Bibliometric analysis is a commonly used tool to assess scientific collaborations within the researchers, community, institution, regions and countries. The analysis of publication records can provide a wealth of information about scientific collaboration, including the number of publications, the impact of the publications, and the areas of research where collaborations are most common. By providing detailed information on the patterns and trends in scientific collaboration, these tools can help to inform policy decisions and promote the development of effective strategies to support and enhance scientific collaborations between countries. This study aimed to analyze and visualize the scientific collaboration between Japan and Russia, using bibliometric analysis of collaborative publications from the Web of Science (WoS) database. The analysis utilized the bibliometrix package within the R statistical program. The analysis covered a period of two decades, from 2000 to 2021. The results showed a slight decrease in co-authored publications, with an annual growth rate of −1.26%. The keywords and thematic trends analysis confirmed that physics is the most co-authored field between the two countries. The study also analyzed the collaboration network and research funding sources. Overall, the study provides valuable insights into the current state of scientific collaboration between Japan and Russia. The study also highlights the importance of research funding sources in promoting and sustaining scientific cooperation between countries. The analysis suggests that more efforts in government funding are needed to increase collaboration between the two countries in various fields.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

This study deals with the impact of Vietnam bank size, loans, credit risk, and liquidity on Vietnam banks’ net interest margin, which are crucial for economic development. High profit margins result in a lower bad debt ratio due to timely loan collection and good liquidity. This study applies a panel data model to evaluate the relationship among bank size, loans, credit risk, liquidity, and marginal profitability, which are increasingly important in commercial bank growth. Data were collected from 2010 to 2022, and test methods were applied to select a good-fit model. Realizing that the factors that have a close correlation and affect the profit margin are 33.6% and 16.07%, 75.2%, 37.51%, 64.30%, and 41.11%, and R2 is 59.04%, respectively, this suggests that financial managers need to develop appropriate strategies and policies to adjust the factors that adversely affect commercial bank profitability.

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

The rapid expansion of smart cities has led to the widespread deployment of Internet of Things (IoT) devices for real-time data collection and urban optimization. However, these interconnected systems face critical cybersecurity risks, including data tampering, unauthorized access, and privacy breaches. This paper proposes a blockchain-based framework designed to enhance the security, integrity, and resilience of IoT data in smart city environments. Leveraging a private blockchain, the system ensures decentralized, tamper-proof data storage, and transaction verification through digital signatures and a lightweight Proof of Work consensus mechanism. Smart contracts are employed to automate access control and respond to anomalies in real time. A Python-based simulation demonstrates the framework’s effectiveness in securing IoT communications. The system supports rapid transaction validation with minimal latency and enables timely detection of anomalous patterns through integrated machine learning. Evaluations show that the framework maintains consistent performance across diverse smart city components such as transportation, healthcare, and building security. These results highlight the potential of the proposed solution to enable secure, scalable, and real-time IoT ecosystems for modern urban infrastructures.

This study analysed the behaviour of both economic and financial profitability of credit unions belonging to segment 1 in Ecuador, as well as its determinants. For this purpose, data from the financial statements of a sample of 30 credit unions between 2016 and 2022 were used by means of a multiple linear regression methodology using panel data with fixed effects after applying the Hausman test. The findings of this research showed that current liquidity and non-performing loans have a negative and significant effect on both economic and financial profitability while the past due portfolio has a positive and significant impact on the generation of profitability of the financial institutions under study. In addition, it was revealed that the rate of outflow absorption has a negative relationship with economic profitability but a positive relationship with financial profitability. Unlike previous research in the Ecuadorian context, this research is pioneering in presenting results that indicate that the determinants traditionally considered for nonfinancial institutions and banks are also valid for credit unions, even though they are organisations with different characteristics from the rest.