Centrality Analysis Research Articles

Examining the spatial networks of an urban agglomeration through the lens of multi-dimensional element flow: Evidence from western China

The Chengdu–Chongqing urban agglomeration (CCUA), as the only national-level city cluster in southwestern China, serves as a strategic support for the Yangtze River Economic Belt and an important demonstration area for promoting new urbanization in the country. The study of the networked characteristics of the CCUA contributes to a systematic understanding of its spatial connectivity patterns, optimization of spatial structure and layout, and promotion of high-quality regional development. In this study, we constructed models for traffic flow, information flow, migration flow, and composite flow to calculate the strength of connections between cities and the total flow of various elements in the CCUA. ArcGIS spatial visualization tools were used to depict the spatial connectivity patterns of the element flows within the CCUA. Additionally, social network analysis methods, including network density, centrality, and cohesive subgroups analysis, were employed to reveal the spatial network structure characteristics of the CCUA. The findings are as follows: (1) The overall structure of the cities within the CCUA is relatively loose, with significant differences in connectivity strength. It exhibits a west-strong and east-weak pattern, with Chengdu-Chongqing, Chengdu-Deyang, Chengdu-Mianyang, and Chengdu-Meishan occupying the top tier, while Zigong and Ya'an have relatively weak connections with other cities. Chengdu and Chongqing have prominent positions in the CCUA, with Chengdu having a more prominent core position compared to Chongqing, resulting in an overall hierarchical distribution of “1 + 1+7 + 7”. (2) The network density of the element flows in the CCUA is relatively low, indicating a generally weak element connectivity. The centrality of cities other than Chengdu and Chongqing is at a moderate to lower level, suggesting a weak overall resource connectivity capacity in the CCUA. (3) Most cities tend to form cohesive subgroups based on geographic proximity, while the cohesive subgroup in Chongqing is still in its early stages of development due to administrative boundaries. The research results quantitatively depict the spatial network structure characteristics of the CCUA, providing theoretical references for its development.

코로나 시대의 근로자 통행행태 및 요인에 관한 연구

COVID-19, which occurred in Wuhan at the end of 2019, spread around the world, and Korea also occurred in January 2020, and its high infectivity greatly affected the use of space in cities. In this study, inflow and outflow factors arising from COVID-19 and social distancing were analyzed using data from the SKT service population in Gyeongsangnam-do. The factors before and after COVID-19 were set as indicators to investigate the factors before and after COVID-19. As an analysis method, 2019 and 2020 were first visualized using Gyeongsangnam-do O/D data, and then community analysis and centrality analysis were used to find out whether the community and centrality actually changed in 2019 and 2020. After that, the spatial measurement model was used to find out the factors of the outflow and inflow population. The inflow of manufacturing, lodging, and service industries increased compared to the previous year, and the inflow of wholesale and retail industries decreased. It was found that universities were significant as a social factor, and the inflow population decreased in the area where the university was located, and the accessibility factor was not significant in 2019, but the inflow increased in the area where the bus stop was located compared to 2020. As for the outflow factors, in terms of land use factors, population density, number of houses, and urban parks were significant. It can be seen that the outflow population has increased in residential areas with high population density and number of houses and many urban parks. As economic factors, wholesale and retail, and educational service industries were significant. As a social factor, universities did not appear significantly, but the inflow factor decreased, and the outflow factor increased. Overall, the inflow is decreasing in the area where the university is located, and the outflow is increasing.

빅데이터를 활용한 무인카페 소비자 인식에 관한 연구

Purpose - The purpose of this study was to investigate the perception of 'unmanned cafes' on the network through big data analysis, and to identify the latest trends in rapidly changing consumer perception. Based on this, I would like to suggest that it can be used as basic data for the revitalization of unmanned cafes and differentiated marketing strategies.
 Design/methodology/approach - This study collected documents containing unmanned cafe keywords for about three years, and the data collected using text mining techniques were analyzed using methods such as keyword frequency analysis, centrality analysis, and keyword network analysis.
 Findings - First, the top 10 words with a high frequency of appearance were identified in the order of unmanned cafes, unmanned cafes, start-up, operation, coffee, time, coffee machine, franchise, and robot cafes. Second, visualization of the semantic network confirmed that the key keyword “unmanned cafe” was at the center of the keyword cluster.
 Research implications or Originality - Using big data to collect and analyze keywords with high web visibility, we tried to identify new issues or trends in unmanned cafe recognition, which consists of keywords related to start-ups, mainly deals with topics related to start-ups when unmanned cafes are mentioned on the network.

