The field of CSR has witnessed considerable growth and established itself as a significant subject in family business literature. However, despite previous reviews exploring this topic from various angles, there remains a crucial gap in understanding the influence of diverse regulatory frameworks and cultural values on the development of literature production and research streams across different regions. This gap holds particular significance for comprehending the latest advancements in this dynamic field, particularly in emerging economies, where cultural norms play a substantial role in shaping the relationship between family firms and CSR. To bridge this gap, this paper conducts a comprehensive review of empirical studies focusing on sustainability in family firms. These studies are organized based on the country of study, and our review encompasses 232 articles published between 1996 and 2023. Utilizing bibliometric software, we systematically group these articles into three distinct clusters: North American studies, European studies, and Asian studies, adhering strictly to our inclusion criteria.

Ants have a very sophisticated olfactory system; their communication is based on the interpretation of chemical trails known as semiochemicals. The escamolera ant (Liometopum apiculatum), has behaviors and physiological responses is induced by semiochemicals, without however, these have not been studied. Using an electronic nose analysis (e-nose) and chromatographic techniques, semiochemicals of L. apiculatum in nests, foraging trails and dissected the gasters were evaluated. Data were analyzed with multivariate statistics and a logistic regression model based on predictors (ant counts) and the semiochemicals identified in pre-season (PRE), season (TEM) and post-season (POS) of larval collecting. From the general pattern of volatiles with a natural separation in POS and a partial distribution between PRE and TEM 32 semiochemicals were identified inside the nests, those who can induce the behavioral responses of trail-following, alarm, aggression, and nestmate recognition. Trail counts indicated that ant traffic was higher in POS and that the presence of certain semiochemicals (response variable) have good fit in the model regression. These findings are intended to provide useful information and support decision-making for the conservation and sustainable use of L. apiculatum in central Mexico.

Microfinance banks and Institutions must simultaneously provide micro financial loans to unprivileged and poor people as well as self-sustaining, which means covering enough costs to eliminate the need for subsidies. To ascertain if Micro-finance Institutions can successfully navigate the double challenge of financial self-sustainability (FSS) as well as outreach to poor clientele and women borrowers i.e. outreach. There is no other alternative but to analyse the balance between FSS and outreach indicators of MFIs. The research goal is to see whether there was any compromise between the self-sustainability and outreach towards poor and female customers. The study used data of 100 MFIs driven from the database of microfinance information’s exchange (MIX) market with the objective to determine trade off between financial sustainability and outreach. The study found financial performance variables are positively and significantly related with average loans size which shows a mission drift, in which MFIs serve wealthy clientele. However, Indian MFIs have a extremely high outreach with their female clientele, confirming MFI's social commitment to objective of women’s empowerment. The research recommends to the Policy-makers that MFIs are compromising their financial services to underprivileged people and women in order to be financially sustainable. To guarantee that institutions are focused on outreach to underserved people in rural areas, the government should reform the policies regarding governing MFIs.

Social network analysis is extensively employed for the examination of relationships and networks among actors across diverse disciplines and thematic research domains. Nevertheless, the utilization of this approach in agriculture and natural resources, particularly pertaining to watershed management, has been relatively limited. The objective of this study is to conduct an analysis of the social interaction and networks of Cidanau Watershed Management (CWM) stakeholders and measure the nodes of units as individuals or organizations in relation to the role and influence of each other through a quantitative approach. The results of the data analysis show that Fkdc has a high degree of centrality and centrality betweenness. This shows that Fkdc has a strong influence and controls CWM network interaction. Conversely, Hs (households) have a low degree of centrality, and centrality betweenness values have a low influence and role in actors’ networks. According to the closeness centrality parameter, it is observed that both Households (Hs) and Universities (Univ) have exhibited the shortest geodesic distance in relation to other stakeholders within the network, thus indicating their highest level of closeness. It means that households and universities had interdependency and were controlled by others. The lowest centrality of Fkdc means that Fkdc has independence of influence and high control access over others. There are four CWM network clusters based on total link strength. Fkdc demonstrates outstanding leadership qualities, enjoys a strong reputation, and possesses a high degree of popularity within the network. Furthermore, Fkdc serves as a positive central node that effectively brings together the interests of all actors, fostering a collective commitment towards shared goals and mutual rewards. In the CWM case, Fkdc is a sustainable watershed organization that acts as a central network body and plays a fundamental role as an intermediary between networks and interactions. Ultimately, with regards to the viewpoint of CWM, the decentralization of watersheds aimed to facilitate wider public engagement by establishing Fkdc as a central network entity and assuming a crucial role as a medium for the dissemination of information, exchange of ideas, and the exertion of influence among its members.

With the proliferation and development of social media platforms, social media data has become an important source for acquiring spatio-temporal information on various urban events. Providing accurate spatio-temporal information for events contributes to enhancing the capabilities of urban management and emergency response. However, existing research on mining spatio-temporal information of events often focuses solely on textual content, neglecting data from other modalities such as images and videos. Therefore, this study proposes an innovative spatio-temporal information extraction method for multi-modal social media data (MIST-SMMD), which extracts the spatio-temporal information of events from multi-modal data on Weibo at coarse and fine-grained hierarchical levels, serving as a beneficial supplement to existing urban event monitoring methods. This paper takes the "July 20th Zhengzhou Heavy Rainfall" incident as an example, to evaluate and analyze the effectiveness of the proposed method. The results indicate that in the coarse-grained spatial information extraction using only textual data, our method achieves a Spatial Precision of 87.54% within a 60m range, and reaches 100% Spatial Precision for ranges beyond 200m. For fine-grained spatial information extraction, the introduction of other modal data such as images and videos results in a significant improvement in Spatial Error. These results demonstrate the ability of the MIST-SMMD method to extract spatio-temporal information from urban events at both coarse and fine levels, and confirms the significant advantages of multi-modal data in enhancing the precision of spatial information extraction.

Communication and effective interactions are inevitable necessities in every organizational setting. In this era of information and communication technology, where limitations and difficulties in proper communication and interactions between different entities of various organizations have been reduced maximally, the government, stakeholders, and citizens of the different nations should also utilize these available tools in a way to improve the maximum performance in governance through interactions and e-participation between the citizens, stakeholders, and the government parastatals. This research focuses on examining the available and most preferred applications or platforms which encourage the best level of communication and interaction through E-participation among the citizens, stakeholders, and government from the local government lev-el taking Nigeria as a case study

Background: The diagnosis of phenylketonuria (PKU) in an infant is a devastating and overwhelming event for their parents. Providing appropriate information and support is paramount, especially at the beginning of a child’s life. Investigating if parents are receiving the right support is important for their continued care. Methodology: An online survey was distributed to explore parents’ perceptions of current support and information provided by their healthcare provider and to rate sources of other support (n=169 participants). Results: Dietitians received the highest (85%) rate of “very helpful” support. Overall, parents found Facebook to be helpful for support, but had mixed reactions when asked if healthcare professionals (HCPs) should provide advice as part of the groups. When rating the most effective learning methods, the top three were: 1:1 sessions (n=109, 70%), picture books (n=73, 50%) and written handouts (n=70, 46%). Conclusion: Most parents were happy with the support and information they received from their dietitian, but required more support from other HCPs. Facebook groups provide parents with the social support that HCPs and their family may be unable to offer, suggesting a place for social media in future PKU care.

Information cascades (tree-like structures formed by posts, comments, likes, replies, etc.) constitute the spine of the public online information environment, reflecting its various trends, evolving with it and, importantly, affecting its development. While users participate in online discussions, they display their views and thus contribute to the growth of cascades. At the same time, users’ opinions are influenced by cascades’ elements. The current paper aims to advance our knowledge regarding this social phenomenon by developing an agent-based model in which agents participate in a discussion around a post on the Internet. Agents display their opinions by writing comments on the post and liking them (i.e., leaving positive assessments). The result of these processes is dual: on the one hand, agents develop an information cascade; on the other hand, they update their views. Our purpose is to understand how agents’ activity, openness to influence, and cognitive constraints (that condition the amount of information individuals are able to proceed with) affect opinion dynamics. We also control for social contagions (when people’ perception of a message may depend not only on the message’s opinion, but also on how other individuals perceive this object, with more positive evaluations increasing the probability of adoption) and ranking algorithms that steer the order in which agents learn new messages. Among other things, we demonstrated that replies to disagreeable opinions are extremely effective for promoting your own position. In contrast, likes have a tiny effect on this issue.

Background: People with Williams syndrome (WS) are characterized with hypersociability, fluency in languages, and advantageous face-processing skills, leading to the proposal of a social module. Previous studies on the mentalizing abilities of people with WS using two-dimensional pictures and mindreading from eyes, including normal-like, delayed, and deviant behaviors, have yielded mixed results. This study thus examined the mentalizing ability of people with WS through structured computerized animations of false belief tasks to investigate whether inferences about other people’s minds can be improved in this population. Method: Participants were shown animations with unexpected location and content changes. After viewing each animation, participants had to answer four types of questions: character identification, reality, memory, and false belief. Their responses were recorded and analyzed. Results: Comprehension of false belief was observed in 4-year-old healthy children, whereas children with WS showed unsuccessful comprehension of false belief (until they attained a mental age of 5.3 years), suggesting an improvement in theory of mind resulting from viewing structured computerized animations. This age is earlier than that reported by previous studies for using theory of mind to pass false belief tests (8.5 years old), even challenging the age at which individuals failed to pass the tests (12.10 years old). Conclusions: Structured computerized animations enhanced the mentalizing ability of people with WS to a certain extent. Compared to the typically developing controls, people with WS presented with a lower developmental level in processing false belief tasks. The educational implication of this study is to develop computerized social skills interventions for people with WS.

The purpose of this paper is to highlight the main themes and insights from the Journal of Orthopedic Research (JOR)/JOR Spine Workshop during the Orthopedic Research Society 2022 Annual Meeting in Tampa Bay, Florida. This workshop, organized by JOR Editor-in-Chief Dr. Linda Sandell, focused on communication strategies (particularly using social media) to broadcast published work in orthopedic research. In this manuscript, we summarize data that support the beneficial impacts of amplifying scholarly works on social media and outline a linearized workflow for constructing a Twitter posts, which can be generalized to other social media platforms, to share academic research. Finally, we identify resources to alleviate barriers to social media use and help promote professionalism and success online in the orthopedic research community. As early career scientists in orthopedics, we see immense value in using social media, particularly Twitter, to communicate our research findings and build our scholarly networks. We hope this information will be persuasive to those in the orthopedic field and be broadly applicable to others in related scientific fields who wish to disseminate findings and engage a public audience on social media. For the orthopedic research society and journal of orthopedic research, social media can assist in accomplishing our mission of creating a world without musculoskeletal limitations via the timely dissemination of orthopedic research findings and news.

