Socialization Development of ESG Business Models in Representative Asia-Pacific Countries:A State-of-the-Art Review and Recommendation

The integration of environmental,social,and governance(ESG)principles has become a pivotal factor in shaping sustainable and responsible corporate practices.The present study investigates the integration of ESG principles within corporate governance models in Asia-Pacific countries,focusing on socialization.By examining the governance culture,legal frameworks,and corporate practices in these representative countries,the paper delineates a strategic framework for embedding social governance into corporate strategies.The study introduces a Cultural,Economic,Legal,and Political(CELP)framework to assess corporate social governance,investigating the correlation between business practices and social changes.Through a systematic literature review and detailed thematic analysis,this paper aims to offer actionable insights and recommendations,guiding corporations in their transition towards more sustainable and socially responsible business practices.

Integrated ribosome and proteome analyses reveal insights into sevoflurane-induced long-term social behavior and cognitive dysfunctions through ADNP inhibition in neonatal mice

A growing number of studies have demonstrated that repeated exposure to sevoflurane during development results in persistent social abnormalities and cognitive impairment.Davunetide,an active fragment of the activity-dependent neuroprotective protein(ADNP),has been implicated in social and cognitive protection.However,the potential of davunetide to attenuate social deficits following sevoflurane exposure and the underlying developmental mechanisms remain poorly understood.In this study,ribosome and proteome profiles were analyzed to investigate the molecular basis of sevoflurane-induced social deficits in neonatal mice.The neuropathological basis was also explored using Golgi staining,morphological analysis,western blotting,electrophysiological analysis,and behavioral analysis.Results indicated that ADNP was significantly down-regulated following developmental exposure to sevoflurane.In adulthood,anterior cingulate cortex(ACC)neurons exposed to sevoflurane exhibited a decrease in dendrite number,total dendrite length,and spine density.Furthermore,the expression levels of Homer,PSD95,synaptophysin,and vglut2 were significantly reduced in the sevoflurane group.Patch-clamp recordings indicated reductions in both the frequency and amplitude of miniature excitatory postsynaptic currents(mEPSCs).Notably,davunetide significantly ameliorated the synaptic defects,social behavior deficits,and cognitive impairments induced by sevoflurane.Mechanistic analysis revealed that loss of ADNP led to dysregulation of Ca^(2+)activity via the Wnt/β-catenin signaling,resulting in decreased expression of synaptic proteins.Suppression of Wnt signaling was restored in the davunetide-treated group.Thus,ADNP was identified as a promising therapeutic target for the prevention and treatment of neurodevelopmental toxicity caused by general anesthetics.This study provides important insights into the mechanisms underlying social and cognitive disturbances caused by sevoflurane exposure in neonatal mice and elucidates the regulatory pathways involved.

Language-socialization-based Analysis of Putonghua Promotion’s Influences on Tujia Cultural Identity-A Case Study of Enshi Tujia and Miao Autonomous Prefecture

Language functions as a carrier of culture,playing a crucial role in individual socialization into his cultural community,thus it is inextricably interconnected with individual cultural identity.Language acquisition and language ability development require necessary sociocultural interactions and practices.China boasts 55 ethnic minorities which have their own distinctive cultures and language varieties.But some of them are experiencing loss of languages and cultural identity.This paper is dedicated to examining the influences of Putonghua Promotion on Tujia cultural identity from the perspectives of Language Socialization.

Spatiotemporal dynamics of the social structure of Indo-Pacific humpback dolphins(Sousa chinensis)in Xiamen waters from 2007 to 2019

As highly social animals,Indo-Pacific humpback dolphins(Sousa chinensis)exhibit community differentiation.Nevertheless,our understanding of the external and internal factors influencing these dynamics,as well as their spatiotemporal variations,is still limited.In the present study,variations in the social structure of an endangered Indo-Pacific humpback dolphin population in Xiamen Bay,China,were monitored over two distinct periods(2007–2010 and 2017–2019)to analyze the effects of habitat utilization and the composition of individuals within the population.In both periods,the population demonstrated a strikingly similar pattern of social differentiation,characterized by the division of individuals into two main clusters and one small cluster.Spatially,the two primary clusters occupied the eastern and western waters,respectively,although the core distribution area of the eastern cluster shifted further eastward between the two periods.Despite this distribution shift,the temporal stability of the social structure and inter-associations within the eastern cluster remained unaffected.A subset of 16individuals observed in both periods,comprising 51.6%and 43.2%of the population in each respective period,emerged as a foundational element of the social structure and may be responsible for sustaining social structure stability,especially during the 2007–2010 period.These observations suggest that the composition of dominant individuals,an internal factor,had a more substantial influence on the formation of the social network than changes in habitat use,an external factor.Consequently,the study proposes distinct conservation measures tailored to each of the two main clusters.