키워드 네트워크 분석을 활용한 창의성과 환경 연구의 지식구조 탐구

The purpose of this study was to investigate the knowledge structure of creativity and environment research by using keyword network analysis and propose a future study direction. For this purpose, we selected a total of 477 keywords from 176 research papers published in South Korea during the past 20 years from 2003 to 2022 that investigated creativity and environment. Then a total of 77 keywords represented more than twice among them were analyzed using the keyword network analysis including degree and betweenness centrality analysis and community analysis. The results showed that the keywords with high degree centrality included creativity, creative environment, creative family environment, family environment, creative thinking, creative leadership, creative personality, and so on. The keywords with high betweenness centrality included creativity, creative family environment, creative environment, creative school environment, research trend, family environment, elementary school teachers, and so on. From the community analysis, nine sub-groups were made focusing on self-leadership (isolated node), creative thinking principles (creative learning principles), creative problem solving (environment education, environmental attitudes, environment literacy etc.), science gifted students (parents, family, school etc.), organizational creativity (individual creativity, group creativity, creative research environment etc.), creativity (creativity characteristics, creative efficacy, classroom environment, creative class environment, reading environment etc.), creative environment (creative thinking, creative product, creative characteristics, creative process etc.), family environment (school environment, task environment, infants creativity etc.), and creative family environment (creative school environment, creative task environment, creative orientation etc.). Theoretical and practical implications and future directions were discussed.

Leak detection in water distribution networks based on graph signal processing of pressure data

Abstract Leakages in water distribution networks (WDNs) affect the hydraulic state of the entire or a large part of the network. Statistical correlation computed among pressure sensors monitoring network nodes aids the detection and localization of such leaks. This opens the possibility to work with water network databases, where graph signal processing (GSP) tools aid in understanding changes in pressure signals due to leakages in the hydraulic system. This paper presents a methodology for time-varying pressure signals on graph structures. The core of this methodology is based on changing of pressure, due to leaks, that modifies the graph structure. Computing for each time step a new topology of the graph and applying centrality analysis based on PageRank, it is possible to identify the presence of new leaks at the water system. A confusion matrix evaluates the precision of the proposed methodology on defining where and when such leakages start and end. Seven leaks are used to validate the process, which presented 86% in accuracy terms. The results show the benefits of the method in terms of speed, computational efficiency, and precision in detecting leakages.

Exploring the Regional Structure of the Worldwide Air Traffic and Route Networks

The topological structure of the world air transportation network has been the subject of much research. However, to better understand the reality of air networks, one can consider the traffic, the number of passengers, or the distance between flights. This paper studies the weighted world air transportation network through the component structure, recently introduced in the network literature, by using the number of flights. The component structure is based on the community or multiple core-periphery structures and splits the network into local and global components. The local components capture the regional flights of these two mesoscopic structures (dense parts). The global components capture the inter-regional flights (links between the dense parts). We perform a comparative analysis of the world air transportation network and its components with their weighted counterparts. Moreover, we explore the strength and the s-core of these networks. Results display fewer local components well delimited and more global components covering the world than the unweighted world air transportation network. Centrality analysis reveals the difference between the top airports with high traffic and the top airports with high degrees. This difference is more pronounced in the global air network and the largest global component. Core analysis shows similitude between the s-core and the k-core for the local and global components, even though the latter includes more airports. For the world air network, the North and Central America-Caribbean airports dominate the s-core, whereas the European airports dominate the k-core.

Modelling and analysis of the complement system signalling pathways: roles of C3, C5a and pro-inflammatory cytokines in SARS-CoV-2 infection.

