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    "# Calculating energy level spacing in strongly correlated systems using tensor-compressed QPE\n",
    "**Combine tensor-network compression and Fire Opal to run QPE-type algorithms**\n",
    "\n",
    "Calculating spectral properties such as energy gaps in quantum many-body systems is one of the most promising applications of quantum computing. These gaps correspond to the allowed excitations of the system, and many quantitative predictions of practical interest, such as relaxation rates and reaction rates, depend on their accurate determination. Quantum phase estimation (QPE) algorithms can in principle determine eigenvalues with polynomial cost, but the circuit depths required have limited experimental implementations to only a few qubits on current noisy devices.\n",
    "\n",
    "In this application note, based on [Kanno et al. (2026)](https://arxiv.org/abs/2601.15616), you will see how tensor-network circuit compression can be combined with [Fire Opal](https://docs.q-ctrl.com/fire-opal) to execute the core quantum phase-difference estimation (QPDE) signal-reconstruction workflow on hardware. The full end-to-end energy-gap extraction workflow, including longer time series and algorithmic error mitigation for larger systems, is discussed in the original paper.\n",
    "\n",
    "Specifically, this application note covers:\n",
    "\n",
    "* An overview of the Quantum Phase-Difference Estimation (QPDE) algorithm\n",
    "* Exemplary construction of the QPDE circuit for a 4-site one-dimensional Hubbard model\n",
    "* Extraction of the complex QPDE time-series signal\n",
    "* A hardware comparison showing improved retained signal amplitude using Fire Opal relative to a standard Qiskit execution without Fire Opal\n"
   ]
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   "source": [
    "## 1. Introduction"
   ]
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    "### 1.1 The energy gap problem\n",
    "\n",
    "Energy gaps, the differences between eigenvalues of a quantum Hamiltonian, encode critical physical information: excitation spectra, phase transitions, and transport properties. Classically, exact diagonalization (ED) or the full configuration interaction (FCI) method scales exponentially with system size, restricting fully exact treatments to small problem sizes. QPE-type algorithms offer a polynomial-cost alternative on a quantum computer, but their use on current devices is limited by hardware noise that accumulates over the required large gate depth.\n",
    "\n",
    "Time-series approaches to QPE extract the phase $\\Theta$ from a signal $\\{e^{i\\Theta t}\\}$ sampled at multiple times $t$, and then apply classical signal processing (Fourier transform, convolution, or direct data fitting) to recover $\\Theta$. These approaches reduce circuit depth compared to textbook QPE, but the resulting circuits remain too deep on current hardware to preserve the reconstructed signal without further compression and error suppression."
   ]
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    "### 1.2 Quantum phase-difference estimation\n",
    "\n",
    "The QPDE algorithm targets the energy gap $\\Delta = E_1 - E_0$ between the ground state and the first excited state of the system Hamiltonian directly, without requiring a controlled time-evolution operator, which is a significant simplification over standard QPE. The algorithm prepares a superposition of the ground state $|\\psi_g\\rangle$ and the first excited state $|\\psi_{\\text{ex}}\\rangle$, evolves it under the Hamiltonian, and reads out the resulting phase oscillation. Under time evolution, the two components acquire a relative phase that grows at rate $\\Delta$, and reversing the preparation maps that oscillation into the all-zeros measurement probability.\n",
    "\n",
    "To turn that oscillation into a complex signal suitable for frequency analysis, the system superposition is entangled with an ancilla qubit in its computational basis. A single-qubit phase gate on the ancilla provides a controllable phase offset $\\theta$, and four measurements at $\\theta = 0,\\,\\pi/2,\\,\\pi,\\,3\\pi/2$ reconstruct the full complex time-series signal $s_t$.\n",
    "\n",
    "The circuit then has four steps:\n",
    "\n",
    "1. $U_{\\text{prep}}$ entangles the ancilla in its computational basis with the ground and first excited state of the modeled system:\n",
    "$$U_{\\text{prep}} |0\\rangle^{\\otimes N+1} = \\frac{1}{\\sqrt{2}} ( |0\\rangle |\\psi_g\\rangle + |1\\rangle |\\psi_{\\text{ex}}\\rangle ).$$\n",
    "2. Time evolution $U_{\\text{evol}}^{t/\\Delta t}$ acts on the system qubits.\n",
    "3. A phase gate $P(\\theta)$ on the ancilla introduces the offset $\\theta$.\n",
    "4. $U^\\dagger_{\\text{prep}}$ undoes the preparation; an all-zeros measurement returns the probability $m(\\theta)$.\n",
    "\n",
    "Combining the four measurements yields the signal\n",
    "\n",
    "$$s_t =  m(0) - m(\\pi) - i\\left[m\\!\\left(\\tfrac{\\pi}{2}\\right) - m\\!\\left(\\tfrac{3\\pi}{2}\\right)\\right] \\approx e^{-i\\Delta t},$$\n",
    "\n",
    "so for perfectly prepared initial states $s_t$ oscillates purely at the target gap $\\Delta$. The gap is then extracted by applying classical frequency estimation to $s_t$ over a long enough time window.\n",
    "\n",
    "The implementation introduces two approximations. First, the exact ground and first excited states are not available, so they are approximated by matrix product states (MPS), obtained from the density matrix renormalization group (DMRG). An MPS is a tensor-network representation of a wavefunction as a chain of local tensors, suited to one-dimensional systems with mostly local entanglement. DMRG is a classical variational method that finds accurate MPS approximations of low-energy wavefunctions for such systems. Second, exact time evolution under the system Hamiltonian produces a deep circuit, so $U_{\\text{evol}}$ is itself approximated by a shallow tensor-network-compressed brick-wall circuit."
   ]
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   "source": [
    "### 1.3 The computation pipeline\n",
    "\n",
    "The figure below visualizes the QPDE algorithm. Ground and excited states are obtained via DMRG, with the resulting MPS used directly as the reference for the state-preparation compression. The Trotter-decomposed time-evolution operator and the state-preparation unitary are each compressed into brick-wall circuits of nearest-neighbor two-qubit gates through MPS/MPO optimization. For each time step $t$, four circuits (one per phase-gate angle $\\theta$) are submitted to IBM hardware through Fire Opal. In the full QPDE workflow presented in the paper, the resulting time-series signal is processed classically to estimate the energy gap. In this application note, you focus on the hardware signal-reconstruction stage and compare the retained amplitude of the reconstructed signal against a noiseless reference."
   ]
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L0vfd7Nbxe36B4DMAAAAAoG4WFhb2DOy63W5NTExoZ2dH8/Pz8vl8dpVDmZubUzKZzKYLBbGTyWQ2IA40KoLOAIC6yR1S+/z580YZALS6H30bwedcmeDz6+/v1/ffcbddDAAAAABAVYyNjamvr88I/GYMDAxoZ2dHw8PDhxreei+pVEpTU1NG3tDQkFZWVgr+vbm5OfX19dnZQEMg6AwAKEoikdDMzIwGBwfV2dmpzs5OjYyMaGZmRolEwq5elDNnzmT/H4lEFI1GjXKgGtLpdEMuOLoywedPP/Zt/dRPX7WLj5zXveFNOt7+s/qBf/JrBJ8BAAAAAFU1Njam8fFxO1tut1srKyuanp4u2PO4UiKRiNG26jiOwuGwAoGAtre3jU47GQsLCy0XeM60Pff392fbnsfGxjQ3N1d22zNqj6AzAGBfyWRSfX19OnHihAYHBzUzM6P19XWtr69rcnJSg4ODOnHihHp7e0u+AfB6vcZwNI888ohRDgBHyW0nvquBD18h+Hzdsde7jODzsdd9n10FAAAAAICy7RVwDoVC2t7eViAQsIsqzu7lHA6Hsz2cXS6X5ufntby8nBf4bpXAcyKRUHd3d7bteW5uLtv2PD4+rv7+/rLbnlF7BJ0BAHva3NzUyZMntbCwIF2/0RkeHtbo6KiGh4fl9XqzdZeWlvac92Q/p0+fzv6fJ9cAgOCzLRN8fv0/+d91w4/eS/AZAAAAAHBoewWc5+fnNT8/L5fLZRdVnD3yo+M4GhoaMupIUjAYbMnA8/r6utrb2xWJRKQi2p7b29u1vr6eswY0GoLOAICCYrGYurq6skHgjo4ObW9va2JiQmNjY5qYmND29rYGBgayr0kmk+ru7i5pmOzcIWJSqVQ2wA0AR10m+PxHn7+kB0+/rBtvPNrDsB87fpNuvOufEHwGAAAAABzKyMjIngHnQsNZV4vdyzkQCBScx1nXy/YKPDdje2o0GjXanv1+v3Z2doy2562tLYXD4exrMr2iY7FYzprQSAg6AwDypFIpdXV1KZlMStfnMFleXi540zM9Pa1gMJhNp1IpdXd3K5VKGfX24na75ff7s+lz584V/VoAOApuedP39CsDL+nTj32b4LMVfL7xzvfpSuq7dhUAAAAAAApaXV3V5OSknV3zgHM0GtXm5qaRNzo6aqRtewWeBwcHm2r0yEQioc7OzmwbcKbt2e5d7jiOZmdnjbbjZDKp3t5eox4aB0FnAECeubk5xePxbHpoaKhgwDljenrauNmJx+Oam5sz6uznzJkz2f8nEgktLS0Z5QCA/ODzTW/4nl3lSDl2/CbdcOf71P6pv9Yn/nuC4DMAAAAAYF+JRKLgcNS1DjhLyutpHQwG5fP5jLxCAoGAFhcXjbxkMqnBwUEjr5FNTU1lOzvpetuzHXDONT09baSj0Wh2SG40FoLOAABDMpk0hnZxHOfAmy6Px6NAIGDk2cPD7CcYDBpB63PnzhnlAIDXZILPn1l+Ub/4y/9w5IPPV1Lf1dSTF9X+qb/Wx574pl73hjfZVQAAAAAAUH9/f16P4NnZ2QPbPiutUND07NmzRno/wWBQPT09Rt7S0pJWV1eNvEaUSCSMzkqO4xhDaBfi8/mM3s4qELRHYyDoDAAwRCIR4+aro6Nj3yfNMu677z4jnUgktL6+buTtxeVyGUN0R6PRprhJAoB6uukN39Mv/ot/IPh83ZXUd3Xui9/Szb/x5/qBrt8k+AwAAAAAyFpYWMgL9Pb09BwY8KyGRx55xEgXCqoeZHp6Om9kyr6+voaftnBhYcF4j4FAIG+48ELstudoNMrczg2IoDMAwPDoo48a6QceeMBI76XQjVGxQWdJOn36tJE+f/68kQYAFJYbfD7T/5JuedPRDj4f+/4b5Zz6ZwSfAQAAAADS9akA+/v7jTy32503bHMt2D19VcRczoW43W5NTEwYeYlEQiMjI0Zeo3n88ceN9GHanum01HgIOgMAslKpVF6g2Ov1Gum9+Hy+vKfSLly4YKT3EwgEjKfzIpGIotGoUQcAsLeb3vA9/cI/f1mffuzb+pUBgs+Z4LPrQ/8v3dQ9pte5ftiuAgAAAAA4AgYHB/N6AM/Pz+f1FK6Fqakp4734fD5jBMhShEKhvCkPl5aW8ra1URQaGdPj8RjpvXR0dNhZeQFs1B9BZwBAVjQazbspKfaLXwXqxuNxI70fx3HybrDsoWYAAAe78ca0HjxN8DnX8fafk+s3Pk/wGQAAAACOmEQikTesdjgczgvW1kIqlcrr5fzwww8b6VIV6u3cqD2ANzc37Sz5fD47qyDHcfIeEmB47cZD0BkAkFWoZ3Ex8zln2L2ik8mkksmkkbefM2fOGOm5uTljfmkAQPFyg88DH76i2058165y5OQGn7//xJ12MQAAAACgxUxNTRlpx3HKGs66Eubm5owOP263W6FQyKhTqkLzQTfqtIWFgsR2IHk/dttzKR2eUBsEnQEAWc8884ydldd7eT+F6pby5d/R0WGsI5VKaWFhwagDACjNjTem9VM/fZXgc47j7T+nH+xf1Bt6f4fgMwAAAAC0qELzJ4fD4ZICnZWSSqXyAuBDQ0N50xWWw+7Is7q62pAdeQq1PZfisG3PqL5j6XQ6bWcWw/2O9xvpxFcfM9IA0ErGxsY0Pj6u0dFRjY2N2cUNJx6Pq62tLZsOhUKan5836hTS2dmZN69GKV8Tmf2Ua21tLe9pu/3Y63C73drZ2TnUDdixY8fsLBShlGNfS4c9nq22XZ/fuGhnNbyfOXXCzipLqx3L2gpJGpX02ndFqyjnvHhle02pLyzo/3r+q3YRgAbxpo9v2VkV952PtNtZqKNqHnOOdWOp1LEu5x6gGRS6t+QcbhyVOn8La81zGnvJ/6xjf6Ojo3ntkPZ3wczMjAYHB7Npx3G0s7NTl6Cz/V5cLpcuXrx4qDbPjFQqpVtuucXoRW23Y9vfJ/a+qoX29nZjpE23262LF4tv17LbjVVG23OrapSYLT2dAQBZ9nzOpQytrT3ql/pUXU9Pj5FOJBJaWloy8gAAh7XQkgHnct14slM/+CuP6I19f6gb2n7cLgYAAAAANJlCPYuDwWBdAs6S9MgjjxjpUChUkYCzrgfT7WG6H330USPdCOxpGCu1/WgcBJ0BAFl2gLhQEHk/herbgeyDeL1edXR0GHnnzp0z0gAAVMMNbT+uN/b9IcFnAAAAAGhyhYaYHhoaMtK1EolEjB6+juNU/L3YQ2zHYrGS22VrrRJBZzuQjfoi6AwA2FMlvvjLcfr0aSMdjUa1urpq5AEAUC2Z4PMP/sojuvFkp10MAAAAAGhwTz75pJH2+/3y+XxGXq3YQ0JXY17pQtsWi8XsrLqyHwIodR8Uqk/QubEwpzMAFOGozOl84sQJ48vf4/FoZ2fHqLOfhYUF9fX1GXnz8/N5w7scJJFI6MQJc87XYDCo5eVlI6/VZeZaqdWcvfY8u2XeIlSdPQfNQfunVbfrKOJYVlZmf1Z6P2a2P/X0n9hFVeHc8/NGutLbI0n/18XnlFr/Q72yvWYXAaiRzJyZ1fiMZ65bzJHaWKpxzDnWjakax7pVcQ43nmqcv6/9nqjcOtEMdo97Jc+lo8D+/Z27/zo7O7W+vp5N16tdd319XZ2d5sPM2/9/9v4/us3rMPd8H1AUBdlSDFVOC3rqZbL3pCLnRDXY2GPSZ1ITZ41rah07AZ14TOVMrsgzbUTmOJGYdCoxMwnB5KyQ6jSh1ORUVHrvITVZE9K3ickk7iXT3LUIN7chM3YquG5HVNw7RMYdC2mtYziSbUiixPsHiVfYGwAJUAD5gvx+1nqXuPe7AeH9QQB8n3fvff686urqjLpiqK+vN4Lm0dFRZyrDlfbVerFfQ3Nzs6an8/87u1jXnjcjt2S29HQGADg2qmezze/3q6WlxahLH4IGAID1VFm9T7sOfkXv6RrVjv2P2asBAAAAAC6STCY1Oztr1DU3Nxvl9WJPGxgKhUoSOGt52sJ0L7/8slF2m2IM/52t9zM2DqEzAKCkss3znA87ZHZLIA4A2Loqq/fpzqf65fvM89rR8CF7NQAAAADABWZnZzMCzWzDT5daNBrVxMSEUdfb22uUi8kOnd02vHZNTY1RtofbXguuGbsLoTMAwGHfGWZ/OVuLtYTOkUgk40tHKBQyygAAbJQK3z26szVM+AwAAAAALpQ+rLaWA+e1XKO8XfZczi0tLSUNv/ft22eUY7GYUQZKjdAZAOCw7wyzg9+1sIPsfJw9e9Yoe71eHTlyxKgDAGCjOeHzH/5Q3qZ/K09lld0EAAAAALDOfvKTnxjljRhaOx6Pa2pqyqg7fPiwUS42uydxIpEwyhvNDv4LfX3ZrlXbvbuxsQidAQAO+4uJcnyY55Lt7rlsz7mSZDKZMexMW1vbmsJrAADWQ8WuvbrjwGd112f+gvAZAAAAADaYPXrj/fffb5TXw4kTJ4zXEQgEtvxIjvZ14kJD57feessoe71erhm7DKEzAMBx33332VUFffjbH/w1NTUZvadXMzExkfF/0ssZAFAO0sPnnR/skMe7224CAAAAACixQjrRlEI8HtfQ0JBRV8q5nFM2ertXY4fOytGJKRd7++jl7D6EzgAAR7YP/rm5ObsqJ/tLwlo++J999lmjXOq5TgAAKLaKXXu189FPyfeZ57UzeJjweR1U1Qe1u+MbKy7b7s78npNy5xOfy2ifvlTVB+2HAAAAAHApu6fzehsaGjJeg9/vX5dezhu93avJ1uHJvp68EkJn9yN0BgA4soW70WjUrsrJbvvYY48Z5dUkEol1n+sEAIBS8Xh3a2fwsHyfeV5/9FdxvZW8YTdBkdy88oZuvhFT5b2/pe21DxjLtvfWaiH2U91MvG4/zLEQ/5kkZTy28t7f0o34z3R9/iX7IQAAAADKxHoOwZxMJnXmzBmj7iMf+YgikUjRl6GhIYXDYWf57ne/a/y/9hzKG+12rz3Pzs4a5UKvPaP0PIuLi4t2ZT78+580yvFXnjPKALCZhMNh9fX1qbe3V+Fw2F7tOrFYTLW1tU65vb1dw8PDRptcamtrjTvMWlpaNDk5abTJJpFIaM+ePUbd/Px81t7TuQwNDamrq8spBwIBnTt3zmizlXg8HknS8zMX7VUl8XhTtVFe41eEkkvtl5TV9s9m3a6tiGNZXKn9Wez9mNr+5IvfsVeVhPfBjxjlYm9PsXg8Hu2orFDHB/bqyMO/pl/dtd1ugiLY9VS/qvabFx7e+tODuhG/YNRl4/Hulu8Pf2jMyX3lz3t07ZUfGO2wMfZ+aek7YSl+x1PvW5c+32CvwgYqxTHnWLtTKY71ZsU57D6lOH9v/T1RvOdEOVg67sU8l7YC++/v1P6rrq42esUGAgEjgJ2bm8voNbsZNTc3a3p6WlphX623PXv2GFMrtrW1aXR01GiTjX29W5IuXry4rjcUuJlbMlt6OgMADM3NzUY5EonkNTSL3UM5EAgUFDgry9Dahw4dMsoAAJSzqws3NfSTf1bga/+7qt7/qDw77rSb4DbdeMMcmm3htVfyCpwlaTF5WYtX3nDKN69c0vXzSxdoAAAAAJQPO1CORqNGD2F7PdaPfe3Zvqaci92usbGRwNmFCJ0BAIYjR44Y5WQymfGhno09fMuxY8eM8mpisZgikYhT9vv96uzsNNoAALAZXF24qe2/8ZB2Pvpp7Wh4Qp6d7hryrJzZ82df/4cfG+VVVe5wfrz+6l9rceGasRoAAAAAsHZ2J6NEImFcE87ldq89Y30QOgMADIFAQKFQyKjr6+szyra5uTlNTEw4Zb/fn/EcqxkZGTHK7e3t8nq9Rh0AAJuJp2KbKu+9X3c8+gzhc5Fsu9scZeXa+dUvXqRU7Nqril17nfJC7G+M9QAAAACwHmpqatTc3HzbS7Y5lDdaKBTKeF2rXXuORqNGp6hs16/hDszpDAB52EpzOmv5g7ypqckYVntwcFBHjx412mm5J/SBAwecO9K8Xq8mJyfVbA2Vspr6+nrNzc1Jy88xPz+/5YdI2eg5ncvFavvH3q41fvUpOXtundW2ayuyj2W5cOuxZE7n9WX/jufaPwuvvazrr/5YN69cslchD3s+94LT23kxeVlvfvkRu0lOVfVB7Tr4Faf81p88mTFcNzZOKebMTGGOVHcqxTHnWLtTKY71ZsU57D6lOH+Z03mrYk7ntbD/zkrtv3x6zt4Oe47ocpBrX22ESCSiAwcOGNeeT58+nXXUy2zXnqenp9XY2Gg33dLcktnS0xkAkCEQCGSE1N3d3Tp58qTxZSAej6ujo8P4Itff319w4Dw7O+sEzpLU1ta25QNnAMDWVHnv/dr5r7u048GntO0uPgsLUeG7xxhee+FifnM5p2zz/6bz82LyMoEzAAAAUKbsHr/FXsotcHab5uZmDQ4OGnWpa8/pYrGYEThL0vDwMIGzixE6AwCyamtr0+joqPElqru7W9XV1QoGg2pqalJ1dbXGxsYkST6fT6Ojo1l7Q6/m2WefNcr2vNIAAGw1ldX75H3k9wifC7C99gGjvBD7qVFeTeWv73d+XnjtFWMdAAAAAKB4Ojs7NTw87EyvmEwm1d3drT179jjXnmtra53A2efzaXJyUm1tbdYzwU0InQEAObW1tencuXPq7Ox0eh4nEglFIhHNzs5Ky/M3Hz9+XOfPn1/zh34quJaklpaWjHk9AADYqlLhs/fhj2vb3ffZq5EmvaeyJN2I/8wor6by3rTQ+R8JnQEAAACglNrb251rz6mOTytde25pabGeAW7DnM4AkIetNqdzLtnmQ2kucCht29TUlA4cOOCUx8fHFQqFjDZb1XrP6bxZ2fMAr/GrT8nZc+tw3FFqzOm8vuzf8bXunxtv/FzXf/ZXuvHGz+1VW95dnxzVNv8+p/zmlx/RYvKy0SaXbXfX6K5P3/qb9vI3n9H1V39stMHGKsWcmSnMkepOpTjmHGt3KsWx3qw4h92nFOcvczpvVczpvBb231nsv9zKYV/Z1569Xi9DaefJLZktPZ0BAHmz5y+53cBZ1tDagUCAwBkAgBVsu/s+eR/+uLwPf1yV1bcC1q3O491tBM433ojlHThLUuW9v2WUGV4bAAAAANaXfd2ZwLn8EDoDADZMMpk0htY+dOiQsR4AAGS37e77tOPBp+R95PcIn5eHIU+38NrfGuXVVP76+52fCw2sAQAAAAAAoTMAYANNTEwomUxKy/NzdHZ22k0AAMAKtt3lJ3yWVFnzAaO88I9/Z5RXY8znXGBgDQAAAAAACJ0BABvo7Nmzzs/t7e3yer3GegAAkJ9U+HzHo8+o8t777dWb3jb/bxrlQobH9lRWGUNzFxpYAwAAAAAAQmcAwAaJx+OampqSJHm9Xh05csRuAgAACuTZ6dOOhie2XPi8vfYB5+fF5GXdiF8w1q+E+ZyXeLy7tb32AaPXNwAAAFBOPB4PS44FWA+EzgCADRGNRp2f29ra5Pf7jfUAAGDt0sPn7b/xkDwV2+wmm8Y2/z55vLud8sLF/ANnWb2kFxeuFRRYl7sdDR/Sr3zxb/QrX/wb7fncC9rd8Q3teqrfbgYAAAAAwKoInQFgE/L5fOrt7XWWD3/4w3aTDdfS0qI333xTp0+f1rFjx+zVAACgCDw7fap6/6Pa+einN234vO29NUZ5IfZTo7ya9Pmgt9p8ztdemdKV0c9uqaAdAAAAAFAahM4AsAn5fD6Fw2FnCYVCdhNX8Pl86uzsVF1dnb0KAAAUkWfHnWb4vN1rNylblb9uDgd9I/4zo7ya9OG1b/zj1hpae3Hhmq6dn9a709+wVwEAAAAAUBBCZwAAAADYIpzw+b95RlX7fmdThM/pw2OrwN7KFb57VLFrr1Ne+Me/M9ZvFTfefN2uAgAAAFxvcXGRZY0LUAqEzgAAAACwxXi2e7V93+9sivB523trnZ9vXrmkm1cuGetXsr32AefnxYVruv7qXxvrt4rF5GW7CgAAAHA9j8fDssYFKAVCZwAAAADYoozw+f2PyrPjTruJ66X3VL7xz/PGutVU1vy28/PCa3+rxYVrxnoAAAAAAJAfQmcAAAAA2OI8273a/hsPaeejny6r8Dk9cFaBPXY9lVWqqg865etzEWN9Pip892h77QPaXvuAPN7d9up1t+3uGuf12PsGAAAAAIBSInQGAAAAAEiSPBXbboXPv3VAnp0+u4mrZITMBfRU3v6+f+UExYsL13TtlR/YTbLyeHfrzic+pz1fmJXvM89rd8c3tLvjG9rzuRfk+8Mfavv7HrYfogrfPfqVL/5NzqXCd09ebXc0fMh4XqVeT2tYv/LFv9Fdn37OeT2+P/yh9nxhVne2hl0RiAMAAAAANjdCZwAAAACAwVOxTdtrPqA7Hn1GOxqecG34vLhwTTfeiDnlbe+tMdavZEfjQefnqy9+O6+5oCvv3a+7PjmqHQ9+VIvJy3r7+1/W5eFP6MroZ3UjfkEVu/Zq98e/rjsO/IHxuJuJ15X46uNaeO0Vo/76qz9W4quP62bidaPtW3/ypK7Pv+TULSYv6/I3n9HVc99z6rQ8J/Vdn35OOxo+pMXkZb3zw6/p8vAnnLaeyirtaPiQ7vr0c8b81QAAAMBmtLi4yJJjAdYDoTMAAAAAIKfKe+93dficPo9zhe+evIaV3tHwISeEvXnlkpI/GrabZKjYtVe7Dn5VFb57dPPKJf3yT9t09cVv6/r8S7p2flq//E+fcIJrb9PHMno830y8rit/3mPUXfu7vzQC55Qbb8R0dXbUKb/9/S/r+qs/Ntp4vLt151P9qti1V4vJy/rlf/qEkj8a1vX5l3T91R/r7fGw3p0+Iy2/9lRbAAAAAABKgdAZAAAAALAqJ3x+8Cltu8tvr94w6eGsx7tbOx/9lLHeVrX/Md3xxOek5Z7SV0Y/k1cvZ+8HO5zQ9p3JP854zGLysjFE944HPmKs13LwfO38tFOu/PX3G+vTVdy91Gv7RvxC1qG/dwYPO6/n6ovf1o34BbuJ3p0+4/QEr9i1V94PdthNAAAAAAAoCkJnAAAAAEDeKqv3yfvI77kmfL4+/5LRU3lHw4e0++NfN4aT9nh3q6o+qN0f/7p2PdUvT2WVFpOXdWX0MxlDXmdT4btH3qaPSavM/7wQ+6nzc67hrK++9B3n5+31QWNduh0NT0iS3v3RiL1qKUBefj2SlHzx1nParr74befnHQ9+VJ7KKmM9AAAAAADFQOgMAAAAACiYET7ffZ+9el29O31G706f0eLCNUnS9vc9rN0d39CvfPFv9Ctf/Bvt+dwL2nXwK86Q19df/fHSvMnWkNW5VFbvc36++cat4bxt6UN9e7y7sw5nff3VHzu9pCt27c0YhluSquqD2nZ3jW5euaTraT2jU7alvZ7F5OWsQ3SnXJ+/FYR7Kqvk2XW3sR4AAAAAgGIgdAYAAAAArFll9T55H/64vA9/fMPC58WFa3p3+oze+uq/0bvTZ3Tt/LSuz7+kxYVruvFG7NY8x9//shJffVyXv/lMxvDYK0kNdS1J2/z7nDDbXu769HPG43IFvNfOfc/5eUdgqUdzutTQ3MkfDTtBerptaa9nte1YvPKGUd625x6jDAAAAABAMRA6AwAAAABu27a777sVPv/q/81evS5uXrmkd6fP6MroZ3V5+BN684uNeutPntTl4U/o8jef0dUXv71ir+Bc0sP0m1cuLYXYeSy5XD33fefn7fVBY8jrbf592v6+h3XzyiVjaOx02/y/6fx887IZKtvsULrCR+gMAAAAACg+QmcAAAAAQNFsu/s+eRsPyvvI7xnDUpe1tN7GC/MvLYXYeSw34heMp0m58UbMmUvaU1mlqv0tzjpv00FJUnLmW1l7OUvm66nYnb03dS5rCd0BAAAArL+xsTGNjY3Z1YBrEToDAAAAAIpu211+7XjwqU0RPt9IXHR+9mSZp3ktrr70Hefnqvf/rrQ8x3PV/hYtLlwzhuC2Ga/Hu9tYZ7PnlU6fdxoAAACAe/X19engwYOqra3V0NCQksmk3QRwFUJnAAAAAEDJpMLnnf+6S5X33m+vLgvpvYMrq/cZw2Gv1bVXppyezJW1D6hi1155P9ghT2XV0jDgK8zVfPONmPNzxa69KwbPFWlzOC8uXFvxeQEAAAC4w8TEhObm5iRJsVhM3d3dSiQSdjPAVQidAQAAAAAlV7Frr3Y0PKE7Hn2m7MLnGxdvDZPt8e7W9vf9K2O9bXvtA6vOnby4cE3XXpmSnCG2H1PV/se0uHBNyR8N280NCxcvGENvV+1/zFifrqqu2fn5+vlpY102KwXYAAAAANZHX1+fUe7s7JTf7zfqALchdAYAAAAArBvPTp8RPnsqttlNXOfGGzElZ77llO848Fltu7vGaJOy48GPatfHv67dH/uKvSrDtejzzs87H/20Knbt1bVz31u1N/LNxOu6OvO/OmXvgx/J2vva492tqoYPScsh9zuTf2w3yUDoDAAAAGysqakpRaNRo+7IkSNGGXAjQmcAAAAAwLpLhc87H/20tv/GQ64Pn5M/GtZi8rIkqcJ3j97zibO649FPydv0MVXVB7UzeFh3ffo53fnE57SYvKwrz4Xtp8hwff4lZ+juVGicfPHWXM8reXf6jBNOb/Pv066DXzWC54pde/Wef/cNZ07ndyb/OGeYXXnvfqO8Wi9tAAAAAKVz5swZoxwKhVRTk/2mV8BNPIuLi4t2ZT78+580yvFXnjPKALCZhMNh9fX1qbe3V+Hw6hcQgWLweDySpOdnLtqrUIDHm6qN8hq/+pRc6nincNxRaqnfjWL/TqTO5XyDs9vlffAjRrnY21Ms9u/4eu2fcrJ49W1df/XHWoi9pMWbN+zVrlCxa6/ubO3T9vc9bK9yXH/1x3p7vDdnwGvbGTysncHDkqRr56d1ZfSzdpOcPN7duuPAZ7VjuTezJC289oo8O3c7PbFvJl7X29//sq6/+uO0Ry5JPb5qf4sRWN+8cknJHw0bvbs32t4vnZNK9Due+v289PkGexU2UCmOOcfanUpxrDcrzmH3KcX5e+t7Y/GeE+Vg6bgX81zaCuy/szZy/0WjUUWjUcViMXuVoa6uToFAQHV1dfYqafl5GhrM9/lz584pEAgYdYVy075C8bklsyV0BoA8EDpjIxA6FwehM5AdofP6sn/H12v/lKPF60kt/B//m67/H/+bFq8n7dWusP19D6vy1/dr29018iz3JL75RkxXo9/Xwmuv2M1XVOG7R3e2Ln2/fGfyK7oRvzV/dL4yXs/CNS384yu6mbioa69MGfM/p/N4d8vb9DG7WpJ0M3FRV899z67eMKW4qJ9CiONOpTjmHGt3KsWx3qw4h92nFOcvofNWRei8FvbfWeu1/+LxuGZnZxWNRvXCCy9odnZWyWThf7s0NzcrEAjo/vvvV1tbm7xer1pbWzUxMeG0CYVCGh8fNx63Fhu1r7A+3JLZEjoDQB4InbERCJ2Lg9AZyI7QeX3Zv+PrtX/KWTmEz1g/pbion0KI406lOOYca3cqxbHerDiH3acU5y+h81ZF6LwW9t