338,817 research outputs found

    On robust stability of stochastic genetic regulatory networks with time delays: A delay fractioning approach

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    Copyright [2009] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Robust stability serves as an important regulation mechanism in system biology and synthetic biology. In this paper, the robust stability analysis problem is investigated for a class of nonlinear delayed genetic regulatory networks with parameter uncertainties and stochastic perturbations. The nonlinear function describing the feedback regulation satisfies the sector condition, the time delays exist in both translation and feedback regulation processes, and the state-dependent Brownian motions are introduced to reflect the inherent intrinsic and extrinsic noise perturbations. The purpose of the addressed stability analysis problem is to establish some easy-to-verify conditions under which the dynamics of the true concentrations of the messenger ribonucleic acid (mRNA) and protein is asymptotically stable irrespective of the norm-bounded modeling errors. By utilizing a new Lyapunov functional based on the idea of “delay fractioning”, we employ the linear matrix inequality (LMI) technique to derive delay-dependent sufficient conditions ensuring the robust stability of the gene regulatory networks. Note that the obtained results are formulated in terms of LMIs that can easily be solved using standard software packages. Simulation examples are exploited to illustrate the effectiveness of the proposed design procedures

    Local sourcing of multinational enterprises in China

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    Purpose – Local sourcing from indigenous firms by multinational enterprises (MNEs) is an important channel through which the former may benefit from the positive externalities generated by the latter. The purpose of this study is to analyze the extent and determinants of local sourcing of MNEs. Design/methodology/approach – Employing a survey dataset covering 493 multinational subsidiaries in China during 1999-2005, this paper applies the two-limit Tobit model. Findings – It is found that an MNE's local sourcing decision is influenced by its strategies, characteristics such as size and learning ability and country-of-origin. More specifically, export-orientation strategy, joint venture strategy and networking with local suppliers positively affect local sourcing. Small and autonomous subsidiaries tend to source more locally. Age has a non-linear effect. The importance of these determinants varies with regions. Research limitations/implications – Aiming at capacity building and competitiveness of indigenous firms, the Chinese government has initiated local content requirement. This study shows that such policy intervention could be counterproductive. The creation of a more competitive business environment by the government could promote more linkages. Originality/value – Given its critical role in economic development, local sourcing by MNEs has attracted much attention. Only limited research has been carried out on FDI linkage effects in China, and the location effect on FDI linkages has not been examined. This study aims to fill the gap by using Chinese survey data

    Mathematical problems for complex networks

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    Copyright @ 2012 Zidong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This article is made available through the Brunel Open Access Publishing Fund.Complex networks do exist in our lives. The brain is a neural network. The global economy is a network of national economies. Computer viruses routinely spread through the Internet. Food-webs, ecosystems, and metabolic pathways can be represented by networks. Energy is distributed through transportation networks in living organisms, man-made infrastructures, and other physical systems. Dynamic behaviors of complex networks, such as stability, periodic oscillation, bifurcation, or even chaos, are ubiquitous in the real world and often reconfigurable. Networks have been studied in the context of dynamical systems in a range of disciplines. However, until recently there has been relatively little work that treats dynamics as a function of network structure, where the states of both the nodes and the edges can change, and the topology of the network itself often evolves in time. Some major problems have not been fully investigated, such as the behavior of stability, synchronization and chaos control for complex networks, as well as their applications in, for example, communication and bioinformatics
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