Herdsmen use different techniques, as per varying geographies and cultures, to keep the cohesion within herds and avoid animals getting lost or predated. However, there is no study on the social behaviour of yaks and on herdsmen management practices. Therefore, this ethology study was initiated by ethnographic inquiries. In Manang, the success of the shepherd is dictated by his personal attribute of “Khula man” or open-heartedness. This attribute refers to good intentions and emotions such as empathy that allows the shepherd to focus more on others than on ownself. This cultural method of assessing the skills required to become a successful and knowledgable shepherd guided us to study the effect of cultural values on the herd’s social behavior. We collected data from two herds living at the same settlement (Yak kharka, 4,100 m altitude, Nepal) by equipping them with loggers. One of the herdsman used the tether rope while other did not. Moreover, the Thaku herd had a more proactive shepherd than the Phurba one. In each herd, 17 animals were equipped with one actigraph wgt3x-BT to measure activity using accelerometer and spatial associations using proximity recorder. One of the herds was equipped with GPS (N=11) as well. Using GPS locations and activity, we showed that the two herds were cohesive and synchronised their activities but the herd with the tether rope was more cohesive. The shepherds also have personal knowledge of the social relationships of their herds and use these relationships to keep the group cohesive and to well manage cattle.

Psycho-social factors are key factors to the powerlessness of infant mothers who breastfeed. As the theory of planned behavior (TPB) is widely used to investigate the correlation between psycho-social factors and breastfeeding behavior, this study aims to examine the utility of TPB constructs to predict breastfeeding behavior and understand TPB-based psycho-social factors’ effectiveness on breastfeeding behavior. A narrative review consulting Web of Science, Scopus, Ebsco, PubMed, and PsycInfo in English and Chinese has been conducted, with the inclusion of twenty studies published between January 2000 and May 2022. It is reported that psycho-social factors based on TPB for breastfeeding such as affective attitude, instrumental attitude, subjective norm, perceived behavioral control (including self-efficacy), moral norm, breastfeeding knowledge, and self-identity can effectively explain the breastfeeding behavior. Future research should improve the theoretical model of TPB-based breastfeeding and implement more localized prediction and intervention studies on breastfeeding.

Despite extensively investigating the impact of social media on fashion products’ marketing, little evidence is available on how the platforms influence sales prediction. Focusing on Lolita fashion, this study investigates the impact of social media marketing on the sales volume prediction of fashion products. Essentially, we analyzed marketing data, including comments, likes, and shares from the Weibo social platform, to forecast future sales, examine how to enhance profit performance, and make production decisions. Using a quantitative approach, we tested three different prediction models, including multiple regression, decision tree, and XGBoost. The results revealed that increasing comments and decreasing the number of likes could significantly improve the sales volumes of Lolita products. In contrast, shares exerted a less significant impact on sales. Regarding prediction models, XGBoost was found to be the best method. In the fashion industry, social media is a useful tool for forecasting market trend. A limitation of this study is that only one social media platform was used to extract data, which might limit the generalization of the findings.

Sociality in animal populations is a continuum, and interactions between conspecifics are meaningful for all vertebrates. Ignorance of social structures can lead to misunderstanding their ecology and, consequently, to unsuccessful species management. Here, we combined genetic and spatial data on radio-collated brown bears (Ursus arctos) to investigate kin-related home range overlap and kin-related centroid distance within central and eastern Finland. We found that the extent of overlap of home ranges was positively linked with relatedness among adult females. A similar positive correlation could be detected between home range centroid distance and relatedness among adult females. Moreover, there were significant differences between the two studied regions: female brown bears in central Finland were more closely related to each other, and the sizes of their home ranges were larger than those in eastern Finland. The smaller home ranges and lower degree of relatedness among bears in eastern Finland might be a result of the substantially higher hunting pressure in the area, combined with immigration of new unrelated individuals from Russia.

Based on 2010, 2013 and 2015 CGSS data, the impact of Internet use and social capital on the income gap among farmers in the past five years is assessed at three time points using the OLS method and a quantile regression method. The study finds that (1) the income gap among farmers increases continuously in the five-year period, while Internet use plays a positive impact on farmers’ income growth in all five quartiles; the coefficient differences are all significantly negative, indicating that Internet use plays a positive role in alleviating the income gap between high-income and low-income farmer subgroups, and (2) social capital plays a positive role in moderating the income gap among farmers and that Internet use by farmers expands the boundary of social capital, which in turn increases the income level of and alleviates the income gap among farmers.

The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced outdoor activities, people are spending more time on social media than usual and expressing their emotion of anxiety, fear, and depression. On a daily basis, about 2.5 quintillion bytes of data are generated on social media, analyzing this big data can become an excellent means to evaluate the effect of COVID-19 on mental health. In this work, we have analyzed data from Twitter microblog (tweets) to find out the effect of COVID-19 on peoples mental health with a special focus on depression. We propose a novel pipeline, based on recurrent neural network (in the form of long-short term memory or LSTM) and convolutional neural network, capable of identifying depressive tweets with an accuracy of 99.42%. Preprocessed using various natural language processing techniques, the aim was to find out depressive emotion from these tweets. Analyzing over 571 thousand tweets posted between October 2019 and May 2020 by 482 users, a significant rise in depressing tweets was observed between February and May of 2020, which indicates as an impact of the long ongoing COVID-19 pandemic situation.

We consider an evolutionary model of social coordination in a 2x2 game where two groups of players prefer to coordinate on different actions. Players can pay a cost to learn their opponent's group: if they pay it, they can condition their actions on the groups. We assess the stability of outcomes in the long-run using stochastic stability analysis. We find that three elements matter for the equilibrium selection: the group size, the strength of preferences, and the information's cost. If the cost is too high, players never learn the group of their opponents in the long-run. If one group is stronger in preferences for its favorite action than the other, or its size is sufficiently large compared to the other group, every player plays that group's favorite action. If both groups are strong enough in preferences, or if none of the group's size is large enough, players play their favorite actions and miscoordinate in inter-group interactions. Lower levels of the cost favor coordination. Indeed, when the cost is low, in inside-group interactions, players always coordinate on their favorite action, while in inter-group interactions, they coordinate on the favorite action of the group that is stronger in preferences or large enough.

One of the unfortunate findings from the ongoing COVID-19 crisis is the disproportionate impact the crisis has had on people and communities who were already socioeconomically disadvantaged. It has, however, been difficult to study this issue at scale and in greater detail using social media platforms like Twitter. Several COVID-19 Twitter datasets have been released, but they have very broad scope, both topically and geographically. In this paper, we present a more controlled and compact dataset that can be used to answer a range of potential research questions (especially pertaining to computational social science) without requiring extensive preprocessing or tweet-hydration from the earlier datasets. The proposed dataset comprises tens of thousands of geotagged (and in many cases, reverse-geocoded) tweets originally collected over a 255-day period in 2020 over 10 metropolitan areas in North America. Since there are socioeconomic disparities within these cities (sometimes to an extreme extent, as witnessed in `inner city neighborhoods’ in some of these cities), the dataset can be used to assess such socioeconomic disparities from a social media lens, in addition to comparing and contrasting behavior across cities.

Social robots may become an innovative means to improve the well-being of individuals. Earlier research showed that people easily self-disclose to a social robot even in cases where that was unintended by the designers. We report on an experiment of self-disclosing in a diary journal or to a social robot after negative mood induction. The off-the-shelf robot was complemented with our inhouse developed AI chatbot and could talk about ‘hot topics’ after having it trained with thousands of entries on a complaint website. We found that people who felt strong negativity after being exposed to shocking video footage benefited the most from talking to our robot rather than writing down their feelings. For people less affected by the treatment, a confidential robot chat or writing a journal page did not differ significantly. We discuss emotion theory in relation to robotics and possibilities for an application in design (the emoji-enriched ‘talking stress ball’). We also underline the importance of - otherwise disregarded - outliers in a data set that is of a therapeutic nature.

This document aims to adapt the dimension of the Socially Responsible Brand Personality (SRBP), proposed by Mayorga (2017), in the midst of the Covid-19 pandemic. The interest of this work is to contribute with the management of the CSR, contributing from a communicational perspective with a tool that optimizes the construction of a socially responsible image by the different stakeholders. The results allow us to conclude that there is a structural modification of the brand personality, which can be presumed to be generated by the current environment and can be established as a pillar of CSR management in the new normality.

The present study aims to analyze scientific production about technological cooperation networks in biotechnology sector, based on bibliometrics network analysis. We used the Gephi software to identify groups of partnerships, proving that the cooperation relationship is a practice used, re-sulting technological development. Findings identify that in the path for new resources that com-plement skills and competences universities are an important player in this cooperative ecosystem, and The United States is a large hub, with an extensive network of global cooperation. A strong role of its researchers in the publication of scientific articles in cooperation is highlighted. This study contributes to the advancement of the discussion about cooperative networks, as well as to the development of policies aimed at the biotechnology sector advancements

Objectives: A wealth of literature has established risk factors for social isolation among older people, however much of this research has focused on community-dwelling populations. Relatively little is known about how risk of social isolation is experienced among those living in long-term care (LTC) homes. We conducted a scoping review to identify possible risk factors for social isolation among older adults living in LTC homes. Methods: A systematic search of five online databases retrieved 1535 unique articles. Eight studies met the inclusion criteria. Results: Thematic analyses revealed that possible risk factors exist at three levels: individual (e.g., communication barriers), systems (e.g., location of LTC facility), and structural factors (e.g., discrimination). Discussion: Our review identified several risk factors for social isolation that have been previously documented in literature, in addition to several risks that may be unique to those living in LTC homes. Results highlight several scholarly and practical implications.

Loneliness is a frequent negative feeling among older people. A programme aimed at alleviating loneliness among older people by promoting social capital, i.e. social support and participation, was conducted in primary health care centres in Spain. We aimed to explore participants’ experiences of loneliness and social participation before the programme, perceived programme effects and contextual influences. A descriptive-interpretative qualitative design was used. 41 persons were included comprising older people, health and social care professionals, and volunteers. Data were collected through three focus groups, 36 semi-structured interviews and participant-observation of the intervention. A thematic content analysis was applied. Older persons with diverse profiles of loneliness and participation decreased their loneliness, increased their knowledge and participation in local community assets, and developed companionship, a sense of belonging, peer support and friendship. Their mental wellbeing increased and participants could deal better with health or family problems. An empowerment process was observed. However, loneliness persisted among some widowed participants and health and social vulnerabilities hampered some impacts. Conflicts and exclusion were occasional unintended effects. The promotion of social capital in ageing to alleviate loneliness involves complex processes interrelated with health and socio-economic factors. Future programmes should be adapted to local contexts and participants’ characteristics.

Social media, traditionally reserved for social exchanges on the net, has been increasingly used by researchers to gain insight into different facets of human life. Unemployment is an area that has gained attention by researchers in various fields. Medical practitioners especially in the area of mental health have traditionally monitored the effects of involuntary unemployment with great interest. In this work, we compare the feedback gathered from social media using crowdsourcing techniques to results obtained prior to the advent of Big Data. We find that the results are consistent in terms of 1) financial strain is the biggest stressor and concern, 2) onslaught of depression is typical and 3) possible interventions including reemployment and support from friends and family is crucial in minimizing the effects of involuntary unemployment. Lastly, we could not find enough evidence to study effects on physical health and somatization in this work.

