Dysfunctional attitudes,social support,negative life events,and depressive symptoms in Chinese adolescents:A moderated mediation model

BACKGROUND Depression is a prevalent psychological issue in adolescents that is significantly related to negative life events(NLEs)and dysfunctional attitudes.High levels of social support can significantly buffer NLEs’effect on depression.Currently,there is limited research on how social support moderates the relationship between NLEs,dysfunctional attitudes,and depression in adolescents in China.It is imperative to investigate this moderating effect to mitigate dysfunctional attitudes in adolescent undergoing depressive mood,ultimately enhancing their overall mental health.AIM To investigate the relationship and underlying mechanisms between specific dysfunctional attitudes,social support,and depression among Chinese adolescents.METHODS This is a cross-sectional study which selected five middle schools in Shandong Province for investigation in March 2022.Participants included 795 adolescents(49.87%male,mage=15.15,SD=1.84,age range=11-18 years old).All participants completed the Dysfunctional Attitude Scale,Adolescent Life Event Scale,Beck Depression Inventory,and Social Support Rating Scale.A moderated mediation model was conducted to examine the relationship between specific dysfunctional attitudes,social support,and depression.RESULTS Results indicated that NLEs affected depression through the mediating role of specific dysfunctional attitudes(autonomy attitudesβ=0.21;perfectionismβ=0.25).Moreover,social support was found to moderate the mediating effect between NLEs,specific dysfunctional attitudes,and depressive symptoms(autonomy attitudes b2=-0.08;perfectionism b2=-0.09).CONCLUSION Dysfunctional attitudes mediated and social support moderated the relationship between NLEs and depression.Social support can buffer depression symptoms among adolescents with autonomy attitudes and perfectionism.

An extended social force model on unidirectional flow considering psychological and behavioral impacts of hazard source

An accurate assessment of the evacuation efficiency in case of disasters is of vital importance to the safety design of buildings and street blocks.Hazard sources not only physically but psychologically affect the pedestrians,which may further alter their behavioral patterns.This effect is especially significant in narrow spaces,such as corridors and alleys.This study aims to integrate a non-spreading hazard source into the social force model following the results from a previous experiment and simulation,and to simulate unidirectional pedestrian flows over various crowd densities and clarity–intensity properties of the hazard source.The integration include a virtual repulsion force from the hazard source and a decay on the social force term.The simulations reveal(i)that the hazard source creates virtual bottlenecks that suppress the flow,(ii)that the inter-pedestrian push forms a stabilisation phase on the flow-density curve within medium-to-high densities,and(iii)that the pedestrians are prone to a less orderly and stable pattern of movement in low clarity–intensity scenarios,possibly with lateral collisions passing the hazard source.

CRB:A new rumor blocking algorithm in online social networks based on competitive spreading model and influence maximization

The virtuality and openness of online social platforms make networks a hotbed for the rapid propagation of various rumors.In order to block the outbreak of rumor,one of the most effective containment measures is spreading positive information to counterbalance the diffusion of rumor.The spreading mechanism of rumors and effective suppression strategies are significant and challenging research issues.Firstly,in order to simulate the dissemination of multiple types of information,we propose a competitive linear threshold model with state transition(CLTST)to describe the spreading process of rumor and anti-rumor in the same network.Subsequently,we put forward a community-based rumor blocking(CRB)algorithm based on influence maximization theory in social networks.Its crucial step is to identify a set of influential seeds that propagate anti-rumor information to other nodes,which includes community detection,selection of candidate anti-rumor seeds and generation of anti-rumor seed set.Under the CLTST model,the CRB algorithm has been compared with six state-of-the-art algorithms on nine online social networks to verify the performance.Experimental results show that the proposed model can better reflect the process of rumor propagation,and review the propagation mechanism of rumor and anti-rumor in online social networks.Moreover,the proposed CRB algorithm has better performance in weakening the rumor dissemination ability,which can select anti-rumor seeds in networks more accurately and achieve better performance in influence spread,sensitivity analysis,seeds distribution and running time.

Dynamics modeling and optimal control for multi-information diffusion in Social Internet of Things

As an ingenious convergence between the Internet of Things and social networks,the Social Internet of Things(SIoT)can provide effective and intelligent information services and has become one of the main platforms for people to spread and share information.Nevertheless,SIoT is characterized by high openness and autonomy,multiple kinds of information can spread rapidly,freely and cooperatively in SIoT,which makes it challenging to accurately reveal the characteristics of the information diffusion process and effectively control its diffusion.To this end,with the aim of exploring multi-information cooperative diffusion processes in SIoT,we first develop a dynamics model for multi-information cooperative diffusion based on the system dynamics theory in this paper.Subsequently,the characteristics and laws of the dynamical evolution process of multi-information cooperative diffusion are theoretically investigated,and the diffusion trend is predicted.On this basis,to further control the multi-information cooperative diffusion process efficiently,we propose two control strategies for information diffusion with control objectives,develop an optimal control system for the multi-information cooperative diffusion process,and propose the corresponding optimal control method.The optimal solution distribution of the control strategy satisfying the control system constraints and the control budget constraints is solved using the optimal control theory.Finally,extensive simulation experiments based on real dataset from Twitter validate the correctness and effectiveness of the proposed model,strategy and method.

