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Xi'an, China

Xidian University, also known as University of Electronic Science and Technology at Xi'an, is a university located in Xi'an, Shaanxi, People's Republic of China. The university is regarded as having strong science and engineering programs, and is particularly famous in Information Technology related disciplines in China. Wikipedia.


Wu S.-L.,Xidian University
Nonlinear Analysis: Real World Applications | Year: 2012

This paper is concerned with entire solutions of a bistable reactiondiffusion system modeling manenvironmentman epidemics, i.e., solutions defined for all times t ∈ R and for all points x ∈ R. It is known that the system has an increasing traveling wave solution with nonzero wave speed under some reasonable conditions. Using the comparison argument and sub-super-solution method, we construct some new entire solutions for the system which behave like two increasing traveling wave solutions propagating from both sides of the x-axis and annihilating at a finite time. © 2011 Elsevier Ltd. All rights reserved. © 2012 Elsevier Ltd. All rights reserved. Source


The accurate identification of protein structure class solely using extracted information from protein sequence is a complicated task in the current computational biology. Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by fusing the sequence information and evolution information to represent a protein sample. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark data-sets, 1189 and 25PDB with sequence similarity lower than 40 and 25%, respectively. Comparison of our results with other methods shows that the proposed method by us is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity data-sets. Source


Zheng Y.,Xidian University | Wang L.,Peking University
Systems and Control Letters | Year: 2012

This paper studies the finite-time consensus problem of heterogeneous multi-agent systems composed of first-order and second-order integrator agents. By combining the homogeneous domination method with the adding a power integrator method, we propose two classes of consensus protocols with and without velocity measurements. First, we consider the protocol with velocity measurements and prove that it can solve the finite-time consensus under a strongly connected graph and leader-following network, respectively. Second, we consider the finite-time consensus problem of heterogeneous multi-agent systems, for which the second-order integrator agents cannot obtain the velocity measurements for feedback. Finally, some examples are provided to illustrate the effectiveness of the theoretical results. © 2012 Elsevier B.V. All rights reserved. Source


Li J.,Xidian University
IET Control Theory and Applications | Year: 2013

In this study, a new consensus problem is introduced for leader-follower multi-agent systems with non-linearity and a distributed adaptive iterative learning control is presented for the consensus problem. With the dynamic of the leader unknown to any of the agent, the proper protocol guarantees that the follower agents can track the leader. The consensus is analysed based on the Lyapunov stability theory. Finally, simulation examples are given to illustrate the effectiveness of the proposed method in this study. © 2013 The Institution of Engineering and Technology. Source


Sun P.G.,Xidian University
New Journal of Physics | Year: 2015

Controllability of a single network often focuses on the determination of the network's minimum dominating set, which aims to elaborate how to control the whole network with minimum driver nodes. This paper proposes a new framework, co-controllability of multiple networks, which stresses the control of one network by another network as well as the mutual control characteristics of multiple networks based on minimum dominating sets. We take a drug-disease-gene network that consists of a drug-drug network, a disease-disease network and a gene-gene network as an example to study co-controllability of multiple networks. The results show that driver nodes tend to be conserved, e.g. diseases highly associated with driver nodes of the drug-drug network tend to be driver nodes in the disease-disease network compared with random networks. In addition, co-controllability of multiple networks is probably associated with the networks' node degree, which is more stringent than controllability of a single network that is mainly determined by the network's degree distribution. We also find that diseases and drugs tend to be mapped as two different subnetworks of human protein-protein interaction (PPI) network, drugs are inclined to dominate diseases by controlling the PPI network, and the coded proteins of disease-related genes exhibit a low tendency to be drug targets for the control of diseases. The results in this paper not only play an important role in understanding co-controllability of multiple networks, but also are helpful for understanding the mechanisms of drug-disease-gene, disease treatments and drug design in a network-based framework. © 2015 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft. Source

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