The complement system is an essential part of innate immunity. It is activated by invading pathogens causing inflammation, opsonization, and lysis via complement anaphylatoxins, complement opsonin's and membrane attack complex (MAC), respectively. However, in SARS-CoV-2 infection overactivation of complement system is causing cytokine storm leading to multiple organs damage. In this study, the René Thomas kinetic logic approach was used for the development of biological regulatory network (BRN) to model SARS-CoV-2 mediated complement system signalling pathways. Betweenness centrality analysis in cytoscape was adopted for the selection of the most biologically plausible states in state graph. Among the model results, in strongly connected components (SCCs) pro-inflammatory cytokines (PICyts) oscillatory behaviour between recurrent generation and downregulation was found as the main feature of SARS-CoV-2 infection. Diversion of trajectories from the SCCs leading toward hyper-inflammatory response was found in agreement with in vivo studies that overactive innate immunity response caused PICyts storm during SARS-CoV-2 infection. The complex of negative regulators FI, CR1 and DAF in the inhibition of complement peptide (C5a) and PICyts was found desirable to increase immune responses. In modelling role of MAC and PICyts in lowering of SARS-CoV-2 titre was found coherent with experimental studies. Intervention in upregulation of C5a and PICyts by C3 was found helpful in back-and-forth variation of signalling pattern linked with the levels of PICyts. Moreover, intervention in upregulation of PICyts by C5a was found productive in downregulation of all activating factors in the normal SCCs. However, the computational model predictions require experimental studies to be validated by exploring the activation role of C3 and C5a which could change levels of PICyts at various phases of SARS-CoV-2 infection.

키워드 네트워크 분석을 활용한 전공특성별 진로준비행동 연구 동향 분석: 간호학과와 체육계열 비교를 중심으로

Objectives The purpose of this study was to examine whether college major characteristics make a difference
 in research on career preparation behavior of college students through network analysis.
 Methods Among the papers published on the academic research information service (www.riss.kr), papers related
 to career preparation behavior of college students were extracted, classified by major, and among them, papers
 related to nursing and physical education were selected for analysis. And keyword frequency analysis and
 centrality analysis were conducted and visualized.
 Results As a result of the network analysis, the influential keywords in the nursing department were ‘self-leadership’,
 ‘major satisfaction’, ‘nursing professionalism’, and ‘career decision self-efficacy’. Compared to other majors,
 the influence of ‘career decision self-efficacy’ was very low, and ‘career barriers’ never appeared. Keywords related
 to majors, such as male nursing college students, appeared frequently. In the field of physical education, influential
 keywords were ‘career decision self-efficacy’, ‘major satisfaction’, ‘grit’, ‘career decision level’, ‘social
 support’, and ‘positive psychological capital’. Physical education majors experienced more difficulties in career
 preparation than other majors due to limited employment opportunities and insufficient career preparation
 education. Therefore, keywords such as ‘grit’, ‘social support’, and ‘positive psychological capital’ often appeared
 to help overcome these difficulties.
 Conclusions Since the career preparation behavior of college students showed a discriminatory aspect according
 to the characteristics of their major, the characteristics of their major should be considered in career preparation
 education and career counseling.

Structural Characteristics of Intergovernmental Water Pollution Control Cooperation Networks Using Social Network Analysis and GIS in Yangtze River Delta Urban Agglomeration, China

Water pollution exhibits distinct negative externalities, necessitating trans-regional collaborative governance among basin governments. However, few studies have examined the structural features of water pollution collaboration networks among local governments in China from a spatial analysis perspective. This study focuses on 27 central cities in the Yangtze River Delta, collecting 109 policy texts and evidence of cooperative actions on water pollution governance among these cities. By utilizing a combination of social network analysis and GIS spatial analysis, the research visualizes the results and delves into the overall structure and internal features of the network. The results indicate that the density of the water pollution cooperation network is 0.75, suggesting that a relatively stable and closely connected network for collaborative governance of water pollution has been formed. Furthermore, the water pollution cooperation in the Yangtze River Delta exhibits a typical “multi-center” network structure, with Shanghai–Suzhou, Nanjing, and Hangzhou as the core, forming three city clusters with tighter cooperation. Suzhou, Shanghai, and Jiaxing have the highest degree of centrality, which are 51, 46, and 44, respectively. The analysis of degree centrality reveals that cities with higher levels of economic development or those that serve as provincial capitals often play a leading role in the cooperation network. The study also observes that adjacent cities or local governments closer to the core cities are more likely to establish cooperative relationships; this phenomenon is not limited by provincial administrative boundaries.

