Search

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]

Social capital is frequently invoked as a reason for engaging in collaborative environmental governance. Yet we have a limited understanding of how collaborative environmental governance mobilizes different types of social capital and how the advantages and costs of social capital accrue for different groups of people. Explicit measures of social capital, such as through social network methods, will help build an understanding of how social capital facilitates collective processes and for whom. We reviewed highly cited articles in Web of Science and Scopus using ‘social capital’ as the search term to identify foundational and emergent social capital concepts. In the context of collaborative environmental governance, we operationalized these social capital concepts with network measures drawn from our expertise and highlighted existing empirical relationships between such network measures and collaborative outcomes. We identified two different perspectives on social capital—one based on social relations that could be readily operationalized with social network measures and the other based on actor characteristics that can further contextualize network data. Relational social capital concepts included social relations among actors; the collective social setting in which relations are embedded; and the advantages and costs that social capital confers to individuals and the collective. Social capital concepts based on actor characteristics included socio-cognitions (e.g. trust, norms, identification with a group, shared meanings) and community engagement (e.g. group membership, civic participation, volunteerism). Empirical evidence using social network approaches to measure social capital reveals patterns in relationship building that influence collaborative and other sustainability outcomes. Social network approaches described here may help define and quantify the social capital mobilized by collaborative governance. Additional research is necessary to track the social capital of collaboratives over time, link it to outcomes, and better understand the social justice implications of collaborative governance.

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]

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.

BackgroundSmoking is a major contributor to morbidity and mortality among individuals with serious mental illness (SMI) and social networks may play an important role in smoking behaviors.AimsOur objectives were to (1) describe the network characteristics of adults with SMI who smoke tobacco (2) explore whether network attributes were associated with nicotine dependence.MethodsWe performed a secondary analysis of baseline data from a tobacco smoking cessation intervention trial among 192 participants with SMI. A subgroup (n = 75) completed questions on the characteristics of their social network members. The network characteristics included network composition (e.g. proportion who smoke) and network structure (e.g. density of connections between members). We used multilevel models to examine associations with nicotine dependence.ResultsParticipant characteristics included: a mean age 50 years, 49% women, 48% Black, and 41% primary diagnosis of schizophrenia/schizoaffective disorder. The median personal network proportion of active smokers was 22%, active quitters 0%, and non-smokers 53%. The density of ties between actively smoking network members was greater than between non-smoking members (55% vs 43%, p = .02). Proportion of network smokers was not associated with nicotine dependence.ConclusionsWe identified potential social network challenges and assets to smoking cessation and implications for network interventions among individuals with SMI.

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.

This study investigates the dynamics of network intermedia agenda-setting (IAS) between televised presidential debate and Twitter, focusing on the 10th Democratic Party Presidential Primary Debate of 2020 and corresponding Twitter discussions. By utilizing a social network analysis, this study uncovers the prioritized issues and sub-issues, as well as the networked relationship between the televised debate and Twitter discussions. The results suggest that certain sub-issues are more salient than main issues in both networks, emphasizing the need to consider detailed discussions alongside overarching issues. A positive, but weak, correlation between the televised debate and Twitter discussion was found. The theoretical, methodological, and practical implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

This study provides a methodological contribution in the analysis of tourists' sustainable behaviour. Traditional methods encounter challenges in addressing the attitude-behaviour gap, prompting the need for new approaches. Using diverse big data sources, the paper introduces a novel methodology for analysing real behaviour patterns, extending Social Network Analysis techniques. The aim is to identify sustainable behaviours that ease overcrowding, distribute visitor flows, and optimize economic diversification. The methodology is valuable for local authorities in monitoring tourist behaviours, aiding in informed decision-making for a more sustainable approach to tourism. [ABSTRACT FROM AUTHOR]