9Zpd5/s7OzOnPmjEZGRuxVt83v9+upp57S1772NaN+fHxcoVDIqFuL9d5XWF9uyWwJnQEgD4TO2AiEzsVB6AxkR+i8vuzf8dX2j71d5WK17VqLxetJXY/9jRb+j59o8erb9mpsEaW4qJ9CiONOpTjmHGt3KsWx3qw4h92nFOcvofNWRei8FvbfWaXYf8lkUmNjYzp16pSi0ai9uqQCgYDOnVt6n7ld67GvsHHcktlW2BUAgPIXj8cVDAadZWBgwG6y4eLxuIaGhhQMBo0hYwAAANJ5tntV9b6HtfPRT6vq/Y/Ks+NOuwkAAAAAFFUymVQ4HFZtba06OjryCpxramrU1tam3t7erEtLS4v8fr/9sJzeeecdzc3N2dWAaxE6A8AmlEwmFYlEnOXChcLnBiy1aDSqrq4uRSIRnThxwl4NAABg8FRs0/bfeOhW+LzTZzcBAAAAgNsWjUbV0NCgvr4+xeNxe7Ukyev1qqWlRb29vZqcnNSbb76p+fl5jY6OKhwOZ10mJyd18eJFXbx4UZOTk+rt7VVzc7P91I6f/exnamhocGWHIiAbhtcGgDyU2/DasVhMtbW1Trm9vV3Dw8NGm42WTCZVW1vrfHE7d+6cAoGA3WxLW+/hte1hqMvFavvH3q41fvUpOXuYo9W2ayuyj2W5cOuxZHjt9WX/jq+2f+ztKherbVexLbz2sq7N/UiL7ybsVdhkSjF8aQrD1bpTKY45x9qdSnGsNyvOYfcpxfm72YbXDgSkwUGzLhg0yxDDa6+R/XdWMfbfwMCA+vr6lEwm7VXScm/mI0eOqL29XT5fcW6EvXDhgn77t39b77zzjr3K0djYqPHx8YJ6Sqcrxb6Ce7gls6WnMwBgQ3i9XoVCIafc19dnrAcAALcsLi66ctlolfferzsefUY7Gp6g5zMAAIALJZNSc/OtpbHRbgG4w9zcnJqamtTT05M1cG5padH4+Ljm5+d19OjRogXOkjQ5OWkEztu2bTPWS9Ls7Kzq6+s1NjZmrwJcg9AZAJC32dlZDQwMOEPCnDx5UrOzs3azvB06dMj5eWpqKudwNQAAACtJhc9VH2jVtrvWduc/AAAAii8WM8tc+oEbTU1NqaGhIet1zsbGRp07d06Tk5NGB5piSSaTGVMPDgwM6Pjx40adJCUSCR08eFAjIyP2qk0hEokY156HhoayHhO4F6EzAGBFyWRSPT09qq6udu726+vrU19fn7q7u9XU1KTa2lqFw+GsdwGupLGxUTU1NVKOL1gAAACF2P5f/Et5H/k97XjwKcJnAAAAF0gmpUTaTCh2CA1stKmpKbW2tma9rnn8+HFNT0+XdErAsbExoyOO1+vVf/ff/Xfq7+/XzMyM6urqjPaS1NHRsWmC52Qyqe7ublVXVysYDBrXnru6upxrzydPnrQfChcidAYA5BSLxdTU1KSBgQHny09zc7N6e3vV1tbmBMaxWEx9fX1qaGhQNBq1nmVl6b2dR0ZGsn7Bw8awh1B1ywKk2OeGWxYAG6+yep8TPlf8yn9hrwYAAMA6Sg+aCZ3hJrkC57q6Os3MzKi/v19er9dYV2ynTp0yyp2dnc68zale1kePHjXaaJMEz9FoVA0NDTp58qTi8bi8Xq9aWlrU29urUCjk7IdYLOZ0forxJuJqhM4AgKzi8biCwaATItfU1Oj8+fOanp5WOBzW6Oiozp8/r7a2Nucxc3Nzam1tLWiY7PTHJxIJDQ0NGesBAADWqrJ6n3b+1x3yPvxxbbv7Pns1AAAA1kF6RvTzn6evATZOrsC5vb1d586dU+M6TEA+MTFhdODxer06duyY0cbr9WpwcFC9vb1Gvco8eI7FYjpw4IDm5uak5aA/NYx5OBzW+Pi4zp8/bwxpPjs7q2AwqET68AlwFUJnAECGZDKpAwcOOHeOeb1eTU5OZgzn4vV6NTo6anwJi8Viam1tNdqtpK6uzni8fXcfAADA7dp2933yPvxxwmcAAIANQE9nuM3ExETOwHl4eLjkvZtT+vr6jHJbW5vTu9cWDodzBs/lNu9x6tpzquOSz+fLeu3Z5/NpfHzcGN680GvPWF+EzgCADENDQ8Zddp2dnRkf+ulOnz5tlGdnZzUxMWHUreTpp592fo7FYgU9FgAAIF9O+PzI76myep+9GgAAACXwi1/c+rmAwfGAkpibm9PBgwdzBs7rJRqNZkxTeOTIEaNsWyl4trfHzYaGhpwezpLU29vrTOOYjX3tORKJcP3YpQidAQCGZDKpEydOGHX2sC62QCCg5uZmo86+U28l6UNsS8r4/wEAAIpp211+7XjwKcJnAACAImlvl6anpeFhKRyWmpulVIaUradzXZ3U0rLUdnR06bFpnRmBkkgmk1kD2vUOnJXl2mkoFDJ69OYSDod1/Phxo25ubk4DAwNGnVvZ1569Xq86OzuNNrbGxsaMfcP1Y3cidAYAGCYmJow5mRsbG3MO65LuscceM8rZ7tbLxe/3q6WlxSnPzs7m/VgAAIC1MsLne++3VwMAACBPNTVLQXN7u9TbuxQiz89Li4tSel+G0dGluvPnpcnJpbZtbUuP9fnSnxEovoGBgYyhqDcicI5Goxk9dbP1YM6lv78/awib3nvYrcbGxjKuPecznHn6tWMtXz9OTQ0J9yB0BgAYfvCDHxhluwdzLvYHvyRNTU3ZVTmlD7GtLHf7AQAAlMq2u/za0fCE7nj0GcJnAACAIkvPxvLoyAmUxOzsbMb1xkAgkDF083o4deqUUQ4EAhkh8mrsuadTvbjd7tlnnzXKH/7wh41yLnaHJy0Psw13IXQGADiSyWTGXXb335/fhddsX4zsAHslbW1txhelqakp4643AACAUvPs9BE+AwAA3Ia5OWlqSurrk1pbpYYGaWTk1vqODqmpaenfvj4pEjGH3wZKIZlM6uDBg0ad1+vNCG7XQzwe19jYmFFXSC/nlEAgoKNHjxp1s7OzGhoaMurcJJFIZATFdXV1RjmXbNeev/vd79pV2GCEzgAARzQaVSKRMOry/eBXlraFDOni9XoVCoWcsj2/BwAAwHpxwufHurX9Nx6Sp2Kb3QQAAABpwmHJ45Hq66UDB5bKExNSttnTZmeXguhwWAoGpdrapcfSaRGl0tPTkzEUc29vb9Ygs9ROnDhhzCkdCASMa6KF6O3tzbgea/eidpPZ2dmM+bTt15+Lz+fLmAKykGvPWB+EzgAAR7Z5lH0FTKhTU1NjlOPxeMYXiZUcOnTIKI+MjBT0eAAAgGLy7LhTVe9/VDsf/TThMwAAAFCGkslkRu/f5uZmHT9+3KhbD/F4POO1HDlyxCgXwuv1qr+/36ibm5vLmLfaLbKFxPb15JXYbe0bCbDxCJ0BAI4LFy7YVRl3kK0k251phXz4Nzc3G/9fIpHI+CIGAACw3tLD56p9vyPP9vUdgg8AAADA2gwNDWV0ahkcHDTK62VsbMx4LX6/X21tbUabQoVCoYxrsmfOnDHKbpHt2nMh7O1MJpNMz+gyhM4AAEe2u80KmdfkrrvusqsK+uD3er0ZX7TcPCQMAADYWjw77tT2fb+jnf/NM4TPAAAAgMtlm74vFAptyLDa2V7LsWPHCrr2mos9eqQdbruF3QO7kBE2Jem+++6zqwrq8ITSI3QGADjsLyOFfvBn+5JkzxG9mqefftoox2IxTUxMGHUAAAAbybPda4bPO+60mwAAAKyrN9+UFhdXXi5etB9l8vszH5NtyTLQHeBKExMTGR1ient7jfJ6GRoaMl6L3+9XZ2en0Wat2tvbjXIymdTIyIhR5wb2deJCrz1nY1/PxsbyLC4uLtqV+fDvf9Iox195zigDwGYSDofV19en3t5ehcNhe7XrxGIx1dbWOuX29nYNDw8bbbKpra017g6rqanR/Py80WYlIyMj6ujoMOqGh4czvvispr6+3uh13djYqJmZGaNNITwej12FPKzxK0LJ3e7x3Gzb9fzMKlcNXOjxpmq7ak0227FEcWzW82KzbVfyxe/YVa7mffAjdpVhR2WFOj6wV0ce/jX96q7t9moUwd4vnbOriu7S5xvsKmygUh5zjrW7FOtYu/Wz8nZl+6zlHHaPYp2/2RV2Tre3Sx/+sBQK2WuWjIxIZ85IK0316vNJbW3S4cNSto6gc3PSiRNLz1WI4eGl1ydJHR2FP35ryPxdx8p6e3vV19dn1NmfBQ0NDYpGo065ublZ09PTRpv1Ul1dbYTOR48eLeow362trUbHHft6qv15Yu+r9WBfe66rq9P58+eNNitJXaNPNzk5qZaWFqNuK3JLZktPZwCA69i9nWdnZ40viAAK94uLr+nxpmpjeeVvfmw3kySd/A9HjXY9/37lsCXf5wWAzerqwk0N/eSfFfja/67/8S//Uf905brdBAAAoKRGRqTWVinbLGdjY0th70qBsyQlEtLQkNTUJNmdB2OxpXoCY5SLSCSScT3xyJEjRnm92D2uvV6vjh07ZrS5XfYQ2/a2u4Hd09nv9xvl1WTrGW33ZMfGInQGALhOtp7R9l1sAAAAbpMePj/zvZ/r/0xcs5sAAACUVCRi1yz1Ti5EMpkZXp86tRRKA+UiYv0y1NXVKZRrKIASs69rdnZ2Fhy4rqa5udkoJ5NJYyRJN7BD50JlC53hLoTOAADXqampUWNjo1HnxrvzAAAAsrm6cFOjL/9nNXzt7wmfAQDAurIzmWh0aSmUnYeljdoLlIWXX37ZKG/UEMwTExMZ1zVL0ePa5/NlBNluC51LgSDaXZjTGQDywJzO+ck2p/Po6Kja2tqMunzY8zoX+lo2g9RcK+s1Z689z+4avyKUnD0HzWr7Z7NuV6F+cfE1/fdP/ldGXf9//I72//bDRp2Wh9f+//zFs055/28/rP7/mHv+VXsf53re22X/P1v1WBZLan8Wez+mtn+95uy159ot9vYUi31erLZ/Nut2bTXpx/Hg/b+iocO/q4WLF4w2yE9qzsxS/C6kzmPmSHWXUhxzjrU7leJYb1acw+5TivP31vertT3n+fNSXd2t8smTUnd3eovV+f3SxbQ/Y2IxKe0yU8GY0zkfS8e9mOfSVmD/PZK+//bs2WP0rB0fH9+Qns4HDhzQ1NSUUw6FQhofHzfaFIv9f/X39+v48ePSKvtqvdjzWhd6vTfbtefp6emMXt5bkVsyW3o6AwAc9p1hSXsCnzWw77DLRzQazbgTbyO+FAIAABTL6Mv/We/pGtWug19RZfU+ezUAAMBt8/nMwFmSrM6eebEGn8s6ZDfgZtFoNGMoZ3tUxfUQjUaNEFiSent7jXIx1VlvAHZv743m9XrtKmwyhM4AAIcdOqffebZW9nPm49lnb/WwTCnFsDPAVvJr1ffq+ZmLxpKrN/LR/+mk0W6lXs5a7smbz/MCAKSq+iDhMwAAKIlAwK5ZW2BsP89PfmKWAbez53OuqalZU8eY22XP5RwKhRSwf8GKaN8+8++L9BEt3cAOne0bA1aTrX1NTY1dhQ1E6AwAcGT78lVI8Jyt7Vo++EescZZCodCangcAAMCtUuHz7o5vaHvtA/ZqAACAgtkdOROJpaGxC3X//WZ5dtYsA273wgsvGOWNGH55bm5OE9Zk6IcOHTLKxWZfP812rXYj2a8vW4i8Eru91+vNeE5sLEJnAIAj24d0IUNsv/XWW0bZ7/cX3NN5amoq4wvRsWPHjDIAAMBmsb32Ae3u+AbhMwAAuG0PPWSW19LLWZLS87lEQopG09cC7meHkw/Zvxzr4MyZM0Y5EAiUfPpAuyex22S79mxfB17Jz3/+c6Oc7fmwsQidAQCO++67z65StIC/LOwhW+x5RPJhD60dCAQ2ZM4VAACA9ZQePlfVB+3VAAAAq7I7c65lWOy6uqW5oVMKuCwEuIYdOq93GBuPxzU0NGTUlXIu5xR7u90m27Vn+3rySuztW8u1Z5QWoTMAwJHtg3pubs6uyslu+8gjjxjl1SSTSY2NjRl16/GFDAAAwC221z6gXQe/ovd0jRI+AwCAvNXUmGGx1jgstn3f/1qeA9hodji53k6cOGGMHllTU1PyXs5ywXavJtt81rMFvMnYbQu99ozSI3QGADiam5szhsN++eWXjXIuyWQyo1d0oV+mJiYmjC9kfr9fLS0tRhsAAICtoLJ6nxM+79j/mL0aAADAYPdyTibXFhjboxCvpbc04Db29c5SSiaTGhkZMeqOHDlilItlbm5OkUjEWS5cuGCsX+8e3qtpbm7OeE35XnuOx+MZQ3EXeu0ZpUfoDAAw2CHv1NSUUc4lYk0U5Pf7s969tpKzZ88a5WPHjmV8EQEAANhKKqv36c6n+uX7zPPa0fAhezUAAICUJSyem1sKngtl93Re67zQgJt897vfVTgcdpaDBw8qGAyWZNm/f39Gj+Pu7m55PJ6iL/X19cb/PTAwYPy/fr/fKG80r9erZusOGfuaci72Neq6ujrmdHYhQmcAgOHpp582yolEIq8Pf3su5mPHjhnl1cTjceP/8Xq9am9vN9oAAABsVRW+e3Rna5jwGQAAZFWMsNjrldL7D8RiUjFG671wYen1RCKS1VERKAl7nuCRkRH19fU5y9jYmNFDuJjLP/zDPxj/N0yHDx82yrFYLGPY7Gx+8IMfGGX7eeAOhM4AAEMoFMrooXzq1CmjbIvH48ZczH6/X52dnUab1YyNjRlDa3d2dq7r0DcAAADlID189jb9W3kqq+wmAABgi/H5zLBYaxwW2w6u88iB8jIwIAWDS0ueA+oB2KSyXXs+ceKEUbbF43FNTEw45bVce8b6IHQGAGQYHBw0yhMTExlDmKTr6+szAuP+/v6Ch8W2e0qXaq4TAACAzaDCd4/uOPBZ3fWZvyB8BgBgi7MDZ60xMLZD57UE1wCwmt7eXqM8MTGhyArDM3R3dxvXnk+fPl3wtWesD8/i4uKiXZkP//4njXL8leeMMgBsJuFwWH19fert7VU4HLZXu04sFlNtba1Tbm9v1/DwsNFmNSdPnlR3d7dT9nq9mpycNObdSCaT6uvrM+YLOXr0aEZovZq5uTnV19c75VAopPHxcaPNVuTxeCRJz89ctFeVxONN1UZ5jV8RSi61X1JW2z+bdbu2Io5lcaX2Z7H3Y2r7ky9+x15VEt4HP2KUi709xWKfF6vtn826XVvNeh7Hm1cu6erMt5R88dtaTF62V5e9vV86J5VoH6bO40ufb7BXYQOV4phzrN2pFMd6s+Icdp9SnL+3vl/l/5zhsJSe4cTjUrX551NexselUOhWuaFBikbTW6B0lo57Mc+lrcD+eyS1/0p9/TYQCJR0hMaampqiz1eca19tlJ6eHuOass/n0/j4eMa1566uLo2MjDh1x48fV39/v1PGErdktoTOAJCHrRg6azl47unpMe4kCwQCCgQCSiaTmp2dNeZIWeuHfmr/pszMzKjRvr12CyJ0zs7+krza/tms27UVcSyLi9B5fdnnxWr7Z7Nu11azEcdxMXlZyZlvKTnzrU0VPpfion4KIY47leKYc6zdqRTHerPiHHafUpy/awmdJyellpZb5YkJqbU1vUV+Ll6U/P6ln5NJac+epX+xHgid18L+e4T9l5sb95V9TVhp154TiYRmZ2cVT5sIvr+/X8ePHzfaY4lbMluG1wYA5HT06FFNT0+rJe0vl2g0qpGREY2NjTmBc0tLi6anp9cUOEvS2bNnnZ8DgQCBMwAAwBp5vLu1M3hYvs88r53Bw/J4d9tNAADAJmNfRnnhBbOcj5qaW4GztNTDmcAZQCmFw2HNzMxkvfY8MTHhBM6hUEgzMzMEzmWA0BkAsKLGxkZNTk7q4sWLGh0dVW9vr7OMjo5qfn4+Y9jtQti9pe05PYBi83g8rlwAFIf9u+WWBdA6n58VO9+jO/51p6p7/7/6H//yH/VPV67bLwcAAGwCNTWSPcpvMeZzXstzAEChUtee5+fnM649j4+P6+LFixofH6eTUpkgdMaGGhkZUTgcdhYA7uX3+9XW1mb8zra1td32/CLpvZz9fr9xZxsAAABuz9WFmxr6yT8r8LX/nfAZAIBNKBCwa9Y2D/NDD5nltfSWBoC1qqmpybj2HAqF5E8fggGuR+iMDXX27Fn19fU5C4CtJZlMamxszCkfO3ZMXq/XaAMAAIDblx4+f/b//Zr+z8Q1uwkAAChDjzxiltc6LLY9gF0kYpYBAFhN2YbOQ0NDxh0P6Uv6MK25JBKJjMelL9G13A4GACjI1NSUEomEJMnr9aq9vd1uAgAAgCK6unBTIz99Qw1f+3s9872fEz4DAFDm7KG15+bMcj58PrPH9OystHy5BgCAvHkWFxcX7cp8+Pc/aZTjrzxnlEutu7tbY2NjzkTi6Xp7exVeZajmWCym7u5uTU1NKWnd+hUKhTQ4OHjbQ8ZidcFgUJG02+bWeDoCJRcOh9XX15fX+4sbxGIx1dbWOuX29nYNDw8bbdzg4MGDTk/no0ePanBw0G6ypXmW5wB9fuaivaokHm+qNspufU9O7ZeU9do/2Hico8WV2p/F3o+p7U+++B17FQrgffAjRrnYx6lY7POd416eFl57Wddf/bFuXrlkr3KNvV86J5XodyF1Hl/6fIO9ChuoFMecY+1OpTjWmxXnsPuU4vy99f0qv+ecnJTSZyo7eVLq7k5vsbpQSBofv1Xu65PK4PLXJrN03It5Lm0F9t8j7L/c2Feb20Zntill29N5cHBQ09PTdrUk6dlnn7WrMtTU1Gh8fFzHjh0z6vv7+zU+Pk7gDAAllkgkNDEx4ZSPHDlirAcAAMD6qLz3fu38113a8eBT2nYXc6YBAFBO7FnK3nrLLOfjscfM8tSUWQYAIB9lGzpLUl1dXdZJxOfm5jSX5zgizWmTVfj9fh09etRYDwAojfSRJkKhEDf7AAAAbLDK6n3yPvJ7hM8AAJSR2x0G2+eT0mc7i0aXhtfOxe+XpqdvLYuLS8v582ab0VHp4sVb68+dk4aHzWG8s0l/7jffvPX4FJ9POn5cmp+/tW5xcfWe2W1tS///zMytx8zPL/Xw7uy0W99S6u1FcXk8HpYcC7Aeyjp01nJPOS0HxulSw7WuJn1o7ZaWFnntW8NQ9hKJhCKRyLrN0z07O6tIJJIxbHs+YrGYIpFI1mHjgUL4fD719vY6y4c//GG7yYZra2vT+fPndfz4cfX29tqrAQAAsEFS4bO38aC23X2fvRoAALiI3ffq137NLK+mvd3sLd3Xl742UzIpxeNSXZ2U1p9LqcuZodBSINvWthTGpgQCS//XzMxSm1xisaW2zc235qtOXWYNBJYe398v5dt3wedbGoJ8dHTp/29slCKRpWC9pmbptZw+vfS8Wfq3lXx7AWAzKds5nSUpHo+runppLrrBwUH19fU5IXRdXZ3Op99ulMPJkyfVvTzJxfDwsNrTb+sqoUgkohMnTiiZTGp2dtYIKGtqapzlwx/+sEJZPpVGRkYUi8Wccltbm+rq6ow2+ZqdndVU2pgpzc3NRg9w2+2+9kgk4szjfPbsWWM7VgueVnttWg6ZR0ZG9IMf/MDYrpTGxkbV1dXp6aefVkv6hCcrSJ/D1+fzGT3iU//f2bNnM4Jtv9+v9vZ2HTlyJOPGiJSJiQk9++yzmpiYMPal1+tVY2Ojjh07lvfrROmU25zO2BxSdyGu13ywzJcLt+McLS7mdHY35nSGm9x44+e6/rO/0o03fm6vWjelmDMzJXUeM0equ5TimHOs3akUx3qz4hx2n1Kcv7e+X+X3nG1tS4Fqyuys1NSU3iK3urqlHrmp0LmQx9bULD02FQyPjUkvvLAU4CYS0sSE9POfSzt2LM05bff4PXAg9zDeNTVmCByLSa2tSz2Nvd6l1+n1LgXIKdnmoa6rW3pM6nns+a5rapbWpwLseFyqr8/ee7yU27uEOZ3Xwv57BPnjXNtc3JDZqtxD54mJCbW2tkqSJicn9eyzz2pkZMRZf/78+VWD2I6ODucx8/PzJR/eNRKJqK+vzwld8+H3+xUKhXT69GmnLj0sl6T29nYNDw875UIEg0Hj9Zw7d04B+1OxiK89Fd6txWqB38jIiHp6evLuKdze3q7BwUH5Ut8Wckj/8KqpqdH8/LySyaROnjypEydOODc75JKaQzx9v0YiEXV3d2cE1dkcP35c/f39djXWEaEzNgKhc3b2HxTrtX+w8ThHi4vQ2d0IneFGN974ua7/w49145/+f/aqkivFRf0UQhx3KsUx51i7UymO9WbFOew+pTh/Cw2d/f6lnrbplzdbW5dC0JX4/Us9gFOXK2MxKRhc+jdfMzO3gt9kcikIHhqSenoyg9vhYXMY70hk6f/LJb19Mrm0zM0tbVvq0m97+1I75Qid01/f0JDU1WWu13KP5HNLh1HKEkynK+X2Ejqvjf33CPLHuba5uCGzVbkPr/2Tn/zE+bmxsVGPPfaYsT6fIbZnlyeo8Pl8JQ+cJyYmMgLefMTjcQ0NDRl17e3txlDgY2NjeQet6aLRqPF6mpubswbOxXztpdLV1aWOjo6C9sPIyIjq6+sL3q65uTkFg0H19PSsGjhredjsAwcOKB6PK5lMqru7W8FgMK/AWZIGBgaMmwwAAAAAYL1tu/s+eRsPyvvI76myep+9GgAAbIB4PDMkPX16qbdtLi0tSwFq6jJwIrEU5hYSOCtt2GtpKYAdGVkKdrNdLu3oMIcCb27O7A2ci9e79JwHDtwKnKWl/6+nZylwti/vtrWZAXGuPlD2HNZtbeZw4+nWa3uRv8XFRZY1LkAplHXonAqMa2pq5PP5MuZkfvbZZ9NaZ0okEppbfudfbcjm2xWNRnXw4EGjrqWlRcPDw5qentbFixd17tw5jY+Pq7e3V6FQaMXetz6fT52dnU45mUwavbzzdebMGaN85MgRo6wSvPbm5mZnnlk76E+fgzbbkus4dXV1ZYTbx48f1/T0tN58803njfT8+fMaHx83esDH43EdPHgw7zmY4/G4mpqanPNPaduU2gctLS0Z+yAejzth88mTJ516n8/n9LgeHR3V0aNH1Zg+NsyyoaGhvF8jAAAAAJTKtrv82vHgU4TPAAC4xMjI0nDPKalezKn5j8PhW8v580vrUpdlU0Nq59k3ZkU9PXaN6exZs/z002Z5JX192cPdgYGl7bJD58HBWz9PTZlhtS09dPb7l4blzkcptxcAylFZD6+9c+dOJZNJhUIhjY+PS1mGil5piO304bn7+/t1/Phxu0nR1NfXOwG31+vV+Pj4qvP0JhIJ9fX1OWGjfaii0agaGm4Np+P3+3XxYv7DNyYSCVVXVztBZq7Hl+K1p9jHK1e7lYyMjKijo8MpNzY2anh4OOdx13JI393dbQTVKw2bnGuYjlAopMHBwYzwXMv74ODBg1nnldbyvjx27JiOHz9u3CyRkn5+pgwODhrzSWP9MLw2NgLDa2dnvyev1/7BxuMcLS6G13Y3htdGOVl8N6Frcz/Swmsv26uKphTDl6YwXK07leKYc6zdqRTHerPiHHafUpy/hQ6vna6lZSlsXeGyqGNubikUHRiw1+RvenqpB6+Wh+eurbVbmOrqlkLvlJGRpR7B2djDU9fW5t8T2++X0i9zZxt6O93Ro2ZI3dW1NGy2rZTby/Daa2P/PYL8ca5tLm7IbFXOPZ2j0agTlt5///1O/Yc//OG0VisPsZ0+tHG2nqXFEo/HndBWkjo7O1cNbbXcA3ZwcFDz8/NqT/+EXRYIBIyev/F4XBOrTdaRZmRkxOg5e+zYMWO9SvjaiyWRSKgrbTIOv9+vycnJFQNnLQe+g4ODxlDiJ06cyHtobp/Pp+npaY2Pj2cNnLXcZnh4WH6/316lxsZGzc/PKxwOZw2clRZop/vud79rlAEAAABgo3l2+rSj4Qnd8egzqrz31t/nAABgfU1NSfX1S3MHpw87HYks9YTu61sairu+fmm5ncB5LezQOMdl1azsx67Eft777zd7e9vLI4+Y7bNczl0T+zXbrwsANpuyDZ3ThzZODw5DoZDzs1YZYvuFF15wfi5l6Jz+WiXpoYceMsqr8fv9Gh4etqulLMNh28NlryS9rdfrVVtbm7FeJX7txWAH56OjoxnDWufi9XrV29vrlPMdotzn8+n8+fM5h/pO5/f7M86tzs5OzczMZA2jbelDqGt5bmgAAAAAcKP08Hl7zQfkqdhmNwEAAOsgErk17HQwuLQcPLhUPnnSnGt4PSWT5rzIpQph7f5IoZDU25t7sSKFolmv7QUAtyjb0PknP/mJ83N6qFdTU2OU5+bmMoLTlFRP50AgkLO3aTHYQWG+vWnz0dLSYoSXU1NTRs/kXOx27e3tWUPQUr72Yjh16pTzc2NjY15BcLpQKGQc+5dfXn04OJ/Pl3Vf5fKIdatcIcG91+s1bqqwjwcAAAAAuI1np09Vv3VAOx/9tLb/xkOEzwAAwLEel5ftcLer61b4ns+SR7+kvK3H9gKAW5Rt6JwKkv1+f0YA+PTTTxvlbEMSR6NRJRIJSSo4qCyUPdTz2bNnjd65t8Pr9erw4cNGXT69ne029nOklPK1365YLGaEsHaP4nylb2MpQl2753Wh/8ftPh4AAAAANoJnx52qev+jhM8AAMCRfim/VJc57eeNx28NM57PYj/+dqzH9uKWxcVFlhwLsB7KMnROJBJOL91sQaM9THS2eZ3Tez+nzwldCo2NjUZv2mg0qqamJk1NTRnt1qqzs9N4fnvIaZs993Nzc7PRmzZdqV/77YhEIkbZ7lGcr/TQOZ9e4oWyQ+NC3e7jAQAAAGAjOeHzY92q2vc78mwv3UhjAADAvXw+KX3A0RJcipWyhLsFDFpZVOu1vQDgFmUZOq8WGPv9fqP3ciwWyxhiO3147lL3dPb5fDp69KhRF41GdeDAAVVXV6u7u1sjIyNrDjz9fr9aWlqcciKRWHFu4vQhqZVlXuh0pX7tt+PChQtGubW1VR6Pp+Al/aaERCLh9IAvltsNjW/38QAAAADgBp7tXm3f9zva+d88Q/gMAMAmk0+wa7exLu8WjR06Z4kQbpu9LdnYbUq1vQDgFmUfOmfr6aw8hthOPYfP51ONPclDCfT29mYNt+PxuE6ePKmOjg7V19dr586dCgaDOnnyZEHDKB87dswo28NnpySTSSOQtgPrbEr92teqVPNLFzt0BgAAAADcYoTP739Unh132k0AAECZ8XqXevauJBQyy9GoWV5Jeo/h1cRi5lzK7e2ZAXA2NTWZ80HnUurtBYByVJahc3ov5Vyhc8h6R7d7s6Z65mYLU0vB6/Vqenpavb29xnDVtmQyqUgkou7ubtXW1qqpqSmvXsSNjY3GENnRaDRj+Gkt74f0sPbIkSMrvh6tw2sHAAAAAGw9nu1ebf+Nh7Tz0U8TPgMAsAmsdqn90KFbP09MLM2fnK98QuN0J07c+tnrlfr709dmamyUZmakycnVw+SUUm4vAJSjsgydU72U6+rqcg49bPfgTR9iO5p2S1G24blLKRwO680339Tw8LBaWlpyvv6U2dlZNTQ0GHMw53L48GGjbA+jbdd5vV61t7cb61dSytdeDJOTk1pcXLztZT16vgMAAAAAlngqtpnh886V/9YEgLUaGBhQMBh0lu7ubruJtDy6Xnq7YDBoXE9MNzIyktdzAluBNRinob9fqqtb+jmZlHp67BYmu+/TKpeiMwwNmXMot7cvBcpp/bak5d7Ng4NLgbPfv/SYZNJsk0sxtxcANoOyC52j0agz/HGuXs4puYbYTv+SuF49ndOlwt7JyUm9+eabOnfunIaHh3X06FHVpT6J0iSTSXV0dKw6nHR7e7sRBE9NTRmPiUajxra3tbXJX+AtYqV67Wth97pejyG9AQAAAAClkQqf73j0Ge1oeILwGUDRXbhwQZFIxFlyBcmp0fzSl1zTscVisbyeE9gKGhulc+eWhpVubl5a2tul8XHp+PGlNsmk1N1tBsK2UChzaOre3vyHvtby/9PRYQ6z3dKy9PoWF5dC5jfflObnpaNHl9YPDUmtrfmHzsXaXgDYLMoydE556KGHjHW2UChkBJOpIbZffvllpy59SOqNEggE1N7ersHBQZ0/f17z8/M6nvpUWpZIJLL2XE5n91xOJpMaGhpyyvbjjxw5YpTXolivfS3uu+8+o3zhwgWjDAAAAAAoT5X33k/4DABAGUkml5ZAYCl0nZ5eWoaHbwXIs7NSQ8NSuJvL+PjSYvd0DoXMgDgfs7NSfb00MmKvWQqMU/23olGpqUnq6rJb5Vas7QVWMjIyopFsJzDgUmUXOuczn3OKz+czejLHYjHNzc05wXUgEFh1iOiNUFNTo/7+fvX29hr12eZottlDbJ85c0bJZFLxeNyY19qeA7pYbue1azmgzpd9/OnpDAAAAACbSyp8BgAA7haPSzt3SgcOSH195tLaKgWDS8Huaj1++/qW2uZaCp3JMZFY6vFcW7v0+J6eW6+rp2cplG5oWAqIC1Gs7QVySSaT6unpUUdHh6qrq3Xy5Ekl8+2GD2yQsgudU/Mye73evEJTe4jtiYkJzS2/09uhpdu0tbUZ5dTrXkldXZ0xl3U8HtfU1JTGxsaMN6RjK004UQT5vnY79C80dE7vyT4xMcEQRgAAAAAAAACwQaampHDYXCYmpDz7JCkaXWqba1lrv6NYbOnxAwO3XtfAwO2Hwre7vUAu6dOnxuNx9fT0FJSfABuhrELnRCLhhIr5BsZtbW1GMHnixAknfF1teO6NVldXZ4Sy+c6/bPd2PnXqlDG8td/vN4LpUsj3tdvzQBfSW9nr9WZsR0dHh1EGAAAAAAAAtNwJY3p62lkGBwftJtLydaz0dtPT0zk7v7S3t+f1nAAAFKKvr88od3Z25sxZALcoq9A5fYjmXF/0bF6vV6HUJApWT9p8g+vb1dXVpYGBAbt6VbFYbE2vNxQKqaamxilHIhEjzD18+LARxK+k1K/dnpc532G4U+xhvKPRqMLhsFGXr5GREfX09NjVAAAAAAAA2ATq6urU3NzsLLmuL3q9XqNdc3Nzxmh9KTU1NXk9JwAA+co2quuRI0eMMuBGZRU6p8/nfP/99xvrVvLhD3/YrpLP58voZVsqqbH36+vrNVHApBNdXV1G+ZFHHjHKK8n1BuT1etXZ2WlX51Tq125/ET9x4oSmpqaMupUEAgEdPXrUqOvr69OBAwdyDultm5iYUH19vTo6OpzhKgAAAAAAAAAAANZb+si1ytLREHCrsgqd08PIQn7BQqFQRs/eXD1vS2lubk6tra1qaGjQyZMns4aiyWRSY2NjCgaDxvY2NzdnzJO8EntY8ZRQKLSmIRhK9dobGxvV3NzslJPJpFpbW9XT06OxsTHNzc1pbm5OU1NT6urq0tDQkPF4Serv7zeeQ8vnSkNDg7q7uzU0NKRIJKJEIqFkMqlIJKKhoSEnTG9tbc26PQAAAAAAAACA7NIv0fv9UpbL0ZvKVtteFC4WiykSiay4rNbxLRqNZowIa4/4CrhV2YTOY2NjxnAC9i/dSuwhtrXB8zlHo1F1d3ervr5eHo9HtbW1ampqksfj0c6dO3Xw4EFj++rq6jQ+Pp41RM7F7/dnDXqPHTtmVxWkFK/dnusmmUxqYGBABw8eVH19verr63XgwAENDQ1lfUP2er0aHx/P2N5kMqmTJ0+qq6tLwWBQe/bs0c6dOxUMBp1hwwmbAQAAAAAAACB/LS3S5KQZwnq90vS01N4u5RiNvmxtte1F/iKRiAYGBtTa2qrq6mrV1tYqGAyuuFRXV6u6ulrBYFDhcFgTExPG9Kj2XM6hUChjxFjArVwfOieTSXV3d6ujo8OoP3XqlLq6uoxfxpUcOnTIKK9nT+enn346I/ROF4vFNDs7a1dLktrb2zU9PZ1z3piV2NscCAQKfnNaj9ceCAQ0PDy8aruV+Hw+jY6O6vTp02vqye3z+dTe3q7Dhw/bq4CylEgkFA6HnaWQ4fHX28TERNYbSgAAAAAAAOA+k5NLQaytsVEaHpYKvATteltte7Gy1GivHo9HwWBQPT09BV/fjMfjikQi6uvrU2trq2pra9Xa2qo//dM/zbiOa+c8gJu5PnSORqOKRqPOMMypJRAIaG5uTslk0n5IVs3NzWppaXEev56hc0tLi8bHx3Xx4kWdPn1azc3NK/b8rampUXt7u+bn5zU8PLymEFVSRhi8ll7O6/Xa29vbdf78ebW1tWUNxtOP/0o6Ozs1Pz+v06dPq6WlZcXXWldXp/b2do2Pj+vNN9/U8PBwzvMi/dzL1SYXn89nPL6QoeElad++fcbjV9omICWRSKivr89Zvvvd79pNNtzExIT27Nmj1tZWnThxwl4NAAAAAAAAF/J4Vl4KGKS0LNjbZy+bbXuRKR6Pa2BgQNXV1RmjvRbLxMSE/v2///dGXSAQWLFTIOA2nsXFxUW7Mh/+/U8a5fgrzxll5CcajSqRSEjLgW2hgWQuyWRStbW1zt01fr9f8/PzRQ0sS/XaU+bm5uT3+2+rB7SWw/f0mxNK8Vqx+YXDYfX19am3t1fhcNhe7TqxWEy1tbVOub29XcPDw0abjRaPx1VdXS0tD5N/8eLF2/5932w8Ho8k6fmZi/aqkni8ael4pKzxK0LJpfZLynrtH2w8ztHiSu3PYu/H1PYnX/yOvaokvA9+xK4qC6vtH3u7in2cisU+31fbrq3GPo7lwo3HMbUvS/G7kDqPL32+wV6FDbT3S+ekIh9zjrU7leJYb1acw+5TivP31ver4j0nysHScS/mubQV2H+PFHv/RaNRnTp1SiMjI/aqnHw+X9aOdcqSVazm6aef1sjISFFynVLvK2wst2S2ru/pvNkFAoE194BdycjIiDGcw+HDh4vyxpSuVK89pa6urigBlN1LvhSvFUDh/H6/mpdHL0gmkwV9eQMAAAAAAACAUkgmk+rp6VFDQ8Oq1ywbGxt19OhRDQ8P6/z583rzzTc1PT2ddXn33Xd1/vx5jY6O6vjx46uObPrss8+qqalJc3Nz9irAlejpvEk1NDQoGo1Kyz0I5+fn8x7qGkAmejqbotGofD7fbd/EMTIyoo6ODqlEIzKUO3o6Z2ffmbna/rG3q5ystm1bjX0sN8s5ulHo6byxVts/9nYV+zgVi32+r7ZdW419HMuFG48jPZ23nlL2HuRYu0spjvVmxTnsPqU4f+npvFXR03kt7L9HirH/otGoDh48uGLQ29jYqCNHjqitrc1eVbBEIqFvfvObOnLkSM7X7/V61dvbq+PHj9ur8laKfVUq0WhUfr+fTKsAbsls6em8CU1MTDiBsySFQiF+OQHcllgspp6eHtXW1srj8aihocH5uba2Vj09PYrFYvbDVhUKhZyQOR6Pa2pqym4CAAAAAAAAACU3MDCQs2ex1+tVe3u7zp07p5mZmaIEzloejvvGjRsrhsCpnte5Xlu5m5ubU09Pj6qrq51rz6mfa2trFQ6H13TtGeuP0HkTOnPmjFE+cuSIUQaAQgwMDKi+vl4DAwNZP9xjsZjRphA+n0+hUMgp9/X1GesBACjU4uKiKxdAnJ8AAACAK8ViMTU1NamnpydjzuVUL+P5+XkNDw/nnK95rZLJpE6cOGHUHT16VHV1dUadlueEbmpqMjodlruBgQE1NDRoYGDAmDI2JRaLqa+vb03XnrH+CJ03mWg0avQUDAQCamxsNNoAQL4OHjzofNnyer06fvy43n33XS0uLurdd9/V6OioM/d66o671tZW+2lWdOjQIefnaDSqSCRirAcAAAAAAACAUojFYgoGg5qdnbVXqbGxUefOnVM4HC7ZaLJDQ0NG2Or1enXs2DGdO3cu63DaiURCwWBwUwTPra2txrXn3t5e49rz8PBwxrXnrq4u+2ngIoTOm4x9Rwy9nAGsVTgc1tjYmFPu7+9Xf3+/Mxy21+tVW1ubZmZmnA9/LQ/x39PT45RX09zcbHxpO3XqlLEeKDa7d5ebFgAAUB6q9j+mncHDORfvBzvshzg8lVUZ7e3F491tPwwbZMeDH804PunLjgc/aj/EUbFrb0Z7ewFKafv7Hs445+xlpfcbb9PHMtqnLxW+e+yHAEBZSQXO2UZ3PH78uGZmZrL2OC6ms2fPGuXOzk75/X55vV719/dnfQ2JREIHDhzI+rrLRXd3tyYmJpzy6dOnFQ6HjWvP7e3tmp6eduq0HNKfPHnSKcNdCJ03kXg8bvyS+v3+os0rAGBriUajxlDXoVBIR48eNdqk1NXVqbe316gbGBjI+247r9drDLE9MTGxKecmAQAAwOZRVdecEb6kL9t81fZDHBW+e1YMrStrPqDF5GX7YdggOwJPZByj1OL9YIcqdu21H+LY9t5a7WjI/fiKFc4ToBi213xgxRsnVnq/8VRWqer9ud+rqvY/Zj8EAMpKrsC5rq5OMzMz6u/vN+pLYWJiwriGmurlnC7V27qlpcWoj8fjWV9/OZiYmDCC41AopPb2dqNNSiAQyNgnPT09WYfixsYjdN5Epqam1NjYqObmZjU3N+vYsWPGHSAAkC97bmX7g9129OhR1dTUGHX2c6wkfYhtZZmbHgAAAHCTK3/eo8RXH7ertZi8rMRXH9fb3/+yvcpx442Y3vqTJ/XOD79mr9Llbz6jy8OfsKuxgX75Z4eyHpObideV+KNH9e507r9drs+/pMRXH9fVF79t1C8uXNNbf3pQb4+HjXqg2N