Housing is one of the most significant components of sustainable development; hence, the need to come up with sustainable housing solutions. Nevertheless, the sales of houses are steadily falling due to the unaffordability of houses to many people. Based on the expanded community acceptance model, this research examines the relationships between sustainable housing and quality of life with the moderating factors of knowledge, technology, and innovation in Shenzhen. Additionally, it aims to delineate the principal dimensions influencing quality of life. The study employs purposive sampling and gathers data from residents of Shenzhen via a Tencent-distributed survey. Analysis was conducted using Smart Partial Least Squares (PLS) 4.0. Results indicate a positive correlation between economic sustainability in housing and quality of life. Contrarily, the social and environmental aspects exhibited negligible impacts on quality of life. Knowledge, technology, and innovation were identified as significant moderators in the correlation among all three sustainable housing dimensions and quality of life. The findings are anticipated to enhance understanding of the perceived impacts of sustainable housing on quality of life in Shenzhen and elucidate the role of knowledge, technology, and innovation in fostering this development.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

Forest is the main carbon sink of terrestrial ecosystem. Due to the unique growth characteristics of plants, the response of their growth status and physiological activities to climate change will affect the carbon cycle process of forest ecosystem. Based on the local scale CO₂ flux and temperature observation data recorded by the FLUXNET registration site and Harvard Forest FLUX observation tower from 2000 to 2012, combined with the phenological model, this paper analyzes the impact of temperature changes on CO₂ flux in temperate forest ecosystems. The results show that: (1) the maximum NEE in 2000–2012 was 298.13 g·m^-2·a^-1, which occurred in 2010. Except in the 2010 and 2011, the annual NEE in other years was negative. (2) NEE, GPP, temperature and phenology models have good fitting effects (R² > 0.8), which shows that the stable period of photosynthesis in temperate mixed forest ecosystem is mainly concentrated in summer, and vegetation growth is the dominant factor of carbon cycle in temperate mixed forest ecosystem. (3) The linear fitting results of the change time points of air temperature (maximum point, minimum point and 0 point date) and the change time points of NEE and GPP (maximum point, minimum point and 0 point date) show that there is a significant positive correlation between air temperature and CO₂ flux (P < 0.01), and the change of air temperature affects the carbon cycle process of temperate mixed forest ecosystem.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

Addressing society’s current ethical dilemmas necessitates urgent reinforcement of moral standards. Transforming student behavior necessitates integrating legal and social measures through proactive youth cultivation within educational institutions. This study focuses on a participatory, strategic approach to managing ethics in private vocational colleges in Thailand. The researchers gathered data through structured, in-depth interviews and opinion surveys from a sample group of 400 individuals, which included directors and department heads of these colleges. The research identified two main dimensions and eleven factors essential for moral education management in private vocational colleges. The first dimension encompasses six critical factors: resource management, structural policies, educational services, personnel, financial management, and materials and equipment management. The second dimension includes five key factors: school strategies, administrative policies, teacher roles, student engagement, and community stakeholder involvement. Statistical analysis supports the causal relationship model, revealing a Chi-Square value of 33.492 and a P-value of 0.055. This comprehensive approach aims to foster sustainable moral development and cultivate desirable societal behaviors among students. It contributes to national stability and aligns with educational and vocational development plans, effectively addressing broader socio-economic challenges. The findings underscore the importance of strategic, ethical management in private vocational education institutions as a cornerstone for nurturing a morally responsible student body and supporting national development goals.

The objectives achieved in the Paris Agreement to reduce greenhouse gas emissions and reduce dependence on fossil fuels have caused, in recent years, a growing importance on sustainability in companies in order to reduce Environmental, social and economic impacts. This study is focused on understanding how the variation in West Texas Intermediate crude oil prices affects the Dow Jones Sustainability Index, and therefore the companies included in it, and vice versa. The research aims to examine the statistical properties of both indices, using fractional integration methods, the fractional cointegration vector autoregressive (FCVAR) approach and the continuous wavelet transform (CWT) technique. The results warn of a change in trend, with the application of extraordinary measures being necessary to return to the original trend, while the analysis of cointegration and wavelet analysis measures reflect that an increase in those adopted based on sustainability by the different companies that make up the index imply a drop in the price of crude oil.