This paper presents the impact of using virtual reality (VR) in education and focuses on the main factors that affect the behavioral intention of using this new technology for the benefit of both professors and students. As part of our research methodology, we conducted a survey based on the Technology Acceptance Model and used partial least squares structural equation modelling. The results show that the intention to use VR technology in education is influenced by both usage satisfaction and the task–technology fit. The system quality of the VR technology proved to be important in influencing both the task–technology fit and perceived usefulness. The findings are useful for professors considering the adoption of new technologies that might improve the efficiency of learning for their students. Additionally, managers of educational institutions can use these insights to direct investments towards this type of innovation, which appeals to newer generations.

The main objective of our research is to identify the impact of recycling and waste reduction behavior on the sustainable tourism decisions of Romanian youngsters (18–25 years old). We used the PLS-SEM method and introduced four variables in the model: sustainable tourism decisions, the interest in recycling, the interest in waste reduction, and the interest in natural and less polluted touristic destinations. The main results emphasize the direct influence of recycling and waste reduction behaviors on the decisions made by Generation Z regarding sustainable tourism and on their preference for destinations that are better preserved and less touched by human intervention. The novelty of our research consists of the fact that we introduced variables such as waste reduction from the perspective of tourists because most studies address it as a management approach of the companies in the tourism sector. The findings are useful for managers in the tourism sector to create better strategies for attracting the younger generation who are preoccupied by environmental issues and sustainability in general.

This paper aims to identify the factors that are positively or negatively impacting students’ well-being and their academic engagement. We used partial least-squares structural equation modeling (PLS-SEM) using the data collected through a questionnaire from four countries: Romania, Turkey, Slovakia, and Bulgaria. The model includes seven factors that influence the well-being of students and indirectly their academic engagement: stressors in the students’ lives; professors’ support; social support from family and friends; the students’ perceived satisfaction in their lives; engaging in activities during their leisure time; self-exploration regarding their careers; and environmental exploration regarding their careers. The results show that all factors, except for stressors and environmental exploration regarding their careers, positively influence the students’ well-being and thus their academic engagement. These findings are useful for university professors and managers in better organizing activities to increase academic performance.

Inadequate air quality has adverse impacts on human well-being and contributes to the progression of climate change, leading to fluctuations in temperature. Therefore, gaining a localized comprehension of the interplay between climate variations and air pollution holds great significance in alleviating the health repercussions of air pollution. This study uses a holistic approach to make air quality predictions and multivariate modelling. It investigates the associations between meteorological factors, encompassing temperature, relative humidity, air pressure, and three particulate matter concentrations (PM10, PM2.5, and PM1), and the correlation between PM concentrations and noise levels, volatile organic compounds, and carbon dioxide emissions. Five hybrid machine learning models were employed to predict PM concentrations and then the Air Quality Index (AQI). Twelve PM sensors evenly distributed in Craiova City, Romania, provided the dataset for five months (22 September 2021–17 February 2022). The sensors transmitted data each minute. The prediction accuracy of the models was evaluated and the results revealed that, in general, the coefficient of determination (R²) values exceeded 0.96 (interval of confidence is 0.95) and, in most instances, approached 0.99. Relative humidity emerged as the least influential variable on PM concentrations, while the most accurate predictions were achieved by combining pressure with temperature. PM10 (less than 10 µm in diameter) concentrations exhibited a notable correlation with PM2.5 (less than 2.5 µm in diameter) concentrations and a moderate correlation with PM1 (less than 1 µm in diameter). Nevertheless, other findings indicated that PM concentrations were not strongly related to NOISE, CO₂, and VOC, and these last variables should be combined with another meteorological variable to enhance the prediction accuracy. Ultimately, this study established novel relationships for predicting PM concentrations and AQI based on the most effective combinations of predictor variables identified.

Air quality, especially particulate matter pollution levels in urban areas, is an essential academic and social topic due to its association with health issues and climate change. In Romania, increasing awareness of urban communities and the availability of low-cost sensors has led to the development of an independent monitoring network currently distributed in over 194 cities and towns. The uRADMonitor^® network consists of 630 sensors measuring PM10 and PM2.5 concentration levels. The spatial distribution of the sensors complements the national air quality network with sensors in residential areas, intense traffic zones, and industrial areas. The data are available through a user-friendly web-based platform from uRADMonitor^®. Based on data collected in 2021, we present an analysis of PM10 pollution levels in Romania’s five most populated urban areas by employing five annual statistical indicators recommended by the European Environmental Agency. For the case of Timișoara, we also compare the data measured by independent sensors with those from the national monitoring network. The results highlight the usefulness of our community-based network as it complements the national one.