The spread of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already taken on pandemic proportions, affecting over 150 countries in a matter of months. Even though containment measures in China have reduced new cases by more than 90%, this reduction is not the case elsewhere, as the US, Spain and Italy have come off worse-off by the spread of the virus, and there is serious concern regarding the national health systems' capacity around the world and Africa, in particular, to effectively respond to the needs of infected patients who require intensive care for the COVID-19 illness. As a result, authorities around the world have resorted to the lockdown strategy to curb the spread of the virus. Therefore, the study aimed at investigating the effect of the lockdown strategy in curbing the spread of the COVID-19 virus in Nigeria. Consequently, the study found daily relative risk increase in cases, and daily relative risk increase in mortality. Also observed is the growth in cases in areas where active measures were not taken. Further, halt in business activities has rendered many penniless and unable to provide for themselves basic amenities. The study recommends that there is need to implement community-level measures of social distancing which may include closing schools, need for individuals with COVID-19 case or respiratory symptoms be properly taken care of, trace and quarantine those who must have come in contact with affected persons and introducing stay at home palliatives for the general public.

With the fast development of the mobile Internet, the online platforms of social networks have rapidly been developing for the purpose of making friends, sharing information, etc. In these online platforms, users being related to each other forms social networks. Literature reviews have shown that social networks have community structure. Through the studies of community structure, the characteristics and functions of networks structure and the dynamical evolution mechanism of networks can be used for predicting user behaviours and controlling information dissemination. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.

Social media has become a powerful tool for spreading information and awareness campaigns on environmental issues, especially as they pertain to the conservation of wild animals. It is a double-edged sword, however, since it also facilitates the legal and illegal trade of wild animal species as well as the propagation of ‘wild animal selfies.’ This review presents some key literature to date which concerns the impact of social media on public perceptions of animals (such as through ‘viral’ videos), changing trends in animal encounters at wildlife tourism destinations, and the wildlife trade as it is facilitated by social media. Finally, avenues for future research are suggested with urgency, since the impact of social media on the welfare and conservation of wild animal species is most likely underestimated yet bears serious consequences.

Despite the expectation that social media use in the public sector contributes to enhancing government's transparency, few studies have been investigated whether social media use actually leads to more disclosure during environmental incidents in practice and how social media influence local governments and their officials' information disclosure. In this article, we model information disclosure during environmental incidents as an evolutionary game process between the central government and local government in social media context, and examine the internal mechanism that how social media influence the progress of information disclosure during environmental incidents. The findings indicate that social media plays an active constructive role in central-local government game relations. Specific- ally, social media can provides an efficient information channels for the central government supervise regional officials in environmental incidents, and thus improves its supervision efficiency, and it also provides an important means for internet mobilization and online-offline interaction by encouraging the public exchange information and express their views, and in turn forces local governments and their officials tend to disclosure ahead.

The focus of this ethnographic expedition is to study the folklore and traditions amongst the existing tribal populations of Barumal village in southern Gujarat, India. The fieldwork revolves around cultural and socio-economic aspects of their livelihood and this paper encompasses the knowledge from one such lens out of many. It tries to identify the importance of mythology and its roots. The data collected from three different population groups are Varlis, Kukanas, Dhodiya Patels that are set within the caste system based hierarchy inhabiting in the same region. The interview method was employed throughout with open-ended questions. The varied customs and traditions appearing in their lifestyle, occupation, and festivals are always associated with one god or another. The key informants felt the need to distinguish the history of their own tribe from the others by taking the help of myths passed down from their ancestors. Most of the key informants were mature adults including both males and females.

The current urban environment is faced with the potential threat of frequent natural disasters, and the sustainable development of post-disaster community has become a global issue. As an intrinsic motivation influencing the social interaction and capital operation of community, social network is an important mechanism promoting such sustainable development. However, the difference in social network caused by different member structure, spatial arrangement and management mechanism of post-disaster communities in different reconstruction modes has influenced such sustainable development process. Therefore, reasonable selection of reconstruction mode is crucial. This paper applied analytic hierarchy process to comprehensively measure and compare the social network strength in post-disaster communities in the four reconstruction modes adopted by the government of China, i.e. unified planning and unified construction, unified planning and independent construction, in situ reconstruction and relocation resettlement, with communities after the 2008 Sichuan earthquake in Chengdu, China as study objects from the perspective of three social relations (the relations between residents and residents, residents and managers, and residents and servers). The results showed that strong connections are generally presented in the social network of post-disaster communities in unified planning modes, that the strength is significantly higher than that of those in non-unified planning modes, and that the strength of UPIC communities is the highest. Meanwhile, government intervention, residents’ free participation and market operation are positively correlated to government trust, community interaction and community service respectively. The positive impact of government intervention is the most significant, but it has a peak value. No government management and excessive government intervention will exert negative impacts. The coordination of government, society and market is the key contents of post-disaster community reconstruction. The reconstruction modes based on “government leadership, resident participation and market operation” may become a feasible path for such sustainable development.

We examined the relationship between living alone and the prevalence of depressive symptoms in older Korean widows and assessed the individual contributions of health, social ties, and socioeconomic factors to the development of depressive symptoms. The study was a secondary analysis using data from widows 65 years of age and older who participated in the Living Profiles of Older People Survey (LPOPS). A logistic regression analysis was used to evaluate the contributions of health, social ties, and socioeconomic factors to the development of depressive symptoms. Working status and equivalent household income were significantly associated with depressive symptoms in both those living with others and those living alone. Adjustment for health status and social ties did not change the impact of living alone on the prevalence of depressive symptoms. However, adjustment for equivalent household income eliminated the negative association between living alone and depressive symptoms. Our findings indicate that economic resources are more important than health and social ties for alleviating the negative impact of living alone on the development of depressive symptoms in older widows.

Appropriate diagnosis, treatment and care contributes to better service engagement, improvements to wellbeing, cost savings, reductions in morbidity and mortality for people with Alcohol Related Brain Damage. In the North-East of England large amounts of alcohol are consumed, reflected in the number of alcohol-related deaths in the region. However, the pathway for people with Alcohol Related Brain Damage to receipt of diagnosis, treatment and care is unknown and could be unwittingly influenced by stigma. Qualitative in-depth semi-structured interviews were completed with 25 health and social care professionals from organisations involved with people with Alcohol Related Brain Damage recruited via snowball-sampling. Interviews were recorded, transcribed verbatim, coded, and analysed. People with Alcohol Related Brain Damage were stigmatised both by society and professionals which inhibited entry into services therefore Alcohol Related Brain Damage remained underdiagnosed and misdiagnosed. There was no dedicated service but silos with revolving doors and underfunded generic care with long waiting lists typically excluding those with alcohol or neurological problems. Professionals need to be challenged on the assumption that Alcohol Related Brain Damage is self-inflicted via training aimed at challenging unconscious bias. Reducing stigmatising processes associated with Alcohol Related Brain Damage could counteract professionals’ reluctance to provide care.

Longitudinal studies tracing high-risk samples over decades have illuminated powerful pathways of resilient adaptation among individuals who overcome adversities faced early in life. This research demonstrates resilience is a common phenomenon driven by dynamic interactions between individual traits and environmental resources that enable positive adjustment despite adversity exposure. For clinical social workers supporting clients impacted by childhood trauma, abuse, violence and other adversities, understanding these resilience processes is vital for developing strengths-based, trauma -informed interventions that enhance positive adaptation. This paper synthesizes key findings from landmark longitudinal resilience studies and explores specific practice implications for resilience-oriented clinical assessment, intervention planning, prevention efforts, and social worker training. By integrating resilience research insights, social workers can more effectively identify and cultivate clients' innate capacities for resilience, facilitating a journey towards posttraumatic growth.

A new intervention model for promoting healthy ageing grounded on integrated value-based care was developed and tested in the city of Valencia (Spain). Its implementation raised relevant barriers for older adults in their access to health, health promotion, and health self-management linked with their health and digital literacy. This new intervention model included several aspects. On the one hand, researchers together with older adults and their informal caregivers participating in the study, designed personalised care plans, based on older adults’ specific needs, to be implemented with the support of a digital solution. On the other hand, researchers and health and social professionals implemented a series of six workshops in different locations of the city to encourage the sense of community among participants, reinforcing their trust in the new care model and increasing their adherence. Social activities were at the core of the workshops to understand older people’s interaction with the health and social services provided in the neighbourhood. Qualitative and quantitative methods were combined to extract information from older participants on how to engage them as active actors of their own health and understanding their values and preferences. Qualitative results show that after a post-pandemic situation, they were more concerned about social isolation and desired face-to-face contact with their professional care team; however, feelings of loneliness and/or sadness were not considered amongst the reasons to visit health professionals. Some of the conclusions reveal that the use of technology as a supportive tool is well received but with a stress on its role as “supportive”, and not replacing the close contact with healthcare professionals. Professionals recognised the benefits of this new approach but required more time and incentives to dedicate the effort needed.

This study investigates the impact of experienced contact on prejudiced attitudes toward individuals with intellectual disabilities (ID), examining beliefs in a just world (BJW) and social dominance orientation (SDO) as potential serial mediators. Data were collected from 224 university students (M = 23.02, SD = 2.48). Path analysis modeling assessed the structural relationships between the study variables. The findings revealed that experienced contact was negatively and significantly associated with BJW and SDO. Additionally, BJW and SDO fully mediated the relationship between experienced contact and overt prejudice. These findings underscore the influence of individual differences on attitudes toward individuals with ID, establishing a crucial foundation for future research and the development of interventions aimed at reducing prejudice and discrimination.

In the current socio-economic context, pension systems have become a crucial topic in the agenda of governments and international bodies. In this paper, an empirical study regarding the economic and social sustainability of pension systems in Central and Eastern European Countries was performed. The goal of the research is to establish the sustainability of pension systems with econometric modeling techniques on three dimensions as the research hypotheses are revealed: first, by strengthening the financial soundness of pension systems; second, consolidating the economic environment of pension systems; third, increasing the degree of social security systems with a direct effect of reducing poverty. The results led to relevant insights emphasizing the need for more inclusive economic, financial, and social reforms. From a policy perspective, these findings could be a starting point for the enhancement of the financial adequacy and economic and social sustainability of pension systems in Central and Eastern European Countries. The econometric techniques in this study highlight those modern pensions systems that need to be addressed in a more complex and empirical context that must encompass the main technical aspects. The conclusions suggest that in the future it needs judicial, economic, and social adjustments for the enhancement of the financial soundness of the pension systems.

In the context of Covid-19 virus containment, there is a lack of acceptance of preventive measures in the population. The present work investigated which factors influence the belief in of scientific propositions compared belief in conspiracy theories. The focus here was on the determinants of conspiracy beliefs in the context of Covid-19 related media content. Using an online questionnaire (N = 175), results indicate that scientific compared to conspiracy theoretical media content led to higher acceptance. Furthermore, Need for Cognition (NFC-K), a conspiracy theoretical worldview (CMQ), and openness to experience (NEO-FFI) were positively associated with conspiracy beliefs derived from Facebook postings. In addition, a conspiracy theoretical worldview was negatively associated with belief in scientific media content. Furthermore, agreeableness was unrelated to conspiracy beliefs, although it was positively associated with conspiracy theoretical worldview. The results imply promising persuasion strategies for reducing conspiracy theoretical beliefs and to increase the acceptance of preventive measures.

