Analysing the Impact of Pedestrian Network Centrality on Segment-Level Pedestrian Density

Studies have shown that street network centrality measures are capable of explaining a significant proportion of pedestrian activity. These studies typically employ street centreline networks that differ significantly from the networks that pedestrians use to traverse the built environment. Presently, centrality approaches are rarely applied to dedicated pedestrian network (DPNs). This creates uncertainty regarding their ability to explain pedestrian activity when derived from DPNs. This study addresses that gap by investigating the extent to which centrality metrics derived from DPNs can explain observed pedestrian densities, both alone and when controlling for other built environment variables in metro station environments in Asia. In total, four DPNs were created centred on metro stations in Bangkok, Manila, Osaka, and Taipei chosen to represent different urban typologies. Multivariate results show that centrality metrics alone explain a mere 6–24% of observed pedestrian densities when calculated on DPNs. When all factors are considered, the contribution of centrality remained consistent in most study sites but is somewhat reduced with land-use variables and proximity to rail transit revealed as the strongest predictors of pedestrian density. Pedestrian design factors were also frequently associated with pedestrian density. Finally, stronger associations between centrality and pedestrian densities were observed in the denser, more complex pedestrian environments. These findings provide insight into the performance of centrality measures applied to DPNs expanding pedestrian network research in this area.

Research on the Performance of Knowledge Co-Creation of Science and Technology Enterprises Based on IUR Network

The current research on knowledge co-creation mostly starts from the perspective of process, studying the process of knowledge co-creation, but there is very little research on the performance of knowledge co-creation. As the carrier of enterprise knowledge co-creation, the industry-university-research network (IUR network) provides a platform for enterprise knowledge co-creation. The purpose of this article is to explore the influence of the centrality of the IUR network on the performance of corporate knowledge co-creation, and the mediating role of corporate absorptive capacity. Technology companies are knowledge-intensive companies and have more knowledge co-creation behaviors. Therefore, this article selects the top 100 technology companies in China’s electronic information industry from 2015 to 2019 as the research sample, and establishes the IUR network based on their cooperative patent data. Our empirical results show that: (1) in the IUR network, the higher the network centrality, the enterprise may have better knowledge co-creation performance; (2) the centrality of the industry-university-research network has a significant role in promoting absorptive capacity of enterprises; (3) the absorptive capacity of enterprises has a complete intermediary effect between the centrality of the IUR network and the knowledge co-creation of technology-based enterprises. This research uses the IUR network to study the performance of knowledge co-creation, which further enriches the related research fields of knowledge co-creation.

EMPLOYEE MOBILITY AND INNOVATION: THE ROLE OF TURNOVER RATE AND NETWORK CENTRALITY

Innovation is crucial to a company’s competitive advantage and employees play an important role in generating innovation within a company. Based on social capital theory, we proposed a new type of social network: the employee mobility network, and explored the impact of employee mobility on innovation. Specifically, we examined the role of both employee turnover rate, and an organisation’s centrality in the employee mobility network in predicting innovation. We collected data from World Intellectual Property Organisation (WIPO), Talentale, and Forbes Global 2000 to test our hypotheses. The results showed that turnover rate had a significantly inverted-U curve relationship with innovation, and both degree and closeness centralities of an organisation in the employee mobility network had a significant positive relationship with innovation. Based on the results, we suggest that companies should find a balanced value for their turnover rate to get the highest return in innovation. Also, we suggest that companies should improve social influence in employee mobility networks in order to attract talent and increase company innovation.

Analysing the determinants of Italian university student mobility pathways

In this paper, we study the mobility choices of Italian students in their transition from a bachelor’s to a master’s degree level with an added emphasis on their overall mobility pathways. We consider individual data from the Italian National Student Archive on two cohorts of students who were enrolled in the academic years 2011–2012 and 2014–2015. We followed both cohorts in Italian universities for six academic years. This allowed us to depict five different profiles of students, categorise them as stayers vs. movers, and work at two different levels. Logit models were then adopted to study the probability to be in mobility at a master’s level, given that a student had been a stayer at bachelor’s degree, and to assess the effect of the field of study. Apart from individual characteristics, network centrality measures were encompassed in the model to assess the university attractiveness in influencing mobility choices.

Relationship between Social Network Characteristics, Flow in Class, Communication Skills, and Problem-solving Skills of Nursing Students in Simulation Nursing Education: Focusing on Social Network Analysis

Purpose: The purpose of this study was to investigate the relationship between social network characteristics, flow in class, communication skills, and problem-solving skills of nursing students in simulation.Methods: For this study a descriptive survey design was used. Participants were 100 nursing students who attended in 2 university and completed the self-report questionnaire. Data were collected from October 14 to December 6, 2019 and were analyzed with SPSS 26.0, AMOS 21.0 and Netminer 4 evaluation version.Results: It was confirmed that the task advice network centrality of nursing students fully mediates their communication skills and affects their problemsolving skills and that friendship network centrality completely mediates flow in class and communication skills, and affects problem-solving skills. Task advice network centrality, friendship network centrality, flow in class, and communication skills were found to explain problem solving skills by 51.8%.Conclusion: In order to increase the problem-solving skills of nursing students in simulation nursing education, a strategy to improve their flow in class and communication skills is essential and it suggests the need to make it part of the curriculum. In addition, in order to increase the communication skills of nursing students, it is necessary to be careful when making a team to minimize the team members isolated within the team so that smooth interaction can occur.

