A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

In this work,the exponential approximation is used for the numerical simulation of a nonlinear SITR model as a system of differential equations that shows the dynamics of the new coronavirus(COVID-19).The SITR mathematical model is divided into four classes using fractal parameters for COVID-19 dynamics,namely,susceptible(S),infected(I),treatment(T),and recovered(R).The main idea of the presented method is based on the matrix representations of the exponential functions and their derivatives using collocation points.To indicate the usefulness of this method,we employ it in some cases.For error analysis of the method,the residual of the solutions is reviewed.The reported examples show that the method is reasonably efficient and accurate.

Estimation of Nonlinear Roll Damping by Analytical Approximation of Experimental Free-Decay Amplitudes

Damping is critical for the roll motion response of a ship in waves. A common method for the assessment of damping in a ship’s rolling motion is to perform a free-decay experiment in calm water. In this paper, we propose an approach for estimating nonlinear damping that involves a linear exponential analytical approximation of the experimental roll free-decay amplitudes, fol- lowed by parametric identification based on the asymptotic method. The restoring moment can be strongly nonlinear. To validate this method, we first analyzed numerically simulated roll free-decay data using rolling equations with two alternative parametric forms: linear-plus-quadratic and linear-plus-cubic damping. By doing so, we obtained accurate estimates of nonlinear damping coefficients, even for large initial roll amplitudes. Then, we applied the proposed method to real free-decay data obtained from a scale model of a bulk barrier, and found the simulated results to be in good agreement with the experimental data. Using only free-decay peak data, the proposed method can be used to estimate nonlinear roll-damping coefficients for conditions with a strongly nonlinear restoring moment and large initial roll amplitudes.

Sparse Signal Recovery via Exponential Metric Approximation

Sparse signal recovery problems are common in parameter estimation, image processing, pattern recognition, and so on. The problem of recovering a sparse signal representation from a signal dictionary might be classified as a linear constraint l_0-quasinorm minimization problem, which is thought to be a Non-deterministic Polynomial-time（NP）-hard problem. Although several approximation methods have been developed to solve this problem via convex relaxation, researchers find the nonconvex methods to be more efficient in solving sparse recovery problems than convex methods. In this paper a nonconvex Exponential Metric Approximation（EMA）method is proposed to solve the sparse signal recovery problem. Our proposed EMA method aims to minimize a nonconvex negative exponential metric function to attain the sparse approximation and, with proper transformation,solve the problem via Difference Convex（DC） programming. Numerical simulations show that exponential metric function approximation yields better sparse recovery performance than other methods, and our proposed EMA-DC method is an efficient way to recover the sparse signals that are buried in noise.

An exponential for the solutions model of collocation method of the HIV infection CD4＋T cells

In this paper, an exponential method is presented for the approximate solutions of the HIV infection model of CD4＋T. The method is based on exponential polynomi- als and collocation points. This model problem corresponds to a system of nonlinear ordinary differential equations. Matrix relations are constructed for the exponential functions. By aid of these matrix relations and the collocation points, the proposed technique transforms the model problem into a system of nonlinear algebraic equations. By solving the system of the algebraic equations, the unknown coefficients are com- puted and thus the approximate solutions are obtained. The applications of the method for the considered problem are given and the comparisons are made with the other methods.