This research explores how regional socioeconomic variables affect the perception of social trust and support networks (PYCC) in Italian regions, and examines the implications for public policy designed to strengthen social cohesion. This study examines the variable "People You Can Count On" (PYCC) from the ISTAT-BES dataset, focusing on its distribution across Italian regions between 2013 and 2022. Using clustering through a k-Means algorithm optimized with the Silhouette coefficient and the Elbow method, three distinct clusters of regions emerged, highlighting significant differences in social support networks. An econometric model was employed to estimate the PYCC variable, factoring in socioeconomic indicators such as employment rates, income inequality, and social participation. The results indicate a complex interplay between socioeconomic conditions and social trust, with regions in the South and Islands showing increased community support, while many Northern regions experienced declines. The study suggests that areas with lower economic conditions often foster stronger social networks, driven by necessity. These findings underline the importance of targeted public policies aimed at fostering social cohesion, particularly in regions facing economic challenges. Policy implications include enhancing education, supporting small enterprises, and promoting social housing and welfare initiatives. Strengthening community participation and volunteering are also highlighted as critical strategies to build resilient support networks. Overall, the research provides valuable insights into the regional disparities of social trust and the role of socioeconomic factors in shaping community support across Italy.

This paper explores the views of a group of young men aged 16–18, studying towards a Level 1 diploma in music in the North East area of England. It uses the conceptualization of “social haunting” to focus our discussions about how Level 1 learners articulated their experiences in the context of their learning journeys and perceived marginalization. The research took place at a large Further Education (FE) college and included a series of five “research encounters” with the group of Level 1 learners. These encounters included a range of activities, resulting in a variety of data being collected, including several activity worksheets, structured and unstructured focus group interviews, and questionnaires. This paper focusses on the outcomes of a single research activity that focused on exploring the topic of marginalization. We present a discussion on social haunting as one of the emerging findings from this research encounter, outlining how the reality of the social and cultural legacy of the UK’s industrial past can shape today’s youth and their futures. We will talk about the young men’s views of marginalization, their school-to-work transitions, the formation of masculinity in post-industrial communities and the role of Further Education in it. The paper concludes by discussing some insights for practice and future research.

Background: Social skills deficits are present in 43.3% of major depressed patients and significantly impact health-related quality of life. However, studies concerning social deficits as state-dependent markers of depression are limited. Objective: To delineate the effects of severity of depression, personality characteristics, family support satisfaction and self-esteem on social skills in clinical depression. Methods: We recruited 150 patients with major depressive disorder or persistent depressive disorder and assessed 1) Beck Depression Inventory-II (BDI-II), 2) Social Skill Inventory (SSI), 3) Family Adaptation, Partnership, Growth, Affection, Resolve (APGAR) Questionnaire 4) Rosenberg Self-Esteem Scale (RSES) and 5) Maudsley Personal Inventory (MPI). Results: Social skills deficits were significantly associated with female gender, age < 40 years, depression severity, introvert / neurotic personality, and lowered family support satisfaction and self-esteem. A large part of the variance (47.2%) in social skills deficits was predicted by satisfaction of family support, introvert personality, and low self-esteem. Exploratory Factor Analysis showed that a bifactorial model best fitted the data with a) a general factor loading highly on BDI-II, SSI emotional and social, APGAR, RSES, MPI_introvert and MPI_neurotic scores, and b) a single-group factor loading on both SSI, RSES, and MPI introvert scores. Conclusion: In clinical depression, social skills deficits covary with increasing severity of depression and thus constitute a state marker of depression, and independently from severity of depression covary with introvert personality features. As such, remission of social skills deficits may emerge as a novel objective for the treatment of depression and prevention of new episodes.

Sustainability experienced significant global momentum in recent decades. The emergence of the sustainability movement in the Financial Sector was prompted by the increasing global climate change resulting from environmental deterioration. Primarily associated with the Financial Sector, it has increasingly been recognised that Financial Institutions are significant contributors to global climate change, both via direct and indirect means. This has resulted in a heightened emphasis and demand from stakeholders for the implementation of Sustainability Principles on a worldwide scale. This study presents a methodology that utilises the various dimensions of Sustainable Finance through the concept of Triple Bottom Line to determine the factors influencing the Sustainability Performance of Financial Institutions. The suggested approach is grounded and supported by the current body of literature. The study presents a hypothesis to investigate the correlation between factors of Green Finance Practices and Sustainability Performance of Banking Sector. The suggested framework can be utilised by regulatory bodies and individual financial institution of a country to identify the elements that influence the Sustainability Performance of Banking Sector.

This review article explores the convergence of virtual reality (VR) and architectural design from an inclusive lens. It underscores the crucial role of virtual reality (VR) in serving as a catalyst for the incorporation of social and cultural sustainability principles in architectural projects. The exploration is guided by inclusive design principles, which encompass several aspects such as accessibility, cultural sensitivity, diversity and inclusivity, user-centered design, and empathy. These concepts serve as the basis for further investigation into the potential of virtual reality (VR) technology to enhance and heighten these features within architectural design, therefore promoting the progress of social and cultural sustainability. The review article highlights the profound impact that virtual reality (VR) may have, going beyond mere visualisation to generate architectural experiences that are immersive, interactive, and empathetic. These experiences are designed to take into account diverse perspectives and needs.

Emotion recognition and social inference impairments are well-documented post-traumatic brain injury (TBI) yet the mechanisms underpinning these are not fully understood. We examined dynamic emotion recognition, social inference abilities, and eye fixation patterns between adults with and without TBI. Eighteen individuals with TBI and 18 matched non-TBI participants were recruited and underwent all three components of The Assessment of Social Inference Test (TASIT). The TBI group were less accurate in identifying emotions compared to the non-TBI group. Individuals with TBI also scored lower when distinguishing sincere and sarcastic conversations but scored similarly to those without TBI during lie vignettes. Finally, those with TBI also had difficulty understanding the actor’s intentions, feelings, and beliefs compared to participants without TBI. No group differences were found for eye fixation patterns and there were no associations between fixations and behavioural accuracy scores. This conflicts with previous studies and might be related to an important distinction between static and dynamic stimuli. Visual strategies appeared goal- and stimulus-driven, with attention being distributed to the most diagnostic area of the face for each emotion. These findings suggest that low-level visual deficits may not be modulating emotion recognition and social inference disturbances post-TBI.

The variations in the socioeconomic status between different social class of a population correspond to differences in accessibility to all resources available and able to improve global health. While socioeconomic status is now known as one of the main determinants for a good health and a good aging, its influence on sleep disorders is not well understood. The socioeconomic status is a concept, not directly observable but estimated using indicators like income, education, occupational status and area of living. This theoretical review explores some theories linking environment of people with occurrence of sleep disturbances, with different patterns associated to socioeconomic status. A model of interaction is proposed to summarize and conceptualizes these interactions and to promote more research on the topic.

This qualitative research study is a part of a larger research project exploring the experiences of young caregivers aged 5-26 years and their families navigating the COVID-19 pandemic between 2020 to 2023. Data was collected from 14 young caregivers who participated in baseline interviews. The central research question guiding this study: What was, is, and will be the impact of changing public health restrictions on young caregivers and their families during the pandemic and pandemic recovery? Seven themes emerged through analysis: 1) Navigating Care During the Height of Public Health Restrictions, 2) Neighbourhood and Built Environment During the Pandemic, 3) Perceptions Towards COVID and Public Health Restrictions/Efforts, 4) The Impact of Public Health Restrictions on Relationships, 5) Mental Health Challenges of Being a Young Caregiver During a Pandemic, 6) Navigating Formal Services and Supports and 7) Recommendations from Young Caregivers. The findings from this empirical research suggest that young caregivers found it easier to navigate their caregiving responsibilities when public health restrictions and work-from-home mandates were initially implemented, however, this later changed due to challenges in finding respite from caregiving, maintaining social connections with friends, creating personal space at home, and finding adequate replacements of programs once offered in-person.

This qualitative research investigates sustainable procurement practices, focusing on the integration of environmental and social criteria in supplier evaluation processes. The study explores how organizations, ranging from large multinational corporations to smaller enterprises, navigate sustainability challenges and opportunities within their procurement strategies. Data were gathered through semi-structured interviews with procurement professionals and sustainability managers across diverse industries. Themes identified include varying organizational commitments to sustainability, from structured policies and resource allocation in larger firms to reactive approaches constrained by resource limitations in smaller entities. Effective supplier relationship management emerged as crucial, facilitating collaborative partnerships aimed at improving environmental and social performance across supply chains. Motivations for sustainable procurement practices encompass regulatory compliance and responding to consumer demands for eco-friendly products, shaping procurement decisions to enhance market competitiveness and stakeholder satisfaction. Environmental and social criteria integration in supplier evaluations highlighted strategies such as lifecycle assessments and sustainable sourcing practices, aimed at minimizing environmental footprints and promoting ethical labor practices. Benefits included cost savings and enhanced brand reputation, tempered by challenges such as higher procurement costs and supplier resistance. Leadership commitment and organizational culture were identified as critical enablers of sustainable procurement, influencing strategic alignment and fostering a culture of sustainability within organizations. The study underscores the transformative potential of sustainable procurement in achieving organizational resilience and contributing to global sustainability goals.

We present the detailed calculations of social distancing requirement. A comparative study of the growth pattern and death tolls in different communities indicates that the growth pattern of infected patients and death rate follow the similar distribution with different parametrizations. Every distribution follows an exponential growth pattern curve, like other microbes, then reaches the saturation point an d eventually decay s However, the argument for the exponential function depends on several parameters unbeknownst, as of yet. However, the slope varies different ial ly for various epicenter s and seems to have a relationship with parameters such as accessibi lity to healthcare facilities, pre existing medical conditions socio economic conditions and lifestyle. The mismatch of the growth pattern is also linked with the impact of various other factors and a premature interpretation of limit ed data. Novel behavi or of the virus brought many surprises, opened up new venues for medical research, and the need for the more detailed study of pathogens in the light of the interaction of RNA and DNA The adaptability to diverse ecological condition s and the relevant modification in the structure is also worth investigation The genetic modification can be studied using quantum mechanical probabilistic approach.

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

COVID-19 severity is heterogeneously distributed over age strata, but current mitigation strategies are homogeneously applied to all population. Social-distancing and stay-home are effective conservative approaches but lack economic sustainability on long term. Conversely, herd-immunity is a nonrestrictive strategy which can cost remarkable number of human lives and can melt the healthcare system down. Here I propose an Age Adaptive Social Distancing (AASD) engineering strategy to mitigate COVID-19 outbreak. AASD is based on the scientific evidence that the fatality rate grows nonlinearly with age, hence also the containing strategy should adapt nonlinearly. Essentially, AASD suggests that ‘silent spreaders’ (age 0-39) should avoid/minimize direct and indirect contacts with individuals in ‘dangerous zone’ (age 40+). The rationale is: 0-19 should follow parents strategy, healthy 20-39 (low fatality rate) might conduct screened life under active surveillance, to sustain economy and acquire rational immunity; 40-59 should respect social distancing (waiting a therapy); 60+ should stay at home (waiting a vaccine). This might save human lives, reduce healthcare demand and improve economic sustainability. The final take-home message is that future studies should design precision and personalized strategies for specific contagious diseases that integrate different social constrains, active surveillance and contact tracing.