Multidimensional proximity has been considered the important factor affecting regional scientific collaboration network (RSCN) and has received extensive attention from scholars. This paper constructs a cross-RSCN based on co-authorship across 31 regions in China, incorporates traditional multidimensional proximity and network embedding proximity into a unified framework and proposes a five-factor driving model for dissecting the mechanism of cross-regional scientific collaborative innovation (CI). The results show that the scientific collaboration network is gradually becoming saturated with connections, and the polarisation of cross-regional collaboration is increasingly serious. The collaboration intensity is affected by the co-movement of several proximity factors, among which geographic proximity (GP) is the main but not the sole one that positively affects the cross-regional scientific collaboration. Based on the decision tree model, we conclude that structure proximity may compensate for low GP and low economic proximity (EP) on cross-regional CI performance. The positive effect of network location proximity on regional scientific collaboration is more sensitive when the EP is not high. The findings provide significant guidance for scientific research institutions, universities and regional policy-makers to select appropriate partners as well as network embedding strategies, and further contribute to long-term improvement of scientific innovation performance and stable regional economic growth. [ABSTRACT FROM AUTHOR]

Space use is widely assumed to be an important predictor of social behaviour given that animals are most likely to interact with nearby conspecifics. In the age of remote-sensing technology, spatial proximity is often used as a proxy for social interaction, although this simplifying assumption has rarely been tested under field conditions. Using 5 years of spatial and social data from direct observations of nearly 200 individually marked free-living California ground squirrels, Otospermophilus beecheyi , we employed social network statistics to evaluate the role of spatial relationships in generating individual differences in sociality. As predicted, range size was positively associated with each of the four social network metrics examined, confirming that more social squirrels generally had larger ranges. The proportion of range overlap between individuals was positively correlated with the strength of their social interactions, with the strongest ties occurring within age and sex classes and with juveniles generally being more social than adults. To determine whether these outcomes varied with interaction type, we examined the effects of spatial relationships on affiliative interactions, agonistic interactions and all interactions combined. We found that spatial relationships better predicted affiliative encounters compared to agonistic encounters. Despite revealing significant links between spatial and social relationships, our models explained only a small proportion of the variation in each social network metric examined. Thus, factors other than space use must contribute to social interactions, suggesting that general assumptions regarding the effects of space use on social interactions need to be evaluated on a species-by-species basis. More generally, these findings highlight the need for explicit consideration of the spatiosocial interface and its implications for multiple aspects of animal behaviour. • We examined spatial and social relationships in California ground squirrels. • Measures of sociality increased with range size. • Range overlap predicted the strength of association between squirrels. • Juvenile squirrels were generally more social than adults. • Space use better predicted affiliative social networks than agonistic networks. [ABSTRACT FROM AUTHOR]

Research has shown that students with disabilities are more likely to maintain a lower social participation status than their peers. One aspect of social participation is the social relationships between these students and their peers. This study examined the friendship and hang out networks of students with and without disabilities in a highly diverse middle school in Southern California. Results indicated that students with disabilities were significantly less nominated as friends and significantly less nominated as hang out partners. Further regression analyses examined the students' friendship and hang out networks. For both, the hang out and friendship in-degrees, results supported the hypothesis that students with disabilities have exhibited lower social participation compared to their peers. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of high-speed rail (HSR) network improvement on the consumption synergy of the Beijing-Tianjin-Hebei region by using the panel data of 13 prefectural-level cities from 2006 to 2021. The results show that: (1) The improvement of the HSR network in the Beijing-Tianjin-Hebei region has a significant positive impact on raising overall regional consumption levels. (2) The HSR network improvement has a heterogeneous impact on consumption in different cities within the Beijing-Tianjin-Hebei region. (3) The promotional effect on peripheral cities is greater than on central cities, thereby promoting the coordinated consumption development of the Beijing-Tianjin-Hebei region. The findings of this study have important policy implications, confirming that the construction of the HSR network can narrow the consumption gap within the region and promote consumption synergy across the region. • This paper investigated the impact of HSR network improvement on consumption synergy in Beijing-Tianjin-Hebei region. • This paper found that the construction of the HSR network can promote consumption synergy across the region. • This paper applied groups of instruments and social network analysis to guarantee the robustness of the results. • The findings have important policy implications to the government and railway department. [ABSTRACT FROM AUTHOR]

