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Water and energy consumption and carbon emissions caused by humans have vital impacts on the natural environment. Due to the dramatic global environmental pollution problem, understanding the relationship between these factors is emerging as an important approach to realising sustainable development. However, with the strengthening of interregional trade links, it is difficult to manage and evaluate their relationship. Therefore, from the perspective of regional and industrial sectors, using the multiregional input-output (MRIO) model and social network analysis (SNA), our research explores an innovative analytical methodology to evaluate the characteristics of the energy-water-carbon spatial network, and a scheme is proposed to improve it. The results demonstrate that the characteristics of water scarcity and energy enrichment could lead to a net inflow of the water footprint and a net outflow of the energy and carbon footprints. Moreover, traditional high-energy-consumption industrial sectors contribute significantly to the energy and carbon footprints. The energy-water-carbon spatial network correlation is low and unstable, and it lacks rationalisation and balance in resource-based areas. Network-based energy-water-carbon research provides more insights toward understanding the carbon emission reduction responsibilities of industrial supply chains. Our findings provide a reference for reducing the energy-water-carbon footprint and achieving the carbon reduction goal of China. [ABSTRACT FROM AUTHOR]

Help Chain (HC) is a problem-solving practice underpinned by lean manufacturing principles and also adopted for health services, which have different complexity characteristics in relation to manufacturing. This study is based on the premise that Social Network Analysis (SNA) might be an effective analytical approach to account for the complexity of HCs in healthcare. Hence, two research questions are addressed: how to model and interpret HCs in health services as a social network and how SNA can either challenge or add to the lean assumptions of HC design. The use of SNA to model HCs was tested in a maternity hospital, where HCs related to five problems were analyzed, concerning the supply of instruments from the centre of sterilised materials to the surgical and obstetrics units. We analyzed the HCs of single problems and then combined the five problem-solving networks to produce a multilayered network that accounted for their interactions. The patterns of social interactions varied according to the problem and three dimensions of actors' performance (i.e. availability, reliability, and agility). SNA unveiled the complexity of HCs and provided guidance for revising the lean assumptions in their design, matching the realities of health services. [ABSTRACT FROM AUTHOR]

BackgroundInfection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2.MethodsWe performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs.ResultsAmong 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001).ConclusionsIP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

While IT workforce research often examines job- and organizational-related reasons for turnover, studies rarely connect the immediate social context of IT work to IT professionals' workplace behavior. To understand the influence of the immediate social context, we develop a social comparison model that connects the social influence of co-worker who have left an organization to turnover among IT professionals who remain. We test our model using a social network of 4,011 IT professionals employed in a large IT firm over four years. We complement our analysis of this data with a multiple-case study with five software development teams. Across the two studies, our results suggest that the departure of an IT professional increases the probability of turnover among remaining coworkers; further, we found that turnover is even more likely when the remaining IT professionals are similar in technical abilities and demographic attributes to the co-worker who left. Our results direct attention to the immediate social context as an influence on the turnover behavior of IT professionals and explain how similarity in domain-specific attributes shapes this turnover behavior. Practitioners should know that a single departure may cause a chain reaction in IT work teams and organizations and find suggestions for assigning new employees. [ABSTRACT FROM AUTHOR]

Brand relationships influence human attitudes, intentions, and behaviors. While past research has generously explored this idea in the customer context, less research has explored the role brand relationships play in the context of frontline employees or salespeople. While much of the customer literature can be used to inform our understanding of salesperson-brand relationships, there are nuances of being an employee, and specifically a salesperson, that have left many research questions unanswered. Consequently, a state of the literature in this domain is warranted to better understand how future research can grow this critical stream. The current research identifies, reviews, and synthesizes 52 salesperson-brand relationship articles. A comprehensive summary of common brand constructs (e.g. brand identification, brand attachment) is employed and supplemented with a social network analysis. To encourage more research in the salesperson-brand domain, we then summarize common themes that pervade the literature and identify a plethora of future opportunities in both established domains (e.g. brand relationships increase effort) and new frontiers (e.g. exploring the dark side of salespeople-brand relationships). [ABSTRACT FROM AUTHOR]

We use a co-authorship network analysis to describe and examine the social network of researchers studying physical education content knowledge worldwide. Co-authorship network analysis is a method for determining the scope, trend, and focus of research around a topic. It provides a way to evaluate both the growth of a field and to examine the extent of research collaboration. Our review consists of 101 articles that were examined in our network analysis. Our results show that the research on content knowledge occurs within and among 13 high-income economies. Research productivity in this area is on an upward trajectory since 1990. The research in physical education occurs in a distributed network with some authors serving as key hubs for research collaboration. From a global perspective, there is a need to create links to low-income economies to strengthen their research capacity. [ABSTRACT FROM AUTHOR]

Trauma-informed communities establish cross-sector coalitions that advocate for the use of trauma-informed practices. Whether and how communication occurs within newly established trauma-informed communities is unclear. We collected qualitative data and social network data from six trauma-informed community partnerships that received funding to build community capacity to address trauma across a range of special populations in Los Angeles county. We identified three communication network sub-types and highlight contributions of network leadership in setting the tone for cross-partnership communication. While partnership leaders shared the goal of strengthening cross-organizational communication, social network results illustrated a need to develop strategies that target these goals.

