Promoting economic resilience: how inter-local collaboration mitigates disaster costs in South Korea

هدف: هدف از پژوهش حاضر بررسی میزان مرکزیت شبکه اجتماعی هم‌نویسندگی موجود در بین مجلات علم اطلاعات نمایه شده در پایگاهتامسون رویترز می باشد. روش‌شناسی:پژوهش حاضر با استفاده از روش تحلیل شبکه ای صورت گرفته است. جامعه پژوهش تمامی مجلات علم اطلاعات است که دارای ضریب تأثیرگذاری بالاتر از 6/0 می باشند. یافته ها: نتایج حاصل از تحلیل نشان داد که گلنزل بالاترین مرکزیت رتبه، بینابینی، بردار ویژه و نزدیکی را در مجله علم‌سنجی دارد و نیکولاس بالاترین مرکزیت رتبه، بردار ویژه و مرکزیت بتا را در مجله علوم اطلاعات دارد. نتایج حاکی از آن است که به طور کلی شبکه اجتماعی هم‌نویسندگی پژوهشگران علم اطلاعات کم تراکممی باشد و از لحاظ سنجه های مرکزیت در مقایسه با سایر رشته های علمی در سطح پایین‌تری قرار دارد.

Objectives The purpose of this study was to analyze the impact of team-based project learning on students' trust in faculty, class participation, and motivation to learn. Quantitative and qualitative data were collected and analyzed in an integrated manner to examine the effects of team-based project teaching on learners. Methods The participants were 26 undergraduate students enrolled in a liberal arts program who participated in a 15-week team-based project learning course. The lessons consisted of a review of the previous lecture, individual activities, paired learning and teamwork, plenary sharing, instructor mini-lectures, and reflection journals. We administered the Faculty Trust Scale, Class Participation Scale, and Learning Motivation Scale at the beginning and end of the semester. A paired sample t-test was conducted to examine pre- and post-test changes, and the students' reflective journals were analyzed for network centrality between keywords using UCINET 6 to present semantic connectivity networks. Results First, students' evaluations of their professors' closeness, expertise, teaching ability, and leadership improved significantly after team-based project learning. In the reflective journals, the main keywords related to professor were ‘thought’, ‘name’, ‘different’, ‘call’, ‘difference’, ‘participation’, ‘time’, ‘activity’, ‘communication’, ‘opinion’. ‘Thought’, ‘difference’, ‘different’, ‘time’, ‘share’, and ‘opinion’ showed a high degree centralty. ‘Thought’, ‘difference’, ‘different’, ‘speaking’, ‘subject’, ‘name’ and ‘call’ were highly closeness centrality, and ‘thought’, ‘difference’, ‘speaking’, ‘name’, and ‘call’ were highly betweenness. Second, after the team-based project learning, the sub-factors of class participation showed statistically significant improvements in expressing, extending, and enthusiasm for the class, and the sub-factors of learning motivation showed statistically significant improvements in attention, confidence, and satisfaction. In the team project learning process, the order of appearance frequency of major keywords was ‘idea’, ‘people’, ‘different’, ‘opinion’, ‘story’, ‘good’, ‘lack’, ‘activity’, ‘difference’, ‘evaluation’, ‘help’, and ‘growth’. The top keywords with high connectivity links were ‘think’, ‘people’, ‘activity’, ‘opinion’, ‘self’, ‘time’, ‘different’, ‘lack’, ‘complement’, ‘help’, ‘write’, and ‘listen’. The top keywords with high degree centrality, closeness centrality, and betweenness centrality were ‘activity’, ‘idea’, ‘person’, ‘different’, and ‘opinion’. Conclusions This study examined the usefulness of team-based project instruction in liberal arts courses and specifically examined the conditions under which it affects students. Implications and recommendations for the implementation of team-based project classes are then discussed.

Air transport network plays a major role in catalyzing globalization through facilitating the movement of goods and people between countries. This study analysed the connectivity of the airline network of the national carrier of Sri Lanka and identified the critical airports and their impact to the network. The study considered the network of Sri Lankan Airlines for the analysis and used flight stats data for data collection. Dubai International Airport (DXB), Delhi International Airport (DEL) and Kuala Lumpur International Airport (KUL) had the highest degree centrality in the network, indicating strong connectivity with other nodes in the network. Thus the most critical airports in the network. While Gan International Airport (GAN) and Seychells International Airport (SEZ) indicated the lowest degree centrality. Degree centrality of the nodes in the networked changed when critical airports were removed from the network. Combaitre International Airport (CJB) is having the highest closeness centrality indicating the ease of movement between the nodes. Removing the critical airports with from the network affected the closeness centrality score of the nodes.