Multilevel models are often used to account for the hierarchical structure of social data and the inherent dependencies to produce estimates of regression coefficients, variance components associated with each level, and accurate standard errors. Social network analysis is another important approach to analysing complex data that incoproate the social relationships between a number of individuals. Extended linear regression models, such as network autoregressive models, have been proposed that include the social network information to account for the dependencies between persons. In this article, we propose three types of models that account for both the multilevel structure and the social network structure together, leading to network autoregressive multilevel models. We investigate theoretically and empirically, using simulated data and a data set from the Dutch Social Behavior study, the effect of omitting the levels and the social network on the estimates of the regression coefficients, variance components, network autocorrelation parameter, and standard errors. [ABSTRACT FROM AUTHOR]

The rapid development of the internet and social network platforms has given rise to a new field of research, social network analysis. This field of research has many fundamental problems, one of which is community detection. The objective of this research is to understand hidden connections among individuals. However, uncovering these connections are still challenging, despite the existence of several methods. In this paper, we propose a new algorithm called MCCD (Modified Cosine for Community Detection) for community detection in social networks based on node similarity. Our algorithm consists of two steps. In the first step, we use a novel cosine similarity formula to identify initial communities. In the second step, we merge these communities based on a new similarity measure. MCCD can be used in two different ways. The first way uses K as an input to identify the exact communities. The second way does not require K and aims to provide the best partitioning by maximizing modularity. Our algorithm has been tested on a variety of artificial and real-world networks, and the experimental results demonstrate its superiority over existing methods in detecting communities. [ABSTRACT FROM AUTHOR]

Online social networks are crucial in propagating information and exerting influence through word-of-mouth transmission. Influence maximization (IM) is the fundamental task in social network analysis to find the group of nodes that maximizes the influence in the social network. IM has different applications like viral marketing, campaigning, advertising, and so on. Literature has presented various algorithms based on different approaches to address the IM problem, including nature-inspired algorithms. Most of the work focuses on the static social network. The proposed work first employs nature-inspired Cuckoo Search Optimization to solve the IM problem in dynamic networks. The proposed algorithm applies the fuzzy-logic-based technique to optimize the nests. We also perform statistical tests to show the effectiveness of the proposed algorithm with the benchmark algorithms. The experimental results are performed on five datasets and compare the results with the state-of-the-art algorithms. The results show that the proposed algorithm gives better results than the nature-inspired state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) is the fundamental study of social network analysis. The IM problem finds the top k nodes that have maximum influence in the network. Most of the studies in IM focus on maximizing the number of activated nodes in the static social network. But in real life, social networks are dynamic in nature. This work addresses the diversification of activated nodes in the dynamic social network. This work proposes an objective function that maximizes the number of communities by utilizing bridge nodes. We also propose a diffusion model that considers the role of inactive nodes in influencing a node. We prove the submodularity, and monotonicity of the objective function under the proposed diffusion model. This work analyzes the impact of different ratios of bridge nodes in the seed set on real-world and synthetic datasets. Furthermore, we prove the NP-Hardness of the objective function under the proposed diffusion model. The experiments are conducted on various real-world and synthetic datasets with known and unknown community information. The proposed work experimentally shows that the objective function gives the maximum number of communities considering bridge nodes compared with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Latin American & Caribbean Studies (Routledge) is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper constructs an institutional investor network based on the heavy holdings of the same stock in China and conducts a social network analysis to investigate the influence of this network on stock price volatility from the perspectives of network structure (density) and location (centrality). The study demonstrates that institutional investor network density is negatively related to stock price volatility, while network centrality is positively related to it. Mechanism analyses further reveal that network density reduces stock price volatility by mitigating private information arbitrage behavior among institutional investors, whereas network centrality increases stock price volatility by creating private information arbitrage opportunities within the network. Additionally, the paper finds that information asymmetry enhances the positive effect of network centrality on stock price volatility. These findings are generally consistent across subsample analyses for different market states, reactions to good and bad news, and types of equity ownership, as well as in other robustness tests. The practical implications of these findings are significant for market stability regulation. [ABSTRACT FROM AUTHOR]

