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The paper deals with evaluation and ranking of students taking into account two main criteria of the learning – theoretical knowledge and practical skills. These criteria are divided into several sub-criteria to reflect different aspects of the learning outcomes. To make such complex evaluation the proper utility function based on simple multi-attribute rating technique is proposed. This new utility function includes not only the evaluation score and weighted coefficients for criteria importance, but considers also additional coefficients that indicate how theoretical knowledge and practical skills will take part in the aggregated final assessment. The formulated model is applied for the assessing of the students on web programming. The students are ranked under three different cases where the theoretical knowledge and practical skills take different part in the aggregated assessment. The obtained results demonstrate the applicability of the described approach by providing different ranking depending on the importance of the theoretical and practical aspects.

Solar and wind energy as environmentally friendly and renewable energy sources, play an essential role in achieving sustainable development of the energy sector. Their inexhaustible resources attract many and different investments. On the other hand, the development of various policies aimed at decreasing carbon footprints also contributes to the attractiveness of such investments. That is why this article analyses some of the main factors influencing the profitability of wind and solar photovoltaic farms. These two factors are the flat cost of electricity, which is the average cost of energy produced over the life of the wind and solar photovoltaic farms, and the efficiency of wind and solar photovoltaic farms, expressed in terms of the energy generated per unit of installed capacity. An analysis of wind and solar photovoltaic farm efficiency was conducted for different countries based on publicly available data from 2011 to 2023. The results show that the wind farms have higher efficiency compared to solar photovoltaic farms, as for both sources of renewable energy, the trend is toward increased efficiency.

Mobile technologies are an essential part of people’s everyday lives since they are utilized for a variety of purposes, such as communication, entertainment, commerce, and education. However, when these gadgets are misused, the human body is exposed to continuous radiation from the electromagnetic field created by them. The communication services available are improving as mobile technologies advance; however, the problem is becoming more severe as the frequency range of mobile devices expands. To solve this complex case, it is necessary to propose a comprehensive approach that combines and processes data obtained from different types of research and sources of information, such as thermal imaging, electroencephalograms, computer models, and surveys. In the present article, a complex model for the processing and analysis of heterogeneous data is proposed based on mathematical and statistical methods in order to study the problem of electromagnetic radiation from mobile devices in-depth. Data science selection/preprocessing is one of the most important aspects of data and knowledge processing aiming at successful and effective analysis and data fusion from many sources. Special types of logic-based binding and pointing constraints are considered for data/knowledge selection applications. The proposed logic-based statistical modeling method provides both algorithmic as well as data-driven realizations that can be evolutionary. As a result, non-anticipated and collateral data/features can be processed if their role in the selected/constrained area is significant. In this research, the data-driven part does not use artificial neural networks; however, this combination was successfully applied in the past. It is an independent subsystem maintaining control of both the statistical and machine-learning parts. The proposed modeling applies to a wide range of reasoning/smart systems.

The mountain hiking destinations become more popular as this is one of the possible ways to cope with workplace stress and to prevent burnout. In contrast to the tourist destinations, mountain hiking requires special attention due to the variety of mountain trails satisfying the same starting and finishing point for a particular route. For the goal, a two-stage search-based approach for a determining of possible routes considering the users’ preferences is developed. The first stage is focused on the determining of possible hiking routes taking into account the requirements and tourists’ preferences, while the second stage concerns the sorting of already determined hiking routes. The applicability of the described approach is illustrated and the obtained results demonstrate the capability in searching and sorting of mountain hiking trails using directed weighted multigraph including tourists’ preferences.

Atwo-stage placement algorithm with multi-objective optimization and group decision making is proposed. The first stage aims to determineaset of design alternatives for objects placement by multi-objective combinatorial optimization. The second stage relies on business intelligence via group decision-making based on solution of optimization task to makeachoice of the most suitable alternative. The design alternatives are determined by means of weighted sum and lexicographic methods. The group decision making is used to evaluate determined design alternatives toward the design parameters. The described algorithm is used for wind farm layout optimization problem. The results of numerical testing demonstrate the applicability of the proposed algorithm.

In the article a combinatorial optimization model for group decision-making problem is proposed. The described model relies on extended simple additive weighting model. A distinctive feature of the proposed model is consideration of the importance of experts’ opinions by introducing weighted coefficient for each of experts. This allows flexible adjustment of differences in knowledge and experience of the group members responsible to determine most preferable alternative to be achieved. The numerical application is illustrated by an example for software engineering adopted from D. Krapohl. The obtained results show the practical applicability of the proposed combinatorial optimization model for group decision-making.

In the paper a business intelligence tool based on group decision making is proposed. The group decision making uses a combinatorial optimization modeling technique. It takes into account weighted coefficients for evaluation criteria assigned by decision makers together with their scores for the alternatives in respect of these criteria. The proposed optimization model for group decision making considers also the knowledge level of the group members involved as decision makers. This optimization model is implemented in three-layer architecture of Web application for business intelligence by group decision making. Developed Web application is numerically tested for a representative problem for software choice considering six decision makers, three alternatives and 19 evaluation criteria. The obtained results show the practical applicability and effectiveness of the proposed approach.

To meet increased demand in energy while reducing greenhouse gas emissions different renewable energy technologies are used. The wind power provides potential benefits as it is a clean, renewable, economic and domestically available power source. Building of sustainable wind farm is subject on many different factors. The aim of the paper is to contribute optimal turbines placement and integration to the electrical grid. For the goal, a mixed-integer non-linear optimization model for determination of maximum wind farm capacity is defined. Another linear optimization model is used to determine the minimum distance of wind turbines in the farm to the point of common coupling. The proposed approach is tested numerically for particular wind turbines type and given wind farm area. The testing results demonstrate the applicability of the proposed optimization models for determination of maximum wind farm capacity and minimum distance between wind farm and point of common coupling.

Predictive Maintenance Sensors Placement by Combinatorial Optimization The strategy of predictive maintenance monitoring is important for successful system damage detection. Maintenance monitoring utilizes dynamic response information to identify the possibility of damage. The basic factors of faults detection analysis are related to properties of the structure under inspection, collect the signals and appropriate signals processing. In vibration control, structures response sensing is limited by the number of sensors or the number of input channels of the data acquisition system. An essential problem in predictive maintenance monitoring is the optimal sensor placement. The paper addresses that problem by using mixed integer linear programming tasks solving. The proposed optimal sensors location approach is based on the difference between sensor information if sensor is present and information calculated by linear interpolation if sensor is not present. The tasks results define the optimal sensors locations for a given number of sensors. The results of chosen sensors locations give as close as possible repeating the curve of structure dynamic response function. The proposed approach is implemented in an algorithm for predictive maintenance and the numerical results indicate that together with intelligent signal processing it could be suitable for practical application.

In traditional and e-learning the tests play an important role to track the learners’ progress. In this regard, the paper deals with the generation of test with different levels of complexity directly associated with particular evaluation. An algorithm for the generation of tests with different levels of complexity using multi-criteria optimization is proposed. It is important to note that all test items could be considered as an element of a level in an educational game or gamified environment, and their weight influences the complexity of the game. The results show that the proposed model allows the generation of tests not only with different levels of complexity, but it also provides additional flexibility in respect of the selected number of questions and their weight. The proposed algorithm could be realized as a separate module for etesting, or it could be integrated in the learning management system.