The coordinated evolution of ecological environment,public service,and tourism economy along the Silk Road Economic Belt,using the Dual-Carbon Targets

Carbon peak and carbon neutrality(dual-carbon)are important targets for the international response to climate change.The Silk Road Economic Belt is a strategic resource region and is important for future ecological environment and tourism development.Based on the“dual-carbon”targets,the Single index quantification,Multiple index synthesis,and Poly-criteria integration evaluation model were used in this study to measure the coordinated development index of the ecological environment,public service,and tourism economy along the Silk Road Economic Belt and to analyze its spatial and temporal evolution.Further,it explores the dynamic evolution and development trend of the three systems using the Kernel Density and Grey Markov Prediction Model.The results show that the coordinated development index along this region needs to be improved during the study period.Furthermore,the coordinated development index of the Southwest region is relatively higher than that of the Northwest region.From the development trend of the three systems,all of them develop in a stable manner;however,the tourism economy system is easily affected by external disturbances.The coordinated development index of the three systems changes dynamically and tends to be in a good state of coordination.There is a certain spatial and temporal heterogeneity.The gravity center of the coordinated development index has been in the Southwest region.During the forecast period,the coordinated development index along this region will improve significantly,while insufficient and unbalanced development will continue.

Research on the Digital Economy's Impact on China's Economic Growth:Based on the Variations in Urbanization Levels Across China's Eastern,Central,and Western Regions and Sectoral Heterogeneity

This paper uses inter-provincial panel data from 2011 to 2017,a linear regression model,and a threshold model to conduct empirical analyses of the impact of the digital economy on China's overall economic growth and the three main sectors of industry.The paper then investigates the impact and effects the digital economy has had on the economic growth of the three main sectors of industry in China's eastern,central,and western regions.Finally,the paper investigates the most significant differences among the various regions and the threshold effects of urbanization levels on the relationship between the digital economy and economic growth.The findings indicate a significantly positive correlation between the digital economy and regional economic growth.Moreover,geographical factors notably influence this correlation.The digital economy exerts a positive effect on all sectors of industry.It may not substantially impact industrial development in regions with highly developed infrastructure.Regarding the other regions,the digital economy exhibits varying degrees of impact due to the differences in the specific indicators.The conclusion drawn by the threshold model is that the magnitude of the threshold effect correlates with geographic factors.No threshold effect was observed in the eastern region,while the threshold effect occurred in the central region when the urbanization levels for the provinces were below 0.6645.Similarly,the threshold effect was noted in the western region when the urbanization level was below 0.3931.Considering all of this,the study also offers policy recommendations that will help balance the regional development of digital economies,accelerate the digital transformation of traditional industries,enhance digital infrastructure construction,refine the formulation and implementation of data policy,and establish relevant incentive mechanisms.

Social and Economic Considerations for Creating Sustainable Climate Change Haven Communities

As Climate Change Haven Communities are constructed across the Northern Hemisphere, it will be necessary to attract two types of migrants to populate them. The first group consists of professionals and companies in eco-sustainable businesses, such as law firms, insurance companies, investment firms, banking, technological innovation, mass media, medical research and pharmaceutical research. The second group will consist of persons engaged in organic/eco-sustainable agriculture whose crops and animal husbandry practices can be transferred successfully to Climate Change Haven regions. The present research focuses on the social and economic variables that must be taken into account to insure that each new Climate Change Haven Community becomes successfully integrated with the local population and forms a cohesive, harmonious social structure. Examples are given from the United States, France, Spain, Portugal and Italy.

National Social Investment Programmes in Nigeria:A Descriptive Analysis of the Socio-Economic Contributions of N-Power Programme in Benue State

This study analysed the socio-economic contributions of N-Power programme amongst the beneficiaries of the scheme in Benue State.Prior to the introduction of N-Power programme,successive administrations in Nigeria have made concerted efforts towards improving the standard of living of the citizenry through the execution of various welfare or social intervention programmes,but not much successes were recorded.Learning from the mistakes of the past regimes,and by way of deliberate state policy,the Buhari’s government initiated a multi-pronged social investment policy,one of which is the N-power programme that came onboard in 2016,which also doubles as the subject of this study.To achieve the goal of this study,a combination of desktop research and survey design was employed.Questionnaires were administered to 390 respondents through a combination of stratified and random sampling techniques.The results of the survey were matched with that of the secondary data obtained through online websites and other related sources.The result indicated that N-Power made positive contributions to the socio-economic life of the beneficiaries in Benue State:specifically,the scheme contributed in poverty eradication,employment generation,skills acquisitions and capacity building.However,some aspect of our findings revealed that the programme has a number of challenges such as:inadequate cash support,delay in monthly cash transfer to beneficiaries,distance participants had to move to their work stations,absence of posting in N-Teach scheme,and lack of adequate working tools amongst others.To salvage this problem the paper recommended the following solutions:expansion of the scheme to cover N-Teach and other aspects,increment in the monthly cash transfer to cushion the high rate of inflation,support for the participants/beneficiaries in transportation and logistics,enrolment of more youth into the various schemes,proper monitoring and evaluation of the implementation of the schemes amongst others.

