Epidemic Spreading with Heterogeneous Awareness on Human Networks

The spontaneous awareness behavioral responses of individuals have a significant impact on epidemic spreading. In this paper, a modified Susceptible-Alert-Infected-Susceptible (SAIS) epidemic model with heterogeneous awareness is presented to study epidemic spreading in human networks and the impact of heterogeneous awareness on epidemic dynamics. In this model, when susceptible individuals receive awareness information about the presence of epidemic from their infected neighbor nodes, they will become alert individuals with heterogeneous awareness rate. Theoretical analysis and numerical simulations show that heterogeneous awareness can enhance the epidemic threshold with certain conditions and reduce the scale of virus outbreaks compared with no awareness. What is more, for the same awareness parameter, it also shows that heterogeneous awareness can slow effectively the spreading size and does not delay the arrival time of epidemic spreading peak compared with homogeneous awareness

Distributed Tracking in Heterogeneous Networks with Asynchronous Sampled-Data Control

This article investigates distributed coordinated tracking problems of networked heterogeneous systems. Based on asynchronous sampling information, distributed sampled-data protocols are employed to realize leader-following synchronization and containment tracking in networked heterogeneous systems. In asynchronous sampled-data protocols, each node has different sampling instants with other nodes and only samples itself information at its own sampling instants. By utilizing the input-delay approach and Lyapunove-Krasovskii functional approach, some sufficient conditions for guaranteeing the coordinated tracking are presented. First, quasi-synchronization criteria are obtained for networked heterogeneous oscillator systems with a dynamic leader over the directed graph. Second, in the presence of multiple heterogeneous leaders for networked heterogeneous systems, sufficient conditions of quasi-containment tracking are derived. In a word, all followers can converge into a bounded level of convex hull spanned by the leader(s). The upper bounds of tracking errors are estimated for both quasi-synchronization and quasi-containment tracking. Finally, two numerical examples are given to verify the theoretical results

Quasi-Synchronization in Heterogeneous Harmonic Oscillators with Continuous and Sampled Coupling

This paper studies quasi-synchronization in networked heterogeneous harmonic oscillators. By introducing a leader, two distributed synchronization protocols are first proposed for heterogeneous networks by utilizing continuous real-time information and aperiodic sampled-data information. Then, the sufficient conditions on quasi-synchronization are established for heterogeneous networks coupled with nonidentical harmonic oscillators. It is found that each follower oscillator can converge to a bounded region of the leader by adopting either a continuous-time protocol or sampled-data protocol. The upper bound of the region is solved for networked heterogeneous harmonic oscillators. Finally, an electrical network is provided to illustrate the applicability of the theoretical results, and two examples are provided to illustrate the effectiveness of the sufficient criteria

Finite-Time Robust Stabilization for Stochastic Neural Networks

This paper is concerned with the finite-time stabilization for a class of stochastic neural networks (SNNs) with noise perturbations. The purpose of the addressed problem is to design a nonlinear stabilizator which can stabilize the states of neural networks in finite time. Compared with the previous references, a continuous stabilizator is designed to realize such stabilization objective. Based on the recent finite-time stability theorem of stochastic nonlinear systems, sufficient conditions are established for ensuring the finite-time stability of the dynamics of SNNs in probability. Then, the gain parameters of the finite-time controller could be obtained by solving a linear matrix inequality and the robust finite-time stabilization could also be guaranteed for SNNs with uncertain parameters. Finally, two numerical examples are given to illustrate the effectiveness of the proposed design method

Efficacy of minimally invasive surgery for the treatment of hypertensive intracerebral hemorrhage: A protocol of randomized controlled trial

Abstract Introduction: Hypertensive intracerebral hemorrhage (HICH) is the most serious complication of hypertension. Clearing intracranial hematoma as soon as possible, reducing brain cell edema, and controlling intracranial pressure could effectively reduce neuron damage, lower patient mortality, and improve patient prognosis. At present, minimally invasive surgery (MIS) has been widely used and plays an important role in the treatment of HICH. However, it is still in controversies about the choice of surgical treatment and medication treatment for HICH. Therefore, we try to conduct a randomized, controlled, prospective trial to observe the efficacy of MIS treatment against HICH compared with medication treatment. Methods: Patients will be randomly divided into treatment group and control group in a 1:1 ratio using the random number generator in Microsoft Excel. Stereotactic soft channel minimally invasive intracranial hematoma puncture and drainage treatment and medication treatment will be applied respectively. The outcomes of intracerebral hemorrhage volume, Glasgow coma scale, National Institutes of Health Stroke Scale will be recorded. Conclusions: The findings of the study will be helpful for the choice of MIS and conservative treatment when treating HICH patients