot(A, x) + np.dot(B, u)\n",
+        "\n",
+        "    return xp\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/DC_motor_w/dc_motor_w.py b/Chapter7/python/DC_motor_w/dc_motor_w.py
new file mode 100644
index 0000000..49b09ec
--- /dev/null
+++ b/Chapter7/python/DC_motor_w/dc_motor_w.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+"""DC_motor_w.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1IzstQqJXO0GyUoHn0iHuNm3-4d_R8JD-
+"""
+
+import numpy as np
+
+def DC_motor_w(t, x):
+    # State variable x=[theta, omega, i, Tl]
+    A = np.array([[0, 1, 0, 0],
+                  [0, 0, 4.438, -7.396],
+                  [0, -12, -24, 0],
+                  [0, 0, 0, -1]])
+
+    B = np.array([[0, 0],
+                  [0, -7.396],
+                  [20, 0],
+                  [0, 0]])
+
+    k = np.array([3.0000, 0.8796, 0.1529, -1.8190])
+    theta_d = 0           # Desired angular position
+    Tl = 0.01             # Step disturbance
+
+    v1 = 2.255 * Tl - k[0] * (x[0] - theta_d) - k[1] * x[1] - k[2] * x[2]
+    v2 = 2.255 * Tl - np.dot(k, x)
+    u = np.array([v1, Tl])
+
+    xp = np.dot(A, x) + np.dot(B, u)
+
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/README.md b/Chapter7/python/README.md
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Chapter7/python/README.md
@@ -0,0 +1 @@
+
diff --git a/Chapter7/python/actives_fb/actives_fb.ipynb b/Chapter7/python/actives_fb/actives_fb.ipynb
new file mode 100644
index 0000000..619211a
--- /dev/null
+++ b/Chapter7/python/actives_fb/actives_fb.ipynb
@@ -0,0 +1,89 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.signal import place_poles, StateSpace, impulse\n",
+        "\n",
+        "# Define matrices\n",
+        "A = np.array([[0, 0, 1, -1, 0],\n",
+        "              [0, 0, 1, 0, 0],\n",
+        "              [-10, 0, -2, 2, 0],\n",
+        "              [720, -660, 12, -12, 0],\n",
+        "              [1, 0, 0, 0, 0]])\n",
+        "b1 = np.array([0, 0, 0.00333, -0.02, 0]).reshape(-1, 1)\n",
+        "b2 = np.array([0, -1, 0, 0, 0]).reshape(-1, 1)\n",
+        "b3 = np.array([0, 0, 0, 0, 1]).reshape(-1, 1)\n",
+        "\n",
+        "# Desired pole locations\n",
+        "pd = np.array([-5, -25+25j, -25-25j, -3+3j, -3-3j])\n",
+        "\n",
+        "# Calculate the state feedback gain matrix k\n",
+        "k = place_poles(A, b1, pd).gain_matrix.flatten()\n",
+        "\n",
+        "# Closed loop system\n",
+        "Acl = A - b1 @ k.reshape(1, -1)\n",
+        "Bcl = 0.1 * b2\n",
+        "C = np.eye(5)\n",
+        "D = np.zeros((5, 1))\n",
+        "ld = 0.1\n",
+        "\n",
+        "# Create state-space system\n",
+        "sys = StateSpace(Acl, Bcl, C, D)\n",
+        "\n",
+        "# Simulate impulse response\n",
+        "t = np.linspace(0, 5, 500)  # 500 points within 5 seconds\n",
+        "t, y = impulse(sys, T=t)\n",
+        "\n",
+        "# Plotting\n",
+        "plt.plot(t, y[:, 0] + ld, 'k', label='$l_1$')\n",
+        "plt.plot(t, y[:, 4] - 0.574 * ld, 'k-.', label='$x$')\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.gca().set_prop_cycle(None)  # Reset the color cycle\n",
+        "for line in plt.gca().get_lines():\n",
+        "    line.set_linewidth(2)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "g0snJl9kz5Qq",
+        "outputId": "0cbffab2-2857-49ff-c235-cf6df7716ec5"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/actives_fb/actives_fb.py b/Chapter7/python/actives_fb/actives_fb.py
new file mode 100644
index 0000000..4462dbc
--- /dev/null
+++ b/Chapter7/python/actives_fb/actives_fb.py
@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+"""actives_fb.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1iTMJN2V-VUYnDhjeYn-H__vX2KmYy_BW
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.signal import place_poles, StateSpace, impulse
+
+# Define matrices
+A = np.array([[0, 0, 1, -1, 0],
+              [0, 0, 1, 0, 0],
+              [-10, 0, -2, 2, 0],
+              [720, -660, 12, -12, 0],
+              [1, 0, 0, 0, 0]])
+b1 = np.array([0, 0, 0.00333, -0.02, 0]).reshape(-1, 1)
+b2 = np.array([0, -1, 0, 0, 0]).reshape(-1, 1)
+b3 = np.array([0, 0, 0, 0, 1]).reshape(-1, 1)
+
+# Desired pole locations
+pd = np.array([-5, -25+25j, -25-25j, -3+3j, -3-3j])
+
+# Calculate the state feedback gain matrix k
+k = place_poles(A, b1, pd).gain_matrix.flatten()
+
+# Closed loop system
+Acl = A - b1 @ k.reshape(1, -1)
+Bcl = 0.1 * b2
+C = np.eye(5)
+D = np.zeros((5, 1))
+ld = 0.1
+
+# Create state-space system
+sys = StateSpace(Acl, Bcl, C, D)
+
+# Simulate impulse response
+t = np.linspace(0, 5, 500)  # 500 points within 5 seconds
+t, y = impulse(sys, T=t)
+
+# Plotting
+plt.plot(t, y[:, 0] + ld, 'k', label='$l_1$')
+plt.plot(t, y[:, 4] - 0.574 * ld, 'k-.', label='$x$')
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.gca().set_prop_cycle(None)  # Reset the color cycle
+for line in plt.gca().get_lines():
+    line.set_linewidth(2)
+plt.show()
\ No newline at end of file
diff --git a/Chapter7/python/ex6_10/ex6_10.ipynb b/Chapter7/python/ex6_10/ex6_10.ipynb
new file mode 100644
index 0000000..f40b050
--- /dev/null
+++ b/Chapter7/python/ex6_10/ex6_10.ipynb
@@ -0,0 +1,74 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "_MIe3jktM744",
+        "outputId": "1c2651b2-f302-491d-d208-87c19c422ded",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Eigenvalues:\n",
+            " [-1.  1. -2.]\n",
+            "Eigenvectors:\n",
+            " [[-0.80178373  0.40824829 -0.66666667]\n",
+            " [-0.26726124  0.40824829 -0.66666667]\n",
+            " [-0.53452248  0.81649658 -0.33333333]]\n",
+            "Inverse of eigenvectors matrix:\n",
+            " [[-1.87082869e+00  1.87082869e+00 -8.30814836e-16]\n",
+            " [-1.22474487e+00  4.08248290e-01  1.63299316e+00]\n",
+            " [-0.00000000e+00 -2.00000000e+00  1.00000000e+00]]\n",
+            "Projection:\n",
+            " [[1.87082869]\n",
+            " [2.04124145]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# System matrices\n",
+        "A = np.array([[-2, -1, 2], [-1, -2, 2], [-2, 0, 2]])\n",
+        "B = np.array([[0, 0], [0, 1], [1, 0]])\n",
+        "\n",
+        "# Vector f and b\n",
+        "f = np.array([[1], [1]])\n",
+        "b = B @ f\n",
+        "\n",
+        "# Eigenvalues and eigenvectors\n",
+        "eigenvalues, eigenvectors = np.linalg.eig(A)\n",
+        "v = np.linalg.inv(eigenvectors)\n",
+        "\n",
+        "# Projection\n",
+        "p = v[:2, :] @ b\n",
+        "\n",
+        "# Display results\n",
+        "print(\"Eigenvalues:\\n\", eigenvalues)\n",
+        "print(\"Eigenvectors:\\n\", eigenvectors)\n",
+        "print(\"Inverse of eigenvectors matrix:\\n\", v)\n",
+        "print(\"Projection:\\n\", p)\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_10/ex6_10.py b/Chapter7/python/ex6_10/ex6_10.py
new file mode 100644
index 0000000..0e097be
--- /dev/null
+++ b/Chapter7/python/ex6_10/ex6_10.py
@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+"""ex6_10.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1bin3KQSn9DaPV2lTjfEfi4QxYsqc915r
+"""
+
+import numpy as np
+
+# System matrices
+A = np.array([[-2, -1, 2], [-1, -2, 2], [-2, 0, 2]])
+B = np.array([[0, 0], [0, 1], [1, 0]])
+
+# Vector f and b
+f = np.array([[1], [1]])
+b = B @ f
+
+# Eigenvalues and eigenvectors
+eigenvalues, eigenvectors = np.linalg.eig(A)
+v = np.linalg.inv(eigenvectors)
+
+# Projection
+p = v[:2, :] @ b
+
+# Display results
+print("Eigenvalues:\n", eigenvalues)
+print("Eigenvectors:\n", eigenvectors)
+print("Inverse of eigenvectors matrix:\n", v)
+print("Projection:\n", p)
\ No newline at end of file
diff --git a/Chapter7/python/ex6_13/ex6_13.ipynb b/Chapter7/python/ex6_13/ex6_13.ipynb
new file mode 100644
index 0000000..0c55534
--- /dev/null
+++ b/Chapter7/python/ex6_13/ex6_13.ipynb
@@ -0,0 +1,169 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "NVRqAuvuLTp7",
+        "outputId": "9ec7b343-e2db-45fd-bb24-9ff58258b0d9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "K1: \n",
+            "[[2.         0.64250517 0.11344729]]\n",
+            "K2: \n",
+            "[[3.         0.87955069 0.15290229]]\n",
+            "K3: \n",
+            "[[4.47213595 1.1848639  0.20208509]]\n",
+            "K4: \n",
+            "[[3.         1.71648224 0.28383787]]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.linalg import solve_continuous_are\n",
+        "from scipy.signal import StateSpace, lsim\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def lqr(A, B, Q, R):\n",
+        "    # Solve Riccati equation\n",
+        "    X = solve_continuous_are(A, B, Q, R)\n",
+        "    # Compute LQR gain\n",
+        "    K = np.linalg.inv(R) @ B.T @ X\n",
+        "    return K\n",
+        "\n",
+        "# System matrices\n",
+        "A = np.array([[0, 1, 0], [0, 0, 4.438], [0, -12, -24]])\n",
+        "B = np.array([[0], [0], [20]])\n",
+        "C = np.array([[1, 0, 0]])\n",
+        "D = np.array([[0]])\n",
+        "x0 = np.array([[-1], [0], [0]])\n",
+        "\n",
+        "R = np.array([[1]])\n",
+        "\n",
+        "# Different Q matrices\n",
+        "Q1 = np.diag([4, 0, 0])\n",
+        "Q2 = np.diag([9, 0, 0])\n",
+        "Q3 = np.diag([20, 0, 0])\n",
+        "Q4 = np.diag([9, 3, 0])\n",
+        "\n",
+        "# LQR gains\n",
+        "K1 = lqr(A, B, Q1, R)\n",
+        "K2 = lqr(A, B, Q2, R)\n",
+        "K3 = lqr(A, B, Q3, R)\n",
+        "K4 = lqr(A, B, Q4, R)\n",
+        "\n",
+        "# Closed-loop systems\n",
+        "Acl1 = A - B @ K1\n",
+        "Acl2 = A - B @ K2\n",
+        "Acl3 = A - B @ K3\n",
+        "Acl4 = A - B @ K4\n",
+        "\n",
+        "# Create state-space systems\n",
+        "sys1 = StateSpace(Acl1, B, C, D)\n",
+        "sys2 = StateSpace(Acl2, B, C, D)\n",
+        "sys3 = StateSpace(Acl3, B, C, D)\n",
+        "sys4 = StateSpace(Acl4, B, C, D)\n",
+        "\n",
+        "# Time vector\n",
+        "t = np.linspace(0, 2, 500)\n",
+        "\n",
+        "# Initial response\n",
+        "t1, y1, x1 = lsim(sys1, U=0, T=t, X0=x0.flatten())\n",
+        "t2, y2, x2 = lsim(sys2, U=0, T=t, X0=x0.flatten())\n",
+        "t3, y3, x3 = lsim(sys3, U=0, T=t, X0=x0.flatten())\n",
+        "t4, y4, x4 = lsim(sys4, U=0, T=t, X0=x0.flatten())\n",
+        "\n",
+        "# Control inputs\n",
+        "u1 = -K1 @ x1.T\n",
+        "u2 = -K2 @ x2.T\n",
+        "u3 = -K3 @ x3.T\n",
+        "u4 = -K4 @ x4.T\n",
+        "\n",
+        "print('K1: ')\n",
+        "print(K1)\n",
+        "print('K2: ')\n",
+        "print(K2)\n",
+        "print('K3: ')\n",
+        "print(K3)\n",
+        "print('K4: ')\n",
+        "print(K4)\n",
+        "\n",
+        "# Plotting\n",
+        "plt.figure(1)\n",
+        "plt.subplot(121)\n",
+        "plt.plot(t1, y1, 'k-.', t2, y2, 'k', t3, y3, 'k--', linewidth=2)\n",
+        "plt.grid()\n",
+        "plt.axis([0, 2, -1, 0.2])\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Angular Error (rad)')\n",
+        "\n",
+        "plt.subplot(122)\n",
+        "plt.plot(t1, u1.T, 'k-.', t2, u2.T, 'k', t3, u3.T, 'k--', linewidth=2)\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Motor Voltage (V)')\n",
+        "plt.legend(['Q_{11}=4', 'Q_{11}=9', 'Q_{11}=20'], loc='best')\n",
+        "\n",
+        "plt.figure(2)\n",
+        "plt.subplot(121)\n",
+        "plt.plot(t2, y2, 'k', t4, y4, 'k-.', linewidth=2)\n",
+        "plt.grid()\n",
+        "plt.axis([0, 2, -1, 0.2])\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Angular Error (rad)')\n",
+        "\n",
+        "plt.subplot(122)\n",
+        "plt.plot(t2, x2[:, 1], 'k', t4, x4[:, 1], 'k-.', linewidth=2)\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Angular Velocity (rad/sec)')\n",
+        "plt.legend(['Q_{22}=0', 'Q_{22}=3'], loc='best')\n",
+        "\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_13/ex6_13.py b/Chapter7/python/ex6_13/ex6_13.py
new file mode 100644
index 0000000..103a5e0
--- /dev/null
+++ b/Chapter7/python/ex6_13/ex6_13.py
@@ -0,0 +1,110 @@
+# -*- coding: utf-8 -*-
+"""ex6_13.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1_7Vg6trWnU6VLOopf3TGTDfAhAYEVrv7
+"""
+
+import numpy as np
+from scipy.linalg import solve_continuous_are
+from scipy.signal import StateSpace, lsim
+import matplotlib.pyplot as plt
+
+def lqr(A, B, Q, R):
+    # Solve Riccati equation
+    X = solve_continuous_are(A, B, Q, R)
+    # Compute LQR gain
+    K = np.linalg.inv(R) @ B.T @ X
+    return K
+
+# System matrices
+A = np.array([[0, 1, 0], [0, 0, 4.438], [0, -12, -24]])
+B = np.array([[0], [0], [20]])
+C = np.array([[1, 0, 0]])
+D = np.array([[0]])
+x0 = np.array([[-1], [0], [0]])
+
+R = np.array([[1]])
+
+# Different Q matrices
+Q1 = np.diag([4, 0, 0])
+Q2 = np.diag([9, 0, 0])
+Q3 = np.diag([20, 0, 0])
+Q4 = np.diag([9, 3, 0])
+
+# LQR gains
+K1 = lqr(A, B, Q1, R)
+K2 = lqr(A, B, Q2, R)
+K3 = lqr(A, B, Q3, R)
+K4 = lqr(A, B, Q4, R)
+
+# Closed-loop systems
+Acl1 = A - B @ K1
+Acl2 = A - B @ K2
+Acl3 = A - B @ K3
+Acl4 = A - B @ K4
+
+# Create state-space systems
+sys1 = StateSpace(Acl1, B, C, D)
+sys2 = StateSpace(Acl2, B, C, D)
+sys3 = StateSpace(Acl3, B, C, D)
+sys4 = StateSpace(Acl4, B, C, D)
+
+# Time vector
+t = np.linspace(0, 2, 500)
+
+# Initial response
+t1, y1, x1 = lsim(sys1, U=0, T=t, X0=x0.flatten())
+t2, y2, x2 = lsim(sys2, U=0, T=t, X0=x0.flatten())
+t3, y3, x3 = lsim(sys3, U=0, T=t, X0=x0.flatten())
+t4, y4, x4 = lsim(sys4, U=0, T=t, X0=x0.flatten())
+
+# Control inputs
+u1 = -K1 @ x1.T
+u2 = -K2 @ x2.T
+u3 = -K3 @ x3.T
+u4 = -K4 @ x4.T
+
+print('K1: ')
+print(K1)
+print('K2: ')
+print(K2)
+print('K3: ')
+print(K3)
+print('K4: ')
+print(K4)
+
+# Plotting
+plt.figure(1)
+plt.subplot(121)
+plt.plot(t1, y1, 'k-.', t2, y2, 'k', t3, y3, 'k--', linewidth=2)
+plt.grid()
+plt.axis([0, 2, -1, 0.2])
+plt.xlabel('Time (sec)')
+plt.ylabel('Angular Error (rad)')
+
+plt.subplot(122)
+plt.plot(t1, u1.T, 'k-.', t2, u2.T, 'k', t3, u3.T, 'k--', linewidth=2)
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('Motor Voltage (V)')
+plt.legend(['Q_{11}=4', 'Q_{11}=9', 'Q_{11}=20'], loc='best')
+
+plt.figure(2)
+plt.subplot(121)
+plt.plot(t2, y2, 'k', t4, y4, 'k-.', linewidth=2)
+plt.grid()
+plt.axis([0, 2, -1, 0.2])
+plt.xlabel('Time (sec)')
+plt.ylabel('Angular Error (rad)')
+
+plt.subplot(122)
+plt.plot(t2, x2[:, 1], 'k', t4, x4[:, 1], 'k-.', linewidth=2)
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('Angular Velocity (rad/sec)')
+plt.legend(['Q_{22}=0', 'Q_{22}=3'], loc='best')
+
+plt.show()
\ No newline at end of file
diff --git a/Chapter7/python/ex6_14/ex6_14.ipynb b/Chapter7/python/ex6_14/ex6_14.ipynb
new file mode 100644
index 0000000..02fc5fb
--- /dev/null
+++ b/Chapter7/python/ex6_14/ex6_14.ipynb
@@ -0,0 +1,61 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import control\n",
+        "\n",
+        "# Define the system matrices\n",
+        "A = np.array([[0, 1, 0, 0],\n",
+        "              [0, 0, -9.8, 0],\n",
+        "              [0, 0, 0, 1],\n",
+        "              [0, 0, 19.6, 0]])\n",
+        "\n",
+        "b = np.array([[0], [1], [0], [-1]])\n",
+        "\n",
+        "# Define the weighting matrices Q and R\n",
+        "Q = np.diag([4, 0, 8.16, 0])\n",
+        "R = 1 / 400\n",
+        "\n",
+        "# Calculate the LQR gain matrix\n",
+        "k, _, _ = control.lqr(A, b, Q, R)\n",
+        "\n",
+        "print(\"LQR gain matrix k:\")\n",
+        "print(k)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SgFugqxV4qrE",
+        "outputId": "b910b227-e6ae-4b42-b2c1-7949457c57e9"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "LQR gain matrix k:\n",
+            "[[ -40.          -37.36929912 -190.66690328  -54.72826817]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_14/ex6_14.py b/Chapter7/python/ex6_14/ex6_14.py
new file mode 100644
index 0000000..1c7f1eb
--- /dev/null
+++ b/Chapter7/python/ex6_14/ex6_14.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+"""ex6_14.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1S_ROFjx4LS56lFoIRBJq8PIG0WgE3O13
+"""
+
+import numpy as np
+import control
+
+# Define the system matrices
+A = np.array([[0, 1, 0, 0],
+              [0, 0, -9.8, 0],
+              [0, 0, 0, 1],
+              [0, 0, 19.6, 0]])
+
+b = np.array([[0], [1], [0], [-1]])
+
+# Define the weighting matrices Q and R
+Q = np.diag([4, 0, 8.16, 0])
+R = 1 / 400
+
+# Calculate the LQR gain matrix
+k, _, _ = control.lqr(A, b, Q, R)
+
+print("LQR gain matrix k:")
+print(k)
\ No newline at end of file
diff --git a/Chapter7/python/ex6_15/ex6_15.ipynb b/Chapter7/python/ex6_15/ex6_15.ipynb
new file mode 100644
index 0000000..19e721e
--- /dev/null
+++ b/Chapter7/python/ex6_15/ex6_15.ipynb
@@ -0,0 +1,61 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import control\n",
+        "\n",
+        "# Define the system matrices\n",
+        "A = np.array([[0, 1, 0, 0],\n",
+        "              [0, 0, 4.438, -7.396],\n",
+        "              [0, -12, -24, 0],\n",
+        "              [0, 0, 0, -1]])\n",
+        "\n",
+        "b = np.array([[0], [0], [20], [0]])\n",
+        "\n",
+        "# Define the weighting matrices\n",
+        "R = 1\n",
+        "Q1 = np.diag([9, 0, 0, 0])\n",
+        "\n",
+        "# Calculate the LQR gain matrix\n",
+        "k, _, _ = control.lqr(A, b, Q1, R)\n",
+        "\n",
+        "print(\"LQR gain matrix k:\")\n",
+        "print(k)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8aSOCYZu5N3Z",
+        "outputId": "48e7ff0e-eec1-4e20-9d5c-a1cb726bc4dd"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "LQR gain matrix k:\n",
+            "[[ 3.          0.87955069  0.15290229 -1.8189703 ]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_15/ex6_15.py b/Chapter7/python/ex6_15/ex6_15.py
new file mode 100644
index 0000000..f1b8ea5
--- /dev/null
+++ b/Chapter7/python/ex6_15/ex6_15.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+"""ex6_15.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1QRD3AeEjjJUEyIb5e-S9xyctth4CHB6G
+"""
+
+import numpy as np
+import control
+
+# Define the system matrices
+A = np.array([[0, 1, 0, 0],
+              [0, 0, 4.438, -7.396],
+              [0, -12, -24, 0],
+              [0, 0, 0, -1]])
+
+b = np.array([[0], [0], [20], [0]])
+
+# Define the weighting matrices
+R = 1
+Q1 = np.diag([9, 0, 0, 0])
+
+# Calculate the LQR gain matrix
+k, _, _ = control.lqr(A, b, Q1, R)
+
+print("LQR gain matrix k:")
+print(k)
\ No newline at end of file
diff --git a/Chapter7/python/ex6_2/ex6_2.ipynb b/Chapter7/python/ex6_2/ex6_2.ipynb
new file mode 100644
index 0000000..417ce91
--- /dev/null
+++ b/Chapter7/python/ex6_2/ex6_2.ipynb
@@ -0,0 +1,56 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "w9djuHcUQ7Ml",
+        "outputId": "29479d61-5e4a-48f1-b65b-6fa1d4d42774"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gain matrix k:\n",
+            "4.867057232987826 1.2251464623704367 0.30000000000000004\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.signal import place_poles\n",
+        "\n",
+        "\n",
+        "# Define matrices and vectors\n",
+        "A = np.array([[0, 1, 0], [0, 0, 4.438], [0, -12, -24]])\n",
+        "b = np.array([[0], [0], [20]])\n",
+        "pd = np.array([-24, -3-3j, -3+3j])\n",
+        "\n",
+        "# Place poles\n",
+        "result = place_poles(A, b, pd)\n",
+        "k = result.gain_matrix\n",
+        "\n",
+        "print(\"Gain matrix k:\")\n",
+        "print(k[0][0], k[0][1], k[0][2])\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_2/ex6_2.py b/Chapter7/python/ex6_2/ex6_2.py
new file mode 100644
index 0000000..c4e3fc4
--- /dev/null
+++ b/Chapter7/python/ex6_2/ex6_2.py
@@ -0,0 +1,17 @@
+
+
+import numpy as np
+from scipy.signal import place_poles
+
+
+# Define matrices and vectors
+A = np.array([[0, 1, 0], [0, 0, 4.438], [0, -12, -24]])
+b = np.array([[0], [0], [20]])
+pd = np.array([-24, -3-3j, -3+3j])
+
+# Place poles
+result = place_poles(A, b, pd)
+k = result.gain_matrix
+
+print("Gain matrix k:")
+print(k[0][0], k[0][1], k[0][2])
\ No newline at end of file
diff --git a/Chapter7/python/ex6_3/ex6_3.ipynb b/Chapter7/python/ex6_3/ex6_3.ipynb
new file mode 100644
index 0000000..f2f0ef6
--- /dev/null
+++ b/Chapter7/python/ex6_3/ex6_3.ipynb
@@ -0,0 +1,117 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 332
+        },
+        "id": "_zy2_mhESJmZ",
+        "outputId": "cb4f8252-147c-49e6-a773-1ae696b3c694"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "expected square matrix",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-1-dcfff65eed5a>\u001b[0m in \u001b[0;36m<cell line: 22>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# Calculating the LQR gain matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Gain matrix k:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/scipy/linalg/_basic.py\u001b[0m in \u001b[0;36minv\u001b[0;34m(a, overwrite_a, check_finite)\u001b[0m\n\u001b[1;32m    943\u001b[0m     \u001b[0ma1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_validated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_finite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0ma1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0ma1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 945\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'expected square matrix'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    946\u001b[0m     \u001b[0moverwrite_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moverwrite_a\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_datacopied\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m     \u001b[0;31m# XXX: I found no advantage or disadvantage of using finv.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: expected square matrix"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.linalg import solve_continuous_are\n",
+        "from scipy.linalg import inv\n",
+        "\n",
+        "\n",
+        "# Define matrices and vectors\n",
+        "A = np.array([[0, 1, 0, 0],\n",
+        "              [0, 0, -9.8, 0],\n",
+        "              [0, 0, 0, 1],\n",
+        "              [0, 0, 19.6, 0]])\n",
+        "b = np.array([[0],\n",
+        "              [1],\n",
+        "              [0],\n",
+        "              [-1]])\n",
+        "Q = np.diag([4, 0, 8.16, 0])\n",
+        "R = 1/400\n",
+        "\n",
+        "# Solving the continuous time Algebraic Riccati Equation (ARE)\n",
+        "P = solve_continuous_are(A, b, Q, R)\n",
+        "\n",
+        "# Calculating the LQR gain matrix\n",
+        "k = np.dot(inv(R), np.dot(b.T, P))\n",
+        "\n",
+        "print(\"Gain matrix k:\")\n",
+        "print(k)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy.linalg import solve_continuous_are\n",
+        "\n",
+        "# Define matrices and vectors\n",
+        "A = np.array([[0, 1, 0, 0],\n",
+        "              [0, 0, -9.8, 0],\n",
+        "              [0, 0, 0, 1],\n",
+        "              [0, 0, 19.6, 0]])\n",
+        "b = np.array([[0],\n",
+        "              [1],\n",
+        "              [0],\n",
+        "              [-1]])\n",
+        "Q = np.diag([4, 0, 8.16, 0])\n",
+        "R = 1/400\n",
+        "\n",
+        "# Solving the continuous time Algebraic Riccati Equation (ARE)\n",
+        "P = solve_continuous_are(A, b, Q, R)\n",
+        "\n",
+        "# Calculating the LQR gain matrix\n",
+        "k = np.dot(np.linalg.inv(np.array([[R]])), np.dot(b.T, P))\n",
+        "\n",
+        "print(\"Gain matrix k:\")\n",
+        "print(k)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vLClEhE1Se6y",
+        "outputId": "5e61b668-8caf-4ab0-8913-47afcb98af29"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gain matrix k:\n",
+            "[[ -40.          -37.36929912 -190.66690328  -54.72826817]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_3/ex6_3.py b/Chapter7/python/ex6_3/ex6_3.py
new file mode 100644
index 0000000..fd3712a
--- /dev/null
+++ b/Chapter7/python/ex6_3/ex6_3.py
@@ -0,0 +1,51 @@
+
+
+import numpy as np
+from scipy.linalg import solve_continuous_are
+from scipy.linalg import inv
+
+
+# Define matrices and vectors
+A = np.array([[0, 1, 0, 0],
+              [0, 0, -9.8, 0],
+              [0, 0, 0, 1],
+              [0, 0, 19.6, 0]])
+b = np.array([[0],
+              [1],
+              [0],
+              [-1]])
+Q = np.diag([4, 0, 8.16, 0])
+R = 1/400
+
+# Solving the continuous time Algebraic Riccati Equation (ARE)
+P = solve_continuous_are(A, b, Q, R)
+
+# Calculating the LQR gain matrix
+k = np.dot(inv(R), np.dot(b.T, P))
+
+print("Gain matrix k:")
+print(k)
+
+import numpy as np
+from scipy.linalg import solve_continuous_are
+
+# Define matrices and vectors
+A = np.array([[0, 1, 0, 0],
+              [0, 0, -9.8, 0],
+              [0, 0, 0, 1],
+              [0, 0, 19.6, 0]])
+b = np.array([[0],
+              [1],
+              [0],
+              [-1]])
+Q = np.diag([4, 0, 8.16, 0])
+R = 1/400
+
+# Solving the continuous time Algebraic Riccati Equation (ARE)
+P = solve_continuous_are(A, b, Q, R)
+
+# Calculating the LQR gain matrix
+k = np.dot(np.linalg.inv(np.array([[R]])), np.dot(b.T, P))
+
+print("Gain matrix k:")
+print(k)
diff --git a/Chapter7/python/ex6_4/ex6_4.ipynb b/Chapter7/python/ex6_4/ex6_4.ipynb
new file mode 100644
index 0000000..632881a
--- /dev/null
+++ b/Chapter7/python/ex6_4/ex6_4.ipynb
@@ -0,0 +1,70 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import control\n",
+        "\n",
+        "# Define the matrices\n",
+        "A = np.array([[0, 1, 0, 0],\n",
+        "              [0, 0, -9.8, 0],\n",
+        "              [0, 0, 0, 1],\n",
+        "              [0, 0, 19.6, 0]])\n",
+        "\n",
+        "b = np.array([[0],\n",
+        "              [1],\n",
+        "              [0],\n",
+        "              [-1]])\n",
+        "\n",
+        "# Controllability matrix\n",
+        "\n",
+        "C = control.ctrb(A,b)\n",