Economic institutional change is a vital driving force behind the rapid rise of China's economy. However, the incremental approach to economic institutional change has caused unbalanced transformation and economic growth. To this end, we adopted the entropy method to measure the economic institutional change index, and employed social network analysis to reveal its spatial correlation characteristics. We then applied QAP analysis to empirically demonstrate the impact of China's economic institutional change on regional disparities in economic growth. The findings indicated a gradual increase in the level of economic institutions over time and a spatial gradient between the eastern, central, and western regions. Moreover, the spatial correlation network of China's economic institutional change is stable and gradually improving. Nevertheless, the role of provinces in the process of economic institutional change varies: the eastern coastal provinces play a dominant role, the central and western provinces benefit to a lesser extent, and some provinces in northeastern China play a "bridging" and "intermediary" role. Regional differences in China's economic institutional change have widened the regional disparities in China's economic growth, and the impact of each dimension of economic institutions on regional disparities in economic growth is characterized by phases. [ABSTRACT FROM AUTHOR]

Adopting smart manufacturing technology (SMT) is critical for the intelligent transformation of manufacturing enterprises. However, an inappropriate SMT adoption (SMTA) path will lead to negligible economic benefits, which seriously restricts intelligent transformation. Using a combined network method based on the Grey-DEMATEL and social network analysis, this paper considers SMTA as a system and identifies the importance of individual SMTA. Firstly, the SMTA network is constructed by identifying 16 SMTs and calculating the interdependencies among individual SMTA. The results show that the SMTA system has a relatively high level of interdependency. The important rankings of SMTs are obtained by single and combined indicators. The roles of SMTA are divided into amplifier, absorber, carrier and neutral. Amplifiers play an important role in the adoption of other SMTs and the paths that successively pass through amplifiers can accelerate the whole SMTA. This paper provides a valuable contribution to SMTA theory from a technological interdependency perspective. This new perspective provides a foundation for designing SMTA paths for manufacturing enterprises and entire industries. [ABSTRACT FROM AUTHOR]

Studying the spatial–social interface requires tools that distinguish between social and spatial drivers of interactions. Testing hypotheses about the factors determining animal interactions often involves comparing observed interactions with reference or 'null' models. One approach to accounting for spatial drivers of social interactions in reference models is randomizing animal movement paths to decouple spatial and social phenotypes while maintaining environmental effects on movements. Here, we update a reference model that detects social attraction above the effect of spatial constraints. We explore the use of our 'wrap-around' method and compare its performance to the previous approach using agent-based simulations. The wrap-around method provides reference models that are more similar to the original tracking data, while still distinguishing between social and spatial drivers. Furthermore, the wrap-around approach results in fewer false-positives than its predecessor, especially when animals do not return to one place each night but change movement foci, either locally or directionally. Finally, we show that interactions among GPS-tracked griffon vultures (Gyps fulvus) emerge from social attraction rather than from spatial constraints on their movements. We conclude by highlighting the biological situations in which the updated method might be most suitable for testing hypotheses about the underlying causes of social interactions. This article is part of the theme issue 'The spatial–social interface: a theoretical and empirical integration'. [ABSTRACT FROM AUTHOR]

This study employs the Standard International Trade Classification (SITC rev.3) to categorize agricultural products and examines the overall and each type of agricultural trade networks in China. It utilizes Social Network Analysis and the Quadratic Assignment Procedure to explore pattern changes and identify influencing factors. The findings indicate that between 2000 and 2019, China's agricultural trade networks experienced expansion in scale and density, with China positioned at the network's centre. However, challenges exist, such as insufficient control and over-reliance on specific trading partners. Key factors influencing the networks include the business environment (BUE), regional trade agreements (RTA), and government governance capacity (GOC). Differences in BUE and GOC are negatively associated with the development of trade networks, while RTAs are positively associated with trade network connectivity. Economic and demographic differences are also positively associated with the closeness of trade networks. Based on these findings, policy recommendations are proposed to address the identified issues. [ABSTRACT FROM AUTHOR]

AbstractDatabase management systems are an essential research field in data engineering, data mining, and computer science. The development and research in this field have evolved from manual bookkeeping to automatic data storage, rapidly growing with time. In this bibliometric study, the articles from four premier venues of publication in the field of database systems have been collected for analysis, including ACM Special Interest Group on Management of Data SIGMOD, a well-reputed symposium PODS (Principles of Database Systems) and two journals of the domain namely: Transactions on Database Systems (TODS) by ACM, and Transactions on Knowledge and Data Engineering (TKDE). This study includes the articles published between 2010 and 2019 in the selected venues to perform bibliometric analysis. Both TKDE and TODS are highly reputed journals in the field of database systems but publish quite different research. The SIGMOD conference began in 1975 and specializes in the research area of databases and data management problems, whereas PODS is the research conference that specializes in research related to database theory. This study presents the analysis of the collected articles from three major aspects: 1) metadata analysis, 2) content-based analysis, and 3) citation-based analysis. Furthermore, it also looked at the leading institutes, countries, and authorities based on article citation and publication count. As a whole, this study proposes a methodology for performing the extensive bibliometric analysis for the database management system and conducts a bibliometric analysis for the aforementioned top publication venues in this area of research. [ABSTRACT FROM AUTHOR]