754df01lf/TdZgebX3m8WFa0vn/zefsVfp7e9/WW/9yZO6mXjdXgUAZSFX4BwIBDQzM7NuU5ba10/b2tqyDuPt9Xo1Pj6eETyntqPcAlh7u+0OTbbjx48bI21mmwcb7kDovImkhhpILbl6JQIoTDKZ1Msvv2xXb1rRaDRj1IR8vmil91bW8h1riUTCqMulsbHRCK3tuUwAAAAAt7mZeF0Lr71i1F099/28Q5jr56eN8sJrr+j6qz826uAO1+df0s0rl4y65My3sgZ52Vybixjlqy9+WzfiF4w6oFQWF65lvLdcOz+dUZfLwvxLRvnmlUsZN1IAQDlJJpM5A+fp6Wkj3CylSCSSMVLkStOlpoLnQCBg1MdiMXV3dxt1bmZvd11dXcY22eyRMrV8/TiZTBp12HiEzgCQQzwe19DQkOrr6zUxMSGv16vOzk672aaTHjhLyriDLpdHHnnErsp4rpWk93ZOJpPGfNIAAACAG3l2mnOhXrfCxZXYj732dz8wynAXe97ba+cLONbWYws5T4BiyDwHXzDKK1lcuGaUr73CexWA8tbV1bXhgbMknTp1yiiHQqG8wtfp6emMdmNjY5qamjLq3OrZZ581ynaYnIt97TmZTCoS4TuV2xA6A8CyWCymiYkJhcNhNTQ0qLq62vkSEggEND4+nnV4k83G/uB/6KGHjHIuzc3NdpVeeCH/P2TtOehPnDjB3WoAAABwtQrfPc7PiwvXtPDa3xrrV1Kx626jfH3+p0YZ7lGxa688lVVO+Wbi9bx7tOs2zxOgGCp2m3OPX7d6L69k293mVFoLMd6rAJSviYkJjYyMGHUbETjbI00qjyGmU3w+nyYnJzOuU3d0dJTFtVQ7HL///vuNci7Zrj3/4AfcCOU2hM4AtpTUHVBTU1MKh8Pq6OhQMBjUnj17VFtbq9bWVvX19Skajcrr9aqtrU3Dw8M6d+5c3j1+y1ksFtPc3JxRV1dXZ5Rz8fl8GV92ZmdnjfJK6urqjGG84/F4xpcvAAAAwC22+fcZQeTCa3+b0SNwJRVpQc5i8jLDLbtY5b2/ZZQLCewkqfLX3+/8fOPihYLOE6AYKu6udX4u9KaJbdX7jHKh5z8AuEU8HldXV5dR5/P5ND4+vq6Bs7LMaRwIBDJ6L6/E7/erv7/fqIvH464fZjsajWb0Ms/32nNNTY28Xq9RR09n9yF0BlBWwuHwikt3d7eCwaCxNDQ0yOPxyOPxaOfOnQoGgzpw4ID6+vo0MjKiSCSiRCKhmpoatbS0qLe3V5OTk3r33Xc1Ojqq9vZ2+2VsWvY8Ilr+QM+X3dYOsFfz9NNPG+UTJ04YZQAAAMAtKq0gptDef+lBJCGOu23z/6ZRXoj9jVFeTfq5svCP5jzgQKnZN8gU+n5T+ev7nZ9vxC/kPZc5ALhNV1eX4vG4UTc4OJhxPbPU4vF4Rm/ffHs5p2tvb8/oJDU0NFRQJ6D1lu3ac76hs7Jce7YDbGw8QmcAZaWvr2/F5eTJk4pEIsaS7cPM7/erublZw8PDmp6e1rvvvqv5+XlNTk4qHA5nfGBvFdlCYvvDfCXZviQU8uHf1tZm3LEWjUa5Yw0AAACulB4aaw1h4vbaB5yfF/7x74x1cJf00E3LvdrzVbFrrzG8dqHnCXC7ttd+wCgX+n6T/viF1zh/AZSnkZGRjBEVQ6HQhnQ2sqcUDAQCec9rbDt9+nRG71+7F7WbvPzyy0bZ6/VmvP6V2NeeE4mEEomEUYeNRegMoKw0NzevuOQ7DEk8HlckEnGG1w4Gg+ro6HD93WClZn/wFzq0zH333WdXFRQ6p24GSHfq1CmjDAAAALhB5b12EJl/GFPhu0ce726nfIMg0tXSj/Vi8rJuvJH/3zj20NyFnCdAMWT01C/gHPRUVhlDcxcaWAOAW9ijKfr9fp0+fdqoWw/xeFxDQ0NG3ZEjR4xyIWpqajKG2Z6amsro0e0Wdocne6rG1WTrHFXItWeUHqEzgLIyPT294nLu3DktLi4aS/r6/v5+9fb2qr29Xc3Nzc4H2+zsrEZGRtTV1aWmpibt3LlTBw8e1MjIyJa6W8r+QlJo6JxN+p17+Th06JBRnpiYyNpbHQAAANgoHu9ubfPfGjK50CFn03s5q8Ces1hf2/z7jBsECh+a+FaP+JtXLhU0ly5QDOnvN4XOH19572+Zc9dfzP+xAOAWExMTGWHn6dOnCw48i2FoaMi4Vur3+9XW1ma0KVRnZ2fGNVw72HYL+zgUegzuuusuu2pLXbsvB4TOADa99J7Qx48fVzgcdobVvnjxohYXFzUzM6PTp0+rvb1dgUBAyWRSY2Nj6ujoUHV1tbq7uzMC2c3odj+ks31RKHS/hUKhjGFVzp49a5QBAACAjZTRy7nAICa95+GN+AUtLlwz1sM97GN9I/4zo7yabWlDc3NzAdabx7vbHN79Nt6rCg2sAcAt7OGmb2c469uRTCZ15swZo+7YsWMZ10EL5fV6M4LrcrmWervbDvfxLC4uLtqV+fDvf9Iox195zigDQDmLxWKamprSd7/7XU1NTUnLger8/HxZfBjGYjHV1t4aAqu9vV3Dw8NGm2xqa2uNIUlqamo0Pz9vtFnJyMiIOjo6jLrh4eGC50fp6OjQyMiIU/Z6vZqfn88aahfC4/HYVVjBGr8ilNztHke3bpfWuG3Pz1y0q1zv8aZqu2pN3Hos13IcUTyb9bzYbNuVfPE7dpWreR/8iF21JpvtOG42lz7fYFfltDN4WDuDh53y2+NhXT33PaPNSu765KjTU/rqi9/W29//st1ky9v7pXN2VdEUcqzvbA1rR8OHnPLl4U8U1Nt5z+decHpKv/PDryn5o9X/LttqinWs3foee7uyvUfnew5X1Qe16+BXnPK702f07rQZeKxk18GvqKo+KC338r88/Am7yZZXrPM3u815TiOXzN91FM7+LIhGo2poMN8zx8fHNyR0PnnypLq7u51yMa81z87OqqmpyaibnJxUS0uLU7Y/T+x9tR6qq6uNDkrNzc2anp422qxkYGBAPT09Rt3o6GhG6L4VuSWzpaezC3g8HmdJD4mKLZlMKhKJGEuhw94CW0VNTY06Ozs1OTmpmZkZ1dXVKR6PG0EoSufpp582yslkkn0PAMAW4n3wI8byVz/9e7uJJOk/fONZo93vdn7BbuKwn/Obz+d/cQOwVab1XlWBvQc9lVXG0NzMkepuGXMyF3Cs7aG5mbsb662y5gNGudCe+unnP+cvgHJk93L2+/1GELtekslkxrzSbW1tRQmcJamxsVGBQMCoc2Nv50JHxLRl65BExuUuhM5bSDweVzAYNJbb/SUHtoLGxkYnBOV3Zn20tLRkfIlw4xclAAAAbE3pQy4XOuTs7YSYWF8e725tu7vGKRc6d7c9NDfHGustfXhsFTgneYXvHlXs2uuUuUEGQLmZm5vTxMSEUVeM4azXYmpqyriu7PV6dezYMaPN7Tp06JBRTo3eudltxPFEboTOyGlkZEThcNhZAGA91dXVGWXuWgMAAIAb2L1XF14rrPdf+hy/hQbWWF/bax8wyoUe68pff7/zc6GBNXC7PJVVZk/lN2IFnYPcNAGg3M3Ozhplv9+vzs5Oo2692D2uOzs7Mzrc3C57yPBEIuG6DlQ+n88oF+P1FXs/4vYwp7MLpI+lX+j8qYWw53iVpPn5edXU3LprN10wGFQkEnHKazxVgE0hHA6rr69Pvb29ZXEThv37nu+czvX19Zqbm3PKhb4nZZvT2Z4/JB+JRELV1dVG0NzS0qLJyUmj3WaX+nxYrzl77Xl23fq+b89Bs9r+KZft0hq2baspl2NZLscxtT+LvR9T279ec/bac+0We3uKxT4vVts/m3W78mVv/18OfVG/84F/adRpeXjt//Bn/y+n/Dsf+Jf6y6EvGm1S7Of8s95n9PHHl+apLBX7/9xqx7GYUvuyFPswtf35zpG648GP6s4nPueUb2uO1Fd/rMvffMZusmndceAPjJ6X2/bco+vzL+nt8cy/sVLzpBbzmBd6rO949FPyfvDW3zfM3Z2/XU/1y5PWS3bbe2t19cVvZ/1dKcWx3qwKOYe3+ffprk+OOuWr576X9XctlzsO/IG8TR+TJN28ckmJP3rUbrJp7Wj4kKoCjzvlit136+blN7LOaV2K8/fW53LxnhPlYOm4F/Nc2grs77Hp+6+jo8OYri/f66PFNjExodbWVqNupVzmduzZs0eJRMIpp1+XXWlfrZfa2lrFYjGnXIxrz9PT02pubjbqtiK3ZLb0dN5CfD6fent7jcW+swTA1mbfGVaMu83WMsTJ2NhYRs/mw4cPG2UAAABgI1Raw9UuFDjPaXrv2UIfW+5uxH+mxeRlba99QNtrH1CF7x67iaukz70tSQuv/a1RXonHu3tLz92dOredY50WQGN9bK8153Mu9BxMf3wh5/5mcDPxum4mXnfO3/Rh9gGUj/QOdZL00EMPGeX1curUKaMcCoVKEjgry8iR6Z2LNisyLnchdN5CfD6fMVx2OBzmFxKAwX5PSCaTGeHvSrK1tb/s5MOevzkQCGQMEQMAADav5IvfMZZsvZwl6X/6xNNGu1y9nJXlOUvdyxmbV8aQswUMuVzhu8cYmvtG/GfG+s3u6rnv6croZ3X91R/bq1wpY+7uN271zFlNZbUVWG+xoYmTM9/S5eFPFLTPUFx2UFrIe5WnskoVd98aPa3QwLrcpUZguPrit+1VAMpELBYzetRKUmNjo1FeD9FoNCP87u3tNcrFZF+H/fnPf26UN5rd4ck+RqvJ1t7eZmwsQmcAgCPbXXaF9Ha223q93owvE6uJxWIZc64cOnTIKAMAAAAbwQ5iCp0j1Z4j+Pr8S0Z5qyiHHt72DQKFBHaSVFlzq5foVp67e6v1kHUT46aJhWsFnYOV9/6WPJVVTvlGGfzOlsJWC9uBzcQOen0+nwKBgFG3Huy5nEOhUElfx7595k1vbuvpnC0gTh8OfDVvvfWWUfb7/WsaZROlQ+gMAHDcd999dlVGkLySX/ziF0Y52xeJ1aTPtaLlLw+dnZ1GHQAAALARKu6uNYOYAnuvps9nfCN+oaDAGuvL7qlcSGAn61hv1ZsLsLGMG2Ru471qceEaNw8AKDsvvPCCUd6IOX+j0agmJiaMulJ3rLGvxbotdL7da892W3t7sfE2TeicTCYViUQyesdtJvF4XJFIRJFIpKC7PwAgX9l6OkejUbsqJ/uLzFru3Hv22WeNcnt7O3esAQAAwBUqdpvz0hY6dHBV/a0LntfOmz1w4C4eaw7iG2/kPzylp7JK29/3r5zy9TnzwjdQahW79po3yLwxb6xfTXpP/euv/rUWF64Z6wHA7exhmB955BGjvB42YvpAe+pEt8kWEt/OteeNGDIdKyvr0Dkej6u7u1u1tbXauXOngsGgmpqa5PF4VFtbq4MHD2pqasppnz6XsT28QrpEImG0te9GycfJkyeN51iraDSqrq4uVVdXq7q6WsFgUMFgUHv27FF9fb0GBgayzqGaS/rrOnnypL1akUjEWW+/MadvT7ZlpX2q5ec+cOCAgsGgdu7cKY/H4yy1tbUKBoPq6OhY0/4GUBzZ7vp7+eWX7aqc7C8JH/7wh43yamZnZ40vD16vV0eOHDHaAAAAABulYtfdRvlm4qJRXkmF7x5V+O5xytf/oTzmNd6qKqzQ+WbidaO8Ento4uuv/rWxHig1j/VetZi8YpRXkz4VwPV/mDHWAUA5Wu8wNh6Pa2hoyKgr5VzO5SJbSJzvtedkMplx7fmxxx4zyth4nsXFxUW7Mh/+/U8a5fgrzxnlUhsYGFBfX19egWtdXZ1CoZAGBgacut7e3pxhcCwWU23trSFo2tvbNTw8bLRZTW1trRHarrSbPR6P83NNTY3m5+cVi8XU3d2dVwBbU1Oj4eHhrGGRLf11pf6vdOFwOGOegXzl2qeRSER9fX2rhtLp/H6/QqGQTp8+ba8CNkTqdyPXee42t/M+FgwGjd/Xuro6nT9/3miTTTQaVUNDg1P2er168803C+ql3N3dbdwQU8jr3oxSnw/Pz+R/MfN2PN5UbZRX+uzaSOmfm8pj/5TLdmkN27bVlMuxLJfjmNqfxd6Pqe1Pvvgde1VJeB/8iFEu9vYUi31erLZ/Nut2bTUcx+JJ7ctS7MPU9l/6/K3vsrnsaPiQ7my99ffAlT/v0bVXfmC0ycX7wQ7d8einpOWhmt/604N2k6w8lVXa8eBHtf1fPKxt761Rhe8e3bxySTf+eV7X/u4vdfXFb9sP0fb3PSzvf91uV0uSbsR/pncm/9gp72j4kKoCjxttUi4Pf8KukiTtePCjqnr/72rbnqUgfTF5WQsXL+hG/Ge6+uK3V+0BvjN4WDuDhyVJV899T2+PZ/6NtfdL56QiH/NCjnX6a5SkX/7Zobzndb7zic9px4MflSRdOz+tK6OftZtk5fHuXj7WTdr23lpV7Nqrm4nXdeOfY7oa/X7Wc22l43f9H2aU/NGtv6e8H+zQ9n/RZLSRpMUrl3Tlz3vs6qVzr+nfmq9n+dy78Y+vKDnzLd28csl+mOHO1rB2NHxIkvTu9Bm9O33GblKSY71Z5XsOV967X+/5/Vs97HLt+2yq6oPadfAr0vLQ2m999d+sepxTqvY/tvTe8N5abbu7xnlvuP4PM7o6879m9Jje5t+nOw5k//2wz8uV2r4z+ZWsQ+BX7X9MOwJPqGLPPUuvZ3mo8JtvxHRtLqLrr65880/6e/71+ZeyvieW4vy99blcvOdEOVg67sU8l7YC+3tsav/Z+czw8LDa27N/NyoF+xpntiymFEZGRtTR0eGU0//fXPtqvTU0NBjhcXNzs6anp4022UQiEQWDQafs8/l08eLFgq49b2YbndmmlGVP546ODvX09OQVOGu5y3164Ox2Y2NjamhoyCtw1nK4dODAgYy7PNxgYmIiI8DKR7Y7gQCsD7t38tzcXMbIB9nY71ktLS0Ff+iPjY0ZZXo5AwAAwE3sIWrt3rC5eCqr5G36mFN+d/obxvpctt1do/d84qzuOPAHqrx3vxYuXlDyR8NavPKGttc+oDuf+Jze8/tnM17HjYsXdPONmLbXPmAsytLDOhXApLerrN6n63OZf8dvu7tG7/n9s7rzic9pe+0DuvHm63p3+owWXntFldX75G36mN7zyTF5P3jrYme5soNzu5d7LhW79qpqOWRVAce68t79uuuTo7rj0U9p23trtTD/kpIz39LiwjVtf9/D2vVUv3Yd/Io83t3G4xZe+1stXrmUcawXk5e1EDPnkl6IvZTRdtt7a3Uty7GuvHe/3vPJMd3x6KdUee9v6cbFC3p3+oxu/PO8Ku/9LXk/2KG7Pv2cEyjDXW78s3n+brs7cyqtXNJvYrj64rfzCpw93t3a/fGva9dT/dr+vn+lm6n3hosXtL32Ad3x6Kf0nk+OaZvfnCv9ZuJ1LcR+qsp7f8s4Lyt27s44L5221fuMtjf+8ZWMkQgqdu1Nez0PSwtXlZz5lq6/+tfa9t5a7Xjwo9r98a/rjgN/YIxKAGDzKvQa5e1IJpMZ2QbXOG9paWkxyvlOJ/uDH5g33zElozuVXU/njo4OjYyMGHU1NTUKhUK6//77nflIE4mEotGovvvd72YNY1fqrXg7PQRT7DtpVtrN9h0mtkAgoMbGRj300EOKx+N6+eWXFY1GM8av9/v9mp+fX/EXbbWezqk5o7U850D6Nqw2/ENzc7PR2zoajaqpqcm4OaClpUVPP/20ampqVFdXp3g8rlgspmg0qpdffjnjDWal/Qasp63U0zmZTKq2tlbxeNypO378uPr7+412NvsutXPnzhU0p/PU1JQOHDjglFtaWjQ5OWm02Wro6Zyd/bm52v4pl+3SGrZtqymXY1kux5GezuvLPi9W2z+bdbu2Go5j8bilp7PHu1t7Pndrft5cvXRt6b1m8+3lvO3uGr3nk2PyVFbpZuJ1Xf5fnjGC0DsO/IETZC+89op++WeH0h69JL2XqSQl/ujRnAHS7o9/fSmcWe7hfH3eDCwrdu3VXZ/5C3kqq7SYvKwro5812mzz79N7/t03nFA0+aNhvfPDr6U9wy3l0NN5m3+f7vrkqFPOt6do+j7Pt5fz9toHtLtjKZy+Eb+gy//LM85x8lRW6c7WPlXtXxo+8torP8jaK3l3xze0ffnGgsXkZSX+6NGMXqUpd336OafX5y//tC0jYE/f9puJ13X5W581epFuf9/D2nXwq05Y9/b3v5y1x73o6Vx0hZzDvj/8oXNDSq73CNtaejl7Kqt016efc0Y9uPzNZ4xRAdJ7C9+8cklvffXfZJyb3qaP6Y4Df+CUL3/zmZy9kNNHjXjnh18zevPLej2S9PZ4WFfPfc9ZX+G7R+/5d99w1q/0e0pPZ6wvejqvhf09NrX/7HxmcHBw1euUc3NzxvXQtZqdnTUC0l27dunhhx/WtWvZP5dvRzKZ1OzsrF3tSO9FnGtfrbd4PK7a2lojNxocHNTRo0eNduns69Ver1fz8/Py+/120y1rozJbW1mFzvbQAJLU1tam4eHhFYPWubk5nTp1yri7ZKXg6HbCmhT7TW2l3Wz/sqd0dnaqv78/53wD9hANyuOXc7XQOZ3dQ3mlbcimvr7eCca9Xq/Gx8cz7mKxJRIJ9fX1aWhoSMlksuD/EyiVrRQ6a3n+9+7ubqe82gf52NiYDh68deGsra1No6O3LtDkw76paHx8XKFQyGiz1RA6Z2d/bq62f8plu7SGbdtqyuVYlstxJHReX/Z5sdr+2azbtdVwHIvHLaGz7HBv4Zre+pMnM3rZpUsP6BYXrumX3ziUdRhY266DX1FV/dIQgtmGdvZUVsn3ub9ygr9sQfG2u2t016dvXa95608P5vy/U/9frgAm/fXkGlY838CqHEJnT2XVUs/M5R6iN69c0lt/8qQWk5ftpo4dD35Udz7xOWm5/S+/cWjFcyPlrk+Oapt/X87zqcJ3j3yfed4pJ776eEab7e97WLs//nWn/OaXH8n5WlP/X3LmW8ZQ6/Z65Tj3ZIWEuYJEEToXXSHncPqNKcrxHpHOvnEk1++5Lf33+Z3JP1Zy5lt2E+OcytbGU1mluz7zF05IvtL/ndquXOfdHY9+yhltIdfNL/ZNJbneGwmdsb4IndfC/h6b2n979uzJq/fsZufG0FlZsi2/36/z58/nzMLsa9VHjx7V4OCg0War24jMNpuyGV47kUgYJ5Uk9ff3a3R0dMXAWcvzkT799NN2tWs1NjZqZmZGp0+fzvlLpuWA2f7FOnHiRN7DjpdSPB43emJ3dnauGjhreRz+wcFBzc/Pr+scCwBMnZ2dxsgFyWRSra2tWd9fotGourq6nHJdXV1BAbeWnz99aO1AILDlA2cAAAC409vjYSfk8FRWaffHvqLKe/fbzVThu0d3PPop7f7412/1CP3znqzBhm37+x52At7r8y9lDf0WF67p+vlb899tr7v1/T3lxhsxo7dgVX1mGy33Yt7+vn8l5RgOOv313Lxyyfh/0107P+2EoZ7KqrIeZntx4ZreHr814lvFrr3adfArWYcp3nZ3jXY91e8EzosL13Rl9DMZwXA2Ox78qBPGXXtlKutjbiZeN86BbMfx+qs/Nnosp46XbZt/nxNw2z1EZb2eG/ELWc89LQ+7nPo9qNi115nDGu7x7g//xDif7kwNNW3xeHfL+8EOvecTZ53A+Z0ffi1n6JuuYtde5/f85pVLOXu8X33lL52fs71XLS5c07W03shV7/9dY3261Pmf/NFwRuC87e4a7Wj6t0756rnvG+tTbsQvGAH8zmBmmAygvBE4u1t/f78aGxudcjw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G/Q6F9f0jtV+ROCdDZQ1HhyN0djqd2rRpkxISEpSVlaXy8vKwrHcdCaNpWwEAACLNE4j5jqAOVklxkfbv+6s3xL2/bH1Qoa+vQ4c+1I/LS6zVQTnlcqnkniLdfkvuoN9Xkh6u2OA3AngwQg12fD288f5+w4+khRdpSdrSPkfJ/vn99/Tt67P6DVtmzjzXb0rxrVt+2ed7BqP2l2ZonbTwon5Hpu1oeUWrli8LGLxOmzZdS9KWKu+WO7QkbWmfI7BL7ikaVBDhazRe56P5mG195qmQ33csGYlz2N97BuvYsaPq+O93rNUBjeR1MpLvHaw/NP9Od9zyrZD/Rgzlc3qk9HcNhvu6//P77+mOW3L7DJu/NP8CLUlbqiVpSwOO3vdwOUffcQYwPjG99jBgeu3BsU4zPZqm1x7KdL/W/Q40vba1T7gEeq+hcDqdKi4u9gtD+xIfH6/s7GzdfffdAcPH4dzvoV4DHsG8znDuV7jPyVB0d3dr9uzZ3nKov+OD+X1zOBzKzc1VY2OjtckrNTVVNptNqampio2NlSQlJibqgQceMIL4/t4nHNNrD9e2AgCA0W28TK8dyJfmX6Crr/2G/sFnqsnDhz7USy++oBeee9boq941Hvfv/TSQ85W08CLl3Hiz8YXyn99/T3XPPuW3TqQkbWt8rd/QUkFu/5VXXaOlX73SeN/2t3aq47/f0SvNv/MLC2NjbdresqvfNT6t7ztn7hd119p7VFJcFHQgEGh6beuUrR65q78TcLrP/fv+qv/3/p6AU8jOnHmuGprf7DOc3tHyilav+rpRV75x86CmmPU4dOhDLUtbZOx7f6/15/ff0/LMy/2OVd4td+jONcUBt9nlcuonAW4KGGg/FeB8BRLN17mi9JjNmftFfS3zWv3TP1/trTt86EN1/Pc7qnnycb/frZkzz1X9i68F/N06NAJrKQcSye0Y7nOoft5z2rTpuuk7t+srFy/W381fYEyR3P7WTv32N7/WC9ueNa7vQNMyD/d14msk39v6WR3s9Nrtb+3UquXLrNWaM/eLWnn9jfqHtKVKWniR97x61q1+9eWX9Py2ZwMGqYHOi6+Rnl67r2swEtf9sWNHtfyqy4zjNK13yuybbr494O/l/n1/1Y6WV/Tb536tPzT/zlsf6nECgHAKZnptQudhQOg8ONYgLtRAyiOY8M9jMH0DGUwIZmXd70DBobVPamqqNmzo/x+0wYiLi1NiYqK1eshaW1v12GOPqb6+3m9kayB2u12VlZV+o3KHc7+Heg14BPM6w7lfHuE6J0NhDZ0TExO1e/duo08wBvP7Zu2r3s+Wm266SZmZmUpNTTXafFmf29/7hCN0tr6fIrStAABgdBuPofOX5l+ge0rKA65f6/HCc89qzR2f/Ts8kCVpS3VPSXm/wVqgNYavvOoaPfrzGqPOqr/tX3n9jbpr7T39fiG++923dXuAEVF5t9yhe0rKjTpf/b2vR9LCi/SP6Vfqiq9eqWnTpmvOvPO1+9235XI59ce3duofekdaeezf91ctv+oyIwSJjbXpnpLyAddpdrmcWr+20C8cHWhN5eWZlxthddLCi7St8TWjTzAee2SjHvQZtTtz5rna3rIr4CiyQKFA0sKLVP5gZb/XiEf7Wzt1xy25Rhg2lPM1Gq7zaDxm31tf5jda3tehQx9q7R35fiNOB3pf9XFDxHt7jxvl4RDO7RiJcxjoPdV7w8QjP68ZMFh1uZyqefJx7/q7gUK4kbxORvK9QwmdXS6nlmde7jfad0naUj3y85o+g1RfO1pe0f1l643P7UDnxddIhs6BrsFIXvf3l603poGfNm26ntzy26DeS70B+cMVG/TCc8+GfJwAIJyCCZ2ZXhsYxRwOh9LT04f86CugHKrU1FRVVVXpyJEjampq0oYNG5Sdna24uDhrV8lnhGd9fb21yRDt+x2q4divSJ2TwYiLi5PN9tmXPV1dXUZ7uG3evNkvxC0pKVFnZ6dKS0v7DXGH22jaVgAAgOF015piNTa92W8Qp95Q87Y71lqrpd4vex/9eU1QX/h+d32ZX5//CmF6VPV++b9l23aVb9zc75fh8oSsL77u9941Tz7uN4I1WMkXL9aTW36rbY2v6bvry7TEZ+TakrSl+sf0r+mutff4jVr+j9J1fqPufv7k1gEDZ/WG0xWPVPmdixeee9YvUPG14pvfMsq7333bb8R0MGp/aU55unLVjQEDZ0n6ScUGv1FoP39yq9856EvyxYt155pio67mycf9grVgjJbrPBqPWX9hnnpvPPj5k1v9gs3fNf7GKI8XI3EOre+p3lkTtmzb7ndeAomNtSnvljvU0PxmUJ9DHrGxNn13fdmIXSfReo3+9JGNfoFz7urv6Mktvw0qcFZvQJ309xdaq6OW9RqM9HVv/Xt355rioN9LvTcrVDxSpZ8/uVUJf+c/KhoAohGhMzCKWKc57ujokHOUrOmRnp6u9evXq66uTgcOHFBnZ6cqKyuVmZlp7ari4mJjv0bzfvdnpPdrKOdkqHz33el0BjXqOlRPPPGEUV6/fv2gRx+H6sMPB/fF4EhuKwAAQDSKjbVpy7btftM+9yfn2/5hhCfMHehLf183fed2o3zs2FG/EHYgM2eeq20vvq7kixdbm/o0bdp0fXe9+W9Al8upnz1aYdQNxDMqecu27X6B8kAOHfpQrzS/ZNRdedU1g36df/nO7X5h7xOPP2qUfeWu/o5f2FH7lLk280D+0Pw7IwCIjbXpXyzn0uPYsaN+a3LeU/qAX+gzkNzV3zGCBJfLqZ8+Gvxa3KPpOh/Nxyw21qaVq8wp1vsKi8aykTiHhw596PeeM2ee2+cI0f5MmzZdZfdv0j2lD1ib/HhGlvZ1k0Yg4bpOovkadbmcfuveh3o+Rovhvu5dLqffTVOD/Rvq8Y/pXxvUNQwAI4nQGRhlAgWVo1F8fLwKCgrU0NCghoYGY+RrR0eHsS6tp7+v0brfVtG0X4M9J0NhHVkdqdHO3d3dam1tNerWrVtnlCNpMEH9SG8rAABANJoxc9agAlv1jiy2fom8JG3pgKOMrQKN3tq/9y/Wqn5Njo31C1GD8Y/pX/MbDbV1yy+Ncn+mTZuuLdt+r7xb7rA2BeX/Pv6o33qXd1lGdwVj5sxz/UYkvvTi832O2v40cPm2Ubd1y1P9hqBW1iBlafqVfteDx9YtvzT2M/nixX2u+zyQnBtvNsr79wYfFI2m63w0HzNJWpx6hbUqIqFeNBuJcxjoM+We0nK/m1IGI5jP1qnTpo/YdRLN1+gLz/3a73P4X29fM6TzEe2G+7rf/e471iq5XC5rFQCMOYTOwChjHYXa2NholEejzMxMv/WOrQHnWNxvRfF+BXNOhsIatlunlA6X7u5uo5ycnCy73W7UhZNvUD9Yw72tAAAAY9mceWbwduzo4GfWsYa+isAX//0JNALVGhL0Zeq06QG3P1jtb+00yldedU3Ir/evt6+xVumPltf3Zf0y3+VyBh24BxqhbT2Ovl59+fdGeelXrzTKg2E9Pv/v/T1GORJG4jof7cfs7+YvsFZpXz8h+1g0EufQ+pmStPAiXX3dN426aDKS18lwvPdLLz5vlAPdIDTWDPd1b/18VoDptgFgLCJ0BkaZ5cuXG+WysjK/sGo0WrJkiVG2jhAdq/sdzfs10DkZCutrW6eVjpShhMLBsIbpQ5k2PNLbCgAAMJZFarTWYEbcDlWgaTgDjaYKN5fL6RcQ/UOAkXfBmjnzXL/Rt//V+qpR9vWl+Rf4TQ9d9+yvjHJfap983BjJlrTwooDH0aP9rf8yyv/QT9+BfGm+ud7m/n1/9RvZGW4jcZ2P9mNmvRY1QMg+Fo3EObR+plz5z1cb5WgzktfJcLy3de32q5d/M2KfJ9FiuK/7mTPP9RuN/38ff9TvdwEAxhpCZ2CUyczMVHJysrfsdDpVWFho9BmM9vb2IQVjvrq7u1VbW2utDoo1aE1NTTXK0bzfQxHp/YrkORmKvLw8Y4rt9vb2QY127ujoCGpK7sTERKM82OnLg30fj7i4OCMsHsw+Dfe2AgAAjGWBvrQPhXX64uE0Y+Ysa5X27wvvaLdAdr/7jv+X57OGdhzmzjvfKP/5/feMspV1ytPd774d1Agx69Ta1lHTvna/+7ZfuOpyObWj5ZWQHrvffdsvYIj0TQLDfZ2PhWM23o3EOdz97tt+nylzLJ8JGD6BroHz5swzymNNoH2O9HWv3jDf16FDH2rV8mVac0e+XnjuWb/fCwAYCwidgVHIus5rfX29CgsLBxWidnV1KTc3VykpKYN6Xn+cTqdyc3OVlZU1qPDL6XRq27Zt3rLNZjOCWI9o3e+hiuR+RfqchMpms+m2224z6nJzc4Oawru8vFwpKSlB7Y91ux0OhzZt2mT0CcTpdKq4uFhJSUlBvY8v3/DY4XCourraaO/LSGwrAAAA+jc5NtZaNWxiY21+X2rvC/CldrgdDjCF95y5QwuIZlhCzX0DhOdXXnWNX6BqDZStrGtFz5x5rlau6nu9TmsAIUm3rF6p1au+HvLD+prBToc+0oK9zq37p3F8zEYr6/HWMJxDa7vCeMMEBi/Q+bDeGDTWBNrnSF/3knTnmuKAN/W88NyzWnNHvi6cP1OrV31dD5aX+P0NA4DRitAZGIVycnJUUFBg1G3evFlJSUkDBlzNzc3Kz89XQkJCyCNgB9LY2KiEhATl5uaqtbXV2mzo6urSihUrjH45OTkBpxaO9v0O1XDsV6TOyVAUFBQYr9nd3a20tDRVV1f7jbJub29XbW2t0tLSVFxcPKipvq3rZhcXF6u0tDTga3R0dKi2tlYJCQkqLy+3NgflpptuMsr5+fl9hsf19fVGebi3FQAAANFthmWE8Qf7Ix86W79IVxhGOluff/yo/3tYWddiHugL+a3PPGWUB5ouNlC4jv5xzEa/kTiHgaaGDhTEYXgEGl071m8CGInrXr3X+YaNlX43kPna0fKKHntko26/JVeXXzxfGWkLVXJPkXYEMbsHAEQjQmdglKqoqFB2drZR193drfz8fMXExCghIUEZGRlas2aNMjIylJCQoJiYGGVkZAwYZIaLJyScPXu28vPzVVpa6n0UFhYqLS1NCQkJamxs9D4nNTVVlZWVxuv4Gg37HYrh2q9InJNQxcXFqaSkxKhzOp3Kz8/X7NmzNXv2bGVkZCgmJkYpKSlGYG63243puftTUlJijD52Op0qKyvzHlPPY8qUKUpKSlJubq439I6Li/Nbp3kgBQUFftu2Zs0a73543u/zn/+8VqxYYfQb7m0FAABAdLMGM64ANyOGW6D3GGogMXXqNKPcX3jssXLVt43Q2OVyausWM1j22L/vr3rpxeeNun+9fY1RtnK5XNYqDIBjNvpFyzmcM29onykI3eFDB61Vfjc4jTUjed3/Y/rXtO3F15V88WJrU0D79/1VNU8+rtWrvq7LL57vd0MVAEQ7QmdglLLZbKqrq/ML7Ty6urrU3NysTZs2qbm5uc9pd+Pj48M2gtVms8lut1ur1d3drerqapWVlXkfmzdv9htxm5ycrLq6un63Jxr3OxwitV/DcU6GYv369X6jvD26u7sDromcmpqq3bt3+62B3Bebzaaqqiq/4+B5fc/DOpo4Ly9Pu3fvHnSQa7PZVFFREfCYedaubm5uDjh9+nBvKwAAAKLbvr3mNNSxAf6NGW6B3iOYkLg/p06ZX/j3NwLZY9q06X7TY9f+8nGj7GGtv/Kqa/wC+2C88/4hvbf3eNgeS9KWWt9izOGYjX4jcQ4DzaiA4TFj5ixrlU6NYCg7Uobzup8z94vasm27GpveVN4tdwR9I9ehQx9q/doCrbkjP+AIdQCIRoTOwChXWlqqzs5O5eXlWZv6FB8fr6KiIrW1tamzs9NvVGao4uLidOTIEdXU1CgnJ8fa3CdPsNnW1hb0tkTTfodTuPdrOM9JqCorKwMGrVbx8fGqq6tTS0vLoLfJE1Rbp68OJDs7W01NTUFtU19ycnLU1tam1NRUa9OAhntbAQAAEL2s09LOmOEfFoRbbID1ffcPcS1p61rUgUKPQHJuvNkoBxrRHGgEdM63zecFEmi60z+//561Cj44ZqPfSJzDQL/vQ/1MQejmzvVfv9l6g9NwCjTyOtxG4roP5EvzL9A9JeVqanlXr731vioeqdJtd6zVkrSl/d6M9cJzz+onFRus1QAQlQidEXXi4uLU1NTkfdTU1Fi7DEpmZqbxev0FOYPpG0hFRYXx/MGw7ve6deusXfoUHx+vqqoqnTx5Ug0NDSopKVFBQYHS09OVnp6uvLw8lZSUqKamxhtMVlRUKDk52fpSYZGTk6OamhodOXJETU1N2rBhg0pKSpSTk2NsT2VlpVpaWtTW1uY3tXQwIrHfQ70GPIbyOpHYr+E6J6HKy8tTZ2enqqqqlJmZ6d3X9PR0FRUVqaWlRZ2dncY2Dfb3LS4uTg0NDd5jmp2drfT0dOXk5HiP5+7du1VXV6f09HTv8wb7Ph6JiYlqaWlRU+/nWElJifdRV1enpqYmnTx50vo0aQS2FQAAANHHGjhL0owQRu8OVqD3GOp6mPv3hTZiO2nhRX6jx6xTjb7S/JIxEjtp4UX6x/SvGX0CmRoghAh0zPEZjtlnhjr6P1wGux0jcQ4DhWnWzwQMn0BTaQ/1M34ohmME70hc9wOZOfNcXX3dN/Xd9WV6cstv9c77h7Rl23bddsfagDN1VP/80REJygFgsGLcbrfbWonwiomJsVYNq6amJiOUAAAAAACMbn/ad8JaFRVWr/q6drS84i0vSVuqJ7f81ujTn4c33q+HfUbzzJn7RTW1vGv0Ccb6tQVGOLny+htVvnGz0ScYGWkLjS+myzdu1srrzSmffYVr+yVpR8srWr3q60bdtsbXlLTwIqNOYX7fY8eO6tKFc426svs3KXf1d4y6wbj84vlGOHblVdfo0Z8Hd4P5Sy8+r9tvyTXqXnvrfe+X8tZr7p6ScuXdcodP78AC7ed315fptjvWGnWREK7zNdzX+Vg4ZpJ0wbypRrmv/fUI9LvY1PJu0FPkhks4tmMkzuFwvudIXicj+d5bn3lK69d+tnTZQO996cK5xhTnwX5uBjLYzyFr/8H+O8Fj/76/KiNtoVHX1+/DcF6D4XDs2FHdX7rO7yaraN5mAOPDl+eeY63yw0hnAAAAAACAKPP7/3zBKE+bNj1g4Bxu06ZN15fmX2DUdex+xygPxqFDH/qNxvyH1CuMcn+Wpl/pN+rr/z7+qNQ7Papv4Dxt2vSgw/FAx3Nn66tGGSaO2eg3Eucw0GfKH9t2GmUMrznzzCm2/yvC14CvmZaR1ta/D5EwEtf9UEybNl1lGzb5/e3r+O/Q/xYDwHAhdAYAAAAAAIgif37/PdU8+bhR9w+WaaYjKTllsVHeuuWpkIOBnz1aYa3ymzK7P7GxNv3Ld2436rZueUoul1O1T/3CqF+56tsBp/Lti3U7/tD8O/2h+XdGHUzj8ZjNtQR0kuRyRn5KYKtwbcdInEPrZ8pLLz6v3e++bdRh+FjDzJdefD7kz/jBOm+OORL5z++/NyxTbI/EdT8UsbE2feVi8/dmJKdBB4BgEToDAAAAAABEkR+Xl/h9CX/XmmKjHEkrLNO4ulzOgOHxQA4d+tAvPE9aeJHfiLOBrFx1oxEmHzr0oV568Xm9sO1Zo1/OjTcb5YGs+Oa3rFUqKS7yO/b4zHg8ZnPmftHvZob2ERipG67tGIlzaP1MkWRMRY3hlfNt/8/KUD7j1TsV9GDMmec//XX7W4O/jvftHdy64MN93Q/2uARi/X23lgEgGhE6AwAAAAAARIkXnntWL734vFGXfPHiQQe1Q7EkbanfdLg1Tz5urPsbjJ9UbPD7Qv/W29cY5WDMnHmurrzqGqNu/dpCv3Wirds8kKSFF/m97v59f9VPhhiG/aH5d8b6qmPJeD1m1qBspKaHDsd2jMQ5XJK21O8z7KUXnzemxx+MPzT/Tg+Wl1irEaQrr7rG73xs3fLLQY0+P3bsqG6/Jdfv79VA5s71H7FfZ1m7eCDVP39Et6xeaa3u13Bf9y+9+LxuWb1y0H83fbW/9V9GOWnhhUYZAKIRoTMAAAAAAEAEHD92dFBfyN9ftl5r7sg36mJjbSp/cLNRNxysI6tdLqdWXbcs6BFpJfcUBRzlbP3SP1jWsNoaZq8MMJIyGN9bX6Zp06YbdY89slGrli/Tn99/z6gfyO5339Ytq1cOOWiIduPxmFmDsponH9fWQQZl4RCu7RiJc3hPSbm1SqtXfV0Pb7zfWt2n/fv+qjV35OuW1St16CBTDQ+F9TP+2LGjWrX8n4L6m1Xz5OPKSr8kqL5Wc+Z90W96763PPBXUdbz73be1etXXdX/Zer+/AcEY7uv+D82/U2b6JXp44/2DHvm89Zmn/F7369d+wygDQDQidAYAAAAAAIgAz0iwSxfO1fq1BQHXMd3R8oqqf/6IVq/6uqp//ojRJknfXV866BG84XD1dd/0C3IPHfrQu519feG/f99ftXrV1/0C59hYmyp+UhXy9KD9Tcv9pfkXhBxmf2n+BSrf6B/qt7+1U5kZl+jB8pJ+R2MeOvShXnjuWd2yeqWWZ14e1WuEhst4PGb/8p1Ca5XWry1QzZOP+4VVLpfz0+nfnzOnfw+HcG3HSJzDJWlLddsda63Verhig1YtX6YdLa/0+bniGU2akbYw4P5g8AKNdna5nLr9llytuSNfjz2y0XtO2t/aqR0tr+j+svXKSFuoknuKQl4DOjbWpn8NMOPF+rUFWnNHvt91cOjQh9r6zFPea62/63IgI3Hdu1xOPVyxQct6j9tAN24dOvShHt54v9/o6auv+6bf+QKAaBTjdrvd1kqEV0xMjLVqWDU1NSk9Pd1aDQAAAAAYpf6074S1atitX1sQ1Mgkq7vWFOuutfdYqyVJD2+831jnc87cL6qp5V2jTzCs27by+hsDftE8kIy0hcZIo/KNm/2CWF/W7R+q/o6VL+v7hnrcrI4dO6rVq77uF5R7fGn+BUr6+4s0d94X1f7WTu1+9+2Ao7liY20q27Cp32MXjK3PPOX3Rbx6R1Dm3XKHtXpQtj7z1IDre86cea5xA8CxY0f7PDZL0pbqyS2/tVZLYTxfI3Wde4zGYyZJF8ybapSD3d/+phKOjbUp+eLF2rf3L95jGer5GEg4t2M4z6F6A7g1d+T3uf3qPbdz5302orv9rZ0Bty/Qfo3kdTKS7239bAz2vXe0vKJbVq8MeHyDkXzxYk2bZjfC10DnJZDlmZf3eR0NJDbWpltvX+P397ap5V3Nmeu/ZrTVcFz31nPia9q06UpaeJG+NP8CzZgxS5L0x7Y3dOyYI2AoPWfuF7Xlue1+I8QBYLh9ee451io/jHQGAAAAAACIgFBH9WoQgXMkTZs2XVu2/V7/mP41a5Mk6c/vv6cXnnvWOyKur8D5kZ//qt/AJFhXX/cNvy/dY2NtWrnq20ZdKFZef6Oe3PJbv9f3dejQh9rR8or30VcAod5Afqwbb8fsrjXFflPzerhcTu1oecVvOtxICOd2DPc5jI216dGf1wQc8eyxf99fjffrKxicOavvbUZwlqQt1SM//1VIf6vuWlOsLdu2a8bMT0PTwbqnpLzP67g/yRcv1rbG14b0uT8c131sbKy1yuvYsaPa0fKKap58XA9XbNDDFRv0h+bfBQyckxZepF8+09DvtgJANCF0BgAAAAAAiIAZM2fptbfe111rioP+wjhp4UXasm37iAfOHrGxNv38ya0q37g56H3wuPq6b6qx+c0+Q+vB+jRgNsPr3NXfCSm4CCT54sXa3rJL95SUD3pf1RvglN2/Sa+99b7K7t9kbR6TxtMxS1p4kRrCeD2HKtzbMRLn8P/P3t8HtXXfeeP323PvtVL3WmOyd6hJ/VDsZV1YGkeQOoZ1nEiOU8sPDeBNKNSlgdrbYNNsgXSmgrnuEcw9C+TaBsi4GLKLC1nq4jquwW5s440blDhZcNwYxSmFuK5NHHwHV50rmGx3pVz9DfcfQSc633MkHT1L8H7NaMbnew7S0XmUv5/z+XyfsTTg5OCbAZfFT0lZjrJ9lRgcehvPWBrE2RSEh4yP4uTgmzDkbBBnqdr9xB4MDY+FfI/amLcZJ8/9h+bPTUlZjuaWThw7+apqkDdQkT7udz72OIaGx/CMpSGo9c3MWo9DXX04OfimpuxtIqJ4wfLaUcDy2kRERERERBRO8VBeW218Yi0eyNuMjXmbxWZgvtTnWx7jKS5NWhZU2WRx3TKz1gcc3ACAnq52fOyRvas2BqYnf+VVT7x0BLc+eB/vjP5KGiczM+te6HR6ZGatR+Hj3/D5/t6Ea7v545ofI/aC7TzOn3tZNbM5M2s9tn51J3Y+9nhQHe3+OBy3cdRjvOhIfY57/7w1fAFvjbwhK1eM+cy2lJTlMORswBdWrMZW8y7NgYtw7a9YHefeJMI2w/x56imY73v61HFcv3YV4795F39w3Ib98iWkzJfjXZq0DJl/e2/Ejk1P4V6PSO5Db8bHrny6f0fewK2pm7Jj2vPzMua/iy+xPE5i+dnjY1dkJcuD/ezxsSvoP/5TjI9dgcNxG3/4/W1kZq3H0qRl2LptFzYbtyr2dzjK/Pf1HsaF187j+u+uysYm1+n02LptF7Zu24mt23bJMrJnZ+/gxa52aRoAntxXGdRDSNE47t2VQW5N3cStqZuyct3usvgA8EDug0Ffy4mIIk1LeW0GnaOAQWciIiIiIiIKp3gIOpOSv6DzQnRr6iamPnjf64MERES0cIUj6CyyX76kOQOaiIiiR0vQmeW1iYiIiIiIiIgoKCtWrmbAmYiIwoYBZyKixMWgMxERERERERERERERERERBY1BZyKiBcBut6O+vh719fVwOp3ibCIiIiIiIiIiIiIioohh0JmIaAF4/vnn0dDQgOeffx56vV6cTUREREREREREREREFDEMOhMRJbiZmRkcPXoUAGA0GsXZREREREREREREREREEcWgMxFRguvs7JRKat93333ibCIiIiIiIiIiIiIioohi0JmIKEE5nU40NzejtrZWanvvvfeksZ3dL5vNJvs7IiIiIiIiIiIiIiKicGLQmYgoAZWXl+Nzn/ucLOAMAEePHkVDQ4PsNT09LVuGiIiIiIiIiIiIiIgonJbMzc3NiY0UXkuWLBGbompoaIjjvBItMHa7HTMzM5iYmMD+/fsBABkZGejo6BAXhcFgQHJysthMRERERAnst1P/KTZRHLg4fAFvDV+QppcmLUPZvkrZMkRERAvF+XMvY3zsijSdmbUeW7ftki1DREQLw9+s/EuxSYFB5yhg0JmIIqWtrQ3V1dUAgKqqKrS2toqLEBEREdECxKAzERERERERRYuWoDPLaxMRJbB33nlH+vfDDz8sm0dERERERERERERERBQNDDoTESUwm80m/dtgMMjmERERERERERERERERRQODzkQJwmazob6+HuXl5TCZTLjrrrtQXl4uLhaQmZkZdHZ2orCwEE6nU5xNHsrLy9Hc3IzJyUlxVkDc+85kMqG8vBz19fWywHEgJicnpfVJS0tDWlqauAgREREREREREREREVHEMehMFOfsdjsKCwthMpnQ0NCAnp4e2Gw2GAwGfOlLXxIX12RmZgb79+/HXXfdhf3792NgYAAjIyPiYjRvenoaPT09qK2txZo1a1BYWBh08NlgMCAjIwM2mw09PT1oaGiAyWRCYWEh7Ha7uLhPnstz3HYiIiIiIiIiIiIiIooVBp2J4lhbWxuys7MxMDAAADCbzRgfH8fc3ByGhoZgsVjEP/Grp6cHa9asQWdnJwCgrKwM4+PjDFr6kJqaig8//BAWiwXJyckYGBhAZmYm6uvrA84Qb21txfDwMObm5jA6Oipt94GBAWRnZ6OtrU38E69ee+016d8cz5mIiIiIiIiIiIiIiGKFQWeiOGW321FbWytNt7a24uzZs8jIyJAtF4jq6mqUl5djZmYGGRkZGB4eRnd3d0jvuVikpqaiqakJ4+PjMJvNcDqdaGhoCKk0ucFgwNDQEKxWq9RWW1urOeM50PGc33jjDSxZskR66XQ6vPXWW/jTn/6Ezs5O/NVf/ZU0b+/evfiv//ov6W9fffVVmEwmaf769esxNjYme381H374ITo7O2E2m2WfvX79eulhCiIiIiIiIiIiIiIiSmwMOhPFIafTifLycimYmZubi6qqKnGxgJSXl0tZtGazGaOjo8jNzRUXIz9SU1Nx9uxZKVA8ODiIvLw8zMzMiItqVl9fLwWNxX3vjdPplILTycnJmoLODz74IG7evInVq1cDAP7H//gfWLlyJXbv3o39+/fjo48+kpb98Y9/jM2bN+O//uu/UFdXh0ceeUQW5H733XeRk5ODt956S2rzdOPGDZhMJnzhC1/ApUuXcOLECczNzeHmzZv42te+hnfffReFhYU4evSo7O+cTie++MUvygLUS5YswV/+5V/C4XAA8+smzq+vr5e9DxERERERERERERERRQ+DzkRxaHBwUJbt2traKpsfqPr6evT09ADzAef+/n7o9XpxMQpAfX09ysrKgPms9PLycnGRgHhmO9vtdgwODsrmizyPj9TUVNk8X1atWoXHH38cAPB//+//xWOPPYZbt27h17/+Nebm5nD9+nXcddddAIBf//rXeOihh/Dcc8+hv78fc3Nz+L//9//ia1/7GgDgk08+QVNTk+z93YaGhmCz2ZCTk4ODBw/iL/7iL4D5zz927JgU+P7nf/5nWUa1Xq/HG2+8IX0GANx77724ceMGUlJSAADf/va3cfjwYWn+v/3bvzHoTEREREREREREREQUQww6E8WhiYkJ2bSWLFZvbDYbGhoagPngZHd3NwPOYdLd3S3tm4GBgYDGYxaJWefiMSDyzIRW25+1tbXSgwbefPLJJ1i1ahXefvttZGVlAQDWrFmDH/7wh9L8iYkJXL58GQUFBQCAP/uzP8Pzzz8vBaZv3rwpCxq7ffvb38bc3BzefvttKeDs9md/9me47777AADvvfce/vjHP8rmr1q1CidOnJACz++99x5u3LghW8Zd2ruvrw+lpaWyeUREREREREREREREFF0MOhPFoXfeeUf6d2pqqmpQUQun04mSkhJp2mq1BpQVK6qurobJZILJZPIbFF0sOjo6pH8HMh6zSNzPnseAGs9l7XY7pqenAY/y3M3NzXjttdc8/kLpz//8z2XjhrutW7dO+vf3v/99KSDtds8992Dp0qWytkD82Z/9Gf7mb/5GbJbxDG5/8skn2LJli6y8dktLC6xWK4qLi8U/JSIiIiIi0uTES0ewbtVS6WXKk//fhyJjfOwKLg5fkL1cLt9DTIXTwZZG7vcgxXrfEVFwYn3uxvrzFxNua4o1Bp2J4pBnQDctLU02LxCdnZ1SMDItLQ0VFRXiIprNzMygs7MTNpsNNpstpOD1QpKbmytlATudTimrPBie29RfUD8jI0MWeF6zZg1MJhPuuusu9PT0oKysDN3d3bK/CRfPTOVIWrNmDU6dOgUA+OMf/4jvfOc7OHjwIPbu3Qur1cqS2kRERERERAmoscGC0qIdstcfHL8XF6M4xH1HlJhife7G+vMXE25rijUGnYni3MzMjNikidPpxLPPPitNh5oROjAwIJV0NhgMSE5OFhdZtL7+9a9L/x4YGAg629mzZLY/ycnJslLpTqcTNpsNycnJ6OjoiFjAORivv/46vv/972PTpk34yle+giVLlqClpUVcTNWDDz4ojd88MDCAf/zHf0RBQQEDzkRERERERIvI7OwdReYWERFFDq+70cNtTQsJg85Eccgzu3lyclI2TyvPLGcAyM/Pl80P1PPPPy/9Wxx/eLErKCiQZR17bqtAeO4vLRnuxcXFuHHjBoaGhtDU1IShoSF8+OGHIWW0h1Nvby/+6q/+Co8++iiSk5Nx6NAhHD9+HHNzc6ipqREX9+pb3/qWNL4zhCA/ERERERERLXzjY1cUmVtERBQ5vO5GD7c1LSQMOhPFIc+Ao9PplAUjtTp58qT077S0tJACxWL27saNG2XzFzu9Xg+j0ShNe2aFayU+XKAl6Iz5ktxGoxEWi0W2DrH2zDPP4Fvf+hY++eQTTE1N4X/9r/+F++67T/P38jQyMoJf/OIX0vSTTz6Jt956S7YMERERERERERERERHFDoPORHHoi1/8omxaDEj6MzMzA5vNJk2HUlrbZrOhvLxc1nbx4kXU19crXgMDA7LlImViYgIDAwOor69HYWEhTCYTCgsLpfUYHBwMOOgbKs/sW3H7ayE+WCAeA4nkxo0bUnnv73//+0hJSZHN/9Of/oTf/va3sjZv3njjDTz22GPIycnB+Pg4Vq9ejU8++QRbtmyBw+EQFyciIiKiGGlssGDdqqV+X/bLl8Q/lSnK36L4G/H1Qru2oVqIKD7VWZvRe+yM7HV3yufFxSgOcd8RJaZYn7ux/vzFhNuaYo1BZ6I4JGaDBjpG8ODgoGw60NLadrsdJpMJ2dnZMJlMinGlOzs70dDQoHgFup6BmpmZQWFhITIzM1FYWIiGhgYMDAzAZrNhYGBAWo/t27fjc5/7nCJYHkkFBQWyac9Mcy3EbSceA4nk1q1b+Oijj8RmyQcffIA33nhDbFa4ceMGHnvsMaxZswYXLlxARkYGjhw5AgD44x//CLPZjP/6r/8S/4yI4lh9fT2WLFkS8VegD/4QEVHoHvnqTpTtq4RO99mwM55SUpbj6epaZGbdK86SKXx8Dx4yPio2AwB0Oj1KSvdiV/7j4iwiSiCZWeuxMW+z7OXt2kHxhfuOKDHF+tyN9ecvJtzWFGsMOhPFIbEUtpgF68/Fixdl06mpqbJpf9yZ1cnJyYqSze5yzmovs9ksWzac7HY78vLyMDAwAL1eD6vVihs3bmBubg5zc3MYGhqK6Of7k5ycLNvOYhDZn/fff182LR4D8eRPf/oT3nnnHbFZsmLFCtx1110AgB/+8IcYGxuT5p07dw7333+/z6A05svKG41GfPLJJxgcHMRf/MVfAAAefPBBHD58GABw+fJl7NmzR/hLIiIiIoqFjXmbUWdtxsa8zeIspKQsx7FTr+Lpmjq/nV4lpXvR1XtCNfDc3NKBhsY2rFi5WpxFREREREREMcagM1EcSk1NRXJysjQtBiT9EQOegWbNFhQUYGhoCENDQ+jr65PNKysrk+aJr0gFSp1OJ7Zv346JiQkAQEdHB+rr62Xfy2g04uzZs7Ig+cMPPyz9Oxo895l7XbXyfLBADGCH040bN3D8+HEAwCeffIJTp07JsoX/9Kc/ob+/X5r+xS9+IQsaA8Brr72GmzdvAgB+/etf49///d9l89esWSNlmf/xj3/El7/8ZSn70Gw24x//8R+lwPEf//hHPP/887J1ePXVV/GFL3wBN2/exJe+9CX8z//5P6V5mA9quw0MDGDv3r3MeCYiIiKKE498dafYhJ35jwccKBbLAGZmrcfOx5jhTEREREREFK8YdCZKAF/60pfEJp88g86hBi9HRkZk0xs3bpRNR8PAwIAUlNXr9T7HqG5tbZX+bTAYZPMizXNbz8zMBBR4DnQfB+PGjRu4//77pYAxAPzTP/0TNm/ejP/6r//Cn/70J+zevRstLZ+NkXf58mXk5OTgrbfeAgD8+Mc/lmWUf/LJJ9J42p6effZZ1NbWShnPmH8I4Ne//rVi2X/6p3+SMpbfeOMNPPLII1Im9OXLl6X1c88XM9p//OMfy5YhIiIiotiZnb0jNqHw8W+ITQELx3sQERERERFR5CyZm5ubExspvJYsWSI2RdXQ0JCiRDLFN7vdjuzsbGA+yDo8PBxQANXzmMvIyMD4+LhsfiCqq6vR1tYmTX/00UeyjN5oaG5uRm1tLTCfBeyvNHNhYSGcTifOnj0rzoqo8vJy9PT0SNOBnHue+xwARkdHA9rnRESJoL6+Hg0NDWJz2AVy/SUiSlS/nfpPsSkuNDZY0NPVLk2npCzHm5evyZbRoih/C+yXL0nTg0NvY236OtkyFLrr167C4bgta9Pp9DDkbJC1RZvDcRvvXL6E8bErAIB3Rn8Fl8spW0an0+O+7K8gM2s97svZgJSU5bL5Wpx46QgsNRXS9IqVqzE0LK/0pNX1a1dx+tRxvDXyBgDg4vAFbMzbjKVJy5D5t/dixaovYudjfy+VmHc4buP6tavS36ekLA/qGHc4buOC7TzOn3sZH8/ewdQH7+PW1E2p1H1m1no88tWdqqXvtbg1dRNTH3xWfU3L8eFyOWG/fAmZWeuRlLRMnK347t7e03O58bEraGywyOb3Hjsjm1ajtl0PtjTiYGuTNO1tv4+PXcH5cy/jrZE34HI5cf3aVWRmrYchZwPW/PU6bDZuDfi407o9x8euoP/4TzE+dgUOx218fOcO1qavw9KkZdj52N9rqvzgcjlx/tzLOH/uNP4wvy3v/vxyrFi5Gpl/ey/uy9mgOpSBL1r3nVbjY1fwuu083njtvKzdfX4vTVqGh4yPKvZhtLxuewVvvPZL6Vpkv3wJSUnLsDZ9HXQ6PR58+JGg1k/rceDJ33kViff0JhGvO6HSuk6Jfu5G6rqr9fO1iOb9NpK/RxJhW8fLuR6J+zEF529W/qXYpMCgcxQw6EyBmJyclJWSbm1tRVVVlbiYV5OTk1izZo00bTQaMTQ0JFsmECaTCTabDZjPHB4dHRUXibienh6pXDPi+Jiura1Fc3OzNB3oera1taG6uhqYf1jg7NmzAZdGJyKKZww6ExGFT7wGncVg8dZtu3CoSz5kjxZfyVopZU0nJS3Dr8amxEUoDCw1FTjx0hFZm7cAXKTNzt5BTWW51FEfqI15m/Hd6tqAOj7DEXS+fu0qGhsseN32ijhLISVlOXYX7cG39h7Avx0+hBfaP6sytfuJPWhu6ZQt74vDcRvPNVkV+8+blJTl+G51LUpK94qzfNISoHUHSE6f+jnOn3tZNg8Anq6uxdM1ddK01u0uLhcMte3q7zu9bnsFzzXXS0EGb5KSlqGu/lnsfuLTyl1aaPnsxgaLLGCgJilpGVrau1UDT7Ozd/Cj1ib09R5WBEdEW7ftQnNLp+YgnbhPxPXX6sRLR/Bck1XzuW7I2YDCx/dgZ/7jmtc1FD1d7fjXQ62a12/rtl14uroWmVnrxVmq/B0HCOK8isR7ihL5uhMqf+u0UM5dcblgqF13xff19vm+ROt+G63fI+I2CYbadxHfN5htHS/neiTvxxQcLUFnltcmihP19fXYvn071qxZg4mJCWRkZGB4eDiggLOaUIKWTqdTVl47UmM2+yNm/JaUlGByclLWFg+WLZP/0PMcp1mLqqoqDA8PIyMjAxMTE8jMzMT27dtlmeZERAtZWVkZ5ubmQn4x4ExEFDvjY+/Kpu/LVmYr+HP92lVZmW5DzgOy+bQwfTx7B6/bXgmqgxfzmU6lRTtgrQvt/9CBeKG9BWbT/Zo6wDHfiftCews25aTjxDFtHblqTrx0BFvyvqy5Mxjzn22tq4LZdD9uTX025FKo3OtSXVmuGvhJJA7HbZQW7cC+0t1+O7gxH5iw1FTAUlPhN0Dkj+dn+wtaYf6z95XuVhwDPV3t2JKXhZ6udk3rdP7cy9huvF/TZ4bD9WtXYTbdD0tNRUDnuv3yJVjrqrApJ11WTSPc3OvX2GAJaP3On3sZ+eZN6Os9LM4KSiTOq1Dfk9cddYvl3I21aN5vE/H3SDjFw7key/sxhY5BZ6I40dDQgMHBQWn67NmzMQvyutntdjidn12oYzGeM+azfj0DCNPT08jOzsbAwIBsuVgTx8/23HZa5ebmSmXBnU4nBgcH8fzzz4uLERFRDExMTMBms0mvSPP8vPr6erS1tUnTMzMz4uJxb3JyUrb94vEBsngX6204PT0t+3zx5a7UEyvxtj6L0fjYFUVHj1qJPH/so59lSgPAfdlfkU0T+dLXeziiQSm3gy2NeK7ZKjZrFmxn9sGWRp+dqplZ67Exb7PX7Lfr167im09s19SJ60uwwcN45Q7gXBy+IM7y68RLR0IKNoby2dbaKtyaugmH4zb2le5GY4NF9tCOFg7HbfwwhGNZq9dtr6Aof0tIQTKXy4mJ38gfbgqXi8MXvK5fUtIybMzbjLJ9ldiYt1lRztbNWlcV0rEQifMqHO/J6466xXLuxlqs7rehitbvkXCKh3M9lPMq1PsxhQfLa0cBy2uTFj09PXjvvffQ09OD6elpJCcno7W1FWVlZeKiPonltYuLi9HXF3g5OwjlngHgxo0bIWVOh2JmZgbZ2dmKztWysjK0trZGfZxpNWLZ2L6+PhQXF8uW8aenpwfV1dWYmZlBamoqysrK8KUvfSng44CIKB6J10lRWVkZuru7xeawamtr8xqwNRgMKCgokLXNzMygra0NP/vZz1QDWGlpacjNzUVra6vi4aNg9PT04Ny5c7DZbH4rZhgMBnz9619HWVlZQJ8d6DZwm56extGjRzEzM4OLFy/C6XSir6/P52c7nU50dnbixRdfhN1uF2cD80OBPPzww6iqqpLu54ODg7JqK57EdXQHGL0Rl9dqYmICR48eFZslZrM5Kg8IRmMbqrHZbLDb7XjnnXdgt9u9frYag8EAo9GI/Pz8gP8fEujxabfb8fzzz2NgYED17wwGA5566imUlZVBr/90XLeFIh7La/f1HlZkdbx7zSGNqaeV2Gl/qKsPW7ftki1D4WGJo/Lat6ZuwpSXJU1vzNuMB3IfxAPz5Sk9y1TOzt7B+NgV3Jq6iYMtjYqsGp1Oj95jZ/w+9HAiyBKUp08dR3XlZ0NAua1NX4fCx/fAkLNBNq7o9fmxs8/84uc4P/iyage4WolMkbi+biWle7Hja3+vKOV5a+omfnftPdXSlCkpy3HW9rbXjmM3tdKXT9fUwVpb5bVTWiSWuRW/h7ft7ojQeJfid1KzYuVqPGr+Gh756k6p7Q+O25j4zbvo6z2sCA6lpCzHwLk3/Y4pqeWz16avw86v/b107GP+s8+fO43Tp47LlsX8wz23Pvg0eOUpM2s9ivd8W/b9r1+7iv7jR2TDILidHHzTb3lorftOZL98CaVFOxTHTFLSMjy59wDuy9mAv07/ElasXC37mzO/+DlOnzwu+25azpVAXb92FfnmTYr1K9tXie9W16qeJy6XEz9qbZKV7YXGc0s8DsJxXkXiPaGyz90S7boTKnGd1CyEczdS112tny+Kxf02Wr9H4m1bi3/nFs1zXU2k7scUHC3ltRl0jgIGnSkQMzMz2L59u9RJGGgHvBh0DmVM58LCQimbOC0tDTdu3BAXiaqZmRmUl5crMpxTU1PR3d0Ns9ksa/fFbrdL72OxWMLSCbl//350dn72oyXQc6+8vBw9PT3A/H7r7++Pi2A6EVG4xEPQec2aNYoHmNzEz29ra0NDQ4NqIEsUzL3I08DAAGpra1UD2/7o9XpUVVXBarVqup8Fsg0wH3ytrq6WVWRx8/VA2tGjR1FdXe03eO6WnJyM733ve6iqqkJhYaHXQLK4jiMjI8jLy5Mt4ynY3zDV1dU+h7jw9d3DJVrbUGQymbz+baAMBgNaW1s1/ybSenxOT0+joaFB9tvLl4yMDPT19SmGbUlk8Rh0FgOYmVnrcXLwTdkyWuSbN8k6r341NuW304qCI+4zBNA5GW4Ox23U1uzHI1/dia3mXQF1FvZ0tSs6aw05G3Ds5KuyNpHYwarluzsct7HdeL+ik3P3E3vQ0NSm6SGL122vSFlublo6wfO3/Z3sc3U6PeqszX7HUHS5nLDU7FcEPXY+9jha273fD6CxQzgzaz0eMm7Fgw9vRVLSMqxY9UWp8sE7ly/hgbzNss7qYLa7u1ypp6sffCyb1srXd1qbvg7ftzT4fNDF4biNmspyRRZW2b5K1FmbZW0if59dZ21WHevVzVsAxtPGvM2oszb7DEI912xVBEu3btuFQ12+kxaC2Xezs3eQv+3vFMEYQ84GtHf1+T3XXS4n+noPS2Ms+ztXAqW2fplZ69H8XIfPbehmv3wJlftKZMEtf8eCr+PALdDzKhLvuZCuO6HytU4L9dwN53U3mM+P1f02Fr9HYr2t4/Fcj/T9mIKjJejM8tpEcSY5OVnWCdjT0xNQp5/Y+emt004Lz4yWeOigS05ORn9/P7q7u2XB2OnpaWzfvl0K2Grx/PPPo6GhAc8//7ymDnottAQlvBkZGZGtv/gdiYgoeiYnJ5GXlydVntDCfS/SGgBzczqdKCkpQWFhYVABZ8y/R3NzM0wmk+b11aq5uRnZ2dmqAWdf6uvrUVJSojlYivn7aENDA9asWRPQb5/c3Fyfv1MmJye9Zvz64i/LWfzNFW7R3IaRZLfbYTKZUF9fL84Kms1mQ3Z2dkDn28TEBEwmU0AZ2xQ4sSx2MJ29LpdTFnBem76OAedFIiVlObp6T6CkdG9AHbyY71zc/cQeWZs4vni4PNdkVXSA11mb0dzSqakDHAAeMj6Klau+KDb79E/1P1B8rnt7+aPT6dHa3o2nKmtk7adPHVd01AbCkLMBvcfO4OTgm3jG0oCNeZuljLONeZvxkPFRPF1TF9S1INp0Oj2esTRgcOhtnx3c8DhWxeP0lcFfyKYD8XR1LQaH3vYZtMJ8J764H92SkpbhUFcfeo+d8Rm0AoBnLA2KZd4K4Vjw5blmqyLgXFK6F8dOvqrYhmp0Oj3K9lXirO1tTcd7oH7U2iRbv6SkZejqPaHYPt4Ycjbgu9W1sra+3sOK76xVJM6rYN+T1x3/FvK5G2uxut8myu+RcIq3c919XsXifkyhY9CZKA5lZGTISlUG2mnoGawMtvN5cnJSFrB++OGHZfNjqaysDOPj44qSluXl5Zo6xWdmZqSOZK1ZN1qIncK+yo2KPNc7OTk54h3ZRESkbnBwENnZ2UEFKQGgoaEBTqe2km/T09PIy8vzGdwMxMjICAoLC8XmoLjXrba2VvP3cfOX0e5PML9dnnzySbFJ5sUXXxSbfBocHFTc1z099dRTYlNYxWIbRlogWcm+1NfXw2Qy+dw/3szMzAT9t+Tf7OwdxTiYwYzFLJaNNGQryxFS+GzdtuvTEqQeryf3HhAXSwjP1Mqvmy6XU3FMhsrhuI3Tp34ua8vMWo+yfZWytnBzOG7jgu28rG3rtl0BB1W+tfeAoqP+xcOHZNNauLOfjp18NeB1iEdJScvQe+yMosPcF51Oj91F8sBCMEFGnU6PYydfDagM8LdUztHMrPU4ee4//HbQexLP9dnZO4qgQ6gcjts4cUxeTSElZXlQGWhJScvQ0NiGuvpnxVlBm529oxj/s67+WUUAw5+S0r2yQKDL5cS/HJJno/oTifMqlPfkdce3hX7uxlqs7rfhEI3fI+EUT+d6MOdVuO7HFD4MOhPFKc/Owvfff182zx/PbJ+ZmZmAO4shZDljPhAeT1JTUzE8PIyKCvlYE7W18qdL1XR2dkrb5L777hNnB80zSJ+cnBzQNvPcx8HsLyIiCt3g4CC2b98eUsBuenpaU2DNneEs3m89VVRUwGq1YmhoCENDQ2htbVXc90Q2my3kjFK73Y68vLygAu8DAwMhBUuD5W+83oGBgYDurz/72c/EJklqamrQZdS1iNU2DITRaITRaAy4KktDQ0NIAd+jR4+GvG08Hz6k8LJffktsgiHnAbHJLzHonPG398qmKby2btuFp2vqZK9E6NBVk5KyXJEVf/134e3kPX3yuGI80aeFDMdI+LfDh8LyuSkpyxVZS+fPqY956U1S0jIcO/nLhD1O1CxNWqY63qY/G3IfFJsC7ui+O+XzAX92SspyRVB0Y95m2XjIWmSqXF9vfRBY/5M/asduXX2zIjARCPE8D8WJYz+RrZ8hZ4MiS1Gr4j3flk3f+kD7sRCJ8yrU91Tbd7zufGahn7uxFqv7bThE4/dIOMXTuR7MeYUw3Y8pfBh0JopDk5OTso7RL34xsDIgYonJYDr3xE5wMas4XrS2tsq+r7jentzlRz0D0++99x7q6+tlr0Azy908gxSBBJwh7GOn0xlwWXS73S6tfyCd6tEQz+tGRPFlenoaNpstpFcogrlfqnn2Wf/ZF7W1tV7XNyMjA8PDw+jo6EB9fb0U4KuqqkJHRweGhoZ8VtN49tlngw6cu8dHDvQ+hPntV17ue5wyzI9BXVZWJguoDw0Nob+/H2VlZT6/mzfJyckoKCgQmyXuY0sLp9PpMyjpL8AdilhuQ29SU1NRUVGBs2fP4saNG5ibm5M+76OPPsLc3Bw+/PBDxW8yNdPT0xgYGBCbNRN/RxiNRunhjOLiYs2/v7ScoxS4d4RgcUrK8oA7UQHgnTCU6KbFa236Otn078KcWfTLfz8tm87MWh9QdlqwxIcxtm7bpSivqtU/HKgWmxTnry9Lk5YF/dkLzV+nf0lswlSUAj8rVsmvr7N3Av/tp7Yfw91JLx67mVnrsfOxx2VtsfTGa7+UTW9+eKtsOhDi9vzdtfdk075E4rwK9T3FfcfrTngkyrkba7G634ZLpH+PhFM8nevBiuX9mJQYdCaKQ2LgVGsHmtvGjRtl08GMEemZeavX6wPOZAkHm82m2BYivV6P/Px8WZtaR3l5eTk+97nPKTKh3Rkznq9ggg7T09OyDn5/na4icR/7+96iSIxRHS7xvG5EFF8GBwdhMplCeoWDXq+HxWLB0NAQ/vu//xtzc3OYm5vDjRs3YLVa/V7LpqenVe9Fbna7HW1tbWIzMP/ZQ0NDPh/2MhqN6OjoEJslTqcTPT09YrMmExMTisCeVj09PX6D3bm5uRgdHUV3d7csoG40GlFQUIDu7m58+OGHQQ0z4a/kta/sZU/+sqL9fU4oYr0N3ZKTk1FWVoazZ8/iww8/REdHh89xrFNTU1FVVYXR0VGUlZWJs2W07gdvkpOTYbVa8dFHH2FoaEh6OKOvrw/j4+MYHh72G3QPNfhN6t4Z/ZVs+r4gshQgjEuo0+mD7vSihc1++RIuDl/A+XMv42BLo/Ry/F6eTfOJkLkTCpfLqeiYfcgYfIBKK7XPfUAlo0crtQdC3hp5QzZN2ojbEVEM/CQlRaaPJtwlesVjd+tXd8qmY02s0vFACA86iUGmW1M3FdmDiYLXnchJlHM3ltSOv2jcb4MRi98j4aS2rRPxXBc/E1G8H5MSg85EcUgMEosBSX/Eko/BdO55dpZ761yfnp5GZmam13GUp6enZYGA5mbtY/ZMTk7CZDJh+/bt4iwFzw5QvV6v2iH6ve99T+qYdMvIyJBlB7lf4vbTQuy4FAPh/oj7WDwGfInUGNXhEM/rRkQk0uv1qKqqwo0bN9DU1ASj0Si7B6alpaG+vh5DQ0Oyv1PjK+j8wgsviE2SqqoqvwEzACgoKPD5gFOgYxj7kpqaKgtsGo1GxX0L8w8Z+VJWVobh4WHVvw0Ho9Go+hvA7ejRoz6DyW6+tp2vwGs4xHobuvX396O7uzuo30QdHR0+j81AfuOIjEYjbty4gfr6eq8PRObm5mraRv62NQVO0XEfRIfVrambsk7TYMrr0cLy6ZiOx2Gtq0Jp0Q5syknHulVLUZS/BaVFO3BgXwkOtjZJr0h2MI6PvasIIH1hhbKTM9zUPjfl84GNNytauUpeTS2ex5okdXenfF5sCopaR324jI9dURy7K4RjL5bGx64oAnUulxMXhy8E9Rofu6IoqRtIie14wutO5CTCuRtrasdfNO63vsTT75FwUtvWPNcpVAw6E8UhcQznQDs3k5OTZQE+b0FhXzw72WdmZhTjOk5OTmL79u2YmJhQzHNzOp2ykqfnzp0TF/HKnek7PT3tN/P4nXfekf7trZPTYDDAaDTKOpvNZrOiE90YxPiEEAL74vbXQgwwiMeAL5Eaozoc4nndiIhExcXFaG1tVVyTRbm5uX6Dcd6CzjMzMz6zkK1Wq9jk1fe+9z2xSWK32/1mzKrR6/UoKChAa2srxsfHpdLJ4gNa7nnu3yg2m83n/Vqv16OpqUlsDjtfWchOp1PxkJjIXxluX+8fqnjZhqHS6/U+fwf5+o7+pKWlafqdlpaW5ndb2Wy2oM4RUqfWcR9MhvJFjyxnBPkeC5nDcRulRTtkr3zzJqxbtVRcNOGdeOkIzKb7sSknHdWV5ejrPYyLwxcCGhcw3MROWaiUSY2EP6h85xUrQwvc3S2MKTo1pf3/n0RaifcFxFmgTG399pXuVlxnA3mJ7xnLa1YoeN2hWIrV/VZNPP4eCSee6xQJDDoTxSHPjurk5GRNnWsiz0xbfx2oasTskNraWgwODmJychK1tbXIzs6G3W5HWVkZ6uvrZcu6iZ32gXTsvfbaa9K/y8vLvWYmjYyMoLOzU5r211nvGaB++OGHZfOCJQblCwoKvGaHeyNuK2/BCk+RHqM6FPG8bkRE4fD1r39dbJLxdh232Wxe72kZGRkB3T/Ee7XI20NhvhQXF6O/vx9VVVV+39+Tv+t5RUWF4l4XCf5KO/vKYoaf0tqpqak+x40OVbxsw3D40peUY2pFm9ls9ru9QgmAk9z4b96VTet0+qCylCeE9wkmW3oh0+n00On0UilHd2bdQjI+dgWmvCxYaioCzoxZm75OkWUYTmodsytD7JjVQgxiIQxZSOLff3xH+RlEoVLL9EsRghGxpHZO06d43aFYUjs3o3G/9RTPv0fCiec6RQKDzkRxyLOj2l9nmTdix+TJkydl8/158sknZdM2mw3bt2/HmjVr0NzcjJmZGVitVnR3d8uW8xRIx7nIc0zjwcFBZGZmoq2tTcqaPnr0KMrLy2EymeB0OpGcnIyzZ8/6zTzz7ND1lhUdKLGD2lfmmS+eGe3eghVukR6jOhTxvG5EROESaBUSN8+HqkSBBHmhYfloXmN9fS+o/K6IFH+BYX/Zrb6C0pHMckYcbcNgeFa3GRwcDKi6TaTo9Xq/DyH4+71F2r0zKh8Lbm36Ouh0gf9fgJnOviUlLUNX7wm8eflaXAVuwuV12ysoyn9ENVCF+eNq9xN78HR1Lbp6T6D32Blc/eBj6TU49HZEjxmXyyU2QRfC/3m1cqk8DBVqtujSpUmy6YWSsUXxL1bZimrUzmn6FK87FEtq52Y07rdu8f57JJx4rlMkMOhMFIc8M5uDyXLGfEfbD37wA2naXzlJkcFgQFNTk+rnGwwGnD171muGs5vYkRdIkPfJJ59EcXGxFDifnJxEdXW1ND50SUkJenp64HQ6kZubi/Hxcb8B58nJSWmd0tLSgg4YiDxLa/sbY1Mrte3uKZQxqu12uyLjOJiXt2MqlHUjIkoUwd5DfGWzTk9PK661vl5tbW3iW8iI9+FI8vdZwW6vYPgKzjqdThw9elRsBua/g6/s8IqKCrEprOJpG3ozMjIiVTIxmUxYs2YNlixZgs997nPSb7Tt27d7/Y0Qbf4yrqP5YMZCZ78sDzpvzNssm9bC5XLKMklWrFwdcqfXQpWUtAybjVvF5oR2a+omairLFSU1V6xcjYbGNrx7zYHBobfR3NKJp2vq8JDx0aCOs1CoHY+zd7w/yBQuah3toXbgfvKJvEM/mIdEiIKhllUXT9695pAFj0J9Rfs6FS687lAsxep+iwT5PRJOPNcpEhh0JopDnkFLfx2QvnhmO09OTvrtnBZZLBaMj4/j7NmzsFqt6OjowOjoKEZHRzUFC8VMokDG9C0rK0NfXx8+/PBDjI+PY2hoCH19fbBarbBarTh79ixGR0cxNzeH4eFhTRnhntnTvsYaDIQ7owfzgX5/5b198ez49Be4DmWMarvdrsg4DublLXs+lHUjosWtrKwMc3NzIb2iJdgAoHhv9DQyMqK41vp7xQtvJakRwlAhwSooKPD5u8DzYTFPvsba9vee4RBP29BTT08PCgsLcddddyEvLw+1tbVobm6GzWYL6XdqNPjbZ/G+/olCDBYDwH3ZX5FNa2G/fEnWwZcoGSKxotYhm8h+2GRVBKMys9bj5Ln/QEnp3rjosFy5SlnaM9SOWS10Op3YhFsfqGdfaTUl/P3dKZ+XTROFg9pxFeqxG05q5W/F+9lixesOxVKs7rdIkN8j4cRznSKBQWeiOPTFL352c52envbZQe2LXq9HX1+fNF1bWxtwRkdqairMZjPq6+tRUVHhNxjqyTNbKDU11W+JQ28yMjJgNBpRXFwsZXeZzeaA1gVC2cpwjOfsdDpRXV0tTTc1NQW8Tm7T09OyzmbPY8CXYMaoTk1NVQR/g3n5yx4KZt2IiBa6hRrk8vX7ItgAfSh8/ebwFiz1FoyGn+zpcIm3bTgwMIA1a9agvLwcAwMDQf8ejSV/2+39998XmygI169dVWSDZP5t4AFjcWzizQ8vrExe8s7lcuL8uZdlbZlZ69F77IxqUChW1AL9f3D8XmwKu7tVSqmrjXcZiFtT8uufWqZTIohWEIKCoxacEY+9WFqqcn3xVk53seF1h7yJxnU3VvfbePs9Eo1tzXOdIoFBZ6I4JI7RODExIZsOhNFolLJvnU4ntm/f7jOTJpw8x0Xs6OiIWYaOW7jHc96/f7+UPV1QUICqqipxEc3Ecp7iMeBNMN/JbDYrylwH87JYLOJbywSzbkREROHgb/xlscS23W73+nvL3zjRC1F9fT0KCwtVg/O+6PV6GI1Gzb9jIk3PDo6oEDvodTo91qavk7Vp8cZrv5RNP2R6VDZNC9fF4QuKBxee3HsgJh28/ogd4Td+F/msSLWsf8fvQ+wQFrKQ1v514OdsPPhEZdxPih9qx66YARdLauv3O2Y6A162Da87hChed2Nxv4233yPR2NY81ykSGHQmikNiFm9tba1sfqDq6+uljB+73Y7CwsKIB57b2tqkQGpFRUXMO2udTqcUIE5OTg45CFpeXi6V4TQYDOju7hYXCYhniVSDwaCpfHmkxqgOh3heNyKieJWWlqaoKhHKK5rXXl+f5SuDN1Lc29IbMatZnPbkL4AdLvGyDTs7OzWVbs/NzYXFYkFfXx+Gh4cxNzeH//7v/8bQ0BB+8IMfiIvHRCJmZycisQRhMCX0XC4nLg5fkKbXpq9TdDbSwqWWvRSv4yOKJT9PHDui6KAOt6SkZYoHOSbG35VNB8LhuK3Innog90HZNFE4qB2774xekk3HUlLSMkXA5dLIG7LpxUpt3/G6Q9EUi/ttIv0eCRee6xQJDDoTxSG9Xo/u7m4pO8Nms/kcZ1CL7u5uKRN3cHAQ2dnZiuzacBkZGZHKTldUVKCjo0NcJOo8x3P2N76fL5OTk8jLy5P2h9lsxvDwcEhZ3M3NzdL6ifvel0iMUR0u8bxuRESx5Ot+YTAYFFUlQnn5KjEdTeIQEtHiK1gsZjZ7+52l1+tRUVEhNkddtLbh9PS0bOgQNVVVVfjwww8xPDyMpqYmFBcXIzc3V1wsLvjL1A7lNyF95tYH8hJ6Scu8X+e8EcdzfsgYeJazw3EbB1saUVq0A/emp2DdqqUoyt+C6spy9HS1e+2orK4sR2nRDq+v122vyJYfH7uiWMbX8p7c63hgXwnyzZuwbtVSrFu1FJty0lFdWY7Tp46Lf7IoiMcQVDKc4sUjX90pm3Y4bqOv97CsLRIM2Rtk0yeOHVF06mr1r4daxaaE6FQXAxAA4IrCvZFCIx6758+9rBhOIZbEY/912ys+r+OLibjvFuN1Z7GL5XU3FvfbWP4eieW25rlO4cagM1GcMhgMaGpqkqbLy8tRWFjotfSjFq2treju7kZycjImJiZgMpmwf//+kN5TjcFgQG5uLrq7u+Mi4Iz5TGc3tYBubW2t1w5nzHda1tbWSsF6vV4Pq9WK/v5+1ffTwm63Y/v27bJM9kDGhQ73GNXhFI51+9Of/oTOzk781V/9laIMKhFRovIVdPZ8YCfR+MrSRZQzdd0KCgp8BhXdw4DYbDav62c2m32+RzjFwzbs6enxGdyuqqpCa2tr1LZJqPwFnZcvV45hRoETx4LT6XSyaS1ePHxINl34+Ddk0/6ceOkIthvvx8HWJlwcviBlr9kvX8LpU8fR2GBBvnmTaqBj5arVGB+7govDFxSv2dk7SBG+nzuoLi57cfgCHI7biuXdPNfRHXRxd8I5HLdx+tRxVFeWo7qyXJE9HknXr11VfA/75ehmIoqZhphfr3hUUrpXMU7t0SM/Dmifzc7egaWmQpbd70/xN/fKpl0up2rHrj9qnfaZWetV90G8WbFytWLb2+Moa5bUPbn3gNiEg62f9XXFmtr9xlpb5fVBpcWE1x2K5XU3FvdbtWMyWr9HYrmtea5TuDHoTBTHqqqqMDo6KpWmHhgYQGZmJu666y6YTCY0NzeLf+JXWVkZbty4gYqKCjidTnR2diIzMzOsgWe9Xo/h4eG4ybCCEGi22+1Sx+3MzAzKy8vR3NwsC5R6cjqdWLNmDZqbmzEzM4OCggKMj4+jvr4+4IBzdXU1TCYTPve5zyE7OxuDg4PAfMf46OhoQONCBztm8uDgIEwmU8gvX8dfsOsGj2Dz5z//eezfvx8fffSRuAgRUcLyNdat59AEicbftX5gYEBsiji9Xo/i4mKxWeJ+oMkdfFbjK1s63OJhG/oqM56cnIzW1sA7H2LpvffeE5tkWI0lPMSOJHEcN39uTd3E+XMvS9O7n9ijeE9fDuwrgaWmArOzd7A2fR2Ghsfw5uVrODn4Jn41NiVlTV+/dhVF+Y8oAqrPWBpw8tx/KDr6yvZV4uTgm4p1WbFyNXqPncGhrj5Z++4n9mBw6G3F8pjPpnavo06nR3NLJ65+8DF6j53Bu9ccqLN+9rv69Knj2G68P2qdnP9yqEWRrV1dGd3/x6mN9ae1g9gt0I7lYOl0epSUyjtnr1+7ivxtf6f6UIMnl8uJnq52bDfejxMvHRFn+2TI2aA4tvp6DyvGVPfnR61NimDadw74rnART1askmecxVOpZlKnFnA4f+7loM/V122v4Llmq9gctMys9di6bZes7dbUTfwoxMD467ZXYKmJfbWcUPC6Q4jhdTcW99tY/x6J1bbmuU7hxqAzUZwzGAzo7+/H0NAQrFYrysrKYDAYYLfb/XakeZOcnIyOjg589NFH6OjoQEFBgc9O8IUgIyNDFiC+5557YDKZcNddd6GnpwcVFRVex2V2l9ZsamrCjRs30N/f7zcbyRu73Q673Y7c3FyUlZXBarViaGgI/f39fjubPYUyRvX09DRsNlvIL2/HX7DrxmAzES0G27ZtE5tk3A8jJZr77rtPbJJ59tlnfWbQRsqTTz4pNkkmJydhs9m8BnPT0tJgNpvF5oiJh23o6yHERAvQTk9P+6xio9frF/zv32gRx4FzOG4HlIXi2Smv0+nxTK3/McXderrapYB1SspyHDv5qqwMYlLSMrR3/VTKPna5nLDWKR/yXLFytaJj8wsrVsmmRQ94lApMSVmOhqY22Xy3Ey8dkcpm63R6HDv5S+x+Yo80X6fTo2xfJbp6T0htDsdt/DCMQZV4tzZ9nSLo39hg0Rx4d2eRB9KxHIp/OFCtWN9bUzdRlP8Inmu24sRLR6QOcYfjNl63vYLGBgu25H0ZjQ2WoEtWPl39WYUszB/PRY9tUTxI4Y21rko1A0kMuMWzlSvl5Uf7eg9Hbb9T8DwfrHErLdqB55qtigCFN9evXUV1ZTn2le6G4/fBnUPefN/SgKSkZbK2F9pbUJS/RfN1yG187Ar2le7GvtLdAQds4hGvOxTL626077ex/j0Sy23Nc53CiUFnogRhNBpRX1+P7u5uDA0N4aOPPvIaJNUqOTkZFRUV6O/vF2ctOMnJyYqxkm02G1JTU9HR0eG3DHhHRwcsFkvQwWY3974bGhpCd3c36uvrg+rEDWWMaoPBAKvVGvIrPz9ffGsghHX7wQ9+gO9973t4/vnn8cc//hEXLgT+VCARUbzzF8Ssra0NS7ZzNEoxe/J3L5uenpYNJ+HLwMAAZmZmxOaguIf88Ka8vNzrZ0UzyxlxsA39jRsdyD09HvgL0pvN5oAr1pC6pKRlMOTIx4LTmiF2sKVRlglSZ232Wp5a5HDclpX++4cD1YqgAeaDujvzH5emx8euqGbIiB2b74z+SjZfZL/8lvRv8W/dHI7beK7ps+Dxzsf+XpFJ4vaQ8VHZWNbxNu5ppJXtk5fgdbmcKMrfgoMtjaoPMVy/dhU9Xe0w5WXBUlMRcMdyKFJSluO7Qucs5tf5hfYWWGoqpDG7N+WkY1/pbvR0tcvWcW36uoDHidy6bZfi4QiH4zZKi3b4HLf81tRNlBbtUHQG63R6tP6oW/XYjVff2rtfbIKlpgJ9vYcVHeMulxOnTx2PWoc9ebcxbzOeqqwRm/FC+6eVFl63vaIaoJ2dvYPTp47jwL4SmE33R2zc+7Xp69Dc0ik2w375Esym+/FcsxUXhy/4PMf6eg+jKH8L8s2bFtSY0LzuUCyvu7G438by90gstzXPdQonBp2JaNEoLi7GjRs3MDQ0hKamJgwNDeHDDz9ERUXilTwKZYxqg8GA+vr6kF/usu+iYNft+9//Pj766COUlpbiL/7iL8TZREQLQkZGhs8KEO5hH0LR3NyMzMxMsTmi0tLSfAZ3AaCtrQ0mk8lrQNxms8FkMqGwsDDggKkv/rKd1ej1+qgPExLrbehrvHEEOOa4r2BvNExMTKCzU9l57CnaDxUsdM3Pdco6lXq62n12gjkct2GpqZCN6/lUZY2is8uXfzt8SNax5+tvH8h9UDatVvIwJWU5Nhu3StPnz73stYMNAI7+5MfA/N95++x/PdQqW0dxzDxR8Te/LZv+lyDG00tU362uVWTNz87ewcHWJnwlayXWrVoKs+l+mE33S/9ubLDIAlWGnA2K94iUpyprFFlBWu1+Yg9ODr6JlavkGU1a1FmbZQ8nYL7jubHBgnvTU2A23Y/qynI812xFadEOfCVrJUx5WYpjXqfTo6GpLWrbK1weMj6qmjVlratCUf4WrFu1FPnmTfhK1krcm56C6spyvKVyvlP0fbe6VnXf2S9fwr7S3TDlZeHe9BSpzH9R/hZ8JWslqivLZUMwRMrWbbvQ3CK/l7m5g+P3pqfI1rG0aAdMeVkw5WXBWlelCAotFIv9urPYxfq6G+37bSx/j8R6W/Ncp3Bh0JmIFpXU1FQYjUZYLBa/GUXxLJQxqiMt2HW75557GGwmWuTsdrviAZdgXt4CifHCX3UNm82GwsLCgIJ8mB+jODs7G7W1tQEHHMPBV3DXzWaz4Z577sGaNWtgMplQXV0Nk8mEz33uczCZTLDZbOKfhKysrEz1IShfzGZzTDJ7Y7kN9Xq9zwci7Ha7pnOrp6cH1dWxG7urra0N2dnZPgPfBoPBb9UBCsza9HV4xlIva7PUVGBf6W4cbGmUSkwfbGmEpaYCW/K+LAWldTo9nrE04BmL9rLaEALHmVnrVQMFbuIYfRO/eVc27eZZ9vrTDJKfy+a7ORy3pSCItyxnCNnQOp1ekREu8gx6A8D132kr57gQ6HR6xcMLouvXrqqWuHQfQ8dOvqo5Uz4cnq6pQ3NLp2qGvZqt23bh5OCbXgNbKZ/3v+46nR7tXT9VdAq7Xb92FadPHccL7S24OHxBNSvL/R6ex3siebq61uc2Hx+7ovq9KbZ0Oj0OdfWpZjy7uVxOXBy+gIvDF3wGcLWcK8HY/cQe9B474/M64rmOF4cvqGZouy2UgAuvOxTr624077ex/j0Sy23Nc53ChUFnIqIEFMoY1ZEWz+tGRPHNbrejoaEh5JeWwFgs5ebmori4WGyWGRgYQHZ2NkpKSmCz2bx+J5vNhvr6eqxZswYlJSUBB6rDqaKiwm+mrpt7POW2tjbYbDafAcJQ6fV6v9tbFKss2FhvQ19jHDudThQWFmJkZEScBcyvz/bt21FeXh6WdVHT09OD7du3S9/Z/erp6UFtbS2ys7NRXV3t9/NbWxdP9mg0le2rxLGTr2Kjx1jHr9tewcHWJlhqKlBdWY6DrU048dIRKYN452OPY9D2ts8ghDeenX0ulxMHWxq9vsSSrN6CBJuNW2WdhP1esrXdZb19ZTlDWMe7Uz4vm6dGp9PLPl+tQ3MhM+RswMnBN/0G5z25s5iCOYbCYfcTe3DW9jaaWzqx87HHZeuembUeDxkfRZ21GYNDb+NQV5/X8uoA8Od/rhObVOl0enT1nkBzS2fAndruc85bh3IiyMxaj7MJ/h0Ws2csDTg5+KZqNp8vKSnLUbavEoNDbwf8kFIgDDkb8OrwrwMa7sHTxrzNaGhsw5uXr6GhsU2cnbAW+3VnsYuH624077ex/D0S623Nc53CYcnc3Nyc2EjhtWTJErEpqoaGhhI6o5OI1B09elTRsZuamgqr1RrzkuHhWLc33ngDmzd/2mnZ19cXcMCAiOJPfX09Ghoi10nk5uu3z5o1a7wGcMvKygJ6KMbXbzyr1Yr6ennWn6eZmRnk5eVhYmJCnOWTwWBAcnIyJiYmvJZYdvP2Mz+c20Bkt9uRl5fnN+jnS2pqKpxOp9ds7WDWcWRkBHl5eWKzqoyMDIyPj4vNURPLbXj06FGUlJSIzQoZGRlITU2FwWDAxMQERkZGvH6WGm/HJvwcn+FQUVHht9pAovjt1H+KTXHDfvkSLthewccfz8rGJV6atAyZf3svMrPWY7Nxq88sEl8cjtvYlJMuNmuWmbUeJwffFJsBAI0NFvR0tUvTb16+Jut0m529g0056XC5nHjG0uC1c1Fcx415m9F77IxsGTX55k2ybSZ+vtvBlkZZefKrH3wsm6+V2tjRS5OWoWxfpawt2vp6D+PML36O69euykqUG3I24O6U5bgvewN2F+1RbJsTLx3BrQ/el6YfyNssexBCND52RVa6N5rffVNOuuy71VmbA/5sl8uJ8+dexgXbeZw/97Jq1lFm1nps/epO7Hzs8aAyLy8OX5CV7wzHNgrXdj996jiuX7uK8d+8iz84buP6tavIzFovXWuWJi3DQ8ZHFd87nN/pYEujbHrrtl0+gx3h+mzx3M3MWh9wIBfzwyF87HHc+Fv/cO07zL/XxeELeGvkDdyauin7PmvT1yElZTkMORuQ8bf3Yudjj8v+NhpcLifsly/hrfl1nPrgfdlDS57r+IUVq7HVvEtxTfImXMeBp0i8p5pEve6EKlzrlOjnbrDX3XB9fjBCud9G6/eImlhv60Q71wO9H1Nw/mblX4pNCgw6R4GvDslo8NXxSkSJbXp6Wurszc3NjatzPdR1Y9CZaOFh0FlucnISJpPJ6/qEytvP/HBuAzWDg4MoLCwMKmhqNpvR19eH7OzssK9jdna2pkzw1tZWVFVVic1RFcttWFhYiIGBAbFZs4yMDBQUFKC5uVmcJfF2bMLP8Rkqs9mM/v7+gMutx6t4DjpH2sXhCygt2iFN735iDwoDKOOn81HqenzsCvLNm6RpMbDc09WOxgYLdDo9Xh3+taKT0U1cR61B59KiHbjo0fk2NDyGFStXy5ZBGIPOiWB29g4+nr2juh0SlcvlxL3pKbK2k4NvhqVz9NbUTUx98H7AndtERMHidYfiVTjvtwvx90igeK6TlqAzy2sTESWweB6jOp7XjRKT3W4PeqxSoniUlpaG0dFRlJWVibMSmtlsxtDQkOYy0Zi/Z/T19eHs2bNITk72mTUb7FjLWsZLDqYUdyTEcht2dHT4nO9LcXExRkdH8aUvfUmcFRbBrhcWYMB5sVu56otiEzbOZ5BoeXkLOGM+Y8OzI7L/+Gcltl0up1Rau6R0r9eAM1TG8lTLDvFHp9Mv6o5Nt6SkZQtuO4yPyccV1+n0imMmWCtWrmZnMBFFFa87FK/Ceb9diL9HAsVznbRg0JmIiIgSwszMDEwmk8/sOaJEk5ycjO7ubvT39wcUYPTFbDbHvHxwbm4uhoeH0dfXh7KyMq/BQqPRiI6ODty4cUMK9s7MzPgMmOp0vsfg8qa4uNhvwLGgoMDrukZbrLZhampqUAHvjo4O9PX1+d3GoTCbzfjwww9htVq9bg9RcnIyOjo6cPbs2YiuG0WX2OH3B8fvZdOhKnz8G9K/r1+7KpXBvGA7D4fjNnQ6Pf7hQLXHXyilpCxHUtIyafoPv/+sJKMvUx6lGFesWtwdmwvZc81W2XQo5eaJiIhIHe+3RNHH8tpR4Kv0YjT4KjFJRBSvWF6bRDabDSaTCZgPDHV3dyM5OVlcjOJYT08PXnzxRbE57FpbW2EwGMRmAEBJSYnXsZC3bdsGi8UiNnvlPh7VPPnkk0FlME9MTODFF1/EyMiI5sz+5ORkFBQUYNu2bSgoKPAbWAvnNgiU3W7HzMwMUlNTkZGRIc4G5pfJzs4WmyXd3d1BbVtoKB2dCL+bo7kNm5ub0dDQoFrmW6/XIzc3F/n5+aioqJAdd4ODg3j22Wdly3saGhoSmyS+ymuLZcF7enpw8eJFaTgPz/U0Go34+te/HlcPEoTbYi6vDQCmvCxpbM216eswOPS2uEjQHI7b2JL3Zbhcnx5TZfsqUWdtlsZbdk/7U5S/BfbLl6Tpd685fHZ0ulxObMpJl7Kit27bhUNdfeJiwCIrr73QnHjpCCw1FbK2rt4TeMj4qKyNiIiIgsf7LVH4aSmvzaBzFDDoTEQUOAadSeQZdMb8uKH9/f1egy5EC4E7wOj+t9PplDJQ09LSkJaWJvxF4hsYGEBhYaHYLBkdHfX6UIE/vgKaGRkZGB8fF5sTUri34fT0NCYmJmC322EwGJCRkRGxQK6vfSQGnRe7xR50rq4sx+lTx6XpQ1192Lptl2wZNeNjVzSN43dgXwnOn3sZmM9a7uo9IY317G2cZZGlpgInXvqsPHdzSyd2+xh7+nXbK9hXuluarrM2o2xfpWwZNzHo7C+gTeH1QnsLdDodHjV/TdOxgPmHCnq6DuFfD7XIyq1nZq3HycE3ZcsSERER77dE8UZL0JnltYmIKO5MTEzgf//v/y1N//M//zPeeecd2TJEExMTyMvL85m1SJToDAYDjEYjjEYjqqqqYLFYpOmFGHAG4DMbXq/XB/2gycDAgNdgJgA89dRTYlPCCvc2TE1NlY5Bo9EYsYAzUSC+I5S3Ptja5HPcZJfLierKchTlPyKVy/bFMzjscNzGgX0lwHz2sdZOz2dqG2SB4KM/OSybLzpz6ufSvzOz1qOkdK9svi/hLjFOvt343VU0NlhgystCvnkTerracXH4Ai4OX5Adh9evXcXF4Qvo6WqH2Xg/nmu2yubrdHq0/ogP0xAREanh/ZYo8TDoTEREceHGjRv4q7/6KyxZsgSZmZn4xS9+Ic27fPkyDAYDlixZgiVLluDo0aOyv6XFa2ZmBoWFhaivrxdnEVECGhwc9PkgSW5urt/y4d688MILYpNEr9cvmIoakdyGRPEkM2u9LDA8PnYFRflbpJLbbi6XEydeOoIteV/G6VPHfY5p7mmzcStSUpZL0+73fbq61mMp31JSlqNs3wFp2n75Eg62NMqWcTvx0hFZVnSdtdln5vInn7hk0y6VMvgUHeNjV9DYYEFp0Q6UFu3AV7JWYt2qpVi3ainMpvtRWrQDjQ0WxbGp0+nR3NKBtenrZO1ERESkxPstUWJg0JmIiOLCmjVr8H/+z//B3Nyc39dCCQxQ+DQ0NKCwsFAqQ0xEsTE5OYklS5Zg+/btaGtrw8TEhLiIqsnJSezfv99nSWgA+N73vic2aWK32zE4OCg2S4qLi+MmezdetyFRPHqmtgGGnA3S9PVrV2HKy8JXslZKHZKbctJhqamAw3Eba9PX4eS5/9BUXlun02Nn/uOytq3bdmn6W0/fra6VrePB1iZYairwuu0VuFxOvG57BY0NFmnMQZ1OjzprMzbmfTrMjJrxsSs4ceyzADUAvPjjQ7Jpim8pKcvRe+wMdj4mP8aIiIgofHi/JYo+jukcBRzTmYiIKHTimM5qOM4zUWxNTk5izZo1sjb32NMPP/wwMF8yfHp6GtPT03C5XLDZbBgZGZH9jRqDwYDR0VGxWZPq6mq0tbWJzZLh4WFprOxYi9dtGC0c01m7xT6ms6cX2lvwo9YmuFzes32fqqzBd6trfWYPi8bHrkjjOCOAcaPVvNDeohhbUGTI2YDm5zp9ZuKIY1l70un0OHbylwEHxilwWvanmpSU5fiHA9UoKd0b0LFIRES0GPF+SxRftIzpzKBzFDDoTEREFDotQWfMl8nt7u5mRjxRDKgFTMNBr9djdHQ0qAdKZmZmcM8998DppfRsvAVi43EbRhODztox6Cx3/dpVTE29j3cuX8L13/0Wf5jPbM7IvBdbzbtkpbID8UJ7Cz5xOfHnOj2eqqwRZwfE4biN8bEreOfyJdyauolbUzfxQO6DWLHqi1ixcrXP7Ga3Ey8dwa0P3hebJbuLvql5zGkK3a2pm7BffgvXr12Vjjs1D+Q+iM3GR2VZ70RERKQN77dE8YFB5zjBoDMREVHotAad3aqqqtDa2io2E1EERSpg2tfXF/SDJG1tbaiurhabJa2traiqqhKbYyYet2E0MeisHYPOREREREREFC1ags4c05mIiIgWpLa2NphMJo7zTJTAUlNTMTQ0FFKw9IUXXhCbJHq9HmVlZWLzghKObUhERERERERE5A+DzkRERLRg2Ww2ZGdnw263i7OIKI6lpqaiqakJ4+PjIVXsGRgYwMTEhNgsKS4uRnJysti8IIRrGxIRERERERERacHy2lHA8tpEREShC7S8tieO80wUPRMTE5icnMTIyAjee+896d++JCcnw2Aw4OGHH4bBYEBBQYG4SFCam5tx7tw5sVnS2toKg8EgNsdcPG3DaCspKcH09LTYDADYtm0bLBaL2Lxosbw2ERERERERRYuW8toMOkcBg85EREShCyXo7MZxnomIaKFg0JmIiIiIiIiiRUvQmeW1iYiIaNFoa2tDXl6e1yw6IiIiIiIiIiIiIgocg85ERES0qIyMjCA7O9tvqVoiIiIiIiIiIiIi0oZBZyIiIlp0pqenYTKZ0NnZKc4iIiIiIiIiIiIiogAx6ExERESLktPpxP79+7F//344nU5xNhERERERERERERFpxKAzERERLWqdnZ0wmUwc55mIiIiIiIiIiIgoSEvm5ubmxEYKryVLlohNUTU0NASj0Sg2ExERJRSbzQaTySQ2h01qair6+/uRm5srziIiIoo7v536T7GJiMir8bErmJ29I2sz5GyATqeXtS0ki/E7RwO3KxEFitcNooXhb1b+pdikwKBzFDDoTEREFLpIB50BQK/Xo6mpCVVVVeIsIiKiuMKgMxEForRoBy4OX5C1DQ2PYcXK1bK2hWQxfudo4HaNnfGxKzjzi5/DfvkSpj54H7embkrzNuZthk6nR/E3v42t23bJ/o4o1njdiJ7Z2Tt4satdbMbdKctRUrpXbA7JxeELeEvYrwCwddsuZGatF5tlvK2nmvs8HlBISlrm972Ddf7cyxgfu4JbUzdxa+qm7GGJzKz1SEpahhUrV2PFytVYmrQMj5q/tuiOYQad4wSDzkRERKGLRtDZrbi4GN3d3dDr+dQtERHFJwadiRa+2dk7GB+7ImvbmLdZNq3VYuzwX4zf2RseS4nN4biNH7U2oa/3sDhLlSFnA56xNAS9j4nCjdeN6Lk1dROmvCyxGQBwcvDNsAZs882bFPcWAGhu6cTuJ/aIzTK+1lOLjXmbYcjZgOJv7g35ODp96jh+2GSVPcijVWbWehQ+/o1FE4DWEnTmmM5EREREgqNHjyIvLw+Tk5PiLCIiIiKiqBgfu4LSoh2yF1EweCwlrvPnXsZ24/2KgPOKlauxMW8zNuZtRlLSMtk8++VLKC3agdOnjsvaiWhxO9jaJDYFzZ0VHCsXhy/ghfYWmPKyUFq0A9evXRUX8ev8uZeRb96E6sryoALOmL+/NjZYYMrLQr55k+JavRgx6ExERESkwm63Izs7GzabTZxFREREREREFFG3pm7CUlMhGwt3bfo6HDv5KoaGx9B77Ax6j53Br8am0NzSqQg+W2r2w375kqyNiBavcAaKwxnADtXF4QvIN29Cj8Zy3QBwsKURB/aV+N0ehpwNmjOYx8eu4F8OtYjNiw7La0cBy2sTERGFLprltUWtra0c55mIiOJKvJXXttZVaXqy/9jJV2HI2SA2S4ryt/jtIH/G0oCnKmvEZqIF5+LwBUVG6tUPPpZNa+U5LqGbwWOMxIVoMX5nb3gsJR6Xy4nSoh2ye2Jm1nocO/lLr9t6fOwKivIfgcvllNpWrFyNoeEx2XJE0cbrRvT4K1u9ddsuHOrqE5sDcv7cyziwr0RslgRbXntj3mY8kPugrM3trZE3AEAxnr2oztqMsn2VYrPMC+0teK7ZKjZDp9OjpHQvHvnqTq/Hp/3yJVwcvoBLI2/gddsr4uwFf83VUl6bQecoYNCZiIgodLEMOoPjPBMRUZyJt6DzxeEL+OW/n0Zf72FZZ7dbSspyFH/z2/hOZY1qB45bX+9h/PLfT6t24uh0euwu2oPvHKjRnHFAlMjCGSikxY3HUuLp6z0Ma538wWct47EebGlUZCAODr2NtenrZG1EtDCpBXN1Or3s97mWa4kv3sZydgs26Px0dS2erqmTtam5NXUTB1sacfrUz1X/39HVewIPGR8Vm4H5oHFR/haxGWX7KvHd6lpFxQhfHI7b+LfDh9DXe1h6qIJBZ5bXJiIiItLEPc7zxMSEOIuIiGjR25i3GXXWZmzM2yzOQkrKchw79SqerqnzGXAGgJLSvV47ippbOtDQ2MaAMxERLXgT4+/Kpg05GzQFibZu2yU2wT7qu4IIES1sJaV7ZdPigymBEEt0q11zIm3FytVobunEq8O/Vn2g5rnmerFJopbhXFK6F3XW5oACzpj/P84zlga8OjzGKkwemOkcBcx0JiIiCl2sM53dkpOT0d3djYKCAnHWouR0OnHPPfdgZmZGnAWLxYKmpuD/M0MUDtXV1bDb7WJz2AwNDYlNFALuL+3iLdPZTS0zq2xfJeqszbI2fyw1FTjx0hFpOjNrPU4OvilbhmihY3YqhQuPpcQjZhKWlO5FQ2ObbBk1LpcT96anyNq0Zg8SUeJTyyAeHHobpUU74HDcltqCzXb2vDbpdHr0HjujyByOdKazp+vXriLfvEmR8az2EKvDcRubctJlbYacDTh28lVZW7Dsly/hR61N6Oo9Ic5aMJjpTERERBRmMzMzKCwsRH299ycnF5OjR4+qBpwxP48o1o4ePQqbzRaRl7djn4LH/ZX4xPH6AKDw8W+ITQELx3sQERElCrF0rb9KIW5qy92dslxsIqJFRKfX4x8OVMvagsl2FrOcS0r3IuXzsb2+rE1fh2csyv65N177pdiEt4YviE3Y/PBWsSlohpwNCzrgrBUznaMg1pnOZWVl+OIXvyg2ExERJZT3338fPT09YnNMFRQUoLu7G8nJyeKsRSMvLw8jIyNis2R4eBi5ubliM1HUjIyMoKGhAYODg+KskFVVVaG1tVVsphBwf2kXr5nOjQ0W9HS1S9MpKcvx5uVrsmW0KMrfAvvlz8qBcjzK2Ll+7aosMwbzQQ1DzgZZWyw5HLdxwXYe58+9jI9n72Dqg/dxa+qmVO49M2s9HvnqTtXy71rcmrqJqQ/el6bVvr/L5ZTW4dbUTVy/dhWY7wxdsXI1tm7bpakEpcNxW/rb8bEraGywyOb3Hjsjm1aTkrJccb54vi+8fAeRlu+N+fXsP/5TjI9dgcNxGx/fuYO16euwNGkZdj7299j52OPinyi4XE6cP/cyzp87jT/Mr+vdn1+OFStXI/Nv78V9ORsUGUv+BPKdxe8aDklJy/xmkTkct/HO5UtSJ/47o79SZGspC22qAABoHUlEQVTpdHrcl/0VZGatx305G5CiMYAYT8eSN/Fw7mJ++5w/9zLeGnkDLpcT169dRWbWehhyNmDNX6/DZuNWzds9XEx5Wbg1dVOafsj4qKZgxvVrV2E23S9rU8v4i7ZA9kU8Xk98ed32Ct547ZfSeWy/fAlJScuwNn0ddDo9Hnz4ETxkfFRxLvmjdZt5crmcsF++hMys9QGVCr5+7SpOnzqOt0beAOarI7jPuwdyH8TdKcuxM/9x6T3F64DatcKTuLyW76L1+0fz/I30dgoHtQzioeEx3J3yeWzJ+7L0m06n0+PV4V8HtG3ELOdXh3+NT1wuxedFM9MZXq57a9PXYXDobVnbC+0tivLavcfOBH2PWYy0ZDoz6BwFsQ46ExERUeRkZGSgv78fGRkZ4qwFzV3+Njs7W5wlwxLbFA/sdrvPY9VoNMJqVY7tBACTk5O4ePEiOjs7xVno7u5GWVmZ2Ewh4v7SJl6DzmKweOu2XTjU1SdbRot701OkwEtS0jL8amxKXISiRCx1jvmx9IaGx2RtseBw3MZzTVbF+nmTkrIc362uVYxt6M/BlkZZRpDn95+dvYMXu9rx4uFDqpn+nlJSlqOppcNnsOPES0dgqakQmwOy+4k9aG6RXwfF99WyD319b8wHWhobLLIOdTVJScvQ0t6t+r1nZ+/gR61N6Os9rAi2irZu24Xmlk7NgZRAvrP4XcNhY95m1cDu7Owd1FSWS0G1QG3M24zvVtf67SQXv38wwnUsieLh3MX8Mfxcc70iq1iUlLQMdfXP+g2ihNO+0t143faKNJ2UtAxnbW/7DRCdPnUc1ZXl0rROp8ebl69pPm8iRcu+iOfriZqernb866FWzefx1m278HR1rd+HUdz8bTN4BNhPn/o5zp97WTYPGoJ4169dRWODRXaseaPT6bG7aA+e/PYB9B8/ghfaW6R5atcKT8FcN/x9/2iev9HaTuGgFswdGh7DipWrFQ+HBjIEzvlzL+PAvhJp2v23ap8X7aAzhP87QOV4gcoxhTh5KCeRaAk6s7w2ERERUQgmJiaQl5eHgYEBcdaC9uKLL+LFF18UmxVYYpvigb+HQrZt2waj0aj6KisrQ0dHh2qw0mg0ik0UBtxfiW187F3Z9H3ZyowUf8bHrsg6jQw5D8jmE2G+A3tL3pc1B60wH+iy1lXBbLpflkEYrPPnXsaWvCwcbG3yG3DG/OfvK92Ngy2N4qyE4nDcRmnRDuwr3e03QIT5QNC+0t2KfdXT1Y4teVno6Wr3GyDC/Pbebrxf02fGs49n7+B12yuaA1Ui9/jM1roqcVZCiIdz1/MY9hewwvwxbKmpwL7S3ZqO1XB48OFHZNPugKo//3JIXtWlpHRvSIHVSEvE64k7q7KxwRLQeXz+3MvIN29CX+9hcVZQ3OdSdWW5asDZnxfaW2A23a8pkIr5AHdf72GYTfeH7TsEI9rnb6JuJzX/cKAaOo8S/H29hzUfw57BWp1OWa471sQMcrV7hdpQA8FcA8g3Bp2JiIiIQrTYxnl2Op3o6enRVO58cnLSZ/ltomiYnJwUm2S0lID/wQ9+IJtOTk5GWlqarI3Cg/srcYnBYsyPbRao8d+IgeuvyKaJDrY0wlJToTje3DKz1mNj3mavgZbr167im09s19RZreYTlwvWuioc2FeiKdgsOtjalLCBZ3eg5qLKuIj+WGurcGvqphR8b2ywBLz9HI7b+KFQGnOx6us9LMtYSwSxPncR4jH8uu0V9HQdEpsjYnfRNxXboa/3sM9rx8GWRtm2cWeIx6tQ9kWsricXhy+gKH+LaqAqKWkZNuZtRtm+SmzM26wIgrlZ66pCCka6g96WmgrNAUPRwZZGRZnhQAS6rcMllGMmmPM3UbeTNykpy2UVI1wuJ/5VeFBFzeu2V2TXlpLSvX6rLkTbH34vPxfU1k+tykD/8Z+KTRQilteOApbXJiIiWjyMRiP6+/sX9DjPPT09KC//rGQbAOj1ejid6p1HLLFNsWaz2WAymcRmyUcffaTpnPX8XV9QUID+/n7ZfAoP7i9t4rG8dk9Xu2LM0HevOWQZFVqI5ZwPdfVpGguXIkPcH/BSsjBaxBKdbiWle7Hja3+vKDl8a+omfnftPdUSnCkpy3HW9rYisCNSK8co2rptFzY/vFUWZLBfvoSJ37yLC7ZXVDue1cYqd0RoHF5xu2nZh1q+99r0ddj5tb/HAx7b/a3hCxj/zbuqmXeGnA249cGngSJPmVnrUbzn27L1vn7tKvqPH5GV7Hc7Ofimauexp0C+szh2aKCsdVWKIJS3cSLFkqIb8zbjgdwHpW3o+Tezs3cwPnYFt6Zu4mBLoyJzS6fTo/fYGdUHfOLpWILK37nF+txdsXI1HjV/DY98dafU9gfHbUz85l309R5WnLvBjIMarIvDF1SzM8v2VeIZS73s/ip+16SkZeg9dsbveRIt4vqpiefrCebfI9+8SXV/fLe6VvV4dLmc+FFrk6zEMjQew+I2W7FyNZ6uqYO1tkqxDt6olSv2di6uTV+Hwsf3wJCzQTYm9PVrV+Fw3MaZX/wc5wdfVmxvaCgbLX6mluuG+P3VRPL8FdfZLZLbKRzEeww8ymtj/t6wJe/L0jGkZZsc2FcinYPi8mqfF+3y2rOzd/CVrJWytsys9Tg5+KaszeG4jU056bI2hPC5i5GW8toMOkcBg85ERESLS1paGvr7+2EwGMRZC0JeXp4sezk1NRVms9lr5nNaWhpu3LghNhNFTVtbG6qr1ct/GQwGjI6Ois2q1qxZI2XhNjU1wWKRd95SeHB/aROPQWcxOKnW2aNFvnmTLMDwq7Epn52yFFnifoXGzuJIuDV1E/nb/k7WiazT6VFnbfY71qvL5YSlZj9Onzoua9/52ONobe+WtYl8dXzvfmIPnq6pkzpz1YyPXcGBfSWKgKG/sRTdZZQ9Xf3gY9m0VmLnuZZ96Ot7r01fhzprs89xEMXxH9VszNuMOmuzz4DPc81WRcBGy3jxwXznYKhtpzprM8r2Vcra3ByO26it2Y9HvroTW827fHb0i9Qe7jHkbMCxk6/K2kSxPpbi8dxdm74O37c0+Hyoyb2vxLK6/s7dcDrx0hHVIOPa9HVofq4Tf3DcxsHWJtl9c8XK1fjJS2d9Xpeizd++iPfryezsHeRv+zvZdTwzaz2an+vw+Xlu9suXULmvRBaI9Hcc+dpmbplZ6/GQcSsefHgrkpKWYcWqL0qVZ965fAkP5G2WPdChdi5i/l7W0NSm6UHB122vSJnmbv6CqcFcN3x9/0ifv7HaTuGgFsz1DDoDCGhs5/GxK8g3b5KmxWXVPi/aQWf75Usoyt8ia/N2j9hXultxTGD+Aag6a7OmfbuYaQk6s7w2ERERUZhNTk4iLy9vQY5nbLfbFeWyy8rK8PWvf13W5okltinWLl68KDZJtJRqduvr68PQ0BCGhoZUxwym8OD+SlxiqUMxa00Ld1af29r0dQw4k+Sf6n+g6ADu6j3hN2iF+QBXa3s3nqqskbWfPnVccexqsWLlahw7+SqaWzr9BnYys9bj5Ln/UAQmAhlLMZ48XV2LwaG3fQaIMB/IEbe3W1LSMhzq6tOUifmMpUGxzFtB7LNIuDh8QREU2bptl9eAM+YzHN3HbSABZ8x39osd+eNj8iEJ4lE8nbvwOIZ9Bawwv6/au36q2E+vDP5CNh1Ju5/Yg95jZxTrcP3aVRTlb8GBfSXSfTMlZTmesTTg5Ln/8HpdOtjSiHWrlkovMegTbYlyPflRa5MseJiUtAxdvScU7+WNIWcDnqltkLX19R5WPIyklSFnA3qPncHJwTfxjKUBG/M2S1m3G/M24yHjo3i6pk7xW+yHTVbFuVhnbUZzS6fmYNtDxkexctUXxeaoicb5uxC2ky+BjO3seY/TxeFYzgDw4mFl2fSt2z7Lfvfk7aGBvt7D2JL3ZVjrqhTVNSgwDDoTERERRYDT6URJSYnXbL1E9eKLL4pNePLJJ2E0Gn2Wuz158qTYRBQ1vh562Lhxo9jkVW5uLoxGI4xGI1JTU8XZFCbcX4lpdvaOouM0mLGYxU4eQ7ayZCxF19Ztuz7NPPF4Pbn3gLhYxDkct3HBdl7WtnXbLkWHuj/f2ntA0dmo1lnpS0rKcpw89x+qJY29SUpahmcs9bI2rWMpxgudTo9jJ18NKAvpWyrHijsI7y9g4Ek85mZn7ygCAtF2a+omairlQ86sWLk64llsYuDK5XIqSnvHk3g6d4M5hnU6PXYXyQP94v0u0gw5G3Ds1KuK7+/p6epavDr8azxVWZMQD2sFsy9idT2Znb2jGIO5rv5ZRTDTn91P7JEFqV0uJ/7lkDzr2h+dTo+GxjYcO/lqwOeQw3FbUaY8M2u9z4dk4kkwx0ww52+ibyctUjSO7Tw+dkW2LYJ5WCrSTrx0RFEJQ6fTY7OXB1lWrFztdax7h+M2+noPI9+8CetWLUVp0Q40Nlhw4qUjiv+jkHcMOhMRERFFUFtbG0wmE2ZmZsRZCcfpdCpKaOfm5iIjIwN6vR5ms1k2z9NCzPqmxDA9PS2VWFazUMvgJyrur8SlliVkyHlAbPJLfJ+Mv71XNk3Rt3XbLjxdUyd7xaLj9d8OH1KUl33aS6ehL2JHK+bLtnrL8FHz5zpdUEGdh4yPKrLiThz7iWw6nt2d8vmAAu2Y395iB/XWr+70moXpTabKteBWCGMwh8rlcqK6skx23Oh0ehzu7Q/q2AhESspyxWdc/138Bp3j6dwN5hgGgA25D4pNfgNX4WS/fAl7SwsV29HTiZeO4A+O34vNcSuYfRGr68mJYz+RbXtDzgZFxQGtivd8WzZ96wPtx1FS0jKcHHxTcR5odeLYEcUxFMy5GCvBHDMI4vxN9O2klZZs53jPcrZfvoTG+h+IzXjGUq+4T3p6qrJGUynti8MX0NPVDktNhSwQ/UJ7i+r48PQpBp2JiIiIIsxmsyE7Oxt2u12clVCOHj2qCJ4/+eST0r/z8/Nl8zyxxDbFiq/jLjk52WsQc3BwEPX19aivr/cZBKXw4v5KXOLT/ykpywPuBAaA8d/Iy8QGmsVDC5fYubd12y5FAFcrtU7Td6LUeaiWYSd28i40K1bJrwUffzwrm9ZCbV/7ChpE2nPN9YpjsqGpDWvT18naIkX8nN/FcaazuJ0S8dz96/QviU2Y8hGkDKeernYU5W+RstlTUpbjUFefohz1ramb+OYT2wM+L1I+H19Zi/7E4nryxmu/lE1vfnirbDoQ4mf/7tp7smlfliYtU5z7gXjjNXnFgcys9QFliCeqQM/fxbKdxAd5xGxnMct5d9EexUMfsXJr6iYaGywoyt+iqFKQmbVe04MZZfsq0XvsTMD/X7k4fAHPNVtRlL8FX8laCWtdVVxXG4kFBp2JiIiIosA9znNnZ2TL7UXSCy+8IP1br9dDr9ejuLhYaisoKIBe7/1JUZbYjk9OpxM2my3qL1/BxXB67bXXxCaJt/GBnU4nysvL0dDQgIaGBp+l4ym8uL8S11sjb8im7wsiEwVCprNOp1d0ztLi5HI5FYGrB1Qyl7RSeyhCPIYjRe1BikAy3RJRUpL8ujx7JzwVgMSO5mg5feo4erraZW0lpXuDznz0xX75Ei4OX8D5cy/jYEuj9HL8Xv6gwic+MmBjaaGcu+Jnwk+QMlx6utrR2GCRpjfmbcZZ26dj2bZ3/TSowPMnn7hk03fHSRBJq1hcT+yX35JNP6ByHddKDBrfmrqpyKqNBLVz8SFj8MHzRBLI+bvYtpOvbGfPLGeoZOlHwq2pm7g4fEHx6ulqx8GWRinj2JSXpbgPY35fd/We8JvB7GbI2YBB29tobe/Gzsce1/x3bu7S+2bT/XihPbBS+QsZg85EREREUeJ0OrF//37s378fTmfk/2MZTna7XRYkLC4uRnFxsSy4o9frYTQapWnRwMCA2ERxYHp6GiaTKeqvkpIScVUiwleFAW/jAzc0NGB6ehqYL+fMIGb0cH8lLjHTOZigwq2pm7JO32BKKNLCND72rqJTPtTsvJWrviibjlaWyt0pnxebcGvKe8bVQqD2nYOhFjiItuvXrsJSs1/WZsjZgDprs6wtUA7HbZw+dRzWuiqUFu3Appx0rFu1FEX5W1BatAMH9pXgYGuT9PIWMIk3C+ncjbbz516WBZxXrFyN9q4+qWSsTqdHV+8JxcMOt6ZuouixLYr7spv4wIK4PeNdtK8n42NXFAFpl8upCIppfY2PXVGU/Y3Gg0dq5+IXVmjbBovJYttO3rKdxSznUCpUBOLES0dQWrRD8WpssOBga5PPsZU35m3GsVOvBpyNrdPpsfOxx9Ha3o1fjX2A1vZuPFVZg415mwMKQj/XbMWBfSWK68VixKAzERERUZR1dnbCZDJJQZJE8OKLL8qmn3zySVlpbbevf/3rYpNkYmICExMTYjNRRPnKqPbMnHVnfO/fvx/NzZ91HHvLrqXI4P5KTGodssF0TF0UxnMO5j1oYfqDSvnpFStDC5SI2X1TUQr86nR6RcBhKgoBBwrd7OwdHPiHEllAIilpGVrbewLqmPZ04qUjMJvux6acdFRXlqOv9zAuDl9YMCXXF9K5G00Ox21UV5bL2g55BJw9Nbd0KgLPDsdtlBbt8Bqc8fSFFavEJvIg/r4BgH2luxVBsUBe4ntG43wXA6lQKVVOi3M7qWU7ez7wgjgf0zolZTmesTSg99iZgAPOIncA2v1+715zYHDo00zosn2VqtVqPJ0/9zIsNRVi86LDoDMRERFRDIyMjCA7O9tngCVeOJ1O9PT0SNNpaWkwGo2qWc1ms1lskjl69KjYRBQxdrvdZ1WB7du3Y8mSJViyZAk+97nPwWQyKUrge8uupfDj/kpcYhlCnU4fVJbyhDCeczDZ0rQwiR30CEO2pPj3H99Rfkak3C189v/vFoPOicBSU6HIqm1p79acMelpfOwKTHlZqu/pz9r0darBx3i00M7daPnXQ62y4NdDxkd9PoilFnienb2jGngW90miZTpHm9qDE4lI7XusDPEBkIVoMW4ntWxnzwdBo5XlHKiNeZvR3NKJV4d/jacqa8TZYbM2fR12PvY46qzN6D12Blc/+FiqMqH2wNn5cy8rrruLDYPORERERDHiLmssBk3iTU9PD2ZmPhsrSy3D2S01NVU1GO32s5/9TGyiGEtLS8Pc3FzUXzdu3BBXJexsNpvYFDBfxzOFF/dX4poYlweL16avU+2E8YeZzuSNS+WBlGACfZ6WLk2STUcj08xNzMRR+34UX3q62mWlRjGf+SWOqavF67ZXUJT/iNcS2WvT12H3E3vwdHUtunpPSJ3c7tfg0NsJc31UO7YT+dyNBpfLib7ew7K2wie+IZtWozXw7PlvnU6P+4J4SGwxcbnkY2AnKrXvsXRZYjy8Ek2LdTuJ2c6eopnlvDFvM56urvX66j12BsdOvoqrH3yM3mNnvAZ+I+0h46NobunEsZO/VL2niZniiw2DzkREREQxlAjjPL/wwguy6YoK3+WCtm3bJjZJIlFie3JyUvN7Tk9Pw2azwWaz+Rw7NhImJyelz/Zc35GREVlQn8LnnXfeEZsknhn7RqMRqamp4iJIS0tDWlqa2BwUb/s/FO4S09F+RapCQzztr1heKxKRmOnsr/ScGpfLKesIX7FytWonDi1OOr2yQzHUQNMnn8g7lqPZaTn1gbwcsNr3o/hxcfgCnmuul7VtzNuMp2vqZG1a3Jq6iZrKckUJ1xUrV6OhsU0q5dnc0omna+rwkPHRoK6p8ULt2E7kczcazp97WXF8GHIekE1709zSqQgQzc7eQVH+I3jd9gocjtuyhx02G7cqHoIh/9695pA9CBLqKxrnuNpvqmiMJZ1oFut2ErOd3aKd5fxA7oN4uqbO62tj3uagqilFSmbWehzq6hObFQ/SLjYMOhMRERHFgc7OThQWForNMWe322UBF7PZrBrs8VRcXCw2yYSrxPbAwACys7OxZs0aVFdXi7MBANnZ2VI53iVLluCee+6ByWSCyWRSBNPD7Z577pF99po1a6TPHhgYkJYrLCzEXXfdhcLCQga3wsxXcPR73/sehoaGpNeHH36I0dFR2TKhZM1q3f+hcFdLiParpKREXJWwiOX+iuW1ItG5XE5Fadj7sr8im9ZCDFxHs4OL4p9OpxObQu4EFsdRvjvl87LpSBIzXO++O3qfTYFxOG4rgsQrVq5GS3u3bDmtfthkVZQ3zsxaj5Pn/gMlpXsXXAB1oZ270SDeUxHgd3y6pk4ReHa5nKjc9w38qLVJ1i5mRpOSWil7tX0U79TKqKuVkl7sFvN2Ust2Fq8lpJSZtR5bt+0SmxPyOhEuDDoTERERxQGDwYCOjg6xOebEYIuv0tpuaWlpMBgMYrMk1BLb09PT2L59uxSkNRqNsFqt4mIAgKeeesprICrSY7/+4Ac/8LodcnNzpX93dHQgLS1NCqJXV1cz8zkMZmZmfGYUqx0XBoNBtm8efvhh2fxAaN3/9KlY769YXisS3fVrVxUZWZl/G3jAWBz7bPPDW2XTtLjdrZKJF2on8K2p2GQbiwFnePl+3oSaJUqBqaksl21znU6P1vaeoLJDXS6nokR3ZtZ69B47oxrYirRoHEtqx3ainrvRonaNEANB/ngLPHuW7U5JWa4aKCG5pSrnpto+indqGbyO34d2Li5Ei3k7idnO0c5yTmT3ZSuzr6//jkFnIiIiIoqR4uJiDA8Ph60sbLg4nU709PRI08nJySgoKJAt401+fr7YJAmlxLbdbkdeXh4GBweRlpaG/v5+DA0NeQ3iVVRUoLW1VWwGohD4q6qqUv1svV4v++yCggLcuHEDra2tSE5ORltbG0wmEwPPIbL5GB9Yr9d7DQhnZGRI//a2jBZa9z99Ktb7K5bXikQndrzqdHqsTV8na9Pijdd+KZt+yBT4OKm0cKl1eobaCSxmW67968CP22CIpbUBBFQq8hOV8SYpMhobLIoSmc9Y6gPaX54uDl9QPKTz5N4DMQk4I0rH0kI6d6NFvK8iyIy5p2vq8IylQWyW7C5ilrMWasfw74LYH/FADKhOjL8rm6ZPLebt9N35cZN7j51BQ1ObOJu8SPm88gGrxYxBZyIiIqIYam1tRV9fH/Rx+IR+T0+PbJzpsrIyzevpLzgdTIntiYkJmEwmTE5OIiMjA+Pj434/B/PBc1FycrIsWBUpap9tMBhUt2NVVRVGR0eRnJwMu93OwHOIfJUq9xVEzM/Ph9VqhdVqDSmIiQD3fzDS0tIwNzcX9deNGzfEVQlZvO6vaF0rEplYJjaQEqBuLpdTFthZm75O0eFHi1tS0jLFwwyhdAI7HLcVWZ4P5D4om46UX/77adl0UtIy1aAGxdb5cy+jp6td1rbzscdRtq9S1haIPzh+LzZFZSzXWFpI5260qGWHB5sx91RlDZpbOsVmAMAXVvA+q4XaNfrSyBuy6UQhlo4+P6gcP5wW93ZKSlqGjXmbsTFvc1AVPRarWyoPFKpdyxcLBp2JiIiIYiA5ORlDQ0OoqqoSZ8WNYEpruxkMBp+Z24GW2HY6nSgsLMTMzAySk5PR39+vOXCnFszyVkY33NQ+21cALS0tDd3dn44RaLfbUV5eLi4Sdk6nEzabLeovX+P3hsNrr70mNkl87YOCggLU19ejvr5enBWwQPf/Yhav+yta14pEJnayJC1Llk1rYb98SdaZ95DRd5ZzdWU5Sot2SK+i/C1Yt2qpLDvs9KnjKC3agXWrlmLdqqUwm+6HpaZCUdpW9EJ7i+y93e9x4qUj0jKv217BvtLd0nuvW7UUpUU7ZO8jun7tKg62NGJf6W6Y8rKkdTqwrwQ9Xe0+OzMj+X0TiUEoXXji2BFF8Emrfz2krGwQjeDf9WtXZeVtAeABH58rdnwDgEvlARkKr1tTN2GpqZC1rU1fh+aW0IbCUcvwjdYDNrE8lhbCuRtNScuUme/vjF4SmzTb/cQe1Yxna12V7N5G3onH2Ou2V/C67RVZWyJ45Ks7ZdMOx23FPYm4nRayEy8d8fmbO1hvCQ+iBFv5aaFg0JmIiIgoygwGA0ZHR+M6mGG322UBGIPBEHAWoa8s5EBLbFdXV0vLd3d3B5R5ePHiRbEJ9913n9gUEWqf7W/c2YKCAmmM6oGBAXR2qmcnhMv09DRMJlPUXyUlJeKqhJVaANEtWmP0BrP/F6t43V/RulYkMvEpfp1OJ5vW4sXDh2TThY9/QzYtSlq2DONjV3Bx+AIuDl+A/fKnnfF/rtNhdvYO9pXuRnVluSx7+vq1qzjx0hEc2FeCF9pbPN5NLilpGa5fuyq9t/s9VqxcDZfLCWtdFfaV7lZ0NquVTHbr6z2MfPMmHGxtwuu2V/CJy4XMrPW4fu0qzp97GY0NFuSbNynGtXaL5PfVQtwenusQTcXf/GycQcxnyKsFoPxR6zzOzFqvyGSLhB82WxWdneK4q55WrFytGMvVHkLwifxzuZzYW1ooq+Kg0+lx6F/7FPsiUH+t0gEdTNnkYMTyWFoI5240iQEvAOjpOqRadlsLl8uJM7/4udgMALDUVDDwrIHa7xJrbZXieh7vSkr3Kq4DR4/8WFG1xpfZ2Tuw1FQohh5YSLidFq6DLY0oyn8krPfeW1M3Fb+LNxu3xmzojHjAoDMRERFRFMXr+M2iULKc3XyN64wASmxPT09LY0sXFxf7DGarUQtmRSvgr5bN6ytr062+vl4K8j/77LPibPLDbrf7LE0e7/t/sYmX/RXLa0UiEzv7xbE2/bk1dVOWjbv7iT2K9xQ1NLbh1eExRQbBx3fuIH/b3+F12ysw5GzAU5U1eKqyRpGh9FyzFc81f/pwj6ikdC9eHf61Ittap9Ojct830Nd7GGvT12Fj3mZFh6SaA/tKYK37tGN6bfo6DA2P4c3L13By8E38amxK+pzr166iKP8RRacVIvx9tfiXQ8rs7+rKMnGxiDPkbFAcG329hwMOxPyotUkRKPjOgWrZdCScPnVckXmu9p1EK1bJM2FDyXgk/6y1VYrO6Nb2bsX5Fwy1sYcDDUiEEsiI1bGkdpwn0rkbbWr3F5fLqci+16q6slz2UJP43gw8+5eZtR5bt+2Std2auokftTbJ2gL1uu2VoPdrMHQ6PUpK5Q+BXL92Ffnb/s7rg29uLpcTPV3t2G68f8EfL9xOC9v42BXkmzf5rTSkhcNxG998YrvifYq/+W3Z9GLDoDMRERFRFOj1enR0dMTt+M2enE6nFOTF/LoXFxfLltHCaDQiNTVVbJacPHlSbFLlObZ0oMHvmZkZ1UCSmLUtln52vyYnJ2XLBWJychLT09OytrS0NJ/bxNPXv/51YP59BgYGxNnkg9o+d0tLS0Nysrbyv9XV1ViyZAmWLFmCu+66S5ztU6j7fzGJh/0Vy2tFohMDMQ7H7YAyQTw7W3U6PZ6pVZYAVZOUtAyFj++RtRXlbwEA9B47g2MnX8UzlgY8Y2lA77EzijEte7oOeV1PnU6PwifkWU3WuirYL7+FQ119GBx6e/4zfqnovPfU09UuBRlTUpbj2MlXZaV0k5KWob3rp9KYee5MajWR/L6JRMwKdrmcKHpsi2qwXo21rko1U1IMJvjz8ewdRQDZl8YGC6or5UNm6HR6ND/nv5rJypXyssh9vYfZkR0hPV3tim1btq8y4OPDm7Xp6xTXjMYGiyLI7c2Jl46EFMiI5bEUL+duItDp9HjGohw25OLwBVhqKjRfyx2O2yjK3yK7VjW3dKK966eK45CBZ/++b2lQZC6+0N6Covwtms9ht/GxK9hXuhv7SncH/PBFqP7hQLVi/9+auomi/EfwXLMVJ146IgVWHY7beN32ChobLNiS92U0NliCLo2faLidFjaXyyntr2CDzxeHL6iewzsfe1zx8Opiw6AzERERUYSlpqZiaGgIFRXRe4o5FJ5BXsyXew42UGY2m8Umid1u1xSoef7554H5cbADzTpUyzQ1GAyyIJbdbleUfna/9u/fL/vbQNhsNrEpoPX3DPS/+OKLsnnhlJaWhrm5uai/bty4Ia5K2PgaH1jrPpienpaVNtf6d26h7v/FJB72VyyvFYkuKWkZDDny8Tq1Zv4cbGmUZevVWZulAKwWKZ+XL7s0aRl+8tJZRaYv5jOoy/ZVStMulxMnjv1EtownsWz4+NgVtLR3ywIcmVnr8d3qWmzM2wxDzgOy5R2O27Lysf9woFrRWY354MLO/Mel6fGxK16zaCL5fRPF1m27FNlHDsdtlBbt8NlpeGvqJkqLdiiCVjqdHq0/6lZ0LPszO3sHB/aV4CtZK6Wxs8X9dnH4Anq62qV1Ez1jqVc8tKHmW3uV1xdLTQX6eg8rAnYulxOnTx1n8CgI9suX8FyzPNCXkrIcDz78iKK8fCAvMUBYtu+AbNrlcqIofwsOtjQqlsV8dl1PVztMeVmw1FSEFMiI5bEUL+duoigp3avIDofHgwfiEA+eHI7beKG9BduN98v2a0NjG3Y/sQcPGR9l4DkIn47rrnxQyH75Esym+/FcsxUXhy/4PJb7eg+jKH8L8s2bfO7DSEpJWY7vqgzr4HI58UJ7Cyw1Fcg3b8K6VUuxKScd+0p3o6erXXbtWZu+Lmrj0ccKt9Pi4HDcRmODBZty0lFdWY6+3sM+HyJxOG7j9KnjyDdvQmnRDsVvv8ys9WhoapO1LUYMOhMRERFFUG5uLkZHRxOqpG44Smu7ubN1vfFXYntgYEDKFi0oKAg4S9xbIEmcdo+h7Emv1+N73/ue2KyZ2viwgYxNm5aWJh03AwMDmgL09Cm1gK+b1jF6a2trZQ9f+CsXLwp1/y8m8bC/YnmtWAian+uUdV6rZQt6cjhuo7qyHAc9gtNPVdYoAhKBenLvAZ+de8V75KXujh75sWzal4eMj6pmLTxVWYPeY2fQ2t4ta/+3w4dkHY++vtsDuQ/Kpi9qLJsbye8bz+qszYp94c5YuTc9BWbT/aiuLMdzzVaUFu3AV7JWwpSXpdiuOp0eDU1tmgK/3szO3sGJ+bGz3Z3P7ldp0Q40NlgUn4v5rE/PhwJ8ecj4qGo2p7WuCkX5W7Bu1VLkmzfhK1krcW96Cqory/GWymeSb+4y+J4cjtvYV7pbUV4+kJfYIf3d6lrFMTc7ewcHW5vwlayVWLdqKcym+2E23S/9u7HBIsukMuRsULyHFrE+luLp3I13Op0eh7r6VK/x7uPSfZ2x1FTgYEsjDuwrQWnRDmzKScdzzVbZQwzPWBpk9yEGnoOzddsuNLfIf/O4vdD+6VAU96an4N70FNl1wJSXBVNe1nzVFG3Z/ZH0VGWNovqAVruf2IOTg29i5Sp55YSFiNtp8ZidvYPTp47DWlcl3X89z+NNOenSAwbikAVumVnr0XvsjOpDposNg85EREREEVJRUYGhoaGgs4RjwW63K0rMPvvss4qsPq2vhgbfZVL9ldj2DID7C2CrUQv8Pfzww2KTYozt5ORkDA8P+8zU9kctiBXowweegTPxYQBSNzk56TNAryUDtqenR1ZiHhr/zlM49v9iEC/7K5bXioVgbfo6RSlQS00F9pXuxsGWRpx46QhOnzqOgy2NsNRUYFNOOk6fOg5IZUQ/LQsdKrWMX09r09fJghR/+L32jMFHvrpTbPLJM0iSmbVetYPaTRzndeI378qmvYnk941nOp0e7V0/VQSv3K5fu4rTp47jhfYW1UxTeLzH7ifkJcu18rU//XnG0oCna+rEZp+erq712Yk5PnZF9XuSdrN3ZsSmiNDNl1X3dQxdv3ZVNdPKfb08dvLVgKpCeIrlsRQP524iWbFyNQ739qtmPLtdHL6AEy8dwcHWJpw/97IiQL9i5WocO/kqnqqskbWDgeeg7X5iD3qPnfF5DrpcTlnFA7H8rqdYPTzxdE0dmls6fV4PPG3dtgsnB9/0GnQXq7EsFNxOC8tPXjqLpyprfJ6/bp7nsb8qI+7rgtbjZKFj0JmIiIgozNzjN3d0dAScmRtraoFNcezSQF5qgTdPIyMjPgNOngFwMetQC7XPF9/H6XTKvndGRgZGR0cVywVCbXzY5OTkgN/TM0gpvh+p85U9r9fr/e6DwcFBRanktLQ0RbDRl3Dt/8UgHvYXYnitWEjK9lXi2MlXZYHQ122v4GBrEyw1FVJms2dH9s7HHseg7W3VzvBgJC3zP/63ZyfT7Owd1aCGmkA7hT0DRi6XEwdbGr2+3AF4N1+d054i+X3jnU6nR1fvCTS3dGrqOPTkPu68Bb60uDvl83jz8jU8XV2r+fMNORu8Bn/8ycxaj7MhrjPFD0POBpwcfFMxNIEv7qy5YI4fT7E+lmJ97iaatenrcOzkL/FUZY1qAMsbnU6PktK9OHnuP3weZww8B8eQswGvDv864GFB3DbmbUZDYxvevHwNDY2xK8W7+4k9OGt7G80tndj52OOyYyUzaz0eMj6KOmszBofexqGuPp8PQPz5n+vEpgWD22nhWLFyNZ6xNODNy9fQ3NKJ3U/sCeocdtv52OPoPXYmoAcTFoMlc3Nzc2IjhdeSJUvEJiIiIlqgUlNT0d/fn5AZjU6nE3fddZesRG00NDU1wWKxiM2A8Dsq0J+tdrsd2dnZsrbk5GR89NFH0rTT6cT+/fulLMni4mJ0dHTIxnENxsDAAAoLC2VtZrMZZ8+elbX54/kdMjIyMD4+Li5CHmZmZpCZmSmVZBdlZGSgo6NDbJb87Gc/U4xpDgBlZWXo7paXzvUlXPt/oYuX/RXLa0Uofjv1n2JT3LBfvoQLtlfw8cezsvJzS5OWIfNv70Vm1npsNm5VdHIH6sRLR2CpqZCmh4bHVEuRehI70geH3lYNKF8cvoDSoh3SdO+xM34zi90cjtvYlJMuNmuWmbUeJwffFJsj+n19URuveGnSMs3loSPN5XLi/LmXccF2HufPvawaWM/MWo+tX92JnY89HvD3x/z4457l4FesXI2h4TFp+sRLR3Drg/fxzuiv4HI5Yb98CZlZ90Kn0yMzaz0KH/+Gz07oQJw+dRzXr13F+G/exR8ct3H92lVkZq2Xzq+lScvwkPFRxfccH7uC8+delqa17MOLwxdk5ZW1/I0a8RjKzFqvWubZn56udnzssX+3btvlc7sG8p3F9w6X3UXf9Hme9vUexplf/BzXr12VZVIZcjbg7pTluC97A3YXKTvF3cec2wN5mzVfo9yieSypica5G65jGPPXAU/+jr9wm529gxPHfoI3XvslLqqMHZyUtAyGnAew47G/x87H/j6ge6y4ndye3FcZtkCK+BnB7otYXU+8cV/z3xq+gLdG3sDUB+/LHh5bm74OKSnLYcjZgC+sWI2t5l2K89mbcG2zSNiUky67ZtVZm32uWzDXjXB+/1idv4Fup3CYnb2DF7vaZW3hPJdFap+nZfuq/V0w97JwGB+7govDF3Dj+m9x/dpVRdUIt8ys9TDkbMB92Ruw2bhV87m8kPzNyr8UmxQYdI4CBp2JiIgWB6PRiL6+voQqp+2pra0N1dXVYnPE5ebmYnh4WGzG5OQk1qxZA8xnLt64cUNcxKeenh6Ul5fL2jwDfxMTEygvL8fIyAj0ej1aW1tRUfFZZ34o6uvrFaXFrVYr6uvl5Wf9mZ6exj333AOoBMFIbnBwEOXl5V4DmKHo7u5GWVmZ2OxVuPb/QhZP+yuW14pQxHPQOVrCEYT1FkwOJegs/u3uJ/agMIBSsDqdXjUzLZLfd6G5NXUTUx+8H7bv6i/oTBQOs7N38PHsHb/n9UIW7nN3obNfvoSUzy9f1McMRZ/L5cS96SmytpODb/oNMi423E60EGkJOrO8NhEREVEYVFVVJdz4zaJ33nlHbIoKb0Enz7LbwWQTvvbaa2ITNm7ciOnpadTX1yM7OxsjIyPSmKzhDCKpfXagY8xiPnPebWZmBjMz0RlvMNFMTExg+/btXo+lUAW678K1/xeqRNhf0bpWUOxFIutj5aovik3YOJ+5oeWlFnAOl0h833i0YuVqBq0o4SQlLVv0wUOeu4Ex5GxY9McMRd/42LuyaZ1OH1QlgoWO24kWK2Y6RwEznYmIiBYuvV6P7u5uFBcXi7MSTnNzM86dOyc2R1xqair6+vrEZln2YTCliTMzMzExMSE2K+j1enz44YdBBbbVeCtT/t///d9BjfF9zz33SMG58fFxZGRkiIssejabTZFZHC7Jycno7+8Xm70K9/5fiOJpfyGG14pQMdNZmfmrpXR0adEOWcm8Ny9fUy2NJ2YrB5ohvG7VUunfDxkfRVfvCdn8YETy+5JvzHQmIiL6lPjbYuu2XTjUpfz//GLH7UQLkZZMZwado8BkMolNREREFKCZmRnY7XaxOabS0tLQ398Pg8EgzqIwaG5uRm1tLRDEGK0zMzO46667xGYYjUaMjIwoAoL9/f0oKCiQtQVLbXxYg8GA0dFRWZtWa9askbK+h4aGAs7ipOgK9/6nyIrltSJUDDorg7BaAsNm0/24fu0q4CdwGGrQ2ZSXJY3puDZ9HQaH3hYXCVgkvy/5xqAzERGR8rcIAHT1nsBDxkdlbYsdtxMtVAw6ExER0YJhs9ni6kEuo9GI/v7+uMl4W4g6Ozuxf/9+IIhM54GBARQWFsra3ONCHz16FCUlJbJ5FRUV6OjokLUFS21s7KqqKrS2tsratPIMOg8PDyM3N1dchOJIuPc/RVYsrxWhYtBZ2aHX3NKJ3T7GTr41dROmvCxpumxfJeqszbJl3MSgc6AdhdWV5Th96rg0fairD1u37ZIto2Z87IrXsf4i+X3JNwadiYhooXihvQU6nQ4PGR/1WzHFzeVyoqfrEP71UAtmZ+9I7ZlZ63Fy8E3ZsgsFtxORkpagM8d0JiIiIgqQe/xmBpwjSxzPOBBqWfHujHSz2SzOwuDgoNgUNG/jwwbLc2zrRB4zfLEI9/6nyIrltYLCb+I38rHzRCeO/UT6t06nxz8ckD8g4ssfHL8Xm3z6jvDeB1ubZJ2PIpfLierKchTlP4LxsSvibFWR/L5ERES0MN343VU0NlhgNt0Ps+l+NDZYcPrUcVwcviD7DXJx+AIuDl/AC+0tMBvvx3PNVtlvGZ1Oj9Yfaa9Glmi4nYiCw6AzERERkUbJycno6+tjxmKUpKWlSf92j2mslVrg7+GHHwbm96M4LvLk5KQsuBuKkZERsSno7GQx2O65TSg+hXP/U+TF8lpB4dfXexgOx22xGZjPIH7x8CFp+rvVtT7HNv5YCBA7fq/+vt5kZq2XZSGPj11BUf4W2dh+mA82n3jpCLbkfRmnTx2HTqeTzfclnN+XiIiIFp/r166ip6sd1ZXlKC3agXzzJqxbtRTrVi1FadEOlBbtwHPNVmnIEDedTo/mlg7NGcCJjtuJSDsGnYmIiIg0yMjIwPDwMIqLi8VZFCGhBJ3Vshc9A39qQcCBgQGxKWAzMzOKdU1OTg46WOz5XsG+B0VPuPc/RV6srhUUGS6XEwXbNuHES0ekcYzHx67gxEtHUFq0Q8o6ecj4KMr2HRD++jOzs3dw9Cc/lrX1H//sPbV6prYBhpwN0vT1a1dRWrQD61YtRVH+FpQW7cCmnHRYairgcNzG2vR1OHnuP7yW1xaF6/sSERERaZWSshy9x85g52OPi7PIA7cTLVYMOhMRERH5UVBQgOHhYUXGG0VWcnIy9Ho9AMDpdCqyfr2x2+2KZfV6vVQyFwDuu+8+2XwAOHfunGy6p6dHNo35966vr0d9fT3a2trE2aoZkJ4BR6fTiaNHj8rm++IZwGRp7fgX7v1PkRXJawXFjsNxG5aaCphN92PdqqXIN2+CpaYCs7N3kJS0DM0tnejqPQGd7tP7i+h12yv4StZKvG57RdZ+/dpVmE33o7qyXNbuS0rKchw7+SqesTQoPs9++RIuDl/A7Owd6HR6PGNpwMnBN7Fi5WrZcv6E+n2JiIhocVnz1+uQlLRMbPYrJWU56qzNeHX417KH6hYqbiei4CyZm5ubExuJiIiI4o3NZoPJZBKbI85qtaK+vl5spijJzMzExMQEAGB0dFQWDPKmra0N1dXycStzc3MxPDwsTU9MTCAzM1O2jF6vx40bN5Camory8nL09PTAYrGgqalJWubo0aMoKSkB5oPAH374occ7qB+nBoMBo6OjmJmZQUlJCQYHB9Hd3Y2ysjLZcmp6enpQXv5pgKO4uBh9fX3iIhRHwr3/KbIiea2Iht9O/afYtOiceOkILDUV0vTQ8Bh+d+09vHP5Ev7wh9/j+rWruDtlOdb+9d/gvpwNyMxa77fE9PjYFZw/97LYLFmx6ouystlaXb92FVNT7+Ody5dw/Xe/xR/mM5vvy96Ardt2aerUjMT3JW0uDl/AWx6l0ZcmLUPZvkrZMkRERInk1tRN2C+/hevXrkq/TdQ8kPsgNhsfXbQBVG4nos/8zcq/FJsUGHQmIiKihKAWzImk5ORkdHd3o6CgQJxFUVRbW4vm5mYggAcA3EEgT1VVVYqxuLOzsxWldd3Z1dPT00hLS8PQ0JAsU1U8DsWf0pOTk1izZo2sDQCMRqOUVWk2m9Hf3y9lcfuyfft2DA4OAgD6+/t5PMa5cO9/iqxIXiuigUFn9SBsoJnCiWSxfV8iIiIiIoofWoLOLK9NREREJHCP38wAX+x973vfk/79s5/9TDbPG5vNJjbh4YcfFpsUgSV4jMlrNpsxPDzsM4ikFjRMS0tTPW5sNhtmZmZgsVg0BxydTqf0XVJTU2E2m8VFKM6Ec/9T5EXrWkFERERERES0GDDoTEREROSB4zfHl9TUVCmINzExoTpmrien04m0tDQYjUbZS60st9FoRH9/P8xmM5KTk5GWloaKigqcPXsWZ8+eVR1D2fPz1d4TALq7u1FRUYG0tDTo9XqYzWZYrVaMj4+jqalJc8BxYGAATqcTAPDUU09p/juKrXDtf4qsSF8riIiIiIiIiBYbltcmIiKihCCWNY6E1tZWVFVVic0UY4ODg9i+fTsAoKmpCRaLRVwkapqbm1FbWwtEYV1KSkpw9OhR2fixRERuLK+9+MpNL7bvS0RERERE8YPltYmIiIg0SE5OxtDQEAPOccpsNkuZ588//zymp6fFRaLGXeLbYDBE9HgZGRnBwMAAMP/9GXAmIlK68bursunfXXtPNr3QLLbvS0REREREiYVBZyIiIlrUDAYDRkdHYTQaxVkURzo6OgAA09PTKCkpEWdHRVtbG+x2O1JTU9HX1xexMskzMzMoKSmB0+lEcnKy6niyRESLmcNxGz1d7ejpOiRrt9ZW4fSp43A4bsvaE91i+75ERERERJSY/l/19fX1YiMRERFRvJmcnMSLL74oNoekuLgYv/jFL3D33XeLsyjOpKWlAQBee+01aVzlaD4oYLfbUVhYiIyMDAwNDSE9PV1cJGxKSkowMjICAOjr60Nubq64CBER/s/sJ2LTovH//f98H/9yqBX/z//zJ1n7x7N3cO7MSfzPv/if2Ji3WTYvkS2270tERERERPHn/53052KTAsd0JiIiooQQ7jGdOX5zYqqurkZbWxswn/1cUfHZ2JaR1tnZieLiYiQnJ4uzwsLpdKK2tlb6ft3d3SgrKxMXIyICOKYzERERERERRRHHdCYiIiIScPzmxNba2oqmpiYAwP79+5GdnQ2bzSYuFhEVFRURCzj39PRgzZo1aGtrQ3JyMs6ePcuAMxERERERERERJQwGnYmIiGjRyM3Nxfj4eFTLMlP4WSwWjI+PIzc3F3a7HSaTCdXV1eJiCSM7Oxvl5eWYnp5GWVkZxsfHYTabxcWIiIiIiIiIiIjiFoPOREREtChUVFRgaGgIqamp4ixKQBkZGRgeHsbo6CgqKirwpS99SVwkYeTm5qKpqQk3btxAd3c3j1EiIiIiIiIiIko4HNOZiIiIEkKwYzrr9Xq0trZGdexfIiKiSOOYzkRERERERBQtHNOZiIiIFrXU1FQMDQ0x4ExEREREREREREQUQQw6ExER0YKUm5uL0dFR5ObmirOIiIiIiIiIiIiIKIwYdCYiIqIFh+M3ExEREREREREREUUPg85ERES0YOj1enR3d6OjowN6vV6cTUREREREREREREQRwKAzERERLQhpaWkYHh5GWVmZOIuIiIiIiIiIiIiIIohBZyIiIkp4RqMRo6OjMBgM4iwiIiIiIiIiIiIiirAlc3Nzc2IjERERUbyx2WwwmUxiM6qqqtDa2io2ExERLWi/nfpPsSkmxseuYHb2jqzNkLMBOh2HuVgseAyoO/HSEVhqKqTpFStXY2h4TLYMEREREVGi+JuVfyk2KTDoTERERAlBDDq7x28uLi6WLUdERLQYxEvQubRoBy4OX5C1DQ2PYcXK1bI2Wrh4DKhj0JmIiIiIFhItQWeW1yYiIqKE4x6/mQFnIiKixDM7ewcXhy/IXhRd3AdERERERBRuDDoTERFRQjGbzRy/mYiIKIGNj11BadEO2Yuii/uAiIiIiIjCjUFnIiIiShhWqxVnz55FcnKyOIuIiIiIiIiIiIiIYoRjOhMRERERERElmHgZ03l87ApmZ+/I2gw5G6DT6WVtni4OX1Bk1l794GPZNEVWOPdBMMfAYsAxnYmIiIhoIeGYzkREREREREQUMZlZ67Exb7PstdiDjYsNjwEiIiIiIgKDzkREREREREREREREREREFAoGnYmIiIiIiIiIiIiIiIiIKGgMOhMRERERERERERERERERUdCWzM3NzYmNRERERERERBS/fjv1n2JTTDgct3H92lVpWqfTw5CzQbYMhOXGx66gscEim9977IxsWk1KynKsTV8nNnv1uu0VvPHaLzE+dgUAYL98CUlJy7A2fR10Oj0efPgRPGR8NKD3BIBbUzcx9cH70rS37+zJ5XLCfvkSMrPWIylpmThbcmvqJsbHrkjr/M7or+ByOWXL6HR63Jf9FWRmrcd9ORuQkrJcNt+bSO0DrcfA+NgVzM7ekaa9LReo69euwuG4LU0H+r6ROk5OvHQElpoKaXrFytUYGh6TLYM43i5ERERERJ7+ZuVfik0KDDoTERERERERJZh4CTprDayJywVj9xN70NzSKTbLuFxO9PUexr8eapUF3HzZum0Xnq6uRWbWenGWqoMtjTjY2iRNq31nl8uJ8+dexulTP8f5cy/L5gHA09W1eLqmDpgP2tZUlsN++ZIiwKzFxrzN+G51LTbmbRZnyURqH4jvq7Y9AOC5ZiteaG+RtQ0OvR1wMFdkNt0vC3rvfOxxtLZ3y5YRReM4CWW7nBx8U/PneBPMdiEiIiIi8kZL0JnltYmIiIiIiIgo4Y2PXcGWvC+jscGiOZAIAOfPvYx88yb09R4WZwXlxEtHsCXvy6iuLFcNOIs+cblwcfhCUAFnALg4fAGlRTtgrasSZ8WVb+09IDbh6JEfi00BuTh8QRZYBYDvHKiWTYvi5ThxU9su/cd/KjYF5HXbKwFvFyIiIiKiUDHovMjZbDbpZbfbxdlEMjxeyB8eI0REREQUC6/bXkFR/iOqQcS16evwkPFRPF1di52PPe41I9haVxVSQPH6taswm+6HpaZCdT0ira/3MHq62sXmuJGSshxbt+2StZ049pOgg+0AcPQn8qB1ZtZ6nxnC8XCciNS2S1/v4ahuFyIiIiKicGB57QRkt9sxMzMjTRsMBiQnJ8uW0WrJkiXSv41GI4aGhmTzybfJyUn09PTI2urr62XTC0kiHi+LbR/FWiIeI0SB4nWFiIjiQaKV147UeMKYH4O3tGiHIkj3VGUNvltdC51OL2t3e6G9BT9qbZL9XUrKcpy1ve1z3GW18tpP19TBWlulWAdvPMtr35q6CVNeljRvY95mPJD7IB6YD3p6Bj9nZ+9gfOwKbk3dxMGWRtyauinNw/y4vb3HzqiO3RupfaD1GIBHVran5pZO7H5ij6xNC4fjNrbkfVm2zVvbu7Hzscdly7lF+zgJdbvUWZtRtq9S1qaFw3Ebm3LSZW2+tgsRERERkRZaymsz6JwAJiYmcPToUZw8edJn5mBubi70ej2+/vWvo7i4WFMgmgGi0NhsNphMJlnbQj6lEvF4WWz7KNYS8RghChSvK0REFA8SLejsSS3AdvWDj2XTWs3O3kH+tr+TBV8zs9aj9UfdiuComuvXrqK0aIcs87VsXyXqrM2y5TyJQWc1mVnr8ZBxKx58eCuSkpZhxaovYnzsClwuJ965fAkP5G2WgskOx23U1uzHI1/dia3mXUhJWS6+nVc9Xe2K4LEhZwOOnXxV1iYK5z4I9BjIN2/C+NgVaTozaz1ODr4pW0aLF9pb8FyzVZpOSVmOV4d/rRo8jsVxEqvtIo4R7Wu7EBERERFppSXozPLaccxut6OwsBCZmZloaGjwGXAGgJGREdhsNuzfvx/33HMPysvLMTIyIi5GREREREREtCD8qLVJFkhMSlqGrt4TmgKJmC+p3NDUJmvr6z2syCDWypCzAb3HzuDk4Jt4xtKAjXmbkZm1HklJy7Axb/On5Ztr6mTZyykpy9HVewIlpXsDCjhjPvApZgmPj70rm443xXu+LZseH7siC7ZqdfQn8hLXu4v2eA2sxttxoqbw8W/IpsfHruDi8AVZmz8ulxMnjh2RtRV/89tetwsRERERUTgx6Byn9u/fj+zsbAwMDIizNHE6nejp6UFeXh4yMzNhs9nERYiIiIiIiIgSlsNxWzG2bl39swEHbrdu2yUb79blcuJfDn2WKaqFTqdHQ2Mbjp181etYwJHyTG2DbNrlckpltOPR7qI9irLUR4/IxyD253XbK7KAr06nx7f2HpAt4xZPx4kvJaV7FcHhFw8fkk37c/rUz2XZ2DqdHsWle2XLEBERERFFCoPOcWZmZgbbt29HZ2enOAsGgwFNTU0YGhrC0NAQ5ubmMDo6iqGhIXR3d3stqT0xMcGgc4SkpaXBarXKXhRfuI+IKNx4XSEiIooPp08el42za8jZoMj61UrMvr31gfYM1qSkZTg5+CZKYhTcS0lZrgjiXv9d/AaddTo9dhd9U9Z24tgRzM7ekbX5cvQn8iD1ZuNWr0HkeDlO/NHp9Ipj6ILtvCyI7I8YpN66LbBy7UREREREoWDQOc4UFhZicHBQ1mYwGDA8PIzR0VFYLBYYjUYYjUZpntFoRFlZGfr6+vDRRx+hu7sbaWlpsvegyEhLS0N9fb3sRfGF+4iIwo3XFSIiovjwy38/LZve/PBW2XQgPDNYAeB3196TTfuyNGmZ5jLNkSJ+/u/iONMZAJ4UspI/LQv9E1mbNw7HbVywnZe1fedAjWzaU7wcJ1qIQW2Xy4mjQpa2N2plyr9zoFo2TUREREQUSQw6x5H6+npFRnJZWRlGR0eRm5sra/elrKwMN27cQHd3t2rmMxEREREREVGiEwNsD4RQ1loM2t6auinLjo0X9suXcHH4As6fexkHWxqll+P38mzYT+Jw3T2tWLkaW7ftkrX1H/+pbNqbo72HZfsmM2s9DDkbZMt4SqTjZG36OjxkfFTWdvQnP9b0GWKWc2bWekWQnIiIiIgokhh0jhM2mw0NDfJxmIqLi9Hd3S1rC0RZWRnGx8dhNpvFWUREREREREQJa3zsiqIcs8vlxMXhC0G9xseuKEpUh7N0cqAcjts4feo4rHVVKC3agU056Vi3aimK8regtGgHDuwrwcHWJunlOb5xohBLXI+PXcHF4QuyNjViaW0xa9pTIh4nxd+UZzurZXaLPj1efi5rY5YzEREREUXbkrm5uTmxkaJv+/btsrLaqampGB0dRWpqqmy5YE1PT6u+15IlS6R/G41GDA0NyeZrMTk5icnJSQBAbm4u9Hq9uEjYuDPBDQZDWLO4I/W+gbLb7ZiZmYFerw8ou92bcO+bcBwvwYr1PpqensbExIQ0nZaWFrUy9iMjI3A6P32yPdyfu5COEU/i/sL8+sQbp9OJkZERWVtycjIMBoOsLVzCfY0BgImJCUxPT0vTkThHI3X+R+p9gxXJc108J8L9/kREFH2/nfpPsSkmTrx0BJaaCml6xcrVGBoe+/+3d7+xbZX5nsC/ebMx0r3gSJutKwo43F5IlJ3WhkG1NeoQo46aDNUk4XJb50KXZGBpQrdq3Fkpcd/YedOk0l4cNEuT7sCN2V6UUGZpiqBJNQib4XJjdu7QUy6oge2SDAOqub0SprNSnX2TfbE5xs9zjp1znHP8J/1+pEicxyePj8/znAP4m99zhH1kHy68j4P7fyq0ff7HPwnb69Hrw2pnzl7ALp2q2F8+fwK/jI3mto18ZqPeeP1V/LdTz+OLDS6PfSQUxpFjx+XmHL3zZ3YMVKXMAVV74EHhsz76s8cRe7HwH9+/c/EtPPdMT267sXEL3l34BPX1+v8fo/c5rVZonmzkvAT8rcIfEuzy78aZsxeEffLFX3oRJ0aGc9vrnRciIiIiIrP+ctufyU0arHSuAoqiaJ7jPDQ0pBsSl8qKvhRFQTwex8DAAPx+P+rq6lBXV4empiYEAgEEAgHcdtttubaOjg7MzMzI3RiWzWYxPj6OQCCQey/1fRoaGlBXV4etW7eiu7sb8Xg890X9eqzuV/3dQCCAUMjcXxKn02mMjY3B6/Wirq4OXq8XgUAgd34bGhoQCAQwNjYmhDqyco+N3appjCYnJ+H3+7F161ahn6amJtTV1QnncnFxUdhHvq5lxY5LURQMDAxg69at8Pv9wvvedttt6OnpKTonZJtpjhQ7b/F4HIFAQDNegbW5EwqFkMlkhN8ppth7qRRFQTQaxcDAALq7uxGNRjE+Pi7vJpiZmUFHRwduu+02zXF6vV5s3boVfX19mkDaLKvuMbLFxUUMDAygqakJLS0tmvOs9huPx+VfXZfV17/K6n7zP3OhuaEqtq/V17rMznsYERFRJf3rdXE56VqnfPQ7BPytGD7Wbzpwvnf7fZrq21ohP8P4nYtv4XqRsX3j9VeF7Uc7Hy8arNbqPJGrt9Uq60J+dSombAef/HnR80JEREREZAdWOleBvr4+4Yt5l8uFpaWlDVccGlFnoioxEAhonjlthNvtxtDQEPr7v/8L3/WMj4/j5MmTpr5odzgc6O3txcTEhPxSjh39mjmHqmw2i3A4jMnJyXWDjXzBYBBHjx7VVCiWa2xK+axmVcsYLS8vo6enx3Do53a70dzcLIQ0U1NT6O3tFfbLp3dc6XQaoVDIUODrdDoRi8WKvodqM80RvfeYn5/HwMBArmq7GJfLhenpaUOVz3rvpZqcnMTJkyd139PtdmNpaUluxvLyMvr6+kyNRTAYxMTEhKlKXKvvMSq13/VC9Xzt7e2Ynp42dPx2XP+wqd9ic0Omt69d17qqHPcwIiKqrFu90ll+XzsUqmC1utL57Td/jeFjAwWf23vv9vvg8T6EO7fdjZ0PPIT6eofmuA7u/yk+zFuaulYqnVdWsvhh613CZ//F8AgOHT4m7Ie15ycH/K1C2wcfXUVj4xahLZ98bHYoNE/k9zZzXq5f/waP+P+9cF56nzmM45ExYT/oVH/X1zvw7sInRc8LEREREZFZrHSuEXL4MDQ0VJbAuVyWl5cxMDBgOKQIhUIIhUKmwgGshSHFqrLs6tesdDoNv9+P8fFxU2EQ1qojT58+LTeXzOzY2K1axkhRFPj9fsNhDdbO5UaPYX5+Hi0tLYZCKADIZDLo6+vD7Oys/JJlqm2OyNQQtKOjQzf81ZNOp9HR0QFFUeSXDEmlUmhpaTEccqsURYHX69Xc89czMzODQCBg+L3susdkMhkEAoGCc6GtrU13uWZ1Xq93Pdl1/dvV70bYfa1X6h5GRERUaf989To+/+OfLPvRCxKt9vVXX+oGznduuxsjJ8bxz1evYz7xe4w9P4kjx47jx20/KctxlUt9vQM9B58W2mb+/mVhWyW379m7r6RgtRbmSWPjFjz6s78S2qbPvKyZJ9B5xnWp54WIiIiIaKMYOldY/vNUVe3t7cJ2NfJ4PIhEIjh37hwSiQS+/fZbLC0tIZFIIJFIoL+/XxOch8PhdUOeeDwuBBoulwv9/f1IJBK4efMmVldXcfPmTSQSCUxMTGB4eNjQ0uF29WtWNptFIBDQnIf29nZEIhHhXJ47dw6RSMT0s13tGhu7VdMYdXR0aAKqYDCIWCyGRCKB1dVVXLt2DYlEAlNTU4YqZteTSqXQ0dGRW/rZ4XAI76nOB737w8jIiNxUVK3OET2pVApjY9//tb/b7UZXV1fuepqYmEB/fz+am5uF38tms+jr6xPajBgbG4Pf79c8L3o9ahCcv7S3w+HIHac6x69du4a5uTkMDg4Kv68oCrq7u4U2PXbdY9R+80NMp9OJiYkJLC0tYXV1FYlEIvfPc3NzwjlPp9MYGBgoGILbdf3b1e9G2H2tV+oeRkREVG56y0mbXZa6GvyX0YgmSGxp3YHzF/8RPQefviWWSO5+/G+E7a+/+hLvXHxLaFtZyeKNs+LS2vIS1HpqeZ7In29lJYvpM2Lw/sXVz/Hb5G+EtmefK/7oFyIiIiIiu3B57QqLx+Oa4OPmzZuawMcuekt+FhIKhXDPPfegq6tLt5pNlslk0N3dLVT1FXuPbDaLhoaGXCjhdruRSCTWfa9sNovJyUmMjIzA6XRqlrS1q1+VmXMoL6UOAJFIBNFoVGiTzc/PIxQKYXFxEb29vZiamhJet3tsVGY+qxnVNEbhcFgIMY0ua7u8vIxQKCRUIq63NG3+cakcDgeGhoYwODhYcDli9dnM+QHeuXPn0NXVJeyXr9bnSD698+Z0OjE6Olp0GfCBgQFMTk4KbeudN733UjU3N+PAgQO5Cl+Hw4HFxUVkMhksLy8LwXEoFBLCT7fbjXPnzhUNfFOpFLq7u4XwcHp6GsFgUNgvn133GPncGVnyO5vNIhQKCb8Xi8U0gbpd179d/arMzHW9eWTXtV7OexgREVXWZltee71lkmV6fZx6aRp79u4T2uxg1fLaektLt7TuwJmzF3TD0mKsWF7b7BioSpkDsmcOPiaEp3v27sOpl6Zz2/IS0i2tO3B+/oPcdiF6n7Nc88SK87K/8xEoH/0uty1/7hMjw4i/9GLB14mIiIiIrMLltWuAXOXscrnKFjibpYYF631hr3I6nZienhaqxuTqu3ypVEr4Yv3o0aOG3svhcGBwcBBLS0sYGhqSX7atX7MURdGEQXNzc+uGQVirUrxy5QomJiZ0q/DsHhu7VcsYpdNpIRh0OBxYWFgwFLq43W7s3LlTbjbF5/Ph0qVLiEajBUMoAOjt7dWEq6+99pqwLav1OVJMMBjElStXNOdENjExgUgkIrQZqRyVud1uTE1N4cqVK4hGo7nQ2eVyoa2tDV1dXUKwuri4KASvLpcLly5dKho4I28+5P87IRQKCddKPrvuMYqiCMfv8/kMPaPZ4XAgFosJ/Y2MjGgqcO26/u3q1wp2XeuVvocRERFtxP9dWZGbimpp3SE34X/XSAWr6sOF9zVVzk89/ZzpwNkqZsfASsEnfy5sv3PxLVy//k1u+5WXTwmvB58Q9y+k1ueJXO185dOPc39c8P+rv/9eeJ1VzkRERERUSQydK+y7774TtuUlYGudGsKoMpmMJnBQyc85XS+QkTmdTs2X87CxX7PkcKurq0t3+dRi+vv7MTr6/V/Ub4SZsbFbtYxROBwWQiq9ZZnt4vF4kEgkDL/fgQMHhG35D1isUE1zpJC2tjZNOF6MvHyyoiiGn5OLtRDw0qVLhkI8lTyvhoaGigaN+dTloFXpdFpTra2y6x5z8uTJ3D+rf4xglBo8qzKZjOZ823X929XvRtl5rctzrZz3MCIionK7/fY7NIHi71L/IGxXu3+9/i9yky3PB64Fes8h/u9rQfMXVz8XqrgbG7fgsf1P5O1ZWK3PE73zogbwb5x9FTdufP+dUmPjlrJUcBMRERERFcLQucLyn++JtUqjzUaunCpULSkH8FaFW3b1a4Ze0CJXXFaC0bGxW7WMUX6VqLr0bbk4nU5Tqxz4fD5hu1gQtRHVMkes4lh7fm6+8+fPC9uF+Hw+TE1NGQ6MsTaX8699vfdfz9DQkDA3Tp8+LbwOG+8xcr9Gl2fPJwfff/jDH4Rtu65/u/rdKLuu9Urfw4iIiMzYdtc9chNWCqzmUowc0P42+RvN822r2fV/+b6SV3XntrvlJltYNQZW+o9Sle4bZ1/FykoWM6/+ndD+aOfjpp51XcvzpL7eoakCfz/5Dq5f/0ZzXoJP/tzUeSEiIiIishpD5ypzzz3a//GrVZlMBslk0vAX//fff7+wffLkyYLLyJphV79myBV37e3tpqvurGR2bOxWDWMkBznBYNBw9Wyl5Id/Vo9dtc0RKx06dEjYlse+EDNBoWpxcVHY7urqMj2v5IpzuU/YeI9JJpPCtfjwww8LrxvhdDqFoF4+fruuf7v6rQQj17o8j2vhHkZERLeuO7fdrQnHlEvfP7fWqO7H/0ZuQiQ8qFmyulr9xfb75CZ8UeLSz2Y/s1VjYCU5TL5+/Ru8c/EtvH3+18J+8pLT66n1eRI8+LRwXlZWsjh2uA9XPv0411Zf70Dw4NO5bSIiIiKiSmDoXGU+++wzuamqKYqC8fFxhMNhBAIBtLS0oK6uDnV1dWhoaEAgEMDY2Jj8a7ryQxWs9e31ejEzM7OhoMCufs147733hO1du3YJ23awcmzsVg1jtF4QVo3MVpzKammOWEkO4uSwzkpWzSv5mOV+7brHyP2WGmTnL/Esn2+7rn+7+q0EI9e6PCdKnWtERETlcuddYkXv5RICz5bWHZrlhL/+6kv819jGHkn02+RvMHzM+sdsyO79C23onL+MtBE3bnyH0OE+KB+ZP39WjIGVGhu34NGf/ZXQNnxsQHi28569+0xXg9f6PGls3ILdbXuENnme6C3DTURERERUbgydK0wOEuQv46tRMplEd3c3Ghoa4PV6EQqFMDY2hmQyqfnS24zm5mYMDg4KbYuLi+jp6UFDQwM6OjoQjUaRTCY1y5IXY1e/ZsjjaiRAKIVdY2O3W2mMKq1W54iV5MpbeeytJC8lXeq8klfBkI9Z3i71fWRyVa3X6839YYKZn1QqletDnmd2Xf929Vut7JoDREREdtm2Tfzvm+kzL+ON118V2oz4z8MjuP32O4S20y8+j/2dj5iuGr7y6cd45uBjeObgY/j6qy/lly137/b7NMd+YmTY8HG/8fqr6Gh7EG+/KVYCG2XVGFhJrmKWq5HlpaaNquV5Ap3zIntWWpqciIiIiKgSGDpXWH19vbAtf2lcTdLpNAKBAAKBAGZnZw19Se90OoUKt/VEIhHN8z8BIJvNYn5+HiMjIwgEAmhoaEBLSwsGBgY0y8rqsatfo+RzZXUYUI6xsVulx+jy5cvCdjWdGytshjliJfkPfuRw1SpywFrqtS8fr9yvPJ6lvo/MjvOiV2Vs1/VvV7/VaLPfw4iIaPP5D08PyE0YPtaP6TMva6p2V1ayePvNX+sGovduvw9jz0/KzVA++h3aAw/ib8ci+HDhfU14qfr6qy8xfeZl7O98BJ3tPyr7s37lMHFlJYv9nY/gl8+fwI0b4qNusLb8dvylFxHwt2L4WL9QBWyWVWNgpZbWHfA88JDcDKy99uO2n8jNhtT6PNnl342W1h1yM7B2Xgq9RkRERERUTgydK0wOBtLptO4X8pW2vLwMv9+v+2W8z+fD4OAgpqamkEgksLS0hNXVVayuruLbb7/F0NCQ/CsFOZ1OzM3NIRKJaEIW2eLiIiYnJ3PLAiuKIu+SY1e/pZLHfSPKNTZ2q/QYyaHdZrJZ5oiV8iudUSAItYI8r0q99uXnScv9ykp9H5l8Xnw+H9ra2jb8I7Pr+rer32q03pwgIiKqNj9u+4lmyWMAiBwfxP7OR3DfXX+OzvYf4Yet2/CD7Y0IHe7D/yyw9PSevfsw9vyk5hnFWKtmPbj/p/jB9kb8YHsjDu7/ae4n4G9FwN+KyPFBTchaLs8ePoZ7pWc737jxHX4ZG8UPW7fhvrv+HO2BB9EeeDD3zydGhoUKW88DD2n6MMLKMbDSs88dk5uAAs9mNqOW5wkABJ/Qr/JmlTMRERERVQuGzhWm9+W7XMFWDfr6+jRV2JFIBNeuXcPCwgJisRh6e3vR1tZmSdgRjUZx7do1zM3NYXBwED6fT95FsLi4CL/fj5mZGfklgV39miWfy40o99jYrVJjJJ8bOyo8K2WzzREryOfDLnK4Xer7yoGi3K+s1PeRyWH3xMQEEonEhn8Ksev6t6vfaiJfu5vpHkZERJvXkVBYs+Rxviuffqxb7avnsb9+AmfOXij6XNuVlSw+XHg/91NsaeRSQtxS1Nc7MPa3+kGo6ourn+suAV1f78Avhkdw9vy7RT93MVaOgVV2t+3RfJ7bb78DPQefFtpKUavzBAAe2/+EZqwaG7fo/uEAEREREVElMHSuMLfbrfmiWK8asZKSyaTmmKamphCNRtetHNuo9vZ2xGIxLCwsYHV1FYlEAqOjowWXSw2FQppwRo9d/RYiB0RWBUKVHBu7lXuM1ntmbq3azHOkVNlsVhPIyfdhq8hLHJc6r+RnQ8v92nWPkfst1x9F2XX929VvNdis9zAiItrcWlp3YC75+5KXTJZ5HngI7y58guORsaKhYiG7/LsxcmIcH3x0FSMnxuWXbeN54CGcn/+g4LLSeh776ydwfv4DHDqsXxVslNVjYIX6eofm2c2P7X+yaDBvRq3Ok/p6B/7tvxOPN/jkzy07L0REREREG8XQuQrI1c7hcFgTiFTSK6+8Imz39/ejt7dXaCuXtrY2DA8PY25uDt9++y0ikYhQiZdOpzE+bv5/+uzqVyWHe1aFAdU0Nnaze4zk0LGarsGNuJXmiFHy9SePvZWsCgLl35OP2a57jF39mmXX9W9Xv5Ugz4nNcg8jIqLqt7ttD86cvZD7ib0Yl3cpqrFxC1468wZiL07hSCiMPXv3wfPAQ7j99juwy78be/buw5FQGMcjYwWXXc5XX+9A7zOH8e7CJzhz9gKOhMLY5d+NO7fdLex37/b7sMu/G4cOH8sFiGfOXkDPwafXDSIf2//khj6znnu334ez59/FyIlx7PLv1hyD54GHsGfvPvxieAQffHQVY89PClW2xyNjwjE9tv9J4feL2egYbHQO6HnqmcNCn/8pFJZ32ZByzBOrz8s7F98SKt7r6x0IWlD9TURERERkFYbOVeDo0aPCdjabxcmTJ4W2SpJDhr179wrbleJ0OhGNRhGLxYT2y5cvC9tm2dHvrl27hO3PPvtM2C5VtY6N3ewYIzmwOX/+vLBdq27VOVKM/Kxe+Q9/rCRXJJd67csVxnK/dt1j5H43co1ZxY7rHzb2Wy6b9R5GRETVr7FxC3b5d+d+zFTr5nv0Z4/jyLHjOPXSNM6efxf/9OlXOHP2Ak69NI0jx46j95nDppYyrq93YJd/N44cO44zZy8gsfApPv/jn3I/84nf48zZC/jF8IihADHfndvutuQz6+k5+DTOnL2ADz66in/69KvccZ89/y5OvTSNQ4eP6R5rS+sO4Zjk8NSIUsfAqjmQTw281R95WWmr2DlPrD4vb7z+qrC9Z+8+U8dDRERERGQ3hs5VwOPxaEKPyclJy6qUMpkMUqmU3GyYHFp5PB5hu9KCwaCwLYczpbKyX3l8Z2dnLRnfah8bu1k5Rs3NzUKFo6IomJ2dFfapRbf6HNEj/1FPZ2ensG0lORwu5dqfnZ0V5rbcJ2y8x8j9zszMaEL7SrHy+s9nV79226z3MCIiolvZ7bffUVJ4TJvPlU8/xjsX3xLann0uJGwTEREREVUaQ+cqMTQ0JGxns1n09fUhm80K7WalUil4vV7Mz8/LLxkmBxxmgwyjn6HU52bmf8kOneO1q18z3G638MxQq6rZ5WOya2zsVg1j5HQ60d/fL7S98MILhs/R+Pg4XnjhBbm54uRzUqtzxCqzs7NCaOrxeNDV1SXsYyWXyyX0n81mTc8TeYn0Q4cOCduw8R7jdrs152dgYEDY3ii7rn+7+q1Wm/UeRkRERETAKy+fErZbWnegpXWH0EZEREREVGkMnatEe3s7BgcHhbb5+Xn4/f6SvjjPZrMYGxuD3+/XVDqaJX/hbjTAzmaziEajCIWM/fVtT08PwuGw4S/IVXIVt7wcrF39miUHRZOTk5r3KEYd0/zzWa6xsVu1jNHQ0JAQOCWTSfj9/qJBbSqVgt/vRygUKulatdtmmSNWSKVSms8jL6Vsh9HRUWFexeNxw/fl+fl5oVrV7XZrgkWVHfcYAIhEIsJ2KpVCNBoV2oxIp9MIhUIYGxsT2u26/u3qt5ptxnsYERER0a3u+vVv8Pab/0NoY5UzEREREVUjhs5VZHR0FD6fT2hTFAVNTU2YnJwU2gvJZrMYHx9HU1MTwuGw/HJJdu7cKWyfPHkSyWRSaJPNz8/D6/ViZGTE8Bf+auDR0tKCeDwuv6wrk8loAhL5HNrVr1ldXV3CUrXZbBZ+v99QeDM7O4uWlhaEw2EhFCjX2NitWsbI5XJpAj1FUeD1etHT04OxsTEkk0mMj4+jr68PXq8Xfr9fE1JVk80yRwpRFAVjY2PrhriTk5MIBALCfvI1aZfm5mZhXqXTaXi93nXnzeTkJDo6OoS2WCymqcRVyZ/HinsM1qrB5etiZGQEfr/f0NLTmUwG0WgULS0tGB8f18wpu65/u/qtZpvxHkZERER0q/vVqRhWVr7/b+jGxi3Ys3efsA8RERERUTWoW11dXZUbqXIymQx6enp0qxGdTifa29uxc+dO4UvwdDqNxcVFvPfeewXDpEgkohs81NXV5f65ra0NiURCeB15wYX8HM+uri7s3bs3V0mpKAo+/PBDKIpSNIiYmppCb2+v3IxAICAcv9vtRjAYRGdnp+ZL/2QyiVQqhdOnTwsh0vDwMEZHR4V97epXZeQcqtSwSa46a25uxoEDB+DxeNDc3IxsNgtFUXD58mUkk0nh3Pf29mJqagoo49iozHxWM6ppjDKZDAKBgOacrsfn88Hj8Qh/IGL3+ZTPm97tfLPMkXz575Gvra0NDz/8MLA2HqlUCpcvX4aiKJpQ2ul0YmFhQVMJLrPq86TTaTQ1NQmBq8PhQH9/Px5++GF4PB643W4oigJFUXDx4kXMzMwIffh8PiwsLAhtMqvvMapsNotAIKAbTgaDQdx///3w+Xxwu93A2nOQ1TmVSqWEIFv+95E8j626/u3qV2VmbpjZV4/8WfSudVU572FERFRZ/+ur/yM3EdEms7KSxY8e2I4bN77LtR0JhXHk2HFhPyIiIiIiu/3ltj+TmzQYOlepgYEBw9XN6+nq6kIkEoHH45FfMvxFuKIo8Pv9mgq19fT29uL+++8Xqq4LfYktf6luVjAYxPT0tNxsW78qo+dQtby8jO7ubtOBgEoOhMoxNiqzn9WoahujTCaD7u5uQ8fkdDpx9OhRRKNRRKNRjIyM5F6bm5sTnrMrM3tcMvm8Fbqdb4Y5kq9Q6GyU0+lEIpHQvSfKrPw8iqIgEAhoKomN8Hg8SCQScDqd8ksaVt9jVNlsFn19fZow3Kz1QmezCl3/dvWrMjM3zOyrR/4sha51VbnuYUREVFkMnYk2v/hLL+LEyHBuu77egXcXPkFj4xZhPyIiIiIiuxkJnbm8dpWamJjApUuX0NXVJb9kiFpBt7S0hHPnzhkKV4rxeDw4d+4cXC6X/JIut9uNubk5TE1NGf6dSCSC3t7egkvHFuJyuRCLxXRDEtjYb6ncbjcSiYTmGd7rUZdNHRoaEtrLMTZ2q7YxUkPJ6elptBVYftnn8yEWi2FpaUl3FQGsnetqsBnmSCHq0s9GwlgAaG9vx5UrVzZ8TyyFGhwXmlOF9Pf3Gw6cYcM9RuVwODA9PY3p6emS5rbH40EsFtP80YJd179d/daCzXYPIyIiIrpV/epUTNjes3cfA2ciIiIiqlqsdK4BiqIgmUzivffew/z8vG61otvthtvtRnNzMzo7Ow1XJuV/0ex2uzVhgCyTySAcDmN2dlazfKvb7YbH48FTTz0lhOWKomB2dja33dXVVTTwyWazmJ2dxWuvvYZMJoNUKqX5zM3NzXC5XOjs7ER/f7+hUMGufs2ew3zLy8uIx+O4ePGi7rK16pK7Bw4cQDAYlF8WlGNsNvJZjajGMVKpFYMOh0OzNK9KXqHg5s2bRY9vo8cVj8eFpYALBUeqzTBHUKBqNJvNYmZmBq+88gqQN15YC9d8Ph8OHDhQcOwKsevzqPN8fn5et/LZ7Xajvb0dhw4dKnq+12PlPUYWj8dzj3WQly+HdK12dXWtG2Dadf3b1a+ZuWFmXz1mr3U9dtzDiIiosljpTLS5vXPxLTz3TI/Qdn7+A7S07hDaiIiIiIjKwUilM0PnGpZMJnNflFeC+izpYl9gW0lRFLhcLss/r139liqVSsHlcq0b0BRT7rGxW7WNUSEdHR2557G7XC5cu3ZN3qVq1PIc0Quda9ny8rIQKHo8HsNVzaWw4h5TiB3/XrLr+rer31pWS/cwIiJi6Ey02b395q/xxdXPc9v/pt6BQ4ePCfsQEREREZULQ2ciojJqamrKhYebIQytVpstdCaqFryHERHVFobOREREREREVC5GQmc+05mIyAKTk5NCtWpnZ6fwOhFRNeM9jIiIiIiIiIiIiDaClc5ERHmy2azpZ5guLy/D6/Xmns3b3NyMS5cume6HjGGlM1FhvIcREd06WOlMRERERERE5cJKZyIik2ZmZrB161YMDAzknm1aSCaTQTgcRktLSy6sAYDR0VGGNURUEbyHERERERERERERUSWw0pmIKE88HkdfX5/Q5vP54HA44HK54HK5oCgK0uk0FhcXhf0AoL29HXNzc3IzWYiVzkSF8R5GRHTrYKUzERERERERlYuRSmeGzkREefQCGyMcDgcikQgGBwdZIWgzhs5EhfEeRkR062DoTEREREREROViJHTm8tpERHlcLpfctK62tjZcunQJw8PDDGuIqKJ4DyMiIiIiIiIiIqJKYKUzEZEORVGgKAqWl5dx+fJl4XmnHo8Hd9xxB9ra2uByudDc3Cz8LtkrGo3m/tntdqO3t1d4nYh4DyMiuhWw0pmIiIiIiIjKxUilM0NnIiIiIiIiohrD0JmIiIiIiIjKxUjozOW1iYiIiIiIiIiIiIiIiIioZAydiYiIiIiIiIiIiIiIiIioZAydiYiIiIiIiIiIiIiIiIioZAydiYiIiIiIiIiIiIiIiIioZAydiYiIiIiIiIiIiIiIiIioZAydiYiIiIiIiIiIiIiIiIioZAydiYiIiIiIiIiIiIiIiIioZAydiYiIiIiIiIiIiIiIiIioZHWrq6urciMREREREREREREREREREZERrHQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKSMXQmIiIiIiIiIiIiIiIiIqKS/T/LExkuwP1GGgAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:7a81a23a-5789-4c51-8bfb-a1cf41f2ffc4.png)\n",
    "\n",
    "Figure 1: Overview of the tensor-based QPDE algorithm. The circuit applies $U_{\\text{prep}}$ on all qubits, $U_{\\text{evol}}$ repeated $t/\\Delta t$ times on the system qubits, a phase gate $P(\\theta)$ on the ancilla, and $U^\\dagger_{\\text{prep}}$, followed by all-zeros measurement. Both $U_{\\text{prep}}$ and $U_{\\text{evol}}$ are brick-wall circuits obtained by tensor-network compression. The energy gap $\\Delta$ is extracted from the measured time-series data with classical signal processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Tensor-network circuit compression\n",
    "\n",
    "QPDE requires two unitaries to be executed on the quantum computer: a state preparation $U_{\\text{prep}}$ that creates the ancilla-system superposition, and a time-evolution operator $U_{\\text{evol}}$ that implements one step of $e^{-iH\\Delta t}$. A direct implementation of either would produce a circuit too deep for current hardware. Tensor-network compression reduces that depth by constructing a much shallower circuit with approximately the same action.\n",
    "\n",
    "A standard technique to approximate a time-evolution operator is Trotterization, where $e^{-iH\\Delta t}$ is broken into a product of exponentials of local terms in $H$. We use the second-order Trotter approximation with $m$ time slices to define a reference operator $U_{\\text{ref}}^\\dagger = S_2(-\\Delta t/m)^m$. $U_{\\text{ref}}^\\dagger$ is represented classically as a matrix product operator (MPO), the operator analogue of an MPS. The task is then to find a brick-wall circuit, whose ansatz consists of alternating layers of nearest-neighbor two-qubit unitaries as in Figure 2(a), whose action on the relevant subspace matches that of the MPO.\n",
    "\n",
    "The match is found by a contraction scheme sketched in Figure 2(b). Each two-qubit unitary of the ansatz is updated one at a time: treating that gate as the unknown, the surrounding circuit is contracted against the MPO reference into an environment tensor $G'$. From the singular value decomposition $G' = U S V^\\dagger$ you can extract the best unitary update $G_{\\text{opt}} = U V^\\dagger$ by setting all singular values to 1. The approximation of the state initialization is conceptually similar and can be found in [Kanno et al. (2026)](https://arxiv.org/abs/2601.15616)\n"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screenshot 2026-06-12 175031.png](attachment:c4eac91a-75b1-4eb0-97e4-2ca90e6bac41.png)\n",
    "\n",
    "Figure 2: Circuit optimization by tensor compression. (a) Brick-wall circuit ansatz for $U_{\\text{evol}}$ and MPO approximation of trotterized time evolution. (b) Tensor product compression reduces full circuit to a four-legged tensor $G^{\\prime\\dagger}$ by iteratively leaving out two-qubit unitaries from the brick-layer ansatz. This tensor is approximated by setting all singular values to 1 yielding $G_\\text{opt}$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Target System: One-Dimensional Hubbard Model\n",
    "\n",
    "The benchmark system is the one-dimensional Hubbard model:\n",
    "\n",
    "$$H = -T \\sum_{q=1}^{n_s-1} \\sum_{\\sigma \\in \\{\\uparrow,\\downarrow\\}} \\left(a^{\\dagger}_{q+1,\\sigma}\\, a_{q,\\sigma} + \\text{h.c.}\\right) + U \\sum_{q=1}^{n_s} n_{q\\uparrow}\\, n_{q\\downarrow} - \\frac{U}{2} \\sum_{q=1}^{n_s} (n_{q\\uparrow} + n_{q\\downarrow}),$$\n",
    "\n",
    "with hopping $T = 1$, on-site Coulomb energy $U = 10$ (strong-correlation regime), and Jordan-Wigner fermion-to-qubit mapping with an up-down qubit ordering ($1\\!\\uparrow, 1\\!\\downarrow, 2\\!\\uparrow, \\ldots, n_s\\!\\downarrow$). The number of system qubits is $N = 2n_s$.\n",
    "\n",
    "The code below reproduces the 4-site system ($N = 8$ system qubits, 9 total with the ancilla). The reference energy gap from exact diagonalization is $\\Delta^{\\text{ref}}_{(0,1)} = 0.254$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Fire Opal execution\n",
    "\n",
    "The QPDE signal $s_t$ is reconstructed from the measurement probability of a single bitstring, namely the all-zeros outcome. This is a useful scalability property of the algorithm: the shot budget needed to resolve $s_t$ does not have to scale with system size. The hardware challenge comes from the circuits themselves. At later time steps, the 9-qubit circuits contain on the order of $10^3$ two-qubit gates after decomposition and transpilation, and noise accumulated across these layers suppresses the all-zero probability relative to the noiseless reference.\n",
    "\n",
    "[Fire Opal](https://docs.q-ctrl.com/fire-opal) is used as the hardware-execution layer for these circuits. It applies automated compilation, error suppression, and measurement-error mitigation before returning corrected output distributions. In the analysis below, we compare standard Qiskit execution and Fire Opal execution through the retained amplitude of the reconstructed QPDE signal $s_t$ relative to a noiseless simulator reference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Running the QPDE Algorithm with Fire Opal\n",
    "\n",
    "The QPDE time-series signal $s_t$ encodes the energy gap in its frequency content. In a full QPDE calculation, a sufficiently long time series is passed to a classical frequency-estimation procedure to estimate the gap. Here, the retained amplitude $\\frac{|s_t^{\\mathrm{hw}}|}{|s_t^{\\mathrm{ideal}}|}$ is used as a signal-quality metric. A larger retained amplitude indicates that less of the reconstructed time-domain signal is lost during hardware execution.\n",
    "\n",
    "Pre-optimized brick-wall gate parameters are loaded from the [published repository](https://github.com/sk888ks/TS-TQPDE_open). The circuits are constructed following the paper's `_build_circuit` logic, then executed through the standard-Qiskit and Fire Opal pipelines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Setup and Authentication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from io import BytesIO\n",
    "from urllib.request import urlopen\n",
    "\n",
    "from qiskit.circuit import QuantumCircuit, Parameter\n",
    "from qiskit.qasm3 import dumps\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    "from qiskit_ibm_runtime import QiskitRuntimeService, SamplerV2 as Sampler\n",
    "import fireopal as fo\n",
    "import qctrlvisualizer as qv\n",
    "\n",
    "plt.style.use(qv.get_qctrl_style())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q-CTRL authentication successful!\n"
     ]
    }
   ],
   "source": [
    "# # Set credentials.\n",
    "token = \"YOUR_IBM_CLOUD_API_KEY\"\n",
    "instance = \"YOUR_IBM_CRN\"\n",
    "\n",
    "credentials = fo.credentials.make_credentials_for_ibm_cloud(\n",
    "    token=token, instance=instance\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Load Pre-Optimized Brick-Wall Gate Parameters\n",
    "\n",
    "The tensor-network optimization of $U_{\\text{prep}}$ and $U_{\\text{evol}}$ is performed classically using [ITensor](https://itensor.org/) (Julia). Here you load the resulting $4 \\times 4$ unitary gate matrices stored as `.npy` files in the [paper's repository](https://github.com/sk888ks/TS-TQPDE_open). This mirrors the `_load_layers` function from the paper's source code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Base URL for raw .npy files from the published repository\n",
    "REPO_RAW = \"https://raw.githubusercontent.com/sk888ks/TS-TQPDE_open/main\"\n",
    "\n",
    "\n",
    "def load_npy_from_github(filepath):\n",
    "    \"\"\"Download a .npy file from GitHub and return the numpy array.\"\"\"\n",
    "    url = f\"{REPO_RAW}/{filepath}\"\n",
    "    with urlopen(url) as response:\n",
    "        return np.load(BytesIO(response.read()), allow_pickle=False)\n",
    "\n",
    "\n",
    "def load_txt_from_github(filepath):\n",
    "    \"\"\"Download a text file from GitHub and return its content.\"\"\"\n",
    "    url = f\"{REPO_RAW}/{filepath}\"\n",
    "    with urlopen(url) as response:\n",