+        "a = np.array([0, -19.6, 0, 0])\n",
+        "\n",
+        "alpha = np.array([12.86, 63.065, 149.38, 157.0])\n",
+        "\n",
+        "Psi = np.array([[1, a[0], a[1], a[2]],\n",
+        "                [0, 1, a[0], a[1]],\n",
+        "                [0, 0, 1, a[0]],\n",
+        "                [0, 0, 0, 1]])\n",
+        "\n",
+        "# Compute k\n",
+        "k = np.dot(alpha - a, np.linalg.inv(np.dot(C, Psi)))\n",
+        "\n",
+        "print(\"k:\", k)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "af4HmmObVeWH",
+        "outputId": "4498bd22-2713-44c9-a7d5-1805aeff1234"
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "k: [-16.02040816 -15.24285714 -98.68540816 -28.10285714]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_4/ex6_4.py b/Chapter7/python/ex6_4/ex6_4.py
new file mode 100644
index 0000000..defe6fb
--- /dev/null
+++ b/Chapter7/python/ex6_4/ex6_4.py
@@ -0,0 +1,32 @@
+
+
+import numpy as np
+import control
+
+# Define the matrices
+A = np.array([[0, 1, 0, 0],
+              [0, 0, -9.8, 0],
+              [0, 0, 0, 1],
+              [0, 0, 19.6, 0]])
+
+b = np.array([[0],
+              [1],
+              [0],
+              [-1]])
+
+# Controllability matrix
+
+C = control.ctrb(A,b)
+a = np.array([0, -19.6, 0, 0])
+
+alpha = np.array([12.86, 63.065, 149.38, 157.0])
+
+Psi = np.array([[1, a[0], a[1], a[2]],
+                [0, 1, a[0], a[1]],
+                [0, 0, 1, a[0]],
+                [0, 0, 0, 1]])
+
+# Compute k
+k = np.dot(alpha - a, np.linalg.inv(np.dot(C, Psi)))
+
+print("k:", k)
diff --git a/Chapter7/python/ex6_5/ex6_5.ipynb b/Chapter7/python/ex6_5/ex6_5.ipynb
new file mode 100644
index 0000000..1f48899
--- /dev/null
+++ b/Chapter7/python/ex6_5/ex6_5.ipynb
@@ -0,0 +1,75 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "l2nWrSyvNeVA",
+        "outputId": "5d3d0038-ca8d-4cdd-815f-fffd6b4df3a0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gain vector k:\n",
+            " [-16.02040816 -15.24285714 -98.68540816 -28.10285714]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.linalg import inv\n",
+        "\n",
+        "# Define matrices\n",
+        "A = np.array([[0, 1, 0, 0], [0, 0, -9.8, 0], [0, 0, 0, 1], [0, 0, 19.6, 0]])\n",
+        "b = np.array([[0], [1], [0], [-1]])\n",
+        "\n",
+        "# Controllability matrix\n",
+        "def ctrb(A, B):\n",
+        "    n = A.shape[0]\n",
+        "    C = B\n",
+        "    for i in range(1, n):\n",
+        "        C = np.hstack((C, np.linalg.matrix_power(A, i) @ B))\n",
+        "    return C\n",
+        "\n",
+        "C = ctrb(A, b)\n",
+        "\n",
+        "# Define vectors\n",
+        "a = np.array([0, -19.6, 0, 0])\n",
+        "alpha = np.array([12.86, 63.065, 149.38, 157.0])\n",
+        "\n",
+        "# Psi_1 matrix\n",
+        "Psi_1 = np.array([\n",
+        "    [1, -a[0], a[0]**2 - a[1], -a[0]**3 + 2*a[0]*a[1] - a[2]],\n",
+        "    [0, 1, -a[0], a[0]**2 - a[1]],\n",
+        "    [0, 0, 1, -a[0]],\n",
+        "    [0, 0, 0, 1]\n",
+        "])\n",
+        "\n",
+        "# Calculate k\n",
+        "k = (alpha - a) @ Psi_1 @ inv(C)\n",
+        "\n",
+        "# Display result\n",
+        "print(\"Gain vector k:\\n\", k)\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_5/ex6_5.py b/Chapter7/python/ex6_5/ex6_5.py
new file mode 100644
index 0000000..8be1ffa
--- /dev/null
+++ b/Chapter7/python/ex6_5/ex6_5.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+"""ex6_5.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1HE0H9YLRCefCzc3LcBsP5FeWrDqJ70k5
+"""
+
+import numpy as np
+from scipy.linalg import inv
+
+# Define matrices
+A = np.array([[0, 1, 0, 0], [0, 0, -9.8, 0], [0, 0, 0, 1], [0, 0, 19.6, 0]])
+b = np.array([[0], [1], [0], [-1]])
+
+# Controllability matrix
+def ctrb(A, B):
+    n = A.shape[0]
+    C = B
+    for i in range(1, n):
+        C = np.hstack((C, np.linalg.matrix_power(A, i) @ B))
+    return C
+
+C = ctrb(A, b)
+
+# Define vectors
+a = np.array([0, -19.6, 0, 0])
+alpha = np.array([12.86, 63.065, 149.38, 157.0])
+
+# Psi_1 matrix
+Psi_1 = np.array([
+    [1, -a[0], a[0]**2 - a[1], -a[0]**3 + 2*a[0]*a[1] - a[2]],
+    [0, 1, -a[0], a[0]**2 - a[1]],
+    [0, 0, 1, -a[0]],
+    [0, 0, 0, 1]
+])
+
+# Calculate k
+k = (alpha - a) @ Psi_1 @ inv(C)
+
+# Display result
+print("Gain vector k:\n", k)
\ No newline at end of file
diff --git a/Chapter7/python/ex6_6/ex6_6.ipynb b/Chapter7/python/ex6_6/ex6_6.ipynb
new file mode 100644
index 0000000..8162fa2
--- /dev/null
+++ b/Chapter7/python/ex6_6/ex6_6.ipynb
@@ -0,0 +1,63 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy.linalg import eig\n",
+        "from control import acker\n",
+        "\n",
+        "# Define matrices\n",
+        "A = np.array([[0, 1, 0, 0], [0, 0, -9.8, 0], [0, 0, 0, 1], [0, 0, 19.6, 0]])\n",
+        "b = np.array([[0], [1], [0], [-1]])\n",
+        "\n",
+        "# Compute eigenvalues of A\n",
+        "e = eig(A)[0]\n",
+        "\n",
+        "# Desired pole locations\n",
+        "pd = [-4.43, -4.43, -2-2j, -2+2j]\n",
+        "\n",
+        "# Pole placement using acker\n",
+        "k = acker(A, b, pd)\n",
+        "\n",
+        "# Display results\n",
+        "print(\"Eigenvalues of A:\\n\", e)\n",
+        "print(\"Gain vector k:\\n\", k)\n"
+      ],
+      "metadata": {
+        "id": "XmBWhO0NOeqi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c17707ff-2981-4886-dae4-893bdb44e9fe"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Eigenvalues of A:\n",
+            " [ 0.        +0.j  0.        +0.j  4.42718872+0.j -4.42718872+0.j]\n",
+            "Gain vector k:\n",
+            " [[-16.02032653 -15.24281633 -98.68522653 -28.10281633]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_6/ex6_6.py b/Chapter7/python/ex6_6/ex6_6.py
new file mode 100644
index 0000000..b3da308
--- /dev/null
+++ b/Chapter7/python/ex6_6/ex6_6.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+"""ex6_6.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1aWwm72XFcUaRJjpULHAiYHpJJ30mw0XZ
+"""
+
+import numpy as np
+from scipy.linalg import eig
+from control import acker
+
+# Define matrices
+A = np.array([[0, 1, 0, 0], [0, 0, -9.8, 0], [0, 0, 0, 1], [0, 0, 19.6, 0]])
+b = np.array([[0], [1], [0], [-1]])
+
+# Compute eigenvalues of A
+e = eig(A)[0]
+
+# Desired pole locations
+pd = [-4.43, -4.43, -2-2j, -2+2j]
+
+# Pole placement using acker
+k = acker(A, b, pd)
+
+# Display results
+print("Eigenvalues of A:\n", e)
+print("Gain vector k:\n", k)
\ No newline at end of file
diff --git a/Chapter7/python/ex6_9/ex6_9.ipynb b/Chapter7/python/ex6_9/ex6_9.ipynb
new file mode 100644
index 0000000..a1077cf
--- /dev/null
+++ b/Chapter7/python/ex6_9/ex6_9.ipynb
@@ -0,0 +1,97 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import control as ctrl\n",
+        "\n",
+        "# Define matrices A, B, and f\n",
+        "A = np.array([[-2, -1, 2], [-1, -2, 2], [-2, 0, 2]])\n",
+        "B = np.array([[0, 0], [0, 1], [1, 0]])\n",
+        "f = np.array([[1], [1]])\n",
+        "\n",
+        "b = np.dot(B, f)\n",
+        "# Calculate controllability matrix C\n",
+        "C = ctrl.ctrb(A, b)\n",
+        "# Desired closed-loop poles (modified to a 1D array)\n",
+        "pd = np.array([-2, -2, -2])  # Changed to a 1D array\n",
+        "\n",
+        "Psi = np.array([[1, 2, -1],[0, 1 ,2],[0, 0, 1]])\n",
+        "\n",
+        "delta = np.array([4, 13, 10]).reshape((-1, 1))\n",
+        "M = np.dot(delta.T, np.linalg.inv(np.dot(C,Psi)))\n",
+        "K1= np.dot(f, M)\n",
+        "\n",
+        "# Calculate state feedback gain K using control.acker\n",
+        "K = ctrl.acker(A, b, pd)\n",
+        "\n",
+        "# Calculate K1\n",
+        "K1 = np.dot(f,M)\n",
+        "# Calculate K1\n",
+        "K2 = np.dot(f,K)\n",
+        "\n",
+        "# Calculate the closed-loop system matrix Ac and its eigenvalues\n",
+        "Ac = A - np.dot(B, K1)\n",
+        "eigenvalues, _ = np.linalg.eig(Ac)\n",
+        "# Print results\n",
+        "print('M: ')\n",
+        "print(M)\n",
+        "print(\" K:\")\n",
+        "print(K)\n",
+        "print(\"\\n K1:\")\n",
+        "print(K1)\n",
+        "print(\"\\n K2:\")\n",
+        "print(K2)\n",
+        "print(\"\\nEigenvalues of Ac:\")\n",
+        "print(eigenvalues)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "p_nQiijpWLml",
+        "outputId": "4a920d31-57c6-4d8b-83b3-67ad824ad990"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "M: \n",
+            "[[-2.2  0.4  3.6]]\n",
+            " K:\n",
+            "[[-2.2  0.4  3.6]]\n",
+            "\n",
+            " K1:\n",
+            "[[-2.2  0.4  3.6]\n",
+            " [-2.2  0.4  3.6]]\n",
+            "\n",
+            " K2:\n",
+            "[[-2.2  0.4  3.6]\n",
+            " [-2.2  0.4  3.6]]\n",
+            "\n",
+            "Eigenvalues of Ac:\n",
+            "[-2.00001117+0.00000000e+00j -1.99999442+9.67093793e-06j\n",
+            " -1.99999442-9.67093793e-06j]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/ex6_9/ex6_9.py b/Chapter7/python/ex6_9/ex6_9.py
new file mode 100644
index 0000000..1f5f785
--- /dev/null
+++ b/Chapter7/python/ex6_9/ex6_9.py
@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+"""ex6_9.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1eUnN9-RmEe9XHoRJRnQwY4Lf41mef1rL
+"""
+
+import numpy as np
+import control as ctrl
+
+# Define matrices A, B, and f
+A = np.array([[-2, -1, 2], [-1, -2, 2], [-2, 0, 2]])
+B = np.array([[0, 0], [0, 1], [1, 0]])
+f = np.array([[1], [1]])
+
+b = np.dot(B, f)
+# Calculate controllability matrix C
+C = ctrl.ctrb(A, b)
+# Desired closed-loop poles (modified to a 1D array)
+pd = np.array([-2, -2, -2])  # Changed to a 1D array
+
+Psi = np.array([[1, 2, -1],[0, 1 ,2],[0, 0, 1]])
+
+delta = np.array([4, 13, 10]).reshape((-1, 1))
+M = np.dot(delta.T, np.linalg.inv(np.dot(C,Psi)))
+K1= np.dot(f, M)
+
+# Calculate state feedback gain K using control.acker
+K = ctrl.acker(A, b, pd)
+
+# Calculate K1
+K1 = np.dot(f,M)
+# Calculate K1
+K2 = np.dot(f,K)
+
+# Calculate the closed-loop system matrix Ac and its eigenvalues
+Ac = A - np.dot(B, K1)
+eigenvalues, _ = np.linalg.eig(Ac)
+# Print results
+print('M: ')
+print(M)
+print(" K:")
+print(K)
+print("\n K1:")
+print(K1)
+print("\n K2:")
+print(K2)
+print("\nEigenvalues of Ac:")
+print(eigenvalues)
\ No newline at end of file
diff --git a/Chapter7/python/fig6_5/fig6_5.ipynb b/Chapter7/python/fig6_5/fig6_5.ipynb
new file mode 100644
index 0000000..c8a2fe7
--- /dev/null
+++ b/Chapter7/python/fig6_5/fig6_5.ipynb
@@ -0,0 +1,108 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "-Z7rmU7Fzyue",
+        "outputId": "35c37e6a-b0bf-4e20-c30f-026764ac2ff0"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Define k values\n",
+        "k = np.arange(0.02, 5.02, 0.02)\n",
+        "x = np.array([1, 0])\n",
+        "\n",
+        "# Calculate J1, J2, J3 for different values of r\n",
+        "r_values = [0, 1, 2]\n",
+        "J1 = []\n",
+        "J2 = []\n",
+        "J3 = []\n",
+        "\n",
+        "for r in r_values:\n",
+        "    p11 = 0.5 / k + 0.5 + (r + 1) * k / 2 + r * k**2 / 2\n",
+        "    p12 = 0 / (5 * k) + r * k / 2\n",
+        "    p22 = 0.5 / k + 0.5 + r * k / 2\n",
+        "    J = p11 * x[0]**2 + 2 * p12 * x[0] * x[1] + p22 * x[1]**2\n",
+        "    if r == 0:\n",
+        "        J1 = J\n",
+        "    elif r == 1:\n",
+        "        J2 = J\n",
+        "    elif r == 2:\n",
+        "        J3 = J\n",
+        "\n",
+        "# Plot J1, J2, J3\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(k, J1, 'k', label='r=0', linewidth=2)\n",
+        "plt.plot(k, J2, '-.', color='black', label='r=1', linewidth=2)\n",
+        "plt.plot(k, J3, '--', color='black', label='r=2', linewidth=2)\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('k')\n",
+        "plt.ylabel('J')\n",
+        "plt.legend()\n",
+        "plt.title('Cost Function J for Different Values of r')\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot J2 and J4\n",
+        "r = 2\n",
+        "x = np.array([0, 1])\n",
+        "p11 = 0.5 / k + 0.5 + (r + 1) * k / 2 + r * k**2 / 2\n",
+        "p12 = 0 / (5 * k) + r * k / 2\n",
+        "p22 = 0.5 / k + 0.5 + r * k / 2\n",
+        "J4 = p11 * x[0]**2 + 2 * p12 * x[0] * x[1] + p22 * x[1]**2\n",
+        "\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.plot(k, J2, 'k', label='J2', linewidth=2)\n",
+        "plt.plot(k, J4, '-.', color='black', label='J4', linewidth=2)\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('k')\n",
+        "plt.ylabel('J')\n",
+        "plt.legend()\n",
+        "plt.title('Comparison of Cost Functions J2 and J4')\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/fig6_5/fig6_5.py b/Chapter7/python/fig6_5/fig6_5.py
new file mode 100644
index 0000000..5c74e80
--- /dev/null
+++ b/Chapter7/python/fig6_5/fig6_5.py
@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+"""fig6_5.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1o16VqdZb4Iy2ioogBamja2bYzAKoU9ZP
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Define k values
+k = np.arange(0.02, 5.02, 0.02)
+x = np.array([1, 0])
+
+# Calculate J1, J2, J3 for different values of r
+r_values = [0, 1, 2]
+J1 = []
+J2 = []
+J3 = []
+
+for r in r_values:
+    p11 = 0.5 / k + 0.5 + (r + 1) * k / 2 + r * k**2 / 2
+    p12 = 0 / (5 * k) + r * k / 2
+    p22 = 0.5 / k + 0.5 + r * k / 2
+    J = p11 * x[0]**2 + 2 * p12 * x[0] * x[1] + p22 * x[1]**2
+    if r == 0:
+        J1 = J
+    elif r == 1:
+        J2 = J
+    elif r == 2:
+        J3 = J
+
+# Plot J1, J2, J3
+plt.figure(figsize=(10, 6))
+plt.plot(k, J1, 'k', label='r=0', linewidth=2)
+plt.plot(k, J2, '-.', color='black', label='r=1', linewidth=2)
+plt.plot(k, J3, '--', color='black', label='r=2', linewidth=2)
+plt.grid(True)
+plt.xlabel('k')
+plt.ylabel('J')
+plt.legend()
+plt.title('Cost Function J for Different Values of r')
+plt.show()
+
+# Plot J2 and J4
+r = 2
+x = np.array([0, 1])
+p11 = 0.5 / k + 0.5 + (r + 1) * k / 2 + r * k**2 / 2
+p12 = 0 / (5 * k) + r * k / 2
+p22 = 0.5 / k + 0.5 + r * k / 2
+J4 = p11 * x[0]**2 + 2 * p12 * x[0] * x[1] + p22 * x[1]**2
+
+plt.figure(figsize=(10, 6))
+plt.plot(k, J2, 'k', label='J2', linewidth=2)
+plt.plot(k, J4, '-.', color='black', label='J4', linewidth=2)
+plt.grid(True)
+plt.xlabel('k')
+plt.ylabel('J')
+plt.legend()
+plt.title('Comparison of Cost Functions J2 and J4')
+plt.show()
\ No newline at end of file
diff --git a/Chapter7/python/inverted_pendulum_k1/inverted_pendulum_k1.ipynb b/Chapter7/python/inverted_pendulum_k1/inverted_pendulum_k1.ipynb
new file mode 100644
index 0000000..ff00797
--- /dev/null
+++ b/Chapter7/python/inverted_pendulum_k1/inverted_pendulum_k1.ipynb
@@ -0,0 +1,55 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "3JYmIX4JrfDB"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "def inverted_pendulum_k1(t, x):\n",
+        "    # Constants\n",
+        "    g = 9.8\n",
+        "    l = 1\n",
+        "    m = 1\n",
+        "    M = 1\n",
+        "\n",
+        "    # State feedback gains\n",
+        "    k = np.array([-16.0203, -15.2428, -98.6852, -28.1028])\n",
+        "\n",
+        "    # Intermediate calculations\n",
+        "    d1 = M + m * (1 - np.cos(x[2]) ** 2)\n",
+        "    d2 = l * d1\n",
+        "\n",
+        "    # State feedback\n",
+        "    F = -np.dot(k, x)\n",
+        "\n",
+        "    # State derivatives\n",
+        "    xp = np.zeros(4)\n",
+        "    xp[0] = x[1]\n",
+        "    xp[1] = (F + m * l * x[3] ** 2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1\n",
+        "    xp[2] = x[3]\n",
+        "    xp[3] = (-F * np.cos(x[2]) - m * l * x[3] ** 2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2\n",
+        "\n",
+        "    return xp\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/inverted_pendulum_k1/inverted_pendulum_k1.py b/Chapter7/python/inverted_pendulum_k1/inverted_pendulum_k1.py
new file mode 100644
index 0000000..0841441
--- /dev/null
+++ b/Chapter7/python/inverted_pendulum_k1/inverted_pendulum_k1.py
@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+"""inverted_pendulum_k1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1d8oeJdHkG77AVqVFhmrIphWQY2D-VY3o
+"""
+
+import numpy as np
+
+def inverted_pendulum_k1(t, x):
+    # Constants
+    g = 9.8
+    l = 1
+    m = 1
+    M = 1
+
+    # State feedback gains
+    k = np.array([-16.0203, -15.2428, -98.6852, -28.1028])
+
+    # Intermediate calculations
+    d1 = M + m * (1 - np.cos(x[2]) ** 2)
+    d2 = l * d1
+
+    # State feedback
+    F = -np.dot(k, x)
+
+    # State derivatives
+    xp = np.zeros(4)
+    xp[0] = x[1]
+    xp[1] = (F + m * l * x[3] ** 2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1
+    xp[2] = x[3]
+    xp[3] = (-F * np.cos(x[2]) - m * l * x[3] ** 2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2
+
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/inverted_pendulum_k2/inverted_pendulum_k2.ipynb b/Chapter7/python/inverted_pendulum_k2/inverted_pendulum_k2.ipynb
new file mode 100644
index 0000000..d4e8042
--- /dev/null
+++ b/Chapter7/python/inverted_pendulum_k2/inverted_pendulum_k2.ipynb
@@ -0,0 +1,55 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "Z5KHXGU9u-fr"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "def inverted_pendulum_k2(t, x):\n",
+        "    # Constants\n",
+        "    g = 9.8\n",
+        "    l = 1\n",
+        "    m = 1\n",
+        "    M = 1\n",
+        "\n",
+        "    # State feedback gains\n",
+        "    k = np.array([-40.0000, -37.3693, -190.6669, -54.7283])\n",
+        "\n",
+        "    # Intermediate calculations\n",
+        "    d1 = M + m * (1 - np.cos(x[2]) ** 2)\n",
+        "    d2 = l * d1\n",
+        "\n",
+        "    # State feedback\n",
+        "    F = -np.dot(k, x)\n",
+        "\n",
+        "    # State derivatives\n",
+        "    xp = np.zeros(4)\n",
+        "    xp[0] = x[1]\n",
+        "    xp[1] = (F + m * l * x[3] ** 2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1\n",
+        "    xp[2] = x[3]\n",
+        "    xp[3] = (-F * np.cos(x[2]) - m * l * x[3] ** 2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2\n",
+        "\n",
+        "    return xp\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/inverted_pendulum_k2/inverted_pendulum_k2.py b/Chapter7/python/inverted_pendulum_k2/inverted_pendulum_k2.py
new file mode 100644
index 0000000..5ee5d4d
--- /dev/null
+++ b/Chapter7/python/inverted_pendulum_k2/inverted_pendulum_k2.py
@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+"""inverted_pendulum_k2.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1d30XrhU8Du5hW2s6wS84jjVjz92XJbgJ
+"""
+
+import numpy as np
+
+def inverted_pendulum_k2(t, x):
+    # Constants
+    g = 9.8
+    l = 1
+    m = 1
+    M = 1
+
+    # State feedback gains
+    k = np.array([-40.0000, -37.3693, -190.6669, -54.7283])
+
+    # Intermediate calculations
+    d1 = M + m * (1 - np.cos(x[2]) ** 2)
+    d2 = l * d1
+
+    # State feedback
+    F = -np.dot(k, x)
+
+    # State derivatives
+    xp = np.zeros(4)
+    xp[0] = x[1]
+    xp[1] = (F + m * l * x[3] ** 2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1
+    xp[2] = x[3]
+    xp[3] = (-F * np.cos(x[2]) - m * l * x[3] ** 2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2
+
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/invpend_solver2/Invpend_solver2.ipynb b/Chapter7/python/invpend_solver2/Invpend_solver2.ipynb
new file mode 100644
index 0000000..0f9716f
--- /dev/null
+++ b/Chapter7/python/invpend_solver2/Invpend_solver2.ipynb
@@ -0,0 +1,95 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449
+        },
+        "id": "A6FCJfB5vYnF",
+        "outputId": "a9b8e964-9dca-4263-d133-3b8dba2032af"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "def inverted_pendulum_k2(t, x):\n",
+        "    # Constants\n",
+        "    g = 9.8\n",
+        "    l = 1\n",
+        "    m = 1\n",
+        "    M = 1\n",
+        "\n",
+        "    # State feedback gains\n",
+        "    k = np.array([-40.0000, -37.3693, -190.6669, -54.7283])\n",
+        "\n",
+        "    # Intermediate calculations\n",
+        "    d1 = M + m * (1 - np.cos(x[2]) ** 2)\n",
+        "    d2 = l * d1\n",
+        "\n",
+        "    # State feedback\n",
+        "    F = -np.dot(k, x)\n",
+        "\n",
+        "    # State derivatives\n",
+        "    xp = np.zeros(4)\n",
+        "    xp[0] = x[1]\n",
+        "    xp[1] = (F + m * l * x[3] ** 2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1\n",
+        "    xp[2] = x[3]\n",
+        "    xp[3] = (-F * np.cos(x[2]) - m * l * x[3] ** 2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2\n",
+        "\n",
+        "    return xp\n",
+        "\n",
+        "# Initial conditions: [x, v, theta, omega]\n",
+        "x0 = [0, 0, 0.6, 0]\n",
+        "\n",
+        "# Time span\n",
+        "t_span = (0, 3)\n",
+        "t_eval = np.linspace(t_span[0], t_span[1], 300)  # 300 points within 3 seconds\n",
+        "\n",
+        "# Solve the ODE\n",
+        "sol = solve_ivp(inverted_pendulum_k2, t_span, x0, t_eval=t_eval, max_step=1e-2)\n",
+        "\n",
+        "# Plotting\n",
+        "plt.plot(sol.t, sol.y[0], 'k', label='x (m)')\n",
+        "plt.plot(sol.t, sol.y[2], '-.k', label='θ (rad)')\n",
+        "plt.grid(True)\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State Variables')\n",
+        "plt.legend()\n",
+        "plt.gca().set_prop_cycle(None)  # Reset the color cycle\n",
+        "for line in plt.gca().get_lines():\n",
+        "    line.set_linewidth(2)\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/invpend_solver2/invpend_solver2.py b/Chapter7/python/invpend_solver2/invpend_solver2.py
new file mode 100644
index 0000000..a6619a0
--- /dev/null
+++ b/Chapter7/python/invpend_solver2/invpend_solver2.py
@@ -0,0 +1,60 @@
+# -*- coding: utf-8 -*-
+"""Invpend_solver2.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1VFkly3yQm9lRWTmqZ3GigS-c7jLDl01D
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+def inverted_pendulum_k2(t, x):
+    # Constants
+    g = 9.8
+    l = 1
+    m = 1
+    M = 1
+
+    # State feedback gains
+    k = np.array([-40.0000, -37.3693, -190.6669, -54.7283])
+
+    # Intermediate calculations
+    d1 = M + m * (1 - np.cos(x[2]) ** 2)
+    d2 = l * d1
+
+    # State feedback
+    F = -np.dot(k, x)
+
+    # State derivatives
+    xp = np.zeros(4)
+    xp[0] = x[1]
+    xp[1] = (F + m * l * x[3] ** 2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1
+    xp[2] = x[3]
+    xp[3] = (-F * np.cos(x[2]) - m * l * x[3] ** 2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2
+
+    return xp
+
+# Initial conditions: [x, v, theta, omega]
+x0 = [0, 0, 0.6, 0]
+
+# Time span
+t_span = (0, 3)
+t_eval = np.linspace(t_span[0], t_span[1], 300)  # 300 points within 3 seconds
+
+# Solve the ODE
+sol = solve_ivp(inverted_pendulum_k2, t_span, x0, t_eval=t_eval, max_step=1e-2)
+
+# Plotting
+plt.plot(sol.t, sol.y[0], 'k', label='x (m)')
+plt.plot(sol.t, sol.y[2], '-.k', label='θ (rad)')
+plt.grid(True)
+plt.xlabel('Time (sec)')
+plt.ylabel('State Variables')
+plt.legend()
+plt.gca().set_prop_cycle(None)  # Reset the color cycle
+for line in plt.gca().get_lines():
+    line.set_linewidth(2)
+plt.show()
\ No newline at end of file
diff --git a/Chapter7/python/train_fb/train_fb.ipynb b/Chapter7/python/train_fb/train_fb.ipynb
new file mode 100644
index 0000000..2e3f869
--- /dev/null
+++ b/Chapter7/python/train_fb/train_fb.ipynb
@@ -0,0 +1,62 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "AUoAcSGtiV5h"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "def train_fb(t, x):\n",
+        "    A = np.array([\n",
+        "        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 0, 1, -1],\n",
+        "        [0, -12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],\n",
+        "        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "        [0, 0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],\n",
+        "        [0, 0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    b1 = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input\n",
+        "    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])  # Constant input\n",
+        "\n",
+        "    vd = 25 * (1 - np.exp(-t / 40))\n",
+        "    # Second Design R=35/120^2\n",
+        "    k = np.array([0.4559, 0.3331, 0.2170, 0.1069, 11.5387, -0.2622,\n",
+        "                  -0.3371, -0.3865, -0.4110, 5.3731])\n",
+        "\n",
+        "    dx = np.array([x[1] - 20, x[2] - 20, x[3] - 20, x[4] - 20])\n",
+        "    dv = np.array([x[5] - vd, x[6] - vd, x[7] - vd, x[8] - vd, x[9] - vd])\n",
+        "    z = x[5] - vd\n",
+        "    X = np.concatenate((dx, dv, [z]))\n",
+        "\n",
+        "    u = -k.dot(X)\n",
+        "    xp = A.dot(x) + b1 * u + b2\n",
+        "    return xp\n",
+        "\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/train_fb/train_fb.py b/Chapter7/python/train_fb/train_fb.py
new file mode 100644
index 0000000..e8c322d
--- /dev/null
+++ b/Chapter7/python/train_fb/train_fb.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""train_fb.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/191baa7TT3w5itsddv3cCymIBrCCkfe1e
+"""
+
+import numpy as np
+from scipy.integrate import solve_ivp
+
+def train_fb(t, x):
+    A = np.array([
+        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 1, -1],
+        [0, -12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],
+        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+        [0, 0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],
+        [0, 0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]
+    ])
+
+    b1 = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input
+    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])  # Constant input
+
+    vd = 25 * (1 - np.exp(-t / 40))
