Energy-entropy inequalities for classical lattice systems

A new characterization of equilibrium states for classical lattice systems is given in terms of correlation inequalities. Their physical meaning is found to express thermodynamic stability. We demonstrate the applicability of the inequalities in specific models.Bevoegdverklaard navorser N.F.W.O.Aangesteld navorser N.F.W.O.     

广东省黄埔港土壤中铬的价态分析研究

采用化学浸提技术对广东省黄埔港土壤中的铬进行了分析,研究了浸提条件及其对测定结果的影响,并运用GF-AAS法定量分析Cr(Ⅵ).检出限为0.03 μg/L,相对标准偏差(RSD,n=12)为1.52%,加标回收率在95.6%-103.6%.     

Dynamic instabilities in scalar neural field equations with space-dependent delays

In this paper we consider a class of scalar integral equations with a form of space-dependent delay. These nonlocal models arise naturally when modelling neural tissue with active axons and passive dendrites. Such systems are known to support a dynamic (oscillatory) Turing instability of the homogeneous steady state. In this paper we develop a weakly nonlinear analysis of the travelling and standing waves that form beyond the point of instability. The appropriate amplitude equations are found to be the coupled mean-field Ginzburg-Landau equations describing a Turing-Hopf bifurcation with modulation group velocity of O(1). Importantly we are able to obtain the coefficients of terms in the amplitude equations in terms of integral transforms of the spatio-temporal kernels defining the neural field equation of interest. Indeed our results cover not only models with axonal or dendritic delays but also those which are described by a more general distribution of delayed spatio-temporal interactions. We illustrate the predictive power of this form of analysis with comparison against direct numerical simulations, paying particular attention to the competition between standing and travelling waves and the onset of Benjamin-Feir instabilities.     

Dynamics of Cohen-Grossberg neural networks with variable and distributed delays

This paper is concerned with the analysis problem for the exponential stability of a class of Cohen-Grossberg neural networks with variable and distributed delays. Some sufficient conditions ensuring the existence, uniqueness and exponential stability of the equilibrium point are obtained by employing Brouwer’s fixed-point theorem and by applying the inequality technique. In the results, we do not assume that the activation function satisfies the boundedness and the Lipschitz condition. Three numerical examples are given to show the effectiveness of the obtained results.     

Inverse chaos synchronization between bidirectionally coupled variable multiple time delay systems

We make first report on inverse chaos synchronization between bidirectionally non-linearly and linearly coupled variable multiple time delay laser systems governed by the Ikeda model. The results are of certain importance in secure chaos-based communication systems.     

Periodic solution of a chemostat model with variable yield and impulsive state feedback control

In this paper, a chemostat model with variable yield and impulsive state feedback control is considered. We obtain sufficient conditions of the globally asymptotical stability of the system without impulsive state feedback control. We also obtain that the system with impulsive state feedback control has periodic solution of order one. Sufficient conditions for existence and stability of periodic solution of order one are given. In some cases, it is possible that the system exists periodic solution of order two. Our results show that the control measure is effective and reliable.     

A birth–death model for Web cache systems: Numerical solutions and simulation

In this paper, we study Web cache hit rates by introducing a birth–death model. A system consisting of a single Web server with a single cache stores Web pages that are classified as hot pages (popular pages) and cold pages (less popular pages). Given requested probabilities for each class, the stochastic model provides the mean hit rate for a random replacement algorithm and the upper and low bounds for other algorithms. Numerical results from the analysis are validated using the output of simulation programs that utilize the LRU algorithm.     

Synchronization of neural networks based on parameter identification and via output or state coupling

For neural networks with all the parameters unknown, we focus on the global robust synchronization between two coupled neural networks with time-varying delay that are linearly and unidirectionally coupled. First, we use Lyapunov functionals to establish general theoretical conditions for designing the coupling matrix. Neither symmetry nor negative (positive) definiteness of the coupling matrix are required; under less restrictive conditions, the two coupled chaotic neural networks can achieve global robust synchronization regardless of their initial states. Second, by employing the invariance principle of functional differential equations, a simple, analytical, and rigorous adaptive feedback scheme is proposed for the robust synchronization of almost all kinds of coupled neural networks with time-varying delay based on the parameter identification of uncertain delayed neural networks. Finally, numerical simulations validate the effectiveness and feasibility of the proposed technique.