    "        return response.read().decode().strip()\n",
    "\n",
    "\n",
    "def load_layers(prefix, depth_max, num_so, is_mpo=False):\n",
    "    \"\"\"Load brick-wall gate parameters from .npy files on GitHub.\n",
    "    For MPS (is_mpo=False): every layer has num_so // 2 gates.\n",
    "    For MPO (is_mpo=True): even-indexed layers have num_so // 2 gates,\n",
    "                           odd-indexed layers have num_so // 2 - 1 gates.\n",
    "    \"\"\"\n",
    "    layers = []\n",
    "    for d in range(depth_max):\n",
    "        layer = []\n",
    "        limit = num_so // 2 if (d % 2 == 0 or not is_mpo) else num_so // 2 - 1\n",
    "        tag = \"MPO\" if is_mpo else \"MPSBW\"\n",
    "        for u in range(limit):\n",
    "            filename = f\"{prefix}_{tag}_depth{d+1}_{u+1}.npy\"\n",
    "            gate = load_npy_from_github(f\"npy/{filename}\")\n",
    "            layer.append(gate)\n",
    "        layers.append(layer)\n",
    "    return layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The state-preparation circuit $U_\\text{prep}$ is optimized to approximate the target MPS superposition\n",
    "\n",
    "$$\n",
    "\\frac{1}{\\sqrt{2}}\\left(|0\\rangle|\\psi_g\\rangle + |1\\rangle|\\psi_\\text{ex}\\rangle\\right).\n",
    "$$\n",
    "\n",
    "The pre-optimized layers loaded below were generated by the tensor-network circuit-optimization pipeline from the original paper. You use the 9-qubit circuit data directly, without reproducing the larger-system iterative-overlap-enhancement procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U_prep: 5 layers, gates per layer: [4, 4, 4, 4, 4]\n",
      "U_evol: 5 layers, gates per layer: [4, 3, 4, 3, 4]\n",
      "|a0|^2 = 0.484241, normalization A = 1.000994\n"
     ]
    }
   ],
   "source": [
    "# 8-qubit Hubbard setup\n",
    "num_sites = 4\n",
    "N = 2 * num_sites\n",
    "num_so = N\n",
    "n_total = num_so + 1\n",
    "\n",
    "d_prep = 5\n",
    "d_evol = 5\n",
    "\n",
    "# Load state-preparation gates (MPS-based, 9-qubit circuit)\n",
    "prep_prefix = f\"Hubbard_site{num_sites}_U10.0_t1.0_depth{d_prep}_sweep1000_cp\"\n",
    "MPS_layers = load_layers(prep_prefix, d_prep, num_so, is_mpo=False)\n",
    "print(f\"U_prep: {d_prep} layers, gates per layer: {[len(l) for l in MPS_layers]}\")\n",
    "\n",
    "# Load time-evolution gates (MPO-based, 8 system qubits)\n",
    "evol_prefix = (\n",
    "    f\"Hubbard_site{num_sites}_U10.0_t1.0_slice100_step0.1_depth{d_evol}_sweep10000_cp\"\n",
    ")\n",
    "MPO_layers = load_layers(evol_prefix, d_evol, num_so, is_mpo=True)\n",
    "print(f\"U_evol: {d_evol} layers, gates per layer: {[len(l) for l in MPO_layers]}\")\n",
    "\n",
    "# Load |a0|^2 normalization coefficient\n",
    "a0_txt = f\"pickle_dat/{prep_prefix}_abs_coeff0.txt\"\n",
    "a0 = complex(load_txt_from_github(a0_txt))\n",
    "A_val = 1.0 / (4.0 * abs(a0) ** 2 * (1.0 - abs(a0) ** 2))\n",
    "print(f\"|a0|^2 = {abs(a0)**2:.6f}, normalization A = {A_val:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Construct the QPDE Circuit\n",
    "\n",
    "The circuit structure follows `_build_circuit()` from the paper's codebase:\n",
    "\n",
    "1. **$U_{\\text{prep}}$** (MPS brick-wall): alternating layers on all 9 qubits, with qubit offset 0 for even layers and 1 for odd layers.\n",
    "2. **$U_{\\text{evol}}^{t/\\Delta t}$** (MPO brick-wall): repeated `num_step` times on system qubits 1-8, with offset 1 for even layers and 2 for odd layers.\n",
    "3. **$P(\\theta)$**: phase gate on qubit 0 (ancilla), parameterized by $\\theta$.\n",
    "4. **$U^{\\dagger}_{\\text{prep}}$**: conjugate-transposed MPS layers applied in reverse order.\n",
    "5. **Measurement** of all qubits in the computational basis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Circuit: 9 qubits, depth 16\n",
      "  Unitary gates: 58\n"
     ]
    }
   ],
   "source": [
    "def build_circuit(MPS_layers, MPO_layers, num_step, eps_param):\n",
    "    \"\"\"Build the QPDE circuit.\n",
    "    Qubit 0 = ancilla, qubits 1..N = system.\n",
    "    \"\"\"\n",
    "    num_depth_prep = len(MPS_layers)\n",
    "    num_so = len(MPO_layers[0]) * 2\n",
    "    qc = QuantumCircuit(num_so + 1)\n",
    "\n",
    "    # --- U_prep: MPS brick-wall on all qubits ---\n",
    "    for d, layer in enumerate(MPS_layers):\n",
    "        offset = 0 if d % 2 == 0 else 1\n",
    "        for u, gate in enumerate(layer):\n",
    "            qc.unitary(gate, [u * 2 + offset, u * 2 + offset + 1])\n",
    "\n",
    "    # --- U_evol^(num_step): MPO brick-wall on system qubits ---\n",
    "    for _ in range(num_step):\n",
    "        for d, layer in enumerate(MPO_layers):\n",
    "            offset = 1 if d % 2 == 0 else 2\n",
    "            for u, gate in enumerate(layer):\n",
    "                qc.unitary(gate, [u * 2 + offset, u * 2 + offset + 1])\n",
    "\n",
    "    # --- P(ε): phase gate on ancilla ---\n",
    "    qc.p(eps_param, 0)\n",
    "\n",
    "    # --- U†_prep: inverse MPS brick-wall ---\n",
    "    for d, layer in enumerate(reversed(MPS_layers)):\n",
    "        logical_d = num_depth_prep - 1 - d\n",
    "        offset = 0 if logical_d % 2 == 0 else 1\n",
    "        for u, gate in enumerate(layer):\n",
    "            qc.unitary(np.matrix.getH(gate), [u * 2 + offset, u * 2 + offset + 1])\n",
    "\n",
    "    qc.measure_all()\n",
    "    return qc\n",
    "\n",
    "\n",
    "# Build template circuit for 1 time step\n",
    "eps = Parameter(\"ε\")\n",
    "qc_test = build_circuit(MPS_layers, MPO_layers, num_step=1, eps_param=eps)\n",
    "print(f\"Circuit: {qc_test.num_qubits} qubits, depth {qc_test.depth()}\")\n",
    "print(f\"  Unitary gates: {qc_test.count_ops().get('unitary', 0)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 Execute with and without Fire Opal\n",
    "\n",
    "At each time step, you execute four circuits, one per phase-gate angle\n",
    "\n",
    "$$\n",
    "\\theta = 0,\\ \\pi/2,\\ \\pi,\\ 3\\pi/2.\n",
    "$$\n",
    "\n",
    "Each circuit returns an all-zero measurement probability $m(\\theta)$, and the four probabilities are combined into the complex QPDE signal\n",
    "\n",
    "$$\n",
    "s_t = A\\left[m(0) - i\\,m(\\pi/2) - m(\\pi) + i\\,m(3\\pi/2)\\right].\n",
    "$$\n",
    "\n",
    "To demonstrate the impact of Fire Opal's error suppression and mitigation, you reconstruct $s_t$ for the first five time steps through two execution paths:\n",
    "\n",
    "1. **Standard Qiskit**: the circuits are transpiled at `optimization_level=3` (the highest preset Qiskit offers) and executed directly on the backend, without further error suppression or mitigation.\n",
    "2. **Fire Opal**: the same circuits are executed through Fire Opal's automated error-suppression and mitigation pipeline.\n",
    "\n",
    "The resulting hardware signals are stored as `signal_raw` and `signal_fo`, and compared with the noiseless reference in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execution parameters\n",
    "backend_name = (\n",
    "    \"ibm_boston\"  # Replace with a backend available to your IBM Quantum instance.\n",
    ")\n",
    "dt = 0.1  # time-step size (Δt)\n",
    "shots = 50_000  # shots per circuit\n",
    "T_max = 5  # time steps to execute\n",
    "eps_vals = [0.0, np.pi / 2, np.pi, 3 * np.pi / 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Qiskit execution (no Fire Opal) ====\n",
      "  Step 1: s_t = 0.6076-0.0060j, |s_t| = 0.6076\n",
      "  Step 2: s_t = 0.5157-0.0737j, |s_t| = 0.5210\n",
      "  Step 3: s_t = 0.3882-0.0293j, |s_t| = 0.3894\n",
      "  Step 4: s_t = 0.3906-0.0877j, |s_t| = 0.4003\n",
      "  Step 5: s_t = 0.3658-0.0134j, |s_t| = 0.3660\n",
      "\n",
      "=== Fire Opal execution ===\n",
      "  Step 1: s_t = 0.7116-0.0615j, |s_t| = 0.7143\n",
      "  Step 2: s_t = 0.6363-0.0300j, |s_t| = 0.6370\n",
      "  Step 3: s_t = 0.4749-0.0476j, |s_t| = 0.4773\n",
      "  Step 4: s_t = 0.4619-0.0403j, |s_t| = 0.4636\n",
      "  Step 5: s_t = 0.4623-0.1019j, |s_t| = 0.4734\n"
     ]
    }
   ],
   "source": [
    "def process_value(A, shots):\n",
    "    \"\"\"Convert count to probability.\"\"\"\n",
    "    if isinstance(A, float):\n",
    "        return A\n",
    "    elif isinstance(A, int):\n",
    "        return A / shots\n",
    "    return 0.0\n",
    "\n",
    "\n",
    "# === Build circuits ===\n",
    "circuits_per_step = []\n",
    "for num_step in range(1, T_max + 1):\n",
    "    qc = build_circuit(MPS_layers, MPO_layers, num_step, eps)\n",
    "    bound = [qc.assign_parameters({eps: theta}) for theta in eps_vals]\n",
    "    circuits_per_step.append(bound)\n",
    "\n",
    "# === PATH 1: Qiskit ===\n",
    "service = QiskitRuntimeService(token=token, instance=instance, channel=\"ibm_cloud\")\n",
    "backend = service.backend(backend_name)\n",
    "pm = generate_preset_pass_manager(optimization_level=3, backend=backend)\n",
    "signal_raw = []\n",
    "print(\"=== Qiskit execution (no Fire Opal) ===\")\n",
    "for num_step, bound_circuits in enumerate(circuits_per_step, start=1):\n",
    "    prob_list = []\n",
    "    for qc_bound in bound_circuits:\n",
    "        qc_t = pm.run(qc_bound)\n",
    "        sampler = Sampler(mode=backend)\n",
    "        sampler.options.default_shots = shots\n",
    "        job = sampler.run([qc_t])\n",
    "        result = job.result()\n",
    "        counts = result[0].data.meas.get_counts()\n",
    "        zero_key = \"0\" * n_total\n",
    "        prob_list.append(counts.get(zero_key, 0) / shots)\n",
    "    s_t = A_val * (prob_list[0] - 1j * prob_list[1] - prob_list[2] + 1j * prob_list[3])\n",
    "    signal_raw.append(s_t)\n",
    "    print(f\"  Step {num_step}: s_t = {s_t:.4f}, |s_t| = {abs(s_t):.4f}\")\n",
    "signal_raw = np.array(signal_raw)\n",
    "\n",
    "# === PATH 2: Fire Opal ===\n",
    "signal_fo = []\n",
    "print(\"\\n=== Fire Opal execution ===\")\n",
    "for num_step, bound_circuits in enumerate(circuits_per_step, start=1):\n",
    "    qasm_list = [dumps(qc_bound) for qc_bound in bound_circuits]\n",
    "    job = fo.execute(\n",
    "        circuits=qasm_list,\n",
    "        shot_count=shots,\n",
    "        credentials=credentials,\n",
    "        backend_name=backend_name,\n",
    "    )\n",
    "    results = job.result()\n",
    "    zero_key = \"0\" * n_total\n",
    "    prob_list = [\n",
    "        process_value(results[\"results\"][i].get(zero_key, 0), shots) for i in range(4)\n",
    "    ]\n",
    "    s_t = A_val * (prob_list[0] - 1j * prob_list[1] - prob_list[2] + 1j * prob_list[3])\n",
    "    signal_fo.append(s_t)\n",
    "    print(f\"  Step {num_step}: s_t = {s_t:.4f}, |s_t| = {abs(s_t):.4f}\")\n",
    "signal_fo = np.array(signal_fo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Signal quality comparison\n",
    "\n",
    "You can compare the standard-Qiskit and Fire Opal results by measuring how much of the noiseless QPDE signal amplitude is retained after hardware execution. For each time step, the retained amplitude is\n",
    "\n",
    "$$\n",
    "\\frac{|s_t^{\\mathrm{hw}}|}{|s_t^{\\mathrm{sim}}|}.\n",
    "$$\n",
    "\n",
    "The table below reports the equivalent decay,\n",
    "\n",
    "$$\n",
    "1 - \\frac{|s_t^{\\mathrm{hw}}|}{|s_t^{\\mathrm{sim}}|}.\n",
    "$$\n",
    "\n",
    "A smaller decay means that the reconstructed hardware signal is closer in magnitude to the noiseless reference. To make a direct energy-gap comparison, you would need a longer hardware time series and the full downstream frequency-estimation procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step |Simulation| |Fire Opal| |Std Qiskit|  FO decay  Std decay\n",
      "--------------------------------------------------------------\n",
      "   1      0.9778      0.7143       0.6076    26.9%     37.9%\n",
      "   2      0.9372      0.6370       0.5210    32.0%     44.4%\n",
      "   3      0.9169      0.4773       0.3894    47.9%     57.5%\n",
      "   4      0.9330      0.4636       0.4003    50.3%     57.1%\n",
      "   5      0.9578      0.4734       0.3660    50.6%     61.8%\n",
      "\n",
      "Mean signal decay:\n",
      "  Fire Opal: 41.6% loss from simulation\n",
      "  Standard Qiskit: 51.7% loss from simulation\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import ast\n",
    "from io import StringIO\n",
    "\n",
    "# Load Simulation reference\n",
    "csv_filename = (\n",
    "    \"execute_Hubbard_site4_U10.0_t1.0_devol5_dprep5_sweep1000_cutoff1e-8_maxdim1_\"\n",
    "    \"cp_step0.10_numslice_100_ratio1_1_numsweep_10000_backendaer.csv\"\n",
    ")\n",
    "csv_url = f\"{REPO_RAW}/csv/{csv_filename}\"\n",
    "with urlopen(csv_url) as response:\n",
    "    df = pd.read_csv(StringIO(response.read().decode()))\n",
    "\n",
    "df[\"result\"] = df[\"result\"].apply(\n",
    "    lambda x: ast.literal_eval(x) if isinstance(x, str) else x\n",
    ")\n",
    "signal_sim = df.groupby(\"k\")[\"result\"].sum().values\n",
    "\n",
    "# Step-by-step comparison\n",
    "print(\n",
    "    f\"{'Step':>4} {'|Simulation|':>11} {'|Fire Opal|':>11} {'|Std Qiskit|':>12} \"\n",
    "    f\"{'FO decay':>9} {'Std decay':>10}\"\n",
    ")\n",
    "print(\"-\" * 62)\n",
    "for i in range(T_max):\n",
    "    s_ideal = abs(signal_sim[i])\n",
    "    s_fo = abs(signal_fo[i])\n",
    "    s_raw = abs(signal_raw[i])\n",
    "    print(\n",
    "        f\"{i+1:>4} {s_ideal:>11.4f} {s_fo:>11.4f} {s_raw:>12.4f} \"\n",
    "        f\"{1 - s_fo/s_ideal:>8.1%} {1 - s_raw/s_ideal:>9.1%}\"\n",
    "    )\n",
    "\n",
    "print(f\"\\nMean signal decay:\")\n",
    "print(\n",
    "    f\"  Fire Opal: {1 - np.mean(np.abs(signal_fo) / np.abs(signal_sim[:T_max])):.1%} loss from simulation\"\n",
    ")\n",
    "print(\n",
    "    f\"  Standard Qiskit: {1 - np.mean(np.abs(signal_raw) / np.abs(signal_sim[:T_max])):.1%} loss from simulation\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "steps = np.arange(1, T_max + 1)\n",
    "amp_raw = np.abs(signal_raw)\n",
    "amp_fo = np.abs(signal_fo)\n",
    "amp_sim = np.abs(signal_sim[:T_max])\n",
    "\n",
    "decay_raw = 1 - amp_raw / amp_sim\n",
    "decay_fo = 1 - amp_fo / amp_sim\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5.5))\n",
    "\n",
    "bw = 0.32\n",
    "x_left = steps - bw / 2\n",
    "x_right = steps + bw / 2\n",
    "\n",
    "ax.bar(x_left, decay_raw, bw, color=\"#999999\", label=\"Without Q-CTRL\", zorder=3)\n",
    "ax.bar(x_right, decay_fo, bw, color=\"#7B2FBE\", label=\"With Q-CTRL\", zorder=3)\n",
    "\n",
    "ax.axhline(0.0, color=\"k\", ls=\"--\", lw=1, alpha=0.4)\n",
    "\n",
    "\n",
    "# Clean bracket on step 5\n",
    "bx = 5 + 0.65\n",
    "y_hi = decay_raw[-1]\n",
    "y_lo = decay_fo[-1]\n",
    "# Vertical line\n",
    "ax.plot([bx, bx], [y_lo, y_hi], color=\"black\", lw=1.5, clip_on=False)\n",
    "# Horizontal ticks\n",
    "ax.plot([bx - 0.06, bx + 0.06], [y_lo, y_lo], color=\"black\", lw=1.5, clip_on=False)\n",
    "ax.plot([bx - 0.06, bx + 0.06], [y_hi, y_hi], color=\"black\", lw=1.5, clip_on=False)\n",
    "# Ratio text\n",
    "r_last = decay_raw[-1] / decay_fo[-1]\n",
    "ax.text(\n",
    "    bx + 0.15,\n",
    "    (y_lo + y_hi) / 2,\n",
    "    f\"{r_last:.1f}X\",\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\",\n",
    "    va=\"center\",\n",
    ")\n",
    "ax.set_xlabel(\"Time step\", fontsize=12)\n",
    "ax.set_ylabel(\"Signal decay (fractional amplitude loss)\", fontsize=12)\n",
    "ax.text(\n",
    "    bx - 0.5,\n",
    "    y_hi + 0.06,\n",
    "    \"↓ Lower is better\",\n",
    "    fontsize=12,\n",
    "    fontstyle=\"italic\",\n",
    "    color=\"black\",\n",
    "    va=\"bottom\",\n",
    ")\n",
    "ax.set_title(\n",
    "    \"Signal Decay – Fractional Loss Relative to Noiseless\",\n",
    "    fontsize=13,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "ax.set_xticks(steps)\n",
    "ax.set_xlim(0.4, 6.5)\n",
    "ax.set_ylim(0, 1.15)\n",
    "ax.legend(fontsize=10, loc=\"upper left\")\n",
    "ax.grid(axis=\"y\", alpha=0.3, zorder=0)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Discussion\n",
    "\n",
    "The hardware results show that Fire Opal improves the quality of the reconstructed QPDE signal for the 9-qubit Hubbard example. Across the five sampled time steps, the Fire Opal execution stays closer to the noiseless simulator reference than standard Qiskit execution. This difference is visible in the retained signal amplitude. With Fire Opal, the reconstructed signal preserves about 58% of the noiseless amplitude on average, compared with about 48% for standard Qiskit execution. In other words, Fire Opal preserves roughly 20% more of the signal amplitude over the sampled time window.\n",
    "\n",
    "The improved signal retention shows that Fire Opal reduces noise-related signal damping and thereby provides longer, usable time series that provide a signal distinguishable from shot-noise or other systematic noise effects. [Kanno et al. (2026)](https://arxiv.org/abs/2601.15616) successfully applied Fire Opal to larger simulations. In their paper, you can find the full process including the downstream frequency analysis to recover the gap, and further refinements such as algorithmic error mitigation and iterative overlap enhancement push the method to larger systems. The signal-quality improvement shown here is what those longer experiments rely on at the hardware level, and extending the reconstruction to the full time window is where higher signal quality translates directly into a more accurate gap estimate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Package               | Version |\n",
      "| --------------------- | ------- |\n",
      "| Python                | 3.12.11 |\n",
      "| matplotlib            | 3.10.3  |\n",
      "| networkx              | 3.2.1   |\n",
      "| numpy                 | 2.3.0   |\n",
      "| qiskit                | 2.3.1   |\n",
      "| qiskit-ibm-runtime    | 0.40.1  |\n",
      "| sympy                 | 1.14.0  |\n",
      "| fire-opal             | 10.0.1  |\n",
      "| qctrl-visualizer      | 9.0.0   |\n",
      "| qctrl-workflow-client | 9.0.1   |\n"
     ]
    }
   ],
   "source": [
    "from fireopal import print_package_versions\n",
    "\n",
    "print_package_versions()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:qiskit2]",
   "language": "python",
   "name": "conda-env-qiskit2-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