The NOSTR is a communication protocol for the social web, based on the w3c websockets standard. Although it is still in its infancy, it is well known as a social media protocol, thousands of trusted users and multiple user interfaces, offering a unique experience and enormous capabilities. To name a few, the NOSTR applications include but are not limited to direct messaging, file sharing, audio/video streaming, collaborative writing, blogging and data processing through distributed AI directories. In this work, we propose an approach that builds upon the existing protocol structure with end goal a decentralized marketplace for federated learning and LLM training. In this proposed design there are two parties: on one side there are customers who provide a dataset that they want to use for training an AI model. On the other side, there are service providers, who receive (parts of) the dataset, train the AI model, and for a payment as an exchange, they return the optimized AI model. The decentralized and censorship resistant features of the NOSTR enable the possibility of designing a fair and open marketplace for training AI models and LLMs.

In the increasingly competitive SVOD market, platforms face high churn rates and substantial revenue losses from SVOD content piracy, all of which limit their ability to invest in acquir-ing/creating content compelling enough to win and retain subscribers. Based on social exchange theory, this study argues that platforms can improve relationships with SVOD content users by offering tiered discounts in exchange for advertising/loyalty and by promoting anti-piracy messages with a prosocial (threatening) approach that emphasizes harm to filmmakers (punishment for pi-rates). We hypothesize that these incentives enhance subscription intention when the incentive specifications (advertising levels, loyalty levels, message approach, and message credibility) match the public’s heterogeneous dispositions (advertising attitude, loyalty attitude, justice sensitivity, and fear of punishment). In a survey on the intention to subscribe to a hypothetical new platform, we confirmed the hypothesized interactions for advertising-based discounts, loyalty-based discounts, and prosocial messages, but did not find support for threatening messages. Further exploration showed that the evaluation of platform content was much more influential than any other incentive and that tiered loyalty discounts had a remarkable capacity to enhance subscription intention. This study’s findings may help shape incentives that are more satisfying to users and ultimately more profitable for platforms.

This study contributes to the Sybil nodes detecting algorithm in Online Social Networks (OSNs). As major communication platforms, online social networks get significantly guarded from malicious activity. A thorough literature review identified various detection and prevention Sybil attack algorithms. Additional exploration of distinct reputation systems and their practical application led to the study’s discovery of machine learning algorithms, i.e., the KNN, Support Vector Machines, and Random Forest algorithms. The study details the data cleansing process for the employed dataset in its process for optimizing the computational demands required to train machine learning algorithms, achieved through dataset partitioning. Such a process led to explaining and analysis of conducting experiments and comparing their results. Such experiments demonstrate the algorithm’s ability to detect Sybil nodes in OSNs (100% accuracy in SVM, 99.6% in Random Forest, and 97% in KNN algorithms); thus concluding by proposing future research opportunities.

The notion of hierarchy is a key characteristic of any complex system. This paper explores which notions of hierarchy are being used in the field of management and organization studies. Four distinct types of hierarchy are identified: a ladder of formal decision-making authority, a ladder of achieved status, a self-organized ladder of responsibility, and an ideology-based ladder. A social mechanism-based perspective serves to define and distinguish these four types. Subsequently, the typology is further developed by comparing the four hierarchy types in terms of their tacit/explicitness, (in)transitivity, and behavior- versus cognition-centeredness. This review article contributes to the literature by dissecting the general metaphor of hierarchy into four different constructs and their social mechanisms, which serves to create a typology of the various ways in which complex systems can be characterized as being hierarchical. This typology can inform future research drawing on any type of hierarchy.

Limited observational studies have described the relationship between sleep duration and overall diet. The present study investigated the association between sleep duration at weekdays and empirically derived dietary patterns in a nationally representative sample of UK adults, aged 19-64 years old, participating in the 2008-2012 UK National Diet and Nutrition Survey Rolling Programme. Survey members completed between three to four days of dietary records. Sleep duration at weekdays was categorised into tertiles to reflect short, normal and long sleep duration. Social jetlag was calculated as the difference between sleep duration at weekends and weekdays. The association between sleep duration/ social jetlag and dietary patterns, derived by principal components analysis, was assessed regressing diet on sleep whilst accounting for the complex survey design and adjusting for relevant confounders. Survey members in the highest tertile of sleep duration had on average 0.45 (95% CI -0.78, -0.12) lower healthy dietary pattern score compared to middle tertile (p =0.007). There was an inverted u-shaped association between social jetlag and a healthy dietary pattern, such that when sleep at weekends exceeded weekday sleep by 1h 45min, scores for indicating a healthy dietary pattern declined (p =0.005). In conclusion, long sleep duration at weekdays and an increased social jetlag are associated with a lower healthy dietary pattern score. Further research is required to address factors influencing dietary patterns in long sleepers.

A regional organisation (RO) is a group of states that, through the formation of mutual relationships, work together for shared regional goals such as economic growth, social welfare, security, and democracy. The Association of South East Asian Nations (ASEAN) is one such organisation, of interest because of its operative paradoxes. However, current theory to study ROs is both fragmented across the field of study, and inadequate. Taking ROs to be complex adaptive systems, diagnosing ROs requires theory that can to explore their hidden aspects. The metacybernetics framework enables the creation of a critical realism configurative a-priori inquiry approach to theory building, the configurations providing coordinative schemas able to respond to predefined complex modelling needs. To analyse ROs using metacybernetics, we offer a derivative schema, Cultural Agency Theory (CAT). Currently this is limited to exploring modes of information acquisition processes through cognitive style, and until now has had no inherent mechanism to explore social relationships as is required to explore ROs. To enable CAT to explore such relationships, we configure the Tönnies social organisation paradigm for CAT. Mindset Agency Theory (MAT) is a specific elaboration of CAT, and creates an empirical setting for the set of formative traits that de-fine an RO character. A related derivative of CAT is that of Multiple Identity Theory (MIT) which enables organisational pathologies to be explored in terms of identity conflicts. This multifaceted paradigm provides a relatively simple approach to the RO diagnosis, in particular in terms of stability/coherence. This paper is in two parts. In this part 1, the CAT theory will be further developed through the joint configuration of Tönnies social organisation paradigm allowing its pathologies to be diagnosed. In part 2 of the paper, the theory will be applied to ASEAN and the diagnosis will show that it is an inherently incoherent organisation.

A regional organisation (RO) is a group of states that, through the formation of mutual relation-ships, work together for shared regional goals such as economic growth, social welfare, security, and democracy. The Association of South East Asian Nations (ASEAN) is one such organisation, of interest because of its operative paradoxes. However, current theory to study ROs is both fragmented across the field of study, and inadequate. Taking ROs to be complex adaptive systems, diagnosing ROs requires theory that can to explore their hidden aspects. The metacybernetics framework enables the creation of a critical realism configurative a-priori inquiry approach to theory building, the configurations providing coordinative schemas able to respond to predefined complex modelling needs. To analyse ROs using metacybernetics, we offer a derivative schema, Cultural Agency Theory (CAT). Currently this is limited to exploring modes of information acquisition processes through cognitive style, and until now has had no inherent mechanism to explore social relationships as is required to explore ROs. To enable CAT to explore such relationships, we configure the Tönnies social organisation paradigm for CAT. Mindset Agency Theory (MAT) is a specific elaboration of CAT, and creates an empirical setting for the set of formative traits that de-fine an RO character. A related derivative of CAT is that of Multiple Identity Theory (MIT) which enables organisational pathologies to be explored in terms of identity conflicts. Thes multifaceted paradigm provides a relatively simple approach to the RO diagnosis, in particular in terms of stability/coherence. This paper is in two parts. In this part 1, the CAT theory will be further developed through the joint configuration of Tönnies social organisation paradigm allowing its pathologies to be diagnosed. In part 2 of the paper, the theory will be applied to ASEAN and the diagnosis will show that it is an inherently incoherent organisation

We present a model of infection dynamics that might explain slower approaching the herd immunity during the covid-19 epidemy in Sweden than it was predicted by a variety of other models; see graphs Fig. \ref{GROWTH2}. The new model takes into account the hierarchic structure of social clusters in the human society. We apply the well developed theory of random walk on the energy landscapes represented mathematically with ultrametric spaces. This theory was created for applications to spin glasses and protein dynamics. To move from one social cluster (valley) to another, the virus should cross a social barrier between them. The magnitude of a barrier depends on the number of social hierarchy's levels composing this barrier. As the most appropriate for the recent situation in Sweden, we consider linearly increasing (with respect to hierarchy's levels) barriers. This structure of barriers matches with a rather soft regulations imposed in Sweden in March 2020. In this model, the infection spreads rather easily inside a social cluster (say working collective), but jumps to other clusters are constrained by social barriers. This model's feature matches with the real situation during the covid-19 epidemy, with its cluster spreading structure. Clusters need not be determined solely geographically, they are based on a number of hierarchically ordered social coordinates. The model differs crucially from the standard models of spread of disease, such as the SIR-model. Our model describes such a specialty of spread of covid-19 virus as the presence of ``super-infectors'' who by performing a kind of random walk on a hierarchic landscape of social clusters spreads infection. In future, this model will be completed by adding the SIR-type counterpart. But, the latter is not a specialty of covid-19 spreading.

Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today's culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings showed how social media has been used as a tool for research, designing and implementing health campaigns, messaging, and health promotion. In addition, social media integration with Artificial Intelligence (AI) and Generative Pre-Trained transformers (GPT) can have a big impact and offer an innovative approach to tackle the problems and difficulties in health informatics. These innovations might improve patient participation, individualized assistance, and care experiences. The research showed how likely social media will keep growing and evolving and, if used effectively, have the potential to help close public health gaps across different cultures and improve population health.

Thailand has formulated her climate change policy and updated relevant plans and policies to align with the goal of achieving carbon neutrality and net zero GHG emissions. This study investigates the optimal level of GHG mitigation in Thailand, taking into account the marginal abatement cost (MAC) and social cost of carbon (SCC). It evaluates how energy efficiency and renewable energy technologies influence GHG reduction in the power and industrial sector and illustrates policy recommendations that align with the 2020 to 2050 policy and plan period. The findings indicate that there may be instances where GHG mitigation potential is insufficient to reach the national mile-stone. In such cases, it becomes imperative to leverage all technologies within the marginal abatement cost curve (MACC) and also utilization of the social cost of carbon for policy decision making and meeting the desired goals. In certain scenarios, the adoption of additional technologies or measures may be necessary, such as flexible power generation and deploying carbon capture and storage or hydrogen which are high-cost technologies. Furthermore, preparations should be made for multiple levels of climate change policies and plans beyond 2030.

Health innovations are generally oriented on a techno-economic vision. In this perspective, technologies are seen as an end in themselves, and there is no arrangement between the technical and the social values of innovation. This vision prevails in sanitary crises, in which management is carried out based on the search for punctual, reactive, and technical solutions to remedy a specific problem without a systemic/holistic, sustainable, or proactive approach. This paper attempts to contribute to the literature on the epistemological orientation of innovations in the field of public health. Taking the Covid-19 and Ebola crises as examples, the primary objective is to show how innovation in health is oriented towards a techno-economic paradigm. Second, we propose a repositioning of public health innovation towards a social paradigm that will put more emphasis on the interaction between social and health dimensions in the perspective of social change.

