Partial synchronization in stochastic dynamical networks with switching communication channels

Modified stochastic diffusion particle tracking model driven by skew Brownian motion: Analysis of sediment particle motion in turbulent flow under the effects of ejection and sweep

Turbulent bursting events have been classified into outward interactions (Q1), ejections (Q2), inward interactions (Q3), and sweeps (Q4) in various studies. Ejections (Q2) and sweeps (Q4) have been identified as significant contributors to time consumption, momentum flux, and sediment flux. Additionally, research has shown that the distribution of these events varies nonuniformly at different bed elevations. Despite extensive investigations into the nonuniform distribution of turbulent bursting events, their impact on sediment transport has been rarely explored. In this work, we developed a modified stochastic diffusion particle tracking model (SD-PTM) driven by skew Brownian motion (SBM) using the stochastic Lagrangian approach to scrutinize sediment particle movement in turbulent flows. The model incorporates turbulent characteristics derived from a direct numerical simulation dataset, allowing for a comprehensive analysis of sediment particle dynamics. Moreover, the proposed model accounts for the nonuniform spatial distribution of ejection and sweep events, as well as the particle movement direction during these events. Numerical simulations of the model were conducted to trace sediment particle trajectories in the streamwise and vertical directions. The analysis of sediment transport involved calculating the variance of particle trajectories to examine anomalous diffusion. The model's performance was evaluated by comparing it with flow velocity and sediment concentration profiles obtained from measurements in previous studies. In conclusion, our study suggests that the motion of sediment particles in turbulent flow can be thoroughly investigated under extreme flow conditions using the modified SD-PTM driven by SBM.

Non-Bayesian social learning model with periodically switching structures

In this article, we investigate the dynamics of non-Bayesian social learning model with periodically switching structures. Unlike the strongly connectedness conditions set for the temporal connecting networks of the non-Bayesian social learning to guarantee its convergence in the literature, our model configurations are essentially relaxed in a manner that the connecting networks in every switching duration can be non-strongly connected. Mathematically and rigorously, we validate that, under relaxed configurations, dynamics of our model still converge to a true state of social learning in a particular sense of probability. Additionally, we provide estimations on the convergence rate for successful social learning in our model. Numerically, we demonstrate the efficacy of the analytically established conditions and estimations by using some representative examples with switching structures. We believe that our results could be potentially useful for illustrating the social activities in the real world.

A new protocol for finite-time consensus of detail-balanced multi-agent networks

In this paper, a finite-time consensus protocol for multi-agent networks is discussed from a new perspective. The order β of the nonlinear function in the protocol is shown to be a crucial parameter in analyzing the finite-time consensus property of multi-agent networks with a detail-balanced communication topology. When β>0, the corresponding protocol can guarantee the consensus of the multi-agent networks. In particular, if β∈(0,1), the consensus can be realized within finite time. A leader-follow model is also investigated in this paper. Finally, several concrete protocols are proposed based on our theoretical analysis, and numerical examples are given to make a comparison among different protocols from the aspect of convergence speed.