An impact of online recommendation network on demand

Campus competition in Central Java creates superior and empowered human resources to make XYZ campus optimize the Knowledge Sharing process. In optimizing the Knowledge Sharing process on the XYZ campus through interaction and communication between students in the study program. This study aims to identify the Knowledge Sharing collaboration of students on the XYZ campus in three study programs with 100 respondents using the Social Network Analysis (SNA) method. The parameters used in this study include density, degree centrality, closeness centrality, betweenness centrality, and clicks (subgroups). Based on the analysis of the results obtained by the level of density level of 4.7% or weak ties because under 50%. Actor 98 has the highest degree of centrality with outdegree value 32 and indegree 7, while actor 65, which has the highest closeness centrality with inCloseness value 16,952 and outCloseness value 1,020. Actor 15 also has the highest centrality betweenness with an amount of Betweenness 2750,148 and nBetweenness 28,346. In this study, it can be concluded that there is collaboration in the Knowledge Sharing of students on the XYZ campus from each divided into three study programs, namely, informatics engineering, accounting computerization, and graphic design.

We explore the impact of individuals’network position toward knowledge sharing intention. In order to identify network positions, we utilize three centrality measures (degree/closeness/ betweenness) of individual network participants. The research findings show that the individual network positions significantly affect knowledge sharing intentions. Since an individual with high degree centrality might be the leader or the hub, one makes considerable effort to maintain the network position by actively participating in intra-team and inter-team knowledge sharing, A participant who can quickly interact with many other participants within a team (high closeness centrality) is more interested in intra-team knowledge sharing than inter-team knowledge sharing. Unlike degree centrality and closeness centrality, the betweenness centrality provides a participant with diverse resources located in multiple sub-groups. Although an individual with high betweenness centrality is not at the center of the networks, one plays a crucial role in disseminating and regulating information. Therefore, the individual is likely to have more positive intention toward inter-team knowledge sharing than intra-team knowledge sharing.

Measurements of node centrality are based on characterizing the network topology structure in a certain perspective. Changing the network topology structure would affect the accuracy of the measurements. In this paper, we employ the Holme-Kim model to construct scale-free networks with tunable clustering, and consider the four measurements of classical centrality, including degree centrality, closeness centrality, betweenness centrality and the eigenvector centrality. For comparing the accuracy of the four centrality measurements, we simulate the susceptible-infected-recovered (SIR) spreading of the tunable clustering scale free networks. Experimental results show that the degree centrality and the betweenness centrality are more accurate in networks with lower clustering, while the eigenvector centrality performs well in high clustering networks, and the accuracy of the closeness centrality keeps stable in networks with variable clustering. In addition, the accuracy of the degree centrality and the betweenness centrality are more reliable in the spreading process at the high infectious rates than that of the eigenvector centrality and the closeness centrality. Furthermore, we also use the reconnected autonomous system networks to validate the performance of the four classical centrality measurements with varying cluster. Results show that the accuracy of the degree centrality declines slowly when the clustering of real reconnected networks increases from 0.3 to 0.6, and the accuracy of the closeness centrality has a tiny fluctuation when the clustering of real reconnected networks varies. The betweenness centrality is more accurate in networks with lower clustering, while the eigenvector centrality performs well in high clustering networks, which is the same as in the tunable clustering scale free networks. According to the spreading experiments in the artificial and real networks, we conclude that the network clustering structure affects the accuracy of the node centrality, and suggest that when evaluating the node influence, we can choose the degree centrality in the low clustering networks, while the eigenvector centrality and the closeness centrality are still in the high clustering networks. When considering the spreading dynamics, the accuracy of the eigenvector centrality and the closeness centrality is high, but the accuracy of the degree centrality and the betweenness centrality is more reliable in the spreading process at high infectious rates. This work would be helpful for deeply understanding of the node centrality measurements in complex networks.

The use of social media as a means of retrieving data because it is considered broader, straightforward, honest, efficient and real time. Not only as an expanding network, social media is also used by most people to actively participate in voicing opinions, getting assumptions, seeing current trends, and even as a forum for obtaining detailed and accurate information. Twitter is one of the social media that is microblogging, and emphasizes the speed of communication. In this era of 4.0, the government also promotes the distribution of information through social media, because it is considered to be more included in the community from various lines. The Ministry of Agriculture is involved in the use of social media as a forum for the socialization of work programs and achievements, as well as public information openly. In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identification in decision making on the application of technology adoption, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development. Besides that, this article will also show the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken to be limited to 3000 nodes, then the results of the Social Network Analysis then calculated the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and the most connections (followers) among other accounts that spread the same information. while the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred to information that has been circulating, and the highest Closeness Centrality is owned by the @baigmac account is the fastest person to retweet the first information written.

