Integral equation models for endemic infectious diseases

Summary Endemic infectious diseases for which infection confers permanent immunity are described by a system of nonlinear Volterra integral equations of convolution type. These constant-parameter models include vital dynamics (births and deaths), immunization and distributed infectious period. The models are shown to be well posed, the threshold criteria are determined and the asymptotic behavior is analysed. It is concluded that distributed delays do not change the thresholds and the asymptotic behaviors of the models.This work was partially supported by NIH Grant AI 13233.     

Permanence of a predator–prey discrete system with Holling-IV functional response and distributed delays

A predator–prey discrete-time model with Holling-IV functional response and distributed delays is investigated in this paper. By using the comparison theorem of the difference equation and some analysis technique, some sufficient conditions are obtained for the permanence of the discrete predator–prey system. Two examples are given to illustrate the feasibility of the obtained result.     

Review Times in Statistical Journals: Tilting at Windmills?

Using limited data, I argue that the review times in statistics are far too long for the field to keep pace with the rapidly changing environment in science. I note that statisticians do not appear to believe in statistics because data on the review process are not widely available to members of the profession. I suggest a few changes that could be made to speed up the review process, although it would appear that a change in our culture is required before the problem will be solved.     

The stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays and reaction–diffusion terms

The global asymptotic stability of impulsive stochastic Cohen–Grossberg neural networks with mixed delays and reaction–diffusion terms is investigated. Under some suitable assumptions and using Lyapunov–Krasovskii functional method, we apply the linear matrix inequality technique to propose some new sufficient conditions for the global asymptotic stability of the addressed model in the stochastic sense. The mixed time delays comprise both the time-varying and continuously distributed delays. The effectiveness of the theoretical result is illustrated by a numerical example.     

On multistability of delayed genetic regulatory networks with multivariable regulation functions

Many genetic regulatory networks (GRNs) have the capacity to reach different stable states. This capacity is defined as multistability which is an important regulation mechanism. Multiple time delays and multivariable regulation functions are usually inevitable in such GRNs. In this paper, multistability of GRNs is analyzed by applying the control theory and mathematical tools. This study is to provide a theoretical tool to facilitate the design of synthetic gene circuit with multistability in the perspective of control theory. By transforming such GRNs into a new and uniform mathematical formulation, we put forward a general sector-like regulation function that is capable of quantifying the regulation effects in a more precise way. By resorting to up-to-date techniques, a novel Lyapunov-Krasovskii functional (LKF) is introduced for achieving delay dependence to ensure less conservatism. New conditions are then proposed to ensure the multistability of a GRN in the form of linear matrix inequalities (LMIs) that are dependent on the delays. Our multistability conditions are applicable to several frequently used regulation functions especially the multivariable ones. Two examples are employed to illustrate the applicability and usefulness of the developed theoretical results.     

一类混合时滞的非线性耦合神经网络的同步

主要讨论了一类混合时滞的非线性耦合神经网络的同步问题.同时,考虑随机扰动以及参数的切换由某个马尔可夫链所确定等方面对其的影响.文中通过构造新的Lyapunov-Krasovskii泛函,运用线性矩阵不等式（LMI）技术并结合Kronecker积来获得神经网络全局同步的充分性判据.由于这样得到的判据是LMI形式,因此可以由数学软件Matlab的LMI Toolbox对所获得的判据进行有效的验证和求解.此外,本文中我们对细胞激活函数做了更为一般的假设,从而使结论在LMI下可以减少保守性.     

Design of delay-dependent state estimator for discrete-time recurrent neural networks with interval discrete and infinite-distributed time-varying delays

The state estimation problem for discrete-time recurrent neural networks with both interval discrete and infinite-distributed time-varying delays is studied in this paper, where interval discrete time-varying delay is in a given range. The activation functions are assumed to be globally Lipschitz continuous. A delay-dependent condition for the existence of state estimators is proposed based on new bounding techniques. Via solutions to certain linear matrix inequalities, general full-order state estimators are designed that ensure globally asymptotic stability. The significant feature is that no inequality is needed for seeking upper bounds for the inner product between two vectors, which can reduce the conservatism of the criterion by employing the new bounding techniques. Two illustrative examples are given to demonstrate the effectiveness and applicability of the proposed approach.     

The asymptotic behavior of a chemostat model with the Beddington-DeAngelis functional response

In this paper, the global asymptotic behavior of a chemostat model with Beddington-DeAngelis functional response is studied. The conditions for the global asymptotical stability of the model with time delays are obtained via monotone dynamical systems. Our results demonstrate that those time delays affect the competitive outcome of the organisms.     

多比例时滞细胞神经网络的指数周期性与稳定性

研究了一类具多比例时滞细胞神经网络的全局指数周期性与稳定性.通过变换y(t)=x(e~t)将具多比例时滞的细胞神经网络变换成具常时滞变系数的细胞神经网络,利用一些分析技巧与构造合适的Lyapunov泛函,得到系统的周期解存在唯一且全局指数周期的时滞依赖的充分条件,判断方法简单易验证.并给出了两个例子及其数值仿真结果以支持所得结论.