+    # Second Design R=35/120^2
+    k = np.array([0.4559, 0.3331, 0.2170, 0.1069, 11.5387, -0.2622,
+                  -0.3371, -0.3865, -0.4110, 5.3731])
+
+    dx = np.array([x[1] - 20, x[2] - 20, x[3] - 20, x[4] - 20])
+    dv = np.array([x[5] - vd, x[6] - vd, x[7] - vd, x[8] - vd, x[9] - vd])
+    z = x[5] - vd
+    X = np.concatenate((dx, dv, [z]))
+
+    u = -k.dot(X)
+    xp = A.dot(x) + b1 * u + b2
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/train_fb1/train_fb1.ipynb b/Chapter7/python/train_fb1/train_fb1.ipynb
new file mode 100644
index 0000000..2dbf4c1
--- /dev/null
+++ b/Chapter7/python/train_fb1/train_fb1.ipynb
@@ -0,0 +1,62 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "fAAii_zHjbHR"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def train_fb(t, x):\n",
+        "    A = np.array([\n",
+        "        [0, 0, 0, 0, 1, -1, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "        [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0, 0],\n",
+        "        [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0, 0],\n",
+        "        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "        [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 0, 0, -1/40]\n",
+        "    ])\n",
+        "\n",
+        "    B = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input\n",
+        "    b1 = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input\n",
+        "    b2 = np.array([0, 0, 0, 0, 250, 0, 0, 0, 0, -1250])  # Constant input\n",
+        "\n",
+        "    vd = 25 * (1 - np.exp(-t / 40))\n",
+        "    k = np.array([54.5333, 16.2848, -1.3027, -4.3607, 191.7414, -40.4841, -34.2067, -29.7070, -27.3437, 52.0886])\n",
+        "\n",
+        "    dx = np.array([x[1] - 20, x[2] - 20, x[3] - 20, x[4] - 20])\n",
+        "    dv = np.array([x[5] - vd, x[6] - vd, x[7] - vd, x[8] - vd, x[9] - vd])\n",
+        "    z = x[5] - vd\n",
+        "    X = np.concatenate((dx, dv, [z]))\n",
+        "\n",
+        "    u = k.dot(X)\n",
+        "    xp = A.dot(x) + b1 * u + b2\n",
+        "    return xp\n",
+        "\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/train_fb1/train_fb1.py b/Chapter7/python/train_fb1/train_fb1.py
new file mode 100644
index 0000000..0126bd8
--- /dev/null
+++ b/Chapter7/python/train_fb1/train_fb1.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""train_fb1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1fosGKMWUDqFkMa_XhAqRJZ_8-T0FzGZI
+"""
+
+import numpy as np
+from scipy.integrate import solve_ivp
+import matplotlib.pyplot as plt
+
+def train_fb(t, x):
+    A = np.array([
+        [0, 0, 0, 0, 1, -1, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],
+        [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0, 0],
+        [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0, 0],
+        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+        [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, -1/40]
+    ])
+
+    B = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input
+    b1 = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input
+    b2 = np.array([0, 0, 0, 0, 250, 0, 0, 0, 0, -1250])  # Constant input
+
+    vd = 25 * (1 - np.exp(-t / 40))
+    k = np.array([54.5333, 16.2848, -1.3027, -4.3607, 191.7414, -40.4841, -34.2067, -29.7070, -27.3437, 52.0886])
+
+    dx = np.array([x[1] - 20, x[2] - 20, x[3] - 20, x[4] - 20])
+    dv = np.array([x[5] - vd, x[6] - vd, x[7] - vd, x[8] - vd, x[9] - vd])
+    z = x[5] - vd
+    X = np.concatenate((dx, dv, [z]))
+
+    u = k.dot(X)
+    xp = A.dot(x) + b1 * u + b2
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/train_fb2/train_fb2.ipynb b/Chapter7/python/train_fb2/train_fb2.ipynb
new file mode 100644
index 0000000..ddca15f
--- /dev/null
+++ b/Chapter7/python/train_fb2/train_fb2.ipynb
@@ -0,0 +1,61 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "ZNaicmn_jkXv"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def train_fb2(t, x):\n",
+        "    A = np.array([\n",
+        "        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 0, 1, -1],\n",
+        "        [0, -12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],\n",
+        "        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "        [0, 0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],\n",
+        "        [0, 0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    b1 = np.array([0, 0, 0, 0, 0, 0.005, 0, 0, 0, 0])  # Force input\n",
+        "    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])  # Constant input\n",
+        "\n",
+        "    vd = 25 * (1 - np.exp(-t / 40))\n",
+        "    k = np.array([54.5333, 16.2848, -1.3027, -4.3607, 191.7414, -40.4841, -34.2067, -29.7070, -27.3437, 52.0886])\n",
+        "\n",
+        "    dx = np.array([x[1] - 20, x[2] - 20, x[3] - 20, x[4] - 20])\n",
+        "    dv = np.array([x[5] - vd, x[6] - vd, x[7] - vd, x[8] - vd, x[9] - vd])\n",
+        "    z = x[5] - vd\n",
+        "    X = np.concatenate((dx, dv, [z]))\n",
+        "\n",
+        "    u = k.dot(X)\n",
+        "    xp = A.dot(x) + b1 * u + b2\n",
+        "    return xp\n",
+        "\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/train_fb2/train_fb2.py b/Chapter7/python/train_fb2/train_fb2.py
new file mode 100644
index 0000000..a81651c
--- /dev/null
+++ b/Chapter7/python/train_fb2/train_fb2.py
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+"""train_fb2.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1erZk9prhRwcpPI2RX_THudsjcgHDM8d9
+"""
+
+import numpy as np
+from scipy.integrate import solve_ivp
+import matplotlib.pyplot as plt
+
+def train_fb2(t, x):
+    A = np.array([
+        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 1, -1],
+        [0, -12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],
+        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+        [0, 0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],
+        [0, 0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]
+    ])
+
+    b1 = np.array([0, 0, 0, 0, 0, 0.005, 0, 0, 0, 0])  # Force input
+    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])  # Constant input
+
+    vd = 25 * (1 - np.exp(-t / 40))
+    k = np.array([54.5333, 16.2848, -1.3027, -4.3607, 191.7414, -40.4841, -34.2067, -29.7070, -27.3437, 52.0886])
+
+    dx = np.array([x[1] - 20, x[2] - 20, x[3] - 20, x[4] - 20])
+    dv = np.array([x[5] - vd, x[6] - vd, x[7] - vd, x[8] - vd, x[9] - vd])
+    z = x[5] - vd
+    X = np.concatenate((dx, dv, [z]))
+
+    u = k.dot(X)
+    xp = A.dot(x) + b1 * u + b2
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/train_lqr/train_lqr.ipynb b/Chapter7/python/train_lqr/train_lqr.ipynb
new file mode 100644
index 0000000..1462932
--- /dev/null
+++ b/Chapter7/python/train_lqr/train_lqr.ipynb
@@ -0,0 +1,88 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5v71kf1ZgdJH",
+        "outputId": "ad9ccf24-ff63-4be6-e8ef-8d24029da83c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "LQR gain K:\n",
+            "[[ 54.5333233   16.28476872  -1.30267828  -4.3607473  191.74140547\n",
+            "  -40.48409407 -34.20666832 -29.70695907 -27.34368401  52.08864446]]\n",
+            "\n",
+            "LQR gain K1 with modified R:\n",
+            "[[ 0.45588579  0.3330732   0.2170438   0.10686101 11.53874117 -0.26220247\n",
+            "  -0.33713495 -0.38652147 -0.41103122  5.37309291]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.linalg import solve_continuous_are\n",
+        "import control\n",
+        "\n",
+        "# State variable\n",
+        "A = np.array([\n",
+        "    [0, 0, 0, 0, 1, -1, 0, 0, 0, 0],\n",
+        "    [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "    [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "    [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "    [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0, 0],\n",
+        "    [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0, 0],\n",
+        "    [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "    [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "    [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75, 0],\n",
+        "    [0, 0, 0, 0, 0, 0, 0, 0, 0, -1/40]\n",
+        "])\n",
+        "\n",
+        "B = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])[:, np.newaxis]  # Force input\n",
+        "\n",
+        "Q = np.diag([3.34**2, 3.34**2, 3.34**2, 3.34**2, 3**2+0.5**2, 2*3**2, 2*3**2, 2*3**2, 3**2, 0.5**2])\n",
+        "Q[5, 4] = Q[4, 5] = -9\n",
+        "Q[6, 5] = Q[5, 6] = -9\n",
+        "Q[7, 6] = Q[6, 7] = -9\n",
+        "Q[8, 7] = Q[7, 8] = -9\n",
+        "Q[9, 4] = Q[4, 9] = 0.5**2\n",
+        "\n",
+        "R = 1 / 120**2\n",
+        "\n",
+        "# Calculate LQR gain\n",
+        "K, S, E = control.lqr(A, B, Q, R)\n",
+        "\n",
+        "R1 = 35 * R\n",
+        "\n",
+        "# Calculate LQR gain with modified R\n",
+        "K1, S1, E1 = control.lqr(A, B, Q, R1)\n",
+        "\n",
+        "print(\"LQR gain K:\")\n",
+        "print(K)\n",
+        "\n",
+        "print(\"\\nLQR gain K1 with modified R:\")\n",
+        "print(K1)\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/train_lqr/train_lqr.py b/Chapter7/python/train_lqr/train_lqr.py
new file mode 100644
index 0000000..23d0c21
--- /dev/null
+++ b/Chapter7/python/train_lqr/train_lqr.py
@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+"""train_lqr.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Eczp7Nq1FTMep8xLMYPGxksBhHbebzFQ
+"""
+
+import numpy as np
+from scipy.linalg import solve_continuous_are
+import control
+
+# State variable
+A = np.array([
+    [0, 0, 0, 0, 1, -1, 0, 0, 0, 0],
+    [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],
+    [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],
+    [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],
+    [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0, 0],
+    [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0, 0],
+    [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+    [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+    [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75, 0],
+    [0, 0, 0, 0, 0, 0, 0, 0, 0, -1/40]
+])
+
+B = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])[:, np.newaxis]  # Force input
+
+Q = np.diag([3.34**2, 3.34**2, 3.34**2, 3.34**2, 3**2+0.5**2, 2*3**2, 2*3**2, 2*3**2, 3**2, 0.5**2])
+Q[5, 4] = Q[4, 5] = -9
+Q[6, 5] = Q[5, 6] = -9
+Q[7, 6] = Q[6, 7] = -9
+Q[8, 7] = Q[7, 8] = -9
+Q[9, 4] = Q[4, 9] = 0.5**2
+
+R = 1 / 120**2
+
+# Calculate LQR gain
+K, S, E = control.lqr(A, B, Q, R)
+
+R1 = 35 * R
+
+# Calculate LQR gain with modified R
+K1, S1, E1 = control.lqr(A, B, Q, R1)
+
+print("LQR gain K:")
+print(K)
+
+print("\nLQR gain K1 with modified R:")
+print(K1)
\ No newline at end of file
diff --git a/Chapter7/python/train_model/train_model.ipynb b/Chapter7/python/train_model/train_model.ipynb
new file mode 100644
index 0000000..de050db
--- /dev/null
+++ b/Chapter7/python/train_model/train_model.ipynb
@@ -0,0 +1,53 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "zjCFOUJmajyG"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "def train_model(t, x):\n",
+        "    A = np.array([\n",
+        "        [0,    0,   0,   0,   0,    1,      0,      0,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    1,     -1,      0,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      1,     -1,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      0,      1,  -1,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      0,      0,   1,  -1],\n",
+        "        [0, -12.5,  0,   0,   0,  -0.75,   0.75,    0,   0,   0],\n",
+        "        [0,  62.5, -62.5, 0,  0,   3.75,  -7.5,   3.75,  0,   0],\n",
+        "        [0,  0, 62.5, -62.5,  0,    0,  3.75,  -7.5,  3.75,  0],\n",
+        "        [0,  0,  0, 62.5, -62.5,    0,     0,  3.75,  -7.5,  3.75],\n",
+        "        [0,    0,   0,   0,  62.5,  0,     0,    0,  3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    b1 = np.array([0, 0, 0, 0, 0, 0.005, 0, 0, 0, 0])  # Force input\n",
+        "    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])  # Constant input\n",
+        "\n",
+        "    u = 750 * np.exp(-t / 10)  # Exponentially decreasing input\n",
+        "    # u = 750  # Constant input\n",
+        "\n",
+        "    xp = A.dot(x) + b1 * u + b2\n",
+        "    return xp\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/train_model/train_model.py b/Chapter7/python/train_model/train_model.py
new file mode 100644
index 0000000..73abe8b
--- /dev/null
+++ b/Chapter7/python/train_model/train_model.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+"""train_model.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/10w8lY8BNvQodIuHxTkKCQj1cFz4BVVDs
+"""
+
+import numpy as np
+from scipy.integrate import solve_ivp
+
+def train_model(t, x):
+    A = np.array([
+        [0,    0,   0,   0,   0,    1,      0,      0,   0,   0],
+        [0,    0,   0,   0,   0,    1,     -1,      0,   0,   0],
+        [0,    0,   0,   0,   0,    0,      1,     -1,   0,   0],
+        [0,    0,   0,   0,   0,    0,      0,      1,  -1,   0],
+        [0,    0,   0,   0,   0,    0,      0,      0,   1,  -1],
+        [0, -12.5,  0,   0,   0,  -0.75,   0.75,    0,   0,   0],
+        [0,  62.5, -62.5, 0,  0,   3.75,  -7.5,   3.75,  0,   0],
+        [0,  0, 62.5, -62.5,  0,    0,  3.75,  -7.5,  3.75,  0],
+        [0,  0,  0, 62.5, -62.5,    0,     0,  3.75,  -7.5,  3.75],
+        [0,    0,   0,   0,  62.5,  0,     0,    0,  3.75, -3.75]
+    ])
+
+    b1 = np.array([0, 0, 0, 0, 0, 0.005, 0, 0, 0, 0])  # Force input
+    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])  # Constant input
+
+    u = 750 * np.exp(-t / 10)  # Exponentially decreasing input
+    # u = 750  # Constant input
+
+    xp = A.dot(x) + b1 * u + b2
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/train_model1/train_model1.ipynb b/Chapter7/python/train_model1/train_model1.ipynb
new file mode 100644
index 0000000..2db8154
--- /dev/null
+++ b/Chapter7/python/train_model1/train_model1.ipynb
@@ -0,0 +1,54 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "jACcAvNoc40M"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "def train_model1(t, x):\n",
+        "    A = np.array([\n",
+        "        [0,    0,   0,   0,   0,    1,      0,      0,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    1,     -1,      0,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      1,     -1,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      0,      1,  -1,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      0,      0,   1,  -1],\n",
+        "        [0, -12.5,  0,   0,   0,  -0.75,   0.75,    0,   0,   0],\n",
+        "        [0,  62.5, -62.5, 0,  0,   3.75,  -7.5,   3.75,  0,   0],\n",
+        "        [0,  0, 62.5, -62.5,  0,    0,  3.75,  -7.5,  3.75,  0],\n",
+        "        [0,  0,  0, 62.5, -62.5,    0,     0,  3.75,  -7.5,  3.75],\n",
+        "        [0,    0,   0,   0,  62.5,  0,     0,    0,  3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    b1 = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input\n",
+        "    b2 = np.array([0, 0, 0, 0, 250, 0, 0, 0, 0, -1250])  # Constant input\n",
+        "\n",
+        "    u = 750  # Constant input\n",
+        "\n",
+        "    xp = A.dot(x) + b1 * u + b2\n",
+        "    return xp\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/train_model1/train_model1.py b/Chapter7/python/train_model1/train_model1.py
new file mode 100644
index 0000000..ac904ca
--- /dev/null
+++ b/Chapter7/python/train_model1/train_model1.py
@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+"""train_model1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1sRy4SbWyjVfN73yFL6SesKHK8FmdgdEX
+"""
+
+import numpy as np
+from scipy.integrate import solve_ivp
+
+def train_model1(t, x):
+    A = np.array([
+        [0,    0,   0,   0,   0,    1,      0,      0,   0,   0],
+        [0,    0,   0,   0,   0,    1,     -1,      0,   0,   0],
+        [0,    0,   0,   0,   0,    0,      1,     -1,   0,   0],
+        [0,    0,   0,   0,   0,    0,      0,      1,  -1,   0],
+        [0,    0,   0,   0,   0,    0,      0,      0,   1,  -1],
+        [0, -12.5,  0,   0,   0,  -0.75,   0.75,    0,   0,   0],
+        [0,  62.5, -62.5, 0,  0,   3.75,  -7.5,   3.75,  0,   0],
+        [0,  0, 62.5, -62.5,  0,    0,  3.75,  -7.5,  3.75,  0],
+        [0,  0,  0, 62.5, -62.5,    0,     0,  3.75,  -7.5,  3.75],
+        [0,    0,   0,   0,  62.5,  0,     0,    0,  3.75, -3.75]
+    ])
+
+    b1 = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input
+    b2 = np.array([0, 0, 0, 0, 250, 0, 0, 0, 0, -1250])  # Constant input
+
+    u = 750  # Constant input
+
+    xp = A.dot(x) + b1 * u + b2
+    return xp
\ No newline at end of file
diff --git a/Chapter7/python/train_solver1/train_solver1.ipynb b/Chapter7/python/train_solver1/train_solver1.ipynb
new file mode 100644
index 0000000..25cc457
--- /dev/null
+++ b/Chapter7/python/train_solver1/train_solver1.ipynb
@@ -0,0 +1,90 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 450
+        },
+        "id": "fSXxnp-6dZBw",
+        "outputId": "26b49baa-1a1e-4f40-9f78-408ecb2b5dfd"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Define the train model function\n",
+        "def train_model1(t, x):\n",
+        "    A = np.array([\n",
+        "        [0,    0,   0,   0,   0,    1,      0,      0,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    1,     -1,      0,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      1,     -1,   0,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      0,      1,  -1,   0],\n",
+        "        [0,    0,   0,   0,   0,    0,      0,      0,   1,  -1],\n",
+        "        [0, -12.5,  0,   0,   0,  -0.75,   0.75,    0,   0,   0],\n",
+        "        [0,  62.5, -62.5, 0,  0,   3.75,  -7.5,   3.75,  0,   0],\n",
+        "        [0,  0, 62.5, -62.5,  0,    0,  3.75,  -7.5,  3.75,  0],\n",
+        "        [0,  0,  0, 62.5, -62.5,    0,     0,  3.75,  -7.5,  3.75],\n",
+        "        [0,    0,   0,   0,  62.5,  0,     0,    0,  3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    b1 = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input\n",
+        "    b2 = np.array([0, 0, 0, 0, 250, 0, 0, 0, 0, -1250])  # Constant input\n",
+        "\n",
+        "    u = 750  # Constant input\n",
+        "\n",
+        "    xp = A.dot(x) + b1 * u + b2\n",
+        "    return xp\n",
+        "\n",
+        "# Define initial conditions and time span for the ODE solver\n",
+        "tspan = (0, 10)  # Define the time range for the simulation\n",
+        "x0 = np.array([0, 20, 20, 20, 20, 0, 0, 0, 0, 0])  # Initial state\n",
+        "\n",
+        "# Solve the differential equation\n",
+        "sol = solve_ivp(train_model1, tspan, x0, method='RK45', t_eval=np.linspace(tspan[0], tspan[1], 100))\n",
+        "\n",
+        "# Extract time points and state variables\n",
+        "t = sol.t\n",
+        "x = sol.y.T  # Transpose for easier indexing\n",
+        "\n",
+        "# Plot the results\n",
+        "plt.plot(t, x[:, 1], 'k', label='x_2')  # x_2 is the second state variable\n",
+        "plt.plot(t, x[:, 4], 'k-.', label='x_5')  # x_5 is the fifth state variable\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter7/python/train_solver1/train_solver1.py b/Chapter7/python/train_solver1/train_solver1.py
new file mode 100644
index 0000000..331f2f1
--- /dev/null
+++ b/Chapter7/python/train_solver1/train_solver1.py
@@ -0,0 +1,55 @@
+# -*- coding: utf-8 -*-
+"""train_solver1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1xjojQFB-rHnz1Lonsz9xyUa4IbNzaJzP
+"""
+
+import numpy as np
+from scipy.integrate import solve_ivp
+import matplotlib.pyplot as plt
+
+# Define the train model function
+def train_model1(t, x):
+    A = np.array([
+        [0,    0,   0,   0,   0,    1,      0,      0,   0,   0],
+        [0,    0,   0,   0,   0,    1,     -1,      0,   0,   0],
+        [0,    0,   0,   0,   0,    0,      1,     -1,   0,   0],
+        [0,    0,   0,   0,   0,    0,      0,      1,  -1,   0],
+        [0,    0,   0,   0,   0,    0,      0,      0,   1,  -1],
+        [0, -12.5,  0,   0,   0,  -0.75,   0.75,    0,   0,   0],
+        [0,  62.5, -62.5, 0,  0,   3.75,  -7.5,   3.75,  0,   0],
+        [0,  0, 62.5, -62.5,  0,    0,  3.75,  -7.5,  3.75,  0],
+        [0,  0,  0, 62.5, -62.5,    0,     0,  3.75,  -7.5,  3.75],
+        [0,    0,   0,   0,  62.5,  0,     0,    0,  3.75, -3.75]
+    ])
+
+    b1 = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0, 0])  # Force input
+    b2 = np.array([0, 0, 0, 0, 250, 0, 0, 0, 0, -1250])  # Constant input
+
+    u = 750  # Constant input
+
+    xp = A.dot(x) + b1 * u + b2
+    return xp
+
+# Define initial conditions and time span for the ODE solver
+tspan = (0, 10)  # Define the time range for the simulation
+x0 = np.array([0, 20, 20, 20, 20, 0, 0, 0, 0, 0])  # Initial state
+
+# Solve the differential equation
+sol = solve_ivp(train_model1, tspan, x0, method='RK45', t_eval=np.linspace(tspan[0], tspan[1], 100))
+
+# Extract time points and state variables
+t = sol.t
+x = sol.y.T  # Transpose for easier indexing
+
+# Plot the results
+plt.plot(t, x[:, 1], 'k', label='x_2')  # x_2 is the second state variable
+plt.plot(t, x[:, 4], 'k-.', label='x_5')  # x_5 is the fifth state variable
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.show()
\ No newline at end of file
diff --git a/Chapter8/python/CL_DCmotor_LTR_solver/CL_DCmotor_LTR_solver.ipynb b/Chapter8/python/CL_DCmotor_LTR_solver/CL_DCmotor_LTR_solver.ipynb
new file mode 100644
index 0000000..1ad907c
--- /dev/null
+++ b/Chapter8/python/CL_DCmotor_LTR_solver/CL_DCmotor_LTR_solver.ipynb
@@ -0,0 +1,145 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Global parameter container\n",
+        "class Parameters:\n",
+        "    def __init__(self, Tl):\n",
+        "        self.Tl = Tl\n",
+        "\n",
+        "# Define the function equivalent to DC_motor_LTR1\n",
+        "def DC_motor_LTR1(t, X, Par):\n",
+        "    # Model of The Real System\n",
+        "    x = X[:3]\n",
+        "    A = np.array([[0, 1, 0],\n",
+        "                  [0, 0, 4.438],\n",
+        "                  [0, -12, -24]])\n",
+        "    B = np.array([[0, 0],\n",
+        "                  [0, -7.396],\n",
+        "                  [20, 0]])\n",
+        "    C = np.array([1, 0, 0])\n",
+        "    y = C @ x\n",
+        "\n",
+        "    # Model of the observer with disturbance Tl\n",
+        "    xh = X[3:]\n",
+        "    Ah = np.array([[0, 1, 0, 0],\n",
+        "                   [0, 0, 4.438, -7.396],\n",
+        "                   [0, -12, -24, 0],\n",
+        "                   [0, 0, 0, -1]])\n",
+        "    Bh = np.array([0, 0, 20, 0]).reshape(-1, 1)\n",
+        "    Ch = np.array([1, 0, 0, 0])\n",
+        "\n",
+        "    # State feedback and state observer gains\n",
+        "    k = np.array([3.0000, 0.8796, 0.1529, -1.8190])\n",
+        "    G = np.array([-1.0000, 235.7440, -978.1707, -20.4870])\n",
+        "\n",
+        "    # Final Equations\n",
+        "    Tl = Par.Tl * np.exp(-t)  # Exponential disturbance\n",
+        "    v = -k @ xh\n",
+        "    u = np.array([v, Tl])\n",
+        "\n",
+        "    xhp = Ah @ xh + Bh.flatten() * v + G * (y - Ch @ xh)\n",
+        "    xp = A @ x + B @ u\n",
+        "    return np.concatenate((xp, xhp))\n",
+        "\n",
+        "# Define the main simulation function\n",
+        "def main():\n",
+        "    # Define initial conditions and parameters\n",
+        "    X0 = np.zeros(7)  # Initial state vector\n",
+        "    Par = Parameters(Tl=0.01)  # Create an instance with disturbance parameter\n",
+        "\n",
+        "    # Define the time span for the simulation\n",
+        "    t_span = (0, 5)\n",
+        "    t_eval = np.linspace(t_span[0], t_span[1], 500)\n",
+        "\n",
+        "    # Solve the differential equations\n",
+        "    sol = solve_ivp(lambda t, X: DC_motor_LTR1(t, X, Par), t_span, X0, t_eval=t_eval, max_step=1e-2)\n",
+        "\n",
+        "    t = sol.t\n",
+        "    x = sol.y.T\n",
+        "\n",
+        "    # Plot the results\n",
+        "    plt.figure(figsize=(12, 10))\n",
+        "\n",
+        "    plt.subplot(221)\n",
+        "    plt.plot(t, x[:, 0], 'k', label=r'$\\theta$')\n",
+        "    plt.plot(t, x[:, 3], '-.k', label=r'$\\theta_h$')\n",
+        "    plt.grid()\n",
+        "    plt.xlabel('Time (sec)')\n",
+        "    plt.ylabel('Angular displacement (rad)')\n",
+        "    plt.legend()\n",
+        "\n",
+        "    plt.subplot(222)\n",
+        "    plt.plot(t, x[:, 1], 'k', label=r'$\\omega$')\n",
+        "    plt.plot(t, x[:, 4], '-.k', label=r'$\\omega_h$')\n",
+        "    plt.grid()\n",
+        "    plt.xlabel('Time (sec)')\n",
+        "    plt.ylabel('Angular velocity (rad/sec)')\n",
+        "    plt.legend()\n",
+        "\n",
+        "    plt.subplot(223)\n",
+        "    plt.plot(t, x[:, 2], 'k', label='i')\n",
+        "    plt.plot(t, x[:, 5], '-.k', label='i_h')\n",
+        "    plt.grid()\n",
+        "    plt.xlabel('Time (sec)')\n",
+        "    plt.ylabel('Motor Current (Amp)')\n",
+        "    plt.legend()\n",
+        "\n",
+        "    Tl = Par.Tl * np.exp(-t)\n",
+        "    plt.subplot(224)\n",
+        "    plt.plot(t, Tl, 'k', label='Tl')\n",
+        "    plt.plot(t, x[:, 6], '-.k', label='Tl_h')\n",
+        "    plt.grid()\n",
+        "    plt.xlabel('Time (sec)')\n",
+        "    plt.ylabel('Disturbance torque (N.m)')\n",
+        "    plt.legend()\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "\n",
+        "if __name__ == \"__main__\":\n",
+        "    main()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "oXuzK-qLVl16",