Influence of Scientific Collaboration Network on Academic Performance

Collaboration in science is a complex phenomenon that affects scientific performance in various ways. Thus, understanding the influences of the research collaboration network is important for researchers. This paper explores the relationship between research collaboration network structural and scientific research performance and conducts an empirical test with data from 416 scholars. Findings revealed that network stability reduces the scholars' research performance, and network centrality promotes research performance. The network structural holes that the scholar spans, moderate the detrimental effects of network stability. This research provides suggestions for scholars to build a reasonable scientific research collaboration network to improve their research performance.

The structure, centrality, and scale of urban street networks: Cases from Pre-Industrial Afro-Eurasia

Cities and towns have often developed infrastructure that enabled a variety of socio-economic interactions. Street networks within these urban settings provide key access to resources, neighborhoods, and cultural facilities. Studies on settlement scaling have also demonstrated that a variety of urban infrastructure and resources indicate clear population scaling relationships in both modern and ancient settings. This article presents an approach that investigates past street network centrality and its relationship to population scaling in urban contexts. Centrality results are compared statistically among different urban settings, which are categorized as orthogonal (i.e., planned) or self-organizing (i.e., organic) urban settings, with places having both characteristics classified as hybrid. Results demonstrate that street nodes have a power law relationship to urban area, where the number of nodes increases and node density decreases in a sub-linear manner for larger sites. Most median centrality values decrease in a negative sub-linear manner as sites are larger, with organic and hybrid urban sites’ centrality being generally less and diminishing more rapidly than orthogonal settings. Diminishing centrality shows comparability to modern urban systems, where larger urban districts may restrict overall interaction due to increasing transport costs over wider areas. Centrality results indicate that scaling results have multiples of approximately ⅙ or ⅓ that are comparable to other urban and road infrastructure, suggesting a potential relationship between different infrastructure features and population in urban centers. The results have implications for archaeological settlements where urban street plans are incomplete or undetermined, as it allows forecasts to be made on past urban sites’ street network centrality. Additionally, a tool to enable analysis of street networks and centrality is provided as part of the contribution.

Peran Media Sosial Terhadap Kompetensi Mahasiswa Ekowisata Unipa Menunjang Promosi Pariwisata Rajaampat

Penggunaan media sosial sangat mudah, hemat biaya, dan efektif dalam mempromosikan objek wisata khususnya Rajaampat yang sudah terkenal hingga internasional. Peranan civitas akademika terhadap promosi pariwisata Rajaampat menjadi sangat penting dikaji, terlebih setelah masa pandemi covid-19 yang membawa kondisi dunia pariwisata terpuruk. Penelitian ini menggunakan metode kualitatif deskriptif. Populasi sasaran dalam penelitian ini adalah mahasiswa yang berstatus aktif sebagai mahasiswa ekowisata Rajaampat Universitas Papua. Penarikan sampel berdasarkan teknik purposif (purposive sampling) kepada mahasiswa aktif yang memiliki media sosial yang berjumlah 10 mahasiswa. Analisis berdasarkan keempat kategori yang telah ditetapkan yaitu tie strength (kekuatan sebuah hubungan), network density (kepadatan jaringan), network centrality (sentralitas jaringan) dan homophile (kesamaan karakteristik). Cara analisis dengan memilah seluruh isi pesan serta mengkategorikannya ke dalam masing – masing karakteristik struktur jaringan media sosial yang sesuai. Hasil penelitian menyimpulkan bahwa kategori tie srenght (kekuatan hubungan), network density (kepadatan jaringan), network centrality (sentralitas jaringan) serta kesamaan karekteristik (homophile) terjalin dalam keseluruhan akun facebook saat memposting sebuah foto.

Centrality of drug targets in protein networks

Background In the pharmaceutical industry, competing for few validated drug targets there is a drive to identify new ways of therapeutic intervention. Here, we attempted to define guidelines to evaluate a target’s ‘fitness’ based on its node characteristics within annotated protein functional networks to complement contingent therapeutic hypotheses. Results We observed that targets of approved, selective small molecule drugs exhibit high node centrality within protein networks relative to a broader set of investigational targets spanning various development stages. Targets of approved drugs also exhibit higher centrality than other proteins within their respective functional class. These findings expand on previous reports of drug targets’ network centrality by suggesting some centrality metrics such as low topological coefficient as inherent characteristics of a ‘good’ target, relative to other exploratory targets and regardless of its functional class. These centrality metrics could thus be indicators of an individual protein’s ‘fitness’ as potential drug target. Correlations between protein nodes’ network centrality and number of associated publications underscored the possibility of knowledge bias as an inherent limitation to such predictions. Conclusions Despite some entanglement with knowledge bias, like structure-oriented ‘druggability’ assessments of new protein targets, centrality metrics could assist early pharmaceutical discovery teams in evaluating potential targets with limited experimental proof of concept and help allocate resources for an effective drug discovery pipeline.