Examining scientific research and publications in criminal psychology from past years can provide a roadmap for a better future. The present research was conducted to perform a bibliometric analysis of publications in the Web of Science database on the topic of criminal psychology, from its inception to the present, aiming to draw a scientific map of global research trends. In a descriptive-analytical study 10,611 scientific resources on criminal psychology were bibliometrically analyzed using the Web of Science database and VOS viewer software, which enables social network analysis. The analyzed sources spanned scientific productions from 1912 to 2023. The study revealed an upward trend in the publication of articles in the field of criminal psychology over the last decade. The evolution of keywords in this research indicated a shift from macro-factors influencing crime occurrence to focusing on legal, contextual, and social-cognitive-personality factors related to criminals, victims, witnesses, judges in sentencing, and neuro-cognitive aspects. [ABSTRACT FROM AUTHOR]

This study provides a quantitative characterisation of the discursive evolution of Artificial Intelligence (AI) on streaming platforms (YouTube), integrating text mining and complex social network analysis. A comprehensive corpus was obtained from leading platforms, and natural language processing algorithms were implemented to analyse the textual content and identify patterns, emerging themes, and changes in AI discourses. Concurrently, a social network analysis was conducted to examine the interaction structures and the influence of different actors in the dissemination of information. The findings reveal significant trends in the representation and perception of AI, indicating the evolution of specific topics, differences in perception between different groups, and the influence of factors such as technological advances and global events. This analysis offers a more profound comprehension of the communication and perception of AI in the digital domain, providing valuable insights for academics, communicators and policymakers engaged in the field of AI. [ABSTRACT FROM AUTHOR]

The main objective of this research is to explore the development and implementation of Artificial Intelligence (AI) in the context of university education at a scientific and social level, through a systematic methodology of review academic articles and scientific papers (scientometrics) from the Web Of Science database for the scientific part and a social listening analysis of the main social networks for the social field. It is concluded that researchers should begin to analyze the effects of the good use of AI tools as a teaching methodology, so that society can also highlight it in it mentions on social networks. [ABSTRACT FROM AUTHOR]

As global economic integration and rapid technological advancements transform international business, international engineering contracting has become essential for achieving sustainable development goals (SDGs). This paper investigates the significant impact of China's strategic initiatives, notably the "Going Global" strategy and the Belt and Road Initiative (BRI), on the operational practices of Chinese enterprises involved in overseas investments. Central to this transformation is the Engineering, Procurement, and Construction (EPC) model, which emphasizes the integration of supply chain management and stakeholder collaboration to enhance performance in international EPC projects and underscores the crucial role of these elements in promoting sustainability. Incorporating insights from social network data analysis, this study reveals that contractors collaborating with various stakeholders—such as owners/consulting engineers, domestic and foreign customs departments, and group headquarters/design parties—exhibit a high degree of similarity in personnel profiles. This suggests that the internal organizational structure and personnel allocation of contractors could be optimized to enhance operational efficiency, aligning with the collaborative patterns identified. This study addresses a critical research gap by exploring how effective supply chain management and collaborative stakeholder engagement within multinational EPC projects contribute to sustainable outcomes. Employing advanced social network analysis software, the research examines the complex interactions among stakeholders and their influence on procurement dynamics. Findings indicate that strong relational networks and strategic collaborations significantly enhance procurement efficiency and project success, underscoring the importance of supply chain integration. Ultimately, integrating supply chain management principles into the EPC model not only offers innovative perspectives for advancing sustainability in international projects but also provides actionable insights for improving project outcomes within the BRI framework. This research underscores the pivotal role of supply chain organization and stakeholder cooperation in achieving sustainability objectives, thereby enriching the discourse on sustainable enterprise operation and supply chain management in the context of global initiatives. [ABSTRACT FROM AUTHOR]

Type 1 diabetes is an invisible disability requiring major life changes and constant monitoring. It can become particularly challenging to control during a major life transition. This study examines the composition and nature of the social networks of college students living with type 1 diabetes. A total of 17 respondents with type 1 diabetes and 18-25 years of age participated in network interviews. Network Canvas facilitated online data collection. The results show that efforts to protect the wellbeing of college students with T1D require active participation of university organizations and certain individuals from students' formal and informal connections. Such connections define students' support networks in college and provide access to social capital. Practical implications and recommendations for university leadership and students' parents are discussed. [ABSTRACT FROM AUTHOR]