Abstract: Background: chronotype is generally defined as the variability of the phase angle of en-trainment, while the latter reflects the relationship between the timing of a certain rhythm (e.g., sleep-wake cycle) and the timing of an external temporal cue. Individuals can be placed on a spectrum from "morning types" (M types), to "evening types" (E types). E-chronotype has been proposed as a transdiagnostic risk factor for psychiatric conditions and associated with psycho-pathological dimensions. Eveningness seems correlated with both suicidal ideation (SI) and suicidal behavioral (SB), through several possible mediating factors. Immunological alterations also have been linked to later chronotypes and SI/SB. Objective: This narrative review aims to summarize the evidence supporting the possible association between chronotypes and suicide, with the eventual mediating role of neuroinflammation and several psychopathological dimensions. Material and methods: A search of literature (2003-2023) was conducted, using various databases: PUBMED, EMBASE, Scopus, UpToDate, PsycINFO, Cochrane Library. English-language articles were col-lected and screened for eligibility. Results: Despite the apparent absence of a direct correlation between E-chronotype and suicidality, E-chronotype promotes a chain of effects that could be in-volved in an increased risk of SB, in which with neuroinflammation possibly play an intriguing role and some psychopathological dimensions may stand out.

The COVID-19 pandemic has had devastating consequences for health, social, and economic domains, but what has received far less focus is the effect on people’s relationship to vital ecological supports, including access to greenspace. We assessed patterns of greenspace use in relation to individual and environmental factors and their relationship with experiencing psychological symptoms under the pandemic. We conducted an online survey recruiting participants from social media for adults in Korea for September–December 2020. The survey collected data on demographics, patterns of using greenspace during the pandemic, and major depression (MD) and generalized anxiety disorder (GAD) symptoms. The Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder 2-item (GAD-2) were applied to identify probable cases of MD and GAD. A logistic regression model assessed the association decreased visits to greenspace after the outbreak compared to 2019 and probable MD and GAD. Among the 322 survey participants, prevalence of probable MD and GAD were 19.3% and 14.9%, respectively. High rates of probable MD (23.3%) and GAD (19.4%) were found among persons currently having job-related and financial issues. Of the total participants, 64.9% reported decreased visits to greenspace after the COVID-19 outbreak. Persons with decreased visits to greenspace had 2.06 higher odds (95% CI: 0.91, 4.67) of probable MD at the time of the survey than persons whose visits to greenspace increased or did not change. Findings suggest that barriers to greenspace use could deprive people of mental health benefits and affect mental health during pandemic; an alternative explanation is that those experiencing poor mental health may be less likely to visit greenspaces during pandemic. This implies the need of adequate interventions on greenspace uses under an outbreak especially focusing on how low-income populations may be more adversely affected by a pandemic and its policy responses.

The study is dedicated to analyzing the complex interaction between Artificial Intelligence (AI) and the Internet of Things (IoT) with institutional structures in the context of sustainable development. This work examines the impact of these technologies on economic and social development, identifying their potential advantages and risks. Methodologically, the research relies on information gathering and case study analysis, assessing the impact of technology in various domains, from smart cities to the manufacturing sector. The findings emphasize the necessity of integrating technological innovations into broader social and environmental initiatives, highlighting the importance of institutional transformations for achieving environmental sustainability.

The relationship between Climate Change and Water is an obvious and key issue within the Sustainable Development Goals. This study aims to investigate the social representation created around this relationship in three different territorial contexts in order to evaluate the influence of the territory on the perception of the risk of Climate Change and its relationship with water. By means of a questionnaire completed by 1709 university students, the climatic literacy of the individual was evaluated in order to relate it to other dimensions on the relationship between Climate Change and Water (information, training previous on climate change and pro-environmental attitudes) in their different dimensions in three different territorial contexts. The results show that the socio-cultural context influences the social representation of Climate Change, but not from the climatological condition, so that it is reasonable to think that the social representation of this relationship is favoured by a common culture around this relationship.

Implementing ethical procurement standards is increasingly recognized as pivotal for organizations aiming to align their practices with principles of social responsibility and sustainability. This qualitative research explores the experiences, challenges, strategies, and impacts associated with ethical procurement across diverse sectors. Motivations for adopting ethical procurement include enhancing corporate reputation, meeting stakeholder expectations, and complying with regulatory frameworks. However, organizations face significant challenges such as balancing ethical considerations with cost-efficiency pressures, navigating complex regulatory landscapes, and ensuring transparency across global supply chains. Strategies for overcoming these challenges emphasize effective supplier relationship management, leveraging technological innovations for transparency, and investing in supplier capacity-building. The study reveals that ethical procurement practices contribute to organizational resilience by improving risk management, enhancing stakeholder engagement, and fostering innovation in procurement processes. Moreover, ethical procurement supports sustainable development goals by promoting environmental sustainability, supporting social equity through fair labor practices, and contributing to economic development. Future research directions include exploring the impact of emerging technologies on ethical procurement, promoting global collaboration for unified standards, and evaluating the long-term sustainability of ethical procurement initiatives.

Less aligned emphasis has been given to the COVID-19 infodemic coordinating with the COVID-19 outbreak. Global profusion of tangled monikers and hashtags has found their ways in daily communication and contributed to backlash against Chinese. Official naming efforts against infodemic should be meet with a fair share of identification. Based on brief critical reviews on previous multifarious naming practices, we punctuate heuristic introspection in scientific conventions and sociocultural paradigms. Infodemiological analysis promises to articulate that people around the globe are divided in their favor stigmatized monikers in the public and scientific communities because of perceptual bias. There is no positive correlation between the degree of infection in their territories and collective perceptual bias to COVID-19. The official portfolio “COVID-19” and “SARS-CoV-2” has not become de facto standard usages, but full-fledged official names are excepted to duly contribute to the resilience of negative perceptual bias and collective behavioral propensities.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational background, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, Natural Language Processing and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of the machine learning experiments with the domain-specific textual data set, considering 2 research directions: a. predictions analysis and b. data balancing. Wrong predictions analysis and the features that contributed to misclassification, along with correct predictions analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel 4-step methodology is proposed in this paper for the first time, consisting of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtains better results than data undersampling in imbalanced data sets, while hybrid approaches perform reasonably well.

Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

(1) Aims: To analyse the relationship between demographic and clinical characteristics and particular kinds of suicidal behaviour and its relationship with a nurse-led suicide prevention program. (2) Methods: A 5-year cross-sectional study design. Study performed in the region of Osona (Catalonia) during 2013-2017. Suicidal behaviour was classified as suicidal ideation, interrupted self-directed violence, suicide attempt and completed suicide. (3) Results: The sample included 753 patients ─ of whom 53 died by suicide─ and encompassed 931 suicidal behaviour episodes. Men represented 38.4% of the sample but 81.1% of deaths by suicide. Mental disorders were associated with suicidal behaviour in 75.4% of the sample. 66.4% of the individuals were engaged in the nurse-led Suicidal Behaviour Case Management Program, of which 0.8% (n=4) died by suicide. (4) Conclusion: The main risk factors were female sex for suicidal behaviour, and male sex and older age for suicide. Mental disorders, widowhood and retirement were also associated with suicide. A high proportion of patients was engaged to the nurse-led suicide prevention program with a low proportion of suicides.

The emergence of the Covid-19 pandemic has created both negative and positive changes, including implementing the circular economy across the globe. This Systematic Review follows the PRISMA statement and employs the Text Mining (Voyant Tools) technique to visualize and analyze the impacts of the Covid-19 on three aspects of the circular economy: economic, social, and environmental. The research employs Latent Dirichlet Allocation (LDA) to identify five major topics: (1) Shortage of medical equipment but high medical waste during Covid-19 due to the high demand in healthcare; (2) The long term negative impacts of lockdown on economic and social activities because of Covid-19 pandemic; (3) The reports on impacts of Covid-19 pandemic on the manufacturing globally, and their coping strategies and new opportunities; (4) The impacts of international restriction on the tourism, trade, shipping, and aviation industry, causing billion-dollar losses; (5) The reduction of pollution with health environment improvements with example cases from China and EU. The research identifies current literature gaps in the circular economy and Covid- 19 topics and encourages the application of text mining tools into researching to stimulate the research process and assist in communicating with the public.

This study examines the factors influencing consumer adoption of on-demand social media platforms characterized by ephemeral content. As these platforms rapidly gain popularity, understanding adoption drivers is crucial for researchers and practitioners. The study develops and tests a comprehensive model integrating traditional technology acceptance factors with platform-specific constructs. Using partial least squares structural equation modelling on data from 180 social media users in Kerala, India, the research reveals that convenience is the strongest predictor of adoption intention, followed by perceived usefulness, interpersonal influence, and enjoyment. Surprisingly, factors like perceived ease of use, social influence, and platform-specific features such as ephemerality and visual focus did not significantly impact adoption directly. Age showed a marginal negative effect, while gender had no significant impact. The findings challenge some prevailing assumptions about on-demand social media adoption and highlight the need for refined models specific to this context. The study contributes to technology adoption literature by demonstrating the primacy of convenience in driving the adoption of these platforms. It also offers practical insights for developers and marketers, suggesting that prioritizing convenience and perceived usefulness in design and marketing may be more effective than emphasizing specific features. Future research directions are proposed to address limitations and further explore this evolving digital landscape.

Leisure education has the potential to improve quality of life and prepares students for social par-ticipation after graduation. This study aimed to elucidate the perspectives of special education teachers regarding leisure instruction. This study surveyed 538 teachers from 10 special needs schools for students with intellectual disabilities in Prefecture A, a rural area of Japan. An online survey was conducted from October 21 to November 17, 2023. The survey items were based on 13 aspects of leisure instruction identified from a literature review related to leisure instruction in Japan and a qualitative analysis of interviews with high school teachers in special needs schools for stu-dents with intellectual disabilities. The results showed that “The ability to pass time during brief periods of free time,” “Various activity experiences that lead to leisure,” and “Knowing one’s strengths and weaknesses in activities” were significantly more important during school. Con-versely, “understanding the psychological importance of leisure,” “connecting with organizations that support leisure activities,” and “finding places to engage in leisure activities” were significantly more important after graduation. The results of this study will be used to identify gaps in percep-tions between special education teachers and vocational rehabilitation practitioners responsible for post-social participation support.

Will human societies accommodate social robots, given the current development rate? Are we under a novelty effect? Can (and should) social robots compete with intelligent agents (e.g., software robots) for the attention of humans? Will the current development of machine learning be the motor of social robotics? What kind of robotics products and culture can we expect that will emerge? These are relevant questions to assess the sustainability of social robots and how they will change business practices and lifestyle. Speculative/exploratory arguments on these topics are possible and have been mostly left out of research papers. This, however, results in an unconventional structure where the novelty comes from the questioning of the role of Social Robotics that has not been duly addressed in the literature. The paper raises questions and (i) presents arguments supporting sustainability grounded on experiments in real-world scenarios, and (ii) identifies challenges according to multiple viewpoints. These are questions for which assertive answers are not found in the literature. Besides the answers to the above questions, the conclusions point to pathways we believe social robotics can evolve, e.g., incorporating maximal knowledge on how humans work.

: Based on fairness perspective, this paper combines the social vulnerability assessment of natural hazards with multi-objective site selection model to propose a new site selection planning model for central refuges. To begin with, the social vulnerability assessment system of regional natural disasters is founded through literature analysis, and the social vulnerability index of natural disasters is constructed; and based on the exponential differentiation of index to depict regional refuge demand, design vulnerability coverage function quantitive site selection fairness; then taking the shortest weighted evacuation distance, maximum vulnerability coverage and construction cost, the coverage population and refuge quantity as constraint conditions to construct multi-objective optimization model; finally, taking Zigong City, Sichuan Province as the object of empirical research, adapting NSGA-II algorithm to get the Pareto optimal solution set, and using TOPSIS method to get the optimal location scheme. The research results can provide a new method for the location layout of central refuge.

