The paper explores the industrial tourism development potential of a former mining area in Petrila, Jiu Valley, Romania, drastically affected by deindustrialization, in order to build a theme park in the old mine site. The research context is determined by the current economic and social state of the area, and also by the potential of the identified industrial heritage elements. For this reason, there was developed an optimal selection algorithm, based on multi-criteria analysis and Greedy approach. This optimal selection algorithm blends the objectivity of the technical-economic studies and the subjectivity of the residents’ perceptions of community satisfaction. After carrying out the Petrila case study, the results of the proposed algorithm application concerning the destination, from the point of view of their use, of each existing building in the Petrila site was determined. Compared with other studies, ours considers different criteria and can always be reapplied to validate or refine the selection based on new emerging inputs.

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine-learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from Internet of Things (IoT) sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R² score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO₂, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air-quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

Exoskeletons are companion devices that help a person to perform various daily activities. These can range from work to medical rehabilitation. The type of activity performed depend on the construction and control of the exoskeleton, so that some devices are for only one arm, others for both, can be driven by motors directly or through cables. Exoskeletons can be driven based on information received from position, force, speed sensors or by using EMG, EEG signals. Exoskeleton wearable devices began to appear around 1980, as an aid in physical work, in the handling of various heavy objects. Over time, they also covered the preventive-rehabilitation medical side, in order to reduce muscle pain or to restore specific movements, attenuated or even missing following accidents or diseases of the muscles. The paper presents an overview of the exoskeletons developed for the human arm.