查看更多>>摘要：By the renormalization method based on the Taylor expansion (TR method, for simplicity), a new direct scheme to compute the singular perturbation of solitons is proposed. The proposed method can also be applied to perturbation of other solvable nonlinear evolution equations. The biggest advantage of the method is that the secular terms in perturbation series need not to be considered at all in our method. But, in other perturbation methods, the most important task is to eliminate the secular terms. In addition, comparing with the other direct approaches, we need not the multi-scale procedure. As an illustration example, the perturbation of shock wave for the perturbed Burgers equation is solved in detail, and a uniform asymptotical solution is obtained to describe the dynamics of the shock wave under the small perturbation and long time. By taking a concrete perturbation and computing the exact solution, we show that the renormalization solution gives exact correction of velocity of shock wave. In final, we compute the perturbation of the single soliton of the damping KdV equation. (c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Fluid-saturated porous media are two-phase media, which are composed of solid and liquid phases. Biot theory for fluid-saturated porous media holds that underground media are composed of porous elastic solid and com-pressible viscous fluid filled with pore space. Compared with the single-phase media theory, the fluid-saturated porous media theory can describe the subsurface media more precisely, and the elastic wave equations in the fluid-saturated porous media contain more parameters used to describe the formation properties. There-fore, fluid-saturated porous media theory is widely used in geophysical exploration, seismic engineering, and other fields.This paper considers the porosity reconstruction problem of the elastic wave equations in the fluid-saturated po-rous media based on Biot theory, which is a typically nonlinear ill-posed inverse problem from mathematical viewpoint. The proposed method is a novel iteration regularization scheme based on the homotopy perturbation technique. To verify the validity and applicability, numerical experiments of two-dimensional and three-dimensional porosity models have been carried out. Numerical results illustrate that this method can overcome the numerical instability and are robust to data noise in the reconstruction procedure. Furthermore, compared with the classical regularized Gauss-Newton method, the homotopy perturbation method greatly widens the convergence region while keeping the fast convergence rate.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：In order to enrich the dynamic characteristics of cellular neural network (CNN) and reveal the influence of memristor nonlinearity on the dynamic behavior of the network, a novel five-dimension memristive cellular neural network hyperchaotic system is designed by replacing the linear resistors in the output module of cellular neural network with two flux-controlled memristors, and the hardware circuit design of the system is completed. Based on Lyapunov exponent spectrum and attractor phase trajectories, the effects of system parameters and initial value on the dynamic characteristics of the memristive cellular neural network model are studied, and the generation conditions of different chaotic attractors and coexisting chaotic attractors are explained. Kolmogorov entropy is used to measure the chaotic degree of the system when the parameters are in different intervals. In this way, the criteria of parameter selection for system application are given. In particular, the effect of Gaussian white noise on the dynamic behavior of memristive CNN chaotic system is studied. The hardware circuit design and characteristic analysis of memristive cellular neural network are completed by the circuit simulation software, and the physical realizability of chaotic characteristics of the memristive cellular neural network model is verified. Furthermore, a secure communication application example based on the hyperchaotic system is given. (c) 2022 Elsevier Ltd.
查看更多>>摘要：Parrondo's paradox (inspired by the flashing Brownian ratchet) and Simpson's paradox (a statistical phenomenon) are two popular paradoxes that have attracted immense interest across many fields ranging from decision theory, evolutionary biology to social dynamics. In this article, we show that risk-taking behaviour through aggregate decision-making on Parrondo's games can lead to the emergence of Simpson's paradox. By partitioning the network of individuals according to risk-taking behaviours, we show that it is possible that the trend of capital losses from playing Parrondo's games reverses when these groups are combined-the signature of Simpson's paradox. This work reports on the emergence of the double paradox on a scale-free network and a social network, with the potential to uncover such instances in other social settings as well.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：We propose a discrete Darboux-Lax scheme for deriving auto-Backlund transformations and constructing solutions to quad-graph equations that do not necessarily possess the 3D consistency property. As an illustrative example we use the Adler-Yamilov type system which is related to the nonlinear Schrodinger (NLS) equation [7]. In particular, we construct an auto-Backlund transformation for this discrete system, its superposition principle, and we employ them in the construction of the one- and two-soliton solutions of the Adler-Yamilov system. PACS numbers: 02.30.Ik, 02.90.+p, 03.65.Fd Mathematics subject classification 2020: 37K60, 39A36, 35Q55, 16T25.