Research on the Socialization of Grassroots Meteorological Services in Agricultural Supply-side Structural Reform

In 2017, the No. 1 Document of the Central Committee proposed that "it is necessary to further promote agricultural supply-side structural reform, accelerate the cultivation of new kinetic energy for agricultural and rural development, and create a new situation in the construction of agricultural modernization". Under the new normal, Zhejiang s agricultural development shows new characteristics and new trends of speed change, structural optimization, and power conversion, and meteorological services for agriculture have stronger demands, higher requirements, and heavier tasks. The socialization of meteorological services is a new type of system arrangement for the provision of meteorological services, and aims to provide high-quality and efficient meteorological service products for the public and promote the equalization of basic meteorological services in urban and rural areas. In this study, through research on the socialization of grassroots meteorological services in a new situation, a new way of the government to provide public weather services is proposed, and a mechanism of the government to purchase public meteorological services is established;social resources and forces are organized and guided to develop the innovation system and mechanism of public weather services, and social resources are fully mobilized to meet the demand for meteorological services in the process of agricultural supply-side structural reform.

Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research focus.This paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this problem.The proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among nodes.Three new metrics are defined:the intensity of node social relationships,node activity,and community connectivity.Within the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity.When a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities.The proposed algorithm was compared to three existing routing algorithms in simulation experiments.Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

The Influence of Watching TV Talent Show on Teenagers Socialization From Marcuse’s Thought of“One Dimensional Person”

Based on Marcuse’s thought of“one-dimensional person”,this paper discusses the influence of TV talent show on teenagers’socialization.By analyzing the characteristics of TV talent shows that emphasize individual competition and success,appearance and image,and instant satisfaction,it is pointed out that these characteristics are easy for teenagers to produce the mentality of pursuing fame and fortune,appearance,and instant satisfaction,ignoring the development of other important values and qualities.Therefore,we need to guide teenagers to maintain a critical attitude toward TV talent shows,and pay attention to cultivating their independent thinking and all-round development.

基于Social Transformer的井下多人轨迹预测方法

目前煤矿井下人员轨迹预测方法中,Transformer与循环神经网络(RNN)、长短期记忆(LSTM)网络相比,在处理数据时不仅计算量小,同时还有效解决了梯度消失导致的长时依赖问题。但当环境中涉及多人同时运动时,Transformer对于场景中所有人员未来轨迹的预测会出现较大偏差。并且目前在井下多人轨迹预测领域尚未出现一种同时采用Transformer并考虑个体之间相互影响的模型。针对上述问题,提出一种基于Social Transformer的井下多人轨迹预测方法。首先对井下每一个人员独立建模,获取人员历史轨迹信息,通过Transformer编码器进行特征提取,接着由全连接层对特征进行表示,然后通过基于图卷积的交互层相互连接,该交互层允许空间上接近的网络彼此共享信息,计算预测对象在受到周围邻居影响时对周围邻居分配的注意力,从而提取其邻居的运动模式,继而更新特征矩阵,最后新的特征矩阵由Transformer解码器进行解码,输出对于未来时刻的人员位置信息预测。实验结果表明,Social Transformer的平均位移误差相较于Transformer降低了45.8%,且与其他主流轨迹预测方法LSTM,S−GAN,Trajectron++和Social−STGCNN相比分别降低了67.1%,35.9%,30.1%和10.9%,有效克服了煤矿井下多人场景中由于人员间互相影响导致预测轨迹失准的问题,提升了预测精度。

Predicting Users’ Latent Suicidal Risk in Social Media: An Ensemble Model Based on Social Network Relationships

Suicide has become a critical concern,necessitating the development of effective preventative strategies.Social media platforms offer a valuable resource for identifying signs of suicidal ideation.Despite progress in detecting suicidal ideation on social media,accurately identifying individuals who express suicidal thoughts less openly or infrequently poses a significant challenge.To tackle this,we have developed a dataset focused on Chinese suicide narratives from Weibo’s Tree Hole feature and introduced an ensemble model named Text Convolutional Neural Network based on Social Network relationships(TCNN-SN).This model enhances predictive performance by leveraging social network relationship features and applying correction factors within a weighted linear fusion framework.It is specifically designed to identify key individuals who can help uncover hidden suicidal users and clusters.Our model,assessed using the bespoke dataset and benchmarked against alternative classification approaches,demonstrates superior accuracy,F1-score and AUC metrics,achieving 88.57%,88.75%and 94.25%,respectively,outperforming traditional TextCNN models by 12.18%,10.84%and 10.85%.We assert that our methodology offers a significant advancement in the predictive identification of individuals at risk,thereby contributing to the prevention and reduction of suicide incidences.