Global tourism development can be seen as a tourism network evolution; however, how the network structure influences the tourism industrial performance has not been clearly outlined. This paper utilizes complex network theory to understand the global tourism network changes and detect the global network structure effects on international tourism industrial performance, aiming to explain the tourism development from a network perspective and help to organize international tourism effectively. Using the data of 222 regions’ statistics from 1995 to 2019, this paper explores the influence of the global-level network structure on the tourism industry through Pearson’s correlations test and the individual-level effects through a combination of the gravity model with the mixed-effect model. At the global level, results indicate that a network structure with a higher density or clustering coefficient can improve the global tourism arrivals, but the high value of the network average path length and small-worldness characteristic have negative effects. At the individual level, the node’s characteristics including the high degree, closeness, and betweenness centrality of a region in the network positively improve its international tourism arrivals, while the eigenvector centrality and local clustering coefficient generate negative effects. Additionally, most network structure measurements of a region show stronger effects on its own tourism performance than the regions with which it connects. This paper verifies that the network structure has significant impacts on tourism performance and development, which can aid international tourism development both globally and individually.

Twitter is one of the micro-blogging social media which emphasizes the speed of communication. In the 4.0 era, the government also promotes the distribution of information through social media to reach the community from various lines. In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identifying the application of technology adoption in decision making, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development. This article also shows the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken limited to 3000 nodes. Then, the measurements of the SNA are represented on the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and has many followers among other accounts that spread the same information. While the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred information, and the highest Closeness Centrality is owned by the @baigmac account because of the fastest to re-tweet the first information.

Background: During the COVID-19 pandemic, telemedicine is applied for various purposes, such as reducing the time of diagnosis and initiating treatment, quarantining and stabilizing patients, enabling the system to closely monitor the citizens at home, and supporting health professionals. Objectives: The present study used Scientometrics analysis to comprehensively analyze the body of research conducted on telemedicine regarding COVID-19. Methods: By using a searching formula, 900 documents were retrieved from the Web of Science. Co-authorship networks were drawn by CiteSpace and Gephi software that are free and powerful illustrating networks. The selected co-authorship indicators were Degree Centrality, Betweenness Centrality, and Closeness Centrality. Results: Andrea M. Russo had by far a high degree of centrality, compared to other authors. Regarding the countries, Belgium and Portugal had a larger node, indicating that they had a higher degree of centrality. Neurosciences had a large node, showing the higher degree of centrality of this subject area. Psychology and Clinical Neurology were also the nodes with a higher degree of centrality. The degree of centrality was high for the University of Zurich, University of Barcelona, and King College London, and the connections of these nodes were more and even stronger, compared to other nodes. Conclusion: This study, which was based on 900 scientific credentials in the field of telemedicine during COVID-19, indicated the level of cooperation among authors, countries, and organizations in 2020. Moreover, by presenting different indicators in these networks’ researchers, countries, and key organizations were introduced for each indicator.

ObjectiveThe huge loss of health insurance funds has been a topic of concern around the world. This study aims to explore the network of moral hazard activities and the attribution mechanisms that lead to the loss of medical insurance funds.MethodsData were derived from 314 typical cases of medical insurance moral hazards reported on Chinese government official websites. Social network analysis (SNA) was utilized to visualize the network structure of the moral hazard activities, and crisp-set qualitative comparative analysis (cs/QCA) was conducted to identify conditional configurations leading to funding loss in cases.ResultsIn the moral hazard activity network of medical insurance funds, more than 50% of immoral behaviors mainly occur in medical service institutions. Designated private hospitals (degree centrality = 33, closeness centrality = 0.851) and primary medical institutions (degree centrality = 30, closeness centrality = 0.857) are the main offenders that lead to the core problem of medical insurance fraud (degree centrality = 50, eigenvector centrality = 1). Designated public hospitals (degree centrality = 27, closeness centrality = 0.865) are main contributor to another important problem that illegal medical charges (degree centrality = 26, closeness centrality = 0.593). Non-medical insurance items swap medical insurance items (degree centrality = 28), forged medical records (degree centrality = 25), false hospitalization (degree centrality = 24), and overtreatment (degree centrality = 23) are important immoral nodes. According to the results of cs/QCA, low-economic pressure, low informatization, insufficient policy intervention, and organization such as public medical institutions, were the high-risk conditional configuration of opportunism; and high-economic pressure, insufficient policy intervention, and organizations, such as public medical institutions and high violation rates, were the high-risk conditional configuration of risky adventurism (solution coverage = 31.03%, solution consistency = 90%).ConclusionThere are various types of moral hazard activities in medical insurance, which constitute a complex network of behaviors. Most moral hazard activities happen in medical institutions. Opportunism lack of regulatory technology and risky adventurism with economic pressure are two types causing high loss of funds, and the cases of high loss mainly occur before the government implemented intervention. The government should strengthen the regulatory intervention and improve the level of informatization for monitoring the moral hazard of medical insurance funds, especially in areas with low economic development and high incident rates, and focus on monitoring the behaviors of major medical services providers.

