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Titlebook: Advances in Applied Mathematics and Approximation Theory; Contributions from A George A. Anastassiou,Oktay Duman Conference proceedings 201

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The Neuron – Building Block of the Brain 1] and vanish at its endpoints. Our extension is threefold: We obtain sharp V.A. Markov-type estimates for all single coefficients as well as sharp Szegö-type estimates for consecutive pairs of coefficients of such polynomials, and both these estimates imply Schur’s inequality for the leading coeff
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Phase Locking via Sinusoidal Couplingsl equations. We study problems connected with solvability, construction of high order finite difference and finite sums schemes, error estimation and investigate the order of arithmetic operations for finding approximate solutions. Corresponding results refined and generalized well-known classical a
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The Neuron – Building Block of the Brainering of a time-harmonic E-polarized electromagnetic plane wave. We propose an inverse algorithm that extends the approach suggested by Kress [14] and further investigated by Kress and Serranho [17, 18] and Serranho [22] for the case of the inverse problem for a perfectly conducting scatterer. It is
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Phase Locking via Sinusoidal Couplings, appears nonlinearly in the boundary conditions. We focus on the case where the boundary condition is given by a cubic equation in .. We first describe the problem by a matrix equation with nonlinear variables such that solving the DSLBVP is equivalent to solving the matrix equation. We develop met
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Integrate and Fire Models (IFM)vergence of the underlying solver methods. To address this complication, we construct a preconditioner that is robust with respect to contrast size and mesh size simultaneously based on the preconditioner proposed by Aksoylu et al. (Comput. Vis. Sci. 11:319–331, 2008). We examine the performance of
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