Upcycling municipal solid waste to sustainable hydrogen via two-stage gasification-reforming

As global municipal solid waste(MSW)quantities continue to escalate,serious socio-environmental challenges arise,necessitating innovative solutions.Waste-to-hydrogen(WTH)via two-stage gasification-reforming(TSGR)presents an emergent technology for MSW upcycling,offering to ease waste management burdens and bolster the burgeoning hydrogen economy.Despite early initiatives to advance TSGR technology,a cohesive and critical analysis of cutting-edge knowledge and strategies to enhance hydrogen production remains lacking.This review aggregates literature on MSW upcycling to hydrogen via TSGR,with a focus on optimizing process control and catalytic efficiency.It underscores technological avenues to augment hydrogen output,curtail catalyst costs,and refine system performance.Particularly,the review illuminates the potential for integrating chemical and calcium looping into TSGR processes,identifying opportunities,and pinpointing challenges.The review concludes with a summary of the current state of techno-economic analysis for this technology,presenting outstanding challenges and future research directions,with the ultimate goal of transitioning WTH from theoretical to practical application.

Social prescribing in the metaverse:a new frontier for primary care practice

The advent of immersive technologies such as the metaverse,extended reality,artificial intelligence,and blockchain offers novel possibilities to transform healthcare services.These innovations coincide with clinicians'aspirations to deliver more comprehensive,patient-centered care tailored to individuals singular needs and preferences.Integration of these emerging tools may confer opportunities for providers to engage patients through new modalities and expand their role.However,responsible implementation necessitates deliberation of ethical implications and steadfast adherence to foundational principles of compassion and interpersonal connection underpinning the profession.While the metaverse introduces new channels for social prescribing,this perspective advocates that its ultimate purpose should be strengthening,not supplanting,human relationships.We propose an ethical framework centered on respect for patients'dignity to guide integration of metaverse platforms into medical practice.This framework serves both to harness their potential benefits and mitigate risks of dehumanization or uncompassionate care.Our analysis maps the developing topology of metaverse-enabled care while upholding moral imperatives for medicine to promote healing relationships and human flourishing.

Social Robot Detection Method with Improved Graph Neural Networks

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

Exploring the Reasons for Selfie-Taking and Selfie-Posting on Social Media with Its Effect on Psychological and Social Lives:A Study among Indian Youths

‘Selfie’taking was introduced to the common people by smartphones and has become a common practice across the globe in no time.With technological advancement and the popularity of smartphones,selfie-taking has grown rapidly within a short time.In light of the new trend set by the generation,this study aimed to explore reasons for selfie-taking and selfie-posting on social media and their effects on the social and psychological lives of young adults.A purposive sampling method was adopted to select 20 Indian citizens,between 18 and 24 years.The data were collected through semi-structured interviews and analysed using thematic analysis.Selfie-taking and posting on social media give positive feelings,and it acts as a mood modifier dependent mostly on the favourability and feedback about the post which in turn affects emotions and self-satisfaction.

Brain region-specific roles of brain-derived neurotrophic factor in social stress-induced depressive-like behavior

Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral response.Recent studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region–specific,particularly involving the corticolimbic system,including the ventral tegmental area,nucleus accumbens,prefrontal cortex,amygdala,and hippocampus.Determining how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress psychopathology.In this review,we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of depression.We focused on associated molecular pathways and neural circuits,with special attention to the brain-derived neurotrophic factor–tropomyosin receptor kinase B signaling pathway and the ventral tegmental area–nucleus accumbens dopamine circuit.We determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature,severity,and duration of stress,especially in the above-mentioned brain regions of the corticolimbic system.Therefore,BDNF might be a biological indicator regulating stress-related processes in various brain regions.

Effects of individual heterogeneity on social contagions

Despite having significant effects on social contagions,individual heterogeneity has frequently been overlooked in earlier studies.To better understand the complexity of social contagions,a non-Markovian model incorporating heterogeneous social influence and adoption thresholds is introduced.For theoretical analysis,a generalized edge-based compartmental theory which considers the heterogeneities of social influence and adoption thresholds is developed.Focusing on the final adoption size,the critical propagation probability,and the phase transition type,social contagions for adoption thresholds that follow normal distributions with various standard deviations,follow various distributions,and correlate with degrees are investigated.When thresholds follow normal distributions,a larger standard deviation results in a larger final adoption size when the information propagation probability is relatively low.However,when the information propagation probability is relatively high,a larger standard deviation results in a smaller final adoption size.When thresholds follow various distributions,crossover phenomena in phase transition are observed when investigating the relationship of the final adoption size versus the average adoption threshold for some threshold distributions.When thresholds are correlated with degrees,similar crossover phenomena occur when investigating the relationship of the final adoption size versus the degree correlation index.Additionally,we find that increasing the heterogeneity of social influence suppresses the effects of adoption threshold heterogeneity on social contagions in three cases.Our theory predictions agree well with the simulation results.