Megaproject governance is a new way to enhance risk prevention and control and improve project performance. However, how to deal with the dilemma of incomplete megaproject governance mechanism and the unclear relationship between elements, and promote the coordination of governance objectives and governance paths, has become a pressing challenge in the process of risk prevention and control of megaproject projects. This study constructs a relational network model of a mega-project governance mechanism based on risk prevention and control by using the social network analysis method. The position and influence of core elements in the relational network are explored, and the relationships between network structures are analysed. It has been shown that risk communication, government coordination, benefit distribution, commitment and risk sharing are at the centre of the relational network of megaproject governance mechanisms. Risk communication and government coordination are key nodes in a relational network of elements that play an important role in the effectiveness of project governance and are closely connected to other elements in the relational network. [ABSTRACT FROM AUTHOR]

Despite the significant global repercussions of terrorism, including loss of lives and economic resources, research on its relationship with crime has been limited to group-level analyses. This study examines the individual criminal activities of Australian Neo-Jihadists, via social network analysis from an individual perspective. Judicial documents and newspaper articles are analysed to determine the criminal activities and operational roles of network actors. Findings suggest a prominence of criminal activities, and a significant relationship between market criminal activities and facilitation of terrorist operations. The study concludes with suggestions for future research, emphasising the potential for disrupting terrorist networks by focusing on the new crime-terror nexus. [ABSTRACT FROM AUTHOR]

In rural China, farmers who have been “lifted out of poverty” still at risk of returning to poverty. The sustainable livelihood and risk coping ability of farmers are of great significance for rural development, and social networks have a distinctive effect on livelihood and risk coping ability of farmers. Based on full-coverage survey in a village, this paper adopts the entropy weight method, Social Network Analysis, and aims to build a social network model and reveal the impact of social networks on farmer’s livelihood and risk coping ability. The results show that the farmers’ livelihood capital based on social network has a significant amplification. Furthermore, social relationships can directly affect farmers’ ability to cope with livelihood risks. And, for farmers with higher social, information, human and financial capital, the effectiveness of social networks in enhancing their risk coping capacity is more significant. Overall, it is suggested that more attention should be paid to the positive role of social networks on the livelihood in the future. For rural development, social relationship networks should be continuously enriched and to strengthen its multiplier effect on farmers’ livelihood. And for farmer development, it should take measures to improve the mobilising capacity of social relations. And to amplify the effectiveness of various types of livelihood capital in risk response. [ABSTRACT FROM AUTHOR]

Tail biting is a multifactorial behavior that causes welfare and economic challenges in swine production. As of 2024, research exploring the influence of pig social structure on the development of tail biting is limited. The objective of this study was to explore whether social structures of pigs from different litter origins can impact tail biting and, ultimately, tail damage. Pigs (n = 96) were grouped (eight pigs/pen) based on their litter origin: non-littermates (NLM), half-littermates, and littermates (LM). Tail injury scores were assessed twice weekly from 10 to 24 weeks of age, with a maximal tail injury score (MTS) over the study period being used to evaluate victimization by tail biting. Pig behavior was video-recorded at 15, 19, and 23 weeks of age. Association networks based on lying behavior and tail biting interaction networks were evaluated at penand pig-levels using social network analysis. Pigs in LM pens experienced higher median MTS compared to pigs in NLM pens (Median = 1.5; Interquartile range = 1-2; p = 0.009). Within association networks, NLM pens had lower degree centralization measures than other pens at both 15 (Estimated marginal mean [EMM] = 0.07; 95% CI = 0.02-0.12; p = 0.003) and 23 weeks (EMM = 0.09; 95% CI = 0.04-0.14; p = 0.01) and pigs in NLM pens had higher weighted degree centrality than those in other pens (EMM = 1.00; 95% CI = 0.90-1.11; p = 0.002), suggesting pigs in NLM pens had more uniform, stronger, and more connections with their pen-mates. In tail biting networks, increased weighted in-degree centrality was associated with increased odds of pigs receiving a more severe MTS (OR = 1.56; 95% CI = 1.08-2.27; p = 0.02). Pigs with increased weighted out-degree centrality tended to have increased odds of receiving a more severe MTS (OR = 1.19; 95% CI = 0.97-1.48; p = 0.09). These preliminary data suggest a potential relationship between social structures and tail biting in growing-finishing pigs. [ABSTRACT FROM AUTHOR]

When a fast kinetic natural disaster occurs, it is crucial that crisis managers quickly understand the extent of the situation, especially through the development of "big picture" maps. For many years, great efforts have been made to use social networks to help build this situational awareness. While there are many models for automatically extracting information from posts, the difficulty remains in detecting and geolocating this information on the fly so that it can be placed on maps. Whilst most of the work carried out to date on this subject has been based on data in English, we tackle the problem of detecting and geolocating natural disasters from French messages posted on the Twitter platform (now renamed "X"). To this end, we first build an appropriate dataset comprised of documents from the French Wikipedia corpus, the dataset from the CAp 2017 challenge, and a homemade annotated Twitter dataset extracted during French natural disasters. We then developed an Entity-Linking pipeline in adequacy with our end-application use case: real-time prediction and peak resiliency. We show that despite these two additional constraints, our system's performances are on par with state-of-the-art systems. Moreover, the entities geolocated by our model show a strong coherence with the spatiotemporal signature of the natural disasters considered, which suggests that it could usefully contribute to automatic social network analysis for crisis managers. [ABSTRACT FROM AUTHOR]