+        "outputId": "3414d847-b5dd-467c-df1b-276248fda13d"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x1000 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/CL_DCmotor_LTR_solver/cl_dcmotor_ltr_solver.py b/Chapter8/python/CL_DCmotor_LTR_solver/cl_dcmotor_ltr_solver.py
new file mode 100644
index 0000000..3ec0e99
--- /dev/null
+++ b/Chapter8/python/CL_DCmotor_LTR_solver/cl_dcmotor_ltr_solver.py
@@ -0,0 +1,110 @@
+# -*- coding: utf-8 -*-
+"""CL_DCmotor_LTR_solver.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1jcHTRJDWgBJ8e96DGoxUVcDP1Q31GNSc
+"""
+
+import numpy as np
+from scipy.integrate import solve_ivp
+import matplotlib.pyplot as plt
+
+# Global parameter container
+class Parameters:
+    def __init__(self, Tl):
+        self.Tl = Tl
+
+# Define the function equivalent to DC_motor_LTR1
+def DC_motor_LTR1(t, X, Par):
+    # Model of The Real System
+    x = X[:3]
+    A = np.array([[0, 1, 0],
+                  [0, 0, 4.438],
+                  [0, -12, -24]])
+    B = np.array([[0, 0],
+                  [0, -7.396],
+                  [20, 0]])
+    C = np.array([1, 0, 0])
+    y = C @ x
+
+    # Model of the observer with disturbance Tl
+    xh = X[3:]
+    Ah = np.array([[0, 1, 0, 0],
+                   [0, 0, 4.438, -7.396],
+                   [0, -12, -24, 0],
+                   [0, 0, 0, -1]])
+    Bh = np.array([0, 0, 20, 0]).reshape(-1, 1)
+    Ch = np.array([1, 0, 0, 0])
+
+    # State feedback and state observer gains
+    k = np.array([3.0000, 0.8796, 0.1529, -1.8190])
+    G = np.array([-1.0000, 235.7440, -978.1707, -20.4870])
+
+    # Final Equations
+    Tl = Par.Tl * np.exp(-t)  # Exponential disturbance
+    v = -k @ xh
+    u = np.array([v, Tl])
+
+    xhp = Ah @ xh + Bh.flatten() * v + G * (y - Ch @ xh)
+    xp = A @ x + B @ u
+    return np.concatenate((xp, xhp))
+
+# Define the main simulation function
+def main():
+    # Define initial conditions and parameters
+    X0 = np.zeros(7)  # Initial state vector
+    Par = Parameters(Tl=0.01)  # Create an instance with disturbance parameter
+
+    # Define the time span for the simulation
+    t_span = (0, 5)
+    t_eval = np.linspace(t_span[0], t_span[1], 500)
+
+    # Solve the differential equations
+    sol = solve_ivp(lambda t, X: DC_motor_LTR1(t, X, Par), t_span, X0, t_eval=t_eval, max_step=1e-2)
+
+    t = sol.t
+    x = sol.y.T
+
+    # Plot the results
+    plt.figure(figsize=(12, 10))
+
+    plt.subplot(221)
+    plt.plot(t, x[:, 0], 'k', label=r'$\theta$')
+    plt.plot(t, x[:, 3], '-.k', label=r'$\theta_h$')
+    plt.grid()
+    plt.xlabel('Time (sec)')
+    plt.ylabel('Angular displacement (rad)')
+    plt.legend()
+
+    plt.subplot(222)
+    plt.plot(t, x[:, 1], 'k', label=r'$\omega$')
+    plt.plot(t, x[:, 4], '-.k', label=r'$\omega_h$')
+    plt.grid()
+    plt.xlabel('Time (sec)')
+    plt.ylabel('Angular velocity (rad/sec)')
+    plt.legend()
+
+    plt.subplot(223)
+    plt.plot(t, x[:, 2], 'k', label='i')
+    plt.plot(t, x[:, 5], '-.k', label='i_h')
+    plt.grid()
+    plt.xlabel('Time (sec)')
+    plt.ylabel('Motor Current (Amp)')
+    plt.legend()
+
+    Tl = Par.Tl * np.exp(-t)
+    plt.subplot(224)
+    plt.plot(t, Tl, 'k', label='Tl')
+    plt.plot(t, x[:, 6], '-.k', label='Tl_h')
+    plt.grid()
+    plt.xlabel('Time (sec)')
+    plt.ylabel('Disturbance torque (N.m)')
+    plt.legend()
+
+    plt.tight_layout()
+    plt.show()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/Chapter8/python/DC_motor_LTR/DC_motor_LTR.ipynb b/Chapter8/python/DC_motor_LTR/DC_motor_LTR.ipynb
new file mode 100644
index 0000000..36ab560
--- /dev/null
+++ b/Chapter8/python/DC_motor_LTR/DC_motor_LTR.ipynb
@@ -0,0 +1,77 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "efeaKduTSK-n",
+        "outputId": "a75ba8c7-1beb-4ffc-ce44-2ee1ec701909"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "State feedback gain matrix, K:\n",
+            "[[ 3.          0.87955069  0.15290229 -1.8189703 ]]\n",
+            "Observer gain matrix, G:\n",
+            "[[  -1.        ]\n",
+            " [ 235.744     ]\n",
+            " [-978.17073888]\n",
+            " [ -20.48698474]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import scipy.linalg\n",
+        "from control import lqr, place\n",
+        "\n",
+        "# Define the system matrices\n",
+        "A = np.array([[0, 1, 0, 0],\n",
+        "              [0, 0, 4.438, -7.396],\n",
+        "              [0, -12, -24, 0],\n",
+        "              [0, 0, 0, -1]])\n",
+        "\n",
+        "B = np.array([[0],\n",
+        "              [0],\n",
+        "              [20],\n",
+        "              [0]])\n",
+        "\n",
+        "C = np.array([[1, 0, 0, 0]])\n",
+        "\n",
+        "# State feedback design using LQR\n",
+        "R = np.array([[1]])\n",
+        "Q1 = np.diag([9, 0, 0, 0])\n",
+        "K, _, _ = lqr(A, B, Q1, R)\n",
+        "\n",
+        "# State observer design using pole placement\n",
+        "pd = np.array([-5-5j, -5+5j, -7+7j, -7-7j])\n",
+        "G = place(A.T, C.T, pd).T\n",
+        "\n",
+        "# Print the results\n",
+        "print(\"State feedback gain matrix, K:\")\n",
+        "print(K)\n",
+        "print(\"Observer gain matrix, G:\")\n",
+        "print(G)\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/DC_motor_LTR/dc_motor_ltr.py b/Chapter8/python/DC_motor_LTR/dc_motor_ltr.py
new file mode 100644
index 0000000..b4eae00
--- /dev/null
+++ b/Chapter8/python/DC_motor_LTR/dc_motor_ltr.py
@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+"""DC_motor_LTR.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1QElNkQI1kkyBvsq8ndJ5mUz7m9uY-WQH
+"""
+
+import numpy as np
+import scipy.linalg
+from control import lqr, place
+
+# Define the system matrices
+A = np.array([[0, 1, 0, 0],
+              [0, 0, 4.438, -7.396],
+              [0, -12, -24, 0],
+              [0, 0, 0, -1]])
+
+B = np.array([[0],
+              [0],
+              [20],
+              [0]])
+
+C = np.array([[1, 0, 0, 0]])
+
+# State feedback design using LQR
+R = np.array([[1]])
+Q1 = np.diag([9, 0, 0, 0])
+K, _, _ = lqr(A, B, Q1, R)
+
+# State observer design using pole placement
+pd = np.array([-5-5j, -5+5j, -7+7j, -7-7j])
+G = place(A.T, C.T, pd).T
+
+# Print the results
+print("State feedback gain matrix, K:")
+print(K)
+print("Observer gain matrix, G:")
+print(G)
\ No newline at end of file
diff --git a/Chapter8/python/DC_motor_LTR1/DC_motor_LTR1.ipynb b/Chapter8/python/DC_motor_LTR1/DC_motor_LTR1.ipynb
new file mode 100644
index 0000000..e313e41
--- /dev/null
+++ b/Chapter8/python/DC_motor_LTR1/DC_motor_LTR1.ipynb
@@ -0,0 +1,71 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "yDxu6_MqXFjy"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "\n",
+        "# Global parameter container\n",
+        "class Parameters:\n",
+        "    def __init__(self, Tl):\n",
+        "        self.Tl = Tl\n",
+        "\n",
+        "# Define the function equivalent to DC_motor_LTR1\n",
+        "def DC_motor_LTR1(t, X, Par):\n",
+        "    # Model of The Real System\n",
+        "    x = X[:3]\n",
+        "    A = np.array([[0, 1, 0],\n",
+        "                  [0, 0, 4.438],\n",
+        "                  [0, -12, -24]])\n",
+        "    B = np.array([[0, 0],\n",
+        "                  [0, -7.396],\n",
+        "                  [20, 0]])\n",
+        "    C = np.array([1, 0, 0])\n",
+        "    y = C @ x\n",
+        "\n",
+        "    # Model of the observer with disturbance Tl\n",
+        "    xh = X[3:]\n",
+        "    Ah = np.array([[0, 1, 0, 0],\n",
+        "                   [0, 0, 4.438, -7.396],\n",
+        "                   [0, -12, -24, 0],\n",
+        "                   [0, 0, 0, -1]])\n",
+        "    Bh = np.array([0, 0, 20, 0])\n",
+        "    Ch = np.array([1, 0, 0, 0])\n",
+        "\n",
+        "    # State feedback and state observer gains\n",
+        "    k = np.array([3.0000, 0.8796, 0.1529, -1.8190])\n",
+        "    G = np.array([-1.0000, 235.7440, -978.1707, -20.4870])\n",
+        "\n",
+        "    # Final Equations\n",
+        "    Tl = Par.Tl * np.exp(-t)  # Exponential disturbance\n",
+        "    v = -k @ xh\n",
+        "    u = np.array([v, Tl])\n",
+        "\n",
+        "    xhp = Ah @ xh + Bh * v + G * (y - Ch @ xh)\n",
+        "    xp = A @ x + B @ u\n",
+        "    return np.concatenate((xp, xhp))\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/DC_motor_LTR1/dc_motor_ltr1.py b/Chapter8/python/DC_motor_LTR1/dc_motor_ltr1.py
new file mode 100644
index 0000000..5afb198
--- /dev/null
+++ b/Chapter8/python/DC_motor_LTR1/dc_motor_ltr1.py
@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+"""DC_motor_LTR1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/19CjaVgcYV6jFcv3PL2UvsJQ5P-mKoGhu
+"""
+
+# Global parameter container
+class Parameters:
+    def __init__(self, Tl):
+        self.Tl = Tl
+
+# Define the function equivalent to DC_motor_LTR1
+def DC_motor_LTR1(t, X, Par):
+    # Model of The Real System
+    x = X[:3]
+    A = np.array([[0, 1, 0],
+                  [0, 0, 4.438],
+                  [0, -12, -24]])
+    B = np.array([[0, 0],
+                  [0, -7.396],
+                  [20, 0]])
+    C = np.array([1, 0, 0])
+    y = C @ x
+
+    # Model of the observer with disturbance Tl
+    xh = X[3:]
+    Ah = np.array([[0, 1, 0, 0],
+                   [0, 0, 4.438, -7.396],
+                   [0, -12, -24, 0],
+                   [0, 0, 0, -1]])
+    Bh = np.array([0, 0, 20, 0])
+    Ch = np.array([1, 0, 0, 0])
+
+    # State feedback and state observer gains
+    k = np.array([3.0000, 0.8796, 0.1529, -1.8190])
+    G = np.array([-1.0000, 235.7440, -978.1707, -20.4870])
+
+    # Final Equations
+    Tl = Par.Tl * np.exp(-t)  # Exponential disturbance
+    v = -k @ xh
+    u = np.array([v, Tl])
+
+    xhp = Ah @ xh + Bh * v + G * (y - Ch @ xh)
+    xp = A @ x + B @ u
+    return np.concatenate((xp, xhp))
\ No newline at end of file
diff --git a/Chapter8/python/DCmotor_Obs/DC_motor_Obs.ipynb b/Chapter8/python/DCmotor_Obs/DC_motor_Obs.ipynb
new file mode 100644
index 0000000..e9550a6
--- /dev/null
+++ b/Chapter8/python/DCmotor_Obs/DC_motor_Obs.ipynb
@@ -0,0 +1,75 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "T-996lW_Mcl-"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Define global parameters\n",
+        "class Parameters:\n",
+        "    def __init__(self, Tl):\n",
+        "        self.Tl = Tl\n",
+        "\n",
+        "Par = Parameters(Tl=0.1)  # Example value for Tl\n",
+        "\n",
+        "def DC_motor_Obs(t, X):\n",
+        "    # Extract state variables\n",
+        "    x = X[:3]\n",
+        "    xh = X[3:]\n",
+        "\n",
+        "    # Real System Matrices\n",
+        "    A = np.array([[0, 1, 0],\n",
+        "                  [0, 0, 4.438],\n",
+        "                  [0, -12, -24]])\n",
+        "    B = np.array([[0, 0],\n",
+        "                  [0, -7.396],\n",
+        "                  [20, 0]])\n",
+        "    C = np.array([1, 0, 0])\n",
+        "\n",
+        "    Tl = Par.Tl  # Step disturbance\n",
+        "    v = 0\n",
+        "    u = np.array([v, Tl])\n",
+        "\n",
+        "    # Real System Model\n",
+        "    xp = A @ x + B @ u\n",
+        "    y = C @ x\n",
+        "\n",
+        "    # Observer Matrices\n",
+        "    Ah = np.array([[0, 1, 0, 0],\n",
+        "                   [0, 0, 4.438, -7.396],\n",
+        "                   [0, -12, -24, 0],\n",
+        "                   [0, 0, 0, 0]])\n",
+        "    Bh = np.array([0, 0, 20, 0])\n",
+        "    Ch = np.array([1, 0, 0, 0])\n",
+        "    G = np.array([0, 234.7440, -936.9136, -27.6050])\n",
+        "\n",
+        "    # Observer Model\n",
+        "    xhp = Ah @ xh + Bh * v + G * (y - Ch @ xh)\n",
+        "\n",
+        "    # Augment the real and estimated states\n",
+        "    Xp = np.concatenate((xp, xhp))\n",
+        "\n",
+        "    return Xp"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/DCmotor_Obs/dc_motor_obs.py b/Chapter8/python/DCmotor_Obs/dc_motor_obs.py
new file mode 100644
index 0000000..5d2f154
--- /dev/null
+++ b/Chapter8/python/DCmotor_Obs/dc_motor_obs.py
@@ -0,0 +1,56 @@
+# -*- coding: utf-8 -*-
+"""DC_motor_Obs.py
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-WXceKJwYXRDZqZqgBy8rX4D5euvHWRN
+"""
+
+import numpy as np
+
+# Define global parameters
+class Parameters:
+    def __init__(self, Tl):
+        self.Tl = Tl
+
+Par = Parameters(Tl=0.1)  # Example value for Tl
+
+def DC_motor_Obs(t, X):
+    # Extract state variables
+    x = X[:3]
+    xh = X[3:]
+
+    # Real System Matrices
+    A = np.array([[0, 1, 0],
+                  [0, 0, 4.438],
+                  [0, -12, -24]])
+    B = np.array([[0, 0],
+                  [0, -7.396],
+                  [20, 0]])
+    C = np.array([1, 0, 0])
+
+    Tl = Par.Tl  # Step disturbance
+    v = 0
+    u = np.array([v, Tl])
+
+    # Real System Model
+    xp = A @ x + B @ u
+    y = C @ x
+
+    # Observer Matrices
+    Ah = np.array([[0, 1, 0, 0],
+                   [0, 0, 4.438, -7.396],
+                   [0, -12, -24, 0],
+                   [0, 0, 0, 0]])
+    Bh = np.array([0, 0, 20, 0])
+    Ch = np.array([1, 0, 0, 0])
+    G = np.array([0, 234.7440, -936.9136, -27.6050])
+
+    # Observer Model
+    xhp = Ah @ xh + Bh * v + G * (y - Ch @ xh)
+
+    # Augment the real and estimated states
+    Xp = np.concatenate((xp, xhp))
+
+    return Xp
\ No newline at end of file
diff --git a/Chapter8/python/DCmotor_Obs_solver/DCmotor_Obs_solver.ipynb b/Chapter8/python/DCmotor_Obs_solver/DCmotor_Obs_solver.ipynb
new file mode 100644
index 0000000..7849632
--- /dev/null
+++ b/Chapter8/python/DCmotor_Obs_solver/DCmotor_Obs_solver.ipynb
@@ -0,0 +1,149 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "class Parameters:\n",
+        "    def __init__(self, Tl):\n",
+        "        self.Tl = Tl\n",
+        "\n",
+        "# Define global parameters\n",
+        "Par = Parameters(Tl=1)\n",
+        "\n",
+        "def DC_motor_Obs(t, X):\n",
+        "    # Extract state variables\n",
+        "    x = X[:3]\n",
+        "    xh = X[3:]\n",
+        "\n",
+        "    # Real System Matrices\n",
+        "    A = np.array([[0, 1, 0],\n",
+        "                  [0, 0, 4.438],\n",
+        "                  [0, -12, -24]])\n",
+        "    B = np.array([[0, 0],\n",
+        "                  [0, -7.396],\n",
+        "                  [20, 0]])\n",
+        "    C = np.array([1, 0, 0])\n",
+        "\n",
+        "    Tl = Par.Tl  # Step disturbance\n",
+        "    v = 0\n",
+        "    u = np.array([v, Tl])\n",
+        "\n",
+        "    # Real System Model\n",
+        "    xp = A @ x + B @ u\n",
+        "    y = C @ x\n",
+        "\n",
+        "    # Observer Matrices\n",
+        "    Ah = np.array([[0, 1, 0, 0],\n",
+        "                   [0, 0, 4.438, -7.396],\n",
+        "                   [0, -12, -24, 0],\n",
+        "                   [0, 0, 0, 0]])\n",
+        "    Bh = np.array([0, 0, 20, 0])\n",
+        "    Ch = np.array([1, 0, 0, 0])\n",
+        "    G = np.array([0, 234.7440, -936.9136, -27.6050])\n",
+        "\n",
+        "    # Observer Model\n",
+        "    xhp = Ah @ xh + Bh * v + G * (y - Ch @ xh)\n",
+        "\n",
+        "    # Augment the real and estimated states\n",
+        "    Xp = np.concatenate((xp, xhp))\n",
+        "\n",
+        "    return Xp\n",
+        "\n",
+        "# Initial conditions\n",
+        "x0 = np.array([1, 0, 0, 0, 0, 0, Par.Tl])\n",
+        "\n",
+        "# Time span\n",
+        "tspan = [0, 2]\n",
+        "\n",
+        "# Solve ODE\n",
+        "sol = solve_ivp(DC_motor_Obs, tspan, x0, method='RK45', t_eval=np.arange(0, 2, 0.01))\n",
+        "\n",
+        "t = sol.t\n",
+        "x = sol.y.T\n",
+        "xh = x[:, 3:7]\n",
+        "\n",
+        "# Plot results\n",
+        "plt.figure(figsize=(10, 8))\n",
+        "\n",
+        "plt.subplot(221)\n",
+        "plt.plot(t, x[:, 0], 'k', label='Real')\n",
+        "plt.plot(t, xh[:, 0], '-.k', label='Estimated')\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('θ (rad)')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.subplot(222)\n",
+        "plt.plot(t, x[:, 1], 'k', label='Real')\n",
+        "plt.plot(t, xh[:, 1], '-.k', label='Estimated')\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('ω (rad/sec)')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.subplot(223)\n",
+        "plt.plot(t, x[:, 2], 'k', label='Real')\n",
+        "plt.plot(t, xh[:, 2], '-.k', label='Estimated')\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('i (Amp)')\n",
+        "\n",
+        "plt.subplot(224)\n",
+        "plt.plot(t, Par.Tl + t * 0, 'k', label='Real')\n",
+        "plt.plot(t, xh[:, 3], '-.k', label='Estimated')\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('T(N.m)')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 713
+        },
+        "id": "QXICquuNK-gN",
+        "outputId": "32b1b8bf-03fd-430e-83b7-01eaa6099dc8"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'T(N.m)')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x800 with 4 Axes>"
+            ],
+            "image/png": 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LfvbfMtczPH/+PFQqFT/YERH9S74vqrS5HkiLFi2+eKUgIyMDr169wvPnzxETE4P79+/j3r176j/fvn2LiIgIRERE4PDhwwAAOzs71KtXDw0bNsTBgweRnp6OOnXqYO7cuaJ8i96iRQs4Ojpi5MiR2Lx5Mxo3bvzZBSxzwxiutIjJGPL3qfVA9IlXqjSjj7VASBw+Pj4wNzfH27dv8eDBA5QqVUrskIiIDEa+L6r0uR6ITCaDm5sb3NzcUK1atSz/JggCHj9+jIsXL+LixYu4dOkSLl++jHfv3uGvv/7CX3/9pd63SJEiCA4ORqNGjUT50DlixAg8efIEc+bMwYABA+Dp6YnatWtrtY28upaFocjL+fvUeiD69LW1LOjL9LEWCGnfhAkTcPr0afzwww+fnezJzMwMVapUwenTp3Hu3DkWVURE/2JUnxgSExMRHh6O8PBwAEBkZCTCw8MRExMjbmDZIJFI4Obmhg4dOmD27Nk4fvw43r59i/Pnz2PatGlZrprt2bMHrVq1QpEiRdCxY0ds3rxZ79/wzpw5Ex07doRCocA333yTJ3JMRESfdvHiRZw5cwZJSUlf3C9zEovz58/rIywiojzDqIqqy5cvo1KlSqhUqRIAYMyYMahUqZJ6+vG8xtTUFNWrV0elSpWQkJAACwsLbNmyBUOHDkWxYsWQlJSEnTt3olu3bnBwcMB3332Hffv2QaFQ6Dw2ExMTrFu3Dr6+vnj16hXatWvH+yGIiPKoRYsW4c8///zsWjWZ/Pz8AADXrl3TR1hERHmGURVV9evXhyAIHz3Wr18vdmi5JggCpkyZAgAYOnQounTpgqVLl+Lx48e4dOkSfvrpJ5QqVQqpqanYtm0bWrduDScnJwwdOhRXr17VaWxWVlb466+/YG9vj6ioKNy9e1en7RERkW5UqFABnTt3hqur6xf3q1ixIgDgxo0b6qGeRERkZEWVMTp48CAuXboECwsL/Pjjj+rtEokEVapUwbRp03Dnzh1cvnwZo0aNgqOjI16/fo3ly5ejcuXKqFq1Kn7//XckJibqJD43Nzf8/fffuHjxIipXrqyTNoiIyDB4eXlBLpcjKSkJkZGRYodDRGQwWFQZOHd3d7Rv3x7Dhg1D0aJFP7mPRCKBn58fFixYgMePH+Pw4cP47rvvIJPJcPnyZfTv3x/Ozs4YPHgw7ty5o/UYa9SoAU9PT/VzMafBJiKinLlx4wYWLVqE06dPf3VfU1NTlCtXDgBw/fp1XYdGRJRnsKgycGXLlsXu3bsxc+bMbO1vamqKgIAAbN26FbGxsZgzZw68vLzw/v17rFixAmXLlkXz5s1x5MgR6GLd58OHD8PLywv37t3T+rmJiEj7jh49ilGjRmHx4sXZ2t/HxwcAiyoion9jUZVH5GZqZ3t7e4wdOxb37t3DsWPH0K5dO0gkEhw6dAhNmzZF+fLl8fvvvyMtLU0rMQqCgFmzZiEmJgYzZszQyjmJ8qv169fDzs5O7DByZP369ShUqJDYYVAOPXz4EACyjDj4ksz7qlhUEeU/7Js+j0WVgTpx4gSGDx+OBw8eaHwuiUSChg0bYs+ePXjw4AFGjhwJa2tr3L59G/3794enpyeWLFmClJQUjdvZunUrxo8fj1WrVmkcN1Fe0qtXL0gkko8ezZo1++qx7u7uWLhwYZZtnTt3xv3793UU7f/Jix0kadejR48AACVLlszW/s2aNcPKlSvxyy+/6DIsItIC9k36w6LKQM2dOxdLly7FkiVLtHrekiVLYuHChXjy5AnmzZsHZ2dnPHnyBCNGjIC7uztmz56N9+/f5/r8Dg4OmDlz5icXVCYyds2aNcOzZ8+yPLZu3Zqrc1lYWHz2PkoibYqNjQWAr878l6l06dIYMGAAfH19dRgVEWkL+yb9YFFloEaPHo1WrVph+PDhOjm/ra0txowZg4iICKxYsQLu7u54+fIlxo8fj+LFi2PKlCl49+6dRm0olUoEBgbi0KFD2gmayMDJ5XI4OjpmeRQsWFC9NIKbmxvkcjmcnZ0xYsQIAB+WgoiOjsbo0aPV3yACH39LN2XKFPj6+mLt2rVwc3ODtbU1hgwZAqVSidmzZ8PR0RFFixbF9OnTs8Q0f/58VKhQAVZWVnB1dcWQIUPUs4GeOHECvXv3Rnx8vLrtzCUc0tLSMHbsWBQrVgxWVlaoXr06Tpw4keXc69evh5ubGywtLdG+fXu8fv1aN4klnXr27BkAwMnJSeRIiEgX2Dfpp29iUWWgGjVqhH/++QdeXl46bUcul2PgwIG4f/8+NmzYgFKlSuHt27eYOnUqPDw8MHv27Fwv6rts2TLMnDkT33//PSIiIrQcOeU3SUlJOX5kZGSoj8/IyEBSUtJHw1w/d6w27dq1CwsWLMDKlSvx4MED7N27FxUqVAAA7N69Gy4uLvjf//6n/gbxcx49eoSDBw/i0KFD2Lp1K9asWYOWLVviyZMnOHnyJGbNmoWff/4ZFy5cUB9jYmKCxYsX49atW9iwYQNCQkIwbtw4AEDNmjWxcOFC2NjYqNseO3YsAGDYsGE4d+4c/vzzT1y/fh0dO3ZEs2bN1EOSL1y4gL59+2LYsGEIDw9HgwYNMG3aNK3mjXQvPT1d/YEjJ0XVjRs3sHr1aly8eFFXoRHlCeyb2DepCZRFfHy8AECIj4/P1fHp6enC3r17hfT0dC1Hph8ZGRnCtm3bhLJlywoABACCk5OT8Ntvv+X4Z0pNTRWqV68uABAqVqwoJCYmfvWYvJ4/sRlD/lJSUoTbt28LKSkpWbZnvh5z8ti+fbv6+O3btwsAhHr16mU5b5EiRT55rFKpzFHcPXv2FKRSqWBlZZXlMX36dGHevHmCt7f3Z38vxYsXFxYsWJBl27p16wRbW1v188mTJwuWlpZCQkKCelvTpk0Fd3f3LLGWKlVKCAoK+mycO3bsEAoXLvzZdgRBEKKjowWpVCrExsZm2d6oUSMhMDBQEARB6NKli9CiRYss/965c2fB1tZWePv27Wfz97nfryBo/v5rrHTZL0VHRwsABJlMJqhUqmyfc+jQoQIAYfz48bmKKa8xhvdWMeX1/H3pfYt9E/umTLxSZWBWrVqFcePGISYmRpT2pVIpOnXqhOvXr2PDhg0oXrw4nj17hsGDB6NMmTLYsmULVCpVts4ll8uxc+dOFC1aFNevX0f//v11Mo07kaFo0KABwsPDszwGDRqEjh07IiUlBR4eHujfvz/27NmT5ZvK7HJ3d0eBAgXUzx0cHFC2bNkss4M6ODjg5cuX6udHjx5Fo0aNUKxYMRQoUADdu3fH69evv3gF+saNG1AqlfD29oa1tbX6cfLkSfWkBnfu3EH16tWzHOfv75/jn4nElfnts6Ojo3p4T3bUqFEDTZs21floCiLSHPsm/fRNpnpphbIlIyMDM2bMQHR0NLy9vdGvXz/RYpFKpejRowc6d+6M1atXY9q0aXj06BG6du2KOXPmYP78+WjQoMFXz+Pi4oIdO3agUaNG2Lp1K6pUqYIxY8bo4ScgY5M51jon/j1hSvv27ZGYmPjR8gRRUVFZnqtUKiQkJOQqRisrq09OS12oUCHcu3cPR48eRXBwMIYMGYI5c+bg5MmTkMlk2T7/f/eVSCSf3Jb5xUdUVBRatWqFwYMHY/r06ShUqBBOnz6Nvn37Ij09HZaWlp9sJzExEVKpFGFhYZBKpVn+zdraOtvxkuHL7f1U3bp1Q7du3XQRElGewr6JfVMmFlUG5O+//0Z0dDQKFy6Mrl27ih0OgA//8YcNG4bevXtj0aJFmD17NsLDw9GwYUO0bdtWvbjwl9StWxfz5s3DyJEjMW7cOFSqVClbBRnRv1lZWWl0vKmpKUxNP37L++95VSoVlEqlRm19ioWFBVq3bo3WrVtj6NChKF26NG7cuIHKlSvDzMxMJ22GhYVBpVJh3rx56g57+/btWfb5VNuVKlWCUqnEy5cvUadOnU+eu0yZMlnGxwPA+fPntRg96QMnqSDSDPumnDPWvonD/wxI5loAAwcOhIWFhbjB/IeVlRUmTpyIhw8fYujQoZBKpfjrr79Qrlw5/PDDD1+dKXD48OHo1q0blEolOnXqJNrwRiJdSktLw/Pnz7M84uLisH79eqxZswY3b95EREQENm3aBAsLCxQvXhzAh6EToaGhiI2NRVxcnNbi8fT0hEKhwJIlSxAREYGNGzdixYoVWfZxd3dHYmIijh07hri4OCQnJ8Pb2xtdu3ZFjx49sHv3bkRGRuLixYsICgrC/v37AQAjRozAoUOHMHfuXDx48ABLly7lTJ95kKZF1du3b3M9mRER6Qf7Jv30TSyqDMSVK1dw6tQpmJqaYsiQIWKH81lFihTB0qVLcf36dTRv3hwKhQLz58+Hp6cnli1b9tmxuBKJBCtXroSvry/i4uLw7bffIjU1Vc/RE+nWoUOH4OTklOVRu3Zt2NnZYfXq1ahVqxYqVqyIo0eP4p9//kHhwoUBAP/73/8QFRWFkiVLwt7eXmvx+Pj4YP78+Zg1axbKly+PzZs3IygoKMs+NWvWxKBBg9C5c2fY29tj9uzZAIB169ahR48e+OGHH1CqVCm0a9cOly5dgpubG4AP99SsXr0aixYtgo+PD44cOYKff/5Za7GTfmhSVAUEBKBQoUI4fPiwtsMiIi1i36SnvumL01jkQ2LN/tejRw8BgPD999/nql2xHDx4MMtMgT4+PsKZM2c+u39kZKRQqFAhAYDQu3fvj2abyuszBInNGPL3pRl49EGpVH5xhiD6sq/lj7P/5Zwu+6UffvhBKF68uLBu3bocn7dr164CgC/O6GUsjOG9VUx5PX9i90uCwL5JU/rom3ilygA8f/5cvbL1yJEjRY4mZ5o1a4Zr165h2bJlKFiwIK5du4ZatWqhT58+WWZ5yeTu7o4///wTJiYmWLdu3UeXe4mISH/mzp2LqKgo9OrVK8fHli5dGgBw9+5dLUdFRJT3sKgyAL/99hsUCgX8/f1RrVo1scPJscwhi/fu3UPfvn0BfLg8W6pUKfz2228f3WjYpEkT9WXewMDAr96PRUREhiezqLpz547IkRARiY9FlchSU1PVV2tGjRolbjAasre3x++//46zZ8/C19cX7969w5AhQ1C9enVcunQpy74//vgjRo0ahRMnTsDOzk6cgImIKNe8vb0BAA8fPhQ5EiIi8bGoEtmff/6Jly9fwsXFBe3btxc7HK3w9/fHpUuXsGTJEtja2iIsLAw1atTAmDFjkJSUBODDxBULFiyAr6+vuMESEeVTz58/R/HixVGrVq1cLczu4eEBAHjz5g3evn2r7fCIiPIUFlUiEgRBPY36sGHDcrTQmqEzNTXFsGHDcO/ePXz//fdQqVRYsGABypcv/8mZoi5cuIApU6boP1Aionzq6dOniImJQWRkJCQSSY6Pt7a2hoODAwDg0aNH2g6PiChPYVElotDQUFy7dg0WFhbo37+/2OHohIODAzZv3owDBw7Azc0NUVFRaNasGXr06KFe8+Dp06eoX78+pk6dip07d4ocMRmK3HxzToaPv1fDUbp0aZw7dw6bNm3K9Tk8PT0BsKii/IHvX8ZLG79bFlUiSk9PR7ly5dCjRw8UKlRI7HB0qnnz5rh16xZGjhwJiUSCjRs3okyZMtiyZQucnJwwceJEtG3bFs2aNRM7VBKZVCoF8OH/BxmfzIVijenKfF5laWmJGjVqoGHDhrk+R8mSJQGwqCLjlvl+xYWujZc2+iZTbQVDOdekSRPcuHEj3/wntba2xsKFC9GlSxf069cPN2/eRNeuXbFp0yYsX74cEydOhEqlEjtMEpmpqSksLS3x6tUryGQymJjo97sflUqF9PR0pKam6r1tY/C5/AmCgOTkZLx8+RJ2dnbq4jm/iI2Nxfjx43Hw4EEkJyfD09MT69atQ5UqVcQOTSMsqig/kEqlsLOzUy8VY2lpmashs5pg36QZffRNLKpEJpFIYGVlJXYYelW9enWEhYVh9uzZ+PXXX3Hw4EFUrFgR8+fPR48ePSAIApYvX46uXbuiSJEiYodLeiaRSODk5ITIyEhER0frvX1BEJCSkgILCwu9d5rG4Gv5s7Ozg6OjowiRieft27eoVasWGjRogIMHD8Le3h4PHjxAwYIFRY3r6NGjuHnzJmrVqoWqVavm6hwsqii/yHzf+tQanPrAvkkz+uibWFSJIC4uDtu2bUPv3r1haWkpdjiiMDMzw88//4wOHTqgb9++OHv2LPr3749du3bBxMQEBw4cwM6dO3H06FHI5XKxwyU9MzMzg5eXlyhDABUKBUJDQ1G3bl0OUcuFL+VPJpPluytUADBr1iy4urpi3bp16m0lSpQQMaIPduzYgVWrVmHq1Km5Lqoy76nitOpk7DK/8CtatCgUCoXe22ffpBl99E0sqkSwbNkyTJkyBXv37kVwcLDY4YiqdOnSCA0NxYIFC/Dzzz/j0KFDsLCwgIWFBU6fPo1+/frhjz/+4Lcy+ZCJiQnMzc313q5UKkVGRgbMzc3ZceUC8/exv//+G02bNkXHjh1x8uRJFCtWDEOGDBF9gqLXr18DAAoXLpzrc2ReqYqNjVV/C0xkzKRSqShfDvG9VTP6yB+LKhGUKFECHh4e6Nu3r9ihGASpVIqxY8eiRYsW6NGjB8LCwgB8+FZo06ZNKF68OKZNmyZylEREuRMREYHffvsNY8aMwcSJE3Hp0iWMGDECZmZm6Nmz50f7p6WlIS0tTf08ISEBwIdvWnPzDXnmMf89NnMGVltb21x/825jY4PDhw+jRIkSMDExEeUbfH34XA4pe5g/zTGHmtEkf9k9hkWVCHr06IGuXbuKHYbBKVu2LEJDQ9G/f39s374dGRkZAIDp06fD0dERw4YNEzlCIqKcU6lUqFKlCmbMmAEAqFSpEm7evIkVK1Z8sqgKCgrC1KlTP9p+5MgRjYaM/3dkRFRUFIAP90MdOHAg1+cFgNu3b+P27dsanSMvyO+jSzTF/GmOOdRMbvKX3QnlWFSJJD/eV5AdMpkMnTp1wqhRo9CvXz9cv34dADB8+HBYW1ujV69e4gZIRJRDTk5OKFu2bJZtZcqUwa5duz65f2BgIMaMGaN+npCQAFdXVwQEBMDGxibH7SsUCgQHB6NJkyZZhr0MHToUANCsWTNUrlw5x+fNTz6XQ8oe5k9zzKFmNMlf5miBr2FRpUfBwcF48uQJvv/+e06+8BW+vr64dOkSpk6diqCgIAiCgN69eyM+Ph4jR44UOzwiomyrVasW7t27l2Xb/fv3Ubx48U/uL5fLP9lHyGQyjT5M/ff4zHuqHBwcNDpvWFgY9u7di5IlSxr9F1+a/g7yO+ZPc8yhZnKTv+zuz4nu9USpVGLUqFHo06cP5s+fL3Y4eYKZmRmmT5+OU6dOqaedz7yCxTHFRJRXjB49GufPn8eMGTPw8OFDbNmyBatWrVJfKRJDcnIyUlNTAWg2UQUAXLlyBdOmTcO2bdu0ERoRUZ7EokpPtmzZgtu3b6NgwYIYPHiw2OHkKbVq1UJkZCQcHBwAAGvWrIGfnx8ePHggcmRERF9XtWpV7NmzB1u3bkX58uXx66+/YuHChaLeW5t5lUomk8Ha2lqjc1WtWhUDBw5Ex44dtREaEVGexOF/epCWloYpU6YAAMaNGwc7OztR48mL7O3tce/ePfj4+CA6Oho3btyAj48PlixZgj59+nDKdSIyaK1atUKrVq3EDkPt39Opa/r+6evrixUrVmgjLCKiPItXqvRgzpw5iIiIgKOjI4YPHy52OHmWra0tLl26BC8vL0gkEqSkpKBfv37o0KGD+gMCERF9XeZ7ZqFChUSOhIjIOLCo0rHIyEhMnz4dADBv3jz1vUGUO/b29jh58iQOHTqE2bNnQyaTYffu3fDx8cGpU6fEDo+IKE/QxsK//5aYmIhr167h5cuXWjkfEVFeY5RF1bJly+Du7g5zc3NUr14dFy9eFCWOjIwM9O3bF6mpqWjQoAG6dOkiShzGxsnJCQEBAfjxxx9x/vx5eHh4IDY2FvXr18e0adOgVCrFDpGIyKBpu6jq3LkzfH19sWfPHq2cj4gorzG6omrbtm0YM2YMJk+ejCtXrsDHxwdNmzYV5duzSZMm4fjx47CyssJvv/3G+350oECBAkhPT4ednR1UKhV++eUXBAQE4NmzZ2KHRkRksORyOTw8PODm5qaV85UsWRLAh4WEiYjyI6MrqubPn4/+/fujd+/eKFu2LFasWAFLS0usXbtWbzEIgoC5c+ciKCgIwIfZ6kqVKqW39vMTiUQClUoFR0dHLFmyBFZWVggJCYGvry+OHDkidnhERAapT58+ePToERYtWqSV83l6egJgUUVE+ZdRFVXp6ekICwtD48aN1dtMTEzQuHFjnDt3Ti8x7Nu3D//73/8wceJEAMCECRPQuXNnvbSdH3l6eiI0NBTHjh3DsGHDcPnyZVSsWBEvX75E06ZNERgYyDWtiIh0jFeqiCi/M6op1ePi4qBUKtXrGWVycHDA3bt3P3lMWloa0tLS1M8TEhIAAAqFIlcfxs+cOYOrV6/CxMQE8+bNw9ChQ/mhPgcyc5WTnGUOX1EoFChZsiSGDx+O4OBgbN++HTNnzsTJkyexceNGrQ1zMWS5yR9lxRxqRpP8Med5V2ZR9fDhQwiCwOHuRJTvGFVRlRtBQUGYOnXqR9uPHDkCS0vLHJ/P1dUVnTt3Rq1ateDm5oYDBw5oI8x8Jzg4OFfHXbhwATNnzoSNjQ169uyJHTt24Ny5c/D19cXw4cNRvXp1LUdqmHKbP/o/zKFmcpO/5ORkHURCn9KhQwdERUVh0aJFqFWrlsbnK1GiBCQSCZKSkvDq1SsULVpUC1ESEeUdRlVUFSlSBFKpFC9evMiy/cWLF3B0dPzkMYGBgRgzZoz6eUJCAlxdXREQEAAbG5scx6BQKODu7o4mTZpAJpPl+Pj8TqFQIDg4ONf5q1atGg4ePIjw8HDs3bsXK1aswOLFi3H58mUEBQVh5MiRmDFjhtH+bjTNHzGHmtIkf5kjBUj3rl+/jgcPHmhttlS5XA5nZ2fExsYiKiqKRRUR5TtGVVSZmZnBz88Px44dQ7t27QAAKpVKfb/Np8jlcsjl8o+2y2QyjT5QaXp8fpfb/Dk5OeH48eNo3bo1Tp8+jX79+mHDhg24dOkS5s+fj0WLFuHy5cvYtm0bihUrpoPIDQNff5pjDjWTm/wx3/qzdetWPH/+HBUqVNDaOd3d3dVFVbVq1bR2XiKivMCoJqoAgDFjxmD16tXYsGED7ty5g8GDByMpKQm9e/cWOzTSEzs7Oxw5cgTt27dHeno6vv/+e5QoUQK7d++GjY0Nzpw5g8qVKyMkJETsUImIROHn54eWLVuiYMGCWjunu7s7ACA6Olpr5yQiyiuMrqjq3Lkz5s6di0mTJsHX1xfh4eE4dOjQR5NXkHGzsLDAjh07MHjwYAiCgOHDh+PixYu4dOmSenbAJk2aICgoCCqVSuxwiYjyvMyiKioqStQ4iIjEYHRFFQAMGzYM0dHRSEtLw4ULF/LN5ASUlVQqxbJlyzBt2jQAwMyZMzF16lQcP34cvXr1gkqlwsSJE9G2bVu8fftW5GiJiPQjISEBixYtwsaNG7V6XhZVRJSfGWVRRZRJIpHgp59+wpo1a2BqaootW7agVatWCAoKwurVqyGXy7Fv3z74+fnhypUrYodLRKRzsbGxGDVqFEaNGqXV87KoIqL8jEUV5Qt9+vTB4cOHYWdnh3PnzqF69eqoXr06zp49ixIlSiAyMhI1a9bE77//DkEQxA6XiEhn4uPjASBXM9x+SWZR9fjxY76PElG+w6KK8o2GDRviwoUL8PLyQkxMDC5cuIDKlSsjLCwMrVu3RlpaGvr3748+ffogJSVF7HCJiHQis6iytbXV6nnd3d3x6NEjxMXFcfFfIsp3WFRRvuLt7Y3z589jxYoV6NevHwCgYMGC2Lt3L2bMmAETExOsX78etWvXRkxMjMjREhFpn66KKlNTU3h4eMDMzEyr5yUiygtYVFG+U6hQIQwcOFD9/NWrVxgxYgSGDRuG4OBgFClSBFeuXIGfnx+OHz8uYqRERNqXuciytof/ERHlZyyqKN/r2bMnli1bhu7du6Nhw4a4fPkyKleujLi4ODRp0gQLFy7k/QFEZDR0daUKAHbt2oUePXrgzz//1Pq5iYgMGYsqyvd++eUXeHt7Y/r06QCA4sWL4/Tp0+jevTuUSiVGjx6N7t27Izk5WeRIiYg0p8ui6urVq9i4cSNOnTql9XMTERkyU7EDIBKbv78/bt++DalUqt52/fp1rFu3DlWqVMGYMWOwefNm3Lp1C3v27FHPcEVElBfpsqhq1qwZChQoAH9/f62fm4jIkPFKFRGQpaA6deoUateujVatWuH777/HsWPHYG9vj/DwcFSpUgVHjx4VMVIiIs3oakp1AKhduzbGjx+PunXrav3cRESGjEUV0X88e/YMpqamOHToEHx9fWFqaoqwsDBUqVIFr1+/RtOmTTFv3jzeZ0VEeVLmRBW6uFJFRJRfsagi+o9OnTrhwoUL8Pb2RmxsLOrVq4etW7fi5MmT6NWrF1QqFcaOHYuuXbvyPisiynN0OfwPAG7duoV9+/YhMTFRJ+cnIjJELKqIPqFixYq4fPkyunTpAqVSifHjx6NTp06YM2cOli1bBlNTU2zduhX+/v6IjIwUO1wiomzTdVHVtGlTtG7dGnfu3NHJ+YmIDBGLKqLPKFCgADZv3oyVK1dCLpdj//79qFSpEnx9fRESEoKiRYvi+vXrqFq1Kk6ePCl2uERE2WJjY4NChQrBzs5OJ+fPnMwnKipKJ+cnIjJELKqIvkAikWDAgAG4cOECvLy88OTJE9StWxfnz5/HpUuX1PdZNW7cGCtXrhQ7XCKirwoJCcHr169Rq1YtnZyfRRUR5UcsqoiywcfHB2FhYfjuu++gVCoxbtw4DBkyBHv27EGXLl2QkZGBQYMGYdiwYVAoFGKHS0QkGhZVRJQfsagiyqYCBQpgy5YtWLFihXo4YJ06dfD7779jxowZkEgkWLZsGZo2bYrXr1+LHS4RkShYVBFRfsSiiigHJBIJBg4ciPPnz8Pb2xuDBw+GpaUlAgMDsXfvXlhbW+P48eOoVq0abt26JXa4RGRgZs6cCYlEglGjRonS/uvXr1GlShU0btxYZ8tCsKgiovyIRRVRLvj6+uLKlSsYO3aselvp0qXxzz//oESJEoiIiIC/vz/++ecfEaMkIkNy6dIlrFy5EhUrVhQthjdv3iAsLAwXLlyARCLRSRv/Lqq4nh8R5RcsqohyycrKCiYmH/4LJSUloV27dvjuu++wcuVK1K9fH+/fv0fbtm0xc+ZMfrAgyucSExPRtWtXrF69GgULFhQtDicnJ+zbtw9//PGHztpwdXWFRCJBcnIy4uLidNYOEZEhYVFFpAVxcXGQSqUwMTGBj48Pjhw5gsGDB0MQBAQGBqJbt25ISUkRO0wiEsnQoUPRsmVLNG7cWNQ4rK2t0bJlS7Rv315nbcjlcjg7OwPgEEAiyj9MxQ6AyBgUL14cFy9eREREBIoWLQoAWL58OUqWLIkJEyZgy5YtuH//Pvbu3YtixYqJHC0R6dOff/6JK1eu4NKlS9naPy0tDWlpaernCQkJAACFQpGr2UUzj9HnzKTFixdHbGwsHj58CF9fX721qyti5NCYMH+aYw41o0n+snsMiyoiLbGwsEC5cuXUzzdv3oygoCD873//w9y5c3H58mVUq1YNf//9N/z8/ESMlIj05fHjxxg5ciSCg4Nhbm6erWOCgoIwderUj7YfOXIElpaWuY4lODgYjx8/xoMHD+Ds7IzSpUvn+lxfI5PJAACHDx/WKGZDExwcLHYIeRrzpznmUDO5yV9ycnK29mNRRaQDgiDg999/x+vXrzFx4kT07dsXZ8+exZ07d1C3bl1s2rRJp8NviMgwhIWF4eXLl6hcubJ6m1KpRGhoKJYuXYq0tDRIpdIsxwQGBmLMmDHq5wkJCXB1dUVAQABsbGxyHINCoUBwcDCaNGmClStXYvHixejQoUOWNrTt/PnzOHnyJCwsLNCiRQudtaMv/85hZsFI2cf8aY451Iwm+cscLfA1LKqIdEAikeDQoUMYP348Fi1ahDVr1sDPzw9169ZFaGgovvnmG8ycORPjxo3T2QxcRCS+Ro0a4caNG1m29e7dG6VLl8b48eM/KqiAD/ckyeXyj7bLZDKNPkzJZDIkJiYCAAoWLKjTD2YlS5aERCJBYmKiUX0A1PR3kN8xf5pjDjWTm/xld39OVEGkI3K5HAsXLsSePXtQsGBBhIWF4dq1a2jWrBkAYMKECejbty/S09NFjpSIdKVAgQIoX758loeVlRUKFy6M8uXL6z2e+Ph4AMjVFa+c6Nq1K1JSUrBx40adtkNEZChYVBHpWLt27RAeHg5/f3/Ex8fj0KFDqFOnDiQSCdatW4cmTZrg9evXYodJRPlA5pUqXRdVFhYWn7zaRkRkrFhUEemBm5sbTp48iQkTJgAATp06hRIlSsDKygqhoaGoXr067t27J3KURKQPJ06cwMKFC0VpO7Oosra2FqV9IiJjxaKKSE9kMhmCgoJw6NAh2NvbIyIiAiqVCkWKFMGjR49Qo0YNHDt2TOwwiYzSu3fvsG7dOvTp0weNGjWCv78/2rRpg8mTJ+Ps2bNih6c3+iyqfvjhB9SrVw/Xrl3TeVtERGJjUUWkZ02bNkV4eDgaNGiAlJQUxMXFwcnJCe/evUOzZs2wevVqsUMkMhpPnz5Fv3794OTkhGnTpiElJQW+vr5o1KgRXFxccPz4cTRp0gRly5bFtm3bxA5X5/RZVF24cAGhoaG4f/++ztsiIhIbZ/8jEoGzszOCg4Mxffp0TJ06FQsWLMA///yDzZs3Y8CAAbh79y5mz579yZnBiCj7KlWqhJ49eyIsLAxly5b95D4pKSnYu3cvFi5ciMePH2Ps2LF6jlJ/9FlUjRs3DklJSfD399d5W0REYmNRRSQSqVSKSZMm4fvvv4enpyc6deqEUqVKYdKkSZg/fz4ePHiALVu28N4HIg3cvn0bhQsX/uI+FhYW6NKlC7p06WL0k8bos6hq06aNztsgIjIUHP5HJDJPT08AH9a26tOnDwoUKAATExP8888/qF27Nh4/fixyhER519cKKk33z2s4UQURkW6wqCIyIKdPn0ZKSgo8PT1RtGhRXLt2DdWrV8eVK1fEDo0ozwsKCsLatWs/2r527VrMmjVLhIj0L7OosrKy0ktb+/fvx6ZNm3TeFhGR2FhUERmQzp0749SpU9i3bx8uXbqE8uXL49mzZ6hTpw72798vdnhEedrKlStRunTpj7aXK1cOK1asECEi/dPnlarnz5+jVatWGDBgAARB0Hl7RERiYlFFZGBq1KgBLy8vuLm54fTp0/Dw8EBycjJat26N5cuXix0eUZ71/PlzODk5fbTd3t4ez549EyEi/VIqlbCwsIBUKtVLUeXq6gqJRIKUlBS8evVK5+0REYmJRRWRAYuPj1ffUyUIAoYOHYqxY8dCpVKJHBlR3uPq6oozZ858tP3MmTNwdnYWISL9kkqlePv2LRQKBQoVKqTz9uRyuTqvkZGROm+PiEhMRlVUTZ8+HTVr1oSlpSXs7OzEDodIY25ubjh8+DDs7e3V2+bNm4eOHTsiOTlZxMiI8p7+/ftj1KhRWLduHaKjoxEdHY21a9di9OjR6N+/v9jh6Y1EIoFEItFLWyVKlAAAREVF6aU9IiKxGFVRlZ6ejo4dO2Lw4MFih0KkNQ0aNMCVK1dQrVo19bbdu3ejQYMGePnypYiREeUtP/74I/r27YshQ4bAw8MDHh4eGD58OEaMGIHAwECxwzNK7u7uAFhUEZHxM6qiaurUqRg9ejQqVKggdihEWuXi4oLQ0FAMHDhQve3ixYuoWrUq7t69K2JkRHmHRCLBrFmz8OrVK5w/fx7Xrl3DmzdvMGnSJLFD04u7d++iSZMmer0qx6KKiPKLfL/4b1paGtLS0tTPExISAAAKhQIKhSLH58s8JjfHEvP3JSYmJliyZAmqVKmCoUOHIj09HTExMahWrRr27NmDunXrMn9awBxqRpP86Svnz58/x5s3b1C3bl3I5XIIgqC34XBievnyJY4ePfrJGRB1JbOo4j1VRGTs8n1RFRQUhKlTp360/ciRI7C0tMz1eYODgzUJK99j/j7P3t4eM2bMwMyZMxEXF4f3798jICAAI0aMQP369QEwf9rAHGomN/nT9X2Cr1+/RqdOnXD8+HFIJBI8ePAAHh4e6Nu3LwoWLIh58+bptH2xeXt7Y9OmTTA3N9dbm7yniojyC4MvqiZMmPDVRRnv3LmT62/eAgMDMWbMGPXzhIQEuLq6IiAgADY2Njk+n0KhQHBwMJo0aQKZTJarmPIz5i/7unbtiu+//x7Hjx+HSqXCwoULYWVlhSpVqiAgIID5yyW+BjWjSf4yRwroyujRoyGTyRATE4MyZcqot3fu3Bljxowx+qLK0dERXbt21WubmVeqoqOj880VQSLKn7ReVL179w579uzBqVOnEB0djeTkZNjb26NSpUpo2rQpatasmaPz/fDDD+jVq9cX9/Hw8Mh1vHK5HHK5/KPtMplMow9Umh6f3zF/X+fo6Ijg4GD8/PPPmDlzJoAPM2A2bNiQBYEW8DWomdzkT9f5PnLkCA4fPgwXF5cs2728vBAdHa3TtvMrV1dXmJiYIDU1FS9evICjo6PYIRER6YTWiqqnT59i0qRJ2Lx5M5ydnVGtWjX4+vrCwsICb968wfHjxzF37lwUL14ckydPRufOnbN1Xnt7+yzTSRPR/5FKpQgKCoKvry8OHDiATZs2ISQkBK1bt8bu3bu5tADRvyQlJX1yWPebN28++eWasbl37x4iIiLg4eGB8uXL66VNmUwGFxcXxMTEICoqikUVERktrc3+V6lSJRQqVAhhYWF49OgRtm7dinnz5mHatGlYvnw5QkNDERcXh19++QULFy7E3LlztdW0WkxMDMLDwxETEwOlUonw8HCEh4cjMTFR620RGZLOnTtjw4YN2Lt3L8zNzXH8+HH4+PggJiZG7NCIDEadOnXwxx9/qJ9LJBKoVCrMnj0bDRo0EDEy/di9ezfatm2LhQsX6rVdTlZBRPmB1q5U3b59G4ULF/7iPhYWFujSpQu6dOmC169fa6tptUmTJmHDhg3q55UqVQIAHD9+XH0DP5Exa9asGSZOnIipU6ciJiYGPj4+OHHiBHx8fMQOjUh0s2fPRqNGjXD58mWkp6dj3LhxuHXrFt68eYMzZ86IHZ7OZX7BaG1trdd2PTw88PDhQ6Snp+u1XSIifdLalaqvFVSa7p8d69evhyAIHz1YUFF+UqZMGXz77bcwMzPDu3fvUKdOHRw9elTssIhEV758edy/fx+1a9dG27ZtkZSUhG+++QZXr15FyZIlxQ5P55KSkgDov6hau3YtYmNj0bNnT722S0SkT1q7UvX3339ne982bdpoq1ki+g+ZTIaNGzciNjYWffr0wYkTJ9C8eXMsXbo0y+LBRPmRra0tfvrpJ7HDEIVYRRVn/COi/EBrRVW7du2yPJdIJBAEIcvzTEqlUlvNEtEnSCQSlChRAocOHUKvXr3w559/YtCgQbh+/TqWLl3KDzmULx06dAjW1taoXbs2AGDZsmVYvXo1ypYti2XLlqFgwYIiR6hbYg3/IyLKD7Q2/E+lUqkfR44cga+vLw4ePIh3797h3bt3OHDgACpXroxDhw5pq0ki+gq5XI4NGzaoZ9Bcvnw5mjZtioyMDJEjI9K/H3/8Ub0W1o0bNzBmzBi0aNECkZGRWdYrNFZiXal68+YNGjRoAG9vb6hUKr22TUSkLzpZ/HfUqFFYsWKF+ttAAGjatCksLS0xYMAA3LlzRxfNEtEnmJmZ4eHDh/D398ft27cRHByMChUq4PLly7CyshI7PCK9iYyMRNmyZQEAu3btQuvWrTFjxgxcuXIFLVq0EDk63RPrSpWNjQ1OnToFpVKJZ8+eoVixYnptn4hIH7R2perfHj169Mn1cWxtbREVFaWLJonoC2xsbHD9+nW0bNkSAHD37l24u7sjNjZW5MiI9MfMzAzJyckAgKNHjyIgIAAAUKhQIfUVLGMmVlFlamqKHTt24MyZMyhSpIhe2yYi0hedFFVVq1bFmDFj8OLFC/W2Fy9e4Mcff0S1atV00SQRfYVUKsW+ffvUw5zi4uLg6emJsLAwkSMj0o/atWtjzJgx+PXXX3Hx4kX1lwz379+Hi4uLyNHpnpj3VLVv3x41a9bMF4ssE1H+pJOiau3atXj27Bnc3Nzg6ekJT09PuLm5ITY2FmvWrNFFk0SUTfPmzcOqVasgkUiQmpqK6tWrY+fOnWKHRaRzS5cuhampKXbu3InffvtNPQzt4MGDaNasmcjR6Z5Y91QREeUHOrmnytPTE9evX0dwcDDu3r0L4MPaOY0bN+asY0QGoH///vDy8kLTpk2Rnp6Ojh07YsaMGQgMDBQ7NCKtCwkJQb169eDm5oZ9+/Z99O8LFiwQISr9E/NK1YMHDxAcHAx7e3t07NhR7+0TEemaTooq4MOUzgEBAeox60RkWOrXr4/bt2/Dz88P8fHxmDhxIu7fv49169aJHRqRVvXr1w/v3r1Ds2bN0K5dOzRv3hwFChQQOyy9EgRB1KLq3LlzGDp0KBo2bMiiioiMks6KqqSkJJw8eRIxMTFIT0/P8m8jRozQVbNElAMlS5ZETEwMfHx8EBUVhfXr18Pc3BzLly/nVWUyGhEREbh+/Tr+/vtvzJ07Fz179kTt2rXRpk0btG3bFm5ubmKHqHP/7ofFKKrc3d0BgJNVEZHR0klRdfXqVbRo0QLJyclISkpCoUKFEBcXB0tLSxQtWpRFFZEBsbGxwcOHD9GgQQOcOnUKK1asQEpKClavXg2ZTCZ2eERaUbFiRVSsWBE///wznj59ir///ht///03xo0bh1KlSqFNmzZo06YNqlSpovW2g4KCsHv3bty9excWFhaoWbMmZs2ahVKlSmm9rc+Ry+VITU1FRkYGLCws9NZuphIlSgAAYmJioFQqIZVK9R4DEZEu6WSiitGjR6N169Z4+/YtLCwscP78eURHR8PPzw9z587VRZNEpAGpVIrQ0FD8/vvvkEql2LBhAxo1aoTo6GixQyPSOmdnZwwaNAgHDhxAXFwcfv75Z0RFRaFZs2aYMWOG1ts7efIkhg4divPnzyM4OBgKhQIBAQHqiSP0RSKRwNLSUpSr0M7OzjA1NUVGRgaePn2q9/aJiHRNJ1eqwsPDsXLlSpiYmEAqlSItLQ0eHh6YPXs2evbsiW+++UYXzRKRhvr27QsnJyd06NABp06dQqlSpXDq1ClUrVpV7NCIdMLKygodOnRAhw4doFQq8ebNG623cejQoSzP169fj6JFiyIsLAx169bVenuGSCqVws3NDREREYiMjISrq6vYIRERaZVOiiqZTAYTkw8XwYoWLYqYmBiUKVMGtra2ePz4sS6aJCItadGiBTZu3IhOnTohLS0NHTp0wLFjx+Dp6Sl2aEQ5tnjx4mztJ5FIMHz4cNjb2+s4IiA+Ph7Ah0WHPyUtLQ1paWnq55kLEysUCigUihy3p1AoEBsbi++++w7u7u6YOXNmLqLWnLu7OyIiIvDw4UP4+/uLEkNuZeY9N/kn5k8bmEPNaJK/7B6jk6KqUqVKuHTpEry8vFCvXj1MmjQJcXFx2LhxI8qXL6+LJolIi7799lucOHECXbp0QUxMDGrVqoUDBw7Az89P7NCIcuS/06W/evUKycnJsLOzAwC8e/dOfb/v8OHDdR6PSqXCqFGjUKtWrc/2h0FBQZg6depH248cOQJLS8tctfvmzRvs3r0brq6uol0dy/yy9ejRoyhcuLAoMWgqODhY7BDyNOZPc8yhZnKTv+Tk5Gztp5OiasaMGXj//j0AYPr06ejRowcGDx4MLy8vrF27VhdNEpGW1alTB5cvX0bz5s0RHh6O2rVrY+DAgVi4cKHYoRFlW2RkpPrvW7ZswfLly7FmzRr1JBH37t1D//79MXDgQL3EM3ToUNy8eROnT5/+7D6BgYEYM2aM+nlCQgJcXV0REBAAGxubHLepUCjw6tUrzJ8/H7a2tmjRokWuYtdUeHg4jh49CplMJloMuaVQKBAcHIwmTZpwAp9cYP40xxxqRpP8ZY4W+BqtF1WCIKBo0aLqb+CKFi360XhyIsobHB0dcfLkSTRt2hTnz5/HokWLEBUVhT179nDKdcpzfvnlF+zcuTPLrHulSpXCggUL0KFDB3Tt2lWn7Q8bNgz79u1DaGgoXFxcPrufXC6HXC7/aLtMJsv1hyl7e3v07NlT1A9jmUOIY2Ji8uyHQk1+B8T8aQNzqJnc5C+7+2t99j9BEODp6cl7p4iMhI2NDY4fP45y5coBAP766y9UrVr1o/XniAzds2fPkJGR8dF2pVKJFy9e6KxdQRAwbNgw7NmzByEhIerpxfMbrlVFRMZM60WViYkJvLy88Pr1a22fmohEYm5ujuvXr6NRo0YAgLCwMHh5eeHt27ciR0aUfY0aNcLAgQNx5coV9bawsDAMHjwYjRs31lm7Q4cOxaZNm7BlyxYUKFAAz58/x/Pnz5GSkqKzNv/r1atXCA0NxYMHD/TW5n9lFlWPHz/+ZHFLRJSX6WSdqpkzZ+LHH3/EzZs3dXF6IhKBiYkJgoOD0adPHwAfhvB4eHjg0aNHIkdGlD1r166Fo6MjqlSpoh5iV61aNTg4OOD333/XWbu//fYb4uPjUb9+fTg5Oakf27Zt01mb/3X27Fk0btz4kxNg6IuTkxPMzMygVCrx5MkT0eIgItIFnUxU0aNHDyQnJ8PHxwdmZmYfrd6ui3VAiEj3JBIJ1qxZA3d3d0yaNAnv3r1DuXLlEBoaimrVqokdHtEX2dvb48CBA7h//z7u3r0LAChdujS8vb112q4gCDo9f3ZkTtGe29kDtcHExATVqlWDUqnU61U6IiJ90ElRxdnBiIzbL7/8AldXV/Tp0wdpaWmoWbMmdu/ejTZt2ogdGtFXeXt767yQMjSGUFQBwKlTp0Rtn4hIV3RSVPXs2VMXpyUiA9KrVy8UK1YMLVq0QEZGBtq1a4fly5dj0KBBYodG9FlPnjzB33//jZiYmI8mW5k/f75IUemeoRRVRETGSmtFVVJSEqysrHS2PxEZniZNmuDy5cvw9/dHSkoKBg8ejMePH2P69Olih0b0kWPHjqFNmzbw8PDA3bt3Ub58eURFRUEQBFSuXFns8HTK0IoqQRC4LAMRGRWtTVTh6emJmTNn4tmzZ5/dRxAEBAcHo3nz5li8eLG2miYiEfn4+ODevXsoVKgQAGDOnDk4duyYyFERfSwwMBBjx47FjRs3YG5ujl27duHx48eoV68eOnbsKHZ4OpV5Ve6/9zjrW0hICDw8PHQ62yIRkRi0dqXqxIkTmDhxIqZMmQIfHx9UqVIFzs7OMDc3x9u3b3H79m2cO3cOpqamCAwM1Nvq9USke66urnj06BFq1aqF27dvo3nz5vjjjz/w3XffiR0akdqdO3ewdetWAICpqSlSUlJgbW2N//3vf2jbti0GDx4scoS6YyhXqqytrREZGamOh4jIWGjtSlWpUqWwa9cu3L9/H506dUJsbCx27tyJ1atX48SJEyhWrBhWr16NqKgoDBkyBFKpVFtNE5EBsLOzw5UrV9CxY0coFAp06dIFzZs35yxfZDCsrKzUV2ycnJyyLAcQFxcnVlh6kflzi11UlS9fHqGhobh06ZKocRARaZvWJ6pwc3PDDz/8gB9++EHbpyYiAyeXy7F161Y4ODhg6dKlOHToEMqXL4+HDx/y/gkSXY0aNXD69GmUKVMGLVq0wA8//IAbN25g9+7dqFGjhtjh6ZShXKmytLREnTp1RI2BiEgXdLL4LxHlX1KpFIsXL0a/fv0AABEREejVqxcUCoXIkVF+N3/+fFSvXh0AMHXqVDRq1Ajbtm2Du7s71qxZI3J0umUoRRURkbHSyZTqRJS/SSQSrF69GlWqVMHQoUPxxx9/IC4uDlu3boWNjY3Y4VE+pFQq8eTJE1SsWBHAh6GAK1asEDkq/TGkouro0aM4cuQI6tati1atWokdDhGRVvBKFRHpzMCBA/HXX3/BwsICBw4cgIODAw4dOiR2WJQPSaVSBAQE4O3bt2KHIgpDmf0P+DC1/Zw5c3D48GGxQyEi0hoWVUSkUy1btkRwcDBkMhlSU1PRokULrF+/XuywKB8qX748IiIixA5DFIZ0papkyZIAkGWiECKivI5FFRHpXK1atXD69GnI5XIIgoDevXtj9uzZYodF+cy0adMwduxY7Nu3D8+ePUNCQkKWhzFbtmwZoqOjUaZMGbFDgYeHBwAWVURkXLR+T9XZs2dx9OhRREREIDk5GZaWlvDw8EBAQIDRz65ERJ9XrVo13LhxA35+fnj//j3Gjx+P58+fY/78+WKHRvlEixYtAABt2rTJMhulIAiQSCRQKpVihaZzVlZWcHJygkwmEzsU9ZWqyMhIKJVKLrFCREZBa0VVXFwcOnTogNOnT6N8+fLw8vLC33//jSZNmuDJkyeYNWsW/P39sW3bNhQuXFhbzRJRHuLl5YX79+/Dx8cHL1++xIIFC/D8+XNs3ryZU66Tzh0/flzsEAiAi4sLZDIZFAoFnjx5guLFi4sdEhGRxrRWVA0cOBBmZmZ4/PgxnJycAAAFChTA4sWL4eHhgcTERPTt2xeDBg3Cjh07tNWsWlRUFH799VeEhITg+fPncHZ2Rrdu3fDTTz/BzMxM6+0RUe44Ojri/v37qFy5MiIiIrB161a8fPkShw8f5jfWpFP16tUTOwRRJCUlYdmyZTh69CgWLlwo+v8zqVSKEiVK4P79+3j06BGLKiIyClq7p+rw4cNYsmSJuqD6L2tra0ybNg0HDx7UVpNZ3L17FyqVCitXrsStW7ewYMECrFixAhMnTtRJe0SUe7a2trh16xYqV64M4MNsYFWrVlXfTE+kLTExMTnaPzY2VkeRiCc+Ph7BwcFYvnw5TEwM41ZqTlZBRMZGa++uhQsXxoMHD764T1RUFAoVKqStJrNo1qwZ1q1bh4CAAHh4eKBNmzYYO3Ysdu/erZP2iEgz5ubmuHjxIpo0aQIAuHr1KsqWLWv0EwaQflWtWhUDBw7EpUuXPrtPfHw8Vq9ejfLly2PXrl16jE4/rKys0LVrV0ycONFghtmyqCIiY6O14X8TJkxAly5dMGLECLRs2RIlS5aERCJBfHw8Hj58iFOnTuGnn37ClClTtNXkV8XHx3+1iEtLS8vy7XjmBzqFQgGFQpHjNjOPyc2xxPxpKi/mb9++fejVqxe2bt2KiIgIeHl5ITw8HEWKFBElnryYQ0OiSf50kfPbt29j+vTpaNKkCczNzeHn5wdnZ2eYm5vj7du3uH37tvqq6ezZs9WTWRgTW1tbdOzY0aB+tsyiKr9OcU9ExkdrRdXgwYPh4OCAGTNmICgoCBKJBIIgoEqVKgAAPz8/LF++HO3atdNWk1/08OFDLFmyBHPnzv3ifkFBQZg6depH248cOaLReh7BwcG5PpaYP03ltfx17twZycnJ+Ouvv/Dy5Ut07NgRo0aNEnWmsryWQ0OTm/wlJydrPY7ChQtj/vz5mD59Ovbv34/Tp08jOjoaKSkpKFKkCLp27YqmTZuifPnyWm+bPo9XqojI2Gh1SvVvvvkG33zzDd69e/fRlOp2dna5OueECRMwa9asL+5z584dlC5dWv08NjYWzZo1Q8eOHdG/f/8vHhsYGIgxY8aonyckJMDV1RUBAQGwsbHJcbwKhQLBwcFo0qSJQUxdm9cwf5rJy/lr0aIFFixYgIkTJ+LMmTOwsrLCtm3bYGVlpdc48nIODYEm+dPl0E8LCwt06NABHTp00Fkbhio+Ph6RkZGIioqCl5eX2OEAyFpUZU5pT0SUl2l9nSoAsLOzU9+ArqkffvgBvXr1+uI+mQsJAsDTp0/RoEED1KxZE6tWrfrq+eVyOeRy+UfbZTKZRh+oND0+v2P+NJNX8zdu3DhUrFgR3377LY4cOYLy5ctj7dq1CAgI0HsseTWHhiI3+WO+dePMmTMYPXo0Nm3ahMuXL4sdDgCgRIkSAD4UfG/evOFSK0SU5+mkqNIme3t72NvbZ2vf2NhYNGjQAH5+fli3bp3BzHJERNnXrFkzHD16FI0bN0ZsbCyaN2+Oy5cvo1KlSmKHRpQnpaSkAPhwtc5QWFhYoEWLFrCxsUFqaqrY4RARaczgi6rsio2NRf369VG8eHHMnTsXr169Uv+bo6OjiJERUU75+/vjxIkTqFOnDtLS0tC2bVscOXIkyzBfIsqezHvVNLlPWBf2798vdghERFpjNJdygoOD8fDhQxw7dgwuLi5wcnJSP4go76latSru3LmDUqVK4fHjx6hduzZOnToldlhEeY4hXqkiIjI2RlNU9erVC4IgfPJBRHlTiRIlcOrUKVSpUgWvX79G/fr10bt3b/6/JsoBQ71SBQBKpRJv3rwROwwiIo0ZzfA/IjJO9vb2CAkJQfXq1XHnzh2sX78eL1++xD///MP7JinHbt68ieDgYDx48AASiQTu7u5o3bq1UQ8tNdSi6u+//0aHDh1Qo0YNhIaGih0OEZFG+ImEiAxegQIFcPXqVfj6+gIADhw4gBo1anCBXsqRypUrw8fHB//73/9w8eJFXLp0CbNnz0b58uXx+++/ix2ezhhqUeXo6AiFQoEnT56IHQoRkcZYVBFRniCXy3H58mU0atQIAHDp0iWUL19eJwvGknFq3LgxLl++jLdv3+Ly5cu4ePEiXr58if/973+YNGmS2OHpTObseubm5iJHkpWPjw+ioqLw8OFDsUMhItIYiyoiyjOkUimCg4PRpUsXAMD9+/fh5eWFd+/eiRsY5QmzZ8/+aGp+iUSChg0bqidzMEaGeqVKLpejePHiHMZLREaB72RElKdIJBJs2bIFI0eOBPBhwe+SJUvi6dOnIkdGecnu3buxcOFCDBgwAK1bt8bkyZN13uayZcvg7u4Oc3NzVK9eHRcvXtR5m4DhFlVERMaERRUR5UkLFy7EjBkzAABv3ryBt7c3Hjx4IHJUlFfs3bsXa9euxcaNG1G9enW0atVKp+1t27YNY8aMweTJk3HlyhX4+PigadOmePnypU7bBQy7qNq7dy86d+6M1atXix0KEZFGWFQRUZ4VGBiIlStXQiKRICkpCRUqVEBYWJjYYVEe8Mcff+D69euIj4+Hr68vOnTooNP25s+fj/79+6N3794oW7YsVqxYAUtLS6xdu1an7QL/V1QZ4jpV9+7dw/bt23HixAmxQyEi0ginVCeiPG3AgAEoXLgwOnXqhLS0NNSoUQPBwcGoX7++2KGRgXn9+jUKFy6cZZuZmRm+/fZbzJ8/X2ftpqenIywsDIGBgeptJiYmaNy4Mc6dO/fR/mlpaUhLS1M/T0hIAAAoFIocz3gpCAJu3rwJAHj27JnBzZhZsmRJAMDdu3cNLrZ/y4zNkGM0ZMyf5phDzWiSv+wew6KKiPK8b7/9FkePHkVAQAAyMjLQu3dvHD9+HO7u7mKHRgakWLFiaNGiBZo0aQJPT09kZGTg/v37WLp0KRo0aKCzduPi4qBUKuHg4JBlu4ODA+7evfvR/kFBQZg6depH248cOZLjIXypqamQSqUAgOnTpwMAKlSokKNz6NLz588BALdv38b+/fshkUhEjujLgoODxQ4hT2P+NMccaiY3+cvuLMMsqojIKDRo0AAXL15EmzZtEBUVhVq1aiE4OBhly5YVOzQyEFu3bsUff/yBCRMm4P379wA+3GfUtm1bLFq0SOTo/k9gYCDGjBmjfp6QkABXV1cEBATAxsYmx+erW7cuAgICcPPmTfz666/YsGEDvv32W22GnGtpaWkYOXIkUlNTUblyZTg5OYkd0icpFAoEBwejSZMmkMlkYoeT5zB/mmMONaNJ/jJHC3wNiyoiMhqVKlXC+fPnERAQgNu3b6NatWr46aefsgy7ovyrffv2aN++PQRBUE8QUbRoUZ1fHSlSpAikUilevHiRZfuLFy/g6Oj40f5yuRxyufyj7TKZLFcfpooUKYJJkyZh69at2LNnD77//nssW7YMgwcPzvG5tE0mk6FEiRJ49OgRIiIi4ObmJnZIX5Tb3wF9wPxpjjnUTG7yl939OVEFERmVYsWKITQ0FGXLlkVSUhImTpyIxYsXix0WGRCJRAIHBwc4ODjoZbiZmZkZ/Pz8cOzYMfU2lUqFY8eOwd/fX+ftZ8awZcsWDBo0CIIgYMiQIZgyZQoEQdBL+1/i7e0N4MOkFUREeRWLKiIyOoULF8bZs2fh7OwMABg7diz27NkjclSUn40ZMwarV6/Ghg0bcOfOHQwePBhJSUno3bu33mKQSqVYvny5ek2uqVOnYsiQIVAqlXqL4VNKlSoF4MNi3kREeRWLKiIySra2tnj06BHat28PhUKBDh06YNWqVQbxzTzlP507d8bcuXMxadIk+Pr6Ijw8HIcOHfpo8gpdk0gkmDJlCpYvXw6JRIIVK1agU6dOSE1N1Wsc/5ZZVPFKFRHlZSyqiMhomZubY/v27ejbty9UKhUGDhyIWrVqQaVSiR0a5UPDhg1DdHQ00tLScOHCBVSvXl20WAYPHozt27fDzMwMu3fvRvPmzfHu3TtRYuHwPyIyBiyqiMiomZqaYvXq1fjuu+8AAOfOnUOFChWQnp4ucmRE4urQoQMOHTqEAgUK4MSJE6hVqxaioqL0HkfmlarIyEj+vySiPItFFREZPYlEgq1bt6JLly4APqyJ4+XlpZ5Wmyi/atCgAUJDQ+Hs7Izbt2+jevXquHjxol5jcHZ2hpWVFZRKJSIiIvTaNhGRtrCoIqJ8Y8uWLRg+fDgAICYmBiVLlsSrV69EjopIXL6+vrhw4QJ8fHzw8uVL1KtXD7t379Zb+xKJhEMAiSjPY1FFRPnK4sWLMWXKFADAq1ev4OnpKcqQJyJD4uLiglOnTqFFixZITU1Fhw4dMGfOHL1N7NK9e3cEBgbCw8NDL+0REWkbiyoiyncmT56MpUuXAviwUnrZsmVx48YNkaMiEleBAgXw119/YejQoRAEAePGjUOvXr30MjPg6NGjMWPGDFSoUEHnbRER6QKLKiLKl4YOHYotW7ZAIpEgJSUFfn5+OH36tNhhEYnK1NQUS5YswaJFiyCVSvHHH3+gbt26ePLkidihEREZNBZVRJRvdenSBQcPHoRUKoVCoUD9+vWxf/9+scMiEpVEIsGIESNw+PBhFCpUCJcuXUKVKlVw9uxZnbUpCAJiY2MRGhqqszaIiHSJRRUR5WtNmzbF2bNnYWZmBqVSiW+++QYhISFih0UkukaNGuHSpUuoUKECXrx4gfr16+tsAe3ExES4uLigXr16oq2XRUSkCRZVRJTvVatWDdeuXYOlpSUEQcDq1atx4cIFscMiEp2HhwfOnj2LDh06QKFQYODAgejVqxeSkpK02k6BAgXg5uYGb29vvHz5UqvnJiLSBxZVREQASpcujXv37qFcuXJISUlBs2bNEBwcLHZYRKKztrbG9u3bERQUBBMTE/zxxx+oVq0a7ty5o9V2Hj16hHv37qmnVyciyktYVBER/X8uLi44ffo0fH19kZSUhBYtWmDw4MFih0UkOolEggkTJiAkJASOjo64ffs2qlatis2bN2utDVNTU62di4hI31hUERH9i5WVFX766Se0aNECGRkZWLFiBQYMGCB2WEQGoV69eggPD0fDhg2RlJSEbt26oX///lofDkhElNewqCIi+g+ZTIadO3fC19cXALB69WrMnj1b3KCIDISDgwOOHDmCSZMmQSKR4Pfff0flypVx+fJljc776NEj1KxZk2tVEVGexKKKiOgTTE1NceXKFfzwww8AgPHjx2PixIlQqVQiR0YkPqlUiqlTp+Lo0aMoVqwY7t+/D39/f8yYMQNKpTJX5yxUqBDOnTuHmzdvIiEhQcsRExHpFosqIqLPkEgkmDt3LmbNmgUACAoKQunSpZGWliZyZESGoWHDhrh+/To6duyIjIwM/PTTT2jQoAGioqJyfK6CBQvCyckJAHD79m0tR0pEpFssqoiIvmLcuHH49ddfAQAPHjyAp6cn3r9/L3JURIahUKFC2LZtG9avXw9ra2ucOnUKFSpUwPLly3N8ZbdcuXIAWFQRUd7DooqIKBt+/vlnjB07FgDw5MkTeHh4IC4uTuSoiAyDRCJBz549ce3aNdSpUweJiYkYOnQoGjZsiIcPH2b7PGXLlgUA3Lp1S1ehEhHpBIsqIqJsmjNnDmbMmAEAiIuLQ8mSJREdHS1yVESGw8PDAydOnMCSJUtgZWWFkydPomLFiliwYEG27rXKvFLFooqI8hoWVUREORAYGIjly5cDABISElCmTBmtL4JKlJeZmJhg2LBhuHHjBho1aoSUlBSMGTMGNWrU+OoMgeXLlwcA3LhxQx+hEhFpDYsqIqIcGjx4MLZu3QqJRIKUlBT4+vri0qVLYodFZFBKlCiB4OBgrFy5Era2trh8+TKqVauGoUOH4t27d588JrOoevr0KV6/fq3HaImINGNURVWbNm3g5uYGc3NzODk5oXv37nj69KnYYRGREfruu++wf/9+SKVSpKenw9/fH8eOHRM7LCKDIpFIMGDAANy9exddu3aFIAhYvnw5SpUqhU2bNkEQhCz729jYoESJEgB4tYqI8hajKqoaNGiA7du34969e9i1axcePXqEDh06iB0WERmp5s2bIzQ0FDKZDEqlEgEBAdi1a5fYYREZHEdHR2zatAkhISEoXbo0Xr58ie7du6N27dq4ePFiln0rVqwIALh+/boYoRIR5YpRFVWjR49GjRo1ULx4cdSsWRMTJkzA+fPnoVAoxA6NiIxUzZo1ceXKFZibm0OlUqFjx45Ys2aN2GERGaQGDRrg2rVrmD59OiwtLXH27FlUr14d3bt3x5MnTwCwqCKivMlU7AB05c2bN9i8eTNq1qwJmUz22f3S0tKyLOSZuYq7QqHIVTGWeQwLudxh/jTD/GkuNzksVaoUrl27Bj8/PyQmJmLYsGFwd3dH3bp1dRWmwdLkNcjXbf5gZmaGiRMnomfPnvjpp5+wYcMGbNq0Cbt27cK4cePg5eUFgEUVEeUtRldUjR8/HkuXLkVycjJq1KiBffv2fXH/oKAgTJ069aPtR44cgaWlZa7jCA4OzvWxxPxpivnTXG5yuHTpUkyaNAkxMTFo0aIFxo0bhypVquggOsOXm/wlJyfrIBIyVMWKFcP69esxbNgwjB49GqdPn8bUqVNhZ2cH4MO06kqlElKpVNxAiYiyweCLqgkTJmDWrFlf3OfOnTsoXbo0AODHH39E3759ER0djalTp6JHjx7Yt28fJBLJJ48NDAzEmDFj1M8TEhLg6uqKgIAA2NjY5DhehUKB4OBgNGnS5ItXyOjTmD/NMH+a0zSH33zzDbp06YL9+/cjKCgIgwcPxvz583UQqWHSJH+ZIwUof6lSpQpCQ0Oxc+dO/Pzzz7h//z4AwMLCAqtWrUK/fv34fkZEBs/gi6offvgBvXr1+uI+Hh4e6r8XKVIERYoUgbe3N8qUKQNXV1ecP38e/v7+nzxWLpdDLpd/tF0mk2n0Jq7p8fkd86cZ5k9zuc2hTCbDnj170K1bN2zfvh1Lly5FQkICNmzYoIMoDVdu8sfXbP4lkUjQsWNHtG/fHn/88QemTp2KmJgYDBkyBHPnzsXUqVPRpUsXXrUiIoNl8BNV2Nvbo3Tp0l98mJmZffJYlUoFAFnumSIi0jWZTIbNmzejQoUKAIA//vgDs2fPFjkqIsNnamqKPn364P79+1i8eDGKFi2KiIgIdO/eHWXKlMGaNWuQnp4udphERB8x+KIquy5cuIClS5ciPDwc0dHRCAkJQZcuXVCyZMnPXqUiItIVU1NThIeHY/jw4QA+3O85ceLEj9blIaKPJSQkoECBApgwYQKCgoJQqFAhPHjwAP369YOHhwcWLFiAxMREscMkIlIzmqLK0tISu3fvRqNGjVCqVCn07dsXFStWxMmTJz85vI+ISNdMTEywePFizJw5E8CHiXEqVqzIWe7ykaioKPTt2xclSpSAhYUFSpYsicmTJ/Nqy1dERESgd+/eCAoKwvjx4xEdHY158+bB2dkZsbGxGDNmDIoXL46pU6fizZs3YodLRGQ8RVWFChUQEhKC169fIzU1FZGRkfjtt99QrFgxsUMjonxu/PjxmDt3LgDg5s2b8PLy4kx3+cTdu3ehUqmwcuVK3Lp1CwsWLMCKFSswceJEsUMzaBUrVkT9+vXRrVs3KBQKWFtbY8yYMYiIiMDq1avh6emJN2/eYMqUKXBxccHAgQNx8+ZNscMmonzMaIoqIiJD9sMPP2DEiBEAgOjoaHh4eODt27ciR0W61qxZM6xbtw4BAQHw8PBAmzZtMHbsWOzevVvs0AyahYUFjh8/jvnz52e5b1oul6Nfv364e/cutm3bhkqVKiElJQWrVq1ChQoV0KhRI/z1119QKpUiRk9E+ZHBz/5HRGQsFi1ahIIFC2Lq1Kl48eIFPDw8cOvWLTg7O4sdGulRfHw8ChUq9Nl/56L02dO+fXu0a9cOZ86cwdKlS/HXX38hJCQEISEhcHd3x6BBg9CjRw8UKVJE47aMNYf6wvxpjjnUjD4WpmdRRUSkR1OmTEGhQoUwcuRIvHv3Dt7e3ggPD4enp6fYoZEePHz4EEuWLFEPB/0ULkr/f5KSkvD69Wu4ubl9cb8ePXqgefPmOHToEI4cOYKoqChMmDABP//8M6pVq4YmTZqgYsWKGk/JnhdzaEiYP80xh5rR5cL0LKqIiPRsxIgRsLOzQ69evZCUlITy5cvj/Pnz8PX1FTs0yqacLkwPALGxsWjWrBk6duyI/v37f/Y4Lkr/wcWLF9GuXTu4urri0aNH2TqmZ8+eSElJwbZt27By5UqEhYXh7NmzOHv2LNzc3NCjRw/07NkTxYsXz1EseTWHhoL50xxzqBl9LEzPooqISAQ9evSAnZ0d2rdvj7S0NFStWhXHjh1D3bp1xQ6NsiGnC9M/ffoUDRo0QM2aNbFq1aovHsdF6T+oWLEiJBIJHj9+jDdv3sDBwSFbx8lkMvTv3x/9+/fHtWvXsGbNGmzatAkxMTGYNm0apk+fjgYNGqBr16745ptvYGdnl+2Y8loODQ3zpznmUDO6XJieE1UQEYmkTZs2CAkJgampKTIyMtCgQQP8888/YodF2ZCTheljY2NRv359+Pn5Yd26dTAxYdebHQUKFEDZsmUBfLhqlRs+Pj5YvHgxnj59ii1btqBRo0YQBAEhISHo27cvHBwc8M0332Dnzp1ISUnRZvhElM/wnZ2ISET16tXDxYsXIZfLoVKp0LZtW2zatEnssEhLMgsqNzc3zJ07F69evcLz58/x/PlzsUPLE6pXrw4AuHDhgkbnMTc3R5cuXXD06FFERkZixowZKFeuHNLT07Fnzx507NgRDg4O6NmzJ/755x+kpqZqI3wiykdYVBERiaxSpUq4fv06rKysIAgC+vTpg5MnT4odFmlBcHAwHj58iGPHjsHFxQVOTk7qB31dtWrVAGheVP2bu7s7AgMDcfPmTVy/fh0TJkyAm5sb3r9/jz/++ANt2rRBkSJF0KlTJ2zdujXb91MQUf7GooqIyAB4e3vj3r178PDwgEKhQLNmzbB//36xwyIN9erVC4IgfPJBX5d5perSpUtQqVRaP3+FChUQFBSEyMhIhIaGYsSIEXBxcUFSUhJ27NiB77//Hvb29mjTpg0OHjyIqKgorcdARMaBRRURkYEoVqwYbt26hTZt2iA1NRVt27bFpEmTxA6LSDTly5eHhYUF4uPjcf/+fZ21Y2Jigjp16mDRokWIiYnBxYsXERgYiFKlSiE9PR2HDh3CypUr4e3tjTJlymDMmDEIDg7Osp4YEeVvLKqIiAyIubk5du7ciS5dukCpVOLXX3/96ixzRMbK1NQUfn5+ALQ7BPBLJBIJqlatihkzZuDu3bu4ffs2/ve//6FMmTKQSqW4e/cuFixYgICAABQqVAitW7fG8uXL8eDBA16BJMrHWFQRERkYmUyGDRs2oEyZMgCADRs2YPbs2SJHRSSOzCGA58+fF6X9MmXKYMKECQgKCsLTp0+xfft29O7dG46OjkhOTsa+ffswdOhQeHt7w9XVFd26dcOaNWvw6NEjFllE+QjXqSIiMkAymQw3b97EgAEDsGbNGowfPx4JCQn49ddfIZFIxA6PSG9q1qyJefPm4cyZM2KHgoIFC6Jjx47o2LEjBEHAtWvXcPDgQRw6dAjnzp1DbGwsNm/ejM2bNwMAXF1dUb9+fTRo0AB16tRByZIldfr/NzU1Febm5urnkyZNwosXL/Dzzz/D1dUVAHDq1CkcOXIEtra2cHR0RPHixeHm5oZixYrB1JQfC4lyi/97iIgMlImJCX7//Xd4e3tj/PjxmD59OoKDg3H27FlIpVKxwyPSi9q1awMAbt68ibdv36JgwYIiR/SBRCKBr68vfH19ERgYiOTkZJw7dw4nTpzA8ePHcfHiRTx+/BgbN27Exo0bAXxY36xGjRrw9/eHv78/qlatCisrq1y1n5GRgbCwMISEhODEiRO4evUqKlasiKNHj6r3WbNmDZ4+fYpBgwapi6rQ0FBMmzbto/NJpVK4uLigdOnSqFixovrx7zXXiOjzWFQRERm4cePGQS6XY9SoUbh48SLKlCmDGzduQC6Xix0akc4VLVoU3t7euH//Ps6ePYuWLVuKHdInWVpaolGjRmjUqBEAICkpCefOncPx48dx4sQJXL58Ga9evcI///yjXuRbKpWiYsWK8Pf3R7Vq1eDn54fSpUt/9oqRUqnEyZMnsXnzZuzatQvx8fFZ/v3u3btZno8cORKpqalwdHRUb6tSpQqGDRuGd+/e4enTp4iOjkZMTAwUCgWio6MRHR2Nw4cPq/eXyWRo3rw5/vrrL63kichYsagiIsoDRo4ciVu3bmH16tV48OABPD09cefOHVhbW4sdGpHO1a5dG8+fP8eLFy/EDiXbrKys0LhxYzRu3BgAkJaWhqtXr+LcuXPqx5MnT3D16lVcvXoVy5cvB/BhshofHx/4+fmhcuXKqFy5MpydnbF27VosW7YMsbGx6jYKFiyI+vXro2HDhvD390fp0qWzxDBu3LiP4mratCmaNm2aZZtKpcKzZ88QGRmJW7du4caNG7h+/TquX7+O+Ph4yGQy9b6CIKBy5crw8PDAkiVL4OzsrLWcEeVlLKqIiPKIVatWwc7ODnPmzMGTJ0/g4eGBO3fuoHDhwmKHRqRT8+fPx6pVq/L0sFe5XI4aNWqgRo0aGD16NADgyZMn6gIrLCwMV69exfv373HhwoXPznZoYWGBunXrok+fPvjmm2+0ch+UiYkJihUrhmLFiqmHWwIfCqioqKgsU8dHREQgPDwct2/fVt87BgDLly/Hq1evUKdOHdSoUQOWlpYax0WUl7CoIiLKQ2bPno2CBQti4sSJePXqFTw8PHDr1i24uLiIHRqRztja2oodgk64uLioJ74APlwxevjwIcLCwnD27FmsWLECGRkZWY5JSUnB4cOHcfjwYRQoUADly5dXP8qVK4fSpUvD2dlZKxNiSCQSlChR4qOYQ0NDERkZmWVSjFWrVuHatWsA/m8q/Dp16qBOnTrqGRyJjBmLKiKiPCYwMBC2trYYOnQoEhISUKpUKVy9ehXe3t5ih0akcwqFIstwNGNiYmICb29veHt7o0uXLlAoFLh69SoGDBiAwoUL49atW7h58yZu3ryJu3fv4v379+orXf9mZWUFLy8v9bkyH15eXihUqJBGMcrlcnWx9G+DBg3CyZMncerUKcTGxqqvts2dOxcA4ObmhgMHDqBevXqoU6eOeuIMImPBooqIKA8aMmQI7Ozs0K1bNyQnJ6NixYo4d+4cKlWqJHZoRDqxZs0aTJ8+HV27dsWvv/4qdjha9+7dO/z444+YMGECSpYsCQBYsGAB5HI5TEw+LCvarl079f7p6el48OCBusi6efMmbt26hYiICCQlJSE8PBzh4eEftVO4cGF4eXmhRIkScHd3h7u7u/rvbm5uuZ4AZ9CgQRg0aJB6yOCpU6dw6tQpnD59Gnfv3kVMTAxWrVqFVatWAQCKFy+O1atXo0mTJrlqj8jQsKgiIsqjvv/+e9jY2KBt27ZIS0tDtWrVcOzYMdStW1fs0Ii0zsTEBJGRkTh+/LjYoejEkCFDsHXrVjx48ADHjx+HRCKBhYXFZ/c3MzNDuXLlUK5cOXTu3Fm9XaFQIDIyEvfv3//oERsbi9evX+P169efXExZIpHA2dlZXWxlPlxcXNT3XBUsWPCLQwszhwyWKFECPXr0AAA8ffoUS5YsQUpKCs6cOYOrV68iOjoaDg4O6uO2bt2K7du3o2vXrujQoUNuUkgkKhZVRER5WKtWrXDixAk0bNgQGRkZaNCgAf766y+0atVK7NCItKpVq1bYv3+/0X5pMGPGDNy/fx9BQUEa3Q8lk8nUw/3+KykpCQ8fPsSDBw8QHR2NyMhIREVFISoqCpGRkUhOTkZsbCxiY2M/u9iyhYWFusD61MPJyQlFixbNMlFF5vpcLVq0gEwmw/v373H+/HmUK1dOvc/Bgwexd+9elC9fXl1UPX/+HD/99JN6JsSKFStyAgwyWCyqiIjyuDp16uDixYvw9/dHWloavv32W5w/f55DAcmo2Nvbo0WLFmKHoTXx8fE4cOAAunTpAgBwd3fHpUuXtDLBxOdYWVnBx8cHPj4+H/2bIAiIi4tTF1n/fjx58kR9lSslJQUPHz7Ew4cPv9iWtbU1ihYtCgcHBxQpUgRpaWm4ePEinJyc4ODggKJFi+LBgwcoWrQo7OzsMGrUKJQvX149BT0AXL58GWvXrsXatWsB/N89Z+XKlUPZsmXVV+q8vb25QDGJjkUVEZERqFSpEq5fv46aNWvi9evXaNCgAQ4cOICaNWuKHRoR/cf9+/fRunVr3L9/HxYWFup7pXRZUH2NRCKBvb097O3tUbVq1U/uk5qaiqdPnyI2NlZdaP378eTJE7x48QJpaWlITExEYmIiIiIi1McfOXLkk+c1MTFBwYIFUahQIezevRuFCxdWT6hRp04dvH79Gk+ePEFCQgLu3r2Lu3fvYteuXerjTU1N4enpiZIlS8LLywvz589X51KpVObpqfgp72BRRURkJLy9vfHo0SO0atUKp0+fRpMmTfDbb7+p72sgyutevnyJ+fPn49GjR9ixY4fY4eTKhQsX0KpVK8TFxcHNzS1PzYJnbm4ODw8PeHh4fHYfQRDw/v17vHjxAi9fvsSLFy/w7NkznD59GnZ2dnj16pV6+8uXL/Hu3TuoVCr1vV65kZGRoS625HI5nj59ChsbG9ja2mL37t148+YN+vbti1q1asHGxgbx8fGIioqCh4cH3N3d4enpiQIFCuQ2LUQAWFQRERkVW1tbHD58GO3bt8eRI0fQs2dP3LlzB0FBQWKHRqQxuVyOefPmISMjAw8fPoSnp6fYIeXI/v370alTJyQnJ6NKlSrYt29flskajIFEIoGNjQ1sbGzg5eUF4MPkGS4uLup7qv4tLS0Nb9++xZs3b/D69Wu8efMmy98/te3NmzdISkr6ZPtpaWnYvn37R9vnz5+P+fPnfzF2ExMTmJqaQiaTwdzcHObm5rCysoK1tTUKFiyI0qVLq58LgoACBQqgcOHCsLa2hrm5OSwsLNTH/fvvmc/NzMxEvRpJusWiiojIyFhaWmLv3r3w8vJCbGwsZs2ahQoVKuD7778XOzQijdja2qJu3boICQnBvn37MGrUKLFDyrbNmzejZ8+eUCqVaNq0KXbu3Alra2uxwxKdXC6Ho6MjHB0dc3Rcamoq3r17h4SEBCQkJCA+Pv6Tf758+RJPnz6FiYkJEhMTkZCQgGfPnuHdu3dQKpVZzqlSqZCeno709PRPFm3Hjh3T6GcFAKlUCplMBlNTU5iZmUEul8PJyUldeGW26+joCDs7O5iZmUGpVOL9+/eIi4vDvn37shRr/y7a5HI5zM3NYWZmpi4I5XI5zMzMPnqYmpp+8iGVStVT+FPOsKgiIjJCFhYWePjwIZo1a4aTJ0+iW7duSExMxIABA8QOjUgjrVu3znNF1a5du9CjRw+oVCr06NEDv//+u9EuYKwv5ubmuSrG/k0QBKSkpOD58+eIjIzEs2fP8Pz5c7x8+RKvXr3C69ev8fbtW8THx8PMzAzNmzdHUlISkpKSsG3bNsTHx6NKlSqQy+VITU1V31P2JUql8qNi7tmzZ9mOOSQkJFc/a25IJBL1lbXMv0skEjg6OkIul8PU1BTv3r1DYmIiihQpAgcHB5iamiIjIwOPHj2CiYlJlkdmwfbvh0QiUf+9SpUqsLGxgYmJCR4/fozHjx/DxcUFpUuXhlQqRUZGBs6fP//RuTL/nvmnqamp+rlUKkWlSpVQrVo1neeLRRURkZEyNzdHSEgIRowYgWXLlmHgwIEICQnBn3/+KXZoRLnWqlUrjB49GidPnkR8fDxsbW3FDumLDh48iC5dukClUqF37974/fffeSXAQEgkElhaWn71PrFPWblyJdLS0iCVSmFq+uHjdExMDG7duoW0tDSkpKQgNTUVKSkpSEpKwvv375GYmIjk5GT1nykpKTA1NcWQIUPU+65ZswZRUVFo1qwZ3NzcoFAocPPmTRw6dAjp6emQSCRQqVRQKpVQqVQQBAEqleqTMZYtWxYKhQLp6el48eIFUlNTIZfLIQgCMjIyPntcJkEQIAjCR9sfP3780bbExERERUXlKIf/dfPmzY+2Xbx4UaNzAsD69esxYsQINGzYUONzfQmLKiIiI2ZiYoIlS5bAysoKs2fPxrZt2xAbG4uTJ0/ygx3lSZ6enihVqhTu3buHw4cPo1OnTmKH9FknTpzAN998A4VCgc6dO2P16tX8f2dE5HJ5ludubm5wc3PT6JyfG6atUChw4MCBT96XBvzfFbB/P/79hcObN2+QnJwMOzs79bDTzNkZ09PT1cXXp/6ekZGh/rtCoYCfnx8kEgkyMjJw69Yt9RUlV1dXZGRk4O3btzh37hwyMjI++VCpVFkKw8yHn58fLCwsoFQq8ejRI0RGRsLJyQkeHh5QKpVISUnBuXPn1IXkvx+Z2zILwX9v9/LyQqlSpTT6vWQHiyoiIiMnkUgwa9YsXL9+HYcOHcLp06fh5+eHS5cuqb9hJcpL2rVrh1mzZmHHjh0GW1SdP38erVq1QmpqKlq3bo2NGzdyam/Smcyhbp9TqFAh9TT1maytrVGxYkWN2q1Xr94nt/ft21ej82pbZlGqS/y6hIgonzh48CA6d+4MAAgPD0fZsmWRnp4uclREOZdZSO3fvx+JiYkiR/Ox58+fo1mzZkhKSkLjxo2xfft23kNFZORYVBER5SN//vmnerKKBw8eoGTJkgb5oZToSypVqgRPT0+kpKTgn3/+ETucjzg6OmLixImoXbs29u7dC3Nzc7FDIiIdY1FFRJTPrFy5EuPGjQMAPHnyBB4eHrledJNIDBKJRH3V1VAnXhk3bhxCQkJgZWUldihEpAcsqoiI8qFZs2Zh2rRpAIBXr16hZMmSePLkichREWVfZlF16NAhg/hS4PHjx+jWrRsSEhLU2zjkjyj/MMqiKi0tDb6+vpBIJAgPDxc7HCIig/TTTz9h6dKlAID4+HiULl0aDx48EDkqouwpX748fH19kZ6ejk2bNokaiyAI6NChAzZv3oxBgwaJGgsRicMoi6px48bB2dlZ7DCIiAze0KFDsWnTJkgkEiQlJaFChQr8MoryBIlEgv79+wMAQkNDRY/lt99+g5+fH2bNmiVqLEQkDqMrqg4ePIgjR45g7ty5YodCRJQndO3aFXv37oWJiQnS0tJQs2ZNREdHix0W0Vd17doVZ86cwc6dO8UOBZUrV8alS5fg6uoqdihEJAKjWqDkxYsX6N+/P/bu3QtLS8tsHZOWloa0tDT188yx0JkLnOVU5jG5OZaYP00xf5rLrzls3rw5Dh8+jJYtWyIlJQW1a9fGwYMHc7xgoib5M/acp6WloXr16rh27RquXr0KX19fsUPK82xtbVGzZk1R2k5KSsJ3332HiRMnwt/fH8CHK1ZElD8ZTVElCAJ69eqFQYMGoUqVKoiKisrWcUFBQZg6depH248cOZLtwuxTgoODc30sMX+aYv40l19zuHz5ckyZMgVPnjxB7dq1MXHixFytRJ+b/CUnJ+f4mLwkc2j6tWvXxA7FKMXHxyMjIwOFCxfWeVupqalo3749goODER4ejocPH0Iul+u8XSIyXAZfVE2YMOGr45Pv3LmDI0eO4P379wgMDMzR+QMDAzFmzBj184SEBLi6uiIgIAA2NjY5jlehUCA4OBhNmjThrD+5wPxphvnTHHP44apVy5YtER4ejgkTJmD58uXo27dvto7VJH//njXN2GQOTd+1axcOHjwodjhGZ82aNRg9ejT69euH+fPn67QthUKBzp07Izg4GFZWVti+fTsLKiIy/KLqhx9+QK9evb64j4eHB0JCQnDu3LmP3tiqVKmCrl27YsOGDZ88Vi6Xf/LNUCaTafSBStPj8zvmTzPMn+bycw6dnZ0REhICDw8PvHv3DsOHD4eXlxcaNWqU7XPkJn/Gmu/cDE2nnHFxccH79+9x9uxZCIKgs2F4SqUSvXv3xt9//w1zc3P8888/6qF/RJS/GXxRZW9vD3t7+6/ut3jxYvWaKwDw9OlTNG3aFNu2bUP16tV1GSIRkdEpWLAg7t27B39/f0RERKBly5bYsWMHWrduLXZoeUpuhqbzXt+ca9CgAQ4dOoT69esjIyND6+dXKBRQKpXo2bMntm/fDplMhm3btqF27dpGnVdtyQ+vQV1jDjWjj/t9Db6oyi43N7csz62trQEAJUuWhIuLixghERHlaUWLFsXt27fx3XffYe/evWjXrh0mTZqEyZMnix2a6HQ5NJ33+ubeoUOHdHJepVKJRYsWITQ0FFKpFD/88AMEQcCBAwd00p6xyg+vQV1jDjWjy/t9jaaoIiIi7ZPL5dixYwd69eqFzZs3Y8qUKXj06BH++OMPsUMTlS6HpvNeX80kJiZi79696Nq1q1aGAWZkZKBPnz4IDQ2FqakptmzZgnbt2mkeaD6S316DusAcakYf9/sabVHl7u4OQRDEDoOIKM8zNTXF+vXrcfXqVdy+fRsbN25EfHw8/vrrL7FDE40uh6bzXt/cS0pKgq+vL2JiYmBqaoru3btrdL7ExER899132L9/P6RSKTZv3oyOHTtqKdr8Jz+8BnWNOdSMLu/3NbrFf4mISPtMTU1x48YNVKtWDQDw999/o379+lCpVCJHZtjc3NxQvnx59cPb2xsAh6bripWVFfr37w8AGDZsmEaLWD9//hz16tXD/v37YW5ujh9//BHt27fXVqhEZGRYVBERUbaYmJjg3LlzaNy4MQDg5MmTqFq1KgsrMigTJkxAjRo1kJCQgBYtWuDt27e5Os/ixYtx5coVFClSBMHBwahRo4aWIyUiY8KiioiIss3ExATBwcH49ttvAQBXrlxB2bJlkZ6eLnJkeUPm0HRfX1+xQzFapqam2L59O4oVK4bbt2+jVatWeP36dY7PM3XqVAwePBjnzp3jLMJE9FUsqoiIKMd27tyJ3r17AwDu3bsHLy8vJCUliRwV0Qeurq44ePAgbG1tcfbsWVSrVg2XL1/+4jFnzpzBd999h9TUVAAf7qNYvnw5PD099REyEeVxLKqIiChX1q5di9GjRwMAYmJi4OHhkeuhVkTaVqFCBZw6dQru7u6IiIhA1apV0alTJ+zZswfR0dF49eqVeuiqUqlEjx49sG3bNsyfP1/kyIkoL2JRRUREuTZ//nz1mkovX75E06ZNkZKSInJURB9UqFABly5dQrdu3SCRSLBjxw588803cHd3R9GiRfH48WMAgFQqxcSJE9G/f3/1FVgiopxgUUVERBqZNGkSFi1aBHNzczg6OsLU1GhX66A8qEiRIti4cSOuXLmCkSNHolSpUjAzMwMAdVEFAH379sWqVavg5OQkVqhElIex5yMiIo2NGDECDRo0gJubG06cOCF2OEQf8fX1xcKFCwEAgiAgIyOD6/0QkdawqCIiIq2oUKECFAqF2GEQfZVEImFBRURaxeF/REREREREGmBRRUREREREpAEWVURERERERBpgUUVERERERKQBFlVEREREREQaYFFFRERERESkARZVREREREREGmBRRUREREREpAEWVURERERERBowFTsAQyMIAgAgISEhV8crFAokJycjISGBq7XnAvOnGeZPc8yhZjTJX+b7bub7MH3Afkl8zKFmmD/NMYea0UffxKLqP96/fw8AcHV1FTkSIqL86f3797C1tRU7DIPBfomISHxf65skAr8SzEKlUuHp06coUKAAJBJJjo9PSEiAq6srHj9+DBsbGx1EaNyYP80wf5pjDjWjSf4EQcD79+/h7OwMExOOTs/Efkl8zKFmmD/NMYea0UffxCtV/2FiYgIXFxeNz2NjY8MXvQaYP80wf5pjDjWT2/zxCtXH2C8ZDuZQM8yf5phDzeiyb+JXgURERERERBpgUUVERERERKQBFlVaJpfLMXnyZMjlcrFDyZOYP80wf5pjDjXD/Bke/k40xxxqhvnTHHOoGX3kjxNVEBERERERaYBXqoiIiIiIiDTAooqIiIiIiEgDLKqIiIiIiIg0wKKKiIiIiIhIAyyqcmjZsmVwd3eHubk5qlevjosXL35x/x07dqB06dIwNzdHhQoVcODAAT1FarhyksP169dDIpFkeZibm+sxWsMSGhqK1q1bw9nZGRKJBHv37v3qMSdOnEDlypUhl8vh6emJ9evX6zxOQ5XT/J04ceKj159EIsHz58/1E7CBCQoKQtWqVVGgQAEULVoU7dq1w7179756HN8HdY99k+bYN+Ue+ybNsG/SjKH0TSyqcmDbtm0YM2YMJk+ejCtXrsDHxwdNmzbFy5cvP7n/2bNn0aVLF/Tt2xdXr15Fu3bt0K5dO9y8eVPPkRuOnOYQ+LD69bNnz9SP6OhoPUZsWJKSkuDj44Nly5Zla//IyEi0bNkSDRo0QHh4OEaNGoV+/frh8OHDOo7UMOU0f5nu3buX5TVYtGhRHUVo2E6ePImhQ4fi/PnzCA4OhkKhQEBAAJKSkj57DN8HdY99k+bYN2mGfZNm2DdpxmD6JoGyrVq1asLQoUPVz5VKpeDs7CwEBQV9cv9OnToJLVu2zLKtevXqwsCBA3UapyHLaQ7XrVsn2Nra6im6vAWAsGfPni/uM27cOKFcuXJZtnXu3Flo2rSpDiPLG7KTv+PHjwsAhLdv3+olprzm5cuXAgDh5MmTn92H74O6x75Jc+ybtId9k2bYN2lOrL6JV6qyKT09HWFhYWjcuLF6m4mJCRo3boxz58598phz585l2R8AmjZt+tn9jV1ucggAiYmJKF68OFxdXdG2bVvcunVLH+EaBb4GtcPX1xdOTk5o0qQJzpw5I3Y4BiM+Ph4AUKhQoc/uw9egbrFv0hz7Jv3ja1A72Dd9mlh9E4uqbIqLi4NSqYSDg0OW7Q4ODp8dw/r8+fMc7W/scpPDUqVKYe3atfjrr7+wadMmqFQq1KxZE0+ePNFHyHne516DCQkJSElJESmqvMPJyQkrVqzArl27sGvXLri6uqJ+/fq4cuWK2KGJTqVSYdSoUahVqxbKly//2f34Pqhb7Js0x75J/9g3aYZ90+eJ2TeZ5vpIIj3w9/eHv7+/+nnNmjVRpkwZrFy5Er/++quIkVF+UKpUKZQqVUr9vGbNmnj06BEWLFiAjRs3ihiZ+IYOHYqbN2/i9OnTYodCpHfsm0hM7Js+T8y+iVeqsqlIkSKQSqV48eJFlu0vXryAo6PjJ49xdHTM0f7GLjc5/C+ZTIZKlSrh4cOHugjR6HzuNWhjYwMLCwuRosrbqlWrlu9ff8OGDcO+fftw/PhxuLi4fHFfvg/qFvsmzbFv0j/2TdrHvkn8volFVTaZmZnBz88Px44dU29TqVQ4duxYlm+r/s3f3z/L/gAQHBz82f2NXW5y+F9KpRI3btyAk5OTrsI0KnwNal94eHi+ff0JgoBhw4Zhz549CAkJQYkSJb56DF+DusW+SXPsm/SPr0HtY99kAH1Trqe4yIf+/PNPQS6XC+vXrxdu374tDBgwQLCzsxOeP38uCIIgdO/eXZgwYYJ6/zNnzgimpqbC3LlzhTt37giTJ08WZDKZcOPGDbF+BNHlNIdTp04VDh8+LDx69EgICwsTvvvuO8Hc3Fy4deuWWD+CqN6/fy9cvXpVuHr1qgBAmD9/vnD16lUhOjpaEARBmDBhgtC9e3f1/hEREYKlpaXw448/Cnfu3BGWLVsmSKVS4dChQ2L9CKLKaf4WLFgg7N27V3jw4IFw48YNYeTIkYKJiYlw9OhRsX4EUQ0ePFiwtbUVTpw4ITx79kz9SE5OVu/D90H9Y9+kOfZNmmHfpBn2TZoxlL6JRVUOLVmyRHBzcxPMzMyEatWqCefPn1f/W7169YSePXtm2X/79u2Ct7e3YGZmJpQrV07Yv3+/niM2PDnJ4ahRo9T7Ojg4CC1atBCuXLkiQtSGIXMa1f8+MnPWs2dPoV69eh8d4+vrK5iZmQkeHh7CunXr9B63ochp/mbNmiWULFlSMDc3FwoVKiTUr19fCAkJESd4A/Cp3AHI8pri+6A42Ddpjn1T7rFv0gz7Js0YSt8k+f/BEBERERERUS7wnioiIiIiIiINsKgiIiIiIiLSAIsqIiIiIiIiDbCoIiIiIiIi0gCLKiIiIiIiIg2wqCIiIiIiItIAiyoiIiIiIiINsKgiIiIiIiLSAIsqIh3r1asX2rVrJ1r73bt3x4wZM/TS1oQJEzB8+HC9tEVERLnHvolIuySCIAhiB0GUV0kkki/+++TJkzF69GgIggA7Ozv9BPUv165dQ8OGDREdHQ1ra2udtxcXFwcPDw+Eh4fDw8ND5+0REdHH2Ddlxb6J9IFFFZEGnj9/rv77tm3bMGnSJNy7d0+9zdraWi8dxuf069cPpqamWLFihd7a7NixI9zd3TFnzhy9tUlERP+HfdPH2DeRrnH4H5EGHB0d1Q9bW1tIJJIs26ytrT8aYlG/fn0MHz4co0aNQsGCBeHg4IDVq1cjKSkJvXv3RoECBeDp6YmDBw9maevmzZto3rw5rK2t4eDggO7duyMuLu6zsSmVyv/X3p3Hx3T1fwD/TPZFFpLIQkhiSSxBrA1CbKGIaouitS+ltCVaktZD0aeppdaqFLVrqVqqKAmxC0XE3iCWRCQhQvZlktzfH34zj0hCJrPcmcnn/XrlJffOued+c5rmzHfOuefgjz/+QGBgYInzP/30Exo0aAAzMzM4OjpiwIAB8teKi4sRGhoKd3d3mJubo3nz5vjjjz9KXH/9+nX07dsX1tbWsLKygp+fH+Li4uSvBwYGYtu2bZVpTiIiUgH2TeybSPOYVBGJYOPGjbC3t8c///yDTz/9FBMnTsTAgQPRvn17REdHIyAgAMOGDUNOTg4A4Pnz5+jatSt8fHxw4cIFHDx4ECkpKRg0aFC597hy5QrS09PRunVr+bkLFy7gs88+w9y5cxEbG4uDBw+iU6dO8tdDQ0OxadMmhIWF4fr165g6dSo++ugjHD9+HACQmJiITp06wdTUFJGRkbh48SJGjx6NwsJCeR1t27bFw4cPcf/+fRW3GhERqRP7JiIlCESkEuvXrxdsbGxKnR8xYoTwzjvvyI87d+4sdOzYUX5cWFgoWFpaCsOGDZOfS0pKEgAIUVFRgiAIwrx584SAgIAS9SYkJAgAhNjY2DLj2b17t2BoaCgUFxfLz+3cuVOwtrYWMjIySpXPy8sTLCwshDNnzpQ4P2bMGGHIkCGCIAhCSEiI4O7uLhQUFJTTCoKQnp4uABCOHTtWbhkiItIM9k0vsG8idTMSM6EjqqqaNWsm/97Q0BB2dnbw9vaWn3N0dAQAPH78GMCLh3qPHj1a5hz4uLg4NGzYsNT53NxcmJqalnhguUePHqhbty48PDzQq1cv9OrVC++++y4sLCxw584d5OTkoEePHiXqKSgogI+PDwAgJiYGfn5+MDY2LvdnMzc3BwD5J5lERKQb2DcRVR6TKiIRvPqHXyKRlDgn62yKi4sBAFlZWQgMDMT8+fNL1eXs7FzmPezt7ZGTk4OCggKYmJgAAKysrBAdHY1jx44hPDwcs2bNwjfffIPz588jKysLALB//37UqlWrRF2mpqYA/tcpvU5aWhoAwMHB4Y1liYhIe7BvIqo8JlVEOqBly5bYuXMn3NzcYGRUsf9tW7RoAQC4ceOG/HsAMDIyQvfu3dG9e3fMnj0btra2iIyMRI8ePWBqaor4+Hh07ty5zDqbNWuGjRs3QiqVlvuJ4LVr12BsbIwmTZoo9DMSEZFuYd9E9D9cqIJIB0yaNAlpaWkYMmQIzp8/j7i4OBw6dAijRo1CUVFRmdc4ODigZcuWOHXqlPzcvn37sHz5csTExODBgwfYtGkTiouL4enpCSsrK3zxxReYOnUqNm7ciLi4OERHR2PFihXYuHEjAGDy5MnIyMjA4MGDceHCBdy+fRubN28usVTvyZMn4efnV6FPDomISHexbyL6HyZVRDrAxcUFp0+fRlFREQICAuDt7Y0pU6bA1tYWBgbl/288duxYbN26VX5sa2uLXbt2oWvXrmjUqBHCwsLw22+/yT+5mzdvHv7zn/8gNDQUjRo1Qq9evbB//364u7sDAOzs7BAZGYmsrCx07twZrVq1wpo1a0p8Mrht2zaMGzdOTS1BRETagn0T0f9w818iPZabmwtPT09s374dvr6+ar/f33//jWnTpuHKlSsVngpCRERVC/sm0kccqSLSY+bm5ti0adNrN2JUpezsbKxfv56dFhERlYt9E+kjjlQREREREREpgSNVRERERERESmBSRUREREREpAQmVUREREREREpgUkVERERERKQEJlVERERERERKYFJFRERERESkBCZVRERERERESmBSRUREREREpAQmVUREREREREpgUkVERERERKQEJlVERERERERKMBI7AG1TXFyMR48ewcrKChKJROxwiIiqDEEQkJmZCRcXFxgY8DM/GfZLRETiqWjfxKTqFY8ePYKrq6vYYRARVVkJCQmoXbu22GFoDfZLRETie1PfxKTqFVZWVgBeNJy1tbXC10ulUoSHhyMgIADGxsaqDk/vsf2Uw/ZTHttQOcq0X0ZGBlxdXeV/h+kF9kviYxsqh+2nPLahcjTRNzGpeoVsaoW1tXWlOy8LCwtYW1vzl74S2H7KYfspj22oHFW0H6e4lcR+SXxsQ+Ww/ZTHNlSOJvomTlonIiIiIiJSApMqIiIiIiIiJTCpIiIiIiIiUgKTKiIiIiIiIiUwqSIiIiIiIlICkyoiIiIiIiIlMKkiIiIiIiJSApMqIiIiIiIiJTCpIoXk5ubi6dOnYodBRESkFe7fv485c+Zg165dSEtLEzscIhIJkyoqV05ODiZPnozY2Fj5uePHj6NBgwbYsGEDBEEQMToiIiLNio6ORo8ePXDo0CH5uRs3buCbb77B+++/j5o1a2LAgAGIiooSMUoiEgOTKirXgAEDsHLlSgwZMgTFxcUAgMuXL+PZs2cYNWoUJk+ezMSKiIiqjMWLF+Pw4cPYvn27/JyLiwvGjBmDxo0bo6ioCDt37kT79u3xwQcfIDExUcRoiUiTmFRRudasWYN+/fph0aJFMDB48asybdo0hIaGQiKR4KeffsKXX34pcpRERESa8e2332LEiBGYNWuW/FyLFi2wdu1aXL9+HVevXsWoUaMgkUjw+++/o1mzZvjzzz9FjJiINIVJFZWrVq1a+PPPP9G1a1f5OSMjIwQHB2P16tUAgB9++AFbtmwRK0QiIiK1yc3Nxe+//y4/dnNzw4YNG+Dm5lZm+aZNm2LdunWIjo5Gq1atkJaWhv79+2P27Nmc2UGk55hUUQlZWVk4c+bMG8uNHTsWs2fPBgB88sknuHv3rrpDIyIi0pji4mIMHjwYH3zwARYuXKjQtS1atMCZM2cwdepUAMDcuXMxfPhwFBYWqiNUItICTKqohCVLlqBDhw747LPP3lh25syZ6NixIzIzMzF69Gh+CkdERHrjhx9+wN69e2FqagpfX1+FrzcxMcHixYvxyy+/wMjICFu2bMGIESOYWBHpKSZVJPf06VP5p3EdOnR4Y3kjIyNs3rwZ5ubmOH78eIkpEkRERLrq3Llz+OqrrwAAy5cvR8eOHStd1+jRo7Fjxw4YGRnh119/xbZt21QVJhFpESZVJLdhwwZkZmaiWbNmGDhwYIWucXNzQ0hICADgiy++QFZWljpDJCIiUiupVIqxY8eisLAQgwYNwrhx45Sus3///tixYwemTZuGDz/8UAVREpG2YVJFAABBEOSLT0yaNEm+2l9FfPHFF3Bzc8PDhw+xfPlydYVIRESkditWrMC1a9dgZ2eHn376CRKJRCX19u/fH4sWLZLXxynzRPqFSRUBeLGp761bt1CtWjUMGTJEoWvNzc3x7bffAgAWLVqE9PR0dYRIRESkVikpKfJFmObPnw87Ozu13CcnJwcDBgyQf5hJRLqPSRUBgPwP+9ChQ2FlZaXw9YMHD4aXlxeePXuGAwcOqDo8IiIitQsNDUVWVhbatGmDUaNGqe0+W7duxa5duxAUFIQnT56o7T5EpDlGYgdA4svJyZFvTjhmzJhK1WFoaIiwsDBYWFigTZs2qgyPiIhI7RISErBq1SoAwHfffafQNHhFjR07FteuXcOAAQPg4OCgtvsQkeYwqSKEh4cjJycHdevWVSoh6ty5swqjIiIi0pzvvvsOBQUF6Ny5M7p166bWe0kkEixbtkyt9yAizeL0P8Lu3bsBvHiIVlUP5D5+/Jh7cRARkU548uQJ1q9fDwCYM2eOyvrCirp69SqmTJmC4uJijd6XiFSHSVUVJ5VKsXfvXgDAe++9p5I6Q0JCUKdOHezcuVMl9REREalTWFgY8vPz0aZNG3Tq1Emj987IyEDnzp2xbNkyzJ8/X6P3JiLVYVJVxR0/fhzPnz+Hg4NDhTb8rQgzMzPk5+cjPDxcJfURERGp0+DBg/HJJ59gxowZGh+lsra2lidTX3/9NSIiIjR6fyJSDSZVVdzff/8NAAgMDIShoaFK6pwwYQLOnj2LtWvXqqQ+IiJtceLECQQGBsLFxQUSiQR79ux5bfljx45BIpGU+kpOTtZMwFQhDRo0wMqVK/H++++Lcv9x48Zh7NixEAQBH330EZKSkkSJg4gqjwtVVHEffPABTE1N0aVLF5XV6ejoCEdHR5XVR0SkLbKzs9G8eXOMHj1aoSnTsbGxsLa2lh/XrFlTHeGRDlu+fDn++ecfXLlyBR999BHCw8NV9mEnEakfk6oqrm3btmjbtq3a6s/MzISxsTHMzMzUdg8iIk15++238fbbbyt8Xc2aNWFra6v6gEgpt27dwrx58zBu3DiNP0v1KnNzc2zfvh2tWrVCZGQkvvvuO/znP/8RNSYiqjgmVaQ28+bNw4IFC7Bs2TKMHj1a7HCIiETTokUL5Ofno2nTpvjmm29e+wxrfn4+8vPz5ccZGRkAXiwsJJVKFb637JrKXKvv1qxZgy1btiAtLQ2+vr7lltNUG9arVw8rVqzAmDFj5L8nfn5+ar2nJvB3UHlsQ+Uo034VvYZJVRX2+++/w8LCAv7+/qhWrZrK6zc1NUVWVhbCwsKYVBFRleTs7IywsDC0bt0a+fn5WLt2Lfz9/XHu3Dm0bNmyzGtCQ0MxZ86cUufDw8NhYWFR6Vi4AEJpzs7O6NGjB1q2bIkDBw68sbwm2tDOzg5dunTB0aNHMXDgQCxZsgQ2NjZqv68m8HdQeWxD5VSm/XJycipUjklVFRYSEoK7d+9i37596NOnj8rrHzVqFGbOnInz58/j4sWLaNWqlcrvQUSkzTw9PeHp6Sk/bt++PeLi4rBkyRJs3ry5zGtCQkIQFBQkP87IyICrqysCAgJKPJdVUVKpFBEREejRoweMjY0V/yH03KeffvrGMppuw06dOsHX1xexsbHYtm0bdu/eDQMD3V1bjL+DymMbKkeZ9pPNFngTJlVVVH5+Pvz9/WFubq62eeQODg4YMGAAfvvtN/z8889YvXq1Wu5DRKRL2rZti1OnTpX7uqmpKUxNTUudNzY2VurNlLLXk+basHr16vj999/Rrl07/P333/jxxx8xbdo0td9X3fg7qDy2oXIq034VLa+7H3uQUkxNTfHLL7/g2rVrsLKyUtt9Pv74YwDAtm3bKjx8SkSkz2JiYuDs7Cx2GFVaQUEBgoKCcOrUKQiCIHY4ZWrWrBmWLl0KAAgODkZMTIyo8RDR63GkitTKz88P7u7uuHfvHvbs2YOhQ4eKHRIRUaVlZWXhzp078uN79+4hJiYGNWrUQJ06dRASEoLExERs2rQJALB06VK4u7ujSZMmyMvLw9q1axEZGcnN0UUWHh6OJUuWYNu2bUhISNDapcvHjx+PyMhI+e8QEWkvjlRVUbGxsSgsLFT7fQwMDDBs2DAAwMaNG9V+PyIidbpw4QJ8fHzg4+MDAAgKCoKPjw9mzZoFAEhKSkJ8fLy8fEFBAaZNmwZvb2907twZly9fxuHDh9GtWzdR4qcXfv/9dwDAwIEDtTahAgCJRILffvsN33//Pad8EWk5jlRVQXl5efD29oaxsTFu374NFxcXtd5v+PDhmDt3Lg4fPozExETUqlVLrfcjIlIXf3//104X27BhQ4nj6dOnY/r06WqOihRRWFiI/fv3AwDef/99kaN5s5cXqJBKpbh48SLeeustESMiorJwpKoKio6OhlQqhaWlpUbm9derVw8dO3ZEcXExtm7dqvb7ERERlefUqVNIS0uDnZ0d2rdvL3Y4FZaWlgY/Pz906dIF165dEzscInoFk6oqKCoqCgDg6+sLiUSikXuOGDECwIspgNr6UDAREem/P//8EwDQt29fGBnpzoQdW1tb2NrawszMDImJiWKHQ0SvYFJVBZ07dw4AXrt7vKoNHDgQZmZmuHHjBi5evKix+xIREckIgiBPqt555x2Ro1GMgYEBNm3ahJiYGPTs2VPscIjoFUyqqqDo6GgAQOvWrTV2TxsbG/Tv3x8AF6wgIiJxXL9+Hffu3YOZmRkCAgLEDkdhNWvWRN26deXHmlhwiogqhklVFZOeno64uDgAkK9epSkjR45Ep06d4Ofnp9H7EhERAcDBgwcBAF26dIGlpaXI0SjnwIED8PLywt27d8UOhYjA1f+qHNnmgXXq1IGdnZ1G792zZ09OWSAiItEcOnQIAHS+LxIEAd9//z3i4uIwePBgnDp1CiYmJmKHRVSlcaSqipFN/WvZsqXIkRAREWlOdnY2Tpw4AQDo1auXyNEoRyKRYMuWLahevTrOnz+PadOmiR0SUZXHpKqKuXTpEgBxk6qUlBQsX74cGRkZosVARERVy7Fjx1BQUAA3Nzc0bNhQ7HCUVqdOHWzatAkA8OOPP2Lz5s0iR0RUtTGpqmJkI1Wafp7qZd27d8fnn3+O3bt3ixYDERFVLS9P/dPUdiLq1rdvX8yaNQsAMH78ePkHp0SkeXymqgrJycnBzZs3AYg7UjV48GDs3bsXtra2osVARERVy4QJE1C7dm2d2vC3ImbPno0LFy7gwIEDeO+993Dx4kXUqFFD7LCIqhwmVVXI1atXUVxcjJo1a8LZ2Vm0OEJCQvD111+Ldn8iIqp6GjdujMaNG4sdhsoZGBhgy5YtaN26Ne7evYuhQ4di//79MDQ0FDs0oiqF0/+qkHv37sHAwADNmzcXdeqDgQF/7YiIiFSlevXq2LVrF8zNzXHo0CHMnj1b7JCIqhydend74sQJBAYGwsXFBRKJBHv27CnxuiAImDVrFpydnWFubo7u3bvj9u3b4gSrhQYPHoysrCxs2LBB7FAAAM+ePcOOHTvEDoOIiPTchg0bsHnzZqSmpoodito0b94ca9asAQD897//xe+//y5yRERVi04lVdnZ2WjevDlWrlxZ5usLFizA8uXLERYWhnPnzsHS0hI9e/ZEXl6ehiPVXubm5nBxcRE7DGRnZ8PV1RWDBg3CjRs3xA6HiIj02Ny5czF8+HCcP39e7FDU6sMPP8TUqVMBAMOHD8fZs2dFjoio6tCppOrtt9/Gt99+i3fffbfUa4IgYOnSpZg5cybeeecdNGvWDJs2bcKjR49KjWiR+CwtLdGlSxcAwNatW0WOhoiI9JVUKsXAgQPRtm1bdOzYUexw1G7hwoUIDAxEfn4+/vrrL7HDIaoy9Gahinv37iE5ORndu3eXn7OxsUG7du0QFRWFwYMHl3ldfn4+8vPz5ceyvZOkUimkUqnCcciuqcy16vT06VO89957aNq0KVasWKEVzzV98MEH2LdvH3799VfMnj0bEolEa9tPV7D9lMc2VI4y7cc2J3UwNjbG/PnzxQ5DYwwNDfHrr79i165dGDZsmNjhEFUZepNUJScnAwAcHR1LnHd0dJS/VpbQ0FDMmTOn1Pnw8HBYWFhUOp6IiIhKX6sO165dQ1RUFOLi4tCnTx+xwwEAmJiYwMzMDPfv38fixYvRqFEj+Wva1n66hu2nPLahcirTfjk5OWqIhKjqqVatGoYPHy4/zs/PR3FxMczNzUWMiki/6U1SVVkhISEICgqSH2dkZMDV1RUBAQGwtrZWuD6pVIqIiAj06NEDxsbGqgxVKW3atEHdunUhlUrRu3dvscOR++uvv7Blyxbcu3cP06ZN09r20xVsP+WxDZWjTPvJZgoQqdKJEyfg4+MDKysrsUMRRWZmJvr3749q1aph586dMDKq8m/9iNRCb/7PcnJyAgCkpKSU2IMpJSUFLVq0KPc6U1NTmJqaljpvbGys1BsqZa9XNRcXF3z00Udih1HK8OHDsWXLFvzxxx9YsWKFvM20rf10DdtPeWxD5VSm/djepGqPHz9G586dYWRkhLS0tCqZWF2/fh2nT5+GsbExbt68CW9vb7FDItJL4j9YoyLu7u5wcnLCkSNH5OcyMjJw7tw5+Pr6ihgZvU7Xrl3h5OSEtLQ0HDx4UOxwiIhIj5w+fRoA4OXlVSUTKgB46623sGPHDkRGRjKhIlIjnUqqsrKyEBMTg5iYGAAvFqeIiYlBfHw8JBIJpkyZgm+//RZ79+7F1atXMXz4cLi4uKB///6ixq0NVq5cib///rvEohzawNDQUL6ICFcBJCIiVTp16hQAVIlV/14nMDAQbdq0kR8/ePAAgiCIGBGR/tGppOrChQvw8fGBj48PACAoKAg+Pj6YNWsWAGD69On49NNPMX78eLRp0wZZWVk4ePAgzMzMxAxbdKmpqZg8eTJ69+6NwsJCscMpRTYtce/evXymgoiIVEaWVPn5+YkcifaIjo5GixYt8PnnnzOxIlIhnXqmyt/f/7V/ACQSCebOnYu5c+dqMCrtFxsbCwBwdXWFpaWlyNGU1rJlS3h6eiI2NhZ79uyBvb292CEREZGOy8nJQXR0NACgQ4cOIkejPa5evYr09HSsWLECUqkUP/74IwwNDcUOi0jn6dRIFVXOv//+CwDw9PQUOZKySSQS+WjVb7/9JnI0RESkDy5evIjCwkK4uLigTp06YoejNUaMGIFffvkFEokEYWFhGDRoEPLy8sQOi0jnMamqAmQjVV5eXiJHUr6hQ4cCACIjI5GWliZyNEREpOvOnj0L4MVCDRKJRORotMuoUaOwbds2mJiYYNeuXQgICMCzZ8/EDotIpzGpqgJkSZW2jlQBgIeHB6ZPn47NmzejWrVqYodDREQ67ty5cwBeJFVU2qBBg3Do0CHY2Njg5MmT6NChA27fvi12WEQ6i0lVFaALSRUAzJ8/H4MGDYKJiYnYoRARkY6TjVS1a9dO5Ei0l7+/P06ePIlatWrh5s2baNOmDfbv3y92WEQ6iUmVnpNKpYiLiwOg3dP/iIiIVOXhw4dITEyEoaEhWrVqJXY4Ws3b2xvnz59H+/btkZ6ejsDAQMydOxdFRUVih0akU5hU6bm7d++isLAQFhYWqFWrltjhvFFCQgJ27NiBVatWiR0KERHpKNkoVbNmzbRy1Vtt4+zsjKNHj+KTTz6BIAiYPXs2unXrhgcPHogdGpHOYFKl52RT/xo2bAgDA+3/zx0VFYWtW7diyZIl3D+DiIgq5eVFKqhiTExMsHLlSmzYsAGWlpY4fvw4tmzZInZYRDpD+99lk1J05Xkqmb59+6J169aYOXMmpx4QEVGlSKVSWFlZoU2bNmKHonNGjBiBy5cv45NPPsGMGTPk5wsLC0WMikj7ManSc3fu3AHwYqRKF1hYWGDmzJkYPnw4jIx0am9qIqoCTpw4gcDAQLi4uEAikWDPnj1vvObYsWNo2bIlTE1NUb9+fWzYsEHtcVZ1y5Ytw/Pnz/Hhhx+KHYpOqlevHlauXCnvh/Pz89GyZUvMnDkTubm5IkdHpJ2YVOk5WVJVv359kSMhItJ92dnZaN68OVauXFmh8vfu3UOfPn3QpUsXxMTEYMqUKRg7diwOHTqk5kjJwMCAq8mqyK5du3D16lWsX79e7FCItBaHAvScg4MDXFxcdC6pSkpKwh9//IHWrVujU6dOYodDRAQAePvtt/H2229XuHxYWBjc3d3xww8/AAAaNWqEU6dOYcmSJejZs6e6wpQTBAHZ2dnIy8tDdnY2jI2N1X5PsQmCoPLNfqVSaZVqw1cFBgbit99+Q25uLoqLi5GdnY2ioiJs3rwZ7777LmxsbF57fVVvP1VgGypHKpWq/Vl9JlV6btu2bWKHUCmLFy/GsmXLMGjQICZVRKSzoqKi0L179xLnevbsiSlTppR7TX5+PvLz8+XHGRkZAF68KZBKpQrdPzs7G9WrV1foGqLXGT16dInjyZMnixQJkWK2bdum8N9QABW+hkkVaaUhQ4Zg2bJl2Lt3LzIyMmBtbS12SERECktOToajo2OJc46OjsjIyEBubi7Mzc1LXRMaGoo5c+aUOh8eHg4LCwuF7p+Xl6dYwEREeiwiIkLha3JycipUjkkVaSUfHx94eXnh33//xfbt2zFu3DixQyIi0oiQkBAEBQXJjzMyMuDq6oqAgACFP2ASBAEBAQEIDw/H4sWLMWrUKFWHq3WePn2Ky5cvo3HjxnByclJJnVKpFJGRkejatSunXr1EEAScOnUKa9aswcGDB0t8ot+wYUN07doVXbp0QcuWLXHx4kWtbj/ZVNmnT58iNTUVqampaNSoEerUqQMAiI6OxoIFC1CrVi35dF4A8PLyQkpKikL3mj17tny0+urVqxg8eDAaNmyI3bt3y8t89dVXePToEczMzGBmZgYTExOkpKSgYcOGsLCwgKmpKYyMjGBoaCj/19vbG82bNwcAZGVl4eTJkzAzM0OXLl3k9V69ehUZGRmQSCRlfgEodc7Ozk7eDoWFhbh58yYkEgmaNGkivyYhIQEZGRkQBEE+zU72fXlf1atXL/F4SlRUFARBQOvWreXPQ965cwePHj16Y12v1vvydgr37t1D7dq1cfr0afTo0UPh30HZbIE3YVKlx1asWIFFixZh9OjRmD17ttjhKEQikWD06NGYPn061q5dy6SKiHSSk5NTqTdcKSkpsLa2LnOUCgBMTU1hampa6ryxsXGl3pDWrFkTwItRK1tbW4Wv1zW2traoV6+eSuuUSqUwMzODra2t1iYFYgkMDERgYCDS0tKwa9cubNu2DUePHsWtW7dw69YthIWFAQBq1aqFOXPmYMyYMQBevDkXBEFt7VlUVARDQ0P58f79+/Ho0SM8efKkxFdqaqr8+1dHdsPCwtCsWTMAL96XHDp0CI0aNSrx/1GtWrUgkUjg4OBQ6svOzg42NjawsrKClZUVrK2tYWVlBScnJ/lzaH5+fkhMTCwV/08//VTiWCqV4sCBA+jdu3eF2szW1hZDhgwpdd7Pz++N175JWXWo4m9LWc+rtm7dWul6fXx8IJVKIZFIKvV3tKLlmVTpsVu3biE+Pl5nlz8dPnw4vvrqK/zzzz+4cuWK/A8bEZGu8PX1xYEDB0qci4iIgK+vr8ZiqFGjBgAgLS1NY/ekqqdGjRoYO3Ysxo4di2fPniEyMhKHDh3CkSNHcPfu3VKJw8mTJ9GtWzf4+/sjMjJSfv6LL76Q7zNmampaYvQEAAoKCpCbm4ucnBzk5OQgNzcX48ePl4/GHDhwAIMGDULz5s1x+vRpeb0ff/xxmcnLq8zMzORJkZWVlfx848aNsXr1ari6upYof+HCBZUvjEK6iUmVHvvmm28wZMgQODg4iB1KpTg6OuKdd97Bzp07sXbtWixfvlzskIioisvKypJvVQG8mFYSExODGjVqoE6dOggJCUFiYiI2bdoEAJgwYQJ+/PFHTJ8+HaNHj0ZkZCR+//137N+/X2MxyxaqeP78ucbuKZb169fj1q1bGDBgAFq1aiV2OFVW9erV8f777+P9998H8GJF31WrViEgIEBe5tatWxAEodSo7C+//KLw76q/v788qbKwsEB2dnapDxH8/f2Rnp5eajTJ3t6+xLGlpWWZSZKjo2OZs2aYUJEMkyo9Zmdnh/bt24sdhlLGjh2LnTt3YvPmzZg/f36502WIiDThwoULJZ5PkD37NGLECGzYsAFJSUmIj4+Xv+7u7o79+/dj6tSpWLZsGWrXro21a9dqZDl1mao0UvXbb78hIiICHh4eTKq0iL29PVq1alXiGbdx48bh3XffLbHSJfDimcLnz58jMzOz1GuCIMDExATm5uawsLCAhYUFzM3NS7zXadu2Le7cuQM7O7sS127ZskUNPxnR/zCpIq3Wo0cP1KlTB/Hx8di1axc+/PBDsUMioirM39//tXudbNiwocxrLl26pMaoXk82UvXs2TPRYtAEQRBw8eJFAEDLli1FjobexMDAQP6838umT5+uVL0WFhYqf6aOqCIMxA6A1CM5ORlffPEF1qxZI3YoSjE0NJTvibF27VqRoyEi0j2ypErfR6oePHiAtLQ0GBsbo2nTpmKHQ0RVDJMqPXXjxg388MMPWLRokdihKG3UqFGQSCQ4duwYbt++LXY4REQ6RTb9T99HqqKjowEATZs2LXP1RCIidWJSpafu3bsH4MV8fl1Xp04d9OrVCwCwevVqkaMhItItVWWkSjb1j89SEZEYmFTpqfv37wPQj6QKACZOnAjgxVLEr3uegYiISpKNVGVlZaGgoEDkaNRHNlLF56mISAxMqvSUPo1UAUDv3r2xd+9e7gdBRKQgGxsb+d9NfZ4CKFsMhEkVEYmBSZWekiVVbm5u4gaiIoaGhggMDISREResJCJShKGhISwtLQHo7xTAJ0+eICUlBRKJhItUEJEo+A5VT+nbSNXLpFIp0tLS4OjoKHYoREQ6oWPHjnB2dtbbvf6uXr0KAPDw8JAnkEREmsSRKj2Um5uLpKQkAPqXVB08eBBubm74+OOPxQ6FiEhnTJgwAT///LPezF541ZUrVwAAzZo1EzkSIqqqmFTpoQcPHgAAqlWrVmpHcV1Xt25dPHr0COfPn0d2drbY4RARkRaQjVR5e3uLHAkRVVVMqvTQyyv/6duiDo0aNUJ4eDju3r3LKR5ERBUkCAIyMzORmZkpdihqwZEqIhIbkyo9pG+LVLyqR48e3NiRiEgBa9asgZ2dHRYuXCh2KGoxePBgDBo0iCv/EZFouFCFHtLnRSpeVlhYiNjYWDRp0kTsUIiItJpsZD89PV3kSNRj2rRpYodARFUcR6r0UFVIqu7duwcvLy906tQJWVlZYodDRKTV3nvvPTx//hxLly4VOxQiIr3EpEoPNWrUCB06dEDjxo3FDkVt6tSpAwMDA6SlpeHnn38WOxwiIq1mbm4OCwsLvXvOFgBiY2Nx9+5dFBcXix0KEVVhTKr00Ny5c3Hq1CkEBASIHYraGBoaIjg4GADwww8/IC8vT+SIiIhIDCEhIahXrx6WL18udihEVIUxqSKd9dFHH8HV1RVJSUnYuHGj2OEQEWmtlJQUjB07FhMmTBA7FJUTBAEmJiZo2rSp2KEQURXGpErPFBUVoaioSOwwNMLExARffvklACA0NBT5+fkiR0REpJ0KCgqwadMm7NixQ+xQVG737t3Izs6Gv7+/2KEQURXGpErPnDx5EqampujSpYvYoWjEmDFj4OLiggcPHmDVqlVih0NEpJWqVasGAHj27JlePntkZGQEIyMuaExE4uFfID0THx+PoqIiGBoaih2KRlhYWGDOnDkYN24c5s2bh5EjR8LW1lbssKgcgiAgJycHRUVFsLa2lp+/fv06cnJyUFBQgPz8fAiC8Ma6GjRogDp16gAAsrOzcefOHVhYWKBBgwbyMk+fPgUAGBsbw9jYWP7GSx8f1id6HVlSJQgC0tPTUb16dZEjIiLSL0yq9MyHH36Ibt26VampcCNHjsSSJUtw48YNzJ8/H6GhoWKHVGXk5+cjKSkJSUlJSE5Oln+flJSEJ0+e4Pnz5/jmm2/kI6fbtm3D0KFD4efnhxMnTsjr6dq1Kx4/fqzQvefPn49PPvkEgiAgKioKPXv2hIeHB86fPw9BECAIAjp37owbN26UutbQ0BBGRkYwNjaWfy/7GjJkCNzd3XHz5k08fvwY06ZNg5WVFX766Sd5vatWrcKdO3dgZGQEQ0PDEl+ycy+/ZmRkhIYNG6Jt27YAgLy8PBw4cAAA0K9fP3mSd+nSJSQlJcHA4MUkAgMDA/lrEomkxHnZuRo1aqBBgwbyRPTChQsAAG9vbxgbG0MQBMTHx+Px48elklXZ8av/Ai/2NZIlqIIg4OrVq5BKpfDy8oKZmRkAyP9by8pYWFjAx8dHof+OpBnGxsawtLREdnY20tLS9CapCg4OxsGDB/Hll1/iww8/FDscIqrCmFTpGUNDQ9SqVUvsMDTKyMgI33//Pfr164elS5fik08+gaurq9hhobi4GFKpFAUFBfIRmIKCAkilUhQWFqKwsLDE94p8lXddfn4+bt26hRMnTqC4uLjEa0VFRSguLi71b1nnyvq3qKgIDx8+RF5envxLKpW+sR3effddmJiYoLi4WL5K4+nTp2FhYQFBEFBcXIyCggKF23fGjBmYMWNGiXN3796FnZ3dG6+V/TxlffiwaNGiMq9p3ry5wjFWxJIlS9RSr1gaN26M7777TuwwqAw1atRAdnY2nj17JnYoKnP+/Hlcvny5Un9DiIhUiUkV6YW+ffuiU6dOOHHiBL766issXboUubm5yMvLQ25uboW+f/lfWSL0pi9ZolTWV2FhodjNohXS09NLnSsuLkZubq5G45BIJCW+Xh7tefV72WpiAFBYWAiJRAIzMzP5tQUFBaUWhJFIJCVGel793tzcHNbW1pBIJCgqKpKPzLm4uMjrffr0KXJycsqMv7wpkZaWlvIPUiQSCf79918AL6ZHGhsbAwCSk5PLfSNd3lRI2UiV7PVr166hsLAQjRo1grm5OSQSCZKSkvDo0SN5PZaWlvLRONI+1atXR0JCAtLS0sQORSUEQcCVK1cAvBiZJSISE5MqPTNt2jQYGhris88+Q+3atcUOp0zFxcXIzMxEVlYWsrKykJ2dLf/3+fPniIqKwv3795Gbm1vitbLKZ2dnyxOh7OxsAMCWLVuwZcsWkX/K0gwNDWFiYlLq+Z7XfVW0nOzLwMAACQkJaNCgAUxMTOTnX56eZmBgIP9XEAQkJCTg9u3b8PLygqenJwwNDREbG4vvv/8eDg4O+PHHH+XXbNu2DRKJBLVq1UKtWrVQu3Zt1KhRQ37vV+uX/VveOdn0tpcTnfKOFSn76rEipFIpDhw4gN69e8uTEqo4WfuR9pFN+dOXpOrJkydITU2FRCLR683uiUg3MKnSI4IgYPXq1cjKysLYsWPVdo/MzEykpaUhPT29Ul+ZmZlqie1VRkZGMDc3h5mZGczNzUt8/7pzpqamMDU1hYmJifzf8r5e9/rLr8me3VG3iiQEd+7cQXh4OMLDwxEZGSn/71GvXj2MGTMGAJCVlYWioiL4+Phg4MCB8sTk3XffVfvPQETqoW9Jlex5SXd3d1hYWIgcDRFVdUyq9Eh6ejqysrIAoEKjVIWFhXj69KlCX2lpaRV6jqYiDA0NUa1aNVSrVg2WlpaoVq0aLCwskJOTA3d3d1hZWZV47eXvXy4vS47Mzc2RkZEBQ0ND1K9fn8vr/j9BEBATE4Ndu3Zh586duHnzZonX7ezs0LVrV3To0EF+rlq1aliwYIGmQyUiNapRowYA/UuqOEpFRNqA7zr1SHx8PADA3t4eqamp8ucdZCt0vfx9UlJSmauBVZSJiQlsbW1hY2NT6S9TU9NSU7M49Uo1BEHApUuXsGXLFuzatQv379+Xv2ZsbIwOHTogICAAAQEB8PHxkT9TRET6S99GqmQfEDGpIiJtoJdJ1cqVK7Fw4UIkJyejefPmWLFihV48PJ2Xl1dmciQ7jo2NBQCkpqaibt26FapTIpGgevXqsLOzk3/VqFGjxPGrXzVq1ICFhYXW7/Vz9OhR/Prrr1i9erXWx6pqq1atwpQpU+THFhYWePvtt/Hee++hT58+sLGxES84IhKFbKRKX1b/k41UNWrUSORIiIj0MKnavn07goKCEBYWhnbt2mHp0qXo2bMnYmNjUbNmTbHDK1N2dvYbR5UePXqE58+fV7hOY2NjODs7w9nZGS4uLmV+7+zsDHt7e73cKDg1NRV9+vRBbm4uOnbsiBEjRogdktoUFhZi7969cHFxQatWrQAAb7/9NoKDg9GvXz988MEH6NWrF585IKri9G2kitP/iEib6F1StXjxYowbNw6jRo0CAISFhWH//v1Yt24dgoOD1Xrv58+f49GjR7h//z7++ecfFBQUIDc3F5mZmXjy5AkeP34s/1f2lZSUpNDCDWZmZuUmS3///Te2b9+OsWPHVsnRmZfZ29sjNDQU165dw4ABA8QOR63mzJmDb7/9Fn379sWuXbsAvHhw+8mTJ6hWrZrI0RGRtmjYsCHee+89tGvXTuxQlPbs2TMkJycD4EgVEWkHvUqqCgoKcPHiRYSEhMjPGRgYoHv37oiKiirzmvz8/BIbgGZkZAB48WyPogsyzJs3D4sXL65E5C/2hHF2doaTk5M8UXr1excXF9jY2JSbLB08eBAAUL9+fZ3dI0nW5qpYDGPixInytlLV4hpiEwQBZ86cgY2NDZo2bQoA+OCDD7BmzRo0bdpUvgGmVCqFqamp3vzcmqTK38GqSJn2Y5urV6dOndCtWzexw1AJ2fNUtWvXhpWVlcjREBHpWVKVmpqKoqIiODo6ljjv6Ogo3xDzVaGhoZgzZ06p8+Hh4QpPlzpy5AiAF1PvqlevLl9O28zMrNQiDba2trC2tkb16tVRo0YNmJubl1tvXl4e7t+/X2KxgbLINkFMTU3V+X1iIiIiVFpfcXExjh8/js6dO+vkogyFhYWIiorC3r17cfv2bbRv3x7Tp0+Xv/7TTz/B0NAQhw8fBqD69quK2IbKqUz7lbfxMdGruEgFEWkbvUqqKiMkJARBQUHy44yMDLi6uiIgIADW1tYK1RUbG4vLly+jffv2OHDggMZXr/v8888BAP369cNbb72l0XurilQqRUREBHr06KGy9hMEAUOHDsXOnTthZGSE0NBQldSrCc+fP8e6deuwcuVKJCQkAHgxBdTb2xtvv/12masnqrr9qhq2oXKUaT/ZTAFSH9leg1ZWVjo9RZzPUxGRttGrpEq26EJKSkqJ8ykpKXBycirzGtlGr68yNjZW+A2B7PmVgoKCSl2vjKKiIjx8+BAA4OHhofNvBlXdfv3798fOnTvxww8/oHbt2iVWxtNG9+7dw7Jly/DLL7/I9x6rWbMmJk2ahIkTJ8LBweG112v6908fsQ2VU5n206X2VmSV2Q0bNsif85UxNTVFXl6eJkKVy87Ohr29PYqKipCVlQVLS0uN3l+Vxo0bB29vbz5PRURaQ6+SKhMTE7Rq1QpHjhxB//79AbyY9nXkyBFMnjxZ7fc3MzMDAPlzLZqUlJSEoqIiGBkZlZtAVmUfffQRHj58iJCQEEydOhVmZmaYMGGC2GGVIAgCoqKisGTJEuzatQvFxcUAgCZNmiAoKAhDhw6V/44RkXgqs8qstbW1fNsLAKKMEllYWMDAwABFRUVIS0vT6aTKy8sLXl5eYodBRCSnV0kVAAQFBWHEiBFo3bo12rZti6VLlyI7O7vUp4TqIHsuSoykSjY1rFatWnq5RLoqzJgxA6mpqfjhhx8wceJE5ObmYurUqWKHBQD4/fff8cMPP+Cff/6RnwsICEBQUBACAgJ0epoOkb6pzCqzEolE9A+8JBIJHjx4AFtb29c+x0tERIpTKKl6/vw5du/ejZMnT+LBgwfIycmBg4MDfHx80LNnT7Rv315dcVbYBx98gCdPnmDWrFlITk5GixYtcPDgwVKLV6iDmEmVhYUFBg8eLN/ckUqTSCRYuHAhDAwMsHDhQgQFBeHevXtYvHgxjIzE/Xxh8+bN+Oeff2BqaoqhQ4ciKChIvrofEWmPyqwyCwBZWVmoW7cuiouL0bJlS3z33Xdo0qRJmWVVuSqt7DrZv/b29iXO6aL79+/j0KFDaNGihcaWh+eqoMph+ymPbagcTaxMW6F3ko8ePcKsWbOwdetWuLi4oG3btmjRogXMzc2RlpaGo0ePYtGiRahbty5mz56NDz74QOGAVWny5Mkame73KjGTqubNm+O3337T+H11jUQiwfz582FnZ4fg4GCsWLECly9fxq+//opatWppJIbo6GisWbMGwcHBqFu3LoAXo2ht2rTBhAkTtHaTaiKq3Cqznp6eWLduHZo1a4b09HQsWrQI7du3x/Xr11G7du1S5VW5Ku3L9GVFy6NHj2LZsmVo2rQpvv32W43eW1/aUCxsP+WxDZWjzpVpK5RU+fj4YMSIEbh48WK5K+3k5uZiz549WLp0KRISEvDFF19UPFo9IWZSRRUnkUgwY8YM1K9fHyNHjsSJEyfQtGlTLFiwAGPGjFH7kuszZ87E33//DSsrKyxYsAAA0LFjR3Ts2FGt9yXSJ/n5+Th37lypWRPu7u5ih1aKr68vfH195cft27dHo0aN8PPPP2PevHmlyqtyVVqg5IqMW7ZswalTpzB48GD06NGjcj+QyAwMDHD79m20atUKvXv31sg9uSqocth+ymMbKkcTK9NWKKm6ceMG7OzsXlvG3NwcQ4YMwZAhQ/D06dMK3VzfiLlQxdOnT2FjYyP6NDZd8v7776N58+YYMmQILly4gPHjx2PVqlXYvn07GjRooHT92dnZ+Pvvv7Fz504sWLAArq6uAIAJEybAxsYGgYGBSt+DqKo5ffo0li1bhr/++gtSqRQ2NjbyWRP5+fnw8PDA+PHjMWHCBLVsCluZVWZfZWxsDB8fH9y5c6fM11W5Ku2r158+fRqbN29G06ZNNZaQqFpgYKBofz+5Kqhy2H7KYxsqR50r01boI/k3JVTKltcXYo5U9ejRA2ZmZvLNX6li6tevL19xz8rKComJiXBxcZG//vTpUwiCUKG6ioqKcOnSJSxZsgT9+vWDg4MDBg4ciG3btmHDhg3ycv369cNvv/0GPz8/Vf84RHqtX79++OCDD+Dm5obw8HBkZmbi6dOnePjwIXJycnD79m3MnDkTR44cQcOGDdUyTeblVWZlZKvMvjwa9TpFRUW4evUqnJ2dVR7fm8ieu01LS9P4vYmI9FmlhjViY2OxYsUK+Y7mjRo1wqeffgpPT0+VBqdrxEyqZEuq83kcxRkZGWHKlCn46KOPcPPmTfkyw8XFxWjevDkEQcD58+flyVZ4eDhu376NvLw8ZGZm4s6dO7h16xb+/fdfZGZmlqjbw8MD77//PkeliFSgT58+2LlzZ7mfGnp4eMDDwwMjRozAjRs3kJSUpJY43rTK7PDhw1GrVi35RuNz587FW2+9hfr16+P58+dYuHAhHjx4gLFjx6olvteRJVW6OqOksLAQaWlpcHBw4KqoRKRVFE6qdu7cicGDB6N169byT+XOnj2Lpk2bYtu2bXj//fdVHqSuEDOpevjwIVJSUt64KSyVz97evsToUWxsLB4/fgxBEEq065o1a/DHH3+UWYeVlRX8/PzQuXNnBAQEoHnz5uz4iVTk448/rnDZxo0bl/sMsLLetMpsfHx8iWcznz17hnHjxiE5ORnVq1dHq1atcObMGbXF9zqy1f90Nam6ceMGmjdvjnr16pU7fZKISAwKJ1XTp09HSEgI5s6dW+L87NmzMX369CqdVMmeqSoqKkJhYaFG57waGhqWmLZGymvUqBGeP3+Oq1evlvhvKfswwczMDBYWFnB3d4enpycaNGgALy8vPtdGpGFZWVnyzbJlKrOggyJet8rssWPHShwvWbIES5YsUWs8FSWbnp+amipyJJUjW2GRHyASkbZR+N1fUlIShg8fXur8Rx99hIULF6okKF1lY2ODI0eOIDo6Wu0ryJFmWFhYlNoH5eVVuYhIHPfu3cPkyZNx7Ngx5OXlyc8LggCJRIKioiIRo9Neuj5SdevWLQCo8o8bEJH2UTip8vf3