Potential use of large language models for mitigating students’problematic social media use:ChatGPT as an example

The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.

Analyzing COVID-19 Discourse on Twitter: Text Clustering and Classification Models for Public Health Surveillance

Social media has revolutionized the dissemination of real-life information,serving as a robust platform for sharing life events.Twitter,characterized by its brevity and continuous flow of posts,has emerged as a crucial source for public health surveillance,offering valuable insights into public reactions during the COVID-19 pandemic.This study aims to leverage a range of machine learning techniques to extract pivotal themes and facilitate text classification on a dataset of COVID-19 outbreak-related tweets.Diverse topic modeling approaches have been employed to extract pertinent themes and subsequently form a dataset for training text classification models.An assessment of coherence metrics revealed that the Gibbs Sampling Dirichlet Mixture Model(GSDMM),which utilizes trigram and bag-of-words(BOW)feature extraction,outperformed Non-negative Matrix Factorization(NMF),Latent Dirichlet Allocation(LDA),and a hybrid strategy involving Bidirectional Encoder Representations from Transformers(BERT)combined with LDA and K-means to pinpoint significant themes within the dataset.Among the models assessed for text clustering,the utilization of LDA,either as a clustering model or for feature extraction combined with BERT for K-means,resulted in higher coherence scores,consistent with human ratings,signifying their efficacy.In particular,LDA,notably in conjunction with trigram representation and BOW,demonstrated superior performance.This underscores the suitability of LDA for conducting topic modeling,given its proficiency in capturing intricate textual relationships.In the context of text classification,models such as Linear Support Vector Classification(LSVC),Long Short-Term Memory(LSTM),Bidirectional Long Short-Term Memory(BiLSTM),Convolutional Neural Network with BiLSTM(CNN-BiLSTM),and BERT have shown outstanding performance,achieving accuracy and weighted F1-Score scores exceeding 80%.These results significantly surpassed other models,such as Multinomial Naive Bayes(MNB),Linear Support Vector Machine(LSVM),and Logistic Regression(LR),which achieved scores in the range of 60 to 70 percent.

Constructing a Triangular Linkage Model of Government Audit,Social Audit,and Internal Audit to Promote High-Quality Development of Audit Services

It is a complex and important topic to study the linkage mechanism of government audit,social audit,and internal audit in the context of China’s high-quality economic development.The implementation of measures,such as establishing a sound and perfect organizational safeguard mechanism,strengthening project collaborative audit mechanism,enhancing the mechanism for utilizing audit results,and establishing an audit and rectification joint mechanism can promote the efficient operation of the audit supervision system and the high-quality development of audit services.

Emoti-Shing: Detecting Vishing Attacks by Learning Emotion Dynamics through Hidden Markov Models

This study examines vishing, a form of social engineering scam using voice communication to deceive individuals into revealing sensitive information or losing money. With the rise of smartphone usage, people are more susceptible to vishing attacks. The proposed Emoti-Shing model analyzes potential victims’ emotions using Hidden Markov Models to track vishing scams by examining the emotional content of phone call audio conversations. This approach aims to detect vishing scams using biological features of humans, specifically emotions, which cannot be easily masked or spoofed. Experimental results on 30 generated emotions indicate the potential for increased vishing scam detection through this approach.

Measurement Research Based on Bayesian Structural Equation Cognitive Model

The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.

Correction of Corporation Value Assessment Model Based on Corporate Social Responsibility

The performance of corporate social responsibility is conducive to the con- tinuous improvement of their profitability, and promotes the upgrading of corporation value. However, it is difficult to confirm, calculate and check the costs and benefits brought by the implementation of corporate social responsibility under the current ac- counting theory system, so it is difficult to estimate whether the fulfillment of corpo- rate social responsibility has any effects on the corporation value assessment. Therefore, based on corporate social responsibility, the correction mode of corpora- tion value assessment is put forward.

Microscopic dynamic simulation model for pedestrian at signalized intersection

Pedestrian's road-crossing model is the key part of micro-simulation for mixed traffic at signalized intersection.To reproduce the crossing behavior of pedestrians,the microscopic behaviors of the pedestrians passing through the crosswalk at signalized intersection were analyzed.A pedestrian's decision making model based on gap acceptance theory was proposed.Based on the field data at three typical intersections in Beijing,China,the critical gaps and lags of pedestrians were calibrated.In addition,considering pedestrian's required space,a modification of the social force model that consists of a self-deceleration mechanism prevents a simulated pedestrian from continuously pushing over other pedestrians,making the simulation more realistic.After the simple change,the modified social force model is able to reproduce the fundamental diagram of pedestrian flows for densities less than 3.5 m-2 as reported in the literature.