Background: Based on the perspective of social network theory, this study explored the network indicator system that facilitated optimal knowledge sharing effect in Medication Therapy Management Services (MTMS) training teams. The aim was to provide a reference for optimizing MTMS training and improving training quality. Methods: Utilizing social network analysis combined with a questionnaire survey, a knowledge sharing matrix for MTMS training teams was constructed. Knowledge sharing behavior was assessed from three perspectives: individual networks, whole networks, and cohesive subgroups. Results: Individual network analysis showed that the knowledge sharing effect within the training team reached its peak when the out-degree centrality was ≥ 3.5, in-degree centrality was ≥ 2.5, eigenvector centrality was ≥ 0.065, and closeness centrality was ≥ 7.86. Whole network analysis indicated that the optimal knowledge sharing effect occurred when the network density of the training team was higher than 0.0343 and the training size was less than 117 participants. Cohesion subgroups analysis demonstrated that knowledge sharing was more effective when members with similar working years participated in training together. Conclusions: The knowledge sharing indicator system developed for MTMS training teams, based on social network analysis, can assist in optimizing the MTMS training model and improving training effectiveness. [ABSTRACT FROM AUTHOR]

Primates live in complex social systems, and social contact and disease interact to shape the evolution of animal (including human) sociality. Researchers use social network analysis (SNA), a method of mapping and measuring contact patterns within a network of individuals, to understand the role that social interactions play in disease transmission. Here, we review lessons learned from SNA of humans and nonhuman primates (NHPs) and explore how they can inform health and wildlife conservation. Utilizing the breadth of knowledge in human systems and outlining how we can integrate that knowledge into our understanding of NHP sociality will add to our comprehension of disease transmission in NHP social networks and, in turn, will reveal more about human disease and well-being. [ABSTRACT FROM AUTHOR]

This study examines the collaboration patterns of authors, affiliations, and countries/regions in articles published in the top fifteen Social Science Citation Index–indexed linguistics journals from 2014 to 2023. Social network analysis is conducted through Gephi and VOSviewer to analyze the collaboration patterns, research foci, and trends of 4,731 co-authored articles using quantitative data and qualitative interpretations. The findings indicate a robust increase in academic collaboration in the top fifteen linguistics journals over the decade, growing by over 10% to encompass almost 80% of the 6,417 articles. Pair collaborations dominate at the author, affiliation, and country levels. Also, the collaboration networks of authors, affiliations, and countries all possess small-world properties, facilitating smooth information flow. Despite their inclusion in the prolific country list, non-native English-speaking Asian countries and regions like China, Japan, Hong Kong (China), and Taiwan (China) lag behind Anglo countries in academic leadership. Major research themes include second language acquisition, second language writing, and English as a foreign language, with significant trends towards sociocultural and multilingual approaches and the broadened application of technology-enhanced measures. The implications and limitations of the findings are also discussed. [ABSTRACT FROM AUTHOR]

In efforts to address the far-reaching effects of climate change and associated impacts in communities, research on environmental philanthropy suggests that more resources are being allocated to environmental societal challenges. However, understandings about which environmental nonprofits benefit from these funding flows is limited. This study integrates resource dependency theory with elitism and pluralism perspectives to analyze a network of environmental nonprofits and their funders in Texas. Resource dependency and a network-analytic approaches share underlying relationality principles, and we connect those dots by conceptualizing the funding dynamics in Texas as a network of funder-grantee relations. Drawing on statewide survey data (n = 114), we use a network analysis technique – exponential random graph modeling (ERGM) – to analyze funding allocations in Texas through the organizational attributes of environmental nonprofits, their funder-grantee relations, and their community context. We specifically observe elitism in funding allocations in Texas, which is evident in network effects (preferential attachment) and the focal areas of environmental work. However, we find limited evidence that age or resources of the nonprofit are predictive of funding and the most influential factor determining a funder-grantee relationship is the natural hazard risk of the community served by the nonprofit. Our findings suggest interconnected funding dynamics of pluralism and elitism in the Texas environmental philanthropy landscape, prompting further discussion about the potential synergies of these patterns and the implications for environmental funding practices. [ABSTRACT FROM AUTHOR]

Locating a set of influential users within a social network, known as the Influence Maximization (IM) problem, can have significant implications for boosting the spread of positive information/news and curbing the spread of negative elements such as misinformation and disease. However, the traditional simulation‐based spread computations under conventional diffusion models render existing algorithms inefficient in finding optimal solutions. In recent years, hop and path‐based approaches have gained popularity, particularly under the cascade models to address the scalability issue. Nevertheless, these existing functions vary based on the considered hop‐distance and provide no guidance on capturing spread sizes beyond two‐hops. In this paper, we introduce Hop‐based Expected Influence Maximization (HEIM), an approach utilizing generalized functions to compute influence spread across varying hop‐distances in conventional diffusion models. We extend our investigation to the Linear Threshold (LT) model, in addition to the Independent Cascade (IC) and Weighted Cascade (WC) models, filling a gap in current literature. Our theoretical analysis shows that the proposed functions preserve both monotonicity and submodularity, and the proposed HEIM algorithm can achieve an approximation ratio of 1−1e under a limited hop‐measures, whereas a multiplicative 1kσh1−e−kσhα‐approximation under global measures. Furthermore, we show that expected spread methods can serve as a better benchmark approach than existing simulation‐based methods. The performance of the HEIM algorithm is evaluated through experiments on three real‐world networks, and is compared to six other existing algorithms. Results demonstrate that the three‐hop based HEIM algorithm achieves superior solution quality, ranking first in statistical tests, and is notably faster than existing benchmark approaches. Conversely, the one‐hop‐based HEIM offers faster computation while still delivering competitive solutions, providing decision‐makers with flexibility based on application needs. [ABSTRACT FROM AUTHOR]