While most published research on COVID-19 focused in a few countries and especially during the second wave of the pandemic and the vaccination period, we turn to the first wave (march-May 2020) to examine sentiment and emotions expressed by Twitter users in Greece. We use computational methods combining opinion mining and the application of a modified emotion lexicon with Social Network Analysis to explore the subtleties behind an overarching negative sentiment. Our analysis has shown that popular sentiment isn’t stable nor monolithic, since underlying emotions may be a better predictor of attitudes during a crisis.

(1) Background: This study aimed to explore how prolonged mask use during the COVID-19 pandemic affected appearance anxiety; (2) Methods: It implemented a quantitative–qualitative mixed-research approach based on a convenience sample of young people in Taiwan; (3) Results: A total of 1,118 participants completed the online questionnaire. A total of 49 participants reached the 55-point threshold of Appearance Anxiety Scale. Higher score of appearance anxiety was correlated to higher state anxiety statistically (r = 0.4, p < 0.001). Ten participants were interviewed and qualitative content analysis revealed 5 main themes and 16 subthemes. Three of the main themes were related to the positive effects of wearing masks for pandemic prevention and reducing appearance anxiety: “Wearing masks indicates compliance with the epidemic prevention requirement,” “Wearing masks functions as protection against lack of confidence in appearance,” and “Wearing masks helps mitigate the fear of being judged by others.” The remaining two “Wearing masks shifts attention from appearance to figure” and “Prolonged mask wearing exacerbates the anxiety about taking it off” were related to the impacts of long-term mask use; (4) Conclusions: results enhance understanding of negative emotional experiences and anxiety related to physical appearance.

Purpose: To examine associations between subjective quality of life and other socio-demographic variables and to explore differences in experiences of people with different levels of quality of life (low, moderate, high). Materials and methods: Semi-structured interviews and standardized measures of mobility, function, health-related quality-of-life, and quality-of-life were used to collect the data for this mixed-method study. Results: Twenty-four participants were interviewed with an average age of 55 years and 54% were male. High quality of life, according to quantitative analysis, was strongly associated with being male, attending rehabilitation, and being married. The qualitative findings supported the quantitative findings and also revealed that people with a low quality of life felt the neighborhood-built environment was not supportive of people with incomplete spinal cord injury who can walk. Participants who reported a low/ moderate quality of life reported feeling devalued by able-bodied people and that their mobility was getting worse over time. Conclusion: Findings suggest that those with incomplete spinal cord injuries who can walk could benefit from improved quality of life by modifying their social and neighborhood's built environment. For instance, sensitivity training for the general population could help to reduce negative attitudes and misperceptions about invisible impairments and promote inclusion.

The role of chemical communication in social relationships of birds is receiving growing attention but our knowledge is still scarce compared to other taxa. Previous evidence suggests that chemical cues emitted by birds may carry information about their characteristics that may be useful in a sexual selection context. However, experimental studies are needed to investigate the role of bird chemical cues in signalling quality of potential partners. We performed an experimental study aimed to disentangle whether female zebra finches, Taeniopygia guttata, use chemical cues to assess the body condition and body size of potential partners. We offered focal females the scent of two males differing in body condition and body size. Our results show that females can assess the body condition of potential partners using olfaction. However, contrary to expected in a mate choice context, our results show that females avoided the scent of the male with greater body condition and body size. Our results therefore suggest that, despite performing the study during breeding period, social interactions may be mediating the avoidance of the scent of the conspecific of the opposite sex with better body condition and body size in this gregarious species.

This empirical research is part of a larger project beginning in 2020 and ongoing until 2023 exploring the impact of the COVID-19 pandemic on young caregivers aged 5-25 years and their families in Canada. Utilizing the social determinants of health as a conceptual framework, this case study emphasizes the voices of professionals offering services to young caregiver clients during the pandemic, and explores their perspectives on the impact of the pandemic on young caregivers and their families. Across three (3) different organizations offering programs and services to young caregiver clients in Ontario, six (6) individual interviews were conducted with directors/program managers and four (4) group interviews were conducted with thirteen (13) staff members who worked directly with young caregivers and their families. Nineteen (19) service providers participated in total. The results of this study highlight five (5) primary themes that emerged through data analysis: i) the role of service providers, ii) the impact of the COVID-19 pandemic on organizations and service providers supporting young caregivers and their families, iii) barriers for service users, iv) helpful resources for service providers and organizations, and v) resources needed/preferred by service providers and organization. The pandemic significantly impacted young caregivers and their families, as reported by professionals, and organizations working with young caregivers and their families were tasked with addressing increased service demands and adapting service delivery to follow public health guidelines.

Sedentary behavior (SB) is an important public health concern. Adults working in desk-based occupations spend a considerable proportion of the workday sitting. More information is needed regarding the factors that contribute to occupational SB. The aim of this study was to assess the utility of social cognitive theory (SCT) to explain work-related SB using a quantitative, cross-sectional design by administering an online questionnaire. Participants included 381 full-time employees at a large, public university in the south-central United States. Hierarchical multiple linear regression was used to determine the relationship between SCT constructs and SB. Mean work-related SB was 6.08 (SD = 1.59) hours/day. In model 1, 9.6% of the total variance in SB was accounted for by standing desk ownership and physical activity level (p=0.001; R2=0.096). In model 2, SCT constructs led to a statistically significant R2 increase of 4.9% (p < 0.001, where standing desk ownership, physical activity, and self-efficacy explained 13.3% of the variance in work-related SB. Findings from this study suggest that self-efficacy may be an important factor in explaining variation in occupational SB. Public health researchers and practitioners should consider strategies to address employees’ self-efficacy when developing workplace interventions to target occupational SB.

Wellness is a multidimensional concept that touches upon the various physical, mental, emotional, spiritual, social and environmental facets of health. Interest towards and importance of wellness have been growing constantly for the past two decades and thus makes it crucial to understand which factors affect public engagement with wellness information for multiple stakeholders. The Instagram account of New York Times (NYT) specifically for sharing wellness content with the handle nyt_well was selected as the object of study. 773 posts from this account between March of 2019 and December of 2022 were collected and analyzed to answer the research question of which factors are most influential to public engagement with wellness content. Two negative binomial regressions were run on features including the type of post, length, word count, sentiment score and topic with number of likes and comments as the dependent variables for each of those regression models. Results indicated that the type of post and its sentiment score were the two most influential determinants of public engagement with p-values smaller than 0.05. While the effects of some of these factors aligned with findings from previous studies conducted on social media content not related to wellness (e.g., marketing), some others affected the two separate public engagement metrics in opposite directions, warranting future studies to investigate further on the cause of this phenomenon.

Social networking services such as Facebook, Twitter, and YouTube are fertile ground for analyzing texts, extracting opinions, and identifying feelings, due to the large number of texts and their diversity in all areas of life. In this manuscript, we apply four algorithms to classify tweets written in the Algerian dialect. To extract feelings, we used six features based on three polarities. In the presented work, we manually annotate a corpus of 2,891 texts and create an Algerian lexicon of idioms that contains 1328 annotated words. Our results show that there are improvements gained on the accuracy of the system, where we have achieved a better accuracy of 85.31%.

There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

The social vulnerability approach (SV) has set up that social inequalities and disadvantages have gone beyond monetary poverty in the last years, since the process is built at different scales. In its objectives, the SV multidimensional measurement is contemplated as a priority tool to monitor the compliance of the first goal – eliminate poverty in all its forms. Therefore, the objective of this research is to calculate the SV of the fishing communities of the Gulf of Ulloa (GU), Mexico by macro-markers to subsequently contrast them with field micro-data, and finally perform a behavior scenario, considering the current public policies restrictive to fishing in such areas. The results showed significant differences depending on the type of information used, obtaining a contingency coefficient of 83.42%, which indicates that the calculus depends strongly on the data used and suggesting that macro-data may be masking the true SV values in the area, in such a way they could be severely underestimated. Even though the context at micro-scale is not the only one, SV should be calculated to analyze the fishing communities since coastal fishery represents almost the total livelihood of the inhabitants. Nevertheless, these communities confront numerous local and global threats, and these pressures on SV put their livelihoods, well-being, food security and traditional lifestyle at risk. Therefore, the role of researching human dimensions and governance is not only basic but also urgent to turn to sustainable socioeconomic management.

Within the field of movement sensing and sound interaction research, multi-user systems have gradually gained interest as a means to facilitate an expressive non-verbal dialogue. When tied with studies grounded in psychology and choreographic theory, we consider the qualities of interaction that foster an elevated sense of social connectedness, non-contingent to occupying one’s personal space. In reflection of the newly adopted social distancing concept, we orchestrate a technological intervention, starting with interpersonal distance and sound at the core of interaction. Materialised as a set of sensory face-masks, a novel wearable system was developed and tested in the context of a live public performance from which we obtain the user’s individual perspectives and correlate this with patterns identified in the recorded data. We identify and discuss traits of the user’s behaviour that were accredited to the system’s influence and construct 4 fundamental design considerations for physically distanced sound interaction. The study concludes with essential technical reflections, accompanied by an adaptation for a pervasive sensory intervention that’s finally deployed in an open public space.

Users on the social networking platform have the freedom to express themselves freely. Towards the same time, this has created a forum for disagreement and hate directed at someone, society, racism, sexual orientation, and so on. Identifying hate online is a challenging task. Researchers from all around the world have contributed major methods for detecting hate speech, but owing to the issue's complexity, there are still many unresolved issues. In this research, we offer a multi-model learning strategy for detecting hate speech on Twitter. We utilised the Kaggle TwitterHate dataset, which had 31962 tweets categorised as binary hate or non-hate, to evaluate our technique. The suggested method is tested using commonly used machine learning classifiers with multi-model technique. Using TF-IDF features, we acquired detection results of 96.29 %, precision of 96%, recall of 96%, and f1-score of 96%.

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

COVID-19 has become a part of everyone's day-to-day life, since the outbreak in 2019 the novel coronavirus disease (COVID-19) has caused more than 4.5 million deaths with over 200 million cases reported globally. Currently, the number of infections and deaths are gradually lowering in different countries however the underlying challenges still exist. COVID-19 threatens human life, social functioning and development. Although numerous studies have been carried out in the past to highlight the key challenges very limited studies have been conducted from an ordinary person's viewpoint. In the fight against COVID-19, humanity has been pushed to a level which cannot be accepted where establishing that balance is a priority. This study focuses on highlighting the common issues faced by the ordinary public in the current era. Five key areas were identified to be the most essential; education, technological adaptation, transportation, mental health and gender-based violence (GBV).

The present study aims to examine the role the social and digital infrastructures might have during the building process of the Smart Regions in the Italian context. Within this framework, it is possible to identify some essential research questions, such as why the same regions are growing faster than the other and which type of effects could be generated from the different connectivity between the regions. Since the Smart Region concept is still composed of technical reports, pilot projects and experiences from a limited number of cities on the international stage, this work it is tried to use a new approach, applying either a neuronal model, the Self-Organizing Maps, and the multivariate regression approach, to extrapolate the existence of possible future conditions for the rising of Smart Regions in Italy, studying the evolution of the used database during the period 2005 – 2016. From the analysis what emerged is that the only bridging social capital dimension, empirically speaking, feed the regional innovation growth because the structure of social relationship facilitates interactions across social, political and economic agents; but there are institutional deficits, most pronounced in Italy and other European countries.

