In-depth analysis of nuclear data flow using graph theory and the Technology, Organization, People Model through the application of betweenness centrality measure and community detection

The incorporation of digital technologies is an essential factor in enhancing the efficiency and performance of nuclear facilities throughout their lifecycle. This encompasses various stages, from the design phase to their operational period. However, the nuclear industry faces significant challenges due to the intricate and diverse nature of its stakeholders, supply chain, and activities.Moreover, the exponential growth of complex information systems has created substantial challenges in data management within the nuclear sector. The traditional approaches to managing data often result in inefficiencies, inconsistencies, and inaccuracies, which can have severe consequences on the performance of nuclear facilities.To address these critical issues, this study proposes the application of graph theory for analyzing nuclear data flow and integrating human system integration (HSI) through the Technology Organization People (TOP) Model. The TOP Model is a comprehensive framework that considers the technological, organizational, and social aspects of complex systems, providing a holistic approach to understanding interactions within the information system.The dataset used in this study was synthetically generated to emulate real-world operations and provide a more accurate representation of the nuclear data information system.This study employs two graph theory methods to analyze the nuclear data flow: betweenness centrality measure and spectral clustering. The betweenness centrality measure is used to identify critical nodes within the data network that are most central or influential in terms of data flow, thereby highlighting the key components involved in data transmission. Spectral clustering is employed to group similar nodes within the data network, sharing common data transmission characteristics, thereby facilitating insight into the underlying structure and dynamics of the nuclear data flow. This comprehensive approach facilitates more informed decisions regarding data management and optimization of data flux. The results of this study demonstrate the potential of this innovative methodology, graph theory, and the TOP Model, offering a new way to address the challenges faced by the nuclear industry in managing nuclear complex information systems. The findings underscore the significance of integrating human factors into data management and provide a framework for enhancing the efficiency of nuclear facilities throughout their lifecycle.This study makes a contribution to the development of more effective strategies for managing complex information systems within the nuclear sector, with implications for enhancing the performance and efficiency of nuclear facilities.