Research on the Model of Consumer Health Information Seeking Behavior via Social Media

Purpose: We aim to create a model of consumer health information seeking behavior via social media, then to have a better understand of it. After that further efforts should be made to provide targeted recommendations for media managers to promote health communication via social media. Methods: Our custom model was derived from literature review, empirical research was tested by the use of questionnaire investigation, and then the collected data were analyzed by structural equation model tool SmartPLS. Finally, the custom model was modified according to the experimental results of SmartPLS. Results: A total of 239 (66.39%) of the respondents were female and 121 (33.61%) were male. The maximum of two stages of age were 18 - 29 (70.56%), 30 - 39 (13.89%). Wechat (60.28%), QQ Zone (55.22%), Micro-blog (48.89%) were the most commonly used social media to obtain health information. About 44.72% of respondents used social media to obtain health information multiple times a day. The whole numerical values of AVE, cronbach’s alpha, CR and square roots of AVE were above the benchmarks of themselves and showed great reliability and validity. All the 11 hypotheses had obvious statistical significance, the P-value of eight path coefficients exhibited <0.001, one path coefficient exhibited <0.01 and two path coefficients exhibited <0.05. Conclusions: A suitable model of consumer health information seeking behavior via social media was created and some inner relationships were found. Namely, gratification of health information and its platform had a positive effect on attitudes toward health information seeking behavior. Health information literacy and health status were proved to have a significant influence on attitudes toward health information seeking behavior, subject norms and perceived behavioral control respectively. In addition, attitudes toward the health information seeking behavior, subject norms and perceived behavioral control were proved to positively associate with health information seeking behavior intention.

Blog acceptance model: An empirical study on exploring users' acceptance and continual usage of blogs

Blogs have permeated into our daily lives at a fast speed, and various kinds of blog spaces have attracted our attention. However, little effort has been made on studying the users' motivation to participate in blog activities. This paper aims to construct a theoretical model about the blog adoption based on technology acceptance model(TAM theory), social capital theory and social exchange theory, and put forward 18 related hypotheses. Then the survey method is adopted to analyze the data from 208 questionnaires using the SPSS and LISREL tools, and to examine the theoretical model and hypotheses. Finally, the paper makes a discussion from five aspects due to the results of data analysis, including individual driving factors, group driving factors, community driving factors, technology acceptance factors and moderating variables. The results show that curiosity/enjoyment, user's experience, social interaction and social identification will greatly affect users' motivation to accept a blog; meanwhile, perceived ease of use, exchange cost and trust will partially influence users' intention to participate in blog activities. The results also suggest that age and education degrees have significant moderating effects on users' acceptance and updating of blogs.

Simulation of Crowd Motion Based on Boids Flocking Behavior and Social Force Model

In the process of crowd movement,pedestrians are often affected by their neighbors.In order to describe the consistency of adjacent individuals and collectivity of a group,this paper learns from the rules of the flocking behavior,such as segregation,alignment and cohesion,and proposes a method for crowd motion simulation based on the Boids model and social force model.Firstly,the perception area of individuals is divided into zone of segregation,alignment and cohesion.Secondly,the interactive force among individuals is calculated based upon the zone information,velocity vector and the group information.The interactive force among individuals is the synthesis of three forces:the repulsion force to avoid collisions,the alignment force to keep consistent with the velocity direction,and the attractive force to get close to the members of group.In segregation and alignment areas,the repulsion force and alignment force among pedestrians are limited by visual field factors.Finally,the interactive force among individuals,the driving force of destination and the repulsion force of obstacles work together to drive the behavior of crowd motion.The simulation results show that the proposed method can not only effectively simulate the interactive behavior between adjacent individuals but also the collective behavior of group.

Meta-analytic structural equation modeling:a primer

Meta-analytic structural equation modeling is a new research field which can help to explain the relationship between a group of variables in several studies by testing structural equation modeling.However,this methodology is still not very popular in many countries and researchers due to lack of promotion.This report aim s to giv e a primer on the principle of m eta-analytic structural equation modeling and provid e a reference for subsequent researchers to conduct m eta-analytic structural equation modeling analysis.

Integrating Multiple Linear Regression and Infectious Disease Models for Predicting Information Dissemination in Social Networks

Social network is the mainstream medium of current information dissemination,and it is particularly important to accurately predict its propagation law.In this paper,we introduce a social network propagation model integrating multiple linear regression and infectious disease model.Firstly,we proposed the features that affect social network communication from three dimensions.Then,we predicted the node influence via multiple linear regression.Lastly,we used the node influence as the state transition of the infectious disease model to predict the trend of information dissemination in social networks.The experimental results on a real social network dataset showed that the prediction results of the model are consistent with the actual information dissemination trends.

