The quality of indoor classroom conditions influences the well-being of its occupants, students and teachers. Especially the temperature, outside acceptable limits, can increase the risk of discomfort, illness, stress behaviors and cognitive processes. Assuming the importance of this, in this quantitative observational study, we investigated the relationship between two environmental variables, temperature and humidity, and students’ basic emotions. Data were collected over four weeks in a secondary school in Spain, with environmental variables recorded every 10 minutes using a monitoring kit installed in the classroom, and students’ emotions categorized using Emotion Recognition Technology (ERT). The results suggest that high recorded temperatures and humidity levels are associated with emotional responses among students. While linear regression models indicate that temperature and humidity may influence students’ emotional experiences in the classroom, the explanatory power of these models may be limited, suggesting that other factors could contribute to the observed variability in emotions. The implications and limitations of these findings for classroom conditions and student emotional well-being are discussed. Recognizing the influence of environmental conditions and monitoring them is a step toward establishing smart classrooms.

Over the course of many years, the Mekong Delta region has experienced relatively low and inconsistent levels of business attraction and low quality of the enterprise environment compared to other regions in Vietnam. To delve into whether this discrepancy reflects a negative perception of the business environment in the area, this study employs a dataset comprising the aggregate Provincial Competitiveness Index (PCI) and nine of its component scores, alongside other significant control variables, to analyze business attraction trends spanning from 2010 to 2020. It based on the modeling analysis for the panel data that includes Pool-OLS, FEM and REM models. The findings indicate that PCI serves as an important indicator influencing the quality of the business environment and plays a role in determining the location preferences of businesses. It is observed that public investment has exerted an impact on enticing new businesses to the region throughout this period. These research outcomes carry several policy implications, suggesting that public policy interventions can positively shape the business environment, consequently bolstering the appeal of business investments in the region.

The relationship between new-quality productivity and educational equity is characterized by close mutual influence and co-evolution. Driven by technological innovation, new-quality productivity is profoundly transforming the economic and social landscape. Educational equity, a crucial component of social justice, is vital for ensuring equal development opportunities for all individuals. The robust growth of new-quality productivity not only optimizes the distribution of educational resources and enhances educational quality but also poses new challenges and demands for equity in education. In turn, the continuous advancement of educational equity provides a solid talent foundation and a conducive environment for innovation to new-quality productivity. These two aspects intertwine and progress together in various domains, including policy systems, cultural values, and educational practices. This interplay highlights the central role of new-quality productivity and educational equity in societal development, while also demonstrating their dynamic and complementary relationship.

Over the past twenty years, service organizations have adopted total quality management to enhance their service quality, significantly impacting business performance, customer satisfaction, and profitability. This study delves into policy development of sustainable quality management theory, benefits, and various service components, while reviewing its implementation in services industries and policy innovation. The concept of Sustainable Quality Management 4.0 (SQM 4.0) integrates sustainable management, traditional quality management, and Quality 4.0 principles to optimize resources, reduce environmental impacts, and enhance decision-making through Industry 4.0, IoT, AI, and big data analytics. The findings offer valuable framework and policy insights for managers and practitioners on quality management and service systems, providing an implementation framework for Sustainable Quality Management in the service sector. The paper outlines comprehensive elements and strategies for implementation as a SQM framework for attaining sustainable quality management in the services industry.

Our study investigates the relationship between firm profitability, board characteristics, and the quality of sustainability disclosures, while examining the moderating effects of financial leverage and external audit assurance. A key focus is the distinction between Big 4 and non-Big 4 audit firms. Using data from Malaysia’s top 100 publicly listed organizations from 2018 to 2020, we analyze sustainability reports based on the Global Reporting Initiative (GRI) standards. Unexpectedly, our results indicate a negative association between firm profitability and board characteristics, challenging traditional assumptions. We find that non-Big 4 audit firms significantly enhance sustainability disclosure quality, contradicting the widely held belief in the superiority of Big 4 firms. Our finding introduces the “Big 4 dilemma” in the Malaysian context and calls for a reassessment of audit firm selection practices. Our study offers new perspectives on the strategic role of board composition and audit firm selection in advancing sustainability disclosures, urging Malaysian organizations to evaluate audit firms on criteria beyond the global prestige of Big 4 firms to improve sustainability reporting.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.