Byzantine Fault-Tolerant Consensus Algorithms: A Survey

The emergence of numerous consensus algorithms for distributed systems has resulted from the swift advancement of blockchain and its related technologies. Consensus algorithms play a key role in decentralized distributed systems, because all nodes in the system need to reach a consensus on requests or commands through consensus algorithms. In a distributed system where nodes work together to reach consensus, there may be Byzantine nodes present. The emergence of Byzantine nodes will affect the consensus of nodes in the distributed system. Therefore, tolerating Byzantine nodes in a distributed system and then reaching a consensus is an essential function of a consensus algorithm. So far, many Byzantine fault-tolerant (BFT) consensus algorithms have emerged, and there are correspondingly many methods to improve the performance of these algorithms. In order to allow researchers to have a clearer understanding of the existing methods, this paper systematically investigated and studied the research progress of the current Byzantine fault-tolerant consensus algorithm. The scope of the research ranged from the classic Byzantine consensus algorithm to some of the latest Byzantine consensus algorithms. The articles were classified according to the methods used to improve the Byzantine consensus algorithm. Through classification and centralized analysis and discussion, we achieved a clearer understanding of the development of Byzantine consensus algorithms and, at the same time, clarified the advantages and disadvantages of this type of method and the latest research progress using this method. At the end of this article, an in-depth discussion and analysis is also presented. By analyzing the impact of the use of these methods on the performance of the BFT consensus algorithm, it is proposed that future research can be improved.

Altered brain degree centrality and functional connectivity in adults with executive dysfunction after trauma exposure

Losing an only child is undoubtedly a huge blow that can adversely affect the prefrontal lobe, a highly sensitive brain region. Neuropsychological evidence emphasizes that executive function (EF) is closely related to the optimal functioning of the frontal cortex. However, the characteristics and potential mechanisms underlying changes in executive function following the huge shock of losing an only child remain insufficiently studied and understood. In this study, we performed degree centrality (DC) and functional connectivity (FC) analyses to explore the organization of the executive function deficits (EFD) network among adults who have lost their only child. In addition, we performed correlation analyses to establish an association between abnormal DC and FC values and post-traumatic stress symptoms. Finally, we used support vector machine analyses to assess the accuracy of abnormal DC and FC values in distinguishing adults with EFD who have lost their only child from those without EFD. Our findings revealed increased DC in the left superior frontal gyrus and right angular gyrus (ANG), whereas decreased DC in the left superior occipital gyrus among adults with EFD. Further FC analysis revealed that the altered FC primarily involved the prefrontal and temporal lobes and cerebellum. Notably, the altered FC between the right ANG and left inferior temporal gyrus exhibited a negative correlation with irritability symptoms (R = -0.047, p = 0.003) in the EFD group. A combined model incorporating altered DC and FC values enabled the classification of 96.69% of adults with EFD, with a sensitivity of 0.8837 and specificity of 0.9558. These findings provide valuable insights into the neural mechanisms underlying distinct EF statuses following trauma exposure, distinguishing adults with and without EFD.

빅데이터 분석을 이용한 코로나 19 이후 경호 관련 학과의 사회적 인식 분석

Objectives The purpose of this study is to analyze society's overall perception of universities' security-related departments through big data, provide basic data for the development of security-related departme.
 Methods Under the keyword “University + Security,” data from major domestic portal sites were collected and refined from January 20, 2020 to March 31, 2023, when the first COVID-19 confirmed case in Korea was found, and word cloud analysis, network analysis and centrality analysis, and CONCOR analysis were conducted.
 Results First, through word cloud analysis of the top 30 keywords of frequency, ‘police’ was found to be an overwhelmingly frequent keyword. Second, in network analysis, “police” was confirmed to be the most important keyword, and words such as “police”, “security”, “administration”, “major”, and “sports” showed high connectivity in connection centrality analysis. In the mediation centrality analysis, it was confirmed that most keywords serve as bridges between nodes and are actively connected. Third, in the CONCOR analysis, it was confirmed that it was clustered into six groups, and the cluster name was determined as ‘public security and After Graduation’, ‘Curriculum and Employment’, ‘University Promotion’, ‘physical education’, ‘Daegu’, and ‘Other’.
 Conclusions It was confirmed that the police and security have a close relationship in people's perceptions, and it is believed that it would be a desirable direction to properly utilize them and reflect them in security-related departments. IIt is necessary to develop and apply a curriculum that can strengthen the systematicity of education and enhance expertise to induce students' interest. It is necessary to use it as an advantage of security-related departments by increasing positive awareness and interest in bodyguards, and it will be helpful to prepare and present specific plans for students' careers and post-graduation plans, and to find and advance to public security.