Economic Growth:The Fundamental Path to the Realization of Social Security Rights for Migrant Workers

The International Labour Organization seems to only see the aspect of social security promoting economic growth,while neglecting the aspect of economic growth supporting social security.From the standpoint of materialism and the practice of social security,the realization of social security rights for migrant workers fundamentally depends on economic growth.The Belt and Road Initiative has provided a Chinese solution for creating a strong material and technological foundation to meet the social security needs of all people,including migrant workers.

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.

Social Enterprises’Institutional Rationale and Its Inspirations for the Modern Market Economy

This study analyzes the ways in which social enterprises contribute to society’s goals.In the new era of human development,many social problems cannot be resolved without the participation of social enterprises(SEs).The study on these enterprises is of great significance for theoretical research and practical observation.In a modern market economy,SEs have pro-social market vitality and compatibility with the market economy;their action gives a glimpse into the truth of human development.As can be seen from the socio-economic reality,numerous entrepreneurs nurture a desire to run SEs at a certain stage of their career development.The tendency of some entrepreneurs to run SEs is of great significance for this study.Social enterprises are hybrid enterprises as opposed to for-profit enterprises and SEs must address complex problems through organizational and institutional innovations.State owned enterprises(SOEs)and SEs share many topics of research and have much to learn from each other,and a key question for SOE reform is what makes SOEs efficient quasi-social enterprises.Both SOEs and SEs assume a social mandate,and their fulfillment of social responsibilities is underpinned by their economic efficiency(market competitiveness).The theoretical rationale and institutional mechanism of SEs-not least their innovation practice and formation of rules-stem from the relationship and coordination between economic and social exchanges under market-based conditions.Social enterprises focus on altruistic social exchange but need to be supported by a certain economic exchange to finance their operations.The biggest social problem for China is its failure to balance economic and social exchanges,as well as the method of reward.Research on SEs should promote the understanding of these questions and the quest for their solutions.Social enterprises may inspire more enterprises to pursue pro-social innovation and development.

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.

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.

Research Progress of High-Quality and Equilibrium Economic Development of Ethnic Regions in the Context of Digital Economy

A new generation of information technology is driving the growth of digital economy across the world.In recent years,China has attached great importance to the development of digital economy and digital technology.With the support of a series of policies,the digital industry has been able to develop at an astounding rate,and at the same time the integration of other industries with digital technology has been strengthened.The profound changes in production factors,production methods,and production approaches have promoted high-quality regional economic development.Literature statistics have shown an increasing trend in digital economy research in recent years.In this paper,literature on digital economy development,digital economy and high-quality economic development,the current situation of economic development in ethnic regions,and digital economy and economic development in ethnic regions is recapitulated in an attempt to learn from existing studies.

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.

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.

Regional economic assessment of a novel place-based model for sustainable food systems

Several actions from both the environmental and human viewpoints have already been made to meet the sustainability goals targeted at food systems.Still,new place-based ideas to improve sustainability are needed.Agroecological symbiosis(AES),a novel food system model,is an example of a suggested system-level change to attain sustainability targets;it is a symbiosis of food production and processing using renewable energy that uses its own feedstock.AES has already been found advantageous from the ecological and biophysical viewpoints,but a regional economic evaluation of the model is still lacking.Thus,the aim of our paper is to assess the regional economic impact of a possible systemic change in the food system using the network of agroecological symbiosis(NAES)as an example.We applied scenarios representing different ways of moving towards envisioned NAES models in Mäntsälä,Finland,and a computable general equilibrium model to evaluate the regional economic impact.According to our results,both regional economy and employment would increase,and the regional production base would diversify with NAES implementation applied to the region,but the extent of the benefits varies between scenarios.The scenario that includes change in both public and private food demand,production of bioenergy and utilization of by-products would cause the largest impacts.However,realizing NAES requires investments that may influence the actual implementation of such models.Nonetheless,a change towards NAES can promote an economically and spatially just transition to sustainability,as NAES seems to be economically most beneficial for rural areas.

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.

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.

Data-Empowered High-Quality Economic Development: Mechanisms and Pathways

To achieve high-quality economic development,it is imperative to prioritize the real economy and foster new factors for economic growth.Data,as a new factor of production,plays a pivotal role in facilitating the seamless integration between digital technology and the real economy.It possesses inherent attributes and techno-economic characteristics that enable the extraction of value across various processes,including production,transaction,consumption,and regulatory supervision.The integration with digital technology enhances the productivity and efficiency of the real economy by facilitating service sector digitalization,accelerating the growth of the new real economy,and supporting the virtual economy in its role of serving the real economy.At present,unleashing the value of data is hindered by inadequate fundamental systems for the data,a lack of activity in the transaction market,and the underutilization of the data as a factor of production by enterprises in the real economy.Therefore,it is advised that data be fully utilized to develop the real economy through the four-pronged approach of“enhancing support for the high-quality provision of the data,expediting the integration of the data into the real economy,promoting the high-quality development of the real economy,and enhancing public service and governance systems”.