Social enterprises are very common in Birmingham having the largest concentrations of social enterprises in the United Kingdom. With the emergence of the Covid-19 pandemic, the operations and management of social enterprises in Birmingham has been greatly affected. This study seeks to analyse the impact of the Covid-19 pandemic on the operations and funding of social enterprises in Birmingham UK. In order to achieve this, the study is going to carry out a qualitative methodology in order to analyse the impact Covid-19 has had on social enterprises. The research is going to select relevant stakeholders through a non-purposive sampling criteria identifying individuals who have direct interest in the functioning of social enterprises. The relevant stakeholders will be expected to respond to semi structured interviews that are structured to evoke responses relevant to this area of research. The research realized that Social enterprises in Birmingham are a critical player in the economy of UK with many individuals depending on the social enterprise industry. In addition to that, the study realised that the Covid-19 pandemic exposed social enterprises to various financial and market risks. Moreover, social enterprises were forced to make a change in their organizational structure through cost management changes and offering alternative services. However, the government came in support through provision of grants and funding to boost the social enterprise economy.

This study aimed to examine trust in information provided by public authorities and financial measures put in place to address the impact of COVID-19. Using a cross-national approach among four Western countries; the United States of America, Norway, Australia, and the United Kingdom provides an analysis of responses related to trust and how they were associated with age group, gender, education level, employment status, size of place of residence, infection sta-tus, and social media use. When controlling for all included variables in logistic regression analyses, the likelihood of having trust in the public authorities’ information was higher for women, those with higher levels of education, and those living in urban areas. Being infected with the coronavirus, and spending more time daily on social media was associated with lower likelihood reporting trust in information. Although policies implemented to respond to eco-nomic concerns varied cross-nationally, higher age, identifying as female, being employed, liv-ing in a city, and lower levels of social media usage were associated with higher likelihood of trusting in the financial measures put in place to counteract the economic effects of COVID-19.

As the coronavirus disease 2019 (COVID-19) continues to rage worldwide, the United States has become the most affected country with more than 34.1 million total confirmed cases up to June 1, 2021. In this work, we investigate correlations between online social media and Internet search for the COVID-19 pandemic among 50 U.S. states. By collecting the state-level daily trends through both Twitter and Google Trends, we observe a high but state-different lag correlation with the number of daily confirmed cases. We further find that the predictive accuracy measured by the correlation coefficient is positively correlated to a state’s demographic, air traffic volume and GDP development. Most importantly, we show that a state’s early infection rate is negatively correlated with the lag to the previous peak in Internet search and tweeting about COVID-19, indicating that earlier collective awareness on Twitter/Google correlates with lower infection rate. Lastly, we demonstrate that correlations between online social media and search trends are sensitive to time, mainly due to the attention shifting of the public.

Multicomponent training is recommended for people with dementia living in long-term care homes. Nevertheless, evidence is limited and people with severe dementia are often excluded from trials. Hence, the aim of this study was to investigate (1) the feasibility and (2) the requirements regarding a multicomponent training for people with moderate to severe dementia. The study was conducted as an uncontrolled single arm pilot study with a mixed methods approach. 15 nursing home residents with a mean age of 82 years (range: 75-90 years; female: 64%) with moderate to severe dementia received 16 weeks of multicomponent training. Feasibility and requirements of the training were assessed by a standardized observation protocol. Eleven participants regularly attended the intervention. The highest active participation was observed during gait exercises (64%), the lowest during strength exercises (33%). It was supportive if exercises were task-specific or related to everyday life. This study confirms that a multicomponent training for the target group is (1) feasible and well accepted. To enhance active participation (2) individual instructions and the implementation of exercises related to everyday life is required. The effectiveness of the adapted training should be tested in future randomized controlled trials.

Studies showed that introversion is the strongest personality trait related to perceived social isolation (loneliness), which can predict various complications beyond objective isolation such as living alone. Lonely individuals are more likely resort to social media for instantaneous comfort, but it is not a perpetual solution. Largely negative implications including poorer interpersonal relationship and depression were reported due to excessive social media usage. Conversational task is an established intervention to improve verbal communication, cognitive and behavioral adaptation among lonely individuals. Despite that behavioral benefits have been reported, it is unclear if they are accompanied by objective benefits underlying physiological changes. Here, we investigate the physiological signals from 28 healthy individuals during a conversational task. Participants were ranked by trait extraversion, where greater introversion is associated with increased susceptibility to perceived social isolation as compared to participants with greater extraversion as controls. We found that introverts had a greater tendency to be neurotic, and these participants also exhibited significant differences in task-related electrodermal activity (EDA), heart rate (HR) and HR variability (HRV) as compared to controls. Notably, resting state HRV among individuals susceptible to perceived loneliness was below the healthy thresholds established in literature. Conversational task with a stranger significantly increased HRV among individuals susceptible to isolation up to levels as seen in controls. Since HRV is also elevated by physical exercise and administration of oxytocin hormone (one form of therapy for behavioral isolation), conversational therapy among introverts could potentially confer physiological benefits to ameliorate social isolation and loneliness. Our findings also suggest that although the recent pandemic have changed how people are interacting typically, we should maintain a healthy dose of social interaction innovatively.

Being a combination of the conference call, talkback radio, audio podcast, and an online video chat, Clubhouse is a new social networking app that gained over 10 million users and over $100 in valuation in just 8 months. Unlike other social networks, it offers a real-time streaming audio chat that does not ask users to share any unnecessary information like exchanging text messages, conducting video calls, or sharing photos. Instead, Clubhouse users can listen to real-time conversations, contribute to these conversations and create their own conversations for the others to listen and to interact with. Often nicknamed a “Silicon Valley’s hottest start-up”, Clubhouse positions itself as an “exclusive” and “alternative” social network that attracts various celebrities and people who just want to talk to each other. Launched in March 2020, amidst the COVID-19 pandemic with its social distancing and lockdowns, Clubhouse offered its users a space for the digital group psychotherapy where people could solve their problems by talking them through with strangers. However, it is unclear what is going to happen to this new social network in the post-pandemic world after all of its hype eventually evaporates. This paper discusses the possible underlying motives for the Clubhouse creation and its real purposes. Moreover, it looks at the three possible scenarios of its further development.

Contact tracing is one of the oldest social network health interventions used to reduce the diffusion of various infectious diseases. However, some infectious diseases like COVID-19 amass at such a great scope that traditional methods of conducting contact tracing (e.g., face-to-face interviews) remain difficult to implement, pointing the need to develop reliable and valid survey approaches. The purpose of this research is to test the effectiveness of three different egocentric survey methods for extracting contact tracing data: (1) a baseline approach, (2) a retrieval cue approach, and (3) a context-based approach. A sample of 397 college students were randomized into one of each condition and were prompted to anonymously provide contacts and populated places visited from the past four days. After controlling for various demographic, social identity, psychological, and physiological variables, participants in the context-based condition were significantly more likely recall more contacts (medium effect size) and places (large effect size) than the other two conditions. Theoretically, the research supports suggestions by field theory that assume network recall can be significantly improved by activating relevant activity foci. Practically, the research contributes to developing innovative social network data collection methods for contract tracing survey instruments.

This paper aimed to use social media impact on student studies in higher education. Apart from enjoyment and academic purposes, many educational practices and processes have been influenced by social networks. This paper highlighted the use of media tools in higher education as well as indicates out some of the factors. Moreover, through a literature review of related articles, we aim to provide insights into the impacts of a social network on educational quality, actual use of social media, and performance impact in higher education. A questionnaire survey on constructivism was circulated among a total of 206 university students as the key method for collecting data. This research hypothesizes educational quality and actual social media use indicates a positive effect in education, all of which also hypothesizes constructivism for educational quality and actual social media that in turn improve students’ satisfaction, and performance impact. Moreover, all research findings were attained through a quantitative method using the Structural Equation Modeling (SEM-AMOS). Findings of this research to indicate a positive effects students on their academic through behavioral intention to utilize social media to actual social media use for teaching and learning on higher education. Moreover, the results mention the use of social media for learning purpose, as well as social media to enables the sharing of knowledge, discussions, and information to enhance students' learning activities, Further studies are recommended which universities educators should take this into consideration when planning their curricula; it comes to the inclusion of technology in the teaching process.

Scientific social networking sites like Researchgate or Academia.edu have become part of the work practice of academic researchers. These digital platforms have been designed precisely to encourage the exchange of knowledge between scholars and to help the expansion of collaborative networks among them. Even if studies on this topic have multiplied in recent years, there is a dearth of research on the actual impact of these platforms on scientific production. The goal of this study is to investigate the relation between the attitude of researchers towards scientific social networks and the use of knowledge in their scientific work. Data from users of the scientific social networking site Researchgate were collected. A total of 143 valid responses were received and structural equation modeling was applied for data analysis. Findings of this study confirmed that researchers use knowledge obtained from scientific social networking sites both incorporating it within their research products and to acquire new competences. In particular approaching the platform as a scientific community with a shared language and a shared vision was found to have a positive impact on knowledge use. To the best knowledge of the authors this is the first study investigating the actual use of knowledge from scientific social networking sites by academic researchers. Results help to understand the impact of these platforms on the work practice of a strategic sector like scientific research.

Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more challenges, given the higher speed of information propagation and the growing impact of social bots and anomalous accounts. Nevertheless, it is crucial to derive a trustworthy information diffusion graph, which is capable of highlighting the importance of specific nodes in spreading the original message. The paper introduces the interaction strength, a novel metric to model retweet cascade graphs by exploring users’ interactions. Initial findings show the soundness of the approaches based on this new metric with respect to the state-of-the-art model, and its ability to generate a denser graph, revealing crucial nodes that participated in the retweet propagation. Reliable retweet graph generation will enable a better understanding of the diffusion path of a specific tweet.

A novel coronavirus (2019-nCoV) responsible for a severe acute respiratory disorder (SARS-CoV-2) in humans, with its epicentre in Wuhan, China emerged in December 2019. This coronavirus, by far, has hit >200 countries, affecting 7 million worldwide accounting 11% death of the affected population. The transmission is majorly caused by human-to-human contact and, through fomite. In view of the increasing number of COVID-19 cases and the absence of definitive treatment or vaccinations, WHO has deemed the viral infection a pandemic of international concern. In such grave situations, there is a need for expanding the health sector workforce, government and police workforce, sanitation and prevention strategies. The current article describes the virology aspect, control of COVID-19 and revisits the various treatment options available at present this deadly infection. Epidemiology of COVID-19 is also discussed to further understand the pandemic status of India. The article also discusses implicating quarantine or social distancing, and in extreme cases, lockdown or alternative approaches such as herd or indirect immunity, as a measure to control the pandemic. Lockdown or social distancing will give rise to economic, emotional, political and social downfall in the country. It is estimated that a lockdown period will set back the country, possibly, by $240 billion, yet it stands unavoidable in the spread of control of infection. Thus, policymakers should strategize economic revival depending upon the best possible data and critical understanding.