A new collision avoidance model for pedestrian dynamics

The pedestrians can only avoid collisions passively under the action of forces during simulations using the social force model, which may lead to unnatural behaviors. This paper proposes an optimization-based model for the avoidance of collisions, where the social repulsive force is removed in favor of a search for the quickest path to destination in the pedestrian＇s vision field. In this way, the behaviors of pedestrians are governed by changing their desired walking direction and desired speed. By combining the critical factors of pedestrian movement, such as positions of the exit and obstacles and velocities of the neighbors, the choice of desired velocity has been rendered to a discrete optimization problem. Therefore,it is the self-driven force that leads pedestrians to a free path rather than the repulsive force, which means the pedestrians can actively avoid collisions. The new model is verified by comparing with the fundamental diagram and actual data. The simulation results of individual avoidance trajectories and crowd avoidance behaviors demonstrate the reasonability of the proposed model.

Empirical analysis of carbon tariff's effect on the export structure of China's manufacturing industry and social welfare based on the GTAP model

In recent years,carbon emissions have gradually evolved from an environment issue into a political and economic one.Carbon tariff has brought about new trade barriers of developed countries,and in order to enhance the industrial competitiveness of developed countries,it will produce unfavorable impact on developing countries.Concentrated on the manufacturing industry,which is the most intensive high-carbon industry in China's export structure,this article studies the relationship between carbon tariff policy and industry structure of export trade and builds up a relation between climate change and international trade.First,by means of establishing a partial equilibrium model,it applies geometric analysis and mathematical analysis to compute the impact on China's manufacturing export trade and the consequences of the introduction of the US carbon tariff to China's manufacturing industry that has already imposed a domestic shipping carbon tax.Furthermore,with the application of the GTAP model,it estimates the overall economic and welfare effects on China's manufacturing industry if the US and Europe introduce carbon tariff by means of four ways,and then analyzes the influence on China's manufacturing industry export structure and social welfare as well.The result shows that the introduction of the US carbon import tariff lowers China's export price and export volume,and the implementation of a domestic carbon tax justifies a higher export price and a lower export volume for China.However,the degree of export reduction is smaller than that under the effect of the US carbon tariff.In the case of developed countries imposing carbon tariff on China's energy-intensive industries,such as chemical rubber products,oil and coal-processing industry and paper industry,whose export would be reduced,the negative impact on the paper industry is the severest,which will decrease the paper industry's export ranging from 1.79%to 6.05%,whereas the other industries' export will increase.Anyhow,it will promote China's manufacturing industry to adjust the export structure to a certain extent.In addition,it will lead to a decrease in China's welfare,with a decrease between $2,134 billion and $8,347 billion.Finally,this paper provides information on international coordination,export structure adjustment and green manufacturing adjustment as a reference for the development of China's manufacturing industry.

Simulation based on a modified social force model for sensitivity to emergency signs in subway station

The subway is the primary travel tool for urban residents in China. Due to the complex structure of the subway and high personnel density in rush hours, subway evacuation capacity is critical. The subway evacuation model is explored in this work by combining the improved social force model with the view radius using the Vicsek model. The pedestrians are divided into two categories based on different force models. The first category is sensitive pedestrians who have normal responses to emergency signs. The second category is insensitive pedestrians. By simulating different proportions of the insensitive pedestrians, we find that the escape time is directly proportional to the number of insensitive pedestrians and inversely proportional to the view radius. However, when the view radius is large enough, the escape time does not change significantly, and the evacuation of people in a small view radius environment tends to be integrated. With the improvement of view radius conditions, the escape time changes more obviously with the proportion of insensitive pedestrians. A new emergency sign layout is proposed, and the simulations show that the proposed layout can effectively reduce the escape time in a small view radius environment. However, the evacuation effect of the new escape sign layout on the large view radius environment is not apparent. In this case, the exit setting emerges as an additional factor affecting the escape time.

Shared Value in Practice： TOMS Business Model

The article deals with the idea of building a company around the corporate social responsibility （CSR） principles and designing a business model that is simultaneously economically viable, lucrative, and of social benefits. TOMS is analyzed as an example of a company that took this approach and succeeded. The main research questions try to examine the basic assumptions of TOMS business model in light of shared value concept and uncover the reasons of company＇s success. The authors claim that two factors played there a crucial role. Firstly the leader whose passion was so contagious that he/she managed to build the team and set up a company based on his/her ideas. Secondly TOMS business model concept （One for One） is convincing for customers who eagerly join the movement. This business case illustrates how a company that is based on social values can grow.