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 selection of an appropriate operating system (OS) in educational environments is a critical decision that impacts the functionality, user experience, and overall efficiency. Financial factors, along with the availability and functionality of tools used on these systems, play a crucial role in this selection. Furthermore, the OS affects the user experience, security, and adaptability to learning. Previous research in this area is scarce, and this paper contributes to a better understanding of the OS selection process in education. This paper proposes a novel multi-criteria decision-making approach to evaluate and select the most suitable OS for educational institutions. The methodology integrates the PIPRECIA method for assessing weighted criteria, alongside utility analysis (NWA), enabling a balanced decision-making process between institutional management and IT experts. The evaluation considered the following criteria: performance, cost, security, usability, implementation, support, and documentation. The study compared three popular operating systems: Microsoft Windows 11, GNU Linux Ubuntu 22.04 LTS, and Apple macOS 12 using the proposed approach. The results, based on the integrated evaluation of all criteria, indicate that GNU Linux Ubuntu 22.04 LTS (0.562) ranked highest, followed by Microsoft Windows 11 (0.553) and Apple macOS 12 (0.543). This paper emphasizes the importance of objective, group-based decision-making in selecting an OS for education, providing practical insights and guidelines for effective technology integration in academic settings.

The main goal of this paper is to develop and prioritize renewable energy scenarios for electricity production. The model proposed in the paper was applied on the example of the municipality of Strpce, enclave in south Kosovo**. The model consists out of three phases, and the decision-making and planning process involves a group of decision makers, i.e. selected experts and stakeholders. In the first phase, a group of decision makers, using a collaborative planning method and Homer pro software, defined eight renewable energy scenarios based on renewable energy potentials and electricity demand in Strpce. In the second phase, a group of decision makers, using the strengths, weaknesses, opportunities, and threats analysis, defined a total of 22 criteria to evaluate scenarios. Finally, in the third phase, a group of decision makers used fuzzy analytical network process methodology to prioritize scenarios. Results show that a scenario that includes installation of photovoltaic panels in Strpce households to cover 50% of the electricity demand is first ranked. The proposed model has a universal character which means that it can be applied in other municipalities/cities and regions. Also, the model enables collaborative planning through the inclusion of different stakeholders whose number and type can be adjusted according to the subject/area of research.

The development of today's economies is inconceivable without energy. However, fossil fuel reserves are declining, climate change is accelerating and some changes in the energy sector are needed. Renewable energy sources are a potential solution for many scientists and practitioners. However, the planning and implementation of renewable energy projects requires consideration of a number of criteria, which is why multicriteria decision-making methods are often used to evaluate renewable energy sources/technologies. Goal of this paper is to evaluate four types of renewable energy sources (photovoltaic, hydro, biomass and wind energy) in Serbia. Analytical hierarchical process and seven criteria were applied. Based on the obtained results, hydro sources are ranked the best. Also, a sensitivity analysis was conducted to determine whether changes in the priority of criteria would cause changes in the range of alternatives. It was found that major changes in priorities are needed for changes to occur, so it can be concluded that the results obtained are relevant.