Three-dimensional printing, or additive manufacturing, represents one of the fastest growing branches of the industry, and fused deposition modeling (FDM) is one of most frequently used technologies. Three-dimensional printing does not provide high-quality surfaces, so finishing is required, and milling is one of the best methods for improving surface quality. The combination of 3D printing and traditional manufacturing technologies is known as hybrid manufacturing. In order to improve quality and determine optimal machining parameters, researchers increasingly use artificial intelligence methods. In the context of manufacturing technologies, both multiple regression analysis (MRA) and artificial neural networks (ANNs) have proven to be highly reliable in predicting and optimizing machining processes. This study focuses on the use of MRA and an ANN to analyze the influence of machining parameters such as feed rate, depth of cut, and spindle speed on the surface roughness of a 3D-printed part in a milling process. The study compares the measured results with the outcomes obtained through MRA and the ANN to assess their effectiveness in predicting and optimizing surface roughness. The results show that higher accuracy was obtained from the ANN method.

Energy is a prerequisite for development of today's economies and societies. Renewable energy sources (RES) are gaining increasing importance and share in the energy systems of many countries. However, assessment and selection among different renewable energy sources request analyzing numerous criteria, limitations, legal regulations etc. For this reason, multi-criteria decision-making methods (MCDM) are often used in the literature and in practice for the assessment of RES projects/technologies. From numerous MCDM methods in this paper analytical hierarchy process (AHP) is selected to assess solar, wind, photovoltaic and biomass sources in Kosovo*. According obtained results first ranked is hydro alternative. Also, for results obtained, sensitivity analysis is conducted. Its show that changes in rank occur exclusively outside the allowable values, so it can be concluded that the results obtained are relevant.

The European Union’s Artificial Intelligence Act (EU AI Act), adopted in 2024, and introduced the first comprehensive legal framework for regulating artificial intelligence across sectors, including education. This paper explores the dual implications of the Act—contractual and pedagogical—within the context of digital education, with a particular emphasis on IT curricula. It examines the risks of diminishing foundational knowledge in algorithmic thinking, programming, and system architecture as AI tools become more prevalent in academic settings. The paper analyzes legal obligations arising from the use of AI in education, such as transparency, informed consent, and liability, situating them within the law of obligations and contract law. It argues that AI integration in education, particularly in high-risk domains, requires more than technical compliance: it demands sustainable, ethical, and pedagogically informed implementation. Drawing on EU policies, UNESCO frameworks, and recent academic literature, the paper proposes strategic recommendations to ensure that digital education not only complies with legal standards but also upholds the core mission of higher education—critical thinking, ethical responsibility, and the cultivation of foundational knowledge.

The basic questions that arise when maintaining an automated electric drive relate to the safety and reliability of the elements in operation. The maintenance strategy of these elements is planned to start from the basic facts related to the plant’s importance, type and purpose. The main purpose of maintenance is the timely detection of a fault and its elimination, i.e. ensuring continuous operation of the plant. In this work, the operation of the cable car was analyzed by monitoring the reliability of the electric drive. An analysis of the significance of reliability coefficients is a potentially new method for maintenance planning. The aim of the research was to define a new strategy for the maintenance of the facility and ensure the maximum safety of skiers through the analysis of previous events. Research is based on coefficients that indicate the reliability, frequency of failures, availability and unavailability of the elements that make up a drive. The essence of the conducted research is based on a new strategy for planning and maintaining such a facility, aiming to ensure skiers’ safety and protection.

This paper investigates the influence of layer height, one of the key parameters in Fused Deposition Modeling (FDM) 3D printing, on the surface roughness of parts manufactured using PETG (Polyethylene Terephthalate Glycol) material. Specimens were printed with different layer heights (0.1 mm, 0.2 mm, and 0.3 mm) under identical printing conditions to evaluate how this parameter affects surface quality. Surface roughness (Ra) was measured using a profilometer, and the results indicate that increased layer height leads to higher surface roughness values, resulting in a rougher surface finish. The findings highlight the importance of selecting an appropriate layer height to achieve the desired surface quality, particularly in applications where aesthetics or functional surface characteristics are critical. This study contributes to the optimization of PETG 3D printing processes in contexts where surface finish is of particular importance.

The aim of the work is to present an innovative solution based on artificial intelligence for examining the condition of railway tracks in real time. The system, based on fuzzy logic and metaheuristics such as Fuzzy Logic, Neural Networks and Bee Behavior Optimization, combines hardware and software to provide reliable data on the technical characteristics of the railway. Installed in rail vehicles, hardware collects this data, while software uses artificial intelligence to improve operational reliability and safety. The aforementioned technology is not only useful for infrastructure diagnostics, but also for urban railways such as trams and metros, ensuring a high level of passenger safety. The introduction of artificial intelligence in the railway sector is a key step towards modernisation, improving efficiency, resource optimization and safety. Although still in its infancy, artificial intelligence already shows great potential in transforming the railway sector towards a more efficient, reliable and sustainable future.