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.

In the present study, conjugate heat transfer and fluid flow performance of microchannel heat sink has been investigated using dimensionless parameters. Novel ribs of four different types are introduced on the side walls of channel, which include trapezoidal ribs, rectangular ribs, hydrofoil ribs, and elliptical ribs. The performance evaluation has been conducted by comparing friction factor (f), Nusselt number ( N u Nu ), fluid bulk temperature ( T f {T_{f}} ), wall shear stress (τ), field synergy number ( F c Fc ), irreversible heat loss ( Q d {Q_{d}} ), and Bejan number ( B e Be ) in a Reynolds number, ranging from Re = 100 \mathit{Re}=100 to Re = 1000 \mathit{Re}=1000 . The results revealed that the addition of these novel ribs are helpful in improving the overall thermal and hydraulic performance of microchannel heat sink. From the results of Bejan number, it has been revealed that more than 96 % of losses are because of heat transfer. However, at low Reynolds number, the frictional losses can be neglected, because of very low fluid velocity. Moreover, it has been revealed that synergetic relation between velocity and temperature gradient becomes weaker at higher Reynolds number. Furthermore, it is clear from this study that elliptical ribs performed better in thermal aspects, whereas hydrofoil ribs performed better at hydrodynamic aspects.

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.