외식산업 이직 관련 연구 동향 분석

In this study, text network analysis, one of the content analysis methods, was used to comprehensively and holistically analyze the cumulative performance of turnover-related studies in the food service industry. For this purpose, 346 articles registered in the academic research information service from 1998, when the first article on turnover in the food service industry was published, to April 2023 were analyzed, divided into the pre-turnover period (1998~2009) and the post-turnover period (2010~2023). Key word frequency analysis, centrality analysis, co-occurrence frequency analysis, and CONCOR analysis were conducted, and the results of this study are as follows. First, the number of turnover studies in the foodservice industry has increased dramatically since 2010. Second, in the later period of turnover research, the topics and subjects of turnover research have become more diverse compared to the earlier period of turnover research. Third, it can be seen that the concepts of turnover intention, job satisfaction, organizational commitment, and job stress are the most important and influential words in turnover research in the foodservice industry.

Exploring a New Methodology for Studying Korean Ancient History Using Network Analysis: Focusing on negotiation data from the Eastern Jin and Sixteen Kingdoms to the Song and Northern Wei period

Network analysis is a methodology that helps understand complex phenomena by visualizing member interactions. In the context of Eastern Jin, Sixteen Kingdoms, Song, Northern Wei period, network analysis can shed light on the position of Paekche and Koguryŏ by analyzing negotiation networks. While existing studies have focused on negotiations between these states and China, few have visualized the entire negotiation network or compared the positions of Paekche and Koguryŏ within the broader East Asian network. This paper explores the network analysis methodology for ancient East Asian negotiation data and conducts a pilot analysis of specific periods.</br>The methodology for applying network analysis to ancient history involves several steps, including evaluating its suitability, quantifying the data, verifying data reliability, and analyzing and visualizing the data. Limitations of using network analysis to study ancient history include obtaining sufficient data and verifying data reliability. Ancient East Asian negotiation data is relatively more abundant than other records, making it a good candidate for network analysis. However, because negotiation data is recorded from the perspective of various actors, it is essential to verify the reliability of the data by ancient history researchers.</br>This paper theoretically analyzes the negotiation data of the Eastern Jin, Sixteen Kingdoms, Song, and Northern Wei periods. The negotiation network analysis reveals that Eastern Jin is the most centralized country, with Koguryŏ actively engaging in negotiations and Paekche focusing on diplomacy with Eastern Jin. The centrality analysis on the negotiation frequency data during Song and Northern Wei period shows that the Song and Northern Wei had the highest centrality in negotiation frequency among 28 countries, with Koguryŏ, Tuyuhun, and Paekche also prominent. The negotiation route analysis reveals the flow of information, with the Song having the highest betweenness centrality, Koguryŏ serving as a conduit to Shilla, and Paekche controlling the route to Mahan, Kaya, and Wa. Paekche plays a crucial role as a conduit between various regions, despite having lower centrality than Koguryŏ.</br>In conclusion, this paper explored the methodology of applying network analysis to ancient East Asian negotiation data and attempted to understand the structural structure of ancient East Asian negotiation networks by analyzing negotiation data from a specific time period.