Chemical looping reforming of the micromolecular component from biomass pyrolysis via Fe_(2)O_(3)@SBA-16

To solve the problems of low gasification efficiency and high tar content caused by solid–solid contact between biomass and oxygen carrier in traditional biomass chemical looping gasification process.The decoupling strategy was adopted to decouple the biomass gasification process,and the composite oxygen carrier was prepared by embedding Fe_(2)O_(3) in molecular sieve SBA-16 for the chemical looping reforming process of pyrolysis micromolecular model compound methane,which was expected to realize the directional reforming of pyrolysis volatiles to prepare hydrogen-rich syngas.Thermodynamic analysis of the reaction system was carried out based on the Gibbs free energy minimization method,and the reforming performance was evaluated by a fixed bed reactor,and the kinetic parameters were solved based on the gas–solid reaction model.Thermodynamic analysis verified the feasibility of the reaction and provided theoretical guidance for experimental design.The experimental results showed that the reaction performance of Fe_(2)O_(3)@SBA-16 was compared with that of pure Fe_(2)O_(3) and Fe_(2)O_(3)@SBA-15,and the syngas yield was increased by 55.3%and 20.7%respectively,and it had good cycle stability.Kinetic analysis showed that the kinetic model changed from three-dimensional diffusion to first-order reaction with the increase of temperature.The activation energy was 192.79 kJ/mol by fitting.This paper provides basic data for the directional preparation of hydrogen-rich syngas from biomass and the design of oxygen carriers for pyrolysis of all-component chemical looping reforming.

Optimizing the sulfur-resistance and activity of perovskite oxygen carrier for chemical looping dry reforming of methane

Perovskite oxides has been attracted much attention as high-performance oxygen carriers for chemical looping reforming of methane,but they are easily inactivated by the presence of trace H_(2)S.Here,we propose to modulate both the activity and resistance to sulfur poisoning by dual substitution of Mo and Ni ions with the Fe-sites of LaFeO_(3)perovskite.It is found that partial substitution of Ni for Fe substantially improves the activity of LaFeO_(3)perovskite,while Ni particles prefer to grow and react with H_(2)S during the long-term successive redox process,resulting in the deactivation of oxygen carriers.With the presence of Mo in LaNi_(0.05)Fe_(0.95)O_(3−σ)perovskite,H_(2)S preferentially reacts with Mo to generate MoS_(2),and then the CO_(2)oxidation can regenerate Mo via removing sulfur.In addition,Mo can inhibit the accumulation and growth of Ni,which helps to improve the redox stability of oxygen carriers.The LaNi_(0.05)Mo_(0.07)Fe_(0.88)O_(3−σ)oxygen carrier exhibits stable and excellent performance,with the CH_(4)conversion higher than 90%during the 50 redox cycles in the presence of 50 ppm H_(2)S at 800℃.This work highlights a synergistic effect in the perovskite oxides induced by dual substitution of different cations for the development of high-performance oxygen carriers with excellent sulfur tolerance.

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.

A Study on the Influence of Social Media Use on Psychological Anxiety among Young Women

To explore the relationship between social influence,social comparison,clarity of self-concept,and psychological anxiety among young women during their usage of social networking sites,our study selected 338 young women aged 14-34 from the social site platforms of Little Red Book and Weibo for questionnaire surveys.The Passive Social Network Utilization Scale,Social Comparison Scale(SCS),Social Influence Questionnaire,Self-Concept Clarity Scale(SCCS),and Generalized Anxiety Disorder Scale(GAD-7)were employed to measure the subjects.Our results show that the frequency of passive social media use is positively related to the level of psychological anxiety.Social comparison,social influence,and unclear self-concepts under social media use are negatively predictive of psychological anxiety.The chain mediation effects indicate that social comparison and social influence under social media use negatively predict the clarity of self-concept,thus having a negative impact on the psychological health of young women.Therefore,young women should strengthen their self-concepts,control their frequency of social media usage,avoid addiction,and pay special attention to their frequency of passive use,in order to protect their psychological health.Our study provides some practical implications and insights regarding the relationship between young women’s social media use and psychological health.

Impact of different interaction behavior on epidemic spreading in time-dependent social networks

We investigate the impact of pairwise and group interactions on the spread of epidemics through an activity-driven model based on time-dependent networks.The effects of pairwise/group interaction proportion and pairwise/group interaction intensity are explored by extensive simulation and theoretical analysis.It is demonstrated that altering the group interaction proportion can either hinder or enhance the spread of epidemics,depending on the relative social intensity of group and pairwise interactions.As the group interaction proportion decreases,the impact of reducing group social intensity diminishes.The ratio of group and pairwise social intensity can affect the effect of group interaction proportion on the scale of infection.A weak heterogeneous activity distribution can raise the epidemic threshold,and reduce the scale of infection.These results benefit the design of epidemic control strategy.