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Zhibin Hu

Publications and source records attributed to Zhibin Hu.

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New Traffic Flow Model with Nonlinear Anticipation and Intelligent-control Boundary: Existence, Long-time Behavior and Large-relaxation-time Limit

In this paper, we propose a novel physical model for traffic flow incorporating nonlinear anticipation effects and an intelligent-control boundary, mathematically formulated as a damping boundary condition: \begin{align*} \begin{cases} u_t^\tau+v_x^\tau=0, & x\in(0,1),\; t>0,\\[1mm] v_t^\tau+g(u_x^\tau)u_x^\tau=\dfrac{f(u^\tau)-v^\tau}{\tau}, & x\in(0,1),\; t>0,\\[1mm] (u^\tau,v^\tau)(x,0)=(u_0^\tau(x),v_0^\tau(x)), & x\in(0,1),\\[1mm] u_x^\tau(0,t)=0,\quad u_x^\tau(1,t)=-ku_t^\tau(1,t), & k>0,\; t>0, \end{cases} \end{align*} where $f$ and $g$ are smooth functions satisfying suitable structural assumptions, and $\tau>0$ is the relaxation time. The primary objective is to rigorously investigate how the intelligent-control boundary suppresses the stop-and-go phenomenon in the large-relaxation-time regime-a mechanism that has not been mathematically addressed in previous studies. Utilizing the energy method, we establish the global well-posedness and exponential time-decay of solutions for the original system under arbitrarily large initial data with small spatial derivatives. Furthermore, we analyze the asymptotic behavior in the large-relaxation-time limit $\tau\to\infty$, by introducing a novel technique that incorporates constant shifts into the initial data of the limiting system. This approach enables us to construct a modified auxiliary system, through which we successfully obtain the global convergence of the original solutions to the asymptotic profiles for all time $t$ as relaxation time $\tau\to\infty$. Numerical simulations further demonstrate that in the large-relaxation-time regime, the stop-and-go density waves emerging at the early stage are gradually suppressed by the damping boundary. This leads the traffic stream to eventually evolve into an essentially uniform profile, which perfectly validates our theoretical results.

math.AP

Implications of the virus-encoded miRNA and host miRNA in the pathogenicity of SARS-CoV-2

The outbreak of COVID-19 caused by SARS-CoV-2 has rapidly spread worldwide and has caused over 1,400,000 infections and 80,000 deaths. There are currently no drugs or vaccines with proven efficacy for its prevention and little knowledge was known about the pathogenicity mechanism of SARS-CoV-2 infection. Previous studies showed both virus and host-derived MicroRNAs (miRNAs) played crucial roles in the pathology of virus infection. In this study, we use computational approaches to scan the SARS-CoV-2 genome for putative miRNAs and predict the virus miRNA targets on virus and human genome as well as the host miRNAs targets on virus genome. Furthermore, we explore miRNAs involved dysregulation caused by the virus infection. Our results implicated that the immune response and cytoskeleton organization are two of the most notable biological processes regulated by the infection-modulated miRNAs. Impressively, we found hsa-miR-4661-3p was predicted to target the S gene of SARS-CoV-2, and a virus-encoded miRNA MR147-3p could enhance the expression of TMPRSS2 with the function of strengthening SARS-CoV-2 infection in the gut. The study may provide important clues for the mechisms of pathogenesis of SARS-CoV-2.

q-bio.GN