Identifying a New Social Intervention Model of Panic Buying Under Sudden Epidemic.

COVID-19 that broke out at the end of 2019 continues to spread globally, with frequent occurrence of variant disease strains, thus epidemic prevention and control become a kind of routine job. At present, due to the prevention and control measures such as maintaining social distance and community blockades, there is a boom in material purchases in many places, which not only seriously endangers social order and public environmental safety, but also easily leads to the interruption of the supply chain and the shortage of social materials. This article aims to study the intervention methods to curb the spread and spread of panic buying behavior. Firstly, through crawler technology and LDA (Latent Dirichlet Allocation) topic model, this article analyzes the intervention measures taken by various social forces in China to curb the spread of panic buying, and summarizes the multi-channel intervention measures including online and offline forms. Secondly, through the multi-Agent Monte Carlo method, the targeted intervention mechanism is supplemented in each propagation link of the panic buying propagation model, and a new social intervention model of panic buying under sudden epidemic is constructed. Then, through MATLAB modeling and simulation, the main factors affecting panic buying intervention are discussed. The simulation results show that: (1) The single plan with the best intervention effect is the supply monitoring. While the official response can play an immediate inhibitory effect, but it is affected by credibility and timeliness. The intervention effect of psychological counseling is limited, and it generally needs to be used in combination with other measures. (2) The combination strategy with the best intervention effect is "supply monitoring + official response + psychological counseling," and the worst is "information review and guidance + psychological counseling." Supply monitoring is a key measure to curb panic buying. At the same time, "information review and guidance" will have a certain counter-effect in the combined strategy. Finally, the effectiveness and universality of the proposed model are verified by examples of China and Britain.

Propagation Model of Panic Buying Under the Sudden Epidemic.

The sudden outbreak of COVID-19 at the end of 2019 has had a huge impact on people's lives all over the world, and the overwhelmingly negative information about the epidemic has made people panic for the future. This kind of panic spreads and develops through online social networks, and further spreads to the offline environment, which triggers panic buying behavior and has a serious impact on social stability. In order to quantitatively study this behavior, a two-layer propagation model of panic buying behavior under the sudden epidemic is constructed. The model first analyzes the formation process of individual panic from a micro perspective, and then combines the Susceptible-Infected-Recovered (SIR) Model to simulate the spread of group behavior. Then, through simulation experiments, the main factors affecting the spread of panic buying behavior are discussed. The experimental results show that: (1) the dissipating speed of individual panics is related to the number of interactions and there is a threshold. When the number of individuals involved in interacting is equal to this threshold, the panic of the group dissipates the fastest, while the dissipation speed is slower when it is far from the threshold; (2) The reasonable external information release time will affect the occurrence of the second panic buying, meaning providing information about the availability of supplies when an escalation of epidemic is announced will help prevent a second panic buying. In addition, when the first panic buying is about to end, if the scale of the second panic buying is to be suppressed, it is better to release positive information after the end of the first panic buying, rather than ahead of the end; and (3) Higher conformity among people escalates panic, resulting in panic buying. Finally, two cases are used to verify the effectiveness and feasibility of the proposed model.

Vertical vs horizontal bullying: A need of socio-interactive ethical trainings at workplace.

Bullying at workplaces is a growing problem associated with several antecedents and outcomes of psychological disorders. Extant literature shows the relationship of victimization with the owning-up curriculum to confront bullying, but so far there is little research to cure the issue of bullying through ethics and there is no study to check the impact of ethical lessons and exercises in training materials to curb bullying at workplace. The current paper is based on a literature review of aggression and bullying, and chiefly addresses the HR practices in rescuing the people from peer victimization. Conceptually segregating supervisor-subordinate (vertical) and peer-peer (horizontal) bullying psychology, this paper attempts to see what possible corrective steps can be taken by senior managers, supervisors and psychologists to resolve this critical problem through interior capacities. In this paper, we have developed three propositions for conducting a future empirical investigation in connection to a cohort study. By addressing the counterproductive/deviant behavior, the present report is likely to be beneficial for psychology scientists and HR practitioners in noticing and resolving the bullying issues at the workplace for the well-being of employees.

Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions.