x8mTJ1G/fv0S50+dOlXlH7w3MjKCn58fMjMzNZpUHT16FD///DP8/PwwadIkjd2XiEgsH330EQRBwLp16+Do6MhpthUkG6nS1aQqNjYWAJMqItI+CidV/fr1w4wZM3Dx4kW89dZbAF48U7Vjxw7MmTMHe/fuLVGW1C8mJgbbt28HACZVRFQlXL58GRcvXuSbawW9nFTJRvV0iWykqmHDhiJHQkRUksJJ1SeffAIA+Omnn/DTTz+V+RqAKjv94pdffsHZs2fRtm1bjS2Xm5iYCACoVauWRu5HRCS2Nm3aICEhgUmVgmRJVWFhITIyMmBjYyNyRBUnCAJHqohIaymcVL36MDCVNGfOHCQnJ2PSpEkaS6oePXoEgEkVEVUda9euxYQJE5CYmIimTZuWWhioWbNmIkWm3czNzWFhYYGcnBykpqbqVFL15MkTpKenQyKRoF69emKHQ0RUApcpU7F3330X//77r9pXnnqZbKSKq/8RUVXx5MkTxMXFyfeGAl7MkOBCFW9mZ2eHnJwcPH36VKeSE9koVZ06deRbmBARaYtKJVXnz5/H0aNH8fjx41IjV4sXL1ZJYLpq2bJlOHDgADw8PDR2T07/I6KqZvTo0fDx8cFvv/3GhSoUZG9vj4SEBJ1brIIr/xGRNlM4qfruu+8wc+ZMeHp6lurI2KlpniAInP5HRFXOgwcPsHfv3lIr0dKbde3aFe7u7qhevbrYoSiEi1QQkTZTOKlatmwZ1q1bh5EjR6ohHN1XXFyM/Px8FBQUaGTz3+fPnyM3NxcANPYMFxGR2Lp27YrLly8zqaqERYsWiR1CpXCRCiLSZgonVQYGBujQoYM6YtELffv2xeHDh7F+/XqNJJ6yqX81atTgHHMiqjICAwMxdepUXL16Fd7e3qU+xOKWHvqHI1VEpM0UTqqmTp2KlStXYunSpWoIR/eZmpoCAPLy8jRyP9nUPy5SQURVyYQJEwAAc+fOLfUaF6p4M0EQkJ+fDzMzM7FDqZDCwkLcuXMHAJMqItJOCidVX3zxBfr06YN69eqhcePGpT4d3LVrl8qC00Wy0SJNJVVcpIKIqiJu71F5a9euxcSJE9G/f3/s2LFD7HAqxNDQEPfv30dsbCzq1KkjdjhERKUonFR99tlnOHr0KLp06QI7OzsuTvEKWVIle85J3ZhUERGRIiwsLFBYWKhTq/9JJBK4uLhwVgYRaS2Fk6qNGzdi586d6NOnjzri0XliJVXsaIiIgAsXLiAnJwedOnUSOxStFRgYiIcPH8LOzk7sUIiI9IbCSVWNGjV0arNATZPNT9dUUtWuXTtkZGSgTZs2GrkfEZE2GzZsGG7dusVnql7DysoKVlZWYoehkDVr1uDu3bsYMGAAWrVqJXY4RESlKJxUffPNN5g9ezbWr18PCwsLdcSk02RJlaaeqRo5ciSXtyci+n9HjhyBVCoVOwxSse3bt+PIkSPw8vJiUkVEWknhpGr58uWIi4uDo6Mj3NzcSi1UER0drbLgdJGmF6ogIqL/4VToNyssLMSMGTOQmpqKVatW6cQHpMOGDYOXlxdat24tdihERGVSOKnq37+/GsLQH5p8pqq4uBiPHj2Ck5MTjIwU/k9JRERVkKGhIX788UcUFBRg3rx5OrGa3ogRIzBixAixwyAiKpfC78Rnz55d7mucw67ZZ6qSkpLg6uoKExMT5ObmwsDAQO33JCISk4GBwRtXnZVIJCgsLNRQRLpHIpHAzs4OSUlJSE1N1YmkiohI26lkeOPWrVv45ZdfsGnTJiQlJamiSp2lyel/jx8/hpGREWrWrMmEioiqhN27d5f7WlRUFJYvX849rCrA3t4eSUlJOrGsenx8PB4/fgxPT0+dW2CDiKqOSidVOTk52L59O9atW4eoqCi0bt0aQUFBqoxNJ2lyoQofHx/k5eUhPT1d7fciItIG77zzTqlzsbGxCA4Oxl9//YUPP/wQc+fOFSEy3SJbTj01NVXkSN5s48aNmDVrFkaOHIn169eLHQ4RUZkUTqrOnj2LtWvXYseOHahTpw5u3ryJo0ePws/PTx3x6RxN71NlaGiIGjVqaOReRETa5NGjR5g9ezY2btyInj17IiYmBk2bNhU7LJ0gS6p0YaTq1q1bAABPT0+RIyEiKl+F54z98MMPaNKkCQYMGIDq1avjxIkTuHr1qnxuNr3Qrl07fPXVV1iwYIHYoRAR6aX09HTMmDED9evXx/Xr13HkyBH89ddfTKgUYG9vD0C3kqqGDRuKHAkRUfkqnFTNmDED/fv3x4MHD7Bw4UI0b95cnXHpLGdnZ7Rt2xbt2rVT+72+++47DBo0CIcPH1b7vYiItMGCBQvg4eGBffv24bfffsOZM2c4U6ISdGX6nyAIiI2NBcCkioi0W4Wn/82bNw/r16/H5s2bMWTIEAwbNoyfCors6NGjOHz4MPr27St2KEREGhEcHAxzc3PUr18fGzduxMaNG8sst2vXLg1Hplt0ZaTqyZMnSE9Ph0QiQf369cUOh4ioXBVOqkJCQhASEoLjx49j3bp1aNeuHerXrw9BEPDs2TN1xqhT0tPTcfToUaSlpWHUqFFqvVdiYiIAoFatWmq9DxGRthg+fPgbl1SnN9OVkSrZKFXdunXlC0EREWkjhReq6Ny5Mzp37owff/wRv/76K9atW4fOnTujbdu2GDBgQJVfAfDx48dYtmwZrK2t1Z5UPXr0CACTKiKqOjZs2CB2CHpBVxaq4PNURKQrKr25kZWVFT7++GOcO3cOly5dQtu2bfH999+rMjadZGNjg+bNm6Nbt25qvU92drZ8KXUXFxe13ouIiPSLrkz/k41UceU/ItJ2Ktkx1tvbG0uXLpVPR6vKatasiTlz5mD79u1qvY+sratVqwZra2u13ouISBtMmDABDx8+rFDZ7du3Y+vWrWqOSHfpyvQ/jlQRka6o0PS/bdu2YfDgwW8sZ2xsjISEBMTHx6NDhw5KB0flk0394ygVEVUVDg4OaNKkCTp06IDAwEC0bt0aLi4uMDMzw7Nnz3Djxg2cOnUK27Ztg4uLC1avXi12yFrLwcEB/v7+sLe3R1FREQwNDcUOqUwcqSIiXVGhkapVq1ahUaNGWLBgAW7evFnq9fT0dBw4cABDhw5Fy5YttX46gT7gIhVEVNXMmzcPsbGx6NChA3766Se89dZbqFOnDmrWrAlPT08MHz4cd+/exerVq3H27Fk0a9ZM7JC1lo2NDY4ePYodO3ZobUJVWFiIuLg4ABypIiLtV6Gk6vjx45g/fz4iIiLQtGlTWFtbo0GDBvD29kbt2rVhZ2eH0aNHo06dOrh27Rr69eun7ri12ogRI2BpaYmEhAS13YNJFRFVRU5OTvj6669x9epVpKamIjo6GqdPn0ZsbCyePXuGP/74A7169VJ7HCtXroSbmxvMzMzQrl07/PPPP68tv2PHDnh5ecHMzAze3t44cOCA2mPUdQ8ePIBUKoWZmRlcXV3FDoeI6LUqvPpfv3790K9fP6SmpuLUqVN48OABcnNzYW9vDx8fH/j4+MDAQCWPaOk8qVQKqVSK3Nxctd2D0/+IqCqaO3cuvvjiC1hYWKB69eqoXr26xmPYvn07goKCEBYWhnbt2mHp0qXo2bMnYmNjUbNmzVLlz5w5gyFDhiA0NBR9+/bFr7/+iv79+yM6Olr0/R6Li4shCIJWjlY9fPgQxsbGaNCgAd9fEJHWU/ivlL29Pfr374/PP/8cwcHBGDt2LFq1asU/eC8xMTEBALUmVRypIqKqaM6cOcjKyhI1hsWLF2PcuHEYNWoUGjdujLCwMFhYWGDdunVlll+2bBl69eqFL7/8Eo0aNcK8efPQsmVL/PjjjxqOvKTAwEAYGxtj9+7dosZRns6dOyMnJweHDx8WOxQiojdSeJ8qejNTU1MAL5Y9VxcmVURUFQmCIOr9CwoKcPHiRYSEhMjPGRgYoHv37oiKiirzmqioqFJ7OPbs2RN79uwps3x+fj7y8/PlxxkZGQD+NwtCUbJrXr1WIpGguLgYKSkplapXU6pXry56fOW1IVUM2095bEPlKNN+Fb2GSZUayHZ9V2dSxel/RFRVSSQS0e6dmpqKoqIiODo6ljjv6OiIf//9t8xrkpOTyyyfnJxcZvnQ0FDMmTOn1Pnw8HBYWFhUMnIgIiKixHH//v3x7rvvwsrKis94VdCrbUiKYfspj22onMq0X05OToXKMalSA00kVR999BHu378Pd3d3td2DiEgbNWzY8I2JVVpamoaiUb2QkJASI1sZGRlwdXVFQEBApfYllEqliIiIQI8ePWBsbKzKUNXqnXfegbW1Nb7//nvRZ2XoahtqC7af8tiGylGm/WSzBd6ESZUaaGL637fffqu2uomItNmcOXNgY2Mjyr3t7e1haGiIlJSUEudTUlLg5ORU5jVOTk4KlTc1NZX3Iy8zNjZW6s2UstdrUnZ2Nv7++28AwE8//aQ1cetSG2ojtp/y2IbKqUz7VbS8ziRV//3vf7F//37ExMTAxMQEz58/L1UmPj4eEydOxNGjR1GtWjWMGDECoaGhMDLS7I+piZEqIqKqavDgwWWusqcJJiYmaNWqFY4cOYL+/fsDeLGC3pEjRzB58uQyr/H19cWRI0cwZcoU+bmIiAj4+vpqIOLyXb58GRs2bEDt2rUxbdo0UWN5laGhIfbs2YN79+6hRo0aYodDRPRGFco2goKCMG/ePFhaWpZ62PZVixcvVklgryooKMDAgQPh6+uLX375pdTrRUVF6NOnD5ycnHDmzBkkJSVh+PDhMDY2xnfffaeWmMqj7pGq58+fIzMzE05OTvy0goiqFDGfp5IJCgrCiBEj0Lp1a7Rt2xZLly5FdnY2Ro0aBQAYPnw4atWqhdDQUADA559/js6dO+OHH35Anz59sG3bNly4cAGrV68W88fAgwcPsHTpUrRp00brkiozMzO88847YodBRFRhFUqqLl26JF/54tKlS+WWU2dnJ3tod8OGDWW+Hh4ejhs3buDw4cNwdHREixYtMG/ePMyYMQPffPONfJlzTVD3SNWOHTswfvx49O3bF3/99Zda7kFEpI3EXv0PAD744AM8efIEs2bNQnJyMlq0aIGDBw/KF6OIj48vsc1I+/bt8euvv2LmzJn46quv0KBBA+zZs0f0Papko31PnjwRNQ4iIn1QoaTq6NGjZX6vTaKiouDt7V1ihaWePXti4sSJuH79Onx8fDQWiyypUtdeKpmZmTAyMuLKf0RU5RQXF4sdAgBg8uTJ5U73O3bsWKlzAwcOxMCBA9UclWIcHBwAAI8fPxY5ktL++usvSKVSdOjQodTKiURE2khnnql6k/KWrJW9Vh517AciS6oyMzPVsp/Ap59+ikmTJiE/P1/v9ivgPgzKYfspj22oHE3sBUKqIRupysnJQXZ2NiwtLUWO6H/mzZuH8+fPY+fOnXjvvffEDoeI6I1ETaqCg4Mxf/7815a5efMmvLy81BaDOvYDkT1TFRsby70/Kon7MCiH7ac8tqFy1LkXCKlGtWrVYGZmhry8PDx58kRrkipBEHDr1i0AgKenp8jREBFVjKhJ1bRp0zBy5MjXlvHw8KhQXU5OTvjnn39KnJMtYVvesrWAevYDuX//Pvz8/NC4cWO0a9dO4TqqMu7DoBy2n/LYhsrRxF4gpBoSiQQODg5ISEjAkydP4ObmJnZIAF5MR0xPT4dEIkG9evXEDoeIqEJETaocHBzkc7qV5evri//+9794/PixfEpDREQErK2t0bhx43KvU8d+IG5ubujdu7fa3pD17t0blpaWWL58OZydndVyD7FxHwblsP2UxzZUjjr3AiHVqVmzJhISErTquSrZKJWbm5t8Oj0RkbbTmWeq4uPjkZaWhvj4eBQVFSEmJgYAUL9+fVSrVg0BAQFo3Lgxhg0bhgULFiA5ORkzZ87EpEmTykyadFV+fr58Q8RVq1aJHA0REeky2Qeb2rQCYGxsLACgYcOGIkdCRFRxOpNUzZo1Cxs3bpQfy1bzO3r0KPz9/WFoaIh9+/Zh4sSJ8PX1haWlJUaMGIG5c+dqPNaMjAzs27cPlpaWCAgIUGndSUlJAF6MsNnZ2am0biIiqlpkMzu0caSKz1MRkS7RmaRqw4YN5e5RJVO3bl2tWBji/v37mDVrFho3bozr16+rtO7ExEQAgIuLi1ZsgklERLpLG0eqZEkVR6qISJfoTFKlS6ytrdG6dWvUr19f5XU/evQIALhHFRERKU0bR6pk0/84UkVEuoRJlRq4ubnhzJkzannoWjZSVatWLZXXTUREVYu2jVQVFhYiLi4OAEeqiEi3GIgdACnm5el/REREyqhXrx66dOmCFi1aiB0KgBfT56VSKczNzVG7dm2xwyEiqjCOVOkY2fQ/jlQREZGyOnXqhMjISLHDkJM9T9WgQQMYGPBzXyLSHfyLpQYFBQVo0KABHBwckJmZqdK6Of2PiIj0FZdTJyJdxZEqNTA2NkZ8fDwEQUB2djasrKxUVjen/xERkaoVFRVBIpGIPjpkZGQEDw8PNG7cWNQ4iIgUxZEqNZBIJLCwsAAAZGdnq6xeQRA4/Y+IiFTK3d0dxsbGuHfvntih4NNPP0VcXBy++eYbsUMhIlIIkyo1qVatGgDVJlXp6enIyckBwJEqIiJSDUEQIAiC1qwACID7MBKRzuH0PzWxtLQEoNqkSjZKZWtrKx8JIyIiUsbhw4dhZWUFe3t7UeMQBIHJFBHpLCZVaqKO6X9WVlaYMWOGyuojIiJSx0b1lRETE4OAgAC89dZb+Ouvv8QOh4hIIUyq1EQdI1Wurq74/vvvVVYfERGRtrh16xZSU1ORlpYmdihERApjUqUmsqQqKytL5EiIiIjKd/z4cezatQstWrTAqFGjRIsjMDAQ0dHRkEqlosVARFRZXKhCTdQx/e/+/fuIj49nh0NERCpz5coVLF++HAcOHBA1DgsLC/j4+KBt27aixkFEVBlMqtREHav/TZ06FXXr1sXatWtVVicREVVtTk5OAIDk5GSRIyEi0l1MqtREHc9UFRcXw9jYmMupExGRymhDUiUIAj7//HMsXbpUpf0mEZGm8JkqNVFHUvXnn3+iuLgYgiCorE4iIqraZElVSkqKaDEkJydj+fLlMDAwwMSJE0WLg4iosjhSpSbqeKYKAAwMDGBoaKjSOomIqOpydHQEAGRmZoo2ShQbGwsAcHNzg6mpqSgxEBEpgyNVatK7d284OzvDx8dH7FCIiIjKZWVlBXNzc+Tm5iIlJQUeHh4aj0GWVHl5eWn83kREqsCRKjV566238Nlnn8HPz08l9V25cgW+vr6YMGGCSuojIiICAIlEIvpzVf/++y8AwNPTU5T7ExEpiyNVOuLu3bs4e/YsioqKxA6FiIj0jKOjI+7duyfac1UcqSIiXcekSk2ePXuG2NhYmJqaqmTPjUePHgEAatWqpXRdREREL+NIFRGRcjj9T03OnDmDTp06YdKkSSqpLzExEQCTKiIiUj0xk6q8vDzcv38fAJMqItJdTKrUxM7ODvXr10fdunVVUp9spIp7VBERkaqJuaz6nTt3IAgCbGxs5CsREhHpGk7/U5O33noLt2/fVll9HKkiIiJ1kSUzYoxUvTz1TyKRaPz+RESqwJEqHcFnqoiIxJeWloYPP/wQ1tbWsLW1xZgxY5CVlfXaa/z9/SGRSEp8adtKrg0bNkS3bt3QokULjd+bi1QQkT7gSJWOkI1UcfofEZF4PvzwQyQlJSEiIgJSqRSjRo3C+PHj8euvv772unHjxmHu3LnyY9kG8dqia9eu6Nq1qyj3liVVfJ6KiHQZkyo1KS4uRsuWLfH8+XNcuHABNWrUqHRd2dnZeP78OQCOVBERieXmzZs4ePAgzp8/j9atWwMAVqxYgd69e2PRokWv/dDLwsJC/twSlcSV/4hIH3D6n5oYGBggNjYW9+7dQ3p6ulJ1PXz4EMCLXe9tbGxUER4RESkoKioKtra28oQKALp37w4DAwOcO3futddu3boV9vb2aNq0KUJCQpCTk6PucCuloKBA4/shdu7cGf7+/mjatKlG70tEpEocqVIja2tr5OTkKJ1UJSQkAABcXV1VERYREVVCcnIyatasWeKckZERatSo8doFHoYOHYq6devCxcUFV65cwYwZMxAbG4tdu3aVWT4/Px/5+fny44yMDACAVCqFVCpVOG7ZNW+61tvbG7Gxsbh48SK8vb0Vvk9lfffdd/LvK/PzaUJF25DKxvZTHttQOcq0X0WvYVKlRjY2NkhOTlbZSFXt2rVVERYREb0kODgY8+fPf22ZmzdvVrr+8ePHy7/39vaGs7MzunXrhri4ONSrV69U+dDQUMyZM6fU+fDwcKWexYqIiHjt63l5eQCAP//8U/5hHpX0pjak12P7KY9tqJzKtF9FZxYwqVIj2VQ92aeMlcWRKiIi9Zk2bRpGjhz52jIeHh5wcnLC48ePS5wvLCxEWlqaQs9LtWvXDsCL/ZnKSqpCQkIQFBQkP87IyICrqysCAgJgbW1d4fvISKVSREREoEePHjA2Ni63nJeXF6ysrGBnZwcDA808HZCWlgZTU1NYWlpq5H6VVdE2pLKx/ZTHNlSOMu1X0ffxTKrUSNb5KTtS5efnh+DgYLRp00YVYRER0UscHBzg4ODwxnK+vr54/vw5Ll68iFatWgEAIiMjUVxcLE+UKiImJgYA4OzsXObrpqamMDU1LXXe2NhYqTdTb7pejIUivv/+eyxduhSzZs0qc3RO2yj736CqY/spj22onMq0X0XLM6lSI1WNVPn7+8Pf318FERERUWU1atQIvXr1wrhx4xAWFgapVIrJkydj8ODB8pX/EhMT0a1bN2zatAlt27ZFXFwcfv31V/Tu3Rt2dna4cuUKpk6dik6dOqFZs2Yi/0Tik+3ByO1CiEjXcfU/NVLVSBUREWmHrVu3wsvLC926dUPv3r3RsWNHrF69Wv66VCpFbGysfA6+iYkJDh8+jICAAHh5eWHatGl4//338ddff4n1I5QrJiYGn332Gb7//nuN3XP79u1ITU3FkCFDNHZPIiJ14EiVGslGqpRNqs6cOYNatWrB1dVVY/PciYiotBo1arx2o183NzcIgiA/dnV1xfHjxzURmtISEhKwYsUKtGrVCsHBwRq7r52dncbuRUSkLkyq1Eg2UqXM9L+srCx06NABwIvkrDIPKRMREb2JbHP5xMREkSMhItI9HPZQI1WMVD179gweHh6wt7dnQkVERGoje64pJSUFhYWFar/fgQMH0KtXL/z4449qvxcRkbpxpEqNVJFUubq6Ii4ursR0EiIiIlWrWbMmjIyMUFhYiOTkZLXvjXju3DkcOnSIezASkV7gSJUaqWL6n4xEIlG6DiIiovIYGBjIl3nXxBTA69evAwCaNGmi9nsREakbR6rUqFmzZpgzZw7q168vdihERERv5OLigoSEBPlS5+okS6oaN26s9nsREakbR6rUyNPTE7NmzcLQoUMrXUdISAjatWuH7du3qzAyIiKi0jS1WEV+fj5u374NgCNVRKQfmFRpucuXL+Off/5BVlaW2KEQEZGee3kTY3W6desWioqKYG1tLU/kiIh0GZMqNSoqKsK1a9dw5syZSi808fDhQwDgg7xERKR2rq6uAP7X96jLjRs3ALwYpeIzw0SkD/hMlRrl5ubC29sbwIv9piwtLRWuIyEhAcD/OjoiIiJ1qVOnDgDgwYMHar0Pn6ciIn3DpEqNLC0t4ejoiGrVqiEnJ0fhpCorKwvPnz8HwJEqIiJSv7p16wIA4uPj1XofrvxHRPpGJ6b/3b9/H2PGjIG7uzvMzc1Rr149zJ49GwUFBSXKXblyBX5+fjAzM4OrqysWLFggUsQvSCQSJCcn486dO3BwcFD4etn0C2tra278S0REaicbqXr48KFaNwBmUkVE+kYnRqr+/fdfFBcX4+eff0b9+vVx7do1jBs3DtnZ2Vi0aBGAF3tBBQQEoHv37ggLC8PVq1cxevRo2NraYvz48SL/BJXD56mIiEiTnJ2d0b17d9SuXRu5ubmwsrJS+T3y8/Nx584dAJz+R0T6QyeSql69eqFXr17yYw8PD8TGxmLVqlXypGrr1q0oKCjAunXrYGJigiZNmiAmJgaLFy/W2aSKz1MREZEmGRgYICIiQq334Mp/RKSPdCKpKkt6ejpq1KghP46KikKnTp1gYmIiP9ezZ0/Mnz8fz549Q/Xq1cusJz8/H/n5+fLjjIwMAIBUKoVUKlU4Ltk1sn+DgoJw6tQpzJs3Dz179lSoLtmDwi4uLpWKRRe92n6kGLaf8tiGylGm/djmVcPLU/+48h8R6QudTKru3LmDFStWyEepACA5ORnu7u4lyjk6OspfKy+pCg0NxZw5c0qdDw8Ph4WFRaVjlH3Sd/bsWcTExODgwYMoKipSqI4zZ84AeLGK4IEDByodiy5S9yel+o7tpzy2oXIq0345OTlqiIQqIy8vD/n5+bCxsVF53c2bN8d3332HmjVrqrxuIiKxiJpUBQcHY/78+a8tc/PmTXh5ecmPExMT0atXLwwcOBDjxo1TOoaQkBAEBQXJjzMyMuDq6oqAgIBKLQ4hlUoRERGBHj16wNjYGDt27MCFCxdQu3Zt9O7dW6G6wsLCAABdunRR+Fpd9Wr7kWLYfspjGypHmfaTzRQgcc2fPx/BwcGYMGECVq1apfL6GzVqhEaNGqm8XiIiMYmaVE2bNg0jR458bRkPDw/5948ePUKXLl3Qvn17rF69ukQ5JycnpKSklDgnO3Zyciq3flNTU5iampY6b2xsrNQbKtn1sk/inj17pnB9soUq3NzcqtybO2Xbv6pj+ymPbaicyrQf21s72NvbAwCSkpJEjoSISHeImlQ5ODhUeKnxxMREdOnSBa1atcL69ethYFByNXhfX198/fXXkEql8o45IiICnp6e5U790wRZ55SamqrwtVz9j4iING3QoEF45513YGdnp/K68/PzceDAATRu3BgNGzbkM1VEpDd0Yp+qxMRE+Pv7o06dOli0aBGePHmC5ORkJCcny8sMHToUJiYmGDNmDK5fv47t27dj2bJlJab2iaGySZUgCPjvf/+LkJAQ+b4hRERE6mZlZQV7e3u1JDyxsbF477330K5dO5XXTUQkJp1YqCIiIgJ37tzBnTt3So3aCIIAALCxsUF4eDgmTZqEVq1awd7eHrNmzRJ9OXVZUvXkyROFrpNIJPjkk0/UERIREZEo8vLy0Lp1a9jY2HCUioj0ik4kVSNHjnzjs1cA0KxZM5w8eVL9ASlAmel/REREYpg9ezYuX76M0NBQlS4q0bZtW5w/f17+gSgRkb7Qiel/ukz2zJiiSdWtW7dw8uRJPH78WB1hERERlSsiIgJ//vknrl27ppb6OUpFRPqGSZWayUaqnj17hsLCwgpft27dOnTq1AnffvutukIjIiIqU/369QG82BdSlQoKClRaHxGRtmBSpWbVq1eXfyKXlpZW4essLS3h4eGBBg0aqCs0IiKiMqkjqXr8+DEsLS3RokULhT5kJCLSBUyq1MzIyEi+pLsii1X85z//QVxcHD799FN1hUZERFQmdSRVly9fRmFhIXJzc2FkpBOPdBMRVRiTKg3gYhVERKRL1JFUxcTEAACaN2+usjqJiLQFPyrSgIkTJyIzMxOurq5ih0JERPRGsqTq0aNHyM7OhqWlpdJ1Xr58GQCTKiLST0yqNGDKlCkKlb9+/Tq6desGHx8f/P333+oJioiIqBw1atRA9erV8ezZM9y9exfe3t5K1ykbqWrRooXSdRERaRtO/9NC9+7dQ0pKClJSUsQOhYiIqihVTgHMycnBv//+C4AjVUSkn5hUaUBGRgauX7+OuLi4CpW/d+8eAMDd3V2dYREREZVLlUnV5cuXUVRUBEdHR9SqVUvp+oiItA2TKg1YvXo1mjZtitmzZ1eo/P379wEAbm5u6guKiIjoNVSZVF24cAEA0Lp1a278S0R6iUmVBtSsWRN2dnYwNTWtUHmOVBERkdhkSdXt27eVruv8+fMAgDZt2ihdFxGRNmJSpQHDhw9HamoqfvnllwqVv3v3LgAmVURE2uS///0v2rdvDwsLC9ja2lboGkEQMGvWLDg7O8Pc3Bzdu3dXSZKiCZ6engAgfxZKGS+PVBER6SMmVVpGEAR5h9ugQQORoyEiIpmCggIMHDgQEydOrPA1CxYswPLlyxEWFoZz587B0tISPXv2RF5enhojVY3GjRtj6NCh+PTTT1FcXFzpejIzM+WJGZMqItJXXFJdyzx69Ag5OTkwNDTkSBURkRaZM2cOAGDDhg0VKi8IApYuXYqZM2finXfeAQBs2rQJjo6O2LNnDwYPHqyuUFXCysoKW7duVbqe6OhoCIIAV1dXODo6qiAyIiLtw6RKAwoKCtC7d2+kpqbi5MmTsLKyKrfsrVu3AAAeHh4wNjbWVIhERKRi9+7dQ3JyMrp37y4/Z2Njg3bt2iEqKqrcpCo/Px/5+fny44yMDACAVCqFVCpVOA7ZNZW5VhXOnTsHAGjVqpVoMShL7DbUdWw/5bENlaNM+1X0GiZVGmBiYoKLFy/i+fPniI+PR5MmTcotK0uqGjZsqKnwiIhIDZKTkwGg1OiMo6Oj/LWyhIaGykfFXhYeHg4LC4tKxxMREaHwNUVFRUhJSUF+fn6lZ0/Uq1cPS5YsAQAcOHCgUnVoi8q0If0P2095bEPlVKb9cnJyKlSOSZWG1KlTp0JJVWxsLID/PSBMRETqExwcjPnz57+2zM2bN+Hl5aWhiICQkBAEBQXJjzMyMuDq6oqAgABYW1srXJ9UKkVERAR69Oih8AyIX3/9FZ988gk6duyIyMhIhe+tL5RpQ2L7qQLbUDnKtJ9stsCbMKnSkLp16+LKlSt48ODBa8txpIqISHOmTZuGkSNHvraMh4dHpep2cnICAKSkpMDZ2Vl+PiUlBS1atCj3OlNT0zK34DA2NlbqzVRlrm/evDnMzc1hamrKN3JQ/r9BVcf2Ux7bUDmVab+KlmdSpSF16tQBAMTHx7+2HJMqIiLNcXBwgIODg1rqdnd3h5OTE44cOSJPojIyMnDu3DmFVhAUU7NmzZCZmQlDQ8NKXf/XX39h586deP/99xEYGKji6IiItAeXVNeQunXrAnhzUrVq1Sr8+OOPaNasmSbCIiKiCoqPj0dMTAzi4+NRVFSEmJgYxMTEICsrS17Gy8sLu3fvBgBIJBJMmTIF3377Lfbu3YurV69i+PDhcHFxQf/+/UX6KRRjYGBQ6YQKAPbt24eNGzfixIkTKoyKiEj7cKRKQ2QjVW+a/tetWzd069ZNEyEREZECZs2ahY0bN8qPfXx8AABHjx6Fv78/gBfPxaanp8vLTJ8+HdnZ2Rg/fjyeP3+Ojh074uDBgzAzM9No7KpQXFwMAwPFPosdNmwYHBwc0KtXLzVFRUSkHZhUaUhFp/8REZF22rBhwxv3qBIEocSxRCLB3LlzMXfuXDVGpl7h4eH44osv0LBhQ/zxxx8KXduxY0d07NhRTZEREWkPJlUaIpv+l5iYiMLCQhgZlW76yMhIJCYmws/PD25ubhqOkIiIqDQLCwtcvXoVT548gSAIkEgkYodERKR1+EyVhjg5OcHY2BhFRUV49OhRmWVWr16N4cOHY9u2bRqOjoiIqGytWrWCkZERkpOTkZCQUOHrfv/9d+zfvx/Z2dlqjI6ISDswqdIQAwMD1K5dG0D5UwBbtGgBPz8/tGvXTpOhERERlcvc3Fy+euHZs2crdI0gCJgxYwb69u2LY8eOqS84IiItwaRKg2RTAMtbrCI4OBgnTpxAly5dNBkWERHRa7311lsAKp5U3blzB/fv34exsTE6d+6sztCIiLQCkyoN4mIVRESki2RJVVRUVIXKh4eHAwA6dOiAatWqqS0uIiJtwYUqNOj999+Hl5cXunbtWuq15ORkWFpawsrKSoTIiIiIyidLqqKjo5GTkwMLC4vXlj906BAAoGfPnmqPjYhIG3CkSoP69euHkJCQMp+Z+vrrr2FjY4PFixeLEBkREVH5PDw84ObmhoKCAhw+fPi1ZdPT0+UjVW+//bYmwiMiEh2TKi0RExMDQRC4lDoREWkdiUSCwMBAAMBff/312rJ79uxBfn4+vLy80KxZM02ER0QkOiZVGpaQkIA//vijxGIVubm5uHbtGgDAx8dHrNCIiIjK9XJSVVxcXG452bYgQ4YM4Z5WRFRlMKnSsPHjx2PgwIHYt2+f/NzRo0dRUFAAV1dXjlQREZFW6ty5M6ysrJCSkoILFy6UWebJkyeIiIgAAAwePFiT4RERiYpJlYZ16NABLVu2hKWlpfycLMHq06cPP9UjIiKtZGJiIl94Ys+ePWWW2bJlC4qKitCyZUs0bNhQg9EREYmLSZWGzZw5ExcvXsTIkSMBvNggUZZU9e3bV8TIiIiIXm/QoEEAXqwC+Kq8vDwsXLgQwItZGUREVQmXVBfZ1atXkZCQAHNz8zKXWiciItIW7777LiIjI+Hv71/qtfXr1yMpKQm1a9eWf3BIRFRVMKkSSWJiIqKjo3H+/HkAQLdu3WBubi5yVEREROUzMjJCly5dynxNtoz69OnTYWpqqsmwiIhEx6RKBLGxsWjbti0KCgoglUoBAAMHDhQ5KiIioopLTEzEf/7zHyxbtgxWVlbYsmULpk2bhrFjx4odGhGRxvGZKhE0bNgQLVu2RF5eHoqKijB8+HAMGzZM7LCIiIgqpLi4GP369cP69etx4sQJAIClpSXCwsI464KIqiQmVSKQSCRYvXo16tWrh0GDBmHNmjVc9Y+IiHSGgYEBli1bhtq1ayM9PV3scIiIRMfpfyJp0KABbt++zWSKiIh0UseOHREfHy92GEREWoFJlYiYUBERkS5jP0ZE9AKn/xERERERESmBSRUREREREZESmFQREREREREpgUkVERERERGREnQmqerXrx/q1KkDMzMzODs7Y9iwYXj06FGJMleuXIGfnx/MzMzg6uqKBQsWiBQtERERERFVFTqTVHXp0gW///47YmNjsXPnTsTFxWHAgAHy1zMyMhAQEIC6devi4sWLWLhwIb755husXr1axKiJiIiIiEjf6cyS6lOnTpV/X7duXQQHB6N///6QSqUwNjbG1q1bUVBQgHXr1sHExARNmjRBTEwMFi9ejPHjx4sYORERERER6TOdSapelpaWhq1bt6J9+/YwNjYGAERFRaFTp04wMTGRl+vZsyfmz5+PZ8+eoXr16mXWlZ+fj/z8fPlxRkYGAEAqlUIqlSocm+yaylxLbD9lsf2UxzZUjjLtxzYnIiJdpVNJ1YwZM/Djjz8iJycHb731Fvbt2yd/LTk5Ge7u7iXKOzo6yl8rL6kKDQ3FnDlzSp0PDw+HhYVFpWONiIio9LXE9lMW2095bEPlVKb9cnJy1BAJERGR+omaVAUHB2P+/PmvLXPz5k14eXkBAL788kuMGTMGDx48wJw5czB8+HDs27dPqR3dQ0JCEBQUJD9OT09HnTp14OvrCysrK4Xrk0qlOHr0KLp06SIfRaOKY/sph+2nPLahcpRpv8zMTACAIAjqCE1nydpDNpNCUVKpFDk5OcjIyODvdCWxDZXD9lMe21A5yrSf7G/vm/omUZOqadOmYeTIka8t4+HhIf/e3t4e9vb2aNiwIRo1agRXV1ecPXsWvr6+cHJyQkpKSolrZcdOTk7l1m9qagpTU1P5sazhXh31IiIizcjMzISNjY3YYWgNWbLp6uoqciRERFXXm/omUZMqBwcHODg4VOra4uJiAJA/D+Xr64uvv/5avnAF8GL6iaenZ7lT/8ri4uKChIQEWFlZVWoELCMjA66urkhISIC1tbXC11d1bD/lsP2UxzZUjjLtJwgCMjMz4eLioqbodBP7JfGxDZXD9lMe21A5muibdOKZqnPnzuH8+fPo2LEjqlevjri4OPznP/9BvXr14OvrCwAYOnQo5syZgzFjxmDGjBm4du0ali1bhiVLlih0LwMDA9SuXVvpmK2trflLrwS2n3LYfspjGyqnsu3HEarS2C9pD7ahcth+ymMbKkedfZNO7FNlYWGBXbt2oVu3bvD09MSYMWPQrFkzHD9+XD51z8bGBuHh4bh37x5atWqFadOmYdasWVxOnYiIiIiI1EonRqq8vb0RGRn5xnLNmjXDyZMnNRARERERERHRCzoxUqVLTE1NMXv27BKLX1DFsf2Uw/ZTHttQOWw/7cP/JspjGyqH7ac8tqFyNNF+EoFr1xIREREREVUaR6qIiIiIiIiUwKSKiIiIiIhICUyqiIiIiIiIlMCkioiIiIiISAlMqhS0cuVKuLm5wczMDO3atcM///zz2vI7duyAl5cXzMzM4O3tjQMHDmgoUu2lSBtu2LABEomkxJeZmZkGo9UuJ06cQGBgIFxcXCCRSLBnz543XnPs2DG0bNkSpqamqF+/PjZs2KD2OLWVou137NixUr9/EokEycnJmglYy4SGhqJNmzawsrJCzZo10b9/f8TGxr7xOv4dVD/2Tcpj31R57JuUw75JOdrSNzGpUsD27dsRFBSE2bNnIzo6Gs2bN0fPnj3x+PHjMsufOXMGQ4YMwZgxY3Dp0iX0798f/fv3x7Vr1zQcufZQtA2BF7tfJyUlyb8ePHigwYi1S3Z2Npo3b46VK1dWqPy9e/fQp08fdOnSBTExMZgyZQrGjh2LQ4cOqTlS7aRo+8nExsaW+B2sWbOmmiLUbsePH8ekSZNw9uxZREREQCqVIiAgANnZ2eVew7+D6se+SXnsm5TDvkk57JuUozV9k0AV1rZtW2HSpEny46KiIsHFxUUIDQ0ts/ygQYOEPn36lDjXrl074eOPP1ZrnNpM0TZcv369YGNjo6HodAsAYffu3a8tM336dKFJkyYlzn3wwQdCz5491RiZbqhI+x09elQAIDx79kwjMemax48fCwCE48ePl1uGfwfVj32T8tg3qQ77JuWwb1KeWH0TR6oqqKCgABcvXkT37t3l5wwMDNC9e3dERUWVeU1UVFSJ8gDQs2fPcsvru8q0IQBkZWWhbt26cHV1xTvvvIPr169rIly9wN9B1WjRogWcnZ3Ro0cPnD59WuxwtEZ6ejoAoEaNGuWW4e+gerFvUh77Js3j76BqsG8qm1h9E5OqCkpNTUVRUREcHR1LnHd0dCx3DmtycrJC5fVdZdrQ09MT69atw59//oktW7aguLgY7du3x8OHDzURss4r73cwIyMDubm5IkWlO5ydnREWFoadO3di586dcHV1hb+/P6Kjo8UOTXTFxcWYMmUKOnTogKZNm5Zbjn8H1Yt9k/LYN2ke+yblsG8qn5h9k1GlryTSAF9fX/j6+sqP27dvj0aNGuHnn3/GvHnzRIyMqgJPT094enrKj9u3b4+4uDgsWbIEmzdvFjEy8U2aNAnXrl3DqVOnxA6FSOPYN5GY2DeVT8y+iSNVFWRvbw9DQ0OkpKSUOJ+SkgInJ6cyr3FyclKovL6rTBu+ytjYGD4+Prhz5446QtQ75f0OWltbw9zcXKSodFvbtm2r/O/f5MmTsW/fPhw9ehS1a9d+bVn+HVQv9k3KY9+keeybVI99k/h9E5OqCjIxMUGrVq1w5MgR+bni4mIcOXKkxKdVL/P19S1RHgAiIiLKLa/vKtOGryoqKsLVq1fh7OysrjD1Cn8HVS8mJqbK/v4JgoDJkydj9+7diIyMhLu7+xuv4e+gerFvUh77Js3j76DqsW/Sgr6p0ktcVEHbtm0TTE1NhQ0bNgg3btwQxo8fL9ja2grJycmCIAjCsGHDhODgYHn506dPC0ZGRsKiRYuEmzdvCrNnzxaMjY2Fq1evivUjiE7RNpwzZ45w6NAhIS4uTrh48aIwePBgwczMTLh+/bpYP4KoMjMzhUuXLgmXLl0SAAiLFy8WLl26JDx48EAQBEEIDg4Whg0bJi9/9+5dwcLCQvjyyy+FmzdvCitXrhQMDQ2FgwcPivUjiErR9luyZImwZ88e4fbt28LVq1eFzz//XDAwMBAOHz4s1o8gqokTJwo2NjbCsWPHhKSkJPlXTk6OvAz/Dmoe+yblsW9SDvsm5bBvUo629E1MqhS0YsUKoU6dOoKJiYnQtm1b4ezZs/LXOnfuLIwYMaJE+d9//11o2LChYGJiIjRp0kTYv3+/hiPWPoq04ZQpU+RlHR0dhd69ewvR0dEiRK0dZMuovvola7MRI0YInTt3LnVNixYtBBMTE8HDw0NYv369xuPWFoq23/z584V69eoJZmZmQo0aNQR/f38hMjJSnOC1QFltB6DE7xT/DoqDfZPy2DdVHvsm5bBvUo629E2S/w+GiIiIiIiIKoHPVBERERERESmBSRUREREREZESmFQREREREREpgUkVERERERGREphUERERERERKYFJFRERERERkRKYVBERERERESmBSRUREREREZESmFQRqdnIkSPRv39/0e4/bNgwfPfddxq5V3BwMD799FON3IuIiCqPfRORakkEQRDEDoJIV0kkkte+Pnv2bEydOhWCIMDW1lYzQb3k8uXL6Nq1Kx48eIBq1aqp/X6pqanw8PBATEwMPDw81H4/IiIqjX1TSeybSBOYVBEpITk5Wf799u3bMWvWLMTGxsrPVatWTSMdRnnGjh0LIyMjhIWFaeyeAwcOhJubGxYuXKixexIR0f+wbyqNfROpG6f/ESnByclJ/mVjYwOJRFLiXLVq1UpNsfD398enn36KKVOmoHr16nB0dMSaNWuQnZ2NUaNGwcrKCvXr18fff/9d4l7Xrl3D22+/jWrVqsHR0RHDhg1DampqubEVFRXhjz/+QGBgYInzP/30Exo0aAAzMzM4OjpiwIAB8teKi4sRGhoKd3d3mJubo3nz5vjjjz9KXH/9+nX07dsX1tbWsLKygp+fH+Li4uSvBwYGYtu2bZVpTiIiUgH2TeybSPOYVBGJYOPGjbC3t8c///yDTz/9FBMnTsTAgQPRvn17REdHIyAgAMOGDUNOTg4A4Pnz5+jatSt8fHxw4cIFHDx4ECkpKRg0aFC597hy5QrS09PRunVr+bkLFy7gs88+w9y5cxEbG4uDBw+iU6dO8tdDQ0OxadMmhIWF4fr165g6dSo++ugjHD9+HACQmJiITp06wdTUFJGRkbh48SJGjx6NwsJCeR1t27bFw4cPcf/+fRW3GhERqRP7JiIlCESkEuvXrxdsbGxKnR8xYoTwzjvvyI87d+4sdOzYUX5cWFgoWFpaCsOGDZOfS0pKEgAIUVFRgiAIwrx584SAgIAS9SYkJAgAhNjY2DLj2b17t2BoaCgUFxfLz+3cuVOwtrYWMjIySpXPy8sTLCwshDNnzpQ4P2bMGGHIkCGCIAhCSEiI4O7uLhQUFJTTCoKQnp4uABCOHTtWbhkiItIM9k0vsG8idTMSM6EjqqqaNWsm/97Q0BB2dnbw9vaWn3N0dAQAPH78GMCLh3qPHj1a5hz4uLg4NGzYsNT53NxcmJqalnhguUePHqhbty48PDzQq1cv9OrVC++++y4sLCxw584d5OTkoEePHiXqKSgogI+PDwAgJiYGfn5+MDY2LvdnMzc3BwD5J5lERKQb2DcRVR6TKiIRvPqHXyKRlDgn62yKi4sBAFlZWQgMDMT8+fNL1eXs7FzmPezt7ZGTk4OCggKYmJgAAKysrBAdHY1jx44hPDwcs2bNwjfffIPz588jKysLALB//37UqlWrRF2mpqYA/tcpvU5aWhoAwMHB4Y1liYhIe7BvIqo8JlVEOqBly5bYuXMn3NzcYGRUsf9tW7RoAQC4ceOG/HsAMDIyQvfu3dG9e3fMnj0btra2iIyMRI8ePWBqaor4+Hh07ty5zDqbNWuGjRs3QiqVlvuJ4LVr12BsbIwmTZoo9DMSEZFuYd9E9D9cqIJIB0yaNAlpaWkYMmQIzp8/j7i4OBw6dAijRo1CUVFRmdc4ODigZcuWOHXqlPzcvn37sHz5csTExODBgwfYtGkTiouL4enpCSsrK3zxxReYOnUqNm7ciLi4OERHR2PFihXYuHEjAGDy5MnIyMjA4MGDceHCBdy+fRubN28usVTvyZMn4efnV6FPDomISHexbyL6HyZVRDrAxcUFp0+fRlFREQICAuDt7Y0pU6bA1tYWBgbl/288duxYbN26VX5sa2uLXbt2oWvXrmjUqBHCwsLw22+/yT+5mzdvHv7zn/8gNDQUjRo1Qq9evbB//364u7sDAOzs7BAZGYmsrCx07twZrVq1wpo1a0p8Mrht2zaMGzdOTS1BRETagn0T0f9w818iPZabmwtPT09s374dvr6+ar/f33//jWnTpuHKlSsVngpCRERVC/sm0kccqSLSY+bm5ti0adNrN2JUpezsbKxfv56dFhERlYt9E+kjjlQREREREREpgSNVRERERERESmBSRUREREREpAQmVUREREREREpgUkVERERERKQEJlVERERERERKYFJFRERERESkBCZVRERERERESmBSRUREREREpAQmVUREREREREr4PzDY0gKfJtZCAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/DCmotor_Obs_solver/dcmotor_obs_solver.py b/Chapter8/python/DCmotor_Obs_solver/dcmotor_obs_solver.py
new file mode 100644
index 0000000..4a347d0
--- /dev/null
+++ b/Chapter8/python/DCmotor_Obs_solver/dcmotor_obs_solver.py
@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+"""DCmotor_Obs_solver.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-dFPgSvavMJedzuPowJLecYXXIQ0RbQV
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+class Parameters:
+    def __init__(self, Tl):
+        self.Tl = Tl
+
+# Define global parameters
+Par = Parameters(Tl=1)
+
+def DC_motor_Obs(t, X):
+    # Extract state variables
+    x = X[:3]
+    xh = X[3:]
+
+    # Real System Matrices
+    A = np.array([[0, 1, 0],
+                  [0, 0, 4.438],
+                  [0, -12, -24]])
+    B = np.array([[0, 0],
+                  [0, -7.396],
+                  [20, 0]])
+    C = np.array([1, 0, 0])
+
+    Tl = Par.Tl  # Step disturbance
+    v = 0
+    u = np.array([v, Tl])
+
+    # Real System Model
+    xp = A @ x + B @ u
+    y = C @ x
+
+    # Observer Matrices
+    Ah = np.array([[0, 1, 0, 0],
+                   [0, 0, 4.438, -7.396],
+                   [0, -12, -24, 0],
+                   [0, 0, 0, 0]])
+    Bh = np.array([0, 0, 20, 0])
+    Ch = np.array([1, 0, 0, 0])
+    G = np.array([0, 234.7440, -936.9136, -27.6050])
+
+    # Observer Model
+    xhp = Ah @ xh + Bh * v + G * (y - Ch @ xh)
+
+    # Augment the real and estimated states
+    Xp = np.concatenate((xp, xhp))
+
+    return Xp
+
+# Initial conditions
+x0 = np.array([1, 0, 0, 0, 0, 0, Par.Tl])
+
+# Time span
+tspan = [0, 2]
+
+# Solve ODE
+sol = solve_ivp(DC_motor_Obs, tspan, x0, method='RK45', t_eval=np.arange(0, 2, 0.01))
+
+t = sol.t
+x = sol.y.T
+xh = x[:, 3:7]
+
+# Plot results
+plt.figure(figsize=(10, 8))
+
+plt.subplot(221)
+plt.plot(t, x[:, 0], 'k', label='Real')
+plt.plot(t, xh[:, 0], '-.k', label='Estimated')
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('θ (rad)')
+plt.legend()
+
+plt.subplot(222)
+plt.plot(t, x[:, 1], 'k', label='Real')
+plt.plot(t, xh[:, 1], '-.k', label='Estimated')
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('ω (rad/sec)')
+plt.legend()
+
+plt.subplot(223)
+plt.plot(t, x[:, 2], 'k', label='Real')
+plt.plot(t, xh[:, 2], '-.k', label='Estimated')
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('i (Amp)')
+
+plt.subplot(224)
+plt.plot(t, Par.Tl + t * 0, 'k', label='Real')
+plt.plot(t, xh[:, 3], '-.k', label='Estimated')
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('T(N.m)')
\ No newline at end of file
diff --git a/Chapter8/python/Invpend_Luen_solver/Invpend_Luen_solver.ipynb b/Chapter8/python/Invpend_Luen_solver/Invpend_Luen_solver.ipynb
new file mode 100644
index 0000000..ef8ecf2
--- /dev/null
+++ b/Chapter8/python/Invpend_Luen_solver/Invpend_Luen_solver.ipynb
@@ -0,0 +1,106 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy.integrate import solve_ivp\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def inverted_pendulum_luenberger(t, X):\n",
+        "    # State variable x = [x; v; theta; omega]\n",
+        "    x = X[:4]\n",
+        "    psi = X[4]\n",
+        "\n",
+        "    # Constants\n",
+        "    g = 9.8\n",
+        "    l = 1\n",
+        "    m = 1\n",
+        "    M = 1\n",
+        "\n",
+        "    d1 = M + m * (1 - np.cos(x[2])**2)\n",
+        "    d2 = l * d1\n",
+        "    k = np.array([-40.0000, -37.3693, -190.6669, -54.7283])\n",
+        "\n",
+        "    dpsi = -40.0 * x[0] - 37.37 * x[1] - 405.9 * x[2] - 58.73 * psi\n",
+        "    omega_h = psi + 4 * x[2]\n",
+        "    xh = np.array([x[0], x[1], x[2], omega_h])\n",
+        "    F = -k @ x  # State feedback\n",
+        "    # F = -k @ xh  # Uncomment for Luenberger Observer Feedback\n",
+        "\n",
+        "    xp = np.array([\n",
+        "        x[1],\n",
+        "        (F + m * l * x[3]**2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1,\n",
+        "        x[3],\n",
+        "        (-F * np.cos(x[2]) - m * l * x[3]**2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2\n",
+        "    ])\n",
+        "    return np.concatenate((xp, [dpsi]))\n",
+        "\n",
+        "\n",
+        "    # Define initial conditions and parameters\n",
+        "X0 = np.array([0, 0, 0.26, 0, 0])  # Initial state vector\n",
+        "\n",
+        "# Define the time span for the simulation\n",
+        "t_span = (0, 3)\n",
+        "t_eval = np.linspace(t_span[0], t_span[1], 300)\n",
+        "\n",
+        "# Solve the differential equations\n",
+        "sol = solve_ivp(inverted_pendulum_luenberger, t_span, X0, t_eval=t_eval, max_step=1e-2)\n",
+        "\n",
+        "t = sol.t\n",
+        "x = sol.y.T\n",
+        "\n",
+        "psi = x[:, 4]\n",
+        "omega = x[:, 3]\n",
+        "omega_h = psi + 4 * x[:, 2]\n",
+        "\n",
+        "# Plot the results\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.plot(t, omega, 'k', label=r'$\\omega$')\n",
+        "plt.plot(t, omega_h, '-.k', label=r'$\\omega_h$')\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('Angular velocity (rad/sec)')\n",
+        "plt.legend()\n",
+        "\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "9pLv46IFZpf9",
+        "outputId": "2a89b46a-982e-43f3-ac09-1e17fe3455e5"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/Invpend_Luen_solver/invpend_luen_solver.py b/Chapter8/python/Invpend_Luen_solver/invpend_luen_solver.py