Aim: This pilot study examines how rural and remote junior doctors' career decisions are influenced by collegial relationships within the discipline of general paediatrics. Methods: Social network analysis (SNA) was undertaken by structured interviews with 10 paediatricians working in regional towns in Western Australia. UNICET software was used to determine the interactions between individual networks to look for overlap and common influencers. Results: Ten rural paediatricians were interviewed. An individual was found to have key measures of centrality at the core of the entire social network of rural general paediatricians. This included a high degree of 'betweenness' (connections within social networks), and a high broker index (connections between separate areas of a network or between networks) demonstrated by that person combining three disconnected networks into a single coherent network. This central individual was a recently appointed consultant with links to senior paediatricians, peers and junior trainees, and may be instrumental in recruitment and retention in the rural paediatric workforce. Conclusion: Improving understanding of the impact of social networks, and decision‐making processes that influence rural career choices, can inform innovative solutions to develop sustainable strategies for recruiting and retaining the rural paediatric workforce. Applying this model on a larger scale may provide more data to support evidence‐based programmes that enable this within the Australian context. [ABSTRACT FROM AUTHOR]

Simple Summary: We review the existing research on social play in humans and other mammals to highlight the complex nature of this behaviour, which is regulated by various hormones and neural circuits. Play can swing from cooperation to competition. Contrary to what has been previously thought, when animals (including humans) play, they might not always be in a positive emotional state or in a relaxed context. By checking examples from human and non-human mammals, we aim to examine current tools and methodological approaches that can give information about the different individual emotional states possibly driving playful interactions and about the individual, socio-ecological, and structural factors potentially revealing the emotional nature of the play. We discuss the context in which play occurs (relaxed/not relaxed) and the structural similarities between play fighting and aggressive behaviour, considering how more competitive forms of play can serve as alternatives to aggression. Additionally, we look at the postures/movements and facial expressions (e.g., play faces) used as signals to indicate the player's intent and at copying behaviours, like rapid motor mimicry, which can enhance synchronization, non-agonistic competition, and possibly emotional sharing between players. This review focuses on social play, a complex behaviour that is often difficult to categorize. Although play has been typically associated with positive emotional states, a thorough examination of the literature indicates that it may relate to different emotional systems, from attachment to conflict. Play oscillates between competition and cooperation, and includes a spectrum in between; thus, quantitatively identifying and demonstrating the emotional nature of play remains challenging. We considered examples from human and non-human animal studies and explored the emotional and neuro-hormonal systems involved in play. We assessed ethological data possibly indicating the emotional states underlying play, and we focused on the cooperative and competitive elements of play. We investigated the relationship between play and affiliative/aggressive behaviours, the communicative meaning of play signals (especially primate play faces), and the motor and possibly emotional contagion function of rapid motor mimicry during play. From all the literature on play, this review selects and combines studies in an innovative way to present the methods (e.g., play indices and social network analysis), tools (e.g., sequential analysis and facial coding software), and evidence indicative of the emotional states underlying play, which is much more complex than previously thought. [ABSTRACT FROM AUTHOR]

The main aim of this study was to investigate adolescents' eustress–distress and mindfulness by examining mental toughness (MT) using network and mediator analyses. The study included 414 adolescents. The results showed that MT was positively related to eustress and mindfulness, but distress was negatively related to MT. Based on the network analysis findings, we tested mindfulness' mediating role in the relationship between eustress–distress and MT. The findings showed that mindfulness played a significant mediating role. However, the mediating role of mindfulness was negative for the relationship between distress and toughness and positive for eustress. These findings advance eustress, distress, and mindfulness as mechanisms for understanding the effects of MT. Considering current knowledge of MT, eustress, distress, and mindfulness, the results are discussed. [ABSTRACT FROM AUTHOR]

In just few years, TikTok has become a major player in the social media environment, especially with regard to teenagers. One of the key factors of this success is the idea of challenges, that is, video competitions/emulations on a certain topic, which a user can launch and other ones can join. Most of the challenges are fun and harmless. However, there are also users who launch challenges that are dangerous, or at least suitable only for an adult audience (and TikTok is the most popular social network for teenagers). This article focuses primarily on this kind of challenge. In particular, it investigates an aspect not yet studied in the literature, which is the different characteristics and evolutionary dynamics of the communities of users participating in non-dangerous and dangerous challenges. Its final goal is the identification of evolutionary patterns that distinguish the communities of users participating in the two types of challenges. The knowledge of these patterns could be a first step in implementing an approach to the early detection of dangerous challenges in TikTok. [ABSTRACT FROM AUTHOR]

