Chaos, Solitons and Fractals: X最新文献

In this work, we propose a strategy based on an analog active network to detect Hopf bifurcations in two–dimensional dynamical systems described by ordinary differential equations. With the proposed strategy, two parameters of the nonlinear system are established in the analog active network by using external controllable voltage levels in order to explore the dynamical evolution of the system in a fast, easy, and accessible way, making our approach a powerful tool to detect Hopf bifurcations in a two-dimensional space. To demonstrate the proposed strategy’s potential and functionality, we electronically implement the kinetics of a reaction-diffusion model proposed by Barrio et al. in 1999 [1], called BVAM model. Hopf bifurcations are detected by following the changes in the system, going from stationary to periodic solutions when varying the control parameters. Local linear stability analysis is performed to show the quantitative agreement between analytical and experimental bifurcations. We additionally found that global effects are detected by the experimental approach, which cannot be predicted from a local analysis. The proposed strategy opens the way to use analog active networks to detect bifurcations in dynamical systems experimentally.

Based on the characteristic of the COVID-19 asymptomatic infection, and due to the shortage of traditional mathematical models of transmission dynamics of infectious diseases, we propose a new SAIR model. This SAIR model fully considers the infectious characteristics of asymptomatic cases and the transformation characteristics between the four kinds case. According to the data released by the National Health Commission of P.R.C, the model parameters are calculated, and the transmission process of the COVID-19 is simulated dynamically. It is found that the SAIR model data are in good agreement with the actual data, and the time characteristics of the infection rate are particularly accurate, proving the accuracy and effectiveness of the model. Then, on the basis of the differences between the model data and the real data, the standard deviation of the error is calculated. From the standard deviation, the functional intervals of the confirmed infection rate and the asymptomatic infection rate, the interval of the total number of cases in the model, and the interval of the number of asymptomatic cases in the society are also calculated. The number of asymptomatic cases in society is of important and realistic significance for the assessment of risk and subsequent control measures. Then, according to the dynamic simulation data of the model with changed value of parameters, the remarkable effects of strict quarantines are discussed. Finally, the possible direction of further study is given.

The 2019 novel coronavirus disease (COVID-19) has spread rapidly to many countries around the world from Wuhan, the capital of China’s Hubei province since December 2019. It has now a huge effect on the global economy. As of 13 September 2020, more than 28, 802, 775, and 920, 931 people are infected and dead, respectively. The mortality of COVID-19 infections is increasing as the number of infections increase. Many countries published control measures to contain its spread. Even though there are many drugs and vaccines under trial by pharmaceutical companies and research groups, no specific vaccine or drug has yet been found. Therefore, it is necessary to explain the behaviour of the case fatality rate (CFR) of COVID-19 using the most updated COVID-19 epidemiological data before 13 September 2020. The dynamics in the CFR were analyzed using the Markov-switching autoregressive (MSAR) models. Results showed that the two-regime and three-regime MSAR approach better captured the non-linear dynamics in the CFR time series data for each of the top heavily infected countries including the world. The results also showed that rises in CFRs are more volatile than drops. We believe that this information can be useful for the government to establish appropriate policies in a timely manner.

This paper considers the fractional-order system in the sense of $\psi$-Hilfer fractional differential equations. In order to investigate the existence and uniqueness of the mild solution, the Banach contraction mapping principle and the measure of non-compactness are applied. As an application, the financial crisis model in the sense of $\psi$-Hilfer fractional differential equation will be used to prove the existence of solution and global stability of it. In addition, to illustrate the feasibility and validity of our results, the numerical simulation of the financial crisis model in the sense of Caputo will be shown in four different cases. Our results indicate that for non-integer order, the system behaves to be asymptotically stable and periodic (chaotic) at a certain limit order and the other part stabilizes to a fixed point.

This paper mainly focuses on the issue of finite-time synchronization of a class of chaotic master and slave systems when they have uncertainties, disturbances, and unknown parameters. It is supposed that Uncertainties and disturbances bounds are unknown. First, using the concept of fractional calculus, a new fractional sliding surface is proposed and its finite-time convergence is also proved. Second, appropriate adaptive laws are introduced to overcome unknown system parameters and these laws correctly estimate the unknown values. With applying the controller, synchronization is achieved within a short time. Also after the synchronization, unstable fluctuations are removed and the controlled system has perfect robustness. The proposed approach is applicable to a wide range of identical or non-identical chaotic master and slave systems. Theoretical analysis and stability examination of the proposed method have been performed utilizing adaptive methods and Lyapunov stability theorem. Thereafter, two practical examples are presented to evaluate the effectiveness and usefulness of the suggested method. Furthermore, this method is compared with methods in recent articles, which shows the superiority of this method.

This work deals with the inverse problem in epidemiology based on a SIR model with time-dependent infectivity and recovery rates, allowing for a better prediction of the long term evolution of a pandemic. The method is used for investigating the COVID-19 spread by first solving an inverse problem for estimating the infectivity and recovery rates from real data. Then, the estimated rates are used to compute the evolution of the disease. The time-depended parameters are estimated for the World and several countries (The United States of America, Canada, Italy, France, Germany, Sweden, Russia, Brazil, Bulgaria, Japan, South Korea, New Zealand) and used for investigating the COVID-19 spread in these countries.