T
Tonghua Zhang
Researcher at Swinburne University of Technology
Publications - 161
Citations - 3834
Tonghua Zhang is an academic researcher from Swinburne University of Technology. The author has contributed to research in topics: Hopf bifurcation & Population. The author has an hindex of 32, co-authored 142 publications receiving 2974 citations. Previous affiliations of Tonghua Zhang include Shanghai Normal University & Shanghai Jiao Tong University.
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Dynamics of a novel nonlinear stochastic SIS epidemic model with double epidemic hypothesis
TL;DR: In this article, the authors proposed new mathematical models with nonlinear incidence rate and double epidemic hypothesis and developed a method to obtain the threshold of the stochastic SIS epidemic model.
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Dynamics analysis and numerical simulations of a stochastic non-autonomous predator–prey system with impulsive effects
TL;DR: In this article, a stochastic non-autonomous Lotka-Volterra predator-prey model with impulsive effects was proposed and its dynamics were investigated, and it was shown that the system has a unique periodic solution which is globally attractive.
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Spatial dynamics in a predator-prey model with herd behavior.
TL;DR: A spatial predator-prey model with herd behavior in prey population and quadratic mortality in predator population is investigated and it is found that the model exhibits complex pattern replication: spotted pattern, stripe pattern, and coexistence of the two.
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Spatio-temporal dynamics of a reaction-diffusion system for a predator–prey model with hyperbolic mortality
TL;DR: In this article, the effects of diffusion on the spatial dynamics of a predator-prey model with hyperbolic mortality in predator population were investigated and the formation of some elementary two-dimensional patterns such as hexagonal spots and stripe patterns.
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Turing–Hopf bifurcation in the reaction–diffusion equations and its applications
TL;DR: The reduced dynamics associated with Turing–Hopf bifurcation is exactly the dynamics of codimension–two ordinary differential equations (ODE), which implies the ODE techniques can be employed to classify the reduced dynamics by the unfolding parameters.