Psychosocial dimensions are essential to guarantee an optimal approach to improve emotional well-being in patients with cardiovascular disease (CVD). There is evidence of sex differences regarding these dimensions. Thus, the connections between them are crucial to implement personalized therapies. Network model analyses were conducted on data from 593 CVD patients. The models included scores from the Hospital Anxiety and Depression Scale (HADS), positive (PA) and negative affect (NA), positivity (PS), satisfaction of life (SLS), social support (SS), self-efficacy on emotion regulation (RESE), cardiac self-efficacy (CSE) and the Short Form-12 Health Survey. The main sex differences were found in: PA-PS (p = 0.03), SS-RESE (p = 0.04), for which the positive associations are stronger for men than for women and PA-RESE (p < 0.01) for which the positive association is stronger for women than for men. These nuances should be considered to implement tailored and integrative therapies for each CVD patient. [ABSTRACT FROM AUTHOR]

Background: The development of drug policies has been a major focus for policy-makers across North America in light of the ongoing public health emergency caused by the overdose crisis. In this context, the current study examined stakeholders' experiences and perceptions of power and value in a drug policy-making process in a North American city using qualitative, questionnaire, and social network data. Methods: We interviewed 18 people who participated in the development of a drug policy proposal between October 2021 and March 2022. They represented different groups and organizations, including government (n = 3), people who use drugs-led advocacy organizations (n = 5), other drug policy advocacy organizations (n = 5), research (n = 3) and police (n = 2). Most of them identified as men (n = 8) and white (n = 16), and their ages ranged between 30 and 80 years old (median = 50). Social network analysis questionnaires and semi-structured qualitative interviews were administered via Zoom. Social network data were analysed using igraph in R, and qualitative data were analysed using thematic analysis. The analyses explored perceptions of value and power within a drug policy-making network. Results: The policy-making network showed that connections could be found across participants from different groups, with government officials being the most central. Qualitative data showed that inclusion in the network and centrality did not necessarily translate into feeling powerful or valued. Many participants were dissatisfied with the process despite having structurally advantageous positions or self-reporting moderately high quantitative value scores. Participants who viewed themselves as more valued acknowledged many process shortcomings, but they also saw it as more balanced or fair than those who felt undervalued. Conclusions: While participation can make stakeholders and communities feel valued and empowered, our findings highlight that inclusion, position and diversity of connections in a drug policy-making network do not, in and of itself, guarantee these outcomes. Instead, policy-makers must provide transparent terms of reference guidelines and include highly skilled facilitators in policy discussions. This is particularly important in policy processes that involve historical power imbalances in the context of a pressing public health emergency. [ABSTRACT FROM AUTHOR]

ABSTRACT Social simultaneity describes individuals active in multiple social contexts. Migrant networks serve as a case study where social network analysis (SNA) has recently been applied to evaluate their assimilation and transnationality. Yet, networks are sometimes insubstantial, as all nominated nodes and bridges are part of the network graph except they are not part of the social grouping. This research attempts to look beyond the social networks metaphor as it reviews critical group size theory and complements SNA with network survey and ethnographic interpretation. The article questions the structural approach and argues that interaction among alters may not be accurately recorded by this method. A respondent‐driven sampling method was used to select and survey 55 egocentric networks out of some 248 Romanian migrant networks in Spain. Results endorse precautions of critical group size theory that do not fit prescriptions of SNA. Resilient social networks and strong reciprocity and exchanges support the moderate transnational space types within the surveyed case. The article concludes that smooth social embeddedness and neoteric cultural diversity eventually reveal characteristics of third‐generation migration in Europe. Despite this, the number of Romanians in Spain decreased at the end of COVID‐19 pandemic. [ABSTRACT FROM AUTHOR]

The article examines how distrust regarding the COVID-19 pandemic has served as a catalyst for the diffusion of conspiracy theories. It discusses the nature of conspiracy theories, their relationship with institutional distrust, and their representation in the social media. The methodology used is based on the analysis of hashtags from over one million tweets to determine if certain theories act as "gateways" to others. The findings suggest that logical ties exist between distinct conspiracy theories, influenced by distrust towards specific institutions and amplified by the COVID-19 crisis. It is concluded that the combination of conspiracy theories is not random, but rather, it follows an internal logic that is based on distrust, since these theories are interconnected in an internally coherent way within the context of the pandemic. [ABSTRACT FROM AUTHOR]