Research on the current situation and development countermeasures of Chinese sports industry companies in the context of big data

Abstract To solve various problems in the development of the sports industry and enhance the pulling effect of the sports industry on the national economy. This paper first analyzes the development scale, the number of institutions and the number of people in China’s sports industry at present through sports industry-related indicators. It further constructs an index system to evaluate the comprehensive strength of the regional sports industry. Secondly, the factors affecting the spatial differences in the comprehensive strength of the sports industry are studied, and the spatial effect test and industrial network centrality analysis are conducted. A geographically weighted regression (GWR) power-driven model was further established for empirical analysis. Finally, the estimated parameters of the model were spatially visualized, and the influence of each influencing factor on the comprehensive strength of the local sports industry was analyzed in depth. The results show that the adjusted goodness-of-fit of the GWR model proposed in this paper is 0.8929, which is closer to 1 than 0.8073 of the OLS regression model, indicating that the GWR model has a better fit. Meanwhile, the AICc value of the GWR model is 5.4177, which is also smaller than that of the OLS regression model. The GWR model has a significant positive contribution to the development of the sports industry in each region in terms of the explanatory variables proposed government input, scientific and technological innovation and talent pool, which are all important to enhance the economic benefits of the sports industry in each region.

팀기반학습으로 운영된 유아동작 모의수업 경험에 대한 의미연결망 분석

Objectives This study examined the meaning and impact of team-based learning (TBL) on early childhood movement education by analyzing the reflective materials on teaching practices written by prospective early childhood teachers participating in TBL.
 Methods The participants of this study were 45 students enrolled in a 4-year early childhood education program in G City. The movement education class they took involved the design and demonstration of simulated lessons, which were conducted using the Team-Based Learning (TBL) approach. After completing a semester of classes, the learners wrote self-reflection materials on TBL and simulated lessons in the context of movement education. The qualitative data from these materials were analyzed using KrKwic and UCINET to extract keywords and perform centrality and semantic network analysis.
 Results Firstly, the common theme keywords that emerged from the TBL activities and simulated teaching demonstration experiences were ‘lesson plan’, ‘activity’, ‘movement’, ‘class’, ‘children’, ‘simulated teaching’, ‘feedback’, and ‘interest’. Secondly, the keywords associated with TBL activities included ‘time’, ‘team members’, ‘groups’, ‘opinions’, ‘active participation’, ‘challenges’, ‘parts’, ‘materials’, ‘expression’, ‘practice’, ‘modification’, and ‘utilization’. The semantic network analysis of TBL activities revealed a meaningful structure related to simulated teaching and the experiences of TBL activities. Thirdly, the keywords identified in relation to simulated teaching experiences were ‘teacher’, ‘thoughts’, ‘progress’, ‘experience’, ‘field’, ‘assistance’, ‘application’, ‘methods’, ‘process’, ‘rules’, ‘improvement’, and ‘demonstration’. The keyword ‘teacher’ showed high values in terms of linkages, degree centrality, closeness centrality, and betweenness centrality. The proximity centrality of ‘progress’, ‘experience’, and ‘application’ was high, while the betweenness centrality was low. The semantic network structure reflected the effectiveness of learning and application through the preparation and implementation phases of simulated teaching.
 Conclusions The semantic network analysis of early childhood movement education simulated teaching experiences conducted through TBL revealed that prospective early childhood educators perceive TBL activities and simulated teaching experiences as highly significant. These findings underscore the importance of learner-engaged instructional approaches in enhancing the teaching competencies of prospective early childhood educators.

Analysis of Weather Factors on Aircraft Cancellation using a Multilayer Complex Network.

Airlines provide one of the most popular and important transportation services for passengers. While the importance of the airline industry is rising, flight cancellations are also increasing due to abnormal weather factors, such as rainfall and wind speed. Although previous studies on cancellations due to weather factors considered both aircraft and weather factors concurrently, the complex network studies only treated the aircraft factor with a single-layer network. Therefore, the aim of this study was to apply a multilayer complex network (MCN) method that incorporated three different factors, namely, aircraft, rainfall, and wind speed, to investigate aircraft cancellations at 14 airports in the Republic of Korea. The results showed that rainfall had a greater impact on aircraft cancellations compared with wind speed. To find out the most important node in the cancellation, we applied centrality analysis based on information entropy. According to the centrality analysis, Jeju Airport was identified as the most influential node since it has a high demand for aircraft. Also, we showed that characteristics and factors of aircraft cancellation should be appropriately defined by links in the MCN. Furthermore, we verified the applicability of the MCN method in the fields of aviation and meteorology. It is expected that the suggested methodology in this study can help to understand aircraft cancellation due to weather factors.