The occurrence of popular social events causes fluctuations and changes of public emotions, while the rapid development of online social platforms and networks has made individual interactions more intense and further escalated public emotions into public opinion. However, there is a lack of consideration of individual emotions in the current research on online public opinion. Based on this, this paper firstly expounds the quantitative representation of attitude and emotion, analyzes the formation and propagation process of online public opinion by combining individual's expression willingness, individual's expression ability, attitude perception value, attitude change probability and other factors, and constructs a network public opinion propagation model that takes individual emotion into consideration. Finally, the main factors affecting the formation and propagation of network public opinion are discussed through simulation experiments. The results demonstrate that: (1) fear is conducive to the formation of online public opinion, but the speed is relatively slow; sadness is not conducive to the formation, but once enough people participate in the exchange of views, the formation of online public opinion will be faster; (2) the influence of online public opinion on individual emotions expands with the increase of the number of individual interactions; (3) different network structures impact differently on the propagation of public opinion. Among them, BA (BA network is a scale-free network model proposed by Barabasi and Albert in order to explain the generation mechanism of power law, BA model has two characteristics: growth and priority connection mechanism) and ER (ER network is a network with random connectivity proposed by Erdös-Renyi) random networks can promote the propagation of online public opinion, which is prone to "one-sided" online public opinion. WS small-world networks (proposed by Watts and Strogatz. It is a kind of network with short average path length and high clustering coefficient) and fully-connected networks have an inhibitory effect on the spread of online public opinion, easily maintaining the multi-dimensional nature of online public opinion.

Screening of long non-coding RNAs markers in plasma of children with chronic gastritis.

OBJECTIVE: The study aimed to detect and analyze long non-coding RNAs (lncRNAs) in plasma of children diagnosed with chronic gastritis, and to explore its biological functions and involved signaling pathways. METHODS: The plasma samples were collected from six children that were diagnosed with chronic gastritis by physical examination, gastroscopy, and pathological examination and six healthy children. The plasma samples were assayed for determining the expression profiles of lncRNA based upon the gen chip detection. The specific expression of lcnRNA in plasma of children with chronic gastritis was analyzed and its biological functions were speculated. RESULTS: Five lncRNAs (RP11-697M17.1, RP11-388M20.9, AFAP1-AS1, BC062758, and XLOC001406) were significantly up-regulated, and five lncRNAs (UNQ697, BX571672.5, CYP4F35P, ANKRD20A5P, and AL832737) were observed to be significantly down-regulated. The lncRNAs RP11-697M17.1, and UNQ697 were detected with the highest up-regulation and down-regulation, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that the up-regulated lncRNAs were significantly enriched in 20 signaling pathways such as phosphoinositide-3-kinase-protein kinase B (PI3K-Akt) pathway, and the down-regulated lncRNAs target genes were significantly enriched in 20 signaling pathways such as the metabolic pathway. CONCLUSION: The analysis of the lncRNA expression profiles in plasma of children with chronic gastritis revealed that the lncRNA RP11-697M17.1, and lncRNA UNQ697 may act as plasma markers for predicting chronic gastritis in children.

Public Opinion Polarization by Individual Revenue from the Social Preference Theory.

Social conflicts occur frequently duringthe social transition period and the polarization of public opinion happens occasionally. By introducing the social preference theory, the target of this paper is to reveal the micro-interaction mechanism of public opinion polarization. Firstly, we divide the social preferences of Internet users (network nodes) into three categories: egoistic, altruistic, and fair preferences, and adopt the revenue function to define the benefits obtained by individuals with different preferences among their interaction process so as to analyze their decision-making behaviors driven by the revenue. Secondly, the revenue function is used to judge the exit rules of nodes in a network, and then a dynamic network of spreading public opinionwith the node (individual) exit mechanism is built based on a BA scale-free network. Subsequently, the influences of different social preferences,as well as individual revenue on the effect of public opinionpolarization, are analyzed through simulation experiments. The simulation results show that(1) Different social preferences demonstrate different influences on the evolution of public opinions, (2) Individuals tend to interact with ones with different preferences, (3) The network with a single preference or a high aggregation is more likely to form public opinion polarization. Finally, the practicability and effectiveness of the proposed model are verified by a real case.

Modeling Periodic Impulsive Effects on Online TV Series Diffusion.

BACKGROUND: Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. METHODS: We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. RESULTS: We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. CONCLUSION: To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public social communities also represents a highly correlated analysis tool to evaluate the advertising value of TV series.