Background: People who inject drugs (PWID) are a well-identified risk population for HIV infection. The risk networks of PWID have been implicated as possible modulators of both HIV risk and educational interventions among this population. In order to further understand the nature of risk networks, we examined how individual characteristics were associated with influential network position based on high closeness, betweenness, or eigenvector network centrality. These centrality measures assess an individual’s importance or potential to influence others based on their connections, closeness is based on proximity to others, betweenness on acting as an intermediary between others, and eigenvector on connection to highly connected peers. Methods: Using data from Athens, Greece collected as part of the Transmission Reduction Intervention Project (TRIP), we constructed a risk network and identified individuals in the top quartile of the distribution for each centrality measure. Using logistic regression, we identified associations between being in the top quartile of each centrality measure and individual characteristics such as demographics, risk behaviors, and altruistic behaviors. We also performed a series of sensitivity analyses to evaluate robustness of the results to the definition of high centrality (e.g., the top 50%, 20%, and 10% of the distribution of the centrality measure). Results: The TRIP study contained a total 356 individuals after restriction to the largest connected component and censoring of individuals with missing covariate information a sample of 231 PWID was extracted from the TRIP study population. Individuals who injected at least once per day were more likely to have high closeness (odds ratio (OR) = 3.36; 95% confidence interval (CI) = 1.57, 8.42), betweenness (OR = 2.22 95% CI = 1.06, 4.67), or eigenvector centrality (OR = 4.50 95% CI = 1.89, 10.68). Individuals who engaged in sex without a condom were less likely to have high closeness centrality (OR = 0.18 95% CI =0.07, 0.45) or high eigenvector centrality (OR = 0.19 95% CI =0.07, 0.49). Individuals who reported higher numbers of sex partners were more likely to have high betweenness centrality (OR = 1.04 95% CI =1.00, 1.08). Years living in the project recruitment area was also associated with high eigenvector centrality (OR = 1.04 95% CI = 1.00, 1.09). Conclusions: Injection frequency was consistently related with network position and likely indicates that individuals who inject more frequently have more interactions with other PWID. Unprotected sex was also related to network centrality and may reflect that less central

Objectives The purpose of this study is to derive key issues related to feedback in the extracurricular curriculum at the university level and suggest directions for system improvements. Methods Therefore, the literature review for this study encompassed 117 academic journal articles published in South Korea during the last decade (2014-2023). Details of each Introduction and Conclusion were extracted, keywords were refined, and the main keywords were identified through TF or TF-IDF. Additionally, a semantic network analysis was conducted to determine connections between keywords. A CONCOR analysis was performed to clarify associations between cooccurring keywords. The results were also visualized. Results First, the top 10 most frequent feedback-related core keywords in non-credit extracurricular programs follow: writing, learning community, core competencies, academic achievement,tutoring, university life, online, self-directed learning, thinking skills, and collaboration. Second, the top five keywords with high degree centrality in the semantic network analysis were learning community-academic achievement-university life-tutoring-collaboration. The top five keywords with closeness centrality were collaboration-creativity-problem solving-career-learning community. The top five keywords with eigenvector centrality were learning community-academic achievement-collaboration-self-directed learning-tutoring. The top five keywords with betweenness centrality were collaboration-creativity-problem-solving-career-learning community. Third, as a result of the CONCOR analysis of the main keywords, the following four clusters were found: demand and performance management, system improvement, content and method improvement, and response to environmental change. Conclusions Based on these findings, the following measures are emphasized: (1) the higher effectiveness of IR related to non-credit extracurricular programs, (2) the preparation of strategies to identify and support prospective transfer students, (3) the examination of management systems to improve the operation of non-credit extracurricular programs, and (4) efforts to ensure the continuity and consistency of policies.

Co-authorship networks and research impact: A social capital perspective