The recent development of characteristic towns has encountered a multitude of challenges and chaos. Nevertheless, there have been many instances of information asymmetry due to the absence of an effective management model and an intuitive digital management system. Consequently, this has caused the erosion of public interests and inadequate supervision by public agencies. As society is progressing at a rapid pace, there is a growing apprehension regarding poor management synergy, outdated management practices, and limited use of technology in traditional construction projects. In today's technologically sophisticated society characterized by the “Internet+” and intelligent management, there is an urgent requirement to identify a more efficient collaborative management model, thereby reducing errors caused by information asymmetry. This paper focuses on the integration of building information modeling (BIM) and integrated project delivery (IPD) for collaborative management within characteristic towns in the PPP mode. By analyzing the available literature on the application status, this study investigates the implementation methods and framework construction of collaborative management while exploring the advantages and disadvantages. On this basis, this study highlights the problems that arise and provides recommendations for improvement. Considering this, the application of the BIM-based IPD model to characteristic towns in PPP mode will enhance the effectiveness of collaborative management among all parties involved, thereby fostering an environment that facilitates decision-making and operational management in the promotion of characteristic industries.

With the continuous development and rapid progress of Internet technology, the technology of “Internet +” has been widely used in almost all walks of life, including education. The new learning mode of “Internet + education” is changing learners’ learning habits, and this learning mode has become a hot issue that scholars pay attention to. Although there is much research on blended learning, the research on the influencing factors of blended learning in Chinese private colleges and universities is limited. In this paper, the questionnaire was designed based on the theory of planning behavior and the technical acceptance model theory, and distribute these questionnaires to undergraduates at Harbin Cambridge University, a private university in China, and 162 valid questionnaires were collected. Analysis was performed by multiple linear regression and structural equation model method. It is found that college students’ blended learning effect is positively correlated with perceived usefulness, interactive behavior, and learning acceptance, while perceived ease of use and learning atmosphere have no significant influence on the learning effect. This study further found that perceived usefulness and interactive behavior can influence the effect of blended learning through the mediating effect of learning acceptance. The results of this study provide a new idea for the study of blended learning; that is, students will know how to improve the effectiveness of blended learning, and also provide a valuable reference for teachers to solve the problem of how to improve the quality and effectiveness of blended classroom teaching.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.

Over the past 50 years, urban planning documents have been drawn up in sub-Saharan African cities without any convincing results. The study of secondary towns in Chad shows that these planning documents have been hampered by natural and man-made factors. The aim of this study is to determine the factors hindering the implementation of planning documents in the town of Pala in Chad. To carry out the study, a methodological approach (using quantitative and qualitative data) based on a questionnaire and interview survey was deployed for data collection. With a sample of 300 households surveyed, the main conclusions of the study show that all the factors identified, such as water erosion with a rate of 17.7 T/Ha/year, expose the town to various risks. Demographics, on the other hand, represent a lesser and therefore acceptable challenge. As far as exogenous factors are concerned, the level of education of the head of household is a determining factor in the implementation and acceptance of urban planning documents in Pala. Confirmatory factor analysis and the Chi² test revealed that consideration of stakeholders’ needs and their inclusion in the process of drawing up these documents are factors that significantly influence their implementation. In contrast, age, gender and other variables did not reveal any significant anomalies in our analyses. Consequently, future efforts to implement Pala’s planning documents must be based on community participation and awareness of the acceptance of these documents, which are necessary in a process of decentralization and urban planning.

The construction industry is responsible for over 40% of global energy consumption and one-third of global greenhouse gas emissions. Generally, 10%–20% of energy is consumed in the manufacturing and transportation stages of materials, construction, maintenance, and demolition. The way the construction industry to deal with these impacts is to intensify sustainable development through green building. The author uses the latest Green Building Certification Standard in Indonesia as the Green Building Guidelines under the Ministry of Public Works and People’s Housing (PUPR) Regulation No. 01/SE/M/2022, as a basis for evaluating existing office buildings or what is often referred to as green retrofit. Structural Equation Modeling-Partial Least Squares (SEM-PLS) is used by the authors to detail the factors influencing the application of green building by analyzing several variables related to the problem studied, which are used to build and test statistical models of causal models. From this study, it is concluded that the most influential factors in the implementation of green retrofitting on office buildings are energy savings, water efficiency, renewable energy use, the presence of green building socialization programs, cost planning, design planning, project feasibility studies, material cost, use of the latest technology applications, and price fluctuations. With the results of this research, there is expected to be shared awareness and concern about implementing green buildings and green offices as an initiative to present a more energy-efficient office environment, save operating costs, and provide comfort to customers.

The objective of this paper is to assess the influence of various types of crises, including the Subprime, COVID-19, and political crises, on corporate governance attributes, regulations, and the association with bank risk. The consecutive occurrences of crises have significantly impacted the global economy, causing substantial disruptions across various facets of the international banking system. Our hypothesis posits that these crises not only influence governance characteristics and regulations but also impact their correlation with the risk and financial distress experienced by banks. Our study is conducted within the Tunisian context spanning from 2000 to 2021, utilizing a GMM regression on a dataset comprising 221 bank-year observations. Our findings indicate that crises have a discernible effect on the relationship between corporate governance and bank risk, as well as between regulation and bank risk. Our results are strong in a range of sensitivity checks, including the use of alternative proxies to measure the bank risks and corporate governance metrics.