This research analyzed the degree of utilization of the agricultural biomass for energy purposes (combustion), in order to indicate the reasons that limit its use. The biomass potential was studied by means of the methodology of the biomass potential, whereas the factors suggesting a low degree of biomass utilization were identified by means of factor analysis. The research results reveal that there is an enormous potential of the unused agricultural biomass. This dissertation research significantly contributes to the establishment of a genuine mathematical model based on multiple linear regression. The solution obtained by this analysis, in both a mathematical and a scientific manner, conveys the primary reasons for an insufficient utilization of the biomass for energy purposes. Moreover, the paper suggests the measures to be applied for a more substantial use of this renewable source of energy and presents the expected benefits to be gained.

Modern development of electric vehicles imposes an extremely important requirement related to their driving range. In addition to traction force and acceleration, driving range is one of the most important performance indicators that contributes to the assessment of the functional quality of a vehicle.Driving range is a measure of energy autonomy, and low autonomy is one of the biggest limitations to the wider adoption of electric vehicles.The main issue behind the limited autonomy lies in the capacity (and design concept) of the embedded energy storage system. The design of such a storage system should enable high operational efficiency with the smallest possible volume and mass of the propulsion battery. Another challenge related to vehicle range concerns the management of available energy, which involves controlling electricity consumption. This means identifying all vehicle usage options that affect energy (and power) consumption, keeping in mind that the battery is the only energy source in fully electric vehicles.

In the era of Industry 4.0, predictive maintenance stands as a critical pillar for efficient and cost-effective operations. This paper conducts a systematic literature review, synthesizing the current state of applying machine learning techniques to predictive maintenance. The study underscores the significance of predictive maintenance in Industry 4.0 and elucidates its potential to revolutionize maintenance practices. It delves into the challenges associated with data quality, model interpretability, and scalability, offering insights through real-world use cases. Additionally, the paper explores the integration of machine learning into Industry 4.0 and its role in sustainable smart manufacturing.

This paper deals with the issue of education during the COVID-19 pandemic in Serbia and Bosnia and Herzegovina and its effects on education development gained during that process. This topic is trending around sustainable education in urban and rural areas also including widespread areas and locations of students and faculties. In this paper, we present a model that was created in the early days of the COVID-19 pandemic and is functional even today. The model is supported on a website and it became a foundation for adding a great deal of material and solutions for more prosperous results in education on several faculties. The key findings imply that in the initial stages of the COVID-19 pandemic with the system establishment of lectures and examinations, the level of adopted skills improved significantly and exceeded those before the COVID-19 pandemic regarding the passing of exams and average grades on subjects. The ANN model was developed, which provides results in terms of successfully passing knowledge tests and average grades by subjects. The innovativeness of the model is a combination of input and output parameters supporting the possibility of its wide application in various branches of science, which resulted in intense application of this model in numerous courses.