A multilayer network model of interaction between rumor propagation and media influence.

Rumors spread among the crowd have an impact on media influence, while media influence also has an impact on rumor dissemination. This article constructs a two-layer rumor media interaction network model, in which the rumors spread in the crowd are described using the susceptibility-apathy-propagation-recovery model, and the media influence is described using the corresponding flow model. The rationality of the model is studied, and then a detailed analysis of the model is conducted. In the simulation section, we undertake a sensitivity analysis of the crucial parameters within our model, focusing particularly on their impact on the basic reproduction number. According to data simulation analysis, the following conclusion can be drawn: First, when the media unilaterally influences the crowd and does not accept feedback from the crowd, the influence of the media will decrease to zero over time, which has a negative effect on the spread of rumors among the crowd (the degree of rumor dissemination decreases). Second, when the media does not affect the audience and accepts feedback from the audience, this state is similar to the media collecting information stage, which is to accept rumors from the audience but temporarily not disclose their thoughts. At this time, both the media influence and the spread of rumors in the audience will decrease. Finally, the model is validated using an actual dataset of rumors. The simulation results show an R-squared value of 0.9606, indicating that the proposed model can accurately simulate rumor propagation in real social networks.

Dynamical behavior mechanism in the network of interaction between group behavior and virus propagation.

This paper introduces a complex network of interaction between human behavior and virus transmission, in which group synchronous behavior influences cure rates. The study examines the influence of individual group behavior on virus transmission, the reciprocal influence of virus transmission on individual group behavior, and the effects of evolving network structures on cluster synchronization. It also analyzes the conditions necessary for virus extinction or the occurrence of a pandemic, as well as the conditions for achieving individual group synchronization. The paper provides discriminant conditions to distinguish between aggregation behavior and virus extinction. The proposed model effectively captures the phenomenon of resurgence observed in many viruses. The conclusions drawn are rigorously validated through simulations conducted under various conditions, confirming the validity and reliability of the findings.

Optimization of drug scheduling for cancer chemotherapy with considering reducing cumulative drug toxicity.

An improved optimal drug scheduling model with considering two control drugs is proposed and the Gauss pseudospectral-based optimization method is studied to decrease the tumor size and drug toxicity in this work. Firstly, the Dexrazoxane drug, which has significant clinical effect to reduce the toxicity of the anticancer drug, is introduced. By analyzing the growth kinetics model of cancer chemotherapy, the toxicity reduction drug is regarded as the second input in the cancer dynamic equations. Correspondingly, the drug scheduling optimization problem with particular optimization goal and necessary constraints is established. Next, a model transformation technique is proposed to reduce the complexity of dynamic equations. With deriving the Gaussian time grid discretization detailly, the Gauss pseudospectral method (GPM)-based cancer chemotherapy drug scheduling algorithm is presented to test the performance of the proposed model within different rates. Finally, the implementation structure of drug scheduling optimization is given in detail. To test and validate the performance of proposed chemotherapy model, extensive simulation results and comparative evaluation are carried out on a specific mathematical model. Simulation results show that the improved optimization model is superior to other literature studies, resulting in the average improvement of performance index by 66.54% and revealing the significant guiding property for cancer chemotherapy.

Effects of heterogeneous susceptibility on epidemiological models of reinfection.

This paper studies an epidemic model with heterogeneous susceptibility which generalizes the SIS (susceptible-infected-susceptible), SIR (susceptible-infected-recovered) and SIRI (susceptible-infected-recovered-infected) models. The proposed model considers the case that some infected people are susceptible again after recovery, some infected people develop immunity after infection, and some infected people are reinfected after recovery. We perform a comprehensive theoretical analysis of the model, showing that under appropriate initial conditions, delayed outbreak phenomenon occurs that can give people false impressions. Moreover, compared with the SIRI model, the proposed model exists the delayed outbreak phenomenon under more probable conditions. Finally, we present a numerical example to illustrate the effectiveness of the theoretical results.

All-atom molecular dynamics simulations of the combined effects of different phospholipids and cholesterol content on electroporation.

The electroporation mechanism could be related to the composition of the plasma membrane, and the combined effect of different phospholipid molecules and cholesterol content on electroporation has rarely been studied nor conclusions drawn. In this paper, we applied all-atom molecular dynamics (MD) simulations to study the effects of phospholipids and cholesterol content on bilayer membrane electroporation. The palmitoyloleoylphosphatidylcholine (POPC) model, palmitoyloleoylphosphatidylethanolamine (POPE) model, and a 1 : 1 mixed model of POPC and POPE called PEPC, were the three basic models used. An electric field of 0.45 V nm-1 was applied to nine models, which were the three basic models, each with three different cholesterol content values of 0%, 24%, and 40%. The interfacial water molecules moved under the electric field and, once the first water bridge formed, the rest of the water molecules would dramatically flood into the membrane. The simulation showed that a rapid rise in the Z-component of the average dipole moment of the interfacial water molecules (Z-DM) indicated the occurrence of electroporation, and the same increment of Z-DM represented a similar change in the size of the water bridge. With the same cholesterol content, the formation of the first water bridge was the most rapid in the POPC model, regarding the average electroporation time (t ep), and the average t ep of the PEPC model was close to that of the POPE model. We speculate that the differences in membrane thickness and initial number of hydrogen bonds of the interfacial water molecules affect the average t ep for different membrane compositions. Our results reveal the influence of membrane composition on the electroporation mechanism at the molecular level.

Vaccination control of an epidemic model with time delay and its application to COVID-19.

This paper studies an SEIR-type epidemic model with time delay and vaccination control. The vaccination control is applied when the basic reproduction number R 0 > 1 . The vaccination strategy is expressed as a state delayed feedback which is related to the current and previous state of the epidemic model, and makes the model become a linear system in new coordinates. For the presence and absence of vaccination control, we investigate the nonnegativity and boundedness of the model, respectively. We obtain some sufficient conditions for the eigenvalues of the linear system such that the nonnegativity of the epidemic model can be guaranteed when the vaccination strategy is applied. In addition, we study the stability of disease-free equilibrium when R 0 < 1 and the persistent of disease when R 0 > 1 . Finally, we use the obtained theoretical results to simulate the vaccination strategy to control the spread of COVID-19.

Control of a multigroup COVID-19 model with immunity: treatment and test elimination.

This paper introduces a multigroup COVID-19 model with immunity, in which the total population of each group is partitioned into five compartments, that is, susceptible, exposed, infective, infective in treatment and recovered compartment. If the basic reproduction number is less than or equal to one, and the infection graph is strongly connected, then the disease-free equilibrium is globally asymptotically stable and the disease dies out. However, the COVID-19 is already in a pandemic state, and the basic reproduction number is large than one. Hence, in order to make the COVID-19 die out in some groups in an area, we design some appropriate control strategies which reduce the number of exposed people and increase the number of people treated. These two methods have been proved to be the most effective methods at present. An effective algorithm is proposed to identify the groups that need to be controlled. Finally, we use the actual limited data of Hubei, Guangdong and Zhejiang provinces in China to illustrate the effectiveness of the obtained results.