查看更多>>摘要：An effective air pollution prediction is of great significance to prevent and control air pollution and protect the health of residents. In order to improve the prediction accuracy of PM2.5, an innovative PM2.5 concentration prediction and early warning system based on optimal feature extraction and intelligent optimization is developed in this study. First, a feedback variational modal decomposition algorithm is designed to decompose the PM2.5 concentration sequence and fuzzy entropy is used to reconstruct the patterns of similar complexity. Then, Copula entropy is used to select the influencing factors with a high impact on PM2.5. Next, the reconstructed components and influencing factors are inputted to three individual prediction models, including long short-term memory neural network, gated recurrent unit neural network, and temporal convolutional network, for training and multi-step short-termprediction. The results of the individual prediction models are nonlinearly combined by Gaussian process regressionwhich is optimized by the multi-objective grey wolf optimization algorithm. Finally, the prediction results of different reconstructed components are nonlinearly integrated to obtain the final PM2.5 prediction results. In an empirical study of two Chinese cities, the combined prediction model proposed in this study outperformed the other six comparative models in terms of prediction accuracy and stability. The experimental results prove that the hybrid prediction model proposed in this paper can make an effective prediction and early warnings of air pollution. (C) 2022 Elsevier Ltd. All rights reserved.
Bekoa, D. J. OwonoKenfack, W. FokouSiewe, M. Siewe
11页
查看更多>>摘要：In this paper, we investigate the effects of harmonic and bounded noise excitations on the saline oscillator dy-namics. To this end, we first of all modify an existing coupled saline oscillators model to account for an external excitation. We next analytically derive the system response to harmonic driving by the mean of the harmonic bal-ance method. Then we study the effects of the excitation amplitude and frequency on the dynamic of a sole os-cillator and two coupled oscillators. We find that over a wide range of the excitation parameters, the oscillator exhibits period-T oscillations whose amplitude increases with the excitation amplitude. This result is supported by simulations. Moreover, simulations show that periodically excited saline oscillator mainly exhibits, in addi-tion, subharmonic and quasi-periodic oscillations. The above oscillatory modes are determined by the values of the excitation amplitude and frequency. By the mean of stochastic averaging method and Monte-Carlo simula-tions, the probabilistic response of the oscillator under bounded noise excitation is determined. We find that the saline oscillator does not display the stochastic P-bifurcation when its physical parameters remain in appro-priate ranges. However, we find that the bounded noise excitation affects the oscillation period, midpoint and amplitude. Both harmonic and random excitations can be used to induce oscillations in the parameter region where free oscillations are dead.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：The paper studies the dynamic properties of a duopoly game in which firms strategically compete in quality enhancing innovation investments and quantities. Market demands are assumed to be isolastic (reciprocal to the price) functions. The non-linearity of the demand functions, as already highlighted by T. Puu in [11], suggests the existence of complicated dynamics (cyclical or chaotic) in a standard dynamic Cournot duopoly. In addition to this, competition in innovation introduces the presence of discontinuities in the best response functions, expanding the set of possible equilibria (including asymmetric and multiple ones) of the standard Cournot duopoly and further enriching the dynamic features of the model. (c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：In this article, we study the dynamics of a Maize streak virus (MSV) epidemic model by using the Caputo fractional derivative. Firstly, we define the dynamics of the given fractional-order model by checking the non negativity and boundedness of the solution, stability of disease-free equilibrium, the existence of a unique solution, and its stability. Then we derive the numerical solution of the proposed model by using an optimized Predictor-Corrector method, which has not yet been applied to solve any kind of epidemic system until now. Our optimized method uses a linear approximation of the proposed nonlinear model to ameliorate the competence of the Predictor-Corrector schemes. To verify the correctness of our results, we plot various graphs by taking different parameter cases at various fractional-order values. Also, Caputo outputs are compared with Atangana-Baleanu-Caputo derivative outputs. Our research shows the usefulness of the proposed optimized Predictor-Corrector method in epidemic studies and for simulating the memory effects in the given MSV system. The solution methodology of the proposed system is the main novelty of this research along with other supporting analyses.(c) 2022 Elsevier Ltd. All rights reserved.
查看更多>>摘要：Protective measures such as wearing mask, quarantine, hand washing and so on can prevent and control the spread of infectious diseases. In this paper, a discrete-time model on networks combines infectious diseases and protective measures is proposed. Protective measures include voluntary protective measures of susceptible individuals and compulsory protective measures of infected individuals. The epidemic threshold is obtained and the time series of optimal control probability of infected individuals are analyzed by optimal control theory. The results of optimal control are compared with other constant control and the influences of protective measures on infectious diseases are analyzed by simulations. The optimal control results indicate that the greater the control proportion of infected individuals, the greater the average social payoff. It is shown that increasing the effectiveness of protective measures could effectively control the spread of infectious diseases. (c) 2022 Elsevier Ltd. All rights reserved.
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Elsevier