The supply chain forms the backbone of the modern consumer economy, weaving an intricate network of stakeholders across geographical and socioeconomic divides. While new technologies have enhanced supply chain management, the market dynamism and network complexities continue to challenge decision-makers. This study employs social network analysis and text mining to unravel technological patterns within the patent landscape of supply chain management. The analysis draws on a dataset of over 32,000 supply chain patents from Lens.org spanning 2000–2022. Network analysis reveals cooperation patterns and key players, while text mining and clustering identify five technology clusters: secure access control, manufacturing, logistics, data management, and RFID. Technology life cycle analysis indicates that secure access control, data management, and RFID have reached maturity, while logistics is still growing and manufacturing faces saturation. The findings highlight that despite maturity, these technologies warrant continued investment to resolve persistent challenges. The technology trends and maturity insights uncovered can help enterprises make informed strategic decisions by aligning R&D initiatives with technology lifecycles. This pioneering study bridges innovation research and technology management, offering a nuanced understanding of supply chain technologies. The framework presented can be extended to analyze other domains, opening avenues for further research. Overall, this study decodes the patent landscape to decode the future. [ABSTRACT FROM AUTHOR]

Clustering validity indices are designed for evaluating the performance of clustering algorithms in terms of quality of clusters. They are also used for detecting the correct number of clusters. Numerous clustering validity indices have been proposed in the literature for different types of data. However, to the best of our knowledge, there are a few well-known clustering validity indices for graph data. In this paper, we propose a new clustering validity index for graph data called the node-based clustering validity index. The main characteristic of our proposed index is that it captures the exclusive contribution of each node to the separation and compactness of the clusters in a graph. This characteristic gives an advantage to the method which is the detection of the correct number of clusters for cases that two sets of nodes are bonded to each other with a single node. We provide an illustrative example to show that many existing validity indices fail to detect the correct number of clusters in such cases. We evaluate the performance of our proposed index with several experiments on different real-world and synthetic graphs. Experiments show that our proposed node-based index outperforms the existing validity indices for different graph data. [ABSTRACT FROM AUTHOR]

Ethnographic fieldnotes can contain richer and more thorough descriptions of social phenomena compared to other data sources. Their open-ended and flexible character makes them especially useful in explorative research. However, fieldnotes are typically highly unstructured and personalized by individual researchers, which make them harder to use as a method for data collection in collaborative and mixed methods research. More precisely, the unstructured nature of ethnographic fieldnotes presents three distinct challenges: 1) Organizability—it can be difficult to search and sort fieldnotes and thus to get an overview of them, 2) Integrability—it is difficult to meaningfully integrate fieldnotes with other more quantitative data types such as more such as surveys or geospatial data, and 3) Computational Processability—it is hard to process and analyze fieldnotes with computational methods such as topic models and network analysis. To solve these three challenges, we present a new digital tool, for the systematic collection, processing, and analysis of ethnographic fieldnotes. The tool is developed and tested as part of an interdisciplinary mixed methods pilot study on attention dynamics at a political festival in Denmark. Through case examples from this study, we show how adopting this new digital tool allowed our team to overcome the three aforementioned challenges of fieldnotes, while retaining the flexible and explorative character of ethnographic research, which is a key strength of ethnographic fieldwork. [ABSTRACT FROM AUTHOR]

Parental phubbing refers to the phenomenon where parents ignore their children due to mobile phone use during parent-child interactions. This study examined the longitudinal mediating role of basic psychological need satisfaction in the relations between parental phubbing and adolescents' core self-evaluation. We recruited 629 adolescents (M age = 15.20 years, SD age = 1.62 years) to participate in our longitudinal survey. Longitudinal mediation analyses revealed that parental phubbing negatively predicted adolescents' core self-evaluation by diminishing their basic psychological need satisfaction. Additionally, longitudinal network analyses identified two significant parental phubbing behaviors that impact adolescents' core self-evaluation: parents keeping mobile phones in hand during interactions with their adolescents and checking mobile phone information during mealtime. Our work provides a novel understanding on how parental phubbing decreases adolescent core self-evaluation. [ABSTRACT FROM AUTHOR]

Game-based learning researchers have been investigating various means to maximise learning in educational games. One promising venue in recent years has been the use of learning analytics in online game-based learning environments. However, little is known about how different elements of learning analytics (e.g. data types, techniques methods, and stakeholders) contribute to game-based learning practices within online learning environments. There is a need for a comprehensive review to bridge this gap. In this systematic review, we examined the related literature in five major international databases including Web of Science, Scopus, ERIC, IEEE, and compiled Proceedings of the International Conference on Learning Analytics and Knowledge. Twenty relevant publications were identified and analysed. The analysis was conducted using four core elements of learning analytics, namely the types of data that the system collects (what), the methods used for performing analytics (how), the reasons the system captures, analyzes, and reports data (why), and the recipients of the analytics (who). This study synthesises the existing literature, provides a conceptual framework as to how learning analytics can enhance online game-based learning practices in higher education, and sets the agenda for future research. [ABSTRACT FROM AUTHOR]

This article aims to open a debate about the diffusion of local integration policies in European small- and medium-sized towns and rural areas (SMsTRAs), shedding light on local policymakers' relational environment and key features of policy networks that influence policy diffusion. Elaborating on data collected through 343 interviews across 34 SMsTRAs, analysed through social network analysis and qualitative content analysis, it concludes that the relational environment in SMsTRAs is unfavourable for integration policy diffusion due to specific characteristics of integration policy networks and the growing politicisation of local integration policymaking. Nonetheless, relevant variation is identified across different types of SMsTRAs. [ABSTRACT FROM AUTHOR]