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.

Japan’s investment in the domestic construction industry has fallen to less than half its peak in 1992. Given the country’s declining population, Japanese construction companies must go global to remain profitable. To what extent the Japanese government and Japanese companies can contribute to meeting the growing infrastructure needs in the region is unclear as Japanese companies have long been operating primarily in Japan. The Japanese government has in recent years passed a series of new laws that encourage private sector participation in financing, building and operating public infrastructure. Through involvement in such public projects, Japanese companies have developed the skills and technologies to build a variety of infrastructures that are resilient to natural disasters and adaptable to various geographical conditions and social and economic development. But the major challenge for Japanese companies is to transform their business model drastically from one that relies on the domestic market to one that contributes to the social and economic development of third countries.

The recession cone and recession function are very important research objects in Convex Analysis. They have extensive applications in the optimization theory. Firstly, we study the properties of the recession cone and recession function. The positive homogeneity and subadditivity of recession function are mainly discussed. And the different methods are considered to prove these properties. Secondly, we discuss the unboundedness of the convex sets and convex functions by using recession cone and recession function.

In the current context of new engineering, the teaching of the course "Civil Engineering Construction Organization and Management" should be targeted and focused. In terms of setting up the course content, schools need to engage in extensive communication and cooperation with enterprises and industry associations, and integrate more practical education elements into the teaching methods to ensure that students can achieve a unity of knowledge and action; In relevant course teaching, teachers should also introduce more ideological and political elements to improve students' ideological and moral literacy. This article analyzes and explores the teaching reform of the course "Civil Engineering Construction Organization and Management" in the context of the new engineering discipline.

With its inherent characteristics of decentralization, immutability, and transparency, blockchain technology presents a promising opportunity to revolutionize the South African food supply chains. Blockchain technology, with its decentralized, immutable, and secure nature, offers solutions to these challenges by improving traceability and accountability across the supply chain. This study investigates the role of blockchain technology in enhancing transparency in the food supply chain among small and medium enterprises in South Africa. SMEs form a critical part of the country's agri-food sector but face challenges such as food fraud, inefficient inventory management, and lack of transparency, which impact food safety and trust. The research adopts a mixed-method approach, utilizing the Technology-Organization-Environment framework and Institutional Theory to explain blockchain adoption among SMEs. The results demonstrate that blockchain-enabled practices, such as smart contracts, records traceability, production tracking, and distribution monitoring, significantly enhance supply chain transparency. The findings highlight blockchain's potential to increase operational efficiency, regulatory compliance, and stakeholder trust. This research provides valuable insights for policymakers and practitioners, emphasizing the need for regulatory support and strategic investment in blockchain solutions to promote sustainability and competitiveness in the agri-food sector.