Community detection in social networks plays a crucial role in understanding various social phenomena. However, accurately identifying communities is challenging due to the complex nature of social networks. To address this challenge, we propose CD-BSO, an innovative metaheuristic based on brainstorming. CD-BSO leverages network knowledge through specialized initialization and solution generation operators designed to capture social network characteristics. In the initialization phase, a technique combining Depth-First Search (DFS) is employed to consider both connectivity and information diffusion, ensuring accurate community representation. The generation step introduces search operators that consider link formation and node similarity, facilitating convergence towards a community structure that closely resembles the network's actual structure. The evaluation of CD-BSO outperforms recent and well-known community detection methods. The evaluation results were further analyzed using visual analysis, providing valuable insights into the network structure. CD-BSO exhibits significant potential for accurately identifying communities and extracting meaningful information from social networks. [ABSTRACT FROM AUTHOR]

AbstractExisting histories of behavioral and experimental economics (BE-XP) are primarily focused on its developments in the United States. While French economist Maurice Allais made significant contributions in the 1950s, the subsequent trajectory of BE-XP in France is understudied. This article addresses this gap by comparing the history of BE-XP in France to international trends previously identified in the literature. We find that, after an initial ambivalent influence from Allais, French BE-XP subsequently aligned with international trends. We nevertheless identify some distinctive national features in utility measurement and social preference modeling that are heterogeneously distributed across the French territory. [ABSTRACT FROM AUTHOR]

Influence maximization is a critical research topic of social network analysis, particularly with the increasing involvement of individuals in the global networked society. The purpose of the problem is to identify k influential nodes from the social network and activate them initially to maximize the expected number of influenced nodes at the end of the spreading process. Although some meta-heuristics based on swarm intelligence or biological evolution have been proposed to tackle this intractable problem, further investigation is required to refine the exploration and exploitation operations based on the iterative information from the evolutionary process. In this paper, an adaptive differential evolution algorithm driven by multiple probabilistic mutation strategies is proposed for the influence maximization problem. In order to enhance the evolutionary capability of the later stages of the discrete differential evolution, the mutation in the framework, consisting of three policies, namely comprehensive learning particle swarm mutation strategy, differential mutation strategy, and perturbation strategy, is implied based on different probabilistic models. An adaptive local search strategy is presented to improve the local optimum results based on a potential alternative library consisting of structural hole nodes, which guides the differential evolution to find a more optimal solution. Experimental results on six real-world social networks demonstrate the competitive performance of the proposed algorithm in terms of both efficacy and efficiency compared to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

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]

Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to 90 % for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent. [ABSTRACT FROM AUTHOR]

Collaborative problem-solving (CPS) involves the interaction and interdependence of students' social and cognitive skills, making it a complex learning process. To delve into the complex dynamics of CPS, previous research has categorized socio-cognitive roles, providing insights into social-cognitive frameworks. However, despite the specific cognitive and social interaction structures employed by roles to engage in CPS interactions, most existing research primarily focuses on individual roles, neglecting inter-role interactions. To fill this gap, twelve triad groups were formed by engaging 36 undergraduate students in online CPS activities to examine differences in social and cognitive interaction structures across different roles and group compositions. Additionally, analyze the differences in CPS processes among various group compositions. The analyses identified five roles (Lurkers, Followers, Drivers, Influential Actors, and Innovators) and three group compositions (Balanced groups, Decentralized groups, and Power Struggle groups). The socio-cognitive structure of Balanced groups, along with other evidence, indicates effective information sharing and negotiation interactions. In contrast, Decentralized and Power Struggle groups exhibited various deficiencies in their socio-cognitive structures, negatively impacting group collaboration processes. These insights provide educators with a comprehensive guide to fostering effective group compositions and role dynamics in online CPS settings, thereby enhancing the overall success of CPS. Additionally, possible activity design considerations and scaffolding strategies are also discussed. [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]