Advance Electric vehicle System for Minimization of wiring Harness Using IoV

The rapid growth of the electric vehicle (EV) market demands innovative solutions to optimize vehicle design, improve efficiency, and enhance user experience. Traditional vehicles heavily rely on extensive wiring harnesses to interconnect various components and systems, leading to increased weight, complexity, and production costs. This abstract presents an advanced electric vehicle system that leverages the power of the Internet of Vehicles (IoV) to minimize wiring harnesses, utilizing components such as Wi-Fi, Thinkspeak cloud, Buzzer, solar plat, charging storing in Battery, LED indicators, voltage and current sensor and other smart electronic devices. The proposed system employs Wi-Fi connectivity as a robust wireless communication technology within the vehicle. By utilizing Wi-Fi, the need for physical wiring is significantly reduced, enabling seamless data transfer between vehicle components and the central control unit using ESP-32 now wireless Communications. This approach streamlines the vehicle's internal architecture, leading to a cleaner and lighter design, ultimately improving energy efficiency and reducing manufacturing complexity. Furthermore, the integration of the Thinkspeak cloud serves as a centralized data storage and analysis platform. Thinkspeak cloud enables real-time monitoring and remote access to critical vehicle parameters and performance metrics, facilitating efficient fleet management and predictive maintenance. The cloud-based solution enhances the vehicle's connectivity and allows for over-the-air updates, enabling continuous improvements and adaptability to emerging technologies.

Cross-market risk spillovers among sovereign CDS, stock, foreign exchange and commodity markets: An interacting network perspective

With the globalisation of the economy and financial markets, cross-market risk spillovers have become increasingly prominent, affecting global financial stability. Using the Diebold-Yilmaz Connectedness Index (DYCI) method, we construct interacting networks to explore these risk spillovers across four multi-country markets: sovereign credit default swap (SCDS), stock, foreign exchange, and commodity markets. We find: First, at the system-level, multiple inter-layer connections between the SCDS and other markets demonstrate the high level of cross-market risk spillovers. They are sensitive to major economic and financial events, such as the COVID-19 pandemic and Russia-Ukraine conflict. SCDS has the closest spillovers with stocks. The block model analysis shows that all relationships between various blocks contain inter-layer connections, and the SCDSs of emerging countries and stocks of developed countries are the sources of risk spillover in the entire system. Second, at the country-level, SCDS' cross-market spillover strength changes by country and time. Five cross-node centrality analysis shows that the SCDSs of emerging countries are the risk spillover engines between the SCDS and other markets. The spillovers of a country's SCDS to gold (oil) are more sensitive to the Fed rate hike in 2016 (the European debt crisis and Russia-Ukraine conflict). Third, to understand the driving factors, we perform time series and panel regressions for cross-market risk spillovers and cross-node centrality, respectively. The influences of economic fundamentals and market sentiment are asymmetric in high- and low-risk spillovers. The greater the total economic linkages, such as trade and capital flows, the greater are the countries' cross-market risk spillovers.

국내 관광산업 생태계 네트워크 분석

The tourism industry is undergoing rapid structural changes due to economic, technological, and social transformations. This study aims to identify the structure and characteristics of the domestic tourism business ecosystem using corporate data. Employing the business ecosystem theory and social network analysis techniques, the influence of key companies and the connectivity between companies are examined. The findings reveal several important insights. First, the analysis of connection centrality indicates that the travel business and the Korea Tourism Organization are core companies that engage in extensive interactions within the ecosystem. Contrary to previous studies categorizing public institutions as secondary stakeholders, this research demonstrates their high influence as core companies within the tourism industry. Second, proximity and centrality of prestige analyses highlight that companies involved in aviation and passenger transport hold top ranks. These transportation-related companies exert rapid and significant ripple effects within the ecosystem. Furthermore, the study identifies the tertiary supply chain through ego-network analysis of key companies such as Korean Air, Hana Tour, and Hotel Lotte. It reveals the involvement of various industries beyond tourism, including system and application software development, supply chain management, and telecommunication sales. This empirical insight provides a glimpse into the digital transformation occurring within the tourism industry. Based on these results, the study puts forth academic and policy implications for the development of a healthy tourism business ecosystem. Understanding the flow within the tourism industry and its interactions with related industries is essential for adapting to ongoing economic and technological changes, thereby fostering a sustainable and competitive tourism sector.