new file mode 100644
index 0000000..51eb260
--- /dev/null
+++ b/Chapter8/python/Invpend_Luen_solver/invpend_luen_solver.py
@@ -0,0 +1,70 @@
+# -*- coding: utf-8 -*-
+"""Invpend_Luen_solver.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1B8iSVdTbFxrcqhmC60yGU4AisvtwoGXl
+"""
+
+import numpy as np
+from scipy.integrate import solve_ivp
+import matplotlib.pyplot as plt
+
+def inverted_pendulum_luenberger(t, X):
+    # State variable x = [x; v; theta; omega]
+    x = X[:4]
+    psi = X[4]
+
+    # Constants
+    g = 9.8
+    l = 1
+    m = 1
+    M = 1
+
+    d1 = M + m * (1 - np.cos(x[2])**2)
+    d2 = l * d1
+    k = np.array([-40.0000, -37.3693, -190.6669, -54.7283])
+
+    dpsi = -40.0 * x[0] - 37.37 * x[1] - 405.9 * x[2] - 58.73 * psi
+    omega_h = psi + 4 * x[2]
+    xh = np.array([x[0], x[1], x[2], omega_h])
+    F = -k @ x  # State feedback
+    # F = -k @ xh  # Uncomment for Luenberger Observer Feedback
+
+    xp = np.array([
+        x[1],
+        (F + m * l * x[3]**2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1,
+        x[3],
+        (-F * np.cos(x[2]) - m * l * x[3]**2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2
+    ])
+    return np.concatenate((xp, [dpsi]))
+
+
+    # Define initial conditions and parameters
+X0 = np.array([0, 0, 0.26, 0, 0])  # Initial state vector
+
+# Define the time span for the simulation
+t_span = (0, 3)
+t_eval = np.linspace(t_span[0], t_span[1], 300)
+
+# Solve the differential equations
+sol = solve_ivp(inverted_pendulum_luenberger, t_span, X0, t_eval=t_eval, max_step=1e-2)
+
+t = sol.t
+x = sol.y.T
+
+psi = x[:, 4]
+omega = x[:, 3]
+omega_h = psi + 4 * x[:, 2]
+
+# Plot the results
+plt.figure(figsize=(8, 6))
+plt.plot(t, omega, 'k', label=r'$\omega$')
+plt.plot(t, omega_h, '-.k', label=r'$\omega_h$')
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('Angular velocity (rad/sec)')
+plt.legend()
+
+plt.show()
\ No newline at end of file
diff --git a/Chapter8/python/README.md b/Chapter8/python/README.md
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Chapter8/python/README.md
@@ -0,0 +1 @@
+
diff --git a/Chapter8/python/ex7_1/ex7_1.ipynb b/Chapter8/python/ex7_1/ex7_1.ipynb
new file mode 100644
index 0000000..4451ccf
--- /dev/null
+++ b/Chapter8/python/ex7_1/ex7_1.ipynb
@@ -0,0 +1,63 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from scipy.signal import place_poles\n",
+        "\n",
+        "# Define the matrix A and vector c\n",
+        "A = np.array([\n",
+        "    [0, 1, 0, 0],\n",
+        "    [0, 0, 4.438, -7.396],\n",
+        "    [0, -12, -24, 0],\n",
+        "    [0, 0, 0, 0]\n",
+        "])\n",
+        "\n",
+        "c = np.array([[1], [0], [0], [0]])\n",
+        "\n",
+        "# Define the desired pole locations\n",
+        "pd = np.array([-5 + 5j, -5 - 5j, -7 + 7j, -7 - 7j])\n",
+        "\n",
+        "# Use the place_poles function to find the gain matrix G\n",
+        "result = place_poles(A.T, c, pd)\n",
+        "G = result.gain_matrix\n",
+        "\n",
+        "# Print the result\n",
+        "print(\"Gain matrix G:\\n\", G)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GaNXcLK1EKCq",
+        "outputId": "f1e03f80-f9dd-4000-fe35-96c673e8a9ff"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gain matrix G:\n",
+            " [[-1.60527254e-13  2.34744000e+02 -9.36913625e+02 -2.76050117e+01]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/ex7_1/ex7_1.py b/Chapter8/python/ex7_1/ex7_1.py
new file mode 100644
index 0000000..3e5b9a2
--- /dev/null
+++ b/Chapter8/python/ex7_1/ex7_1.py
@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+"""ex7_1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1WZFnFbs-vv8Ef7Awsfd5dn_HOLtNrqnF
+"""
+
+import numpy as np
+from scipy.signal import place_poles
+
+# Define the matrix A and vector c
+A = np.array([
+    [0, 1, 0, 0],
+    [0, 0, 4.438, -7.396],
+    [0, -12, -24, 0],
+    [0, 0, 0, 0]
+])
+
+c = np.array([[1], [0], [0], [0]])
+
+# Define the desired pole locations
+pd = np.array([-5 + 5j, -5 - 5j, -7 + 7j, -7 - 7j])
+
+# Use the place_poles function to find the gain matrix G
+result = place_poles(A.T, c, pd)
+G = result.gain_matrix
+
+# Print the result
+print("Gain matrix G:\n", G)
\ No newline at end of file
diff --git a/Chapter8/python/inverted_pendulum_Luenburger/inverted_pendulum_Luenburger.ipynb b/Chapter8/python/inverted_pendulum_Luenburger/inverted_pendulum_Luenburger.ipynb
new file mode 100644
index 0000000..4f50205
--- /dev/null
+++ b/Chapter8/python/inverted_pendulum_Luenburger/inverted_pendulum_Luenburger.ipynb
@@ -0,0 +1,56 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "XIeBjYirZ0Y5"
+      },
+      "outputs": [],
+      "source": [
+        "def inverted_pendulum_luenberger(t, X):\n",
+        "    # State variable x = [x; v; theta; omega]\n",
+        "    x = X[:4]\n",
+        "    psi = X[4]\n",
+        "\n",
+        "    # Constants\n",
+        "    g = 9.8\n",
+        "    l = 1\n",
+        "    m = 1\n",
+        "    M = 1\n",
+        "\n",
+        "    d1 = M + m * (1 - np.cos(x[2])**2)\n",
+        "    d2 = l * d1\n",
+        "    k = np.array([-40.0000, -37.3693, -190.6669, -54.7283])\n",
+        "\n",
+        "    dpsi = -40.0 * x[0] - 37.37 * x[1] - 405.9 * x[2] - 58.73 * psi\n",
+        "    omega_h = psi + 4 * x[2]\n",
+        "    xh = np.array([x[0], x[1], x[2], omega_h])\n",
+        "    F = -k @ x  # State feedback\n",
+        "    # F = -k @ xh  # Uncomment for Luenberger Observer Feedback\n",
+        "\n",
+        "    xp = np.array([\n",
+        "        x[1],\n",
+        "        (F + m * l * x[3]**2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1,\n",
+        "        x[3],\n",
+        "        (-F * np.cos(x[2]) - m * l * x[3]**2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2\n",
+        "    ])\n",
+        "    return np.concatenate((xp, [dpsi]))"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/inverted_pendulum_Luenburger/inverted_pendulum_luenburger.py b/Chapter8/python/inverted_pendulum_Luenburger/inverted_pendulum_luenburger.py
new file mode 100644
index 0000000..0a9523f
--- /dev/null
+++ b/Chapter8/python/inverted_pendulum_Luenburger/inverted_pendulum_luenburger.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+"""inverted_pendulum_Luenburger.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1y3pMZJ6PdmmxEDIfRgBoxEEX1ayl1emP
+"""
+
+def inverted_pendulum_luenberger(t, X):
+    # State variable x = [x; v; theta; omega]
+    x = X[:4]
+    psi = X[4]
+
+    # Constants
+    g = 9.8
+    l = 1
+    m = 1
+    M = 1
+
+    d1 = M + m * (1 - np.cos(x[2])**2)
+    d2 = l * d1
+    k = np.array([-40.0000, -37.3693, -190.6669, -54.7283])
+
+    dpsi = -40.0 * x[0] - 37.37 * x[1] - 405.9 * x[2] - 58.73 * psi
+    omega_h = psi + 4 * x[2]
+    xh = np.array([x[0], x[1], x[2], omega_h])
+    F = -k @ x  # State feedback
+    # F = -k @ xh  # Uncomment for Luenberger Observer Feedback
+
+    xp = np.array([
+        x[1],
+        (F + m * l * x[3]**2 * np.sin(x[2]) - m * g * np.sin(x[2]) * np.cos(x[2])) / d1,
+        x[3],
+        (-F * np.cos(x[2]) - m * l * x[3]**2 * np.sin(x[2]) * np.cos(x[2]) + (M + m) * g * np.sin(x[2])) / d2
+    ])
+    return np.concatenate((xp, [dpsi]))
\ No newline at end of file
diff --git a/Chapter8/python/train_lqe/train_lqe.ipynb b/Chapter8/python/train_lqe/train_lqe.ipynb
new file mode 100644
index 0000000..489c376
--- /dev/null
+++ b/Chapter8/python/train_lqe/train_lqe.ipynb
@@ -0,0 +1,91 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OMXPGjbYF4MH",
+        "outputId": "5ba37ad7-d47f-4ef4-ba52-47fc2cda7131"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rank of observability matrix: 9\n",
+            "Observer gain matrix G:\n",
+            "[[1.99058981e+00 1.08544451e-01]\n",
+            " [1.49345465e+00 5.01549260e-02]\n",
+            " [9.57650109e-01 1.97194771e-02]\n",
+            " [4.65320387e-01 6.03124367e-03]\n",
+            " [1.08544451e+01 2.01348387e+00]\n",
+            " [8.28412633e+00 1.31076277e+00]\n",
+            " [4.35271167e+00 8.64094750e-01]\n",
+            " [1.54241826e+00 6.05910738e-01]\n",
+            " [2.19774769e-01 4.87575800e-01]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from scipy.linalg import solve_continuous_are, eigvals\n",
+        "\n",
+        "# Define the system matrix A\n",
+        "A = np.array([\n",
+        "    [0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "    [0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "    [0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "    [0, 0, 0, 0, 0, 0, 0, 1, -1],\n",
+        "    [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],\n",
+        "    [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "    [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "    [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],\n",
+        "    [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]\n",
+        "])\n",
+        "\n",
+        "# Define the output matrix C\n",
+        "C = np.array([\n",
+        "    [1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+        "    [0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
+        "])\n",
+        "\n",
+        "# Compute the observability matrix\n",
+        "O = np.hstack([np.dot(np.linalg.matrix_power(A, i), C.T) for i in range(A.shape[0])])\n",
+        "\n",
+        "# Check the rank of the observability matrix\n",
+        "rank_O = np.linalg.matrix_rank(O)\n",
+        "print(\"Rank of observability matrix:\", rank_O)\n",
+        "\n",
+        "# Define the weight matrices W and V\n",
+        "W = np.diag([0, 0, 0, 0, 9, 0, 0, 0, 0])\n",
+        "V = np.diag([1e-2, 1])\n",
+        "\n",
+        "# Solve the continuous-time Algebraic Riccati Equation (ARE)\n",
+        "P = solve_continuous_are(A.T, C.T, W, V)\n",
+        "\n",
+        "# Compute the observer gain matrix G\n",
+        "G = np.dot(np.linalg.inv(V), np.dot(C, P)).T\n",
+        "\n",
+        "print(\"Observer gain matrix G:\")\n",
+        "print(G)\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/train_lqe/train_lqe.py b/Chapter8/python/train_lqe/train_lqe.py
new file mode 100644
index 0000000..47da4ad
--- /dev/null
+++ b/Chapter8/python/train_lqe/train_lqe.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+"""train_lqe.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1QLEQKSrPfS6e-4ArrL074TgkFzCXxo2b
+"""
+
+import numpy as np
+from scipy.linalg import solve_continuous_are, eigvals
+
+# Define the system matrix A
+A = np.array([
+    [0, 0, 0, 0, 1, -1, 0, 0, 0],
+    [0, 0, 0, 0, 0, 1, -1, 0, 0],
+    [0, 0, 0, 0, 0, 0, 1, -1, 0],
+    [0, 0, 0, 0, 0, 0, 0, 1, -1],
+    [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],
+    [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+    [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+    [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],
+    [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]
+])
+
+# Define the output matrix C
+C = np.array([
+    [1, 0, 0, 0, 0, 0, 0, 0, 0],
+    [0, 0, 0, 0, 1, 0, 0, 0, 0]
+])
+
+# Compute the observability matrix
+O = np.hstack([np.dot(np.linalg.matrix_power(A, i), C.T) for i in range(A.shape[0])])
+
+# Check the rank of the observability matrix
+rank_O = np.linalg.matrix_rank(O)
+print("Rank of observability matrix:", rank_O)
+
+# Define the weight matrices W and V
+W = np.diag([0, 0, 0, 0, 9, 0, 0, 0, 0])
+V = np.diag([1e-2, 1])
+
+# Solve the continuous-time Algebraic Riccati Equation (ARE)
+P = solve_continuous_are(A.T, C.T, W, V)
+
+# Compute the observer gain matrix G
+G = np.dot(np.linalg.inv(V), np.dot(C, P)).T
+
+print("Observer gain matrix G:")
+print(G)
\ No newline at end of file
diff --git a/Chapter8/python/train_model_Obs/train_model_Obs.ipynb b/Chapter8/python/train_model_Obs/train_model_Obs.ipynb
new file mode 100644
index 0000000..bb92c65
--- /dev/null
+++ b/Chapter8/python/train_model_Obs/train_model_Obs.ipynb
@@ -0,0 +1,115 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "wCKYcsXeOY2F"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "class Parameters:\n",
+        "    def __init__(self, F):\n",
+        "        self.F = F\n",
+        "\n",
+        "# Define global parameters\n",
+        "Par = Parameters(F=1)\n",
+        "\n",
+        "def train_model1(t, X):\n",
+        "    # Extract state variables\n",
+        "    x = X[:10]\n",
+        "    xh = X[10:]\n",
+        "\n",
+        "    # Real System Matrices\n",
+        "    A = np.array([\n",
+        "        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 0, 1, -1],\n",
+        "        [0, -12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],\n",
+        "        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "        [0, 0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],\n",
+        "        [0, 0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    b1 = np.array([0, 0, 0, 0, 0, 0.005, 0, 0, 0, 0])\n",
+        "    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])\n",
+        "\n",
+        "    if t < 10:\n",
+        "        u = Par.F\n",
+        "        uh = 0.5 * u\n",
+        "    else:\n",
+        "        u = 0\n",
+        "        uh = u\n",
+        "\n",
+        "    # Real System Model\n",
+        "    xp = A @ x + b1 * u + b2\n",
+        "    C = np.array([\n",
+        "        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
+        "    ])\n",
+        "    y = C @ x\n",
+        "    dy = np.array([y[0] - 20, y[1]])\n",
+        "\n",
+        "    # Observer Matrices\n",
+        "    Ah = np.array([\n",
+        "        [0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 1, -1],\n",
+        "        [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],\n",
+        "        [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],\n",
+        "        [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    Bh = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0])\n",
+        "    Ch = np.array([\n",
+        "        [1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
+        "    ])\n",
+        "\n",
+        "    yh = Ch @ xh\n",
+        "    G = np.array([\n",
+        "        [10.5008, 0.0472],\n",
+        "        [4.0624, 0.0100],\n",
+        "        [1.2245, 0.0004],\n",
+        "        [0.3222, -0.0007],\n",
+        "        [118.1098, 1.1441],\n",
+        "        [60.1867, 0.5240],\n",
+        "        [16.7939, 0.3003],\n",
+        "        [-0.0227, 0.2370],\n",
+        "        [-4.2587, 0.2213]\n",
+        "    ])\n",
+        "\n",
+        "    xhp = Ah @ xh + Bh * uh + G @ (dy - yh)\n",
+        "\n",
+        "    # Augment the real and estimated states\n",
+        "    Xp = np.concatenate((xp, xhp))\n",
+        "\n",
+        "    return Xp"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/train_model_Obs/train_model_obs.py b/Chapter8/python/train_model_Obs/train_model_obs.py
new file mode 100644
index 0000000..75a7b41
--- /dev/null
+++ b/Chapter8/python/train_model_Obs/train_model_obs.py
@@ -0,0 +1,96 @@
+# -*- coding: utf-8 -*-
+"""train_model_Obs.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1_Xb_z0AgP-MAWO0OW8xhJBAT5DIj1z1N
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+class Parameters:
+    def __init__(self, F):
+        self.F = F
+
+# Define global parameters
+Par = Parameters(F=1)
+
+def train_model1(t, X):
+    # Extract state variables
+    x = X[:10]
+    xh = X[10:]
+
+    # Real System Matrices
+    A = np.array([
+        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 1, -1],
+        [0, -12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],
+        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+        [0, 0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],
+        [0, 0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]
+    ])
+
+    b1 = np.array([0, 0, 0, 0, 0, 0.005, 0, 0, 0, 0])
+    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])
+
+    if t < 10:
+        u = Par.F
+        uh = 0.5 * u
+    else:
+        u = 0
+        uh = u
+
+    # Real System Model
+    xp = A @ x + b1 * u + b2
+    C = np.array([
+        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
+    ])
+    y = C @ x
+    dy = np.array([y[0] - 20, y[1]])
+
+    # Observer Matrices
+    Ah = np.array([
+        [0, 0, 0, 0, 1, -1, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, -1, 0, 0],
+        [0, 0, 0, 0, 0, 0, 1, -1, 0],
+        [0, 0, 0, 0, 0, 0, 0, 1, -1],
+        [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],
+        [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],
+        [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]
+    ])
+
+    Bh = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0])
+    Ch = np.array([
+        [1, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0]
+    ])
+
+    yh = Ch @ xh
+    G = np.array([
+        [10.5008, 0.0472],
+        [4.0624, 0.0100],
+        [1.2245, 0.0004],
+        [0.3222, -0.0007],
+        [118.1098, 1.1441],
+        [60.1867, 0.5240],
+        [16.7939, 0.3003],
+        [-0.0227, 0.2370],
+        [-4.2587, 0.2213]
+    ])
+
+    xhp = Ah @ xh + Bh * uh + G @ (dy - yh)
+
+    # Augment the real and estimated states
+    Xp = np.concatenate((xp, xhp))
+
+    return Xp
\ No newline at end of file
diff --git a/Chapter8/python/train_obs_solver1/train_obs_solver1.ipynb b/Chapter8/python/train_obs_solver1/train_obs_solver1.ipynb
new file mode 100644
index 0000000..645b267
--- /dev/null
+++ b/Chapter8/python/train_obs_solver1/train_obs_solver1.ipynb
@@ -0,0 +1,168 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "qgldOGgvPovR",
+        "outputId": "beac5749-4ee5-45d2-f189-69f41e3e36d9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x800 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.integrate import solve_ivp\n",
+        "\n",
+        "class Parameters:\n",
+        "    def __init__(self, F):\n",
+        "        self.F = F\n",
+        "\n",
+        "# Define global parameters\n",
+        "Par = Parameters(F=1000)\n",
+        "\n",
+        "def train_model1(t, X):\n",
+        "    # Extract state variables\n",
+        "    x = X[:10]\n",
+        "    xh = X[10:]\n",
+        "\n",
+        "    # Real System Matrices\n",
+        "    A = np.array([\n",
+        "        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 0, 1, -1],\n",
+        "        [0, -12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],\n",
+        "        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "        [0, 0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],\n",
+        "        [0, 0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    b1 = np.array([0, 0, 0, 0, 0, 0.005, 0, 0, 0, 0])\n",
+        "    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])\n",
+        "\n",
+        "    if t < 10:\n",
+        "        u = Par.F\n",
+        "        uh = 0.5 * u\n",
+        "    else:\n",
+        "        u = 0\n",
+        "        uh = u\n",
+        "\n",
+        "    # Real System Model\n",
+        "    xp = A @ x + b1 * u + b2\n",
+        "    C = np.array([\n",
+        "        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
+        "    ])\n",
+        "    y = C @ x\n",
+        "    dy = np.array([y[0] - 20, y[1]])\n",
+        "\n",
+        "    # Observer Matrices\n",
+        "    Ah = np.array([\n",
+        "        [0, 0, 0, 0, 1, -1, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 1, -1, 0, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 1, -1, 0],\n",
+        "        [0, 0, 0, 0, 0, 0, 0, 1, -1],\n",
+        "        [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],\n",
+        "        [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],\n",
+        "        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],\n",
+        "        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],\n",
+        "        [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]\n",
+        "    ])\n",
+        "\n",
+        "    Bh = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0])\n",
+        "    Ch = np.array([\n",
+        "        [1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
+        "        [0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
+        "    ])\n",
+        "\n",
+        "    yh = Ch @ xh\n",
+        "    G = np.array([\n",
+        "        [10.5008, 0.0472],\n",
+        "        [4.0624, 0.0100],\n",
+        "        [1.2245, 0.0004],\n",
+        "        [0.3222, -0.0007],\n",
+        "        [118.1098, 1.1441],\n",
+        "        [60.1867, 0.5240],\n",
+        "        [16.7939, 0.3003],\n",
+        "        [-0.0227, 0.2370],\n",
+        "        [-4.2587, 0.2213]\n",
+        "    ])\n",
+        "\n",
+        "    xhp = Ah @ xh + Bh * uh + G @ (dy - yh)\n",
+        "\n",
+        "    # Augment the real and estimated states\n",
+        "    Xp = np.concatenate((xp, xhp))\n",
+        "\n",
+        "    return Xp\n",
+        "\n",
+        "# Initial conditions\n",
+        "x0 = np.array([0, 20, 20, 20, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
+        "\n",
+        "# Time span\n",
+        "tspan = [0, 20]\n",
+        "\n",
+        "# Solve ODE\n",
+        "sol = solve_ivp(train_model1, tspan, x0, method='RK23', t_eval=np.arange(0, 20, 0.1))\n",
+        "\n",
+        "t = sol.t\n",
+        "x = sol.y.T\n",
+        "xh = x[:, 10:19]\n",
+        "\n",
+        "# Plot results\n",
+        "plt.figure(figsize=(10, 8))\n",
+        "\n",
+        "plt.subplot(211)\n",
+        "plt.plot(t, x[:, 1] - 20, 'k', label='Real x2')\n",
+        "plt.plot(t, xh[:, 0], 'k-.', label='Est x2')\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.setp(plt.gca().lines, linewidth=2)\n",
+        "\n",
+        "plt.subplot(212)\n",
+        "plt.plot(t, x[:, 5], 'k', label='Real v1')\n",
+        "plt.plot(t, xh[:, 4], 'k-.', label='Est v1')\n",
+        "plt.grid()\n",
+        "plt.xlabel('Time (sec)')\n",
+        "plt.ylabel('State variables')\n",
+        "plt.legend()\n",
+        "plt.setp(plt.gca().lines, linewidth=2)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Chapter8/python/train_obs_solver1/train_obs_solver1.py b/Chapter8/python/train_obs_solver1/train_obs_solver1.py
new file mode 100644
index 0000000..52f4a89
--- /dev/null
+++ b/Chapter8/python/train_obs_solver1/train_obs_solver1.py
@@ -0,0 +1,133 @@
+# -*- coding: utf-8 -*-
+"""train_obs_solver1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-UqM89zJVKYpzUKJ_9ek6meluX9wWqTE
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import solve_ivp
+
+class Parameters:
+    def __init__(self, F):
+        self.F = F
+
+# Define global parameters
+Par = Parameters(F=1000)
+
+def train_model1(t, X):
+    # Extract state variables
+    x = X[:10]
+    xh = X[10:]
+
+    # Real System Matrices
+    A = np.array([
+        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, -1, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 1, -1, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 1, -1, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 1, -1],
+        [0, -12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],
+        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+        [0, 0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],
+        [0, 0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]
+    ])
+
+    b1 = np.array([0, 0, 0, 0, 0, 0.005, 0, 0, 0, 0])
+    b2 = np.array([0, 0, 0, 0, 0, 250, 0, 0, 0, -1250])
+
+    if t < 10:
+        u = Par.F
+        uh = 0.5 * u
+    else:
+        u = 0
+        uh = u
+
+    # Real System Model
+    xp = A @ x + b1 * u + b2
+    C = np.array([
+        [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
+    ])
+    y = C @ x
+    dy = np.array([y[0] - 20, y[1]])
+
+    # Observer Matrices
+    Ah = np.array([
+        [0, 0, 0, 0, 1, -1, 0, 0, 0],
+        [0, 0, 0, 0, 0, 1, -1, 0, 0],
+        [0, 0, 0, 0, 0, 0, 1, -1, 0],
+        [0, 0, 0, 0, 0, 0, 0, 1, -1],
+        [-12.5, 0, 0, 0, -0.75, 0.75, 0, 0, 0],
+        [62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0, 0],
+        [0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75, 0],
+        [0, 0, 62.5, -62.5, 0, 0, 3.75, -7.5, 3.75],
+        [0, 0, 0, 62.5, 0, 0, 0, 3.75, -3.75]
+    ])
+
+    Bh = np.array([0, 0, 0, 0, 0.005, 0, 0, 0, 0])
+    Ch = np.array([
+        [1, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0]
+    ])
+
+    yh = Ch @ xh
+    G = np.array([
+        [10.5008, 0.0472],
+        [4.0624, 0.0100],
+        [1.2245, 0.0004],
+        [0.3222, -0.0007],
+        [118.1098, 1.1441],
+        [60.1867, 0.5240],
+        [16.7939, 0.3003],
+        [-0.0227, 0.2370],
+        [-4.2587, 0.2213]
+    ])
+
+    xhp = Ah @ xh + Bh * uh + G @ (dy - yh)
+
+    # Augment the real and estimated states
+    Xp = np.concatenate((xp, xhp))
+
+    return Xp
+
+# Initial conditions
+x0 = np.array([0, 20, 20, 20, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+# Time span
+tspan = [0, 20]
+
+# Solve ODE
+sol = solve_ivp(train_model1, tspan, x0, method='RK23', t_eval=np.arange(0, 20, 0.1))
+
+t = sol.t
+x = sol.y.T
+xh = x[:, 10:19]
+
+# Plot results
+plt.figure(figsize=(10, 8))
+
+plt.subplot(211)
+plt.plot(t, x[:, 1] - 20, 'k', label='Real x2')
+plt.plot(t, xh[:, 0], 'k-.', label='Est x2')
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.setp(plt.gca().lines, linewidth=2)
+
+plt.subplot(212)
+plt.plot(t, x[:, 5], 'k', label='Real v1')
+plt.plot(t, xh[:, 4], 'k-.', label='Est v1')
+plt.grid()
+plt.xlabel('Time (sec)')
+plt.ylabel('State variables')
+plt.legend()
+plt.setp(plt.gca().lines, linewidth=2)
+
+plt.tight_layout()
+plt.show()
\ No newline at end of file