A Time-Varying Complex Dynamical Network Model And Its Controlled Synchronization Criteria

Today, complex networks have attracted increasing attention from various fields of science and engineering. It has been demonstrated that many complex networks display various synchronization phenomena. In this paper, we introduce a time-varying complex dynamical network model. We then further investigate its synchronization phenomenon and prove several network synchronization theorems. Especially, we show that synchronization of such a time-varying dynamical network is completely determined by the inner-coupling matrix, and the eigenvalues and the corresponding eigenvectors of the coupling configuration matrix of the network.Comment: 13 page

Network analysis of chaotic dynamics in fixed-precision digital domain

When implemented in the digital domain with time, space and value discretized in the binary form, many good dynamical properties of chaotic systems in continuous domain may be degraded or even diminish. To measure the dynamic complexity of a digital chaotic system, the dynamics can be transformed to the form of a state-mapping network. Then, the parameters of the network are verified by some typical dynamical metrics of the original chaotic system in infinite precision, such as Lyapunov exponent and entropy. This article reviews some representative works on the network-based analysis of digital chaotic dynamics and presents a general framework for such analysis, unveiling some intrinsic relationships between digital chaos and complex networks. As an example for discussion, the dynamics of a state-mapping network of the Logistic map in a fixed-precision computer is analyzed and discussed.Comment: 5 pages, 9 figure

The Evolution of Health Outcomes from Childhood to Adolescence

NOTE: The peer-reviewed version of this paper is available online from the Journal of Health Economics at: http://dx.doi.org/10.1016/j.jhealeco.2010.10.007 The authors\u27 manuscript of it is available as the additional file listed below. Using data from the Canadian National Longitudinal Survey of Children and Youth (NLSCY), this study examines how and why health outcomes exhibit persistence during the period from childhood to adolescence. We examine the distribution of health outcomes and health transitions using descriptive analysis and explore the determinants of these distributions by estimating the contributions of family SES, unobserved heterogeneity and state dependence and also allowing for heterogeneity of state dependence parameters across categories of neighborhood status. Our analysis indicates that children living in poorer neighborhoods and in neighborhoods with lower education level tend to experience poor health status for longer after a transition to it, while children tend to experience multiple health drops living in poorer neighborhoods, in neighborhoods with less educated people, in neighborhoods with more families headed by lone-parents and in neighborhoods with more families living in rental accommodations. Junhu Li is a Ph.D. candidate in the Department of Economics at McMaster University. Her research fields are health economics and applied econometrics. She is interested in the analysis of dynamics and determinants of child health outcomes, and policy analysis of health care financing and funding reforms. Her recent research is on two topics. The first topic is on the evolution of health outcomes from childhood to adolescence using Canadian survey data NLSCY. The second topic is on the empirical identification of physician response to pay-for-performance incentives by exploiting the quasi-natural-experiment in the primary care reform models in Ontario, Canada